rhone_sa.ipynb

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    "# -*- coding: utf-8 -*-\n",
    "# This is a report using the data from IQAASL.\n",
    "# IQAASL was a project funded by the Swiss Confederation\n",
    "# It produces a summary of litter survey results for a defined region.\n",
    "# These charts serve as the models for the development of plagespropres.ch\n",
    "# The data is gathered by volunteers.\n",
    "# Please remember all copyrights apply, please give credit when applicable\n",
    "# The repo is maintained by the community effective January 01, 2022\n",
    "# There is ample opportunity to contribute, learn and teach\n",
    "# contact dev@hammerdirt.ch\n",
    "\n",
    "# Dies ist ein Bericht, der die Daten von IQAASL verwendet.\n",
    "# IQAASL war ein von der Schweizerischen Eidgenossenschaft finanziertes Projekt.\n",
    "# Es erstellt eine Zusammenfassung der Ergebnisse der Littering-Umfrage für eine bestimmte Region.\n",
    "# Diese Grafiken dienten als Vorlage für die Entwicklung von plagespropres.ch.\n",
    "# Die Daten werden von Freiwilligen gesammelt.\n",
    "# Bitte denken Sie daran, dass alle Copyrights gelten, bitte geben Sie den Namen an, wenn zutreffend.\n",
    "# Das Repo wird ab dem 01. Januar 2022 von der Community gepflegt.\n",
    "# Es gibt reichlich Gelegenheit, etwas beizutragen, zu lernen und zu lehren.\n",
    "# Kontakt dev@hammerdirt.ch\n",
    "\n",
    "# Il s'agit d'un rapport utilisant les données de IQAASL.\n",
    "# IQAASL était un projet financé par la Confédération suisse.\n",
    "# Il produit un résumé des résultats de l'enquête sur les déchets sauvages pour une région définie.\n",
    "# Ces tableaux ont servi de modèles pour le développement de plagespropres.ch\n",
    "# Les données sont recueillies par des bénévoles.\n",
    "# N'oubliez pas que tous les droits d'auteur s'appliquent, veuillez indiquer le crédit lorsque cela est possible.\n",
    "# Le dépôt est maintenu par la communauté à partir du 1er janvier 2022.\n",
    "# Il y a de nombreuses possibilités de contribuer, d'apprendre et d'enseigner.\n",
    "# contact dev@hammerdirt.ch\n",
    "\n",
    "# sys, file and nav packages:\n",
    "import datetime as dt\n",
    "from datetime import date, datetime, time\n",
    "from babel.dates import format_date, format_datetime, format_time, get_month_names\n",
    "import locale\n",
    "\n",
    "# math packages:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# charting:\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import ticker\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "# from matplotlib import colors as mplcolors\n",
    "\n",
    "# build report\n",
    "import reportlab\n",
    "from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\n",
    "from reportlab.lib import colors\n",
    "\n",
    "from reportlab.platypus.flowables import Flowable\n",
    "from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak, KeepTogether, Image\n",
    "from reportlab.lib.pagesizes import A4\n",
    "from reportlab.lib.units import cm\n",
    "from reportlab.platypus import Table, TableStyle\n",
    "\n",
    "# the module that has all the methods for handling the data\n",
    "import resources.featuredata as featuredata\n",
    "from resources.featuredata import makeAList, small_space, large_space, aSingleStyledTable, smallest_space\n",
    "from resources.featuredata import caption_style, subsection_title, title_style, block_quote_style, makeBibEntry\n",
    "from resources.featuredata import figureAndCaptionTable, tableAndCaption, aStyledTableWithTitleRow\n",
    "from resources.featuredata import sectionParagraphs, section_title, addToDoc, makeAParagraph, bold_block\n",
    "from resources.featuredata import makeAList\n",
    "\n",
    "# home brew utitilties\n",
    "import resources.sr_ut as sut\n",
    "\n",
    "# images and display\n",
    "from PIL import Image as PILImage\n",
    "from IPython.display import Markdown as md\n",
    "from myst_nb import glue\n",
    "\n",
    "def convertPixelToCm(file_name: str = None):\n",
    "    im = PILImage.open(file_name)\n",
    "    width, height = im.size\n",
    "    dpi = im.info.get(\"dpi\", (72, 72))\n",
    "    width_cm = width / dpi[0] * 2.54\n",
    "    height_cm = height / dpi[1] * 2.54\n",
    "    \n",
    "    return width_cm, height_cm\n",
    "\n",
    "# chart style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# a place to save figures and a \n",
    "# method to choose formats\n",
    "save_fig_prefix = \"resources/output/\"\n",
    "\n",
    "# the arguments for formatting the image\n",
    "save_figure_kwargs = {\n",
    "    \"fname\": None,\n",
    "    \"dpi\": 300.0,\n",
    "    \"format\": \"jpeg\",\n",
    "    \"bbox_inches\": None,\n",
    "    \"pad_inches\": 0,\n",
    "    \"bbox_inches\": 'tight',\n",
    "    \"facecolor\": 'auto',\n",
    "    \"edgecolor\": 'auto',\n",
    "    \"backend\": None,\n",
    "}\n",
    "\n",
    "## !! Begin Note book variables !!\n",
    "\n",
    "# There are two language variants: german and english\n",
    "# change both: date_lang and language\n",
    "date_lang =  'de_DE.utf8'\n",
    "locale.setlocale(locale.LC_ALL, date_lang)\n",
    "\n",
    "# the date format of the survey data is defined in the module\n",
    "date_format = featuredata.date_format\n",
    "\n",
    "# the language setting use lower case: en or de\n",
    "# changing the language may require changing the unit label\n",
    "language = \"de\"\n",
    "unit_label = \"p/100 m\"\n",
    "\n",
    "# the standard date format is \"%Y-%m-%d\" if your date column is\n",
    "# not in this format it will not work.\n",
    "# these dates cover the duration of the IQAASL project\n",
    "start_date = \"2020-03-01\"\n",
    "end_date =\"2021-05-31\"\n",
    "start_end = [start_date, end_date]\n",
    "\n",
    "# the fail rate used to calculate the most common codes is\n",
    "# 50% it can be changed:\n",
    "fail_rate = 50\n",
    "\n",
    "# Changing these variables produces different reports\n",
    "# Call the map image for the area of interest\n",
    "bassin_map = \"resources/maps/rhone_city_labels.jpeg\"\n",
    "\n",
    "# the label for the aggregation of all data in the region\n",
    "top = \"Alle Erhebungsgebiete\"\n",
    "\n",
    "# define the feature level and components\n",
    "# the feature of interest is the rhone (rhone) at the river basin (river_bassin) level.\n",
    "# the label for charting is called 'name'\n",
    "this_feature = {'slug':'rhone', 'name':\"Erhebungsgebiet Rhône\", 'level':'river_bassin'}\n",
    "\n",
    "# the lake is in this survey area\n",
    "this_bassin = \"rhone\"\n",
    "# label for survey area\n",
    "bassin_label = \"Erhebungsgebiet Rhône\"\n",
    "\n",
    "# these are the smallest aggregated components\n",
    "# choices are water_name_slug=lake or river, city or location at the scale of a river bassin \n",
    "# water body or lake maybe the most appropriate\n",
    "this_level = 'water_name_slug'\n",
    "\n",
    "# the doctitle is the unique name for the url of this document\n",
    "doc_title = \"rhone_sa\"\n",
    "\n",
    "# identify the lakes of interest for the survey area\n",
    "lakes_of_interest = [\"lac-leman\"]\n",
    "\n",
    "# !! End note book variables !!\n",
    "## data\n",
    "# Survey location details (GPS, city, land use)\n",
    "dfBeaches = pd.read_csv(\"resources/beaches_with_land_use_rates.csv\")\n",
    "# set the index of the beach data to location slug\n",
    "dfBeaches.set_index(\"slug\", inplace=True)\n",
    "\n",
    "# Survey dimensions and weights\n",
    "dfDims = pd.read_csv(\"resources/corrected_dims.csv\")\n",
    "\n",
    "# code definitions\n",
    "dxCodes = pd.read_csv(\"resources/codes_with_group_names_2015.csv\")\n",
    "dxCodes.set_index(\"code\", inplace=True)\n",
    "\n",
    "# columns that need to be renamed. Setting the language will automatically\n",
    "# change column names, code descriptions and chart annotations\n",
    "columns={\"% to agg\":\"% agg\", \"% to recreation\": \"% recreation\", \"% to woods\":\"% woods\", \"% to buildings\":\"% buildings\", \"p/100m\":\"p/100 m\"}\n",
    "\n",
    "# !key word arguments to construct feature data\n",
    "# !Note the water type allows the selection of river or lakes\n",
    "# if None then the data is aggregated together. This selection\n",
    "# is only valid for survey-area reports or other aggregated data\n",
    "# that may have survey results from both lakes and rivers.\n",
    "fd_kwargs ={\n",
    "    \"filename\": \"resources/checked_sdata_eos_2020_21.csv\",\n",
    "    \"feature_name\": this_feature['slug'], \n",
    "    \"feature_level\": this_feature['level'], \n",
    "    \"these_features\": this_feature['slug'], \n",
    "    \"component\": this_level, \n",
    "    \"columns\": columns, \n",
    "    \"language\": 'de', \n",
    "    \"unit_label\": unit_label, \n",
    "    \"fail_rate\": fail_rate,\n",
    "    \"code_data\":dxCodes,\n",
    "    \"date_range\": start_end,\n",
    "    \"water_type\": None,    \n",
    "}\n",
    "\n",
    "fdx = featuredata.Components(**fd_kwargs)\n",
    "\n",
    "# call the reports and languages\n",
    "fdx.adjustForLanguage()\n",
    "fdx.makeFeatureData()\n",
    "fdx.locationSampleTotals()\n",
    "fdx.makeDailyTotalSummary()\n",
    "fdx.materialSummary()\n",
    "fdx.mostCommon()\n",
    "fdx.codeGroupSummary()\n",
    "\n",
    "# !this is the feature data!\n",
    "fd = fdx.feature_data\n",
    "\n",
    "# !keyword args to build period data\n",
    "# the period data is all the data that was collected\n",
    "# during the same period from all the other locations\n",
    "# not included in the feature data. For a survey area\n",
    "# or river bassin these_features = feature_parent and \n",
    "# feature_level = parent_level\n",
    "period_kwargs = {\n",
    "    \"period_data\": fdx.period_data,\n",
    "    \"these_features\": this_feature['slug'],\n",
    "    \"feature_level\":this_feature['level'],\n",
    "    \"feature_parent\":this_bassin,\n",
    "    \"parent_level\": \"river_bassin\",\n",
    "    \"period_name\": bassin_label,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"most_common\": fdx.most_common.index\n",
    "}\n",
    "period_data = featuredata.PeriodResults(**period_kwargs)\n",
    "\n",
    "# the rivers are considered separately\n",
    "# select only the results from rivers\n",
    "# this can be done by updating the fd_kwargs\n",
    "fd_rivers = fd_kwargs.update({\"water_type\":\"r\"})\n",
    "fdr = featuredata.Components(**fd_kwargs)\n",
    "fdr.makeFeatureData()\n",
    "fdr.adjustForLanguage()\n",
    "fdr.makeFeatureData()\n",
    "fdr.locationSampleTotals()\n",
    "fdr.makeDailyTotalSummary()\n",
    "fdr.materialSummary()\n",
    "fdr.mostCommon()\n",
    "\n",
    "# collects the summarized values for the feature data\n",
    "# use this to generate the summary data for the survey area\n",
    "# and the section for the rivers\n",
    "admin_kwargs = {\n",
    "    \"data\":fd,\n",
    "    \"dims_data\":dfDims,\n",
    "    \"label\": this_feature[\"name\"],\n",
    "    \"feature_component\": this_level,\n",
    "    \"date_range\":start_end,\n",
    "    **{\"dfBeaches\":dfBeaches}\n",
    "}\n",
    "admin_details = featuredata.AdministrativeSummary(**admin_kwargs)\n",
    "admin_summary = admin_details.summaryObject()\n",
    "\n",
    "# update the admin kwargs with river data to make the river summary\n",
    "admin_kwargs.update({\"data\":fdr.feature_data})\n",
    "admin_r_details = featuredata.AdministrativeSummary(**admin_kwargs)\n",
    "admin_r_summary = admin_r_details.summaryObject()\n",
    "\n",
    "# this defines the css rules for the note-book table displays\n",
    "header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF;text-align:right;'}\n",
    "even_rows = {\"selector\": 'tr:nth-child(even)', 'props': f'background-color: rgba(139, 69, 19, 0.08);'}\n",
    "odd_rows = {'selector': 'tr:nth-child(odd)', 'props': 'background: #FFF;'}\n",
    "table_font = {'selector': 'tr', 'props': 'font-size: 12px;'}\n",
    "table_data = {'selector': 'td', 'props': 'padding:6px;'}\n",
    "table_css_styles = [even_rows, odd_rows, table_font, header_row, table_data]\n",
    "\n",
    "# this defines the css rules for the note-book table displays\n",
    "header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF;text-align:right;'}\n",
    "table_font = {'selector': 'tr', 'props': 'font-size: 12px;'}\n",
    "table_data = {'selector': 'td', 'props': 'padding:6px;'}\n",
    "heat_map_css_styles = [table_font, header_row, table_data]\n",
    "\n",
    "# this is the numeric formatting for the dimensions table\n",
    "dims_table_formatter = {\n",
    "    \"Plastik (Kg)\": lambda x: featuredata.replaceDecimal(x, language),\n",
    "    \"Gesamtgewicht (Kg)\": lambda x: featuredata.replaceDecimal(x, language),\n",
    "    \"Fläche (m2)\": lambda x: featuredata.thousandsSeparator(int(x), language),\n",
    "    \"Länge (m)\": lambda x: featuredata.thousandsSeparator(int(x), language),\n",
    "    \"Erhebungen\": lambda x: featuredata.thousandsSeparator(int(x), language),\n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), language)\n",
    "}\n",
    "\n",
    "# formatting for mpl charts\n",
    "months = mdates.MonthLocator(interval=1)\n",
    "months_fmt = mdates.DateFormatter(\"%b\")\n",
    "days = mdates.DayLocator(interval=7)\n",
    "\n",
    "# pdf download is an option \n",
    "# the .pdf output is generated in parallel\n",
    "# this is the same as if it were on the backend where we would\n",
    "# have a specific api endpoint for .pdf requests. \n",
    "# reportlab is used to produce the document\n",
    "# the components of the document are captured at run time\n",
    "# the pdf link gives the name and location of the future doc\n",
    "pdf_link = f'resources/pdfs/{this_feature[\"slug\"]}_sa.pdf'"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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     "name": "#%% md\n"
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   "source": [
    "(rhonesa)=\n",
    "# Rhône\n",
    "\n",
    "| {Download}`Download </resources/pdfs/rhone_sa.pdf>` | [English](https://www.plagespropres.ch/rhone_sa.html) |"
   ]
  },
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   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Die Erhebungsorte sind für die Analyse nach Erhebungsgebiet gruppiert. Die Grösse der Markierung gibt einen Hinweis auf die Anzahl Abfallobjekte, die gefunden wurden.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_city_map_caption"
      }
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "' '"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "blank_caption"
      }
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# pdf components\n",
    "# the components are stored in an array and collected as the script runs\n",
    "pdfcomponents = []\n",
    "\n",
    "# pdf title\n",
    "pdf_title = Paragraph(this_feature[\"name\"], featuredata.title_style)\n",
    "\n",
    "# caption for the opening map\n",
    "map_caption = featuredata.defaultMapCaption(language=language)\n",
    "f1cap = Paragraph(map_caption, style=caption_style)\n",
    "\n",
    "# images need to be converted to pixels for reportlab\n",
    "o_w, o_h = convertPixelToCm(bassin_map)\n",
    "\n",
    "# figure arguments\n",
    "figure_kwargs = {\n",
    "    \"image_file\":bassin_map,\n",
    "    \"caption\": f1cap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 16,\n",
    "    \"caption_height\":1,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "# a table with a caption for pdf\n",
    "f1 = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "# collect the components\n",
    "new_components = [\n",
    "     pdf_title,    \n",
    "    small_space,\n",
    "    f1\n",
    "]\n",
    "# add those sections\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "# end pdf\n",
    "\n",
    "# capture the graphics for markdown\n",
    "glue(f'{this_feature[\"slug\"]}_city_map_caption', map_caption, display=False)\n",
    "glue(\"blank_caption\", \" \", display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{figure} resources/maps/rhone_city_labels.jpeg\n",
    "---\n",
    "name: rhone_survey_area_city_labels_map\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_city_labels_map>` {glue:text}`rhone_city_map_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Erhebungsorte und Landnutzungsprofile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Im Zeitraum von März 2020  bis Mai 2021 wurden im Rahmen von 106 Datenerhebungen insgesamt 28 454 Objekte entfernt und identifiziert.  Die Ergebnisse des Erhebungsgebiet Rhône umfassen 32 Orte, 18 Gemeinden und eine Gesamtbevölkerung von etwa 488 138 Einwohnenden.\n",
       "\n",
       "*Seen*\n",
       "\n",
       ">Lac Léman\n",
       "\n",
       "*Fliessgewässer*\n",
       "\n",
       ">Rhône\n",
       "\n",
       "*Gemeinden*\n",
       "\n",
       ">Allaman, Bourg-en-Lavaux, Genève, Gland, La Tour-de-Peilz, Lausanne, Lavey-Morcles, Leuk, Montreux, Préverenges, Riddes, Saint-Gingolph, Saint-Sulpice (VD), Salgesch, Sion, Tolochenaz, Versoix, Vevey\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the admin summary can be converted into a standard text\n",
    "an_admin_summary = featuredata.makeAdminSummaryStateMent(start_date, end_date, this_feature[\"name\"], admin_summary=admin_summary)\n",
    "\n",
    "# pdf components\n",
    "new_components = [    \n",
    "    small_space,\n",
    "    Paragraph(\"Erhebungsorte\", section_title), \n",
    "    small_space,\n",
    "    Paragraph(an_admin_summary , featuredata.p_style),\n",
    "]\n",
    "\n",
    "# add the admin summary to the pdf\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "# end pdf\n",
    "\n",
    "# collect component features and land marks\n",
    "# this collects the components of the feature of interest (city, lake, river)\n",
    "# a comma separated string of all the componenets and a heading for each component\n",
    "# type is produced\n",
    "feature_components = featuredata.collectComponentLandMarks(admin_details, language=language)\n",
    "\n",
    "# markdown output\n",
    "components_markdown = \"\".join([f'*{x[0]}*\\n\\n>{x[1]}\\n\\n' for x in feature_components])\n",
    "\n",
    "# put that all together:\n",
    "lake_string = F\"\"\"\n",
    "{an_admin_summary}\n",
    "\n",
    "{\"\".join(components_markdown)}\n",
    "\"\"\"\n",
    "\n",
    "# display the string\n",
    "md(lake_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Kumulative Gesamtmengen nach Gewässer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "source_hidden": true
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     "name": "#%%\n"
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     "hide-input"
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   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Erhebungsgebiet Rhône: kumulierten Gewichte  und Masse für die Gemeinden'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_dims_table_caption"
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    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_92260 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_92260 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_92260 tr {\n  font-size: 12px;\n}\n#T_92260 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_92260 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_92260\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_92260_level0_col0\" class=\"col_heading level0 col0\" >Gesamtgewicht (Kg)</th>\n      <th id=\"T_92260_level0_col1\" class=\"col_heading level0 col1\" >Plastik (Kg)</th>\n      <th id=\"T_92260_level0_col2\" class=\"col_heading level0 col2\" >Fläche (m2)</th>\n      <th id=\"T_92260_level0_col3\" class=\"col_heading level0 col3\" >Länge (m)</th>\n      <th id=\"T_92260_level0_col4\" class=\"col_heading level0 col4\" >Erhebungen</th>\n      <th id=\"T_92260_level0_col5\" class=\"col_heading level0 col5\" >Objekte (St.)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_92260_level0_row0\" class=\"row_heading level0 row0\" >Erhebungsgebiet Rhône</th>\n      <td id=\"T_92260_row0_col0\" class=\"data row0 col0\" >151,31</td>\n      <td id=\"T_92260_row0_col1\" class=\"data row0 col1\" >45,97</td>\n      <td id=\"T_92260_row0_col2\" class=\"data row0 col2\" >25 986</td>\n      <td id=\"T_92260_row0_col3\" class=\"data row0 col3\" >4 911</td>\n      <td id=\"T_92260_row0_col4\" class=\"data row0 col4\" >106</td>\n      <td id=\"T_92260_row0_col5\" class=\"data row0 col5\" >28 454</td>\n    </tr>\n    <tr>\n      <th id=\"T_92260_level0_row1\" class=\"row_heading level0 row1\" >Lac Léman</th>\n      <td id=\"T_92260_row1_col0\" class=\"data row1 col0\" >60,82</td>\n      <td id=\"T_92260_row1_col1\" class=\"data row1 col1\" >32,0</td>\n      <td id=\"T_92260_row1_col2\" class=\"data row1 col2\" >19 786</td>\n      <td id=\"T_92260_row1_col3\" class=\"data row1 col3\" >4 520</td>\n      <td id=\"T_92260_row1_col4\" class=\"data row1 col4\" >98</td>\n      <td id=\"T_92260_row1_col5\" class=\"data row1 col5\" >27 462</td>\n    </tr>\n    <tr>\n      <th id=\"T_92260_level0_row2\" class=\"row_heading level0 row2\" >Rhône</th>\n      <td id=\"T_92260_row2_col0\" class=\"data row2 col0\" >90,48</td>\n      <td id=\"T_92260_row2_col1\" class=\"data row2 col1\" >13,97</td>\n      <td id=\"T_92260_row2_col2\" class=\"data row2 col2\" >6 200</td>\n      <td id=\"T_92260_row2_col3\" class=\"data row2 col3\" >391</td>\n      <td id=\"T_92260_row2_col4\" class=\"data row2 col4\" >8</td>\n      <td id=\"T_92260_row2_col5\" class=\"data row2 col5\" >992</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff18468fbe0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_dims_table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the basic summary of dimensional data is available in the AdministrativeSummary class\n",
    "dims_table = admin_details.dimensionalSummary()\n",
    "dims_table.sort_values(by=[\"quantity\"], ascending=False, inplace=True)\n",
    "\n",
    "# apply language settings\n",
    "dims_table.rename(columns=featuredata.dims_table_columns_de, inplace=True)\n",
    "\n",
    "# convert to kilos\n",
    "dims_table[\"Plastik (Kg)\"] = dims_table[\"Plastik (Kg)\"]/1000\n",
    "\n",
    "# save a copy of the dims_table for working\n",
    "# formatting to pdf will turn the numerics to strings\n",
    "# which eliminates any further calclations\n",
    "dims_df =  dims_table.copy()\n",
    "\n",
    "# pdf\n",
    "# these columns need formatting for locale\n",
    "thousands_separated = [\"Fläche (m2)\", \"Länge (m)\", \"Erhebungen\", \"Objekte (St.)\"]\n",
    "replace_decimal = [\"Plastik (Kg)\", \"Gesamtgewicht (Kg)\"]\n",
    "\n",
    "# format the dimensional summary for .pdf and add to components\n",
    "dims_table[thousands_separated] = dims_table[thousands_separated].applymap(lambda x: featuredata.thousandsSeparator(int(x), language))\n",
    "dims_table[replace_decimal] = dims_table[replace_decimal].applymap(lambda x: featuredata.replaceDecimal(str(round(x,2))))\n",
    "\n",
    "# subsection title\n",
    "subsection_title1 = Paragraph(\"Kumulative Gesamtmengen nach Gewässer\", subsection_title)\n",
    "\n",
    "# a caption for the figure\n",
    "dims_table_caption = f'{this_feature[\"name\"]}: kumulierten Gewichte  und Masse für die Gemeinden'\n",
    "dims_table_captionpdf = Paragraph(dims_table_caption, style=caption_style)\n",
    "# pdf table\n",
    "colWidths=[3.5*cm, 3*cm, *[2.2*cm]*(len(dims_table.columns)-1)]\n",
    "d_chart = aSingleStyledTable(dims_table, colWidths=colWidths)\n",
    "\n",
    "atable = tableAndCaption(d_chart, dims_table_captionpdf, colWidths)\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    subsection_title1,\n",
    "    small_space,\n",
    "    atable \n",
    "]\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "# end pdf\n",
    "\n",
    "# this formats the table through the data frame\n",
    "dims_df[\"Plastik (Kg)\"] = dims_df[\"Plastik (Kg)\"].round(2)\n",
    "dims_df[\"Gesamtgewicht (Kg)\"] = dims_df[\"Gesamtgewicht (Kg)\"].round(2)\n",
    "dims_df[thousands_separated] = dims_df[thousands_separated].astype(\"int\")\n",
    "\n",
    "# set the index name to None so it doesn't show in the columns\n",
    "dims_df.index.name = None\n",
    "dims_df.columns.name = None\n",
    "\n",
    "# give the figure a name\n",
    "figure_name=f'{this_feature[\"slug\"]}_dims_table'\n",
    "\n",
    "# use the caption from the .pdf for the online figure\n",
    "glue(\"rhone_dims_table_caption\",dims_table_caption, display=False)\n",
    "\n",
    "# apply formatting and styles to dataframe\n",
    "q = dims_df.style.format(formatter=dims_table_formatter).set_table_styles(table_css_styles)\n",
    "\n",
    "# capture the figure for display\n",
    "glue(figure_name, q, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_dims_table\n",
    "---\n",
    "name: 'rhone_dims_table'\n",
    "---\n",
    "{glue:text}`blank_caption`\n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_dims_table>` {glue:text}`rhone_dims_table_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "### Landnutzungsprofil der Erhebungsorte\n",
    "\n",
    "Das Landnutzungsprofil zeigt, welche Nutzungen innerhalb eines Radius von 1500 m um jeden Erhebungsort dominieren. Flächen werden einer von den folgenden vier Kategorien zugewiesen:\n",
    "\n",
    "* Fläche, die von Gebäuden eingenommen wird in %\n",
    "* Fläche, die dem Wald vorbehalten ist in %\n",
    "* Fläche, die für Aktivitäten im Freien genutzt wird in %\n",
    "* Fläche, die von der Landwirtschaft genutzt wird in %\n",
    "\n",
    "Strassen (inkl. Wege) werden als Gesamtzahl der Strassenkilometer innerhalb eines Radius von 1500 m angegeben.\n",
    "\n",
    "Es wird zudem angegeben, wie viele Flüsse innerhalb eines Radius von 1500 m um den Erhebungsort herum in das Gewässer münden.\n",
    "\n",
    "Das Verhältnis der gefundenen Abfallobjekte unterscheidet sich je nach Landnutzungsprofil. Das Verhältnis gibt daher einen Hinweis auf die ökologischen und wirtschaftlichen Bedingungen um den Erhebungsort.\n",
    "\n",
    "Für weitere Informationen siehe *[Landnutzungsprofil](luseprofile)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# pdf\n",
    "section_titlex =  \"Landnutzungsprofil der Erhebungsorte\"\n",
    "\n",
    "section_summary = [\n",
    "    \"Das Landnutzungsprofil zeigt, welche Nutzungen innerhalb eines Radius \",\n",
    "    \"von 1500 m um jeden Erhebungsort dominieren. Flächen werden einer von \",\n",
    "    \"den folgenden vier Kategorien zugewiesen:\"\n",
    "]\n",
    "\n",
    "section_definition = [\n",
    "    \"Fläche, die von Gebäuden eingenommen wird in %\",\n",
    "    \"Fläche, die dem Wald vorbehalten ist in %\",\n",
    "    \"Fläche, die für Aktivitäten im Freien genutzt wird in %\",\n",
    "    \"Fläche, die von der Landwirtschaft genutzt wird in %\"\n",
    "]\n",
    "\n",
    "p_one = [\n",
    "    \"Strassen (inkl. Wege) werden als Gesamtzahl der Strassenkilometer \",\n",
    "    \"innerhalb eines Radius von 1500 m angegeben.\"\n",
    "]\n",
    "\n",
    "p_two = [\n",
    "    \"Es wird zudem angegeben, wie viele Flüsse innerhalb eines Radius von \",\n",
    "    \"1500 m um den Erhebungsort herum in das Gewässer münden.\"\n",
    "]\n",
    "\n",
    "p_three = [\n",
    "    \"Das Verhältnis der gefundenen Abfallobjekte unterscheidet sich \",\n",
    "    \"je nach Landnutzungsprofil. Das Verhältnis gibt daher einen Hinweis \",\n",
    "    \"auf die ökologischen und wirtschaftlichen Bedingungen um den Erhebungsort.\",\n",
    "]\n",
    "\n",
    "# make paragraphs\n",
    "land_use_subtitle = Paragraph(section_titlex, section_title)\n",
    "land_use_summary = Paragraph(''.join(section_summary), featuredata.p_style)\n",
    "definition_list = makeAList(section_definition)\n",
    "para_one = Paragraph(\"\".join(p_one), featuredata.p_style)\n",
    "para_two = Paragraph(\"\".join(p_two), featuredata.p_style)\n",
    "para_three = Paragraph(\"\".join(p_three), featuredata.p_style)\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([        \n",
    "        land_use_subtitle,\n",
    "        small_space,\n",
    "        land_use_summary,\n",
    "        smallest_space,\n",
    "        definition_list,\n",
    "        smallest_space,\n",
    "        para_one,\n",
    "        smallest_space,\n",
    "        para_two,\n",
    "        smallest_space,\n",
    "        para_three\n",
    "    ])\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "# end pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 900x800 with 6 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_survey_area_landuse"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "'Landnutzungsprofil der Erhebungsorte. Verteilung der Erhebungen in Bezug auf die Landnutzung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_land_use_caption"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this gets all land use the data for the project\n",
    "# the land use profile at 1500 m is stored with \n",
    "# location data and the survey data\n",
    "land_use_kwargs = {\n",
    "    \"data\": period_data.period_data,\n",
    "    \"index_column\":\"loc_date\",\n",
    "    \"these_features\": this_feature['slug'],\n",
    "    \"feature_level\":this_feature['level']   \n",
    "}\n",
    "\n",
    "# the landuse profile of the project, the profile of all survey locations\n",
    "project_profile = featuredata.LandUseProfile(**land_use_kwargs).byIndexColumn()\n",
    "\n",
    "# update the kwargs for the feature data\n",
    "land_use_kwargs.update({\"data\":fdx.feature_data})\n",
    "\n",
    "# build the landuse profile of the feature\n",
    "feature_profile = featuredata.LandUseProfile(**land_use_kwargs)\n",
    "\n",
    "# this is the component features of the report\n",
    "feature_landuse = feature_profile.featureOfInterest()\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(9,8), sharey=\"row\")\n",
    "\n",
    "for i, n in enumerate(featuredata.default_land_use_columns):\n",
    "    r = i%2\n",
    "    c = i%3\n",
    "    ax=axs[r,c]\n",
    "    \n",
    "    # the value of land use feature n\n",
    "    # for each element of the feature\n",
    "    for element in feature_landuse:\n",
    "        # the land use data for a feature\n",
    "        data = element[n].values\n",
    "        # the name of the element\n",
    "        element_name = element[feature_profile.feature_level].unique()\n",
    "        # proper name for chart\n",
    "        label = featuredata.river_basin_de[element_name[0]]\n",
    "        # cumulative distribution        \n",
    "        xs, ys = featuredata.empiricalCDF(data)\n",
    "        # the plot of landuse n for this element\n",
    "        sns.lineplot(x=xs, y=ys, ax=ax, label=label, color=featuredata.bassin_pallette[element_name[0]])\n",
    "    \n",
    "    # the value of the land use feature n for the project\n",
    "    testx, testy = featuredata.empiricalCDF(project_profile[n].values)\n",
    "    sns.lineplot(x=testx, y=testy, ax=ax, label=top, color=\"magenta\")\n",
    "    \n",
    "    # get the median landuse for the feature\n",
    "    the_median = np.median(data)\n",
    "    \n",
    "    # plot the median and drop horizontal and vertical lines\n",
    "    ax.scatter([the_median], 0.5, color=\"red\",s=40, linewidth=1, zorder=100, label=\"Median\")\n",
    "    ax.vlines(x=the_median, ymin=0, ymax=0.5, color=\"red\", linewidth=1)\n",
    "    ax.hlines(xmax=the_median, xmin=0, y=0.5, color=\"red\", linewidth=1)\n",
    "    \n",
    "    if i <= 3:\n",
    "        if c == 0:            \n",
    "            ax.yaxis.set_major_locator(MultipleLocator(.1))\n",
    "        ax.xaxis.set_major_formatter(ticker.PercentFormatter(1.0, 0, \"%\"))        \n",
    "    else:\n",
    "        pass      \n",
    "    \n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.get_legend().remove()    \n",
    "    ax.set_title(featuredata.luse_de[n], loc='left')\n",
    "    \n",
    "# filename and figure tag\n",
    "figure_name = \"rhone_survey_area_landuse\"\n",
    "land_use_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":land_use_file_name})\n",
    "    \n",
    "plt.subplots_adjust(top=.91, hspace=.4)\n",
    "plt.suptitle(\"Landnutzung im Umkries von 1 500 m um den Erhebungsort\", ha=\"center\", y=1, fontsize=16)\n",
    "fig.legend(handles, labels, bbox_to_anchor=(.5,.5), loc=\"upper center\", ncol=3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=.91, hspace=.4)\n",
    "\n",
    "# save figure\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "\n",
    "# capture the output\n",
    "figure_caption = \"Landnutzungsprofil der Erhebungsorte. Verteilung der Erhebungen in Bezug auf die Landnutzung.\"\n",
    "glue(figure_name, fig, display=False)\n",
    "glue(f\"{this_feature['slug']}_land_use_caption\", figure_caption, display=False)\n",
    "\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_survey_area_landuse\n",
    "---\n",
    "name: 'rhone_survey_area_landuse'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_landuse>` {glue:text}`rhone_land_use_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Verteilung der Erhebungsergebnisse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Links: Erhebungsgebiet Rhône, März 2020 bis Mai 2021, n = 106. Rechts: empirische Verteilungsfunktion der Erhebungsergebnisse Erhebungsgebiet Rhône.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_sample_total_notes"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 1000x500 with 2 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_sample_totals"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pdf\n",
    "o_w, o_h = convertPixelToCm(land_use_file_name)\n",
    "f3cap = Paragraph(figure_caption, caption_style)\n",
    "figure_kwargs = {\n",
    "    \"image_file\":land_use_file_name,\n",
    "    \"caption\": f3cap, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\":1,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f3 = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    f3\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "# end pdf\n",
    "\n",
    "# the sample totals of all other locations than the feature data\n",
    "dx = period_data.parentSampleTotals(parent=False)\n",
    "\n",
    "# get the monthly or quarterly results for the feature\n",
    "rsmp = fdx.sample_totals.set_index(\"date\")\n",
    "resample_plot, rate = featuredata.quarterlyOrMonthlyValues(rsmp, this_feature[\"name\"], vals=unit_label)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10,5))\n",
    "\n",
    "ax = axs[0]\n",
    "\n",
    "# feature surveys\n",
    "sns.scatterplot(data=dx, x=\"date\", y=unit_label, label=top, color=\"black\", alpha=0.4,  ax=ax)\n",
    "# all other surveys\n",
    "sns.scatterplot(data=fdx.sample_totals, x=\"date\", y=unit_label, label=this_feature[\"name\"], color=\"red\", s=34, ec=\"white\", ax=ax)\n",
    "# monthly or quaterly plot\n",
    "sns.lineplot(data=resample_plot, x=resample_plot.index, y=resample_plot, label=F\"{this_feature['name']}: monatlicher Medianwert\", color=\"magenta\", ax=ax)\n",
    "\n",
    "ax.set_ylabel(unit_label, **featuredata.xlab_k14)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.xaxis.set_minor_locator(days)\n",
    "ax.xaxis.set_major_formatter(months_fmt)\n",
    "ax.set_ylim(-50, 2000)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "# the cumlative distributions:\n",
    "axtwo = axs[1]\n",
    "\n",
    "# the feature of interest\n",
    "feature_ecd = featuredata.ecdfOfAColumn(fdx.sample_totals, unit_label)    \n",
    "sns.lineplot(x=feature_ecd[\"x\"], y=feature_ecd[\"y\"], color=\"darkblue\", ax=axtwo, label=this_feature[\"name\"])\n",
    "\n",
    "# the other features\n",
    "other_features = featuredata.ecdfOfAColumn(dx, unit_label)\n",
    "sns.lineplot(x=other_features[\"x\"], y=other_features[\"y\"], color=\"magenta\", label=top, linewidth=1, ax=axtwo)\n",
    "\n",
    "axtwo.set_xlabel(unit_label, **featuredata.xlab_k14)\n",
    "axtwo.set_ylabel(\"Verhältnis der Erhebungen\", **featuredata.xlab_k14)\n",
    "axtwo.set_xlim(0, 3000)\n",
    "axtwo.legend(bbox_to_anchor=(.4,.5), loc=\"upper left\")\n",
    "axtwo.xaxis.set_major_locator(MultipleLocator(500))\n",
    "axtwo.xaxis.set_minor_locator(MultipleLocator(100))\n",
    "axtwo.yaxis.set_major_locator(MultipleLocator(.1))\n",
    "axtwo.grid(which=\"minor\", visible=True, axis=\"x\", linestyle=\"--\", linewidth=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "figure_name = f\"{this_feature['slug']}_sample_totals\"\n",
    "sample_totals_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":sample_totals_file_name})\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "\n",
    "# figure caption\n",
    "sample_total_notes = [\n",
    "    f'Links: {this_feature[\"name\"]}, {featuredata.dateToYearAndMonth(datetime.strptime(start_date, date_format), lang=date_lang)} ',\n",
    "    f'bis {featuredata.dateToYearAndMonth(datetime.strptime(end_date, date_format), lang=date_lang)}, n = {admin_summary[\"loc_date\"]}. ',\n",
    "    f'Rechts: empirische Verteilungsfunktion der Erhebungsergebnisse {this_feature[\"name\"]}.'\n",
    "]\n",
    "\n",
    "sample_total_notes = ''.join(sample_total_notes)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_sample_total_notes', sample_total_notes, display=False)\n",
    "glue(\"rhone_sample_totals\", fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_sample_totals\n",
    "---\n",
    "name: 'rhone_survey_area_sample_totals'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_sample_totals>` {glue:text}`rhone_sample_total_notes`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Zusammengefasste Daten und Materialarten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Zusammenfassung der Daten aller Erhebungen am Erhebungsgebiet Rhône. Gefunden Materialarten am Erhebungsgebiet Rhône in Stückzahlen und als prozentuale Anteile (stückzahlbezogen).'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_sample_summaries_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 640x480 with 2 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_survey_area_sample_material_tables"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csx = fdx.sample_summary.copy()\n",
    "\n",
    "\n",
    "combined_summary =[(x, featuredata.thousandsSeparator(int(csx[x]), language)) for x in csx.index]\n",
    "\n",
    "# the materials table\n",
    "fd_mat_totals = fdx.material_summary.copy()\n",
    "fd_mat_totals = featuredata.fmtPctOfTotal(fd_mat_totals, around=0)\n",
    "\n",
    "# applly new column names for printing\n",
    "cols_to_use = {\"material\":\"Material\",\"quantity\":\"Objekte (St.)\", \"% of total\":\"Anteil\"}\n",
    "fd_mat_t = fd_mat_totals[cols_to_use.keys()].values\n",
    "fd_mat_t = [(x[0], featuredata.thousandsSeparator(int(x[1]), language), x[2]) for x in fd_mat_t]\n",
    "\n",
    "# make tables\n",
    "fig, axs = plt.subplots(1,2)\n",
    "\n",
    "# summary table\n",
    "# names for the table columns\n",
    "a_col = [this_feature[\"name\"], \"Total\"]\n",
    "\n",
    "axone = axs[0]\n",
    "featuredata.hide_spines_ticks_grids(axone)\n",
    "\n",
    "table_two = sut.make_a_table(axone, combined_summary,  colLabels=a_col, colWidths=[.75,.25],  bbox=[0,0,1,1], **{\"loc\":\"lower center\"})\n",
    "table_two.get_celld()[(0,0)].get_text().set_text(\" \")\n",
    "table_two.set_fontsize(12)\n",
    "\n",
    "# material table\n",
    "axtwo = axs[1]\n",
    "axtwo.set_xlabel(\" \")\n",
    "featuredata.hide_spines_ticks_grids(axtwo)\n",
    "\n",
    "table_three = sut.make_a_table(axtwo, fd_mat_t,  colLabels=list(cols_to_use.values()), colWidths=[.4, .4,.2],  bbox=[0,0,1,1], **{\"loc\":\"lower center\"})\n",
    "table_three.get_celld()[(0,0)].get_text().set_text(\" \")\n",
    "table_three.set_fontsize(12)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.2)\n",
    "\n",
    "# figure caption\n",
    "summary_of_survey_totals = [\n",
    "    f'Zusammenfassung der Daten aller Erhebungen am {this_feature[\"name\"]}. ',\n",
    "    f'Gefunden Materialarten am {this_feature[\"name\"]} in Stückzahlen und ',\n",
    "    f'als prozentuale Anteile (stückzahlbezogen).'\n",
    "]\n",
    "\n",
    "summary_of_survey_totals = ''.join(summary_of_survey_totals)\n",
    "glue(f'{this_feature[\"slug\"]}_sample_summaries_caption', summary_of_survey_totals, display=False)\n",
    "\n",
    "figure_name = f'{this_feature[\"slug\"]}_sample_summaries'\n",
    "sample_summaries_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":sample_summaries_file_name})\n",
    "\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "glue('rhone_survey_area_sample_material_tables', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_survey_area_sample_material_tables\n",
    "---\n",
    "name: 'rhone_survey_area_sample_material_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "{numref}`Abbildung %s: <rhone_survey_area_sample_material_tables>` {glue:text}`rhone_sample_summaries_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Die am häufigsten gefundenen Objekte\n",
    "\n",
    "Die am häufigsten gefundenen Objekte sind die zehn mengenmässig am meisten vorkommenden Objekte und/oder Objekte, die in mindestens 50 % aller Datenerhebungen identifiziert wurden (Häufigkeitsrate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Häufigste Objekte im Erhebungsgebiet Rhône: d. h. Objekte mit einer Häufigkeitsrate von mindestens 50% und/oder Top Ten nach Anzahl. Zusammengenommen machen die häufigsten Objekte 79% aller gefundenen Objekte aus. Anmerkung: p/100 m = Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_most_common_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_510f4 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_510f4 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_510f4 tr {\n  font-size: 12px;\n}\n#T_510f4 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_510f4 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_510f4\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_510f4_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_510f4_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_510f4_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_510f4_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_510f4_level0_row0\" class=\"row_heading level0 row0\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_510f4_row0_col0\" class=\"data row0 col0\" >4 220</td>\n      <td id=\"T_510f4_row0_col1\" class=\"data row0 col1\" >14%</td>\n      <td id=\"T_510f4_row0_col2\" class=\"data row0 col2\" >93%</td>\n      <td id=\"T_510f4_row0_col3\" class=\"data row0 col3\" >48,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row1\" class=\"row_heading level0 row1\" >Expandiertes Polystyrol</th>\n      <td id=\"T_510f4_row1_col0\" class=\"data row1 col0\" >3 589</td>\n      <td id=\"T_510f4_row1_col1\" class=\"data row1 col1\" >12%</td>\n      <td id=\"T_510f4_row1_col2\" class=\"data row1 col2\" >80%</td>\n      <td id=\"T_510f4_row1_col3\" class=\"data row1 col3\" >17,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row2\" class=\"row_heading level0 row2\" >Zigarettenfilter</th>\n      <td id=\"T_510f4_row2_col0\" class=\"data row2 col0\" >3 169</td>\n      <td id=\"T_510f4_row2_col1\" class=\"data row2 col1\" >11%</td>\n      <td id=\"T_510f4_row2_col2\" class=\"data row2 col2\" >90%</td>\n      <td id=\"T_510f4_row2_col3\" class=\"data row2 col3\" >41,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row3\" class=\"row_heading level0 row3\" >Snack-Verpackungen</th>\n      <td id=\"T_510f4_row3_col0\" class=\"data row3 col0\" >1 737</td>\n      <td id=\"T_510f4_row3_col1\" class=\"data row3 col1\" >6%</td>\n      <td id=\"T_510f4_row3_col2\" class=\"data row3 col2\" >92%</td>\n      <td id=\"T_510f4_row3_col3\" class=\"data row3 col3\" >19,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row4\" class=\"row_heading level0 row4\" >Industriepellets (Nurdles)</th>\n      <td id=\"T_510f4_row4_col0\" class=\"data row4 col0\" >1 387</td>\n      <td id=\"T_510f4_row4_col1\" class=\"data row4 col1\" >4%</td>\n      <td id=\"T_510f4_row4_col2\" class=\"data row4 col2\" >43%</td>\n      <td id=\"T_510f4_row4_col3\" class=\"data row4 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row5\" class=\"row_heading level0 row5\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_510f4_row5_col0\" class=\"data row5 col0\" >1 180</td>\n      <td id=\"T_510f4_row5_col1\" class=\"data row5 col1\" >4%</td>\n      <td id=\"T_510f4_row5_col2\" class=\"data row5 col2\" >76%</td>\n      <td id=\"T_510f4_row5_col3\" class=\"data row5 col3\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row6\" class=\"row_heading level0 row6\" >Wattestäbchen/Tupfer</th>\n      <td id=\"T_510f4_row6_col0\" class=\"data row6 col0\" >1 112</td>\n      <td id=\"T_510f4_row6_col1\" class=\"data row6 col1\" >3%</td>\n      <td id=\"T_510f4_row6_col2\" class=\"data row6 col2\" >75%</td>\n      <td id=\"T_510f4_row6_col3\" class=\"data row6 col3\" >10,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row7\" class=\"row_heading level0 row7\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_510f4_row7_col0\" class=\"data row7 col0\" >1 112</td>\n      <td id=\"T_510f4_row7_col1\" class=\"data row7 col1\" >3%</td>\n      <td id=\"T_510f4_row7_col2\" class=\"data row7 col2\" >71%</td>\n      <td id=\"T_510f4_row7_col3\" class=\"data row7 col3\" >7,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row8\" class=\"row_heading level0 row8\" >Styropor < 5mm</th>\n      <td id=\"T_510f4_row8_col0\" class=\"data row8 col0\" >718</td>\n      <td id=\"T_510f4_row8_col1\" class=\"data row8 col1\" >2%</td>\n      <td id=\"T_510f4_row8_col2\" class=\"data row8 col2\" >28%</td>\n      <td id=\"T_510f4_row8_col3\" class=\"data row8 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row9\" class=\"row_heading level0 row9\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_510f4_row9_col0\" class=\"data row9 col0\" >614</td>\n      <td id=\"T_510f4_row9_col1\" class=\"data row9 col1\" >2%</td>\n      <td id=\"T_510f4_row9_col2\" class=\"data row9 col2\" >65%</td>\n      <td id=\"T_510f4_row9_col3\" class=\"data row9 col3\" >5,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row10\" class=\"row_heading level0 row10\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_510f4_row10_col0\" class=\"data row10 col0\" >554</td>\n      <td id=\"T_510f4_row10_col1\" class=\"data row10 col1\" >1%</td>\n      <td id=\"T_510f4_row10_col2\" class=\"data row10 col2\" >54%</td>\n      <td id=\"T_510f4_row10_col3\" class=\"data row10 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row11\" class=\"row_heading level0 row11\" >Getränke-Deckel, Getränkeverschluss</th>\n      <td id=\"T_510f4_row11_col0\" class=\"data row11 col0\" >425</td>\n      <td id=\"T_510f4_row11_col1\" class=\"data row11 col1\" >1%</td>\n      <td id=\"T_510f4_row11_col2\" class=\"data row11 col2\" >51%</td>\n      <td id=\"T_510f4_row11_col3\" class=\"data row11 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row12\" class=\"row_heading level0 row12\" >Ringe von Plastikflaschen/Behältern</th>\n      <td id=\"T_510f4_row12_col0\" class=\"data row12 col0\" >362</td>\n      <td id=\"T_510f4_row12_col1\" class=\"data row12 col1\" >1%</td>\n      <td id=\"T_510f4_row12_col2\" class=\"data row12 col2\" >68%</td>\n      <td id=\"T_510f4_row12_col3\" class=\"data row12 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row13\" class=\"row_heading level0 row13\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_510f4_row13_col0\" class=\"data row13 col0\" >350</td>\n      <td id=\"T_510f4_row13_col1\" class=\"data row13 col1\" >1%</td>\n      <td id=\"T_510f4_row13_col2\" class=\"data row13 col2\" >67%</td>\n      <td id=\"T_510f4_row13_col3\" class=\"data row13 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row14\" class=\"row_heading level0 row14\" >Schrotflintenpatronen</th>\n      <td id=\"T_510f4_row14_col0\" class=\"data row14 col0\" >347</td>\n      <td id=\"T_510f4_row14_col1\" class=\"data row14 col1\" >1%</td>\n      <td id=\"T_510f4_row14_col2\" class=\"data row14 col2\" >50%</td>\n      <td id=\"T_510f4_row14_col3\" class=\"data row14 col3\" >0,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row15\" class=\"row_heading level0 row15\" >Tabak; Kunststoffverpackungen, Behälter</th>\n      <td id=\"T_510f4_row15_col0\" class=\"data row15 col0\" >296</td>\n      <td id=\"T_510f4_row15_col1\" class=\"data row15 col1\" >1%</td>\n      <td id=\"T_510f4_row15_col2\" class=\"data row15 col2\" >51%</td>\n      <td id=\"T_510f4_row15_col3\" class=\"data row15 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row16\" class=\"row_heading level0 row16\" >Strohhalme und Rührstäbchen</th>\n      <td id=\"T_510f4_row16_col0\" class=\"data row16 col0\" >278</td>\n      <td id=\"T_510f4_row16_col1\" class=\"data row16 col1\" >0%</td>\n      <td id=\"T_510f4_row16_col2\" class=\"data row16 col2\" >67%</td>\n      <td id=\"T_510f4_row16_col3\" class=\"data row16 col3\" >3,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row17\" class=\"row_heading level0 row17\" >unidentifizierte Deckel</th>\n      <td id=\"T_510f4_row17_col0\" class=\"data row17 col0\" >263</td>\n      <td id=\"T_510f4_row17_col1\" class=\"data row17 col1\" >0%</td>\n      <td id=\"T_510f4_row17_col2\" class=\"data row17 col2\" >54%</td>\n      <td id=\"T_510f4_row17_col3\" class=\"data row17 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row18\" class=\"row_heading level0 row18\" >Schleckstengel, Stengel von Lutscher</th>\n      <td id=\"T_510f4_row18_col0\" class=\"data row18 col0\" >238</td>\n      <td id=\"T_510f4_row18_col1\" class=\"data row18 col1\" >0%</td>\n      <td id=\"T_510f4_row18_col2\" class=\"data row18 col2\" >62%</td>\n      <td id=\"T_510f4_row18_col3\" class=\"data row18 col3\" >2,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row19\" class=\"row_heading level0 row19\" >Einwegartikel; Tassen/Becher & Deckel</th>\n      <td id=\"T_510f4_row19_col0\" class=\"data row19 col0\" >201</td>\n      <td id=\"T_510f4_row19_col1\" class=\"data row19 col1\" >0%</td>\n      <td id=\"T_510f4_row19_col2\" class=\"data row19 col2\" >55%</td>\n      <td id=\"T_510f4_row19_col3\" class=\"data row19 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row20\" class=\"row_heading level0 row20\" >Spielzeug und Partyartikel</th>\n      <td id=\"T_510f4_row20_col0\" class=\"data row20 col0\" >194</td>\n      <td id=\"T_510f4_row20_col1\" class=\"data row20 col1\" >0%</td>\n      <td id=\"T_510f4_row20_col2\" class=\"data row20 col2\" >60%</td>\n      <td id=\"T_510f4_row20_col3\" class=\"data row20 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row21\" class=\"row_heading level0 row21\" >Verpackungen aus Aluminiumfolie</th>\n      <td id=\"T_510f4_row21_col0\" class=\"data row21 col0\" >178</td>\n      <td id=\"T_510f4_row21_col1\" class=\"data row21 col1\" >0%</td>\n      <td id=\"T_510f4_row21_col2\" class=\"data row21 col2\" >52%</td>\n      <td id=\"T_510f4_row21_col3\" class=\"data row21 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_510f4_level0_row22\" class=\"row_heading level0 row22\" >Medizin; Behälter/Röhrchen/Verpackungen</th>\n      <td id=\"T_510f4_row22_col0\" class=\"data row22 col0\" >177</td>\n      <td id=\"T_510f4_row22_col1\" class=\"data row22 col1\" >0%</td>\n      <td id=\"T_510f4_row22_col2\" class=\"data row22 col2\" >57%</td>\n      <td id=\"T_510f4_row22_col3\" class=\"data row22 col3\" >2,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff115d37bb0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_most_common_tables"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add summary tables to pdf\n",
