geneve.ipynb

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    "%load_ext watermark\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from IPython.display import Markdown as md\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "import seaborn as sns\n",
    "import json\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: 10px;'}\n",
    "table_data = {'selector': 'td', 'props': 'padding: 6px;'}\n",
    "table_caption = {'selector': 'caption', 'props': 'font-size: 14px; font-style: italic; caption-side: bottom; text-align: left; margin-top: 10px'}\n",
    "table_css_styles = [even_rows, odd_rows, table_font, header_row, table_caption]\n",
    "\n",
    "table_large_data = {'selector': 'tr', 'props': 'font-size: 14px;'}\n",
    "table_large_font = [even_rows, odd_rows, table_large_data, header_row, table_caption]\n",
    "\n",
    "def rotateText(x):\n",
    "    return 'writing-mode: vertical-lr; transform: rotate(-180deg);  padding:10px; margins:0; vertical-align: baseline;'\n",
    "\n",
    "canton = \"Genève\"\n",
    "cantons_avoisinante = \"Valais, Vaud\"\n",
    "other_cantons = [\"Valais\", \"Vaud\"]\n",
    "\n",
    "def save_json(filename, data):\n",
    "    with open(f\"resources/{filename}\", \"w\") as afile:\n",
    "        json.dump(data, afile)\n",
    "\n",
    "def open_json(filename):\n",
    "    with open(f\"resources/{filename}\", \"r\") as afile:\n",
    "        data = json.load(afile)\n",
    "        return data\n",
    "    \n",
    "\n",
    "display_names = open_json(\"french_display_names.json\")\n",
    "\n",
    "land_use_names = open_json(\"french_land_use_names.json\")\n",
    "\n",
    "material_names = open_json(\"french_material_names.json\")\n",
    "\n",
    "group_names = open_json(\"french_group_names.json\")\n",
    "\n",
    "french_names = open_json(\"french_code_translations.json\")\n",
    "\n",
    "# land use\n",
    "land_use = pd.read_csv(\"resources/work_land_use.csv\")\n",
    "\n",
    "start_columns = ['loc_date', 'slug', 'city', 'water_name_slug', 'date', 'doy', 'Project', 'code', 'pcs/m', 'quantity', 'canton', 'zone']\n",
    "\n",
    "all_data = pd.read_csv(\"resources/survey_work_data.csv\", low_memory=False)\n",
    "all_data = all_data[start_columns].copy()\n",
    "all_data[\"date\"] = pd.to_datetime(all_data[\"date\"])"
   ]
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    "codes_of_interest = [\n",
    "    \"G95\",\n",
    "    \"G70\",\n",
    "    \"Gfrags\",\n",
    "    \"Gfoam\",\n",
    "    \"G112\",\n",
    "    \"G27\",\n",
    "    \"G30\",\n",
    "    \"G67\",\n",
    "    \"G10\",\n",
    "    \"Gcaps\",\n",
    "]\n",
    "\n",
    "all_data = all_data[all_data.code.isin(codes_of_interest)].copy()\n",
    "this_canton =  all_data[all_data.canton == canton].copy()\n",
    "this_canton.drop_duplicates([\"code\", \"loc_date\"], inplace=True)\n",
    "\n",
    "cantons_voisine = all_data[all_data.canton.isin(other_cantons)].copy()\n",
    "\n",
    "operations={\n",
    "    \"loc_date\":\"nunique\", \n",
    "    \"city\":\"nunique\", \n",
    "    \"quantity\":\"sum\",\n",
    "    \"slug\":\"nunique\"\n",
    "}\n",
    "summary_data = this_canton.groupby([\"zone\"]).agg(operations)\n",
    "\n",
    "summary_data = summary_data.astype(\"int\")"
   ]
  },
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   "source": [
    "# Genève"
   ]
  },
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   "source": [
    "## Historique\n",
    "\n",
    "Le Valais dispose d'une côte réduite au lac Léman. C'est pourquoi, par rapport aux autres cantons riverains du lac, il y a relativement peu d'échantillons. Le premier échantillon valaisan a été prélevé à Saint-Gingolph en 2016. La masse d'échantillons décrivant les conditions valaisannes a été collectée dans le cadre du recensement fédéral des déchets sauvages. Cette publication présente les résultats pour le lac ([Le Léman](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lac-leman.html)) et le Rhône ([Rhône](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/rhone_sa.html#)). Les résultats des Alpes ont été considérés séparément ([Alpen und der Jura](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/alpes_valaisannes.html)).\n",
    "\n",
    "Nous considérons ici les résultats comme des zones d'intérêt pour les acteurs du canton. Nous définissons donc trois zones qui représentent les différentes régions où des données ont été collectées dans le canton.\n",
    "\n",
    "* lac\n",
    "* montagne\n",
    "* rivière\n",
    "\n",
    "### Definitions/abbreviations\n",
    "\n",
    "1. __pcs/m:__ nombre de déchets pour un mètre de littoral ou sentier pédestre\n",
    "2. __quantité:__ le nombre de déchets identifier\n",
    "3. __échantillons/inventaire:__ L'action de ramasser les déchets et de les compter à un endroit donné.\n",
    "4. __utilsation du sols:__ Cela correspond aux couches cartographiques disponibles sur swiss GeoAdmin.\n",
    "5. __taux d'échec:__ Rapport entre le nombre de fois où un objet spécifique a été trouvé et le nombre total d'échantillons. Un peu comme la fréquence."
   ]
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   "source": [
    "### Objets d'intérêt"
   ]
  },
  {
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    "Les objets d'intérêt sont ceux qui ont été trouvés dans au moins 50 % des enquêtes ou qui figurent parmi les dix objets les plus abondants en termes de quantité.\n",
    "\n",
    "```{note}\n",
    "Le choix du mode de regroupement des différents objets doivent être représentatifs des groupes cibles ou des domaines de préoccupation. Il peut y avoir de nombreux groupes différents qui sont corrects, cela dépend du cas d'utilisation. Dans le cas présent, nous nous intéressons aux objets qui ont été trouvés souvent et en grande quantité.\n",
    "```"
   ]
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      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a963a tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_a963a tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_a963a tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_a963a th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_a963a caption {\n",
       "  font-size: 14px;\n",
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       "  caption-side: bottom;\n",
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       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a963a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a963a_level0_col0\" class=\"col_heading level0 col0\" >quantité</th>\n",
       "      <th id=\"T_a963a_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n",
       "      <th id=\"T_a963a_level0_col2\" class=\"col_heading level0 col2\" >% de totale</th>\n",
       "      <th id=\"T_a963a_level0_col3\" class=\"col_heading level0 col3\" >taux d'échec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row0\" class=\"row_heading level0 row0\" >Plastiques fragmentés</th>\n",
       "      <td id=\"T_a963a_row0_col0\" class=\"data row0 col0\" >2,872</td>\n",
       "      <td id=\"T_a963a_row0_col1\" class=\"data row0 col1\" >0.81</td>\n",
       "      <td id=\"T_a963a_row0_col2\" class=\"data row0 col2\" >0.32</td>\n",
       "      <td id=\"T_a963a_row0_col3\" class=\"data row0 col3\" >0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row1\" class=\"row_heading level0 row1\" >Mégots et filtres à cigarettes</th>\n",
       "      <td id=\"T_a963a_row1_col0\" class=\"data row1 col0\" >2,000</td>\n",
       "      <td id=\"T_a963a_row1_col1\" class=\"data row1 col1\" >0.27</td>\n",
       "      <td id=\"T_a963a_row1_col2\" class=\"data row1 col2\" >0.22</td>\n",
       "      <td id=\"T_a963a_row1_col3\" class=\"data row1 col3\" >0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row2\" class=\"row_heading level0 row2\" >Emballages de bonbons, de snacks</th>\n",
       "      <td id=\"T_a963a_row2_col0\" class=\"data row2 col0\" >1,036</td>\n",
       "      <td id=\"T_a963a_row2_col1\" class=\"data row2 col1\" >0.17</td>\n",
       "      <td id=\"T_a963a_row2_col2\" class=\"data row2 col2\" >0.12</td>\n",
       "      <td id=\"T_a963a_row2_col3\" class=\"data row2 col3\" >0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row3\" class=\"row_heading level0 row3\" >Polystyrène expansé fragmenté</th>\n",
       "      <td id=\"T_a963a_row3_col0\" class=\"data row3 col0\" >942</td>\n",
       "      <td id=\"T_a963a_row3_col1\" class=\"data row3 col1\" >0.05</td>\n",
       "      <td id=\"T_a963a_row3_col2\" class=\"data row3 col2\" >0.11</td>\n",
       "      <td id=\"T_a963a_row3_col3\" class=\"data row3 col3\" >0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row4\" class=\"row_heading level0 row4\" >Pellets industriels (GPI)</th>\n",
       "      <td id=\"T_a963a_row4_col0\" class=\"data row4 col0\" >681</td>\n",
       "      <td id=\"T_a963a_row4_col1\" class=\"data row4 col1\" >0.0</td>\n",
       "      <td id=\"T_a963a_row4_col2\" class=\"data row4 col2\" >0.08</td>\n",
       "      <td id=\"T_a963a_row4_col3\" class=\"data row4 col3\" >0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row5\" class=\"row_heading level0 row5\" >Couvercles de bouteilles en plastique</th>\n",
       "      <td id=\"T_a963a_row5_col0\" class=\"data row5 col0\" >486</td>\n",
       "      <td id=\"T_a963a_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
       "      <td id=\"T_a963a_row5_col2\" class=\"data row5 col2\" >0.05</td>\n",
       "      <td id=\"T_a963a_row5_col3\" class=\"data row5 col3\" >0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row6\" class=\"row_heading level0 row6\" >Coton-tige</th>\n",
       "      <td id=\"T_a963a_row6_col0\" class=\"data row6 col0\" >484</td>\n",
       "      <td id=\"T_a963a_row6_col1\" class=\"data row6 col1\" >0.04</td>\n",
       "      <td id=\"T_a963a_row6_col2\" class=\"data row6 col2\" >0.05</td>\n",
       "      <td id=\"T_a963a_row6_col3\" class=\"data row6 col3\" >0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row7\" class=\"row_heading level0 row7\" >Bâche, feuille plastique industrielle</th>\n",
       "      <td id=\"T_a963a_row7_col0\" class=\"data row7 col0\" >290</td>\n",
       "      <td id=\"T_a963a_row7_col1\" class=\"data row7 col1\" >0.0</td>\n",
       "      <td id=\"T_a963a_row7_col2\" class=\"data row7 col2\" >0.03</td>\n",
       "      <td id=\"T_a963a_row7_col3\" class=\"data row7 col3\" >0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row8\" class=\"row_heading level0 row8\" >Cartouches de fusil de chasse</th>\n",
       "      <td id=\"T_a963a_row8_col0\" class=\"data row8 col0\" >70</td>\n",
       "      <td id=\"T_a963a_row8_col1\" class=\"data row8 col1\" >0.0</td>\n",
       "      <td id=\"T_a963a_row8_col2\" class=\"data row8 col2\" >0.01</td>\n",
       "      <td id=\"T_a963a_row8_col3\" class=\"data row8 col3\" >0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a963a_level0_row9\" class=\"row_heading level0 row9\" >Emballage fast food</th>\n",
       "      <td id=\"T_a963a_row9_col0\" class=\"data row9 col0\" >52</td>\n",
       "      <td id=\"T_a963a_row9_col1\" class=\"data row9 col1\" >0.0</td>\n",
       "      <td id=\"T_a963a_row9_col2\" class=\"data row9 col2\" >0.01</td>\n",
       "      <td id=\"T_a963a_row9_col3\" class=\"data row9 col3\" >0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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   ],
   "source": [
    "# find the ojects that were in at least 50% of the surveys\n",
    "this_canton[\"fail\"] = this_canton['pcs/m'] > 0\n",
    "fail_rates = this_canton.groupby(\"code\", as_index=False).fail.sum()\n",
    "n_samps = this_canton.loc_date.nunique()\n",
    "fail_rates[\"rate\"]= fail_rates.fail/n_samps\n",
    "fail_rates = fail_rates.sort_values(by=\"rate\", ascending=False)\n",
    "failed = fail_rates[fail_rates.rate >= .5].code.unique()\n",
    "\n",
    "# find the top ten items\n",
    "abundant = this_canton.groupby(\"code\", as_index=False).quantity.sum()\n",
    "abundant_c = abundant.sort_values(by=\"quantity\", ascending=False)[:10].code.unique()\n",
    "\n",
    "# combine them\n",
    "most_common_codes = list(set([*abundant, *failed]))\n",
    "# display_names.update({\"fail_rate\": \"taux d'échec\"})\n",
    "\n",
    "# combine and display\n",
    "mc_data = this_canton[this_canton.code.isin(codes_of_interest)].groupby(\"code\", as_index=False).agg({\"quantity\":\"sum\", \"pcs/m\":\"median\", \"fail\":\"sum\"})\n",
    "mc_data[\"fail_rate\"] = (mc_data.fail/n_samps).round(2)\n",
    "mc_data[\"q_weight\"] = (mc_data.quantity/abundant.quantity.sum()).round(2)\n",
    "mc_data[\"object\"] = mc_data.code.apply(lambda x: french_names[x])\n",
    "mc_data = mc_data[[\"object\", \"quantity\", \"pcs/m\", \"q_weight\", \"fail_rate\"]]\n",
    "\n",
    "mc_data = mc_data.sort_values(by=\"quantity\", ascending=False)\n",
    "mc_data[\"quantity\"] = mc_data.quantity.astype(\"int\")\n",
    "mc_data[\"pcs/m\"] = mc_data[\"pcs/m\"].round(2)\n",
    "mc_data.set_index(\"object\", drop=True, inplace=True)\n",
    "mc_data.index.name = None\n",
    "mc_data.rename(columns=display_names).style.set_table_styles(table_large_font).format('{:,}')"
   ]
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   "source": [
    "dims = pd.read_csv(\"resources/work_dimensions.csv\")\n",
    "\n",
    "land_use_of_interest = [\n",
    "    'Siedl', \n",
    "    'Wald',\n",
    "    'Stadtzentr',\n",
    "    'Reben', \n",
    "    'Obstanlage',\n",
    "    'undefined',\n",
    "    'infrastructure',\n",
    "    'recreation',\n",
    "    'Strasse'\n",
    "]\n",
    "\n",
    "\n",
    "def dimensional_summary(dims, survey_data, zones=[\"lac\", \"rivière\", \"montagne\"]):\n",
    "    dfs = []\n",
    "    for label in zones:\n",
    "        d_f = dims[dims.slug.isin(this_canton[this_canton.zone == label].slug.unique())].copy()\n",
    "        d_f[\"zone\"] = label\n",
    "        dfs.append(d_f)\n",
    "    dimensions = pd.concat(dfs)\n",
    "    d_sum = dimensions.groupby(\"zone\").agg({\"area\":\"sum\", \"length\":\"sum\", \"time\":\"sum\"})\n",
    "    return dimensions, d_sum\n",
