residuos1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contenedores para basura en la Ciudad de México\n",
    " Proyecto Prototípico\n",
    "\n",
    "---\n",
    "Licenciatura Ciencias de Datos para Negocios - 6to semestre\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "En este proyecto, abordaremos el desafío de la mala gestión de residuos en la Ciudad de México utilizando técnicas de programación. Basándonos en la información proporcionada por el Instituto Nacional de Estadística y Geografía (INEGI), estamos desarrollando un código que nos permitirá analizar y visualizar datos relacionados con la generación y gestión de residuos.\n",
    "\n",
    "A través de este código, buscamos identificar patrones, tendencias y anomalías en los datos, lo que nos ayudará a comprender mejor la situación actual y tomar decisiones informadas para mejorar la gestión de residuos en nuestra comunidad.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopandas in c:\\users\\quetz\\miniconda3\\lib\\site-packages (0.14.4)\n",
      "Requirement already satisfied: fiona>=1.8.21 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from geopandas) (1.9.6)\n",
      "Requirement already satisfied: numpy>=1.22 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from geopandas) (1.26.4)\n",
      "Requirement already satisfied: packaging in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from geopandas) (23.2)\n",
      "Requirement already satisfied: pandas>=1.4.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from geopandas) (2.2.2)\n",
      "Requirement already satisfied: pyproj>=3.3.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from geopandas) (3.6.1)\n",
      "Requirement already satisfied: shapely>=1.8.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from geopandas) (2.0.4)\n",
      "Requirement already satisfied: attrs>=19.2.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (23.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (2024.2.2)\n",
      "Requirement already satisfied: click~=8.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (8.1.7)\n",
      "Requirement already satisfied: click-plugins>=1.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (1.1.1)\n",
      "Requirement already satisfied: cligj>=0.5 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (0.7.2)\n",
      "Requirement already satisfied: six in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2024.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from click~=8.0->fiona>=1.8.21->geopandas) (0.4.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMPORTAR PAQUETES NECESARIOS\n",
    "import pandas as pd\n",
    "import requests\n",
    "import geopandas as gpd\n",
    "import json\n",
    "import numpy as np\n",
    "from scipy.optimize import linprog\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib.patches import Patch\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cartografía para los mapas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVE_ENT</th>\n",
       "      <th>CVE_MUN</th>\n",
       "      <th>NOM_MUN</th>\n",
       "      <th>OID_1</th>\n",
       "      <th>cov_</th>\n",
       "      <th>cov_id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09</td>\n",
       "      <td>002</td>\n",
       "      <td>Azcapotzalco</td>\n",
       "      <td>337</td>\n",
       "      <td>337</td>\n",
       "      <td>338</td>\n",
       "      <td>POLYGON ((-99.18245 19.50756, -99.18231 19.507...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09</td>\n",
       "      <td>005</td>\n",
       "      <td>Gustavo A. Madero</td>\n",
       "      <td>338</td>\n",
       "      <td>338</td>\n",
       "      <td>339</td>\n",
       "      <td>POLYGON ((-99.11789 19.59059, -99.11860 19.584...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09</td>\n",
       "      <td>016</td>\n",
       "      <td>Miguel Hidalgo</td>\n",
       "      <td>339</td>\n",
       "      <td>339</td>\n",
       "      <td>340</td>\n",
       "      <td>POLYGON ((-99.19044 19.47046, -99.19058 19.467...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09</td>\n",
       "      <td>006</td>\n",
       "      <td>Iztacalco</td>\n",
       "      <td>340</td>\n",
       "      <td>340</td>\n",
       "      <td>341</td>\n",
       "      <td>POLYGON ((-99.05579 19.42214, -99.05584 19.421...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09</td>\n",
       "      <td>017</td>\n",
       "      <td>Venustiano Carranza</td>\n",
       "      <td>341</td>\n",
       "      <td>341</td>\n",
       "      <td>342</td>\n",
       "      <td>POLYGON ((-99.10946 19.45292, -99.10895 19.452...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>09</td>\n",
       "      <td>007</td>\n",
       "      <td>Iztapalapa</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>343</td>\n",
       "      <td>POLYGON ((-99.05813 19.40072, -99.05814 19.400...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>09</td>\n",
       "      <td>012</td>\n",
       "      <td>Tlalpan</td>\n",
       "      <td>2448</td>\n",
       "      <td>2448</td>\n",
       "      <td>2449</td>\n",
       "      <td>POLYGON ((-99.19671 19.30240, -99.19629 19.302...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>09</td>\n",
       "      <td>013</td>\n",
       "      <td>Xochimilco</td>\n",
       "      <td>2449</td>\n",
       "      <td>2449</td>\n",
       "      <td>2450</td>\n",
       "      <td>POLYGON ((-99.09880 19.32045, -99.09870 19.319...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09</td>\n",
       "      <td>008</td>\n",
       "      <td>La Magdalena Contreras</td>\n",
       "      <td>2450</td>\n",
       "      <td>2450</td>\n",
       "      <td>2451</td>\n",
       "      <td>POLYGON ((-99.20819 19.33674, -99.20859 19.336...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>09</td>\n",
       "      <td>014</td>\n",
       "      <td>Benito Juárez</td>\n",
       "      <td>2451</td>\n",
       "      <td>2451</td>\n",
       "      <td>2452</td>\n",
       "      <td>POLYGON ((-99.13659 19.40281, -99.13679 19.401...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>09</td>\n",
       "      <td>015</td>\n",
       "      <td>Cuauhtémoc</td>\n",
       "      <td>2452</td>\n",
       "      <td>2452</td>\n",
       "      <td>2453</td>\n",
       "      <td>POLYGON ((-99.12951 19.46265, -99.12919 19.462...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>09</td>\n",
       "      <td>010</td>\n",
       "      <td>Álvaro Obregón</td>\n",
       "      <td>2453</td>\n",
       "      <td>2453</td>\n",
       "      <td>2454</td>\n",
       "      <td>POLYGON ((-99.18906 19.39559, -99.18871 19.394...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>09</td>\n",
       "      <td>004</td>\n",
       "      <td>Cuajimalpa de Morelos</td>\n",
       "      <td>2454</td>\n",
       "      <td>2454</td>\n",
       "      <td>2455</td>\n",
       "      <td>POLYGON ((-99.25738 19.40112, -99.25698 19.400...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>09</td>\n",
       "      <td>003</td>\n",
       "      <td>Coyoacán</td>\n",
       "      <td>2455</td>\n",
       "      <td>2455</td>\n",
       "      <td>2456</td>\n",
       "      <td>POLYGON ((-99.13984 19.35692, -99.13923 19.356...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09</td>\n",
       "      <td>011</td>\n",
       "      <td>Tláhuac</td>\n",
       "      <td>2456</td>\n",
       "      <td>2456</td>\n",
       "      <td>2457</td>\n",
       "      <td>POLYGON ((-98.97893 19.32393, -98.97856 19.323...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>09</td>\n",
       "      <td>009</td>\n",
       "      <td>Milpa Alta</td>\n",
       "      <td>2457</td>\n",
       "      <td>2457</td>\n",
       "      <td>2458</td>\n",
       "      <td>POLYGON ((-98.99718 19.22747, -98.99723 19.227...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CVE_ENT CVE_MUN                 NOM_MUN  OID_1  cov_  cov_id  \\\n",
       "0       09     002            Azcapotzalco    337   337     338   \n",
       "1       09     005       Gustavo A. Madero    338   338     339   \n",
       "2       09     016          Miguel Hidalgo    339   339     340   \n",
       "3       09     006               Iztacalco    340   340     341   \n",
       "4       09     017     Venustiano Carranza    341   341     342   \n",
       "5       09     007              Iztapalapa    342   342     343   \n",
       "6       09     012                 Tlalpan   2448  2448    2449   \n",
       "7       09     013              Xochimilco   2449  2449    2450   \n",
       "8       09     008  La Magdalena Contreras   2450  2450    2451   \n",
       "9       09     014           Benito Juárez   2451  2451    2452   \n",
       "10      09     015              Cuauhtémoc   2452  2452    2453   \n",
       "11      09     010          Álvaro Obregón   2453  2453    2454   \n",
       "12      09     004   Cuajimalpa de Morelos   2454  2454    2455   \n",
       "13      09     003                Coyoacán   2455  2455    2456   \n",
       "14      09     011                 Tláhuac   2456  2456    2457   \n",
       "15      09     009              Milpa Alta   2457  2457    2458   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((-99.18245 19.50756, -99.18231 19.507...  \n",
       "1   POLYGON ((-99.11789 19.59059, -99.11860 19.584...  \n",
       "2   POLYGON ((-99.19044 19.47046, -99.19058 19.467...  \n",
       "3   POLYGON ((-99.05579 19.42214, -99.05584 19.421...  \n",
       "4   POLYGON ((-99.10946 19.45292, -99.10895 19.452...  \n",
       "5   POLYGON ((-99.05813 19.40072, -99.05814 19.400...  \n",
       "6   POLYGON ((-99.19671 19.30240, -99.19629 19.302...  \n",
       "7   POLYGON ((-99.09880 19.32045, -99.09870 19.319...  \n",
       "8   POLYGON ((-99.20819 19.33674, -99.20859 19.336...  \n",
       "9   POLYGON ((-99.13659 19.40281, -99.13679 19.401...  \n",
       "10  POLYGON ((-99.12951 19.46265, -99.12919 19.462...  \n",
       "11  POLYGON ((-99.18906 19.39559, -99.18871 19.394...  \n",
       "12  POLYGON ((-99.25738 19.40112, -99.25698 19.400...  \n",
       "13  POLYGON ((-99.13984 19.35692, -99.13923 19.356...  \n",
       "14  POLYGON ((-98.97893 19.32393, -98.97856 19.323...  \n",
       "15  POLYGON ((-98.99718 19.22747, -98.99723 19.227...  "
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leer el archivo GeoJSON CDMX\n",
    "cdmx = gpd.read_file(\"https://raw.githubusercontent.com/JuveCampos/Shapes_Resiliencia_CDMX_CIDE/master/Shape%20Ciudad%20de%20M%C3%A9xico/CDMX_mpal.geojson\")\n",
    "cdmx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVEGEO</th>\n",
       "      <th>CVE_ENT</th>\n",
       "      <th>CVE_MUN</th>\n",
       "      <th>CVE_LOC</th>\n",
       "      <th>CVE_AGEB</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0901000011716</td>\n",
       "      <td>09</td>\n",
       "      <td>010</td>\n",
       "      <td>0001</td>\n",
       "      <td>1716</td>\n",
       "      <td>POLYGON ((-99.25882 19.32558, -99.25834 19.325...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0901000012150</td>\n",
       "      <td>09</td>\n",
       "      <td>010</td>\n",
       "      <td>0001</td>\n",
       "      <td>2150</td>\n",
       "      <td>POLYGON ((-99.19170 19.37893, -99.19170 19.378...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0901000011133</td>\n",
       "      <td>09</td>\n",
       "      <td>010</td>\n",
       "      <td>0001</td>\n",
       "      <td>1133</td>\n",
       "      <td>POLYGON ((-99.17760 19.35182, -99.17766 19.351...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0901000011307</td>\n",
       "      <td>09</td>\n",
       "      <td>010</td>\n",
       "      <td>0001</td>\n",
       "      <td>1307</td>\n",
       "      <td>POLYGON ((-99.20805 19.31277, -99.20768 19.312...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0901000010281</td>\n",
       "      <td>09</td>\n",
       "      <td>010</td>\n",
       "      <td>0001</td>\n",
       "      <td>0281</td>\n",
       "      <td>POLYGON ((-99.24228 19.38451, -99.24233 19.384...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2426</th>\n",
       "      <td>0900700012564</td>\n",
       "      <td>09</td>\n",
       "      <td>007</td>\n",
       "      <td>0001</td>\n",
       "      <td>2564</td>\n",
       "      <td>POLYGON ((-99.00253 19.35744, -99.00255 19.355...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>0900700012615</td>\n",
       "      <td>09</td>\n",
       "      <td>007</td>\n",
       "      <td>0001</td>\n",
       "      <td>2615</td>\n",
       "      <td>POLYGON ((-98.99932 19.35467, -98.99889 19.354...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>0900700012969</td>\n",
       "      <td>09</td>\n",
       "      <td>007</td>\n",
       "      <td>0001</td>\n",
       "      <td>2969</td>\n",
       "      <td>POLYGON ((-98.98392 19.34561, -98.98367 19.345...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>0900700013721</td>\n",
       "      <td>09</td>\n",
       "      <td>007</td>\n",
       "      <td>0001</td>\n",
       "      <td>3721</td>\n",
       "      <td>POLYGON ((-99.06016 19.36342, -99.06042 19.362...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>0900700011034</td>\n",
       "      <td>09</td>\n",
       "      <td>007</td>\n",
       "      <td>0001</td>\n",
       "      <td>1034</td>\n",
       "      <td>POLYGON ((-99.05680 19.36313, -99.05760 19.359...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2431 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "             CVEGEO CVE_ENT CVE_MUN CVE_LOC CVE_AGEB  \\\n",
       "0     0901000011716      09     010    0001     1716   \n",
       "1     0901000012150      09     010    0001     2150   \n",
       "2     0901000011133      09     010    0001     1133   \n",
       "3     0901000011307      09     010    0001     1307   \n",
       "4     0901000010281      09     010    0001     0281   \n",
       "...             ...     ...     ...     ...      ...   \n",
       "2426  0900700012564      09     007    0001     2564   \n",
       "2427  0900700012615      09     007    0001     2615   \n",
       "2428  0900700012969      09     007    0001     2969   \n",
       "2429  0900700013721      09     007    0001     3721   \n",
       "2430  0900700011034      09     007    0001     1034   \n",
       "\n",
       "                                               geometry  \n",
       "0     POLYGON ((-99.25882 19.32558, -99.25834 19.325...  \n",
       "1     POLYGON ((-99.19170 19.37893, -99.19170 19.378...  \n",
       "2     POLYGON ((-99.17760 19.35182, -99.17766 19.351...  \n",
       "3     POLYGON ((-99.20805 19.31277, -99.20768 19.312...  \n",
       "4     POLYGON ((-99.24228 19.38451, -99.24233 19.384...  \n",
       "...                                                 ...  \n",
       "2426  POLYGON ((-99.00253 19.35744, -99.00255 19.355...  \n",
       "2427  POLYGON ((-98.99932 19.35467, -98.99889 19.354...  \n",
       "2428  POLYGON ((-98.98392 19.34561, -98.98367 19.345...  \n",
       "2429  POLYGON ((-99.06016 19.36342, -99.06042 19.362...  \n",
       "2430  POLYGON ((-99.05680 19.36313, -99.05760 19.359...  \n",
       "\n",
       "[2431 rows x 6 columns]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leer el archivo JSON CDMX\n",
    "cdmx1 = gpd.read_file(\"https://datos.cdmx.gob.mx/dataset/d2ccf6ae-fdf4-407c-a15f-e7dfac2d509d/resource/7b0b7a89-d92e-46ec-9286-018e849f8123/download/lmites-de-ageb-urbanas-en-la-ciudad-de-mxico.geojson\")\n",
    "cdmx1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ver claves de los municipios ['002' '005' '016' '006' '017' '007' '012' '013' '008' '014' '015' '010'\n",
      " '004' '003' '011' '009']\n"
     ]
    }
   ],
   "source": [
    "print(\"Ver claves de los municipios\",cdmx[\"CVE_MUN\"].unique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 2431 entries, 0 to 2430\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype   \n",
      "---  ------    --------------  -----   \n",
      " 0   CVEGEO    2431 non-null   object  \n",
      " 1   CVE_ENT   2431 non-null   object  \n",
      " 2   CVE_MUN   2431 non-null   object  \n",
      " 3   CVE_LOC   2431 non-null   object  \n",
      " 4   CVE_AGEB  2431 non-null   object  \n",
      " 5   geometry  2431 non-null   geometry\n",
      "dtypes: geometry(1), object(5)\n",
      "memory usage: 114.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cdmx1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1) INVESTIGACIÓN DE OPERACIONES** \n",
    "Modelo de Optimización de Recursos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clave Entidad</th>\n",
       "      <th>Entidad federativa</th>\n",
       "      <th>Clave Municipio</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Total</th>\n",
       "      <th>Confianza</th>\n",
       "      <th>Base o sindicalizado</th>\n",
       "      <th>Eventual</th>\n",
       "      <th>Otros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>2</td>\n",
       "      <td>Azcapotzalco</td>\n",
       "      <td>412</td>\n",
       "      <td>3</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>3</td>\n",
       "      <td>Coyoacán</td>\n",
       "      <td>2164</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>4</td>\n",
       "      <td>Cuajimalpa de Morelos</td>\n",
       "      <td>486</td>\n",
       "      <td>4</td>\n",
       "      <td>306</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>5</td>\n",
       "      <td>Gustavo A. Madero</td>\n",
       "      <td>2943</td>\n",
       "      <td>2</td>\n",
       "      <td>1686</td>\n",
       "      <td>1255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>6</td>\n",
       "      <td>Iztacalco</td>\n",
       "      <td>990</td>\n",
       "      <td>1</td>\n",
       "      <td>573</td>\n",
       "      <td>0</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>7</td>\n",
       "      <td>Iztapalapa</td>\n",
       "      <td>4035</td>\n",
       "      <td>3</td>\n",
       "      <td>2185</td>\n",
       "      <td>65</td>\n",
       "      <td>1782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>8</td>\n",
       "      <td>La Magdalena Contreras</td>\n",
       "      <td>550</td>\n",
       "      <td>3</td>\n",
       "      <td>330</td>\n",
       "      <td>0</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>9</td>\n",
       "      <td>Milpa Alta</td>\n",
       "      <td>418</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>10</td>\n",
       "      <td>Álvaro Obregón</td>\n",
       "      <td>950</td>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>11</td>\n",
       "      <td>Tláhuac</td>\n",
       "      <td>653</td>\n",
       "      <td>1</td>\n",
       "      <td>358</td>\n",
       "      <td>0</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>12</td>\n",
       "      <td>Tlalpan</td>\n",
       "      <td>1066</td>\n",
       "      <td>3</td>\n",
       "      <td>634</td>\n",
       "      <td>0</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>13</td>\n",
       "      <td>Xochimilco</td>\n",
       "      <td>780</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>14</td>\n",
       "      <td>Benito Juárez</td>\n",
       "      <td>1540</td>\n",
       "      <td>0</td>\n",
       "      <td>926</td>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>15</td>\n",
       "      <td>Cuauhtémoc</td>\n",
