linth_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/linth_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 linth (linth) at the river basin (river_bassin) level.\n",
    "# the label for charting is called 'name'\n",
    "this_feature = {'slug':'linth', 'name':\"Erhebungsgebiet Linth\", 'level':'river_bassin'}\n",
    "\n",
    "# the lake is in this survey area\n",
    "this_bassin = \"linth\"\n",
    "# label for survey area\n",
    "bassin_label = \"Erhebungsgebiet Linth\"\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 = \"linth_sa\"\n",
    "\n",
    "# identify the lakes of interest for the survey area\n",
    "lakes_of_interest = [\"zurichsee\", \"walensee\", \"vierwaldstättersee\", \"Zugersee\"]\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",
    "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 heatmap 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": [
    "(linthsa)=\n",
    "# Linth\n",
    "\n",
    "| {Download}`Download </resources/pdfs/linth_sa.pdf>` | [English](https://www.plagespropres.ch/linth_sa.html) |"
   ]
  },
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   "execution_count": 2,
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     "remove-input"
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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": "linth_city_map_caption"
      }
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     "output_type": "display_data"
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    {
     "data": {
      "application/papermill.record/text/plain": "' '"
     },
     "metadata": {
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       "mime_prefix": "application/papermill.record/",
       "name": "blank_caption"
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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/linth_city_labels.jpeg\n",
    "---\n",
    "name: linth_survey_area_city_labels_map\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_city_labels_map>` {glue:text}`linth_city_map_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Erhebungsorte und Landnutzungsprofile"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Im Zeitraum von März 2020  bis Mai 2021 wurden im Rahmen von 112 Datenerhebungen insgesamt 9 412 Objekte entfernt und identifiziert.  Die Ergebnisse des Erhebungsgebiet Linth umfassen 40 Orte, 17 Gemeinden und eine Gesamtbevölkerung von etwa 612 111 Einwohnenden.\n",
       "\n",
       "*Seen*\n",
       "\n",
       ">Zürichsee, Walensee, Vierwaldstättersee, Zugersee\n",
       "\n",
       "*Fliessgewässer*\n",
       "\n",
       ">Jona, Dorfbach, Limmat, Linthkanal, Escherkanal, Reuss, Seez, Sihl\n",
       "\n",
       "*Gemeinden*\n",
       "\n",
       ">Dietikon, Freienbach, Glarus Nord, Hünenberg, Küsnacht (ZH), Merenschwand, Quarten, Rapperswil-Jona, Richterswil, Schmerikon, Stäfa, Unterengstringen, Walenstadt, Weesen, Weggis, Zug, Zürich\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the admin summary can be converted into a standard text\n",
    "an_admin_summary = featuredata.makeAdminSummaryStateMent(start_date, end_date, this_feature[\"name\"], admin_summary=admin_summary)\n",
    "\n",
    "# pdf components\n",
    "new_components = [    \n",
    "    small_space,\n",
    "    Paragraph(\"Erhebungsorte\", section_title), \n",
    "    small_space,\n",
    "    Paragraph(an_admin_summary , featuredata.p_style),\n",
    "]\n",
    "\n",
    "# add the admin summary to the pdf\n",
    "pdfcomponents = addToDoc(new_components, pdfcomponents)\n",
    "# end pdf\n",
    "\n",
    "# collect component features and land marks\n",
    "# this collects the components of the feature of interest (city, lake, river)\n",
    "# a comma separated string of all the componenets and a heading for each component\n",
    "# type is produced\n",
    "feature_components = featuredata.collectComponentLandMarks(admin_details, language=language)\n",
    "\n",
    "# markdown output\n",
    "components_markdown = \"\".join([f'*{x[0]}*\\n\\n>{x[1]}\\n\\n' for x in feature_components])\n",
    "\n",
    "# put that all together:\n",
    "lake_string = F\"\"\"\n",
    "{an_admin_summary}\n",
    "\n",
    "{\"\".join(components_markdown)}\n",
    "\"\"\"\n",
    "\n",
    "# display the string\n",
    "md(lake_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Kumulative Gesamtmengen nach Gewässer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "hide-input"
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   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Erhebungsgebiet Linth: kumulierten Gewichte  und Masse für die Gemeinden'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_dims_table_caption"
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    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_87a40 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_87a40 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_87a40 tr {\n  font-size: 12px;\n}\n#T_87a40 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_87a40 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_87a40\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_87a40_level0_col0\" class=\"col_heading level0 col0\" >Gesamtgewicht (Kg)</th>\n      <th id=\"T_87a40_level0_col1\" class=\"col_heading level0 col1\" >Plastik (Kg)</th>\n      <th id=\"T_87a40_level0_col2\" class=\"col_heading level0 col2\" >Fläche (m2)</th>\n      <th id=\"T_87a40_level0_col3\" class=\"col_heading level0 col3\" >Länge (m)</th>\n      <th id=\"T_87a40_level0_col4\" class=\"col_heading level0 col4\" >Erhebungen</th>\n      <th id=\"T_87a40_level0_col5\" class=\"col_heading level0 col5\" >Objekte (St.)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_87a40_level0_row0\" class=\"row_heading level0 row0\" >Erhebungsgebiet Linth</th>\n      <td id=\"T_87a40_row0_col0\" class=\"data row0 col0\" >35,96</td>\n      <td id=\"T_87a40_row0_col1\" class=\"data row0 col1\" >12,77</td>\n      <td id=\"T_87a40_row0_col2\" class=\"data row0 col2\" >25 637</td>\n      <td id=\"T_87a40_row0_col3\" class=\"data row0 col3\" >5 323</td>\n      <td id=\"T_87a40_row0_col4\" class=\"data row0 col4\" >112</td>\n      <td id=\"T_87a40_row0_col5\" class=\"data row0 col5\" >9 412</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row1\" class=\"row_heading level0 row1\" >Zürichsee</th>\n      <td id=\"T_87a40_row1_col0\" class=\"data row1 col0\" >12,64</td>\n      <td id=\"T_87a40_row1_col1\" class=\"data row1 col1\" >4,49</td>\n      <td id=\"T_87a40_row1_col2\" class=\"data row1 col2\" >7 409</td>\n      <td id=\"T_87a40_row1_col3\" class=\"data row1 col3\" >2 212</td>\n      <td id=\"T_87a40_row1_col4\" class=\"data row1 col4\" >56</td>\n      <td id=\"T_87a40_row1_col5\" class=\"data row1 col5\" >5 192</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row2\" class=\"row_heading level0 row2\" >Walensee</th>\n      <td id=\"T_87a40_row2_col0\" class=\"data row2 col0\" >10,99</td>\n      <td id=\"T_87a40_row2_col1\" class=\"data row2 col1\" >6,69</td>\n      <td id=\"T_87a40_row2_col2\" class=\"data row2 col2\" >13 236</td>\n      <td id=\"T_87a40_row2_col3\" class=\"data row2 col3\" >2 225</td>\n      <td id=\"T_87a40_row2_col4\" class=\"data row2 col4\" >31</td>\n      <td id=\"T_87a40_row2_col5\" class=\"data row2 col5\" >3 694</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row3\" class=\"row_heading level0 row3\" >Limmat</th>\n      <td id=\"T_87a40_row3_col0\" class=\"data row3 col0\" >3,33</td>\n      <td id=\"T_87a40_row3_col1\" class=\"data row3 col1\" >0,18</td>\n      <td id=\"T_87a40_row3_col2\" class=\"data row3 col2\" >1 111</td>\n      <td id=\"T_87a40_row3_col3\" class=\"data row3 col3\" >237</td>\n      <td id=\"T_87a40_row3_col4\" class=\"data row3 col4\" >6</td>\n      <td id=\"T_87a40_row3_col5\" class=\"data row3 col5\" >219</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row4\" class=\"row_heading level0 row4\" >Linthkanal</th>\n      <td id=\"T_87a40_row4_col0\" class=\"data row4 col0\" >2,84</td>\n      <td id=\"T_87a40_row4_col1\" class=\"data row4 col1\" >0,06</td>\n      <td id=\"T_87a40_row4_col2\" class=\"data row4 col2\" >797</td>\n      <td id=\"T_87a40_row4_col3\" class=\"data row4 col3\" >106</td>\n      <td id=\"T_87a40_row4_col4\" class=\"data row4 col4\" >2</td>\n      <td id=\"T_87a40_row4_col5\" class=\"data row4 col5\" >84</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row5\" class=\"row_heading level0 row5\" >Zugersee</th>\n      <td id=\"T_87a40_row5_col0\" class=\"data row5 col0\" >0,35</td>\n      <td id=\"T_87a40_row5_col1\" class=\"data row5 col1\" >0,14</td>\n      <td id=\"T_87a40_row5_col2\" class=\"data row5 col2\" >412</td>\n      <td id=\"T_87a40_row5_col3\" class=\"data row5 col3\" >104</td>\n      <td id=\"T_87a40_row5_col4\" class=\"data row5 col4\" >3</td>\n      <td id=\"T_87a40_row5_col5\" class=\"data row5 col5\" >62</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row6\" class=\"row_heading level0 row6\" >Vierwaldstättersee</th>\n      <td id=\"T_87a40_row6_col0\" class=\"data row6 col0\" >0,3</td>\n      <td id=\"T_87a40_row6_col1\" class=\"data row6 col1\" >0,16</td>\n      <td id=\"T_87a40_row6_col2\" class=\"data row6 col2\" >283</td>\n      <td id=\"T_87a40_row6_col3\" class=\"data row6 col3\" >83</td>\n      <td id=\"T_87a40_row6_col4\" class=\"data row6 col4\" >3</td>\n      <td id=\"T_87a40_row6_col5\" class=\"data row6 col5\" >58</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row7\" class=\"row_heading level0 row7\" >Seez</th>\n      <td id=\"T_87a40_row7_col0\" class=\"data row7 col0\" >1,0</td>\n      <td id=\"T_87a40_row7_col1\" class=\"data row7 col1\" >0,05</td>\n      <td id=\"T_87a40_row7_col2\" class=\"data row7 col2\" >642</td>\n      <td id=\"T_87a40_row7_col3\" class=\"data row7 col3\" >51</td>\n      <td id=\"T_87a40_row7_col4\" class=\"data row7 col4\" >2</td>\n      <td id=\"T_87a40_row7_col5\" class=\"data row7 col5\" >42</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_87a40_row8_col0\" class=\"data row8 col0\" >0,0</td>\n      <td id=\"T_87a40_row8_col1\" class=\"data row8 col1\" >0,0</td>\n      <td id=\"T_87a40_row8_col2\" class=\"data row8 col2\" >326</td>\n      <td id=\"T_87a40_row8_col3\" class=\"data row8 col3\" >85</td>\n      <td id=\"T_87a40_row8_col4\" class=\"data row8 col4\" >3</td>\n      <td id=\"T_87a40_row8_col5\" class=\"data row8 col5\" >21</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row9\" class=\"row_heading level0 row9\" >Sihl</th>\n      <td id=\"T_87a40_row9_col0\" class=\"data row9 col0\" >1,97</td>\n      <td id=\"T_87a40_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_87a40_row9_col2\" class=\"data row9 col2\" >152</td>\n      <td id=\"T_87a40_row9_col3\" class=\"data row9 col3\" >42</td>\n      <td id=\"T_87a40_row9_col4\" class=\"data row9 col4\" >2</td>\n      <td id=\"T_87a40_row9_col5\" class=\"data row9 col5\" >15</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row10\" class=\"row_heading level0 row10\" >Dorfbach</th>\n      <td id=\"T_87a40_row10_col0\" class=\"data row10 col0\" >0,34</td>\n      <td id=\"T_87a40_row10_col1\" class=\"data row10 col1\" >0,0</td>\n      <td id=\"T_87a40_row10_col2\" class=\"data row10 col2\" >1 093</td>\n      <td id=\"T_87a40_row10_col3\" class=\"data row10 col3\" >129</td>\n      <td id=\"T_87a40_row10_col4\" class=\"data row10 col4\" >1</td>\n      <td id=\"T_87a40_row10_col5\" class=\"data row10 col5\" >12</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row11\" class=\"row_heading level0 row11\" >Escherkanal</th>\n      <td id=\"T_87a40_row11_col0\" class=\"data row11 col0\" >1,0</td>\n      <td id=\"T_87a40_row11_col1\" class=\"data row11 col1\" >1,0</td>\n      <td id=\"T_87a40_row11_col2\" class=\"data row11 col2\" >39</td>\n      <td id=\"T_87a40_row11_col3\" class=\"data row11 col3\" >11</td>\n      <td id=\"T_87a40_row11_col4\" class=\"data row11 col4\" >1</td>\n      <td id=\"T_87a40_row11_col5\" class=\"data row11 col5\" >10</td>\n    </tr>\n    <tr>\n      <th id=\"T_87a40_level0_row12\" class=\"row_heading level0 row12\" >Reuss</th>\n      <td id=\"T_87a40_row12_col0\" class=\"data row12 col0\" >1,2</td>\n      <td id=\"T_87a40_row12_col1\" class=\"data row12 col1\" >0,0</td>\n      <td id=\"T_87a40_row12_col2\" class=\"data row12 col2\" >137</td>\n      <td id=\"T_87a40_row12_col3\" class=\"data row12 col3\" >38</td>\n      <td id=\"T_87a40_row12_col4\" class=\"data row12 col4\" >2</td>\n      <td id=\"T_87a40_row12_col5\" class=\"data row12 col5\" >3</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f638b8ca8e0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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(\"linth_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} linth_dims_table\n",
    "---\n",
    "name: 'linth_dims_table'\n",
    "---\n",
    "{glue:text}`blank_caption`\n",
    "```\n",
    "{numref}`Abbildung %s: <linth_dims_table>` {glue:text}`linth_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": "linth_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": "linth_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 = \"linth_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} linth_survey_area_landuse\n",
    "---\n",
    "name: 'linth_survey_area_landuse'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_landuse>` {glue:text}`linth_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 Linth, März 2020 bis Mai 2021, n = 112. Rechts: empirische Verteilungsfunktion der Erhebungsergebnisse Erhebungsgebiet Linth.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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": "linth_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(\"linth_sample_totals\", fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_sample_totals\n",
    "---\n",
    "name: 'linth_survey_area_sample_totals'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_sample_totals>` {glue:text}`linth_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 Linth. Gefunden Materialarten am Erhebungsgebiet Linth in Stückzahlen und als prozentuale Anteile (stückzahlbezogen).'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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": "linth_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('linth_survey_area_sample_material_tables', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_survey_area_sample_material_tables\n",
    "---\n",
    "name: 'linth_survey_area_sample_material_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "{numref}`Abbildung %s: <linth_survey_area_sample_material_tables>` {glue:text}`linth_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 Linth: d. h. Objekte mit einer Häufigkeitsrate von mindestens 50% und/oder Top Ten nach Anzahl. Zusammengenommen machen die häufigsten Objekte 66% aller gefundenen Objekte aus. Anmerkung: p/100 m = Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_most_common_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_5e292 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_5e292 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_5e292 tr {\n  font-size: 12px;\n}\n#T_5e292 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_5e292 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_5e292\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_5e292_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_5e292_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_5e292_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_5e292_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_5e292_level0_row0\" class=\"row_heading level0 row0\" >Zigarettenfilter</th>\n      <td id=\"T_5e292_row0_col0\" class=\"data row0 col0\" >2 012</td>\n      <td id=\"T_5e292_row0_col1\" class=\"data row0 col1\" >21%</td>\n      <td id=\"T_5e292_row0_col2\" class=\"data row0 col2\" >87%</td>\n      <td id=\"T_5e292_row0_col3\" class=\"data row0 col3\" >23,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row1\" class=\"row_heading level0 row1\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_5e292_row1_col0\" class=\"data row1 col0\" >917</td>\n      <td id=\"T_5e292_row1_col1\" class=\"data row1 col1\" >9%</td>\n      <td id=\"T_5e292_row1_col2\" class=\"data row1 col2\" >75%</td>\n      <td id=\"T_5e292_row1_col3\" class=\"data row1 col3\" >10,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row2\" class=\"row_heading level0 row2\" >Expandiertes Polystyrol</th>\n      <td id=\"T_5e292_row2_col0\" class=\"data row2 col0\" >898</td>\n      <td id=\"T_5e292_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_5e292_row2_col2\" class=\"data row2 col2\" >65%</td>\n      <td id=\"T_5e292_row2_col3\" class=\"data row2 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row3\" class=\"row_heading level0 row3\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_5e292_row3_col0\" class=\"data row3 col0\" >621</td>\n      <td id=\"T_5e292_row3_col1\" class=\"data row3 col1\" >6%</td>\n      <td id=\"T_5e292_row3_col2\" class=\"data row3 col2\" >66%</td>\n      <td id=\"T_5e292_row3_col3\" class=\"data row3 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row4\" class=\"row_heading level0 row4\" >Snack-Verpackungen</th>\n      <td id=\"T_5e292_row4_col0\" class=\"data row4 col0\" >558</td>\n      <td id=\"T_5e292_row4_col1\" class=\"data row4 col1\" >5%</td>\n      <td id=\"T_5e292_row4_col2\" class=\"data row4 col2\" >85%</td>\n      <td id=\"T_5e292_row4_col3\" class=\"data row4 col3\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row5\" class=\"row_heading level0 row5\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_5e292_row5_col0\" class=\"data row5 col0\" >462</td>\n      <td id=\"T_5e292_row5_col1\" class=\"data row5 col1\" >4%</td>\n      <td id=\"T_5e292_row5_col2\" class=\"data row5 col2\" >55%</td>\n      <td id=\"T_5e292_row5_col3\" class=\"data row5 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row6\" class=\"row_heading level0 row6\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_5e292_row6_col0\" class=\"data row6 col0\" >238</td>\n      <td id=\"T_5e292_row6_col1\" class=\"data row6 col1\" >2%</td>\n      <td id=\"T_5e292_row6_col2\" class=\"data row6 col2\" >39%</td>\n      <td id=\"T_5e292_row6_col3\" class=\"data row6 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row7\" class=\"row_heading level0 row7\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_5e292_row7_col0\" class=\"data row7 col0\" >228</td>\n      <td id=\"T_5e292_row7_col1\" class=\"data row7 col1\" >2%</td>\n      <td id=\"T_5e292_row7_col2\" class=\"data row7 col2\" >38%</td>\n      <td id=\"T_5e292_row7_col3\" class=\"data row7 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row8\" class=\"row_heading level0 row8\" >Tabak; Kunststoffverpackungen, Behälter</th>\n      <td id=\"T_5e292_row8_col0\" class=\"data row8 col0\" >178</td>\n      <td id=\"T_5e292_row8_col1\" class=\"data row8 col1\" >1%</td>\n      <td id=\"T_5e292_row8_col2\" class=\"data row8 col2\" >48%</td>\n      <td id=\"T_5e292_row8_col3\" class=\"data row8 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_5e292_level0_row9\" class=\"row_heading level0 row9\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_5e292_row9_col0\" class=\"data row9 col0\" >173</td>\n      <td id=\"T_5e292_row9_col1\" class=\"data row9 col1\" >1%</td>\n      <td id=\"T_5e292_row9_col2\" class=\"data row9 col2\" >52%</td>\n      <td id=\"T_5e292_row9_col3\" class=\"data row9 col3\" >1,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f6381744850>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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('linth_most_common_caption', mc_caption_string, display=False)\n",
