python_Máximas_Empirica_Gumbel.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/barbedorafael/Python_workshop_HGE/blob/main/python_M%C3%A1ximas_Empirica_Gumbel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " HIDROLOGIA\n",
        "\n",
        " Ingrid Petry\n",
        " 10/08/2021\n",
        "\n",
        "######################################################################\n",
        "\n",
        "Ajuste da curva de probabilidade das precipitaçoes máximas anuais "
      ],
      "metadata": {
        "id": "9LwpTsfukK6S"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tyH7izIB3wH2"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 39,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "C0bxKqSn22tI",
        "outputId": "40b8ce8f-e9e3-4738-9e08-1e2a641f4071"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5db3ae3d-407e-4b37-a157-a859f3430abc\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5db3ae3d-407e-4b37-a157-a859f3430abc\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Adiciona o arquivo (formato, CSV, onde a primeira linha contém o nome das colunas)\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload() # adicionar o arquivo P_max.csv na pasta do Drive\n",
        "\n",
        "# Os dados são extraídos pelo nome da coluna, dessa forma, pode-se usar outro \n",
        "# de dados, basta que a primeira linha contenha o nome das colunas. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XaIjb5w23se",
        "outputId": "44d593ce-9a15-4e52-d30d-9f2892ae6ab6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Ano  Pmax(mm/dia)\n",
            "0  1961          68.0\n",
            "1  1962          52.8\n",
            "2  1963          66.8\n",
            "3  1964          58.7\n",
            "4  1965          98.8\n"
          ]
        }
      ],
      "source": [
        "table = pd.read_csv('P_max.csv')\n",
        "\n",
        "dados = pd.DataFrame(table)\n",
        "\n",
        "print(dados.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "STHa8-s6maDV",
        "outputId": "acc25e92-43a7-4ec6-e928-75bca16feead"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71cc854810>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EEN1CiPcD9wO1wJNCiD1CiG8ASCn3A/8FHAB+DXxQSjl3fpsmxJeQqZg1ex/2hwlFYtMqZWAyuCsLAkU+JGfuoPnMzKXgbst2Bynle1Jc/WCG+38B+EIxi1KUDl+CYZgvFKFeD5ZmwvBxn58ic69zKQsCRX6EIjEiMTk9uNvn1sAO1aE6y/ElVAeYNWvpGdO6U9tTyTLK9leRJ3FHyCRZZq6N2lPBfZbjC0Zo1LN1s2YtPaPaYOxUmfukM6SyIFDkhj9pUIeBZ47JMiq4z3J8oSittU7tskk93XtGJ7AIaPU6p93W4NaamlTmrsgVI4lxJQV3l92mgrtidiClxBeM0FarZcRmzdzPjwZorXXG7VkTqavRTq3VhqoiV4wA7kmSZbTM3ZyfgXKggvssJqhvLBmZu1mzlp6xQHwodjI2q4Vap01ZEChyJp0so0ohFbMG4408GdzNmbX0jAaYVzddkjGod9tVnbsiZyZH7CXXuduU/YBidmBUyrSZXnMPMD9N5g5arbuSZRS5kjFzD0eZbKif3ajgPosxGpjMnLmPByNcCEZSdqca1LuUeZgid4xh8MnB3eWwIiUEwubs1C41KrjPYozMvcHtwGYRptQbe9L4uCfS4HIw4lelkIrcSFfnHneGNGGSUw5UcJ/FGIM6vE6raTeT0k1gSkTZ/irywfBsT5W5g3kLC0qNCu6zGGPzyO2w4XHapnSrmoV0E5gSMWSZuaKVKorDH4xiEeC0TQ1vk5m7Cu6KKscYju112kybuffog7EzZe4NLjvhqDTl+hXmQzMNs00b/DI5as98SU45UMF9FmNk6h6nlrmb8U3dOxakwW2nxp5+1K5yhlTkg2b3O/39NNfmqKrgPovxJZSEuexWU05jGguE4/4x6ahX5mGKPEhl9wuTG6wquCuqHl8wgs0icNosps3cfcHotKqGZOpdmr+MMg9T5II/FMGV4j3ldipZRlEBHt93nn94/FBJn9PIYIQQmuZuwiYmfyiCJ0WWlYiRuasuVUUu+EPRlO8pJcsoKsJv9vfw/e2nS/qc48EIXr0F2+MwpyOeLxSd1iaeTFxzV7KMIgf8oeg0R0jQhnUYt88FVHA3Cb5QtOSlir5gBI8eOF0O65SRe2bBH8yeuasNVUU+aGeD0xOGeJ27CUuCy4EK7ibBF4wQjkqCkdJlFYlZsTGowGy14kbZWiZcdit2q1CNTIqcSLeh6rBZsFtFvMlptqOCu0kwsvbxQOmyCl8wglffRHI7bERjkmDEXL4avlAEjzNz5i6EoN7lULKMIif8oWh88zSZcjtDnh+d4I77n+f0oL9sr5ErKribBKNMsZTOjb7g5Ompx6SbSf4cqmVAk2ZGVbWMIge0OvfU76lyN/O9cmKIvd2j/GhXd9leI1dUcDcJ8cy9hFmFlhVrb3K3CU2TQpEYoWgsq+YOyhlSkRvRmCQQjuFK0xRX7uB+tG8c0AokKo0K7ibBCOql3PT0BaNxyWOyxtc8mfuE0WSVpVoGNAsCJcsosjGh6+nppD63o7z9Hsf6teB+qOdCxaUZFdxNgJSTviklzdynyDK2+HVmwfgiyylzd6vgrsjO5HDs1AmDq8yZ+7E+H6vn1QKVz95VcDcBwUiMaEyrYinVhmokGiMYiSXIMubL3I0PoieHzL3epUbtKbJjNOqlSxg8ZQzukWiMEwM+rl3Vypr5dSq4K6Zm66XKrI2NWU+8FNJ8DRyTa8yeuTe4HFwIRohEzVXtozAX6UbsGZRTlukeniAUjbGs1csta9vZeXqY/gvBsrxWLmQN7kKIh4QQfUKI1xKuaxJCPCmEOKL/36hfL4QQ/yKEOCqEeFUIsbmci58tJNoClEqWSZY8XCa0O/WlmZiTCqORaayEpaKK2Uc2WaacG6qG3r68zcsta+chJTx5oLcsr5ULuWTujwC3Jl33CeBpKeUK4Gn9Z4DbgBX6vw8AXy/NMmc3UzP30rzxEu1+IVFzN0/mPnkKnZssA6hxe4qMGIE7nSwzE8F9WYuX1fNqWdTkrqg0kzW4SymfBYaSrr4DeFS//Cjw1oTrvyU1XgIahBDzS7XY2UpihUypqmUSB3WAOR3x4pl7DrJMvbIgUOSAEbhTecuAVplVrs/A0b5xWrxO6t12hBDcsradF44NMBaozHu2UM29XUp5Xr/cA7TrlxcCZxLu161fp8hAos5+oUSyQ7L26LabcUM1/8xd1borMhHfpE8ny9ithKOScBn2bo71+1je5on/fMvaeYSjkv851Ffy18qFojdUpWZWkrdhiRDiA0KIHUKIHf39/cUuo6pJlEpKtaE6niTL2KwWHDaLqczDjGPNJXNvUAM7FDmQbUO1XEOypZQc7RtnWas3ft3mRY20eJ08sb8yunuhwb3XkFv0/42vprNAZ8L9OvTrpiGlfEBKuVVKubW1tbXAZZSPz/zsNf7x16X1V0+HEeTqXfaSBfdUZYYek3m6xz+IGUbsGTS49YEdSnNXZMAfl/pSZ+6TVWOlTXIGfSFGJ8JTgrvFIrjponaeeb2PQAXMygoN7j8H7tIv3wX8LOH69+lVM5cDownyTVXx3JEBfryre0ZcFI0se15dTcmqZcZTlBm6HTZzZe6hCE6bBZs1+9uwrkb7UI5OmGf9CvMR19wz2A8k3q9UHNNtB5a1eadcf8vadnyhKNuODpT09XIhl1LI7wEvAquEEN1CiPcD/wDcJIQ4Atyo/wzwK+A4cBT4JvBnZVn1DDDkD9E7FuTsyETZX8vIItrqnCULvr6kDVXQAv2EmTT3YDSnBibQZKVap02N2lNkZCIUxWmzYLWIlLfHPZZKfAZ7rN8HaGWQiVy5rIVap60iVTNZP1lSyvekuemGFPeVwAeLXVSliURj8Y27naeG6Wh0l/X1xoNR7FZBg9vBmaHS+FH4gxGEmJrBuBw2Uw3J9qWZUp+Oerddae6KjCSa5aXCXaZ+j2P947jsVubX1Uy53mGzcN3qNp462EckGsvpLLVUqA7VFIxMhDHUmF2nhsv+esbEJK/TGpdTimU8GMXjsCHEZAajae7mkTX8+hpzRTlDKrLhD0XTSjJQPlnmaN84S1s9WFKcMdyydh5DvhA7ZiCWJKKCewqGfdqpvxDMyB/Ep48F8zptJbQfmD4Ew23GzD2HShmDBrdd1bkrMuJPcEJNxaT1dallmamVMom8cVUrDptlxqUZFdxTMKQH902dDRw8P5Y14EaisaKqOIxA7HHamAhH4yZixWB8YSSiae4mytxD+WXuDS6HytxniFdODFWlj48/HE1rPQDlkWUmQlHOjkxM09sNPE4b1yxvmXErAhXcUzCsB+qbLppHTMLeMyMZ7/+vvz3KdV96hlCBI+x8+saisflZik3VxOHYBqbL3IP5ae51Jvd0PzsyQd9YoNLLKJqTAz7e+e8v8stXq6/QzR+MZCytLYcsc3xgHClJm7kDXLuqle7hiZLtqeWCCu4pGPJpAeSGNW2AtqmaDiklv9h7jmF/mMO9Fwp6PV8ogtdpiwfjUtj++lIMCXabTXMP5V4tA5Oj9sw25NvgD/7jZa76x9/yiR+9yokBX6WXUzDnR7UvqNcLfD9XEu09NbOyjFEpsyyhOzWZK5Y2A/DiscGSvW42VHBPwZBPs+lc1ORmZbuXnafTB/dj/eMc1z/Ie7Jk+OkwhmoYga4Uurs2HDtJlnFY8YejpgmO/jyrZRpcdsJRGZ+2YyaiMcnpIT+djW5+vPssN/zzM3zou7s4cG6s0kvLG+PM1ajdriYmssgyNXYLQpRWljnWN45FQFdz+uC+vM1Li9fJC8dmrt5dBfcUDPnCeBxWauxWtixuZNepYWJpdPDf6K3FHoc1q3yTDl9Qm9Zea2TuJQru02QZpw0pIRA2h5bqy6POHRKdIc0nzQyOB4nGJPdc1cXzH7+OP3rDUp55vZ83/ctz/K9Ht5tqAlY2BvU9J8PlsJrQEqX0CYMQAre9tM6Qx/rH6WxyU5NBDhJCcMWyZl48PjhjyZUK7ikY9odo9Gjt7lsWNzEWiHA0zRv9iQO9bOio57KlzeztLjC4J8kypbDl9aU4PTXe9GboUo3GtAw8n8y92esEoK+CAxDS0aNr7e11NbTV1vDJ29aw7RPX86HrlvPUwb6K+nrny9C4FtxPD/nLYrBVTiZC0bSOkAaldoZM9pRJxxVLm+kdC86YZKeCewqGfCGa48G9EUitu/eMBth7ZoSb185jQ0cDR/rGC8q6jSzbCMbjweIz08T5qQauMnXnFUJ8kHEe1TJLW7XT3qMmlAt6x7QvnPaEJpZ6l52P3LQSt8NasGRXCQxZMhyVM7oBWCxSypwa40rp6R6NSU4M+FjWml6SMbhimaa7vzBDursK7ilIzNy7mt00eRwpg/uTB7Vs7Ja17WzorEdK2Nc9mtdrBSNRwlGJx2GNa+TFNjLFYjLlZqWZMvd8HCENFje5sVuFKYO7kbnPq5/aoWi1CNYvrGd3FQV3Q5aByc3CaiAYiRGT2Sd7aaP2ShPcz41MEIzE0pZBJtLV7GZeXQ0vHlfBvWIM+UI06S6EQgg2L2pMGdyf2N/D0hYPy1q9bOhoAMhbmvEnzDot1YaqPzzdNAwmnfLMMLAjPikqj8zdZrWwpMVjyuDeNxbAIqBFl44S2biogYPnxghGKn/GlAtDvhAr9GBVTbr7RBa7XwMtcy/NZ8B4L+YiywghuHJZMy8dmxndXQX3FAz5JjN3gK1djZwY8DE4Pqn1jk6EefHYIDetbUcIQaPHweJmd96bqom+694Sbagmj9gz8JSp9boQsvlup2N5m9eUAadnNEBrrTOlYdWmzgZC0VjVVM4M+UIsbvbQWuvkuAl/1+nwZRnUYVBKWSY+Wi+H4A5w+bJmBn0hDveW//eqgnsSgXAUfyhKU0JwN3T3XacnA/czr/cRiUluvmhe/LoNHQ15B/fEN6TTZsFmEUVn7umyYmOjyQxzVNN9AWVjeVstpwZ9FfHHzkTvhSDzkkyjDDZ2au+fatHdB/U9p2WtnqqSZYzMPeuGagnnGhzrH6fJ45iSDGZist69/CWRKrgnYdT4Nron/1jrF9Zjt4op0swT+3tp8TrZ1NkQv25DZwPnRgN5dSlOBjkrQgg8TlsJMvdJqScRj8M8skwxmXtMwslBcwWd3tEAbWmC+7z6GubV1VRFcJdSMuwL0eR1sLTVnGdJ6TC6rzM1MYGuuYdL8xk41udjeY