well_test_geothermal.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "fE07fvbOZXnG23CTJ1J4",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          },
          "outputId": null
        },
        "id": "UaR8V7v3dUNF"
      },
      "outputs": [],
      "source": [
        "import openpyxl\n",
        "import pandas as pd\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "XAwQM8ffvMIGuJgSJGr9",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": null
        },
        "id": "GZCGuK_hdUNH"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "flowrate = pd.read_excel(r'Data-FlowRate.xlsx', header=1) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "Ck6YMVamlovJ0JAVaDbr",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "OHZeaGR0WZdLclWhdc4O",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ihirMQJ7dUNK",
        "outputId": "b43b9c35-7a5d-40f9-a52d-53803417a5b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                raw_datetime  flow_Lpm\n",
              "0  2020-12-11T06:00:00+13:00       0.0\n",
              "1  2020-12-11T06:01:00+13:00       0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb68ce18-1402-4d36-ad0f-fd5bb4f3b293\">\n",
              "    <div 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>raw_datetime</th>\n",
              "      <th>flow_Lpm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-11T06:00:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11T06:01:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb68ce18-1402-4d36-ad0f-fd5bb4f3b293')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-cb68ce18-1402-4d36-ad0f-fd5bb4f3b293 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-cb68ce18-1402-4d36-ad0f-fd5bb4f3b293');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "flowrate.columns = ['raw_datetime','flow_Lpm'] \n",
        "\n",
        "flowrate.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "Mj04KyYGfEwQ2rZNKAm2",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "ZWLsh0igdUNL"
      },
      "source": [
        "### 2.1.3 Convert datetime string into Python datetime \n",
        "\n",
        "The datetime is in [ISO format](https://en.wikipedia.org/wiki/ISO_8601) and, because it includes the timezone, this is the most globally useful datetime format. \n",
        "\n",
        "> 2020-12-11T06:00:00+13:00 \n",
        "\n",
        "where T indicates the time data and +13:00 is the time shift between UTC and New Zealand where the pumps were recording data. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "hnDxNSzTzX6ZCuTyvz1h",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "wyjsp4o9p6ICo3S4GOpo",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_um7gkbadUNL",
        "outputId": "cfbf6339-53db-4fd5-c5dc-6a43e75a536c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 711 entries, 0 to 710\n",
            "Data columns (total 2 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   raw_datetime  711 non-null    object \n",
            " 1   flow_Lpm      711 non-null    float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 11.2+ KB\n"
          ]
        }
      ],
      "source": [
        "flowrate.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "HytLUJyBpSF8it24TH8E",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "TIcMtiRedUNM"
      },
      "source": [
        "Look at the output above and note that the _raw_datetime_ data type is _object_ and not _datetime_\n",
        "\n",
        "We will use a two-step process to parse this date:\n",
        "\n",
        "1. Convert it to a datetime object with the default UTC timezone\n",
        "2. Convert the datetime object to local time in New Zealand so it can be combined with the pts tool data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "MyHm58bTP0oSeh1EbL0v",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "NTMqZWwSzDNjcxDOF54j",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "V6IdHTvHdUNN",
        "outputId": "c7e804fc-993c-4226-d65b-fee43e96f230"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  raw_datetime  flow_Lpm              ISO_datetime\n",
              "709  2020-12-11T17:59:00+13:00       0.0 2020-12-11 17:59:00+13:00\n",
              "710  2020-12-11T18:00:00+13:00       0.0 2020-12-11 18:00:00+13:00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db6ba2e4-9d1d-4abc-b252-82c4b751a75d\">\n",
              "    <div 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>raw_datetime</th>\n",
              "      <th>flow_Lpm</th>\n",
              "      <th>ISO_datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>2020-12-11T17:59:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 17:59:00+13:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>710</th>\n",
              "      <td>2020-12-11T18:00:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 18:00:00+13:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db6ba2e4-9d1d-4abc-b252-82c4b751a75d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-db6ba2e4-9d1d-4abc-b252-82c4b751a75d 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-db6ba2e4-9d1d-4abc-b252-82c4b751a75d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# use a method that recognises the ISO format to parse the datetime\n",
        "\n",
        "list = []\n",
        "for date in flowrate['raw_datetime']:\n",
        "    newdate = datetime.fromisoformat(date)\n",
        "    list.append(newdate)\n",
        "flowrate['ISO_datetime'] = list\n",
        "\n",
        "flowrate.tail(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "n1FGWrr9U5cTFkTzFauT",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "51nPhD8tdUNO"
      },
      "source": [
        "The method fromisoformate() is new in version Python 3.7\n",
        "\n",
        "More information can be found [here](https://docs.python.org/3/library/datetime.html) and [here](https://pythontic.com/datetime/date/fromisoformat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "HVm6Xfee9rCcySq8FrcM",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "7ZZ614JH8uV2QNWG0qyC",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aV5Wy7f8dUNP",
        "outputId": "137e7c31-3dba-4baf-b1d2-41621deabb3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 711 entries, 0 to 710\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype                    \n",
            "---  ------        --------------  -----                    \n",
            " 0   raw_datetime  711 non-null    object                   \n",
            " 1   flow_Lpm      711 non-null    float64                  \n",
            " 2   ISO_datetime  711 non-null    datetime64[ns, UTC+13:00]\n",
            "dtypes: datetime64[ns, UTC+13:00](1), float64(1), object(1)\n",
            "memory usage: 16.8+ KB\n"
          ]
        }
      ],
      "source": [
        "flowrate.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "YpzJ4SDmAJKvvKrq7W9a",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "L0WoQMprc81JUoXjWc2x",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nmZpHDBWdUNQ",
        "outputId": "0ec3b6fb-12ab-46f6-856e-fa6f9dc9bd16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                raw_datetime  flow_Lpm              ISO_datetime  \\\n",
              "0  2020-12-11T06:00:00+13:00       0.0 2020-12-11 06:00:00+13:00   \n",
              "1  2020-12-11T06:01:00+13:00       0.0 2020-12-11 06:01:00+13:00   \n",
              "2  2020-12-11T06:02:00+13:00       0.0 2020-12-11 06:02:00+13:00   \n",
              "3  2020-12-11T06:03:00+13:00       0.0 2020-12-11 06:03:00+13:00   \n",
              "4  2020-12-11T06:04:00+13:00       0.0 2020-12-11 06:04:00+13:00   \n",
              "\n",
              "             datetime  \n",
              "0 2020-12-11 06:00:00  \n",
              "1 2020-12-11 06:01:00  \n",
              "2 2020-12-11 06:02:00  \n",
              "3 2020-12-11 06:03:00  \n",
              "4 2020-12-11 06:04:00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-645e7d26-353e-4645-8df9-b4ade0112a92\">\n",
              "    <div 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>raw_datetime</th>\n",
              "      <th>flow_Lpm</th>\n",
              "      <th>ISO_datetime</th>\n",
              "      <th>datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-11T06:00:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:00:00+13:00</td>\n",
              "      <td>2020-12-11 06:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11T06:01:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:01:00+13:00</td>\n",
              "      <td>2020-12-11 06:01:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-11T06:02:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:02:00+13:00</td>\n",
              "      <td>2020-12-11 06:02:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-12-11T06:03:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:03:00+13:00</td>\n",
              "      <td>2020-12-11 06:03:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-12-11T06:04:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:04:00+13:00</td>\n",
              "      <td>2020-12-11 06:04:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-645e7d26-353e-4645-8df9-b4ade0112a92')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-645e7d26-353e-4645-8df9-b4ade0112a92 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-645e7d26-353e-4645-8df9-b4ade0112a92');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "list = []\n",
        "for date in flowrate.ISO_datetime:\n",
        "    newdate = pd.to_datetime(datetime.strftime(date,'%Y-%m-%d %H:%M:%S'))\n",
        "    list.append(newdate)\n",
        "flowrate['datetime'] = list\n",
        "\n",
        "flowrate.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "WvDArWfvoM5ImPYB2YW0",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "lLo98YRejPuSHAM7WjSR",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XP1t9AJ5dUNR",
        "outputId": "298819e0-4e74-467b-da85-a5d0d35f8e68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 711 entries, 0 to 710\n",
            "Data columns (total 4 columns):\n",
            " #   Column        Non-Null Count  Dtype                    \n",
            "---  ------        --------------  -----                    \n",
            " 0   raw_datetime  711 non-null    object                   \n",
            " 1   flow_Lpm      711 non-null    float64                  \n",
            " 2   ISO_datetime  711 non-null    datetime64[ns, UTC+13:00]\n",
            " 3   datetime      711 non-null    datetime64[ns]           \n",
            "dtypes: datetime64[ns, UTC+13:00](1), datetime64[ns](1), float64(1), object(1)\n",
            "memory usage: 22.3+ KB\n"
          ]
        }
      ],
      "source": [
        "flowrate.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "6BFTl8xFoDEaZNGUic1c",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "GeZmvtGpdUNR"
      },
      "source": [
        "Now we have a Python datetime object that we can combine with the PTS data which does not have timezone information.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "soaYNI5DXzIquCDLvcXP",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "ptiX3erqdUNS"
      },
      "source": [
        "### 2.1.4 Add a flowrate field in our desired units\n",
        "\n",
        "Conversions for typical geothermal flowrate units:\n",
        "\n",
        "- L/sec to t/hr 3.6\n",
        "- L/min to t/hr 0.060"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "MaZLlmJ5MsVfCyoSxsRE",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "zTLrvR0D11VlqNTG67S5",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "kfm4dSkCdUNS",
        "outputId": "41d9961e-107d-45a3-e74c-c64ab8085457"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                raw_datetime  flow_Lpm              ISO_datetime  \\\n",
              "0  2020-12-11T06:00:00+13:00       0.0 2020-12-11 06:00:00+13:00   \n",
              "1  2020-12-11T06:01:00+13:00       0.0 2020-12-11 06:01:00+13:00   \n",
              "\n",
              "             datetime  flow_tph  \n",
              "0 2020-12-11 06:00:00       0.0  \n",
              "1 2020-12-11 06:01:00       0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d966aa33-921d-4ce2-b468-a50792b98d9d\">\n",
              "    <div 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>raw_datetime</th>\n",
              "      <th>flow_Lpm</th>\n",
              "      <th>ISO_datetime</th>\n",
              "      <th>datetime</th>\n",
              "      <th>flow_tph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-11T06:00:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:00:00+13:00</td>\n",
              "      <td>2020-12-11 06:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11T06:01:00+13:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-12-11 06:01:00+13:00</td>\n",
              "      <td>2020-12-11 06:01:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d966aa33-921d-4ce2-b468-a50792b98d9d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d966aa33-921d-4ce2-b468-a50792b98d9d 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-d966aa33-921d-4ce2-b468-a50792b98d9d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "flowrate['flow_tph'] = flowrate.flow_Lpm * 0.060\n",
        "\n",
        "flowrate.head(2) # 23 to see the non-zero values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "EI77qWidXjIrwTdTxzeq",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "Z-nIlDcIdUNT"
      },
      "source": [
        "### 2.1.5 Trim excess columns from the dataframe\n",
        "\n",
        "Now we just drop the columns that we don't want from the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "Vaaev33GaFciqtS3yEu5",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "M5MWzVSwPhEXTXioMhMm",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "5Xdl370qdUNT",
        "outputId": "40e16e9e-0ed8-4788-9e2d-412e42f25ceb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             datetime  flow_tph\n",
              "0 2020-12-11 06:00:00       0.0\n",
              "1 2020-12-11 06:01:00       0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b20df30-a149-4da9-8c0e-a70bb2a65981\">\n",
              "    <div 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>datetime</th>\n",
              "      <th>flow_tph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-11 06:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11 06:01:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b20df30-a149-4da9-8c0e-a70bb2a65981')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9b20df30-a149-4da9-8c0e-a70bb2a65981 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-9b20df30-a149-4da9-8c0e-a70bb2a65981');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "flowrate.drop(columns = ['raw_datetime', 'flow_Lpm', 'ISO_datetime'], inplace = True)\n",
        "\n",
        "flowrate.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "hf7FUr31yjWOIPj7fVVa",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": null
        },
        "id": "pRkUQA61dUNW"
      },
      "outputs": [],
      "source": [
        "pts = pd.read_excel(r'Data-PTS.xlsx') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "fJXX8wbHbsydE9x2RLXE",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "bFwYj2XhS1scgduCBN9t",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qN5RtlWmdUNX",
        "outputId": "f793973c-3983-4fc8-bd4a-860fb4b5cbda"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(101294, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "pts.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "OdgCM1r85MBVNnJYmqlW",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "eNdu1lweTTSSL0DPKCiy",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "gB7PEC7cdUNY",
        "outputId": "382feb68-d518-48cc-e55d-bf7997726907"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date             Time    Timestamp    DEPTH SPEED  \\\n",
              "0           yyyy-mm-dd     hh:mm:ss.sss         days        m   m/s   \n",
              "1  2020-12-11 00:00:00  00:26:44.800000  44176.39357 -6.32025   NaN   \n",
              "2  2020-12-11 00:00:00         00:26:45  44176.39358 -6.32025   NaN   \n",
              "\n",
              "  Cable Weight       WHP Temperature  Pressure  Frequency  ... SGS Voltage  \\\n",
              "0           kg      barg          ∞C      bara         Hz  ...           V   \n",
              "1    24.957275 -0.607066   19.702093  0.498792  40.648876  ...           0   \n",
              "2    25.211151 -0.606777    19.68925  0.485864  41.483227  ...           0   \n",
              "\n",
              "  Internal Temp 1 Internal Temp 2 Internal Temp 3 Cal Temp Error Code 1  \\\n",
              "0              ∞C              ∞C              ∞C       ∞C        Count   \n",
              "1       24.999414       24.999414         21.8269        0            0   \n",
              "2       24.999414       24.999414         21.8269        0            0   \n",
              "\n",
              "  Error Code 2 Error Code 3 Records Saved Bad Pages  \n",
              "0        Count        Count         Count     Count  \n",
              "1            0            0       7948034         0  \n",
              "2            0            0       7948034         0  \n",
              "\n",
              "[3 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-87b63461-806b-491b-9fb2-fc2204893b0f\">\n",
              "    <div 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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DEPTH</th>\n",