    "glue('linth_most_common_tables', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_most_common_tables\n",
    "---\n",
    "name: 'linth_survey_area_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_most_common_tables>` {glue:text}`linth_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 Linth.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_mc_heat_map_caption"
      }
     },
     "output_type": "display_data"
    },
    {
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background-color: #fff9c9;\n  color: #000000;\n}\n#T_effac_row9_col8 {\n  background-color: #feeba2;\n  color: #000000;\n}\n#T_effac_row9_col9 {\n  background-color: #fea231;\n  color: #000000;\n}\n#T_effac_row9_col10 {\n  background-color: #fed069;\n  color: #000000;\n}\n#T_effac_row9_col11 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_effac_row9_col12 {\n  background-color: #f3801c;\n  color: #f1f1f1;\n}\n#T_effac_row9_col13 {\n  background-color: #fe9829;\n  color: #000000;\n}\n#T_effac_level0_col0, #T_effac_level0_col1, #T_effac_level0_col2, #T_effac_level0_col3, #T_effac_level0_col4, #T_effac_level0_col5, #T_effac_level0_col6, #T_effac_level0_col7, #T_effac_level0_col8, #T_effac_level0_col9, #T_effac_level0_col10, #T_effac_level0_col11, #T_effac_level0_col12, #T_effac_level0_col13 {\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_effac\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_effac_level0_col0\" class=\"col_heading level0 col0\" >Dorfbach</th>\n      <th id=\"T_effac_level0_col1\" class=\"col_heading level0 col1\" >Escherkanal</th>\n      <th id=\"T_effac_level0_col2\" class=\"col_heading level0 col2\" >Jona</th>\n      <th id=\"T_effac_level0_col3\" class=\"col_heading level0 col3\" >Limmat</th>\n      <th id=\"T_effac_level0_col4\" class=\"col_heading level0 col4\" >Linthkanal</th>\n      <th id=\"T_effac_level0_col5\" class=\"col_heading level0 col5\" >Reuss</th>\n      <th id=\"T_effac_level0_col6\" class=\"col_heading level0 col6\" >Seez</th>\n      <th id=\"T_effac_level0_col7\" class=\"col_heading level0 col7\" >Sihl</th>\n      <th id=\"T_effac_level0_col8\" class=\"col_heading level0 col8\" >Vierwaldstättersee</th>\n      <th id=\"T_effac_level0_col9\" class=\"col_heading level0 col9\" >Walensee</th>\n      <th id=\"T_effac_level0_col10\" class=\"col_heading level0 col10\" >Zugersee</th>\n      <th id=\"T_effac_level0_col11\" class=\"col_heading level0 col11\" >Zürichsee</th>\n      <th id=\"T_effac_level0_col12\" class=\"col_heading level0 col12\" >Erhebungsgebiet Linth</th>\n      <th id=\"T_effac_level0_col13\" class=\"col_heading level0 col13\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_effac_level0_row0\" class=\"row_heading level0 row0\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_effac_row0_col0\" class=\"data row0 col0\" >0,0</td>\n      <td id=\"T_effac_row0_col1\" class=\"data row0 col1\" >0,0</td>\n      <td id=\"T_effac_row0_col2\" class=\"data row0 col2\" >0,0</td>\n      <td id=\"T_effac_row0_col3\" class=\"data row0 col3\" >8,5</td>\n      <td id=\"T_effac_row0_col4\" class=\"data row0 col4\" >10,5</td>\n      <td id=\"T_effac_row0_col5\" class=\"data row0 col5\" >0,0</td>\n      <td id=\"T_effac_row0_col6\" class=\"data row0 col6\" >0,0</td>\n      <td id=\"T_effac_row0_col7\" class=\"data row0 col7\" >7,0</td>\n      <td id=\"T_effac_row0_col8\" class=\"data row0 col8\" >0,0</td>\n      <td id=\"T_effac_row0_col9\" class=\"data row0 col9\" >1,0</td>\n      <td id=\"T_effac_row0_col10\" class=\"data row0 col10\" >0,0</td>\n      <td id=\"T_effac_row0_col11\" class=\"data row0 col11\" >10,5</td>\n      <td id=\"T_effac_row0_col12\" class=\"data row0 col12\" >4,0</td>\n      <td id=\"T_effac_row0_col13\" class=\"data row0 col13\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row1\" class=\"row_heading level0 row1\" >Expandiertes Polystyrol</th>\n      <td id=\"T_effac_row1_col0\" class=\"data row1 col0\" >0,0</td>\n      <td id=\"T_effac_row1_col1\" class=\"data row1 col1\" >0,0</td>\n      <td id=\"T_effac_row1_col2\" class=\"data row1 col2\" >0,0</td>\n      <td id=\"T_effac_row1_col3\" class=\"data row1 col3\" >0,0</td>\n      <td id=\"T_effac_row1_col4\" class=\"data row1 col4\" >0,5</td>\n      <td id=\"T_effac_row1_col5\" class=\"data row1 col5\" >0,0</td>\n      <td id=\"T_effac_row1_col6\" class=\"data row1 col6\" >12,5</td>\n      <td id=\"T_effac_row1_col7\" class=\"data row1 col7\" >0,0</td>\n      <td id=\"T_effac_row1_col8\" class=\"data row1 col8\" >4,0</td>\n      <td id=\"T_effac_row1_col9\" class=\"data row1 col9\" >9,0</td>\n      <td id=\"T_effac_row1_col10\" class=\"data row1 col10\" >0,0</td>\n      <td id=\"T_effac_row1_col11\" class=\"data row1 col11\" >3,0</td>\n      <td id=\"T_effac_row1_col12\" class=\"data row1 col12\" >3,0</td>\n      <td id=\"T_effac_row1_col13\" class=\"data row1 col13\" >5,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row2\" class=\"row_heading level0 row2\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_effac_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_effac_row2_col1\" class=\"data row2 col1\" >0,0</td>\n      <td id=\"T_effac_row2_col2\" class=\"data row2 col2\" >3,0</td>\n      <td id=\"T_effac_row2_col3\" class=\"data row2 col3\" >2,0</td>\n      <td id=\"T_effac_row2_col4\" class=\"data row2 col4\" >7,0</td>\n      <td id=\"T_effac_row2_col5\" class=\"data row2 col5\" >0,0</td>\n      <td id=\"T_effac_row2_col6\" class=\"data row2 col6\" >7,5</td>\n      <td id=\"T_effac_row2_col7\" class=\"data row2 col7\" >0,0</td>\n      <td id=\"T_effac_row2_col8\" class=\"data row2 col8\" >0,0</td>\n      <td id=\"T_effac_row2_col9\" class=\"data row2 col9\" >12,0</td>\n      <td id=\"T_effac_row2_col10\" class=\"data row2 col10\" >0,0</td>\n      <td id=\"T_effac_row2_col11\" class=\"data row2 col11\" >18,0</td>\n      <td id=\"T_effac_row2_col12\" class=\"data row2 col12\" >10,5</td>\n      <td id=\"T_effac_row2_col13\" class=\"data row2 col13\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row3\" class=\"row_heading level0 row3\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_effac_row3_col0\" class=\"data row3 col0\" >0,0</td>\n      <td id=\"T_effac_row3_col1\" class=\"data row3 col1\" >9,0</td>\n      <td id=\"T_effac_row3_col2\" class=\"data row3 col2\" >0,0</td>\n      <td id=\"T_effac_row3_col3\" class=\"data row3 col3\" >0,0</td>\n      <td id=\"T_effac_row3_col4\" class=\"data row3 col4\" >5,5</td>\n      <td id=\"T_effac_row3_col5\" class=\"data row3 col5\" >0,0</td>\n      <td id=\"T_effac_row3_col6\" class=\"data row3 col6\" >0,0</td>\n      <td id=\"T_effac_row3_col7\" class=\"data row3 col7\" >0,0</td>\n      <td id=\"T_effac_row3_col8\" class=\"data row3 col8\" >0,0</td>\n      <td id=\"T_effac_row3_col9\" class=\"data row3 col9\" >5,0</td>\n      <td id=\"T_effac_row3_col10\" class=\"data row3 col10\" >0,0</td>\n      <td id=\"T_effac_row3_col11\" class=\"data row3 col11\" >3,0</td>\n      <td id=\"T_effac_row3_col12\" class=\"data row3 col12\" >2,0</td>\n      <td id=\"T_effac_row3_col13\" class=\"data row3 col13\" >5,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row4\" class=\"row_heading level0 row4\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_effac_row4_col0\" class=\"data row4 col0\" >0,0</td>\n      <td id=\"T_effac_row4_col1\" class=\"data row4 col1\" >0,0</td>\n      <td id=\"T_effac_row4_col2\" class=\"data row4 col2\" >0,0</td>\n      <td id=\"T_effac_row4_col3\" class=\"data row4 col3\" >5,0</td>\n      <td id=\"T_effac_row4_col4\" class=\"data row4 col4\" >0,5</td>\n      <td id=\"T_effac_row4_col5\" class=\"data row4 col5\" >0,0</td>\n      <td id=\"T_effac_row4_col6\" class=\"data row4 col6\" >0,0</td>\n      <td id=\"T_effac_row4_col7\" class=\"data row4 col7\" >1,5</td>\n      <td id=\"T_effac_row4_col8\" class=\"data row4 col8\" >0,0</td>\n      <td id=\"T_effac_row4_col9\" class=\"data row4 col9\" >1,0</td>\n      <td id=\"T_effac_row4_col10\" class=\"data row4 col10\" >0,0</td>\n      <td id=\"T_effac_row4_col11\" class=\"data row4 col11\" >4,0</td>\n      <td id=\"T_effac_row4_col12\" class=\"data row4 col12\" >1,0</td>\n      <td id=\"T_effac_row4_col13\" class=\"data row4 col13\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row5\" class=\"row_heading level0 row5\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_effac_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_effac_row5_col1\" class=\"data row5 col1\" >0,0</td>\n      <td id=\"T_effac_row5_col2\" class=\"data row5 col2\" >0,0</td>\n      <td id=\"T_effac_row5_col3\" class=\"data row5 col3\" >0,0</td>\n      <td id=\"T_effac_row5_col4\" class=\"data row5 col4\" >0,0</td>\n      <td id=\"T_effac_row5_col5\" class=\"data row5 col5\" >0,0</td>\n      <td id=\"T_effac_row5_col6\" class=\"data row5 col6\" >0,0</td>\n      <td id=\"T_effac_row5_col7\" class=\"data row5 col7\" >0,0</td>\n      <td id=\"T_effac_row5_col8\" class=\"data row5 col8\" >0,0</td>\n      <td id=\"T_effac_row5_col9\" class=\"data row5 col9\" >4,0</td>\n      <td id=\"T_effac_row5_col10\" class=\"data row5 col10\" >0,0</td>\n      <td id=\"T_effac_row5_col11\" class=\"data row5 col11\" >0,0</td>\n      <td id=\"T_effac_row5_col12\" class=\"data row5 col12\" >0,0</td>\n      <td id=\"T_effac_row5_col13\" class=\"data row5 col13\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row6\" class=\"row_heading level0 row6\" >Snack-Verpackungen</th>\n      <td id=\"T_effac_row6_col0\" class=\"data row6 col0\" >1,0</td>\n      <td id=\"T_effac_row6_col1\" class=\"data row6 col1\" >18,0</td>\n      <td id=\"T_effac_row6_col2\" class=\"data row6 col2\" >5,0</td>\n      <td id=\"T_effac_row6_col3\" class=\"data row6 col3\" >6,0</td>\n      <td id=\"T_effac_row6_col4\" class=\"data row6 col4\" >5,5</td>\n      <td id=\"T_effac_row6_col5\" class=\"data row6 col5\" >0,0</td>\n      <td id=\"T_effac_row6_col6\" class=\"data row6 col6\" >1,0</td>\n      <td id=\"T_effac_row6_col7\" class=\"data row6 col7\" >0,0</td>\n      <td id=\"T_effac_row6_col8\" class=\"data row6 col8\" >5,0</td>\n      <td id=\"T_effac_row6_col9\" class=\"data row6 col9\" >6,0</td>\n      <td id=\"T_effac_row6_col10\" class=\"data row6 col10\" >3,0</td>\n      <td id=\"T_effac_row6_col11\" class=\"data row6 col11\" >11,5</td>\n      <td id=\"T_effac_row6_col12\" class=\"data row6 col12\" >6,0</td>\n      <td id=\"T_effac_row6_col13\" class=\"data row6 col13\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row7\" class=\"row_heading level0 row7\" >Tabak; Kunststoffverpackungen, Behälter</th>\n      <td id=\"T_effac_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_effac_row7_col1\" class=\"data row7 col1\" >0,0</td>\n      <td id=\"T_effac_row7_col2\" class=\"data row7 col2\" >0,0</td>\n      <td id=\"T_effac_row7_col3\" class=\"data row7 col3\" >0,0</td>\n      <td id=\"T_effac_row7_col4\" class=\"data row7 col4\" >0,0</td>\n      <td id=\"T_effac_row7_col5\" class=\"data row7 col5\" >0,0</td>\n      <td id=\"T_effac_row7_col6\" class=\"data row7 col6\" >0,0</td>\n      <td id=\"T_effac_row7_col7\" class=\"data row7 col7\" >0,0</td>\n      <td id=\"T_effac_row7_col8\" class=\"data row7 col8\" >0,0</td>\n      <td id=\"T_effac_row7_col9\" class=\"data row7 col9\" >0,0</td>\n      <td id=\"T_effac_row7_col10\" class=\"data row7 col10\" >0,0</td>\n      <td id=\"T_effac_row7_col11\" class=\"data row7 col11\" >3,0</td>\n      <td id=\"T_effac_row7_col12\" class=\"data row7 col12\" >0,0</td>\n      <td id=\"T_effac_row7_col13\" class=\"data row7 col13\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row8\" class=\"row_heading level0 row8\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_effac_row8_col0\" class=\"data row8 col0\" >0,0</td>\n      <td id=\"T_effac_row8_col1\" class=\"data row8 col1\" >0,0</td>\n      <td id=\"T_effac_row8_col2\" class=\"data row8 col2\" >0,0</td>\n      <td id=\"T_effac_row8_col3\" class=\"data row8 col3\" >0,0</td>\n      <td id=\"T_effac_row8_col4\" class=\"data row8 col4\" >0,0</td>\n      <td id=\"T_effac_row8_col5\" class=\"data row8 col5\" >0,0</td>\n      <td id=\"T_effac_row8_col6\" class=\"data row8 col6\" >0,0</td>\n      <td id=\"T_effac_row8_col7\" class=\"data row8 col7\" >0,0</td>\n      <td id=\"T_effac_row8_col8\" class=\"data row8 col8\" >4,0</td>\n      <td id=\"T_effac_row8_col9\" class=\"data row8 col9\" >0,0</td>\n      <td id=\"T_effac_row8_col10\" class=\"data row8 col10\" >7,0</td>\n      <td id=\"T_effac_row8_col11\" class=\"data row8 col11\" >2,0</td>\n      <td id=\"T_effac_row8_col12\" class=\"data row8 col12\" >0,0</td>\n      <td id=\"T_effac_row8_col13\" class=\"data row8 col13\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_effac_level0_row9\" class=\"row_heading level0 row9\" >Zigarettenfilter</th>\n      <td id=\"T_effac_row9_col0\" class=\"data row9 col0\" >1,0</td>\n      <td id=\"T_effac_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_effac_row9_col2\" class=\"data row9 col2\" >3,0</td>\n      <td id=\"T_effac_row9_col3\" class=\"data row9 col3\" >9,0</td>\n      <td id=\"T_effac_row9_col4\" class=\"data row9 col4\" >0,5</td>\n      <td id=\"T_effac_row9_col5\" class=\"data row9 col5\" >0,0</td>\n      <td id=\"T_effac_row9_col6\" class=\"data row9 col6\" >3,5</td>\n      <td id=\"T_effac_row9_col7\" class=\"data row9 col7\" >0,0</td>\n      <td id=\"T_effac_row9_col8\" class=\"data row9 col8\" >8,0</td>\n      <td id=\"T_effac_row9_col9\" class=\"data row9 col9\" >19,0</td>\n      <td id=\"T_effac_row9_col10\" class=\"data row9 col10\" >13,0</td>\n      <td id=\"T_effac_row9_col11\" class=\"data row9 col11\" >40,0</td>\n      <td id=\"T_effac_row9_col12\" class=\"data row9 col12\" >23,0</td>\n      <td id=\"T_effac_row9_col13\" class=\"data row9 col13\" >20,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f6381559a60>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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} linth_most_common_heat_map\n",
    "---\n",
    "name: 'linth_survey_area_most_common_heat_map'\n",
    "---\n",
    "{glue:text}`blank_caption`  \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_most_common_heat_map>` {glue:text}`linth_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 Linth, monatliche Durchschnittsergebnisse p/100 m'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_monthly_results_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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color: #000000;\n}\n#T_626dc_row8_col1 {\n  background-color: #f88a21;\n  color: #f1f1f1;\n}\n#T_626dc_row8_col2 {\n  background-color: #d65808;\n  color: #f1f1f1;\n}\n#T_626dc_row8_col3 {\n  background-color: #f78921;\n  color: #f1f1f1;\n}\n#T_626dc_row8_col4 {\n  background-color: #dd5f0c;\n  color: #f1f1f1;\n}\n#T_626dc_row8_col5 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_626dc_row8_col6 {\n  background-color: #fff2b1;\n  color: #000000;\n}\n#T_626dc_row8_col8, #T_626dc_row8_col10 {\n  background-color: #fff6b9;\n  color: #000000;\n}\n#T_626dc_row9_col5 {\n  background-color: #fed36f;\n  color: #000000;\n}\n#T_626dc_row9_col9 {\n  background-color: #fff7bd;\n  color: #000000;\n}\n#T_626dc_row9_col10, #T_626dc_row9_col11 {\n  background-color: #fffcd7;\n  color: #000000;\n}\n</style>\n<table id=\"T_626dc\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_626dc_level0_col0\" class=\"col_heading level0 col0\" >Apr.</th>\n      <th id=\"T_626dc_level0_col1\" class=\"col_heading level0 col1\" >Mai</th>\n      <th id=\"T_626dc_level0_col2\" class=\"col_heading level0 col2\" >Juni</th>\n      <th id=\"T_626dc_level0_col3\" class=\"col_heading level0 col3\" >Juli</th>\n      <th id=\"T_626dc_level0_col4\" class=\"col_heading level0 col4\" >Aug.</th>\n      <th id=\"T_626dc_level0_col5\" class=\"col_heading level0 col5\" >Sept.</th>\n      <th id=\"T_626dc_level0_col6\" class=\"col_heading level0 col6\" >Okt.</th>\n      <th id=\"T_626dc_level0_col7\" class=\"col_heading level0 col7\" >Nov.</th>\n      <th id=\"T_626dc_level0_col8\" class=\"col_heading level0 col8\" >Dez.</th>\n      <th id=\"T_626dc_level0_col9\" class=\"col_heading level0 col9\" >Jan.</th>\n      <th id=\"T_626dc_level0_col10\" class=\"col_heading level0 col10\" >Feb.</th>\n      <th id=\"T_626dc_level0_col11\" class=\"col_heading level0 col11\" >März</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_626dc_level0_row0\" class=\"row_heading level0 row0\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_626dc_row0_col0\" class=\"data row0 col0\" >5,3</td>\n      <td id=\"T_626dc_row0_col1\" class=\"data row0 col1\" >5,2</td>\n      <td id=\"T_626dc_row0_col2\" class=\"data row0 col2\" >9,9</td>\n      <td id=\"T_626dc_row0_col3\" class=\"data row0 col3\" >9,1</td>\n      <td id=\"T_626dc_row0_col4\" class=\"data row0 col4\" >8,6</td>\n      <td id=\"T_626dc_row0_col5\" class=\"data row0 col5\" >6,6</td>\n      <td id=\"T_626dc_row0_col6\" class=\"data row0 col6\" >1,4</td>\n      <td id=\"T_626dc_row0_col7\" class=\"data row0 col7\" >1,8</td>\n      <td id=\"T_626dc_row0_col8\" class=\"data row0 col8\" >0,7</td>\n      <td id=\"T_626dc_row0_col9\" class=\"data row0 col9\" >1,6</td>\n      <td id=\"T_626dc_row0_col10\" class=\"data row0 col10\" >1,4</td>\n      <td id=\"T_626dc_row0_col11\" class=\"data row0 col11\" >1,6</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row1\" class=\"row_heading level0 row1\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_626dc_row1_col0\" class=\"data row1 col0\" >0,3</td>\n      <td id=\"T_626dc_row1_col1\" class=\"data row1 col1\" >18,2</td>\n      <td id=\"T_626dc_row1_col2\" class=\"data row1 col2\" >11,0</td>\n      <td id=\"T_626dc_row1_col3\" class=\"data row1 col3\" >14,9</td>\n      <td id=\"T_626dc_row1_col4\" class=\"data row1 col4\" >12,3</td>\n      <td id=\"T_626dc_row1_col5\" class=\"data row1 col5\" >31,4</td>\n      <td id=\"T_626dc_row1_col6\" class=\"data row1 col6\" >7,3</td>\n      <td id=\"T_626dc_row1_col7\" class=\"data row1 col7\" >14,0</td>\n      <td id=\"T_626dc_row1_col8\" class=\"data row1 col8\" >59,0</td>\n      <td id=\"T_626dc_row1_col9\" class=\"data row1 col9\" >58,4</td>\n      <td id=\"T_626dc_row1_col10\" class=\"data row1 col10\" >9,3</td>\n      <td id=\"T_626dc_row1_col11\" class=\"data row1 col11\" >10,4</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row2\" class=\"row_heading level0 row2\" >Snack-Verpackungen</th>\n      <td id=\"T_626dc_row2_col0\" class=\"data row2 col0\" >19,2</td>\n      <td id=\"T_626dc_row2_col1\" class=\"data row2 col1\" >18,3</td>\n      <td id=\"T_626dc_row2_col2\" class=\"data row2 col2\" >16,0</td>\n      <td id=\"T_626dc_row2_col3\" class=\"data row2 col3\" >26,6</td>\n      <td id=\"T_626dc_row2_col4\" class=\"data row2 col4\" >11,4</td>\n      <td id=\"T_626dc_row2_col5\" class=\"data row2 col5\" >40,5</td>\n      <td id=\"T_626dc_row2_col6\" class=\"data row2 col6\" >11,4</td>\n      <td id=\"T_626dc_row2_col7\" class=\"data row2 col7\" >6,4</td>\n      <td id=\"T_626dc_row2_col8\" class=\"data row2 col8\" >8,8</td>\n      <td id=\"T_626dc_row2_col9\" class=\"data row2 col9\" >10,8</td>\n      <td id=\"T_626dc_row2_col10\" class=\"data row2 col10\" >8,0</td>\n      <td id=\"T_626dc_row2_col11\" class=\"data row2 col11\" >4,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row3\" class=\"row_heading level0 row3\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_626dc_row3_col0\" class=\"data row3 col0\" >2,0</td>\n      <td id=\"T_626dc_row3_col1\" class=\"data row3 col1\" >9,2</td>\n      <td id=\"T_626dc_row3_col2\" class=\"data row3 col2\" >13,6</td>\n      <td id=\"T_626dc_row3_col3\" class=\"data row3 col3\" >3,9</td>\n      <td id=\"T_626dc_row3_col4\" class=\"data row3 col4\" >10,0</td>\n      <td id=\"T_626dc_row3_col5\" class=\"data row3 col5\" >14,5</td>\n      <td id=\"T_626dc_row3_col6\" class=\"data row3 col6\" >1,8</td>\n      <td id=\"T_626dc_row3_col7\" class=\"data row3 col7\" >3,4</td>\n      <td id=\"T_626dc_row3_col8\" class=\"data row3 col8\" >2,5</td>\n      <td id=\"T_626dc_row3_col9\" class=\"data row3 col9\" >0,6</td>\n      <td id=\"T_626dc_row3_col10\" class=\"data row3 col10\" >3,4</td>\n      <td id=\"T_626dc_row3_col11\" class=\"data row3 col11\" >1,2</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row4\" class=\"row_heading level0 row4\" >Expandiertes Polystyrol</th>\n      <td id=\"T_626dc_row4_col0\" class=\"data row4 col0\" >41,2</td>\n      <td id=\"T_626dc_row4_col1\" class=\"data row4 col1\" >37,8</td>\n      <td id=\"T_626dc_row4_col2\" class=\"data row4 col2\" >54,4</td>\n      <td id=\"T_626dc_row4_col3\" class=\"data row4 col3\" >10,0</td>\n      <td id=\"T_626dc_row4_col4\" class=\"data row4 col4\" >9,4</td>\n      <td id=\"T_626dc_row4_col5\" class=\"data row4 col5\" >42,8</td>\n      <td id=\"T_626dc_row4_col6\" class=\"data row4 col6\" >12,0</td>\n      <td id=\"T_626dc_row4_col7\" class=\"data row4 col7\" >6,5</td>\n      <td id=\"T_626dc_row4_col8\" class=\"data row4 col8\" >5,4</td>\n      <td id=\"T_626dc_row4_col9\" class=\"data row4 col9\" >6,2</td>\n      <td id=\"T_626dc_row4_col10\" class=\"data row4 col10\" >10,6</td>\n      <td id=\"T_626dc_row4_col11\" class=\"data row4 col11\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row5\" class=\"row_heading level0 row5\" >Industriefolie (Kunststoff)</th>\n      <td id=\"T_626dc_row5_col0\" class=\"data row5 col0\" >3,8</td>\n      <td id=\"T_626dc_row5_col1\" class=\"data row5 col1\" >8,8</td>\n      <td id=\"T_626dc_row5_col2\" class=\"data row5 col2\" >11,9</td>\n      <td id=\"T_626dc_row5_col3\" class=\"data row5 col3\" >6,0</td>\n      <td id=\"T_626dc_row5_col4\" class=\"data row5 col4\" >8,0</td>\n      <td id=\"T_626dc_row5_col5\" class=\"data row5 col5\" >15,9</td>\n      <td id=\"T_626dc_row5_col6\" class=\"data row5 col6\" >14,8</td>\n      <td id=\"T_626dc_row5_col7\" class=\"data row5 col7\" >12,1</td>\n      <td id=\"T_626dc_row5_col8\" class=\"data row5 col8\" >4,3</td>\n      <td id=\"T_626dc_row5_col9\" class=\"data row5 col9\" >2,1</td>\n      <td id=\"T_626dc_row5_col10\" class=\"data row5 col10\" >16,7</td>\n      <td id=\"T_626dc_row5_col11\" class=\"data row5 col11\" >6,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row6\" class=\"row_heading level0 row6\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_626dc_row6_col0\" class=\"data row6 col0\" >24,3</td>\n      <td id=\"T_626dc_row6_col1\" class=\"data row6 col1\" >39,9</td>\n      <td id=\"T_626dc_row6_col2\" class=\"data row6 col2\" >36,0</td>\n      <td id=\"T_626dc_row6_col3\" class=\"data row6 col3\" >39,0</td>\n      <td id=\"T_626dc_row6_col4\" class=\"data row6 col4\" >24,0</td>\n      <td id=\"T_626dc_row6_col5\" class=\"data row6 col5\" >64,8</td>\n      <td id=\"T_626dc_row6_col6\" class=\"data row6 col6\" >14,5</td>\n      <td id=\"T_626dc_row6_col7\" class=\"data row6 col7\" >6,6</td>\n      <td id=\"T_626dc_row6_col8\" class=\"data row6 col8\" >9,1</td>\n      <td id=\"T_626dc_row6_col9\" class=\"data row6 col9\" >8,1</td>\n      <td id=\"T_626dc_row6_col10\" class=\"data row6 col10\" >12,0</td>\n      <td id=\"T_626dc_row6_col11\" class=\"data row6 col11\" >8,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row7\" class=\"row_heading level0 row7\" >Tabak; Kunststoffverpackungen, Behälter</th>\n      <td id=\"T_626dc_row7_col0\" class=\"data row7 col0\" >10,8</td>\n      <td id=\"T_626dc_row7_col1\" class=\"data row7 col1\" >3,4</td>\n      <td id=\"T_626dc_row7_col2\" class=\"data row7 col2\" >2,8</td>\n      <td id=\"T_626dc_row7_col3\" class=\"data row7 col3\" >12,4</td>\n      <td id=\"T_626dc_row7_col4\" class=\"data row7 col4\" >2,7</td>\n      <td id=\"T_626dc_row7_col5\" class=\"data row7 col5\" >13,8</td>\n      <td id=\"T_626dc_row7_col6\" class=\"data row7 col6\" >1,3</td>\n      <td id=\"T_626dc_row7_col7\" class=\"data row7 col7\" >1,5</td>\n      <td id=\"T_626dc_row7_col8\" class=\"data row7 col8\" >2,5</td>\n      <td id=\"T_626dc_row7_col9\" class=\"data row7 col9\" >7,6</td>\n      <td id=\"T_626dc_row7_col10\" class=\"data row7 col10\" >3,4</td>\n      <td id=\"T_626dc_row7_col11\" class=\"data row7 col11\" >2,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row8\" class=\"row_heading level0 row8\" >Zigarettenfilter</th>\n      <td id=\"T_626dc_row8_col0\" class=\"data row8 col0\" >53,7</td>\n      <td id=\"T_626dc_row8_col1\" class=\"data row8 col1\" >68,4</td>\n      <td id=\"T_626dc_row8_col2\" class=\"data row8 col2\" >89,1</td>\n      <td id=\"T_626dc_row8_col3\" class=\"data row8 col3\" >69,0</td>\n      <td id=\"T_626dc_row8_col4\" class=\"data row8 col4\" >85,9</td>\n      <td id=\"T_626dc_row8_col5\" class=\"data row8 col5\" >125,9</td>\n      <td id=\"T_626dc_row8_col6\" class=\"data row8 col6\" >20,0</td>\n      <td id=\"T_626dc_row8_col7\" class=\"data row8 col7\" >13,5</td>\n      <td id=\"T_626dc_row8_col8\" class=\"data row8 col8\" >17,0</td>\n      <td id=\"T_626dc_row8_col9\" class=\"data row8 col9\" >31,4</td>\n      <td id=\"T_626dc_row8_col10\" class=\"data row8 col10\" >17,1</td>\n      <td id=\"T_626dc_row8_col11\" class=\"data row8 col11\" >26,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_626dc_level0_row9\" class=\"row_heading level0 row9\" >Verpackungsfolien, nicht für Lebensmittel</th>\n      <td id=\"T_626dc_row9_col0\" class=\"data row9 col0\" >0,0</td>\n      <td id=\"T_626dc_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_626dc_row9_col2\" class=\"data row9 col2\" >0,0</td>\n      <td id=\"T_626dc_row9_col3\" class=\"data row9 col3\" >2,9</td>\n      <td id=\"T_626dc_row9_col4\" class=\"data row9 col4\" >6,4</td>\n      <td id=\"T_626dc_row9_col5\" class=\"data row9 col5\" >39,5</td>\n      <td id=\"T_626dc_row9_col6\" class=\"data row9 col6\" >4,0</td>\n      <td id=\"T_626dc_row9_col7\" class=\"data row9 col7\" >1,6</td>\n      <td id=\"T_626dc_row9_col8\" class=\"data row9 col8\" >3,2</td>\n      <td id=\"T_626dc_row9_col9\" class=\"data row9 col9\" >15,4</td>\n      <td id=\"T_626dc_row9_col10\" class=\"data row9 col10\" >5,9</td>\n      <td id=\"T_626dc_row9_col11\" class=\"data row9 col11\" >5,8</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f63815025e0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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(\"linth_monthly_results_caption\", monthly_data_caption, display=False)\n",
    "glue(\"linth_monthly_results\", mcdm, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} linth_monthly_results\n",
    "---\n",
    "name: 'linth_monthly_results'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_monthly_results>` {glue:text}`linth_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 Linth. Für alle gültigen Erhebungen an Seen n=112. 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": "linth_spearmans_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 300x500 with 60 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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 Linth. 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('linth_survey_area_spearmans', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} linth_survey_area_spearmans\n",
    "---\n",
    "name: 'linth_survey_area_spearmans'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_spearmans>` {glue:text}`linth_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": {
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     "remove-input"
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   "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",
    "\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"
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   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Verwendungszweck oder Beschreibung der identifizierten Objekte in % der Gesamtzahl nach Gemeinden im Erhebungsgebiet Erhebungsgebiet Linth. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_codegroup_percent_caption"
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     },
     "output_type": "display_data"
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#000000;\n}\n#T_43082_row9_col3 {\n  background-color: #fff1ae;\n  color: #000000;\n}\n#T_43082_level0_col0, #T_43082_level0_col1, #T_43082_level0_col2, #T_43082_level0_col3, #T_43082_level0_col4, #T_43082_level0_col5, #T_43082_level0_col6, #T_43082_level0_col7, #T_43082_level0_col8, #T_43082_level0_col9, #T_43082_level0_col10, #T_43082_level0_col11, #T_43082_level0_col12, #T_43082_level0_col13 {\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_43082\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_43082_level0_col0\" class=\"col_heading level0 col0\" >Dorfbach</th>\n      <th id=\"T_43082_level0_col1\" class=\"col_heading level0 col1\" >Escherkanal</th>\n      <th id=\"T_43082_level0_col2\" class=\"col_heading level0 col2\" >Jona</th>\n      <th id=\"T_43082_level0_col3\" class=\"col_heading level0 col3\" >Limmat</th>\n      <th id=\"T_43082_level0_col4\" class=\"col_heading level0 col4\" >Linthkanal</th>\n      <th id=\"T_43082_level0_col5\" class=\"col_heading level0 col5\" >Reuss</th>\n      <th id=\"T_43082_level0_col6\" class=\"col_heading level0 col6\" >Seez</th>\n      <th id=\"T_43082_level0_col7\" class=\"col_heading level0 col7\" >Sihl</th>\n      <th id=\"T_43082_level0_col8\" class=\"col_heading level0 col8\" >Vierwaldstättersee</th>\n      <th id=\"T_43082_level0_col9\" class=\"col_heading level0 col9\" >Walensee</th>\n      <th id=\"T_43082_level0_col10\" class=\"col_heading level0 col10\" >Zugersee</th>\n      <th id=\"T_43082_level0_col11\" class=\"col_heading level0 col11\" >Zürichsee</th>\n      <th id=\"T_43082_level0_col12\" class=\"col_heading level0 col12\" >Erhebungsgebiet Linth</th>\n      <th id=\"T_43082_level0_col13\" class=\"col_heading level0 col13\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_43082_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_43082_row0_col0\" class=\"data row0 col0\" >33%</td>\n      <td id=\"T_43082_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_43082_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_43082_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_43082_row0_col4\" class=\"data row0 col4\" >1%</td>\n      <td id=\"T_43082_row0_col5\" class=\"data row0 col5\" >0%</td>\n      <td id=\"T_43082_row0_col6\" class=\"data row0 col6\" >2%</td>\n      <td id=\"T_43082_row0_col7\" class=\"data row0 col7\" >0%</td>\n      <td id=\"T_43082_row0_col8\" class=\"data row0 col8\" >2%</td>\n      <td id=\"T_43082_row0_col9\" class=\"data row0 col9\" >2%</td>\n      <td id=\"T_43082_row0_col10\" class=\"data row0 col10\" >2%</td>\n      <td id=\"T_43082_row0_col11\" class=\"data row0 col11\" >3%</td>\n      <td id=\"T_43082_row0_col12\" class=\"data row0 col12\" >2%</td>\n      <td id=\"T_43082_row0_col13\" class=\"data row0 col13\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_43082_row1_col0\" class=\"data row1 col0\" >8%</td>\n      <td id=\"T_43082_row1_col1\" class=\"data row1 col1\" >40%</td>\n      <td id=\"T_43082_row1_col2\" class=\"data row1 col2\" >38%</td>\n      <td id=\"T_43082_row1_col3\" class=\"data row1 col3\" >36%</td>\n      <td id=\"T_43082_row1_col4\" class=\"data row1 col4\" >40%</td>\n      <td id=\"T_43082_row1_col5\" class=\"data row1 col5\" >33%</td>\n      <td id=\"T_43082_row1_col6\" class=\"data row1 col6\" >12%</td>\n      <td id=\"T_43082_row1_col7\" class=\"data row1 col7\" >67%</td>\n      <td id=\"T_43082_row1_col8\" class=\"data row1 col8\" >36%</td>\n      <td id=\"T_43082_row1_col9\" class=\"data row1 col9\" >13%</td>\n      <td id=\"T_43082_row1_col10\" class=\"data row1 col10\" >19%</td>\n      <td id=\"T_43082_row1_col11\" class=\"data row1 col11\" >27%</td>\n      <td id=\"T_43082_row1_col12\" class=\"data row1 col12\" >22%</td>\n      <td id=\"T_43082_row1_col13\" class=\"data row1 col13\" >19%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_43082_row2_col0\" class=\"data row2 col0\" >0%</td>\n      <td id=\"T_43082_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_43082_row2_col2\" class=\"data row2 col2\" >0%</td>\n      <td id=\"T_43082_row2_col3\" class=\"data row2 col3\" >2%</td>\n      <td id=\"T_43082_row2_col4\" class=\"data row2 col4\" >2%</td>\n      <td id=\"T_43082_row2_col5\" class=\"data row2 col5\" >0%</td>\n      <td id=\"T_43082_row2_col6\" class=\"data row2 col6\" >2%</td>\n      <td id=\"T_43082_row2_col7\" class=\"data row2 col7\" >0%</td>\n      <td id=\"T_43082_row2_col8\" class=\"data row2 col8\" >3%</td>\n      <td id=\"T_43082_row2_col9\" class=\"data row2 col9\" >4%</td>\n      <td id=\"T_43082_row2_col10\" class=\"data row2 col10\" >2%</td>\n      <td id=\"T_43082_row2_col11\" class=\"data row2 col11\" >6%</td>\n      <td id=\"T_43082_row2_col12\" class=\"data row2 col12\" >5%</td>\n      <td id=\"T_43082_row2_col13\" class=\"data row2 col13\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_43082_row3_col0\" class=\"data row3 col0\" >0%</td>\n      <td id=\"T_43082_row3_col1\" class=\"data row3 col1\" >0%</td>\n      <td id=\"T_43082_row3_col2\" class=\"data row3 col2\" >19%</td>\n      <td id=\"T_43082_row3_col3\" class=\"data row3 col3\" >4%</td>\n      <td id=\"T_43082_row3_col4\" class=\"data row3 col4\" >29%</td>\n      <td id=\"T_43082_row3_col5\" class=\"data row3 col5\" >33%</td>\n      <td id=\"T_43082_row3_col6\" class=\"data row3 col6\" >38%</td>\n      <td id=\"T_43082_row3_col7\" class=\"data row3 col7\" >13%</td>\n      <td id=\"T_43082_row3_col8\" class=\"data row3 col8\" >14%</td>\n      <td id=\"T_43082_row3_col9\" class=\"data row3 col9\" >27%</td>\n      <td id=\"T_43082_row3_col10\" class=\"data row3 col10\" >8%</td>\n      <td id=\"T_43082_row3_col11\" class=\"data row3 col11\" >7%</td>\n      <td id=\"T_43082_row3_col12\" class=\"data row3 col12\" >15%</td>\n      <td id=\"T_43082_row3_col13\" class=\"data row3 col13\" >18%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_43082_row4_col0\" class=\"data row4 col0\" >0%</td>\n      <td id=\"T_43082_row4_col1\" class=\"data row4 col1\" >30%</td>\n      <td id=\"T_43082_row4_col2\" class=\"data row4 col2\" >10%</td>\n      <td id=\"T_43082_row4_col3\" class=\"data row4 col3\" >1%</td>\n      <td id=\"T_43082_row4_col4\" class=\"data row4 col4\" >5%</td>\n      <td id=\"T_43082_row4_col5\" class=\"data row4 col5\" >0%</td>\n      <td id=\"T_43082_row4_col6\" class=\"data row4 col6\" >0%</td>\n      <td id=\"T_43082_row4_col7\" class=\"data row4 col7\" >13%</td>\n      <td id=\"T_43082_row4_col8\" class=\"data row4 col8\" >0%</td>\n      <td id=\"T_43082_row4_col9\" class=\"data row4 col9\" >10%</td>\n      <td id=\"T_43082_row4_col10\" class=\"data row4 col10\" >0%</td>\n      <td id=\"T_43082_row4_col11\" class=\"data row4 col11\" >4%</td>\n      <td id=\"T_43082_row4_col12\" class=\"data row4 col12\" >6%</td>\n      <td id=\"T_43082_row4_col13\" class=\"data row4 col13\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_43082_row5_col0\" class=\"data row5 col0\" >0%</td>\n      <td id=\"T_43082_row5_col1\" class=\"data row5 col1\" >0%</td>\n      <td id=\"T_43082_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_43082_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_43082_row5_col4\" class=\"data row5 col4\" >0%</td>\n      <td id=\"T_43082_row5_col5\" class=\"data row5 col5\" >0%</td>\n      <td id=\"T_43082_row5_col6\" class=\"data row5 col6\" >12%</td>\n      <td id=\"T_43082_row5_col7\" class=\"data row5 col7\" >0%</td>\n      <td id=\"T_43082_row5_col8\" class=\"data row5 col8\" >0%</td>\n      <td id=\"T_43082_row5_col9\" class=\"data row5 col9\" >6%</td>\n      <td id=\"T_43082_row5_col10\" class=\"data row5 col10\" >0%</td>\n      <td id=\"T_43082_row5_col11\" class=\"data row5 col11\" >2%</td>\n      <td id=\"T_43082_row5_col12\" class=\"data row5 col12\" >3%</td>\n      <td id=\"T_43082_row5_col13\" class=\"data row5 col13\" >8%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_43082_row6_col0\" class=\"data row6 col0\" >42%</td>\n      <td id=\"T_43082_row6_col1\" class=\"data row6 col1\" >0%</td>\n      <td id=\"T_43082_row6_col2\" class=\"data row6 col2\" >0%</td>\n      <td id=\"T_43082_row6_col3\" class=\"data row6 col3\" >9%</td>\n      <td id=\"T_43082_row6_col4\" class=\"data row6 col4\" >5%</td>\n      <td id=\"T_43082_row6_col5\" class=\"data row6 col5\" >0%</td>\n      <td id=\"T_43082_row6_col6\" class=\"data row6 col6\" >5%</td>\n      <td id=\"T_43082_row6_col7\" class=\"data row6 col7\" >0%</td>\n      <td id=\"T_43082_row6_col8\" class=\"data row6 col8\" >7%</td>\n      <td id=\"T_43082_row6_col9\" class=\"data row6 col9\" >2%</td>\n      <td id=\"T_43082_row6_col10\" class=\"data row6 col10\" >6%</td>\n      <td id=\"T_43082_row6_col11\" class=\"data row6 col11\" >5%</td>\n      <td id=\"T_43082_row6_col12\" class=\"data row6 col12\" >4%</td>\n      <td id=\"T_43082_row6_col13\" class=\"data row6 col13\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_43082_row7_col0\" class=\"data row7 col0\" >0%</td>\n      <td id=\"T_43082_row7_col1\" class=\"data row7 col1\" >0%</td>\n      <td id=\"T_43082_row7_col2\" class=\"data row7 col2\" >10%</td>\n      <td id=\"T_43082_row7_col3\" class=\"data row7 col3\" >6%</td>\n      <td id=\"T_43082_row7_col4\" class=\"data row7 col4\" >6%</td>\n      <td id=\"T_43082_row7_col5\" class=\"data row7 col5\" >0%</td>\n      <td id=\"T_43082_row7_col6\" class=\"data row7 col6\" >17%</td>\n      <td id=\"T_43082_row7_col7\" class=\"data row7 col7\" >0%</td>\n      <td id=\"T_43082_row7_col8\" class=\"data row7 col8\" >2%</td>\n      <td id=\"T_43082_row7_col9\" class=\"data row7 col9\" >12%</td>\n      <td id=\"T_43082_row7_col10\" class=\"data row7 col10\" >0%</td>\n      <td id=\"T_43082_row7_col11\" class=\"data row7 col11\" >9%</td>\n      <td id=\"T_43082_row7_col12\" class=\"data row7 col12\" >10%</td>\n      <td id=\"T_43082_row7_col13\" class=\"data row7 col13\" >14%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_43082_row8_col0\" class=\"data row8 col0\" >8%</td>\n      <td id=\"T_43082_row8_col1\" class=\"data row8 col1\" >0%</td>\n      <td id=\"T_43082_row8_col2\" class=\"data row8 col2\" >24%</td>\n      <td id=\"T_43082_row8_col3\" class=\"data row8 col3\" >30%</td>\n      <td id=\"T_43082_row8_col4\" class=\"data row8 col4\" >1%</td>\n      <td id=\"T_43082_row8_col5\" class=\"data row8 col5\" >0%</td>\n      <td id=\"T_43082_row8_col6\" class=\"data row8 col6\" >7%</td>\n      <td id=\"T_43082_row8_col7\" class=\"data row8 col7\" >0%</td>\n      <td id=\"T_43082_row8_col8\" class=\"data row8 col8\" >19%</td>\n      <td id=\"T_43082_row8_col9\" class=\"data row8 col9\" >19%</td>\n      <td id=\"T_43082_row8_col10\" class=\"data row8 col10\" >40%</td>\n      <td id=\"T_43082_row8_col11\" class=\"data row8 col11\" >27%</td>\n      <td id=\"T_43082_row8_col12\" class=\"data row8 col12\" >24%</td>\n      <td id=\"T_43082_row8_col13\" class=\"data row8 col13\" >17%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_43082_row9_col0\" class=\"data row9 col0\" >8%</td>\n      <td id=\"T_43082_row9_col1\" class=\"data row9 col1\" >0%</td>\n      <td id=\"T_43082_row9_col2\" class=\"data row9 col2\" >0%</td>\n      <td id=\"T_43082_row9_col3\" class=\"data row9 col3\" >11%</td>\n      <td id=\"T_43082_row9_col4\" class=\"data row9 col4\" >4%</td>\n      <td id=\"T_43082_row9_col5\" class=\"data row9 col5\" >0%</td>\n      <td id=\"T_43082_row9_col6\" class=\"data row9 col6\" >0%</td>\n      <td id=\"T_43082_row9_col7\" class=\"data row9 col7\" >0%</td>\n      <td id=\"T_43082_row9_col8\" class=\"data row9 col8\" >14%</td>\n      <td id=\"T_43082_row9_col9\" class=\"data row9 col9\" >4%</td>\n      <td id=\"T_43082_row9_col10\" class=\"data row9 col10\" >18%</td>\n      <td id=\"T_43082_row9_col11\" class=\"data row9 col11\" >9%</td>\n      <td id=\"T_43082_row9_col12\" class=\"data row9 col12\" >7%</td>\n      <td id=\"T_43082_row9_col13\" class=\"data row9 col13\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_43082_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_43082_row10_col0\" class=\"data row10 col0\" >0%</td>\n      <td id=\"T_43082_row10_col1\" class=\"data row10 col1\" >10%</td>\n      <td id=\"T_43082_row10_col2\" class=\"data row10 col2\" >0%</td>\n      <td id=\"T_43082_row10_col3\" class=\"data row10 col3\" >1%</td>\n      <td id=\"T_43082_row10_col4\" class=\"data row10 col4\" >7%</td>\n      <td id=\"T_43082_row10_col5\" class=\"data row10 col5\" >33%</td>\n      <td id=\"T_43082_row10_col6\" class=\"data row10 col6\" >5%</td>\n      <td id=\"T_43082_row10_col7\" class=\"data row10 col7\" >7%</td>\n      <td id=\"T_43082_row10_col8\" class=\"data row10 col8\" >3%</td>\n      <td id=\"T_43082_row10_col9\" class=\"data row10 col9\" >1%</td>\n      <td id=\"T_43082_row10_col10\" class=\"data row10 col10\" >5%</td>\n      <td id=\"T_43082_row10_col11\" class=\"data row10 col11\" >1%</td>\n      <td id=\"T_43082_row10_col12\" class=\"data row10 col12\" >1%</td>\n      <td id=\"T_43082_row10_col13\" class=\"data row10 col13\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f63818a5970>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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(\"linth_codegroup_percent_caption\", code_group_percent_caption, display=False)\n",