5ZO0Bnk5vOJteM6O4quCdh+MokZu41ditrF9Sz85Q2JzwQjvLM633cdFH7FBe4jZ31AOzNY1PVCMSGJOMtQXAfT/jCSMTYvDSDBYEvxaSoXDA+SGbT3XsvBNJm7gAbOxuqIriPTUSIxKSeuXsZ8YfjnwmzYyQtLnsOskyJMvej/eMZO1NTccXSZl46PpS2d6ZUqOCexLBuPdCUdJq1ZXEje7tHCUVivHhsEF8oys1r26fcZ+2CeqwWkZc0E68a0bNqj9Na/IZqGu3R+NkM5mHGhyvfzH1pqwch4MgMaJa5EghHGfGHaa+bvplqsHFRA6cG/aYPlIN6GWSTLstA9Wyq5vqeKpXmPuQLMeQL5ay3G1yxrJnRiTAHzpd3D0YF9yQm39z2KddvXdxIKBJj/7lRnjjQg9dp40q9btWgxm5l9bzavCpmjCw7MXMvVhNPHo5tYPhcm0Jzz3HzK5kau5VFTe60TWWVoC9FjXsyG3X5bs+ZmfX0zpfEM1cjaFWLDUG6KrFk3A7NfbXYzPl4npupBlcsbQHgpTJLMyq4J2F4uSdq7gCb9U3V7SeHePJAL29c1YrTNv1NtKFT21TN9Y3jT9IJS6G5G8+Z7C1jsQhc9tKVgRVDXHPPo87dYHmr11QBJ7E7NR3rF9ZjEbDntLmlGSO4N3ucLGhw4bRZ4t5JZsc4I83kLQOTmX2xHkWGNJhLjXsi8+prWNriKbuJmAruSQz5wwgBDUnBvb2uho5GF9968RQD4yFuXjsv5eM3djQwFojkvOGXnGWXQnPP1CDkcVrNobkHI9gsAkcBY8eWt3k53u8zjd94b5oGpkQ8Thsr22tN38wUz9y9DqwWwZIWj6m+SDMRLyTIQZaB4kuCj/WP47RZWNDgyvuxly9r5uUye+ar4BXY6zwAACAASURBVJ7EsC9Eg8uesl55y+JGuocnsFsFb1zVmvLxGzrza2byBSNYLQKnTftTeEowjWk8Q4OQ22EzxZBsv25JnDgGMFeWtXkJRWOcGS7/8PJcMIJ7e2364A6waVF+Z3WVwOhONZr4llVRxYyRiWcvhSxN1dixfh9LWjxph3Fn4oqlzYwHI7xWxt4HFdyTGPKH0tasbtWlmSuWtVBXY095n+VtXtwOK3vP5FYxo00jmgxyuWTuRrlapuessaeeAO92FL9hWwpSuVbmitE9aZaKmd6xADV2C3WuzMezsVM7qzthsjLORIZ8IVx2azxALmv1cHrIXxXdtUailO1ssBSZeywmOXh+LG9JxuDyGfB3V8E9iaHxSeuBZC5dov1BbluXWpKBSS+RXMvexpN81405qpnak188NsglX3gqralT8nMmUsruvGLId1BHIstMFtx7xoK019VkPQuJNzOZWHcf8oWmVIot0/sKTg+a30As17PBUthwvHRikPOjgfhAn3xprXWyst1bVn93FdyTGM6Qua+aV8svPnQ179zamfE5NnY2cODcWE5j93zBSPzNBposE8viuX6sf5xITKad/JQpK/Y4beYwDgtl9t3ORF2NnfY6J0f6zDEpqHc0kHEz1WB5mxePyR0iB30hmr2T7/+lLZXzmBmdCPON3x3LeabwRIrpY6koReb+wx3d1NbYuG3d/IKf44qlzew4OVzweM5sqOCeRKJpWCrWd9Rn1dg26F4ih3qy62m+pAzWq2+CXshg+2vooukyd18wmtYZz+2wmkNzz7DGXFjRVmuajb7eC7kFd6tFsMHkzUxDvuCUzH1pvNZ95qWk/9p+hn94/BC7M0xCSyTVUPhUFBvcRyfC/GrfeW7fsCDjgI5sXLGsmYlwlFcLnAORDRXcE5BSMuzXWq+LIb6pmsOHWBuHN/kGyWVgh1HRcHoo9YZi8nMm4nGYKHMvoAzSYHmbl6N943m760VjkpMlLO2TUtIzGmBehgamRDZ2NnDw/JjpvHEMhsanyjIep4359TUVydy36ZJFriZbuQzqgOI3VH++9xzBSIx3XZL5DD4bly1pRojy+bur4J7AeDBCOCozZu65sKC+hhavgz05bKr6gpEpGaw3HtzTv/EGfZOTclLhD6WXZVwlbL0uBk0fLTxzX9bmxReKxoc558o3nzvOTV/5XcYN6XwYm4gQjMRyytxBC+6RmGT/ufx8/2cCKWXcNCyRpRUwEAtHY7xyQrP7yHXw/Exl7j/ccYbV82pZv7C+oMcbNHocfPi65Wxa1JD9zgWggnsChvVArg5v6RBCaA6ROZxuGSP2DHKx/TXGoGXaUE33JjeN5h4sMnMvwGNGSsl/7ThDOCpLlonm0sCUyEb9g7zbhJuq/lCUYCRGk2fqWciyVi/HCzhLKoa9Z0bwh6JYBDnvreSeuevBvYAk5+D5MV7tHuWdWzsLKuNN5i9vXsU1K1KXVRdLUcFdCPERIcR+IcRrQojvCSFqhBBLhBAvCyGOCiF+IIQoLlLOIOmsBwphQ2cDx/rHGQtkHpmnDYlOJctkCO4JmXuqD1zycybidlgJhGM5b1KB9qH5yA/2lHTkWrGZ+4r2/IP7q92jHNcz0FLNscylgSmRttoaFja4TKm7T3anTv3ILmv1ciEYoT+P2bX9F4J88Du76MnzzMpg29FBhIDrV7fnLMv4ct5QNWSZ/IP7D7afwWG18LZNC/N+7ExTcHAXQiwE/hzYKqVcB1iBdwP/CHxFSrkcGAbeX4qFzgSp7H4LZUNnA1LCa1kcIpOzbE8OmfugL4TVIpgIRxkYny4vaHMk02TuhnlYHprvyycG+cnuszx/tDRlW8asy0KrZUALQA1uO0fyCO4/2X0Wh02r/y+VZXBPjg1MiZjVIXIwhSMqFLap+t2XT/Pf+87zH88dL2gt244NsHZBHZcuaaT/QpARf3YZbSLHhMFoGsxXcw9Govx0z1luWtte9Nn9TFCsLGMDXEIIG+AGzgPXA4/ptz8KvLXI15gxhtI4QhbChg5Nj9uTQZoJR2OEIrGkapnMwT0W0zZ9V7XXAtN1dymlvqGaXnMH8OfRyGRICImTqIohEI4hJVNKQPNFCJGXx0w4GuMXe89x45o2OhtdnBwozVmI4d3fluOGKmjBvXt4goES/T5LhTEYO7mgIG4glqOUFYtJHtt1BtAy3XztNPyhCLtPD3PVshZW6O/zXL7E/TkMxzYopN/jyQO9jPjDvCtLKbRZKDi4SynPAl8CTqMF9VFgJzAipTT+mt1AyvMXIcQHhBA7hBA7+vv7C11GSRlOk7kUQoPbwcIGF6/3pNcL/SmGanhrMssyY4Ew0ZiMa7fJUkkgrA0JTl/nnr+nu5FlpjpLKIRJR8jCM3fQK2ZyDDjPHu5n0BfibZs66GrxlEyW6RkL0OC251USZ/ztzNbMZCQ3ybLMvLoa3A5rzsH9pRODnBma4J6rurgQjPDDHWfyWsf2k8OEo5Irl7ewUg/uuWyq+kLRnI3oEodkhyIxdpwc4v7fHuFD393FC2nOUH+w/QwL6mu4anlLjkdSWYqRZRqBO4AlwALAA9ya6+OllA9IKbdKKbe2tpZnQyFfhvwh7FaRNuvNl7Y6J4MZAuJ4iiDn1oPEeJrNHuPU2TgzSM7cJ4dgpNPc8ysDk1LGg/tgiSpMJn23i/s9L2/zMuQL5XRG8ePdZ2l027l2ZStdzR5ODvpKskHYO5Z+vF461um+/2aTZoYSvNwTsegGYsdzlGWMBp+P37qaLYsbeXjbybz2eF44OoDdKrikq5EF9TV4HNas/v0R/