2_1.ipynb

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    "colab": {
      "name": "2.1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOmVN1oUrWWaC7G54Fe3j5G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fimoziq/unecozik/blob/main/2_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "m5s305IDoIbo",
        "outputId": "a5722019-64ee-47fc-b4cb-a0009696995b"
      },
      "source": [
        "from pandas import read_csv\n",
        "df = read_csv('_pythza2F.csv',parse_dates = True, index_col = 0,delimiter=',',decimal=',')\n",
        "df.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pr</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d;</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-01</th>\n",
              "      <td>4.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-02-01</th>\n",
              "      <td>3.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-03-01</th>\n",
              "      <td>3.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-04-01</th>\n",
              "      <td>3.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-05-01</th>\n",
              "      <td>3.69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              pr\n",
              "d;              \n",
              "2007-01-01  4.20\n",
              "2007-02-01  3.93\n",
              "2007-03-01  3.64\n",
              "2007-04-01  3.53\n",
              "2007-05-01  3.69"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nei6x3rSobWR"
      },
      "source": [
        "\"\"\"тренд-сезонная модель\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "BoHzkD5Hoaas",
        "outputId": "95addf38-e8e8-41c9-90f0-f140784e4031"
      },
      "source": [
        "df_columns, df.columns = df.columns, ('y', )\n",
        "df_index, df.index.name = df.index.name, 'date'\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-01</th>\n",
              "      <td>4.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-02-01</th>\n",
              "      <td>3.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-03-01</th>\n",
              "      <td>3.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-04-01</th>\n",
              "      <td>3.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-05-01</th>\n",
              "      <td>3.69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               y\n",
              "date            \n",
              "2007-01-01  4.20\n",
              "2007-02-01  3.93\n",
              "2007-03-01  3.64\n",
              "2007-04-01  3.53\n",
              "2007-05-01  3.69"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZwjC9paorkN"
      },
      "source": [
        "\"\"\"float64!!!!!!!!!!!! delimiter=',' ^ decimal=','\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cmZvgiRoqg2"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize = (10, 6))\n",
        "\n",
        "plt.xlabel(df_index)\n",
        "plt.ylabel(df_columns[0])\n",
        "\n",
        "ax.xaxis.set_major_locator(mpl.dates.YearLocator())\n",
        "ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y'))\n",
        "\n",
        "ax.plot(df['y'], marker = 'o', color = (0, 0, 0, 0), markerfacecolor = 'blue')\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "0P0_0XtloyVf",
        "outputId": "0a677f09-2f9c-4703-f176-ab93df8a9d38"
      },
      "source": [
        "import numpy as np\n",
        "df['t'] = np.arange(1, len(df) + 1)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>y</th>\n",
              "      <th>t</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-01</th>\n",
              "      <td>4.20</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-02-01</th>\n",
              "      <td>3.93</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-03-01</th>\n",
              "      <td>3.64</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-04-01</th>\n",
              "      <td>3.53</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-05-01</th>\n",
              "      <td>3.69</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               y  t\n",
              "date               \n",
              "2007-01-01  4.20  1\n",
              "2007-02-01  3.93  2\n",
              "2007-03-01  3.64  3\n",
              "2007-04-01  3.53  4\n",
              "2007-05-01  3.69  5"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATkYwREco0S7",
        "outputId": "1fdaee8c-a510-4f20-eaf4-179dfa38cf50"
      },
      "source": [
        "import numpy as np\n",
        "coef = np.polyfit(df['t'], df['y'], deg = 1)\n",
        "coef"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.0066815 , 4.07421986])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmzJs0NLo2WI",
        "outputId": "4813e213-2233-4654-c0c7-6bfdfdbbf485"
      },
      "source": [
        "df['y^'] = coef[0] * df['t'] + coef[1]\n",
        "df['y^'].head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2007-01-01    4.080901\n",
              "2007-02-01    4.087583\n",
              "2007-03-01    4.094264\n",
              "2007-04-01    4.100946\n",
              "2007-05-01    4.107627\n",
              "Name: y^, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "yZd8blDTo3pv",
        "outputId": "57b541cb-f3da-4c18-96f7-69bf60cad031"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (12, 7))\n",
        "\n",
        "plt.xlabel(df_index)\n",
        "plt.ylabel(df_columns[0])\n",
        "\n",
        "ax.plot(df['y^'], color = 'orange')\n",
        "\n",
        "ax.plot(df['y'], marker = 'o', color = (0, 0, 0, 0), markerfacecolor = 'blue')\n",
        "\n",
        "ax.xaxis.set_major_locator(mpl.dates.YearLocator())\n",
        "ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y'))\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "P8kpyqnCo6Tm",
        "outputId": "741c3f78-80be-4dd7-fc33-fb5742945e0a"
      },
      "source": [
        "from pandas.plotting import autocorrelation_plot\n",
        "\n",
        "fig, ax = plt.subplots(figsize = (10, 6))\n",
        "\n",
        "autocorrelation_plot(df['y'], ax = ax, marker = '.')\n",
        "ax.xaxis.set_major_locator(plt.MultipleLocator(6))\n",
        "ax.set_xlim(0, 48)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTz2b7Y1o9av"
      },
      "source": [
        "\"\"\"по графику автокорреляции для ряда данных можно предположить, что лаг **сезонности** равен 12\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "7gWkiaIEo_II",
        "outputId": "d4938b80-ad88-4ebf-ea01-4107ab37dbd3"
      },
      "source": [
        "import pandas as pd\n",
        "s = 12\n",
        "ses = (df['y']-df['y^'])\n",
        "ses = np.array([np.mean(ses.iloc[k::s]) for k in range(s)])\n",