              "      <th>SPEED</th>\n",
              "      <th>Cable Weight</th>\n",
              "      <th>WHP</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>...</th>\n",
              "      <th>SGS Voltage</th>\n",
              "      <th>Internal Temp 1</th>\n",
              "      <th>Internal Temp 2</th>\n",
              "      <th>Internal Temp 3</th>\n",
              "      <th>Cal Temp</th>\n",
              "      <th>Error Code 1</th>\n",
              "      <th>Error Code 2</th>\n",
              "      <th>Error Code 3</th>\n",
              "      <th>Records Saved</th>\n",
              "      <th>Bad Pages</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yyyy-mm-dd</td>\n",
              "      <td>hh:mm:ss.sss</td>\n",
              "      <td>days</td>\n",
              "      <td>m</td>\n",
              "      <td>m/s</td>\n",
              "      <td>kg</td>\n",
              "      <td>barg</td>\n",
              "      <td>∞C</td>\n",
              "      <td>bara</td>\n",
              "      <td>Hz</td>\n",
              "      <td>...</td>\n",
              "      <td>V</td>\n",
              "      <td>∞C</td>\n",
              "      <td>∞C</td>\n",
              "      <td>∞C</td>\n",
              "      <td>∞C</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:44.800000</td>\n",
              "      <td>44176.39357</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.957275</td>\n",
              "      <td>-0.607066</td>\n",
              "      <td>19.702093</td>\n",
              "      <td>0.498792</td>\n",
              "      <td>40.648876</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:45</td>\n",
              "      <td>44176.39358</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.211151</td>\n",
              "      <td>-0.606777</td>\n",
              "      <td>19.68925</td>\n",
              "      <td>0.485864</td>\n",
              "      <td>41.483227</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87b63461-806b-491b-9fb2-fc2204893b0f')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-87b63461-806b-491b-9fb2-fc2204893b0f 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-87b63461-806b-491b-9fb2-fc2204893b0f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "pts.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "3TBoCJrLMKA7WHfBZeCw",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "fnFVhFuFev20Ibcv5SjU",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "nDv0dZdAdUNZ",
        "outputId": "4fa9bdfb-d0cb-4710-eaf7-ba823d061e36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date             Time    Timestamp  depth_m speed_mps  \\\n",
              "0           yyyy-mm-dd     hh:mm:ss.sss         days        m       m/s   \n",
              "1  2020-12-11 00:00:00  00:26:44.800000  44176.39357 -6.32025       NaN   \n",
              "2  2020-12-11 00:00:00         00:26:45  44176.39358 -6.32025       NaN   \n",
              "\n",
              "  cweight_kg  whp_barg  temp_degC pressure_bara frequency_hz  ... SGS Voltage  \\\n",
              "0         kg      barg         ∞C          bara           Hz  ...           V   \n",
              "1  24.957275 -0.607066  19.702093      0.498792    40.648876  ...           0   \n",
              "2  25.211151 -0.606777   19.68925      0.485864    41.483227  ...           0   \n",
              "\n",
              "  Internal Temp 1 Internal Temp 2 Internal Temp 3 Cal Temp Error Code 1  \\\n",
              "0              ∞C              ∞C              ∞C       ∞C        Count   \n",
              "1       24.999414       24.999414         21.8269        0            0   \n",
              "2       24.999414       24.999414         21.8269        0            0   \n",
              "\n",
              "  Error Code 2 Error Code 3 Records Saved Bad Pages  \n",
              "0        Count        Count         Count     Count  \n",
              "1            0            0       7948034         0  \n",
              "2            0            0       7948034         0  \n",
              "\n",
              "[3 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-817c5818-4df4-4e88-b73e-b859feafd74c\">\n",
              "    <div 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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>depth_m</th>\n",
              "      <th>speed_mps</th>\n",
              "      <th>cweight_kg</th>\n",
              "      <th>whp_barg</th>\n",
              "      <th>temp_degC</th>\n",
              "      <th>pressure_bara</th>\n",
              "      <th>frequency_hz</th>\n",
              "      <th>...</th>\n",
              "      <th>SGS Voltage</th>\n",
              "      <th>Internal Temp 1</th>\n",
              "      <th>Internal Temp 2</th>\n",
              "      <th>Internal Temp 3</th>\n",
              "      <th>Cal Temp</th>\n",
              "      <th>Error Code 1</th>\n",
              "      <th>Error Code 2</th>\n",
              "      <th>Error Code 3</th>\n",
              "      <th>Records Saved</th>\n",
              "      <th>Bad Pages</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yyyy-mm-dd</td>\n",
              "      <td>hh:mm:ss.sss</td>\n",
              "      <td>days</td>\n",
              "      <td>m</td>\n",
              "      <td>m/s</td>\n",
              "      <td>kg</td>\n",
              "      <td>barg</td>\n",
              "      <td>∞C</td>\n",
              "      <td>bara</td>\n",
              "      <td>Hz</td>\n",
              "      <td>...</td>\n",
              "      <td>V</td>\n",
              "      <td>∞C</td>\n",
              "      <td>∞C</td>\n",
              "      <td>∞C</td>\n",
              "      <td>∞C</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "      <td>Count</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:44.800000</td>\n",
              "      <td>44176.39357</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.957275</td>\n",
              "      <td>-0.607066</td>\n",
              "      <td>19.702093</td>\n",
              "      <td>0.498792</td>\n",
              "      <td>40.648876</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:45</td>\n",
              "      <td>44176.39358</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.211151</td>\n",
              "      <td>-0.606777</td>\n",
              "      <td>19.68925</td>\n",
              "      <td>0.485864</td>\n",
              "      <td>41.483227</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-817c5818-4df4-4e88-b73e-b859feafd74c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-817c5818-4df4-4e88-b73e-b859feafd74c 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-817c5818-4df4-4e88-b73e-b859feafd74c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# dictionary method for renaming some columns\n",
        "dict = {\n",
        "    'DEPTH':'depth_m',\n",
        "    'SPEED': 'speed_mps',\n",
        "    'Cable Weight': 'cweight_kg',\n",
        "    'WHP': 'whp_barg',\n",
        "    'Temperature': 'temp_degC',\n",
        "    'Pressure': 'pressure_bara',\n",
        "    'Frequency': 'frequency_hz'\n",
        "}\n",
        "\n",
        "pts.rename(columns=dict, inplace=True)\n",
        "\n",
        "pts.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "NVriHzL7wjFkFtjeke5c",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "7Cne0nowdUNa"
      },
      "source": [
        "### 2.2.3 Drop units row\n",
        "\n",
        "Note that we are using the index value to drop the row of units and then we remake the index. Therefore, if we run this cell over and over, it will keep dropping the first row of data. \n",
        "\n",
        "If you are unsure what state your dataframe is in or how many times you have run the cell below, it is useful to run the process from the start again (i.e., go back to step _2.1.1 Import pts data using Pandas_ and work down from there). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "y0gzm2QBEHr5yGDvnNO0",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          },
          "outputId": null
        },
        "id": "VX47LlPddUNb"
      },
      "outputs": [],
      "source": [
        "# drop units row using the index value of that row\n",
        "pts.drop(0, inplace=True)\n",
        "\n",
        "# re-index the dataframe\n",
        "pts.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# WARNING if you run this cell again, it will drop the first line of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "8sp0DJpYqDxS3LcIJZW6",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "fbtwJRhlyDRYz3z1OwD9",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "zC5uWIfydUNb",
        "outputId": "a6fd225c-3dec-4a62-ded3-fe2e362ca08d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date             Time    Timestamp  depth_m speed_mps  \\\n",
              "0  2020-12-11 00:00:00  00:26:44.800000  44176.39357 -6.32025       NaN   \n",
              "1  2020-12-11 00:00:00         00:26:45  44176.39358 -6.32025       NaN   \n",
              "\n",
              "  cweight_kg  whp_barg  temp_degC pressure_bara frequency_hz  ... SGS Voltage  \\\n",
              "0  24.957275 -0.607066  19.702093      0.498792    40.648876  ...           0   \n",
              "1  25.211151 -0.606777   19.68925      0.485864    41.483227  ...           0   \n",
              "\n",
              "  Internal Temp 1 Internal Temp 2 Internal Temp 3 Cal Temp Error Code 1  \\\n",
              "0       24.999414       24.999414         21.8269        0            0   \n",
              "1       24.999414       24.999414         21.8269        0            0   \n",
              "\n",
              "  Error Code 2 Error Code 3 Records Saved Bad Pages  \n",
              "0            0            0       7948034         0  \n",
              "1            0            0       7948034         0  \n",
              "\n",
              "[2 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4dfac89-b9f5-411a-800a-f530b19419e9\">\n",
              "    <div 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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>depth_m</th>\n",
              "      <th>speed_mps</th>\n",
              "      <th>cweight_kg</th>\n",
              "      <th>whp_barg</th>\n",
              "      <th>temp_degC</th>\n",
              "      <th>pressure_bara</th>\n",
              "      <th>frequency_hz</th>\n",
              "      <th>...</th>\n",
              "      <th>SGS Voltage</th>\n",
              "      <th>Internal Temp 1</th>\n",
              "      <th>Internal Temp 2</th>\n",
              "      <th>Internal Temp 3</th>\n",
              "      <th>Cal Temp</th>\n",
              "      <th>Error Code 1</th>\n",
              "      <th>Error Code 2</th>\n",
              "      <th>Error Code 3</th>\n",
              "      <th>Records Saved</th>\n",
              "      <th>Bad Pages</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:44.800000</td>\n",
              "      <td>44176.39357</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.957275</td>\n",
              "      <td>-0.607066</td>\n",
              "      <td>19.702093</td>\n",
              "      <td>0.498792</td>\n",
              "      <td>40.648876</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:45</td>\n",
              "      <td>44176.39358</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.211151</td>\n",
              "      <td>-0.606777</td>\n",
              "      <td>19.68925</td>\n",
              "      <td>0.485864</td>\n",
              "      <td>41.483227</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4dfac89-b9f5-411a-800a-f530b19419e9')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e4dfac89-b9f5-411a-800a-f530b19419e9 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-e4dfac89-b9f5-411a-800a-f530b19419e9');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "pts.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "nW1vhrhloQP4Yoi0ArA4",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "omH3UpRjD6TndRrUDrk3",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZxcgfbJPdUNd",
        "outputId": "5bdec2b0-3b06-462a-d757-f6b3fd70bb0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This workbook is using the 1900 date system.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "wb = openpyxl.load_workbook(filename=r'Data-PTS.xlsx')\n",
        "\n",
        "if wb.epoch == openpyxl.utils.datetime.CALENDAR_WINDOWS_1900: \n",
        "    print(\"This workbook is using the 1900 date system.\")\n",
        "if wb.epoch == openpyxl.utils.datetime.CALENDAR_MAC_1904: \n",
        "    print(\"This workbook is using the 1904 date system.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "YClw7gmigjTCS9W0DmDF",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          },
          "outputId": null
        },
        "id": "ULOUI3xtdUNe"
      },
      "outputs": [],
      "source": [
        "list = []\n",
        "for date in pts.Timestamp:\n",
        "    newdate = openpyxl.utils.datetime.from_excel(date)\n",
        "    list.append(newdate)\n",
        "pts['datetime'] = list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "rIKpo6SSbQw1qs8n1QBa",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "z0ESi4Y3Ixl714DzupuK",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "hXG6mkkudUNf",
        "outputId": "b08ab0ca-a38d-4adb-b937-102e6e55f5a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date             Time    Timestamp  depth_m speed_mps  \\\n",
              "0  2020-12-11 00:00:00  00:26:44.800000  44176.39357 -6.32025       NaN   \n",
              "1  2020-12-11 00:00:00         00:26:45  44176.39358 -6.32025       NaN   \n",
              "2  2020-12-11 00:00:00  00:26:45.200000  44176.39358 -6.32025       NaN   \n",
              "\n",
              "  cweight_kg  whp_barg  temp_degC pressure_bara frequency_hz  ...  \\\n",
              "0  24.957275 -0.607066  19.702093      0.498792    40.648876  ...   \n",
              "1  25.211151 -0.606777   19.68925      0.485864    41.483227  ...   \n",
              "2  24.981985 -0.606582   19.69439      0.475091     42.96333  ...   \n",
              "\n",
              "  Internal Temp 1 Internal Temp 2 Internal Temp 3 Cal Temp Error Code 1  \\\n",
              "0       24.999414       24.999414         21.8269        0            0   \n",
              "1       24.999414       24.999414         21.8269        0            0   \n",
              "2       24.999414       24.999414         21.8269        0            0   \n",
              "\n",
              "  Error Code 2 Error Code 3 Records Saved Bad Pages                datetime  \n",
              "0            0            0       7948034         0 2020-12-11 09:26:44.448  \n",
              "1            0            0       7948034         0 2020-12-11 09:26:45.312  \n",
              "2            0            0       7948034         0 2020-12-11 09:26:45.312  \n",
              "\n",
              "[3 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b34578cb-9b85-42f2-ba8f-6ca812312b3e\">\n",
              "    <div 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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>depth_m</th>\n",
              "      <th>speed_mps</th>\n",
              "      <th>cweight_kg</th>\n",
              "      <th>whp_barg</th>\n",
              "      <th>temp_degC</th>\n",
              "      <th>pressure_bara</th>\n",
              "      <th>frequency_hz</th>\n",
              "      <th>...</th>\n",
              "      <th>Internal Temp 1</th>\n",
              "      <th>Internal Temp 2</th>\n",
              "      <th>Internal Temp 3</th>\n",
              "      <th>Cal Temp</th>\n",
              "      <th>Error Code 1</th>\n",
              "      <th>Error Code 2</th>\n",
              "      <th>Error Code 3</th>\n",
              "      <th>Records Saved</th>\n",
              "      <th>Bad Pages</th>\n",
              "      <th>datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:44.800000</td>\n",
              "      <td>44176.39357</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.957275</td>\n",
              "      <td>-0.607066</td>\n",
              "      <td>19.702093</td>\n",
              "      <td>0.498792</td>\n",
              "      <td>40.648876</td>\n",
              "      <td>...</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-12-11 09:26:44.448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:45</td>\n",
              "      <td>44176.39358</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.211151</td>\n",
              "      <td>-0.606777</td>\n",
              "      <td>19.68925</td>\n",
              "      <td>0.485864</td>\n",
              "      <td>41.483227</td>\n",
              "      <td>...</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-12-11 09:26:45.312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-11 00:00:00</td>\n",
              "      <td>00:26:45.200000</td>\n",
              "      <td>44176.39358</td>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.981985</td>\n",
              "      <td>-0.606582</td>\n",
              "      <td>19.69439</td>\n",
              "      <td>0.475091</td>\n",
              "      <td>42.96333</td>\n",
              "      <td>...</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>24.999414</td>\n",