    "\n",
    "dimensional_summary, d_sum = dimensional_summary(dims, this_canton, zones=[\"lac\", \"rivière\", \"montagne\"])\n",
    "\n",
    "summary_by_zone = pd.concat([summary_data, d_sum], axis=1, ignore_index=False)\n",
    "summary_by_zone[\"hours\"] = (summary_by_zone[\"time\"]/60).round(2)\n",
    "summary_by_zone.drop(\"time\", inplace=True, axis=1)\n",
    "summary_by_zone.loc[\"total\", : ] = summary_by_zone.sum()"
   ]
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   "source": [
    "### Résumé des échantillons novembre 2015 à décembre 2022\n",
    "\n",
    "A compléter"
   ]
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       "</style>\n",
       "<table id=\"T_94674\">\n",
       "  <caption>Historique des échantillonnages Genève : Le nombre d'échantillons et l'ampleur de l'effort par contexte. Remarque : les heures correspondent au temps passé à compter et à collecter.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_94674_level0_col0\" class=\"col_heading level0 col0\" >échantillons</th>\n",
       "      <th id=\"T_94674_level0_col1\" class=\"col_heading level0 col1\" >municipalités</th>\n",
       "      <th id=\"T_94674_level0_col2\" class=\"col_heading level0 col2\" >quantité</th>\n",
       "      <th id=\"T_94674_level0_col3\" class=\"col_heading level0 col3\" >site</th>\n",
       "      <th id=\"T_94674_level0_col4\" class=\"col_heading level0 col4\" >m²</th>\n",
       "      <th id=\"T_94674_level0_col5\" class=\"col_heading level0 col5\" >mètres</th>\n",
       "      <th id=\"T_94674_level0_col6\" class=\"col_heading level0 col6\" >heures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_94674_level0_row0\" class=\"row_heading level0 row0\" >lac</th>\n",
       "      <td id=\"T_94674_row0_col0\" class=\"data row0 col0\" >50</td>\n",
       "      <td id=\"T_94674_row0_col1\" class=\"data row0 col1\" >4</td>\n",
       "      <td id=\"T_94674_row0_col2\" class=\"data row0 col2\" >8,692</td>\n",
       "      <td id=\"T_94674_row0_col3\" class=\"data row0 col3\" >11</td>\n",
       "      <td id=\"T_94674_row0_col4\" class=\"data row0 col4\" >16,119</td>\n",
       "      <td id=\"T_94674_row0_col5\" class=\"data row0 col5\" >4,432</td>\n",
       "      <td id=\"T_94674_row0_col6\" class=\"data row0 col6\" >108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94674_level0_row1\" class=\"row_heading level0 row1\" >rivière</th>\n",
       "      <td id=\"T_94674_row1_col0\" class=\"data row1 col0\" >9</td>\n",
       "      <td id=\"T_94674_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_94674_row1_col2\" class=\"data row1 col2\" >221</td>\n",
       "      <td id=\"T_94674_row1_col3\" class=\"data row1 col3\" >2</td>\n",
       "      <td id=\"T_94674_row1_col4\" class=\"data row1 col4\" >100</td>\n",
       "      <td id=\"T_94674_row1_col5\" class=\"data row1 col5\" >20</td>\n",
       "      <td id=\"T_94674_row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94674_level0_row2\" class=\"row_heading level0 row2\" >total</th>\n",
       "      <td id=\"T_94674_row2_col0\" class=\"data row2 col0\" >59</td>\n",
       "      <td id=\"T_94674_row2_col1\" class=\"data row2 col1\" >6</td>\n",
       "      <td id=\"T_94674_row2_col2\" class=\"data row2 col2\" >8,913</td>\n",
       "      <td id=\"T_94674_row2_col3\" class=\"data row2 col3\" >13</td>\n",
       "      <td id=\"T_94674_row2_col4\" class=\"data row2 col4\" >16,219</td>\n",
       "      <td id=\"T_94674_row2_col5\" class=\"data row2 col5\" >4,452</td>\n",
       "      <td id=\"T_94674_row2_col6\" class=\"data row2 col6\" >110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fda4c2478b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caption_french = f\"Historique des échantillonnages {canton} : Le nombre d'échantillons et l'ampleur de l'effort par contexte. Remarque : les heures correspondent au temps passé à compter et à collecter.\"\n",
    "\n",
    "summary_by_zone.index.name = None\n",
    "summary_by_zone.rename(columns=display_names, inplace=True)\n",
    "summary_by_zone = summary_by_zone.astype(\"int\")\n",
    "summary_by_zone.style.set_table_styles(table_large_font).format('{:,}' ).set_caption(caption_french)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504cb022-4091-462a-b3f4-ac2a4b7e11fa",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Communes et zones d'intérêt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f6ca782-d64a-430f-b9cc-6aae5b021746",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "__Municipalités:__ Genève, Hermance, Versoix, Savonière, Veyrier"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_in_canton = ', '.join(this_canton.city.unique())\n",
    "\n",
    "munix = f'__Municipalités:__ {cities_in_canton}'\n",
    "md(munix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6b842b3-221d-4098-8423-1aac61440ab1",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "__Lacs:__ lac-leman"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lakes = ', '.join(this_canton[this_canton.zone == 'lac'].water_name_slug.unique())\n",
    "lakex = f'__Lacs:__ {lakes}'\n",
    "md(lakex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2fba0b0b-12d8-48b1-98df-40fe6ca55888",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "__Rivières:__ arve, rhone"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rivers = ', '.join(this_canton[this_canton.zone == 'rivière'].water_name_slug.unique())\n",
    "riverx = f'__Rivières:__ {rivers}'\n",
    "md(riverx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8565c8-03f3-40b8-bb37-80afa76212db",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Utilisation des sols: (m² utilisé)/(m² disponible)\n",
    "\n",
    "L'utilisation du sol des sites étudiés en par rapport à l'utilisation du sol d'autres cantons sur le lac. \n",
    "\n",
    "Chaque lieu d'inventaire est géolocalisé au centre d'un hexagone de 3 000 mètres. L'ampleur des attributs de l'occupation et de l'utilisation des sols à l'intérieur de cet hexagone est calculée. Cette procédure peut améliorer le choix des lieux d'échantillonnage. Les résultats ont déjà été utilisés pour identifier les caractéristiques d'un lieu d'inventaire qui peuvent conduire à l'accumulation de certains déchets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7317f264-93ba-42ef-8562-58ce651da694",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 5, figsize=(7,6), sharey=True)\n",
    "\n",
    "valais_locs = this_canton.slug.unique()\n",
    "region_locs = cantons_voisine.slug.unique()\n",
    "\n",
    "index_order = [\n",
    "    'Siedl',\n",
    "    'infrastructure', \n",
    "    'Wald',\n",
    "    'Obstanlage',\n",
    "    'Undefined',\n",
    "    'recreation',\n",
    "    'Reben',\n",
    "    'Stadtzentr',\n",
    "  \n",
    "]\n",
    "\n",
    "ax_s = {\n",
    "    'Siedl':[0,0],\n",
    "    'infrastructure':[0,1], \n",
    "    'Wald':[0,2],\n",
    "    'Obstanlage':[0,3],\n",
    "    'undefined':[0,4],\n",
    "    'recreation':[1,0],\n",
    "    'Reben':[1,1],\n",
    "    'Stadtzentr':[1,2],\n",
    "}\n",
    "\n",
    "l_used = land_use[land_use.use.isin(land_use_of_interest)]\n",
    "v_land_use = land_use[land_use.slug.isin(valais_locs)].copy()\n",
    "r_land_use = land_use[land_use.slug.isin(region_locs)].copy()\n",
    "\n",
    "no_streets = [x for x in land_use_of_interest if x != \"Strasse\"]\n",
    "\n",
    "for i, name in enumerate(ax_s.keys()):\n",
    "    \n",
    "    row = ax_s[name][0]\n",
    "    column = ax_s[name][1]\n",
    "    ax = axs[row, column]\n",
    "    sns.stripplot(data = v_land_use[v_land_use.use == name], y = \"scaled\", color=\"magenta\", zorder=20, size=6, ax=ax)\n",
    "    sns.boxplot(data= r_land_use[r_land_use.use == name], y=\"scaled\",  ax=ax, color='slategray')\n",
    "    ax.set_xticks([0], [land_use_names[name]])\n",
    "    ax.set_ylabel(\"proportion\")\n",
    "\n",
    "axs[1, 4].axis('off')\n",
    "a_space = axs[1, 3]\n",
    "a_space.axis('off')\n",
    "h = [ Line2D([0], [0], marker='o', color='w', label='Scatter', markerfacecolor='magenta', markersize=10), Patch(facecolor='slategray', edgecolor='black', label='Color Patch')]\n",
    "l = [canton, cantons_avoisinante ]\n",
    "a_space.legend(h,l, bbox_to_anchor=(0,1), loc=\"upper left\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf84ded-25e9-4cd5-9265-f75bd0a2d831",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Résultats des inventaires: pcs/m\n",
    "\n",
    "La densité est calculée en additionnant tous les objets trouvés pour chaque jour et lieu d'échantillonnage. La somme pour chaque échantillon est ensuite divisée par la longueur du rivage, de la rive ou du tronçon de sentier. Pour un exemple, voir le rapport fédéral sur le calcul des lignes de base. [Baselines](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/baselines.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70cae240-e3f1-4da4-9081-7212867684e5",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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338Xs2bM77QobqCxmI9w+f9KuJo7nYTF3f/p8d7399ttoaWnBBRdcAKfTGbft1FNPxRNPPIHLL78ckydPhsViwZ/+9CdcccUV+OKLLxJmPI0YMQJbt27FunXrMHToUNjt9pRT/m+55Rb8+te/RnV1NU477TTwPI9vvvkGGzZswB133IGnn34aqqpGP/fZZ5+F2WzG8OHDe73Pc+bMwYIFCxAOh3HfffelfJ7dbse1116L66+/HhzH4YgjjoDH48GqVatgtVpTBjiR11111VXQNA2//OUv4Xa7sXr1athsNpx33nm45ZZbMHHiROy///4IhUJ4++23sd9++/V63wghPUNjZtIkCAL+/ve/A0B0WmtE5N/3338/BCHzd+E9MWfOHKxfvx7Tpk3DsGHDUj6P4zi8++67OOqoo3DhhRdizJgxOOOMM7B7925UVVV1+brDDz8cc+fOxejRo3HGGWdg27ZtKe+UBzpRFFHktIPrsKggx/Moctohipm/h3jiiSdwzDHHJAQygN4ys27dOnz11VcoKirCc889h3fffTc6PXnBggUJz//Vr36FI488EqWlpXFTmDs69thj8fbbb2PZsmU45JBDcOihh+Lee++NBisFBQV4/PHHcdhhh2H8+PH48MMP8Z///AfFxcW93ufTTjsNTU1N8Pv9Cdl+O7r99tuxYMEC3H333Rg7diyOPfZYvPfee122Qt5+++245ZZbsGjRIuy333449thj8Z///CfaXWUwGDB//nyMHz8ehx9+OARBwIsvvtjrfSOE9AzHkjUrDCButxtOpxMulyshUVYwGMTWrVsxcuRImEymtN7/9ddfx5VXXhk3GLi6uhr3339/0nEEZPBQFAX+QAiqpkLgBVjMxj4JZEj+yESdQ8hg0dn1uyOqWXvp5JNPxqxZs/Dxxx+jtrYWlZWVmDZtWtZbZEj2iaIIh51OMUII6WtU02aAIAiYPn16totBCCGEDEo0ZoYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJjJU9OnT8e8efOyXQxCOkXHKSGkP1AwkwGqxrDuhyA+XOPDuh+CULUBvUIEyVHnn39+l2sU9YWZM2dCEAR89tln/f7ZhBACUAbgXlv1tR8Pv9KChlY1+lhpgYDLTivE4Qdbslgykm1KOISg1wVNkcGLEkw2J0RD5lfMzqaamhp8+umnuPzyy/HEE0/g0EMPzXaRCCGDELXM9MKqr/1Y8HhjXCADAA2tKhY83ohVX/v7pRzPPfccJk2aBLvdjoqKCpx11lmor6+Pe87GjRtxwgknwOFwwG63Y9q0afj555/7pXyDUdDTiuYdP8DbuBv+1gZ4G3ejeccPCHpas1amTZs24fjjj4fNZkN5eTnOOeccNDY2Rre/+uqrGDduHMxmM4qLi3HMMcfA5/N1+p5PPfUUfv3rX+OSSy7BSy+91OXzR4wYgdtvvx1nnXUWbDYbqqqq8OCDD8Y9x+Vy4fe//z3KysrgcDhw1FFHYf369dHt69evx5FHHgm73Q6Hw4GJEyfiyy+/TOMbIYQMFBTMpEnVGB5+paXT5zz8aku/dDmFw2HcfvvtWL9+Pd58801s3boV559/fnT7rl27cPjhh8NkMuGjjz7C2rVrMXfuXCiK0udlG4yUcAju+h3Q1PggV1NVuOt3QAmH+r1MtbW1OOKII3DQQQfhyy+/xPvvv489e/bgt7/9bXT7mWeeiblz5+K7777DihUrcPLJJ4Ox1McvYwxPPfUUzj77bOy7774YPXo0Xn755S7Lcvfdd2P8+PH46quvMH/+fFx11VVYtmxZ9D1POOEE1NXV4d1338XatWsxYcIEHH300WhubgYAzJkzB0OHDsWaNWuwdu1a3HjjjZAkKQPfEiEkX1E3U5o2/BRKaJHpqKFFxYafQjhotKlPyzJ37tzo/++111544IEH8Itf/AJerxc2mw0PP/wwnE4nXnzxxWilP3r06D4t02AW9LoSApkITVUR9LpgKyrr1zI98sgjmDBhAhYuXBh97Mknn0R1dTV++OEHeL1eKIqCk08+GcOHDwcAjBs3rtP3/OCDD+D3+3HssccCAM4++2w88cQT+N3vftfp6w477DDceOONAPTj8JNPPsF9992HGTNmYPny5diwYQPq6+thNOpdcn/729/w5ptv4tVXX8Xvf/971NTU4LrrrsO+++4LABg1alR6X8oAE5YZPAEVqgIIImA3CzBIXLaLRUi/oJaZNDW5Og9kevq83vj6668xa9YsDB8+HHa7PbqCd01NDQBg3bp1mDZtGt299hNNkXu1vS+sXbsWy5cvh81mi/5FgoGff/4ZBx54II4++miMGzcOp512Gh5//HG0tHTe8vjEE0/g9NNPhyjq90RnnnkmPv/8c2zevLnT102ZMiXh39999120nF6vF8XFxXFl3bp1a7Rb9Oqrr8aFF16IY445BnfeeSd1lwLw+FTU1MloatXQ6tXQ1Kqhpk6Gx9f39Q8huYCCmTQVO4WMPi9dPp8PM2fOhM1mw3PPPYc1a9bgjTfeAKB3PwGA2Wzu0zKQeLzYedDY1fa+oGkaTjzxRKxbty7u78cff8Thhx8OQRCwbNkyvPfeexg7diwefPBBjBkzBlu3bk36fs3NzXjzzTfxj3/8A6IoQhRFDBkyBIqi4Mknn+xx+TiOi5azsrIyoZybN2/GddddBwBYsGBBdAzYRx99hLFjx0aP+cEoLDPsaVbRsUdbY8CeZhVhmWZXkoGPupnSNG4fI0oLhE67mkoLBYzbp29nr3z//fdobGzEnXfeierqagBIGAw5fvx4LFmyBLIsU+tMPzDZnPC37Ena1cQLAkw2Z7+XacKECXjttdcwYsSIaEtKRxzH4bDDDsNhhx2GW265BcOHD8cbb7yBq6++OuG5//rXvzB06FC8+eabcY9/+OGHWLRoEf7617+m/JyOU7g/++yzaCvRhAkTUFdXB1EUMWLEiJT7M3r0aIwePRpXXXUVzjzzTDz11FP4zW9+08k3MHB5AomBTITG9O3FElX1ZGCjlpk0CTyHy04r7PQ5l51aCIHv2z7rYcOGwWAw4MEHH8SWLVvw1ltv4fbbb497zuWXXw63240zzjgDX375JX788Uc8++yzXXYHkPSIBiMcZdXghfhWOV4Q4Cir7tPp2S6XK6FVo6amBpdddhmam5tx5pln4osvvsCWLVuwdOlSzJ07F6qq4vPPP8fChQvx5ZdfoqamBq+//joaGhqw3377Jf2cJ554AqeeeioOOOCAuL+5c+eitbUV77zzTsoyfvLJJ1i8eDF++OEHPPzww3jllVdw5ZVXAgCOOeYYTJkyBbNnz8Z///tfbNu2DatXr8bNN9+ML7/8EoFAAJdffjlWrFiB7du345NPPsGaNWtSlnMwULsYx9/VdkIGAgpmeuHwgy1YcFEJSgviL1qlhQIWXFTSL3lmSktL8fTTT+OVV17B2LFjceedd+Jvf/tb3HOKi4vx0Ucfwev14ogjjsDEiRPx+OOPUytNHzLZC1BUPRq2kipYCkphK6lCUfVomOwFffq5K1aswMEHHxz3d8stt6CqqgqffPIJVFXFscceiwMOOABXXnklnE4neJ6Hw+HAqlWrcPzxx2P06NG4+eabcc899+C4445L+Iy1a9di/fr1OOWUUxK22e12zJw5E0888UTKMl5zzTVYu3YtDj74YNx+++245557ooOIOY7Du+++i8MPPxxz587F6NGjccYZZ2Dbtm0oLy+HIAhoamrCueeei9GjR+O3v/0tjjvuONx2222Z+xLzjNBFo0tX2wkZCDjW2dzLAcDtdsPpdMLlcsHhcMRtCwaD2Lp1K0aOHAmTKf0ZR6rGsOGnEJpcKoqdetdSX7fIEJKPRowYgXnz5g3aJQ4yVefECssMNXVy0q4mngOGVUg0q4nkpc6u3x1RzJ4BAs/1+fRrQghJxiBxKC8SEgYB8xxQXkTTs8ngQMEMIYTkObtVgNHAU54ZMmhRMEMI6Tfbtm3LdhEGLIPE0awlMmjRAGBCCCGE5DUKZgghhBCS1yiYIYQQQkheo2CGEEIIIXmNghlCCCGE5DUKZkiPhMNhLFy4MLrKMSGEEJJtFMyQHrn22muxYcOG6MKAhBBCSLZRMJOHzj//fMyePTtj77dixQpwHIfW1tZOn/faa6/h22+/xZIlS8BxlIyLEEJIbshqMLNgwQJwHBf3V1FREd3OGMOCBQtQVVUFs9mM6dOnY+PGjVkscXJhmaHJraC+WUGTW0FYHpjLXZ1yyin46KOPYDAYsl0UQgghJCrrLTP7778/amtro38bNmyIblu8eDHuvfdePPTQQ1izZg0qKiowY8YMeDyeLJY4nsenoqZORlOrhlavhqZWDTV1Mjw+NWtl2rRpE44//njYbDaUl5fjnHPOQWNjY9rvFw6Hcf3112PIkCGwWq2YPHkyVqxYEd3+9NNPo6CgAG+//TbGjBkDi8WCU089FT6fD0uWLMGIESNQWFiIK664Aqra/r0899xzmDRpEux2OyoqKnDWWWehvr4+uj3SYvThhx9i0qRJsFgsmDp1KjZv3pz2vhBCCBl4sh7MiKKIioqK6F9paSkAvVXm/vvvx0033YSTTz4ZBxxwAJYsWQK/34/nn38+5fuFQiG43e64v74SllnC4m4AoDFgT7OalRaa2tpaHHHEETjooIPw5Zdf4v3338eePXvw29/+Nu33/N3vfodPPvkEL774Ir755hucdtpp+NWvfoUff/wx+hy/348HHngAL774It5//32sWLECJ598Mt599128++67ePbZZ/HYY4/h1Vdfjb4mHA7j9ttvx/r16/Hmm29i69atOP/88xM+/6abbsI999yDL7/8EqIoYu7cuWnvCyGEkAGIZdGtt97KLBYLq6ysZCNGjGCnn346+/nnnxljjP38888MAPvqq6/iXnPSSSexc889t9P3BJDw53K5Ep4bCATYpk2bWCAQSKv8jS6Zbd4eSvnX6JLTet+unHfeeWzWrFlJt/35z39mM2fOjHtsx44dDADbvHlz0tcsX76cAWAtLS0J23766SfGcRzbtWtX3ONHH300mz9/PmOMsaeeeooBYD/99FN0+8UXX8wsFgvzeDzRx4499lh28cUXp9yvL774ggGIviZSrg8++CD6nHfeeYcBSPs3IySbelvnEDKYuFyulNfvjrLaMjN58mQ888wz+O9//4vHH38cdXV1mDp1KpqamlBXVwcAKC8vj3tNeXl5dFsy8+fPh8vliv7t2LGjz8qvKr3b3hfWrl2L5cuXw2azRf8iM49+/vnnHr/fV199BcYYRo8eHfeeK1eujHs/i8WCvffeO/rv8vJyjBgxAjabLe6x2G6kr7/+GrNmzcLw4cNht9sxffp0AEBNTU1cGcaPHx/9/8rKSgCIex9CCCGDW1aXWD3uuOOi/z9u3DhMmTIFe++9N5YsWYJDDz0UABJmzTDGOp1JYzQaYTQa+6bAHQhdfHtdbe8LmqbhxBNPxF133ZWwLRII9PT9BEHA2rVrIQhC3LbYQEWSpLhtHMclfUzTNACAz+fDzJkzMXPmTDz33HMoLS1FTU0Njj32WITD4bjXxb5P5LePvA8hhBCSU+vFW61WjBs3Dj/++GN06nFdXV3cRbi+vj6htSZb7GYBLS4tYcwMAPCcvr2/TZgwAa+99hpGjBgBUez9z3vwwQdDVVXU19dj2rRpGSih7vvvv0djYyPuvPNOVFdXAwC+/PLLjL0/IYSQwSPrA4BjhUIhfPfdd6isrMTIkSNRUVGBZcuWRbeHw2GsXLkSU6dOzWIp2xkkDuVFAvgODUU8B5QXCTBIfZeLxeVyYd26dXF/NTU1uOyyy9Dc3IwzzzwTX3zxBbZs2YKlS5di7ty5cTOJktmwYUPCe44ePRpz5szBueeei9dffx1bt27FmjVrcNddd+Hdd99Nu/zDhg2DwWDAgw8+iC1btuCtt97C7bffnvb7EUIIGbyy2jJz7bXX4sQTT8SwYcNQX1+PO+64A263G+eddx44jsO8efOwcOFCjBo1CqNGjcLChQthsVhw1llnZbPYcexWAUYDD09AharoXUt2c98GMoA+bfnggw+Oe+y8887D008/jU8++QQ33HADjj32WIRCIQwfPhy/+tWvwPOdx66HH354wmOMMTz11FO44447cM0112DXrl0oLi7GlClTcPzxx6dd/tLSUjz99NP405/+hAceeAATJkzA3/72N5x00klpvychhJDBiWOMZS3D2xlnnIFVq1ahsbERpaWlOPTQQ3H77bdj7NixAPQL6W233YZHH30ULS0tmDx5Mh5++GEccMAB3f4Mt9sNp9MJl8sFh8MRty0YDGLr1q0YOXIkTCZTRveNEEI6ojqHkO7r7PrdUVaDmf5AwQwhJFdQnUNI9/UkmMmpMTOEEEIIIT1FwQwhhBBC8hoFM4QQQgjJaxTMQB9oTAghfY3qGkL6xqAOZiKZZf1+f5ZLQggZDCJ1Tcfs2ISQ3smpDMD9TRAEFBQURNf5sVgsnS6VQAgh6WCMwe/3o76+HgUFBQlLgxBCemdQBzMAUFFRAYAWLiSE9L2CgoJonUMIyZxBH8xwHIfKykqUlZVBluVsF4cQMkBJkkQtMoT0kUEfzEQIgkAVDSGEEJKHBvUAYEIIIYTkPwpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheS1ngplFixaB4zjMmzcv+hhjDAsWLEBVVRXMZjOmT5+OjRs3Zq+QhBBCCMk5ORHMrFmzBo899hjGjx8f9/jixYtx77334qGHHsKaNWtQUVGBGTNmwOPxZKmkhBBCCMk1WQ9mvF4v5syZg8cffxyFhYXRxxljuP/++3HTTTfh5JNPxgEHHIAlS5bA7/fj+eefz2KJCSGEEJJLsh7MXHbZZTjhhBNwzDHHxD2+detW1NXVYebMmdHHjEYjjjjiCKxevTrl+4VCIbjd7rg/QgghhAxcYjY//MUXX8RXX32FNWvWJGyrq6sDAJSXl8c9Xl5eju3bt6d8z0WLFuG2227LbEF7ISwzeAIqVAUQRMBuFmCQuGwXixBCCBkwstYys2PHDlx55ZV47rnnYDKZUj6P4+Iv/IyxhMdizZ8/Hy6XK/q3Y8eOjJW5pzw+FTV1MppaNbR6NTS1aqipk+HxqVkrEyGEEDLQZK1lZu3ataivr8fEiROjj6mqilWrVuGhhx7C5s2bAegtNJWVldHn1NfXJ7TWxDIajTAajX1X8G4Kywx7mlVoLP5xjQF7mlUYDTy10BBCCCEZkLWWmaOPPhobNmzAunXron+TJk3CnDlzsG7dOuy1116oqKjAsmXLoq8Jh8NYuXIlpk6dmq1id5snkBjIRGhM304IIYSQ3stay4zdbscBBxwQ95jVakVxcXH08Xnz5mHhwoUYNWoURo0ahYULF8JiseCss87KRpF7RFV6t50QQggh3ZPVAcBduf766xEIBHDppZeipaUFkydPxtKlS2G327NdtC4JXXyzXW0nhBBCSPdwjLEUnSEDg9vthtPphMvlgsPh6LfPDcsMNXVy0q4mngOGVUg0ZoYQQghJoSfX76znmRmoDBKH8iIBfId4heeA8iKank0IIYRkCnV29CG7VYDRwFOeGUIIIaQPUTDTxwwSh2KJvmZCCCGkr1A3EyGEEELyGgUzhBBCCMlrFMwQQgghJK9RMEMIIYSQvEbBDCGEEELyGgUzhBBCCMlrFMwQQgghJK9RMEMIIYSQvEbBDCGEEELyGgUzhBBCCMlrFMwQQgghJK9RMEMIIYSQvEbBDCGEEELyGgUzhBBCCMlrFMwQQgghJK9RMEMIIYSQvEbBDCGEEELympjtAhBCCBl8wjKDJ6BCVQBBBOxmAQaJy3axSJ6iYIYQQki/8vhU7GlWobH2x1pcGsqLBNitQvYKRvIWBTOEEEK6rbctKmGZJQQyAKAxYE+zCqOBpxYa0mMUzBBCCOmWTLSoeAKJgUyExvTtxRJdmkjP0BFD8hr1uxPSPzLVoqIqvdtOSDIUzJC8Rf3uhPSfTLWoCF08pavthCRDU7NJXurqLjEsp6h1CSFpyVSLit0sgE/RgMNz+nZCeoqCGZKXunOXSAjJnEy1qBgkDuVFiQENzwHlRdRNTNJDDXokL1G/OyH9y24W0OLSkt5E9LRFxW4VYDTwNN6NZAwFMyQvUb87If0r0qLSsXs33RYVg8TRrCWSMXQkkbyUybtEQkj3UIsKyVUUzJC8lOm7REJI91CLCslFdESSvEV3iYTEo7xLZLCiYIbkNbpLJERHeZfIYEZTswkhJM9R3iUy2NEtbR6hJmRCSDK03hEZ7OjozhPUhEwISYXyLpHBjrqZ8gA1IRNCOkN5l8hgR8FMHuiv1P1hmaHJraC+WUGTW6EgiZA8QesdkcGO4vU80B9NyNSNRUj+orxLZLBLO5j54osvsGLFCtTX10PTtLht9957b68LRtr1dRNyV91YRgNPlSEhOY7yLpHBLK3L4MKFC3HzzTdjzJgxKC8vB8e1nyyx/08yo69T99NMCEIGBsq7RAartI76v//973jyySdx/vnnZ7g4JJm+bkKmmRCEEELyWVrBDM/zOOywwzJdFtKJvmxCppkQhBBC8llas5muuuoqPPzww5kuC+mCQeJQ7BBRViSi2CFmrC+cZkIQQgjJZ2ndc1977bU44YQTsPfee2Ps2LGQJClu++uvv56RwpH+QTMhCCGE5LO0gpkrrrgCy5cvx5FHHoni4mIa9DsA0EwIQggh+SqtYOaZZ57Ba6+9hhNOOCHT5SFZRDMhCCGE5KO0xswUFRVh7733znRZCCGEEEJ6LK1gZsGCBbj11lvh9/szXR5CCCGEkB5JK5h54IEH8N5776G8vBzjxo3DhAkT4v6665FHHsH48ePhcDjgcDgwZcoUvPfee9HtjDEsWLAAVVVVMJvNmD59OjZu3JhOkQkhhBAyQKU1QGL27NkZ+fChQ4fizjvvxD777AMAWLJkCWbNmoWvv/4a+++/PxYvXox7770XTz/9NEaPHo077rgDM2bMwObNm2G32zNSBkIIIYTkN44x1u2lkX/44QeMHj26L8uDoqIi3H333Zg7dy6qqqowb9483HDDDQCAUCiE8vJy3HXXXbj44ouTvj4UCiEUCkX/7Xa7UV1dDZfLBYfD0adlJ4QQQkhmuN1uOJ3Obl2/e9TNdPDBB2O//fbDDTfcgE8//bRXhexIVVW8+OKL8Pl8mDJlCrZu3Yq6ujrMnDkz+hyj0YgjjjgCq1evTvk+ixYtgtPpjP5VV1dntJyEEEIIyS09CmaampqwePFiNDU14Te/+Q3Ky8txwQUX4K233kIwGEyrABs2bIDNZoPRaMQf/vAHvPHGGxg7dizq6uoAAOXl5XHPLy8vj25LZv78+XC5XNG/HTt2pFUuQgghhOSHHgUzJpMJJ554Iv7v//4PtbW1eOONN1BaWoobb7wRxcXFmDVrFp588knU19d3+z3HjBmDdevW4bPPPsMll1yC8847D5s2bYpu75iQjzHWaZI+o9EYHVAc+SOEEELIwJXWbCZADzKmTp2KO++8E5s2bcK6detw+OGH4+mnn0Z1dXW3124yGAzYZ599MGnSJCxatAgHHngg/v73v6OiogIAElph6uvrE1prCCGEEDJ4pR3MdDRq1Chcc801WLVqFXbv3h031qUnGGMIhUIYOXIkKioqsGzZsui2cDiMlStXYurUqZkqNiGEEELyXFrBzJIlS/DOO+9E/3399dejoKAAU6dOxfbt21FcXIxRo0Z1+T5/+tOf8PHHH2Pbtm3YsGEDbrrpJqxYsQJz5swBx3GYN28eFi5ciDfeeAPffvstzj//fFgsFpx11lnpFJsQQgalsMzQ5FZQ36ygya0gLHd7EisheSGtYGbhwoUwm80AgE8//RQPPfQQFi9ejJKSElx11VXdfp89e/bgnHPOwZgxY3D00Ufj888/x/vvv48ZM2YA0IOkefPm4dJLL8WkSZOwa9cuLF26lHLMEEJIN3l8KmrqZDS1amj1amhq1VBTJ8PjU7NdNEIypkd5ZiIsFgu+//57DBs2DDfccANqa2vxzDPPYOPGjZg+fToaGhr6oqxp6ck8dUIIGUjCMkNNnQwtSS3Pc8CwCgkGKfWECkKyqc/yzETYbDY0NTUBAJYuXYpjjjkGgD7bKRAIpPOWhBBCMswTUJMGMgCgMX07IQNBWssZzJgxAxdeeCEOPvhg/PDDDzjhhBMAABs3bsSIESMyWT5CCCFpUpXebSckX6TVMvPwww9jypQpaGhowGuvvYbi4mIAwNq1a3HmmWdmtICEEELSI3Rxu9rVdkLyRVpjZvIJjZkhhAxWNGaG5LM+HzPz1FNP4ZVXXkl4/JVXXsGSJUvSeUtCCCEZZpA4lBcJ4DvEKzwHlBcJFMiQASOtYObOO+9ESUlJwuNlZWVYuHBhrwtFCCEkM+xWAcMqJBQX8Ciw8Sgu4DGsQoLdKmS7aIRkTFo9ptu3b8fIkSMTHh8+fDhqamp6XShCCCGZY5A4FEs0QIYMXGm1zJSVleGbb75JeHz9+vXRwcCk9yhrJyGEENK1tEL1M844A3/84x9ht9tx+OGHAwBWrlyJK6+8EmeccUZGCzhYeXwq9jTH54hocWkoLxKoeZgQQgiJkVYwc8cdd2D79u04+uijIYr6W6iqivPOO4/GzGRAWGYJgQygJ7na06zCaOBp4B4hhBDSpldTs3/88Ud8/fXXMJvNGD9+PIYPH57JsmVEPk7NbnIraGrVUm4vLuBR7KD+b0IIIQNXT67faV8Rn3jiCdx333348ccfAQCjRo3CvHnzcOGFF6b7lqQNZe0khBBCui+tYObPf/4z7rvvPlxxxRWYMmUKAH317Kuuugrbtm3DHXfckdFCDjaUtZMQQgjpvrS6mUpKSvDggw8mLF3wwgsv4IorrkBjY2PGCthb+dTNFJYZPAEV4TDD7iYZJkmAJMaPjaGsnYQQQgaDPu9mUlUVkyZNSnh84sSJUBTqA0lHx9lLJklAQ4sCp42H1azPXqKsnYQQQkiitPLMnH322XjkkUcSHn/ssccwZ86cXhdqsEk2e0kSOVSWiBB4DlYzR1k7CSGEkBR6NQB46dKlOPTQQwEAn332GXbs2IFzzz0XV199dfR59957b+9LOcB5AonTsAGA4ziYjPofzV4ihBBCkkvrCvntt99iwoQJAICff/4ZAFBaWorS0lJ8++230edxHHWHdAfNXiKEEELSl1Yws3z58kyXY1Cj2UuEEEJI+tIaM0Myy24WwKdoxOI5fTshhBBCkqNgJgcYJA7lRYkBDc1eIoQQQrpGHRg5wm4VYDTw8ARUqIretWQ3UyBDCCGEdIWCmRxikDgUS/STEEIIIT1B3UyEEEIIyWsUzBBCCCEkr1EwQwghhJC8RsEMIYQQQvIaBTOEEEIIyWsUzBBCCCEkr9E84BwQlhnllyGEEELSRMFMlnl8KvY0x6+a3eLSUF4kwG6lZQwIIYSQrlA3UxaFZZYQyACAxoA9zSrCMkv+QkIIIYREUTCTRZ5AYiAToTF9OyGEEEI6R8FMFqlK77YTQgghhIKZrBK6GLHU1XZCCCGEUDCTVXazAD7FpCWe07cTQgghpHMUzGSRQeJQXpQY0PAcUF5E07MJIYSQ7qCOjCyzWwUYDTzlmSGEEELSRMFMDjBIHIol+ikIIYSQdFA3EyGEEELyGgUzhBBCCMlrFMwQQgghJK/RQA1CsowWGiWEkN6hYIaQLKKFRgkhpPeom4mQLKGFRgkhJDMomCEkS2ihUUIIyQwKZgjJElpolBBCMoOCGUKyhBYaJYSQzKBghpAsoYVGCSEkM7IazCxatAiHHHII7HY7ysrKMHv2bGzevDnuOYwxLFiwAFVVVTCbzZg+fTo2btyYpRITkjm00CghhGRGVoOZlStX4rLLLsNnn32GZcuWQVEUzJw5Ez6fL/qcxYsX495778VDDz2ENWvWoKKiAjNmzIDH48liyQnJDLtVwLAKCcUFPApsPIoLeAyrkGhaNiGE9ADHGMuZ+Z8NDQ0oKyvDypUrcfjhh4MxhqqqKsybNw833HADACAUCqG8vBx33XUXLr744i7f0+12w+l0wuVyweFw9PUuEEIIISQDenL9zqkxMy6XCwBQVFQEANi6dSvq6uowc+bM6HOMRiOOOOIIrF69Oul7hEIhuN3uuD8yeIVlhia3gvpmBU1uhXK3EELIAJQzwQxjDFdffTV++ctf4oADDgAA1NXVAQDKy8vjnlteXh7d1tGiRYvgdDqjf9XV1X1bcJKzPD4VNXUymlo1tHo1NLVqqKmT4fFR/hZCCBlIciaYufzyy/HNN9/ghRdeSNjGcfEDIRljCY9FzJ8/Hy6XK/q3Y8eOPikvyW2UXZcQQgaPnMhkccUVV+Ctt97CqlWrMHTo0OjjFRUVAPQWmsrKyujj9fX1Ca01EUajEUajsW8LTHJed7LrFks5cfgTQgjppay2zDDGcPnll+P111/HRx99hJEjR8ZtHzlyJCoqKrBs2bLoY+FwGCtXrsTUqVP7u7gkj1B2XUIIGTyyemt62WWX4fnnn8e///1v2O326DgYp9MJs9kMjuMwb948LFy4EKNGjcKoUaOwcOFCWCwWnHXWWdks+qAWlhk8ARWqomeptZtzLycKZdclhJDBI6tV+iOPPAIAmD59etzjTz31FM4//3wAwPXXX49AIIBLL70ULS0tmDx5MpYuXQq73d7PpSWAPqi241iUFpeG8iIhp3Kj2M0CWlxa0q4myq5LCCEDS07lmekLlGcmc8IyQ02dnDJAGFYh5VQLTbLAK5JdN5cCL0IIIYl6cv2mxnbSbfk2qNZuFWA08DnfJUYIIaR3cufKQ3JePg6qNUhcTgVYhBBCMi9n8syQ3EeDagkhhOQiuvyQpJLNWKJBtYQQQnIRBTMkQWczlsqLhJSDamksCiGEkGygYIbE6WoZgGEVEoZV0KBaQnJVPuSBIiTTKJghcbo1Y8kh0qBaQnJQvuSBIiTTaAAwiZOPM5YIIbS4KhncKJgZ4Bhj8AZUBIJat55PM5YIyU/daVUlZKCiS9MAtqdZwSfrA1i+1gezkccpR9oxepgBhY7Uzc00Y4mQ/EStqmQwo2AmR/V2EF9dk4J59+5BfUv73diX3wVx5EQLLv9tIQrtyYMSg8T1yYwlGpRISN+iVlUymNHhnYN6O4hPljW8+pEnLpCJWL7Wj99Mt6UMZoDMLwNAgxIJ6XvUqkoGMxozk2MyMYiv1ath2ee+lNvf+zT1tgiDxKHYIaKsSESxQ+xViwwNSiSk70VaVfkOp2pf5YEKywxNbgX1zQqa3AqdyySrqGUmx2RqMUetk8XQVbX/Kp18W5ySkHzWX4urUmsryTXUMpNjMjGIz2nlceRES8rtv5pi62Gp0keDEgnpX5lqVU2FWltJLqJgJsdkYhCfwcDjjJkOFNgSf95DxhoxrEJKs3Q9R4MSCckf3ek6oingJBfRpSTHZGoQX1WJhH/cUIH3Vnux6ms/TEYepxxpw8FjTCjqZGp2ptGgRELyQ3e7jqi1leQiCmZyTCanRlcUizj3eCdmT7eD5wFnFvqy+2qqNyEkc7rqOjIa+Oi5Sq2tJBfRYZeDMjmITxC4Tqdh94f+GpRICElPTwbqU2tr7huMeb0omMlRBokbULN8Btr+EDKQ9KTriFpbc9tgnWlGVxdCBqjBeHdG0tPTriNqbc1NPekuHGgomCF5hy7SXRusd2ckPel0HVFra+4ZzHm9BuZekQGLLtJdG8x3ZyRRd4J/6joaGAbzTDMKZkjeoIt09wzmuzMSryfBP3Ud5b/BPNNsAO8aGWjoIt09g/nujLRLJ/inrqP8NphnmlEGYJI36CLdPYP57oy0o0y9g09/LzaaS6haI3mDLtLdM5jvzki7vgz+aRB+7hqs3YVU/ZO8QRfp7qHBnATou+CfBuHnvsHYXTi49jYPuLwqWjwqfAEGu4VHgZ2HgyoIAO0X6bomBb4Qg6oyCAIHq5FDeVHmVwfOZ4P17oy064vgnwbhk1xFwUwO2dOk4I6nGrFxSzj62C/GmnDNnCKUFtJPFaGoQFjWoKmAqjEYRQr2khmMd2eDSVddPX3RQkeD8EmuoqMuR7R6VNz+ZCM2bQ3HPf7FpiAefLkF159bBNsg70aJ3BUKAocCW/yhS3eFZDDpbldPplvoaBA+yVUUzOSIVo+aEMhEfPJNAK0ebdAHM3RXSEjnXT31LSp4noOsMpgNXDTAz9R5QYPwSa6iQy9HtHq1lNsYA3yB1NsHC7orJCR1UG+QAH+Q4Z5/NaFmj4K9h0j47QwHhpSIMBkzk4WDBuGTXEXBTI4otKeuBDgOsJkpJRDdFRKSPGg3iMBPO2QsfLop+tiWXTI+XOPHHX8owS/2N4PvmHwkDTRTjuQqukLmiAI7j/H7GJNuO+JgMwqSBDthmaHJraC+WUGTW0FYTtEHM0DYzYnJoCLorpAMFsmCdo7ncP+LzQmPawxY/GwzGl2ZS5BntwoYViGhuIBHgY1HcQGPYRUSTcsmWUXBTI5w2gT86fxiTNy3PaDhOODwg8249NRCWDu0zHh8KmrqZDS1amj1amhq1VBTJ8PjG7hZPQdzdktCIpIF9W6vCn8w+c1Mq1eDy5PZesEgcSh2iCgrElHsGDhpEQbbDeJAQg3zOaSsSMQtF5SgxavBH9Bga8sz03Hg72DO9WA08HDYNHh8GjQADjOPwgFUmRLSlVRdPZ3hODo/ukLJAPMbBTM5xm7t+sQZrLN64isbvXL2+BksJg0GiSobMnh0nHItqxwsJi5p60yhnYfDRo3wnRnMN4gDBR3heWgwzurpqrKh5mAy2MR29ZQVirj27CJ0bIDhOeD6c4pR4qRgvzO0KGf+G3i374PAYJzVM1hbowjpDknkMHl/M/55YwVeXObCjjoFew814LSj7RhSKmZkJtNANhhvEAcaqv3z0GDM9UCVDSGdMxt5jKo24PqzixEM60nzDAZqfO+OwXiDONDQkZ6HBuOsHqpsCOkeo4GH0yZQINMDlPYh/9ElIE/19arIXS1i198GY2sUIYNRNuoeSgaY/yiYyWN9tSpyLk5RpMqGkIEvm3VPX98gkr5FwQyJk8tTFKmyIWTgyoW6p69uEEnfo18tD7i9KmSVwWrmYerjfvBcnzVElQ0hA1Ou1z35KNeGC/QlOjJyWItHxYafQnhhqRutHhXj9zHizGMdGFIqQRL75oCkWUOE5J7BcFGiuiezcnG4QF+iYCZHeXwqlrzdirc+9kUfW/aFHyu+8uOBa8oxZnjyRSk7cnlVhMIMPA8UOYQu803QrCFCcktnF6WB1O1KdU/m5EKXXX+jwyNHNbm1uEAmQlaA+15owV2Xl8JpSx1d+4MaftoZxiOvtWLz9jAKHTxOP8aBYw6xoqiTbKA0a4iQ3NHZRWnHHgUcBwhC+0Upn++8B3rd05+ta4Oxy25g7c0Asv7HYMptP9SE4fVrnQYzG34KYf4/GqL/bnFr+Ofrrfj25xCumVMEszH5HR3NGiIkd6S6KDHGsKdZgdXCocAmRh/zhhg8O1RUlYo9XoA1211ZA7nu6e8un8HYZZfVrEqrVq3CiSeeiKqqKnAchzfffDNuO2MMCxYsQFVVFcxmM6ZPn46NGzdmp7D9rKsxMZ0tgtvkUvHgyy1Jt/1vfQANrSpq6mQ0tWpo9WpoatVQUyfD49PXH7FbBQyrkFBcwKPAxqO4gMewCikv7/YIyWepLjqBMIOiAVrbkkGywrC7QUFDi4pGt4bte+S4c7orHl/ndUJ/GYh1TzbWlct0l11YZmhyK6hvVtDkVnJyLbysBjM+nw8HHnggHnrooaTbFy9ejHvvvRcPPfQQ1qxZg4qKCsyYMQMej6efS9pz6f74kdftP9KQMmCZsK8RDiuf8jN8QQ27G1OH3hu3hCB0qBs6nlixi9gV9/AOjxCSGakuOqqqn6e8oN/01TcrULT27Zra/Ytlri3iOtDqnmwsYpnJjMa5Euh2JavdTMcddxyOO+64pNsYY7j//vtx00034eSTTwYALFmyBOXl5Xj++edx8cUX92dReyTdJsXY1xkkDhfNcuKxN11xz7FbeFxxWhHAgJo6OelnCLzecsNSnEA2M59020DtSyUkX6UaRyIIHHgAVpMQbaWJxbdVM905pwfj+Ir+lI0un0x12eXTQOKcPUK3bt2Kuro6zJw5M/qY0WjEEUccgdWrV6cMZkKhEEKhUPTfbre7z8saK90fP/I6VWMIhBk8foZJ+5lwwN5GvLXKiyaXikPGmnDEBAuKHQJq9igpP8Nh5TB5fxM++zZx3I0kAnsNkSCnOIEGYl9qpmR7TAEZfFJdlKxGDnwhD0nkEAzHRzKRICeiuxdTxvS6R1UZBIGD2cCB4ziqE3opW7O0MpFkNJ8C3dwoRRJ1dXUAgPLy8rjHy8vLsX379pSvW7RoEW677bY+LVtn0v3xPQEVITm+ubjZrcEoAmf9ygG7hUehXZ9a3eRODGRiP8MX0PD73xRgy64G1Le0NwXyPDD/vGIEQxoEIXkPI01/TK6/BvBRwEQ6SnVRCoU17GlW42Yz8QCKnHzcmLvuXExlJbGrSuSBsiKR6oReyuYsrd4mGc2ngcQ5f5hyHQaOMMYSHos1f/58XH311dF/u91uVFdX91n5Okr3xw+HEysTAAgpwNZdMvYdYYjmiOnqMxQGCBqw+Ioy/LQzjHU/hFBeJGDqODNUxuAPsIQxM0DvTqyBfBHur6bWwZbkinRfsouSQdKDnFavAkXWoEFvkYkNZLpzTpskHg0toYS6R9GAhhYFIyqlTO3GoJTPs7TyKfdPDhUlXkVFBQC9haaysjL6eH19fUJrTSyj0QijsXsJ5fpCuj9+UNYSKpMIRdO3d/czHGYeHj9DUGUYWSVhzDADVA0Ihhl4jkN1uYCmDncKvTmxBvpFuD+aWvOpb5rkDoPEoaxQgtnAp32xDMoanDYezS49IIq+HoDTxiMoa7CaszpXJO/l67py+ZT7J2eDmZEjR6KiogLLli3DwQcfDAAIh8NYuXIl7rrrriyXLrV0f3yDyIMHkCye4du2d/czCh0iLCa9CVpW9CbkyLZIgGE1Z6YlZTBchPujqTWf+qZJ7unNxVJVAKtZgEHi4Quq0FR9AHGklSeXuhLyWT6uK5dPrUpZ/Wa9Xi9++umn6L+3bt2KdevWoaioCMOGDcO8efOwcOFCjBo1CqNGjcLChQthsVhw1llnZbHUnUv3xzcZORQ5k98dFTl5mIztr+vOZ0SaoFNVbpk6sQbDRbg/mlrzqW+a5KaentORrmGPX0WrV4XVJEQT8MXKpa4E0v/ypVUpq4fpl19+iSOPPDL678hYl/POOw9PP/00rr/+egQCAVx66aVoaWnB5MmTsXTpUtjt9mwVuVvS+fHtZgF2i5b07sgocQktOt35jP64ExgMF+H+aGrNp75pkh86G8cW2zXMGODzM3i8CoqcPKwxx3OudSWQ/pcv4yE5xlJlIxkY3G43nE4nXC4XHA5HtovTqWRjT2K7hnJRk1tBU2uKwT4Aigt4FDvy/0rc179NWGYJeYNiP2dYhZSTFQjJTZ0dr0YDn3CsRWYzaRpQUSJCErmcr3tI5nUMXDhA7y3I0jWpJ9fv/L/KDCD50pwXK58GiPVGX/82+dQ3TXJbV+PYHLbE81USOVSVigiEGQwGoMjB53zdQzKrYwDMGENtowKnLb61LlfHQ1Iwk2PybZDYYLoI9/Vvk4/BLMk9XY5j82nQ77njcRwHi5GD0zIwWlNJ9yULgANhBlnVW2YMUnzuolwcD5k7JSEZkY3+TboIZ06+BbMku5Kd712NU0vdKayj8VmDT7IAOLL+lwbAF1QTBofn2nhIOmwHkGzme+nPi3C+DEgjpC+lOt9Nhs5fF8lDNdC7hkn3JQtMYjNLa0nWlMy1oDfHikPSNRjyvQDJK/DmVhVmIweDxPc6uKFAieSDzs73QIhBVeMvRhEd81AN9K7hfOcLaKhvlREOAQYjUFYg9UkCw2SBidnAQeT1pK18h/g2F4NeCmb6WH9dHAdDvpdkFXiyWRjptkYN9EzGZODo7Hxn4GC3AsEQ0s5DRbKvrimM77fJkGNaRbbvVrDvCAkVxV00v/VQsokcHMehrEhEQ4sSt3Bprga9+X11y3H9eXEcDPleOlbgjMWvZxXp1+1Oa1THINMkJaaDBwZeyxYZGLo6nw0ij7JCIet5qEh6fAEtIZABAFkFvt8mw24RM9pCk2oih1HicOAoIzQg54NeOpIzIFnrC4B+vTgOhqRrHSvwQJjFrWcV26/b6QrlSYLMYEiBqrG4KYjdeS8ysOVqt2N3zveBHKzk6u+SKfWtiYFMhKzq20eak69BmO53k+8TOQbmkd6PUg7CM6Jfu336M9+L168iJDOYDHy/LkDXsQKPjLaP6Nivm+zuNdVYA1llaHUnTkHs7L3IwJbL3Y6DJb9TMn35u+RKkBQOpbe9t99NPgfA+VnqHNHZILy6RgV2qwCOS34ipLo4pnsy9Ue+F69fxc+7ZDzzjgs7GxSMqJRw3glOjKiQYOllUNOd/e5YgccOcOSBuH5dIPnda6qxBoLApZyCmOq9yMCVDwPqTQagoUUFJ3AwGzhwHJez4xkypS9/l84CAZOBQ5NbQ7NLBccDRQ4BxU4BYpJB1plgSN7o0un2fDhm+xJV0b3Q6aBb6N0gFmPygyfphbaHUXVjq4LttTI2bgmjslTEAXsZMbRMhC+kZfzOIiRr+OhLP+5/sSX6WEOLijWbgvjzBcU4/CBL0tkT3dHd/e4YsEVG22uavhhnbItKqrvTVEFk9L2SNO0O9DtdkiiXB9THni8Gg76WmyLrA+DLCgf2shd99bt0Fgg0u1Vs2hbGgy+1IBjWn2Azc7jxvGJMGGOCyZj51umyAgnbdytJu5okQd/eUS4fs/1h4O5ZPwiHGfwhDarKIMTcHQF6K0E4nDw9VbKLY7KTyShxABh2NyqQWlU4bfrdAADUNiq47sF67G5Q4p5/1xWl2H+kMe3AIpUWt4ZHXmtNuu3vL7Zg/5FGlBVlthJJdjfRsV/Xaubg8bH4VppO7k5TtbBERu4HO9QeA/1OlySXqwPqO54vkshFWxKDXXRN5JJ0W6D76ndJFQjwPNDi0XD3s81xj3sDDLc82oj/u6kCI6oyO7MIAKxmHvuOkBIGAUsCsO+I5NOzc/WY7S8UzKTJ41Oxu0lGs6v9DBB5oKxInx4siRycVgHBcOrpkXHv1+FkMohAXZOC+15oRn2LfjQPLRNx43nFqC4T8cBLzXGBDACEZIY//aMBT9xcifI0AovONLkUhOTkYb/bp6HVq6YVzKRzN9GxX7cnFWNnYw2MEocRlSYE5cy3bJH8kqsD6vvy