AOSC650RNN.ipynb

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        "<a href=\"https://colab.research.google.com/github/BrendanGramp/UMDAOSC650/blob/main/AOSC650RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# LSTM\n",
        "\n",
        "Data being read includes precipitation, gage height, river discharge, and longitude/latitude\n"
      ],
      "metadata": {
        "id": "2wbxelrgNBGe"
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Dropout, Activation, TimeDistributed, RepeatVector\n",
        "import keras\n",
        "from keras.src.preprocessing.sequence import TimeseriesGenerator\n",
        "#from google.colab import drive\n",
        "#drive.mount('/Colab_Notebooks')"
      ]
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        "pwd"
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      "source": [
        "# reading dataset from NOAA online\n",
        "dataset = pd.read_csv(\n",
        "    '01302020PowerPrecipitation2010-2023.csv',\n",
        "    delimiter=',')\n",
        "datasettwo = pd.read_csv(\n",
        "    '01302020Discharge2010-2023.csv',\n",
        "    delimiter=',')\n",
        "datasetthree = pd.read_csv(\n",
        "    '01302020GageHeight2010-2023.csv',\n",
        "    delimiter=',')"
      ],
      "metadata": {
        "id": "iKKGgzDJmBMD"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data features include seasons, year, total SST (C), and ONI (ENSO index)\n",
        "dataset.head()"
      ],
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      "execution_count": 84,
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              "   YEAR  MO  DY  HR  PRECTOTCORR  Time2\n",
              "0  2009  12  31  19         0.14      0\n",
              "1  2009  12  31  20         0.28      4\n",
              "2  2009  12  31  21         0.33      8\n",
              "3  2009  12  31  22         0.28     12\n",
              "4  2009  12  31  23         0.22     16"
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              "  agency_cd  site_no       datetime tz_cd  106410_00060 106410_00060_cd  \\\n",
              "0      USGS  1302020  1/1/2010 0:00   EST          94.9               A   \n",
              "1      USGS  1302020  1/1/2010 0:15   EST          94.9               A   \n",
              "2      USGS  1302020  1/1/2010 0:30   EST          94.9               A   \n",
              "3      USGS  1302020  1/1/2010 0:45   EST          94.9               A   \n",
              "4      USGS  1302020  1/1/2010 1:00   EST          94.9               A   \n",
              "\n",
              "           Time  Time2  \n",
              "0  40179.000000    0.0  \n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d0dea0a-9ae7-4d7e-ba24-e77c88110d93 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d0dea0a-9ae7-4d7e-ba24-e77c88110d93');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0ba48d21-1c15-4d3f-986a-70d5420e853e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ba48d21-1c15-4d3f-986a-70d5420e853e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ba48d21-1c15-4d3f-986a-70d5420e853e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "datasettwo"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasetthree.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7loWSTi9e60f",
        "outputId": "c1e0cad3-1164-4aab-8bad-5af4c3189be1"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  agency_cd  site_no       datetime tz_cd  106409_00065 106409_00065_cd  \\\n",
              "0      USGS  1302020  1/1/2010 0:00   EST          0.97               A   \n",
              "1      USGS  1302020  1/1/2010 0:15   EST          0.97               A   \n",
              "2      USGS  1302020  1/1/2010 0:30   EST          0.97               A   \n",
              "3      USGS  1302020  1/1/2010 0:45   EST          0.97               A   \n",
              "4      USGS  1302020  1/1/2010 1:00   EST          0.97               A   \n",
              "\n",
              "           Time  Time2  \n",
              "0  40179.000000    0.0  \n",
              "1  40179.010416    1.0  \n",
              "2  40179.020833    2.0  \n",
              "3  40179.031250    3.0  \n",
              "4  40179.041666    4.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f501ad4-edbd-4676-adcc-f258943a1cfd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>agency_cd</th>\n",
              "      <th>site_no</th>\n",
              "      <th>datetime</th>\n",
              "      <th>tz_cd</th>\n",
              "      <th>106409_00065</th>\n",
              "      <th>106409_00065_cd</th>\n",
              "      <th>Time</th>\n",
              "      <th>Time2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USGS</td>\n",
              "      <td>1302020</td>\n",
              "      <td>1/1/2010 0:00</td>\n",
              "      <td>EST</td>\n",
              "      <td>0.97</td>\n",
              "      <td>A</td>\n",
              "      <td>40179.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USGS</td>\n",
              "      <td>1302020</td>\n",
              "      <td>1/1/2010 0:15</td>\n",
              "      <td>EST</td>\n",
              "      <td>0.97</td>\n",
              "      <td>A</td>\n",
              "      <td>40179.010416</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>USGS</td>\n",
              "      <td>1302020</td>\n",
              "      <td>1/1/2010 0:30</td>\n",
              "      <td>EST</td>\n",
              "      <td>0.97</td>\n",
              "      <td>A</td>\n",
              "      <td>40179.020833</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USGS</td>\n",
              "      <td>1302020</td>\n",
              "      <td>1/1/2010 0:45</td>\n",
              "      <td>EST</td>\n",
              "      <td>0.97</td>\n",
              "      <td>A</td>\n",
              "      <td>40179.031250</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>USGS</td>\n",
              "      <td>1302020</td>\n",
              "      <td>1/1/2010 1:00</td>\n",
              "      <td>EST</td>\n",
              "      <td>0.97</td>\n",
              "      <td>A</td>\n",
              "      <td>40179.041666</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f501ad4-edbd-4676-adcc-f258943a1cfd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f501ad4-edbd-4676-adcc-f258943a1cfd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f501ad4-edbd-4676-adcc-f258943a1cfd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cb665c50-c075-4c9a-b64d-a8b9469547a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb665c50-c075-4c9a-b64d-a8b9469547a0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cb665c50-c075-4c9a-b64d-a8b9469547a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "datasetthree"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ep-nRwNHrnsk",
        "outputId": "da26bab2-930c-4c58-8415-f46faadc16ec"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        YEAR  MO  DY  HR  PRECTOTCORR   Time2\n",
              "122707  2023  12  31  14          0.0  490828\n",
              "122708  2023  12  31  15          0.0  490832\n",
              "122709  2023  12  31  16          0.0  490836\n",
              "122710  2023  12  31  17          0.0  490840\n",
              "122711  2023  12  31  18          0.0  490844"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YEAR</th>\n",
              "      <th>MO</th>\n",
              "      <th>DY</th>\n",
              "      <th>HR</th>\n",
              "      <th>PRECTOTCORR</th>\n",
              "      <th>Time2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>122707</th>\n",
              "      <td>2023</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122708</th>\n",
              "      <td>2023</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122709</th>\n",
              "      <td>2023</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122710</th>\n",
              "      <td>2023</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122711</th>\n",
              "      <td>2023</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490844</td>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-e3bd3b9a-3f23-4fa0-95be-7b831eccb05e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e3bd3b9a-3f23-4fa0-95be-7b831eccb05e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a1c25d66-549c-4578-9769-c14a90b907e4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"YEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 12,\n        \"max\": 12,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 31,\n        \"max\": 31,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 14,\n        \"max\": 18,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRECTOTCORR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 490828,\n        \"max\": 490844,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          490832\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasettwo[\"106410_00060\"].tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmn3qcLH8Z0t",
        "outputId": "3d6dfe0a-0966-4b65-e8a2-4bbfff72c8ba"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "489972    109.0\n",
              "489973    109.0\n",
              "489974    109.0\n",
              "489975    109.0\n",
              "489976    109.0\n",
              "Name: 106410_00060, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasetthree[\"106409_00065\"].tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_QUWZpuODzC",
        "outputId": "015737fa-063f-4388-b3fb-d3e27818dba8"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "490179    0.93\n",
              "490180    0.93\n",
              "490181    0.93\n",
              "490182    0.93\n",
              "490183    0.93\n",
              "Name: 106409_00065, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop any nans\n",
        "dataset = dataset.dropna(subset=[\"PRECTOTCORR\"])"
      ],
      "metadata": {
        "id": "5RcTG8sEqmbb"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# quick stats\n",
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EipN-1z-vGAZ",
        "outputId": "e59df08f-c85d-41d0-eeba-c7dd55f1bb3b"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                YEAR             MO            DY             HR  \\\n",
              "count  122712.000000  122712.000000  122712.00000  122712.000000   \n",
              "mean     2016.499136       6.523372      15.72834      11.500000   \n",
              "std         4.030828       3.448596       8.79958       6.922215   \n",
              "min      2009.000000       1.000000       1.00000       0.000000   \n",
              "25%      2013.000000       4.000000       8.00000       5.750000   \n",
              "50%      2016.000000       7.000000      16.00000      11.500000   \n",
              "75%      2020.000000      10.000000      23.00000      17.250000   \n",
              "max      2023.000000      12.000000      31.00000      23.000000   \n",
              "\n",
              "         PRECTOTCORR          Time2  \n",
              "count  122712.000000  122712.000000  \n",
              "mean        0.143851  245422.000000  \n",
              "std         0.533112  141696.189815  \n",
              "min         0.000000       0.000000  \n",
              "25%         0.000000  122711.000000  \n",
              "50%         0.000000  245422.000000  \n",
              "75%         0.030000  368133.000000  \n",
              "max        19.180000  490844.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d0c0381d-edad-4c77-bbc5-d10551bd6ab7\" class=\"colab-df-container\">\n",
              "    <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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YEAR</th>\n",
