ticino_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/ticino_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 ticino (ticino) at the river basin (river_bassin) level.\n",
    "# the label for charting is called 'name'\n",
    "this_feature = {'slug':'ticino', 'name':\"Erhebungsgebiet Ticino\", 'level':'river_bassin'}\n",
    "\n",
    "# the lake is in this survey area\n",
    "this_bassin = \"ticino\"\n",
    "# label for survey area\n",
    "bassin_label = \"Erhebungsgebiet Ticino\"\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 = \"ticino_sa\"\n",
    "\n",
    "# identify the lakes of interest for the survey area\n",
    "lakes_of_interest = ['lago-di-lugano','lago-maggiore']\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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     "name": "#%% md\n"
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   "source": [
    "(ticinosa)=\n",
    "# Ticino\n",
    "\n",
    "| {Download}`Download </resources/pdfs/ticino_sa.pdf>` | [English](https://www.plagespropres.ch/ticino_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": "ticino_city_map_caption"
      }
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     "output_type": "display_data"
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    {
     "data": {
      "application/papermill.record/text/plain": "' '"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "blank_caption"
      }
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     "output_type": "display_data"
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   ],
   "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"
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   },
   "source": [
    "```{figure} resources/maps/ticino_city_labels.jpeg\n",
    "---\n",
    "name: ticino_survey_area_city_labels_map\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_city_labels_map>` {glue:text}`ticino_city_map_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Erhebungsorte und Landnutzungsprofile"
   ]
  },
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    {
     "data": {
      "text/markdown": [
       "\n",
       "Im Zeitraum von März 2020  bis Mai 2021 wurden im Rahmen von 28 Datenerhebungen insgesamt 3 031 Objekte entfernt und identifiziert.  Die Ergebnisse des Erhebungsgebiet Ticino umfassen 20 Orte, 7 Gemeinden und eine Gesamtbevölkerung von etwa 141 943 Einwohnenden.\n",
       "\n",
       "*Seen*\n",
       "\n",
       ">Lago Maggiore, Lago di Lugano\n",
       "\n",
       "*Fliessgewässer*\n",
       "\n",
       ">Ticino, Cassarate, Maggia\n",
       "\n",
       "*Gemeinden*\n",
       "\n",
       ">Ascona, Bellinzona, Brissago, Gambarogno, Locarno, Lugano, Minusio\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",
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    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Kumulative Gesamtmengen nach Gewässer"
   ]
  },
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   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Erhebungsgebiet Ticino: kumulierten Gewichte  und Masse für die Gemeinden'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_dims_table_caption"
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    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a611d tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a611d tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a611d tr {\n  font-size: 12px;\n}\n#T_a611d th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_a611d td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_a611d\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a611d_level0_col0\" class=\"col_heading level0 col0\" >Gesamtgewicht (Kg)</th>\n      <th id=\"T_a611d_level0_col1\" class=\"col_heading level0 col1\" >Plastik (Kg)</th>\n      <th id=\"T_a611d_level0_col2\" class=\"col_heading level0 col2\" >Fläche (m2)</th>\n      <th id=\"T_a611d_level0_col3\" class=\"col_heading level0 col3\" >Länge (m)</th>\n      <th id=\"T_a611d_level0_col4\" class=\"col_heading level0 col4\" >Erhebungen</th>\n      <th id=\"T_a611d_level0_col5\" class=\"col_heading level0 col5\" >Objekte (St.)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a611d_level0_row0\" class=\"row_heading level0 row0\" >Erhebungsgebiet Ticino</th>\n      <td id=\"T_a611d_row0_col0\" class=\"data row0 col0\" >46,26</td>\n      <td id=\"T_a611d_row0_col1\" class=\"data row0 col1\" >4,03</td>\n      <td id=\"T_a611d_row0_col2\" class=\"data row0 col2\" >7 974</td>\n      <td id=\"T_a611d_row0_col3\" class=\"data row0 col3\" >1 517</td>\n      <td id=\"T_a611d_row0_col4\" class=\"data row0 col4\" >28</td>\n      <td id=\"T_a611d_row0_col5\" class=\"data row0 col5\" >3 031</td>\n    </tr>\n    <tr>\n      <th id=\"T_a611d_level0_row1\" class=\"row_heading level0 row1\" >Lago Maggiore</th>\n      <td id=\"T_a611d_row1_col0\" class=\"data row1 col0\" >28,11</td>\n      <td id=\"T_a611d_row1_col1\" class=\"data row1 col1\" >1,98</td>\n      <td id=\"T_a611d_row1_col2\" class=\"data row1 col2\" >4 767</td>\n      <td id=\"T_a611d_row1_col3\" class=\"data row1 col3\" >1 118</td>\n      <td id=\"T_a611d_row1_col4\" class=\"data row1 col4\" >18</td>\n      <td id=\"T_a611d_row1_col5\" class=\"data row1 col5\" >1 810</td>\n    </tr>\n    <tr>\n      <th id=\"T_a611d_level0_row2\" class=\"row_heading level0 row2\" >Lago di Lugano</th>\n      <td id=\"T_a611d_row2_col0\" class=\"data row2 col0\" >5,56</td>\n      <td id=\"T_a611d_row2_col1\" class=\"data row2 col1\" >1,01</td>\n      <td id=\"T_a611d_row2_col2\" class=\"data row2 col2\" >338</td>\n      <td id=\"T_a611d_row2_col3\" class=\"data row2 col3\" >108</td>\n      <td id=\"T_a611d_row2_col4\" class=\"data row2 col4\" >4</td>\n      <td id=\"T_a611d_row2_col5\" class=\"data row2 col5\" >925</td>\n    </tr>\n    <tr>\n      <th id=\"T_a611d_level0_row3\" class=\"row_heading level0 row3\" >Ticino</th>\n      <td id=\"T_a611d_row3_col0\" class=\"data row3 col0\" >7,4</td>\n      <td id=\"T_a611d_row3_col1\" class=\"data row3 col1\" >0,98</td>\n      <td id=\"T_a611d_row3_col2\" class=\"data row3 col2\" >1 265</td>\n      <td id=\"T_a611d_row3_col3\" class=\"data row3 col3\" >161</td>\n      <td id=\"T_a611d_row3_col4\" class=\"data row3 col4\" >4</td>\n      <td id=\"T_a611d_row3_col5\" class=\"data row3 col5\" >187</td>\n    </tr>\n    <tr>\n      <th id=\"T_a611d_level0_row4\" class=\"row_heading level0 row4\" >Maggia</th>\n      <td id=\"T_a611d_row4_col0\" class=\"data row4 col0\" >4,9</td>\n      <td id=\"T_a611d_row4_col1\" class=\"data row4 col1\" >0,01</td>\n      <td id=\"T_a611d_row4_col2\" class=\"data row4 col2\" >900</td>\n      <td id=\"T_a611d_row4_col3\" class=\"data row4 col3\" >60</td>\n      <td id=\"T_a611d_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_a611d_row4_col5\" class=\"data row4 col5\" >55</td>\n    </tr>\n    <tr>\n      <th id=\"T_a611d_level0_row5\" class=\"row_heading level0 row5\" >Cassarate</th>\n      <td id=\"T_a611d_row5_col0\" class=\"data row5 col0\" >0,29</td>\n      <td id=\"T_a611d_row5_col1\" class=\"data row5 col1\" >0,05</td>\n      <td id=\"T_a611d_row5_col2\" class=\"data row5 col2\" >704</td>\n      <td id=\"T_a611d_row5_col3\" class=\"data row5 col3\" >70</td>\n      <td id=\"T_a611d_row5_col4\" class=\"data row5 col4\" >1</td>\n      <td id=\"T_a611d_row5_col5\" class=\"data row5 col5\" >54</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7fafc87b3e20>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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(\"ticino_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} ticino_dims_table\n",
    "---\n",
    "name: 'ticino_dims_table'\n",
    "---\n",
    "{glue:text}`blank_caption`\n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_dims_table>` {glue:text}`ticino_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": "ticino_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": "ticino_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 = \"ticino_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} ticino_survey_area_landuse\n",
    "---\n",
    "name: 'ticino_survey_area_landuse'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_landuse>` {glue:text}`ticino_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 Ticino, März 2020 bis Mai 2021, n = 28. Rechts: empirische Verteilungsfunktion der Erhebungsergebnisse Erhebungsgebiet Ticino.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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": "ticino_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(\"ticino_sample_totals\", fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_sample_totals\n",
    "---\n",
    "name: 'ticino_survey_area_sample_totals'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_sample_totals>` {glue:text}`ticino_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 Ticino. Gefunden Materialarten am Erhebungsgebiet Ticino in Stückzahlen und als prozentuale Anteile (stückzahlbezogen).'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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": "ticino_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('ticino_survey_area_sample_material_tables', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_survey_area_sample_material_tables\n",
    "---\n",
    "name: 'ticino_survey_area_sample_material_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "{numref}`Abbildung %s: <ticino_survey_area_sample_material_tables>` {glue:text}`ticino_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 Ticino: d. h. Objekte mit einer Häufigkeitsrate von mindestens 50% und/oder Top Ten nach Anzahl. Zusammengenommen machen die häufigsten Objekte 74% aller gefundenen Objekte aus. Anmerkung: p/100 m = Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_most_common_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b1482 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_b1482 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_b1482 tr {\n  font-size: 12px;\n}\n#T_b1482 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_b1482 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_b1482\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b1482_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_b1482_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_b1482_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_b1482_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b1482_level0_row0\" class=\"row_heading level0 row0\" >Zigarettenfilter</th>\n      <td id=\"T_b1482_row0_col0\" class=\"data row0 col0\" >743</td>\n      <td id=\"T_b1482_row0_col1\" class=\"data row0 col1\" >24%</td>\n      <td id=\"T_b1482_row0_col2\" class=\"data row0 col2\" >96%</td>\n      <td id=\"T_b1482_row0_col3\" class=\"data row0 col3\" >15,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row1\" class=\"row_heading level0 row1\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_b1482_row1_col0\" class=\"data row1 col0\" >274</td>\n      <td id=\"T_b1482_row1_col1\" class=\"data row1 col1\" >9%</td>\n      <td id=\"T_b1482_row1_col2\" class=\"data row1 col2\" >85%</td>\n      <td id=\"T_b1482_row1_col3\" class=\"data row1 col3\" >8,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row2\" class=\"row_heading level0 row2\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_b1482_row2_col0\" class=\"data row2 col0\" >259</td>\n      <td id=\"T_b1482_row2_col1\" class=\"data row2 col1\" >8%</td>\n      <td id=\"T_b1482_row2_col2\" class=\"data row2 col2\" >85%</td>\n      <td id=\"T_b1482_row2_col3\" class=\"data row2 col3\" >9,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row3\" class=\"row_heading level0 row3\" >Industriepellets (Nurdles)</th>\n      <td id=\"T_b1482_row3_col0\" class=\"data row3 col0\" >247</td>\n      <td id=\"T_b1482_row3_col1\" class=\"data row3 col1\" >8%</td>\n      <td id=\"T_b1482_row3_col2\" class=\"data row3 col2\" >17%</td>\n      <td id=\"T_b1482_row3_col3\" class=\"data row3 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row4\" class=\"row_heading level0 row4\" >Expandiertes Polystyrol</th>\n      <td id=\"T_b1482_row4_col0\" class=\"data row4 col0\" >237</td>\n      <td id=\"T_b1482_row4_col1\" class=\"data row4 col1\" >7%</td>\n      <td id=\"T_b1482_row4_col2\" class=\"data row4 col2\" >57%</td>\n      <td id=\"T_b1482_row4_col3\" class=\"data row4 col3\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row5\" class=\"row_heading level0 row5\" >Snack-Verpackungen</th>\n      <td id=\"T_b1482_row5_col0\" class=\"data row5 col0\" >130</td>\n      <td id=\"T_b1482_row5_col1\" class=\"data row5 col1\" >4%</td>\n      <td id=\"T_b1482_row5_col2\" class=\"data row5 col2\" >71%</td>\n      <td id=\"T_b1482_row5_col3\" class=\"data row5 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row6\" class=\"row_heading level0 row6\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_b1482_row6_col0\" class=\"data row6 col0\" >80</td>\n      <td id=\"T_b1482_row6_col1\" class=\"data row6 col1\" >2%</td>\n      <td id=\"T_b1482_row6_col2\" class=\"data row6 col2\" >82%</td>\n      <td id=\"T_b1482_row6_col3\" class=\"data row6 col3\" >3,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row7\" class=\"row_heading level0 row7\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_b1482_row7_col0\" class=\"data row7 col0\" >72</td>\n      <td id=\"T_b1482_row7_col1\" class=\"data row7 col1\" >2%</td>\n      <td id=\"T_b1482_row7_col2\" class=\"data row7 col2\" >57%</td>\n      <td id=\"T_b1482_row7_col3\" class=\"data row7 col3\" >2,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row8\" class=\"row_heading level0 row8\" >Styropor < 5mm</th>\n      <td id=\"T_b1482_row8_col0\" class=\"data row8 col0\" >65</td>\n      <td id=\"T_b1482_row8_col1\" class=\"data row8 col1\" >2%</td>\n      <td id=\"T_b1482_row8_col2\" class=\"data row8 col2\" >32%</td>\n      <td id=\"T_b1482_row8_col3\" class=\"data row8 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row9\" class=\"row_heading level0 row9\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_b1482_row9_col0\" class=\"data row9 col0\" >64</td>\n      <td id=\"T_b1482_row9_col1\" class=\"data row9 col1\" >2%</td>\n      <td id=\"T_b1482_row9_col2\" class=\"data row9 col2\" >67%</td>\n      <td id=\"T_b1482_row9_col3\" class=\"data row9 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row10\" class=\"row_heading level0 row10\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_b1482_row10_col0\" class=\"data row10 col0\" >54</td>\n      <td id=\"T_b1482_row10_col1\" class=\"data row10 col1\" >1%</td>\n      <td id=\"T_b1482_row10_col2\" class=\"data row10 col2\" >50%</td>\n      <td id=\"T_b1482_row10_col3\" class=\"data row10 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b1482_level0_row11\" class=\"row_heading level0 row11\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_b1482_row11_col0\" class=\"data row11 col0\" >42</td>\n      <td id=\"T_b1482_row11_col1\" class=\"data row11 col1\" >1%</td>\n      <td id=\"T_b1482_row11_col2\" class=\"data row11 col2\" >57%</td>\n      <td id=\"T_b1482_row11_col3\" class=\"data row11 col3\" >2,5</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7faf5a52b670>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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('ticino_most_common_caption', mc_caption_string, display=False)\n",
    "glue('ticino_most_common_tables', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_most_common_tables\n",
    "---\n",