7rvf0kj0YlxHX/0uqS70BhF44b/upNs0Bryx0oPLTytKOs6utyqKDbBbxG7nmcnVY7a/DPDd6xuRuyOTJICHEk0PrmhAfbOCqlIRAs+hrEhvCuxOBRF7MnGcHpjc9EhD3Am/s17BVfftwaPzK/DFpmDSsvmD+urLmQ5meL7zk7Wr7al0526iq0o21aC1VK/rbGyR1dy/g5pJbsrVAbbdvfvuaWDSX4Odezuuo69+l1QXek0DdjWk/tK37VYQllmfBDOA3kKTatZSR7l6zPYXCmbSELk7kkQORU5eXyK9bZuiAaEww8gqMXpSdudOKfZkMho4PPUfV9KDUlb0Lh+W4u4MAFy+rlZf6blihwCbmYM3kPjBpYUCHFYeTW6lx3d1Xd0thGUNNXVajyvZrirnfJ6CSPperi6g2p27754GJh0DDMYYAmEGVWXwBzXsPZTrcYDvD2pobFWx8ms/mt0qphxgxl5DJPBC72Z59tXvkioQ4HkOwytF7G5MHtCMqpZgNORGvZGrx2x/oWAmDbF3R1azAIOkD8LTVH16cHFBz+9mYk8mTWX4cYec8rn+oIZCO48WT/KgZa8qvUUok83GxU4BN/2uBDf9swFazMdKIvCn84oRCGoIxRS5u3d1nd1NqCpDIKRnNI3V1V1cd+/+BvJgONJ7uRj0dnX3bZJ41DYqPWr5iO26imTUjs3fJGtBjB5q7Had5g9qWPmVH3c/1z7O5N8rvRhRKeKOP5R2+trujOvoi98lVSCgKAxnH+fEpxsSW8IFHjjpcHufzWhKRy4es/2FavM0dLw7ih2EBwCmFDOYOhN7MvG8/v8dx8REbNsdxu9/U4C7nmlO2DZlnBnFTiHjzcaCwOGg0UY8cXMl3v7Ygy27ZYwZZsBxU23QNBYXyADdbzbu7G7CYuXgT96b1nlSvEE+qp9kTq4FvV3dfQfl5IEOoHdd72oMw2oU4i5ykQCiY0btCDncs6m9TS4Vf/tXYt20rVbBSx+4ccqRdgTDyV/b3XEdffG7pAoEFJXh1guLcd8LLXC3tXoXOwXMP78IFcW513WTa8dsfxl8e5wBkbujkMziWmSsJgFGiYPdLKTVKhI5mbxBFXOOdeDrzQ1Jnzd5nAVlhQJuvbAYj73RitomFVYTh1lH2DHrcBvMRh41dXLG8w0YDTyGV/D4w8mFCMsMBgOHVq+Kptb4D4ptpt7VyDCkxNB5P3iKSqTVo8KP1F1mqe7iMjmqP1eSaBES0dndd31z8oPbF1DR7NJQ4OAhW/TjN3JzEwkgOmbUjoh0DXXnJkBWGD79NpCyG/y/n/lxynQreI5P7NLJgXEdyQIBg8ThlwdasN9II1o9KjiOQ6GdR7EzdR6xXDcQ6zUKZtJgkDjYLMDObfF5APwBfRGwUDhxRdrutooYJA5FkgipmsMlpxTgsTdaoWrt266dU4SqEhEWE49J+5kwoqpUXyGX58BxgMurIdTJ3VkmWiYEgYO5rWm1Y2DQsZlalhlURe5y35NVIumOzs/UqP5czgBLBodUF51Ud9/Jjm1ZYdFxfbEpDCI3N5UlInhOS8ioDcQno+zqJsDjU+HyaXCl6P6O7E9NzTYUF4gw2oZBaas/c31chyBwKCsUUVbYP5fMFo+KYJhB4PXxiplMtTFQ6zUKZtIQlhm8fqCyRIy2QETyzHh8DC1uJeHg62mriN0q4MRf2nDYgWbs3KNAEIAhJSKKnPoYnbDMUNvYfkAqMRVRutmH0xFbeSZrpo7c1aXTIpTu6PxMjOof7Nk0Sfalc9FJduz7gio06KkjzIbEeiko6+/pD2pQFIZAWAPTAFEEqtpWogc6vwmInC/ggIn7GvHC0uTPGzNMxMcrl+HhB+7Cqv99ggLHkAHTMpAJvoCGTVtDeOS1VmyrlWE1c/jNdDtmHW6P5hjrjYFcr9Ec1DRExmRwHAeLkYfdIsBi5MFxHHwh/U4qmUirSHeZjDyqSiT8Yn8z9qoyQFaBxlYVXr8Kb7Dr7MOpZCLfQFhmaHIr8Ac0tHoUuH0qWr0q5JigKvaurqf7DrSPD+g467uru7h0XxerO+NuMi3yndY3K2hy61M+yeDU1UUn1bGR7NjX1PYcWJEbHMb0hJ8ev4pWt34RqyoRwHN6a4DBwMFs5OH2MvgCapc3AZHzRdOAkgIRY0cmJpLjOeCUaSH848HF2LVrF1b/72OUFYkodoh5ewFNJd1z+esfgrjhoQZsq9UHIfoCDM+958biZxvR6km/zomUZ/ueMLxBDTzHYDVzsJq46LHSV/Vaf6GWmTR01rKhqixpSvzuvDaZUFjD99vDuO+FZtTU6S+euK8Rl51WCEnUp2p31NPswz0VuWMMyXpLjMevwhtgsFk4eH0MhQ4BhrZp67H5F9JpEerJ6PyOTfKVJWLaCfD6O5vmQG36JenpzSD2jueMJAkIhxENZGSFYU+zDF+QQdMY3H4e/hCDwHEYWm6Ia13VoHddD6/sfJmE2PMhJDPceF4x/vupF/9e5YU3wLD/SBG/+WUID9x1HXbu3AkA+Pe//42zzjoLPD+w7qnTPZebWhU8/EpL0m1rNoXQ2KqiwN7zuiC2PC1uFQ6rgBaPhn+v8oLnORx9iAVOK4+QnN9ZgimYSYMgxg9yjV3KQBA4qKlqIfS8VaSmTsE199fHnRhrvw/hmvvrcd9V5ZCR+Fk9zT7cE5E7RlVr71IymwRIIkNY0SBJHMKKhqoSA8ym+EqqO/ueaoxAV2N8klUgPNdWgTjSmL3Vj9k0B3LTL0lPb4Pp2HMmLAvRCQGMMexulNHQrELR9NZTgwhs8YZhMfEYVi6hqjSx+7yrdoX47mY9eeeMSSqKhS3geAFfr/0Uc06+F3V1ddHnjRgxYsAFMr05l32htq66FL7bFsI+1T1bOqFjeUoLRTz+Zis+39g+TfQ/H3txzCEWnH2cM6+zBOdx0bOHg742Uuzg30gzrtXIwSgmv3j2ZLyGJ6BCUYAn3mpNeofW4tGw7ocgDhxlSlhmgOfQo+zDPRG5Y+w480EUORgNAnwBFSajgJCiwRzTi9mdfY9t8YnMEpMMwMhKA4ocqQ/VvggG+jObJk0lJx1lMpiOnc7tDWpxgYzDxkEU9RQIDc0qSgsEWExCwgK5XQVPyc4XxjuwY9u3+OMVlyd9zfnnn9/9ncgTkXPZZOCgMQYO+n9lpetzWRL0uiVVXeCw6mMle1KXxdYtBhHYXBOOC2QiPljjx1GTLJiwr7nb751rBlZY3A/Csj4zwGnj4748RQMaWhSUFAgYWiamPV7D41NRUyejqVVDIKRh07bUq8d9+V0QHc+Lvp4VEKnUks18UDWgxCkkrD7dnTJFAhKPX0VdowKXl8ETYGh2MXzzYwgt7tS1aabGt8T2c3sCKoqdfK/G3XTXYF8gjiSymxPHfEWkE0zbrQKGVUjgOD3DuM3CobhAgLltTBvH63WYL5j8ROoqeIodqyMrDK1eBbWNCo49bjZOPvnk+PLzPB5//HFUV1f3aB/ygsZglDi88z8Pbnu8EX/7VxO21cowGTkwxtDqVlOOo3HaBPzyoOTBhCQCIyoNqKmT4fF1f1xLx2Vy3lzpTfncN1d5oXWWWj7H0e1eD0UunMky/1pNAjSkn4UxckGXRIABaPVomH9eMbwBhlc/dOPnXTKK7AICYQ2+AEN5kQCTgUNI0cCDgwYGk8QjrOozqppcPV8GoCuRSi3VVEFB5FDlFGAwAFYj3+199wT0FpnYpSEiZBXYsUdp+847X9cqme4EA8m7qYAiJw/W9h59NetisC8QRxL1RWp6g8TBZubhsCWe/2YDD79fBc8lmZ7dzeDJbtUTzO3Yo0Dg9enjYRTj1jsewg033oyPV62A3WHHL395BMrKyuFwOHq8D7nOE2C44m974ItZ9mXjliYcOdGCU460weXToGrxeX4i9bHFpOfw+mmnHJcwVeCBBReWtNX7Wo+WmOjY/RcIpZ427wto0Vmx+ZiHhqrJHoq9MHbM/Bu7PZ0sjJ6ACkni8PHXfjz7nis6uLfAxuOGc4tgNvH4cYcMu4WHUeIwtFxAXZMKBqChRYbTxsNq5iC7NTS0KCgtFOMG4GZiDEakOdls4CC23c1FRGYvCTyHISWdDxjsSFXap5AmI6ssZRNtb4OBzrqpml0ahlX0bF96arAvEEeSS3ZTZJJ4BGUN9c1KjwbDR55nt/IJ5y2gdxMXF3Cwdhjn1pPgKSwzNLk0mIw8TGgbn9OgQNEcEDg7jjlxXzAAXoWBcwtwOHrWZZLr/EENT/67NS6QiVi+1o+TDrfFncvJ6uOKYhH3XVWGLTtlrPsxiPIiEQeOMqLZpWLHnvaLT3eXmIitW3iew5RxZtTUeZI+98iJVlhNfN5ORqBgpociF8a4AcA8wHEM/iBDIMTB7VNhNvEwGbmeRbQasK1WxpP/ccU93OrV8MJSNy7/bRFKnAIEAfhuawhOuxmyrKGxVYEoCmh2adHWIlltX8E7Nt9Mb8dgxN4xlhWJ0UHAPPRWDGPb9p5WUoKITmeBCQKXsoWlt8FAtsesdOcuPB/vlEjvxd4UeXxqwrpLyS4ynV2MCmwiyos07OmQD0rkgSEVRgyvlNKeAdjxPAqENHiDGmSZwe1T0eLRoq1CHq8Cu0WfPTVQePwaVidZwyni828DOG6qDYFQ+5eUrH4pLRDBMaCqVEQorOHbn4MIy3pXoNnAQxS5bi8xEVu3BMMMv5piw3urfdFlGSJKCgQcdqAZsoK8nYxAwUwP2c0C9jSq0cpAUfQuHU9AQ5lTgD/EwBiHQgeHqlIJdkvXEW3kQuUNanj2XVfC9lOPsqO8WMSND9Wj2a2B44BD9jPhoNEmFDkEbN4mo7iAQRQ5+IIqwjLg8atgGiBJDIV2qddTpOO+g5g7xiKHfqdoENMI3mLf0yxAMgAIJG6LJPtK1cLS2yb52LVpks1QS/Z9ZTq46KxrMl/vlPJVLgaO3R3k3tXzhlXwqC4XIYn67JnIsW416mNoIoEMxzMEgsD23UGYDDxKCgQUF+itrqnEnieywlDXpMDt1dDsViErQDisQBL12Y8agIYWFWVF7a0znX3vufibdMRBH5eSaupXqu8uWf3CCUBjq4KGFgUtnsgb6mNuTEYOoigiJKdurY4VW7cwleGBa8vx4n/dWPGVHzwPHPMLK04/xoHyIhFNbgUMgNmAaGtOMMzAWO5PRsjNUuU4RdWgafrFr9WrosmtgQewq0FGSaGEsAK0uBlEQYVB4juNaGMvVAYRCYtLjqySMKxCwr3Pty/cxhjwxaYgdtYruHluMWxWHoGwBrsoIBBk8Po1eP36CeD2MoRCCoqcPKxtLRSZGIOR6cXMDBKHkZUGeLyhpLPEBJ7rtIUlVTAAAE1updNKUBCTrxYc/ewOu9lXwUWy7zSdmVr5UPHnqlwNHD0BvTvZYtJ/x2CIRcsYe5HpViujQ0w4V3ggOsbObOTw9scevLbcG30vh5XHgotKMGa4AWZj8rEasa3W9c0KOE6vKyPd5Rr0+kgS9RsvTuDay93J9w4kthbkwm/Skd3K4/CDLVi+1p90+9TxZgSTJDNNVh+bJB4urxZd7kFVGTw+DYoGGIIMBXaGukYFVhNQ3MlMz4iOdcsfzyjE7050AgCcNh4GSf9NVQXQNIbn3vegZo+CvYZImH2EHQYJCOd4HhqazdRDnrapxxUlIiQJ4MHa7jY4hFU+uo6SBsAX0vRxIClm1HS8UDEAwyqkuOf8+pc2vLjMnbQsuxsV7GlWYLfyYDHBlSTp41kAQBD0sjS7NMgKy+kxGEUOvX+4vFBAkYNHaaGAqlKx211XBolDsUOMZhUNhbXozLBWr4amVi3pbACTxKOhJXG14MgMNZPUfpqkm5m1O5JlDe3pTK3Y2XCd7TNJ1Je/bW/JYb18/17lwRsrPPAEVHBgcHkVyAprn2XYzcHwseeK3SxEAxmjxOGbn0J45SNv3Pfg9mm44aF67GlWUh5LkRlYkbQNkshDaDt1eABGiW/LTq5FW1tVpfPvfWe9gromJSd/k47MRh5zT3SiwJZ4Wf31L62wmLiEBThT1cdBWUNpoQijCBQ7edjMPHhen9XktPEIhpkeHPpYWt+BycCjtFBEaaEYDWQUleH77WFcungPln3hx+btYby32odL7qpDbaMCg5TbkxFyuGi5KVIZSCIHs8TDZOJhUTgobVfCuDwLWvs4kGSVTOyFijGGukYV5xznwJ8fbYx2JY2uNiS01sT6aUcYo4YZwPH6oo4GUV9WodAhwONTIYl6gKUBCMoqhpUbc/ouvdAhwmoWet2y0JMWjaCsT7WPLsbHA7KigamAw6KPQYrMHOir8TWp7ky7eqvY44qS7/VOtsdOpdLqUfHc+y68u9oXfeyNFV5MHW/COcc5sXOPAqsZaHIDLr8Kf4hFu0g7SnYxit9vhn+9n9jVDejZxr/cFMTk/c1Jj6VId69nh17pqRpQXiTC7ZUhCEBI1sDCelBT5DToSUbF9pmMHWeGSqLecmOQ+IS8N0BudnsMKZPw0HXl+OhLPz7dEIDDymP2dDuKHTz2NKkwGNu7rzvrBlcVwGrmMGKIEZ99G4A/qOHQcWbwPLBpa1hfO4vXp9l39h2EwhoaXSrW/RBCi1vFgaOMGFImoShJItEml4q7n21KCLg0Dfjbc82496qyhMArl1qBc+coyBOxlQEv6H2KAIPQ9hvzHBBJwcLx+nM6vi4i9kIUCDN4g/p06xvPLUKhQ8AXG4NociuwmriU+R8qSiQIvN4MLPKArOoHksnAobrcCEVDtF+8vDD7zbLdOfgz0YXVkwuTXnHoU+09fgV7mlSoTB9s5/YDNbUyzAYedqvQJzlhOgtCGt0KbKbUi4bGHle5ejHOF7ma7+fnXeG4QCZi9TdBTDvIArNRzxReXgQoKtDUooJv6yKNHSvHgUFpW4Ik9tyLz0XCdZqFdmutjCnjzSmPJbtVb02VNRmaCgiigJFDGFrdDApra4mw8WhsUVFayGA3S9jdEEZdoxI3k9Hj1bvGNRVQ+dQtD7nW7RGW9YkgU8ebcchYIxgDaupkNLv04MUJHooMVJSIKCvsZJYkx/DphgAef7M9sHxhqQeHjDXh3OOd2LIrFF1rK9V3EApr+GJjELc90Qgt5svdd4QBt11UgtIOK4A3u9WU15kWj4ZQOH72Wa51yVLN1kOxM2esJgE2kwa/H1A0HgV2FSYD4Au2TVM28rCahJRNibEXIlVlUBSGn3eFcdAoE254sAGNLhW7G804dooNry9PnE4nicCYYQYomobqMgPqW2U0u7S4wauxcwUMht5HzL2JxPvz4O/JhSnyO4gCEAgClg6/lYb2lo1M5YSJ/R59IRUhGXEXngiTJCAUZjAluTPteFzl6sU4X+Rivp9gWMOrHyafSgsA/17lxaWnFGDHHgUNrTJEnodBQnTdtMhsRkVhUFQNDO0jVJtbVZiNHAIyi7bmMAYMr5CiCx12NHakEbIC8BxLORat0CHC49fH9MgKg0HUwAkqmKwPkBV5DjwPiAIPWWFw+1hCSoZI17jFxKXMaQXkXreHJ6An/mRMXyg4FNYAcGhqVeGwceB5Hg6biGDqXKgAAJeXxQUyEWs2BTFpPxMOHm2AxvTvJdV30OhSEwIZAPh+WxgvfeDBxbOdkGK60Ds+rzO52ApMY2Z6qOOqtL6AhhavirrGMBgD6ptUmCSg0MGhrEjodLxHbJZPRdH0A97M451PfGh06XdHn38bwAF7GTBpP1Pca81GDn+9pBSCwFBkF2E18ygrkCAJHFRVn5XDYtoLM7XAZLrjMfp7PEKXlRzXPj5FVVn0O1Paxh4Fwxr8QRXhsAajyEdbNjKRmbXj99js0lDXqMCXZFyVJHJwWLluZSLOxYtxPsl01t1MUFQGb6DzRGehsB64uNuyZgdlQFUBUdQD9AI7B44DTMb28ssKw856BZu3y/D7GZpaVOxuUBBWgLltA0M7slt4HLC3Eb6Aht1N+vHb0Kri551hfPNjADvrw9F0+5E6Us8dxaGsUERVMY+9hxpQViSiqlSEKHKob5VhNLSP8YulQb9hsyYJ5IHczMEkywxOm4DPNwZx2/814u7nmrGjXsEh+5sQCuuDaIHOM5MzxvDOJ6kz9f57pRdi2xfW2Xew9vtgygDl3U+8aPG0bwzLDDYLD3OK79ph5WE18WmP5esPVL2lwW4VwPMcvvkxAI0BQ0pFcJyIYJiBs3Fw2oAxw02wmPlOWy4iJ/3OegX+ICBKgNMu4ON17RE5zwPb62RcOMuJc493YnudjCIHjyGlEjSNIdx2ZxRp9VA1hla3BpXpK+IWOQSYDPp0zEwsMJluJN7fXSCd5Z5RFIamVhZ3x8cYEAiobVPtY9euARpbVRQ5GQpsfK+ngSf9Hpk+1sHjV1FRwmC3CHGtNHabAHs3xhFR8r3e6Yusu71lMeozZDZuCSfdPnl/M3bWy2hxaygt5KFpGlRVH/ivKBxMRh6CwHU41uNn7oUUDUVOfczYzj0y9hoiYd4ZhXg8JgHciEoJN55XBFVtT8jpC6hxGbs9fhmBIENFsRidXchxDAIfaS0WE7pLwyGA47m4nFURIq8vjFjkyK3fpDOyAtz4UEP0ZhQA/vW+G6u+8uOG84qxp6W9xStVS6merDN1MOBuu3ns6jtoak39HsEwi05WAdpmyzGGK08vxJ3PNMc9l+OAeWcWQlH1erOnY/n6CwUzafL6VfiDiK5twhgDgx5AhMIcBJ7r1pQ5o4GHwcBgMgJDS/W1gCJ3hzwP/On8Yiz93Icl77ghiUCxU4DRwOG6s4thNup37CaJjybTspr1lOK7G/WFKmVFwcgqA5pcGkRBTbs7p7fBSH93gaS6MHHQm9tj71IBPQOq2cShrkmF0cDB1JagCmgPNBxWHgV2Ie3lKoDE79EXUOH2aQjLDGEZEFpV+PwsOpU+EoR0ZxxRLl6M801vftu+wPMcDj/Igpc/8KCpwwXOZuZwzC8seOcTH5w2Djv3KJAkPhq4GCQNVaUCpA5Nch0XidVUwGprX55FURmmHWzGxP1McHs1iKK+NIGq6mkfnG2zdTouPaJo7Ss/R25uChxCNH1/MgYjILd1scau1i0K+g1GWGUIqwyVJWLSZH5KOISg1wVNkcGLEkw2J0SDsXdfeppUleHDNb64QCZiR72CzdvDGFnV/lukaikVeA7TDjbjf+uTJN0CcNBoE2xmDg5r5zeoB48x4dn3ks+EHVklxbXC6LPKgDHDjXjw2nK8tMyNHXtkjKiUcMpRdoQVDVt2hSFJQIFNhMuvdXssX3+hYCZN7kD7iRx7N69j2LI7pC870EnFGGlNaWzV0OphaGxVUORgmDHZiufec+Ow8Was+zGEz77Vs0rKClDXpJ8oNz5UjwevLUdJkZ7oKnLxkhUGt5fBYmq/WCta7/syexuMZKMLJNmFSZFZ27iBRBzHQRTbm+MDQRVur96fb5AAb0DvVouM8ekquAjL+oJ7Hp9+rDjMfNxFRFba16IqcQpodKnQNBYdL2Ay8Kgs61mLWq5djPNRpnMo9VZ5sYi/X12G595348M1PmgaMO0gM86Y6cDOPWEUOoBQmMEXBCzQYGmbjaCp+vpuhY747maTgYPdwsPj1w/GyCSFyPIsdguPQruIQjtQWhA7Ro6D0cDBF9CP62Q9GKrK4m5uUrUWMsYQCjMYJR7BkAZj2xg/i5GL5nzS2qZ3N7Vq4Lm2sXUxs3CCnla463dAU9uDB3/LHjjKqmGyF2Tku+8Jt0/Diq+S55gBgJVf+7H/XgUAum4pPXAfU/TGJJYkAuee4GybeNK56nIR+wyV8NPOxPFPl59WiAJ7++dH6t+QrC+UeeGsAj2Ds1/Fp9/4YTBw4Dg9iDVKKkZUigjx3RvL119y54zNM5EuXsY6BjI6Dvq06ZDM4qYaRga8Gg18+x00B7S0ZcncXKPg+KlWrPzKjyMmWPC355qRjDfAsKNewfBKA+qb2yOJZOsbRVa47k13Tm+DkWx1gXS8MOnfVfImJkXVg4pgWG+GjQ1kSpwCFDV5UJhsUHQorGHHHiUubbzIA5KogeN4WC0CZKU9IGZt4wqsZg5iW7dAcQGfVktarl2MSe94fCoCIYYzZthx6lF2qBqDL6DC41MxpNyAIWUGNLlU/LhDhj+oX/xEHih0CBBFfWCnIOgBj8nAoXaPgoZWFQeNNsHr14/lWLHncsdjaU+TjFavgla3vlRBJL1+9LVtrUKx+WzKiwTUNSnRjMMA4PGqsFl47GnR4G8b9zOkTM9UHglkipx8tLu143mnhEMJgQwAaKoKd/0OiEZzv7fQ6HlgOmmFEjkYRA6K2nVLaVmRiHvnlWPJO6346Es/FBUYv48RF59cALORQ7NLH2vX2eSJYqeIOy4pxfPvu/D+Z36EZYYRlRIuP60A+44wxNVb4NqzQWtMr/+aXCrWbQ5CZYDF3J5DLSQD22oVTNhXAAfkTCsw1XhpiizY5g3qTbYCr2e7VFWgwMahxaPgxx1hGAw8Cm0iPCJDsZMDeB6eHSocNv3g4DhOz0fTVuGYjMD7q7245qyiaCrpVGob9RojtvJJtr5RbH95ut05vQ1GcqULpLOgSxC46KrfDa0yGOMhCIjm6ol8j3EZV5PM0GpuVREIqWhytTfnc2DY06Ln0VCZihInQ1jWYDXz0ZYiDkCBLabpllFrymAXO8ZKDQP+kIaGFhUlBQJUjeGBF1uwdbfeWnj6DAfKiwzY2aDAKHEwSRxKnCLueqYZ63/Up89IInDsoTaMGW7ApYvrcNUZhdh/b2N0YGpn57LHp3eJ+vwMIYXB62fw+/VZOmaTEE2ExxiDL6Sivlk/3zjoNwphWQPTgPoWBYwBwbDe2gPoF9Dte8IYUSbBauGiN3+xYs+7oNeVEMhEn6eqCHpdsBWVZepn6BanTcCsw+1x2dpjnTjNBquZg9PWvdbWIgePC2cV4OzjnFA1Pbh46j+tMBl4nHaMHXZL59nlAaCsUMSlpxTizGOdUFUGs4lHoV3PQbarOT4ZIWNAMKTCbBJgNAA2K4cRQyS0JrlZD8n6sTmqWsqZVmAKZtIUWbDNXxsCB4Zmt562u8gJ7KjXB0jJMmA0aggHZRQ6edQ1tY/VMBr15FFVJRI4DnDYOHj9PERBRZ1Xw4qv/TjiIDMK7XzcqPNYew/VJ17HrYzaoR6KVDAR6XbnZCIYyYUukM6CMquRg1HUgwmR5yFJ+pNUDXEVdSDMwJr1nB1un5bQbeULMdQ3qVA1DqLIQeCB+hY1esGwmjiEFQ0q06dPlhWK4IBo3ogImn1EPAEVHA+YeA4a9G4ZaPpCkXfFDNTcXqdg8bPNOH2GHb/Y34zmVhVDyyXc9n9NcdOsZQV4+39eGCQ7Dhtvxj3Pt+CfN5a3TbcGLFYOLq8KRWPYuUfB/9YH4LTxOHKCBTyvH9tWM4eQzCDwDKrGwe1lsFkYHFYBrR4NXr+C8mIJsqyBMYbaRgVWc6QVU0NYBrx+DZLIUFLYdr6JHAABbj+DxcQnTaAHtN+MaUryqeMRXW3vK4ceYMLYkQZs2ho/YHvSfiaUFQrwBxlMBg0GKfXNH2MMuxsVbNklIxDS8O4nPrS4Vew30ojTjnbgmXdduPKeelx1ZiHG7W3ssrXdYOBRXtR1FnNR5GAxCwjLDP96z4PtdQpGVoo4cZodssLg8WvwBjTUt6gQeYDnuJxqBc6NUuQhg8ShulyEoqrwBWUUcAKMkh7ZhsL6eIdtDTKsZh7FBQJ2/CgDHFBezGAyCFAZh1aXCkXRUFIowmwSIIkMkgS4PCoURcUPNSH89hgHHn2jNeHzh5SKqC4To2WJBBpWkwBPW392ZG2hyAWyt905mQhGsn3wdx6U6eXa06zGBYWR71FpW4lc0QCnjYPLrw/W7ZicTFUZFFXPLGwX9e6kcFzdyukr4wJoaNUr+rgWGdDsI6KTZX1my7/ed2N3g4IRlSIuObUQtz1Rn/T5r3zowTG/sMIocfAGtJT5Yt79xIvrzynCx+sC+GR9ABP3MyCsAoIggucY/rc+AIuJR4lTwNLPfXjuPTcumuXEviONcPkYDJKA8kIOYYVBEjn4gxqCYYa6JhlmowB/KIxhFQYw6LNvdjcylBQI+vhCrwqvl8FqAewyD1PMzZaiMmyrC8eN+Ysk0LOahWiAz4sSOtPV9r5SUiDi+nOKsf7HID7/Ngieb1uTKcTwx3v24P6ry6G0dN6asrtRwXufeNHq1eKSJe6oV7DiKz9uuaAY97/QgodfbcWjN1b0uLU9MglBVuKzLhc5RGzdLeOOJ9uzAO+/lwFNbhUfr/OjsVXF+H2MmDLOjPpmBSZTbrUcUzDTC3argJICCcZaBYLegwRfgAPPcWhyM3j8DKLIEAgyNLlVWM0CXB4VpmIeZgMHRQaaWhmKnPoYHFHk4LDqaxJp4NDi1XDwKAPmnuTEi0vd8LdlZzx4jBFXnl4EQUQ0r0NsoGE16Wt2GGNSmmeqOydVMJJLaa270lVQZjTwsFs47OYVcG0JCAF9EdDIlG2rSW+qVTTEJScD2rqrBIC1Jcbq2BrO8XoOkAKbqA+gY0gIZHJt9lE+/b4DRUjW8OkGPx58uTX6WKNLRU2dApc3eWut1nY8lhYI2Fab+ioXDLPoMdfsUdHiYdhVr2DKOAG7GlR89m0AW3bJKCsUcNoxdnh8Gh7/twsPXVcOjtNbK8HxsFv1GXkc9MUv9UR4QIuHwR8MobJEQGOr3tJjMnCwmLjoIHefH5DDDKa2zJ6M6YlDOw5pix0QHwnwTTYn/C17knY18YIAky15rpy+FgpreO49Fzb8HMLBo01QGfDEW67oTLRXP3LjvBMKUramhGUNr33kxi/2t+BP/2hIsp3hX++7ccIvbVjyjgsNLQpGDu1Z4KYqSJhWDwCFNuDefzVHA5ljfmGBwyrg1scao89Z90MIry334O4rylBWkJ2AMRUKZnpJFDgMLxdR36IiENabVQWRQ8ivwm5ta1pVGEJhwGRgYIzT1yhhPKwWDl6/hmBYQ5FTQLNLg8HAo9gposXDYDAAtU0K9h0u4W9/LItmghV4/aBubGFo5toHgUUCjWKH2K8Xn1xLa90dnbUQGSQO5cUSLKb2Qdr+kBYNZCIDEyOtN4qmT3c1G9qmvSoMBgEwtlXSQsxXEJsZGtCn0jutHMS2tPK5GCjk4+87ELS4VDz6hivhcYFP8uQYZiMPSQIqi1NX75Ko33wBwKR9TWhsUeG08aipU3Db/zVFn+f2abj/hRacOdOO+68qRV2jgiI7jya3fhn0B/XudY1FjvP249YfZNi5J4xWrz7LQeRVaCoPQ9showFQNA1A2+wrjYEJPMqL9UzcseM0eB6wW7noeSEajHCUVScMAuYFAY6y6qxNzw6GGbbWytjTrOL9zxKXoNheq0BTGVQl+fnt9mnYVqug0J48rxAAfLctjDNmOvR/pNGCyxiDrABlRQIUjcHv11DvUuENCHHLGRw9yYqbHkkMqHwBhn++3orbLy7p0ef2NQpm0hSZdrurQcbPu8LgOR7BsAa3V9MH8/JAWaGEsKyB08e4QdMYFJVBEri2E1wf/W436UnwqkoYOF5PdvTTzjA4cAirDNtrFYiioi+2xuktN5GF11JNue6v7pxcTGudTDrBXWwLDmtmcNriBybGdumFwxpaYgb8qgoHsxHQGIMk8jBIGhS5PTN05D14Tk//3p1ke9loGcmX33cganInz4xd16RgRGXyJQesJg6VJXoXZpGToaxQQH1LYuvFkROt+GR9ANVlIsqLRWyvUzBqmAF/+b/GhOcCwEvLPBg/yoT/fePHOcc54QsyPfGaqrc8lhaKCCv6Kt46Bo9PA2fTA5HIBAd/GChycuBYEEFZBcdEhMKA0aAnx/P4GOwWfVp4JOcMGKCBwefXs3ZHjn3RaIbJUYyguwUMDGZbASyFpV0GMn2Zm8Zs4DCySsLPSaZDA8CIShE8z6UcD8fzegs9143c/EaJQ1VJz+sOVQN21st4+38+hML6ApYzJ1sRltujx6pSEdtq5ZS5xb75KQSXT0NYZTlzE0bBTBo8PrVtLRQFuxtlNDRr0DQNBiOD0cjDKHFo9mgwaRpUTe8GKnYIEAUgGNJH9kdmQ3IcAB4osLcfCAU2EbKioa5J1Q98QV+8ck+ThuICBrMh/mfL5iKC+bC4YXdbFlwuF+rr67F582ZYLBYMH7EX7M4KCJKIAjsPjtdzYLR6lWg/s9PGweNnaHarENpumXkAJcV6ErKQrC+BUF4swO/XYDHHBzLd6U7KZstIPvy+A1WqXCKvLffgyjOK8NcnG+PupAUeuGluMZxWHr6QBjXA8NdLS7HgsUbsamjvcjr0ABOmjjfji41+3HpRCbbV6q0AqgY0u1N0XzGgsUXF2u9C+PanBtz+hxL8uCOEkZUGhGSGTVvDcFh5TN7fhNoGP3Y3+DFyiAk2swSrS0OzW4Mo8DBIMhqb3BB4DSaJg88fgsAH4bQ4ARjipmNbjJzeHeLW2pY2YGhq1dDi0lBo9ED17I5rlQl6W2Cw2DoNTPo8Nw3H4dQj7dheK0NW9OztkW4bjgNOOcoBWWGwm5OfMwU2AYfsZ8KwitRdOONHGbF5exhX/LYwOrA6lY51h8nA4d7nm/HNT+2LQ72xwosP1/hx/9VlsFv0+kzg9PFLnQmFWXToA5D91lqqhXooLDPUNen5Q8KKBlnhYLfy8LRNWTQa9ICmskhfRC2kcFA1hqFlPH7epUDgeTS7FFQUizAYeJQ6BRglIeGiIAr6gOBIhB4MqhAlwCDyUFQkpJPO1iKCXX1uOJx6QbqeUBQF/kAIqqZC4AVYzEaIYteHb3daFgCgvqEe99x9F/7+9/uja1rZbDa88OLLGHvgNKiahFaPAo8Pcf3MPACrhemZoFni7AtJFFFUwMd1/QVDDGFFg0ni9QynMkv5nWS7ZYQWr8yeYocAm5mDNxD/4+9pVvHaR248fH0FvtgUwLc/hzCsQsIvD7RAVTXsrFdQ7OShafqd/h2XlCIQ1ODxayhxCuB4fazK0b+wYetuWV+QVuq6+8pu5WA161myv9sWxiFjzVj8TBM2/NzeJSLwwOIrSlFaZML/1gXgDYTxi7FmHDhKRG1jEC0tXoBnMBh4mAwCrCYO4ABV86HAaoDLx9DsUmAwADaTAF+ARSczRMauMTWM7T9vR5mTR2wVoKkqXHXbEfZ7AY5LaHXpj9w0LR4FDMD0iRaoKjCq2oCVX/mxekMAV/y2EL6gAs4s6AtwJjlveZ7DkRMteP9TH357jB0vfxC/yKjVxOH8E5wwGTiYjBwaWjSYjcnfq2PdIQj64OLYQCbC7dMXNL31whJc+0ADdjUq0dmyyew9VIIk6ukCIrLdWkvBTA95Aip8Ib07QVH1i5Km6SPzzRqPsMxQYBVQYOGxo16BUdJXh/UHgSHFIhx2Af4Qg90qoMAmoMCu34nEXhQ8ARVih/TeDqueeVPV9MR4Bbb4ny5b03g7+1xZYdjdpMTdhfQkeg8EAmhubgZjDJLJgnDMlCC3z48ipx0Ws74Ap6qq2L17N7Zt24aWlhaMGjUazsJSBBQb3H4VBVYNHFTwogWywsBrXoR8Lfj5Zw02exE++mAp7r//vrjP93q9+M3sk/DVum/hZ9Vo9qrgGBdtgQH0iwUPAVYLnzJhVmwCMUOYQ0tQg8Y4yDKDEGJoaaqH19UAt9uF8vJylJeXo6CgAED2W0Zo8crsKXYKuHluCf70SEPcgoGSCJx5rANhmWFYuYRR1QbICsP2OhmlhQIEAWhyta+3pKgMPM+hwC5A1QCzBGxrVCBHrukMKC/UW45TdV+ZjBzsFgG/+3UBWr0qPtsQgM3MYePWMMxGPR8WY8BxU21Y/2MIz7zbnkb/wzUB/P43BRi3txlGScTIYRw0VYbAhWE2W/Wgi2PwBhQIooQCi4g9zQo2/hxEVamAkZUGMOjjDTktjHDLdgRb9qBVkeBwWqCGQ9BUBeB4BN3NsBSUwOwoBBDf6tLXuWma3SqefMuFZV+0ZwHmOOCi2U6cclQpvtsWgi8AmE1AXWMA+46QUFGcGDCUFIiYeagVTS599tD7n/rQ4lExYYwJR060ICRr4Ln2RStT1QEd6w6TgcMHXySO44lY9bUfs4+w4aHryvHyB25s2RXGr39pxdv/i3+NKABXnFYIJclXmc3WWqqKekhV2qbeKgxen54lNiKadVPgEAwBVSUSwoqejMhh5eDxafAENAh825iamCm9sReFyMUvkt4b0Adt+QP6ho6J8bI5jbezdOWRBelidSd6V1UVP//8MxYtWoS3334bVqsVl156KU477bcwmq0IhfxoaWrG+q9qUT10KCorK/HTTz/h17/+NZqb23NvzJlzNhbcdgcaarbir/98EK2trZh7wYUYP348rr3maixbtgyMMTz11FNYtGhR0rKUlJSgdlcNSkplWOQACouKIBhLEFQMbYvncQiENNS3yjBLfFzLTGTqo8DrU0pNEh93pyQKQMC9DaedMhvfffdd9DNPOeUUPPDAA6iqqsp6ywgtXpk9gsDhwNFGPHlzJd75xIstu2SMGW7A0YdYAOjT+l0+BrRda2JbL7S2SUHDKpInNbOYeOzYo0Buy/pqNnAwiMD15xTh2gfq47oPeA647NRCPPueC+t+COGXB5oxe7oNHIAFF5bA49dQ5BCwuSaMMcMMmB8zC8cocbh5bjFeW+7BAy+16O/H66/bXKPhjRVN8AcZnDYe5x7vwMGjRdz4cEPcOB+bmcPiK8pgZB401W6H6mtFwNMK5gsg2BCGwWQFBAFBd4u+sKUkRYOZ2FaXvs5N89X3gbhABtDr/sfecGFUtQFbd+tpGCpEfWzB99tk2C0irObEJjFB0L+7EVUSLjzJAbefIRDSsGFLCJqmL+YZSeeQqg5IeJwltujHEgUOksghICu4+swi+EN6BD1xPxNeXuZBs1vF2L2MOGumAwYJCCQ28CT/3H5CwUwPCaIeiLS4VfA8YJT0bIhAZFaLhr2cBvhDCgIB/flmAw+jgYM/1L5mkt3Gx42diL0oJLvb5bj2lWVjc6BkexpvqrwtobBeQSVrregqev/pp59wyCGHwOPRm1gbGxtxww034IMPPsBDDz2EM844A19//XX0+UuXLsXs2bPh98dXJCNGDMfdixfiscceiz529dVX48jpR6CpqX3GhiiKqKmpSSjHiBEj8M9//hPz59+Ir776CgBgsVhww4034cw5F4CJhZAVBpdXg9ujQWnrv/Z4FZhN+owMngecVqCpVYMnEIZJ0u+AA2EGI2vAiSf8Clu3bo373Ndeew3FxcW4//77IXSRL6OvW0ZyJXPzYGWUeAyr4PH72QWQ1bacLgFN/z1ibmo65pQC9ItKqokAhQ4R1iQrsbu9Ch6+rgKrv/Hj2y1hlBUKOPQAM95d7cW6H/Sr1//WB3DiNBueetuF77e19zNM2s+Eifua9IShbRe0E6fZ8M4nPny9uf3Kd/J0O5av9WP52vbz1eXV4AswLH6uOWHAsjfAcMujjbjzYjNY0AtOC4FXfOAlGa21NVDCQZid+kBg0WiCyV4EVZGj506k1aUvc9O0elS8uMyTcvvSz33Yu0rEzgYVgYDcNp2aR32rjJHmxK4tVdHHVnrbxuPVNytodOkrW0siB6PEwR/Qc1ylXLCyw+Mev4ajD7Hiv58lXz9q+kQL3vrYg+Om2rCtVobDymPt90G8scKLoyZZYLPw2FYrY/4/GnDHH0og8BwUlUV7DyJBcbZaa7sxZjr7/vGPf2DkyJEwmUyYOHEiPv7446yVxd62kjEvAGEFKC7Qk+UB+pdpMvAwSsCB+5jhsAmwWwSIIgdV09f3MUj6HXlRWzdRsotC5DM6kkQOQ8tEDC+XUGDjUVzAY1iFlPXpsXargGEVEooL+Gi5igsEWDu5a08Vvft8Ptx2223RQCbWUUcdhXPOOScukBk1ahQ2bNiQEMhYLBZMnDgxLpCZMmUKPv/887hABtCDpwMPPDDh8+68807MnTs3GsgAgN/vx6233IQPlv4bRklfEC+sAOXFejp3AFAZw7ZaBRpY3AVGDgP1zTJ2Nchocqmo3V2TEMhEPP3006irq0t5LAD91zKS7PfNheNuMBEEDiYDD4Hnor9HeYmAIoc+u7GqVEy4cejqoqJPTBBRVqSnczBIHDiBQzDoxyH7MVSXi6htVHDzPxvw8br4FZzf/p8X1eXxF/8vvwvi36s8mPELa/Sxg0Yb8dm38a+duK8p6YKMI6ukuOAoVqNLhSfAw9O4GwGfFyzkwp7v1sDvakTQ04JAayNCnlaooSB8rQ0IB+K7RjRFhsnmBC8kP2Z7m5tGURlaPMm7sAB9wU9R0iuIkKLnAmKMIZyidSPy2/kCKupbFHz7cwjbahVsr1OxrVaBx69C1fTWb5OU/DIeW3dEMjGbTXy0ZS9WVamIyfub8e+VPoTDgKwyMACPvNaK3Y0KnnvfjX++3or3P/WhsVXFI6+16pmKGxQ0tOiDtBtaVNQ2KimW8e17OR/MvPTSS5g3bx5uuukmfP3115g2bRqOO+64pHfS/cEgtSW2awtMwrLet11ZJGCvagH7jzRiSJmEsiIJ+46QEJu1mkGfSnfIWCMqS8SUF4XI3XDHixjPARXFIsqLpbgKKBd0rBiTraYaK1VF29LSgrfeeivptgMOOABffPFF3GPl5eVJj4WpU6di2bJlcY+NGzcOn376acJzn3zyScybNy/usaqqKrjdbuzevTtpWW7/ywL4PfXR3DN2i4iqUhGlhYKe9dnJR8dDRQgisKdFhS+o313t3JH6GA6Hw/D5fJ0eC/3ZMpLswkeyxyBxGFJigNMqwGLk41pkgPQDXbPE0NjUBI9fwfK1fnyxKRhdYDBWWGYQk7z98i/9OPQAc/Tfcls3e2y53T4t7rGIZGMwYvkCmh6MqDKMBh5KyI+QpxV8ZBQwz4EXRKjhMOQOwQwvStHcNB0DmkzkprGaeRw0ypRy+0GjjGhsUaMzhMIKQ0hmSPWRdrMAVdXXY6pvViAI7RdrkefQ4tHQ7FJRYOMQlJPPQIutOwJhfZxnbYOCM2Y6cON5xZg6zoyDxxjx+98U4PezC7D42aa2VnMNZhOP77aGU47XW/9jCCGZxeU35AE4bfo4rWQpBfpazncz3Xvvvbjgggtw4YUXAgDuv/9+/Pe//8UjjzySdJxDKBRCKNQe7rrd7oTn9JbdJqCiBHDYeQSD+rRBm0mAzaJXKoa2fuuKYgPsFhH1rTLCIcBgBMoKpKR9pAmfkQPrGPVGumMtOI6DyWSCz+dLeDwQCCQ8f8uWLTjvvPMSHhcEAbIc3wfe2tqK8vLyhOfu2LEDH330ER599FH8+c9/Rn19PYYNG4Yff/wx5f7V1tZCkYNxSfQAfTqpqjL4g3xC6xMPPeuzZmSQZYYRI/dK+f5msxk2mw1A/h8LpG/0RRegooRhMjL4/V5MHWfDv1cl75L4xf5mvLkisfVU1dqT8QHtyfkiA5gVhcGcIg0+x+mDVJMtrstxQGkB0GxQITEZXo8PJkchAAaTrQBGewEknweqHIIqh8BY+wU+ttXFZC+AaDRnPM+M2cjjnOMd+N96f7SLLaLQzmP/vYx4dbkHlrZ4h+c5iBxLmUVXz+rOQanTE9xJogDBwsBxDEVOAaGwnuEdfGI9EytSd2zfE0YwqCf6ZBrw+Jst+MX+ZpSIAj5c44vLi+Ow8jBJ+pi/zjAABolB5OPHCmZrEHBOt8yEw2GsXbsWM2fOjHt85syZWL16ddLXLFq0CE6nM/pXXV2d8XLZzQKMEgenVUR5sQGVxQbYrfpgrI4XaquZx8hKI8aMMGJkpbFbgUxEPt8Np9uiUFZWFg1cYzHGYLEkNo/u3r0bNpsNQ4cOjXv8iy++wFFHHRX32DvvvINTTjkl6ec+/vjjGDZ8JP63eg2+XLsOTy95FhMnTkq5f0VFRTCajKgoERO60yKra3dc9BOcvqCoyOsnfEHxEIwdOzbp+19++eWorKyM/jufjwXSdzLdBahqKqxmERzz4/ipZhTYEuurvaokOKz6bM2OTAa9K/z0Y/QVotdsCuLICe3nrcb08THlRYnl+3CND6ceZU9arhmHmCEFd8Nm5sFBgWQyQwsHoISDAGPgeQFmZ2G0lcZo1TPkJmt1EQ1G2IrK4CgbAltRWcYS5g0plXDfVeUYM1yfocRxwOT9TbjjklKsXq8HhTzHQeQBu5nDqGGdXw8MIo+yQgEVxXqLb2WJiCGlEgSe18decvpklO50JxY5BBQ5RRTYRKiMYeK+Jry32oe3VnnjApnJ+5vAcwyyCuw3MvXU7L2GSFBVwGxsf9+49emyMAg4p4OZxsZGqKqacDddXl6Ourq6pK+ZP38+XC5X9G/Hjh0ZL1euNP3nunQqWkmScNlll2HfffdN2Ob3+3HSSSclPH7dddfhxRdfxC9/+cvoY4wxVFcPw3HHHR99zOfzYfny5bjlllsSmuXnXnABxu4/DkWlVRg3bjzGjN4Hh/ziF9Ep0h1df/31GF49BMYkv7XZwEFqu1OJJQh6BuGhZRKcNg5hVoTXXv8Ppk2bFn2OKIq49NJLcc0118BozE5KdpJfMhnoCrwAk1GC0SDA1bIbi68oxOwjLChxCqgoFvC7Xztx8wXF+M/HyQe7/ma6DQbBh1nTOPzj+mKccqQZc09y4JhDLNH68oWlblx3TjGcHQKlH2rCOPZQK648oxCFDn2b1czhvOPtOO9YEXy4FTzPgxNE8KIB5oIy8IIIk82pn88MsBVVoHTkWBRUjoStpApF1aMzkwyvGySRwz5DDZh/XjH+76YK/N+fKvCbI2xY90MQdiuPkVUC9qqSMG6UhHF7mzCsInWwAOgtsLzAw2TgwTG91SscEyTwPAdJ4LrVnRg7fkZRgNOOcuD4qdZobiGeA46caMalpxaCsbYlIwQOJxxmTXgvgQcuOaUALW41euOWrOz9jWMsWe9lbti9ezeGDBmC1atXY8qUKdHH//rXv+LZZ5/F999/3+V7uN1uOJ1OuFwuOByOjJaPFt/rOzt37sRnn32GZ599FgUFBbjkkkswatQoBINBXHvttXj55ZehaRoEQcCcOXNw1113wWAwoLGxEYFAAAUFhXAUVsDd2oiPPtRzyLhcLpx44om46qqroCgKVq1ahWAwiOnTp6OqqgpFRUVxZdA0DevWrcdJJ52IXbt2AdC7uy666CL85S9/QXl5edLsvDwH2CyA14+4xzno0ytNxvbKh+cBTnXB72lEKOhDYWEhKioqYLUmViKE9DVFUVDX2IJAIAi31w9F1mCy2GAw2CDwesZrRZEB3o6/v+TGl98FAejdSbMPt+G0Y2wwCHJccksAaGgJICSL8AUZLEYOFqOKsCKgZo+K2kYNew2RUF0uobRQhKbpY0VCMoNB5FBg1dC6czM0VYWqyAh6XXDVbgMvigh6XDBYbNAUBbwgwGwvRPVB03qVL6a3YuuEsKxhZ72MsMxQaNdbMYxtN8NdtZ6FZYbttWHsrNcH/Lq9LJqwU+SBqhIBB442odDRvcghtly+gAqe42A2cZAVvXtcFLloS1zkeZLI8EONjNeXe9Di1rD/Xgb8doYDTGOob1H1pTOSjNcaViFl5FrYk+t3Tgcz4XAYFosFr7zyCn7zm99EH7/yyiuxbt06rFy5ssv36MtghvQ9RVHAcRyEmEF7Ho8He/bsgdvthsPhQEVFRXR8SSoNDQ1QVRWFhYU9avFgjGH37t2ora2Fx+PB0KFDUVZWBqezfeZDqqA22eOhsJZyjAPNDiK5wB8IotnlgSLLCIb0wEQSRFSWFcNut8IfCKLF7QXHGRGSRQTDDFYzj0I7B7s1+bkVeU8Wk/2P4/m4xJed6bgMQdjvRcDdBGtxBTRFgabIEI1GFA3ZB5bC0sx8Eb0Qe+6DY2CafiPU05veyNI5e5oVBMMMgbAGHnp9MWaEEUXdDGSSlYuxtgzzLLFcsc+TFQ3+oD7YV2MMTNPz0SS7Yct0XTZgghkAmDx5MiZOnIh//OMf0cfGjh2LWbNmpUx0FouCGZJrqEWP5Lqulg9JZ3mRdJckib6+wwKRoskMJRjokwUjc0lkUWOPT59s4jDz3VqcNtNl6O4NWybL1ZPrd87PZrr66qtxzjnnYNKkSZgyZQoee+wx1NTU4A9/+EO3Xh+J1fpiVhMh6ZLQno0zGND/CMk1AgeAqQl5nLq7PVOviRJNgGjSV6pXWNy//cGQvpLvAGQSAFPMtTwbdUaqOqsv67LIdbs7bS45H8ycfvrpaGpqwl/+8hfU1tbigAMOwLvvvovhw4d36/WR5Gt9MauJEEIIIX3L4/HEde0nk/PdTL2laRp2794Nu92eMFCpr7jdblRXV2PHjh2DsmuL9p/2n/af9n8w7v9g3ncg8/vPGIPH40FVVRV4vvPJ1znfMtNbPM8n5CDpLw6HY1Ae0BG0/7T/tP+0/4PRYN53ILP731WLTERO55khhBBCCOkKBTOEEEIIyWsUzPQBo9GIW2+9ddBmcKX9p/2n/af9H4z7P5j3Hcju/g/4AcCEEEIIGdioZYYQQggheY2CGUIIIYTkNQpmCCGEEJLXKJghhBBCSF6jYIYQQggheY2CmRQWLVqEQw45BHa7HWVlZZg9ezY2b94c9xzGGBYsWICqqiqYzWZMnz4dGzdujHvOY489hunTp8PhcIDjOLS2tib9vHfeeQeTJ0+G2WxGSUkJTj755L7atW7pr/1fsWIFOI5L+rdmzZq+3s2k+vO3/+GHHzBr1iyUlJTA4XDgsMMOw/Lly/ty97rUn/v/1VdfYcaMGSgoKEBxcTF+//vfw+v19uXudSkT+9/c3IwrrrgCY8aMgcViwbBhw/DHP/4RLpcr7n1aWlpwzjnnwOl0wul04pxzzklZR/SX/tz/v/71r5g6dSosFgsKCgr6Y/e61F/7v23bNlxwwQUYOXIkzGYz9t57b9x6660Ih8P9tq8d9edvf9JJJ2HYsGEwmUyorKzEOeecg927d6dfeEaSOvbYY9lTTz3Fvv32W7Zu3Tp2wgknsGHDhjGv1xt9zp133snsdjt77bXX2IYNG9jpp5/OKisrmdvtjj7nvvvuY4sWLWKLFi1iAFhLS0vCZ7366qussLCQPfLII2zz5s3s+++/Z6+88kp/7GZK/bX/oVCI1dbWxv1deOGFbMSIEUzTtP7a3Tj9+dvvs88+7Pjjj2fr169nP/zwA7v00kuZxWJhtbW1/bGrSfXX/u/atYsVFhayP/zhD+z7779nX3zxBZs6dSo75ZRT+mtXk8rE/m/YsIGdfPLJ7K233mI//fQT+/DDD9moUaMS9u1Xv/oVO+CAA9j/t3e3IU31fxzHP8vLTcs1mjEnFqVFkKmQPvAmyZQ0IykoemCSCQZZGIEWQUVaPZEiI4zIbhgKkRUYilimaHbj1KgjGqKUaaK5tBkqiU7z+39w/R15V17bOUcH3xf4wOPPs++7sfXjtLWamhqqqakhPz8/iouLk7V3Ojn7z58/T9nZ2ZSWlkYajUbOzDnJ1f/06VNKSkqisrIyamtro6KiItLpdJSeni578yQ57/vs7GwyGo3U0dFBb968odDQUAoNDbV5dt7MzFNvby8BoOrqaiIimpiYIL1eT1lZWdY1IyMjpNFo6NatWzN+v6qqatYn9LGxMfLy8qK7d+9KOr+9pOqfzmKxkE6no4sXL4o6vz2kau/r6yMA9PLlS+uxwcFBAkAVFRXSxNhAqv7c3FzS6XT069cv6zFBEAgAffz4UZoYG9jbP+nRo0ekVCppbGyMiIiam5sJANXW1lrXGI1GAkAtLS0S1fx3UvX/zmAwLJrNzHRy9E+6fPkyeXt7ize8neRsLyoqIoVCQRaLxaZZ+Z+Z5mnyEplWqwUAtLe3w2QyISYmxrpGpVIhIiICNTU18z7v+/fv0d3djSVLlmDz5s3w9PTEzp07Z1yyX2hS9U9XXFyM79+/Iykpya55xSRVu7u7OzZu3Ij8/Hz8/PkT4+PjyM3NhYeHB4KCgsSNsINU/aOjo1AqlVM+DdfV1RUA8Pr1azFGF4VY/QMDA1i+fDn++effz/c1Go3QaDQIDg62rgkJCYFGo7HrMSQ2qfodhZz9AwMD1ttZDORq7+/vx/379xEWFgZnZ2ebZuXNzDwQEdLS0hAeHg4/Pz8AgMlkAgB4eHhMWevh4WH92Xx8/vwZAJCZmYlz586hpKQEK1asQEREBPr7+0UqsI+U/dPdu3cPO3bswOrVq20fWERStisUCpSXl0MQBKjVari4uODatWt49uzZonn9gJT9UVFRMJlMuHLlCiwWC378+IEzZ84AAHp6ekQqsI9Y/WazGZcuXcKRI0esx0wmE3Q63Yy1Op3OrseQmKTsdwRy9re1tSEnJwcpKSkiTW8fOdpPnz6NZcuWwd3dHZ2dnSgqKrJ5Xt7MzENqaioaGxvx4MGDGT9TKBRTvieiGcf+ZGJiAgBw9uxZ7Nu3D0FBQTAYDFAoFHj8+LF9g4tEyv7fdXV1oaysDMnJyTb9vhSkbCciHDt2DDqdDq9evUJ9fT327NmDuLi4RfOXuZT9mzZtQl5eHq5evYqlS5dCr9fDx8cHHh4ecHJysnt2MYjRPzg4iF27dsHX1xcZGRl/PMefzrMQpO5f7OTq//r1K2JjY7F//34cPnxYnOHtJEf7qVOnIAgCnj9/DicnJyQmJoJs/IQl3sz8xfHjx1FcXIyqqiqsWrXKelyv1wPAjN1ob2/vjF3rn3h6egIAfH19rcdUKhV8fHzQ2dlpz+iikLr/dwaDAe7u7ti9e7ftA4tI6vbKykqUlJSgoKAAW7ZsQWBgIG7evAlXV1fk5eWJE2EHOe77AwcOwGQyobu7G2azGZmZmejr64O3t7f9AXYSo39oaAixsbFwc3PDkydPplxC1+v1+Pbt24zb7evrs/kxJCap+xc7ufq/fv2KyMhIhIaG4vbt2xKU/Hdyta9cuRIbNmxAdHQ0CgoKUFpaitraWptm5s3MHIgIqampKCwsRGVl5YwnV29vb+j1epSXl1uPWSwWVFdXIywsbN63ExQUBJVKNeXtb2NjY+jo6MCaNWvsD7GRXP2/357BYEBiYuKCP+HJ1T48PAwAU14zMvn95BW7hSD3fQ/8e5nazc0NDx8+hIuLC6Kjo+1qsIdY/YODg4iJiYFSqURxcTFcXFymnCc0NBQDAwOor6+3Hqurq8PAwIDNf45ikKt/sZKzv7u7G9u2bUNgYCAMBsOM5wK5LeR9P3lFZnR01Obh2SyOHj1KGo2GXrx4MeVtw8PDw9Y1WVlZpNFoqLCwkJqamig+Pn7G21N7enpIEAS6c+eO9Z0rgiCQ2Wy2rjlx4gR5eXlRWVkZtbS0UHJyMul0Ourv75e1+Xdy9hMRVVRUEABqbm6WrXEucrX39fWRu7s77d27lxoaGqi1tZVOnjxJzs7O1NDQIHv3JDnv+5ycHHr37h21trbSjRs3yNXVla5fvy5r73Ri9A8ODlJwcDD5+/vTp0+fppxnfHzcep7Y2FgKCAggo9FIRqOR/P39F/yt2XL2f/nyhQRBoAsXLpCbmxsJgkCCINDQ0JDs3ZPk6u/u7qb169dTVFQUdXV1TVmzUORqr6uro5ycHBIEgTo6OqiyspLCw8Np3bp1NDIyYtPsvJmZA4BZvwwGg3XNxMQEZWRkkF6vJ5VKRVu3bqWmpqYp58nIyPjreSwWC6Wnp5NOpyO1Wk3bt2+nDx8+yFQ6Ozn7iYji4+MpLCxMhrK/k7P97du3FBMTQ1qtltRqNYWEhFBpaalMpbOTs//gwYOk1WpJqVRSQEAA5efny1Q5NzH6J9+OPttXe3u7dZ3ZbKaEhARSq9WkVqspISHhr/99gdTk7D906NCsa6qqquQLnkaufoPBMOeahSJXe2NjI0VGRpJWqyWVSkVr166llJQU6urqsnl2xf8DGGOMMcYcEr9mhjHGGGMOjTczjDHGGHNovJlhjDHGmEPjzQxjjDHGHBpvZhhjjDHm0HgzwxhjjDGHxpsZxhhjjDk03swwxhhjzKHxZoYxxhhjDo03M4wxxhhzaLyZYYwxxphD+x/KDe1SR+aY7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # make the survey total from the list of available codes\n",
    "vs_jt = this_canton.groupby([\"loc_date\", \"date\", \"zone\"], as_index=False)[\"pcs/m\"].sum()\n",
    "\n",
    "# all other locations\n",
    "# lake\n",
    "lake_jt=cantons_voisine[cantons_voisine.zone == \"lac\"].groupby([\"loc_date\", \"date\"], as_index=False)[\"pcs/m\"].sum()\n",
    "\n",
    "# rivers\n",
    "rhn_jt = cantons_voisine[cantons_voisine.zone == \"rivière\"].groupby([\"loc_date\", \"date\"], as_index=False)[\"pcs/m\"].sum()\n",
    "\n",
    "#alpes\n",
    "alp_jt = cantons_voisine[cantons_voisine.zone == \"montagne\"].groupby([\"loc_date\", \"date\"], as_index=False)[\"pcs/m\"].sum()\n",
    "\n",
    "french_caption = \"La densité totale par échantillon pour les 74 objets d'intérêt. \\\n",
    "Le premier prélèvement en Valais date de 2016. Les prélèvements en rivière \\\n",
    "et en montagne ont été réalisés lors du rapport fédéral.\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.scatterplot(data=vs_jt, x=\"date\", y=\"pcs/m\", hue=\"zone\", zorder=20, palette={'lac':'royalblue', 'montagne':'saddlebrown', 'rivière': 'black'}, ax=ax)\n",
    "sns.scatterplot(data=lake_jt,  x=\"date\", y=\"pcs/m\", zorder=0, color='royalblue', alpha=0.2, ax=ax, label='Le Léman')\n",
    "sns.scatterplot(data=rhn_jt, x=\"date\", y=\"pcs/m\", zorder=1, color='slategray', alpha=0.2, ax=ax, label='Autres rivières')\n",
    "sns.scatterplot(data=alp_jt, x=\"date\", y=\"pcs/m\", zorder=1, color='saddlebrown', alpha=0.2, ax=ax, label='Les Alpes')\n",
    "ax.set_ylim(-1, 60)\n",
    "ax.legend(bbox_to_anchor=(0, 1), loc=\"upper left\", ncol=2)\n",
    "ax.set_xlabel(\"\")\n",
    "plt.show()"
   ]
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   "source": [
    "### Répartition de la densité des déchets\n",
    "\n",
    "__Répartition de la densité des déchets à l'intérieur du canton: pcs/m__"
   ]
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       "<table id=\"T_2c55a\">\n",
       "  <caption>La répartition de la densité de déchets en Genève selon le domaine d'étude.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2c55a_level0_col0\" class=\"col_heading level0 col0\" >Lac</th>\n",
       "      <th id=\"T_2c55a_level0_col1\" class=\"col_heading level0 col1\" >Montagne</th>\n",
       "      <th id=\"T_2c55a_level0_col2\" class=\"col_heading level0 col2\" >Rhône</th>\n",