    "glue(\"linth_codegroup_percent\", ptd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_codegroup_percent\n",
    "---\n",
    "name: 'linth_survey_area_codegroup_percent'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_codegroup_percent>` {glue:text}`linth_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 Linth. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, werden weiterhin nach ihrer Grösse klassifiziert.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_codegroup_pcsm_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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#000000;\n}\n#T_b2258_row8_col11 {\n  background-color: #742905;\n  color: #f1f1f1;\n}\n#T_b2258_row8_col13 {\n  background-color: #fe9f2e;\n  color: #000000;\n}\n#T_b2258_row9_col10, #T_b2258_row9_col12 {\n  background-color: #feeca5;\n  color: #000000;\n}\n#T_b2258_row9_col11 {\n  background-color: #fecd63;\n  color: #000000;\n}\n#T_b2258_row10_col5 {\n  background-color: #fffcd6;\n  color: #000000;\n}\n#T_b2258_level0_col0, #T_b2258_level0_col1, #T_b2258_level0_col2, #T_b2258_level0_col3, #T_b2258_level0_col4, #T_b2258_level0_col5, #T_b2258_level0_col6, #T_b2258_level0_col7, #T_b2258_level0_col8, #T_b2258_level0_col9, #T_b2258_level0_col10, #T_b2258_level0_col11, #T_b2258_level0_col12, #T_b2258_level0_col13 {\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_b2258\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b2258_level0_col0\" class=\"col_heading level0 col0\" >Dorfbach</th>\n      <th id=\"T_b2258_level0_col1\" class=\"col_heading level0 col1\" >Escherkanal</th>\n      <th id=\"T_b2258_level0_col2\" class=\"col_heading level0 col2\" >Jona</th>\n      <th id=\"T_b2258_level0_col3\" class=\"col_heading level0 col3\" >Limmat</th>\n      <th id=\"T_b2258_level0_col4\" class=\"col_heading level0 col4\" >Linthkanal</th>\n      <th id=\"T_b2258_level0_col5\" class=\"col_heading level0 col5\" >Reuss</th>\n      <th id=\"T_b2258_level0_col6\" class=\"col_heading level0 col6\" >Seez</th>\n      <th id=\"T_b2258_level0_col7\" class=\"col_heading level0 col7\" >Sihl</th>\n      <th id=\"T_b2258_level0_col8\" class=\"col_heading level0 col8\" >Vierwaldstättersee</th>\n      <th id=\"T_b2258_level0_col9\" class=\"col_heading level0 col9\" >Walensee</th>\n      <th id=\"T_b2258_level0_col10\" class=\"col_heading level0 col10\" >Zugersee</th>\n      <th id=\"T_b2258_level0_col11\" class=\"col_heading level0 col11\" >Zürichsee</th>\n      <th id=\"T_b2258_level0_col12\" class=\"col_heading level0 col12\" >Erhebungsgebiet Linth</th>\n      <th id=\"T_b2258_level0_col13\" class=\"col_heading level0 col13\" >Alle Erhebungsgebiete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b2258_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_b2258_row0_col0\" class=\"data row0 col0\" >3,0</td>\n      <td id=\"T_b2258_row0_col1\" class=\"data row0 col1\" >0,0</td>\n      <td id=\"T_b2258_row0_col2\" class=\"data row0 col2\" >0,0</td>\n      <td id=\"T_b2258_row0_col3\" class=\"data row0 col3\" >0,0</td>\n      <td id=\"T_b2258_row0_col4\" class=\"data row0 col4\" >0,5</td>\n      <td id=\"T_b2258_row0_col5\" class=\"data row0 col5\" >0,0</td>\n      <td id=\"T_b2258_row0_col6\" class=\"data row0 col6\" >1,0</td>\n      <td id=\"T_b2258_row0_col7\" class=\"data row0 col7\" >0,0</td>\n      <td id=\"T_b2258_row0_col8\" class=\"data row0 col8\" >0,0</td>\n      <td id=\"T_b2258_row0_col9\" class=\"data row0 col9\" >1,0</td>\n      <td id=\"T_b2258_row0_col10\" class=\"data row0 col10\" >0,0</td>\n      <td id=\"T_b2258_row0_col11\" class=\"data row0 col11\" >2,0</td>\n      <td id=\"T_b2258_row0_col12\" class=\"data row0 col12\" >0,5</td>\n      <td id=\"T_b2258_row0_col13\" class=\"data row0 col13\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_b2258_row1_col0\" class=\"data row1 col0\" >1,0</td>\n      <td id=\"T_b2258_row1_col1\" class=\"data row1 col1\" >36,0</td>\n      <td id=\"T_b2258_row1_col2\" class=\"data row1 col2\" >9,0</td>\n      <td id=\"T_b2258_row1_col3\" class=\"data row1 col3\" >27,5</td>\n      <td id=\"T_b2258_row1_col4\" class=\"data row1 col4\" >26,5</td>\n      <td id=\"T_b2258_row1_col5\" class=\"data row1 col5\" >3,0</td>\n      <td id=\"T_b2258_row1_col6\" class=\"data row1 col6\" >5,0</td>\n      <td id=\"T_b2258_row1_col7\" class=\"data row1 col7\" >17,0</td>\n      <td id=\"T_b2258_row1_col8\" class=\"data row1 col8\" >12,0</td>\n      <td id=\"T_b2258_row1_col9\" class=\"data row1 col9\" >20,0</td>\n      <td id=\"T_b2258_row1_col10\" class=\"data row1 col10\" >12,0</td>\n      <td id=\"T_b2258_row1_col11\" class=\"data row1 col11\" >52,0</td>\n      <td id=\"T_b2258_row1_col12\" class=\"data row1 col12\" >30,5</td>\n      <td id=\"T_b2258_row1_col13\" class=\"data row1 col13\" >37,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_b2258_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_b2258_row2_col1\" class=\"data row2 col1\" >18,0</td>\n      <td id=\"T_b2258_row2_col2\" class=\"data row2 col2\" >0,0</td>\n      <td id=\"T_b2258_row2_col3\" class=\"data row2 col3\" >1,0</td>\n      <td id=\"T_b2258_row2_col4\" class=\"data row2 col4\" >1,0</td>\n      <td id=\"T_b2258_row2_col5\" class=\"data row2 col5\" >0,0</td>\n      <td id=\"T_b2258_row2_col6\" class=\"data row2 col6\" >1,0</td>\n      <td id=\"T_b2258_row2_col7\" class=\"data row2 col7\" >0,0</td>\n      <td id=\"T_b2258_row2_col8\" class=\"data row2 col8\" >3,0</td>\n      <td id=\"T_b2258_row2_col9\" class=\"data row2 col9\" >5,0</td>\n      <td id=\"T_b2258_row2_col10\" class=\"data row2 col10\" >0,0</td>\n      <td id=\"T_b2258_row2_col11\" class=\"data row2 col11\" >6,5</td>\n      <td id=\"T_b2258_row2_col12\" class=\"data row2 col12\" >4,0</td>\n      <td id=\"T_b2258_row2_col13\" class=\"data row2 col13\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_b2258_row3_col0\" class=\"data row3 col0\" >0,0</td>\n      <td id=\"T_b2258_row3_col1\" class=\"data row3 col1\" >0,0</td>\n      <td id=\"T_b2258_row3_col2\" class=\"data row3 col2\" >3,0</td>\n      <td id=\"T_b2258_row3_col3\" class=\"data row3 col3\" >4,0</td>\n      <td id=\"T_b2258_row3_col4\" class=\"data row3 col4\" >12,0</td>\n      <td id=\"T_b2258_row3_col5\" class=\"data row3 col5\" >3,0</td>\n      <td id=\"T_b2258_row3_col6\" class=\"data row3 col6\" >18,0</td>\n      <td id=\"T_b2258_row3_col7\" class=\"data row3 col7\" >3,0</td>\n      <td id=\"T_b2258_row3_col8\" class=\"data row3 col8\" >9,0</td>\n      <td id=\"T_b2258_row3_col9\" class=\"data row3 col9\" >20,0</td>\n      <td id=\"T_b2258_row3_col10\" class=\"data row3 col10\" >3,0</td>\n      <td id=\"T_b2258_row3_col11\" class=\"data row3 col11\" >13,5</td>\n      <td id=\"T_b2258_row3_col12\" class=\"data row3 col12\" >12,5</td>\n      <td id=\"T_b2258_row3_col13\" class=\"data row3 col13\" >20,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_b2258_row4_col0\" class=\"data row4 col0\" >0,0</td>\n      <td id=\"T_b2258_row4_col1\" class=\"data row4 col1\" >27,0</td>\n      <td id=\"T_b2258_row4_col2\" class=\"data row4 col2\" >3,0</td>\n      <td id=\"T_b2258_row4_col3\" class=\"data row4 col3\" >0,0</td>\n      <td id=\"T_b2258_row4_col4\" class=\"data row4 col4\" >11,0</td>\n      <td id=\"T_b2258_row4_col5\" class=\"data row4 col5\" >0,0</td>\n      <td id=\"T_b2258_row4_col6\" class=\"data row4 col6\" >0,0</td>\n      <td id=\"T_b2258_row4_col7\" class=\"data row4 col7\" >3,0</td>\n      <td id=\"T_b2258_row4_col8\" class=\"data row4 col8\" >0,0</td>\n      <td id=\"T_b2258_row4_col9\" class=\"data row4 col9\" >8,0</td>\n      <td id=\"T_b2258_row4_col10\" class=\"data row4 col10\" >0,0</td>\n      <td id=\"T_b2258_row4_col11\" class=\"data row4 col11\" >4,0</td>\n      <td id=\"T_b2258_row4_col12\" class=\"data row4 col12\" >3,0</td>\n      <td id=\"T_b2258_row4_col13\" class=\"data row4 col13\" >7,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_b2258_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_b2258_row5_col1\" class=\"data row5 col1\" >0,0</td>\n      <td id=\"T_b2258_row5_col2\" class=\"data row5 col2\" >0,0</td>\n      <td id=\"T_b2258_row5_col3\" class=\"data row5 col3\" >0,0</td>\n      <td id=\"T_b2258_row5_col4\" class=\"data row5 col4\" >0,0</td>\n      <td id=\"T_b2258_row5_col5\" class=\"data row5 col5\" >0,0</td>\n      <td id=\"T_b2258_row5_col6\" class=\"data row5 col6\" >5,5</td>\n      <td id=\"T_b2258_row5_col7\" class=\"data row5 col7\" >0,0</td>\n      <td id=\"T_b2258_row5_col8\" class=\"data row5 col8\" >0,0</td>\n      <td id=\"T_b2258_row5_col9\" class=\"data row5 col9\" >3,0</td>\n      <td id=\"T_b2258_row5_col10\" class=\"data row5 col10\" >0,0</td>\n      <td id=\"T_b2258_row5_col11\" class=\"data row5 col11\" >0,0</td>\n      <td id=\"T_b2258_row5_col12\" class=\"data row5 col12\" >0,0</td>\n      <td id=\"T_b2258_row5_col13\" class=\"data row5 col13\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_b2258_row6_col0\" class=\"data row6 col0\" >5,0</td>\n      <td id=\"T_b2258_row6_col1\" class=\"data row6 col1\" >0,0</td>\n      <td id=\"T_b2258_row6_col2\" class=\"data row6 col2\" >0,0</td>\n      <td id=\"T_b2258_row6_col3\" class=\"data row6 col3\" >4,0</td>\n      <td id=\"T_b2258_row6_col4\" class=\"data row6 col4\" >6,5</td>\n      <td id=\"T_b2258_row6_col5\" class=\"data row6 col5\" >0,0</td>\n      <td id=\"T_b2258_row6_col6\" class=\"data row6 col6\" >17,0</td>\n      <td id=\"T_b2258_row6_col7\" class=\"data row6 col7\" >0,0</td>\n      <td id=\"T_b2258_row6_col8\" class=\"data row6 col8\" >6,0</td>\n      <td id=\"T_b2258_row6_col9\" class=\"data row6 col9\" >4,0</td>\n      <td id=\"T_b2258_row6_col10\" class=\"data row6 col10\" >3,0</td>\n      <td id=\"T_b2258_row6_col11\" class=\"data row6 col11\" >6,0</td>\n      <td id=\"T_b2258_row6_col12\" class=\"data row6 col12\" >4,0</td>\n      <td id=\"T_b2258_row6_col13\" class=\"data row6 col13\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_b2258_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_b2258_row7_col1\" class=\"data row7 col1\" >0,0</td>\n      <td id=\"T_b2258_row7_col2\" class=\"data row7 col2\" >3,0</td>\n      <td id=\"T_b2258_row7_col3\" class=\"data row7 col3\" >2,0</td>\n      <td id=\"T_b2258_row7_col4\" class=\"data row7 col4\" >7,0</td>\n      <td id=\"T_b2258_row7_col5\" class=\"data row7 col5\" >0,0</td>\n      <td id=\"T_b2258_row7_col6\" class=\"data row7 col6\" >7,5</td>\n      <td id=\"T_b2258_row7_col7\" class=\"data row7 col7\" >0,0</td>\n      <td id=\"T_b2258_row7_col8\" class=\"data row7 col8\" >0,0</td>\n      <td id=\"T_b2258_row7_col9\" class=\"data row7 col9\" >12,0</td>\n      <td id=\"T_b2258_row7_col10\" class=\"data row7 col10\" >0,0</td>\n      <td id=\"T_b2258_row7_col11\" class=\"data row7 col11\" >18,0</td>\n      <td id=\"T_b2258_row7_col12\" class=\"data row7 col12\" >10,5</td>\n      <td id=\"T_b2258_row7_col13\" class=\"data row7 col13\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_b2258_row8_col0\" class=\"data row8 col0\" >1,0</td>\n      <td id=\"T_b2258_row8_col1\" class=\"data row8 col1\" >0,0</td>\n      <td id=\"T_b2258_row8_col2\" class=\"data row8 col2\" >3,0</td>\n      <td id=\"T_b2258_row8_col3\" class=\"data row8 col3\" >11,5</td>\n      <td id=\"T_b2258_row8_col4\" class=\"data row8 col4\" >0,5</td>\n      <td id=\"T_b2258_row8_col5\" class=\"data row8 col5\" >0,0</td>\n      <td id=\"T_b2258_row8_col6\" class=\"data row8 col6\" >3,5</td>\n      <td id=\"T_b2258_row8_col7\" class=\"data row8 col7\" >0,0</td>\n      <td id=\"T_b2258_row8_col8\" class=\"data row8 col8\" >8,0</td>\n      <td id=\"T_b2258_row8_col9\" class=\"data row8 col9\" >20,0</td>\n      <td id=\"T_b2258_row8_col10\" class=\"data row8 col10\" >16,0</td>\n      <td id=\"T_b2258_row8_col11\" class=\"data row8 col11\" >50,0</td>\n      <td id=\"T_b2258_row8_col12\" class=\"data row8 col12\" >26,5</td>\n      <td id=\"T_b2258_row8_col13\" class=\"data row8 col13\" >25,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_b2258_row9_col0\" class=\"data row9 col0\" >1,0</td>\n      <td id=\"T_b2258_row9_col1\" class=\"data row9 col1\" >0,0</td>\n      <td id=\"T_b2258_row9_col2\" class=\"data row9 col2\" >0,0</td>\n      <td id=\"T_b2258_row9_col3\" class=\"data row9 col3\" >7,5</td>\n      <td id=\"T_b2258_row9_col4\" class=\"data row9 col4\" >6,0</td>\n      <td id=\"T_b2258_row9_col5\" class=\"data row9 col5\" >0,0</td>\n      <td id=\"T_b2258_row9_col6\" class=\"data row9 col6\" >0,0</td>\n      <td id=\"T_b2258_row9_col7\" class=\"data row9 col7\" >0,0</td>\n      <td id=\"T_b2258_row9_col8\" class=\"data row9 col8\" >12,0</td>\n      <td id=\"T_b2258_row9_col9\" class=\"data row9 col9\" >5,0</td>\n      <td id=\"T_b2258_row9_col10\" class=\"data row9 col10\" >10,0</td>\n      <td id=\"T_b2258_row9_col11\" class=\"data row9 col11\" >17,5</td>\n      <td id=\"T_b2258_row9_col12\" class=\"data row9 col12\" >10,0</td>\n      <td id=\"T_b2258_row9_col13\" class=\"data row9 col13\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_b2258_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_b2258_row10_col0\" class=\"data row10 col0\" >0,0</td>\n      <td id=\"T_b2258_row10_col1\" class=\"data row10 col1\" >9,0</td>\n      <td id=\"T_b2258_row10_col2\" class=\"data row10 col2\" >0,0</td>\n      <td id=\"T_b2258_row10_col3\" class=\"data row10 col3\" >0,0</td>\n      <td id=\"T_b2258_row10_col4\" class=\"data row10 col4\" >3,0</td>\n      <td id=\"T_b2258_row10_col5\" class=\"data row10 col5\" >2,5</td>\n      <td id=\"T_b2258_row10_col6\" class=\"data row10 col6\" >2,0</td>\n      <td id=\"T_b2258_row10_col7\" class=\"data row10 col7\" >4,0</td>\n      <td id=\"T_b2258_row10_col8\" class=\"data row10 col8\" >4,0</td>\n      <td id=\"T_b2258_row10_col9\" class=\"data row10 col9\" >1,0</td>\n      <td id=\"T_b2258_row10_col10\" class=\"data row10 col10\" >3,0</td>\n      <td id=\"T_b2258_row10_col11\" class=\"data row10 col11\" >0,0</td>\n      <td id=\"T_b2258_row10_col12\" class=\"data row10 col12\" >0,0</td>\n      <td id=\"T_b2258_row10_col13\" class=\"data row10 col13\" >0,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f63805ee130>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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(\"linth_codegroup_pcsm_caption\", code_group_pcsm_caption, display=False)\n",
    "glue(\"linth_codegroup_pcsm\", cgp, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_codegroup_pcsm\n",
    "---\n",
    "name: 'linth_survey_area_codegroup_pcsm'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_codegroup_pcsm>` {glue:text}`linth_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 Linth Fliessgewässer, April 2020 bis März 2021, n = 19. Rechts: Zusammenfassung der Daten.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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": "linth_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('linth_survey_area_rivers_summary_caption', rivers_caption, display=False)\n",
    "\n",
    "glue('linth_survey_area_rivers_summary', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_survey_area_rivers_summary\n",
    "---\n",
    "name: 'linth_survey_area_rivers_summary'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_survey_area_rivers_summary>` {glue:text}`linth_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 Linth: Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_rivers_most_common_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_da5ba tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_da5ba tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_da5ba tr {\n  font-size: 12px;\n}\n#T_da5ba th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_da5ba td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_da5ba\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_da5ba_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_da5ba_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_da5ba_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_da5ba_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_da5ba_level0_row0\" class=\"row_heading level0 row0\" >Zigarettenfilter</th>\n      <td id=\"T_da5ba_row0_col0\" class=\"data row0 col0\" >72</td>\n      <td id=\"T_da5ba_row0_col1\" class=\"data row0 col1\" >17%</td>\n      <td id=\"T_da5ba_row0_col2\" class=\"data row0 col2\" >57%</td>\n      <td id=\"T_da5ba_row0_col3\" class=\"data row0 col3\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row1\" class=\"row_heading level0 row1\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_da5ba_row1_col0\" class=\"data row1 col0\" >51</td>\n      <td id=\"T_da5ba_row1_col1\" class=\"data row1 col1\" >12%</td>\n      <td id=\"T_da5ba_row1_col2\" class=\"data row1 col2\" >42%</td>\n      <td id=\"T_da5ba_row1_col3\" class=\"data row1 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row2\" class=\"row_heading level0 row2\" >Snack-Verpackungen</th>\n      <td id=\"T_da5ba_row2_col0\" class=\"data row2 col0\" >28</td>\n      <td id=\"T_da5ba_row2_col1\" class=\"data row2 col1\" >6%</td>\n      <td id=\"T_da5ba_row2_col2\" class=\"data row2 col2\" >68%</td>\n      <td id=\"T_da5ba_row2_col3\" class=\"data row2 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row3\" class=\"row_heading level0 row3\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_da5ba_row3_col0\" class=\"data row3 col0\" >27</td>\n      <td id=\"T_da5ba_row3_col1\" class=\"data row3 col1\" >6%</td>\n      <td id=\"T_da5ba_row3_col2\" class=\"data row3 col2\" >47%</td>\n      <td id=\"T_da5ba_row3_col3\" class=\"data row3 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row4\" class=\"row_heading level0 row4\" >Kronkorken, Lasche von Dose/Ausfreisslachen</th>\n      <td id=\"T_da5ba_row4_col0\" class=\"data row4 col0\" >17</td>\n      <td id=\"T_da5ba_row4_col1\" class=\"data row4 col1\" >4%</td>\n      <td id=\"T_da5ba_row4_col2\" class=\"data row4 col2\" >31%</td>\n      <td id=\"T_da5ba_row4_col3\" class=\"data row4 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row5\" class=\"row_heading level0 row5\" >Industrieschrott</th>\n      <td id=\"T_da5ba_row5_col0\" class=\"data row5 col0\" >15</td>\n      <td id=\"T_da5ba_row5_col1\" class=\"data row5 col1\" >3%</td>\n      <td id=\"T_da5ba_row5_col2\" class=\"data row5 col2\" >26%</td>\n      <td id=\"T_da5ba_row5_col3\" class=\"data row5 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row6\" class=\"row_heading level0 row6\" >Expandiertes Polystyrol</th>\n      <td id=\"T_da5ba_row6_col0\" class=\"data row6 col0\" >13</td>\n      <td id=\"T_da5ba_row6_col1\" class=\"data row6 col1\" >3%</td>\n      <td id=\"T_da5ba_row6_col2\" class=\"data row6 col2\" >15%</td>\n      <td id=\"T_da5ba_row6_col3\" class=\"data row6 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row7\" class=\"row_heading level0 row7\" >Geschirr aus Keramik oder Glas, Tassen, Teller, Stücke</th>\n      <td id=\"T_da5ba_row7_col0\" class=\"data row7 col0\" >13</td>\n      <td id=\"T_da5ba_row7_col1\" class=\"data row7 col1\" >3%</td>\n      <td id=\"T_da5ba_row7_col2\" class=\"data row7 col2\" >10%</td>\n      <td id=\"T_da5ba_row7_col3\" class=\"data row7 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row8\" class=\"row_heading level0 row8\" >Zeitungen oder Zeitschriften</th>\n      <td id=\"T_da5ba_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_da5ba_row8_col1\" class=\"data row8 col1\" >2%</td>\n      <td id=\"T_da5ba_row8_col2\" class=\"data row8 col2\" >10%</td>\n      <td id=\"T_da5ba_row8_col3\" class=\"data row8 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_da5ba_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen aus Aluminiumfolie</th>\n      <td id=\"T_da5ba_row9_col0\" class=\"data row9 col0\" >10</td>\n      <td id=\"T_da5ba_row9_col1\" class=\"data row9 col1\" >2%</td>\n      <td id=\"T_da5ba_row9_col2\" class=\"data row9 col2\" >31%</td>\n      <td id=\"T_da5ba_row9_col3\" class=\"data row9 col3\" >0,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f63814661f0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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('linth_rivers_most_common_caption', mc_caption_string, display=False)\n",