SzYnWVQh4HbYWXX6WH+8MGX2fB3T/COb7zIl544zLOH+/mDB1/mX54+MsUDqHvYz/NHB3jH1s6CvGQqQTGyzI3ACSllv5QyDPwYuApo0GUagA7gbJFrnDGGxkM0uh0l2QUHzTY106l3qjmiFovAk8H/xdj0ml/vor3OOS1zTzc/1cCT52bSiQEfoxNaRlcqWSbbF1CuLM/RhmAsEObJA728ZcMCHDYLS1o8+EPRvDYI09E7ln68XjpcDs3332zBfdAXwmG1pExucjUQGwuEefy1yQaf91+9hNNDfp4+2JvzOrYdG2BTZyNuh02T39prs2buY4H83lPzG1ycGPDRNxbknVs7+MYfbGb339zEi5+8gbdsWMCXnzzM3Y9sj7/nH9vZDcCdWzpyPo5KU0xwPw1cLoRwCy0a3gAcAP4HeId+n7uAnxW3xJljyB8qiSRj0FrryChlGIE4+cPkcdoYD6QO7saZQJPHwaIm9/TMPc38VIP4qL0cdVAjAHU1uzOeheSDcdZQiswdyCrNPL7vPKFIjLdv1j6YXS3aBmEppJnesdwbmBLZsriRXaeHTWXINTQeotFjT5ncLGv1cnZkImvz1S/3nicQjnGnrkvffFE7CxtcPPj8iZzWMOIPsf/cGFcunxyEs7LNm7Vixig7vmhBXU6v8/X3bmbnp2/kNx95A393xzpuXTefRo8Dj9PGV9+1kS+8bR0vHR/kzf/yPNtPDvHDHd1ctayFziZ3Ts9vBorR3F9G2zjdBezTn+sB4OPAXwohjgLNwIMlWOeMMOwLlaRSxqDZ42TIF0xr8epLMxbM67TFJZtk4uVqXgedTe7pmXtWWSa/Bo7dp0fwOm1ctqQ5XipaLJNfQMVl7gvqXbgd1qyZ+493nWVpiycuZS1p1oJ7sRUzkWiM/gvBnGvcE7luVRv+UJSXjg8VtYZSopmGpf6iWtbmQcrsX4g/3HmGle3e+O/aZrVw15WLefnEEK+dzd649dLxQaRkiq69sr2WgfFgxsazHSeHsFpEfOJVNjxOG83e1McqhOC9ly3mx396JQ6bhTu/8SJnRya4c2v1ZO1QZLWMlPIzUsrVUsp1Uso/lFIGpZTHpZSXSimXSynvlFKaqyQgA0MlsB5IpNnrICYnSyyTSTcOz1uT3tM9URdd1OTm/FhgSvaX7jkNDFkmn8z94o562uqcDPlCeWmn6ShV5m6xCJa1ejMG9+5hPy+fGOJtmxbGM9IFDTXYrYITRVbMDPpCxGTuDUyJXLGsGZfdylMHcpcryk2q7lSDXAzEjvZdYPfpEe7cMrXB512XLMLtsPLwtpNZ17Dt6CBuh5UNHZNB2uhpyCTN7Dg5zLoFdUW/pxJZt7CeX/751bx5/XyWtHi4Jc2AHrOiOlQTGM5iGpYvLXpmkG4j0ghy02QZR/rgPugL4XXacNq0WaJSwtmEoRXxCpwMxmGQW517IBzl4PkxNi1qoNmjfVHlUm+cDV+WNeaD4TGTjp/tOQfAWxOaTmxWC51N7qI9ZowGnUKCe43dytUrWnj6YO+Mdn5mItnuN5ElLdpg8mN96X9nP9zRjc0ipvyuAepddu7c0sEv9p6j70LmpqZtxwa4dEkTDttkaFqZpRwyFImx58wIWxY3ZXzuQqirsfP/3ruZ33702qJMwiqBCu460ZhkZCJc0uYEwzp1IM3GXaoNVePnC2k098QP4CJd/0vU3SefM41xWA7GZAavnR0lEpNs7GyMn8KWomImnrkXKcuAFtzPjwa4kKITWErJj3Z1c2lX0zStdIleMVMMRgNTvtUyBjeuaePcaICD581hXZwpuLscVhY2uNJm7uFojB/tOst1q9torZ0ud9x91RLCsRj/+dLptK/fMxrgeL+Pq5ZNLTWcX1+D12njSJrMff+5UYKRGJd0NaZ97mIpVZHFTKKCu86IP4SU0OQu3nrAoFUPiANpAuJ4Gu3Z67Sm9X9JFdwTdffxNJu0Bk6bBSFyK4U0mpc2djZMflGVoGLGqLEvVeYOqbsnDbuBt22e3mrR1aIF92JG3hkNTO31+W+oAly3Whv0kE8lSbkIRqKMByNpZRmApa1ejg+kDu6/e72fgfFg2lkHS1o83LC6je+8dCrtpuw2vb48cTMV9Ia1DJuqO05qlsBbyhjcqxEV3HXi1gMlzdz1bDdNQPSHIlgEuJJO97Q5qmnq3McnddHWWidOm2VK5p5NzxZC6LJP9sx9z5kROhpdtNY64xLTUCky92AEIaDGXvzbL1M5pGE38Kb10wcqdLV4CIRj9GaRCTLRMxbAahE0p9mEzEZbbQ0bOht46lBfwWsoFYmDsdOxvNXL4Z5xHtl2YtqAif/acYYWryPtbGGAe69awqAvxM91qSyZbccGaPI4WDNvesXLynZvWgOxHaeGWNzspi0PC4i5gAruOpPdeYV9UFNhDNpOl+0avjLJp3zemvRzVBMzdyEEnUnlkOPBKA6rZYpmmYzbYWUinD1z33NmJF59YHyhlKIc0heKpjzuQljc5MZuFfHgPuwL8dtDvfzzE6/z413d3LimjXrX9LMxo2KmmHLI3rEgbbXOoppablzdxt4zI1m16HJj/F0zZe7/65olbFncyGd/cYAbv/w7frbnLLGYZGA8yG8P9fH2zR3YM8wvvWJZM6vn1fK1p49w8PzUQTZSSl44OsgVS5uxpPh9ahUzoWnJhZSSHSeH2bJYZe3JqOCuY7xpGkvgCGlgsQiaPI60AVEbsTddd/Y6bIQisWnZkZRSC+4J2ZVW656woZrDEIxMZwYGfWMBzo5MsGmR9qFpcDuwiNI0MuXjAZINm1VrSvrp7rNc/6Vn2PT5J7n3kR382zPHWNzs4cPXr0j5uK4WTdI6VcRs0EIamJK5YU07AP9T4ew9nrlnSG4WNLj47h9dxqP3XorHaeO+7+/hLfc/z9//6hCRmMza4COE4AtvW0coGuOO+7fx7wkj9E4M+OgZC0yTZAxWpLEhODnoZ9AX4pKu0m+mVjsquOsYskwpm5hAy4TSZe7JI/YM0tn+jgcjhKKxKdnVoiY33QnWv+PB9I6QBi67NavmvvvMpN4OmpNek8eRdv8gHzRL4tKVrF23qo1ITLK01cvHbl3F9z9wOfs+ezO/+PDVrJmfuqllQb0Lh81SVMVMoQ1MiayZX8uC+hqeOljZ4J7r+18IwbUrW/nvD1/NV9+1kbFAmB/t6mZjZ0M8AGdiy+ImfvMXb+D61W38/eOHeM83X+LMkJ9t+jSiK5el9m1ZqZdDJm+qbj+p9QlsVZn7NEr3Caty4pl7CUshQdPF03WppnNvTHSGTNwDSJVddTa5uRCMMOLXKn0yOUIaeJzWrJn77tMj2K2CtQkdf80ep+kyd4BPvmkNn3zTmrweY7EIFje5i5JlekYDXL40daaZK0IIbryonR/u6CYQjlas3C4XWSYRi17y+Kb18/nZnrOsXZD7VKImj4Ov/8FmfrTrLJ/9+X5u+9pzzKuvYUF9DV3NqTtA59XVUOu0TSuH3HlymAa3Pe5cqZhEZe46Q74Qboe15B+uZo8jbWenNmJv+uvFM/ek7HogxQcwuRzSn4Mzntthw5+lzn3PmWEuml835feRSWLKh0yTomYSo2KmECZCUcYCkYJq3JO5YU07E+EoLxwrjV9+IQzpMwJS7U9kwmGzcOfWzpzb/g2EELxjSweP33cNF82v42jfOFcub0m7D6N5zHinyTLbTw2xZVFjSp1+rqOCu85whhrfYmjxOhm4kC5zj6bO3NPY/k5m7umD+3iOmXsmP/doTPJq9+i0Vu5mr6NEde65W7OWkyUtHk4N+gsqh+zNc7xeJi5f2oTHYa2oNDPoC9Hots94kOxscvO9D1zOv75nEx+9eWXG+65sq53iDjk4HuR4v4+tSm9PiQruOqU2DTNo9jqZCEdTaty+NIOsvc7Utr+pLFk7m1zAZHD35ZAVJ3pZp+Jw7wX8oWh8M9WgxZvZ5TJXclnjTLC42U0wEuP8WP6VKr1FNjAl4rRZuWZFK7892Jdzt6qUksHxYN6DMNIx5AuWXJLMFatF8JYNC5hf78p4vxXtXgYTLJ53ntLq27eq+vaUVP4TZhJKbRpm0BLvUg2xqHnqr9uXZvMzPmovqUvVyJqbE6pl3A4bLV5HvJHJF8xFlknfJAWTTpDTMnePgwuBCMFIFKet8Mzbn+Osy3ITNxAb8LGwIXNgSWZyMHZpSmdvWNPGr/f3sP/cGOsWTtWvw9EY244OcKR3nKN94xzrH+do/zgj/jCLm938/ENX5y2nJJOpO9UsTA7uGOcKr5Mdp4ZxWC2sX5i73j+XUJm7zmAZZRmAgRS6uyahpNDc05h7DY2HqLFbpn0hJNa6+0LZZZlsmfvu08M0uu0sTtrcai5RI5MvmPqMZaYpxvq3b0z7e7bnOBg7G9etbkMIeCqpW3XvmRFuv38bdz+8nS/86iBPH+rFYhG8af18/uLGFZwdnuDjj71atD/NoC80JWkwI5MeM5ruvuPkEOs76qvO82WmqPwnzCSUK3NP5y8TjUkC4VgaWSb1HNUhXyhlk9WiJnf8FNWfQ5mhx2ElFIkRjsZSNp0YzUvJm1vGsQyOh7KeQqdDSmmazH1eXQ3OAsshe8YCuOxWakv0JdXidbKps4GnD/bxFzeuZDwY4Z+feJ1HXjhJW62Tf3nPJt6wooWGpPeox2HjC786yCMvnOSeq5YU/PrVkLm31zmpddo43HuBQDjKvrOj3Ht14cc826n6zL3vQqDorCUQjuILRWkqYQOTQTpnSF8aR0hIX+ee7uxiUZObcyMT+ENaHXy22aTGYOpU2fuFQJgjfeNs7JyuY7aUwF8mFI0RiUlTZO4Wi4iP3MuX3rEA8+prSmoodcOadvadHeUH209z85d/xyMvnOQPL1/Mk395LbdvWDAtsIPWNXrjmja++KuDBU92ikRjjPjDGRuYzIAQghXtXo70jvNq9yjhqGRrGZwgZwtVHdx/vKubS7/wNCeL6DIEGPFr1gPleHM3xdv2pwZEI3Cn0twdNs0+IHlgR7rsqrPJTUwSryTIFjgnB3ZM191f7R5FSti0aPrQA+OsoZhySH+aASWVoqulsFr33rEAbSncD4vhRr1b9eM/2oe3xsZjf3Iln7tjHXU16ZMOIQRfunMDbbU1fOi7uxj1T3fHzMawP/VgbDOysr2WI33j8eYlZTuQnqoO7qt1g6FXu4ubRTlZYlj6zL3GbqW2xjatkSnbNCJvilF7Q2mGKRjlkId6NL+O7NUy6acx7T6tyTsbUky0icsyRUxkik+KMkG1DGi6+5mhibyHkPSOBZlXIr3dYGW7l/detoj/fcsqfvnha3IOXA1uB/f//iZ6xwL81WN78z6TTVVia1ZWtNcy5AvxxP4elrd5q2LNlaKqg/vKdi81dgt7z2Qf3xWMRHn7v21L6eERd4QsUylYqhLCdPNTDbQu0mRZJphWlgHivuDZNXddlknRpbrnzAjLWj0pqy+8ThsOm6W4zD2UecbrTLOk2UMoGuPcyET2O+tIKekZC5SkDDIRzXtlPR+8bnlG47dUbFrUyCduW8OTB3pznldqYHxZV0fmrnWi7u0eVZYDWajq4G6zWli3oD4+HDcT+7pH2XV6hK8/c2zabYNlzlxS+ctkkmVAC8CJde7+UIRAOJbSkrW9rgaH1cIB3Wkvm3FYfEh2kiwTjGgzPS9dkrqlXghBi6e4Rqb4cZugiQkKq5gZ8YcJRWJFm4aVmnuv6uLmi9r5h8cPsTcP/X1Yd0Qt5YjJcrEywb9GNS9lpqqDO8DFHQ3sPzdKJBrLeD+jmuSVk0PTpskMxx0hy5e5J2e7xsCKdJl7bdIc1UzeH1aLYGGji0Px4J69FBKma+4vHx9iPBjhpova0j622Vucv4y/hIM6SsGSlvyHZRse8KXO3ItFCMH/fccGmjwO/u4X+3OWZ1I1x5mVtlontXoHt8rcM1P1wX1DZz2BcCztlBaDnaeGadW9t/9rx5kptw35Qgih+a+Xg1Rt+7mMw0vMrLNZsnY2uRkL5KZne9Jo7k8d7MVlt6Z15oPiLQgmz1jMkbm31TpxO6x5Ze6Ts1PNV11S77bzlzetZNfpEX6zP7cJT4NlMs0rB0IIVrbX0uJ1TuvDUEyl6oP7xfqU9EzSjJSSXadHuGZFCzesbuNHO7sJJ2T6w/4Q9S47tgyDBoqhxetk2B+acnYxHg/u6TT3qRuq2Ta9FjVN1p1nbWJyTtfcpZQ8daCXa1a0ZGwK0ZwhZ4/mLoRgcbMnr1r3eAOTyTJ3g3ds6WBFm5d/+vWhKe/zdAz5tPd/pkEbZuKjN63k/7x1XVXONZ1JquOvmYGuZjd1NbaMFTNnhiYYGA+yZXEj7760k4HxEE8nmDQN+UI0lTFrafE6kFLzrzEwJJF0Qc7rmDqNKW49kDa4T2Yx2fRsI3NPPDM4cH6Mc6MBbryoPeNjW7za/kGhvQWT1TLmyNwBlrS48yqnNawH2kyYuYO2F/XxW1dzfMDH97efyXr/wTRVWGblyuUt3LpuXqWXYXqqPrgLIdjQ2ZCxYmbn6cma2DesaGVeXQ0/2D45hX3IFyqb3g4JFgQJ7pDGZqk7TZasTUtKzNx1XTTNpldicM+WubtSyDJPHehDCLh+dXq9HTRZJhiJxfcM8iVe526SzB2gq9nDmSF/1n0bg9NDftrrnEX565SbG9a0cemSJr721OGs5mJD4+bvTlXkT9UHd4CLO+p5XW9JTsXOU8PUOm2saKvFZrVw59YOfne4P17+NlQm6wGD+KDshPpww8s9ncWqt8aGLxSN29EO+kI4rJa07e6denC3CHBmKaNzWC3YLGLKl8dTB3vZ1NkQ/yJKR5Mn89DvbBiZe/JQ8ErS1eIhEpN0D+dWDnlywMdi3XTMrAgh+ORtqxkYD/HNZ49nvG+5kxtFZZglwb2BaEyy/1zq7H3nqRE2LmqIDzJ+59ZOYhIe29kNaJp7OU9LEz1ZDPxp7H4NDEMxY6iGkV2l0xmN4O5xZh88LYTA7bDGM/fzoxPsOzuaVZJJPJZ006Wy4Q9FcdmtRQ2VLjX5VsycHPTFHSXNzKZFjbx5/Xy++dzxjAO4q02WUeTGrAjuhjVtKmlmPBjh9Z4xNid4k3c2ubl6eQs/2H6GaEwy7AvPjCyTkO2OpxnUYZBs+5vN2Kmuxk6j255Vkkl8fkP3N/YfblqTPbi3FJu5B7MP8J5puhKsf7NxIRBmYDwUr483O//7llWEIjG+9tSRlLfHYpLhMs0yUFSWooK7EKJBCPGYEOKQEOKgEOIKIUSTEOJJIcQR/f+yF6O219XQXudMuam698wIMTndg+Jdl3RydmSCJw/0EIrGymI9YFBXY8NhtUzJdtON2DNIdobMxZJ1UZM75xJDzdNdy9yfOtjL4mY3y9uyz6GctCAoPHPPNsB7pmnxOvA6bTltqp7S77OkpTrK8