       "      <th id=\"T_2c55a_level0_col3\" class=\"col_heading level0 col3\" >Genève</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th id=\"T_2c55a_level0_row0\" class=\"row_heading level0 row0\" >moyenne</th>\n",
       "      <td id=\"T_2c55a_row0_col0\" class=\"data row0 col0\" >3.46</td>\n",
       "      <td id=\"T_2c55a_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_2c55a_row0_col2\" class=\"data row0 col2\" >0.81</td>\n",
       "      <td id=\"T_2c55a_row0_col3\" class=\"data row0 col3\" >3.06</td>\n",
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       "      <td id=\"T_2c55a_row1_col0\" class=\"data row1 col0\" >3.72</td>\n",
       "      <td id=\"T_2c55a_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
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       "      <td id=\"T_2c55a_row2_col0\" class=\"data row2 col0\" >0.13</td>\n",
       "      <td id=\"T_2c55a_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
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       "      <th id=\"T_2c55a_level0_row3\" class=\"row_heading level0 row3\" >25%</th>\n",
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       "      <td id=\"T_2c55a_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "      <td id=\"T_2c55a_row3_col2\" class=\"data row3 col2\" >0.19</td>\n",
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       "      <th id=\"T_2c55a_level0_row4\" class=\"row_heading level0 row4\" >50%</th>\n",
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       "      <td id=\"T_2c55a_row4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "      <td id=\"T_2c55a_row4_col2\" class=\"data row4 col2\" >0.25</td>\n",
       "      <td id=\"T_2c55a_row4_col3\" class=\"data row4 col3\" >1.69</td>\n",
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       "      <th id=\"T_2c55a_level0_row5\" class=\"row_heading level0 row5\" >75%</th>\n",
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       "      <td id=\"T_2c55a_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_2c55a_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
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       "      <td id=\"T_2c55a_row6_col1\" class=\"data row6 col1\" >nan</td>\n",
       "      <td id=\"T_2c55a_row6_col2\" class=\"data row6 col2\" >5.20</td>\n",
       "      <td id=\"T_2c55a_row6_col3\" class=\"data row6 col3\" >16.70</td>\n",
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   ],
   "source": [
    "summaries = {\n",
    "    canton:vs_jt[\"pcs/m\"].describe(),\n",
    "    \"Le Léman\": lake_jt[\"pcs/m\"].describe(),\n",
    "    \"Autres Rivières\":rhn_jt[\"pcs/m\"].describe(),\n",
    "    \"Alpes\": alp_jt[\"pcs/m\"].describe()\n",
    "    \n",
    "}\n",
    "\n",
    "zone_summaries = {\n",
    "    \"Lac\":vs_jt[vs_jt.zone == \"lac\"][\"pcs/m\"].describe(),\n",
    "    \"Montagne\":vs_jt[vs_jt.zone == \"montagne\"][\"pcs/m\"].describe(),\n",
    "    \"Rhône\":vs_jt[vs_jt.zone == \"rivière\"][\"pcs/m\"].describe(),\n",
    "    canton:vs_jt[\"pcs/m\"].describe()\n",
    "}\n",
    "\n",
    "valais_pcsm_total = pd.DataFrame(zone_summaries)\n",
    "valais_pcsm_total = valais_pcsm_total.T\n",
    "valais_pcsm_total[\"count\"] = valais_pcsm_total[\"count\"].astype(\"int\")\n",
    "valais_pcsm_total.rename(columns=display_names, inplace=True)\n",
    "valais_pcsm_total.drop(\"echantillons\", inplace=True, axis=1)\n",
    "valais_pcsm_total = valais_pcsm_total.T\n",
    "\n",
    "english_caption_2 = f\"La répartition de la densité de déchets en {canton} selon le domaine d'étude.\"\n",
    "\n",
    "dis_total = valais_pcsm_total.style.set_table_styles(table_large_font).format(precision=2).set_caption(english_caption_2)\n",
    "dis_total.background_gradient(axis=None, vmin=valais_pcsm_total.min().min(), vmax=valais_pcsm_total.max().max(), cmap=\"YlOrBr\")"
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    "__Répartition de la densité des déchets à l'extérieur du canton: pcs/m.__"
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       "</style>\n",
       "<table id=\"T_9f4fc\">\n",
       "  <caption>La répartition de la densité de déchets en Genève comparé à d'autres localités ayant des domaines d'études similaires</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9f4fc_level0_col0\" class=\"col_heading level0 col0\" >Genève</th>\n",
       "      <th id=\"T_9f4fc_level0_col1\" class=\"col_heading level0 col1\" >Le Léman</th>\n",
       "      <th id=\"T_9f4fc_level0_col2\" class=\"col_heading level0 col2\" >Autres Rivières</th>\n",
       "      <th id=\"T_9f4fc_level0_col3\" class=\"col_heading level0 col3\" >Alpes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row0\" class=\"row_heading level0 row0\" >moyenne</th>\n",
       "      <td id=\"T_9f4fc_row0_col0\" class=\"data row0 col0\" >3.06</td>\n",
       "      <td id=\"T_9f4fc_row0_col1\" class=\"data row0 col1\" >5.92</td>\n",
       "      <td id=\"T_9f4fc_row0_col2\" class=\"data row0 col2\" >0.34</td>\n",
       "      <td id=\"T_9f4fc_row0_col3\" class=\"data row0 col3\" >0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row1\" class=\"row_heading level0 row1\" >écart-type</th>\n",
       "      <td id=\"T_9f4fc_row1_col0\" class=\"data row1 col0\" >3.60</td>\n",
       "      <td id=\"T_9f4fc_row1_col1\" class=\"data row1 col1\" >8.05</td>\n",
       "      <td id=\"T_9f4fc_row1_col2\" class=\"data row1 col2\" >0.28</td>\n",
       "      <td id=\"T_9f4fc_row1_col3\" class=\"data row1 col3\" >0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row2\" class=\"row_heading level0 row2\" >min</th>\n",
       "      <td id=\"T_9f4fc_row2_col0\" class=\"data row2 col0\" >0.11</td>\n",
       "      <td id=\"T_9f4fc_row2_col1\" class=\"data row2 col1\" >0.02</td>\n",
       "      <td id=\"T_9f4fc_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_9f4fc_row2_col3\" class=\"data row2 col3\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row3\" class=\"row_heading level0 row3\" >25%</th>\n",
       "      <td id=\"T_9f4fc_row3_col0\" class=\"data row3 col0\" >0.44</td>\n",
       "      <td id=\"T_9f4fc_row3_col1\" class=\"data row3 col1\" >1.54</td>\n",
       "      <td id=\"T_9f4fc_row3_col2\" class=\"data row3 col2\" >0.08</td>\n",
       "      <td id=\"T_9f4fc_row3_col3\" class=\"data row3 col3\" >0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row4\" class=\"row_heading level0 row4\" >50%</th>\n",
       "      <td id=\"T_9f4fc_row4_col0\" class=\"data row4 col0\" >1.69</td>\n",
       "      <td id=\"T_9f4fc_row4_col1\" class=\"data row4 col1\" >3.36</td>\n",
       "      <td id=\"T_9f4fc_row4_col2\" class=\"data row4 col2\" >0.38</td>\n",
       "      <td id=\"T_9f4fc_row4_col3\" class=\"data row4 col3\" >0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row5\" class=\"row_heading level0 row5\" >75%</th>\n",
       "      <td id=\"T_9f4fc_row5_col0\" class=\"data row5 col0\" >4.04</td>\n",
       "      <td id=\"T_9f4fc_row5_col1\" class=\"data row5 col1\" >6.93</td>\n",
       "      <td id=\"T_9f4fc_row5_col2\" class=\"data row5 col2\" >0.49</td>\n",
       "      <td id=\"T_9f4fc_row5_col3\" class=\"data row5 col3\" >0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f4fc_level0_row6\" class=\"row_heading level0 row6\" >max</th>\n",
       "      <td id=\"T_9f4fc_row6_col0\" class=\"data row6 col0\" >16.70</td>\n",
       "      <td id=\"T_9f4fc_row6_col1\" class=\"data row6 col1\" >58.41</td>\n",
       "      <td id=\"T_9f4fc_row6_col2\" class=\"data row6 col2\" >0.80</td>\n",
       "      <td id=\"T_9f4fc_row6_col3\" class=\"data row6 col3\" >2.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd9fe1f0d90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valais_compare = pd.DataFrame(summaries)\n",
    "valais_compare = valais_compare.T\n",
    "valais_compare[\"count\"] = valais_compare[\"count\"].astype(\"int\")\n",
    "valais_compare.rename(columns=display_names, inplace=True)\n",
    "valais_compare.drop(\"echantillons\", inplace=True, axis=1)\n",
    "valais_compare = valais_compare.T\n",
    "\n",
    "english_caption_3 = f\"La répartition de la densité de déchets en {canton} comparé à d'autres localités ayant des domaines d'études similaires\"\n",
    "valais_compare.style.set_table_styles(table_large_font).format(precision=2).set_caption(english_caption_3)\n",
    "dis_total = valais_compare.style.set_table_styles(table_large_font).format(precision=2).set_caption(english_caption_3)\n",
    "dis_total.background_gradient(axis=None, vmin=valais_compare.min().min(), vmax=valais_compare.max().max(), cmap=\"YlOrBr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dff034e-a94f-4abd-af22-af0f45ca98cb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "__Répartition des objets selon matériel.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c592827-ec8a-45c6-b646-530dbb177b78",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_dec04 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_dec04 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_dec04 tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_dec04 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_dec04 caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_dec04\">\n",
       "  <caption>Total des objets par type de matériau. Tous les échantillons.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dec04_level0_col0\" class=\"col_heading level0 col0\" >quantité</th>\n",
       "      <th id=\"T_dec04_level0_col1\" class=\"col_heading level0 col1\" >% de totale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dec04_level0_row0\" class=\"row_heading level0 row0\" >Plastique</th>\n",
       "      <td id=\"T_dec04_row0_col0\" class=\"data row0 col0\" >8,913</td>\n",
       "      <td id=\"T_dec04_row0_col1\" class=\"data row0 col1\" >1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fda4c2396d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCodes = pd.read_csv(\"resources/codes.csv\")\n",
    "dfCodes.set_index(\"code\", drop=True, inplace=True)\n",
    "dfCodes.loc[\"Gcaps\", [\"material\", \"description\", \"parent_code\", \"groupname\"]] = \"Plastic\", \"Plastic bottle tops\", \"Parent code\", 'food and drink'\n",
    "\n",
    "code_d = dfCodes[\"description\"]\n",
    "code_m = dfCodes[\"material\"]\n",
    "code_g = dfCodes[\"groupname\"]\n",
    "\n",
    "caption_material_fr = \"Total des objets par type de matériau. Tous les échantillons.\"\n",
    "\n",
    "this_canton[\"material\"] = this_canton.code.apply(lambda x: code_m.loc[x])\n",
    "material_totals = this_canton.groupby(\"material\", as_index=False).quantity.sum()\n",
    "material_totals[\"quantity\"] = material_totals.quantity.astype(int)\n",
    "material_totals[\"material\"] = material_totals.material.apply(lambda x: material_names[x])\n",
    "material_totals[\"q_weight\"] = (material_totals.quantity/material_totals.quantity.sum()).round(3)\n",
    "material_totals = material_totals.set_index(\"material\", drop=True)\n",
    "material_totals.sort_values(by=\"quantity\", ascending=False, inplace=True)\n",
    "material_totals.index.name = None\n",
    "material_totals.rename(columns=display_names).style.set_table_styles(table_large_font).format('{:,}').set_caption(caption_material_fr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004331c7-5926-4791-95b7-20a996b35003",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Objets d'intérêt résultats municipaux: pcs/m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1bc5978f-01ab-487f-a016-33953670a1c7",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "  background-color: #fed676;\n",
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       "#T_44521_row6_col3, #T_44521_row8_col1 {\n",
       "  background-color: #fee89d;\n",
       "  color: #000000;\n",
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       "#T_44521_row7_col0 {\n",
       "  background-color: #fff7bc;\n",
       "  color: #000000;\n",
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       "#T_44521_row8_col0 {\n",
       "  background-color: #d95b09;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_44521_row8_col2 {\n",
       "  background-color: #f07818;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_44521_row8_col3 {\n",
       "  background-color: #662506;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_44521_row9_col0 {\n",
       "  background-color: #f88a21;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_44521_row9_col3 {\n",
       "  background-color: #fffcd7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_44521_level0_col0, #T_44521_level0_col1, #T_44521_level0_col2, #T_44521_level0_col3, #T_44521_level0_col4 {\n",
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       "  vertical-align: baseline;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_44521\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_44521_level0_col0\" class=\"col_heading level0 col0\" >Genève</th>\n",
       "      <th id=\"T_44521_level0_col1\" class=\"col_heading level0 col1\" >Hermance</th>\n",
       "      <th id=\"T_44521_level0_col2\" class=\"col_heading level0 col2\" >Savonière</th>\n",
       "      <th id=\"T_44521_level0_col3\" class=\"col_heading level0 col3\" >Versoix</th>\n",
       "      <th id=\"T_44521_level0_col4\" class=\"col_heading level0 col4\" >Veyrier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row0\" class=\"row_heading level0 row0\" >Bâche, feuille plastique industrielle</th>\n",
       "      <td id=\"T_44521_row0_col0\" class=\"data row0 col0\" >0.24</td>\n",
       "      <td id=\"T_44521_row0_col1\" class=\"data row0 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row0_col2\" class=\"data row0 col2\" >0.0</td>\n",
       "      <td id=\"T_44521_row0_col3\" class=\"data row0 col3\" >0.1</td>\n",
       "      <td id=\"T_44521_row0_col4\" class=\"data row0 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row1\" class=\"row_heading level0 row1\" >Cartouches de fusil de chasse</th>\n",
       "      <td id=\"T_44521_row1_col0\" class=\"data row1 col0\" >0.02</td>\n",
       "      <td id=\"T_44521_row1_col1\" class=\"data row1 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row1_col2\" class=\"data row1 col2\" >0.02</td>\n",