    "name: 'ticino_survey_area_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_most_common_tables>` {glue:text}`ticino_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 Ticino.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_mc_heat_map_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_4bccb tr {\n  font-size: 12px;\n}\n#T_4bccb th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_4bccb td {\n  padding: 6px;\n}\n#T_4bccb_row0_col0, #T_4bccb_row8_col6 {\n  background-color: #fffdd9;\n  color: #000000;\n}\n#T_4bccb_row0_col1, #T_4bccb_row1_col2, #T_4bccb_row2_col5, #T_4bccb_row11_col2 {\n  background-color: #fffcd8;\n  color: #000000;\n}\n#T_4bccb_row0_col2, #T_4bccb_row1_col5, #T_4bccb_row3_col0, #T_4bccb_row10_col0, #T_4bccb_row10_col1 {\n  background-color: #fffddd;\n  color: #000000;\n}\n#T_4bccb_row0_col3, #T_4bccb_row1_col0, #T_4bccb_row1_col3, #T_4bccb_row1_col4, #T_4bccb_row4_col0, #T_4bccb_row4_col2, #T_4bccb_row4_col3, #T_4bccb_row4_col4, #T_4bccb_row4_col5, #T_4bccb_row4_col6, #T_4bccb_row5_col2, #T_4bccb_row5_col3, #T_4bccb_row6_col0, #T_4bccb_row6_col3, #T_4bccb_row6_col4, #T_4bccb_row7_col0, #T_4bccb_row7_col4, #T_4bccb_row8_col3, #T_4bccb_row8_col4, #T_4bccb_row9_col0, #T_4bccb_row9_col1, #T_4bccb_row9_col2, #T_4bccb_row9_col3, #T_4bccb_row9_col4, #T_4bccb_row9_col5, #T_4bccb_row9_col6, #T_4bccb_row10_col2, #T_4bccb_row10_col6 {\n  background-color: #ffffe5;\n  color: #000000;\n}\n#T_4bccb_row0_col4, #T_4bccb_row8_col1 {\n  background-color: #fff9c5;\n  color: #000000;\n}\n#T_4bccb_row0_col5, #T_4bccb_row5_col1, #T_4bccb_row6_col1 {\n  background-color: #fffddb;\n  color: #000000;\n}\n#T_4bccb_row0_col6, #T_4bccb_row2_col3, #T_4bccb_row3_col5, #T_4bccb_row7_col2, #T_4bccb_row7_col5, #T_4bccb_row8_col0, #T_4bccb_row10_col3 {\n  background-color: #fffee1;\n  color: #000000;\n}\n#T_4bccb_row1_col1, #T_4bccb_row2_col0, #T_4bccb_row3_col2, #T_4bccb_row3_col4, #T_4bccb_row6_col2, #T_4bccb_row6_col5, #T_4bccb_row10_col5 {\n  background-color: #fffee2;\n  color: #000000;\n}\n#T_4bccb_row1_col6, #T_4bccb_row3_col3, #T_4bccb_row3_col6, #T_4bccb_row7_col1 {\n  background-color: #fffedf;\n  color: #000000;\n}\n#T_4bccb_row2_col1 {\n  background-color: #fff1ae;\n  color: #000000;\n}\n#T_4bccb_row2_col2, #T_4bccb_row11_col4 {\n  background-color: #fffcd4;\n  color: #000000;\n}\n#T_4bccb_row2_col4, #T_4bccb_row5_col5, #T_4bccb_row5_col6, #T_4bccb_row6_col6, #T_4bccb_row7_col6 {\n  background-color: #ffffe4;\n  color: #000000;\n}\n#T_4bccb_row2_col6 {\n  background-color: #fffacd;\n  color: #000000;\n}\n#T_4bccb_row3_col1, #T_4bccb_row5_col0, #T_4bccb_row5_col4, #T_4bccb_row8_col5, #T_4bccb_row10_col4 {\n  background-color: #fffee0;\n  color: #000000;\n}\n#T_4bccb_row4_col1 {\n  background-color: #ea6e13;\n  color: #f1f1f1;\n}\n#T_4bccb_row7_col3, #T_4bccb_row8_col2 {\n  background-color: #fffddc;\n  color: #000000;\n}\n#T_4bccb_row11_col0, #T_4bccb_row11_col6 {\n  background-color: #fffaca;\n  color: #000000;\n}\n#T_4bccb_row11_col1 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_4bccb_row11_col3 {\n  background-color: #fffbd2;\n  color: #000000;\n}\n#T_4bccb_row11_col5 {\n  background-color: #fffbd0;\n  color: #000000;\n}\n#T_4bccb_level0_col0, #T_4bccb_level0_col1, #T_4bccb_level0_col2, #T_4bccb_level0_col3, #T_4bccb_level0_col4, #T_4bccb_level0_col5, #T_4bccb_level0_col6 {\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_4bccb\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_4bccb_level0_col0\" class=\"col_heading level0 col0\" >Cassarate</th>\n      <th id=\"T_4bccb_level0_col1\" class=\"col_heading level0 col1\" >Lago di Lugano</th>\n      <th id=\"T_4bccb_level0_col2\" class=\"col_heading level0 col2\" >Lago Maggiore</th>\n      <th id=\"T_4bccb_level0_col3\" class=\"col_heading level0 col3\" >Maggia</th>\n      <th id=\"T_4bccb_level0_col4\" class=\"col_heading level0 col4\" >Ticino</th>\n      <th id=\"T_4bccb_level0_col5\" class=\"col_heading level0 col5\" >Erhebungsgebiet Ticino</th>\n      <th id=\"T_4bccb_level0_col6\" class=\"col_heading level0 col6\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_4bccb_level0_row0\" class=\"row_heading level0 row0\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_4bccb_row0_col0\" class=\"data row0 col0\" >9,0</td>\n      <td id=\"T_4bccb_row0_col1\" class=\"data row0 col1\" >9,5</td>\n      <td id=\"T_4bccb_row0_col2\" class=\"data row0 col2\" >6,0</td>\n      <td id=\"T_4bccb_row0_col3\" class=\"data row0 col3\" >0,0</td>\n      <td id=\"T_4bccb_row0_col4\" class=\"data row0 col4\" >24,5</td>\n      <td id=\"T_4bccb_row0_col5\" class=\"data row0 col5\" >8,0</td>\n      <td id=\"T_4bccb_row0_col6\" class=\"data row0 col6\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row1\" class=\"row_heading level0 row1\" >Expandiertes Polystyrol</th>\n      <td id=\"T_4bccb_row1_col0\" class=\"data row1 col0\" >0,0</td>\n      <td id=\"T_4bccb_row1_col1\" class=\"data row1 col1\" >2,0</td>\n      <td id=\"T_4bccb_row1_col2\" class=\"data row1 col2\" >9,5</td>\n      <td id=\"T_4bccb_row1_col3\" class=\"data row1 col3\" >0,0</td>\n      <td id=\"T_4bccb_row1_col4\" class=\"data row1 col4\" >0,0</td>\n      <td id=\"T_4bccb_row1_col5\" class=\"data row1 col5\" >6,0</td>\n      <td id=\"T_4bccb_row1_col6\" class=\"data row1 col6\" >5,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row2\" class=\"row_heading level0 row2\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_4bccb_row2_col0\" class=\"data row2 col0\" >2,0</td>\n      <td id=\"T_4bccb_row2_col1\" class=\"data row2 col1\" >40,5</td>\n      <td id=\"T_4bccb_row2_col2\" class=\"data row2 col2\" >12,5</td>\n      <td id=\"T_4bccb_row2_col3\" class=\"data row2 col3\" >3,0</td>\n      <td id=\"T_4bccb_row2_col4\" class=\"data row2 col4\" >1,0</td>\n      <td id=\"T_4bccb_row2_col5\" class=\"data row2 col5\" >9,5</td>\n      <td id=\"T_4bccb_row2_col6\" class=\"data row2 col6\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row3\" class=\"row_heading level0 row3\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_4bccb_row3_col0\" class=\"data row3 col0\" >6,0</td>\n      <td id=\"T_4bccb_row3_col1\" class=\"data row3 col1\" >4,5</td>\n      <td id=\"T_4bccb_row3_col2\" class=\"data row3 col2\" >2,5</td>\n      <td id=\"T_4bccb_row3_col3\" class=\"data row3 col3\" >5,0</td>\n      <td id=\"T_4bccb_row3_col4\" class=\"data row3 col4\" >2,5</td>\n      <td id=\"T_4bccb_row3_col5\" class=\"data row3 col5\" >3,5</td>\n      <td id=\"T_4bccb_row3_col6\" class=\"data row3 col6\" >5,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row4\" class=\"row_heading level0 row4\" >Industriepellets (Nurdles)</th>\n      <td id=\"T_4bccb_row4_col0\" class=\"data row4 col0\" >0,0</td>\n      <td id=\"T_4bccb_row4_col1\" class=\"data row4 col1\" >153,0</td>\n      <td id=\"T_4bccb_row4_col2\" class=\"data row4 col2\" >0,0</td>\n      <td id=\"T_4bccb_row4_col3\" class=\"data row4 col3\" >0,0</td>\n      <td id=\"T_4bccb_row4_col4\" class=\"data row4 col4\" >0,0</td>\n      <td id=\"T_4bccb_row4_col5\" class=\"data row4 col5\" >0,0</td>\n      <td id=\"T_4bccb_row4_col6\" class=\"data row4 col6\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row5\" class=\"row_heading level0 row5\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_4bccb_row5_col0\" class=\"data row5 col0\" >4,0</td>\n      <td id=\"T_4bccb_row5_col1\" class=\"data row5 col1\" >8,5</td>\n      <td id=\"T_4bccb_row5_col2\" class=\"data row5 col2\" >0,0</td>\n      <td id=\"T_4bccb_row5_col3\" class=\"data row5 col3\" >0,0</td>\n      <td id=\"T_4bccb_row5_col4\" class=\"data row5 col4\" >4,5</td>\n      <td id=\"T_4bccb_row5_col5\" class=\"data row5 col5\" >1,0</td>\n      <td id=\"T_4bccb_row5_col6\" class=\"data row5 col6\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row6\" class=\"row_heading level0 row6\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_4bccb_row6_col0\" class=\"data row6 col0\" >0,0</td>\n      <td id=\"T_4bccb_row6_col1\" class=\"data row6 col1\" >8,0</td>\n      <td id=\"T_4bccb_row6_col2\" class=\"data row6 col2\" >2,5</td>\n      <td id=\"T_4bccb_row6_col3\" class=\"data row6 col3\" >0,0</td>\n      <td id=\"T_4bccb_row6_col4\" class=\"data row6 col4\" >0,0</td>\n      <td id=\"T_4bccb_row6_col5\" class=\"data row6 col5\" >2,5</td>\n      <td id=\"T_4bccb_row6_col6\" class=\"data row6 col6\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row7\" class=\"row_heading level0 row7\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_4bccb_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_4bccb_row7_col1\" class=\"data row7 col1\" >5,0</td>\n      <td id=\"T_4bccb_row7_col2\" class=\"data row7 col2\" >3,5</td>\n      <td id=\"T_4bccb_row7_col3\" class=\"data row7 col3\" >7,0</td>\n      <td id=\"T_4bccb_row7_col4\" class=\"data row7 col4\" >0,0</td>\n      <td id=\"T_4bccb_row7_col5\" class=\"data row7 col5\" >3,0</td>\n      <td id=\"T_4bccb_row7_col6\" class=\"data row7 col6\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row8\" class=\"row_heading level0 row8\" >Snack-Verpackungen</th>\n      <td id=\"T_4bccb_row8_col0\" class=\"data row8 col0\" >3,0</td>\n      <td id=\"T_4bccb_row8_col1\" class=\"data row8 col1\" >24,0</td>\n      <td id=\"T_4bccb_row8_col2\" class=\"data row8 col2\" >7,0</td>\n      <td id=\"T_4bccb_row8_col3\" class=\"data row8 col3\" >0,0</td>\n      <td id=\"T_4bccb_row8_col4\" class=\"data row8 col4\" >0,0</td>\n      <td id=\"T_4bccb_row8_col5\" class=\"data row8 col5\" >4,0</td>\n      <td id=\"T_4bccb_row8_col6\" class=\"data row8 col6\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row9\" class=\"row_heading level0 row9\" >Styropor < 5mm</th>\n      <td id=\"T_4bccb_row9_col0\" class=\"data row9 col0\" >0,0</td>\n      <td id=\"T_4bccb_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_4bccb_row9_col2\" class=\"data row9 col2\" >0,0</td>\n      <td id=\"T_4bccb_row9_col3\" class=\"data row9 col3\" >0,0</td>\n      <td id=\"T_4bccb_row9_col4\" class=\"data row9 col4\" >0,0</td>\n      <td id=\"T_4bccb_row9_col5\" class=\"data row9 col5\" >0,0</td>\n      <td id=\"T_4bccb_row9_col6\" class=\"data row9 col6\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row10\" class=\"row_heading level0 row10\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_4bccb_row10_col0\" class=\"data row10 col0\" >6,0</td>\n      <td id=\"T_4bccb_row10_col1\" class=\"data row10 col1\" >6,5</td>\n      <td id=\"T_4bccb_row10_col2\" class=\"data row10 col2\" >0,0</td>\n      <td id=\"T_4bccb_row10_col3\" class=\"data row10 col3\" >3,0</td>\n      <td id=\"T_4bccb_row10_col4\" class=\"data row10 col4\" >4,5</td>\n      <td id=\"T_4bccb_row10_col5\" class=\"data row10 col5\" >2,5</td>\n      <td id=\"T_4bccb_row10_col6\" class=\"data row10 col6\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_4bccb_level0_row11\" class=\"row_heading level0 row11\" >Zigarettenfilter</th>\n      <td id=\"T_4bccb_row11_col0\" class=\"data row11 col0\" >20,0</td>\n      <td id=\"T_4bccb_row11_col1\" class=\"data row11 col1\" >242,0</td>\n      <td id=\"T_4bccb_row11_col2\" class=\"data row11 col2\" >9,5</td>\n      <td id=\"T_4bccb_row11_col3\" class=\"data row11 col3\" >15,0</td>\n      <td id=\"T_4bccb_row11_col4\" class=\"data row11 col4\" >13,0</td>\n      <td id=\"T_4bccb_row11_col5\" class=\"data row11 col5\" >15,5</td>\n      <td id=\"T_4bccb_row11_col6\" class=\"data row11 col6\" >20,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7faf645b09d0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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} ticino_most_common_heat_map\n",
    "---\n",
    "name: 'ticino_survey_area_most_common_heat_map'\n",
    "---\n",
    "{glue:text}`blank_caption`  \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_most_common_heat_map>` {glue:text}`ticino_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 Ticino, monatliche Durchschnittsergebnisse p/100 m'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_monthly_results_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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background-color: #fff9c7;\n  color: #000000;\n}\n#T_3651e_row3_col3, #T_3651e_row6_col10, #T_3651e_row10_col10 {\n  background-color: #fff8c0;\n  color: #000000;\n}\n#T_3651e_row3_col5 {\n  background-color: #fff9c6;\n  color: #000000;\n}\n#T_3651e_row3_col6, #T_3651e_row9_col10 {\n  background-color: #fff9c9;\n  color: #000000;\n}\n#T_3651e_row4_col0 {\n  background-color: #fffbd0;\n  color: #000000;\n}\n#T_3651e_row4_col6 {\n  background-color: #fffddb;\n  color: #000000;\n}\n#T_3651e_row4_col10, #T_3651e_row9_col5, #T_3651e_row11_col5 {\n  background-color: #fffcd8;\n  color: #000000;\n}\n#T_3651e_row5_col0 {\n  background-color: #fee79a;\n  color: #000000;\n}\n#T_3651e_row5_col6 {\n  background-color: #b13f03;\n  color: #f1f1f1;\n}\n#T_3651e_row5_col10 {\n  background-color: #feab39;\n  color: #000000;\n}\n#T_3651e_row6_col0 {\n  background-color: #fffbd2;\n  color: #000000;\n}\n#T_3651e_row6_col3 {\n  background-color: #fffacd;\n  color: #000000;\n}\n#T_3651e_row6_col5 {\n  background-color: #fffbcf;\n  color: #000000;\n}\n#T_3651e_row6_col6 {\n  background-color: #fff7be;\n  color: #000000;\n}\n#T_3651e_row7_col3, #T_3651e_row8_col0 {\n  background-color: #fffee0;\n  color: #000000;\n}\n#T_3651e_row7_col6 {\n  background-color: #fffee2;\n  color: #000000;\n}\n#T_3651e_row8_col3 {\n  background-color: #fffdd9;\n  color: #000000;\n}\n#T_3651e_row8_col6 {\n  background-color: #fec859;\n  color: #000000;\n}\n#T_3651e_row9_col0 {\n  background-color: #fee392;\n  color: #000000;\n}\n#T_3651e_row9_col3 {\n  background-color: #fe9c2c;\n  color: #000000;\n}\n#T_3651e_row9_col6 {\n  background-color: #feb23f;\n  color: #000000;\n}\n#T_3651e_row10_col0 {\n  background-color: #ab3c03;\n  color: #f1f1f1;\n}\n#T_3651e_row10_col3 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_3651e_row10_col5 {\n  background-color: #feeaa1;\n  color: #000000;\n}\n#T_3651e_row11_col3 {\n  background-color: #fff8c1;\n  color: #000000;\n}\n#T_3651e_row11_col10 {\n  background-color: #ffface;\n  color: #000000;\n}\n</style>\n<table id=\"T_3651e\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_3651e_level0_col0\" class=\"col_heading level0 col0\" >Mai</th>\n      <th id=\"T_3651e_level0_col1\" class=\"col_heading level0 col1\" >Juni</th>\n      <th id=\"T_3651e_level0_col2\" class=\"col_heading level0 col2\" >Juli</th>\n      <th id=\"T_3651e_level0_col3\" class=\"col_heading level0 col3\" >Aug.</th>\n      <th id=\"T_3651e_level0_col4\" class=\"col_heading level0 col4\" >Sept.</th>\n      <th id=\"T_3651e_level0_col5\" class=\"col_heading level0 col5\" >Okt.</th>\n      <th id=\"T_3651e_level0_col6\" class=\"col_heading level0 col6\" >Nov.</th>\n      <th id=\"T_3651e_level0_col7\" class=\"col_heading level0 col7\" >Dez.</th>\n      <th id=\"T_3651e_level0_col8\" class=\"col_heading level0 col8\" >Jan.</th>\n      <th id=\"T_3651e_level0_col9\" class=\"col_heading level0 col9\" >Feb.</th>\n      <th id=\"T_3651e_level0_col10\" class=\"col_heading level0 col10\" >März</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_3651e_level0_row0\" class=\"row_heading level0 row0\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_3651e_row0_col0\" class=\"data row0 col0\" >2,0</td>\n      <td id=\"T_3651e_row0_col1\" class=\"data row0 col1\" >0,0</td>\n      <td id=\"T_3651e_row0_col2\" class=\"data row0 col2\" >0,0</td>\n      <td id=\"T_3651e_row0_col3\" class=\"data row0 col3\" >1,8</td>\n      <td id=\"T_3651e_row0_col4\" class=\"data row0 col4\" >0,0</td>\n      <td id=\"T_3651e_row0_col5\" class=\"data row0 col5\" >0,0</td>\n      <td id=\"T_3651e_row0_col6\" class=\"data row0 col6\" >1,7</td>\n      <td id=\"T_3651e_row0_col7\" class=\"data row0 col7\" >0,0</td>\n      <td id=\"T_3651e_row0_col8\" class=\"data row0 col8\" >0,0</td>\n      <td id=\"T_3651e_row0_col9\" class=\"data row0 col9\" >0,0</td>\n      <td id=\"T_3651e_row0_col10\" class=\"data row0 col10\" >3,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row1\" class=\"row_heading level0 row1\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_3651e_row1_col0\" class=\"data row1 col0\" >16,7</td>\n      <td id=\"T_3651e_row1_col1\" class=\"data row1 col1\" >0,0</td>\n      <td id=\"T_3651e_row1_col2\" class=\"data row1 col2\" >0,0</td>\n      <td id=\"T_3651e_row1_col3\" class=\"data row1 col3\" >10,7</td>\n      <td id=\"T_3651e_row1_col4\" class=\"data row1 col4\" >0,0</td>\n      <td id=\"T_3651e_row1_col5\" class=\"data row1 col5\" >0,0</td>\n      <td id=\"T_3651e_row1_col6\" class=\"data row1 col6\" >12,7</td>\n      <td id=\"T_3651e_row1_col7\" class=\"data row1 col7\" >0,0</td>\n      <td id=\"T_3651e_row1_col8\" class=\"data row1 col8\" >0,0</td>\n      <td id=\"T_3651e_row1_col9\" class=\"data row1 col9\" >0,0</td>\n      <td id=\"T_3651e_row1_col10\" class=\"data row1 col10\" >7,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row2\" class=\"row_heading level0 row2\" >Snack-Verpackungen</th>\n      <td id=\"T_3651e_row2_col0\" class=\"data row2 col0\" >9,7</td>\n      <td id=\"T_3651e_row2_col1\" class=\"data row2 col1\" >0,0</td>\n      <td id=\"T_3651e_row2_col2\" class=\"data row2 col2\" >0,0</td>\n      <td id=\"T_3651e_row2_col3\" class=\"data row2 col3\" >10,2</td>\n      <td id=\"T_3651e_row2_col4\" class=\"data row2 col4\" >0,0</td>\n      <td id=\"T_3651e_row2_col5\" class=\"data row2 col5\" >0,0</td>\n      <td id=\"T_3651e_row2_col6\" class=\"data row2 col6\" >15,3</td>\n      <td id=\"T_3651e_row2_col7\" class=\"data row2 col7\" >0,0</td>\n      <td id=\"T_3651e_row2_col8\" class=\"data row2 col8\" >0,0</td>\n      <td id=\"T_3651e_row2_col9\" class=\"data row2 col9\" >0,0</td>\n      <td id=\"T_3651e_row2_col10\" class=\"data row2 col10\" >6,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row3\" class=\"row_heading level0 row3\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_3651e_row3_col0\" class=\"data row3 col0\" >6,7</td>\n      <td id=\"T_3651e_row3_col1\" class=\"data row3 col1\" >0,0</td>\n      <td id=\"T_3651e_row3_col2\" class=\"data row3 col2\" >0,0</td>\n      <td id=\"T_3651e_row3_col3\" class=\"data row3 col3\" >8,5</td>\n      <td id=\"T_3651e_row3_col4\" class=\"data row3 col4\" >0,0</td>\n      <td id=\"T_3651e_row3_col5\" class=\"data row3 col5\" >7,0</td>\n      <td id=\"T_3651e_row3_col6\" class=\"data row3 col6\" >6,3</td>\n      <td id=\"T_3651e_row3_col7\" class=\"data row3 col7\" >0,0</td>\n      <td id=\"T_3651e_row3_col8\" class=\"data row3 col8\" >0,0</td>\n      <td id=\"T_3651e_row3_col9\" class=\"data row3 col9\" >0,0</td>\n      <td id=\"T_3651e_row3_col10\" class=\"data row3 col10\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row4\" class=\"row_heading level0 row4\" >Kunststoff-Bauabfälle</th>\n      <td id=\"T_3651e_row4_col0\" class=\"data row4 col0\" >4,7</td>\n      <td id=\"T_3651e_row4_col1\" class=\"data row4 col1\" >0,0</td>\n      <td id=\"T_3651e_row4_col2\" class=\"data row4 col2\" >0,0</td>\n      <td id=\"T_3651e_row4_col3\" class=\"data row4 col3\" >3,3</td>\n      <td id=\"T_3651e_row4_col4\" class=\"data row4 col4\" >0,0</td>\n      <td id=\"T_3651e_row4_col5\" class=\"data row4 col5\" >0,0</td>\n      <td