       "      <td>2065</td>\n",
       "      <td>3</td>\n",
       "      <td>1361</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>16</td>\n",
       "      <td>Miguel Hidalgo</td>\n",
       "      <td>1057</td>\n",
       "      <td>15</td>\n",
       "      <td>573</td>\n",
       "      <td>469</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>17</td>\n",
       "      <td>Venustiano Carranza</td>\n",
       "      <td>1551</td>\n",
       "      <td>3</td>\n",
       "      <td>1017</td>\n",
       "      <td>14</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clave Entidad Entidad federativa  Clave Municipio   \\\n",
       "0               9   Ciudad de México                 2   \n",
       "1               9   Ciudad de México                 3   \n",
       "2               9   Ciudad de México                 4   \n",
       "3               9   Ciudad de México                 5   \n",
       "4               9   Ciudad de México                 6   \n",
       "5               9   Ciudad de México                 7   \n",
       "6               9   Ciudad de México                 8   \n",
       "7               9   Ciudad de México                 9   \n",
       "8               9   Ciudad de México                10   \n",
       "9               9   Ciudad de México                11   \n",
       "10              9   Ciudad de México                12   \n",
       "11              9   Ciudad de México                13   \n",
       "12              9   Ciudad de México                14   \n",
       "13              9   Ciudad de México                15   \n",
       "14              9   Ciudad de México                16   \n",
       "15              9   Ciudad de México                17   \n",
       "\n",
       "                 Municipio  Total  Confianza  Base o sindicalizado  Eventual  \\\n",
       "0             Azcapotzalco    412          3                   229         0   \n",
       "1                 Coyoacán   2164          1                  1343       820   \n",
       "2    Cuajimalpa de Morelos    486          4                   306       176   \n",
       "3        Gustavo A. Madero   2943          2                  1686      1255   \n",
       "4                Iztacalco    990          1                   573         0   \n",
       "5               Iztapalapa   4035          3                  2185        65   \n",
       "6   La Magdalena Contreras    550          3                   330         0   \n",
       "7               Milpa Alta    418          1                   289       128   \n",
       "8           Álvaro Obregón    950          4                   561       385   \n",
       "9                  Tláhuac    653          1                   358         0   \n",
       "10                 Tlalpan   1066          3                   634         0   \n",
       "11              Xochimilco    780          1                   500       279   \n",
       "12           Benito Juárez   1540          0                   926       614   \n",
       "13              Cuauhtémoc   2065          3                  1361       701   \n",
       "14          Miguel Hidalgo   1057         15                   573       469   \n",
       "15     Venustiano Carranza   1551          3                  1017        14   \n",
       "\n",
       "    Otros  \n",
       "0     180  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4     416  \n",
       "5    1782  \n",
       "6     217  \n",
       "7       0  \n",
       "8       0  \n",
       "9     294  \n",
       "10    429  \n",
       "11      0  \n",
       "12      0  \n",
       "13      0  \n",
       "14      0  \n",
       "15    517  "
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ruta archivos CSV\n",
    "personal = 'https://raw.githubusercontent.com/Quetzaltlali/PP_LCDN-Residuos_CDMX/main/tipo_contrato.csv'\n",
    "personal = pd.read_csv(personal)\n",
    "personal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   Clave Entidad         16 non-null     int64 \n",
      " 1   Entidad federativa    16 non-null     object\n",
      " 2   Clave Municipio       16 non-null     int64 \n",
      " 3   Municipio             16 non-null     object\n",
      " 4   Total                 16 non-null     int64 \n",
      " 5   Confianza             16 non-null     int64 \n",
      " 6   Base o sindicalizado  16 non-null     int64 \n",
      " 7   Eventual              16 non-null     int64 \n",
      " 8   Otros                 16 non-null     int64 \n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 1.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Verificar la estructura del DataFrame\n",
    "print(personal.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "### Función Objetivo\n",
    "La función objetivo busca minimizar la suma de empleados de confianza, eventuales y otros en todos los municipios. \n",
    "En términos matemáticos, esto se expresa como:\n",
    "\n",
    " Minimizar Z = Suma total ∑{i=1}^{n} (x_i + y_i + z_i) \n",
    "\n",
    "donde:\n",
    "- \\( x_i \\): empleados de confianza en el municipio \\( x1 \\)\n",
    "- \\( y_i \\): empleados eventuales en el municipio \\( y1\\)\n",
    "- \\( z_i \\): otros empleados en el municipio \\( z1 \\)\n",
    "- \\( n \\): es el número total de municipios \\( n \\)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "### Restricciones de Igualdad\n",
    "Las restricciones de igualdad aseguran que la suma de empleados de confianza, eventuales y otros en cada municipio \n",
    "sea igual a la cantidad total de empleados menos los empleados de base:\n",
    "\n",
    "( x_i + y_i + z_i ) = Total_i - Base_i \n",
    "\n",
    "En el código, se construyen las matrices ( A_eq ) y ( b_eq) \n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "### Restricciones de Desigualdad\n",
    "Las restricciones de desigualdad aseguran que la suma de empleados de confianza, eventuales y otros en cada municipio\n",
    "sea al menos igual a un mínimo operativo, ajustado por los empleados de base:\n",
    "\n",
    "( x_i + y_i + z_i ) ≥ min_operacion - Base_i\n",
    "\n",
    "En el código, se construyen las matrices ( A_ub ) y ( b_ub ).\n",
    "\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "### Los datos deben estar en formato adecuado para su procesamiento.\n",
    "Las matrices A_eq y A_ub, y los vectores b_eq y b_ub deben ser convertidos a matrices numpy antes de pasarlos a la función linprog.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "### Impresión de Resultados\n",
    "Finalmente, se imprimen los resultados encontrados con una solución óptima:\n",
    "\n",
    "Se muestra cada tipo de empleado para cada municipio según la solución encontrada por el algoritmo de programación lineal.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusión de la Optimización de Empleados por Municipio\n",
    "\n",
    "La optimización se realizó con el objetivo de minimizar la cantidad total de empleados en los municipios del sector de residuos sólidos, respetando las siguientes consideraciones:\n",
    "\n",
    "- **Restricciones:**\n",
    "  - Se mantuvieron constantes los empleados de base o sindicalizados, no pudiendo ser reducidos.\n",
    "  - Se estableció un límite mínimo de operación de 200 empleados en total por municipio, contabilizando empleados de confianza, eventuales y otros.\n",
    "\n",
    "- **Función Objetivo:**\n",
    "  - Minimizar la suma de empleados de confianza, eventuales y otros empleados, reflejando una optimización en la gestión de recursos humanos y posiblemente una reducción en costos operativos.\n",
    "\n",
    "Los resultados obtenidos muestran una asignación eficiente de empleados, donde cada municipio tiene asignados únicamente empleados de confianza, cumpliendo con las restricciones impuestas. Esto sugiere que el modelo de optimización aplicado fue efectivo para alcanzar el objetivo planteado, asegurando una gestión eficiente de recursos humanos en el sector de residuos sólidos de los municipios analizados.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado de la función objetivo (Z): 8789.0\n",
      "Resultados optimizados por tipo de contrato y alcaldía:\n",
      "Municipio: Azcapotzalco, Confianza: 0.0, Eventual: 0.0, Otros: 183.0\n",
      "Municipio: Coyoacán, Confianza: 0.0, Eventual: 0.0, Otros: 821.0\n",
      "Municipio: Cuajimalpa de Morelos, Confianza: 0.0, Eventual: 0.0, Otros: 180.0\n",
      "Municipio: Gustavo A. Madero, Confianza: 0.0, Eventual: 0.0, Otros: 1257.0\n",
      "Municipio: Iztacalco, Confianza: 0.0, Eventual: 0.0, Otros: 417.0\n",
      "Municipio: Iztapalapa, Confianza: 0.0, Eventual: 0.0, Otros: 1850.0\n",
      "Municipio: La Magdalena Contreras, Confianza: 0.0, Eventual: 0.0, Otros: 220.0\n",
      "Municipio: Milpa Alta, Confianza: 0.0, Eventual: 0.0, Otros: 129.0\n",
      "Municipio: Álvaro Obregón, Confianza: 0.0, Eventual: 0.0, Otros: 389.0\n",
      "Municipio: Tláhuac, Confianza: 0.0, Eventual: 0.0, Otros: 295.0\n",
      "Municipio: Tlalpan, Confianza: 0.0, Eventual: 0.0, Otros: 432.0\n",
      "Municipio: Xochimilco, Confianza: 0.0, Eventual: 0.0, Otros: 280.0\n",
      "Municipio: Benito Juárez, Confianza: 0.0, Eventual: 0.0, Otros: 614.0\n",
      "Municipio: Cuauhtémoc, Confianza: 0.0, Eventual: 0.0, Otros: 704.0\n",
      "Municipio: Miguel Hidalgo, Confianza: 0.0, Eventual: 0.0, Otros: 484.0\n",
      "Municipio: Venustiano Carranza, Confianza: 0.0, Eventual: 0.0, Otros: 534.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\quetz\\AppData\\Local\\Temp\\ipykernel_1180\\978148225.py:81: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  X[i] = xi\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import linalg\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    \"Clave Entidad\": [9]*16,\n",
    "    \"Entidad federativa\": [\"Ciudad de México\"]*16,\n",
    "    \"Clave Municipio\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
    "    \"Municipio\": [\"Azcapotzalco\", \"Coyoacán\", \"Cuajimalpa de Morelos\", \"Gustavo A. Madero\", \"Iztacalco\", \"Iztapalapa\", \n",
    "                  \"La Magdalena Contreras\", \"Milpa Alta\", \"Álvaro Obregón\", \"Tláhuac\", \"Tlalpan\", \"Xochimilco\", \n",
    "                  \"Benito Juárez\", \"Cuauhtémoc\", \"Miguel Hidalgo\", \"Venustiano Carranza\"],\n",
    "    \"Total\": [412, 2164, 486, 2943, 990, 4035, 550, 418, 950, 653, 1066, 780, 1540, 2065, 1057, 1551],\n",
    "    \"Confianza\": [3, 1, 4, 2, 1, 3, 3, 1, 4, 1, 3, 1, 0, 3, 15, 3],\n",
    "    \"Base o sindicalizado\": [229, 1343, 306, 1686, 573, 2185, 330, 289, 561, 358, 634, 500, 926, 1361, 573, 1017],\n",
    "    \"Eventual\": [0, 820, 176, 1255, 0, 65, 0, 128, 385, 0, 0, 279, 614, 701, 469, 14],\n",
    "    \"Otros\": [180, 0, 0, 0, 416, 1782, 217, 0, 0, 294, 429, 0, 0, 0, 0, 517]\n",
    "}\n",
    "\n",
    "personal = pd.DataFrame(data)\n",
    "\n",
    "# Parámetros del problema\n",
    "num_municipios = len(personal)\n",
    "Total = personal[\"Total\"].values\n",
    "Base = personal[\"Base o sindicalizado\"].values\n",
    "\n",
    "# Construir matrices A, b y c\n",
    "A_eq = []\n",
    "b_eq = []\n",
    "c = []\n",
    "\n",
    "for i in range(num_municipios):\n",
    "    # Restricción: x_i + y_i + z_i = Total_i - Base_i\n",
    "    a_eq = [0] * (3 * num_municipios)\n",
    "    a_eq[3*i] = 1\n",
    "    a_eq[3*i + 1] = 1\n",
    "    a_eq[3*i + 2] = 1\n",
    "    A_eq.append(a_eq)\n",
    "    b_eq.append([Total[i] - Base[i]])\n",
    "\n",
    "    # Coeficientes de la función objetivo (minimizar x_i + y_i + z_i)\n",
    "    c.extend([1, 1, 1])\n",
    "\n",
    "# Convertir a matrices numpy\n",
    "A_eq = np.array(A_eq)\n",
    "b_eq = np.array(b_eq)\n",
    "c = np.array(c).reshape(-1, 1)\n",
    "\n",
    "# Variables básicas y no básicas iniciales\n",
    "XBasicas = [3*i + 2 for i in range(num_municipios)]\n",
    "XNo_basicas = [3*i for i in range(num_municipios)] + [3*i + 1 for i in range(num_municipios)]\n",
    "\n",
    "# Construcción de la matriz B (variables básicas)\n",
    "def construir_B(A_eq, XBasicas):\n",
    "    B = []\n",
    "    for i in range(len(A_eq)):\n",
    "        for j in XBasicas:\n",
    "            B.append(A_eq[i][j])\n",
    "    fila = len(A_eq)\n",
    "    col = len(XBasicas)\n",
    "    B = [B[col*i : col*(i+1)] for i in range(fila)]\n",
    "    return np.asmatrix(B)\n",
    "\n",
    "# Verificación de optimalidad\n",
    "def interruptor(lista_CN_sombrero):\n",
    "    for i in lista_CN_sombrero[0]:\n",
    "        if i < 0:\n",
    "            return 'negativo encontrado'\n",
    "    return 'todos positivos o cero'\n",
    "\n",
    "# Algoritmo Simplex\n",
    "def simplex(A_eq, b_eq, c, XBasicas, XNo_basicas):\n",
    "    while True:\n",
    "        # Construir B\n",
    "        B = construir_B(A_eq, XBasicas)\n",
    "        # Calcular Xb\n",
    "        Xb = linalg.inv(B) @ b_eq\n",
    "        \n",
    "        # Construir solución básica\n",
    "        X = np.zeros(len(c))\n",
    "        for i, xi in zip(XBasicas, Xb):\n",
    "            X[i] = xi\n",
    "        \n",
    "        # Calcular CtB\n",
    "        CtB = c[XBasicas].T\n",
    "        \n",
    "        # Calcular N\n",
    "        N = A_eq[:, XNo_basicas]\n",
    "        \n",
    "        # Calcular Yt\n",
    "        Yt = CtB @ linalg.inv(B)\n",
    "        \n",
    "        # Calcular CN_sombrero\n",
    "        CtN = c[XNo_basicas].T\n",
    "        CN_sombrero = CtN - Yt @ N\n",
    "        lista_CN_sombrero = CN_sombrero.tolist()\n",
    "        \n",
    "        # Verificar optimalidad\n",
    "        if interruptor(lista_CN_sombrero) == 'todos positivos o cero':\n",
    "            break\n",
    "        \n",
    "        # Ubicar al menor en CN_sombrero\n",
    "        min_val = min(lista_CN_sombrero[0])\n",
    "        indice = lista_CN_sombrero[0].index(min_val)\n",
    "        \n",
    "        # Variable entrante\n",
    "        n_entrante = XNo_basicas[indice]\n",
    "        \n",
    "        # Construir An\n",
    "        An = A_eq[:, n_entrante].reshape(-1, 1)\n",
    "        \n",
    "        # Calcular An_sombrero y b_sombrero\n",
    "        An_sombrero = linalg.inv(B) @ An\n",
    "        b_sombrero = linalg.inv(B) @ b_eq\n",
    "        \n",
    "        # Ratio test\n",
    "        ratios = np.divide(b_sombrero, An_sombrero, out=np.full_like(b_sombrero, np.inf), where=An_sombrero>0)\n",
    "        \n",
    "        # Variable saliente\n",
    "        min_ratio = np.min(ratios)\n",
    "        indice = np.argmin(ratios)\n",
    "        s_saliente = XBasicas[indice]\n",
    "        \n",
    "        # Actualizar variables básicas y no básicas\n",
    "        XBasicas[indice] = n_entrante\n",
    "        XNo_basicas[XNo_basicas.index(n_entrante)] = s_saliente\n",
    "    \n",
    "    # Calcular Z (función objetivo)\n",
    "    Z = CtB @ linalg.inv(B) @ b_eq\n",
    "    Z_lista = Z.tolist()\n",
    "\n",
    "    # Extraer resultados optimizados de las variables\n",
    "    resultados = {}\n",
    "    for i in range(num_municipios):\n",
    "        resultados[personal[\"Municipio\"][i]] = {\n",
    "            \"Confianza\": X[3*i],\n",
    "            \"Eventual\": X[3*i + 1],\n",
    "            \"Otros\": X[3*i + 2]\n",
    "        }\n",
    "    \n",
    "    return Z_lista[0][0], resultados\n",
    "\n",
    "# Ejecutar el algoritmo simplex\n",
    "resultado, resultados_por_tipo = simplex(A_eq, b_eq, c, XBasicas, XNo_basicas)\n",
    "\n",
    "print(\"Resultado de la función objetivo (Z):\", resultado)\n",
    "print(\"Resultados optimizados por tipo de contrato y alcaldía:\")\n",
    "for municipio, valores in resultados_por_tipo.items():\n",
    "    print(f\"Municipio: {municipio}, Confianza: {valores['Confianza']}, Eventual: {valores['Eventual']}, Otros: {valores['Otros']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos de ejemplo (empleados de confianza por municipio)\n",
    "municipios = ['Azcapotzalco', 'Coyoacán', 'Cuajimalpa de Morelos', 'Gustavo A. Madero', \n",
    "              'Iztacalco', 'Iztapalapa', 'La Magdalena Contreras', 'Milpa Alta',\n",
    "              'Álvaro Obregón', 'Tláhuac', 'Tlalpan', 'Xochimilco', 'Benito Juárez',\n",
    "              'Cuauhtémoc', 'Miguel Hidalgo', 'Venustiano Carranza']\n",
    "empleados_confianza = [183, 821, 180, 1257, 417, 1850, 220, 129, 389, 295, 432, 280, 614, 704, 484, 534]\n",
    "\n",
    "# Configurar la fuente y otros parámetros estéticos\n",
    "plt.figure(figsize=(12, 8))\n",
    "bars = plt.barh(municipios, empleados_confianza, color='#d4c19c')  # Color de las barras verde\n",
    "\n",
    "# Añadir las etiquetas con los valores en cada barra\n",
    "for bar, emp_conf in zip(bars, empleados_confianza):\n",
    "    plt.text(bar.get_width() + 10, bar.get_y() + bar.get_height()/2, f'{emp_conf:,}', \n",
    "             va='center', ha='left', fontsize=12)\n",
    "\n",
    "plt.xlabel('Fuente: Elaboración propia con datos del Censo Nacional de Gobiernos \\n Municipales y Demarcaciones Territoriales de la \\n Ciudad de México 2023. INEGI', fontsize=14, fontname='Century Gothic')  # Fuente de la etiqueta del eje x\n",
    "plt.title('Asignación de Minimo de empleados por Municipio', fontsize=18, fontweight='bold', fontname='Century Gothic')  # Título con negrita y fuente Century Gothic\n",
    "\n",
    "# Subtítulo debajo del título principal\n",
    "plt.text(0.5, 0.92, 'Cantidad de Personas', fontsize=16, color='black', style='italic',\n",
    "         transform=plt.gca().transAxes, ha='center', fontname='Century Gothic')  # Ajuste de la posición vertical\n",
    "\n",
    "plt.gca().invert_yaxis()  # Invertir el eje y para que el municipio con más empleados esté arriba\n",
    "plt.grid(axis='x', color='gray', linestyle=':')  # Líneas de la cuadrícula en gris y punteadas en el eje x\n",
    "\n",
    "# Eliminar bordes superior y derecho\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "\n",
    "# Ajustar el tamaño de la fuente en los ejes\n",
    "plt.xticks(fontsize=12, fontname='Century Gothic')  # Fuente de los ticks del eje x\n",
    "plt.yticks(fontsize=12, fontname='Century Gothic')  # Fuente de los ticks del eje y\n",
    "\n",
    "# Guardar el gráfico como imagen PNG\n",
    "plt.tight_layout()  # Ajustar automáticamente los márgenes para evitar recorte de etiquetas\n",
    "plt.savefig('grafico_mejorado.png', bbox_inches='tight', dpi=300)  # Guardar el gráfico como imagen\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datos originales (empleados de confianza por municipio)\n",
    "municipios = ['Azcapotzalco', 'Coyoacán', 'Cuajimalpa de Morelos', 'Gustavo A. Madero', \n",
    "              'Iztacalco', 'Iztapalapa', 'La Magdalena Contreras', 'Milpa Alta',\n",
    "              'Álvaro Obregón', 'Tláhuac', 'Tlalpan', 'Xochimilco', 'Benito Juárez',\n",
    "              'Cuauhtémoc', 'Miguel Hidalgo', 'Venustiano Carranza']\n",
    "empleados_confianza = [183, 821, 180, 1257, 417, 1850, 220, 129, 389, 295, 432, 280, 614, 704, 484, 534]\n",
    "\n",
    "# Leer el archivo GeoJSON de los municipios de la Ciudad de México\n",
    "url_geojson = \"https://raw.githubusercontent.com/JuveCampos/Shapes_Resiliencia_CDMX_CIDE/master/Shape%20Ciudad%20de%20M%C3%A9xico/CDMX_mpal.geojson\"\n",
    "cdmx = gpd.read_file(url_geojson)\n",
    "\n",
    "# Crear un diccionario con los datos originales\n",