    "sample_summary_subsection = Paragraph(\"Verteilung der Erhebungsergebnisse\", subsection_title)\n",
    "\n",
    "sample_total_notes_pdf = [\n",
    "    f'<b>Links:</b> {this_feature[\"name\"]}, {featuredata.dateToYearAndMonth(datetime.strptime(start_date, date_format), lang=date_lang)} ',\n",
    "    f'bis {featuredata.dateToYearAndMonth(datetime.strptime(end_date, date_format), lang=date_lang)}, n = {admin_summary[\"loc_date\"]}. ',\n",
    "    f'<b>Rechts:</b> empirische Verteilungsfunktion der Erhebungsergebnisse {this_feature[\"name\"]}.'\n",
    "]\n",
    "s_totals_caption = makeAParagraph(sample_total_notes_pdf, style=caption_style)\n",
    "\n",
    "samp_mat_subsection = Paragraph(\"Zusammengefasste Daten und Materialarten\", style=subsection_title)\n",
    "samp_material_caption = Paragraph(summary_of_survey_totals, style=caption_style)\n",
    "\n",
    "o_w, o_h = convertPixelToCm(sample_totals_file_name)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":sample_totals_file_name,\n",
    "    \"caption\": s_totals_caption, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 15,\n",
    "    \"caption_height\":1,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f4 = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "o_w, o_h = convertPixelToCm(sample_summaries_file_name)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":sample_summaries_file_name,\n",
    "    \"caption\": samp_material_caption, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 11,\n",
    "    \"caption_height\":1,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "f5 = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    sample_summary_subsection,\n",
    "    small_space,\n",
    "    f4,\n",
    "    small_space,\n",
    "    samp_mat_subsection,\n",
    "    small_space,\n",
    "    f5,\n",
    "    PageBreak()\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# the most common objects results\n",
    "most_common_display = fdx.most_common\n",
    "\n",
    "# language appropriate columns\n",
    "cols_to_use = featuredata.most_common_objects_table_de\n",
    "cols_to_use.update({unit_label:unit_label})\n",
    "\n",
    "# data for display\n",
    "most_common_display.rename(columns=cols_to_use, inplace=True)\n",
    "most_common_display = most_common_display[cols_to_use.values()].copy()\n",
    "most_common_display = most_common_display.set_index(\"Objekte\", drop=True)\n",
    "\n",
    "# .pdf output\n",
    "data = most_common_display.copy()\n",
    "data[\"Anteil\"] = data[\"Anteil\"].map(lambda x: f\"{int(x)}%\")\n",
    "data['Objekte (St.)'] = data['Objekte (St.)'].map(lambda x:featuredata.thousandsSeparator(x, language))\n",
    "data['Häufigkeitsrate'] = data['Häufigkeitsrate'].map(lambda x: f\"{x}%\")\n",
    "data[unit_label] = data[unit_label].map(lambda x: featuredata.replaceDecimal(round(x,1)))\n",
    "\n",
    "# make caption\n",
    "# get percent of total to make the caption string\n",
    "m_common_percent_of_total = fdx.most_common['Objekte (St.)'].sum()/fdx.code_summary['quantity'].sum()\n",
    "\n",
    "mc_caption_string = [\n",
    "    f'Häufigste Objekte im {this_feature[\"name\"]}: ',\n",
    "    'd. h. Objekte mit einer Häufigkeitsrate von mindestens 50% und/oder ',\n",
    "    f'Top Ten nach Anzahl. Zusammengenommen machen die häufigsten Objekte {int(m_common_percent_of_total*100)}% ',\n",
    "    f'aller gefundenen Objekte aus. Anmerkung: {unit_label} = Medianwert der Erhebung.'\n",
    "]\n",
    "\n",
    "mc_caption_string = \"\".join(mc_caption_string)\n",
    "\n",
    "colwidths = [4.5*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm]\n",
    "\n",
    "mc_caption_string = \"\".join(mc_caption_string)\n",
    "d_chart = aSingleStyledTable(data, colWidths=colwidths)\n",
    "d_capt = featuredata.makeAParagraph(mc_caption_string, style=caption_style)\n",
    "mc_table = tableAndCaption(d_chart, d_capt, colwidths)\n",
    "\n",
    "most_common_display.index.name = None\n",
    "most_common_display.columns.name = None\n",
    "\n",
    "# set pandas display\n",
    "aformatter = {\n",
    "    \"Anteil\":lambda x: f\"{int(x)}%\",\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, language),\n",
    "    \"Häufigkeitsrate\": lambda x: f\"{int(x)}%\",   \n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), language)\n",
    "}\n",
    "\n",
    "mcd = most_common_display.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "glue('rhone_most_common_caption', mc_caption_string, display=False)\n",
    "glue('rhone_most_common_tables', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_most_common_tables\n",
    "---\n",
    "name: 'rhone_survey_area_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_most_common_tables>` {glue:text}`rhone_most_common_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Die am häufigsten gefundenen Objekte nach Gewässer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Median p/100 m der häufigsten Objekte am Erhebungsgebiet Rhône.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_mc_heat_map_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_54c49 tr {\n  font-size: 12px;\n}\n#T_54c49 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_54c49 td {\n  padding: 6px;\n}\n#T_54c49_row0_col0, #T_54c49_row0_col3, #T_54c49_row7_col0, #T_54c49_row7_col2, #T_54c49_row12_col0 {\n  background-color: #fffcd6;\n  color: #000000;\n}\n#T_54c49_row0_col1, #T_54c49_row1_col1, #T_54c49_row1_col3, #T_54c49_row2_col1, #T_54c49_row4_col1, #T_54c49_row4_col3, #T_54c49_row6_col0, #T_54c49_row6_col1, #T_54c49_row6_col2, #T_54c49_row6_col3, #T_54c49_row7_col1, #T_54c49_row9_col1, #T_54c49_row9_col3, #T_54c49_row10_col1, #T_54c49_row10_col3, #T_54c49_row11_col1, #T_54c49_row12_col1, #T_54c49_row12_col3, #T_54c49_row13_col1, #T_54c49_row13_col3, #T_54c49_row15_col1, #T_54c49_row15_col3, #T_54c49_row16_col1, #T_54c49_row16_col3, #T_54c49_row17_col0, #T_54c49_row17_col1, #T_54c49_row17_col2, #T_54c49_row17_col3, #T_54c49_row18_col1, #T_54c49_row18_col3, #T_54c49_row19_col1, #T_54c49_row19_col3, #T_54c49_row20_col1, #T_54c49_row21_col1, #T_54c49_row22_col1, #T_54c49_row22_col3 {\n  background-color: #ffffe5;\n  color: #000000;\n}\n#T_54c49_row0_col2, #T_54c49_row1_col0, #T_54c49_row1_col2, #T_54c49_row4_col0, #T_54c49_row9_col0, #T_54c49_row9_col2, #T_54c49_row15_col2, #T_54c49_row18_col0, #T_54c49_row19_col0, #T_54c49_row22_col0, #T_54c49_row22_col2 {\n  background-color: #fffddb;\n  color: #000000;\n}\n#T_54c49_row2_col0 {\n  background-color: #feb340;\n  color: #000000;\n}\n#T_54c49_row2_col2 {\n  background-color: #fedb80;\n  color: #000000;\n}\n#T_54c49_row2_col3, #T_54c49_row5_col3 {\n  background-color: #fffacb;\n  color: #000000;\n}\n#T_54c49_row3_col0 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_54c49_row3_col1, #T_54c49_row13_col2 {\n  background-color: #fffee2;\n  color: #000000;\n}\n#T_54c49_row3_col2 {\n  background-color: #c04602;\n  color: #f1f1f1;\n}\n#T_54c49_row3_col3, #T_54c49_row5_col1 {\n  background-color: #fed97c;\n  color: #000000;\n}\n#T_54c49_row4_col2, #T_54c49_row7_col3, #T_54c49_row8_col3, #T_54c49_row11_col3, #T_54c49_row13_col0, #T_54c49_row14_col1, #T_54c49_row18_col2, #T_54c49_row19_col2, #T_54c49_row20_col3 {\n  background-color: #fffee0;\n  color: #000000;\n}\n#T_54c49_row5_col0, #T_54c49_row5_col2, #T_54c49_row14_col3 {\n  background-color: #fff4b5;\n  color: #000000;\n}\n#T_54c49_row8_col0, #T_54c49_row8_col2 {\n  background-color: #fff9c9;\n  color: #000000;\n}\n#T_54c49_row8_col1, #T_54c49_row16_col2 {\n  background-color: #fffbd3;\n  color: #000000;\n}\n#T_54c49_row10_col0, #T_54c49_row10_col2, #T_54c49_row16_col0 {\n  background-color: #fffbd0;\n  color: #000000;\n}\n#T_54c49_row11_col0 {\n  background-color: #fff1b0;\n  color: #000000;\n}\n#T_54c49_row11_col2 {\n  background-color: #fff8c0;\n  color: #000000;\n}\n#T_54c49_row12_col2, #T_54c49_row15_col0 {\n  background-color: #fffcd8;\n  color: #000000;\n}\n#T_54c49_row14_col0 {\n  background-color: #feca5d;\n  color: #000000;\n}\n#T_54c49_row14_col2 {\n  background-color: #fed471;\n  color: #000000;\n}\n#T_54c49_row20_col0 {\n  background-color: #feeca5;\n  color: #000000;\n}\n#T_54c49_row20_col2 {\n  background-color: #fff0ad;\n  color: #000000;\n}\n#T_54c49_row21_col0 {\n  background-color: #c64902;\n  color: #f1f1f1;\n}\n#T_54c49_row21_col2 {\n  background-color: #df620d;\n  color: #f1f1f1;\n}\n#T_54c49_row21_col3 {\n  background-color: #fed069;\n  color: #000000;\n}\n#T_54c49_level0_col0, #T_54c49_level0_col1, #T_54c49_level0_col2, #T_54c49_level0_col3 {\n  writing-mode: vertical-lr;\n  transform: rotate(-180deg);\n  padding: 10px;\n  margins: 0;\n  vertical-align: baseline;\n}\n</style>\n<table id=\"T_54c49\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_54c49_level0_col0\" class=\"col_heading level0 col0\" >Lac Léman</th>\n      <th id=\"T_54c49_level0_col1\" class=\"col_heading level0 col1\" >Rhône</th>\n      <th id=\"T_54c49_level0_col2\" class=\"col_heading level0 col2\" >Erhebungsgebiet Rhône</th>\n      <th id=\"T_54c49_level0_col3\" class=\"col_heading level0 col3\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_54c49_level0_row0\" class=\"row_heading level0 row0\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_54c49_row0_col0\" class=\"data row0 col0\" >3,0</td>\n      <td id=\"T_54c49_row0_col1\" class=\"data row0 col1\" >0,0</td>\n      <td id=\"T_54c49_row0_col2\" class=\"data row0 col2\" >2,0</td>\n      <td id=\"T_54c49_row0_col3\" class=\"data row0 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row1\" class=\"row_heading level0 row1\" >Einwegartikel; Tassen/Becher & Deckel</th>\n      <td id=\"T_54c49_row1_col0\" class=\"data row1 col0\" >2,0</td>\n      <td id=\"T_54c49_row1_col1\" class=\"data row1 col1\" >0,0</td>\n      <td id=\"T_54c49_row1_col2\" class=\"data row1 col2\" >2,0</td>\n      <td id=\"T_54c49_row1_col3\" class=\"data row1 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row2\" class=\"row_heading level0 row2\" >Expandiertes Polystyrol</th>\n      <td id=\"T_54c49_row2_col0\" class=\"data row2 col0\" >26,0</td>\n      <td id=\"T_54c49_row2_col1\" class=\"data row2 col1\" >0,0</td>\n      <td id=\"T_54c49_row2_col2\" class=\"data row2 col2\" >17,5</td>\n      <td id=\"T_54c49_row2_col3\" class=\"data row2 col3\" >5,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row3\" class=\"row_heading level0 row3\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_54c49_row3_col0\" class=\"data row3 col0\" >61,5</td>\n      <td id=\"T_54c49_row3_col1\" class=\"data row3 col1\" >0,5</td>\n      <td id=\"T_54c49_row3_col2\" class=\"data row3 col2\" >48,0</td>\n      <td id=\"T_54c49_row3_col3\" class=\"data row3 col3\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row4\" class=\"row_heading level0 row4\" >Getränke-Deckel, Getränkeverschluss</th>\n      <td id=\"T_54c49_row4_col0\" class=\"data row4 col0\" >2,0</td>\n      <td id=\"T_54c49_row4_col1\" class=\"data row4 col1\" >0,0</td>\n      <td id=\"T_54c49_row4_col2\" class=\"data row4 col2\" >1,0</td>\n      <td id=\"T_54c49_row4_col3\" class=\"data row4 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row5\" class=\"row_heading level0 row5\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_54c49_row5_col0\" class=\"data row5 col0\" >9,0</td>\n      <td id=\"T_54c49_row5_col1\" class=\"data row5 col1\" >18,0</td>\n      <td id=\"T_54c49_row5_col2\" class=\"data row5 col2\" >9,0</td>\n      <td id=\"T_54c49_row5_col3\" class=\"data row5 col3\" >5,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row6\" class=\"row_heading level0 row6\" >Industriepellets (Nurdles)</th>\n      <td id=\"T_54c49_row6_col0\" class=\"data row6 col0\" >0,0</td>\n      <td id=\"T_54c49_row6_col1\" class=\"data row6 col1\" >0,0</td>\n      <td id=\"T_54c49_row6_col2\" class=\"data row6 col2\" >0,0</td>\n      <td id=\"T_54c49_row6_col3\" class=\"data row6 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row7\" class=\"row_heading level0 row7\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_54c49_row7_col0\" class=\"data row7 col0\" >3,0</td>\n      <td id=\"T_54c49_row7_col1\" class=\"data row7 col1\" >0,0</td>\n      <td id=\"T_54c49_row7_col2\" class=\"data row7 col2\" >3,0</td>\n      <td id=\"T_54c49_row7_col3\" class=\"data row7 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row8\" class=\"row_heading level0 row8\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_54c49_row8_col0\" class=\"data row8 col0\" >5,5</td>\n      <td id=\"T_54c49_row8_col1\" class=\"data row8 col1\" >3,5</td>\n      <td id=\"T_54c49_row8_col2\" class=\"data row8 col2\" >5,5</td>\n      <td id=\"T_54c49_row8_col3\" class=\"data row8 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row9\" class=\"row_heading level0 row9\" >Medizin; Behälter/Röhrchen/Verpackungen</th>\n      <td id=\"T_54c49_row9_col0\" class=\"data row9 col0\" >2,0</td>\n      <td id=\"T_54c49_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_54c49_row9_col2\" class=\"data row9 col2\" >2,0</td>\n      <td id=\"T_54c49_row9_col3\" class=\"data row9 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row10\" class=\"row_heading level0 row10\" >Ringe von Plastikflaschen/Behältern</th>\n      <td id=\"T_54c49_row10_col0\" class=\"data row10 col0\" >4,0</td>\n      <td id=\"T_54c49_row10_col1\" class=\"data row10 col1\" >0,0</td>\n      <td id=\"T_54c49_row10_col2\" class=\"data row10 col2\" >4,0</td>\n      <td id=\"T_54c49_row10_col3\" class=\"data row10 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row11\" class=\"row_heading level0 row11\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_54c49_row11_col0\" class=\"data row11 col0\" >10,0</td>\n      <td id=\"T_54c49_row11_col1\" class=\"data row11 col1\" >0,0</td>\n      <td id=\"T_54c49_row11_col2\" class=\"data row11 col2\" >7,0</td>\n      <td id=\"T_54c49_row11_col3\" class=\"data row11 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row12\" class=\"row_heading level0 row12\" >Schleckstengel, Stengel von Lutscher</th>\n      <td id=\"T_54c49_row12_col0\" class=\"data row12 col0\" >3,0</td>\n      <td id=\"T_54c49_row12_col1\" class=\"data row12 col1\" >0,0</td>\n      <td id=\"T_54c49_row12_col2\" class=\"data row12 col2\" >2,5</td>\n      <td id=\"T_54c49_row12_col3\" class=\"data row12 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row13\" class=\"row_heading level0 row13\" >Schrotflintenpatronen</th>\n      <td id=\"T_54c49_row13_col0\" class=\"data row13 col0\" >1,0</td>\n      <td id=\"T_54c49_row13_col1\" class=\"data row13 col1\" >0,0</td>\n      <td id=\"T_54c49_row13_col2\" class=\"data row13 col2\" >0,5</td>\n      <td id=\"T_54c49_row13_col3\" class=\"data row13 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row14\" class=\"row_heading level0 row14\" >Snack-Verpackungen</th>\n      <td id=\"T_54c49_row14_col0\" class=\"data row14 col0\" >21,5</td>\n      <td id=\"T_54c49_row14_col1\" class=\"data row14 col1\" >1,0</td>\n      <td id=\"T_54c49_row14_col2\" class=\"data row14 col2\" >19,0</td>\n      <td id=\"T_54c49_row14_col3\" class=\"data row14 col3\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row15\" class=\"row_heading level0 row15\" >Spielzeug und Partyartikel</th>\n      <td id=\"T_54c49_row15_col0\" class=\"data row15 col0\" >2,5</td>\n      <td id=\"T_54c49_row15_col1\" class=\"data row15 col1\" >0,0</td>\n      <td id=\"T_54c49_row15_col2\" class=\"data row15 col2\" >2,0</td>\n      <td id=\"T_54c49_row15_col3\" class=\"data row15 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row16\" class=\"row_heading level0 row16\" >Strohhalme und Rührstäbchen</th>\n      <td id=\"T_54c49_row16_col0\" class=\"data row16 col0\" >4,0</td>\n      <td id=\"T_54c49_row16_col1\" class=\"data row16 col1\" >0,0</td>\n      <td id=\"T_54c49_row16_col2\" class=\"data row16 col2\" >3,5</td>\n      <td id=\"T_54c49_row16_col3\" class=\"data row16 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row17\" class=\"row_heading level0 row17\" >Styropor < 5mm</th>\n      <td id=\"T_54c49_row17_col0\" class=\"data row17 col0\" >0,0</td>\n      <td id=\"T_54c49_row17_col1\" class=\"data row17 col1\" >0,0</td>\n      <td id=\"T_54c49_row17_col2\" class=\"data row17 col2\" >0,0</td>\n      <td id=\"T_54c49_row17_col3\" class=\"data row17 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row18\" class=\"row_heading level0 row18\" >Tabak; Kunststoffverpackungen, Behälter</th>\n      <td id=\"T_54c49_row18_col0\" class=\"data row18 col0\" >2,0</td>\n      <td id=\"T_54c49_row18_col1\" class=\"data row18 col1\" >0,0</td>\n      <td id=\"T_54c49_row18_col2\" class=\"data row18 col2\" >1,0</td>\n      <td id=\"T_54c49_row18_col3\" class=\"data row18 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row19\" class=\"row_heading level0 row19\" >Verpackungen aus Aluminiumfolie</th>\n      <td id=\"T_54c49_row19_col0\" class=\"data row19 col0\" >2,0</td>\n      <td id=\"T_54c49_row19_col1\" class=\"data row19 col1\" >0,0</td>\n      <td id=\"T_54c49_row19_col2\" class=\"data row19 col2\" >1,0</td>\n      <td id=\"T_54c49_row19_col3\" class=\"data row19 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row20\" class=\"row_heading level0 row20\" >Wattestäbchen/Tupfer</th>\n      <td id=\"T_54c49_row20_col0\" class=\"data row20 col0\" >12,0</td>\n      <td id=\"T_54c49_row20_col1\" class=\"data row20 col1\" >0,0</td>\n      <td id=\"T_54c49_row20_col2\" class=\"data row20 col2\" >10,5</td>\n      <td id=\"T_54c49_row20_col3\" class=\"data row20 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row21\" class=\"row_heading level0 row21\" >Zigarettenfilter</th>\n      <td id=\"T_54c49_row21_col0\" class=\"data row21 col0\" >47,0</td>\n      <td id=\"T_54c49_row21_col1\" class=\"data row21 col1\" >0,0</td>\n      <td id=\"T_54c49_row21_col2\" class=\"data row21 col2\" >41,5</td>\n      <td id=\"T_54c49_row21_col3\" class=\"data row21 col3\" >20,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_54c49_level0_row22\" class=\"row_heading level0 row22\" >unidentifizierte Deckel</th>\n      <td id=\"T_54c49_row22_col0\" class=\"data row22 col0\" >2,0</td>\n      <td id=\"T_54c49_row22_col1\" class=\"data row22 col1\" >0,0</td>\n      <td id=\"T_54c49_row22_col2\" class=\"data row22 col2\" >2,0</td>\n      <td id=\"T_54c49_row22_col3\" class=\"data row22 col3\" >0,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff11fdc9b20>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_most_common_heat_map"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add new section to pdf\n",
    "mc_section_title = Paragraph(\"Die am häufigsten gefundenen Objekte\", section_title)\n",
    "para_g = \"Die am häufigsten gefundenen Objekte sind die zehn mengenmässig am meisten vorkommenden Objekte und/oder Objekte, die in mindestens 50 % aller Datenerhebungen identifiziert wurden (Häufigkeitsrate)\"\n",
    "mc_section_para = Paragraph(para_g, featuredata.p_style)\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        mc_section_title,\n",
    "        small_space,\n",
    "        mc_section_para\n",
    "    ]),\n",
    "    small_space,\n",
    "    mc_table,\n",
    "       \n",
    "]\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "mc_heat_map_caption = f'Median {unit_label} der häufigsten Objekte am {this_feature[\"name\"]}.'\n",
    "\n",
    "# calling componentsMostCommon gets the results for the most common codes\n",
    "# at the component level\n",
    "components = fdx.componentMostCommonPcsM()\n",
    "\n",
    "# map to proper names for features\n",
    "feature_names = admin_details.makeFeatureNameMap()\n",
    "\n",
    "# pivot that and quash the hierarchal column index that is created when the table is pivoted\n",
    "mc_comp = components[[\"item\", unit_label, this_level]].pivot(columns=this_level, index=\"item\")\n",
    "mc_comp.columns = mc_comp.columns.get_level_values(1)\n",
    "\n",
    "# insert the proper columns names for display\n",
    "proper_column_names = {x : feature_names.loc[x, 'water_name'] for x in mc_comp.columns}\n",
    "mc_comp.rename(columns = proper_column_names, inplace=True)\n",
    "\n",
    "# the aggregated total of the feature is taken from the most common objects table\n",
    "mc_feature = fdx.most_common[unit_label]\n",
    "mc_feature = featuredata.changeSeriesIndexLabels(mc_feature, {x:fdx.dMap.loc[x] for x in mc_feature.index})\n",
    "\n",
    "# the aggregated totals of all the period data\n",
    "mc_period = period_data.parentMostCommon(parent=False)\n",
    "mc_period = featuredata.changeSeriesIndexLabels(mc_period, {x:fdx.dMap.loc[x] for x in mc_period.index})\n",
    "\n",
    "# add the feature, bassin_label and period results to the components table\n",
    "mc_comp[this_feature[\"name\"]]= mc_feature\n",
    "mc_comp[top] = mc_period\n",
    "\n",
    "caption_prefix =  f'Median {unit_label} der häufigsten Objekte am '\n",
    "col_widths=[4.5*cm, *[1*cm]*(len(mc_comp.columns))]\n",
    "mc_heatmap_title = Paragraph(\"Die am häufigsten gefundenen Objekte nach Gewässer\", subsection_title)\n",
    "tables = featuredata.splitTableWidth(mc_comp, gradient=True, caption_prefix=caption_prefix, caption=mc_heat_map_caption,\n",
    "                    this_feature=this_feature[\"name\"], vertical_header=True, colWidths=col_widths)\n",
    "\n",
    "# identify the tables variable as either a list or a Flowable:\n",
    "if isinstance(tables, (list, np.ndarray)):\n",
    "    grouped_pdf_components = [*tables]\n",
    "else:\n",
    "    grouped_pdf_components = [tables]\n",
    "    \n",
    "\n",
    "new_components = [\n",
    "    small_space,\n",
    "    mc_heatmap_title,\n",
    "    small_space,\n",
    "    *grouped_pdf_components\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# notebook display style\n",
    "aformatter = {x: featuredata.replaceDecimal for x in mc_comp.columns}\n",
    "\n",
    "mcd = mc_comp.style.format(aformatter).set_table_styles(heat_map_css_styles)\n",
    "mcd = mcd.background_gradient(axis=None, vmin=mc_comp.min().min(), vmax=mc_comp.max().max(), cmap=\"YlOrBr\")\n",
    "\n",
    "# remove the index name and column name labels\n",
    "mcd.index.name = None\n",
    "mcd.columns.name = None\n",
    "\n",
    "# rotate the text on the header row\n",
    "# the .applymap_index method in the\n",
    "# df.styler module is used for this\n",
    "mcd = mcd.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "# display markdown html\n",
    "glue(f'{this_feature[\"slug\"]}_mc_heat_map_caption', mc_heat_map_caption, display=False)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_most_common_heat_map', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_most_common_heat_map\n",
    "---\n",
    "name: 'rhone_survey_area_most_common_heat_map'\n",
    "---\n",
    "{glue:text}`blank_caption`  \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_most_common_heat_map>` {glue:text}`rhone_mc_heat_map_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "### Die am häufigsten gefundenen Objekte im monatlichen Durchschnitt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Erhebungsgebiet Rhône, monatliche Durchschnittsergebnisse p/100 m'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_monthly_results_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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#000000;\n}\n#T_11937_row14_col7 {\n  background-color: #fff3b4;\n  color: #000000;\n}\n#T_11937_row15_col1 {\n  background-color: #fffbd0;\n  color: #000000;\n}\n#T_11937_row16_col5, #T_11937_row16_col6, #T_11937_row20_col3, #T_11937_row21_col7 {\n  background-color: #fffaca;\n  color: #000000;\n}\n#T_11937_row16_col11 {\n  background-color: #febf4b;\n  color: #000000;\n}\n#T_11937_row17_col3 {\n  background-color: #fee596;\n  color: #000000;\n}\n#T_11937_row17_col7, #T_11937_row21_col11 {\n  background-color: #fee595;\n  color: #000000;\n}\n#T_11937_row18_col2 {\n  background-color: #fec14c;\n  color: #000000;\n}\n#T_11937_row19_col0 {\n  background-color: #fff9c7;\n  color: #000000;\n}\n#T_11937_row19_col1 {\n  background-color: #fed97c;\n  color: #000000;\n}\n#T_11937_row19_col2 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_11937_row19_col3 {\n  background-color: #b13f03;\n  color: #f1f1f1;\n}\n#T_11937_row19_col4 {\n  background-color: #d95b09;\n  color: #f1f1f1;\n}\n#T_11937_row19_col6 {\n  background-color: #fee79a;\n  color: #000000;\n}\n#T_11937_row19_col8 {\n  background-color: #de610c;\n  color: #f1f1f1;\n}\n#T_11937_row19_col9 {\n  background-color: #fedb80;\n  color: #000000;\n}\n#T_11937_row19_col11 {\n  background-color: #fec95b;\n  color: #000000;\n}\n#T_11937_row21_col0 {\n  background-color: #8e3104;\n  color: #f1f1f1;\n}\n#T_11937_row21_col1 {\n  background-color: #feeaa1;\n  color: #000000;\n}\n#T_11937_row21_col2 {\n  background-color: #feb643;\n  color: #000000;\n}\n#T_11937_row21_col3 {\n  background-color: #e3660f;\n  color: #f1f1f1;\n}\n#T_11937_row21_col4 {\n  background-color: #feae3b;\n  color: #000000;\n}\n#T_11937_row21_col5 {\n  background-color: #fede86;\n  color: #000000;\n}\n#T_11937_row21_col10 {\n  background-color: #fff0ad;\n  color: #000000;\n}\n</style>\n<table id=\"T_11937\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_11937_level0_col0\" class=\"col_heading level0 col0\" >Apr.</th>\n      <th id=\"T_11937_level0_col1\" class=\"col_heading level0 col1\" >Mai</th>\n      <th id=\"T_11937_level0_col2\" class=\"col_heading level0 col2\" >Juni</th>\n      <th id=\"T_11937_level0_col3\" class=\"col_heading level0 col3\" >Juli</th>\n      <th id=\"T_11937_level0_col4\" class=\"col_heading level0 col4\" >Aug.</th>\n      <th id=\"T_11937_level0_col5\" class=\"col_heading level0 col5\" >Sept.</th>\n      <th id=\"T_11937_level0_col6\" class=\"col_heading level0 col6\" >Okt.</th>\n      <th id=\"T_11937_level0_col7\" class=\"col_heading level0 col7\" >Nov.</th>\n      <th id=\"T_11937_level0_col8\" class=\"col_heading level0 col8\" >Dez.</th>\n      <th id=\"T_11937_level0_col9\" class=\"col_heading level0 col9\" >Jan.</th>\n      <th id=\"T_11937_level0_col10\" class=\"col_heading level0 col10\" >Feb.</th>\n      <th id=\"T_11937_level0_col11\" class=\"col_heading level0 col11\" >März</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_11937_level0_row0\" class=\"row_heading level0 row0\" >Medizin; Behälter/Röhrchen/Verpackungen</th>\n      <td id=\"T_11937_row0_col0\" class=\"data row0 col0\" >5,0</td>\n      <td id=\"T_11937_row0_col1\" class=\"data row0 col1\" >4,3</td>\n      <td id=\"T_11937_row0_col2\" class=\"data row0 col2\" >13,8</td>\n      <td id=\"T_11937_row0_col3\" class=\"data row0 col3\" >6,3</td>\n      <td id=\"T_11937_row0_col4\" class=\"data row0 col4\" >6,4</td>\n      <td id=\"T_11937_row0_col5\" class=\"data row0 col5\" >4,2</td>\n      <td id=\"T_11937_row0_col6\" class=\"data row0 col6\" >7,1</td>\n      <td id=\"T_11937_row0_col7\" class=\"data row0 col7\" >4,7</td>\n      <td id=\"T_11937_row0_col8\" class=\"data row0 col8\" >9,1</td>\n      <td id=\"T_11937_row0_col9\" class=\"data row0 col9\" >4,0</td>\n      <td id=\"T_11937_row0_col10\" class=\"data row0 col10\" >2,0</td>\n      <td id=\"T_11937_row0_col11\" class=\"data row0 col11\" >8,6</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row1\" class=\"row_heading level0 row1\" >Wattestäbchen/Tupfer</th>\n      <td id=\"T_11937_row1_col0\" class=\"data row1 col0\" >7,0</td>\n      <td id=\"T_11937_row1_col1\" class=\"data row1 col1\" >31,9</td>\n      <td id=\"T_11937_row1_col2\" class=\"data row1 col2\" >56,8</td>\n      <td id=\"T_11937_row1_col3\" class=\"data row1 col3\" >80,3</td>\n      <td id=\"T_11937_row1_col4\" class=\"data row1 col4\" >38,8</td>\n      <td id=\"T_11937_row1_col5\" class=\"data row1 col5\" >23,0</td>\n      <td id=\"T_11937_row1_col6\" class=\"data row1 col6\" >26,2</td>\n      <td id=\"T_11937_row1_col7\" class=\"data row1 col7\" >11,4</td>\n      <td id=\"T_11937_row1_col8\" class=\"data row1 col8\" >36,0</td>\n      <td id=\"T_11937_row1_col9\" class=\"data row1 col9\" >26,4</td>\n      <td id=\"T_11937_row1_col10\" class=\"data row1 col10\" >14,6</td>\n      <td id=\"T_11937_row1_col11\" class=\"data row1 col11\" >33,6</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row2\" class=\"row_heading level0 row2\" >Strohhalme und Rührstäbchen</th>\n      <td id=\"T_11937_row2_col0\" class=\"data row2 col0\" >16,0</td>\n      <td id=\"T_11937_row2_col1\" class=\"data row2 col1\" >9,7</td>\n      <td id=\"T_11937_row2_col2\" class=\"data row2 col2\" >13,8</td>\n      <td id=\"T_11937_row2_col3\" class=\"data row2 col3\" >16,6</td>\n      <td id=\"T_11937_row2_col4\" class=\"data row2 col4\" >6,0</td>\n      <td id=\"T_11937_row2_col5\" class=\"data row2 col5\" >8,7</td>\n      <td id=\"T_11937_row2_col6\" class=\"data row2 col6\" >5,1</td>\n      <td id=\"T_11937_row2_col7\" class=\"data row2 col7\" >2,3</td>\n      <td id=\"T_11937_row2_col8\" class=\"data row2 col8\" >3,8</td>\n      <td id=\"T_11937_row2_col9\" class=\"data row2 col9\" >8,5</td>\n      <td id=\"T_11937_row2_col10\" class=\"data row2 col10\" >0,8</td>\n      <td id=\"T_11937_row2_col11\" class=\"data row2 col11\" >13,9</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row3\" class=\"row_heading level0 row3\" >Einwegartikel; Tassen/Becher & Deckel</th>\n      <td id=\"T_11937_row3_col0\" class=\"data row3 col0\" >9,0</td>\n      <td id=\"T_11937_row3_col1\" class=\"data row3 col1\" >6,6</td>\n      <td id=\"T_11937_row3_col2\" class=\"data row3 col2\" >3,4</td>\n      <td id=\"T_11937_row3_col3\" class=\"data row3 col3\" >12,7</td>\n      <td id=\"T_11937_row3_col4\" class=\"data row3 col4\" >5,1</td>\n      <td id=\"T_11937_row3_col5\" class=\"data row3 col5\" >3,8</td>\n      <td id=\"T_11937_row3_col6\" class=\"data row3 col6\" >4,2</td>\n      <td id=\"T_11937_row3_col7\" class=\"data row3 col7\" >1,5</td>\n      <td id=\"T_11937_row3_col8\" class=\"data row3 col8\" >3,6</td>\n      <td id=\"T_11937_row3_col9\" class=\"data row3 col9\" >6,7</td>\n      <td id=\"T_11937_row3_col10\" class=\"data row3 col10\" >3,4</td>\n      <td id=\"T_11937_row3_col11\" class=\"data row3 col11\" >11,9</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row4\" class=\"row_heading level0 row4\" >Schleckstengel, Stengel von Lutscher</th>\n      <td id=\"T_11937_row4_col0\" class=\"data row4 col0\" >9,0</td>\n      <td id=\"T_11937_row4_col1\" class=\"data row4 col1\" >8,1</td>\n      <td id=\"T_11937_row4_col2\" class=\"data row4 col2\" >10,6</td>\n      <td id=\"T_11937_row4_col3\" class=\"data row4 col3\" >16,3</td>\n      <td id=\"T_11937_row4_col4\" class=\"data row4 col4\" >9,5</td>\n      <td id=\"T_11937_row4_col5\" class=\"data row4 col5\" >11,2</td>\n      <td id=\"T_11937_row4_col6\" class=\"data row4 col6\" >3,8</td>\n      <td id=\"T_11937_row4_col7\" class=\"data row4 col7\" >1,3</td>\n      <td id=\"T_11937_row4_col8\" class=\"data row4 col8\" >10,9</td>\n      <td id=\"T_11937_row4_col9\" class=\"data row4 col9\" >5,8</td>\n      <td id=\"T_11937_row4_col10\" class=\"data row4 col10\" >2,3</td>\n      <td id=\"T_11937_row4_col11\" class=\"data row4 col11\" >6,6</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row5\" class=\"row_heading level0 row5\" >Ringe von Plastikflaschen/Behältern</th>\n      <td id=\"T_11937_row5_col0\" class=\"data row5 col0\" >5,0</td>\n      <td id=\"T_11937_row5_col1\" class=\"data row5 col1\" >5,9</td>\n      <td id=\"T_11937_row5_col2\" class=\"data row5 col2\" >12,8</td>\n      <td id=\"T_11937_row5_col3\" class=\"data row5 col3\" >18,1</td>\n      <td id=\"T_11937_row5_col4\" class=\"data row5 col4\" >10,2</td>\n      <td id=\"T_11937_row5_col5\" class=\"data row5 col5\" >5,5</td>\n      <td id=\"T_11937_row5_col6\" class=\"data row5 col6\" >10,1</td>\n      <td id=\"T_11937_row5_col7\" class=\"data row5 col7\" >5,3</td>\n      <td id=\"T_11937_row5_col8\" class=\"data row5 col8\" >5,8</td>\n      <td id=\"T_11937_row5_col9\" class=\"data row5 col9\" >8,2</td>\n      <td id=\"T_11937_row5_col10\" class=\"data row5 col10\" >11,0</td>\n      <td id=\"T_11937_row5_col11\" class=\"data row5 col11\" >22,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row6\" class=\"row_heading level0 row6\" >Snack-Verpackungen</th>\n      <td id=\"T_11937_row6_col0\" class=\"data row6 col0\" >91,0</td>\n      <td id=\"T_11937_row6_col1\" class=\"data row6 col1\" >20,0</td>\n      <td id=\"T_11937_row6_col2\" class=\"data row6 col2\" >75,3</td>\n      <td id=\"T_11937_row6_col3\" class=\"data row6 col3\" >94,3</td>\n      <td id=\"T_11937_row6_col4\" class=\"data row6 col4\" >39,5</td>\n      <td id=\"T_11937_row6_col5\" class=\"data row6 col5\" >45,8</td>\n      <td id=\"T_11937_row6_col6\" class=\"data row6 col6\" >39,9</td>\n      <td id=\"T_11937_row6_col7\" class=\"data row6 col7\" >13,5</td>\n      <td id=\"T_11937_row6_col8\" class=\"data row6 col8\" >17,5</td>\n      <td id=\"T_11937_row6_col9\" class=\"data row6 col9\" >28,4</td>\n      <td id=\"T_11937_row6_col10\" class=\"data row6 col10\" >34,8</td>\n      <td id=\"T_11937_row6_col11\" class=\"data row6 col11\" >138,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row7\" class=\"row_heading level0 row7\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_11937_row7_col0\" class=\"data row7 col0\" >9,0</td>\n      <td id=\"T_11937_row7_col1\" class=\"data row7 col1\" >6,3</td>\n      <td id=\"T_11937_row7_col2\" class=\"data row7 col2\" >6,2</td>\n      <td id=\"T_11937_row7_col3\" class=\"data row7 col3\" >5,0</td>\n      <td id=\"T_11937_row7_col4\" class=\"data row7 col4\" >11,6</td>\n      <td id=\"T_11937_row7_col5\" class=\"data row7 col5\" >15,5</td>\n      <td id=\"T_11937_row7_col6\" class=\"data row7 col6\" >32,1</td>\n      <td id=\"T_11937_row7_col7\" class=\"data row7 col7\" >8,0</td>\n      <td id=\"T_11937_row7_col8\" class=\"data row7 col8\" >6,0</td>\n      <td id=\"T_11937_row7_col9\" class=\"data row7 col9\" >4,7</td>\n      <td id=\"T_11937_row7_col10\" class=\"data row7 col10\" >36,9</td>\n      <td id=\"T_11937_row7_col11\" class=\"data row7 col11\" >25,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row8\" class=\"row_heading level0 row8\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_11937_row8_col0\" class=\"data row8 col0\" >5,0</td>\n      <td id=\"T_11937_row8_col1\" class=\"data row8 col1\" >9,4</td>\n      <td id=\"T_11937_row8_col2\" class=\"data row8 col2\" >11,6</td>\n      <td id=\"T_11937_row8_col3\" class=\"data row8 col3\" >13,6</td>\n      <td id=\"T_11937_row8_col4\" class=\"data row8 col4\" >7,1</td>\n      <td id=\"T_11937_row8_col5\" class=\"data row8 col5\" >2,0</td>\n      <td id=\"T_11937_row8_col6\" class=\"data row8 col6\" >4,6</td>\n      <td id=\"T_11937_row8_col7\" class=\"data row8 col7\" >2,5</td>\n      <td id=\"T_11937_row8_col8\" class=\"data row8 col8\" >5,0</td>\n      <td id=\"T_11937_row8_col9\" class=\"data row8 col9\" >5,0</td>\n      <td id=\"T_11937_row8_col10\" class=\"data row8 col10\" >23,7</td>\n      <td id=\"T_11937_row8_col11\" class=\"data row8 col11\" >3,6</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen aus Aluminiumfolie</th>\n      <td id=\"T_11937_row9_col0\" class=\"data row9 col0\" >16,0</td>\n      <td id=\"T_11937_row9_col1\" class=\"data row9 col1\" >2,9</td>\n      <td id=\"T_11937_row9_col2\" class=\"data row9 col2\" >7,0</td>\n      <td id=\"T_11937_row9_col3\" class=\"data row9 col3\" >13,0</td>\n      <td id=\"T_11937_row9_col4\" class=\"data row9 col4\" >3,1</td>\n      <td id=\"T_11937_row9_col5\" class=\"data row9 col5\" >0,8</td>\n      <td id=\"T_11937_row9_col6\" class=\"data row9 col6\" >3,6</td>\n      <td id=\"T_11937_row9_col7\" class=\"data row9 col7\" >2,9</td>\n      <td id=\"T_11937_row9_col8\" class=\"data row9 col8\" >1,0</td>\n      <td id=\"T_11937_row9_col9\" class=\"data row9 col9\" >2,0</td>\n      <td id=\"T_11937_row9_col10\" class=\"data row9 col10\" >2,7</td>\n      <td id=\"T_11937_row9_col11\" class=\"data row9 col11\" >6,4</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row10\" class=\"row_heading level0 row10\" >Getränke-Deckel, Getränkeverschluss</th>\n      <td id=\"T_11937_row10_col0\" class=\"data row10 col0\" >7,0</td>\n      <td id=\"T_11937_row10_col1\" class=\"data row10 col1\" >13,0</td>\n      <td id=\"T_11937_row10_col2\" class=\"data row10 col2\" >14,4</td>\n      <td id=\"T_11937_row10_col3\" class=\"data row10 col3\" >34,6</td>\n      <td id=\"T_11937_row10_col4\" class=\"data row10 col4\" >8,5</td>\n      <td id=\"T_11937_row10_col5\" class=\"data row10 col5\" >12,3</td>\n      <td id=\"T_11937_row10_col6\" class=\"data row10 col6\" >8,3</td>\n      <td id=\"T_11937_row10_col7\" class=\"data row10 col7\" >1,3</td>\n      <td id=\"T_11937_row10_col8\" class=\"data row10 col8\" >9,0</td>\n      <td id=\"T_11937_row10_col9\" class=\"data row10 col9\" >15,7</td>\n      <td id=\"T_11937_row10_col10\" class=\"data row10 col10\" >3,2</td>\n      <td id=\"T_11937_row10_col11\" class=\"data row10 col11\" >25,1</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row11\" class=\"row_heading level0 row11\" >Spielzeug und Partyartikel</th>\n      <td id=\"T_11937_row11_col0\" class=\"data row11 col0\" >0,0</td>\n      <td id=\"T_11937_row11_col1\" class=\"data row11 col1\" >1,4</td>\n      <td id=\"T_11937_row11_col2\" class=\"data row11 col2\" >10,9</td>\n      <td id=\"T_11937_row11_col3\" class=\"data row11 col3\" >12,0</td>\n      <td id=\"T_11937_row11_col4\" class=\"data row11 col4\" >8,2</td>\n      <td id=\"T_11937_row11_col5\" class=\"data row11 col5\" >4,8</td>\n      <td id=\"T_11937_row11_col6\" class=\"data row11 col6\" >4,2</td>\n      <td id=\"T_11937_row11_col7\" class=\"data row11 col7\" >2,0</td>\n      <td id=\"T_11937_row11_col8\" class=\"data row11 col8\" >3,1</td>\n      <td id=\"T_11937_row11_col9\" class=\"data row11 col9\" >8,0</td>\n      <td id=\"T_11937_row11_col10\" class=\"data row11 col10\" >3,1</td>\n      <td id=\"T_11937_row11_col11\" class=\"data row11 col11\" >7,1</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row12\" class=\"row_heading level0 row12\" >Schrotflintenpatronen</th>\n      <td id=\"T_11937_row12_col0\" class=\"data row12 col0\" >5,0</td>\n      <td id=\"T_11937_row12_col1\" class=\"data row12 col1\" >13,6</td>\n      <td id=\"T_11937_row12_col2\" class=\"data row12 col2\" >9,0</td>\n      <td id=\"T_11937_row12_col3\" class=\"data row12 col3\" >24,0</td>\n      <td id=\"T_11937_row12_col4\" class=\"data row12 col4\" >12,9</td>\n      <td id=\"T_11937_row12_col5\" class=\"data row12 col5\" >9,7</td>\n      <td id=\"T_11937_row12_col6\" class=\"data row12 col6\" >10,1</td>\n      <td id=\"T_11937_row12_col7\" class=\"data row12 col7\" >1,3</td>\n      <td id=\"T_11937_row12_col8\" class=\"data row12 col8\" >7,6</td>\n      <td id=\"T_11937_row12_col9\" class=\"data row12 col9\" >9,2</td>\n      <td id=\"T_11937_row12_col10\" class=\"data row12 col10\" >4,2</td>\n      <td id=\"T_11937_row12_col11\" class=\"data row12 col11\" >12,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row13\" class=\"row_heading level0 row13\" >Expandiertes Polystyrol</th>\n      <td id=\"T_11937_row13_col0\" class=\"data row13 col0\" >52,0</td>\n      <td id=\"T_11937_row13_col1\" class=\"data row13 col1\" >62,9</td>\n      <td id=\"T_11937_row13_col2\" class=\"data row13 col2\" >143,4</td>\n      <td id=\"T_11937_row13_col3\" class=\"data row13 col3\" >186,6</td>\n      <td id=\"T_11937_row13_col4\" class=\"data row13 col4\" >225,4</td>\n      <td id=\"T_11937_row13_col5\" class=\"data row13 col5\" >140,5</td>\n      <td id=\"T_11937_row13_col6\" class=\"data row13 col6\" >123,3</td>\n      <td id=\"T_11937_row13_col7\" class=\"data row13 col7\" >306,5</td>\n      <td id=\"T_11937_row13_col8\" class=\"data row13 col8\" >72,1</td>\n      <td id=\"T_11937_row13_col9\" class=\"data row13 col9\" >31,3</td>\n      <td id=\"T_11937_row13_col10\" class=\"data row13 col10\" >21,8</td>\n      <td id=\"T_11937_row13_col11\" class=\"data row13 col11\" >56,6</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row14\" class=\"row_heading level0 row14\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_11937_row14_col0\" class=\"data row14 col0\" >0,0</td>\n      <td id=\"T_11937_row14_col1\" class=\"data row14 col1\" >3,7</td>\n      <td id=\"T_11937_row14_col2\" class=\"data row14 col2\" >37,5</td>\n      <td id=\"T_11937_row14_col3\" class=\"data row14 col3\" >98,6</td>\n      <td id=\"T_11937_row14_col4\" class=\"data row14 col4\" >105,6</td>\n      <td id=\"T_11937_row14_col5\" class=\"data row14 col5\" >44,5</td>\n      <td id=\"T_11937_row14_col6\" class=\"data row14 col6\" >24,4</td>\n      <td id=\"T_11937_row14_col7\" class=\"data row14 col7\" >49,5</td>\n      <td id=\"T_11937_row14_col8\" class=\"data row14 col8\" >22,2</td>\n      <td id=\"T_11937_row14_col9\" class=\"data row14 col9\" >14,1</td>\n      <td id=\"T_11937_row14_col10\" class=\"data row14 col10\" >2,8</td>\n      <td id=\"T_11937_row14_col11\" class=\"data row14 col11\" >22,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row15\" class=\"row_heading level0 row15\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_11937_row15_col0\" class=\"data row15 col0\" >25,0</td>\n      <td id=\"T_11937_row15_col1\" class=\"data row15 col1\" >21,0</td>\n      <td id=\"T_11937_row15_col2\" class=\"data row15 col2\" >13,7</td>\n      <td id=\"T_11937_row15_col3\" class=\"data row15 col3\" >36,7</td>\n      <td id=\"T_11937_row15_col4\" class=\"data row15 col4\" >22,4</td>\n      <td id=\"T_11937_row15_col5\" class=\"data row15 col5\" >13,8</td>\n      <td id=\"T_11937_row15_col6\" class=\"data row15 col6\" >12,1</td>\n      <td id=\"T_11937_row15_col7\" class=\"data row15 col7\" >4,2</td>\n      <td id=\"T_11937_row15_col8\" class=\"data row15 col8\" >15,4</td>\n      <td id=\"T_11937_row15_col9\" class=\"data row15 col9\" >16,5</td>\n      <td id=\"T_11937_row15_col10\" class=\"data row15 col10\" >12,4</td>\n      <td id=\"T_11937_row15_col11\" class=\"data row15 col11\" >22,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row16\" class=\"row_heading level0 row16\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_11937_row16_col0\" class=\"data row16 col0\" >9,0</td>\n      <td id=\"T_11937_row16_col1\" class=\"data row16 col1\" >24,7</td>\n      <td id=\"T_11937_row16_col2\" class=\"data row16 col2\" >31,1</td>\n      <td id=\"T_11937_row16_col3\" class=\"data row16 col3\" >92,1</td>\n      <td id=\"T_11937_row16_col4\" class=\"data row16 col4\" >22,4</td>\n      <td id=\"T_11937_row16_col5\" class=\"data row16 col5\" >27,7</td>\n      <td id=\"T_11937_row16_col6\" class=\"data row16 col6\" >28,1</td>\n      <td id=\"T_11937_row16_col7\" class=\"data row16 col7\" >12,7</td>\n      <td id=\"T_11937_row16_col8\" class=\"data row16 col8\" >17,4</td>\n      <td id=\"T_11937_row16_col9\" class=\"data row16 col9\" >25,2</td>\n      <td id=\"T_11937_row16_col10\" class=\"data row16 col10\" >13,8</td>\n      <td id=\"T_11937_row16_col11\" class=\"data row16 col11\" >128,1</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row17\" class=\"row_heading level0 row17\" >Styropor < 5mm</th>\n      <td id=\"T_11937_row17_col0\" class=\"data row17 col0\" >0,0</td>\n      <td id=\"T_11937_row17_col1\" class=\"data row17 col1\" >4,0</td>\n      <td id=\"T_11937_row17_col2\" class=\"data row17 col2\" >16,1</td>\n      <td id=\"T_11937_row17_col3\" class=\"data row17 col3\" >78,0</td>\n      <td id=\"T_11937_row17_col4\" class=\"data row17 col4\" >9,0</td>\n      <td id=\"T_11937_row17_col5\" class=\"data row17 col5\" >12,8</td>\n      <td id=\"T_11937_row17_col6\" class=\"data row17 col6\" >7,6</td>\n      <td id=\"T_11937_row17_col7\" class=\"data row17 col7\" >78,5</td>\n      <td id=\"T_11937_row17_col8\" class=\"data row17 col8\" >35,4</td>\n      <td id=\"T_11937_row17_col9\" class=\"data row17 col9\" >10,6</td>\n      <td id=\"T_11937_row17_col10\" class=\"data row17 col10\" >16,1</td>\n      <td id=\"T_11937_row17_col11\" class=\"data row17 col11\" >3,9</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row18\" class=\"row_heading level0 row18\" >Industriepellets (Nurdles)</th>\n      <td id=\"T_11937_row18_col0\" class=\"data row18 col0\" >0,0</td>\n      <td id=\"T_11937_row18_col1\" class=\"data row18 col1\" >0,9</td>\n      <td id=\"T_11937_row18_col2\" class=\"data row18 col2\" >126,2</td>\n      <td id=\"T_11937_row18_col3\" class=\"data row18 col3\" >140,6</td>\n      <td id=\"T_11937_row18_col4\" class=\"data row18 col4\" >12,0</td>\n      <td id=\"T_11937_row18_col5\" class=\"data row18 col5\" >18,5</td>\n      <td id=\"T_11937_row18_col6\" class=\"data row18 col6\" >31,1</td>\n      <td id=\"T_11937_row18_col7\" class=\"data row18 col7\" >13,1</td>\n      <td id=\"T_11937_row18_col8\" class=\"data row18 col8\" >35,2</td>\n      <td id=\"T_11937_row18_col9\" class=\"data row18 col9\" >8,1</td>\n      <td id=\"T_11937_row18_col10\" class=\"data row18 col10\" >15,4</td>\n      <td id=\"T_11937_row18_col11\" class=\"data row18 col11\" >9,4</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row19\" class=\"row_heading level0 row19\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_11937_row19_col0\" class=\"data row19 col0\" >30,0</td>\n      <td id=\"T_11937_row19_col1\" class=\"data row19 col1\" >96,0</td>\n      <td id=\"T_11937_row19_col2\" class=\"data row19 col2\" >328,0</td>\n      <td id=\"T_11937_row19_col3\" class=\"data row19 col3\" >267,4</td>\n      <td id=\"T_11937_row19_col4\" class=\"data row19 col4\" >229,2</td>\n      <td id=\"T_11937_row19_col5\" class=\"data row19 col5\" >91,7</td>\n      <td id=\"T_11937_row19_col6\" class=\"data row19 col6\" >74,0</td>\n      <td id=\"T_11937_row19_col7\" class=\"data row19 col7\" >46,1</td>\n      <td id=\"T_11937_row19_col8\" class=\"data row19 col8\" >222,4</td>\n      <td id=\"T_11937_row19_col9\" class=\"data row19 col9\" >93,1</td>\n      <td id=\"T_11937_row19_col10\" class=\"data row19 col10\" >26,6</td>\n      <td id=\"T_11937_row19_col11\" class=\"data row19 col11\" >115,4</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row20\" class=\"row_heading level0 row20\" >Tabak; Kunststoffverpackungen, Behälter</th>\n      <td id=\"T_11937_row20_col0\" class=\"data row20 col0\" >11,0</td>\n      <td id=\"T_11937_row20_col1\" class=\"data row20 col1\" >3,4</td>\n      <td id=\"T_11937_row20_col2\" class=\"data row20 col2\" >8,7</td>\n      <td id=\"T_11937_row20_col3\" class=\"data row20 col3\" >27,1</td>\n      <td id=\"T_11937_row20_col4\" class=\"data row20 col4\" >10,0</td>\n      <td id=\"T_11937_row20_col5\" class=\"data row20 col5\" >2,7</td>\n      <td id=\"T_11937_row20_col6\" class=\"data row20 col6\" >8,4</td>\n      <td id=\"T_11937_row20_col7\" class=\"data row20 col7\" >1,1</td>\n      <td id=\"T_11937_row20_col8\" class=\"data row20 col8\" >1,9</td>\n      <td id=\"T_11937_row20_col9\" class=\"data row20 col9\" >4,8</td>\n      <td id=\"T_11937_row20_col10\" class=\"data row20 col10\" >2,0</td>\n      <td id=\"T_11937_row20_col11\" class=\"data row20 col11\" >33,1</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row21\" class=\"row_heading level0 row21\" >Zigarettenfilter</th>\n      <td id=\"T_11937_row21_col0\" class=\"data row21 col0\" >295,0</td>\n      <td id=\"T_11937_row21_col1\" class=\"data row21 col1\" >66,7</td>\n      <td id=\"T_11937_row21_col2\" class=\"data row21 col2\" >136,8</td>\n      <td id=\"T_11937_row21_col3\" class=\"data row21 col3\" >216,3</td>\n      <td id=\"T_11937_row21_col4\" class=\"data row21 col4\" >144,6</td>\n      <td id=\"T_11937_row21_col5\" class=\"data row21 col5\" >89,3</td>\n      <td id=\"T_11937_row21_col6\" class=\"data row21 col6\" >35,8</td>\n      <td id=\"T_11937_row21_col7\" class=\"data row21 col7\" >27,3</td>\n      <td id=\"T_11937_row21_col8\" class=\"data row21 col8\" >19,6</td>\n      <td id=\"T_11937_row21_col9\" class=\"data row21 col9\" >19,6</td>\n      <td id=\"T_11937_row21_col10\" class=\"data row21 col10\" >55,9</td>\n      <td id=\"T_11937_row21_col11\" class=\"data row21 col11\" >78,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_11937_level0_row22\" class=\"row_heading level0 row22\" >unidentifizierte Deckel</th>\n      <td id=\"T_11937_row22_col0\" class=\"data row22 col0\" >0,0</td>\n      <td id=\"T_11937_row22_col1\" class=\"data row22 col1\" >14,1</td>\n      <td id=\"T_11937_row22_col2\" class=\"data row22 col2\" >13,3</td>\n      <td id=\"T_11937_row22_col3\" class=\"data row22 col3\" >4,7</td>\n      <td id=\"T_11937_row22_col4\" class=\"data row22 col4\" >9,2</td>\n      <td id=\"T_11937_row22_col5\" class=\"data row22 col5\" >8,7</td>\n      <td id=\"T_11937_row22_col6\" class=\"data row22 col6\" >7,6</td>\n      <td id=\"T_11937_row22_col7\" class=\"data row22 col7\" >0,9</td>\n      <td id=\"T_11937_row22_col8\" class=\"data row22 col8\" >6,2</td>\n      <td id=\"T_11937_row22_col9\" class=\"data row22 col9\" >7,4</td>\n      <td id=\"T_11937_row22_col10\" class=\"data row22 col10\" >3,0</td>\n      <td id=\"T_11937_row22_col11\" class=\"data row22 col11\" >9,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff115c53340>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_monthly_results"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect the survey results of the most common objects\n",