        "ses = pd.DataFrame(ses - np.mean(ses), index = np.arange(1, s + 1))\n",
        "ses"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.031332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.257850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.587031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.621213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.552894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.359576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.021257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.364561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.575380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.611198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.532017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.285335</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           0\n",
              "1   0.031332\n",
              "2  -0.257850\n",
              "3  -0.587031\n",
              "4  -0.621213\n",
              "5  -0.552894\n",
              "6  -0.359576\n",
              "7  -0.021257\n",
              "8   0.364561\n",
              "9   0.575380\n",
              "10  0.611198\n",
              "11  0.532017\n",
              "12  0.285335"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "csaKQqOLpA9B",
        "outputId": "72d65b84-dffa-46b7-ae0c-9da0a6931ef8"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (10, 6))\n",
        "ax.set_xlim(0, s + 1)\n",
        "plt.plot(ses)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaLCyUwEpDML"
      },
      "source": [
        "tmp = pd.concat([ses for _ in range(4)], ignore_index = True)\n",
        "tmp.index = df.index"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_67VQlJApEx2"
      },
      "source": [
        "df['s'] = tmp\n",
        "df['y^s'] = df['y^'] + df['s']\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "g_DN8LwipGtA",
        "outputId": "cb4d211e-7c14-4f45-838c-4581b64b511c"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (12, 7))\n",
        "\n",
        "plt.xlabel(df_index)\n",
        "plt.ylabel(df_columns[0])\n",
        "\n",
        "ax.plot(df['y^s'], color = 'orange')\n",
        "\n",
        "ax.plot(df['y'], marker = 'o', color = (0, 0, 0, 0), markerfacecolor = 'blue')\n",
        "\n",
        "ax.xaxis.set_major_locator(mpl.dates.YearLocator())\n",
        "ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y'))\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgUSQKDmpJWK"
      },
      "source": [
        "n = len(df)\n",
        "\n",
        "x = np.arange(48, 60)\n",
        "\n",
        "df_fv = pd.DataFrame({'y' : coef[0] * x + coef[1] + df['s'].iloc[:12].array}, index = pd.date_range(start = df.index[-1] + np.timedelta64(1, 'M'), \n",
        "                                    end   = df.index[-1] + np.timedelta64(13, 'M'), \n",
        "                                    freq  = 'MS'))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTOXLVBWpK5_"
      },
      "source": [
        "\"\"\"точечный и интервальный прогноз>\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPZUIcmRpKIj",
        "outputId": "98e43e87-b318-44dc-960d-67f946bc5403"
      },
      "source": [
        "variance = np.sum(np.power(df['y'] - df['y^s'], 2)) / (n-1)\n",
        "sigma =np.power(variance,0.5)\n",
        "sigma"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09807964224129358"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0eSTy5OpNFH",
        "outputId": "631de531-67b2-4a60-d69f-6146c56e7c27"
      },
      "source": [
        "from scipy.stats import t\n",
        "tstud=t.interval(0.95, len(df)-1)\n",
        "list(tstud)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-2.0117405104757546, 2.0117405104757546]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmlS8ZVdpOQs",
        "outputId": "e9aa264d-c5cc-42cd-9de1-83b40fadfe89"
      },
      "source": [
        "lower=list(tstud)[0]*sigma\n",
        "lower"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.19731078954977932"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIzVXFFepQEw",
        "outputId": "d4ae42df-b26f-4f46-d360-4f7d766b06bc"
      },
      "source": [
        "upper=list(tstud)[1]*sigma\n",
        "upper\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19731078954977932"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89s5s6qKpSUy"
      },
      "source": [
        "df_fv['y-'] = df_fv['y'] + lower\n",
        "df_fv['y+'] = df_fv['y'] + upper"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "40Aj7yx8pUaR",
        "outputId": "f876a8c6-b579-4075-d13a-75eeccb53dfe"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (12, 7))\n",
        "\n",
        "plt.title('Доверительные интервалы прогноза с вероятностью 0.95')\n",
        "plt.xlabel(df_index)\n",
        "plt.ylabel(df_columns[0])\n",
        "\n",
        "ax.plot(df_fv['y-'], color = 'green')\n",
        "ax.plot(df_fv['y'],  color = 'orange')\n",
        "ax.plot(df_fv['y+'], color = 'green')\n",
        "\n",
        "ax.plot(df['y'], marker = 'o', color = (0, 0, 0, 0), markerfacecolor = 'blue')\n",
        "ax.plot(df['y^s'], color = 'orange')\n",
        "\n",
        "ax.xaxis.set_major_locator(mpl.dates.YearLocator())\n",
        "ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y'))\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kVFdfFx09HXY3Iy2yUrlxpVQt0A70A31a6+URy9cBXwaUtd5ntNZ7E7mvEEIIIYRwvn31+7j28WtRSrFz/U6m5E6xu0kplY6e68u11ktjBMcHgTVa6wuArwEPjeC+QgghhBDCwX79+q9Z9aNVnO47zaG2Q3zi959Aa213s1LK1rQQrfVLWusW69+tQJmd7RFCCCGEEGMX1EHu+es9XPer61gyfQm71u/i/nfez5NvPskD2x6wu3kplergWgPPKqV2KqXWD7PurcCfRnpfpdR6pdQOpdSOxsbGJDRZCCGEEEKM1snuk1z7+LX8+4v/zq2Vt/L8zc8zs2Amd3jv4H0L38ddz97FjmM77G5myqhUds0rpUq11keVUtOA54DbtdYvRFnvcuD7wGqtdfNI7htu+fLleseO8XuwhBBCCCGc7K3mt7j28Wt5q/ktvvuu7/K5iz+HUmpgeeB0gMofVJKpMtn1qV2uzb9WSu2Mlbac0p5rrfVR63cD8CSwIkrjLgQeBj4QCqwTva8QQgghhHCGp2ueZsUPV9DY2cifb/ozn1/x+UGBNUDxxGIe/9DjHD55mNueum1c5l+nLLhWSuUrpQpCfwNXAfsj1pkD/Bb4O631WyO5rxBCCCGEsJ/Wmm9u+Sbv3vRuKqZUsP2T21lbsTbm+qtmr+LrV36d37zxG76//fvpa2iapLIU33TgSeuMJQv4hdb6aaXUpwG01g8C9wIlwPet9UIl96LeN4VtFUIIIYQQI9TZ28ltT93GY/sf47rzr+PHH/gx+RPyh73fnavuxFfr485n72TV7FUsm7ksDa1Nj5TmXKeb5FwLIYQQQqTPNZuu4ZmaZ9h4xUb+afU/DUkDiae5s5mlP1hKTmYOO9fvpDC3MIUtTS7bcq6FEEIIIcT4FDgd4JmaZ7h79d3cfdndIwqsAUrySvjlh39JbWstn/zDJ8dN/rUE10IIIYQQYsRerHsRjeZd89816m1cMvsS7rvyPn71+q94cMeDSWydfSS4FkIIIYQQI+ar9ZGblcuK0rEVdLvrkru4Zv413PHMHew+vjtJrbOPBNdCCCGEEGLEfHU+VpWtIicrZ0zbyVAZ/Oxvf4Ynz8NHfv0RTnafTFIL7SHBtRBCCCGEGJHA6QB7T+yNW3JvJDx5Hh7/8OMcbDnI+j+sd3X+tQTXQgghhBBiREL51skKrgFWz1nN1y7/Gr987Zc8d+C5pG033SS4FkIIIYQQI1JVV5WUfOtI/+D9B7IysvDV+pK63XSS4FoIIYQQQoyIr9aHt8xLblZuUrc7MXsiS2csZeuRrUndbjpJcC2EEEIIIRLWcrqFPSf2sLZ8bUq27y31su3oNvqD/SnZfqpJcC2EEEIIIRL24qHk51uH85Z5OdV7itcaX0vJ9lNNgmshhBBCCJGwqtoqcjJzWFm2MiXb95Z5AVybGiLBtRBCCCGESJivzseq2auSnm8dck7ROXjyPBJcCyGEEEKI8a21q5Xdx3ezpnxNyvahlMJb5uWVo6+kbB+pJMG1EEIIIYRISCrqW0fjLfXyeuPrtHa1pnQ/qSDBtRBCCCGESIiv1kdOZs5AXnSqhLa//ej2lO4nFSS4FkIIIYQQCamqq0pJfetIF5dejEK5Mu9agmshhBBCCDGs1q5Wdp/YnfKUEIDJOZNZNG0RW49KcC2EEEIIIcahzYc2E9TBtATXACtLV7L1yFa01mnZX7JIcC2EEEIIIYblq/UxIXMCK0tTU986krfMS+B0gJpATVr2lywSXAshhBBCiGGF8q0nZk9My/7cOpmMBNdCCCGEECKutq42dh3fxdrytWnb53me8yiYUCDBtRBCCCGEGF/SnW8NkJmRyYrSFa4b1CjBtRBCCCGEiCuUb53q+taRvGVe9p7YS2dvZ1r3OxYSXAshhBBCiLiq6qpYWboybfnWId4yL/26n53HdqZ1v2MhwbUQQgghhIjpZPdJdh7fmdaUkJBQZRI35V1LcC2EEEIIIWKyI986ZGr+VOYVzXNV3rUE10IIIYQQIia78q1DvGVeXj78smsmk5HgWgghhBBCxOSr9bGydCV52Xm27N9b5uV4x3GOnDxiy/5HSoJrIYQQQggR1cnuk+w6vos15Wtsa4PbJpOR4FoIIYQQQkS15dAW+nX/2PKtezvg5Zthy8cg2D/iu184/UJys3JdE1xn2d0AIYQQQgjhTL5aH9kZ2ayavWp0G+g4AC9cC62vmv8Lz4fF/zKiTUzInMBFMy9yzaBG6bkWQgghhBBR+ep8rCwbZb71ib/A0xdD5xG4/Fko/xi8+hWorxrxprxlXnYe20lPf8/I25FmElwLIYQQQogh2rvb2XlsJ2vL147sjlrDm9+F56+GiTPh6u0w8//Aigdh0nx46QboahjRJr1lXrr7u9l7Yu/I2mIDCa6FEEIIIcQQWw6bfOs1FSMYzNjfBVs/DrvugNL3wVUvQ8E8syy7AFY/Ad0BeOnvQAcT3mxoUOMrR18ZyUOwhQTXQgghhBBiiIF867IE8607j8Kf18DBn8IFX4XLfmMC6nBFS2D59+DEs/D6NxJuS9nkMkoLSl0xqFEGNAohhBBCiCF8tT5WlK4gf0L+8Cs3vgwvfhD6OuCyJ2H2tbHXnfdJqH8e9t0DUy+DaZcl1J6VZStdEVxLz7UQQgghhBikvbudHcd2JFaCz/8j+MtayMozaSDxAmsApWDFD2DSPNhyPXQ1JtQmb6kXf4ufxlOJrW8XCa6FEEIIIcQgLx1+KbH61nv/BV65DaatMQMXpyxObAfZk63862Z4+aaE8q/dknctwbUQQgghhBjEV+sjKyMrfr51fxe88S2Y/SFY+0fIKR7ZToqWwkXfgeNPwxv/MezqF826iEyV6fjUEAmuhRBCCCHEIL66BPKtm16BYDfMvQkyRjmMb/6nYc5HYO8GaNgcd9W87DyWzFhydgfXSqlapdSrSqk9SqkdUZYrpdT3lFI1Sql9SqllYctuVkq9bf3cnMp2CiGEEEIIo6Ong+1Htw9f37rBB6iEByRGpRSs/CHkV1j1r5viru4t9bLt6Db6RzGNerqko+f6cq31Uq318ijLrgEWWD/rgf8HoJQqBr4CrARWAF9RShWloa1CCCGEEGe1hPOtG6pMab0JYwzRQvnXXQ2w9ea4+dfeMi/tPe280fTG2PaZQnanhXwA+Jk2tgJTlFIzgauB57TWAa11C/Ac8C47GyqEEEIIcTbYV78PgOWzovWLWvq7oOllmLY2OTstXgbL/hOO/RHe+HbM1UKDGp2cGpLq4FoDzyqldiql1kdZXgocDvv/iHVbrNuHUEqtV0rtUErtaGx0dmkWIYQQQlgO/xZe/6aZKls4Sk2ghuKJxRRNjNMj3bzNBNjT1yZvxws+C7M/DHvvhtZXo64yv3g+xROLz+rgerXWehkm/eNzSqm/SfYOtNYPaa2Xa62XT506NdmbF0IIIUQyBfth95fhxQ/Bni+D/4d2t0hE8Lf4mV88P/5K9T5AmUlgkkUpuPj/gu43PdhRV1F4y7xnb3CttT5q/W4AnsTkT4c7CswO+7/Mui3W7UIIIYRwq54WqHovvPFNWPAZmHEV7Px7aNlnd8tEGH/Az7yiefFXCuVbj7T83nByp0HBAmiKHTx7S7283vg6bV1tyd13kqQsuFZK5SulCkJ/A1cB+yNWewq4yaoa4gXatNbHgWeAq5RSRdZAxqus24QQQgjhRm1vwDMrof4vZna+i78Pl/zcDIbbfB30ttvdQgH09PdQ11YXP7ju74aml8zEMalQ4jXBdYyUIW+ZF41m+7Htqdn/GKWy53o6sFkptRfYBvyv1vpppdSnlVKfttb5I3AAqAF+CHwWQGsdAL4GbLd+/s26TQghhBBuc+QPJrDubYMr/grzrWFYudPgksegowa2f0byrx2grrWOoA7GTwsJ5VsnazBjJI8Xuk5A56Goi1eUrkChHJsaMsqK38PTWh8AlkS5/cGwvzXwuRj3fwR4JFXtE0IIIUSKaQ2vbYR995pqEJc9CfmzB68zfQ1c8K+w7x6YfjnMu9WetgrA5FsDzCuO03MdyreelvShdIbHVAShaSvklw9ZXJhbyHlTz3NscG13KT4hhBBCjEe9HSbdY989ULEO3vni0MA65Py7YcY7YcfnY1aJEOlRE6gBiJ8W0lAFUy5Mfr51yJQLIHPisHnXW49sRTvwaocE10IIIYRIro6D8NwlcORJqPwWrPoZZE2MvX5GJqx6FLKnwOaPmMBc2MIf8JOXnceMSTOirxDKt05mCb5IGdlQvDx+cF3mpfl080BPu5NIcC2EEEKI5Gl4EZ5eDqcOw5o/wnlfNCXWhjNxOlz6C2h/C7Z/VvKvbeJvMZVCVKxj1rwd+k+nbjBjiMcLLbtMMB+FkyeTkeBaCCGEEMmz/bMwYQq8azvMunpk951+OSz+CtT+HA78JCXNE/HVBGri51s3+MzvVOVbh3i8EOyBlj1RF58/9Xw8eR6Otx9PbTtGIWUDGoUQQghxlulqhLb9sOTrUDDMJCSxLNpgcnp3fA5KVsCURclto4gpqIMcaDnAexa8J/ZK9T4r37oktY0pCRvU6Fk5ZHFmRibHv3icrAznhbLScy2EEEKI5Gh4wfweSz5uRiZcsgmyJ5sBkX2nktI0Mbxj7cfo7u+O3XPd32PVt16b+sbkzYK82dAcO+3DiYE1SHAthBDCATZteoyKisVkZGRSUbGYTZses7tJYjQafJCVD8UXjW07E2eYAPvkm6aCiEiLYSuFBKx861QOZgzn8cYd1OhUElwLIYSw1aZNj7F+/Qbq6h5A6y7q6h5g/foNEmC7Ub0PPJeaag9jNeNKWHyPyb0+8NOxb08Myx8wlTdiTiBT7zO/p16WngaVeOFULZw+kZ79JYkE10IIIWy1YcNGOjt/BFwOZAOX09n5IzZs2Ghzy8SIhPKtk9mrufhek4Kw/bNwsjp52xVR+Vv8ZGVkMbswRj3yBp+pQZ3rSU+DQpPJNL+Snv0liQTXQgghbHXo0BvAagBysrusW1dbtwvXCOVbJzMfNyPTlOcLdsPBR5O3XRFVTaCGuVPmRs9l7u+BxjTlW4cUVZqrIE0SXAshhBAJmzPnPLIzn+f7H/8MrQ9NYc15PmAzc+acZ3fTxEg0VNGnc5i/8rbk5s5PnGmqU8QZ2CaSw9/ijz2YMbAD+jvTl28NZuKhKUtcd+ydOcxSCCHEWePbGz/PjJr3c+mCbpraS3jscx/ikq9N4t83fsPupokRaKn+HbteV/gP/jewmrq6zaxffysA69bdMLaNe7ym5zrYb3qzRdJprfEH/KwqWxV9hVB966kprm8dyeOFAz921bGXnmshhOtJpQkXC+zkQ/kbWTlfc/vjZVx5X4CiSS289O1C1n3so3a3TiSqq4kidZi/7F9HIrnzI37Plnihr91UDxEp0Xy6mbbutviDGQsXpy/fOqTEa8oxtr2W3v2OgQTXQghXk0oTLlb7C3huNZBB1jVbeeCpw+ytC5J76Q+YqV6F175udwtFohpNvrXvjZsiFgzNnR/Ve3ZgYJu70gPcJFQpJGoZvmAvNG5Jb0pIiAuPvQTXQghXk0oTLhTsh93/CC+tMzPwvWs7FFeeWT7vNii/AV69F+qr7GunSFy9j84exY4DXRELhubOj+o9W7AAJhS5suaxW/hbrOA6Ws51s5Vvnc7BjCGTzoEcj6uOvQTXQghXC680cYZUmnCsnhaoeg+88S1Y8Fm44s+QO23wOkrBih/ApHnw0sdMiTfhbA1VtGUvJjvn08DzQC/wPHl5t7Jx44ZBq47qPauUSQ9wUYDlNjWBGhSKc4rOGbowlG89Lc351uDKYy/BtRDC1Uyv2OaIW6XShCO1vQ5Pr4D6v8KKh+Di/xt7spHsAlj9K+huhpf/DnQwvW0VietuhtZ9zFzyUR56aCPl5bejVC7l5bfz0EMbhwxmHPV71uM1ebe9J5PafGH4W/yUTi4lNyt36MKBfOupaW8XYI79yTegp9We/Y+QBNdCCFfbuHEDeXm3MlxvmbDZkafgmZVmUNqVz8P8Tw5/n6IlcNF/wfFn4PX7U99GMTqh+tbT17Ju3Q3U1u4nGOyntnZ/1Coho37PeryAhphcQGQAACAASURBVObtSX4AAkzOdcx866YtMG1N+hsVMpB3vc2+NoyABNdCiLRKdmWPdetuSKi3TNjo0K/hhQ/A5HfAu3bA1EsTv+/89TDno7DvHmh4MXVtFKNX74PMiVB8cUKrj/o9W7LC/HbZbH1uUROoiV4pJLDTVOsYw2BGrTU/2/szHt718Og2UHIxoFyTGiJ1roUQaROqEmAGMyWvDu66dTdIMO1kB38O+RXwzhfMpBAjoRSsfMh8wW+5Aa7Zk/5SYCK+Bp85YcqckPBdRvWenTAFJp/nmgDLTTp6Oqg/VR+957reZ36PMt+6u6+bz//x8zy82wTWMyfN5D0L3zOyjWRPhsJFrjn20nMthEgbqexxFtJBaHwRZlw58sA6JHsyrH4Cuhvh5Zsk/9pJugPQ+mr6qkh4rIFtWqdnf2eJgTJ80SqFNPhMYBs58DgBJzpOcMXPruDh3Q9z9+q7WTpjKTf97iYOtx0eeSM9XlOOzwXHXoJrIUTaSGWPs1DrPlMhZKzBV3ElLPsOHP+TqTQinKHhBUCnLx/X4zUnWacOpmd/Z4lQGb4haSHBXmjcPKr37/aj21n+0HL2nNjDEx9+gvuuvI8nPvwEPf09XP+b6+nt7x3ZBj1e81nS/vaI25JuElwLV5GZ+NxtpFUC5HiPAwOXlJMQfC34DMy5Dvb+s5nQQtivwWfyrUsSy7cesxJrYJtL0gPcIuYEMgP51iN7//5s78+47MeXkZ2ZzUufeInrFl0HwIKSBfzwfT/kpcMvcc/z94yskS469hJcC9eQmfjcbyRVAuR4jxMNPlOvOn/22LelFKz4IeSXw5brTQk4Ya96H3gugcyc9OyvcBFk5bsiwHKTmkANJRNLKMwtHLygwZrEKcGT475gH3c+cyc3/+5mLpl9Cds/uZ0lM5YMWuf6xdfzqYs+xf1b7uePb/8x8UYWnmdSxFwwU6ME18I1JF/X/UZSJUCO9ziggyZtIJlTJk8oNPnXXQ2w5+7kbVeMXHfApP2keErsQVewzlnCid5yCa6TzN/ij14ppN4HhecnlG8dOB3gmk3X8J2t3+H2FbfzzI3P4MmLPvj4O1d/hwunX8hNT97EkZNHEmukyjAVY1xw7CW4Fq4h+brjQyJ1cMEc74kTlnHnu79NWXFo8Iscb1dpfdXKt05yPm7xRVD6XjMZjbBP44ukOt862hWsR/90lGDzLug7nbL9nm38Lf6hgxlHkG+9v2E/F//wYl6oe4FH3v8I37vme2RnxpggCpiYPZEnPvwEXX1d3PCbG+gL9iXW0BKvOaHrO5XY+jaR4Fq4hszEd3bxXjCPzfcu59vr7uJ3d17LhKxu5Hi7TDLzrSOVeKHDL1Oj26neB5m5Z+pPp0C0K1gvvnkXGaofWnanbL9nk57+Hg61HYqSb70L+jqGvTLxh+o/4H3YS2dvJ1W3VPHxyo8ntN9zPefyg/f+gM2HNnPv8/cm1liPF3S/yQV3MAmuhWvITHxnkYYX+OuX6pk33c/Xn7qBi+bu4lsfu16Ot9s0+GDSOZA/J/nbHpixTSYUsU2DL+X51tGuWL5Sc7P5wwXpAW5Q21pLUAeHpoWE8q2nxq5vHdRBPvHUJ5hfPJ8dn9yBt8w7on2vu3Adt1Xextc3f51nap4Z/g4lK81vhx97Ca6F7RKtCCEz8Z0FtIa3/x/85UpyC2dSNfE/+MHWfXznT3D71b/jTw99QI63W+ig+XJOVf3j4otAZTr+S3bc6g5Ay96U17eOdsWyvq2Gw4FsVwxsc4OYlULqfWbSnonTY953f8N+mjqbuMN7B6WTS0e1/+9d8z0umHYBNz55I0dPHo2/cq4HJs13/Ptegmthq5FWhEg0X1e4UH8PbPsUbP8szLwarnqF96/7IrW1+7njJ91QsoK/mfAIdBywu6UiEaF861QNdsvKgylLHP8lO26F8q1TPJgx1hXL/qLlcuyTpCZQA0RMIBPsM8d4mOPrq/UBsKZi9KlfE7Mn8sR1T3C69zQf++3Hhs+/dsFkMhJcC1tJRQgBwOkT8JfLwf9DWPTP8De/N1UhQjInwKW/BDJg80ehv9u2pooEjbCE16h4vNC8DYL9qduHiK6+KuX51hD7imXFRR+FzsPQOUxPpxiWv8VPfnY+0/PDeqhD+dbDXJmoqquiYkoFFVMqxtSGd3jewYPvfZAX6l7gq76vxl+5ZCWcPg6dCVYZsYEE18JWUgFE0LwDnl4OLXtMAL1kI2RkDl1vUgV4fwyBHbDny2lvphiheh/kz01NvnWIxwt97XBSPi/SrsEHnlVpqW8d9Yql5NwnTahSiFLqzI2B7eb31Eti3i+og1TVVrGmPDkn0DdeeCOfWPoJ7nvxPp7zPxd7xYFj79wrFxJcC1tJBZCz3MGfw3OrISMLrtoC5R+Jv/7sa+Hcv4fq/4LDT6anjWLkQvnWKU4ZcNOMbeNKT4s5GU5xvnVcRUshY4Ic+ySoCdQMzbdu90NmHkyMnUf9WsNrNJ9uZm3F2qS15YF3P8D5U89n3W/XUd9RH32lKReaqyYOPvYSXAtbSQWQs5QOwq674OWbTO/X1dvNl2Uiln4TipfD1k9AR21KmylGqXU/9ARSH3wVzIcJxY7uwRqXGtKTbx1XZg4ULXN0gOUGQR3kYMvBoZVCOmpMpZ/w3uwIoXzrZAbXedl5PPrBR2nsbOS3b/w2+kqZE8yAZgcfewmuha2kAshZ6tjT8Oa3YcFn4IpnIXdq4vfNnACrfwlo2PJRMxBSOEuDz/yensJ8azBf/B6vo79kx6U01LdOiMdr0sSCvfa2w8WOnjxKd3/30J7rDr85eY3DV+ejvLB8zPnWkZZMX8L0/Om8cjROyk+J19S6dujnvwTXwnZSAeQsVP8X8+W87D8hI/YsXjFNOge8j5jBbHv+KfntE2PTUAX5FZBfnvp9lXih7XXoaUv9voTRUGWe98xce9vh8UL/aVOZRoxK1EohOmiqMk2aF+Nepsf7hboXktprHaKUYmXZSrYeiXPS7PFCsBta9yZ9/8kgwbUQIv3qfWP/cp79QVh4O1R/B478PmlNE2OUrnzrEI8X0GcGYInU6mk1MyPamRIS4pGc+7Hyt5ga14PSQk4fg/4uKIgdXL/e+DpNnU1JG8wYyVvqpbq5msDpQPQVHH7sJbgW41KiE9MIGyTzy7nyP0zu3cu3wKm6sW9PjF3ba9DdnL7BbiUrAOXYL9lxJ5RvbedgxpC8OZA7Q479GPgDfrIzspk9efaZG9tNwM2k2Gkhqci3Dhea6XHb0W3RV8grM4MtHXrsJbgW485IJ6YRaZbML+fMHKv+dRA2Xy+5l05Q7zO/U1nfOtyEQig8z7FfsuNOgw8ycsCz0u6WnMm5lwGto1bTUkPFlAoyw8ufdljBdZyea1+tjzmFc5Kebx2yfNZyMlTG8KkhDj32KQ+ulVKZSqndSqn/ibLsO0qpPdbPW0qp1rBl/WHLnkp1O8X4IRPTOFyyv5wL5sHKH5kP2b1SZcZ2DT6Tbz2pAkjTVaQS58/YNm7U+6z61jbnW4d4vND+trlaIkbMH/APrRTSXgMqy1wZiEJrTVVdFWsr1g6ujZ1EBTkFLJ62ePjguuMAdDWkpA1jkY6e678Holb411rfobVeqrVeCjwAhNddOR1aprV+fxraKdximC9QmZjG4Rqqkv/lPOfDMPcmqP6eY0ePnxV0EBpeGOi1TttVJI/XBFehHjeRGj2t0LonfVclEhGqdd4cI31AxKS1NhPIRKsUkl9h5h+IIpRvvbZ8bUrb5y318srRVwjqYPQVBurcO28ioZQG10qpMuA9wMMJrH4DINftRXwHfw5PzoDDMepfIhPTOFoo3zoVX86l73f06PGzQtvr0N00kE+ftqtIHud+yY4rjZvNCZQTBjOGFF8EKkPSgkahqbOJk90nB1cKAasMX/yUEIA1Fak9yfKWeWntauWt5reir1C8zPSwOzA1JNU9198FvgTEOO0wlFLlwFzgr2E35yqldiiltiqlro1z3/XWejsaGxuT0mjhQME+2HmnmXSkp80MYGuP3kslE9M4WCq/nB0+evysMJBvvRZI41WkyedD1iRHfsmOK/U+K6XLa3dLzsieBIUXyPt+FKJWCtHapIXEKcPnq/Mxe/Js5k6Zm9L2hQY1xkwNycqD/7MZzndeOdaUBddKqfcCDVrrnQmsfj3wa611f9ht5Vrr5cDHgO8qpaIeaa31Q1rr5Vrr5VOnjmAiCuEe3QHwXWNKri28Hd6zH1QmbP4I9HcPWV0mpnGwVH4555WaEeTyJWufBp+pbW3lW6ftKlJGJpRcLMc+1Rp85r3rlHzrEI8Xml8xJ+4iYf6ACa4HpYX0BKC3LeYEMlprqmpTm28dcq7nXApzCofJu14J2QUpbcdopLLn+lLg/UqpWuBx4Aql1KMx1r2eiJQQrfVR6/cBwAdUpqylwrla98MzF5s8zpWPwPLvmTf9qp9Ayy7Y/Y9R7yYT0zhUqr+cS5w7enzc09rk04dVgUnrVaQSL7Tsgb7Tyd+2MFcMW3Y7owRfJI/XBIQnq+1uiavUBGpQKOYWhfVAD5Thi95z/UbTGzR2NqasBF+4DJUx/GQyDpWy4FprfbfWukxrXYEJnv+qtb4xcj2l1DuAIuDlsNuKlFI51t8eTKD+eqraKhzq8G/hWWsGrndWwbyPn1lW9gE49x/grQfg0G/sa6NIXDq+nD0rHTt6fNwL5VuH5dOn6ipS1AokHi/oPnPSLZIv1DM87TK7WzJUiaSEjYa/xU/Z5DJys8I6OzrMjI2xgutU17eO5C318mrDq3T0dKRlf8mS9jrXSql/U0qFV/+4Hnhc60ElIM4Ddiil9mK6PL6htZbg+myhg7DvXnjxQ1C4GK7eET2NYOn9UHwxvHKrCaiEsw3kW6dwEIyDR4+Pew0+8zsinz7ZV5FiVSD5TdUxs4IEWKnR/rb5XXi+ve2IZvJCyJ4iV61GyN/iHzqYcaDn+pyo9/HV+iibXJbyfOsQb5mXoA6y49iOtOwvWdISXGutfVrr91p/36u1fips2Ve11v8Usf5LWusLtNZLrN8/Skc7hQP0noQX/hb2fw3OuQXe6YO8WdHXzZwAq39p/t58vZRgc7oGH2RMOBMAp4KDR4+Pe/U+Uxc3vyKlu4lVgeSLG/4b8udKcJ0q7TWQmWdmRHQalWGuWsmxH5GaQA3ziyJyqzv8ZubDrIlD1k9HfetIK0pXAHEGNTqUzNAonOPk2/CMF479L1z0XybHerjc3ElzwftjCGyHPV9KTzvF6NT7zBWIKB/aSZOVB0VL5Es23bQ2J0/T15pZ81IobgUSB8/Y5nqh8mxpCqpGrMQLbfuht93ulrhCe3c7DacaopThq4lZhu/NpjdpONWQ8vrW4UrySlhYslCCayFGpe1NeGYFdNXD5c/CuV9I/EN89t/Cwi9A9X/B4d+ltp1idHraTC5sOgZDlXjNhBLB/uHXFckxkG+9NuW7iluBxOOFziPmRyRXhz9ueTbbebwm7SzgrvQBuxxoMamUQyaQaffDpOiVQtKdbx3iLfOy9chWtItmYJXgWjhD3WPQdxKu3gYzrhj5/Su/aSYT2Ppx6KhNevPEGDVuSd/kEx4v9HXASRmmkTYNVeZ3Go5v3AokknOfGjpoBV0ODq5LTPqAXLVKTE3ADFwcVOO6twO6TsTsufbVmXzrc4qi52OnirfUS/2peura6tK637GQ4Fo4Q4MPipbFnRUqrswcWP0EEIQtH5X8a6dJR751iEwmk34NPsibnfJ8aximAknRUlNHXVJDkuv0MTP7aYzax46QUwyTz5X3fYJCE8gMSgsJFQaI0nOttcZX62NN+Zq05VuHDDuZjANJcC3s13fafCCOtddr0jkmT7t5G+y9OylNE0lS74OSlanNtw6ZNA9ySuRLNl20Nsd32tq05ePGrECSOcEMapVjn1zD1D52jFCdexelD9ilJlCDJ8/D5JzJZ27ssI5zlE6ugXzrNKeEAFww/QImZk3klSPuuSIlwbWwX/NWCPYkJ19zzodgwefgzf+EI08Nv75Ivd6T0LIzPSkhYAI8mUwmfU6+Ad2N6Tu+wynxmrzbYK/dLRk/QrWPR3tlMV08XlPj/lSt3S1xPH+Lf3BKCJiKMBD1JKqqzqR+2RFcZ2VksXzWcrYedc9nugTXwn71PlNKaWpkBYBRWvYtk2Ky9RY45Z4crXGrwapvnc6Z3TxeM8iupzV9+zxb1fvMb6cE1x4v9HdB6z67WzJ+tPtNicu8OXa3JD5JCUuYP+AfOpixw2+u+k2YMmR9X62P0oLSofdJE2+Zl13Hd9Hd123L/kdKgmthv4YqEwxPKEzO9jJzTf3rYB9s+1RytilGr6HK5FtHmwgoVUL7at6evn2erRqqrHzr9EwqMSwJsJKvw2/y6TOy7G5JfIWLTS1uOfZxdfd1c6jtUPTgOkqvdSjfOp31rSN5y7z09Pew58QeW/Y/UhJcC3v1d5kPwmlJnrWvYD4s+DTUPw/97jjTHbcafGYkf1Ze+vZZfDGg5Es21UL1raetcU7947zZMHGmHPtkao9d+9hRMrLMlcvZH7S7JY5W21qLRkdPC4kSXFc3V1N/qp415SmcXXcYbhvUKMG1sFfTVjMKPRWXlD2rTC53y+7kb1skpvckBHamNyUEzFWQwvOH5F1v2vQYFRWLycjIpKJiMZs2PZbedo03J980Oa5OSQmBMzn3Elwnh9ZWj6aDK4WEW/AZmG5fEOgGUSuF9PdA56GoFWGqau3Ltw6ZVTCL2ZNnuybvWoJrYa9k51uHK1lpfsuXrH0at4Dutyf48lgBllU5YNOmx1i/fgN1dQ+gdRd1dQ+wfv0GCbDHosFnfqf75Gk4Hq8ZhNfVZHdL3K8nAL1t7ui5FgkJ1bgelBZyqs6MjYnSc+2r8zGrYNbQnu40C00m4wYSXAt7NfigqDLqAIoxy5tlBuBIcG2feh9kZJurCOlW4jWBgTUCfsOGjXR2/gi4HMgGLqez80ds2LAx/W0bL+p9kFdmymA6yUDOvXtKdzlWnAoSwp38AT+TJkxiWv60Mzd2RD/OTsi3DvGWealtreVExwlb25EICa6FfQbyrdembh8eKclmq4Yqq751GvOtQwYCLHP8Dx16A4i8QrLaul2MmNbm+KaxvnXCii8ClSnBdTIM1D52SVqIGJa/xVQKGRQst0c/zm81v8WJjhOsLV+bvgbGEMq7dkO9awmuhX2aXjH51skezBjO4zWXu04fT90+RHS97abecCqPbzyTz4OsgoGpsOfMOQ/YHLHSZut2MWJd9eYnNO20k2Tlw5QL5KpVMoR6rp1SDUaMWU2gZnC+NZiTqKx8yJ0+6GZfrQ+wN986pHJGJdkZ2a5IDZHgWtinwQcomHZZ6vYRmm67yflnum4XOVjwr09stC/fGiAj0wR+Vs/1xo0byMu7FXge6AWeJy/vVjZu3GBP+9zO6T2aJV7Tc62DdrfE3Tr8JvUnHbOripTrD/ZzsPUg84uiVQo5Z8hVqKq6KmZOmml7vjXAxOyJLJ2x1BWDGiW4Fvap96Uu3zqkuNLk/Mrl4ZSKNlhw99MP0q8z7cm3DvF4oWUv9HWybt0NPPTQRsrLb0epXMrLb+ehhzaemTpbjIzTc3E9XlOt5uSbdrfE3WLUPhbudLT9KD39PdF7riMqwjgp3zrEW+Zl+9Ht9AX77G5KXBJcC3v0d0HTy6nv1czMhSlL5fJwikUbLHjpglnsqssxlxrtUrISdB8EdgGwbt0N1NbuJxjsp7Z2vwTWY/Dqy7+nPwg5Je9wZllDmUwmOWLUPhbuFLVSiA5Cx4EhFWHeDrzN8Y7jjkgJCfGWeTnVe4rXGl6zuylxSXAt7NG8zcq3Xpv6fXm8ENhuZmwUKRE5WDA/p4Plc9/i2T2d9jUKwGOVY5RBrUm1adNjvL7tWQ41zaCnr9uZZQ0LFsCEIgmux6K3w+TVOzX1Z4waTzXS099jdzPSyh8w6VyD0jw6j5rv44iTKCflW4e4ZTIZCa5FysSdsKPeR8rzrUM8Xug7BW3OPtN1s8jBgpcu3EJWZj9vBMrtaxRA7jSTRygBVlJt2LCRipLZ+BsW4diyhirDXLmQE6vR6zhgfo/Dnuuna55m2remsePYDrubklb+Fj/ZGdmUTS47c2OM8ROhfOsFxQvS2ML45k6Zy9S8qY7Pu5bgWqTEsBN2NPigaGlq861D5PJwykUOFlx7/k/p7YP3fvwrdjdNZutLgUOH3mDe9EZq6sO/jB1Y1tDjhdb9pnKNGLlQ7eNxOIHMoqmLANh9/OyawbcmUMPcorlkZmSeuTEUXIedRIXyrddUrHFMvjWAUsoVk8lIcC1SIu6EHaF863TN6pY/F3KmSg9WCkUOFrx66e9ozVzI9es+bnfTTIB1+ih0HrG7JePG4oUL8RQ0468PD7ocWNawxAtoaN5ud0vcqX1o0DVelE0uo2RiCbtPnF3Btb/FP7TyR3sNqCzImz1wU02ghmPtxxxR3zqSt8zLm01v0nK6xe6mxCTBtUiJuBN2NG8zAXa6SrQpdWYqbJEyA4MFu9tYVt7D1MUftrtJhly5SLr777kRAH99J44ua+ixanDLifXodPghpyQ9VxjTTCnFspnLzqrgWmtNTaBmaBm+Dj9MmgsZWQM3bTm8BYC/Kf+bdDYxIaG8621Ht9ncktgkuBYpEXfCjvoq0pZvHeLxmpJcPc490x03GreY+tbpujIxnClLICNHguskuma1ycHszPy5s8saTiiCye+QYz9aVqWQuONnHGSk7aycUcn+hv1nzaDGY+3H6Ojp4FzPuYMXtA8tt/h289tkZWSxoMQ5+dYhF8+6GIVydGqIBNciJeJO2NHgg6Il5osvXQYmk3Hume640VBlLjFOvcTulhiZE8x02NJ7mTxWjubTL+5xflnD0FUrre1uift0+DnYlBV//IxDDDvOJ4rKmZX09PfweuPraWypfaqbqwE4tyQsuNba5NZHBNf+Fj/lheVkhfVmO0VBTgGLpi3ilaPOnb9CgmuREjEn7Lj+g9D0Uvp7NUsuBpQEWOnQ4DPPt531rSN5vBDYCWdJD1XKtdeYaZKzJ9ndkuGVeKG7EU4dtLsl7tLfA52HeOr5t2OPn3GQuON8YqicUQmcPYMaq5us4Dq857q72Uy2FFEpJOoU6Q7iLTWDGrVDT5oluBYpE3XCjnTnW4dkF8CUxXJ5ONV6O8zgMaekhIR4vOZ117rP7paMDx1+99Q+lpz70TlVCzrI7rebiDl+xkHijvOJYUHJAvKz88+avOvq5mrys/MpLSg9c2OUSiFgDXyMzM12EG+Zl5auFt4OvG13U6KS4FqkV6i+9dQ05luHlHjNNOg6mP59ny2aXjIzIqb75Gk4JRJgJZWbpsQuXGSuosixHxkr6GrXFcQcP+Mgccf5xJChMlgyY8lZFVwvLFk4uLRe+9Byi4HTAVq7Wp3dc+3wyWQkuBbp1VBl8q1zitO/b4/XDGhsd+aZ7rhQ7zP51h6H5FuH5JXBxFmSFpQMfadNWUO3BNcZWVB8sQTXI2WV4fvYp/4x9vgZB4k7zieOyhmV7Dmxh+BZ0OlS3VQ9dDBjhx9QZrItS9Qp0h3mvKnnMTlnsgTXQtDfbeVbr7Fn/3J5OPWatpjBg07LxZVyjMkTyl12S1oImGPfstucGIjEdNRAVj4f+tino4+fcdgA1pjjfIZpZ+WMSjp6OgamBR+vuvq6qG2tZWHxwsELOvyQVwqZuQM3RZ0i3WEyVAYrSldIcC0Ezduh/7R9+biT3wHZkyXASqWT1Sa33YlKvOaLpKvR7pa4W+gyslt6rgE8q0y6Ussuu1viHqHybEpFHz/jQKNpZ+VMa1DjOE8NqQnUoNFRyvDVwKTBQbS/xQTX5xSdg5N5S73sq9/HqZ5TdjdlCAmuRfo0+DD1rW0qSq8yoGSlpAakSm8HdNU7N+gKXblodm75JlcIDYByU891yUrzW06sE+emvPoxWDR1EdkZ2eO+YshApZCSKGkhEdPb1wRqmFUwi4nZE9PVvFHxlnnp1/3sPL7T7qYMIcG1SJ96H0y50J586xCP11SM6HPema7rxRh17hjFF4HKlABrrNr9kF0IE2x8H4/UxOmQP1eOfaJ0EDoODAm6xqOcrBwWTVs07nuu32p+C4CFJWFpITE6RKJOke5AK8vMSbMTU0MkuBbp0d9j8q3triJR4jVfHM077G3HeOT0Hs2sPDNbowRYYxOacCK84oAbeLxy1SpRnUch2D0kXWC8qpxRya7juxxbMzkZqpurmVUwi4KcgjM3xvjM9gf8jh7MGOLJ83D/O+9nbcVau5syhATXIj0CoXxrmwYzhnisy8PyJZt87Q7vuQYrwNoGwX67W+Je7S6qcR3O4zVVTjqP2N0S5xsIuhz8Xk6iyhmVNHY2cqz9mN1NSZnq5uroKSEw6DP7VM8pjnccd0VwDfClS7/EitIVdjdjCAmuRXrU+8xvu/KtQ3JKoGCB9F6mQkeNeX4nFNrdktg8Xuhrh5Nv2t0Sdwr2mclFnHwCFctArXPJuR+WGwetjsF4H9SotTZl+CKD6yjH+UDLAcDZlULcQIJrkR4NPivfusTulpjBTU1bYRxfArRFu9/5l5FL5MrFmHQeMlU33NijWbQUMnLk2Ceiww8Z2ZA32+6WpMWS6UtQqHE7qLGps4mWrpboNa5zPIM6RAZqXDt4Ahk3kOBapF5/DzRucc6U2B4vdJ0wgYJIHjdUFyhYABOK5MrFaA2k/jj8JCqazAlQvEyOfSI6/JBfYSbgOQsU5BQwv3j+uO25rm6OUSmkfehndqgMn1vSQpxKgmuReoEdJt/a7sGMIR65PJx0/T3mZMXpPZpKmfQACbBGx+25uCVe83kU7LW7Jc7WXuP8E+Ukq5xZOX6D61AZviE910OPsz/gp3hiMUUTi9LVfbhYhAAAIABJREFUvHFJgmuReg0+83vqZbY2Y8CUC81sVBJgJc+pWlOFxQ09mh4vtL0GvSftbon7tNeY1IqJs+xuSUI2bXqMiorFZGRkUlGxmBff7If+LlOOU0SntVX72AXv5SSqnFFJbWstLadb7G5K0lU3V5OTmUN5YfmZG/t7oPPwkONc01IjvdZJIMG1SL16H0y5AHI9drfEyMiG4uWSe5lMburR9HgBbWYMFSMTmnBCOf+rY9Omx1i/fgN1dQ+gdRd1dQ9w65efNAsbX7a3cU7W3WxOPM+ynutlM5cBsOfEHptbknzVzdXML55PZkbmmRsHOkSG9lzLYMaxS/knpFIqUym1Wyn1P1GW3aKUalRK7bF+bgtbdrNS6m3r5+ZUt1OkiNPyrUM8Xgjsgv5uu1syPripukCJVbapSQKsEXNDXr1lw4aNdHb+CLgcyAYu5+2jP+FEW5acWMfT4aL3chJVzjAVQ3Yd32VzS5Kvuql68OQxcOYzO6xDpKe/h7q2Oum5ToJ0dD/8PfBGnOW/1FovtX4eBlBKFQNfAVYCK4CvKKUkAciNAjugv9M5+dYhJV4zSULLXrtbMj50+CErH3Kn292S4U2YAoXnS1rQSGkddQCUUx069AawOuLWy9hS3SfHPp52h08GlSJT86dSWlA67vKue/t78bf449S4PnOc61rrCOqgVApJgpQG10qpMuA9wMMjvOvVwHNa64DWugV4DnhXstsn0qChyvyeanN960ihQY3Sg5UcoaDLLbP2lViz9Uk5xsR1nTAnyi4JuubMOQ/YHHHrZqqbp5vAoqvRjmY5X4cfUDBprt0tSbvxOKjxYOtB+oJ90cvwZeVD7rSBm0KVQiQtZOxS3XP9XeBLQDDOOh9SSu1TSv1aKRUqqlkKHA5b54h12xBKqfVKqR1KqR2NjfJh6Tj1Pihc7Jx865C8Usgrkx6sZIky6tzRPF6TWxrqvRHDc8MMnGE2btxAXt6twPNAL/A8eXm3svxdnzIrNEu1oKjaa8xnY2au3S1Ju8oZlbzZ9CadvZ12NyVpBiqFRJtAJqJDxB+QMnzJkrLgWin1XqBBa70zzmp/ACq01hdieqd/OtL9aK0f0lov11ovnzp16ihbK1KmZeeZXmKnKfHSXvvnQdUENm16zO5WuY8OQsdB1/RoAmHlGOXkKmEuy8Vdt+4GHnpoI+Xlt6NULuXlt/PQQxu56qNfBpUpxz4WF+XVJ1vljEqCOsir9a/a3ZSkeav5LSBaGb6hFWFqAjXkZecxY9KMdDVv3Eplz/WlwPuVUrXA48AVSqlHw1fQWjdrrUMjyh4GLrL+PgqETw1VZt0m3KSnzfQOFiwcfl0b7DyUQ4Fq5HTLvw5UE1i/foME2CPVedTkr7vpC3ny+ZA1SQKskWj3m6A0v3z4dR1i3bobqK3dTzDYT23tftatuwGy8mDKEjn2sXTUuKPqTwqMx2nQq5ur8eR5KJ5YfObGYD90HIg6gcy8onkot6T3OVjKgmut9d1a6zKtdQVwPfBXrfWN4esopWaG/ft+zgx8fAa4SilVZA1kvMq6TbiJw8uz3feDlwBYOT+bUDWBzs4fsWHDRlvb5TqhHk039VxnZJqqIZJzn7gOP+TNMTMdup3HC83bTJAhzuhth64Gd9SrT4HywnKKcovG1TTo1c3VQ1NCTh+FYE/04FoGMyZF2ouVKqX+TSn1fuvfLyilXlNK7QW+ANwCoLUOAF8Dtls//2bdJtzE4eXZ/vRKLb19WXjnhwdYq60qAyJhLsvFHeDxmmoxfeMnvzKl2sdRj6bHC33tcFLe64N0HDC/x8txHiGlFEtnLB1fPddNUYLrjqEVYYI6iD/gl3zrJElLcK219mmt32v9fa/W+inr77u11ou01ku01pdrrd8Mu88jWuv51s+P09FOkWQdzg66ps08nz118yKC681WlQGRsA6/mZgnb/bw6zpJiRd0n6l3LobX4R8/PZolknMflcM7RNJh2cxl7KvfR29/r91NGbO2rjbqT9UPzbeO0iFyrP0Y3f3dUikkSZw/zZZwrw6/qXucPcnulkS1ceMGdtQdZ8W8l8lQXYSqCWzcuMHuprlLew3kzzWpFm7iWWl+S2rI8HpaoCcwfno0C+ZDTolMJBTJ4R0i6VA5o5Lu/m7ebHpz+JUdrro5RqWQjpohHSJSKSS5JLgWqdPu7PJs69bdwIWX38ik3C4Wz84bqCawbt0NdjfNXdxaXSB3Gkw6R3ovE+HW1J9YlDpT61yc0eGHHA9MKLS7JbYZT4MaB8rwReu5jugQqQmYqxaSc50cElyL1IlS6sdpLn3/nQDs/cuDZ6oJiMRpbR1nl34ge1aZ3kuZTCa+KDmarufxQtvr0NNqd0ucw+EdIulwbsm5TMyaOC4GNVY3V5OpMjmn6JzBC6J0iPhb/GRlZDGncE4aWzh+SXAtUqPvNHQecf4H9aRzTE+N9F6OTncT9J50by5uiRdOHzOvVRHbQLrAOfHXc5OBWVq329sOJ3FBh0iqZWZkcuH0C8dHz3VzNecUncOE8Ao/WkcdnOxv8VMxpYKsjKw0t3J8kuBapMapg+a304Pr0OVhCa5Hx+HlFoc1EGDJ8Y+rvQZyZ5jpkseL4osBJe/9kP5u6Dzs/M/sNKicUcmeE3vQLr+iVd1UzcKSiHkmuptMpZxJQyeQkXzr5JHgWqRGu4suI3u8piSXXB4eObfn4k650EzzLAFWfOOxR3NCIRSeLydWIadqzWyrbn0vJ1HlzErauts42HrQ7qaMWlAHeTvwdpwyfGeOs9Yaf8AvlUKSSIJrkRpuGnU+0Hu5zd52uFFHDaBg0ly7WzI6mROg+CIJrofT7tJBq8PxWFetXN5DmRRu6hBJscoZ1qBGF+ddH2o7RFdfV5TBjEPLLQZOB2jrbpOe6ySS4FqkRnsNZE825a6crkQuD49aux/yykzvr1uVeCGwE/p77G6JM/WdNjO6jcegq8RrSgyGAo6zmZs6RJKh8WX44xJo3T9k0QXTLyBTZbLruHtr4L/V/BYQrQyfn8gOEakUknwSXIvUCE04oZTdLRle9mQoXCTB9Wh0jIPqAh4vBLuhda/dLXGm0Kx9bj/O0UjO/RntNSanPnea3S1JjwmF0Lov6iRSuVm5nDf1PFcPaoxbhi+iQ8TfYk6sJC0keSS4FqnhtvJsHqvmrVweHpnxkIvrkdn64hrPPZqTz4OsAjn24K4OkWQoOBcyJ0JL9AB62cxl7g6um6uZnDOZ6fnTBy/oqBnymR2aQGbuFJem9zmQBNci+YJ90HHQXV/GHq+Zha79bbtb4h697dDV4K7jHE1eGUwslQArllDKhNtPoqLJyDQzdcpMje7rEBmrjEwzoDlGcF05o5ITHSc40XEizQ1Ljurmas4tORcVebIUpZZ5TUsNpQWlTMyemMYWjm8SXIvk6zwMus9dX8Yl0ns5Ym4vwxfOI7P1xdThh+wpkFNsd0tSo8Rr0gP6TtndEvsE+036j9tPlEeqeJkJrnVwyCK3D2qsbqoemhLS0wLdjTB58O1SKST5JLgWyefGy8iF55ncawmwEjdQhm8cfCh7vCa46GqwuyXOE2XCiXHF4wXdbwa1nq1OH4Vgj7s6RJKhqNJMgtUxtOTe0hlLAXdOg36q5xSHTx4eOpjxpMnD/v/s3Xl8nFd97/HPmdG+jFbbcrxI3jR24jge4yTOAtlDAklKLtx7kxpuSEPdFhpoaemCuwCtC7Q0QHO5pYZQaGJCKRSahk3KHodsTuIkdpKxJdmSF8mWJVkz2qWZc/84M7KWGWkkzTPPMzO/9+vll+yZkeYkj2bm95znd76H0inFdU+zJIUkmRTXIvliRP04nnJB1SUycz0XGTVzfZn5Ksd/umgvbqaqutR8zeZjn44TIslQYWanY7WGlBWUsbpidVoW14e7TXtj3OJ6wsx130gfHX0dkhSSZFJci+TrawZXPhQts3skcyOXh+cm2GS2js/12D2ShavYAionuwusWMKj0N+aGSdQ8RRUm5OHbD726TghkgzlG0G5Z+y7Tse2kLhJIQG/eZ+bEMPX0mPSgKQtJLmkuBbJF2yGktVmNjidRC8Pd+2zeyTpIZNmNHMKoWKztAVN1d9m1k9ketFVvc0saszWtKC+ZnDlQtEKu0eSWu4Cs0tnd/ziurmnmd6h3hQPbGH8Xaa4Xle5bvIdQb/5bHbljt8UTQqRtpDkmrX6UUq5lFL/KxWDERkiXbOPJfN2bjItXaB6m9mlMxyyeyTO0Zclu/ZVb4OhDrMYOxsFm6F4lUnQyDYVvvgz10tN28j+jv2pHNGC+bv8rCxbOT39I+CftphRNpCxxqzFtdY6DPxJCsYiMoHWZmFYOn4Y51dB6brsvjycqNCwmdVMx5OoeKq2mZag3oN2j8Q5sqUXN9uzztN1QiQZKnzmxGpweuTeeGJImvVd+8/4p/dbh0Om/WdqUkhPM1WFVZQXlKdwhJkv0ev2jyml/lgptUIpVRn9Y+nIRHoaOmUKlHR9o67aZj5gs/XycKL6jwI6PU+i4pErF9MFm8yl88Kldo/EWuWbzH9nNhbXWpuZ60x6Lc/FDIsal5YupaakJq2Ka631eMb1JANtZifaWEkhMmuddIkW1/8b+DjwNLBvwh8hJhvfcCJNX6zjl4fb7B6JswUzcEazZLVZoJmNBVY8fc3mGKfb+om5cuVC5dbsPLEaPgNjwcx6Lc9FhYnci7UNOqTfosaOvg76RvpiL2aEmG0hspgx+RJ9xzwf+AbwOrAfuB+4wKpBiTTWl+bZx9l+eThRfRmYLqDUuSsXwgg2Z9Yxnkn1ZSbrOjRs90hSK5N34ExEXpn5HZ8hMeStzrcYGhtK8cDmJ7qYMZEYvpHQCG29bbKY0QKJFtffAzYA/4QprM+P3CbEZMEmM8tVXGv3SOan/EJwF0qBNZtgM+SUQMFiu0eSXNXbIPA2jJy1eyT20/rczHU2qN5mNlLpSa/FawuWLX31M5llUWNIhzhw+kCKBzU/cWP4gv7ITquLxm9qPdtKWIeluLZAosX1Rq31x7TWT0b+/Daw0cqBiTTV1wxFteDOs3sk8xO9PCzF9czG2wWU3SNJrvG+65fsHYcTDLZDaDB7ZjSrLjFfu162dxyp1tcMqEnZx1mn0mcW4o9Mj9xLt23Q/V1+CnMKWe5ZPvmOaFLIhPfsaFKItIUkX6LF9atKqW3RfyilLkV6rkUsmRDPVr0Nel7NvsvDc9GXoVtiV10CKJN5nO2ybUazcJnpuY8zg5mxgk0m39qdb/dI7DO+qHH6VYtVFavw5HvSZlGjv8tPfVU9rqnrJGLE8DX3RDKuZUFj0iVaXL8L+LVS6qhS6ijwPHCxUupNpdQblo1OpJ9gBkQ6jV8eTo8305QLh6DvSPr21c8kt9Ts2iZXLtJ/cfJcKWV26sy2130mTIgs1AyJIS7lYnPN5rTJuvaf8U9vCRntg8ETMRczFucWs6R4SQpHmB0SLa5vAlYBV0X+rIrcdgtwqzVDE2ln5CyMdKf/ZeQqWdQ4o8ET5uQjUz+Qq7ZB14ugw3aPxF59zWZr6HRdPzEflT7oPQChEbtHkjoBP5TW2z0KexXWQEFN3BOr9VXrxxcKOtnw2DBHzh6ZvpgxeMh8jRPDpzKtvc8BEiqutdatM/2xepAiTWTKZeSi86BopRTX8QQzMClkouptMNIDwcN2j8Refc2msJ6wVXIm27PnYX7/L/dAeJT3XbmBPXsetntI1hs6YyZEpsxoZqUZFjV6q710D3ZzZuBMigc1N809zYR1eIakkMknUc3dzbKY0SIZHl4qUiqTiq7qbdmZeZuITN8SW+IYjUxo8UrQnj0Ps2PHThr27QKgJv+D7NixM/ML7GCk6CqV4tpctXgLQtMj96LFajSJw6niJoUE/JhFq+fes8M6TEtPixTXFpHiWiTPeNGVAS/W6m3Q32oSE8Rkfc1mNrNw+eyPTUee9ZDrkeK6L3t27du5cxcDAw/QdOrDBAdL8NUOMjDwADt37rJ7aNaKs7FIVqrYAjoEZ9+cdle0WD3UdSjVo5qTaOtKfdWUNp+g31yFyikcv+lE4ATDoWFJCrGIFNcieYLNpm8tp9jukSzceN/1i/aOw4mCTWY3Q5fb7pFYQ7mg6tKsvHKxZ8/D1NVtpKrUBSM9vHIoYPeQUqKt7W3gSrR28XrbRfjqXgOujNyewQJ+c6JcXGf3SOxXGX9RY115HbmuXMf3Xfu7/NSU1ODJ90y+Q5JCUk6Ka5E8mRTPVukzHzpZWGDNKhs2FqneBmffgLF+u0eSMtHWiNbW+1m9+DkA/v7/NWZ+awSwcuUGYC8Arx31cdHK11HqmcjtGSzoN60CmXqiPBfFqyC3DLqnF9c5rhzWVK5xfnF9xj+931prs6CxdHpSCCBtIRaR4lokT7A5c+LZ3AVmgUu2twZMpXV2bIldtc2khXRlT5x/tDUCrmHNYrNO/WDbFzO/NQLYtWsnRUX3AE/yWusmSgv7uLDuo+zatdPuoVkrxoxm1lIKKjbHX9RY5XV+z3VXjOJ68KSZJJg6c93dTK4rlxVlK1I4wuwhxbVIjrFBE9GWSUVX1TazW1t4zO6ROMdwJ4wFM78Xt/pS8zWLrlxEWyMA1iwxl4xbTn8w81sjgO3b72T37l3U1t7L/tbfAeAbf3M727ffafPILBQeM1ehpLg+p8JnrliFQ9Pu8lZ5aepuYsyhnwddA110D3bHWcxIzLaQuvI6clw5KRphdpHiWiRHX4v5miltIWBaA0IDJvdWGMEMiVucTX4VlK7LqisXE1sj1i5p4mTPUgZHXs381oiI7dvv5OjRA7zaNASuXK68oMjuIVmr/yiERyUpZKIKH4QGz6WoTOCt9jIaHuXo2aOpH1cCoi0r0zOuYxfXTd1N0m9tISmuRXKMZ1xn0IymRLJNlylZ5omovsxsg6613SNJiYmtEWuWNNF0qpqionsyvzViKncelG2M2XubUSQpZLroosYYx97pcXwzxvDlFEPhsvGbtNY09zSztiKDPq8dRoprkRyZFMMXVVwHBYtNgSWMYBMmL3WV3SOxXvU2GDplIhmzwMTWiDWL93Kq/xi7d+/K7NaIeCp80PNqZp9YSXE9nWc9uPJj9l07PY7P3+Un15VLXXnd5DuiO3BO2IWxa7CLwHBAZq4tJMW1SI5gk1lpnVdp90iSR6nI7KXMXI/ra4aiFeDOt3sk1qvKvisX27ffydGml1hWCf/z7j/OzsIaTHE9fMasI8lUQb9pf8qvsnskzuHKhfILzYnVFNVF1VQWVjo2McTf5Wdt5drpPdQxFq1KUoj1pLgWyRHdcGLC2XFGqNpmYoyGu+weiTP0NWfW1YmZlF8I7qKsWtQInFs/kQ2tP/HM0B6QMQJ+6beOpXKLOe4xrlp4q7zOLa7P+Ke3hISGTG996fSkEEA2kLGQFNciOTI1ni3ad931kr3jcIpgU2b11c/ElQMVF0H39FmsjJaJLV5zVX4RoOLGsmUEieGLrcIHo2djtoPVV9U7sud6LDxGU3dTjMWMTYCOmRSiUKyqyIL2PptYXlwrpdxKqdeUUo/GuO/TSqm3lFJvKKUeV0rVTrgvpJTaH/nziNXjFAsQHjNnx5lYXFduNTv2ZVFrQFyjARPFl01FV8UW6NlvMq+zRdBcMs74uMWZ5JaYtJhMLa5HAzDUIcV1LBXxd2r0Vnlp72snMOysnUtfOfkKo+FRfDW+yXeM99VP3g69qbuJZZ5lFOQUpGiE2ScVM9efAuIFpb4GbNVabwJ+BPz9hPsGtdabI39us3qQYgEG2kCPZeaHcW4JlF0oxTVkTwzfRJU+k+sdbZXIBsHDkFdh/mSzCl/mFtfRokvaQqYrv9BMqKTRosaG5gYUiutWXzf5jmgMX+nk4rq5p1laQixmaXGtlFoOvB/4dqz7tdZPaq0HIv98AVhu5XiERTK96KreBl0vZtfsZSzj7QJZ9KY8wyxWxpJeXKPSZ1oDhrvtHknySVJIfDlFJjUkjeL4Glsa2bJ0C9VF1ZPvCPih8DzILZ10c3N3syxmtJjVM9dfA/4ESKQquQf4xYR/Fyil9imlXlBKfSDeNymldkQet6+zs3OBwxXz0he9jJyhL9bqbTDae+4DKVtlU8Z1VNkFoHKyq+86KL24wIQTq/32jsMKAb+ZnS1ZbfdInCnOVYu1lWtxKZejZq6Dw0GeP/48N6y+YfqdMfrqg8NBTvWfkuLaYpYV10qpW4DTWutXEnjsh4GtwD9MuLlWa70V+E3ga0qpmL8JWuvdWuutWuutixYtSsbQxVwFm8FdYM6QM1Ekku35R79GXd1GXC43dXUb2bPnYZsHlmLBJshfNG0WJKO5802BnS0z16MBGGyX4hoy+6pF0A/Fq7IjUnM+KnwmhnHo9KSb83PyqSuvc1RiyFNHn2IsPMaNa26cfIfWMa9CtfSYFjdpC7GWlTPXVwC3KaWOAj8ArlVKPTT1QUqp64GdwG1a6+Ho7VrrE5GvLcBTgG/q9wqH6Gs2MyAqQ8NnPPUM6yLeeuZhWlvvR+shWlvvZ8eOndlVYEfjFrNNZWQWK5M3FIkKRGbkpLiGgkVQtDwzr1pIUsjMKmbeqdFJxXVjSyNFuUVcvuLyyXcMnzGpJzGSQgDZQMZillVDWus/11ov11rXAXcAT2itPzzxMUopH/AvmML69ITbK5RS+ZG/V2MK9besGqtYoGBTZrcKKBcvHHZx8apFwDVALnANAwMPsHPnLpsHl0KZGrc4mwqfmcEabLd7JNYLRopr6bk2MnFRow6bRatZfowPdR3i07/6NCcCMTYKqthsvsY49vVV9RzqOkTYIWtwGlsauar2KvJzplyFiNNXLxvIpEbKpxqVUl9QSkXTP/4BKAH+Y0rk3gZgn1LqdeBJ4EtaaymunUhrk6SQ4dnHT77Zz8blRygpCE649Ura2uIF4WSY0DAMHMve4hoyr8iKJdqLm41XKGKp8JkWirGB2R+bLgaOQ2gw62euewZ7+OoLX+WlEzH2MMivhOK6uHF8A6MDsYvyFDvWe4x3zrwTu986GLu4bu5uprqomrKCshSMMHulpLjWWj+ltb4l8ve/0lo/Evn79VrrJVMj97TWv9ZaX6i1vijy9YFUjFPMbs+ehyf1Hf/4+/8MoYHMXcwY0RJYiculuXj1yxNu3cvKlRtsG1NK9R0BdHYWXRUXma/ZUlwX10kvblSFz8z0nn3D7pEkjySFALBpySbcys1rHXFe13GuWkTj+JzQGtLY0gjADWviLGZ05UNR7aSbm3skKSQVMrRJVlhhz56H2bFj56S+42/+w9+aOzN85vrWj+4EYNva7wOjwJMUFd3Drl07bR1XymRjUkhUrsf8fmdDcR2UGL5JKjPwqoUU1wAU5hayvnr9zMV18DCMBifd7KQ4vobmBpaWLOWCRRdMvzPgN5MhLvekm5u6m6TfOgWkuF6AqbO4mb64befOXQwMPMDEvuNlZR8xd2b4zPX/3P7b9OrzuGbTD1GqgNrae9m9exfbt99p99AsFf0d/4OP3QrAjxpet3lENqncEnNxU0bRYbOgMcuLrkmKVprNdDLp2Af9kFMKBTV2j8R2vqU+XmuPc2zHT6wmv+edV3oeJXklts9ch3WYx488zg1rbkApNf0BMSI1B0cHaetto76yfvrjRVJJcT1PsWZxMz09wvQXXznptjWL3YyFgOLamN+TScrW3MgNmwsIh8Y4evRAVhTW0d/x1Ys/TmCwiLt2fDmjf8fjqvBB/xEY6bF7JNYZOGFavKS4PkepzFvUGE0KiVWQZRlfjY8TwROc7j89/c44ay2UUuOLGu20v2M/ZwbOxO63Do+aBehTrkId7j6MRo+3tgjrSHE9T7FmcTM9PcL0F++ddNvaJS9w4mwuuHLtGVQqVW2D4U5TZGWBib/jaxYfoflUPQMD38no3/G4MnlDkag4C6CyXoUPzr5pCpZMIDF843w15nUdc/a68DyT6x9nUaPdM9cNzQ0AXL/6+ul39h0BPQaeyTPU0VaWaGuLsI4U1/MUaxY309Mjdu3aSVHRPZgAF9N3vG7pc+RVrrd5ZClSbTaT4cwL9o4jRSb+jq9d0kTz6TVk+u94XJXxc28zRrQXV3quJ6vwQXgYAu/YPZKFGxuAgTY5xhGba0zkXsy+6+hVixg5594qL61nWxkcHbR6iHE1tjSyackmakpitPfEeS1HZ9vrq6QtxGpSXM9TrFncTE+P2L79Tnbv3kVt7b3jfccX1uWydN0Vdg8tNcouAHcRdMWIbspA0d9xlwqxavERmjrWkum/4xNNWlOx/loGdEVmtQdMFfBDTgkULrV7JM5SucV8zYTNZIKHzVeZuQagorCCuvK6+IsaK33Qe9BEkU5QX1WPRo9nRqfawOgAe9v2cuPqG2M/IM5VKH+Xn+We5RTnFVs8QiHF9TzFmsXNhvSI7dvv5OjRA4TDIY4eepZ81Z898WyuHBPLlskF1gTR3/HlVT8kL2eU5tOjWfE7DrHXVDz95hBnjzxl99CsE/BDab304k5VWm9OqjPhdT+eFCIzl1G+mhkWNVb4THtF78FJN9sdx/dM6zOMhEZiR/CBOc751SavewJ/l19aQlJEiut5ijWLmw3pEZMEszCercJn+m4dsjuXlaK/41ds+nMA+vhx1vyOx1pTsa/5g5SEj8OYfZeCLRUjXUBgoszKN2VWcV26Dsi+xKtYfDU+DncfJjgcnH5nReSqxZRjH22rsCuOr7G5kXx3Pu9e+e7YDwhOT/3RWuM/I8V1qkhxvQCTZnGzID1immDkkliGx/BNUuGD0YDZlTILbN9+J9//5p8C8PCje7PmdzzWmorXjt5KjhuzuC3TjA1Cf5sU1/FUZshJddAPRSsgpzgrE69i2bLUFNCvn4oRM1q6xsQWTllrUZJXwrLSZbbNXDe0NPDu2ncZceTiAAAgAElEQVRTmFsY+wGB6Xn1p/tP0zvcK0khKSLFtZi/8Y1FVts7jlTKxE0lZhPwQ04xFC6zeyQpE2tNxWutY+YvmXjs+5owO3DKB29M4yfVaZ4UNCEpJBsTr2LxLTXv6a+2x+ipV664rYDeaq8tcXztwXYOnD4QO4IPYKQXhk7F7LcGSQpJFSmuxfz1NZvFTzlZtDiibCOonMxOjZgqC3txY62pON2/k2GdIb23U8mufTOLk3mcVrQ27QKRE6hsTLyKZWnJUhYXL555p8azr0M4NOnmaByf1joFozznsZbHALhxTZzFjHFey+MxfDJznRJSXIv5CzZlV781gDsfys5P7w/ZucrCXNzYayr+jvyaSzMjNWIqWeg2s/KNoNzp/bofOmVm3yOv5WxMvIpFKTX7osax/sjVnXO8VV7ODp2lc6AzBaM8p6GlgUVFi9i0ZFPsBwRjx/D5u/wU5BSwsmylxSMUIMW1WIi+5uxJCpko03Zsm0loCPqPZmW7QMw1FRU+6H0TwmN2Dy+5An4oWp5dV6Hmwl1gTqrT+YrVlBnNbE28isVX4+Ng50GGx4an3zmecT/5pNqORY1aax5reYzrV1+PS8Up3wJ+cyI4pV3T3+VnXeW6+N8nkkr+L4v5GRuAwZPZN3MNpsAaOgWD7XaPxHrBSC9uls1cx1XhMyccmbChyETB6QugxBQVPuhJ46sWU7KPJfHqHN9SH2PhMQ52Hpx+p+d8swPxlAkVO+L4Dpw+QEdfR/yWEDDFdclqcOdNutl/xi+bx6SQFNdifqJpGdlYXGfDbn1R0os7WSb03k6ldVa2/sxZup9UB/zgLjRpIRFZn3gVMeM26O48s9amZ/+km2vLasl356d05jq65XncxYwQ80R5JDRCS0+LLGZMISmuxfxEk0Kysi3EbJmbUQVWPOP9e+vsHYdTeLymRSCTTqyGTsNorxTXsxnfqTFNj33Ab17H0hYwzZrKNZTmlcZf1Fh2AQQmL/R0u9ysrVyb0pnrxpZGNlRvYJknTnKTDptdOKesnWjpaSGkQ7KYMYXkVSbmJxs3kInK9UDJ2vS+RJyogB8Kz4PcUrtH4gyunMzZUCQqzgIoMUW6n1TL1Ym4XMrFRTUXxS+uPV4YOG4WNk6Qyji+obEhnm59euaWkP4207Y25ThHxygz16kjxbWYn74myC2ftr1q1qj0pe8M1lzIB/J047t0pjaCyzLS+pOYXI+ZTEjH4jo0Av1H5ARqBltqtrC/Yz+hKZF7wLnXRvDwpJu9VV6ae5oZDY1aPr7n2p5jaGxo5paQQJykEInhSzkprsX8BLM0KSSqwmc+rEbO2j0S60R7ceUDebIKH4yeNSkqmSDgB1f+pF5cEUe6JgX1NYMOSdTiDHxLfQyMDnC4+/D0O6PvgYHJLSDeKi9j4TGOnLV+c6HGlkZyXblcVXdV/AcFY58o+7v8LC5eTHlBuYUjFBNJcZ0MmTKDNRd9zdnZEhI1vrBt/8yPS2fDZ0wRKTOak4333mZIW1C0F9fltnskzlfpM4u5R3rtHsncxJnRFOfMuKgxOpE0pbhOZRxfQ3MDl6+4nJK8kvgPCvjNFZaCJZNu9nf5pSUkxaS4XqhnPwTP3m73KFIrPBrJPpbiOi1nsRIl7QKxlV+Y/huKTBSU1p+EpetJdZwZTXHO+YvOJ8+dF7vvOqcIilZOn7lOURxfZ38nr3W8NnNLCJzbgXPKbrr+M1Jcp5oU1wvlLoKul+weRWr1t5lLjCVZ3BZSuMRs/Z7JfdfygRybuwA8GzKjuA6NmJlYOcaJSdeT6oDfzGbmldk9EsfKdeeycfHGmRc1BicX0ZWFlVQXVVs+c/34kceBGbY8j4qxRqZnsIfOgU7pt04xKa4XqtJnck8HT9k9ktQZj+HL4plrSN/+y0SN9+LW2j0S58mUY99/xJwoS7tAYgproKAm/VqCZGFyQqLboOtYrZ4er/n/OOU+b5XX8pnrhuYGKgoq2LJ0S/wHjfXDwLGY/dYgSSGpJsX1QqXrTMZCRFdMZ/PMNZhjH3gbxgbtHok1An7Tayi9uNNlykm1tP7MXTqeWEXbBcSMfDU+uga7OB44Pv3OUi+M9U3bRMhbZW0cn9aaxpZGrlt9He6Z3oujn8tTi2tJCrGFFNcLNZ59mmYzGQsR8ENOiWmLyGaVW8ysX+8Bu0diDenFjS9TTqqluJ67yi3pdVI93G0WJ8sxnpVvaWRRY6zWkPE4vsmFtLfay6n+U/QOWbPI9Z0z73A8cJwbVyfQEgLTY/i6/OS4clhVvsqS8YnYpLheqLxyKFmd2b23U0UvMU5ZNJF1ogVWul0iTkR4NBK3KB/IMaX7hiJRQT8ULDbvYyIxFb70OqmWE6iEbVqyCYXi1fYY7+nR/38x4vjAukWNjS2NANywZpbFjAE/oKbtpuvv8rO6YjW57lxLxidik+I6GdLxMuFCBCX7GIDiOrORTiYe+74joMfkAzmevHIoXpX+x15yzOeuMs2uWsgOnAkrySuhvqo+9sx10XJwF8ZPDLFoUWNjSyPrKtdRV1438wMDfiheCTmFk26WpBB7SHGdDBU+s8gv3bJP52Ns0KSFyGYEZua+YnP6fMjOhcx2za7Sl/5XLWSh29wVr4LcsvS5WhnwgysXSqQtIBFblm6JnXWtXGZWeEpiyOqK1biV25KZ65HQCE8eeXL2CD6ITHpN/lwOhUM0dTdJcW0DKa6TIdoecPZ1e8eRCsHDgJZZkKgKH5x9A8Jjdo8kucZnu+QkKq50P6ke6YHhTimu5yrdTqoDfrPhlyvH7pGkBV+Nj2OBY3QNdE2/M5oYMkGeO49VFassKa5fOP4C/aP9s7eERHfTnfJabuttYzg0LIsZbSDFdTJELxOmy0zGQkj28WSVPggNTXvDTXsBP+RXQ36l3SNxropILFa6nlTLrn3zV34h9B5Mj915ZWHynMy4qLHUa+IrQ8OTbvZWeS1pC/nRWz/CrdxcU3fNzA8MNpkkk7KNk26WGD77SHGdDIVLTUB/usxkLEQgslJa2kKMTEmNmEraBWaX7ifV460/8lqeszixbI4TDpnCS65AJWzGbdA9XtDhc3s9RHirvBzuPkxYh5M2jr1te/nGy9/g7s13U1Ywy+Y/HWbRIzXXTbpZYvjsI8V1smTLosaA3yzsyCm2eyTO4FlvduzLtGMvubizS/eT6oAfVI5JOxJzMx7L5vArVv1HITwiJ8pzUFVUxQrPipnj+ALT4/iGxoY41nssKWMIDAf4yE8+Ql15Hfe9977Zv6GjwawFKJm8sZu/y095QTmLihYlZVwicVJcJ0vlFuh9y7QIZDJJCpnMlQNlF6ZvgRXLSC8MnZIP5ESk80l10G8Ka5dEdM1ZnCLLcaT1Z158S31x2kIiVwCmnFQlO47vU7/8FG29bTx0+0OU5pfO/ODwKJx6EpbeMC0e199lkkJUtsfm2kCK62Sp8JnosrNpkn06H3EWTWS9Sp9pDUiH/stESFJI4ip8kZPq4dkf6zTyWp6/OLFsjiNrZObFV+PDf8ZP30jf5DvyyszVKgvj+H781o/57v7v8tkrP8tlKy6b/Ru6XoLRANRM32TGf8YvLSE2keI6WTK193aiodMw2itv1FNVbIHRs+YSbCaQXNzEVUZOqtNlQ5GoaC+uvJbnJ04sm+ME/JBXYRYni4T5anxoNG+cemP6nTESQ5YUL6E0r3TBM9cngyfZ8egOtp63lb+66q8S+6b2RvP7WHPtpJv7Rvo4ETwhixltIsV1spSsglxPZhfXUnTFlmknVgE/KLf04iaiIk0XNQ60QXhYXssLEaPIcpzo2glpC5iT8cSQWIsaS73TTqqUUnirvQsqrrXW/NZ//RaDo4M8dPtDie+o2NEIlVvNSdQEh7pMy5IU1/aQ4jpZlMtkn6bbh+xcSLtAbOUXmmI0U459INKL686zeyTOV7IackqhJ802k5HX8sLFiWVzFGn9mZcVnhVUFVbFX9Q43GX+TLDQOL5vvPwNftX8K75y41cSb+UY6YWuF+O2hIAkhdhFiutkGt9QJGT3SKwR8JtkjOKVdo/EWXIKTWpIpsxcy6LVxKXrSbUU1wsXJ5bNMUaDMHhSjvE8KKXiL2ocX8w6fVHjscAxBkYH5vx8b3e+zWcaP8PNa2/m97b+XuLfeOpJ0CGzmHEKf5cfhWJt5do5j0csnBTXyVThg9CAuRSXiQJ+02eo5NdmmnROjZhIh80unPKBnLjKLel3Uh30Q2455EtE17zFKbIcI/o5JK/lefHV+Dhw+gCjodHJd0QnHoLT4/gA9ryxZ0551yOhET78kw9TnFvMA7c9MLdkj45GyCmBqm3T7vJ3+aktr6UgpyDxnyeSRqqkZMq03tupgn7ZjCCeCp+ZJRo6bfdIFqa/zcRJygdy4tLxpDraLiC9uPPniV1kOYbE8C2Ir8bHSGiEtzrfmnxHySoTXznlpOr61ddzwaIL2PHoDnz/4uOn7/wUnUCC1Oef+jyvtr/Kt279FktLl85tkO0NsPjqmC18/jN+6be2keXFtVLKrZR6TSn1aIz78pVS/66UalJKvaiUqptw359Hbvcrpd5r9TiTomwDuPIzs7gOjUBfixRd8UzYrW/Pnoepq9uIy+Wmrm4je/Y8bO/Y5mL8A1lOohKWjifV0ou7cLkeKKhx7sx1wB9JNZG2gPmIuw26K8ds1jLluFcWVvL6777OQ7c/xODoILf/++1s/dZWHj30aNwi+7m25/jSc1/i7s13c/uG2+c2wL4j0NcUsyVEa82hrkNSXNsoFTPXnwLejnPfPUCP1not8FXgywBKqfOBO4ALgJuA/6eUcqdgrAvjyjWL27rTbHFTIvpaTG+XzILEVrEZgP1P/is7duyktfV+tB6itfV+duzYmT4FtuTizl26nVSP9sHgCTnGyeCpd3ZxXVwH7ny7R5KW1lWuoyi3iFfbY3yel9bHjGF0u9xs37Sdtz7xFt/9je9ydugstz58K5d++1J+2fTLSUV2dBfG2rJavn7T1+c+wPEtz6cvZjwZPEn/aL8sZrSRpcW1Umo58H7g23Ee8hvA9yJ//xFwnTINR78B/EBrPay1PgI0AZdYOdakifbeZsqGIlFSdM0srwKK6zh+4BcMDDwAXAPkAtcwMPAAO3fusnmACQr4IzNyS+weSfpw5UL5xvRZ1BhtY5CrEwsXI5bNMaSNb0HcLjcXLbko/qLGYFPcdRY5rhzu2nwX73ziHb5967c53X+am/fczBXfuYLHWh5Da82nfvkpWntbefD2B2ffhTGW9kazmVGMz+RoJKDMXNvH6pnrrwF/AsTr7l8GHAPQWo8BvUDVxNsjjkdum0YptUMptU8pta+zszNZ456/Sh+M9Jgc2UwSkMUxs6rw4V0UAK6ccseVtLXFu3jjMAG/5OLORzqdVEtSSPLEiWWzXWgk0vqzwe6RpDVfjY/9HfunL1D0eCE8MuvGYbnuXO7Zcg+H7j3EN9//TY4FjnHDgzfg+xcf393/Xf7sij/jipVXzH1g4RCcehxqpm95DhLD5wSWFddKqVuA01rrV6x6DgCt9W6t9Vat9dZFixyw8j1dN5WYTdAPBYshr9zukThX5RbW1UBp4a+m3LGXlSvT5EMueEiKrvmo8MFINwwct3skswv4AQUl0ou7YE5NDDnzPIQGYclVdo8krfmW+ugb6aO5e0rcYuncjnueO4/f2fo7NN3bxP+9+f/SOdDJpcsu5a+v/uv5Daz7FTOJF6MlBMzMdXFuMctKY85JihSwcub6CuA2pdRR4AfAtUqph6Y85gSwAkAplQOUAV0Tb49YHrnN+co3mUUk6dJ/majojKaIL3JidWn9DuBJYBR4kqKie9i1a6edI0vMWD8MHJPiej7KIidPTm0RmCjoh+Jak88uFmaORVbKdDSaja0WX233SNLalqVbgBiLGueZFJOfk88nLvkEbX/Qxt7f2kvefDfqGu+3vi7m3f4uP/VV9XOL9RNJZVlxrbX+c631cq11HWZx4hNa6w9PedgjwF2Rv38o8hgduf2OSJrIKmAd8JJVY02qnCLzhpuJxbUUXTOLFNdf/MwN1Nbei1IF1Nbey+7du9i+/U6bB5eA4GHzVY7z3Dm1yIpFXsvJE41lc1ocX3sDVG+DvDK7R5LWLlh0ATmunOnboOdXm3U283y9u11uclw58x9YRyNUbIGC2Ffr/Wf80hJis5TnXCulvqCUui3yzweAKqVUE/Bp4M8AtNYHgR8CbwG/BD6htU6fHRoyZUORqJEeGO6UD+TZFC6FgsVsXePi6NEDhMMhjh49kB6FNUgu7kIULjWbOTi9uNbaFIJyjOdtUtTm6s30hhY567gPd0P3PtOPKxYkPyefCxZdMH3mWin7FrOO9sGZX8eM4AMYGhvi6NmjspjRZgs4dUqc1vop4KnI3/9qwu1DwP+M8z27gDSJWJii0get34ehzrhnlmlFiq7EKGVOrNK13z7ai1u6zu6RpB+lTDKDk4qsWAZPmvYfOVGelz17HmbHjp2RRKAraW3dyzP7b+HdvIRjVqOcegLQUlwniW+pj58d+hla68ltFp566Hgs9QM6/TSER+Me36buJjRaimubyQ6NVkjHTSVmIukCiavwQe9BCA3bPZK5C/iheKX04s6Xx8GxbFHyWl6QnTt3TYvafPvErRSGTsaNZUu59gYTp1mVHum1Tuer8dE50MnJ4MnJd3i85mR1NJjaAbU3gLsQFsVOGZGkEGeQ4toKmVhcqxzTXyhmVuEDPQa9B+weydwFZdHqgni8Zvv4sUG7RxKf5NUviInUnBy16T95Lfk5etZYtpTQ2vTjLrnW7CQoFsxXE2enxtL5LWpcsI5GWPwecBfEvDuacb2uUq5A2kmKayvkV5rV+GnUHjDjlt1BP5SsNgt3xMzSNYpRa1notlClXkCfWxjqRAE/5BRDoUR0zYeJ1Nw76TZ/+5D5ixNagvqaTZG/NHZEm5i7i2ouApi+qNGOGMaB4xB4e8aWH3+Xn/NKz5vfxjQiaaS4tkoaLWqM9hHG3bJbiq7Ela6BnNK0OfbjBtthrE+O80LMM54rpQKRXfskomtedu3aSVHRPUyM2jx29h/NnU5oCWpvMF+l3zppPPke1laujTFzvRZQqS2u2yMRfDOcPPnP+KXf2gGkuLZKhc/MYI32AbPMDNssVh/h+Jbd4ZDZ5lWKrsQoF1RsTr/iOloYyHbJ8xddCOqEGcx45ER5QbZvv5Pdu3dNitr8u/u+GIllc8BJVUcjFNdByRq7R5JRNi3ZxMHOg5NvdBeY/9epPJnuaISCGijbGPNurTX+LimunUCKa6tU+AANZ1+ffWbYZrH6CMe37B5ohfCw9OLORYUPel53zgKnRMhCt4XLLTHtFk4trkNDpmVATqAWZPv2O6dEbf6mfbFsE4XHTFLI0hvlykSSeau8tPS0MBoanXyHx5u617sOm3SSOFueA5wZOMPZobOymNEBpLi2SuW