disaster_risk.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "disaster_risk.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "z5jWQn05pfT0",
        "F5oZLw9DE4Ca",
        "DXLjUpS5E4KJ",
        "IS_upuaDGvRO"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Viiic98/disaster-risk-management/blob/main/disaster_risk.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxihtrR2xne6"
      },
      "source": [
        "# **Disaster Risk Management**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3JxKBf0zIrn"
      },
      "source": [
        "## **Drive Mount**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zurlh6BQzT-H",
        "outputId": "88e32e7a-c73c-4c3c-9fe0-77d6e915f2a4"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "path_file = '/content/drive/MyDrive/project/data.csv'\n",
        "file_dead_by_event = '/content/drive/MyDrive/project/DeadsByEventYear.csv'\n",
        "file_event_by_dep_year = '/content/drive/MyDrive/project/NumberEventsByDepartmentByYear.csv'\n",
        "file_event_by_dep_month = '/content/drive/MyDrive/project/NumberEventsByDepartmentByMonth.csv'\n",
        "file_event_by_city_year = '/content/drive/MyDrive/project/CityYear.csv'\n",
        "file_event_by_city_month = '/content/drive/MyDrive/project/CityMonth.csv'"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZfw93FTx1VZ"
      },
      "source": [
        "## **Necessary imports**\n",
        "\n",
        "##### Add here all the necessary imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjCg6BPrxUzd"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "from matplotlib.gridspec import GridSpec\n",
        "import seaborn as sns\n",
        "import pprint"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mP3FrP0u0Yx3"
      },
      "source": [
        "## **Data cleaning with SageMaker Studio**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJ_g2GwwaWWE"
      },
      "source": [
        "We had 22 files of data, the files were divided into 3 batches to do the transformation and unification of the data.</br>\n",
        "The files were distributed as follows:</br>\n",
        "- Amine from 1998 to 2004</br>\n",
        "- Pablo from 2005 to 2011</br>\n",
        "- Victor from 2012 to 2019"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcSlgxjfcS3I"
      },
      "source": [
        "The new dataframes should have the next structure:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7HRu07ScR89"
      },
      "source": [
        "columns = [\"DATE\",\t\"DEPARTAMENT\",\t\"CITY / TOWN\",\t\"EVENT\",\t\"DEAD\",\t\"INJURED\",\t\"MISSING\",\t\"PERSONS\",\t\"FAMILIES\",\t\"DESTROYED\", \"HOUSES\",\t\"DAMAGED\", \"HOUSES\",\t\"TRACKS\",\t\"VEHICULAR BRIDGES\",\t\"PEDESTRIAN BRIDGES\",\t\"AQUEDUCT\",\t\"SEWERAGE\",\t\"HEALTH CENTER\",\t\"SCHOOL\",\t\"COMMUNITY CENTER\",\t\"MENAGES\",\t\"FOOD SUPPORT\",\t\"SUPPORT SUPLIES(ROOF TILES, COATS)\",\t\t\"OTHERS\",\t\"ECONOMIC TRANSFERS\", \"TOTAL AMOUNT\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ovuy2jpFdfN2"
      },
      "source": [
        "Steps that every one of us used to transform the data we had"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3867T3VbZY8"
      },
      "source": [
        "### **Amine transformation - 1998 to 2004**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWjFRIMkd166"
      },
      "source": [
        "####**1998**</br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsRkF8E1h3U9"
      },
      "source": [
        "Rename column STATE to DEPARTMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QLVhb3kh8JV"
      },
      "source": [
        "Drop column OTHERS19"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkkz-8IhiMWZ"
      },
      "source": [
        "Drop column _C28"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzIbmKlCiQFT"
      },
      "source": [
        "Drop column OBSERVATIONS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuY6PFbYiVdV"
      },
      "source": [
        "Rename column OTHERS24 to OTHERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsOR8cQQiZJb"
      },
      "source": [
        "Rename column TOTAL to TOTAL AMOUNT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2zG2oEveVKO"
      },
      "source": [
        "####**1999**</br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYQyEOs_i8Ng"
      },
      "source": [
        "Rename column DATE0 to DATE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QHG3wvXi8Yk"
      },
      "source": [
        "Rename column DEPARTAMENT to DEPARTMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cl7RdhqCi8bm"
      },
      "source": [
        "Drop column OTHERS19"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2358RWbdi8fe"
      },
      "source": [
        "Drop column DATE SUPPORT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNQpJxLei8o5"
      },
      "source": [
        "Rename column OTHERS25 to OTHERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEgrb0DFjgRr"
      },
      "source": [
        "Drop columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ah62KgLe7mu"
      },
      "source": [
        "df.drop(['DATE28', 'COMMENTS', \"_c30\", \"_c31\"],\n",
        "        axis='columns',\n",
        "        inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAGo0nX1eZZB"
      },
      "source": [
        "####**2000**</br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkfb1TisjqjG"
      },
      "source": [
        "Rename column DATE0 to DATE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMnWsE6Ujs6g"
      },
      "source": [
        "Rename column DEPARTAMENT to DEPARTMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1D20qa0jxRy"
      },
      "source": [
        "Drop columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJU47TRvfVam"
      },
      "source": [
        "df.drop([\"OTHERS19\", \"DATE SUPPORT\", \"DATE28\", \"COMMENTS\",\n",
        "         \"CANTIDAD30\", \"VALOR31\", \"_c32\",\"VALOR33\", \"CANTIDAD34\",\n",
        "         \"VALOR35\", \"CANTIDAD36\", \"VALOR37\", \"CANTIDAD38\",\n",
        "         \"VALOR39\", \"CANTIDAD40\", \"VALOR41\", \"CANTIDAD42\",\n",
        "         \"VALOR43\", \"CANTIDAD44\", \"VALOR45\", \"CANTIDAD46\",\n",
        "         \"VALOR47\", \"CANTIDAD48\", \"VALOR49\", \"CANTIDAD50\",\n",
        "         \"VALOR51\", \"CANTIDAD52\", \"VALOR53\", \"CANTIDAD54\",\n",
        "         \"VALOR55\", \"CANTIDAD56\", \"VALOR57\", \"CANTIDAD58\",\n",
        "         \"VALOR59\", \"CANTIDAD60\", \"VALOR61\", \"CANTIDAD62\",\n",
        "         \"VALOR63\", \"CANTIDAD64\", \"VALOR65\",\"CANTIDAD66\",\n",
        "         \"VALOR67\", \"CANTIDAD68\", \"VALOR69\", \"CANTIDAD70\",\n",
        "         \"VALOR71\", \"CANTIDAD72\", \"VALOR73\", \"CANTIDAD74\",\n",
        "         \"VALOR75\", \"CANTIDAD76\", \"VALOR77\", \"CANTIDAD78\",\n",
        "         \"VALOR79\", \"CANTIDAD80\", \"VALOR81\", \"CANTIDAD82\",\n",
        "         \"VALOR83\", \"CANTIDAD84\", \"VALOR85\", \"CANTIDAD86\",\n",
        "         \"VALOR87\", \"CANTIDAD88\", \"VALOR89\", \"VALOR TOTAL\",\n",
        "         \"CANTIDAD91\", \"VALOR92\", \"CANTIDAD93\", \"VALOR94\",\n",
        "         \"CANTIDAD95\", \"VALOR96\"],\n",
        "        axis='columns',\n",
        "        inplace=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lALGU8Lsj4du"
      },
      "source": [
        "Rename column OTHERS25 to OTHERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMyxVmqLeZfg"
      },
      "source": [
        "####**2001**</br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BINRA1UUj7XY"
      },
      "source": [
        "Rename column DEPARTAMENT to DEPARTMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFcMkrnAkCWP"
      },
      "source": [
        "Drop columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1r_WH5jf17E"
      },
      "source": [
        "df.drop([\"DATE SUPPORT\", \"DATE29\", \"COMMENTS\", \"CANTIDAD31\", \"VALOR32\",\n",
        "         \"_c33\", \"VALOR34\", \"CANTIDAD35\", \"VALOR36\", \"VALOR38\",\n",
        "         \"CANTIDAD37\",\"VALOR40\", \"CANTIDAD39\", \"VALOR42\", \"CANTIDAD41\",\n",
        "         \"VALOR44\", \"CANTIDAD43\", \"VALOR46\", \"CANTIDAD45\", \"VALOR48\",\n",
        "         \"CANTIDAD47\", \"VALOR50\", \"CANTIDAD49\", \"VALOR52\", \"CANTIDAD51\",\n",
        "         \"VALOR54\", \"CANTIDAD53\", \"VALOR56\", \"CANTIDAD55\", \"VALOR58\",\n",
        "         \"CANTIDAD57\", \"VALOR60\", \"CANTIDAD59\", \"VALOR62\", \"CANTIDAD61\",\n",
        "         \"VALOR64\", \"CANTIDAD63\", \"VALOR66\",\"CANTIDAD65\", \"VALOR68\",\n",
        "         \"CANTIDAD67\", \"VALOR70\", \"CANTIDAD69\", \"VALOR72\", \"CANTIDAD71\",\n",
        "         \"VALOR74\", \"CANTIDAD73\", \"VALOR76\", \"CANTIDAD75\", \"VALOR78\",\n",
        "         \"CANTIDAD77\", \"VALOR80\", \"CANTIDAD79\", \"VALOR82\", \"CANTIDAD81\",\n",
        "         \"VALOR84\", \"CANTIDAD83\", \"VALOR86\", \"CANTIDAD85\", \"VALOR88\",\n",
        "         \"CANTIDAD87\", \"VALOR90\", \"TOTAL VALUE\", \"AMOUNT92\", \"VALUE93\",\n",
        "         \"AMOUNT94\", \"VALUE95\", \"AMOUNT96\", \"VALUE97\", \"CANTIDAD89\"],\n",
        "        axis='columns',\n",
        "        inplace=True)\n",
        "df.drop([\"HECTARES\", \"OTHERS20\"],\n",
        "        axis=\"columns\",\n",
        "        inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dq53bPkRj7cF"
      },
      "source": [
        "Rename column OTHERS26 to OTHERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1mQa7Kkj7ek"
      },
      "source": [
        "Rename column DATE0 to DATE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jEJmmrmXeZm3"
      },
      "source": [
        "####**2002**</br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gbyqjmk7kbo3"
      },
      "source": [
        "Drop columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COfek9ligbfT"
      },
      "source": [
        "df.drop([\"DATE SUPPORT\", \"DATE29\", \"COMMENTS\", \"CANTIDAD31\", \"VALOR32\",\n",
        "         \"_c33\", \"VALOR34\", \"CANTIDAD35\", \"VALOR36\", \"VALOR38\",\n",
        "         \"CANTIDAD37\",\"VALOR40\", \"CANTIDAD39\", \"VALOR42\", \"CANTIDAD41\",\n",
        "         \"VALOR44\", \"CANTIDAD43\", \"VALOR46\", \"CANTIDAD45\", \"VALOR48\",\n",
        "         \"CANTIDAD47\", \"VALOR50\", \"CANTIDAD49\", \"VALOR52\", \"CANTIDAD51\",\n",
        "         \"VALOR54\", \"CANTIDAD53\", \"VALOR56\", \"CANTIDAD55\", \"VALOR58\",\n",
        "         \"CANTIDAD57\", \"VALOR60\", \"CANTIDAD59\", \"VALOR62\", \"CANTIDAD61\",\n",
        "         \"VALOR64\", \"CANTIDAD63\", \"VALOR66\",\"CANTIDAD65\", \"VALOR68\",\n",
        "         \"CANTIDAD67\", \"VALOR70\", \"CANTIDAD69\", \"VALOR72\", \"CANTIDAD71\",\n",
        "         \"VALOR74\", \"CANTIDAD73\", \"VALOR76\", \"CANTIDAD75\", \"VALOR78\",\n",
        "         \"CANTIDAD77\", \"VALOR80\", \"CANTIDAD79\", \"VALOR82\", \"CANTIDAD81\",\n",
        "         \"VALOR84\", \"CANTIDAD83\", \"VALOR86\", \"CANTIDAD85\", \"VALOR88\",\n",
        "         \"CANTIDAD87\", \"VALOR90\", \"TOTAL VALUE\", \"AMOUNT92\", \"VALUE93\",\n",
        "         \"AMOUNT94\", \"VALUE95\", \"AMOUNT96\", \"VALUE97\", \"CANTIDAD89\"],\n",
        "        axis='columns',\n",
        "        inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-aUfCeRQkfYv"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taeADexwkg7n"
      },
      "source": [
        "Rename column DATE0 to DATE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1BqqV1qkkIQ"
      },
      "source": [
        "Rename column DEPARTAMENT to DEPARTMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4xCwyhAkkKx"
      },
      "source": [
        "Rename column OTHERS26 to OTHERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7mXpwUakn2K"
      },
      "source": [
        "Drop column OTHERS20"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GXk3YHukrxg"
      },
      "source": [
        "Drop column HECTARES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6avUG9meZup"
      },
      "source": [
        "####**2003**</br>\n",
        "Rename was done using the next pandas code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jsKQ8FEgssG"
      },
      "source": [
        "df.rename(columns={\"DATE0\":\"DATE\", \"DEPARTAMENT\":\"DEPARTMENT\", \"OTHERS26\":\"OTHERS\"},\n",
        "          inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzjmznQ2g-C5"
      },
      "source": [
        "Drop was done using the next pandas code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPeyfbpWhC3G"
      },
      "source": [
        "df.drop([\"REQUEST/INFORMATION\", \"HECTARES\"],\n",
        "        inplace=True,\n",
        "        axis=1)\n",
        "df.drop([\"OTHERS20\", \"DATE SUPPORT\", \"DATE29\", \"COMMENTS\", \"CANTIDAD32\",\n",
        "         \"VALOR33\",\"_c34\", \"VALOR35\", \"CANTIDAD36\", \"VALOR37\", \"CANTIDAD38\",\n",
        "         \"VALOR39\", \"CANTIDAD40\", \"VALOR41\", \"CANTIDAD42\", \"VALOR43\",\n",
        "         \"CANTIDAD44\", \"VALOR45\", \"CANTIDAD46\", \"VALOR47\", \"CANTIDAD48\",\n",
        "         \"VALOR49\", \"CANTIDAD50\", \"VALOR51\", \"CANTIDAD52\", \"VALOR53\",\n",
        "         \"CANTIDAD54\", \"VALOR55\", \"CANTIDAD56\", \"VALOR57\", \"CANTIDAD58\",\n",
        "         \"VALOR59\", \"CANTIDAD60\", \"VALOR61\", \"CANTIDAD62\", \"VALOR63\",\n",
        "         \"CANTIDAD64\", \"VALOR65\",\"CANTIDAD66\", \"VALOR67\", \"CANTIDAD68\",\n",
        "         \"VALOR69\", \"CANTIDAD70\", \"VALOR71\", \"CANTIDAD72\", \"VALOR73\",\n",
        "         \"CANTIDAD74\", \"VALOR75\", \"CANTIDAD76\", \"VALOR77\", \"CANTIDAD78\",\n",
        "         \"VALOR79\", \"CANTIDAD80\", \"VALOR81\", \"CANTIDAD82\", \"VALOR83\",\n",
        "         \"CANTIDAD84\", \"VALOR85\", \"CANTIDAD86\", \"VALOR87\", \"CANTIDAD88\",\n",
        "         \"VALOR89\", \"CANTIDAD90\", \"VALOR91\", \"TOTAL VALUE\", \"AMOUNT93\",\n",
        "         \"VALUE94\", \"AMOUNT95\", \"VALUE96\", \"AMOUNT97\", \"VALUE98\", \"_c99\"],\n",
        "        axis='columns',\n",
        "        inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-PrFdmYeaA7"
      },
      "source": [
        "####**2004**</br>\n",
        "Rename was done using the next pandas code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfGTIr7u1BvV"
      },
      "source": [
        "df.rename(columns={\"DATE0\":\"DATE\", \"DEPARTAMENT\":\"DEPARTMENT\", \"OTHERS26\":\"OTHERS\"}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OU7EJzO8h5nC"
      },
      "source": [
        "The columns to be used were selected with the next SQL sentence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6q3i4hXiA5m"
      },
      "source": [
        "select DATE,\n",
        "       DEPARTMENT,\n",
        "       `CITY / TOWN`,\n",
        "       EVENT,\n",
        "       DEAD, \n",
        "       INJURED,\n",
        "       MISSING, \n",
        "       PERSONS,\n",
        "       FAMILIES, \n",
        "       `DESTROYED HOUSES`,\n",
        "       `DAMAGED HOUSES`,\n",
        "       TRACKS, \n",
        "       `VEHICULAR BRIDGES`,\n",
        "       `PEDESTRIAN BRIDGES`, \n",
        "       AQUEDUCT, SEWERAGE,\n",
        "       `HEALTH CENTER`,\n",
        "       SCHOOL,\n",
        "       `COMMUNITY CENTER`, \n",
        "       MENAGES, \n",
        "       `FOOD SUPPORT`,\n",
        "       `ROOF TILES`, \n",
        "       COATS, \n",
        "       OTHERS, \n",
        "       `ECONOM.`, \n",
        "       `TOTAL AMOUNT`\n",
        "       from df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHs5CrHIpGkR"
      },
      "source": [
        "#### **Concatenated dataframe 1998 - 2004**\n",
        "Once all of these transformation were done, the new data frames were concatenated using SageMaker<br/>\n",
        "After that, there has been some other transformations to unify the structure with Victor and Pablo's structure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hYLlCVLFlpd8"
      },
      "source": [
        "Replace the name of the month with the correspondent number using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEiIjxZNpkaj"
      },
      "source": [
        "for index in df.index:\n",
        "  if len(df.loc[index, \"DATE\"]) == 8:\n",
        "    df.loc[index, \"DATE\"] = \"0\" + df.loc[index, \"DATE\"]\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Jan\", \"01\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Feb\", \"02\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Mar\", \"03\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Apr\", \"04\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"May\", \"05\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Jun\", \"06\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Jul\", \"07\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Aug\", \"08\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Sep\", \"09\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Oct\", \"10\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Nov\", \"11\")\n",
        "    df.loc[index, \"DATE\"]= df.loc[index, \"DATE\"].replace(\"Dec\", \"12\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "px49ZReymAqK"
      },
      "source": [
        "Putting DATE column into format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7K4VwQhp3ls"
      },
      "source": [
        "for index in df.index:\n",
        "  if df.loc[index, \"DATE\"][6] == \"9\":\n",
        "    df.loc[index, \"DATE\"] = df.loc[index,\"DATE\"][:6] + \"19\" + df.loc[index,\"DATE\"][6:]\n",
        "  elif df.loc[index, \"DATE\"][6] != \"9\":\n",
        "    df.loc[index, \"DATE\"] = df.loc[index,\"DATE\"][:6] + \"20\" + df.loc[index,\"DATE\"][6:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7HwDIzMmdTT"
      },
      "source": [
        "Parse column date from string to date using SageMaker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBxTUBzTnuvf"
      },
      "source": [
        "Fix values of some columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iseimXY7qHX8"
      },
      "source": [
        "for index in df.index:\n",
        "  if df.loc[index, \"ROOF TILES\"] is None:\n",
        "    df.loc[index, \"ROOF TILES\"] = \"0\"\n",
        "  if df.loc[index, \"COATS\"] is None:\n",
        "    df.loc[index, \"COATS\"] = \"0\"\n",
        "  if df.loc[index, \"ROOF TILES\"][0] == \"$\":\n",
        "    df.loc[index, \"ROOF TILES\"] = df.loc[index, \"ROOF TILES\"][1:]\n",
        "  if df.loc[index, \"COATS\"][0] == \"$\":\n",
        "    df.loc[index, \"COATS\"] = df.loc[index, \"COATS\"][1:]\n",
        "  df.loc[index, \"ROOF TILES\"] = df.loc[index, \"ROOF TILES\"].replace(\",\", \"\")\n",
        "  df.loc[index, \"COATS\"] = df.loc[index, \"COATS\"].replace(\",\", \"\")\n",
        "  df['SUPPORT SUPPLIES'] = df['ROOF TILES'].astype(float) + df['COATS'].astype(float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBO80L_-nxQI"
      },
      "source": [
        "Drop columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82PNBKRMqcFJ"
      },
      "source": [
        "df.drop([\"ROOF TILES\", \"COATS\"],\n",
        "        axis=\"columns\",\n",
        "        inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDqgsySOmiLn"
      },
      "source": [
        "Rename column ECONOM. to ECONOMIC TRANSFERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vw32Lh3tnifm"
      },
      "source": [
        "Move column SUPPORT SUPPLIES after FOOD SUPPORT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAkdg5t0nrKw"
      },
      "source": [
        "Fix values of some columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AT1fZfzJqiSE"
      },
      "source": [
        "for index in df.index:\n",
        "  if df.loc[index, \"MENAGES\"] is None:\n",
        "    df.loc[index, \"MENAGES\"] = \"0\"\n",
        "  if df.loc[index, \"FOOD SUPPORT\"] is None:\n",
        "    df.loc[index, \"FOOD SUPPORT\"] = \"0\"\n",
        "  if df.loc[index, \"OTHERS\"] is None:\n",
        "    df.loc[index, \"OTHERS\"] = \"0\"\n",
        "  if df.loc[index, \"ECONOMIC TRANSFERS\"] is None:\n",
        "    df.loc[index, \"ECONOMIC TRANSFERS\"] = \"0\"\n",
        "  if df.loc[index, \"TOTAL AMOUNT\"] is None:\n",
        "    df.loc[index, \"TOTAL AMOUNT\"] = \"0\"\n",
        "  df.loc[index, \"MENAGES\"] = df.loc[index, \"MENAGES\"].replace(\"$\", \"\").replace(\",\", \"\")\n",
        "  df.loc[index, \"FOOD SUPPORT\"] = df.loc[index, \"FOOD SUPPORT\"].replace(\"$\", \"\").replace(\",\", \"\")\n",
        "  df.loc[index, \"OTHERS\"] = df.loc[index, \"OTHERS\"].replace(\"$\", \"\").replace(\",\", \"\")\n",
        "  df.loc[index, \"ECONOMIC TRANSFERS\"] = df.loc[index, \"ECONOMIC TRANSFERS\"].replace(\"$\", \"\").replace(\",\", \"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sARLglNwq-vB"
      },
      "source": [
        "for index in df.index:\n",
        "  df.loc[index, \"TOTAL AMOUNT\"] = df.loc[index, \"TOTAL AMOUNT\"].replace(\"$\", \"\").replace(\",\", \"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuCDWJb8oLnI"