    "# and aggregate code with groupname for each sample\n",
    "# use the index from the most common codes to select from the feature data\n",
    "# the aggregation method and the columns to keep\n",
    "agg_pcs_quantity = {unit_label:\"sum\", \"quantity\":\"sum\"}\n",
    "groups = [\"loc_date\",\"date\",\"code\", \"groupname\"]\n",
    "# make the range for one calendar year\n",
    "start_date = \"2020-04-01\"\n",
    "end_date = \"2021-03-31\"\n",
    "# aggregate\n",
    "m_common_m = fd[(fd.code.isin(fdx.most_common.index))].groupby(groups, as_index=False).agg(agg_pcs_quantity)\n",
    "# set the index to the date column and sort values within the date rage\n",
    "m_common_m.set_index(\"date\", inplace=True)\n",
    "m_common_m = m_common_m.sort_index().loc[start_date:end_date]\n",
    "\n",
    "# set the order of the chart, group the codes by groupname columns and collect the respective object codes\n",
    "an_order = m_common_m.groupby([\"code\",\"groupname\"], as_index=False).quantity.sum().sort_values(by=\"groupname\")[\"code\"].values\n",
    "\n",
    "# use the order array and resample each code for the monthly value\n",
    "# store in a dict\n",
    "mgr = {}\n",
    "for a_code in an_order:\n",
    "    # resample by month\n",
    "    a_cell = m_common_m[(m_common_m.code==a_code)][unit_label].resample(\"M\").mean().fillna(0)\n",
    "    a_cell = round(a_cell, 1)\n",
    "    this_group = {a_code:a_cell}\n",
    "    mgr.update(this_group)\n",
    "\n",
    "# make df form dict and collect the abbreviated month name set that to index\n",
    "by_month = pd.DataFrame.from_dict(mgr)\n",
    "by_month[\"month\"] = by_month.index.map(lambda x: get_month_names('abbreviated', locale=date_lang)[x.month])\n",
    "by_month.set_index('month', drop=True, inplace=True)\n",
    "\n",
    "# transpose to get months on the columns and set index to the object description\n",
    "by_month = by_month.T\n",
    "by_month[\"Objekt\"] = by_month.index.map(lambda x: fdx.dMap.loc[x])\n",
    "by_month.set_index(\"Objekt\", drop=True, inplace=True)\n",
    "\n",
    "# pdf components\n",
    "# gradient background for .pdf table\n",
    "monthly_heat_map_gradient = featuredata.colorGradientTable(by_month)\n",
    "\n",
    "# subsection title and figure caption\n",
    "mc_monthly_title = Paragraph(\"Die am häufigsten gefundenen Objekte im monatlichen Durchschnitt\", subsection_title)\n",
    "monthly_data_caption = f'{this_feature[\"name\"]}, monatliche Durchschnittsergebnisse p/100 m'\n",
    "figure_caption = Paragraph(monthly_data_caption, caption_style)\n",
    "\n",
    "# make pdf table\n",
    "col_widths = [4.5*cm, *[1*cm]*(len(mc_comp.columns))]\n",
    "\n",
    "d_chart = aSingleStyledTable(by_month, vertical_header=True, gradient=True, colWidths=col_widths)\n",
    "d_capt = featuredata.makeAParagraph(monthly_data_caption, style=caption_style)\n",
    "mc_table = tableAndCaption(d_chart, d_capt, colwidths)\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        featuredata.large_space,\n",
    "        mc_monthly_title,\n",
    "        featuredata.large_space,\n",
    "        mc_table\n",
    "    ])\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# remove the index names for .html display\n",
    "by_month.index.name = None\n",
    "by_month.columns.name = None\n",
    "\n",
    "aformatter = {x: featuredata.replaceDecimal for x in by_month.columns}\n",
    "\n",
    "mcdm = by_month.style.format(aformatter).set_table_styles(heat_map_css_styles).background_gradient(axis=None, cmap=\"YlOrBr\", vmin=by_month.min().min(), vmax=by_month.max().max())\n",
    "glue(\"rhone_monthly_results_caption\", monthly_data_caption, display=False)\n",
    "glue(\"rhone_monthly_results\", mcdm, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} rhone_monthly_results\n",
    "---\n",
    "name: 'rhone_monthly_results'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_monthly_results>` {glue:text}`rhone_monthly_results_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Erhebungsergebnisse und Landnutzung\n",
    "\n",
    "Das Landnutzungsprofil ist eine Darstellung der Art und des Umfangs der wirtschaftlichen Aktivität und der Umweltbedingungen rund um den Erhebungsort. Die Schlüsselindikatoren aus den Ergebnissen der Datenerhebungen werden mit dem Landnutzungsprofil für einen Radius von 1500 m um den Erhebungsort verglichen.\n",
    "\n",
    "Eine Assoziation ist eine Beziehung zwischen den Ergebnissen der Datenerhebungen und dem Landnutzungsprofil, die nicht auf Zufall beruht. Das Ausmass der Beziehung ist weder definiert noch linear.\n",
    "\n",
    "Die Rangkorrelation ist ein nicht-parametrischer Test, um festzustellen, ob ein statistisch signifikanter Zusammenhang zwischen der Landnutzung und den bei einer Abfallobjekte-Erhebung identifizierten Objekten besteht.\n",
    "\n",
    "Die verwendete Methode ist der Spearmans Rho oder Spearmans geordneter Korrelationskoeffizient. Die Testergebnisse werden bei p < 0,05 für alle gültigen Erhebungen an Seen im Untersuchungsgebiet ausgewertet.\n",
    "\n",
    "1. Rot/Rosa steht für eine positive Assoziation\n",
    "2. Gelb steht für eine negative Assoziation\n",
    "3. Weiss bedeutet, dass keine statistische Grundlage für die Annahme eines Zusammenhangs besteht"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "land_use_section_title = \"Erhebungsergebnisse und Landnutzung\"\n",
    "luse_section_summary = [\n",
    "    \"Das Landnutzungsprofil ist eine Darstellung der Art und des Umfangs der wirtschaftlichen Aktivität \",\n",
    "    \"und der Umweltbedingungen rund um den Erhebungsort. Die Schlüsselindikatoren aus den Ergebnissen der \",\n",
    "    \"Datenerhebungen werden mit dem Landnutzungsprofil für einen Radius von 1500 m um den Erhebungsort verglichen.\"\n",
    "]\n",
    "\n",
    "p_one = [\n",
    "    \"Eine Assoziation ist eine Beziehung zwischen den Ergebnissen der Datenerhebungen und \",\n",
    "    \"dem Landnutzungsprofil, die nicht auf Zufall beruht. Das Ausmass der Beziehung ist \",\n",
    "    \"weder definiert noch linear.\"\n",
    "]\n",
    "\n",
    "p_two = [\n",
    "    \"Die Rangkorrelation ist ein nicht-parametrischer Test, um festzustellen, ob ein \",\n",
    "    \"statistisch signifikanter Zusammenhang zwischen der Landnutzung und den bei einer \",\n",
    "    \"Abfallobjekte-Erhebung identifizierten Objekten besteht.\"\n",
    "]\n",
    "\n",
    "p_three = [\n",
    "    \"Die verwendete Methode ist der Spearmans Rho oder Spearmans geordneter Korrelationskoeffizient. \",\n",
    "    \"Die Testergebnisse werden bei p < 0,05 für alle gültigen Erhebungen an \",\n",
    "    \"Seen im Untersuchungsgebiet ausgewertet.\"\n",
    "]\n",
    "\n",
    "values = [\n",
    "    \"Rot/Rosa steht für eine positive Assoziation\",\n",
    "    \"Gelb steht für eine negative Assoziation\",\n",
    "    \"Weiss bedeutet, dass keine statistische Grundlage für die Annahme eines Zusammenhangs besteht\"\n",
    "]\n",
    "\n",
    "\n",
    "a_list_groups = makeAList(values)\n",
    "\n",
    "section_titlex = Paragraph(land_use_section_title, section_title)\n",
    "section_summary = Paragraph(''.join(luse_section_summary), featuredata.p_style)\n",
    "para_one = Paragraph(''.join(p_one), featuredata.p_style)\n",
    "para_two = Paragraph(''.join(p_two), featuredata.p_style)\n",
    "para_three = Paragraph(''.join(p_three), featuredata.p_style)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    section_titlex,\n",
    "    small_space,\n",
    "    section_summary,\n",
    "    smallest_space,\n",
    "    para_one,\n",
    "    smallest_space,\n",
    "    para_two,\n",
    "    smallest_space,\n",
    "    para_three,\n",
    "    smallest_space,\n",
    "    a_list_groups\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Ausgewertete Korrelationen der am häufigsten gefundenen Objekte in Bezug auf das Landnutzungsprofil im Erhebungsgebiet Rhône. Für alle gültigen Erhebungen an Seen n=106. Legende: wenn p > 0,05 = weiss,  wenn p < 0,05 und Rho > 0 = rot, wenn p < 0,05 und Rho < 0 = gelb.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_spearmans_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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qcCsbgUCA0qVLw8bGBgMGDECDBg2K1RZ7EImIiH5gL168gJ6enrLDoCIYOnQoKlSoAAMDA3To0AH9+/eHt7c3DAwMUL58ebRv3x6xsbHo27cvTp8+Xay22INIRET0HdqzZw/27NkjfT1jxgyUKlVKpo5YLMbdu3fh6Oio6PDoKxgYGMDU1BQRERHQ1dWVHn///j369u2LsmXLYu/evejTpw9WrlyZ736JRcUEkYiISA7ev3+P//77D2KxOE+ZtbW1wuNJS0tDQkKC9PW7d++QmZkpU0dDQwOtW7fGsGHDFB0efYXQ0FCMGzdOJjkEAD09PfTt2xcLFixA//790b17d0ydOrVYbTFBJCIiKob4+HhMnToVx48fz1MmkUggEAiU8iQVBwcHdOzYEdra2vD09ERwcDCsrKwUHgfJz6tXr6Cunn/qJhQK8ebNGwAfnsuclZVVrLaYIBIRERXDtGnTcO7cOfj7+6N69erQ0NBQdkgAAB8fH2zfvh12dnYwNzdXmbjo61WtWhWbNm2Cu7u7TKKYlZWFTZs2oWrVqgCAuLg4GBsbF6stJohERETFcO7cOUycOBFdunRRdigyNDU1pSuXL168iPfv3ys5Iiqu4cOHY/jw4WjRogWaNm0KExMTvHnzBseOHUNsbCyWLl0KADh9+jTq1q1brLaYIBIRERWDjo4OKlSooOww8qhUqRLWr18vHXY8f/48Xr16VWD9Fi1aKCo0+krNmjXDypUrsXTpUmzevFk6hcHGxgYzZ86Em5sbAGDOnDnFbotPUiEiIiqGhQsXIjExEbNnz1Z2KDIOHTqE8ePHIysrCwKBAJ/7ca+seZL09dLS0pCcnAx9fX3o6OjI/fpMEImIiIohKysLU6ZMQUpKCjw8PGBgYJCnjrJ65+Lj4/HkyRP07NkT06ZNQ40aNQqs6+TkpMDISNVxiJmIiKgYoqOjcf36dTx9+hTHjh3LU67M3jljY2MYGxvDx8cHbm5uqFSpklLiIPmJjo7G4cOH8fLlS6Snp8uUCQQCzJ07Vy7tsAeRiIioGPz8/PDkyRMMHDiwwFXM7J0jeYiMjERgYCBycnJgbGwMTU1NmXKBQJDvLylfgwkiERFRMdSpUwdz5sxB27ZtlR3KZ2VnZ+PEiRN49OhRvj1PQ4cOVVJkVFS5j9VbvHgxypQp803b4hAzERFRMZQpUwalS5dWdhiflZCQgJ49e+Lx48cyC1YEAoG0DhNE1ffs2TMEBwd/8+QQANS+eQtERETfse7du2P79u3KDuOzFi9eDC0tLRw/fhwSiQQ7duzAkSNH0Lt3b1SpUgWRkZHKDpGKoEKFCkhNTVVIW+xBJCIiKgY1NTXcu3cPPj4+cHd3h6GhoUy5QCBA7969lRJbrnPnzmHo0KEwMzMD8CFmCwsLTJgwARkZGViwYAGCgoKUGiMVbtCgQfjjjz/g7u7+Tba2+RgTRCIiomL47bffAAAxMTH5rlZWhQTx1atXMDc3h1AohJqaGtLS0qRlTZo0wZgxY5QYHRXVzZs38fbtWzRv3hzOzs4wMjLKU2fq1KlyaYsJIhERUTHIa9Xot2RkZISUlBQAgJmZGe7fvw9HR0cAQFJSErKzs5UZnkrKyMjAmTNnEBMTI31kYS5lJf2bN2+W/v3gwYN5ygUCARNEIiIiVWBubq7sEAplbW2NBw8ewMPDA+7u7li+fDlKlSoFDQ0NBAUFoU6dOsoOUaXcunULAQEBePv2bb5PoFFWgnj37l2FtcVtboiIiL5zZ86cwfPnz9GtWzfEx8ejT58+uHfvHgDAwsICK1euRLVq1ZQcpero3Lkz3r17h3HjxhW4t2VJ+MWgOJggEhERfSF/f39Mnz4d1atXh7+//2frCgQCbNy4UUGRFY1EIsH9+/chEAhQrVo1qKtzQPFj9vb2WLhwIZo2barsUJSG3wgiIqIv9HHfSmH9LKrYDyMQCCASiZQdhsoqX768skPIl5WVlczelfmR12Md2YNIRET0A5BIJLh58yZiYmIgFovzlHfo0EHxQamo8PBw7N69G+vXr8/zODtlCg4OzpMgxsfH4/Tp08jOzkaHDh0QGBgol7bYg0hERPSde/LkCQYPHoxnz54VuOiCCeL/dOzYETExMWjWrBmcnJzy7G0JyG87mS8xbNiwfI9nZGSgX79+MDY2lltb7EEkIiL6zvXp0wfPnj3D+PHjIRKJ8u0V+94XXXyJyMhIBAYGIisrK99ygUAgt6FceTly5AgWLlyII0eOyOV67EEkIiL6zt24cQO//PILWrZsqexQSoRff/0V1tbWmDlzZoGrmFWNlpYW4uLi5HY9JohERETfOV1dXZQqVUrZYZQYMTExCAkJgZWVlbJDKZL4+HisW7cOVatWlds1mSASERF95zp27IgDBw7A3d1d2aGUCNWqVZM+eUaVeHp65lmkkpGRgfj4eAgEAqxYsUJubTFBJCIi+g59PBdNJBLhwIEDCAgIgKenZ76LLlq0aKHA6FTbiBEjEBQUhPr168PU1FTZ4Ug5OTnlSRA1NTVhbm6O1q1bo2LFinJri4tUiIiIvkO5e+ZJJBLpfwuiiosulCkgIAB3795FUlISrKysYGBgIFMu7946VcQeRCIiomLIzMzEmjVrcODAAbx8+TLPHoMCgQBRUVEKj2vTpk0Kb/N7cf/+faipqcHIyAixsbGIjY1VdkgKxx5EIiKiYliwYAE2bNgAd3f3AreQkdfmxfTjCQkJKXJdgUCAoUOHyqVdJohERETF4OHhAV9fX5VOAuPj45GUlJTvKtcnT57AwMBArpssl3Tp6enQ1tYusDw6Olqu8/0+59OV1PlNF/h4XqK8pgqoyeUqREREP6ikpCTUr19f2WF81syZM7Fu3bp8y9avX4/Zs2crOCLVNnLkyALnbMbGxqJ3794Ki+Xu3bvSP3/++ScqVqyIUaNG4dixY7hx4waOHTuGESNGoGLFijh8+LDc2mWCSEREVAyOjo64e/eussP4rCtXrsDV1TXfMldXV1y+fFnBEam2a9eu5Zs0x8fHo3fv3tDS0lJCVMCcOXPg7e2NQYMGwdzcXLqCOSAgAN7e3nJN9JkgEhERFcPUqVOxa9cuHDlyBBkZGcoOJ18JCQn5bm0DAPr6+oiPj1dsQCpu2bJl2LlzJ/744w/pseTkZPTp0weZmZlYv369UuK6dOkS6tWrl2+Zg4ODXBN9rmImIiIqBm9vb2RlZWHEiBEQCAR55q4JBAKl99CZmJjg/v37aNCgQZ6y+/fvF5g8/qjq1auH+fPnY9y4cTA3N4ebmxv69++PhIQEbNmyBWZmZkqJS1NTEzdv3oSLi0ueslu3buW7QOprMUEkIiIqBi8vrzybF6saNzc3rFy5Em5ubjILVZ4+fYrVq1fDw8NDecGpqNatWyM6Ohrjxo2DpaUl/vvvP4SGhipscUp+mjdvjmXLlkFPTw9t27aFgYEBkpKSEBERgWXLlqFdu3Zya4urmImIiL5zsbGx6NSpE5KSkuDs7Ixy5crh1atXOH/+PIyMjLBz506ULVtW2WGqpGnTpuHPP//Epk2blP5s5pSUFAwZMgQXLlyAQCCAUChEdnY2JBIJHB0dsWLFCrk9c5sJIhER0Q8gNjYWv//+O06ePImEhAQYGRnB3d0dw4cPZ3IIwN7ePt+eYIlEgszMTJnhW2VPGzhx4gQuXLiAxMREGBoawtnZGa6urnLtyWaCSEREVEyPHj3CsmXLpD+0t2/fDmtra4SEhKB+/fr5zv0j1TJx4sQvSrDmzZv3DaNRPs5BJCIiKoY7d+6gR48e0NPTg5OTk8xedO/fv8e2bdtUMkH877//8ODBA9ja2sLIyEjZ4Sjd/PnzlR3CF4mPj0d6enqe4xUqVJDL9ZkgEhERFcPChQshEomwfv16aGho4NChQ9IyOzs7HDlyRInRfbB48WKkpaVh8uTJAIAzZ84gICAAGRkZMDAwwObNm2FpaankKKkwKSkpmDdvHg4ePJjnmd+55PUkFSaIRERExXDlyhX89ttv0NHRQXZ2tkyZiYkJ3rx5o6TI/ufIkSPo06eP9PWSJUsgEokQEBCAkJAQrFixAkFBQUqMkIpi7ty5OHDgAHx9fQt87re8MEEkIiIqJg0NjXyPJyUlfdMf4kUVGxsLCwsLAB82zb558yZWr14NNzc3iMViLFiwQMkRUlH8+++/GDNmDHr16vXN2+KTVIiIiIpBJBLh6NGj+ZadPHkS1tbWCo4oL4lEIn228JUrVyAUCuHo6AgAMDMzQ0JCgjLDoyISi8WoWbOmQtpigkhERCXKu3fvcPLkSezfvx9JSUnKDgf+/v7YtWsX5s6dK30m83///Yd169Zh9+7d8Pf3V3KEgIWFBY4fPw4AOHToEGxtbaVPfHn9+jX09fWVGR4VUePGjRW2vQ6HmImIqMRYtmwZ1qxZg/T0dAgEAuzatQsGBgbo1asXGjVqhIEDByo8ptatW+P58+cICQlBaGgoAGDYsGEQCoUYPnw4PD09FR7Tp7p27YpZs2Zh3759SE5Oxty5c6VlV65cQY0aNZQYHRXV4MGDMXz4cOjp6aFJkyb5PiJRXo9N5D6IRERUIoSFhWHOnDno0aMH3NzcMGjQIOzevRvW1tbYsGEDjhw5gi1btigtvlevXuHkyZN4+/YtjIyM4OrqCnNzc6XF86mDBw/i6tWrsLW1hbe3t/T4zz//jHr16qFDhw7KC05FvX37FjExMfmuGM4dolek3Ce5fG6/Rq5iJiKiH0pYWBh69+6N8ePH51ktXLlyZTx79kwpcV28eBG1a9dGuXLl0LlzZ5my9+/fIyoqSinJxKfatGmDNm3a5Dn+yy+/KCEa1fb69WuMHz8e58+fz1MmkUggEAjkloh9iaFDhyrsud9MEImIqER48eIF3Nzc8i3T09NDcnKygiP6wN/fH9u3b4ednV2esidPnsDf318pycTHatWqVWCMt27dQufOnZUeoyr55ZdfcOfOHYwdO/abbyfzJYYNG6awtpggEhFRiVC6dOkC9xSMiYlBmTJlFBzRB5+bqZWVlQU1NeWvB/1cjDk5OQqMpGS4cOECxo8fj06dOik7FKVhgkhERCWCi4sL1q5di6ZNm0JLSwvAh7lYWVlZ2Lp1K1xdXRUWS0pKikyPZVxcHF6+fClTJz09HXv27IGJiYnC4voat2/f5irmTwgEApQvX17ZYeQREhLy2XKBQIChQ4fKpS0uUiEiohLh2bNn8PX1RalSpdCsWTNs3rwZPj4+uHPnDl6+fIk9e/bI7Tm0hQkJCcGyZcsKrSeRSDBo0CCMGjVKAVHJ2rhxIzZt2gQAePnyJUxMTPIMlYrFYrx9+xZeXl5YsmSJwmNUVTNmzICGhgamTJmi7FBk5C5SKYg850ayB5GIiEqEypUrY+vWrZg3bx62bt0KiUSCffv2wdnZGQsXLlRYcggAjRo1gq6uLiQSCX777Tf89NNPedrX1NREzZo14eTkpLC4PlamTBnp85VjYmJQqVKlPD2FuTGqwl6NqqRVq1b4+eefIZFICtxORlEboH88fzR3n82PJSYm4ujRo9i4cSNWr14tt3bZg0hERCVORkYGEhISYGBgIN3wWVlCQkLQuXNnlC1bVqlxfI6fnx9mzJiB6tWrKzuUEuHjnrpPVw0rehXzL7/8gn79+hX6C9Dq1atx/fr1IvVsFwV7EImIqMTR1NRUmYQsMDBQ2SF8Vnp6OjIzMxEbG8sEsYjmzZun7BCkfv755yLVs7W1xcqVK+XWLhNEIiJSWYVNyv+YPCfof6ns7GycOHECjx49Qnp6usrEBQDa2tq4f/8+hEKh0mIoaXx8fJQdglS7du2waNGiQp/BfO/ePejq6sqtXSaIRESksj5NEAUCQZ4tWz4eAlRGIvb69Wv07t0bjx8/lolP2XF9zN7eHjdu3ICzs7NS4yiJHj9+jISEBNSqVUuuCVhRGRoaSpP7vXv35inPyMjAvXv3sHv3brRv315u7XIOIhERlQhPnz7FgAED4Ovri7Zt28LU1BRxcXGIiIjA7t27sWbNGlSpUkUhsTg4OGDTpk2wsbHB+PHj8eDBAyxfvhxNmjTBjh07YGhoiK1btyIyMhIbNmxQ+nD4/fv3MWTIEAQGBqJ58+bQ09NTajwlwd69exEUFIS4uDgAwK5du2BtbY0RI0agUaNG6NKli8JjKmgVs5aWFtq3b48JEyagVKlScmmLCSIREZUIAwYMQJ06dfKd8xcSEoJr165h7dq1Coll0qRJGDJkCCpVqoRGjRph/PjxaNu2LaytrbFr1y7Y2NgA+LDAICEhAUFBQQqJqyD29vbIzMyUPqJQW1tbpodTIBDg8uXLygpP5Rw+fBijRo2Ch4cH3N3dMWvWLOlzv1evXo2zZ89i/fr1Co8rJiYmzzEtLa1vstcmh5iJiKhEuHTpEvr27ZtvmYODA9atW6ewWD5exJCcnAxzc3MIhUKoqakhLS1NWtakSROMGTNGYXEVxMvLS2HP8P0erF69Gh07dsTcuXORnZ2NWbNmScuqVauG0NBQpcRlbm6usLaYIBIRUYmgqamJmzdvwsXFJU/ZrVu3FPq83ICAAEycOBFVqlSBsbExUlJSAABmZma4f/8+HB0dAQBJSUnSXjtlmj9/vrJDKFEePXqEsWPH5ltmaGiIxMRExQaUj7S0NIjF4jzH89uz8WswQSQiohKhefPmWLZsGfT09NC2bVsYGBggKSkJERERWLZsGdq1a6ewWO7fvy9drVy7dm08fPhQOhy5fPlylCpVChoaGggKCkKdOnUUFhfJh46ODt69e5dvWWxsLAwMDBQc0QdpaWkICgpCREQEkpKS8q3DJ6kQEdEPZeLEiXj+/Dl++eUXzJ49G0KhENnZ2ZBIJHB0dMTEiRMVFss///wj/XvPnj3x7NkzAMDIkSNx/fp1TJgwAQBgYWGhtMe1Xbx4EbVr14aenh4uXrxYaP3cXk/6MGczLCwMXl5eecrCw8OV9nScWbNmYf/+/WjSpAmqV68ODQ2Nb9YWF6kQEVGJcuLECVy4cAGJiYkwNDSEs7MzXF1dVWaOnUQiwf379yEQCFCtWjWoqyunL8bKygo7duyAnZ0drKysCrw/in4ySElw8+ZN9OjRAzVr1kS7du2wYMECDBw4EPfu3cPZs2exc+fOQvcl/BYaNGiAAQMGoF+/ft+8LSaIRERExbB37140btwYRkZGecoSExMRGRmJDh06KDyuCxcuwNraGnp6erhw4UKh9ZXVK6aqzp07h5kzZ+LJkyfSY5UrV8asWbOUtp9kgwYNsHjx4nzn4cobE0QiIqJiqFWrFrZv3w47O7s8Zbdu3ULnzp3ZO1eCPX/+HG/evIGRkRGqVq2q1FimTZuG0qVLY9y4cd+8Lc5BJCKiEuFzw6S5lJGIfa6fRSwWq8Qj7iIjI+Hk5KSUJ4GUdBYWFrCwsADw4fPU0tJSWiwTJkzA8OHDMW/ePDRu3DjfxTLW1tZyaYsJIhERlQhDhw7NkyDGx8fj9OnTyM7OVugw7suXL2U2LY6Kisqz5Uh6ejp27NiB8uXLKyyuggQEBEBdXR12dnZo2LAhXFxcULduXZVIXlXRoUOHkJCQgJ49ewIAnj17hsGDB+PJkyewt7fHihUrlLKSWSwWIysrCxs3bsSmTZtkyuQ9l5RDzEREVKJlZGSgX79+aNWqFXr06KGQNkNCQhASEvLZHs3cH69TpkyBn5+fQuIqSFRUFE6fPo1z587h8uXLSE9Ph56eHhwdHeHi4oKGDRvC0tJSqTGqkk6dOqFVq1bo378/gA+/nFy/fh1t2rTBvn374OPjI12prkgBAQG4cOECOnfuXOAqZh8fH7m0xQSRiIhKvCNHjmDhwoU4cuSIQtp79OgRHj58CIlEgpEjR2L06NGoXLmyTB1NTU1YWlqiYsWKCompqDIyMnDlyhWcPXsWp0+fxu3btyEQCBAVFaXs0FSGs7Mzfv31VzRu3BhisRhOTk6YOXMmOnTogG3btuGPP/5Q2HftY/b29pg0aZJCngPNIWYiIirxtLS0EBcXp7D2qlevjurVqwP48Ng9Dw+PfFcxq6LY2Fg8f/4cT548wYsXLyCRSKRz7OiDtLQ06XzN69evIyMjA+7u7gCAGjVqIDY2Vilx6enpKexxe0wQiYioRIuPj8e6deuUtsJUXkN639Jff/2F06dP4+zZs3jx4gVMTEzg7OyM8ePHo2HDhioxT1KVmJqa4s6dO3B0dMTJkydRtWpVGBsbA/jw+ERtbW2lxNWhQwccPHgQjRo1+uZtMUEkIqISwdPTM8+cv4yMDMTHx0MgEGDFihUKiyUkJASdO3dG2bJlERIS8tm6AoEAQ4cOVVBk+RsxYgR0dHTw008/oX379pxvWIgWLVpg8eLFuHjxIk6cOIEBAwZIy+7du6e0HlcrKyssXrwYQ4cOhYeHR74LZVq0aCGXtjgHkYiISoSJEyfmSRA1NTVhbm6O1q1bK3Su36dPKfkcVXhKSUBAAC5evIjU1FTUrFkTjRo1QqNGjVC/fn2lbtuiqtLT0zF37lxcvXoVdnZ2+Pnnn6W9hl26dIGLiwtGjRql8LgU+V1jgkhERPQDyM7OxrVr13D27FmcOXMGN27cgJqaGhwcHNCwYUMMHDhQ2SFSIRT5RBwmiERERD+gGzdu4Pfff8fp06dVopdT1f3333948OABbG1tS8yCpOLgHEQiIioxsrOzceLECTx69Ajp6ekyZcqa6zd8+HB06tQJbm5uUFNTU3j7RfXmzRucOXMGZ8+exdmzZ6UrcWvWrImGDRsqOTrVsnjxYqSlpWHy5MkAgDNnziAgIACZmZkoXbo0wsLCVGYe5/Xr1xEVFQUnJyfpynp5YA8iERGVCK9fv0bv3r3x+PFjCAQC6UbUH89LVEYvmJubG968eYMyZcrAx8cHHTp0kOsPanlo27YtHj16BIlEAnNzc7i4uEj/5K7Opf9p1aoV+vTpI91vsEuXLpBIJAgICEBISAiqVq2KoKAghcc1ZcoUZGdnY/78+QCAgwcPYuzYsZBIJNDQ0MCmTZtgb28vl7ZU91cdIiL64Tk4OODWrVsAgIULF0JLSwvHjx+HRCLBjh07cOTIEfTu3RtVqlRBZGSkUmL8999/sXLlStSvXx8bN25E27Zt0a1bN+zcuRMpKSlKielT1atXx4wZM/D333/j2LFjmD17Ntq0acPksACxsbHSlcoJCQm4efMmhg8fjqZNm2LAgAG4fPmyUuI6f/48GjRoIH29cuVKuLq6Yu/evbC3t8eqVavk1haHmImISGV5eXlJt/I4ffo0xo8fDzMzMwCAmpoaLCwsMGHCBGRkZGDBggVK6dVRU1ND48aN0bhxYyQnJyMiIgLh4eH4+eefMXfuXLRo0QILFixQeFwf+/3335Xafq69e/d+UX1FPl/7YxKJRNpDfeXKFQiFQjg6OgIAzMzMkJCQoJS43rx5gwoVKgD4kMQ+ePAA06dPh5WVFfz9/TFjxgy5tcUEkYiIVNa8efOkf09OToa5uTmEQiHU1NSQlpYmLWvSpAnGjBmjjBBl6Ovro2fPnujZsycuXbqEsWPHYv/+/UpPEFXFxIkTZV7nTg/4eLbbx1MGlJUgWlhY4Pjx43BxccGhQ4dga2sr3ebm9evX0NfXV0pc6urqEIvFAD4krlpaWqhTpw4AwMDAAMnJyfJrS25XIiIikrOAgABMnDgRVapUgbGxsXTI1szMDPfv35f26iQlJSE7O1uZoUqdPn0a4eHhOHr0KMRisdzmhH0pKyurPPtGfo4i5m8eO3ZM+vc3b95g1KhRcHV1Rdu2bWFiYoI3b94gIiICp0+fxuLFi795PAXp2rUrZs2ahX379iE5ORlz586Vll25cgU1atRQSlzVqlXDvn37YG9vj127dsHBwQEaGhoAgFevXsl1ygATRCIiUln379+XrlauXbs2Hj58CA8PD7i7u2P58uUoVaoUNDQ0EBQUJO1JUYbnz58jPDwce/fuRWxsLExNTeHv749OnTqhSpUqSolp6NChMglieHg43r9/D09PT5iYmCAuLg7Hjx+Hrq4uOnXqpJCYPn6O8KJFi9CsWTPpSmHgQwLk5OSEuXPnYv369ViyZIlC4vpUjx49YGBggKtXr8LW1hbe3t7SMrFYrLTHK/bp0wejR4/GwYMHAQDLli2Tlp09exYikUhubXEVMxERlQinTp3Cs2fP0LNnT8THx6NPnz64d+8egA9DgitXrkS1atUUHtdPP/2Ey5cvQ11dHU2aNFHJLW/++OMP/Pnnn1i/fj309PSkx1NSUtCnTx94eXmhf//+Co2pfv36WLp0ab5b7Jw+fRojRozApUuXFBpTSXD16lVcu3YNtra2qF+/vvT40qVLUadOHTRu3Fgu7TBBJCKiEkkikeD+/fsQCASoVq0a1NWVMyjm4+ODjh07ol27djA0NFRKDIVp1qwZxo8fn+9zev/66y8sWLAA//zzj0JjcnBwwNixY9GjR488ZWFhYQgKClLaamHiEDMREZVQAoFArkNqX2vPnj3KDqFQsbGxEAqF+ZYJhUK8efNGwRF92D9yyZIlqFChAjw8PKTHjx8/jt9//x2urq4Kj+ljFy9eRGhoaL6bsgOy8ykVJTY2Fu/fv5f2lGdlZWH9+vWIiopCo0aN4OvrK7e2mCASEVGJkZKSgrCwMJw/fx6JiYkwNDSEs7MzunfvrrSVpSVB9erVsWHDBri7u0sXNQBARkYG1q9fr5Sh+SlTpqB3794YPHgw9PT0UKZMGbx9+xbv379H5cqVMWXKFIXHlOvSpUvo3bs3nJyc8OjRI7i5ueH9+/e4du0aKlWqBAcHB6XENX36dJQvXx7Tp08HAKxYsQLLli2Dvr4+/vzzT2hoaMjMlywODjETEVGJ8OLFC/Tq1QsvX75EhQoVYGpqiri4OLx8+RLly5fHpk2bUKlSJYXE0rRpUyxbtgxWVlbw9PT87GphgUCAo0ePKiSugkRGRmLo0KEwMTFB8+bNpffu77//xps3b7Bs2TKZXjxFEYvFCA8Px4ULF2QS/g4dOki3lVGGXr16wcLCAjNmzIC1tTV2794Na2tr3L17F/3798esWbPg6emp8Ljc3NwwZcoUtGzZUvq6TZs2mDhxIqZPn467d+9i+/btcmmLPYhERFQizJkzB2KxGFu3bpXZOubKlSsYNmwY5syZg5UrVyokFicnJ+liDycnpy/aTkYZPDw8sHbtWixevBhbtmxBTk4OBAIB7OzsMG/ePKU9i1lLSwvdu3dH9+7dldJ+QR48eIC+fftKP9fcLZSsrKwwZMgQLFu2TCkJYmJiIkxMTAAAjx49QlxcnHRFtZeXFw4fPiy3tpggEhFRiXD+/HlMnjw5z76CDg4OGDlypMxedd/axxt45z4XV9XlPns5LS0NycnJ0NfXh46OjrLDUklpaWnQ09ODmpoaNDU1ZZ6cUq1aNTx69EgpcZUuXRrx8fEAPsyRNDAwkM7DFQgEyMzMlFtbTBCJiKhE0NTURPny5fMtK1++PDQ1NRUc0Qd79+5F48aNYWRklKcsMTERkZGRSnsiSH50dHSUlhj6+/sXua5AIMDGjRu/YTQFq1ChgnThTo0aNfDvv/9Kt4+5ePGi0lar29nZYc2aNVBXV8emTZtkFvK8ePFC+hhKeWCCSEREJYKnpyf+/PPPfFe3/vnnn0qZQwcAkyZNwvbt2/NNEKOjozFp0iSVSBCjo6Nx+PBhvHz5Ms+qXIFAoJAe2C9Z9qDMJRJOTk64cOECWrZsic6dO2PmzJl49OgRNDU1cfr0afTp00cpcY0YMQJ9+vTBkCFDYGBggICAAGnZ0aNHYWtrK7e2mCASEZHKun37tvTv7dq1w5QpUzB8+HC0a9dO5tFst27dwpw5c5QS4+cSGbFYXOD2MooUGRmJwMBA5OTkwNjYOE9vq6LmUIaGhiqkneIaNmwYkpKSAADdu3dHeno6IiIiAACDBw+WScwUqVatWjh+/DgeP36MypUro1SpUtKyHj16oHLlynJri6uYiYhIZX36POHcH1kFHVPE84QB4OXLl4iJiQEA+Pn5YcaMGahevbpMnfT0dOzYsQN3797F33//rZC4CuLt7Q0DAwMsXrwYZcqUUWosqi47OxvPnz9HmTJlfuitk9iDSEREKuvjxSCqJDw8HCEhIRAIBBAIBJg5c2aeOrmJqzL388v17NkzBAcHq2xyGB8fn+9m1BUqVFB4LBKJBG3atMGKFSvk9tg6ebl48WKhdRwdHeXSFhNEIiJSWblbeKiaVq1awdLSEhKJBCNHjsTo0aPzDO9pamrC0tISFStWVFKU/1OhQgWkpqYqO4w8li9fjtDQUCQmJuZbrqge4Y+pq6vDxMREqXMgC+Ln51fodAB53TMmiEREVOI8fvwYCQkJqFWrFnR1dRXefvXq1aVDyvPmzYOHh0e+i1RUxaBBg/DHH3/A3d1dZba22bVrF9asWYMBAwZg6dKlCAgIgEQiwf79+6GlpYUBAwYoLbY2bdpg7969Slv4VJBNmzblOZaQkIBjx47hypUrmDZtmtzaYoJIREQlxt69exEUFIS4uDgAH5IMa2trjBgxAo0aNUKXLl0UHpOxsfFnk8OQkBAEBgYqMKK8bt68ibdv36J58+ZwdnbON96pU6cqNKYtW7Zg0KBB0gSxefPmsLa2xuDBg9GzZ0+ZvQcVzcrKCocOHYK/vz9atGgBU1PTPD13LVq0UHhcTk5O+R738vLCtGnTcPLkSbi7u8ulLS5SISKiEuHw4cMYNWoUPDw84O7ujlmzZkkfgbZ69WqcPXsW69evV3hcDg4OCAsLQ61atfKUrVu3DgsXLlTKUOnHrKysPluuyAU+uerVq4eQkBA0aNAAtWrVwrZt21C3bl0AwMGDB/H777/jyJEjCo0plyrer8KcPHkSEydOxOnTp+VyPfYgEhFRibB69Wp07NgRc+fORXZ2NmbNmiUtq1atmtK2UGnevDkGDhyInTt3oly5ctLjW7duxW+//YYJEyYoJa6P3b17V9kh5KGu/iEFEQgEKFWqFF69eiUtMzIyQmxsrLJCw8aNG1X+8YmfSk5ORkZGhtyuxwSRiIhKhEePHmHs2LH5lhkaGha40OFbmzNnDvr27YsBAwZg69atKFWqFPbu3YtZs2YhMDBQaZsqq7rKlStLk0JbW1vs3LkTTZs2hZqaGrZv3w5zc3Olxebs7Ky0tj/n5cuXeY5lZGTg3r17WLRoEerUqSO3tpggEhFRiaCjo4N3797lWxYbGwsDAwMFR/SBuro6QkJC0L17dwwbNgy+vr6YMmUKevfurfS5h6rM3d0dFy9ehI+PDwYOHIj+/fvD0dERQqEQqampCn229qeaNm2KZcuW5TvUfP/+fQwePBjHjh1TeFyenp759mxKJBJUrVqVi1SIiOjHY29vj7CwMHh5eeUpCw8PL3ACvyLo6+tj9erV6Nq1K8aOHYsuXbqoxNDyx/bt24eNGzfi8ePHEIvFecoVMaeuY8eOaNCgAZydndG7d2/pk0BcXFywdetWHDx4EGpqamjcuDEaNGjwzeMpSExMTIHDtWKxON+ePEWYO3dungRRS0sL5ubmsLW1hZqamtzaYoJIREQlwtChQ9GjRw/4+vqiXbt2EAgEOHLkCIKDg3Hp0iXs3LlTYbEUtBjG2dkZZ8+ehYWFhbSOQCBA7969FRZbfo4dO4bJkyfDx8cHUVFR6NSpE8RiMf755x+YmZmhbdu2CokjOTkZf/zxB9avXw+hUAhra2s4OTnB2dkZ9erVw6RJkxQSR3G8ePECenp6Smm7Y8eOCmuLq5iJiKjEOHfuHGbOnIknT55Ij1WuXBmzZs1S6Lyxwla5fkwVVrx269YN9erVw+jRo2FtbS1d/R0XF4eePXti4MCB8PX1VUgssbGxOHfuHM6fP48LFy4gOjoaAoEAQqEQtra2cHZ2hrOzMxwcHKClpaWQmABgz5492LNnDwDgwoULqF27tsyzjoEPvYd3796Fo6Mj1q5dq7DYlIEJIhERlTjPnz/HmzdvYGRkhKpVqyq8/dznMBeVMhdcAB96NoOCguDi4oLatWtjx44dsLOzA/AhMVq3bh0OHDiglNhevXolkzDGxMRAIBBAU1MT169fV1gcW7ZswdatWwEADx8+RMWKFaGtrS1TR0NDAyKRCMOGDVPYYwC/pFdVIBDIbe4mh5iJiEilvXjxAtra2jA1NZUe+3iBQGRkJEqVKoXOnTsrLKYvSfji4+O/YSRFk52dDQ0NDaipqUFHR0e60TgAlC9fHi9evFBabOXKlUOHDh3QrFkzXLx4EXv27MHff/8t1y1biqJHjx7o0aMHgA+LQYKDg7+op/hbOX/+fJHrynNrHiaIRESksm7duoXOnTtjyZIl0sUp2dnZWLBggUw9gUAACwsLldmeRCKR4MSJE9i1axeOHz+OW7duKTWeihUr4vXr1wA+DI8fPHgQTZs2BQD89ddfMsm3oqSmpuLSpUs4f/48zp8/Lx2GF4lE6NWrFxwdHRUeU65//vlHaW1/SlmxMEEkIiKVtWPHDtjb2+e7cnnlypWwtLSERCLBr7/+ir179yo9QXz+/Dl2796NPXv2IC4uDhoaGvnGrmguLi44c+YM2rZtC39/f4waNQo3b96EhoYGnjx5gjFjxigkjtOnT0sTwlu3bkEgEKB27dpwdnbGsGHDUK9evTzz/pQtMTERa9euxYMHD1C2bFn4+fnB0tJS2WF9c0wQiYhIZZ0/fx69evXKt8zU1FQ61Ovl5YWlS5cqMjQpsViMw4cPY/fu3bh06RIkEol05fLAgQM/+5xmRRk1apR0yLZVq1YQCoWIiIiAQCBAz5490aRJE4XE0a9fP+jq6sLX1xfDhg2Dg4MDdHV1FdJ2YRYsWIDDhw8jMjJSeiw1NRW+vr6IiYlB7pKNgwcPYufOnahWrZpC4vr111/h7+8v85SenJwcuW5pk59ve3UiIqJiePXqFapXry5zTCAQwMrKSmYBgampqcIfzXbjxg1MmzYNjRo1wqRJk3D79m34+Phg1apVkEgk8PT0VInkEAA0NTVleuZatGiB4OBgLF26FCYmJtLh5m+tZs2aSEtLw9atW7F8+XKsXr0aZ86cQVpamkLa/5yrV6+idevWMsc2b96M6Oho9OrVC5cuXcK2bdugq6uLNWvWKCyu9evXS6cHAB+mWFhbW+P27dvftF32IBIRkUr7dLMNNTU17N27V+ZYTk5OnnrfUrt27fDw4UMAQN26ddGpUye0bt0aurq6BT7thYD9+/cjKSkJFy9exPnz5/HPP/9g1apVEAqFsLGxgaOjI5ycnODg4KDwvQZfvHgBf39/mWPHjx+HsbExxo0bB6FQiLp166JPnz7YvHmzwuLK73utiO86E0QiIlJZZmZmePjwYaFP1Xj48CHMzMwUFBXw4MEDCAQCNG7cGGPHjkWNGjUU1nZJZ2BggGbNmqFZs2YAPszxu3DhAi5cuIDIyEisW7cOampq0u14FCU5OVnmO5SVlYWbN2+iWbNmEAqF0uO1atWSWQX+veIQMxERqSxHR0ds374dWVlZBdbJysrC9u3bFfqovcmTJ0MkEiEyMhLt2rVD165dsXPnTqSkpCgshu+FoaEhWrRogUGDBmHQoEFo2rQpsrOzcfPmTYXGYWJiIjOUGxUVhaysLNjY2MjUU1NTg6ampkJjUwb2IBIRkcry9/eHr68vRowYgVmzZqFMmTIy5W/evMH06dPx5MkTLFy4UKFx+fv74+bNm9i1axcOHTqEn3/+GXPnzkXjxo0hEAjkuifd9+jNmze4cOGCdIPsp0+fAviQgNnY2Ch8Rbq1tTV27tyJVq1aQSAQYP/+/RAIBHBxcZGp9/jxY4VvC/T48WNpL2Z2drb0WH6sra3l0iafpEJERCpt27ZtmDVrlnSeWu4TLF6+fIlbt24hOzsb06ZNQ7du3ZQWY34rmStXroyuXbvCx8dHKYtVirqI4ezZs1i0aJFCHgd4+PBhaUL45MkTSCQSqKmpwcrKSvqIvfr16ytlq5vr16+je/fuqFSpEoyMjHDt2jXUr18/z3zDgQMHwsDAAL/99ptC4rKyssrzy0buSvn8jsnrc2SCSEREKu/y5ctYtWoVLly4gPT0dACAtrY2GjRogIEDB8LBwUHJEf7P8+fPsWvXLuzduxevX7+GlpaWQh8Zlyu/xCI/8k4sihKTpaWlNCF0cnKCvr7+N2+7KHLnQCYmJqJu3boYM2YMDA0NpeVxcXHo168fhgwZgpYtWyokptznQxeVj4+PXNplgkhERCVGTk4OEhISAABGRkbffC+44sjJycG///6L3bt3IyQkROHtKyux+Jy//voLTk5OKrP9DxWMCSIRERERyVDdX72IiIiISCmYIBIRERGRDG5zQ0REcpOTk4OsrCyoqamp3DYv2dnZyMjIgKampszGx6pAVWNT1bgAxva1JBIJcnJyoK6u/tk5vEwQiYhIbnKfPkFEqs3W1vazG34zQSQiIrnJ7ZGoUU0TQjXV6kHMyMzGk2dZsLW1VbleHbFYjDt37qhcbKoaF/C/2KpVUYeGumrFpsrftdyn1BS2AwATRCIikpvcYWWhmgBCoWoliMKcDz8QhUKhyv3QVlf/8ONY1WJT1biAj2Ljd+2rFDYFhItUiIiIiEgGE0QiIiIiksEEkYiIiIhkMEEkIiIiIhlMEImIiIhIBhNEIiIiIpLBBJGIiIiIZDBBJCIiIiIZTBCJiIiISAYTRCrRwsPDIRKJ8v2zYMECZYenEv79918EBwfnW+bp6YmJEyfKtb20tDQEBwfj/Pnzcr0u8CHeQYMG5Tm+c+dO1KpVCwEBARCLxXJvtyhiY2MRHByMO3fufNX5YWFhCA8PL1YM0dHRGDhwIJycnCASiTBnzhwAQFRUFH766SfUq1cPIpEIGzZsKFY7RPT946P26Lswb948VKtWTeaYmZmZkqJRLf/++y/CwsIwbNiwPGUhISEoVaqUXNtLS0tDSEgIAgMD4ezsLNdr52ft2rX47bff4O3tjblz50ofv6Vor1+/RkhICMzNzVGrVq0vPn/r1q0wMjJCx44dvzqGefPm4fr165g7dy5MTExgamoKAJg8eTLS0tIQFBQEAwMDmJubf3UbRPRjYIJI3wVLS0vY2toWqW5mZiYEAoHSEglVUrt2bbldSyKRKLz3LigoCKtWrYKfnx+mTJlS6LNFv3cPHjyAnZ0dmjVrlud4586d0bhxYyVFRkQlDYeY6bt2/vx5iEQi7N27F/Pnz4ebmxtsbW3x7NkzxMfHY8aMGWjdujXs7e3h4uICf39/XLp0Kc91Xr16heHDh8Pe3h7169fHmDFjcOPGDYhEIplhwYkTJ8Le3h6PHj1Cv379ULduXbi6umL16tUAgGvXrqF79+6oW7cuvLy8sGfPnjxtxcXFYdq0aXB3d4eNjQ08PT0REhKCrKwsaZ3o6GiIRCKsW7cO69evh6enJ+zt7dG1a1dcu3ZNJp6wsDAAkBl+j46OBpD/EHNKSgoWLFgAT09P2NjYwM3NDXPmzEFqaqpMPZFIhFmzZmHr1q1o1aoVbG1tsWfPHri4uAD40DuZ297HbTx9+hRjxoyBi4sLbGxs0KpVK2mMRZWTk4Pp06dj1apVGDp0KKZOnSpNDnPvTX7DtSKRSGa4PTg4GCKRCA8ePMDo0aNRr149NGzYEJMmTcK7d+9kzj18+DA6d+6MevXqoU6dOmjatCkmTZoE4MP3zNfXFwAwadIk6fvObevFixcYNWoUXF1dYWNjg4YNG6JXr17S4WhPT088ePAAFy5ckJ7r6ekpbfvly5cYO3aszD37448/kJOTI21fJBLh2bNnOHHihPQauVMwsrKysHXrVunxXEX5rhHRj4ldKPRdyMnJyfND7eMewqCgINStWxczZ86EmpoaypQpg/j4eABAYGAgTExMkJqair///ht+fn7YsGGDdHg0NTUV/v7+SEpKwtixY1G5cmWcPHkSo0aNyjeWzMxMDBs2DN26dUO/fv0QERGBRYsWISUlBX/99RcGDBiAcuXKYfPmzZg4cSIsLS1hY2MD4MMP7M6dO0NNTQ1Dhw6FhYUFrl69ihUrViAmJgbz5s2TaSssLAzVqlXD5MmTAQC///47Bg4ciGPHjqF06dIYMmQIUlNT8ddff2H79u3S8woafk9LS8NPP/2EV69eISAgQJo8LV26FPfv38eGDRtkeumOHj2KS5cuYejQoTAxMYGhoSHWrl2L/v37w9fXF507dwYAGBsbAwAePnyIbt26oXz58pgwYQJMTU1x6tQpzJ49GwkJCQgMDCzkkwaysrIwduxYHDp0CFOmTIG/v3+h5xRm2LBhaN26NXx9fXH//n0sWrQIAKT3++rVqxg1ahRat26NwMBAaGlp4eXLlzh37hwAwNraGvPmzcOkSZMwePBgeHh4AADKlSsHABgwYABycnIwbtw4VKhQAQkJCbh69SqSk5MBfEimhw8fjtKlS2P69OkAAE1NTQBAfHw8unXrhszMTIwYMQLm5uaIjIzEggUL8Pz5c8yYMQPW1tbYvn07AgMDUalSJUyYMAEAULFiRWzfvh1du3aFl5cX+vbtK33PX/pdI6IfCxNE+i506dIlz7Hbt29L/25hYYGlS5fKlBsaGmLGjBnS19nZ2XB1dUVMTAxCQ0OlCeKePXvw7NkzrFmzBu7u7gAAV1dXpKWlySRduTIzMzFy5Ei0aNECAODk5ITIyEisWrUKe/bskQ7r5vYkHThwQJogBgcHIykpCQcPHkSFChUAAC4uLtDW1saCBQvQr18/1KhRQ9qWnp4eVq1aBaFQCOBD4te5c2ecOHECbdq0gYWFBUxMTAAAdevWLfQ+hoaG4t69e9ixY4d0yN7FxQVly5bF8OHDceLECZlhytTUVERERMDAwEB6LDf5LFeuXJ42582bBz09PWzdulU697FRo0bIyMjA6tWr4efnJ3Ot/Jw6dQoAEBAQIJfkEAB8fX3Rv39/AEDDhg3x7Nkz7N69G3PnzoVAIMDVq1chkUgwc+ZMlC5dWnpe7nzBUqVKwdLSEsCH79rH7zshIQFPnjzB5MmT4e3tLT2e+/0APgz1a2tro1SpUnnu2fr16xEbG4udO3fCzs4OAODm5obs7Gxs27YNvXr1QtWqVVG3bl1oampCX19f5hq5n7+JiYnM8S/9rhHRj4VDzPRdWLBgAXbt2iXz5+MexI9/GH9s69at8PHxga2tLWrXrg1ra2ucPXsWjx49kta5ePEi9PT0pMlhrrZt2+Z7TYFAIJNEqauro3LlyjA1NZWZ82doaAhjY2O8fPlSeiwyMhLOzs4wMzNDVlaW9E9u2xcuXJBpy8PDQ5ocAoCVlRUAICYmJv8bVYjjx4/D0tIStWrVkmnf1dUVAoEgT/sNGjQoNKHLJRaLce7cOTRv3hza2tp53p9YLJYZHi9IrVq1UKFCBWzevLlI9Yvi4+Fc4MNQtFgsxtu3bwFAmiyPHDkShw4dQmxsbJGvbWhoCAsLC+l0gKioKOnQcFGcO3cONWrUkCaHuTp27AiJRCLtxfxSX/pdI6IfC3sQ6btQvXr1zy5SyV3N+bH169dj/vz56NatG0aMGAEjIyOoqanh999/x+PHj6X1EhMTpb0wHytTpky+beno6EBLS0vmmIaGBgwNDfPU1dTUlFnY8fbtWxw/fhzW1tb5XjshIUHm9afXzB2W/NrFIm/fvsWzZ8+K3H5+97UgiYmJyMrKQmhoKEJDQ4t0/fyULVsWwcHB6NWrF/r164e1a9fC3t6+yHHkp6D7mJ6eDgBwdHTEsmXLEBoaigkTJiAjIwOWlpYICAgo8BeFXAKBABs2bMCyZcuwdu1azJ8/H4aGhmjXrh1GjhxZ6CryxMTEfFcd5/bUJiYmFvFdyvrS7xoR/ViYINIPIb/Vrfv374eTkxNmzpwpc/z9+/cyrw0NDXHjxo08579580a+QQIwMjKCSCTCyJEj8y3/1lv3GBkZQUtLC3Pnzi2w/GNfsmpYX18fQqEQ3t7e6NGjR751KlasWKRrVapUCZs2bYK/v780SXRwcAAAaXKekZEhc05xE55mzZqhWbNmyMjIwLVr17Bq1SqMGTMG5ubmhSao5ubm0nv65MkTHD58GCEhIcjIyMCsWbM+e66hoSHi4uLyHH/9+jWAvJ9JUSn7u0ZEqo0JIv2wBAKBtKco1927d3Ht2jWUL19eeszR0RGHDx/Gv//+KzN0fPDgQbnH5OHhgX///RcWFhZFHrotzMe9Ydra2oW2v2rVKhgaGqJSpUrFbu9jOjo6cHZ2RlRUFEQiUZ57/6UqVqyI0NBQ+Pv7o3///lizZg3q1asHExMTaGlp4d69ezL1jx07Vqz2cmlqasLJyQn6+vo4deoUoqKiYG9vX+D7/lTVqlUxZMgQHDlyBFFRUTLXze9cFxcXrFq1Crdv35bp7du7dy8EAsFX7zX5Lb5rRPT9YIJIPywPDw8sX74cS5cuhaOjI548eYLly5ejYsWKyM7Oltbz8fHBxo0bMX78eIwYMQKVK1fGiRMnpIsl1NTkN5V3+PDhOHPmDLp16wY/Pz9UrVoVGRkZiI6OxokTJzBz5kzpytiiqlmzJgBIF9moqakVmKD16tULR44cwU8//YTevXtDJBIhJycH//33H06dOoW+ffuiTp06n22vVKlSMDc3x7Fjx+Di4gIDAwMYGRmhYsWKmDJlCnr06IGePXuie/fuMDc3x/v37/H8+XP8888/2LRp0xe9N3Nz8zxJYv369dG+fXvs3r0bFhYWsLKywo0bN3DgwIEvuvbHfv/9d7x69QouLi4oV64ckpOTsWnTJmhoaMDJyQnAh8Up2traiIiIQPXq1aGrqwszMzMkJCTgl19+QcuWLVG5cmVoaGjg3LlzuHfvHgYOHChto2bNmjh48CAOHTqEihUrQktLCyKRCL1798bevXsxaNAgDB8+HBUqVEBkZCS2bNmC7t27o2rVql/1nr7Fd42Ivh9MEOmHFRAQgLS0NOzatQtr165FjRo1MGPGDBw9elRmgr6uri42btyIuXPn4rfffoNAIICrqyumT5+OgQMHyqxqLS4zMzPs2rULy5cvx7p16xAbGws9PT2Ym5vDzc0N+vr6X3zNtm3b4sqVK9iyZQuWLVsGiUSCY8eO5Tucq6uri7CwMKxevRrbt29HdHQ0tLW1Ub58eTRs2LDIT+CYM2cOfv31VwwePBgZGRnw8fHB/PnzUaNGDYSHh2P58uVYsmQJ4uPjUbp0aVSuXPmrN3GuUKGCNEkcMGAAVq9eLd13ce3atUhNTYWzszNWrlyZZzFKUdWpUwe3bt3CwoULER8fD319fdjY2GDDhg3S1cs6OjqYO3cuQkJC0K9fP2RmZiIwMBA9evSAhYUFtmzZglevXgGAdCsaPz8/aRvDhg1DXFwcpk6divfv38Pc3Bz//PMPjI2NsW3bNixatAiLFi3C+/fvUbFiRYwbNw59+vT5qvcDfJvvGhF9PwQSiUSi7CCISqKVK1diyZIliIyMZE8L0f/Lzs7GtWvXIKqhBaFQtZ5sk5GZg4ePM1C3bl2Z1f+qICMjAzdv3lS52FQ1LuB/sVlW04CGhorFpsLftdx/o4XFxh5EoiLYvHkzAKBatWrIzMzEuXPnEBoaivbt2zM5JCKi7w4TRKIi0NbWxsaNGxEdHY3MzEyUL18eAwYMwODBg5UdGhERkdwxQSQqAl9fX+mzdomIiL53fJIKEREREclggkhEREREMpggEhEREZEMJohEREREJIMJIhERERHJYIJIRERERDKYIBIRERGRDCaIRERERCSDCSIRERERyeCTVIiISG6ys7MBABmZ2RDmqFYfRHp6BgAgLS0NmpqaSo5GVmpqKgDVi01V4wL+F1u6OBMSCJQcjSxV/q5lZWUB+PBvVSgUFlhPIJFIJIoKioiIvm8pKSm4d++essMgokKIRCKUKlWqwHL2IBIRkdzk9pZUq6IOoZpq9epkZGbh2QsJqlgIoKGuWj/+cmOztbX9bK+OoonFYty5cweW8bHQFKpYj7BEgEeGJir9earifcuGAHcNTArt2VStO0pERCVabnKjoS6EUKhaCeKHYcgMaKirQ0NDdZIw4H+xCYVClUoQ1f8/8dIUqkFDxRJ+yf8vo1Dlz1MV75va/w/HF/Y9U620loiIiIiUjgkiEREREclggkhEREREMpggEhEREZEMJohEREREJIMJIhERERHJYIJIRERERDKYIBIRERGRDCaIRERERCSDCSLRJ8LDwyESiQr8c/78eWWHKBd+fn7w8/OTvo6OjoZIJEJ4eLhc23n48CGCg4MRHR0t1+sWxaefZe3ateHu7o5JkyYhNjb2i6/36T2TpytXriA4OBjJycnf5PpFcf78+e/qO05EX4+P2iMqwLx581CtWrU8x2vUqKGEaL49MzMzbN++HRYWFnK97sOHDxESEgInJydUrFhRrtcuqtzPMj09HZcuXcKqVatw4cIFREREQFdXVykxferq1asICQmBj48P9PX1lR0OEf3gmCASFcDS0hK2trbKDkNhNDU1UbduXbldLzMzEwKBajyD9OPPskGDBsjOzsby5ctx9OhRtG/fXsnRfTsSiQRisRja2trKDoWIShgOMRN9pYMHD0IkEmHz5s0yx5cuXYpatWrh9OnTAP43dLtmzRqsWLECHh4esLW1RceOHXH27FmZc589e4ZJkyahRYsWqFOnDtzc3BAQEIB79+7J1MsdCjxw4AAWL14MV1dXODg4oHfv3nj8+LFMXYlEgjVr1qBJkyawtbWFj48P/v333zzvp6Ah5qdPn2LMmDFwcXGBjY0NWrVqhbCwsHzj2bt3L+bPnw83NzfY2tpi586dGDFiBADA399fOtT7cRtnzpxBr1694ODggDp16qBbt2557kt8fDx+/vlnNG7cGDY2NmjQoAG6deuGM2fOFPj5fE5uIvzy5UsAgFgsxqJFi+Dp6QkbGxu4ublh5syZnx3ulUgkaNGiBfr165en7P3796hXrx5mzpwJAMjJycHy5cvh5eUFOzs71K9fH+3atcPGjRsBAMHBwfj1118BAE2bNpWZzjB58mQ4OTkhLS0tTzv+/v5o06aN9LVIJMKsWbOwdetWtGrVCra2ttizZw8A4NKlS+jVqxfs7e2l9zkyMvLLbx4R/RDYg0hUgJycHGRlZckcEwgEEAqFAIA2bdrgwoULmD9/PurUqQNbW1ucPXsWK1aswKBBg9CoUSOZc8PCwlChQgVMnjwZOTk5WLt2LQYMGIDQ0FDY29sDAF6/fg1DQ0OMGTMGxsbGSEpKwp49e9ClSxfs2bMnz5B3UFAQHBwcMGfOHKSkpGDhwoUYPHgwDh06JI0zJCQEISEh8PX1hZeXF169eoWff/4ZOTk5qFq16mfvwcOHD9GtWzeUL18eEyZMgKmpKU6dOoXZs2cjISEBgYGBeeKpW7cuZs6cCTU1NdjY2CA5ORlBQUGYNm0arK2tAUA6jL1v3z5MmDABTZs2xYIFC6Curo7t27ejX79+WLduHVxcXAAA48aNQ1RUFEaNGoUqVaogOTkZUVFRSExMLOrHKePZs2cAAGNjY0gkEgwZMgTnzp3DwIEDUb9+fdy7dw/BwcG4du0atm/fDk1NzTzXEAgE+OmnnzB37lw8ffoUVapUkZbt3bsXKSkp6NmzJwBg7dq1CAkJweDBg1G/fn1kZWXh8ePHePfuHQCgc+fOSEpKQmhoKEJCQmBqagrgw3QGAwMD7N69GwcOHEDnzp1lPpvz589j2rRpMnEdPXoUly5dwtChQ2FiYoIyZcrgwoUL6Nu3L2rWrIk5c+ZAU1MTW7duRUBAAIKCgtC6deuvuo9E9P1igkhUgC5duuQ5JhQKERUVJX09ZcoU3LhxAyNHjsTq1asxduxY1K9fH8OGDctzbnZ2NtavXw8tLS0AgKurK5o2bYqlS5di/fr1AABHR0c4OjrKnNO4cWO0bdsW27dvx6RJk2SuWaNGDSxcuFD6Wk1NDSNHjsTNmzdRt25dJCcnY82aNWjevDnmzJkjc1737t0LTRDnzZsHPT09bN26FaVKlQIANGrUCBkZGVi9ejX8/PxgYGAgrW9hYYGlS5fKXKNy5crSNj8ewk5LS8PcuXPh4eGBZcuWSY83btwYPj4+CAoKws6dOwF8WMDRuXNnmc+kWbNmn439Y7nJvlgsxsWLF7FixQro6enB09MTp06dwqlTpzBu3Dj0799f+h7LlSuHUaNGYe/evfl+FwCgY8eOWLJkCcLCwjBlyhTp8S1btsDZ2Vk6X/XKlSuoWbOmzPfCzc1N+vdy5cqhfPnyAIBatWrJzNW0srKCk5MTwsLCZBLEzZs3o1SpUujQoYNMTKmpqYiIiJD5XLp27Qp9fX2EhoZCT08PANCkSRN06NABCxYsQKtWrVRmOgARqQYOMRMVYMGCBdi1a5fMnx07dsjU0dTUxJIlS5CYmAgfHx9IJBIsWrRI2nv3sRYtWkiTQwAoVaoUmjRpgosXLyI7OxsAkJWVhZUrV6J169awsbFB7dq1YWNjg6dPn+LRo0d5runp6SnzWiQSAfjf0OnVq1chFovRrl07mXoODg4wNzf/7PsXi8U4d+4cmjdvDm1tbWRlZUn/uLu7QywW49q1a3neY1FdvXpVet8+vnZOTg7c3Nxw8+ZNpKamAgDs7OywZ88eLF++HNeuXUNmZmaR2wE+JPvW1tZwcHDAoEGDYGJigjVr1sDExATnzp0D8CHZ+1irVq2gq6ubZ7j7Y6VKlULHjh2xZ88eaaxnz57Fw4cP8dNPP0nr2dra4u7du5gxYwZOnjyJlJSUL4rf398fd+7cweXLlwEAKSkp2LdvH3x8fKQJX64GDRrIJIepqam4fv06vLy8ZOoKhUK0b98er169yjMtgYiIPYhEBahevXqRFqlUrlwZ9evXR2RkJLp37w4zM7N865mYmOR7LDMzE6mpqShdujTmz5+PsLAwDBgwAI6OjjAwMIBAIMDUqVMhFovznG9oaCjzOncoND09HQCkQ7AFtf05iYmJyMrKQmhoKEJDQ/Otk5CQIPM6d2i0KN68eQMAGD58eIF1kpKSoKuri8WLF2PFihXYtWsXfv/9d+jq6qJ58+YYN25ckdpcsGABqlevDnV1dZQpU0bmM0pMTIS6ujqMjY1lzhEIBDAxMSl0GNvPzw9hYWGIiIhA165dERYWhnLlyqFp06bSOoMGDYKuri7279+Pbdu2QSgUon79+hg7dmyRvmNNmzaFubk5tmzZgnr16iE8PBxpaWno0aNHnrqf3o/k5GRIJJJ871PuffjaoXoi+n4xQSQqpp07dyIyMhJ2dnYICwtD69atUadOnTz1chOiT49paGhIt1rZv38/OnTogNGjR8vUS0hI+KqtT3ITyILa/lwvor6+PoRCIby9vfNNRADk2bbmS4YpjYyMAAA///xzvvcLAMqUKQPgw1zBKVOmYMqUKXj58iX++ecfLFq0CG/fvsW6desKbetzyb6hoSGysrIQHx8vkyRKJBK8efOm0ASucuXKcHd3R1hYGNzd3fHPP/9g2LBhMr3I6urq6NOnD/r06YPk5GScOXMGixcvRv/+/REZGQkdHZ3PtqGmpoaePXti8eLFmDBhArZs2QIXF5d8t2H69DPQ19eHmpoa4uLi8tR9/fo1gP99FkREuTjETFQM9+7dw+zZs9GhQweEhYWhZs2aGDVqFJKSkvLUPXLkiEwvYEpKCo4fP4769etLkwmBQAANDQ2Z8yIjI79qU2fgw2pdLS0tREREyBy/cuUKYmJiPnuujo4OnJ2dERUVBZFIBFtb2zx/ipJYfNqrmcvBwQH6+vp4+PBhvte2tbXNd3FIhQoV8NNPP6Fhw4Yy80G/Vu5CmP3798sc/+uvv5Camiot/xx/f3/cu3cPEyZMgJqaWoFzFoEPCVvLli3Ro0cPJCYmSj+H3PeaX08x8GEhi4aGBsaOHYsnT55IF8AURldXF3Xq1MHff/8t8xnk5ORg//79KFeuXKFzUYnox8MeRKICPHjwQDo38GMWFhYwNjZGamoqRo4ciYoVK2L69OnQ1NTE77//Dh8fH0yaNAnLly+XOU8oFEp7kXJycrBmzRqkpKTILFzw8PCQrlYWiUS4ffs21q1bh3Llyn3VezAwMEDfvn2xYsUKTJkyBS1btsSrV68QHBxcpKHZKVOmoEePHujZsye6d+8Oc3NzvH//Hs+fP8c///yDTZs2FXoNS0tLAMCOHTugp6cHLS0tVKxYEUZGRpg6dSomTpyIpKQkeHl5oUyZMoiPj8fdu3cRHx+PmTNn4t27d/D390fbtm1RrVo16Onp4ebNmzh58iSaN2/+VfflY40aNYKrqysWLlyIlJQUODg44N69e1i6dClq164Nb2/vIl2jRo0aOH/+PNq3by/t+cwVEBAAS0tL2NjYwNjYGDExMdi4cSPMzc2li3hq1qwJANi4cSN8fHygrq6OqlWrShcH6evrw9vbG1u3boW5uXme+aefM3r0aPTt2xf+/v7o27cvNDQ0sGXLFjx48ABBQUFcoEJEeTBBJCrApyuGc82ePRudO3fG9OnT8d9//2HXrl3SIeJKlSph9uzZGDFiBDZs2IDevXtLz+vZsyfEYjFmz56Nt2/fwtLSEqtWrUK9evWkdaZMmQJ1dXWsXr0aqampqF27NoKDg/H7779/9fsYMWIEdHV1sWXLFuzbtw/VqlXDzJkz8ccffxR6bo0aNRAeHo7ly5djyZIliI+PR+nSpVG5cmU0bty4SO1XqlQJkydPxqZNm+Dv74/s7GzMmzcPHTt2hLe3NypUqIC1a9di+vTpeP/+PYyNjVGrVi34+PgAALS0tGBnZ4d9+/YhJiYGWVlZKF++PAYMGCBddVwcAoEAy5cvR3BwMMLDw7Fy5UoYGhrC29sbo0ePzrcXMz+tWrVCcHCwzOKUXM7Ozvjrr7+wc+dOpKSkwNTUFA0bNsSQIUOkPcbOzs4YNGgQ9uzZg507dyInJwebNm2Cs7Oz9DqtW7fG1q1b0a1bN6ipFX0AyMnJCRs2bEBwcDAmTZqEnJwcWFlZYcWKFWjSpEmRr0NEPw6BRCKRKDsIou9ZdHQ0mjZtivHjx+e7qTJ9Hzp27AiBQIDdu3d/szbmz5+PrVu3IjIyUmXnDWZnZ+PatWsQ1dCCUKhaPZMZmTl4+DgDltU0oKGRd6cBZcqNrW7duvnugqAsGRkZuHnzJqyT4qChpmKfJ9QQVdpYpT9PVbxv2RDgZukyhX7X2INIRPSVUlJScP/+fURGRuL27dsy+znK07Vr1/D06VNs2bIFXbt2VdnkkIi+H0wQiYi+0u3bt+Hv7w9DQ0MEBgZ+0ebdX6Jr167Q0dGBh4cHRo4c+U3aICL6GBNEom+sYsWKeZ6lTN8HZ2dnhXy2/P4QkaJxmxsiIiIiksEEkYiIiIhkMEEkIiIiIhlMEImIiIhIBhNEIiIiIpLBBJGIiIiIZDBBJCIiIiIZTBCJiIiISAYTRCIiIiKSwQSRiIiIiGTwUXtERCQ32dnZAICMzGwIc1SrDyI9PePDf8WZkECg5Ghk5caWlpYGTU1NJUfzP6mpqQCANKip3D1L/f//qvTnqYL3Lev//5udnQ2hUFhgPYFEIpEoJiQiIvrepaSk8NnRRCWASCRCqVKlCixnDyIREclNbu+Xra3tZ3snlEEsFuPOnTuwjI+FplDFejclAjwyNFG5+5Z7z6pYCKChrlopQ0ZmFp69kKjcPQP+d99UMbbs7GzcvHmz0J5q1fq0iYioRMv9YSgUClXuB6P6/yc4mkI1aKip1rCf5P+XBKjafcu9Zxrq6tDQUJ24APz/0G2Gyt0z4H/3TRVjy1VYXKr1KxQRERERKR0TRCIiIiKSwQSRiIiIiGQwQSQiIiIiGUwQiYiIiEgGE0QiIiIiksEEkYiIiIhkMEEkIiIiIhlMEImIiIhIBhNEIioSkUhU6J/g4GBp3dy/q7KwsDCEh4fnOR4bG4vg4GDcuXNHIXFkZGRg2rRpcHV1Ra1ateDt7Q0A8PT0xMSJE6X1oqOjIRKJZGK+cuUKgoODkZycrJBYiejHwEftEVGRbN++Pd/jWVlZmDBhAmJjY9G4cWNp3XLlyikyvK+ydetWGBkZoWPHjjLHX79+jZCQEJibm6NWrVoKiWP79u34+eefYW1tDV1dXQBASEgISpUq9dlzr169ipCQEPj4+EBfX/+bx0pEPwYmiERUJHXr1s33+OzZsxEdHY1Zs2bBzs7us3W/tbS0NOjo6Cil7eJ48OABtLW18dNPP8kcr127tpIiKrn3kojkg0PMRPTV9u7di9DQUPj6+qJr167S4/kNMV+6dAldu3aFra0t3NzcsGTJEuzcuRMikQjR0dHSeocOHULfvn3h6uoKOzs7tGrVCgsXLkRqaqrM9SZOnAh7e3vcu3cPffv2hb29PXr37g3gw5Dt8uXL0bJlS9jY2KBBgwaYNGkS4uPjped7enriwYMHuHDhgnSI3NPTE+fPn4evry8AYNKkSXmGzwHg5s2bCAgIgJOTE2xtbdGhQwccOnRIJr7w8HCIRCKcO3cO06dPh7OzM5ydnREYGIjY2FiZe7Vz506kp6dL28odQv50iPlTwcHB+PXXXwEATZs2lZ5//vx5mfvZtWtX1K1bF/b29ujXrx+ioqKKfC+J6MfEHkQi+ipRUVGYPn06bG1tMX369M/WvXv3Lvr27YsqVapgwYIF0NbWxrZt27B///48dZ8+fQp3d3f06tULOjo6ePz4MdasWYMbN25g06ZNMnUzMzMxePBgdOvWDQMGDEB2djZycnIwZMgQXL58Gf369YODgwNiYmIQHByMGzduYPfu3dDW1kZISAiGDx+O0qVLS+PX1NSEhYUF5s2bh0mTJmHw4MHw8PAAAOmQ+blz59C/f3/UqVMHM2bMQOnSpXHo0CGMGjUK6enpeYarp06dCg8PDyxatAj//fcffvvtN4wbN076XrZv347ly5fj/Pnz2LhxIwDAwsKiSJ9B586dkZSUhNDQUISEhMDU1BQAUKNGDQDAypUrsWTJEnTs2BGDBw9GZmYm1q1bh549e2Lnzp3SegXdSyL6cTFBJKIvFh8fj8DAQOjq6iI4OBiampqfrb9ixQoIhUJs2LABxsbGAAAPDw+0a9cuT90hQ4ZI/y6RSODg4IDq1avjp59+wt27d2FlZSUtz8zMxNChQ9GpUyfpsYMHD+LkyZMIDg5GixYtpMetrKzg6+uL8PBw9OjRA7Vr14a2tjZKlSqVZ0jc0tISwIdE7dOymTNnwtLSEhs3boS6+of/hbq5uSEhIQFBQUHo0KED1NT+Nzjj5uaGqVOnSl8nJSXht99+Q1xcHExNTVG3bl0YGxtDTU3ti4fmy5Urh/LlywMAatWqhYoVK0rL/vvvPwQHB+Onn36Sab9hw4bw8vJCSEgIlixZ8tl7SUQ/Lg4xE9EXyc7OxujRo/Hq1SssXrxYmqB8zsWLF+Hs7CxNDgFATU0NrVq1ylP3xYsXGDNmDBo1aoRatWrB2tpaOjfv8ePHeep7eXnJvD5+/Dj09fXRpEkTZGVlSf/UqlULpqamuHDhwpe+Zalnz57h8ePH0sT24+u7u7sjLi4OT548kTnH09NT5rVIJAIAvHz58qvjKIpTp04hKysL3t7eMnFqaWnB0dEx3/vw6b0koh8XexCJ6Iv89ttvOHv2LCZMmIAGDRoU6ZzExESYmJjkOV6mTBmZ1+/fv0ePHj2gpaWFkSNHokqVKtDW1sarV68QGBiI9PR0mfo6Ojp5Vvm+ffsWycnJsLGxyTeWhISEIsWcnzdv3gAAFixYgAULFhTp+oaGhjKvc3tbP30v8pYba+58yk993MsJ5H8viejHxQSRiIrswIEDWL9+PVq3bo2+ffsW+TxDQ0NpwvKxT4+dO3cOr1+/RmhoKJycnKTH3717l+91BQJBnmNGRkYwNDTE2rVr8z1HT0+vyHHnd20AGDRoEJo3b55vnapVq3719eUpN9alS5eiQoUKhdbP714S0Y+LCSIRFcndu3cxdepU1KxZE3PmzPmicx0dHXHixAnEx8dLh5lzcnLw559/ytTLTVI+ndO4bdu2Irfl4eGBgwcPIicnB3Xq1PlsXU1NzXx78grq5atWrRqqVKmCu3fvYvTo0UWO6VvKjVUsFsscd3V1hbq6Op4/f86hYyL6YkwQiahQSUlJGDp0KDIyMtC/f3/cv38/33rGxsb5rsAdPHgwjh8/jt69eyMgIEC6ijktLQ3A/4Y77e3tYWBggOnTpyMwMBDq6uqIiIjAvXv3ihxrmzZtEBERgYEDB8LPzw92dnbQ0NDAq1evcP78eTRt2lTa+1ezZk0cPHgQhw4dQsWKFaGlpQWRSAQLCwtoa2sjIiIC1atXh66uLszMzFC2bFnMnDkTAwYMQL9+/eDj44OyZcsiKSkJjx49wu3bt7F06dIvvb3FUrNmTQDAxo0b4ePjA3V1dVStWhUVK1bE8OHDsWTJErx48QLu7u7Q19fHmzdvcPPmTejo6GD48OEKjZWISg4miERUqLt370r3Khw/fnyB9Xx8fDB//vw8x62srPDHH3/g119/xYQJE2BgYID27dvD0dERCxcuROnSpQF8GBZdtWoVFixYgHHjxkFHRwdNmzbF4sWL4ePjU6RYhUIhVqxYgU2bNmHfvn1YvXo1hEIhypUrB0dHR2lCBQDDhg1DXFwcpk6divfv38Pc3Bz//PMPdHR0MHfuXISEhKBfv37IzMxEYGAghg0bhgYNGmDnzp1YuXIl5s6di+TkZBgaGqJ69er5Lrr51pydnTFo0CDs2bMHO3fuRE5ODjZt2iQ9Xr16dWzatAkHDx5ERkYGTE1NYWNjg+7duys8ViIqOQQSiUSi7CCI6MfUt29fxMTE4K+//lJ2KCQn2dnZuHbtGurWrQuhUKjscGRkZGTg5s2bsE6Kg4aaas25zIAaokobq9x9y71nltU0oKGhOnEBQEZmDh4+zlC5ewb8776pYmxF/TfKHkQiUoh58+ahVq1aKF++PJKSkhAREYHTp09/8XxGIiL69pggEpFCZGdnY+nSpXjz5g0EAgGqV6+OX3/9Fd7e3soOjYiIPsEEkYgUYurUqTJP9CAiItXFJ6kQERERkQwmiEREREQkgwkiEREREclggkhEREREMpggEhEREZEMJohEREREJIMJIhERERHJYIJIRERERDKYIBIRERGRDD5JhYiI5CY7OxsAIBaLoa6uWj9iUlNTAQBpUIMEAiVHIyv1//+blpYGTU1Npcbysdx7li7OVLl7lp6eAUD17hnw0XdNBWPLysoC8OHfqlAoLLCeQCKRSBQVFBERfd9SUlJw7949ZYdBRIUQiUQoVapUgeWq9esdERGVaLm9Jba2tp/tnVAGsViMO3fuqHRsVSwE0FChnteMzCw8eyFR6XvG2L5MdnY2bt68WWjPpup8C4mIqMTL/WEoFApV7gdj7pC3Ksemoa4ODQ3Vie3DsHKGSt8zxvZ1CouLi1SIiIiISAYTRCIiIiKSwQSRiIiIiGQwQSQiIiIiGUwQiYiIiEgGE0QiIiIiksEEkYiIiIhkMEEkIiIiIhlMEImIiIhIBhNEIgW6fv06hg4dCg8PD9jY2KBhw4bo2rUr5s+fr+zQIBKJMGvWrC865+jRoxCJRNi6dWuBdU6fPg2RSIT169cXN0Sl8vT0xKBBg5QdBhGRQjBBJFKQyMhIdOvWDSkpKRg3bhz++OMPTJkyBQ4ODjh06JCyw/sqHh4eMDU1xe7duwusEx4eDg0NDXh7eyswMiIiKg4+i5lIQdauXYuKFSti3bp10ud0AkCbNm0wbtw4JUb29dTV1eHt7Y21a9fi/v37qFmzpkx5cnIy/v77b3h6esLY2LhYbaWlpUFHR6dY1yAioqJhDyKRgiQmJsLIyEgmOcylpva/f4q5Q5knTpyAj48P7Ozs0LJlS+zatUvmnPj4eMyYMQOtW7eGvb09XFxc4O/vj0uXLuW5fkZGBkJCQtCqVSvY2trC2dkZfn5+uHLlSoHxSiQSBAUFwdraGjt27Ciwnq+vL4APPYWfOnDgAMRiMTp16iS9ZlhYGLy9vWFnZwdHR0cMHz4cL168kDnPz88Pbdu2xcWLF9GtWzfUqVMHkydPlrk/f//9N9q1awdbW1s0bdoUmzZtkrmGWCzG/Pnz4e3tjXr16sHJyQldu3bF0aNH88SZk5OD0NBQaVz169dHly5dcOzYsQLfNwCEhYWhdu3aWLp0qTRuPz+/PPUmTpwIT09P6evo6GiIRCKsW7cO69evh6enJ+zt7dG1a1dcu3Ytz/k7duyAl5cXbGxs0Lp1a0REROS5JvDhc16+fDlatmwJGxsbNGjQAJMmTUJ8fLxMvaJ+x4jox8UeRCIFqVu3Lnbu3InZs2ejXbt2qF27NjQ0NPKte/fuXSxYsAADBgyAiYkJdu7ciSlTpqBy5cpwdHQE8CHhBIDAwECYmJggNTUVf//9N/z8/LBhwwY4OzsDALKystC/f39cvnwZ/v7+aNCgAbKzs3H9+nX8999/+bafkZGBiRMnIjIyEitWrIC7u3uB76tq1aqoV68e9u/fjzFjxsi8p/DwcJQtWxaurq4AgGnTpmHPnj3w8/PD2LFjkZSUhGXLlqFbt27Yt28fTExMpOfGxcVh3Lhx6N+/P0aNGiWTRN+5cwdz586VvveIiAjMmTMHmZmZ6Nevn/Q9JCUloW/fvihbtiwyMzNx5swZDBs2DPPmzUOHDh2k15s4cSL2798PX19fDB8+HBoaGoiKikJMTEy+71kikeDXX39FaGgoZs+ejY4dOxZ4fz4nLCwM1apVkya/v//+OwYOHIhjx46hdOnSAIDt27dj2rRp8PLywqRJk/Du3TuEhIQgMzNT5lo5OTkYMmQILl++jH79+sHBwQExMTEIDg7GjRs3sHv3bmhra0vrF+U7RkQ/LiaIRAoyZswYPH78GKGhoQgNDYWGhgZsbGzg6emJnj17Qk9PT1o3ISEBW7duRYUKFQAAjo6OOHfuHCIiIqQ/vKtVq4YZM2ZIz8nOzoarqytiYmIQGhoqTRAPHDiA8+fPY/bs2ejcubO0/qe9T7kSExMxZMgQREdHY8uWLbCysir0vfn6+mLSpEmIjIxE8+bNAQAPHjzAzZs3ERAQAKFQiGvXrmHHjh2YOHEi+vTpIz23fv368PLywvr162WG2hMTE7FkyRK4uLjkae/169fYu3evNLbGjRsjPj4ey5cvR48ePaCjo4PSpUtj3rx5MvfHxcUFycnJ2LhxozRBvHTpEvbt24eAgACMGjVKWr+gpDg9PR3jx4/HmTNnsGbNmnzjKyo9PT2sWrUKQqEQAGBmZobOnTvjxIkTaNOmDXJychAcHIw6depIeykBoF69emjRogXMzMykxw4fPoyTJ08iODgYLVq0kB63srKCr68vwsPD0aNHD+nxonzHiOjHxSFmIgUxMjLCli1bsGvXLowZMwaenp54+vQpFi1ahHbt2skMA9aqVUv6gxsAtLS0UKVKFbx8+VLmmlu3boWPjw9sbW1Ru3ZtWFtb4+zZs3j06JG0zsmTJ6GlpSUd5v2c6OhodO3aFSkpKdixY0ee5DA7OxtZWVnSPzk5OQCAVq1aQU9PT2axyu7duyEQCKTtHj9+HAKBAO3bt5e5homJCaysrHDhwgWZtgwMDApMviwtLfPE1rZtW6SkpOD27dvSY4cPH0a3bt1gb28vvT+7du2SuT8nTpwAAPTs2bPQ+5OYmIhevXrhxo0b2LJlS7GSQ+DDIp/c5BCA9D3l9lw+efIEcXFxaNWqlcx5FSpUgL29vcyx48ePQ19fH02aNJG5v7Vq1YKpqWme+1vU7xgR/ZjYg0ikYLa2trC1tQUAZGZmYuHChdiwYQPWrl2L8ePHAwAMDQ3znKepqQmxWCx9vX79esyfPx/dunXDiBEjYGRkBDU1Nfz+++94/PixtF58fDzMzMxkhmgLcuPGDSQkJGDUqFEoV65cnvLmzZvLDLsGBgZi2LBh0NHRQZs2bRAeHo64uDgYGRlh//79cHR0hIWFBQDg7du3kEgkaNiwYb5tV6pUSea1qalpgXF+PBT96bHcofcjR45g5MiRaNmyJfr37w8TExMIhUJs3bpVJpGNj4+HUCj8bHu5nj59iqSkJHTp0iXPgpyv8ennrKmpCQDSzzkhIQEAUKZMmTznmpiYyHwWb9++RXJyMmxsbPJtK/daBbWd2/7H3zEi+nExQSRSIg0NDQQGBmLDhg148ODBF527f/9+ODk5YebMmTLH379/L/Pa2NgYly9fRk5OTqFJYuvWrWFiYoLFixdL57R9bMWKFcjIyJC+/niI09fXFzt27MDevXtRtWpVvH37FhMmTJCWGxkZQSAQICwsTJoIfezTYwKBoMA437x5U+Cx3MRn//79qFixIpYsWSJzrY0bN8qcZ2xsjOzsbMTFxcm8n/zUrVsXLVu2xJQpUwAAM2bMkLmnmpqaSElJyXPep8lZURkZGQH4kPx96tN7YGRkBENDQ6xduzbfa308hYGIqDAcYiZSkNevX+d7PHe4s7Dk5FMCgSBPUnX37t08q2Dd3NwgFovzXWWcnyFDhmDy5MlYunQpFi1aJFMmEomkPaC2trYoW7astKxOnTqoWbMmwsPDsXv3bpQuXRpeXl7Scg8PD0gkEsTGxspcI/ePSCQq8nt/8OAB7t69K3PswIED0NPTg7W1NYAP90dDQ0MmOYyLi8uzMjl3ruHnNvv+mI+PD4KCghAeHo7x48cjOztbWmZubo6nT5/KJNEJCQm4evVqkd/bx6pWrQpTU1McPnxY5vjLly/zXNPDwwOJiYnIycnJ9/5Wq1btq2Igoh8TexCJFKRfv34oV64cmjRpgmrVqkEikeDOnTv4448/oKurC39//y+6noeHB5YvX46lS5fC0dERT548wfLly1GxYkWZpKVt27YIDw/HjBkz8OTJEzg7O0MikeD69euoXr062rRpk+favXr1gq6uLqZNm4bU1FRMnTr1sz16uTp16oR58+bhyZMn6Nq1q8yq2Xr16qFr166YPHkybt26BUdHR+jo6CAuLg6XL19GzZo1ZRZRfI6ZmRkGDx6MwMBAmJqaYv/+/Th9+jTGjh0r3SvRw8MDR44cwYwZM+Dl5YVXr15h+fLlMDMzw9OnT6XXql+/Pry9vbFixQq8ffsWHh4e0NTURFRUFHR0dPLdtqZly5bQ0dHB8OHDIRaLsWjRImhqasLb2xvbt2/H2LFj0aVLFyQmJmLt2rUoVapUkd7Xp9TU1DBs2DBMmzYNw4cPR6dOnZCcnIyQkBCYmprKfCZt2rRBREQEBg4cCD8/P9jZ2UFDQwOvXr3C+fPn0bRpU+kCIiKiwjBBJFKQwYMH49ixY9i4cSNev36NzMxMmJqaomHDhhg0aBCqV6/+RdcLCAhAWloadu3ahbVr16JGjRqYMWMGjh49KrMgQV1dHWvWrMGqVatw8OBBbNy4EXp6erCysoKbm1uB1+/cuTN0dXUxfvx4pKamYs6cOYUOUbdv3x4LFy5EZmZmvotiZs2ahTp16mD79u3YunUrcnJyYGZmBgcHB9jZ2RX5vdeqVQsdO3ZEcHAwnj59CjMzM0yaNAm9e/eW1unUqRPevn2Lbdu2Yffu3ahUqRIGDhyIV69eISQkROZ68+fPR+3atbF7926Eh4dDW1sbNWrU+Oyj9Ro3bozVq1cjICAAQ4YMQUhICOrVq4cFCxZg9erVGDJkCCpVqoShQ4fixIkTeRaJFFXXrl0hEAiwdu1aDB06FObm5tKtcD7epkgoFGLFihXYtGkT9u3bh9WrV0MoFKJcuXJwdHSUy5xJIvpxCCQSiUTZQRARFZWnpycsLS2xatUqZYeiNMnJyfDy8kKzZs3wyy+/KDscGdnZ2bh27Rrq1q0rs0JbFWRkZODmzZsqHZtlNQ1oaKhObBmZOXj4OEOl7xlj+zJF/TfKHkQiIhUWFxeHlStXwtnZGYaGhnj58iU2bNiA9+/ff/G0BCKiomKCSESkwjQ1NRETE4OZM2ciKSkJ2traqFOnDmbOnAlLS0tlh0dE3ykmiERUovzzzz/KDkGhDAwMsHLlSmWHQUQ/GG5zQ0REREQymCASERERkQwmiEREREQkgwkiEREREclggkhEREREMpggEhEREZEMJohEREREJIMJIhERERHJYIJIRERERDKYIBIRERGRDD5qj4iI5CY7OxsAIBaLoa6uWj9iUlNTAQBpaWnQ1NRUcjSycmNLF2dCAoGSo/mf9PQMAKp9zxjbl8nKygLw4d+qUCgssJ5AIpFIFBUUERF931JSUnDv3j1lh0FEhRCJRChVqlSB5ar16x0REZVoub0l1aqoQ6imOj1hAJCRmYVnLySwjI+FplC1ZlilSwR4ZGgCW1vbz/bqKJpYLMadO3dULi6gZMRWxUIADRXrSc/OkeDx06xCezZVK2oiIirRcn9Qa6gLIRSqVoL4Yeg2A5pCNWioWPIq+f8lAUKhUKWSndxpAqoWF1AyYtNQV4eGhmrFppYtAZBV6D1TrV+hiIiIiEjpmCASERERkQwmiEREREQkgwkiEREREclggkhEREREMpggEhEREZEMJohEREREJIMJIhERERHJYIJIRERERDKYINJ3KTw8HCKRCDdv3pTbNaOjoyESiRAeHi63a37s4cOHCA4ORnR09Bed5+fnBz8/v28S09fw9PTExIkTpa+Lc99iY2MRHByMO3fuyDNEAMCkSZPQr18/6evcOEUiEQ4ePJinfnBwMEQiEeLj4+UeS34mTpwIT09PmWMikQjBwcHfpL2zZ8/C3t4esbGx3+T6RFSyMEEkUhEPHz5ESEgIYmJivui86dOnY/r06d8oKuV6/fo1QkJC5J4gRkVFYe/evRg5cmS+5YsXL0ZmZqZc21R1Li4usLW1RVBQkLJDISIVwASRqIRKS0sDANSoUQM1atRQcjQly+rVq2FnZwdbW9s8Ze7u7njx4gW2bdv2TWPIzMxEVlbWN23jS/Xs2RMRERH477//lB0KESkZE0T6YUycOBH29vZ49uwZBgwYAHt7ezRu3Bjz589HRkaGTN3Y2FiMGDEC9vb2qFevHkaOHIk3b97kuWZBw7v5DQ9u2bIF7du3h729Pezt7dGyZUtpb014eDhGjBgBAPD395cOdeYOy/r5+aFt27a4ePEiunXrhjp16mDy5MkFxpCRkYHly5ejZcuWsLGxQYMGDTBp0qQ8w6Oenp4YNGgQ/v77b7Rr1w62trZo2rQpNm3alOc9paSkYMGCBfD09ISNjQ3c3NwwZ84cpKamfva+F+Tp06cYM2YMXFxcYGNjg1atWiEsLExafv78efj6+gL4MByce09yh1hfvHiBUaNGwdXVFTY2NmjYsCF69epVaG/jmzdvcPToUbRv3z7f8gYNGsDV1RXLly9HSkrKZ6/16XB6rk8/k/Pnz0MkEmHv3r2YP38+3NzcYGtri2fPngH48Pl7eXlJ78PevXs/2+7H4uLiMG3aNLi7u8PGxgaenp4ICQnJk3x+