       "      <td id=\"T_44521_row1_col3\" class=\"data row1 col3\" >0.03</td>\n",
       "      <td id=\"T_44521_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row2\" class=\"row_heading level0 row2\" >Coton-tige</th>\n",
       "      <td id=\"T_44521_row2_col0\" class=\"data row2 col0\" >0.14</td>\n",
       "      <td id=\"T_44521_row2_col1\" class=\"data row2 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row2_col2\" class=\"data row2 col2\" >0.15</td>\n",
       "      <td id=\"T_44521_row2_col3\" class=\"data row2 col3\" >0.35</td>\n",
       "      <td id=\"T_44521_row2_col4\" class=\"data row2 col4\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row3\" class=\"row_heading level0 row3\" >Couvercles de bouteilles en plastique</th>\n",
       "      <td id=\"T_44521_row3_col0\" class=\"data row3 col0\" >0.19</td>\n",
       "      <td id=\"T_44521_row3_col1\" class=\"data row3 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row3_col2\" class=\"data row3 col2\" >0.13</td>\n",
       "      <td id=\"T_44521_row3_col3\" class=\"data row3 col3\" >0.26</td>\n",
       "      <td id=\"T_44521_row3_col4\" class=\"data row3 col4\" >0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row4\" class=\"row_heading level0 row4\" >Emballage fast food</th>\n",
       "      <td id=\"T_44521_row4_col0\" class=\"data row4 col0\" >0.03</td>\n",
       "      <td id=\"T_44521_row4_col1\" class=\"data row4 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row4_col2\" class=\"data row4 col2\" >0.0</td>\n",
       "      <td id=\"T_44521_row4_col3\" class=\"data row4 col3\" >0.01</td>\n",
       "      <td id=\"T_44521_row4_col4\" class=\"data row4 col4\" >0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row5\" class=\"row_heading level0 row5\" >Emballages de bonbons, de snacks</th>\n",
       "      <td id=\"T_44521_row5_col0\" class=\"data row5 col0\" >0.51</td>\n",
       "      <td id=\"T_44521_row5_col1\" class=\"data row5 col1\" >0.03</td>\n",
       "      <td id=\"T_44521_row5_col2\" class=\"data row5 col2\" >0.35</td>\n",
       "      <td id=\"T_44521_row5_col3\" class=\"data row5 col3\" >0.41</td>\n",
       "      <td id=\"T_44521_row5_col4\" class=\"data row5 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row6\" class=\"row_heading level0 row6\" >Mégots et filtres à cigarettes</th>\n",
       "      <td id=\"T_44521_row6_col0\" class=\"data row6 col0\" >0.83</td>\n",
       "      <td id=\"T_44521_row6_col1\" class=\"data row6 col1\" >0.29</td>\n",
       "      <td id=\"T_44521_row6_col2\" class=\"data row6 col2\" >0.48</td>\n",
       "      <td id=\"T_44521_row6_col3\" class=\"data row6 col3\" >0.34</td>\n",
       "      <td id=\"T_44521_row6_col4\" class=\"data row6 col4\" >0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row7\" class=\"row_heading level0 row7\" >Pellets industriels (GPI)</th>\n",
       "      <td id=\"T_44521_row7_col0\" class=\"data row7 col0\" >0.2</td>\n",
       "      <td id=\"T_44521_row7_col1\" class=\"data row7 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row7_col2\" class=\"data row7 col2\" >0.0</td>\n",
       "      <td id=\"T_44521_row7_col3\" class=\"data row7 col3\" >0.08</td>\n",
       "      <td id=\"T_44521_row7_col4\" class=\"data row7 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row8\" class=\"row_heading level0 row8\" >Plastiques fragmentés</th>\n",
       "      <td id=\"T_44521_row8_col0\" class=\"data row8 col0\" >1.1</td>\n",
       "      <td id=\"T_44521_row8_col1\" class=\"data row8 col1\" >0.34</td>\n",
       "      <td id=\"T_44521_row8_col2\" class=\"data row8 col2\" >0.95</td>\n",
       "      <td id=\"T_44521_row8_col3\" class=\"data row8 col3\" >1.58</td>\n",
       "      <td id=\"T_44521_row8_col4\" class=\"data row8 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_44521_level0_row9\" class=\"row_heading level0 row9\" >Polystyrène expansé fragmenté</th>\n",
       "      <td id=\"T_44521_row9_col0\" class=\"data row9 col0\" >0.86</td>\n",
       "      <td id=\"T_44521_row9_col1\" class=\"data row9 col1\" >0.0</td>\n",
       "      <td id=\"T_44521_row9_col2\" class=\"data row9 col2\" >0.02</td>\n",
       "      <td id=\"T_44521_row9_col3\" class=\"data row9 col3\" >0.07</td>\n",
       "      <td id=\"T_44521_row9_col4\" class=\"data row9 col4\" >0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd9fe1aed90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objects_city = this_canton[this_canton.code.isin(codes_of_interest)].groupby([\"city\",\"code\"], as_index=False)[\"pcs/m\"].mean()\n",
    "objects_city[\"object\"] = objects_city.code.apply(lambda x: french_names[x])\n",
    "municipal_results = objects_city.pivot(columns=\"city\", index=\"object\", values=\"pcs/m\").fillna(0)\n",
    "municipal_results = municipal_results.round(2)\n",
    "municipal_results.index.name = None\n",
    "municipal_results.columns.name = None\n",
    "dist = municipal_results.style.set_table_styles(table_large_font).format('{:,}').applymap_index(rotateText, axis=1)\n",
    "dist.background_gradient(axis=None, vmin= municipal_results.min().min(), vmax= municipal_results.max().max(), cmap=\"YlOrBr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488e32ba-dc11-4bc4-8ad1-136a240a4a4e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Résultats par utilité d'objet: pcs/m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4383ec10-a952-4a2d-83aa-9c3eccf96255",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "L'utilité d'un objet est basée sur l'utilisation de l'objet avant qu'il ne soit jeté ou sur la description de l'objet si l'utilisation d'origine n'est pas déterminée. \n",
    "\n",
    "* __eaux usées :__ objets rejetés par les stations d'épuration des eaux, y compris les objets susceptibles d'être jetés dans les toilettes\n",
    "* __microplastiques (< 5 mm) :__ plastiques fragmentés et résines plastiques de préproduction\n",
    "* __infrastructures :__ éléments liés à la construction et à l'entretien des bâtiments, des routes et de l'approvisionnement en eau et en électricité\n",
    "* __alimentation et boissons :__ tous les matériaux liés à la consommation d'aliments et de boissons\n",
    "* __agriculture :__ principalement les feuilles industrielles, c'est-à-dire le paillage et les couvertures de rangs, les serres, la fumigation des sols, l'enrubannage des balles. Comprend les plastiques durs pour les clôtures agricoles, les pots de fleurs, etc.\n",
    "* __tabac :__ principalement les filtres de cigarettes, y compris tout le matériel lié au tabagisme\n",
    "* __Loisirs :__ objets liés aux sports et aux loisirs, tels que la pêche, la chasse, la randonnée, etc.\n",
    "* __Emballages non alimentaires et boissons :__ matériaux d'emballage non identifiés comme étant liés à l'alimentation, aux boissons ou au tabac.\n",
    "* __fragments de plastique :__ morceaux de plastique d'origine ou d'utilisation indéterminée\n",
    "* __articles personnels :__ accessoires, hygiène et vêtements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f321bbd-8c1b-4b02-93e0-8e3352758288",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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       "  background-color: #fee596;\n",
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       "  background-color: #fffdd9;\n",
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       "}\n",
       "#T_586d2_level0_col0, #T_586d2_level0_col1, #T_586d2_level0_col2, #T_586d2_level0_col3, #T_586d2_level0_col4 {\n",
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       "</style>\n",
       "<table id=\"T_586d2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_586d2_level0_col0\" class=\"col_heading level0 col0\" >Genève</th>\n",
       "      <th id=\"T_586d2_level0_col1\" class=\"col_heading level0 col1\" >Hermance</th>\n",
       "      <th id=\"T_586d2_level0_col2\" class=\"col_heading level0 col2\" >Savonière</th>\n",
       "      <th id=\"T_586d2_level0_col3\" class=\"col_heading level0 col3\" >Versoix</th>\n",
       "      <th id=\"T_586d2_level0_col4\" class=\"col_heading level0 col4\" >Veyrier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row0\" class=\"row_heading level0 row0\" >agriculture</th>\n",
       "      <td id=\"T_586d2_row0_col0\" class=\"data row0 col0\" >0.02</td>\n",
       "      <td id=\"T_586d2_row0_col1\" class=\"data row0 col1\" >0.0</td>\n",
       "      <td id=\"T_586d2_row0_col2\" class=\"data row0 col2\" >0.0</td>\n",
       "      <td id=\"T_586d2_row0_col3\" class=\"data row0 col3\" >0.03</td>\n",
       "      <td id=\"T_586d2_row0_col4\" class=\"data row0 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row1\" class=\"row_heading level0 row1\" >alimentation et boissons</th>\n",
       "      <td id=\"T_586d2_row1_col0\" class=\"data row1 col0\" >0.34</td>\n",
       "      <td id=\"T_586d2_row1_col1\" class=\"data row1 col1\" >0.03</td>\n",
       "      <td id=\"T_586d2_row1_col2\" class=\"data row1 col2\" >0.45</td>\n",
       "      <td id=\"T_586d2_row1_col3\" class=\"data row1 col3\" >0.45</td>\n",
       "      <td id=\"T_586d2_row1_col4\" class=\"data row1 col4\" >0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row2\" class=\"row_heading level0 row2\" >eaux usées</th>\n",
       "      <td id=\"T_586d2_row2_col0\" class=\"data row2 col0\" >0.04</td>\n",
       "      <td id=\"T_586d2_row2_col1\" class=\"data row2 col1\" >0.0</td>\n",
       "      <td id=\"T_586d2_row2_col2\" class=\"data row2 col2\" >0.15</td>\n",
       "      <td id=\"T_586d2_row2_col3\" class=\"data row2 col3\" >0.33</td>\n",
       "      <td id=\"T_586d2_row2_col4\" class=\"data row2 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row3\" class=\"row_heading level0 row3\" >fragments de plastique</th>\n",
       "      <td id=\"T_586d2_row3_col0\" class=\"data row3 col0\" >0.79</td>\n",
       "      <td id=\"T_586d2_row3_col1\" class=\"data row3 col1\" >0.25</td>\n",
       "      <td id=\"T_586d2_row3_col2\" class=\"data row3 col2\" >0.78</td>\n",
       "      <td id=\"T_586d2_row3_col3\" class=\"data row3 col3\" >1.6</td>\n",
       "      <td id=\"T_586d2_row3_col4\" class=\"data row3 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row4\" class=\"row_heading level0 row4\" >infrastructures</th>\n",
       "      <td id=\"T_586d2_row4_col0\" class=\"data row4 col0\" >0.1</td>\n",
       "      <td id=\"T_586d2_row4_col1\" class=\"data row4 col1\" >0.0</td>\n",
       "      <td id=\"T_586d2_row4_col2\" class=\"data row4 col2\" >0.02</td>\n",
       "      <td id=\"T_586d2_row4_col3\" class=\"data row4 col3\" >0.02</td>\n",
       "      <td id=\"T_586d2_row4_col4\" class=\"data row4 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row5\" class=\"row_heading level0 row5\" >loisirs</th>\n",
       "      <td id=\"T_586d2_row5_col0\" class=\"data row5 col0\" >0.0</td>\n",
       "      <td id=\"T_586d2_row5_col1\" class=\"data row5 col1\" >0.0</td>\n",
       "      <td id=\"T_586d2_row5_col2\" class=\"data row5 col2\" >0.01</td>\n",
       "      <td id=\"T_586d2_row5_col3\" class=\"data row5 col3\" >0.02</td>\n",
       "      <td id=\"T_586d2_row5_col4\" class=\"data row5 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row6\" class=\"row_heading level0 row6\" >microplastiques (< 5 mm)</th>\n",
       "      <td id=\"T_586d2_row6_col0\" class=\"data row6 col0\" >0.0</td>\n",
       "      <td id=\"T_586d2_row6_col1\" class=\"data row6 col1\" >0.0</td>\n",
       "      <td id=\"T_586d2_row6_col2\" class=\"data row6 col2\" >0.0</td>\n",
       "      <td id=\"T_586d2_row6_col3\" class=\"data row6 col3\" >0.02</td>\n",
       "      <td id=\"T_586d2_row6_col4\" class=\"data row6 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_586d2_level0_row7\" class=\"row_heading level0 row7\" >tabac</th>\n",
       "      <td id=\"T_586d2_row7_col0\" class=\"data row7 col0\" >0.48</td>\n",
       "      <td id=\"T_586d2_row7_col1\" class=\"data row7 col1\" >0.12</td>\n",
       "      <td id=\"T_586d2_row7_col2\" class=\"data row7 col2\" >0.19</td>\n",
       "      <td id=\"T_586d2_row7_col3\" class=\"data row7 col3\" >0.38</td>\n",
       "      <td id=\"T_586d2_row7_col4\" class=\"data row7 col4\" >0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd9fe1df250>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_canton[\"groupname\"] = this_canton.code.apply(lambda x: code_g.loc[x])\n",
    "\n",
    "cg_agg = this_canton.groupby([\"loc_date\", \"city\", \"groupname\"], as_index=False)[\"pcs/m\"].sum()\n",
    "cg_agg[\"groupname\"] = cg_agg.groupname.apply(lambda x: group_names[x])\n",
    "cg_agg_group = cg_agg.groupby([\"city\",\"groupname\"], as_index=False)[\"pcs/m\"].median()\n",
    "\n",
    "cg_display = cg_agg_group.pivot(columns=\"city\", index=\"groupname\", values=[\"pcs/m\"])\n",
    "cg_display=cg_display.round(2)\n",
    "cg_display.index.name = None\n",
    "cg_display.columns = cg_display.columns.droplevel()\n",
    "cg_display.fillna(0, inplace=True)\n",
    "cg_display.columns.name = None\n",
    "\n",
    "dist = cg_display.style.set_table_styles(table_large_font).format('{:,}').applymap_index(rotateText, axis=1)\n",
    "dist.background_gradient(axis=None, vmin=cg_display.min().min(), vmax= cg_display.max().max(), cmap=\"YlOrBr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b980747-c52a-4f2d-b936-d7bc8c2e1a55",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Lieux d'enquêtes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "873c291a-19f7-4654-8e3a-fbfbfd809b47",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "<table id=\"T_8854f\">\n",
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "      <th id=\"T_8854f_level0_col3\" class=\"col_heading level0 col3\" >municipalités</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row0\" class=\"row_heading level0 row0\" >baby-plage</th>\n",
       "      <td id=\"T_8854f_row0_col0\" class=\"data row0 col0\" >Baby Plage</td>\n",
       "      <td id=\"T_8854f_row0_col1\" class=\"data row0 col1\" >46.208783</td>\n",
       "      <td id=\"T_8854f_row0_col2\" class=\"data row0 col2\" >6.162850</td>\n",