id=\"T_3651e_row4_col6\" class=\"data row4 col6\" >2,3</td>\n      <td id=\"T_3651e_row4_col7\" class=\"data row4 col7\" >0,0</td>\n      <td id=\"T_3651e_row4_col8\" class=\"data row4 col8\" >0,0</td>\n      <td id=\"T_3651e_row4_col9\" class=\"data row4 col9\" >0,0</td>\n      <td id=\"T_3651e_row4_col10\" class=\"data row4 col10\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row5\" class=\"row_heading level0 row5\" >Expandiertes Polystyrol</th>\n      <td id=\"T_3651e_row5_col0\" class=\"data row5 col0\" >16,3</td>\n      <td id=\"T_3651e_row5_col1\" class=\"data row5 col1\" >0,0</td>\n      <td id=\"T_3651e_row5_col2\" class=\"data row5 col2\" >0,0</td>\n      <td id=\"T_3651e_row5_col3\" class=\"data row5 col3\" >7,2</td>\n      <td id=\"T_3651e_row5_col4\" class=\"data row5 col4\" >0,0</td>\n      <td id=\"T_3651e_row5_col5\" class=\"data row5 col5\" >0,0</td>\n      <td id=\"T_3651e_row5_col6\" class=\"data row5 col6\" >59,3</td>\n      <td id=\"T_3651e_row5_col7\" class=\"data row5 col7\" >0,0</td>\n      <td id=\"T_3651e_row5_col8\" class=\"data row5 col8\" >0,0</td>\n      <td id=\"T_3651e_row5_col9\" class=\"data row5 col9\" >0,0</td>\n      <td id=\"T_3651e_row5_col10\" class=\"data row5 col10\" >32,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row6\" class=\"row_heading level0 row6\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_3651e_row6_col0\" class=\"data row6 col0\" >4,3</td>\n      <td id=\"T_3651e_row6_col1\" class=\"data row6 col1\" >0,0</td>\n      <td id=\"T_3651e_row6_col2\" class=\"data row6 col2\" >0,0</td>\n      <td id=\"T_3651e_row6_col3\" class=\"data row6 col3\" >5,5</td>\n      <td id=\"T_3651e_row6_col4\" class=\"data row6 col4\" >0,0</td>\n      <td id=\"T_3651e_row6_col5\" class=\"data row6 col5\" >5,0</td>\n      <td id=\"T_3651e_row6_col6\" class=\"data row6 col6\" >8,7</td>\n      <td id=\"T_3651e_row6_col7\" class=\"data row6 col7\" >0,0</td>\n      <td id=\"T_3651e_row6_col8\" class=\"data row6 col8\" >0,0</td>\n      <td id=\"T_3651e_row6_col9\" class=\"data row6 col9\" >0,0</td>\n      <td id=\"T_3651e_row6_col10\" class=\"data row6 col10\" >8,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row7\" class=\"row_heading level0 row7\" >Industriepellets (Nurdles)</th>\n      <td id=\"T_3651e_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_3651e_row7_col1\" class=\"data row7 col1\" >0,0</td>\n      <td id=\"T_3651e_row7_col2\" class=\"data row7 col2\" >0,0</td>\n      <td id=\"T_3651e_row7_col3\" class=\"data row7 col3\" >1,2</td>\n      <td id=\"T_3651e_row7_col4\" class=\"data row7 col4\" >0,0</td>\n      <td id=\"T_3651e_row7_col5\" class=\"data row7 col5\" >0,0</td>\n      <td id=\"T_3651e_row7_col6\" class=\"data row7 col6\" >0,7</td>\n      <td id=\"T_3651e_row7_col7\" class=\"data row7 col7\" >0,0</td>\n      <td id=\"T_3651e_row7_col8\" class=\"data row7 col8\" >0,0</td>\n      <td id=\"T_3651e_row7_col9\" class=\"data row7 col9\" >0,0</td>\n      <td id=\"T_3651e_row7_col10\" class=\"data row7 col10\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row8\" class=\"row_heading level0 row8\" >Styropor < 5mm</th>\n      <td id=\"T_3651e_row8_col0\" class=\"data row8 col0\" >1,3</td>\n      <td id=\"T_3651e_row8_col1\" class=\"data row8 col1\" >0,0</td>\n      <td id=\"T_3651e_row8_col2\" class=\"data row8 col2\" >0,0</td>\n      <td id=\"T_3651e_row8_col3\" class=\"data row8 col3\" >2,8</td>\n      <td id=\"T_3651e_row8_col4\" class=\"data row8 col4\" >0,0</td>\n      <td id=\"T_3651e_row8_col5\" class=\"data row8 col5\" >0,0</td>\n      <td id=\"T_3651e_row8_col6\" class=\"data row8 col6\" >26,0</td>\n      <td id=\"T_3651e_row8_col7\" class=\"data row8 col7\" >0,0</td>\n      <td id=\"T_3651e_row8_col8\" class=\"data row8 col8\" >0,0</td>\n      <td id=\"T_3651e_row8_col9\" class=\"data row8 col9\" >0,0</td>\n      <td id=\"T_3651e_row8_col10\" class=\"data row8 col10\" >0,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row9\" class=\"row_heading level0 row9\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_3651e_row9_col0\" class=\"data row9 col0\" >18,0</td>\n      <td id=\"T_3651e_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_3651e_row9_col2\" class=\"data row9 col2\" >0,0</td>\n      <td id=\"T_3651e_row9_col3\" class=\"data row9 col3\" >35,7</td>\n      <td id=\"T_3651e_row9_col4\" class=\"data row9 col4\" >0,0</td>\n      <td id=\"T_3651e_row9_col5\" class=\"data row9 col5\" >3,0</td>\n      <td id=\"T_3651e_row9_col6\" class=\"data row9 col6\" >31,0</td>\n      <td id=\"T_3651e_row9_col7\" class=\"data row9 col7\" >0,0</td>\n      <td id=\"T_3651e_row9_col8\" class=\"data row9 col8\" >0,0</td>\n      <td id=\"T_3651e_row9_col9\" class=\"data row9 col9\" >0,0</td>\n      <td id=\"T_3651e_row9_col10\" class=\"data row9 col10\" >6,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row10\" class=\"row_heading level0 row10\" >Zigarettenfilter</th>\n      <td id=\"T_3651e_row10_col0\" class=\"data row10 col0\" >60,3</td>\n      <td id=\"T_3651e_row10_col1\" class=\"data row10 col1\" >0,0</td>\n      <td id=\"T_3651e_row10_col2\" class=\"data row10 col2\" >0,0</td>\n      <td id=\"T_3651e_row10_col3\" class=\"data row10 col3\" >72,8</td>\n      <td id=\"T_3651e_row10_col4\" class=\"data row10 col4\" >0,0</td>\n      <td id=\"T_3651e_row10_col5\" class=\"data row10 col5\" >15,0</td>\n      <td id=\"T_3651e_row10_col6\" class=\"data row10 col6\" >12,7</td>\n      <td id=\"T_3651e_row10_col7\" class=\"data row10 col7\" >0,0</td>\n      <td id=\"T_3651e_row10_col8\" class=\"data row10 col8\" >0,0</td>\n      <td id=\"T_3651e_row10_col9\" class=\"data row10 col9\" >0,0</td>\n      <td id=\"T_3651e_row10_col10\" class=\"data row10 col10\" >8,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_3651e_level0_row11\" class=\"row_heading level0 row11\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_3651e_row11_col0\" class=\"data row11 col0\" >0,0</td>\n      <td id=\"T_3651e_row11_col1\" class=\"data row11 col1\" >0,0</td>\n      <td id=\"T_3651e_row11_col2\" class=\"data row11 col2\" >0,0</td>\n      <td id=\"T_3651e_row11_col3\" class=\"data row11 col3\" >8,0</td>\n      <td id=\"T_3651e_row11_col4\" class=\"data row11 col4\" >0,0</td>\n      <td id=\"T_3651e_row11_col5\" class=\"data row11 col5\" >3,0</td>\n      <td id=\"T_3651e_row11_col6\" class=\"data row11 col6\" >3,3</td>\n      <td id=\"T_3651e_row11_col7\" class=\"data row11 col7\" >0,0</td>\n      <td id=\"T_3651e_row11_col8\" class=\"data row11 col8\" >0,0</td>\n      <td id=\"T_3651e_row11_col9\" class=\"data row11 col9\" >0,0</td>\n      <td id=\"T_3651e_row11_col10\" class=\"data row11 col10\" >5,2</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7faf5a498820>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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(\"ticino_monthly_results_caption\", monthly_data_caption, display=False)\n",
    "glue(\"ticino_monthly_results\", mcdm, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} ticino_monthly_results\n",
    "---\n",
    "name: 'ticino_monthly_results'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_monthly_results>` {glue:text}`ticino_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=28. 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": "ticino_spearmans_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 300x600 with 72 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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('ticino_survey_area_spearmans', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} ticino_survey_area_spearmans\n",
    "---\n",
    "name: 'ticino_survey_area_spearmans'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_spearmans>` {glue:text}`ticino_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": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "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]],\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",
    "# 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 Ticino. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_codegroup_percent_caption"
      }
     },
     "output_type": "display_data"
    },
    {
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#000000;\n}\n#T_966ab_row2_col4, #T_966ab_row4_col0, #T_966ab_row6_col4, #T_966ab_row9_col2 {\n  background-color: #fff5b8;\n  color: #000000;\n}\n#T_966ab_row3_col0, #T_966ab_row6_col0, #T_966ab_row7_col5, #T_966ab_row9_col0 {\n  background-color: #ffefaa;\n  color: #000000;\n}\n#T_966ab_row3_col2 {\n  background-color: #feaa38;\n  color: #000000;\n}\n#T_966ab_row3_col3 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_966ab_row3_col5 {\n  background-color: #fed36f;\n  color: #000000;\n}\n#T_966ab_row3_col6, #T_966ab_row8_col2 {\n  background-color: #fec95b;\n  color: #000000;\n}\n#T_966ab_row4_col3 {\n  background-color: #fed87a;\n  color: #000000;\n}\n#T_966ab_row5_col1, #T_966ab_row8_col0 {\n  background-color: #f78921;\n  color: #f1f1f1;\n}\n#T_966ab_row5_col5 {\n  background-color: #feeca4;\n  color: #000000;\n}\n#T_966ab_row5_col6 {\n  background-color: #fff2b1;\n  color: #000000;\n}\n#T_966ab_row7_col6 {\n  background-color: #fedd84;\n  color: #000000;\n}\n#T_966ab_row8_col1 {\n  background-color: #9e3604;\n  color: #f1f1f1;\n}\n#T_966ab_row8_col3 {\n  background-color: #febe4a;\n  color: #000000;\n}\n#T_966ab_row8_col4 {\n  background-color: #fee596;\n  color: #000000;\n}\n#T_966ab_row8_col5 {\n  background-color: #fd9627;\n  color: #000000;\n}\n#T_966ab_row8_col6 {\n  background-color: #fece65;\n  color: #000000;\n}\n#T_966ab_row9_col4 {\n  background-color: #fee28e;\n  color: #000000;\n}\n#T_966ab_level0_col0, #T_966ab_level0_col1, #T_966ab_level0_col2, #T_966ab_level0_col3, #T_966ab_level0_col4, #T_966ab_level0_col5, #T_966ab_level0_col6 {\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_966ab\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_966ab_level0_col0\" class=\"col_heading level0 col0\" >Cassarate</th>\n      <th id=\"T_966ab_level0_col1\" class=\"col_heading level0 col1\" >Lago di Lugano</th>\n      <th id=\"T_966ab_level0_col2\" class=\"col_heading level0 col2\" >Lago Maggiore</th>\n      <th id=\"T_966ab_level0_col3\" class=\"col_heading level0 col3\" >Maggia</th>\n      <th id=\"T_966ab_level0_col4\" class=\"col_heading level0 col4\" >Ticino</th>\n      <th id=\"T_966ab_level0_col5\" class=\"col_heading level0 col5\" >Erhebungsgebiet Ticino</th>\n      <th id=\"T_966ab_level0_col6\" class=\"col_heading level0 col6\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_966ab_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_966ab_row0_col0\" class=\"data row0 col0\" >6%</td>\n      <td id=\"T_966ab_row0_col1\" class=\"data row0 col1\" >2%</td>\n      <td id=\"T_966ab_row0_col2\" class=\"data row0 col2\" >2%</td>\n      <td id=\"T_966ab_row0_col3\" class=\"data row0 col3\" >4%</td>\n      <td id=\"T_966ab_row0_col4\" class=\"data row0 col4\" >2%</td>\n      <td id=\"T_966ab_row0_col5\" class=\"data row0 col5\" >2%</td>\n      <td id=\"T_966ab_row0_col6\" class=\"data row0 col6\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_966ab_row1_col0\" class=\"data row1 col0\" >22%</td>\n      <td id=\"T_966ab_row1_col1\" class=\"data row1 col1\" >11%</td>\n      <td id=\"T_966ab_row1_col2\" class=\"data row1 col2\" >25%</td>\n      <td id=\"T_966ab_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_966ab_row1_col4\" class=\"data row1 col4\" >45%</td>\n      <td id=\"T_966ab_row1_col5\" class=\"data row1 col5\" >21%</td>\n      <td id=\"T_966ab_row1_col6\" class=\"data row1 col6\" >19%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_966ab_row2_col0\" class=\"data row2 col0\" >2%</td>\n      <td id=\"T_966ab_row2_col1\" class=\"data row2 col1\" >1%</td>\n      <td id=\"T_966ab_row2_col2\" class=\"data row2 col2\" >3%</td>\n      <td id=\"T_966ab_row2_col3\" class=\"data row2 col3\" >2%</td>\n      <td id=\"T_966ab_row2_col4\" class=\"data row2 col4\" >7%</td>\n      <td id=\"T_966ab_row2_col5\" class=\"data row2 col5\" >2%</td>\n      <td id=\"T_966ab_row2_col6\" class=\"data row2 col6\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_966ab_row3_col0\" class=\"data row3 col0\" >9%</td>\n      <td id=\"T_966ab_row3_col1\" class=\"data row3 col1\" >3%</td>\n      <td id=\"T_966ab_row3_col2\" class=\"data row3 col2\" >23%</td>\n      <td id=\"T_966ab_row3_col3\" class=\"data row3 col3\" >51%</td>\n      <td id=\"T_966ab_row3_col4\" class=\"data row3 col4\" >6%</td>\n      <td id=\"T_966ab_row3_col5\" class=\"data row3 col5\" >16%</td>\n      <td id=\"T_966ab_row3_col6\" class=\"data row3 col6\" >18%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_966ab_row4_col0\" class=\"data row4 col0\" >7%</td>\n      <td id=\"T_966ab_row4_col1\" class=\"data row4 col1\" >2%</td>\n      <td id=\"T_966ab_row4_col2\" class=\"data row4 col2\" >4%</td>\n      <td id=\"T_966ab_row4_col3\" class=\"data row4 col3\" >15%</td>\n      <td id=\"T_966ab_row4_col4\" class=\"data row4 col4\" >6%</td>\n      <td id=\"T_966ab_row4_col5\" class=\"data row4 col5\" >4%</td>\n      <td id=\"T_966ab_row4_col6\" class=\"data row4 col6\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_966ab_row5_col0\" class=\"data row5 col0\" >0%</td>\n      <td id=\"T_966ab_row5_col1\" class=\"data row5 col1\" >28%</td>\n      <td id=\"T_966ab_row5_col2\" class=\"data row5 col2\" >3%</td>\n      <td id=\"T_966ab_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_966ab_row5_col4\" class=\"data row5 col4\" >0%</td>\n      <td id=\"T_966ab_row5_col5\" class=\"data row5 col5\" >10%</td>\n      <td id=\"T_966ab_row5_col6\" class=\"data row5 col6\" >8%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_966ab_row6_col0\" class=\"data row6 col0\" >9%</td>\n      <td id=\"T_966ab_row6_col1\" class=\"data row6 col1\" >1%</td>\n      <td id=\"T_966ab_row6_col2\" class=\"data row6 col2\" >4%</td>\n      <td id=\"T_966ab_row6_col3\" class=\"data row6 col3\" >2%</td>\n      <td id=\"T_966ab_row6_col4\" class=\"data row6 col4\" >7%</td>\n      <td id=\"T_966ab_row6_col5\" class=\"data row6 col5\" >3%</td>\n      <td id=\"T_966ab_row6_col6\" class=\"data row6 col6\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_966ab_row7_col0\" class=\"data row7 col0\" >4%</td>\n      <td id=\"T_966ab_row7_col1\" class=\"data row7 col1\" >5%</td>\n      <td id=\"T_966ab_row7_col2\" class=\"data row7 col2\" >11%</td>\n      <td id=\"T_966ab_row7_col3\" class=\"data row7 col3\" >4%</td>\n      <td id=\"T_966ab_row7_col4\" class=\"data row7 col4\" >1%</td>\n      <td id=\"T_966ab_row7_col5\" class=\"data row7 col5\" >9%</td>\n      <td id=\"T_966ab_row7_col6\" class=\"data row7 col6\" >14%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_966ab_row8_col0\" class=\"data row8 col0\" >28%</td>\n      <td id=\"T_966ab_row8_col1\" class=\"data row8 col1\" >44%</td>\n      <td id=\"T_966ab_row8_col2\" class=\"data row8 col2\" >18%</td>\n      <td id=\"T_966ab_row8_col3\" class=\"data row8 col3\" >20%</td>\n      <td id=\"T_966ab_row8_col4\" class=\"data row8 col4\" >12%</td>\n      <td id=\"T_966ab_row8_col5\" class=\"data row8 col5\" >26%</td>\n      <td id=\"T_966ab_row8_col6\" class=\"data row8 col6\" >17%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_966ab_row9_col0\" class=\"data row9 col0\" >9%</td>\n      <td id=\"T_966ab_row9_col1\" class=\"data row9 col1\" >2%</td>\n      <td id=\"T_966ab_row9_col2\" class=\"data row9 col2\" >7%</td>\n      <td id=\"T_966ab_row9_col3\" class=\"data row9 col3\" >4%</td>\n      <td id=\"T_966ab_row9_col4\" class=\"data row9 col4\" >13%</td>\n      <td id=\"T_966ab_row9_col5\" class=\"data row9 col5\" >6%</td>\n      <td id=\"T_966ab_row9_col6\" class=\"data row9 col6\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_966ab_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_966ab_row10_col0\" class=\"data row10 col0\" >4%</td>\n      <td id=\"T_966ab_row10_col1\" class=\"data row10 col1\" >0%</td>\n      <td id=\"T_966ab_row10_col2\" class=\"data row10 col2\" >0%</td>\n      <td id=\"T_966ab_row10_col3\" class=\"data row10 col3\" >0%</td>\n      <td id=\"T_966ab_row10_col4\" class=\"data row10 col4\" >1%</td>\n      <td id=\"T_966ab_row10_col5\" class=\"data row10 col5\" >0%</td>\n      <td id=\"T_966ab_row10_col6\" class=\"data row10 col6\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7faf5a531b80>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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(\"ticino_codegroup_percent_caption\", code_group_percent_caption, display=False)\n",
    "glue(\"ticino_codegroup_percent\", ptd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_codegroup_percent\n",
    "---\n",