      },
      "source": [
        "Parse columns using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMo97hoerDMd"
      },
      "source": [
        "df[\"FOOD SUPPORT\"] = df[\"FOOD SUPPORT\"].astype(float)\n",
        "df[\"OTHERS\"] = df[\"OTHERS\"].astype(float)\n",
        "df[\"MENAGES\"] = df[\"MENAGES\"].astype(float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSxUj-q_rgeI"
      },
      "source": [
        "for index in df.index:\n",
        "  try:\n",
        "    _ = float(df.loc[index, \"ECONOMIC TRANSFERS\"])\n",
        "  except:\n",
        "    df.loc[index, \"ECONOMIC TRANSFERS\"] = \"0\"\n",
        "    df[\"ECONOMIC TRANSFERS\"] = df[\"ECONOMIC TRANSFERS\"].astype(float)    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUpEJep3oWC-"
      },
      "source": [
        "Drop column TOTAL AMOUNT (going to be re-calculated later)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDppN4usogMS"
      },
      "source": [
        "Rename column CITY / TOWN to CITY"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NnvjWvvdDb5w"
      },
      "source": [
        "### **Pablo transformation - 2005 to 2011**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5jWQn05pfT0"
      },
      "source": [
        "#### **Concatenated files from 2005 to 2008 directly**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tzBdhhGJ1M4"
      },
      "source": [
        "Get all the columns needed using the next SQL sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TASr20oRJxuK"
      },
      "source": [
        "select DATE,\n",
        "       DEPARTMENT,\n",
        "       CITY, \n",
        "       EVENT, \n",
        "       DEAD, \n",
        "       INJURED,\n",
        "       MISSING,\n",
        "       PERSONS,\n",
        "       FAMILIES,\n",
        "       `DESTROYED HOUSES`,\n",
        "       `DAMAGED HOUSES`,\n",
        "       TRACKS,\n",
        "       `VEHICULAR BRIDGES`,\n",
        "       `PEDESTRIAN BRIDGES`,\n",
        "       AQUEDUCT,\n",
        "       SEWERAGE,\n",
        "       `HEALTH CENTER`,\n",
        "       SCHOOL,\n",
        "       `COMMUNITY CENTER`,\n",
        "       MENAGES,\n",
        "       `FOOD SUPPORT`, \n",
        "       `ROOF TILES`+ COATS AS `SUPPORT SUPLIES`,\n",
        "       `OTHERS SUPPORTS` as OTHERS,\n",
        "       `ECONOMIC TRANSFERS`,\n",
        "       `TOTAL AMOUNT`\n",
        "       from df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5oZLw9DE4Ca"
      },
      "source": [
        "#### **Concatenated files 2009 and 2011 directly**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uE-aaRt_Kub2"
      },
      "source": [
        "Get all the columns needed using the next SQL sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Lwl7SsrKWyG"
      },
      "source": [
        "select DATE, \n",
        "       DEPARTMENT, \n",
        "       CITY,\n",
        "       EVENT, \n",
        "       DEAD,\n",
        "       INJURED,\n",
        "       MISSING,\n",
        "       PERSONS,\n",
        "       FAMILIES,\n",
        "       `DESTROYED HOUSES`,\n",
        "       `DAMAGED HOUSES`,\n",
        "       TRACKS,\n",
        "       `VEHICULAR BRIDGES`,\n",
        "       `PEDESTRIAN BRIDGES`,\n",
        "       AQUEDUCT,\n",
        "       SEWERAGE,\n",
        "       `HEALTH CENTER`,\n",
        "       SCHOOL,\n",
        "       `COMMUNITY CENTER`,\n",
        "       MENAGES,\n",
        "       `FOOD SUPPORT`,\n",
        "       `CONSTRUCTION SUPPLIES`+COATS AS `SUPPORT SUPLIES`,\n",
        "       `OTHERS SUPPORTS` AS OTHERS,\n",
        "       `ECONOMIC TRANSFERS`,\n",
        "       `TOTAL AMOUNT`\n",
        "       from df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXLjUpS5E4KJ"
      },
      "source": [
        "#### **2010**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_H07jNHGEjN"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',\n",
        "        'MUERTOS': 'DEAD',\n",
        "        'HERIDOS': 'INJURED',\n",
        "        'DESAPA.': 'MISSING',\n",
        "        'PERSONAS': 'PERSONS',\n",
        "        'FAMILIAS': 'FAMILIES',\n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',\n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',\n",
        "        'VIAS': 'TRACKS',\n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',\n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',\n",
        "        'ACUED.': 'AQUEDUCT',\n",
        "        'ALCANT.': 'SEWERAGE',\n",
        "        'C. SALUD': 'HEALTH CENTER',\n",
        "        'C.EDUCAT.': 'SCHOOLS',\n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',\n",
        "        'MENAJES': 'MENAGES',\n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',\n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',\n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',\n",
        "        'OTROS.1': 'OTHERS',\n",
        "        'GIRO DIRECTO': 'ECONOMIC TRANSFERS',\n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',\n",
        "        }\n",
        "df.drop(columns=df.iloc[:, 27:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['DATE'] = '01-01-2010'\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'TOTAL AMOUNT'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJO3bedhL1d3"
      },
      "source": [
        "Parse DATE column from string to date using SageMaker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxZSIs5zMV6Z"
      },
      "source": [
        "Get all the columns needed using the next SQL sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZak5CFEL8n2"
      },
      "source": [
        "select DATE,\n",
        "       DEPTO as DEPARTMENT,\n",
        "       `CITY / TOWN` AS CITY,\n",
        "       EVENT,\n",
        "       DEAD,\n",
        "       INJURED,\n",
        "       MISSING,\n",
        "       PERSONS,\n",
        "       FAMILIES,\n",
        "       `DESTROYED HOUSES`,\n",
        "       `DAMAGED HOUSES`,\n",
        "       TRACKS,\n",
        "       `VEHICULAR BRIDGES`,\n",
        "       `PEDESTRIAN BRIDGES`,\n",
        "       AQUEDUCT,\n",
        "       SEWERAGE,\n",
        "       `HEALTH CENTER`,\n",
        "       SCHOOLS as SCHOOL,\n",
        "       `COMMUNITY CENTER`,\n",
        "       MENAGES,\n",
        "       `FOOD SUPPORT`,\n",
        "       `SUPPORT SUPLIES(ROOF TILES, COATS)` as `SUPPORT SUPLIES`,\n",
        "       OTHERS,\n",
        "       `ECONOMIC TRANSFERS`,\n",
        "       '' as `TOTAL AMOUNT`\n",
        "       from df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IS_upuaDGvRO"
      },
      "source": [
        "#### **Concatenated dataframe 2005 - 2011**\n",
        "Once all of these transformation were done, the new data frames were concatenated using SageMaker, and then adding another transformation to this new dataframe."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahce5300M_1k"
      },
      "source": [
        "Get all the columns needed using the next SQL sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHWqO5QGMvK8"
      },
      "source": [
        "select to_date(DATE) as DATE,\n",
        "       DEPARTMENT,\n",
        "       CITY,\n",
        "       EVENT,\n",
        "       DEAD,\n",
        "       INJURED,\n",
        "       MISSING,\n",
        "       PERSONS,\n",
        "       FAMILIES,\n",
        "       `DESTROYED HOUSES`,\n",
        "       `DAMAGED HOUSES`,\n",
        "       TRACKS,\n",
        "       `VEHICULAR BRIDGES`,\n",
        "       `PEDESTRIAN BRIDGES`,\n",
        "       AQUEDUCT,\n",
        "       SEWERAGE,\n",
        "       `HEALTH CENTER`,\n",
        "       SCHOOL,\n",
        "       `COMMUNITY CENTER`,\n",
        "       MENAGES,\n",
        "       `FOOD SUPPORT`,\n",
        "       `SUPPORT SUPLIES`,\n",
        "       OTHERS,\n",
        "       `ECONOMIC TRANSFERS`,\n",
        "       `TOTAL AMOUNT`\n",
        "       FROM DF"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyC3UhNfNB2K"
      },
      "source": [
        "Drop column TOTAL AMOUNT because is going to be re calculated later"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aV8DmO9aNKMh"
      },
      "source": [
        "Rename column SUPPORT SUPLIES to SUPPORT SUPPLIES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvxGvIMANT99"
      },
      "source": [
        "Fill NaN values from columns OTHERS and ECONOMIC TRANSFERS with 0, then parse them from string to float"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4IZ1NDNNN5X"
      },
      "source": [
        "df['OTHERS'][df['OTHERS'].isnull()] = '0'\n",
        "df['ECONOMIC TRANSFERS'][df['ECONOMIC TRANSFERS'].isnull()] = '0'\n",
        "df['OTHERS'] = df['OTHERS'].astype(float)\n",
        "df['ECONOMIC TRANSFERS'] = df['ECONOMIC TRANSFERS'].astype(float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lv4RiBgKDoQc"
      },
      "source": [
        "### **Victor transformation - 2012 to 2019**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9JDqJuIDnWJ"
      },
      "source": [
        "#### **2012**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zSy1IjhEnMY"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',\n",
        "        'MUERTOS': 'DEAD',\n",
        "        'HERIDOS': 'INJURED',\n",
        "        'DESAPA.': 'MISSING',\n",
        "        'PERSONAS': 'PERSONS',\n",
        "        'FAMILIAS': 'FAMILIES',\n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',\n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',\n",
        "        'VIAS': 'TRACKS',\n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',\n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',\n",
        "        'ACUED.': 'AQUEDUCT',\n",
        "        'ALCANT.': 'SEWERAGE',\n",
        "        'C. SALUD': 'HEALTH CENTER',\n",
        "        'C.EDUCAT.': 'SCHOOLS',\n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',\n",
        "        'MENAJES': 'MENAGES',\n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',\n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',\n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',\n",
        "        'OTROS.1': 'OTHERS',\n",
        "        'GIROS DIRECTOS': 'ECONOMIC TRANSFERS',\n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',\n",
        "        }\n",
        "df.drop(columns=df.iloc[:, 29:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS', 'FECHA TRAMITE '], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['MENAGES'].fillna(0)\n",
        "df['FOOD SUPPORT'].fillna(0)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0)\n",
        "df['OTHERS'].astype(float).fillna(0)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0)\n",
        "df['TOTAL AMOUNT'].fillna(0)\n",
        "df['OTHERS'] = df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoUuMm1wEpWQ"
      },
      "source": [
        "#### **2013**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbMbtFFxFEfb"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'OTROS.1': 'OTHERS',        \n",
        "        'TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 29:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2BForZdEp6T"
      },
      "source": [
        "#### **2014**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZskPwar7FE3h"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'OTROS.1': 'OTHERS',        \n",
        "        'TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 29:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueUd6JcFEp9D"
      },
      "source": [
        "#### **2015**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrJa_6JCFFQ2"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'OTROS.1': 'OTHERS',        \n",
        "        'TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 29:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHyA0PrcEqBQ"
      },
      "source": [
        "#### **2016**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yL75-H6kFFqk"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'OTROS.1': 'OTHERS',        \n",
        "        'TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 29:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11Oh_69FEqD6"
      },
      "source": [
        "#### **2017**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vVJwMfsFGFY"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'OTROS.1': 'OTHERS',        \n",
        "        'TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 29:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRoabAqXEqMo"
      },
      "source": [
        "#### **2018**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UB7dwkApFGjs"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS - BIGBAG': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'SOPORTE AEREO': 'AIR SUPPORT',        \n",
        "        'CARROTANQUES - MOTOBOMBAS-PLANTA POTABILIZADORA': 'TANKS - MOTOR PUMPS-POTABILIZATION PLANT',        \n",
        "        'HORAS MAQUINA RETROEXCAVADORA BULLDOCER INTERVENTORIA': 'INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS',        \n",
        "        'OTROS.1': 'OTHERS',        \n",
        "        'TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 32:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS', 'OTROS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['AIR SUPPORT'].fillna(0, inplace=True)\n",
        "df['TANKS - MOTOR PUMPS-POTABILIZATION PLANT'].fillna(0, inplace=True)\n",
        "df['INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['AIR SUPPORT'] + df['TANKS - MOTOR PUMPS-POTABILIZATION PLANT'] + df['INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS'] + df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY', 'AIR SUPPORT', 'TANKS - MOTOR PUMPS-POTABILIZATION PLANT', 'INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CN-bm9dxEqkl"
      },
      "source": [
        "#### **2019**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG8q03RxFHKk"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "cols = {'FECHA': 'DATE',\n",
        "        'DEPARTAMENTO': 'DEPARTAMENT',\n",
        "        'MUNICIPIO': 'CITY / TOWN',\n",
        "        'EVENTO': 'EVENT',        \n",
        "        'MUERTOS': 'DEAD',        \n",
        "        'HERIDOS': 'INJURED',        \n",
        "        'DESAPA.': 'MISSING',        \n",
        "        'PERSONAS': 'PERSONS',        \n",
        "        'FAMILIAS': 'FAMILIES',        \n",
        "        'VIV.DESTRU.': 'DESTROYED HOUSES',        \n",
        "        'VIV.AVER.': 'DAMAGED HOUSES',        \n",
        "        'VIAS': 'TRACKS',        \n",
        "        'PTES.VEHIC.': 'VEHICULAR BRIDGES',        \n",
        "        'PTES.PEAT.': 'PEDESTRIAN BRIDGES',        \n",
        "        'ACUED.': 'AQUEDUCT',        \n",
        "        'ALCANT.': 'SEWERAGE',        \n",
        "        'C. SALUD': 'HEALTH CENTER',        \n",
        "        'C.EDUCAT.': 'SCHOOLS',        \n",
        "        'C.COMUNIT.': 'COMMUNITY CENTER',        \n",
        "        'SUBSIDIO DE ARRIENDO': 'RENTAL SUBSIDY',        \n",
        "        'MENAJES': 'MENAGES',        \n",
        "        'AP.ALIMENT.': 'FOOD SUPPORT',        \n",
        "        'MATERIALES CONSTRUCCION': 'CONSTRUCTION MATERIALS',        \n",
        "        'SACOS - BIGBAG': 'SUPPORT SUPLIES(ROOF TILES, COATS)',        \n",
        "        'APOYO AEREO / TERRESTRE': 'AIR SUPPORT',        \n",
        "        'CARROTANQUES - MOTOBOMBAS-PLANTA POTABILIZADORA': 'TANKS - MOTOR PUMPS-POTABILIZATION PLANT',        \n",
        "        'HORAS MAQUINA RETROEXCAVADORA BULLDOCER INTERVENTORIA': 'INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS',        \n",
        "        'OTROS.1': 'OTHERS',\n",
        "        'INFRAESCTRUCTURA TECNOLOGICA': 'TECHNOLOGICAL INFRASTRUCTURE',\n",
        "        'OBRAS DE EMERGENCIA': 'EMERGENCY WORKS',\n",
        "        'ASISTENCIA NO ALIMENTARIA': 'MENAGES',\n",
        "        'FIC / TRANSFERENCIAS ECONOMICAS': 'ECONOMIC TRANSFERS',        \n",
        "        'RECURSOS EJECUTADOS': 'TOTAL AMOUNT',        \n",
        "}\n",
        "df.drop(columns=df.iloc[:, 33:], inplace=True)\n",
        "df.drop(columns=['HECTAREAS'], inplace=True)\n",
        "df.rename(cols, axis='columns', inplace=True)\n",
        "df['RENTAL SUBSIDY'].fillna(0, inplace=True)\n",
        "df['MENAGES'].fillna(0, inplace=True)\n",
        "df['FOOD SUPPORT'].fillna(0, inplace=True)\n",
        "df['CONSTRUCTION MATERIALS'].fillna(0, inplace=True)\n",
        "df['SUPPORT SUPLIES(ROOF TILES, COATS)'].fillna(0, inplace=True)\n",
        "df['OTHERS'].fillna(0, inplace=True)\n",
        "df['EMERGENCY WORKS'].fillna(0, inplace=True)\n",
        "df['TECHNOLOGICAL INFRASTRUCTURE'].fillna(0, inplace=True)\n",
        "df['TANKS - MOTOR PUMPS-POTABILIZATION PLANT'].fillna(0, inplace=True)\n",
        "df['INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS'].fillna(0, inplace=True)\n",
        "df['ECONOMIC TRANSFERS'].fillna(0, inplace=True)\n",
        "df['TOTAL AMOUNT'].fillna(0, inplace=True)\n",
        "df['OTHERS'] = df['AIR SUPPORT'] + df['TANKS - MOTOR PUMPS-POTABILIZATION PLANT'] + df['INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS'] + df['RENTAL SUBSIDY'] + df['CONSTRUCTION MATERIALS'] + df['OTHERS']\n",
        "df['OTHERS'] = df['OTHERS'] + df['EMERGENCY WORKS'] + df['TECHNOLOGICAL INFRASTRUCTURE']\n",
        "df.drop(columns=['CONSTRUCTION MATERIALS', 'RENTAL SUBSIDY', 'AIR SUPPORT', 'TANKS - MOTOR PUMPS-POTABILIZATION PLANT', 'INTERVENTORY BULLDOCER BACKHOE MACHINE HOURS'], inplace=True)\n",
        "df.drop(columns=['TECHNOLOGICAL INFRASTRUCTURE', 'EMERGENCY WORKS'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z25Yh0qeHCSR"
      },
      "source": [
        "#### **Concatenated dataframe 2012 - 2019**\n",
        "Once all of these transformation were done, the new data frames were concatenated using SageMaker, and then adding another transformation to this new dataframe."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbK7usCQHrUU"
      },
      "source": [
        "Fix some values in column DATE and give the format to all the column dd-mm-yyyy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAEEuPs7HVvn"
      },
      "source": [
        "df['DATE'].iloc[[25570, 25571, 25572, 25573, 25944, 25945, 25988, 25989, 25990, 25991, 25992]] = '2018-01-01'\n",
        "x = df['DATE'].iloc[25876].split('/')\n",
        "df['DATE'].iloc[25876] = '{}-{}-{}8'.format(x[2], x[1], x[0])\n",
        "x = df['DATE'].iloc[26081].split('/')\n",
        "df['DATE'].iloc[26081] = '{}-{}-{}'.format(x[2], x[1], x[0])\n",
        "df['DATE'] = [x.split(' ')[0] for x in df['DATE']]\n",
        "df['DATE'] = ['{}-{}-{}'.format(x.split('-')[2], x.split('-')[1], x.split('-')[0]) for x in df['DATE']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6JtiKs_ICjl"
      },
      "source": [
        "Parse column DATE from string to datetime using SageMaker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgaPM_ucIl1Q"
      },
      "source": [
        "Drop column TOTAL AMOUNT because is going to be re calculated later"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfbFoXIWIvHS"
      },
      "source": [
        "Rename column CITY / TOWN to CITY"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Al4d9EnsIvXo"
      },
      "source": [
        "Rename column SUPPORT SUPLIES to SUPPORT SUPPLIES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3V6S2G0-I8Wa"
      },
      "source": [
        "Rename column DEPARTAMENT to DEPARTMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRvHjeXaJDrJ"
      },
      "source": [
        "Rename column SCHOOLS to SCHOOL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UtEmqjWDo62t"
      },
      "source": [
        "### **New dataframe with data from 1998 to 2019**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7GTAa_9qYuk"
      },
      "source": [
        "Handle missing using SageMaker for the next columns:</br>\n",
        "DEAD, INJURED, MISSING, PERSONS, FAMILIES, DESTROYED HOUSES, DAMAGED HOUSES, TRACKS, VEHICULAR BRIDGES, PEDESTRIAN BRIDGES, AQUEDUCT, SEWERAGE, HEALTH CENTER, SCHOOL, COMMUNITY CENTER"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RL4OmL6Vq-dA"
      },
      "source": [
        "Fix values in columns to remove '.' and ','</br>\n",
        "Check that values in a column are only numbers to finally convert the column to integer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTzpIlLyrAWN"
      },
      "source": [
        "import pandas as pd\n",
        "def check_rows(column):\n",
        "  \"\"\" Remove special character from values in a column\n",
        "      and let them with only numbers\n",
        "      Return: column parsed to integer\n",
        "  \"\"\"\n",
        "  for i, x in enumerate(df[column]):\n",
        "    if ',' in x:\n",
        "      df[column][i] = x.replace(',', '')\n",
        "    if '.' in x:\n",
        "      df[column][i] = x.replace('.', '')\n",
        "    if not x.isdecimal():\n",
