taruma_hk102_upsampling.ipynb

taruma_hk102_upsampling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "taruma_hk102_upsampling.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/taruma/96c321175ecac3e51350ef4c94f3d7d4/taruma_hk102_upsampling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQKA_rNamLfF",
        "colab_type": "text"
      },
      "source": [
        "Berdasarkan [#102](https://github.com/taruma/hidrokit/issues/102): **upsampling data**\n",
        "\n",
        "Referensi Isu:\n",
        "- `hidrokit.contrib.taruma.hk98`. ([manual/notebook](https://gist.github.com/taruma/aca7f90c8fbb0034587809883d0d9e92)\\). **buat ringkasan/rekap data deret waktu**\n",
        "\n",
        "Deskripsi permasalahan:\n",
        "- Mengubah data bulanan menjadi data harian sehingga dapat di rekap menggunakan fungsi yang tersedia di `hidrokit.contrib.taruma.hk98`.\n",
        "\n",
        "Strategi penyelesaian:\n",
        "- Pada melakukan upsampling, nilai yang kosong bisa diisi menggunakan metode interpolasi atau diisi dengan nilai pada awal bulan (_default_)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PHw6EI0nTW8",
        "colab_type": "text"
      },
      "source": [
        "# PERSIAPAN DAN DATASET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuJOIRNQlMXA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLyf2uqBx7b0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = [15, 5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKtlRK6LrqAU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a866c9bb-38f2-4c5d-cd92-f85075e1ee69"
      },
      "source": [
        "try:\n",
        "    import hidrokit\n",
        "except ModuleNotFoundError:\n",
        "    !pip install git+https://github.com/taruma/hidrokit.git@latest -q\n",
        "    import hidrokit\n",
        "print(f'hidrokit version: {hidrokit.__version__}')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hidrokit version: 0.3.5-beta.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YJorL_WnWoY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget -O pet.xlsx \"https://taruma.github.io/assets/hidrokit_dataset/fjmock_sample.xlsx\" -q\n",
        "!wget -O sta.xlsx \"https://taruma.github.io/assets/hidrokit_dataset/data_daily_sample.xlsx\" -q\n",
        "sta_path = 'sta.xlsx'\n",
        "pet_path = 'sample.xlsx'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlXyd1idr_yF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "96e98887-f420-48dc-99ee-092f5ccd8f54"
      },
      "source": [
        "df_pet = (pd.read_excel(pet_path, usecols=[3])\n",
        "            .set_index(\n",
        "                pd.date_range('20050101', '20160101', freq='MS', closed='left')\n",
        "            )\n",
        "         )\n",
        "df_pet.head()"
      ],
      "execution_count": 5,
      "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>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-02-01</th>\n",
              "      <td>3.145340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-03-01</th>\n",
              "      <td>3.213324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-04-01</th>\n",
              "      <td>3.474628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-05-01</th>\n",
              "      <td>3.597509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 PET\n",
              "2005-01-01  2.988970\n",
              "2005-02-01  3.145340\n",
              "2005-03-01  3.213324\n",
              "2005-04-01  3.474628\n",
              "2005-05-01  3.597509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpvfR3rdsxGD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a6c60fed-fc2a-42aa-f862-667670c4318a"
      },
      "source": [
        "from hidrokit.contrib.taruma import hk88\n",
        "\n",
        "df_sta = hk88.read_workbook(sta_path, ['STA_B'])\n",
        "df_sta.head()"
      ],
      "execution_count": 6,
      "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>STA_B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2001-01-01</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-02</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-03</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-04</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-01-05</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           STA_B\n",
              "2001-01-01     0\n",
              "2001-01-02     0\n",
              "2001-01-03    45\n",
              "2001-01-04     0\n",
              "2001-01-05     5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZR7CEznXowWW",
        "colab_type": "text"
      },
      "source": [
        "# KODE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6G3lJXQpWYE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ref: https://stackoverflow.com/q/29612705/4886384\n",
        "def upsampling(df, freq='D', fill_method='ffill', \n",
        "               use_inter=False, inter_method='linear', inter_keys={}, \n",
        "               reindex=False):\n",
        "    start = df.index.min() - pd.DateOffset(day=1)\n",
        "    end = df.index.max() - pd.DateOffset(day=31)\n",
        "    date = pd.date_range(start, end, freq=freq)\n",
        "\n",
        "    newdf = df.reindex(date)\n",
        "\n",
        "    if reindex:\n",
        "        return newdf\n",
        "\n",
        "    if use_inter:\n",
        "        return newdf.interpolate(method=inter_method, **inter_keys)\n",
        "    else:\n",
        "        return newdf.fillna(method=fill_method)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBU4ydgyuY7H",
        "colab_type": "text"
      },
      "source": [
        "# PENERAPAN\n",
        "\n",
        "Pada _notebook_ ini terdapat dua dataset yaitu `df_sta` (data hujan stasiun B) dan `df_pet` (data evapotranspirasi). Dua dataset ini memiliki perbedaan dari jumlah data dan observasinya, pada `df_sta` data dalam bentuk observasi harian, sedangkan pada `df_pet` data berupa observasi bulanan. \n",
        "\n",
        "Dalam pemodelan F.J. Mock (`hidrokit.contrib.taruma.hk96`) data masukan bisa memiliki sembarang periode (per 3 hari, 15 hari, dll). Untuk data `df_sta` bisa dilakukan rekap data menggunakan modul `contrib.taruma.hk98`, sedangkan data `df_pet` tidak bisa menyesuaikan dengan periode tersebut karena data bukan data harian.\n",
        "\n",
        "Fungsi `upsampling` menangani permasalahan tersebut dengan mengubah data bulanan menjadi harian dengan pengisian berbagai metode (interpolasi, atau isian dari awal bulan). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCNqM-YLuap0",
        "colab_type": "text"
      },
      "source": [
        "## Fungsi `upsampling`\n",
        "\n",
        "Fungsi ini mengubah data bulanan/periode ke data harian dengan menggunakan berbagai metode (isian atau interpolasi). \n",
        "\n",
        "Argumen yang harus diberikan:\n",
        "\n",
        "- `df`: dataset dalam objek `pandas.DataFrame` dengan index berupa objek `DateTimeIndex`/`Timestamp`.\n",
        "\n",
        "Argumen opsional:\n",
        "\n",
        "- `fill_method='ffill'`: mengisi data yang kosong dengan metode _forward fill_. Metode merupakan isian valid untuk argumen `method` pada metode `pandas.DataFrame.fillna()`.\n",
        "- `freq='D'`: mengubah dalam bentuk harian.\n",
        "- `use_inter=None`: isi `True` jika ingin menggunakan `inter_method`.\n",
        "- `inter_method='linear'`: metode interpolasi.\n",
        "- `inter_keys={}`: _keywords_ untuk metode interpolasi.\n",
        "- `reindex=False`: isi `True` jika hanya mendapatkan keluaran tanpa mengisi data yang kosong. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcFCVaZSyDmT",
        "colab_type": "text"
      },
      "source": [
        "### Dataset `df_pet`\n",
        "\n",
        "Berikut informasi `df_pet` sebelum dilakukan _upsampling_."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJ_Jx_pbyOnz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "4c1c337d-3d32-4e6a-c7c7-891cf9138be3"
      },
      "source": [
        "df_pet.info()\n",
        "df_pet.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 132 entries, 2005-01-01 to 2015-12-01\n",
            "Freq: MS\n",
            "Data columns (total 1 columns):\n",
            "PET    132 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 2.1 KB\n"
          ],
          "name": "stdout"
        },
        {
          "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>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-02-01</th>\n",
              "      <td>3.145340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-03-01</th>\n",
              "      <td>3.213324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-04-01</th>\n",
              "      <td>3.474628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-05-01</th>\n",
              "      <td>3.597509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 PET\n",
              "2005-01-01  2.988970\n",
              "2005-02-01  3.145340\n",
              "2005-03-01  3.213324\n",
              "2005-04-01  3.474628\n",
              "2005-05-01  3.597509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nru8FlJzyR1J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "0fe72809-e5e6-480b-c660-84907cf343f2"
      },
      "source": [
        "df_pet.plot();"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEvCAYAAAA0ITL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqSZ2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzRxj_sQydVh",
        "colab_type": "text"
      },
      "source": [
        "Sebelum dilakukan _upsampling_ data hanya memiliki $132$ data observasi dengan frekuensi bulanan `MS`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IedqLBTjxZED",
        "colab_type": "text"
      },
      "source": [
        "### Menggunakan _forward fill_ (_default_)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4ML4tEUwHNT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "d94ce1d1-9552-43e9-83a5-689154d3a23d"
      },
      "source": [
        "df_pet_ffill = upsampling(df_pet)\n",
        "df_pet_ffill.info()\n",
        "df_pet_ffill.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 4017 entries, 2005-01-01 to 2015-12-31\n",
            "Freq: D\n",
            "Data columns (total 1 columns):\n",
            "PET    4017 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 62.8 KB\n"
          ],
          "name": "stdout"
        },
        {
          "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>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>2.98897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-02</th>\n",
              "      <td>2.98897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-03</th>\n",
              "      <td>2.98897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-04</th>\n",
              "      <td>2.98897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-05</th>\n",
              "      <td>2.98897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                PET\n",
              "2005-01-01  2.98897\n",
              "2005-01-02  2.98897\n",
              "2005-01-03  2.98897\n",
              "2005-01-04  2.98897\n",
              "2005-01-05  2.98897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1x0wASsgxc2h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "2a677b63-fe43-44d6-a2e8-6f14776dd17f"
      },
      "source": [
        "df_pet_ffill.plot();"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEvCAYAAAA0ITL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZwcxXn3f9Wzs6dW930hIYEk7kNg\nYyMQ+ML4vs/YTsDg13aM7eR1jO34TN7YMYmTTxyCMbaDb2PHBwYTjM1lcwgEiBsEOpAEOlfHSivt\nMdP1/tHTPTvTVb1TvdXV1d3P9/Phs6JrZrqmp7q6nnqe5/cwzjkIgiAIgiAIgiAIe3DS7gBBEARB\nEARBEATRCBlqBEEQBEEQBEEQlkGGGkEQBEEQBEEQhGWQoUYQBEEQBEEQBGEZZKgRBEEQBEEQBEFY\nBhlqBEEQBEEQBEEQltGW1omnT5/OFy1alNbpCYIgCIIgCIIgUuWBBx7YwzmfIWpLzVBbtGgR1q5d\nm9bpCYIgCIIgCIIgUoUx9pysjUIfCYIgCIIgCIIgLIMMNYIgCIIgCIIgCMsgQ40gCIIgCIIgCMIy\nUstREzEyMoJt27ZhcHAw7a5oo7OzE/Pnz0e5XE67KwRBEARBEARBZASrDLVt27aht7cXixYtAmMs\n7e6MG845+vr6sG3bNixevDjt7hAEQRAEQRAEkRGsCn0cHBzEtGnTcmGkAQBjDNOmTcuVh5AgCIIg\nCIIgiOSxylADkBsjzSdv34cgCIIgCIIgiOSxKvTRBkqlEk488URUKhWsWLEC1157Lbq7u4PjPu98\n5zuxZs0abNq0CYcOHcLu3buD8MYrr7wSL3nJS9L6CgRBEARBEARBZBwy1Jro6urCunXrAADvec97\ncNVVV+GTn/xkw/Fmbr/9dlxxxRW44YYbTHaVIAiCIAiCIIicQoZaBKtWrcIjjzySdjcIgiAIgiAI\ngrCAvQPDuOWJHXB5uK2now2vOXEOSo6e1Ccy1CRUKhXcdNNNuOCCCwAAR44cwSmnnBK0X3755XjH\nO96RVvcIgiAIgiAIgjDM9+7ahP+49Vlp+4IpXTh14RQt57LWUPvSbx/HEy/0a/3M4+ZOxBded3zk\na0YbZKtWrcJFF10EAJGhjwRBEARBEARB5J++gWFM7WnH7z62quH4/Zv34q9/8hBGqgJXW0ysNdTS\nggwygiAIgiAIgig2I1UXuw4OhY7v6h/E5K4yZk/qbDg+tacdgFdHWRfWGmpjeb4IgiAIgiAIgiCS\n4KM/fhA3P75T2LbyqHBoYxIFuaw11GyjOUftggsuwFe/+tUUe0QQBEEQBEEQRBLsOjiEY2ZOwAdX\nHR1qO3XhZOn79PnTyFALcejQIeHxarUqfc/q1auxevXqhHpEEARBEARBEIRJXA7MmdyFt5+xoLU3\n1FxqGiMf4ej7KIIgCIIgCIIgiBzAOTSp7MeGDDWCIAiCIAiCIIhRuFwt74zVXs01Bj+SoUYQBEEQ\nBEEQBDEKDg6HpetSs85Q0ylpaQN5+z4EQRAEQRAEkXdcF1Cx04LX5jVHrbOzE319fbkxbjjn6Ovr\nQ2dn59gvJgiCIAiCIAjCCjgAlrJHzSrVx/nz52Pbtm3YvXt32l3RRmdnJ+bPn592NwiCIAiCIAiC\naBHOuWKOWu19GvtglaFWLpexePHitLtBEARBEARBEESB4RyUo0YQBEEQBEEQBGETLueKOWo11ce8\n5qgRBEEQBEEQBEGkjctJ9ZEgCIIgCIIgCMIqOKBUSM236aiOGkEQBEEQBEEQREKo5qgl4XsjQ40g\nCIIgCIIgCGIUnHM4MawvylEjCIIgCIIgCIJICJerecnqoY/6IEONIAiCIAiCIAhiFBwkJkIQBEEQ\nBEEQBGEVrgvFxDNfnp/ERAiCIAiCIAiCIBKDPGoEQRAEQRAEQRAW4XJOOWoEQRAEQRAEQRA2oSrP\nnwRkqBEEQRAEQRAEQYzC5RwqdlrwUpLnJwiCIAiCIAiCSAaXA4w8agRBEARBEARBEDahVvDaN+q4\nRpcaGWoEQRAEQRAEQRCj8Dxq6faBDDWCIAiCIAiCIIhRcK5W8Np/pcYyamSoEQRBEARBEARBjMbl\navWuA3l+MtQIgiAIgiAIgiCSgXOeuphIW6pnJwgikj8+uRN/eHKXsO28ZTPwyuNnG+4RQRAEQRBE\n/uGKOWoMvpiIPshQIwiLueqODVi3dT8md7c3HD9weARP7egnQ40gCIIgCCIBONIveE2GGkFYTNXl\neNHiafjhxS9qOP7+796H/UdGUuoVQRAEQRBEvnE5j5mjloI8P2OsxBh7iDF2g6DtA4yx3YyxdbX/\nLtbWQ4IoMDJpWMagN1uVIAiCIAiCCHA5h6NSSC0BVDxqlwF4EsBESfvPOOcfHX+XiCJyaKiCdVv2\nC4sETuoq46T5k1PoVfrI3O4MemOgCYIgCIIgiDpcUfUxeJ/GPrRkqDHG5gN4DYB/BPBJjecnCADA\nf/zxGXzrzo3S9j996jwsmNptsEd24CkOhY8zxsihRhAEQRAEkRCemEg2PGr/BuBTAHojXvMWxtg5\nANYD+ATnfOt4O0cUh4NDFUzsbMN3P3BGw/F7N/bhit+vx8BwJaWepQvnco+aS5YaQRAEQRBEbPYN\nDOMjP34Qh4bC68zhqqum+phAHbUxDTXG2GsB7OKcP8AYWy152W8B/IRzPsQYuxTAtQDOF3zWJQAu\nAYCFCxfG7jSRPzjn6CiXsHLR1Ibjuw8O1drT6FX6yBJZGSvuNSEIgiAIgtDBM7sO4e4NfTh5wWRM\n7S43tL1s+Uy88rhZKfXMoxWP2ksBvJ4xdiGATgATGWM/5Jy/138B57xv1OuvAfDPog/inF8N4GoA\nWLlyJS0ziQDXBUoiz1ECuxNZwpW63RnlqBEEQRAEQYwDX6HxU69ahpcunT6uz2LB1rpB1UfO+eWc\n8/mc80UA3gng1tFGGgAwxuaM+t/XwxMdIYiWqXIOsbCOXzywmGYJl1wXz6NWzGtCEARBEAShA7e2\nlNKRipZEOlvsOmqMsS8DWMs5vx7AxxhjrwdQAbAXwAf0dI8oCi7nQs9R0T1qXCbPb74rBEEQBEEQ\nucJ3BOgsbG00R63xxPx2ALfX/v35UccvB3C5vm4RRYNzwBH4d4tukLici8VEKEeNIAiCIAhiXPhr\nKR3rzcC5oOGzfFoueE0QSeJyLslRK7apJq+jxgobDkoQBEEQBKED31BLu7C1DDLUCCuouhLPUe1v\nUb1HrqTaInnUCIIgCIIgxodf6kiLR83XVUgr9JEgkkKaixW4kYtplUjrqDG9rnWCIAiCIPLN//35\nw7jlyZ3Ctr98yWJc9vJjDPcoffy1lK0RXGSoEVYgy8XyKar3SKr6CEaqjwRBEARBtMx9m/dianc7\nVh3TKEN/02M7cN/mPgDFM9QCj5pG1UedzgUy1AgrcDlHSWCRJJGYmSVcceQjQB41giAIgiAUGKm4\nOHPpdHzpDSc0HN+4ZwAHBysp9Spl/Bw18qgRhBxZYed6vG8xzRKOiNy9Yl4SQiP3bdqLj/3kIYxU\n3VDb1J52/PojL0VPBz0mCIIg8sBwlaPcFpan6Glvw67+oRR6lD56c9Q8KEeNyB2uKyl4XXSPmguJ\nmAgr7DUh9PHk9n7s6B/EW0+fj85y/eG9cfcA7t7Qh90Hh8hQIwiCyAkjVRftpbCh1t1ewp5DQ7j+\n4RdCbZO7yjjn2BkmupcKnDxqBDE20tDH2t+COtRqOWri61JULyOhj6rrjaHPvWYFJne3B8d//dDz\nuHtDH20GEARB5IiRqotyKbymmDO5E30Dw/jYTx4Svu/O/3seFk7rTrp7qZBMjpo+yFAjrEAa+hgc\nK+aS0aujFj5Oqo+EDuoPqMZB5v+vS5sBBEEQucEz1MIetU++YhnefNr80Kb4Het34ys3PIGhStVQ\nD81TV31MtRtSyFAjrMCVqht6FHW96HIe5OmNxvOome8PkS98Q6zZm+0bbjTGCIIg8gHnHCNVLjTU\nSg7DkhkTQsef3nHQe2/ivUsPHuSo6ctS0xnxRIYaYQUyeX5SfQQcQVl6L0etqFeF0IWvIVJq9qjV\n/lJ4LUEQRPYYHKmGRKJGqt583i4QEykyQY6ahsuShFeODDXCClw3vFgEkqnyniW4VA2zuNeE0Ifv\nUWt+QPmbJi6NMaIFNu8ZwP2b9wrb5k3pwkuWTBe2EQShn0e3HcCbrrwLFckE3lkutfxZwWZ5jp8F\n/mXS41HTDxlqhBW4nAt3ImyNGTYF51xeRy3HEydhBl9MJORRS6BoJ5FfvnLDE/jjU7uEbSWH4dEv\nvhLd7bTcIAgT7OgfRMXluOjsxZgzqbOhreQwvOGUeSn1zE7855xQeVwRkucncgvn4TyZxvZiLhil\nIaGW7vwQ2SLwqDWNMf9WdMPl1QgixGClihPnTcKV7zmt4fjNj+/AP9z4JG55Yidm9HY0tHW3t+Hk\n+ZOEEQMEQcTHXy+96dR5OGHepHF9Vl3OLb9rsMCjZulURIYaYQUu5yhHyfOb7Y41RKo+FtR4JfTh\nun7oo0RMpLB3HqEC50BHm4MFUxvlu09eMBkAcNlP1wnf9/MPnYUzFk1NvH8EUSRsVzG0DS5RP45D\nEs9OMtQIK6hKPEcoQHx0FK7L5Tlq5rtD5Iwq1S8kNODl0oaPrzxqCq7/6EtxeLhR2nvD7kP47K8e\nw8HBEUM9JAgiDkXIUeNBjpqdkKFGGOW6tVvx52f2hI5v2jOAk+dPDh0PxEQKapZwiBdAjHLUCA1U\nJSI+DsnzEwpwSMqIMIaTBPP6hA5v6VGp0gAjCN1wy8UxbEOWAhAHylEjMs+379yI5/cfwayJjQmu\nU7rbseqYsDJYwetdezvVgsnWIXl+QgMu55LyD/V2ghgLzqG0HV1XFaXxRRD68UP5dHxW/jftOOWo\nEUQdDuC85TPxn+8+bczXApSjJi0ETh41QgOuKw45DjxqpjtEZBJFOw1tJe/VVRKrIQjt0NpADa0e\ntQRCRanqHWEUVQEMVvAQLM7DQg8ejBbRxLipci4MfQR51AgVJDlqMvwFUYVkRQkiMXR4iIpQqsV2\n8RXyqBHGUbkXijBJAMChoQqGRqqh41VJHTXyqBE6cF0u3Aio56jRICPGhoPDYa3v+/oCNrQRQBD6\nCQwPylFrCa2qj9AfjUKGGmEU1cFbBPW55/oG8LJ/uQMVV/wl29vCCyAKTCN0QKqPhA5kqo8y2mpj\njsREwtyxfjc27T4UOs4Yw6uOn43ZTQWMCaIZnfN2EZ4F/nfTUfA6CchQI4yjsmtR96jll90Hh1Bx\nOd531lFYOnNCQxtjDBccPzv0HvKoETqouuK4/LrYg+keEVnEy1FrfV53yKMm5YPfX4vhijgk9Pn9\nR/CZC1cY7hGRVWwN5bONoOC1Bg9kPUeN6qgRWUV57OY/BMufJF553GycLVC+FMEoR43QAJeI1TgJ\nPGyI/MI5j+VRIzGRRlyXY7ji4tJzj8aHzlnS0Hbu128ThscTRDN+qogOO01HOKDt+NeLPGoEUSPO\nvZDn5WJdcaj19zBGu9FE6/yfHz6A25/eHTo+XHUxe6IglCoQE0m4Y0QuUB0mvse2SmIiDVRrc/qE\n9jZM6WlvaGsrOUE7QURBw0SN4Dmn0VCjHDUisyjnqFm6w6ETN0YiKwNNxkTrPLLtABZM7cLqZTND\nbacuCBckrsvz0yAjxsbLUWt9/qp71Gh8jca/HqVS+FqWHEYeSEIJLaqPtb+5Xm8kIM+v89FJhhph\nHJV7IXhpjieJOImsjDEKSyNahnOOk+dPbjm/pRAPZ0IbqnXU/Bw1mYBSUfENtTbBw6DEGNyCXq+R\nqov7Nu0V5u61tzk4c/FUlEtUbcqnPkoKsNOtgXqOmp2QoUYYJXYdtRxbaoGhphggnd8rQujGVa1z\n5RS7fiGhSMwcNQrfbsQ3XEU7+yWHFTb08feP78RHfvygtP2b7z4Vrz1prsEe2Y3OTdwilEjiWj1q\n+tesZKgRxlGqo1b7m+fnU9wctRzPm4RmvDpXaqG1AC2kidZQ9aiVSExEiBvhUXMcFNajNjBcAQBc\n/RenY+aonNo9B4dw8ffX4tBgJa2uWU0RUkd0EHjULL1eZKgRRombo5bn9WJ9MayykCbVR6J1VD1q\n9V1Bghgb1Rw131D77l2bcP3DL4Ta33LaPFy86mht/csKvkdNVNuwxIrrUfMnouPmTsT8Kd3B4Z39\ngwBQ3OsyBnpUH72/ebjE//aH9bj6zo2h4yO1HSPVqCYRSTgXyFAjjKMmmpH/BWO8HDWSTidaR3Uh\n7b+UPGpEK3BwpUVhueTg0nOPxsbdA6G2+zfvxR+f3FVIQy0QE3HC+VaOwworvhLIzTfNYUG9x4Je\nFxk0bYt5dNsBdJVLePNp80Jt8yZ3YWJnOYVejQ0ZaoRRVCeQJIoH2oYbIz6aIh8JFThXW0gHqo85\nvu8IfXBFjy0AXP5qsbDN26+6R0OPsonvGZKJiRTVUJPhBBtK6fbDVnTUQMvTZrnLOeZO7sJnX3Nc\nYueo5/Tpg2RyCONQHbVG3MCjpubxoDU00SouV8tRc3IU7kIkjzdO9CV45Fm4IIpqtbZpJzLUiuxR\nk6jylajMgxCdBa/zhMv1hDeahgw1wiiqD+A8xUfL8L0WqjlERV3MEOpwqJbF8FX5kukPkS9Ux1ck\n2VtHaaNSKwAu9Kg5rLChyEEWd9NlcUg9VIjWy5GjqCZvwzLZcwQeSI2Xiww1wjyxbpTsTxIyYnnU\nkG/jldCL6yqqPubo4Uwkj2po7difp/HDMkQQBk8etZYoMfKoiQg8kAXe9BDBuR4JftOQoUYYRTlH\nLYHdCdsIanio3I0sz6YroRtljxrlfhAKxMlRk5G9ZZQ+KlHy/IyhWtD7sR762HhdgtDHPC8QxkHz\n9Yr3GR55uMJGPGoJ1J0jMRHCOCqTRxKJmbbhSh5CUTCy1AgFOFcbX/VdRxpkxNh4qo86c9TyzYNb\n9uGL1z+OSpPlNVipApAXvC6qumFd9bHxeF30yHSP7IYuhxiXcy0CK1GQPD+ReeKrPurviy3ELXhN\nOWpEq3DFnUTyqBEq6PSoFYG1m/fikW0HcN6yGSEp/uPnTsLpR00JvYdUH8P4cxpdl0bi5L3LYDky\nhl2uts6yBTLUCOPEETXIM24wqVKOGpEMqgWvaaeaUEGnmAhjAHf1fJat+HbFle85HV3tpZbe4zhA\n38AQ/vjkzlDbnEldOG7uRJ1dtApSfSR0wDmHo5RjEoMEosDIUCOsJol4X1tR96gRRGtwKIqJ1P7+\n1x3P4lcPbQu1/tVLF+ElS6dr6x+RbTwxkfxvqunCNyxU1oxTuttx78a9uOjataG29jYHT375gsBw\nyRvBs67p6zHGwBipPjaTgOij5k9NBzejYiJkqBHGUblNkoj3tY14Ba+9MJgv//aJUNuEjhI+fN5S\ndJZb26kl8o/LoXTjzZ/SjbOXTse+w8PYfmCwoe2pHQcxo7eDDDUiQGcZNQYGjny71HiMOf+Kt52M\n/7N6Sej4dWu34of3bkHV5bk11KKgkFABpPoopOryxK9JsGGlcdFKhhphFFW570KIidTWJCoP7ePm\nTsSkrjJ+vnZrw/GKy3FkpIqzj5mBMxdP1dlNIsso7iR2tZfww4tfJGw74x//gHzfkYQyvNhqjapU\nY8z5PR1tOGn+5NDxPz2zB0DOo0789ADBKHMcRqqPEnQIZ+RJJ8DL1c7eTEWGGmEctfvEz5XJwSwh\nwY2R+HvhiXNw4YlzQsfv3rAH7/72GgoFIRpwqc4VkSBejpqeEcZY/sdXHAGpIiMreA141zDv40WV\nXBvt48DlSNzrnIRzgQw1wiiqgzeDmx/KJFGckh5cxGg49MXmF+CWJBTRXfA673gy4eTx0IEs9PH+\nzXtx/boXhO+ZPakTH169JHGp9rSQia/EIahlq+Gz0sZEHbUkaNlQY4yVAKwF8Dzn/LVNbR0Avg/g\ndAB9AN7BOd+ssZ9EjoiTdJ7nh1CcHDUZRUno/8MTO/H0zoOh4weOjODpHQeFD+55k7vw1becmNuH\ncxT+wlAHRfB4EGpoV33U81HW4nKOkraNk/zPZ1GGh+OIDbWrbt+A29fvxqSucsPxwZEqDg9X8faV\nCzCjtyOB3tpDAR91kXjqx/muo3YZgCcBiDRgLwKwj3O+lDH2TgBfA/AODf0jcoZyHTX/fTl+dPvP\nGC2GWkFUMv/m5w/jwJERYduM3g4snNrdcGxn/yD+/OwefObCFZjUXRa+L89wjQ8oT+wh3+OLUINT\njpoSOtXniuBRq9cFExcCf2jLPnzjlvUNx5/Y3o9Vx0zHf//lmQ3Hf3jvc/jcrx/L9RwWhIpquCvz\nNL5U64naQkuGGmNsPoDXAPhHAJ8UvOQNAL5Y+/cvAHyTMcZ4nhOLiNgo1VErQD0n/4GhdQLJ8fUC\ngOGKi7966WJ8+tXLQ23lEgs90L/750348g1P5PrhLCNY5Gj9TI0fRmQeDq53IyDnA8x1uZI0fysU\ncW4DgGWzerFm0148vO1AqO3dZy6UvzHHlyvnt09sXANiIvU1q3nVx38D8CkAvZL2eQC2AgDnvMIY\nOwBgGoA94+4hkStUHybFkOf3/mrJVxj3J2QDl3OU2xja21pb7eRpV1AVnR5bgMJpiDDkUVND54Kx\nCM/IuocozM8uPctkVzKBv87SMcTq48uuATZSdfHgc/swUg33q6Ps4LSFU0LCIbmto8YYey2AXZzz\nBxhjq8dzMsbYJQAuAYCFCyN2Oohco+ZR8/7aNUXopV5TZ/yfFezmjP+jrEZ1oVMPoS0e9bAhPZ/H\nUMzrSMjhinX6osjgOkqZqptA6KOWT7MbLYZHka5X2h1IkBsf2Y6P/2ydtP3qvzgdrzx+dsMxnbna\nMpJYa7TiUXspgNczxi4E0AlgImPsh5zz9456zfMAFgDYxhhrAzAJnqhIA5zzqwFcDQArV64swn1C\nNKGeo5Yfef7hiovn+gZCx3f2ewWFSQGsdVwOpWT8JMIRskLdo6bn8xhjuR9fhDo6RS3yPrx0qs8V\nSUyEaA2t18tSw/bgUAUA8O33rcTkUXnnuw8O4cM/ehD7BTns3IBHLYk12JiGGuf8cgCXex1gqwH8\nbZORBgDXA3g/gHsAvBXArZSfRshRWWB7f/MwmD7360dx3dptwjaHoeUwvlbIe75C1VVb6ORpHKlS\nD4PRuZAu4pUkZHADO9V5wuUcjiZLrb4wzO89qVUcI9j8HfdHWUvw1QpwT56yYHKDeueug97G91DF\nDb029/L8zTDGvgxgLef8egDfAfADxtizAPYCeKem/hE5Q3Vu9GOMP/WLR/CpXzzS0NbmMHz7/Stx\n3rKZmnqXLHsHRjBvchcuvzAsgDF7YicmdIy/rGEG5yBlglBRhRm3CHkcMpKo00cQo9EY+eh9Xs7v\nU53y/D45v2QeNIcpkWvDVhLS39FWAuBFMDVjREwkgbpzSitDzvntAG6v/fvzo44PAnibxn4ROUbl\nPpkzqRNfeeMJ2HNwqOH44EgV37pzI57bMwAs09zBhOCcY3J3Ga89aW5i5yhC6KNfM0dpwg1y93J8\nYSTwIPRR4w5+8S4jEYFX/kHPZxWhzmES9ZzyPOfr9BYWYHjlezDUkH3Djlpk0lClGmpz3WzOL+Pf\nwicIBZRz1BjDX7z4qNDxfQPD+NadGzO1XtRZFLaVc+UVP+eqWdEpiuCVeb4wEvyC6rqGXhEKEhNq\ncHDKUVPAdTlKmiLdWYHiunU+P/O8aReEimoVX7HzejV/xcBQGwl71DjXd9+N1aE05PkJQhs65tos\neo64Abd7EWJDXEnIQxQFWsuE8L+zPjnw/Ne5ItTQ6VErAonI8xdgdtOydtDwGVkhz9+1HtLf+C0Z\n88r2PLR1P3605rmGtkNDlXzK8xOEXvL/MJHhGqg1VITEct9QU1J9tDXO3gBxDFuCUEFntAADcn+j\nJiHPn2eSGA55HmI6v5ut0Sg8IlJk/pQu3Ll+N+5cvzvUNmtiZ6L98u/HO5/Zg8PDjeGXJYfhLafN\nx+xJan0gQ40wjp4ijNmrF+YtZpJOZK2fK6/EyVGzPXwjSWQ7j3Gh0EeiGW+MFcBi0ATnHI6u0Mfg\nM/V8no3oVK4thmGrX+k3S/zuY6vQL5DnBwNmTOgIH9dIV7mEuZM6pYYiAHzkvKVKn0mGGmEUbQ+T\nDHqOjEpYZ+eyKBPUBSPVx5aI2nmMA0MxryMRhb65rQgbAVpDH1n2Ni3jolVZVONn2YqeNBM7x1dU\nHl5nuYTOcslof3zKJQd3ffr8YJ3i43KOYz57EypV9StJhhphnDwU6KxUXTy0dT9GBBKwHWUHpyyY\nEhK74EZCH7N/bcfCDTxqrb+nCDlqlaqL+zbtxWCT2tXBQa8wqNaC13o+isgJJua2LPK+796Hezf0\nhY6PuC6Ont6j9VxZ2rRUJcdfLRGKcLmCSBELZx7GGEpN3WLhpWLLkKFGGEWbQy3le/PGR7fjsp+u\nk7Zf/Ren45XHz244ZqaGh0eeQ/z8nCul0McgRy2/1+XOZ3bjr/57rbR9YlfZYG+IIqE7Ry0vt+nj\nzx/A0pkTcO6yGaG2lUdN0XKOImxCaVUxLMCzQGftTFuF2yzrzpiMJ/2CDDXCOHomW4+0Jo9DQ56X\n4qr3noYp3e3B8R39g7jsp+swMFwJvceEMpqtk6pOqjEKXqMA1+XQkOdJ++a7T8WCKd0NbW0lhhWz\nJ2o5j7eQzvGFJJThXK88f17gAE4/agr+7oLliZ0j7WchYS+FuCcz9hXj3KdkqBFGycsCz/8apy2c\ngpmjVIS27j0MwCusGHoPeCFCE5OmXsC59fcU4ar799aKOROxZMaE5E5UgBwiQg2tHjXGchMR4JrI\nSy7AM0VrmFsBNu10fjVbo3Tqgikpd6RFxrP2S7r0G0GE0Jvgms7kEZy1OQ659v+u4ClgRJ6/ADL0\nvuqjkjw/y/91qRuwBsJrc3wdCXUoR02Mkbxk/1w5vinrqo8pdyQj8LplS1hGnLuUPGqEUTSLPqa+\n8G7e4XOiDAKuGK4Xpz8FyFeIJc9f+5vnxUw9dy/ljhC5pFJ1cdUdG9A/GA7rPjJS1Vf+AenP67rw\nlH7NzPk5ntq0UqTpMc85auL2awcAACAASURBVD6Z+z1jXEgy1Ajj5CL8T+J2940HsUeNo83Qd89L\niKmIwHOkIs9v+cNGB26wiZr0wjA/oWlE6zy14yCu+P16tJeckKJtm8OwYk5vSj2zFxN5ycG5zJwm\nFfI8bxPx0F0f1ARxu0qGGmEUXRNu2p6jQIWq6Xg99FH8nqTD0vJG1eUho3O46iUAkjx/I66hmP08\neTyI1hkc8cRqvvOBlVh1TFjFUBd5miJNzPlFCHf30eMhytEAk6A38tHWOmp664OagIFCH4kCkfbD\nSXbe6By15BPL82SQ3Ll+N/7yv+8PQh2bKZdaT7EthiRzDDXMGDBWjEUh0chQrWZku8J9F5e8jC+X\ncwO1M72/5OVWIy9jTEQ9py9LZgwhgww1wih5WSjXFYdkOWrh72niq6dtwOrkub4BVF2OD527BD3t\npYa2jrKD1YLaRDLyZMDKiKOGSRCtMlQrpN5RLo3xyvGSnwFspCRLsh9vBcHzthDf1i7qaQN2PT11\n1oozBWOM5PmJbKA1wTVl1cfmr1LPURO/x1zoo12Tahz8a3jJOUdjak979ItbxLJnjVbcwFBLPtSK\ndu+Lx3DNo9bRZsCjlvgZzMDBk78fC5B/66O1BmtuRlmYIog+1tdg2fmWXuij+rgjeX7CKLqnxrQf\nTmExEe+vKPSRGwx9zAN1dcfxf1bd85nfh7OxHDUKfSwkQ4YMtTzNYS5H4qtlZmkOkU5ovlEjMGK0\nGrZ2UaQxQR41wjhZ2gGRISvAGVWvi3MD3o4c7a66GnOubCnnkCQmw4NyfBkLz7qt+/GF3zyGkWrj\nr7z/8DAAoN2ERy0vN6qBOb9ewDkn10yALIIlDnl6RhLZ2tiJu8lJhhphFs2qj2khK3gd5VEzkVju\nk4dnkM4CzkXIUauHPiZ7HkpQzzdrN+/Fw9sOYPWyGWhz6kbZ3MldOHfZTMyZ1JVi77KF0Tk/z5Nb\nDZp7WkO2kRwHZunDM4uhq17agDpkqBHG0eOOT1fFj0vCzKIKXnuJ5WakmvNAVWMB5zyJrMioF7w2\n4FHL8XUsOv5v+x/vOhW9nWXj58/PDOatbUlMZPzonG8stTu0Uld9TLkjCVKkZxAZaoRR8n5vjVXw\n2pg8fw4utE7DI23xGROYExMB8n8nFxeS9hYzVKli/Y5DwrbJ3WUsmNodOs65CTGR/G9CZbFmVl6w\nXXwlU9MUhT4SWSEPceYyVaWogtei1+vG9klVBdfVaKjV/uZ6MeN7eRNOISIxkXyTtmKcrePrqzc9\nhe/dtVnY5jDgvs++HNMndDQcd7m5Ob8I6IzGyTM23j9JkaXfM67qIxlqhFF0hSqmrUQk23Ueq+C1\nKXn+PEzUvrFb0iEmkiNPowydOX1RZGoHk1DGv0XMlRLJBrv6hzB3Uie+/IYTGo7fu7EP1/x5Ew4N\nVhoMteBZZ0pAKgebczKSmLfzLL7io7UUkmWXS5Z+kkfIUCOMk6cbS1ZHTVbwmuT5W0enPH89YM+y\np41G6qGiyZ8rv1eRMFXmQYatdfoOD1cwbUIHXn7crIbjA8MVAOHNOVMF6G1dSOukLjevb9OOIEzD\nPJeaMlRHjTCKrmdJ2nH5ss3S9Ate5yeDqL5jRh61VvDHXNKhIAysELvRRYV+WjGHh6voai+Fjsvy\nkk0X5KWfTY08Xy+dpVpsFeJKO0Q7LqT6SGQCLQvv2t+0dl5lD+Gx5PlNzSp5WEhXOdcS9ghkbzKP\ng9GC18megrCA1DxqKeeobek7jA27w6Ihuw8OCQVDZJtz3JCHO20FZCPk+bslgKGo21TR6WU1RVzD\nmQw1wih5mW9lEyGL8KiZKH6aoTlrTFyub5GTtgfWBPWFoQnVRyKvmCycbiMXXXs/ntklVnc87agp\noWP+HFVtmvRdQ4vlYsjN67+OuX4WaPwsW8dXFn8/bxOKxESIgpB2KFuUJ89hEHbMRPHTPC2tXFef\n+EraHlgTmMqJGX0uIn+YHEci0vbYHh6u4vzlM/Gxlx0Tajt21oTQMceRhT5SmQOd0FVUpwjXLGvf\nkeT5Cevh0GOsBB4SDZ8Vh6ibzWFMmqOW/O5qfjxHOlUy0zbsTWCqjhoYy7G56/HJn63DLx96Xth2\n8vxJ+M1HzzbcI3PUPUFZWwLpweUc03raccqCyS29Pgh9dBuPmwo/y9OcL0Nvweu0NaOThydwD9sW\nWpvFot5xu0qG2jjhnEsnESetLUnCGKJJwjPUUpbnz8FDqOrqkeYH7A3f0ImxHDXY99DWzdM7D2Lx\n9B687uS5Dcf//MxuPLn9YEq9MkPaxYXTDrlUVeeV5SXXxQ5MhSLbdU9et3Yr/v7XjwnXR72dbbjp\nslWYObGz5c8r6sZBHNK+h02QxUcQi7nJSYbaOHnbVfdg7XP7hG2ffMWxwvCJIsM59M4eKd+toocw\nYxLVRyp+qoTLuTajowgJ9zpVMqMownqJc2DJjB588hXHNhwfHKniie39KfXKDDYIEaR5n6puqI0V\n+mhqv9a2qe3pHQfhco6LVx3dcPy5vgH87tEd2NE/2LKhpisSBxgVBm/Z9bIV2zc5s2bAU+hjQuw4\nMIhv3LIew9XG2AbOOdY+tw+rjpmOlUdNbWj73t2bhMpRhD7SzGWIKrboMLF8ubdTa6j4qWWz6nDF\nxSU/WIs9h4aE7ReffTTeeOq8hmNcpwfS8oeNDnSKrxQd2X502oqEJkhdTS3lMawaoi6T5y+6mIjL\nOTrLJfzdBcsbjt/61E787tEdSvdR3u853diw2ZI0WRwSFPqYIH9+dg9+tnYr5k7qRFupsfTckhk9\n+JtXLgvFs/9m3fNi5b+C4znUdMZNa/uoWOcVfRPPoyYy1PR5iGTYWvNkZ/8gbn96N1bMmYi5kxp3\nUdds2ovfP7EjZKglIc9v23XRianQWolWTq7wNgnCx70acub7YxQD89SYXUjz3IobaqUx5PlN1DX0\nzpfoaZThEpXjoL+Kn6dtzy7HxouPzs0WW8eXDfOUMixeWgoZai3g1mbg6z50FuZPCddRESFbrBP6\nSPMejZoIHcawd2AEzzZJPA9XxYu/JLBt5PnS1RefvRhvOX1+Q9ubrrwLBwcrofckIc9v35XRh2ug\n/APgx9nn9zoC8jwlFvNBmyVcAyHaNsMV1Xltkee3DVey2eGjEt6qe4PX/0yCyAJkqLWAb3Apxa1L\nwt8Kj2KidhRpLhijftrOcgn/8+A2/M+D24RtSWLrQ7tau2AiD9mEjjbsOTSMLX2HG473HxnRL8+f\ng1tyV8072Tz2n9zeb+T3t3SIacXLiRF5A/IxhqLg4KnmfTCvE6nBofasr9fObHapNbYnha2lR6Qe\n/hiXQ6vqYxFmMI0XrP4T2jW+dMsdmCDu84MMtRaII3vtMBaS67WB69ZuxS8eCBsQAPCy5TNx6blL\nDPdofKRdR000Iv77L8/Axj0DoeMMwFlLpiXbsRq2bRL4XmmREuq0nnb86Zk9OOfrt4XaFk5tzYM9\nFrbmccThqjs24rt3bRK2LZjalfj5C5GnJdlQcgpQmsCE6JHNqIoY+ZtPIXl+Q2IituYlu5IQ0tix\nDZqvo23XKw6b9gzgoS1hMbsndxy0dtNWFyZy/nUTt7+5NdR+vnYrPvvrx4QL1t7OMn73sVWYPak1\nxaG6R63189sa+vjbh1/AEy/044R5ExuOr995CAcOjyRuqCWh3pQGUcm6J8ybhBPmTTLboSZsG3mB\nR01wwf7u1cux6pgZwvctm92r5fzWxtnHYLBSxdSedtzw1+FaXpO7y0b6kIfrGIVMUMLWeV0nqmIa\nuokrYa0LVUNVJs8fhD7q6daY2DYspXmeMeq+6Vw35InP/fpR3PVsn7Bt+oR2LeewdSPANg9ykuTW\nUHtqx0FwzvHBZmnYvYdx4yPbsf3AkZYNNd/YU6mLJit6nDacA8tn9+Knl5zVcPxDP3gAmwReIJtJ\nU/Wx3ge7Hh+WdSfAz99o0uIBAMyZ1BXKW9ONEzxs0h4x44dzoM1hmDs5ee+ZCIYi5KiJw/8KEfqY\nwZ1qnch+exmy0EdT5TJ8U9C2e9J1x4pCUrLUtFGPrrDresVhcMTF6UdNwb++/eRQ25QePYaazWRt\nlvKiUUhMJKDqcnSVS/hUkzTs7U/vwo2PbFe6RWOFPjp2LgqjwjpMTFyqxURb+bw0sO+X9WD1p5BV\n+GFBpgp+h7DzssRCa9mCOGTt6RgDaf5DAQwYVTEN3TCk++xUfUaVpHXUPIob+ihTTo1H3m+99TsP\n4srbnkVV8Dt2l0v4zIUrMKkpYsLlHBM62nDUtJ7E+hVXpTNpdK8lTcAQ7zrm1lCruC7Kgu37utu9\n9cvlewNUJlzPo2bb0PYNNcFOccYGPJByUrCl0rC2JpZHiYmYIE+hjzoLgcehyF6l0Z7ZvHqd0g59\nTBtVMZEg9LEpRy14/hsSE7ENaY5aDMNSp+qjrcJSNz+2A79e9wIWT+9p+KaDI1W8cGAQF540B+ce\n25giYNt3MEmRvnpuDbWqK67BFOcmdWOEMDCLQx9la2UTN71WmV2WnkFiu+KQbRN4NUJMxAR5Cncx\nJcMfRfavYjQyr5I/d7kcKNk8AYwD77unqPqYcki7qyzP77262jzpBpE4evolw9YNA845HEGoexwP\njY3RSboZqXqW/q1/c27Db/rgln1485V3i2uzIvlnga0eWyB7Cp6MxavDKbiN8kGlytEmmCH9Qa02\nSfjvbf09jqVJ516idLYGdyRphT7a99MCsHcn3L8XRGIiJgjOaunvpkLqHrW48RsZIkpMBMj3wjGL\nIUU64Vw9H917n0xMxIxHzbYhKZXnr6Ha37wXvB5xvTVrs+Ht1HcZQ3BLI3tMwG3fLRfgPTrVb9T8\nGmouR0mw5ekPalfB3RUsMhUnb9smTqA2eYp2uQztYuqcWNK8R9OuNSTD1njyuphIWh41O69LLFL2\nqOVqo0eCTPmvHlqcX0zs0keRdmitskfN8d/XeDwo4WIqR82yUSkbR3GuRxIlI2xbn1WqLtpEa9ba\nX6FHLYHrIju/feOrOEqgY4Y+MsY6AdwJoKP2+l9wzr/Q9JoPAPg6gOdrh77JOb9Gb1fVqLgcbQKL\nJI7WQrw6anZ61Mba5VLhX29Zj1+s3SpsWzqrF9//qzO1nEdGmiEyttcasm3oBXXU0vKoWRq+sbN/\nEJf/8lEcGa6G2no62vD1t54UUu+SJembgqUYcmwKDvE86XtabBtHOlE1VPIGB5Qmd3+d8dEfP9gw\nZvwhYmpzyrYx6Uo8s3UPYOsd1vvV7BzdI1WOsmDNWvfYht8jm6cIO4lbg7SVHLUhAOdzzg8xxsoA\n/swYu4lzfm/T637GOf+oeheSoeq6wtDHeo6BukdN5X5gloqJeCE94uuiGs5zz4Y9GHE5VjcluD7+\nQj/uXL9bmHCv21udZgiSjfOjjX0C0hcT8U/78Z+tQ2fZaWpj+PvXHodXHDfLeL/Wbd2PW5/ahePn\nTkRPe3067h8cwT0b+/DUjoOhIumyJH2TWDi1acV1ETlR2Ti36yLtkKK0x7aqx/ro6T343GtWYN/h\n4VBbe6mEV6xIdl6xVOhXvikcs7+6x4Vtm00VV+JR86PABHOO6xr02Np1uYCMhmgnovrIvZXwodr/\nlmv/2faThahUJWIiMWJX4ngDPI9a6+cwhavRE8Q5cOysCfj62xprePz7H57BE9v7E891SDMES6so\nikZsDVOIqqNmguPmTML7zzoKB4cqobZfPfQ8HtyyLxVDrVLTYv7Xt5/SUNx7zcY+vOPqe8UPZwty\n1OwaXckgur+zuDCIQ9pfM835Sz30keHipnqtJqkr2tp1V8oKXtfbVT5L35i09R6uVDnaIh6Qossl\n23gvArauwaKJ19+WVB8ZYyUADwBYCuA/OedrBC97C2PsHADrAXyCcy6OiTNE1eXC3Qnf2FIxovzX\nqgghOIyh2qzXawHSyTPG4suVqIM5o3aAnKZ2LsvSj4HMjbzr4CC+d9dmjFTC17+j7OCDq47G5O7x\nFYNMe9dZiqW7X77BkVaYRld7CV96wwnCthse3m64N3UqtTmiea6S1WYCbMghsnHg60WWS5unMg8y\n0i49kPboSvv+UsbSrsoKXtfzqNO9iWy7hysuRzlKAE+Yo2YiTNnO/G7bNiZaIcnQR3DOqwBOYYxN\nBvArxtgJnPPHRr3ktwB+wjkfYoxdCuBaAOeHO8kuAXAJACxcuFC9twqMuBylyBy1ZEMfHUvl+XXm\nqMlsLidYZGo5zZh9aOaWJ3biv27fgK5yqcEodTlwZKSKZbMn4vUnzx3nee3O47Bt6NWUh1MLfYwk\n5uSpg5GaR605N8FfKFcFN9FYO9VJ4z1sbBthepHtwzgxnh9ZwzNUUu5D2mIiFk5TMmIECRlBZ81W\nDsuTwjXgiYlErFmFqo/2eghNkM3vrn6nKtVR45zvZ4zdBuACAI+NOt436mXXAPhnyfuvBnA1AKxc\nuVLLvFJ1OfqPjISOD45UJfL8fl9aP4e/u6pWR83OPAbZjc0A5fEzVkFL2ffXFsIgOe4vbv/0d+dh\n+oSO4PjG3Ydw/r/coaT4KcW+nxaAHd6OStUNGenDNe+mjTvVDOktvCs1C7bcJvaoiW4h2U61SSwd\n/trgkjyl+txmuEMGkS2wjWGBkWjfLCUnULS1bEx69R7Dx4NDaYU+avoc3Yy4HGXFKDATYiK2liTJ\n2n0KxO9vK6qPMwCM1Iy0LgCvAPC1ptfM4Zz78UOvB/BkzP4o8+EfPYCbH98pbHvp0mmCo/KQIhlx\nCsza61HTGNMs2dmPUinSiax4oCynsF5Db/wd0xjBqZV6DmY6g+/ejX147zVrUJEM/o42+yqCpFkX\nzC9y2qxQ699XIo+aDRtAFnQhUbzyG+HjtuYD6cSGBVBal9f/XbOY9/Prh57H/Zv3Nhzrbi/hHWcs\nQEdbyXh/+Fh11Az2xSb+8cYncO09z4WOj1RdLJ89MXQ8KgpMpqxZBLI4fpIMfZwD4NpanpoD4DrO\n+Q2MsS8DWMs5vx7AxxhjrwdQAbAXwAfUuxKP5/cfwbGzJuDdZ4ZDKV90dNhQq4eutE4cOWzH0vAg\nWdhUnAeTTJhkdI5a87m9cymfSopo8vKPNH/Peg09DeeV5OfZQlojb+vew6i4HB9ctTiUBzipq4wl\nMyak1LNo0rpeQehjSbypIBYTSTlHrQArA6m0eIznR9awdRPKBP7tlqXvP2dSJ9pLDn5wb3jxDwBL\nZ0zAS5ZOH9c5tu07jPd/9z4cFpQRcRjDP7zxBJy3fGbDcdm6Ka4HUNe8k7YH8uFtBzBjQgdeJ0i/\nePHRU0PHoqLATKxDTN0K92/ei75DQ8K2MxZNxbRR0VE+WXwWJWKocc4fAXCq4PjnR/37cgCXq59+\n/LgusHBqDz7w0sUtvb5+k7Z+taox8rocW+X5uTw8TrW30lpDEYtMnch+Ed8J0fw9dReDtnGOSLtL\n/rV9/0sWYf6U7lT70ipxSlPooi4mEi4ZAMjuIXHRelOk6IA0hnfZRaGPdoaZ6UT23U2RtpovkH5o\nsQrHzurFY196Vcj7/tCWfXj3NWswoiG0Z+PuAWzYPYDzl8/E9AmNG3A/f2AbHnhun8BQi06NUEEm\n7pNFhkaqWDpzAj796uUtvkP+LMjLpsrug0N421X3SNvfdeYC/NObT2o4ZoPnX5W4c5tSjpqNqHq7\n4kSGyfIVIs/DmBbPjW5cLl7kxRk+Xg2PiMVM03EuMaBiI3EjB567pu85Vu6cCrZOEmkvJLMYOhQ3\nHEEHMo9aKUKQJ32PGvJtqQCANPSx1prr75+uWE2aBMJhKfdDlXZBSHlHrV6kjrHq18H86PlLcdrC\nKQ1tv314O4YqYU+b1KNW+5tWXnDav+1QxQ3V8oxirBIHST9rTawpBke88fM3rzgWL28qk3PxtWsx\nMBQeX1kUmPFK2yQsJmIjqqo3cfKnXDde6KONHrWoRHHVCV22mxO46lMyVP2v0byYjVNDT3oO2GmM\npL2QrBvj2cGEh+jGR7bj5sd3hI6v33kQQFj1cawctbSvr30zm15kGzHWFn/ViIkiulGkqSoaPDty\nYanq+w5+3reoRFFH2cHgSPhhP9YGt9JmOfQ/U9IyFAdHqko5g/46Q+hRs+BZoAP/u82b0oUVcxrz\n9DrKjpVraZNk3lBTlZuP41WJKyZi49jS6QmS5bvJwrb8/9O1CJCHPvpiIuJ+aRETsXwzJ62h50qM\nZJuRidLo5Nq7N+Phbfsxd3JXqO0Vx80KLQzrJS5kCeRphqblHw7xGK7PIfnFKz1ShF85TJ4WhDrz\nKX1xKFF5lc62UoRHTR76KOrXrv5BbNg9EDq+48Cgxhw1LR8Tm7geNWH0EKI9bjow4QGtRowvWRqR\n7WswEXGDUQprqImu1Ybdh/Dk9v7Q8Y17Dinf3I5j56QvW4B4LlnFz5Lku8nERHTjLbDFC1lAkKPG\nGtvHg611lNJ+CPnXJe1+qOB51JL9PV3OcfpRU/DjD764pdeXxthFTbeOmp2bUDqR1dLSGT5tK2nX\nZrIhBzJL89eYaLiYbpShJvGoudKVtPzifvD7a/HwtgPCtgVTw5tc4yHJW/jIcBWX//IR9A9WQm19\nh4bVPGpBjlq4LfVSGpoI1myC71KKSCPK2ndnjMW6HTNvqKk+VKLklT/+03V49HnxJLF4eo9Svxhj\neGH/EVz6g7WhtlkTO/HF1x2fSnhFHAXLyM8S5btJ6n4E+Ut6Tu99pqRfXj8aj+ssG8CTiMXQQH18\np3P+LKqmmSh4rfrxgVda8ICKkzOrEwZg+4FBfO1/nwq1nbFoCs5fPiv8powhDX0c1Z5XLJ3ajCAL\nm88iOr+Bn6MmMtQ62kq4e8Me/MV31jQcf+KFfhw3N0JuXnATHRyq4EWLp+LjLz821LZwWjbEqQDg\n2V2H8Ot1L+Coad2Y2FluaFs+pxerl81o+bOirpcJr5KJcG//u4lVQuvjT/SeIpB5Qy22R03wGw8M\nV3DusTPwudesCLXNnNip1K/Vx87Ahl2H8Fzf4Ybj+w4PY2f/ED68eilmT1L7TB1IwxGgfiO6EmnY\nukGUtEctuj2Uo1b7q2s33MpHucZwlzjUjXErr44QEz1VVS3zN0BEDyjVOU83K+ZMxJ+e2YPv/GlT\nw/ER18XvH+/JiaEm2anWGD5tKyYECqJIU9wnq2IiIpjGsVqV1CYFgDedNg+/f3wHBoYavUdLZ07A\na06cI/1MYa84MKO3A2ctEdXA1YOJEhu+mu8XX388zls2c4xXRxO1Zk37XtVFVMpEyZGrMmfxqxc0\n9FEtRjeqyrvrckzqKuOYWb3j7tfbVi7A21YuCB3/yX1bcPkvH03tQe+60PYUGktMJORRq/3VmaMm\nGvR+mEbzeWRqlEOVKt73nfuwW1LD40PnLMHbzwj/ljZPkKkl49f+ZikX38Tv6HkpWj9P3aMmNtTS\nXEn+7auW4W9ftSx0/LKfPoSHt+5PoUf6kd09wbjOr52WKyl0VerzV/YvgE7vb1QO0YfOXYIPnbtE\nuV+ie8hbU2T/2vvXq1kkKg5RufUm7lUTUTr1jYBwm8OYUFQri57/uKqPKVbj0UP8HLXwxapyLpyI\ndBKVGGoKcY4aUx5Ast0cU3XUAPHvKNudkYUQ7Dk0jDWb9qK3ow3HzZnY8N/OA4O485nd4fNauphJ\nu091I9nCiyPBlMqcyiUZW55fU6c04iV9p90LTUjCS6PyRfKCbAPOFAzqzyJdyMLms4xOQ61Nw8QT\n9WwwoWgblf6iC7/sio71ZFRuvQkxERPUBeAE60mHoSrxJhaFzHvUVF2/UYaS6ya/k1Z/0Kf3INJ1\nY8tEDWQJ9/X8JT0dkJYZgHh3Rpaj5hsX733xUSEv6Cu/cQcqglnC1t2ctPsUeE1T7YUaDMk7SFRv\nd39oi0Ifwe2UD2csPyIbMmMlaqMvL3BJSHsRyMnwBaDX2AwW0joMj9pfsYcoH0ZyYNiWdFwvuUdL\nln6iE//3WL/rIO5+dk9DW1vJwakLJ6NcGp/PJyo3tBSxkZqlDWGgdi2LGfqoanjIDSWdRoz07Cl7\n1GQ3drwctTEkrBP+jtLQR4lBKMtRi5ok2hwniDdvfo/Nk8RzfYexZmNfw7GSw3DS/MnC4qi6yGIy\nvgkVQ9WQnlJwD8nmKfuCIWwtSRIH2c6+znAyW0ndo5biueuiBtmZv2QEC3wNn1WtPQJFddRUicy5\ngoG6YAZz1HR4IKNUtE0Ytt3tJTAGfOuOjfjWHRtD7Ve87WS89fT54zpHfSMg3CYPfcxeDTkvWkCd\nnBhq6h41EVU3+dBHE1Xeo3C5+GaIg+xG8T8/XEdN/5cWfaIsLFHqUYtQtCqXWBDG0HheO1dq5ZKD\n9pKDH9z7HH5w73Oh9s+9ZgUuXnV0YucPfvOMzaCJ/56KN7w/VqUFry28vo6hEFITyBZAeaqj9snr\n1mHNxr2h43sHhlMRuhoNqdaOnyi1QFWqNcND19oBkItj5MFI9qNw2nRcsAjD0kRO37QJHfj9x8/B\n3oHhhuP9gxV88PtrQyIycfAjR4SpNE5EHbXsD5WWyLyhxhUNj6gq757cvKEctZQe9VJPUIyv7bpj\n5aipf6YKsptUOtlLdqbqk0T4LW0lByNViUdNqbdm6CyX8LvLVmFX/2Co7d3XrMFBQV2XJLAwMk9K\nHG+yKqqhss4YOWo2enPTzlF7aMs+bNl7WNh20vzJSiVWOGSqj94fkchL1rhz/R5M7GzDaUdNCbW9\n+OjkVPfGwsTQdl2Ox1/oDxVq7h8cAZAPY8FHj0dNn+ERFapnouyNiV82qkC4Kk6EC9JUrrxIYM83\n3HRsBPifIfLYOkxcpiaLxM2Hz7yhprpoiQpdqbpci2s/8vwRiaEm4BHJunHuN3Eeh9gY1r9bKQ61\nkoWwyubMqHCXconJc9QsfZYvnTkBS2dOELYl7fFwI3bGbMVTYkoW1d0/f6ze+tRO7GvayXxh/xFM\n7i4L3pUuaeaocc7xagcGZgAAIABJREFUnmvW4PBwVdh+5uKpuO7SsxQ+T7ygy86oHhuXc7xk6TT8\nwxtPTLsrIZIeRXc+sxsf+N790vaejswvjbTiPwK1hj7K2g3dZSZUDMtactQ8hGIiKW4Y13MNx89Y\n8vwV0WZ5Qy+yQyFDH2WCFjKi8qdMhD6aqjEmQ15HTf17ywyiujFs4juKPKPi7yMzIKMmiXLJwaGK\nzAuVrUnClEECZO7KGAm1UrkmXeUSFk/vwb0b9+JeQXjastnjLyGiG5aiR2246uLwcBUXnb0Y73nR\nwoa2z/7qscBT0iqykKK0Q9d14vLkNybjkXyf+muRBV97y4mYO7mroa3NcbByUdjLmDV05sP7HmS9\noY/phHWb2ET0c9R0etSEBa+RnvdX5/gaU54/J6GPcbubeUMtrjy/OPTR3KBPckHDOcfNj+/AgSPh\nxcngiKtR9VFsEI0V+qhrx0we+iie7GXe1Lo0bPg9bY7Eo5bBScKE2EOU0WsrXleTvTDSUDoJbSUH\nt/3t6uQ6lABp5qgdqXnS5k3uwtEzGr3Jk7rK6BsQ10iUIc9zrbXnIEvNddXGpEkSD0WuneCMRVND\n4yUvsJg+j989uj0UOr9mk7dZpHMjW5xfbnKTL7lBpjNHLSoKK818ZZ1iNVGROA5jklDz7M3BcYXL\ncmCoqS0Ko9zunkdNT79k1Pua3CBbv/MQPvTDB6XtMyfqSRT3cvrCx6NUinQiyy2S7TLJDEg//lk0\nSchy1JBJxaHkfxN/AWvp+k+IkRw1S3MadeLlqKXz8ByoGWrd7aVQW5skfDkKaehjyqHrOnG53oV3\nloiq25Q3VG7JvkND+PCPxGuH6RM60K5hgRR1yb0NrXGfwii3PLEzlBu7but+AJrk+SMEjFJ9rgQe\nNR05at5f0XRUcuSRGhkbKgAKGvqouqNQD10JX66qoncuDiYe9PsOezkt//GuU3F6U6I4Y8BsgaEW\nJ8lRFiMchBg22Te6c9S8PoePezvF4tcD4d3wKI9ae8nB9gOD+PtfP9Zw/P7N+2L1OU2Mhj5maAaV\njSOd5CFUbiwcFk96WIUDR0bwmV89isNNSmN+blq3ILeoveRgWLjZIkeWg2qiWK4pTJSjiYMJD7f/\nbMqzoRbnq/n3yedeswJvOa1Rcr2rvYQ2HYZaRF0wE6qPssiaOAxXXFz6g7XC9VxvZxsmdY0/lzhK\nvdPz/Kcb+qiDKOVth4nVj7MY1VTY0EfVGzvqJuVGVB+Tz3E4VIu/XzStJxR/rxNZfqApj5oMmZdV\nFlMdNUmcdtQU3LOxDzc+uj3UdtaS9JTR4mCkXpgfwpChvS4TfbVZfEYXjOlTQ/zxmi3C8hJPbu8H\nAMzs7QhJyJ+5aCpOmT859J54HjVJvcmIiIxt+w5j/2FxLtzSmRPQWQ57+9Kk6ia/MWkrUXWb8sJY\noh0i/Nu3t7MNU3ratfcJGD0PinOusjQkXc7hcuCylx2Dvzp7cUNbZ9lBR9v47/moNWOaxorOtaxv\niElDHyU5apkjZnpA5g011V3BqDo4RlQfa391GTHfvPUZbNwz0HBs294jAIAJna3/vAzqe5gyI9k/\n9vWbn8aUUep0vmStrivsFQ8UTfayHDXxbnhUXtVFZy/GRU0TcFbxQvwSDn2MCGGwFc/TmPR14chm\noEbr6MyB/OOTO7Ft3+GQTPz8KV2YM6kTX3jd8S2H7ZWl4ctyvPDp8HF/IdF3aBi9nfU8nv4jFbz6\n3+8U1lwEgHeesQBffctJSn1IGq+0jX1j0kQoMoU+iglEQwxcF5nhkfQ8GceAleF/h+72khbvmYio\nNaMszcME9SSeZEMfo0Lqs7Qh7FPY0EcdYiK8tjOS9INLp2rYSNXFFb9fj4mdbZjYNEmcunAy5iRc\ntFQWdnrsrF4sn92LzX0D2NzX2Hb0jB6cJNj1joMsZE1uQNbbR1NPZNXSLWsxEZrmG722ihSIMLEw\nBPI/vnTmQI64HEfPmIBvv2/luD8rlqEm2dr3c3Te/q17hO/7zIXLsXh6ozjFF37zmFDYKW1sDX00\nQRZFj1Spiz20fk9yA9clMkctRXGMOJgw+KOcCy5PL1deq6poxHUsOQzP9R3Gm668q+H4lr7DaG/L\nlku8sKGPruJGtTz8zftrro7a+Ee37y7+0Ool+PDqpeP6rDh5OrLdnIXTuvG/Hz9nXP0ZDzIDsi7P\n33g8qo5antAZmiaDSzMX7YUZMGCBbF2TODgRSd+qVKouypqsiHKJST1dIurhu2FWL5uBr7/1JAxV\nwoZfb2cbXnfS3NBm3xU3P21lmE7VUnl+E7m0UXnJeSHOT2siJDRKLdBE3EFUjpwqJjZ5o9aMaYY+\n6lV99P6KoiRef8pcYXmV4+ZOxIsWT9VwdnOwmJNb5g01rupRk4S/+UaPOdXH8ZN2+EbSi/6xkIVr\nyrysMmntagESy4F44a2qZFFMBDCk+pixa6IKY/o8apUq16KYBngetSMjVbzuP/4capvcXcZZS6Y1\nGCz+NxD9Xp3lEt62coHS+XVeF11wzmtjMueDUkIWPf+qROXjy6gaWFNEeWJU13NpY3IciRWu07te\nOj1q9Ry1cNurjp+NVx0/e/wnyTCZN9Q84YjWX++/9po/b8JvH6kLRARelaRDH2t/dTy4dXoBZfle\nUaQZHw3IxTG4ZEzIPGpF2F0FzNRRC7wRGXrYAgZy1CAWp8gTOsfXiOtiQlnP4+mVx8/G+p0HQ/f9\nwcER3LuxD396Zo/wfQundms5vymPrQomQtziwsAM5NIWY84H1DbnTM7f4vzy5De0tH6+gZxs/x59\ncns/fvvwCw1tlSpPPVRD9dn5vbs24YkX+huObdvn6SrYOB/pxNssL6yYSOs/7pTudrzp1HnYfuBI\nYwNjOHvpdKxaOkNzDxvxQwqS3oUwga1eAjciIZkJEpKiii3mCgM7+zIRBpthBlyNljlUEsHRKMpS\nqXK0aRpIpyyYjGvef4awbahSFUo/O4xpU2n0phy7BoDvOUk6gsRW3CCCJmOTlQLxQh+9v0lelqiP\n5gbtDh33ZLB2GPcnyWlzGCZ2tuGGR7bjhkfC6tNTu5NR5xyLuB61f/7fp+EwhMRXls/uTVSl3Abi\nlgLKhKF2cHAEX/vfp3B4qBpqUw3fcByGb7zjFJ3dU8LfVdexYOZc38Mmbjx72s854a5cRL88wYPm\n13t/8/zQBszsVrkp1nWJi4mcGBM7xWnjqXPp+ayRqqulZtNY6JDPHou4D2cRnHM8v/+I0LicN7mr\n5Wtm8+aUifvRT1m08fvrQ5zmEYWJdIqxQh+T/k10fno9py9BMRGH4Y7/ex76BoZCbYwxLJ7Wk9i5\no4hbU9LlHO87axEuv3BFEt3KJZkw1B7eegA/vHcLZvZ2oKPc+CBaNK0bpy3UoyJoAp3SsFXNUrqy\n+61SdYUFY9POcWDMK0XQHA7wXN9h6TXxlA/FHrWc22lGcmVM7ojqwkSoFZB/Q41FyCirUnE5yppy\n1NJGp9rqLx98Hn/z84eFbe86cyH+6c0ntvQ5RSj4HEURQh9jbb4aGRcRYiIGHc86TlXPZ012IE3p\naU+srl1c4nrUvE3LHN94EXhrDfX3ZcJQG6p4nrRvv28lTl6QHaNMRF2eX1+Omo7dHNl9wznH6itu\nD2KIm0lzMTWlux33bd6L+zbvDbWtmDNR+B7PWGk8FlVsMU8Ioj61k3beYhyMeNR4/nPU/PGlY1e8\nUnXRlpNqxDo3SPYc8nbV/+nNJ6JjlDT1FTc/HbS1gt8fG0MfTdwlaQtxmUSpjppBA1a0Bsra86Mo\nm7wiArEaxfdlrQSDTljM9ICMGGreNk/WaiaIkNXyioPuSUKsoOgleq46ZjrOXjq9oc1hDK89eY6e\nk8fgJ5e8GDuacw1rzJooriEnEiCJKraYJ0TeRN24GXSpmTJgs3ZdVAnq/Sjmrg5X3NC4HNGo+pg2\nOseXv8n0xlPmoau9HrZ5zZ82WRfiNh6Svh+LUUdNnULUUdO4BgtUjvM+uQuIWxNYVQCQyIihNlwz\n1DpyYKjVc9TG/1nBrmiCM5v/8D9j0VRceu6SxM4ThwkdbVg6s1fpPaLEftsXLboQeRO1k8FJ2Igq\nX/bsV2X8393lHE6L3/a6+7fiU//ziLDNRP6YCXSOr6BOYdPldRy1e9vm0EcTkQ0m6l+lTbCQVhh9\nZuqoyeFjtNsGedTUPURFiC6JIvehj/nyqGkMfdTytJFI3Qet+UAU5laE3VWP5OX53QxOwiZ6a5fm\nXzL4Idgq33XjngGUHIZPvuLYhuOMAa8+IT1vvU48MRE9I0BWp9BRzA+0fYGZvDy/9zfPAlLBQjpG\n6GOSxnKUJ8aEknS9ULMOQTfvb/7XDmHi5qgV2aMWd9MuE4Za3aOWgx3WYNd5/B/lSwwnqThkmar0\nuHEYw5Pb+3Hd2q3BscefP+C1ZX8fIBJvmBgQE8naJKxxIS3DhJpZ2rBgblMzGMolho+ctzShXqWP\nzvpyQZ2rpu0FhzGhEqSMoLBxQVdMuoW4bEbNUPP+mrgu4jpq2Zong7kuO13WRt1j2zpZrbOaNtYZ\naj+9bwt29A82HHtwy34AefGo6dvN0bkrysZYxOflvprZ24E/PbMnVOS25DBMtUxVSTeM1UOeksLb\nLcvWYGEwJM+f8DnSxonYKZdRdXmiods2wKBPTERW58phcUUj7Lz2Sd+PtnsUdRC35A6QXh0114RH\nTePnF9mjFqAw8cgiAopC3Hxlqwy1vkND+PQvHxW2zZ/ShZ727HvU4oQjyKgaKNpZz4nIx531u8tW\nYe/AcOh4T3ubdfK3ujEhJsLBM2eQMBOyj8j/w8n/eipGSdXluffq6K2j5v1tXhhGhT7e9vQu7Gua\n8w4cGRF+TlFwgwVjfr9/PcSvdYJC4CnVUfNyeQ39JhrFRHI+hUlRfXT6ry3qvMMYcODIMB7csk/p\nfVYZan6trv/3phPxrjMXhNrzMKkGeRxaxES8vzqui8zSz1voY3d7G7rbrRr2xvB29sPHX9h/BF+4\n/vFAXXU0ZYfh8guXtyzaksnQR+jxcEd+fiHERLxveNlP14XKdnSWS/jMhSswfUJHw3GvOH2+rwzT\nuEEiE8FwHLGhtnXvYfzl9+6Xft70CfZtTjEDLm5v3CV7jrSpG0Qqocj+exM01CBfA3mhj4mdunZ+\nfRRBlCYKVQ9RcL2S6Y719LS34b7Ne/HmK+9Wep9VK1bfQ9TmsFwYZSLi7DrLMKH6SOQHUWkCAFj7\n3D7c8sROLJ/di85y3WtdcV089nw/zjl2hoKhlq0cA8CUPH/2rosqpx01GSvmTMTmPQMNx4cqLrbs\nPYxXHjcbF5wwu6GtynmuBR0A+QZJHAJxp5BHTXyOw8OeENeXXn88Vi+b0dBWLjmYO7lLT8cyhluA\nDQKfODlERuqoCc9vbhGv45a0PYQ4aVQ3oQIPZM7nfBn//q5T8PSOg8K2874mf59VhlogGZzjHzFO\nAqYM3TlqUX0q6DyUK2TFFqu1G++/3ns6Fk/vCY7vGxjGqV+5Rbgj+41b1uPWp3aFjr+w/0jmxorO\n0DQZefNMizj9qKm46bJVoeNP7ziIV/3bncLNqaqb/0UOYwDXlBsqqzPlMIZKNXwSf/Nz1sROHDWt\nJ9RuIwzJl8tweb7XGXFxDSykI+uojdFuG7KNk6IQ16NWVOZM6sKcSeqbY1YZapXagrGUfc0QKXGU\n0WT4D+EkJ4kiF3TMGzKDpFKte7KbXw+Id+pvfHQ7DhwZwYnzJjUcn9HbgVMWTNbSX1N4C0O1+/Gx\n5w9gx4FBYdvJCyZjRm9ziF9xwz18j1lFMJBcl+d6vgdqiozQY6lxiViPLEctiLooqFGy//AwfrRm\nSyis+75NewsT+qgytZkUWRFtAJoIhY5bqFmESQ+kjSjnqJH4SiysMtTqD5X8PrmD4akxkVXHQ1hm\niMkKrBLZw5MJly/mmndR/QeaaAHIOceLFk/FN999WgI9NYuqR+3IcBVv/M+7hIYHALzmpDn4T9F1\nKeg95G8AVAWSo1VeANVHSVhiHLw6hWG8HLXw8UogOKXn/CZgDBipuvjDEztDbfOndmH57Iktf9Yf\nn9yFr9/8tLDtpPmThMfzwngKXidbR837K+qVm7ENrSCnL1O91gdTrM1K68l4WGWo+ZEbeX5wOxGL\nX1XqtWDG/VEAZDtcej6bSB9ZrkxlVG7oaKLGlWxnP6uoDPPhqouKy3Hx2YvxhlPmNbR94rp1ODxU\nEb6vqA/zwKNWFXvU8h6C5i1m9EykHDKPmnj+risDZ8dSm9hZxlDFxcXfXxtq624v4YkvX9DyZ/lR\nOvdcfn6skKM8oLSQNunxkPUrcY+af3p9OgE5n8LkSNIpZMjKixDRWGaoZW/3TxV/kjg0VMGBwyMN\nbW0lhp6O1n8SmSdkPP2Sto/7DETaeIm/YYLC6QLJb0C8qeDmSDVNJrIiw18Qz5vShRObduV72kuS\nJPnk1cxspa3ke9Rk4yjfF0a3R000GTuMBUWsQ69HtjY/P3zeErxsxczQPfmT+7fgx2u2KBn3Jgs4\n20bUN959cAi3PbUrtMh+crsndJCsPL8keieDioD1umBZ6rU+GKC0y1kPFS3m9YqLpYZafi01v2j3\nZT9dF2pjDPjJB1+MFx89reH4bU/vwqf/5xE054qP1A7omlSFC8xRfSOyDZPsuss8alE5al5h0nwM\nCtVvUQ93EXwWE4eg8RjnyQtROWpVnv/8KdkGSSy4eDfaYUxYzN73YmbpkVouOThhXjgs8Y71nQC8\ncFmnxbupyPLpUSGG1/x5I751x0bh+0oOw9QEyzbUU+cae2aqGHJwfg03ZZHHF6Ceo6Zrw6po2GWo\nBTlqKXckQY6d2Ysr3nYy+o80etP2Dgzjm7c9KxQoeGzbAezsH8K7zlwQWhz3tJdw2lFTEuuvrpAd\nIn1kCk3BBklT7auxPGp5eTgxxW3BKE+2LAStyLdRW81KEHrU3Px4ZmU4qtJoEXg5auJxFyUm0pYl\nS01CadQ4GlVFJJIie9R8RENvaMRFb0cbbv7EOaG2nvY2TOouJ9YfWcHrYFM4Q1taRRfHUA7rLvj1\niotdhpokBCtPOA7DW0+fHzq+ec8Avnnbs8J4X//IV95wAtoSsmLHWktkafIkxMhqngSGWtN9J3ug\n+sfycp+qiolEhbswifqeiUKutuKPK5GhVnWpjpoKPMKjJlR0zVE6gf8dRONIRpFDraKe2ZxzlEos\n1Tp6zb+iqXwvnUOhbqjp+8wsofrsLLoHMi5WTd9FlhIOwswE4Stp7tpQ6GN+cJh4fFUl953/oJcp\nReblNlWt2xSVSyG7xl5qUU4umCKliBy1aiFy1NTLP8iQhRw7DsQ5ajna/PQ9ajK1VRH1759Il6wm\nSjTDTXGjTaowbSj0MTifhs8ofMFrKMrz1/4W9XrFZUxDjTHWyRi7jzH2MGPsccbYlwSv6WCM/Ywx\n9ixjbA1jbFGczvjx9IU01PxFsaDNxC4Ek0i3FzlkK2/I6oVVJXXU/P8V5lzlzqMWR7mqdY+af54i\n0haRo+YWwKPmKO46RyHzzMrqqNXzvrN/jetlHtTv1bzk06oQlYslK/NgAnnoo7/OSbpn+j4/uOeK\nN7wAqAtxkUctHq141IYAnM85PxnAKQAuYIy9uOk1FwHYxzlfCuAbAL4WpzNZVKjSRX3ykoc+pvKw\nIUMtN0gLXksWc74hIn3Q5+g+VdsVlO/SyxblHMV9OJWCBXbY1VgE1UdALDATB1nh9LFDH7N/jZ1Y\nhlpxPWo+4s3f9IzXscRETKEj/77oOZCeR039fszT2sEEY+aocW80H6r9b7n2X/Mv8wYAX6z9+xcA\nvskYY1zxTsjT7p8q0flAZvJbon4surGyj0yR0A9jbP6N66qPstCZJHppHlWth/oufbjNYQxVhA0S\n7/NzcsEU8TfeZKqPua+jpuixjYJzsTT9WGIieXimxvGoFVrsIfIrpx+6LrslbPypjgxXce+mviCU\n1ufZXd7SOO1rmRaqOWp1MZFEupNbWhITYYyVADwAYCmA/+Scr2l6yTwAWwGAc15hjB0AMA3AHtln\nVl2OfQPDDccO1JQQ8/7gFuEvkoViIpJdVBME4QgpnZ/Qhyy4tuJyoSpcMCaFIbE5EsdQlE/3H9bC\nXCGJMVxk17TjMJQchmv+tAnX3b+1oW3PwDBOFEix5wmdjzM3wqMmMtQCj1oObtZ6mQdBEqiEIucQ\nBblgwtzFFK/JqA3A0UZ3xVA+ZVTZAhk/WvMc/uHGJ6XtEzuTU8m0GdUN/Hppm+Ldj+OhJUONc14F\ncApjbDKAXzHGTuCcP6Z6MsbYJQAuAYD22Utx6lduEb6uo80qjRMjOPI5FRzJhwcxSVao6QRfIjna\nSgy3Pb0bJ3zh5objQ5UqyhJZOG+nPnw8TyFrnkdN3ZAS56iJPRucF/se+vQFy7F+50Fh28tWzDLc\nG7MwyPMWVZE9CxyHYf/ACL5601MNx5/dVStgnIPNT9/YVLDTIr3feSfKIEmzvEq5tin4+d88js//\n5vFQe3OutA0cHKwAAH7zkZeGrlt3exuWzpyQQq/SRzVaICptgJCjJM/POd/PGLsNwAUARhtqzwNY\nAGAbY6wNwCQAfYL3Xw3gagBYvPxE/oXXHRc6R29nGStmT1TpVi7wdxjkxYUNd6hGcf0A+eMTrzgW\nf35G7OReNrtXeNyRKNalqRqmG9WvEcTZCz+LCl6L+OA5R6fdhdRwHH35N7JnwYo5E/Hbh1/Ad+/a\nFGqbM6kT03s79HQgRdpK5FGLgzjHOL1rMqWnHf/8lpOwoz9cM7bkMLz+lLmJnj/41ooiGA4DTl4w\nOYkuZRZV1ccib5yMhzENNcbYDAAjNSOtC8ArEBYLuR7A+wHcA+CtAG4dKz9t2oQOfOCli+P1OodE\nSemakPYe6/Ppvso+5y2bifOWzVR6jyyUL0+hjwzA4EgV2/YdDrXN7O1Ee5OHP0ggFzghZQWvAXo4\nFRWtHjWJCMRFZy/GRWfn+3nqGxbx6qgl0iWrifrKadd1fPsZC9I7eQwqBVCnjYOq6mNQ2oYehkq0\n4lGbA+DaWp6aA+A6zvkNjLEvA1jLOb8ewHcA/IAx9iyAvQDemViPc0pduCHcZmpSFd1vdGMVnIhQ\nvrzsUne0lXDPxj6c/bXbQm0vXzEL17x/ZcOxqCK6slwhzjnF5RcVpi8ygacoq542gZiIplIaeSc6\nx7iY1wSI1gOQ4br5CfXXiarqY8GrGcSmFdXHRwCcKjj++VH/HgTwNr1dKxZRib8m8ltkn8/HaCfy\njUxuPk8Fr7/yxhPw4JZ9oePf+dMm7D4YDs+Jqs0kLXgNuoeKisP0WWpFXmD7Ho2rbt+AaRMaQznb\nHIb3vWQR5k3uajhe5LpN/le2LUcti1RdbmXuXNqoqj4WWoV1HCjlqBHJEYiJCNpM7cZTwWuiGUdS\nCD3NOjy6WTpzgjAZ/KZHt2P3oaHQ8cDLLPgsJlGQpPuouDCIvdJxKPIC++gZPZjR24E/PLmr4Tjn\nHAPDVUyb0I5LzlnS0Obn9OVlroqDbTlqaRPnW1clZTEIRcXkAm+cjAcy1CzBf5A01+kA/F3UhM8/\nznYin3iLzPDxIiwYS46DqsQ7BshCHyPKGWjuH5ENNDrUwFHcBfbSmb24/7MvDx0frrg49nM3Ybgi\nql9Y3JC1qK+dpxzjuCjVzqQcNSGqqo8k7hOP4ungW0p0mIKZHUGhJ8A/SjdWIZHnXOV/si054o2T\n+sMm/B7p9UKxd/WLjKOYcB+FLs9cnijX1CBHquJ7tajraz8KR+bhz/v8LSPO165ynotahLrxStu0\n/npKpYkHedQswZ80ZXXUUhvYlPxZaGQx6EVYAJUcJhQu8HPQRPekV0ct4Y4RmYIB2Hd4GFfc/HSo\n7fi5E/HqE+e0/mFcrDZaZBjzCqqLZPvzFKKtTJD2LjZgC3pVAr5312bc/PiOhmMdbSVc9vJjML0p\nB7LqgkIfBcjWB32HhvDjNVsw0vQw3DcwXHsfXUsVyFCzhUD1USbPn/DpJTccrTmLjeOIc9SKEILl\nMBbpURM9bJjEo4aCF7wuMstm9+I3D7+A/7pjQ8PxqssxraddyVBzST1USJvDUCGPWsu4BQ4JnT+l\nG8tm9WLD7kPYsPtQcLzicuw+OISVi6bgDafMa3iP65JHTQSDuM7qbx9+Af9yy3rhezraHCyc2p10\n13IFGWqWMHY8ebqTBM1RxSQ6Ry3fg0LmUfORyfPLNjxogV1MLj13CS49d0no+Bevfxy/fHCb0md5\nGySaOpYjyiVHGPpIIX5iTChJ28rUnnbc/IlzQsef338EL/3qrRgaCXtmqY6aGNkG//4jIwCADf/v\nQrpuGiBDzRKiQx8NiIkw8c5Ive4F3WxFRJRzxTk34uVNmxJjwuK6gUdN8J4oMRGCGI2qtDVQ8FC+\nCNpKktDHAte/CvLeSfWxJdpLXkzxkEBByuWcQo4FMAB9A8N4qKm8zcbdA+jtaCMjTRNkqFmCP5xF\nYVNpei9UihkS+UMkN1+UWiiOIwt99NsF72FMUrS+uDvYhBiZ8AwAXPTf92PNpr2h40dGqlg0jcKG\nmmlzxB41t8CeIx/xBiypPjbT3uZN6CL1UK+OGllqzfR0tOHWp3bh1qd2hdqOntGTQo/yCRlqluBP\nmjKFpsRz1BC9u0uTejEReYiiVA/zRImJQx+DOmrCHLX08kyJbBEVWvvgln1YMLUbL1kyLdR25uKp\nSXctc5RLDBWZJ6SgDy8WEaVT5Osio6NmqA1VqqG2KuU6CvnO+8/Ahj2HhG1LpodrkxLxIEPNEsYK\nfUzNo0aqj4WGsbrKoU/do5TvUeE4TFhHzY24J6Kk2GldRIwmSiG06nK8aPFU/P1rjzPbqYzihT6K\nN1VyPk1JifralOsYxg99FHnUqI6amIXTurGQPPyJQ4aaZYh34w2EKUg+n+peFBthjhp8j1IaPTJH\nyZHfj4A49NO1gpIzAAAL2klEQVQTXxGpZOZffIVQwzPqxZZakcUe4tDRVsJv1j2Pmx7b3nB8pMox\npbucUq/SJSpKh3IdwzgOQ7nE8MyuQ7jt6cZQvp39g+SBJFKDDDVLiJoETIVNicMuKUetyDiM4cZH\nt+PuDX3CtjxTYuJwKv+OkKk+Uugj0QoysRqACuyq8ukLluP+zeGcPgA4Yd4kw72xC2GUDuWoCZna\n044bH9mOGx/ZHmp7EYUcEylBhpol+JOmSLzAhMTwWKqOpPpYTP76/KW4f/O+0PE2h+GVx81KoUfm\ncByxMIh/j4puSccB+o9U8NWbnmo4LgrLIoqNM0boI4Vatc7Lj5uFl+d8PlIl6plNOWpirv/o2Xhh\n/xFh2+LpJI5BpAMZapYQSOkK2lxTu1/CnbfaP2hOLyTvPHMh3nnmwrS7kQpyeX7vr+ieXDFnIn75\n4PP47l2bGo6XSwzLZvcm0U0io9TFHsJhsZ4cOE26RHzqoY+yzV/DHcoAsyZ2YtbEzrS7QRANkKFm\nCWPXUUvYozbGx9OcThQNmSqfv/AR3ZPvO2sR3nfWoqS7RuQA32PmcqDUNJS8YzTrEuNHpvpIOWoE\nkQ3IULOEIPRRUkfNBFEFrwmiaJQchuGKi7f8190Nx/uPjACgzQtifPgeDVGYY9UtrlohkTwu5cwS\nRGYgQ80SgjAYUaMFCmC0+0YUjZetmIlHnz8Q2qzoKpewZMYELJ89MZ2OEbnACTxqTXUKa7G1FPpI\njIeoRzbnHCUq4EwQmYAMNYtgkqrTRkIfxaeuS7EnenaCsI/Tj5qKH1z0orS7QeQUWbi7H25LoY/E\nePDH15W3PYtr797c0Lbv8DAVTieIjECGmkV4NZjCx42JiQjgEcIJBEEQRDyC0Mdmjxonjxoxfsol\nB5+5cDk27RkQtr/q+NmGe0QQRBzIULMIxpg0TyzxetdMUkct4fMSBEEUEd/jEQ59bGwniLhccs6S\ntLtAEMQ4oSBli3BYeqqP/7+9e4uxq6rjOP79004RuUmlAUMZWrUijTEB6wgCcklMEIloYgwaFXmp\nUaMQaQzy4JsvosQYvISIBpTEmEBQiIqAPGAChGlFSTshEoKKFioQWyoELPx9OHsunZ49c067bzPn\n+0kmc3r2nnXWnvy7z/zOXmvtxfg3gyRVZ2bo47x7qs8MffTdWZJGnm8FHRKU3GC3gUtqQZD95sdl\nzmyXJFWjbOjj9L372v5wTpLUPoNal0T/JfJpcSndmftd+zeDJFVmxSKrPs5fsl+SNHoMah1yWNB3\nUliSrd3w2vuoSVL1omyOWnpFTZLUY1DrkN7Qxz43vH69mStaZjJJasbMYiIlc9Rc9VGSZFDrkPLF\nRBq4ola6pZij5qe7klSZ6cVCylZ99D5qkiSX5++QiOCp5//L3duf2e/5Z/a80sjr9w2JXmaTpMpN\nf/j1+Z9t5fCVs5+ZvvpaL6m56qMkyaDWIcceMca9U7u4d2rXAdvet351vS++yKe3frYrSdWZWLea\nC05dMxPMph0+dhgXnLqGifVvbqlnkqSuMKh1yJ1fPoedu1/uu2189Rsb7k2Pqz5KUvXWHX8kP71i\nou1uSJI6zKDWIauPXMXqI1e13Y39TA999D5qkiRJUnMcBS9gdmjj/Jte972vmyRJkqRaGdQ0EIc+\nSpIkSc0xqAmYDWLzV3mcHfooSZIkqSkGNS1oJqiZ1CRJkqTGGNQEzC4WMn9GmnPUJEmSpOYZ1DQg\nL6lJkiRJTTGoCSgf2ujQR0mSJKl5BjXtZ/7y/NPMaZIkSVJzDGoCyoNYSW6TJEmSVCODmvZTlsvC\nsY+SJElSYwxqAhaYo1ZEN2OaJEmS1ByDmvZTdsNrSZIkSc0xqGlB0znNkY+SJElScxYNahFxckTc\nHxE7ImJ7RFzZZ5/zI2J3RDxafH2jnu6qLtNz0MpucG1QkyRJkpqzcoB99gFXZ+a2iDga2BoR92Tm\njnn7PZCZl1TfRbVpern+cJaaJEmS1JhFg1pm7gR2Fo9fjIgp4CRgflDTMnDf1C7GVsxeaH3y33tb\n7I0kSZI0mga5ojYjItYBpwMP99l8VkT8GfgXsCUztx9y79SYY48YA+CLt27ru/2YYrskSZKk+g0c\n1CLiKOA24KrM3DNv8zbglMzcGxEXA3cAG/q0sRnYDDA+Pn7QnVb1PjUxzntOOY7XXj9wjtoRq1bw\ntjVHtdArSZIkaTRFDrD+ekSMAXcBd2fm9QPs/xSwKTOfK9tn06ZNOTk5OURXJUmSJGn5iIitmbmp\n37ZBVn0M4CZgqiykRcSJxX5ExETR7vMH32VJkiRJGl2DDH08G/gM8FhEPFo8dy0wDpCZPwI+Dnwh\nIvYBLwOX5SCX6iRJkiRJBxhk1cc/wsJrs2fmDcANVXVKkiRJkkbZokMfJUmSJEnNMqhJkiRJUscY\n1CRJkiSpYwxqkiRJktQxBjVJkiRJ6hiDmiRJkiR1jEFNkiRJkjom2rovdUS8CDzeyosvTccDz7Xd\niSXkWGB3251YQqyv4Vljw7HGhmN9Dcf6Go71NRzrazjW13BOzcyj+21Y9IbXNXo8Mze1+PpLSkRM\n+vsaXETcmJmb2+7HUmF9Dc8aG441NhzrazjW13Csr+FYX8OxvoYTEZNl2xz6qOXqzrY7oGXPGlOd\nrC/VyfpSnayvihjUtCxlpicJ1coaU52sL9XJ+lKdrK/qtBnUbmzxtZcif1+qk/WlulljqpP1pTpZ\nX6pTaX21tpiIJEmSJKk/hz5KkiRJUscY1FoSESdHxP0RsSMitkfElcXzqyPinoj4a/H9uOL5iIjv\nRcQTEfGXiDhjTlvjEfH7iJgq2lvXzlGpSyqusW8VbUwV+0Rbx6VuOIj6emdEPBgRr0TElnltXRQR\njxe1d