              "      <td>21.8269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7948034</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-12-11 09:26:45.312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b34578cb-9b85-42f2-ba8f-6ca812312b3e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b34578cb-9b85-42f2-ba8f-6ca812312b3e 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-b34578cb-9b85-42f2-ba8f-6ca812312b3e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "pts.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "OpPeMqBmsoBWPIfUwl6C",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "1Yc5PQ9b6p4bzJPy9op7",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cQTbLPWdUNg",
        "outputId": "54750c26-2299-47fc-b234-f920cedefbc7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Time', 'Timestamp', 'depth_m', 'speed_mps', 'cweight_kg',\n",
              "       'whp_barg', 'temp_degC', 'pressure_bara', 'frequency_hz', 'Reed 0',\n",
              "       'Reed 1', 'Reed 2', 'Reed 3', 'Battery Voltage', 'PRT Ref Voltage',\n",
              "       'SGS Voltage', 'Internal Temp 1', 'Internal Temp 2', 'Internal Temp 3',\n",
              "       'Cal Temp', 'Error Code 1', 'Error Code 2', 'Error Code 3',\n",
              "       'Records Saved', 'Bad Pages', 'datetime'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "pts.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "WE10NayG3VA9MsIXcf1n",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          },
          "outputId": null
        },
        "id": "1zN7fah3dUNg"
      },
      "outputs": [],
      "source": [
        "pts.drop(columns = ['Date', 'Time', 'Timestamp','Reed 0',\n",
        "       'Reed 1', 'Reed 2', 'Reed 3', 'Battery Voltage', \n",
        "       'PRT Ref Voltage','SGS Voltage', 'Internal Temp 1', \n",
        "       'Internal Temp 2', 'Internal Temp 3','Cal Temp', \n",
        "       'Error Code 1', 'Error Code 2', 'Error Code 3',\n",
        "       'Records Saved', 'Bad Pages',], inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "KEHcfVKDdWIgeF75xFSy",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "8Ji3lJ15kErZLF1sQ1HM",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Xvc6nzGfdUNh",
        "outputId": "5067fc98-4aff-4c54-d946-0b19293020cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   depth_m speed_mps cweight_kg  whp_barg  temp_degC pressure_bara  \\\n",
              "0 -6.32025       NaN  24.957275 -0.607066  19.702093      0.498792   \n",
              "1 -6.32025       NaN  25.211151 -0.606777   19.68925      0.485864   \n",
              "2 -6.32025       NaN  24.981985 -0.606582   19.69439      0.475091   \n",
              "\n",
              "  frequency_hz                datetime  \n",
              "0    40.648876 2020-12-11 09:26:44.448  \n",
              "1    41.483227 2020-12-11 09:26:45.312  \n",
              "2     42.96333 2020-12-11 09:26:45.312  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af3edafd-2cbf-4c74-a777-ff7ab4a48dea\">\n",
              "    <div 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>depth_m</th>\n",
              "      <th>speed_mps</th>\n",
              "      <th>cweight_kg</th>\n",
              "      <th>whp_barg</th>\n",
              "      <th>temp_degC</th>\n",
              "      <th>pressure_bara</th>\n",
              "      <th>frequency_hz</th>\n",
              "      <th>datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.957275</td>\n",
              "      <td>-0.607066</td>\n",
              "      <td>19.702093</td>\n",
              "      <td>0.498792</td>\n",
              "      <td>40.648876</td>\n",
              "      <td>2020-12-11 09:26:44.448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.211151</td>\n",
              "      <td>-0.606777</td>\n",
              "      <td>19.68925</td>\n",
              "      <td>0.485864</td>\n",
              "      <td>41.483227</td>\n",
              "      <td>2020-12-11 09:26:45.312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-6.32025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.981985</td>\n",
              "      <td>-0.606582</td>\n",
              "      <td>19.69439</td>\n",
              "      <td>0.475091</td>\n",
              "      <td>42.96333</td>\n",
              "      <td>2020-12-11 09:26:45.312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af3edafd-2cbf-4c74-a777-ff7ab4a48dea')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-af3edafd-2cbf-4c74-a777-ff7ab4a48dea 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-af3edafd-2cbf-4c74-a777-ff7ab4a48dea');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "pts.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "VrTqGcBLQkTRAtyEJBNE",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "3I55AzB3dtQDECrcD0dr",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GM6H9m3BdUNi",
        "outputId": "d5848039-5d59-4f79-ae6a-53e987bc9e00"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "depth_m                  object\n",
              "speed_mps                object\n",
              "cweight_kg               object\n",
              "whp_barg                 object\n",
              "temp_degC                object\n",
              "pressure_bara            object\n",
              "frequency_hz             object\n",
              "datetime         datetime64[ns]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "pts.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "ast0rBbFWN2hY4a5VVJp",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "YZeDCTXqqut22C8b6b6Z",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoTrj_JDdUNi",
        "outputId": "5e6da90a-8c0b-481b-fdd7-27a10b0c12a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "depth_m                 float64\n",
              "speed_mps               float64\n",
              "cweight_kg              float64\n",
              "whp_barg                float64\n",
              "temp_degC               float64\n",
              "pressure_bara           float64\n",
              "frequency_hz            float64\n",
              "datetime         datetime64[ns]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "pts[\n",
        "    ['depth_m', 'speed_mps','cweight_kg','whp_barg','temp_degC','pressure_bara','frequency_hz']\n",
        "] = pts[\n",
        "    ['depth_m','speed_mps','cweight_kg','whp_barg','temp_degC','pressure_bara','frequency_hz']\n",
        "    ].apply(pd.to_numeric)\n",
        "\n",
        "pts.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "a1xftRDFvOnbT1c865Lw",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "pEEK0fMlPNYlQoNCp5Ck",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrLO-qJtdUNj",
        "outputId": "cd59ed66-5825-4aa9-eed4-e7181f7118c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['depth_m', 'speed_mps', 'cweight_kg', 'whp_barg', 'temp_degC',\n",
              "       'pressure_bara', 'frequency_hz', 'datetime'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "pts.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "nCM6b2FwsVWFFKSfJyRT",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          },
          "outputId": null
        },
        "id": "a0JIuWSodUNk"
      },
      "outputs": [],
      "source": [
        "def timedelta_seconds(dataframe_col, test_start):\n",
        "    '''\n",
        "    Make a float in seconds since the start of the test\n",
        "\n",
        "    args:   dataframe_col: dataframe column containing datetime objects\n",
        "            test_start: test start time formatted '2020-12-11 09:00:00'\n",
        "\n",
        "    returns: float in seconds since the start of the test\n",
        "    '''\n",
        "    test_start_datetime = pd.to_datetime(test_start)\n",
        "    list = []\n",
        "    for datetime in dataframe_col:\n",
        "        time_delta = datetime - test_start_datetime\n",
        "        seconds = time_delta.total_seconds()\n",
        "        list.append(seconds)\n",
        "    return list\n",
        "\n",
        "test_start_datetime = '2020-12-11 09:26:44.448'\n",
        "\n",
        "flowrate['timedelta_sec'] = timedelta_seconds(flowrate.datetime, test_start_datetime)\n",
        "pts['timedelta_sec'] = timedelta_seconds(pts.datetime, test_start_datetime)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "oIjYzYwSCbZ6mGWyH4fE",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "Uw3t0K484Z068mzpJzrw",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "kPc0HmLKdUNo",
        "outputId": "58e4ddc1-972c-41de-e3ef-3293c19464b9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt \n",
        "import matplotlib.dates as mdates\n",
        "\n",
        "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1,figsize=(18,12),sharex=True)\n",
        "\n",
        "ax1.plot(flowrate.datetime, flowrate.flow_tph, label='Surface pump flowrate', \n",
        "    c='k', linewidth=0.8, marker='.')\n",
        "ax1.set_ylabel('Surface flowrate [t/hr]')\n",
        "ax1.set_ylim(0,150)\n",
        "\n",
        "for ax in [ax2, ax3, ax4]:\n",
        "    ax.plot(pts.datetime, pts.depth_m, label='PTS tool depth', \n",
        "        c='k', linewidth=1.5)\n",
        "    ax.set_ylabel('PTS tool depth [m]')\n",
        "    ax.set_ylim(1000,0)\n",
        "\n",
        "ax4.set_xlabel('Time [hh:mm]')\n",
        "ax4.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
        " \n",
        "injectivity_index = [ # purple\n",
        "    (pd.to_datetime('2020-12-11 10:28:00'),pd.to_datetime('2020-12-11 10:30:00')),\n",
        "    (pd.to_datetime('2020-12-11 12:51:00'),pd.to_datetime('2020-12-11 12:53:00')),\n",
        "    (pd.to_datetime('2020-12-11 15:20:00'),pd.to_datetime('2020-12-11 15:23:00')), # pumps shut off too soon\n",
        "    ]\n",
        "injectivity_index_alt = [ # light purple \n",
        "    (pd.to_datetime('2020-12-11 14:38:00'),pd.to_datetime('2020-12-11 14:40:00')) # alternate used because of pump issue\n",
        "    ]\n",
        "pressure_transient = [ # green\n",
        "    (pd.to_datetime('2020-12-11 10:36:00'),pd.to_datetime('2020-12-11 12:10:00')),\n",
        "    (pd.to_datetime('2020-12-11 12:55:15'),pd.to_datetime('2020-12-11 14:40:00')), \n",
        "    ]\n",
        "temperature_profile = [ # red\n",
        "    (pd.to_datetime('2020-12-11 10:03:00'),pd.to_datetime('2020-12-11 10:11:30')),\n",
        "    (pd.to_datetime('2020-12-11 12:28:30'),pd.to_datetime('2020-12-11 12:36:00')),\n",
        "    (pd.to_datetime('2020-12-11 14:57:30'),pd.to_datetime('2020-12-11 15:05:00')),\n",
        "    (pd.to_datetime('2020-12-11 16:09:00'),pd.to_datetime('2020-12-11 16:15:30'))\n",
        "    ]\n",
        "fluid_velocity = [ # yellow\n",
        "    (pd.to_datetime('2020-12-11 09:46:00'),pd.to_datetime('2020-12-11 10:25:00')),\n",
        "    (pd.to_datetime('2020-12-11 12:10:30'),pd.to_datetime('2020-12-11 12:51:00')),\n",
        "    (pd.to_datetime('2020-12-11 14:40:30'),pd.to_datetime('2020-12-11 15:19:00')),\n",
        "    ]\n",
        "\n",
        "ax1.set_title('Pressure transient analysis (not included in this tutorial)')\n",
        "for top, bottom in pressure_transient:\n",
        "    ax1.axvspan(top, bottom, color='#369a33', alpha=0.3) # green\n",
        "\n",
        "ax2.set_title('Temperature profile analysis - Notebook 2')\n",
        "for top, bottom in temperature_profile:\n",
        "    ax2.axvspan(top, bottom, color='#9a3336', alpha = 0.5) # red \n",
        "\n",
        "ax3.set_title('Determine well capacity (injectivity index) - Notebook 3')\n",
        "for top, bottom in injectivity_index:\n",
        "    ax3.axvspan(top, bottom, color='#33369a', alpha=.6) # purple\n",
        "\n",
        "for top, bottom in injectivity_index_alt:\n",
        "    ax3.axvspan(top, bottom, color='#33369a', alpha=.3) # light purple\n",
        "    \n",
        "ax4.set_title('Feed zones from fluid velocity analysis - Notebook 4')\n",
        "for top, bottom in fluid_velocity:\n",
        "    ax4.axvspan(top, bottom, color='#ffc000', alpha=.3) # yellow \n",
        "\n",
        "for ax in [ax1, ax2, ax3, ax4]:\n",
        "    ax.grid()\n",
        " \n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "Edapbj09achzt97OCl4D",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "l5_Xya9SdUNp"
      },
      "source": [
        "#### Overview of the completion test (plot above)\n",
        "\n",
        "The highlighted areas on the plot above are the data we will extract for analysis during this tutorial. \n",
        "\n",
        "The zig-zags in the PTS tool depth show the tool going up and down inside the well. Injectivity index (well capacity) uses pressure and flow rate just after these tool passes are complete and before the pump rate is changed (dark purple). However, after the third set of passes (i.e., during the third flowrate) the pumps were shut off too soon, so we will use the pressure and flow rate just prior to the passes (light purple).\n",
        "\n",
        "#### Use the plot below to explore the entire dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "uoigj1oiuRgDYiaeXzDj",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          },
          "outputId": null
        },
        "id": "Ww0MpKlrdUNq"
      },
      "outputs": [],
      "source": [
        "def overview_fig(pts_df,flowrate_df,title=''):\n",
        "    fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6, 1,figsize=(10,15),sharex=True)\n",
        "    ax1.set_title(title,y=1.1,fontsize=15)\n",
        "\n",
        "    ax1.plot(flowrate_df.datetime, flowrate_df.flow_tph, label='Surface pump flowrate', \n",
        "        c='k', linewidth=0.8, marker='.')\n",
        "    ax1.set_ylabel('Surface flowrate [t/hr]')\n",
        "    ax1.set_ylim(0,150)\n",
        "    \n",
        "    ax2.plot(pts_df.datetime, pts_df.depth_m, label='PTS tool depth', \n",
        "        c='k', linewidth=0.8)\n",
        "    ax2.set_ylabel('PTS tool depth [m]')\n",
        "    ax2.set_ylim(1000,0)\n",
        "    \n",
        "    ax3.plot(pts_df.datetime, pts_df.pressure_bara, label='PTS pressure', \n",
        "        c='tab:blue', linewidth=0.8)\n",
        "    ax3.set_ylabel('PTS pressure [bara]')\n",
        "    \n",
        "    ax4.plot(pts_df.datetime, pts_df.temp_degC, label='PTS temperature', \n",
        "        c='tab:red', linewidth=0.8)\n",
        "    ax4.set_ylabel('PTS temperature')\n",
        "    \n",
        "    ax5.plot(pts_df.datetime, pts_df.frequency_hz, label='PTS impeller frequency', \n",
        "        c='tab:green', linewidth=0.8)\n",
        "    ax5.set_ylim(-30,30)\n",
        "    ax5.set_ylabel('PTS impeller frequency [hz]')\n",
        "    # 1 hz = 60 rpm\n",
        "\n",
        "    ax6.plot(pts_df.datetime, pts_df.speed_mps, label='PTS tool speed', \n",
        "        c='tab:orange', linewidth=0.8)\n",
        "    ax6.set_ylim(-2,2)\n",
        "    ax6.set_ylabel('PTS tool speed [mps]')\n",
        "    \n",
        "    ax6.set_xlabel('Time [hh:mm]')\n",
        "    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
        "    \n",
        "    for ax in [ax1,ax2,ax3,ax4,ax5,ax6]:\n",
        "        ax.grid()\n",
        "    \n",
        "    return plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "iooxa": {
          "id": {
            "block": "A4zY8RFp07RNhFRXswcP",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 2
          },
          "outputId": {
            "block": "YteUPsYCdASYQu8NdNvo",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "WTtENJF-dUNs",