    "datos_originales = dict(zip(municipios, empleados_confianza))\n",
    "\n",
    "# Agregar una nueva columna al GeoDataFrame\n",
    "cdmx['Empleados de confianza'] = cdmx['NOM_MUN'].map(datos_originales)\n",
    "\n",
    "# Definir los colores para cada rango\n",
    "paleta = [\"#e3c8a6ff\", \"#c88f80ff\", \"#ac2540ff\", \"#901d3dff\"]\n",
    "niveles = ['< 220', '220 - 432', '432 - 614', '> 614']\n",
    "\n",
    "# Convertir lista de colores a un objeto de paleta de colores\n",
    "cmap_paleta = ListedColormap(paleta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configurar el tamaño de la figura\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Crear el mapa temático de empleados de confianza por municipio\n",
    "cdmx.plot(column='Empleados de confianza', cmap=cmap_paleta, linewidth=0.8, edgecolor='none', legend=False)\n",
    "\n",
    "# Quitar marcos de latitud y longitud\n",
    "plt.axis('off')\n",
    "\n",
    "# Añadir título con la fuente 'Century Gothic'\n",
    "plt.title('Asignación de empleados de confianza por \\n municipio en la Ciudad de México', fontsize=18, fontweight='bold', fontname='Century Gothic')\n",
    "\n",
    "# Crear leyenda personalizada y ubicarla a la derecha\n",
    "legend_elements = [Patch(facecolor=color, edgecolor='gray', label=label) for color, label in zip(paleta, niveles)]\n",
    "plt.legend(handles=legend_elements, title='Rangos de empleados', loc='center left', bbox_to_anchor=(1, 0.20), fontsize=10, prop={'family': 'Century Gothic'})\n",
    "\n",
    "# Agregar fuente debajo del mapa con la fuente 'Century Gothic'\n",
    "plt.figtext(0.5, -0.1, 'Fuente: Elaboración propia con datos del Censo Nacional de Gobiernos \\n Municipales y Demarcaciones Territoriales de la \\n Ciudad de México 2023. INEGI', fontsize=10, ha='center', fontname='Century Gothic')\n",
    "\n",
    "# Ajustar automáticamente los márgenes para evitar recorte de etiquetas\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardar el gráfico como imagen PNG\n",
    "plt.savefig('grafica_cdmx.png', bbox_inches='tight', dpi=300)\n",
    "\n",
    "# Mostrar el mapa\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2) VISUALIZACIÓN DE DATOS** \n",
    "Diseño de gráficos eficientes para describir la situación de contenedores de basura en tu comunidad.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>67.328591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>60.969462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>65.034225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>66.399147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>70.495388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>72.645835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha  Porcentaje\n",
       "0 2013-01-01   67.328591\n",
       "1 2015-01-01   60.969462\n",
       "2 2017-01-01   65.034225\n",
       "3 2019-01-01   66.399147\n",
       "4 2021-01-01   70.495388\n",
       "5 2023-01-01   72.645835"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URL con token del la variación anual del igae\n",
    "url1 = \"  https://www.inegi.org.mx/app/api/indicadores/desarrolladores/jsonxml/INDICATOR/6200032275/es/0700/false/BISE/2.0/ef8c858c-1f87-6408-5630-fb5496f609bf?type=json\"\n",
    "\n",
    "# Hacer la solicitud HTTP a la URL\n",
    "response_resid= requests.get(url1)\n",
    "\n",
    "# Convertir la respuesta en JSON\n",
    "data_resid = response_resid.json()\n",
    "\n",
    "# Usar las columnas TIME_PERIOD y OBS_VALUE\n",
    "data_resid = pd.DataFrame(data_resid[\"Series\"][0][\"OBSERVATIONS\"])\n",
    "\n",
    "#Convertir los tipos de datos de caracteres a fecha y numericas\n",
    "data_resid['TIME_PERIOD'] = pd.to_datetime(data_resid['TIME_PERIOD'])\n",
    "data_resid['OBS_VALUE'] = pd.to_numeric(data_resid['OBS_VALUE'])\n",
    "\n",
    "# Filtrar las columnas 'TIME_PERIOD' y 'OBS_VALUE' solamente\n",
    "data_resid = data_resid[['TIME_PERIOD', 'OBS_VALUE']]\n",
    "\n",
    "# Renombrar las columnas en los DataFrames\n",
    "# Porcentaje de la población de 18 años y más satisfecha con el servicio de recolección de basura Nacional\n",
    "data_resid.rename(columns={'TIME_PERIOD': 'Fecha', 'OBS_VALUE': 'Porcentaje'}, inplace=True)\n",
    "data_resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Crear la gráfica\n",
    "plt.figure(figsize=(7, 2.7))\n",
    "plt.plot(data_resid['Fecha'], data_resid['Porcentaje'], marker='o', linestyle='-', color='#d4c19c')\n",
    "plt.title('Grafica 2. Población de 18 años y más satisfecha con el \\n servicio de recolección de basura a nivel nacional.', fontsize=12, fontweight='bold', fontname='Century Gothic', ha='center', pad=20)\n",
    "plt.xlabel('Año', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "plt.ylabel('Porcentaje', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "\n",
    "# Agregar porcentajes a cada punto\n",
    "for i, porcentaje in enumerate(data_resid['Porcentaje']):\n",
    "    plt.annotate(f'{porcentaje:.2f}%', (data_resid['Fecha'][i], porcentaje), textcoords=\"offset points\", xytext=(0,5), ha='center', fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Agregar fuente debajo del mapa con la fuente 'Century Gothic'\n",
    "plt.figtext(0.5, -0.1, 'Fuente: Elaboración propia con datos de la API de INEGI', fontsize=10, ha='center', fontname='Century Gothic')\n",
    "\n",
    "# Formatear los números en el eje Y con comas y dos decimales\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: f'{x:,.2f}'))\n",
    "\n",
    "# Configurar fuente y tamaño de fuente para los números en los ejes X y Y\n",
    "plt.xticks(fontsize=8, fontname='Century Gothic')\n",
    "plt.yticks(fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Quitar el marco de la parte superior y derecha\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Añadir líneas de fondo de color gris\n",
    "ax.yaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "ax.xaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Rotar las etiquetas del eje X y aplicar la misma fuente\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardar la imagen\n",
    "plt.savefig('grafica_2.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Cantidad de personas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>29.641314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>28.051457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>31.060849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>33.307948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>36.165551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>38.152521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha  Cantidad de personas\n",
       "0 2013-01-01             29.641314\n",
       "1 2015-01-01             28.051457\n",
       "2 2017-01-01             31.060849\n",
       "3 2019-01-01             33.307948\n",
       "4 2021-01-01             36.165551\n",
       "5 2023-01-01             38.152521"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URL con token del la variación anual del igae\n",
    "url2 = \"https://www.inegi.org.mx/app/api/indicadores/desarrolladores/jsonxml/INDICATOR/6207020194/es/0700/false/BISE/2.0/ef8c858c-1f87-6408-5630-fb5496f609bf?type=json\"\n",
    "# Hacer la solicitud HTTP a la URL\n",
    "response_resid= requests.get(url2)\n",
    "\n",
    "# Convertir la respuesta en JSON\n",
    "data_resid = response_resid.json()\n",
    "\n",
    "# Usar las columnas TIME_PERIOD y OBS_VALUE\n",
    "data_resid = pd.DataFrame(data_resid[\"Series\"][0][\"OBSERVATIONS\"])\n",
    "\n",
    "#Convertir los tipos de datos de caracteres a fecha y numericas\n",
    "data_resid['TIME_PERIOD'] = pd.to_datetime(data_resid['TIME_PERIOD'])\n",
    "data_resid['OBS_VALUE'] = pd.to_numeric(data_resid['OBS_VALUE'])\n",
    "\n",
    "# Filtrar las columnas 'TIME_PERIOD' y 'OBS_VALUE' solamente\n",
    "data_resid = data_resid[['TIME_PERIOD', 'OBS_VALUE']]\n",
    "\n",
    "# Renombrar las columnas en los DataFrames\n",
    "# Porcentaje de la población de 18 años y más satisfecha con el servicio de recolección de basura\n",
    "data_resid.rename(columns={'TIME_PERIOD': 'Fecha', 'OBS_VALUE': 'Cantidad de personas'}, inplace=True)\n",
    "\n",
    "# Convertir la cantidad de personas a millones\n",
    "data_resid['Cantidad de personas'] = data_resid['Cantidad de personas'] / 1000000\n",
    "\n",
    "data_resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+wYIFBh8Qtyc4mzdvVpbTJ9GDBw+udL+mXFt9+vQxeu3e/rd///4qj1OIyhPZ3bt3Kx+4S5cuFfHx8eLLL78UarVaeHp6iuTkZKPb+9///qckhvokQ0//xc5YIlvVufruu++ULzTltzl06FABiGeeeUYIIcTy5csFlP2S8vLLL4ukpKQqj7+4uFg0bNhQAOKHH34QpaWlIjAwUABi+fLlla53e9l/++03ZfoLL7ygHH9JSYkQouxXIRcXFwGIn376SQhhWqz//e9/Cyj7JeOtt95SanUrU9XypsRHCKHU9ul/FSmfyCYnJwt3d3cBZbXQe/fuNdim/vmmv+diYmLueN0GBgYKIYSYP3++gLIvj4WFhVUes/469vX1VZ7XOp1O+UVt5cqVJp/38q5du3bHZfRMuWb1nzUajUakpaUpy+krLvRfkssrKCgw+ivW7X/29vaiqKhIJrJmJNvISn+Zs7MzjRo1Aqj2i0hNmzZV/t/Hxwe1+talqO/8vkOHDgbrNGnSpEbd5uh7DOjRo0eVy+n3q1KpeOCBBwzmlf/3nTzyyCP873//U9qFfv7551W2uavMM888Q3FxMdeuXePzzz83eONa3xVX+fPk4+OjtM0t3y6x/LINGzZUljU26EBqaqrSFVT5dcufk+TkZPLy8oA7n+vy9MdS1bk31u739q653N3dARBVdI1tyrVVnXhV1ha5OjZv3gzA0KFDlfZ3//d//8eQIUO4ceOG0g6yPP0xtGzZEq1WazCvquu0qnOlP/+tWrWqdJv6Y33qqaf47LPPsLGx4eOPP6Z58+ZV9hBibW3N//3f/wFlbdnXrl1LdHQ0vr6+TJo0qdL1bqdvHw7g7e0NQGBgoNKGVKVS4enpCdxqj25KrN966y3mzZtHfn4+b775Js2bN2fv3r2Vlqeq5Wsan8p4enqyZ88eOnbsSFhYGL179+axxx6rtN29Kdet/tnYuXNnbGxsqlWe++67T3leq1Qq3NzcgFvX0l95fsfGxiKEqLLttJ4p16xew4YNlfLCnZ8ZqampFBYWVlmO/Px8kpOTq1xGqlsykZVqxeTJk4Gyl75uf/Fjz549iLKaf+VDq1u3bvj4+CjLWFtbG2xLP+/kyZMG0+Pi4gxe4KoufaJWfnvl6fcrhODUqVMG8+60LsCYMWPo1KmT0h8klH1AqFQqhBA17gO2MvovD2fOnDGYfv36daVnCD8/P4N55Y/j2rVryrK3x0TP3d1debO9/Lrl/+3m5oatra3ReXfSsGFDoOzcl+9CSL+t8scCGLxIpU9qqmLKtaVPmN59913lbf/yf2PHjr3jPiuj75Wg/Mty+oTiypUrVR7DpUuXKry4U/66vV1V50p/LYWHh5Ofn28wz9j5f/bZZ7ly5QovvfQS2dnZPP7441Xem1OmTMHa2pojR47wyiuvADB9+vRqJ0/66+r2st8+zdgxmRJrtVrNm2++SXR0NP/4xz9ITk5m/PjxBi8ZVnf5msanKm3btuX48eP88ssveHl58cMPP/Dll18aXVZ/3VpbWxMREWH0utW/pKR/Np45c6bSYy2vfE8X5f/9V57f+vIYewaXZ+o1C1R48fJOzww3Nzfl+Pbu3Vvpc0Bfbsk8ZCIr1YpXXnmFFi1akJ6eTteuXfnggw+4dOkSycnJHD16lIkTJ/LDDz9gZWXFu+++W+W29N0uffnll3z88cfExcVx+PBhRo0aVaOy/fOf/wTKalGWLl1KTEwMf/75J3PnzqVTp06cPn0aKKvhadu2rbJOWFgYV69e5euvv+aDDz6o1r5OnjzJ66+/zuHDh4mLi2PatGkIIbCzs1NqlYQQtdLh/T/+8Q8AvvjiCxYvXkx8fDxHjx7lkUceobS0lJYtW9K1a1eDdb799lsWLFhAXFycwbLe3t5069atwj7s7e2Vvlz/9a9/sX79ehISEli3bh2vvvqqwbK2trZKYjdt2jR+/PFHrl69yunTp3n22Wfp168f0dHRRo8lICBA2f+kSZP4/fffSUhI4LvvvuPtt98Gyvpc/atMubbGjBkDlHVrtHHjRnJzc4mNjeX111/ntdde4+rVq9VOxIzR11ht2bKF5cuXK30F69+4btmypdH1QkNDsbW1JSUlhbFjx3L27FkuX77Ma6+9xu7du2tUlmHDhuHs7MyNGzeYMGEC58+f5/Lly7zxxhv89ttvwK3zf/DgQXr06EF4eDhPPvkkWq2WgoIC4uLiKt1+w4YNlc764+PjcXZ2rvMBGUyJ9dq1axk0aBBpaWlK7XFSUhKZmZlGt13V8rUdn9zcXAYNGsSXX35J165dCQkJASAiIsLo8oGBgbRv356SkhKeeOIJLl68SF5eHocPH2bChAl88803So3qhAkTsLKy4s8//+Qf//gHZ86cIT4+nrVr19K6dWuWLFlS5S8cxvyV57e/v7/ya87EiRPZuHEj165d49ixY4wePZpx48YptZ+mXLM1ZW9vz9ChQ4GybtFOnDhBfn4+f/zxB4899hiLFi2iqKjI4BdFyQzM0Z5BujdFREQoL0EY+7OxsRH/+9//lOUra1NUUFAgunXrVmW7JFPayObl5VW5vWeffVZZ98iRI0o7T2N/VbWRPXnypLCzszO63ieffKKUpUWLFsLJyUmcOXOm0m3p28jq23gZU1paKkaNGmV0f46OjgZtTfVtZPUvodz+Z21tbfD2un66vn1tdHS0wYtdxv70bty4IYKCgipd7oMPPhBCGH/ZKzw83OBFntv/evbsqbThu73t5PXr15X1q3qpSs+Ua6uoqEj069fP6DJqtVqsWrWq0v3crrJy5eTkVHqu/P39RXp6eqXb/PTTT6s8BmNtZO90rlavXl3pG+AzZ84UQpS1idS3K7z9z8/PT3mRqzK7du1Sln/11VfveN4qK7u+Xaf+DX298m1FqxvrnJwcpdeV2/86d+5stFzVWb668RHizm1kK+sBYcuWLUaPW4iyZ5Gzs7PR9Zo2bWrQ/nrhwoWVljMgIEAkJiYKISq/jsuX35R7zJiIiIhKnwNqtVr8/PPPyrLVuWaFqPyzRv9cnD9/fqXliY2NFY0aNTK6D3d3d+W9ENlG1nzk1wip1rRo0YLjx4+zdOlSevbsqfRfGBAQwJNPPsmxY8d4+umn77gdW1tbduzYwauvvkrDhg3RaDR06NCB9evXV+vn4/Ls7e0JCwvj3XffVfpbdXR0JCQkhO+++47PPvtMWbZLly4cOnSIIUOG4ODggLOzM+PHj+e999674346dOjA3r17GThwIA4ODtjZ2dGlSxfWrFnDiy++CJT9JOnk5IRWq/3LndGr1WpWr17NJ598QnBwMDY2Nnh7e/Poo49y6tQpHnzwwQrrvP7667zwwgt4eHhgZ2dHz549CQsLY/jw4ZXup1mzZhw9epRx48bh4uKCVqslNDSUzz//vMKyHh4eHD16lJkzZxIUFISNjQ0uLi7069ePzZs3VzliVVBQECdPnmTy5MnKtXPffffx7rvvsmvXrr9U+6lX2bX166+/VvipWqPRsG3bNubPn6+cX1dXVwYNGsTvv/9u0GdnTTg4OPD777/zz3/+E29vbzQajRK//fv34+rqWum6L7zwAmvXrqVDhw7Y2Njg6+vLjBkzePTRR2tcnjFjxrBv3z4GDx6Mk5MTjo6OdOvWjZUrVypDG+v7dB0zZozST25oaCjbt2+vcP7K07dhtbGxUe6HulTd54iDgwNhYWEMGjQIBwcHPDw8mDBhAr/++qvR7VZn+dqMzyuvvMK7775LQEAAdnZ2tG7dmu+++47Q0NBK1+nQoQMnT55k0qRJyrXl5+fHtGnTOHHihBILKGvisWPHDgYNGoSbmxs2NjY0a9aMl156iZMnTypNFarrrz6/W7RoofyK4+/vj0ajwd3dnWHDhnHgwAEmTpyoLFuda/av8vPz4+TJkzz77LM0btwYjUaDj48Pjz32GKdPnyYoKKhW9iPVnEoIE383kCRJkiQTPfroo/z888888cQTlb7IJkmSZCqZyEqSJEl1KiYmhhYtWlBaWsr58+cJDg42d5EkSbpHyKYFkiRJUp366KOPKCkpYejQoTKJlSSpVskaWUmSpDpQXFxMmzZtiIiIYOrUqUbbFEuSJEl/jayRlSRJqgNffPEFERERPPTQQyxevNjcxZEkSbonyURWkqoghGDs2LG4urpWOXqRMQUFBfTu3RsvLy/2799fRyUsM3jwYFQqFfPmzavT/ZhTnz59UKlUtfY28l/1ySef4OTkxMsvv1xhXmZmJv/5z38IDg7ml19+qTDoR11YuXIlKpWKgICAWtneE088gUqlqvP+Xv9O/srzpLoCAgJQqVQGA7PUJ6tWrcLV1fUv9/pRW+R1bvlkIitJVUhLS2PNmjVkZmbyww8/mLTun3/+yb59+7hx40adfWhJ5vP111+Tk5PD//73vwrzoqOjef7559m8eTMuLi5mKJ1UH/2V58m94scffyQzM5PVq1eTmppq7uJI94C6ryaQJAvWoEEDZs2axfbt25WhNaurffv2TJ48mQsXLvDMM8/UUQklc5k7dy7vvfee0dGDOnToUGGseUn6K8+Te8Vrr73G9evX6d+/Pw0aNDB3caR7gKyRleq9//73vwQEBKDVaunZsyeHDx82mH/58mUmTZqkdKLfokUL3njjDYNxzvU/S//vf/+jefPmNG3alMmTJ6NSqSokIuvWrUOlUuHj40NRURHr1q3j1KlTxMTEVFhO/zB2cHCgY8eOLF68WBmz3MrKip9++okjR44YjDFeVFTEO++8Q1BQkDJgxEsvvVSt2gkhBJ988omybnBwcJV9cq5bt45u3bqh1WpxcnKiV69ebNiwocp9zJs3D5VKxdy5c+nduzfW1tbKsefk5PD666/TrFkzpaP3J554osK5gbIhO1966SUCAwOxs7PD19eXRx99lIsXL1ZYdufOnfTt2xcHBwfc3d0ZOnQoBw4cuOP5KC4u5v333+e+++7D1tYWDw8PxowZw9mzZyssm52dzdy5cwkODkar1eLp6cnIkSM5dOhQhWWjoqL4v//7P/z8/LCzs8PPz49nnnmGq1evKsuEhYVx5swZzpw5Y/Kx6M/xgAED+PDDDwkODsbZ2ZkBAwZw4cKFOx73sWPH6N+/Pw4ODnh6evLss8+Snp5udNnq3B9V0el0LFiwgGbNmmFra0u7du346aefKix3/fp1HnvsMRo0aIBWq2XgwIHKvaRSqZRrJDExkXHjxuHm5oaHhwdTp04lIyMDgD179qBSqbCzszPYdvmmM8bu5/LnRqvV4u7uzuOPP86CBQuq1eyi/P0VFBTEokWL6N+//x2b7uh/op40aRKvv/46gYGBuLm5MWrUKIPr5vbnyb59+1CpVDg5OZGVlaUsU1hYiJeXFyqVio0bNxqsa+r9bIw+Jl9++SUjRozA3d2dgIAA3nvvPeX5pbdt2za6du2Kvb09Pj4+/Otf/2LGjBmoVCr69OljsGx17vlDhw5x8uRJjh49arDu8ePHGTZsGC4uLjg7O9O3b19luGa9b7/9FpVKRcuWLVm+fDkPPPAATk5OdO3alYMHDxosW1RUxJw5c/D398fW1pb777+fn376iebNm6NSqfj2229NPm9SPWWuIcUkqTp+/PHHCsMC+vj4iLy8PCGEEOfPnxdubm5Ghw/s0KGDyM7OFkLcGorQ3t5eAGL69OnixIkTAhC2trbixo0byj779u0rADFnzhwhhPEhIJ999tlKh1986KGHlOFUjQ2X27NnT6PrNWnSRMTHx1d5PqZOnWp0Xf1+bh+q99133620jO+++26l+9APRak/V/fff78QQojs7GzRoUMHo9tzc3MT58+fV7Zx/vz5Soe1tbW1Fdu3b1eWXbx4sVCpVEaHo/zqq6+U5coPJ1lSUiKGDBlidB92dnYiLCxMWffatWuiWbNmlQ57+fXXXyvLhoWFCQcHB6PLurq6itOnTwshhHjmmWcqDDda3WPRn+PKroPc3NxK47N3714l3saugduHUa7u/WGMfphkY/uCW8MNCyFEcnKyMlTp7X+3n4srV64IIYQYMGBAheX+9a9/CSGE+P3335V93q78UNTG7ueqzo2+HFUNMS1E5feXfv3b76/Kzpexv06dOgmdTieEqPg80Q/r/emnnyrb+u677wSUDRFbWloqhKj+/Vx+yFhjKttO+XL8/PPPRq9n/bTbhwmu7j1vbIjhX3/9tdKhZufNm6cspx8G1tifo6OjuHbtmhCibPjuoUOHVhlL/fmvznDgUv0mE1mpXps2bZoAxKhRo0RUVJR44YUXxLFjx5T5Xbp0EYB44YUXxLlz50RycrI4cuSIkuDMmjVLCHHrg8/b21ucPXtWpKSkGKz/3nvvCSGE+PPPPwUgrK2txdWrV4UQFT94fvnlF+Wh+Nprr4kLFy6Iy5cvi6+++kp06NBBrFy5UvnQKp/Izp07V3nofv311yI+Pl6EhYUp+xg8eHCl5+Lo0aPKfidPnizCw8PFxYsXxT//+U9luv6D9s8//xRqtVo0aNBAfP/99yIuLk4kJCSIFStWCG9vb6FWq8WFCxeM7uf2JGv9+vXKeZg5c6YARP/+/cWBAwdEUlKSuHDhgnjttdcEIEJCQoQQQuh0OtG6dWsBZeOOb968WVy9elUcPnxYPPLII2Ly5MnKNsPDw5UPsMcff1xcunRJhIeHi8mTJysfgLGxsQYx1Ceyn3/+uQBE+/btxc6dO0ViYqKIiooSH3zwgbCyshJ+fn6iqKhICCFEaGioAETDhg3FihUrRFxcnDh16pR4+umnxSOPPCIuXbokhBAiNzdX+TDu0qWL+P3330VCQoL4/fffRf/+/cUrr7wi0tLShBAVE1lTjuX2c/zCCy+Iq1evGox5v2/fvkqvg+DgYOXchoWFifj4ePHVV18JrVZbIVmr7v1hzO2J2auvviqioqLEH3/8oaxrY2OjxFH/xc7JyUl89913Ij4+XuzYsUMEBgZWSGQdHR0FIFatWiX27t0r/u///k/5YmpqIlv+fjbl3JRnyv11p/M1f/58kZCQIF599VVlWlxcnBCi4vNEfx23atVKeW48+OCDAhDvv/++EMK0+9mURNbX11fs27dPREREKPdsv379hBBlX7obNGggANG1a1dx5MgRERMTIxYsWCDUarVBMmrKPV8+kU1NTRWurq4CEEOGDBFnzpwRly9fFrNnz1YSz8OHDwshDBPZMWPGiJiYGLF69WolOf3++++FEEKsWrVKWW727Nni8uXL4vTp0+Khhx5SpstE9t4hE1mpXtu5c6dQqVTC2tpaPPXUUyI6OlqZFxMTU2XNAiACAwOFELc++J599lmD7X///fcCEH5+fqKkpEQ899xzAhCjR49Wlin/waP/UL29Jq4y5RPZpk2bCkB8+OGHBssdP35cKbO+VqG8N954QwCiTZs2Si2NEGU1k0FBQQYftPPmzbvjuXn77beN7kefZN13330G0wMCAu64zfj4eHH48GHlA+j2eBnz1ltvGT2m0tJS0bZtW4PEtXwi26dPnzuWZ//+/eLatWvKB50+DpVZsWKFgLKaV33CWpnyiawpx6I/x97e3qKkpEQIUZYM6GuCV65caXSfkZGRyrHd/oVOCCHefvttg2TNlPvDGP0HfP/+/Q2m5+TkCA8PDwGI5cuXCyGEaNSokdHrev/+/RUSWX1i2LBhQ7Fo0SJRUFCgLL9nz54qE1l97Zyx+9mUc2OMKfdXVeerU6dOyrTk5GSlTEeOHBFCVHyeZGdnC2dnZwGInTt3imPHjinnQP9LkSn3symJ7JIlS5RpH374oZJQC1H27AWElZWVuH79usH6Tz31lEEyaso9Xz6R1dc+e3h4VPglQl+rqk8y9YmsRqMxuD/1X2D0if+jjz4qABEaGmqwvaysLCVplonsvUO2kZXqtQEDBrBmzRo8PT356quvuO+++1i0aBEACQkJd1w/NjbW4N8NGzY0+Pe4cePw8PAgLi6On3/+WXmT+Pnnn690m/q2fj169DDlUACUtnLlXwRq3749KpUKgLi4OKPrJiUlKcuq1bduXSsrK9q1a2ewbE3OTXnlz1V1t6k/Pw0bNqRZs2ZVLq8/H+WPSa1Wc//991dZzuqWJzY2FiEEKpWK7t27V7m8vuytW7fGzc3tjtu/XU2OpVWrVlhZWQFl7Rb1+xSVjFOjvwZUKhUPPPCAwbzy/66NawAqXqsODg60atXKoDz6/5ZftnyZAJYvX85zzz1HUlIS06dPJzg4mD///BNA6aasfDtNnU4HgEajMZh++zVqyrkxxpT7qyqtW7dW/t/d3V35/8pi6ujoqLTTX7JkCUuWLAFg/PjxeHh4ALUXy+qUVV9O/fnw9fXFx8fHYL3y59OUe748/X3TqlUrtFqt0f0Ye47ffn9WVvby16OTkxPNmzc3qXxS/ScTWaneGzVqFFFRUcqLCC+++CLnzp3D29sbKPvwi4iIID09vcLfjRs3DLZVvj9PW1tbnn76aQCmTp1KdnY2wcHB9O3bt9Ly+Pr6AnDy5EmTj6VRo0YAFV4QOnXqlPIQbtKkidF19R8mZ86cUT7YoexD/48//jBYVn9uhgwZYvS8pKenK18IKlP+XOm3+cMPP1S6zS5duijnJzEx8Y4fwPrz8ccffxh80Ot0OuUc+fn5GV1XX55333230vKMHTtWKY8QglOnTlVZHv2y4eHh5OTkVLlsbRxL+Zea9EltZfTXgLFjKX891uT+MKb8dnNzc7l06ZJBefT/Lb+ssXvE1taWpUuXcvHiRQYOHMjly5eVRM7V1RWAkpIS5QUwQHmRTT9f7/Zr1JRzY4wp91dVbo/pneKp99xzzwGwefNmVq1aBRh+ma6N+9nUsurPx/Xr17l+/brBvPLn05R7vjz9fRMeHk5+fr7R/Zh631R2PWZnZxMZGWlS+aT6TyayUr0WHh5OSEgIBw8eZOzYscpDLzIyksDAQNq3b09JSQlPPPEEFy9eJC8vj8OHDzNhwgS++eYbg5qVykydOhW1Wk1eXh5w60OlMv/85z8B+PLLL3n77beJiooiMjKShQsX0qZNG8LCwipd9x//+AcA//nPf/jxxx9JSEggLCyMxx57DICBAwdWqAnVGzlyJADnz59n8uTJREREEB4ezhNPPFHh4Tx69GhUKhW//fYbCxYsIC0tjZSUFJYvX86kSZM4cuRIhdqPOxkzZgwAM2bMYM+ePeTn5xMREcGzzz7L22+/TUZGBtbW1nTv3p2mTZtSWlrKsGHD2L17N9euXWP//v306dOH559/nuzsbKCs1snKyopz584xZcoU5Ziefvppzp8/j0ajUc5ZZeV5//332bhxI7m5ucTGxvL666/z2muvcfXqVWxsbPD391dqzydOnMjGjRu5du0ax44dY/To0YwbN47k5GQAhg8fjouLCykpKTz88MMcPnyYhIQEtm/fTseOHZk3bx5FRUVGy/NXjqW6AgMDadu2LVB2HYaFhXH16lW+/vprPvjggwrL1sb9sXv3bl577TUuX77MuXPnGDt2LCkpKdjZ2REaGgrAI488AsDbb7/Nt99+S0JCArt27WLy5MkVtrdw4UImTpyovOEPEBERAUDz5s1xcnIC4PXXX+f69eusWrVKSUzbtGlTK+fGGFPur9rWqlUr+vfvT2lpKYWFhXTq1InOnTsr8+vifr6T7t274+npSWlpKSNHjuTIkSPExcXx0Ucf8d1331VYtrr3fHnDhg3D2dmZGzduMGHCBM6fP8/ly5d54403+O233wCMdnFXFf31+NtvvzFr1iyuXLnCH3/8wdixY8nMzLzj+sXFxSbtTzIzMzVpkKRqGTx4cIW2YC4uLkqbrZMnTyrty8r/NW3aVCQnJwshKravLG/EiBECyl5WycrKMphXvk1baWmpGD16dKVt1UJDQyt92SsvL0+EhIQYXc/b21tpR1iZl19+ucp2cre34fv3v/9d6XLPP/98pfvQt98cNGiQwfT09HTlDevyf3Z2dmL//v3KsgcPHlRe6jG27J49e5RlP/nkE6PLqVQqsXTpUmW58jEsKioS/fr1M7quWq0Wq1atUtaNiIgQXl5elS77888/K8uuWbOm0jeo3d3dlZdqjPVaUN1jqewcV6d945EjR5SXl4z93d4OtLr3hzH6toMajcbo+rcfT2pqqmjRokWV1+aVK1dEXFycsLGxqTBv3Lhxyrb0bY3L/+lfQjJ2LdTk3Bhjyv1V2fkq39ZSv67+hSVjvaAIIcS6deuUZcvPE6L697MpbWT1ZRLiVvvTli1bKtN+/fVXYWVlVel+b+95oLr3vLFeC1avXl3pPTdz5swqyyhExetBp9OJMWPGVBnLytrIbtmyRdjY2IhevXpVev6k+kXWyEr12o8//shTTz1FgwYNcHR0pFevXmzbtk356ahDhw6cPHmSSZMm4e3tjUajwc/Pj2nTpnHixAk8PT2rtR/9z3iTJk1SaoQqo1ar+eWXX1i6dCkPPvggWq0WrVZL+/btWbhwIRs3blTau5Znb2/P7t27mTt3Ls2aNUOj0dC4cWOmTp3KmTNn7tjP5X//+1+++OILWrVqhUajISAggPfff58BAwZUWPbtt99m9erVdO/eHQcHBxwcHOjUqRPLly9X2uGZwtXVlUOHDil9ZGo0Gjw8PBg1ahTHjh0zaDPcrVs3Tpw4waRJk2jYsCEajQZvb28mTJjAyZMn6d27t7Lsiy++yJYtW+jZsyf29va4uLgwYMAAtm/fXmXtuEajYdu2bcyfP5/g4GBsbGxwdXVl0KBB/P777wZDYLZo0YLTp0/z7LPP4u/vj0ajwd3dnWHDhnHgwAEmTpyoLDt69GgOHTrEqFGj8PT0VGL09NNP88cff3DfffdVWqaaHospunTpwqFDhxgyZAgODg44Ozszfvx4o7WOtXF/PP7447z55ps0bNgQGxsbOnTowLp16wyOx93dnUOHDvH000/j4eGBra0tPXv2ZMWKFQbbatKkCTt27KB79+7Y29vj6+vL1KlT+fLLL5Vl5syZw3//+1+lL1dfX1+mTZtWrf5SKzs377333h3Xhcrvr/L9pdaFESNG0KRJExo0aMCECRMqzK/t+7k6Ro4cya5du+jRowd2dnY0aNCAKVOm8NJLL1VY1pR7vrwxY8awb98+Bg8ejJOTE46OjnTr1o2VK1fWaEhqlUrFihUrePfdd2natCkajYZWrVqxfPlyAgMDq1zX1tYWe3v7O34OSPWHSohKWqBLkiRJkiRJUj0ma2QlSZIkSZIkiyQTWUmSJEmSJMkiyURWkiRJkiRJskgykZUkSZIkSZIskkxkJUmSJEmSJIskE1lJkiRJkiTJIslEVpIkSZIkSbJIMpGVJEmSJEmSLJJMZCVJkiRJkiSLJBNZSZIkSZIkySLJRFaSJEmSJEmySDKRlSRJkiRJkiySTGQlSZIkSZIkiyQTWUmSJEmSJMkiyURWkiRJkiRJskgykZUkSZIkSZIskkxkJUmSJEmSJIskE1lJkiRJkiTJIslEVpIkSZIkSbJIMpGVJEmSJEm6By1btoyuXbvSvXt3PvjgA2X60aNH6d27N7179+af//wnubm5Rtd/99138fHxISoqymD6p59+ygMPPECfPn3o06cPmZmZFdb9+uuvjU5PTU3lhx9++ItHdotMZCVJkiRJku4xsbGxbNq0iYMHD3Lw4EFOnDjBhQsXAJg2bRqbN29m7969TJw4kXfeecfoNt544w0GDx5cYfq5c+dYtWoVe/bsYc+ePbi4uFRY5vjx43z66acVpn/88cecPn36Lx7dLTKRlSRJkiRJusf4+/uzefNmrKysyMrKIj09HTc3NwAcHBxITEwEIDIyErXatHQwIiKCZs2aVblMbGwsO3bsICsrS5mWkZFBWFhYhRrev0ImspIkSZIkSfeobdu24efnh6+vLz4+PgDMmTOH+++/n5YtW/LFF18wa9Ysk7YZHh5OaGgoDz30EPv37ze6THFxMdOmTWPx4sXKtIULFzJz5kyjTQ5qSiaykiRJkiRJ96jBgweTlpaGv78/8+fP59q1a/z73/8mPDyc8PBwPvzwQ15//fVqb6+0tJRz586xY8cOfvjhB2bMmEF6errBMoWFhWg0GsaOHcu2bdvIyckhKyuLPXv2MHLkSNRqNTqdrlaOTyaykiRJkiRJ95iIiAjS0tIAUKvVzJo1i71793LgwAHGjBlDkyZNABgxYgQnT56s9natrKzw9PQEwNvbm27dunHp0iWDZS5fvkxAQABqtZrnnnuOJUuWsHjxYqZNm4ZKpaJx48bExcXVynHKRFaSJEmSJOkeExUVxezZs5V/r1ixgpCQENq3b8+OHTsoLCwEyl7csrGxAaCoqIi8vLwqtzt8+HBOnToFlPVAsHfvXoKDgyvsOzAwEIDx48ezZcsWdu7cyejRowEIDAwkMjKyVo7Tula2cg8QQpCdnY2TkxMqlcrcxZEkSZIkSaqx0NBQTp8+Tbdu3VCr1bRp04ZFixZha2vLpEmT6N27N/b29tjb2/PVV18BsGjRIuLj4432NqD38ccfM3XqVCURXrBgQYVeCyIjI5WXwdRqNVOnTsXa2lrJrwIDA4mKimLgwIF/+ThVQgjxl7dyD8jKysLFxYXMzEycnZ3NXZx7wrlz52jbtq25iyHVgIydZZPxs1wydpZLxs48ZNMCqc6cP3/e3EWQakjGzrLJ+FkuGTvLJWNnHrJpgVRnbG1tzV0EqYZk7CybjJ/lkrGzXPdq7ITQkZeZRElRHtY2WrQu3qhU9aceVDYtuEk2LZAkSZIkSbolKyWG61FHKCm6NYSttY0Dvs274uwRYL6C3ab+pNTSPWfVqlXmLoJUQzJ2lk3Gz3LJ2Fmuey12WSkxxF8IM0hiAUqKcom/EEZWSox5ClaOTGSlOlNbnR1Ld5+MnWWT8bNcMnaW616KnRA6rkcdqXKZ69FHEML8xywTWanO6PuQkyyPjJ1lk/GzXDJ2luteil1Zm9jcKpcpKcwlLzPpLpWocvJlL6nONGrUyNxFkGpIxs6yyfhZLhk7y3UvxK6kKJ/cjOukJ4ZXc/mqB0+4G2SNrFRn9u3bZ+4iSDUkY2fZZPwsl4yd5bLE2JUU5ZN54wrXIg8RdWIt4Ud+5uql38nNuFat9a1ttHVcwmqUoTY2kp+fj729fW1sSpIkSZIkSaoDJcUF5GVcJzczkdyMaxTmZVRYxs7BHXsXH7KSoyktKax0W9a2DmhdvOuwtNVjciL7v//9j7S0NGbOnAnA888/z8qVK7Gzs+P777+nf//+tV5IyTL16dPH3EWQakjGzrLJ+FkuGTvLVR9jV1JcSF7mdXJvJq+FuWkVlrF1cMPBxRcHV1+0Lr5Ya8r6w3V09SX+Qlil2/YN7Fov+pM1OZGdN28ehw4dAsq6moiMjCQpKYnDhw8zefJkIiMja72QkmWKi4vD19fX3MWQakDGzrLJ+FkuGTvLVR9iV1pcSG5WIrkZ18nLuE6BscRV64qDa0McXHzQuvpirbEzui1njwCaBPev2I+srQO+gfWnH1mTE1m1Wo1Op+PatWu88cYb7Nq1C2tra9q3b8+NGzfqooyShbp8+TJdunQxdzGkGpCxs2wyfpZLxs5ymSN2pSVF5GUm3qxxvU5BTmqFZWy1rmhdfJTk1dqm+k1BnT0CcGrgV69H9jI5kV26dCkDBw4E4P333ycgIACA+fPn8+STT5q0rWXLlvHNN99gZWXFiBEjmDlzJkOGDCE/Px+AnJwccnJy+PPPP7GysjJY9+WXX+bw4cPY2tpib2/Pb7/9Rl5eHlOmTCEmJgaAt99+m759+5p6iFItsbaWnWJYKhk7yybjZ7lk7CzX3YhdaUkReVlJZYlrhj5xNRyg1cbeBQdXXxxcfNG6+qD5iy9kqVRqHFzr768EtTZEbXJyMg0aNKiQcFYmNjaW559/ng0bNmBlZcXYsWN56623CA4OVpZ54403eOCBBxgzZkyF9QcPHsz69euxs7tVJT5jxgxatWrF5MmTSUtLY/DgwezYsQNXV9c7lkcOUStJkiRJUn1SWlpMfmYSuTfbueZnp1AxcXW+rY2rDxpbB/MU1kxMrhuOi4sz+ldQUEBCQkK1t+Pv78/mzZuxsrIiKyuL9PR03NzclPlJSUkcOHCA0aNHG10/OzvbIIkFOHDgAJMnTwbA3d2dCRMmsGPHDlMPUaola9euNXcRpBqSsbNsMn6WS8bOctVG7HSlxeSkJ5B05QSXz2zi0qEfiD2/nZT4s+Rn3wAEGjsnXH2CaNSyN0FdJtDiwbE0DOqBi1fg3y6JhRo0LXj44YdJSkrixo0bBAUFYWtry9mzZ/H09MTX15dTp06ZtL1t27YxYcIEhg8fjo+PjzL93Xff5Y033kClUlVYJzU1lfDwcAYMGIBWq+W9996jTZs2FWqDg4KCuHDhgtH9FhYWUlh4q1uJrKwsk8ot3VlRUZG5iyDVkIydZZPxs1wydparJrHTlZaQl5VMbmbZy1n52TcqDPuqsXUsaypws1cBGzvH2iryPcHkRPbbb79l8uTJbNu2jXbt2gEQGRnJpEmTWL58uckFGDx4MGlpacydO5f58+cze/ZsYmNjiYiI4KGHHjK6jr29PdHR0bi4uBAZGcmjjz7K0aNHKyS9VbWamD9/Pm+99VaF6atXr0ar1TJq1CjCwsLIzMzEy8uLzp07s3nzZgA6dOiATqfjzJkzQFlyf+DAAVJTU3F3d6dXr16sX78egHbt2qHRaDh58iQAQ4cO5cSJEyQlJeHs7MxDDz3EmjVrAGjdujWOjo4cPXoUgEGDBnH+/HkSEhJwcHBg2LBhrFq1CoCWLVvi4eHBwYMHARgwYAARERHExcVha2vLqFGjWLVqFTqdjsDAQBo1aqR01tynTx/i4uK4fPky1tbWjB07lrVr11JUVIS/vz+BgYHs3r0bgB49epCcnExERAQAEydOZMOGDeTl5dG4cWOCg4OVWu+QkBAyMzOVLw+NGzdm8+bNZGdn4+PjQ4cOHdi6dSsADz74IAUFBZw7dw6ARx55hD179pCeno6HhwchISFs2rQJgAceeACA06dPAzB8+HAOHz5MSkoKbm5u9OnTh19//RWAtm3bYmdnx/HjxwEIDQ3l1KlTJCYm4uTkxODBg1m9ejUAwcHBuLi4cPjwYQAeeughLly4wNWrV9FqtTz88MOsWLECKPtS5OXlxYEDBwDo168f0dHRxMbGYmNjw+jRo1m9ejUlJSU0a9YMPz8/9uzZA0CvXr1ISEggOjoatVrN+PHjWbduHYWFhfj5+REUFMSuXbsA6N69OykpKYSHl42qMn78eDZv3kxubi6NGjWiTZs2bN++HYAuXboobcgBxowZw44dO8jKysLb25tOnTqxZcsWADp27EhxcTFnz54FYOTIkezbt4+0tDQaNGhAjx492LBhAwDt27dHq9Uqxz5s2DCOHTtGcnIyLi4u9O/fn3Xr1gHQpk0btFotx44dA2DIkCH88ccfXLt2DUdHR0JDQ/nll18AaNWqFe7u7kqvJwMHDuTSpUvEx8djb2/PyJEjWblyJUIIWrRogY+PD/v37wegb9++xMTEcOXKFTQaDWPGjGHNmjUUFxfTtGlTAgIC+P333wHo2bMniYmJREZGolKpmDBhAuvXryc/P58mTZrQqlUrdu7cCUC3bt1IS0vj0qVLAIwbN46tW7eSk5NDw4YNuf/++/ntt98A6Ny5M3l5eZw/fx6gXj8j0tLSOHXqVL1/RowdO5Zt27bJZ8Rtz4iMjAyAev+MUKvVSsWVfEaUPSMcHR05d+5clc+ILZs3obXR0TzAC1GUia4oC3W5+roSnRWunn5cjEogp1DNfa3bkKvSsG/3CeDvk0dMnDiR6jC5jWz//v2ZM2dOhf7SDhw4wFtvvaUE/04iIiLw8PDA3d0dgNzcXEaNGsX27dt58sknmTJlCiEhIdXa1ujRo1myZAljx45VHiIAH330EX5+fowbN67COsZqZJs0aSLbyNaipKQkvL3N31myZDoZO8sm42e5ZOwsl7HY6XSl5N+scc3NuE5+1g2EKDVYxtpGe7PGtaxXAY2dk9FfoyXjTG4je+zYMXr06FFhevfu3Tly5Ei1txMVFcXs2bOVf69YsYKQkBAuXrxIVlZWhSS2qKiIvLyyMX27du1KfHw8AFeuXCEuLg5vb2+6devGDz/8AEB6ejpr1qxh0KBBRvdva2uLs7OzwZ9Uu/TfxiTLI2Nn2WT8LJeMneXavXs3Ol0puZmJJMeeJubsVi4d+oGYs1u5EXuavMxEhCjF2kaLi2cgvi260/zBsQR1mUDjVn1w8wnCxt5ZJrEmMrlpgaurKwkJCfj7+xtMv3z5sknJYGhoKKdPn6Zbt26o1WratGnDokWLmD59utGf/BctWkR8fDyffvopixYtYsKECajVajQaDcuXL0etVjNv3jymTJnCl19+iUqlYsGCBbi4uJh6iJIkSZIkVdOlS5do1aqVuYthFkKnIz/7BrmZ12naoIBLh35A6MrVuGrs0br64ODSEAdXX5ms1jKTmxa8+eabnDt3jtWrVysvVxUVFTFq1CgeeOAB3n777b9UoNLS0mp34VWbZPdbtS8+Pp4mTZqYuxhSDcjYWTYZP8tV32JnrL/324WFhfH5558rbTTLe/fdd1m8eDEHDhygefPmQFnFlL7NMsCJEyfYtm1bhV97v/76a0aPHl2hQio1NZWtW7cyadKk2jhEkwidjvycFGUAgrzMJISuxGAZK42d0h1WWeLqIhPXOmRyjezcuXN5/PHHCQ4OZtSoUQghWLNmDV27dmXevHl/uUDmSGKlupGcnFyvHshS9cnYWTYZP8tVn2IXGxvLpk2bOHjwoNLf+4ULF5T+3rOzs5kxY4bygp4xb7zxRoWh66dPn8706dOBstrcmTNnGm2yePz4cRISEpgzZ47B9I8//pj8/Py7ksgKoaMgJ1UZgCAvKwldabHBMlbWtji4+pKaVUyrtl2w1brKxPUuMjmRtbKy4scff+TYsWPK24IrVqzgwQcfrPXCSZYtIiKCjh07mrsYUg3I2Fk2GT/LVZ9ip+/vHTDa3/srr7zCq6+++pdeTps7dy7/+c9/jM6LjY3l/PnzTJ8+XfmlNCMjg7CwMLy8vGq8z6qUJa5pt9W4JhpNXMuGfC2rcbXVuqFSqTiwYgXtu7pVsmWprtR4PLXOnTvTuXPn2iyLJEmSJEn1jLH+3nfv3s13331HZGQkW7Zs4dNPP1V6IaqukydPotFouP/++43OLy4uZtq0aSxevJg33ngDgIULFzJz5kwWLlz4l45JTwgdBblliWteRiK5mYnoSg37g1Vb2+Dg4qM0F7B1cJc1rvVIjYao3bJlC5s3byYpKYmCggKDeVX9xFCfyTaykiRJkmScTqdj7ty5aLVaZs+eTd++fXnrrbfo1asXq1atYuvWrXz33XdG133iiSf497//rbSR1Rs2bBgLFy6sMB3Kush85JFH2Lx5M7179+a3335Dp9MxYsQIfv/9d/r160dYWBhqtWmdLwkhKMw1rHEtLSmXuFppbtW4uvhi5+iOSmVyJ0/SXWJyjexHH33EN998w0svvURAQAAajaYuyiXdAzZs2MDDDz9s7mJINSBjZ9lk/CxXfYrd7f29q9VqZs2axahRo5g9ezaFhYX06tULKBuUwdQa0r1799K4cWOjSSyU9YQUEBCAWq3mueeeY8mSJZSWljJt2jRUKhWNGzcmLi6OgICAKvcjhKAwL/1WG9fMREpLCg2WKUtcvZUaVzvHBjVKXOtT7P5OapTIHj16tEL3W5JUnr7fX8nyyNhZNhk/y1WfYhcVFcXHH3/MF198Adzq7x3AxsaG8PBwWrZsyf79+2ncuDFQ1otRSUkJWq22ym2//fbbfP/991XuOzAwEChLlHv37o2VlZXS/3xgYCCRkZEVEtmyxDVDGfI1NzOR0mLDX47Vamu0Lj5Kl1j2TjVLXMurT7H7OzE5kS0tLcXX17cuyiLdY/QPNsnyyNhZNhk/y1WfYldZf+8A3333HY899hilpaW4ubnx5ZdfAoZ9vlfm4MGDdOzYkYYNG1a6TGRkJM2aNQNArVYzdepUrK2tlbapgYGBREVFMWDAAIryM5WmArkZ1yskriq1NVpnr7KRs1x9sHf0RGVik4TqqE+x+zsxuY3sSy+9RMuWLZk6dWpdlcksZBvZ2peamkqDBg3MXQypBmTsLJuMn+X6u8Supn3GCyEoys9Skta8zOuUFOUbLKNSW6F19r7VxtXJA7W67rv2/LvErr4xuUZ25MiRTJ8+nYsXLzJkyBBcXFwMLkbZk4Gkt2PHDiZOnGjuYkg1IGNn2WT8LNe9GjshdORlJlFSlIe1jRatS/W67BJCUFSQXdZM4Gata0mR4U/4KpUVWmcvtDe7w7J38rwriWt592rs6juTE9l58+bh5ubG2bNnOXv2rME8lUolx4mWJEmSJEmRlRLD9agjlBTlKtOsbRzwbd4VZ48Ag2WFEBQXZJObmUhuxjVyMxIN1gNQqdTYO3spL2fZO3uiVte4N1HJwtWo+617kWxaUPtiYmLu+EapVD/J2Fk2GT/Lda/FLislhvgLYZXObxLcHzvHBkqPArkZ1yguNJK4OnkqAxDYO3mhtqp/ieu9FjtLUeMr4bfffmPv3r0IIejTpw9DhgypzXJJ94DMzExzF0GqIRk7yybjZ7nupdgJoeN61JEql4m/sBsoV5+mUpUlrjdrXLXO3vUycS3vXoqdJTH5tb3CwkJCQ0OZM2cObm5uNGzYkPfee49BgwZVGBxB+nu7cOGCuYsg1ZCMnWWT8bNc91LsytrE5t5hqbIk1t7JE48m9+PfZhD3dZtEs/bD8W7aCUe3RhaRxMK9FTtLYvLVMWfOHLRaLUePHlVe8po+fTpTpkxh1qxZfPLJJ7VeSEmSJEmSLEt+dkq1lmvYogduvi3ruDTSvcrkNrKNGzfm4MGDFQZEuH79Oh07duTatWu1WsC7RbaRrX0lJSVYW1vGN2nJkIydZZPxs1yWHruSonwyk6PJSI6mIKd6iWxAu1AcXC2/f3pLj52lMrlpQXp6Oj4+PhWme3t7y/YhkoFt27aZuwhSDcnYWTYZP8tlibHTlZaQkRxN7LnthB9ZQeLlo0oSq1JV3Q2Wta1Dtbviqu8sMXb3ApO/OrRr147t27czYsQIg+l79uyhTZs2tVYwyfJlZ2ebuwhSDcnYWTYZP8tlKbETQkduRiKZyVFkpcSgKy1W5tk7eeLi1RwXz6bkZSVV2WuBb2DXWhketj6wlNjda0xOZN99910ee+wxtFotAwYMAODw4cM8/fTTLFu2rNYLKFkuYzX3kmWQsbNsMn6Wq77HriA3jYykKDKTow0GJtDYOuLi3RxXr0Bsta7KdGePAJoE96/Yj6ytA76BFfuRtWT1PXb3qhr1I7tz505efvllrl+/jq2tLe7u7nz00UcMGjSoLsp4V8g2srUvMzMTFxcXcxdDqgEZO8sm42e56mPsigtzyUy+TGZyFAW5acp0tbUNLh5NcfFujtbZG5VKVek2jI3sda/UxOrVx9j9HdSoVfLAgQM5d+4caWlpCCHk2MKSUVu3bpXD9VkoGTvLJuNnuepL7EpLi8lOiSEjKYrcjOvou8lSqdQ4ujfB1bs5ju6Nqz2ilkqlvide6KpKfYnd343JiWx2djaZmZk0btwYd3d3UlJS+OKLL2jSpAlDhw6tizJKkiRJklTHhNCRm36NjOQoslJiEboSZZ69sxeuXs1x9myKtcbOjKWUJEMmJ7LPPvssHTt25KWXXiIrK4vu3bszcOBAvvvuO3bv3s1///vfuiinZIEefPBBcxdBqiEZO8sm42e57nbshBAU5KaSmRRF5o3LlBTlK/Ns7Jxx8Q7E1as5Nvayyd2dyPvOPExOZDdu3Mjy5cuBssERHn/8cWbPnk1GRgbNmjWTiaykkCO9WS4ZO8sm42e57lbsigtyyEiOJjM5isK8DGW6lbUtzp7NcPVujr2TZ5XtXiVD8r4zD5MT2Q4dOvDJJ5+g1WrZt28fx48fByA+Ph43N7daL6Bkuc6dOye7ZLNQMnaWTcbPctVl7EpLisi62e41L/O6Ml2lssKpQRNcvJvj6NYYtbrqvl8l4+R9Zx4mJ7KrV69m4cKFZGVlsW3bNmUUi/Xr17NkyZJaL6AkSZIkSTUjdDpy0q+SkRxFdmocQleqzNO6+Nxs9xqAlbWtGUspSTVncvdbU6ZM4bPPPrvnhmGT3W/VvoKCAuzs5EsBlkjGzrLJ+Fmu2oidEIKCnJSy/l5vXKa0+NZP3jb2Lrh6N8fFKxAbO6e/WlzpNvK+Mw+TO3HbtWsXGRkZtbLzZcuW0bVrV7p3784HH3ygTO/WrRt9+vShT58+zJo1q8J6N27cYNiwYfTq1Ys+ffpw7tw5AEpLS3nxxRcJCQkhJCSEX375pVbKKdXMnj17zF0EqYZk7CybjJ/l+iuxKyrI5kbsaaJOrOXy6Y2kXbtAaXEBVho73Bu1ptkDI2jeaTSefu1lElsH5H1nHiZXq37wwQeMGDGCV155hXbt2uHk5IRafSsf9vLyqtZ2YmNj2bRpEwcPHsTKyoqxY8dy4cIFWrZsib29PWFhlQ9pN23aNF588UUGDBhATEwM48eP59ChQyxbtgxnZ2cOHz5MQUEBgwcPpmPHjgQGBpp6mFItSE9PN3cRpBqSsbNsMn6Wy9TYlRYXkplyhcykKPKykpTpKrUVzg38y9q9ujZCpb63Bh+oj+R9Zx4mJ7IzZswA4NVXX60wT6VScfny5Wptx9/fn82bNwNlP+unp6fj5uZGVFQU/v7+la5XXFzM9evXleFxAwIC6NKlCydPnmTDhg2sXr0aADs7O/71r3+xdu1apczS3eXh4WHuIkg1JGNn2WT8LFd1YqfTlZKTFk9mcnRZu1ehU+Y5uPri4tUcZ48ArKxt6rKoUjnyvjMPkxPZK1eu1GoBtm3bxoQJExg+fDg+Pj4cOnSIgwcP0qdPH5o3b86CBQsMekNITU3F29vbYBtBQUHExMSQl5dn0L41KCiIXbt2Gd1vYWEhhYWFyr+zsrJq9bgkCAkJMXcRpBqSsbNsMn6Wq7LYCSHIz0ouG6zgxhVKS259ftlq3ZR2rxpbh7tVVKkced+ZR43e2BJCsHr1ak6dOsX7779PdHQ0fn5+aDQak7c1ePBg0tLSmDt3LvPnz+eZZ57h/PnzaDQaNmzYwPTp0/n+++8N1infr53+fbXKphszf/583nrrrQrTV69ejVarZdSoUYSFhZGZmYmXlxedO3dWapA7dOiATqfjzJkzADz88MMcOHCA1NRU3N3d6dWrF+vXrwegXbt2aDQaTp48CcDQoUM5ceIESUlJODs789BDD7FmzRoAWrdujaOjI0ePHgVg0KBBnD9/noSEBBwcHBg2bBirVq0CoGXLlnh4eHDw4EEABgwYQEREBHFxcdja2jJq1ChWrVqFTqcjMDCQRo0asW/fPgD69OlDXFwcly9fxtramrFjx7J27VqKiorw9/cnMDCQ3bt3A9CjRw+Sk5OJiIgAYOLEiWzYsIG8vDwaN25McHAwO3bsAMpu4szMTC5cuACATqfDxcWF7OxsfHx86NChA1u3bgXKOo4uKChQ2jc/8sgj7Nmzh/T0dDw8PAgJCWHTpk0APPDAAwCcPn0agOHDh3P48GFSUlJwc3OjT58+/PrrrwC0bdsWOzs7pVu40NBQTp06RWJiIk5OTgwePFiptQ8ODsbFxYXDhw8D8NBDD3HhwgWuXr2KVqvl4YcfZsWKFUDZlyIvLy8OHDgAQL9+/YiOjiY2NhYbGxtGjx7N6tWrKSkpoVmzZvj5+SntpXr16kVCQgLR0dGo1WrGjx/PunXrKCwsxM/Pz+ALV/fu3UlJSSE8PByA8ePHs3nzZnJzc2nUqBFt2rRh+/btAHTp0oWcnBz+/PNPAMaMGcOOHTvIysrC29ubTp06sWXLFgA6duxIcXExZ8+eBWDkyJHs27ePtLQ0GjRoQI8ePdiwYQMA7du358CBAzg6OgIwbNgwjh07RnJyMi4uLvTv359169YB0KZNG7RaLceOHQNgyJAh/PHHH1y7dg1HR0dCQ0OVtuqtWrXC3d2dQ4cOAWVDXV+6dIn4+Hjs7e0ZOXIkK1euRAhBixYt8PHxYf/+/QD07duXmJgYrly5gkajYcyYMaxZs4bi4mKaNm1KQEAAv//+OwA9e/YkMTGRyMhIVCoVEyZMYP369eTn59OkSRNatWrFzp07gbL2+GlpaVy6dAmAcePGsXXrVnJycmjYsCH3338/v/32GwCdO3cmLy+P8+fPA9TrZ0RCQgL9+vWr98+IsWPHsm3bNvmMuO0Zce3aNV555RXlGRHQxAcfd2vSEyOxtb71mVZcqiIjz4rOPUPZtecQuZF/0qhRxl17RqjVak6dOgXIZ4T+GZGbm0uXLl0s4hkB9T+PqO5wvyb3WpCRkcGQIUNo0aIFq1evJj8/n3//+99s3bqVvXv34uRUvQbkEREReHh44O7uDpRdAKNGjVJuQL1u3bopFzWUNS0YOHCgQaPqadOm8dhjjzFnzhzWrl2rfACvWbOGy5cvG21aYKxGtkmTJrLXglq0YsUKOe60hZKxs2wyfpZrxYoVjB3zCFk3LpORFEV+9g1lnlptjZNHAK5egTi4NUSlku1e6xN535mHyXfB7NmzGTRokEEt6TvvvEP37t15/fXXq72dqKgoZs+erfx7xYoVNGrUiL59+yqjY2zcuJEWLVoAUFRURF5eHhqNBh8fH+VbQVxcHCdPnqRTp048/PDDfPLJJ0BZovrZZ58xZswYo/u3tbXF2dnZ4E+qXfpaEsnyyNhZNhk/y6PTlZB54wrtmtoRfuRnrkcdvpnEqnBwa0Sjlr0JCvkHjVv1xtG9sUxi6yF535mHyU0L1q1bp/wsdPtP+TNnzqR9+/YsXbq0WtsJDQ3l9OnTdOvWDbVaTZs2bfj888/5+eef6dGjB3Z2djRq1IgvvvgCgEWLFhEfH8+nn37K4sWLmTx5MnPmzMHa2pqvvvoKtVrNM888wyuvvEK3bt1QqVS88sorNGvWzNRDlCRJkqQ6J4QgLzORzORoMm9cQVdapMyzc3DHxbs5Lp6BaGy1ZiylJNVvJjctcHJyIj09HWtra7RaLXl5eUBZDai7uzu5ubl1UtC6JgdEqH3yZxbLJWNn2WT86rfCvIyywQqSoykuzFGmW9s4cC21kJA+w7FzcDdjCaWakPedeZhcI9uhQwd27tzJkCFDDF6m+u233+jQoUOtFk6SJEmS7gUlRflk3mz3WpCTokxXW2lw9gjA1bs5WhdfTq1cKZNYSTKByTWyR48eZdKkSSxZsoSRI0eSkpLChg0bmDFjBqtWraJbt251VdY6JWtka19OTo7y4p1kWWTsLJuMX/2gKy0hOzWWjOQoctISAP3HrQpH98a4ejXHqYEfaqtbdUoydpZLxs48TG4t3qVLF9atW8eyZctwdHTEx8eHH3/8kQ0bNlhsEivVDX2XNZLlkbGzbDJ+5iOEICf9Ggnh+wg/8jNXL+0hJ+0qILB38sAnsCstu07Ev81DuHg1M0hiQcbOksnYmUeN+pFt06YNa9eure2ySPeYlJSUOy8k1UsydpZNxu/uK8hNu9nu9TIlRbfeFdHYOuLiFYird3Nsta533I6MneWSsTOPGvffsWXLFmbMmMHMmTOVzoAl6Xa3j8gmWRYZO8tW3+K3bNkyunbtSvfu3fnggw+U6QcPHqRnz5706tWLxx57TOl68Xa5ubmMGzeO3r1706dPH6VTeICTJ0/Svn17/v3vf1e671OnTrF3716j8xYuXFjzgwKKC/NIuXqO6JO/En3yV1KvnqOkKBe1lQ1uPi0JuH8oLTqPw7tpp2olsVD/YidVn4ydeZjcRraoqIiRI0eSlpbGhAkT0Ol0/PTTT3h7e/Prr79ia2tbV2WtU7KNbO0rKCjAzs7O3MWQakDGzrLVp/jFxsby/PPPs2HDBqysrBg7dixvvfUW9913H3379uWXX37By8uLJUuWoFaree655wzW//DDD/H09OTJJ58kOTmZcePGGQyIs2fPHnbt2sU777xjdP+rVq3iyy+/JCwszGD6zp07mTlzpjI6VXXpSovJSilr95qbfg19u1eVSq20e3Vs0AS1ukY/eNar2EmmkbEzD5NrZOfMmUODBg04fPgwL774Ii+//DLHjh3DycmJN954oy7KKFko/ZCQkuWRsbNs9Sl+/v7+bN68GSsrK7KyskhPT8fNzQ2VSsWePXvw8vKitLSUK1eu4O3tXWH9GTNm8OSTTwJlA+CYWusVFRVFamqqMnSs3kcffURpaWm1tiGEjpy0q1y9tIfwwz+TEL6X3PSyl7fsnb3wbR5CUNeJ+LUeiLNn0xonsVC/YieZRsbOPEy+23788UeOHTtmMBiClZUVH374IV27duWjjz6q1QJKkiRJlm/btm1MmDCB4cOH4+Pjo0y/ceMGHTp0wNHRkXnz5lW6/lNPPcX333/Pli1bTNpvZGQkn3/+Oe+8846y7p49e2jVqhWurq4kJSUZTaCFEBTkppGZFEXmjcuUFOUp82zsnHDxao6Ld3Ns7eUveJJkTibXyGZlZeHl5VVhepMmTcjJyTGyhvR31bZtW3MXQaohGTvLVh/jN3jwYNLS0vD392f+/PnKdE9PT+Lj41mwYAHjx4+vdP2vvvqK+Ph45s6dy5UrV6q936tXr9K1a1eaNGmivFW+YMECZs6cSWBgIJGRkQbLFxfmciP+D6JP/srlU+tJTThPSVEeVta2uPm2omn7YTR/cCxeAR3qJImtj7GTqkfGzjxMTmRDQkLYt29fhen79u2T3W9JBmRbIcslY2fZ6lP8IiIiSEtLA0CtVjNr1iz27t1LSkoKUVFRynLDhg0jMzOzwvpHjhxR/t/Hx4dJkyYZTLuT0tJSVCoVs2bNYv78+Rw6dIhmzZrRsGFDAgMDiYqKorSkiPTECGLObiXi6EqSr5ygMC8dlUqNs0cATYIHENR1Ig1bdEfr7G3wi2Rtq0+xk0wjY2ceJieyixYtYtasWfzxxx/KtIiICF5//XUWLVpUq4WTLNvx48fNXQSphmTsLFt9il9UVBSzZ89W/r1ixQpCQkIAePzxx5Vhzs+dO6e8aKvT6cjKygLgk08+4dChQ0DZUOibNm3iwQcfrNa+s7OzlQ7q/f398fHx4dlnn2XmzJkInQ5fTydOHt5F+JGfuRaxn9yM6wBonb3xbdGdll3/QZPg/jh7+KNWW9XC2biz+hQ7yTQyduZhchvZUaNGkZqaSqdOnQgMDMTW1pY///wTNzc3Ro0aBZS1LVKpVFy4cKHWCyxJkiRZjtDQUE6fPk23bt1Qq9W0adOGRYsWYWtry6xZsxg4cCBWVla4uLiwbNkyANasWcNPP/3Ehg0b+PTTT5kyZQqZmZkUFxfz4osv0rx582rtOzIykmbNmin/njVrFh99OB+rgnjCj+7FpugakZHhCF1fbOxdcPVujotnIDb2TnVyLiRJqn0md78VGxtb7WX9/f1NLpC5yO63al9mZiYuLi7mLoZUAzJ2lk3Gz1BRQTaZydFkJEVRlH+r+YKVxg4Xz2a4ejfHztGjTpsMVJeMneWSsTMPk2tkLSk5lczr1KlT9O3b19zFkGpAxs6yyfhBaUkhmTeukJkcTV5mojJdpbbCqYE/rl6BOLo1RqWu8bhAdULGznLJ2JlHzTu7k6Q7SExMvPNCUr0kY2fZ7sX4CaEjLzOJkqI8rG20aF28UakMk1CdrpSc9KtkJkWRnRqPELf6iXVw8cXFuznOHgFYWdvc7eJX270Yu78LGTvzkImsVGecnGQ7M0slY2fZ7rX4ZaXEcD3qCCVFuco0axsHfJt3xamBP/nZN8hIiiLrxmVKSwqVZWy1rrh4N8fVMxCNnaM5im6yey12fycyduZhchvZe5VsI1v7SkpKsLaW35UskYydZbuX4peVEkP8hbBK51tr7Ckpzr/1bxt7XDwDcfEOxM6hQb1o92qKeyl2fzcydubxlxoH5ebm3nkh6W9r9erV5i6CVEMydpbtXomfEDquR1XdZ2xJcT6orHDxCsS/zSCCukzAJ7AL9vXk5S1T3Sux+zuSsTOPGiWymzZtonXr1jRo0ACAN998UwZQkiRJqlW5GdcNmhNUpknr/jRu1QdH98YV2s1KknRvM7kOfNOmTcyZM4cNGzYow7FNmDCBYcOG4erqysCBA2u9kJJlCg4ONncRpBqSsbNslhy/4sI8ctKvkpN2ley0uGqtI0qK6rhUd48lx+7vTsbOPExOZN9++22WLl1K8+bNlZ9t7rvvPpYsWcKMGTM4ffp0rRdSskyyPz3LJWNn2SwpfkLoyMtKJiftKjlp8RTkppm8DWsbbR2UzDwsKXaSIRk78zA5kb1w4QLdu3evML1fv36Eh4fXSqGke8Phw4cJCAgwdzGkGpCxs2z1PX7FhbnkpCeQkxZPTvo1dKWGNap2jh44uTfGwa0RVy/+TklRXqXbsrZ1QOviXddFvmvqe+ykysnYmYfJiayrqyvXrl2jYcOGBtOTk5Nxc3OrtYJJkiRJ9wah05GXlaQ0GShf62plbYujWyMc3Rvj6NYYaxt7ZZ5v85Aqey3wDewq28VK0t+Yyd1vzZ49m+TkZJYvX45WqyUvLw+dTsejjz5K48aNWbBgQV2VtU7J7rdqX2pqqvJCoGRZZOwsW32IX3FhbllzgfSrRmtd7Z08cHRrgqN7Y+ydPKpMRo32I2vrgG9gV5w9AurqEMyiPsROqhkZO/MwOZEtLi7m6aefJioqimPHjvHiiy+ydetWWrduzY8//oiNTf0dMaUqMpGtffv376dnz57mLoZUAzJ2ls0c8bu91jU77SqFRmtdG9+sdW1kUOtare1XY2Sve4G89yyXjJ15mNy0QKPR8N1333H27Fn2799PQUEB33zzDZ07d66L8kkW7OrVq+YuglRDMnaW7W7Fz7DWNQFdabHBfHsnTyV5vVOt652oVGocXH3/apHrPXnvWS4ZO/Oo8RAU7dq1o127dn9p58uWLeObb77BysqKESNGMHPmTE6fPs3MmTMpLi7GwcGBL7/8skJ7XICXX36Zw4cPY2tri729Pb/99ht5eXlMmTKFmJgYoKyHhb59+/6lMko1p9XeO28S/93I2Fm2uorfHWtdNXZlbV3dalbrKsl7z5LJ2JlHtZoWDBkypNojpGzdurVay8XGxvL888+zYcMGrKysGDt2LPPmzWPy5Mls3LgRb29vjh49ykcffWR0sIXBgwezfv167OzslGkzZsygVatWTJ48mbS0NAYPHsyOHTtwdXW9Y3lk0wJJkqSK9LWu2elXya2s1tW9MY5uTbB3anBP/twvSVL9Va0a2ddff135/6KiIl588UW6devG2LFjsbOzY/Xq1ezatYvFixdXe8f+/v5s3rwZKEsi09PTcXZ25oknnsDbu6wrlS5dunDt2jWj62dnZxsksQAHDhzgww8/BMDd3Z0JEyawY8cOxo0bV+1ySbVnxYoVTJw40dzFkGpAxs6y/ZX46Wtds9PiyUm/SmFuusF8pdbVvUlZravGrpItSTUh7z3LJWNnHtVKZHv37q38/7///W9GjRrF22+/rUzr1asX77zzDvv27WPAgAEmFWDbtm1MmDCB4cOH07hxY5599lll3pYtW+jUqVOFdVJTUwkPD2fAgAFotVree+892rRpg5WVlcFyQUFBXLhwweh+CwsLKSwsVP6dlZVlUrklSZLuFcWFuWWJa9pVcjOuVVLr2gQn98bYOXpU+xc6SZKkumZyrwWNGjXijz/+wMPDw2B6RkYGbdq0qVFjZ51Ox9y5c9FqtcyePRuA8+fP8/zzz7N582acnJwMls/Ly6O4uBgXFxciIyN59NFHOXr0KL1792bfvn3Kcps2beLixYvMmDGjwj7nzZvHW2+9VWG6vluxUaNGERYWRmZmJl5eXnTu3FmpQe7QoQM6nY4zZ84A8PDDD3PgwAFSU1Nxd3enV69erF+/HihrS6zRaDh58iQAQ4cO5cSJEyQlJeHs7MxDDz3EmjVrAGjdujWOjo4cPXoUgEGDBnH+/HkSEhJwcHBg2LBhrFq1CoCWLVvi4eHBwYMHARgwYAARERHExcVha2vLqFGjWLVqFTqdjsDAQBo1aqScmz59+hAXF8fly5extrZm7NixrF27lqKiIvz9/QkMDGT37t0A9OjRg+TkZCIiIgCYOHEiGzZsIC8vj8aNGxMcHMyOHTsACAkJITMzU/nyEBgYSHJyMtnZ2fj4+NChQwel6cmDDz5IQUEB586dA+CRRx5hz549pKen4+HhQUhICJs2bQLggQceAFBGjRs+fDiHDx8mJSUFNzc3+vTpw6+//gpA27ZtsbOz4/jx4wCEhoZy6tQpEhMTcXJyYvDgwUpTleDgYFxcXDh8+DAADz30EBcuXODq1atotVoefvhhVqxYAZR9KfLy8uLAgQNA2QAg0dHRxMbGYmNjw+jRo1m9ejUlJSU0a9YMPz8/9uzZA5R90UtISCA6Ohq1Ws348eNZt24dhYWF+Pn5ERQUxK5duwDo3r07KSkpyuAi48ePZ/PmzeTm5tKoUSPatGnD9u3bgbJfLHJycvjzzz8BGDNmDDt27CArKwtvb286derEli1bAOjYsSPFxcWcPXsWgJEjR7Jv3z7S0tJo0KABPXr0YMOGDQC0b9+eK1eukJmZCcCwYcM4duwYycnJuLi40L9/f9atWwdAmzZt0Gq1HDt2DChrhvTHH39w7do1HB0dCQ0N5ZdffgGgVatWuLu7c+jQIQAGDhzIpUuXiI+Px97enpEjR7Jy5UqEELRo0QIfHx/2798PQN++fYmJieHKlStoNBrGjBnDmjVrKC4upmnTpgQEBPD7778D0LNnTxITE4mMjESlUjFhwgTWr19Pfn4+TZo0oVWrVuzcuROAbt26kZaWxqVLlwAYN24cW7duJScnh4YNG3L//ffz22+/AdC5c2fy8vI4f/48QL1+RmRmZtK5c+cqnhE2DOoXwvFDu3C0LcFeY/gxoLKyoUhouZ6SR36JDaPGjK+TZ8TYsWPZtm2bfEbc9ozIysrimWeeqffPCLVazalTpwD5jNA/I9zc3GjUqJFFPCOg/ucR1a3dNjmRdXR0JD09HY1GYzC9sLAQd3d3cnNzK1nTUEREBB4eHri7uwOQm5vLqFGj2L59O9HR0Tz22GOsXr3a6Ite5Y0ePZolS5YwduxY5SEC8NFHH+Hn52e0aYGxGtkmTZrINrK1KD4+niZNmpi7GFINyNhZNmPxKy7IIfvmgAQVa11V2DuX9TAga13NS957lkvGzjxMbpUfEhKiZP63W7lyJV26dKn2dqKiopTaVyhrWxISEkJCQgKTJk3i+++/N0hii4qKyMsrG6awa9euxMfHA3DlyhXi4uLw9vamW7du/PDDDwCkp6ezZs0aBg0aZHT/tra2ODs7G/xJtev2LxWSZZGxs2wHDhxApyslJ/0aiZePEXViLRHHVnE98iDZqbHoSoux0tjh4tWcxq360DLkHzRrPxwv/wewd/KUSawZyXvPcsnYmYfJ3W99+OGHDB06lOLiYsaNG4cQgh9//JE5c+Yo1evVERoayunTp+nWrRtqtZo2bdrw1ltv0bdvXwoLC3nqqaeUZffs2cOiRYuIj4/n008/ZdGiRUyYMAG1Wo1Go2H58uWo1WrmzZvHlClT+PLLL1GpVCxYsAAXFxdTD1GSJMkiFRXkkJN2FX/3QsIP/2S01tXJrTGO7k2wc2wgE1ZJkiyeyU0LAP744w9mz57N/v37UalU9OrVi/fee4+2bdvWRRnvCtn9Vu1LSkpSeqCQLIuMnWXQ6UrJy0wiJ73sRa3CvAyD+dYa+5tdYzXGwa0R1hpb8xRUqjZ571kuGTvzqNGACPfff7/SQFySKhMdHS1vagv1d4vdtWvXcHJyqvBiaX1UVJCtjKaVm34Nna7ktrkqtM5epOXoCL6/m6x1tUB/t3vvXiJjZx6y52qpzsTGxpq7CFIN1bfYLVu2jK5du9K9e3c++OADZXppaSkTJ06kefPmla5748YNhg0bRq9evejTp4/yFryeEILHHnuMyMhIo+tv3LiRy5cvV5heXFzM0qVLa3hE1VfW1jWBxOijRJ1YS+SxX7gedYjs1Dh0uhKsbexx9W5B4/v60irkUZq2H0ZEfO7NIWFlEmtp6tu9J1WfjJ151HiIWkm6ExsbG3MXQaqh+hS72NhYNm3axMGDB5VRAC9cuEBwcDBWVlasWLGCHj16VLr+tGnTePHFFxkwYAAxMTGMHz+eQ4cOKf1OL168mJCQEDp06GB0/YsXL7Jhwwa++uorg+nffvstYWFhPP/887V3sDcpta76HgaM1Lo6ujfG0b0xdg4Va13rU/wk08jYWS4ZO/OQiaxUZ0aPHm3uIkg1VJ9iZ2wUQDc3t2qtW1xczPXr15WBWgICAujSpQsnT56kc+fOREVF8fPPPxv0P11eVFQUZ8+e5cqVKzRt2hSAkpISvv32W6yta+cRWtbWNZGctHiy065SlJ9pMN/axh7Hmy9pObo2xOoObV3rU/wk08jYWS4ZO/OQTQukOqPvVFyyPPUxdtu2bcPPzw9fX198fHyqtU5qamqFNmtBQUHExMQA8NRTT5GdnU2/fv2UTsLLi4uLY9GiRbz33nvKtB9//JExY8agVqupwfuyABTlZ5N27QKx53dw6dCPxJ7bRmrCnzeTWBVaZ2+8AjrRrMNIgrpMpFHLXrh4Nr1jEgv1M35S9cjYWS4ZO/MwOZG9dOmSMsIIlPWbNmHCBF577TUyMjJqs2yShSspKbnzQlK9VB9jN3jwYNLS0vD392f+/PnVXq/8z+76xHPXrl24urpy6tQptmzZwpIlS5SRZ25XUlJCt27dSExMJDY2ltLSUr7++mumTp2Kr68vCQkJ1SqHTldCTtpVrkcfIfL4GiKP/8L1qMPkpMUjdCVY22hx9Q6i8X39aNWtrK2rp9/92Nfgha36GD+pemTsLJeMnXmYnMg+/fTTyuhdkZGRTJo0iTFjxpCfn8+jjz5a6wWULFezZs3MXQSphupT7CIiIkhLSwNArVYza9Ys9u7dW611GzRoQHJyssG08PBwAgICOHXqFJMnT8bW1hYXFxfGjx/PiRMnDJbNy8vDzs4OgNmzZzN//nxWrlzJiBEjsLe3JzAwkKioqEr3X5SfRapS6/oTsee3k3Z7rauLD14BnQjsMJKgLhNo1LJnWa2r9V/rJqs+xU8yjYyd5ZKxMw+TE9mzZ88SGhoKwAsvvMB///tfxowZw8cff1xlOzPp78fPz8/cRZBqqD7FrrJRAKuiHwlQo9Hg4+OjPJvi4uI4efIknTp1om3btmzcuBEhBCUlJWzdupU2bdoYbCc6Olr5cAoJCeHq1assXryYZ599FoDAwECD3g4q1rquJrF8ratPEE3u60erbv+k6f1D8fS7v9a7yapP8ZNMI2NnuWTszMPkNxXGjRvHqFGj0Gg0uLm5MWrUKAC2bNli0QMiSLVvz549TJw40dzFkGqgPsXO2CiAixYtqnKd20cCXLx4MZMnT2bOnDlYW1vz1VdfoVarGTJkCGfPnqVz585YWVkxefJk2rVrZ7CdyMhIg1qWWbNmcfDgQRwcHICyRHbD+rWkJlwgJz2e3IzrCF3prQ2oytq6Oro3xsmtCbYObnelS6z6FD/JNDJ2lkvGzjxqNLLXzp070el0DBw4ELW6rFJ31apVdOrUicDAwFov5N0gR/aqfStWrJA3tYWSsaucrrSE3Js9DOSkX6UoP8tgvrWNtixxdW+Cg2tDrKzvfpc8Mn6WS8bOcsnYmUeNElm93NxcpWbC0slEtvYlJCTQqFEjcxdDqgEZO0OF+VlliWvaVXIzjdW6+uB0s19XW+3dqXWtioyf5ZKxs1wyduZRo04QN23axOuvv050dDQFBQW8+eabtG3bljFjxtR2+SQLJm9qy3Wvxk4IHXmZSZQU5WFto0Xr4o1KVfFVgbJa1+vKoARFBfWv1rUq92r8/g5k7CyXjJ15mJzIbtq0iTlz5rBhwwalTeyECRMYNmwYLi4uDBw4sNYLKVmm6OhoOnfubO5iSDVwL8YuKyWG61FHKCnKVaZZ2zjg27wrzh4BFOZn3hpNy0itq4OzjzKaVn2oda3KvRi/vwsZO8slY2ceJieyb7/9NkuXLqV58+bKg/y+++5jyZIlzJgxg9OnT9d6ISXLpG8/LVmeey12WSkxxF8IqzC9pCiX+AthWGvsKCkuMJinsXW4OZpW43pZ61qVey1+fycydpZLxs48TG4j6+joSE5ODgBarZa8vDwACgsLcXNzU/5taWQbWUm6NwmhI+LoLwY1scapcHD1wdGtyc1aV9d6XesqSZIk1aAfWVdXV65du1ZhenJycrXHP5f+HtatW2fuIkg1dC/FLjcjsRpJLDRpPYCAdqF4NGmL3V3qJquu3Evx+7uRsbNcMnbmYXIi+9hjjzF37lyDaTqdjhkzZvCPf/yj1gomWb7CwkJzF0GqoXshdkX52STHnOLqxd3VWl6UFtdxie6eeyF+f1cydpZLxs48TE5k33rrLYqLi+nevTvFxcW89tprtG3bltLSUt599926KKNkoeQoJ5bLUmOnKy0hIymKmD+2Enn8F27Enaa0pHofLtY22jou3d1jqfGTZOwsmYydedS4H9mzZ8+yf/9+CgoK6Nmzp8W/qSfbyNa+Gzdu4Onpae5iSDVgSbETQpCffYOMxAgyb1xGd1vNqoNrQ1y9W5B05TglRZW337e2dSCo8zijXXFZIkuKn2RIxs5yydiZR42f2u3ateP555/nlVdesfgkVqobu3btMncRpBqyhNiVFOWTEn+O6JPruHJmE+mJ4ehKi9HYOeHp34EWnccT0G4Irt7N8W0eUuW2fAO73jNJLFhG/CTjZOwsl4ydedRoQIQtW7awefNmkpKSKCgw7LJm69attVKwe114eDgtW7Y0dzEkyaIInY7stHgykiLITo0Hyn5QUqmtcPZoiptPEFoXnwovajl7BNAkuH/FfmRtHfANLOtHVpIkSbI8JieyH330Ed988w0vvfQSAQEBaDSauihXvbVixQo+//xzAIKDg1myZAnW1tZ89913LFu2jJKSEvr378/8+fMr3caFCxd45pln2LNnD1ZWVgbzzp07x7lz54y+OLd48WKee+65CutcvHiRuLg4Bg0aVAtHWHu6d+9u7iJINVTfYleQm05GUgQZSVGU3tbfq72TJ64+Qbh4NrtjP6/OHgE4NfCr1shelq6+xU+qPhk7yyVjZx41SmSPHj2Kv79/XZSnXrt48SI//fQTYWFhaDQaFixYwOeff86AAQNYtWoVe/fuxdramilTprBx40ZGjBhRYRulpaU888wzfPHFFxUS0pycHMaOHcurr75qdP9bt27F1dWVSZMmGUx/55136NKlS71LZFNSUmTjdwtVH2JXWlJEZvJlMpIiyM++oUy31tjj4t0cN58gbLWuJm1TpVLj4OpbyyWtf+pD/KSakbGzXDJ25mFyVURpaSm+vvf+B4ExycnJvPbaa0otdGhoKOfPn+fcuXM89NBDaDQaVCoVI0aM4M8//zS6jQ8++IDQ0FBat25dYd7UqVOVYX+NycvLY9myZZSW3ho6MzIykrNnzxIZGfkXj672hYeHm7sIUg2ZK3ZCCHLSr3H10h7Cj/zM9aiDZUmsSoVTA3/8Wg8kqMsEfJp1NjmJ/TuR957lkrGzXDJ25mFyIvvPf/6Tr7/+ui7KUu/17t2b3r17K//+9ttvGTx4MJ06dWLNmjVkZmZSWFjIV199RceOHSusf+nSJf7zn/+wfft2Ro4cSWxsrDJv2bJl+Pn5MXToUKP7LikpQavVMnz4cFauXKlMf++99/j888+Jjo6uxSOVpLurqCCb5NhTRB77hdhzv5GZHI3QlWKrdcW7WWdadpmIX+sBODXwQyWHgZQkSZJuMrlpwciRI5k+fToXL15kyJAhuLi4GPxE/nfpweCHH34gLS2NRx55BIAXX3yRYcOGkZGRgYODAwMHDqywzjvvvMM333zDxIkT2b9/P08//TQ7d+7kjz/+YMOGDWzatIkffvjB6P5iYmLw8/Pj+eefZ8iQIUycOJHY2FgyMzPp0aOHMmxwfTJ+/HhzF0GqobsRO11pCVmpsWQkRpCbcWu0QLWVBhevQNx8grBz9LDoEbbMRd57lkvGznLJ2JmHyYnsvHnzcHNz4+zZs5w9e9ZgnkqlYvfu6o2iA2W1kN988w1WVlaMGDGCmTNncuzYMV5++WXUajVeXl4sX74cV1dXg/Xy8vKYMmUKMTExALz99tv07dsXgPnz5/Prr7+iVqt59NFHeeGFF0w9xDvauHEja9euZc2aNcq0MWPGMHLkSHr37s2XX35p9MP38uXLTJw4EYCePXuSm5tLUVERr732GhkZGfTv35/ExESEEPj7+xskw5GRkQQGBuLo6EhoaCi//PILu3fvZvbs2QBYWVlRUlKCtXWNOqKoE5s3bzbaTliq/+oqdkIICnJSSE+MIDP5MrrSImWeg2tDXH2CcG7gj9qq/lzHlkjee5ZLxs5yydiZh8mfFr///nut7Dg2NpZNmzZx8OBBrKysGDt2LGfPnuW5555jw4YNNGrUiI0bN/Lyyy9XaMowb948+vXrx+TJk0lLS2Pw4MHs2LGDQ4cOcenSJY4ePYoQgvHjx3P//ffTq1evWikzwO7du1m6dCnr16+vkDR+8MEHDB8+3KD9a25uLhqNBhsbG/z8/Ni7dy+9e/cmPDwca2trbGxs2LFjh7L8t99+S0lJSYUa3aioKAIDAwGYNm0a/fr1o3HjxnTq1AkAf39/rly5QosWLWrtWP+q3Nw7j28v1U+1HbuSonwykqPISIykMC9dma6xdcTVpwWu3i2wsXOq1X3+ncl7z3LJ2FkuGTvzqHG1x2+//cbevXsRQtCnTx+GDBli0vr+/v5s3rwZKBtVKz09nczMTPr27UujRo0AGDFiBB988AGlpaUGzRcOHDjAhx9+CIC7uzsTJkxgx44d7NixgxkzZqBSqVCpVMyaNYvly5fXWiJ79OhRHn74YYKCgpTjbd++PQsXLuTPP/9k+/btFRL9mTNn0rRpU1555RU+//xz/vnPf/LGG29gZ2fHl19+We19R0ZG0qNHDwCcnJwYOXIkAwYMUOYHBgYSFRVVrxJZfRwly1MbsRNCR07aVdITI8hOiwNxe5+vAbh6B+Hg6iubDtQBee9ZLhk7yyVjZx4mD1FbWFjII488QnJyMmPHjsXOzo41a9ag1WrZsGEDdnZ2JhVg27ZtTJgwgeHDhxMaGkpaWhrPP/+8Mn/MmDF89tlneHl5KdN69uzJ/v37lX9v3ryZCxcusHv3btauXYuDgwNQ1p3VuHHjjA7SUFhYSGHhrTHYs7KyaNKkSY2HqM3OziY9PV12vXGbtLQ03N3dzV0MqQb+SuwK8zJuNh2IoqQoX5lu7+SJq3cLXLyaYWVtW1tFlYyQ957lkrGzXDJ25mFyjeycOXPQarUcPXpUqSWdPn06U6ZMYdasWXzyyScmbW/w4MGkpaUxd+5crly5UqE9rLE8u3wNzu3L3D6vqhx9/vz5vPXWWxWmr169Gq1Wy6hRowgLCyMzMxMvLy86d+6s1CB36NABnU7HmTNnAHj44Yc5fPgwqampXLx4kV69erF+/XqgbChfjUbDyZMnARg6dCgnTpwgKSkJZ2dnHnroIaWtbevWrXF0dOTo0aMADBo0iPPnz5OQkICDgwPDhg1j1apVALRs2RIPDw8OHjwIwIABA4iIiCAuLg5bW1tGjRrFqlWr0Ol0BAYG0qhRI/bt2wdAnz59iIuL4/Lly1hbWzN27FjWrl1LUVER/v7+BAYGKm2de/ToQXJyMhEREQBMnDiRDRs2kJeXR+PGjQkODlaaRoSEhJCZmcmFCxcA0Ol0uLi4kJ2djY+PDx06dFC+VDz44IMUFBRw7tw5AB555BH27NlDeno6Hh4ehISEsGnTJgAeeOABAE6fPg3A8OHDOXz4MCkpKbi5udGnTx9+/fVXANq2bYudnR3Hjx8HyrpIO3XqFImJiTg5OTF48GBWr14NlA1o4eLiwuHDhwF46KGHuHDhAlevXkWr1fLwww+zYsUKAIKCgvDy8uLAgQMA9OvXj+joaGJjY7GxsWH06NGsXr2akpISmjVrhp+fH3v27AGgV69eJCQkEB0djVqtZvz48axbt47CwkL8/PwICgpShjbs3r07KSkpSjcu48ePZ/PmzeTm5tKoUSPatGnD9u3bAejSpQs5OTlKV29jxoxhx44dZGVl4e3tTadOndiyZQsAHTt2pLi4WGnXPnLkSPbt20daWhoNGjSgR48ebNiwASj7leHAgQM4OjoCMGzYMI4dO0ZycjIuLi7079+fdevWAdCmTRu0Wi0njh/F1b6UZo0cKcpLU+4nK40diWklpOVZ09SpCVbFWravLlt34MCBXLp0ifj4eOzt7Rk5ciQrV65ECEGLFi3w8fFRvrD27duXmJgYrly5gkajYcyYMaxZs4bi4mKaNm1KQECA8mtIz549SUxMJDIyEpVKxYQJE1i/fj35+fk0adKEVq1asXPnTgC6detGWloaly5dAlC++Obk5NCwYUPuv/9+fvvtN6DsRda8vDzOnz8PYPIz4sCBA6SmpuLu7l7nz4iEhAT69etX758RY8eOZdu2bfIZcdsz4tq1a7zyyiv1/hmhVqs5deoUUL1nxLFjxwAYMmQIf/zxB9euXTN45wOgVatWuLu7c+jQIcDynhG5ubl06dLFIp4RUP/zCP07RXdico1s48aNOXjwYIUBEa5fv07Hjh25du1aJWsaioiIwMPDQ/n2kpuby4gRI3jwwQd5//33leVCQkI4cOCAQdOCHj16KA8LKBukwc/Pjx07dvDqq6/SqlUrAE6cOMHXX3/NZ599VmH/tV0jawohdH+L0YVWrFhR7QtRql+qEzshBHmZiaQnRpCVcgWh0/dvrMKpQRNcvVvg5C67yzIHee9ZLhk7yyVjZx4m18imp6fj4+NTYbq3tzeZmZnV3k5UVBQff/wxX3zxBVB2AXTp0oWwsDCSkpLw9vZm8+bNtG7dGisrK3Q6HTk5OTg7O9OtWzd++OEHJk2aRHp6OmvWrGH79u04Ojry0Ucf8b///Q8hBB9++CH/+te/jO7f1tYWW9u7//NmVkpMxfHebRzwbX7vjffepUsXcxdBqqGqYldckENGUiTpSZEUF2Qr023sXXDzCcLFuzkaG+3dKKZUCXnvWS4ZO8slY2ceJiey7dq1Y/v27RW6mNizZw9t2rSp9nZCQ0M5ffo03bp1Q61W06ZNGxYtWsTIkSMZM2YMarUaX19f5YWoNWvW8NNPP7FhwwbmzZvHlClTlG6uFixYgIuLC6Ghofzxxx+EhISgVquZNGmS8oJUfZCVEkP8hbAK00uKcom/EEaT4P73VDJbH/u2laqnfOx0uhKyU2JJT4okNz1Bma620uDi2QxXnyDsnTzli1v1hLz3LJeMneWSsTMPk5sW7N69m8cee4xvv/1WeWv+8OHDPProoyxbtszoQACWICsrCxcXlzprWiCEjoijvxjUxJZnbetAUOdx90wzA/kzi+VasWIFEyZMoCAnlYykCDKSo9GV3OrzVevii5tPC5w9AlBbacxYUskYee9ZLhk7yyVjZx4m18j269ePb775hpdeeonr169ja2uLu7s7n3/+ucUmsXdDWZvYqvuYKynM5XrUYVw8m2Hn2AAra5u7VDpJuqWkuIAGDsVEn1pPYe6tF7c0tg64et/s89W+btuRS5IkSVJ1mFwje7u0tDSEEDRo0KA2y2QWdV0jm5kczdVLe0xax8beBXtHD+ycPLB38ihLbi2o9qu4uBiNxnLK+3cmhI6c9AQyEiPITo1DCB0AKpUVTh7+uPno+3y9N34tuNfJe89yydhZLhk786hWjay+24zKREdHK//fuXPnv1aie5R1NV9+sXf2pqQwh+LCXIryMynKzyTzxq3za6t1xc7RA3unBtg7emLn6F5vf9rdsWMHQ4cONXcxpCoU5mWWNR1IiqKkKO/W9FJrAlo+iItnIFYa2eerpZH3nuWSsbNcMnbmUa1EdubMmdXamEqlUvoNkwxpXbyxtnG4YxvZpveHolKpKSnKJz8nlYLsG+TnpJCfnUpJUS6FeRkU5mWQmRx1cy0VtloX7J08sXNsUFZz69CgXoxVn5WVZe4iSEaUlhSRlRJDRmIEeVlJynQra1tcvJvj5h3Er5u206FvsBlLKf0V8t6zXDJ2lkvGzjyqle2UH3ZVMp1Kpca3eVejvRbo+QZ2VX66tbaxx8m9MU7ujZX5xUV5FGSn3ExwU8jPSaGkKE9JbkmK1O8NWwdX7B31TRI8ympu1Xc3ufX29r6r+5MqJ4QgLyuJjMQIsm5cQacruTlHhaN7Y9x8gnB0b4JaXdZfs4ydZZPxs1wydpZLxs48qtVG9sMPP6z2BmfMmPGXCmQudd1GVtmPsX5kbR3wDaxZP7LFhbkU5KSSfzOxLchOoaQ4v+KCKhV2Wrey9rY3E1xbB3clcakLWVlZdT64hFS14sJcMpIiyUiMpKjgVm2Bjb0Lrj4tcPVqjsbWocJ6MnaWTcbPcsnYWS4ZO/OoVhVdfr6RxEiqEWePAJwa+NXayF4aWwc0tg44NfADymreSoryDBLb/JwUSosLKMhNoyA3jQzKholTqdTYOrjdeqHM0QNbB7daS263bNkiuyIxA52ulOzUODISI8hJTwDKvquqrTQ4ezbFzTsIe2evKvt8lbGzbDJ+lkvGznLJ2JlHtRLZN998s67L8beiUqlxcPWto22rlOTW2aNsGGEhxM2a2xSDBLe0pJCCnFQKclIhMVwpm62DO/Y3E1s7Jw/stG5ymFELkJ+TSkZiBJnJ0ZSW3Bp+Wevig5t3EE6eARbV64UkSZIk3Um1EtnOnTvTqlUrvv/+e5588skqa3K+/vrrWiucVDtUKhU2do7Y2DkqzRfKktsc8rNTDBJcXUkRBTll09KV9a2wc3S/2VuCvubW9Y61yB07dqzbAzPRihUr+PzzzwEIDg5myZIlWFtbs2XLFubPn49areb+++/nk08+wdra8NaIiopiypQplJaWYm9vz//+9z+aNGkCwKeffso333yDi4sLABs2bFD+X+/rr79m9OjRFaanpqaydetWJk2aVKNjKikuIDM5mozECApu6/PV2kZb1uerTxC2Nejztb7FTjKNjJ/lkrGzXDJ25lGtRPaxxx5TGjEPHjy4Tgsk3R1lya0TNnZOuHg2BW4mtwXZN3tJ0Ce4qehKi8jPvkF+9g3Sr99cX21V1kuC462aW1uti0FyW1xcbI5DM+rixYv89NNPhIWFodFoWLBgAZ9//jmPP/447733Hnv37sXa2pqlS5fy1Vdf8cwzzxis/9RTT7F06VLatGnDyZMnefrpp9m+fTsA586dY9WqVQQFBVW6/+PHj5OQkMCcOXMMpn/88cfk5+eblMiW9fl67Wafr7G39fmqxsnDH1fvIBzdGv6lPl/rU+wk08n4WS4ZO8slY2ce1Upkp02bpvz/+PHj66wwknmpVCps7J2xsXfGxbMZUJbcFhVkG3QDVpCTgq60mPysZPKzkm+tr7bG3rGB0g1Y+IUzBAffVy860U9OTua1115TOqsODQ3l008/xdbWFiEEqampeHt7ExUVRXCwYbdTV69excvLizZt2gBl37rt7OxISkrC29ubiIgImjVrVuX+Y2NjOX/+PNOnT1deBsjIyCAsLAwvL69qHUNhfhYZiRFkJEUa9Plq59gAV+8gXLyaYa2xq/Y5qcrZs2dp3bp1rWxLuvtk/CyXjJ3lkrEzD5P7YyopKSEmJoYrV65w+fJl5b+XL18mJiaGlJSUuiinZCYqlQpbe2ds7Z1x8QoEbia3+Zm3+rnNTqEgNw1daTF5WUlK36QtveHSwR9u9W97s2mCjb1Llc1T6kLv3r0N/v3tt98yePBgbG1tmTJlCoGBgXh5edGkSRM++ugjg2WvXr1KQECAwbQWLVoQGxuLt7c34eHhhIaGAjBnzhx69uxZYf/FxcVMmzaNxYsX88YbbwCwcOFCZs6cycKFCystd2lpMVk3rhjv89UrEFefIOwdLX9kPUmSJEmqiWolsosWLeLXX3/lypUrFBQU4Ofnh7+/PwEBAQQEBNCjRw8CAgJo2rRpXZdXqgdUKhW2Wldsta6gJLc6ivKzKvSWoNOVGCS3UPb2vD651TdLsLFzvmvJ7Q8//EBaWhqPPPIIp0+f5scffyQuLg53d3c+++wzFixYwOuvv17hmG+n77WutLSUc+fO4enpSVJSEiNHjmTr1q24ubkpyxYWFqLRaBg7diy9e/dm+vTp6HQ69uzZw5tvvsmnn36KTqdDffOFOiEE+VnJpCdGkJVyBV2p/ucqFY5ujXD1CcKpgV+ddp02cuTIOtu2VPdk/CyXjJ3lkrEzj2olstOnT6dNmzZ8/PHHREVF8eSTT/Lkk09ib29f1+WTLIRKpVaSW1fv5gBs376N3j26KEltfk4KBTmpZTW3mYnkZSYq66utbMqaJTjdeqFMY+dU68ntxo0bWbt2LWvWrAFg586dPPvss7i7uwPw3HPPERISYpDINm7cmLi4OIPtRERE4O/vj5WVFZ6enkBZZ9jdunXj0qVLhISEKMtevnyZgIAA1Go1zz33HEuWLKG0tJRp06ahUqmU7Tfy9SQjKYqMpEiK8jOV9W3snMv6fPVuYbTP17qwb98+Bg0adFf2JdU+GT/LJWNnuWTszKPaTQv69+9P//79CQ8P55NPPqFly5ZMmjSJF154AR8fn7oso2Sh0tLSsXNww87BDVdaAGU1t4V5GWU1tzdfKCvISUNXWkRu5nVyM68r61tZ2yjNEfT/1dg61ji53b17N0uXLmX9+vVKrwSdOnXi22+/ZdSoUahUKnbt2oWfX1mfvLm5uWg0Gho3bkxSUhLh4eG0bNmS06dPU1JSgre3N8OHD+ett96iQ4cOpKamsnfvXubOnWuw36ioKAIDy2qux48fT+/evbGysmL27NnodKU09nHnwI4VPNDKG6XPV7U1zp5NcfUJQuvsfdebYqSlpd15IanekvGzXDJ2lkvGzjxMbiPbsmVLvvjiC9LS0vj888/p2rUrffr04dVXX1VehpEkgAYNKrbdVKnU2Dm4Y+fgjptP2Vv+QqejMC/9VrOEm8ltaUkRuRnXyM24pqxvZW1rMDqZnaMHGluHOyZ6R48e5eGHHyYoKIghQ4YA0L59exYuXMipU6cICQnB3t6eBg0asHTpUgBmzpxJ06ZNeeWVV1i+fLnS/ZaDgwNfffUVUNbrwNSpUyksLOu3dcGCBRW62IqMjFReBlOr1UydOhVdSQGJl4+SmRyNs00u4Zf+5IFWXmidvXH1CcLZIwAra5uanPZaYSx2kuWQ8bNcMnaWS8bOPKo1RG1ViouLefPNN/nggw8YN24cK1asqK2y3VV3a4jav5O8vDy0Wm2N1tXpSinMTTdob1uYm650NXU7K42dwehk9k4eWNto73ot5p2UFheScSOajMRICnJuvRRZ1udrc1y9g7DVulSxhbvnr8ROMj8ZP8slY2e5ZOzMw+Qa2dvt3buXjz/+mPDwcJYsWcKTTz5ZW+WS7gEbNmyo8XB9arVVWVtZJw+4OQhaWXKbZvBCWUFeOqXFBeSkXyUn/aqyvrXGvlzNbYO71r70dkLoyE2/RnpSJNkpsQhRCtzs87WBH64+QTi6NaoXXZTd7q/ETjI/GT/LJWNnuWTszMPkRLa0tJRVq1bx3//+F61WyyuvvMLDDz9c72q/pHtPWXLrib2TpzJNpyuhICedgpwb5GenKjW3JcX55KTFk5MWryxrbaO9WXN7cyAHJ0+sbUx/YVEIHXmZSZQU5WFto0Xr4l0hES3KzyIjKZKMpEiKC3OV6bYO7rj5tMDFq3mt9fkqSZIkSX9X1U5ks7Ky+PLLL/nss8/o1KkTn332GV26dKnLskkWrn379nW+D7XaGq2zJ1rn25Lb0hIKbtbc6offLczLoKQoj+y0OLLTbvVAYG2jNegGzN7Ro8rkNislhutRRygpyr1tGw74Nu+Ko1sjslJiSE+MJK/cS2suXoG4egdh59jAIr703Y3YSXVHxs9yydhZLhk786hWIvvRRx/xzjvvkJWVRffu3bnvvvu4cOECubm5+Pv74+fnp4yYJEl6+n5R7/p+razROnuhdb41YpautJiCnDSD4XeV5DY1juzUW8mtxtZB6SVBn+Baa+zISokh/kJYhf2VFOUSfyEMldoKoStVpju4NcLNOwgnDz/U/9/efUdFcbZtAL/osCxFRMEWsSHWGBO7lKix8WpQExuKqBFiJ7HGY32xEE3UCLZYMCrqm9jAgmIEERWxxBArNsTYFVSKLMjufH8Y5mPdBRcElzHX7xzPcWeemblnbma5efaZZw3fahTPO6ev3FHpYP6ki7mTLuZOP3T67Tpx4kSMGzcOKSkpSE5Oxq1bt3D16lUcPHgQycnJuH37NoyMjFCzZk0cP368rGMmifjjjz9Qv359fYcB4NWXMMhsHCCzcRCXKZUvochM/f95bjOeIDf7OV7mZOFlThYyUlPEtsamllDmKYo8hqBSwthMDjtHZ9g41IOpubzMzqeslafcUfExf9LF3EkXc6cfOncTmZqaol69eqhXr57W9S9evEBycnKpBUZU1oyMTGBp4whLm/+fB1mZlwtFZqrabAm52elqQwmKUs3ZFfIKVcsqZCIiIirgraffel9w+q3Sl5GRASsrK32H8daUeTl48vd5PPk78Y1tq7t4wOafr+2Vsvcld/9WzJ90MXfSxdzpBwd0UJk5deqUvkMoFUbGZpBXqKZTW2PT92MOwfcld/9WzJ90MXfSxdzpBwtZKjOPHj3SdwilRmbjAGPTouehNTazVBuDK2XvU+7+jZg/6WLupIu50w8WslRmXv+qVikzMDBElbqti2xTpU7rcvfFBiX1PuXu34j5ky7mTrqYO/3gGNl/cIxs6cvJyYGZmZm+wyhVWueRNbNElTqtYW3vpL/AStn7mLt/E+ZPupg76WLu9OP96D6icmnnzp36DqHUWds7wblVXzg17Y7qLh5watodzi37vldFLPB+5u7fhPmTLuZOupg7/ZDWLO1lKL9jOj09Xc+RvD9evHjx/l5PQ0sYmFtCCSAjI1Pf0ZS69zp3/wLMn3Qxd9LF3JU+KyurN34bJocW/OPOnTuoUaOGvsMgIiIiIkCn4Z4sZP+hUqlw7949nap/erP09HTUqFEDf//9N8ccSwxzJ23Mn3Qxd9LF3JUNXWoyDi34h6GhIapXr67vMN471tbWvKklirmTNuZPupg76WLu3j0+7EVEREREksRCloiIiIgkiYUslQkzMzPMmjWLc+pJEHMnbcyfdDF30sXc6Q8f9iIiIiIiSWKPLBERERFJEgtZIiIiIpIkFrJEREREJEksZImIiIhIkljI0jvx6NEjZGVl6TsMon8tlUql7xCI/jUEQRDvOT5TX7b4zV70TiQkJCA3Nxc9evSAqampvsMhHUVERCAmJgZLlizRdyhUTFFRUThx4gRevHiBr7/+GrVr19Z3SFQMUVFRiImJwaNHjzBq1CjUqVMHtra2+g6LdHDw4EFERkYiNTUVkydPRpMmTaBSqWBoyL7DssCrSu9EXFwcQkJCEB4ejoyMDH2HQzpq0aKF1t4E9u6VbzExMZg/fz46d+6M6tWrY+7cufoOiYohP3/dunVD+/bt8euvvyI8PBzXr1/Xd2j0BnFxcVi4cCG8vb3RuXNnzJs3D8nJySxiyxCvLL0T7du3h62tLVJTUxEXF4fk5GQ8f/5c32HRG+Tk5ODu3bs4ceIEli5dilmzZiEpKQmGhob8uKwcO3v2LKZMmYK2bduiXbt2yM7Oxtq1a5GYmMg/JCUgISEBfn5+cHNzw9ChQzFo0CCkp6cjPj4emZmZvPfKsT/++AMjR45EixYtMHjwYHh4eCA6OlrfYb3XWMjSO+Ho6IjKlSujZcuWCA8PR79+/XD//n19h0VFEAQBTk5OqFKlCsaPHw8LCwtYW1tj1qxZuHDhAgwMDPQdIr0mv8DJzc3F5cuXsXv3bvj7+8PJyQnGxsbYsGEDzpw5o+co6U2MjY3V3h+bNGkCd3d3nD59Gjdv3uS9V469fPkSqampAF7djzKZDCkpKeJ6fppV+ljIUpkpeMPK5XI4OjrCzMwMZ8+eRbNmzXDlyhU+AFZOqVQq8ZelgYEBPvjgA/j7+2PChAno3LkzVq9erecISZv8nHl7e8PQ0BAHDx6Em5sbFixYAF9fX7Rr1w5btmzhL9Ny6Pjx49i3bx/27t0LPz8/xMTEYNeuXQBeFURNmzZFkyZNsGXLFj1HSq8rmLvRo0fD2NgYOTk5MDAwQO3atWFjYwMA2L17N3tnywALWSpV+/fvR9++fQEAhoaG4i/Mhg0b4uTJk/Dz88O8efMwbNgw3LlzB0qlUp/hUgGv5y4vLw8A0Lt3bzRs2FBsZ2VlBWtra73ESNoVzB0A1KxZEwEBAWjVqhUcHBzE5YaGhsxdORQdHY3Jkyfj2bNnWLJkCcLCwhAcHIzw8HBERESIf6DI5XLmr5wpmLulS5dizZo1GD58OMzMzAC8+uNSLpcjPDwcQUFBcHJy0m/A7yEWslRqcnJycOzYMaSnpyMgIADAq1+cSqUS2dnZsLW1RWBgILp06YJWrVph6NChfFMuJ7Tlztj41aQmLi4uuHjxIhYuXIgZM2Zg+fLlGDhwoB6jpYK05S5f69atcfLkSfzyyy/YvHkzVqxYgeHDh/PBk3JEoVBg5cqV+OGHH+Dt7Y29e/ciOjoagiBg7NixCAkJwdKlS7F06VKsW7cOvXr10nfI9I/Xc7dnzx7ExcUhKSkJKpUKgiBAEAT88MMPWLlyJTZu3Ii6devqO+z3Dt/N6K3lj8szMzPD/PnzceDAAeTl5WHUqFEAACMjI1hYWCA0NBQdOnQQP7a2tLTUZ9iEN+cOABwcHBASEoKmTZvCxcUF69atQ6NGjfQVMv1Dl9y5uLggMDAQf/31F5KSkhAcHKzWu076k58/IyMjWFlZiXmxsLBAhQoVkJ6ejo8//hhr1qyBXC6HkZERVqxYgQYNGugzbMKbc6dQKGBoaAgDAwMYGBjA0NAQy5Ytg7Ozsz7Dfm8ZCHz8kd7S48ePUalSJbx48QIymUxcPnbsWCiVSqxYsQIAkJeXJ/byUfmga+6o/GHupC0/fwCwfv16GBoawtfXFwAwY8YMeHt7w8XFBVeuXIGzszN70cuRonI3c+ZMDBw4EC4uLkhMTISdnR0EQcAHH3ygx4jfb7wz6K0cPnwYw4cPx9SpUzF69Gi1eQ6Dg4NhZGSEcePGAQCL2HKmOLmj8kWX3I0dO1aPEVJR8vM3adIk+Pn5oXPnzmIhBABpaWnIy8vD9u3bMXjwYDx69Eh/wZKaN+UuNTVVzN2wYcNgYWHBIrasCUQllJiYKDRt2lSIj48XHj9+LGzZskX49NNPhRs3bqi18/X1FSZMmKCnKEkb5k66mDtpez1/mzdvFj799FPh+vXrYps5c+YIQ4cOFbp37y5cvnxZj9FSQcxd+cQuMiqxrKwseHl5oXXr1hAEAQMGDIBKpcLQoUOxbt06cVB7aGgoHj58qOdoqSDmTrqYO2l7PX/e3t4AgGHDhon5y8jIQHx8PCIiIlCvXj09R0z5mLvyiYUsFdvp06dx//59PH36FHFxcbh//z6qVKkC4NX8lSqVCkuXLsXixYthYGAAExMTtSmASH+YO+li7qRNl/wtWbIES5cuRd26dbFjxw4WQuUEc1e+cYwsFcuBAwcwadIk8RtmHj58iGPHjuHevXtiG1dXV9jZ2cHU1BQmJiZ6jJYKYu6ki7mTtuLkz8TEBP7+/pxdopxg7so/9siSzjIyMhAWFoaff/4Zzs7OSEtLQ3p6Op4/f474+Hg0atQILi4uOHPmDK5du4asrCzIZDJ+nWI5wNxJF3MnbcXJ3/Xr15GZmQlLS0vmrxxg7qSBhSzpzMjICLm5ucjNzQUA2NnZITs7G48fP0Zubi5CQ0PRuHFjHDhwANu2beM8seUIcyddzJ20FTd/crlczxFTPuZOGljIks5kMhn69OmDly9finPCVqxYEV5eXmjQoAEcHBzg6OiIb775hmPzyhnmTrqYO2lj/qSLuZMGjpGlYunatSsaNWokzglrZmYGc3NzREREYNOmTahTpw5v6HKKuZMu5k7amD/pYu7KP/bIUrFYW1sDePUVfQYGBjA3N8eECRPw/PlzLF++HI6OjnqOkArD3EkXcydtzJ90MXflHwtZKpH8wez37t3D+fPnERUVhVq1auk5KtIFcyddzJ20MX/SxdyVXxxaQG+lRYsW2LlzJ29oCWLupIu5kzbmT7qYu/LHQBAEQd9BEBEREREVF3tkiYiIiEiSWMgSERERkSSxkCUiIiIiSWIhS0RERESSxEKWiIiIiCSJhSwR0XsmOzsb165d03cYRERljoUsEZFEXLx4EYGBgVrX9ezZE3K5HObm5qhYsSImT578jqMjInr3OI8sEZFE+Pv7Y+/evUhJSRG/+52I6N+MPbJERBLw9OlTbN++HVWrVsXOnTv1HQ4RUbnAQpaISALWrl2L3r17Y9asWQgODlZb5+HhgSNHjiAgIAD29vaoXLkyFi1apNZGqVRizpw5qFmzJiwsLPDRRx9h9+7d7/AMiIhKHwtZIqJyTqlUYuXKlfjmm2/g6emJx48fIzExUa3NuHHj4OzsjDt37iAhIQGhoaHYs2ePuH7YsGE4fvw4Dh48iGfPniEoKAjjx4/H1q1b3/XpEBGVGhayRETl3J49e1C/fn00bNgQBgYGCAgIQEhIiFobd3d3jBo1Cubm5qhVqxa++eYb7N27FwBw5swZHDt2DOHh4XBxcYGZmRm6dOmCHTt2YOLEiVAqlfo4LSKit8anBYiIyrlly5bh9OnTcHR0BPCqhzYrKwsLFy5EhQoVAADdunVT28bJyQn79u0DAMTGxqJnz56wsLBQa/PJJ5/AysoKSUlJaNiw4Ts4EyKi0sVCloioHLtw4QIePnyoMS/sggULsG7dOkycOBEAYGNjo7bexMQECoUCACAIAszNzbXu39zcHJy8hoikikMLiIjKsWXLlsHPzw+Ojo5q/8aMGYNVq1ZBpVIBAIyMjArdh7u7OyIiIpCXl6e2/MqVK3j48CFcXFzK9ByIiMoKC1kionLq6dOn2LFjB3x8fDTW1atXDx988IE4fKAoLVq0QLNmzeDt7Y2UlBSoVCrEx8fDy8sLCxcuLLIIJiIqz1jIEhGVU2vXrkX37t3FcbCv8/f313joqzAbNmxAvXr10K5dO8hkMvj7++O///0vBg8eXJohExG9U/xmLyIiIiKSJPbIEhEREZEksZAlIiIiIkliIUtEREREksRCloiIiIgkiYUsEREREUkSC1kiIiIikiQWskREREQkSSxkiYiIiEiSWMgSERERkSSxkCUiIiIiSWIhS0RERESSxEKWiIiIiCSJhSwRERERSRILWSIiIiKSJBayRERERCRJLGSJiIiISJJYyBIRERGRJLGQJSIiIiJJYiFLRERERJLEQpaIiIiIJImFLBERERFJEgtZIiIiIpIkFrJEREREJEksZImIiIhIkljIEhEREZEksZAlIiIiIkliIUtEREREksRCloiIiIgkiYUsEREREUkSC1kiIiIikiQWskREREQkSSxkiYiIiEiSWMgSERERkSSxkCUiIiIiSWIhS0RERESSxEKWiEpNcHAwfv/9d32HQURE/xIsZOmd8PDwgJmZGczNzcV/p06deqcxbNiwAV9//XWp7nP27NkwMTFROy9zc3P89ddfJTqek5MTHjx4UKoxlrYWLVogODhYY3lISAh2796Ntm3b6iGq0nPv3j1UqlQJZ8+e1XcopcrX1xfbtm0r1jZHjhxB165dyygi7d72Pp09ezaCgoJKLZ7ly5fj6NGjWtflv6/JZDJYWVmhefPmOHjwoLheH9f81q1bcHFxEV+PGjUKKpVKa1tzc3Px/76+vjA1NdV4L3v+/LnY5sWLFwgKCsKHH34Ia2tr2NnZoVOnTvjf//5XZAxEZYmFLL0zsbGxUCgU4r+WLVvqO6RSERgYqHZeCoUCTZs21XdYZcbZ2RkODg5qy/Ly8pCbm4s9e/ZAJpPpKbLSYWFhgfr168Pa2lrfoZCeCYKAkJAQhISEFNomNjYWL168wPPnz/HTTz/Bx8cHKSkp7zDKou3fvx+zZs3Sqe3GjRs13stsbGwAAPfv30erVq2QkJCA1atX48mTJ0hJScGIESMwc+ZMeHp6IiMjoyxPhUgrFrJEVCxhYWHo27ev2jJjY2N8++23ki9iAaBChQo4duwY6tWrp+9QSM+ioqJgYmKCmJgY3L17t8i2hoaGcHV1xRdffIEDBw68owh1s2TJEoSHh7/VPry9veHp6Yldu3ahdevWMDU1hZWVFfr164fExERUr14dz549K52AiYqBhSzpVWEfI77+Efv+/fvx8ccfQyaToXr16pg6dSoUCoW43sPDA0eOHEFAQADs7e1RuXJlLFq0SFw/cOBAjBgxAmvXroW5uTkePnwI4FUvw8CBA1GhQgVYW1vD09MTly5dErfLyclBjRo1sHv37rc+10ePHsHHxwe2traoUKECfHx8cP36dTg5Oam1e/78Ob7++mvY29vD2toaffr00ejhefz4MXx9fVGxYkXI5XJ07doVFy5cENf7+vpi8eLFaNeuHRo0aCB+tBgcHIw6depAJpOhRYsWiI6OhpeXF44cOaIW54gRI+Do6AgbGxu0bdsWu3btEtd37dpVrf25c+fQqVMnWFpawt7eHiNGjEBaWppaLFu2bEFgYCCqVq0KW1tbTJw4EUqlUut1yv9Y8tKlS/jss88gl8tRpUoVTJo0CTk5OWI7c3NzbN++HQ4ODpg7d65O1yX/Y9vjx4+jTZs2kMlkcHJyQlBQkNrHrwU/cgWAbdu2oXHjxrCwsEDjxo3x66+/IiAgABs2bNB6DrpcRwC4efMmevfuDWtra9ja2qJv3764ffu2uD7/Y/JVq1ahVq1asLa2hq+vL168eFHocYFXwzzq1q0LCwsLNG/eHPv379doc+PGDXz++eewsrJCpUqVMH78eJw4cQIeHh4AgO+++w6dO3fGoUOH1IYCZWVlISAgAI6OjpDJZGjfvj3i4uLE/WZnZ+Pzzz+HXC5Ht27diiwADx8+jFatWol5+PHHHyEIglqb9PR0jBo1CpUqVYJcLoebmxuOHz9e5PkX5zyLsmzZMkyZMgVDhgzBqlWrdDqeIAgwMDDQqW3Be6dKlSqYNm0acnNz1drk5uZi2rRpqFatGmQyGT755BNERETotP98q1atwtChQ5GUlFSs7fIdP34c9+7dw/z587WuNzc3x+rVq1GjRo0S7Z/obbCQpXLv0KFDmDRpEpYsWYL09HQkJCTg2bNnGDhwoFq7cePGwdnZGXfu3EFCQgJCQ0OxZ88eAMCWLVuwZs0afPXVV1AoFHBwcIBCoUCnTp3QsmVLpKSk4NGjRxg4cCA8PT3FwtHY2BguLi6oWLHiW51DRkYGPDw84OTkhOTkZNy+fRtubm7o16+fRtv+/fujefPmuH37Nv7++280a9YMrq6uePToEYBXhYSHhwcqVqyIS5cu4d69e+jZsyc6deqk9osqKCgIS5YsQWJiIgwNDTF9+nRs27YN4eHhSE9Px4oVKzBt2jScP39e3CYtLQ1t27aFg4MDzp07h7S0NCxatAizZ8/Gr7/+qhHr+fPn0a1bNwwYMAAPHjzA+fPnYWJigo4dO6r9oTFv3jzk5OQgKSkJV69exenTp7FixYpCr1dqaip8fHwwadIkpKam4tSpU7h//z68vLzEQufly5fYsGEDrl69iilTpuh8XZKSkvDtt9+KP08HDx5ETEwMRo0apTWWtWvXYs6cOVizZg0yMzPx22+/Yc2aNUX2uulyHe/fvw93d3e0bt0aKSkpuHHjBho3bgxXV1c8fvxY3NemTZtw+vRpnDlzBnfu3EF2djZmzJhR6LGnT5+OLVu2YOfOncjMzMT69euxYMECnDhxQmxz9+5deHh4oFOnTnjw4AGSkpJQpUoV+Pn5iW0WLFiAqKgofPbZZ+JQIJVKhR49euDx48dISEjAkydPMHbsWHz55ZfiONJNmzbB0dERqamp6NOnj9q5FHTw4EH4+vpi1qxZSE9PR1xcHC5fvoyFCxeKbQRBQI8ePWBra4vLly/j2bNnYmF5+vTpQq9Bcc6zMNevX8f58+fRt29fjBs3DqGhoRpFZkEvX77EoUOHsH37dp3GuCYmJqJ79+7w9fVFWlqaeJ8GBASotfvqq6/w4MEDnDx5EpmZmViyZAmmT59erB7WTp06Ydq0aejVq1eJPv6PiopC3759YWjIkoHKIYHoHXB3dxdMTU0FMzMz8V9CQoIQGhoq+Pv7a7SvWbOmcP/+fUEQBKF58+bCxYsXNdq4uroK8fHx4v7HjBmjtv7nn38W/Pz8xNevH+unn34SpkyZorHftWvXao1Jm1mzZgnGxsZq52VmZibcuHFD7Xjff/+94OPjo7F9cHCwULNmTbXzXr58uUa7sWPHChMmTBAEQRAWLVok9O/fX6PNjz/+KHzxxReCIAjCkCFDhJEjR4rr7ty5I9jb2wupqalq29y9e1ewsrISYmJiBEEQhEmTJonHKSgtLU1QKBSCIAhCly5dxPaenp7CqlWrNNr36NFDCAkJEWP5z3/+o7Y+KipK6Ny5s8Z2giAIycnJgqGhoXD27Fm15UqlUmjRooUQEREhCIIgABASEhLE9bpcl5iYGEEulwt///23WpusrCyhRo0awl9//SUIgiCYmZkJgiAICoVCsLe3F65evarWPjMzU/jggw+E0NBQreegy3UcPXq0MHXqVI02Y8eOFSZOnCgIwqufr6ZNmwpKpVJcf/XqVcHZ2VnrcQvLc2pqqlCpUiVh69atgiAIwsiRI4WZM2dqbD9hwgTB3d1dfB0TEyN06dJFfP3bb78Jbdu2FVQqldp2O3bsED755BNBEARh5cqVwrhx47TGV1CDBg2E6OhojeUdO3YU75vw8HChX79+Gm1+//13tbgKmjVrlrBgwQJBEHQ/T23Gjx8vBAUFia/79esnbN68Wa1N/vuaTCYTKlSoILi5uQlHjhwR1w8ZMkS85q/r1q2bsH79eo3lw4cPF8/tzz//FNq0aaNxva9evSo0aNBA636Tk5OF+vXri68Lvpf2799f6N27t7gu/+c8P1YTExON97K8vDzBz89PWLlypdbj6RIDUVnin1f0zmh72Kuoj+AMDAyQmZmJc+fOoXnz5hpP0548eVLtyfJu3bqpbe/k5CQOIdDm6NGjWLx4scZ+R48ejT/++EPn89L2sFft2rU1jtW/f3+Nbb/88kuNZdp6aX19fREbGwvg1XXUtq+BAweKbQDgww8/FP8fHx+PDh06wM7OTm2bqlWrol27duLrQ4cOYfDgwRr7rlChAszMzDSW6xpLcXNTp04dNG/eXG2ZoaEhBgwYUOg56hpLmzZtUL16dbU2MpkMvXr1UmsHvOpxrl27tsZ4WUtLS3Tv3r3Q+HW5jrrG26VLF7WesKKuXWF5trOzQ8eOHcXXxfl5LCg2NhZ9+/bVuG+9vLxw6dIlZGVlYfDgwUhMTMSAAQNw//59rft58uQJ0tLS8OmnnxYZw9GjR7Fjxw6Ne9TT0xPnzp0rMta3Oc/MzEyEhoZi4cKFcHR0hKOjIyIjI7XO1hEbG4usrCykpaUhNjYW7u7ub4wLAOLi4rTe6wVji4uLw6lTp2BhYaF2/k2aNMG1a9eK3bu6bt063LhxAwsWLNC6XtvDXkZGRrC3t8etW7c02h89elQtLj4gSfrAQpb0SiaTaf24Ljc3F5aWllAoFLCzs9N4c1UoFMjNzcXo0aPFbfKfrs1nYmKi9vH26xQKBXbs2KF132UxNZjw2tg/QHMsJgC1caCF7Ufbdubm5mrHsLKyeuPxX48hNzcXJiYmRR7/ddoK3NdjKW5utO0TAFQqlVhEmZiYqLXT9brosu+CdLlur9PlOuoab3Gvna7x6vrzqEvMhoaGMDU1hSAIsLS0RHR0NDw8PNC6dWtcuXKlxHEqFAosXrxY6z1a1B9CbzrOm85zw4YN6Nu3Ly5evIg///wTf/75J5KSkqBUKkt1WrY3xaZQKDB+/Hit5//y5UuNe/xNZDIZdu/ejaVLl6pNE/YmXbp0wdatWzXeq93c3MR4IiIiULdu3WLFQ1QaWMiSXtWqVQvXrl1TW5aRkQGlUgm5XA57e3tUqlRJbXwf8OoXwOjRo/HkyRNxmZGRUbGO7erqKo6hLSgsLKxUHu4qyM3NDZs3b9ZYvmPHDo1l2saibt68GW5ubgAAd3f3Qrcr2BtUsBevbdu2iI6OVrtewKuxnDExMeJrd3d3bNy4UWPfT58+1Vpgu7m5YefOnW+Mpbi5uXTpkkYBpFQqERYWJu739fF6ul6X2NhYjeuQmZmJXbt2afSmNWnSBDdv3tSIJScnB3v37i00fl2uo67xFufa5ef59XGpmZmZaoVLcX4eC3J3d9ea76ioKNSpUwdyuRzAq9z4+/tjzJgxWL9+vUZ7e3t72NvbIyoqqsgYCrtHo6OjdXr4qiTnKfwz5daYMWPE3tj8f/7+/lp7ZUvCzc0NYWFhGsu3b98u/t/V1RWRkZHIy8tTa3P58mXMnDmzRMd1cnJCWFiY1k8Mioq1fv36CAgI0Fp85+bmYu7cuRg0aFCJYiJ6GyxkSa8++ugjpKamYsmSJVAoFHjy5An8/f3Vpnf6/vvvMWjQIERHRyM3NxfXrl1D//79kZycDHt7+xIfe+TIkYiJiUFQUBCePn2KZ8+eYeXKlZg4cSIaNWoE4FXx9Nlnn6k9lV3SY509exZTp05FWloacnJysGnTJq0PPG3cuBGrV69Geno60tPTsXDhQuzYsQNTpkwR9xUXF4d58+bh+fPnyM3NxZYtWzB79mwEBgZqPX7VqlXh5+eH7t27IzExESqVComJiejVqxdsbW3FdtOnT0dYWBjmz5+PtLQ0KJVKxMXFFVqwzp8/H5MmTcL27dvx8uVLPH36FDNmzMD58+cxfPjwEl+v/FkdYmJioFQqkZKSggEDBqBSpUrw9PTUuo2u18XW1hZeXl44e/YsVCoVLl++jB49eqB79+5o3Lix2j7NzMwwf/589OjRA3FxcVCpVLh+/fobP5rW5TpOmzYNq1atws8//4zs7GxkZWVh2bJlCAsLw+TJk0t03apWrYqRI0eiW7duOHfuHFQqFS5evAgvLy9YWFiI7aZNm4YNGzbgxx9/RGZmJjIzM7F06dI3FrK9e/dGdnY2xo0bh0ePHiEvLw+RkZEYPny4OEtISEiIOLfq8ePHC32SffHixRgyZAjCw8ORl5eHBw8eYOzYsWp/NPTp0wfp6ekICAjAw4cPkZWVhW3btmHQoEEaudKmJOd58OBB2NjYqA1byTdgwADs379f4w+hkliwYAGmT5+O0NBQKBQKPH36FIGBgTh06JDYplWrVmjYsCG8vb1x+/ZtKBQKREZGwtPTU3yPKolOnTqJ7ye62rp1K+Lj49G9e3ccPXpU/JmNjY1Fx44dIZPJMG7cuBLHRFRSLGRJr4yMjLB3715ERkaiUqVKcHFxga2trdrUWT179sTixYsxYcIEWFtbw9XVFXXr1tWYyqi4rK2tER0djbNnz6JWrVqoVq0aIiIiEBsbK46JzMvLw5UrV5CamlrofmbMmKExhu/mzZtqbaysrBAbG4vbt2+jVq1aqFKlCg4fPqzxjTjAqx7h+Ph41KxZE9WrV8epU6cQFxeHypUrAwDkcjliYmJw+fJlODk5wc7ODuvXr8e+ffvQoEGDQuOcP38+BgwYAC8vL8jlcvj5+WHOnDlo1qyZ2KZq1ao4fvw4Lly4gLp168LGxgYTJ07E7NmzMWDAAI19fvjhh9i9ezeWL18OW1tb1KlTB7dv30Z0dLRa4VRc9vb22LBhAxYsWAAbGxu0bNkS1apVw+7duwsdV63rdWnYsCG+//57jB07FnK5HJ07d0aHDh2wcuVKrfv18/PD7Nmz4efnB0tLS/Tq1QtDhgwptKAGdLuO1apVw+HDh7F37144ODigSpUqiImJQUxMjJjrkggMDISPjw/69OkDS0tL+Pj4YOLEiXB1dRXbVK9eHbGxsYiJiYGDgwNq166NlJQUrF27tsh9GxkZYd++fVAqlWjcuDGsra0xZ84c/PLLL+J411atWmH06NFwcHCApaVloTMEdOnSBRs3bsTcuXNhZWWFNm3aoHbt2vjuu+/ENsbGxoiMjERmZiYaNWoEe3t7BAcHIzw8HO3bt3/jtSjJeQYHBxcas6WlJfr06YM1a9a88dhv0rRpU0RGRmLjxo2ws7NDo0aNkJOTg2XLlqm127x5M2rUqIHWrVvD1tYWU6dOxYoVK7SOry2OCRMmaMz84uPjo/Felj9NXsWKFXHy5El06NAB48ePR+XKlVG5cmUEBASgd+/e2Lt3L4yNjd8qJqKSMBAKG6hERKQHt27dQteuXQsdW/k2jhw5gqCgoHI3YT0REZUMe2SJiIiISJJYyBIRERGRJHFoARERERFJEntkiYiIiEiSWMgSERERkSSxkCUiIiIiSWIhS0RERESSxEKWiIiIiCSJhSwRERERSRILWSIiIiKSJBayRERERCRJLGSJiIiISJL+D5JnGvSR5NmMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Crear la gráfica\n",
    "plt.figure(figsize=(7, 3))\n",
    "plt.plot(data_resid['Fecha'], data_resid['Cantidad de personas'], marker='o', linestyle='-', color='#d4c19c')\n",
    "plt.title('Grafica 3. Población de 18 años y más satisfecha con el \\n servicio de recolección de basura a nivel nacional.', fontsize=12, fontweight='bold', fontname='Century Gothic', ha='center', pad=20)\n",
    "plt.xlabel('Año', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "plt.ylabel('Millones de personas', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "\n",
    "# Agregar cantidad de personas en millones a cada punto\n",
    "for i, cantidad in enumerate(data_resid['Cantidad de personas']):\n",
    "    plt.annotate(f'{cantidad:.2f} M', (data_resid['Fecha'][i], cantidad), textcoords=\"offset points\", xytext=(0,5), ha='center', fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Agregar fuente debajo del mapa con la fuente 'Century Gothic'\n",
    "plt.figtext(0.5, -0.1, 'Fuente: Elaboración propia con datos de la API de INEGI', fontsize=10, ha='center', fontname='Century Gothic')\n",
    "\n",
    "# Formatear los números en el eje Y con comas y dos decimales\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: f'{x:,.2f}'))\n",
    "\n",
    "# Configurar fuente y tamaño de fuente para los números en los ejes X y Y\n",
    "plt.xticks(fontsize=8, fontname='Century Gothic')\n",
    "plt.yticks(fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Quitar el marco de la parte superior y derecha\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Añadir líneas de fondo de color gris\n",
    "ax.yaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "ax.xaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Rotar las etiquetas del eje X y aplicar la misma fuente\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardar la imagen\n",
    "plt.savefig('grafica_3.png', dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>59.410699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>65.187023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>65.059493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>69.466055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>75.149520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>74.974844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha  Porcentaje\n",
       "0 2013-01-01   59.410699\n",
       "1 2015-01-01   65.187023\n",
       "2 2017-01-01   65.059493\n",
       "3 2019-01-01   69.466055\n",
       "4 2021-01-01   75.149520\n",
       "5 2023-01-01   74.974844"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URL con token del la variación anual del igae\n",
    "url3 = \"https://www.inegi.org.mx/app/api/indicadores/desarrolladores/jsonxml/INDICATOR/6200032275/es/07000009/false/BISE/2.0/ef8c858c-1f87-6408-5630-fb5496f609bf?type=json\"\n",
    "# Hacer la solicitud HTTP a la URL\n",
    "response_resid= requests.get(url3)\n",
    "\n",
    "# Convertir la respuesta en JSON\n",
    "data_porcentaje = response_resid.json()\n",
    "\n",
    "# Usar las columnas TIME_PERIOD y OBS_VALUE\n",
    "data_porcentaje = pd.DataFrame(data_porcentaje[\"Series\"][0][\"OBSERVATIONS\"])\n",
    "\n",
    "#Convertir los tipos de datos de caracteres a fecha y numericas\n",
    "data_porcentaje['TIME_PERIOD'] = pd.to_datetime(data_porcentaje['TIME_PERIOD'])\n",
    "data_porcentaje['OBS_VALUE'] = pd.to_numeric(data_porcentaje['OBS_VALUE'])\n",
    "\n",
    "# Filtrar las columnas 'TIME_PERIOD' y 'OBS_VALUE' solamente\n",
    "data_porcentaje = data_porcentaje[['TIME_PERIOD', 'OBS_VALUE']]\n",
    "\n",
    "# Renombrar las columnas en los DataFrames\n",
    "# Porcentaje de la población de 18 años y más satisfecha con el servicio de recolección de basura Ciduad de México\n",
    "data_porcentaje.rename(columns={'TIME_PERIOD': 'Fecha', 'OBS_VALUE': 'Porcentaje'}, inplace=True)\n",
    "data_porcentaje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear la gráfica\n",
    "plt.figure(figsize=(7, 2.7))\n",
    "plt.plot(data_porcentaje['Fecha'], data_porcentaje['Porcentaje'], marker='o', linestyle='-', color='#d4c19c')\n",
    "plt.title('Grafica 4. Población de 18 años y más satisfecha con el \\n servicio de recolección de basura en Ciudad de México.', fontsize=12, fontweight='bold', fontname='Century Gothic', ha='center', pad=20)\n",
    "plt.xlabel('Año', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "plt.ylabel('Porcentaje', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "\n",
    "# Agregar porcentajes a cada punto\n",
    "for i, porcentaje in enumerate(data_porcentaje['Porcentaje']):\n",
    "    plt.annotate(f'{porcentaje:.2f}%', (data_porcentaje['Fecha'][i], porcentaje), textcoords=\"offset points\", xytext=(0,5), ha='center', fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Agregar fuente debajo del mapa con la fuente 'Century Gothic'\n",
    "plt.figtext(0.5, -0., 'Fuente: Elaboración propia con datos de la API de INEGI', fontsize=10, ha='center', fontname='Century Gothic')\n",
    "\n",
    "# Formatear los números en el eje Y con comas y dos decimales\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: f'{x:,.2f}'))\n",
    "\n",
    "# Configurar fuente y tamaño de fuente para los números en los ejes X y Y\n",
    "plt.xticks(fontsize=8, fontname='Century Gothic')\n",
    "plt.yticks(fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Quitar el marco de la parte superior y derecha\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Añadir líneas de fondo de color gris\n",
    "ax.yaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "ax.xaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Rotar las etiquetas del eje X y aplicar la misma fuente\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardar la imagen\n",
    "plt.savefig('grafica_4.png', dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Cantidad de personas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>3.927573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>4.664842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>4.495028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>4.820981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.228718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>5.679804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha  Cantidad de personas\n",
       "0 2013-01-01              3.927573\n",
       "1 2015-01-01              4.664842\n",
       "2 2017-01-01              4.495028\n",
       "3 2019-01-01              4.820981\n",
       "4 2021-01-01              5.228718\n",
       "5 2023-01-01              5.679804"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URL con token del la variación anual del igae\n",
    "url3 = \"https://www.inegi.org.mx/app/api/indicadores/desarrolladores/jsonxml/INDICATOR/6207020194/es/07000009/false/BISE/2.0/ef8c858c-1f87-6408-5630-fb5496f609bf?type=json\"\n",
    "# Hacer la solicitud HTTP a la URL\n",
    "response_resid= requests.get(url3)\n",
    "\n",
    "# Convertir la respuesta en JSON\n",
    "data_resid = response_resid.json()\n",
    "\n",
    "# Usar las columnas TIME_PERIOD y OBS_VALUE\n",
    "data_resid = pd.DataFrame(data_resid[\"Series\"][0][\"OBSERVATIONS\"])\n",
    "\n",
    "#Convertir los tipos de datos de caracteres a fecha y numericas\n",
    "data_resid['TIME_PERIOD'] = pd.to_datetime(data_resid['TIME_PERIOD'])\n",
    "data_resid['OBS_VALUE'] = pd.to_numeric(data_resid['OBS_VALUE'])\n",
    "\n",
    "# Filtrar las columnas 'TIME_PERIOD' y 'OBS_VALUE' solamente\n",
    "data_resid = data_resid[['TIME_PERIOD', 'OBS_VALUE']]\n",
    "\n",
    "# Renombrar las columnas en los DataFrames\n",
    "# Porcentaje de la población de 18 años y más satisfecha con el servicio de recolección de basura Ciduad de México\n",
    "data_resid.rename(columns={'TIME_PERIOD': 'Fecha', 'OBS_VALUE': 'Cantidad de personas'}, inplace=True)\n",
    "\n",
    "# Convertir la cantidad de personas a millones\n",
    "data_resid['Cantidad de personas'] = data_resid['Cantidad de personas'] / 1000000\n",
    "data_resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Crear la gráfica\n",
    "plt.figure(figsize=(7, 3))\n",
    "plt.plot(data_resid['Fecha'], data_resid['Cantidad de personas'], marker='o', linestyle='-', color='#d4c19c')\n",
    "plt.title('Grafica 5. Población de 18 años y más satisfecha con el\\nservicio de recolección de basura en la Ciudad de México.', fontsize=12, fontweight='bold', fontname='Century Gothic', ha='center', pad=20)\n",
    "plt.xlabel('Año', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "plt.ylabel('Millones de personas', fontsize=10, fontname='Century Gothic', ha='center')\n",
    "\n",
    "# Agregar cantidad de personas en millones a cada punto\n",
    "for i, cantidad in enumerate(data_resid['Cantidad de personas']):\n",
    "    plt.annotate(f'{cantidad:.2f} M', (data_resid['Fecha'][i], cantidad), textcoords=\"offset points\", xytext=(0,5), ha='center', fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Agregar fuente debajo del mapa con la fuente 'Century Gothic'\n",
    "plt.figtext(0.5, -0.1, 'Fuente: Elaboración propia con datos de la API de INEGI', fontsize=10, ha='center', fontname='Century Gothic')\n",
    "\n",
    "# Formatear los números en el eje Y con comas y dos decimales\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: f'{x:,.2f}'))\n",
    "\n",
    "# Configurar fuente y tamaño de fuente para los números en los ejes X y Y\n",
    "plt.xticks(fontsize=8, fontname='Century Gothic')\n",
    "plt.yticks(fontsize=8, fontname='Century Gothic')\n",
    "\n",
    "# Quitar el marco de la parte superior y derecha\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Añadir líneas de fondo de color gris\n",
    "ax.yaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "ax.xaxis.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Rotar las etiquetas del eje X y aplicar la misma fuente\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardar la imagen\n",
    "plt.savefig('grafica_5.png', dpi=300)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clave Entidad</th>\n",
       "      <th>Entidad federativa</th>\n",
       "      <th>Clave Municipio</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Cantidad promedio diaria de residuos sólidos urbanos recolectados</th>\n",
       "      <th>Recolección Casa por casa</th>\n",
       "      <th>Recolección en un punto establecido</th>\n",
       "      <th>Recolección por sistema de contenedores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>2</td>\n",
       "      <td>Azcapotzalco</td>\n",
       "      <td>712328</td>\n",
       "      <td>0</td>\n",
       "      <td>712328</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>3</td>\n",
       "      <td>Coyoacán</td>\n",
       "      <td>1100000</td>\n",
       "      <td>440000</td>\n",
       "      <td>660000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>4</td>\n",
       "      <td>Cuajimalpa de Morelos</td>\n",
       "      <td>610296</td>\n",
       "      <td>0</td>\n",
       "      <td>598090</td>\n",
       "      <td>12206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>5</td>\n",
       "      <td>Gustavo A. Madero</td>\n",
       "      <td>3021000</td>\n",
       "      <td>90630</td>\n",
       "      <td>2869950</td>\n",
       "      <td>60420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>6</td>\n",
       "      <td>Iztacalco</td>\n",
       "      <td>880000</td>\n",
       "      <td>264000</td>\n",
       "      <td>572000</td>\n",
       "      <td>44000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>7</td>\n",
       "      <td>Iztapalapa</td>\n",
       "      <td>1070500</td>\n",
       "      <td>267625</td>\n",
       "      <td>749350</td>\n",
       "      <td>53525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>8</td>\n",
       "      <td>La Magdalena Contreras</td>\n",
       "      <td>450000</td>\n",
       "      <td>0</td>\n",
       "      <td>450000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>9</td>\n",
       "      <td>Milpa Alta</td>\n",
       "      <td>84900</td>\n",
       "      <td>0</td>\n",
       "      <td>84051</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>10</td>\n",
       "      <td>Álvaro Obregón</td>\n",
       "      <td>851000</td>\n",
       "      <td>510600</td>\n",
       "      <td>85100</td>\n",
       "      <td>255300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>11</td>\n",
       "      <td>Tláhuac</td>\n",
       "      <td>293000</td>\n",
       "      <td>293000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>12</td>\n",
       "      <td>Tlalpan</td>\n",
       "      <td>1126400</td>\n",
       "      <td>1092608</td>\n",
       "      <td>22528</td>\n",
       "      <td>11264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>13</td>\n",
       "      <td>Xochimilco</td>\n",
       "      <td>671000</td>\n",
       "      <td>0</td>\n",
       "      <td>657580</td>\n",
       "      <td>13420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>14</td>\n",
       "      <td>Benito Juárez</td>\n",
       "      <td>550000</td>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>15</td>\n",
       "      <td>Cuauhtémoc</td>\n",
       "      <td>1760000</td>\n",
       "      <td>0</td>\n",
       "      <td>1760000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>16</td>\n",
       "      <td>Miguel Hidalgo</td>\n",
       "      <td>977000</td>\n",
       "      <td>293100</td>\n",
       "      <td>586200</td>\n",
       "      <td>97700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>17</td>\n",
       "      <td>Venustiano Carranza</td>\n",
       "      <td>1400000</td>\n",
       "      <td>686000</td>\n",
       "      <td>714000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>15557424</td>\n",
       "      <td>3937563</td>\n",
       "      <td>11071177</td>\n",
       "      <td>548684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clave Entidad Entidad federativa  Clave Municipio               Municipio  \\\n",
       "0               9   Ciudad de México                2            Azcapotzalco   \n",
       "1               9   Ciudad de México                3                Coyoacán   \n",
       "2               9   Ciudad de México                4   Cuajimalpa de Morelos   \n",
       "3               9   Ciudad de México                5       Gustavo A. Madero   \n",
       "4               9   Ciudad de México                6               Iztacalco   \n",
       "5               9   Ciudad de México                7              Iztapalapa   \n",
       "6               9   Ciudad de México                8  La Magdalena Contreras   \n",
       "7               9   Ciudad de México                9              Milpa Alta   \n",
       "8               9   Ciudad de México               10          Álvaro Obregón   \n",
       "9               9   Ciudad de México               11                 Tláhuac   \n",