    "name: 'ticino_survey_area_codegroup_percent'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_codegroup_percent>` {glue:text}`ticino_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 Ticino. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_codegroup_pcsm_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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#T_ce816_row10_col1, #T_ce816_row10_col2, #T_ce816_row10_col3, #T_ce816_row10_col4, #T_ce816_row10_col5, #T_ce816_row10_col6 {\n  background-color: #ffffe5;\n  color: #000000;\n}\n#T_ce816_row1_col0 {\n  background-color: #fffbd2;\n  color: #000000;\n}\n#T_ce816_row1_col1 {\n  background-color: #fec857;\n  color: #000000;\n}\n#T_ce816_row1_col2, #T_ce816_row3_col1 {\n  background-color: #fff9c6;\n  color: #000000;\n}\n#T_ce816_row1_col4 {\n  background-color: #fff2b1;\n  color: #000000;\n}\n#T_ce816_row1_col5 {\n  background-color: #fff8c2;\n  color: #000000;\n}\n#T_ce816_row1_col6 {\n  background-color: #fff6b9;\n  color: #000000;\n}\n#T_ce816_row2_col1, #T_ce816_row2_col6, #T_ce816_row3_col4, #T_ce816_row4_col0, #T_ce816_row4_col5, #T_ce816_row6_col2, #T_ce816_row6_col6, #T_ce816_row9_col5 {\n  background-color: #fffedf;\n  color: #000000;\n}\n#T_ce816_row2_col4 {\n  background-color: #fffddc;\n  color: #000000;\n}\n#T_ce816_row3_col0, #T_ce816_row4_col6, #T_ce816_row6_col0, #T_ce816_row6_col5, #T_ce816_row9_col0 {\n  background-color: #fffddd;\n  color: #000000;\n}\n#T_ce816_row3_col2 {\n  background-color: #fff8c0;\n  color: #000000;\n}\n#T_ce816_row3_col3 {\n  background-color: #ffefac;\n  color: #000000;\n}\n#T_ce816_row3_col5 {\n  background-color: #fffaca;\n  color: #000000;\n}\n#T_ce816_row3_col6 {\n  background-color: #ffface;\n  color: #000000;\n}\n#T_ce816_row4_col1, #T_ce816_row9_col4 {\n  background-color: #fffcd4;\n  color: #000000;\n}\n#T_ce816_row4_col3 {\n  background-color: #fffcd6;\n  color: #000000;\n}\n#T_ce816_row4_col4, #T_ce816_row6_col4, #T_ce816_row9_col2 {\n  background-color: #fffee0;\n  color: #000000;\n}\n#T_ce816_row5_col1 {\n  background-color: #c94b02;\n  color: #f1f1f1;\n}\n#T_ce816_row6_col1 {\n  background-color: #fffdd9;\n  color: #000000;\n}\n#T_ce816_row7_col1 {\n  background-color: #fff3b4;\n  color: #000000;\n}\n#T_ce816_row7_col2, #T_ce816_row8_col2 {\n  background-color: #fffcd7;\n  color: #000000;\n}\n#T_ce816_row7_col6, #T_ce816_row8_col3, #T_ce816_row8_col5 {\n  background-color: #fffbd0;\n  color: #000000;\n}\n#T_ce816_row8_col0 {\n  background-color: #fffacd;\n  color: #000000;\n}\n#T_ce816_row8_col1 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_ce816_row8_col4 {\n  background-color: #fffbd3;\n  color: #000000;\n}\n#T_ce816_row8_col6 {\n  background-color: #fff9c7;\n  color: #000000;\n}\n#T_ce816_row9_col1 {\n  background-color: #fff9c9;\n  color: #000000;\n}\n#T_ce816_level0_col0, #T_ce816_level0_col1, #T_ce816_level0_col2, #T_ce816_level0_col3, #T_ce816_level0_col4, #T_ce816_level0_col5, #T_ce816_level0_col6 {\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_ce816\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_ce816_level0_col0\" class=\"col_heading level0 col0\" >Cassarate</th>\n      <th id=\"T_ce816_level0_col1\" class=\"col_heading level0 col1\" >Lago di Lugano</th>\n      <th id=\"T_ce816_level0_col2\" class=\"col_heading level0 col2\" >Lago Maggiore</th>\n      <th id=\"T_ce816_level0_col3\" class=\"col_heading level0 col3\" >Maggia</th>\n      <th id=\"T_ce816_level0_col4\" class=\"col_heading level0 col4\" >Ticino</th>\n      <th id=\"T_ce816_level0_col5\" class=\"col_heading level0 col5\" >Erhebungsgebiet Ticino</th>\n      <th id=\"T_ce816_level0_col6\" class=\"col_heading level0 col6\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_ce816_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_ce816_row0_col0\" class=\"data row0 col0\" >4,0</td>\n      <td id=\"T_ce816_row0_col1\" class=\"data row0 col1\" >9,0</td>\n      <td id=\"T_ce816_row0_col2\" class=\"data row0 col2\" >2,0</td>\n      <td id=\"T_ce816_row0_col3\" class=\"data row0 col3\" >3,0</td>\n      <td id=\"T_ce816_row0_col4\" class=\"data row0 col4\" >0,0</td>\n      <td id=\"T_ce816_row0_col5\" class=\"data row0 col5\" >2,0</td>\n      <td id=\"T_ce816_row0_col6\" class=\"data row0 col6\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_ce816_row1_col0\" class=\"data row1 col0\" >17,0</td>\n      <td id=\"T_ce816_row1_col1\" class=\"data row1 col1\" >98,0</td>\n      <td id=\"T_ce816_row1_col2\" class=\"data row1 col2\" >26,5</td>\n      <td id=\"T_ce816_row1_col3\" class=\"data row1 col3\" >0,0</td>\n      <td id=\"T_ce816_row1_col4\" class=\"data row1 col4\" >43,5</td>\n      <td id=\"T_ce816_row1_col5\" class=\"data row1 col5\" >29,0</td>\n      <td id=\"T_ce816_row1_col6\" class=\"data row1 col6\" >37,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_ce816_row2_col0\" class=\"data row2 col0\" >1,0</td>\n      <td id=\"T_ce816_row2_col1\" class=\"data row2 col1\" >6,0</td>\n      <td id=\"T_ce816_row2_col2\" class=\"data row2 col2\" >3,5</td>\n      <td id=\"T_ce816_row2_col3\" class=\"data row2 col3\" >2,0</td>\n      <td id=\"T_ce816_row2_col4\" class=\"data row2 col4\" >8,0</td>\n      <td id=\"T_ce816_row2_col5\" class=\"data row2 col5\" >3,5</td>\n      <td id=\"T_ce816_row2_col6\" class=\"data row2 col6\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_ce816_row3_col0\" class=\"data row3 col0\" >7,0</td>\n      <td id=\"T_ce816_row3_col1\" class=\"data row3 col1\" >26,0</td>\n      <td id=\"T_ce816_row3_col2\" class=\"data row3 col2\" >31,0</td>\n      <td id=\"T_ce816_row3_col3\" class=\"data row3 col3\" >47,0</td>\n      <td id=\"T_ce816_row3_col4\" class=\"data row3 col4\" >6,0</td>\n      <td id=\"T_ce816_row3_col5\" class=\"data row3 col5\" >22,5</td>\n      <td id=\"T_ce816_row3_col6\" class=\"data row3 col6\" >20,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_ce816_row4_col0\" class=\"data row4 col0\" >6,0</td>\n      <td id=\"T_ce816_row4_col1\" class=\"data row4 col1\" >14,5</td>\n      <td id=\"T_ce816_row4_col2\" class=\"data row4 col2\" >2,5</td>\n      <td id=\"T_ce816_row4_col3\" class=\"data row4 col3\" >13,0</td>\n      <td id=\"T_ce816_row4_col4\" class=\"data row4 col4\" >4,5</td>\n      <td id=\"T_ce816_row4_col5\" class=\"data row4 col5\" >6,0</td>\n      <td id=\"T_ce816_row4_col6\" class=\"data row4 col6\" >7,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_ce816_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_ce816_row5_col1\" class=\"data row5 col1\" >206,0</td>\n      <td id=\"T_ce816_row5_col2\" class=\"data row5 col2\" >1,0</td>\n      <td id=\"T_ce816_row5_col3\" class=\"data row5 col3\" >0,0</td>\n      <td id=\"T_ce816_row5_col4\" class=\"data row5 col4\" >0,0</td>\n      <td id=\"T_ce816_row5_col5\" class=\"data row5 col5\" >0,0</td>\n      <td id=\"T_ce816_row5_col6\" class=\"data row5 col6\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_ce816_row6_col0\" class=\"data row6 col0\" >7,0</td>\n      <td id=\"T_ce816_row6_col1\" class=\"data row6 col1\" >10,0</td>\n      <td id=\"T_ce816_row6_col2\" class=\"data row6 col2\" >6,0</td>\n      <td id=\"T_ce816_row6_col3\" class=\"data row6 col3\" >2,0</td>\n      <td id=\"T_ce816_row6_col4\" class=\"data row6 col4\" >4,5</td>\n      <td id=\"T_ce816_row6_col5\" class=\"data row6 col5\" >6,5</td>\n      <td id=\"T_ce816_row6_col6\" class=\"data row6 col6\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_ce816_row7_col0\" class=\"data row7 col0\" >2,0</td>\n      <td id=\"T_ce816_row7_col1\" class=\"data row7 col1\" >40,5</td>\n      <td id=\"T_ce816_row7_col2\" class=\"data row7 col2\" >12,5</td>\n      <td id=\"T_ce816_row7_col3\" class=\"data row7 col3\" >3,0</td>\n      <td id=\"T_ce816_row7_col4\" class=\"data row7 col4\" >1,0</td>\n      <td id=\"T_ce816_row7_col5\" class=\"data row7 col5\" >9,5</td>\n      <td id=\"T_ce816_row7_col6\" class=\"data row7 col6\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_ce816_row8_col0\" class=\"data row8 col0\" >21,0</td>\n      <td id=\"T_ce816_row8_col1\" class=\"data row8 col1\" >272,5</td>\n      <td id=\"T_ce816_row8_col2\" class=\"data row8 col2\" >12,5</td>\n      <td id=\"T_ce816_row8_col3\" class=\"data row8 col3\" >18,0</td>\n      <td id=\"T_ce816_row8_col4\" class=\"data row8 col4\" >15,5</td>\n      <td id=\"T_ce816_row8_col5\" class=\"data row8 col5\" >18,0</td>\n      <td id=\"T_ce816_row8_col6\" class=\"data row8 col6\" >25,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_ce816_row9_col0\" class=\"data row9 col0\" >7,0</td>\n      <td id=\"T_ce816_row9_col1\" class=\"data row9 col1\" >23,5</td>\n      <td id=\"T_ce816_row9_col2\" class=\"data row9 col2\" >4,5</td>\n      <td id=\"T_ce816_row9_col3\" class=\"data row9 col3\" >3,0</td>\n      <td id=\"T_ce816_row9_col4\" class=\"data row9 col4\" >14,5</td>\n      <td id=\"T_ce816_row9_col5\" class=\"data row9 col5\" >5,5</td>\n      <td id=\"T_ce816_row9_col6\" class=\"data row9 col6\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce816_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_ce816_row10_col0\" class=\"data row10 col0\" >3,0</td>\n      <td id=\"T_ce816_row10_col1\" class=\"data row10 col1\" >0,0</td>\n      <td id=\"T_ce816_row10_col2\" class=\"data row10 col2\" >0,0</td>\n      <td id=\"T_ce816_row10_col3\" class=\"data row10 col3\" >0,0</td>\n      <td id=\"T_ce816_row10_col4\" class=\"data row10 col4\" >1,0</td>\n      <td id=\"T_ce816_row10_col5\" class=\"data row10 col5\" >0,0</td>\n      <td id=\"T_ce816_row10_col6\" class=\"data row10 col6\" >0,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7faf5a52bfd0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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(\"ticino_codegroup_pcsm_caption\", code_group_pcsm_caption, display=False)\n",
    "glue(\"ticino_codegroup_pcsm\", cgp, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_codegroup_pcsm\n",
    "---\n",
    "name: 'ticino_survey_area_codegroup_pcsm'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_codegroup_pcsm>` {glue:text}`ticino_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 Ticino Fliessgewässer, April 2020 bis März 2021, n = 6. Rechts: Zusammenfassung der Daten.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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": "ticino_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('ticino_survey_area_rivers_summary_caption', rivers_caption, display=False)\n",
    "\n",
    "glue('ticino_survey_area_rivers_summary', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_survey_area_rivers_summary\n",
    "---\n",
    "name: 'ticino_survey_area_rivers_summary'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_survey_area_rivers_summary>` {glue:text}`ticino_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 Ticino: Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_rivers_most_common_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_dde92 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_dde92 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_dde92 tr {\n  font-size: 12px;\n}\n#T_dde92 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_dde92 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_dde92\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_dde92_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_dde92_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_dde92_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_dde92_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_dde92_level0_row0\" class=\"row_heading level0 row0\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_dde92_row0_col0\" class=\"data row0 col0\" >56</td>\n      <td id=\"T_dde92_row0_col1\" class=\"data row0 col1\" >18%</td>\n      <td id=\"T_dde92_row0_col2\" class=\"data row0 col2\" >66%</td>\n      <td id=\"T_dde92_row0_col3\" class=\"data row0 col3\" >10,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row1\" class=\"row_heading level0 row1\" >Zigarettenfilter</th>\n      <td id=\"T_dde92_row1_col0\" class=\"data row1 col0\" >42</td>\n      <td id=\"T_dde92_row1_col1\" class=\"data row1 col1\" >14%</td>\n      <td id=\"T_dde92_row1_col2\" class=\"data row1 col2\" >100%</td>\n      <td id=\"T_dde92_row1_col3\" class=\"data row1 col3\" >15,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row2\" class=\"row_heading level0 row2\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_dde92_row2_col0\" class=\"data row2 col0\" >16</td>\n      <td id=\"T_dde92_row2_col1\" class=\"data row2 col1\" >5%</td>\n      <td id=\"T_dde92_row2_col2\" class=\"data row2 col2\" >83%</td>\n      <td id=\"T_dde92_row2_col3\" class=\"data row2 col3\" >4,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row3\" class=\"row_heading level0 row3\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_dde92_row3_col0\" class=\"data row3 col0\" >16</td>\n      <td id=\"T_dde92_row3_col1\" class=\"data row3 col1\" >5%</td>\n      <td id=\"T_dde92_row3_col2\" class=\"data row3 col2\" >83%</td>\n      <td id=\"T_dde92_row3_col3\" class=\"data row3 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row4\" class=\"row_heading level0 row4\" >Papierbecher, Lebensmittelverpackungen aus Papier/ Karton</th>\n      <td id=\"T_dde92_row4_col0\" class=\"data row4 col0\" >11</td>\n      <td id=\"T_dde92_row4_col1\" class=\"data row4 col1\" >3%</td>\n      <td id=\"T_dde92_row4_col2\" class=\"data row4 col2\" >50%</td>\n      <td id=\"T_dde92_row4_col3\" class=\"data row4 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row5\" class=\"row_heading level0 row5\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_dde92_row5_col0\" class=\"data row5 col0\" >10</td>\n      <td id=\"T_dde92_row5_col1\" class=\"data row5 col1\" >3%</td>\n      <td id=\"T_dde92_row5_col2\" class=\"data row5 col2\" >66%</td>\n      <td id=\"T_dde92_row5_col3\" class=\"data row5 col3\" >3,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row6\" class=\"row_heading level0 row6\" >Kabelbinder</th>\n      <td id=\"T_dde92_row6_col0\" class=\"data row6 col0\" >10</td>\n      <td id=\"T_dde92_row6_col1\" class=\"data row6 col1\" >3%</td>\n      <td id=\"T_dde92_row6_col2\" class=\"data row6 col2\" >33%</td>\n      <td id=\"T_dde92_row6_col3\" class=\"data row6 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row7\" class=\"row_heading level0 row7\" >Industrieschrott</th>\n      <td id=\"T_dde92_row7_col0\" class=\"data row7 col0\" >9</td>\n      <td id=\"T_dde92_row7_col1\" class=\"data row7 col1\" >3%</td>\n      <td id=\"T_dde92_row7_col2\" class=\"data row7 col2\" >50%</td>\n      <td id=\"T_dde92_row7_col3\" class=\"data row7 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row8\" class=\"row_heading level0 row8\" >Taschentücher, Toilettenpapier, Servietten, Papierhandtücher</th>\n      <td id=\"T_dde92_row8_col0\" class=\"data row8 col0\" >8</td>\n      <td id=\"T_dde92_row8_col1\" class=\"data row8 col1\" >2%</td>\n      <td id=\"T_dde92_row8_col2\" class=\"data row8 col2\" >50%</td>\n      <td id=\"T_dde92_row8_col3\" class=\"data row8 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row9\" class=\"row_heading level0 row9\" >Getränkedosen (Dosen, Getränke)</th>\n      <td id=\"T_dde92_row9_col0\" class=\"data row9 col0\" >6</td>\n      <td id=\"T_dde92_row9_col1\" class=\"data row9 col1\" >2%</td>\n      <td id=\"T_dde92_row9_col2\" class=\"data row9 col2\" >50%</td>\n      <td id=\"T_dde92_row9_col3\" class=\"data row9 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row10\" class=\"row_heading level0 row10\" >Windeln – Feuchttücher</th>\n      <td id=\"T_dde92_row10_col0\" class=\"data row10 col0\" >6</td>\n      <td id=\"T_dde92_row10_col1\" class=\"data row10 col1\" >2%</td>\n      <td id=\"T_dde92_row10_col2\" class=\"data row10 col2\" >50%</td>\n      <td id=\"T_dde92_row10_col3\" class=\"data row10 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row11\" class=\"row_heading level0 row11\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_dde92_row11_col0\" class=\"data row11 col0\" >6</td>\n      <td id=\"T_dde92_row11_col1\" class=\"data row11 col1\" >2%</td>\n      <td id=\"T_dde92_row11_col2\" class=\"data row11 col2\" >66%</td>\n      <td id=\"T_dde92_row11_col3\" class=\"data row11 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row12\" class=\"row_heading level0 row12\" >Schnur < 1cm</th>\n      <td id=\"T_dde92_row12_col0\" class=\"data row12 col0\" >5</td>\n      <td id=\"T_dde92_row12_col1\" class=\"data row12 col1\" >1%</td>\n      <td id=\"T_dde92_row12_col2\" class=\"data row12 col2\" >50%</td>\n      <td id=\"T_dde92_row12_col3\" class=\"data row12 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row13\" class=\"row_heading level0 row13\" >Robidog Hundekot-Säcklein, andere Hundekotsäcklein</th>\n      <td id=\"T_dde92_row13_col0\" class=\"data row13 col0\" >4</td>\n      <td id=\"T_dde92_row13_col1\" class=\"data row13 col1\" >1%</td>\n      <td id=\"T_dde92_row13_col2\" class=\"data row13 col2\" >50%</td>\n      <td id=\"T_dde92_row13_col3\" class=\"data row13 col3\" >0,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row14\" class=\"row_heading level0 row14\" >Papier &Karton;Zigarettenschachteln, Papier/Karton Verpackung für Tabakwaren</th>\n      <td id=\"T_dde92_row14_col0\" class=\"data row14 col0\" >4</td>\n      <td id=\"T_dde92_row14_col1\" class=\"data row14 col1\" >1%</td>\n      <td id=\"T_dde92_row14_col2\" class=\"data row14 col2\" >50%</td>\n      <td id=\"T_dde92_row14_col3\" class=\"data row14 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dde92_level0_row15\" class=\"row_heading level0 row15\" >Papierfragmente</th>\n      <td id=\"T_dde92_row15_col0\" class=\"data row15 col0\" >3</td>\n      <td id=\"T_dde92_row15_col1\" class=\"data row15 col1\" >1%</td>\n      <td id=\"T_dde92_row15_col2\" class=\"data row15 col2\" >50%</td>\n      <td id=\"T_dde92_row15_col3\" class=\"data row15 col3\" >0,5</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7faf593d4d30>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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('ticino_rivers_most_common_caption', mc_caption_string, display=False)\n",