    "glue('linth_rivers_most_common_tables', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{glue:figure} linth_rivers_most_common_tables\n",
    "---\n",
    "name: 'linth_rivers_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_rivers_most_common_tables>` {glue:text}`linth_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 LinthMedian p/100 m, Anzahl der gefundenen Objekte und Prozent der Gesamtmenge.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_frag_table_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_52bf2 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_52bf2 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_52bf2 tr {\n  font-size: 12px;\n}\n#T_52bf2 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_52bf2 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_52bf2\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_52bf2_level0_col0\" class=\"col_heading level0 col0\" >p/100 m</th>\n      <th id=\"T_52bf2_level0_col1\" class=\"col_heading level0 col1\" >Objekte (St.)</th>\n      <th id=\"T_52bf2_level0_col2\" class=\"col_heading level0 col2\" >Anteil</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_52bf2_level0_row0\" class=\"row_heading level0 row0\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_52bf2_row0_col0\" class=\"data row0 col0\" >0,0</td>\n      <td id=\"T_52bf2_row0_col1\" class=\"data row0 col1\" >30</td>\n      <td id=\"T_52bf2_row0_col2\" class=\"data row0 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row1\" class=\"row_heading level0 row1\" >Objekte aus Kunststoff/Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_52bf2_row1_col0\" class=\"data row1 col0\" >0,0</td>\n      <td id=\"T_52bf2_row1_col1\" class=\"data row1 col1\" >2</td>\n      <td id=\"T_52bf2_row1_col2\" class=\"data row1 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row2\" class=\"row_heading level0 row2\" >Objekte aus Kunststoff/Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_52bf2_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_52bf2_row2_col1\" class=\"data row2 col1\" >17</td>\n      <td id=\"T_52bf2_row2_col2\" class=\"data row2 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row3\" class=\"row_heading level0 row3\" >Objekte aus Kunststoff 0,5 - 2,5 cm</th>\n      <td id=\"T_52bf2_row3_col0\" class=\"data row3 col0\" >3,5</td>\n      <td id=\"T_52bf2_row3_col1\" class=\"data row3 col1\" >455</td>\n      <td id=\"T_52bf2_row3_col2\" class=\"data row3 col2\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row4\" class=\"row_heading level0 row4\" >Objekte aus Kunststoff 2,5 - 50 cm</th>\n      <td id=\"T_52bf2_row4_col0\" class=\"data row4 col0\" >4,5</td>\n      <td id=\"T_52bf2_row4_col1\" class=\"data row4 col1\" >442</td>\n      <td id=\"T_52bf2_row4_col2\" class=\"data row4 col2\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row5\" class=\"row_heading level0 row5\" >Objekte aus Kunststoff > 50 cm</th>\n      <td id=\"T_52bf2_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_52bf2_row5_col1\" class=\"data row5 col1\" >1</td>\n      <td id=\"T_52bf2_row5_col2\" class=\"data row5 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row6\" class=\"row_heading level0 row6\" >Objekte aus expandiertem Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_52bf2_row6_col0\" class=\"data row6 col0\" >1,0</td>\n      <td id=\"T_52bf2_row6_col1\" class=\"data row6 col1\" >506</td>\n      <td id=\"T_52bf2_row6_col2\" class=\"data row6 col2\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row7\" class=\"row_heading level0 row7\" >Objekte aus expandiertem Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_52bf2_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_52bf2_row7_col1\" class=\"data row7 col1\" >390</td>\n      <td id=\"T_52bf2_row7_col2\" class=\"data row7 col2\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_52bf2_level0_row8\" class=\"row_heading level0 row8\" >Objekte aus expandiertem Polystyrol > 50 cm</th>\n      <td id=\"T_52bf2_row8_col0\" class=\"data row8 col0\" >0,0</td>\n      <td id=\"T_52bf2_row8_col1\" class=\"data row8 col1\" >2</td>\n      <td id=\"T_52bf2_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 0x7f6380477580>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "linth_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(\"linth_frag_table_caption\", frag_captions, display=False)\n",
    "glue(\"linth_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} linth_frags_table\n",
    "---\n",
    "name: 'linth_frags_table'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <linth_frags_table>` {glue:text}`linth_frag_table_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Die Erhebungsorte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} resources/maps/linth_location_labels.jpeg\n",
    "---\n",
    "name: linth_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>limmat_dietikon_keiserp</th>\n",
       "      <td>47.407989</td>\n",
       "      <td>8.409540</td>\n",
       "      <td>Dietikon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zugerseecholler_cham_blarerm</th>\n",
       "      <td>47.178216</td>\n",
       "      <td>8.480013</td>\n",
       "      <td>Zug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limmat_unterengstringen_oggierbuhrer</th>\n",
       "      <td>47.409400</td>\n",
       "      <td>8.446933</td>\n",
       "      <td>Unterengstringen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limmat_zuerich_wagnerc</th>\n",
       "      <td>47.403496</td>\n",
       "      <td>8.477770</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limmat_zurich_mortensena_meiera</th>\n",
       "      <td>47.400252</td>\n",
       "      <td>8.485411</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sihl_zuerich_eggerskoehlingera</th>\n",
       "      <td>47.381898</td>\n",
       "      <td>8.538328</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasi-strand</th>\n",
       "      <td>47.128442</td>\n",
       "      <td>9.110831</td>\n",
       "      <td>Weesen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rastplatz-stampf</th>\n",
       "      <td>47.215177</td>\n",
       "      <td>8.844286</td>\n",
       "      <td>Rapperswil-Jona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuerichsee_richterswil_benkoem_2</th>\n",
       "      <td>47.217646</td>\n",
       "      <td>8.698713</td>\n",
       "      <td>Richterswil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walensee_walenstadt_wysse</th>\n",
       "      <td>47.121828</td>\n",
       "      <td>9.299552</td>\n",
       "      <td>Walenstadt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pfafikon-bad</th>\n",
       "      <td>47.206766</td>\n",
       "      <td>8.774182</td>\n",
       "      <td>Freienbach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zurichsee_kusnachterhorn_thirkell-whitej</th>\n",
       "      <td>47.317685</td>\n",
       "      <td>8.576860</td>\n",
       "      <td>Küsnacht (ZH)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zurichsee_wollishofen_langendorfm</th>\n",
       "      <td>47.345890</td>\n",
       "      <td>8.536155</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>untertenzen</th>\n",
       "      <td>47.115260</td>\n",
       "      <td>9.254780</td>\n",
       "      <td>Quarten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zurcher-strand</th>\n",
       "      <td>47.364200</td>\n",
       "      <td>8.537420</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jona-river-rastplatz</th>\n",
       "      <td>47.216100</td>\n",
       "      <td>8.844430</td>\n",
       "      <td>Rapperswil-Jona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reuss_hunenberg_eberhardy</th>\n",
       "      <td>47.232976</td>\n",
       "      <td>8.401012</td>\n",
       "      <td>Hünenberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reuss_ottenbach_schoenenbergerl</th>\n",
       "      <td>47.278354</td>\n",
       "      <td>8.394224</td>\n",
       "      <td>Merenschwand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuerichsee_staefa_hennm</th>\n",
       "      <td>47.234643</td>\n",
       "      <td>8.769881</td>\n",
       "      <td>Stäfa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linthdammweg</th>\n",
       "      <td>47.131790</td>\n",
       "      <td>9.091910</td>\n",
       "      <td>Weesen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mols-rocks</th>\n",
       "      <td>47.114343</td>\n",
       "      <td>9.288174</td>\n",
       "      <td>Quarten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seeflechsen</th>\n",
       "      <td>47.130223</td>\n",
       "      <td>9.103400</td>\n",
       "      <td>Glarus Nord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seemuhlestrasse-strand</th>\n",
       "      <td>47.128640</td>\n",
       "      <td>9.295100</td>\n",
       "      <td>Walenstadt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muhlehorn-dorf</th>\n",
       "      <td>47.118448</td>\n",
       "      <td>9.172124</td>\n",
       "      <td>Glarus Nord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuerichsee_waedenswil_colomboc_1</th>\n",
       "      <td>47.219547</td>\n",
       "      <td>8.691961</td>\n",
       "      <td>Richterswil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>murg-bad</th>\n",
       "      <td>47.115307</td>\n",
       "      <td>9.215691</td>\n",
       "      <td>Quarten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schmerikon-bahnhof-strand</th>\n",
       "      <td>47.224780</td>\n",
       "      <td>8.944180</td>\n",
       "      <td>Schmerikon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aabach</th>\n",
       "      <td>47.220989</td>\n",
       "      <td>8.940365</td>\n",
       "      <td>Schmerikon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sihl_zuerich_eichenbergerd</th>\n",
       "      <td>47.339588</td>\n",
       "      <td>8.516697</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kusnachter-dorfbach-tobelweg-1-4</th>\n",
       "      <td>47.317770</td>\n",
       "      <td>8.588258</td>\n",
       "      <td>Küsnacht (ZH)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flibach-river-right-bank</th>\n",
       "      <td>47.133742</td>\n",
       "      <td>9.105461</td>\n",
       "      <td>Weesen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linth_route9brucke</th>\n",
       "      <td>47.125730</td>\n",
       "      <td>9.117340</td>\n",
       "      <td>Glarus Nord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seez_spennwiesenbrucke</th>\n",
       "      <td>47.114948</td>\n",
       "      <td>9.310123</td>\n",
       "      <td>Walenstadt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linth_mollis</th>\n",
       "      <td>47.131370</td>\n",
       "      <td>9.094768</td>\n",
       "      <td>Glarus Nord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seez</th>\n",
       "      <td>47.119830</td>\n",
       "      <td>9.300251</td>\n",
       "      <td>Walenstadt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>limmat_zuerich_suterdglauserp</th>\n",
       "      <td>47.394539</td>\n",
       "      <td>8.513443</td>\n",
       "      <td>Zürich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strandbeiz</th>\n",
       "      <td>47.215862</td>\n",
       "      <td>8.843098</td>\n",
       "      <td>Rapperswil-Jona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vierwaldstattersee_weggis_schoberls_1</th>\n",
       "      <td>47.044532</td>\n",
       "      <td>8.418569</td>\n",
       "      <td>Weggis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vierwaldstattersee_weggis_schoberls_2</th>\n",
       "      <td>47.043953</td>\n",
       "      <td>8.417308</td>\n",
       "      <td>Weggis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vierwaldstattersee_weggis_schoberls_3</th>\n",
       "      <td>47.029957</td>\n",
       "      <td>8.410801</td>\n",
       "      <td>Weggis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           latitude  longitude   \n",
       "standort                                                         \n",
       "limmat_dietikon_keiserp                   47.407989   8.409540  \\\n",
       "zugerseecholler_cham_blarerm              47.178216   8.480013   \n",
       "limmat_unterengstringen_oggierbuhrer      47.409400   8.446933   \n",
       "limmat_zuerich_wagnerc                    47.403496   8.477770   \n",
       "limmat_zurich_mortensena_meiera           47.400252   8.485411   \n",
       "sihl_zuerich_eggerskoehlingera            47.381898   8.538328   \n",
       "gasi-strand                               47.128442   9.110831   \n",
       "rastplatz-stampf                          47.215177   8.844286   \n",
       "zuerichsee_richterswil_benkoem_2          47.217646   8.698713   \n",
       "walensee_walenstadt_wysse                 47.121828   9.299552   \n",
       "pfafikon-bad                              47.206766   8.774182   \n",
       "zurichsee_kusnachterhorn_thirkell-whitej  47.317685   8.576860   \n",
       "zurichsee_wollishofen_langendorfm         47.345890   8.536155   \n",
       "untertenzen                               47.115260   9.254780   \n",
       "zurcher-strand                            47.364200   8.537420   \n",
       "jona-river-rastplatz                      47.216100   8.844430   \n",
       "reuss_hunenberg_eberhardy                 47.232976   8.401012   \n",
       "reuss_ottenbach_schoenenbergerl           47.278354   8.394224   \n",
       "zuerichsee_staefa_hennm                   47.234643   8.769881   \n",
       "linthdammweg                              47.131790   9.091910   \n",
       "mols-rocks                                47.114343   9.288174   \n",
       "seeflechsen                               47.130223   9.103400   \n",
       "seemuhlestrasse-strand                    47.128640   9.295100   \n",
       "muhlehorn-dorf                            47.118448   9.172124   \n",
       "zuerichsee_waedenswil_colomboc_1          47.219547   8.691961   \n",
       "murg-bad                                  47.115307   9.215691   \n",
       "schmerikon-bahnhof-strand                 47.224780   8.944180   \n",
       "aabach                                    47.220989   8.940365   \n",
       "sihl_zuerich_eichenbergerd                47.339588   8.516697   \n",
       "kusnachter-dorfbach-tobelweg-1-4          47.317770   8.588258   \n",
       "flibach-river-right-bank                  47.133742   9.105461   \n",
       "linth_route9brucke                        47.125730   9.117340   \n",
       "seez_spennwiesenbrucke                    47.114948   9.310123   \n",
       "linth_mollis                              47.131370   9.094768   \n",
       "seez                                      47.119830   9.300251   \n",
       "limmat_zuerich_suterdglauserp             47.394539   8.513443   \n",
       "strandbeiz                                47.215862   8.843098   \n",
       "vierwaldstattersee_weggis_schoberls_1     47.044532   8.418569   \n",
       "vierwaldstattersee_weggis_schoberls_2     47.043953   8.417308   \n",
       "vierwaldstattersee_weggis_schoberls_3     47.029957   8.410801   \n",
       "\n",
       "                                                      stat  \n",
       "standort                                                    \n",
       "limmat_dietikon_keiserp                           Dietikon  \n",
       "zugerseecholler_cham_blarerm                           Zug  \n",
       "limmat_unterengstringen_oggierbuhrer      Unterengstringen  \n",
       "limmat_zuerich_wagnerc                              Zürich  \n",
       "limmat_zurich_mortensena_meiera                     Zürich  \n",
       "sihl_zuerich_eggerskoehlingera                      Zürich  \n",
       "gasi-strand                                         Weesen  \n",
       "rastplatz-stampf                           Rapperswil-Jona  \n",
       "zuerichsee_richterswil_benkoem_2               Richterswil  \n",
       "walensee_walenstadt_wysse                       Walenstadt  \n",
       "pfafikon-bad                                    Freienbach  \n",
       "zurichsee_kusnachterhorn_thirkell-whitej     Küsnacht (ZH)  \n",
       "zurichsee_wollishofen_langendorfm                   Zürich  \n",
       "untertenzen                                        Quarten  \n",
       "zurcher-strand                                      Zürich  \n",
       "jona-river-rastplatz                       Rapperswil-Jona  \n",
       "reuss_hunenberg_eberhardy                        Hünenberg  \n",
       "reuss_ottenbach_schoenenbergerl               Merenschwand  \n",
       "zuerichsee_staefa_hennm                              Stäfa  \n",
       "linthdammweg                                        Weesen  \n",
       "mols-rocks                                         Quarten  \n",
       "seeflechsen                                    Glarus Nord  \n",
       "seemuhlestrasse-strand                          Walenstadt  \n",
       "muhlehorn-dorf                                 Glarus Nord  \n",
       "zuerichsee_waedenswil_colomboc_1               Richterswil  \n",
       "murg-bad                                           Quarten  \n",
       "schmerikon-bahnhof-strand                       Schmerikon  \n",
       "aabach                                          Schmerikon  \n",
       "sihl_zuerich_eichenbergerd                          Zürich  \n",
       "kusnachter-dorfbach-tobelweg-1-4             Küsnacht (ZH)  \n",
       "flibach-river-right-bank                            Weesen  \n",