00bx6Nuqaq+ytrRaKvy/FVsXxERf4qIu5o+Fi1vBrX27AOuzsyNwJnAlyJiI3ANcF9mbgDu\nK/4N8CFgQ/G1GfjhnLZuAa7LzNOACWBXM4egjqukxiLi/cDZwLuBdwHvBc5r8DjUTcPW1wvAV4Bv\nz20kIlYA36dXfxuBTxbtaLRVUl8LtKPRVlV9TbsSmKq3yxpFBrWWZObOzNxWPH6R3n/wk4BLgZuL\n3W4GPlo8vhS4JXseAt4UEW8pTiwrM/Oeoq29mflSk8eibqqqxoAE3gCsAg4HxoBnGzsQddKw9ZWZ\nuzLzEeB/85qaAJ7IzCcz81XgF0UbGmFV1dcC7WiEVXj+IiLWAh8GftxA1zViDGodUAxVPB14GDgh\nM3cWm54BTigenwT8Y86PPV089w7gPxFxe3HZ/briE2ppxqHUWGY+CNwP7Cy+7s5MPznUjAHrq0zZ\nuU0CDrm+ytqRgErq67vA14DX6+ifRptBrWURcRRwG3BVZu6Zuy17S3IutiznSuBcYAu9IWlvBT5X\nfU+1VB1qjUXE24HTgLX0/oC+MCLOram7WmIqOIdJpaqqr4Xa0eiq4P3xEmBXZm6tr5caZQa1FkXE\nGL0TxK2ZeXvx9LPFcDOK79Pzzf4JnDznx9cWzz0NPFoMG9oH3AGcgURlNfYx4KFiWO1e4LfAWU30\nX902ZH2VKas7jbiK6qusHY24iurrbOAjEfEUvWHbF0bEz2vqskaQQa0lxap5NwFTmXn9nE2/Bi4v\nHl8O/GrO858tVuY7E9hdXJ5/hN5cojXFfhcCO2o/AHVehTX2d+C8iFhZvLGdh5OmR95B1FeZR4AN\nEbE+IlYBlxVtaIRVVV8LtKMRVlV9ZebXM3NtZq6jd+76Q2Z+uoYua0R5w+uWRMQ5wAPAY8yOa76W\n3hjpXwLjwN+AT2TmC8VJ5QbgIuAl4IrMnCza+iDwHSCArcDmYlK+RlhVNVbMefwB8AF6w0B+l5lf\nbfRg1DkHUV8nApPAMcX+e4GNmbknIi6mN89jBfCTzPxmowejzqmqvuitVntAO5n5m4YORR1U5flr\nTpvnA1sy85KmjkPLn0FNkiRJkjrGoY+SJEmS1DEGNUmSJEnqGIOaJEmSJHWMQU2SJEmSOsagJkmS\nJEkdY1CTJEmSpI4xqEmSJElSxxjUJEmSJKlj/g9V5XGtybzF2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3M3SYbcy2H4",
        "colab_type": "text"
      },
      "source": [
        "Dataframe `df_pet` setelah dilakukan _upsampling_ menjadi $4017$ data observasi dengan frekuensi harian `D`. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnTdHDCRzFYj",
        "colab_type": "text"
      },
      "source": [
        "### Menggunakan interpolasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edNAZzRMneEa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "0d5a14c1-def0-4acf-c25c-c4042c5a8859"
      },
      "source": [
        "df_pet_inter = upsampling(df_pet, use_inter=True, inter_method='linear')\n",
        "df_pet_inter.info()\n",
        "df_pet_inter.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 4017 entries, 2005-01-01 to 2015-12-31\n",
            "Freq: D\n",
            "Data columns (total 1 columns):\n",
            "PET    4017 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 62.8 KB\n"
          ],
          "name": "stdout"
        },
        {
          "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>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-02</th>\n",
              "      <td>2.994014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-03</th>\n",
              "      <td>2.999058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-04</th>\n",
              "      <td>3.004102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-05</th>\n",
              "      <td>3.009146</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 PET\n",
              "2005-01-01  2.988970\n",
              "2005-01-02  2.994014\n",
              "2005-01-03  2.999058\n",
              "2005-01-04  3.004102\n",
              "2005-01-05  3.009146"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4g-q5kU7zVdO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "692d07ee-858a-40c4-823d-d43b6fff7eb3"
      },
      "source": [
        "df_pet_inter.plot();"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEvCAYAAAA0ITL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUsd8AlGfiNOjt30WAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGmH6kzxzkFp",
        "colab_type": "text"
      },
      "source": [
        "Perbedaan dengan _forward fill_ dapat dilihat dari grafiknya."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxEkf-A_zoZB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "9d7b4fd9-7cce-4e74-d817-5cbd852ffaca"
      },
      "source": [
        "df_pet_quad = upsampling(df_pet, use_inter=True, inter_method='quadratic')\n",
        "df_pet_quad.info()\n",
        "df_pet_quad.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 4017 entries, 2005-01-01 to 2015-12-31\n",
            "Freq: D\n",
            "Data columns (total 1 columns):\n",
            "PET    3987 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 62.8 KB\n"
          ],
          "name": "stdout"
        },
        {
          "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>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-02</th>\n",
              "      <td>2.996154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-03</th>\n",
              "      <td>3.003196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-04</th>\n",
              "      <td>3.010095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-05</th>\n",
              "      <td>3.016851</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 PET\n",
              "2005-01-01  2.988970\n",
              "2005-01-02  2.996154\n",
              "2005-01-03  3.003196\n",
              "2005-01-04  3.010095\n",
              "2005-01-05  3.016851"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91R-aRuEz9Wx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "b45e4615-edef-42d6-e8c3-0489c2d7cf2c"
      },
      "source": [
        "df_pet_quad.plot();"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEvCAYAAAA0ITL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNFZoGFkz9NB",
        "colab_type": "text"
      },
      "source": [
        "Jika menggunakan metode `quadratic`, grafik menjadi lebih halus."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJcTuPM60HxJ",
        "colab_type": "text"
      },
      "source": [
        "### Melakukan _reindex_ saja\n",
        "\n",
        "Argumen ini disediakan agar memberi kebebasan jika tidak ingin menggunakan metode interpolasi/isian."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0umWFuZn8Ei",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "62da127b-a50c-4057-ee6d-35a58bef4164"
      },
      "source": [
        "df_pet_re = upsampling(df_pet, reindex=True)\n",
        "df_pet_re.info()\n",
        "df_pet_re.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 4017 entries, 2005-01-01 to 2015-12-31\n",
            "Freq: D\n",
            "Data columns (total 1 columns):\n",
            "PET    132 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 62.8 KB\n"
          ],
          "name": "stdout"
        },
        {
          "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>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>2.98897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-02</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-03</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-04</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-05</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                PET\n",
              "2005-01-01  2.98897\n",
              "2005-01-02      NaN\n",
              "2005-01-03      NaN\n",
              "2005-01-04      NaN\n",
              "2005-01-05      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNQaT2jU0lLE",
        "colab_type": "text"
      },
      "source": [
        "## Menggabungkan dengan modul `taruma.hk98` (rekap data harian)\n",
        "\n",
        "Modul ini bisa digabungkan dengan modul `taruma.hk98` yang digunakan untuk merekap data harian.\n",
        "\n",
        "Karena `df_pet` sudah diubah menjadi data harian (`df_pet_ffill`), maka bisa digabungkan dengan `df_sta`. Dataframe `dataset` dimulai dari tahun 2005 karena `df_pet` hanya dimulai dari tahun 2005.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9GPGfaR0tvB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "93cab4f9-876d-4e00-95d2-69dc33d3c877"
      },
      "source": [
        "dataset = pd.concat([df_sta, df_pet_ffill], axis=1).loc['20050101':]\n",
        "dataset"
      ],
      "execution_count": 17,
      "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>STA_B</th>\n",
              "      <th>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>14</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-02</th>\n",
              "      <td>0</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-03</th>\n",