        "      df[column][i] = '0'\n",
        "  return pd.to_numeric(df[column], downcast='integer')\n",
        "df['DEAD'] = check_rows('DEAD')\n",
        "df['INJURED'] = check_rows('INJURED')\n",
        "df['MISSING'] = check_rows('MISSING')\n",
        "df['PERSONS'] = check_rows('PERSONS')\n",
        "df['FAMILIES'] = check_rows('FAMILIES')\n",
        "df['DESTROYED HOUSES'] = check_rows('DESTROYED HOUSES')\n",
        "df['DAMAGED HOUSES'] = check_rows('DAMAGED HOUSES')\n",
        "df['TRACKS'] = check_rows('TRACKS')\n",
        "df['VEHICULAR BRIDGES'] = check_rows('VEHICULAR BRIDGES')\n",
        "df['PEDESTRIAN BRIDGES'] = check_rows('PEDESTRIAN BRIDGES')\n",
        "df['AQUEDUCT'] = check_rows('AQUEDUCT')\n",
        "df['SEWERAGE'] = check_rows('SEWERAGE')\n",
        "df['HEALTH CENTER'] = check_rows('HEALTH CENTER')\n",
        "df['SCHOOL'] = check_rows('SCHOOL')\n",
        "df['COMMUNITY CENTER'] = check_rows('COMMUNITY CENTER')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KVJGnz3sONe"
      },
      "source": [
        "Remove rows where date is null</br>\n",
        "Create new columns YEAR, MONTH and DAY from DATE column</br>\n",
        "Using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hL38DrLfsM7N"
      },
      "source": [
        "rows = list(df['DATE'][df['DATE'].isnull()].index)\n",
        "df.drop(rows, axis=0, inplace=True)\n",
        "new_df = df['DATE'].astype(str)\n",
        "df['YEAR'] = 0\n",
        "df['MONTH'] = 0\n",
        "df['DAY'] = 0\n",
        "for i, x in enumerate(new_df):\n",
        "  date = x.split('-')\n",
        "  df['YEAR'][i] = int(date[0])\n",
        "  df['MONTH'][i] = int(date[1])\n",
        "  df['DAY'][i] = int(date[2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5e0KvRJsv_e"
      },
      "source": [
        "Move column YEAR after DATE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUaB7dY3stsy"
      },
      "source": [
        "Move column MONTH after YEAR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRr70AvRs1Eu"
      },
      "source": [
        "Move column DAY after MONTH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0fBm5Bys8T0"
      },
      "source": [
        "Calculate and create the column TOTAL AMOUNT using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l66L3sOZs7X1"
      },
      "source": [
        "df['TOTAL AMOUNT'] = df['MENAGES'] + df['FOOD SUPPORT'] + df['SUPPORT SUPPLIES'] + df['OTHERS'] + df['ECONOMIC TRANSFERS']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKNS3n9ftF-0"
      },
      "source": [
        "Remove left and right spaces from column EVENT using SageMaker Format string"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQi_YFFetVPF"
      },
      "source": [
        "Drop rows where event is null using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClLHJ27UtPDp"
      },
      "source": [
        "rows = list(df['EVENT'][df['EVENT'].isnull()].index)\n",
        "df.drop(rows, axis=0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLne8YQ7tujJ"
      },
      "source": [
        "Normalize values removing special characters like \"ñ\" using python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQug2S1Ftmqa"
      },
      "source": [
        "def simplify(text):\n",
        "\timport unicodedata\n",
        "\ttry:\n",
        "\t\ttext = unicode(text, 'utf-8')\n",
        "\texcept NameError:\n",
        "\t\tpass\n",
        "\ttext = unicodedata.normalize('NFD', text).encode('ascii', 'ignore').decode(\"utf-8\")\n",
        "\treturn str(text)\n",
        "\n",
        "for i, x in enumerate(df['EVENT']):\n",
        "  df['EVENT'][i] = simplify(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypS9AeJwuELD"
      },
      "source": [
        "Fix values in EVENT columns and translate them to Spanish"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9ja94w2uCA8"
      },
      "source": [
        "right_events = {'STRUCTURAL FIRE': 'FUEGO ESTRUCTURAL',\n",
        "                'FROST': 'HELADA',\n",
        "                'ERUPTION': 'ERUPCION',\n",
        "                'STORM-STRUCTURAL FIRE': 'FUEGO ESTRUCTURAL DE TORMENTA',\n",
        "                'CRASH ACCIDENT': 'CHOQUE',\n",
        "                'STORM': 'TORMENTA',\n",
        "                'DROUGHT/FOREST FIRE': 'SEQUÍA / INCENDIO FORESTAL',\n",
        "                'EROYESON': 'EROSION',\n",
        "                'EXPLOTION': 'EXPLOSION',\n",
        "                'MAR DELEVA': 'MAR DE LEVA',\n",
        "                'GLIDE': 'DESLIZAMIENTO',\n",
        "                'MAREJADAS': 'MAREJADA',\n",
        "                'ELECTRIC STORM': 'TORMENTA ELECTRICA',\n",
        "                'EPIDEMIC': 'EPIDEMIA',\n",
        "                'EROSION-STRUCTURAL FIRE': 'FUEGO ESTRUCTURAL POR EROSIÓN',\n",
        "                'DROUGHT': 'SEQUIA',\n",
        "                'STRUCTURAL INCIDENT': 'INCIDENTE ESTRUCTURAL',\n",
        "                'SEA OF LEVA': 'MAR DE LEVA',\n",
        "                'DAMMING': 'EMBALSE',\n",
        "                'STRUCTURAL COLLAPSE': 'COLAPSO ESTRUCTURAL',\n",
        "                'VOLCANO EVENT': 'EVENTO VOLCANICO',\n",
        "                'AVALANCHE': 'AVALANCHA',\n",
        "                'FLOOD': 'INUNDACION',\n",
        "                'CONTAMINATION': 'CONTAMINACION',\n",
        "                'HAILSTORM': 'GRANIZADA',\n",
        "                'GALE': 'VENDAVAL',\n",
        "                'SWELLS': 'MAREJADA',\n",
        "                'VARIOUS': 'VARIOS',\n",
        "                'EROTION': 'EROSION',\n",
        "                'SEA OF LEADS': 'MAR DE PLOMOS',\n",
        "                'COLLAPSE': 'COLAPSO ESTRUCTURAL',\n",
        "                'COLAPSO': 'COLAPSO ESTRUCTURAL',\n",
        "                'SEISM': 'SISMO',\n",
        "                'POLLUTION': 'POLUCION',\n",
        "                'FOREST FIRE': 'INCENDIO FORESTAL',\n",
        "                'EARTHQUAKE': 'TERREMOTO',\n",
        "                'VOLCANIC ERUPTION': 'ERUPCION VOLCANICA',\n",
        "                'AIRCRAFT ACCIDENT': 'ACCIDENTE DE AVIACION',\n",
        "                'ACCIDENT': 'ACCIDENTE',\n",
        "                'CRECIENTE SUBITA': 'CRECIENTE',\n",
        "               }\n",
        "for i, x in enumerate(df['EVENT']):\n",
        "  if x == '0' or x == 'AR' or x == 'YESSMO':\n",
        "    df.drop(i, axis=0, inplace=True)\n",
        "    continue\n",
        "  if x in right_events.keys():\n",
        "    x = right_events[x]\n",
        "  if 'ERUPCION' in x:\n",
        "    x = 'ERUPCION'\n",
        "    df['EVENT'][i] = x\n",
        "  x = x.upper()\n",
        "  if '.' in x:\n",
        "    x = x.replace('.', ' ')\n",
        "  if 'INC' in x and\\\n",
        "    not 'INCENDIO' in x and\\\n",
        "    not 'INCIDENT' in x:\n",
        "    x = x.replace('INC', 'INCENDIO')\n",
        "  df['EVENT'][i] = x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yEDsuLKuZI4"
      },
      "source": [
        "Remove left and right spaces from column DEPARTMENT using SageMaker Format string"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joWlpJiXuchM"
      },
      "source": [
        "Remove left and right spaces from column CITY using SageMaker Format string"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5F24EbXGum0M"
      },
      "source": [
        "Fix values for DEPARTMENT column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDeQFnyWuh2e"
      },
      "source": [
        "for i, x in enumerate(df['DEPARTMENT']):\n",
        "  x = x.upper()\n",
        "  if '_' in x:\n",
        "    x = x.replace('_', ' ')\n",
        "  if x == 'VALLE':\n",
        "    x = 'VALLE DEL CAUCA'\n",
        "  if x == 'SAN ANDRES':\n",
        "    x = 'SAN ANDRES ISLAS'\n",
        "  df['DEPARTMENT'][i] = x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivFsIFJfvN61"
      },
      "source": [
        "Normalize values removing special characters like \"ñ\" using python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFHoi4TDu1SM"
      },
      "source": [
        "def simplify(text):\n",
        "\timport unicodedata\n",
        "\ttry:\n",
        "\t\ttext = unicode(text, 'utf-8')\n",
        "\texcept NameError:\n",
        "\t\tpass\n",
        "\ttext = unicodedata.normalize('NFD', text).encode('ascii', 'ignore').decode(\"utf-8\")\n",
        "\treturn str(text)\n",
        "\n",
        "for i, x in enumerate(df['CITY']):\n",
        "  if x == None:\n",
        "    df.drop(i, axis=0, inplace=True)\n",
        "    continue\n",
        "  df['CITY'][i] = simplify(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50VBYN3OvYa9"
      },
      "source": [
        "Fix null values from OTHERS column and recalculate values for TOTAL AMOUNT column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbh37d16vXoq"
      },
      "source": [
        "df['OTHERS'][df['OTHERS'].isnull()] = 0\n",
        "df['TOTAL AMOUNT'] = df['MENAGES'] + df['FOOD SUPPORT'] + df['SUPPORT SUPPLIES'] + df['OTHERS'] + df['ECONOMIC TRANSFERS']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7ER51SFv1yL"
      },
      "source": [
        "Remake the values of columns YEAR, MONTH and DAY because some of them were corrupted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmO1cX5Vvw6M"
      },
      "source": [
        "new_df = df['DATE'].astype(str)\n",
        "for i, x in enumerate(new_df):\n",
        "  date = x.split('-')\n",
        "  df['YEAR'][i] = int(date[0])\n",
        "  df['MONTH'][i] = int(date[1])\n",
        "  df['DAY'][i] = int(date[2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCX5tFmvwJN6"
      },
      "source": [
        "Fix cities from 'Valle del Cauca' department"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHsvPNWVwISf"
      },
      "source": [
        "cities = {'BUGA LA GRANDE': 'BUGALAGRANDE',\n",
        "          'CALIMA': 'CALIMA (DARIEN)',\n",
        "          'CALIMA - DARIEN': 'CALIMA (DARIEN)',\n",
        "          'CALIMA DARIEN': 'CALIMA (DARIEN)',\n",
        "          'CALIMA EL DARIEN': 'CALIMA (DARIEN)',\n",
        "          'CALIMA-DARIEN': 'CALIMA (DARIEN)',\n",
        "          'EL DARIEN': 'CALIMA (DARIEN)',\n",
        "          'CERRIT0': 'EL CERRITO',\n",
        "          'CERRITO': 'EL CERRITO',\n",
        "          'GUADALAJARA DE BUGA': 'BUGA',\n",
        "          'JUMBO': 'YUMBO',\n",
        "          'OVANDO': 'OBANDO',\n",
        "          'VARIOUS': 'VARIOS',\n",
        "         }\n",
        "for i, x in enumerate(df['DEPARTMENT']):\n",
        "  if df['CITY'][i] == 'DEPARTAMENTO':\n",
        "    df.drop(i, axis=0, inplace=True)\n",
        "    continue\n",
        "  if x == 'VALLE DEL CAUCA':\n",
        "    if df['CITY'][i] in cities.keys():\n",
        "      df['CITY'][i] = cities[df['CITY'][i]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfY3PvsWwZqQ"
      },
      "source": [
        "**Code to move the dataframe to S3 from SageMaker in order to download the dataframe as csv**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPS3i_PqwZCO"
      },
      "source": [
        "from io import StringIO # python3; python2: BytesIO \n",
        "import boto3\n",
        "def _write_dataframe_to_csv_on_s3(DESTINATION, dataframe, filename):\n",
        "    \"\"\" Write a dataframe to a CSV on S3 \"\"\"\n",
        "    print(\n",
        "        \"Writing {} records to '{}' in bucket '{}'.\".format(\n",
        "            len(dataframe), filename, DESTINATION\n",
        "        )\n",
        "    )\n",
        "    # Create buffer\n",
        "    csv_buffer = StringIO()\n",
        "    # Write dataframe to buffer\n",
        "    dataframe.to_csv(csv_buffer, sep=\",\", index=False)\n",
        "    # Create S3 object\n",
        "    s3_resource = boto3.resource(\"s3\")\n",
        "    # Write buffer to S3 object\n",
        "    s3_resource.Object(DESTINATION, filename).put(Body=csv_buffer.getvalue())\n",
        "_write_dataframe_to_csv_on_s3('sagemaker-us-east-2-710303918543', df, 'data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7146kiB901hU"
      },
      "source": [
        "## **Data Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYr_0gtGgYl-"
      },
      "source": [
        "### **Read files**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exUWd46XgYHJ"
      },
      "source": [
        "dfevent = pd.read_csv(file_dead_by_event)\n",
        "df = pd.read_csv(path_file)\n",
        "dfevent_dep_year = pd.read_csv(file_event_by_dep_year)\n",
        "dfevent_dep_month = pd.read_csv(file_event_by_dep_month)\n",
        "dfevent_city_year = pd.read_csv(file_event_by_city_year)\n",
        "dfevent_city_month = pd.read_csv(file_event_by_city_month)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4_Pu5gHI_SQ"
      },
      "source": [
        "### **DataFrame summary**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpeehsEpUbKf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "45095fcb-d2c6-46e5-fd00-cdc759b9f362"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>DAY</th>\n",
              "      <th>DEAD</th>\n",
              "      <th>INJURED</th>\n",
              "      <th>MISSING</th>\n",
              "      <th>PERSONS</th>\n",
              "      <th>FAMILIES</th>\n",
              "      <th>DESTROYED HOUSES</th>\n",
              "      <th>DAMAGED HOUSES</th>\n",
              "      <th>TRACKS</th>\n",
              "      <th>VEHICULAR BRIDGES</th>\n",
              "      <th>PEDESTRIAN BRIDGES</th>\n",
              "      <th>AQUEDUCT</th>\n",
              "      <th>SEWERAGE</th>\n",
              "      <th>HEALTH CENTER</th>\n",
              "      <th>SCHOOL</th>\n",
              "      <th>COMMUNITY CENTER</th>\n",
              "      <th>MENAGES</th>\n",
              "      <th>FOOD SUPPORT</th>\n",
              "      <th>SUPPORT SUPPLIES</th>\n",
              "      <th>OTHERS</th>\n",
              "      <th>ECONOMIC TRANSFERS</th>\n",
              "      <th>TOTAL AMOUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.000000</td>\n",
              "      <td>45802.00000</td>\n",
              "      <td>4.580200e+04</td>\n",
              "      <td>4.580200e+04</td>\n",
              "      <td>4.580200e+04</td>\n",
              "      <td>4.580200e+04</td>\n",
              "      <td>4.580200e+04</td>\n",
              "      <td>4.580200e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2012.503144</td>\n",
              "      <td>5.959609</td>\n",
              "      <td>14.902734</td>\n",
              "      <td>0.084669</td>\n",
              "      <td>0.134077</td>\n",
              "      <td>0.008187</td>\n",
              "      <td>173.984608</td>\n",
              "      <td>19.098380</td>\n",
              "      <td>0.600934</td>\n",
              "      <td>11.272565</td>\n",
              "      <td>0.073119</td>\n",
              "      <td>0.008406</td>\n",
              "      <td>0.014366</td>\n",
              "      <td>0.024016</td>\n",
              "      <td>0.001768</td>\n",
              "      <td>0.001856</td>\n",
              "      <td>0.026636</td>\n",
              "      <td>0.00941</td>\n",
              "      <td>4.290663e+06</td>\n",
              "      <td>4.212859e+06</td>\n",
              "      <td>6.879935e+05</td>\n",
              "      <td>7.790341e+06</td>\n",
              "      <td>3.950921e+06</td>\n",
              "      <td>2.093278e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.183091</td>\n",
              "      <td>3.480793</td>\n",
              "      <td>9.245370</td>\n",
              "      <td>1.064087</td>\n",
              "      <td>2.840381</td>\n",
              "      <td>0.293222</td>\n",
              "      <td>1650.353156</td>\n",
              "      <td>174.615401</td>\n",
              "      <td>8.978960</td>\n",
              "      <td>134.174096</td>\n",
              "      <td>1.130472</td>\n",
              "      <td>0.208955</td>\n",
              "      <td>0.406508</td>\n",
              "      <td>0.529837</td>\n",
              "      <td>0.083959</td>\n",
              "      <td>0.052625</td>\n",
              "      <td>0.401997</td>\n",
              "      <td>0.21691</td>\n",
              "      <td>4.886292e+07</td>\n",
              "      <td>4.336869e+07</td>\n",
              "      <td>8.999221e+06</td>\n",
              "      <td>3.126398e+08</td>\n",
              "      <td>5.080348e+07</td>\n",
              "      <td>3.740825e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1998.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2010.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>398.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>208000.000000</td>\n",
              "      <td>26000.000000</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>8592.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>19.00000</td>\n",
              "      <td>4.558554e+09</td>\n",
              "      <td>5.176118e+09</td>\n",
              "      <td>1.058436e+09</td>\n",
              "      <td>5.991488e+10</td>\n",
              "      <td>3.450680e+09</td>\n",
              "      <td>7.084852e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               YEAR         MONTH  ...  ECONOMIC TRANSFERS  TOTAL AMOUNT\n",
              "count  45802.000000  45802.000000  ...        4.580200e+04  4.580200e+04\n",
              "mean    2012.503144      5.959609  ...        3.950921e+06  2.093278e+07\n",
              "std        5.183091      3.480793  ...        5.080348e+07  3.740825e+08\n",
              "min     1998.000000      1.000000  ...        0.000000e+00  0.000000e+00\n",
              "25%     2010.000000      3.000000  ...        0.000000e+00  0.000000e+00\n",
              "50%     2013.000000      6.000000  ...        0.000000e+00  0.000000e+00\n",
              "75%     2016.000000      9.000000  ...        0.000000e+00  0.000000e+00\n",
              "max     2019.000000     12.000000  ...        3.450680e+09  7.084852e+10\n",
              "\n",
              "[8 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZc-VlHPzHcp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "c3d45b27-327b-4b4a-d6fe-2771224bef97"
      },
      "source": [
        "dead = [df['DEAD'].values]\n",
        "sns.displot(dead)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f7c3090db50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 402.25x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3HuSm1-lZHY"
      },
      "source": [
        "### **Dead by Event**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snBeYfwwk9F1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "32556ea3-d2d9-4c24-a973-c2eb3844163b"
      },
      "source": [
        "plt.figure(figsize=(50,50))\n",
        "events=sns.load_dataset=df\n",
        "sns.barplot(x='EVENT', y='DEAD', data=df)\n",
        "plt.title(\"Dead by Event\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Dead by Event')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 3600x3600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdcvAtdklmM-"
      },
      "source": [
        "### **Invest by year**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWl72-Iyp94D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6650ac01-f105-423a-ca5f-6013f8d225d7"
      },
      "source": [
        "plt.figure(figsize=(30,30))\n",
        "events=sns.load_dataset=df\n",
        "sns.barplot(x='YEAR', y='TOTAL AMOUNT', data=df)\n",
        "plt.title(\"INVEST BY YEAR\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'INVEST BY YEAR')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypbPjYJ1ltBk"
      },
      "source": [
        "### **Invest by event**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9sgaKNpEqngt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "outputId": "a4df2039-ce4f-4644-dd1c-abad32dd83ba"
      },
      "source": [
        "plt.figure(figsize=(50,30))\n",
        "events=sns.load_dataset=df\n",
        "sns.barplot(x='EVENT', y='TOTAL AMOUNT', data=df)\n",
        "plt.title(\"INVEST BY EVENT\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'INVEST BY EVENT')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 3600x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39b9hf8BlyIB"
      },
      "source": [
        "### Heat map of correlation of features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3osTJuJlObK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "eebde5d8-8a69-4fed-98ef-96e0202bd458"
      },
      "source": [
        "correlation_matrix = df.corr()\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "sns.heatmap(correlation_matrix,square = True,cmap=\"YlGnBu\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "4c8vf1rxX1KJ",
        "outputId": "8dd6622d-d00e-422e-b34f-65a181fe4990"
      },
      "source": [
        "sns.set()\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "events = sns.load_dataset=dfevent\n",