              "      <th>MO</th>\n",
              "      <th>DY</th>\n",
              "      <th>HR</th>\n",
              "      <th>PRECTOTCORR</th>\n",
              "      <th>Time2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>122712.000000</td>\n",
              "      <td>122712.000000</td>\n",
              "      <td>122712.00000</td>\n",
              "      <td>122712.000000</td>\n",
              "      <td>122712.000000</td>\n",
              "      <td>122712.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2016.499136</td>\n",
              "      <td>6.523372</td>\n",
              "      <td>15.72834</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>0.143851</td>\n",
              "      <td>245422.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.030828</td>\n",
              "      <td>3.448596</td>\n",
              "      <td>8.79958</td>\n",
              "      <td>6.922215</td>\n",
              "      <td>0.533112</td>\n",
              "      <td>141696.189815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2009.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.00000</td>\n",
              "      <td>5.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>122711.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>16.00000</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>245422.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2020.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>23.00000</td>\n",
              "      <td>17.250000</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>368133.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2023.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.00000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>19.180000</td>\n",
              "      <td>490844.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0c0381d-edad-4c77-bbc5-d10551bd6ab7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d0c0381d-edad-4c77-bbc5-d10551bd6ab7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d0c0381d-edad-4c77-bbc5-d10551bd6ab7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1ec3cfe6-85c4-41ea-b286-7f2989086d62\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ec3cfe6-85c4-41ea-b286-7f2989086d62')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1ec3cfe6-85c4-41ea-b286-7f2989086d62 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"YEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42779.8194769193,\n        \"min\": 4.0308278084018925,\n        \"max\": 122712.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2016.4991361887999,\n          2016.0,\n          122712.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43383.02289199855,\n        \"min\": 1.0,\n        \"max\": 122712.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.523371797379229,\n          7.0,\n          122712.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43380.015712564535,\n        \"min\": 1.0,\n        \"max\": 122712.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.728339526696656,\n          16.0,\n          122712.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43381.40958951836,\n        \"min\": 0.0,\n        \"max\": 122712.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          122712.0,\n          11.5,\n          17.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRECTOTCORR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43384.23973636147,\n        \"min\": 0.0,\n        \"max\": 122712.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          122712.0,\n          0.14385072364560922,\n          19.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 155833.03152129325,\n        \"min\": 0.0,\n        \"max\": 490844.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          122712.0,\n          245422.0,\n          368133.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasettwo.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "jLJIhKXnfn5y",
        "outputId": "c4c413f7-11e4-4bfb-969a-77f8a42ca092"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         site_no  106410_00060           Time          Time2\n",
              "count   489977.0  489977.00000  489977.000000  489977.000000\n",
              "mean   1302020.0      76.70612   42732.821357  245166.850337\n",
              "std          0.0     128.36378    1475.420189  141640.338186\n",
              "min    1302020.0       7.10000   40179.000000       0.000000\n",
              "25%    1302020.0      28.90000   41455.479166  122542.000000\n",
              "50%    1302020.0      45.80000   42731.750000  245064.000000\n",
              "75%    1302020.0      76.60000   44008.822916  367663.000000\n",
              "max    1302020.0    3000.00000   45291.989583  490847.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d688b77c-d4f9-4cf1-b86e-cfad4a50114c\" class=\"colab-df-container\">\n",
              "    <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>site_no</th>\n",
              "      <th>106410_00060</th>\n",
              "      <th>Time</th>\n",
              "      <th>Time2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>489977.0</td>\n",
              "      <td>489977.00000</td>\n",
              "      <td>489977.000000</td>\n",
              "      <td>489977.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1302020.0</td>\n",
              "      <td>76.70612</td>\n",
              "      <td>42732.821357</td>\n",
              "      <td>245166.850337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>128.36378</td>\n",
              "      <td>1475.420189</td>\n",
              "      <td>141640.338186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1302020.0</td>\n",
              "      <td>7.10000</td>\n",
              "      <td>40179.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1302020.0</td>\n",
              "      <td>28.90000</td>\n",
              "      <td>41455.479166</td>\n",
              "      <td>122542.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1302020.0</td>\n",
              "      <td>45.80000</td>\n",
              "      <td>42731.750000</td>\n",
              "      <td>245064.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1302020.0</td>\n",
              "      <td>76.60000</td>\n",
              "      <td>44008.822916</td>\n",
              "      <td>367663.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1302020.0</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>45291.989583</td>\n",
              "      <td>490847.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d688b77c-d4f9-4cf1-b86e-cfad4a50114c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d688b77c-d4f9-4cf1-b86e-cfad4a50114c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d688b77c-d4f9-4cf1-b86e-cfad4a50114c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cf857880-ac19-49fe-9068-e86e0de2d77f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf857880-ac19-49fe-9068-e86e0de2d77f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf857880-ac19-49fe-9068-e86e0de2d77f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"datasettwo\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"site_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 506530.51216132153,\n        \"min\": 0.0,\n        \"max\": 1302020.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          489977.0,\n          1302020.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"106410_00060\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173066.20918671534,\n        \"min\": 7.1,\n        \"max\": 489977.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          76.70611973623255,\n          45.8,\n          489977.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 160864.80738875235,\n        \"min\": 1475.4201894399837,\n        \"max\": 489977.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          42732.821357345754,\n          42731.75,\n          489977.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176737.23227731156,\n        \"min\": 0.0,\n        \"max\": 490847.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          245166.85033705665,\n          245064.0,\n          489977.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[dataset[\"MO\"]==1].plot(\"YEAR\",\"PRECTOTCORR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "wNj_dv2SiQxD",
        "outputId": "082823c3-e2e1-462b-c291-d586e15ee905"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='YEAR'>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(122712), dataset[\"PRECTOTCORR\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "_aN7-gCI_GZv",
        "outputId": "55f09e54-5324-4727-c125-1e4ba98dbeae"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bd8221f3040>]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(489977), datasettwo[\"106410_00060\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "-St3xupmYVTC",
        "outputId": "3f5eeb6f-f9af-46a6-e829-34c01c32dcc2"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bd82213f1c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(490184), datasetthree[\"106409_00065\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "uLKyqa_a84MF",
        "outputId": "f3c81482-fe6d-4208-b4e8-a1dd53891889"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bd8217bcb80>]"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# quick viz\n",
        "# djf only\n",
        "#dataset[dataset[\"SEAS\"]==\"DJF\"].plot(\"YR\",\"ANOM\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "3QEaavkavtQG",
        "outputId": "f6e0e55e-4d3c-4322-edd3-08348bf1e1dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='YR'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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BNmeKyGBvFgAVImFjvIB1Q/svycyXG+3wQk6viBgLyiRArRnCF/NCa6YbRkqjpNvMqqzIHergQuSF8bnYZHYY8yco5j3eMH9ej3FqJibvJzfw1gwpIoQX2Cx7ta7yniURDrrWTAJPNm5hZtXBngyAzgtqeuT5cbzin+/FP/0k2pFFBplVkwW7CSxkkx9oRooI4QvxTT9VpPZMmIjPdRLvetzS6a2ZrfumAQDP7J+O+Ega6cjGC1g3qG5JhrVmenMdYFal8V3CD/OCCkKTM+HSbdtRWa97sKdRiJQ6rPhihftsDJREsehgF7QqeURiDVNEeow5IkksRGhqhvCDuAFyco4KkTApddlSMuYRERWRTvIhTbNCpBR9Bo+osPXlGxe2bih2kwxrhRc6wCNS5FMz1JohPKArREgRCZVy15lVWWsmw7/WSY97ar5RgMyVoy9ExEmLQq7xfCfxgtZNGJNVkx1oRooI4ZF6XdW96SldNVz0UzPJO9m4RTOrZvnXOinUTFNEon9MrOjIphV+MSBFJN60bN9NskekSh4RwiPGCG5SRMJFH2iWvJONW3hrpkdTRJJ4krViulm4z1dqke91Yc9rLp1ClnlEuqDYTTLGZFU2+ppEJYumZgjPzJepEGkn3Zcj0ni8vbkMsulmumoCT7JWiFNmUbdn2Hsrl0kh04HPdScyb9y+m24mqyawWKccEcIz84YEP0pXDZfuS1ZlUwGpRMvOVkwLrcyoM3jKwh1pJk2tmSTAc0RyHZAjQuO7hFeMS8moEAkXfaBZ518kis0Tak8mnejRRCvEz8tMxJMzoiKSbe726Yb2X5JhikhnjO9Sa4bwSLFCrZl20m2BZiUhJyHJd3tW6BSRiA2rrMjNZTSPSLkLit0koykiDQ8V3zWTwM8Ie//1UGuGcIuxNUOFSLiIdzpdoYiIrZkOK0RqdRXTggoStSJSFqYWmEeEzKrxZt5gVtUUkeinsNxCigjhGaMiQhHv4dJ147vN1l9PNs1PUEmUnc0wFh5Rm1XLYmuGPCKxp1Kro9qctGrJEUnguYE8IoRnWEXOJEFSRMKl65JVqyZm1Q553NOGoj3qmPeSbny3oYhUyCMSW0Rzc68xWTVhxXpjzxFNzRAeYa2Z0cE8ACpEwqb7xndZ2qLgEakkT3Y2g6WqMqKOeS/rxncbz3UlYRe0boJ9NtIphReOrFivq8lqq1XrKliMDrVmCNewD8PYYE/zv+sdlXwZN/RL75JzovGCqqq61kyS92iY0aKIRF2I1LQevTY10/nFblJhanQhm4aiNF4vUU1I0udEbPFTa4ZwDbtQjAzk0fwstNzpEcHRTa0ZUf3pyaYSvUfDDOM6hKhzREpcfRIUkS5Q3bzwy8f34nVfvh87Ds1Gdgzs/dKT0xQEpogAyfqciJ91KkQI14jjYwP5xggZtWfCQ9ea6fC7VVH9aZhVO6sQiasiIppVO73Y9cqdj+7Gn1+YxIZnDkZ2DPOGhXcAkEmn0BSzEjVdJmbYMHUnKqgQSSBaxHCKr2qnyZnwKFW7pzXDjKqNHniq41ozxvC/2ZhMzeQzmlm1099jXmFFQJRFcdGQqspIomFVVOOiJvojIFzDcx4yaQw1CxFSRMKhWqvrFqN1umyuvbcapwYe1lRJzgnWjumi0awak6mZTAqZVFMR6XDVzSvsuYpSdZgzpKoykhhqFpcMEYAKkURSFNZQs0KEYt7DwagEdLpsLhpVAXScIsLCzPqad7SRt2bE8d0MKSJ2lGKgiBj3zDByCczbKQlqXNREfwSEa+aFCO7BHlJEwsSoBHR+IaK/42N3S0m607ODFexjQ42Js8jNquxikE0hmyKzqh3suYqyKJ5vtvJ6DYpIEkPNeGsm4gwRgAqRRDLP1rRnSREJG+MFuNNTL4uGk1MSe992sNYMK0Qij3hnZtV0mke8d3qx65ViHBQRk6kZIJmfE3Zu64lBayYT9QEQ7hG3Pw4VSBEJE2M+S5LueLxQNJyckniCtYOZuscGewFEH/HOFLdcJoW62ihyO73Y9QpXRCJtzTR+d8HCI5Kkz4moxkUNFSIJhF0ce3MpMquGTLcqImwbp2bC64zAPJYjsrSpiERtVtUCzVKoNqPdSRExJx6KSLM1Y1BE2MW8XEvO54THu8fAI0KFSAIRtz8O9jReQgo0C4du94h0miIybfCIRD++29wblUkBzUOhqRlzYuERMckRARKqiPCFd9G3ZqIvhQjX6MyqCVZE/vvxvbjuP/8U+eSCHcY7nE6/SJQMUzNJNOHZYVRE5mI0vks5ItY0Vg80FZEIn5+5sjaxKMJ3MiWoECnGSBGJ/ggI14h3rUluzdy84Tn84vG9+MOzh6M+FEuMikinXyTEzbsAOjZZlSki5Vo90semDzSjZFUrxAVtcRjfbVFEEliIcEUkS4oI4YGiMDWTZEWEKSFxVkSMJ5ZOv0iIYXlAZ7VmStUafz3ZwkggWsOqWIjQrhlrxAVtsUxWTWJrhnJECD/MmwWaJTDinXldou7T22E0aXb6RaJouEtK4p2eFWKq6nAhxx/bbIRZIqatmXryn+ugKQrKZJQXe8tk1QQW7HEyq0Z/BIRrRLMqK0Smi1VdFHkSYHcX8xGHStnRtYoIb80kLzHSCpa1M5DPIJ1SYpGuqikiaaE1k6zPcTsQbwiiDTSz94gkyUtFEe+EZ1RV1UxG2RRPVgVaN4vGHabsRJ1uaYfmLG98VDp/fNcQ8c7GdxN0grWCKSIDzUmzQq7xv5EWIsL23UyKAs2siIsiYhXxnkQvleYRib4MiP4ICFeUqnU0c4/Qm00jl0lx41SSRnhVVU1GIdIs+tjFq9MvEtys2oEeEda+ZL6q/nzjNY3y/cditnNpzaza6cWuF3SKSBySVTthfJdaM4RXRNMW+zAkcXJGLKiiTre0g8mXfc2LVqXD+/ctgWbcIxLfYlGWFkUk3/j8RBnzzgPNsimKeLdBVESifH40RUQfwcU8VdSa8QYVIgmDfRAyKYXfQSWxEBF9IfFWRBofVnb3XKl29t2qMUekkxQR1rpk7UxNEYmuEOFm1TSN79ohFsJRGqdFf55IMhUR9lmPvgyI/ggIV4iju4zB3sYJNVGFiKDsJMGsyhSRTp9oaDWrJu8EawVrXWoeEaaIxG1qprOLXS+IeT6xTFZNoHLIt++SIkK4xWz7YxJHeEUVJAnju+zuuRMuyHZogWadl6zKFJGBpiLS15TX5yJqzaiqqpuayaTII2JFHDwioq+tE5JVKUeE8IxZRZ7EUDPR6xLr1kzFqIh09kWiaNg/kUuHN7777797Dm/66h/a9r5l8e5MQWSvaVQ5IuKYbo6SVW2Jw9SMblCgIwLNtOnLqIn+CAhXGKVzIKEekYS1ZvqbxsZOv1u1NqsGf4L93kM78cjz4/jt1gOB/2wzpgyKCDOrRjW+K97l54XWDBUircQhR0Q8T1m1ZpJUiBhvOqKECpGEUTRTRJon1qkkFSI6s2qCWjO1OlS1c4sR4/ZdJtvW6mrggXlMCduydzrQn2sF84hws2ouWrOqeNHKpbWI904vdr0gKiJhvBdlmGOj1pkU0s3MF0ayA82iLwOiPwLCFfOGCwWQTEVkLmFTM0zGB5C4BFs3tASaCSepoO/2mBLx5N6pQH+uFZpHhI3vNv43KrMqu2hl0wpSKUULNOtwQ7QXjCbQKJQHq4kZIJmmbsoRITxjFjGcxEIkKR6RsmF8F+jsCO5S1bw1AwR/kmVF9ZN72lWI6KdmWMR7VGZV5j9i/gL2XHfy+8srRcMW7CiUB3buNaaqAgn1iND2XcIrRTb7nWk1q04V49viMJI8j4hQiHTwHStXRJrvr0xKgdJUoUu14F6ncrXOL7iHZko4MF0M7GdbYUxW1cyqEbVmavoLAVNEanW1o9t/XhBvXICIFBGL0V1AM3xSa8Yb0R8B4YqijSKSVI9IuVaPrUGPzdqLrZlKgu563GL0iCiKou2bqQT3uI2+jHaoIkwRGWSKCDerRtSaqeoVEeYRAUgVMWI0S0eiiFiM7gLhTpeFBbVmCM90ikdk3nCH47c9E5aqoqUPprlBrVNHeKu1On9s4lRWGFkixtc7bMOqqqotyap86V1EiogYZgZoBQlAkzNGYqGINN8nZopIonNEqDVDuMVMHhQLkaRIusbCwU8h8asn9uHkG+/C9x7a6fewWhDlSzZemaS7HjcUhcclFrq5TPB3ey2KSMiG1dlyDax+HDBGvEekiLA7UnYRY7tmAJqcMdKiiETZmjFTRBJmVhXD9HpIESHcYpYjwgKaanU11sZPkVZFxPtd6aM7J1BXgfufOeT3sFoQ5cssS77sUEVEvOsU5dowJgKM7ZAn90wG9rPNYG3LbFrhnx1mOowqR6Rs6NFnhJHQTvYheSEeikjreg0Gb18mpBARj5MUEcI1Zjkivdk0v1tPSnsmyNYMk0xfGJ/zdUxmiM7yTt+OWqxoRZeiaBfFMGRn9noPFxrqxPZDs6GalrWJmSx/bP2CWTUKJbFsaM0oijbCS4qInlaPSPtvuNjNkr0ikowbQV0hQooI4Raz8V1FURLnEymWgytEWET3C+Pzvo7JDH1rprMjuI0ZIowwRhPZSf3ohQUs7s+hrgJb94fnEzFmiABajkhdbR0PbQclg1kVQMe/x7xiVESiUB7YMZiN7yZtJxNTelOKXomLCipEEoaZWRWIR7rq4y9MSrdHjIWHn9YMK84Oz5YDl9nLYmumw5Mvzdp+gDiaGNzd3qyQybB26SCAcCdnpgxGVQAoCJ+hKAyrZROzYKerbl4xFh5RTBWxc5bx3AskzyNSEuLdRfUzKqgQSRhWd61xWHx35W1/xLtvfQjjs2XH7zW2ZvzI8mIRs3siWFVEdJZ3+i4QzQ8TviLC2ml9uQxOWtYsRPaG5xMxhpkBQCql8LvbKAyr7O7ZTBHpVB+SV0px8IjY5Iiw17CuNqbP4k6cFt4BVIgkDqsPQ9StmUqtjkMzJdTqKg7Plhy/31iI+NmAKv7bIH0iqqrq5PMMl8078yKhFbn600IYHhFmVu3NpXFSOxSR+VZFBNBGeGciMKyWBE8Oo9Mns7wSh6kZ29aM8JlJQntGW3gXjxIg1KPYsGEDXvva12LZsmVQFAU//vGPw/x1XQE3q+b0L13UhYjYEpEJiCoaTsLzAbRmAGDXkeAUEfGEks92g0fEXHoOx6wqKCLNQuSpfdOoh6QETJkoIoC2VTmKxXc8WVW4GGQ6fDLLK0aTfjRmVZvWjKBqJaGI1Lxv0U/MACEXIrOzszjttNPwla98Jcxf01Xwi0XG2JppnGCjinmfFn6vTL+dFQ+L+nIA/JlVxYtIkIqI0VnO7larHTpaaYx3Z4Qxvste70I+jWMX9yGfSWGuXMPzR4KffAJa490ZWqhZBK0Zw9QMoCkiSZD32wn7LLJCMsqld+w9I5JJp8A8n0kY4Y1TqioAtD6jAXLppZfi0ksvDfNXdB3crJozb81EZVYVpW2Zfju7EC3sz2HPZNFnISK2ZoJTRMRI81xaU0TK1c68W+UqVUtrJoxAM03mzqRTWDM2gMdemMSTe6Zw7OK+wH4Pw8wjAogx7xG0ZkwLkc5u/3mFvTcHe7M4MF2KONDM/OKdy6RQrNSTpYiQR6SVUqmEqakp3R9Cj1WoTqxaMxKKCDuxLOrLA/AnjYuFyK5AFRF9rgbPeOhURaRq0ZphBViAd+ns/cLuLsM2rIo5IiJ88V0EhYgx0AyA4EPqzPeYV4yKSBSqA49OsAgAS1KomTg1EwdiVYjcdNNNGBoa4n9WrFgR9SHFDrNAMyD68d1plx4RdnexqN9/a2Y+JEXEeKHofI+IRY5IGK0ZtkywqeyxEd6wds5oZlWDIpKLrhAxV0Q6u9j1gqqq/LzHCskoFCNNETFvJIShHIZF3Foz8TiKJuvXr8fk5CT/s2vXrqgPKXZYGQqjVkRmBI+Ik7qhqqpWiDQ9Il7Hdyu1uu5OfWKuwsOr/GJcCqWN73ambK75jww5ItysGpyPYs6oiIQ8OaMFmhk9Is3WTAQeEW0iS8gRSXX2e8wLlZrK9wQNxsAjYqWIJCnUTFzmGQdiVYjk83kMDg7q/hAaFWE7aqxbMw6KSKlaB0vUXthszXgNlBKVFBbZHZQqUjIoIpkODzQrWRS5oeyaEcyqALCmWYjsmyri8Izz+LdbmImbmboZrDXjpTWoqiqu/+6f8NEf/dnTMZVN+vSdrrp5QSyAWSEZxdSMXY4IkKxQM+O5LWricRSEFGL2Ro/BMMWmAaYCUgPcojOrOpzURfXDb2uG/ax0SuEmx8AKkYp+O2quwy8SRX6XZJ4jEmygGWvNNAqB/nwGxywqAAinPTNtkqwKiGZV9++/vZNF/PzPe/H9P+7ypBbZBpp1aLHrBTF+P8qpGXaOMts1A4QT/BcWZhk2URLqUczMzGDTpk3YtGkTAGD79u3YtGkTdu4Mfl17N8D2s6QU/ckLiF4RcTO+ywqqXDrFpVavrRn2uwrZNFYs7AUA7ApoBNQ4a9/p8duWOSJhmFVNFogxw+qWvcG3Z6bmraZmvHtEDs9oCcJe3r/GQhfo/PeYF0Q/QxjqnCyaR8SiNRPCKoSw6KockYcffhhnnHEGzjjjDADABz7wAZxxxhn4+Mc/Huav7VhEM6FxPwBTRIqVeqC9fFlmXYzvavtyUtz45VcRKeTTWL6gcUcdVmum00crHQPNAlwMZ1REAGDtGJucCbYQqdTq/D3Xooj4eP8dEhKEvfx7s0Czdr/HJucreN93HsFdm/e15fd5QUwBDaMolqFWV3nxU3CYmkmUIhKT8d1Qc0TOO++8SNZrdyp2PcqBfAaKAqhq4+QyOtDeSnfGxfiuuEGY7/rw6REp5DJYsaChiAQVamZ0lnd62JRV7DMvRMIY3823KiJBG1ZFI3W/QRFh7z8vEe+iIuKpEInB1Mx9Tx/ELzfvw67xOVxyylhbfqdbSsJYeRgpvzKIbXHL1kxEx+YF8ogQnrHavAs0FngNNGVmJkO3k2mdR0ROEenNioWIv9ZMb1ZTRHYF5hExTs10uEfE0qwabqAZgxUi2w7OtKx99wPzTRVyaf4aMvp9mFUPCaZaL8drdjFgEe/tUkSONB/D84fmYnvTyAvkbCoyQyi7eVIU64t3MguRLmjNEMEy72CWGipE5xPRT83IKSI92TQf3/TqEeESf17ziASliBilc36R6NA9IEWLkb6gT/7lqjb9JcZljw32YEEhi1pdxbYDM4H8LsA6VRUACnm29M79+0+c7vGjiIgXg0ybVbeJ5rliulTFYYmt2VHAFZFMOrKbAXF019gWZySqNUM5IoRXtORL85ctyph3fY6InCJSEFozs+WqpzsyzcmewVHDDUVkulgNpBgzOsuzmaaRMAEnGi8UK+bvr6ALEVF9EBURRVF4sFmQ7RmrzbuAFqjmRRHRt2bc/3uz1ky7J7Mm5rTPyfOHZ9vyO91SioMi4jC6CyRsfFd4TuNAPI6CkKLoEKgzFOEIrxuPSFFwnzN1p656kzTnhamZ3lwai5vjwEFMzhiTL7MdvhmV54hkzKdmgjJBswwRcX8PgwebBWhYtdq8C4hTM+4f20FBEfGi6PHxXdOpmfa8x8bntGJq+6FwFg76RVREogoNc5qYAYRCJAGtW2rNEJ6x84gA2h1fFK0ZN0vv5oSCSnSgezmZG4OxgpycsRrfTcKJxgtWEe/aWGIwj5sXj/nW93EYhtUpi1RVwF/Eu25814tHxCTLIcOL3fa8x8YToIjoPCIRtT/mBC+aFVGOFruFWjOEZ9gH0kkRmZyLtyIiekQyaU1unfNwMjeaHpcHODnTOjXDwqbif6LxglXrLx/wyZ+pD30mOzvELJGgzJPTPFXVpBBpFkPzlRpqLpWuwwGN7+paM5k2j+/qFJF4FiKiIhJV+6Moo4gkyiNCrRnCI06KSFShZqqq6u4oi5W67cVa9IiI/zvn4a6U3103L2qBKiKGPqo2vtuZrRmnHJHAChGTMDPGcSP9yKVTmC5VA8uD0fbMWLdmAHeqhqqq/gPN+K4ZURFpb6CZXhGJZ2vGbGqm3ZMpcw5tcUCbrkuCYsrObcY2bFRQIZIgrDbvMqKKeS9V6y13cHbqhvFx+AmVmjUoIkFOzhhbM9mIwpTahdaaCdesqoWZtb6Ps+kUjl/SDyC4hFU2zm5mVs1nUmhe+121ZybnKzqvkJf3rnGpItD+fUaiR2THodlYjvAWBe9SVIFmThOLQNIUkXgFmsXjKAgpnD4MgxEpImJbhp3U7Xwi7KTd03wcvT6yROZbWjPNLJEjQXhE9K2ZTl96V+SeBYtk1cAUES2EzozRgcYixKDex3aKiKIonmLeD83oR13nKu7UPFXVkjp1u2ZS7Qs0q9bqutUM06UqjsRwhFcs2LIRt2YKEmbVKJKt3UJmVcIzRYdY3qhaM+wE3pdLayd1G5+IcRSOfbjnXZ7MAcFExlszmiLi9+5Oy3lgS+86dw+IqqqWq8GDDjRjLTirkzr7fUEVPkwhNPOIAKJhVf4CYtwQXHRZRIsKoi5ZlV9owy92xfPEksFG8bcjhobVomDqjWrx5FzZvi0OJDXQLB4lQDyOogPZOzmPzbsnA/2ZTrPsWo5Ie5NV2V1Vf09Ga7PYnNSNY8jsf72MUM4ZZP6jhhuFyGy5pstI8IJROu/kQDPx5GnVmgkq4p0bjPPmiggrtIM6oXOzqokiAggbeF1kgbQoIi4LEfGuWT810z5FhPlDBnsyOG6k0Q7bEcMRXrFAjmoyRSpHJEGtGSv1MyqoEAmJd97yEF775fuxb7IY2M90+jCwE21UrZm+fEbqpG40qzIVxYvhzzg105NNc2l/l0+fCC9E0izQrFmIJOBE4xYxotxy+261HoiHgKlYZh4RQLswByVxT9m0ZgDt/ecmlEycmAHcT3yJFytda6aN7b+Jpj9kuJDDykV9ABKgiEQc8S7TmvFybJt3T+L2P+5sm0eHpma6gP1TRWw7MANVBXZPBOP8B5xHyKJKVmWtmYF8Ruqkbpz+6fWRbikmqzK09oy/595o6Gpn/77dMKNqOqW0hIyJrYMgxkpnHbxOvDUT0LZfTRExb81oi+/kiwmmiLAiwm0RzcyW2bSCVEqLDG+nIZopIgsKWRy7uOGt2hHDyRlREYkqNMxVsqqHY/vHOx/HR370OO556oC3A3SJWYZNlMTjKDqMR3dO8P/vZaunFUWHkStWiEyXqq4zEfzAHmN/T0aLbJcwq7ILEQs1m/WkiLTeXa9YyEZ4fSoihm202kWi81oz2mRC6ylBPFkFoVJoUzMWrZmAe+3arhnzQoQvvnPxWWUeEVb0ui1E2HsrZyj62rlrxkwRiWOomblHREW9jee4eZMbHiN+2kYHpxvvp19u3ufh6NxDZtUu4NFd4/z/iztY/DJvmDYxIprxpts4wstO9H05wSNio24Yx3e5WdVHa0a8u2YXB7+TM1bJqmFcJPZMzOOHj7yALXun2nqCZRSrepVKRLxYBiGJMwXNLFkVED0i/oseVVW1XTO95hcRNr3jphBmm3eXN4te162Zmt5/xOBZNW14D0zoFJFGIbI9hiO8ZooI0F5VZI6fs6wvmX48Iuy995st+0MvQqs1belkXBQR6/KO8IxeEQmuIHCSB7PpFAq5NObKNUzOVzBcyAX2u+2YFRQRJt3bKSLGMeRC3nuOyLzJKKgWaubXI2KerBqGY/+TP3sCv3piP4DGhWHdcYuwbtUirDtuMY4b6bPc+BkUVvHuAJBKKcimFVRqaiAnf+7rsXgfBzk1U6xoJ10rRYT7mlwpIg01YQVXRNzdcJiN7gKaIbodHohxQRE5eqG2MHJ8roKFfe05d8hg5hEBGoWI3RRLoMcgkSPiZxUCu3Ebn6vg4efH8bJVizwcpRzi8cXFI0KFSMBUa3U8/oI2LTMdoCLiFGgGNNozrBBpFzOCR4RdPGQ8IlwRyXob31VVlZtida0ZliXi0yNSNhi6wjQSMlNzSmmcjP778X3478cbMu1Rw7247cqX4PglA4H/XobTaHgunUKlVgvkAjnHd83Yt2aKLlUGM5hRNaVYm2P5+K4rs2qzEGGKiMepmZzhjpS/x9qhiDTPEcOFLHqyaSwb6sGeySK2H5qNVSEiKiJs8STQXtO4tvTO+pKZS3sbcy8bAiH/54n9oRYioveKWjMdytb907qo6GA9Iua7QESiGOEVp2ZkZG6jItIr4Ssxo1Stg6nIZq0Zv1kiVq2ZSghmVaZI3HLFS/Cj963DB19zAs45bhFymRR2T8zjf57cH/jv1P9+8827jCAzEmbb6BGZFhbeWalKBU+BZo3WDCt6XXtELHIcshF4RBY0ldO4+kREYyVT54A2t2YkIt69fkaMN23/8+S+UNtj7PiyaQXpVLhKqyxUiASM2JYBAvaIOOyaAaLZwDsj5ojknffGtAaaeWvNiN8vtmaWDvdAURoX98M+kiL5LhAeaMbGd4M/STCPxkA+g7NWLsT7Lzge373mZXjvuasABDt9Zfr7LeLdGUGGmnFFxGpqJhvc1MzkPDOqWt/J9vP3rNz7r1ipcaWTtTTcbt8tG95bjIxgxgyb8VlNEQGAY5o+kR0BL7/b8PRB/PCRFzz/e2PQXhR5HW6SVd0eFyvMMykFPdkUXhifx5MBrTcwQ2s5x0MNAagQCRxWiLATepCKiMy+gyhi3vnUjKCIWI1CqqoqyJwsR8Rba4bdweYzKV1ln8+kMTbYAwDYdcS7T8Q44sbNqqEoIuZF5lFNdWdPyIVIycasCgSriBizX4wEmSPCFBGr0d3GcbhrzbAY9ExKwZKhRmbNfKXm6i7WWOQysm1M7xU9IgBwzKJwRnhvuH0TPnTHYzgw5S1Tydg2jCJLRCpZNe1RESlpxfK5x48AaLRnwiJuqaoAFSKBwyZmzj620eObDrI103wDOXlEgPYuvhMLEa6IWJzUde0UQ7KqW0XEGIwmEkSWiOXSuxBOgFaKxLLh9hQiVoUQI8iTP1Me+iw9IsGZVbXRXWtFRDOryr3/mFF1UX+OFzGqqr2GMhjXBzDa6RFhNysLjIqITWtmrlzFJf9vA6777p+kfketrvLCzevNkTG2IIoodVc5Ii4LaHH30sUnjwFAqK1YYyxBHIjPkXQAk3MVPHew8SF+xerFAIJrzdTq2pIs29ZMb/vTVc0UESuPiNhL7zG2Zlx6ROZMJmYYK/jkjLcLuNmIGzPKhXGRsJJLWSGye3w+1L6xU2smyK2ns3w/kJMi4v93Oe2ZAdybVZk/ZFFfXndhchPIp7Vm9M8Bi3hvpyKygCsiziO8G54+iKf2TeNuyQul+Jy6KdREjMGCUYSaFR1UPEDIEXF5XHPcY5fGq9eMIp1SsGXvlC811w7t+aTWTEey6YUJAA2Jk7npg2rNiBMEMopIJB6RfIZPJlh5RNidRTatJXjyZFWXrRm75Wk8S8TjCK/ZiFs2E85FQlVVfpI2Tq2Iu3PCNCBLm1V9TrKoquocaJYN5ncBsoqIu0KYFyL9OaRTCr8AuVH0eGsmba6IhF2IFCs1/p4baioixhFeM+596iCARiElo46JN2JFj622FkWkzR4RVVW1HJEQPSKFXAYL+nJ46TELAQC/eiKccLMiKSKdzaM7G22ZM45ewE98QSkiohnO7g3E7m7G27jOW8wR0bbvWigiJi0A7hHxaFY1L0T8KSKiUZKd+PjSu5oaqDqhXzinfyw92TQWNUcpZQ2rXoLhtELIXqXwexdaFpQmy0CzAI2xPMzMxiPC3rOyNw3MAD3S3/CHsPefm3HjsuEun9GuXTNMDcmkFAw0H39vLo2lQw1vlVl7RlVV3LtViyCXmTISv8eL+bixFVo/LRhmi9SMSk3lSdW2hUjzuOqqu6knng7d/DxcdPISAOG1Z4z5SHEgPkfSATCj6ukrhnlsdNCKCBths2JJ06S5z6MxzAvTLjwiZsujCln3a9gB2N6lLF+ojfB6gV1wMymFTzIwIyEQbHtGPEGbKRK8PSNRiPxmy36cfONd+NYfdrg6hqLhZG8kKI+IqDpYB5oFOb5rv3kX0PJFZFsrhwVFBPA29cWTVS0i3sNWRFiq6nBBP9a8khlWTSZnntgzhQPT2rI/mXOb+D1ecmEqNRXso8YKVPb+aEf7CtAX9nZqtFhUuinY2XmPvY9ec1KjEHl4xxH+XguSuMW7A1SIBIaqqti0awIAcMbRw+hvnviCilp3WnjHYHc0QW79tUNVVU0RET0iFkWFWSgbe0zzlZqreHPeW3XwiHiJTOe7QIS7BnEZXJB3rKwISCn6YodxlAvD6u+eOYS6Cjy4/bC7Y3Awqwa1fp0Vj7lMihd4Lb8rwIj3KSFHxAotR0S2NcPMqg1FhBVvrgoRq6mZEH1IIsaJGcax3LDaWsD/VlBDAA+FiIfXU3wPRDU1w1TcjMlCSBGvqxCM+7KWLyjglKMGUVeB34SwBM/ouYkD8TmShLP90Cwm5yvIZ1JYMzbI5c7ZsruxPivmy84TM4BWiOyfKrYlFGm+UuN3LP09zrtmzMbg+gSJ3s3JymzPDGNsqAcppXFCOOThrsJMvswIRUKQoWZiEWAWuuVmcoaFUR2adteac1qoGJRBUCserd/HPQFu3+WKiMWeGQDob75nyzU53wN7Py3mrZnGv3czfm4ZaBaSD8kIV0QMJl67ULN7tx7U/Xc7WjNFXQpoNGZVY9yAFZl0CkysdlOIcEVEmCK76KTm9EwIPhGamulgWFvm1KOGkMukuCJSq6ue3eIiMuNjQOPkmEkpqKvAwRBkPSPMA5NSGsdW4K0Zc3XD7EMtXvzc3FXaje9m0yksHfJuWDWTL8OKlzYGNhlhWSIvSBUijcfq9rUvVexbM/mAioNZm0kn/ruyQbZmnBUR8b0o468Rx3fFf89uFmSwDDQLyYdkxEoRYZMzxtbM+GyZe+BYASYTTSDmCflRRPKZFC/SveZ1eIXdVDmdewFvo8VmG8SZT2TDM4dcJf7KQK2ZBPPHHUd0O2SMsLbM6SuGATTetKw6ng5g8Z0W6mP/5kmlFO4T2duG9owY764oiq5NYpY2aZZQmEopWpaIC58I3+JqcVHzkyViJl+mUlokcpDSuej/MeOo4cbr6aSIVGt1XnQdmnZXiNht3wWCG991SlUFBLNqre57EzGbNLIzq+aE9fIzEj4Rroj06c2qbsZ3nQLNAHCDZBiIm3dFjllsHmq24ZmDqKvAmrEBHDfSKFbcKiJebsjMJjza3Zph76F+G58Rw0uRNFtuPY+duGQARy8soFyt43fPHLT6p54gs2pCmSpW8PZ/fxBv+uof8NzBGdPvYUFmZxy9AAAaF+V8cJMzmiLi/JK10yciLrwDGnfUrLtglsswb7GzoeBhhNcpodPP5IyVfMn3XAR4ErTbfAvos0Ts2DtZ5NHg06Wqq+kZxxyRgAykcyYytBHxOfdb+GiKiP1FRGY1AQDUhYAurojwpY3ux3eNd6WiByHMmHe+Z8aw3G7lwkaRMTlf0U3e3dv0Kpx34ih/LmUKEb9mVbPE31yAU1UyMPVokcQiQC/HpgX8aY9RURRc1DStBp2yWnKYkIsCKkQkODxTbszN1+q48adPtEim8+UatuydBtAwqjIGApyckTWrAg1/BBB+GiegFVms6BJVETN1wyoqWWzpyGI2gSOyojk54yUYyPJCEYKZ0FkRaTyOA9MlWwOnceTSjTemXcmq/O7PbvpAeB78toKmJHJEADgG8Wk/r8Jfe2NrJgizalg+JCMsJ2TI4BHpzWnrEdj7qVZXcd/Tjbvy808c4Z91mc3i4rnPSxFrlq8TZLieDGxcW2YjsZcxdzNFBAAuPqXhE/n1lv2BeoYo4j2hiJMvv3vmUEvQzOO7J1GrqxgdyHM1AtCkvCAUEbNpEyuiUERE2ZIVBqaKiMXjYCO8bu7irT7ADNai8mZWtTITBj866FQELOzLcaVi/6T1Y3neIKe78YloxZDTrhl/kyw8zMwiQwRomP5YC8zP76vVVf7+tEtWBcDH7Z3u8tnEzEBPhj9XBS+FSM0i0EzwIYWZJWLcvCuitWcahcimXRMYn6tgoCeDs1YuEJ4r58erN6v6UEQyoiLSNPS2SRE5MiNfiHgp2OcsPhNnHr0Ai/pymCpW8ccdR6R/nhM0NZNQjJX/p372pK4fvIm3ZYZ1Uw/sAxvEvhl2ApeR05hJc28bskTEeHdGn82JymqLJburdGPMclJECjn3kjnD6sMaRgR3sWrfFlEUhbdnXpiwVneMkw4HXfhEnLfvBqWIOJtVxd/npxUk3o07KiJ5ufefcWIG0B6Lm9YDX6hoeL5TKYV7y8KcnLHyiACiYbXxXmNju+eeMIJMOiVkJDl733TJql4+hzFQRI7MNl5zqULEQ9ialdctnVJwylFDAJzbsm4gs2pCYYrISUsHsXxBL/ZMFvGle7bxv2cTM8wfwuhvGuSC8YjIje8CESkieUlFhLVmDMWDl6LBbnwX0BQGt8v0AJkI7iADzewVEUDMErF+TY0GQ1etGcntu/4DzZzNqkAwG3hZqmo+k3I86crum2ETM4v7tYuStrTRxa4ZC0UEaE/Mu9XUDNC6/O6epj/k/BNHAYhJtM6vjd/WjLki0l6z6pFm0bawL+/wnVrBVK65P4+Z5SHJKnVuoPHdhML6zCMDeXzitScDAP79d89h24GGcVVMVBUJ0iMiO74LaB6Rve3wiJgpInYeEavWjId0Sm3szfxulxc3PgqRVjNhc2omBEXE7sSwbMg5S4QpIqxocaOIlBwMs4FNzVRkFREWm+7992l7ZuzbMoD8Bt7Ds9rCO0aQHhGgPTHvYrKqkWMWaZMz+6eKeGLPFADgvBMbK+rdpEbrl975WT3QOjXTrvHdI/w1D0cRYc+j2coD/r70cA6zokhTM8lEXJx14UlLcMGaUVRqKj7x0yewd3Ie+6aKSCnAi5YP6f5dkDHvJRdmVdaa2T9dCnUEEBAW3vWIrRlrRaToMDXjpvJ3mprx1ZqxkM4zIcjCUorIAvvJmXpd5R6RlxzTUObctWacckSCVUTsPCJAMFkifPOuxNilUxAfg41FLxIUES8Fr508zgyr1ZDMqqqqYmKetWZsFJFDs7ivGWJ22vIh3o5yc5eub814MauaKCJpbby7HTAVzDhhZIa3HBFrRcTtHiQZaGomocwY7qxufO3JyGVSuH/bIXz6F1sAAGvGBlvu8rSY9+AUkR6JKnZkII90SkGtrnoyarrBtDXDt5namFWtWjOuFBH74qw3615lYViaVUO4W3XafAsI6aqT5oVIY6KmjnRKwWlNZc7T1IzFMfBAs4A8Ik4FdRCtGX4D4WBUBbS7Uad2w6FZ1poRFBEP47tlGxVMW+oWzk3EdKnKb1DMFBFxhPe/Hn0BQGNsl+HGhO8/4t1aEYnn+K6HHBGbdmUorRlSRJLJtOHO6uhFBfzteccBAH7+570AgNOFsV2GG1OXE1beCjPSKQVLBhonyrBDzcxbM9ZyolWLicvbrjwi+tFhI71htmYCjXi3N4oCwLJmqJmVIsL6+csX9HKPkCtFxCHdNaiT/7zN3Z9IEIWPtnlXQhGxKZ5FDnOzqqiIeFh6Z9eaSYWriEzMNp6XnmzK9PUWR3gfeK4xrfHqNVoh4uYuXWx1eYt4j9YjoqpabkwYZtVqrc7f42bnsVAUERrfTSbTJlkE733VcTh6YYH/9xkGf4j4/cEGmsnJae3yiZi1Zgo2Mve8xR0xuzB5UkQsnhMx8dJtXLbVXUMYd6tO47sAsHy48V7bPTFv+liYP2Tloj5+t85GTZ2o1Or8Djns7buzEsmqgKCI+PCITM7LhZkBolnVwSNiWHgHAL25xrG6a81Y35VmQjBEi4zbjO4y2BZeoKEEnHqU1nZ25REJTBERWzPBhwpaMV2q8tchjPFd8cbL7DPRF4oiQlMziYRFtIumt55sGp943Un8v40TM0CwHpGii6kZQBjhjUIRsTH+OSkish+4Wl3lHyirixr7mXXVfT+5bCIJA9r4bpB3q2YnWyNjQz1QlMb3HpltLTDYxMwxiwoYaaphB6dLUgWYaCJ0MquWfEe8S5pVA9jA+0hzN8pxI/2O3yt7wmftLlGm11qALqZmbBQR5hEJa2rGbmKGwbbwAsCrThxBKtUaS+D0XKmqqovM9+MRMYt4D3sxIACeLlvIpW1vFBhuF/IxQ386pZgWpf2SJmo3xDFHxPlWgeCKSL9BOnv1miX4yCVrUKzU+P4FEd5LDTBZVebDAAgjvCFnicyaeURkFBHLiHe5D5z4sy1bM8LvmC/XXN0BaOO7+n8TxknQySjKfu/oQB77p0rYPTGvuyMHzBWR+UoNs+Vay/u29fe3bjg1+/1AAGZVpog4mVV9tmaqtTo2NNNARX+DFX2CemYHH98dEHNE3LcA+fiuyfOdC3lqZnLeOkOEwbbwAvq2DCB43xzOa3PlGsQ62FugWWvLsJ3bd92kqgLuTd2iQmi2eZspdaGYVWPUmqFCRAK7mOj3Nb0iZgSpiMy7LER4ayZkRWS61OrTkPKI+DSrsu9TFOsPVDadQjatoFJTMVeuYbhg+m2mmAUpAWKgWYCtGUmpdNlwL/ZPlbBnYh4vWj6s+zs2MbNyYQF9+QwKuTTmyjUcmi5JFCLaXafZyZD9HeA/WZXv1Qg50OyR58cxXaxiQSHbMlZvRkHis1qs1Pj7fXGfSSHiKtDMeWomrIh3dpdvZlRlsBHedErBK1eP6P6uv/nalat1VGp13X4cEaNi4i3i3UQRSQdjnJbBTaoq4N4j4vR5CMesau8Hi4L4lEQxRmaVuBmBekQkpw0YvDUTskfEVBGxMf5Z7cyxU1HM4BJ/1vxOgtHrMdTMySPSbkUEEJbfGULNVFUb3WXx3Lw9IzE5Y7ZYzEhwioj9yDVD84h4K3zubY6dvuqEER4Xb0e/xK4jdnecTSsY7NXe715yREo2ikgmFa4iMs4zRKwvri89diHGBntw+RlHYchQsIij13YXSKNi4m3pnY0i0o5CZM6lIpJ1N1o866AQchN1gDkicZyaIUVEghnJxVlGgnQ88+RLyTdPuxQRs+fGzvhnZTB1u69D+wA7LzObKlZdnwSdtqO2e3wXAJZbbOE9PFvGTKkKRdE2Di/uz+P5w3M898L+9ztP7bTfrOrvrpfFkp+/xrkt0zge5zvPwzNamJlY/LL3cqnaMP06FT6qqmoeERM1IRdysqq2Z8b6xmpRfx4P/J8LTP8uk06hJ5tCsVLHdLFqWdAYn0tvgWbWHpG2FCIuWzOuFRGHUMY+PlZOZtWuh3lEBl0qInzXTBBL71wqImzcc/9UEfWQQs3qdZUXG306RcS8366qqvP4rsvWjNMFzcvdKmCXIxK8kdAp1ZTBs0QMKhfzhywb6uU/Y6RfXhGR8R952SpqRFVVYcGXrFnV/e/bMzGPp/ZNQ1GAc48fcf4HgNQiN21iRn9REo23Mu0Z8Tk0MwyGbVa1CzOTpT/fOBfaReKziye7SfEW8W6iiLRx1wwvRCSfK7c5Iuz9ZnUeE03Ubif/rIijRyQ+RxJTKrU6P7m4VUQGmh/WUrXuP3/B5fjuSH8eKaWxrv7QbDihZuJJyCzi3XhSL1Xr3Lxm1ZqR9Yg4je4yvOwBAZyTVQPdNcNbI7KtGX0hwpaTiSOX4uSME1wRsblD4n15H+O0ZWFMOMxAs3ubasgZK4al0jAB+/1IDD4xYzAK92RTYAKJzPtMPBeYKSKZkM2qrDUzJBH0ZgVrZdm1ndnnn5mnq3XV9WqE2Cgi/W4LEXeme6vCnH29KkwJ+kFVVS3iPUZTM/E5kpgiftD6Xbdm5HqpMhQl75oZmXQKowMsSySc9gy748mm9aNnfRabTO3GRPskLgQiTh9gBrvAuJWF2d1WW3bNSL62R1kpIkdaCxEtS8SNImJ9OtCWeXl/3OLuoYLDY+WtGQ+Fz71PNfwhxmkPO8RevNWdJ8tlWWwobhRF4QVvsex8vE6FSC5sRUQiR8QJmbYzC3IUw9+KLi+mtopIGwsRmVRVwP2xOSoigtoWhGG1UlP5zSC1ZhIEa6v0ZFOW7nArMukUP0H56fGJLQ03TuewfSKsSOvLZ3Q98z6LpEn2GLJppeW5dNtCkTU9em7NWMiXoZhVJc1jrBA5PFvWFVbi6C7DlSLC75DsFJHGsdXqquf9RazIzGVS/K7fCq9TM6VqDb/fdgiA3Ngug11YazZ3njxVdaB1C6s2fi6hiDTfO9m0osvnYDCzaiWklipbeLegz48iIlOINN5XYsHj2qsVcY4IMyjLFm2uA80cPCLplFbkBpElIio11JpJEFMeJ2YYQeybEU+Msh4RQPOJ7LPYT+IXszAzQPOIzBoSTVkxYFZMFYSRQJkLnfvWjLepGeNUQxgXCdmMmMHeDH+uxfaMGGbGYHehByXSVWUUGfF58HonqsW7O7+HvQaaPfjcEcxXahgdyOPkZYPS/058H1ndeR62uTvucfE+sxvdBYSldyEHmg31+vGIOJt7+URdT8ZzYWn23mxna4aNOht9QVa4zThhHju7XJ0ghx7E558KkQRhFu/uhoEA3kSib0J2agYAxgabI7whhZpZFSKsuldVfViWVZgZoFc2ZPrsblszbvfNWJpVM03ZPMCToMzUCtBoAbDiUmzP2CkiclMzzhNZQRQi/KTrkCECeJ+aYf6Q808ctR3rNiJz52nlEQHcvc/swsyAcFQ3RrVW5+c0u6kZJ2RusPhEXV4rRNxPr7X6GbgPo51m1b7W19wMt4FmfBu1zWei32abuVt4UKNNZlAUUCHigOb89qeI+Fl8x6TzXNpZ0hZZOhSyR8QicVZ3d6mLeLZup+QzKaS44c/5ZOW0eZcfCzPBBjW+yzIeQlBEZHq2xsmZybkKl9qtzKpObnsZRSaTUvjr4zXUjJ10ndppgPddM79t5oecv0ZuWkaETyhYnPC5R8Tk7rjXhdnabnQXECezgm/NsIkZwJ9ZtU9iyoidO/vyGf7ecluImBmpRR9GUJMkZpSqNf4YpKdm3HpE2q2ImLS64kC8jiaGGDfvuiWIEV52cnPrcmYekX1heUQE6VUklVK0nrlworLzuSiK4mqLKR/fDas1YzE1kw14dFBVVVNDnhVHGbJEnj/SUENGBvI6pYGZVcu1Ok8GtkL7/dbvL0VRPK04F+G+HgcVC/DWmtl+aBbbD80ik1Lw8tWLXR9fn8XYOUPbvGuiiLD3mcSF1mnXR5hTM6xoHezJuLqpMaIpvdY3WLOmhYh7zw+gf2+KSlJYiwEBYLy5pTiT0gfY2eF614yDRwQIdvFdHDNEACpEHPHbmgki5t3t6C6DKyJT7fWIAEJAlHBSd1IxNGOpxFZP6UAzJpm7HN+1aM0E3b8XL+pOrRmgNV3VzB/S+Flp/p51MqzKelT85jfM8pOujCLivjVz71ONtsxLjlnoScHU9nq0FhP1urYO3swv0OvifVZyUkRCWKzImJBYeCeDdpduXXix13tA5xHxpoiIF07xMxlmlsjhZuzBgr6cdBuDHWdQUzNAsDHvVue1qInX0cQQHu+e99ea8fMmsopFd2Jp86K1f7IUSqiZVWsGML+7LDoUVH0u+uxup2bctGZElcJ455ALOEekpFs45/z6Ll+gb808f6jVH8IYkRzhlS5EXJ5kjci+ZoC3qRnmD3EztivC37Mmn9XJ+Qpvx5mlbLqZzrLbvAsEr7qJsAwRP/4QQO4Gi93E9eU0RcRNq63xOWxVRMSJuzANq27DzAAfUzM2N1QyRZ8sJYlR/SiI19HEEL55169Z1Udrht0VuFVERgfyUJTGCe2wyep4v7AV37aKiNiacbgQ9dpEwxuRTVZ1Gx0P6IuM1kCzYDMemP8npWjeADuMoWZWigigjZk6KyKsDy83Uuu/EHFhVnWxjfnB544A8OYPEY/L7P3H7o4HezKmBWPBzdSMw11pmK2Z8YAUETdTM43WjHuzaqWmom6SeZFOKTxGP8wRXrfx7kDwyaqAYFal1kz3Yrd5VwbZldl2aB4Rd2+ebDrF74rD8ImIOSJGzNaqO2WhuGmjyE5gsOLNzdSMKB8b5fNMwBMNohohI/+yQmTv5DzqddV0YoYhmyXCzbKOIWM+PSL8wiQ/vitb9Px+22GUa3UsX9CL40b6PR2f3cVVM6qaT0+4Cc5zVkTCG9+dDEoR6XFWRNhFtl/wiLh57+gyLww3BO0INXObqgpoxyX7OKUUEYk9SLLwQoQUkWThdfMug+1k8KOIaB4R9y8X94mEkCUyW7Iu0gomrnqn7A836gUrVsIINLObtc/xi0Qwd6tuE3OXDOSRTimo1FQcnClxRWSliSIi3ZqRNMv6zW+Y5a+/jCLi7oTudWxXxC7m3WrPDKPXhdFaK0QsckRSrDUTX0WkT0LpnTHJEXGjiBQrNp9Dn0WxDP5aM3KPc1ZC2Q02R4SmZhKJcXGTW/oD2J7o1awKAEuHGnfQ+0LIErEzq5opIk5eF+1CENz4bsHD+K7drH3QgWYll1uVM+kUxgYbxeUz+2d4kbFyoX9FxKlvrE0EeOtVz5ddKCIZeYVBVVX89il//pDGcdkpItYTM4C76Syn1gzLqglDEWEekWHfHhHnbAvt/JDmapubQkS8aBo/h+0INfPSmnG7HFIuRyQ4RcTM/BsHqBBxQNu8668146cQKXk0qwLaCO+eELJEpu1aMyYGq3mH4kFbfCcTaMZSOoMPNLObtc+yeOmAToBuFRFAS8zd+FwjynxBIYshkwsLy7uQNqs6nJz8yuHuAs3k73if3j+DPZNF5DMpvGzVIk/HBog7kkw8IjzMzPyi5KatWLZI7WWEkVXDCGLPDOCs9KqqqveIsMLSxXunaLMlth0beO2mpKxwUyDV6yof95bJEZG5QXOCFJGEElRrxleOiOSFwoylQ+HFvLO7ITMjr51HxErZcdNGmZNszfDobYkdIAw7Q1fQo5WsCLC6KJnBskR+v+0wAHN/CCAoIg6FSEmyGPKfI+Ii0MxFjsh9TzfUkHXHLfJUrDO0HBsTRYTHu1soIi6ms/hCRYvxXWaIDuMiOxGQItLnoPSWqnVeSPXnM9rr6WJqRpuYMQ9ABMJVRNzumQG0AqmuOitaxWqNL6CzzxEJ0KzKijsP6nqYUCHiQCxyRJobPXt8KCJhLL4TI5yNmHlEnAqRggtjqewoqHan6uYEaH0npo1WBuURkRudFWGG1cd3TwIwn5gBgJH+xmvv2JoxGZE0w7dZ1dX4buN7KjXnJXtM7TtpqfxuGTP6Td6zDBaVb7bwDnDnb9IuBvbju+G0ZoLxiLA4g1K1bmrcFs93fTlREXHvETF7ntrRmhl3uXkXMKxCcHj9xPeZXds9FLMqKSLJQitEvN1BsALG1/hu1btHhF20wvSISE/NOLVmHCK2GaqqSo+Cegk0K9s4y4MONNOMoi4UkWaWCLtAH22hiCweaJxAD8+UbXNk5HNEfI7vNk+8TvuBAENolcPvc6O02GFrVm1elBZbXJTcTGfxXTMOEe9hJqv6nZoRfT5mF0j2tUIujVRK8TS+W7JRgrUbAv/tCiu8TM14fd+abWFmkFm1y6nVVVtDpgzBKCLeQ2iYsXHvZDHwvQxyyapyEe+NfyN3V1muaRt6HXfNCNHbso+/JOz2MZILcXxXFlZcMqwUEdZGqNZV3Y6R1mOQK4b8BpqxC7xM+0Q8UTq1ZzTjsrfPKIOd8OdsPSL2rZkgAs3C2PDMGA/II5JJp/j7xaztbLxJ8Ta+K6OIhBPxXq+r/LlyMzWTSWs7s5w+J1qGiP371k6pcwvliCQQ8c7Ib47ITKnqOd3UKZHUjiXNQqRcrfMKPwgqtTq/gJk9N2YplU4hZLLGUvHvZcd3VVX+JGjXRw06bIrveXFxYjjKUIhYeURymRT3AtgZVmWX7vk1CM5LGoyBxvPMQqucXjfZcDsn2An/0V3j+LvvPYr7nznEP7NO47tuprOcLgY8NC/gtkOxUuO/28zc7JZ+GwXT2Lb1Nr5rrYiEbVadnK/wMLUFLlozgLyXak5yiiwcj0i8Lv3xOpqYwSr9XDrl6o5VRFQLvK5xdlIS7MhlUnzkMEifiPihMJPazXbNOBVUsiOQTGXJpVO6uGczxLsN2ckZe49IsEZCL5HLsooIoGWJ2PlEZFUZL4ZDkVmXLRTZDbxuvCd2nLZiGKctH0KlpuKnj+3BO255EK/8l3vxr/+zlQcSOgWaybzHSg6KCLvIBr1rht3hZ1KKqa/LLf02WSKzZQtFxJVZNTqPCGvFDfZkHM8xRmRDzWSnyMSCz6+qrcUFkCKSGLSJGe8f2nwmxS9eXtszXnfNMNi4Z5DpquyxNB5f69tI2zUjH2jGpXGHgm3ehcSfTmlbY2U2owL2fdRswIqIrBoh0p/P8BXuA/mMbc6BTJaIrE8l57MvPycRZy0iuyhtzodiKDLUm8WPr3s5fnr9y/GOlx2NgZ4Mdk/M40v3bAPQKEKtxvjdLGx0bM0EvM+IwbbJDheynkPfROy8CzPcD9R4Xrx4RGwVkZALES8ZIgzZFqaWIeKkiDSe57rqLg/JDEpWTSB+J2aAxvp0uzsHGeYr7uV7Ec0nYj7Cu3n3pGPWhBEn70zBxOnNlR3H7btyngCZLa6AaCSUe/7tpPPAd81I+jOMMFXk6EUF24vKYod01Xpd5SdMR0XEx8lfVbXMBBmzauP3yfkKtJRd/3f5iqLgRcuH8X/fcCr++I8X4ov/63S8YvViAI2tvlbPNX+PSbVm7A2DQb/HGEFt3mXY+d9m+fmhUTB72aZcsnlfuk0wdYufQkQ21IwrIhIbxNnbzq9htaunZr7yla/gmGOOQU9PD84++2w89NBD7fi1vvGbIcLwG2pWlEwRtWKpzQjvb7cewF9+6X5c8+2HXf1MfqKxKNL6TCKv2R2OpUdEcvKAmbZknw+3i+94oJlJcZCNgVkV0Hwix1j4QxhOioh4YQhzaqZU1QzG0oqIZJbIvE/F0IqebBqvP/0ofOdvzsZjH78It135UsvvZY+pUlMd3xtOikguYNWNwQzLw73+/SGAfeKntpnbvyJidtHMh+wR0QoR81acHbKfE+4RcXjfKooijPD6VEQ8KLDtIPRC5Pbbb8cHPvAB3HjjjfjTn/6E0047DRdffDEOHDgQ9q/2TRCKCCCkEHosRPxEvAPAGIt5NxQi1Vod//cXWwAAj+6cwH4XI758K7GVImJisJp3aM2Y+UrMmK+4uwPuddG/Bxw8IgGnXrLRbLcBQ6tGGgXI8UvsF7wxRcQq1Ey8MDjFzPsxCM7pDMayioicRyQos6odQ4WsbeicWATJTH0B1uO7GbZZNiSPSGCKCL/Ban28xqkZHvHuQsHQ2ghRtGYan5eFfe6LNtkEYtmpGSA4w2rXKiKf//zncc011+DKK6/ESSedhK997WsoFAr45je/Gfav9s2Uw8VWlgGfrRmvd80M5hExKiLf++MubDsww//7vqcPSv9MuwwRQK+IqKqqk+YtCxETX4kZbs2J4givDGWb1gzbAxKUIlLy2Jq59txV+MRrT8KV5xxr+31Oigi7MGRSCvcmWOHHrMru/vIZbRrGCVk5Pyizqh9ywpSP011/2eYCCwQ/mcUIKkOEYbf4btbQuuWBZi7eO3aKSFbyYu+Vw0EoIg5eKtmpmcb3BJMlUrRRe6Mk1KMpl8t45JFHcOGFF2q/MJXChRdeiI0bN7Z8f6lUwtTUlO5PlMz4DDNjsDuHaZ+KiJccEcDcIzJdrOD/3f00AG3qwk0hwjfvWhUizQ9Xta6iXKujVK3zOGMrj4js5IHbC4/bfTN2dw0846GmBpLLwo2iLqXSRf15XPHyYx3HMB0LERe7bvg0gA9FRNYfAsiZVVVVDa014wZFUXhr0amQ5lMzFoVf0Fk1DJYU6nYc1Qrb8V1DIeImsp8h5REJIfQN8JaqypD1UrlRRIJafNeVOSKHDh1CrVbDkiVLdF9fsmQJ9u3b1/L9N910E4aGhvifFStWhHl4jgQxNQPIrcy2w+/UDNvAK4aa/dtvn8Xh2TJWjfThn9/0IgDA/c8ccozTZvDWjMVzI364Zks13V2idcR7499UBQOlGcxtLt+aYcv0/E/NiBePINozYd+haIvvzDNkZDfvAv4CzdgJ1E17Ubt4Wf++YkUrcIMwq/qhR3Jypuwgj2tm1XA8IkMBe0SkAs0CVkTaNb7rpWhznSMicV5nCjOZVdvA+vXrMTk5yf/s2rUr0uPxu3mX4Tdd1clb4cToYOOuuFStY2Kugl1H5nDL/dsBAP/n0rU4a+UCDPZkMDlfwWMvTEj9TKfWTFqIdZ4tVfldazatWM7l6/vs1s/VnIPp1UjBZWvGVhFJa22FIKRz2c23XmGKyJHZkmmR6WZ82M/Jn4eZScjQDN6asbl4ie8Tv+O7fpFV3pwKER7xHrBHJKjNu4w+m7v0ltaMl4h3O0Uk5Ij3Iz4UEdlCRHZqBhCfa7/ju10Y8b548WKk02ns379f9/X9+/djbGys5fvz+TwGBwd1f6IkqKmZAR9TM6qqcvne64m2J5vmH6i9k0V89ldbUa7WsW7VIlywdhSZdAqvPH4EALBBsj3j1JoB9D4RLabe+jHkhMwVO3nbrTlRM6tKju/aJKuKRVQQjn3ZzbdeWVjIQVEaGQRmybpuxof9jO/KhjeZ/T47OZ+9T9x4T8JCdoSXrxBwingP+G5/PGCPyIDt+G7jMTLF1EvEe5SKyLifHBFJ/4psjgigTR/5bs104/bdXC6Hs846C7/5zW/41+r1On7zm99g3bp1Yf7qQAhuasZawnRC3D7q582ztGlYvWvzXvz0sT1QFOAfL1vLcxHOPaGRlSDrE5HZwcMnZ8pVxzAzhky6qja+625qRnp81zbQTFRE/J8EZTffeiWTTvEi1MwnwiYpZNp+OYnCwAovi+lkLl7zLtWxMJHeleQwvpvNhLNrJugcETsD5bRxakYoHGRXXdgpIn6KYidUVRXMqt4VEUePSNleVRbps/HjuKFrWzMf+MAH8I1vfAPf+ta3sGXLFrzvfe/D7OwsrrzyyrB/tW/8bt5l+GnNzEt4K2QYG2z4RL7y22cBAG86czlOOWqI//25JzQUkcd2TfATlh3subH7EHFFRPCIOF3wChJ+Dja+KxtoVpC8U2XYfVgVRdHGKwNozYStiAD2I7y/eHwvAODFKxc6/hzNIOjdrOpNEbFrzbj/uWEhOybOnj+rdlg2FeyGZwabmhkOSBHpt9ksrrVmWI6I9lhlVREpRSSEHJG5sraTx1ch4nBscy7alXZtMDfEtTUT+qf3rW99Kw4ePIiPf/zj2LdvH04//XTcddddLQbWODLVbM1YGTJl0T6w1htQrWABNOmUorsbdwsLNavVVfRm0/jQRSca/r4XJy4ZwNb907h/2yH85YuW2f48p0AzQL9WXUXj/zsVU6KKYoW2bTWcqRm78V2g4ROp1p2Dq2QIWxEBGj6Rp/ZN45BBEZmcq+DuJxtt0786a7njz8n7GJmc5XfIbjwibFzYrjUjH/cfNr3Z1hA/M1jx6RTxXlcbn9cgWk6qqm1gDsoj0m/zWZ21UESARoEh83rZFelhju+yFmY+k/KktMlPzcib7jWzajAR72He+HihLWXR9ddfj+effx6lUgkPPvggzj777Hb8Wt8wBcNva8aul+qEGGbmZz/EWLMQAYBrzl2l+28Gb89sdW7PyLRmxN0x85LFg0zR4GbsDRCnGdwpIpbSeYChZl52zbhlxEIR+dmf96BcrWPN2ABOXubsxwrCrOrmxJ6Xac3EIEOEUZCcmmHjz5bvL+GGI6gR3qlilbd4A1NEWFCjiSIybTg/ZNIpriRKb8GWmF4LY3xXjHf3cs6V9oi42EYdRKCZqqqORumoiNfRxIzApmZYjogHj4jfDBHG0QsbWSGjA3lce+4q0+951QmjAIANzxx0zMhwSlYFBEWkVJNOhy1I3FXy1ozk3bX71oy9fMl7+EEoIh4DzdywuDk5Y1REfvSnFwA01BCZE66XfSEMf2ZV59ZM1BMzgPZ+t5sMES8GVjki2YBHxIGG+gU03mdB3Q2zz5/xBqtSq/PHKJ4f2O+VnZyxy7gJc9eMnz0zgHxrhisiEuexIHJExM9R3Myq0TdWY4qqqoIiEqFHRGLaRIaLTx7D+1+9Gq85aYmlr+PFxyxATzaF/VMlbN0/jTVj1nfJTI61a81oUzNVfjfkaFbNWcu9DLcXHxnfiYjThkrNIxJEIRKNIvLswRk8unMC6ZSC159+lNTP8aOIuMlMYMhMzcRJEZExRYsXJ6f3FxCcT2Q84NFdABhoKiKlah2VWp0XUOLFsk9XiKQwU5KPeS/ZtC3DnJrxW4jkJfJ2VFV1qYj4zxHRFSKkiCSDuXKNS5m+WzM+xnfZXYHfO75cJoUPXnQiXrR82PJ7erJprFu1CIBze2ZGRhHJmygijiuvnVszc55bMy7Hd63MhAGtaVdVtS0928UDrVMzP3qkoYacd8IIzxpxwl8h4m7SCXCXIxILs6rExJf43FkpIqInJCgzZtB7ZgC9IikWH+w8l8+kdOqOzOspUrT5HIZpVg1KEbFT8sq1Ole7XCkiPqZmWGGXUvTFbhygQsQC1npIpxTfRQDrpc6Wqq5jwf3umXHLq5rTM05jvDwnQCpHpCrdmpEx/M1V5CVNQH6rL8OxNZMOZqpBv/k2RLNqf8MPdKipiNTqKu58dDcA4E0SJlUGP8F6mprxYFaVSFad85k6HCQy/iaZQkRRlMA38Aa9ZwZo+D7Y+3bGpBAxnhvyLkPN7BQRP8ZpJ/yM7gJyY+5zgum0IHFuDyLQTLzB8uM3DAMqRCwQ4939vmisfVGpqa77634377qFjfE+vGPcsh9Zqtb4nYhta4ZX8TUUXZtVrSt/t3I8/5mux3ftFRG/d2PinWGYhaZx38wfnj2EvZNFDPVmccHaUemfI5rw3BbUbg3GQBJbM80WoM37TNwzk7K5K83wYjeYQoQVoUG2ZgDztrNxYobBY96lx3clFJEQChE/e2YAObPqrLAE0mnZJGDtx3EDv8GK2cI7gAoRS4yubz8UsmmwWsbtG4l7RNp0oj12cR9WLOxFuVbHA88dNv0e0SVv19/kUwQl+UAzbeRXYmomG1agmYNHJKC7VdYrb4xmh2hWbe6bGZ+roFKr87bM605b5sqbIj4fboswT1MzEuZYt6PcYSITaOYUZsZg0nlQrYcdh2cBACubCy6Dwizfgo2YthQiLhSRRtsyGo+Inz0zgNyxuV0CGYRZVSvs4nfZj98RxYSgwswAIJVS0M/mwF1OzrCLVW+bqlhFURzbM9rdbdo240AzWMl7RAo5+9ZMva5tW5VtzcgGTTGcRtxy6WDMqnaBTUGyoJDjr9Pzh2dx1xONhZNu2jKAvpXg9gIw6yFZVcsRkQg0kyxKw0SLeLf+jJcdRncZ7O+D2jez49AcgMaNRpCYpUbzreXG1oyLqatKTQUbGDIrloNSJc04MttQjzwrIhL+FS1DRO7zoEUh1KSTaY3EdfMuQIWIJUFt3mX0ezSsBjU144ZzHfbOTJeaQW8O1byYqyA9vuvQmhEd9/JL75wlcxHHXSABmVXtxhODJJVS+En1tj/sQLFSx+rRfpy2fMjhX+rxU4i4vQME5FbHz3socMJCRnkrSd6Vsn0zQbVmth9qKCJBFyJm3gWr8Do3ioj4mpspk6G2Zpp+moV9ciZuIzKBZm4mZgD9udarYTWuqaoAFSKWBJUhwugzuXOQodhmjwgAnLN6MTIpBTsOz2FH8wQmIjMxA2gfstmyfMS708lcPOHJbqztFTwiTt6GWl3lBYZlsmpA47tcem7DiYH5RO54uNGWedOZctkhIikh3dftnShPQHXxPk5qa8bWrFqzL3IZmYBUN6BxDtk9MQ8g+EJEC2vUUqOtNnPz3UEShUhRUMHsA81CaM00/TQL+7yp4VIeERcZIoB+qaNXw6pTyzlK4ndEMSGozbsMr1ki7bprFunPZ/DiYxYAaISbGZHJEAG0D5noEXF6HE59dtFrYGf2E2EXKVXVn+DMKEvM2gclm7fztWWFSKlaR0oB3niGXHaIEZmMBDPmLHwD9r/LeWqm3WZuO2RM0aJZ1Y6gRsQBzR8y2JPxPAlihdh+ZfDN3D3mhYjTZxDQ372bFcxhLb2r1OqYat5oeVVEZFozbhURRVF4Bo9Xw6pTLEGUUCFiQVCbdxlaloi7fTOy3oqgYdMzZu0ZmVRVQBzfrWkR746FiDbyawYf3XXxfIi/0zF+W5SELde0B7P0jntE2nARZYvvAOAVx4+YRvzLIJORYERVVW3TqBezqu2umRhNzUiMnsvelQY1Ig4A2w822zIj/YGPbZotvuOKSM7oEXFutTGcjJXsfVhXg10OyPJWUgow1OvtJlSmWPfimfJrWLUz/0ZN/I4oJgRdiHBFxGVrhke8t7mKZYbVDc8cwoGpou7vrKRXI33CUix20XX64DkpIkyWdFOYpVMKP6E5+UTYhSKdUizH6rS71WDMqu04MYihZTIL7qyQ3aMhUqrWufHQzesmlSMSo9aMjCnaKd6dwTwilQAi3p9rtldXBdyWAcyDtpxaMzKKiFN+ktjaCrI9w8LMhgWDt1ukpmY8KIR+N/CSWTWBBDk1AwjucpdvIla4uAmCCoKTlg7izKOHUa7W8fUNz+n+jkuvjmbV5h2im10zkq0ZWUnT+HOdJmdkzITZwMZ32zdOxxSRgZ4MLjrJ++ZrL4qI+FoGvWtmPkbJqqI528qLJDu+yxSRSgCth7CMqoD51IxVa8ZNoJmTcuTHOG3HkRl/YWaA3GfEiyLiN+a91MbzjVvid0QxgXlEgsgRAcwlTBlYEJFsDHdQKIqC919wPADgPx/cyQ1cgFgcybVmyrU6puYb/8YpD8XJrOp17btM/DYg5yzPBjy+2w6PyCuPX4xCLo1rz13l6/d5mVZgF6aebMrVXaZmVrWZmpFU2toBe0/WVeuLUFnyrpQXuwGM7zLD+TEhFCLmOSLm5wc347slByU4nVJ4NlOghchcAIVIWl7Jc6eIuMtDMlJqU1yAF+J3RDEhcI+Ix2qWpWGKPf52cd4JI3jR8iHMV2r49/u386/zsDeH50YsFg43Z/OdFJE+vqDOwiPi0RPQK2EkBAQzoc2HNajxXb5npg1S6QlLBvDEJy/Gdeev9vVz3PT5GVqx4O6zxH5XpabyvU9G4tSaEaO6re76Zd5fgDg14781sz3E1ozZec26NROcIiLG4HvZBm0F3zPjI4FWZjOw2xwRQDs3+ldEov+sGKFCxAKWlTEYVGvGpyISRSGiKAre/+qGKvLtP+zg0cezkqmzuUyKnyzYCVXaI2IxaqsVIm5bM3IbeGU+rEF5REpt9IgAjdfTr1nRjyLitnjUJbla/L44Rbxn0tr73equteyQUcMI6j02OVfhSaFhKiL6iHe2h8qQI+Ii4r0o4Y3LZYJ5jkQOs9ZMv/dCJB/C1AwQgFmVm+Pjd9mP3xHFhODNqo2Cxo1HpFKr83CddrdmGBeuHcXapYOYLddw6+8bqojVUiszjN4Wx6V3DqO2cx4DrGRj3t20Zvy69du90DAIvOQ3eDnpir8LMFdgylVhg2kMklUBrai0LERqcn36oHxI25uju6MD+cDazCJmN1jajYr+Jk4zq8orInafDZkLvlvY1IzXVFVArlh3myMCBGlWjd9lP35HFBMCN6v2uH8Tseo8nVIw7HGUzC+KouDvXt2Q82/9/Q5Mzld4ZoDMic2oXDh5RMTvNxu19dya4R4Rp/Fd5/FKLV46GcmqQeJFEfHaPsmkU3xU2kx+F9WtOLRmAGflTT5Ztdma8ekR2X5oBkA4RlVAUz3EqZlp3prRvyYy5mOGzPoDLxNcTvA9M35aM2nn0WIvxblZZosbqDWTMFRVDTzi3YtHhLVlFvXlpMO7wuDik8dwwpJ+TJeq+NYfdmCGGXklnhu3iog4amt2V+m9NSN3NyYT+pMJWBGJ4x2KFV4CzVjx52Xyy27fDMuUyaQUx1ZHu3AKNeO7ZiQDzXwrIs0MkVUj4RQiZnfpVq3boBWRMGLe2dTMIh+tGZnRYm85Is2iz2eOSBzPN/E7ohhQqta5pyHwXTMuPCIHI/SHiKRSCjc53nL/duyfahyXW0Ukm5bbMmt3Mve6WyTQ1gzLePBbiPC13PG7Q7HCzV0tQ1uS6P6zxJ6boklrJk5GVUZvzl5541MzDq95UBHv2w+Hs+yOYRzfrdVV/rq0FiKsqJQJNJNQREIoRMYDmJrpyWrLQCfnzQMs/eSIzHjdNVNxVnujIn5HFAPYh0pR3Pe1rfCSI8ImZqLyh4j85YuWYdXiPkzOV/jeCrceEdkWBLtgmVX+sx4vPvLju/I5In7Dpkq8NZOcj6G31oz3xXR2ikicjKoMvoHXqjXT5oh31po5ZlG4hUjj5q2ua9H4Gt+VKNh4XkeQZtVZ/4VIOqXgqOFeAMDOZiFoxE+OCAWadQk8QySXCawl4iVZNcqJGSNpQRVhyLRmxLtg2X0gduFjvgPNHO7GZAKnAmvNtHF8Nyj8mFU9KSI248J+fm5YOClvsuO7QRiiVVVtW2sGaFwg2UUyI7RYGW7Gd2UUkWzAHhFVVflkoN+dPCsXFQAAO4+YFyJeckQo4r3LCHpiBtDeRPOVmvTJ5dB040OxeCDYRVVeef3py3D0wgL/bylFRKj4ZVUMu3RVz4FmAY7vGkeSvZLIqRkvrRkPe2YYdnfRXjb6ho04fm5GWXJyIRNA++/gdAmz5RpSCrBC+NwGSTad4o9lRihE+vKZllFxd0vvJDwiAY04M6aKVT6F5bcQYedJq0LEU44ImVW7i6AnZgDjnYPcG4l5REZioIgAjSmG684/jv+3TDVfyLtXRFiRMWvSC531KMc7RcczZDwiQfXv27lrJii8bD3100LR9s1Yq2Nxas0wdaZoqYi4zBHx0f5jO2aWLyiEevHRFnpWbRdickVEauld+z0iLMysP5/x/XzZFSLVWp0XBe5yRHyaVSUntqIgPppmjAh6YgZofGjymRRK1TqmSxUMFZyLnEMx8ogw3njGcvxy8z4M9mSl7nC9KCLsJMZODCJeWzO8d19xGN+VMHQFF2gW3zsUK3I2rRIruFnVQ46F7dRMrM2q9opIO1ozYe6YEenLZ3BopozZUhXz5cbxmhUi2jZlmfFdFzkiPgqR+XINuyfmsOvIPB7acQQAsKDP/w0oa808b+IREdWy9uaIxDfQjAoRE8JozbCfV5opS4/wxskjwshlUrjtypdKf78Xj8gZRy/Ar7ccwG+2HMCVLz9W93feWzOSS++kklXZRcLv0rvkKSJ+zKqBt2ZitGeGwU3RFgWvFmgmOzXj/T3WrkJEnJxhBYTZqHZeUERUVbVN+ZVRJvl70WWxtm+yiA/d8Rie2jfNz7Eiy4Z6Xf08M1bYKCJsYiaTUhxNyyKdHPFOhYgJbLIlyNYM0PjAHpopSxtWD0a08C5IxBOSbCHyly9ais/+aiv+8OwhHJop6Qoxr4FmQbZmsh4Mm6a/q+sCzYI1q8Zp8y7Dacsze80dd80E4BFppyICNJQv1lLpNzl3sve5qjY+O3YXRBlFxGug2U827cb92w7x/x7IZ7B8YQHLF/RixYIC/tdLV7j6eWaw1syR2TKmixXdtUScmHGzckGcUKrW6nznlSxxzi2Kzyc4Rky7COxyA/t5MiO8lVodE8149zgpIm4RfSROqaqMlYv6cOpRQ3h89yR+uXkf3vmylfzvvE5KaK0Z/4pIJqCwqUQqIp6mZnwoItwjkqzWjFUhIhtoxgoVP++xdhUiWlhjhRcQxj0zgP4CWKzYFyJuFBG3S++27J0CAFz9imPxd68+HoO9rcZavwz0ZLGwL4cjs2XsPDKHk5cN8b/zkiFi/P7ZUg1DBXfnjTgrIsk5A7aRsFozbkZ44xDvHgSil6Pg4s7/L1+0FADw88f26L7u1aAoHWgm4xFJBWtWjeOJwQqW6+Bu6Z0fj4i1r4C/F2KkKBV4a8Zhasah+PQb8V6rq3j+cHsVkZlSTdu8a3KjkEunwK73Th4jGbXQ6/juU/umAQDrVi3CUCEbeBHCYKrILkN7xkuGCKBfIuol1ExmfUVUxO+IYgBTRILavMtgS6BkenwszGxxf7Tx7n4peDCrAsBlzULkoR1HsH+qCKBx4Wd3lO5bM3LjuzJ3rEEFmiVx10zew+r1eR9eDrkckfg8f06KCL/Td1BEMj5HxHePz6NSU5HLpLBs2L/nwY4+4QZLHN81oigKz8xxMqzKZF548YiUqjVsO9AIeVu7bFD633nByrCqrTxwX5gzc+ucB59IKcatmfgdUQwI06wKyCkicTSqeqHPw/gu0Bg5PPPoYagq8Is/7wWgVzO87ppxbM1IrMoOetdMolozHjwi7OLkJe/DLlLej/ckLNixOEW8O3lEcj7fY8/xRNUCjxsPC3Zemy1X+U2W1blTNtSsKDFRxp7Diov34rMHZlGtqxjsyWDZUI/0v/MCU0SeNyoiJe8FtB/DKrVmEkborRkZRaRDChHxw+b2zv8vX7QMAPDzPzfaM+wu08uSM9fbd6UCzfylXsb5xGCF20KkXK3zotqL6Zq1gky371a8R8eHRcEh4p0VT85TM/4UkXb5QwD9xXHGRhEB5GPeZRSRvAe/EvOHrFk6GFpLhmHVmtE8U+6vL/2CMdgN1Vqdh7WRIpIQ+NRMPuDWTI+LQiSGGSJeEE9Ibi8Yl71oKRQF+NPOCeyemOe9VS/mRPZvipU66jYtFblAM/9GQvFEnCRFhCsUkif/XeNzqKuN137USyHCc0SSsfTOTnmbnKvgQPNzffQi+6TTjE8f0o5mIXJMGwoRcaGnXWsGCEcRcaPOsULkpKXhtmUAQRE5bKGIeGjNsClEt4qIWKyRRyQhhBFoBrgzq3ZKa8arRwQAlgz24CXHLAQA/OLPezyHmRmPwy7ZUW7pXeMiYXcnNlOq8mLS9PdUxEIkPhdSJ3I2hYEZzDC5clGfpztQmdZMnBQRO1P05j2TABoXqCEHAzrzIVU9mlVZquqqdhQiQuInu8gOWBYicjHvrjwiLgoRZlRdMzYg/W+8srK5aHD3xLyuxeZnisxrqJl4vomjAkuFiAk8pjgsj4jEm+hQc2pmcX889sx4RSwavFxwX8umZ/6819eFR1wsZzc5o03NOLv17RSRt3xtI1712Xst14CzYiidUvjPSwJux3e3H2rcDR7joABYYSflxzHi3c6sunl3oxA55Sjnu3G/23e11ky/p3/vBmbCny5VuZps3ZoJUBFJu1PnVFXlisjaNigiowN55DMp1Ooq9kwU+ddnPUYQAEJrxuXUDPv8ZNNK6J4hLyTnDNhGwpuakc8ROTjdeOMmvTVT8BBoJnLpqUuRUoA/vzDJTyJepPhUSuF3V3aTM24Czaxkc1VVsXX/NObKNcs14NyoGsN+rR1u70KZIuK1RWC3a0Zbehcfs2ohy8yqZopI4/0rZkpY4ccQXazUsHtiHkCbPCI6RaSq+5oRO88Po+GfklFE3I2SH5wu4fBsGSkFOGFJ+IpIKqWYJqzOOTxHdvS58BmKaOe1+BTtIsk6C7aBSq3Oq/HwWjPmd8kiTBGJy8I7r+TSKd7v9lKILO7P45zjFgMA7nhkFwBvrRlAGOG1uRvj47s+lt7NlWuoNX0o43Ot+3KAZI7uAu4LEXZn7l0Rsd41w57DOHlEegWPiNGL9ARXRJwLkayPiPedR+agqo32SDsUVVHpZYWI1WZurTVj/Rms1FSwp05m1YLse3FLsy1zzOK+tr1nVvLJmVn+tUAUEdeFSHwX3gFUiLQwLfg3ZNbcu8HN1Az3iCRcEVEUhUvnXiV0Fm62ebd3RQQQJ2ckWjN2iY4Osrn4HrIuROI7028HuzDItmaYUe+YRR4VEbtdMx6DocJEPBbxmKeLFe7bOFkiv8LPYsXnDjbbMiPefDluEX0LMzbbdwFNAbT3aWl/Z2esdFsUt7Mtw+CKiKCMajkiXhQRpj65m5qJ8+ZdgAqRFlhbppBLu87yd6JfMkekXO2MeHcG27PgJcAHAC45ZYyrKoD3C49mJLR+/uUi3puyuYWRcEpQvMw2CIu/J2mKiJ1CYaRcreOF8WYh4rU1IxFo5kVpCwvx9RTfZ1v2Nu7Glw71SH2m2a6ZqofQvHaO7gJa0TFVrHLvglUhkpcwqxZ1xkqb8d2Mu2KtnRMzDBZqJrZmtBwRL1Mz3lozRb55Nz6fFREqRAyElSECyHtEDs821JBMwuPdGR+6+AS842VHS0nSZgwXcnjF8Yv5f3tdclbIOcvCUuO7KU0RUdXWC8W0UIiMz1mYVSvxPjFYwcegqzXHO9EXmqO7vVlvo7uA9a6ZWl3LYYmTIpJOKfy9Iypvm120ZQCxNeNeEeGjux5VKLew81q5WuctFcvxXZvCkiGqhXaKjttk1af2tm9ihmE2wutnasZza4YUkWShFSLBFwBijojZBYxxaLpxF70o4fHujDeesRz/9w2n+nJrv7YZbgb4UEQcWjO6kDE7SVhQyszuWKfE1oyFIpLEVFUAWNSXw2BPBqoKPHtwxvZ72cl35aKC5xaB1a4Z0ecTp+27gHmWCBvdPUXCqAr4y6phisiqkfYUIsaiQ1GsP6My47uyfoZcWt6sWqrW+Pu1na0ZpojsOjLHz/m+ckQ8JqvK3GBFSTyPKkLCyhABtIA0VbX3KRycaUzMdEJbJihec/ISXgB4LUQKNhkPAFM4Gv9fpjUDmF8opoSR3SNWHhHWmompi90KRVGwZqxxIn9q35Tt9wbRIrBqzbC7SkWJXzFnttfIzegu4E8Rea7NrZlsOqW7wPXnrLfZ2gXUMbQi3f6z4cYj8sz+GVTrKoZ6s1gacrS7yPIFjUJkulTl6mgkOSIxT3GO1yc4Bkw7mK380JNNcVXArqJlikjSR3eDZLAni/NOHAEALOjzNgkgu5AMkBvfBcxlYdGsOuFgVo3bRVSGNUsb0jYLh7JCDDPzipVZdV7wh7TDkOkGY6jZfFlbtCbfmvHmEZkqVrjRvR2pqgzxfGnnBeuRGN+V3RLLw/UkChExyKyd75eebBpjg43Ch30egpmacWlW5R6ReJ5v4nlUERJWhgjQuJvkPhEbw2qn7JkJmk++/mRcf/5qvPXFKzz9e5Y3YTW+K57QZJJVAfOcB71Z1dwjom3CjOcdih0nNnvsrOduxfZma+bYxd5GdwFrj0gcU1UZrAXIduFs2TeFutr4PMt6ZXjEu8sV98wfsrg/H8o5zAox/NFuGkQm4r3EM3bsX1uZhGNGFBMzjKMNhlV/OSLeIt7JI5IwwjSrAnIjvJ0S7x40S4d68aGLT/SsiDi1Zvhm1LS9SU5RFGEXiJlZ1dkjok3NJO8jKNuaCUIR0dbGG1sz8dszwzAqIk8IbRnZu3E+vusy4n17G6PdRcRsn36bAkgmR0R2oizvojXD3qtrl7bPqMo4WhjhrddVzFWiS1aN641P8s6CIcMX3oVUiLCfaxX9DXTOwru4obVmzD/EbkJ/7ELNRI/I+FzZ1Jgs2wePI0wR2T9Vsiy0KrU6XhhvpHv6md6wUkR4vHuMUlUZxoKX5d/IGlUBuTUCZuxgkfo+VCgviIpIv82dvhbxbje+K2esZGZVJx9NI9q9od5FoYisFNJVi9Ua96F5UUSYwXXOZWuGKf3UmkkIYU7NAJqLmvWMzdAUkWTvmYkbTlMzbvqodoFToiJSqtZNW0FJTVYFGndlKxb2ArD2ibwwPo9aXUVPNoUlg94LanYxqtZV08VhcVREjGPifGLGxfi6llVjPiJuBTO6LxlsnyETMHhEbO70tYh3/4qIrFn1wHQJR9oY7W6EtWaePzLHvR2K4s2o3t98bsu1uqtlf3/cMQ6gvRkqbqBCxECYUzOAJmuznqUZnRLvHjfsVrQDYh/V+QRhZyacNkT4m4WaaTkiyfwIsvfxVov2jJhl4cccKL4WohdgvhJnj4i2b6ZUreHp/Y1iTXZiBtAbot3EvI83PUkLPbYvvSIWInbLQmXGd6UVEaFINcbpi7Bz7bGL+yIp/FlrZteROS0NOJv2FM0gqiiykzOlag0Pbj8MALo8pjiRzLNgiIStiDBp0K6/Tq2ZcCg4TM2wgmFQIkTObs/FlMGIPGESasaSDpM2vstgoVBWisiOw8GEaok7f8QskVibVXNaoNkz+2dQqakYLmRx1HCv9M/QGaJd+ETYe7jdhYg4KWM3cZiXinh3p4gA9obVKNsygOaR2jdV5K+PlwwRoJEvw55DWcPqI8+Po1ipY2QgjxMjUIRkoELEALubDWN8F9Cksaf3z5hOXJSrde4fIbNqsPQ4tGZeaG4slblg2EVwyykiciOKcYUre1aFSFMRWenTq5BOKfyiLPpENLNqHD0iLEekisd3a0FmbpQh9v4CgEpVXhGJqhAZ6HE5vhuIR0QoUm3aFFFOzADAgkIW/flGCODW5ufFS4YIw61h9f5nDgEAXrF6cexG3RnJPAuGCFNEBkNqzSxf0Iu+XBrlap0HD4mI8e5DHRDvHiectu/uHmeFiHN/3S5wamq+qao1Txhmi+9KCQ00YzDD6tP7pk1l8R1sdDeAmHEtS0R73eYFiTtuaOO7NR5kdrKLtgygV0TcTM6wAL0FhTYrIjk5RURu6R0r0uXGdwF7n0iUEzNAY8qOtWeYgugnDdhtqNnvhEIkrlAhYiDs1kwqpWDNUmufCGvLdEq8e5xwas3sYYrIAmdFxN6s2lBEmBpgNlmS5KkZADhmUQH5TArzlZpuoRcjiNFdRt4kuCrO47vi1MzmPe4nZgD9iLjs5Iyqqvy9tqjNRnf91Ex7FBFFUbgqYtWaKVZqeLa5jTgqRQTQfCJPNs/5XiZmGNriO+fJmfHZMjdLx9UfAlAh0kLYZlVAq8y3mARCsYkZ8ocEj9P23d28NePcTrDaBVKt1Xly4sqFjYvwETOPSIKTVYHG42cTCEafSKVWx66muhREzLjZxt94e0QaxzRdrPKbDS8LH+1GxM2YKlZ5q7Ddiog4smvXmslLBJq5mShzmpzZdmAGtXrDozPW5kkiETYt+VTz/eBHEWHPtYwi8vtnD0FVgROXDLR9ksoNyTwLhkStrvKLSJiFiN3kDIt3J39I8HDJ3EIR4a0ZCUUkZ3GREA1kKxZaKyKlBI/vMnjCqsF4vVsY3fW6dVfEbORzPs6FSPN4H39hEuVqHQP5DM+ScEM2Za26mcH8IX25dNvfV/35rPD/bZJVLSL7RdwsaGOFiNVzxM6x7Y52N8JGeJmRPRhFxLkQ4f6QGKshABUiOsQXNqzWDGA/OUPx7uFhN75brtaxf7qRwSBlVuWtGb0iwlp7vdk0vwibeUT41ExCFRFAm5zZalBEtgsTM0G0F01bM5X4m1X3TTXeTyctG/T0PGSF8VQZWCHiNXnYD+KFVSxKjMgkq7pSRNL2ikjUEzOMow2FaDs8Iqqqav4QKkSSA0+fy6R0o2FBs0ZIpjROVNDobngYo7dF9k0WoaqN114mSM7KrMomngZ6MnxywbQQSfCuGYYW9a4vRJ5nEzOLgkn3NNvAy82qMVREjMfkpS0DCPtmXCoiiyIoRPRL75yTVY0BdSJeFBErhYVPzIxFW4iwNi3D19RMTq4Q2XF4Drsn5pFLp3D2sQs9/752QIWIAJ92CHlZVF8+w0/SxvYM7ZkJj15h82fNcJf5wkTDcHnUcK+UhKsFmulPgHzqqjeL4ULjfWS2+C7Ju2YYbAvvjsOzOt8Nm5gJavsrn5pJmEeE4SbITCRrobpZMR6hIuLWrAoARYviISiPiKqqwsRMtIXI0uEevnkd8J4jAsibVX/3zEEAwJkrh30pMO0guWfBENg/xeKRwy8C1lr4RJgiQvHuwSN+GI3S8J6Jxmu/TDJ0il8kDBkPU8VWRWSiQxWRxf15LO7PQ1WBZ/ZrKwuCCjNjmO2b4VMzMfTYGI/J7cQMg8e8Syoih1mGSJuNqoBx6Z1zoBnQusiQf92NImIzNbN/qoTxuQpSCnD8kn7HnxUm2XRK1/L1lyMiZ1ZlbZlXHj/i+Xe1CypEBPZONi5GS4fCdxev5SO8elmbpmbCoyebAhM7jO0ZLUNErhDhsrmVItKT5ZMLR2ZbF98ledeMyBoTw6oY7x4E5q0Z7xtMw0ZUaXqyKawa8XYRdK2IzEUTZgbIB5qlUgpXMawUETdGbjtFhN3krRrpj8XnTPSJhO0RqdbqeODZRqz7K2PuDwGoENGxb7JxMRprQyGyho/wGlsztGcmLBRFsZyc2c1aMxITM4CoiOhPgFOCR4RJ5GaL75I+vsswRr3rtu4GtAE2bzJpMc/NqtFfYIyIx3TS0kGdJO8GniMiGWh2eCa61sxQbxYXrBnFhWuX8CA/K7QNvOaKCCuoZAId7cyq7D3J3qNRc7TgmQp7auaxFyYwXapiuJDFyR4VuXYSv9uJCNnDFRH5nRBeYVHv2w7MoFKrI5tOoVStUbx7yPRm05gr1zBX0X+Id7uIdwc0s6pxokH0iPTl0silUyjX6hifq/C7IFVVpfdpxB0+wttU9naPz6PKtu4OBFPQm1244uwREe92vRpVAfvQPDPYBTwKs6qiKLjlipdIfW9PNo3pYtUy1Iy1yEclWuRcEam1FjVMmVs9Gm1bhrEyIEVEJuKdtWVeftxiz4VwO0n27VjA7Gtja+ao4V705zMo1+p4rpn8x+5osmmKdw+LXot0VTcZIoDd+K6miCiKggV9jddRzBIR7+xl+uBxRhxFV1WV+0NWLgxmdBcQPCLChSvOUzOiR8RfIcKmZuI/vusGpgKaxbwXKzWetSFTyPIcEZN9PEF7lfwitmb8KCLsPT9rY1ZNSn4II9lnwYDZ28bWTCqlcMmQtWeYP2RRX57i3UPCLOa9Xle5WVVeETG/W2Vm1cHm5JXoE2GIF9SkKyKrR/uRUoDxuQoOTpfwfHNiJqjRXaC1NaOqqpAjEr/nryebAvv4ejWqAtbpvVZEOb7rBvZ6mrVmmFk/l0lhsNdZNWCtmZKJahTGe9EPYmsmEEXEojUzVazg0V0TAOK9X0aECpEmqqoKZtXwWzOAYFhtGv34xMxAvE8kSabXZAPvoZkSyrU6Uop8EZq1mGgwLk1khYiYJcLuBBubZZP9EezJpnmM+5Z909jelMODiHZnGM2qpWodzPsbx6kZRVHwN69chTeecZQvf4LdYkUzohzfdUOPicLFECcXZcborcyqxUqNB8rFURHxs93dyaz6wLOHUaurOGZRgac7xx3yiDSZKlb5xaldOwnWGHbO8IkZ8oeERq9JuirzhywZ7JEuDLJ8bNBqfLehiPBQM0ER4UbVhLdlGGuWDuLZg7PYum8q0GV3DGOyqlhExnFqBgD+z1+s9f0zMi4i3kvVGqabF6a4KyI9JtuUGQeaN2Ojkv4iq0KELWIc6MnwPJ+oGejJYsXCXuwen/e1+sDJrHr/tuSM7TLi+SmOAOYPWVDItk3uXWvYwssmZsioGh7swiW2ZtwaVQHrjAfNrNr4PTzUTFh81ymju4w1SwbwC+zFU3unhTCzAFszho2tLDwtl0klwojnFS00z7k1M9F8f6VTCm8LxhUt5t1aEZG9UFsVIuIIeZQ7ZozcduVLcWi6hFEfN7uaWbUGVVVbHl/S/CEAFSIczR/SnrYM0NiIqCiNlsyhmZLQmqFCJCzMNvC6NaoCWm/aeLfKChGjIiKGmmmjux1SiDQL6s17JrGreScapBxubM3EeeFdkLhpzfDR3UI29v4yu/FdpojIborVAs30Pytu/hDGcSP9OM5jrgyDGV1r9cb0nXgeeWF8Ds8dmkU6pWDdcYt8/Z52Epo2/OlPfxrnnHMOCoUChoeHw/o1gdHOiRlGn7CV86m907Twrg1wj4hJa8aVIsJkc8PdKssRsTOr8lTVhGeIMJgP4un9M6jWVeQzqUDbm1atmUKHFHJWWE1mmcE8SAsiSFV1S09Wbz4WOTDlLtDRUhGJ2cRMkIgptsb2zD1PHQAAnLZ8KPbKmEhoZ8JyuYw3v/nNeN/73hfWrwgUliHSjokZEbE9c4gW3oUOu4suiq0ZD4pINtO8WxVOgKqqCopI06zKxnfnWsd3kxzvLsJG0RkrFxUCvSs3Ts3wePcuUURkIt55vHvM/SGAVoCbKyLMrOpOETEWa3FVRIIglVKEEV6tENn47GH8f/+9BQBwwdolkRybV0IrRD75yU/i7//+73HqqaeG9SsChaWqLouqENk3JSgi8T+ZJBWzDbxeFJFsqrV/X6rW+c6LAePUzKzoEemMVFVGKqXwYDMg+LtQbddMszVTYRkind1ZNnuPWTGepEIkY+0RYYqIW4+IUV15/kjwpuk4YTSsPrT9CK667Y8oVuo4/8QR/M0rj43y8FwTqzNhqVTC1NSU7k+72MsVkfZ5RAAIWSLTmiJCrZnQMG3NuNwzA2hmVXHZFpuYSSmafMqnZnTju02zaocoIgD0hUiAo7uA0JqpdJciwt9jFjtZRJKkiNgFmjFFRCZVFTBvzZSrdf6ZPqYDFRFAzBKp4ZHnx3HlrQ9hvlLDK49fjK++46zEqa2xKkRuuukmDA0N8T8rVqxo2+9u58I7kbU86n2aJwpSayY8jIFmU8UKH3t01ZrhYVNCITLf+Dn9+QxvTdh5RDpFEQGAtWEqIhatmc43qzJFxLkQSZIi0mOYgmKUqjWMN6d/ZNcDmG3ffWF8DnW1cdPRqedSZlj9w7OHcMU3H8JsuYaXr16Eb7zrxYk0wbs6E370ox+Foii2f5566inPB7N+/XpMTk7yP7t27fL8s9yyLyKPyPIFvRjIZ3iPk+Ldw6XXML7L7pwWFLKupH6tf6/J5izefVB4/XSL75q/s9RhUzMAcOLYIP//Qd+FdvvUjEyy6pEkFSKsNWNQRHiqajolnf2hKSLazxL9IXEa3Q0Sprj+v18/g+lSFWcfuxD//q6XJPac4qrJ+sEPfhBXXHGF7fesWrXK88Hk83nk8+2vYKeLFd5ra7cioigK1iwdwB93jANoxLt36ocnDhQMrRkvRlVADDTT7sSMo7sAdIvvjsyVcVSut+NyRICQWzNZ86mZ3mxne0QyJu8xK5JUiFiZVQ8IZn3Zc6BZa6aTJ2YYojn8xSsX4JtXvCTRrUpXn+SRkRGMjCQnrU0WpoYM9bq7Kw6KtUsHeSHSqVJiXNCW3jWKBi9GVcB8D4i2Z0Z7DymKguFCFgemSxifLeOo4V5+Z5/0hXciQ71ZfPTSNZgpVrHM5XPpBG/NNAu4OC+8C5JsSl4RYR6kJBQiPQbPD+OAi627DLPWTCdPzDDYc3TG0cO49cqXcPNqUgnt6Hfu3IkjR45g586dqNVq2LRpEwBg9erV6O+Px1pmxp6I/CGMNYKsTRMz4WKMeNcKEXcnrZxJ2JSZIgI0Lg4Hpkv8YtGJiggAvPdVx4Xyc42tGfKItMLMqsnKETFXRNzEn5spImGsGYgbf3fB8Thp6SDecMZRLeebJBJaIfLxj38c3/rWt/h/n3HGGQCAe++9F+edd15Yv9YT+9q4ddeMtUs1WZvCzMKlYFh657U1YxZopoWZ6T9WRsNqpwWahU2LWTXGm3eDRDbQTFVVblZdlIAbGa01oy+wtIV38ufhnMnOJ6aIdOrEDNBYzPrOdcdEfRiBEdqZ8LbbboOqqi1/4laEAGj71l0jJ441ot4Bas2EjXHXzAtcEXFXhGabd2JioJm2Z0Z/h8JDzVghUmVL7zr7QhoURo9It5lVnSLep4pVnjWSCEWE54gYFBGXGSJAqyJSrdWxa7zZmgnYq0SEB92SIZp4d5FCLsONVaSIhEtvrvGWZ4rIHo+tGd6/r7fmiAxYKCJsNLFTWzNhwVoztbqKaq3O9wT1dnqgmYkPyQxW4BZy6US8p6wi3nlrxo0iYpia2TtZRKWmIpdJYWmbtqgT/qFCBNHFu4uce/xiKArwouVDkR1DN8DHdys1FCs1PjLoemom0yqbc0XExCMCQPCIdF6OSJiI4Uylar2Lds3IKSJJCjMDrKdm3G7eBYRCpPkcsYmZoxcGu2aACJfOvqWQhHlEolJEAODG156M919wPCkiIcMuXuVqHS80/SG92TQWSOYWMDIpM7OquSIybPCIdNqumbDJCdNFYh5Lx7dmmA/JoRBJUpgZIES8W+SIjEqGmQGCR6T5meITMws71x/SidAtGaL3iACNfR1UhISPaHDcdmAGQEMNcZvdYiabs2RVo0dkYdMjMsFbM6SIuCGdUrhfolStdV3Eu9OumSRliABCxLtgVq3U6lzZWeJmfDdjLEQ6f2KmE+n6M+FMqcol9ShbM0R7yGdS3Bi87cA0APcZIoBWiFRMds1YeUS4IkIeEdeIWSJs9Lrjl96ZvMfMOMIyRBJgVAXEiHdNEWFqSCaluDLcGhWRHWxiZjEpIkmi6wsR1pYZ6Mno0uqIzkRRFN6eERURt5j1761yRDSzqmFqhhQRafLCltWuac3w91inKSKsNaN9dsRUVTfeDqNHhBSRZNL1Z8Kolt0R0cEMq8+wQsSDIpIzyXgwS1YFbMyq5BGRRgw106ZmOvv5Y1k1VSdFhIWZJaQQyQvtlHqz7cSNqi4nXXKCabxWV7siQ6QToUIkBv4Qor2wEd5nD3ovRLT+feMiUa+rfF9RiyLSvEAUK427edYbz1NrRpq8MPKptWY6+/kzm8wygxUiixJSiIgtSaZkeElVBfRG5t3j8yhV60inlMDXDBDh0vWFSNQZIkT7KTSXpbGCwEtrJisoIqqqYqZchdq8Xhg9In25NJfZj8yVO3LXTNiw52qmVOUX5kKHL70zy6oxI2mKSI/wvmfq4EGequqyEElrP+vp/Q3P1/IFvfzzSSSDrn+19kYc7060H6Ok78msmtI+OtW6yv0huUyqxYSqKJoBb3y2TIFmHmCFyESzvQV0QWtGMuKdtfySoohk0imkm0UW+yzsn3I/ugsYCpGm+Zz8IcmDChFSRLqOXqEAyKQUV7stGNmMZqir1OrCnhnzPBLRJ0Lju+5hUzPjs43nOZNSdLJ8JyIbaHZkJlmKCKCpIuyzcGDafZgZ0Ig9YGrjM/sbrVbyhySPzv4kS7CPPCJdh+gtGBvq4XdnbsgIikilpgqpqubtguFmYNqR2TIPNCNFRB6WxskUkU5XQwDtbt8u4r1crWO66U1KiiICtMa8M4+Ip5uC5vPEWjOkiCSPri9ESBHpPsSLmJe2DKCNVgJ6RcToD2EwRYQVvgAVIm5grRm2r6e3C547GUWEtWXSKcVSjYsjfIS3qYiw1oyXpZ9MGWPmc0pVTR5dXYjMlauYbF5AyCPSPRQCKEQUReEx79WaiulSszXTa34xYDHve8VCpMNbC0HCWzNz2oK3TicjEfHOjaqFbKJ2q+SF1ky1VsfhWe+KCFOOmN+EwsySR1efCdlFoT+faRm5JDoX8W7ay8QMQ7xj1cLMLBQRXog0zNHplMLNiIQzWmumqYh0eKoqILRmbCLetUIkOW0ZQBvHLlbrODRThqo2PhNe2kuiV0hRgOULqBBJGl19JqTR3e5EvIh5VUQAfQS3k1l1gaE1Q2qIO7pSEUlripsVSUtVZTCjdqlS40bVxf05T6qOWIgsG+qllmcC6eqzIVNEqC3TXehaMz4Ukaxwx+qkiLDtvntYIUInS1do47uNgq+bCpFyrQ5VNS9GklqI8NZMtY4DU97bMoB+hHclTcwkks7XN23YO9GQyUkR6S6C8IgAmmG1XK0L8e72isihmcZJlwoRd7DWDFNEusGsKl5ga3WVFyYiSS1ERLPqdPOz43Z0lyEGA9LETDLpbkVkiikiNLrbTYhFgJ8oaL4LpK5iStIjwm5s85Qh4grWmpnrkoV3AHQeIiufSGILkYw2vssUEbd7ZhhZUkQST1efDVm/fhkpIl0Fu4gt7s/7Uia0hVvi+K6FImIwE9LCO3cY4/C7wayaEfwSZYvJmSNzCS1EdB4Rb3tmGKJHhMLMkklXFyLkEelOWLjY0Qv9KWHsQiFOzViN7y7o03+dUlXdYSxEukEREe/0rQyrLFU1aYUIU7iKlRoOsM27LuPdGTlqzSSezr+tsIGNUlKqanfx8tWL8f5Xr8Z5J476+jni4jvW57ZqzfTnM8imFb43JE+KiCuMm4q7oRBJpxSkFKCuAlULRYR5ZpI2vssK8WKlLqSqelREqDWTeLq2EJkv17gDnxSR7iKfSeODF53o++dk+XhlnXtErMyqiqJguJDDwWlmViVFxA2trZnOL0SAhk+kXK1bt2aS6hHhEe817A9IERkdyKPQBS27TqRrz4b7mm/+vlzacj8IQdiREXJEnBQRQDOsAjQ145aW1kyXPH/ZlHWWiKqqXBFJWiHCXs+5co1PknlWRJo/i9SQ5NK1hQgb3R0b6oGiJCcamYgPTBGZK9d4vLSVRwTQ+0SoEHGHsZXVLXe+2QybzGpVRKZLVd7qS1wh0nz/756YR10FUgqwqN/f+C75Q5JL9xYitHWX8AnziDB5HGh4QaxYoFNEuvaj5wnjuHPXtGZSmg/JCDOqFnLpxBW27Hh3HpkD0ChCvGzBBrRI99NXDAdybET76Y7bChP2TdHEDOEPYyHSn8/YnkwXCHetZFZ1RzdOzQCa6ma2+C6po7uAVoi/cKShTHttywDAteeuwiuPX4yTlw0FcmxE++naQoRNzFCGCOEVNr7LChEnr5HoEaFAM3cYC7euUUR4IWKtiCSxEGGvJzPhejWqAg2v1ouWDwdxWEREdO3ZcO8EpaoS/mD9+8PNQsRpgzPLLwEo0MwtrYpId9xD8X1GHaqIMPwoIkTy6d5ChDbvEj7JGhWRXgdFpI+mZrxivHB1TWvGziPCRncTliECtBbiIz4UESL5dG0hQh4Rwi9Gj4iTIrKgj8yqXmlpzXRJIcdbMyZTM+MJzRABWluTXuPdic6gK8+GxUqNXzyWUWuG8AjLETnczEFw8ogsoBwRz3SvWZW1ZloVEdYSXJDAQsT4/l/iceEd0Rl0ZSHClt31ZtOOcjpBWJFr3q1qm3ftFZGFNL7rma7NERHSe40wRWRREgsRw+tJikh305VnQ9EfQmFmhFfENe2AfaoqoA80o/Fdd4hSvqJ0TyHHckTMIt6TrYgYWjNkVu1quuO2wkBdVXHCkn5K4iN8wfr3DLtUVaCRM5JJKajW1a65kAaFuNisN5vumhuITNo64p3FuydRERELcUUBFntMVSU6g64sRF6+ejH+5+9fFfVhEAkn51IRURQFC/oai+9ofNcdqZSCXDqFcq3eNf4QQCsyfvrYHlx+5lG6AuxIhygii/py3AtDdCf06hOER5hszrDavCty2vJh5NIprBrpD+uwOhZmWO2WMDMA+NvzVyOfSeG+pw/itj/s4F8vV+uYbnqTEqmICGZVP2FmRGdAhQhBeCSb0bcHnBQRAPjaO87Eg//nAhob9wDziRSy3SPknrBkAP942VoAwE2/fApP7ZsCAEw02zLplCJVAMcNcQqK/CEEFSIE4ZGsURFx8IgADYNrEqX0OMB8BT1dpIgAwDtfthLnnziCcrWO//29TShWappRtZBFyuOyuCjJZ1JgXSaamCGoECEIj2SNZlUJRYTwDruLLnRZBouiKPiXvzoNi/tz2Lp/Gv9811N8dHdBAlNVgcZjYq8nZYgQVIgQhEdax3eTJ5EniRwrRLpMEQGAkYE8PvtXpwEAbv39Dtz56G4AyUxVZbBQM1JECCpECMIjxqmZJPbqkwQzOHaTWVXk/DWjuOKcYwAAdzzyAoBkFyJMERklRaTroUKEIDwi5ohkUgplg4RMvosVEcZHL12DE5ZoE1dJ9huNNQuQ40Yoz6nboTMnQXhEbM0M9ma7JmQrKrRCpHu9OD3ZNL74v87galwSR3cZX/7rM/Gff3M2Vo8ORH0oRMRQIUIQHskJiojM6C7hDzY1062tGcbapYP4/y4/FSsXFXDB2iVRH45nViws4OWrF0d9GEQMoLMnQXhEDDQjf0j4aDki3V2IAMBfnbUcf3XW8qgPgyACgRQRgvBIVghlIkUkfFYuLDT+dzF5Cgiik6CzJ0F4JJui1kw7ueHCE/AXpy7FSUsHoz4UgiAChM6eBOERURGh1kz45DIpnHLUUNSHQRBEwFBrhiA8ktEpIlSIEARBeIEKEYLwSFY3vkviIkEQhBeoECEIj4iFCCkiBEEQ3qBChCA8Ii69o4V3BEEQ3qBChCA8QooIQRCEf6gQIQiPZEgRIQiC8A0VIgThkaxh1wxBEAThHipECMIj2RQlqxIEQfiFChGC8Eg2QzkiBEEQfqHbOILwSG82jdectAT1uooFBSpECIIgvECFCEF4RFEUfONdL476MAiCIBINtWYIgiAIgogMKkQIgiAIgogMKkQIgiAIgogMKkQIgiAIgogMKkQIgiAIgogMKkQIgiAIgogMKkQIgiAIgoiM0AqRHTt24Oqrr8axxx6L3t5eHHfccbjxxhtRLpfD+pUEQRAEQSSM0ALNnnrqKdTrddx8881YvXo1Nm/ejGuuuQazs7P43Oc+F9avJQiCIAgiQSiqqqrt+mWf/exn8dWvfhXPPfec6d+XSiWUSiX+31NTU1ixYgUmJycxODjYrsMkCIIgCMIHU1NTGBoakrp+t9UjMjk5iYULF1r+/U033YShoSH+Z8WKFW08OoIgCIIg2k3bCpFt27bhS1/6Eq699lrL71m/fj0mJyf5n127drXr8AiCIAiCiADXhchHP/pRKIpi++epp57S/Zvdu3fjkksuwZvf/GZcc801lj87n89jcHBQ94cgCIIgiM7FtUfk4MGDOHz4sO33rFq1CrlcDgCwZ88enHfeeXjZy16G2267DamUfO3jpsdEEARBEEQ8cHP9dj01MzIygpGREanv3b17N84//3ycddZZuPXWW10VIQDAaqSpqSm3h0kQBEEQRESw67aM1hHa+O7u3btx3nnnYeXKlfjc5z6HgwcP8r8bGxuT+hnT09MAQKZVgiAIgkgg09PTGBoasv2e0AqRu+++G9u2bcO2bduwfPly3d/JdoOWLVuGXbt2YWBgAIqiBHp8bDR4165dXdP2ocdMj7lT6bbH3G2PF6DHnLTHrKoqpqensWzZMsfvDa0QueKKK3DFFVf4+hmpVKqliAmabjTF0mPuDugxdz7d9ngBesxJwkkJYdCuGYIgCIIgIoMKEYIgCIIgIqNrC5F8Po8bb7wR+Xw+6kNpG/SYuwN6zJ1Ptz1egB5zJ9PWXTMEQRAEQRAiXauIEARBEAQRPVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGYkuRDZs2IDXvva1WLZsGRRFwY9//GPd3+/fvx9XXHEFli1bhkKhgEsuuQTPPPOM7nvOO++8lu3B733ve3Xfs3PnTlx22WUoFAoYHR3Fhz/8YVSr1bAfnilBPGYA2LhxI1796lejr68Pg4ODOPfcczE/P8///siRI3j729+OwcFBDA8P4+qrr8bMzEzYD88Uv495x44dlpui77jjDv59cXmdg3iN9+3bh3e+850YGxtDX18fzjzzTPzoRz/SfU8nvcYA8Oyzz+KNb3wjRkZGMDg4iLe85S3Yv3+/7nvi9JhvuukmvOQlL8HAwABGR0fxhje8AVu3btV9T7FYxHXXXYdFixahv78fb3rTm1oek8z79re//S3OPPNM5PN5rF69GrfddlvYD8+UoB7z3/3d3+Gss85CPp/H6aefbvq7/vznP+OVr3wlenp6sGLFCvzLv/xLWA/LliAe82OPPYa3ve1tWLFiBXp7e7F27Vp88YtfbPldcXmd3ZLoQmR2dhannXYavvKVr7T8naqqeMMb3oDnnnsOP/nJT/Doo49i5cqVuPDCCzE7O6v73muuuQZ79+7lf8Q3bK1Ww2WXXYZyuYw//OEP+Na3voXbbrsNH//4x0N/fGYE8Zg3btyISy65BBdddBEeeugh/PGPf8T111+vW0r49re/HU888QTuvvtu/PznP8eGDRvwnve8py2P0Yjfx7xixQrd67t371588pOfRH9/Py699FIA8Xqdg3iN3/Wud2Hr1q346U9/iscffxyXX3453vKWt+DRRx/l39NJr/Hs7CwuuugiKIqCe+65B7///e9RLpfx2te+FvV6nf+sOD3m++67D9dddx0eeOAB3H333ahUKrjooot0r+Pf//3f42c/+xnuuOMO3HfffdizZw8uv/xy/vcy79vt27fjsssuw/nnn49NmzbhhhtuwN/8zd/gV7/6VVsfLxDMY2ZcddVVeOtb32r6e6ampnDRRRdh5cqVeOSRR/DZz34Wn/jEJ/D1r389tMdmRRCP+ZFHHsHo6Ci+853v4IknnsA//uM/Yv369fjyl7/MvydOr7Nr1A4BgHrnnXfy/966dasKQN28eTP/Wq1WU0dGRtRvfOMb/GuvetWr1P/9v/+35c/97//+bzWVSqn79u3jX/vqV7+qDg4OqqVSKdDH4Bavj/nss89WP/axj1n+3CeffFIFoP7xj3/kX/vlL3+pKoqi7t69O9gH4RKvj9nI6aefrl511VX8v+P6Ont9vH19feq3v/1t3c9auHAh/55Oe41/9atfqalUSp2cnOTfMzExoSqKot59992qqsb7Mauqqh44cEAFoN53332qqjaOP5vNqnfccQf/ni1btqgA1I0bN6qqKve+/Yd/+Af15JNP1v2ut771rerFF18c9kNyxMtjFrnxxhvV0047reXr//Zv/6YuWLBA99n9yEc+op544onBPwiX+H3MjL/9279Vzz//fP7fcX6dnUi0ImJHqVQCAPT09PCvpVIp5PN53H///brv/c///E8sXrwYp5xyCtavX4+5uTn+dxs3bsSpp56KJUuW8K9dfPHFmJqawhNPPBHyo3CHzGM+cOAAHnzwQYyOjuKcc87BkiVL8KpXvUr3nGzcuBHDw8N48YtfzL924YUXIpVK4cEHH2zTo5HDzevMeOSRR7Bp0yZcffXV/GtJeZ1lH+8555yD22+/HUeOHEG9Xsf3v/99FItFnHfeeQA67zUulUpQFEUX/NTT04NUKsW/J+6PeXJyEgCwcOFCAI33aaVSwYUXXsi/Z82aNTj66KOxceNGAHLv240bN+p+Bvse9jOixMtjlmHjxo0499xzkcvl+NcuvvhibN26FePj4wEdvTeCesyTk5P8ZwDxfp2d6NhChL2Q69evx/j4OMrlMv75n/8ZL7zwAvbu3cu/76//+q/xne98B/feey/Wr1+P//iP/8A73vEO/vf79u3TfcgB8P/et29fex6MJDKP+bnnngMAfOITn8A111yDu+66C2eeeSYuuOAC3nPft28fRkdHdT87k8lg4cKFiXzMRm655RasXbsW55xzDv9aUl5n2cf7gx/8AJVKBYsWLUI+n8e1116LO++8E6tXrwbQea/xy172MvT19eEjH/kI5ubmMDs7iw996EOo1Wr8e+L8mOv1Om644Qa8/OUvxymnnAKgcby5XA7Dw8O6712yZAk/Xpn3rdX3TE1N6Xxh7cbrY