        "outputId": "f43a18a6-ed88-4433-c0c3-950606b23484"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1080 with 6 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAANgCAYAAACcPkStAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3xcd33n/9dHd/kqyRfJduzYIU7SJA4kcUhMKCgECqEBuoRlYWmT0hS3u/S2v+1mYX/L0m1LAyktJd0lYDbQpFugtBCS7ULulgOJIXES52bjS4idWPJF1sWWbF1Hn/1j5ihjaW6aOTNzNHo/H495aObMmXO+Omdmzmc+35u5OyIiIiISPVXlLoCIiIiIpKZATURERCSiFKiJiIiIRJQCNREREZGIUqAmIiIiElEK1EREREQiquSBmpl9w8yOmdmLScv+xMw6zWxn4vbepOc+bWb7zWyPmb271OUVERERKRcr9ThqZvY2YBC4290vTiz7E2DQ3b84Zd0LgW8DbwZWAg8D57l7rKSFFhERESmDkmfU3P0xoDfH1T8AfMfdR9z9FWA/8aBNREREpOLVlLsASX7PzG4EdgD/0d37gFXAT5PWOZRYNo2ZbQY2AzQ2Nl6+evXqIhcXJiYmqKpSM7+w6HiGT8c0XDqe4dMxDZeOZ/hKcUz37t173N2XpXouKoHaHcCfAZ74+1fAb81kA+6+BdgCsHHjRt+xY0fYZZymo6OD9vb2ou9nrtDxDJ+Oabh0PMOnYxouHc/wleKYmtnBdM9FIux296PuHnP3CeDrvF692Qkkp8bOSiwTERERqXiRCNTMbEXSw38FBD1C7wM+Ymb1ZrYOWA88WeryiYiIiJRDyas+zezbQDuw1MwOAZ8F2s3sTcSrPg8AvwPg7i+Z2XeBXcA48En1+BQREZG5Iq9Azcxaclhtwt37py5094+mWPfOdBtx988Bn5tB8UREREQqQr4Zta7EzTKsUw2syXP7IiIiInNevoHabne/NNMKZvZsntsWEREREfLvTLAppHVEREREJI28AjV3HzazajP7eaZ18i+WiIiIiOQ9PEei9+UeM1M7NBEREZEiKHR4jmbgJTN7EjgVLHT39xe4XREREZE5r9BA7TOhlEJEREREpikoUHP3bWEVRERERETOVNAUUmb2QTPbZ2YnzOykmQ2Y2cmwCiciIiIylxVa9Xkb8D533x1GYURERETkdYVOyn5UQZqIiIhIceQ71+cHE3d3mNk/Aj8ARoLn3f37IZRNREREZE7Lt+rzfUn3TwO/kvTYAQVqIiIiIgXKN1B7CHjA3XvCLIyIzD7bt2/ntttuY8+ePdTX1zMyMsKyZcu48MILufTSS3n22fi0v4sWLaKjo4OVK1dyyy23sGnTJrZs2cL3vvc9li1bRnd3N29605vYu3cve/bsYdmyZbS0tADQ29tLd3f35PaT99PS0kJbW9vkvnbt2sXw8DA333wzGzZs4O677+bIkSOT6/zoRz+aLGtdXd0Z6wWvbW9vp6mpifb2dgA6OjpYsmQJ//Iv/8KXv/xl2trauPHGGyefa29vZ9OmTZPHY+oyEZF85RuorQb+ycxqgUeAHwFPuruHVjIRibzt27fz1re+lYmJiTOW7969m8ceeyzt637wgx/Q0tJCb2/vGcsffPDBM7aRTaZ1nnzyyayvT7desMzMAEj11fbVr36VqqoqJiYmMDPOPvtsamtrefnll5mYmKCqqoo1a9bQ1NQ0GVQCaQPOqc+lC0K7u7vPeG7RokXs3LmTG264gc2bN+f0P4vI7JFXoObuXwC+YGYLgXcCvwV81cx2A/cTz7YdDa+YIhJFHR0d04K0XE0N0qIo22/P4H93dw4cODDtueRlmYLKVM/NdP0gyFWwJlJZCur16e4D7n6Pu/+Ou18K/DmwDLg7lNKJSKS1t7dPZp2k/L73ve+VuwgiErKCxlEzs0fc/drgsbvvMrO/TV4mIpVr06ZNXH/99QwMDHD06FHq6+vp6+vj4MGDeW+zqqqK5cuXc/ToUdydqqoqLrnkkjOqCpP/Pvfcc1kzX8mSqyn37dt3xnPr16+npqaGPXv25J0pLKcbbrih3EUQkZDlOzxHAzAPWGpmzUDwk3oRsCqksonILNDS0sLnPvc5NmzYMLls+/bt3H13PLGe3KEgaIAfdD44//zzue666+jp6WHJkiX09PRMNsLPtVF+sK+gw0Cwj6mdCJLLkNzw/7bbbqOrq4ubb755stoweZvJYrEY119/Pc8+++wZzyV3dqirq6O9vX2yU0SmdmjZnksVhK5du5ampqZpz91yyy2q9hSpQPlm1H4H+CNgJfA0rwdqJ4H/EUK5RGSWGB8fp6bmzK+STZs2ZQyu7rnnnqzbzbaNbOvl+tpUZUm3zSBwLJVUQWhykBkEw48++ihf+MIXSlYuESmdfDsTfBn4spn9vrv/bchlEpFZZGxsjNra2nIXoyJlClaTn7v88stLWSwRKaG8OhOY2WUAmYK0YB0RqWypMmoiIhKOfL9dv2lm7bxe5ZnKncCleW5fRGYJZdRERIon30BtMWe2TUulO89ti8gsooxaNLi7hkoRqUD5tlFbG3I5RGSWUkat/Orq6hgbG6Ourq7cRRGRkBU04K2IiDJq5dfQ0MDw8HC5iyEiRaBATUQKooxa+SlQE6lcCtREpCBjY2PKqJWZAjWRylVQoGZxv25m/y3xeI2ZvTmcoonIbKCqz/JToCZSuQrNqH0F2AR8NPF4APifBW5TRGYZ9TYsLwVqIpWr0EDtSnf/JDAM4O59QNZuR2b2DTM7ZmYvJi1rMbOHzGxf4m9zYrmZ2e1mtt/MntdAuiIiZ1KgJlK5Cg3UxsysGnAAM1sGTOTwur8D3jNl2aeAR9x9PfBI4jHAdcD6xG0zcEeBZRYRqSgK1EQqV6GB2u3APcByM/sc8BPg1mwvcvfHgN4piz8A3JW4fxfwa0nL7/a4nwJNZraiwHKLiFQMBWoilaugFsDu/g9m9jRwLfFZCn7N3XfnublWdz+cuH8EaE3cXwW8lrTeocSyw0nLMLPNxDNutLa20tHRkWcxcjc4OFiS/cwVOp7hK8UxHRgYmDPnLarv0SNHjvDUU0/NygFvo3pMZysdz/CV+5gWFKiZ2d+7+28AP0+xLG/u7mbmM3zNFmALwMaNG729vb2QIuSko6ODUuxnrtDxDF8pjunChQvnzHmL6nv0Zz/7GevWrYtk2bKJ6jGdrXQ8w1fuY1po1edFyQ8S7dUuz3NbR4MqzcTfY4nlncDqpPXOSiwTERFU9SlSyfIK1Mzs02Y2AFxiZifNbCDx+Bhwb55luQ+4KXH/pqTt3AfcmOj9eRVwIqmKVERkzlOgFrd9+3ZuvfVWtm/fXu6iiIQm30nZbwVuNbNb3f3TM329mX0baAeWmtkh4LPA54HvmtnNwEHgw4nVfwi8F9gPnAY+nk+ZRUQq1aFDh7j33nv5m7/5G5YtWwZAd3c39fX1jIyMpFyW6bmZrp9tWy0tLQD09vZOe66vr4/x8fEZbz/YbvI2n3/+eSYmJjAzzj77bNasWZNxW1PLVYz/u62tjRtvvJFNmzYV6/RLhSu0M8GnE+OdrQcakpY/luV1H03z1LUp1nXgk4WUU0SkUgVZpFgsBsDu3dP7c6Valum5ma4f5rZyXT/Teu7OgQMHOHDgQFH2PdNtffOb32Tr1q0K1iQvhU4h9dvAY8ADwH9P/P2TwoslIrNB/HeUlFNHR8dkkCbRNDo6qp6YkrdCOxP8IXAFcNDdrwEuBfoLLpWIzAoTExNUV1eXuxhzWnt7u+Zajbi6ujr1xJS8FfrpHnb3YTPDzOrd/edmdn4oJRORyBsbG6O2trbcxZjTNm3axGOPPcZtt93Gnj175mwbtXzanBW7jdpzzz3HRz/6UbVRk4IUGqgdMrMm4AfAQ2bWR7wjgIjMAePj48rmRMCmTZu45557yl2MvJR7jKpiuvzyy7njDs16KIUptDPBv0rc/RMz2wosBu4vuFQiMisooyYiUlx5B2qJwW1fcvcLANx9W2ilEpFZYWxsTBk1EZEiyrszgbvHgD1mtibE8ojILDI+Pq6MmohIERX6U7gZeMnMngROBQvd/f0FbldEZgFl1ETSMzPcHTMrd1FkFiv0G/YzoZRCRGYlZdRE0quuriYWi+nHjBSk0HfPucBj7r4vjMKIyOyijJpIelVVVUxMTJS7GDLLFfoNuwb4mpmtBZ4mPkvBj919Z4HbFZFZQBk1kfSCjJpIIQqamcDdP+vu7wAuAn4M/CfiAZuIzAHKqImkp4yahKGgb1gz+6/A1cAC4Fngj4kHbCIyByijJpKeMmoShkJ/Cn8QGAf+L7AN2O7uIwWXSkpm+/btkyODB1OcpFom4R2XbNsJnu/v72fnzp0sW7aMffv2sXLlSs4777wzlo2Ojmac3qavr4+zzz572nOpps85//zzueWWW9i0adMZZQS4++672bVr1+Q2grkL9+zZw/79+9m+fbveKyJTKKMmYSh0ZoLLzGwR8azau4AtZnbM3d8aSukq3JYtW7jzzjuzXmyLNR/fwYMHee2115iYmKCqqopLLrmEvr4+Xn311cku5WeffTZr1qwJbd/nn38+5513Hh0dHdP+74MHD9Lc3DzjuQOHh4dZv349+/bto6GhoSjHcHx8nJdffnnyWK1Zs4ampqZQjnny/3TgwAGef/75GX+57969O+1zBw9On9Ut1fq7d+/mBz/4AevXr5/8XzN58sknJ+9fc801bN26VcGaSBJl1CQU7p73DbgY+HfAd4D9wFbgTwvZZhi3yy+/3Eth69ateb/2lltucUA33SriZmb+F3/xF+F9uCKqkM+8pFbJx/Td7363Hzt2rKT7rOTjWS6lOKbADk8T0xRa9fl54m3SbgeecvexArc3J2zfvp2//Mu/LHcxREJTVVVVsRNri+RLGTUJQ6FVn9ebWR1wHnC+me1RsJZdR0dHkJEUydnU9i5mxrnnnktNTc2M26jV19dPVrGaGc3NzfT29mbc//r166mpqWF8fJz9+/dPvoerqqr4yle+ompPkSnURk3CUGivz7cDdwMHAANWm9lN7v5YCGWrWO3t7VRVVU1e6LJdbIvVRq27uztlo/JgWfLjsPY9W9uoJTeg37t3L3v27CnaMR8eHqa9vZ2mpiaWLFlCT0/PZLaqo6PjjGXZgqPkDgFTTe3UELSZbGhomHZ8b775ZjZv3nzGa++++24AbrzxRgVpIikooyZhKLTq86+BX3H3PQBmdh7wbeDyQgtWyTZt2sQFF1zARz7yEd75znfqIpeQKaiQuDDfK5s2bTpje5s3bz4jGJvJa0VkOmXUJAyFBmq1QZAG4O57zUyDKuWgvr6ez3xGU6WKiFQqZdQkDIUGajvM7H8B/zvx+GPAjgK3WfGCdkEiIlK5lFGTMBQ0hRTxoTl2AX+QuO1KLJMMfvSjHzEwMMD27dvLXRQRESmS6upqduzYwa233qrve8lbob0+R4i3U/vrcIpT+bZv384NN9zAyMgI1157LY888oja+oiIVKCenh4+/vGPMzY2Rl1dnb7vJS95ZdTM7AUzez7dLexCVpKOjg7GxuIjmIyOjtLR0VHeAomISFF0d3czMjJCLBbT973kLd+M2r8GhsIsyFzR3t5ObW0to6Ojk8M9iIhI5VmxYgXPP/887q7ve8lbvm3UvuXuB4E/d/eDU29hFrDSbNq0iS9+8YtcffXVSoOLiFSwFStWcPXVV/PBD35Q3/eSt3wzanVm9m+Bt5jZB6c+6e7fL6xYle3888/nLW95iz60IiIVrKqqigULFnDdddfp+17ylm+g9rvEh+JoAt435TkHFKhlMD4+Tk1NoSOjiIhIlFVXVzMyMsL4+Hi5iyKzWF7Rgrv/BPiJme1w9ztDLlPFi8ViCtRERCpcVVUVo6OjCtSkIIUOzxF6kGZmB4ABIAaMu/tGM2sB/hFYS3xe0Q+7e1/Y+y4VZdRERCqfMmoShkIHvC2Wa9z9Te6+MfH4U8Aj7r4eeCTxeNYaHx+nurq63MUQEZEiCjJqmkZKChHVQG2qDwB3Je7fBfxaGctSMGXUREQqX3V1tao+pWDm7vm/OD5h5ceAc9z9T81sDdDm7k8WsM1XgD7inRK+5u5bzKzf3ZuS9tkXPE563WZgM0Bra+vl3/nOd/ItQs4GBwdZsGDBjF/30EMP0dfXx4c//OEilGr2yvd4Sno6puHS8QxfJR/Tr371q/zkJz/huuuu42Mf+1hJ9lnJx7NcSnFMr7nmmqeTahHPUGha5yvABPAO4E+Jty37HnBFAdt8q7t3mtly4CEz+3nyk+7uZjYtunT3LcAWgI0bN3opBhbs6OjIawDDgwcPcuLECQ1+OEW+x1PS0zENl45n+Cr5mN5///088cQTrFmzpmT/YyUfz3Ip9zEttOrzSnf/JDAMkGjgX1fIBt29M/H3GHAP8GbgqJmtAEj8PVbIPspNVZ8iIpVPVZ8ShkIDtTEzqyZeTYmZLSOeYcuLmc03s4XBfeBXgBeB+4CbEqvdBNxbSKHLTZ0JREQqX1VVlXp9SsEKTevcTjzrtdzMPgd8CPivBWyvFbgn3gyNGuJTVd1vZk8B3zWzm4GDwKxu3BWLxaivry93MUREpIiCjJp6fUohCh1H7R/M7GngWsCAX3P33QVs7xfAG1Ms70nsoyKMj48zf/78chdDRESKSAPeShgKCtTM7CrgJXf/n4nHi8zsSnf/WSilq1BqoyYiUvmqq6sZHx9XoCYFKbSN2h3AYNLjwcQyyUBt1EREKl9VVfwSq0BNClFooGaeNBCbu09QeLu3iqe5PkVEKl/wg1yBmhSi0EDtF2b2B2ZWm7j9IfCLMApWyVT1KSJS+YKMmjoTSCEKDdR+F3gL0AkcAq4kMTuApKdATUSk8imjJmEotNfnMeAjIZVlzlAbNRGRyqc2ahKGQnt9NgA3AxcBDcFyd/+tAstV0dRGTUSk8imjJmEotOrz74E24N3ANuAs4vN9Sgaq+hQRqXzKqEkYCg3UznX3zwCn3P0u4FeJt1OTDBSoiYhUPmXUJAwFz/WZ+NtvZhcDi4HlBW6z4qmNmohI5VOvTwlDoWmdLWbWTHx+z/uABcBnCi5VhVMbNRGRyqeMmoQhr2jBzP7Q3b8M7Hb3PuAx4JxQS1bBVPUpIlL51EZNwpBv1efHE3//NqyCzCUK1EREKp8yahKGfKOF3Wa2D1hlZs8nLTfA3f2SwotWuWKxmNqoiYhUuKqqKhoaGhSoSUHyCtTc/aNm1gY8ALw/3CJVPmXUREQqX3V1NQ0NDepMIAUpJFroBl5094NhFWauUKAmIlL5lFGTMOQ9PIe7x4A1ZlYXYnnmBAVqIiKVr7q6mvr6egVqUpBCo4VXgMfN7D7gVLDQ3f+6wO1WNA3PISJS+aqqqqitrcXdy10UmcUKjRZeTtyqgIWFF2du0IC3IiKVr7q6Wt/1UrCCAjV3/+9hFWQuUdWniEjlq6qq0ne9FKygd5CZbQWm5XTd/R2FbLfSKVATEal81dXV1NTUYGblLorMYoVGC3+cdL8BuAFQq8ks1EZNRKTyBRk1tVGTQhRa9fn0lEWPm9mThWxzLlAbNRGRyhdk1EZHR8tdFJnFCq36bEl6WAVcDiwuqERzgKo+RUQqX5BRU6AmhSg0WniaeBs1I17l+Qpwc6GFqnTKqImIVD71+pQwFFr1uS6sgsw1VVV5jzUsIiKzgHp9ShjyihbM7IrEXJ/B4xvN7F4zu31KdaikoIalIiKVT70+JQz5pnW+BowCmNnbgM8DdwMngC3hFE1ERGT2Uq9PCUO+Odlqd+9N3P83wBZ3/x7wPTPbGU7RREREZq8goyZSiHwzatVmFrz7rgUeTXpO78oslAYXEal8yYGasmqSr3wDtW8D28zsXmAI+DGAmZ1LvPozdGb2HjPbY2b7zexTxdhHqegDKyJS+aqqqiZ7fk5MTJS7ODJL5ZX9cvfPmdkjwArgQX898qgCfj+swgXMrBr4n8C7gEPAU2Z2n7vvCntfudqyZQtf+tKXWL58OQDd3d3U19czMjIy+XfZsmUADA8Ps379evbt28fo6Ch79+5ly5YtbN68uVzFFxGRInvxxRfZs2cP3d3dXHXVVYyPj59xbUi+bqRals9zfX19nH322TPaVrbtt7TE+wj29vZmfd1M/4+w/u9irr9kyRJuu+02Nm3aFObbI3fuHvkbsAl4IOnxp4FPp1v/8ssv92L62te+5sTHjyvo9rWvfa2o5Zxttm7dWu4iVBwd03DpeIavUo/pE0884Q0NDaFcK3Qr/622ttafeOKJor1fgB2eJqYxnwXVcGb2IeA97v7bice/AVzp7r+XtM5mIEhRnQ/sKWKR1gOLQtjOSWBfCNupFEuB4+UuRIXRMQ2Xjmf4KvWYtgGryl0ICVUncKRI2z7b3ZeleqJiGv67+xZKPDSIme1w942l3Gcl0/EMn45puHQ8w6djGi4dz/CV+5jOluHxO4HVSY/PSiwTERERqVizJVB7ClhvZuvMrA74CHBfmcskIiIiUlSzourT3cfN7PeAB4Bq4Bvu/lKZiwWahSFsOp7h0zENl45n+HRMw6XjGb6yHtNZ0ZlAREREZC6aLVWfIiIiInOOAjURERGRiFKgJiIiIhJRCtREREREIkqBmoiIiEhEKVATERERiSgFaiIiIiIRpUBNREREJKIUqImIiIhElAI1ERERkYhSoCYiIiISUQrURERERCJKgZqIiIhIRClQExEREYmoWROomdl7zGyPme03s0+VuzwiIiIixWbuXu4yZGVm1cBe4F3AIeAp4KPuvqusBRMREREpotmSUXszsN/df+Huo8B3gA+UuUwiIiIiRVVT7gLkaBXwWtLjQ8CVySuY2WZgM0BjY+Plq1evLnqhJiYmqKqaLbFu9M3F4zkxMUFnZyfFer/OxWNaTDqe4dMxDVcxjmcsFqOrq6to31NRV4r36N69e4+7+7JUz82WQC0rd98CbAHYuHGj79ixo+j77OjooL29vej7mSvm4vF0dy677DKeffbZomx/Lh7TYtLxDJ+OabiKcTyfeuopvv71r7Nly5ZQtztblOI9amYH0z03W37GdALJofxZiWUis5qZYWblLoaISFpdXV2sXLmy3MWYs2ZLoPYUsN7M1plZHfAR4L4yl0lERKTiKVArr1kRqLn7OPB7wAPAbuC77v5SeUslEo6GhgaGhobKXQwRkZQUqJXXrGmj5u4/BH5Y7nKIhK2lpYXe3l5WrVpV7qKIiEyjQK28ZkVGTaSSNTc309/fX+5iiIikpECtvBSoiZRZU1MTfX195S6GiEhK3d3dLF++vNzFmLMUqImUWUtLiwI1EYksjXVXXjryImWmjJqIRNXIyAh1dXXlLsacpkBNpMyUURORqDp8+DArVqwodzHmNAVqImWmzgQiElXqSFB+CtREyqy5uZne3t5yF0NEZBpl1MpPgZpImTU3N6vqU0QiSRm18ks74K2ZteTw+gl3V52NSAEUqIlIVHV2dhZ9QnLJLNPMBF2JW6YZo6uBNaGWSGSOUWcCEYmqw4cPK6NWZpkCtd3ufmmmF5vZsyGXR2TO0VyfIhJVCtTKL1MbtU05vD6XdUQkA7NMSWsRkfI5fvw4S5YsKXcx5rS0GTV3Hw7um1kzsDp5fXd/JnkdERERqSzurh+TZZap6hMAM/sz4DeBlwFPLHbgHcUrlsjcUl9fz/DwMA0NDeUuiogIgL6TIiKX4Tk+DLzB3dvd/ZrELe8gzcxWm9lWM9tlZi+Z2R8mlreY2UNmti/xtzmx3MzsdjPbb2bPm9ll+e5bJKo0jZSIRI3GUIuGXAK1F4GmEPc5DvxHd78QuAr4pJldCHwKeMTd1wOPJB4DXAesT9w2A3eEWBaRSGhpadGgtyISKQrUoiFr1SdwK/Csmb0IjAQL3f39+ezQ3Q8DhxP3B8xsN7AK+ADQnljtLqAD+M+J5Xe7uwM/NbMmM1uR2I5IRdBYaiISNRrsNhpyCdTuAr4AvABMhLlzM1sLXAr8DGhNCr6OAK2J+6uA15Jediix7IxAzcw2E8+40draSkdHR5hFTWlwcLAk+5kr5vLxPHHiBD/+8Y8ZHx8Pdbtz+ZgWg45n+HRMwxXm8dy2bRvz5s2b8+en3O/RXAK10+5+e9g7NrMFwPeAP3L3k8m9StzdzczTvjgFd98CbAHYuHGjl2Ik5Y6ODo3YHKK5fDx37txJS0tL6P//XD6mxaDjGT4d03CFeTzvv/9+rrnmmjl/fsr9Hs0lUPuxmd0K3MeZVZ/P5LtTM6slHqT9g7t/P7H4aFClaWYrgGOJ5Z3EhwYJnJVYJlIxmpqa6O/XbGwiEh2q+oyGXAK1YHaCq5KW5T08h8VTZ3cSn/ngr5Oeug+4Cfh84u+9Sct/z8y+A1wJnFD7NKk0LS0tHDhwoNzFEBGZpEAtGrIGau5+Tcj7vBr4DeAFM9uZWPZfiAdo3zWzm4GDxIcFAfgh8F5gP3Aa+HjI5REpu+bmZmXURCRSent7aWlpKXcx5ry0gZqZXe/u/5LpxbmsM5W7/4T0E71fm2J9Bz45k32IzDbNzc309PSUuxgiIpM0K0E0ZMqo/aWZdZI+qAL4C2BGgZqITNfS0qLhOUQkMk6fPs28efPKXQwhc6B2FPjrDM8D7AuxLCJzlsZRE5Eo0WC30ZFpUvb2EpZDZE5rbGxkeHi43MUQEQEUqEVJLlNIiYiIyBzS1dWlQC0iFKiJREi874yISHkdPnyYVatWlbsYggI1kchQ9aeIREVnZ6fGUIuIXAa8xczeAqxNXt/d7y5SmUTmpObmZnp7e/UrVkTK7vDhwwrUIiJroGZmfw+8AdgJxBKLHVCgJhKioOenAjURKTdl1KIjl4zaRuBCV+MZkaIKMmoiIuV24sQJmpqayl0MIbc2ai8CbcUuiMhcp7HURCQqNCtBdGSaQur/EK/iXAjsMrMngZHgeXd/f/GLJzJ3KFATkSg4deoU8+fPL3cxJCFT1ecXS1YKEaGlpYXjx4+XuxgiMsdpDLVoSVv16e7b3H0b8N7gfvKy0hVRZG5oamqiv+bSMmYAACAASURBVL+/3MUQkTmuq6tLHQkiJJc2au9Ksey6sAsiMte1tLSoM4GIlJ0CtWjJ1Ebt3wH/HjjHzJ5Pemoh8HixCyYy16iNmohEgao+oyVTRu1bwPuA+xJ/g9vl7v7rhe7YzKrN7Fkz+5fE43Vm9jMz229m/2hmdYnl9YnH+xPPry103yJRpOE5RCQKurq6NJ5jhGRqo3bC3Q+4+0eBJcAHgPcTn6EgDH8I7E56/AXgS+5+LtAH3JxYfjPQl1j+pcR6IhWnpaVFbdREpOw02G20ZG2jZmafAe4iHqwtBb5pZv+1kJ2a2VnArwL/K/HYgHcA/5xY5S7g1xL3P5B4TOL5a02Du0gFqq+vZ2RkJPuKIiJFpOmjoiWXmQl+HXijuw8DmNnniU8n9ecF7PdvgFuIt3eDeBDY7+7jiceHgCDvugp4DcDdx83sRGL9M8YxMLPNwGaA1tZWOjo6CihebgYHB0uyn7lCxxMGBgZmfAx+8pOfsGnTJqqrq6c9Nzg4yPe//326u7s5//zzc97mtm3beOtb35pym3PZTN6j27Zt44ILLqC1tTXl86dPn2bXrl1s3Lgx5/0/8cQTXHzxxSxatCjn10RdOT/3jzzyCFdddVXaMcNGRkbYu3cvGzZsyHmbjz32GFdccQWNjY1hFXNG0h3PV155hVgsxrnnnpt1G11dXTz99NMa8Dah7Ncmd894A7YCTUmPm4BHs70uw/auB76SuN8O/AvxTN3+pHVWAy8m7r8InJX03MvA0kz7uPzyy70Utm7dWpL9zBU6nu6XXXaZT0xMzOg1gL/44ospn9u6dat/4hOf8PhHPTcTExMO+M6dO2dUjrlgJu9RwG+99da0z3d0dMzovATb/P73vz+j10RdOT/3gD/88MNpn//Hf/zHvM7Rgw8+WGjR8pbueH7wgx/0Cy64IOvrJyYm/NJLLw25VLNbKd6jwA5PE9PkklE7AbxkZg8Rn6ngXcCTZnZ7ItD7gxnGhlcD7zez9wINwCLgy0CTmdV4PKt2FtCZWL8zEbgdMrMaYDHQM8N9iswK8+bN4/Tp0zMeFTxTJ4T6+voZbevkyZMA9PToY1ao0dHRtM8FxzlXnphuWYMihyvT+3ymTRGCc9Td3V1QmYphYGCA06dP57ReJWVsK0Eugdo9iVugo5AduvungU8DmFk78Mfu/jEz+yfgQ8B3gJuAexMvuS/xeHvi+Uc9+DSIVJhgiI5cA7VYLAZkDtRmWn3R1dWVdZuSm0zDrQTHNxaL5VTFHAR2CtTCEQTRmc7RTIfLCToDRfFHTlVVFRMTE1nX6+zsVI/PiMkaqLn7XWbWCKxx9z1FLMt/Br5jZn8OPAvcmVh+J/D3ZrYf6AU+UsQyiJRVMOjtWWedldP6wYUhU2/REydOUFOTy2+yuK6uLubNm6ceqAVwd2pqajIGu0EQ0N3dTVtbW9ZtdnZ20tbWFskgYDYKjmMugdrJkydzyjJ1dXWxePHiSAbTExMTOf1o02C30ZNLr8/3Ee88cH/i8ZvM7L4wdu7uHe5+feL+L9z9ze5+rrv/a3cfSSwfTjw+N/H8L8LYt0gUzXQstWDdbAFBW1tbztU4hw8f5qKLLlJGrQCDg4OsXr2aEydOpF0nOC+dnZ1p10nW1dXFm970Jo4dOxZWMee048ePs2LFiqyfnVWrVnH48OGcttnZ2ckll1wSyUBtZGSEhoaGrOspUIueXKaQ+hPgzUA/gLvvBM4pYplE5qyZzk4QZN8yXWx6e3u56KKLZnSxufjiizVLQgGOHz/O+vXrswYBF1544WRVczadnZ288Y1vVAAdkp6eHs4999yMmeO+vj4uvvjinM/R4cOH2bBhQyQDtYGBARYsWJB1PVV9Rk8ugdqYu0/9WZi9oltEZmym83329/ezbt26jBeboaEh3vCGN+R8senq6uKiiy5SoFaAnp4e1qxZw6lTpzKuM5MAuqurK7JBwGx0/Phxzj333Kw/cvIJpmdz9bQGu42eXAK1l8zs3wLVZrbezP4WeKLI5RKZk5qammbUNqyvr49zzjkna3C3YsWKGQUEF198sTI3BTh+/DhLly7NuM5MM2pdXV2sXbs2Y09Syd3x48c577zzsmY9L7744hlVT//SL/1SJNt3mhmNjY0MDQ1lXE9Vn9GTS6D2+8BFwAjwbeAk8EfFLJTIXDXTjFoQqGW6MJjZjAO1Cy+8UBm1AvT09OQUqM00o6aJssPT09PDOeeck3GYlCAbPZNA7ayzziJqAxOMjY1RU1OTU9MKBWrRkzVQc/fT7v7/u/sV7r4xcX+4FIUTmWvyaaOWqfpmdHSUuro6Vq5cmXPm5vTp06xcuTKSWYHZoru7m6VLl1JXV5e2E8fIyAjr1q2bUUZtxYoV1NbWMjY2FmZx56Senh5aW1szDlnh7qxatWpGbdRWrFiRPMB7JPT19dHc3JzTD8Hh4eGcOh1I6aTts29m/4f4ALcpufv7i1IikTksnzZqra2taYOBkydP0tLSknNGzd0xM6qrq3Mac0lSCwLo4Hymy4S1trZy9OjRnLY5PDxMY2MjS5YsoaenJ6chPSS948ePs2TJkoxDVsw0Gx0EOfPnz+fUqVM5Nd4vhSBQy9a0YmJigqqqXCrapJQynZEvAn8FvAIMAV9P3AaJT+MkIiFrbm6eUSarp6eHJUuWpP31PjAwQHNzc84Ztb6+PlpaWnLev6QWtFFLlyENzleu2bHkC+iSJUvUoSAEwTkys8mBo5MNDQ1NBl25jOiffI6WLl0aqXOUa0YtyARLtKTNqLn7NgAz+yt3T541+P+Y2Y6il0xkDmpqappR1WfwBWxmk9mwZIODg7S0tOR84Uju8WVm+oWdp6CNWrpx8U6dOjU5+0R1dTXj4+MZByVO7pywdOnSWd2rMCp6e3tpaWmhqamJEydOTPuBEny2gLSfr2TJ5ygIpteuXVu08s9E8L9k+yHY1dWloTkiKJdv4PlmNjlumpmtA2Y2EaGI5KSuro7x8fGc1w++gBcsWJByKIiBgQGampqoqqrKqc3M4cOHJwO1RYsWzXg+SokLqtVaWlpSBt7Jmcvly5dnnRsyuYF31LI1s1XQfjNd8JJ8jmbaCD9qwXTwv2T7IaiOBNGUS6D2H4AOM+sws23AVuAPi1ssEcnFwMAACxcuTJu5OXny5GRWoK6uLuvQDslf1OmCDMmuv7+fpqamtBf4vr4+mpqaAHKqlk4ehFSBWrjSfXaSM2qrVq3K2vMz+UdO1M5R8H7LFnAqUIumXHp93g+sJx6c/QFwvrs/WOyCicxlM+kxZmZpswKDg4OTF5vW1laOHDmScVvJQ0DMdDoreV1QTZYpUAuyNbk0Vg96EwKTnQkkf0E2DdK3C00OpnM5R8mfnahm1LL9+FKgFk05NT5x9xF3fy5xy23CQBHJy8KFCxkYGMhp3SCgSxdUBZ0JIJ65yeViE2RugrY7MjPJbZnSBWpB+yjIPQgILqDqTFC43t7eyc9Fpoxa8jnKJaMW1WA6OaOW6ceXArVoUithkYjJdYiO0dFRamtrJ1+TKiAYGBjIOyBQRi0/yR0F0p3L5Gq1fKo+oxQEzEbHjx9n2bJlQPpgOjmjdtZZZ80oUFu6dGnWdoelFPQOb2lpyfje0Tyf0aRATSRich2iI/lin+5iMzg4eEb1TbaAIPliM9PBdyUuufdfunPZ398/GUDnEqipM0G4ks9RurHFZlo9nXyOli1bFqlArb+/f7LXZ6Ys+dGjR2ltbS1hySQXaQM1M7ss062QnZpZk5n9s5n93Mx2m9kmM2sxs4fMbF/ib3NiXTOz281sv5k9X+i+RaIu10xWLr3SggFvIbcG0cPDw9TX1wMzH3xX4oLsBWSuVlu8eDGQWxCQfAGNWrXabJQcqGXKeub7Iydqn53e3l6amppoaGjIOCZctmFipDwynZG/yvCcA+8oYL9fBu539w+ZWR0wD/gvwCPu/nkz+xTwKeA/A9cR78ywHrgSuCPxV6Qi5ZrJSr6QNDc3s3v37mnrJHcmaGtry9iZYOqYac3NzezcuXOmxZ/zpmbUUp3L5GCura0t6+wEY2Njk9XcufTelcymBtPpqj5n0r5zZGRk8kdO1Np3Dg4OTs6SkG4suOT3mERLpgFvrynGDs1sMfA24DcT+xkFRs3sA0B7YrW7gA7igdoHgLs93mr6p4ls3Ap3z21OD5FZJtdALblBerpf8KOjozQ2NgLZMzfJAUZQjihlBWaL5CCgsbGR4eHpUyMnBwHZZidQliN8PT09nHfeeUBuHT7q6+szBsdTf+RUVVVFbgq25AAt1eC9qvaMrqyffjOrBf4d8eAK4gHU19w931mB1wHdwDfN7I3A08SH/mhNCr6OAME7ZhXwWtLrDyWWnXHFMbPNwGaID0PQ0dGRZ/FyNzg4WJL9zBU6nnFHjhzh6NGjnHvuuRnXe+KJJ+jt7aWjo4NXX32VPXv2TDt+sVhsclksFuPll19Oe4z37dtHVVXV5POdnZ0ptzmX5fIefeKJJ1iwYMHkegMDA9Ne8/LLL7Nr167JwPnUqVM88sgjVFdXT9ted3c3NTU1Z2xjaGiIhx9+uCICuHJ87p999lnmzZtHR0cHJ0+e5JVXXplWhgMHDvDCCy+wd+9eIH6OHn300ZQzdfT19Z3x2YH4/7V169aMsxkUQ6rjmbxsbGyMBx54YNrE67t378bd9XlPodzXplw+5XcAtcBXEo9/I7HstwvY52XA77v7z8zsy8SrOSe5u5tZ7gNJxV+zBdgCsHHjRm9vb8+zeLnr6OigFPuZK3Q844aHh9m2bVvWY/HCCy/whje8gfb2do4dO8a3vvWtM17j7lRXV5+xbN68eWm3e/r0aS677LLJ53t6erjrrrt0TpLk8h599NFHefOb3zy53sKFC3n7299+xgW7qqqK66+/frKqad26dVx88cUpMxpPPvkkl1xyyRn7Xbt2LRs2bKiIDEg5Pvff+MY3uPbaa9mwYQOxWIw/+7M/m1aGhoYG3v3ud08+Ds7R8uXLp23vueee46KLLjpjG21tbVxxxRUln5h96vEcHR2lubl5ctnZZ5/Nhg0bpvXu7Ovr44orrtDnPYVyX5ty6fV5hbvf5O6PJm4fB64oYJ+HgEPu/rPE438mHrgdNbMVAIm/xxLPdwKrk15/VmKZSEXKtSFy0JMLUvcuDCaVTlZTU5O2mm3qGEqLFy+e0QTxEpdc9Qnx6s+pDbiTh1aBzD0/U41tFbVehbNNcjV/dXV1yknZpw46nakzTqo5MqPSOze50xGk/35JnllBoiWXQC1mZm8IHiTm/Zz+rs6Rux8BXjOz8xOLrgV2AfcBNyWW3QTcm7h/H3BjovfnVcAJtU+TSjaT4TmCzgSp2jn19fVN+zWfqeH61ItNTU1NyguYZDY1UEt1PqdWh2VqP5g84n1APT8L09vbe8Y5ykWmYDq5x2cgKuco+XsC0g9Hkup/kGjIJVD7T8DWpLk+HwX+Y4H7/X3gH8zseeBNwF8AnwfeZWb7gHcmHgP8EPgFsB/4OvDvC9y3SKTl2og/ubEzTM8A9Pb2snDhwjOWZQsIpv6inslUVhLX3d09OZgqTD+fqY5ptiBg6nmJSrZmtkqeQgrigXNy4/9U2eiZfnaico6mBqXpMmqalSC6srZRc/dHzGw9EGTA9hQ6jZS77wQ2pnjq2hTrOvDJQvYnMpvk2rU/uedgILk3V39//7SMWqaA4MiRI7S1tZ2xLOi9lqoBtaR24sSJyTHSIH5hTM5gJA+VEFixYgUvvfRSyu2luoBGJVtTKRYtWsTJkycnM09Tqwsh/tl57rnnUr6+q6uLa6898/IVlam+pn5PpMvYK6MWXVm/fRO9Pn8H+G+J2ycSy0SkCGpqahgfH8+6XnIbNZg+R2hvby+LFi064zWZxoNK9UW9ePFiTp48OZPiz3mpxqNLzmCkCrBXrVo1o2q1qE1RNJtMbR8I8aAq+RwlzwUayNRGLcpZz1SBWqqM2rFjx1J2lJDyy+Vn8h3A5cR7fX4lcf+OYhZKZK7LpUv/1MzM1Pk+U7VRy1R9k2rAS42lNjOpqjWnVjVNrbKGzJnO7u7uaRdQzfeZv56enjPGC4TpWaZUgdpM26hFJVCb+n5LN25cLBZLOTyMlF8uw3Nc4e5vTHr8qJmlzv+KSCiCNjOZqhynDloZBFVnn302kD6jlupiMzExkTI41HyfM3Pq1Klp7QKbm5s5duzY5OOpmVCIj/2YrpNHqguoMmr5mzqwM0z/QTLTczS1zRtE5xz19fVNfifA9B90oFkJoq7kvT5FJLt0PbOSTQ2spgZVqTJq6apvpjaAD0RtzsKo6+7untabMFVGbWoQkG5aqHSzEkQlWzMbpQrUpgYvqc5RuiYJ6X5QRSXrOTWjluoznap9qkRHLhm1oNfnLwADzgZ+q6ilEpnjgqqYqVVkgVTtbFIFalMHRE03/la6hsTKqM3M8ePHpwW8U4PuVA3VId5xY2r27PDhwykHtY1KEDAbpQrUmpqapn12pq4DrwdrycFzurZdUcqoJQedqX4EqsdntOWSUfsJ8QnR/4D4sBrnA48Xs1Aic122tmGpGqRP/aXc19c3rRou3eCe6Qa7VEZtZo4fPz4toza1h+bUca0Cy5cvn3ZhT3decu1wItOly6hN/eykOkdtbW0cOXLkjGXpfuREpSNOLhk19fiMtlwCte3uPuLuzyduI8D2YhdMZC7LlslKFajlUvUJqWcn6OzsTBkQ5Dr4rsSlyqhNPS9TB8QNpOro0dnZOW3EeylMuoza1M4EqbKeqZoOpDtHZhaJcQinflekmmBe77NoSxuomVmbmV0ONJrZpWZ2WeLWDswrWQlF5qBsmaxU1Wepen1OzahB6kbRmao+lVHLXaqM2tRALVVDdUjd0SNTpqOuro6RkYKGtJyTuru7s7ZRS/VDCFKfo87OzrTnKArB2tDQEI2NjRnXUdVntGXKqL0b+CLxuTX/Kun2H4D/UvyiicxduWTUplbNTH1Nqp5oEL/YTM0KpPuiVtXnzKTKqNXW1p5RTdnT05MyW7NixYoZBWpqp5afdO0Ip3YmSHeOpmY9M82RuWjRojPGNoyS5ABSgVq0pQ3U3P0ud78G+E13f4e7X5O4fcDdv1/CMorMOdkCpFQXkqmvSfdLPtWgt+kCglx6n8rrUlWrTZUuozbTqs+ojHw/26TKeqZqo5YqUEuVUcsU5JS7d26674D58+dz6tSpyccK1KItaxs1d/9eKQoiIq/LllFLdbFPbk+Wqbol1cUmXfd8TVU0M5kCteCc9Pf3nzHFVCBVpjNTRm3ZsmUK1PKQKghbvHjxGdO2jY2NFZyNhvS9rEvl9OnTzJ8/f9ryqYFpqkGAJTo0gZ9IBOVT9bl48eLJQO3UqVMpv6AhdaA2MjJCfX39tHWnTkslmaXK1sCZxzEWi6UcGy3VeUk3vh0oiM5XqgGE0/WGnipVNvro0aMph1CB8mc90/VenZop13y+0aYzIxJB+XQmqKmpmbzYpGtjA9PbQmX6kq6qqip7Y+jZZHBwMGUHjlza+rW1tU3r5JFpWp+ojNNVaTK931NlyNINShysX85ALd33QPL7Md2PNImOTL0+rzCztqTHN5rZvWZ2u5mlvgKISCiytQ3r7e1N+Us50N/fn/b5qVmBVI2rJT9Tp/UKBOcz3VRdEO90kDxsSrrOIAF1Jpi506dPM29e6kELgmnbBgYGUgbbEP/hMjExMfk429RL5Q6m0/VeTW4moVkJoi/TzARfA94JYGZvAz5PfMDbNwFbgA/lu1Mz+w/AbwMOvAB8HFgBfAdYAjwN/Ia7j5pZPXA38cnge4B/4+4H8t23yGywaNEidu/ezbe+9S3Gx8en3Z5++umUwVVwscmUUZuaFcjWkLixsZGvf/3r07Jr2e4X+nwU133llVd4/PHH066bLjMZZDBOnDiRsn1aIKiCq66u5siRIxkHIW1ra+MrX/kKd95557Ry5Pq/RWG9/fv388wzz5Rkv11dXdPmvw0EwfTg4GDK4CZQX18/mYU6evRoxiBnyZIl/N3f/d3kOQ+C9Fz+zmTd5L+7du1iYGCA973vfWm/B5KH3enq6tJgtxGXKVCrdvcgV/9vgC2JjgXfM7Od+e7QzFYRn+XgQncfMrPvAh8B3gt8yd2/Y2ZfBW4G7kj87XP3c83sI8AXEuURqVhVVVV8/vOf5/Dhw9TU1Eze6uvrmT9/Pp/5zGcyjoae7pc0xIOB5KzA4cOHM15sbr/9dn76059OPk7OCE29SExdlun5bK+P4vOxWIwLL7ww7fO/+qu/SipBm8NMATTEZyc4duzYZPV0pgD60ksv5YYbbjhjLLV0/0em56YGl/lso5D1jh8/zvLly0uy3/Xr1/O7v/u7pBJkmU6ePJkxUAt6565duzbrOXrb297Ga6+9RiwWm/bjINPfmaw79e/Ro0f5zd/8TV5++eW0PYybm5snq9nV4zP6MgZqZlbj7uPAtcDmHF+X634bzWyM+OC5h4F3AP828fxdwJ8QD9Q+kLgP8M/A/zAzczWckQp30003zfg1zc3N9PT0ZAzU4PVJwOvq6rJOH3PZZZdx2WWXzbgslailpYX29vYZvy4I1DJVScPr1dJBoJbpvDQ0NKQNOmaTjo6OvI5p2IIs08mTJzMG00GnjyBQy3SO5s2bxyc+8YliFDetjo4O+vv7OXbsGH19fSnnIW1qauLnP/85oEBtNsgUcH0b2GZmx4Eh4McAZnYucCLD6zJy904z+yLwamK7DxKv6uxPBIUAh4Bg8KBVwGuJ146b2Qni1aNntNA0s80kgsnW1lY6OjryLWLOBgcHS7KfuULHs3DDw8M8/PDD7Ny5k5aWFtatW5fymNbU1HDPPffQ2trK448/zpo1a3Tsc5Dve/TIkSMcPXp0smot3TZGR0d54IEHOHnyJNu2baO+vr7iz0tUPvcDAwNs27aNwcFB+vv705ZpZGSEBx98kNHRUTo6OqipqYlE+QODg4OMjIxMfg9ceOGF08r3yiuv8OKLL9LR0cFPf/pT3vjGN0bqf4iacr9HMwVqXwAeId527MGkDFYV8bZqeTGzZuJZsnVAP/BPwHvy3V7A3bcQbzvHxo0bvRS/0KLyS7BS6HgW7oc//CHnnHMOr776KldffTXz5s1LeUw3bNjA2rVrufLKK/mnf/on3vWud/GWt7yl9AWeZfJ9jw4PD/PYY4+xevVqYrFY2m3s2rWLmpoa2tvbefDBB/nlX/7liv9MROVz/8QTT7BmzRr6+/tZvnx52jK98sorDAwM0N7ezkMPPcTVV18difIHOjo6uOiii1i3bh27du3ibW9727TPdmtrK9u3b6e9vZ1vfvObvOc97+GSSy4pU4mjr9zv0UzDczzp7j9193vcfXIIY3ff6+7PZHhdNu8EXnH3bncfA74PXA00mVkQOJ4FBKMKdgKrARLPLybeqUBEpkiuYsul+gZU9VEKQWeCbFXSyT1ydV5Ka8mSJTmdo+QZJKLaED/4Hkg3w0Ly8Bx6n0VfpkAtdR/ywr0KXGVm8yzewvNaYBewldd7kt4E3Ju4f1/iMYnnH1X7NJHUkr+gM7WFSh5LLVsbNSlcrudFAXT5BL0+e3t7Uw5aHFi1atXk7ASZ5vksp6BjRC4D3vb09GT8f6X8MlV9LjOz/y/dk+7+1/ns0N1/Zmb/DDwDjAPPEq+y/L/Ad8zszxPL7ky85E7g781sP9BLvIeoiKTQ0tLCyy+/PNm78MiRIynXW7lyJY8//jigAS9LITnTmWu2Jpd5QyU8zc3NPPvsszMKpjPNHFFOzc3N7Nq1K212sL6+ntHRUSD92H8SHRl7fQILKEJmzd0/C3x2yuJfAG9Ose4w8K/DLoNIJQp+KefSu7Crq0tTx5RIcrYmU5V0W1vbZHCtC2hpBVmobOcoudowqp+f4H8ZHR3N+CNsaGiIxsbGEpZM8pEpUDvs7n9aspKISMFaWlro6+tLO6l0IAjUVO1RGjU1NYyPj2cNoIPZCXQBLb1geI5s58jMcPdIZ6KD/yVbK6HOzk5WrVqVcR0pv3K0URORIsk29VRg6dKlHD9+XO3TSsjMsmZrID4g8aFDh3ReSizIQvX392ecPQLi46Pt378/sucoqGrPlJGdP39+pP8HeV2mjNqvmtkfAecSn+bpzqRxzkQkgpKnhskkmLOwq6tLv6hLxMxyCgKWL1/Ozp07I9lIvZItXryY/v5+YrFY2knWA6tWrWLHjh2RPUdNTU0cOnSI+fPnZ1xn165dkf0f5HWZMmpfAjYSD9KuA/6qJCUSkbw1NTXx2muvpZ1UOll9fT2vvPKKvqhLpKmpid7e3qxtmlauXBnpIKBS1dTUEIvFclo3OEdR/ZGzaNEiDhw4kLHjypIlS3jhhRci+z/I6zJ9Y1zo7r/u7l8jPizGL5eoTCKSp+rqanp6ejJ+QQdWrFjBM888o6qPEsm1WnrlypU8+eSTnHXWWSUolSSLxWI5dQ6IejBtZlnHg2tubuall16K7P8gr8v0jhwL7qjKU2T2yPYFHVi5ciVPP/20ArUSWbx4MSdOZJ99L+pBQCUbGBjIKRsd9apPiAedmdpDKlCbPTJVxL/RzE4m7hvxSdRPJu67uy8qeulEZMYmJiZYtCj7x3PVqlU8++yzqvookaC3YDYrV65kcHBQF9AyyHW4jZUrVzI+Ph75z86CBQvSPtfc3Mzp06f1PpsF0gZq7l5dyoKISHhyudgEmbS2trZiF0dmILhwKtNZermOXReco6gHOZm+B4Ksey4/6qS8ojdSn4gUZPHixTmN77R69WqAyI4FVWmWL1/OOeeck3W9IEuTrXeohG/JkiU5VX0G7QczjbdWbjU1NRk/28uXLwfQoMqzQOY+yCIy6zz11FM5Zcne/va388wzz5SgRALwx3/8x2zevDnrem1tADgY7QAAIABJREFUbTz//PO6gJbBd7/73ZwGGp4/fz67d++O9Dnas2fPZDCWylvf+laee+65EpZI8qVATaTCrF+/Pqf1amtrufTSS4tcGgm0tLRkHewW4hmODRs2lKBEMtUb3vCGnNe94IILiliSwmXL3jY2NnLJJZeUqDRSCFV9ioiIiESUAjURERGRiFKgJiIiIhJRRQvUzOwbZnbMzF5MWtZiZg+Z2b7E3+bEcjOz281sv5k9b2aXJb3mpsT6+8zspmKVV0RERCRqiplR+zvgPVOWfQp4xN3XA48kHkN8LtH1idtm4A6IB3bAZ4ErgTcDnw2COxEREZFKV7RAzd0fA3qnLP4AcFfi/l3AryUtv9vjfgo0mdkK4N3AQ+7e6+59wENMD/5EREREKlKp26i1uvvhxP0jQGvi/irgtaT1DiWWpVsuIiIiUvHKNo6au7uZZZ/4Lkdmtpl4tSnAoJntCWvbGSwFjpdgP3OFjmf4dEzDpeMZPh3TcOl4hq8Ux/TsdE+UOlA7amYr3P1womrzWGJ5J7A6ab2zEss6gfYpyztSbdjdtwBbwi5wJma2w903lnKflUzHM3w6puHS8Qyfjmm4dDzDV+5jWuqqz/uAoOfmTcC9SctvTPT+vAo4kagifQD4FTNrTnQi+JXEMhEREZGKV7SMmpl9m3g2bKmZHSLee/PzwHfN7GbgIPDhxOo/BN4L7AdOAx8HcPdeM/sz4KnEen/q7lM7KIiIiIhUpKIFau7+0TRPXZtiXQc+mWY73wC+EWLRwlTSqtY5QMczfDqm4dLxDJ+Oabh0PMNX1mNq8RhJRERERKJGU0iJiIiIRJQCNREREZGIUqAmIiIiElEK1EREREQiSoGaiIiISEQpUBMRERGJKAVqIiIiIhGlQE1EREQkohSoiYiIiESUAjURERGRiFKgJiIiIhJRCtREREREIkqBmoiIiEhEKVATERERiSgFaiIiIiIRpUBNREREJKIUqImIiIhEVE25C1AMS5cu9bVr1xZ9P6dOnWL+/PlF389coeMZPh3TcOl4hk/HNFw6nuErxTF9+umnj7v7slTPVWSgtnbtWnbs2FH0/XR0dNDe3l70/cwVOp7h0zENl45n+HRMw6XjGb5SHFMzO5juOVV9ioiIiESUAjURERGRiFKgJiJSAuOxCUbGYzmtOzSa23oiUvkUqIlUmNd6T3Pi9FjW9cZiEzz7al8JSiQAf/fEAX7jziezrndqZJxf+m/3l6BEMtW9Ozv54QuHc1r3yw/vK3JpROIUqIlUmF++bStffezlrOs9fbCPf/WVJ0pQIgF4ufsUT77Sm3W9vtOjALh7sYskU9x2/x7+fnvaNt2ThkZjfOnhvSUoUf70/qkcCtREKlAuVWcnh7Jn3aT0BobHARgcGS9zSeaemmrDyR7g9A/Fg+lcq7LLYeOfP8y3n3w17fN7jw7woTv0Q202UKAmMkedTAQEUb7YVBKz3NZToFY+OZ6iyXN0cii656jn1CiH+4fSPt/VP8SOg2r6MBsoUBOZowaG4xm1KF9sKkmuQUCQ6QyCASkty+FMBZ+d4G9UZcoNnlaHlVlDgZpIBcqlfcrA8Dh1NVWRv9jMNQMj8fNxQlXTkRX8uIn6Ocr0NRAEamrLFn2hzkxgZh/MYbVhd/9hmPsVkTMNj01kXefE0BhnNTdOVoFKcY3Fsp8TiAcBrYvqFUCXgZkxkUPgcnJ4jCqLftYzU3X70Fg8UBscGWdhQ22JSiT5CHsKqa8D95I5y/82QIGaSJHMq6vm9Fj2ao2B4TFWNTUqICiRobEJ5tVVZ11vcGScVU2NqpIug/qaKobHswfUJ4fGWN0yL/IZtbFY+qBzOJFROzmsQC3qwg7UfuTuv5VpBTP73yHvU0SSNNZWM5xDoHZyaDwRqCkgKIWh0VhOgdrJ4TFWNc9TAF0GtdVVxCZyCNSGE8F0xM/R6dH0n+3TozEWNtQk3meNpSuUzFiobdTc/dfDWEdE8tdYl1ugNjASr/qMelagUgyPxaivySFQGwqCAAXQpeZ4xnZdgZPDY6yM+I+cKsvcYWBoLEbrogZlbmeBsDNqk8zsYuBCoCFY5u53F2t/IgITEx6v+syhR9fg8DhtixvpOzVagpLJ6dFxGnOs+rxw5SIO9Z4uQalkqlyGURkYHmdNy7xIj0XYkCWzPjwWo3VRfaT/B4krSq9PM/ss8LeJ2zXAbcD7i7EvEXnd0FiMpsa6nMZGi7nTPK9WVWwlMhqbYH59TdZOBSeGxjhLGbWSc3cMyymjNjA8zorFDZEe6y5bE4ih0RjLFtRP9jKW6CrW8BwfAq4Fjrj7x4E3AouLtC8RSTg9GqOxrjqniw3AosZaBQQl1FhbNdnbLp2TQ0G1mi6gpTQyPkFdTVVOGbUTQ2Osam6MbDZqLDbBwoaajO+102MxWher6nM2KFagNuTuE8C4mS0CjgGri7QvEUkYHovRWFud9WITjJ20qKE2shebSuOevToK4hfZJQvqFECXWPDZqa6qypr1HBweY3XzvMieo6GxGEsW1GduozYao3Vhg34QzALFCtR2mFkT8eE6ngaeAbYXaV8ikjA0lltGbXhsgoaaahY31qozQYmYxaujcpmHdWFDDYO6gJZUkI3OJes5mgimByMaqA2PxljcWJsx4BwOOhNE9H+Q14XemcDMDLjV3fuBr5rZ/cAid38+7H2JyJmCi002A8NjLGyoYUFDTaTb2VSaxrrqrEEAQH1NNaM5DpAr4RhKZNRiE87waIxFWcYWa6yt5vRYND87w2MTNNZm/sE2NBZjuToTzAqhB2ru7mb2Q2BD4vGBsPchIqkNjcYvNjVVxniGC/3J4TEWNtQyrza3HqJSmNiEU2WWNaM2FpugpirXWUElTMFnZ3xiIqdg2nJpzFYmp8fGqa/NXGE2Mp7oTKCMWuQVq+rzGTO7okjbFpE0hsfjF5v62uqMI6zHRyOvoarK8IxTN0sYhsdiNNRUJ3ripT8vgxolvmyGx2I01FbRUJs56zkem6C6KtrTZJ8ejTG/LnMexh01fZglijWO2pXAx8zsIHCK+JRS7u6XFGl/IkI8K9BQW5UYSy39L+UBBQQlFbQdjFd9Zj4vC+rjX8tBo/ba6mgHBZUifo5qiLlnzDIPDI+zqCF+jqrMiE041RHLgg7nOAvGwoYaBtT0IfKKFai9u0jbFZEMhkbjF5t5dZmr2E7+P/buPEyOu7oX/vdUVa/Ts0gz0kiyZO2y5d0gg7GNkZe8BrMYeA0YwmoIycMSuAHCkvAmJITAvW9CIEBiE8AmIQbCasBmMx7bGGzjXd607xppNDOapfdafvePquqp7q6qru6unqmZOZ/n0aPp7uqq31R1T5/+LecUVPSmZgI1IUSkh3LmO3tYLRmTUSj7D0n3pMw/y90JBbmShr50fLaauajZ10g3jEodTDdT1vxOAOiKm3M8ne+lKCioOhLWFAivYJ8IUAKWzGJzqyNf1YQQB4UQBwEUAAjHP8ZYBxVU85t0UvEfYnN+2KRjCs9T67CidV0aJSHNlmZ6OruTCue4mkVmj5pkziP0uUbTRQ3dCfMa9aaimd4mb/WoBV28wqKtU5UJXkVEuwHsB3APgAMA7gz4XJmIHiOin1q31xPRg0S0h4i+Q0T89ZIxD3YuqGSDP9DTRQ09Vi9AT0qJfHHp+c7u4UjFZeQbXBd76LMnGePrMovs904q7v/FZaqoImN9yTGLmkcvmC5YgVo6rrj2Dqo18+xE0AzZbE50avLD3wO4GMAuIcR6mFUKHgj43A8AeNZx+3MAPi+E2ATgFIB3htlQxhaSvD1HLeY/Ry1b1NCdsD9sYpH8sFlI8pUPThl5nzlBU4WZns5MkgPo2WS+d+QA0wZmhjozSSWSCWPtVCMpj1XdBVVHOmbOYeMe9ejrVKCmCiHGAEhEJAkh7gawrdGTiGg1gJcD+A/rNgG4EsD3rE1uBfDqzjSZsfnPnhDd6MMmW9IqvQKZRDQ/bBYS+4Mz2WDV53RRnenp5AB6VhVVw0p426g3Wq0sJuhJRnPVZCV5b9wjUHMsNuAe9ejr1GKCCSLKALgPwLeIaATm6s9G/gXAXwLotm73A5gQQth/rY4AOM3tiUT0bgDvBoDBwUEMDQ213vqAstnsrBxnseDz2b49+0voyx/FsQkd2vhhnJUpup7TXQdK2EwnkDsg4dRIGfc/dBzT+zv152DhaPU1+shxDcdyBpLTEnaOGRhSjrpu9+TeMlZ2SRjK7sWJIyqyJ4DEyefabHW0ReV9/9zuMoq9Eko6MF40MFQ+4LrdowdUZGLAkHoQJ46oyI8A8Qhdo2w2i2eH96LUK+HUqI77HxzFyJLqFaDHcwamJ8oYGhpCYbKEX9/7e6zp5tXFXub6Ndqpv8zXASgC+CCAP4ZZkP3v/J5ARK8AMCKEeISItjd7QCHEzQBuBoBt27aJ7dub3kXThoaGMBvHWSz4fLbv52NP4pIXrEXswDh6UzFkpve4ntP/Ovgwrrz8bKzsTeFZ7MXqJSlsP3/V7Dd4nmn1NXrqsSPoy5Zx3uo+jO4YxvbtZ7tu97v8s7hkyzJcumkAJx8+jGxJw/ZL17fZ6miLyvv+9/ln8YIty5Av63jq6CS2b9/iut3jv96Fs1f1YvtZgxjpOox8OVrXaGhoCMtXLscLtw6isGsEWzctw2WbB6q2eebYFB7J78f27edjaOppnHnuSly0bukctTj65vo12pFATQiRI6IVAF4AYBzAL6yhUD+XAngVEV0LIAmgB8AXAPQRkWL1qq0G4P5VlDFWWbmWtiat93tsN1VQKyVyelLRnBC9kDjnP/nnt5uZo9adjGF4sjhbTVz0cmWtUn6tUQ7CTGJmMcGJqehdo0rePo+5qgV15nftS8cwkeehzyjr1KrPdwF4CMBrAVwP4AEiutHvOUKIjwshVgsh1gG4AcBvhBB/DOBuax8A8DYAP+5EmxlbCOw8aqm4jILPh42dxgOI7jybhaRQ1tGVsNMlNKoYYc9R47mDs6lQNszUNg3mEU4VHLnukrFIJox1Ll5xm2+XK+lIxx2ri/n9H2mdGvr8CIAL7V40IuoH8DsAX29hXx8F8G0i+jSAxwB8LbRWMhZhuiEqySolClZbsG7SukdycoGZBLfdSQXZkvcfajsZrmEIEAFl3YBhAHHFbJedmX26pOHEZBEf+d6T2DKYQV86jhNTRWwZ7May7oR9YAiISrFoAbOUTe19EKLymN0GAeDEVAmPHjqFh/aPAzCDmamihuufvxpj2RKu2joI+zQRCERmWRQA1s+VB0GYOadkP17zXFSeaz7zmWEN2SePVT1uPxcgrF6Swjmn9dadw+oSUg1ydNkT1VP+iwlU3cBN9+zFkq44VvWmEJMlKDIhJkuQJYIiEQwhsCQdR0JxfCd3/l6OG1W/r+M4ztdd9f3V7SG476DRfguaQM4R7FCA9lUd12N7r2PLErm+l+xrZBjwXUwwVaztjfZ/75ycLkGz3suKLOG0vpTn9mHJljR0JRTPVCNVX9S4jFTkdSpQGwMw7bg9bd0XiBBiCMCQ9fM+mEOojC0qGz9xB5akYzh9aRq6mAlkAPPD4ucfuBxdieq3sL2aq7Is3+Mzwbmv7mTMN7Hq//P5e7F7JAsAOOe0HsRkCTuPT/su6d+0PIPhySIOjOaQSShIxeRKsFkJnmqCJInIJciaibQIwHEUMZotVY6TtT7gj00UIEsEydqBgIDhPGcegZ99LgSqc0nZwWP148BowcCh8XzV+RPWcQwB/Ps9e/Gj915adz4Kqo5kgIS3044goLtBeo5dJ6bx//9yF957xUYcnyyirBtQNQHNMHDvrpM4MJYHAJy3uhcDmUSlrZV2O/ZV9ftU3e+eX6t6e+F6f9DtJiaK+MbeP9Qd3Gt7r/YF+X1Kqo7zVvfif19/PmrlraFPAfiumDZLSM2szPULch47PIHXfuV3WD/QhaKqY3iyiBeuX4pt65ZgSTqOVFxGXJYQVyQkFAmqLhBXJKv9M68rUfnZ+ZoUMIyZ1679/3OHVex3vO/cesucqz57UzEcHs97/g5s7oUaqBHRX1g/7gHwIBH9GObr6DoAT4Z5LMYWMiEEzjmtBz99/4tdH3/XrQ9jsqDWBWolzUBCkZCKWwGBS6Bm9pDN3O5JKpWAx006LmPPP7wMSkRqTn78ZVvn7NhDOIzt2zd5Pv6rZ4+73p8vm3mrvNIl2FTdQNzq/WoUQE8VNNx46Xp85JozA7Y+msyJ2i+alWOdmCri4z/Y4fqYPa/L/tmLM7VNo4S3kwUV77tiEz58zRkAzOu7ZySL45NFnMqXUVB1ZEsayjkD00UNimx+2Zjp4Z35wmJ+manu7ZUcX3zsbXUB/OnlG9CTNNP0uM2hs+dM2r8Dp+eItrB71Oy0GnutfzaeV8ZYE8yAy7uociYhVw0X2ewhTa9JxIA1LBKfeeubCW+9/1DrQkQmSJuvzBJSChKKhLLmPf+puqfTv2B2zhEwsGC6Et5fSoqqgaT1nvPrUTPETBH27mQMUz6BWr6kV32ZiskStq7swdaVPa00P5Ch0gFs37YGgPkly+3vRL6sYWmXWeSnl4c+Iy/Ud7kQ4lNh7o+xxcouZ+OlUZkbv0nrzvJRgJXwkmtKdpS5yENqqvB9rEHB7Lyqoyvu/Rph9dI+Q8/mfFACkX+ZL2cwHVckaLr3NcqVtcoQ41zoSiiuvYPFmjlqvOo72kL9mkxEfxvGNowtds6hCTeNsqenYt6VCZwT1u1t8yr/oQ6L27yugqpXekgF3Od9abpR6akJojDHQcB8JFkLLNzYQ41+AbLbtfWLvfOlmTQYc8Hr70BBnfn7wj1q0Rd2v/m7iGjK53GCmXrjb0M+LmMLinNVlptUXPIN1NJx754D54R1INhqUhZMXJZQ0oy6ILugGg2DqmypOoAG6ifnV2+vo8/RM8qCqV0Z2sw2RbX+2vpdo1xZx7LuZFPtC5PXnMicY0i2q0FuPzb3wg7UvoqZeWp+2zDGfBTK/kOf6bhS903ZMETlAyYVl5Hz+OM7XdLQlQj2Ld9r1R9z15VQkCtpdR/mxfLMRHWvIMC5mtAWkyWUNN11vmKupOG0vrkLAuYrrx7NIKaLaqUgu83vGjlXV84FrwTLeUdvLBH5Bpts7vEcNcYiyBya8J6ZkHb5plzUZp4Tl70nrU8V6j9sADPQk2qG3hotamDV7OtSWxGiYOXoAsyhMrdzPeWoSmDrTirIFjUkMvXXwExqyosJWmHnBvR83COYm7bykznZKz+9rtFcDn2mY+5z1Aqq4ftFkEULL+ViLILsCgNeki6Top0f3H4fQs7yUbauuOLaA9eoZ49VS3ss8jDETGCWVGQUtfptzNJE1delJ+W9qpDnqLUmqdRXHlB1A7JU/XHo1ptsfsmpfl/6TcbPlWbKTc2FVFxGruQSqJW1qiBfkQiqz6IINrc4UGMsghrNUUvGpLpALWhQ5SxTZDNTdLjVBJzbHoH5pivhPeRsS3rMG5ou1qfb6LF61NzkuEetJemEUjccaOe5s8VkCWWXwGXa5b3T45PeZq5XfcYVCZrLwohcSUfaMf2BqxNEGwdqjEVQscHQp9tqrmLAoMpMz1HbK+Ce9NK5Oow11uUyd7BWKia5brN7ZLou+PbLcZcvB59ryGZ0uU0bqHnvpDzqfU4V1boeMr/0NlEdni7UpP/pTXG9zyjrVFH2LUR0FxE9Zd0+j4j+uhPHYmwhyjfoHUvF6+ee1M5r8xr9NOdCufUKuA99cq6u4NJxxTXBqJNXGanJQn0Q0J3wzho/1/Of5qu0yzB/bd5Cr7QWUwWtbn6nXxmpXBMLd2aTZpg1hG29qRgmOFCLrE71qH0VwMcBqAAghHgSZloOxlgAjeaouS0mMIc+Z54jkXvOqKmC+6R1t56bRvncWLWuRP11qS3Z5ZWsuDuhYPPyTPV9PiWKotpbE3Vpl3lbBVVHwvElx214FDBXfda+d8wSbO5BTkHVq6qARBX3qEVbpwK1tBDioZr7OFELYwE1zKMWk1GqmZCeV83s985t3D7j3dJAeAUExQbtYNXc0qKUtJnSRIAZKLitxPv508dRqlmp61eiqFAzr4oFk47X95bVptFIxdzzFE4V6xfiZHyCaecikrkiEcEw/PNv8By1aOtUoDZKRBthFmQHEV0PYLhDx2JswWlUQirpNketZrg0HZdRdvkD7dYr4BUQ8By15ngFAc4hyqRHELC6L43T+9NV9/X49HREIQiYj3admMb+0WzVfYWyXhVMu62qBjzmd/oE01HIT+a1ytiJe9SirVOB2nsB3ATgTCI6CuCDAP6sQ8dibMEplKuHYmopEuGOHcern6NWD5cmYzLc5rWruqgLvryGPhsFjKwagXB8slh1X22w+8NHj+I3z47UPVeWCUvS8ar7/IY+WWsu3TRQt7q2tgc7FZdRKAdb9dmd9A5yolD0o3aVsTMxti2TUJB1SePBoiH0wXMikgG8RwhxNRF1AZCEENNhH4exhSyv+s8/6knFsKlmPlPtSq5UTIbb3163ZJ49yZjryrV8WUeShz4DG8gkIMvVH4K11+WV56/Csu5E3XPN/HbV1zyTcA+gWevMBR+1qz4NJGK1qz7dFhN4JCV2WUAihIhEj1q6pvfdLHFW/SWwJxXD5DG/6o9sLoXeoyaE0AFcZv2c4yCNseYVG5SeScakutVk5hDbzFs6GZeh6sE+KRSJ8ND+sfp2qDwPqhmpuIx87UT1miHptf1drsPJ2ZJbHrWYZxDAWmMu+Kg+p7W9nsmYjLzX0GftHLWEgt0n6j/mynr1ysq5kqqZE+k2naHbZ0EEm3udehU9RkS3E9FbiOi19r8OHYuxBae2F6ZWUpFxaDxf/Zxy4x41TTcgu4zH9KRirnmjeI5acwwh8J8PHKy6rzZp8OFTedy/Z7TuuY8dmqgr19WTch/6dCv8zoLpitcP89UOfabjMopuxcxdEth6VY8olPU5rUpgq53P6va3pSfpnQuOzb1OvYqSAMYAXOm4TwD4QYeOx9iC0ihAkiRCf1f18Fntc1IxGeVCdc9LrqTXzbEBgGXdCazorS/wPddFpeebFT1JvPTsFVX3FWpSnJyzqhcxl0UA55zWU3ef19Bnnq9Ly8wFHzV51Mo6lvfMvJ/cFuvYasuzdScU14AsF5FrlIrV9KiVtbr8e+aCCO5Ri6qOBGpCiHd0Yr+MLRbmh7t/h3dtx1ihZl5bKi4jX/NZM11SXRNwdicV/Oa5+gnunFS1OWmX9By1PRhdCRlag3QJNkWW8MSRybr7cyWu89mqREzGsQn/BR+pmIyxbDnQ/iSJXOd95iNyjWpXIhfKRl27upMxz1JlbO51JFAjom8A9a9cIcSNnTgeYwtNo6FPN7UrNJMxGRM1c9TcVq0B5geT2wR3HvpsTleivjJBsSa/HRFh14nq9BBeQ9IAXHtraoNyFpwQou5LidtCnNoUKoYh8HQTE+7zZR3pCAx9Bpmj5pUyhkVDp15FP3X8nATwGgDHOnQsxhYc3RBQGkxErutRK9cnvK0dvTFXFtYHakSEFT31Q5+NFjWwagmlvph37dxBTTew4+hE1TbZknsADQDrBtJ193GPWutW9CZx2eaBqvtqc92lXIZHs2UNF29Y6rlfswLFzJvyVL5clZttrtTmhHML1IgoEqlEmLtODX1+33mbiG4D8NtOHIuxhSjIH02CmXHcTnpaW+4pFZdQmwrK7FFzf9u7Dd8UVD0SHzbzBRFh70iu6r6CWj0vcG1/Ghdv6K/aZqrgfV3isoRizYcrzx1snVvqjbqFOPH6ouxevdEz+zSqgj1VF4jJcx/9pGLVedQKZY1Xcs8zs7V2eDOA5bN0LMbmvSDZF2qHK9zzqFXv6L7dJz2HbwhUl/aB56g1r3YIqfa6JBS5LinulEu1CFu3S4qOHNf5bFkqLuO+3dWrbmt7mdxq6U7mVc95XN0uk/HzZQ1LuuKu288m83eZaXftKmSbRAQ94NxJNrs6NUdtGtVz1I4D+GgnjsXYQhSkR80e0uiy5sEU1dpSRfVDn6f3d+GMFfWrCwG7TmV1SgGvP+rM29mrqs9vsXZIOi5jPFc9Ud1v6DOTVJAtahjIzMwhzLukiWDBpGIyzlzRXXVfXWWCmIyCWh2UjWZLUDx6yOwKEoOOSx+VXs/a3sGi6p7aJZMwX2e9affXIZs7nRr67G68FWPMTdBkpsmaCc9Ftbr4t1uPWqGsYZVLGg7AzqWkVgVqXEKqeUTV85Xcemv6aj4Mpwoqujw+1LsT9bnUojJRfT4iIsg16VF2nZiu642uHfrUDAMXrOlz3Wd3UqnrUbt390ms7E2F1OrW1Q996uhJ1QdjdooODtSipyNDn0R0qVU+CkT0ZiL6ZyJa24ljMbbQlHUD8QAZzWs/TASqi3Sn4vUJbx8/PFH3IWXrScbqAgLdEJ7bM3dJpfq61A59JhUZJa06CPj9vjHPIenpkoZjk4Wq+/JlrhjRjtpzfXAsj7hSXdWjNo+aW1UCWyZR/97ZuqIH29YuCanFrTN71KqnSLj19LkFmywaOjVH7d8A5InofAAfArAXwDc7dCzGFpRi2Qg03Fg7Kbq2Iy4Vqy8htePoJCSPcVW3wuy8Eqx56YRSlUvtmWNTVUGAJFHdtVq7NI1rzqlOlGs7eqqAncerSxQVypprPjwWzIaBrqrb55zWUzf0WdSqAzW3Op+2vnQMkzWF2fMRmTZg9qjNvB7zLglvAXOenVsVDDb3OhWoacIcv7kOwJeEEF8GwMOhjAUQNIdabX6kWsmYjKlydUSwqi+F9cu6XLd3+0PNJSWbV1sE+9FDE9Aa1FzNljTPckOvufC0uqoRubKOFC8maNm+0RwMx8R5IaorDsRkCWpNmpUH9o271l0FzC85tfnzolRCqnaOmtvfF69yZWzudSpQmyaijwN4M4CW/b7ZAAAgAElEQVSfEZEEgAe+GQsgaJJZvzI3gPlh8/RY9YfNY4cmPPfNQx/hSCeqqxNsP2MZVva5zwu0+Q+rKXWrDQtl3XNOG2vsBeuWVvWYufUcP3W0eni0J6Vgbb/7l5xD43n8/KnjVfeZue7mPlBL1axgrU3jYzO/qPH7P4o6Fai9AUAJwDuFEMcBrAbwfzp0LMYWlNqcTl6c2dMNQ9QNafakFGxZUv0Wf97pfRjIuKcMMFMMzAQE5oT4ZlvPalM71OZAczPlk99OFwKPHa5OkJsruQ9fsWBqgxc3tbVXdUOg3+O9c/H6fpx7Wm/VfXmPuWCzLV0zRaLoN0etwIFaFHWsRw3AF4QQ9xHRFgAXALitQ8dibEEJmhIjFZcqf4BLmlFXGzSp1KfnKOsGEh4JbLuT1T03Zd1ALMCiBlatK65U11b0GGpyru6d9smjtrI3Wbe4JM8lpNrSlajujQ4yxP/dh494zu/sSih1w6JRqfVZV0LK44ug22IiFg2d+it8L4AEEZ0G4JcA3gLgFr8nENEaIrqbiJ4hoqeJ6APW/UuJ6FdEtNv6f+6X0TDWQUFTYjiX3bsNl5rFooMzhMDdO2dqIBbL7gEG85eKy1XzlXSjPuCN15Sa8sujJgTw/UePVN2XL2k89NmGdHwmsPLqOa4N3rafsQyrPIaw7TxqTlFJFp1QJJQcw7xFzWvoU8G0xxw8Nrc6FaiRECIP4LUAviKEeB2Acxo8RwPwISHEWQAuBvBeIjoLwMcA3CWE2AzgLus2YwtWwWMOSS1nDb98WQsUVNXOu3FaszSNLYOZym2vP+jMX1dcaVjgOhWTUXTU9/Ir7XX60jReVrMiNCpBwHzlzNZf0tx7mRWJqhYUDO08ie6EezDdk6qf31WISK8nUfUqY/PvS/1Hf08yxkOfEdWxQI2IXgTgjwH8LMixhBDDQohHrZ+nATwL4DSYK0dvtTa7FcCrO9JixiIi6NCnczVXkHlQWs0qtlqZhIKDY/nKbZ4H1RqzR81/WK0+WbH39etJ1ZeQikoQMF+l40rlGnlVEKgtZr5hoMs1wAGsZNE1gdqTRyYjmYPQ67XDPWrR1al3+gcBfBzAD4UQTxPRBgB3B30yEa0DcCGABwEMCiGGrYeOAxj0eM67AbwbAAYHBzE0NNRq2wPLZrOzcpzFgs+n6fEjKko6MCQO+2739LCGneM6tuIwDkzqmBjT6s6foeuV+3KqwJlLJc9zPJI3cN/uQuXxHSc1/OzJEl63yn37xSjIa/TQMQ3TZYGhwj7rOYW650yfKmLot7/Dii7JcxubZggcGSlWPT45VcB9997T6q8RKXPxvj9xtIw/TB2BcUzBWMFAdrJc14bCdBF33XMf+hLmNdLLBdxzj/s5N4TA4ZHqa7i2x/u91klu59P5+vJ67UyVBQ4eLfHfYBdz/tkkhOjYPwDpFp6TAfAIgNdatydqHj/VaB/Pf/7zxWy4++67Z+U4iwWfT9Otv9svvvPQoYbb3b/npPiHnz0jhBDiof1j4m9+/FTdNpf/wx3CMAwhhBCHx3PiT279g+f+ciVVvO7ffle5/fCBMfHJH+1otvkLWpDX6J07hsUXfr1LCCGEYRji2i/cW7fNR/7ncfH00cnKbbdtnGofb7T9fDIX7/sv/HqXuOX+/UIIIfaMTIv3/fejddv82X8+LA6O5iq3X/5F/3O+9qM/rbo9V9fI7Xw62+L1e5Q1XVz3pd92qlnz2my8RgE8LDximk6VkHoRET0D4Dnr9vlE9JUAz4sB+D6AbwkhfmDdfYKIVlqPrwQw4vV8xhaCQllHMuDQpz3PxmteW0xCpVzReK6MsZpi4E6pmIy8oxC1V/Fm5q8rMbPIQ9WF68pZ59Cn7pJahXWW7Jh/Zq6CrL9GzvdDkGt09qoe38fnkiwRdCvBr9cK15gsQTP8p0ewudGpOWr/AuAaAGMAIIR4AsDlfk8gMy301wA8K4T4Z8dDtwN4m/Xz2wD8OPTWMhYh+YB51FTNwK+fGak8x22eTUJGZZ7NeK4MvykzVPNBVFR1JBVOz9Es50R1ryoTzvJf2RKXg5ptp/XNFEv3WmWdjM/MAfWrHGFzvn3KWrRS29TOiWTzS8deSULUTbBp9Cq5FGYajyuJ6HHr37UAPgvgj4hoN4CrrduMLVhB03Os6E3ieWv7AAAlzf05cZmqem5etKE/cDvyXKaoJem4UulRG8+V8ft9Y3XbOKtKTOTL2HUi67vPuCPFQlkzoEQoCJiPEoqEibw5+b+oGq492M5gerqoNgzUdp3IVq5pPmK1WO3fRXAS63mpU3+FDxPRJQCENZz5AZirOD0JIX4LwOsldFXI7WMsssxVn40/iJ2rPr2GS+MSKkGDX64uG4HMKgcSWb1BHBA0y9mjRgBeft7Kum2cSUgLqo5LNvoH0HYd1kRG5vJRIZguarh75wg+fM0Znr2eyZhUuUZm+hT/986lG/sxXVQrVQ9Sseh8yUlZXwxKCaMuebJTTDa/EHglxWZzo1N/hf8MwHthptc4CrMywXs7dCzGFpRm8qjtHpkGAIzny67fcuIyVXoFpooaMh65umzp+Eydyt/vHcOek/49Payes0etqOlIunzoOct/jefKDYfJnOV98mo0Mt7PZxuXZ/D8tWbudK9ALS7LGM+aczr98tzZzBJs1jXymIowV+ygs9gg9U83VyeIpNBDfiKSYZaP+uOw983YYuD1wVErFZNxeLwAwJwgvCRdX4cw7pijli1qWL0kVbeNk/2HujsZw5krurGyz397Vi8dl5G3cnSZCzLcJ6rb1+WZY1N4vKaWZy1neZ9ciXOotSsdl3HklPneKXp8MZIlVCp7TBVU9KT8e9Sc1QkKZT1aQ59WWbNG81/t32Egk5jF1rFGQu9RE0LoANYSkXv1WsaYr5HpUqBEs3FFqhSO3j+ag3ApGJWQqdK7M11UG/aoORN3Bg0YWbVUTEZRm5mr5JpM1VEUPJNQcO25K+q2ceqpCQKi1FszHzkDZa/X+ZKu+MwctZKKngbvnYzjGpmVQqITTNs9uGaPmne7zC8EXJ0gajr1StoH4H4iuh1Azr6zZjUnY8zFQ/vHm07XcGg8X1l+77RjVMemA6fw4s3LMFVU0dugV8AshTMTEHCg1jxJIhhWDgSvc+gMFB47NIF8gxV53Y4AOlfWuEetTZmkUlmQUVR1LOmq71dIxWSMW+ls7thxvOFigp7kTAWJfOR61CQUVd2sCeyzkrvHpWYpm3uderfvtf5JALo7dAzGFqQLT+9zHcb0s2UwgzVL03X3X7JKwcZlXQDMeTaNegXMoQ9Hj1qARQ3M22i2VMlj5+ScqL5+WRdW9roX+7Z1O3o6uEetfem4jIIjhYpb8JKKyZVrd8GaPqwf6PLdZ3dSQdYeni5Hq/yavZig0KBGrHMuJIuOjgRqQohPAQAR9Zg3xXQnjsPYQmTmYArWo2Ynr/yvBw7h6q311dViUvUctUYr15zzbIoqr/5q1+4TWYxMl+ruT1tzhgBzSPqMQf/vsz0pBWPWxPZcWUM6Qr0181Gqqk6u4Rq8JOMzKVTMXHf+H5cHRvPYP5rF6y9aY/aoRajX00yOrTecztCTitXVLGVzryOvJCLaBuAbsHrTiGgSwI1CiEc6cTzGFhKi+uSzjfzRWYPYMJCpuz8uwxEQNE7aaa5cswM1rkzQrtP709jiEoSlHHnUAq0oTMSwfzQPwFpRyNelLURUmdNZKGuuwYtEhONTRQDARL7xtIEXbliKmELWPv17rmZbKm7Om/QKSm2ZhFIZ7mXR0alxja8DeI8QYp0QYh3M1Bzf6NCxGFtQvEq8uFEkgqYb0HT31YUTJYH7do8CCJbWwTn0WVR1132yxmKyhLJm4OBY3jU7ZDImoWgNqwXJ0VWVnqOkId0g4GbBFVT3VZ99qRgUq5THVLHxYoIl6TjK1jWNWnoOuwfR63e19Ti+qLHo6NRfYV0IcZ99w0pmy1efsQCa6UyzS8MUVB0Jlz/AqzMSNi2f6Wlr1FPXnYxhqjAzd4cnrbcmbQ2bCYHKh72TLBF+8sQxAObQZ6MeNecij7warSBgvit49DKl4jNz1IIE05mEc2WuFqmhT7MHV/PsPbSZrzMe+oyaTgVq9xDRTUS0nYheYhVkHyKi5xHR8zp0TMYWHTvDfUE13NNAKNTUN+SSquNrv90HIHq9AvOJXdA7rkhY1l2fk6onGcOlm8xqBFOBkqnO9HTmS9Ga/zSfCSHwkyeOuQbTSUWumkfYzDXKRWzo004H02gld09KqXxRY9HRqXf7+db/f1Nz/4Uwcwhe2aHjMjav6YZArhS8eHIqJqNYNoc+3bLbT5UFbnviED7zmnMC7W/9QBcu3TQAwFzUkOCi7C0Zy5UxVdDwxOGJSkDmlHIU/D4ynm8YeNVmvY9SEDBfxWUJZd3AZZsG0OPSW2bP6wIAVW88X9OZlDgfwR61omqgqBnoz/hXJuDFBNHTqVWfV3Riv4wtdNmShv2jucYbWu586jjefPFaz8cH04TLtywzhzEDJODMJBUM7TwJANhxdLLpRQ3M9NihCew9mcWy7gSWuuToSjiKrB+bLEJy6dFx6k4olQC+oEYrCJiv7JW3XmWVUnG58l4IIpNUKnnUcqVoBdN2wttG5emcvYIsOvjrMmMRUtYMXHHGssDbv/ni032HJ7tiVEm50agqAQD0peKVGohx7k1r2bsuW48l6TgMIVyHmpwB8Nmrehruz5lEN2pBwHxl1rXVUdR010Ll6ZhcNb+zEdlxjaKX8FauJLz1G/rMxJWmevTZ7OC/xIxFSLHBqqxazuLebuKSWctwz0gWv3lupOH+kjEJJ628X1sGg39IsWoPHRjHc8encHAs73s9RTNLfC23e8ypYs0xa7KaPWBuPZqSREjGJKi6AVlq7qMyH7HqEamYjHxZa5g2RJLItRQdm1scqDEWIQWPYRgvzlJEbuIyoaDqiCsSzl/T13B/RIRD4/nAx2fuXnrOCizrTmDH0Unfj71Ckys4hRB40YZ+15JHrDnphIJ8WcdTR6c8t3nq6BRyJQ3dTaZDyUVsIU7SnqMWoH5vC98dWIeFGqgR0UVEtMJx+61E9GMi+iIRLQ3zWIwtRPkm62smYjKmi1qlbmGtuGwGA08cnsCoS4Z8L7ohmq43ymZkEgryJR3PO70P/T5B1Vi2jD8cOBVon2lrhS+n5whHOiYjV/Zf4WjXvmyUKNoWl82amrrhvrhnrtglywoBciMStdbTyzon7FfSTQDKAEBElwP4LIBvApgEcHPIx2Jswfn2Q4fwrQcPBd7eLhyd8vjjaybEFehLx3HN2Stct3GTL2tIcvmoltn57cq698pZiczezu0B5yTaOe6iFgTMV+mEuZjgnNO85wie3p/GZEFFTypYoNbtWPkZJem4Ys5RC7BiOB1TkCvzPLUoCfvdLgshxq2f3wDgZiHE94UQnwSwKeRjMbbgPLBvrKntU3EZp3Llhr1wqm5g/TL/otJOR04VkORem5aVNQO7TkxDCO8kw8mYjMPjeSxNBxvG5BV54UrHZWRLGsitdITlqaNTeGDfGH702LFA+4zqNZIlgm6IQKu/zVxq0fsdFrPQAzUisl8FVwH4jeOx6MysZCyihieLTW2fiskYz5cbfkueDlACx3bmim5MFlQkedVnywYycRgCePqY9/yndFzGZEFFImCZLi7vE66uuIyxrP+XnAtP70M6ruCyzQOB9mkWNY/uNSqoOpJx/9dbVHsFF7Owg6fbYFYlGAVQAHAfABDRJpjDn4wxHx+8eguOTgSfzJ+MyTiVUxuuFM0WNWxcFuztvvdkFs8NT3EKiDYsSceRjEk497Rez21SMRlPHplEIuAQc1R7a+arVFzB8dGsb8+xPS/QTgLdSE8yhol8ObLzO8ua4ZqKxKmHX2eRE/ZX5s8B+BCAWwBcJmZmJEoA3h/ysRhbcDJJBees8v5wr7V/NIef7RhuOPQ5FaBWoW0gk8B4XuU5am2wk6nuOOr9/TQVk/HIwVMYz5UD7bM7GcNEXoUc0SBgvumK+8/vBOxrNO67stqpO6ngxFQx0os9AtX75UAtUsLuUXtICFFXy1MIsSvk4zC2IBXLOnp66mtDerlo3RJsGcz4fjBIEmGy0LhWoe3129agqHFS1XakE3IlS72XZFzGjqOTWL0kFWifPSk7COBZJGFIxWWMTJWgGt4rHBMxGSenS+hLB/uS05NUcHyyFMlrZK7mbLydnSCbRUfYPWr8VY+xNuTLeuChMMD8sBlvsJggFZMwMl0MHKg9eugUfvX0CQ7U2pC2rssL1ntnJUrFZAxk4njpOcFW43YnYzge8d6a+aQroeBUvuw7FPizJ4fx0P7xwClzelLRvUYx2Uze20hvKsaLCSIm7LB/GRH9hdeDQoh/Dvl4jC0ozfZkpeMKxnJl33k2qZiMI6cK6E4E6xV4xXkr8dMnGw+nMm+pmIzf7R3DS7Z4p95IxWSMZsvoTQW7Lt1JBccni0g3mXyVubNT2/glgk4oEuKKFLiUVHdSwchUEf2Z6CUkTlm9g410J2M4MBa83jDrvNBXfQLIAOj2+McY81FU9aZWW6ZiMoQAHtg37r1NXMbJbClQrU/A/EM9MlXiQK0N9tCXb0+nFVwP9iQD7bMnGcOJqSK6IthbMx91JRSMZcu+C3Hefsk6SESBg+meZAzDk9Ecno4rEspa4x41HvqMnrBfTcNCiL8LeZ+MLRrfuP8AXrShP/D29of9y3yGz5IxGfmSDjlgfcjupIIT08WGGcyZt7giIaFIvr2jdoAQNOt9T1LBialSoFJgrLGuuIzpkuYbTCdjsjW/M2ivpxVMR6gge7O6kwovJogYnqPGWIQM9iSwsjfY5HIAlbkwfkFVKiajHGBuiq2sGZjIq0hFsFdgPilphm9vTVw2/1wGnTvYnYzh6EQhkvOf5iM7iA7S6xn0GvWkzKkIUexRC4rzqEVP2K+mlxPRB2FWIdgB4GtCCL7ijAV0/uo+LG1ifotdSsgvb1OzQ5hr+80KBhwQtM/v3B+ZKAAwh8sC7cu6HvM5CIgS+zw2+pIDIHDJLrvnrVFewyjjygTRE3aP2ucBbIMZpL0MwD+FvH/GFrRCk3PUbH0+ZYiCDnnallpFxHmOWvtSPlngn3/6EgBm+pQg7MoSPEctHPb74tC4d4LpZt8D9rXJRHToM0gKvp5kjBPeRkzYgdpZQog3CyFuAnA9gBeHvH/GFrRCWUdXC6v6NJ+hzWaGPQFU5tfM516BqPiP+/Z7PtbsdbYTlfKqz3BdaAXMbppNUVO5RhHt9fSra2pLKBJKARYdsNkTdqBWCcN5yJOx5hU1HYkWetT8PhjGssEy39vsPG68mKB9fh94reap64poEDBf+fWaGUEyxLoIukBkNuk+iX2diChQzxubPWG/ms4nIrsKMQFIWbcJgBBC9IR8PMYWFCEal3hxs7LXO8XDaLZx7iQ3/K26s1r9LFRk/hQNk1/A3PJcrQheoo9cc0aghLcsekIN1IQQHRsrIaKXAvgCzFxt/yGE+GynjsXYXGnlm+xP3ncZTl+a9nz8M685F8OTxab2+clXnIUXrPPOqs8a+8INF+ACn1Qa56/uw+8/fmVT+3zkr6+uzCFk7fvxey/Fuad519Z91fmn4bSAJb5s//2uF+Iin4oUc+W81cHTusiSWcUg6CIK1lnR6591QUQygC8D+CMARwD8gYhuF0I8M7ctY6x1QgicnC5hecCEp17OXe1fxH3dQBfWDXQ1tc93Xra+nSYxANddcJrv45JETaViAYD+TPA6sKyxRjnpetMxXHnmYFP7vGTTQDtNioSuuIwz/vpO3PCC0x31QQU2L+/Gjfy3YdbNi0ANwAsA7BFC7AMAIvo2gOsAcKDG5q18Wcdbv/4Q3n7JOvSl4/jlM8cDp2pgjLFO+efXX4Cnj02iP5MAYaanP2jiXxYuEi1OlpxNRHQ9gJcKId5l3X4LgBcKId7n2ObdAN4NAIODg8//9re/3fF2ZbNZZDLBasCxxhbj+TyWNfDAsAZDABcsk7GhT/LNidasxXhOO4nPZ/j4nIaLz2f4ZuOcXnHFFY8IIba5PTZfetQaEkLcDOBmANi2bZvYvn17x485NDSE2TjOYrFYz+ebOrjvxXpOO4XPZ/j4nIaLz2f45vqczpeZgkcBrHHcXm3dxxhjjDG2YM2XQO0PADYT0XoiigO4AcDtc9wmxhhjjLGOmhdDn0IIjYjeB+AXMNNzfF0I8fQcN4sxxhhjrKPmxWKCZhHRSQAHZ+FQAwBGZ+E4iwWfz/DxOQ0Xn8/w8TkNF5/P8M3GOV0rhFjm9sCCDNRmCxE97LVKgzWPz2f4+JyGi89n+PichovPZ/jm+pzOlzlqjDHGGGOLDgdqjDHGGGMRxYFae26e6wYsMHw+w8fnNFx8PsPH5zRcfD7DN6fnlOeoMcYYY4xFFPeoMcYYY4xFFAdqjDHGGGMRxYEaY4wxxlhEcaDGGGOMMRZRHKgxxhhjjEUUB2qMMcYYYxHFgRpjjDHGWERxoMYYY4wxFlEcqDHGGGOMRRQHaowxxhhjEcWBGmOMMcZYRHGgxhhjjDEWURyoMcYYY4xFFAdqjDHGGGMRxYEaY4wxxlhEcaDGGGOMMRZRHKgxxhhjjEUUB2qMMcYYYxGlzHUDOmFgYECsW7eu48fJ5XLo6urq+HEWCz6f4eNzGi4+n+HjcxouPp/hm41z+sgjj4wKIZa5PbYgA7V169bh4Ycf7vhxhoaGsH379o4fZ7Hg8xk+Pqfh4vMZPj6n4eLzGb7ZOKdEdNDrMR76ZIwxxhiLKA7UGGOMMcYiigM1xhhjbBEr7twJIcRcN4N54ECNMcYYW0TKR45C6Hrl9v7rXg19YmIOW8T8cKDGGGOMLVBuPWV7r74a2bvvrr7TMGapRaxZHKgxxhhjC5BRLuO5rWe5P0hUdVMUi7PQItYKDtQYY4yxBUiUSp6PUTxeddvgQC2yOFBjjDHGFiBRLvs8WtOj5hPUsbnFgRpjjDG2AAlN93u0+pbuty2bSxyoMcYYYwuST8qN2kUGHKhFFgdqjDHG2ELkt5KzJlATvOozsjhQY4wxxhYin+CrLm0HB2qRxYEaY4wxtgA1U21A6ByoRRUHaowxxthC1MTQJwyeoxZVHKgxxhhjC5FvoGb9Z23DPWrRxYEaY4wxtgAJw2/o03rMXu3JPWqR1VagRkRriehq6+cUEXWH0yzGGGOMtadxeg47fxqv+oyulgM1IvoTAN8DcJN112oAPwqjUYwxxhhrk19uNCtQ0yenzJuqOhstYi1op0ftvQAuBTAFAEKI3QCWh9EoxhhjjLXHL/iyA7TygQPWHTz0GVXtBGolIUSlkBgRKfDtZ2WMMcbYbDF86ndqJ08CAEq7diG+dm39KlAWGe0EavcQ0ScApIjojwD8D4CfhNMsxhhjjLVDlLyLsmtjY+b/o6NQli2brSaxFrQTqH0UwEkAOwD8KYA7APx1GI1ijDHGWHv0iQn3BxQFRjZrbjM1Camvl3vUIqylQI2IZADPCiG+KoR4nRDieuvnhleaiL5ORCNE9JTjvr8loqNE9Lj171rHYx8noj1EtJOIrmmlvYwxxthCp46MoPemm/DsmVuhnToFdXjYdTupqwtGPg8AMLI5yBlO2BBlLQVqQggdwE4iOr2Fp98C4KUu939eCHGB9e8OACCiswDcAOBs6zlfsYJExhhjbNETuo6pX/4Sz565FXsufwlK55yLnle+EuqRo5Ves1pSPA5hzV8zcjlIXV1NlZtis6udoc8lAJ4moruI6Hb7X6MnCSHuBTAe8BjXAfi2EKIkhNgPYA+AF7TeZMYYY2z+E0Jg+JOfxHNnn4OJ730Pa772H9j63LMoXnoJlKVLYeTzMIoF9yfHFAhNM/dTKkFKJWex5axZShvP/WRorTC9j4jeCuBhAB8SQpwCcBqABxzbHLHuq0NE7wbwbgAYHBzE0NBQyM2rl81mZ+U4iwWfz/DxOQ0Xn8/w8TltXuaHP0LXL34BI53GqY/+JU6sX499qgoMDSGbzeLQyRGoDz2IxL79SAN157df1ZA7eRL7hoaw5ORJlHt7oQuBYlfXnPw+UTfXr9GWAzUhxD0htuPfAPw9zPQefw/gnwDc2GR7bgZwMwBs27ZNbN++PcTmuRsaGsJsHGex4PMZPj6n4eLzGT4+p8GV9u/Hkfe9H5SIY/Uvfm6m1agxNDSE9Zs3I75hI3JjY5gA6s7v3u5uyN0ZXLB9Ow7cdDMyWzYjtnoNevk6uJrr12g7lQmmiWjK+lckIp2IplrZlxDihBBCF0IYAL6KmeHNowDWODZdbd3HGGOMLRpTv/oV9r3sWvTf+A5s+MEPXIM0G8XjEKpaGd6se1xRANUa+lRVUCzGqz4jrJ0etcoyESIimPPJLm5lX0S0UghhL095DQB7RejtAP6biP4ZwCoAmwE81GqbGWOMsflm7zUvRfngQWz06EWrRbGYWZVA1cyfhYD5MW09rszMUYNhANRW2W/WYe3MUauw0nL8iIj+BsDH/LYlotsAbAcwQERHAPwNgO1EdAHMoc8DMPOyQQjxNBF9F8AzADQA77VWnDLGGGML3skv/ivKBw9i7W3/HShIAwBIMmDoEJoZqNVxBG0zuEctqloO1IjotY6bEoBtAIqNnieEeKPL3V/z2f4fAPxD0w1kjDHG5jF9ehqjX/kKVv7jPyJ94YWBn0eyBKGbgRrsYc3a4Mw51OkauLGoaKdH7ZWOnzWYPWHXtdUaxhhjjAEADr7pTRh4z3vQ95pXN/dESQYMA0LXzPlobuzgzP6f56hFVjuB2n8IIe533kFElwIYaa9JjDHG2OJWePxxlPbsxfrb39f0c0mWYJRUQDdAsuwehNn3CQFwh1qktTOD8F8D3scYY4yxJhz90Iex7rvfqVoEEJgkA9tsQUYAACAASURBVLoBYeiAHLCYD/eoRVbTPWpE9CIAlwBYRkR/4XioBwCXd2KMMcbaUNq/H8rAAFLnntvS80mWzCDNr0eNyJzDJssgIi4hFWGtDH3GAWSs5zoruU4BuD6MRjHGGGOL1eiXv4LM1Ve1vgOrRw2GDsjeA2eVHGq8mCDSmg7UrIoE9xDRLUKIgx1oE2OMMbYoGcUiSrt3Y9XnPtvyPuweNaEbIFnxnqNmGCDJCuS4Qy2y2llMkCei/wPgbACViq5CiCvbbhVjjDG2CGXvvReZyy83hyxbJSuArpurPn32IwzDmsPGPWpR1s5igm8BeA7AegCfgpme4w8htIkxxhhblKbuvBM9176srX3YedSgG4Di0aMGALoOSJyeI+raCdT6hRBfA6AKIe4RQtwIgHvTGGOMsRYYuRym7/w5Emee2d6O5JlVn36rRoVhgCSZ56hFXDtDn6r1/zARvRzAMQBL228SY4wxtvjkHnwQS9/+9tZScjiQLJs9aoYAFAUCHoObhjGz2IB71CKrnUDt00TUC+BDMPOn9QD4X6G0ijHGGFtkJv7ne+i/8R3t70iWAV0DdN291qdF6NyjNh+0FKgRkQxgsxDipwAmAVwRaqsYY4yxRaawYwdSF1zQ9n6q8qIRefeWVSXE5R61qGppjpoQQgfgVlydMcYYY01Sh4eR3LrVtwcsMKKZuMutt8wO3Oz0HNyhFmntDH3eT0RfAvAdADn7TiHEo223ijHGGFtEsvfci9R554WzM5L855xZvWzCMACJ56hFXTuBmt0/+3eO+wR45SdjjDHWlIkf/gBL3/yWcHZGZC4UsHkOfRpmeg6eoxZpLQdqQgiel8YYY4yFgEhC5oqQPlYJAAQEhPcKUiJzMQGZPWpc6zO6Ws6jRkSDRPQ1IrrTun0WEb0zvKYxxhhjC59RLEIYBuRMVyj7sxcTEHyS2RKZiwmI2k4HwjqrnYS3twD4BYBV1u1dAD7YboMYY4yxxaT03HNInnFGeDuUJP/FBNb9Quc5avNBO4HagBDiuwAMABBCaAD0UFrFGGOMLRKFp55G8txzwtthkDlqVo8a8Ry1yGsnUMsRUT+suJ2ILoaZU40xxhhjAZV2Podku2Wjqjhyp3nFYGQVZa8Mj4Z4eBaqdlZ9/gWA2wFsJKL7ASwDcH0orWKMMcYWidLefUhs3BjeDq3FBBUuPWoEq9dNklAV2LHIaWfV56NE9BIAZ8B8WewUQqgNnsYYY4wxixACRj4PqSuchQQAQJJk9ZZhZkFB3UZWoEbkX72AzbmWAzUiSgJ4D4DLYIbu9xHRvwshimE1jjHGGFvItJERKMuXhbtTZ2UCeMRgRFbCWzuQ40AtqtqZo/ZNAGfDLMj+Jevn/wzyRCL6OhGNENFTjvuWEtGviGi39f8S634ioi8S0R4iepKIntdGmxljjLHIKO/fj8T6DeHu1O4howYLBYSVZ43XEkRaO4HaOUKIdwoh7rb+/QnMYC2IWwC8tOa+jwG4SwixGcBd1m0AeBmAzda/dwP4tzbazBhjjEVGad8+xNevD3endSWkvFZ9OhcTcI9aVLUTqD1qrfQEABDRCwE8HOSJQoh7AYzX3H0dgFutn28F8GrH/d8UpgcA9BHRyjbazRhjjEVC8YknEF+3NtydEiCElZ4jSB41Ts8Rae2s+nw+gN8R0SHr9ukAdhLRDgBCCNFsddlBIcSw9fNxAIPWz6cBOOzY7oh137DjPhDRu2H2uGFwcBBDQ0NNHr552Wx2Vo6zWPD5DB+f03Dx+QzfYj+nS556GnsuughGSOcgm83ikUOHkT42DGV6GkJVceC++yBSqco2S6enIUolHH3sUSSHj0GLKYCmo7CIr4OfuX6NthOo1Q5dhkYIIYioqX5YIcTNAG4GgG3btont27d3omlVhoaGMBvHWSz4fIaPz2m4+HyGb7Gf031f/CLOe+1rQVI7A1wzhoaGsG3jRow/9xxKk5OQMhmcc9llkLu7K9vs/+K/QkqnsfHcc5EdGUHyzDNhlMtYuoivg5+5fo22k57joDXhf41zP0KIR1vc5QkiWimEGLaGNkes+49ax7Cttu5jjDHG5i27EHpYQVqFVDNHzbMygQDPUYu+dtJz/D2AtwPYi5mZigLAlS3u8nYAbwPwWev/Hzvufx8RfRvACwFMOoZIGWOMsXlJPXoMpWee7cCeHSWk3OafVXKnGY1XhrI5187Q5+sBbBRClJt9IhHdBmA7gAEiOgLgb2AGaN8loncCOGjtHwDuAHAtgD0A8gDe0UabGWOMsUjQThzHkje9Kfwd25UJ7F4yjx41oeuOouzhN4OFo51A7SkAfZgZogxMCPFGj4euctlWAHhvs8dgjDHGokw9cgSxNWsab9gkszJBo1qfdq41v41YFLQTqP0jgMespLUl+04hxKvabhVjjDG2wKnDw4ivCzmHGgDIMqBrM7dde9QAoesgkry3YZHQTqB2K4DPAdgBwAinOYwxxtjioB4/jq6LL268YZMoFoPQdPPnmvlnlQUM9mICnqMWee0EankhxBdDawljLBQnv/xlpM47D5kXv9h3O21sDPmHH0Hmiu2Q4vFZah1jzKYeOQpl1arQ90uyDKHN9KiJ2hWgRO4/s0hqJ1C7j4j+EeaqTOfQZ6vpORhjIRi/9ZtIbNncMFAb/uT/B/XIEeinxrHkhhtmqXWMMZs2PgZlYCD0/ZKiQFSGPj1WfYIAQzeLsnOPWqS1E6hdaP3v7LdtJz0HY6xNolxGfP06GPl8w22148ex7M/fj+JzOzvfMMaYq9BzqAGAYlYacGX3nBFBGGZRdlIUCFUNvx0sFO0kvL0izIYwxtqn53KQu7qgTUw03FZAQO7vhzY2OgstY4w56dPTkDPdjTdsQdXQZ21vmT3cKUsQmgoQQUolYRQLHWkLa1/LoTwRDRLR14joTuv2WVYONMbYHFGPHIXcPwApnYZ26pTndvrEBEiJIb52LXK/vb/p4whVhdB1CMOAkc9DaBr0bBba2Bi0UQ78GGtEHR5GbOWKjuy7MvTpNv/MSskhpdIwsjlAlkGpFESx2JG2sPa1M/R5C4BvAPgr6/YuAN8B8LU228QYA5C977co7doFoetQli+DKJUhSkVoo2PQxkax5IY3InXuOVXPOfC612H5Rz8KKdOFA2+4AfjEx+v2K8pl7Hvta7HkjW+EsnQpIASePXMrznxqB0ip/5Pw7Jlb3Rsoy4DuPryy4Se3I7F5c/O/NGOLhHZiBMrywc7sXFEA1btHjUCQUikY2SxIkiGlUtBGxzrTFta2dgK1ASHEd4no4wAghNCIyGNQnDHWDPXYMRz9wAew5E1vhFA1SIk4KJGE1NMDWVUxdvPNUI8ew9pbvlF5jlEy1/QsfetboE9MYOK2b7vu+/hnPgPt2DD6b7wRALD+Rz/Crm3bYGSzkPv6XJ+z5eGHIcVjgKI0nFMz8vl/gXr8OAdqjPlQh49B6WiPmvvHsQDM4c50CvrkJOTeXqjHhjH5wx9i1T9+piPtYe1pJ1DLEVE/KtedLgYwGUqrGFvkjGIJmSuuwPIPf9j1cbm/H8bUVNV9QtWQvugikCxD6e9H8qyzXJ+b2LABK/7uUyBZNveV6UJ800YYpRLkmm2FYSB59tmQM12B2y53Z2BMTwfenrHFSDt+HMlzzu3IvkmSzFqfnkOfBEqloJ08idjKFchcdikAoHzgAOLr1nWkTax17Sw3+QuYqTk2EtH9AL4J4M9DaRVji5xQVddhSBvJCoSqVd+pa4BSG2rVM0olyJlM1X2JjZsgyvVle4Wm+bbDjZTphj6dbeo5jC025cNHEFt9Wsf2b5RK5vSEmqHP6V//GoXHHoOcyWDq9p/AyBcqPenDn/pUx9rDWtdOoPY0gJcAuATAnwI4G8BzYTSKscVOaCoQ8wnUYkpVQkvAKgcjO57jkRtJFEugZLJ6f4k4RKlUv7GqgmKx4A0HIHVnYGS5R40xP4VHH0X89NM7tv/y3r1IbNlcl/z22Ef+EgAQW2Meu/+d5hSIpe94R10VAxYN7QRqvxdCaEKIp4UQTwkhVAC/D6thjC1qmuYbIFUntDQJTasMZ5p3uGcan7rjDqjDw9X7i8crc9yq9qn6B4xu5O5u6FMcqLH5J/fgQ8g9+FDD7aZ+/gsceMMNyD3wQEvHMcplaCdPQqr5whSmzNVXoXv7dhi5HDTH+33gPe+BMjiI5NlnofulL638ncm85HIYLr3qbO41HagR0Qoiej6AFBFdSETPs/5tB5AOvYWMLULm0KdPT5YsA1rt0KdurvZqIHP55Uhsqp7oT7JizmmpbUeDgNFNbPUaZO++u6nnMOZFlMsQLq/NlvfnkdhVz+Zw6G1vw6G3va1hwDJ2880oPPEExm+5FbkHHqgu0eRDO3UKhSeewMhnP9fxxTZrvvQldF99NfJ/+AMO3PDGmQcIGPyrTyCxfj1W/8vnK3fHVq+GsrS/o21irWllMcE1AN4OYDWAf8JMfYopAJ8Ip1mMLW6N5oZRLF73gVPbo1Z85hn34Estg+LVwRfJsusHmNmO5gK1xIb1KO3aBe3UKShLljT1XBYNRrmM6V/8EjB0CE2D3N+P+OrVMAoFxNeuBcXj0EZHUdq1G1I6hfL+/RCGAVEsQZ+chDo8jPyDDyK+di1iq1ZCGz+F3H33mTtXFEiJBCAEjHwegwAOXfIiqEePoXzwICiRQHzdOpR2ulfMSGzeDCgK1CNHEF+3DqJYQGn3HgCAPDAAua8XoliCeuRI1fPkpUuhj48DgGsqmqP/639Vft553vnY+tyzdccWhoGRz/1v6FNTOOPRR3DsE3+FQ29/Bzb++leIr15dt/34f30LJz79acgDA5ASCeinTiF10TYkNm3C6Y4V252kDA5CO3Gicnv0X7+E02+5pW47OZOBXrNAiUVD04GaEOJWALcS0f8rhPh+B9rE2KIn1AZDn/FY3eR/M6iqXkxAhfps40a5DKopwi5lMjByOZd2ND9HDQAGP/EJTP30Z1j6ljc3/VzWeXo2B/XIYeQeeAClPXtQePgRUMoKuIpFUDJpviamp5HYsgXFHTuQ2LrV/CIgBIRhQEqnITQViQ0bkdi00Uwd09uH+NrT0fvKV0C97jqQRJD7+yHKKgb/8iOALEPu64Pc02M2RJbx2//5H2y85BIoy5ZBSiQgVBVGqQQplar64mHk89BPnQJiMcAwIPf1wcjloB4/juTWrdBGR6H095vvCyHMRTM9PTDyeUiZDIT1uj/4pj+GUSxWLagRQiD/4INInn024hs3YOonP4UwjLpUNNrJUUz/5jfYeMfPQPE4Vv/L5zH8qT5oJ0+6Bmr5Rx7Gmq9+FZkXXwYhRCTmgFEshvia+rZ6/Q1gc6+dElIcpDHWIY1WfUqJRP2cspqhz/RFF5lDpDUKjz+OpW95S9V907/6FcqHD9UVchdq86s+AaDnla/A7hddwoFahKgjI8gODWH0y18xq0foOgb+/P3ofdWr0HvttZAHBpBYtw6lvXuR3OqR5LgJQYf29OXLq4IcisUgu3w5kNJpSOnq2TVSMgml3xyuiy1fbj4/lapsD5hzJgGAEgnz/u6MuXDGufJZ05A480yc/tWbIaVSUI8dgygUQF3VaWm0kRF0XfzCqi86UirtmdVfiieg2O2aoyBtzU3/jv2vfk3lduqCCyD11udLJEXxTGDN5lY7edQYYx0iNBXku+ozVj/0WbvqU5JcFxSQrECq+QCCEKH2qNlDntN33YXuq65q+vksPKX9+zH8ib+CPjWFnpdfi9Vf+TKSZ53lGTiEEaRFmZRI1AVW9lQDubfX3CaegFEu171P9PExyDXzuEipX4Fdtd8mF+OELbFpE5LnzuRrM/J5SKnOLWJg4eNAjbEoajSJX5YBozoIqxv6lMg1UIutXl29OhTAkre+BVLNcKi5T/+A0c+Gn/0U+17+Cqz7zreROv/86v0KAZHPI/u73yH7619j2Qc/iNjKlS0dZ77QszmIQh7KsmWzetx9L7u2MvzGrC85taltauaEun0RAqxpA4nq94m5rUeg1uIXnTDV9pQJYdS9/yuPIdiiCDa7Wln1eRERrXDcfisR/ZiIvkhES8NtHmOLk9A0/xWcRIBRM0yh61VDnUTuPWowDLO3zbk7SXJfWdeoHT4SGzdizc034cAbbsCuSy9DcecuAED+0Uex75WvxM7nb0P+wYcg9fRizxVXovDEEy0dZ74Y+dznsPvFlzfcrnzkCI5/5jNQT4y0fcz8H/4AABykOSmK+0IcZ6AWj0GUXVaHugRe+sQEtNGTrodqJWF0pxG8h2AJFHgFK5s9rbyCbgJwNQAQ0eUAPgvg/QAuAHAzgOtDax1ji1Sj9BwkyxB1PWrBhj7hMkkaJNX10FXa0UaPQObyy7Hp3ntw5P3vx/7rrjPbZBjYcOcdSKxfX9mu6+IX4uDb34GNP/spYqtWtXy8KCsfOQwlQK/hqdtuQ/HpZzD1k9vR/653tXXM4U99Cqs+99m29rHQSIkkjELN0GdNvkBzVbVXpY7q90Np505zAcPrX++yvf9c07ng12tm9yTWLjZic6uVV5AshBi3fn4DgJuthQXfJ6LHw2saY4tXo1WfIKl+4q/uMvTplp7DpUcNEgFa/UTiVtJz1IotX4713/kOjHIZRi7nmrKj+6qr0Hf99dhz5VU448knXIdh5ztRLEHOdFlzCb1Lfenjp9D9R1dDG3HvpWlGbOUqpC9+Udv7WUikdApGIV99Z83r3Gvo0+2LS891r/LOzTY6VlnEMKesHvO6L2g1KB43g854HMc++jEgpmDVpz89S41kXlqpTCATkR3gXQXgN47H2v7qQEQHiGgHET1ORA9b9y0lol8R0W7rf07OxBa0hnnUZAlCVAdhQtcBR48akQRqc+iz1VWfbqR43Dev2oq/+gSWf/hD2HP5S5B/+OEFNwQjSiVIPb2eKwRt+vQUYitWwsi2Vy9VCIHSzp1Qls/unLioo1isLll03fstVj88Cpj1M2vnqEmJBETJPUGu0DRI1irUuUSJRKVEnO/QZzIJYaX0Ke7ahZI1XYHNrVb+At8G4B4iGgVQAHAfABDRJgCTIbXrCiHEqOP2xwDcJYT4LBF9zLr90ZCOxVjkCFUF9fZ4byBJgF4TqGk1PTVeQ5/CqF/x5zX0OctDN/3vehcSW7bg4JvN9CErP/33yGzfDmVgYNba0ElSOg2jUKhfdeugn5pA6sILMP7Nbzbcnx3MClWFPj6O4lNPgeJxSJkMTn3rv9F9zTWRyN0VKSRB1L13auaoSfWLdQBAlMqVdB8Vklw/X9TeTzxe33s9B6SuNIxstq7Gb/12Xebq72XLzFT2IVaEYK1r5S/w5wDcBWAlgF+Kma+9Esy5ap1wHYDt1s+3AhgCB2psAWsUIJk9YPVDn3AOfZL7qk9hiPr8ah4rROcivUDm8sux9blnoQ4PY+zr38DwX3/STKba1wd9fBx9r3sdIBHkTAbxDRsRW7USyXPOmdWARAgBoaqQrBqp+uio2Wuh61CWL69ri1EsQkBAGRjA7stejDOffqpu+FOoKqbuvBPl/fsRGxyE/n/Zu+swucrz4ePfZ2bdIpuNEOLEhSQkwQIsUFxLgeKBFvdC0RYa+iuFFwot2pJCaXB3D4RNgCJxI4G4J5tssu4zz/vH7MyOnLEzZ2R37s917bUzR+85Y/c8uncvK0eMZL85ZWT26hUQQ8V//0v5/f8PgMwBrgm2WzZuAiBnzBhyxo6h1513xOPhd2x2W0BiFVBybDPorANtg+b6VmUqe5COOGBYep0MtuxsV5Vma2vIuXttOTme8RmVze4qpRdJZ+YT+Aet9UT/hVprq8pINfCZUkoDT2mtZwC9tNbuWWV3AIGfWkJ0JuF6WwYbnsPu92UTrOpTBVZ9ooP0+rQnpzF0Zp8+9P7DnfT+w53o5mbXgK2zv0Q3N1G/YCEqM5Par76mYeFCwFValdm3LyonB0d1FZk9e+Go3IvWmuY1awHIGTvWVaWlNdhsOKoqyejWncYVK1xjaGVmegZOBXA2NNC8fj0ZJSVklJTQvbaG9Y8+hm5pobW8HJWbS+uOHYBr+iLH7t30/++z5B90kM9jWXfqaXQ771wKf/ELqt5+m9aKCp/zANQvWMi2W2+j37//DcDg995l7Ukn07J1q2Gi5qjYI8NumOBKQPxL1Pw6ExhsA22Jmn/bURVYuu2zOgVKNN1t7sJ1DlI57WPMqYwMT3WpSC4zn8DxftVN1VpvVUr1BGYppVZ5r9Ra67YkzjcopS4HLgfo1asXZWVlcQ4TamtrE3KedCHXs13+6jW0tLTSHORD1VZZSdHOnaz1ul7ZixdjLy9nRduyLhUV1Btc02579rD+m6/Bq5FzzqpV2KqrqffbNmfZMmxV1SxPleelfz/X//32C1zndJKxbRv2HTtxFuTjLCpC5+WBUjhzclzt9RwOVGur67/DgTM3F+VwoO12dGamzzUxUltbS4H3iPZ+8t95lyXz5tPs1w6tu3ayZeBAWLOGosmT+W72bBy9e/tsk71gIfbTTmW+oxXarnf+qFGUf/01zQZzMBatWEF9SQmtDuMxvDqKRL/v8zdvotXppMnW/lWWsX4DuTvL+bktjsLt21m7cAEt1b6tefLXraVFQbPXvtkrV5KxbZvhe6R7bU3CP9OMrmdRRQXr/vc/nF260CXE9S7YXcG6b7+lZe9eutXVYauuks9kkv/dZCZRK1FK3RRspdb64RjiQWu9te1/uVLqbWAKsFMp1UdrvV0p1QcIGGCoreRtBsCkSZN0aWlpLGFEpKysjEScJ13I9Wy39f0P6HHC8UGn4XHU1LDh6acZ73W99mzbhurbl25ty37+4110W7+eAy+6yLON1prVt93OuCOP9OmCX5uRSdVbb9HX7/rv2bgRZ9du9JDnBQj/Gt29ejVZfftS5LfNukcfZVzbsnUPPczwgQMDSt327toF+/TxPH8AP992O9kVFQy4/vqAc21+5VV6H3sMmX4JX0eT6Pf97pUryRoyxOc5qi9aRPWWLfRuW7bz228pGDeO/ClTfPYtX7CAvPETKJh6qGdZjdNJY0YGJQaPYf2jj3me90Qxup7bZs2i+/jx2It7sOP9D3w+N7ztXrWKrEGDKSotZePMmbTU18tnMsn/bjLV6xMoAAqD/JmmlMpXShW6bwPHAsuB94BpbZtNA96N5TxCpLrmDRvIGjgw6Hp7YaGrK71X77XGJUt9porJ6N6d7OXLffarfO11HFVVAeMk5R0wkeqPPgo4z+4n/0nrrtiHiUgXtpzcgDG6wLenXe7EiYbDc9R9/U3ANF4lN99E4XHHGZ7L2dSYEj0KOxybPbCRfMDQNgbbANrhRNn9h7YJ0mwghbiH3dDNTSGHC7F36YKjqhIAW14+zvr6oNuKxDFTorZda/1nyyNx6QW83VannwG8pLX+RCk1D3hNKfVbYCMQOLKgEJ2IdjrDDjSb0aMHjr17PVMSte4q9xkstuctt7Dnjdd99rHl5tDj6qsCjuX+wnfW1/tMfJ17wAEUX3Kx2YeRdoymJ/Jny8szbP+UuW9fsrwGAQZXI3BH23AJAVodSWs/2JEpuy2gkXzA0DZ2m3GPR4fDlcR5H88W2Is01XjmLm1uDjmYra2wiNadOwFXG9Hava42nqnQzi6dmSlRi9szprVep7Xev+1vtNb63rblFVrro7XWQ7XWv/AacFeITsfZ0IAtTDd6cI2w3lLe3grAUVmFvWtXz/2Mnj2xVfm2sWnZviNodaq9e3eafvbtE9S6a1fC56bs0Ow2dJg2YyrDeDgHZc8I/BK1h5jw2+HwLQUSkTEoLQsY2sZgCA8Ics1t9sABdFOMyslBNzW19VoN/gPQVpCPs841fp+9qAiVnc2O6fckKkwRhJlE7SSl1I1KqceVUld4DX4rhLBA6+7dEY0bVr9oEQ0LFnrua3x/+dq7dcVW4ztoasv2bUEnP+92/nm0+FVz6nCTwwsfyp4ROL6df8/bYMMeOANnLFAZGcEn/Ha0hpzhQBhrWruGbbfc6rvQb2ibimefZcef/hSwr25qRGX7/ohSGXb2Pve84blSZZJzW24uzvoGnLW1Icfws3fpQuseVzmIdjro+9DfqHz1VZo3b05UqMKAmUTt78AkYBlwAvCQpREJkeZad+4kw2A4Bn8ZPUuo/eorwDVOV9OPK33Xd+2Kqvdt89SyfTsZfYzn0szs3YfW7dt9F0qNR1SUUYmaw+Ez1IqyGydqutWgWi0zeImaq+pTErVotW7fEbCsYdlyWnfs9NzP2ndfWrZtC9jO2diELce3jZe9Sxfrg7SYraAAZ20Njrq6kIla9n770bh8heuOU6Oyc+j373+z+/EnEhSpMGImURultb5Aa/0UrgnYD7M4JiHSmqPKtwozmK5nnkne5MkAOGtryT/M962osrJQfgmBY9duMkqMS+vs3bpS9f4HnvvOpiYpTYuWUYma/9yedoN5WnGVYPg3VFcZGcGrUrUOO3ejCFR08skBy6rffx+H1xAove76I8WXXRqwXWt5ObYi3xlDsocPx1ZQEFBy6qispGXLVouijo1rxoF6tF8bVH/2wkIaly+n+qOPPOMT5k89lJadO2lavTpR4Qo/Zt7lngnQtNYdewAfIVKQsyGy3nwZ3Ys9bW2cDQ1B9vEtEtME/3LP6t/fp6epY88eMoo7x9RNiaIy7AEzRuimJp+2Z8qe4So98+dwBnYOsGcEzEvpOW6KVKt1NHlTplB4zC98lhUeeyzdzjnHc99o7lutNfXffx8wULGy2cgaPDgg+W7evIWiID12E81eWED9woXsff11bLnBEzWA3AkT2HrTzdTPn0/elMkopSi5/nrW//octtz4uwRFLLyZSdT2V0pVt/3VAOPct5VSgaMyCiGiohsbUDmhB14FsOXl4mzrcCXSDgAAIABJREFUEeisrzeu0vD6lW84qrr38YqKcFRWeu4HPaYIyllXT7VXqSS4Sla8S0ib1q2l7rtvA/Z1tTnz/Uh21tdR8e+nDc8VanJtEZxrlH6DSdm9p1ay2V1Vy95aWsjZf5zxMe32wMSuNfQsAImUPXw4NZ98Qv2335E7fnzIbXtPnw5Aj+uvw9Y2lEfexAno+np6/v7meIcqDESdqGmt7Vrrora/Qq11htftELNICyEi4axvwJYXPkGy5eZ6eps564