       "      <td id=\"T_8854f_row0_col3\" class=\"data row0 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row1\" class=\"row_heading level0 row1\" >hermance</th>\n",
       "      <td id=\"T_8854f_row1_col0\" class=\"data row1 col0\" >Hermance</td>\n",
       "      <td id=\"T_8854f_row1_col1\" class=\"data row1 col1\" >46.303583</td>\n",
       "      <td id=\"T_8854f_row1_col2\" class=\"data row1 col2\" >6.240833</td>\n",
       "      <td id=\"T_8854f_row1_col3\" class=\"data row1 col3\" >Hermance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row2\" class=\"row_heading level0 row2\" >port-choiseul</th>\n",
       "      <td id=\"T_8854f_row2_col0\" class=\"data row2 col0\" >Port Choiseul</td>\n",
       "      <td id=\"T_8854f_row2_col1\" class=\"data row2 col1\" >46.289667</td>\n",
       "      <td id=\"T_8854f_row2_col2\" class=\"data row2 col2\" >6.170717</td>\n",
       "      <td id=\"T_8854f_row2_col3\" class=\"data row2 col3\" >Versoix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row3\" class=\"row_heading level0 row3\" >savoniere</th>\n",
       "      <td id=\"T_8854f_row3_col0\" class=\"data row3 col0\" >Savonière</td>\n",
       "      <td id=\"T_8854f_row3_col1\" class=\"data row3 col1\" >46.264133</td>\n",
       "      <td id=\"T_8854f_row3_col2\" class=\"data row3 col2\" >6.199733</td>\n",
       "      <td id=\"T_8854f_row3_col3\" class=\"data row3 col3\" >Savonière</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row4\" class=\"row_heading level0 row4\" >versoix-p</th>\n",
       "      <td id=\"T_8854f_row4_col0\" class=\"data row4 col0\" >Versoix-p</td>\n",
       "      <td id=\"T_8854f_row4_col1\" class=\"data row4 col1\" >46.274050</td>\n",
       "      <td id=\"T_8854f_row4_col2\" class=\"data row4 col2\" >6.171317</td>\n",
       "      <td id=\"T_8854f_row4_col3\" class=\"data row4 col3\" >Versoix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row5\" class=\"row_heading level0 row5\" >villa-barton</th>\n",
       "      <td id=\"T_8854f_row5_col0\" class=\"data row5 col0\" >Villa Barton</td>\n",
       "      <td id=\"T_8854f_row5_col1\" class=\"data row5 col1\" >46.222350</td>\n",
       "      <td id=\"T_8854f_row5_col2\" class=\"data row5 col2\" >6.152500</td>\n",
       "      <td id=\"T_8854f_row5_col3\" class=\"data row5 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row6\" class=\"row_heading level0 row6\" >arve_carouge_battistellak</th>\n",
       "      <td id=\"T_8854f_row6_col0\" class=\"data row6 col0\" >arve_carouge_battistellak</td>\n",
       "      <td id=\"T_8854f_row6_col1\" class=\"data row6 col1\" >46.178491</td>\n",
       "      <td id=\"T_8854f_row6_col2\" class=\"data row6 col2\" >6.154319</td>\n",
       "      <td id=\"T_8854f_row6_col3\" class=\"data row6 col3\" >Veyrier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row7\" class=\"row_heading level0 row7\" >pont-sous-terre</th>\n",
       "      <td id=\"T_8854f_row7_col0\" class=\"data row7 col0\" >Pont sous-terre</td>\n",
       "      <td id=\"T_8854f_row7_col1\" class=\"data row7 col1\" >46.202960</td>\n",
       "      <td id=\"T_8854f_row7_col2\" class=\"data row7 col2\" >6.131577</td>\n",
       "      <td id=\"T_8854f_row7_col3\" class=\"data row7 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row8\" class=\"row_heading level0 row8\" >baby-plage-geneva</th>\n",
       "      <td id=\"T_8854f_row8_col0\" class=\"data row8 col0\" >Baby Plage Geneva</td>\n",
       "      <td id=\"T_8854f_row8_col1\" class=\"data row8 col1\" >46.208558</td>\n",
       "      <td id=\"T_8854f_row8_col2\" class=\"data row8 col2\" >6.162923</td>\n",
       "      <td id=\"T_8854f_row8_col3\" class=\"data row8 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row9\" class=\"row_heading level0 row9\" >baby-plage-ii-geneve</th>\n",
       "      <td id=\"T_8854f_row9_col0\" class=\"data row9 col0\" >Baby-Plage II Genève</td>\n",
       "      <td id=\"T_8854f_row9_col1\" class=\"data row9 col1\" >46.208940</td>\n",
       "      <td id=\"T_8854f_row9_col2\" class=\"data row9 col2\" >6.164330</td>\n",
       "      <td id=\"T_8854f_row9_col3\" class=\"data row9 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row10\" class=\"row_heading level0 row10\" >jardin-botanique</th>\n",
       "      <td id=\"T_8854f_row10_col0\" class=\"data row10 col0\" >Jardin Botanique</td>\n",
       "      <td id=\"T_8854f_row10_col1\" class=\"data row10 col1\" >46.225909</td>\n",
       "      <td id=\"T_8854f_row10_col2\" class=\"data row10 col2\" >6.149357</td>\n",
       "      <td id=\"T_8854f_row10_col3\" class=\"data row10 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row11\" class=\"row_heading level0 row11\" >rocky-plage</th>\n",
       "      <td id=\"T_8854f_row11_col0\" class=\"data row11 col0\" >Rocky plage</td>\n",
       "      <td id=\"T_8854f_row11_col1\" class=\"data row11 col1\" >46.209737</td>\n",
       "      <td id=\"T_8854f_row11_col2\" class=\"data row11 col2\" >6.164952</td>\n",
       "      <td id=\"T_8854f_row11_col3\" class=\"data row11 col3\" >Genève</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8854f_level0_row12\" class=\"row_heading level0 row12\" >versoix</th>\n",
       "      <td id=\"T_8854f_row12_col0\" class=\"data row12 col0\" >Versoix</td>\n",
       "      <td id=\"T_8854f_row12_col1\" class=\"data row12 col1\" >46.289194</td>\n",
       "      <td id=\"T_8854f_row12_col2\" class=\"data row12 col2\" >6.170569</td>\n",
       "      <td id=\"T_8854f_row12_col3\" class=\"data row12 col3\" >Versoix</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd9f5bafd60>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beaches = pd.read_csv(\"resources/beaches.csv\")\n",
    "\n",
    "survey_locations = this_canton.slug.unique()\n",
    "\n",
    "canton_beaches = beaches[beaches.slug.isin(survey_locations)].copy()\n",
    "canton_beaches.set_index(\"slug\", inplace=True)\n",
    "canton_beaches.index.name = None\n",
    "canton_beaches[[\"location\", \"latitude\", \"longitude\", \"city\"]].rename(columns=display_names).style.set_table_styles(table_large_font)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708769b8-fa03-46c0-89c4-6b006ccde2e3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Inventaire complet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2bc71e72-cb2c-43c8-a38c-8ce27acf937f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "operators = {\n",
    "    \"quantity\":\"sum\",\n",
    "    \"pcs/m\": \"median\",\n",
    "}\n",
    "\n",
    "this_canton[\"fail\"] = this_canton[\"quantity\"] > 0\n",
    "code_fails = this_canton.groupby(\"code\").fail.sum()\n",
    "code_fail_rate = code_fails/this_canton.loc_date.nunique()\n",
    "\n",
    "inventory = this_canton.groupby(\"code\", as_index=False).agg(operators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a5de6413-e813-4f08-8e66-34b27f24f3e3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_905d1 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_905d1 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_905d1 tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_905d1 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_905d1 caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_905d1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_905d1_level0_col0\" class=\"col_heading level0 col0\" >object</th>\n",
       "      <th id=\"T_905d1_level0_col1\" class=\"col_heading level0 col1\" >quantité</th>\n",
       "      <th id=\"T_905d1_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n",
       "      <th id=\"T_905d1_level0_col3\" class=\"col_heading level0 col3\" >taux d'échec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row0\" class=\"row_heading level0 row0\" >Gfrags</th>\n",
       "      <td id=\"T_905d1_row0_col0\" class=\"data row0 col0\" >Plastiques fragmentés</td>\n",
       "      <td id=\"T_905d1_row0_col1\" class=\"data row0 col1\" >2872</td>\n",
       "      <td id=\"T_905d1_row0_col2\" class=\"data row0 col2\" >0.809</td>\n",
       "      <td id=\"T_905d1_row0_col3\" class=\"data row0 col3\" >0.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row1\" class=\"row_heading level0 row1\" >G27</th>\n",
       "      <td id=\"T_905d1_row1_col0\" class=\"data row1 col0\" >Mégots et filtres à cigarettes</td>\n",
       "      <td id=\"T_905d1_row1_col1\" class=\"data row1 col1\" >2000</td>\n",
       "      <td id=\"T_905d1_row1_col2\" class=\"data row1 col2\" >0.273</td>\n",
       "      <td id=\"T_905d1_row1_col3\" class=\"data row1 col3\" >0.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row2\" class=\"row_heading level0 row2\" >G30</th>\n",
       "      <td id=\"T_905d1_row2_col0\" class=\"data row2 col0\" >Emballages de bonbons, de snacks</td>\n",
       "      <td id=\"T_905d1_row2_col1\" class=\"data row2 col1\" >1036</td>\n",
       "      <td id=\"T_905d1_row2_col2\" class=\"data row2 col2\" >0.170</td>\n",
       "      <td id=\"T_905d1_row2_col3\" class=\"data row2 col3\" >0.831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row3\" class=\"row_heading level0 row3\" >Gfoam</th>\n",
       "      <td id=\"T_905d1_row3_col0\" class=\"data row3 col0\" >Polystyrène expansé fragmenté</td>\n",
       "      <td id=\"T_905d1_row3_col1\" class=\"data row3 col1\" >942</td>\n",
       "      <td id=\"T_905d1_row3_col2\" class=\"data row3 col2\" >0.052</td>\n",
       "      <td id=\"T_905d1_row3_col3\" class=\"data row3 col3\" >0.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row4\" class=\"row_heading level0 row4\" >G112</th>\n",
       "      <td id=\"T_905d1_row4_col0\" class=\"data row4 col0\" >Pellets industriels (GPI)</td>\n",
       "      <td id=\"T_905d1_row4_col1\" class=\"data row4 col1\" >681</td>\n",
       "      <td id=\"T_905d1_row4_col2\" class=\"data row4 col2\" >0.000</td>\n",
       "      <td id=\"T_905d1_row4_col3\" class=\"data row4 col3\" >0.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row5\" class=\"row_heading level0 row5\" >Gcaps</th>\n",
       "      <td id=\"T_905d1_row5_col0\" class=\"data row5 col0\" >Couvercles de bouteilles en plastique</td>\n",
       "      <td id=\"T_905d1_row5_col1\" class=\"data row5 col1\" >486</td>\n",
       "      <td id=\"T_905d1_row5_col2\" class=\"data row5 col2\" >0.086</td>\n",
       "      <td id=\"T_905d1_row5_col3\" class=\"data row5 col3\" >0.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row6\" class=\"row_heading level0 row6\" >G95</th>\n",
       "      <td id=\"T_905d1_row6_col0\" class=\"data row6 col0\" >Coton-tige</td>\n",
       "      <td id=\"T_905d1_row6_col1\" class=\"data row6 col1\" >484</td>\n",
       "      <td id=\"T_905d1_row6_col2\" class=\"data row6 col2\" >0.037</td>\n",
       "      <td id=\"T_905d1_row6_col3\" class=\"data row6 col3\" >0.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row7\" class=\"row_heading level0 row7\" >G67</th>\n",
       "      <td id=\"T_905d1_row7_col0\" class=\"data row7 col0\" >Bâche, feuille plastique industrielle</td>\n",
       "      <td id=\"T_905d1_row7_col1\" class=\"data row7 col1\" >290</td>\n",
       "      <td id=\"T_905d1_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
       "      <td id=\"T_905d1_row7_col3\" class=\"data row7 col3\" >0.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row8\" class=\"row_heading level0 row8\" >G70</th>\n",
       "      <td id=\"T_905d1_row8_col0\" class=\"data row8 col0\" >Cartouches de fusil de chasse</td>\n",
       "      <td id=\"T_905d1_row8_col1\" class=\"data row8 col1\" >70</td>\n",
       "      <td id=\"T_905d1_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
       "      <td id=\"T_905d1_row8_col3\" class=\"data row8 col3\" >0.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_905d1_level0_row9\" class=\"row_heading level0 row9\" >G10</th>\n",
       "      <td id=\"T_905d1_row9_col0\" class=\"data row9 col0\" >Emballage fast food</td>\n",
       "      <td id=\"T_905d1_row9_col1\" class=\"data row9 col1\" >52</td>\n",
       "      <td id=\"T_905d1_row9_col2\" class=\"data row9 col2\" >0.000</td>\n",
       "      <td id=\"T_905d1_row9_col3\" class=\"data row9 col3\" >0.322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd9f5cbeeb0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inventory[\"fail_rate\"] = inventory.code.apply(lambda x: code_fail_rate.loc[x])\n",
    "inventory = inventory[inventory.quantity > 0]\n",
    "inventory[\"object\"] =inventory.code.apply(lambda x: french_names[x])\n",
    "inventory = inventory[[\"code\", \"object\", \"quantity\", \"pcs/m\", \"fail_rate\"]]\n",
    "inventory.set_index(\"code\", inplace=True)\n",
    "inventory[\"quantity\"] = inventory.quantity.astype(\"int\")\n",
    "\n",
    "inventory.sort_values(by=\"quantity\", inplace=True, ascending=False)\n",
    "inventory.rename(columns=display_names, inplace=True)\n",
    "inventory.index.name = None\n",
    "inventory.style.set_table_styles(table_large_font).format(precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5bec42d8-ed97-4004-828b-409022b85d7c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "<br />\n",
       "<br />\n",
       "Ceci est un ébauche : le script a été mis à jour le 22/09/2023 in Biel, CH\n",
       "\n",
       "Contactez analyst@ pour plus d'informations\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "today = dt.datetime.now().date().strftime(\"%d/%m/%Y\")\n",
    "where = \"Biel, CH\"\n",
    "\n",
    "my_block = f\"\"\"\n",
    "<br />\n",
    "<br />\n",
    "Ceci est un ébauche : le script a été mis à jour le {today} in {where}\n",
    "\n",
    "Contactez analyst@ pour plus d'informations\n",
    "\"\"\"\n",
    "\n",
    "md(my_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11142d5a-9945-4ed7-8ed6-1aae1d07a1af",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Git repo: https://github.com/hammerdirt-analyst/cantonal_reports.git\n",
      "\n",
      "Git branch: main\n",
      "\n",
      "seaborn   : 0.12.2\n",
      "numpy     : 1.25.2\n",
      "pandas    : 2.0.3\n",
      "matplotlib: 3.7.1\n",
      "json      : 2.0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark --iversions -b -r"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}