        "events = events.pivot(\"EVENT\",\"YEAR\" ,\"DEAD\")\n",
        "ax = sns.heatmap(events,cmap=\"YlGnBu\" )\n",
        "plt.title(\"DEAD BY EVENT\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "8SzOozAmmS6U",
        "outputId": "b655d7ed-e2be-4938-f924-0e9b7ec8678f"
      },
      "source": [
        "sns.set()\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "events = sns.load_dataset=dfevent_dep_year\n",
        "events = events.pivot(\"DEPARTMENT\" ,\"YEAR\", \"EVENTS\")\n",
        "ax = sns.heatmap(events,cmap=\"YlGnBu\" )\n",
        "plt.title(\"EVENT BY DEPARTMENT\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "uAbZimdAmvnR",
        "outputId": "23bacaac-f2cf-4d39-b649-9bb85e5824e7"
      },
      "source": [
        "sns.set()\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "events = sns.load_dataset = dfevent_dep_month\n",
        "events = events.pivot(\"DEPARTMENT\" ,\"MONTH\", \"EVENTS\")\n",
        "ax = sns.heatmap(events,cmap=\"YlGnBu\" )\n",
        "plt.title(\"EVENT BY DEPARTMENT\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9+1mwYAFeXl5YrVbat2/P66+/XrGbI4QQQggh7ku9evXKnXX2lx555BHOnTvHf/7zH37zm99w8uRJLl26REBAAIGBgRiNRrRaLUajkcDAQBo2bAhwx33lUdmdGeAghBuz2t1rggAPldrVIVR7slL3g+GO99nd2OwWV4fgNJXKvX5Ldcf3sTv+vXBHKko/Rv+g1fvNy5XexpX/rKnwsZs2bWL16tXKUzwJCQn07duXkydPMnXqVK5cuUK9evVYsGABv/nNbwDuuK88ksiIGuPD3G2uDsFpTzQodnUITqnj5X5/Ttzvqwm8nFn/7gdVIR+EF7o6BKddNrnXO+M/V90rKQD4MNfX1SE47aW21+5+kLgvj9dzr889gB5NB7g6hCqXyDwo7veXT4gawt2SGHfkXl9VS7hbEuOO3C2JEQ+GJDGVzx2TmKqjZk5EXDOvWgghhBBCCOHWJJGppoqKiujYsSNz585VygwGAxqNhi1btjiUJSQkcPz4cbRaLVqtlj59+tClSxfl9aeffqocd9vZs2dJSEggPDycfv36MWrUKHJzcx1iyMjIIDY2lqioKMLDw5k7dy63bt1S9ms0Gq5fv+5wTvfu3ZXZ1F544QW+/PJLh/0TJ04kJCQEi8X9ngcXQgghhKgMD2IdmaqoakYl7pvRaCQ4OJj09HTMZrNS/uijj7J48WKKix27bzUaDWlpaaSlpZGQkEBoaKjy+n9nKrNYLLz88st07tyZzMxMtm/fzpAhQ3jppZcoKioC4ODBg8yZM4f58+ezdetWtmzZwsWLF5k5c+Y9X1NhYSF79+4lICCArKyse65HCCGEEEK4P0lkqim9Xk98fDwajYbMzEylvH379rRq1YqUlJR7rjs9PV1ZC+a2qKgounbtyieffAJAUlIS48aNUxbEVKvVJCYm8sUXX/Dzzz/fU7ubN2+md+/eDB8+HL1ef8/xCyGEEEJUJ9IjI6qNY8eOUVhYSEhICHFxcaW+9E+aNIkVK1Y4PObljOPHjxMcHFyqvFOnThw/flw5plOnTg7769evT4sWLZRjnKXX64mLiyMiIoLDhw+Tn59/T/UIIYQQQgj3J4lMNZSSkoJWq0WlUhEREUF2drbDl36NRkPXrl35+OOP76n++52x+/ac4s7sz8nJ4cqVK4SEhFC7dm0iIiJITU29rziEEEIIIaoDFR6VvlVFMv1yNWM2mzEajfj4+JCWlgaUjGkxGAw0bdpUOe71119n2LBhjBkzxuk22rVrx9q1a0uVHz58mLZt2wIlydLhw4cJDAxU9hcWFnL69Gk6dOgAQMOGDSksLKRu3boAFBcXc+3atTJXcdXr9Vy5coXw8HDlOuvWrcurr77qdPxCCCGEEML9Vc30StyzzMxMWrVqxa5du8jKyiIrK4s1a9awceNGh+Mee+wxIiMjSU5OdrqN6OhoioqK+PDDD5WyrVu3cuDAAUaMGAHA+PHjWbFihfIYmclkIjExkREjRtCkSRMAQkNDWb9+vVLH+vXrCQ4Opnbt2g7t3U7O9Hq9ck179uwB4JtvvnE6fiGEEEKI6kTGyIhqQa/XExMT41DWuXNnbDYbZ86ccSiPj4/n8uXLTrfh4+PDmjVr+Ne//kVYWBh9+/Zl/fr1rFmzhvr1Sxbr6969OzNmzGDKlClERkbSrVs3mjdvzqRJk5R6pk+fzunTp4mJiUGr1bJ7924WLlxYqr2MjAxatGhBQECAQ3lMTIwM+hdCCCGEqKFU9vsd8CBEBWRkZDBnzhyWL19O+/btXRLDh7nbXNLuvXqigfutcFzHy73+nLjj+u0vZ9Z3dQhO+yC80NUhOOWyyf3eGf+56n5Pin+Y6+vqEJzyUttrrg6h2nu8nvt97gH0aDrA1SHQqG3C3Q+6T5dyl1R6G85yv798wi317duXvn37ujoMIYQQVcSOAQ+7OgQnPUzy9z+4OgghylRVH/2qbJLIiBpjYINQV4dQ/bnnj2luxdjb1RHcAzd7XzT1dHUEzmvnfh11wFlXB+C06PryOSJEVSKJjBBCCCGEEG5M5ZYPS9+/B9YPVVRURMeOHZk7d65SZjAY0Gg0bNmyxaEsISGB48ePo9Vq0Wq19OnThy5duiivP/30U+W4286ePUtCQgLh4eH069ePUaNGkZub6xBDRkYGsbGxREVFER4ezty5cx0WhdRoNFy/ft3hnO7du5OXlwfACy+8wJdffumwf+LEiYSEhGCxWMq99qlTp9KrVy+0Wi0xMTGMHDmSs2f/+0vUN998w3PPPUdkZCTh4eFMmzaNoqIiZX9Z7d6u95NPPuHIkSP07NkTq9XqsN9gMPDCCy8or4cMGYJWq3U4Ji8vj6CgICW2uLg4/vWvf5V7LZ9++ikxMTEMGjSIqKgoFixYoNTTvXt3h2OvX7+ORqNRXlssFhYvXkxkZCQxMTHodDrmz5+PxWJh//79BAcHo9VqGThwICNGjODkyZNl3sPb29atW8uNUwghhBBCVG8PLJExGo0EBweTnp6O2WxWyh999FEWL15McbHjswcajYa0tDTS0tJISEggNDRUef388887HGuxWHj55Zfp3LkzmZmZbN++nSFDhvDSSy8pCcHBgweZM2cO8+fPZ+vWrWzZsoWLFy8yc+bMe76mwsJC9u7dS0BAAFlZWXc8dsyYMaSlpbF582aCgoJYuXIlAD/++COvvfYakyZNYtu2bWzfvp2HHnqI119/vcJxBAcH4+fnx1dffeVQbjAYGDx4MADff/89Fy9e5ObNm3z33XcOxz300ENKbM888wzTp08vs53s7GySk5P59NNP2bRpE+np6aUSozuZNm0aJ06cQK/Xs3nzZlJSUmjVqpXyfmjdujVpaWnKe+Xtt992OP/2Pby9RUVFVbhtIYQQQojqSqZfrmR6vZ74+Hg0Gg2ZmZlKefv27WnVqhUpKSn3XHd6ejoPPfQQL730klIWFRVF165d+eSTTwBISkpi3LhxSg+BWq0mMTGRL774gp9//vme2t28eTO9e/dm+PDhFZ4G2Gazcf36dfz8/ABYuXIlzzzzDN26dQPAw8ODP/7xj/zwww9OrZESFxeHwWBQXv/0008cO3ZM+bKv1+vRarXodLo7xtq9e3eH3qJfys/Px9fXlzp16gDg6elJu3btKhTfqVOnyMjIYO7cufj6lsxU4+XlxbPPPqssiPlL3bp1KzcOIYQQQgghHkgic+zYMQoLCwkJCSEuLq7UF+lJkyaxYsUKh8e8nHH8+HGCg4NLlXfq1ElZkPH48eN06tTJYX/9+vVp0aKFcoyz9Ho9cXFxREREcPjwYfLz88s9dtWqVWi1Wnr27Mm+ffsYOXJkuXF5e3sTFBTEsWPHKhyLVqtl586dSg+UwWAgOjqaWrVqYbFY2Lx5M3FxccTGxrJlyxZMJlOZ9Wzfvp3o6Ogy9/Xo0QMvLy+efvpp3nzzTdavX8/NmzcrFF9OTg4BAQFKAncnNpuNzMzMUnHcvoe3t6NHj1aobSGEEEKI6kx6ZCpRSkoKWq0WlUpFREQE2dnZDl/6NRoNXbt25eOPP76n+u93KRyV6s4DpMran5OTw5UrVwgJCaF27dpERESQmppabh23H4v66quvGDRoEDNmzLivmP9Xo0aNCA0NxWg0YrPZSE1NVR4r27FjBy1btqRFixY0a9aMoKAgtm/frpx79epVJcn68MMPGTt2bJlt1KlTh/Xr17Ns2TLat2/Phg0bePbZZzGbzXe9hxVx8uRJtFotoaGh7N27l+HDhzvs/99HywIDA++7TSGEEEII4Z4qPZExm80YjUb0ej1hYWFER0djsVgcHoMCeP311/noo4+4evWq0220a9eOI0eOlCo/fPgwbdu2BUqSpcOHDzvsLyws5PTp03To0AGAhg0bUlj434XbiouLuXbtGg0bNixVt16v58qVK4SHhxMWFsaOHTuUaxo/frzSa3DtWukFtKKioti7d2+5cVksFnJychwGylfE4MGD0ev1fP311/j6+iq9VHq9nhMnThAWFkZYWBhHjx516BW7PUZmx44dxMTE8Oabb2K321mxYoVyHfv27QNKkrqOHTvy0ksvsXbtWs6cOcP3339PgwYNuHbtmsOEA5cvX6ZRo0YABAUF8eOPPzpMYvC/bo+R2bVrF+3atSMxMdGp6xdCCCGEqImkR6aSZGZm0qpVK3bt2kVWVhZZWVmsWbOGjRs3Ohz32GOPERkZSXJystNtREdHU1RUxIcffqiUbd26lQMHDjBixAigJLlYsWKF8hiZyWQiMTGRESNG0KRJEwBCQ0NZv369Usf69esJDg6mdu3aDu39Mjm7fU179uwBSmYgW7ZsmdJrcHs8yC/t27ePli1bAiW9DBs2bODAgQNAyWNVixYtIiAggK5duzp1H3r16sX58+d59913iYuLA+DChQscOHCAzMxMJdadO3fy3XffcebMGYfzPT09+eMf/8iFCxfIzMxk3LhxynWEhIRw8uRJh5ngfvjhBywWC4888gh16tShQ4cODmOd1q1bR48ePQBo2bIlYWFhzJw5U0nurFYrGzZsKDVTnI+PD4mJiezevZucnByn7oEQQgghhKgZKn0dGb1eT0xMjENZ586dsdlspb5Ix8fHl0pwKsLHx4c1a9Ywf/58Pv74Yzw8PHjsscdYs2YN9euXrBLWvXt3ZsyYwZQpU7h58ybnzp3j+eefZ9KkSUo906dPZ968ecTExODh4UGzZs1YuHBhqfYyMjJo0aIFAQEBDuUxMTHo9Xq6dOlS6pxVq1axYcMGbDYbvr6+zJ8/H4BWrVqRlJTEu+++y+XLl7FYLHTr1o0lS5Y4nD916lTUarVDff/Ly8sLrVZLcnKysn/jxo306tXLIaFSq9X07dsXg8GATqdzqEOtVjNx4kSWLl1KeHi4wyNjt27d4i9/+QuXLl1CrVbj6enJokWLlF6XRYsWMXfuXNauXYvdbqdNmzYOj9DNnz+fZcuWMXjwYLy9vbHZbPTu3RsfH59S19K4cWNefvllli5dyvLlyx3u4W3Dhg3jueeeK3WuEEIIIUTNUjV7TCqbyn6/A0zcVEZGBnPmzGH58uW0b9/e1eGIB+DCBecfWxRCCFE5Hn7Y/WamvHChmatDEFXQww8/5OoQeCRoWqW3cS7n7bsf9IDV2ERG1DwW2yFXh+AUT5X67gdVMb/GpA8P0s3ii64OwWleHnVcHYLTbPbyFwyumtzvY9FsKz0es6rz8Sj96HVVpvYsPV62qrPbrXc/qIpxt88RABWun3yo2RNlrwH4azr773mV3oazamY/lBBCCCGEqFLcMYkRrlXpY2SEEEIIIYQQlaeqzipW2WrmVYu7KioqomPHjsydO1cpMxgMdOnSBa1WS3R0NGPGjOHChQsO561duxaNRlNqtrGwsDCHGc8A4uLi2L9/v/J606ZN6HQ6oqKiiIuLY9KkSQ4TQpw8eRKNRsNHH330K16pEEIIIYRwR5LIiDIZjUaCg4NJT0/HbDYr5aGhoaSlpZGenk7dunVZunSpw3l6vZ6QkBCHdWoqYsOGDaxcuZK//e1vbN26FYPBwLBhw7h48b9jGFJSUu6pbiGEEEKI6kyFR6VvVVHVjEq4nF6vJz4+Ho1GQ2ZmZqn9KpWKrl27cvbsf2edyc3NpaCggHnz5pVKgO5m6dKlTJs2TVlfB0qmzO7YsSNQsjjp5s2bmTNnDiaTiezs7Hu/OCGEEEII4fYkkRGlHDt2jMLCQkJCQoiLiyuzB8RsNrNr1y6io6OVspSUFHQ6Hf7+/gQGBpKRkVGh9i5dusS5c+cIDg4u95gdO3YQEBBAQEAAsbGx0isjhBBCCPH/qVQelb5VRVUzKuFSKSkpaLVaVCoVERERZGdnk5+fD8DevXvRarX89re/paCggP79+wNgsVgwGo3ExsYCVDjZqOgMJXq9Xqlbp9OxdetWTCbTvVyeEEIIIYSoBmTWMuHAbDZjNBrx8fEhLS0NKElSDAYDTZs2JTQ0lCVLlnDt2jVGjRrF4sWLmTx5MllZWVy9epWRI0cCYLPZuHjxImfPnqVZs2Y0bNiQwsJCh7YKCwtp1KgRjRo1omnTpmRnZ/O73/2uVEwXL15kz549HD16lOXLlwNw8+ZNttIh3UgAACAASURBVG3bxqBBgyr3hgghhBBCVHE1depq6ZERDjIzM2nVqhW7du0iKyuLrKws1qxZw8aNGx2O8/X1Zfbs2axbt47z58+j1+uZOXOmcs6OHTuIi4vDYDAA0KNHDzZs2IDVWrI4186dO/Hw8KBFixYAxMfHM3/+fH766SeljYMHD5KdnU1qaiqRkZHs2LFDqf8vf/mLPF4mhBBCCFGDSSIjHOj1emJiYhzKOnfujM1mc5gKGaBdu3ZERUWRlJTEgQMHiIyMdNgfExPDxo0bsdvtjB07Fl9fX3Q6HVqtlg8++IClS5fi7e0NwLBhwxg9ejQJCQlERUURHR3NunXrePjhhzEYDKViCg8P57vvviMvL68S7oIQQgghhPuoqWNkVHa73e7qIIR4ECy2Q64OwSmeKrWrQ3Cau3Vt3yy+ePeDqhgvjzquDsFpNrvF1SE4yf0+Fs22a64OwWk+Hr6uDsEpas+Grg7BaXa71dUhOMXdPkNuUxHo6hAICP5Lpbfx45G3Kr0NZ8kYGVFjWNzsg/6G7byrQ3Car/ejrg7BKZ6qWq4OwWkXb525+0FVTD3vuq4OwSl2N0xk3C0pADDbrrs6BKfcKL5w94OqmOvF7pcYPOTtfl9N6/u4PpGpqdzv3SKEEEIIIaodd0xiqoqq+uhXZauZVy2EEEIIIYRwa5L6VlNFRUX07NmToUOH0rt3b9555x2gZCpjm81GkyZNAHjttdc4duwYN27cYMqUKWXWtXPnTsaMGcPSpUvp16+fUj516lTS09PZtm0bzZs3V8rat29P7dq1+cc//gHA2bNnqVWrFg0aNABgzpw5rFu3jvbt2zNixAgAsrOzee+99/j555/x8/Ojbt26TJgwga5du2Kz2Vi1ahUbN27Ew8MDlUrFyJEjGTp0aOXcPCGEEEIIN1JTe2QkkammjEYjwcHBpKenM3nyZGVNmKSkpFJJy7Fjx+5Yl16vJyQkBL1e75DIADz88MMkJSXx9ttvO5QPHjyYwYMHA/9Nbm4nLQDr1q1T/v/48eO8+uqrLFy4kJ49ewLw008/cfToUQBWrFjBvn37+Oyzz/Dz8+Ps2bOMHj0atVqNVqt19tYIIYQQQohqoGambzWAXq8nPj4ejUZDZmbmPddz+fJl9u3bx1//+lcOHTrEhQuOgx2HDRvGV199xYkTJ+65jdWrV/PMM88oSQxAixYtiIyMxGQysWrVKhITE/Hz8wOgWbNmTJ48maSkpHtuUwghhBCiulDhUelbVVQ1oxL35dixYxQWFhISEkJcXNx9LRy5adMm+vTpQ+PGjenXr1+phTHr1KnDq6++ynvvvXfPbeTk5BAcHFzmvh9//BFvb29at27tUN6pUyd+/vlnrl93r1lvhBBCCCHEr0MSmWooJSUFrVaLSqUiIiKC7Oxs8vPz76kug8FAbGwsALGxsRgMhlLHDB06lOPHj3PkyJH7irssd1vmSJZBEkIIIUSNp/Ko/K0KkjEy1YzZbMZoNOLj46OMi7FYLBgMBsaNG+dUXd999x0nTpxg+vTpStn58+f59ttveeqpp5Qyb29vJkyYwLvvvkuzZs2cjjkoKIjs7Gz69u1bal/Lli2xWCycPHnSoVfm8OHD+Pv74+vrfmsnCCGEEEKI+1c10ytxzzIzM2nVqhW7du0iKyuLrKws1qxZU+qRsIrQ6/WMHj1aqScrK4sJEyaU+ahaTEwMBQUFHDhwwOl2Ro8ezWeffcbevXuVsp9//plt27ahVqt55ZVXSExMpKioCCiZBW3RokVMmDDB6baEEEIIIaoblcqj0reqSHpkqhm9Xk9MTIxDWefOnbHZbHdMMv75z3+Snp6uvB41ahTp6ekOs4sBDBw4kEGDBjFjxgyHcg8PD9544w3Gjh3rdMzt2rVj5cqVvPfee8ycOZPatWvToEEDEhISAIiPj8fDw4MhQ4bg6ekJwIsvvohOp3O6LSGEEEIIUT2o7DLIQNQQN4p3uzoEp1hsN1wdgtN8vR91dQhOKbbddHUITrtkOufqEJxWz7uuq0Nwih33+1j09qjj6hCcZra512QtxW74N/l6scrVITjlIW/3/H29vk9/V4fA410WV3obJ755vdLbcJZ7vmOEuAeeqlquDsEp3l7uN/7H3T7orXaTq0OoEWp5NnJ1CE6xY3N1CE5zx/dyLc/6rg7BKVaV+yWLvt4+rg7BaZ4q94tZuI4kMkIIIYQQwuUkibl3VXWdl8pWM69aCCGEEEII4dakR0Y4CAsLw8fHB7VajclkokuXLsyaNQtvb28+++wzkpOTsdls2Gw2YmNjGTNmDB4eJfnwtWvX+Nvf/sbOnTupVavkMa6goCAmTZrEI488AkBGRgbLli3j5s2bFBcX07dvX9544w18fHwYMmQIZrMZi8XCqVOnaNOmjVLH22+/zcmTJ4mOjmbatGmMHDnSJfdHCCGEEKKqqaqzilU2SWREKUuWLKFt27ZYrVaef/55tm/fjtlsJjk5mdWrV9O8eXOKiooYP348NpuN+Ph47HY7r776Km3atMFoNKJWq7FarWzevJm8vDweeeQRDh48yJw5c1i9ejUajQaTycSUKVOYPXs28+bNY8OGDQDk5eUxePBgZR2c21JSUggJCUGv10siI4QQQghRBeXl5TF+/Hjl9dWrV7l27RoHDhzghx9+YOrUqRQWFlK/fn0WLFhAy5YtAe64rzw1M30TFWIymTCZTNSrV4+kpCSmTJlC8+bNAfDz82P27Nm8//77mM1mvv76a06fPs306dNRq9UAeHp6otPp6NKlCwBJSUmMGzcOjUYDgFqtJjExkS1btnD69Ok7xlJcXMzmzZuZM2cOJpOJ7OzsSrxyIYQQQgg3olJV/lZB/v7+pKWlKVt4eDgDBw4EYNasWQwfPpxt27YxfPhwZs6cqZx3p33lkURGlJKQkIBWq6VHjx74+/vTqVMn8vLy6NSpk8NxrVu3xsvLi1OnTvHvf/+boKAgvL29y633+PHjpeqoX78+jz32GLm5uXeMaceOHQQEBBAQEEBsbGyZi3IKIYQQQoiqw2w2s3nzZgYPHsylS5fIyclRkpqBAweSk5NDQUHBHffdiSQyopQlS5aQlpbGvn37MJlMJCcnl3usqpwMfe/evWi1Wvr27csHH3xw3zHp9XpiY2MB0Ol0bN26FZPJ/aYbFUIIIYT41XlU/nblyhXy8vJKbVeuXCk3rKysLJo2bcoTTzzB2bNnadq0qbK4uaenJ02aNOHs2bN33He3yxaiTGq1mj59+nDkyBH8/f05fPiww/6TJ09isVgICAggKCiIo0ePUlxcDEBoaChpaWn069ePGzdK1hbRaDSl6igsLOTnn39WBvaX5eLFi+zZs4elS5cSFhbG888/z82bN9m2bduvfMVCCCGEEG7oATxalpycTHh4eKntTj946/V6Bg8eXGmXLYP9RblsNhsHDx6kZcuW9O/fn4ULF9KmTRuaNWtGUVERiYmJvPLKK6jVakJDQ2natClvv/02kydPVsbJ3Lx5kzp1ShYRGz9+PH/84x958sknlcH+iYmJREVF4e/vX24cqampREZG8s477yhlRqORDRs2MGjQoMq9CUIIIYQQgt///vfK0zG/VK9evTKPz8/P5+DBgyxcuBCAZs2akZ+fj9VqxdPTE6vVyvnz52nWrBl2u73cfXciiYwoJSEhAbVajcVioU2bNowfPx4/Pz9u3brFqFGjsNvtWK1WtFot48aNA0oeMfv73//Oe++9x4ABA6hduzZ169alVatWSrLRvXt3ZsyYwZQpU7h16xYWi4Xw8HDeeOONO8ZjMBiYMmWKQ1l4eDizZs0iLy/vjkmQEEIIIUS158Rg/HtVr169cpOWsmzcuJHevXvToEEDABo1akRgYCBGoxGtVovRaCQwMJCGDRsC3HFfeVR2u91+75ckhPswWQ+6OgSneKjc73cGm93i6hCcYrW73zirQnOhq0NwWpNaLVwdglPs2FwdgtPc8b3sbn/jrDazq0NwmqeHj6tDcIqnyr3ivc1T1dHVIdA2dGWlt5G7d6xTx0dGRjJ9+nR69eqllJ08eZKpU6dy5coV6tWrx4IFC/jNb35z133lkURG1BjXi3e5OgSnmK3lD56rqmp53vmXk6rGQ1X+LHtVVd71c64OwWkN1K6OoPqr7dnI1SE47cyNS64OwSknr3q6OgSnafysrg7BKQ/Xcq/PkNvqePVwdQi0/d0DSGT2OJfIPAgy2F8IIYQQQgjhdtyrX1cIIYQQQgjhwP4AxshURdIjIxRhYWFERUWh1WqJiopixowZWCwlYx4+++wzBgwYQP/+/YmMjGTlypXYbP99jlyj0XD9+vUy68zNzWXVqlVMmjSp1P6pU6eydOlSAIqKiujYsSNz5851OMZgMNClSxe0Wi3R0dGMGTOGCxcu/JqXLoQQQggh3IwkMsLB7cUw09PTOXHiBNu3byc1NZXk5GRWr17N559/zmeffcaePXtYubLiz2PqdDp27NjhsGjS9evX+eKLL5Sp/IxGI8HBwaSnp2M2Ow6qvL0uTXp6OnXr1lWSHyGEEEKIGk/1ALYqSBIZUSaTyYTJZKJevXokJSUxZcoUmjdvDoCfnx+zZ8/m/fffL5VwlKdJkyZ0794do9GolH3++ecEBwfz6KOPAiWLJsXHx6PRaMjMzCyzHpVKRdeuXe+60qsQQgghhKjeJJERDhISEtBqtfTo0QN/f386depEXl4enTp1cjiudevWeHl5cerUqQrXPXjwYAwGg/LaYDAoq70eO3aMwsJCQkJCiIuLQ6/Xl1mH2Wxm165dREdHO39xQgghhBDVkYeq8rcqSBIZ4eD2o2X79u3DZDKRnJxc7rEqJweW9enThzNnzvD9999z6tQpTpw4Qb9+/QBISUlBq9WiUqmIiIggOzub/Px85dy9e/ei1Wr57W9/S0FBAf3797+3CxRCCCGEENWCJDKiTGq1mj59+nDkyBH8/f05fPiww/6TJ09isVgICAiocJ3e3t4MGjQIg8HAxo0bGTBgAGq1GrPZjNFoRK/XExYWRnR0NBaLxaH35vYYmZ07d6JSqVi8ePGvdq1CCCGEEG5Npar8rQqSREaUyWazcfDgQVq2bMlrr73GwoULlXEpRUVFJCYm8sorr6BWO7fS3eDBg9m0aROpqanKY2WZmZm0atWKXbt2kZWVRVZWFmvWrGHjxo2lzvf19WX27NmsW7eO8+fP3/+FCiGEEEIItyTryAgHCQkJqNVqLBYLbdq0Yfz48fj5+XHr1i1GjRqF3W7HarWi1WoZN26cw7lRUVHK42a1a9dm27Ztpepv06YNzZs3x2Qy0b59e6BkkH9MTIzDcZ07d8Zms3HgwIFSdbRr146oqChWr17N9OnTf61LF0IIIYRwT1Wzw6TSqex2u93VQQjxIFwv3uXqEJxitl65+0FVTC3Phq4OwSkeKm9Xh+C0vOvnXB2C0xo413Er7kFtz0auDsFpZ25ccnUITjl51dPVIThN42d1dQhOebiWe32G3FbHq4erQ6BN2OpKb+P7rFcqvQ1nSY+MqDH+fvyWq0Nwii7A/X5jOHOjwNUhOGXFsbquDsFpXx7zc3UITts2tMjVIVR7X+S53w8fbf3c6+n21g+5V1IA4Olmv9L/eO2yq0O4J4H1XR0BVXZWscrmXn9FhBBCCCGEEALpkRFCCCGEEMK9VdFZxSqb9MiIcoWFhREVFYVWqyUqKooZM2ZgsVgA+OyzzxgwYAD9+/cnMjKSlStXYrPZSE1NZdiwYdhsNqWeZcuWMXHiRACsVis9e/YsNVEAwO7duxk2bBgRERHExcXx6quvcvz4cWV/UVERHTt2ZO7cuZV85UIIIYQQoqqTHhlxR0uWLKFt27ZYrVaef/55tm/fjtlsJjk5mdWrV9O8eXOKiooYP348NpuN+Ph4tm3bxocffsioUaM4fvw4GzZsUKZS3r17N02aNOFf//oXFy9epHHjxgDs2bOH6dOns2zZMjp06ADA0aNHuXDhAhqNBgCj0UhwcDDp6elMnjwZHx8f19wUIYQQQoiqpGZ2yEiPjKgYk8mEyWSiXr16JCUlMWXKFJo3bw6An58fs2fP5v3338dsNvPnP/+Z5ORkcnNzmTZtGjNmzKBBgwZAyVTLw4YNo2/fvqSmpir1L1u2jPj4eCWJAQgMDOR3v/ud8lqv1xMfH49GoyEzM/MBXbkQQgghRBXnoar8rQqSREbcUUJCAlqtlh49euDv70+nTp3Iy8ujU6dODse1bt0aLy8vTp06RePGjZk6dSrPPfccjz/+OH379gWgoKCAffv20b9/f+Li4jAYDMr5OTk5BAcHlxvHsWPHKCwsJCQkhLi4OPR6feVcsBBCCCGEcAuSyIg7WrJkCWlpaezbtw+TyURycnK5x6p+MdAsOjoaX19fXn75ZaVs06ZNPP300/j6+vLUU09htVo5dOhQheJISUlBq9WiUqmIiIggOzub/Pz8e78wIYQQQojqQvUAtipIEhlRIWq1mj59+nDkyBH8/f05fPiww/6TJ09isVgICAhQyjw9PfHw+O9bTK/X89VXXxEWFkZYWBgFBQVKz0pQUBDZ2dlltm02mzEajej1esLCwoiOjsZisTj06AghhBBCiJpFBvuLCrHZbBw8eJCWLVvSv39/Fi5cSJs2bWjWrBlFRUUkJibyyiuvoFaXvYR3dnY2V69eZc+ePUrPTX5+PgMGDGD69OmMGzeOP/3pT7Rv354nnngCKHmc7OLFi1y9epVWrVqxbt06pb5Dhw4xZcqUMmc/E0IIIYSoSew1dPplSWTEHSUkJKBWq7FYLLRp04bx48fj5+fHrVu3GDVqFHa7HavVilarvWNSodfrGTBggMPjZ02bNiUoKIitW7cSGxvLnDlzmDNnDoWFhXh5eeHv78+bb77JRx99RExMjEN9nTt3xmazceDAAbp161Zp1y+EEEIIIaomld1ut7s6CCEehMX//sLVIThFF2BydQhOu2Zxr1+EVhyr6+oQnPblMU9Xh+C0bUOLXB1CtbfnnLerQ3BaWz+rq0Nwip+P7e4HVTFqN/tzcdXNPkNuC6w/0NUh8PjAjyq9jRPGkZXehrOkR0bUGM+1vuXqEJxSx9PX1SE4rb6P2dUhOGXeU+6XLNqeLHZ1CE6r7fWwq0Nwis1ucXUITot67JqrQ3Cays2+gphs7vf3oq6Xn6tDcEp9H/ltXTjHvf6KCCGEEEIIIRy5Z2fWfZNZy4QQQgghhBBuRxIZUS6LxcLixYuJjIwkJiYGnU7H/PnzsVhKHrtYu3YtGo2GnJwch/NeeOEFvvzyy1L1TZ06lV69eqHT6YiIiOC5554jNTW11HGLFi2iffv2XLp0yaH8888/R6fTodVqiYqK4s033/wVr1YIIYQQwk2pVJW/VUHyaJko17Rp0zCZTOj1enx9fSkuLkav12M2m/H29kav1xMSEoJerycoKKhCdY4ZM4YRI0YAcPToUSZOnMjly5d56aWXALBaraSlpfHkk0+SlpamLKh5/vx5Zs+ezcaNG2nWrBl2u52jR49WzoULIYQQQogqT3pkRJlOnTpFRkYGc+fOxde3ZNC5l5cXzz77LHXr1iU3N5eCggLmzZtHeno6ZrPzg7wDAwOZPn06q1ev5vbkeTt37qRFixYkJCQ4LHh58eJFvLy8qF+/PgAqlarCyZMQQgghRLXmoar8rQqSREaUKScnh4CAAPz8yp7xJCUlBZ1Oh7+/P4GBgWRkZNxTO8HBwVy6dImCggKgZL2ZuLg4unTpgsVi4ciRIwC0a9eOjh070qdPHxISEvjoo4+4fPnyvV2cEEIIIYRwe5LICKdZLBaMRiOxsbEAxMbGotfr76muXy5jdOnSJQ4cOEBUVBQAOp1OqdfDw4Ply5fz8ccf0717d3bu3MmgQYMoLCy8z6sRQgghhHBzqgewVUEyRkaUKSgoiB9//JGioqJSvTJZWVlcvXqVkSNHAmCz2bh48SJnz56lWbNmTrXzf//3fzRq1IhGjRqxZs0aiouLGTRoEADFxcXcvHmTt956i1q1agHQtm1b2rZty/PPP090dDQHDhwgIiLi/i9YCCGEEEK4FemREWVq2bIlYWFhzJw5k2vXShZas1qtbNiwgbVr1zJz5kyysrLIyspix44dxMXFOYxpqYhjx47xl7/8hVdeeQUAg8HAsmXLlHp37dpFx44d2bp1K/n5+Rw6dEg599y5cxQUFODv7//rXbQQQgghhDuSWcuEcDR//nyWLVvG4MGD8fb2xmazERwczJEjR0hKSnI4NiYmhmnTphEfHw+UTLWsVquV/atWrVL+u2HDBm7dukXDhg159dVX0el0HDlyhMLCQkJCQkrVq9fr6dq1K0lJSZw+fZpatWphs9mYOHGiDPgXQgghhKihVPZfDlIQoho7f2uTq0NwSh1PX1eH4DSr3fnZ64RzbPZiV4fgtNpejV0dglNsdourQ3Ca2XbN1SE4TeVmD4WYbCZXh+C0ul5lT9hTVbnrV1Jf7z6uDoHHB39S6W2c0I+o9DacJT0yosbw9Wrk6hCc4u1R19UhOM1D5V5fTG4UX3B1CE6zY3N1CPfAvb6ceKi8XR2C0+p6OTc+sSoott1wdQhO8fV2v3vsbmx2q6tDEG5GEhkhhBBCCCHcmXv9jvirkURGCCGEEEIId1ZFB+NXNklkajCLxcLy5cvZsmULPj4+eHp6EhISwptvvom3tzdr165l9uzZbNy40WFQfVFREQsWLGD//v14eXlRv359Jk2apAzUT0pK4saNG0yZMkU555NPPuG7775j/vz57N+/nzFjxtCyZUtlv0ajISYmhnfeeQeAixcvYrPZaNKkCQCvvfYa/fr1Y9GiRSQnJ7Nz504aNXKvR8WEEEIIIcSvRxKZGmzatGmYTCb0ej2+vr4UFxej1+sxm814e3uj1+sJCQlBr9c7JDKvv/46bdu25YsvvsDT05MDBw4wYcIE1q5dS+vWrSvUduvWrcucrrlnz55A2cmQ1WolLS2NJ598krS0NF5++eX7vANCCCGEENVAzeyQqalP1IlTp06RkZHB3Llz8fUtmR3Ly8uLZ599lrp165Kbm0tBQQHz5s0jPT0ds7lkNqqDBw/yww8/8Mc//hFPT08AunXrxjPPPMP7779fqTHv3LmTFi1akJCQ4PSaNUIIIYQQonqRRKaGysnJISAgAD+/sqdmTElJQafT4e/vT2BgIBkZGQAcP36cJ554Am9vx1l9OnXqRG5uboXbP3nyJFqtVtmWLl1613P0ej1xcXF06dIFi8XCkSNHKtyeEEIIIUR1ZfdQVfpWFcmjZaIUi8WC0Wjkn//8JwCxsbHo9Xqio6PvOMe76v8PNFOVM+Dsl+XlPVpWnkuXLnHgwAEWLFgAgE6nQ6/XExwcXOE6hBBCCCFE9SGJTA0VFBTEjz/+SFFRUalemaysLK5evcrIkSMBsNlsXLx4kbNnz9KuXTv+/ve/Y7FYHHplDh8+TOfOnQFo0KABp0+fdqjz8uXLNGzY8J7jTUtLo7i4mEGDBgFQXFzMzZs3eeutt6hVq9Y91yuEEEII4fZq6Kxl8mhZDdWyZUvCwsKYOXMm166VrAhttVrZsGEDa9euZebMmWRlZZGVlcWOHTuIi4vDYDDQtWtXAgICWLRoEVZrycJVBw8eZPv27YwZMwaAkJAQdu/ezblz5wAoLCxky5Yt/O53v7vneA0GA8uWLVNi2rVrFx07dmTr1q33eSeEEEIIIYQ7kh6ZGmz+/PksW7aMwYMH4+3tjc1mIzg4mCNHjpCUlORwbExMDNOmTSM+Pp4lS5awYMEC+vXrh91u58aNG6SlpfHII48AJY+NvfXWW8THx2O1WrHb7YwYMYLf/va3Sn23x8jc1qRJE1avXl1mnEeOHKGwsFCZ3vmXMen1enQ63a91S4QQQggh3E/N7JBBZb/ToAch7uLatWtMmjSJhg0b8vbbb+PhUXU7+W4Uf+XqEJzi7VHX1SE4zUNVdf/9y3Kj+IKrQ3Ca1W5ydQhOq+XZwNUhOMn9vhF4qtzvEdti2w1Xh+AUH09fV4dQ7dnsVleHcE+8PTq7OgRaP7+u0ts4+elzld6Gs6RHRtwXX1/fcntSqh6bqwNwyi3rJVeH4DS1Z31Xh+CUv7jhxHdDW7nX+xigpa97vZdVbpaQA5y74X7vi2Z1vO9+UBXioXK/r0weKve6x3bcM5GpEqrorGKVzf3+WgshhBBCCCFqPElkqjGLxcLixYuJjIwkJiYGnU7H/PnzsVgsAKxduxaNRkNOTo7Defv27WPIkCFotVr69+/Piy++iM3m+GvfxIkTCQkJUeq6TaPRMHr06FJl169fV15brVZ69uzJuHHjHI7bv38/wcHBaLVaBg4cyIgRIzh58qSyf+rUqfTq1cth/RkZ7C+EEEKIGk+lqvytCnK/flJRYdOmTcNkMqHX6/H19aW4uBi9Xo/ZbMbb2xu9Xk9ISAh6vZ6goCCgZFrjhIQE/vGPf9CuXTugZPHMX64BU1hYyN69e2nVqhVZWVlERkY6tPuf//yHgwcP0rVr1zLj2r17N02aNOFf//oXFy9epHHjxsq+X64vs2jRIt5++23+/ve/K/vHjBnDiBEjfp0bJIQQQggh3Jb0yFRTp06dIiMjg7lz5+LrWzJA0cvLi2effZa6deuSm5tLQUEB8+bNIz09HbPZDMD169e5ceOGQ3IRFBTkkMhs3ryZ3r17M3z4cPR6fam2J0yYwF//+tdyY9Pr9QwbNoy+ffuSmppa7nHdunXj7NmzTl+7EEIIIUSNonoAWxUkiUw1lZOTQ0BAQKnFLm9LSUlBp9Ph7+9PYGAgGRkZAPj5+TF06FAiIiIYO3Ysq1atKpVM6PV64uLiiIiI4PDhw+Tn5zvsj4iIoLi4WKnzlwoKCti3bx/9+/dX1qYp+g9WawAAIABJREFUi81mIzMzk+joaIfyVatWOTxadvTo0QrfEyGEEEIIUflMJhOzZs0iIiKCmJgY/vSnPwHwww8/8OyzzxIZGcmzzz7LqVOnlHPutK88ksjUQBaLBaPRSGxsLACxsbEOPSszZ84kLS2N8PBw/u///o+BAwcqb6acnByuXLlCSEgItWvXJiIiosxelTfeeIPFixeXGluzadMmnn76aXx9fXnqqaewWq0cOnRI2X97fZnQ0FD27t3L8OHDHc4fM2YMaWlpyhYYGPhr3RYhhBBCCPfkoar8zQmLFi1CrVazbds2Nm/ezOuvvw7ArFmzGD58ONu2bWP48OHMnDlTOedO+8q9bOfuknAXQUFB/PjjjxQVFZXal5WVxdWrVxk5ciRhYWG8++677N+/36Hn5bHHHmPIkCEkJSXRuXNnvvzyS6CkN+bKlSuEh4cTFhbGjh07yuxVCQ0NpVGjRmzatMmhXK/X89VXXxEWFkZYWBgFBQUOSVTr1q1JS0tj165dtGvXjsTExF/pjgghhBBCiMp2/fp1UlNTef3115WhCY0bN+bSpUvk5OQwcOBAAAYOHEhOTg4FBQV33HcnMti/mmrZsiVhYWHMnDmTefPm4evri9VqxWAwYDQamTlzJkOGDFGOnzlzJgaDgZEjR3Lo0CF6/D/27j0uqjp//PhrQEARwbtrqeAlWdGvpqFSXjBQwAtX86uW7s/MTM1Yq61EDS+rZmUXb9la22Zr6iaHi4BaAaGSq+Z6yyhZ3TQtvAAiKMLAzPz+4OtsE5dhlGHmyPv5eJzHds75nM/nfWZH4H0+5/P5DBmCRqOhqKiIixcv0qlTJ7RaLSkpKSiKgqenp/Ha4OBgjhw5gq+vr0kML774IvPmzTPunzx5kuLiYrKysoxf7MuXLzN27FgWLlxocq2zszNLliwhKCiI7Oxs42QEQgghhBDiNxpgHZmioiKKioqqHHd3d8fd3d24f+HCBVq2bMn69es5dOgQzZs3549//CNNmzalQ4cOODo6AuDo6Ej79u3Jzc3FYDDUeK5169Y1xiQ9MvewVatW4eXlxfjx4xk3bhyhoaEcPXqUEydOVJlpLDQ0lISEBAwGA59++ikhISGEhYUxefJkQkNDGTVqFGlpaXTp0sUkibl9bXWD/v/nf/6H3r17G/cVRWHs2LEmEwd06NABHx+faqdRbtu2LdOnT2f9+vXGY78dI7Ntm/VXshVCCCGEaOw2b95MYGBglW3z5s0m5XQ6HRcuXMDHx4f4+Hj+9Kc/8dxzz1FSUlLvMWkMBoOh3msVwg6VVOy3dQgW0Rm0tg7BYi6OLW0dgkUWHy20dQgW+9+ut2wdgsW83GwdgWU0GvU947tUojdfyM50dFXXqvNNHVvZOgSLOWjU9Rkb0Nk6hDvi7OBrvpCVdZuxw+ptHH87uE49MgUFBQwbNoxTp04ZH16PGTOGVatWMX36dA4dOoSjoyM6nY7BgwfzxRdfYDAYCA4Orvac9MgIIYQQQggh7pi7uzudOnWqsv06iQFo3bo1gwcP5uuvvwYqZyPLz8/Hy8uLXr16kZKSAkBKSgq9evWidevWtGnTpsZztZEeGdFobD1b9fU1e9a+qfqesLZvpq6YnVT4KOfAZXU9YQUY3VldvYsV6voaA3ClVH1f5qaO6vrzQ6uz04U0atFWZb9Hvr2mzqHbYzuPtnUIdJsZZ/U2/rPpsTqXvXDhAgsWLKCwsJAmTZowb948/P39OXv2LPPnz6eoqAh3d3def/11unXrBlDruZqo8xsjhBBCCCGEqKSxr0S7c+fO/P3vf69yvHv37uzYUf1rcLWdq4n6HuEImysvL2fNmjUEBwcTGhpKREQEq1atory8HICtW7fi7e1Ndna2yXUBAQHk5OSYHIuKiuLQoUPG/Z07dxIREUFISAhRUVE8//zz/PLLL8bzZ8+exdvbm48//th6NyiEEEIIIeye9MgIi8XExFBWVoaiKLi5uVFRUYGiKGi1WpycnFAUBT8/PxRFsWja5B07dvC3v/2N9957Dy8vLwAOHTpEXl4e9913HwBxcXHGuqdNm2aFuxNCCCGEUJkGmH7ZHkmPjLDIuXPnSEtLY/ny5bi5VU5F1KRJEyZOnEjz5s3JycmhoKCAFStWkJqailZb93fj169fT0xMjDGJARg8eDB9+/YFoKKiguTkZJYtW0ZZWRknT56s13sTQgghhBDqIYmMsEh2djaenp54eHhUez4uLo6IiAg6depEr169SEtLq1O9+fn5XLp0iX79+tVYJjMzE09PTzw9PYmMjKx27RohhBBCiEbHoQE2O2SnYQk1Ki8vJyUlhcjISIA6JxuaOg5QUxTFWHdERAR79uyhrKzszgMWQgghhBCqJWNkhEV8fHw4f/48169fr9Irk5GRQXFxsXHsil6vJy8vj9zcXDp27Ejr1q0pLDRdgLCwsJA2bdrQpk0bOnTowMmTJxk6dGiVdvPy8sjKyuL777/nvffeA+DWrVt8/vnnhIWFWedmhRBCCCHUwM5mLWso0iMjLOLl5UVAQACxsbHcuHEDAJ1Ox44dO9i6dSuxsbFkZGSQkZFBZmYmUVFRxMfHAzBkyBB27NiBTle5cu/evXtxcHCgS5cuAMyZM4dVq1bx008/Gdv75ptvOHnyJImJiQQHB5OZmWmsf+XKlfJ6mRBCCCFEIyWJjLDYqlWr8PLyYvz48YwbN47Q0FCOHj3KiRMnCA4ONikbGhpKQkICBoOBWbNm4ebmRkREBOHh4fz1r39l/fr1ODlVLvA3adIkZsyYQXR0NCEhIYwZM4Zt27bRrl074uPjCQ0NNak7MDCQU6dOcfHixQa7dyGEEEIIu+Ogsf5mhzQGg0FdS+sKcYe2nt1j6xAs0l5lKzIDtG+mrpidVPgo58BlJ1uHYLHRnes+e6E9qFDX1xiAK6Xq+zI3dVTXnx9anX3+IVebtir7PfLtNXWOeBjbebStQ6BbdKLV2/jP2girt2EpdX5jhBBCCCGEEAAYGukYGUlkRKMR4dnc1iFYpMJQausQLNbUsZWtQ7BIfmmurUOwmH9HdfVuALR0bm3rECyiU+G/vSYOJbYOwWJ6dXXIoFVbwEATlXXUDf+ds61DECojiYwQQgghhBBqprKktb5IIiPuSHl5Oe+99x67du3C2dkZR0dH/Pz8GDZsGHPmzMHLy8tY1tvbmzfeeAOtVsvKlSv55ptvcHBwwGAw8Mwzz5gM4j979ixjxowhJibGOI0zwLp169i6dSvt27enrKyM3r178+c//xlXV9cGvGshhBBCCGEvJJERdyQmJoaysjIURcHNzY2KigoURUGr1dK9e3fjlMu/9sknn1BYWMjOnTtxdHTk5s2bXL161aRMXFwcfn5+KIpikshA5SKYr7zyClqtlmnTprFlyxZmzpxpzdsUQgghhLB/djqrmLU10o4ocTfOnTtHWloay5cvx83NDYAmTZowceLEWntILl26RNu2bXF0dASgefPmJj03FRUVJCcns2zZMsrKyjh58mS19Tg7O9O/f39yc9U3vkEIIYQQQtQP6ZERFsvOzsbT0xMPD49qz589e5bw8HDj/qhRo5g7dy4TJkzgqaee4tChQ/Tv35/hw4czcuRIY7nMzEw8PT3x9PQkMjISRVHo27dvlfpv3LjBN998w/PPP1//NyeEEEIIoTYya5kQ9aOmV8u8vb1JT0/nm2++4ejRo/z5z39m3759LFu2DABFUYiMjAQqXyOLiIhgwYIFuLi4AJCYmMjXX3/N+fPnGTp0KH5+fg13U0IIIYQQwq7Iq2XCYj4+Ppw/f57r169bfK2LiwtDhw4lOjqatWvXkpKSAkBeXh5ZWVmsX7+egIAAnnjiCW7dusXnn39uvDYiIoKdO3eSlpZGTk4OW7durbd7EkIIIYRQLQeN9Tc7JImMsJiXlxcBAQHExsZy48YNAHQ6HTt27KCkpOa1DI4cOUJ+fr5x/7vvvqNTp05AZW9LcHAwmZmZZGRkkJGRwcqVK1EUpUo97dq1Y+HChWzcuJHSUvWt9yCEEEIIIe6evFom7siqVavYsGED48ePx8nJCb1ej7+/P/fdd1+VMTLt27fngw8+4OLFiyxfvpzy8nIcHBxo06YNb775JgDx8fG88sorJm0EBgayePFiLl68WKX9ESNG0K1bN7Zv315ldjMhhBBCiEbFPjtMrE5jMBjUt1StEHegpGK/rUOwSIUKVxdv6tjK1iFYJL9UfTPf3aywdQSWu8+1ta1DsIhOhf/2istr7g23V3qV/fWh1ds6Ass1dbR1BJZp4aTOteFaOAXaOgS8YlKt3sa518ZavQ1LyatlQgghhBBCCNWRV8tEo1FQVmjrECyixqd/LZ3V1cPR3KmprUOwWJlOfb0FNyvyzReyI7cq1PeOhoujyro3gFKduj5nJxU++tWp7GuhM5TZOgT1stPB+Namwn+WQgghhBBCiMZOemREnZSXl/Pee++xa9cunJ2dcXR0xM/Pj2HDhvHWW2+ZrBuTk5PDrFmzyMjIAECr1fL222+TlpZGkyZNaNq0KXPnzjVZDPPHH39k9erV/PDDD3h4eODs7MyMGTOMZT777DM2b96MXq9Hr9cTGRnJzJkzcXCQXFwIIYQQjZwsiClEzWJiYigrK0NRFNzc3KioqEBRFLRardlrlyxZQklJCampqbi4uJCTk8OMGTPw8PBg4MCBXLlyhSlTpvDSSy+xYcMGAK5evcrXX38NVE7NvHnzZj744APuu+8+rl+/zrPPPoter2fOnDlWvW8hhBBCCGGf5HG2MOvcuXOkpaWxfPly3NzcAGjSpAkTJ07E1bX2GUZ+/vlndu/ezZIlS3BxcQGgZ8+ezJo1i/Xr1wPw6aefMnjwYCIiIozXtWvXzri/bt06XnnlFe677z4APDw8WLp0KX/5y1/qlEgJIYQQQtzTHBpgs0PSIyPMys7OxtPTEw8Pj2rP/3bdmLKy/w7Wy8nJoUuXLrRs2dLkmgcffJA1a9YY6x8yZEi1dd+4cYOLFy/y4IMPmhzv3r07TZo04dy5c/Ts2fOO7ksIIYQQQqiXJDLirnXv3r3aMTIA1lymSNNI3wcVQgghhDDRSP8mstOOImFPfHx8OH/+PNevX7f42p49e/LTTz9RWGg69fHx48fx9vY21v/tt99We72bmxudOnXi+PHjJsfPnj1LeXk5np6eFsckhBBCCCHUTxIZYZaXlxcBAQHExsZy48YNAHQ6HTt27KCkpPbVpDt16kRISAhLliwxvnKWk5PD+++/z9y5cwF4/PHH+ec//0lycrLxuvz8fBITEwGYO3cub7zxBrm5lWuUXL9+nSVLlvD0008bx90IIYQQQjRaDhrrb3ZIXi0TdbJq1So2bNjA+PHjcXJyQq/X4+/vbxyAX5vFixfz9ttvM2bMGJycnHBxcWHhwoUMGjQIgA4dOvD3v/+d1atX8+677+Lq6oqrqytPP/00AJGRkZSWlvLUU09hMBjQ6XSEh4cze/Zsq96zEEIIIYSwXxqDNQcxCGFHLt5MNl/Ijmj1to7Aci2dbR2BZZo4NLV1CBYrKC21dQgWa+6krl8ztyrs88ljbVwc1fUZA9xU2efsJO+wWF0LJ0dbh3BHWjqPsXUIeP35C6u3ce7VIKu3YSn5ZymEEEIIIYRQHXm1TDQarVyqnz7aXpVWFJovZGdaOHW2dQgW0RsqbB2Cxdo1u2XrECx2rczyiUJsqXkT9fVuuDi62zoEizk73LB1CBZxVuFnfFNlv0ccNE62DkG1DDJrmRBCCCGEEEKog/TINCLl5eW899577Nq1C2dnZxwdHfHz8+PFF1/kxx9/5PXXX+enn35Cr9fTu3dvYmJi6NixIwDz58/nwIEDtGrVitLSUoYOHcrChQtxcKjMhb29vY0LU2q1Wnr37s2cOXPo0aOHsf20tDQ2bNjArVu3qKioYOTIkbzwwgs4O1cOrAgICMDZ2RkXFxfKysrw9fVl8eLFODn99wnNm2++yebNm9m7dy9t2rRpqI9OCCGEEMJ+NdKuiUZ6241TTEwMZ86cQVEUkpOTiYuLo2vXruTl5fHkk0/y2GOP8eWXX5Kens6AAQOYPn065eXlxutnzpxJUlISiqKwb98+9uzZY1L/9u3bSU5OZvfu3QwcOJDJkydz4cIFAL755huWLVvGqlWr2LNnD6mpqVy6dImlS5ea1LF27VqSkpJITU3lzJkzfPnll8ZzOp2OpKQkBgwYQFJSkhU/KSGEEEIIYe8kkWkkzp07R1paGsuXL8fNzQ2AJk2aMHHiRBRFYdCgQYwePdpYftq0abi5uZGamlqlLjc3N3r37s0vv/xSbVsODg5MnjyZoUOHsnXrVgDWrVvH7NmzjYtguri4sGTJEnbt2sXPP/9cpY6ysjLKyspwd//vO8l79+6lS5cuREdHEx8ff+cfhhBCCCHEvUSjsf5mhySRaSSys7Px9PTEw6PqgPecnBz69etX5Xi/fv04ffp0leP5+fmcPn2aRx99tNY2+/Xrx5kzZwA4ffo0Dz74oMn5li1b0rlzZ3JycozHoqOjCQ8PZ8iQIXTq1ImhQ4cazymKQlRUFL6+vpSXl3PixInab1oIIYQQQtyzJJER1HUpoU2bNhEaGoq/vz9Dhw6le/fu9VLvr91+tezgwYOUlZXx8ccfA5XJ0+HDhwkJCQEgIiICRVEsrl8IIYQQ4p7joLH+ZockkWkkfHx8OH/+PNevV50G1dvbu9rejZMnTxpfBYPKMTLJycns3LmTnTt3snfv3lrb/Pbbb3nggQeMbRw/ftzkfGFhIRcuXDCW+TUXFxdGjBjBgQMHAEhKSqKiooKwsDACAgLYtm0bu3fvplSFiwMKIYQQQtQrSWTEvczLy4uAgABiY2O5caNy7n6dTseOHTuIiori0KFD7N6921j+448/pqioiLFjx1apq1u3bkRHR/POO+9U2+ui1+v57LPP2L9/P5MnTwbg2WefZePGjcZX1crKyliyZAkhISF06tSp2jq++eYbvLy8AIiPj2fDhg1kZGSQkZHBvn376Nu3b5UJB4QQQgghROMg0y83IqtWrWLDhg2MHz8eJycn9Ho9/v7+RERE8NFHH/HGG2/w1ltvYTAY6NWrF3/9619Npj7+tYkTJ/LJJ5+Qnp7OyJEjAZg0aRJQOf2yj48P27Zto3PnygUSBw8ezKJFi3jllVcoLS2lvLycwMBAXnjhBZN6o6OjcXFxoby8nAceeIBnn32WEydOUFhYiJ+fn0nZ0NBQFEUhIiKivj8qIYQQQgj1sM8OE6vTGO5kIIMQKnSzYp+tQ7BIqcpWZAZwd+5s6xAsojdU2DoEi1UYbtk6BItdK6v6Sqs9a+aovl+LLipcdb5cf8PWIVjEWYWf8U2V/R5p6tjM1iHcEXenUbYOAc/VGVZv4/yfAqzehqWkR0YIIYQQQggVM9jpGBZrk0RGNBo+76rr6XvJB1/ZOgSLffRlkK1DsMi8+ep6WgnwS9ZOW4dgsYlbZtk6BIv8O9/R1iFY7FJKrq1DsJjXY/fZOgSLtHTW2zoEizVvUnXJBXt24MXj5gvZoTNHbN8j01hJIiOEEEIIIYSa2emCldYms5ZZWXl5OWvWrCE4OJjQ0FAiIiJYtWoVn332GdHR0SZlv/rqK6ZOnQrAxYsX8fb2ZvHixcbzFy9eZPDgwcb9gIAAQkJCCAsLY9SoUcyePZujR48az8fHxxvbqEt9ANevX6dv374sX77c5Hh8fDze3t58+umnxmMGg4HAwECL6vD19SU8PJwxY8Ywc+ZMrl69ajz/448/8uyzzxIYGEhUVBSTJk0iLS3NpI4JEyYQHh7+249ZCCGEEEI0MpLIWFlMTAxnzpxBURSSk5OJi4uja9euaLVas9e6urqSnp7OTz/9VGOZtWvXsnPnTr788ksiIyOZOXNmjSve16W+lJQU+vXrR2pqapUYfXx8SExMNO4fOnQID4+q3da11fHII4+QlJREamoqzZs3Z/369QBcuXKFKVOmMGrUKNLT04mPj2fdunXGqaIB/v3vf5OXl8etW7c4depUjfcghBBCCNGoyDoyor6dO3eOtLQ0li9fjpubGwBNmjRh4sSJuLq6mr3e2dmZ6dOn8+6779apvaCgICZNmsRf//rXO65PURTmzJmDt7c36enpJuc6d+5M06ZNOXPmDAAJCQlERkZaVMdtGo2GgQMHkptb+V73p59+yuDBg02mUm7Xrp3JvqIohIeHExERgaIoNd6DEEIIIYS490kiY0XZ2dl4enpW22tRV0888QTHjh3j+++/r1P5fv36GRMNS+v74YcfjOu1REVFVZssREREkJCQwM2bN/nXv/7F8OHDLa4DKtea2bdvH2PGjAEqP6u+ffvWGHd5eTnJyclERUURGRnJrl27KCsrq7G8EEIIIUSjoWmAzQK3hz+Eh4cTHh7O/v37ATh+/DhhYWEEBwczffp08vPzjdfUdq4mksjYiKaOg7JcXFyYM2cOb7/9dp3Km1sWqLb64uLiCA8PR6PREBQUxMmTJ7l8+bJJmZCQENLS0ti1axcjR47E0dHRojoOHDhAeHg4Dz/8MAUFBYwePbpO95WZmYmXlxddunShY8eO+Pj48OWXX9bpWiGEEEII0bDWrl1LUlISSUlJDBs2DL1ez0svvURsbCyff/45vr6+rF69GqDWc7WRRMaKfHx8OH/+PNevV10MrnXr1ly7ds3k2LVr12jTpk2VslFRUVy4cIEjR46YbfPbb7/lgQceqLVMdfVptVpSUlJQFIWAgADGjBlDeXk58fHxJtc2b96cBx98kNWrV1d5rawuddweI7N37140Gg1r1qwBKj+rb7/9tsaYFUXhzJkzBAQEEBAQwPfffy+vlwkhhBBCAA4O1t/u1qlTp3BxccHX1xeASZMmsWfPHrPnar3vuw9L1MTLy4uAgABiY2ONg9Z1Oh07duzA29ubM2fOcPr0aQBKS0tJSEhgyJAhVepxdHRk3rx5rF27ttb20tLS2LZtG9OnT6+1XHX1paen07VrV/bt20dGRgYZGRl89NFHJCQkVLn+6aef5rnnnsPb29vkuCV1uLm5sXTpUrZt28aVK1d4/PHH+ec//0lycrKxTH5+PomJiVy9epXDhw+Tnp5urHfv3r2cOnWKX375pdZ7FUIIIYQQd6+oqIiLFy9W2YqKiqot/6c//YnQ0FCWLFlCUVERubm53Hfff9ePat26NXq9nsLCwlrP1UbWkbGyVatWsWHDBsaPH4+TkxN6vR5/f38iIiJ4++23iY2NpbS0FL1ez6hRoxg/fny19YSEhPDBBx9w8+ZNk+PR0dE4Oztz69YtunfvzqZNm+jXr5/ZuH5bn6IohIaGmpTp378/er2ew4cPmxzv0aMHPXr0qFKnJXUA/P73vzfGsXDhQv7+97+zevVq3n33XVxdXXF1deXpp58mISGB4cOHGydMgMpX5EaOHEl8fDxz5841e79CCCGEEPeqhlhGZvPmzcbZZn9t7ty5PPfccybHPv30Uzp27IhWq2XFihUsW7aMUaPqf+FQjcHcoAoh7hGeqzNsHYJFSj5INl/Iznz0ZZCtQ7DIvPm1P+mxR79k7bR1CBabuGWWrUOwyL/zHc0XsjOXUnJtHYLFvB67z3whO9LSWW/rECzWvIm6Yj7w4nFbh3BHzhz5o61DoOuGvVZv48TU/tX2vri7u+Pu7l7jdadPn2b27Nm8++67LFiwgJSUFAAKCgoIDAzk2LFjnDx5ssZztZEeGdFonP9TJ1uHYBH9izNsHYLFNBp1/UgZtxX0hnJbh2ERDTXP7mevNBo1vsWsrpgNEa1tHYLFRu25Zr6QHVECO9g6BIsZUNmz6iMhaCydHksADdMjYy5hua2kpASdTkeLFi0wGAzs2rWLXr160adPH0pLSzly5Ai+vr5s376dkJAQgFrP1UZdf3UIIUQ9UlsSIxqKupIYtfoypBX5eZ1tHYYF1NfrpTaSxNwb8vPzee6559DpdOj1erp3787ixYtxcHDgjTfeYPHixZSVlXH//ffz5ptvAtR6rjbyaploRHJsHYBF1PhHttp6ZFT5GavwF736emTUFi8YDDpbh3BH1JTItGunvkRGbT0yavz5VqmnrQOg+8Z9Vm/j7Ozh5gs1MPX9tBZWV15ezrp16wgODmbs2LGEhYURHR3NmTNnWLduHa+//rpJ+S1btjB//nyTY1u3bsXb25vs7GyT41OnTuWrr74CYN26dXh7e3PixAnj+V/Xf+jQIaKiokyuP3v2LN7e3nz88cf1dbtCCCGEEEKFJJERVcTExHD69Gl27NhBamoqSUlJREVF8eOPP9a5DkVR8PPzM7vWy/33389bb71V53rj4uLqVK8QQgghRGOh0Vh/s0eSyAgT586dIy0tjRUrVhgHdGk0GkaMGFHnafNycnIoKChgxYoVpKamotVqaywbFBREYWEh+/fvN1tvRUUFycnJLFu2jLKyMk6ePFm3mxJCCCGEuIdJIiMEkJ2djaenJx4eHndcR1xcHBEREXTq1IlevXqRlpZWY1mNRsMLL7zAO++8g7nhWpmZmXh6euLp6UlkZKT0ygghhBBCNGKSyIhanTlzhvDwcIKDg1m+fDmaGlLy28fLy8tJSUkhMjISoE4Jx4gRI3BxcWH37t21llMUxVhvREQEe/bsoayszNJbEkIIIYS4p2gcrL/ZI3VNMSSszsfHh/Pnz1NUVIS7uzs9evQgKSmJLVu2cOrUKbp27crPP/9scs21a9do3bpyDYOMjAyKi4uZNm0aAHq9nry8PHJzc+nYsWON7b744ossXLiwxjnD8/LyyMrK4vvvv+e9994D4NatW3z++eeEhYXVw50LIYQQQgg1sdP8StiKl5cXgYGBLFq0iOLiYuPxkpISAPz8/Ni/fz+XLl0CoLCwkF27djF06FA/tcXyAAAgAElEQVSgstckNjaWjIwMMjIyyMzMJCoqivj4+Frb9fX1xcvLi+Tk6lezT0xMJDg4mMzMTGPdK1eulNfLhBBCCNHoyRgZIf7Pa6+9Rrdu3XjssccYO3YskydP5rvvvmPq1Kl0796dBQsWMGfOHMLDw/nDH/7AlClTePjhh7l8+TKHDx8mODjYpL7Q0FASEhLMjoF5/vnnyc2tfp7++Ph4QkNDTY4FBgZy6tQpLl68eHc3LIQQQgghVEcWxBSNiCyIaW2yIKb1qXHBOFkQ0/pkQUzrkwUxrU+NP98q2X5BzF5/tf6CmN8/ZX8LYqrrrw4h7oLeUGHrECzioHGydQj3PEeNMzpDzdOD2yP5Xlif2n5WADio7CECVP6R3bbdL7YOo860+mLzheyMwaC3dQgWa+LgausQLOao1vzrHqC+n3xCCFGPCvI8bR2CRdT4VFgIIepCjUmMvbDXMSzWpr7+cyGEEEIIIUSjJ4mMqFZ5eTnr1q0jODiYsWPHEhYWRnR0NGfOnGHdunW8/vrrJuW3bNnC/PnzTY5t3boVb29vsrOzTY5PnTqVr776CsCkrkOHDtGvXz/Cw8MZN24cU6ZM4ezZsybX7t27F29vb7788sv6vmUhhBBCCFWSWcuE+JWYmBhOnz7Njh07SE1NJSkpiaioKH788cc616EoCn5+fhZNkdy9e3eSkpJISUmhX79+vPbaa3ddpxBCCCGEuPeYTWRurxciGo9z586RlpbGihUrcHd3B0Cj0TBixAhGjRpVpzpycnIoKChgxYoVpKamotVaPqB60KBBJtMxX7t2jYMHD/LWW29x7Ngxrl69anGdQgghhBD3Go1GY/XNHplNZMaMGdMQcQg7kp2djaenJx4eHndcR1xcHBEREXTq1IlevXqRlpZm0fV6vZ709HST79/OnTsZMWIEbdu2ZdSoUSQkJNxxfEIIIYQQQt3MJjKyzIw4c+YM4eHhBAcHs3z58hqz8tvHy8vLSUlJITIyEoDIyMg6vwp29uxZwsPDeeSRRzhw4ACPP/648Vx8fLxJnfHx8XdzW0IIIYQQ9wSNg/U3e2R2+mWNRoPBYKg1oXFwsNO7E3fEx8eH8+fPU1RUhLu7Oz169CApKYktW7Zw6tQpunbtys8//2xyzbVr12jdujUAGRkZFBcXM23aNKCydyUvL4/c3Fw6duxYa9vdu3cnPj4erVbLCy+8wJIlS1izZg2nTp3izJkzLFy40Fj2ypUr/Otf/+Khhx6q3w9ACCGEEELYPbOJTElJCT4+PtWeMxgMaDQavv/++3oPTNiOl5cXgYGBLFq0iBUrVtCiRQug8rsA4Ofnx8aNG7l06RK/+93vKCwsZNeuXcTGxgKVA/JjY2OZMGGCsc7Y2Fji4+N59tln6xSDs7MzS5YsISgoiOzsbBRFYcaMGTz//PPGMn/5y19QFEUSGSGEEEI0anY6hMXqzCYyzZo1IyUlpSFiEXbktdde47333uOxxx6jSZMmuLu70759e2bOnEn37t1ZsGABc+bMQafTYTAYmDJlCg8//DCXL1/m8OHDrF692qS+0NBQYmJimDNnDhUVFbi4uJiNoW3btkyfPp233nqLb7/9lm3btpmcHzduHGFhYSxatAhXV1lESwghhBCiMdEYzAyCGTBgAEePHm2oeMQ97tatWwQGBvLZZ5/RqVOnBm1bb8g2X8iOOGjMPmcQ9eDq1dpfd7Q37drlmi8k7oreUGHrECymxp8XBtQ1BrdcX2zrECxmMOhtHYJFmjio86Gko6aPrUOg35b9Vm/jxJRhVm/DUmZ/8slgf1Ff9uzZw7vvvsvkyZMbPIkB0GgcG7zNu6G2X/IAGtTVt12uL6Flm7PmC9oRnUF9YxIdNOZ7YO2JvU4zWhs1Jl9q4+TQwtYhNBLqSr6EbZlNZD744IOGiEM0AiEhIYSEhNg6DCGEEELYJUli7pQKn7/UC7OJjKIotU6dq9FoWLlyZb0GJYQQQgghhBC1MfuOQocOHardPDw8yMjIkEUJVaS8vJx169YRHBzM2LFjCQsLIzo6mjNnzrBu3Tpef/11k/Jbtmxh/vz5Jse2bt2Kt7c32dmV40127drF//7v/1Zpa926dcTExADg7e3NzZs3jed0Oh3Dhg1j9uzZJtccOnSIfv36ER4ezrhx45gyZQpnz/73tZ/58+czfPhwwsPDjduePXvu7kMRQgghhFA5B431N3tktkdm3rx5JvsVFRVs376d999/n969e1c5L+xXTEwMpaWl7NixA3d3dwwGA3v37uXHH3+scx2KouDn54eiKPj4+DBy5EiWLl3K2bNn6d69O1A5riohIYE33nij2jr2799P+/btOXr0KHl5ebRt29Z47vY6MgBvvvkmr732Gh9++KHx/MyZM5kyZcqd3L4QQgghxD2psb5aVudRo3q9nri4OIKCgti1axdvv/02H330EX379rVmfKKenDt3jrS0NFasWIG7uztQ+VrgiBEjGDVqVJ3qyMnJoaCggBUrVpCamopWq8XZ2Zlx48YZkw+AgwcP4uTkhK+vb7X1KIrCpEmTGDlyJImJiTW2N2jQIHJzZYYmIYQQQghRVZ0SmdTUVEaPHs22bdtYvHgxW7duZdCgQdaOTdSj7OxsPD098fDwuOM64uLiiIiIoFOnTvTq1Yu0tDQAxo8fT1JSEjqdDoD4+HiioqKqraOgoICDBw8yevRooqKiTBKgX9Pr9aSnpzNmzBiT45s2bTJ5tUwWYxVCCCFEY6fRWH+zR2ZfLQsLC+PKlSvMmDGDoKAgNBoNFy5cMCnTuXNnqwUorOPMmTO8+OKLlJaWMmzYMFq2bFltudvTkJaXl5OSksL27dsBiIyMRFEUxowZg4+PD23btmX//v34+vqSnp7On/70p2rr27lzJ48++ihubm489NBD6HQ6jh07Rv/+/QE4e/Ys4eHhXL58GTc3N3bs2GFyvbxaJoQQQgghoA6JTE5ODgCrV6/mrbfeqrKujEajkafiKuDj48P58+cpKirC3d2dHj16kJSUxJYtWzh16hRdu3bl559/Nrnm2rVrtG7dGoCMjAyKi4uZNm0aUNljkpeXR25uLh07dmT8+PHEx8dz5coVfH196dChQ7VxKIpCQUEBAQEBABQXF6MoijGRuT1GRqvV8sILL7BkyRLWrFljpU9FCCGEEEL9NPY6Gt/KzCYyP/zwQ0PEIazMy8uLwMBAFi1axIoVK2jRonJhr5KSEgD8/PzYuHEjly5d4ne/+x2FhYXs2rWL2NhYoDIBiY2NZcKECcY6Y2NjiY+P59lnnyU0NJR33nmHH3/8kblz51Ybw8mTJykuLiYrK8vY03P58mXGjh3LwoULTco6OzuzZMkSgoKCyM7OxsfHp94/EyGEEEIIoV53vUR0QUFBfcQhGsBrr71Gt27deOyxxxg7diyTJ0/mu+++Y+rUqXTv3p0FCxYwZ84cwsPD+cMf/sCUKVN4+OGHuXz5MocPHyY4ONikvtDQUBISEjAYDLRs2ZLhw4dz9epVY2/LbymKwtixY01Wze7QoQM+Pj7VTqPctm1bpk+fzvr1643HfjtGZtu2bfX06QghhBBCqFNjHSOjMfz2XbHfGDRoEIcPHzbu/7//9//YvHmzcX/AgAEcPXrUehEKUU8MnLZ1CPc8DXb6k64G5foSW4dgMQfNXT9/anAOGhdbh2Ah9a0ubuZXuagHGo2jrUNoBNT3bw9AQy9bh8CgHVlWb+PwhKFWb8NSZl8tKy8vN9n/7XgY+eEp1OJ04b9tHYJF9l1ysnUIFnukQ7n5QnbEWX05AakX1JYUwKMdy2wdgkV6erSydQgWO3/jmq1DsFjzJur6++FGhboe1ACU6WwdgeUSzze1dQgWWzrA9omMvfaYWJvZX+MaM5+MufNCCCGEEEKYo8YkRtiW2R4ZIYQQQgghhP1qrP0KZhMZrVbLyy+/bNwvKSkx2ddqtdaJTKhGQEAA77//Pj179jQei4qK4pVXXiEhIYE+ffqYrP3y+uuv4+rqynPPPUd8fDyZmZmsXbuWixcvMn78eA4dOlRtOzqdjhEjRtCnTx82btxo9fsSQgghhBD2y2wiM2vWLIv2hbCW/fv30759e44ePUpeXh5t27a1dUhCCCGEEDbXSJeRMZ/I1LQmiBANTVEUJk2axPHjx0lMTGTGjBm2DkkIIYQQQtiI2UTmm2++MVvJwIED6yUYoV7R0dG4uPx3NqVz587Va/0FBQUcPHjQuBbOq6++KomMEEIIIQQyRqZGU6dOpU2bNjg5OVU71bJGoyEzM9MasQkVWbt2bZUxMlDzrHaWzna3c+dOHn30Udzc3HjooYfQ6XQcO3aM/v3733nQQgghhBBCtcwmMoGBgZw4cYJHH32UiIgI+vXr1xBxiXtEq1atKCwsNDl27do1OnfubFE9iqJQUFBAQEAAAMXFxSiKIomMEEIIIRo9Fa6VXC/M3vaGDRtISUmhR48eLF++nJCQEDZu3Ehubm5DxCdUbsiQIezevZvr168DkJubS1ZWFn5+fnWu4+TJkxQXF5OVlUVGRgYZGRmkpKSwZ88ebt26Za3QhRBCCCGEHavTOjItW7bkiSee4IknnuA///kPn376KSNHjuTvf/87AwYMsHaMQsWGDBnCpEmTmDp1KhqNBkdHRxYtWkS3bt2qLV9UVMTw4cON+926dcPT05OxY8eavI7WoUMHfHx82LNnD5GRkVa/DyGEEEIIe9VYx8hoDNUNfKmGwWAgKyuLxMREDh48yPDhw3nhhRdo166dtWMUol78UJhi6xAssu+Sk61DsNgjHcptHYJFnFXYFZ96wcV8ITvzaMcyW4dgkZ4erWwdgsXO37hm6xAs1rxJnf78sBs3KtT3l2KZztYRWCbxfFNbh3BHlg4YaesQGLYzy+pt7A8bavU2LGW2R+b06dMkJiaye/duunfvTkREBCtWrKBpU3V+2YQQQgghLOXTsqf5QnboWH6OrUMQDcDSSZTuFWZ7ZH7/+9/TtWtXQkNDad++fbVlHnvsMasEJ0R9unq12NYhCCGEUKl27dQ5Nvjq1Y62DuGe165dC1uHgH/K11ZvY++4IVZvw1Jme2RurxHzz3/+s9rzGo1GEhkhhBBCCCFEgzKbyLz55pv87ne/a4hYhJUEBATw/vvv07NnT+bPn8+BAwdo1aoVpaWlDB06lIULF+Lg8N/BAvPmzePgwYPs378fJ6f/jtPw9vbm6NGjNG/enIKCAp566in8/f2ZN29ejW3Hx8eTmZnJ2rVruXjxIkFBQTzwwAPo9XqcnJxYtGiRyYQRZ8+eZcyYMcTExDBt2jTj8XXr1rF+/Xo+++wz4xTg69ato6SkhFdeeaUePy0hhBBCCHVppG+WmZ9+ecyYMQ0Rh2hAM2fOJCkpCUVR2LdvH3v27DGeKyws5MCBA3h6epKRkVHt9bm5uTzxxBOEhYXVmsRUp0WLFiQlJZGcnMxjjz3GwoULTc7HxcXh5+eHoihVrr3//vt56623LGpPCCGEEELcm8wmMnWc1EyokJubG7179+aXX34xHktOTsbf35/HH3+82mTip59+YurUqUyfPp0nn3zyrtofPHiwyXpEFRUVJCcns2zZMsrKyjh58qRJ+aCgIAoLC9m/f/9dtSuEEEIIcS/RaKy/2SOziYxGo8FgMKDX62vchDrl5+dz+vRpHn30UeMxRVGIiooiKCiI48ePc/nyZZNrpk2bxqxZs5gwYcJdt//ll1+a9PhlZmbi6emJp6cnkZGRVRIpjUbDCy+8wDvvvCMJthBCCCGEnVu/fj3e3t7k5FTOnnf8+HHCwsIIDg5m+vTp5OfnG8vWdq4mZhOZkpISfHx86N27d5Xt9nGhLps2bSI0NBR/f3+GDh1K9+7dAcjOzqaoqAg/Pz+aNWtGUFAQiYmJJteOGDGCuLg4bty4cUdtFxcXEx4ezrBhw/jb3/7GrFmzjOcURTEubhkREcGePXsoKzNdf2LEiBG4uLiwe/fuO2pfCCGEEOJe46Cx/map7777juPHj3P//fcDoNfreemll4iNjeXzzz/H19eX1atXmz1X632bK9CsWTPS0tKq3dLT00lLS7P8zoRNzZw5k+TkZHbu3MnOnTvZu3cvUJlIFBUVERgYSEBAAJmZmcTHx5tc++qrr9KzZ09mzJjBjRs3UBSF8PBwwsPD2blzp9m2b4+RyczMJDQ0lBdffBGDwUBeXh5ZWVmsX7+egIAAnnjiCW7dusXnn39epY4XX3yRNWvWUFFRUT8fiBBCCCGEqDdarZZly5axZMkS47FTp07h4uKCr68vAJMmTTKO067tXG3Mzlqm0WiMmZS4t3Tr1o3o6GjeeecdHn74YVJSUlAUBU9PT2OZ4OBgjhw5YvxiaTQali5dyuLFi5kxYwYffvgh48ePt7htR0dHXnrpJYKDg0lPT+fcuXMEBwebZN8pKSns2LGDsLAwk2t9fX3x8vIiOTmZ4ODgO7x7IYQQQoh7w530mFiqqKiIoqKiKsfd3d1xd3c3ObZmzRrCwsLo1KmT8Vhubi733Xefcb9169bo9XoKCwtrPdeyZcsaY7rrwf4FBQXmqhB2bOLEidy6dYu0tDS6dOliksQAhIaGVjtWZenSpTzwwAPGnpk74eLiwrx581i/fj2KohAaGmpyPjAwkFOnTnHx4sUq1z7//PMmEwUIIYQQQgjr2bx5M4GBgVW2zZs3m5Q7duwYp06d4vHHH7d6TBqDmUzl9tP4a9eu4eHhYVxvJC8vjw8++IDPPvuMY8eOWT1QIe7W1avFtg5BCCGESrVrp86HZ1evdrR1CPe8du1a2DoEgj/PsnobOx7uW6cemU2bNvHJJ5/g7OwMwKVLl2jTpg1Tp05l586dpKSkAJWdIYGBgRw7doyTJ0+yYMGCas/VxuyrZU2aNMHf358rV67QsmVL1q5dy6lTp1i3bh3Dhg2rkoUJYa9atjlj6xAsojeobwyQk4OrrUOwSIXhlq1DsJhWd2c9oLbU1LGVrUOwiMZe5xmtRbn+pq1DsJijpqmtQ7CIzuBkvpCdqdDfwr31dVuHUWcajdkXhezUQ7YOoEFU9wpZdWbOnMnMmTON+7cXZu/RowefffaZsZNk+/bthISEANCnTx9KS0urPVcbs4nM66+/TkREBGFhYSQkJPDcc8/xwAMPoCgKXbt2NduAEEIIIYQQwnoaYozM3XJwcOCNN95g8eLFlJWVcf/99/Pmm2+aPVcbs6+WDRo0iIMHD+Lg4IBWq+XBBx/kwIEDtQ68EcIelevV9Qqk9MhYn/TINAzpkbE+6ZGxPgeNOntk1EStPTLODrbvkRn9hfVfLdsdNNTqbVjK7DemoqLCOC7G2dkZNzc3SWLsWEBAgHHRoerMmzcPPz8/ysvLa60nKyuLxx9/nMDAQKKiopgwYYJxKuaLFy8yePBgk/I3b97E29vb5Nj169fp27cvy5cvNzkeHx9PdHR0tXUFBAQQEhJCWFgYo0ePZseOHSbX6nQ6hg0bxuzZs2uNXwghhBCisXBogM0emX21TKvV8vLLLxv3S0pKTPYB3njjjfqPTNS7wsJCDhw4QNeuXcnIyKhx6uKsrCwWLFjA2rVrefDBB4HKhCMpKcmi9lJSUujXrx+pqam8/PLLxkFf5qxdu5aePXuSk5NDVFQUw4cPp0OHDgDs37+f9u3bc/ToUfLy8mjbtq1FMQkhhBBCiHuD2QRr1qxZdOnSxbj9dr9Lly4NEaeoB8nJyfj7+/P4449XmVL51zZs2MCcOXOMSQxAp06dePbZZy1qT1EU5syZg7e3N+np6RbH27NnT9zd3bl8+bJJnZMmTWLkyJEkJiZaXKcQQgghxL3GQWOw+maPzPbIzJ07t9bz1U3DJuyToii88sorPPjgg6xYsYLLly8bezp+LTs7m9jY2Ltq64cffqCwsBA/Pz+uXr2KoiiMHj3aojr+9a9/0apVK37/+98DlVPxHTx4kNdee41u3brx6quvMmPGjLuKUwghhBBCqNMdvfKm0+nIyMggOjqaoUPtb+CPqCo7O5uioiL8/Pxo1qwZQUFBde7RiI6OJiwsjCFDhgB1GwgbFxdHeHg4Go2GoKAgTp48adKzYq694OBgpkyZwh//+EfjK2k7d+7k0Ucfxc3NjYceegidTidrGAkhhBCi0XPQWH+zR2Z7ZH4tOzubxMREUlJSuHbtGmPHjmXLli3Wik3UI0VRKCoqIjAwEKgc+9S8eXOeeeaZKmV79erFt99+S69evYDKMSs3b95kwIABALRq1YobN26g0+lwdHQE4Nq1a7Rp08ZYd0pKCs7OzsZxNeXl5cTHx9dpkP7tMTK7d+8mJiaGAQMG0LZtWxRFoaCggICAAACKi4tRFIX+/fvf5acjhBBCCCHUxmyPTF5eHh999BGhoaFMmDCB//znP7z88st4eHgQExND3759GyJOcRduJxaKopCRkUFGRgZZWZXT9B05cqRK+Tlz5rBhwwZOnjxpPHbr1n+ncHR1deV//ud/iIuLMx7btm2bsccmPT2drl27sm/fPmN7H330EQkJCRbFPXr0aIYMGcJf/vIXTp48SXFxMVlZWcY6U1JS2LNnj0lsQgghhBCNjcxaVgN/f39atGjBs88+y5gxY4xP3VevXm314MSdefLJJ409JQDz58+nS5cueHp6mpQLDQ1FURR8fX1Njg8fPpxly5axcuVKrl69Stu2bXF2dmbZsmXGMm+++SbLly9n69atGAwGHnjgARYtWgRU9v6Ehoaa1Nm/f3/0ej2HDx9Gp9PVeQazF198kaioKG7cuMHYsWNNXmvr0KEDPj4+7Nmzh8jIyLp9OEIIIYQQ9xh7ffXL2swuiDl//ny++OILPDw8GDduHOPGjcPb25uhQ4eSlJRkTGyEqKulS5fSpk0bsxNJ1DdZENP6ZEFM65MFMa1PFsRsGLIgpvXJgpgNwx4WxByfvt/qbSiBw6zehqXM9sisWrWKxYsX8/nnn5OYmMiHH35Ijx49uHHjhsm4CCHM0Wq1jB8/nlatWjV4EgNQUnGlwdu8G44aF1uHYDG1/RK6VJJr6xAsVlyuvj+yvVqo63uhQX2f8aVbhbYOwWKtXRzNF7Ij+aU6W4dwz2vfTF0Pw25ztoMfcRo7nR7Z2uo02L9Zs2ZEREQQERHBL7/8QlJSEklJSURERBAYGMiaNWusHae4Bzg7O5OcnGzrMIQQQgghxD2gTolMVlYW//73v/Hx8WHw4MHMnj2b2bNnc+zYMVmUUAghhBBCCBtqrGNkzHaGbdq0iblz55KamsqsWbP49NNPjef69+/P0qVLrRqgqCogIIChQ4ei0/23mzs+Ph5vb2+T6bDffPNN+vTpQ35+vsn1eXl5zJ8/n8DAQMLDwwkNDWXZsmUUFxcDleOihg8fTkREBEFBQUyePLnahLWm+gMCAsjJyalSfurUqcY2b29Hjx41nnv44Ye5efOmSfmvvvrKpI558+bh5+dHeXl5XT8uIYQQQghxDzKbyPzjH//g448/Ji4ujg8//JCtW7c2RFzCjPbt2xunUAZISEigd+/exn2dTkdSUhIDBgwwruUCldMoP/HEE3h6evLFF1+QlJSEoih06dLFJCGZOXMmiYmJfPHFF8TGxrJx40b+9re/ma3fnEWLFhlfTbx9/W3NmjUzaeO3CgsLOXDgAJ6enmRkZNS5TSGEEEKIe1ljnX7ZbFzXrl3jwQcfBOChhx4iLy/P6kEJ8yIjI4mPjwfgwoULlJSU0LNnT+P5vXv30qVLF6Kjo43lAFJSUmjZsiWzZ882TtHs7OzMtGnT8PLyqratXr16sXDhQj744ANuT3JXU/13Y+bMmWzdupWCgoJqzycnJ+Pv78/jjz+Ooij10qYQQgghhFCnOiVYBoMBvV6PTqfDYDAY929vouENGjSInJwcrl+/TkJCAhERESbnFUUhKioKX19fysvLOXHiBADffffdHS1i2q9fP/Lz841JRk31m7N8+XKTV8t+3QvUoUMHwsPDef/996u99nabQUFBHD9+nMuXL1t8H0IIIYQQ9xoHjcHqmz0ym8iUlJTg4+ND79696dOnD0VFRcb92/8rGp5Go2H06NGkpqaSmprKuHHjjOfy8/M5fPgwISEhAERERNTYg5GYmEh4eDgBAQHs2rWrxvZ+vdyQJfX/1m9fLfvt9N0zZ84kJSWF3FzTaXGzs7MpKirCz8+PZs2aERQUJBNNCCGEEEI0YmZnLUtPT2+IOMQdiIyMZMKECQwcOJBWrf674FxSUhIVFRWEhYUBUFFRwa1bt1iwYAE+Pj4mr4LdnlY7Ojqa0tLSGtv69ttvadOmDW3atOGjjz6qsf6mTe9ugbNWrVoxZcoU1q5da3JcURSKiooIDAwEKtekad68Oc8888xdtSeEEEIIoXaNddYys4nM/fff3xBxiDvQuXNnnn/++SqvisXHx7NhwwYeeeQR47GnnnqKPXv2MG7cOD788EM2bdrEU089haOjIwaDodYk5ocffmDlypU8/fTTZuv/7Stud2LatGkEBwcbx/BotVpSUlJQFAVPT09jueDgYI4cOYKvr+9dtymEEEIIIdTF7Ktlv/3DNCYmxmT/4Ycfrt+IhEUmTpxIr169jPuFhYUUFhbi5+dnUi40NBRFUXB1dWXLli2cPXuWUaNGERERweTJk+nSpQvDhg0zlt+0aRPh4eEEBwezdOlSnnnmGZ588klOnDhRa/23PfnkkwwfPty4Xb9+Hag6Rqa6Hj9XV1eeeeYZ4+tlaWlpdOnSxSSJqa5NIYQQQojGqLHOWqYx/HrwQzX69+/PsWPHjPuDBg3i8OHDNZ4Xwl5d135u6xAs4qhxsXUIFnN2bGHrECxyqeQXW4dgseJy9b0/4NXC3dYhWESD+j7jS7cKbR2CxVq7ONo6BIvkl+rMFxJ3pX0zV1uHcEdaOOPST7oAACAASURBVAXaOgT+sHev1dv4xN/f6m1YyuyrZRqN6Q/03+Y9vz0vhL0yoK5fQs6ObrYOwWJ6g7oWKs267GTrECx2tdRen4vV7MmeWluHYJGb5bdsHYLFWjmr73dxkVZdP5M7ura0dQgWc3JQ18MlsM+ZsdSgsY6Rsfg3oiQuQgghhBBCCFsz2yOj1WpZs2aNcb+0tNRkv7xcXU9ghRBCCCGEuJfY6zov1mY2kQkNDeXSpUvG/bFjx5rs/3r9EiEAAgIC0Gq17N271zjzWHx8PDExMbz66qu4urqycuVKkxnxhg0bRteuXfnkk08AyM3NpWnTpsZppZctW0a/fv2YN28eBw8eZP/+/Tg5qe+1ICGEEEIIUT/MJjKvvfYaAGfOnOHIkSNcv34dDw8PHnroIR544AGrByjUqX379mRlZeH/fwPDEhISTBZPfeSRR6qsFQMwfvx4AObPn0+fPn2YMmWK8VxhYSEHDhyga9euZGRkEBwcbOW7EEIIIYSwf411jIzZRMZgMLBw4UISExPp0KED7du35/Lly1y5coXw8HBWrlwp42ZEFZGRkcTHx+Pv78+FCxcoKSmhZ8+ed1VncnIy/v7+DB06FEVRJJERQgghhGjEzA72/8c//sGhQ4fYvn07X331Ff/4xz/IzMxk+/btHDlyhO3btzdEnEJlBg0aRE5ODtevXychIaHKekQHDhwwWU9mx44dZutUFIWoqCiCgoI4fvw4ly9ftlb4QgghhBCq0VjXkTEbV1JSEosWLaqyenzfvn1ZsGABSUlJVgtOqJdGo2H06NGkpqaSmppaZSzVI488QlJSknGbMGFCrfVlZ2dTVFSEn58fzZo1IygoiMTERGveghBCCCGEKjhoDFbf7JHZV8vOnj3LwIEDqz03cOBAXn755XoPStwbIiMjmTBhAgMHDjQO2r9TiqJQVFREYGDlolNarZbmzZvzzDPP1EeoQgghhBBCZcwmMjqdDje36hfmc3NzQ6/X13tQ4t7QuXNnnn/++Sq9eZbSarWkpKSgKAqenp7G48HBwRw5cgRfX9+7DVUIIYQQQrVksH8NKioqOHjwIAZD9V1KOp26VuYVDWvixInVHr89Rua2Pn36sGLFimrLpqWl0aVLF5MkBiqnBlcURRIZIYQQQohGSGOoKUP5PwEBAWYrycjIqLeAhLCWQu0uW4dgEdcm7W0dgsX0hgpbh2CR+HOFtg7BYldL7XXIZc2e7KmumG+W37J1CBZr6qi+x7HF5fb5zn1N2jZtaesQLObk0MLWIVhIXd+J25wc+ts6BOb+8yurt7H+4Uet3oalzPbISJIi7hUtnXvYOgSLafVFtg5BCCGEEMIumU1khBC2cz3f29Yh3NNGuds6gjugwpjLVNbxpcZfjOrqC63UzNYBWOim+jrqRANp187WEdjv9MjW1ljvWwghhBBCCKFianzwpHoBAQFotVr27t2Lo6MjAPHx8cTExPDqq6/i6urKypUruf/++43XDBs2jK5du/LJJ58AkJubS9OmTY3TGi9btox+/foxb948Dh48yP79+3FycjJp97333sPd3Z0pU6bw448/snr1an744Qc8PDxwdnZmxowZjBw50lh+woQJaLXaKmsFHTlyhLfeeouCggLKy8sZOHAg8+fPN8YSEBDA+++/T8+ePY3XREVF8corrzB48GDmz5/PgQMHTKZknj17NocOHeLo0aNA5bTfnTp1wsXFxfj5AIwYMYI+ffqwcePGu/h/QAghhBDi3mGv67xYmyQyNtK+fXuysrLw9/cHICEhgd69exvPP/LII6xdu7bKdePHjwdg/vz59OnThylTphjPFRYWcuDAAbp27UpGRgbBwcEm16anp7N+/XquXLnClClTeOmll9iwYQMAV69e5euvvzaW/fe//01eXh5OTk6cOnWKPn36AHD+/Hnmzp3L2rVrGTRoEHq9nlWrVvHcc8+xZcuWOt//zJkzTWIHCAkJMf53QEAAa9euNUmGMjMzad++PUePHiUvL4+2bdvWuT3x/9m787Aoy/3x4+8BAVNU3KUMTY+gyFFzw3IfUBDBAcwkl1JzSUMkPaall2Km5lYp1HGpY3ZMSx0EcU0hSL8maGlKuB874YYiIAjKOr8//PkcJ9YxcWbg87quua647/u5n888TsCHexNCCCGEqFpkapmR+Pn5KaMMycnJ5OTk6P3S/jiioqLo27cvI0aMQKvV6tXduHEDnU6Hvb0933zzDa6urvj6+ir1jRs31vtaq9Wi0Wjw9fXV62vNmjW88sordO/eHQALCwtmzpxJcnIyP/3001+KvzxarZaAgADc3d2JiIio1HsJIYQQQpgLC1Xlv0yRJDJG0r17d86fP8+dO3fYsWOHXhIB/ztn5eFr27Zt5fap1Wrx9/dn4MCBnDx5kpSUFKUuOjpa2Uo7KSmpzEMq8/PziYqKwt/fHz8/P/bs2UNubi4A586do1OnTnrtrayscHZ25vz58xV+/+vWrdN7f2fOnCmzfVpaGkePHmXQoEH4+/srSaAQQgghhKieZGqZkahUKgYNGsTu3bvZvXs33377Lb/99ptSX9rUstIkJSWRmZlJjx49UKlUDBw4kIiICCZNmgQ8SGTefffdCvUVGxtLy5YtcXBwAMDZ2ZkDBw7g7e1d7nuqaH1JU8vKsnPnTvr374+trS1dunShsLCQEydO8OKLxt+7XQghhBDCmKrryIQkMkbk5+fHsGHD6Natm97C98eh1WrJzMzEzc0NgLy8PGrXrs2kSZPIysri6tWrtG3bFniQmJw+fbrMvi5evKiM4OTk5KDVavH29sbJyYmTJ0/qbQqQn59PUlISU6ZMAaBBgwZkZOjvt5qRkUHDhg3/0vtLS0tTYsrKykKr1UoiI4QQQghRTUkiY0TPP/8877zzTpnTvCoiLy+PXbt2odVqadGihVLu4eHB8ePHuXHjBr1791bKR4wYgZ+fH1FRUfj4+ABw+/ZtDh06RM+ePUlISODHH3/E1tYWgNzcXHr16sW1a9eYOHEiAQEB9OnTR1nsv3z5crp168bf//53AHr27Mm2bdvo0qULlpaWxMXFYWFhoYzwGOrUqVNkZWVx+PBhZVQnJSWFwYMHM2fOHJ55xtxOIxBCCCGEeHJMdQ1LZZNExsiGDx9eYvnDNTIPubi4sGjRohLbHjx4EAcHB70kBsDHxwetVsu9e/f07tO0aVP+/e9/s2LFCj799FNq1apFrVq1mDBhAjt27KBPnz5KEgNgY2ODu7s74eHhBAYGEhoayscff0x6ejqpqan07duX5cuXK+3feustli1bhq+vLxYWFtSrV4+wsDC97aDXrVunt+4nICCA1157rcT3p9VqGTx4sN7UtKZNm+Ls7My+ffvw8/Mr8TohhBBCCFF1qXQ6XfXceLqayMvLw9PTk++//54aNZ583vrLL7/wzjvv8MEHHyhbSZuuim9GYCpu3bI3dghCCCGEKEPjxnWMHQKzjkVX+j2WdnOr9HsYShIZUW3oOGfsEKo8FeY1tl2kKzB2CAYr0uUbOwSDWVrUNHYIVZ65/b9njnTIr0uVzXw/x3/t+IwnobomMjK1TAghhBBCCDNWXdfIVNfd2kQFqNVqvL29KSoq0it79LyYYcOG6a3leejUqVOMHTsWd3d3hg4dyuuvv86xY8cAGD16ND/88IPS9uDBg/j5+eHp6Ym7uzsfffQReXl5BsUhhBBCCCGqF0lkRJlycnKIjIwsse7ChQukpqZy7949EhMTlfJz584xadIkxo0bx8GDB9FqtXz44YekpaUV6+PYsWN88MEHfPTRR+zbt4/du3dz48YNFixYUOE4hBBCCCGqM4un8DJFphqXMBGBgYGEhYXpjZA8pNVq0Wg0+Pr6otVqlfL169fzyiuv6G357ODggIeHR7E+QkNDmTx5Mk5OTsCDHdJCQkLYs2cPV69erVAcQgghhBCi+pFERpTJxcWF9u3bs2XLFr3y/Px8oqKi8Pf3x8/Pjz179pCbmwtAUlISHTt2rFD/586do1OnTnpldnZ2PP/883pTx0qLQwghhBCiurNQ6Sr9ZYokkRHlCg4OZv369WRnZytlsbGxtGzZEgcHB+zt7XF2dubAgQNPPQ4hhBBCiOrOQlX5L1MkiYwoV6tWrejbty8bNmxQyrRaLRcvXkStVqNWqzlz5owyvczZ2ZlTp05VqG8nJydOnjypV5aRkUFycjJt2rQpNw4hhBBCCGFapkyZwpAhQ/D19WXEiBGcOXMGgMuXLzN8+HA8PDwYPnw4v//+u3JNWXWlkURGVMjUqVPZvHkz2dnZZGZmkpCQQHR0NDExMcTExBAXF0diYiLXrl1j/PjxbN26lSNHjijXJycns3///mL9vv322/zzn//k3LkHZ7zk5uYSEhKCp6cnzZs3LzMOIYQQQghheiMyS5cuZefOnURERDBu3Djef/99AObPn8+IESPYv38/I0aMYN68eco1ZdWV+r4NC0tUV82aNUOj0ZCRkcHRo0fp06cPtra2Sr2NjQ3u7u6Eh4fTtm1b1qxZw9q1a3F3d8fHx4c5c+bQsGHDYv26uroyd+5cZs2ahaenJ15eXjRp0oT58+eXG4cQQgghhDA9derUUf777t27qFQqbt++TVJSEt7e3gB4e3uTlJREWlpamXVlUel0OtNcvSPEE6bjnLFDqPLM7VTmIl2BsUMwWJEu39ghGMzSoqaxQ6jyzO3/PXOkQ35dqmzm+zl2NHYAfHjiYKXfI6h1dzIzM4uV161bl7p16xYrnzNnDv/3f/+HTqfjiy++IDc3l1mzZrF7926ljZeXF8uXL0en05Va1759+1JjqvEX35MQQgghhBCiitu4cSNhYWHFygMDA5k6dWqx8kWLFgEQERHBsmXLmDZt2hOPSRIZUW2Y41965C+AQghhGszxZwjIz5Hq4mlsj/zGG2/g5+dXrLyk0ZhH+fr6Mm/ePJo1a0ZKSgqFhYVYWlpSWFjIzZs3sbe3R6fTlVpXFklkhDBhqbeeNXYIQggz0LjxdWOH8Fhu3Sr7lxRTYq7P2Jx+jpjrM64uSptC9mcPN4Z6mITExMRQr149GjZsSLt27di1axcajYZdu3bRrl07GjRoAFBmXWlksX81oVar8fb2pqioSK/s0UMnhw0bhkajKXbtqVOnGDt2LO7u7gwdOpTXX3+dY8eOATB69Gh++OEHpe3Bgwfx8/PD09MTd3d3PvroI/Ly8gyKo7RYrly5gqur62M+ASGEEEKIqsmUdi27d+8e06ZNw8fHB41Gw1dffcWaNWtQqVSEhISwadMmPDw82LRpEwsWLFCuK6uuNDIiU43k5OQQGRlZ4rDghQsXSE1NxcrKisTERFxcXAA4d+4ckyZNYtmyZfTu3RuAP/74Q9kP/FHHjh3jgw8+YP369Tg5OSmLuhYsWKDMkywvjrJiEUIIIYQQpq1Ro0Zs3bq1xLrWrVuzbds2g+tKIyMy1UhgYCBhYWF6IyQPabVaNBoNvr6+ysGWAOvXr+eVV15RkhgABwcHPDw8ivURGhrK5MmTcXJyAh5syRwSEsKePXu4evVqheIoKxYhhBBCCFGcKY3IPE2SyFQjLi4utG/fni1btuiV5+fnExUVhb+/P35+fuzZs4fc3FwAkpKS6NixY4X6P3fuHJ06ddIrs7Oz4/nnn9ebOlZaHOXFIoQQQgghxEOSyFQzwcHBrF+/nuzsbKUsNjaWli1b4uDggL29Pc7Ozhw4cOCpx2GsWIQQQgghzJmlqvJfpkgSmWqmVatW9O3blw0bNihlWq2WixcvolarUavVnDlzRpnS5ezszKlTpyrUt5OTEydPntQry8jIIDk5mTZt2pQbR3mxCCGEEEII8ZAs9q+Gpk6dir+/P4WFhWRmZpKQkMCPP/6Ira0tALm5ufTq1Ytr164xfvx4xowZQ48ePXj55ZcBSE5OJikpqdg6mbfffpuZM2fSuXNnZbF/SEgInp6eNG/evMw4AG7dulVmLEIIIYQQojhTXcNS2WREphpq1qwZGo2GjIwMjh49Sp8+fZTEAR4s0nd3dyc8PJy2bduyZs0a1q5di7u7Oz4+PsyZM4eGDRsW69fV1ZW5c+cya9YsPD098fLyokmTJsyfP7/cOAB27NhRZiwAmZmZ9OnTR3mNGTPmCT4ZIYQQQghhLlQ6nU6OfBXVxPnym5gYczosTghhPOZ6kKA5fY+TZ1z5zPUZg6OxA2DVb99X+j2mtR9Y6fcwlIzICCGEEEIIIcyOrJERJmH58uVcvnyZevXq4e/vT7du3Z74PeJvXnjifVYmKwtAZV4x17MuMnYIBskrMr9JxZ+dqW3sEAz2Qee7xg7BIHlF5jdR4fwd8/u75Km0GsB/jB1GhXWwLjB2CAZrVFOHZd1zxg6jwhJumd/nGKB7Y+OPyFTXNTKSyAiTMHPmTGOHIIQQQgghzIh5pr7iqVCr1Xh7e1NUVKRX9ujhlsOGDUOj0ehdd+XKFZydndFoNPj4+ODv788vv/yi1IeGhrJ06VIA4uPjcXJyYt26dUp9fHw8/v7+ytd37tzh/fffx83NDQ8PDwICAjh+/PgTf79CCCGEEObI8im8TJEkMqJMOTk5REZGllh34cIFUlNTuXfvHomJiXp1derUITIykqioKF555RXmzJlT6j0aN27Mxo0byczMLLF+2rRp2Nra8v3337N//36mT59OYGAg//3vfx//jQkhhBBCVBEWqsp/mSJJZESZAgMDCQsLIy8vr1idVqtFo9Hg6+tb5qGVrq6uXL9e+k4kTZo0wdPTk/Xr1xerO3bsGJcvX2bmzJlYWj74e0D37t0ZOnQoa9eufYx3JIQQQgghqgJJZESZXFxcaN++PVu2bNErz8/PJyoqCn9/f/z8/NizZw+5ubkl9nHgwAG8vLzKvM/kyZPZvn07N2/e1Cs/d+4c7du3x8rKSq+8U6dOnD179jHekRBCCCFE1WKh0lX6yxRJIiPKFRwczPr168nOzlbKYmNjadmyJQ4ODtjb2+Ps7MyBAweU+qysLDQaDb1792bDhg289dZbZd6jUaNGvPrqq3z++ed65XLMkRBCCCGEKIkkMqJcrVq1om/fvmzYsEEp02q1XLx4EbVajVqt5syZM3rTyx6ukYmNjcXHx4cZM2aUm5SMHz+egwcP8scffyhlbdu25bfffiM/P1+v7cmTJ3FycnpC71AIIYQQwnxZqir/ZYokkREVMnXqVDZv3kx2djaZmZkkJCQQHR1NTEwMMTExxMXFkZiYyLVr1/Sus7S0ZObMmdy6dYvo6Ogy71GnTh3Gjh3LP//5T6WsW7dutGjRguXLl1NYWAg8WDezfft2Jk2a9OTfqBBCCCGEMAuSyIgKadasGRqNhoyMDI4ePUqfPn2wtbVV6m1sbHB3dyc8PLzYtTY2NgQHBxMWFlbuqMyoUaOUhOWh1atXk5WVxYABA/Dw8GDFihWsXr2ali1bPpH3JoQQQghhzqrrrmUqnSxCENVE/M3dxg7BIFZm+GeGetZF5TcyIXlFJvqduQyfnalt7BAM9kHnu8YOwSB5Reb3YzEzz/y+YZxKM68zuTs0KDB2CAZrVNO8PssXM83vcwzQvfFgY4fAhvP7K/0eYx09Kv0ehjKv7yJCCCGEEEIIPaY6YlLZJJER1UbTZ8xrtMDaVI/RLUN964bGDsEguUV3jB2CwSY6ZZffSPwlz1jaGDsEg1nalLz9vSnr2TS//EYmxNLCvEY3AArMLOS/1S0sv5EQj5BERgghhBBCCDMmIzJVgFqtplatWuzcuRMLCwulbM2aNTg6OgKwdetWNm7cSFFREUVFRfj5+TFx4kSlvZOTE46OjqhUKnQ6HcHBwTRv3px3330XgDt37nD37l2ee+45AF599VWeeeYZFi9erJQB9O7dm3/84x8lxmhtbY21tTX37t3jb3/7GxMmTKBz584AhIeHV7ivvXv3snbtWnQ6Hbm5ubRv356VK1fqtRk2bBh5eXlERkYqZVeuXMHNzY2AgAAWLFiglA0dOpT4+Hjlmvz8fH7//XfatGkDgLOzM0uWLOHSpUt4eXnx3nvvMWbMGKXf0NBQwsLC2Lp1Kx07dlTKcnJymDVrFvHx8UycOJEXXnhB2U65V69eTJkyhXr16gEwevRorl27preRwPz58+ncubNe3f3799FoNEyZMqWkj4IQQgghhKjiqlQiA5CTk0NkZCR+fn7F6iIiIti4cSPr16/n2Wef5c6dO7z99tsUFRXp/UL87bffUrt2beLi4ggODubYsWNKIhAeHk5sbCyrV69W2oeHh/Pyyy/rlZVl9erVSmL1/fffM3HiRL788kvll/+K9HXz5k0WLFjAjh07sLe3R6fTcebMGb02Fy5cIDU1FSsrKxITE3FxcVHqatWqRXR0NG+++SYODg56123btg34X3LzaBIEsH37dnr06IFWq9VLZACee+45Vq5cyddff11i3K1bt1Z2Nrt79y4fffQRY8aMYfv27VhaPphLNXfuXPr371/i9Q/rbt68iZeXFz179lSemxBCCCFEdWSpMrN5hE+IeW4PUYbAwEDCwsLIy8srVhcaGsqsWbN49tlnAahXrx4LFixg7dq1JbZ3dXUlJyeHzMzMSot34MCBBAQE8OWXXxp0XWpqKjVq1MDOzg4AlUqFs7OzXhutVotGo8HX11fvsEoAa2trxo0bx6effmrQfQsKCoiKiuKDDz4gNzeXU6dOFXs/GRkZHDp0qNy+bG1tmT9/Punp6RVq/6gmTZrwwgsvFDu3RgghhBBCVA9VLpFxcXGhffv2bNmyRa/87t27XLlyhU6dOumVt27dmho1avD7778X6+vAgQP06NGDBg0alHvfI0eOoNFolNfDUY2K6NixIxcvXjSor7Zt29KhQwf69etHUFAQX331Fenp6Up9fn4+UVFR+Pv74+fnx549e8jN1V8MOnLkSE6cOFFsJKcssbGxtGjRghYtWuDn51csQVKpVEyfPp1PPvmk3DNjAKysrGjXrh0XLlxQyj788EO993/79u1i112+fJmMjAxcXV0rHLsQQgghRFVk8RRepqjKTS0DCA4O5vXXX+eVV16pUHuVSn+FVEBAANnZ2aSmprJx48YK9WHI1LI/+/Mv/BXpy8LCgs8//5zz589z7NgxDh48yJdffklUVBR2dnbExsbSsmVLZdqYs7MzBw4cwNvbW+nDxsaGKVOm8PHHHzN//vwKxarVapVpe76+vvj6+vL+++9jY/O/XX769evH2rVr2bt372O9/7Kmln344YesWLGC//znP8yaNatCSaYQQgghhKh6TDXB+ktatWpF37592bBhg1Jma2tL8+bNOXnypF7bS5cukZ+fT4sWLZSyb7/9lpiYGKZNm8b06dOLjWQ8aadPn1YW1BvK0dGRkSNHsmHDBurUqUNCQgLwIOG4ePEiarUatVrNmTNnio2eAPj7+5OcnMzx48fLvVdqaiqHDx8mLCwMtVrNyJEjuXfvHvv3Fz+EacaMGaxatYqCgrIPEMvPz+fs2bPKmqHyzJ07l927d7Np0yY++eQTzp07V6HrhBBCCCGqKgtV5b9MUZVMZACmTp3K5s2byc7+35kLgYGBLFu2jOvXrwMPdiALCQlhwoQJeiMKD40bN46GDRsWm6b2JB08eJAtW7Ywbtw4g65LSUnhxIkTytc3btwgLS2N5s2bc+vWLRISEoiOjiYmJoaYmBji4uJITEwstqbE0tKS4ODgCo0mRURE4OHhQWxsrNLv4sWLS0yQunbtSsuWLYmKiiq1v+zsbBYuXEj9+vXp1auXAe8eunTpwogRI1i1apVB1wkhhBBCiKqhSk4tA2jWrBkajYZ//etfSpmfnx/379/nzTffRKfTUVhYiEajYfLkySX2oVKpmDVrFu+88w4BAQHUrFmz1Ps9XNfykIuLC4sWLSqxbVBQkLL9cuvWrVm3bp3ezlsV6augoIDQ0FCuXr1KzZo1KSoqIjg4GGdnZ9atW0efPn30tjC2sbHB3d2d8PBwfH199fry9PRk/fr1eklfScLDw5k1a5ZemZubG/Pnz+fKlSvF2r/zzjvFdo+7dOkSGo2GgoICdDodvXr14quvvlJ2LIMH08ce3YQgKCgINze3Yv1PnjyZAQMGkJSUVGyjAyGEEEKI6sJUR0wqm0pXkRXZQlQBv2eVPjpkiqwty29jaupbNzR2CAbJLbpj7BAM9sdd8zv5unlt8/oxY6myNnYIBssrqtwp0JWhoMi8fvOytDCvz7E5qmFeHwlFA5shxg6B7Zf3Vfo9XnnBs9LvYagqOyIjhBBCCCFEdVBdz5GRREYIIYQQT519LSdjh2Cwm/fPGjuEKq1AZ76jMsZWXaeWSSIjqo3a9/sZO4Qq727Zy6zEE2Bv7AAeQ2HlnSlcKcxv8p6Zqnnd2BEYTJVV8tEA4skxy///Ghs7gOpLEhnxxKnVaqytrbG2tqaoqIjJkyfTqFEjJk6cSMuWLZV2Tk5OLFu2jPj4eKWusLAQOzs7FixYQOvWrQEYPXo048aN0ztbJigoiH79+uHv7/+0354QQgghhEmRERkhnqDVq1fj6OhIUlISAQEBLFu2jNatWxMeHl5i+0frli9fzpIlS/jiiy+eZshCCCGEEMKMVNlzZIRpcHZ2pnbt2iVuz1ya7t27K2f9CCGEEEKIslXXAzFlREZUqqNHj5Kbm0vLli2VM2QeGjBgAIGBgXrti4qKiI6OxsvL62mHKoQQQgghzIgkMqJSBAUFYWNjg62tLaGhodSoUaPMqWUPk5yUlBRsbW3Ztm1bufdQqUz0zwNCCCGEEE+RZTX9lUgSGVEpHq6ReSg+Pr7M9g+TnLy8PKZPn05ISAirVq0CoEGDBmRkZOi1T09Pp0GDBk8+cCGEEEIIYRZkjYwwKdbW1oSEhHDo0CGSkpIA6NmzJxEREeTmz2aGjwAAIABJREFUPji5+uzZs1y6dIkOHToYM1QhhBBCCJNgodJV+ssUyYiMeGr+vEamSZMmrF+/vli7Ro0aMW7cOMLCwvj888955ZVXuHbtGq+88goWFhbUrFmTTz75hPr16z/N8IUQQgghhAlR6XQ600yxhHjCbt3KMnYIQggh/r/Gjc1vd8pbt8zxSFpR2Ro3rmPsEDh4dU+l38P9OdPbiEmmlgkhhBBCCCHMjkwtE9WGXcNLxg7BIEW6PGOHYDCVyrz+NmKBlbFDMFihGX4ucgpSjB2CQSwtaho7BIPVtLQzdggGu1dgXp9lSwsb6jU0r5F9FZbGDsEgKsx1662Oxg7AZM95qWzm9VuHEEIIIYQQQiCJTJWlVqvx9PRkyJAheHt7s3v3bsLDwwkKCtJr98MPPzB69GjOnTuHRqNBo9HQr18/unbtqnz9zTffEBoaytKlS/Wu3bRpE7NnzwYebK/s5ORUrM3o0aNxcnIiOztbKSssLKR3795MnjwZgLy8PAYPHkxcXJzSJj09nb59+3L27Fnu3LnD+++/j5ubGx4eHgQEBHD8+PEn+ryEEEIIIcyVparyX6ZIppZVYQ/PcklKSiIgIIBp06aV2tbJyYnIyEgAwsPDiY2NZfXq1Up9aGhoufd74YUXiI6O5h//+AeWlpYkJyeTk5NTrN2hQ4do0qQJv/zyC6mpqTRq1IglS5YQHBxMZGQkderUYeHChQwfPpy2bdsyZswYHB0d+f7777G0tCQhIYHAwEC+++47WrRo8RhPRgghhBBCmDsZkakGnJ2dqV27NpW9QV2tWrV48cUXOXz4MAA7duzA19e3WDutVktAQADu7u5EREQA0KFDB3x8fFi8eDEHDx7kv//9LxMnTuTYsWNcvnyZmTNnYmn5YK5v9+7dGTp0KGvXrq3U9yOEEEIIYQ6q6zkykshUA0ePHiU3N5caNSp/AM7Pz48dO3ag0+nYvXs33t7eevVpaWkcPXqUQYMG4e/vT3h4uFL39ttv89tvvzFv3jyWLl1KjRo1OHfuHO3bt8fKSn9RdqdOnTh79mylvx8hhBBCCGGaZGpZFRYUFISNjQ22traEhoZy8+bNx+5LpSp5cuSfy11dXVmwYAEHDx7E0dGx2KGVO3fupH///tja2tKlSxcKCws5ceIEL774ItbW1owePZrY2Fj+9re/AVT6KJIQQgghhLmrrruWSSJThT1cI/NQXFwc6enpem3S09Np2LBhuX3Vr1+fq1evFru2QYMGemUqlYpBgwYxd+5clixZUqwfrVZLWloaarUagKysLLRaLS+++CIAlpaWyhQygLZt2/LFF1+Qn5+vNypz8uRJnJycyo1bCCGEEEI8Penp6bz77rv88ccfWFtb06JFCz744AMaNGjAyZMnmTdvHrm5uTz33HMsX75c+T20rLrSyNSyauTvf/87Fy9e5Ny5cwDcv3+fHTt20LNnz3Kv7dGjB4cOHeLGjRsAZGRksGfPHnr16lWs7fDhwxk/fjx9+vTRKz916hRZWVkcPnyYmJgYYmJi2LVrF/v27ePevXsl3rdbt260aNGC5cuXU1hYCMCxY8fYvn07kyZNMuj9CyGEEEJURRaqyn9VlEqlYvz48ezfv5+oqCief/55VqxYQVFRETNnzmTevHns37+frl27smLFCoAy68oiIzLVSIMGDfj444+ZN28e9+/fp6ioiAEDBjB06NByr23dujXvv/8+U6ZMobCwEJ1Ox6hRo3jppZeKtW3atCkTJkwoVq7Vahk8eLDedLSmTZvi7OzMvn378PPzK/Heq1evZunSpQwYMAArKyvs7OxYvXo1LVu2rPibF0IIIYSookxpZMLOzg5XV1fl606dOrFlyxYSExOxsbGha9euAAQEBODm5saSJUvKrCuLSieLEEQ1kV900tghGKTIDE9wV6lM6Vtp+SywKr+RiSk0w89FTkGKsUMwiKVFTWOHYLCalnbGDsFghUXm9Vm2tLAxdggGU2FZfiMTosI8F3rUsOho7BCIv7m70u/RrmZvMjMzi5XXrVuXunXrlnhNUVER48aNQ61W07RpU7RaLevWrVPqO3bsSFxcHPHx8aXW2dmV/v1NRmREtXHrfrKxQzBI7RrWxg7BYOb4S7aNZcnffE3V1ewsY4dgsOa16xk7BINYqMzvR2NeYfFfLkxdflHxc8ZMWX5BkbFDMJi1hfl9lnMKCo0dgsGerWX8RKaUPZmeqI0bNxIWFlasPDAwkKlTp5Z4zcKFC6lVqxajRo3iwIEDTzwm8/uECyHEE2JuSYwQQlRl5pjEVCdvvPFGicsAShuNWbp0Kf/9739Zs2YNFhYW2Nvbc+3aNaU+LS0NCwsL7OzsyqwriyQyQgghhBBCmLGnMSmvrClkf/bxxx+TmJjIunXrsLZ+MMPExcWF+/fvc/z4cbp27cq3336Lp6dnuXVlMa8J7aLS5OXl8dFHH+Hu7o6npycajYa9e/cCEB4eTlBQkF77H374gdGjRwNw5coVvUVdarUab29vioqK9MrOnz8PwOzZs+nTpw++vr4MHDiQ1157jYiICKXtn/v7c2y+vr4cPHjwyT8EIYQQQgjxl1y4cIG1a9dy8+ZNAgIC0Gg0vP3221hYWLBs2TIWLFjAwIEDOXbsGDNmzAAos64sMiIjAAgJCSEnJ4fdu3djY2PD+fPnefPNN8sd0itNTk4OkZGRpe5ENnHiREaNGgXAmTNnCA4OJj09nbFjx1YotvHjx1OvXj26dev2WPEJIYQQQlQVT2ONTEW1adNGOerjzzp37kxUVJTBdaWRERnB1atX2bt3LyEhIdjYPNiVxdHRkcmTJ5e4qKsiAgMDCQsLIy+v/MXf7dq1Y86cOaxfv54/b6JXWmxvvfXWY8cmhBBCCCHMnyQygvPnz+Pg4FBs9KVTp07KdDBDubi40L59e7Zs2VKh9h07duT27dukpaVVOLazZ88+VmxCCCGEEFWJxVN4mSJTjUs8RWUdJaRSqfQOsDREcHAw69evJzs7+7FjkGOOhBBCCCFESSSRETg6OvLHH3+QkZGhV37y5ElefPFFGjRoQHp6ul5deno6DRs2LLPfVq1a0bdvXzZs2FBuDKdPn6Zhw4bF+iwrNicnp3L7FUIIIYSo6lQqXaW/TJEkMoLmzZvj6elJSEgIubm5wIMpXRs3biQ4OJi///3vXLx4UVm4df/+fXbs2EHPnj3L7Xvq1Kls3ry5zFGZs2fPsnjxYiZMmFDh2NasWUNgYODjvF0hhBBCCFEFyK5lAoD58+fz8ccf4+XlhUqlIiUlha1bt9KuXTvgwX7g8+bN4/79+xQVFTFgwACGDh1abr/NmjVDo9Hwr3/9S6983bp1bNu2jfv379OgQQMmTZqEr69vubFZWVlhY2PDnDlz6N69+19/40IIIYQQZs6ENi17qlQ6WYQg/iQ/P5958+Zx48YN1qxZo+wWZu6u5Ri2pZ+x1a5hbewQDFaoK3+XOlNiY1mxg71MydXsLGOHYLDmtesZOwSDWKjM7298BUX3jB2CwfKLcowdgkHydUXlNzIx1hbm9VnOKSg0dgiP5dlaPsYOgZO3d1X6PTo19K70exjKvD7h4qmwsrJiyZIlxg7jiUvPNa+/V1ioco0dgsEszOsRU1Bwx9ghGOzWfUtjh2Cwutbp5TcyIWb2MQYgM8/8Zoqnmdn3ZFsr83vGOswt+VJRz1r+vv44TOkcmafJ/P6vFEIIIYQQVY4kMcJQMiIjhBBCCCGEGaumAzIyIlPVqNVqPD09GTJkCIMGDWLbtm0AxMfH4+/vr7T75ptv8PHxYciQIXh6erJ06VK9fgoLC+nduzeTJ0/WK4+Pj6djx45oNBq8vb0ZNWoUly5d0msTFxeHk5MTBw4c0CufPXs2mzZtKhZzaGgoL730Er6+vnh4eDB06FA2btxIYaH+XNnNmzfj5OREUlKS4Q9GCCGEEEJUKTIiUwWtXr0aR0dHzp8/j7+/P3369NGrP3XqFBs3bmT79u3UrVuXwsJCLly4oNfm0KFDNGnShF9++YXU1FQaNWqk1LVu3Zrw8HAAli9fzpIlS/jiiy+Ueq1WS48ePdBqtQwYMKBCMfv6+jJr1iwAkpOTmTlzJsnJycydO7fEfp2dnQ17KEIIIYQQVZS5rVF9UmREpgpzdHSkbt26pKSk6JWnpKRga2tLrVq1ALC0tKRt27Z6bbRaLQEBAbi7uxMREVHqPbp3787169eVr9PT0zl69CgrV67kxIkT3Lp1y+C4n3/+eRYtWsSWLVvIynqwQ9P58+dJS0tj0aJF7N69m7w889odSwghhBBCPFmSyFRhP//8M/Xr1y+WpPTs2ZMaNWrQv39/ZsyYwXfffce9e//bujMtLY2jR48yaNAg/P39ldGXPysqKiI6OhovLy+lbOfOnfTr149GjRoxYMAAduzY8Vixt27dmpo1a3L58mUAtm/fjq+vL82bN6ddu3YcPHjwsfoVQgghhKhqVE/hZYokkamCgoKC8PDwYNSoUUybNg1ra/3zSGrVqsV3333HZ599houLC9u2bWP48OHKKMfOnTvp378/tra2dOnShcLCQk6cOKFcf+nSJTQaDS+//DJHjhxhxIgRSl14eDh+fn4A+Pn5lZoEVcTDI47y8/PZtWuXXr9arfax+xVCCCGEEOZP1shUQQ/XyOzdu5f33nuPzp07F2ujUqno0KEDHTp0YOTIkbz88stcuHCB9u3bo9VqSUtLQ61WA5CVlYVWq+XFF18E/rdGJi8vj+nTpxMSEsKqVatITEzk4sWLzJkzR7nPzZs3+fnnn+nSpYtB7+E///kPubm5tGrVipiYGLKyshgzZgzwYCQoNTWV69evY29v/5hPSQghhBCiapBzZESVM2jQIHr27MnatWv1yi9dusT58+eVry9fvkx+fj7NmjXj1KlTZGVlcfjwYWJiYoiJiWHXrl3s27dPb/oZgLW1NSEhIRw6dIikpCS0Wi3jx49XrouJiWHq1KkGj55cuXKFOXPm8Nprr2Fra4tWq2XevHlKn7GxsWVOeRNCCCGEqE6q69QyGZGp4mbMmIG/vz8dO3ZUyu7fv8/ixYu5ffs2NjY2WFpasnz5cho2bMjq1asZPHgwqkdS+6ZNm+Ls7My+fft49tln9fpv1KgR48aNY+XKlZw+fZotW7bo1Xt7ezNkyBBl97FVq1axbt06pX7hwoUARERE8NNPP3Hv3j1sbW3x8fFh9OjRpKSkkJCQwIoVK/T69fHx4b333mPKlCl6sQohhBBCiOpBpXu4EEGIKu639F3GDsEgDWsWGTsEg1XX7R+fpgt3LI0dgsFa1y0sv5EJMcePcWae+U2wSMs1rydta2V+vy6ZW8T1rM0t4geer+1j7BA4m1H5v+O0tfOu9HsYSkZkhBBCVKpmz7Qtv5GJSbl31tghCCGEKIckMqLaaFLQ19ghGOausQMQpsjR/P7wDlwvv4mJsbjb39ghGMTO2AE8BjvzGpCBAmMHUA2Y6zOubewAqu+MCLP8kSiEEEIIIYSo3iSRKYFarcbT05MhQ4YwaNAgtm3bBkB8fDz+/v5Ku2+++QYfHx+GDBmCp6cnS5cu1eunsLCQ3r17M3nyZL3y+Ph4OnbsiEajwdvbm1GjRnHp0iW9NnFxcTg5OXHgwAG98tmzZ7Np06ZiMYeGhvLSSy/h6+uLh4cHQ4cOZePGjRQW6s9N37x5M05OTiQlJRXr4/jx4wQFBQFw9+5dFi5cyIABA9BoNPj7+7NmzRq99suXL8fFxYXbt28Xe36P7or20OjRo3Fzc0Oj0TBgwADGjh1LbGxssXbBwcH06NGD/Px8vfLynrcQQgghRHUku5YJPQ/PYjl//jz+/v706dNHr/7UqVNs3LiR7du3U7duXQoLC7lw4YJem0OHDtGkSRN++eUXUlNTadSokVL38CwWeJAQLFmyhC+++EKp12q19OjRA61Wy4ABAyoUs6+vL7NmzQIgOTmZmTNnkpycrOwY9ud+nZ2d9a6Pjo7Gzc0NnU7HxIkTadu2Lbt378ba2prc3Fy2bt2qtC0sLCQyMpLOnTsTGRnJuHHjKhTj3Llz6d//wZSN+Ph4pk+fzvz58xk4cCAAGRkZHDlyhBdeeIGYmBg8PDyAij1vIYQQQghRfciITDkcHR2pW7cuKSkpeuUpKSnY2tpSq1YtACwtLWnbVn9Bq1arJSAgAHd3dyIiIkq9R/fu3bl+/X9zyNPT0zl69CgrV67kxIkT3Lp1y+C4n3/+eRYtWsSWLVvIysoC4Pz586SlpbFo0SJ2795NXl6e3jWxsbH069ePn376iWvXrvHee+9hbW0NgI2NDaNHj1baxsXF4eDgQFBQ0GOf5+Lq6kpgYKDeOTdRUVH07duXESNG6J0/U5HnLYQQQghRHalUukp/mSJJZMrx888/U79+/WK/NPfs2ZMaNWrQv39/ZsyYwXfffad3YGRaWhpHjx5l0KBBZR7eWFRURHR0NF5eXkrZzp076devH40aNWLAgAHs2LHjsWJv3bo1NWvW5PLlywBs374dX19fmjdvTrt27Th48KDS9sKFCzRu3Jh69erx22+/4ezsjJWVVal9a7Va/P396dq1K/n5+fz666+PFWPHjh25ePFisX4HDhzIyZMnlQSyvOcthBBCCCGqF0lkShEUFISHhwejRo1i2rRpysjEQ7Vq1eK7777js88+w8XFhW3btjF8+HBllGPnzp30798fW1tbunTpQmFhISdOnFCuv3TpEhqNhpdffpkjR44wYsQIpS48PBw/Pz8A/Pz8/tIJ9g+PCcrPz2fXrl16/T464vFwWllF3L59m4SEBDw9PYEHU9oe7etx4gNISkoiMzOTHj168MwzzzBw4EBlJKu85y2EEEIIUV3JGhmh5+Eamb179/Lee+/RuXPnYm1UKhUdOnSgQ4cOjBw5kpdffpkLFy7Qvn17tFotaWlpqNVqALKystBqtbz44ovA/9bI5OXlMX36dEJCQli1ahWJiYlcvHiROXPmKPe5efMmP//8M126dDHoPfznP/8hNzeXVq1aERMTQ1ZWFmPGjAEejASlpqZy/fp17O3tiY6O5pNPPgGgffv2bN68mYKCAmrUKP4RiYyMpKCggCFDhgBQUFDAvXv3eP/996lZs6ZBMZ4+fZo2bdoAD0ZjMjMzlYQqLy+P2rVrM2nSJKDs5y2EEEIIIaoXGZEpx6BBg+jZs6feOg54MKLy6M5cly9fJj8/n2bNmnHq1CmysrI4fPgwMTExxMTEsGvXLvbt21dsOpS1tTUhISEcOnSIpKQktFot48ePV66LiYlh6tSpBo94XLlyhTlz5vDaa69ha2uLVqtl3rx5Sp+xsbHKlLeUlBTy8vJo3rw5AC+99BJNmzblo48+UkY88vLylN3SwsPD+eyzz5S+fvzxRzp06MC+ffsMivH48eOEhYUxYcIE8vLy2LVrF1qtVun38OHDSruynrcQQgghRHWmUlX+yxTJiEwFzJgxA39/fzp27KiU3b9/n8WLF3P79m1sbGywtLRk+fLlNGzYkNWrVzN48GBUj/yrN23aFGdnZ/bt28ezzz6r13+jRo0YN24cK1eu5PTp02zZskWv3tvbmyFDhii7j61atYp169Yp9QsXLgQgIiKCn376iXv37mFra4uPjw+jR48mJSWFhIQEVqxYodevj48P7733HvXr11dGjuDByMcXX3zBypUr8fLy4plnnlHa//rrr2RkZNCjR49ifWm1Wnx9fQEYO3YslpaWSn1UVBQAH374IZ9++in37t3j2WefZeHChfTv3589e/bg4OBAixYtSux31KhRpT5vIYQQQghR/ah0jy5SENXSm2++yTvvvIOLi4uxQ6lUt25lGTsEIaqlxo2vl9/IxNy6ZW/sEIQQZqJx4zrGDoHfs6Iq/R4t6/hU+j0MJYmMqDZ0nDN2CFWeymSXA1YdOuRbdmUzx8+xOX4uzO056ygsv5HJMb8VBOb2uXjA0dgBVNtERqaWCSGEEEIIozPPJMY0mOoalspmfqm6EEIIIYQQotqTRKYMe/fuxdfXF41Gg6enJzNmzCjWZtiwYWg0Gr2yK1eu4OTkxPz58/XKXF1dy7zf8uXLcXFx4fbt23rlarUab29vioqK9Moe7uI1e/Zs+vTpg6+vLwMHDuS1115Tzl95eG9nZ2c0Gg0+Pj74+/vzyy+/KPWhoaG89NJLaDQa5fX1118DcPXqVSZOnIiPj49y7aO7hz3KycmJ7OxsAI4ePao8m0GDBvH6668r8Y8ePZoffvjB4OdQkX8PIYQQQojqRs6REXpu3rzJggUL2LFjB/b29uh0Os6cOaPX5sKFC6SmpmJlZUViYqLeYvlatWoRHR3Nm2++iYODQ7n3KywsJDIyks6dOxMZGcm4ceP06nNycoiMjFQOtPyziRMnMmrUKADOnDlDcHAw6enpjB07FoA6deoQGRkJwObNm5kzZw579+5Vrvf19WXWrFnF+l2wYAF9+vRR+k5JSSnxbJlHFRQUEBQUxNdff03btm2BB4ddqiow7lnac6jIv4cQQgghhKg+ZESmFKmpqdSoUQM7OzvgwZbEzs7Oem20Wi0ajabEk+2tra0ZN24cn376aYXuFxcXh4ODA0FBQYSHhxerDwwMJCwsrEIn2bdr1445c+awfv16StrLwdXVlevXK7aL0I0bN2jatKnyddOmTcvd8jg7O5ucnBwaNWqklDk7O1cokSntOVTk30MIIYQQojqqrufISCJTirZt29KhQwf69etHUFAQX331Fenp6Up9fn4+UVFR+Pv74+fnx549e8jNzdXrY+TIkZw4caJCIwdarRZ/f3+6du1Kfn4+v/76q169i4sL7du3L3bGTGk6duzI7du3SUtLK1Z34MABvLy89MoiIiL0ppbFxcUBMH78eN59911GjRrF8uXLOXXqVLn3rlevHq+++ioDBw7krbfeYt26dRVOnEp7DuX9ewghhBBCVFfVdWqZJDKlsLCw4PPPP+ff//43rq6uxMXFMWTIEDIyMgCIjY2lZcuWODg4YG9vj7OzMwcOHNDrw8bGhilTpvDxxx+Xea/bt2+TkJCAp6cnQIkjPADBwcGsX79eWYdSlj+PxGRlZaHRaOjduzcbNmzgrbfe0qv39fUlMjJSefXt2xeAIUOGEBMTw4gRI7h37x5vvPEGu3btKvf+8+bNIzIyEjc3N06fPo23tze///57mdeU9RzK+/cQQgghhBDViyQy5XB0dGTkyJFs2LCBOnXqkJCQADwYObh48SJqtRq1Ws2ZM2dKTD78/f1JTk7m+PHjpd4jMjKSgoIChgwZglqtZsuWLezdu5f79+/rtWvVqhV9+/Zlw4YN5cZ9+vRpGjZsqEwDe7hGJjY2Fh8fH2bMmFHitLOS1K9fHy8vL+bNm8fkyZMrlMgAPP/88wwbNozQ0FBefPHFMhf4Q8WeQ2n/HkIIIYQQ1ZWFqvJfpkgSmVKkpKRw4sQJ5esbN26QlpZG8+bNuXXrFgkJCURHRxMTE0NMTAxxcXEkJiZy7do1vX4sLS0JDg5m9erVpd4rPDyczz77TOnrxx9/pEOHDuzbt69Y26lTp7J58+YyR2XOnj3L4sWLmTBhQrE6S0tLZs6cya1bt4iOji73OcTGxipT5goLCzl37hzNmzcv85rs7GwOHz6sJEqZmZlcuXKl3OvKeg5l/XsIIYQQQojqR3YtK0VBQQGhoaFcvXqVmjVrUlRURHBwMM7Ozqxbt44+ffpga2urtLexscHd3Z3w8HB8fX31+vL09Cx1Stivv/5KRkYGPXr00Cv38fFBq9UW66tZs2ZoNBr+9a9/6ZWvW7eObdu2cf/+fRo0aMCkSZOKXftorMHBwYSFheHm5gY8WCNz5MgRpY1arWbatGnEx8ezdOlSatSoQWFhIS4uLkybNq3MZ6fT6fjmm29YuHAhNjY2FBYW4uPjw4ABA5Q2s2fPxsbGRvl6xYoVZT6Hbt26lfrvIYQQQghRnZnogEmlU+kqOr9ICDOn45yxQ6jy5FTmyqdDvmVXNnP8HJvj58LcnrOOQmOH8BjMa+KNuX0m/sfR2AFwPSeq0u9hX8un0u9hKBmREdXG8lN/GDsEg/g45JbfyMQ0faao/EYmZMoRO2OHYLArmeb1iwnA3C5Zxg7BIA1tzOtzDLAr2ab8RibG3sy+X9S2Mr9k0alegbFDMFhdM3zObe2Mn8ioVOb33J4E8/uJKIQQQgghqhxzTGKEccmIjBBCCCGEEGbMXCfl/VWSyJixvXv3snbtWnQ6Hbm5ubRv356VK1cq9cOGDSMvL4/IyEil7MqVK7i5uREQEMCCBQuUsqFDhxIfH69ck5+fz++//06bNm0AcHZ2ZsmSJVy6dAkvLy/ee+89xowZo/QbGhpKWFgYW7dupWPHjkpZTk4Os2bNIj4+nokTJ/LCCy+Qn58PQK9evZgyZQr16tUDYPTo0Vy7dk1vE4X58+fTuXNnvbr79++j0WiYMmVK5TxYIYQQQghh8iSRMVM3b95kwYIF7NixA3t7e3Q6HWfOnFHqL1y4QGpqKlZWViQmJuLi4qLU1apVi+joaN58800cHBz0+t22bRvwv+Tm0SQIYPv27fTo0QOtVquXyAA899xzrFy5kq+//rrEmFu3bk14eDgAd+/e5aOPPmLMmDFs374dS0tLAObOnUv//v1LvP5h3c2bN/Hy8qJnz55K0iSEEEIIUV2pqumQjKyRMVOpqanUqFEDO7sHi5VVKpXeVsRarRaNRoOvr2+xgzqtra0ZN24cn376qUH3LCgoICoqig8++IDc3FxOnTqlVz9w4EAyMjI4dOhQuX3Z2toyf/580tPTK9T+UU2aNOGFF14odmaPEEIIIYSoPiSRMVNt27alQ4cO9OvXj6CgIL766ivS09MByM/PJyoqCn9/f/z8/NizZ49yqOUXE+3WAAAgAElEQVRDI0eO5MSJE3qjOOWJjY2lRYsWtGjRAj8/v2IJkkqlYvr06XzyySdUZFdvKysr2rVrx4ULF5SyDz/8EI1Go7xu375d7LrLly+TkZGBq6trhWMXQgghhKiqVE/hZYokkTFTFhYWfP755/z73//G1dWVuLg4hgwZQkZGBrGxsbRs2RIHBwfs7e1xdnbmwIEDetfb2NgwZcoUPv744wrfU6vV4ufnB4Cvry/79u0rliD169cPGxsb9u7dW6E+/5zwzJ07l8jISOXVsGFDpe7DDz9k8ODBeHl5MXLkSBo0aFDh2IUQQgghRNUiiYyZc3R0ZOTIkWzYsIE6deqQkJCAVqvl4sWLqNVq1Go1Z86cKTZ6AuDv709ycjLHjx8v9z6pqakcPnyYsLAw1Go1I0eO5N69e+zfv79Y2xkzZrBq1SoKCsrevz4/P5+zZ8/i6Fix/dfnzp3L7t272bRpE5988gnnzskBl0IIIYQQFk/hZYpMNS5RjpSUFE6cOKF8fePGDdLS0mjcuDEJCQlER0cTExNDTEwMcXFxJCYmFltTYmlpSXBwMKtXry73fhEREXh4eBAbG6v0u3jx4hITpK5du9KyZUuioko/ZTY7O5uFCxdSv359evXqZcA7hy5dujBixAhWrVpl0HVCCCGEEKJyLV26FLVajZOTE+fPn1fKL1++zPDhw/Hw8GD48OH8/vvvFaoriyQyZqqgoIDQ0FA8PDzQaDRMmDCB4OBgjh07Rp8+ffS2MLaxscHd3V3ZMexRnp6e1K9fv9z7hYeH4+Pjo1fm5uZGYmIiV65cKdb+nXfe4fr163plly5dQqPRMHjwYIYNG0bNmjX56quvlB3LoPgamejo6BLjmTx5Mj///DNJSUnlxi6EEEIIUZWpVJX/qig3Nze++eYbnnvuOb3y+fPnM2LECPbv38+IESOYN29eherKfN+6iqzKFqIKWHbqQPmNTIiPQ275jUxM02eKjB2CQaYcsTN2CAa7kml+f3+a2yXL2CEYpKGNeX2OAXYl2xg7BIPZm9n3i9pmeOq8U72yp3ibmrpm+IwB2tp5GzsE0nJ3Vvo9GtgMMai9Wq1mzZo1ODo6cvv2bTw8PIiPj8fS0pLCwkJcXV35/vvv0el0pdaVtx5azpER1UawS/kjT6bkXkGqsUMwWO0a9sYOwSAb+5hfsphflGPsEAymw9rYIRhEhWX5jUxMhwa1jB2CwQp0940dgkFqqGoaOwSDmdv3CwuV+f2/Zzoqf1+xzMxMMjMzi5XXrVuXunXrlnnt9evXadq0qTILx9LSkiZNmnD9+nV0Ol2pdZLICCGEEEIIIf6SjRs3EhYWVqw8MDCQqVOnGiEiSWSEEEIIIYQwa6qnMCLzxhtvKMdwPKq80RgAe3t7UlJSKCwsVKaP3bx5E3t7e3Q6Xal15ZFERjyWvXv3snbtWnQ6Hbm5ubRv356VK1fi5OTEL7/8Qu3atZW2rq6uaLVamjdvjk6n4+uvv2br1q3Ag0MxXVxcePfdd8nMzGTgwIG0adOGoqIirKysmDt3Lp07dwYgNDSUzZs306RJE6XvoUOH8vrrrz/dNy+EEEIIYUJUqspfP1mRKWSladiwIe3atWPXrl1oNBp27dpFu3btlKljZdWVRRIZYbCbN2+yYMECduzYoWTSZ86cqdC1n376KceOHWPjxo00atQInU7HgQMHuHPnDiqVijp16hAZGQnA5s2bmTNnjt7hmr6+vsyaNatS3pcQQgghhPhrPvzwQ77//ntSU1MZO3YsdnZ27N69m5CQEGbPns3nn39O3bp1Wbp0qXJNWXVlkURGGCw1NZUaNWpgZ/dgxyeVSoWzs3O512VnZ7NhwwYiIiJo1KiRcu3AgQMBim3j7OrqyrJly55w9EIIIYQQVU3lTy2rqLlz5zJ37txi5a1bt2bbtm0lXlNWXVkkkREGa9u2LR06dKBfv364urrSuXNnNBpNuefRXLp0CWtra1q1alWh+xw4cAAvLy+9soiICI4cOaJ8PX36dPr27Wv4mxBCCCGEEGZNEhlhMAsLCz7//HPOnz/PsWPHOHjwIF9++SVRUVGlXqOq4ElKWVlZaDQa0tLSyMvLK5ady9QyIYQQQgh9T2Oxvykyv5PVhMlwdHRk5MiRbNiwgTp16pCQkECDBg3IyMhQ2hQUFHD37l0aNGhA69atyc3N5fLly6X2+XCNTGxsLD4+PsyYMQM5s1UIIYQQQvyZJDLCYCkpKZw4cUL5+saNG6SlpdG8eXNefvllvvvuO6Xuu+++o2PHjjzzzDPUrl2bMWPGMG/ePG7fvg2ATqfj4MGDJCcn693D0tKSmTNncuvWLaKjo5/OGxNCCCGEMEuqp/AyPTK1TBisoKCA0NBQrl69Ss2aNSkqKiI4OBhnZ2fmzJnDokWL8PHxwcLCAnt7e70F+9OnT+err75i9OjRwINEpmvXrnTv3r3YabE2NjYEBwcTFhaGm5sbUHyNjFqtZtq0aU/hXQshhBBCCFOi0sm8HVFN5BUdN3YIBrlXkGrsEAxWu0b5h1eZkkJdrrFDMFh+UY6xQzCYjkJjh2AQFZbGDsFgVha1jB2CwQp0940dgkFqqGoaOwSDmdv3CwuV+f2/B1CrRm9jh0Bm/oFKv0ddqwGVfg9DyYiMqDbm/3LH2CEY5LuvzeuHPEDckj+MHYJBrueY3+za/0uxNnYIBvvplo2xQzDI3I6Z5TcyMXfyso0dgsF2/PcZY4dgkNf/dtPYIRjsxj3zSgwmfW2ev5b+931jR1B9mecnRgghhBBCCPH/meYalspmfn+OFEIIIYQQQlR7MiIjyjRs2DDy8vLIz8/n999/p02bNgA4OzsTFBTEkiVL+O2337CwsMDBwYFZs2bh6OgIQGhoKDk5OcXOfQkPDyc2NpbVq1dz5coV3Nzc8PDwYPXq1Uqb2bNns2PHDqKiopT+Ho0nMjLyKbx7IYQQQgjTV13PkZFERpTp4YGUV65cYejQoUoCkZ+fz5AhQ3j11VeVBGTfvn2MHTuWPXv2UK9evQrfw87OjvPnz3Pnzh3q1atHdnY2x48fp2nTpnrtLly4QGpqKlZWViQmJuLi4vKE3qUQQgghhDA3MrVMPJbdu3dTp04dxo4dq5R5enrSrVs3Nm3aZHB/Xl5e7Nq1C3iQELm7/7/27jwsqrp/H/g9qLiRe+5rqOCOO6mhgagRyMxABoFLj4Rb4vqouX1d0tJyCdwqFc1KTWZYFHcscEkpRUkTEjKfzAKSRVFkWOb3hxfnxzgDAgLnHLhfXXM15xyYucdrODPv89lGomZNwzpbo9HA1dUVSqUSGo3mxV4AERERURWhqIT/pIiFDJVJfHw8+vTpY7TfxsYG8fHxpX48lUoltPaEhIRApVIZHM/JycHhw4ehVquhUqlw9OhRZGfLb+pcIiIiIiofLGSoTMp7+aF27dqhdu3aiIyMxOPHj2FlZWVw/IcffkDHjh3Rvn17tGrVCt27d8epUxU/ZzoRERGR9JlVwk16pJmKJM/a2hrXrl0z2n/16lWDwfmloVQqsWDBAiiVSqNjGo0GCQkJsLe3h729PW7evMnuZURERETVGAf7U5k4OTlh+/btCAwMFMbJHD9+HNHR0VixYkWZHnPMmDG4f/8+XFxcDPanpKQgOjoaUVFRsLCwAABkZ2dj2LBhuHfvHlq3bv1Cr4WIiIhIzhQKaY5hqWgsZKhMzM3NsXv3bnz88cfYt28fzMzM0K5dO+zevRuNGjUSfu7AgQMIDw8XtqdPnw5zc9Mrk9evXx++vr5G+4ODg2FnZycUMQBQu3ZtjBw5ElqtFu+//345vjIiIiIikgOFvrwHOxBJ1Ac/R4gdoVQOfvVQ7AilFvlRDbEjlMrfj+XXu/Z8kukLAVL2Y0ptsSOUytI+D8SOUGoZOvldjQ2+U1fsCKUyofNjsSOU2j9Z8jonT/lKntfX7yx2FDsCHuVGVfhz1K9pV+HPUVryfMcQlUH/pjliRygV5dJaYkcoNV1evtgRSsWilvyu43S0yBU7Qql5WmaJHaFUapvJr8DNkt/bAj0ayeucbCa/WhGWL+WJHaFUdk2U3zlZKqQ6PXJFk9/ZmoiIiIiIqj22yBARERERyVr1bJuonq/6Bfn4+GD//v0G+/R6PRwcHBAdHQ0AiIyMhJWVldFaJ4sWLTK58n1AQADWrVtntF+r1WLAgAFwdXUVbp9++qnJXPb29hgzZgzGjh0LR0dHTJs2DVeuXCnRY2m1Wvj5+T33tev1euzduxdvvvkm3nzzTSiVSixduhQPHvz/PuVFvfbx48fj+++/N9jn5+cHrVYrbJ89exYeHh4YNWoU1Go1pkyZYrDAZkZGBnr37o0PP/zwuVmJiIiIqOpii0wZuLm5ITAwEJ6ensK+S5cuwczMDAMHDgTwdN0TW1tbaDQaODq+2CCwIUOGwN/fv0Q/6+/vL6zjcvLkSfj6+mLXrl3o06dPqR/LlM2bN+Onn37C3r170axZM+j1epw6dQoZGRlo0KABgLK/9nPnzmHJkiXYunUrevXqBQC4efMmUlJShAUyjxw5gj59+iA8PBwLFiwocgY0IiIiouqCY2SoxBwcHHDnzh0kJiYK+7RaLdRqNRQKBdLS0nDx4kVs2LABMTExSElJESXnqFGj4OHhgV27dpXL4z169AiBgYH48MMP0axZMwBP5y0fNWoU2rVrBwAv9Nq3bt2K6dOnC0UMAHTr1g3Dhg0TtjUaDaZPnw4rKytERMhrFjIiIiIiKj8sZMrA3NwcLi4uwsrymZmZOH36NFQqFQAgLCwMI0aMQLNmzeDo6Ijg4OAXer4LFy4YdAc7dOhQiX+3T58+SEhIKJfHSkxMhLm5OV555ZUif+ZFXvuvv/4qtByZEhcXh/T0dNja2kKtVgv//kRERETVmUKhqPCbFLGQKSN3d3eEhYUhLy8Px44dQ79+/dCyZUsAT1tnCooalUplMAakLIYMGYLQ0FDh9tZbb5X4d59dJuhFHqskyvLaS/rHERQUBFdXV6EVKDY2FklJSS+Ul4iIiIjkiWNkysja2hrNmzdHVFQUNBoNJk6cCAC4fv06EhISsGTJEuFnk5OTcfnyZfTv37/Sc/7yyy/o0qVLuTyWpaUlsrOzcfv2bXTq1Mno+PNee5MmTZCenm7wO2lpaWjSpAkAoHv37oiNjUW3bt2MHlun0+HIkSMwNzdHaGgoACAnJwdarRbTpk0rl9dHREREJE/SbDGpaGyReQFubm4ICAjAH3/8AQcHBwBPx3D4+PjgzJkzwm3mzJmidIM6ffo09u/fj//85z/l8nj169fHpEmTsHz5cty/fx/A0xaf06dP488//3zuax86dChCQkKQnZ0N4GlXscTERPTu3RsAMG3aNGzbtg03btwQnjMuLg7nzp1DREQEOnXqhKioKOGxd+/e/cLd9oiIiIhIntgi8wKcnZ2xbt06jBs3Dubm5sjOzkZ4eLjR1MzOzs4YO3Ysli5dCgD47LPP8MUXXwjHV69eDQA4cOAAwsPDhf3Tp0+Hubm5MK6lQM+ePbFmzRqTmfz8/GBubo6srCxYWlriiy++MBh3UtxjRUZGws7OTjimVqsxe/Zsg8efO3cu9uzZg/HjxwN4WsgMGDBAmEmsuNfu7u6Oe/fuwd3dHWZmZqhTpw42bdqExo0bAwDs7OywatUqrFq1Cunp6ahZsybatm2LefPmYc+ePXBxcTF47L59+yI/Px/R0dEYNGiQyX8PIiIioqpOUU3bJhT6ZwdREFVRQbePix2hVNrVzxM7Qqk1q5MvdoRSyc6XX1N8fHoNsSOU2qstcsSOUCq1zeT3heC3DPm9l6+nyeta6sCX5fU+BoC6Mjtd3MmU398eAIxs4yR2BGTnRVf4c9SuIb2LxixkqBr5TewARNWSHnIryuX3Zaq6riFRmfSQ39club0v5PhvDAAKWIkdAdl5P1X4c9SuMbDCn6O05He2JiIiIiKiak9e7bpERERERGRAquu8VDS2yFCRfHx8jAbv6/V6ODg4IDo6GpGRkbCyssKpU6cMfmb8+PH4/vvvDfb5+fkJa8oEBATg1VdfhaurK8aMGYPFixdDp9MBAOzt7TFmzBiDRTvv3r0LADh27BiUSqXwe/Pmzauol05EREREEscWGSqSm5sbAgMD4enpKey7dOkSzMzMMHDgQMyaNQu2trbQaDRwdHQs1WMrlUosXLgQOp0O48ePx4EDBzBhwgQAgL+/P7p27Wrw88nJyVi5ciWCg4PRqlUr6PV63Lx588VfJBEREZHssUWGyICDgwPu3LmDxMREYZ9Wq4VarUZ6ejouXryIDRs2ICYmBikpKWV6DnNzc/Tv3x+3b98u9uf+/fdf1KxZE40aNQLwtAm1e/fuZXpOIiIiIpI/FjJUJHNzc7i4uAgLWmZmZuL06dNQqVQICwvDiBEj0KxZMzg6OpZ5YcqHDx/i/PnzBkWJn5+f0K1MrVYDAKytrdG7d2+MGDECfn5+2LNnD9LS0l78RRIRERHJnAJmFX6TImmmIslwd3dHWFgY8vLycOzYMfTr1w8tW7aEVquFSqUCAKhUKmH8S3EKD0QLCQmBq6srJkyYgBEjRggFC/C0a1loaChCQ0OFxzUzM8O2bduwb98+DB48GJGRkRg7dizS09PL+RUTERERkRxwjAwVy9raGs2bN0dUVBQ0Gg0mTpyI69evIyEhAUuWLBF+Ljk5GZcvX0b//v3RpEkTowIjLS0NTZo0EbYLxsiUVteuXdG1a1d4eXnByckJ0dHRGDVqVNlfIBEREZHscYwMkUlubm4ICAjAH3/8AQcHB2g0Gvj4+ODMmTPCbebMmUIXtKFDhyIkJATZ2dkAgLi4OCQmJqJ3795lzpCUlISYmBhh+59//kFqairatm37Yi+OiIiIiGSJLTL0XM7Ozli3bh3GjRsHvV6P8PBwo2mZnZ2dMXbsWCxduhTu7u64d+8e3N3dYWZmhjp16mDTpk1o3LhxiZ7Pz88PtWvXFrY//PBDNGnSBAEBAfjrr79Qp04d5OfnY/bs2RzwT0RERNWeopq2yCj0er1e7BBEleM3sQMQVUt65IkdoZTk11mhun6JqUx6yO/rktzeF3L8NwYABazEjoA8fWyFP0cNRdl71lQUtsgQUbmR2xdWXd5DsSOUWi0zC7EjlFqeXid2hFIxU8jvozEnP1PsCKVWu0bJWump+pBb4UXik9/ZmoiIiIiICpFfS3J5qJ6vmgQ+Pj5G4130ej0cHBywZcsW+Pn5CftTUlKwYMECODg4QKVSwd3dHd999x0AICAgAOvWrTN4nK+//hqLFi0y2BcZGQkrKyucOnXKYP+iRYtgZ2cHpVKJUaNGwdPTEyEhIUZ5P/nkE/Ts2RP3799/oddNRERERPLGQqaac3NzM1rM8tKlSzAzM0OrVq2EfVlZWfD29oalpSVOnTqF4OBgBAYGIj8/v1TPp9FoYGtrK8xwVpivry9CQkJw8uRJLF++HNu3b0dgYKBwPC8vD6GhoejXrx9CQ0NL+UqJiIiIqiZFJfwnRSxkqjkHBwfcuXMHiYmJwj6tVgu1Wm2wgOWRI0fQqFEjTJkyBWZmT982L730Ejw8PEr8XGlpabh48SI2bNiAmJgYpKSkFPmz3bp1w5IlS/Dll1+iYD6KyMhItG/fHn5+fiVagJOIiIiIqi4WMtWcubk5XFxchBaSzMxMnD59GiqVyuDnbty48dx1YEJCQuDq6ircvvjiC4PjYWFhGDFiBJo1awZHR0ejlqBn9enTB/fv30dqaiqAp605arUaAwYMQE5ODq5du1bal0tERERUBSkq4SY9LGQI7u7uCAsLQ15eHo4dO4Z+/fqhZcuWpX4cpVKJ0NBQ4ebr62twXKvVCgWSSqV6bqtK4ZnB79+/j+joaIwZM0Z4LlPd04iIiIioeuCsZQRra2s0b94cUVFR0Gg0mDhxotHP9OjR44UKh+vXryMhIQFLliwR9iUnJ+Py5cvo37+/yd/55Zdf0LRpUzRt2hS7d+9Gbm4uxo4dCwDIzc1FVlYWFi9ejDp16pQ5FxEREZHcFR4OUJ2wRYYAPB30HxAQgD/++AMODg5Gx998802kpqZi586dQktJZmYmDh48WKLH12g08PHxwZkzZ4TbzJkziyyO4uLisHbtWrz33nsAnrbmbN26VfjdqKgo9O7dG8ePHy/jKyYiIiIiOWMhQwAAZ2dnJCQkwNnZGebm5kbH69Wrh3379iEuLg4ODg5wcXHB+PHjhYH/xcnOzkZ4eLjQmlL4OU+cOIHHjx8DAL744gu4urpi9OjRWLlyJaZMmYJ3330X165dQ3p6OmxtbQ1+v/DYHiIiIqLqy6wSbtKj0BceiEBUpf0mdoAqT488sSOUii7vodgRSq2WmYXYEUotT68TO0KpmCnk1+s6Jz9T7AilVrtGY7EjVHlSnTK36ukqdgDoEV/hz6GAVYU/R2nJ72xNRERERESC6lq0skWGiIiIiIhkR5od3oiIiIiIiIrBQoaIiIiIiGSHhQwREREREckOCxkiIiIiIpIdFjJERERERCQ7LGSIiIiIiEh2WMgQEREREZHssJAhIiIiIiLZYSFDRERERESyw0KGiIiIiIhkh4UMUTl4+PCh2BFM0uv1YkcgiZk7dy4ePHggdoxSOXDggNgRiIhIgljIEJUDFxcXsSOYpFarxY5AEmNpaQmlUonvv/9e7CglduLECUyePBlJSUliRymxRYsWCfe3bdtmcGzKlCmVHYeo3MXFxeH9998XO0ap6HQ6fPXVV2LHoHLEQoaoHEi15UOquV5EXFyc2BGMyKnFYMaMGdi+fTu2bNmCDz74APHx8UhISBBuUhQYGAhHR0e8/fbbCA4OFjtOicTHxwv3T506ZXBMTgWZHGzYsAE5OTlixygXUjy/3b59G++99x5cXFywY8cOZGRkYPbs2fD29kb37t3FjmfSkydPsHPnTqxevRrR0dEAgP3798Pe3h5nzpwROR2Vp5piByCqChQKhdgRTNLpdEhMTCyyoOncuXMlJyq52NhY/P333xg0aBAaN26MW7duYfPmzbhy5Qp+/PFHseMZOHHiBE6dOoW1a9eiRYsWYsd5LisrK3z88cfw9PTExYsXhfevQqFARESEyOlM8/DwgK2tLdzd3fHxxx/DzMwMer0eCoVCcu8HwPAiwrN/f1I9XwCAtbW1yXwF/9Y3b94UIVXx7t+/D7VajXXr1kn2i/Wz5HR+W758Ofr06YPx48cjIiICb731FqytrXHixAk0bdpU7HgmLVmyBP/88w/69u2LDRs2oHnz5oiPj8eaNWswfPhwseNROWIhQ1RCxV2tzs3NrcQkJfe///0Pvr6+JgsZKX9p3b59O3bv3o1OnTph48aN8PLyEv7/8ccfix3PSGBgIA4cOIC3334bs2bNgkqlEjtSsXbv3o2vvvoKq1atgpOTk9hxSiQ2NhaLFy+Gs7MzJk+eDDMzaXcoKFwMSLlwedaVK1cAPC1cPD09ZdHauHbtWpw9exZ+fn5QKpWYNGkSatSoIRyvW7euiOmMye38lp6ejvnz5wMAhg0bhqFDh2Ljxo2oWVO6XyF//fVXHD58GDVr1kRmZiaGDRuGiIgIyRZeVHbSfRcSSYyvr2+Rx2rXrl2JSUquc+fOCAkJETtGqYWFheHo0aN4+eWXcfv2bTg7O2Pfvn3o16+f2NGKJJcWAw8PDzRt2hQajUY2H+qffvopjh8/jlWrVmHIkCEGx7777juMGzdOpGRFu337Ntzd3Y3u6/V6/PHHHyImK169evWE+zVq1DDYlrLXXnsNgYGBUKlU2Lp1KwDptiLJ7fxWuGAxMzNDy5YtJV3EAECdOnWEjBYWFujYsaNszndUOtJ+JxJJCPvVVp46derg5ZdfBgB06tQJnTp1kuyHfAG5tBh4eHhAqVQa7dfpdDhw4AAmTJggQqripaamIiQkBBYWFkbHtm3bJslC5osvvhA7wguT0xi7kydP4qOPPsKUKVMk/fcHyO/8VrgQN7UdFBQkRqxiJSUlYf369cJ2cnKywfaCBQvEiEUVgIUMURUm5Q/H4jx8+BCRkZHCdnZ2tsG21Po4F9diIDWjR4/Gzp078ffff2PUqFEYPHgw9u/fj61bt6Jz586SLGTWrl1b5DGpftkeNGiQ2BFemFy6xM2ZMwf/+9//8OWXX0p63F8BuZ3f5FiUv/POO8VuU9Wh0Ev1U4BIYmxtbYsdBCul7kMFUlNTUbduXaGP+M8//4wTJ06gbdu28Pb2NuhHLiXjx48v8phCoZDc9JmLFy/G4sWLTbYYxMXFwdraWoRUps2fPx9///03+vbti59++kkYBLtkyRLJfYEqiREjRuCHH34QO4aRwld/n6VQKPDf//63EtOUXOHz3IMHD9CgQQMA0j7Pbd26FVOnTjU4n+l0Ohw7dgxarRZ79+4VMZ0xuZ3fiKSMhQxRCf3111/FHm/Tpk0lJSk5T09PrFu3Du3bt8fvv/8ONzc3uLq6IjExEd27d8cHH3wgdsQqQy6zEL3xxhuyGwRbVFGg1+vx3Xff4fLly5Wc6Pm2bNlitC83NxeHDx/GP//8gxs3boiQ6vnkeJ4rLDY2FkFBQThx4gR69eoFZ2dnk10pqeQWLVokTEKwbds2TJ8+XTg2ZcoUfP7552JFK9LXX38Nb29vAEBUVBTs7OyEYwEBAZg5c6ZY0aicSbcTKZHEtGnTpsjbw4cPxY5n0oMHD9C+fXsAQHh4OMaMGYMVK1Zg586dOH/+vMjpinb16lXhfmpqqsExKS7kuGPHDkyePBm7du2Ch4cHvvrqK7z11lvo2LEjTp48KXY8A3IcBFuvXj2Tt/r16+Pdd98VO55J77//vsHN0tISx48fR5cuXaDVasWOV6SGDRtCr9cbneP0ej0aNmwodjyTUlNTERgYCBcXF3iUF78AAB5iSURBVCxYsACtW7dG3bp1sXPnTkkWMXI7vxVe20YuayJpNBrh/qZNmwyOcbxr1cIxMkSlIJer7gXMzc2F+1evXhU+1GvXri3pWWdWrlwpLHw4efJkg0UQ/f398frrr4sVzaTQ0FDZzEIkx0Gwcls9vLDz589jw4YNqFu3LtasWYP+/fuLHalY69evh52dHdq2bWuwPy4uDlFRUVi1apVIyYpmZ2eHAQMGYOXKlcLf3KFDh0ROVTS5nd8Kk8uaSMWt48SOSFWLdL/JEEmM3Ob+B55ecY+MjESLFi1w5coVIWd+fj6ys7NFTlc0uX0IyWkWIjkOgjU1hXjTpk3Ru3dvybYSxMbGYuPGjUhPT8ecOXNkM/7o+vXrJouVkSNHGl3ZloqJEyciLCwMGzduhJubG0aPHi12pGLJ7fwmxzWRisssl9dAJcNChqiE5Db3P/B0deP58+cjKSkJM2bMEL5sf//99+jZs6fI6Yomtw8hOc1CVFzrxr179yoxScmZ6gqSlpaG27dvY/PmzRgwYIAIqYo3btw4tGnTBqNGjcKlS5dw6dIlg+NSbPkCgJycnCKPSfFvDwD++9//Yt68efjhhx+g1Wrx0UcfITc3FxcvXoStra3Y8YzI7fwmxzWRCrc8F76v1+uRnJwsZjQqZyxkiEpITlfdC1hbW+PIkSNG+x0cHODg4CBCopIpXBhkZmYaFAWZmZlixSpSq1atsHPnTmG7ZcuWwrZCoZBUIfMsnU6HkydPIigoCDdu3MBPP/0kdiQj/v7+JvfHxcVhxYoVklx9fsaMGZL8Uvo8er0eqampaNKkicH+1NRUSbYWFDAzM4O9vT3s7e2RmpqK0NBQrFmzBhkZGYiKihI7ngG5nd/kPv3ys63Onp6elR2HKhBnLSMqoZEjR2LZsmXC9ocffoilS5cK21L8svrsl1KFQoEmTZqgQ4cOkp16GSh+elIA2LdvXyUlqbquX7+OoKAgHDt2DNnZ2VizZg0cHBxQp04dsaOVikqlMhhjQC9m7969OHXqFNasWYMOHToAAO7cuYNly5bB3t4ekyZNEjegCd98802Rx9LT0zFjxoxKTPN8PL8RlR8WMkQlJMe5/93c3Iz2paWloWbNmggICICVlZUIqaqHpKQkaDQahISESGrmsr1790Kr1eLJkydQq9UYO3YsvLy8ZDmTj06ng5ubGw4fPix2FCNyHNdTYNOmTdizZw9q164N4GlXyYkTJ2Lu3LkiJzPN2toaPXr0QNeuXU0e/+ijjyo5UdWSmZmJAwcOoGHDhlAqlfjkk0/w448/omPHjli8eDFatWoldkSTYmNj0aBBA3Ts2BERERG4cOECOnXqBA8PD0lPdkOlw0KGqBqKjIxEYGAg9uzZI3aUEklOTkZMTAw6duwo6eIrJycHp0+fRlBQEKKjo6FWq+Hi4iKpMRzW1tZ49dVXsWrVKrRr1w7A066GERERIicrWuGuNwXS0tIQGhoKGxsbzJo1S4RUxfPz8zPaJ/VxPYU9fvwYCQkJAIDOnTujXr16+Pfff9GsWTORkxnTarUIDg5GVlYWVCoVnJ2dJV8sFib185ufnx9q1KiBrKwspKeno0uXLnB0dMSlS5dw69YtSXY927RpEw4fPoz8/Hy88cYbuHTpEoYMGYKYmBh06dIFK1asEDsilRMWMkQl9ODBA2zfvh23b99G9+7d4evrK7tuOIVJuUvO0aNHsWTJEjRo0ABLlizBypUr0aZNG9y5cwezZs2S3ExbcXFxCAoKQnh4OLp37w6lUomNGzdKck2IxMREaDQahIWFoVOnTlCpVNiyZYukW2SebQ0t6CI5dOhQuLu7y2osipTH9RRISUlBUlISrK2tUbNmTaSmpmLHjh0IDg6W5BiqAn/++SdCQkJw9OhRdO3aFdOmTYO1tbXYsYzI7fz25ptvIjw8HDk5ORg2bBguXrwo/M05OzubHIcpNicnJwQHB+PRo0ewt7fHuXPnYGFhAZ1OB6VSiaNHj4odkcoJ29aISqhgPIydnR3OnDmDTz75xGDMjJzk5+cjLy9P7BhF2rFjB4KCgvDgwQO8++670Gg0sLS0RFJSEiZPniy5D3qlUolXX30VGo0GrVu3BgBs3rxZ5FSmWVpaYsGCBcIsTxqNBv/++y8WLFgAFxcXvPbaa2JHNLJ8+fIii5W7d++iRYsWBmsmSZm1tbWkpz4/dOgQVq5ciYYNG6JJkyaYNWsWFi1ahGHDhhksMihF7dq1w6RJk9CsWTP4+/tj2LBhkixk5HZ+K/jbqlWrFlq1amXwt1irVi2xYhXL3NwctWvXRu3atdG+fXtYWFgI++VyrqCSYSFDVEKJiYkIDw8HALi7u+Ptt98WOdHzFXQNKSw9PR2HDh2SdNcWMzMzWFpaAgBat24t3G/RooUkJylYvnw5tFotvL29oVar4erqKnak56pRo4Ywe939+/cRHByMdevWSbKQmTJlislCRq/XIy8vD48fP8bChQuFaWGlTKfTITc3V+wYRdqzZw+Cg4PRpUsXXL58GRMmTMCGDRswZswYsaMVSa/X4+zZs9Bqtbh16xbeeOMNfPfdd0LXSamR2/mt8Cxrjx49kvwsa8DT98STJ0+Qn58PhUKBJ0+eCLPusSNS1cJChqiECl/FkcsVHV9fX4Ptgi45Q4YMwbRp00RK9XyFv7QWDDguYGZmVtlxnuudd97BO++8g99++w0ajQYeHh7IzMyERqPB6NGjhauBUtW0aVP4+PjAx8dH7CgmPa/bW3JyMiZNmiSpQqa4cT0jR44UIVHJ1KxZE126dAEA9O/fH+3atZN0EQM8bSVv3rw51Gq1MO11dna2wRgfKZHb+a3w9PKFp5Yv2Jai+Ph49O3bVyhabGxsoFAooNfrZdUVlZ6PY2SISmjgwIEYMmSIsH3hwgWD7c8++0yMWFWSjY2N8OUjISFBuK/X6/H7778jJiZGzHjPlZubi4iICGi1WkRHR0sqr729vdGCfE2bNsWwYcMwZcoU2RTpz9q3b99zp7WtTHId1+Pk5ISAgADhC6Cfn5/BttSKAuDpe7pAwZfVwttSm8hC7uc3IilhIUNUQsUNjL93757k1ioAnn4w6nQ64arf1atXhZW7u3fvjvr164sZr0jR0dHFHh80aFAlJXlxycnJaN68udgxBLdu3TLal5aWhoMHD6Jx48YGayNR2d26davIYqV27dqSHddTuCh4lhSLAjmS2/nt3r17BtsFRfmzrUlEYmAhQ1RGycnJCA4OhlarhV6vl9RaIQU2bdoEhUKB2bNnAwBGjBiBpk2bQqfTYdSoUZg5c6bICauGDz74wGC7cCvH4MGDRUpVOnl5eVCr1QgNDRU7SpXwbMtXATmO66HqzdbW1qilKzMzEzY2Nli/fr0wwYmUPO/vKigoqJKSUEXjGBmiUijoMqTRaHDt2jXk5uZi165dsLGxETuaSZGRkfj222+F7UaNGkGj0SAvLw/e3t6SLWTWr19f7PEFCxZUUpKS6dmzp9G+tLQ0rFy5EhMmTICHh4cIqUqnRo0akuyfL1dyHNdTFJ1Oh+PHj0Oj0WDv3r1ix5E9uZ3fLl68aLQvLy8PBw4cwOrVq7F9+3YRUhUvMTERr7zyCtRqtTDmi6omFjJEJbR27VqEh4fDysoKKpUK/v7+cHJykmwRAzwdOFqvXj1h28nJCcDTL60FXcykaPfu3ejRowfs7OwkOYvPs7y8vEzu9/b2xqRJk2RRyJw/fx4vvfSS2DGqjebNm8PT01PsGMWKjY1FUFAQTpw4gV69ekGlUokdqUqQ2/nNlBo1asDLy0uyLRvnz5/H8ePHERwcjJCQECiVStktlEolw0KGqIQOHjwIGxsb+Pr6wtbWFgAkO2C3wIMHDwy2C89ilpaWVtlxSmzv3r0IDg7GkSNHMHLkSNleVWvUqJHk3iNubm5GmdLS0qBQKLBlyxaRUlVPUpqcoEBqairCwsKg0WiQk5MDpVKJunXrGsxURS+mqpzfAEh2PbJ69epBrVZDrVYLC6W6uLhg/vz5GDt2rNjxqByxkCEqobNnz+Lw4cNYv349MjIyoFQqJXsSL9C9e3fhalRhYWFh6N69u0ipnm/w4MEYPHgwHj9+jOPHj2P16tV48uQJFixYIOn1b56VlJQkuUJm4cKFBtsFA3c7dOiAmjX5kVDd2dnZYcCAAVi5ciX69esH4OkimVR+5HZ+y8rKMtqXnp6OAwcOSL4Ae/jwIc6dO4fz58+jQ4cO6NChg9iRqJzxU4uohBo0aAAvLy94eXkhLi4OGo0G2dnZ8PLygouLiyS7D82dOxfe3t44e/Ys+vTpA+Bpd5GLFy/i66+/Fjnd89WrVw82Nja4ffs2wsPDkZKSInYkk0z1eU9PT8e5c+eMJgIQm9RmRCJpmThxIsLCwrBx40a4ublh9OjRYkeqsuRyfuvbt6/BYP/C65EtWbJE5HSmRUZGIiQkBPHx8XBycsKnn36Ktm3bih2LKgBnLSN6ATk5OTh9+jS0Wi2+/PJLseOYlJycjG+//Ra//vorAKBbt27w8vKS1JTAz3rw4AHCw8MRGhoKc3NzqFQqjB492mC8j5SY6pLVpEkTDB48WFi1Wyr8/PyKbCVSKBTYvHlzJSciqcnPz8cPP/wgrIOUm5uLbdu2CV1q6cXI7fyWn58vu4lArK2t0aNHDwwaNMjk+U5qEypQ2bGQISLJ6dWrF7p27QqVSoV27doZHR8+fLgIqcpGp9NJar0QU+shPXz4EHv37kVaWhquXLkiQiqSqtTUVISGhkKr1SIjIwNRUVFiR5I9uZ3fVCpVseuoSVFAQECx3Xrff//9SkxDFYldy4iqsKLWNxk6dKikr64WzAR34sQJo2MKhUJyH/TA06uWGRkZaNy4MYCnBcz+/fuxa9cuSX35KzzzlE6nw1dffYU9e/Zg9OjRmD59uojJSAq++eYbo33m5ubw8PBAenq6CImqHrmd3+R4vVuqSwtQ+WMhQ1SFFbW+yapVqyS9vsm+ffvEjlAqZ86cwfz585GVlQVbW1vMnj0bc+bMQbNmzbBx40ax4xnJz8/HoUOHsH37dgwaNAgHDhxg/3ECAKxevRo9evRA165dxY5SZcnt/KbT6ZCYmFhkQdO5c+dKTvR8X3/9Nby9vQEAUVFRsLOzE44FBASw0KlC2LWMqBpKT0/HpEmTEBISInYUk65evSpctUxNTUWTJk2EY99//z1ef/11saKZ5Orqinnz5mHw4ME4efIkli1bBj8/P/znP/8RO5qRY8eO4bPPPkOnTp0wd+5cyc86RJVLq9UiODgYWVlZUKlUXHujAsjt/NazZ0+0aNHCZCGjUCgQEREhQqriFe4O92zXODl2laOisUWGqBqS4vomha1cuVL4oJk8ebLBh46/v7/kPugBCFf8XFxcsHnzZkkWMQAwZ84ctG7dGrVq1TI5ScFnn30mQiqSimfX3vDw8EDXrl0xbdo0WFtbix2vSpDb+a1z586SvehVlMJF17MFGK/fVy0sZIiqISmub1KYHD+Enjx5ImSzsLAw2K5bt66Y0Qx89NFHYkcgGWjXrh0mTZqEZs2awd/fH8OGDWMhU07keH6Tm8Kfb89+1kn5s49Kj4UMURVman2TtLQ0nD9/XnLrmxQmtw+h+Ph49O3b1+BLSEHXEYVCgZs3b4oVzUjhwf7P+vnnnysxCUmRXq/H2bNnodVqcevWLbzxxhv47rvvTM6uRWUjt/NbwcKocpKUlCR8/hW+r9frkZycLGY0KmcsZIiqMFPrErRt2xY+Pj5IS0sTIVHJPHz4EJGRkQCAzMxM4X7BttTExcWJHaHMkpOTERwcDK1WC71ej5MnT4odiURkZ2eH5s2bQ61WY8aMGVAoFMjOzkZCQgIAaQ7slhu5nd+GDBmC0NBQuLq6GuwPCQlBgwYNYG9vL1Kyor3zzjsm7wOAp6dnZcehCsTB/kTVSHJysjCYV8pfWsePH1/scTnM+pOWliZMxSw1ubm5iIiIQFBQEGJjY5Gbm4tdu3YJrUhUfRX+Ulp4NfeCbSkO7JYbuZ3f3nnnHQQEBKBp06YG+1NTUzF9+nQcOHBApGRFO336NEaOHCl2DKoELGSIqriCL60ajQbXrl3jl9YKEBcXhw0bNqBhw4aYOXMmZs+ejZs3b6Jx48bw9/fHwIEDxY4oWLt2LcLDw2FlZQWVSgVHR0c4OTnhzJkzYkcjIglyd3dHUFCQyWNjx45FWFhYJSd6Ps5MVn2YiR2AiCrO2rVrMXz4cBw8eBAuLi6IjIxEw4YNJV/E/Pzzzzh16pTR/lOnTkly5fkVK1bg9ddfR9euXeHt7Y2JEyfi2rVr+Pjjj7Fu3Tqx4xk4ePAgOnfuDF9fX7i4uKBOnTqS7JdPVFU5Ojpix44dSEpKEjtKiWRkZBR5LCsrqxKTlByv0VcfHCNDVIUdPHgQNjY28PX1ha2tLQBpDiZ91pYtW7B69Wqj/d26dcPSpUuxZ8+eyg9VjKysLKEf9v79+6FUKgEAw4cPl9yCmGfPnsXhw4exfv16ZGRkQKlUIi8vT+xYRNXGhx9+iODgYDg5OaFv375wc3PDyJEjUatWLbGjmWRlZYXDhw/DxcXFYH94eLhk16FKTk42OdlNgQULFlRiGqpILGSIqjC5fml99OiRyVmS2rZti9TUVBESFc/M7P83bjdq1KjIY1LQoEEDeHl5wcvLC/Hx8QgKCkJ2dja8vLzg4uICDw8PsSMSVWmDBw/G4MGDsWzZMhw7dgz79u3DypUr4ezsDHd3d8lNcz1v3jyMHz8eP/zwA/r06QMAuHbtGi5duiS58TwFzMzMTE52Q1UPx8gQVRNxcXHQaDQ4cuQIXnnlFUl/aR01alSRExEUd0wsQ4YMEVphQkJChPt6vR5hYWE4f/68mPEMFMw+VVheXh4iIiJw9epVbNmyBebm5iIkI6q+EhMTsXDhQty4cUNS07UXSElJwTfffCNk6969Ozw9PdG8eXORk5nGMTLVB1tkiKoJa2trLFmyBAsWLMDp06eh1WolW8i0bdsWFy5cwJAhQwz2//jjj2jdurVIqYomp6k+fX19jfYVzE6Vl5eHoUOHYuHChXB3dxchHVH1kpiYCK1Wi7CwMLRo0QLLly8XO5JJd+7cwdWrV4Wp5rOzszF06FDJFjK8Rl99sEWGiCQnNjYWU6dOxVtvvYXevXsL+w4dOoQdO3YI+6j8JScnY9KkSTh69KjYUYiqpMzMTISHh0Oj0eCvv/6Cs7Mz3Nzc0LVrV7GjmXT69GmsXr0aU6dOFSaKiYmJweeff45ly5ZJcprjc+fOoWfPnrh79y46duwICwsLsSNRBWEhQ0SSFB8fj507d+LXX38F8LQrw+TJkyXXfxwAvv76a3h7ewMAoqKiYGdnJxwLCAjAzJkzxYpWJvv27XvuWhdEVDZ9+/bF4MGDoVarYW9vj5o1pd05Rq1WY926dUYD+3/77TcsXLhQkl24jh49ig8++AD169eHTqdDQEAAXn31VbFjUQVgIUNE9IIK98d+tm82+2oTUWHJyclo3rw5cnJycOfOHQBA+/btJTs2zcnJqcgW2uKOicnFxQXr169Ht27dcPHiRWzdulWyExPQi5HWdDpERIDBlMXPLsS2bNmyyo7zXIWvBz17bYjXioiosObNm2PHjh0YOHAgPDw84OnpicGDB2P79u1iRzMpJycHOTk5Rvt1Oh10Op0IiZ7PzMwM3bp1AwDY2toiMzNT5ERUUVjIEJHknD17Vrj/zTffGBy7fv16Zcd5rsJr8zy7To8c1u0hosrz+eef4/z589BoNPj555/x008/ISgoCBcuXMCOHTvEjmfEwcEBCxcuxMOHD4V9Dx48wKJFi+Dg4CBisqLl5OQgMTERCQkJSEhIQHZ2tsE2VR3S7phJRNWS3Fo4kpKShMXXCt/X6/VITk4WMxoRSUxwcDAOHTqEl156SdhnaWmJLVu2YNy4cZg6daqI6YzNnTsXK1aswPDhw9GhQwcAT2cxGzNmDObNmydyOtOePHmC9957z2BfwbZCoUBERIQYsagCsJAhIsmRWwuHnKZfJiJx1ahRw6CIKdCwYUNJDvw3NzfH2rVr8f777+O3336DXq9H165d0aZNG7GjFenMmTNiR6BKIr2/GCKq9u7evYtZs2ZBr9cL94GnLRx//fWXyOmMZWZmYtGiRQCA8+fPY+jQoSInIiKpUigUSEpKQosWLQz2//PPPyIlKpnWrVtLch0vU+7du1dk1hs3bqBHjx6VnIgqCgsZIpKcxYsXC/dff/11YcHGgm2puXTpknD/008/ZSFDREWaMGECfHx8sGjRIvTp0wcAcPXqVaxbtw4TJkwQOV3VMGPGDGG2SHd3d4NJY5YuXcqZJKsQFjJEJDkqlQqxsbHYtWsXEhMTAQBdunTBu+++K8nFMIsb00NEVNi4ceNgbm6O//u//8Pdu3ehUCjQtm1bzJgxA0qlUux4VULh83Bubm6Rx0j+WMgQkeTExMTA19cXHh4ecHFxgV6vxy+//AIfHx98+eWXwlVMqdDpdEhMTIRerze4X6Bz584ipiMiKUlISEDPnj2xfft24TyhUCigUCiQkJDA80U5kNs4Syo7FjJEJDk7d+7E2rVr4ejoKOxzdHRE79698fnnn2Pbtm0ipjP27Aw5he9zhhwiKszX19fkl+lHjx4hIyMDN2/eFCFV1VIw3bJerze4X3CMqg6Fnm1sRCQxo0ePxokTJ0p9jIhIbh4/fozAwEB8++23GDt2LBYuXCh2JNmzt7c32ld4rCVnNas62CJDRJJTp06dMh0jIpKL3Nxc7N+/H19++SWGDx8OrVZrNJMZlc2zhUpGRgaio6PRrl07WFtbi5SKKgILGSKSnIJVmU01GOfk5IiQiIio/ISEhGDLli3o2bMn9u7di06dOokdqUqZP38+fHx8YG1tjfT0dLi6usLCwgJpaWmYM2cO3nrrLbEjUjlhIUNEkmNqVeYCHKhJRHLm4uKCx48fY+bMmejZsyfy8vKQkJAgHOdg/xd348YNoeUlNDQUlpaW2L17N/755x9MmTKFhUwVwkKGiCSH/ZeJqKp69OgRAMDf399g3AbAyUHKS+EuyJcvX8bIkSMBAC1btuTFsCqGhQwRERFRJeGFmsqRlJSEhg0bIjo6Gn5+fsJ+zlpWtbCQISIiIqIqw9fXF0qlErVq1UL//v2F7npXr15F69atRU5H5YnTLxMRERFRlZKSkoJ///0X1tbWQneypKQk5OXlsZipQljIEBERERGR7JiJHYCIiIiIiKi0WMgQEREREZHssJAhIiIiIiLZYSFDRESyZW9vj549eyI1NdVgv1KphJWVFe7evQsAuHLlCiZMmIC+ffuif//+mDp1qsEihJcuXYKVlRVWrFhh8Dienp7QarXYsWMH+vbti759+6JXr17o1q2bsP3mm28CAKysrHDnzh2D3w8ICMD8+fMr4JUTERELGSIikrU2bdogPDxc2I6Pj0dWVpawHRMTg8mTJ8PBwQFnz55FREQErKys4OnpiT///FP4uXr16iE0NFQofgqbOnUqYmJiEBMTg5UrV8LGxkbYLvzcRERUeVjIEBGRrLm6uiIkJETYDgkJgVKpFLY/+eQTuLq6YuLEibCwsECjRo0wZ84c9OnTBwEBAcLPvfTSS1Cr1di6dWul5iciorJhIUNERLJmY2ODzMxMJCYmIi8vD+Hh4Rg7diwA4MmTJ4iJicGYMWOMfu+NN97AhQsXDPZNnToVJ06cwO+//14p2YmIqOxqih2AiIjoRRW0ygwcOBCWlpZo0aIFACA9PR35+fl4+eWXjX7n5ZdfRlpamtE+Dw8P+Pv7Y/PmzaXOoVKpYGb2/68RZmdnY/To0aV+HCIiej4WMkREJHuurq7w9vbG3bt34erqKuxv2LAhzMzMkJKSAktLS4PfSUlJQePGjY0e67333oOjoyPi4uJKnSM4OBgdOnQQtgMCAowmACAiovLBrmVERCR7bdq0Qdu2bREZGYlRo0YJ++vWrQsbGxscP37c6HeOHTsGW1tbo/2NGzfGxIkTy9QiQ0RElYctMkREVCWsWbMGGRkZqFevHnJzc4X98+bNg4+PD1555RWo1Wrk5eVh9+7duHr1KoKCgkw+1rvvvgsHB4fKik5ERGXAFhkiIqoS2rdvj169ehntHzBgAHbu3IlTp07htddew+uvv46bN2/i22+/RceOHU0+loWFBXx8fJCenl7BqYmIqKwUer1eL3YIIiIiIiKi0mCLDBERERERyQ4LGSIiIiIikh0WMkREREREJDssZIiIiIiISHZYyBARERERkeywkCEiIiIiItlhIUNERERERLLDQoaIiIiIiGSHhQwREREREcnO/wMd3HO24B8JtAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "YeEl4WvBmwCX",
        "outputId": "6f45c333-30bd-414f-8755-4f725545b6cd"
      },
      "source": [
        "sns.set()\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "events = sns.load_dataset = dfevent_city_year\n",
        "events = events.pivot(\"CITY\" ,\"YEAR\", \"EVENTS\")\n",
        "ax = sns.heatmap(events,cmap=\"YlGnBu\" )\n",
        "plt.title(\"EVENT BY CITY\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "Os340Ob_njxu",
        "outputId": "a10ab41d-5e81-40d8-a2cb-bdb082216446"
      },
      "source": [
        "sns.set()\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "events = sns.load_dataset = dfevent_city_month\n",
        "events = events.pivot(\"CITY\" ,\"MONTH\", \"EVENTS\")\n",
        "ax = sns.heatmap(events,cmap=\"YlGnBu\" )\n",
        "plt.title(\"EVENT BY CITY\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfq4BKMQbKkp"
      },
      "source": [
        "## **Government analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-aUVf_Pbhzy"
      },
      "source": [
        "**Spanish:**<br>\n",
        "con el fin de consolidar un análisis sobre los eventos amenazantes en el Valle del Cauca, es importante realizar los siguientes cuestionamientos:\n",
        "\n",
        "¿Cuales son los eventos que se presentan más de una vez al año?<br>\n",
        "¿Cuales son los eventos que han generado mayor pérdidas y daños a la vida humana?<br>\n",
        "¿Cuales son los eventos que han generado mayores daños y pérdidas a la infraestructura?<br>\n",
        "¿Cuales son los eventos que han generado mayores pérdidas económicas?<br>\n",
        "¿Cuales son los eventos que han generado mayores pérdidas o daños ecológicos? <br>\n",
        "¿Cuales eventos han superado la magnitud de eventos anteriores?<br>\n",