    "glue('ticino_rivers_most_common_tables', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} ticino_rivers_most_common_tables\n",
    "---\n",
    "name: 'ticino_rivers_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_rivers_most_common_tables>` {glue:text}`ticino_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 TicinoMedian p/100 m, Anzahl der gefundenen Objekte und Prozent der Gesamtmenge.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_frag_table_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_2379d tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_2379d tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_2379d tr {\n  font-size: 12px;\n}\n#T_2379d th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_2379d td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_2379d\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_2379d_level0_col0\" class=\"col_heading level0 col0\" >p/100 m</th>\n      <th id=\"T_2379d_level0_col1\" class=\"col_heading level0 col1\" >Objekte (St.)</th>\n      <th id=\"T_2379d_level0_col2\" class=\"col_heading level0 col2\" >Anteil</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_2379d_level0_row0\" class=\"row_heading level0 row0\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_2379d_row0_col0\" class=\"data row0 col0\" >0,0</td>\n      <td id=\"T_2379d_row0_col1\" class=\"data row0 col1\" >11</td>\n      <td id=\"T_2379d_row0_col2\" class=\"data row0 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row1\" class=\"row_heading level0 row1\" >Objekte aus Kunststoff/Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_2379d_row1_col0\" class=\"data row1 col0\" >0,0</td>\n      <td id=\"T_2379d_row1_col1\" class=\"data row1 col1\" >28</td>\n      <td id=\"T_2379d_row1_col2\" class=\"data row1 col2\" >1%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row2\" class=\"row_heading level0 row2\" >Objekte aus Kunststoff/Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_2379d_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_2379d_row2_col1\" class=\"data row2 col1\" >2</td>\n      <td id=\"T_2379d_row2_col2\" class=\"data row2 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row3\" class=\"row_heading level0 row3\" >Objekte aus Kunststoff 0,5 - 2,5 cm</th>\n      <td id=\"T_2379d_row3_col0\" class=\"data row3 col0\" >1,5</td>\n      <td id=\"T_2379d_row3_col1\" class=\"data row3 col1\" >121</td>\n      <td id=\"T_2379d_row3_col2\" class=\"data row3 col2\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row4\" class=\"row_heading level0 row4\" >Objekte aus Kunststoff 2,5 - 50 cm</th>\n      <td id=\"T_2379d_row4_col0\" class=\"data row4 col0\" >3,5</td>\n      <td id=\"T_2379d_row4_col1\" class=\"data row4 col1\" >108</td>\n      <td id=\"T_2379d_row4_col2\" class=\"data row4 col2\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row5\" class=\"row_heading level0 row5\" >Objekte aus Kunststoff > 50 cm</th>\n      <td id=\"T_2379d_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_2379d_row5_col1\" class=\"data row5 col1\" >0</td>\n      <td id=\"T_2379d_row5_col2\" class=\"data row5 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row6\" class=\"row_heading level0 row6\" >Objekte aus expandiertem Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_2379d_row6_col0\" class=\"data row6 col0\" >2,0</td>\n      <td id=\"T_2379d_row6_col1\" class=\"data row6 col1\" >164</td>\n      <td id=\"T_2379d_row6_col2\" class=\"data row6 col2\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row7\" class=\"row_heading level0 row7\" >Objekte aus expandiertem Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_2379d_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_2379d_row7_col1\" class=\"data row7 col1\" >73</td>\n      <td id=\"T_2379d_row7_col2\" class=\"data row7 col2\" >2%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2379d_level0_row8\" class=\"row_heading level0 row8\" >Objekte aus expandiertem Polystyrol > 50 cm</th>\n      <td id=\"T_2379d_row8_col0\" class=\"data row8 col0\" >0,0</td>\n      <td id=\"T_2379d_row8_col1\" class=\"data row8 col1\" >0</td>\n      <td id=\"T_2379d_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 0x7faf593fbb50>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "ticino_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(\"ticino_frag_table_caption\", frag_captions, display=False)\n",
    "glue(\"ticino_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} ticino_frags_table\n",
    "---\n",
    "name: 'ticino_frags_table'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <ticino_frags_table>` {glue:text}`ticino_frag_table_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Die Erhebungsorte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} resources/maps/ticino_location_labels.jpeg\n",
    "---\n",
    "name: ticino_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>caprino</th>\n",
       "      <td>45.987963</td>\n",
       "      <td>8.986241</td>\n",
       "      <td>Lugano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foce-del-cassarate</th>\n",
       "      <td>46.002411</td>\n",
       "      <td>8.961477</td>\n",
       "      <td>Lugano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lido</th>\n",
       "      <td>46.002004</td>\n",
       "      <td>8.962156</td>\n",
       "      <td>Lugano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lugano-centro</th>\n",
       "      <td>46.002627</td>\n",
       "      <td>8.950724</td>\n",
       "      <td>Lugano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spiaggia-parco-ciani</th>\n",
       "      <td>46.002510</td>\n",
       "      <td>8.960820</td>\n",
       "      <td>Lugano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>via-brunari-spiaggia</th>\n",
       "      <td>46.202350</td>\n",
       "      <td>9.016910</td>\n",
       "      <td>Bellinzona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golene-gudo</th>\n",
       "      <td>46.170655</td>\n",
       "      <td>8.946657</td>\n",
       "      <td>Bellinzona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isole-di-brissago-porto</th>\n",
       "      <td>46.132760</td>\n",
       "      <td>8.735030</td>\n",
       "      <td>Brissago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magadino-lido</th>\n",
       "      <td>46.149349</td>\n",
       "      <td>8.855663</td>\n",
       "      <td>Gambarogno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rivapiana</th>\n",
       "      <td>46.172079</td>\n",
       "      <td>8.813255</td>\n",
       "      <td>Minusio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>via-mirasole-spiaggia</th>\n",
       "      <td>46.199530</td>\n",
       "      <td>9.014930</td>\n",
       "      <td>Bellinzona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiero-giro-del-golf-spiaggia</th>\n",
       "      <td>46.145770</td>\n",
       "      <td>8.790430</td>\n",
       "      <td>Ascona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vira-gambarogno</th>\n",
       "      <td>46.143343</td>\n",
       "      <td>8.838759</td>\n",
       "      <td>Gambarogno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canale-saleggi</th>\n",
       "      <td>46.200625</td>\n",
       "      <td>9.015853</td>\n",
       "      <td>Bellinzona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route-13</th>\n",
       "      <td>46.162521</td>\n",
       "      <td>8.782579</td>\n",
       "      <td>Locarno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ascona-traghetto-spiaggia</th>\n",
       "      <td>46.153882</td>\n",
       "      <td>8.768480</td>\n",
       "      <td>Ascona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pacha-mama-gerra</th>\n",
       "      <td>46.123473</td>\n",
       "      <td>8.783508</td>\n",
       "      <td>Gambarogno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gerra-gambarogno</th>\n",
       "      <td>46.123366</td>\n",
       "      <td>8.785786</td>\n",
       "      <td>Gambarogno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>san-nazzaro-gambarogno</th>\n",
       "      <td>46.130443</td>\n",
       "      <td>8.798657</td>\n",
       "      <td>Gambarogno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vira-spiaggia-left-of-river</th>\n",
       "      <td>46.142900</td>\n",
       "      <td>8.838407</td>\n",
       "      <td>Gambarogno</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  latitude  longitude        stat\n",
       "standort                                                         \n",
       "caprino                          45.987963   8.986241      Lugano\n",
       "foce-del-cassarate               46.002411   8.961477      Lugano\n",
       "lido                             46.002004   8.962156      Lugano\n",
       "lugano-centro                    46.002627   8.950724      Lugano\n",
       "spiaggia-parco-ciani             46.002510   8.960820      Lugano\n",
       "via-brunari-spiaggia             46.202350   9.016910  Bellinzona\n",
       "golene-gudo                      46.170655   8.946657  Bellinzona\n",
       "isole-di-brissago-porto          46.132760   8.735030    Brissago\n",
       "magadino-lido                    46.149349   8.855663  Gambarogno\n",
       "rivapiana                        46.172079   8.813255     Minusio\n",
       "via-mirasole-spiaggia            46.199530   9.014930  Bellinzona\n",
       "sentiero-giro-del-golf-spiaggia  46.145770   8.790430      Ascona\n",
       "vira-gambarogno                  46.143343   8.838759  Gambarogno\n",
       "canale-saleggi                   46.200625   9.015853  Bellinzona\n",
       "route-13                         46.162521   8.782579     Locarno\n",
       "ascona-traghetto-spiaggia        46.153882   8.768480      Ascona\n",
       "pacha-mama-gerra                 46.123473   8.783508  Gambarogno\n",
       "gerra-gambarogno                 46.123366   8.785786  Gambarogno\n",
       "san-nazzaro-gambarogno           46.130443   8.798657  Gambarogno\n",
       "vira-spiaggia-left-of-river      46.142900   8.838407  Gambarogno"
      ]
     },
     "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>G27</th>\n",
       "      <td>Zigarettenfilter</td>\n",
       "      <td>743</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>96</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G200</th>\n",
       "      <td>Getränkeflaschen aus Glas, Glasfragmente</td>\n",
       "      <td>274</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>85</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfrags</th>\n",
       "      <td>Fragmentierte Kunststoffe</td>\n",
       "      <td>259</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>85</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G112</th>\n",
       "      <td>Industriepellets (Nurdles)</td>\n",
       "      <td>247</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfoam</th>\n",
       "      <td>Expandiertes Polystyrol</td>\n",
       "      <td>237</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>57</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G30</th>\n",
       "      <td>Snack-Verpackungen</td>\n",
       "      <td>130</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G67</th>\n",
       "      <td>Industriefolie (Kunststoff)</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G941</th>\n",
       "      <td>Verpackungsfolien, nicht für Lebensmittel</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G117</th>\n",
       "      <td>Styropor &lt; 5mm</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G74</th>\n",
       "      <td>Schaumstoffverpackungen/Isolierung</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G178</th>\n",
       "      <td>Kronkorken, Lasche von Dose/Ausfreisslachen</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G89</th>\n",
       "      <td>Kunststoff-Bauabfälle</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G149</th>\n",
       "      <td>Papierverpackungen</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G921</th>\n",
       "      <td>Keramikfliesen und Bruchstücke</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>Lebensmittelbehälter zum einmaligen Gebrauch a...</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G153</th>\n",
       "      <td>Papierbecher, Lebensmittelverpackungen aus Pap...</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G177</th>\n",
       "      <td>Verpackungen aus Aluminiumfolie</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G95</th>\n",
       "      <td>Wattestäbchen/Tupfer</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G923</th>\n",
       "      <td>Taschentücher, Toilettenpapier, Servietten, Pa...</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G21</th>\n",
       "      <td>Getränke-Deckel, Getränkeverschluss</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G152</th>\n",
       "      <td>Papier &amp;Karton;Zigarettenschachteln, Papier/Ka...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G156</th>\n",
       "      <td>Papierfragmente</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G66</th>\n",
       "      <td>Umreifungsbänder; Hartplastik für Verpackung f...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G186</th>\n",
       "      <td>Industrieschrott</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G25</th>\n",
       "      <td>Tabak; Kunststoffverpackungen, Behälter</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G73</th>\n",
       "      <td>Gegenstände aus Schaumstoff &amp; Teilstücke (nich...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>Einkaufstaschen, Shoppingtaschen</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G137</th>\n",
       "      <td>Kleidung, Handtücher und Lappen</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G98</th>\n",
       "      <td>Windeln – Feuchttücher</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G93</th>\n",
       "      <td>Kabelbinder</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G24</th>\n",
       "      <td>Ringe von Plastikflaschen/Behältern</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</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>21</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G22</th>\n",
       "      <td>Deckel für Chemikalien, Reinigungsmittel (Ohne...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G922</th>\n",
       "      <td>Etiketten, Strichcodes</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G100</th>\n",
       "      <td>Medizin; Behälter/Röhrchen/Verpackungen</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G33</th>\n",
       "      <td>Einwegartikel; Tassen/Becher &amp; Deckel</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G32</th>\n",
       "      <td>Spielzeug und Partyartikel</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</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>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G50</th>\n",
       "      <td>Schnur &lt; 1cm</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G198</th>\n",
       "      <td>Andere Metallteile &lt; 50 cm</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G35</th>\n",
       "      <td>Strohhalme und Rührstäbchen</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G70</th>\n",
       "      <td>Schrotflintenpatronen</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G191</th>\n",
       "      <td>Draht und Gitter</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G159</th>\n",
       "      <td>Kork</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G211</th>\n",
       "      <td>Sonstiges medizinisches Material</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G31</th>\n",
       "      <td>Schleckstengel, Stengel von Lutscher</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>G148</th>\n",
       "      <td>Kartonkisten und Stücke</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G146</th>\n",
       "      <td>Papier, Karton</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G204</th>\n",
       "      <td>Baumaterial;  Ziegel, Rohre, Zement</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101</th>\n",
       "      <td>Robidog Hundekot-Säcklein, andere Hundekotsäck...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G23</th>\n",
       "      <td>unidentifizierte Deckel</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G87</th>\n",
       "      <td>Abdeckklebeband/Verpackungsklebeband</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G175</th>\n",
       "      <td>Getränkedosen (Dosen, Getränke)</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G908</th>\n",
       "      <td>Klebeband; elektrisch, isolierend</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G142</th>\n",
       "      <td>Seil, Schnur oder Netze</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G914</th>\n",
       "      <td>Büroklammern, Wäscheklammern, Gebrauchsgegenst...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G96</th>\n",
       "      <td>Hygienebinden/Höscheneinlagen/Tampons und Appl...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G90</th>\n",
       "      <td>Blumentöpfe aus Plastik</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G135</th>\n",