       "10              9   Ciudad de México               12                 Tlalpan   \n",
       "11              9   Ciudad de México               13              Xochimilco   \n",
       "12              9   Ciudad de México               14           Benito Juárez   \n",
       "13              9   Ciudad de México               15              Cuauhtémoc   \n",
       "14              9   Ciudad de México               16          Miguel Hidalgo   \n",
       "15              9   Ciudad de México               17     Venustiano Carranza   \n",
       "16              9   Ciudad de México                0                   Total   \n",
       "\n",
       "    Cantidad promedio diaria de residuos sólidos urbanos recolectados  \\\n",
       "0                                              712328                   \n",
       "1                                             1100000                   \n",
       "2                                              610296                   \n",
       "3                                             3021000                   \n",
       "4                                              880000                   \n",
       "5                                             1070500                   \n",
       "6                                              450000                   \n",
       "7                                               84900                   \n",
       "8                                              851000                   \n",
       "9                                              293000                   \n",
       "10                                            1126400                   \n",
       "11                                             671000                   \n",
       "12                                             550000                   \n",
       "13                                            1760000                   \n",
       "14                                             977000                   \n",
       "15                                            1400000                   \n",
       "16                                           15557424                   \n",
       "\n",
       "    Recolección Casa por casa  Recolección en un punto establecido  \\\n",
       "0                           0                               712328   \n",
       "1                      440000                               660000   \n",
       "2                           0                               598090   \n",
       "3                       90630                              2869950   \n",
       "4                      264000                               572000   \n",
       "5                      267625                               749350   \n",
       "6                           0                               450000   \n",
       "7                           0                                84051   \n",
       "8                      510600                                85100   \n",
       "9                      293000                                    0   \n",
       "10                    1092608                                22528   \n",
       "11                          0                               657580   \n",
       "12                          0                               550000   \n",
       "13                          0                              1760000   \n",
       "14                     293100                               586200   \n",
       "15                     686000                               714000   \n",
       "16                    3937563                             11071177   \n",
       "\n",
       "    Recolección por sistema de contenedores  \n",
       "0                                         0  \n",
       "1                                         0  \n",
       "2                                     12206  \n",
       "3                                     60420  \n",
       "4                                     44000  \n",
       "5                                     53525  \n",
       "6                                         0  \n",
       "7                                       849  \n",
       "8                                    255300  \n",
       "9                                         0  \n",
       "10                                    11264  \n",
       "11                                    13420  \n",
       "12                                        0  \n",
       "13                                        0  \n",
       "14                                    97700  \n",
       "15                                        0  \n",
       "16                                   548684  "
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ruta archivos CSV\n",
    "resid = 'https://raw.githubusercontent.com/Quetzaltlali/PP_LCDN-Residuos_CDMX/main/resi_solidos_cdmx.csv'\n",
    "resid = pd.read_csv(resid)\n",
    "resid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3) MINERÍA DE DATOS** \n",
    "El impacto del uso de contenedores de basura de la CDMX, en la contaminación del aire y agua.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: rpy2 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (3.5.16)\n",
      "Requirement already satisfied: cffi>=1.15.1 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from rpy2) (1.16.0)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from rpy2) (3.1.4)\n",
      "Requirement already satisfied: tzlocal in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from rpy2) (5.2)\n",
      "Requirement already satisfied: packaging in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from rpy2) (23.2)\n",
      "Requirement already satisfied: pycparser in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from cffi>=1.15.1->rpy2) (2.21)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from jinja2->rpy2) (2.1.5)\n",
      "Requirement already satisfied: tzdata in c:\\users\\quetz\\miniconda3\\lib\\site-packages (from tzlocal->rpy2) (2024.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install rpy2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Datos iniciales\n",
    "# datos_reales_basura <- matrix(c(\n",
    "#   2016, 8918653, 12600, 1.41, 10500, 1.18, 2500, 0.28, 10000, 1.12,\n",
    "#   2017, 8973908, 12900, 1.43, 10800, 1.20, 2600, 0.29, 10200, 1.14,\n",
    "#   2018, 9035951, 13200, 1.46, 11100, 1.23, 2700, 0.30, 10400, 1.15,\n",
    "#   2019, 9099589, 13500, 1.48, 11400, 1.25, 2800, 0.31, 10600, 1.16,\n",
    "#   2020, 9161421, 13200, 1.44, 10800, 1.18, 2700, 0.29, 10100, 1.10,\n",
    "#   2021, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "#   2022, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "#   2023, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "#   2024, 0, 0, 0, 0, 0, 0, 0, 0, 0), nrow = 9, byrow = TRUE)\n",
    "\n",
    "# colnames(datos_reales_basura) <- c(\"Year\", \"Population\", \"Ton_per_day\", \"Ton_per_capita\", \"Residential\", \"Res_per_capita\", \"Commercial\", \"Com_per_capita\", \"Industrial\", \"Ind_per_capita\")\n",
    "# datos_reales_basura <- as.data.frame(datos_reales_basura)\n",
    "\n",
    "# # Columna de interés\n",
    "# ton_per_day <- as.numeric(datos_reales_basura$Ton_per_day[1:5])\n",
    "\n",
    "# # Calcular la media y la desviación estándar\n",
    "# media <- mean(ton_per_day)\n",
    "# desviacion_estandar <- sd(ton_per_day)\n",
    "\n",
    "# media\n",
    "# desviacion_estandar\n",
    "\n",
    "# # Configurar la semilla\n",
    "# set.seed(123)\n",
    "\n",
    "# # Generar valores simulados\n",
    "# valores_simulados <- runif(5, min = media - 3 * desviacion_estandar, max = media + 3 * desviacion_estandar)\n",
    "# valores_simulados\n",
    "\n",
    "# # Asignar valores simulados\n",
    "# datos_reales_basura$Ton_per_day[6:9] <- valores_simulados[1:4]\n",
    "# datos_reales_basura\n",
    "\n",
    "# # Construir matrices de distancia\n",
    "# library(stats)\n",
    "\n",
    "# distancias <- list()\n",
    "# for (n in 9:1) {\n",
    "#   distancias[[n]] <- dist(datos_reales_basura$Ton_per_day[1:n])\n",
    "# }\n",
    "\n",
    "# distancias\n",
    "\n",
    "# # Construir jerarquía de clusters\n",
    "# jerarquia <- hclust(dist(datos_reales_basura$Ton_per_day))\n",
    "# jerarquia\n",
    "\n",
    "# # Dendograma\n",
    "# plot(jerarquia, main = \"Dendograma de la jerarquía de clusters\", xlab = \"Años\", ylab = \"Distancia\", sub = \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4) MERCADOTECNIA DIGITAL** \n",
    "Cambio de cultura cívica en la gestión de residuos como herramienta del fomento de prácticas sostenibles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clave Entidad</th>\n",
       "      <th>Entidad federativa</th>\n",
       "      <th>Clave Municipio</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Cantidad promedio diaria de residuos sólidos urbanos recolectados</th>\n",
       "      <th>Recolección Casa por casa</th>\n",
       "      <th>Recolección en un punto establecido</th>\n",
       "      <th>Recolección por sistema de contenedores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>2</td>\n",
       "      <td>Azcapotzalco</td>\n",
       "      <td>712328</td>\n",
       "      <td>0</td>\n",
       "      <td>712328</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>3</td>\n",
       "      <td>Coyoacán</td>\n",
       "      <td>1100000</td>\n",
       "      <td>440000</td>\n",
       "      <td>660000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>4</td>\n",
       "      <td>Cuajimalpa de Morelos</td>\n",
       "      <td>610296</td>\n",
       "      <td>0</td>\n",
       "      <td>598090</td>\n",
       "      <td>12206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>5</td>\n",
       "      <td>Gustavo A. Madero</td>\n",
       "      <td>3021000</td>\n",
       "      <td>90630</td>\n",
       "      <td>2869950</td>\n",
       "      <td>60420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>6</td>\n",
       "      <td>Iztacalco</td>\n",
       "      <td>880000</td>\n",
       "      <td>264000</td>\n",
       "      <td>572000</td>\n",
       "      <td>44000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>7</td>\n",
       "      <td>Iztapalapa</td>\n",
       "      <td>1070500</td>\n",
       "      <td>267625</td>\n",
       "      <td>749350</td>\n",
       "      <td>53525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>8</td>\n",
       "      <td>La Magdalena Contreras</td>\n",
       "      <td>450000</td>\n",
       "      <td>0</td>\n",
       "      <td>450000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>9</td>\n",
       "      <td>Milpa Alta</td>\n",
       "      <td>84900</td>\n",
       "      <td>0</td>\n",
       "      <td>84051</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>10</td>\n",
       "      <td>Álvaro Obregón</td>\n",
       "      <td>851000</td>\n",
       "      <td>510600</td>\n",
       "      <td>85100</td>\n",
       "      <td>255300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>11</td>\n",
       "      <td>Tláhuac</td>\n",
       "      <td>293000</td>\n",
       "      <td>293000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>12</td>\n",
       "      <td>Tlalpan</td>\n",
       "      <td>1126400</td>\n",
       "      <td>1092608</td>\n",
       "      <td>22528</td>\n",
       "      <td>11264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>13</td>\n",
       "      <td>Xochimilco</td>\n",
       "      <td>671000</td>\n",
       "      <td>0</td>\n",
       "      <td>657580</td>\n",
       "      <td>13420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>14</td>\n",
       "      <td>Benito Juárez</td>\n",
       "      <td>550000</td>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>15</td>\n",
       "      <td>Cuauhtémoc</td>\n",
       "      <td>1760000</td>\n",
       "      <td>0</td>\n",
       "      <td>1760000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>16</td>\n",
       "      <td>Miguel Hidalgo</td>\n",
       "      <td>977000</td>\n",
       "      <td>293100</td>\n",
       "      <td>586200</td>\n",
       "      <td>97700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>17</td>\n",
       "      <td>Venustiano Carranza</td>\n",
       "      <td>1400000</td>\n",
       "      <td>686000</td>\n",
       "      <td>714000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>15557424</td>\n",
       "      <td>3937563</td>\n",
       "      <td>11071177</td>\n",
       "      <td>548684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clave Entidad Entidad federativa  Clave Municipio               Municipio  \\\n",
       "0               9   Ciudad de México                2            Azcapotzalco   \n",
       "1               9   Ciudad de México                3                Coyoacán   \n",
       "2               9   Ciudad de México                4   Cuajimalpa de Morelos   \n",
       "3               9   Ciudad de México                5       Gustavo A. Madero   \n",
       "4               9   Ciudad de México                6               Iztacalco   \n",
       "5               9   Ciudad de México                7              Iztapalapa   \n",
       "6               9   Ciudad de México                8  La Magdalena Contreras   \n",
       "7               9   Ciudad de México                9              Milpa Alta   \n",
       "8               9   Ciudad de México               10          Álvaro Obregón   \n",
       "9               9   Ciudad de México               11                 Tláhuac   \n",
       "10              9   Ciudad de México               12                 Tlalpan   \n",
       "11              9   Ciudad de México               13              Xochimilco   \n",
       "12              9   Ciudad de México               14           Benito Juárez   \n",
       "13              9   Ciudad de México               15              Cuauhtémoc   \n",
       "14              9   Ciudad de México               16          Miguel Hidalgo   \n",
       "15              9   Ciudad de México               17     Venustiano Carranza   \n",
       "16              9   Ciudad de México                0                   Total   \n",
       "\n",
       "    Cantidad promedio diaria de residuos sólidos urbanos recolectados  \\\n",
       "0                                              712328                   \n",
       "1                                             1100000                   \n",
       "2                                              610296                   \n",
       "3                                             3021000                   \n",
       "4                                              880000                   \n",
       "5                                             1070500                   \n",
       "6                                              450000                   \n",
       "7                                               84900                   \n",
       "8                                              851000                   \n",
       "9                                              293000                   \n",
       "10                                            1126400                   \n",
       "11                                             671000                   \n",
       "12                                             550000                   \n",
       "13                                            1760000                   \n",
       "14                                             977000                   \n",
       "15                                            1400000                   \n",
       "16                                           15557424                   \n",
       "\n",
       "    Recolección Casa por casa  Recolección en un punto establecido  \\\n",
       "0                           0                               712328   \n",
       "1                      440000                               660000   \n",
       "2                           0                               598090   \n",
       "3                       90630                              2869950   \n",
       "4                      264000                               572000   \n",
       "5                      267625                               749350   \n",
       "6                           0                               450000   \n",
       "7                           0                                84051   \n",
       "8                      510600                                85100   \n",
       "9                      293000                                    0   \n",
       "10                    1092608                                22528   \n",
       "11                          0                               657580   \n",
       "12                          0                               550000   \n",
       "13                          0                              1760000   \n",
       "14                     293100                               586200   \n",
       "15                     686000                               714000   \n",
       "16                    3937563                             11071177   \n",
       "\n",
       "    Recolección por sistema de contenedores  \n",
       "0                                         0  \n",
       "1                                         0  \n",
       "2                                     12206  \n",
       "3                                     60420  \n",
       "4                                     44000  \n",
       "5                                     53525  \n",
       "6                                         0  \n",
       "7                                       849  \n",
       "8                                    255300  \n",
       "9                                         0  \n",
       "10                                    11264  \n",
       "11                                    13420  \n",
       "12                                        0  \n",
       "13                                        0  \n",
       "14                                    97700  \n",
       "15                                        0  \n",
       "16                                   548684  "
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ruta archivos CSV\n",
    "resid = 'https://raw.githubusercontent.com/Quetzaltlali/PP_LCDN-Residuos_CDMX/main/resi_solidos_cdmx.csv'\n",
    "resid = pd.read_csv(resid)\n",
    "resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convertir variable CVE_MUN en nuúmerica\n",
    "cdmx1['CVE_MUN'] = pd.to_numeric(cdmx1['CVE_MUN'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVE_MUN</th>\n",
       "      <th>num_ageb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CVE_MUN  num_ageb\n",
       "0         2       103\n",
       "1         3       156\n",
       "2         4        31\n",
       "3         5       305\n",
       "4         6       110\n",
       "5         7       458\n",
       "6         8        52\n",
       "7         9        42\n",
       "8        10       199\n",
       "9        11       110\n",
       "10       12       207\n",
       "11       13       122\n",
       "12       14       102\n",
       "13       15       153\n",
       "14       16       130\n",
       "15       17       151"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupar por 'CVE_MUN' y contar el número de 'CVE_AGEB'\n",