        "\n",
        "<br><br>\n",
        "**English:**<br>\n",
        "In order to consolidate an analysis of threatening events in Valle del Cauca, it is important to ask the following questions:\n",
        "\n",
        "What are the events that occur more than once a year?<br>\n",
        "What are the events that have generated the greatest loss and damage to human life?<br>\n",
        "What are the events that have generated the greatest damage and loss to infrastructure?<br>\n",
        "What are the events that have generated the greatest economic losses?<br>\n",
        "  What are the events that have generated the greatest ecological loss or damage?<br>\n",
        "What events have exceeded the magnitude of previous events?<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dt7VGmfypXMX"
      },
      "source": [
        "### **Constants**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUnq3BLopWbC"
      },
      "source": [
        "EVENTS = list(df.EVENT.unique())\n",
        "YEARS = list(df.YEAR.unique())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R93_dfv9Cykm"
      },
      "source": [
        "### **Most frequent events**\n",
        "Questions answered: What are the events that occur more than once a year?\n",
        "\n",
        "---\n",
        "Description: <br />\n",
        "`list_events`: list of lists, every list in list_events contains `event`, `number_of_occurences`. <br />\n",
        "`df_event`: Pandas dataframe containing the rows in the column `EVENT` are equal to `event` variable. <br />\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6hbH57_CgSA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "02b72484-d637-485a-cad1-62aae0bf354f"
      },
      "source": [
        "list_events = []\n",
        "for event in EVENTS:\n",
        "    df_event = df[df['EVENT'] == event]\n",
        "    num_of_occurences = len(df_event.index)\n",
        "    list_events.append([event, num_of_occurences])\n",
        "list_events = sorted(list_events, key=lambda i: i[1], reverse=True)\n",
        "# Plotting\n",
        "num_of_occurences = [x[1] for x in list_events][:5]\n",
        "events = [x[0] for x in list_events][:5]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.bar(events, num_of_occurences)\n",
        "plt.setp(ax.get_xticklabels(), rotation=20, horizontalalignment='right')\n",
        "plt.xlabel(\"Event\")\n",
        "plt.ylabel(\"Number of occurences\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRgYYoYwIsED"
      },
      "source": [
        "### **Most frequent events per year**\n",
        "Question answered: What are the events that have generated the greatest loss and damage to human life?\n",
        "\n",
        "---\n",
        "Description: <br />\n",
        "`list_events`: list of lists, every list in list_events contains `event`, `number_of_occurences`. <br />\n",
        "`df_event`: Pandas dataframe containing the rows in the column `EVENT` are equal to `event` variable. <br />\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5V5WC4pWH9l-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc3935e8-5153-4124-cce9-15db428e1ec5"
      },
      "source": [
        "list_events_per_year = []\n",
        "for year in YEARS:\n",
        "    df_year = df[df['YEAR'] == year]\n",
        "    list_events = []\n",
        "    for event in EVENTS:\n",
        "        df_event_per_year = df_year[df_year['EVENT'] == event]\n",
        "        list_events.append([event, len(df_event_per_year.index)])\n",
        "        list_events = sorted(list_events, key= lambda i: i[1], reverse=True)[:10]\n",
        "    list_events_per_year.append([year, list_events])\n",
        "pprint.pprint(list_events_per_year)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1998,\n",
            "  [['INUNDACION', 326],\n",
            "   ['TORMENTA', 72],\n",
            "   ['DESLIZAMIENTO', 71],\n",
            "   ['AVALANCHA', 17],\n",
            "   ['INCENDIO FORESTAL', 16],\n",
            "   ['SEQUIA', 15],\n",
            "   ['INCIDENTE ESTRUCTURAL', 11],\n",
            "   ['POLUCION', 5],\n",
            "   ['MAREJADA', 4],\n",
            "   ['EPIDEMIA', 4]]],\n",
            " [1999,\n",
            "  [['INUNDACION', 666],\n",
            "   ['DESLIZAMIENTO', 183],\n",
            "   ['VENDAVAL', 74],\n",
            "   ['AVALANCHA', 37],\n",
            "   ['INCENDIO ESTRUCTURAL', 14],\n",
            "   ['VARIOS', 7],\n",
            "   ['SISMO', 4],\n",
            "   ['INCENDIO FORESTAL', 3],\n",
            "   ['EXPLOSION', 2],\n",
            "   ['COLAPSO ESTRUCTURAL', 2]]],\n",
            " [2000,\n",
            "  [['INUNDACION', 308],\n",
            "   ['DESLIZAMIENTO', 141],\n",
            "   ['TORMENTA', 46],\n",
            "   ['FUEGO ESTRUCTURAL', 21],\n",
            "   ['AVALANCHA', 6],\n",
            "   ['POLUCION', 5],\n",
            "   ['VARIOS', 4],\n",
            "   ['INCENDIO FORESTAL', 3],\n",
            "   ['TERREMOTO', 2],\n",
            "   ['COLAPSO ESTRUCTURAL', 1]]],\n",
            " [2001,\n",
            "  [['INCENDIO FORESTAL', 182],\n",
            "   ['INUNDACION', 104],\n",
            "   ['TORMENTA', 103],\n",
            "   ['DESLIZAMIENTO', 26],\n",
            "   ['FUEGO ESTRUCTURAL', 23],\n",
            "   ['SEQUIA', 6],\n",
            "   ['AVALANCHA', 3],\n",
            "   ['VARIOS', 2],\n",
            "   ['POLUCION', 1],\n",
            "   ['COLAPSO ESTRUCTURAL', 1]]],\n",
            " [2002,\n",
            "  [['INUNDACION', 220],\n",
            "   ['TORMENTA', 94],\n",
            "   ['DESLIZAMIENTO', 55],\n",
            "   ['INCENDIO FORESTAL', 33],\n",
            "   ['SEQUIA', 18],\n",
            "   ['FUEGO ESTRUCTURAL', 10],\n",
            "   ['AVALANCHA', 7],\n",
            "   ['VARIOS', 5],\n",
            "   ['CONTAMINACION', 3],\n",
            "   ['GRANIZADA', 2]]],\n",
            " [2003,\n",
            "  [['INUNDACION', 261],\n",
            "   ['INCENDIO FORESTAL', 86],\n",
            "   ['TORMENTA', 81],\n",
            "   ['DESLIZAMIENTO', 52],\n",
            "   ['FUEGO ESTRUCTURAL', 28],\n",
            "   ['AVALANCHA', 10],\n",
            "   ['SEQUIA', 9],\n",
            "   ['SISMO', 4],\n",
            "   ['CONTAMINACION', 4],\n",
            "   ['EXPLOSION', 2]]],\n",
            " [2004,\n",
            "  [['INUNDACION', 398],\n",
            "   ['TORMENTA', 136],\n",
            "   ['DESLIZAMIENTO', 94],\n",
            "   ['INCENDIO FORESTAL', 29],\n",
            "   ['SISMO', 20],\n",
            "   ['FUEGO ESTRUCTURAL', 19],\n",
            "   ['SEQUIA', 17],\n",
            "   ['AVALANCHA', 17],\n",
            "   ['COLAPSO ESTRUCTURAL', 8],\n",
            "   ['EXPLOSION', 3]]],\n",
            " [2005,\n",
            "  [['INUNDACION', 554],\n",
            "   ['DESLIZAMIENTO', 160],\n",
            "   ['TORMENTA', 154],\n",
            "   ['AVALANCHA', 27],\n",
            "   ['FUEGO ESTRUCTURAL', 25],\n",
            "   ['EVENTO VOLCANICO', 9],\n",
            "   ['VARIOS', 8],\n",
            "   ['INCENDIO FORESTAL', 7],\n",
            "   ['SEQUIA', 4],\n",
            "   ['TORMENTA ELECTRICA', 4]]],\n",
            " [2006,\n",
            "  [['INUNDACION', 514],\n",
            "   ['DESLIZAMIENTO', 247],\n",
            "   ['VENDAVAL', 164],\n",
            "   ['FUEGO ESTRUCTURAL', 31],\n",
            "   ['AVALANCHA', 21],\n",
            "   ['MAREJADA', 12],\n",
            "   ['EVENTO VOLCANICO', 9],\n",
            "   ['EXPLOSION', 6],\n",
            "   ['GRANIZADA', 5],\n",
            "   ['VARIOS', 4]]],\n",
            " [2007,\n",
            "  [['INUNDACION', 600],\n",
            "   ['VENDAVAL', 140],\n",
            "   ['DESLIZAMIENTO', 137],\n",
            "   ['FUEGO ESTRUCTURAL', 36],\n",
            "   ['AVALANCHA', 13],\n",
            "   ['INCENDIO FORESTAL', 7],\n",
            "   ['EXPLOSION', 7],\n",
            "   ['COLAPSO ESTRUCTURAL', 6],\n",
            "   ['ERUPCION', 6],\n",
            "   ['ACCIDENTE', 4]]],\n",
            " [2008,\n",
            "  [['INUNDACION', 1045],\n",
            "   ['DESLIZAMIENTO', 322],\n",
            "   ['VENDAVAL', 181],\n",
            "   ['FUEGO ESTRUCTURAL', 44],\n",
            "   ['AVALANCHA', 23],\n",
            "   ['COLAPSO ESTRUCTURAL', 18],\n",
            "   ['SISMO', 16],\n",
            "   ['ERUPCION', 14],\n",
            "   ['EXPLOSION', 8],\n",
            "   ['VARIOS', 8]]],\n",
            " [2009,\n",
            "  [['INUNDACION', 410],\n",
            "   ['VENDAVAL', 272],\n",
            "   ['DESLIZAMIENTO', 168],\n",
            "   ['FUEGO ESTRUCTURAL', 73],\n",
            "   ['AVALANCHA', 23],\n",
            "   ['COLAPSO ESTRUCTURAL', 21],\n",
            "   ['EXPLOSION', 17],\n",
            "   ['SISMO', 6],\n",
            "   ['ERUPCION', 6],\n",
            "   ['EROSION', 5]]],\n",
            " [2011,\n",
            "  [['INUNDACION', 1576],\n",
            "   ['DESLIZAMIENTO', 907],\n",
            "   ['VENDAVAL', 355],\n",
            "   ['FUEGO ESTRUCTURAL', 132],\n",
            "   ['COLAPSO ESTRUCTURAL', 33],\n",
            "   ['AVALANCHA', 26],\n",
            "   ['EXPLOSION', 19],\n",
            "   ['TORMENTA ELECTRICA', 18],\n",
            "   ['GRANIZADA', 6],\n",
            "   ['ACCIDENTE', 4]]],\n",
            " [2010,\n",
            "  [['INUNDACION', 1143],\n",
            "   ['DESLIZAMIENTO', 582],\n",
            "   ['VENDAVAL', 262],\n",
            "   ['SEQUIA', 150],\n",
            "   ['INCENDIO ESTRUCTURAL', 101],\n",
            "   ['COLAPSO ESTRUCTURAL', 45],\n",
            "   ['AVALANCHA', 28],\n",
            "   ['INCENDIO FORESTAL', 16],\n",
            "   ['EXPLOSION', 15],\n",
            "   ['SISMO', 13]]],\n",
            " [2012,\n",
            "  [['INUNDACION', 1198],\n",
            "   ['INCENDIO FORESTAL', 1149],\n",
            "   ['VENDAVAL', 586],\n",
            "   ['DESLIZAMIENTO', 544],\n",
            "   ['INCENDIO ESTRUCTURAL', 222],\n",
            "   ['SISMO', 43],\n",
            "   ['COLAPSO ESTRUCTURAL', 32],\n",
            "   ['TORMENTA ELECTRICA', 28],\n",
            "   ['EXPLOSION', 21],\n",
            "   ['ERUPCION', 16]]],\n",
            " [2013,\n",
            "  [['INCENDIO FORESTAL', 1320],\n",
            "   ['VENDAVAL', 685],\n",
            "   ['INUNDACION', 658],\n",
            "   ['DESLIZAMIENTO', 427],\n",
            "   ['INCENDIO ESTRUCTURAL', 390],\n",
            "   ['ACCIDENTE DE TRANSITO', 89],\n",
            "   ['SISMO', 88],\n",
            "   ['ACCIDENTE', 67],\n",
            "   ['COLAPSO ESTRUCTURAL', 61],\n",
            "   ['AVALANCHA', 36]]],\n",
            " [2014,\n",
            "  [['INCENDIO FORESTAL', 1489],\n",
            "   ['VENDAVAL', 502],\n",
            "   ['INUNDACION', 460],\n",
            "   ['INCENDIO ESTRUCTURAL', 360],\n",
            "   ['DESLIZAMIENTO', 346],\n",
            "   ['ACCIDENTE DE TRANSITO', 104],\n",
            "   ['ACCIDENTE', 100],\n",
            "   ['COLAPSO ESTRUCTURAL', 79],\n",
            "   ['SEQUIA', 39],\n",
            "   ['CRECIENTE', 35]]],\n",
            " [2015,\n",
            "  [['INCENDIO FORESTAL', 2120],\n",
            "   ['VENDAVAL', 411],\n",
            "   ['INUNDACION', 253],\n",
            "   ['INCENDIO ESTRUCTURAL', 248],\n",
            "   ['DESLIZAMIENTO', 198],\n",
            "   ['SISMO', 78],\n",
            "   ['ACCIDENTE', 78],\n",
            "   ['ACCIDENTE DE TRANSITO', 62],\n",
            "   ['COLAPSO ESTRUCTURAL', 52],\n",
            "   ['SEQUIA', 28]]],\n",
            " [2016,\n",
            "  [['INCENDIO FORESTAL', 1867],\n",
            "   ['INUNDACION', 565],\n",
            "   ['VENDAVAL', 445],\n",
            "   ['DESLIZAMIENTO', 327],\n",
            "   ['INCENDIO ESTRUCTURAL', 202],\n",
            "   ['CRECIENTE', 66],\n",
            "   ['ACCIDENTE DE TRANSITO', 54],\n",
            "   ['COLAPSO ESTRUCTURAL', 52],\n",
            "   ['ACCIDENTE', 52],\n",
            "   ['SISMO', 29]]],\n",
            " [2017,\n",
            "  [['INCENDIO FORESTAL', 1019],\n",
            "   ['INUNDACION', 634],\n",
            "   ['DESLIZAMIENTO', 479],\n",
            "   ['VENDAVAL', 349],\n",
            "   ['INCENDIO ESTRUCTURAL', 271],\n",
            "   ['CRECIENTE', 138],\n",
            "   ['ACCIDENTE DE TRANSITO', 81],\n",
            "   ['COLAPSO ESTRUCTURAL', 59],\n",
            "   ['ACCIDENTE', 58],\n",
            "   ['ACCIDENTE MINERO', 29]]],\n",
            " [2018,\n",
            "  [['INCENDIO FORESTAL', 1373],\n",
            "   ['INUNDACION', 487],\n",
            "   ['DESLIZAMIENTO', 395],\n",
            "   ['VENDAVAL', 322],\n",
            "   ['INCENDIO ESTRUCTURAL', 268],\n",
            "   ['REMOCION EN MASA', 106],\n",
            "   ['CRECIENTE', 97],\n",
            "   ['OTROS', 82],\n",
            "   ['ACCIDENTE DE TRANSITO', 75],\n",
            "   ['COLAPSO ESTRUCTURAL', 50]]],\n",
            " [2019,\n",
            "  [['INCENDIO DE COBERTURA VEGETAL', 2453],\n",
            "   ['VENDAVAL', 430],\n",
            "   ['MOVIMIENTO EN MASA', 400],\n",
            "   ['INUNDACION', 398],\n",
            "   ['INCENDIO ESTRUCTURAL', 245],\n",
            "   ['ACCIDENTE TRANSPORTE TERRESTRE', 130],\n",
            "   ['AVENIDA TORRENCIAL', 84],\n",
            "   ['COLAPSO ESTRUCTURAL', 63],\n",
            "   ['EXPLOSION', 28],\n",
            "   ['ACCIDENTE TRANSPORTE MARITIMO O FLUVIAL', 24]]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KD0zwbQMtgL"
      },
      "source": [
        "### **Most frequent event per city**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvQO1WDgNijp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "6644f787-a8c6-4a9e-8e75-6547ccd94d6d"
      },
      "source": [
        "# we only worked on Valle del Cauca cities\n",
        "list_city = []\n",
        "df_valle_del_cauca = df[df['DEPARTMENT'] == \"VALLE DEL CAUCA\"]\n",
        "cities = list(df_valle_del_cauca.CITY.unique())\n",
        "for city in cities:\n",
        "    df_city = df[df['CITY'] == city]\n",
        "    list_city.append([city, len(df_city.index)])\n",
        "    # print(\"{}: {}\".format(city, len(df_city.index)))\n",
        "list_city = sorted(list_city, key=lambda i: i[1], reverse=True)\n",
        "# Plotting\n",
        "num_of_occurences = [x[1] for x in list_city][:5]\n",
        "cities = [x[0] for x in list_city][:5]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.bar(cities, num_of_occurences)\n",
        "plt.setp(ax.get_xticklabels(), rotation=20, horizontalalignment='right')\n",
        "plt.xlabel(\"City\")\n",
        "plt.ylabel(\"number of events from 1998 to 2019\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veNmNEF50OWK"
      },
      "source": [
        "### **Most events that had danger on humankind**\n",
        "question answered: What are the events that have generated the greatest loss and damage to human life?\n",
        "\n",
        "---\n",
        "Description: <br />\n",
        "`list_persons_per_event`: list of lists, every list in list_events contains `event`, `averages of deaths+injuries+missings`. <br />\n",
        "`df_event`: Pandas dataframe containing the rows in the column `EVENT` are equal to `event` variable. <br />"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxDqRo6HOCHb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "f66cd860-5ce2-41b9-fd99-f32d001a72d7"
      },
      "source": [
        "list_persons_per_event = []\n",
        "for event in EVENTS:\n",
        "    df_event = df[df['EVENT'] == event]\n",
        "    mean = df_event[\"PERSONS\"].mean() \n",
        "    if mean != 0:\n",
        "      list_persons_per_event.append([event, mean])\n",
        "list_persons_per_event = sorted(list_persons_per_event, key= lambda i: i[1], reverse=True)\n",
        "\n",
        "# Plotting\n",
        "persons = [x[1] for x in list_persons_per_event][:5]\n",
        "events = [x[0] for x in list_persons_per_event][:5]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.bar(events, persons)\n",
        "plt.setp(ax.get_xticklabels(), rotation=20, horizontalalignment='right')\n",
        "plt.xlabel(\"Event\")\n",
        "plt.ylabel(\"Individuals Harmed\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4C6yXzRI3BOY"
      },
      "source": [
        "### **Events with greatest economic losses**\n",
        "Question answered: What are the events that have generated the greatest economic losses? <br />\n",
        "\n",
        "---\n",
        "Description: <br />\n",
        "`list_events`: list of lists, every list in list_events contains `event`, `average_total_amount`. <br />\n",
        "`df_event`: Pandas dataframe containing the rows in the column `EVENT` are equal to `event` variable. <br />"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaUWiPr3y_EO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "6463727f-7e51-4a3f-e06c-81788d7ab4c1"
      },
      "source": [
        "list_events = []\n",
        "for event in EVENTS:\n",
        "    df_event = df[df['EVENT'] == event]\n",
        "    average_total_amount = df_event[\"TOTAL AMOUNT\"].mean()\n",
        "    list_events.append([event, average_total_amount])\n",
        "list_events = sorted(list_events, key=lambda i: i[1], reverse=True)\n",
        "# Plotting\n",
        "losses = [x[1] for x in list_events][:5]\n",
        "events = [x[0] for x in list_events][:5]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.bar(events, losses)\n",
        "plt.setp(ax.get_xticklabels(), rotation=20, horizontalalignment='right')\n",
        "plt.xlabel(\"Event\")\n",
        "plt.ylabel(\"losses\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4Jh4Pp-sd6C"
      },
      "source": [
        "### **Events with greatest infrastructure damages**\n",
        "Question answered: What are the events that have generated the greatest damage and loss to infrastructure? <br />\n",
        "\n",
        "---\n",
        "Description: <br />\n",
        "I made a new column 'INFRASTRUCTURE' which is sum of columns from `DESTROYED HOUSES` to `COMMUNITY CENTER`. <br />\n",
        "`list_events`: list of lists, every list in list_events contains `event`, `average_infrastructure`. <br />\n",
        "`df_event`: Pandas dataframe containing the rows in the column `EVENT` are equal to `event` variable. <br />"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DA2DCtJwE_W8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "bbf4c871-453c-4677-bbfa-779146f22cbe"
      },
      "source": [
        "df[\"INFRASTRUCTURE\"] = df[\"DESTROYED HOUSES\"]+ df[\"DAMAGED HOUSES\"]+ df[\"TRACKS\"]+ \\\n",
        "                       df[\"VEHICULAR BRIDGES\"]+ df[\"PEDESTRIAN BRIDGES\"]+ df[\"AQUEDUCT\"]+ \\\n",
        "                       df[\"SEWERAGE\"]+ df[\"HEALTH CENTER\"]+ df[\"SCHOOL\"]+\tdf[\"COMMUNITY CENTER\"]\n",
        "list_events = []\n",
        "for event in EVENTS:\n",
        "    df_event = df[df['EVENT'] == event]\n",
        "    avg = df_event[\"INFRASTRUCTURE\"].mean()\n",
        "    list_events.append([event, avg])\n",
        "list_events = sorted(list_events, key=lambda i: i[1], reverse=True)\n",
        "# Plotting\n",
        "infra_dmg = [x[1] for x in list_events][:5]\n",
        "events = [x[0] for x in list_events][:5]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.bar(events, infra_dmg)\n",
        "plt.setp(ax.get_xticklabels(), rotation=20, horizontalalignment='right')\n",
        "plt.xlabel(\"Event\")\n",
        "plt.ylabel(\"Infrastructure damage\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9PQjfEYERF3"
      },
      "source": [
        "# Use SVM Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggyyJzuyEb02"
      },
      "source": [
        "# libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRIJvi_eF-3-"
      },
      "source": [
        "X = df.iloc[:,7:-1]\n",
        "y = df['TOTAL AMOUNT']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-xMMDOSH0K-"
      },
      "source": [
        "#Split Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLhwBCbsHyi-"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.30)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lhJEThw1IOzQ"
      },
      "source": [
        "# Build the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLrh8uS9FqyZ"
      },
      "source": [
        "svregression = SVR(kernel='linear', verbose=True)\n",
        "svregression.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsDCyw12IqkG"
      },
      "source": [
        "# Make Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4Hd_FeoImkX"
      },
      "source": [
        "y_pred = svregression.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQ6VWNfKIoqA"
      },
      "source": [
        "# Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xa2U5g17I98Y"
      },
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}