       "      <td>Kleidung, Fussbekleidung, Kopfbedeckung, Hands...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G927</th>\n",
       "      <td>Plastikschnur von Rasentrimmern, die zum Schne...</td>\n",
       "      <td>5</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>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G936</th>\n",
       "      <td>Folien für Gewächshäuser</td>\n",
       "      <td>5</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G34</th>\n",
       "      <td>Besteck, Teller und Tabletts</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G154</th>\n",
       "      <td>Zeitungen oder Zeitschriften</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G7</th>\n",
       "      <td>Getränkeflaschen &lt; = 0,5 l</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G28</th>\n",
       "      <td>Stifte, Deckel, Druckbleistifte usw.</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G919</th>\n",
       "      <td>Nägel, Schrauben, Bolzen usw.</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G931</th>\n",
       "      <td>(Absperr)band für Absperrungen, Polizei, Baust...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G208</th>\n",
       "      <td>Glas oder Keramikfragmente &gt;2.5 cm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G36</th>\n",
       "      <td>Säcke aus strapazierfähigem Kunststoff für 25 ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G65</th>\n",
       "      <td>Eimer</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G903</th>\n",
       "      <td>Behälter und Packungen für Handdesinfektionsmi...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G901</th>\n",
       "      <td>Medizinische Masken, synthetische</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G928</th>\n",
       "      <td>Bänder und Schleifen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G26</th>\n",
       "      <td>Feuerzeug</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G125</th>\n",
       "      <td>Luftballons und Luftballonstäbchen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G170</th>\n",
       "      <td>Holz (verarbeitet)</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G106</th>\n",
       "      <td>Kunststofffragmente eckig &lt;5mm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G53</th>\n",
       "      <td>Netze und Teilstücke &lt; 50cm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G201</th>\n",
       "      <td>Gläser, einschliesslich Stücke</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G904</th>\n",
       "      <td>Feuerwerkskörper; Raketenkappen</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G195</th>\n",
       "      <td>Batterien (Haushalt)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G906</th>\n",
       "      <td>Kaffeekapseln Aluminium</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G19</th>\n",
       "      <td>Autoteile</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G182</th>\n",
       "      <td>Angeln; Haken, Gewichte, Köder, Senkblei, usw.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G40</th>\n",
       "      <td>Handschuhe Haushalt/Garten</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G165</th>\n",
       "      <td>Glacestengel (Eisstiele), Zahnstocher, Essstäb...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G136</th>\n",
       "      <td>Schuhe</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G155</th>\n",
       "      <td>Feuerwerkspapierhülsen und -fragmente</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G925</th>\n",
       "      <td>Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G929</th>\n",
       "      <td>Elektronik und Teile; Sensoren, Headsets usw.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G905</th>\n",
       "      <td>Haarspangen, Haargummis, persönliche Accessoir...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G907</th>\n",
       "      <td>Kaffeekapseln aus Kunststoff</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G115</th>\n",
       "      <td>Schaumstoff &lt;5mm</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G91</th>\n",
       "      <td>Bio-Filtermaterial  / Trägermaterial aus Kunst...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G141</th>\n",
       "      <td>Teppiche</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G92</th>\n",
       "      <td>Köderbehälter</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G52</th>\n",
       "      <td>Netze und Teilstücke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>3</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G37</th>\n",
       "      <td>Netzbeutel, Netztasche, Netzsäcke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G918</th>\n",
       "      <td>Sicherheitsnadeln, Büroklammern, kleine Gebrau...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G4</th>\n",
       "      <td>Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G41</th>\n",
       "      <td>Handschuhe Industriell/Professionell</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G62</th>\n",
       "      <td>Schwimmer für Netze</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G902</th>\n",
       "      <td>Medizinische Masken, Stoff</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G197</th>\n",
       "      <td>sonstiges Metall</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G128</th>\n",
       "      <td>Reifen und Antriebsriemen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gummi</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>3</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G942</th>\n",
       "      <td>Kunststoffspäne von Drehbänken, CNC-Bearbeitung</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G933</th>\n",
       "      <td>Taschen, Etuis für Zubehör, Brillen, Elektroni...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G107</th>\n",
       "      <td>Zylindrische Pellets &lt;5mm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G99</th>\n",
       "      <td>Spritzen - Nadeln</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>Säcke, Taschen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G124</th>\n",
       "      <td>Andere Kunststoff</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G939</th>\n",
       "      <td>Kunststoffblumen, Kunststoffpflanzen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G131</th>\n",
       "      <td>Gummibänder</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G167</th>\n",
       "      <td>Streichhölzer oder Feuerwerke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G129</th>\n",
       "      <td>Schläuche und Gummiplatten</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G132</th>\n",
       "      <td>Schwimmer (Angeln)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G134</th>\n",
       "      <td>Sonstiges Gummi</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gummi</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>G8</th>\n",
       "      <td>Getränkeflaschen &gt; 0,5L</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>G913</th>\n",
       "      <td>Schnuller</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>G126</th>\n",
       "      <td>Bälle</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>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>G9</th>\n",
       "      <td>Flaschen und Behälter für Reinigungsmittel und...</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>G12</th>\n",
       "      <td>Kosmetika, Behälter für Körperpflegeprodukte, ...</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>G900</th>\n",
       "      <td>Handschuhe Latex, persönliche Schutzausrüstung</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>G123</th>\n",
       "      <td>Polyurethan-Granulat &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>G119</th>\n",
       "      <td>Folienartiger Kunststoff (&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>G176</th>\n",
       "      <td>Konservendosen (Lebensmitteldosen)</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>G915</th>\n",
       "      <td>Reflektoren, Mobilitätsartikel aus Kunststoff</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>G916</th>\n",
       "      <td>Bleistifte und Bruchstücke</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>G102</th>\n",
       "      <td>Flip-Flops</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>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>G103</th>\n",
       "      <td>Kunststofffragmente rund &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>G97</th>\n",
       "      <td>Behälter von Toilettenerfrischer</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>G104</th>\n",
       "      <td>Kunststofffragmente abgerundet / rundlich  &lt;5m...</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>G105</th>\n",
       "      <td>Kunststofffragmente subangulär &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>G945</th>\n",
       "      <td>Rasierklingen</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>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>G940</th>\n",
       "      <td>Schaumstoff EVA (flexibler Kunststoff)</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>G938</th>\n",
       "      <td>Zahnstocher, Zahnseide Kunststoff</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>G937</th>\n",
       "      <td>Pheromonköder für Weinberge</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>G108</th>\n",
       "      <td>Scheibenförmige 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>G935</th>\n",
       "      <td>Gummi-Puffer für Geh- und Wanderstöcke und Tei...</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>G932</th>\n",
       "      <td>Bio-Beads, Mikroplastik für die Abwasserbehand...</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>G109</th>\n",
       "      <td>Flache 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>G930</th>\n",
       "      <td>Schaumstoff-Ohrstöpsel</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>G11</th>\n",
       "      <td>Kosmetika für den Strand, z.B. Sonnencreme</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>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>G113</th>\n",
       "      <td>Fäden &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>G926</th>\n",
       "      <td>Kaugummi, enthält oft Kunststoffe</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>G114</th>\n",
       "      <td>Folien &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>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>G116</th>\n",
       "      <td>Granulat &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>G118</th>\n",
       "      <td>Kleine Industrielle Kügelchen &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>G917</th>\n",
       "      <td>Blähton</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>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>G68</th>\n",
       "      <td>Fiberglas-Fragmente</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>G202</th>\n",
       "      <td>Glühbirnen</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>G150</th>\n",
       "      <td>Milchkartons, Tetrapack</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G151</th>\n",
       "      <td>Tetrapack, Kartons</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G158</th>\n",
       "      <td>Sonstige Gegenstände aus Papier</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</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>G161</th>\n",
       "      <td>Verarbeitetes 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>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>G213</th>\n",
       "      <td>Paraffinwachs</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>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>G210</th>\n",
       "      <td>Sonstiges Glas/Keramik Materialien</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>G166</th>\n",
       "      <td>Farbpinsel</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>G207</th>\n",
       "      <td>Tonkrüge zum Fangen von Kraken</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>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>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>G17</th>\n",
       "      <td>Kartuschen von Kartuschen-spritzpistolen</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>G29</th>\n",
       "      <td>Kämme, Bürsten und Sonnenbrillen</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>G20</th>\n",
       "      <td>Laschen und Deckel</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>G199</th>\n",
       "      <td>Andere Metallteile &gt; 50 cm</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>G196</th>\n",
       "      <td>Grosse metallische Gegenstände</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>G171</th>\n",
       "      <td>Sonstiges Holz &lt; 50cm</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>G193</th>\n",
       "      <td>Teile von Autos und Autobatterien</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>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>G173</th>\n",
       "      <td>Sonstiges</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>G185</th>\n",
       "      <td>Behälter mittlerer Grösse</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>G183</th>\n",
       "      <td>Teile von Angelhaken</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>G181</th>\n",
       "      <td>Geschirr aus Metall, Tassen, Besteck usw.</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>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>G179</th>\n",
       "      <td>Einweg Grill</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>G147</th>\n",
       "      <td>Papiertragetaschen, (Papiertüten)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G144</th>\n",
       "      <td>Tampons</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>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>G63</th>\n",
       "      <td>Bojen</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>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>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>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>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>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>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>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>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>G13</th>\n",
       "      <td>Flaschen, Behälter, Fässer zum Transportieren ...</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>G174</th>\n",
       "      <td>Aerosolspraydosen</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>G133</th>\n",
       "      <td>Kondome einschließlich Verpackungen</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>G64</th>\n",
       "      <td>Kotflügel</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>G61</th>\n",
       "      <td>Sonstiges Angelzubehör</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>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>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>G59</th>\n",
       "      <td>Monofile Angelschnur (Angeln)</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>G55</th>\n",
       "      <td>Angelschnur (verwickelt)</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>G51</th>\n",
       "      <td>Fischernetz</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>G5</th>\n",
       "      <td>Plastiksäcke/ Plastiktüten</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>G48</th>\n",
       "      <td>Seile, synthetische</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>G43</th>\n",
       "      <td>Sicherheitsetiketten, Siegel für Fischerei ode...</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>G39</th>\n",
       "      <td>Handschuhe</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>G38</th>\n",
       "      <td>Abdeckungen; Kunststoffverpackungen, Folien zu...</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>G139</th>\n",
       "      <td>Rucksäcke</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>G14</th>\n",
       "      <td>Flaschen für Motoröl (Motorölflaschen)</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>G140</th>\n",
       "      <td>Sack oder Beutel (Tragetasche), Jute oder Hanf</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>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",
       "G27                                      Zigarettenfilter           743  \\\n",
       "G200             Getränkeflaschen aus Glas, Glasfragmente           274   \n",
       "Gfrags                          Fragmentierte Kunststoffe           259   \n",
       "G112                           Industriepellets (Nurdles)           247   \n",
       "Gfoam                             Expandiertes Polystyrol           237   \n",
       "G30                                    Snack-Verpackungen           130   \n",
       "G67                           Industriefolie (Kunststoff)            80   \n",
       "G941            Verpackungsfolien, nicht für Lebensmittel            72   \n",
       "G117                                       Styropor < 5mm            65   \n",
       "G74                    Schaumstoffverpackungen/Isolierung            64   \n",
       "G178          Kronkorken, Lasche von Dose/Ausfreisslachen            54   \n",