JUaSjl+QJxVFVhLwreniajW7fARE3G6YqKLTcnoOeso7LSpx1T4ZFHkmnQTrBhydKAarXcccaJgW5uDjlnowjOlpuD02/mCJwOnzk5jdoGunp8Bkm87LbAks9Wv7l3kyirXz/P7dwxo0NumzN8GACFfgO8Dv3ma7L23dfy2ER40sBBiBQTaYKkcvPQ3iVqeYH76Kys9m3q6rAXBp/+SGVm+rSHclWnhh8mRLTLO/DAgLaCjpoabF7XXWXneOZT9Na8cSPZfuOoBZt1wFFTEzLpFsHZcnI87wk310C27UmVygiSqAXpvKHsGYElaqESuw7Af0y/jOLiJEUiJFETIsVEWuVoy8vDWddWotZgXKKms7M9o4s7autQIRoSg291mrO+Puz2wpfKzg5IwnRzs6cKybVNFro5cLLr7BAzUfjTTU2o7OADl4oQ2iYo9+FfopaR4Zqo3Vtra9Bx65RBiZpuSa3hU7r86oyIx0Qc8vnnUpqeQqTsXIgU46ytiShRsxcW4Kipce1TV2eYqDnz83FUVZFRUkLjihU4a+sCtvHZvqEB3eJqW+Osq8MeYgJyEciWlYWzyTcJc19PzzY5OTibmiM+ZvaokQHL/I8pImc0yr5/iRoZgZ04dGtr8JkgDMbG047UqfoE6H3nnejbDDqxGMjat2+coxHRkBI1IVJM0+o1ZPYN/0FpLy6m7quv0FrTsnmL4a9l1dpK40rX+GqNK38k12suUCOO2hoaV60CoHVnOfbuUt0RlczMgJIY3dzi2+szy7jUrfHHHw0P6Zo/tCFge1uIqYBEaP7X2lFdhc3rR4ktLx+H32DRrXv2YCsw/gGlsrKo+/Y7n2XNa9ZgT6HOOLb8fOxF0oy8I5JETYgUUr9oEU1r15LZq2fYbZXNRtZ+Q6h4+ml2P/EE+YcfEbBN45QpbP/TdOoXLqJp9Rq6T7so5DH7Pfkk226/A0dlJeUPPED+oYeYfizpSCmF/6gZzvo6lFc1ki03JyDxctTVkX+I8bW2+3XyAFfJp/8I+SJyGfv0YfNVV6NbWqgpK6N29pc+Q9Nk7tOH5k0bqfv+BwBq58xh2y230u3ccw2P1/N3N7Lt97+n4tn/op1Oqt5/n12PPkbhsccm4uGITk4SNSFShG5uZstVV9P/GePhGIz0++c/2fXQw/S57z7sBr/2m8bvT9Fxx7HxvPPoc890VEbo1g65Y8dSdOIJ/HzQwfT8/c0BY0aJ8JpWraJ+wQIAtNNJ7Zy55IwY4VmfUVJC46qVtFZUANC6dy+7HnmE7KH7GR4ve9hQdtx7L80bNgCu3rubr7yK7BHD4/tAOrH9Pv8cW2EBGy+5hC1XXsXQOWU+4wsqm40Bzz5L+UMPsfbEk9gz8zn6P/M0+VOmGB4ve+hQhs37gfr581k1ajTbbrmV/cq+JKt//0Q9JNGJSRs1IVLEqnH7A5AzMrBNUjBZ/foxYuWPhu1u3Prc+xd6T/+TT4P2UEquuYbiSy4JOYK5CG7whx+w9YYbyTvoIJpWrSJ3/HhyJ0zwrLd36UKv225n9aFT6fn7m6l6/wOKL7uMohNPMDxeyXXXUf3Rx6w9/gS6nHYajStXUvK7G+l21lmJekidjrLZ2Oe++1xJ9MMPG84EklFSQv9/z6B58xZyRo8K+R4D16j+/Z54nIalS8no1ZuM7t3jFb5IM5KoCZEkurmZnX/7GzkjRtKyfRsFvziafR97LOrjhPsCUTYbKsIkzU2SNPOyBw9mwMsvs+sf/yD/0EPpceUVAdsUHDaVQe+9S23ZHAa8+KJhaaibstnocvJJFB5Zyt6XX6b7JRf7lNAJc5TdTuFRR4bcxt6lC7lRzuUZbOw7IcySRE2IJFJKsf3OOwEYvmB+2KRLdAz2gnx6//EPIbfJGTaMnGHDIj6mLT+f4ksD558UQnRukqgJkSQqK4uet91G9oiRFBx+mEzXJIQQIoAkakIkkbLZ6PrL05MdhhBCiBQlvT6FEEIIIVKUJGpCCCGEEClKEjUhhBBCiBSltNbht+pglFK7gI0JOFUPYHcCzpMu5HpaT66pteR6Wk+uqbXkelovEdd0gNY6cB5AOmmilihKqfla60nJjqOzkOtpPbmm1pLraT25ptaS62m9ZF9TqfoUQgghhEhRkqgJIYQQQqQoSdRiMyPZAXQycj2tJ9fUWnI9rSfX1FpyPa2X1GsqbdSEEEIIIVKUlKgJIYQQQqQoSdSEEEIIIVKUJGpCCCGEEClKEjUhhBBCiBQliZoQQgghRIqSRE0IIYQQIkVJoiaEEEIIkaIkURNCCCGESFGSqAkhhBBCpKiMSDdUSp0RwWaNWuuPYohHCCGEEEK0iXgKKaVUBfAuoEJsdrjWeogVgQkhhBBCpLuIS9SAj7XWvwm1gVLqhRjjEUIIIYQQbWRSdiGEEEKIFBV1ZwKl1Fql1JV+yz6wLiQhhBBCCAHmen22AEcqpZ5VSmW1LetrYUxCCCGEEAJziVq91vrXwErgK6VUf0DqT4UQQgghLBZNZwI3BaC1fkAptRD4DOhuaVRCCCGEEMJUona3+4bW+nOl1LHAxZZFJIQQQgghAJO9PpVSfYEBeCV6Wuu5FsYVkx49euiBAwfG/Tx1dXXk5+fH/TzpIh2v5676XZQ3lDO6eDQrKlYAMLp4tGXHT8drGk9yPa0n19Racj2tl4hrumDBgt1a6xKjdVGXqCml7gfOAX4EHG2LNZAyidrAgQOZP39+3M9TVlZGaWlp3M+TLtLxev5z8T95csmTzJ82n7EzxwIwf5p1r910vKbxJNfTenJNrSXX03qJuKZKqY3B1pmp+vwlMFxr3WQ+JCGEEEIIEY6ZXp/rgEyrAxEiLYWakE0IIUTai2ZS9sdwVXHWA4uVUl8AnlI1rfX11ocnROemJFMTQggRQjRVn+6GMwuA9+IQixBpa3vt9mSHIIQQIgVFnKhprWfGMxAh0pG7RO2cD8+Jet/F5YsZ02MMGTbjt7HWmhbdElN8wnpNjiay7dnJDkMI0UFE3EZNKTXDim2EEO2UciVq1c3VUe974ccX8viix4Ou/2TDJ9y06SbTsYn4mPTCJJbvXp7sMIQQHUQ0VZ+nK6UaQ6xXwJExxiNEWnGXqLU6W03t/8zyZ7jxgBsN190691bTcYn4Wl+1njE9xiQ7DBHEg/Me5IaJN5Blzwq/sRBxFk2idksE23xlNhAhROQqGyuTHYKIwffbv+eUIackOwwRxHM/PkeP3B5cMuaSZIcihLRREyKZ3FWf0bp29rUWRyISqaG1IdkhiDD2Nu1NdghCAObGURNCJNmSXUuSHYKIgV3ZGTtzLN9v/z7ZoYgg6prrkh2CEIAkakJ0Sg6nI/xGwnL/Xf5fvtn6DXsb93LNF9cE3e7jDR8DcOXnVyYqNNFma+1WnNoZdjszHXyEiIeoEzWl1Nh4BCJEOorXgLf1rfXtt1vqQ2wprPTQgoe48vMrmbNlDnO3hJ/+2GwnEmHe8W8ez4KdC8Ju98mGT6hqqqLFKUPciOQyU6L2pFLqB6XU1UqpLpZHJISImXdytrh8cRIjSU93fXNXskMQIUTaRnDqK1OZ+PzEOEcjRGhRJ2pa68OA84F+wAKl1EtKqWMsj0yINGC2M4G3sTMDC7lrmms8t6/4/IqIqkIfWfgI22q3xRyPsE5dS52UusXBF5u+SHYIQkTMVBs1rfVq4I/AbcARwKNKqVVKqTPMBqKU6qeU+lIp9aNSaoVS6oa25d2VUrOUUqvb/nczew4hUk28qj79e6xVNVeF3efpZU+zdNfSuMQjzDnopYP4y3d/SXYYnc66ynVordlQtSHZoQgRlpk2auOUUn8HVgJHAadorUe23f57DLG0AjdrrUcBBwHXKKVGAbcDX2ithwJftN0XQoSwt9E3UTvi1SMi2u+WuZEMlygipbWO+Rhvrn7TgkiEt+W7l/PSqpc45Z3OP5bd4vLFvLLqlWSHIWJgpkTtMWAhsL/W+hqt9UIArfU2XKVspmitt3sdqwZXItgXOA1wj+E2Ezjd7DmESDXxKlG7ec7NUW3f5GiKSxzpzqF9q5xjaS/Y0NrA5BcmxxqSAPIy87j/h/stOdaGqg0p3eHgwo8v5N7v7012GCIG0cxM4HYS0KC16xNIKWUDcrTW9Vrr560ISik1EJgAfA/00lpvb1u1A+gVZJ/LgcsBevXqRVlZmRWhhFRbW5uQ86SLdLyea6vXBiwzcw0i2SfUNtdtvC6m86eLaF+jX875kkyV6bn/SkVgyUakz12do45GR2One36S8b73HnrD/9yNzsCZEsO9d8bljuOynpdZFV5Mgl3Pzva6SaRkfzeZSdQ+B34B1LbdzwM+Aw6xIiClVAHwJnCj1rrau7G11lorpQzrErTWM4AZAJMmTdKlpaVWhBNSWVkZiThPukjH67lxxUaY77ssomswE7Lt2Z6SsIB9DOYRCXlcr+3T7TmIRtjXqN91P2TqIeRn5nvuXzfzOvxFcrwxB47hmWXPhN++A0r4+97vOfI/t1HnnHDP0dKGpSnzvHhfT601POdanirxdUTJ/m4yU/WZo7V2J2m03c6zIhilVCauJO1FrfVbbYt3KqX6tK3vA5RbcS4hOrrh3YYbLl++e7nn9v37WlO9I8wJ1WPzsrGXMbXv1IiOc+JbJ/LCyhesCkukiVSukhWRM5Oo1SmlPAPLKKUOAGKeuE65is6eAVZqrR/2WvUeMK3t9jTg3VjPJURn8OsRvzZcfu6H53pu59vzDbcRieH9Rdni8P3SHNNjDD3zekZ0HJkbNPWl4nMUzWDX135xrSR2KcpM1eeNwOtKqW2AAnoDxt8Y0TkUuBBYppRyt7i9E7gfeE0p9VtgI3C2BecSokM7ZsAxTCiZEHKbAUUDALht8m3YbfZEhCX8NDuaPbf9vwSLsooSHU7ai6QXbl5Gns/MHpFqam0iNyOXH7b/wJQ+U8yEZ7lIhuVxm7NlDnd9cxf3Hyal8KnGzIC384ARwFXAlcBIrXX4+TjCH/drrbXSWo/TWo9v+/tIa12htT5aaz1Ua/0LrfWeWM8lRKow2+tz1sZZ7G7cHXKbGcfMACDLnkWGzcxvMhEr7+Rsc81mn3XebddEYoTr3ay19knSph88ncP6HhbRsR3awe6G3fz2s9/GFKOVTn77ZM/tSOY3/XDdh/EMR5hkdlL2ycA4YCJwrlLqIutCEiJ9xDIzwa76XSHX71Owj+e2FeN5xduavWsMG3J3ZJm29h6fO+t3JjESAVDbUhtyvf9A0QOKBnhKpsMpfa2UI1870nRsVltftd7nvgzB03GZGfD2eeBvwFRcCdtkYJLFcQmRFmIZR82mjN++fz7kz9w48cb2c0SZDN7z7T3MXGHQbTTOvIdM6AxK+5X6PEfu6blumHhDskJKe2Wbyzy3RxePDlj/+k+vA5ChXCXQVkzxlizP/ficz/0pL6ZGdayInpn6kEnAKN0RfqILkeIW7DTfaiDYl4hGk5fZ3hFbodBE/nZ94+c3AJg2elqYLUU0suxZAEwbNY39S/ZPcjTpyXsO3KvHX80Ti5/wWV/ZVAnACye9wDkfnBP1eyeVuN/H3vY27qVbTuAsjJHMBSySx0zV53JcHQiEEDHaVmd+EvRQpXH+60L9rpr+v+mmYxC+RnYf6bnt/xz0znN9bGbaM5ncO/wMA94dEQCWTVtmQYTpLVgptJu76nN4t+E8dtRjKKU6RLOBSC3bbfwaWlO5JsGRiGiYSdR6AD8qpT5VSr3n/rM6MCHSQaju8+HaoBVkFhgu9/9iCVe96p5LUhKB+CrJK+GUwZHPLXnACwfEMZr01CuvfWKbTzd8GrB+SfkSADJsGZT2K0WhImqE31Fc88U1hssvn3V5giMR0TCTqE3HNd/mX4GHvP6EEFEK1QYm2IcqwEF9DmJI1yHBj+tfotYBqm86cnugSGh0p3+Mqc577tXvtn8XsP6s4WcFLAv13rnrm7usCSwO7j74bm6fcjvLpi3jrGGBj8vbnkYZTCGVmRmeYw6wAchsuz0P1yTtQogo2ZW58c3syh6yjZq3UMlBZWOlz/2r97/aVDzC16VjL42q+tlfXUtd0HWdqSrOKgt3LgyoKjbiXTp29f5XB1zL8SXjOW/EeZ774RLrd9a843N/9lmzIwk3IbTWnl7Hdx98N0DEPVhFajHT6/My4A3gqbZFfYF3gu8hhAgmXJuZYDQaheKVk18xnEoq0pKbw171HSPqqvFXMf+C+UG2FpFYfOHioD07I03eLv30UsPlo4pHxRZcJ/WvJf+KaBR+70TtjKFnBKz3L/VURNdGrSSvJOJtE8H786Ugs4Cj+h2VxGiEWWa+Ja7BNYtANYDWejUQ2TwoQggfZkvU3EYXjw5I9sxUcy68sL1QPNue7dMoXkQn2CwQAUlAiGR6eYVrvlb/KquO3AsxFbgTte/O+87w+mutfZJpM9e7b0Hf2IK0iP9j+e3Y3zK6R+CQJCL1mUnUmrTWnjJmpVQGyCeHEGaYLlHz+xCOlffArMli5eNJBv+Sl0vGXEKX7C4+66N9jEZDKQjz3IlasFkhjJoNBEvU3Mf6Rf9f+CwvyipKieppJ86AZNTosdzx1R0+9//49R/jGpeInplviTlKqTuBXKXUMcDrwPvWhiVEejBbohaqYXoqfEmkI//nZHzP8eRm5PpsE21ngrOHuaY2vvmAm137R1kVJ3x5dyYA4+fDv9Qz2PV295S8dfKtLLpwEUsvWhqw/9wtcznvw/MM9483o9JBIx+s+8Dn/rtr36WqKfI5QkX8mUnUbgd2AcuAK4CPAEnBhTDBbIlaKLM3z/YZ2DMc9xdMMMe9cVxUx2tsbWRt5dqIt+8swpWYudsVRqNnnqtVif+E7gDfb/+eSS9ENynMuR+cm9aDm0aS5EaS3IDr+gP0KehDhi3DJ0Fzl1ytq1wXdOyyeDMsCfRb5F+a5jb1lalxiEiYZabXp1Nr/W+t9Vla6zPbbstPPCFMCDVZeqjSl1DtZr7Z+g1zt8wNu73WmhHdR4Qt5dlWt43a5tBzJHr7cN2HnP7u6RFv31mES8Sibev02smveZ4b9/9Ve1Z55qssry+Pev7GqftO5YtNX0S1T2fiX6Lm/9VlNAZhtM+bT6KX5OFYvH8IGsXiXZrmnjbLTYbsSB1men2uV0qt8/+LR3BCdHbBGp6HpSP/EgiWPCzdvZRVe1ZFdAwnkQ/6Of3b6YC109I4tZP/bfufZceLh7DjpEXxnPXK68XI4vYOHd2yXW3VWnUrb61+CzCXBCzauYi317wd9X6dRbikyz/ZNjszQSqUXWgd+HoM9fi/OucrLht7mee+VH+mDjP1LpNon4z9MOBR4AUrgxIiXYQqUQsl3BeO1b/kzYzOPv758eyo22HJ+fc07uGKWVdYcqy40aGryqKp+uye093nvnfVp7ta2Uzni+93fE9ja2PU+3UW4V7HhsNzhHivvXFK4HyakVadxptTOwN7sAZJIJdctISCrAKun3i9Z5n/a1Akj5mqzwqvv61a638AJ8UhNiE6vSxbVtB14X6Vx/olENX+JgsIjnnjGHM7dkDalakFXx9DKYt3ouZOflOpl2yrs7VDJIBhEzWDdobBnreR3UcyvHvgGIaTere3G4xHG9RIBfRgNXi9jOg+gqUXLfWJ88uzv4x7bCI6Zqo+J3r9TVJKXQmYKxYQIs3FOuBtMFZ/iUdT9envL9/9Jebzh3o8Dy94OCWqaSJpo2bmeXnuhOc4dcipnvvupM1sqWk8xmH755J/8qv3fmX5cSN1ZP8jybIH/9HjFkmJmvdTFGp4jmBunnSzp0lDKrVRA+PkzT/GHrk9OKjPQSlRfStczHxLeM/veR9wAHC2lUEJIUJL9IdoNOe7cv8rycvI89x/9adXI5reJ5RQX3jPLn+WdVXJbyYbUa/PCNsMeW83oecEn/HYttRsiTFS61U0VLCpZlPSzn/uiHPJy8wLu51/Z4IAOvaqy7Ezx7K7YXfU+1ktYHiOKJJGGVg5tZip+jzS6+8YrfVlWuuf4hGcEJ1d1+yupvcN9cEbyYdyNF9C0ZSoFWUV8ceD/ujTMPmAFw5gzuY5IRM+d8wPznswoNdquFhTYcgJ/0Rse+12n8dr9Ng3VUef3JQ3lAPmS2tK9y01tV9n4P8crNyz0qc0NqAzgclx6xbsXAAkv+ozms4E3mzKJolaCjFT9XlTqL94BClEZzW59+Sg68INzxEqeSnILPDd3uDLZkyPMRFPFRXNl1Wzo5lMW6ZPw2SAa2dfy7jnxgXd74KPLgDguR+f45ovruHpZU/T4mjB4XSETdRS4UvFvwTj2DePZf5O33lTvdev3ruaheULMcts9fZZw88Kv1GUinOLGdRlkOXHtZpRidrexr2e20bTfJmp9vdUTyexHaHRZ0TE72OVGj1XhYvZXp9X4ZqMvS9wJTARKGz7E0LEWbgP0UjmG4ymRCaaD+0WZwuZdteUVMumLeP4gcf7rDcaZNPo+I8sfISJL0xk/PPjqWyqDHlOM71SE8E9KKqb9zVvdbaaOmZRVpHrWAZJgNHAuIkwpngMJww8ISnnBjji1SMiqm40ep14V5sbzvVpImFpcSTnefDm1E7f9nZRJI1S9ZlazCRq+wITtdY3a61vxtVGrb/W+h6t9T3WhieECGdv097wG0XptP1O87kfTalCi7PFZ+7QB494kLsPvttz3z3I5uaazRz92tFsrt4csqQN4JR3TgHg5rKbPV+c3l+g0QzIGy9GVU1PLX3Kc3tN5RpW7F7hs33QY4VIDg7e52AgMNH+bvt3THx+Ytg4zU5blsr2NO6hoqEi7HZGidpPe9tb7jy++HEWlS9qX6nMlda6E+ZkVn0C2Ly+4vsW9vXMdBGOTFWWWsz01uwFeLcMbm5bJoSIUixVI+4v6h11O9hRt4Pe+b0D1gFsqtnEmr1rOG9k4JyDwUrVJvXynZoomg/tysbKgEnezxp2FmcNO4uxM8cC8PnGz2l1tlLeUM61s6/1bFeUVUR1czW3Tr6VB+Y9EHDszzZ+ZpjU3Vh2o2Esy6Yldvoe/+cz257tuf3AvAdoaG3w3A92TZsdzazcszLoOdzt8fyTjkhKlMaVjEtoolbfUh9RI38rRFJCaZSoeT9Hja2NPveVK1OLOZYT3jyBN099M2HXAgIHvD26/9ER72umt6uIHzOJ2nPAD0op9/DWpwMzrQtJCAGutjN1LXUoVMAHvP+HqP8Xg3fC8NyK52h2Rtfr8sz3z2T+BfN9vrQijfm1n1+jR24PT8mPt49++REnvn0ivyv7nWeZu+rpndPeYUjXIYydOZZjBhzDhaMuBPAkd2bEsm8wp319Gj1ye/D+uvcpr3c17L983OV8uuFTNlZv9NnWe4on/9KVYD0Qw31BtmrXc72mcg0Q+Bjd97/69Vd0zTHfWaWqqQqllKeqNZT3173PrI2zuGr8VZ5l9S31HPjSgWGT5SZndNNgBRNJtW+4RM1o+IpYStTcidKW2i3UtdQlNFF7dNGjXL3/1RwzIPqxDKVELbVEnahpre9VSn2Ma1YCgEu01otC7SOEiN72uu0c9NJBQdd7J2OhxksKVmq2YOcCfqz4Mejxq5qqPFUlZ75/JscMOIZRxaO4dOylAPzm098wb8c8/nTwn7jnW99WD+75KP31K+oX9HxDug7x3Pb+8lw2bRmbazZz4lsnBt03kd5d+27AshlLZ4Tdz+bX0iTYF6H/dv4O6uN6TRzc52D+teRfQbc77NXDApKkpbuWUt1cTXFuMXsa93hGn99Zt5Py+nKKsos4+e2TWXrRUq754hpqm2u5cNSFnmnBvO3XdT8eOPwB5u2Yx+LyxQD8bd7fmPnjTB458hE212wGIkuWfz/z90zoOYFpo6b5lI4+cPgDDO82nMFdB4c9xoUfXxiwLENl0Kpb6VfYzxOPP+/SX6MhLcwkau7Xsvfr+KjXj+LUIady79R7oz6eWYt3LTa1XyoNpizMD1SbB1RrrZ9VSpUopQZprddbGZgQ6WBYt2Gm9/VJxvw+WD9Z/wm3Tr415P4bqjaEXF/bXOvTpmXWxlnM2jiLRxY+4rOdf5IGcM34a4Ie95EjH+GGL2/wWXbx6Is9t41KYNwzOBitGztzLC+e+CLjSnyrRLfWbuWijy7yDGeRDONLxrff8fvui2QMNX+l/Uo9r5ncjFxTMZW+Vhp2G+/qZaMkDVwleme8d4bPspk/uipX/J/fSCwqX+TbPgy4da7rNXzZ2MsCehFHwl36GCxJg9DX0WzJkrvXtf/wO++tfY/ph0wPaBoQL7EkXFL1mTqiTtSUUn/C1fNzOPAskIlrrs9DrQ1NiM5vv277BV037/x53Dr3Vs4efjZ/X/B3HjziQU57x9XIv3tOd58Pe/8vd+91Jw0+yTORdzSqm6uj3sctVBXPUf2P4vGjHqe2pZbbv7qd2WfNpiSvJOTxeub15O1TjScTn9hzok/7PLe+BX354uwvoop7b+NeuuV0C7q+rKyMI444gpqWmoiqBPsVtpcg+pd6BuupGqoBev/C/j6JwxlDz+CeQ1yJssPpYHfDbnrl96KmuYZDXj6ESz+9lKePezpsnMl0acmlPL3LN8bpB0/3SRDPGHoG0RjZfWTIdn7evF+rGbYMn/l3zVR95mXkMaBoABA4TA7AxOcn8s2530T0+omV6ZkRpEC5ryOKAAAgAElEQVQtpZgpUfslMAFYCKC13qaUkmE5hLBYTkYOjx71KABT+04FgjeO9//lvG/hvp7bwb74w32I1zTXAK6OBY8f/Tj5mfm0OFuob6mn1dlK95zuKKVwOB3YlI1W3cr22u30L+of9rEd0e8Ilu9eTn5mftgkzR1rsKR25gnWNZENlaR5xxLpl6x3lZ1/lWawxx3p1GD+CYTdZqdXvqtfV2GW6yP5+x3fM3bmWMPXzT+O/Ac3fnkjfz7kz2TaM5nYcyL1LfUM6TqEKS9OYXDXwdwx5Q5GF4/2DLfiz5003vPtPby5+k3PeZocTWyr3caAogFhez6WlZV59mt1tnoSpV8N+xVaazQ67DEO3edQ/nrYX+ma3TXqnpZDurRXuV834Tp+2uM1fruJhGVwl8Ge66/RnD/yfG6fcjvQXg186MuH8nDpwwzpMiSial2zQpXYVzVVBU1mpY1aajGTqDVrrbVSSgMopfItjkkIESX/pKvR0T5Bdo/cHp5qQYfTgVM7ybRn+kzzZMSonVmmLdNnOiPAM69hpsqMKElzG9NjDN+d913E23c0v5/0e0py25Mx/+fowD4HMqX3lID9wiXQoaq8IzG+ZDzPn/g8EDzxn3fBvIiO5Y7VPznKtmebGgDXuzTLffxoHmO0SdqBvQ/0ec3uqNvh894xk7AUZhUGnYZq7q/ncvirhwNwU5nv+PD7l+zPfYfdx74F+1o2R6hP0unnnTXvBF0n46ilFjOJ2mtKqaeArkqpy4DfAP+2NiwhRCy8x+tqaGmgd56ranD88642U8umLQv7QTy82/D4BZgGzhh6hs9QGP5JRFFWEc8c90xUx+xf1N9TWhPO4gsXo5QKOG+yJwqPB7Ntvvyrhe/74T4AfneAq1dycW4xl4y5JKpjzjg2eMeSbjnd+OxXn3Hsm8fyxNFPAHDNF672nEt2LbGsw8yBnx4IhJ7bNGTJrQzPkVKiStSU6x3+KjACqMbVTu1urfWsOMTmf+7jgUcAO/C01vr+eJ9TiER48cQXOf+j803vf8ukWwKG0fD+gD524LHUt9YH7DdrY/C37csnvRzXKpl04J9QDe8+nN1bw491FqoE5+zhZ0d8fndJp7dRxaO4+6C7DbaOzaF9D6U4t9jy40bqsaMfi8txczNyAzqpuEXaBs5fn4I+PiWZ/qWaG6o2MGfLHP42/2+mjg+uKm8IncDWtdQFXTep1yTTnVWE9aJK1NqqPD/SWo8F4p6cuSml7MATwDHAFmCeUuo9rXXwsQWE6CDGlYzj5ZNe5qklT1G2pSzq/S8afVHY4xu5fcrtQZO1MT3GeG7LL2trHNP/GCaUTEhqDBm2DAZ0GWD5cW3Ykj4KvxVOGXyKqbk9rTSwy0AGdhnItNHTTO1fVlbGsEnDePWnVzmw94FBtzu076E8ueRJw3UXjLrA1LlFfJip+lyolJqstY6sEYM1pgBrtNbrAJRSrwCnAZKoiU5hTI8xTO492VSi5u+4gcf5DHfh7boJ1zGqeBTg6kk5ovuIiI4p4yrFLsueFbI37Obqzexq2MXgLoMjmlEhlRLorbVb+XF3x/84PnrA0Wyq3hTRtrPOTFhZRdT2KdjHU33rVtVURUNrg6eH9Oji0Z5ODiK1mUnUDgTOV0ptBOpw9YvRWuvQk/XFpi/gPRDOlrY4PJRSlwOXA/Tq1YuysrI4huNSW1ubkPOki3S/nmuqXSPNx3oN9u7ey9KFS9mdtTvgmvZx9OHvK/9Oay/X+FLXFF4T9nxVlVV89dVXZNuim6WgM4rlNbqydiV1zjrKyo33v27jdQAcXng4Z3U/K+zxNjVtYnvt9qjiqa6qZu6cuZZPIzVn9xx+qPvB1LVJpfe9HTuDGBQ0np8afmJR/SLOKT4HgFWsCnm8NY1r2FK/hbJ64+PFQ21tLZ/M/oRsle3THnF5/XI2Nm/kpK4neZb1pS9lOxMXW0eV7NdoxIma16C2x8UxHtO01jOAGQCTJk3SpaWlcT9nWVkZiThPukj367lpxSaYT9TX4Kaym7hl0i30KegDwKyvZzFp9CSGdRtGWVkZB049kNrmWlfvz7aBTHuN7cXI4pERHf+/n/yXww47LKHT36SqWF6jNWtrqGyqpHRUkP3bRhnZZ599KD3IeJuvt35N34K+DOoyiBUVK1j30zpKD4k8nn9/9G8OP+Jwywdcnf3NbGxrbaauTTLf92+vfpsJPScwsMvAiLa/bqYrmf7Xr4LPCOGtcGchFRsrKJ1SajLC6JWVlTGjbgbPnfCcTy/ad758hzm75/Dg6Q8mLJbOItnfTdE0Knij7f9/tNYb/f/iEZyXrYD33DP7ti0TotMw2xvPPUm3z7G8qipX7VnFM8uf8Tn+g/PlwzrZGlobuO/7+wKWh6rSXLhzIVtr5aPPKiv3rKSyqTKibTvauGL+r6O9jXs9MzWIjiWaRM2mlLoTGKaUusn/L14BtpkHDFVKDVJKZQHnAO/F+ZxCJEyLo4XVe1eb3j/Ul7tC4dROHE4HQ7sNBdqnZBKJ4/8caa15adVLjJs5LmB5yOPEkjDEKddIpfZy0QjX9tLhdFDb7BpPsNUZfZKTrOROoQKe6+LcYgozZWz6jiiaRO0cwIGrurTQ4C9utNatwLXAp8BK4DWt9YrQewnRcdS01LBsd/gG5EbClcTZlA2ndlLXWudJBpM5/2W6WFmxkrWVa32WGSUGGs2LK1/03I+01+HCnQtpcbbEFqQImWTuatjFnV/fCRDxtX562dMhB5pNCBXkcUmfoA4p4jZqWuufgP+nlFqqtf44jjEFO/9HwEeJPq8QibKmco3nttbasoFJbcqG1pqqxirPslhK70RkFpYvJD8znyFdh4Td9v4f2oeFjLQU5oF5D9AtO/yUVyI4pULPPOA9M4F/teGnGz7luIGBTbZ31e/yjFsYasDZeDKaWUBrLb23O6ioB75JRpImRGfnPw+ku9F/JNwfyisrVhp+6SilcOJkV8OuqOMyU90jXNwJcrTCVSN6r9/btDfq44vI2ZSNlXtc7yv/98Izy4xnlfBO/i797FK+3fZt3OMMiCFIQub9429n3U7m7UjkKFvCrI4/QqEQnUDXnK5MP3g6ANXN1aaOcfYHZwdUtYErCdRa868l7T3VMlRkhekTnp/A4vLFpuJJd+62gdEKldw5tIM9jXs82xRkFpiOrzN5b+17NDmaot4v3JyWSil21u/kkk8vMey0E+qYTy9zTU8VaWcFq/m/jjS+JWpbarcwe9PsRIclTIgqUVNK2ZRSh8QrGCHSmXtkd3fj5Uh5/4L/ZMMnhsd1aiffbm//ZR+u99cbP7/B7gbXdEfJqr7p6GzKFjYJMBIquXv9p9e565u7WFHhaqIrz43Le2vMJWrgm9Csq1xHfUv7dGvu9+SCnQu44csbIjqeUq4E/c2f3/TcTzSlFN9v/56KhgrPMqn67LiiStS01k5cUzkJISx2QK8DAEx3KgDXkA/+3F8c0fhh+w9RJ4zCV6TXfcEFC3zuO7XT5wvWW4PD9fy6q+FaHNKZwEqnvXsaL616yXPfu0lCpO9LdzJ0RL8jADw/eBJJofhw3YeU17d3GtJY1+5VJJaZqs8vlFK/UvKMC2GpPvmuAWt/P+f3ADw0/6Goj+GeHsabjdAlO4aC9RoTEfNuiH7nV3cargfX1FJ9C/oyptg1v2pDawM3z7nZ8Jju47lL0mRcrNgo5Vv1uX/J/gwsGui5b6bE8u3Vb/PM8mci6kSSSEadC0THYCZRuwJ4HWhWSlUrpWqUUuYa1QghAvQv7A/AR+sj7+Ts/tAdUBQ44Xajo5H31kY37KBCsa12W1T7CF/uIVGc2sn7694Pue2Ouh0sr1gOQLOzOeh27sThjZ9d449H2tYwITrBT/fRxaMpzi323A9WHrFid/DRoWpaavhm6zdJr2YM+KFlNFqHlLd0CGZ6fRZqrW1a60ytdVHb/aJ4BCdEWmn7zDxnhGsewWja3NS2uKop8zLyAn4pr9yzMvpQlMJus3Y+yHTzn+X/4e8L/s6H6z4Mu613yc3cLXPDfsl/sO4DALpkd4k6rmQnEKnEfS1aHC0c+dqRAevdVZ8LL1jos/znvT+HPba7fVteRuKnXvMvKYTAzgSi44g6UVMuFyil7mq7308pNcX60IRIT7vqXcNo1DXXRbS9Qnl6lrmrwrw/kL3b2dw+5XbP7XDjR5npsSjauXvv3vXNXYbrrahabnQ0xnyMdOaunn5p1UuGbcncJU6Zdt+5UT9c/2HY0ij3e9CuEv+Dx7va3e2EQSd42s2JjsVM1eeTwMHAeW33a5EOBkLEzP3B/u7adwF8JlQOZ2+jazwtoyoZd2kbwJCuQ1hy0RIATn3n1JCx/LwnfKmBCM7d0N/TnsxvHK6QiXKEVVJ1LZEl8yK0v83/m+Fy/+FP3M0Svt/+fdjSKfcPnUhnmrCSu0TN+3XUPae7TwmstEHtOMwkagdqra8BGgG01nsBmThQCItcN+E6IPLSEvdYTwD/WPiPgPXjStoHzx3RbYSnSmZD9YaQx0zGF0xn4v/8+ffIzcvM47NffQbAsmnLWDatvVehDESaGEZVhN7sNjuPHvkoAG+e+iYzT5gZ8bHfXvM2kJxkOmgSqWFz9WbPzCRSFdoxmEnUWpRSdtqaJiqlSkA+0YWwSn5mflTbf7rhU+bvnB90vbsUYNm0ZXTN6eqzrqa5Juh+i3YuiioOEZrRF3afgj5JiES4tTpbw7YFPbK/q+3asG7D6JHbg2mjpgHhk5wlu5ZYE6RJ/iW27tK1cz48hzPeOyMZIQmTzCRqjwJvAz2VUvcCXwN/tTQqIdKQ+4N/a+3WqPfdXLM56LqirMC+PlP7TgXgtZ9eCxpLll0Kyq2UCuPSNTmaWFkRfeeSzuqFlS9wzRfXeO5HUh3ont/T3Us3FQWbcUGjTc98IpLHTK/PF4FbgfuA7cDpWuvXrQ5MiHT1yMJHot5nd33wQTXdVZ3ecuw5gHFVKbh+fU/uPdnnvohNKnxBVjZV8uSSJ5MdRkoLV1LWM69ngiKJgXK1kfN+LEYdDETHEHFrZaVUd6+75cDL3uu01nusDEwIEbmaluBVmHab3af9E8DgroNhU/DjKZRMyG6xReWLGNptaLLDEDHqnts9/EZJZlSiZpSAShu1jiGaErUFwPy2/wv87gdvICOEiIi71Or8kedHtd8pg0+J+lzXjr82bCxG01EJ89zDrlgp2qEfpKdf7DJtmeE3SrJgCZg8/x1TxCVqWutB8QxECOGytSa6NmpmqiXD7bNqzyreWv1W1McV7QZ1GcT6qvWe+1a0+RvbYyw/7/3Z0wB+8UWLoz6GlKIkzriScZw74tzknNwvJ/N/z0s1aMcRTdXnxFDrtdYLQ60XQoTm/gIt21IW1X5GbdBi9WPFj5YfM90c1vcwn0StotF4onV/N0y8IWg7xTsPvJPKpko+Xv+xTxvCdDeuZFxCp9O6YtwVPLX0qZDb+Dc3SKRgw44EVIdK29MOIZpXdqgZojVwVIyxCCFMeGfNOz73I63eePDwB/nP8v/EIyQBpqfgKs4pDvolP6aHa+J2d6/dqHXSQpTrJ15vaj+bsvnMwFHZWBnRfqftd1rQRK1nbk+m7mvy+bGIzCzSuURT9Rk4EZoQIiVF8kv5+EHHc/yg4xMQTXpKxtRB4dS01PDV1q8sP+5BfQ4iNyPX8uPGm38y8822bzhv5HlBtm7Xr7Bf0GT6/V++H9WsInGhjH+wuas7s+3ZiY5IxMDMXJ95Sqk/KqVmtN0fqpQ62frQhEgvVlVDSCeA1HDGfmfw0BGhKiISz+F0hN/IhJMGn8SdB94Zl2MnkhVDqORl5iV9DMJwPTy7ZHWRjgUdiJnGLc8CzcAhbfe3An+xLCIhBK+fEv3QhBN7upqRLtttXduYbtndAGl4bEa/on4cO/DYZIfh49njn+Wbc79JdhgpozinmMKswmSHEReh2qPtbtztmgtUOpZ0CGYStSFa6weAFgCtdT3Isy2ElUZ0H8EJA0+Iap+nj30agPL6csviuHZC6GE8RHSqm8KX2MSzgfe4knGGM1Wkq555PRnatfONbadcdZ8B3MmbUztZXB59j2GRHGYStWalVC7tc30OAUJPliaEiNiHv/wQgAeOeCCi7WccM4NXTnqFTLv14zudNewsvjvvO/Iy8yw/djq5av+rAPho/UdJjkR4m3HMDB496tFkh2E5pVydCbyTfv/Ssz2NMkZ9R2GmxeOfgE+AfkqpF4FDgYutDEqIdBZtb8GD9znY8hgGdRnEb8b8BqVU1JPEi0CDuriGodxQvSHstlIdlThdc7omO4S4mLtlLuN6jAtY7t2EodnR7JlKTqS2qBM1rfUspdRC4CBcVZ43aK2DTzQohIhKIseDCua9099Ldgidxrge4xjbYyzgahMlUtNjRz3GdbOvS3YYllm5Z6XPff+x1VqcLdJoqYMwO1LmEcDRwJHAYdaFI4QwO/6Wt8qmyMaDEvH34kkv0regLxD5oLcisW6bfBvLdy8H2hKYTmBgl4EBy7zn712wc0ECoxGxMDM8x5PAlcAyYDlwhVLqCasDEyJdxTLTwF+n/hUIHB9KJFc0HQRktPjEu2DUBczePBuALTVbkhxN7E4dcioZKsOnGl2hcOj24Vm21kY3VZ1IHjPfCEcBx2mtn9VaPwuciMxKIIRlCjILTO/bNdvV5ubbbd9aFY6wUP/C/skOQQQxbdQ0ADbXbE5yJLHLsGUEVH3urN8ZMH/vkvIliQxLmGSmMcwaoD+wse1+v7ZlQggLxDJY5uCugwHond/bqnCERab2neqZTD0U6UyQHKX9SoHOkai5EzLvkvXZm2YHbLewXKbo7gjMJGqFwEql1A9t9ycD85VS7wForU+N9oBKqQeBU3ANpLsWuERrXdm27g7gt4ADuF5r/amJmIVIC30L+nLDxBs4sp/M+JZqHjriIamSTmFdsrsA0L+o85R6/rz3Z4Z3Hw5AfWt9kqMRZplJ1O62PAqYBdyhtW5VSv0/4A7gNqXUKOAcYDSwD/C5UmqY1jo+86AI0QlcOvbSZIcgDMhYdKlvcu/J5ie8T0HeY6Ud0PMAyjaXcfuU27n/h/uTGJWIlpnhOeYAKKWKvPfXWpsePU9r/ZnX3e+AM9tunwa8orVuAtYrpdYAUwBpgCOEEMJS/znuP8kOwRJnDD2Dt1a/xf4l+3uWnTnsTKqbqzl/5PmeRO3/Dv2/ZIUoohB1oqaUuhz4M9AIOHGNxKKBwRbF9Bvg1bbbfXElbm5b2pYJIYQQwsA9h9zD9IOn+/QgLsgq4PqJ1wPw/AnPM77n+GSFJ6JkpurzFmBMtIPcKqU+B4xaOP9Ba/1u2zZ/AFqBF6MNqi2BvBygV69elJWVRXuIqNXW1ibkPOlCrqeLlddArqm1EnE9V61cReHmzjlRuBF5jVor0utZ9mP4bYRLsl+jZhK1tUDUrRK11r8ItV4pdTFwMnC0bp/nYiuuXqVu+7YtMzr+DGAGwKRJk3RpaWm0IUatrKyMRJwnXcj1BGZi6TWQa2qtuF/PmTBy1EhKB8fxHClGXqPWkutpvWRfUzOJ2h3A/5RS3+M1GbvW+nqzQSiljgduBY7QWnsnge8BLymlHsbVmWAo8IPBIYQQolOQ4TmEEN7MJGpPAbNxzUxgVV/zx4FsYFZbnfp3WusrtdYrlFKvAT/iqhK9Rnp8CiGEECJdmEnUMrXWN1kZhNZ6vxDr7gXutfJ8QgiRqqRETQjhzcwUUh8rpS5XSvVRSnV3/1kemRBCCCFEmjNTonZu2/87vJZZOTyHEEIIIYTA3IC3g+IRiBBCCHzGvhJCiIgTNaXUUVrr2UqpM4zWa63fsi4sIdLTxaMvTnYIQgghUkg0JWpH4OrteYrBOg1IoiZEjG6edHOyQxBJJp0JhBDeIk7UtNZ/avt/SfzCEUIIIYQQbmZ6fQohhIgXKVATQniRRE0IIYQQIkVJoiaEEEIIkaIiTtSUUpOVUr297l+klHpXKfWoDHgrhBDWkM4EQghv0ZSoPQU0AyilDgfuB54DqoAZ1ocmhBBCCJHeohmew6613tN2+9fADK31m8CbSqnF1ocmhBDpR0rUhBDeoilRsyul3Ind0bjGVHMzMxWVEEIIPzkZOckOQQiRQqJJsF4G5iildgMNwFcASqn9cFV/CiGEiEHZ2WV0z5Emv0KIdkprHdmGrtK0SUAf4DOtdV3b8mFAgdZ6YdyijJJSahewMQGn6gHsTsB50oVcT+vJNbWWXE/ryTW1llxP6yXimg7QWpcYrYgmUVuotZ5oaVgdnFJqvtZ6UrLj6CzkelpPrqm15HpaT66pteR6Wi/Z1zSaNmrSwlUIIYQQIoGiaaNWopS6KdhKrfXDFsQjhBBCCCHaRDU8B1CAlKx5k/HjrCXX03pyTa0l19N6ck2tJdfTekm9ptJGTQghhBAiRUkbNSGEEEKIFBVNido+wNnAfsAy4BmtdWscYxNCCCGESGvRJGqvAi24Bro9Adiotb4hjrEJIYQQQqS1aBK1ZVrrsW23M4AfpM2aEEIIIUT8RNNGrcV9Q6o8hRBCCCHiL5oSNQdQ574L5AL1bbe11rooLhEKIYQQQqSpiBM1IYQQQgiRWNFUfQohhBBCiASSRE0IIYQQIkVJoiaEEEIIkaIkURNCCCGESFGSqAkhhBBCpChJ1IQQQgghUpQkakIIIYQQKapDJGpKqX5KqS+VUj8qpVYopWSOUSGEEEJ0eh1iwFulVB+gj9Z6oVKqEFgAnK61/jHJoQkhhBBCxE2HKFHTWm/XWi9su10DrAT6JjcqIYQQQoj4ykh2ANFSSg0EJgDf+y2/HLgcIDc394B+/frFPRan04nN1iFy3Q5Brqf15JpaS66n9eSaWkuup/UScU1//vnn3VrrEqN1HaLq000pVQDMAe7VWr8VbLtJkybp+fPnxz2esrIySktL436edCHX03pyTa0l19N6ck2tJdfTeom4pkqpBVrrSUbrOkzarZTKBN4EXgyVpAkhhBBCdBYdIlFTSingGWCl1vrhZMcjhBBCCJEIHSJRAw4FLgSOUkotbvs7MdlBCSGEEELEU4foTKC1/hpQyY5DCCGEECKROkqJmhBCCCFE2pFETQghhBAiRVlS9amUei+CzfZorS+24nxCCCGEEOnAqjZqI4FLQ6xXwBMWnUsIIYQQIi1Ylaj9QWs9J9QGSql7LDqXEEIIIURasKSNmtb6Nf9lSimbUqoo1DZCCCGEECI4SzsTKKVeUkoVKaXygeXAj0qpW6w8hxBCCCFEurC61+corXU1cDrwMTAI10C1QohgHC1QswPMzrvbVAs7lrfft2r+3uZ6KF9pzbHSUWsTLHkl2VGIcHb9BLPvdb0HV89KdjTWCfc54GhNTBwiZlYPeJvZNifn6cDjWusWpVTHmfVdiGT4vx7B102vCr//F/fADzN8l925DbLyzce0+CV456r2+3fvBZuM5hOVle/D21e4/tzOex2GHZu8mISvL++DOfe7bs99wPX/qv9Br9HJi8kK07u4/l8zD0qGuW5rDfd09d1u6u/gF9MTGZkwwepP3qeADUA+MFcpNQCotvgcQnQuvcfBSTFMYZuZF7iscpP54wFsXwKTL2u/39oY2/HSUV73wGUb5iY+DhFcThGc/k/45Qy4+CPXsvo9yY3JCuPOCVxWvc31/5Dr25d9/ffExCNiYmmJmtb6UeBRr0UblVJHWnkOITodpWDyb11/3p46PLL9i/aB056ECee77r91OTHPuNZ9MGQVwE2r4I1LYjtWOtv/XPjlv1y3F73gqk4WqaO1ETJzYfQvXfcnXwb2rOTGZIWMLCge6rts90+QmQ/H/p/rb10ZrPk8KeGJ6FjdmaBYKfWoUmqhUmqBUuoRoIuV5xCi04m1TVlzrUE1Z4zHdDpA2aCojzXHS0eOFuiyb7KjEKE4HWDLbL+vOsmU0o5WsGf6LsvpCmPP9FrQSR5rGrC66vMVYBfwK+DMttuvWnwOIYS35jpX6ZeHBR/A2gk2u3XHS0fOVrD5V1pIwptSrOp4k2qcLQavPQIT0c76+DsZqxO1Plrr/9Nar2/7+wvQy+JzCNG5xPorvrkOsgt8l8X6AazbStSsOl46cjpA2b0WWJjw1lW4ji9ipA3efxa81rWGpprkvW8cLW1VuN7n94uls5QepgGre31+ppQ6B3APbnsm8KnF5xBCeGtpgIzs9vtWfABrZ3uiJh/o5miH9T1ld/0ET0xpv7/vZLhU2hnFRgW5bdLS1+EtrxkVz5oJo0+P/bjR0A6vEnFv8l7uiKwuUbsMeAlobvt7BbhCKVWjlJLen0LEg3dSZRWnX4maVNlFL6BEjdhLWL55BEaeCndshcmXQnZR+H1EcEbPR6zP0VuXwshT4LIvXfeXvBzb8czQ2vXa834sGr8fXZK0dRRW9/ostPJ4QnR6llWNWFx9o7W0UYuVTzs/rCmZLBnh6uWbXQDjz4OFz8d+zLTmV/VpxXN08LXQ/2DoOxEu/hBWfRj7MaPl/9pzLTTYTn6AdQRWV32ilBoHDPQ+ttb6LavPI0SnYFTqEjX/D1srqj6ljVrMLHluw5HnJXYW/8jJzPXqcamS897RziArLE5KRUJYmqgppf4DjANWAO5XigYkURPCSNC2JNEcA+t7c2lne5IhH+gmaeurpH2SiCQlAZ1JwPWz4keObj9Ost472v3a077LAjdMVEQiBlaXqB2ktR5l8TGF6Ly8P9RjYvEvZWmjFrt4fDFqr6o6pWI/Xtoz6PVpRfLrc8wklagZ/UiQNmodktU/975VSlmeqCml/qOUKldKLQ+/tRAdidHwACaOYTXtlLk9Y+b/3Frxxeid2MsXrTWsrg5MkVJP5X9ugx+FUiLbIVj9SfwcrmTtJ6XUUqXUMqXUUguO+1/geAuOI0Rq0RZUj2mjZNLkksoAACAASURBVM+Kqk9poxaTcKWlTie8ekH0x/V+ruV5iU3cqgOTXPUZSVImTRo6DKurPp8BLgSW0d5GLWZa67lKqYFWHU+IlKGdWF71aXS8xyZBxWrX7esXuWYyqK+AniODxOXVEF4+0M0LVa2mnbDyfZjeBYr3g5Megtpy16TgB11pfDzv/aXq0wJxKPXU2u8wyaj6NGijBvEZ3FfEndWJ2i6t9XsWHzMiSqnLgcsBevXqRVlZWdzPWVtbm5DzpIt0vJ721nrGVlWx2OBxH1BTy4IIrseIHdvZPG8edQXlAAzfsZ2t8+ZRW1hBbW0tc7/4lMMrVuOwZWN3NsGjEzz7bt73VNbu99uAYw7ZtImKuqVUbtLsv7eSFV9/TWtmQcB26Saa12jv7auwOZvZVu/avteOVWS21LClyXVfOR0c0batY+8m7M+d1r7zJ7dRVvpuwDH7b1xHw/YGdu3qRkHNOvpu385PHfw9k8z3/aCNG6mqLGDPVlcvzSFbNlPRsIjKDa2ebUrLTqOi+0SK9yz02bc2fxDzJ/8j8JibNlJZXcjeLRkUVa2k184trE7g46utrWXPngq0srNu3jzqCnYBUFS1kp7lW1nTFkuXyh8p2bXFc18El+zvJqsTtUVKqZeA94Em98JEDM+htZ4BzACYNGmSLi0tjfcpKSsrIxHnSRdpeT0bq2FLd+PH/VNBZNdjz8v0njKlvXSs6nX6TJoEfcZRVlbG4ROHw87jsZ/nN+3uwufpt2Mp/YzO0fAJ/UYdAAMOgY3dmDr1UMjtFuWD63yieo0u2AiOZoZNadt+yQ6o38N+B7fdd7TCXGB6FT79fivWwttXGJ9n7nzoMRRGlcKOYnDMp08Hf88k9X3fOgcGjIOhbedvmkW/YeNh0GGu+1pDmaL4oudcs0IsfhFO/Bs8OJiCuvXGcbeWMWDg/rBfKWzOg6Vr6ZvAx1dWVkb37t3Blknx5MnQa7RrxaYcWL6efd2xbMyGFf+/vXsPk6sq8z3+fbuTQAKBcDNgQMFwGW5ySRBQwaiIKArKwWeYZ3QURnFwzjjqjB6QGQ/ieI7i7eh4RkAF8RmPihcEBEVAOugMyM2EhAmBEFACHYQASTr3pN/zx96drt69L3VZXXtX1e/zPPV01d6111699qraq9b1idHXkqnse1PogtpUogLaqTXbND2HSJbM+Y4aldOksWV9NLdT0p4Hw+DC7Hipj1qLigaKOMyaM37zTnvCpB1zjqsZTKDr0qJkM2W8bcS2zbDvXJixX/Q46JRo+6eeh2+9MSPI5DUpq+kzkT+SfVnVpaFjhF6Z4NyQ4Yl0v4kY9ZkIb3gr9KV81PP6OKmPWiA5BeimpmZJ9lGT1uUN+Ghy0mIruzCdlrc0j1qnCjLqM+4f1vJ7co79PnAXcIiZrTCz8Z1qRDpRiHnUUkd91nOOvGMmYP3QXlPPZKqp1y3n5j5ucmPdaFtSuNZn1mcrrwCWnPC2QoMJQi9AL20RqkbtQjN7Lme/AX9P3IesUe7+F03FSqQTtFwgKhqKn1OQy7rZDG+rmUdNX+jNqaPpM01hTVnZtTXdpKA5MOtHTm5tdEWuSdr/MhGT+8qEC1VQmw+8veA9twY6l0j38OEwTVjBbzbqo9aytDny6l6yKK+2ZuRQTc8RRl4TYUZhu+gzW4mmT/LPrabzjhGkoKa+aSJNCtX0OX7j2OdZzTeZYWqtzyAKC9CpB+U0fXoFCgFdpKjg3HQ/wpGmz5HXbZY5CbbmUetE6oQiUqpQgwly+p7k3WwyCwTJPmr6Qm9cUZrl1dZUuP9TVylqDmz181nmj5yipk/9AOsUKqiJlCnEElJQ3BcqdYHmvEOGoa+/jjdKprpqYxqs6YSxNWrSmuQ1CtGHK1kgKqPWM3MwQcr7pPJUUBMpU4glpIpGrvlwzilyatQo+WbTDfKW7MlL07ymz3reJ/XLXVapxRq1Mms9i5aLUpeGjhGkj5qZfSxvv7t/OcR5RLpP3o2g3i/Sgi/gvOk56ulsrC/0FhQ0Nanps2JC9FGrDaesz04c78KCvPJPJwg16nN6/PcQ4DhgZL3PtwP3BDqHSPcJMZgAmqsVKJx7LSs8qUvTN8mC66LBBOGkff6SadroD5XKNH2qj1q3CDXq89MAZnYncKy7r41fXwLcFOIcIt0pQB+1lkau5d1Eyq4V6HT1zFvV4CCP2mNUoxZGM1PbNBR2SdNzjOujVkehVCopdB+1mcDmmteb420ikibIPGpFN5MmpudQAaB1zXZUL2z6lHCaHJlbGGYVfuQUDUppTyykdaEXZf8ucI+ZXRe/fgdwTeBziHSPUL9oxxUCks+bqLkpu/mmGxSOxm2w72AVmtW6SWqNWXLAR6uDfVo7vLlzZgwwKhpgIJUUelH2z5rZL4CT4k3nuvvvQ55DpKsMb4X+yRk76/wSLexT08R8XbVhajBBk+q5fo3Oeq/BBMHl9ttqokattkBUdtNnck44rfXZkSZieo5pwBp3/yqwwswOmIBziHSH2hUAmg+E5moFGvmiVoGgYXXV1mQenL3Lap6oRq1FaTVPyQ0tNn1WZTBB1vuk8oIW1MzsfwL/A7go3jQZ+PeQ5xDpKmMmlm1Bs7UCmd/TtTv0y7s5ddwsm2n6HHOsbrQtmYgJbysjkT+ShTfVlHeM0DVq7wTOANYBuPvTjE7dISJJw9smYNRnYlvW6geN3Og7+oZVpiYGbNQ7j5pq1MIIPeFtZZo+64m38k8nCF1Q2+zuzkj3ZbOdAocv0l3GranZrLxagazVD+qcr0u/vJtTTwE6la5L+7QytU2eCjR9pja7q49aJwpdULvWzK4AZpjZB4DbgG8GPodI9whVUGtqdCEN3ET0y7txaU1NdU6mWs88atK6wglvm52eI1ZaYTqO97ixBN3UtNs7Qo/6/KKZvQlYQ7RKwafc/daQ5xDpKiEKaqlftnUMJqh3dKEKBs0Zl+7J2pVW51FT02cQoSe8HdeRv6KDCVQj2zFCz6MG8Ajg7n6bmU0zs+kjKxWISEKQGrWiJo28L231UZtQRTVqjRagK9H/qZsU/MhpdVH2UgvTyfxRMApZKiv0qM8PAD8Grog3zQJ+FvIcIl0lt6DWwA0ir0mjmUXZNY9aAMlmzr6U69JMmBpMEExarWfu/roCHT2m1KbP5DxqFIwOl6oK3Uftb4HXEDV94u6PAi8JfA6R7jERTZ8NTXibo+zmm06Xtq5nqwt+1x6jGrUwcvttNVmjVvZnxz2a9seHx24b9772RUmaF7qgtsndt6/1aWaTCJQVzOw0M1tqZsvM7MIQYYqUbkKaPpO7m5lYVfOota6OwQQNB6nrElZBwbmZGsvkNSql1nNkSp6cpk/VlHeM0AW1+Wb2SWBqPKjgR8CNrQZqZv3A/wXeAhwG/IWZHdZquCKlm7BRn7VNbFnnKGj6rD1GTWxNyhlM0PLUD+i6tMqHJ2AeteFErWdJLFGjBgX/q1RV6ILahcCzwCLgg8DNwD8FCPdVwDJ3Xx7X2P0AODNAuCLlmpBRn2lNn1kT3uaEOXKMfnk3J7WZs8Wmz9r8oqbP1o2bDDrEqM/kZ7qka2RW0PTZYG3fV46EhT8IEjVpTOjpOYbN7Brgd0S5c2k8AW6rZgFP1rxeARwfIFyR8mzbCjf9AxzznvT9G1+EK05O3ze4EP75uWhB90dvgb6ahd2X3Bj1T3nZCdHrFfdFj6TJ0+CxX6eHv/QmOPPrcTxWw/85AvY5qr7/q1sNLmQewNKjazbWfL1t3QRbN8LfL4xeLx9IXNvEjXHji/DEf2SeK9V9V8GRZ0fPJ+0Ij/4qO490iDlrh2DpzmEDHVyYnl/XPB1tf/dPotcPXAMnfKjmuAWw8kE45C3R642rySxoZV2jBd+D4/8mej55Giz+Caxa1tS/Ubea/3fO2iHYugr2P2lsfltx79h4WB8s/H/wzm/Ud47VT8Id/wuOOidgxKUeQQtqZnY6cDnwGNHPkAPM7IPu/ouQ58k49/nA+QAzZ85kYGBgok/J0NBQW87TK3oyPQ//PGwG0v7vo76aedgx6z7BgvkDeN9kjtnlEBb87vd4X/Rx3m+vU9m0eip/GhhgaGiIh9ZvYsf+2TyZco45fbtyf9r2nWdz/z0PRi/2/8fo0esOifLozjunFyr6t67jiMX/m4Vxeh6yYTIr/jjEuuej13s89xDT1z7OE/H+qetXsP8ex7MkK/1Tth+902wefPhZhh+N9827vrX/qQLy0rRph6Rv3nntcmY99XOWxml77PSD+f3iFXjfSgD2mvRKpm4Y5I/x/ulrHmXv1Vt4tJFrNP1gHlz8JMNL/hRtaMc1qvl/R9Jz9rKrWbX+97z4h20A7PPMeuClDMZxtuEtHDP9YB6o8zv34H1OZXCfN7G2176jqcC9yd2DPYCHgQNrXs8GHg4Q7onALTWvLwIuynr/nDlzvB3uuOOOtpynVyg9G/DNU9y3bIyfv9F96+bRfb/9qvvCa909TtNFP3b/zVfSw7n8pMa297jcPLphtfvVp4++vu5D7oMPjr5e+kv32z8z+vrZR9x/dF56WFnp/+3T3DcN1R3fTtDWz/3gg9F1GXHlG8Z+dhZf537nl0Zfr7jP/caPpIeVdY2+dar7pnWtx7VJ29Pzln9yf+yO0R33XhU9Rmzd7H7l6+sP+Gd/6/70ggAx7DztyKPAfZ5RpgndR22tu9fW8S4HQkx2ey9wkJkdYGZTgHOAGwKEK9K5Rpo1ivpCqcN5iYoGE9RzXCP7pFjR6hCev7seVejXmeyjVjSvX5FnH0Z5rxyhVya4z8xuBq4lyhXvAu41s7MA3P2nzQTq7lvN7L8DtwD9wFXu/lCgOIt0nnEdyQsmsqzCjaPbWcESUSEGE2jwQIvSOtAnpqwoHJxTpCLXqJ4Jb5OjQvOsexam7BQkatKY0AW1HYFngNfFr58FpgJvJ8q9TRXUANz9ZqJRpCIy5oaTNpFlyPm6pD4pNZnjbowB0lWF7ua1Le0qcI2KaswaHTW8+2zYcdeWoyWNCz3q89yQ4YlIhrwbTohpG1QYaFxdKz2oAFy+vObntPUxO1XK9Bxjfjc0+Bnfugn6pwSJmTQm9Fqfl5nZLmY22cxuN7NnzezdIc8hIiOy1uPMWNdTJt64ps9ks1pOn6GGw5fGFSzjZTb+sjQ8111FrtG4lQkgfZ64Ov3htzB5aquxkiaEHkxwqruvAd4GPAEcCHw88DlEpPaGk7qGX0VuFj2lqMYsRP+nZo8RoPFazWY/R1Woka7nh0Ej8dz7ldG8jdJ2oQtqI02ppwM/cvfVgcMXEaij6bOGCm3tkeyInuyjFqTpU9eyNUVLKHXRNWp0VGdheBUofPao0IMJfm5mDwMbgAvMbC9gY+BziMi4G0rBDUhfsm1Q1OTcyPQceafRtWzJhI/6bPaY0FJGdSrvdKSgNWrufiHwamCuu28B1qM1OUUmRuaoz0CjC6UJRdNzULwtN3hd15Y0XKvZwf0Ik33UqhIvaVjoGjXc/fma5+uAdaHPIdLz8m44427++oJui7TamDFNn6GaolQr0ryibgGBRuZWoeZqXB+1FgwPo3xXntB91ESkDIWFM33JTryiFSFC3DhV6G5Z4ajPLpmUOLWg1uT3wPAWDSQokQpqIp2o9oYSpE+NtCx1ZYK8wQTqo9Z27Rr1WYXPYMjBBMPbwPrDhCUNC9L0aWbH5u139wdCnEdEauV8CYfotC4NKro5h+qoLs0rWOYrVNNnJQRs+vRh6FNBrSyh+qh9KWefA28IdB4RAcbe4OuYBkK1MG0ywdNzqNDdmqIBHY2M+sy6FlW5RpbSYNbs94Crj1qZghTU3P31IcIRkTql3lCkVEXXJNkU1cnNah1tgkd9QjV+GCX7qLX0fZFct1baKeioTzObDFwAnBxvGgCuiKfqEJFgCpaMym3ekYmR1gctOY9aq/Na6Vq2LLfA0kCtZ+a1q8g1CtlHzYfTa+ikLUJPz/ENYDLwb/Hr98Tb3h/4PCKSNY9a6g1Ev4YnXLsWZVfNRvPqSbu81SXqP1ETxwRmfSlLSDXb9KkatTKFLqgd5+5H1bz+tZktDHwOESn80tRgglLk3uSLOrI3GL40Ka95OkBhpDLXKORgAleNWolCp/w2M5s98sLMXgFsC3wOERnTRJO8uYSYC0oaVlijBmHmt9O1bF5R384uqvVMrkzQCjV9lip0jdrHgTvMbDlRjn85cG7gc4jImI7rKpRVU/K6hOgzVJXamg5VT/Nz3lx4danINUobTNDKqE8V1EoTtKDm7reb2UHAIfGmpe6+KeQ5RKQeGkxQuglZmQAVxFtST61niNNU4BqFHBmu6TlKNRGjPj9IzahPM9OoT5Hg8moG0vbpS7YUefOoVaYvU49pR9NnJQTso4b6qJVJoz5FOlHRr2UVAiogrUYtuanBArSua2vaNuqzAsb1UWtl1KeaPstU+VGfZvYu4BLgUOBV7n5fK+GJdIecX/7JQpxu7iUKvDLB9nCkOfVModIlrC9aTD0EH+6utOkwnTDqczFwFnBni+GI9IiCZXKkPVqZnmNwAWxTj5EJ0StNn0H7qHVwzWIXCF1QGxn1OWBm84FfA//YSoDuvsTdlwaJnUi3CNX0mXyfOwwPqxZuIqROl5Bz87v90tHnT94LC38IKx+ciJj1jpCjPgcXwvzLYNvW0WOGh2HLhgARDSBtybJkYWtwIaxcNPp6/hfgkl3hh+8e+77N62Dr5omLq+QK3fT5W2DMqM/A4YsIUNj0OUbOjWnlg9EXs0yAxE0+OV1Ckf/8WvSQgIrWY22w1uiOz0YPgCnTYfPa5qMWXJ357fLXjt+25Mbx3ws7vSRMtKRhoQtqd7n7scD2n31m9gBwbN5BZnYbsHfKrovd/fp6Tmxm5wPnA8ycOZOBgYF649y0oaGhtpynVyg963fEqlU8ctddbN5hD+asHeL+mnTbe3ApfcObeXr9AENDQzw8+DD92zbx1IaBceFMes33eO1//OX21wuOupQXdzuKXVYvYY2uxThFeXTO2rXbr8Xhzz7HY3ffzcapjwMwbd0fefnKlSyJ9++8djmzVg6yNCW8ecDCV36azVNmMPOZ+azaYy6rZxyODW/Bu+y6tPNzP2XTKg5e9RyL4/MlPzszXniQ3Z//I8vjbTNeWMQeq57ksbT4zRu9NU3e/CK7rHmUdTu9jL7hTawv8RqNpOfMlUuZtHUdT22M4rLvk8vYMnk6z7xQE7d514M7/ds2MPuxq9kyeVcef0VUmzZ584tMW/8UOw89zlP7vi16f5flvXqVfW8KUlAzs72BWcBUMzuG0Z+RuwDTio5391NajYO7XwlcCTB37lyfN29eq0EWGhgYoB3n6RVKzwYMXsGeJ5wAu86CpTuPTbcHnoQtGzj4+HkMDAzwZ7P+DDav56Dj56WH9abV258evf1Zxnt7XGEeXTp9dP/Kb7LXiSfCjJdFr59dChvvZObI/sHdYfh+9kkL75iHOGqXWXENz/t4WbD/oHra+rlf8zSsunb0fMnPznKDR5/hZSPbHu+HR1ayXwd9L21Pz4UrYf0qDjpxXrTjrodg2p4cetS89ANPeSsQzVKfdNAExLOTlH1vClWj9mbgfcC+wJcYLaitAT4Z6Bwisl0DTZ/qb1YRDXTu3nXfiY1Kzwrc9FllQVbCkCoIUlBz92uAa8zsv7n7T0KEOcLM3gn8K7AXcJOZLXD3N4c8h0jHKRzRpWWlqqG2j1rAtRelOQ1/Djr4elnf+CWkpCOFXkIqaCEtDvM64LrQ4Yp0LxXKypMzf924wQS6cZajYHqObirQJAcT6AdbR9JUwyKdyPrAc6YorHe+LplYhc3QunG2V481feqz3xVUUBPpRJN2yJ7XKNQM+NKE2ht9WvOzrkup6roG3bKElGpwu0WQgpqZHReP/Bx5/Vdmdr2Zfc3Mdg9xDhGpMWkH2LoxY2eH3li6weACuPOL0fMt6xl7LbS0V+l23BWW3QaXn5Txhi5q+kwdTKDvhk4UqkbtCmAzgJmdDHwO+C6wmnjKDBEJaNKOsC1npnAVCMpx+Fnw689Ek4UuHxjbPJ1Wm9OptTWdatIOMG3P0YmeBxNLUXfV9eiiQmePC1VQ63f35+Pnfw5c6e4/cfd/Bg4MdA4RGdGfU6OmAkF53nU1vDseU3X8BaNzqIGmS6iKTzwG58+Prk/f5LH7xvX9zFlCquqSfdQ6uRm3x4Ua9dlvZpPcfSvwRuIVAgKfQ0RG5DZ9SqkOPAUuWZ2yQzUclfHSo6PHWz43dnv/ZNi2pZw4hZacnkM6VqhC1PeB+Wb2HLAB+A2AmR1I1PwpIiFN2hG2bsrYmSwQqHBQCWZjF8DWdame/iljC2qdXLBudG1ZqaxQBbXPA7cD+wC/ct+eu/uAvwt0DhEZkVejltq8oSaP8hms/uP4bVId/VOivp/Dw9AX9wzq1ObCcU3tHVzo7HGhCmr3xIuxj+HujwQKX0RqTdoBtmyIOkQn9U2CX10M+x7H7GVXwZr74cA3tD+OkhDfKJPX7IyvtT8qkq5/Miy6Nnoc9g5YfgcccHLZsWrOhhfgzsuix277w9zzyo6RNClUQa1Df3KIdKi7vwEv/iFjZ1wguOpU9hvZtGltGyIluSbtmLJRX52VMlIDdfqX4aaPRc+X3FhefFoxa+7o8xeegFs/BXP/Go48u7QoSXNCFdT2MrOPZe109y8HOo+IAHx4Adz4YTj4NDj0bWP3HX5WtH3rJpbc8FUOPefT5cRRxtphl+jvJath5WLY+4hy4yPj7bRnVMCZex4ccVZUuO7Ufmp7HghnXw37Hgcz9oO1K2HytLJjJU0INuoT2Bn9PBRpj74+OPPr6fvMYMpOMGUnntn79Rza3phJlklTRkeDqpBWTTvuCh+4PXo+dbdy4xLCEWeNPp++d/b7pNJCFdQG3f3SQGGJiIiICOEmvFVNmoiIiEhgoWrUTjezjxCtQrAI+HY8+a2IiIiINClUjdpXgLlEhbS3AF8KFK6IiIhIzwpVo3aYux8JYGbfBu4JFK6IiIhIzwpVo7Z9zQ01eYqIiIiEEapG7SgzWxM/N2Bq/NoAd/ddAp1HREREpGcEKai5e3+IcERERERkVKimTxEREREJrPIFNTP7gpk9bGYPmtl1Zjaj7DiJiIiItEPlC2rArcAR7v5K4BHgopLjIyIiItIWlS+oufuvakaS3g3sW2Z8RERERNol1KjPdjkP+GHaDjM7Hzg/fjlkZkvbEJ89gefacJ5eofQMT2kaltIzPKVpWErP8NqRpi/P2mHuPsHnLmZmtwF7p+y62N2vj99zMdHqB2d5FSINmNl97j637Hh0C6VneErTsJSe4SlNw1J6hld2mlaiRs3dT8nbb2bvA94GvLEqhTQRERGRiVaJgloeMzsN+ATwOndfX3Z8RERERNql8oMJgK8D04FbzWyBmV1edoRqXFl2BLqM0jM8pWlYSs/wlKZhKT3DKzVNK9FHTURERETG64QaNREREZGepIKaiIiISEWpoBYzs9PMbKmZLTOzC+NtZmafNbNHzGyJmX0449j3mtmj8eO9NdvnmNmiOMyvmZm16/+pgow0faOZPRD3N/ytmR2YcexF8XFLzezNeWH2CjO7ysz+ZGaLa7btbma3xnnvVjPbLeNY5dGEjPSsa8m6rHxoZgeY2e/i7T80synt+F+qIi1Na/b9g5m5me2ZcazyaEJWeprZ38X59CEzuyzjWOXRFBmf+6PN7O74vnSfmb0q49hy8qi79/wD6AceA14BTAEWAocB5wLfBfri970k5djdgeXx393i57vF++4BTgAM+AXwlrL/1wqk6SPAofF7PgR8J+XYw+L37wAcEIfTnxVm2f9rG9P0ZOBYYHHNtsuAC+PnFwKfTzlOebT+9DwVmBQ//3xGembmQ+Ba4Jz4+eXABWX/n2Wnabx9P+AW4A/AninHKY/WmZ7A64HbgB3i12n3JeXRxtL0VyP5CngrMJByXGl5VDVqkVcBy9x9ubtvBn4AnAlcAFzq7sMA7v6nlGPfDNzq7s+7+wtEa5OeZmb7ALu4+90eXcnvAu9oxz9TEVlp6sAu8Xt2BZ5OOfZM4AfuvsndHweWxeFlhdkT3P1O4PnE5jOBa+Ln15Cex5RHU6Slp9e3ZF1qPox/Rb8B+HH8vqzr0bUy8ijAV4imWcoavaY8miIjPS8APufum+L3pN2XlEczZKRpPfel0vKoCmqRWcCTNa9XxNtmA38eV4X+wswOAjCzuWb2rYJjZ8XPk9t7RVa6vB+42cxWAO8BPgdgZmeY2aUFx2Zt72Uz3X0wfr4SmAnKo4GcR/TrGDN7qZndHG/PSs89gBdrCnpKT8DMzgSecveFie3Ko805GDgpbr6cb2bHgfJoiz4CfMHMngS+CFwE1cmjKqjl2wHY6NHSEd8ErgJw9/vc/f2lxqxzfRR4q7vvC1wNfBnA3W9w90+VGrMOF/+a8/i58mgLLFqybivwPQB3f9rd31purDqPmU0DPgmM+2wrjzZtElHz2wnAx4FrzcyUR1tyAfBRd9+P6B71bahOHlVBLfIUUR+KEfvG21YAP423XQe8soFjn2Jss8nI9l6Rli7PAEe5++/ibT8EXl3nsU/lbO9lz8RV78R/05pBlEcbYKNL1v1lXPhNykrPVcAMM5uU2N7LZhP1M11oZk8QpckDZpZc21l5tH4rgJ965B5gmGjR8FrKo415L6P3+h8RNR0nlZZHVVCL3AscFI+GmQKcA9wA/Iyo4ybA64g6wifdApxqZrvFI+5OBW6Jm6PWmNkJcb+AvwKun+h/pEKy0nRXMzs4fs+bgCUpx94AnGNmO5jZAcBBRJ01s8LsZTcQfckQ/03LY8qjdbLRJevO8Owl61LzYVyouwM4O35f1vXoGe6+yN1f4u77u/v+RIWMY919ZeKtyqP1235fir9LpwDPHNc1JwAAA3ZJREFUJd6jPNqYp4nu8RD14Xs05T3l5dGQIxM6+UE00uMRopEyF8fbZgA3AYuAu4hqgwDmAt+qOfY8og7vy4Bza7bPBRbHYX6deCWIXnlkpOk74/RcCAwAr4i3n0E0cGPk2Ivj45ZSM4ImLcxeeQDfBwaBLUQ3vL8m6nNyO9EXy23A7jV5T3m08fRcRtQPZUH8uDx+70uBm4vyIdEou3vicH5EPDKvVx5paZrY/wTxqE/l0abz6BTg3+M0eQB4Q/xe5dHm0/S1wP3xfel3wJwq5VEtISUiIiJSUWr6FBEREakoFdREREREKkoFNREREZGKUkFNREREpKJUUBMRERGpKBXURERERCpKBTURqTQz28PMFsSPlWb2VPx8yMz+bQLO9x0ze9zM/qbm9dkp75tnZj8Pff6CuJ1kZv9lZovbeV4RKc+k4reIiJTH3VcBRwOY2SXAkLt/cYJP+3F3//EEn6Nh7v4bM3sr0NYCooiURzVqItKRamu0zOwSM7vGzH5jZn8ws7PM7DIzW2RmvzSzyfH75pjZfDO738xuGVkntQ4nm9l/mtnyRO3azmb2YzN72My+Fy8hk4znd8zsG2Z2d3z8PDO7ysyWmNl3at43ZGZfMLOHzOw2M3uVmQ3Ex5zRQlKJSAdTQU1EusVsonX6ziBaYucOdz8S2ACcHhfW/hU4293nAFcBn60z7H2Ilpl5G/C5mu3HAB8BDiNamuc1AGZ2aaJwtRtwIvBRovVZvwIcDhxpZkfH79kJ+LW7Hw6sBf6FaD3cdwKX1hlPEekyavoUkW7xC3ffYmaLgH7gl/H2RcD+wCHAEcCtccVXP9Gaf/X4mbsPA/9lZjNrtt/j7isAzGxBfJ7fuvunEsff6O4ex+0Zd18UH/NQfMwCYHMizptq/p/964yniHQZFdREpFtsAnD3YTPb4qMLGQ8TfdcZ8JC7n9hs2DHL2L6N7O/UkfcNJ44ZiRtAMs61/4++q0V6lJo+RaRXLAX2MrMTAcxsspkdXnKcRERyqaAmIj3B3TcDZwOfN7OFRM2Nr56Ic6X0URMRaYqN1rSLiEg8EvPnVZyeA8DM9ieK3xElR0VE2kA1aiIiY60GPjMy4W2VmNlJwI3Ac2XHRUTaQzVqIiIiIhWlGjURERGRilJBTURERKSiVFATERERqSgV1EREREQq6v8D5N33cK6FSdoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "overview_fig(pts,flowrate);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "xad220gtnyM9R5vYGPFG",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "NMdesRKKdUNs"
      },
      "source": [
        "Note that I sometimes use a ; at the end of the code in the cell to suppress extra output, as is has been done above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "iooxa": {
          "id": {
            "block": "F3gJD6r2sfnWV5gdoJJF",
            "project": "mmReuqVTAa9JzPpNr22I",
            "version": 1
          }
        },
        "id": "CWzfiXCLdUNt"
      },
      "source": [
        "*** negrito\n",
        "\n",
        "###  references\n",
        "\n",
        "Kaya, E., Zarrouk, S.J., and O’Sullivan, M.J. (2011): Reinjection in geothermal fields: a review of worldwide experience. Renew. Sustain. Energy Rev. 15 (1), 47-68. \n",
        "\n",
        "Zarrouk, S.J. and McLean, K. (2019): Geothermal well test analysis: fundamentals, applications, and advanced techniques. 1st edition, Elsevier. \n",
        "\n",
        "***\n",
        "\n",
        "© 2021 [Irene Wallis](https://www.cubicearth.nz/) and [Katie McLean](https://www.linkedin.com/in/katie-mclean-25994315/) \n",
        "\n",
        "Licensed under the Apache License, Version 2.0\n",
        "\n",
        "\n",
        "***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "iooxa": null,
        "id": "_pV0bBf2dUNu"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "iooxa": {
      "id": {
        "block": "xsmu9oKVnHvU3tpyB9XG",
        "project": "mmReuqVTAa9JzPpNr22I",
        "version": 2
      }
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}