7vuXq0mTJtDV1cWOHTuK3D4RfZ/UlR0AkSJlZmZi8ODB8PX1Rd++fXHx4kUsX74cpUqVQmBgIAAgPT0dffr0wevXrzFmzBhUqVIFkZGRGDVq1Fe3e/DgQcycORN+fn6YMGEC1NTU8OzZMzx8+BAA4OHhgdGjRyMoKAjTpk2DtbU1AMDCwkJ6jbi4OIwbNw79+/fHqFGjoKaW/+93OTk5GDJkCC5fvox+/frBwcEBMTExCA4Oxo0bN7B7925oa2tL69+5cwdz585FYGAgTExMEBERgTlz5iAzM1M6Ry8tLQ0//fQTXr16hYCAAIhEIjx48ABLly7F/fv3sWHDBggEgiLfj4cPH6Jbt24oX748JkyYAFNTU5w6dQqzZ89GQkICAgMDYW1tjXnz5mHSpEkYPHgwPDw8AADlypUDAAwYMAA5OTkYN24cKlSogISEBFy9ehXJycmfbfvUqVPIzMxEgwYNCqwzbtw4dOjQAevWrZMm7vIQFBSEunXrYubMmVBTU0OZMmUQHh6OSZMmoWnTppg4cSLevXuHkJAQZGRkFPgZ54qLi0Pnzp2hpqaGoUOHwsLCAlevXsWKFSsQExODefPmASj8+5dLU1MT9vb2+Pfff+X6vomo5GGCSD+UzMxMDBs2DK1atQLwYd7VrVu3cODAAWmCuGfPHjx69AjLly9H06ZNAQCurq4Qi8Vf3bNy5coV6OvrY+rUqdJjLi4u0r8bGxujcuXKAD4MGdetWzfPNRITE7FkyRKZ8/Jz+PBhnDx5EsHBwWjRooX0uJWVFXx9fREeHo4ePXpIj79+/Rp79+6FlZUVAKBx48aIj4/H8uXL0aNHD+jo6CA0NBT37t3Djh07pMOyLi4uKFu2LIYPH44TJ06gcePGRb4f8+bNg56eHrZu3YpSpUoBABo1aoSMjAysXr0afn5+MDAwgKWlJYAPifLH9yQhIQFPnjzB5MmT4e3tLT3+8fstyLVr16CtrY1q1aoVWMfKygpt27bFhg0b0KNHD5iamhb5vX2OhYUFli5dKn2dk5ODxYsXw9raGsuWLZMm2fXq1YOXlxfMzMw+e73g4GAkJSXh4MGDqFChAoAPn4u2tjYWLFiAfv36oUaNGoV+/z5Wu3ZtrF69GqmpqdDV1S3uWyaiEopDzPRDEQgE+a4MffnypfT1+fPnoaenJ00Oc7Vt2/ar27W1tUVycjJGjx6No0ePftVKWAMDg0KTQwA4fvw49PX10aRJE2RlZUn/1KpVC6amprhw4YJMfUtLS2lymKtt27ZISUnB7du3pde0tLRErVq1ZK7p6uoKgUCQ55qfIxaLce7cOTRv3hza2toy13N3d4dYLMa1a9c+ew1DQ0NYWFhg3bp1WL9+PaKiopCTk1Ok9l+/fg1jY+NCezxHjhyJzMxMhISEFPWtFerTBPbJkyd4/fo12rZtKxOPubk57O3tC71eZGQknJ2dYWZmluc+ApB+Ll/y/StTpgxycnLynVJBRD8O9iDSD0VHRwdaWloyxzQ1NSEWi6WvExMTYWJikufc/I4VVYcOHZCdnY2dO3di+PDhyMnJga2tLUaOHIlGjRoV6RpF7cV6+/YtkpOTYWNjk295QkKCzOvPvdfExETpNZ89eyYd+i7smp+TmJiIrKwshIaGIjQ09KuuJxAIsGHDBixbtgxr167F/PnzYWhoiHbt2mHkyJHSXsn8iMViaGpqFhpnxYoV0aNHD4SFhaFPnz6F1i+KTz/D3PdZ0GdQ2Ir2t2/f4vjx44V+Ll/y/cv995Genl60N0VE3yUmiESfMDQ0xI0bN/Icz69HRVNTM9+FDPklOJ06dUKnTp2QmpqKixcvIjg4GIMGDcJff/0Fc3PzQuMq6hw/IyMjGBoaYu3atfmW6+npybzO733lHjM0NJReU0tLC3Pnzi2wzaLS19eHUCiEt7e3zFD3xypWrFjodczNzaXxPHnyBIcPH5bO3Zs1a1aB5xkaGkp7RgszePBg7N69G0FBQdLh7o9pamrmWeAEfPj887snn36GuXU+9xl8jpGREUQiUYHb9Xw8RF3U719SUpJMbET0Y2KCSPQJZ2dnHD58GMeOHZMZZj5w4ECeuubm5vjrr7+QkZEh7ZXKXSxRUC+Wrq4uGjdujMzMTAwdOhQPHz6Eubm59Pzi9tx4eHjg4MGDyMnJQZ06dQqt/+DBA9y9e1dmmPnAgQPQ09OT9kx5eHhg1apVMDQ0RKVKlYoVn46ODpydnREVFQWRSPTZ3ryi3pOqVatiyJAhOHLkCKKioj5bt1q1ajh48CDevXuH0qVLf7aukZERBgwYgMWLF0u3FfqYubk57t27J3PsyZMnePLkSZESrKpVq8LU1BQHDhxAnz59pAlkTEwMrl69WugcRA8PD/z777+wsLCAgYFBoe0BBX//cr148QKGhobF6jEnopKPCSLRJzp06IANGzZgwoQJGDVqFCpXrox///0Xp06dylPX29sb27dvx9ixY9GlSxckJiZi7dq1eZLDqVOnQltbGw4ODjA1NUVcXBxWr16N0qVLSxd95PZQ7dixA3p6etDS0kLFihW/uCenTZs2iIiIwMCBA+Hn5wc7OztoaGjg1atXOH/+PJo2bYrmzZtL65uZmWHw4MEIDAyEqakp9u/fj9OnT2Ps2LHQ0dEBAPTq1QtHjhzBTz/9hN69e0MkEiEnJwf//fcfTp06hb59+xYpGc01ZcoU9OjRAz179kT37t1hbm6O9+/f4/nz5/jnn3+k2+xYWFhAW1sbERERqF69OnR1dWFmZoaEhAT88ssvaNmyJSpXrgwNDQ2cO3cO9+7dw8CBAz/btrOzM5YuXYrr16/D1dW10Fh79eqFsLAwnDhxIk+Zt7c3xo0bhxkzZsDLywsxMTFYu3ZtkT8zNTU1jBgxAlOnTsXQoUPRpUsXJCcnIyQkpEgJ2vDhw3HmzBl069YNfn5+qFq1KjIyMhAdHY0TJ05g5syZKFeuXJG+f7muXbsGJyenL1qVTkTfHyaIRJ/Q0dHBpk2bMGfOHCxcuBACgQCurq4ICgpCt27dZOrWq/d/7N15XBXV+8DxD1w2ARUQcMFd4bqwiAu4oYSVmruGO+YuKpJo5FK5lZrlDuKGuZBbKmaippmalomWmpRLrqWYiiKigiyX+/uD752fI6CAlJTP+/XipXfmzJlnzszlPpwzZ24DZs2axbJlyxgxYgSVKlVi5MiRHDx4UDVxo2HDhkRHR7Nr1y7u3buHra2tsq2dnR0AlSpVYuLEiaxZs4Z+/fqh0+mYOXMmXbt2LVD8Go2GxYsXs2bNGrZt28ayZcvQaDSUK1eORo0a4eLioipfu3ZtunbtSlhYGFeuXMHR0ZEJEybQv39/pYylpSVr165l2bJlbNy4kWvXrmFhYUH58uVp2rRpvobIH1ezZk2io6OJiIhg/vz5JCYmUrJkSapUqaKaDV2iRAlmzJhBeHg4gwYNIiMjg6CgIHr37k3lypVZt24dN27cUNpv3Lhxz/zawfr16+Pk5MS3336brwSxRIkSjBo1ig8++CDHug4dOnDr1i02bNhAdHQ0zs7OTJkyhUWLFuW7Lfz9/QGIjIwkKCgIJycnhg0bxrFjx545+cfR0ZHNmzcTERHBihUruHnzJlZWVjg5OeHj40OpUqWA/F1/AH/++Se///47o0aNynf8Qoj/JiO9Xq9/0UEIIV4MPz8/nJ2dWbp06YsO5R/12WefsWTJEg4ePKh6JuTLbv78+Wzbto1vvvkGE5PC9R/odDpOnjyJtqY5Gk3x6oVMz8jiwqV06t5LwNS4mMWGMadL2lGvXj00Gs2LDkeRnp5OXFxcsYsL/h2xOVc3xdS0eMWm0+k5dyHtme0mj7kRQrx0+vTpg7W1teqbW152ycnJrFu3jpCQkEInh0KI/w5JEIUQLx1zc3M++eSTfD3u5mVx7do1hg4dSocOHV50KEKIYkD+TBTiJbZv374XHcIL07BhQxo2bPiiwyg26tSpQ506dV50GEKIYkJ6EIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEinyTihBCiCKj0+kASM/QockqXn0Qjx6lA5CKMXqMXnA0ain/+zc1NbVYfQVkSkp2ZMUtLvh3xPYoLaPYXWs6Xdb//tWh0WjyLGek1+v1/1RQQggh/tsePHjAuXPnXnQYQohn0Gq1WFtb57leehCFEEIUGUNPTvWqJmiMi1fPSXpGJn9c1VO1shGmJsXr488Qm5ub21N7df5paWlpnDlzBufEm5hpilmPsN6Iizb2xfp8Fsd202HE2dL2z+x1LV4tKoQQ4l/NkNyYmmjQaIpXgpg91JeOqYkJpqbFJwmD/49No9EUqwTR5H+Jl5nGGNNilvDr/zeNojifz+LYbsb/G/J+1nVWvNJaIYQQQgjxwkmCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEU/0nR0dFotVri4uKKrM5r166h1WqJjo4usjofd+HCBcLCwrh27VqBtgsICCAgIKBQ+0xPT2fSpEk0b96c2rVr06lTp+fet1arJSwsrFDx5CUjI4M2bdqwbNkyZVle5zgxMZGuXbvi6enJDz/8UKRxFMT27dtZtWpVobYt7LXwpKioKF577TVcXV3RarUkJycDMG/ePHx9falTpw4NGzYEoE+fPkyfPv259ieE+O+Q72IWopi4cOEC4eHheHl5UbFixXxvN3ny5ELvc/369WzcuJEPPviAunXrYmlpWei6DDZu3Ei5cuWeu57HrVu3juTkZPr27fvUcjdu3GDAgAHcuXOHlStXUq9evSKNoyBiYmI4f/48/fv3L/C2hb0WHnfmzBk++ugj/P396dy5MyYmJlhZWbF3716WLFlCYGAgLVq0wMzMDIC3336bgQMH0qtXL6pXr16ofQoh/jskQRTiXyo1NZUSJUpQs2bNQtdx/vx5LCwsnpl4FURRJ2WZmZmsWLGCbt26PTWBvXLlCgMGDCAjI4OoqCi0Wm2RxvFvc/78eQC6d++Ou7t7juX9+vWjTJkyynIvLy+qVavGypUr+fDDD//ZYIUQxY4MMYuXxvjx4/H09OSPP/5gyJAheHp60rJlSz7++GPS09NVZW/evMnbb7+Np6cnDRo0YPTo0dy+fTtHnXkN744fPx4/Pz/VsnXr1tGxY0c8PT3x9PSkTZs2zJ07F8geLn377beB7A9urVarGs4OCAigffv2HDt2jJ49e+Lh4cHEiRPzjCE9PZ2IiAjatGmDq6srjRs3ZsKECSQmJipltFotmzZt4tGjRzn2l5aWxpw5c/Dz88PV1RUfHx+mTp2qDFE+TW5DzAkJCUyaNIkWLVrg6uqKn58f4eHhZGZmPrO+ffv2cfPmzacOf585c4bevXuj0WhYv369KjnM7VwAhIWF5UgitVot06ZN48svv6Rt27Z4eHjQsWNH9u/fryqXmJjIBx98QMuWLZX27dmzJ4cPHwayz8mBAweIj49X2vbxfT3PtQCwefNmOnbsiJubG15eXowcOZKLFy8q6wMCAggNDQXA398frVartMP8+fMBaNq0aY5z1bFjR2JiYnjw4EGebS2EeDlID6J4qWRkZDB8+HDefPNNBg4cyLFjx4iIiMDa2pqgoCAAHj16xIABA7h16xZjx46latWqHDhwgJCQkELvd8eOHUydOpWAgADGjRuHsbExf/zxBxcuXADA19eXMWPGMHfuXCZNmkTdunUBqFy5slJHQkICoaGhDB48mJCQEIyNc//7LisrixEjRvDzzz8zaNAg6tevT3x8PGFhYZw6dYotW7ZgYWHBxo0biYiIIDY2ltWrVyv70+v1jBgxgiNHjjB06FAaNmzIuXPnCAsL4+TJk2zcuFEZlsyPhIQE/P39MTY2ZuTIkVSuXJkTJ06wePFi4uPjmTlz5lO3P3DgAGXKlMmzp/Tnn38mPDyc8uXLs2LFChwdHfMdW177i4uLIzg4GEtLSyIjIwkKCuLrr7+mUqVKAISGhnL69GlCQkKoWrUqycnJnD59mqSkJCB72P+DDz7g6tWrhIeHq+p/3mth6dKlzJ07l/bt2zN27Fju3r1LeHg4PXr0YPPmzVStWpXJkycTExPD4sWLmTlzJtWrV8fOzo5+/fqxdu1aNm/eTGRkJCVLllTdDuDt7c3s2bM5evRorkm1EOLlIQmieKlkZGQwatQo2rZtC0CTJk349ddfiYmJURLErVu3cvHiRSIiImjVqhUAzZs3Jy0tjS+++KJQ+z1+/DilSpXi/fffV5Y1adJE+b+dnR1VqlQBoGbNmrkO0yYlJTF//nzVdrnZtWsXhw4dIiwsjNdff11ZXqtWLd58802io6Pp3bs39erVw87ODmNjY9X+Dh06xPfff68kowDNmjWjXLlyhISE8OWXX9K9e/d8H3tYWBj37t1jx44dVKhQQTl2CwsLZs2axaBBg546TH7y5Enq1KmT5/qZM2dSsmRJVq9ejZ2dXb7jyktaWhorV67E2toagLp16+Lj48OuXbsYOnQokH0+/f39Ve3w6quvKv+vWbMmpUqVwszMLMe5fJ5rITk5mYiICFq2bMmcOXOU5d7e3rz++uuEhYUxZ84catasqSSUzs7OuLm5KWUNCWHdunVztFft2rUxMjLi+PHjkiAK8ZKTIWbxUjEyMsrxwafVarl+/bryOjY2FisrKyU5NGjfvn2h9+vm5kZycjJjxoxh7969qqHe/CpduvQzk0OA/fv3U6pUKV555RUyMzOVn9q1a+Pg4MDRo0efuv2RI0cA6Nq1q2p527ZtsbS05McffyxQ3AcOHMDb2xtHR0dVPC1atAB4Zjy3bt1S3Sv3JD8/P+7fv8/06dPR6XQFii033t7eSnIIYG9vT5kyZYiPj1eWubu7s3XrViIiIjh58iQZGRn5rv95roUTJ07w6NEjunTpolpevnx5GjdurJy7wjI1NaVUqVLcvHnzueoRQvz7SQ+ieKmUKFECc3Nz1TIzMzPS0tKU10lJSdjb2+fYNrdl+dW5c2d0Oh2bNm0iODiYrKws3NzcGD16NM2aNctXHQ4ODvkqd+fOHZKTk3F1dc11/d27d5+6fVJSEiYmJjl6l4yMjLC3t1eGUfPrzp077N+/XxkqLWg8jx49ynHOHjdixAhq167NokWL0Ov1fPrpp2g0mgLF+DgbG5scy568RubNm8fixYvZvHkzCxYswNLSktdee43Q0NBnnqfnuRYMbZ/bPhwdHZV7IJ/Hk8cqhHg5SYIoxBNsbGw4depUjuW5TVIxMzPL9Yb+3JKebt260a1bN1JSUjh27BhhYWEMGzaM3bt34+Tk9My4jIyM8hW/ra0tNjY2REZG5rreysrqqdvb2NiQmZlJYmKiKknU6/Xcvn1bNVyZ33i0Wi2jR4/Odf2z7hm0tbV9ZlIaHByMkZER4eHhZGVlMXv2bExMsn+9mZmZ5ZiEBM9OTJ/Gzs6O9957j/fee4/r16+zb98+5syZw507d1ixYsUzty/stWBIXhMSEnKsu3XrFra2toU+JoPk5ORck2QhxMtFhpiFeIK3tzcPHz7k22+/VS2PiYnJUdbJyYkrV66oEpC7d+9y4sSJPOu3tLSkZcuWBAYGkpGRoUxOMEz8ePTo0XPF7+vrS1JSktIz9eTPs55xZxjG/uqrr1TLd+/eTUpKSr6GuZ+M5/fff6dy5cq5xlO2bNmnbl+9enWuXr36zP2MGjWKUaNGsWvXLsaOHavMkK5YsSJ37txRJfjp6el8//33BTqOvFSoUIG+ffvStGlTTp8+rSw3MzN75rks6LXg6emJhYVFjnNz48YNjhw5QuPGjZ/rWG7evElaWtpzPTpJCPHfID2IQjyhc+fOrFq1inHjxhESEkKVKlX47rvvck0oOnXqxMaNG3nnnXfo3r07SUlJREZGqu5hA3j//fexsLCgfv36ODg4kJCQwLJlyyhZsqTSI+fs7AzAF198gZWVFebm5lSsWLHAvULt2rVj+/btDB06lICAANzd3TE1NeXGjRvExsbSqlUrXnvttTy3b9asGc2bN2f27Nk8ePCA+vXrc+7cORYuXEidOnUK/G0rwcHBHD58mJ49exIQEEC1atVIT0/n2rVrHDx4kKlTpz71wdpeXl5EREQoz318mqCgIIyNjVmwYAF6vZ65c+fStm1bFi5cSEhICIMHDyYtLY2oqKhC3694//59+vXrR/v27alevTpWVlbExcVx6NAhVbu6uLiwZ88e1q1bh6urK0ZGRri5uT33tTBixAjmzp3Lu+++S7t27UhKSmLRokWYm5srE60K65dffgGy/0gSQrzcJEEU4gklSpRgzZo1TJ8+ndmzZ2NkZETz5s2ZO3cuPXv2VJVt0KABs2bNYtmyZYwYMYJKlSoxcuRIDh48qJp80bBhQ6Kjo9m1axf37t3D1tZW2dYwjFupUiUmTpzImjVr6NevHzqdjpkzZ+aYLPIsGo2GxYsXs2bNGrZt28ayZcvQaDSUK1eORo0a4eLi8tTtjYyMiIiIICwsjOjoaJYsWYKNjQ2dOnVizJgxBXrEDWQPIW/evJmIiAhWrFjBzZs3sbKywsnJCR8fH0qVKvXU7Tt06EBYWBgHDhxQZp8/zYgRIzA2NmbevHmEhIQwd+5cFi1axLx58wgODsbBwYEBAwaQmJiY4xE0+WFubo67uzvbtm0jPj6ezMxMypcvz5AhQ5RZ35D9DMPz588zb9487t+/j16v59y5c899LQwbNgw7OzuioqLYuXMnFhYWeHl5MWbMGKpWrVrg43nc3r17cXFxeekfMi6EACO9Xq9/0UEIIcTTBAYGkpmZmed9leL5PXjwAB8fHyZMmFCgxxg9SafTcfLkSbQ1zdFo8nff7D8lPSOLC5fSca5uiqlp4Scy/R0MsdWrV++5JlkVtfT0dOLi4qh7LwFT42J2PjHmdEm7Yn0+i2O76TAirmSZZ15rcg+iEKLYGzNmDD/++GOuk4dE0Vi1ahXly5cvcI+1EOK/SRJEIUSx5+LiwowZM3KdSS6KhrW1NR9//LEy+1sI8XKT3wRCiH+Fgk6OEQXTv3//Fx2CEKIYkR5EIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEi36QihBCiyOh0OgDSM3RosopXH8SjR+nZ/6ZloMfoBUejZogtNTUVMzOzFxzN/0tJSQEgFeNi12Yp//u3WJ/PYthumf/7V6fTodFo8ixnpNfr9f9MSEIIIf7rHjx4wLlz5150GEKIZ9BqtVhbW+e5XnoQhRBCFBlD71f1qiZojItXz0l6RiZ/XNXj5ub21J6TFyEtLY0zZ84Uu9gMcTkn3sRMU8x6hPVGXLSxp2plI0xNilc6U5yvNZ1OR1xc3DN7qotXiwohhPhXM3wYmppo0GiKV4KYPdSXjkajKXYf2ib/S3CKW2yGuMw0xpgWs4Rf/79pFKYmJpiaFp82g+J9rRk8K67i9eeAEEIIIYR44SRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUogh8/fXXaLVadu7cmWNdx44d0Wq1HDp0KMe6V199lS5duuR7P2vXriU6OjrH8ps3bxIWFsaZM2cKFngBfffdd4SFheW67tq1a2i1WlasWAGAn58fw4YNy1EuICCA9u3bF1lMWq2WadOmFVl9hXH27Fm0Wi3Dhw9Hq9U+82f8+PFFHsPp06fp27cvDRo0QKvVsmrVqiLfhxDi5SFftSdEEfDy8sLIyIgjR47wxhtvKMuTkpL4/fffsbS0JDY2Fh8fH2XdjRs3uHr1KgMGDMj3ftavX4+trS1du3ZVLb916xbh4eE4OTlRu3bt5z+gPHz33XesXbuWUaNG5Vjn6OjIxo0bqVChAgDh4eFYWFj8bbEUJ3v27KFixYpMmDBBlRT/9ttvTJs2jTFjxuDt7a0st7OzK/IYJk6cSGpqKnPnzqV06dI4OTkV+T6EEC8PSRCFKAJ2dnY4Oztz9OhR1fJjx45hYmJCt27diI2NVa07cuQIgCpx+DczMzOjXr16yus6deq8uGD+Ybt37+b111+ncuXKVK5cWVmelpYGQJUqVVRt83c4f/48/v7+tGzZskjq0+l06HQ6zMzMiqQ+IcS/iwwxC1FEvL29uXz5Mrdu3VKWxcbG4urqSsuWLfntt9948OCBsu7o0aNoNBoaNmxIeHg4/v7+eHl5Ub9+fbp06cKmTZvQ6/VKeT8/P86fP8/Ro0eVoUo/Pz9iY2N58803AZgwYYKy7vGh4Li4OAIDA/Hy8sLNzY3OnTvnGA5PTU1l1qxZ+Pn54ebmhpeXF127diUmJgaA8ePHs3btWgDVcOm1a9cAiIqKonfv3jRu3BhXV1deffVV5s2bR3p6eq7t9dNPP9G9e3fc3d3x8fFh/vz56HQ6VZn09HTCw8Np27Ytbm5ueHt7ExAQwPHjx3PU9+WXX9K2bVs8PDzo2LEj+/fvz1HmypUrjB07liZNmuDq6krbtm2VY3r8nGm1WmJiYpg3bx7Nmzenfv369O/fn0uXLuWo8+LFi1y4cIHWrVvnepxPCgsLQ6vV5lgeHR2tak/4/2H6b775hg4dOuDm5karVq1Ys2ZNju0yMzNZv369cl4MEhISmDRpEi1atMDV1RU/Pz/Cw8PJzMxUyhhuD1i+fDkRERHKNWD4I0YI8fKRHkQhikjjxo2Jiori6NGjyj12sbGxvPLKK9SvXx8jIyN+/vlnpYcnNjaWOnXqULJkSeLj4+nRo4cyPHvy5Ek++ugjbt68SVBQEJA9ZBscHEzJkiWZPHkykN1rV7lyZWbOnMmECRMYPnw4vr6+AJQrVw7I7qkcPHgwHh4eTJkyhZIlS7Jz505CQkJ49OiRMlw9c+ZMvvrqK0aPHk3t2rVJTU3l999/JykpCYARI0aQkpLC7t272bhxo3Lcjo6OAPz555906NCBihUrYmJiwrlz51iyZAm3bt1i5syZqrZKSEggJCSEoUOHEhwczIEDB1i8eDHJyclMmjQJgMzMTAYPHszPP/9Mv379aNy4MTqdjl9++YW//vpLVd+BAweIi4sjODgYS0tLIiMjCQoK4uuvv6ZSpUoAXLhwgZ49e1K+fHnGjRuHg4MD33//PR999BF3795V2tlg7ty51K9fn+nTp/PgwQNmz57N8OHD2blzJxqNRim3Z88eypYti4eHR4Gul/w6c+YMM2bMICgoCHt7e7Zv38706dPJyMhg0KBB+Pr6snHjRnr06EHr1q0ZOHCgqp39/f0xNjZm5MiRVK5cmRMnTrB48WLi4+NznJeoqCiqVq3KuHHjsLa2pkqVKn/LMQkhij9JEIUoIo0aNcLY2FhJEO/evcv58+d59913sbKyok6dOhw5coSWLVvy119/ce3aNdq0aQOg+qDOysrCy8sLvV7PmjVrGDlyJEZGRtSpUwcLCwusra1zDFc6OzsDULly5Rzrpk6dirOzM6tXr8bEJPst7+Pjw927d5k7dy6dO3fG2NiYEydO0KxZM/r3769sa0g2DXXb29sD5Dpc+t5776mOoVGjRtjY2DBx4kTGjx9P6dKllfVJSUlERETQqlUrAJo3b05aWhrr169n8ODBVKhQgZiYGGJjY/noo4/w9/dXtvXz88ux77S0NFauXIm1tTUAdevWxcfHh127djF06FClja2srFi/fr1SrlmzZqSnp7Ns2TICAgJUMdasWZPZs2crr42NjRk9ejRxcXGq4zcMLxsZGeWIqyjcunWLL7/8klq1agHQsmVLEhMTiYiIoHfv3tjZ2Sn3NNrb26tiCwsL4969e+zYsUP546NJkyZYWFgwa9YsBg0aRM2aNZXy5ubmrFixAlNT07/lWIQQ/x4yxCxEESldujS1atVS7jU8duwYGo2G+vXrA9kJpGGd4V/D/Yc//vgj/fv3p0GDBtSuXZu6deuycOFCkpKSuHPnTqFj+uOPP7h06RIdOnQAsnvlDD8tWrQgISGBy5cvA+Dm5sbBgweZPXs2sbGxPHr0qED7On36NIGBgXh7eyvHMG7cOHQ6HVeuXFGVtbKyUpJDg/bt25OVlcWxY8cAOHToEObm5nTr1u2Z+/b29laSPshOlMqUKUN8fDyQnUAeOXKE1157DQsLixztkJaWxsmTJ1V1PpmIGoZtr1+/riy7evUqZ86c4fXXX39mjIXl7OysJIcG7du358GDB/z2229P3fbAgQN4e3vj6OiY45iBHPfM+vn5SXIohACkB1GIIuXt7c3KlSu5efMmsbGx1K1bFysrKyB7pvPKlSu5f/8+sbGxmJiY0KBBA06dOsWgQYPw8vLiww8/pFy5cpiamrJ3716WLFlS4ETtcbdv3wZg1qxZzJo1K9cyd+/eBeD999+nXLly7Ny5k+XLl2Nubk7z5s159913qVq16lP3c/36dfr06UO1atWYOHEiTk5OmJubc+rUKaZNm5bjGAw9kbktMwxpJyYm4ujoiLHxs/+OtbGxybHMzMxMmSSSlJREZmYmUVFRREVF5VqHoR3yqtMwWePxY/n6668pU6YMDRo0eGaMhZWftsrLnTt32L9/P3Xr1s11/ZPH7ODgULgghRD/OZIgClGEDAni0aNHOXr0qNJTAyhJxLFjx4iNjcXNzQ0rKyt27NiBiYkJS5cuxdzcXCm/d+/e547H1tYWgGHDhvHaa6/lWqZatWoAWFpaEhwcTHBwMLdv3+bgwYPMmTOHwMBAvv7666fuZ+/evaSkpBAWFqZ6vMrZs2dzLW9IXHNbZkjM7Ozs+Pnnn8nKyspXkvg0pUqVQqPR0KlTJ3r37p1rmYoVKxa43j179vDqq6+q7kl8FsM5Tk9PV80QfjJZM8hPW+XF1tYWrVbL6NGjc11vuH/U4O8aJhdC/PtIgihEEWrUqBEajYbdu3dz/vx53nnnHWVdyZIlqV27Nl9++SXx8fHKsK+RkREajUaVBD169IivvvoqR/1mZma59ijm1rsFUL16dapWrcrZs2cZM2ZMvo/D3t6erl27cvbsWVavXk1qaiolSpRQ7efxZxwaEovHEx69Xs8XX3yRa/0PHz7k22+/VQ0zx8TEYGxsTKNGjYDs+yRjYmKIjo5WZmkXVokSJfD29ub06dNotdoieXTLX3/9RVxcHG+//XaBtjMk0GfPnsXd3V1Zntusa8h+fM3Zs2dVw8wxMTFYWVnl2TNo4Ovry3fffUflypVV91cKIcSzSIIoRBGytramTp067N27F2Nj4xxDj40aNWL16tXA/99/2LJlS1auXMnYsWPp0aMHSUlJrFixItckxsXFhR07drBz504qVqyIubk5Wq2WypUrY2Fhwfbt26lRowaWlpY4OjpStmxZpk6dypAhQxg0aBBdunShbNmy3Lt3j4sXL/Lbb7+xcOFCAPz9/fH19UWr1VK6dGkuXrzItm3b8PT0pESJEsr+AZYvX06LFi0wNjZGq9XStGlTTE1NGTNmDIMHDyY9PZ3169eTnJycazvZ2NgwZcoU/vrrL6pWrcp3333HF198Qa9evZTJFO3btyc6OpopU6Zw+fJlvL290ev1/PLLL9SoUYN27doV6Ny899579O7dmz59+tCrVy+cnJx4+PAhf/75J/v27VM9OiY/du/eTalSpQr8HMuWLVtiY2PDe++9x9tvv41Go2Hr1q05ZmYbODo6Mnz4cIKCgnBwcOCrr77ihx9+4J133lHOS16Cg4M5fPgwPXv2JCAggGrVqpGens61a9c4ePAgU6dOVWa7CyHE4yRBFKKIeXt7ExcXR+3atVUTJyA7QVy1ahWmpqZ4enoC2bNKZ8yYwfLlywkMDKRs2bJ0794dOzs71cxggFGjRpGQkMD777/Pw4cPcXJyYt++fZQoUYIZM2YQHh7OoEGDyMjIICgoiFGjRtG4cWM2bdrEkiVLmDFjBsnJydjY2FCjRg3atm2r1N24cWP27dun9BiWLVuWzp07ExgYqJRp3749x48fZ926dSxatAi9Xs+3335LjRo1CAsLY/78+YwaNQobGxvat29P//79GTJkSI42cnBwYNKkScyaNYvff/+d0qVLExgYqPqGFhMTE5YvX87SpUvZsWMHq1evxsrKilq1aqm+kSa/atasSXR0NBEREcyfP5/ExERKlixJlSpVCvVw6T179hRqUoe1tTXLly9nxowZhIaGUrJkSfz9/fHx8eH999/PUb527dp07dqVsLAwrly5gqOjIxMmTFDNNs+Lo6MjmzdvJiIighUrVnDz5k2srKxwcnLCx8eHUqVKFSh2IcTLw0j/+JN4hRBCPFNCQgItWrRg0aJFuT52p6j4+fnh7OzM0qVL/7Z9FDWdTsfJkyfR1jRHoyle9zSmZ2Rx4VI69erVK9B9o/+E9PR05RFKxSk2Q1x17yVgalzMzifGnC5ph3N1U0xNi0+bQfG+1gzv0WfFJj2IQghRQA4ODpw5c+ZFhyGEEH8beQ6iEEIIIYRQkR5EIYQopvbt2/eiQxBCvKSkB1EIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqMg3qQghhCgyOp0OgPQMHZqs4tUH8ehROgCpqamYmZm94GjUUlJSgOIXmxIXxugxesHRqKX8799HaRnFLrbifK1lZmYC2e9VjUaTZzkjvV6v/6eCEkII8d/24MEDzp0796LDEEI8g1arxdraOs/10oMohBCiyBh6S6pXNUFjXLx6ddIzMvnjqh43N7en9py8CGlpaZw5c6bYxVZc4wKJrbB0Oh1xcXHP7NmUBFEIIUSRMXwYmppo0GiKV4KYPQyZjkajKXYf2iYm2R/HxS224hoXSGzP61lxFa8bRIQQQgghxAsnCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJYhH75ZdfGDlyJL6+vri6utK0aVN69OjBxx9/XKB6AgICaN++/d8U5Yt3/PhxwsLCSE5Ofq565s2bh6+vL3Xq1KFhw4YApKenM2nSJJo3b07t2rXp1KlTUYT8r6DVapk2bdqLDuOF0Ol0NGnShFWrVgF//3tIq9USFhamvI6NjUWr1RIbG/u37VMIIf4p8lV7RejAgQMMHz4cLy8vQkNDcXBwICEhgV9//ZUdO3Ywfvz4Fx1isXHixAnCw8Pp0qULpUqVKlQde/fuZcmSJQQGBtKiRQvleyXXr1/Pxo0b+eCDD6hbty6WlpZFGboopo4dO0ZiYiKvvfbaC9l/3bp12bhxIzVr1nwh+xdCiKIkCWIRioyMpGLFiqxYsUL5HkaAdu3aERoa+gIj+286f/48AP369aNMmTKq5RYWFvTt2/cfj+nRo0dYWFj84/sVsHv3blxdXXFycnoh+7e2tqZevXpFVt+jR48wNzfHyKh4fZ+xEOLlIEPMRSgpKQlbW1tVcmhgbKxu6u3bt9OjRw88PT3x9PSkU6dObNq0Kcd2p06donfv3nh4eNCqVSuWLVtGVlaWsj4tLY2PP/6YTp060aBBA7y8vOjRowd79+5V1XPt2jW0Wi3R0dE59vHkUFlYWBharZazZ88SHBys1Dtz5kwyMzO5dOkSgwYNwtPTEz8/P5YvX66qLysri4iICFq3bo27uzsNGzakQ4cOrF69Wqn/k08+AaBVq1ZotVrV0FxWVhbLly+nTZs2uLq60qRJE959911u3Lih7MPPz4/58+cD0LRpU+UYtFotmzZt4tGjR0q90dHRdO7cmd69e+c4dp1Oh4+PD0FBQcqy9PR0IiIilP03btyYCRMmkJiYqNrWz8+PYcOGsWfPHjp37oybmxvh4eFKm06bNo0NGzbQunVrXF1deeONN9ixY4eqjsTERKZMmcIbb7yBp6cnTZo0oV+/fvz00085Yk1PTyc8PJy2bdvi5uaGt7c3AQEBHD9+PEdZA71ez9y5c6lbty5ffPGFEnduvdkBAQEEBAQorw1DpjExMcybN4/mzZtTv359+vfvz6VLl3LsZ8mSJbzyyiu4ubnRtWtXfvjhhxx1Ajx48IBZs2bh5+eHq6srPj4+TJ8+nZSUFFU5Qxt++eWXtG3bFg8PDzp27Mj+/ftzPc5vvvmG1q1b59kWALt27cLf358GDRoo76kJEyaoyly/fp133nmHJk2a4OrqStu2bfnss89U77vc5DXEHBcXR2BgIF5eXri5udG5c2d27typKhMdHY1Wq+X7779nwoQJNG7cGA8PD9LT0xk/fjx+fn459me43gvbZnv37qVDhw64urrSqlUrVq9enWudQoiXk/QgFqF69eqxadMmPvroIzp06ECdOnUwNTXNUW7BggVERETw+uuvM2DAAEqWLMn58+e5fv26qlxCQgKhoaEMGDCAoKAgvvnmG+bMmYOjoyOdO3cGspOGe/fuMXDgQMqWLUtGRgaHDx9m1KhRzJw5UylXGKNHj6Zjx4707NmTH374gcjISDIzMzl8+DC9e/dm0KBBbN++ndmzZ1OlShVef/11ILsnNTw8nOHDh9OwYUMlqbx//z4A/v7+3Lt3j6ioKMLDw3FwcABQhuamTJnCxo0b6du3L76+vsTHx7NgwQKOHj1KdHQ0dnZ2hIeHs3btWjZv3kxkZCQlS5akXLly+Pj4EBERQWxsrJKQVq5cmQcPHjB9+nSuXLlC1apVlWP8/vvvuXXrFl27dgWyk9MRI0bw888/M2jQIOrXr098fDxhYWGcOnWKLVu2qHoIf/vtNy5evMjw4cOpWLEiJUqUUNbt27eP2NhYgoODKVGiBOvWrWPMmDFoNBratGkDZP9RARAUFIS9vT0pKSl88803BAQEsGrVKry9vQHIzMxk8ODB/Pzzz/Tr14/GjRuj0+n45Zdf+Ouvv3I9f4bk4sCBAyxevJgWLVoU6jqYO3cu9evXZ/r06Tx48IDZs2czfPhwdu7ciUajAbLvBV26dCk9evTgtdde48aNG7z//vtkZGRQrVo1pa7U1FT69u3LjRs3CAwMRKvVcv78eRYuXMjvv//OqlWrVD1mBw4cIC4ujuDgYCwtLYmMjCQoKIivv/6aSpUqKeWOHz9OQkKCcg3m5sSJE4SEhPDGG28QFBSEubk5169f58iRI0qZxMREevbsSUZGBm+//TZOTk4cOHCAWbNm8eeffzJlypQCtd2RI0cYPHgwHh4eTJkyhZIlS7Jz505CQkJ49OiRct0ZTJw4EV9fXz755BNSU1Nz/WPzWfLTZgcPHmTUqFE0bNiQ+fPnk5mZyWeffcbt27cLvD8hxH+TJIhFaOzYsVy6dImoqCiioqIwNTXF1dUVPz8/+vTpg5WVFVevXmXp0qV06NCB2bNnK9s2a9YsR31JSUksX74cd3d3ILun7OjRo2zfvl1J/EqWLMnMmTOVbQw36icnJ7N69ernShB79OjBgAEDlH3/8MMPfP7554SHhyv3eXl5eXHgwAG2b9+ufDgfP34cFxcXRo0apdTl4+Oj/L9cuXKUL18egNq1a1OxYkVl3cWLF9m4cSO9e/fmgw8+UJbXqVMHf39/Vq9eTUhICHXq1KFcuXJA9r1fdnZ2St12dnYYGxurhvs6dOjAJ598wtatWwkJCVGWb926FXt7eyV52rVrF4cOHSIsLEyVbNSqVYs333yT6OhoVU9kYmIiO3bsUCVBBnfv3mXz5s3Y29sD0LJlS9q3b8/cuXOVBLF69eqqpEOn09G8eXPi4+OJiopSEsSYmBhiY2P56KOP8Pf3V8rn1rME2dfOiBEjuHbtGuvWraNWrVq5lsuPmjVrqq5VY2NjRo8eTVxcHPXq1ePevXusXLmSN954QzVBxtnZmR49eqjaJioqinPnzvHFF1/g5uYGQJMmTShbtizBwcEcPHiQli1bKuXT0tJYuXIl1tbWQPa59vHxYdeuXQwdOlQpt3v3blxcXFTJ/5NOnDiBXq9n6tSplCxZUln+eJK2cuVKbt68yaZNm5T3nY+PDzqdjg0bNvDWW2/leq7zMnXqVJydnVm9erWS7Pn4+HD37l3mzp1L586dVaMLTZo0ee5JRvlps4ULF1K2bFlWrFih3Lvr4+OT5/UkhHj5yBBzEbK1tWXdunVs3ryZsWPH4ufnx5UrV5gzZw4dOnQgMTGRw4cPo9Pp6NOnzzPrc3BwUD6kDLRabY6exl27dtGzZ088PT2pU6cOdevWZfPmzVy8ePG5jsfX11f1ukaNGhgZGal6okxMTKhSpQrx8fHKMjc3N86ePcuUKVM4dOgQDx48yPc+DcNzXbp0US13d3enRo0a/Pjjj4U4kuxz4+fnx9atW5Whwnv37vHtt9/SqVMn5cN7//79lCpVildeeYXMzEzlp3bt2jg4OHD06FFVvVqtNs+EoUmTJkpyCKDRaHjjjTf4448/VMPl69evp0uXLri5uSnn78cff1Sdv0OHDmFubk63bt2eeazXrl2jR48ePHjwgC+++OK5kkPImYQahiAN1+HJkydJT0+nbdu2qnL16tXLcT/g/v37cXZ2pnbt2qr2bd68OUZGRjna19vbW0l0AOzt7SlTpozqegPyNbxsSEhHjx7Nzp07uXnzZo4yR44coWbNmjned127dkWv16t6G5/ljz/+4NKlS3To0AFAdbwtWrQgISGBy5cvq7Z5Wg9ofj2rzVJSUvj111959dVXleQQwMrKShJEIYRCehD/Bm5ubsqHUUZGBrNnz2bVqlXKUCig9H49jY2NTY5lZmZmpKWlKa/37NnD6NGjadOmDYMHD8be3h6NRsP69evZsmXLcx1H6dKlVa9NTU0pUaIE5ubmOZY/ngQOGzYMS0tLvvrqKzZs2IBGo6Fhw4a88847SrvkxTDk6ujomGOdo6NjjuS4ILp168bu3bv54Ycf8PHxISYmhvT0dFUP0p07d0hOTsbV1TXXOu7evat6bRgez83jyeGTy5KSkihXrhwrV67k448/pmfPnrz99tvY2tpibGzMggULVPf5JSYm4ujomONe1tycOnWKu3fvEhISkq/r7FmevA4NScWjR4+UYwFUE4UMnmyDO3fu8Mcff1C3bt1c9/Vk++bnPXDq1CmuX7/+zOSqUaNGLFq0iKioKMaNG0d6ejrOzs4EBgYqj8NJSkrKdZKL4Xo0HGt+GIZrZ82axaxZs3ItU5DrKb+e1WbJycno9fpcz1duy4QQLydJEP9mpqamBAUFsWrVKs6fP8+rr74KwI0bN5Rh1ufx1VdfUbFiRebPn6+6d8tw/52BIalLT09XLX/yA6oomJiYMGDAAAYMGEBycjKHDx9m3rx5DB48mAMHDqju03uS4cPt1q1bOZKbW7duYWtrW+i4mjdvjqOjI9HR0fj4+BAdHY2Hh4fqsSS2trbY2NgQGRmZax1WVlaq10+bYZrb/VyGZYbj/Oqrr/Dy8mLq1Kmqcg8fPlS9trOz4+effyYrK+uZSeIbb7yBvb098+bNU+6pfJyZmVmO6wCyr4XCtK/hWO7cuZNj3e3bt1UJl62tLebm5syYMSPXugqz/927d1O1alVcXFyeWfbVV1/l1VdfJT09nZMnT7J06VLGjh2Lk5MTnp6e2NjYkJCQkGO7W7duFTg+Q9lhw4bl+eidJ3ufc7uenna+CqNUqVIYGRnleb6EEAJkiLlIGT5EnmQYKnR0dKRZs2ZKD19RMDIywtTUVPXBkpCQwLfffqsqZ29vj7m5OefOnVMtf7JcUStVqhRt2rShd+/eJCUlKcNchl6ox3uCABo3bgxkJ06PO3XqFBcvXlTWF4ZGo6FTp07s3buXn376iV9//TXHkK2vry9JSUlkZWUpPcGP/1SvXj3f+/vxxx9VH7g6nY6dO3dSuXJlJfk1MjJSDfMBnD17lpMnT6qW+fj4kJaWluss9NyMGDGCiRMnsnDhQubMmaNa5+TklOM6uHz5co7hzvzy8PDAzMwsx8zckydP5hgK9vX15erVq9jY2OTavo/fj5pfe/bseebw8pPMzMyU55UCnD59Gsi+LeDChQv89ttvqvJffvklRkZGyj2h+VG9enWqVq3K2bNncz1WNzc31VBwXipWrMidO3dU11J6ejrff/99vmN5nKWlJa6uruzdu1eVeD58+DDX2c5CiJeT9CAWoUGDBlGuXDleeeUVqlevjl6v58yZM3z22WdYWlrSr18/KlasyLBhw4iIiODRo0e0b9+ekiVLcuHCBe7evUtwcHCB9unr68uePXuYMmUKrVu35saNG0RERODo6MiVK1eUckZGRnTs2JEtW7ZQuXJlatWqxalTp4iJiSniVoDAwECcnZ1xdXXFzs6O+Ph4Vq9ejZOTE1WqVAFQentWr15Nly5dMDExoVq1alSvXp0ePXrw+eefY2xsTIsWLZRZzOXLl6d///7PFVu3bt1Yvnw5Y8eOxcLCgjfeeEO1vl27dmzfvp2hQ4cSEBCAu7s7pqam3Lhxg9jYWFq1apXvBzHb2try1ltvMWLECGUW86VLl5g3b55SxtfXl4iICBYuXEijRo24fPkyERERVKxYEZ1Op5Rr37490dHRTJkyhcuXL+Pt7Y1er+eXX36hRo0atGvXLsf+33rrLSwtLZk0aRIpKSm8//77GBkZ0alTJ0JDQ5VrJj4+nsjIyEL3ztrY2DBgwACWLl1KqVKllFnMixYtwsHBQfXHy1tvvcWePXvo27cv/fv3R6vVkpWVxV9//cX333/PwIED8fDwyPe+z5w5w59//pmve/cWLFjAjRs3aNKkCeXKlSM5OZk1a9ZgamqKl5cXAP379+fLL79k2LBhBAcHU6FCBQ4cOMC6devo1atXgSaoQPYklSFDhjBo0CC6dOlC2bJluXfvHhcvXuS3335j4cKFz6yjbdu2LFy4kJCQEAYPHkxaWhpRUVGq66OggoODGTZsGIMGDeKtt95Cp9OxYsUKrKysuHfvXqHrFUL8d0iCWISGDx/Ot99+y+rVq7l16xYZGRk4ODjQtGlThg0bRo0aNQB4++23qVKlCp9//jnvvPMOGo2GqlWr5nheXH5069aNO3fusGHDBrZs2UKlSpUYOnQoN27cUJ7JZ2B49l1kZCQpKSl4e3uzZMmSIr8x3dvbm927d7Np0yYePHigtMGIESOUx/54e3szbNgwtm7dyqZNm8jKymLNmjV4e3szZcoUKlWqxObNm1m3bh3W1tb4+PgwduzY5xpihuwhPU9PT06cOEGHDh1Us1khu5dx8eLFrFmzhm3btrFs2TI0Gg3lypWjUaNG+RrGNPDz86NmzZrMnz+fv/76i0qVKjF79mxVUhoYGEhqaqryuJ6aNWsyZcoU9u7dq5qwYWJiwvLly1m6dCk7duxg9erVWFlZUatWLdUM8Sf5+/tjaWnJu+++S0pKCtOnT6dDhw7cunWLDRs2EB0djbOzM1OmTGHRokUFaEm1kJAQSpQoodRpmJ09b9481TflWFpasnbtWpYtW8bGjRu5du0aFhYWlC9fnqZNmxb4Ide7d+/Gyckpz3tGH+fh4cGvv/7K7NmzSUxMpFSpUri6urJq1SqcnZ2B7KH8DRs2MGfOHObMmcPDhw+pWLGi8ripgmrcuDGbNm1iyZIlzJgxg+TkZGxsbKhRo0aOST15qVSpEosWLWLevHkEBwfj4ODAgAEDSExMzPEez68WLVoQFhbGggULGD16NA4ODvTq1Ytbt27l6L0XQrycjPR6vf5FByHEf41Wq6VPnz5MmjTpRYfywly9epW2bdsSFBREYGDg37KPN954gxYtWsjXWBaBjIwMOnfuTNmyZfnss88KXY9Op+PkyZNoa5qj0RSvb4FJz8jiwqV06tWrpzzDs7hIT09XHh1VnGIrrnGBxFZYhvfos2KTHkQhxHM7e/YsMTExeHp6Ym1tzeXLl4mMjMTa2po333zzb9vvk/c9ivybOHEizZo1w8HBgdu3b7N+/XouXrzIe++996JDE0IUA5IgCiGeW4kSJfj111/ZvHkz9+/fx9raGm9vb0aPHp3r437Ei/fw4UNmzZpFYmIipqam1KlTh2XLltG0adMXHZoQohiQBFGIv8GTs4T/66pUqcKqVatedBiiABYsWPCiQxBCFGPymBshhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJGv2hNCCFFkdDodAOkZOjRZxasP4tGjdABSU1MxMzN7wdGopaSkAMUvtuIaF0hshZWZmQlkv1c1Gk2e5Yz0er3+nwpKCCHEf9uDBw9euu8iF+LfSKvVYm1tned66UEUQghRZAy9JW5ubk/tnXgR0tLSOHPmjMRWAMU1Lvj/2KpWNsLUpHilM+kZmfxxVV8s202n0xEXF/fMns3i1aJCCCH+1QwfhhqNpth9MJr8L4mQ2PKvuMYF/x+bqYkJpqbFKzY9RkB6sWw3g2fFVbxuEBFCCCGEEC+cJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIp8k4oQL6lffvmFZcuW8dtvv3H79m1KlSpFpUqV8PT0ZPz48Uq5tWvXUqJECbp27foCo/1viY6OZsKECbmu+/7773FwcPiHIxJCCDVJEIV4CR04cIDhw4fj5eVFaGgoDg4OJCQk8Ouvv7Jjxw5Vgrh+/XpsbW0lQfwbzJw5k+rVq6uW2djYvJhghBDiMZIgCvESioyMpGLFiqxYsUL5PlOAdu3aERoa+rfv/9GjR5ibm2NkZPS37yu/9Ho9aWlpWFhYPFc9ly9fplq1avkq6+zsjJub23PtTwgh/g5yD6IQL6GkpCRsbW1VyaGBsfH//1rw8/Pj/PnzHD16FK1Wi1arxc/Pj4cPH9KwYUMmTZqUY/tr165Ru3ZtIiMjgezhVK1Wy/fff8+ECRNo3LgxHh4epKenk5WVxfLly2nTpg2urq40adKEd999lxs3bqjqDAgIoH379vz00090794dd3d3fHx8mD9/PjqdLsexTZkyBR8fH1xdXWnVqhXz5s0jPT1dVU6r1TJt2jTWr19P27ZtcXNzY+vWrYVqz/PnzzNv3jxatWrF6NGjC1VHXgxxbtmyhdatW+Pu7k7Xrl05efIker2eyMhI/Pz88PT0pF+/fvzxxx+q7Q1td+LECXr27Im7uzt+fn5s2bIFyO5N7tKlCx4eHnTo0IGDBw8WafxCiH8n6UEU4iVUr149Nm3axEcffUSHDh2oU6cOpqamOcqFh4cTHBxMyZIlmTx5MgBmZmZYWVnRrVs3vvjiC0JDQylZsqSyzbp16zA1NaVbt26quiZOnIivry+ffPIJqampmJiYMGXKFDZu3Ejfvn3x9fUlPj6eBQsWcPToUaKjo7Gzs1O2T0hIICQkhKFDhxIcHMyBAwdYvHgxycnJSqKalpZGv379uHr1KqNGjUKr1fLTTz+xbNkyzpw5w7Jly1Qx7d27l59++omRI0dib29PmTJl8t2G165dY8eOHcTExPD7779ToUIF2rRpQ8eOHfNdR2BgIImJiZQsWRIvLy+Cg4NxcXHJUe7AgQOcPn2ad955ByMjIz799FOGDRtG586duXr1KpMmTeL+/ft8/PHHjBo1im3btql6ZxMSEpgwYQKDBw+mXLlyfP7550ycOJG//vqL3bt3ExgYiLW1NREREYwcOZK9e/dStmzZfB+HEOK/RxJEIV5CY8eO5dKlS0RFRREVFYWpqSmurq74+fnRp08frKysAKhTpw4WFhZYW1tTr149VR19+/ZlzZo1bNmyhf79+wPZCdqWLVto164dtra2qvJNmjRh2rRpyuuLFy+yceNGevfuzQcffKAsr1OnDv7+/qxevZqQkBBleVJSEhEREbRq1QqA5s2bk5aWxvr16xk8eDAVKlRg69atnDt3jvnz59O2bVsAmjVrhqWlJbNnz+aHH36gWbNmSp0pKSls376d0qVL56vdbt++za5du9ixYwcnTpzAwcGBNm3aMHXqVDw9PfM9ZG5vb09gYCD16tXD2tqa33//nWXLltGjRw/Wr19PrVq1VOXT09P57LPPsLS0VJaNHDmS2NhYtm7dquw3MTGRGTNm8Pvvv6PValVtt2LFClxdXQFwdXWladOmLF++nD179ijJYNmyZenUqRN79uwhICAgX8cihPhvkiFmIV5Ctra2rFu3js2bNzN27Fj8/Py4cuUKc+bMoUOHDiQmJj6zjkqVKuHr68v69evR6/UAbN++naSkJPr27Zuj/Ouvv656HRsbC0CXLl1Uy93d3alRowY//vijarmVlZWSHBq0b9+erKwsjh07BsCRI0ewtLSkTZs2qnKGCTZP1tm4ceN8J4fh4eG0aNGC8PBwXFxcWL16NQcPHuT999+nfv36BbqfskWLFoSEhPDKK6/QqFEj+vTpw9q1awFYsGBBjvLe3t6q5LBGjRpKPY/v17D8+vXrqu0dHByU5BCyJ8LY2dlRu3ZtVU+hYcLMk9sLIV4+kiAK8RJzc3Nj6NChLFy4kEOHDtG/f3/i4+OV+wefpV+/fly5coUffvgByB5e9vT0pG7dujnKPvnolqSkJAAcHR1zlHV0dFTWG9jb2+coZ1hmKJuUlIS9vX2OZK1MmTKYmJjkqLMgj5OxtLTEzMyM9PR07t+/z/3798nIyMj39s9SsWJFGjRowC+//JJj3ZNJrOF2gLyWp6WlqZbnNjPazMwsx/ZmZma5bi+EePlIgiiEALKTi6CgICB70kV+NGnSBBcXFz7//HOOHz/Ob7/9Ru/evXMt+2TSZkhabt26laPsrVu3cgxR3759O0c5wzJDXTY2Nty+fVvp0TS4c+cOmZmZOeosSK/fwIEDOXz4MFOnTuXhw4eEhITQpEkTQkNDOXDgQI5JMIWh1+tVk4SEEOJFkd9EQryEckvKIPu+QFD36pmZmfHo0aM86woICOC7775j7ty52Nvb5xjezUvjxo0B+Oqrr1TLT506xcWLF5X1Bg8fPuTbb79VLYuJicHY2JhGjRoB2QlrSkoKe/fuVZX78ssvlfXPw9LSko4dO7Js2TIOHTpEaGgo169fJzAwkObNmzNx4kRl6Lygrl69yvHjx/Hw8HiuGIUQoijIJBUhXkKDBg2iXLlyvPLKK1SvXh29Xs+ZM2eUiRD9+vVTyrq4uLBjxw527txJxYoVMTc3V02A6NixI3PmzOHYsWMMHz5cGaZ8lurVq9OjRw8+//xzjI2NadGihTKLuXz58srEFwMbGxumTJnCX3/9RdWqVfnuu+/44osv6NWrFxUqVACgc+fOrF27lnHjxhEfH4+Liws///wzS5cupWXLljRt2vT5G+9/bG1t6dWrF7169eLGjRvExMQQExPD6dOnlYQ0L/3796dhw4bUqlULKysrfv/9dyIjIzEyMuLtt98ushiFEKKwJEEU4iU0fPhwvv32W1avXs2tW7fIyMjAwcGBpk2bMmzYMGWyA8CoUaNISEjg/fff5+HDhzg5ObFv3z5lvYWFBX5+fnz11Vf06tWrQHFMmTKFSpUqsXnzZtatW4e1tTU+Pj6MHTs2x3Cwg4MDkyZNYtasWfz++++ULl2awMBARo0apZQxNzdnzZo1zJs3j8jISO7evUvZsmUZOHCgMnz+dyhXrhyDBw9m8ODBefbOPs7FxYVdu3bx2WefkZaWhp2dHY0bN2bEiBH5fsi2EEL8nYz0T96sI4QQBZCeno6fnx8NGjTIdQZuUQgICODu3bvExMT8LfWLoqPT6Th58iT16tVDo9G86HBU0tPTiYuLk9gKoLjGBf8fm3N1U0xNi1lsGVlcuJReLNstv+9R6UEUQhRKYmIily5dIjo6mjt37jB06NAXHZIQQogiIgmiEKJQDhw4wIQJE3BwcGDy5Mm5PtpGCCHEv5MkiEKIQunatavyAOq/W1RU1D+yHyGEENnkMTdCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkW+SUUIIUSR0el0AKSlpWFiUrw+YlJSUgBITU3FzMzsBUejVlxjK65xwf/H9igtAz1GLzgatUeP0oHi2W6ZmZlA9ntVo9HkWc5Ir9fr/6mghBBC/Lc9ePCAc+fOvegwhBDPoNVqsba2znN98frzTgghxL+aobekelUTNMbFq1cnPSOTP67qqVrZCNNi1rtpiM3Nze2pvTr/tLS0NM6cOYNz4k3MNMXrrrRHeiMu2tgX6/NZHNtNhxFnS9s/s2ezeLWoEEKIfzVDcmNqokGjKV4JYvYwZDqmJiaYmhafJAz+PzaNRlOsEkTDbQJmGmNMi1nCr//fNIrifD6LY7sZ/284/lnXWfFKa4UQQgghxAsnCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohB/s+joaLRaLXFxcarliYmJdO3aFU9PT3744YcXEtv27dtZtWpVoba9cOECYWFhXLt27bliiIqK4rXXXsPV1RWtVktycjIA8+bNw9fXlzp16tCwYcNct7127RparVb1U79+fTp27MiqVavQ6XTPFVtR8vPzY9iwYfkqe/r0afr27UuDBg3QarXKOQoLC0Or1ZKYmEhsbCxarZavv/46x/ZarZawsDDltaFsbGxskRyLEOK/T75qT4gX4MaNGwwYMIA7d+6wcuVK6tWr90LiiImJ4fz58/Tv37/A2164cIHw8HC8vLyoWLFiofZ/5swZPvroI/z9/encuTMmJiZYWVmxd+9elixZQmBgIC1atHjmd4YGBATQvn17AJKTk9m3bx8zZ87kxo0bjB8/vlCxvUgTJ04kNTWVuXPnUrp0aZycnADw9/fHx8eHUqVKUbduXTZu3EjVqlVfbLBCiP8kSRCF+IdduXKFAQMGkJGRQVRUFFqt9kWH9MKcP38egO7du+Pu7p5jeb9+/ShTpswz6ylfvrwqyW7RogXnz58nJibmX5kgnj9/Hn9/f1q2bKlaXq5cOcqVKweAtbX1C/vDQgjx3ydDzEL8g86cOUPv3r3RaDSsX79eSQ7Hjx+Pn59fjvKGIcXHabVapk2bxpdffknbtm3x8PCgY8eO7N+/X1UuMTGRDz74gJYtW+Lq6krjxo3p2bMnhw8fBrJ73Q4cOEB8fLxqiNZg3bp1dOzYEU9PTzw9PWnTpg1z584FsofN3377bSA7iTNsGx0drWy/efNmOnbsiJubG15eXowcOZKLFy8q6wMCAggNDQWye8a0Wq3SDvPnzwegadOmOYZL86tkyZKYmpqqlu3cuZOBAwfSvHlz3N3dadu2LbNnzyYlJUVVLiAggICAgBx15naewsPD8ff3x8vLi/r169OlSxc2bdqEXq/PNa5vvvmGDh064ObmRqtWrVizZo2yznA7QmZmpnJ9GM7J7du3mTx5Mm3btqVevXrUq1ePnj178t133xW4bQzi4uIIDAzEy8sLNzc3OnfuzM6dOwtdnxDiv0N6EIX4h/z888+Eh4dTvnx5VqxYgaOjY6HrOnDgAHFxcQQHB2NpaUlkZCRBQUF8/fXXVKpUCYDQ0FBOnz5NSEgIVatWJTk5mdOnT5OUlATA5MmT+eCDD7h69Srh4eGq+nfs2MHUqVMJCAhg3LhxGBsb88cff3DhwgUAfH19GTNmDHPnzmXSpEnUrVsXgMqVKwOwdOlS5s6dS/v27Rk7dix3794lPDycHj16sHnzZqpWrcrkyZOJiYlh8eLFzJw5k+rVq2NnZ0e/fv1Yu3YtmzdvJjIykpIlSyq9ZnnJysoiMzMTgPv37/Ptt99y6NAhBg8erCp35coVWrRowVtvvUWJEiW4dOkSy5cv59SpU6pErSDi4+Pp0aMHFSpUAODkyZN89NFH3Lx5k6CgIFXZM2fOMGPGDIKCgrC3t2f79u1Mnz6djIwMBg0ahK+vLxs3bqRHjx60bt2agQMHKtsmJSWh0WgYNWoU9vb2PHz4kL179xIYGMiqVavw9vYuUNxHjhxh8ODBeHh4MGXKFEqWLMnOnTsJCQnh0aNHdO3atVDtIYT4b5AEUYh/yMyZMylZsiSrV6/Gzs7uuepKS0tj5cqVWFtbA1C3bl18fHzYtWsXQ4cOBeD48eP4+/vTvXt3ZbtXX31V+X/NmjUpVaoUZmZmOYYqjx8/TqlSpXj//feVZU2aNFH+b2dnR5UqVZR6Ht8+OTmZiIgIWrZsyZw5c5Tl3t7evP7664SFhTFnzhxq1qypJJTOzs64ubkpZQ0JYd26dfPVVrNnz2b27NmqZV27diU4OFi1bMSIEcr/9Xo99evXp0aNGvTt25ezZ89Sq1atZ+7rSTNnzlT+n5WVhZeXF3q9njVr1jBy5EiMjIyU9bdu3eLLL79U9tOyZUsSExOJiIigd+/e2NnZKcdrb2+vateaNWsyadIk5bVOp8PHx4f4+HiioqIKnCBOnToVZ2dnVq9ejYlJ9keBj48Pd+/eZe7cuXTu3BljYxlkEuJlJQmiEP8QPz8/9u3bx/Tp0/nkk0/QaDSFrsvb21tJDiE7mShTpgzx8fHKMnd3d7Zu3YqNjQ1Nmzalbt26OYZc8+Lm5sbnn3/OmDFjeOONN6hfv36+k9oTJ07w6NEjunTpolpevnx5GjduzJEjR/JVz+P0en2OGcmGpAayh7k7duwIQEpKCidPnmTx4sWkpKSwYMECpdzVq1eZP38+R44c4c6dO6ph4EuXLhUqQfzxxx9ZunQpcXFxPHjwQLXuzp072NvbK6+dnZ1z7KN9+/b88MMP/Pbbb3nO1jZYv349X3zxBRcuXCA9PV1ZXr169QLF/Mcff3Dp0iXGjRsHoPS+Qvb9m/v37+fy5cvUqFGjQPUKIf47JEEU4h8yYsQIateuzaJFi9Dr9Xz66aeFThJtbGxyLDMzMyMtLU15PW/ePBYvXszmzZtZsGABlpaWvPbaa4SGhuLg4PDU+jt37oxOp2PTpk0EBweTlZWFm5sbo0ePplmzZk/d1jCEnds+HB0dlXsgC2Lr1q1MmDBBtezcuXPK/8uVK6fqgfT29sbIyIg5c+Zw6NAhfHx8ePjwIb1798bc3JzRo0dTtWpVLCwsuHHjBkFBQTx69KjAcZ06dYpBgwbh5eXFhx9+SLly5TA1NVVmYT9Z5+PJ4pPLDO2Wl5UrV/Lxxx/Ts2dP3n77bWxtbTE2NmbBggVcunSpQHHfvn0bgFmzZjFr1qxcy9y9e7dAdQoh/lskQRTiHxQcHIyRkRHh4eFkZWUxe/ZsTExMMDMzU/UIGTzPh7SdnR3vvfce7733HtevX2ffvn3MmTOHO3fusGLFimdu361bN7p160ZKSgrHjh0jLCyMYcOGsXv3buWxK7kxJK8JCQk51t26dQtbW9sCH8srr7zC5s2bC7SNYXLH2bNn8fHx4ciRI9y6dYuoqCi8vLyUcvfv38+xrZmZWY7eQMh5Pnbs2IGJiQlLly7F3NxcWb53795cYzIkZrktyy3pf9xXX32Fl5cXU6dOVS1/+PDhU7fLjeEcDBs2jNdeey3XMtWqVStwvUKI/w5JEIX4h40aNQojIyPCwsLQ6/XMmTOHihUrcufOHW7fvq30KKWnp/P9998XyT4rVKhA3759+fHHHzl+/Liy3MzM7Jk9Z5aWlrRs2ZKMjAxGjhzJhQsXcHJyUp5N+OT2np6eWFhY8NVXX9G2bVtl+Y0bNzhy5AitW7cucPy2trYFTizPnDkDoDwmx3Av4JPPVNywYUOObZ2cnNi9ezfp6elK+bt373LixAnV0L6RkREajUZ1r96jR4/46quvco3p/PnzOe51jImJwcrKSpnokxcjI6McsZ89e5aTJ09Svnz5p277pOrVq1O1alXOnj3LmDFjCrStEOLlIAmiEC9AUFCQMjyo1+sZM2YMCxcuJCQkhMGDB5OWlkZUVFShvwnk/v379OvXj/bt21O9enWsrKyIi4vj0KFDqh4jFxcX9uzZw7p163B1dcXIyAg3Nzfef/99LCwsqF+/Pg4ODiQkJLBs2TJKliypDOU6OzsD8MUXX2BlZYW5uTkVK1bE1taWESNGMHfuXN59913atWtHUlISixYtwtzcPMfM3qLw119/cfLkSQBSU1M5ceIEy5Ytw8nJiddffx3ITlxLly7N5MmTCQoKwsTEhO3bt6uGqg06derExo0beeedd+jevTtJSUlERkaqkkPInmSycuVKxo4dS48ePUhKSmLFihV5Ptjb0dGR4cOHExQUhIODA1999RU//PAD77zzDiVKlHjqMfr6+hIREcHChQtp1KgRly9fJiIigooVKxbqOpk6dSpDhgxh0KBBdOnShbJly3Lv3j0uXrzIb7/9xsKFCwtcpxDiv0MSRCFekBEjRmBsbMy8efPQ6/UsXLiQhQsXEhwcjIODAwMGDCAxMTHHI2jyw9zcHHd3d7Zt20Z8fDyZmZmUL1+eIUOGqB790q9fP86fP8+8efO4f/8+er2ec+fO0bBhQ6Kjo9m1axf37t3D1taWBg0aMGvWLGWySqVKlZg4cSJr1qyhX79+6HQ6Zs6cSdeuXRk2bBh2dnZERUWxc+dOLCws8PLyYsyYMX/LN39ERUURFRWlHHv58uXp3r07Q4YMUZI6W1tbli5dyqxZswgNDaVEiRK0atWKefPm5ZhQYzjWZcuWMWLECCpVqsTIkSM5ePAgR48eVco1adKEGTNmsHz5cgIDAylbtizdu3dXhvefVLt2bbp27UpYWBhXrlzB0dGRCRMm5OubbAIDA0lNTVUe/1OzZk2mTJnC3r17VTHlV+PGjdm0aRNLlixhxowZJCcnY2NjQ40aNVQ9v0KIl5ORPq+nuQohhBAFpNPpOHnyJNqa5mg0Rs/e4B+UnpHFhUvpOFc3xdS08E8R+DsYYqtXr95zPeGgqKWnpxMXF0fdewmYGhez84kxp0vaFevzWRzbTYcRcSXLPPNak4dcCSGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFAxedEBCCGE+O/Q6XQApGfo0GQVrz6IR4/Ss/9Ny0CP0QuORs0QW2pqKmZmZi84mv+XkpICQCrGxa7NUv73b7E+n8Ww3TL/969Op0Oj0eRZzkiv1+v/mZCEEEL81z148IBz58696DCEEM+g1WqxtrbOc730IAohhCgyht4vNze3p/ZOvAhpaWmcOXNGYiuA4hoXSGyFpdPpiIuLe2ZPtSSIQgghiozhw1Cj0RS7D0YTk+yPPIkt/4prXCCxPa9nxVW8bhARQgghhBAvnCSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYRKsUwQb9++zdy5c+nUqRP169fH1dWVFi1aEBQUxLfffqt8GXxBpKamEhYWRmxsbIG3DQgIoH379gCEhYWh1WpJTEwscD0Gfn5+DBs2rNDbP2nnzp20a9cOd3d3tFotZ86cUeJ8kR5vN5G7s2fPMnHiRF599VXc3d1xd3fn9ddfZ9KkScTFxanKFodz+riCvE9jY2PRarWFev/lV1RUFK+99hqurq5otVqSk5P/tn39WzzP7z0hxMut2H3V3smTJxk+fDh6vZ5evXpRr149LC0tuX79Ovv372fUqFFMnToVf3//AtWbmppKeHg4QUFBeHt7F2jbyZMnk5mZCYC/vz8+Pj6UKlWqQHX8XRITE3n33Xdp3rw5kydPxszMjKpVq77osEQ+bNiwgQ8//JBq1arRr18/nJ2dAbh06RIxMTG8+eabfPPNN1SuXPkFR5rT3/U+LawzZ87w0Ucf4e/vT+fOnTExMcHKyuof2Xdx9jy/94QQL7dilSAmJyczcuRILC0tWb9+PY6Ojqr1nTp14uzZsyQlJf3tsaSmplKiRAkAatasqSwvV64c5cqV+9v3n1+XL18mIyODjh074uXl9aLDEfn0888/M3XqVFq2bMnChQtVX5repEkT+vTpw65duzA3N3+BUeauOL1PDc6fPw9A9+7dcXd3f2rZx9/bQgghcleshpi/+OILbt++TWhoaI4PHYNatWrRuHFj1bKEhAQmTZpEixYtcHV1xc/Pj/DwcKXX79q1azRp0gSA8PBwtFotWq2W8ePHA/8/dPfbb78RHBxMo0aNeO211wA4deoUo0ePxs/PD1dXV7y9vRk5ciQXL15UxRAdHY1Wq+XIkSNMnjwZb29vvL29CQoK4ubNm8889rVr11KnTh0WLlyoLDt8+DBvvfUW9evXx8PDg549e/Ljjz8q68ePH0/v3r0BCAkJQavVEhAQkOc+du7cycCBA2nevDnu7u60bduW2bNnk5KSoip39epVQkJCaN68Oa6urjRt2pS33nqLM2fOqMpt376dHj164OnpiaenJ506dWLTpk059nvq1Cl69+6Nh4cHrVq1YtmyZWRlZanKPHjwgFmzZint7OPjw/Tp03PEptVqmTZtGl9++SVt27bFw8ODjh07sn///me0MKSlpfHxxx/TqVMnGjRogJeXFz169GDv3r2qcteuXUOr1RIdHZ2jDq1WS1hYmPI6MTGRDz74gJYtW+Lq6krjxo3p2bMnhw8ffmosS5cuRaPRMG3aNFVy+Li2bdtStmzZp9ZT1Oc0Pwr7Pn1SXFwcISEh+Pn54e7ujp+fH2PGjCE+Pl5VLjU1Vbk23Nzc8PLyomvXrsTExADZtzKEhoYC2T38j7+3Dbc5HDt2jJ49e+Lh4cHEiRML1HaGY27dujWurq688cYbbN++nfHjx+Pn56eUMVw3kZGRLFu2TDmugIAA5Q+52bNn07x5cxo0aMDIkSO5c+dOjn3t3LmTHj16UK9ePTw9PRk0aBCnT59WlRk/fjyenp788ccfDBkyBE9PT1q2bMnHH39Menq6Es/Tfu8BXLlyhbFjx9KkSRNcXV1p27Yta9eufep5E0K8HIpVD+Lhw4fRaDS0bNky39skJCTg7++PsbExI0eOpHLlypw4cYLFixcTHx/PzJkzcXR0JDIyksGDB/Pmm28qw152dnaqukaNGsUbb7xBz549lQ+Ja9euUaNGDdq3b0/p0qVJSEhg/fr19O3blx07duSo4/3338fX15c5c+bw119/8emnnxIaGsqaNWtyjV+v1/PJJ58QFRXFRx99RNeuXQHYtm0b48aNo1WrVsyaNQsTExM2btzIoEGDWLFiBU2aNGHEiBG4ubkxbdo0xowZg7e3N9bW1nm21ZUrV2jRogVvvfUWJUqU4NKlSyxfvpxTp06p4hsyZAhZWVmEhoZSoUIF7t69y4kTJ1T3dC1YsICIiAhef/11BgwYQMmSJTl//jzXr1/PcX5CQ0MZMGAAQUFBfPPNN8yZMwdHR0c6d+4MZCcAffv25caNGwQGBqLVajl//jwLFy7k999/Z9WqVRgZGSl1HjhwgLi4OIKDg7G0tCQyMpKgoCC+/vprKlWqlOfxp6enc+/ePQYOHEjZsmXJyMjg8OHDjBo1ipkzZyrxFERoaCinT58mJCSEqlWrkpyczOnTp5/ae6bT6YiNjcXV1TXPBCu/ivKc5ldh3qe5iY+Pp1q1arRr10713nrzzTdV762ZM2fy1VdfMXr0aGrXrk1qaiq///670saTJ08mJiaGxYsXM3PmTKpXr656XxquwcGDBxMSEoKxcfbfxfltu40bNzJp0iRat27NhAkTuH//PuHh4WRkZOR6XOvWrcPFxYVJkyaRnJzMrFmzCAwMxMPDAxMTE2bMmMH169eZNWsW7733HkuWLFG2XbJkCfPnz6dr164MHz6cjIwMVqxYQZ8+fdi0aZNqNCMjI4Phw4fz5ptvMnDgQI4dO0ZERATW1tYEBQU98/fehQsX6NmzJ+XLl2fcuHE4ODjw/fff89FHH3H37l2CgoKe6/wKIf7l9MVImzZt9M2aNcuxXKfT6TMyMpQfnU6nrPvggw/09erV08fHx6u2WbFihd7FxUV//vx5vV6v19+5c0fv4uKiX7hwYY76Fy5cqHdxcdEvWLDgmTFmZmbqHz58qK9Xr55+9erVyvItW7boXVxc9FOmTFGVX758ud7FxUV/69YtZdkrr7yiHzp0qD41NVU/atQofYMGDfSHDx9W1qekpOi9vLz0w4YNy9EOHTt21L/55pvKsiNHjuhdXFz0u3btyvWY8pKVlaXPyMjQHz16VO/i4qI/c+aMXq/X6xMTE/UuLi76VatW5bntn3/+qa9du7Z+7NixeZbR6/X6vn376l1cXPS//PKLavkbb7yhHzhwoPJ66dKl+lq1aulPnTqlKvf111/rXVxc9AcOHFCWubi46Js2baq/f/++siwhIUFfq1Yt/dKlS58az5MyMzP1GRkZ+okTJ+o7d+6sLL969arexcVFv2XLlhzbPHkN1atXTz99+vQC7TchIUHv4uKiDwkJyTMmw09WVpay7u88pwVRmPep4To9cuRInvXm9d5q3769fsSIEU+NyfD+e/IaMlyDj7+/cpNX2+l0On2zZs30/v7+qvLx8fH6unXr6l955RVlmeG66dixo+rYV61apXdxcdEHBgaq6pg+fbrexcVFuZavX7+ur1Onjv7DDz9UlXvw4IG+WbNm+rfffltZNm7cOL2Li4t+586dqrJDhgzRt27dWnn9tN97AwcO1Ldo0UL1XtLr9fpp06bp3dzc9ElJSXm219NkZmbqf/rpJ31mZmahtv87paWlSWwFVFzj0usltsLK73u0WPUg5mXmzJmqv+h9fX1ZunQpkN2b5O3tjaOjozKkDNCiRQtmzZrF0aNHVX91P83rr7+eY9nDhw+JiIhgz549xMfHq2ZmPjnMDKiGnABl1un169dxcHBQliclJfHWW29x8+ZNpcfB4MSJEyQlJdGlSxfVMQH4+PgQGRlJSkoKlpaW+Toug6tXrzJ//nyOHDnCnTt30Ov1yrpLly5Rq1YtbGxsqFy5MitWrCArKwtvb29q1aql9LpAdg+STqejT58+z9yng4NDjnvCDDOtDfbv34+zszO1a9dWHW/z5s0xMjLi6NGjqt6qJ3tK7e3tKVOmTI6hydzs2rWL1atXc+7cOdVQYmHv9XN3d2fr1q3Y2NjQtGlT6tati6mpaaHqAujatStnz55VXr/77rsMGjQoz/JFdU6LwtPep7nJ73vLzc2N7du3M3v2bHx8fPDw8MDCwiLfcZUuXVoZan1cftru8uXLJCQk5DgHFSpUwNPTM9drrmXLlqq2rVGjBpDdHo8zLL9+/TouLi58//33ZGZm0qlTJ9X7wNzcnEaNGuWYiWxkZJTr75sjR448rTmA7Nstjhw5Qq9evbCwsMjxu/Pzzz/n5MmTz91LLIT49ypWCWKFChX48ccfc9xEPnDgQDp27AjA8OHDVdvcuXOH/fv3U7du3VzrvHv3br73n9tw39ixYzly5IgynGtlZYWRkRFDhw4lLS0tR3kbGxvVa8P9ZY8ePVItv3LlCvfu3aN79+6q5BCyHx8CEBwcnGes9+7dK1CC+PDhQ3r37o25uTmjR4+matWqWFhYcOPGDYKCgpT4jIyMWLVqFYsWLSIyMpKPP/4YGxsbOnTowOjRo7G2tlYe8ZOfyTpPtgdkt8njbXfnzh3++OOPfJ/D/NSZmz179jB69GjatGnD4MGDsbe3R6PRsH79erZs2fLMY8nNvHnzWLx4MZs3b2bBggVYWlry2muvERoaqvqD4HG2trZYWFjkGI4HmDNnDqmpqSQkJOS41p9UlOe0IArzPs1Nft9b77//PuXKlWPnzp0sX74cc3NzmjdvzrvvvpuvGfu5nYf8tp3h2itTpkyOOuzt7XNNEEuXLq16bfiDIa/lhmM1vO/ffPPNXI/jyYS+RIkSOf6wyc/7ALL/QM3MzCQqKoqoqKhcyxTkd6cQ4r+nWCWITZs25fvvv+e7776jTZs2yvLy5ctTvnx5gBw39Nva2qLVahk9enSudT7PPV7379/nwIEDBAUFMXToUGW54V6251GvXj3atGnDe++9B8CUKVOUDwBbW1sAPvjgAzw8PHLdPrcPrKc5cuQIt27dIioqSjXb+f79+znKOjk5MWPGDCB7lvSuXbsIDw8nPT2dadOmKfcw3bhxQzkvz8PW1hZzc3Nln7mtLwpfffUVFStWZP78+ap7GlevXq0qZ/jQNdzsb5DbB6adnR3vvfce7733HtevX2ffvn3MmTOHO3fusGLFilzj0Gg0NG7cmB9++IFbt26prlFDb/e1a9eeeTxFeU4LojDv0ycV5L1laWlJcHAwwcHB3L59m4MHDzJnzhwCAwP5+uuvnxnv4+faIL9tZ7j2cptMYkjoiophXwsXLqRChQpFWveTSpUqhUajoVOnTspEtydVrFjxb41BCFG8FasE0d/fn88++4xPP/2U+vXr5yu58/X15bvvvqNy5co5/kJ/XF49eU9jZGSEXq/P8WG3adOmQj2s+0ldunShRIkSvPPOO6SkpDBr1iw0Gg3169enVKlSXLhwgb59+z73fuD/PySfPJYNGzY8dbtq1aoxYsQI9uzZo8ykbNasmdLz5unp+dyxGYYibWxsnjrJ5HkZGRlhamqqShgSEhL49ttvVeXs7e0xNzfn3LlzquVPlntShQoV6Nu3Lz/++CPHjx9/atmhQ4dy8OBBJk+ezMKFCws1LF2U57QgCvM+fVJh31v29vbKMPzq1asL/cia/LZdtWrVcHBwYNeuXQwYMEBZfv36dU6cOPHck4we17x5c0xMTPjzzz9p3bp1kdSZ1++9EiVK4O3tzenTp9Fqtc9M6IUQL59ilSCWKlWKRYsWERgYSKdOnejVqxceHh5YWVmRlJTEsWPHSEhIUCUlwcHBHD58mJ49exIQEEC1atVIT0/n2rVrHDx4kKlTp1KuXDmsra1xcnLi22+/pUmTJpQuXRpbW9un/pVsbW1No0aNWLFiBba2tjg5OXH06FE2b95cZA/KbtOmDSVKlCA4OJi0tDTmzJmDlZUV77//PuPHj+fevXu0bt2aMmXKkJiYyNmzZ0lMTGTq1KkF2o+npyelS5dm8uTJBAUFYWJiwvbt23MkQWfPnuXDDz+kTZs2VKlSBVNTU44cOcK5c+eUnp6KFSsybNgwIiIiePToEe3bt6dkyZJcuHCBu3fvPnVoPDdvvfUWe/bsoW/fvvTv3x+tVktWVhZ//fUX33//PQMHDsyzJ7UgfH192bNnD1OmTKF169bcuHGDiIgIHB0duXLlilLOyMiIjh07smXLFipXrkytWrU4deqU8lgVg/v379OvXz/at29P9erVsbKyIi4ujkOHDimPScpLgwYNmDRpkjJzvXv37tSsWRNjY2MSEhLYs2cPwFOHf4vynEL2o5omTJjAzJkzldn0uSnM+/RJBXlv+fv74+vri1arpXTp0ly8eJFt27bh6elZ6OcZ5rftjI2NGTVqFJMmTSI4OJhu3bqRnJxMeHg4Dg4OufZOFlbFihUJDg5m/vz5XL16lRYtWlCqVClu375NXFyc8nuiIJ72e++9996jd+/e9OnTh169euHk5MTDhw/5888/2bdvX55PXhBCvByKVYII2UOvMTExrFmzhr1797Jy5UoyMjKwtbWlbt26fPjhh6qvb3N0dGTz5s1ERESwYsUKbt68iZWVFU5OTjm+8WT69Ol88sknDB8+nPT0dLp06cLHH3/81HjmzJnD9OnT+fTTT8nMzKR+/fqsXLmySL8qr2XLlixbtozAwEBGjBhBeHg4nTp1okKFCkRGRjJ58mQePnyInZ0dtWvXpkuXLgXeh62tLUuXLmXWrFmEhoZSokQJWrVqxbx581T1OTg4ULlyZdatW8eNGzcAqFSpEuPGjVM9Y/Htt9+mSpUqfP7557zzzjtoNBqqVq361Ocw5sXS0pK1a9eybNkyNm7cyLVr17CwsKB8+fI0bdoUJyenAteZm27dunHnzh02bNjAli1bqFSpEkOHDuXGjRuEh4eryhqeFWeYEOTt7c2SJUtUkwLMzc1xd3dn27ZtxMfHk5mZSfny5RkyZAiDBw9+Zjy9evXC09OT1atXs2rVKm7duoWRkRHlypXD09OTVatW5Tq5wqCoz6lh0k5e904+rqDv09zk973VuHFj9u3bp/QYli1bls6dOxMYGPjMOPOS37YD6NGjB0ZGRkRGRjJy5EicnJwYOnQo3377LX/99VehY8jNsGHDqFGjBmvWrGHHjh2kp6fj4OCAq6srvXr1KlSdef3eq1mzJtHR0URERDB//nwSExMpWbIkVapUkckpQgiM9I9P3RNCvLTefvttrl27VugJOy+T5ORkWrduzauvvsqHH374osMpVnQ6HSdPnqRevXpoNJoXHY5Keno6cXFxElsBFNe4QGIrrPy+R4tdD6IQ4p+n1+s5evQon3766YsOpdhJSEhgyZIleHt7Y2Njw/Xr11m1ahUPHz6kX79+Lzo8IYT4W0iCKITAyMhI9TWO4v+ZmZkRHx/P1KlTuXfvHhYWFnh4eDB16lScnZ1fdHhCCPG3kARRCCGeonTp0qqvwxNCiJdB0X6VghBCCCGE+NeTBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJFvUhFCCFFkdDodAGlpaZiYFK+PmJSUFABSU1MxMzN7wdGoFdfYimtcILEVVmZmJpD9XtVoNHmWM9Lr9fp/KighhBD/bQ8ePODcuXMvOgwhxDNotVqsra3zXF+8/rwTQgjxr2boLale1QSNsdELjkYtPSOTP67qqVrZCNNi1rtpiM3Nze2pvTr/tLS0NM6cOYNz4k3MNMXrrrRHeiMu2tgX6/NZHNtNhxFnS9s/s2ezeLWoEEKIfzVDcmNqokGjKV4Joh4jIB1TExNMTYtPEgb/H5tGoylWCaLhNgEzjTGmxSzh1/9vGkVxPp/Fsd2MyY7nWddZ8UprhRBCCCHECycJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgliETh79iwTJ07k1Vdfxd3dHXd3d15//XUmTZpEXFxcoeq8cOECYWFhXLt2rcDbarVapk2bBkBAQADt27cvVAwA165dQ6vVsmLFikLXkR9hYWFotVrlx8PDgxYtWjBo0CCioqJ48ODB37r/6OhotFptoc/Xk/z8/Bg/fvxz1fHTTz8xZswYfH19cXV1pV69erRr146PP/6YixcvFqrO48ePExYWRnJycoG2e/I68PPzY9iwYYWKASA2NhatVsvXX39d6Dr+7R5/nz6N4doszO8CIYQoLPmqvee0YcMGPvzwQ6pVq0a/fv1wdnYG4NKlS8TExPDmm2/yzTffULly5QLVe+HCBcLDw/Hy8qJixYoF2nbjxo3Y29sDMHnyZDIzMwu0/YsUGRlJyZIlycjI4NatW/z44498+umnrFixgiVLllCrVq0XHeI/Yt68eSxZsgRPT0+GDx9OlSpV0Ol0nDt3jq1bt7Jy5UpOnz5d4K/kOnHiBOHh4XTp0oVSpUrleztHR0c2btxIhQoVAAgPD8fCwqJA+xZCCPHvIQnic/j555+ZOnUqLVu2ZOHChaovvm7SpAl9+vRh165dmJub/+2xpKamUqJECQDq1aunLK9Zs+bfvu+iVLduXezs7JTX7dq1o2/fvvTt25fhw4eze/fuZ37B+L9dTEwMS5YsoWfPnkyZMgUjo///Hs9mzZoxYMAA1q1b94/E8ujRIywsLDAzM1NdV3Xq1PlH9l+cPP4eE0KI/zoZYn4OS5cuRaPRMG3atDyTlrZt21K2bFnVsri4OAIDA/Hy8sLNzY3OnTuzc+dOZX10dDRvv/02AP369VOGXaOjo4H/HzY+duwYPXv2xMPDg4kTJwKwfft2BgwYQPPmzXF1daVly5ZMnTqV+/fvq2IYP348np6e/PHHHwwZMgRPT09atmzJxx9/THp6+lOPOyMjg3HjxuHp6cn+/fsB0Ov1rF27lk6dOuHu7k6jRo0IDg7m6tWrBWjR3NWqVYvAwECuX7+uaieAw4cP89Zbb1G/fn08PDzo2bMnP/74Y446Ll68yJgxY2jatCmurq74+vry7rvvPvVYb926RdeuXXn99de5cuUKAA8ePGDWrFn4+fnh6uqKj48P06dPJyUl5bmP02Dx4sXY2toyceJEVXJoYGRkRJ8+fXL0Hj6rLcLCwvjkk08AaNWqlXJdxcbGAv8/bLxnzx46d+6Mm5sb4eHhAERFRdG7d28aN26Mq6srr776KvPmzcvRfoZr89SpU/Tu3RsPDw9atWrFsmXLyMrKeupxP3jwgEGDBtG0aVNOnToFQHp6OhEREbRp0wZXV1caN27MhAkTSExMVLYbMWIEr7zySq71+/v706VLF+V1fq/Tp73HfvzxRwICAvD29sbd3R1fX19GjRpFamqqsn16ejrh4eG0bdsWNzc3vL29CQgI4Pjx4zli/PLLL2nbti0eHh507NhReU89TV63MAQEBBAQEKC8zsrKIiIigtatW+Pu7k7Dhg3p0KEDq1evVsokJibywQcf0LJlS6WNe/bsyeHDh58ZhxDiv0t6EAtJp9MRGxuLq6srjo6O+d7uyJEjDB48GA8PD6ZMmULJkiXZuXMnISEhPHr0iK5du+Lr68uYMWOYO3cukyZNom7dugCqYeqEhARCQ0MZPHgwISEhGBtn5/p//PEHvr6+9O/fnxIlSnDp0iWWL1/OxYsXWbNmjSqWjIwMhg8fzptvvsnAgQM5duwYERERWFtbExQUlGv8ycnJBAUFcenSJaKionB1dQVg0qRJbN26lYCAAN555x3u3bvHokWL6NmzJ9u2bVOGvAvLz8+PTz/9lGPHjtG5c2cAtm3bxrhx42jVqhWzZs3CxMSEjRs3MmjQIFasWEGTJk2A7HtEe/Xqha2tLcHBwVSpUoWEhAT27dtHenp6rsn977//ztChQylXrhwbNmzAzs6O1NRU+vbty40bNwgMDESr1XL+/HkWLlzI77//zqpVq3JN6Ari5s2bXLhwgfbt2xeo5zk/beHv78+9e/eIiooiPDwcBwcHQN3L/Ntvv3Hx4kWGDx9OxYoVlR6zP//8kw4dOlCxYkVMTEw4d+4cS5Ys4datW8ycOVMVi+HaHDBgAEFBQXzzzTfMmTMHR0dH5dw96caNGwwZMoSMjAw2btxIpUqVyMrKYsSIEfz8888MGjSI+vXrEx8fT1hYGKdOnWLLli1YWFjQrVs3RowYwZEjR2jatKlS58WLFzl16hTvv/++sqwg12lu77Fr164xbNgwGjZsyPTp0ylVqhQ3b97k0KFDZGRkUKJECTIzMxk8eDA///wz/fr1o3Hjxuh0On755Rf++usv1XEfOHCAuLg4goODsbS0JDIykqCgIL7++msqVaqU7/Ofl8jISMLDwxk+fDgNGzYkMzOTS5cuqf5gDA0N5fTp04SEhFC1alWSk5M5ffo0SUlJz71/IcS/lySIhXT37l0ePXqk3JP1OJ1Oh16vV15rNBolcZg6dSrOzs6sXr0aE5Ps5vfx8eHu3bvMnTuXzp07Y2dnR5UqVYDsD+/Hh/YMkpKSmD9/vpIEGTye2On1eurXr0+NGjXo27cvZ8+eVd3Dl5GRwahRo2jbti2QPSz+66+/EhMTk2uCaPhwhOz7HJ2cnAA4efIkX3zxBePHj2fAgAFK+YYNG9K6dWtWrlxJaGjoU1rz2Qz7unXrFpA93Ddjxgx8fX1ZtGiRUq5ly5Z06dKFuXPnsmnTJgBmzpyJiYkJmzdvVg1fd+zYMdd9HT58mFGjRtGsWTM+/fRTJVGLiori3LlzfPHFF7i5uQHZbVa2bFmCg4M5ePAgLVu2fK7jvHHjBkCBrqv8tkW5cuUoX748ALVr18713tbExER27NhBtWrVVMvfe+895f9ZWVk0atQIGxsbJk6cyPjx4yldurSyPikpieXLl+Pu7g5A06ZNOXr0KNu3b881QTxz5gxDhw6lcuXKLFq0CBsbGwB27drFoUOHCAsL4/XXX1fK16pVizfffJPo6Gh69+5Ny5Ytsbe3Z8uWLaoEMTo6GlNTU2WSVkGv09zeY7t37yYtLY13331X9V7q0KGD8v+YmBhiY2P56KOP8Pf3V5b7+fnlOPa0tDRWrlyJtbU1kH2LhY+PD7t27WLo0KE5yhfU8ePHcXFxYdSoUcoyHx+fHGX8/f3p3r27suzVV1997n0LIf7dZIj5b9C1a1fq1q2r/Hz22WdAdu/epUuXlA+TzMxM5adFixYkJCRw+fLlfO2jdOnSOZJDgKtXrzJ27FiaNWtG7dq1qVu3Ln379gWyJ848zsjIKMeHllar5fr16znqPX36ND169KBMmTKsX79eSdgA9u/fj5GRER07dlQdk729PbVq1eLo0aNAdsL6+PqCTJ55PDGC7MkWSUlJdOnSRVVfVlYWPj4+xMXFkZKSQmpqKseOHaNt27aq5DAvX375JUOHDsXf358FCxaoevH279+Ps7MztWvXVu2zefPmGBkZKcf5d/H29lZdV7t37wby3xb5odVqcySHkH3+AwMD8fb2Vq6rcePGodPplOF3AwcHByU5fLze3K6r77//nt69e9OoUSNWrlypJIeQ3d6lSpXilVdeUR1X7dq1cXBwUNrbxMSEjh078s033yg9Yzqdjm3bttGqVStsbW2V+vJznRrk9h6rXbs2pqamfPDBB2zdujXXWygOHTqEubk53bp1y6OV/5+3t7eSHALY29tTpkwZ4uPjn7ltfri5uXH27FmmTJnCoUOHcn0agLu7O1u3biUiIoKTJ0+SkZFRJPsWQvy7SQ9iIdna2mJhYZHrh96cOXNITU0lISGB4cOHK8tv374NwKxZs5g1a1au9d69ezdf+zcMDz7u4cOH9O7dG3Nzc0aPHk3VqlWxsLDgxo0bBAUF8ejRI1X5EiVK5BjGNDMzIy0tLUfdP/zwA3fv3mX8+PE5Zr/euXMHvV6v6r15nGGobOvWrUyYMEG17ty5c88+WFA+MA3D+Ya2DA4OznObe/fuYWxsjE6ny3EfaF527NiBubk5/v7+OYaL79y5wx9//KEM+T8pv+fuacqVKweQ63UVFRVFZmYmv/32G5MnT1aW57ctLC0tn7n/3K6r69ev06dPH6pVq8bEiRNxcnLC3NycU6dOMW3atBzX1eNJnkFe19XevXtJS0ujV69eOYb679y5Q3JysnIbw5Meb+9u3brx2WefsWPHDnr27Mn3339PQkICXbt2VdWXn+vUILe2qFy5MqtWrSIyMpJp06aRkpJCpUqVCAgI4K233gKye2EdHR2V2z6epiBtVRjDhg3D0tKSr776ig0bNqDRaGjYsCHvvPOO0gs+b948Fi9ezObNm1mwYAGWlpa89tprhIaG5toGQoiXgySIhaTRaGjcuDE//PADt27dUt2HaLin68nnlhl6MoYNG8Zrr72Wa7259d7kJrd73Y4cOcKtW7eIiorCy8tLWf7kBJXCGDRoEFevXlV6jR4fKrS1tcXIyIi1a9fmej+fYdkrr7zC5s2bC7X/ffv2ASjHZWjLDz74AA8Pj1y3KVOmDFlZWWg0Gm7evJmv/cyePZsFCxbQt29fPvvsM2rXrq2ss7W1xdzcnBkzZuS6rSGm51G2bFmcnZ354YcfSEtLUyXwhlie7A3Mb1vkR27X1d69e0lJSSEsLEzVc3z27Nl81fk0EyZMYNeuXQwZMoTw8HCaN2+urLO1tcXGxobIyMhct7WyslL+X7NmTdzd3YmOjqZnz55s2bIFR0fHHPXl5zo1yOt+0oYNG9KwYUN0Oh2//vorUVFRzJgxA3t7e9q1a4ednR0///wzWVlZ+UoSC8PMzCzXCVZ3795VXYcmJiYMGDCAAQMGkJyczOHDh5k3bx6DBw/mwIEDlChRAjs7O9577z3ee+89rl+/zr59+5gzZw537tz5259/KoQoviRBfA5Dhw7l4MGDTJ48mYULF2JqavrU8tWrV6dq1aqcPXuWMWPGPLWs4cPqyd6ZpzF8oD35Qbdhw4Z815EXY2Njpk2bhqWlJePHjyclJYXevXsD4Ovry7Jly7h58yZvvPFGnnXY2toWKok6e/YsS5cuxcnJSblfsn79+pQqVYoLFy4oQ+h5adSoEV9//TWjR49+5jBz6dKlWblyJYGBgfTr14/ly5cr94D6+vqydOlSbGxsimQCQV4CAwMZO3YsM2fOZPLkyc+c+FKQtjBcGwXpocrtutLr9XzxxRf5riMv5ubmhIWFERoayvDhw5k3b55y/5uvry87duwgKysrz8T3cV27dmXKlCn89NNP7N+/nwEDBqhmeuf3Os0vjUaDh4cH1atXZ/v27fz222+0a9cOHx8fYmJiWqCRwAAA33dJREFUiI6O5s0333zu/eTGyckpR+/75cuXuXz5cp7vsVKlStGmTRtu3rzJjBkziI+Pz/EYrAoVKtC3b19+/PHHXGdcCyFeHpIgPocGDRowadIkPvroI7p27Ur37t2pWbMmxsbGJCQksGfPHgDVPUZTp05lyJAhDBo0iC5dulC2bFnu3bvHxYsX+e2331i4cCGA8sDtL774AisrK8zNzalYseJTEyxPT09Kly7N5MmTCQoKwsTEhO3bt+d7GDc/xo8fj5WVFVOnTiUlJYXBgwfToEEDevTowcSJE/n1119p1KgRJUqUICEhgZ9//hkXFxclmXyW3377jZIlS5KZmak8KHvbtm2UKVOGJUuWKEmKlZUV77//PuPHj+fevXu0bt2aMmXKkJiYyNmzZ0lMTGTq1KlAdi9Vr1696N69uzIZ4s6dO+zbt4+pU6eqzg9kny/DbNIBAwawePFiGjduzFtvvcWePXvo27cv/fv3R6vVkpWVxV9//cX333/PwIEDn5rIBAQEcPTo0Weej/bt23P+/HmWLFnC2bNn6dq1K1WqVCErK4sbN26wbds2pQ0K2hYuLi4ArF69mi5dumBiYkK1atVytMHjmjZtiqmpKWPGjGHw4MGkp6ezfv36An8bS15MTU2ZO3cu7733Hm+//TazZs2iffv2tGvXju3btzN06FACAgJwd3fH1NSUGzduEBsbS6tWrVQ98e3bt+fjjz9m7NixpKenqx5vAxTJdbp+/XqOHDmCr68v5cuXJy0tjS1btijtZIgjOjqaKVOmcPnyZby9vdHr9fzyyy/UqFGDdu3aPXebderUidDQUKZMmULr1q2Jj48nMjIyx++HwMBAnJ2dcXV1xc7Ojvj4eFavXo2TkxNVqlTh/v379OvXj/bt21O9enWsrKyIi4vj0KFDeY5yCCFeDpIgPqdevXrh6enJ6tWrWbVqFbdu3cLIyIhy5crh6enJqlWrVDe6N27cmE2bNrFkyRJmzJhBcnIyNjY21KhRQ+kdg+z7oSZOnMiaNWvo168fOp2OmTNnqu6pepKtrS1Lly5l1qxZhIaGUqJECVq1asW8efNyfFg+j1GjRmFpacknn3xCSkoKwcHBTJs2DQ8PDzZu3Mj69evJysrC0dGR+vXr55iw8DSDBw8GsnurSpcujVar5Z133qFr1645kphOnTpRoUIFIiMjmTx5Mg8fPsTOzo7atWurjrdWrVps3ryZhQsXMmfOHB4+fIiDgwONGzfO8/mVFhYWRERE8M477zB06FDCwsJo2bIla9euZdmyZWzcuJFr165hYWFB+fLladq0qWr4NTeG/eZHSEgIPj4+rF27lkWLFnHnzh1MTExwcnKiUaNGvPPOO6p78/LbFt7e3gwbNoytW7eyadMmsrKyWLNmDd7e3nnGUqNGDcLCwpg/fz6jRo3CxsaG9u3b079/f4YMGZKv43kWY2NjZsyYgZWVFaGhoaSmpuLv78/ixYtZs2YN27ZtY9myZWg0GsqVK0ejRo2UZNegZMmSvPrqq8TExFC/fv1cb9d43uu0du3a/PDDD4SFhZGQkIClpSUuLi4sXrxYGc42MTFh+fLlLF26lB07drB69WqsrKyoVatWjhnEhdWhQwdu3brFhg0biI6OxtnZmSlTpqhmsUP2+d69ezebNm3iwYMHODg40LRpU0aMGIGpqSl6vR53d3e2bdtGfHw8mZmZlC9fniFDhijvRSHEy8lI/+T0UCFEkXvw4AHe3t5MnDiRPn36vOhwhPjb6HQ6Tp48ibamORrN8z0XtKilZ2Rx4VI6ztVNMTUt2NdU/t0MsdWrV6/AX6H5d0pPTycuLo669xIwNS5m5xNjTpe0K9bnszi2mw4j4kqWeea1Jo+5EeIf8NNPP1G2bFnVc/GEEEKI4kqGmIX4B/j6+uLr6/uiwxBCCCHyRXoQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEi38UshBCiyOh0OgDSM3RosopXH8SjR+nZ/6ZloMfoBUejZogtNTUVMzOzFxzN/0tJSQEgFeNi12Yp//u3WJ/PYthumf/7V6fTodFo8ixnpNfr9f9MSEIIIf7rHjx4wLlz5150GEKIZ9BqtVhbW+e5XnoQhRBCFBlD71f1qiZojItXz0l6RiZ/XNVTtbIRpibF6+PPEJubm9tTe3X+aWlpaZw5cwbnxJuYaYpZj7DeiIs29nI+C0in0xEXF/fMnuri1aJCCCH+1QwfhqYmGjSa4pUgZg/1pWNqYoKpafH60DbEptFoilVCYfK/xMtMY4xpMUv49f+bRiHns3CeFVfx+nNACCGEEEK8cJIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiC+ANHR0Wi1WuWnTp06NG/enJCQEK5cuZKjfEBAAAEBAf98oP9iAQEBqjZ2d3enY8eOrFq1iqysLKXc+PHj8fPz+9viWLJkCXv37s2xPDY2Fq1WS2xsrCoWT0/PZ9aZnp7OpEmTaN68ObVr16ZTp04A+Pn5MX78+KILvoCuXbuGVqtlxYoVLywGgG+//ZY6deqQmJioxPT4T/369ZVrQafTFWof+T1X8P/n+uuvvwayv/902rRpOco9+T5PTU0lLCxMdY0IIcQ/Rb5q7wWaOXMm1atXJy0tjePHj7NkyRJiY2PZtWsXpUuXVspNnjz5BUb571WpUiVmz54NwJ07d9iwYQMzZ84kISGB0NDQfySGpUuX0rp1a1599VXV8rp167Jx40Zq1qxZ4DrXr1/Pxo0b+eCDD6hbty6WlpZFFe5/wp49e2jYsCF2dnakpKQA2clX+/btAUhOTmbfvn3MnDmTGzdu/O1JteFcV61aFYCNGzdib2//zO1SU1MJDw8nKCgIb2/vvzVGIYR4kiSIL5CzszNubm4AeHt7o9PpCAsLY+/evXTr1k0p93/s3XdYFFfbwOEfHRRpCqJgJboaQUAFxEpQo8ZesWFDQRSJXSyxJbbYBbFhFIkaG8aeGFs0r/VLNJrYNRo1FixAlA77/cG7E0dAwZDImzz3dXHpzpw588yZWfbZc+YMb5JECDA1NcXNzU153ahRI1q2bMm6desYNmwYRkZGby02c3NzVWwFcfXqVUxNTenVq1fhBvUPkJ6ezsGDBxk2bJhqeZkyZXJcC1evXmXXrl1/eYL48rl+0/NeWNLT09HT01P+xq4QQuRGhpiLEF2y+PjxY9Xyl4eeXhzKW716Nb6+vri7u+Pn58fZs2dz1Ltp0yaaN2+Os7MzH3zwATt37sx1aDUtLY3IyEhatGiBs7MzdevWZdy4cTx58uSVca9ZswaNRsOtW7dyrJszZw7Ozs6qOrZs2ULbtm1xcXHB09OTIUOGcP36ddV2uiG8W7duMXDgQNzd3WncuDGzZs0iLS3tlfHkxcjIiBo1apCcnPzKY1q3bh09e/bE29sbNzc32rRpw8qVK0lPT1eVu3DhAkFBQXh7e+Ps7EyDBg0IDAzk/v37QPZQYlJSEtu2bVOGN3XnMbch5tx8//33eHl5ERQURFJSEhqNhs2bN5OSkqLUGRsbm+u2qampzJo1i3bt2lG7dm08PT3x8/PLdch77969dOnShdq1a+Pq6kqTJk0YN26cqkxiYiKzZs2iSZMmODs74+3tzcCBA3OcOyBf1+X58+cZNGgQnp6euLi40L59e/bs2aMqo7sd48SJE0yePBkvLy+8vLwICQnhwYMHOeo8fvw4v//+e44e29yUKFEi1y8Je/bswc/PDzc3N9zd3QkICODChQu51pGf63PRokV07twZT09PnJ2dadmyJatXr0ar1eYZ2507d/D29gYgIiJCOdcvJrM3b95k5MiRyvWn+/LzIt119uWXXzJr1iwaNmyIi4sLt27d+kveY0KIfw75ClmE3LlzB0AZinqddevWUblyZcaPHw9kfxAFBgZy4MABSpQoAWQPZ02aNInmzZszbtw4fv/9dyIiInIkO1lZWQwePJjvv/+egIAAatWqxd27dwkPD+fcuXNs3boVU1PTXONo27Ytc+fOJTY2luHDhyvLMzMz2bFjB++99x42NjZA9pDr/Pnzad26NSNHjuTp06dERETg5+fHli1bVMeenp5OcHAwnTt3pn///pw+fZrIyEjMzc0JCQnJVxu97Pbt2xgaGqqG8F/266+/0rp1axwdHTEyMuLSpUssW7aMGzduMHPmTACSkpLo168fjo6OTJo0iVKlShEXF8fJkyd5/vy50vZ9+vTBy8uLwYMHA9m9Sfm1Z88exo4dS6dOnfjoo48wMDBg48aNREZGcvLkSaKjowEoX758rtunpaWRkJBA//79KV26NOnp6Rw7doyhQ4cyc+ZM2rdvD8CZM2cYPnw4H3zwASEhIZiYmPDbb79x4sQJpa5nz57Ro0cP7t69y4ABA3B1dSUpKYnTp08TFxeHk5OTUjY/1+WJEyeUeqZMmUKJEiXYs2cPw4cPJyUlhY4dO6qOZeLEifj4+DBv3jzu3bvHnDlzGD16NGvXrlWV27dvH25ubpQuXVq1PCsri4yMDAB+//13Dhw4wNGjRxkwYICq3LJly1i4cCEdO3YkODiY9PR0Vq1aRc+ePdm8ebOqNz+/1+dvv/1G9+7dKVOmDFqtlh9//JGFCxfy/PnzPK9jOzs7oqKiGDBgAJ07d6ZLly4Ayvvo2rVrdOvWjTJlyjB27FhsbW357rvv+OSTT3j69GmOeufPn4+bmxtTp05FX1+fkiVLFugYhBD/PpIgvkW6Dy3dPYhLly7Fw8Mj35MmihcvzvLlyzEwMACyP1S6dOnCkSNHaNWqFVlZWYSHh+Pq6srixYuV7WrXrs3777+PnZ2dsmzv3r0cPXqU8PBw3n//fWV5tWrV6Ny5M7GxsfTo0SPXOGxsbHjvvff48ssv+fDDD9HXz+6Y/u6773j48KHyYZ+YmEhkZCSNGzdm3rx5yvZeXl68//77hIeHq5anp6czdOhQWrZsCYC3tzc//fQTu3btyveHly4pePLkCTExMfz888+0aNEiz2QXUPWcZWVlUadOHaysrBg/fjxhYWFYWlpy48YN4uPjmT59uqq36oMPPlD+7+bmhr6+PjY2NgUeVlyxYgULFy5k+PDhDBw4UFWnjY0N+vr6r62zRIkSSkIL2Qm7t7c3iYmJREdHqxJErVbL1KlTlQQOUCVp0dHRXL16ldWrV1OvXj1l+YvXis7rrkuAqVOnUqVKFaKjo5WhzoYNG/L06VPmz59P+/btletIt27ixInK64SEBObMmUNcXBy2trbK8e3fv5+goKAcMc2dO1e5H/XF4wsNDVVe37t3j/DwcHr16qXaV7169WjevDkREREsXLhQWZ7f63P27NnK/7OysvDy8kKr1bJ27VqGDBmCnp5ejniNjY2pUaMGAPb29jnO9cyZMylevDgbNmxQvnTUr1+ftLQ0VqxYgb+/v+pLUPny5VW/Awp6DEKIfx9JEN+irl27ql47OTkRGRmZ73uDfHx8lA9hyE7mAO7evQvAL7/8QlxcHAEBAartypYti7u7u1IO4NChQ1hYWPDee+8pSRVA9erVsbW15dSpU3kmiJD9Ybtv3z6OHTtGgwYNgOzhQVtbWxo1agRkJyIpKSl06NBBtW2ZMmWoW7euqscKQE9PL0eyrBtuzI+rV68qH7KQPcTcpk2b1076uXDhAosXL+bMmTPEx8er1t28eRNXV1cqVKiApaUlc+fOJS4uDg8Pj0K5V1Sr1TJp0iS2bdvG3LlzVQnnm9i7dy/R0dFcvnxZmbABYGJiovxfd2vDsGHD6NSpE7Vr187RA3f06FEqVqyoSg7z8rrr8tatW9y4cYOxY8cCqK63Ro0acejQIX755RdVr2Ru1wFk987pEsRTp07x9OlTmjVrliOm3r1707ZtWyC79/fs2bMsXbqUpKQkFi1aBGR/ocnIyKBdu3aqmExMTPDw8MhxO0B+r8/jx4+zfPlyzp8/z7Nnz1TrHj9+nK8JKy9KTU3lxIkTdO/eHVNT0xzt9/nnn3P27FkaN26sLM8tkS/IMQgh/n0kQXyLZs+ejZOTE8+fP2fPnj1s3LiRESNGEBUVla/traysVK+NjY2B7A8QgKdPnwIow0kvKlWqlCpBfPz4MYmJiTg7O+e6L11deWnUqBG2trbExsbSoEEDEhISOHjwIL1791aSBV2ypftAf5GdnR3Hjh1TLTMzM1MlMrpj1B3f65QvX5758+ejp6eHiYkJjo6OmJmZvXKb3377jZ49e1KpUiXGjx+Pg4MDJiYmnDt3jmnTppGSkgJk987FxMSwbNkyFixYQEJCAra2tnTt2pXg4OA3ngCTnp7Onj17qFKlipJYv6l9+/YxbNgwWrRowYABAyhVqhQGBgZs2LCBrVu3KuU8PDxYsmQJMTExjB07lrS0NKpUqcKgQYOUmb9PnjyhTJky+drv667LR48eAdnX/4u9ay96+XrLq07d+QD4+uuvqVGjBo6Ojjnqs7e3VxJhyO611tPTY968eRw9epSGDRsqcXXu3DnXmF7s0YT8XZ/nzp0jICAAT09PPv74Y+zt7TEyMmL//v0sW7ZMFX9+xcfHk5GRQUxMDDExMbmWebn9cnvP5fcYhBD/TpIgvkVOTk7Kh1bdunXJyspi8+bNfPXVV7Ro0eJP129tbQ3knPQCf3xIv1jWysoqz+S0ePHir9yXgYEB7dq1IyYmhsTERHbt2kVaWppqmFL3IR8XF5dj+4cPHyrxFhYTExNVUpAf+/fvJykpifDwcBwcHJTlly5dylFWo9GwYMECtFotly9fJjY2liVLlmBqakpgYOAbxWxsbMzatWsJCAigX79+REVFvfJ+yVfZsWMHjo6OLFy4UDWMqbt38UVNmzaladOmpKWlcfbsWZYvX87IkSNxcHDA3d0dGxsbZfLNn6U7z0FBQbn29gFUqlSpQHVmZWWxf//+Aj0vVNcLeenSJRo2bKjEtXjxYsqWLVug/edl9+7dGBoasnz5clUilttEofyysLBQ3m959eq/nCTnNowthBCvIrOYi5DRo0djaWnJ4sWLVQ9zflOVKlXC1taWvXv3qpb/9ttvnDlzRrXMx8eH+Ph4srKycHFxyfFTuXLl1+6vY8eOpKamsmvXLmJjY3F3d1cNE7q7u2NqasqOHTtU292/f58TJ05Qt27dP3G0hUP3QarroYLsYd9Nmza9cptq1aoxfvx4LCws+Pnnn5V1xsbGBe4levfdd/n888+5f/8+/v7+uSb4+aGnp4eRkZEqOYiLi+PAgQN5bmNsbIynp6fynEjd7N2GDRty8+ZNjh8//kaxvKhy5cpUrFiRS5cu5Xqtubi4FGgyD8APP/xAXFxcnkOpubl48SLwRw97gwYNMDQ05Ndff80zroLS09PDwMBA1fuYkpKS4z2Qm9x6SSG718/Ly4sLFy6g0WhyjbOwv2wJIf59pAexCLG0tCQwMJA5c+awc+dO5S9kvCl9fX2GDh3KpEmTCA0NpVOnTiQmJhIREYGtra0qcWjVqhU7d+4kMDAQf39/atasiZGREffv3+fkyZM0adIkz94eHScnJ9zd3VmxYgX37t3j448/Vq23sLBg8ODBzJ8/nzFjxtCqVSvi4+NZsmQJJiYmReKm+Hr16mFkZMSIESMYMGAAaWlpbNiwgcTERFW5Q4cOsX79epo2bUq5cuXQarXs27ePxMRE6tevr5SrWrUqp06d4uDBg9ja2lK8ePF8JdtOTk6sX7+evn370rNnT9asWYO9vX2BjsXHx4d9+/YxZcoUmjdvzv3794mMjMTOzk71F3sWLVrE/fv38fb2xt7ensTERNauXYuRkRGenp4A9OnTh7179zJ48GACAwOpWbMmKSkpnD59Gh8fnwIn91OnTmXgwIEEBATQoUMHSpcuTUJCAtevX+fnn3/OdULFq3z99ddUrVo1z57He/fuKY/aSU5O5syZM6xYsQIHBwclqXR0dCQ0NJSFCxdy+/ZtGjVqhIWFBY8ePeL8+fOYmZmpJrXkR+PGjVm9ejUjR47Ez8+P+Ph4Vq1apfoCkhdzc3McHBw4cOAA3t7eWFpaYm1tjaOjIxMmTKBHjx707NmT7t274+DgwPPnz/n11185ePBgjtndQghRUJIgFjH+/v6sW7eOyMhIWrdurbrZ/034+fmhp6dHVFQUQ4YMwcHBQXnkyL1795RyBgYGLF26lLVr17J9+3ZWrFiBgYEB9vb2eHh4ULVq1Xztr2PHjnz00UeYmprmOsEiKCgIGxsbYmJi2LNnD6ampnh6ejJixIh8P97nr+Tk5ER4eDgLFy5k6NChWFlZ0bp1a/r27auaTVyhQgUsLCyIiori4cOHGBkZUalSJWbNmqWahDNhwgSmTp3KiBEjSE5OxtPTM8/7xl5Wrlw5JUns0aMH0dHRlCtXLt/H0qlTJ+UvyGzdupVy5copz2mMiIhQyrm6uvLTTz8xd+5cnjx5goWFBc7OzqxZs4YqVaoA2cnK+vXrCQ8PZ9OmTSxZsgQLCwtcXFxyTLbKj7p167J582aWLVvGjBkzSExMxMrKCicnJ2VGbUF88803qofLv+zF+/VMTEwoU6YMXbt2ZeDAgareyqCgIJycnFi7di27d+8mLS0NW1tbnJ2d6d69e4Hj8vb2ZsaMGaxcuZJBgwZRunRpunbtio2NDRMmTHjt9tOnT+fTTz8lODiYtLQ0OnTowKxZs3jnnXeIjY0lMjKShQsX8uTJE0qUKEGFChVUk1OEEOJN6Wlf9bRW8Y+UmJio/Pm3l3v5hPhfc+7cObp06cKOHTuU+wrF25OZmcnZs2fRvGOCgUHRuvcxLT2LazfSqFLZCCOjP/flu7DpYnNzc/vTHQOFKS0tjfPnz1MjIQ4j/SJ2PtHnQgkbOZ8FpHuPvi426UH8h4uLi2PZsmV4eXlhZWXFb7/9xpo1a3j+/Dm9e/d+2+EJ8afVrFmTy5cvv+0whBDiH0USxH84Y2Nj7t69y9SpU0lISMDU1BRXV1flQcVCCCGEEC+TBPEfztLSkmXLlr3tMIQQQgjxP0QecyOEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVOQvqQghhCg0mZmZAKSlZ2KQVbT6IFJS0rL/TU1Hi95bjkZNF1tycjLGxsZvOZo/JCUlAZCMfpFrs6T//ivns2AyMjKA7PeqgYFBnuX0tFqt9u8KSgghxD/bs2fPuHz58tsOQwjxGhqNBnNz8zzXSw+iEEKIQqPrLXFxcXll78TbkJqaysWLF4t0bBXL62FkWHQ+mtPSM7h1W1vk4oI/Yqvy5AHGBkWst1qrx3WrUkXyWsvMzOT8+fOv7dksWmdbCCHE/zTdh6GBgUGR+2A0/G+CU5RjMzI0xMio6MSWPXSbVuTigj9iMzbQx0i/aA0xa/87xaMoXms6r4uraKXcQgghhBDirZMEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQKVCCGBsbi0aj4fz586rlT548oWPHjri7u/Of//ynUAPMj/DwcDQaDU+ePPlL95PX8Rcl/v7+tG7d+m2HofJ3nZ+iJiwsDI1Go/y4ubnh6+vLoEGD2Lp1K2lpaW87RJVLly6h0Wi4cOGCavmTJ09wdnb+W6/9PXv20KpVK2rWrIlGo+HixYuFUq+/vz/+/v6FUteLdNf4X+Xf+h4SQrw9f/pP7d2/f59+/frx+PFjVq9ejZubWyGEJcQ/g6mpKdHR0QCkpKRw//59jhw5wkcffcTq1auJiorC3t7+LUeZbd++fTg6OvLuu++qlu/YsYP09HQAtmzZgouLy18ax5MnTxgzZgwNGjRg8uTJGBsbU7FixUKpe/LkyYVSjxBC/NP9qQTx5s2b9OvXj/T0dGJiYl77DTo5ORkzM7M/s8u3Ij09HT29ovV3HsX/Bn19/Rxfmtq3b0/Hjh0ZNGgQoaGhbNq06e0E95Kvv/6a999/P8fyrVu3UrJkScqWLcvu3bsZN24cpqamf1kcv/zyC+np6bRt2xZPT89Xli3o75R33nnnz4YnhBD/Cm98D+LFixfp0aMHBgYGbNiwIUdyGBYWhru7O5cvX6Z///64u7vTt29fAOLj45kyZQoNGzbE2dmZJk2asGDBghxDbhqNhmnTpvHll1/SsmVLXF1dadu2LYcOHXptfNevX6dJkyZ06dKFx48fA3DlyhWCg4Px8PDAxcWFdu3asW3bNtV2J0+eRKPR8OWXXzJr1iwaNmyIi4sLt27dynU/Dx8+pGPHjrz//vvcvHkTgGfPnjF79mx8fX1xdnamYcOGTJ8+naSkpEI7vj/j/PnzDB8+HF9fX2rWrImvry8jRozg7t27qnLJycnKcbi4uODp6UnHjh3ZtWuXqtyPP/7IoEGD8PLywsXFhaZNmzJ9+vQc+338+DEjRoygdu3a1KtXj3HjxvH777+rymi1WtatW0e7du2oWbMmHh4ehIaGcvv27Vce0/79+9FoNBw/fjzHuvXr16PRaLh06ZKy7MCBA/j5+eHq6oq7uzv9+vXjzJkzqu10w3pXr159bdwF1aBBA7p06cKPP/7I6dOnleVZWVmsXLmSFi1a4OzsjLe3N2PGjOH+/fuq7S9cuEBQUBDe3t44OzvToEEDAgMDVeUK0pbXr1/n2rVrNG/eXLX8xx9/5MqVK7Rr146uXbvy+++/8/XXX+fY3tfXl7CwsBzLXx7SzcrKIjIykubNm1OzZk3q1KlDmzZtlF7WsLAwevToAcDw4cPRaDTK9q/6nZKWlkZkZKTSbnXr1mXcuHE5hmRzG2Jev349bdu2xd3dHXd3d1q0aMH8+fOV9fl9H7xsz5499O/fnwYNGlCzZk1atmzJ3Llzc/we0LXz3/0e0t2Ocu7cOXr06IGrqytNmjRhxYoVZGVlvfLYhBD/fG/Ug/j9998TERFBmTJlWLVqFXZ2drmWS09PJzg4mG7dujFw4EAyMzNJTU2ld+/e3L59m6FDh6LRaPi///s/VqxYwcWLF1mxYoWqjsOHD3P+/HlCQ0MpVqwYUVFRhISE8NVXX1GuXLlc93vq1ClCQkKoU6cO8+bNw8zMjBs3btCtWzdKlizJhAkTsLa2ZseOHYSFhfHo0SMGDhyoqmP+/Pm4ubkxdepU9PX1KVmyZI79XLlyhcDAQOzt7fniiy+wsbEhOTmZXr16cf/+fQYNGqQkGIsXL+bKlSusWbNG1Rv5Jsf3Z929e5dKlSrRqlUrLC0tiYuLY8OGDXTu3Jndu3djY2MDwMyZM9mxYwfDhg2jevXqJCcnc+XKFeLj45W6jh49SnBwMJUrVyYsLIwyZcpw9+7dXO9FHTp0KB988AGdO3fmypUrzJs3T9mPzqRJk9i2bRv+/v6MGjWKhIQElixZQrdu3di+fTulSpXK9Zh8fHwoWbIkW7duxdvbW7Vu27Zt1KhRg2rVqgGwc+dORo0aRYMGDZg3bx5paWlERUXh7+/PmjVrqFOnToHjfhO+vr6sX7+e06dP4+HhAcCUKVPYuHEjvXr1wsfHh7t377Jo0SJOnTpFbGwsNjY2JCUl0a9fPxwdHZk0aRKlSpUiLi6OkydP8vz58zdqy3379lG6dGlcXV1VMW7ZsgWATp06YW9vz4wZM9iyZQvt2rV7o2OOiooiIiKC4OBg6tSpQ0ZGBjdu3FCSnMGDB+Pi4sK0adMYMWIEXl5emJubK9vn9jslKyuLwYMH8/333xMQEECtWrW4e/cu4eHhnDt3jq1bt+bZ47l7926mTp2Kv78/Y8eORV9fn1u3bnHt2jWlTH7eB7m5efMmjRo1ok+fPsrvoJUrV3Lu3DnWrl2rlHub76G4uDhGjx5Nv379CAkJ4ZtvvmHevHnY2dnRvn37Vx6fEOKf7Y0SxJkzZ1KiRAmio6OVZCI36enpDBkyhE6dOinLvvjiCy5fvszChQtp2bIlAPXr16dYsWLMnTuX//znP9SvX18pn5qayurVq5UPiRo1atCwYUP27t1LYGBgjn1u376dCRMm0K1bN8aPH4++fnYnaUREBOnp6axdu5YyZcoA0LhxYxITE5VfniVKlFDqKV++PIsXL87z2I4dO8bQoUOpX78+c+bMwcTEBICYmBguX77Mpk2blHu1vL29KV26NKGhoRw5coTGjRu/8fEVhhYtWtCiRQvldWZmJj4+PtSvX59du3bRu3dvAM6cOUP9+vWVXhrITsReNG3aNMqUKcPmzZuVNgBU51ync+fODBgwAIB69epx69Yttm7dyowZM9DT0+Ps2bNs2rSJsLAw+vXrp2xXp04dmjdvzurVqxk9enSux2RoaEjbtm3ZsGEDv//+u3Iur1+/zrlz5/joo4+A7B6sTz/9lKpVq7Jy5Url+mjcuDHNmjVj7ty5fPHFFwWK+02VLVsWyO6F1sW6ceNGevToocQL8O6779KlSxeio6MZPnw4N27cID4+nunTp9O0aVOl3AcffKD8v6BtqRtefvF4kpOT2bNnD25ubsrQbIsWLfjyyy/59ddfKV++fIGP+YcffqBq1aoMHTpUWdawYUPl/+XLl1f2VaFChRzD87n9Ttm9ezdHjx4lPDxcNURerVo1OnfuTGxsrNIrmVs8FhYWTJw4UVn28heM/LwPcjN48GDl/1qtllq1auHk5ESvXr24dOmS8oXlbb6H4uPjWblyJTVr1lTqPHXqFDt37pQEUYh/uTcaYvb19eX3339n+vTpZGZmvrLsy0NWJ06coFixYqoEBaBjx44AOYYIX+5BKFWqFCVLlswxHAqwbNkyxo0bx6hRo5g4caLy4a/br7e3t5Ic6nTo0IHk5OQcw4u53Yul8+WXXxIYGEiXLl1YtGiR6pf6oUOHqFKlCtWrVycjI0P5adCgAXp6epw6deqNj6+wPH/+nDlz5tCsWTPeffdd3n33Xdzd3UlKSuL69etKORcXF44cOcLcuXM5efIkKSkpqnp++eUXfv31Vzp37qxqg7z4+vqqXms0GlJTU5VbAA4dOoSenh5t27ZVtV2pUqWoVq1ajrZ7WadOnUhJSWHPnj3Ksq1bt2JsbKzM7P7ll194+PAh7dq1U10fxYsX5/333+fHH38kOTm5QHEXlpMnTwLZ1+SLatasiZOTk/LeqFChApaWlsydO5cNGzaoert0CtKWt2/f5uLFizmu+b179/Ls2TNVotKpUye0Wi1bt259o2N0cXHh0qVLTJkyhaNHj/Ls2bMC1/Hy75RDhw5hYWHBe++9pzrW6tWrY2tr+8rrxsXFhcTEREaMGMH+/ftznSX8uvdBXm7fvs3IkSOpX78+1atXp0aNGvTq1QuAGzduAG//PWRra6skhy/W+dtvv+XrGIUQ/1xv1IM4ePBgqlevzpIlS9BqtcyZMwcDA4Mc5czMzFTJD2R/Yy1VqlSOnpeSJUtiaGiYY9jGysoqR73GxsakpqbmWL5jxw5Kly6t6kl5cb+2trY5luuGx1/eb25ldXbv3o2JiQldunTJcRyPHz/m1q1b1KhRI9dtnz59qnpdkOMrLCNHjuTEiRPKcF7x4sXR09MjMDBQtd+JEydib2/Pnj17WLlyJSYmJjRo0IAxY8ZQsWJF5cO0dOnS+drvy8dqbGwMoHzgPn78GK1WS7169XLd/nVD7lWqVMHFxYXY2Fj8/PzIzMxkx44dNGnSRNm3rv3zuhaysrJITExUTXx4XdxvSvcl4OVrMLdbNuzs7JQP7RIlShATE8OyZctYsGABCQkJ2Nra0rVrV4KDgzEyMipQW3711VeULFmS2rVrq8ps2bIFExMTGjZsSGJiIpCdPDg4OLBt2zZCQ0Nzfd+/SlBQEMWKFWPHjh188cUXGBgYUKdOHUaNGpWv2dG5/U55/PgxiYmJODs757rNy++5F7Vv357MzEw2b95MaGgoWVlZuLi4MGzYMGUk43Xvg9w8f/6cHj16YGJiwrBhw6hYsSKmpqbcv3+fkJAQ5dp52++ht/H7Rwjxv+GNZzGHhoaip6dHREQEWVlZzJ07F0NDdXW5Db9ZWVnx448/otVqVesfP35MRkYG1tbWbxoSUVFRDB8+nJ49e7JmzRocHBxU+42Li8uxjW547+X9vmrocO7cuSxatIhevXrx2WefUb16dWWdtbU1JiYmzJgxI9dt/8zxFYbff/+dw4cPExISohrCTktLIyEhQVW2WLFihIaGEhoayqNHjzhy5Ajz5s1j0KBBfPXVV8rtBQ8ePCiU2KytrdHT02PdunXKB9+Lclv2so4dOzJ16lSuX7/O7du3iYuLU3qndfsA8rwW9PX1sbCw+BNHkX8HDx4EUGbq6j6sHz58mOPRNw8fPlRdOxqNhgULFqDVarl8+TKxsbEsWbIEU1NTAgMDC9SW+/bto2nTpqpk75dffuH7778H8h5O/e6775TbJYyNjXN9ruPTp09VcRsaGtKvXz/69etHYmIix44dY8GCBQwYMIDDhw+/dkZybu9La2trrKysiIqKynWb4sWLv7LOTp060alTJ5KSkjh9+jTh4eEEBQXx9ddf4+Dg8Nr3QW5OnDjBw4cPiYmJUc3EfnlCSVF8DwkhBPzJv6QydOhQhg4dyt69exk5ciQZGRmv3cbb25ukpCT279+vWv7ll18q699U2bJllV+MPXv2VGYV6+o9ceJEjl/E27dvx8zMrEDPb7S0tGT16tU4OTnRu3dvzp49q6zz8fHh9u3bWFlZ4eLikuPH0dHxjY+vMOjp6aHVanN8UGzevPmVtwuUKlWKjh070qpVK3755ReSk5OpVKkS5cuXL7SHPvv4+KDVannw4EGubZefBxG3bt0aExMTYmNjiY2NpXTp0jRo0EBZX6lSJUqXLs2uXbvQarXK8qSkJPbt24ebm9vf8iim//znP2zZsgV3d3dlUkzdunWB7J7wF507d47r168r61+kp6dHtWrVGD9+PBYWFvz8889A/tvy3r17nD9/Psfwsm5yyieffMLatWtVPytWrMDIyEg1zOzg4MDly5dVdfzyyy/88ssvebaBhYUFLVq0oEePHsTHx7/xbRU+Pj7Ex8crvX8v/1SuXDlf9RQrVozGjRszaNAg0tPTcx26z+19kBtdIvvy++zl+1uL4ntICCGgEB6UHRISgr6+PosWLUKr1TJ//vwcPYkvat++PevWrWPs2LHcvXuXqlWr8v3337N8+XIaN26c59BIftnZ2RETE8OAAQOUHr6qVasyZMgQDh06RO/evRkyZAiWlpbs3LmTw4cPM3r0aNUElfwwNzdXZhz369ePpUuXUrduXfr06cO+ffvo1asXffv2RaPRkJWVxb179/juu+/o379/jpmi+aHRaPD09CQmJua1ZZ89e5Zrz4aNjQ2enp54eHiwatUqrK2tcXBw4NSpU2zZsiVHz1mXLl3w8fFBo9FgaWnJ9evX2b59O+7u7koSNWnSJIKDg+natSt9+/alTJky3Lt3j6NHjyozLPOrdu3a+Pn5MX78eH766Sc8PDwwMzMjLi6O77//nqpVq+Y52UDHwsKCZs2asW3bNhITEwkICFDda6ivr8/o0aMZNWoUQUFB+Pn5kZaWxqpVq0hMTGTkyJEFivl1srKylC8QaWlp/Pbbbxw9epS9e/fi5OTEokWLlLKVK1fGz8+Pzz//HH19fRo1aqTMYi5TpowySeLQoUOsX7+epk2bUq5cObRaLfv27SMxMVEZFs1vW3799ddYWFjg5eWlxJGRkcH27dtxcnKiS5cuuR7Xe++9x8GDB3ny5Ak2Nja0a9eO0aNHM2XKFJo3b87du3eJiorK0WM+aNAgqlSpgrOzMzY2Nty9e5fo6GgcHByoUKHCG7Vxq1at2LlzJ4GBgfj7+1OzZk2MjIy4f/8+J0+epEmTJjRr1izXbSdOnIipqSm1atXC1taWuLg4VqxYQYkSJZQh7/y8D17m7u6OpaUlkydPJiQkBENDQ3bu3JkjiYai9x4SQggohAQRsu9J1NfXZ8GCBQwfPlz1DLGXmZiYsHbtWhYsWEBUVBRPnz6ldOnS9O/fn5CQkMIIBxsbG6Kjo5UPjKioKFxcXPjiiy+YP38+06ZNIyUlBScnJ2bOnKkagiwIU1NTIiMjGTVqFIGBgYSHh9O4cWPWrVvHihUr2LhxI3fu3MHU1JQyZcpQr1491bB3fukeXfKq+yJfdO/ePT788MMcy3UJ5rx585g+fTpz5swhIyODWrVqsXr1aoKCglTl69aty8GDB4mOjiY5OZnSpUvTvn17Bg0apJRp2LAhn3/+OUuWLOGTTz4hNTUVe3v7HDfT59e0adNwdXVl48aNbNiwgaysLOzs7KhVq1aOm+nz8uIz6l6e8AHQpk0bzMzMWLFiBcOHD8fAwABXV1fWrl1LrVq13ijuvKSkpODn5wdkXy82NjZoNBo+/vhj2rRpk6OHacqUKZQrV44tW7awfv16zM3NadiwISNHjlSSrQoVKmBhYUFUVBQPHz7EyMiISpUqMWvWLNXx5qct9+3bh6+vL0ZGRsp2hw8fJi4uLsejn17UtWtX9u3bx/bt2+nXrx9t2rTh4cOHfPHFF8TGxlKlShWmTJnCkiVLVNt5eXnx9ddfs3nzZp49e4atrS316tVj8ODBqhgKwsDAgKVLl7J27Vq2b9/OihUrMDAwwN7eHg8PD6pWrZrntnXq1CE2Npa9e/eSkJCAtbU1tWvXZvbs2crwb37eBy+ztrZm+fLlzJ49m9GjR2NmZqY87/Xla7IovoeEEEJP++I4myiSvv32W4KCgti+fbsMEYlCExcXR6NGjViyZMkbJyNCvCwzM5OzZ8/i5uZW4ElMf7W0tDTOnz9fpGOrUtkII6OiE1taehbXbqQVubjgj9hqJMRhpF+0/tpZGvpcKGFTJK+1/L5HC6UHUfy1Tpw4QatWrSQ5FIXK1taWixcvvu0whBBCFEGSIP4PGDt27NsOQQghhBD/In9qFrMQQgghhPjnkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVTkbzELIYQoNJmZmQCkpqZiaFi0PmKSkpIASE5OxtjY+C1Ho6aLLSU1HS16bzmaP6SkpGX/W8Tigj9iS0a/yMWW9N9/i+K1lpGRAWS/Vw0MDPIsp6fVarV/V1BCCCH+2Z49e8bly5ffdhhCiNfQaDSYm5vnub5ofb0TQgjxP03XW1K5oiEG+kWrVyctPYNbt7VULK+HURHr3dTF5uLi8spenb9bamoqFy9epMqTBxgbFK270lK0ely3KlWkz2dRbLdM9LhkWeq1PZtFq0WFEEL8T9MlN0aGBhgYFK0EMXsYMg0jQ0OMjIpOEgZ/xGZgYFCkEkTdbQLGBvoYFbGEX/vfaRRF+XwWxXbT/+9w/Ouus6KV1gohhBBCiLdOEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCEK0Y8//siQIUPw8fHB2dmZevXq4efnx6xZswpUj7+/P61bt/6LolT79ttvCQ8Pz3VdfHw8w4cPx9vbG41Gw+DBg4Hsv+H54jYnT55Eo9Fw8uTJAu8/OTmZ8PDwN9q2qFi3bh2xsbFvOwwhhCg0kiAKUUgOHz5Mt27dePbsGaNHj+azzz5jwoQJ1KpViz179rzt8PL07bffEhERkeu6yMhIvvnmG8aNG8fGjRsZPXp0ruVq1KjBxo0bqVGjRoH3n5ycTEREBKdOnSrwtkXFhg0b2LZt29sOQwghCo38LWYhCklUVBSOjo6sWrVK+fulAK1atcozsforpKeno6enp4rhTV29epXy5cvTtm3bV5YzNzfHzc3tT+/v36Awz48QQvxVpAdRiEISHx+PtbV1rh/8+vrqt9rOnTvx8/PD3d0dd3d32rVrx+bNm3Nsd+7cOXr06IGrqytNmjRhxYoVZGVlKet1Q7tffvkls2bNomHDhri4uHDr1i0AtmzZQtu2bXFxccHT05MhQ4Zw/fp1ZfuwsDDWrVsHZA8b637u3LmDRqPh2LFjXL9+XVme1zBwbkPMYWFhuLu7c+vWLQYOHIi7uzuNGzdm1qxZpKWlAXDnzh28vb0BiIiIUPYTFham1HPz5k1GjhyJt7c3zs7OtGzZUon55f3v2rWLBQsW0KBBA2rVqkXfvn25ceOGqqxu+P7//u//6Nq1KzVr1qRhw4YsXLiQzMxMVdmIiAi6dOmCp6cntWrVokOHDmzevBmtVquU8fX15erVq5w6dUqJ39fXt1DOT37bUSctLY3IyEhatGiBs7MzdevWZdy4cTx58kRVztfXl6CgII4cOUKHDh2oWbMmLVq0YMuWLbmeXyHEv498hRWikLi5ubF582Y++eQT2rRpw7vvvouRkVGOcosWLSIyMpL333+ffv36UaJECa5evcpvv/2mKhcXF8fo0aPp168fISEhfPPNN8ybNw87Ozvat2+vKjt//nzc3NyYOnUq+vr6lCxZkuXLlzN//nxat27NyJEjefr0KREREfj5+bFlyxYqVqzI4MGDSUpK4uuvv2bjxo1KfXZ2dmzcuJGpU6fy+++/M3fuXADeeeedArVJeno6wcHBdO7cmf79+3P69GkiIyMxNzcnJCQEOzs7oqKiGDBgAJ07d6ZLly4A2NjYAHDt2jW6detGmTJlGDt2LLa2tnz33Xd88sknPH36lJCQkBztUKtWLaZPn86zZ8+YO3cuwcHB7NmzBwMDA1XbDh8+nMDAQEJDQzl8+DBLly4lMTGRSZMmKeXu3r2Ln58fZcuWBeDs2bN88sknPHjwQNl3REQEoaGhlChRgsmTJwNgbGxcKOcnv+0IkJWVxeDBg/n+++8JCAigVq1a3L17l/DwcM6dO8fWrVsxNTVV6rx06RKzZ89m4MCBlCpVis2bNzNhwgQqVKiAh4dHgc6zEOKfRxJEIQrJyJEjuXHjBjExMcTExGBkZISzszO+vr707NmT4sWLc/v2bZYvX06bNm2UpAugfv36OeqLj49n5cqV1KxZE4B69epx6tQpdu7cmSNBLF++PIsXL1ZeJyYmEhkZSePGjZk3b56y3MvLi/fff5/w8HDmzZtH+fLlKVWqFECOIWI3NzfMzc1JT09/4+Hj9PR0hg4dSsuWLQHw9vbmp59+YteuXYSEhGBsbKzct2hvb59jPzNnzqR48eJs2LABc3NzILut0tLSWLFiBf7+/lhaWirl33nnHVW76uvrM2zYMM6fP6+qOz4+nsjISJo0aQJAgwYNSE1NZcOGDQwYMEBJCGfOnKlsk5WVhaenJ1qtlrVr1zJkyBD09PR49913MTU1feUw+5uen/y2I8DevXs5evQo4eHhvP/++8q21apVo3PnzsTGxtKjRw9l+dOnT9mwYYNyrB4eHpw4cYKdO3dKgiiEkCFmIQqLtbU169evZ8uWLYwcORJfX19u3rzJvHnzaNOmDU+ePOHYsWNkZmbSs2fP19Zna2urJIc6Go0mR08joEoIAM6cOUNKSgodOnRQLS9Tpgx169blxIkTb3CEBaenp6cMt+rkdQwvS01N5cSJEzRr1gxTU1MyMjKUn0aNGpGamsrZs2dV2+S2LyDH/ooXL64khzqtW7cmKyuL06dPK8uOHz9O3759qV27NtWrV6dGjRosXryY+Ph4Hj9+/Npj0Pmz5yc/7Xjo0CEsLCx47733VG1VvXp1bG1tc0wCql69upIcApiYmFCxYsV8nRshxD+f9CAKUchcXFxwcXEBsnt+5s6dy5o1a4iKiqJEiRJAdm/Z61hZWeVYZmxsTGpqao7ltra2qtfx8fG5Lofs4eNjx469dv+FwczMDBMTE9WyvI7hZfHx8WRkZCg9srl5+vSp6vXLbaYb6k1JSVEt1/Wa5rZM13bnzp0jICAAT09PPv74Y+zt7TEyMmL//v0sW7YsR52v8mfPT37a8fHjxyQmJuLs7JxrDK9rq9zqFEL8e0mCKMRfyMjIiJCQENasWcPVq1dp2rQpAPfv36dMmTKFth89PT3Va92Hf1xcXI6yDx8+xNrautD2/VexsLDAwMCAdu3aqYZGX+To6PhGdT969CjPZbq22717N4aGhixfvlyVnO3fv7/A+/s7zo+1tTVWVlZERUXlur548eIFrlMI8e8lCaIQheThw4fY2dnlWK6blWpnZ0f9+vUxMDBgw4YNuLu7/2WxuLu7Y2pqyo4dO5T71iA7MT1x4gTNmzdXlr3Yy/biJIa/S169fGZmZnh5eXHhwgU0Gk2OiR9/xvPnzzlw4IBqmHnXrl3o6+sr99/p6elhYGCgmoGekpLCjh07cj2GgvQoFuT85JePjw+7d+8mKysLV1fXAm8vhBAvkgRRiEISEBCAvb097733HpUrV0ar1XLx4kU+++wzihUrRu/evXF0dCQoKIjIyEhSUlJo3bo1JUqU4Nq1azx9+pTQ0NBCicXCwoLBgwczf/58xowZQ6tWrYiPj2fJkiWYmJioZv9WrVoVgJUrV9KoUSP09fULPSF7FXNzcxwcHDhw4ADe3t5YWlpibW2No6MjEyZMoEePHvTs2ZPu3bvj4ODA8+fP+fXXXzl48CBr1659o31aWVkxZcoU7t27R8WKFfn222/ZtGkT3bt3V+7La9y4MatXr2bkyJH4+fkRHx/PqlWrcm2XqlWrsnv3bvbs2YOjoyMmJibK/Y+5Kcj5ya9WrVqxc+dOAgMD8ff3p2bNmhgZGXH//n1OnjxJkyZNaNasWYHrFUL8O0mCKEQhCQ4O5sCBA0RHR/Pw4UPS09OxtbWlXr16BAUF4eTkBMCHH35IhQoV+Pzzzxk1ahQGBgZUrFgRf3//Qo0nKCgIGxsbYmJi2LNnD6ampnh6ejJixAjVI1Rat27NDz/8wPr161myZAlarZYDBw688fDtm5g+fTqffvopwcHBpKWl0aFDB2bNmsU777xDbGwskZGRLFy4kCdPnlCiRAkqVKhA48aN33h/tra2TJo0idmzZ3PlyhUsLS0ZNGgQQ4cOVcp4e3szY8YMVq5cyaBBgyhdujRdu3bFxsaGCRMmqOobOnQocXFxTJw4kefPn+Pg4MDBgwdfGUN+z09+GRgYsHTpUtauXcv27dtZsWIFBgYG2Nvb4+HhoXwREEKI/NDTvvjEVyGE+Ifz9/fn6dOn7Nq1622H8o+UmZnJ2bNn0bxjgoGB3us3+BulpWdx7UYaVSobYWRk8PoN/ka62Nzc3FTP7Hzb0tLSOH/+PDUS4jDSL2LnE30ulLAp0uezKLZbJnqcL1HytdeaPOZGCCGEEEKoSIIohBBCCCFU5B5EIcS/Sl7PVBRCCPEH6UEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECryl1SEEEIUmszMTADS0jMxyCpafRApKWnZ/6amo0XvLUejpostOTkZY2PjtxzNH5KSkgBIRr/ItVnSf/8t0uezCLZbxn//zczMxMDAIM9yelqtVvv3hCSEEOKf7tmzZ1y+fPlthyGEeA2NRoO5uXme66UHUQghRKHR9X65uLi8snfibUhNTeXixYsSWwEU1bjgj9gqltfDyLBopTNp6Rncuq0tku2WmZnJ+fPnX9tTXbRaVAghxP803YehgYFBkftgNPxvEiGx5V9RjQv+iM3I0BAjo6IVW/awclqRbDed18VVtG4QEUIIIYQQb50kiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEyj8iQdRoNPn6OXny5Gvr8vf3p3Xr1oUan6+vL0FBQYVap0ajYdq0aTmWL168GI1Gw+TJk8nKyirUfebXtWvXCA8P586dO2+0/bJly9i/f/+fiuHChQv06tWL2rVro9FoWLNmDQDHjx+nY8eOuLm5odFo/vR+/leEh4ej0Wh48uTJ2w6lSDp58mSO3xceHh506dKFbdu2vXG9Bfl9Ehsbi0aj4fz589y5cweNRsOqVatylPP19SUsLEx5/eDBA8LDw7l48eIbxymEEC/7R/ypvY0bN6peR0ZGcvLkSaKjo1XL33nnnb8zrL+VVqtl+vTpxMTEEBgYyMiRI99aLNeuXSMiIgJPT08cHR0LvP3y5ctp3rw5TZs2feMYxo8fT3JyMvPnz8fS0hIHBwe0Wi3Dhg2jYsWKLF26FDMzMypVqvTG+xD/PCNGjMDLywuAp0+f8uWXXxIWFsazZ8/w9/f/S/ft4+PDxo0bcXJywtDQkI0bN1K2bNnXbvfw4UMiIiJwcHCgevXqf2mMQoh/j39Egujm5qZ6bWNjg76+fo7l/1QZGRmMHz+e7du3M2bMGAICAt52SG/d1atX6dKlC40bN1aWPXjwgPj4eJo2bYq3t/ffGk9KSgomJibo6en9rfsVBVOhQgXV743GjRtz/vx5du/e/ZcniDY2NtjY2Civ3/bvL7lmhfh3+0cMMefHunXr6NmzJ97e3ri5udGmTRtWrlxJenp6ruX/7//+j65du1KzZk0aNmzIwoULyczMVJWJiIigS5cueHp6UqtWLTp06MDmzZvRarX5iufdd99l8eLFf+q4UlNTGTp0KLt27eKTTz5RJYe6YbOXh9Z1w1exsbHKsrCwMNzd3bl16xYDBw7E3d2dxo0bM2vWLNLS0lTbr1+/nrZt2+Lu7o67uzstWrRg/vz5QPYw2YcffghA7969leE63b4uXLhAUFAQ3t7eODs706BBAwIDA7l//z6QPXSelJTEtm3blG1f/GC+cuUKwcHBeHh44OLiQrt27VRDgLphuoyMDDZs2KDUER4eTqNGjQCYO3cuGo0GX19f9u/fj0aj4fjx4znadv369Wg0Gi5duqQsO3/+PIMGDcLT0xMXFxfat2/Pnj17VNvpYvjuu+8YN24cdevWxdXVlbS0NGWo98KFC4SEhFCrVi1q167NqFGjcgz/7tmzh/79+9OgQQNq1qxJy5YtmTt3LklJSTli/fHHHxk0aBBeXl64uLjQtGlTpk+fnqPci65fv06TJk3o0qULjx8/VuJ++daA3K4j3dDpuXPn6NGjB66urjRp0oQVK1bkuLXh6tWr9O/fH1dXV+rWrcvUqVM5fPhwrtfmsWPH6NOnD7Vq1cLV1ZVu3brlODe6Nrx69SojRoygdu3a1KtXj3HjxvH777+/8pgLSl9fn+LFi2NoqP4urdVqWbduHe3ataNmzZp4eHgQGhrK7du3c63nde2UkpLCzJkzadu2LbVr16ZmzZp06NCBL7/88pXxnTx5ks6dOwMwbtw41fWu82evWSHEv9M/ogcxP3799Vdat26No6MjRkZGXLp0iWXLlnHjxg1mzpypKhsXF8fw4cMJDAwkNDSUw4cPs3TpUhITE5k0aZJS7u7du/j5+SnDQGfPnuWTTz7hwYMHhISE5BqHVqvl008/JSYmhk8++YSOHTsq68LCwti2bRsHDhzI19Ds8+fPGTBgAGfOnGHBggU0b978TZpGkZ6eTnBwMJ07d6Z///6cPn2ayMhIzM3NlePZvXs3U6dOxd/fn7Fjx6Kvr8+tW7e4du0akD1MNmLECObPn8+kSZOoUaMGAOXLlycpKYl+/frh6OjIpEmTKFWqFHFxcZw8eZLnz58D2bcL9OnTBy8vLwYPHgyAubk5ADdu3KBbt26ULFmSCRMmYG1tzY4dOwgLC+PRo0cMHDhQGabz8/OjefPm9O/fHwB7e3uqVatGSEiIktwYGxtTtWpVSpYsydatW3P0Km7bto0aNWpQrVo1AE6cOMGAAQNwdXVlypQplChRgj179jB8+HBSUlJU5xKyh7l9fHz49NNPSU5OViUZISEhtGjRgm7dunHt2jUWLVrE9evX2bRpE0ZGRgDcvHmTRo0a0adPH8zMzLhx4wYrV67k3LlzrF27Vqnr6NGjBAcHU7lyZcLCwihTpgx3797lP//5T57n+tSpU4SEhFCnTh3mzZuHmZlZQS4VIPt9Mnr0aPr160dISAjffPMN8+bNw87Ojvbt2wPZw5+9evWiWLFiTJkyBRsbG3bv3p3r/bPbt29n7NixNGnShNmzZyvDrAEBAaxatSrH+Rk6dCgffPABnTt35sqVK8ybNw8gx/u5ILKyssjIyAAgPj6e2NhYrly5wscff6wqN2nSJLZt24a/vz+jRo0iISGBJUuW0K1bN7Zv306pUqUK1E4pKSkkJiYyYMAA7OzsSEtL4/jx44wbNw5AKfeyGjVqMHPmTMaNG0dwcDA+Pj5A9vUOhXvNCiH+Xf41737dL1rI/hCoU6cOVlZWjB8/nrCwMCwtLZX18fHxREZG0qRJEwAaNGhAamoqGzZsYMCAAUpC+OIHUVZWFp6enmi1WtauXcuQIUNyDM2kpKQwZswYjh07xsqVK3N84Onr62NgYJDvIR1d78LHH3/8p5NDyE4Qhw4dSsuWLQHw9vbmp59+YteuXUqC+MMPP2BhYcHEiROV7V48DhsbGypUqABk3/P54jDZTz/9RHx8PNOnT1fdX/jBBx8o/3dzc0NfXx8bG5scQ2wRERGkp6ezdu1aypQpA2QPASYmJiofzi8O05UqVUpVh+6Dv0yZMqrlbdu2ZcOGDfz++++UKFECyO5dO3fuHB999JFSburUqVSpUoXo6Gjlg7Nhw4Y8ffqU+fPn0759e/T1/+iU9/b2zjURAmjWrBljxowBsq+vkiVLMmrUKPbu3Uvbtm0BlAQZsr9Y1KpVCycnJ3r16sWlS5eUxHXatGmUKVOGzZs3Y2JiomzTqVOnXPe9fft2JkyYQLdu3Rg/frwq5oKIj49n5cqV1KxZE4B69epx6tQpdu7cqSQ0a9asISEhgXXr1in3ADdu3JiAgADu3r2r1JWcnMyMGTPw8fFhyZIlyvLGjRvToUMH5s+fz+bNm1X779y5MwMGDFD2fevWLbZu3cqMGTPeeFh0+PDhqtf6+voMGjSIrl27KsvOnj3Lpk2bCAsLo1+/fsryOnXq0Lx5c1avXs3o0aML1E5WVlaq3yeZmZnUq1ePxMREoqOj80wQzc3NqVKlCpD9Jezl90xhXrNCiH+Xf80Q84ULF5QhuOrVq1OjRg3Gjh1LZmYmN2/eVJUtXry4khzqtG7dmqysLE6fPq0sO378OH379qV27dpKnYsXLyY+Pp7Hjx+rto+Pj6dPnz6cO3eO9evX53oP3IwZM7hw4QIODg75OqY6depgYWFBREQEt27dymdL5E1PTw9fX1/VMo1Gw2+//aa8dnFxITExkREjRrB///4CzYqtUKEClpaWzJ07lw0bNii9jvl14sQJvL29leRQp0OHDiQnJ3PmzJkC1afTqVMnUlJSVMNuW7duxdjYWJmBeuvWLW7cuEGbNm2A7GRT99OoUSPi4uL45ZdfVPW+//77ee5TV49Oy5YtMTQ0VA253r59m5EjR1K/fn3l+urVqxeQ3ZsK8Msvv/Drr7/SuXNnVXKYl2XLljFu3DhGjRrFxIkT3zg5BLC1tVWSHp2Xr5fTp09TpUqVHBPEXp7Ze+bMGeLj4+nQoYOqbbOysmjYsCHnz5/PMbSe27Wampqa471XEKNGjWLLli1s2bKF1atXM2DAAFauXMns2bOVMocOHUJPT4+2bduqYi1VqhTVqlXj1KlTqjrz004Ae/fupVu3bri7u/Puu+9So0YNtmzZwvXr19/oWAr7mhVC/Lv8K3oQf/vtN3r27EmlSpUYP348Dg4OmJiYcO7cOaZNm0ZKSoqq/IvDQy8vi4+PB7LvKQoICMDT05OPP/4Ye3t7jIyM2L9/P8uWLctR582bN0lISKBr165UrVq1UI5Lo9Ewbtw4+vfvT69evVi7du2fmpVrZmaWI8kwNjYmNTVVed2+fXsyMzPZvHkzoaGhZGVl4eLiwrBhw6hfv/4r6y9RogQxMTEsW7aMBQsWkJCQgK2tLV27diU4OFgZWs1LfHw8tra2OZbb2dkp699ElSpVcHFxITY2Fj8/PzIzM9mxYwdNmjTBysoKgEePHgEwe/ZsVbLwoqdPn6pe5xZrXusMDQ2xsrJSjuH58+f06NEDExMTZea1qakp9+/fJyQkRLm+dAl66dKl83WsO3bsoHTp0qpe2zela5sXvXy9xMfH53q7RMmSJVWvde0bGhqa5/4SEhIoVqxYnvs3NjYGyPHeK4hy5crh4uKivK5Xrx4JCQmsXr2azp074+TkxOPHj9FqtdSrVy/POl6Un3bat28fw4YNo0WLFgwYMIBSpUphYGDAhg0b2Lp16xsdS2Ffs0KIf5d/RYK4f/9+kpKSCA8PV/XOvTj54EW6X6y5LdP9st+9ezeGhoYsX75clVTl9Vw9Nzc3WrRowYQJEwCYMmXKn+q90XF2dmb16tX079+f3r17Ex0dTeXKlQGUuF6+0fzlD4WC6tSpE506dSIpKYnTp08THh5OUFAQX3/99Wt7PzUaDQsWLECr1XL58mViY2NZsmQJpqamBAYGvnJbKysr4uLicix/+PAhANbW1m98TB07dmTq1Klcv36d27dvExcXp7o/S1d3UFAQzZo1y7WOl5PzVw1zxsXFqZK6jIwM4uPjlevrxIkTPHz4kJiYGDw9PZVyL0/C0A2nP3jwIB9HCVFRUQwfPpyePXuyZs0a1fn6K64XKyurV76fdHTt+9FHH+Hq6pprXS8nlX8XjUajXK9OTk5YW1ujp6fHunXrlKT0Rbkte50dO3bg6OjIwoULVdfNy4/qKojCvmaFEP8u/4ohZt0vvRd/cWu1WjZt2pRr+efPn3PgwAHVsl27dqGvr4+Hh4dSp4GBgSrJS0lJYceOHXnGobuXKjY2ljFjxuSYFf2matSowZo1a0hLS6N3797KkJTuw//y5cuq8gcPHiyU/RYrVozGjRszaNAg0tPTlSHj/PTk6OnpUa1aNcaPH4+FhQU///yzss7Y2DjXbb29vTlx4kSOZGj79u2YmZn9qceCtG7dGhMTE2JjY4mNjaV06dI0aNBAWV+5cmUqVqzIpUuXcHFxyfVHN5kmP3bu3Kl6vXfvXjIyMpRkMLdrFuCLL75Qva5UqRLly5dn69at+ZpxWrZsWSWx6dmzp+r2ir/ievHw8ODq1as5bifYvXu36nWtWrWwsLDg2rVrebbvmyRehUH3RVKXoPr4+KDVannw4EGucWo0mgLvQ09PDyMjI1WCFhcXl+P3UG7yer8V9jUrhPh3+Vf0INarVw8jIyNGjBjBgAEDSEtLY8OGDSQmJuZa3srKiilTpnDv3j0qVqzIt99+y6ZNm+jevbsyQaVx48asXr2akSNH4ufnR3x8PKtWrXrth1iLFi0wMzMjNDSU1NRU5s2bp2xT0FnML6pevTrR0dH06dOHPn36EB0djZOTE/Xq1WPFihVYWlpStmxZjh8/zjfffFOgul80ceJETE1NqVWrFra2tsTFxbFixQpKlCihDM3pbprftGkTxYsXx8TEBEdHR86ePcv69etp2rQp5cqVQ6vVsm/fPhITE1XD01WrVuXUqVMcPHgQW1tbihcvTuXKlRkyZAiHDh2id+/eDBkyBEtLS3bu3Mnhw4cZPXq0MsHkTVhYWNCsWTO2bdtGYmIiAQEBOXp4p06dysCBAwkICKBDhw6ULl2ahIQErl+/zs8//1ygRxZ98803GBgYUL9+fa5evcqiRYuoVq2aMkHI3d0dS0tLJk+eTEhICIaGhuzcuTNH8gbZM2qDg4Pp2rUrffv2pUyZMty7d4+jR48qM3tfZGdnR0xMDAMGDKBXr1589tlnVK1aFRcXFypVqsSnn35KZmYmFhYW7N+/n++//76ArfmHPn36sHXrVgYOHEhoaCglS5Zk165dyr1vujYuXrw4EydOJCwsjISEBJo3b07JkiV58uQJly5d4smTJ0ydOrXA+w8PDyciIoK1a9cqD8B+lVu3bnH27Fkgu7f22LFjbNmyBWdnZ+rUqQNA7dq18fPzY/z48fz00094eHhgZmZGXFwc33//PVWrVqVHjx4FitPHx4d9+/YxZcoUmjdvzv3794mMjMTOzi7HPdIvK1++PKampuzcuRMnJyeKFSuGnZ0dpUuXLtRrVgjx7/KvSBCdnJwIDw9n4cKFDB06FCsrK1q3bk3fvn0ZOHBgjvK2trZMmjSJ2bNnc+XKFSwtLRk0aBBDhw5Vynh7ezNjxgxWrlzJoEGDKF26NF27dsXGxkYZRs5L48aNWbFiBYMGDWLw4MFERERgampKUlISpqamWFhYvNFxVqtWjejoaPr27asMN3/66ad8/PHHzJ07l8zMTN577z3mzZuX5wzX16lTpw6xsbHs3buXhIQErK2tqV27NrNnz1aGO8uVK8f48eNZu3YtvXv3JjMzk5kzZ+Lm5oaFhQVRUVE8fPgQIyMjKlWqxKxZs+jQoYOyjwkTJjB16lRGjBhBcnIynp6exMTEULlyZb744gvmz5+v3Dvq5OTEzJkzczyu40107NiRXbt2Aaji0albty6bN29m2bJlzJgxg8TERKysrHByclISu/wKDw8nPDycDRs2KJODxo8fr3xZsLa2Zvny5cyePZvRo0djZmZGkyZNWLBgQY7YGjZsyOeff86SJUv45JNPSE1Nxd7ePsckjhfZ2NgQHR1NYGAg/v7+REVF4eLiwrJly/j444+ZPHkyxsbGtGrVikmTJr12+D8vpUuX5vPPP2fGjBlMnjwZMzMzmjZtSmhoKGPHjlUl9e3ataNs2bJERUUxefJknj9/jo2NDdWrV8/1fORHUlISenp6ud5XnBvd8zwhu4e8bNmyDB48mH79+mFgYKCsmzZtGq6urmzcuJENGzaQlZWFnZ0dtWrVyjEhJT86derE48eP+eKLL9i6dSvlypVTng8aERHxym3NzMyYMWMGERERBAQEkJ6eTkhICEOHDi3Ua1YI8e+ip83PU53F36J+/fq0bduWsWPHvu1QxF9E16N1/Phx1V/N+Lf56KOP2LVrFydPnvxLh447d+5M2bJlpafsb5SZmcnZs2dxc3NTJdVFQVpaGufPn5fYCqCoxgV/xFalshFGRkUstvQsrt1IK5Ltlt/36L+iB/F/wdWrV0lOTs61R1OI/2URERGULl0aR0dHkpKSOHz4MJs3byY4OPgvTQ6fPXvGpUuX8pzBK4QQIm+SIBYRVapU4YcffnjbYQhR6IyMjIiKiuLBgwdkZGRQsWJFwsLC6NOnz1+6X3Nzc3766ae/dB9CCPFPJQmiEH+joUOHqu5l/TcICgoiKCjobYchhBCiAP4Vj7kRQgghhBD5JwmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRP7UnhBCi0GRmZgKQmpqKoWHR+ohJSkoCIDk5GWNj47ccjVpRja2oxgV/xJaSmo4WvbccjVpKShpQNNstIyMDyH6vGhgY5FlOT6vVav+uoIQQQvyzPXv2jMuXL7/tMIQQr6HRaDA3N89zfdH6eieEEOJ/mq63pHJFQwz0i1avTlp6Brdua6lYXg+jIta7qYvNxcXllb06f7fU1FQuXrxY5OKCP2IryuezypMHGBsUrbv5MtHjkmWp1/ZsFq0WFUII8T9Nl0QYGRpgYFC0EsTsYcg0jAwNMTIqWsmOLjYDA4MilYjpbhMoanHBH7EV5fNpbKCPURH7oqT/3+H4153PopXWCiGEEEKIt04SRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEUSA//vgjQ4YMwcfHB2dnZ+rVq4efnx+zZs1SlVu3bh2xsbF/SQxhYWG4u7u/ttydO3fQaDSsWrWq0PZ98uRJNBoNJ0+eLLQ6/5f4+voSFhb22nIajUb1U6tWLbp168auXbvyLD9t2jQAWrdujb+/f44y/v7+tG7d+s8dQD58++23hIeH57ru5WvK19eXoKCgHOUKO9YX20cIIf4O8qf2RL4dPnyY4OBgPD09GT16NLa2tsTFxfHTTz+xe/duVeKwYcMGrK2t6dix41uMWLxNzZs3p3///mi1Wu7cucPy5csZOXIkWq2WNm3aqMpu3LiRUqVKATB37lzlT2i9Dd9++y3r1q1j6NChOdbZ2dmxceNGypYtC0BERASmpqZ/d4hCCPGXkwRR5FtUVBSOjo6sWrVK9QHeqlUrRo8e/cb1pqeno6en91aTAlH4SpUqhZubGwDu7u7UqlULX19fNm7cmCNB1JUDqFatWqHHUljXmLGxsSrWd999909GJoQQRZMMMYt8i4+Px9raOtcPWX39Py4lX19frl69yqlTp5QhRl9fX+CPIdovv/ySWbNm0bBhQ1xcXLh16xYAW7ZsoW3btri4uODp6cmQIUO4fv16rvHcunWLgQMH4u7uTuPGjZk1axZpaWm5ll29ejW+vr64u7vj5+fH2bNnVevPnz/P8OHD8fX1pWbNmvj6+jJixAju3r372nbRDXlfv36dgIAA3NzcaNCgAStWrADg7NmzdO/eHTc3N5o3b862bdty1BEXF8ekSZNo1KgRzs7O+Pr6EhERQUZGxmv3r9Foch0SfXk4ODY2Fo1Gw4kTJ5g8eTJeXl54eXkREhLCgwcPVNump6fz6aefUr9+fVxdXenevTvnzp17bSyv4uDggI2NDY8ePcoR0507d1RlXzWUf+7cOXr06IGrqytNmjRhxYoVZGVl5dg2t2ssOTmZ2bNn4+vrq1xjHTt2VIa+w8LCWLduHaAeJtfFFxMTQ48ePahbty7Ozs40bdqUBQsW5Hnd/d///R9du3alZs2aNGzYkIULF5KZmakqk5aWRkREBC1btsTFxQUvLy/8/f354YcfctT35Zdf0rJlS1xdXWnbti2HDh3KUebmzZuMHDkSb29vnJ2dadmypXJML7fRrl27WLBgAQ0aNKBWrVr07duXGzdu5HosQoh/F+myEfnm5ubG5s2b+eSTT2jTpg3vvvsuRkZGOcpFREQQGhpKiRIlmDx5MpDd8/Ki+fPn4+bmxtSpU9HX16dkyZIsX76c+fPn07p1a0aOHMnTp0+JiIjAz8+PLVu2ULFiRWX79PR0goOD6dy5M/379+f06dNERkZibm5OSEiIal/r1q2jcuXKjB8/HoBFixYRGBjIgQMHKFGiBAB3796lUqVKtGrVCktLS+Li4tiwYQOdO3dm9+7d2NjYvLJt0tPTGTp0KN26dSMgIICdO3cyb948nj17xtdff83AgQOxt7fn888/JywsjCpVquDs7AxkJ4ddunRBX1+fIUOGUL58ec6cOcPSpUu5e/cuM2fOLNiJeo2JEyfi4+PDvHnzuHfvHnPmzGH06NGsXbtWKfPRRx/x5Zdf0r9/f+rXr8/Vq1cJCQnh+fPnb7zf33//nYSEBFxdXd+4jri4OEaPHk2/fv0ICQnhm2++Yd68edjZ2dG+fXtV2dyusZkzZ7Jjxw6GDRtG9erVSU5O5sqVK8THxwMwePBgkpKS+Prrr9m4caNSl52dHQC//vorbdq0wdHREUNDQy5fvsyyZct4+PBhjvMUFxfH8OHDCQwMJDQ0lMOHD7N06VISExOZNGkSABkZGQwYMIDvv/+e3r17U7duXTIzM/nxxx+5d++eqr7Dhw9z/vx5QkNDKVasGFFRUYSEhPDVV19Rrlw5AK5du0a3bt0oU6YMY8eOxdbWlu+++45PPvmEp0+f5nhvzJ8/n1q1ajF9+nSePXvG3LlzCQ4OZs+ePRgYGLzxeRJC/O+TBFHk28iRI7lx4wYxMTHExMRgZGSk9Hb17NmT4sWLA9nDbqamppibm6uG415Uvnx5Fi9erLxOTEwkMjKSxo0bM2/ePGW5l5cX77//PuHh4arluoSsZcuWAHh7e/PTTz+xa9euHB+CxYsXZ/ny5coHnp2dHV26dOHIkSO0atUKgBYtWtCiRQtlm8zMTHx8fKhfvz67du2id+/er2yb9PR0hg0bxvvvvw+Ap6cnhw8fZvny5Wzbtk0ZitRN7Nm1a5eSIIaHh5OQkMDu3buVe9u8vb0xNTVl9uzZBAQE8M4777xy/wXRsGFDJk6cqLxOSEhgzpw5xMXFYWtry/Xr19m2bRt9+/ZlzJgxANSvX5+SJUsyatSofO9Hq9WSkZGBVqvl7t27zJ49G1NT0xznpyDi4+NZuXIlNWvWBKBevXqcOnWKnTt35kgQX77GAM6cOUP9+vXp27evsszHx0e1je5eyNyu3QkTJij/z8rKwsPDAysrK8aPH09YWBiWlpaqWCMjI2nSpAkADRo0IDU1lQ0bNjBgwADKli3Lrl27OHnyJJ988gldunRRttX1uL8oNTWV1atXY25uDkCNGjVo2LAhe/fuJTAwEICZM2dSvHhxNmzYoJSrX78+aWlprFixAn9/f1WM77zzDnPnzlVe6+vrM2zYMM6fP5/ne1cI8e8gQ8wi36ytrVm/fj1btmxh5MiR+Pr6cvPmTebNm0ebNm148uRJvuvSJVI6Z86cISUlhQ4dOqiWlylThrp163LixAnVcj09vRwfohqNht9++y3Hvnx8fFS9Ibp73F4cPn7+/Dlz5syhWbNmvPvuu7z77ru4u7uTlJSU5xD3y/E0btxYeW1oaEiFChWwtbVV3admZWWFjY2NKs7Dhw/j5eWFnZ0dGRkZyk+jRo0AOHXq1Gv3XxC5tRugxKQb1n35PsGWLVsW6B6+9evXU6NGDZydnWnevDlHjx5l/vz5SmL8JmxtbZXk8MX4czvvL19jAC4uLhw5coS5c+dy8uRJUlJSCrT/CxcuMGjQILy8vKhevTo1atRg7NixZGZmcvPmTVXZ4sWLK8mhTuvWrcnKyuL06dMAHD16FBMTEzp16vTafXt5eSlJH2Tf41myZEnlOk5NTeXEiRM0a9YMU1PTHNdSampqjlsrXnctCCH+vaQHURSYi4sLLi4uQHbP2dy5c1mzZg1RUVFKj9Pr2Nraql7rhvheXg7ZPX7Hjh1TLTMzM8PExES1zNjYmNTU1BzbW1lZ5SgHqMqOHDmSEydOMHjwYFxcXChevDh6enoEBgbmWufLcovHyMgox75zi/Px48ccOnSIGjVq5Fr306dPX7v/gsirPXTJUl7nwtDQMNfjyUvLli0JCAggIyODK1euMH/+fIYPH862bdtUtwv8mdgh7/Oe27U0ceJE7O3t2bNnDytXrsTExIQGDRowZsyY18b022+/0bNnTypVqsT48eNxcHDAxMSEc+fOMW3atBzJpq4nMrdlujZ+8uQJdnZ2qnt48/K6Y4+PjycjI0Pp4c/Ny9fS664FIcS/lySI4k8xMjIiJCSENWvWcPXq1Xxvp6enp3qt+6CKi4vLUfbhw4dYW1v/qThf5ffff+fw4cOEhIQoQ3WQPXkgISHhL9uvjrW1NRqNhmHDhuW6Xnf/W16MjY1znSTxponli+eidOnSyvKMjAwlsckPGxsb5YuEu7s7Tk5O9OrVi5kzZ7J8+XIAJal+Of7CSIpfvsYAihUrRmhoKKGhoTx69IgjR44wb948Bg0axFdfffXK+vbv309SUhLh4eE4ODgoyy9dupRr+Rcn47y8TNfGNjY2fP/992RlZeUrSXwVCwsLDAwMaNeuHT169Mi1jKOj45/ahxDi30OGmEW+PXz4MNfluiHYFxMZY2PjAvVCuLu7Y2pqyo4dO1TL79+/z4kTJ6hbt+4bRJw/enp6aLXaHBNpNm/enGPG6V/Bx8eHK1euUL58eaV39sWfF5O03Dg4OHD58mXVsuPHj5OUlPRG8Xh5eQGwc+dO1fK9e/fma1Z1XurUqUP79u05fPgwZ86cAVASrZfjP3jw4BvvJ79KlSpFx44dadWqFb/88gvJyclA3r1ouoTzxetEq9WyadOmXOt//vw5Bw4cUC3btWsX+vr6eHh4ANn3g6amphbKQ+XNzMzw8vLiwoULaDSaXK+lv/KLlhDin0V6EEW+BQQEYG9vz3vvvUflypXRarVcvHiRzz77jGLFiqkmclStWpXdu3ezZ88eHB0dMTExUe5vyo2FhQWDBw9m/vz5jBkzhlatWhEfH8+SJUswMTH5UxMbXsfc3BwPDw9WrVqFtbU1Dg4OnDp1ii1btmBhYfGX7VcnNDSUY8eO0a1bN/z9/alUqRJpaWncuXOHI0eOMHXqVOzt7fPcvl27dixatIhFixbh6enJtWvX+Pzzz5UZ2gXl5ORE27ZtiY6OxtDQkHr16nH16lVWrVqlugfuTXz44Yfs2bOHRYsWsWbNGlxcXKhUqRKffvopmZmZWFhYsH//fr7//vs/tZ+8dOnSBR8fHzQaDZaWlly/fp3t27fj7u6OmZkZkH3tAqxcuZJGjRqhr6+PRqOhXr16GBkZMWLECAYMGEBaWhobNmwgMTEx131ZWVkxZcoU7t27R8WKFfn222/ZtGkT3bt3VyYjtW7dmtjYWKZMmcIvv/yCl5cXWq2WH3/8EScnJ2USVX5NmDCBHj160LNnT7p3746DgwPPnz/n119/5eDBg6qZ6kII8SqSIIp8Cw4O5sCBA0RHR/Pw4UPS09OxtbWlXr16BAUF4eTkpJQdOnQocXFxTJw4kefPn+Pg4PDaXqGgoCBsbGyIiYlhz549mJqa4unpyYgRI974nrX8mjdvHtOnT2fOnDlkZGRQq1YtVq9eneufUStsdnZ2bNmyhcjISFatWsWDBw8oXrw4Dg4ONGzY8LVJakBAAM+ePWPbtm189tln1KxZk0WLFjF48OA3jmn69OmUKlWKbdu2ERMTQ/Xq1QkPD2fEiBFvXCdkTzrq1asXq1at4vTp03h4eLBs2TI+/vhjJk+ejLGxMa1atWLSpEmq4f7CUrduXQ4ePEh0dDTJycmULl2a9u3bM2jQIKVM69at+eGHH1i/fj1LlixBq9Vy4MABnJycCA8PZ+HChQwdOhQrKytat25N3759GThwYI592draMmnSJGbPns2VK1ewtLRk0KBBqr/QYmhoyMqVK1m+fDm7d+8mOjqa4sWLU61aNRo2bFjg43vnnXeIjY0lMjKShQsX8uTJE0qUKEGFChVUk6iEEOJ19LRarfZtByGEEOKfITMzk7Nnz6J5xwQDg5z3gb5NaelZXLuRRpXKRhgZFa3nPOpic3NzK1LPoExLS1Mee1SU4oI/YivK57NGQhxG+kXrfZCJHudLlHztOZV7EIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQMXzbAQghhPjnyMzMBCAtPRODrKLVB5GSkpb9b2o6WvTecjRqutiSk5MxNjZ+y9H8ISkpCSh6ccEfsRXp84l+kYst47//ZmZmYmBgkGc5Pa1Wq/17QhJCCPFP9+zZMy5fvvy2wxBCvIZGo8Hc3DzP9dKDKIQQotDoepkqVzTEQL9o9ZykpWdw67aWiuX1MDIsWh9/uthcXFxe2avzd0tNTeXixYtUefIAY4Mi1iOs1eO6VakifT6LYrtloscly1Kv7REuWi0qhBDif5ouuTEyNMDAoGgliNlDfWkYGRpiZFR0kjD4IzYDA4MilSAa/jfxMjbQx6iIJfza/06jKMrnsyi2m/5/h7xfd50VrbRWCCGEEEK8dZIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKUQTExsai0Wi4c+fOa8v6+/vj7+//l8bzww8/EB4eTmJiYr72f+fOHQIDA/H09ESj0TB9+nTu3LmDRqMhNja2QPsODw9Ho9H8qfhzs3PnTtasWVPo9eri1f24urrSqFEjAgICiImJ4dmzZ4W+zxfprp3z588XSn2+vr6EhYUVSl1CiP9d8qf2hCgCfHx82LhxI3Z2dm87FADOnDlDREQEHTp0wMLCQrVu8uTJOcrPnDmTH3/8kRkzZlCqVClsbW2xtbVl48aNlC9fvkD77tKlCw0bNvxT8edm165dXL16lb59+xZ63QBRUVGUKFGC9PR0Hj58yPHjx5kzZw6rVq1i2bJlVKtW7S/ZrxBC/BUkQRSiCLCxscHGxuZth5Ev77zzTo5lV69epWbNmjRt2lS13M3NrcD129vbY29v/6bh5ZCcnIyZmVmh1ZeXGjVqqM5hq1at6NWrF7169SI4OJivv/4aY2PjvzwOIYQoDDLELEQBhYWF4evrm2N5bkOjGo2GadOm8eWXX9KyZUtcXV1p27Ythw4dUpXLbYhZq9WycuVK3nvvPVxcXOjQoQPffvttrjE9e/aM2bNn4+vri7OzMw0bNmT69OkkJSUVOJ7w8HA+/fRTAJo0aaIMnZ48eRJQDzGfPHkSjUbDrVu3OHLkiFL2zp07uQ4xvzgU+/KP7tjzGmLes2cPfn5+uLm54e7uTkBAABcuXMhxbtzd3bl8+TL9+/fH3d2dvn374u/vz+HDh7l7965qnzppaWlERkbSokULnJ2dqVu3LuPGjePJkye5tnd+VatWjUGDBvHbb7+xZ88e1bpjx47Rp08fatWqhaurK926deP48eM56rh+/TojRoygXr16ODs74+Pjw5gxY0hLS8tzvw8fPqRjx468//773Lx5E8j/NSKEECA9iEL85Q4fPsz58+cJDQ2lWLFiREVFERISwldffUW5cuXy3C4iIoKIiAg6d+5M8+bNuX//Ph999BFZWVlUqlRJKZecnEyvXr24f/8+gwYNQqPRcPXqVRYvXsyVK1dYs2YNenp6+Y6nS5cuJCQkEBMTQ0REBLa2tkDuPYc1atRg48aNhISEUK5cOcaOHQuAnZ0dDx8+zFF+48aNqtcpKSmMGTOGrKwsrKys8myLZcuWsXDhQjp27EhwcDDp6emsWrWKnj17snnzZlVs6enpBAcH061bNwYOHEhmZib29vZ89NFH3L59m4iICFXdWVlZDB48mO+//56AgABq1arF3bt3CQ8P59y5c2zduhVTU9M8Y3sdX19f5syZw+nTp2nfvj0A27dvZ+zYsTRp0oTZs2djaGjIxo0bCQgIYNWqVXh7ewNw6dIlunfvjrW1NaGhoVSoUIG4uDgOHjxIWlparj2SV65cITAwEHt7e7744gtsbGwKfI0IIYQkiEL8xVJTU1m9ejXm5uZAdlLVsGFD9u7dS2BgYK7bJCYmsnLlSpo1a8b06dOV5e+88w7du3dXJYgxMTFcvnyZTZs24eLiAoC3tzelS5cmNDSUI0eO0Lhx43zHY29vT5kyZQCoXr06jo6OeR6bubk5bm5uGBsbY2Fh8doh5RfXZ2ZmMnToUH7//Xc+//xzJZ6X3bt3j/DwcHr16sXEiROV5fXq1aN58+ZERESwcOFCZXl6ejpDhgyhU6dOqnosLCwwNjbOEePevXs5evQo4eHhvP/++8ryatWq0blzZ2JjY+nRo8crj+tVHBwcAJSEOTk5mRkzZuDj48OSJUuUco0bN6ZDhw7Mnz+fzZs3A9n3dhoaGrJlyxbV8HXbtm1z3dexY8cYOnQo9evXZ86cOZiYmAAFv0aEEEKGmIX4i3l5eamSn1KlSlGyZEnu3r2b5zZnzpwhNTWVNm3aqJbXqlVLSTh0Dh06RJUqVahevToZGRnKT4MGDdDT0+PUqVN/Op6/wrRp0/j2229ZtGgRNWrUyLPcd999R0ZGBu3atVMdn4mJCR4eHjmOD6B58+b5juPQoUNYWFjw3nvvqeqvXr06tra2udZfEFqtVvX6zJkzxMfH06FDB9X+srKyaNiwIefPnycpKYnk5GROnz5Ny5Yt83V/6pdffklgYCBdunRh0aJFSnKoO8aCXCNCCCE9iEL8xXIbOjU2NiY1NTXPbeLj44Hs5O1lLy97/Pgxt27dyjPJevr06Z+Op7BFRkbyxRdfMH36dBo1avTKso8ePQKgc+fOua7X11d/zzUzM8uzNzI3jx8/JjExEWdn51zXv9x+BaVLvHUz1HXHExoamuc2CQkJ6Ovrk5mZSenSpfO1n927d2NiYkKXLl1yDBcX9BoRQghJEIUoIGNj41wnCBTmh6wuidMlEy969OiRqhfR2toaExMTZsyYkWtd1tbWhRZXYYiNjWXRokUMHTo0z6TvRbr4Fy9eTNmyZV9bvqD30llbW2NlZUVUVFSu64sXL16g+l528OBBADw9PZX9AXz00Ue4urrmuk3JkiXJysrCwMCABw8e5Gs/c+fOZdGiRfTq1YvPPvuM6tWrK+v+164RIcTbJwmiEAXk6OjI48ePefTokdKbl5aWxnfffVdo+3Bzc8PExISdO3eqhkt/+OEH7t69q0oQfXx8WL58OVZWVq+c9FIQuskPhd2reOTIET766CM6depESEhIvrZp0KABhoaG/PrrrwUaOn6ZsbExKSkpOZb7+Piwe/dusrKy8kzY3tSlS5dYvnw5Dg4OtGzZEsi+TcDCwoJr167Rq1evV27v4eHBV199xbBhw147zGxpacnq1asZNGgQvXv3ZuXKlcr9ln/FNSKE+GeTBFGIAmrZsiWLFy9m+PDhDBgwgNTUVGJiYsjMzCy0fVhaWtK/f3+WLl3KhAkTaNGiBffv3yc8PFyZVazTp08f9u3bR69evejbty8ajYasrCzu3bvHd999R//+/Quc+FStWhWA6OhoOnTogKGhIZUqVSrQ0O3Lbt++zYcffoijoyMdO3bk7NmzqvXvvvturrNyHR0dCQ0NZeHChdy+fZtGjRphYWHBo0ePOH/+PGZmZq8crn3xmPbt28f69etxdnZGT08PFxcXWrVqxc6dOwkMDMTf35+aNWtiZGTE/fv3OXnyJE2aNKFZs2avrf/nn3+mRIkSZGRkKA/K3r59OyVLlmTZsmXKsRUvXpyJEycSFhZGQkICzZs3p2TJkjx58oRLly7x5MkTpk6dCsC4cePo3r07Xbt2JTAwkPLly/P48WMOHjzI1KlTc5wPc3NzZVZ6v379WLp0KXXr1v1LrhEhxD+bJIhCFFC5cuVYsmQJCxYsIDQ0FFtbW/r168eTJ09yPELlz/jwww8pVqwY69evZ/v27VSuXJmpU6fy2WefqcoVK1aMdevWsWLFCjZu3MidO3cwNTWlTJky1KtXL8eklvzw8vIiKCiIbdu2sXnzZrKysli7di1eXl5vfDy//fYbSUlJ3Lx5k549e+ZYf+DAgTxnTAcFBeHk5MTatWvZvXs3aWlp2Nra4uzsTPfu3fO1/969e3P16lUWLFjA77//jlar5fLlyxgYGLB06VLWrl3L9u3bWbFiBQYGBtjb2+Ph4aEky68zYMAAILun0tLSEo1Gw6hRo+jYsWOORK5du3aULVuWqKgoJk+ezPPnz7GxsaF69ep06NBBKVetWjW2bNnC4sWLmTdvHs+fP8fW1pa6devm+dBtU1NTIiMjGTVqFIGBgYSHh9O4ceNCv0aEEP9setqXp9gJIYQQbygzM5OzZ8+ieccEA4Oi9WzFtPQsrt1Io0plI4yMDN52OCq62Nzc3DAwKDqxpaWlcf78eWokxGGkX8TOJ/pcKGFTpM9nUWy3TPQ4X6Lka681ecyNEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBXDtx2AEEKIf47MzEwA0tIzMcgqWn0QKSlp2f+mpqNF7y1Ho6aLLTk5GWNj47cczR+SkpIASEa/yLVZ0n//LdLnswi2W8Z//83MzMTAwCDPcnparVb794QkhBDin+7Zs2dcvnz5bYchhHgNjUaDubl5nuulB1EIIUSh0fV+ubi4vLJ34m1ITU3l4sWLElsBFNW44I/YKpbXw8iwaKUzaekZ3LqtpcqTBxgbFK2e9Ez0uGRZ6rU91UWrRYUQQvxP0yURBgYGRS6hMPxvEiGx5V9RjQv+iM3I0BAjo6IVW/awchrGBvoY6RetIWb9/w55v+58Fq20VgghhBBCvHWSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgFpIff/yRIUOG4OPjg7OzM/Xq1cPPz49Zs2YVuC5/f39at2792nJ37txBo9EQGxv7JiHnu/5Vq1b96bq+/fZbwsPDCyGqtyM2NhaNRsOdO3feaPunT58yb948PvjgA9zc3KhduzYtWrRg9OjRXLp0SSn3ww8/EB4eTmJiYmGF/rc6efIkGo2GkydPvu1QCiQ8PByNRsOTJ0/+dF3JycmEh4f/pW0QFhaGu7v7X1a/EEJIglgIDh8+TLdu3Xj27BmjR4/ms88+Y8KECdSqVYs9e/a87fCKhG+//ZaIiIi3HcZb8fz5c/z8/Ni2bRtdunRh6dKlzJ07Fz8/P+7cuaNKEM+cOUNERMT/bIIoshPEiIgITp069bZDEUKINyZ/i7kQREVF4ejoyKpVq5S/DQnQqlUrRo8e/RYjE0XBV199xa1bt4iOjqZu3bqqdf369SMrK+stRSbEq6WkpGBqavq2wxBCvAXSg1gI4uPjsba2ViWHOvr6OZt4586d+Pn54e7ujru7O+3atWPz5s05yp07d44ePXrg6upKkyZNWLFiRb6SiZs3bzJy5Ei8vb1xdnamZcuWrFu3Lke5xMREZs2aRZMmTXB2dsbb25uBAwdy/fr1POtOT09n7NixuLu7c+jQISC7x2T27Nn4+vri4uKCp6cnHTt2ZNeuXUD2cJhu/xqNRvnRDddqtVrWrVtHu3btqFmzJh4eHoSGhnL79m3VvnVD7/lpl6tXr9K/f39cXV2pW7cuU6dO5fDhw7kOfx47dow+ffpQq1YtXF1d6datG8ePH39tO+dXfHw8ALa2trmu110j4eHhfPrppwA0adJEaacX492zZw9+fn64ubnh7u5OQEAAFy5cUNWnG368desWAwcOxN3dncaNGzNr1izS0tJUZe/fv09oaCju7u7UqVOHkSNHcu7cuVxvXTh//jyDBg3C09MTFxcX2rdv/0Y95JcuXUKj0eR6zX/77bdoNBoOHDigLPu///s/+vTpg7u7u3J+Dh8+rNpOdwvAiRMnmDx5Ml5eXnh5eRESEsKDBw8KHGNu/P398ff3z7E8LCwMX19fIPu2DG9vbwAiIiKUcxgWFgbAkydP+Oijj2jcuDHOzs7UrVuXbt26cezYMVWdR44coU+fPtSuXRtXV1datmzJ8uXLc+w7P+c4LS2NyMhIWrRooexz3LhxOYbTfX19CQoKYt++fbRv3x4XF5d/ba+/EEJ6EAuFm5sbmzdv5pNPPqFNmza8++67GBkZ5Vp20aJFREZG8v7779OvXz9KlCjB1atX+e2331Tl4uLiGD16NP369SMkJIRvvvmGefPmYWdnR/v27fOM5dq1a3Tr1o0yZcowduxYbG1t+e677/jkk094+vQpISEhADx79owePXpw9+5dBgwYgKurK0lJSZw+fZq4uDicnJxy1J2YmEhISAg3btwgJiYGZ2dnAGbOnMmOHTsYNmwY1atXJzk5mStXriiJ0eDBg0lKSuLrr79m48aNSn12dnYATJo0iW3btuHv78+oUaNISEhgyZIldOvWje3bt1OqVKkCtcvDhw/p1asXxYoVY8qUKdjY2LB7926mTZuW45i2b9/O2LFjadKkCbNnz8bQ0JCNGzcSEBDAqlWrlA/7P8PNzQ2AsWPHMmjQIGrXro21tXWOcl26dCEhIYGYmBgiIiKUhPKdd94BYNmyZSxcuJCOHTsSHBxMeno6q1atomfPnmzevFkpB9mJfHBwMJ07d6Z///6cPn2ayMhIzM3NlWsgKSmJ3r17k5CQwKhRo6hQoQJHjx5l+PDhOWI7ceKEcp1MmTKFEiVKsGfPHoYPH05KSgodO3bMd3tUq1aNd999l9jYWLp06aJat23bNkqWLEnjxo0BOHXqFP3796dq1apMnz4dY2NjNmzYwKBBg5g/fz4ffPCBavuJEyfi4+PDvHnzuHfvHnPmzGH06NGsXbs23/H9GXZ2dkRFRTFgwAA6d+6sHJ+NjQ0Ao0eP5sKFCwwfPpyKFSuSmJjIhQsXlPcKwObNm/noo4/w8PBg6tSplCxZkl9++YWrV6+q9pWfc5yVlcXgwYP5/vvvCQgIoFatWty9e5fw8HDOnTvH1q1bVT2EP//8M9evXyc4OBhHR0fMzMz+4hYTQhRVkiAWgpEjRypJU0xMDEZGRjg7O+Pr60vPnj0pXrw4ALdv32b58uW0adOGuXPnKtvXr18/R53x8fGsXLmSmjVrAlCvXj1OnTrFzp07X5kgzpw5k+LFi7NhwwbMzc2V+tPS0lixYgX+/v5YWloSHR3N1atXWb16NfXq1VO2f//993Ot986dOwQFBQGwceNGHBwclHVnzpyhfv369O3bV1nm4+Oj/L98+fJKkqdLlnTOnj3Lpk2bCAsLo1+/fsryOnXq0Lx5c1avXq0aps9Pu6xZs4aEhATWrVunJE2NGzcmICCAu3fvKnUlJyczY8YMfHx8WLJkibK8cePGdOjQgfnz5+fay1VQtWvXJjQ0lKVLlzJkyBAAHB0dadCgAd27d6datWoA2NvbU6ZMGQCqV6+Oo6OjUse9e/cIDw+nV69eTJw4UVler149mjdvTkREBAsXLlSWp6enM3ToUFq2bAmAt7c3P/30E7t27VKSh23btnHr1i1WrlxJo0aNAGjQoAHJycmqRB5g6tSpVKlShejoaKWnvGHDhjx9+pT58+fTvn37XHvL89KxY0c++eQTfvnlFypVqgRAQkICBw4coFevXso+5s2bh4WFBTExMcr76L333qN9+/bMnj2bli1boqenp9TbsGFDVfskJCQwZ84c4uLi8uzBLUzGxsbUqFEDyD6fL1/vP/zwA126dKFr167KsqZNmyr/f/78ObNmzaJWrVqsXbtWObbcvqjk5xzv3buXo0ePEh4ernpvV6tWjc6dOxMbG0uPHj2U5U+ePGH37t3KORFC/HvJEHMhsLa2Zv369WzZsoWRI0fi6+vLzZs3mTdvHm3atFGGco4dO0ZmZiY9e/Z8bZ22trZKEqSj0Why9DS+KDU1lRMnTtCsWTNMTU3JyMhQfho1akRqaipnz54F4OjRo1SsWFGVHOblwoUL+Pn5UbJkSTZs2KBKDgFcXFw4cuQIc+fO5eTJk6SkpLy2Tp1Dhw6hp6dH27ZtVfGWKlWKatWq5bjRPz/tcvr0aapUqaLqUQNyzAw/c+YM8fHxdOjQQbXvrKwsGjZsyPnz50lKSsr3sbzKkCFDOHz4MDNmzMDPz49ixYrxxRdf0KlTJ2Uo/lW+++47MjIyaNeunSpWExMTPDw8crSTnp6eMuypk1s7FS9eXEkOdV5up1u3bnHjxg3atGkDkOO6iouL45dffilQe7Rp0wZjY2O2bdumLNu1axdpaWlKb2RSUhI//vgjzZs3V5JDAAMDA9q2bcv9+/e5ceOGqt7cjhl45fvm71SzZk22bdtGZGQkZ8+eJT09XbX+zJkzSu/+i4lvbvJzjg8dOoSFhQXvvfee6rxVr14dW1vbHNeNRqOR5FAIAUgPYqFycXHBxcUFyP52P3fuXNasWUNUVBRjxoxREkV7e/vX1mVlZZVjmbGxMampqXluEx8fT0ZGhtKTmZunT58C2T0Fut6q1/nPf/7D06dPCQsLw8LCIsf6iRMnYm9vz549e1i5ciUmJiY0aNCAMWPGULFixVfW/fjxY7RabZ6Jarly5VSv89Mu8fHxqt43nZIlS6peP3r0CIDQ0NA840tISKBYsWJ5ri+IUqVK0alTJzp16gRkJ2gDBw5k+vTpr32skS7Wzp0757r+5d47MzMzTExMVMtya6cXh+918mqn2bNnM3v27Fz3r7uu8svKygpfX1++/PJLPvzwQwwMDNi2bRs1a9akSpUqQPYtDVqtNteeP93tCS8OzerqfZGxsTFAgb60/JUWLFjA0qVL2bJlC4sWLaJYsWI0a9aM0aNHY2trW6DfEfk5x48fPyYxMVG5HeRlL5+3v6OXVQjxv0ESxL+IkZERISEhrFmzRrl3SHcf0v379/OdnBWEhYUFBgYGtGvXTjVs9CJd4mRjY8P9+/fzVW9AQAC3b99m7NixZGZm5hjiLlasGKGhoYSGhvLo0SOOHDnCvHnzGDRoEF999dUr67a2tkZPT49169YpH+Yvym3Z61hZWSlJzYteXqa7D/Cjjz7C1dU117peTpYKk4eHB/Xr12f//v08fvz4lfvSxbp48WLKli1bKPu3srLi3LlzOZbn1U5BQUE0a9Ys17repNepY8eOfPXVV/znP/+hbNmynD9/nilTpijrLSws0NfXJy4uLse2Dx8+VMX2dzA2NubZs2c5lhckObaxsWHChAlMmDCB3377jYMHDzJv3jweP37MqlWrVL8jCoO1tTVWVlZERUXluv7Fnlngtb2WQoh/D0kQC8HDhw+VHo0X6WYD69bVr18fAwMDNmzY8Jc85NbMzAwvLy8uXLiARqN5ZXLVsGFDFi9ezPHjx187EUNfX59p06ZRrFgxwsLCSEpKyjMBLVWqFB07duTSpUtER0eTnJyMmZmZqifnxZvifXx8WLFiBQ8ePMgx4eBNeXh48Nlnn3Ht2jXVMPPu3btV5WrVqoWFhQXXrl2jV69ehbLv3Dx69AgbG5scvXyZmZncunULMzMzSpQoAfyREL/cU9ygQQMMDQ359ddfad68eaHE5eHhwd69e/n222+VSSGQs50qV65MxYoVuXTpEiNGjCiUfUP2MZUuXZrY2FjKlCmDiYmJqie1WLFiuLq68s033zB27FjlusnKymLHjh3Y29v/rcOhDg4OfP3116SlpSnn6enTp5w5c0a53xfy32tZtmxZevXqxfHjx/nhhx8AcHd3p0SJEnzxxRe0atXqTydsPj4+7N69m6ysrDy/BAkhRG4kQSwEAQEB2Nvb895771G5cmW0Wi0XL17ks88+o1ixYvTu3RvI7r0LCgoiMjKSlJQUWrduTYkSJbh27RpPnz595VBnfk2YMIEePXrQs2dPunfvjoODA8+fP+fXX3/l4MGDymzOPn36sHfvXgYPHkxgYCA1a9YkJSWF06dP4+Pjk+N5fZD9OI/ixYszdepUkpKSGDBgAJA9+9bHxweNRoOlpSXXr19n+/btuLu7K7Mgq1atCqBMiNDX10ej0VC7dm38/PwYP348P/30Ex4eHpiZmREXF8f3339P1apV80xG89KnTx+2bt3KwIEDCQ0NpWTJkuzatUu5T06XqBUvXpyJEycSFhZGQkICzZs3p2TJkjx58oRLly7x5MkTpk6dmud+Tp48Se/evQkJCWHo0KF5ltu+fTsbN26kdevWuLi4UKJECe7fv8/mzZu5evUqQ4YMUZIKXTtFR0fToUMHDA0NqVSpEo6OjoSGhrJw4UJu375No0aNsLCw4NGjR5w/fx4zM7MCXz8dOnQgOjqaMWPG8OGHH1KhQgWOHDnCd999p2onyJ6kMnDgQAICAujQoQOlS5cmISGB69ev8/PPP7N48eIC7Ruy7yVs3749q1evxtzcnGbNmimJss6IESPo378/vXv3pn///hgZGbF+/XquXr3K/PnzC73H69ChQzl61QBatGhBu3bt2LhxI6NGjaJr167Ex8cTFRWlSg4BzM3NcXBw4MCBA3h7e2NpaYm1tTWWlpb07t2b1q1bU7lyZYoXL8758+c5evSo0jNbvHhxxo4dy8SJE+nbty9du3alZMmS/Prrr1y6dIlJkyYV6HhatWrFzp07CQwMxN/fn5o1a2JkZMT9+/c5efIkTZo0ybNXWAjx7yYJYiEIDg7mwIEDREdH8/DhQ9LT07G1taVevXoEBQWpHhmj+yD+/PPPGTVqFAYGBlSsWDHX56u9iXfeeYfY2FgiIyNZuHAhT548oUSJElSoUEHVS2Rubs769esJDw9n06ZNLFmyBAsLC1xcXFQzLF82dOhQihUrxqeffkpSUhKhoaHUrVuXgwcPKj2GpUuXpn379gwaNEjZrnXr1vzwww+sX7+eJUuWoNVqOXDgAI6OjkybNg1XV1c2btzIhg0byMrKws7Ojlq1auWYkJIfpUuX5vPPP2fGjBlMnjwZMzMzmjZtSmhoKGPHjlUlIe3ataNs2bJERUUxefJknj9/jo2NDdWrV6dDhw6v3I9uAsvr7tvy8fHh0aNHfPvtt2zYsIHExESKFy+ORqPh008/pV27dkpZLy8vgoKC2LZtG5s3byYrK4u1a9cqy52cnFi7di27d+8mLS0NW1tbnJ2d6d69e4HbqVixYkRHRzNjxgzmzJmDnp4eDRo0YPLkyQQGBqraqW7dumzevJlly5YxY8YMEhMTsbKywsnJSZlF+yY6duzI8uXLefLkiXJv5os8PT1Zs2YN4eHhjBs3jqysLKpVq8bSpUt577333ni/eRk/fnyuyy9fvkzt2rWZPXs2K1asYPDgwZQrV44hQ4Zw5MiRHJM9pk+fzqeffkpwcDBpaWl06NCBadOmUbNmTbZv387du3fJyMigTJkyDBw4UPmyBdlfuHSPy5k4cSJarRYHB4dXPr0gLwYGBixdupS1a9eyfft2VqxYgYGBAfb29nh4eChfSIQQ4mV6Wq1W+7aDEOLv8NFHH7Fr1y5Onjz5Rvc2vuzTTz9l9+7d7Nu3L8dkgf9luuctHj58OF+TJYR4UWZmJmfPnsXNzQ0DA4O3HY5KWloa58+fl9gKoKjGBX/EVqWyEUZGRSy29Cyu3UijRkIcRvpF697eTPQ4X6Lka8+p9CCKf6SIiAhKly6No6MjSUlJHD58mM2bNxMcHFwoySFkDzEPHjz4fzo5/Pzzz4Hs+wzT09M5ceIEMTExtG3bVpJDIYT4F5MEUfwjGRkZERUVxYMHD8jIyKBixYqEhYXRp0+fQtvH1q1bC62ut8XU1JTo6Gju3LlDenq6MuQZHBz8tkMTQgjxFkmCKP6RgoKClL/8IvLWuXPnPJ+tKIQQ4t9L/pKKEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRv6QihBCi0GRmZgKQmpqKoWHR+ohJSkoCIDk5udD+JnthKaqxFdW44I/YUlLT0aL3lqNRS0lJAyAZ/SIXW8Z//83MzMTAwCDPcnparVb794QkhBDin+7Zs2dcvnz5bYchhHgNjUaDubl5nuuL1tc7IYQQ/9N0vUyVKxpioF+0ek7S0jO4dVtLxfJ6GBWx3k1dbC4uLq/s1fm7paamcvHiRao8eYCxQdG6Ky1Fq8d1q1JF+nwWxXbLRI9LlqVe2yNctFpUCCHE/zRdcmNkaICBQdFKELOH+tIwMjTEyKjoJGHwR2wGBgZFKkHU3SZgbKCPURFL+LX/nUZRlM9nUWw3/f8Oeb/uOitaaa0QQgghhHjrJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQcxDbGwsGo0mz5+TJ08qZX19fQkLC3uL0RYNDx48IDw8nIsXL+ZYFx4e/v/t3Xtcjvf/wPFX3R2kA1Exchhxo3NUimiZOYyRY1gOyyGWzGmEOc2pzbnkVHNoNJOc+TI2M8cdvnyF8XOYDduIlCk63PX7w/e+vi6Vymxi7+fj4cF93Z/rc72vz3Xlfvc5XDdarVa1LSAggCFDhjyTY+uv17Vr10q13/Hjx594nR/98zI4d+4cWq2Ws2fPAhQ4Rzc3N9q1a0d0dDSZmZl/WRz665WcnPyXHaOkDhw4QNeuXXFzc8PX15fBgwdz7ty5Eu//+D3k5ORE06ZNCQoKYsGCBVy/fv0vjB6uXbuGVqslLi7umdQ3fvx4AgICnkldQogXl3zVXjFmz55NnTp1Cmx3cHBQ/h0dHf3EL7z+p7h58ybR0dFUr16dhg0bqt7r3r07fn5+zymyojk6OrJhwwbVtrCwMGrUqMG4ceOeU1R/nb1792Jvb0+jRo2UbW3atOGdd94BIDMzk2+//ZYlS5Zw/vx5oqKinleof4tTp04xbNgw/Pz8GDlyJH/88QdfffUVFy5coEGDBqWqa9SoUXh7e6PT6UhLS+PUqVNs2rSJ1atX8+GHH/LWW2/9RWchhBDPniSIxahXrx7Ozs5PLPPoh+0/kU6nQ6fTPbFM1apVqVq16t8UUclZWFjg5uam2mZiYoKVlVWB7S+DPXv28MYbb6i22djYqM7V19eXX3/9le3bt5OVlYWpqenfHOWzc//+fczMzIp8f8+ePeTn57Nw4UKlXLt27Z7qWLVq1VK1Y6tWrRgwYAADBgwgIiLipeqJFkK8/GSI+Rl4fIhZP+S0Y8cOFixYQPPmzfHw8KB///5cvnxZKbdu3ToaNGjA7du3lW2ffPIJWq2WadOmKdvy8vLw9PRkzpw5yrbs7GxiYmJo27atMqQVERFBamqqKrbs7GzmzJlDs2bNcHV1pU+fPpw+fbpAzKmpqUydOpX27dvj7u6Oj48Pffv25fvvv1fVpx/OWrlyJTExMQQEBODs7MyxY8fo1q0bgOrDUN8DVdgQc2HWrVtHo0aNWLx4sbLtyJEj9OvXDw8PD1xdXQkKCuLo0aPF1vWsZGVlMWfOHDp16kTjxo3x8vKiZ8+e7Nu3r0DZ3bt30717dxo3boyrqyutWrUiIiJCeT8vL4+YmBjatGmDi4sLTZo0oWPHjqxZs0ZVz5UrVxg9ejQ+Pj44OTnRrl071q1bpypT0vtM79KlS1y8eJE2bdoUe86WlpYYGBhgaKj+L6Kk1+LSpUuMGjUKX19fnJyc8Pf35/333yc7O1tVLiMjgylTpuDt7Y23tzdhYWHcuHGjQH27du2iZ8+euLm54e7uTkhIiDJMrjd+/Hjc3d05f/4877zzDu7u7vTv3/+J56nRaMjLy+Pnn38utk2eRsWKFZk2bRq5ubmsXr1a9V5JrjHA3bt3mTNnDq1atcLJyQkfHx8GDRrEpUuXijxuTk4O48aNw93dna+++gqA/Px81q1bR6dOnXBxccHT05Pw8HCuXr36TM9ZCPFykB7EYuTl5ZGbm6vaZmBggEajKXbf+fPn4+HhwcyZM7l37x5z585l6NCh7Nq1C41Gg4+PD/n5+Rw9epQOHToADz+Ay5Urx5EjR5R6Tp8+zd27d/Hx8VFiGjZsGD/88AMhISF4eHhw/fp1oqKilGGtcuXKAQ+TtV27djFw4ECaNm3KxYsXCQsL4969e6pY09LSgIfDqzY2NmRmZvLFF18QHBzM6tWr8fb2VpWPj4+ndu3ajBs3DgsLCypXrszs2bOJiIhg6NCh+Pv7A5S41zA/P5+PPvqI+Ph4ZsyYQZcuXQDYunUr48aNo1WrVkRGRmJkZMSGDRsICQkhLi5OaZPCHD9+nL59+xIWFsbw4cNLFEdhsrOzSU9P55133qFKlSrk5ORw5MgRhg8fzuzZs+ncuTMAJ06cYOTIkbRv356wsDBMTU359ddfOXbsmFJXbGws0dHRDB06lCZNmpCbm8vly5f5448/lDIXL14kKCiIV155hXHjxmFra8uhQ4eYMWMGd+7cISwsTBVfcfeZ3t69e6lSpQqurq4F2l5/j+uHmDdv3kz79u0xNjZWypX0Wpw7d45evXphbW1NeHg4tWrVIiUlhS+//JLs7GxMTEyUOidNmoS/vz/z5s3jt99+4+OPP2bs2LGsXbtWKbNs2TIWLlxIly5dGDp0KDk5OcTFxdGnTx82btyomu6Rk5PD0KFDCQoKYtCgQcX2bAcGBvLJJ58wcuRI1q1bR6VKlZ5Y/mm4uLhga2ur+mWrpNf43r179O7dm+vXrzNw4EBcXV3JzMzku+++IyUlhbp16xY43t27dwkLC+Py5cvEx8fj5OQEwOTJk9m8eTPBwcGMGTOG9PR0lixZQlBQEFu3bsXGxuaZn7sQ4sUlCWIxevToUWCbRqMp0HtRGAcHB+bOnau8NjQ05L333iM5ORk3Nzfq1KlD1apVOXLkCB06dCA7O5sffviB4OBgVq5cya+//kq1atU4cuQIxsbGeHp6Ag97qb755huioqJUw4UNGjSgW7duJCUl0bt3by5evMiOHTsYNGgQo0ePBqBZs2bY2NgwatQoVax16tRh6tSpymudTkfz5s25fv068fHxBRJEU1NT4uLiVAlETk4OADVr1izV8OyDBw94//33OXLkCCtXrlQSjfv37zNr1iz8/f1ZsmSJUr5ly5YEBgYyf/58Nm7cWGS9+kTewMCgxLEUxtLSktmzZyuvdTodPj4+3L17lzVr1qgSxPz8fKZNm4alpaVSXp/sAvz73/+mfv36qoT18bmZs2fPxtzcnISEBGVua7NmzcjOzmbFihUEBwdToUIFpXxx95mefnj58fZYv34969evV21r0aIF06dPV16X5lrMnj0bIyMjEhMTVQlXYXPw/Pz8mDRpkvI6PT2djz/+mJSUFGxtbfntt9+Iiori7bffVpXz9fWlTZs2REdHs3DhQmV7Tk4O7777Ll27di1wrMJ89913VKxYkdTUVPr378+aNWuwtrYu0b6lUa1aNc6fP6+8Luk1XrNmDRcuXGDVqlX4+voq+z8+TUDv2rVrysKvDRs2UL16dQBOnjzJ559/zvjx4xkwYIBSvkmTJrRp04ZVq1YxduzYZ37eQogXlwwxFyMyMpLExETVn88//7xE+z6+ElA/xPrrr78q23x8fJQhuhMnTnD//n0GDBiAtbU1hw8fBh72Krq5uVG+fHkAvvrqK6ysrHjttdfIzc1V/jRs2BBbW1u+/fZbAOXvx+dUtWnTBiOjgr8bJCQkEBgYiLOzM40aNcLR0ZGjR48WOpQVEBCgSg6fVlpaGv369ePUqVOsX79e1SN44sQJ0tLSCAwMVJ1nXl4efn5+JCcnP3GlrZeXF2fPni3Q4/Y0du/eTVBQEO7u7krbJCYmqtpGP1f1vffeY9euXYUOlTo7O3Pu3DmmTp3KN998U6AnNysri2PHjtG6dWvKlSunOu8WLVqQlZXFyZMnVfuU5D67evUqP/74Y6GJRbt27ZR7e926dUyaNInTp08zcOBAZUi4pNfi/v37fPfdd7Rr165EvXHFxX7o0CFyc3Pp1KmT6rimpqZ4enoq9/ijSjKEDg+v6fTp04mJiWHNmjXcvHmTAQMGKL3pAP379+ftt98uUX1Pkp+fr/y7NNf4m2++oXbt2qrksChnz56lZ8+eVK5cmYSEBCU5hIf/ZxgYGPDWW2+pjmdjY0ODBg0KbUchxD+b9CAWo27dusUuUilKxYoVVa/1Q2sPHjxQtvn4+LB582auXLnCkSNHaNSoEZUrV6Zp06YcPXqUjh07cuLECUJDQ5V9bt++zd27d5Who8fduXMH+N+w8eNDR0ZGRgViW7VqFXPmzCEoKIgRI0ZgbW2NoaEhixYtKnQ+m62tbYnaoDhXrlwhPT2dHj16UL9+fdV7t27dAiA8PLzI/dPT05XE+a+yd+9e3nvvPdq2bcvAgQOxsbFBo9GQkJDApk2blHKenp4sWbKE+Ph4xo0bR3Z2NvXq1SM0NFSZQjBkyBDKly/Ptm3b+Oyzz9BoNDRp0oQxY8bg7OxMWloaubm5xMfHEx8fX2g8+uurV5L77F//+heVK1emcePGBeqrVKmS6h5v0qQJlSpVYtSoUSQlJREUFFTia2FoaIhOp6NKlSpFlitN7Prj6ue3Pu7xOZJmZmYlfqJAdHQ0zZs3x8XFBYDVq1fTv39/BgwYwOrVqzE2Nub06dPP5FFMv/32G3Z2dgClusapqam88sorJTrG4cOHuXPnDuPHj8fKykr13u3bt8nPzy8y0axRo0ZJT0UI8Q8hCeJzpu8xO3LkCEeOHFH+A/fx8WHhwoV89913ZGdnq/5jt7a2pmLFisTGxhZap7m5OfC/D99bt26pPrBzc3NVvSQA27Ztw8vLS7U4Bh4uIijMnx221XNzc6Nt27ZMnDgRgKlTpyof+vqhvg8++KDAvDm9ypUrP5M4nmTbtm3Y29uzcOFC1Xk/vrAE4PXXX+f1118nOzubkydPsnz5ckaPHk316tVxd3fHyMhIWdl69+5djhw5woIFCxg4cCAHDhzAysoKjUZDp06d6N27d6Hx2Nvbl/oc9u7dy+uvv16iubPwv548/fMAS3ot8vLy0Gg0hfaePg39cRcvXky1atWKLV+a+/KXX35RPcqmQYMGrF27ln79+jFgwAAaN26MkZERQUFBpQ/8EadOnSIlJUVJcktzjStVqsTvv/9eouOEhIRw9epVxo0bh06nU6Y+wMN2NDAwYN26dao5oHqFbRNC/LNJgvic2dnZ4eDgwN69ezlz5owyN9DX15fJkyezevVqLCwsVD08/v7+7Ny5k7y8vCI/rAFlzuKuXbtwdHRUtu/Zs6fQhTePf0icO3eOkydPlrgHo7Ceq5IIDAzEzMyMMWPGkJmZSWRkJBqNBg8PD6ysrLh48eIzGeZ7WgYGBhgbG6uSj5SUFPbv31/kPiYmJnh5eWFlZcWhQ4c4e/Ys7u7uqjJWVla0bduWGzduMGvWLK5fv46DgwPe3t6cPXsWrVb7TD64f/vtN5KTkxkxYkSJ99E/7FyfgJfmWnh6evKvf/2L9957708v+mjevDlGRkb88ssvJR46Lql69epx5MgRZb4jQP369Vm7di29e/fmzJkzzJ49WzWftLTS0tKYMmUKxsbGyopqMzOzEl9jPz8/Fi9ezNGjR5+4IAse9qZOnz6d8uXLM378eDIzM5UE1N/fnxUrVnDjxg3at2//1OcjhPjnkASxGBcuXCh0JWTNmjWf2YpHHx8f4uPjKVeuHB4eHsDDIR97e3sOHTpEQECAas7gm2++yfbt2xk8eDDBwcG4uLhgbGzM77//zvHjx2nVqhWtW7emXr16dOjQgVWrVqHRaGjatKky4V3/GBM9f39/YmJiWLx4MZ6envz000/ExMRgb29f7ErQR9ukXLlybN++nbp161K+fHns7OxKNNzYtm1bzMzMCA8PJysri3nz5mFubs6kSZMYP3486enptGnThsqVK5Oamsq5c+dITU0t0OP5qGe1itnf35+9e/cydepU2rRpw++//05MTAx2dnZcuXJFKbdo0SJ+//13fHx8qFq1Knfv3mXt2rUYGxvj5eUFQGhoKPXq1cPJyYlKlSpx/fp11qxZQ/Xq1alVqxYAEydOpHfv3vTp04devXpRvXp1MjIy+OWXX/jyyy9VK3xLYs+ePVhZWRVYaKR369YtZc5bVlYWP/74I0uXLsXKykpZYFOaaxEREUGvXr3o0aMHgwcPpmbNmty+fZsvv/ySadOmleqh8vb29oSHh7Nw4UKuXr1KixYtsLKy4tatWyQnJyv3zNOIiIjgnXfeoWfPnoSEhFC3bl1SUlLYvXs39+/fx8bGhuXLl9O8eXNlePhJfv75Z06ePEleXh5paWn85z//YdOmTdy7d4/IyEjq1aunlC3pNe7Xrx+7d+9m2LBhDB48GBcXFx48eMB3332Hv78/TZs2LRDH+PHjMTc3Z9q0aWRmZjJw4EAaN25Mz549mTBhAqdPn8bT0xMzMzNSUlL44YcfqF+/fpG9mUKIfyZJEIvx6DPsHjVjxgy6d+/+TI6hTxAbN26seiixr68vn3/+eYF5QxqNhqVLl7J27Vq2bt3KihUr0Gg0VK1aFU9PT9VcvtmzZ2Nra0tiYiKrV6+mYcOGLFy4kIEDB6rmKYWGhnL//n0SExOJjY3FwcGBqVOnsm/fvhJPYDczM2PWrFlER0cTEhJCTk5OqZKzli1bsmLFCkJDQxk2bBjR0dF06tSJatWqERsby5QpU8jIyKBSpUo0bNiQwMDAJ9anX8DyZ+dLdu3aldu3b/PZZ5+xadMmatSoweDBg/n999+Jjo5Wyrm6unL69Gnmzp1LamoqVlZWODk5sXr1aiU58Pb2Zs+ePWzcuJF79+5ha2uLr68vw4YNUxb9ODg4kJSURExMDAsXLiQ1NRVLS0tq1apFy5YtSx3/3r17n7ioaM+ePezZswcAY2NjqlatSkBAAEOHDlUtdCjptWjQoAGJiYksXryYefPmkZGRga2tLU2bNn2qHtEhQ4ZQt25d1q5dy86dO8nOzsbW1hYnJyd69epV6vr0PD092bhxIzExMURHR3P37l1sbGzw9fVly5YtaDQaevXqRb9+/YiPjy/2MTDz588HHs7xtbCw4NVXX6Vr16706NFD1Y5Q8mtsYWHB+vXriYqK4vPPP2fJkiVYWVnh7Oxc6BMW9IYPH0758uX56KOPyMzMJDw8nOnTp+Pq6sqGDRtISEggLy8POzs7PDw8lHmYQgihZ5D/6PI68Y/w73//m169ejF37lw6duz4vMP5y3z00Ufs3LmTvXv3vtDfBvJnpKSk0KJFC5YsWSLfryv+FjqdjpMnT6J1MEWjeTZzlZ+V7Jw8Ll7Opl4dY4yNSzYf9++ij83Nza3Ec4X/DtnZ2SQnJ+OYnoKxYRm7nhhy1rJSmb6eZbHddBiQbFm52HtNehBfcocPH+bEiRM4OTlhamrK+fPnWbFiBbVr1y7yWWovi+PHjzNs2LB/bHIID