       "G89                                 Kunststoff-Bauabfälle            42   \n",
       "G149                                   Papierverpackungen            36   \n",
       "G921                       Keramikfliesen und Bruchstücke            34   \n",
       "G10     Lebensmittelbehälter zum einmaligen Gebrauch a...            30   \n",
       "G153    Papierbecher, Lebensmittelverpackungen aus Pap...            27   \n",
       "G177                      Verpackungen aus Aluminiumfolie            26   \n",
       "G95                                  Wattestäbchen/Tupfer            23   \n",
       "G923    Taschentücher, Toilettenpapier, Servietten, Pa...            22   \n",
       "G21                   Getränke-Deckel, Getränkeverschluss            20   \n",
       "G152    Papier &Karton;Zigarettenschachteln, Papier/Ka...            18   \n",
       "G156                                      Papierfragmente            18   \n",
       "G66     Umreifungsbänder; Hartplastik für Verpackung f...            17   \n",
       "G186                                     Industrieschrott            17   \n",
       "G25               Tabak; Kunststoffverpackungen, Behälter            16   \n",
       "G73     Gegenstände aus Schaumstoff & Teilstücke (nich...            16   \n",
       "G3                       Einkaufstaschen, Shoppingtaschen            15   \n",
       "G137                      Kleidung, Handtücher und Lappen            15   \n",
       "G98                                Windeln – Feuchttücher            15   \n",
       "G93                                           Kabelbinder            15   \n",
       "G24                   Ringe von Plastikflaschen/Behältern            14   \n",
       "G203    Geschirr aus Keramik oder Glas, Tassen, Teller...            14   \n",
       "G22     Deckel für Chemikalien, Reinigungsmittel (Ohne...            13   \n",
       "G922                               Etiketten, Strichcodes            12   \n",
       "G100              Medizin; Behälter/Röhrchen/Verpackungen            12   \n",
       "G33                 Einwegartikel; Tassen/Becher & Deckel            12   \n",
       "G32                            Spielzeug und Partyartikel            12   \n",
       "G194    Kabel, Metalldraht oft in Gummi- oder Kunststo...            11   \n",
       "G50                                          Schnur < 1cm            11   \n",
       "G198                           Andere Metallteile < 50 cm            10   \n",
       "G35                           Strohhalme und Rührstäbchen            10   \n",
       "G70                                 Schrotflintenpatronen            10   \n",
       "G191                                     Draht und Gitter             9   \n",
       "G159                                                 Kork             9   \n",
       "G211                     Sonstiges medizinisches Material             9   \n",
       "G31                  Schleckstengel, Stengel von Lutscher             9   \n",
       "G148                              Kartonkisten und Stücke             8   \n",
       "G146                                       Papier, Karton             8   \n",
       "G204                  Baumaterial;  Ziegel, Rohre, Zement             7   \n",
       "G101    Robidog Hundekot-Säcklein, andere Hundekotsäck...             7   \n",
       "G23                               unidentifizierte Deckel             6   \n",
       "G87                  Abdeckklebeband/Verpackungsklebeband             6   \n",
       "G175                      Getränkedosen (Dosen, Getränke)             6   \n",
       "G908                    Klebeband; elektrisch, isolierend             6   \n",
       "G142                              Seil, Schnur oder Netze             6   \n",
       "G914    Büroklammern, Wäscheklammern, Gebrauchsgegenst...             6   \n",
       "G96     Hygienebinden/Höscheneinlagen/Tampons und Appl...             5   \n",
       "G90                               Blumentöpfe aus Plastik             5   \n",
       "G135    Kleidung, Fussbekleidung, Kopfbedeckung, Hands...             5   \n",
       "G927    Plastikschnur von Rasentrimmern, die zum Schne...             5   \n",
       "G157                                               Papier             5   \n",
       "G936                             Folien für Gewächshäuser             5   \n",
       "G943                     Zäune Landwirtschaft, Kunststoff             4   \n",
       "G34                          Besteck, Teller und Tabletts             4   \n",
       "G154                         Zeitungen oder Zeitschriften             4   \n",
       "G7                             Getränkeflaschen < = 0,5 l             4   \n",
       "G28                  Stifte, Deckel, Druckbleistifte usw.             3   \n",
       "G919                       Nägel, Schrauben, Bolzen usw.              3   \n",
       "G931    (Absperr)band für Absperrungen, Polizei, Baust...             3   \n",
       "G208                   Glas oder Keramikfragmente >2.5 cm             3   \n",
       "G36     Säcke aus strapazierfähigem Kunststoff für 25 ...             3   \n",
       "G65                                                 Eimer             3   \n",
       "G903    Behälter und Packungen für Handdesinfektionsmi...             3   \n",
       "G901                    Medizinische Masken, synthetische             3   \n",
       "G928                                 Bänder und Schleifen             3   \n",
       "G26                                             Feuerzeug             3   \n",
       "G125                   Luftballons und Luftballonstäbchen             3   \n",
       "G170                                   Holz (verarbeitet)             3   \n",
       "G106                       Kunststofffragmente eckig <5mm             3   \n",
       "G53                           Netze und Teilstücke < 50cm             2   \n",
       "G201                       Gläser, einschliesslich Stücke             2   \n",
       "G904                      Feuerwerkskörper; Raketenkappen             2   \n",
       "G195                                 Batterien (Haushalt)             2   \n",
       "G906                              Kaffeekapseln Aluminium             2   \n",
       "G19                                             Autoteile             2   \n",
       "G49                                           Seile > 1cm             2   \n",
       "G182       Angeln; Haken, Gewichte, Köder, Senkblei, usw.             2   \n",
       "G40                            Handschuhe Haushalt/Garten             2   \n",
       "G165    Glacestengel (Eisstiele), Zahnstocher, Essstäb...             2   \n",
       "G136                                               Schuhe             2   \n",
       "G155                Feuerwerkspapierhülsen und -fragmente             1   \n",
       "G925    Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...             1   \n",
       "G929       Elektronik und Teile; Sensoren, Headsets usw.              1   \n",
       "G905    Haarspangen, Haargummis, persönliche Accessoir...             1   \n",
       "G6      Flaschen und Behälter aus Kunststoff, nicht fü...             1   \n",
       "G907                         Kaffeekapseln aus Kunststoff             1   \n",
       "G115                                     Schaumstoff <5mm             1   \n",
       "G145                                     Andere Textilien             1   \n",
       "G91     Bio-Filtermaterial  / Trägermaterial aus Kunst...             1   \n",
       "G141                                             Teppiche             1   \n",
       "G92                                         Köderbehälter             1   \n",
       "G52                                  Netze und Teilstücke             1   \n",
       "G138                                  Schuhe und Sandalen             1   \n",
       "G37                     Netzbeutel, Netztasche, Netzsäcke             1   \n",
       "G918    Sicherheitsnadeln, Büroklammern, kleine Gebrau...             1   \n",
       "G4      Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...             1   \n",
       "G41                  Handschuhe Industriell/Professionell             1   \n",
       "G62                                   Schwimmer für Netze             1   \n",
       "G902                           Medizinische Masken, Stoff             1   \n",
       "G197                                     sonstiges Metall             1   \n",
       "G128                            Reifen und Antriebsriemen             1   \n",
       "G172                               Sonstiges Holz > 50 cm             1   \n",
       "G942      Kunststoffspäne von Drehbänken, CNC-Bearbeitung             1   \n",
       "G933    Taschen, Etuis für Zubehör, Brillen, Elektroni...             1   \n",
       "G107                            Zylindrische Pellets <5mm             1   \n",
       "G99                                     Spritzen - Nadeln             1   \n",
       "G2                                         Säcke, Taschen             1   \n",
       "G124                                    Andere Kunststoff             1   \n",
       "G939                 Kunststoffblumen, Kunststoffpflanzen             1   \n",
       "G131                                          Gummibänder             1   \n",
       "G167                        Streichhölzer oder Feuerwerke             1   \n",
       "G129                           Schläuche und Gummiplatten             1   \n",
       "G132                                   Schwimmer (Angeln)             1   \n",
       "G134                                      Sonstiges Gummi             1   \n",
       "G84                                      CD oder CD-Hülle             0   \n",
       "G8                                Getränkeflaschen > 0,5L             0   \n",
       "G913                                            Schnuller             0   \n",
       "G122                           Kunststofffragmente (>1mm)             0   \n",
       "G126                                                Bälle             0   \n",
       "G88                                   Telefon inkl. Teile             0   \n",
       "G9      Flaschen und Behälter für Reinigungsmittel und...             0   \n",
       "G12     Kosmetika, Behälter für Körperpflegeprodukte, ...             0   \n",
       "G900       Handschuhe Latex, persönliche Schutzausrüstung             0   \n",
       "G123                           Polyurethan-Granulat < 5mm             0   \n",
       "G119                      Folienartiger Kunststoff (>1mm)             0   \n",
       "G176                   Konservendosen (Lebensmitteldosen)             0   \n",
       "G915        Reflektoren, Mobilitätsartikel aus Kunststoff             0   \n",
       "G916                           Bleistifte und Bruchstücke             0   \n",
       "G102                                           Flip-Flops             0   \n",
       "G999    Keine Gegenstände bei dieser Erhebung/Studie g...             0   \n",
       "G103                        Kunststofffragmente rund <5mm             0   \n",
       "G97                     Behälter von Toilettenerfrischer              0   \n",
       "G104    Kunststofffragmente abgerundet / rundlich  <5m...             0   \n",
       "G105                  Kunststofffragmente subangulär <5mm             0   \n",
       "G945                                        Rasierklingen             0   \n",
       "G944              Pelletmasse aus dem Spritzgussverfahren             0   \n",
       "G940               Schaumstoff EVA (flexibler Kunststoff)             0   \n",
       "G94                                             Tischtuch             0   \n",
       "G938                    Zahnstocher, Zahnseide Kunststoff             0   \n",
       "G937                          Pheromonköder für Weinberge             0   \n",
       "G108                         Scheibenförmige Pellets <5mm             0   \n",
       "G935    Gummi-Puffer für Geh- und Wanderstöcke und Tei...             0   \n",
       "G934    Sandsäcke, Kunststoff für Hochwasser- und Eros...             0   \n",
       "G932    Bio-Beads, Mikroplastik für die Abwasserbehand...             0   \n",
       "G109                                  Flache Pellets <5mm             0   \n",
       "G930                               Schaumstoff-Ohrstöpsel             0   \n",
       "G11            Kosmetika für den Strand, z.B. Sonnencreme             0   \n",
       "G111                           Kugelförmige Pellets < 5mm             0   \n",
       "G113                                           Fäden <5mm             0   \n",
       "G926                    Kaugummi, enthält oft Kunststoffe             0   \n",
       "G114                                          Folien <5mm             0   \n",
       "G712                                        Skihandschuhe             0   \n",
       "G116                                        Granulat <5mm             0   \n",
       "G118                   Kleine Industrielle Kügelchen <5mm             0   \n",
       "G917                                              Blähton             0   \n",
       "G713                                        Skiförderband             0   \n",
       "G68                                   Fiberglas-Fragmente             0   \n",
       "G711                                          Handwärmer              0   \n",
       "G202                                           Glühbirnen             0   \n",
       "G150                              Milchkartons, Tetrapack             0   \n",
       "G151                                   Tetrapack, Kartons             0   \n",
       "G158                      Sonstige Gegenstände aus Papier             0   \n",
       "G160                                             Paletten             0   \n",
       "G161                                   Verarbeitetes Holz             0   \n",
       "G214                                              Öl/Teer             0   \n",
       "G213                                       Paraffinwachs              0   \n",
       "G162                                               Kisten             0   \n",
       "G210                   Sonstiges Glas/Keramik Materialien             0   \n",
       "G166                                           Farbpinsel             0   \n",
       "G207                       Tonkrüge zum Fangen von Kraken             0   \n",
       "G205                                    Leuchtstoffröhren             0   \n",
       "G168                                          Holzbretter             0   \n",
       "G17              Kartuschen von Kartuschen-spritzpistolen             0   \n",
       "G29                      Kämme, Bürsten und Sonnenbrillen             0   \n",
       "G20                                    Laschen und Deckel             0   \n",
       "G199                           Andere Metallteile > 50 cm             0   \n",
       "G196                       Grosse metallische Gegenstände             0   \n",
       "G171                                Sonstiges Holz < 50cm             0   \n",
       "G193                    Teile von Autos und Autobatterien             0   \n",
       "G190                Farbtöpfe, Farbbüchsen,  ( Farbeimer)             0   \n",
       "G188                       Andere Kanister/Behälter < 4 L             0   \n",
       "G173                                            Sonstiges             0   \n",
       "G185                            Behälter mittlerer Grösse             0   \n",
       "G183                                 Teile von Angelhaken             0   \n",
       "G181            Geschirr aus Metall, Tassen, Besteck usw.             0   \n",
       "G180                                      Haushaltsgeräte             0   \n",
       "G179                                         Einweg Grill             0   \n",
       "G147                    Papiertragetaschen, (Papiertüten)             0   \n",
       "G144                                              Tampons             0   \n",
       "G710    Ski / Snowboards (Ski, Befestigungen und ander...             0   \n",
       "G63                                                 Bojen             0   \n",
       "G71                                       Schuhe Sandalen             0   \n",
       "G709    Teller von Skistock (runder Teil aus Plastik u...             0   \n",
       "G708                                            Skistöcke             0   \n",
       "G707                                Skiausrüstungsetikett             0   \n",
       "G706                                        Skiabonnement             0   \n",
       "G705                                 Schrauben und Bolzen             0   \n",
       "G704                                      Seilbahnbürsten             0   \n",
       "G703                   Pistenmarkierungspfosten (Plastik)             0   \n",
       "G702                      Pistenmarkierungspfosten (Holz)             0   \n",
       "G13     Flaschen, Behälter, Fässer zum Transportieren ...             0   \n",
       "G174                                    Aerosolspraydosen             0   \n",