       "linth_route9brucke                             Glarus Nord  \n",
       "seez_spennwiesenbrucke                          Walenstadt  \n",
       "linth_mollis                                   Glarus Nord  \n",
       "seez                                            Walenstadt  \n",
       "limmat_zuerich_suterdglauserp                       Zürich  \n",
       "strandbeiz                                 Rapperswil-Jona  \n",
       "vierwaldstattersee_weggis_schoberls_1               Weggis  \n",
       "vierwaldstattersee_weggis_schoberls_2               Weggis  \n",
       "vierwaldstattersee_weggis_schoberls_3               Weggis  "
      ]
     },
     "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>2 012</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>87</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfrags</th>\n",
       "      <td>Fragmentierte Kunststoffe</td>\n",
       "      <td>917</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfoam</th>\n",
       "      <td>Expandiertes Polystyrol</td>\n",
       "      <td>898</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G200</th>\n",
       "      <td>Getränkeflaschen aus Glas, Glasfragmente</td>\n",
       "      <td>621</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G30</th>\n",
       "      <td>Snack-Verpackungen</td>\n",
       "      <td>558</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G67</th>\n",
       "      <td>Industriefolie (Kunststoff)</td>\n",
       "      <td>462</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G941</th>\n",
       "      <td>Verpackungsfolien, nicht für Lebensmittel</td>\n",
       "      <td>238</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G74</th>\n",
       "      <td>Schaumstoffverpackungen/Isolierung</td>\n",
       "      <td>228</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G25</th>\n",
       "      <td>Tabak; Kunststoffverpackungen, Behälter</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G178</th>\n",
       "      <td>Kronkorken, Lasche von Dose/Ausfreisslachen</td>\n",
       "      <td>173</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G156</th>\n",
       "      <td>Papierfragmente</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G117</th>\n",
       "      <td>Styropor &lt; 5mm</td>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G89</th>\n",
       "      <td>Kunststoff-Bauabfälle</td>\n",
       "      <td>117</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G177</th>\n",
       "      <td>Verpackungen aus Aluminiumfolie</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G923</th>\n",
       "      <td>Taschentücher, Toilettenpapier, Servietten, Pa...</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G73</th>\n",
       "      <td>Gegenstände aus Schaumstoff &amp; Teilstücke (nich...</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G96</th>\n",
       "      <td>Hygienebinden/Höscheneinlagen/Tampons und Appl...</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G24</th>\n",
       "      <td>Ringe von Plastikflaschen/Behältern</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G21</th>\n",
       "      <td>Getränke-Deckel, Getränkeverschluss</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G112</th>\n",
       "      <td>Industriepellets (Nurdles)</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G211</th>\n",
       "      <td>Sonstiges medizinisches Material</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G155</th>\n",
       "      <td>Feuerwerkspapierhülsen und -fragmente</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G95</th>\n",
       "      <td>Wattestäbchen/Tupfer</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G31</th>\n",
       "      <td>Schleckstengel, Stengel von Lutscher</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G23</th>\n",
       "      <td>unidentifizierte Deckel</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G904</th>\n",
       "      <td>Feuerwerkskörper; Raketenkappen</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G153</th>\n",
       "      <td>Papierbecher, Lebensmittelverpackungen aus Pap...</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G106</th>\n",
       "      <td>Kunststofffragmente eckig &lt;5mm</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>Lebensmittelbehälter zum einmaligen Gebrauch a...</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G922</th>\n",
       "      <td>Etiketten, Strichcodes</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G125</th>\n",
       "      <td>Luftballons und Luftballonstäbchen</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G35</th>\n",
       "      <td>Strohhalme und Rührstäbchen</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G32</th>\n",
       "      <td>Spielzeug und Partyartikel</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G940</th>\n",
       "      <td>Schaumstoff EVA (flexibler Kunststoff)</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G928</th>\n",
       "      <td>Bänder und Schleifen</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G66</th>\n",
       "      <td>Umreifungsbänder; Hartplastik für Verpackung f...</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G148</th>\n",
       "      <td>Kartonkisten und Stücke</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G159</th>\n",
       "      <td>Kork</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G203</th>\n",
       "      <td>Geschirr aus Keramik oder Glas, Tassen, Teller...</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G936</th>\n",
       "      <td>Folien für Gewächshäuser</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G50</th>\n",
       "      <td>Schnur &lt; 1cm</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G115</th>\n",
       "      <td>Schaumstoff &lt;5mm</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G165</th>\n",
       "      <td>Glacestengel (Eisstiele), Zahnstocher, Essstäb...</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G208</th>\n",
       "      <td>Glas oder Keramikfragmente &gt;2.5 cm</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G33</th>\n",
       "      <td>Einwegartikel; Tassen/Becher &amp; Deckel</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G91</th>\n",
       "      <td>Bio-Filtermaterial  / Trägermaterial aus Kunst...</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G186</th>\n",
       "      <td>Industrieschrott</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G131</th>\n",
       "      <td>Gummibänder</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G87</th>\n",
       "      <td>Abdeckklebeband/Verpackungsklebeband</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G917</th>\n",
       "      <td>Blähton</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G146</th>\n",
       "      <td>Papier, Karton</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>Einkaufstaschen, Shoppingtaschen</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G34</th>\n",
       "      <td>Besteck, Teller und Tabletts</td>\n",
       "      <td>22</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>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G905</th>\n",
       "      <td>Haarspangen, Haargummis, persönliche Accessoir...</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G152</th>\n",
       "      <td>Papier &amp;Karton;Zigarettenschachteln, Papier/Ka...</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G98</th>\n",
       "      <td>Windeln – Feuchttücher</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G22</th>\n",
       "      <td>Deckel für Chemikalien, Reinigungsmittel (Ohne...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G942</th>\n",
       "      <td>Kunststoffspäne von Drehbänken, CNC-Bearbeitung</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G154</th>\n",
       "      <td>Zeitungen oder Zeitschriften</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G135</th>\n",
       "      <td>Kleidung, Fussbekleidung, Kopfbedeckung, Hands...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G149</th>\n",
       "      <td>Papierverpackungen</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G100</th>\n",
       "      <td>Medizin; Behälter/Röhrchen/Verpackungen</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G137</th>\n",
       "      <td>Kleidung, Handtücher und Lappen</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G90</th>\n",
       "      <td>Blumentöpfe aus Plastik</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G170</th>\n",
       "      <td>Holz (verarbeitet)</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G201</th>\n",
       "      <td>Gläser, einschliesslich Stücke</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G210</th>\n",
       "      <td>Sonstiges Glas/Keramik Materialien</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G213</th>\n",
       "      <td>Paraffinwachs</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G921</th>\n",
       "      <td>Keramikfliesen und Bruchstücke</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G26</th>\n",
       "      <td>Feuerzeug</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G142</th>\n",
       "      <td>Seil, Schnur oder Netze</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G191</th>\n",
       "      <td>Draht und Gitter</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G175</th>\n",
       "      <td>Getränkedosen (Dosen, Getränke)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G59</th>\n",
       "      <td>Monofile Angelschnur (Angeln)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G4</th>\n",
       "      <td>Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</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>8</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G93</th>\n",
       "      <td>Kabelbinder</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G925</th>\n",
       "      <td>Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...</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>G20</th>\n",
       "      <td>Laschen und Deckel</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G927</th>\n",
       "      <td>Plastikschnur von Rasentrimmern, die zum Schne...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G919</th>\n",
       "      <td>Nägel, Schrauben, Bolzen usw.</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G194</th>\n",
       "      <td>Kabel, Metalldraht oft in Gummi- oder Kunststo...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G28</th>\n",
       "      <td>Stifte, Deckel, Druckbleistifte usw.</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G943</th>\n",
       "      <td>Zäune Landwirtschaft, Kunststoff</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G161</th>\n",
       "      <td>Verarbeitetes Holz</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G914</th>\n",
       "      <td>Büroklammern, Wäscheklammern, Gebrauchsgegenst...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G167</th>\n",
       "      <td>Streichhölzer oder Feuerwerke</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G134</th>\n",
       "      <td>Sonstiges Gummi</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G7</th>\n",
       "      <td>Getränkeflaschen &lt; = 0,5 l</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G65</th>\n",
       "      <td>Eimer</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G933</th>\n",
       "      <td>Taschen, Etuis für Zubehör, Brillen, Elektroni...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101</th>\n",
       "      <td>Robidog Hundekot-Säcklein, andere Hundekotsäck...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G908</th>\n",
       "      <td>Klebeband; elektrisch, isolierend</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G126</th>\n",
       "      <td>Bälle</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G124</th>\n",
       "      <td>Andere Kunststoff</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G929</th>\n",
       "      <td>Elektronik und Teile; Sensoren, Headsets usw.</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G926</th>\n",
       "      <td>Kaugummi, enthält oft Kunststoffe</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G197</th>\n",
       "      <td>sonstiges Metall</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G68</th>\n",
       "      <td>Fiberglas-Fragmente</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G103</th>\n",
       "      <td>Kunststofffragmente rund &lt;5mm</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G204</th>\n",
       "      <td>Baumaterial;  Ziegel, Rohre, Zement</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G901</th>\n",
       "      <td>Medizinische Masken, synthetische</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G171</th>\n",
       "      <td>Sonstiges Holz &lt; 50cm</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G930</th>\n",
       "      <td>Schaumstoff-Ohrstöpsel</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G119</th>\n",
       "      <td>Folienartiger Kunststoff (&gt;1mm)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G133</th>\n",
       "      <td>Kondome einschließlich Verpackungen</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G49</th>\n",
       "      <td>Seile &gt; 1cm</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G43</th>\n",
       "      <td>Sicherheitsetiketten, Siegel für Fischerei ode...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G48</th>\n",
       "      <td>Seile, synthetische</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G70</th>\n",
       "      <td>Schrotflintenpatronen</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G29</th>\n",
       "      <td>Kämme, Bürsten und Sonnenbrillen</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G931</th>\n",
       "      <td>(Absperr)band für Absperrungen, Polizei, Baust...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G114</th>\n",
       "      <td>Folien &lt;5mm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11</th>\n",
       "      <td>Kosmetika für den Strand, z.B. Sonnencreme</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G145</th>\n",
       "      <td>Andere Textilien</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G939</th>\n",
       "      <td>Kunststoffblumen, Kunststoffpflanzen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G99</th>\n",
       "      <td>Spritzen - Nadeln</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G916</th>\n",
       "      <td>Bleistifte und Bruchstücke</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G938</th>\n",
       "      <td>Zahnstocher, Zahnseide Kunststoff</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6</th>\n",
       "      <td>Flaschen und Behälter aus Kunststoff, nicht fü...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G5</th>\n",
       "      <td>Plastiksäcke/ Plastiktüten</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G12</th>\n",
       "      <td>Kosmetika, Behälter für Körperpflegeprodukte, ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G128</th>\n",
       "      <td>Reifen und Antriebsriemen</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G8</th>\n",
       "      <td>Getränkeflaschen &gt; 0,5L</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G111</th>\n",
       "      <td>Kugelförmige Pellets &lt; 5mm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G176</th>\n",
       "      <td>Konservendosen (Lebensmitteldosen)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>Säcke, Taschen</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G934</th>\n",
       "      <td>Sandsäcke, Kunststoff für Hochwasser- und Eros...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G108</th>\n",
       "      <td>Scheibenförmige Pellets &lt;5mm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G107</th>\n",
       "      <td>Zylindrische Pellets &lt;5mm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G38</th>\n",
       "      <td>Abdeckungen; Kunststoffverpackungen, Folien zu...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G999</th>\n",
       "      <td>Keine Gegenstände bei dieser Erhebung/Studie g...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Unbekannt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G138</th>\n",
       "      <td>Schuhe und Sandalen</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G918</th>\n",
       "      <td>Sicherheitsnadeln, Büroklammern, kleine Gebrau...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G118</th>\n",
       "      <td>Kleine Industrielle Kügelchen &lt;5mm</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G900</th>\n",
       "      <td>Handschuhe Latex, persönliche Schutzausrüstung</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G913</th>\n",
       "      <td>Schnuller</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G902</th>\n",
       "      <td>Medizinische Masken, Stoff</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G903</th>\n",
       "      <td>Behälter und Packungen für Handdesinfektionsmi...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G92</th>\n",
       "      <td>Köderbehälter</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G109</th>\n",
       "      <td>Flache Pellets &lt;5mm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G71</th>\n",
       "      <td>Schuhe Sandalen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G935</th>\n",
       "      <td>Gummi-Puffer für Geh- und Wanderstöcke und Tei...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G173</th>\n",
       "      <td>Sonstiges</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G907</th>\n",
       "      <td>Kaffeekapseln aus Kunststoff</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G129</th>\n",
       "      <td>Schläuche und Gummiplatten</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G144</th>\n",
       "      <td>Tampons</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G185</th>\n",
       "      <td>Behälter mittlerer Grösse</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G214</th>\n",
       "      <td>Öl/Teer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G147</th>\n",
       "      <td>Papiertragetaschen, (Papiertüten)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G140</th>\n",
       "      <td>Sack oder Beutel (Tragetasche), Jute oder Hanf</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G14</th>\n",
       "      <td>Flaschen für Motoröl (Motorölflaschen)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G139</th>\n",
       "      <td>Rucksäcke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G39</th>\n",
       "      <td>Handschuhe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G40</th>\n",
       "      <td>Handschuhe Haushalt/Garten</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G41</th>\n",
       "      <td>Handschuhe Industriell/Professionell</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G195</th>\n",
       "      <td>Batterien (Haushalt)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G190</th>\n",
       "      <td>Farbtöpfe, Farbbüchsen,  ( Farbeimer)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G132</th>\n",
       "      <td>Schwimmer (Angeln)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</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>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G53</th>\n",
       "      <td>Netze und Teilstücke &lt; 50cm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G151</th>\n",
       "      <td>Tetrapack, Kartons</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G17</th>\n",