3tP9fMJhRBCiJKSBPElZ2FhweHDh1m7di0ZGRlYW1vTokULRo0a9dInTo8+gkYIIYQQJScJ4kvO1dWVhISE5x2GEEIIIV4g8k0qQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiX7UnhBDimdHpdABk5+jQ5JWtPogHD7If/p2VQz4GzzkaNX1s9+/fx8TE5DlH8z+ZmZkA3MewzLVZ5n//LtPXswy2W+5//9bpdGg0miLLGeTn5+f/PSEJIYR42d27d4/z588/7zCEEMXQarVYWFgU+b70IAohhHhm9L1fdWoboTEsWz0n2Tm5/Hw1n9o1DTA2Klsff/rYnJ2dn9ir83fLysrixx9/pF7qDUw0ZaxHON+ASxVtyvT1LIvtpsOAcxVsiu2pLlstKoQQ4oWmT26MjTRoNGUrQXw41JeNsZERxsZlJwmD/8Wm0WjKVIJo9N/Ey0RjiHEZS/jz/7uMoixfz7LYbob/HfIu7j4rW2mtEEIIIYR47iRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUL7T//Oc/vPvuu/j7++Pk5ISvry89e/Zkzpw5T1XftWvX0Gq1JCUl/a37/lOVtM2OHz+OVqtV/jRs2BBfX1/Cw8O5dOnSM41p3bp1z/0a3rhxg6ioKH788ccC70VFRaHValXbAgICGDJkyDM5dlJSElqtlmvXrj2T+oQQLyb5qj3xwjpw4ABDhw7Fy8uLsWPHYmtrS0pKCqdPn2bnzp2MHz++1HXa2dmxYcMGatas+RdELP6sUaNG4e3tTU5ODqdPn2bJkiUcO3aM7du3U6VKlWdyjISEBKytrenSpcszqe9p3Lx5k+joaKpXr07Dhg1V73Xv3h0/P7/nFJkQ4p9CEkTxwoqNjcXe3p64uDjl+0IB3nzzTcaOHftUdZqYmODm5vaMIhTPWq1atZTr4+npiaWlJRMnTiQpKYmhQ4f+qbrv37+PmZnZM4jy6el0OnQ63RPLVK1alapVq/5NEQkh/qlkiFm8sNLS0rC2tlYlh3qGhupbWz8E98UXX9CxY0ecnZ1p1aoVa9euVZUrasjzypUrjB49Gh8fH5ycnGjXrh3r1q0rNsZHh0Uf//PoEF5ycjKhoaF4eXnh7OxM586d2bVrl6quwoYWofAhwezsbObMmUOzZs1wdXWlT58+nD59moCAgGJ7VvXDucePHy+2bcaPH4+7uzs///wzgwYNwt3dnZYtWzJnzhyys7NV+9+4cYMRI0bg7u5O48aNee+997h161axbfgk+mTx119/BR4OD/fp0wcfHx/c3Nzo2LEjK1euJCcnR7VfcHAwHTp04LvvviMoKAhXV1cmTJhAQEAAFy5c4Ntvv1WuU0BAABkZGTRp0oTJkycXiOHatWs0bNiQ2NhYAFJTU5k6dSrt27fH3d0dHx8f+vbty/fff19oe65cuZKYmBgCAgJwdnbm2LFjdOvWDYCIiAgljqioKKDo++Bx69ato1GjRixevFjZduTIEfr164eHhweurq4EBQVx9OjREra2EOKfRHoQxQvLzc2NjRs3MmPGDDp27EijRo0wNjYusvyPP/7IrFmzCAsLw8bGhu3btzNz5kxycnIICQkpcr+LFy8SFBTEK6+8wrhx47C1teXQoUPMmDGDO3fuEBYWVuS+GzZsUL1+8OAB77//Pnl5eVSsWBGAY8eOMXDgQFxdXZk6dSqWlpbs2rWLkSNH8uDBg6ca6oyIiGDXrl0MHDiQpk2bcvHiRcLCwrh3716p6ypOTk4OQ4cOpVu3brzzzjt89913xMTEYGFhobTNgwcPGDBgADdv3mT06NHUrl2bAwcOMHLkyD917J9//hmASpUqAfDLL7/QoUMH7O3tMTY25ty5cyxbtozLly8ze/Zs1b4pKSmMHTuWgQMHMnLkSAwNDRk0aBDh4eFYWloyZcoU4GGvsrm5OV27duXzzz9n7NixWFpaKvWsX78eY2NjunbtCjz8xQVQ7rPMzEy++OILgoODWb16Nd7e3qo44uPjqV27NuPGjcPCwoLKlSsze/ZsIiIiGDp0KP7+/gAl7jXMz8/no48+Ij4+nhkzZij3z9atWxk3bhytWrUiMjISIyMjNmzYQEhICHFxcfj4+JSi5YUQLztJEMULa/To0Vy+fJn4+Hji4+MxNjbGycmJgIAA+vTpg7m5uar8zZs32bJlCw0aNACgZcuWpKamEhMTQ+/evYscXpw9ezbm5uYkJCRgYWEBQLNmzcjOzmbFihUEBwdToUKFQvd9dLhap9MxfPhw/vjjDz799FOlrmnTplGvXj3WrFmj9Ib6+flx584d5s+fT+fOnQv0iD7JxYsX2bFjB4MGDWL06NFKvDY2NowaNarE9ZRUTk4Ow4cPp127dgD4+Phw+vRpduzYoSSImzdv5tKlS8TExNCqVSsAmjdvTlZWFp9//nmJj5WXl0dubi65ubmcPn2ayMhINBqNcuyIiAhV2SZNmlCxYkUmTJjA+PHjVdcpLS2NhQsXFkiMypUrh4WFRYGpBm+//TZr165l06ZN9O/fH4CsrCw2bdrEm2++ibW1NQB16tRh6tSpyn46nY7mzZtz/fp14uPjCySIpqamxMXFqX650fd41qxZs1RTHvS/gBw5coSVK1cq53b//n1mzZqFv78/S5YsUcq3bNmSwMBA5s+fz8aNG0t8HCHEy0+GmMULy9ramvXr15OYmMjo0aMJCAjgypUrzJs3j44dO5KamqoqX69ePSU51OvQoQP37t3jzJkzhR4jKyuLY8eO0bp1a8qVK6ckJ7m5ubRo0YKsrCxOnjxZoninT5/O119/zaJFi3B0dAQe9oBdvnyZjh07AhSoPyUlhZ9++qlU7fLtt98CKEmTXps2bQodjv+zDAwMCAgIUG3TarXKsC88HLY2NzdXkkO9Dh06lOpYI0eOxNHRURk21+l0LF68WLmuZ8+eJTQ0FG9vbxo2bIijoyPjxo1Dp9Nx5coVVV0VKlQoVa9ZjRo18Pf3JyEhgfz8fAC2b99OWloab7/9tqpsQkICgYGBODs706hRIxwdHTl69GihK64DAgKe2PNdUmlpafTr149Tp06xfv161bmdOHGCtLQ0AgMDVfdYXl4efn5+JCcnk5mZ+adjEEK8PKQHUbzwnJ2dcXZ2Bh72vMydO5fVq1cTGxvL+++/r5SzsbEpsK9+m35Y8HFpaWnk5uYqvZSFuXPnTrExxsTE8NlnnzFz5kxatGihbNfPwYuMjCQyMvKp6388Zih4vkZGRsqw9rNkZmaGqampapuJiQlZWVmqmJ7U/iU1ZswYmjZtikajwdramldeeUV579dff6VPnz68+uqrTJgwgerVq2NqasqpU6eYPn06Dx48UNVla2tbqmMD9O3bl/79+3P48GGaN2/O+vXrcXd3VxJ+gFWrVjFnzhyCgoIYMWIE1tbWGBoasmjRIi5fvlygzqeJozBXrlwhPT2dHj16UL9+fdV7+vssPDy8yP3T09MpX778M4lFCPHikwRRvFSMjY0JCwtj9erVXLhwQfVeYQsi9NuKSpysrKzQaDR06tSJ3r17F1rG3t7+iTElJSWxaNEihg8friw+0NMPSw4ZMoTWrVsXuv+rr74KoCRh2dnZmJiYKO8/nkDqz+XWrVuqR7/k5uYWmQg/6tHjPKq0ierjMZ06darA9tIuUqlRo4byy8Dj9u3bR2ZmJlFRUVSvXl3Zfu7cuULLGxgYlOrY8HD4vH79+nz66aeUL1+eM2fO8PHHH6vKbNu2DS8vL6ZNm6banpGR8cziKIybmxtt27Zl4sSJAEydOlWZmqC/zz744ANcXV0L3b9y5crPJA4hxMtBEkTxwrp58yZ2dnYFtuuH8R5/78KFC5w7d041zLxjxw7Mzc1VPUCPMjMzw9vbm7Nnz6LValWJWUkcPHiQDz74gK5duxa6mKVOnTrUrl2bc+fOFTs/UJ/0nDt3DhcXF2X7V199pSrn6ekJwK5du1TntWfPHnJzc4uNWX+c8+fPq5639+WXXxa7b1G8vb3ZvXs3+/fvVw0z79ix46nrfJw+0Xr0GuXn55dqjqN+/8d7Gx8VHBzMlClTuHfvHjY2NrRt27ZAHI/fJ+fOnePkyZOqHs/iYgCeGEdhAgMDMTMzY8yYMWRmZipzND08PLCysuLixYsFhsOFEKIwkiCKF1ZISAhVq1bltddeo06dOuTn5/Pjjz/yySefUL58efr27asqb2dnx9ChQwkLC8PW1pZt27Zx+PBhxowZ88Tn302cOJHevXvTp08fevXqRfXq1cnIyOCXX37hyy+/LPCoHL2rV68yYsQI7O3t6dKlS4G5io0aNcLExIRp06YxaNAgQkJCCAwMpEqVKqSnp3Pp0iXOnDmjPKakZcuWVKxYkYkTJzJixAg0Gg2bN2/mt99+U9Vbr149OnTowKpVq9BoNDRt2pQLFy6watUqLC0ti+2xsrW1xdfXlxUrVlChQgWqVavG0aNH+eKLL56435N07tyZ1atXM27cOEaOHEmtWrX4+uuvOXTo0FPX+ThfX1+MjY0ZNWoUAwcOJDs7m4SEBO7evVuqeurXr8/OnTvZtWsX9vb2mJqaqh4r89ZbbzFv3jy+++47hg4dWiAZ9Pf3JyYmhsWLF+Pp6clPP/1ETEwM9vb2xT7jUK9mzZqUK1eO7du3U7duXcqXL4+dnV2JHgbetm1bzMzMCA8PJysri3nz5mFubs6kSZMYP3486enptGnThsqVK5Oamsq5c+dITU0t0OMphPhnkwRRvLCGDh3K/v37WbNmDTdv3iQnJ0dJboYMGULdunVV5Rs2bEiXLl2IioriypUr2NnZERERoaxILYqDgwNJSUnExMSwcOFCUlNTsbS0pFatWrRs2bLI/X799VcyMzO5cuUKffr0KfD+/v37sbe3p2nTpmzcuJFly5Yxa9Ys7t69S8WKFalbt65qoYmFhQUrV65k1qxZyqNW9N+qMWnSJFXds2fPxtbWlsTERFavXk3Dhg1ZuHAhAwcOxMrKqti2/eijj/jwww+ZO3cuOp2O1157jXnz5imPciktMzMz1q5dy8yZM5k7dy4GBgY0b96c+fPnExQU9FR1Pq5u3bpERUWxcOFChg8fTsWKFenQoQP9+/dn0KBBJa5n+PDhpKSkMGnSJDIyMqhevbqq97RcuXIEBASwbds2evXqVWD/0NBQ7t+/T2JiIrGxsTg4ODB16lT27dunLCAqjpmZGbNmzSI6OpqQkBBycnIICwtj+PDhJdq/ZcuWrFixgtDQUIYNG0Z0dDSdOnWiWrVqxMbGMmXKFDIyMqhUqRINGzYkMDCwZI0jhPjHMMjXL8cT4iUWEBBAvXr1WL58+fMO5bn597//Ta9evZg7d66yalqUXnZ2NgEBATRu3JhFixY973DKHJ1Ox8mTJ9E6mKLRPJv5lc9Kdk4eFy9nU6+OMcbGmucdjoo+Njc3NzSashNbdnY2ycnJOKanYGxYxq4nhpy1rFSmr2dZbDcdBiRbVi72XpMeRCFeQocPH+bEiRM4OTlhamrK+fPnWbFiBbVr1+aNN9543uG9kFJTU7l8+TJJSUncvn2bwYMHP++QhBDiLyMJohAvIQsLCw4fPszatWvJyMjA2tqaFi1aMGrUqAKPpBElc+DAASIiIrC1tWXKlClFLmwSQoiXgSSI4h/hz6zAfRG5urqSkJDwvMN4qXTp0uWpvvZQCCFeRPJNKkIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRb5JRQghxDOj0+kAyM7RockrW30QDx5kP/w7K4d8DJ5zNGr62O7fv4+JiclzjuZ/MjMzAbiPYZlrs8z//l2mr2cZbLfc//6t0+nQaDRFljPIz8/P/3tCEkII8bK7d+8e58+ff95hCCGKodVqsbCwKPJ96UEUQgjxzOh7v5ydnZ/YO/E8ZGVl8eOPP1Iv9QYmmjLWu5lvwKWKNtSuaYCxUdn5aM7OyeXnq/llLi74X2xyPUtHl5fP5Su5xfZUl62ohRBCvND0SaFGoylzCaLRfz+oTTSGGBuWrWG//P8uCTA2MsLYuOy028Ph0ewyFxf8Lza5nqVjqMsHcov9+SxbKbcQQgghhHjuJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQXzBvPvuu7i4uHD37t0iy4wePRpHR0du3br1N0b2bB0/fhytVsu//vWv5x2KKEJYWBharZbp06cX+r7+Gh4/fvxvjuyh4OBggoODn2rfqKgotFrtM46o5K5du8bgwYPx8vJCq9Uyc+bMUu2v1WqJiopSXj/vayGEePHIV+29YLp168a+ffvYvn07ffr0KfD+H3/8wb59+/D398fGxuY5RCj+CW7fvs2BAwcA2L59O+PGjcPU1PT5BvWYKVOmPPW+3bt3x8/P7xlGUzqzZ8/mP//5D7NmzcLGxgZbW9s/VZ+joyMbNmzAwcHhGUUohHjZSQ/iC6ZFixbY2dmxadOmQt/fsWMHDx48oFu3bn/qODqdjuzs7D9Vh3h5bdmyhZycHPz9/bl79y579+593iEV4ODg8NQJUdWqVXFzc3u2AZXChQsXcHFx4fXXX8fNzY3q1av/qfosLCxwc3PDwsLiGUUohHjZSYL4gtFoNAQGBnLmzBnOnz9f4P2kpCRsbW1p0aIFACkpKUyePJkWLVrg5OREQEAA0dHR5ObmKvtcu3YNrVbLypUriYmJISAgAGdnZ44dO6YMTW3dupXZs2fTrFkzXFxcePvttzl79qzq2MnJyYwcOZKAgABcXFwICAhg1KhRXL9+vUCcN27c4IMPPqBly5Y4OTnRvHlzwsPDnzgsfu/ePUJCQvD19eXUqVNK3ElJSQXKPj7Eph8yvHDhAqNGjaJx48b4+voSERHBH3/8odr37t27TJgwAS8vL9zd3Rk8eDBXr14tUCfAlStXGD16ND4+Pjg5OdGuXTvWrVunKqNvwx07drBgwQKaN2+Oh4cH/fv35/Lly0Wer97PP/9MREQEb7zxBq6urvj5+REaGlrg+iclJaHVarl27Vqhx390ePHs2bMMGTJEibt58+YMHjyY33//vdh4ADZt2oSNjQ1z5syhXLlyRf7C8riihn3Hjx9PQECA8lp/bWNjY1mxYoVyTwUHB/PTTz+Rk5PD3Llzad68OY0bN+bdd9/l9u3bTzyWvs64uDhWrVpFQEAA7u7u9OzZk5MnT6r2LWyIubDrDxAQEMD48eOV1/rrcPToUSZNmoS3tzceHh68//77ZGZmkpKSwogRI2jSpAnNmzcnMjKSnJwc4H/X6ueff+bgwYNotVrVNf31118ZM2aM6n775JNPyMvLe2K7FzXEnJycTGhoKF5eXjg7O9O5c2d27dr1xLqEEP8MMsT8AuratSsrVqxg06ZNTJgwQdl+8eJFTp06xeDBg9FoNKSkpNC9e3cMDQ159913qVmzJidOnGDp0qVcv36d2bNnq+qNj4+ndu3ajBs3DgsLC2rVqqUkdwsWLKBRo0bMmDGDP/74g+joaIKDg9myZQs1atQA4Pr167z66qu8+eabVKhQgZSUFBISEujWrRs7d+6kUqVKwMPksGvXruTm5hIaGopWq+XOnTscOnSI9PT0QofGf//9dwYNGkROTg4bNmygRo0aBRKhkhg+fDjt27enW7du/N///R/z5s0DUNoiLy+P0NBQTp8+TVhYGI6Ojpw4cYKBAwcWqOvixYsEBQXxyiuvMG7cOGxtbTl06BAzZszgzp07hIWFqcrPnz8fDw8PZs6cyb1795g7dy5Dhw5l165daDSaImO+efMmFStWZPTo0VSqVIn09HQ2b95Mjx492Lx5M3Xq1ClVG2RmZjJgwADs7e2ZPHkyNjY2pKSkcPz4cTIyMord/9///jeXLl0iJCQEa2tr3njjDbZv387Vq1eVe+FZWb9+PfXr12fy5MncvXuXyMhIQkNDcXV1xcjIiFmzZvHrr78SGRnJxIkTWbZsWbF1rlu3jjp16ig/O4sWLWLw4MHs378fS0vLZxb7pEmTeOONN5g/fz5nz55lwYIF6HQ6fvrpJ1q3bk3Pnj05cuQIK1euxM7OjgEDBihDwWFhYdSoUYNx48YBYGdnR2pqKkFBQeTk5DBixAiqV6/OgQMHiIyM5JdffmHq1Kmliu/YsWMMHDgQV1dXpk6diqWlJbt27WLkyJE8ePCALl26PLO2EEK8eCRBfAHVqlULT09Ptm3bxtixYzE2NgZQenG6du0KPOwFSU9PZ+fOnVSrVg0AHx8fypUrR2RkJCEhIaohOFNTU+Li4pT6ACVBrFSpEkuWLMHAwACAxo0b06ZNG5YvX86MGTMAaNu2LW3btlX21el0+Pv706xZM3bs2EHfvn2Bhx/IaWlpbN26lbp16yrl27dvX+j5/vjjjwwePJiaNWuyZMkSKlas+NRt161bNyXZ8/X15eeff2bTpk3MmjULAwMDvvnmG3744QemTp1Kr169AGjWrBkmJiZKMqk3e/ZszM3NSUhIUIbumjVrRnZ2NitWrCA4OJgKFSoo5R0cHJg7d67y2tDQkPfee4/k5OQnDmd6enri6empvNbpdLRs2ZIOHTqwYcMGIiIiStUGly9fJi0tjZkzZ/L6668r24tq/8clJiYC/7vPunXrxrZt20hKSmLEiBGliqU4lpaWxMTEYGj4cLDjzp07zJo1izp16rB06VKl3OXLl1mzZg337t0rdhjV3Nyc5cuXK0m5nZ0d3bt35+DBg7z55pvPLPbXXntNSfCaNWvGyZMn2bFjBxEREfTv3x94eA8eOnSI7du3M2DAAGUo2MTEBCsrK9V9sWrVKm7cuMHGjRtxcXEBwM/PD51Ox2effUa/fv149dVXSxzftGnTqFevHmvWrMHIyEip786dO8yfP5/OnTsr7S6E+OeRn/4XVNeuXblz5w5ffvklALm5uWzbto0mTZpQu3ZtAA4cOIC3tzd2dnbk5uYqf/TDz99++62qzoCAAFVy+KgOHTooySFA9erVcXd3Vw1ZZWRk8PHHH9O6dWsaNWpEo0aNcHd3JzMzk0uXLinlDh48iLe3tyo5LMqhQ4fo3bs3np6erFq16k8lh4BqGBMeDhtmZWUpw5P6NmnXrp2q3OOJQ1ZWFseOHaN169aUK1euQPtmZWUVGLYs7NjwcNjwSXJzc1m2bBnt27fHycmJRo0a4eTkxJUrV1TtWlK1atWiQoUKzJ07l4SEBC5evFjifTMyMti9ezfu7u7K9fPy8qJmzZps3ry52KHO0mrZsqUqSdEf09/fX1VOv724ttTv+2iPbYMGDQAKnQrxZxQVY8uWLQtsL8mxjx07hoODg5Ic6nXp0oX8/HyOHTtW4th+/vlnLl++TMeOHQEK3L8pKSn89NNPJa5PCPHykR7EF1Tbtm2ZMWMGSUlJtGnThq+//ppbt24xZswYpczt27f56quvcHR0LLSOO3fuqF4/aaVkYcO+NjY2nDt3Tnk9evRojh07xrBhw3B2dsbc3BwDAwMGDx5MVlaW6rhVqlQp0Xnu27ePrKwsevXqhYmJSYn2eZLHE0x9nQ8ePAAgLS0NIyOjAuUeP/+0tDRyc3OJj48nPj6+0GM93r7FHbsoc+bMYd26dQwaNAhPT08qVKiAgYEBkyZNUrVrSVlaWhIfH8+yZctYsGAB6enp2Nra0qNHD4YOHVrkLwkAu3btIjMzk3bt2qketdSuXTuWL1/O4cOHn+nq30d7YAEltqK2l6Q9iroOT9OWT1Ka2EuyICwtLa3QxSp2dnbK+yWln+sbGRlJZGRkoWUev3+FEP8skiC+oMqVK8ebb77Jxo0buXnzJps2bcLc3Fw1xGttbY1Wq+W9994rtA79B4veoz2Ejyts8citW7eUD9s//viDAwcOEBYWxuDBg5Uy2dnZpKenq/aztrbmxo0bxZ0iABEREezevZtBgwYRHR1N8+bNlff0j1V5/MP1z3ywVaxYkdzcXNLS0lSJREpKiqqclZUVGo2GTp060bt370Lrsre3f+o4HrVt2zY6d+7MqFGjVNvv3LmDlZWV8ro07aHValmwYAH5+fmcP3+epKQklixZQrly5VTX73H6aQyzZs1i1qxZBd5PTEx8YoJoYmLCvXv3Cmx/EZIRExOTQhO5vyv2ihUrFrgP4eEcVXj4c1VS+rJDhgyhdevWhZYpzXC1EOLlIwniC6xbt2589tlnxMXFcfDgQQIDAzEzM1Pe9/f35+uvv6ZmzZoFei1Ka8eOHQwYMEBJIq9fv86JEyfo1KkT8DC5zM/PL9DLt3HjRnQ6nWpbixYt2LZtG5cvXy52gYWpqSlRUVGMHTuWoUOHsmDBAmXenI2NDaampgVW8+7fv/+pz9PLy4vY2Fh27dqlSvweX9lpZmaGt7c3Z8+eRavVPpPezaIYGBgU6NU7cOAAN27coFatWso2fe/S+fPnVe2qn4ZQVN0NGjRgwoQJbN68mTNnzhRZ9tKlS5w4cYI2bdoU+gzOpUuXsn//fu7cuVNkslK9enX27NlDdna20mZ37tzhxIkTZf4RLNWrVy9wrx09epTMzMy/5fg+Pj4sX76cM2fOqEYFtmzZgoGBAd7e3iWuq06dOtSuXZtz584V+MVDCCFAEsQXmrOzM1qtljVr1pCfn1/g2Yfh4eEcOXKEoKAggoODefXVV8nOzubatWscPHiQadOmUbVq1RIdKzU1lXfffZcePXrwxx9/EBUVhYmJCUOGDAEePmfN09OTuLg4rK2tqV69Ot9++y2JiYmqXi6AESNGcPDgQd5++22GDBlC/fr1+eOPP/jmm2/o379/gbmJxsbGzJ8/n4kTJzJixAgiIyOVOZFvvfUWmzZtombNmjRo0IBTp06xY8eOp25TPz8/PDw8iIyM5N69ezg5OXHixAm2bt0KqHtZJ06cSO/evenTpw+9evWievXqZGRk8Msvv/Dll1+ydu3ap47jUf7+/spqZa1Wy5kzZ4iLiytw7ZydnXn11Vf56KOP0Ol0WFlZsW/fPn744QdVua+++or169fz+uuvU6NGDfLz89m7dy93796lWbNmRcahX5wycODAAvPg4OH8xKNHj7Jt2zb69etXaB2dOnViw4YNjBkzhh49epCWlkZsbGyZTw7hYeyLFi1i0aJFeHl5cfHiRT799NNnuvL5Sfr378+WLVsYMmQI4eHhVKtWjQMHDrB+/Xp69epV6h6/adOmMWjQIEJCQggMDKRKlSqkp6dz6dIlzpw5w+LFi/+iMxFCvAgkQXzBdevWjZkzZ+Lg4ICrq6vqPTs7OxITE4mJiSEuLo4bN25gbm5O9erV8fPzK5C4PcnIkSNJTk4mIiKCe/fu4eLiwvz586lZs6ZSZt68ecycOZOPP/6Y3NxcPDw8WLVqlZJE6lWpUoXExEQWL17MypUrSUtLw9ramsaNGxe5CMXQ0JBZs2Zhbm7O2LFjuX//Pt27d1eePxcbG0tmZibe3t4sW7aswIKQkjI0NGTZsmXMmTOHlStXkpOTg4eHBx9//DE9evRQtZmDgwNJSUnExMSwcOFCUlNTsbS0pFatWgUWIvwZEydOxMjIiBUrVpCZmUmjRo2Iiopi0aJFqnIajYZly5bx4YcfMmXKFExMTHjzzTeZPHmyati4Vq1aWFlZERsby82bNzE2NubVV19lzpw5BAYGFhpDTk4OW7dupWHDhoUmh/Bw8UXVqlVJTEwsMkFs3LgxkZGRrFixgmHDhlGjRg3effddDh48WGDRVFkTEhLCvXv32Lx5M5988gkuLi4sWrSIYcOG/S3Hr1SpEp999hnz5s1j3rx5ZGRkYG9vz9ixYxkwYECp62vatCkbN25k2bJlzJo1i7t371KxYkXq1q1bYJGWEOKfxyA/Pz//eQchyq7jx4/Tt29fFi1apJrf+E+zfft2xowZQ0JCAh4eHs87HCHKLJ1Ox8mTJ3Fzc3vi8z2fh+zsbJKTk3FMT8HYsOg5189DNoactaxEvTrGGBuXnXbLzsnj4uXsMhcX/C82uZ6lo9Plc/5iVrE/o9KDKMRjduzYwY0bN6hfvz6GhoacPHmSuLg4PD09JTkUQgjxjyAJohCPMTc3Z+fOnSxdupT79+9ja2tLly5dilwNLoQQQrxsJEEUT+Tt7V3odz6/zF577TVee+215x2GEEII8dzIN6kIIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIp81Z4QQohnRqfTAZCVlYWRUdn6iMnMzATgPobkY/Cco1HL/O/fD7JyylRsDx5kP/y7jMUF/4tNrmfp6HR5//1bh0ajKbKcQX5+fv7fFZQQQoiX27179/5x398uxItIq9ViYWFR5Ptl69c7IYQQLzQTExMA6tQ2QmNYtnpOsnNy+flqPs7Ozk/sOXkesrKy+PHHH8tcbGU1LpDYnpZOpyM5OVn5WS2KJIhCCCGeGf2HobGRBo2mbCWID4f6stFoNGXuQ1s/HF/WYiurcYHE9mcVF5csUhFCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKmU6QUxKSkKr1aLVajl+/HiB9/Pz82ndujVarZbg4OBneuyAgADGjx+vvD5+/HiRcTzJ+PHjCQgIeKaxFebRttL/adq0KcHBwXz11VdPXW9AQABDhgwpUdmoqCi0Wi2pqalKe/3rX/8qUE6r1RIVFaW8vnjxIlFRUVy7du2p4yyJtLQ0HB0d+eKLL4CH5/Zoe7m5uREYGMinn35KYV9R7u7urtwTwcHBdOjQoUAZrVbL9OnT/9Lz0Mde0uvyV9G3Z3R0NFqtlrlz5xZZ9sqVK2i1WmbMmPE3RvjsFXXdhRDiZfNCfNWeubk5iYmJeHt7q7Z/++23/PLLL5ibm//lMTg6OrJhwwYcHBxKtd+wYcPo27fvXxRVQbNnz6ZOnTrk5+dz69YtPv30U0JDQ1m6dOlfnqh2794dPz8/rKyslPaqXbt2sftdvHiR6OhovLy8sLe3/8vi279/P8bGxvj5+SnbPDw8GDduHAA3b95k1apVfPjhh9y7d4/Q0FDV/mvWrKFixYoATJkyhdzc3L8s1heBvj0HDhzIl19+yZYtWxg5cmShX9+UlJQEQLdu3f7uMIUQQjyFMt2DqNe+fXv27t3LvXv3VNsTExNxd3enWrVqf3kMFhYWuLm5YWFhUar9atasSaNGjf6iqAqqV68ebm5uuLu707p1a5YvX46JiQk7d+78y49dtWpV3NzcMDIyUtpLn1A9D/fv31e93rNnDy1atKBcuXLKNisrK9zc3HBzc+ONN95g5cqVWFpasmHDhgL1ubi4ULNmTQAcHBxo0KDBXxpvWfdoe3br1o2UlBQOHjxYoJxOp2PLli04Ojr+6TZ70dpICCFeVC9Egvjmm28CsGPHDmXbH3/8wd69e+natWuh+2RnZxMTE0Pbtm1xcnKiadOmREREkJqaqiqXk5PDRx99RLNmzXB1daVXr16cOnWqQH2PDzFfu3atwJDuo3/0Chti1g9DbtmyhXbt2uHq6spbb731p4aCi2JqaoqxsbHyxeF6JW0fvYMHDxIYGIiLiwtt27YlMTFR9f6tW7eYMmUK7dq1UxKuoKAgvv766yfGl5SUxIgRIwDo27ev0n76HieAI0eO0K9fPzw8PHB1dSUoKIijR4+q6tEPb585c4bw8HA8PT1p3bq18v69e/c4cuQIbdq0eWI8FhYW1K5dm1u3bqm2p6WlMXXqVPz8/HBycqJVq1YsWLCA7OzsQusp7to+Kd68vDzi4+Pp1KkTLi4uNGnShB49erB///4CxynuugCkpKQwefJkWrRogZOTEwEBAURHR6t6QPX3c1xcHKtWrSIgIAB3d3d69uzJyZMnC9T5eHt27NiRcuXKqa6b3qFDh7hx44bqZ3XXrl307NlT+WUmJCSEs2fPqvYbP3487u7unD9/nnfeeQd3d3f69+8P/G+o9/vvv6dHjx64uLjg5+fHwoUL0el0qnqio6Pp3r07Xl5eeHh4EBgYyMaNGwudRrB9+3Z69uyJu7s77u7udOrUiY0bNxYo96gvvvgCV1dXJk6cSG5ubpHTSvTX/FGl+b9g3759dOzYUbn/1qxZU2id+fn5rFu3Trl/PD09CQ8P5+rVq6py+jY8deoUvXv3xtXVlVatWrFixQry8vKeeM5CiJffCzHEbGFhQZs2bdi0aRNBQUHAw2TR0NCQdu3asWbNGlX5vLw8hg0bxg8//EBISAgeHh5cv36dqKgoTp06xaZNm5RepA8++IAtW7bwzjvv0KxZMy5cuEBYWBgZGRlPjMnOzq5AL1Nqaipjx46lSpUqxZ7TgQMHSE5OJjw8nPLlyxMbG0tYWBj/+te/qFGjhlJOq9Xi5eVFfHx8idoqLy+P3Nxc8vPzuX37NrGxsdy/f181b6o07QNw7tw5IiMjGTRoEDY2NmzcuJGJEydSq1YtPD09gYcJlEajYfjw4djY2JCRkcG+ffsIDQ1l9erVBaYH6Pn7+zNq1Cjmz5/P5MmTcXR0BFB66rZu3cq4ceNo1aoVkZGRGBkZsWHDBkJCQoiLi8PHx0dV3/Dhw2nfvj1BQUFkZmYq27/88ksMDAxo2bLlE9svNzeX33//XTU0npWVRd++fbl69SrDhw9Hq9Xy/fffs2LFCn788UdWrFihqqOk17aoeMePH8+2bdvo1q0b4eHhGBsbc/bsWa5fv67atyTXJSUlhe7du2NoaMi7775LzZo1OXHiBEuXLuX69evMnj1bVee6deuoU6cOEyZMAGDRokUMHjyY/fv3Y2lpWWR7Wlpa8sYbb7B7925SU1OpVKmSUjYpKQlTU1M6duwIwLJly1i4cCFdunRh6NCh5OTkEBcXR58+fdi4caNqGkdOTg5Dhw4lKCiIQYMGqZK/lJQURo4cyeDBgwkPD+fAgQMsXbqUu3fvMnnyZKXc9evX6dmzpzLScPLkSWbMmMGNGzcICwtTyi1atIiYmBjeeOMNBgwYgKWlJRcuXODXX38teKP81+rVq/noo48ICwtj2LBhRZZ7kpLcLwcPHmT48OE0adKEhQsXkpubyyeffFLgFxmAyZMns3nzZoKDgxkzZgzp6eksWbKEoKAgtm7dio2NjaoNx44dy4ABAwgLC+OLL75g3rx52NnZ0blz56c6HyHEy+GFSBABunbtSt++fblw4QL16tVj06ZNtG3bttAh3927d/PNN98QFRXFG2+8oWxv0KAB3bp1Iykpid69e3Pp0iU2b95M//79ef/99wFo1qwZlStXZsyYMU+Mx8TEBDc3N+X1/fv36du3L2ZmZqxcubLY88nKymLVqlVK/I6Ojvj5+bF7924GDx6slNNoNBgalryjt0ePHgXi/OCDD1Tz7kraPnp37twhISFB+YD19PTk2LFjbN++XUlEHBwcVB/KOp0OPz8/rl+/Tnx8fJEJYqVKlahVq5ZSx+NtOmvWLPz9/VmyZImyvWXLlgQGBjJ//vwCvTudO3cmPDy8wHH27NlDs2bNCtwv+fn5Sk/azZs3Wbp0KWlpaarFFJs3b+b8+fMsXLiQdu3aAQ/vk/LlyzN37lwOHz5Ms2bNlPIlvbaFxfv999+zdetWQkNDGTlypLK9RYsWBc6pJNclKiqK9PR0du7cqZTz8fGhXLlyREZGEhISokrIzM3NWb58uTKP0M7Oju7du3Pw4EGlJ7+o9uzWrRvbtm1j27ZtSk9fWloaX375JW3atMHKyorffvuNqKgo3n77bSZNmqTs6+vrS5s2bYiOjmbhwoXK9pycHN59991CRwrS0tKIiYmhVatWADRv3pysrCwSEhIYOHCgcr6PJsF5eXl4eXmRn5/P2rVreffddzEwMODq1assX76cjh07qhbbPHpdH5WXl8fMmTPZsGEDc+bM4a233iq0XEmU5H5ZvHgxVapUIS4uDhMTEwD8/PwK9FSePHmSzz//nPHjxzNgwABle5MmTWjTpg2rVq1i7NixqjZcuXIlLi4uwMPr8O2337J9+3ZJEIX4h3shhpgBvLy8qFmzJps2beL8+fMkJycXObz81VdfYWVlxWuvvUZubq7yp2HDhtja2vLtt98CKMPF+p4NvXbt2hUYkn0SnU7HyJEjuXTpEitXrqR69erF7uPt7a36cLWxsaFy5coFeonOnj1boIf0SSIjI0lMTCQxMZGVK1cSGBjI9OnT+fTTT5UyJW0fvYYNG6rmeZqamlK7du0CPSsJCQkEBgbi7OxMo0aNcHR05OjRo1y6dKnE8T/qxIkTpKWlERgYqIozLy8PPz8/kpOTVb2EgCrh1cvMzOTQoUOFDi9//fXXODo64ujoyGuvvcbnn3/OpEmT8Pf3V8ocO3aM8uXL07ZtW9W+Xbp0ASgw3F3Sa1tYvPo5fH369CmsSVRKcl0OHDiAt7c3dnZ2qjbUJ5yPX2t/f3/VIhP9nMFHYy+qPR/9GdXbvn072dnZys/qoUOHyM3NpVOnTqp4TE1N8fT0LBAPUOS0AHNzcyU51OvQoQN5eXl89913yrajR4/Sv39/GjduTMOGDXF0dGTx4sWkpaVx+/Zt4OE0Bp1OV6J2z8rKYtiwYWzfvp1PPvnkTyWHUPz9kpmZyenTp3n99deV5FB//o8niF999RUGBga89dZbqva1sbGhQYMGBdrX1tZWSQ71tFrtE3tNhRD/DC9MD6KBgQFdunQhPj6erKwsateuTZMmTQote/v2be7evYuTk1Oh79+5cwd4+NszPPxP8lFGRkalWlwxZcoUvvnmG5YvX07Dhg1LtE9h9ZuYmJCVlVXi4xambt26ODs7K69btGjB9evX+fjjj3nrrbewsrIqcfuUJtZVq1YxZ84cgoKCGDFiBNbW1hgaGrJo0SIuX778VOeiHz4rrEdQLz09nfLlyyuv7ezsCpQ5cOAAubm5hc4La9y4MREREeTl5XHlyhUWLVrEhx9+iIODg3J/paWlYWNjg4GBgWrfypUrY2RkpNxHeqW5to/Hm5qaikajKXBPFqYkx7l9+zZfffWVMnT/uOKutT4hebTOotrTwMCArl27smDBApKTk3F2diYpKQl7e3uaNm0K/O+aFrWa+fHecjMzsyIXhj06VPr4Nv01OXXqFCEhIXh5efHhhx9StWpVjI2N2bdvH8uWLePBgwcAytzbqlWrFnqsR92+fZvffvsNX19f3N3diy1fnOKu4927d8nPz6dy5coFyj2+7fbt2+Tn5+Pr61vosR6f4vBX/T8khHjxvTAJIjzssVm8eDGfffaZavjtcdbW1lSsWJHY2NhC39c/Fkf/n2NKSopq3mBubm6BD/2iREVFsXHjRmbPnk3z5s1LdiJ/M61Wy6FDh7hy5QouLi4lbp/S2LZtG15eXkybNk21vbi5nE9ibW0NPJwn6urqWmiZwj40H7d3716aNm1KhQoVCrxnaWmpJNSurq64urrSqVMnpk2bxtatWzE0NKRixYr85z//IT8/X5Uk3r59m9zcXCXOZ6FSpUrodDpSUlIKTXZLy9raGq1Wy3vvvVfo+09zjCe1Z2BgIIsXL2bTpk3K3MkRI0Yo7aZvq8WLF5fo6QOPJ+WPKmz+nX6b/md7586dGBkZsXz5ckxNTZVy+/btU+2nnzP5+++/88orrzwxpmrVqjF+/HjCwsIYPnw4ixYtUtVtYmJS6OKlx5PxkrKyssLAwEDp7XzU421gbW2NgYEB69atU/U2PhqbEEKUxAuVIFapUoWQkBAuX778xPkx/v7+7Ny5k7y8vCITC0CZF7d9+3ZVb9ru3btL9Iy7jRs3Eh0dTXh4uDLcWBadO3cO+N+Hc0nbpzQMDAwKfPicO3eOkydPFvuBq99P35uj5+HhgZWVFRcvXuTtt99+qriysrL4+uuvVQ89f5LatWszcOBAoqOj2bVrFx06dMDHx4fdu3ezb98+1croLVu2ABRYKPNntGjRguXLl5OQkKCs7v4z/P39+frrr6lZs2ahCV1pFdeeVapUwc/Pj507d6LT6TA0NCQwMFB5v3nz5hgZGfHLL78Uu6K8OBkZGezfv181zKxfvKafg2lgYFBgHu+DBw/Ytm2bqq5mzZqh0WhISEgoUa9g8+bNiY2NZciQIQwZMoSYmBilJ9ve3p7bt29z69YtpUczOzubQ4cOPdV5li9fHicnJ/bt28f777+v/LxkZGQUWO3s7+/PihUruHHjBu3bt3+q4wkhBLxgCSJQ7OIRePhYnO3btzN48GCCg4NxcXHB2NiY33//nePHj9OqVStat25N3bp1eeutt1izZg1GRkb4+vpy4cIF4uLiin3e4YkTJ5g6dSoeHh40a9aswKNAHl1s8WeUdhXzhQsXlJWeaWlp7N27l8OHD9O6dWtleKmk7VMa/v7+xMTEsHjxYjw9Pfnpp5+IiYnB3t6+wGNHHlevXj0APv/8c8zNzTE1NcXe3h5ra2smTZrE+PHjSU9Pp02bNlSuXJnU1FTOnTtHampqgR7Lx33zzTc8ePCA119/vcTn8s477/DZZ58RHR1Nu3bt6Ny5M+vWrWPcuHFcv36d+vXr88MPP7B8+XJatmxZ5HDe02jSpAmdOnVi6dKl3L59G39/f0xMTDh79ixmZmal/sag8PBwjhw5QlBQEMHBwbz66qtkZ2dz7do1Dh48yLRp00o0rKpXkvbs1q0bBw4cYOPGjTRv3lz1C4K9vT3h4eEsXLiQq1ev0qJFC6ysrLh16xbJycmYmZk9cUrBoypWrMjUqVP57bffqF27Nl9//TWff/45vXr1UnonW7ZsyapVqxg9ejQ9e/YkLS1NtdDj0bj0id6DBw/o0KEDlpaWXLx4kTt37hQaU5MmTVi9ejUDBw7knXfeUZ6h2a5dOxYvXszIkSMZOHAgWVlZxMfHF/tz8CTh4eEMGTKEkJAQ+vXrh06nIy4uDnNzc9LT05VyjRs3pmfPnkyYMIHTp0/j6emJmZkZKSkp/PDDD9SvX1+1AE0IIYrywiWIJaHRaFi6dClr165l69atrFixAo1GQ9WqVfH09KR+/fpK2ZkzZ2JjY8PmzZuJj4+nYcOGREVFMWrUqCce46effiI3N5d///vf9OzZs8D758+f/9PnoR+eLcl8NL2IiAjl35aWltjb2xMREaH6UChN+5RUaGgo9+/fJzExkdjYWBwcHJg6dSr79u0rdOHBo2rUqMGECRNYu3Ytffv2RafTMXv2bLp06UKnTp2oVq0asbGxTJkyhYyMDCpVqkTDhg1VPVNF2bNnD02aNCnRULSeubk5w4YNY/r06cpqzrVr17JgwQJiY2O5c+cOVapU4Z133lE9JuVZmTNnDo0aNWLTpk0kJSVRrlw5HBwcnuqr9ezs7EhMTCQmJoa4uDhu3LiBubk51atXV771pjRK0p7+/v7Y2Nhw69atQheSDRkyhLp167J27Vp27txJdnY2tra2ODk50atXrxLHYmtry+TJk4mMjOT//u//qFChAqGhoQwfPlwp4+Pjw6xZs1i5ciWhoaFUqVKFHj16UKlSJSZOnKiqb8SIEdSqVYtPP/2UMWPGoNFoqF279hOTcmdnZz799FMGDBhAv379iI2NpUaNGixZsoQFCxYQHh6Ora0tAwYMIDU1lejo6BKf36NatGhBVFQUixYt4r333sPW1pZevXpx8+bNAr2h06dPx9XVlQ0bNpCQkEBeXh52dnZ4eHgUWJAihBBFMcgv7Gmxokz4+uuvGTJkCFu3bi3wMFxRvOzsbHx9fRkxYsQz/67uf6Ky1J7BwcHcuXNH9fD8f5qcnBw6d+5MlSpV+OSTT553OAqdTsfJkyfROpii0RQ9h/R5yM7J4+LlbNzc3Ar9SsjnKTs7m+Tk5DIXW1mNCyS2p6X/GS0utpeyB/FlcezYMd58801JDp+SiYkJ33///fMO46Uh7fl8TZgwgWbNmmFra8utW7dISEjg0qVLBXpChRDiWZAEsQwbN27c8w5BCFFGZGRkEBkZSWpqKsbGxjRq1IgVK1Y80zmwQgihJwmiEOKFU9JFWy+TRYsWPe8QhBD/IC/MN6kIIYQQQoi/hySIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKfJOKEEKIZyY/Px8AXV7+c46kIJ0u779/655zJAXl5uYCZS+2shoXSGxPSx+T/me1KAb5xZUQQgghSig7O5vk5OTnHYYQohjOzs6YmJgU+b4kiEIIIZ6ZvLw8cnNzMTQ0xMDA4HmHo6LT6cjOzsbExASNRvO8w1Epq7GV1bhAYnta+fn55OXlYWRkhKFh0TMNJUEUQgghhBAqskhFCCGEEEKoSIIohBBCCCFUJEEUQgghhBAqkiAKIYQQQggVSRCFEEIIIYSKJIhCCCGEEEJFEkQhhBBCCKEiCaIQQgghhFCRBFEIIYQQQqhIgiiEEEIIIVQkQRRCCCGEECqSIAohhBBCCBVJEIUQQgghhIokiEIIIYQQQkUSRCGEEEIIoSIJohBCCCGEUJEEUQghhBBCqEiCKIQQQgghVCRBFEIIIYQQKpIgCiGEEEIIFUkQhRBCCCGEiiSIQgghhBBCRRJEIYQQQgihIgmiEEIIIYRQkQRRCCGEEEKoSIIohBBCCCFUJEEUQgghhBAq/w+j0mM+s7zn0AAAAABJRU5ErkJggg==",
      "application/papermill.record/text/plain": "<Figure size 300x1150 with 138 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_survey_area_spearmans"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_data = fd[(fd.code.isin(fdx.most_common.index))&(fd.water_name_slug.isin(lakes_of_interest))].copy()\n",
    "land_use_columns = featuredata.default_land_use_columns\n",
    "code_description_map = fdx.dMap\n",
    "\n",
    "# chart the results of test for association\n",
    "fig, axs = plt.subplots(len(fdx.most_common.index),len(land_use_columns), figsize=(len(land_use_columns)*.5,len(fdx.most_common.index)*.5), sharey=\"row\")\n",
    "\n",
    "# the test is conducted on the survey results for each code\n",
    "for i,code in enumerate(fdx.most_common.index):\n",
    "    # slice the data\n",
    "    data = corr_data[corr_data.code == code]\n",
    "    \n",
    "    # run the test on for each land use feature\n",
    "    for j, n in enumerate(land_use_columns):       \n",
    "        # assign ax and set some parameters\n",
    "        ax=axs[i, j]\n",
    "        ax.grid(False)\n",
    "        ax.tick_params(axis=\"both\", which=\"both\",bottom=False,top=False,labelbottom=False, labelleft=False, left=False)\n",
    "        \n",
    "        # check the axis and set titles and labels       \n",
    "        if i == 0:\n",
    "            ax.set_title(f\"{featuredata.luse_de[n]}\", rotation=90, ha=\"left\", fontsize=12)\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        if j == 0:\n",
    "            ax.set_ylabel(f\"{code_description_map[code]}\", rotation=0, ha=\"right\", va=\"center\", labelpad=10, fontsize=12)\n",
    "            ax.set_xlabel(\" \")\n",
    "        else:\n",
    "            ax.set_xlabel(\" \")\n",
    "            ax.set_ylabel(\" \")\n",
    "        # run test\n",
    "        ax = featuredata.make_plot_with_spearmans(data, ax, n, unit_label=unit_label)\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "# figure caption\n",
    "caption_spearmans = [\n",
    "    \"Ausgewertete Korrelationen der am häufigsten gefundenen Objekte in Bezug auf das Landnutzungsprofil \",\n",
    "    f'im Erhebungsgebiet Rhône. Für alle gültigen Erhebungen an Seen n={str(admin_summary[\"loc_date\"])}. Legende: wenn p > 0,05 = weiss,  ',\n",
    "    \"wenn p < 0,05 und Rho > 0 = rot, wenn p < 0,05 und Rho < 0 = gelb.\",  \n",
    "]\n",
    "\n",
    "spearmans = ''.join(caption_spearmans)\n",
    "glue(f'{this_feature[\"slug\"]}_spearmans_caption', spearmans, display=False)\n",
    "\n",
    "figure_name = f'{this_feature[\"slug\"]}_survey_area_spearmans'\n",
    "sample_summaries_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":sample_summaries_file_name})\n",
    "\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "glue('rhone_survey_area_spearmans', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} rhone_survey_area_spearmans\n",
    "---\n",
    "name: 'rhone_survey_area_spearmans'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_spearmans>` {glue:text}`rhone_spearmans_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "## Verwendungszweck der gefundenen Objekte\n",
    "\n",
    "Der Verwendungszweck basiert auf der Verwendung des Objekts, bevor es weggeworfen wurde, oder auf der Artikelbeschreibung, wenn die ursprüngliche Verwendung unbestimmt ist. Identifizierte Objekte werden einer der 260 vordefinierten Kategorien zugeordnet. Die Kategorien werden je nach Verwendung oder Artikelbeschreibung gruppiert.\n",
    "\n",
    "* Abwasser: Objekte, die aus Kläranlagen freigesetzt werden, sprich Objekte, die wahrscheinlich über die Toilette entsorgt werden\n",
    "* Mikroplastik (< 5 mm): fragmentierte Kunststoffe und Kunststoffharze aus der Vorproduktion\n",
    "* Infrastruktur: Artikel im Zusammenhang mit dem Bau und der Instandhaltung von Gebäuden, Strassen und der Wasser-/Stromversorgung\n",
    "* Essen und Trinken: alle Materialien, die mit dem Konsum von Essen und Trinken in Zusammenhang stehen\n",
    "* Landwirtschaft: Materialien z. B. für Mulch und Reihenabdeckungen, Gewächshäuser, Bodenbegasung, Ballenverpackungen. Einschliesslich Hartkunststoffe für landwirtschaftliche Zäune, Blumentöpfe usw.\n",
    "* Tabakwaren: hauptsächlich Zigarettenfilter, einschliesslich aller mit dem Rauchen verbundenen Materialien\n",
    "* Freizeit und Erholung: Objekte, die mit Sport und Freizeit zu tun haben, z. B. Angeln, Jagen, Wandern usw.\n",
    "* Verpackungen ausser Lebensmittel und Tabak: Verpackungsmaterial, das nicht lebensmittel- oder tabakbezogen ist\n",
    "* Plastikfragmente: Plastikteile unbestimmter Herkunft oder Verwendung\n",
    "* Persönliche Gegenstände: Accessoires, Hygieneartikel und Kleidung\n",
    "\n",
    "Im Anhang befindet sich die vollständige Liste der identifizierten Objekte, einschliesslich Beschreibungen und Gruppenklassifizierung. Das Kapitel [16 Codegruppen](codegroups) beschreibt jede Codegruppe im Detail und bietet eine umfassende Liste aller Objekte in einer Gruppe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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     "source_hidden": true
    },
    "tags": [
     "remove-input"
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   },
   "outputs": [],
   "source": [
    "o_w, o_h = convertPixelToCm(sample_summaries_file_name)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":sample_summaries_file_name,\n",
    "    \"caption\":None,\n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 10,\n",
    "    \"caption_height\":0,\n",
    "    \"hAlign\": \"TOP\",\n",
    "}\n",
    "\n",
    "side_by_side_style = TableStyle([\n",
    "    ('BOTTOMPADDING', (0, 0), (-1, -1), 0),\n",
    "    ('TOPPADDING', (0, 0), (0, 0), 0),\n",
    "    ('TOPPADDING', (0, 1), (0, 1), 0),\n",
    "    ('VALIGN', (0, 0), (-1, -1), \"BOTTM\"),\n",
    "    ('VALIGN', (0, 0), (0, 0), \"BOTTOM\"),\n",
    "    ('ALIGN', (0,0), (-1,-1), 'LEFT'),\n",
    "    ('LEFTPADDING', (0,0), (0, 0), 0),\n",
    "    ('LEFTPADDING', (0,1), (0, 1), 8),\n",
    "])\n",
    "\n",
    "f5 = figureAndCaptionTable(**figure_kwargs)\n",
    "caption_spearmans = Paragraph(spearmans, caption_style)\n",
    "figure_text = figure_text = [\n",
    "    [f5, [featuredata.large_space,  caption_spearmans, featuredata.large_space]],\n",
    "   \n",
    "]\n",
    "side_by_side = Table(figure_text, style=side_by_side_style, colWidths=[10*cm, 4.4*cm])\n",
    "\n",
    "new_components = [\n",
    "    featuredata.large_space,\n",
    "    side_by_side\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "\n",
    "# make pdf out put\n",
    "cone_group_subtitle = Paragraph(\"Verwendungszweck der gefundenen Objekte\", style=section_title)\n",
    "\n",
    "paragraph_one = [\n",
    "    \"Der Verwendungszweck basiert auf der Verwendung des Objekts, bevor es weggeworfen wurde, \",\n",
    "    \"oder auf der Artikelbeschreibung, wenn die ursprüngliche Verwendung unbestimmt ist. \",\n",
    "    \"Identifizierte Objekte werden einer der 260 vordefinierten Kategorien zugeordnet. \",\n",
    "    \"Die Kategorien werden je nach Verwendung oder Artikelbeschreibung gruppiert.\"\n",
    "]\n",
    "\n",
    "group_names_list = [\n",
    "    \"Abwasser: Objekte, die aus Kläranlagen freigesetzt werden, sprich Objekte, die wahrscheinlich über die Toilette entsorgt werden\",\n",
    "    \"Mikroplastik (< 5 mm): fragmentierte Kunststoffe und Kunststoffharze aus der Vorproduktion\",\n",
    "    \"Infrastruktur: Artikel im Zusammenhang mit dem Bau und der Instandhaltung von Gebäuden, Strassen und der Wasser-/Stromversorgung\",\n",
    "    \"Essen und Trinken: alle Materialien, die mit dem Konsum von Essen und Trinken in Zusammenhang stehen\",\n",
    "    \"Landwirtschaft: Materialien z. B. für Mulch und Reihenabdeckungen, Gewächshäuser, Bodenbegasung, Ballenverpackungen. Einschliesslich Hartkunststoffe für landwirtschaftliche Zäune, Blumentöpfe usw.\",\n",
    "    \"Tabakwaren: hauptsächlich Zigarettenfilter, einschliesslich aller mit dem Rauchen verbundenen Materialien\",\n",
    "    \"Freizeit und Erholung: Objekte, die mit Sport und Freizeit zu tun haben, z. B. Angeln, Jagen, Wandern usw.\",\n",
    "    \"Verpackungen ausser Lebensmittel und Tabak: Verpackungsmaterial, das nicht lebensmittel- oder tabakbezogen ist\",\n",
    "    \"Plastikfragmente: Plastikteile unbestimmter Herkunft oder Verwendung\",\n",
    "    \"Persönliche Gegenstände: Accessoires, Hygieneartikel und Kleidung\"\n",
    "]\n",
    "\n",
    "paragraph_three = [\n",
    "    \"Im Anhang befindet sich die vollständige Liste der identifizierten Objekte, \",\n",
    "    \"einschliesslich Beschreibungen und Gruppenklassifizierung. \",\n",
    "    \"Das Kapitel [16 Codegruppen](codegroups) beschreibt jede Codegruppe im Detail und bietet eine \",\n",
    "    \"umfassende Liste aller Objekte in einer Gruppe.\"\n",
    "]\n",
    "\n",
    "\n",
    "# make paragraphs\n",
    "code_group_para_one = ''.join(paragraph_one)\n",
    "code_group_para_three = ''.join(paragraph_three)\n",
    "\n",
    "cgroup_pone = Paragraph(code_group_para_one, featuredata.p_style)\n",
    "\n",
    "cgroup_pthree = Paragraph(code_group_para_three, featuredata.p_style)\n",
    "a_list_groups = makeAList(group_names_list)\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([        \n",
    "        cone_group_subtitle,\n",
    "        small_space,\n",
    "        cgroup_pone,\n",
    "        smallest_space,\n",
    "        a_list_groups,\n",
    "        smallest_space,\n",
    "        cgroup_pthree,\n",
    "    ])\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Verwendungszweck oder Beschreibung der identifizierten Objekte in % der Gesamtzahl nach Gemeinden im Erhebungsgebiet Erhebungsgebiet Rhône. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_codegroup_percent_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_3b45c tr {\n  font-size: 12px;\n}\n#T_3b45c th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_3b45c td {\n  padding: 6px;\n}\n#T_3b45c_row0_col0, #T_3b45c_row0_col2, #T_3b45c_row4_col3, #T_3b45c_row6_col1 {\n  background-color: #fee596;\n  color: #000000;\n}\n#T_3b45c_row0_col1, #T_3b45c_row1_col1 {\n  background-color: #792b05;\n  color: #f1f1f1;\n}\n#T_3b45c_row0_col3, #T_3b45c_row4_col0, #T_3b45c_row4_col2, #T_3b45c_row8_col1, #T_3b45c_row9_col3 {\n  background-color: #feeda6;\n  color: #000000;\n}\n#T_3b45c_row1_col0, #T_3b45c_row1_col2, #T_3b45c_row3_col3 {\n  background-color: #b34003;\n  color: #f1f1f1;\n}\n#T_3b45c_row1_col3 {\n  background-color: #a03704;\n  color: #f1f1f1;\n}\n#T_3b45c_row2_col0, #T_3b45c_row2_col2, #T_3b45c_row2_col3, #T_3b45c_row9_col2 {\n  background-color: #fff4b6;\n  color: #000000;\n}\n#T_3b45c_row2_col1, #T_3b45c_row6_col0, #T_3b45c_row6_col2, #T_3b45c_row10_col1 {\n  background-color: #fffcd6;\n  color: #000000;\n}\n#T_3b45c_row3_col0, #T_3b45c_row3_col2 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_3b45c_row3_col1, #T_3b45c_row8_col2 {\n  background-color: #fa9125;\n  color: #f1f1f1;\n}\n#T_3b45c_row4_col1, #T_3b45c_row7_col3 {\n  background-color: #ed7215;\n  color: #f1f1f1;\n}\n#T_3b45c_row5_col0 {\n  background-color: #fea231;\n  color: #000000;\n}\n#T_3b45c_row5_col1, #T_3b45c_row6_col3, #T_3b45c_row9_col0 {\n  background-color: #fff9c6;\n  color: #000000;\n}\n#T_3b45c_row5_col2 {\n  background-color: #feb23f;\n  color: #000000;\n}\n#T_3b45c_row5_col3 {\n  background-color: #fece65;\n  color: #000000;\n}\n#T_3b45c_row7_col0, #T_3b45c_row7_col2 {\n  background-color: #e1640e;\n  color: #f1f1f1;\n}\n#T_3b45c_row7_col1, #T_3b45c_row10_col0, #T_3b45c_row10_col2, #T_3b45c_row10_col3 {\n  background-color: #ffffe5;\n  color: #000000;\n}\n#T_3b45c_row8_col0, #T_3b45c_row9_col1 {\n  background-color: #f4811d;\n  color: #f1f1f1;\n}\n#T_3b45c_row8_col3 {\n  background-color: #c64902;\n  color: #f1f1f1;\n}\n#T_3b45c_level0_col0, #T_3b45c_level0_col1, #T_3b45c_level0_col2, #T_3b45c_level0_col3 {\n  writing-mode: vertical-lr;\n  transform: rotate(-180deg);\n  padding: 10px;\n  margins: 0;\n  vertical-align: baseline;\n}\n</style>\n<table id=\"T_3b45c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_3b45c_level0_col0\" class=\"col_heading level0 col0\" >Lac Léman</th>\n      <th id=\"T_3b45c_level0_col1\" class=\"col_heading level0 col1\" >Rhône</th>\n      <th id=\"T_3b45c_level0_col2\" class=\"col_heading level0 col2\" >Erhebungsgebiet Rhône</th>\n      <th id=\"T_3b45c_level0_col3\" class=\"col_heading level0 col3\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_3b45c_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_3b45c_row0_col0\" class=\"data row0 col0\" >6%</td>\n      <td id=\"T_3b45c_row0_col1\" class=\"data row0 col1\" >21%</td>\n      <td id=\"T_3b45c_row0_col2\" class=\"data row0 col2\" >6%</td>\n      <td id=\"T_3b45c_row0_col3\" class=\"data row0 col3\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_3b45c_row1_col0\" class=\"data row1 col0\" >18%</td>\n      <td id=\"T_3b45c_row1_col1\" class=\"data row1 col1\" >21%</td>\n      <td id=\"T_3b45c_row1_col2\" class=\"data row1 col2\" >18%</td>\n      <td id=\"T_3b45c_row1_col3\" class=\"data row1 col3\" >19%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_3b45c_row2_col0\" class=\"data row2 col0\" >4%</td>\n      <td id=\"T_3b45c_row2_col1\" class=\"data row2 col1\" >2%</td>\n      <td id=\"T_3b45c_row2_col2\" class=\"data row2 col2\" >4%</td>\n      <td id=\"T_3b45c_row2_col3\" class=\"data row2 col3\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_3b45c_row3_col0\" class=\"data row3 col0\" >22%</td>\n      <td id=\"T_3b45c_row3_col1\" class=\"data row3 col1\" >12%</td>\n      <td id=\"T_3b45c_row3_col2\" class=\"data row3 col2\" >22%</td>\n      <td id=\"T_3b45c_row3_col3\" class=\"data row3 col3\" >18%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_3b45c_row4_col0\" class=\"data row4 col0\" >5%</td>\n      <td id=\"T_3b45c_row4_col1\" class=\"data row4 col1\" >14%</td>\n      <td id=\"T_3b45c_row4_col2\" class=\"data row4 col2\" >5%</td>\n      <td id=\"T_3b45c_row4_col3\" class=\"data row4 col3\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_3b45c_row5_col0\" class=\"data row5 col0\" >11%</td>\n      <td id=\"T_3b45c_row5_col1\" class=\"data row5 col1\" >3%</td>\n      <td id=\"T_3b45c_row5_col2\" class=\"data row5 col2\" >10%</td>\n      <td id=\"T_3b45c_row5_col3\" class=\"data row5 col3\" >8%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_3b45c_row6_col0\" class=\"data row6 col0\" >2%</td>\n      <td id=\"T_3b45c_row6_col1\" class=\"data row6 col1\" >6%</td>\n      <td id=\"T_3b45c_row6_col2\" class=\"data row6 col2\" >2%</td>\n      <td id=\"T_3b45c_row6_col3\" class=\"data row6 col3\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_3b45c_row7_col0\" class=\"data row7 col0\" >15%</td>\n      <td id=\"T_3b45c_row7_col1\" class=\"data row7 col1\" >1%</td>\n      <td id=\"T_3b45c_row7_col2\" class=\"data row7 col2\" >15%</td>\n      <td id=\"T_3b45c_row7_col3\" class=\"data row7 col3\" >14%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_3b45c_row8_col0\" class=\"data row8 col0\" >13%</td>\n      <td id=\"T_3b45c_row8_col1\" class=\"data row8 col1\" >5%</td>\n      <td id=\"T_3b45c_row8_col2\" class=\"data row8 col2\" >12%</td>\n      <td id=\"T_3b45c_row8_col3\" class=\"data row8 col3\" >17%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_3b45c_row9_col0\" class=\"data row9 col0\" >3%</td>\n      <td id=\"T_3b45c_row9_col1\" class=\"data row9 col1\" >13%</td>\n      <td id=\"T_3b45c_row9_col2\" class=\"data row9 col2\" >4%</td>\n      <td id=\"T_3b45c_row9_col3\" class=\"data row9 col3\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_3b45c_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_3b45c_row10_col0\" class=\"data row10 col0\" >1%</td>\n      <td id=\"T_3b45c_row10_col1\" class=\"data row10 col1\" >2%</td>\n      <td id=\"T_3b45c_row10_col2\" class=\"data row10 col2\" >1%</td>\n      <td id=\"T_3b45c_row10_col3\" class=\"data row10 col3\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff115e1ac10>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_codegroup_percent"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the results are a callable for the components\n",
    "components = fdx.componentCodeGroupResults()\n",
    "\n",
    "# pivot that and reomve any hierarchal column index\n",
    "pt_comp = components[[this_level, \"groupname\", '% of total' ]].pivot(columns=this_level, index=\"groupname\")\n",
    "pt_comp.columns = pt_comp.columns.get_level_values(1)\n",
    "pt_comp.rename(columns = proper_column_names, inplace=True)\n",
    "\n",
    "# the aggregated codegroup results from the feature\n",
    "pt_feature = fdx.codegroup_summary[\"% of total\"]\n",
    "pt_comp[this_feature[\"name\"]] = pt_feature\n",
    "\n",
    "# the aggregated totals for the period\n",
    "pt_period = period_data.parentGroupTotals(parent=False, percent=True)\n",
    "pt_comp[top] = pt_period\n",
    "\n",
    "# caption\n",
    "code_group_percent_caption = [\n",
    "    'Verwendungszweck oder Beschreibung der identifizierten Objekte in % der ',\n",
    "    f'Gesamtzahl nach Gemeinden im Erhebungsgebiet {this_feature[\"name\"]}. '\n",
    "    'Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, ',\n",
    "    'werden weiterhin nach ihrer Grösse klassifiziert.'\n",
    "]\n",
    "\n",
    "code_group_percent_caption = ''.join(code_group_percent_caption)\n",
    "\n",
    "# format for data frame\n",
    "pt_comp.index.name = None\n",
    "pt_comp.columns.name = None\n",
    "aformatter = {x: '{:.0%}' for x in pt_comp.columns}\n",
    "ptd = pt_comp.style.format(aformatter).set_table_styles(heat_map_css_styles).background_gradient(axis=None, vmin=pt_comp.min().min(), vmax=pt_comp.max().max(), cmap=\"YlOrBr\")\n",
    "ptd = ptd.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "# the caption prefix is used in the case where the table needs to be split horzontally\n",
    "caption_prefix =  'Verwendungszweck oder Beschreibung der identifizierten Objekte in % der Gesamtzahl nach Gemeinden: '\n",
    "\n",
    "col_widths = [4.5*cm, *[1*cm]*(len(pt_comp.columns))]\n",
    "cgpercent_tables = featuredata.splitTableWidth(pt_comp.mul(100).astype(int), gradient=True, caption_prefix=caption_prefix, caption= code_group_percent_caption,\n",
    "                    this_feature=this_feature[\"name\"], vertical_header=True, colWidths=col_widths, rowends=-2)\n",
    "\n",
    "if isinstance(tables, (list, np.ndarray)):\n",
    "    grouped_pdf_components = [*tables]\n",
    "else:\n",
    "    grouped_pdf_components = [tables]\n",
    "\n",
    "\n",
    "glue(\"rhone_codegroup_percent_caption\", code_group_percent_caption, display=False)\n",
    "glue(\"rhone_codegroup_percent\", ptd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_codegroup_percent\n",
    "---\n",
    "name: 'rhone_survey_area_codegroup_percent'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_codegroup_percent>` {glue:text}`rhone_codegroup_percent_caption`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Verwendungszweck der gefundenen Objekte Median p/100 m am Erhebungsgebiet Rhône. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'"
     },
     "metadata": {
      "scrapbook": {
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       "name": "rhone_codegroup_pcsm_caption"
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     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_50fbf tr {\n  font-size: 12px;\n}\n#T_50fbf th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_50fbf td {\n  padding: 6px;\n}\n#T_50fbf_row0_col0 {\n  background-color: #fee79a;\n  color: #000000;\n}\n#T_50fbf_row0_col1 {\n  background-color: #fffaca;\n  color: #000000;\n}\n#T_50fbf_row0_col2, #T_50fbf_row2_col0 {\n  background-color: #fee89d;\n  color: #000000;\n}\n#T_50fbf_row0_col3 {\n  background-color: #fffddb;\n  color: #000000;\n}\n#T_50fbf_row1_col0 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_50fbf_row1_col1 {\n  background-color: #fff4b6;\n  color: #000000;\n}\n#T_50fbf_row1_col2 {\n  background-color: #9b3504;\n  color: #f1f1f1;\n}\n#T_50fbf_row1_col3 {\n  background-color: #feb340;\n  color: #000000;\n}\n#T_50fbf_row2_col1 {\n  background-color: #fffcd7;\n  color: #000000;\n}\n#T_50fbf_row2_col2 {\n  background-color: #feeda6;\n  color: #000000;\n}\n#T_50fbf_row2_col3, #T_50fbf_row6_col3 {\n  background-color: #fffbcf;\n  color: #000000;\n}\n#T_50fbf_row3_col0 {\n  background-color: #b03f03;\n  color: #f1f1f1;\n}\n#T_50fbf_row3_col1 {\n  background-color: #fff5b8;\n  color: #000000;\n}\n#T_50fbf_row3_col2 {\n  background-color: #e96d13;\n  color: #f1f1f1;\n}\n#T_50fbf_row3_col3 {\n  background-color: #fee799;\n  color: #000000;\n}\n#T_50fbf_row4_col0, #T_50fbf_row4_col2, #T_50fbf_row9_col1 {\n  background-color: #fff2b1;\n  color: #000000;\n}\n#T_50fbf_row4_col1 {\n  background-color: #fee697;\n  color: #000000;\n}\n#T_50fbf_row4_col3 {\n  background-color: #fffacb;\n  color: #000000;\n}\n#T_50fbf_row5_col0 {\n  background-color: #ffeea9;\n  color: #000000;\n}\n#T_50fbf_row5_col1, #T_50fbf_row8_col1, #T_50fbf_row10_col3 {\n  background-color: #ffffe5;\n  color: #000000;\n}\n#T_50fbf_row5_col2, #T_50fbf_row6_col1 {\n  background-color: #fff6ba;\n  color: #000000;\n}\n#T_50fbf_row5_col3 {\n  background-color: #fffee2;\n  color: #000000;\n}\n#T_50fbf_row6_col0, #T_50fbf_row6_col2 {\n  background-color: #fff8c0;\n  color: #000000;\n}\n#T_50fbf_row7_col0 {\n  background-color: #d85a09;\n  color: #f1f1f1;\n}\n#T_50fbf_row7_col1, #T_50fbf_row10_col1 {\n  background-color: #ffffe4;\n  color: #000000;\n}\n#T_50fbf_row7_col2 {\n  background-color: #f78921;\n  color: #f1f1f1;\n}\n#T_50fbf_row7_col3 {\n  background-color: #feeaa1;\n  color: #000000;\n}\n#T_50fbf_row8_col0 {\n  background-color: #f07919;\n  color: #f1f1f1;\n}\n#T_50fbf_row8_col2 {\n  background-color: #f4811d;\n  color: #f1f1f1;\n}\n#T_50fbf_row8_col3 {\n  background-color: #feda7e;\n  color: #000000;\n}\n#T_50fbf_row9_col0, #T_50fbf_row9_col2 {\n  background-color: #fff3b4;\n  color: #000000;\n}\n#T_50fbf_row9_col3 {\n  background-color: #fff8c4;\n  color: #000000;\n}\n#T_50fbf_row10_col0, #T_50fbf_row10_col2 {\n  background-color: #fffedf;\n  color: #000000;\n}\n#T_50fbf_level0_col0, #T_50fbf_level0_col1, #T_50fbf_level0_col2, #T_50fbf_level0_col3 {\n  writing-mode: vertical-lr;\n  transform: rotate(-180deg);\n  padding: 10px;\n  margins: 0;\n  vertical-align: baseline;\n}\n</style>\n<table id=\"T_50fbf\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_50fbf_level0_col0\" class=\"col_heading level0 col0\" >Lac Léman</th>\n      <th id=\"T_50fbf_level0_col1\" class=\"col_heading level0 col1\" >Rhône</th>\n      <th id=\"T_50fbf_level0_col2\" class=\"col_heading level0 col2\" >Erhebungsgebiet Rhône</th>\n      <th id=\"T_50fbf_level0_col3\" class=\"col_heading level0 col3\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_50fbf_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_50fbf_row0_col0\" class=\"data row0 col0\" >19,5</td>\n      <td id=\"T_50fbf_row0_col1\" class=\"data row0 col1\" >7,5</td>\n      <td id=\"T_50fbf_row0_col2\" class=\"data row0 col2\" >19,0</td>\n      <td id=\"T_50fbf_row0_col3\" class=\"data row0 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_50fbf_row1_col0\" class=\"data row1 col0\" >87,5</td>\n      <td id=\"T_50fbf_row1_col1\" class=\"data row1 col1\" >12,5</td>\n      <td id=\"T_50fbf_row1_col2\" class=\"data row1 col2\" >76,0</td>\n      <td id=\"T_50fbf_row1_col3\" class=\"data row1 col3\" >37,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_50fbf_row2_col0\" class=\"data row2 col0\" >19,0</td>\n      <td id=\"T_50fbf_row2_col1\" class=\"data row2 col1\" >4,0</td>\n      <td id=\"T_50fbf_row2_col2\" class=\"data row2 col2\" >16,5</td>\n      <td id=\"T_50fbf_row2_col3\" class=\"data row2 col3\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_50fbf_row3_col0\" class=\"data row3 col0\" >71,5</td>\n      <td id=\"T_50fbf_row3_col1\" class=\"data row3 col1\" >12,0</td>\n      <td id=\"T_50fbf_row3_col2\" class=\"data row3 col2\" >55,5</td>\n      <td id=\"T_50fbf_row3_col3\" class=\"data row3 col3\" >20,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_50fbf_row4_col0\" class=\"data row4 col0\" >14,0</td>\n      <td id=\"T_50fbf_row4_col1\" class=\"data row4 col1\" >20,5</td>\n      <td id=\"T_50fbf_row4_col2\" class=\"data row4 col2\" >14,0</td>\n      <td id=\"T_50fbf_row4_col3\" class=\"data row4 col3\" >7,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_50fbf_row5_col0\" class=\"data row5 col0\" >16,0</td>\n      <td id=\"T_50fbf_row5_col1\" class=\"data row5 col1\" >0,0</td>\n      <td id=\"T_50fbf_row5_col2\" class=\"data row5 col2\" >11,5</td>\n      <td id=\"T_50fbf_row5_col3\" class=\"data row5 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_50fbf_row6_col0\" class=\"data row6 col0\" >10,0</td>\n      <td id=\"T_50fbf_row6_col1\" class=\"data row6 col1\" >11,5</td>\n      <td id=\"T_50fbf_row6_col2\" class=\"data row6 col2\" >10,0</td>\n      <td id=\"T_50fbf_row6_col3\" class=\"data row6 col3\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_50fbf_row7_col0\" class=\"data row7 col0\" >61,5</td>\n      <td id=\"T_50fbf_row7_col1\" class=\"data row7 col1\" >0,5</td>\n      <td id=\"T_50fbf_row7_col2\" class=\"data row7 col2\" >48,0</td>\n      <td id=\"T_50fbf_row7_col3\" class=\"data row7 col3\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_50fbf_row8_col0\" class=\"data row8 col0\" >52,0</td>\n      <td id=\"T_50fbf_row8_col1\" class=\"data row8 col1\" >0,0</td>\n      <td id=\"T_50fbf_row8_col2\" class=\"data row8 col2\" >50,0</td>\n      <td id=\"T_50fbf_row8_col3\" class=\"data row8 col3\" >25,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_50fbf_row9_col0\" class=\"data row9 col0\" >13,0</td>\n      <td id=\"T_50fbf_row9_col1\" class=\"data row9 col1\" >14,0</td>\n      <td id=\"T_50fbf_row9_col2\" class=\"data row9 col2\" >13,0</td>\n      <td id=\"T_50fbf_row9_col3\" class=\"data row9 col3\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_50fbf_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_50fbf_row10_col0\" class=\"data row10 col0\" >2,0</td>\n      <td id=\"T_50fbf_row10_col1\" class=\"data row10 col1\" >0,5</td>\n      <td id=\"T_50fbf_row10_col2\" class=\"data row10 col2\" >2,0</td>\n      <td id=\"T_50fbf_row10_col3\" class=\"data row10 col3\" >0,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff115d91f10>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_codegroup_pcsm"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pivot that\n",
    "grouppcs_comp = components[[this_level, \"groupname\", unit_label ]].pivot(columns=this_level, index=\"groupname\")\n",
    "\n",
    "# quash the hierarchal column index\n",
    "grouppcs_comp.columns = grouppcs_comp.columns.get_level_values(1)\n",
    "grouppcs_comp.rename(columns = proper_column_names, inplace=True)\n",
    "\n",
    "# the aggregated codegroup results from the feature\n",
    "pt_feature = fdx.codegroup_summary[unit_label]\n",
    "grouppcs_comp[this_feature[\"name\"]] = pt_feature\n",
    "\n",
    "# the aggregated totals for the period\n",
    "pt_period = period_data.parentGroupTotals(parent=False, percent=False)\n",
    "grouppcs_comp[top] = pt_period\n",
    "\n",
    "# color gradient of restults\n",
    "code_group_pcsm_gradient = featuredata.colorGradientTable(grouppcs_comp)\n",
    "grouppcs_comp.index.name = None\n",
    "grouppcs_comp.columns.name = None\n",
    "\n",
    "# pdf and display output\n",
    "code_group_pcsm_caption = [\n",
    "    f'Verwendungszweck der gefundenen Objekte Median {unit_label} am ',\n",
    "    f'{this_feature[\"name\"]}. Fragmentierte Objekte, die nicht eindeutig ',\n",
    "    'identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'\n",
    "]\n",
    "code_group_pcsm_caption = ''.join(code_group_pcsm_caption)\n",
    "\n",
    "caption_prefix =  f'Verwendungszweck der gefundenen Objekte Median {unit_label} am '\n",
    "col_widths = [4.5*cm, *[1*cm]*(len(grouppcs_comp.columns))]\n",
    "cgpcsm_tables = featuredata.splitTableWidth(grouppcs_comp, gradient=True, caption_prefix=caption_prefix, caption=code_group_pcsm_caption,\n",
    "                    this_feature=this_feature[\"name\"], vertical_header=True, colWidths=col_widths)\n",
    "\n",
    "if isinstance(cgpcsm_tables, (list, np.ndarray)):\n",