LpaPLz3skV8f/uZaf0dAGOBMNGYVMF9FlJs5v414NdSytXABuAg8AngaSnlCuBp/eeyc3FHA692T8/cd54aRgjYmGSEdfPadhrc9njHajk1dyEEzd6pXarZPM2TPd1zsWS9dd38rBuiBm6HDX8wgi8Y4YVjg9y4pj2n0rJ0Rmi5ku1LrRIIIVjS4kkZ/JIx6uGrJXMH+PANK3DZrfzj44em3ZZqAIxidlBwcBdC1ANvAB4EkFKGpJQjwB3Ao/rdHgXeWuwic2FjZwPHB3yMTkx1xdt5aphV7bXTnPWcNitv39TBXv0LodyZS7PXMSUgZhuqkTxHNZfg/qdvXMZfv/minNZjaO7PHRkgFInFHQmzUaN7mBejuZulxj2R5W1ejvZlD+6GdLO4qXqCe4vXyZ9cu5QnDvTy413dU27L1hynqF6KydyXAP3Aw0KI3UKI/xBCeIB2KeV5/T49QMqoIYT4gBBihxBiR39/fxHL0Li4Qxu1tS8he4/FJLtOD0+bBWrwrks645fLXS2gmYclyjKZg1yiLBOMRBkPRkq66aVp7lGeOthLvcue1xzKYrpUfSHzZe6gzec8PxpgLJDZMvfEoI95dTXxctJq4Y+vXcYVS5v5xI/3TfGdyWRroahuignuNmAz8HUp5SbAR5IEI7VOl5TdLlLKB6SUpSNeEgAAEVpJREFUW6WUW1tbW4tYhsbFC6d3qh7tH+dCIJLWOnXVvNq4b3m539xaZ2fihmokYyOPJyG4lyO7cjusXAiE+e2hPq5b1ZpXd2Ix/jL+YNQ0vjKJrGzTR7hlsbE4OeCjq0r09kTsVgv/772baat18sff3hmvnjEcUZXmPvsoJrh3A91Sypf1nx9DC/a9Qoj5APr/0z12y0C9205Xs3vKpuou3Swsky/2B65ZSkejq+yt5C1eBwO+ULyz059NczecG4OReHZVyg+g22Hl5KCfIV8opxLIRJo9joItCHyhiGkcIROJz+fsvZDxfqcG/fHSyWqjyePggT/cyuhEmD/9z10EI9Gq8nJX5EfBwV1K2QOcEUKs0q+6ATgA/By4S7/uLuBnRa0wD5I7VXeeGqbJ46Arg8HQbevn8/zHry/7kN0Wr5NQJMaFYIRYTOLLoj1PyjKTH8BSbnoZFSs2i+ANK/M7c2r2OouqljFj5t7R6MJlt2Y0oBsLhBn0heKlk9XIRQvq+NKdG9h5apjP/Gw/g+MhPA6rGjI9Cyn2U/Zh4DtCCAdwHLgH7Qvjv4QQ7wdOAe8s8jVy5uKOBn625xx9YwHa6mrYeXqYzYsaTWEwlNjIZKnV1pNJlnHZrViElrmXI7syzgwuX9qccYxbKlq8DoZ8QWIxmbbDNh2+oDkzd4tFsLzNy5G+9Jl7fDO1ioM7wJsvns/B88u5/3+O0uxxVIWPuyJ/igruUso9wNYUN91QzPMWygZ9U3Vv9yhbFls43u/jzi2dWR41MyQ2MhlBPVPmLoTQnCGDkaymYYVgZO43rsmtdDKRZo+DmISRiXBeXziRaIxgJGbKzB20TdVtRwfS3m6UQVarLJPIX960koPnx3j6UB8dTdNHKiqqn1nRoWqwdkE9Votg75mR+CzQzSkGPVeCycw9GG9MytZN6tWD+5AviNUi8s6wM9Hq1bztb8ixBDKRuFdOnpuqhpWCGatlQNPde8eCaYdMGw1Ms8FH3GIRfOXdG1nZ7mVZa/V/WSmmY84UqkBcDisr22vZ2z1CTEpsFhH3e680rfHMPcTChtyCnOEMOeQTNLodeUsgmbhj0wI2L26Me9LkQ6K/zIo8vhv8QXN5uSdjzOc83HeBS1KMcDs54GNBfc2s0afrauz88sPXmMrnR1E6ZlXmDpo0s+/sKDtPDbN2QZ1p6pGNOvqB8WDcGTFb5h6XZcZLb+zktFlzshtIRUsKl8tcMI7brJn7Cr0c8nCaipkTg+Z3g8wXh82igvssZdYF94s7Ghjxh9l+cojNJpqxaLdaaHDbGRwPJYzYyxzca+OZu7mMnZo9k5vD+RDP3E2quS9scOFxWNPWums17rMruCtmL7MwuGubqqnMwiqN1qUaTBixl02WscabmMxU0dDgdmAR+Wvu8czdhNUyoFfMtNemzNxH/WGG/eGqMQxTKGZdcF81rzY+rMJswd1o/sl1jqimuUcZGA+aqj3catH2AAbyrHWPjxY0aeYOsLLNm7LW3fB6r+Yad8XcYtYFd7vVwtoFdcyvr2F+vSv7A2aQllotc89VlvE6bYz4Q4wFIqaSZWC6EVou+OIbqubM3EGrmBkYDzKc9MVlBPfZUAapmBuYN4Uqgr99y1r8WeZGVoIWj2OKLJNufqqBx2mLe66bKXMHwysnv8zd+FIzm597IiuMipneC1y2tDl+/YkBH0JQUHWRQlEJZl3mDpr975XLWyq9jGm0eJ2MBSKM+MPU2C3Ysph1JVbTmM2StRBnSOOLytSyjO4xczjJ/lcrg3TNmjJIxexnVgZ3s2I0/5wZ8uc0Dm9qcDdX5m5sDueDcTZllvLUVMyvr6HWaZtmIHZi0F+VbpCKuYsK7jOI0fxzctCXUyNP4n3MNimn2ePgQkDzms8VXyiKw2rBYTPv204IwfJ277SKmVODPrWZqqgqzPspm4UYzT9nhidy0p29CRuPZsvcjbOQoTykGb9J7X6TWdlWO6XWfcQfYsQfVpupiqpCBfcZpEXPvkOR2JTAnQ4jcxeivDNeCyHR5TJXfCYd1JHMinYvg75QXHY6MUvcIBVzCxXcZxAjc4fc/FUMzb3BZTddi7jxRZXPpqo/FDF1GaRBfFNVl2YmyyCV5q6oHlRwn0HcDis1du1XnksGawR3s0kyoJVCQn5dqr5Q1NRlkAaTU5k0aebEgB+LKoNUVBkquM8gQoh4UMwlgzWy+2aTlUFCYbKMNlrQ/Jl7e52T2hpbPHM/NehjQYMLp838a1coDFRwn2Faao3gnnu1jBkzd6/ThsNmYSAPZ8hqydyFEKxsn9xUPTmgKmUU1YcK7jNMix6oc5FljIlNZjINMxBC0JLnoGx/KJJxtKCZWNnu5XDfBaSUnBjwqRp3RdWhgvsMY2yq5pK526wWrlvVyhUJbfBmotnrzE9zD0Zxm3RQRzIr2moZ8Yc50jfOWCCiMndF1VEdn7RZhKFV51IKCfDwPZeWczlFka8FQXVl7tqm6hP7ewBlGKaoPlTmPsMYmXs1aM/ZyMc8LBaT+KtEc4fJkXtPHOgFUEM6FFWHCu4zjJG5m3WOaD60eB30X5h0uczERNj8dr+JtNY6qXfZebV7VCuDbFSau6K6UMF9hjE85s1YAZMvt66bRyga44Fnj2e97+T81Or4UtMqZrTsfWGjy9R+OApFKtQ7doa5pKuRR++9lEu6zDUlqhA2LWrk9y6ezwPPHqNnNJDxvv4qGNSRjKG7q81URTWigvsMI4Tg2pWtCGEuO4FC+fitq4nF4MtPvp7xftWWucNkcFebqYpqRAV3RVF0Nrm568rF/HBnNwfPj6W9354zIwDU1lRPcDemMqnMXVGNFB3chRBWIcRuIcQv9Z+XCCFeFkIcFUL8QAhR/eKyIiMfum4FdTV2vvirgylvf+5IP5/9+X4uW9LE1sVNM7y6wtnUqclON6xpq/RSFIq8KUXmfh+Q+Kn+R+ArUsrlwDDw/hK8hsLE1Lvt/PkNK3juyAC/O9w/5bZXu0f4k2/vZHlbLd+8a2tVbUy6HFbu//3NyupXUZUU9UkTQnQAbwb+Q/9ZANcDj+l3eRR4azGvoagO/vDyxSxudvPF/z5INCYBzQf9noe30+hx8Og9l1BXY6/wKhWKuUOxadRXgY8BMf3nZmBESmkUPncDC4t8DUUV4LBZ+Pitq3m99wKP7TxD34UA73voZSTwrXsvpa2uptJLVCjmFAUHdyHE7wF9UsqdBT7+A0KIHUKIHf39/dkfoDA9t62bx5bFjXzpicPc/dB2BsdDPHT3JSxt9VZ6aQrFnKOYzP0q4HYhxEng+2hyzNeABiGEURLRAZxN9WAp5QNSyq1Syq2tra1FLENhFoQQ/PWb19B/Icjh3gt8/Q+2sLGzodLLUijmJAUHdynlJ6WUHVLKLuDdwG+llO8F/gd4h363u4CfFb1KRdWweVEjf3f7Wh543xauXam+tBWKSlGOouOPA98XQvwfYDfwYBleQ2Fi7rqyq9JLUCjmPCUJ7lLKZ4Bn9MvHAfP61CoUCsUcoHqKjhUKhUKRMyq4KxQKxSxEBXeFQqGYhajgrlAoFLMQFdwVCoViFqKCu0KhUMxCVHBXKBSKWYiQUlZ6DQgh+oFTOdy1BRgo83IqyWw/Ppj9x6iOr/qppmNcLKVM2QpuiuCeK0KIHVLKrZVeR7mY7ccHs/8Y1fFVP7PlGJUso1AoFLMQFdwVCoViFlJtwf2BSi+gzMz244PZf4zq+KqfWXGMVaW5KxQKhSI3qi1zVygUCkUOqOCuUCgUs5CKBnchxENCiD4hxGsJ120QQrwohNgnhPiFEKIu4baL9dv267fX6Ndv0X8+KoT4FyGEqMTxpCKfYxRCvFcIsSfhX0wIsVG/zZTHmOfx2YUQj+rXHxRCfDLhMbcKIV7Xj+8TlTiWVOR5fA4hxMP69XuFEG9MeIxZ/36dQoj/EUIc0D9X9+nXNwkhnhRCHNH/b9SvF/r6jwohXhVCbE54rrv0+x8RQtxVqWNKpoBjXK3/fYNCiL9Kei5Tvk9TIqWs2D/gDcBm4LWE67YD1+qX7wU+r1+2Aa8CG/SfmwGrfvkV4HJAAI8Dt1XyuAo9xqTHrQeOJfxsymPM82/4+8D39ctu4CTQBViBY8BSwAHsBS6q9LEVcHwfBB7WL7cBOwGLyf9+84HN+uVa4DBwEfBPwCf06z8B/KN++U36+oV+PC/r1zcBx/X/G/XLjZU+vgKPsQ24BPgC8FcJz2Pa92mqfxXN3KWUzwJDSVevBJ7VLz8J/H/65ZuBV6WUe/XHDkopo0KI+UCdlPIlqf0FvgW8tfyrz408jzGR96ANHsfMx5jn8UnAow9QdwEhYAxtctdRKeVxKWUI7bjvKPfacyHP47sI+K3+uD5gBNhq8r/feSnlLv3yBeAgsBDt9/+ofrdHmVzvHcC3pMZLQIN+fLcAT0oph6SUw2i/l1tn8FDSku8xSin7pJTbgXDSU5n2fZoKM2ru+5n8hd0JdOqXVwJSCPEbIcQuIcTH9OsXAt0Jj+/WrzMz6Y4xkXcB39MvV9sxpju+xwAfcB44DXxJSjmEdixnEh5frce3F7hdCGETQiwBtui3VcXfTwjRBWwCXgbapZTn9Zt6gHb9crq/VVX8DXM8xnRUxTEamDG43wv8mRBiJ9opVEi/3gZcDbxX//9tQogbKrPEokl3jAAIIS4D/FLK11I9uApId3yXAlFgAbAE+KgQYmllllgU6Y7vIbQP/A7gq8ALaMdreoQQXuBHwF9IKccSb9PPNqq+ZnouHGMiJRmQXUqklIfQJBiEECuBN+s3dQPPSikH9Nt+haaF/ifQkfAUHcDZGVtwAWQ4RoN3M5m1g3Y8VXOMGY7v94FfSynDQJ8QYhuwFS0bSjx7qcrjk1JGgI8Y9xNCvICm7w5j4r+fEMKOFvS+I6X8sX51rxBivpTyvC679OnXnyX13+os8Mak658p57rzIc9jTEe6YzclpsvchRBt+v8W4NPAN/SbfgOsF0K4dc32WuCAflo1JoS4XK9AeB/wswosPWcyHKNx3TvR9XbQNEOq6BgzHN9p4Hr9Ng/ahtwhtA3KFUKIJUIIB9qX289net25ku749PemR798ExCRUpr6Paqv50HgoJTyywk3/RwwKl7uYnK9Pwfep1fNXA6M6sf3G+BmIUSjXnVys35dxSngGNNRVe/TSu9ifw9Nfw2jZebvB+5Dy3YOA/+A3kWr3/8P0PTO14B/Srh+q37dMeD+xMdU+l8Bx/j/t3f3rFFEYRiG76eysUplIy4iaJNCtDNKwC6NFoJaWMTOzsbK32ErglhFsbJTULAQG8WAGD9Ik9bGSgtzLM6JLJKgMbI7nr0vGIYdhuG8zO6zw3y8swi82GY7g6xxN/UB+4GVtg/fAjfGtrPU1v8E3Jx2XX9Z3whYo16we0xtxzr0/bdAPR3xBnjdpiXq3WhPgA+tlrm2foBbrY5V4OTYtq4CH9u0PO3a9lDjgbavv1Avim9QL4gP9nu63WT7AUnq0OBOy0iS9s5wl6QOGe6S1CHDXZI6ZLhLUocMd828JOeTlCTHpj0W6V8x3KXapO15m0tdMNw101q/kQXqw0mX2rLFJE+T3E/yLsm9rf7rSc4meZXam/12kn1THL60I8Nds+4ctd/Ne+BzkhNt+XHgOrWN72HgVOrLYe4AF0sp89TeTNcmP2Tp9wx3zbqfffPbfOvUzMtSykYpZZP6uPoIOAqstz8CqD3Az0xwrNIfG1xXSGlSksxRG5nNJynUN+0U4BHwbWzV7/hb0X/GI3fNsgvA3VLKoVLKqJRyEFgHTu+w/howSnKkfb4CPJvAOKVdM9w1yy4DD39Z9oAd7poppXwFloGVJKvAJmPtmqUhsSukJHXII3dJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjr0A6vitfMhVYjEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "dados.plot('Ano', 'Pmax(mm/dia)')\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "pc7Fix40F5KU",
        "outputId": "85073489-2235-4215-9d4b-a2b1b726d1b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '(mm)')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "dados.plot('Ano', 'Pmax(mm/dia)', style='.-', kind=\"bar\", color='blue', label='Chuva máxima diária anual', figsize=(16,3))\t\n",
        "\n",
        "plt.title('Hietograma')\n",
        "\n",
        "plt.xlabel('Ano')\n",
        "\n",
        "plt.ylabel('(mm)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uod18UoBf8Gq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9019aa3c-b758-4b6e-df19-1e90e6becab1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Ano  Pmax(mm/dia)  ordem  Prob_Weibul         TR\n",