       "G133                  Kondome einschließlich Verpackungen             0   \n",
       "G64                                             Kotflügel             0   \n",
       "G61                                Sonstiges Angelzubehör             0   \n",
       "G143                                  Segel und Segeltuch             0   \n",
       "G60                    Lichtstab, Knicklicht, Glow-sticks             0   \n",
       "G59                         Monofile Angelschnur (Angeln)             0   \n",
       "G56                                     Verhedderte Netze             0   \n",
       "G55                              Angelschnur (verwickelt)             0   \n",
       "G51                                           Fischernetz             0   \n",
       "G5                             Plastiksäcke/ Plastiktüten             0   \n",
       "G48                                   Seile, synthetische             0   \n",
       "G43     Sicherheitsetiketten, Siegel für Fischerei ode...             0   \n",
       "G39                                            Handschuhe             0   \n",
       "G38     Abdeckungen; Kunststoffverpackungen, Folien zu...             0   \n",
       "G139                                            Rucksäcke             0   \n",
       "G14                Flaschen für Motoröl (Motorölflaschen)             0   \n",
       "G140       Sack oder Beutel (Tragetasche), Jute oder Hanf             0   \n",
       "G1                                          Sixpack-Ringe             0   \n",
       "\n",
       "        Anteil  p/100 m  Häufigkeitsrate     Material  \n",
       "code                                                   \n",
       "G27         24       15               96      Plastik  \n",
       "G200         9        8               85         Glas  \n",
       "Gfrags       8        9               85      Plastik  \n",
       "G112         8        0               17      Plastik  \n",
       "Gfoam        7        6               57      Plastik  \n",
       "G30          4        4               71      Plastik  \n",
       "G67          2        3               82      Plastik  \n",
       "G941         2        2               57      Plastik  \n",
       "G117         2        0               32      Plastik  \n",
       "G74          2        3               67      Plastik  \n",
       "G178         1        1               50       Metall  \n",
       "G89          1        2               57      Plastik  \n",
       "G149         1        0               14       Papier  \n",
       "G921         1        0               35         Glas  \n",
       "G10          0        0               32      Plastik  \n",
       "G153         0        0               28       Papier  \n",
       "G177         0        0               39       Metall  \n",
       "G95          0        0               39      Plastik  \n",
       "G923         0        0               39       Papier  \n",
       "G21          0        0               28      Plastik  \n",
       "G152         0        0               28       Papier  \n",
       "G156         0        0               42       Papier  \n",
       "G66          0        0               32      Plastik  \n",
       "G186         0        0               25       Metall  \n",
       "G25          0        0               35      Plastik  \n",
       "G73          0        0               28      Plastik  \n",
       "G3           0        0               21      Plastik  \n",
       "G137         0        0               17        Stoff  \n",
       "G98          0        0               35      Plastik  \n",
       "G93          0        0               17      Plastik  \n",
       "G24          0        0               25      Plastik  \n",
       "G203         0        0               21         Glas  \n",
       "G22          0        0               17      Plastik  \n",
       "G922         0        0               17      Plastik  \n",
       "G100         0        0               21      Plastik  \n",
       "G33          0        0               21      Plastik  \n",
       "G32          0        0               28      Plastik  \n",
       "G194         0        0               21       Metall  \n",
       "G50          0        0               21      Plastik  \n",
       "G198         0        0               21       Metall  \n",
       "G35          0        0               21      Plastik  \n",
       "G70          0        0               14      Plastik  \n",
       "G191         0        0               10       Metall  \n",
       "G159         0        0               28         Holz  \n",
       "G211         0        0               21      Plastik  \n",
       "G31          0        0                7      Plastik  \n",
       "G148         0        0               10       Papier  \n",
       "G146         0        0                3       Papier  \n",
       "G204         0        0               10         Glas  \n",
       "G101         0        0               21      Plastik  \n",
       "G23          0        0               14      Plastik  \n",
       "G87          0        0               14      Plastik  \n",
       "G175         0        0               10       Metall  \n",
       "G908         0        0               10      Plastik  \n",
       "G142         0        0               21        Stoff  \n",
       "G914         0        0               17      Plastik  \n",
       "G96          0        0               14      Plastik  \n",
       "G90          0        0               17      Plastik  \n",
       "G135         0        0               14        Stoff  \n",
       "G927         0        0               14      Plastik  \n",
       "G157         0        0               14       Papier  \n",
       "G936         0        0               10      Plastik  \n",
       "G943         0        0               10      Plastik  \n",
       "G34          0        0               14      Plastik  \n",
       "G154         0        0                7       Papier  \n",
       "G7           0        0               10      Plastik  \n",
       "G28          0        0                7      Plastik  \n",
       "G919         0        0                3       Metall  \n",
       "G931         0        0                3      Plastik  \n",
       "G208         0        0                7         Glas  \n",
       "G36          0        0                7      Plastik  \n",
       "G65          0        0               10      Plastik  \n",
       "G903         0        0                7      Plastik  \n",
       "G901         0        0               10      Plastik  \n",
       "G928         0        0                7      Plastik  \n",
       "G26          0        0               10      Plastik  \n",
       "G125         0        0               10        Gummi  \n",
       "G170         0        0               10         Holz  \n",
       "G106         0        0                7      Plastik  \n",
       "G53          0        0                3      Plastik  \n",
       "G201         0        0                7         Glas  \n",
       "G904         0        0                3      Plastik  \n",
       "G195         0        0                3       Metall  \n",
       "G906         0        0                3       Metall  \n",
       "G19          0        0                3      Plastik  \n",
       "G49          0        0                3      Plastik  \n",
       "G182         0        0                7       Metall  \n",
       "G40          0        0                7      Plastik  \n",
       "G165         0        0                3         Holz  \n",
       "G136         0        0                7        Stoff  \n",
       "G155         0        0                3       Papier  \n",
       "G925         0        0                3      Plastik  \n",
       "G929         0        0                3       Metall  \n",
       "G905         0        0                3      Plastik  \n",
       "G6           0        0                3      Plastik  \n",
       "G907         0        0                3      Plastik  \n",
       "G115         0        0                3      Plastik  \n",
       "G145         0        0                3        Stoff  \n",
       "G91          0        0                3      Plastik  \n",
       "G141         0        0                3        Stoff  \n",
       "G92          0        0                3      Plastik  \n",
       "G52          0        0                3      Plastik  \n",
       "G138         0        0                3        Stoff  \n",
       "G37          0        0                3      Plastik  \n",
       "G918         0        0                3       Metall  \n",
       "G4           0        0                3      Plastik  \n",
       "G41          0        0                3      Plastik  \n",
       "G62          0        0                3      Plastik  \n",
       "G902         0        0                3        Stoff  \n",
       "G197         0        0                3       Metall  \n",
       "G128         0        0                3        Gummi  \n",
       "G172         0        0                3         Holz  \n",
       "G942         0        0                3      Plastik  \n",
       "G933         0        0                3      Plastik  \n",
       "G107         0        0                3      Plastik  \n",
       "G99          0        0                3      Plastik  \n",
       "G2           0        0                3      Plastik  \n",
       "G124         0        0                3      Plastik  \n",
       "G939         0        0                3      Plastik  \n",
       "G131         0        0                3        Gummi  \n",
       "G167         0        0                3         Holz  \n",
       "G129         0        0                3        Gummi  \n",
       "G132         0        0                3        Gummi  \n",
       "G134         0        0                3        Gummi  \n",
       "G84          0        0                0      Plastik  \n",
       "G8           0        0                0      Plastik  \n",
       "G913         0        0                0      Plastik  \n",
       "G122         0        0                0      Plastik  \n",
       "G126         0        0                0        Gummi  \n",
       "G88          0        0                0      Plastik  \n",
       "G9           0        0                0      Plastik  \n",
       "G12          0        0                0      Plastik  \n",
       "G900         0        0                0        Gummi  \n",
       "G123         0        0                0      Plastik  \n",
       "G119         0        0                0      Plastik  \n",
       "G176         0        0                0       Metall  \n",
       "G915         0        0                0      Plastik  \n",
       "G916         0        0                0         Holz  \n",
       "G102         0        0                0      Plastik  \n",
       "G999         0        0                0    Unbekannt  \n",
       "G103         0        0                0      Plastik  \n",
       "G97          0        0                0      Plastik  \n",
       "G104         0        0                0      Plastik  \n",
       "G105         0        0                0      Plastik  \n",
       "G945         0        0                0       Metall  \n",
       "G944         0        0                0      Plastik  \n",
       "G940         0        0                0      Plastik  \n",
       "G94          0        0                0      Plastik  \n",
       "G938         0        0                0      Plastik  \n",
       "G937         0        0                0      Plastik  \n",
       "G108         0        0                0      Plastik  \n",
       "G935         0        0                0      Plastik  \n",
       "G934         0        0                0      Plastik  \n",
       "G932         0        0                0      Plastik  \n",
       "G109         0        0                0      Plastik  \n",
       "G930         0        0                0      Plastik  \n",
       "G11          0        0                0      Plastik  \n",
       "G111         0        0                0      Plastik  \n",
       "G113         0        0                0      Plastik  \n",
       "G926         0        0                0      Plastik  \n",
       "G114         0        0                0      Plastik  \n",
       "G712         0        0                0        Stoff  \n",
       "G116         0        0                0      Plastik  \n",
       "G118         0        0                0      Plastik  \n",
       "G917         0        0                0         Glas  \n",
       "G713         0        0                0      Plastik  \n",
       "G68          0        0                0      Plastik  \n",
       "G711         0        0                0      Plastik  \n",
       "G202         0        0                0         Glas  \n",
       "G150         0        0                0       Papier  \n",
       "G151         0        0                0       Papier  \n",
       "G158         0        0                0       Papier  \n",
       "G160         0        0                0         Holz  \n",
       "G161         0        0                0         Holz  \n",
       "G214         0        0                0  Chemikalien  \n",
       "G213         0        0                0  Chemikalien  \n",
       "G162         0        0                0         Holz  \n",
       "G210         0        0                0         Glas  \n",
       "G166         0        0                0         Holz  \n",
       "G207         0        0                0         Glas  \n",
       "G205         0        0                0         Glas  \n",
       "G168         0        0                0         Holz  \n",
       "G17          0        0                0      Plastik  \n",
       "G29          0        0                0      Plastik  \n",
       "G20          0        0                0      Plastik  \n",
       "G199         0        0                0       Metall  \n",
       "G196         0        0                0       Metall  \n",
       "G171         0        0                0         Holz  \n",
       "G193         0        0                0       Metall  \n",
       "G190         0        0                0       Metall  \n",
       "G188         0        0                0       Metall  \n",
       "G173         0        0                0         Holz  \n",
       "G185         0        0                0       Metall  \n",
       "G183         0        0                0       Metall  \n",
       "G181         0        0                0       Metall  \n",
       "G180         0        0                0       Metall  \n",
       "G179         0        0                0       Metall  \n",
       "G147         0        0                0       Papier  \n",
       "G144         0        0                0        Stoff  \n",
       "G710         0        0                0      Plastik  \n",
       "G63          0        0                0      Plastik  \n",
       "G71          0        0                0      Plastik  \n",
       "G709         0        0                0      Plastik  \n",
       "G708         0        0                0       Metall  \n",
       "G707         0        0                0      Plastik  \n",
       "G706         0        0                0      Plastik  \n",
       "G705         0        0                0       Metall  \n",
       "G704         0        0                0      Plastik  \n",
       "G703         0        0                0      Plastik  \n",
       "G702         0        0                0         Holz  \n",
       "G13          0        0                0      Plastik  \n",
       "G174         0        0                0       Metall  \n",
       "G133         0        0                0        Gummi  \n",
       "G64          0        0                0      Plastik  \n",
       "G61          0        0                0      Plastik  \n",
       "G143         0        0                0        Stoff  \n",
       "G60          0        0                0      Plastik  \n",
       "G59          0        0                0      Plastik  \n",
       "G56          0        0                0      Plastik  \n",
       "G55          0        0                0      Plastik  \n",
       "G51          0        0                0      Plastik  \n",
       "G5           0        0                0      Plastik  \n",
       "G48          0        0                0      Plastik  \n",
       "G43          0        0                0      Plastik  \n",
       "G39          0        0                0      Plastik  \n",
       "G38          0        0                0      Plastik  \n",
       "G139         0        0                0        Stoff  \n",
       "G14          0        0                0      Plastik  \n",
       "G140         0        0                0        Stoff  \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/ticino_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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}