       "      <td>Kartuschen von Kartuschen-spritzpistolen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G61</th>\n",
       "      <td>Sonstiges Angelzubehör</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G179</th>\n",
       "      <td>Einweg Grill</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>G182</th>\n",
       "      <td>Angeln; Haken, Gewichte, Köder, Senkblei, 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>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>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>G19</th>\n",
       "      <td>Autoteile</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>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>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>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>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>G62</th>\n",
       "      <td>Schwimmer für 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>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>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>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>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>G704</th>\n",
       "      <td>Seilbahnbürsten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G705</th>\n",
       "      <td>Schrauben und Bolzen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G706</th>\n",
       "      <td>Skiabonnement</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G707</th>\n",
       "      <td>Skiausrüstungsetikett</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G708</th>\n",
       "      <td>Skistöcke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G709</th>\n",
       "      <td>Teller von Skistock (runder Teil aus Plastik u...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>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>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>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>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>G84</th>\n",
       "      <td>CD oder CD-Hülle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G88</th>\n",
       "      <td>Telefon inkl. Teile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>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>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>G37</th>\n",
       "      <td>Netzbeutel, Netztasche, Netzsäcke</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>G136</th>\n",
       "      <td>Schuhe</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>G906</th>\n",
       "      <td>Kaffeekapseln Aluminium</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>G141</th>\n",
       "      <td>Teppiche</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>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>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>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         2 012  \\\n",
       "Gfrags                          Fragmentierte Kunststoffe           917   \n",
       "Gfoam                             Expandiertes Polystyrol           898   \n",
       "G200             Getränkeflaschen aus Glas, Glasfragmente           621   \n",
       "G30                                    Snack-Verpackungen           558   \n",
       "G67                           Industriefolie (Kunststoff)           462   \n",
       "G941            Verpackungsfolien, nicht für Lebensmittel           238   \n",
       "G74                    Schaumstoffverpackungen/Isolierung           228   \n",
       "G25               Tabak; Kunststoffverpackungen, Behälter           178   \n",
       "G178          Kronkorken, Lasche von Dose/Ausfreisslachen           173   \n",
       "G156                                      Papierfragmente           151   \n",
       "G117                                       Styropor < 5mm           134   \n",
       "G89                                 Kunststoff-Bauabfälle           117   \n",
       "G177                      Verpackungen aus Aluminiumfolie           112   \n",
       "G923    Taschentücher, Toilettenpapier, Servietten, Pa...           106   \n",
       "G73     Gegenstände aus Schaumstoff & Teilstücke (nich...            95   \n",
       "G96     Hygienebinden/Höscheneinlagen/Tampons und Appl...            93   \n",
       "G24                   Ringe von Plastikflaschen/Behältern            85   \n",
       "G21                   Getränke-Deckel, Getränkeverschluss            80   \n",
       "G112                           Industriepellets (Nurdles)            72   \n",
       "G211                     Sonstiges medizinisches Material            71   \n",
       "G155                Feuerwerkspapierhülsen und -fragmente            70   \n",
       "G95                                  Wattestäbchen/Tupfer            70   \n",
       "G31                  Schleckstengel, Stengel von Lutscher            63   \n",
       "G23                               unidentifizierte Deckel            61   \n",
       "G904                      Feuerwerkskörper; Raketenkappen            59   \n",
       "G153    Papierbecher, Lebensmittelverpackungen aus Pap...            58   \n",
       "G106                       Kunststofffragmente eckig <5mm            58   \n",
       "G10     Lebensmittelbehälter zum einmaligen Gebrauch a...            56   \n",
       "G922                               Etiketten, Strichcodes            54   \n",
       "G125                   Luftballons und Luftballonstäbchen            50   \n",
       "G35                           Strohhalme und Rührstäbchen            49   \n",
       "G32                            Spielzeug und Partyartikel            47   \n",
       "G940               Schaumstoff EVA (flexibler Kunststoff)            46   \n",
       "G928                                 Bänder und Schleifen            43   \n",
       "G66     Umreifungsbänder; Hartplastik für Verpackung f...            41   \n",
       "G148                              Kartonkisten und Stücke            38   \n",
       "G159                                                 Kork            38   \n",
       "G203    Geschirr aus Keramik oder Glas, Tassen, Teller...            38   \n",
       "G936                             Folien für Gewächshäuser            37   \n",
       "G50                                          Schnur < 1cm            36   \n",
       "G115                                     Schaumstoff <5mm            34   \n",
       "G165    Glacestengel (Eisstiele), Zahnstocher, Essstäb...            31   \n",
       "G208                   Glas oder Keramikfragmente >2.5 cm            29   \n",
       "G33                 Einwegartikel; Tassen/Becher & Deckel            28   \n",
       "G91     Bio-Filtermaterial  / Trägermaterial aus Kunst...            28   \n",
       "G186                                     Industrieschrott            28   \n",
       "G131                                          Gummibänder            26   \n",
       "G87                  Abdeckklebeband/Verpackungsklebeband            26   \n",
       "G917                                              Blähton            23   \n",
       "G146                                       Papier, Karton            23   \n",
       "G3                       Einkaufstaschen, Shoppingtaschen            22   \n",
       "G34                          Besteck, Teller und Tabletts            22   \n",
       "G157                                               Papier            22   \n",
       "G905    Haarspangen, Haargummis, persönliche Accessoir...            21   \n",
       "G152    Papier &Karton;Zigarettenschachteln, Papier/Ka...            20   \n",
       "G98                                Windeln – Feuchttücher            20   \n",
       "G22     Deckel für Chemikalien, Reinigungsmittel (Ohne...            19   \n",
       "G942      Kunststoffspäne von Drehbänken, CNC-Bearbeitung            18   \n",
       "G154                         Zeitungen oder Zeitschriften            18   \n",
       "G135    Kleidung, Fussbekleidung, Kopfbedeckung, Hands...            16   \n",
       "G149                                   Papierverpackungen            16   \n",
       "G100              Medizin; Behälter/Röhrchen/Verpackungen            15   \n",
       "G137                      Kleidung, Handtücher und Lappen            14   \n",
       "G90                               Blumentöpfe aus Plastik            14   \n",
       "G170                                   Holz (verarbeitet)            14   \n",
       "G201                       Gläser, einschliesslich Stücke            13   \n",
       "G210                   Sonstiges Glas/Keramik Materialien            12   \n",
       "G213                                       Paraffinwachs             12   \n",
       "G921                       Keramikfliesen und Bruchstücke            12   \n",
       "G26                                             Feuerzeug            12   \n",
       "G142                              Seil, Schnur oder Netze            12   \n",
       "G191                                     Draht und Gitter            11   \n",
       "G175                      Getränkedosen (Dosen, Getränke)            11   \n",
       "G59                         Monofile Angelschnur (Angeln)            11   \n",
       "G4      Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...            11   \n",
       "G198                           Andere Metallteile < 50 cm            10   \n",
       "G93                                           Kabelbinder            10   \n",
       "G925    Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...             9   \n",
       "G20                                    Laschen und Deckel             9   \n",
       "G927    Plastikschnur von Rasentrimmern, die zum Schne...             9   \n",
       "G919                       Nägel, Schrauben, Bolzen usw.              9   \n",
       "G194    Kabel, Metalldraht oft in Gummi- oder Kunststo...             9   \n",
       "G28                  Stifte, Deckel, Druckbleistifte usw.             9   \n",
       "G943                     Zäune Landwirtschaft, Kunststoff             9   \n",
       "G161                                   Verarbeitetes Holz             8   \n",
       "G914    Büroklammern, Wäscheklammern, Gebrauchsgegenst...             8   \n",
       "G167                        Streichhölzer oder Feuerwerke             8   \n",
       "G134                                      Sonstiges Gummi             8   \n",
       "G7                             Getränkeflaschen < = 0,5 l             8   \n",
       "G65                                                 Eimer             8   \n",
       "G933    Taschen, Etuis für Zubehör, Brillen, Elektroni...             8   \n",
       "G101    Robidog Hundekot-Säcklein, andere Hundekotsäck...             8   \n",
       "G908                    Klebeband; elektrisch, isolierend             7   \n",
       "G126                                                Bälle             7   \n",
       "G124                                    Andere Kunststoff             7   \n",
       "G929       Elektronik und Teile; Sensoren, Headsets usw.              6   \n",
       "G926                    Kaugummi, enthält oft Kunststoffe             6   \n",
       "G197                                     sonstiges Metall             5   \n",
       "G68                                   Fiberglas-Fragmente             5   \n",
       "G103                        Kunststofffragmente rund <5mm             5   \n",
       "G204                  Baumaterial;  Ziegel, Rohre, Zement             5   \n",
       "G901                    Medizinische Masken, synthetische             5   \n",
       "G171                                Sonstiges Holz < 50cm             4   \n",
       "G930                               Schaumstoff-Ohrstöpsel             4   \n",
       "G119                      Folienartiger Kunststoff (>1mm)             4   \n",
       "G133                  Kondome einschließlich Verpackungen             4   \n",
       "G49                                           Seile > 1cm             4   \n",
       "G43     Sicherheitsetiketten, Siegel für Fischerei ode...             4   \n",
       "G48                                   Seile, synthetische             4   \n",
       "G70                                 Schrotflintenpatronen             4   \n",
       "G29                      Kämme, Bürsten und Sonnenbrillen             4   \n",
       "G931    (Absperr)band für Absperrungen, Polizei, Baust...             4   \n",
       "G114                                          Folien <5mm             3   \n",
       "G11            Kosmetika für den Strand, z.B. Sonnencreme             3   \n",
       "G145                                     Andere Textilien             3   \n",
       "G939                 Kunststoffblumen, Kunststoffpflanzen             3   \n",
       "G99                                     Spritzen - Nadeln             3   \n",
       "G916                           Bleistifte und Bruchstücke             3   \n",
       "G938                    Zahnstocher, Zahnseide Kunststoff             3   \n",
       "G6      Flaschen und Behälter aus Kunststoff, nicht fü...             3   \n",
       "G5                             Plastiksäcke/ Plastiktüten             3   \n",
       "G36     Säcke aus strapazierfähigem Kunststoff für 25 ...             3   \n",
       "G12     Kosmetika, Behälter für Körperpflegeprodukte, ...             3   \n",
       "G128                            Reifen und Antriebsriemen             2   \n",
       "G8                                Getränkeflaschen > 0,5L             2   \n",
       "G111                           Kugelförmige Pellets < 5mm             2   \n",
       "G176                   Konservendosen (Lebensmitteldosen)             2   \n",
       "G2                                         Säcke, Taschen             2   \n",
       "G934    Sandsäcke, Kunststoff für Hochwasser- und Eros...             2   \n",
       "G108                         Scheibenförmige Pellets <5mm             2   \n",
       "G107                            Zylindrische Pellets <5mm             2   \n",
       "G38     Abdeckungen; Kunststoffverpackungen, Folien zu...             2   \n",
       "G999    Keine Gegenstände bei dieser Erhebung/Studie g...             2   \n",
       "G138                                  Schuhe und Sandalen             2   \n",
       "G918    Sicherheitsnadeln, Büroklammern, kleine Gebrau...             2   \n",
       "G118                   Kleine Industrielle Kügelchen <5mm             2   \n",
       "G900       Handschuhe Latex, persönliche Schutzausrüstung             1   \n",
       "G913                                            Schnuller             1   \n",
       "G902                           Medizinische Masken, Stoff             1   \n",
       "G903    Behälter und Packungen für Handdesinfektionsmi...             1   \n",
       "G92                                         Köderbehälter             1   \n",
       "G109                                  Flache Pellets <5mm             1   \n",
       "G71                                       Schuhe Sandalen             1   \n",
       "G935    Gummi-Puffer für Geh- und Wanderstöcke und Tei...             1   \n",
       "G173                                            Sonstiges             1   \n",
       "G907                         Kaffeekapseln aus Kunststoff             1   \n",
       "G129                           Schläuche und Gummiplatten             1   \n",
       "G172                               Sonstiges Holz > 50 cm             1   \n",
       "G144                                              Tampons             1   \n",
       "G185                            Behälter mittlerer Grösse             1   \n",
       "G214                                              Öl/Teer             1   \n",
       "G147                    Papiertragetaschen, (Papiertüten)             1   \n",
       "G140       Sack oder Beutel (Tragetasche), Jute oder Hanf             1   \n",
       "G14                Flaschen für Motoröl (Motorölflaschen)             1   \n",
       "G139                                            Rucksäcke             1   \n",
       "G39                                            Handschuhe             1   \n",
       "G40                            Handschuhe Haushalt/Garten             1   \n",
       "G41                  Handschuhe Industriell/Professionell             1   \n",
       "G195                                 Batterien (Haushalt)             1   \n",
       "G190                Farbtöpfe, Farbbüchsen,  ( Farbeimer)             1   \n",
       "G132                                   Schwimmer (Angeln)             1   \n",
       "G52                                  Netze und Teilstücke             1   \n",
       "G53                           Netze und Teilstücke < 50cm             1   \n",
       "G151                                   Tetrapack, Kartons             1   \n",
       "G17              Kartuschen von Kartuschen-spritzpistolen             1   \n",
       "G61                                Sonstiges Angelzubehör             1   \n",
       "G179                                         Einweg Grill             1   \n",
       "G94                                             Tischtuch             0   \n",
       "G196                       Grosse metallische Gegenstände             0   \n",
       "G113                                           Fäden <5mm             0   \n",
       "G174                                    Aerosolspraydosen             0   \n",
       "G160                                             Paletten             0   \n",
       "G207                       Tonkrüge zum Fangen von Kraken             0   \n",
       "G205                                    Leuchtstoffröhren             0   \n",
       "G162                                               Kisten             0   \n",
       "G202                                           Glühbirnen             0   \n",
       "G166                                           Farbpinsel             0   \n",
       "G102                                           Flip-Flops             0   \n",
       "G199                           Andere Metallteile > 50 cm             0   \n",
       "G97                     Behälter von Toilettenerfrischer              0   \n",
       "G183                                 Teile von Angelhaken             0   \n",
       "G180                                      Haushaltsgeräte             0   \n",
       "G104    Kunststofffragmente abgerundet / rundlich  <5m...             0   \n",
       "G945                                        Rasierklingen             0   \n",
       "G932    Bio-Beads, Mikroplastik für die Abwasserbehand...             0   \n",
       "G193                    Teile von Autos und Autobatterien             0   \n",
       "G944              Pelletmasse aus dem Spritzgussverfahren             0   \n",
       "G181            Geschirr aus Metall, Tassen, Besteck usw.             0   \n",
       "G158                      Sonstige Gegenstände aus Papier             0   \n",
       "G188                       Andere Kanister/Behälter < 4 L             0   \n",
       "G105                  Kunststofffragmente subangulär <5mm             0   \n",
       "G182       Angeln; Haken, Gewichte, Köder, Senkblei, usw.             0   \n",
       "G937                          Pheromonköder für Weinberge             0   \n",
       "G168                                          Holzbretter             0   \n",
       "G19                                             Autoteile             0   \n",