    "    new_components = [\n",
    "        featuredata.large_space,\n",
    "        *cgpercent_tables,    \n",
    "        featuredata.larger_space,\n",
    "        *cgpcsm_tables,\n",
    "        featuredata.larger_space\n",
    "    ]\n",
    "else:\n",
    "    new_components = [\n",
    "    featuredata.large_space,\n",
    "    cgpercent_tables,    \n",
    "    featuredata.larger_space,\n",
    "    cgpcsm_tables,\n",
    "    featuredata.larger_space\n",
    "    ]\n",
    "    \n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "aformatter = {x: featuredata.replaceDecimal for x in grouppcs_comp.columns}\n",
    "cgp = grouppcs_comp.style.format(aformatter).set_table_styles(heat_map_css_styles).background_gradient(axis=None, vmin=grouppcs_comp.min().min(), vmax=grouppcs_comp.max().max(), cmap=\"YlOrBr\")\n",
    "cgp= cgp.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "\n",
    "glue(\"rhone_codegroup_pcsm_caption\", code_group_pcsm_caption, display=False)\n",
    "glue(\"rhone_codegroup_pcsm\", cgp, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_codegroup_pcsm\n",
    "---\n",
    "name: 'rhone_survey_area_codegroup_pcsm'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_codegroup_pcsm>` {glue:text}`rhone_codegroup_pcsm_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Fliessgewässer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Links: Erhebungsgebiet Rhône Fliessgewässer, April 2020 bis März 2021, n = 8. Rechts: Zusammenfassung der Daten.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_survey_area_rivers_summary_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 1100x600 with 2 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_survey_area_rivers_summary"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary of sample totals\n",
    "csx = fdr.sample_summary.copy()\n",
    "combined_summary =[(x, featuredata.thousandsSeparator(int(csx[x]), language)) for x in csx.index]\n",
    "\n",
    "# the lake and river sample totals\n",
    "rivers = fdr.sample_totals\n",
    "lakes = fdx.sample_totals\n",
    "\n",
    "# make the charts\n",
    "fig = plt.figure(figsize=(11,6))\n",
    "\n",
    "aspec = fig.add_gridspec(ncols=11, nrows=3)\n",
    "\n",
    "ax = fig.add_subplot(aspec[:, :6])\n",
    "\n",
    "line_label = F\"{rate} median:{top}\"\n",
    "\n",
    "sns.scatterplot(data=lakes, x=\"date\", y=unit_label, color=\"black\", alpha=0.4, label=\"Seen\", ax=ax)\n",
    "sns.scatterplot(data=rivers, x=\"date\", y=unit_label, color=\"red\", s=34, ec=\"white\",label=\"Fliessgewässer\", ax=ax)\n",
    "\n",
    "ax.set_ylabel(unit_label, labelpad=10, fontsize=14)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.xaxis.set_minor_locator(days)\n",
    "ax.xaxis.set_major_formatter(months_fmt)\n",
    "# ax.margins(x=.05, y=.05)\n",
    "ax.set_ylim(-50, 2000)\n",
    "\n",
    "a_col = [this_feature[\"name\"], \"Total\"]\n",
    "\n",
    "axone = fig.add_subplot(aspec[:, 6:])\n",
    "featuredata.hide_spines_ticks_grids(axone)\n",
    "\n",
    "table_five = sut.make_a_table(axone, combined_summary,  colLabels=a_col, colWidths=[.75,.25],  bbox=[0,0,1,1], **{\"loc\":\"lower center\"})\n",
    "table_five.get_celld()[(0,0)].get_text().set_text(\" \")\n",
    "table_five.set_fontsize(12)\n",
    "\n",
    "rivers_caption = [\n",
    "    f'Links: {this_feature[\"name\"]} Fliessgewässer, {featuredata.dateToYearAndMonth(datetime.strptime(start_date, date_format), lang=date_lang)} ',\n",
    "    f'bis {featuredata.dateToYearAndMonth(datetime.strptime(end_date, date_format), lang=date_lang)}, n = {len(rivers.loc_date.unique())}. ',\n",
    "    'Rechts: Zusammenfassung der Daten.'\n",
    "]\n",
    "\n",
    "rivers_caption = ''.join(rivers_caption)\n",
    "\n",
    "\n",
    "figure_name = f'{this_feature[\"slug\"]}river_sample_totals'\n",
    "river_totals_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":river_totals_file_name})\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "\n",
    "glue('rhone_survey_area_rivers_summary_caption', rivers_caption, display=False)\n",
    "\n",
    "glue('rhone_survey_area_rivers_summary', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_survey_area_rivers_summary\n",
    "---\n",
    "name: 'rhone_survey_area_rivers_summary'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_survey_area_rivers_summary>` {glue:text}`rhone_survey_area_rivers_summary_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Die an Fliessgewässern am häufigsten gefundenen Objekte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Häufigste Objekte p/100\\xa0m an Fliessgewässern im Erhebungsgebiet Rhône: Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_rivers_most_common_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_d554a tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_d554a tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_d554a tr {\n  font-size: 12px;\n}\n#T_d554a th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_d554a td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_d554a\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_d554a_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_d554a_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_d554a_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_d554a_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_d554a_level0_row0\" class=\"row_heading level0 row0\" >Windeln – Feuchttücher</th>\n      <td id=\"T_d554a_row0_col0\" class=\"data row0 col0\" >182</td>\n      <td id=\"T_d554a_row0_col1\" class=\"data row0 col1\" >18%</td>\n      <td id=\"T_d554a_row0_col2\" class=\"data row0 col2\" >62%</td>\n      <td id=\"T_d554a_row0_col3\" class=\"data row0 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row1\" class=\"row_heading level0 row1\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_d554a_row1_col0\" class=\"data row1 col0\" >122</td>\n      <td id=\"T_d554a_row1_col1\" class=\"data row1 col1\" >12%</td>\n      <td id=\"T_d554a_row1_col2\" class=\"data row1 col2\" >62%</td>\n      <td id=\"T_d554a_row1_col3\" class=\"data row1 col3\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row2\" class=\"row_heading level0 row2\" >Snack-Verpackungen</th>\n      <td id=\"T_d554a_row2_col0\" class=\"data row2 col0\" >58</td>\n      <td id=\"T_d554a_row2_col1\" class=\"data row2 col1\" >5%</td>\n      <td id=\"T_d554a_row2_col2\" class=\"data row2 col2\" >50%</td>\n      <td id=\"T_d554a_row2_col3\" class=\"data row2 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row3\" class=\"row_heading level0 row3\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_d554a_row3_col0\" class=\"data row3 col0\" >54</td>\n      <td id=\"T_d554a_row3_col1\" class=\"data row3 col1\" >5%</td>\n      <td id=\"T_d554a_row3_col2\" class=\"data row3 col2\" >37%</td>\n      <td id=\"T_d554a_row3_col3\" class=\"data row3 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row4\" class=\"row_heading level0 row4\" >Zigarettenfilter</th>\n      <td id=\"T_d554a_row4_col0\" class=\"data row4 col0\" >53</td>\n      <td id=\"T_d554a_row4_col1\" class=\"data row4 col1\" >5%</td>\n      <td id=\"T_d554a_row4_col2\" class=\"data row4 col2\" >37%</td>\n      <td id=\"T_d554a_row4_col3\" class=\"data row4 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row5\" class=\"row_heading level0 row5\" >Einkaufstaschen, Shoppingtaschen</th>\n      <td id=\"T_d554a_row5_col0\" class=\"data row5 col0\" >50</td>\n      <td id=\"T_d554a_row5_col1\" class=\"data row5 col1\" >5%</td>\n      <td id=\"T_d554a_row5_col2\" class=\"data row5 col2\" >50%</td>\n      <td id=\"T_d554a_row5_col3\" class=\"data row5 col3\" >3,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row6\" class=\"row_heading level0 row6\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_d554a_row6_col0\" class=\"data row6 col0\" >44</td>\n      <td id=\"T_d554a_row6_col1\" class=\"data row6 col1\" >4%</td>\n      <td id=\"T_d554a_row6_col2\" class=\"data row6 col2\" >12%</td>\n      <td id=\"T_d554a_row6_col3\" class=\"data row6 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row7\" class=\"row_heading level0 row7\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_d554a_row7_col0\" class=\"data row7 col0\" >42</td>\n      <td id=\"T_d554a_row7_col1\" class=\"data row7 col1\" >4%</td>\n      <td id=\"T_d554a_row7_col2\" class=\"data row7 col2\" >25%</td>\n      <td id=\"T_d554a_row7_col3\" class=\"data row7 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row8\" class=\"row_heading level0 row8\" >Baumaterial;  Ziegel, Rohre, Zement</th>\n      <td id=\"T_d554a_row8_col0\" class=\"data row8 col0\" >36</td>\n      <td id=\"T_d554a_row8_col1\" class=\"data row8 col1\" >3%</td>\n      <td id=\"T_d554a_row8_col2\" class=\"data row8 col2\" >12%</td>\n      <td id=\"T_d554a_row8_col3\" class=\"data row8 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row9\" class=\"row_heading level0 row9\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_d554a_row9_col0\" class=\"data row9 col0\" >30</td>\n      <td id=\"T_d554a_row9_col1\" class=\"data row9 col1\" >3%</td>\n      <td id=\"T_d554a_row9_col2\" class=\"data row9 col2\" >50%</td>\n      <td id=\"T_d554a_row9_col3\" class=\"data row9 col3\" >3,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_d554a_level0_row10\" class=\"row_heading level0 row10\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_d554a_row10_col0\" class=\"data row10 col0\" >8</td>\n      <td id=\"T_d554a_row10_col1\" class=\"data row10 col1\" >0%</td>\n      <td id=\"T_d554a_row10_col2\" class=\"data row10 col2\" >50%</td>\n      <td id=\"T_d554a_row10_col3\" class=\"data row10 col3\" >0,5</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff115afd160>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_rivers_most_common_tables"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the most common objects results\n",
    "most_common_display = fdr.most_common.copy()\n",
    "\n",
    "# language appropriate columns\n",
    "cols_to_use = featuredata.most_common_objects_table_de\n",
    "cols_to_use.update({unit_label:unit_label})\n",
    "\n",
    "# data for display\n",
    "most_common_display.rename(columns=cols_to_use, inplace=True)\n",
    "most_common_display = most_common_display[cols_to_use.values()].copy()\n",
    "most_common_display = most_common_display.set_index(\"Objekte\", drop=True)\n",
    "\n",
    "# .pdf output\n",
    "data = most_common_display.copy()\n",
    "data[\"Anteil\"] = data[\"Anteil\"].map(lambda x: f\"{int(x)}%\")\n",
    "data['Objekte (St.)'] = data['Objekte (St.)'].map(lambda x:featuredata.thousandsSeparator(x, language))\n",
    "data['Häufigkeitsrate'] = data['Häufigkeitsrate'].map(lambda x: f\"{x}%\")\n",
    "data[unit_label] = data[unit_label].map(lambda x: featuredata.replaceDecimal(round(x,1)))\n",
    "\n",
    "# make caption\n",
    "# get percent of total to make the caption string\n",
    "m_common_percent_of_total = fdx.most_common['Objekte (St.)'].sum()/fdx.code_summary['quantity'].sum()\n",
    "\n",
    "mc_caption_string = f'Häufigste Objekte p/100 m an Fliessgewässern im {this_feature[\"name\"]}: Medianwert der Erhebung.'\n",
    "\n",
    "\n",
    "# pdf_mc_table  = featuredata.aStyledTable(data, caption=mc_caption_string, colWidths=[4.5*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm])\n",
    "\n",
    "col_widths = [4.5*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm]\n",
    "\n",
    "d_chart = aSingleStyledTable(data, vertical_header=False, gradient=False, colWidths=col_widths)\n",
    "d_capt = featuredata.makeAParagraph([monthly_data_caption], style=caption_style)\n",
    "pdf_mc_table = tableAndCaption(d_chart, d_capt, colwidths)\n",
    "\n",
    "most_common_display.index.name = None\n",
    "most_common_display.columns.name = None\n",
    "\n",
    "# set pandas display\n",
    "aformatter = {\n",
    "    \"Anteil\":lambda x: f\"{int(x)}%\",\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, language),\n",
    "    \"Häufigkeitsrate\": lambda x: f\"{int(x)}%\",   \n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), language)\n",
    "}\n",
    "\n",
    "mcd = most_common_display.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "glue('rhone_rivers_most_common_caption', mc_caption_string, display=False)\n",
    "glue('rhone_rivers_most_common_tables', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} rhone_rivers_most_common_tables\n",
    "---\n",
    "name: 'rhone_rivers_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_rivers_most_common_tables>` {glue:text}`rhone_rivers_most_common_caption`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "rivers_section_title = Paragraph(\"Fliessgewässer\", section_title)\n",
    "rivers_caption_pdf = [\n",
    "    f'<b>Links:</b> {this_feature[\"name\"]} Fliessgewässer, {featuredata.dateToYearAndMonth(datetime.strptime(start_date, date_format), lang=date_lang)} ',\n",
    "    f'bis {featuredata.dateToYearAndMonth(datetime.strptime(end_date, date_format), lang=date_lang)}, n = {len(rivers.loc_date.unique())}. ',\n",
    "    '<b>Rechts:</b> Zusammenfassung der Daten.'\n",
    "]\n",
    "\n",
    "r_totals_caption = makeAParagraph(rivers_caption_pdf, style=caption_style)\n",
    "r_mc_subsection = Paragraph(\"Die an Fliessgewässern am häufigsten gefundenen Objekte\", subsection_title)\n",
    "r_mc_caption = Paragraph(mc_caption_string, caption_style)\n",
    "\n",
    "\n",
    "o_w, o_h = convertPixelToCm(river_totals_file_name)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\":river_totals_file_name,\n",
    "    \"caption\": r_totals_caption, \n",
    "    \"original_width\":o_w,\n",
    "    \"original_height\":o_h,\n",
    "    \"desired_width\": 13,\n",
    "    \"caption_height\":1,\n",
    "    \"hAlign\": \"CENTER\",\n",
    "}\n",
    "\n",
    "r_totalsx = figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    rivers_section_title,\n",
    "    small_space,\n",
    "    r_totalsx,\n",
    "    small_space,\n",
    "    small_space,\n",
    "    r_mc_subsection,\n",
    "    small_space,\n",
    "    pdf_mc_table,        \n",
    "    PageBreak()\n",
    "  \n",
    "]\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Anhang\n",
    "\n",
    "### Schaumstoffe und Kunststoffe nach Grösse\n",
    "\n",
    "Die folgende Tabelle enthält die Komponenten «Gfoam» und «Gfrag», die für die Analyse gruppiert wurden. Objekte, die als Schaumstoffe gekennzeichnet sind, werden als Gfoam gruppiert und umfassen alle geschäumten Polystyrol-Kunststoffe > 0,5 cm. Kunststoffteile und Objekte aus kombinierten Kunststoff- und Schaumstoffmaterialien > 0,5 cm werden für die Analyse als Gfrags gruppiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Fragmentierte und geschäumte Kunststoffe nach Grösse am Erhebungsgebiet RhôneMedian p/100 m, Anzahl der gefundenen Objekte und Prozent der Gesamtmenge.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_frag_table_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_6be63 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_6be63 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_6be63 tr {\n  font-size: 12px;\n}\n#T_6be63 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_6be63 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_6be63\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_6be63_level0_col0\" class=\"col_heading level0 col0\" >p/100 m</th>\n      <th id=\"T_6be63_level0_col1\" class=\"col_heading level0 col1\" >Objekte (St.)</th>\n      <th id=\"T_6be63_level0_col2\" class=\"col_heading level0 col2\" >Anteil</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6be63_level0_row0\" class=\"row_heading level0 row0\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_6be63_row0_col0\" class=\"data row0 col0\" >0,0</td>\n      <td id=\"T_6be63_row0_col1\" class=\"data row0 col1\" >121</td>\n      <td id=\"T_6be63_row0_col2\" class=\"data row0 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row1\" class=\"row_heading level0 row1\" >Objekte aus Kunststoff/Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_6be63_row1_col0\" class=\"data row1 col0\" >0,0</td>\n      <td id=\"T_6be63_row1_col1\" class=\"data row1 col1\" >443</td>\n      <td id=\"T_6be63_row1_col2\" class=\"data row1 col2\" >2%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row2\" class=\"row_heading level0 row2\" >Objekte aus Kunststoff/Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_6be63_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_6be63_row2_col1\" class=\"data row2 col1\" >83</td>\n      <td id=\"T_6be63_row2_col2\" class=\"data row2 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row3\" class=\"row_heading level0 row3\" >Objekte aus Kunststoff 0,5 - 2,5 cm</th>\n      <td id=\"T_6be63_row3_col0\" class=\"data row3 col0\" >18,0</td>\n      <td id=\"T_6be63_row3_col1\" class=\"data row3 col1\" >2 035</td>\n      <td id=\"T_6be63_row3_col2\" class=\"data row3 col2\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row4\" class=\"row_heading level0 row4\" >Objekte aus Kunststoff 2,5 - 50 cm</th>\n      <td id=\"T_6be63_row4_col0\" class=\"data row4 col0\" >12,5</td>\n      <td id=\"T_6be63_row4_col1\" class=\"data row4 col1\" >1 659</td>\n      <td id=\"T_6be63_row4_col2\" class=\"data row4 col2\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row5\" class=\"row_heading level0 row5\" >Objekte aus Kunststoff > 50 cm</th>\n      <td id=\"T_6be63_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_6be63_row5_col1\" class=\"data row5 col1\" >0</td>\n      <td id=\"T_6be63_row5_col2\" class=\"data row5 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row6\" class=\"row_heading level0 row6\" >Objekte aus expandiertem Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_6be63_row6_col0\" class=\"data row6 col0\" >6,0</td>\n      <td id=\"T_6be63_row6_col1\" class=\"data row6 col1\" >2 476</td>\n      <td id=\"T_6be63_row6_col2\" class=\"data row6 col2\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row7\" class=\"row_heading level0 row7\" >Objekte aus expandiertem Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_6be63_row7_col0\" class=\"data row7 col0\" >4,0</td>\n      <td id=\"T_6be63_row7_col1\" class=\"data row7 col1\" >1 108</td>\n      <td id=\"T_6be63_row7_col2\" class=\"data row7 col2\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_6be63_level0_row8\" class=\"row_heading level0 row8\" >Objekte aus expandiertem Polystyrol > 50 cm</th>\n      <td id=\"T_6be63_row8_col0\" class=\"data row8 col0\" >0,0</td>\n      <td id=\"T_6be63_row8_col1\" class=\"data row8 col1\" >5</td>\n      <td id=\"T_6be63_row8_col2\" class=\"data row8 col2\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7ff115ce59d0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "rhone_frags_table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "annex_title = Paragraph(\"Anhang\", section_title)\n",
    "frag_sub_title = Paragraph(\"Schaumstoffe und Kunststoffe nach Grösse\", subsection_title)\n",
    "\n",
    "frag_paras = [\n",
    "    \"Die folgende Tabelle enthält die Komponenten «Gfoam» und «Gfrag», die für die Analyse gruppiert wurden. \",\n",
    "    \"Objekte, die als Schaumstoffe gekennzeichnet sind, werden als Gfoam gruppiert und umfassen alle geschäumten \",\n",
    "    \"Polystyrol-Kunststoffe > 0,5 cm. Kunststoffteile und Objekte aus kombinierten Kunststoff - und Schaumstoffmaterialien > 0,5 \",\n",
    "    \"cm werden für die Analyse als Gfrags gruppiert.\"\n",
    "]\n",
    "\n",
    "frag_p = \"\".join(frag_paras)\n",
    "frag = Paragraph(frag_p, featuredata.p_style)\n",
    "\n",
    "frag_caption = [\n",
    "    f'Fragmentierte und geschäumte Kunststoffe nach Grösse am {this_feature[\"name\"]}',\n",
    "    f'Median {unit_label}, Anzahl der gefundenen Objekte und Prozent der Gesamtmenge.'\n",
    "]\n",
    "frag_captions = ''.join(frag_caption)\n",
    "\n",
    "# collect the data before aggregating foams for all locations in the survye area\n",
    "# group by loc_date and code\n",
    "# Combine the different sizes of fragmented plastics and styrofoam\n",
    "# the codes for the foams\n",
    "before_agg = pd.read_csv(\"resources/checked_before_agg_sdata_eos_2020_21.csv\")\n",
    "some_foams = [\"G81\", \"G82\", \"G83\", \"G74\"]\n",
    "before_agg.rename(columns={\"p/100m\":unit_label}, inplace=True)\n",
    "\n",
    "# the codes for the fragmented plastics\n",
    "some_frag_plas = list(before_agg[before_agg.groupname == \"plastic pieces\"].code.unique())\n",
    "mask = ((before_agg.code.isin([*some_frag_plas, *some_foams]))&(before_agg.location.isin(admin_summary[\"locations_of_interest\"])))\n",
    "agg_pcs_median = {unit_label:\"median\", \"quantity\":\"sum\"}\n",
    "\n",
    "fd_frags_foams = before_agg[mask].groupby([\"loc_date\",\"code\"], as_index=False).agg(agg_pcs_quantity)\n",
    "fd_frags_foams = fd_frags_foams.groupby(\"code\").agg(agg_pcs_median)\n",
    "fd_frags_foams[\"item\"] = fd_frags_foams.index.map(lambda x: fdx.dMap.loc[x])\n",
    "fd_frags_foams[\"% of total\"] = (fd_frags_foams.quantity/fd.quantity.sum()).round(2)\n",
    "\n",
    "# table data\n",
    "data = fd_frags_foams[[\"item\",unit_label, \"quantity\", \"% of total\"]]\n",
    "data.rename(columns={\"item\":\"Objekt\", \"quantity\":\"Objekte (St.)\", \"% of total\":\"Anteil\"}, inplace=True)\n",
    "data.set_index(\"Objekt\", inplace=True, drop=True)\n",
    "data.index.name = None\n",
    "\n",
    "aformatter = {\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, language),\n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), language),\n",
    "    \"Anteil\":'{:.0%}'   \n",
    "}\n",
    "\n",
    "frags_table = data.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "\n",
    "glue(\"rhone_frag_table_caption\", frag_captions, display=False)\n",
    "glue(\"rhone_frags_table\", frags_table, display=False)\n",
    "\n",
    "# frag_table = featuredata.aStyledTable(data, caption=frag_captions, colWidths=[7*cm, *[2*cm]*(len(dims_table.columns)-1)])\n",
    "col_widths = [7*cm, *[2*cm]*(len(data.columns)-1)]\n",
    "\n",
    "d_chart = aSingleStyledTable(data, vertical_header=False, gradient=False, colWidths=col_widths)\n",
    "d_capt = featuredata.makeAParagraph(frag_caption, style=caption_style)\n",
    "pdf_mc_table = tableAndCaption(d_chart, d_capt, colwidths)\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        annex_title,\n",
    "        small_space,\n",
    "        frag_sub_title,\n",
    "        featuredata.smaller_space,\n",
    "        frag,\n",
    "        small_space\n",
    "    ]),\n",
    "    pdf_mc_table\n",
    "]\n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} rhone_frags_table\n",
    "---\n",
    "name: 'rhone_frags_table'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <rhone_frags_table>` {glue:text}`rhone_frag_table_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Die Erhebungsorte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} resources/maps/rhone_location_labels.jpeg\n",
    "---\n",
    "name: rhone_location_map\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standort</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>maladaire</th>\n",
       "      <td>46.446296</td>\n",
       "      <td>6.876960</td>\n",
       "      <td>La Tour-de-Peilz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preverenges</th>\n",
       "      <td>46.512690</td>\n",
       "      <td>6.527657</td>\n",
       "      <td>Préverenges</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vidy-ruines</th>\n",
       "      <td>46.516221</td>\n",
       "      <td>6.596279</td>\n",
       "      <td>Lausanne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baby-plage-geneva</th>\n",
       "      <td>46.208558</td>\n",
       "      <td>6.162923</td>\n",
       "      <td>Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grand-clos</th>\n",
       "      <td>46.387746</td>\n",
       "      <td>6.843686</td>\n",
       "      <td>Saint-Gingolph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quai-maria-belgia</th>\n",
       "      <td>46.460156</td>\n",
       "      <td>6.836718</td>\n",
       "      <td>Vevey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anarchy-beach</th>\n",
       "      <td>46.447216</td>\n",
       "      <td>6.859612</td>\n",
       "      <td>La Tour-de-Peilz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lavey-les-bains-2</th>\n",
       "      <td>46.207726</td>\n",
       "      <td>7.011685</td>\n",
       "      <td>Lavey-Morcles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leuk-mattenstrasse</th>\n",
       "      <td>46.314754</td>\n",
       "      <td>7.622521</td>\n",
       "      <td>Leuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pont-sous-terre</th>\n",
       "      <td>46.202960</td>\n",
       "      <td>6.131577</td>\n",
       "      <td>Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cully-plage</th>\n",
       "      <td>46.488887</td>\n",
       "      <td>6.741396</td>\n",
       "      <td>Bourg-en-Lavaux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preverenges-le-sout</th>\n",
       "      <td>46.508905</td>\n",
       "      <td>6.534526</td>\n",
       "      <td>Préverenges</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>la-pecherie</th>\n",
       "      <td>46.463919</td>\n",
       "      <td>6.385732</td>\n",
       "      <td>Allaman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villa-barton</th>\n",
       "      <td>46.222350</td>\n",
       "      <td>6.152500</td>\n",
       "      <td>Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oyonne</th>\n",
       "      <td>46.456682</td>\n",
       "      <td>6.852262</td>\n",
       "      <td>La Tour-de-Peilz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lavey-la-source</th>\n",
       "      <td>46.200804</td>\n",
       "      <td>7.021866</td>\n",
       "      <td>Lavey-Morcles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lavey-les-bains</th>\n",
       "      <td>46.205159</td>\n",
       "      <td>7.012722</td>\n",
       "      <td>Lavey-Morcles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baby-plage-ii-geneve</th>\n",
       "      <td>46.208940</td>\n",
       "      <td>6.164330</td>\n",
       "      <td>Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>les-glariers</th>\n",
       "      <td>46.176736</td>\n",
       "      <td>7.228925</td>\n",
       "      <td>Riddes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiger-duck-beach</th>\n",
       "      <td>46.518256</td>\n",
       "      <td>6.582546</td>\n",
       "      <td>Saint-Sulpice (VD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>le-pierrier</th>\n",
       "      <td>46.439727</td>\n",
       "      <td>6.888968</td>\n",
       "      <td>Montreux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tschilljus</th>\n",
       "      <td>46.304399</td>\n",
       "      <td>7.580262</td>\n",
       "      <td>Salgesch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boiron</th>\n",
       "      <td>46.491030</td>\n",
       "      <td>6.480162</td>\n",
       "      <td>Tolochenaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rocky-plage</th>\n",
       "      <td>46.209737</td>\n",
       "      <td>6.164952</td>\n",
       "      <td>Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lacleman_gland_lecoanets</th>\n",
       "      <td>46.402811</td>\n",
       "      <td>6.281959</td>\n",
       "      <td>Gland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baye-de-montreux-g</th>\n",
       "      <td>46.430834</td>\n",
       "      <td>6.908778</td>\n",
       "      <td>Montreux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>les-vieux-ronquoz</th>\n",
       "      <td>46.222049</td>\n",
       "      <td>7.361664</td>\n",
       "      <td>Sion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versoix</th>\n",
       "      <td>46.289194</td>\n",
       "      <td>6.170569</td>\n",
       "      <td>Versoix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parc-des-pierrettes</th>\n",
       "      <td>46.515215</td>\n",
       "      <td>6.575531</td>\n",
       "      <td>Saint-Sulpice (VD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plage-de-st-sulpice</th>\n",
       "      <td>46.513265</td>\n",
       "      <td>6.570977</td>\n",
       "      <td>Saint-Sulpice (VD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bain-des-dames</th>\n",
       "      <td>46.450507</td>\n",
       "      <td>6.858092</td>\n",
       "      <td>La Tour-de-Peilz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolochenaz</th>\n",
       "      <td>46.497509</td>\n",
       "      <td>6.482875</td>\n",
       "      <td>Tolochenaz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           latitude  longitude                stat\n",
       "standort                                                          \n",
       "maladaire                 46.446296   6.876960    La Tour-de-Peilz\n",
       "preverenges               46.512690   6.527657         Préverenges\n",
       "vidy-ruines               46.516221   6.596279            Lausanne\n",
       "baby-plage-geneva         46.208558   6.162923              Genève\n",
       "grand-clos                46.387746   6.843686      Saint-Gingolph\n",
       "quai-maria-belgia         46.460156   6.836718               Vevey\n",
       "anarchy-beach             46.447216   6.859612    La Tour-de-Peilz\n",
       "lavey-les-bains-2         46.207726   7.011685       Lavey-Morcles\n",
       "leuk-mattenstrasse        46.314754   7.622521                Leuk\n",
       "pont-sous-terre           46.202960   6.131577              Genève\n",
       "cully-plage               46.488887   6.741396     Bourg-en-Lavaux\n",
       "preverenges-le-sout       46.508905   6.534526         Préverenges\n",
       "la-pecherie               46.463919   6.385732             Allaman\n",
       "villa-barton              46.222350   6.152500              Genève\n",
       "oyonne                    46.456682   6.852262    La Tour-de-Peilz\n",
       "lavey-la-source           46.200804   7.021866       Lavey-Morcles\n",
       "lavey-les-bains           46.205159   7.012722       Lavey-Morcles\n",
       "baby-plage-ii-geneve      46.208940   6.164330              Genève\n",
       "les-glariers              46.176736   7.228925              Riddes\n",
       "tiger-duck-beach          46.518256   6.582546  Saint-Sulpice (VD)\n",
       "le-pierrier               46.439727   6.888968            Montreux\n",
       "tschilljus                46.304399   7.580262            Salgesch\n",
       "boiron                    46.491030   6.480162          Tolochenaz\n",
       "rocky-plage               46.209737   6.164952              Genève\n",
       "lacleman_gland_lecoanets  46.402811   6.281959               Gland\n",
       "baye-de-montreux-g        46.430834   6.908778            Montreux\n",
       "les-vieux-ronquoz         46.222049   7.361664                Sion\n",
       "versoix                   46.289194   6.170569             Versoix\n",
       "parc-des-pierrettes       46.515215   6.575531  Saint-Sulpice (VD)\n",
       "plage-de-st-sulpice       46.513265   6.570977  Saint-Sulpice (VD)\n",
       "bain-des-dames            46.450507   6.858092    La Tour-de-Peilz\n",
       "tolochenaz                46.497509   6.482875          Tolochenaz"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the survey locations\n",
    "disp_columns = [\"latitude\", \"longitude\", \"city\"]\n",
    "disp_beaches = admin_details.df_beaches.loc[admin_summary[\"locations_of_interest\"]][disp_columns]\n",
    "disp_beaches.reset_index(inplace=True)\n",
    "disp_beaches.rename(columns={\"city\":\"stat\", \"slug\":\"standort\"}, inplace=True)\n",
    "disp_beaches.set_index(\"standort\", inplace=True, drop=True)\n",
    "\n",
    "# make this into a pdf table\n",
    "location_subsection = Paragraph(\"Die Erhebungsorte und Inventar der Objekte\", subsection_title)\n",
    "col_widths = [6*cm, 2.2*cm, 2.2*cm, 3*cm]\n",
    "pdf_table = aStyledTableWithTitleRow(disp_beaches, title=\"Die Erhebungsorte\", colWidths=col_widths)\n",
    "\n",
    "disp_beaches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "### Inventar der Objekte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Objekte</th>\n",
       "      <th>Objekte (St.)</th>\n",
       "      <th>Anteil</th>\n",
       "      <th>p/100 m</th>\n",
       "      <th>Häufigkeitsrate</th>\n",
       "      <th>Material</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gfrags</th>\n",
       "      <td>Fragmentierte Kunststoffe</td>\n",
       "      <td>4 220</td>\n",
       "      <td>14</td>\n",
       "      <td>48</td>\n",
       "      <td>93</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfoam</th>\n",
       "      <td>Expandiertes Polystyrol</td>\n",
       "      <td>3 589</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>80</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G27</th>\n",
       "      <td>Zigarettenfilter</td>\n",
       "      <td>3 169</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>90</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G30</th>\n",
       "      <td>Snack-Verpackungen</td>\n",
       "      <td>1 737</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>92</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G112</th>\n",
       "      <td>Industriepellets (Nurdles)</td>\n",
       "      <td>1 387</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G67</th>\n",
       "      <td>Industriefolie (Kunststoff)</td>\n",
       "      <td>1 180</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>76</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G95</th>\n",
       "      <td>Wattestäbchen/Tupfer</td>\n",
       "      <td>1 112</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G74</th>\n",
       "      <td>Schaumstoffverpackungen/Isolierung</td>\n",
       "      <td>1 112</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G117</th>\n",
       "      <td>Styropor &lt; 5mm</td>\n",
       "      <td>718</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G89</th>\n",
       "      <td>Kunststoff-Bauabfälle</td>\n",
       "      <td>614</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>65</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G200</th>\n",
       "      <td>Getränkeflaschen aus Glas, Glasfragmente</td>\n",
       "      <td>554</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G106</th>\n",
       "      <td>Kunststofffragmente eckig &lt;5mm</td>\n",
       "      <td>427</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G21</th>\n",
       "      <td>Getränke-Deckel, Getränkeverschluss</td>\n",
       "      <td>425</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G24</th>\n",
       "      <td>Ringe von Plastikflaschen/Behältern</td>\n",
       "      <td>362</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G178</th>\n",
       "      <td>Kronkorken, Lasche von Dose/Ausfreisslachen</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G70</th>\n",
       "      <td>Schrotflintenpatronen</td>\n",
       "      <td>347</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G25</th>\n",
       "      <td>Tabak; Kunststoffverpackungen, Behälter</td>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G98</th>\n",
       "      <td>Windeln – Feuchttücher</td>\n",
       "      <td>295</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>Lebensmittelbehälter zum einmaligen Gebrauch a...</td>\n",
       "      <td>288</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G35</th>\n",
       "      <td>Strohhalme und Rührstäbchen</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G23</th>\n",
       "      <td>unidentifizierte Deckel</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G31</th>\n",
       "      <td>Schleckstengel, Stengel von Lutscher</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G103</th>\n",
       "      <td>Kunststofffragmente rund &lt;5mm</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>Einkaufstaschen, Shoppingtaschen</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G33</th>\n",
       "      <td>Einwegartikel; Tassen/Becher &amp; Deckel</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G32</th>\n",
       "      <td>Spielzeug und Partyartikel</td>\n",
       "      <td>194</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G177</th>\n",
       "      <td>Verpackungen aus Aluminiumfolie</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G100</th>\n",
       "      <td>Medizin; Behälter/Röhrchen/Verpackungen</td>\n",
       "      <td>177</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G921</th>\n",
       "      <td>Keramikfliesen und Bruchstücke</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G22</th>\n",
       "      <td>Deckel für Chemikalien, Reinigungsmittel (Ohne...</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G73</th>\n",
       "      <td>Gegenstände aus Schaumstoff &amp; Teilstücke (nich...</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G38</th>\n",
       "      <td>Abdeckungen; Kunststoffverpackungen, Folien zu...</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G941</th>\n",
       "      <td>Verpackungsfolien, nicht für Lebensmittel</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G66</th>\n",
       "      <td>Umreifungsbänder; Hartplastik für Verpackung f...</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G211</th>\n",
       "      <td>Sonstiges medizinisches Material</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G156</th>\n",
       "      <td>Papierfragmente</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G922</th>\n",
       "      <td>Etiketten, Strichcodes</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G91</th>\n",
       "      <td>Bio-Filtermaterial  / Trägermaterial aus Kunst...</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G159</th>\n",
       "      <td>Kork</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G208</th>\n",
       "      <td>Glas oder Keramikfragmente &gt;2.5 cm</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G904</th>\n",
       "      <td>Feuerwerkskörper; Raketenkappen</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G90</th>\n",
       "      <td>Blumentöpfe aus Plastik</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G125</th>\n",
       "      <td>Luftballons und Luftballonstäbchen</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G96</th>\n",
       "      <td>Hygienebinden/Höscheneinlagen/Tampons und Appl...</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G204</th>\n",
       "      <td>Baumaterial;  Ziegel, Rohre, Zement</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G165</th>\n",
       "      <td>Glacestengel (Eisstiele), Zahnstocher, Essstäb...</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G124</th>\n",
       "      <td>Andere Kunststoff</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G914</th>\n",
       "      <td>Büroklammern, Wäscheklammern, Gebrauchsgegenst...</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G34</th>\n",
       "      <td>Besteck, Teller und Tabletts</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G50</th>\n",
       "      <td>Schnur &lt; 1cm</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G153</th>\n",
       "      <td>Papierbecher, Lebensmittelverpackungen aus Pap...</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G105</th>\n",
       "      <td>Kunststofffragmente subangulär &lt;5mm</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G908</th>\n",
       "      <td>Klebeband; elektrisch, isolierend</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G198</th>\n",
       "      <td>Andere Metallteile &lt; 50 cm</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G28</th>\n",
       "      <td>Stifte, Deckel, Druckbleistifte usw.</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G137</th>\n",
       "      <td>Kleidung, Handtücher und Lappen</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G923</th>\n",
       "      <td>Taschentücher, Toilettenpapier, Servietten, Pa...</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G93</th>\n",
       "      <td>Kabelbinder</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G149</th>\n",
       "      <td>Papierverpackungen</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G905</th>\n",
       "      <td>Haarspangen, Haargummis, persönliche Accessoir...</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G26</th>\n",
       "      <td>Feuerzeug</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G155</th>\n",
       "      <td>Feuerwerkspapierhülsen und -fragmente</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G131</th>\n",
       "      <td>Gummibänder</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G115</th>\n",
       "      <td>Schaumstoff &lt;5mm</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G146</th>\n",
       "      <td>Papier, Karton</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G152</th>\n",
       "      <td>Papier &amp;Karton;Zigarettenschachteln, Papier/Ka...</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G937</th>\n",
       "      <td>Pheromonköder für Weinberge</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G7</th>\n",
       "      <td>Getränkeflaschen &lt; = 0,5 l</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G157</th>\n",
       "      <td>Papier</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G939</th>\n",
       "      <td>Kunststoffblumen, Kunststoffpflanzen</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G4</th>\n",
       "      <td>Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G20</th>\n",
       "      <td>Laschen und Deckel</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G135</th>\n",
       "      <td>Kleidung, Fussbekleidung, Kopfbedeckung, Hands...</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G142</th>\n",
       "      <td>Seil, Schnur oder Netze</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G207</th>\n",
       "      <td>Tonkrüge zum Fangen von Kraken</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G191</th>\n",
       "      <td>Draht und Gitter</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G65</th>\n",
       "      <td>Eimer</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G175</th>\n",
       "      <td>Getränkedosen (Dosen, Getränke)</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G213</th>\n",
       "      <td>Paraffinwachs</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G144</th>\n",
       "      <td>Tampons</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G12</th>\n",
       "      <td>Kosmetika, Behälter für Körperpflegeprodukte, ...</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G148</th>\n",
       "      <td>Kartonkisten und Stücke</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G48</th>\n",
       "      <td>Seile, synthetische</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G99</th>\n",
       "      <td>Spritzen - Nadeln</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>Säcke, Taschen</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G134</th>\n",
       "      <td>Sonstiges Gummi</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G936</th>\n",
       "      <td>Folien für Gewächshäuser</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G943</th>\n",
       "      <td>Zäune Landwirtschaft, Kunststoff</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G114</th>\n",
       "      <td>Folien &lt;5mm</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6</th>\n",
       "      <td>Flaschen und Behälter aus Kunststoff, nicht fü...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G8</th>\n",
       "      <td>Getränkeflaschen &gt; 0,5L</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G194</th>\n",
       "      <td>Kabel, Metalldraht oft in Gummi- oder Kunststo...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G126</th>\n",
       "      <td>Bälle</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G123</th>\n",
       "      <td>Polyurethan-Granulat &lt; 5mm</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G927</th>\n",
       "      <td>Plastikschnur von Rasentrimmern, die zum Schne...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G87</th>\n",
       "      <td>Abdeckklebeband/Verpackungsklebeband</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G918</th>\n",
       "      <td>Sicherheitsnadeln, Büroklammern, kleine Gebrau...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G43</th>\n",
       "      <td>Sicherheitsetiketten, Siegel für Fischerei ode...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G182</th>\n",
       "      <td>Angeln; Haken, Gewichte, Köder, Senkblei, usw.</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G186</th>\n",
       "      <td>Industrieschrott</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G203</th>\n",
       "      <td>Geschirr aus Keramik oder Glas, Tassen, Teller...</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G113</th>\n",
       "      <td>Fäden &lt;5mm</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G145</th>\n",
       "      <td>Andere Textilien</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G199</th>\n",
       "      <td>Andere Metallteile &gt; 50 cm</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G201</th>\n",
       "      <td>Gläser, einschliesslich Stücke</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G930</th>\n",
       "      <td>Schaumstoff-Ohrstöpsel</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G59</th>\n",
       "      <td>Monofile Angelschnur (Angeln)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G170</th>\n",
       "      <td>Holz (verarbeitet)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G901</th>\n",
       "      <td>Medizinische Masken, synthetische</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G928</th>\n",
       "      <td>Bänder und Schleifen</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G64</th>\n",
       "      <td>Kotflügel</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G942</th>\n",
       "      <td>Kunststoffspäne von Drehbänken, CNC-Bearbeitung</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11</th>\n",
       "      <td>Kosmetika für den Strand, z.B. Sonnencreme</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G158</th>\n",
       "      <td>Sonstige Gegenstände aus Papier</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G210</th>\n",
       "      <td>Sonstiges Glas/Keramik Materialien</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G940</th>\n",
       "      <td>Schaumstoff EVA (flexibler Kunststoff)</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G931</th>\n",
       "      <td>(Absperr)band für Absperrungen, Polizei, Baust...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G938</th>\n",
       "      <td>Zahnstocher, Zahnseide Kunststoff</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G176</th>\n",
       "      <td>Konservendosen (Lebensmitteldosen)</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G906</th>\n",
       "      <td>Kaffeekapseln Aluminium</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G119</th>\n",
       "      <td>Folienartiger Kunststoff (&gt;1mm)</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G926</th>\n",
       "      <td>Kaugummi, enthält oft Kunststoffe</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G133</th>\n",
       "      <td>Kondome einschließlich Verpackungen</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G900</th>\n",
       "      <td>Handschuhe Latex, persönliche Schutzausrüstung</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G118</th>\n",
       "      <td>Kleine Industrielle Kügelchen &lt;5mm</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G128</th>\n",
       "      <td>Reifen und Antriebsriemen</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101</th>\n",
       "      <td>Robidog Hundekot-Säcklein, andere Hundekotsäck...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G136</th>\n",
       "      <td>Schuhe</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G36</th>\n",
       "      <td>Säcke aus strapazierfähigem Kunststoff für 25 ...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G195</th>\n",
       "      <td>Batterien (Haushalt)</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G929</th>\n",
       "      <td>Elektronik und Teile; Sensoren, Headsets usw.</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G933</th>\n",
       "      <td>Taschen, Etuis für Zubehör, Brillen, Elektroni...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G68</th>\n",
       "      <td>Fiberglas-Fragmente</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G61</th>\n",
       "      <td>Sonstiges Angelzubehör</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G916</th>\n",