    "conteo_ageb_por_mun = cdmx1.groupby('CVE_MUN')['CVE_AGEB'].count().reset_index()\n",
    "\n",
    "# Renombrar la columna de conteo para mayor claridad\n",
    "conteo_ageb_por_mun = conteo_ageb_por_mun.rename(columns={'CVE_AGEB': 'num_ageb'})\n",
    "conteo_ageb_por_mun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17 entries, 0 to 16\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                                             Non-Null Count  Dtype \n",
      "---  ------                                                             --------------  ----- \n",
      " 0   Clave Entidad                                                      17 non-null     int64 \n",
      " 1   Entidad federativa                                                 17 non-null     object\n",
      " 2   Clave Municipio                                                    17 non-null     int64 \n",
      " 3   Municipio                                                          17 non-null     object\n",
      " 4   Cantidad promedio diaria de residuos sólidos urbanos recolectados  17 non-null     int64 \n",
      " 5   Recolección Casa por casa                                          17 non-null     int64 \n",
      " 6   Recolección en un punto establecido                                17 non-null     int64 \n",
      " 7   Recolección por sistema de contenedores                            17 non-null     int64 \n",
      "dtypes: int64(6), object(2)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "resid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hacer cruce de la tabla conteo_ageb_por_mun la columna CVE_MUN y Clave Municipio para pegar en resid el num_ageb a la tabla resid\n",
    "\n",
    "# Crear el DataFrame de conteo de AGEB por municipio a partir de 'cdmx1'\n",
    "conteo_ageb_por_mun = cdmx1.groupby('CVE_MUN')['CVE_AGEB'].count().reset_index()\n",
    "conteo_ageb_por_mun = conteo_ageb_por_mun.rename(columns={'CVE_AGEB': 'num_ageb'})\n",
    "\n",
    "# Asegurarse de que las columnas sean del mismo tipo\n",
    "resid['Clave Municipio'] = pd.to_numeric(resid['Clave Municipio'], errors='coerce')\n",
    "conteo_ageb_por_mun['CVE_MUN'] = pd.to_numeric(conteo_ageb_por_mun['CVE_MUN'], errors='coerce')\n",
    "\n",
    "# Renombrar la columna 'Clave Municipio' a 'CVE_MUN' en resid para hacer el cruce\n",
    "resid = resid.rename(columns={'Clave Municipio': 'CVE_MUN'})\n",
    "\n",
    "# Hacer el cruce con resid para pegar num_ageb\n",
    "resid = resid.merge(conteo_ageb_por_mun, on='CVE_MUN', how='left')\n",
    "\n",
    "# Renombrar la columna 'CVE_MUN' de nuevo a 'Clave Municipio' si es necesario\n",
    "resid = resid.rename(columns={'CVE_MUN': 'Clave Municipio'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clave Entidad</th>\n",
       "      <th>Entidad federativa</th>\n",
       "      <th>Clave Municipio</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Cantidad promedio diaria de residuos sólidos urbanos recolectados</th>\n",
       "      <th>Recolección Casa por casa</th>\n",
       "      <th>Recolección en un punto establecido</th>\n",
       "      <th>Recolección por sistema de contenedores</th>\n",
       "      <th>num_ageb</th>\n",
       "      <th>Demanda de contenedores por AGEB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>2</td>\n",
       "      <td>Azcapotzalco</td>\n",
       "      <td>712328</td>\n",
       "      <td>0</td>\n",
       "      <td>712328</td>\n",
       "      <td>0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>6915.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>3</td>\n",
       "      <td>Coyoacán</td>\n",
       "      <td>1100000</td>\n",
       "      <td>440000</td>\n",
       "      <td>660000</td>\n",
       "      <td>0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7051.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>4</td>\n",
       "      <td>Cuajimalpa de Morelos</td>\n",
       "      <td>610296</td>\n",
       "      <td>0</td>\n",
       "      <td>598090</td>\n",
       "      <td>12206</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19686.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>5</td>\n",
       "      <td>Gustavo A. Madero</td>\n",
       "      <td>3021000</td>\n",
       "      <td>90630</td>\n",
       "      <td>2869950</td>\n",
       "      <td>60420</td>\n",
       "      <td>305.0</td>\n",
       "      <td>9904.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>6</td>\n",
       "      <td>Iztacalco</td>\n",
       "      <td>880000</td>\n",
       "      <td>264000</td>\n",
       "      <td>572000</td>\n",
       "      <td>44000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>8000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>7</td>\n",
       "      <td>Iztapalapa</td>\n",
       "      <td>1070500</td>\n",
       "      <td>267625</td>\n",
       "      <td>749350</td>\n",
       "      <td>53525</td>\n",
       "      <td>458.0</td>\n",
       "      <td>2337.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>8</td>\n",
       "      <td>La Magdalena Contreras</td>\n",
       "      <td>450000</td>\n",
       "      <td>0</td>\n",
       "      <td>450000</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8653.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>9</td>\n",
       "      <td>Milpa Alta</td>\n",
       "      <td>84900</td>\n",
       "      <td>0</td>\n",
       "      <td>84051</td>\n",
       "      <td>849</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2021.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>10</td>\n",
       "      <td>Álvaro Obregón</td>\n",
       "      <td>851000</td>\n",
       "      <td>510600</td>\n",
       "      <td>85100</td>\n",
       "      <td>255300</td>\n",
       "      <td>199.0</td>\n",
       "      <td>4276.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>11</td>\n",
       "      <td>Tláhuac</td>\n",
       "      <td>293000</td>\n",
       "      <td>293000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2663.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>12</td>\n",
       "      <td>Tlalpan</td>\n",
       "      <td>1126400</td>\n",
       "      <td>1092608</td>\n",
       "      <td>22528</td>\n",
       "      <td>11264</td>\n",
       "      <td>207.0</td>\n",
       "      <td>5441.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>13</td>\n",
       "      <td>Xochimilco</td>\n",
       "      <td>671000</td>\n",
       "      <td>0</td>\n",
       "      <td>657580</td>\n",
       "      <td>13420</td>\n",
       "      <td>122.0</td>\n",
       "      <td>5500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>14</td>\n",
       "      <td>Benito Juárez</td>\n",
       "      <td>550000</td>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5392.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>15</td>\n",
       "      <td>Cuauhtémoc</td>\n",
       "      <td>1760000</td>\n",
       "      <td>0</td>\n",
       "      <td>1760000</td>\n",
       "      <td>0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>11503.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>16</td>\n",
       "      <td>Miguel Hidalgo</td>\n",
       "      <td>977000</td>\n",
       "      <td>293100</td>\n",
       "      <td>586200</td>\n",
       "      <td>97700</td>\n",
       "      <td>130.0</td>\n",
       "      <td>7515.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>17</td>\n",
       "      <td>Venustiano Carranza</td>\n",
       "      <td>1400000</td>\n",
       "      <td>686000</td>\n",
       "      <td>714000</td>\n",
       "      <td>0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>9271.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>Ciudad de México</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>15557424</td>\n",
       "      <td>3937563</td>\n",
       "      <td>11071177</td>\n",
       "      <td>548684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clave Entidad Entidad federativa  Clave Municipio               Municipio  \\\n",
       "0               9   Ciudad de México                2            Azcapotzalco   \n",
       "1               9   Ciudad de México                3                Coyoacán   \n",
       "2               9   Ciudad de México                4   Cuajimalpa de Morelos   \n",
       "3               9   Ciudad de México                5       Gustavo A. Madero   \n",
       "4               9   Ciudad de México                6               Iztacalco   \n",
       "5               9   Ciudad de México                7              Iztapalapa   \n",
       "6               9   Ciudad de México                8  La Magdalena Contreras   \n",
       "7               9   Ciudad de México                9              Milpa Alta   \n",
       "8               9   Ciudad de México               10          Álvaro Obregón   \n",
       "9               9   Ciudad de México               11                 Tláhuac   \n",
       "10              9   Ciudad de México               12                 Tlalpan   \n",
       "11              9   Ciudad de México               13              Xochimilco   \n",
       "12              9   Ciudad de México               14           Benito Juárez   \n",
       "13              9   Ciudad de México               15              Cuauhtémoc   \n",
       "14              9   Ciudad de México               16          Miguel Hidalgo   \n",
       "15              9   Ciudad de México               17     Venustiano Carranza   \n",
       "16              9   Ciudad de México                0                   Total   \n",
       "\n",
       "    Cantidad promedio diaria de residuos sólidos urbanos recolectados  \\\n",
       "0                                              712328                   \n",
       "1                                             1100000                   \n",
       "2                                              610296                   \n",
       "3                                             3021000                   \n",
       "4                                              880000                   \n",
       "5                                             1070500                   \n",
       "6                                              450000                   \n",
       "7                                               84900                   \n",
       "8                                              851000                   \n",
       "9                                              293000                   \n",
       "10                                            1126400                   \n",
       "11                                             671000                   \n",
       "12                                             550000                   \n",
       "13                                            1760000                   \n",
       "14                                             977000                   \n",
       "15                                            1400000                   \n",
       "16                                           15557424                   \n",
       "\n",
       "    Recolección Casa por casa  Recolección en un punto establecido  \\\n",
       "0                           0                               712328   \n",
       "1                      440000                               660000   \n",
       "2                           0                               598090   \n",
       "3                       90630                              2869950   \n",
       "4                      264000                               572000   \n",
       "5                      267625                               749350   \n",
       "6                           0                               450000   \n",
       "7                           0                                84051   \n",
       "8                      510600                                85100   \n",
       "9                      293000                                    0   \n",
       "10                    1092608                                22528   \n",
       "11                          0                               657580   \n",
       "12                          0                               550000   \n",
       "13                          0                              1760000   \n",
       "14                     293100                               586200   \n",
       "15                     686000                               714000   \n",
       "16                    3937563                             11071177   \n",
       "\n",
       "    Recolección por sistema de contenedores  num_ageb  \\\n",
       "0                                         0     103.0   \n",
       "1                                         0     156.0   \n",
       "2                                     12206      31.0   \n",
       "3                                     60420     305.0   \n",
       "4                                     44000     110.0   \n",
       "5                                     53525     458.0   \n",
       "6                                         0      52.0   \n",
       "7                                       849      42.0   \n",
       "8                                    255300     199.0   \n",
       "9                                         0     110.0   \n",
       "10                                    11264     207.0   \n",
       "11                                    13420     122.0   \n",
       "12                                        0     102.0   \n",
       "13                                        0     153.0   \n",
       "14                                    97700     130.0   \n",
       "15                                        0     151.0   \n",
       "16                                   548684       NaN   \n",
       "\n",
       "    Demanda de contenedores por AGEB  \n",
       "0                            6915.81  \n",
       "1                            7051.28  \n",
       "2                           19686.97  \n",
       "3                            9904.92  \n",
       "4                            8000.00  \n",
       "5                            2337.34  \n",
       "6                            8653.85  \n",
       "7                            2021.43  \n",
       "8                            4276.38  \n",
       "9                            2663.64  \n",
       "10                           5441.55  \n",
       "11                           5500.00  \n",
       "12                           5392.16  \n",
       "13                          11503.27  \n",
       "14                           7515.38  \n",
       "15                           9271.52  \n",
       "16                               NaN  "
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear la nueva columna como la división de 'Cantidad promedio diaria de residuos sólidos urbanos recolectados' entre 'num_ageb'\n",
    "resid['Demanda de contenedores por AGEB'] = (resid['Cantidad promedio diaria de residuos sólidos urbanos recolectados'] / resid['num_ageb']).round(2)\n",
    "resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17 entries, 0 to 16\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                                             Non-Null Count  Dtype  \n",
      "---  ------                                                             --------------  -----  \n",
      " 0   Clave Entidad                                                      17 non-null     int64  \n",
      " 1   Entidad federativa                                                 17 non-null     object \n",
      " 2   Clave Municipio                                                    17 non-null     int64  \n",
      " 3   Municipio                                                          17 non-null     object \n",
      " 4   Cantidad promedio diaria de residuos sólidos urbanos recolectados  17 non-null     int64  \n",
      " 5   Recolección Casa por casa                                          17 non-null     int64  \n",
      " 6   Recolección en un punto establecido                                17 non-null     int64  \n",
      " 7   Recolección por sistema de contenedores                            17 non-null     int64  \n",
      " 8   num_ageb                                                           16 non-null     float64\n",
      " 9   Demanda de contenedores por AGEB                                   16 non-null     float64\n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "resid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 8 columns):\n",
      " #   Column                  Non-Null Count  Dtype   \n",
      "---  ------                  --------------  -----   \n",
      " 0   CVE_ENT                 16 non-null     object  \n",
      " 1   CVE_MUN                 16 non-null     int64   \n",
      " 2   NOM_MUN                 16 non-null     object  \n",
      " 3   OID_1                   16 non-null     int64   \n",
      " 4   cov_                    16 non-null     int64   \n",
      " 5   cov_id                  16 non-null     int64   \n",
      " 6   geometry                16 non-null     geometry\n",
      " 7   Empleados de confianza  16 non-null     int64   \n",
      "dtypes: geometry(1), int64(5), object(2)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cdmx['CVE_MUN'] = pd.to_numeric(cdmx['CVE_MUN'], errors='coerce')\n",
    "cdmx.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Realizar la unión, asegurándose de que 'Clave Municipio' y 'CVE_MUN' coincidan\n",
    "cdmx_merged = cdmx.merge(resid, left_on='CVE_MUN', right_on='Clave Municipio', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definir los colores para cada rango\n",
    "paleta = [\"#e3c8a6ff\", \"#c88f80ff\", \"#ac2540ff\", \"#901d3dff\"]\n",
    "niveles = ['< 220', '220 - 432', '432 - 614', '> 614']\n",
    "\n",
    "# Convertir lista de colores a un objeto de paleta de colores\n",
    "cmap_paleta = ListedColormap(paleta)\n",
    "\n",
    "# Configurar el tamaño de la figura\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Crear el mapa temático de demanda de contenedores por AGEB\n",
    "cdmx_merged.plot(column='Demanda de contenedores por AGEB', cmap=cmap_paleta, linewidth=0.8, edgecolor='none', legend=False)\n",
    "\n",
    "# Quitar marcos de latitud y longitud\n",
    "plt.axis('off')\n",
    "\n",
    "# Añadir título con la fuente 'Century Gothic'\n",
    "plt.title('Demanda de contenedores por AGEB en la Ciudad de México', fontsize=18, fontweight='bold', fontname='Century Gothic')\n",
    "\n",
    "# Crear leyenda personalizada y ubicarla a la derecha\n",
    "legend_elements = [Patch(facecolor=color, edgecolor='gray', label=label) for color, label in zip(paleta, niveles)]\n",
    "plt.legend(handles=legend_elements, title='Rangos de demanda', loc='center left', bbox_to_anchor=(1, 0.20), fontsize=10, prop={'family': 'Century Gothic'})\n",
    "\n",
    "# Agregar fuente debajo del mapa con la fuente 'Century Gothic'\n",
    "plt.figtext(0.5, -0.1, 'Fuente: Elaboración propia con datos de tu fuente de datos', fontsize=10, ha='center', fontname='Century Gothic')\n",
    "\n",
    "# Ajustar automáticamente los márgenes para evitar recorte de etiquetas\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardar el gráfico como imagen PNG\n",
    "plt.savefig('mapa_demanda_contenedores.png', bbox_inches='tight', dpi=300)\n",
    "\n",
    "# Mostrar el mapa\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}