       "G64                                             Kotflügel             0   \n",
       "G150                              Milchkartons, Tetrapack             0   \n",
       "G711                                          Handwärmer              0   \n",
       "G13     Flaschen, Behälter, Fässer zum Transportieren ...             0   \n",
       "G62                                   Schwimmer für Netze             0   \n",
       "G60                    Lichtstab, Knicklicht, Glow-sticks             0   \n",
       "G56                                     Verhedderte Netze             0   \n",
       "G702                      Pistenmarkierungspfosten (Holz)             0   \n",
       "G703                   Pistenmarkierungspfosten (Plastik)             0   \n",
       "G704                                      Seilbahnbürsten             0   \n",
       "G705                                 Schrauben und Bolzen             0   \n",
       "G706                                        Skiabonnement             0   \n",
       "G707                                Skiausrüstungsetikett             0   \n",
       "G708                                            Skistöcke             0   \n",
       "G709    Teller von Skistock (runder Teil aus Plastik u...             0   \n",
       "G55                              Angelschnur (verwickelt)             0   \n",
       "G710    Ski / Snowboards (Ski, Befestigungen und ander...             0   \n",
       "G712                                        Skihandschuhe             0   \n",
       "G116                                        Granulat <5mm             0   \n",
       "G713                                        Skiförderband             0   \n",
       "G51                                           Fischernetz             0   \n",
       "G84                                      CD oder CD-Hülle             0   \n",
       "G88                                   Telefon inkl. Teile             0   \n",
       "G123                           Polyurethan-Granulat < 5mm             0   \n",
       "G9      Flaschen und Behälter für Reinigungsmittel und...             0   \n",
       "G122                           Kunststofffragmente (>1mm)             0   \n",
       "G37                     Netzbeutel, Netztasche, Netzsäcke             0   \n",
       "G136                                               Schuhe             0   \n",
       "G906                              Kaffeekapseln Aluminium             0   \n",
       "G141                                             Teppiche             0   \n",
       "G143                                  Segel und Segeltuch             0   \n",
       "G63                                                 Bojen             0   \n",
       "G915        Reflektoren, Mobilitätsartikel aus Kunststoff             0   \n",
       "G1                                          Sixpack-Ringe             0   \n",
       "\n",
       "        Anteil  p/100 m  Häufigkeitsrate     Material  \n",
       "code                                                   \n",
       "G27         21       23               87      Plastik  \n",
       "Gfrags       9       10               75      Plastik  \n",
       "Gfoam        9        3               65      Plastik  \n",
       "G200         6        4               66         Glas  \n",
       "G30          5        6               85      Plastik  \n",
       "G67          4        2               55      Plastik  \n",
       "G941         2        0               39      Plastik  \n",
       "G74          2        0               38      Plastik  \n",
       "G25          1        0               48      Plastik  \n",
       "G178         1        1               52       Metall  \n",
       "G156         1        0               41       Papier  \n",
       "G117         1        0               18      Plastik  \n",
       "G89          1        0               41      Plastik  \n",
       "G177         1        0               39       Metall  \n",
       "G923         1        0               28       Papier  \n",
       "G73          1        0               11      Plastik  \n",
       "G96          0        0               13      Plastik  \n",
       "G24          0        0               38      Plastik  \n",
       "G21          0        0               34      Plastik  \n",
       "G112         0        0               19      Plastik  \n",
       "G211         0        0               25      Plastik  \n",
       "G155         0        0                9       Papier  \n",
       "G95          0        0               32      Plastik  \n",
       "G31          0        0               30      Plastik  \n",
       "G23          0        0               29      Plastik  \n",
       "G904         0        0               24      Plastik  \n",
       "G153         0        0               23       Papier  \n",
       "G106         0        0               13      Plastik  \n",
       "G10          0        0               21      Plastik  \n",
       "G922         0        0               22      Plastik  \n",
       "G125         0        0               23        Gummi  \n",
       "G35          0        0               31      Plastik  \n",
       "G32          0        0               27      Plastik  \n",
       "G940         0        0                8      Plastik  \n",
       "G928         0        0                9      Plastik  \n",
       "G66          0        0               25      Plastik  \n",
       "G148         0        0               11       Papier  \n",
       "G159         0        0               15         Holz  \n",
       "G203         0        0               11         Glas  \n",
       "G936         0        0               12      Plastik  \n",
       "G50          0        0               24      Plastik  \n",
       "G115         0        0                4      Plastik  \n",
       "G165         0        0               13         Holz  \n",
       "G208         0        0               13         Glas  \n",
       "G33          0        0               16      Plastik  \n",
       "G91          0        0               12      Plastik  \n",
       "G186         0        0               15       Metall  \n",
       "G131         0        0               16        Gummi  \n",
       "G87          0        0               16      Plastik  \n",
       "G917         0        0                6         Glas  \n",
       "G146         0        0               10       Papier  \n",
       "G3           0        0               13      Plastik  \n",
       "G34          0        0               14      Plastik  \n",
       "G157         0        0                5       Papier  \n",
       "G905         0        0               10      Plastik  \n",
       "G152         0        0                9       Papier  \n",
       "G98          0        0               13      Plastik  \n",
       "G22          0        0               10      Plastik  \n",
       "G942         0        0                8      Plastik  \n",
       "G154         0        0                5       Papier  \n",
       "G135         0        0                8        Stoff  \n",
       "G149         0        0                4       Papier  \n",
       "G100         0        0                8      Plastik  \n",
       "G137         0        0               10        Stoff  \n",
       "G90          0        0                8      Plastik  \n",
       "G170         0        0                5         Holz  \n",
       "G201         0        0                8         Glas  \n",
       "G210         0        0                3         Glas  \n",
       "G213         0        0                5  Chemikalien  \n",
       "G921         0        0                6         Glas  \n",
       "G26          0        0                9      Plastik  \n",
       "G142         0        0                9        Stoff  \n",
       "G191         0        0                9       Metall  \n",
       "G175         0        0                8       Metall  \n",
       "G59          0        0                8      Plastik  \n",
       "G4           0        0                7      Plastik  \n",
       "G198         0        0                8       Metall  \n",
       "G93          0        0                8      Plastik  \n",
       "G925         0        0                7      Plastik  \n",
       "G20          0        0                4      Plastik  \n",
       "G927         0        0                6      Plastik  \n",
       "G919         0        0                3       Metall  \n",
       "G194         0        0                4       Metall  \n",
       "G28          0        0                6      Plastik  \n",
       "G943         0        0                4      Plastik  \n",
       "G161         0        0                6         Holz  \n",
       "G914         0        0                5      Plastik  \n",
       "G167         0        0                2         Holz  \n",
       "G134         0        0                6        Gummi  \n",
       "G7           0        0                6      Plastik  \n",
       "G65          0        0                4      Plastik  \n",
       "G933         0        0                5      Plastik  \n",
       "G101         0        0                7      Plastik  \n",
       "G908         0        0                6      Plastik  \n",
       "G126         0        0                5        Gummi  \n",
       "G124         0        0                5      Plastik  \n",
       "G929         0        0                5       Metall  \n",
       "G926         0        0                4      Plastik  \n",
       "G197         0        0                4       Metall  \n",
       "G68          0        0                4      Plastik  \n",
       "G103         0        0                2      Plastik  \n",
       "G204         0        0                3         Glas  \n",
       "G901         0        0                4      Plastik  \n",
       "G171         0        0                1         Holz  \n",
       "G930         0        0                2      Plastik  \n",
       "G119         0        0                1      Plastik  \n",
       "G133         0        0                2        Gummi  \n",
       "G49          0        0                2      Plastik  \n",
       "G43          0        0                2      Plastik  \n",
       "G48          0        0                2      Plastik  \n",
       "G70          0        0                3      Plastik  \n",
       "G29          0        0                3      Plastik  \n",
       "G931         0        0                2      Plastik  \n",
       "G114         0        0                2      Plastik  \n",
       "G11          0        0                2      Plastik  \n",
       "G145         0        0                1        Stoff  \n",
       "G939         0        0                2      Plastik  \n",
       "G99          0        0                2      Plastik  \n",
       "G916         0        0                2         Holz  \n",
       "G938         0        0                2      Plastik  \n",
       "G6           0        0                1      Plastik  \n",
       "G5           0        0                1      Plastik  \n",
       "G36          0        0                2      Plastik  \n",
       "G12          0        0                2      Plastik  \n",
       "G128         0        0                1        Gummi  \n",
       "G8           0        0                1      Plastik  \n",
       "G111         0        0                1      Plastik  \n",
       "G176         0        0                1       Metall  \n",
       "G2           0        0                0      Plastik  \n",
       "G934         0        0                0      Plastik  \n",
       "G108         0        0                1      Plastik  \n",
       "G107         0        0                0      Plastik  \n",
       "G38          0        0                1      Plastik  \n",
       "G999         0        0                1    Unbekannt  \n",
       "G138         0        0                1        Stoff  \n",
       "G918         0        0                1       Metall  \n",
       "G118         0        0                1      Plastik  \n",
       "G900         0        0                0        Gummi  \n",
       "G913         0        0                0      Plastik  \n",
       "G902         0        0                0        Stoff  \n",
       "G903         0        0                0      Plastik  \n",
       "G92          0        0                0      Plastik  \n",
       "G109         0        0                0      Plastik  \n",
       "G71          0        0                0      Plastik  \n",
       "G935         0        0                0      Plastik  \n",
       "G173         0        0                0         Holz  \n",
       "G907         0        0                0      Plastik  \n",
       "G129         0        0                0        Gummi  \n",
       "G172         0        0                0         Holz  \n",
       "G144         0        0                0        Stoff  \n",
       "G185         0        0                0       Metall  \n",
       "G214         0        0                0  Chemikalien  \n",
       "G147         0        0                0       Papier  \n",
       "G140         0        0                0        Stoff  \n",
       "G14          0        0                0      Plastik  \n",
       "G139         0        0                0        Stoff  \n",
       "G39          0        0                0      Plastik  \n",
       "G40          0        0                0      Plastik  \n",
       "G41          0        0                0      Plastik  \n",
       "G195         0        0                0       Metall  \n",
       "G190         0        0                0       Metall  \n",
       "G132         0        0                0        Gummi  \n",
       "G52          0        0                0      Plastik  \n",
       "G53          0        0                0      Plastik  \n",
       "G151         0        0                0       Papier  \n",
       "G17          0        0                0      Plastik  \n",
       "G61          0        0                0      Plastik  \n",
       "G179         0        0                0       Metall  \n",
       "G94          0        0                0      Plastik  \n",
       "G196         0        0                0       Metall  \n",
       "G113         0        0                0      Plastik  \n",
       "G174         0        0                0       Metall  \n",
       "G160         0        0                0         Holz  \n",
       "G207         0        0                0         Glas  \n",
       "G205         0        0                0         Glas  \n",
       "G162         0        0                0         Holz  \n",
       "G202         0        0                0         Glas  \n",
       "G166         0        0                0         Holz  \n",
       "G102         0        0                0      Plastik  \n",
       "G199         0        0                0       Metall  \n",
       "G97          0        0                0      Plastik  \n",
       "G183         0        0                0       Metall  \n",
       "G180         0        0                0       Metall  \n",
       "G104         0        0                0      Plastik  \n",
       "G945         0        0                0       Metall  \n",
       "G932         0        0                0      Plastik  \n",
       "G193         0        0                0       Metall  \n",
       "G944         0        0                0      Plastik  \n",
       "G181         0        0                0       Metall  \n",
       "G158         0        0                0       Papier  \n",
       "G188         0        0                0       Metall  \n",
       "G105         0        0                0      Plastik  \n",
       "G182         0        0                0       Metall  \n",
       "G937         0        0                0      Plastik  \n",
       "G168         0        0                0         Holz  \n",
       "G19          0        0                0      Plastik  \n",
       "G64          0        0                0      Plastik  \n",
       "G150         0        0                0       Papier  \n",
       "G711         0        0                0      Plastik  \n",
       "G13          0        0                0      Plastik  \n",
       "G62          0        0                0      Plastik  \n",
       "G60          0        0                0      Plastik  \n",
       "G56          0        0                0      Plastik  \n",
       "G702         0        0                0         Holz  \n",
       "G703         0        0                0      Plastik  \n",
       "G704         0        0                0      Plastik  \n",
       "G705         0        0                0       Metall  \n",
       "G706         0        0                0      Plastik  \n",
       "G707         0        0                0      Plastik  \n",
       "G708         0        0                0       Metall  \n",
       "G709         0        0                0      Plastik  \n",
       "G55          0        0                0      Plastik  \n",
       "G710         0        0                0      Plastik  \n",
       "G712         0        0                0        Stoff  \n",
       "G116         0        0                0      Plastik  \n",
       "G713         0        0                0      Plastik  \n",
       "G51          0        0                0      Plastik  \n",
       "G84          0        0                0      Plastik  \n",
       "G88          0        0                0      Plastik  \n",
       "G123         0        0                0      Plastik  \n",
       "G9           0        0                0      Plastik  \n",
       "G122         0        0                0      Plastik  \n",
       "G37          0        0                0      Plastik  \n",
       "G136         0        0                0        Stoff  \n",
       "G906         0        0                0       Metall  \n",
       "G141         0        0                0        Stoff  \n",
       "G143         0        0                0        Stoff  \n",
       "G63          0        0                0      Plastik  \n",
       "G915         0        0                0      Plastik  \n",
       "G1           0        0                0      Plastik  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None)\n",
    "complete_inventory = fdx.code_summary.copy()\n",
    "complete_inventory.sort_values(by=\"quantity\", ascending=False, inplace=True)\n",
    "complete_inventory[\"quantity\"] = complete_inventory[\"quantity\"].map(lambda x: featuredata.thousandsSeparator(x, language))\n",
    "complete_inventory[\"% of total\"] = complete_inventory[\"% of total\"].astype(int)\n",
    "complete_inventory[unit_label] = complete_inventory[unit_label].astype(int)\n",
    "complete_inventory.rename(columns=featuredata.inventory_table_de, inplace=True)\n",
    "    \n",
    "inventory_subsection = Paragraph(\"Inventar der Objekte\", subsection_title)\n",
    "col_widths=[1.2*cm, 4.5*cm, 2.2*cm, 1.5*cm, 1.5*cm, 2.4*cm, 1.5*cm]\n",
    "inventory_table = aStyledTableWithTitleRow(complete_inventory, title=\"Inventar der Objekte\", colWidths=col_widths)\n",
    "\n",
    "new_map_image =  Image('resources/maps/linth_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)"
   ]
  }
 ],
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