       "      <td>Bleistifte und Bruchstücke</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G37</th>\n",
       "      <td>Netzbeutel, Netztasche, Netzsäcke</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G919</th>\n",
       "      <td>Nägel, Schrauben, Bolzen usw.</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G917</th>\n",
       "      <td>Blähton</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G167</th>\n",
       "      <td>Streichhölzer oder Feuerwerke</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G17</th>\n",
       "      <td>Kartuschen von Kartuschen-spritzpistolen</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G19</th>\n",
       "      <td>Autoteile</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G97</th>\n",
       "      <td>Behälter von Toilettenerfrischer</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G147</th>\n",
       "      <td>Papiertragetaschen, (Papiertüten)</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G29</th>\n",
       "      <td>Kämme, Bürsten und Sonnenbrillen</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G181</th>\n",
       "      <td>Geschirr aus Metall, Tassen, Besteck usw.</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G915</th>\n",
       "      <td>Reflektoren, Mobilitätsartikel aus Kunststoff</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G913</th>\n",
       "      <td>Schnuller</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G116</th>\n",
       "      <td>Granulat &lt;5mm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G129</th>\n",
       "      <td>Schläuche und Gummiplatten</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G13</th>\n",
       "      <td>Flaschen, Behälter, Fässer zum Transportieren ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G92</th>\n",
       "      <td>Köderbehälter</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G63</th>\n",
       "      <td>Bojen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G161</th>\n",
       "      <td>Verarbeitetes Holz</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G197</th>\n",
       "      <td>sonstiges Metall</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G108</th>\n",
       "      <td>Scheibenförmige Pellets &lt;5mm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G104</th>\n",
       "      <td>Kunststofffragmente abgerundet / rundlich  &lt;5m...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G62</th>\n",
       "      <td>Schwimmer für Netze</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G40</th>\n",
       "      <td>Handschuhe Haushalt/Garten</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G109</th>\n",
       "      <td>Flache Pellets &lt;5mm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G907</th>\n",
       "      <td>Kaffeekapseln aus Kunststoff</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G151</th>\n",
       "      <td>Tetrapack, Kartons</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G925</th>\n",
       "      <td>Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G107</th>\n",
       "      <td>Zylindrische Pellets &lt;5mm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G140</th>\n",
       "      <td>Sack oder Beutel (Tragetasche), Jute oder Hanf</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G139</th>\n",
       "      <td>Rucksäcke</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G150</th>\n",
       "      <td>Milchkartons, Tetrapack</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G55</th>\n",
       "      <td>Angelschnur (verwickelt)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G932</th>\n",
       "      <td>Bio-Beads, Mikroplastik für die Abwasserbehand...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G49</th>\n",
       "      <td>Seile &gt; 1cm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G5</th>\n",
       "      <td>Plastiksäcke/ Plastiktüten</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G138</th>\n",
       "      <td>Schuhe und Sandalen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G174</th>\n",
       "      <td>Aerosolspraydosen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G179</th>\n",
       "      <td>Einweg Grill</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G183</th>\n",
       "      <td>Teile von Angelhaken</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G185</th>\n",
       "      <td>Behälter mittlerer Grösse</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G171</th>\n",
       "      <td>Sonstiges Holz &lt; 50cm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G102</th>\n",
       "      <td>Flip-Flops</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G193</th>\n",
       "      <td>Teile von Autos und Autobatterien</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G196</th>\n",
       "      <td>Grosse metallische Gegenstände</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G39</th>\n",
       "      <td>Handschuhe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G202</th>\n",
       "      <td>Glühbirnen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G154</th>\n",
       "      <td>Zeitungen oder Zeitschriften</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G172</th>\n",
       "      <td>Sonstiges Holz &gt; 50 cm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G51</th>\n",
       "      <td>Fischernetz</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G9</th>\n",
       "      <td>Flaschen und Behälter für Reinigungsmittel und...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G935</th>\n",
       "      <td>Gummi-Puffer für Geh- und Wanderstöcke und Tei...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G945</th>\n",
       "      <td>Rasierklingen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G166</th>\n",
       "      <td>Farbpinsel</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G14</th>\n",
       "      <td>Flaschen für Motoröl (Motorölflaschen)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G173</th>\n",
       "      <td>Sonstiges</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G999</th>\n",
       "      <td>Keine Gegenstände bei dieser Erhebung/Studie g...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unbekannt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G111</th>\n",
       "      <td>Kugelförmige Pellets &lt; 5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G94</th>\n",
       "      <td>Tischtuch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G934</th>\n",
       "      <td>Sandsäcke, Kunststoff für Hochwasser- und Eros...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G944</th>\n",
       "      <td>Pelletmasse aus dem Spritzgussverfahren</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G160</th>\n",
       "      <td>Paletten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G60</th>\n",
       "      <td>Lichtstab, Knicklicht, Glow-sticks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G903</th>\n",
       "      <td>Behälter und Packungen für Handdesinfektionsmi...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G702</th>\n",
       "      <td>Pistenmarkierungspfosten (Holz)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G162</th>\n",
       "      <td>Kisten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G168</th>\n",
       "      <td>Holzbretter</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G180</th>\n",
       "      <td>Haushaltsgeräte</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G188</th>\n",
       "      <td>Andere Kanister/Behälter &lt; 4 L</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G190</th>\n",
       "      <td>Farbtöpfe, Farbbüchsen,  ( Farbeimer)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G205</th>\n",
       "      <td>Leuchtstoffröhren</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G214</th>\n",
       "      <td>Öl/Teer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G143</th>\n",
       "      <td>Segel und Segeltuch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G141</th>\n",
       "      <td>Teppiche</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G132</th>\n",
       "      <td>Schwimmer (Angeln)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G41</th>\n",
       "      <td>Handschuhe Industriell/Professionell</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G52</th>\n",
       "      <td>Netze und Teilstücke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G53</th>\n",
       "      <td>Netze und Teilstücke &lt; 50cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G56</th>\n",
       "      <td>Verhedderte Netze</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G703</th>\n",
       "      <td>Pistenmarkierungspfosten (Plastik)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G902</th>\n",
       "      <td>Medizinische Masken, Stoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G704</th>\n",
       "      <td>Seilbahnbürsten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G705</th>\n",
       "      <td>Schrauben und Bolzen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G706</th>\n",
       "      <td>Skiabonnement</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G707</th>\n",
       "      <td>Skiausrüstungsetikett</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G708</th>\n",
       "      <td>Skistöcke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G709</th>\n",
       "      <td>Teller von Skistock (runder Teil aus Plastik u...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G71</th>\n",
       "      <td>Schuhe Sandalen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G710</th>\n",
       "      <td>Ski / Snowboards (Ski, Befestigungen und ander...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G711</th>\n",
       "      <td>Handwärmer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G712</th>\n",
       "      <td>Skihandschuhe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G713</th>\n",
       "      <td>Skiförderband</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G84</th>\n",
       "      <td>CD oder CD-Hülle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G88</th>\n",
       "      <td>Telefon inkl. Teile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G122</th>\n",
       "      <td>Kunststofffragmente (&gt;1mm)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>Sixpack-Ringe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Objekte Objekte (St.)   \n",
       "code                                                                      \n",
       "Gfrags                          Fragmentierte Kunststoffe         4 220  \\\n",
       "Gfoam                             Expandiertes Polystyrol         3 589   \n",
       "G27                                      Zigarettenfilter         3 169   \n",
       "G30                                    Snack-Verpackungen         1 737   \n",
       "G112                           Industriepellets (Nurdles)         1 387   \n",
       "G67                           Industriefolie (Kunststoff)         1 180   \n",
       "G95                                  Wattestäbchen/Tupfer         1 112   \n",
       "G74                    Schaumstoffverpackungen/Isolierung         1 112   \n",
       "G117                                       Styropor < 5mm           718   \n",
       "G89                                 Kunststoff-Bauabfälle           614   \n",
       "G200             Getränkeflaschen aus Glas, Glasfragmente           554   \n",
       "G106                       Kunststofffragmente eckig <5mm           427   \n",
       "G21                   Getränke-Deckel, Getränkeverschluss           425   \n",
       "G24                   Ringe von Plastikflaschen/Behältern           362   \n",
       "G178          Kronkorken, Lasche von Dose/Ausfreisslachen           350   \n",
       "G70                                 Schrotflintenpatronen           347   \n",
       "G25               Tabak; Kunststoffverpackungen, Behälter           296   \n",
       "G98                                Windeln – Feuchttücher           295   \n",
       "G10     Lebensmittelbehälter zum einmaligen Gebrauch a...           288   \n",
       "G35                           Strohhalme und Rührstäbchen           278   \n",
       "G23                               unidentifizierte Deckel           263   \n",
       "G31                  Schleckstengel, Stengel von Lutscher           238   \n",
       "G103                        Kunststofffragmente rund <5mm           229   \n",
       "G3                       Einkaufstaschen, Shoppingtaschen           206   \n",
       "G33                 Einwegartikel; Tassen/Becher & Deckel           201   \n",
       "G32                            Spielzeug und Partyartikel           194   \n",
       "G177                      Verpackungen aus Aluminiumfolie           178   \n",
       "G100              Medizin; Behälter/Röhrchen/Verpackungen           177   \n",
       "G921                       Keramikfliesen und Bruchstücke           176   \n",
       "G22     Deckel für Chemikalien, Reinigungsmittel (Ohne...           176   \n",
       "G73     Gegenstände aus Schaumstoff & Teilstücke (nich...           166   \n",
       "G38     Abdeckungen; Kunststoffverpackungen, Folien zu...           152   \n",
       "G941            Verpackungsfolien, nicht für Lebensmittel           139   \n",
       "G66     Umreifungsbänder; Hartplastik für Verpackung f...           135   \n",
       "G211                     Sonstiges medizinisches Material           103   \n",
       "G156                                      Papierfragmente           101   \n",
       "G922                               Etiketten, Strichcodes            99   \n",
       "G91     Bio-Filtermaterial  / Trägermaterial aus Kunst...            96   \n",
       "G159                                                 Kork            90   \n",
       "G208                   Glas oder Keramikfragmente >2.5 cm            87   \n",
       "G904                      Feuerwerkskörper; Raketenkappen            85   \n",
       "G90                               Blumentöpfe aus Plastik            80   \n",
       "G125                   Luftballons und Luftballonstäbchen            79   \n",
       "G96     Hygienebinden/Höscheneinlagen/Tampons und Appl...            77   \n",
       "G204                  Baumaterial;  Ziegel, Rohre, Zement            68   \n",
       "G165    Glacestengel (Eisstiele), Zahnstocher, Essstäb...            68   \n",
       "G124                                    Andere Kunststoff            68   \n",
       "G914    Büroklammern, Wäscheklammern, Gebrauchsgegenst...            65   \n",
       "G34                          Besteck, Teller und Tabletts            65   \n",
       "G50                                          Schnur < 1cm            62   \n",
       "G153    Papierbecher, Lebensmittelverpackungen aus Pap...            62   \n",
       "G105                  Kunststofffragmente subangulär <5mm            59   \n",
       "G908                    Klebeband; elektrisch, isolierend            58   \n",
       "G198                           Andere Metallteile < 50 cm            58   \n",
       "G28                  Stifte, Deckel, Druckbleistifte usw.            57   \n",
       "G137                      Kleidung, Handtücher und Lappen            54   \n",
       "G923    Taschentücher, Toilettenpapier, Servietten, Pa...            51   \n",
       "G93                                           Kabelbinder            51   \n",
       "G149                                   Papierverpackungen            50   \n",
       "G905    Haarspangen, Haargummis, persönliche Accessoir...            48   \n",
       "G26                                             Feuerzeug            45   \n",
       "G155                Feuerwerkspapierhülsen und -fragmente            44   \n",
       "G131                                          Gummibänder            43   \n",
       "G115                                     Schaumstoff <5mm            43   \n",
       "G146                                       Papier, Karton            40   \n",
       "G152    Papier &Karton;Zigarettenschachteln, Papier/Ka...            39   \n",
       "G937                          Pheromonköder für Weinberge            38   \n",
       "G7                             Getränkeflaschen < = 0,5 l            35   \n",
       "G157                                               Papier            35   \n",
       "G939                 Kunststoffblumen, Kunststoffpflanzen            33   \n",
       "G4      Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...            33   \n",
       "G20                                    Laschen und Deckel            31   \n",
       "G135    Kleidung, Fussbekleidung, Kopfbedeckung, Hands...            31   \n",
       "G142                              Seil, Schnur oder Netze            30   \n",
       "G207                       Tonkrüge zum Fangen von Kraken            30   \n",
       "G191                                     Draht und Gitter            29   \n",
       "G65                                                 Eimer            28   \n",
       "G175                      Getränkedosen (Dosen, Getränke)            28   \n",
       "G213                                       Paraffinwachs             28   \n",
       "G144                                              Tampons            27   \n",
       "G12     Kosmetika, Behälter für Körperpflegeprodukte, ...            25   \n",
       "G148                              Kartonkisten und Stücke            23   \n",
       "G48                                   Seile, synthetische            23   \n",
       "G99                                     Spritzen - Nadeln            21   \n",
       "G2                                         Säcke, Taschen            20   \n",
       "G134                                      Sonstiges Gummi            20   \n",
       "G936                             Folien für Gewächshäuser            20   \n",
       "G943                     Zäune Landwirtschaft, Kunststoff            19   \n",
       "G114                                          Folien <5mm            18   \n",
       "G6      Flaschen und Behälter aus Kunststoff, nicht fü...            18   \n",
       "G8                                Getränkeflaschen > 0,5L            17   \n",
       "G194    Kabel, Metalldraht oft in Gummi- oder Kunststo...            17   \n",
       "G126                                                Bälle            17   \n",
       "G123                           Polyurethan-Granulat < 5mm            17   \n",
       "G927    Plastikschnur von Rasentrimmern, die zum Schne...            16   \n",
       "G87                  Abdeckklebeband/Verpackungsklebeband            16   \n",
       "G918    Sicherheitsnadeln, Büroklammern, kleine Gebrau...            16   \n",
       "G43     Sicherheitsetiketten, Siegel für Fischerei ode...            15   \n",
       "G182       Angeln; Haken, Gewichte, Köder, Senkblei, usw.            15   \n",
       "G186                                     Industrieschrott            14   \n",
       "G203    Geschirr aus Keramik oder Glas, Tassen, Teller...            14   \n",
       "G113                                           Fäden <5mm            14   \n",
       "G145                                     Andere Textilien            14   \n",
       "G199                           Andere Metallteile > 50 cm            13   \n",
       "G201                       Gläser, einschliesslich Stücke            13   \n",
       "G930                               Schaumstoff-Ohrstöpsel            12   \n",
       "G59                         Monofile Angelschnur (Angeln)            11   \n",
       "G170                                   Holz (verarbeitet)            11   \n",
       "G901                    Medizinische Masken, synthetische            11   \n",
       "G928                                 Bänder und Schleifen            11   \n",
       "G64                                             Kotflügel            11   \n",
       "G942      Kunststoffspäne von Drehbänken, CNC-Bearbeitung            10   \n",
       "G11            Kosmetika für den Strand, z.B. Sonnencreme            10   \n",
       "G158                      Sonstige Gegenstände aus Papier            10   \n",
       "G210                   Sonstiges Glas/Keramik Materialien            10   \n",
       "G940               Schaumstoff EVA (flexibler Kunststoff)            10   \n",
       "G931    (Absperr)band für Absperrungen, Polizei, Baust...            10   \n",
       "G938                    Zahnstocher, Zahnseide Kunststoff             9   \n",
       "G176                   Konservendosen (Lebensmitteldosen)             9   \n",
       "G906                              Kaffeekapseln Aluminium             8   \n",
       "G119                      Folienartiger Kunststoff (>1mm)             8   \n",
       "G926                    Kaugummi, enthält oft Kunststoffe             8   \n",
       "G133                  Kondome einschließlich Verpackungen             8   \n",
       "G900       Handschuhe Latex, persönliche Schutzausrüstung             8   \n",
       "G118                   Kleine Industrielle Kügelchen <5mm             8   \n",
       "G128                            Reifen und Antriebsriemen             8   \n",
       "G101    Robidog Hundekot-Säcklein, andere Hundekotsäck...             8   \n",
       "G136                                               Schuhe             7   \n",
       "G36     Säcke aus strapazierfähigem Kunststoff für 25 ...             7   \n",
       "G195                                 Batterien (Haushalt)             7   \n",
       "G929       Elektronik und Teile; Sensoren, Headsets usw.              7   \n",
       "G933    Taschen, Etuis für Zubehör, Brillen, Elektroni...             7   \n",
       "G68                                   Fiberglas-Fragmente             7   \n",
       "G61                                Sonstiges Angelzubehör             7   \n",
       "G916                           Bleistifte und Bruchstücke             7   \n",
       "G37                     Netzbeutel, Netztasche, Netzsäcke             6   \n",
       "G919                       Nägel, Schrauben, Bolzen usw.              5   \n",
       "G917                                              Blähton             5   \n",
       "G167                        Streichhölzer oder Feuerwerke             5   \n",
       "G17              Kartuschen von Kartuschen-spritzpistolen             5   \n",
       "G19                                             Autoteile             5   \n",
       "G97                     Behälter von Toilettenerfrischer              5   \n",
       "G147                    Papiertragetaschen, (Papiertüten)             5   \n",
       "G29                      Kämme, Bürsten und Sonnenbrillen             4   \n",
       "G181            Geschirr aus Metall, Tassen, Besteck usw.             4   \n",
       "G915        Reflektoren, Mobilitätsartikel aus Kunststoff             4   \n",
       "G913                                            Schnuller             3   \n",
       "G116                                        Granulat <5mm             3   \n",
       "G129                           Schläuche und Gummiplatten             3   \n",
       "G13     Flaschen, Behälter, Fässer zum Transportieren ...             3   \n",
       "G92                                         Köderbehälter             3   \n",
       "G63                                                 Bojen             3   \n",
       "G161                                   Verarbeitetes Holz             3   \n",
       "G197                                     sonstiges Metall             3   \n",
       "G108                         Scheibenförmige Pellets <5mm             3   \n",
       "G104    Kunststofffragmente abgerundet / rundlich  <5m...             3   \n",
       "G62                                   Schwimmer für Netze             3   \n",
       "G40                            Handschuhe Haushalt/Garten             3   \n",
       "G109                                  Flache Pellets <5mm             2   \n",
       "G907                         Kaffeekapseln aus Kunststoff             2   \n",
       "G151                                   Tetrapack, Kartons             2   \n",
       "G925    Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...             2   \n",
       "G107                            Zylindrische Pellets <5mm             2   \n",
       "G140       Sack oder Beutel (Tragetasche), Jute oder Hanf             2   \n",
       "G139                                            Rucksäcke             2   \n",
       "G150                              Milchkartons, Tetrapack             2   \n",
       "G55                              Angelschnur (verwickelt)             2   \n",
       "G932    Bio-Beads, Mikroplastik für die Abwasserbehand...             2   \n",
       "G49                                           Seile > 1cm             2   \n",
       "G5                             Plastiksäcke/ Plastiktüten             2   \n",
       "G138                                  Schuhe und Sandalen             1   \n",
       "G174                                    Aerosolspraydosen             1   \n",
       "G179                                         Einweg Grill             1   \n",
       "G183                                 Teile von Angelhaken             1   \n",
       "G185                            Behälter mittlerer Grösse             1   \n",
       "G171                                Sonstiges Holz < 50cm             1   \n",
       "G102                                           Flip-Flops             1   \n",
       "G193                    Teile von Autos und Autobatterien             1   \n",
       "G196                       Grosse metallische Gegenstände             1   \n",
       "G39                                            Handschuhe             1   \n",
       "G202                                           Glühbirnen             1   \n",
       "G154                         Zeitungen oder Zeitschriften             1   \n",
       "G172                               Sonstiges Holz > 50 cm             1   \n",
       "G51                                           Fischernetz             1   \n",
       "G9      Flaschen und Behälter für Reinigungsmittel und...             1   \n",
       "G935    Gummi-Puffer für Geh- und Wanderstöcke und Tei...             1   \n",
       "G945                                        Rasierklingen             1   \n",
       "G166                                           Farbpinsel             1   \n",
       "G14                Flaschen für Motoröl (Motorölflaschen)             1   \n",
       "G173                                            Sonstiges             1   \n",
       "G999    Keine Gegenstände bei dieser Erhebung/Studie g...             0   \n",
       "G111                           Kugelförmige Pellets < 5mm             0   \n",
       "G94                                             Tischtuch             0   \n",
       "G934    Sandsäcke, Kunststoff für Hochwasser- und Eros...             0   \n",
       "G944              Pelletmasse aus dem Spritzgussverfahren             0   \n",
       "G160                                             Paletten             0   \n",
       "G60                    Lichtstab, Knicklicht, Glow-sticks             0   \n",
       "G903    Behälter und Packungen für Handdesinfektionsmi...             0   \n",
       "G702                      Pistenmarkierungspfosten (Holz)             0   \n",
       "G162                                               Kisten             0   \n",
       "G168                                          Holzbretter             0   \n",
       "G180                                      Haushaltsgeräte             0   \n",
       "G188                       Andere Kanister/Behälter < 4 L             0   \n",
       "G190                Farbtöpfe, Farbbüchsen,  ( Farbeimer)             0   \n",
       "G205                                    Leuchtstoffröhren             0   \n",
       "G214                                              Öl/Teer             0   \n",
       "G143                                  Segel und Segeltuch             0   \n",
       "G141                                             Teppiche             0   \n",
       "G132                                   Schwimmer (Angeln)             0   \n",
       "G41                  Handschuhe Industriell/Professionell             0   \n",
       "G52                                  Netze und Teilstücke             0   \n",
       "G53                           Netze und Teilstücke < 50cm             0   \n",
       "G56                                     Verhedderte Netze             0   \n",
       "G703                   Pistenmarkierungspfosten (Plastik)             0   \n",
       "G902                           Medizinische Masken, Stoff             0   \n",
       "G704                                      Seilbahnbürsten             0   \n",
       "G705                                 Schrauben und Bolzen             0   \n",
       "G706                                        Skiabonnement             0   \n",
       "G707                                Skiausrüstungsetikett             0   \n",
       "G708                                            Skistöcke             0   \n",
       "G709    Teller von Skistock (runder Teil aus Plastik u...             0   \n",
       "G71                                       Schuhe Sandalen             0   \n",
       "G710    Ski / Snowboards (Ski, Befestigungen und ander...             0   \n",
       "G711                                          Handwärmer              0   \n",
       "G712                                        Skihandschuhe             0   \n",
       "G713                                        Skiförderband             0   \n",
       "G84                                      CD oder CD-Hülle             0   \n",
       "G88                                   Telefon inkl. Teile             0   \n",
       "G122                           Kunststofffragmente (>1mm)             0   \n",
       "G1                                          Sixpack-Ringe             0   \n",
       "\n",
       "        Anteil  p/100 m  Häufigkeitsrate     Material  \n",
       "code                                                   \n",
       "Gfrags      14       48               93      Plastik  \n",
       "Gfoam       12       17               80      Plastik  \n",
       "G27         11       41               90      Plastik  \n",
       "G30          6       19               92      Plastik  \n",
       "G112         4        0               43      Plastik  \n",
       "G67          4        9               76      Plastik  \n",
       "G95          3       10               75      Plastik  \n",
       "G74          3        7               71      Plastik  \n",
       "G117         2        0               28      Plastik  \n",
       "G89          2        5               65      Plastik  \n",
       "G200         1        2               54         Glas  \n",
       "G106         1        0               22      Plastik  \n",
       "G21          1        1               51      Plastik  \n",
       "G24          1        4               68      Plastik  \n",
       "G178         1        3               67       Metall  \n",
       "G70          1        0               50      Plastik  \n",
       "G25          1        1               51      Plastik  \n",
       "G98          1        0               35      Plastik  \n",
       "G10          1        0               47      Plastik  \n",
       "G35          0        3               67      Plastik  \n",
       "G23          0        2               54      Plastik  \n",
       "G31          0        2               62      Plastik  \n",
       "G103         0        0                5      Plastik  \n",
       "G3           0        0               24      Plastik  \n",
       "G33          0        2               55      Plastik  \n",
       "G32          0        2               60      Plastik  \n",
       "G177         0        1               52       Metall  \n",
       "G100         0        2               57      Plastik  \n",
       "G921         0        0               23         Glas  \n",
       "G22          0        0               31      Plastik  \n",
       "G73          0        0               38      Plastik  \n",
       "G38          0        0               11      Plastik  \n",
       "G941         0        0               17      Plastik  \n",
       "G66          0        0               41      Plastik  \n",
       "G211         0        0               41      Plastik  \n",
       "G156         0        0               25       Papier  \n",
       "G922         0        0               31      Plastik  \n",
       "G91          0        0               34      Plastik  \n",
       "G159         0        0               38         Holz  \n",
       "G208         0        0               14         Glas  \n",
       "G904         0        0               23      Plastik  \n",
       "G90          0        0               25      Plastik  \n",
       "G125         0        0               26        Gummi  \n",
       "G96          0        0               30      Plastik  \n",
       "G204         0        0               15         Glas  \n",
       "G165         0        0               22         Holz  \n",
       "G124         0        0               16      Plastik  \n",
       "G914         0        0               24      Plastik  \n",
       "G34          0        0               25      Plastik  \n",
       "G50          0        0               29      Plastik  \n",
       "G153         0        0               17       Papier  \n",
       "G105         0        0                7      Plastik  \n",
       "G908         0        0               19      Plastik  \n",
       "G198         0        0               25       Metall  \n",
       "G28          0        0               28      Plastik  \n",
       "G137         0        0               17        Stoff  \n",
       "G923         0        0               18       Papier  \n",
       "G93          0        0               24      Plastik  \n",
       "G149         0        0               13       Papier  \n",
       "G905         0        0               29      Plastik  \n",
       "G26          0        0               22      Plastik  \n",
       "G155         0        0                2       Papier  \n",
       "G131         0        0               26        Gummi  \n",
       "G115         0        0                3      Plastik  \n",
       "G146         0        0                9       Papier  \n",
       "G152         0        0               13       Papier  \n",
       "G937         0        0               16      Plastik  \n",
       "G7           0        0               14      Plastik  \n",
       "G157         0        0               10       Papier  \n",
       "G939         0        0               13      Plastik  \n",
       "G4           0        0               13      Plastik  \n",
       "G20          0        0               12      Plastik  \n",
       "G135         0        0               15        Stoff  \n",
       "G142         0        0               15        Stoff  \n",
       "G207         0        0                0         Glas  \n",
       "G191         0        0               13       Metall  \n",
       "G65          0        0               13      Plastik  \n",
       "G175         0        0               12       Metall  \n",
       "G213         0        0               17  Chemikalien  \n",
       "G144         0        0               10        Stoff  \n",
       "G12          0        0               13      Plastik  \n",
       "G148         0        0                6       Papier  \n",
       "G48          0        0               14      Plastik  \n",
       "G99          0        0               16      Plastik  \n",
       "G2           0        0               10      Plastik  \n",
       "G134         0        0               13        Gummi  \n",
       "G936         0        0               10      Plastik  \n",
       "G943         0        0                8      Plastik  \n",
       "G114         0        0                1      Plastik  \n",
       "G6           0        0                5      Plastik  \n",
       "G8           0        0                8      Plastik  \n",
       "G194         0        0               15       Metall  \n",
       "G126         0        0               10        Gummi  \n",
       "G123         0        0                0      Plastik  \n",
       "G927         0        0                8      Plastik  \n",
       "G87          0        0                7      Plastik  \n",
       "G918         0        0                8       Metall  \n",
       "G43          0        0                5      Plastik  \n",
       "G182         0        0                7       Metall  \n",
       "G186         0        0                5       Metall  \n",
       "G203         0        0                5         Glas  \n",
       "G113         0        0                3      Plastik  \n",
       "G145         0        0                9        Stoff  \n",
       "G199         0        0                3       Metall  \n",
       "G201         0        0                5         Glas  \n",
       "G930         0        0                9      Plastik  \n",
       "G59          0        0                6      Plastik  \n",
       "G170         0        0                6         Holz  \n",
       "G901         0        0                8      Plastik  \n",
       "G928         0        0                4      Plastik  \n",
       "G64          0        0                1      Plastik  \n",
       "G942         0        0                7      Plastik  \n",
       "G11          0        0                7      Plastik  \n",
       "G158         0        0                2       Papier  \n",
       "G210         0        0                3         Glas  \n",
       "G940         0        0                3      Plastik  \n",
       "G931         0        0                4      Plastik  \n",
       "G938         0        0                7      Plastik  \n",
       "G176         0        0                4       Metall  \n",
       "G906         0        0                3       Metall  \n",
       "G119         0        0                0      Plastik  \n",
       "G926         0        0                5      Plastik  \n",
       "G133         0        0                6        Gummi  \n",
       "G900         0        0                6        Gummi  \n",
       "G118         0        0                3      Plastik  \n",
       "G128         0        0                5        Gummi  \n",
       "G101         0        0                7      Plastik  \n",
       "G136         0        0                3        Stoff  \n",
       "G36          0        0                1      Plastik  \n",
       "G195         0        0                6       Metall  \n",
       "G929         0        0                5       Metall  \n",
       "G933         0        0                2      Plastik  \n",
       "G68          0        0                5      Plastik  \n",
       "G61          0        0                5      Plastik  \n",
       "G916         0        0                5         Holz  \n",
       "G37          0        0                2      Plastik  \n",
       "G919         0        0                3       Metall  \n",
       "G917         0        0                2         Glas  \n",
       "G167         0        0                2         Holz  \n",
       "G17          0        0                1      Plastik  \n",
       "G19          0        0                2      Plastik  \n",
       "G97          0        0                4      Plastik  \n",
       "G147         0        0                4       Papier  \n",
       "G29          0        0                3      Plastik  \n",
       "G181         0        0                3       Metall  \n",
       "G915         0        0                3      Plastik  \n",
       "G913         0        0                2      Plastik  \n",
       "G116         0        0                0      Plastik  \n",
       "G129         0        0                1        Gummi  \n",
       "G13          0        0                1      Plastik  \n",
       "G92          0        0                2      Plastik  \n",
       "G63          0        0                1      Plastik  \n",
       "G161         0        0                1         Holz  \n",
       "G197         0        0                2       Metall  \n",
       "G108         0        0                1      Plastik  \n",
       "G104         0        0                1      Plastik  \n",
       "G62          0        0                1      Plastik  \n",
       "G40          0        0                1      Plastik  \n",
       "G109         0        0                0      Plastik  \n",
       "G907         0        0                1      Plastik  \n",
       "G151         0        0                1       Papier  \n",
       "G925         0        0                1      Plastik  \n",
       "G107         0        0                0      Plastik  \n",
       "G140         0        0                0        Stoff  \n",
       "G139         0        0                0        Stoff  \n",
       "G150         0        0                1       Papier  \n",
       "G55          0        0                1      Plastik  \n",
       "G932         0        0                0      Plastik  \n",
       "G49          0        0                1      Plastik  \n",
       "G5           0        0                1      Plastik  \n",
       "G138         0        0                0        Stoff  \n",
       "G174         0        0                0       Metall  \n",
       "G179         0        0                0       Metall  \n",
       "G183         0        0                0       Metall  \n",
       "G185         0        0                0       Metall  \n",
       "G171         0        0                0         Holz  \n",
       "G102         0        0                0      Plastik  \n",
       "G193         0        0                0       Metall  \n",
       "G196         0        0                0       Metall  \n",
       "G39          0        0                0      Plastik  \n",
       "G202         0        0                0         Glas  \n",
       "G154         0        0                0       Papier  \n",
       "G172         0        0                0         Holz  \n",
       "G51          0        0                0      Plastik  \n",
       "G9           0        0                0      Plastik  \n",
       "G935         0        0                0      Plastik  \n",
       "G945         0        0                0       Metall  \n",
       "G166         0        0                0         Holz  \n",
       "G14          0        0                0      Plastik  \n",
       "G173         0        0                0         Holz  \n",
       "G999         0        0                0    Unbekannt  \n",
       "G111         0        0                0      Plastik  \n",
       "G94          0        0                0      Plastik  \n",
       "G934         0        0                0      Plastik  \n",
       "G944         0        0                0      Plastik  \n",
       "G160         0        0                0         Holz  \n",
       "G60          0        0                0      Plastik  \n",
       "G903         0        0                0      Plastik  \n",
       "G702         0        0                0         Holz  \n",
       "G162         0        0                0         Holz  \n",
       "G168         0        0                0         Holz  \n",
       "G180         0        0                0       Metall  \n",
       "G188         0        0                0       Metall  \n",
       "G190         0        0                0       Metall  \n",
       "G205         0        0                0         Glas  \n",
       "G214         0        0                0  Chemikalien  \n",
       "G143         0        0                0        Stoff  \n",
       "G141         0        0                0        Stoff  \n",
       "G132         0        0                0        Gummi  \n",
       "G41          0        0                0      Plastik  \n",
       "G52          0        0                0      Plastik  \n",
       "G53          0        0                0      Plastik  \n",
       "G56          0        0                0      Plastik  \n",
       "G703         0        0                0      Plastik  \n",
       "G902         0        0                0        Stoff  \n",
       "G704         0        0                0      Plastik  \n",
       "G705         0        0                0       Metall  \n",
       "G706         0        0                0      Plastik  \n",
       "G707         0        0                0      Plastik  \n",
       "G708         0        0                0       Metall  \n",
       "G709         0        0                0      Plastik  \n",
       "G71          0        0                0      Plastik  \n",
       "G710         0        0                0      Plastik  \n",
       "G711         0        0                0      Plastik  \n",
       "G712         0        0                0        Stoff  \n",
       "G713         0        0                0      Plastik  \n",
       "G84          0        0                0      Plastik  \n",
       "G88          0        0                0      Plastik  \n",
       "G122         0        0                0      Plastik  \n",
       "G1           0        0                0      Plastik  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None)\n",
    "complete_inventory = fdx.code_summary.copy()\n",
    "complete_inventory.sort_values(by=\"quantity\", ascending=False, inplace=True)\n",
    "complete_inventory[\"quantity\"] = complete_inventory[\"quantity\"].map(lambda x: featuredata.thousandsSeparator(x, language))\n",
    "complete_inventory[\"% of total\"] = complete_inventory[\"% of total\"].astype(int)\n",
    "complete_inventory[unit_label] = complete_inventory[unit_label].astype(int)\n",
    "complete_inventory.rename(columns=featuredata.inventory_table_de, inplace=True)\n",
    "    \n",
    "inventory_subsection = Paragraph(\"Inventar der Objekte\", subsection_title)\n",
    "col_widths=[1.2*cm, 4.5*cm, 2.2*cm, 1.5*cm, 1.5*cm, 2.4*cm, 1.5*cm]\n",
    "inventory_table = aStyledTableWithTitleRow(complete_inventory, title=\"Inventar der Objekte\", colWidths=col_widths)\n",
    "\n",
    "new_map_image =  Image('resources/maps/rhone_location_labels.jpeg', width=cm*16, height=12*cm, kind=\"proportional\", hAlign= \"CENTER\")\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        featuredata.large_space,\n",
    "        location_subsection,\n",
    "        small_space,\n",
    "        new_map_image,\n",
    "        Paragraph(map_caption, caption_style),\n",
    "        featuredata.smaller_space,\n",
    "        pdf_table,\n",
    "        \n",
    "    ]),\n",
    "    featuredata.large_space,\n",
    "    inventory_table\n",
    "]    \n",
    "\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "complete_inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "hide-input",
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "doc = SimpleDocTemplate(pdf_link, pagesize=A4, leftMargin=2.5*cm, rightMargin=2.5*cm, topMargin=2.5*cm, bottomMargin=1*cm)\n",
    "pageinfo = f'IQAASL/Erhebungsgebiete/Zusammengefasste/{this_feature[\"name\"]}'\n",
    "source_prefix = \"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/\"\n",
    "source = f\"{this_feature['slug']}_sa.html\"\n",
    "\n",
    "link_to_source = f'{source_prefix}{source}'\n",
    "\n",
    "def myLaterPages(canvas, doc):\n",
    "    canvas.saveState()\n",
    "    canvas.setLineWidth(.001*cm)\n",
    "    canvas.setFillAlpha(.8)\n",
    "    canvas.line(2.5*cm, 27.6*cm,  18.5*cm, 27.6*cm) \n",
    "    canvas.setFont('Times-Roman',9)\n",
    "    canvas.drawString(2.5*cm, 1*cm, link_to_source)\n",
    "    canvas.drawString(18.5*cm, 1*cm,  \"S.%d \" % (doc.page,))\n",
    "    canvas.drawString(2.5*cm, 27.7*cm, pageinfo)\n",
    "    canvas.restoreState()\n",
    "    \n",
    "doc.build(pdfcomponents,  onFirstPage=myLaterPages, onLaterPages=myLaterPages)"
   ]
  }
 ],
 "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": "3.9.16"
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