5Yhrp/noB7zH/7wB9x+++26tmJcX2cZOrYQyWaz+K//+i88/fTTWLhwIQqFAu69915ceumlOi/Ee97zHlx88cU49dRT8fa3vx3f/va3ceedd+LZZ5+N8Oi9IfOYWb/82muvxZVXXokzzjgDX/jCF3DiiSfim9/8ZpSH7wnZ15kxPz+P7373uzo1JEnIPt5/+qd/wsTEBH7961/j4Ycfxgc+8AG85S1vweOPPx7h0XtD5jGPjIzgjjvuwM9+9jP09/djaGgIExMTOPPMM03fB3Hjuuuuw+bNm/H9738/6kNpG/SYvbF582a8/vWvx4033oiLLroowKOLjkzUBxAmZ511FjZt2oTJyUmUy2WMjIzg7LPP1kmzRs4++2wAwLZt23DcccdhbGwMDz30kO57mJt5bGwsvIP3iNNjXrp0KQDgpJNO0v27tWvXYufOnQAaj+vAgQO6v69Wqzhy5EgiH7PID3/4Q8zNzeFd73qX7utJep2dHu+zzz6LL3/5y9i8eTNOPvlkAMBpp52G3/3ud/jKV76Cr33tax35Gl900UV49tlncejQIWQyGQwPD2NsbAyrVq0CEN/39fXXX8+Ns8uXL+dfHxsbQ7lcxsTEhO5uef/+/fx4Zd63Y2NjLVMn+/fvx+DgIHp7e8N4SI74ecwyWD1m9ndREMRjfvLJJ3HBBRfgPe95Dz72sY/p/i6Or7Ms8b9VCIChoSGMjIzgmWeewcMPP4zXv/71lt+7adMmANoFe926dXj88cd1J7C7774bg4ODLRfzOGH1mI855hgsW7asZXzs6aefxsqVKwE0HvPExAQeeeQR/vf33HMP6vU6L9TiiMzrfMstt+B1r3sdRkZGdF9P4uts9XiZx8moBKTTaa6IdfJrvHjxYgwPD+Oee+7BgQMH8LrXvQ5A/B6zqqq4/vrrceedd+Kee+7Bscceq/v7s846C9lsFr/5zW/417Zu3YqdO3di3bp1AOTet+vWrdP9DPY97Ge0kyAeswzr1q3Dhg0bUKlU+NfuvvtunHjiiViwYIH/B+KCoB7zE088gfPPPx/vfve78elPf7rl98TpdXZNxGZZX0xPT6uPPvqo+uijj6oA1M9//vPqo48+qj7//POqqqrqD37wA/Xee+9Vn332WfXHP/6xunLlSvXyyy/n/37btm3qpz71KfXhhx9Wt2/frv7kJz9RV61apZ577rn8e6rVqnrKKaeoF110kbpp0yb1rrvuUkdGRtT169e3/fGqqv/HrKqq+oUvfEEdHBxU77jjDvWZZ55RP/axj6k9PT3qtm3b+Pdccskl6hlnnKE++OCD6v33368ef/zx6tve9ra2PlZGEI9ZVVX1mWeeURVFUX/5y1+2/F2cXme/j7dcLqurV69WX/nKV6oPPvigum3bNvVzn/ucqiiK+otf/IJ/X6e9xt/85jfVjRs3qtu2bVP/4z/+Q124cKH6gQ98QPc9cXrM73vf+9ShoSH1t7/9rbp3717+Z25ujn/Pe9/7XvXoo49W77nnHvXhhx9W161bp65bt47/vcz79rnnnlMLhYL64Q9/WN2yZYv6la98RU2n0+pdd93V1serqsE8ZlVtfJYfffRR9dprr1VPOOEE/t5hUzITExPqkiVL1He+853q5s2b1e9///tqoVBQb7755rY+XlUN5jE//vjj6sjIiPqOd7xD9zMOHDjAvydOr7NbEl2I3HvvvSqAlj/vfve7VVVV1S9+8Yvq8uXL1Ww2qx599NHqxz72Md04186dO9Vzzz1XXbhwoZrP59XVq1erH/7wh3UjgKqqqjt27FAvvfRStbe3V128eLH6wQ9+UK1UKu18qBy/j5lx0003qcuXL1cLhYK6bt069Xe/+53u7w8fPqy+7W1vU/v7+9XBwUH1yiuvVKenp9vxEFsI6jGvX79eXbFihVqr1Ux/T1xe5yAe79NPP61efvnl6ujoqFooFNQXvehFLeO8nfYaf+QjH1GXLFmiZrNZ9fjjj1f/9V//Va3X67rvidNjNnu8ANRbb72Vf8/8/Lz6t3/7t+qCBQvUQqGgvvGNb1T37t2r+zky79t7771XPf3009VcLqeuWrVK9zvaSVCP+VWvepXpz9m+fTv/nscee0x9xSteoebzefWoo45SP/OZz7TpUeoJ4jHfeOONpj9j5cqVut8Vl9fZLYqqqmpQ6gpBEARBEIQbusIjQhAEQRBEPKFChCAIgiCIyKBChCAIgiCIyKBChCAIgiCIyKBChCAIgiCIyKBChCAIgiCIyKBChCAIgiCIyKBChCAIgiCIyKBChCAIgiCIyKBChCCIUFBVFRdeeCEuvvjilr/7t3/7NwwPD+M73/kOFEXhf0ZGRvAXf/EXePzxxyM4YoIgooAKEYIgQkFRFNx666148MEHcfPNN/Ovb9++Hf/wD/+AL33pS3wd+tatW7F371786le/QqlUwmWXXYZyuRzVoRME0UaoECEIIjRWrFiBL37xi/jQhz6E7du3Q1VVXH311bjooovwzne+k3/f6OgoxsbGcOaZZ+KGG27Arl278NRTT0V45ARBtItM1AdAEERn8+53vxt33nknrrrqKlx++eXYvHkznnjiCdPvnZycxPe//30AQC6Xa+dhEgQREVSIEAQROl//+tdx8sknY8OGDfjRj36EkZER3d+zFs3s7CwA4HWvex3WrFnT9uMkCKL9UGuGIIjQGR0dxbXXXou1a9fiDW94Q8vf/+53v8MjjzyC2267DSeccAK+9rWvtf8gCYKIBFJECIJoC5lMBpmM+Snn2GOPxfDwME488UQcOHAAb33rW7Fhw4Y2HyFBEFFAighBELHiuuuuw+bNm3HnnXdGfSgEQbQBKkQIgogVhUIB11xzDW688Uaoqhr14RAEETJUiBAEETuuv/56bNmyBXfccUfUh0IQRMgoKt1yEARBEAQREaSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGVSIEARBEAQRGf8/nOiGLsfZJ0QAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = 0\n",
        "for x in dataset[\"Time2\"]:\n",
        "  if x in datasettwo[\"Time2\"]:\n",
        "    k = k+1\n",
        "    if x+1 in datasettwo[\"Time2\"]:\n",
        "      if x+2 in datasettwo[\"Time2\"]:\n",
        "        if x+3 in datasettwo[\"Time2\"]:\n",
        "          k=k+1\n",
        "print(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRuvtcV0w95f",
        "outputId": "0b52ac49-3696-4595-ec52-6bd1fe8be3f5"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "244989\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WjYxiKmSx4UO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train test split by years\n",
        "trainds = datasetthree[datasetthree[\"Time2\"]<=392169]\n",
        "testsds = datasetthree[datasetthree[\"Time2\"]>392169]"
      ],
      "metadata": {
        "id": "NtRnXk5nwQbP"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# grab anoms columns (ONI) and reset index\n",
        "# reshape to be (samples, features)\n",
        "# number of features here is just 1 (ONI)\n",
        "trainds = trainds.reset_index(drop=True)[\"106409_00065\"].values.reshape(-1, 1)\n",
        "testsds = testsds.reset_index(drop=True)[\"106409_00065\"].values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "_O7iYwVZvEyB"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking shapes -- make sure (samples, features)\n",
        "print(trainds.shape, testsds.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qRzFiAK5oln",
        "outputId": "6b89674f-3841-4de8-e784-dcc91b168855"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(392000, 1) (98184, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "sc = MinMaxScaler(feature_range=(0,1))"
      ],
      "metadata": {
        "id": "7S-QdRBHmCjm"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# do rescaling\n",
        "trainds_scaled = sc.fit_transform(trainds)\n",
        "testsds_scaled = sc.transform(testsds)"
      ],
      "metadata": {
        "id": "a6M2k9RF8cXZ"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Many-to-One LSTM"
      ],
      "metadata": {
        "id": "_1L5-a5eb3TK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# for help with data manipulation for time series rnn training; we can use the time series generator from keras\n",
        "\n",
        "# dummy input for understanding\n",
        "# data = np.array([[i] for i in range(50)])\n",
        "\n",
        "# here goes our training data\n",
        "data = trainds\n",
        "\n",
        "# the shape of your target is your prediction shape (e.g., here we make it 1)\n",
        "target = data\n",
        "\n",
        "# the length of the input feature\n",
        "n_input=4 # you can change this! doesn't HAVE to be 4 months input\n",
        "n_features=data.shape[1]\n",
        "\n",
        "# this is the generator that will go into our rnn for training\n",
        "data_gen = TimeseriesGenerator(data,\n",
        "                               target,\n",
        "                               length=n_input,\n",
        "                               sampling_rate=1,\n",
        "                               batch_size=1000, # you can try different batch sizes\n",
        "                               shuffle=True)"
      ],
      "metadata": {
        "id": "zUb6c5liFwG5"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shapes\n",
        "print(data.shape, target.shape, n_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FreObmKXt8Fc",
        "outputId": "c80f31a5-b5ba-441e-8101-1a7fb1c49b1e"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(392000, 1) (392000, 1) 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# quick check of output\n",
        "batch_0 = data_gen[0]\n",
        "x, y = batch_0\n",
        "print(x[0], y[0])\n",
        "print(x.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KThyhWlzXq6",
        "outputId": "200690fe-1d70-42ed-eb3d-c5cf33fde2e6"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.14]\n",
            " [1.13]\n",
            " [1.13]\n",
            " [1.13]] [1.12]\n",
            "(1000, 4, 1) (1000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# many-to-one example\n",
        "# lets make our rnn!\n",
        "regressor = Sequential()\n",
        "\n",
        "# LSTM; input shape is 1 (for one time step at a time) and n_features which is also 1 for our toy example\n",
        "regressor.add(keras.layers.Input(shape=(1, n_features), batch_size=1000))\n",
        "\n",
        "# don't return sequence; just the last output of the input sequence\n",
        "regressor.add(LSTM(units=100, return_sequences=False))\n",
        "\n",
        "# one neuron for output prediction\n",
        "regressor.add(Dense(1))\n",
        "regressor.add(Activation('linear'))\n",
        "\n",
        "# compile model\n",
        "regressor.compile(optimizer='adam', loss='mean_squared_error', metrics=['mse', 'mae'])\n",
        "\n",
        "# what does graph look like?\n",
        "regressor.summary()"
      ],
      "metadata": {
        "id": "dCWIVnYxmCau",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f0747a0-2eda-4d54-cf42-224219c687dd"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (1000, 100)               40800     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (1000, 1)                 101       \n",
            "                                                                 \n",
            " activation_2 (Activation)   (1000, 1)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 40901 (159.77 KB)\n",
            "Trainable params: 40901 (159.77 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train the lstm!\n",
        "regressor.fit(data_gen, validation_split=0.1, epochs=5, shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "Aue4JDCWyy9L",
        "outputId": "58c7ce24-89f3-4562-9001-dd1b953df44c"
      },
      "execution_count": 129,
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "`validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'keras.src.preprocessing.sequence.TimeseriesGenerator'>]",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-129-faa226b8ad73>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# train the lstm!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/data_adapter.py\u001b[0m in \u001b[0;36mtrain_validation_split\u001b[0;34m(arrays, validation_split)\u001b[0m\n\u001b[1;32m   1774\u001b[0m     \u001b[0munsplitable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mflat_arrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_can_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0munsplitable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1776\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1777\u001b[0m             \u001b[0;34m\"`validation_split` is only supported for Tensors or NumPy \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1778\u001b[0m             \u001b[0;34m\"arrays, found following types in the input: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munsplitable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'keras.src.preprocessing.sequence.TimeseriesGenerator'>]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# here goes our testing data that was already scaled\n",
        "testdata = testsds_scaled\n",
        "\n",
        "testtarget = testdata\n",
        "\n",
        "testdata_gen = TimeseriesGenerator(\n",
        "    testdata,\n",
        "    testtarget,\n",
        "    length=n_input,\n",
        "    sampling_rate=1,\n",
        "    batch_size=testdata.shape[0], # all samples\n",
        "    shuffle=False)"
      ],
      "metadata": {
        "id": "VvqyVkKR9OgI"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# quick check of output\n",
        "batch_0 = testdata_gen[0]\n",
        "x, y = batch_0\n",
        "print(x[0], y[0])\n",
        "print(x.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yD-8nXD4-rZu",
        "outputId": "49d04590-682c-4f9f-9b6b-930b0c3f836c"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.09513742]\n",
            " [0.09513742]\n",
            " [0.09513742]\n",
            " [0.09513742]] [0.09513742]\n",
            "(98350, 4, 1) (98350, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict using test data\n",
        "oni_predictions = regressor.predict(testdata_gen)"
      ],
      "metadata": {
        "id": "DPA3UGBsmCXX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd2d113b-367d-48c8-f2cd-94c7c531da45"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 6s 6s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# quick check that we have a match of shapes\n",
        "print(oni_predictions.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHY10TFu_Hp5",