              "      <td>7</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-04</th>\n",
              "      <td>37</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-05</th>\n",
              "      <td>0</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-27</th>\n",
              "      <td>6</td>\n",
              "      <td>3.133179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-28</th>\n",
              "      <td>32</td>\n",
              "      <td>3.133179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-29</th>\n",
              "      <td>0</td>\n",
              "      <td>3.133179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-30</th>\n",
              "      <td>0</td>\n",
              "      <td>3.133179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-31</th>\n",
              "      <td>0</td>\n",
              "      <td>3.133179</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4017 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           STA_B       PET\n",
              "2005-01-01    14  2.988970\n",
              "2005-01-02     0  2.988970\n",
              "2005-01-03     7  2.988970\n",
              "2005-01-04    37  2.988970\n",
              "2005-01-05     0  2.988970\n",
              "...          ...       ...\n",
              "2015-12-27     6  3.133179\n",
              "2015-12-28    32  3.133179\n",
              "2015-12-29     0  3.133179\n",
              "2015-12-30     0  3.133179\n",
              "2015-12-31     0  3.133179\n",
              "\n",
              "[4017 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo4TG8Sz17YU",
        "colab_type": "text"
      },
      "source": [
        "Setelah mempersiapkan `dataset`, dapat dilakukan rekap menggunakan modul `taruma.hk98`. Pada data curah hujan, akan dilakukan penjumlahan curah hujan, jumlah hari hujan, jumlah hari periode. Sedangkan pada data PET, hanya merekap dengan merata-ratakan nilainya setiap periode. Periode yang akan digunakan adalah per 11 hari (3 periode)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNmleXTr1WOE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "b443d1c6-9896-42ed-a2d1-478a6707a971"
      },
      "source": [
        "from hidrokit.contrib.taruma import hk98\n",
        "\n",
        "NDAYS = '11D'\n",
        "n_rain = lambda x: (x>0).sum()\n",
        "ufunc, ufunc_col = [np.sum, n_rain, len], ['SUM', 'NRAIN', 'NDAYS']\n",
        "\n",
        "dataset_pet = hk98.summary_station(dataset, 'PET', np.mean, 'PET', \n",
        "                                   n_days=NDAYS)\n",
        "dataset_prep = hk98.summary_station(dataset, 'STA_B', ufunc, ufunc_col, \n",
        "                                    n_days=NDAYS)\n",
        "\n",
        "data_input = pd.concat([dataset_prep, dataset_pet], axis=1)\n",
        "data_input.columns = data_input.columns.droplevel(0)\n",
        "data_input.head(10)"
      ],
      "execution_count": 18,
      "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>SUM</th>\n",
              "      <th>NRAIN</th>\n",
              "      <th>NDAYS</th>\n",
              "      <th>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-01-01</th>\n",
              "      <td>102.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-01-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.988970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-02-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.145340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-02-12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.145340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-02-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.145340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-03-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.213324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-03-12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.213324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-03-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>3.213324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-04-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.474628</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              SUM  NRAIN  NDAYS       PET\n",
              "2005-01-01  102.0    6.0   11.0  2.988970\n",
              "2005-01-12    0.0    0.0   11.0  2.988970\n",
              "2005-01-23    0.0    0.0    9.0  2.988970\n",
              "2005-02-01    0.0    0.0   11.0  3.145340\n",
              "2005-02-12    0.0    0.0   11.0  3.145340\n",
              "2005-02-23    0.0    0.0    6.0  3.145340\n",
              "2005-03-01    0.0    0.0   11.0  3.213324\n",
              "2005-03-12    0.0    0.0   11.0  3.213324\n",
              "2005-03-23    0.0    0.0    9.0  3.213324\n",
              "2005-04-01    0.0    0.0   11.0  3.474628"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_UHNrpF1358",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "1244f47f-9dbd-4855-be56-42bf5f99b1d1"
      },
      "source": [
        "data_input.sample(10)"
      ],
      "execution_count": 19,
      "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>SUM</th>\n",
              "      <th>NRAIN</th>\n",
              "      <th>NDAYS</th>\n",
              "      <th>PET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-07-12</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.601199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-12-12</th>\n",
              "      <td>190.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.987743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-06-12</th>\n",
              "      <td>72.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.243436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-12-01</th>\n",
              "      <td>86.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.987743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-03-01</th>\n",
              "      <td>113.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.463320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-07-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.970709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-03-12</th>\n",
              "      <td>245.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.386749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-11-23</th>\n",
              "      <td>69.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.908408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>3.240201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-10-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.060168</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              SUM  NRAIN  NDAYS       PET\n",
              "2014-07-12   32.0    4.0   11.0  3.601199\n",
              "2007-12-12  190.0    7.0   11.0  2.987743\n",
              "2010-06-12   72.0    2.0   11.0  3.243436\n",
              "2007-12-01   86.0    5.0   11.0  2.987743\n",
              "2013-03-01  113.0    4.0   11.0  3.463320\n",
              "2007-07-01    0.0    0.0   11.0  3.970709\n",
              "2007-03-12  245.0    4.0   11.0  3.386749\n",
              "2013-11-23   69.0    2.0    8.0  2.908408\n",
              "2010-07-23    0.0    0.0    9.0  3.240201\n",
              "2007-10-01    0.0    0.0   11.0  4.060168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DuXy3-Wu5Moc",
        "colab_type": "text"
      },
      "source": [
        "# Changelog\n",
        "\n",
        "```\n",
        "- 20191219 - 1.1.0 - Initial\n",
        "```\n",
        "\n",
        "#### Copyright &copy; 2019 [Taruma Sakti Megariansyah](https://taruma.github.io)\n",
        "\n",
        "Source code in this notebook is licensed under a [MIT License](https://choosealicense.com/licenses/mit/). Data in this notebook is licensed under a [Creative Common Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). \n"
      ]
    }
  ]
}