            "0  2008         149.6      1     0.017857  56.000000\n",
            "1  1982         138.8      2     0.035714  28.000000\n",
            "2  1974         125.1      3     0.053571  18.666667\n",
            "3  1981         109.5      4     0.071429  14.000000\n",
            "4  1970         107.7      5     0.089286  11.200000\n"
          ]
        }
      ],
      "source": [
        "# Distribuição empírica\n",
        "\n",
        "dados = dados.sort_values('Pmax(mm/dia)',ascending=False)   # organiza os dados de chuva em ordem decrescente \n",
        "dados = dados.reset_index(drop=True)              \n",
        "\n",
        "nt = len(dados)                                             # número de intervalos de tempo \n",
        "\n",
        "dados['ordem'] = pd.Series(range(1,nt+1))                   # cria coluna com a ordem dos valores\n",
        "\n",
        "dados['Prob_Weibul'] = dados['ordem'] / (nt+1)              # cria coluna com a probabilidade de excedência \n",
        "\n",
        "dados['TR'] = 1 / dados['Prob_Weibul']                      # cria coluna com a ordem dos valores de TR\n",
        "\n",
        "print(dados.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8bxS-OsfmM7",
        "outputId": "70204b22-d00d-4f1e-c6fa-4ddfee627f9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Média: 77.29\n",
            "Desvio Padrão: 22.53\n",
            "Variança: 507.73\n"
          ]
        }
      ],
      "source": [
        "# Distribuição de máximas de Gumbel \n",
        "\n",
        "u = dados['Pmax(mm/dia)'].mean()      # média\n",
        "s = dados['Pmax(mm/dia)'].std()       # desvio padrão\n",
        "v = s**2                              # variança ( ** = ^ )\n",
        "\n",
        "print('Média:', round(u,2))\n",
        "print('Desvio Padrão:', round(s,2))\n",
        "print('Variança:', round(v,2))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuHyQValoapP",
        "outputId": "f54f545b-2b19-42de-faf8-7e8dc55cd515"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Ano  Pmax(mm/dia)  ordem  Prob_Weibul         TR  Pmax_Gumbel\n",
            "0  2008         149.6      1     0.017857  56.000000   137.708772\n",
            "1  1982         138.8      2     0.035714  28.000000   125.370448\n",
            "2  1974         125.1      3     0.053571  18.666667   118.083903\n",
            "3  1981         109.5      4     0.071429  14.000000   112.864105\n",
            "4  1970         107.7      5     0.089286  11.200000   108.775512\n"
          ]
        }
      ],
      "source": [
        "dados['Pmax_Gumbel'] = u-s*(0.45+0.7797*np.log(np.log(dados['TR']/(dados['TR']-1)))) \n",
        "\n",
        "print(dados.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "IMQ1h8GIodfM",
        "outputId": "7713fd5e-8d26-4a42-9b59-6a9dfd6eda86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ajuste das precipitações máximas à distribuição Gumbel')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plotando os gráficos \n",
        "\n",
        "ax = dados.plot('TR', 'Pmax_Gumbel', kind='line', color='red', label='Gumbel', figsize=(8,4))\n",
        "\n",
        "dados.plot('TR', 'Pmax(mm/dia)', kind = 'scatter', color='k', label='Empírica', ax=ax)\t\n",
        "\n",
        "plt.xscale('log')\n",
        "\n",
        "plt.xlabel('TR')\n",
        "\n",
        "plt.ylabel('Chuva máxima diária anual')\n",
        "\n",
        "plt.title('Ajuste das precipitações máximas à distribuição Gumbel')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "python_Máximas_Empirica_Gumbel.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}