        "outputId": "2d8a28cd-ba35-413f-aa9e-e49dd8909c98"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(98350, 1) (98350, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# how did we do!?\n",
        "plt.scatter(\n",
        "    y,\n",
        "    oni_predictions\n",
        "    )\n",
        "#plt.xlim([-3,3])\n",
        "#plt.ylim([-3,3])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ZPeSf6qzGrd1",
        "outputId": "5cfb9c9d-97dc-4de6-88b6-e87a2f8d2dde"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Many-to-Many LSTM"
      ],
      "metadata": {
        "id": "ydKfkPRzfuLD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# redoing data preprocessing steps here more quickly\n",
        "\n",
        "# reading dataset from NOAA online\n",
        "dataset = pd.read_csv(\n",
        "    'http://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt',\n",
        "    delim_whitespace=True)\n",
        "\n",
        "# drop any nans\n",
        "dataset = dataset.dropna(subset=[\"ANOM\"])\n",
        "\n",
        "# train test split by years\n",
        "trainds = dataset[dataset[\"YR\"]<=2010]\n",
        "testsds = dataset[dataset[\"YR\"]>2010]\n",
        "\n",
        "# grab anoms columns (ONI) and reset index\n",
        "# reshape to be (samples, features)\n",
        "# number of features here is just 1 (ONI)\n",
        "trainds = trainds.reset_index(drop=True)[\"ANOM\"].values.reshape(-1, 1)\n",
        "testsds = testsds.reset_index(drop=True)[\"ANOM\"].values.reshape(-1, 1)\n",
        "\n",
        "# Feature Scaling\n",
        "sc = MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "# do rescaling\n",
        "trainds_scaled = sc.fit_transform(trainds)\n",
        "testsds_scaled = sc.transform(testsds)"
      ],
      "metadata": {
        "id": "HyKsK1p2frBp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for help with data manipulation for time series rnn training; we can use the time series generator from keras\n",
        "\n",
        "# dummy input for understanding\n",
        "# data = np.array([[i] for i in range(50)])\n",
        "\n",
        "# here goes our training data\n",
        "data = trainds_scaled\n",
        "\n",
        "# the shape of your target is your prediction shape\n",
        "target = np.concatenate((np.roll(data, 0, axis=0),\n",
        "                         np.roll(data, -1, axis=0),\n",
        "                         np.roll(data, -2, axis=0),\n",
        "                         #np.roll(data, -3, axis=0)\n",
        "                        ), axis=1)\n",
        "\n",
        "# the length of the input feature\n",
        "n_input=4\n",
        "n_features=data.shape[1]\n",
        "n_pred=target.shape[1]\n",
        "\n",
        "# this is the generator that will go into our rnn for training\n",
        "data_gen = TimeseriesGenerator(data,\n",
        "                               target,\n",
        "                               length=n_input,\n",
        "                               sampling_rate=1,\n",
        "                               batch_size=16,\n",
        "                               shuffle=True)"
      ],
      "metadata": {
        "id": "780eNEwefrJt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# quick check of output\n",
        "batch_0 = data_gen[0]\n",
        "x, y = batch_0\n",
        "print(x[0], y[0])\n",
        "print(x.shape, y.shape, n_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFMqsQTdfrM2",
        "outputId": "3b36c3c0-76c0-4a28-feec-b049909d4cea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.37923251]\n",
            " [0.4717833 ]\n",
            " [0.55079007]\n",
            " [0.60948081]] [0.66591422 0.71331828 0.76749436]\n",
            "(16, 4, 1) (16, 3) 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# many-to-one example\n",
        "\n",
        "# lets make our rnn!\n",
        "regressor = Sequential()\n",
        "\n",
        "# LSTM; input shape is 1 (for one time step at a time) and n_features which is also 1 for our toy example\n",
        "regressor.add(keras.layers.Input(shape=(1, n_features), batch_size=16))\n",
        "\n",
        "# encoder is needed because we are using many to predict future manys\n",
        "# return sequence\n",
        "regressor.add(LSTM(units=50, return_sequences=False))\n",
        "\n",
        "# one neuron for output prediction needs to be repeated for each future many prediction\n",
        "regressor.add(RepeatVector(n_pred))\n",
        "\n",
        "# decoder to predict; return each prediction so return_sequence=True\n",
        "# return sequence\n",
        "regressor.add(LSTM(units=50, return_sequences=True))\n",
        "\n",
        "# predict 1 value per returned sequence value\n",
        "regressor.add(TimeDistributed(Dense(1)))\n",
        "regressor.add(Activation('linear'))\n",
        "\n",
        "# compile model\n",
        "regressor.compile(optimizer='adam', loss='mean_squared_error', metrics=['mse', 'mae'])\n",
        "\n",
        "# what does graph look like?\n",
        "regressor.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FVy06vcfrPe",
        "outputId": "29636342-b45f-4831-b945-ddb633d27d89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_1 (LSTM)               (16, 50)                  10400     \n",
            "                                                                 \n",
            " repeat_vector (RepeatVecto  (16, 3, 50)               0         \n",
            " r)                                                              \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (16, 3, 50)               20200     \n",
            "                                                                 \n",
            " time_distributed (TimeDist  (16, 3, 1)                51        \n",
            " ributed)                                                        \n",
            "                                                                 \n",
            " activation_1 (Activation)   (16, 3, 1)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 30651 (119.73 KB)\n",
            "Trainable params: 30651 (119.73 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train the lstm!\n",
        "regressor.fit(data_gen, epochs=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaipkME-hYxO",
        "outputId": "d3b9c523-3e5a-47a0-db8d-11ae679b27c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "46/46 [==============================] - 6s 13ms/step - loss: 0.0764 - mse: 0.0764 - mae: 0.2227\n",
            "Epoch 2/20\n",
            "46/46 [==============================] - 1s 14ms/step - loss: 0.0258 - mse: 0.0258 - mae: 0.1262\n",
            "Epoch 3/20\n",
            "46/46 [==============================] - 1s 15ms/step - loss: 0.0155 - mse: 0.0155 - mae: 0.0965\n",
            "Epoch 4/20\n",
            "46/46 [==============================] - 1s 13ms/step - loss: 0.0146 - mse: 0.0146 - mae: 0.0930\n",
            "Epoch 5/20\n",
            "46/46 [==============================] - 1s 14ms/step - loss: 0.0157 - mse: 0.0157 - mae: 0.0954\n",
            "Epoch 6/20\n",
            "46/46 [==============================] - 1s 15ms/step - loss: 0.0142 - mse: 0.0142 - mae: 0.0920\n",
            "Epoch 7/20\n",
            "46/46 [==============================] - 1s 16ms/step - loss: 0.0125 - mse: 0.0125 - mae: 0.0860\n",
            "Epoch 8/20\n",
            "46/46 [==============================] - 1s 19ms/step - loss: 0.0109 - mse: 0.0109 - mae: 0.0814\n",
            "Epoch 9/20\n",
            "46/46 [==============================] - 1s 12ms/step - loss: 0.0103 - mse: 0.0103 - mae: 0.0787\n",
            "Epoch 10/20\n",
            "46/46 [==============================] - 1s 15ms/step - loss: 0.0068 - mse: 0.0068 - mae: 0.0642\n",
            "Epoch 11/20\n",
            "46/46 [==============================] - 0s 8ms/step - loss: 0.0061 - mse: 0.0061 - mae: 0.0596\n",
            "Epoch 12/20\n",
            "46/46 [==============================] - 0s 10ms/step - loss: 0.0066 - mse: 0.0066 - mae: 0.0626\n",
            "Epoch 13/20\n",
            "46/46 [==============================] - 1s 14ms/step - loss: 0.0054 - mse: 0.0054 - mae: 0.0573\n",
            "Epoch 14/20\n",
            "46/46 [==============================] - 1s 14ms/step - loss: 0.0052 - mse: 0.0052 - mae: 0.0550\n",
            "Epoch 15/20\n",
            "46/46 [==============================] - 1s 11ms/step - loss: 0.0057 - mse: 0.0057 - mae: 0.0572\n",
            "Epoch 16/20\n",
            "46/46 [==============================] - 1s 11ms/step - loss: 0.0059 - mse: 0.0059 - mae: 0.0590\n",
            "Epoch 17/20\n",
            "46/46 [==============================] - 0s 9ms/step - loss: 0.0058 - mse: 0.0058 - mae: 0.0579\n",
            "Epoch 18/20\n",
            "46/46 [==============================] - 0s 7ms/step - loss: 0.0056 - mse: 0.0056 - mae: 0.0572\n",
            "Epoch 19/20\n",
            "46/46 [==============================] - 0s 7ms/step - loss: 0.0053 - mse: 0.0053 - mae: 0.0551\n",
            "Epoch 20/20\n",
            "46/46 [==============================] - 0s 7ms/step - loss: 0.0052 - mse: 0.0052 - mae: 0.0550\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f82d76d6d10>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# here goes our testing data\n",
        "testdata = testsds_scaled\n",
        "\n",
        "# make sure you make this match your training target\n",
        "testtarget = np.concatenate((\n",
        "    np.roll(testdata, 0, axis=0),\n",
        "    np.roll(testdata, -1, axis=0),\n",
        "    np.roll(testdata, -2, axis=0),\n",
        "    #np.roll(testdata, -3, axis=0)\n",
        "    ), axis=1)\n",
        "\n",
        "testdata_gen = TimeseriesGenerator(\n",
        "    testdata,\n",
        "    testtarget,\n",
        "    length=n_input,\n",
        "    sampling_rate=1,\n",
        "    batch_size=testdata.shape[0], # all samples\n",
        "    shuffle=False)"
      ],
      "metadata": {
        "id": "3_b36aCChZPy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# quick check of output\n",
        "batch_0 = testdata_gen[0]\n",
        "x, y = batch_0\n",
        "print(x[0], y[0])\n",
        "print(x.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pa9VaqHPiDoE",
        "outputId": "ff6da512-1858-4061-f51a-0153990fc1ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.13769752]\n",
            " [0.18961625]\n",
            " [0.248307  ]\n",
            " [0.29345372]] [0.33408578 0.35891648 0.34988713]\n",
            "(153, 4, 1) (153, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make predictions\n",
        "oni_predictions = regressor.predict(testdata_gen)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZQlHOxuh0Zy",
        "outputId": "c845e2e4-e9b6-432a-e951-7753e5219060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check shapes match\n",
        "print(oni_predictions.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIHWVyFIFBxt",
        "outputId": "4093be74-8b58-4622-bdb1-d48b0f0beeca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(153, 3, 1) (153, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0, n_pred):\n",
        "    # how did we do!?\n",
        "    plt.scatter(\n",
        "        sc.inverse_transform(y[:, i].reshape(-1, 1)).reshape(-1),\n",
        "        sc.inverse_transform(oni_predictions[:, i].reshape(-1, 1)).reshape(-1)\n",
        "        )\n",
        "    plt.xlim([-3,3])\n",
        "    plt.ylim([-3,3])\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WMaxU9aZkjFP",
        "outputId": "221d0cf8-8071-4c0f-9510-2b0c664ae98f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aZz0E1tTmlaI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}