53tsZZ4YdIFYf4fiW3dHZGCm6Elfpg9CAs3tvpwr4zQr0ouV2jyS9OTkxJNgEaHktWyGVRVY8XS/BaEBaQixQX1XPWHiMI2ePTL6jtN7MXGtt/SB6Xjf7TcTJt4Zzixll5tp+UlxbpXCZ2cWp57WZZ4YdIFYf4fiW3TKjOXfpmBYTPBTpxZW3hAWJFlmp+LCdK3ktW8dTb08s20QdjYAySSEiqaLFajTmbpzHa3LjB09YP4jxLc+vj/sQieFzDvkktUp0Q5GeV2eeGXaAWH2E41t2B/2QWw75GbAZTqqUnQ+uvPQqrqUXd94mrqf4/Fd/AqO9MHTa7mFNJ3311rErlm2i9gaoutikVYmkihar0ZnhcalMDGlvgPILzW6wcfi7/OS786ktq7V+PGJGUlxbKbKhyBd3/Wn8mWGHmN5HGNmyO1p0SQ9f4ly5ZsFJ96t2jyQxoWHoPyLF9TxMXU/x/ME/AKDxP79h88hiCPih8DzIlYiupLMjlm2ikV7oelFaQixSWVhJdVF17JlrsP64jw1C595Zj6+/y8/ayrW4XW5rxyNmJcW1lSq3QHiUO2/eGH9m2OlkRnN+KiNRjE5sD5iqr9kslpFFq3M2dT2Fv/1/AfDYTx6wdVwxnXnBJNmI5LMjlm2i00+BDkm+tYW8Vd7pM9eFy0xuvNXHvfNZEyxQM/Px9Z/xS0uIQ0hxbaUJvbdxZ4adbLTP9JJJcT135RfBSDcMddg9ktlJL+68TV1P0XZmJUMj+SzKPxn/m+zQ32paFmRm0xp2xLJN1N5giryqbfY8fxaIWVwrdW5Ro5XaG0yr4eJ3x33IaGiU5p5mWczoEFJcW6l0LeSUpFfv7UTRNwyZ0Zw7z3rzNfCOveNIxHhxLb24czV1PUVYuzl86jwuWu2w1osENp8QC2RnYkhHIyy+Gtx59jx/FvBWezndf5qzQ2cn35GK497RCIuuhJyiuA85cvYIY+ExKa4dQoprKykXVFyUvsW1zGjOX1lksWo6FNdBv1kkk+uxeyRpJ1bSTvPpU1y6vsTmkU3R0Wj6rT3OWESdkTze1MWyTdR31MR+yomTpeqrzOTDoa4ps9SlXpMfHxqy5okHO+DsG7MeX0kKcRYprq0W3VBEh+0eydwF/ICS3b7mI9qL15sGxXXAL1cn5ilW0s7aLdfjcXVCeHT2H5AK4RDDrT/nR3sHcLlzHLdDbMZIZSzbROMRbdLyY6X4cXz1gI7kyFug4zHzNYHFjHDuJEDYS4prq1X4YKzPuheelYJ+KK6FnEK7R5J+lDKtIQFnZJnPSBatLsjU9RQbL/sfoMegr8XuoQHwi4e/RL7q4z9f+IQjd4jNGNGIw1S3hnQ0mpP5aCuasMSayjW4lTv1cXwdjWbPjFkWI/vP+KkuqqayUKIYnUCKa6tFFzWmSyzbRFJ0LYxnvfPbQobOmIWXkn2cPHbHsk3xZsM/AfDYgU/ixB1iM4Ydxz0cMjObsuW55fLceayqWDW9uI6+d1qxM6vWpriuuX7WDb78XX7pt3YQKa6tVnaByT1Ot75rrSO79smLdd48G2DgmEldcaqg9NUnXfT/pZ0bikxw8YrTvHb0IjoDiyfc6pwdYjNGqmLZJup5FUZ6pCUkRbxV3ultIbmlZj2DFce99yAMtid0fA91HZLi2kGkuLaaO88U2OlWXA+eMP2DUnTNX/QyrUOKrJhk0Wry5VWYHU2dMHM91s8V9YrGA1NbBpyzQ2zGGI9lS+FxT2BLbJE83iovh7sPE566hsrjhYAF7/Mnf2G+zlJcd/Z3cqr/FOurpTXIKaS4ToWKLemzoUhU9I1Ciq75ixbXvQ6eIQz4zZWV4jq7R5JZ7Ixlm+j0M+TlaJ499DRO3iE2Y1hVZMXT3mBaDwsWpe45s5i32svQ2BDHeo9NvqPUa06qkvkZP3AcDv4dLHo3FK+Y8aGPH3kcgKvqrkre84sFkeI6FSp8MHwm9avIF0LaBRaudK3pk3Ny33XwEJSsBVeO3SPJLKmewYynvQHcBWz/wy+l5w6x6cbqWLaJRvvgzK9jzmru2fMwdXUbcbnckg6TRNEkjpiLGkd6zOd8MugwPP9R0KOw7TuzPryxuZGKggretfRdyXl+sWBSXKdCZXRRYxq1hgT8pn+wcJndI0lf7nwTY+jk4loWrVrD44Wh0zBydvbHWqmjERa9mzu235V+O8SmI48XS2PZJjr9tIl7nJJ/vGfPw+zYsZPW1vslHSbJ4sbxJTspxv91OPU4bPmqmaSZgdaahpYGrlt9HW6XOznPLxZMiutUKL8IUOnVdx3wmzcMWYG+ME5ODAmPQV+TFNdWcEJiyMBJsyBKFruljieFcXwdjWbb9UVXTLp5585dDAw8AFyDpMMkV01JDaV5pfHj+JJxtersAdj/57DsNljzsVkf7u/yczxwnBtWy+vcSaS4ToXcEihdl37FtRRdC+dZb1ovwmN2j2S6/qNm5ksSYZKv1AHFdYdseZ5yVsayTdXRCIuvMgX2BCYF5sopD5Z0mGRQSuGt9k4vrovrwJW38Nd7aBh+vR3yyuDSbyU0udXQ3AAgxbXDSHGdKhW+9Mm6Dg2ZwkuKroXzrIfwiPn/6SB79jzM3R+6DoD/cfffyCXjZCtZDcptb991RyMULIbyC+0bQ7axMpZtooHj0PtWzKsSJgVm75RbJR0mWWLG8bncpn1jocf9jb80W51f+oB57SagsaWRtZVrWVWxamHPLZJKiutUqfTBQBsMd9k9ktkFmwAtM9fJ4Il8oDmoNSTak7ko7yYAnn7tPunJTDZ3nimwU5kcMZEORzafuGHWzSdEkqUiKWaGLbF37dpJUdE9SDqMNbxVXo4FjtE/0j/5jlLvwmJXTz0Nb38F1u6AZbck9C0joRGeOvqUzFo7kLzrpkp0p8ae1+0dRyLGs49l174FG++9dU5xHe3JvPb8Vt46sYHuvg9IT6YVovFcdjj7pllQKf3WqVcaKa6tjF5tb4SCJTGvSmzffie7d++SdBiLeKvNe3pT95RFqx4v9DXPrwVwpBee/z9mAbzvHxP+theOv0DfSB83rpHWL6eR4jpVys43Xx1UZMUVLQhkS+yFy680l/ccdNzb2t4mP3cr71n/DI1vRosv6clMOk89BA+bWeRUk81F7OPxwujZ5MWyTTXpqkTsntzt2++UdBiLjCeGxFrUGB41J7Zzte/3TVTv5Q+ZNVoJamxuxK3cXFN3zdyfU1hKiutUiW6N6+Td+qICftM3mFtq90gyg2e9ozaSWblyA1fU76Yof5CGN6MzHtKTmXQeb2T9Qlvqn7u9wewMWyRRmilndVLM2TdguFOuSthkbaWJxpvWd119BbjyofFyeOUPYbAjsR/Y+kM4+hBc8BdQfemcxtLY0silyy+lrKBsTt8nrCfFdapEt8Z1wq5ts5GkkOTybIDA247ZoXPXrp28z/c3jIzl8BgpSMMAAB+CSURBVPTblyM9mRaxKzEkNASdz0rxZZdkxrLF0m7SIVgqx9cOxXnFrPCsiDFzvQ5ueQtq74RD98Mjq+G1z8BQZ/wfNnAcXv5dqLoENs7t/bdnsIeXT74s/dYOJcV1KjllS+SZaB3JuJbiOmk865O7e9cCbd9+J3ffVMGrbXkMjFRLT6ZVrC6y4uncawpsieCzR1FtcmLZ4ulohLKNULjUmp8vZhUzjg/MIuZt34H3vw0rPgTv3AePrDK51VPDDKK7MIaG4bKHwJU7pzE8ceQJwjosxbVDSXGdSqX1qdsad76Gz5h+QZm5Th7PevPVKX3XQ51UqqNs+8BnpSfTSgVLINeT+hPq9gZT3C1+T2qfVxjJimWLZWwQTj8rJ042i8bx6XhXIz3r4PJ/g/cdNJvBvPVl+K86eP0vzUQLgP+fIrsw3mceP0cNzQ148j1csuyS+f+HCMtIcZ1Kqdwad77Gk0KkuE6a8eLaIX3XHY+br9I2YC2lFh7PNR8djVB9uVnjIexhVVJM57MQHpbXrs28VV6CI0E6+mbpqy5bD1d8H973Jpx3Mxz8W1Nkv/pHsP/P4LxbTPTePDS2NHJN3TXkuuc24y1SQ4rrVHLClsizCUpxnXTFK8FdCL0OmbnuaIC8Cqh8l90jyXypbgUbPAU9+2Vm024eLwTnGcsWTzgEB3eZqyFyVcJW0Ti+Q10JnjiXXwBX/hBuft0k+LxznzmOl347oV0Yp2rububI2SPSEuJgUlynUiq3xp2vgN+seC6qtXskmUO5IkWWA4prrc3M5pLrzOVrYS2PFwaOwVj/7I9NhlNyVcIRPF7QY9B3JHk/851/hNPPwLv+CXKKkvdzxZzFjeObTcUmePePzUz2Dc9B4ZJ5PX90y3PJt3YuKa5TKVVb4y5EwG/6BaXwSi7PemcU1wG/WaEuM5upMX5CfTg1z9fRCHmV5zatEvYoTfJi1p798MZfwIoPwqr/k5yfKeZtRdkKCnIKpsfxJap847z6rKMaWxqpLasdjwUUziPFdap5vPZtiZyIoMTwWcKz3ixmHRu0dxwdkRgvmdlMjVS2gmltFjPWXC8nx3ZL5nEPDcGvPwz51XDJv8yrjUAkl0u5WFe5bu4z10kwFh7jiSNPcOOaG1Hyu+BYUlynWnShi0MyjycJj5o+QYnhSz7PBsxiVptPrNoboWQtlNTZO45sURqZnUpFcR14GwZPyomTE+RXQn5Vco77/j+H3oNw6b+anykcIW4cn8VePvEyvcO90m/tcFJcp5qn3lGZx5P0HTF9gh7Z9jzpnBDHFxqB009KS0gq5RRB0crUFNftkS3PZXMRZ0hGYkjHY+D/GtT/Ppz33uSMSySFt8rLkZ4jjIRGUvq8jS2NKBTXrro2pc8r5say4lopVaCUekkp9bpS6qBS6vMxHvNVpdT+yJ9DSqmzE+4LTbjvEavGmXJ27dqWiOiYZOY6+UrXAcre4rrrBbOwTmY2U8uToji+jgbT410si5EdYaFJMcPd8Pxd5sR885eTNy6RFN4qLyEdorm7OaXP29DcwNbztlJVJFcxnMzKmeth4Fqt9UXAZuAmpdS2iQ/QWv+h1nqz1nozcD/wnxPuHozep7W+zcJxppZdu7YlQmL4rJNTCMV10Gtj1nV7Iyg3LLnGvjFko2iRZWUrWGgETj8tJ05O4vHC0CkY6Z3792oNL/8eDJ2Gy/dIOogDzTmOLwkCwwFeOP6CtISkAcuKa230Rf6ZG/kz06fLncDDVo3HMYrrrN0adyG6XobCZaZfUCRf2QZ7Z647GqDqUsgrs28M2ajUC2NBGJplw4mFOPO8uSohLT/OMZ4YMo/i6+geaPshbPo8VG5J7rhEUsw7jm8Bnjr6FCEdkgi+NGBpz7VSyq2U2g+cBhq11i/GeVwtsAp4YsLNBUqpfUqpF5RSH5jhOXZEHrevs7MzqeO3hJVb4y5EOGT6+2qut3skmcuzPrKYNZz65x7pge59MrNph+gaBitf8x0NkasSV1v3HGJuKjYBCn69HY7sMe+xiehvhX2fgEVXwIY/tXSIYv7KCspYUrxk/nF889DQ3EBxbjGXrbgsZc8p5sfS4lprHYq0fCwHLlFKbYzz0DuAH2mtJ7771GqttwK/CXxNKbUmznPs1lpv1VpvXbRoUVLHbxk7tkSeTc9rMNItM19W8qw3sVr9bal/7o4nTFEvxzf1UhHH194I1ZeZXd+EM5SshqseMbuzPv9h+PlGaP33mU+uwyHTZ63DcNmDEqnocPVV9SmduW5saeSquqvIc+el7DnF/KQkLURrfRZ4ErgpzkPuYEpLiNb6RORrC/AUkDm7Ini80JfkrXEXqiOSNCAz19YZTwyxoe+6o8EUXlWXpP65s13RClNgWVVcD3fJVQmnWnYL3PwaXPkfZqfW5+6An2+Cth/HLrLfuc/0zr/rn6BkVerHK+bEW5W6OL7Ws60c6jrEjatlgiQdWJkWskgpVR75eyFwAzCt4VQptR6oAJ6fcFuFUio/8vdq4ArgLavGmnKl9SZTOplb4y5UewNUbIaCxXaPJHN5Npivqe67jm4usuQacOWk9rmFKapK11m3iPnUE4CW4tqplAtWfghufgMuf9jEne79EPxiCxz/r3MLXXtehzd2wvLbYfVHbR2ySIy32suZgTN0D3Zb/lyNLWYC7IY18jpPB1bOXC8FnlRKvQG8jOm5flQp9QWl1MT0jzuAH2g9aSn9BmCfUup1zIz3l7TWmVNcOy0xZKwfzjwnH85WK6iObCyR4uK6r9nsDlkjMx62sXJn1vZGyC2Dqout+fkiOVxuqLsD3nfQtHyM9cMzH4BfboVjPzW92XlVcMlu2YUxTYwvakxB33VjSyPLSpexoXqD5c8lFs6yaSyt9RvEaOXQWv/VlH9/LsZjfg1caNXYbDexB3PZLfaOBeD0M2YmXYpr63nWp764Hm/5keNrm1IvHPtPE5mXzH5JrU3Lz5Jr5apEunC5YdWHofYOOPoQvPkFePZ2c9/VPzcn4SItTIzjs3KRYSgc4rGWx7jNe5tseZ4mZIdGO+RXJW9r3GRobwB3ASy60u6RZD7P+tRnXbc3mgjI0rWpfV5xjscLOmSuIiRTz36TLiG7MqYfV45p/7jVD5d+By75Fpx3s92jEnOwqnwVOa4cy/uuX+t4je7Bbsm3TiNSXNslGVvjJktHIyx6t9noRFjLswGGO80itFQIj8Gpx82stcx42KfUgji+0Ai8+FuQvwhWfCh5P1eklisX1twNaz9m90jEHOW6c1lTscby4rqx2Vx9vH61BA6kCymu7WJlD+ZcDJyA3oPSMpAq44khKTqx6noZRgMys2k3K9ZZvPnXZub60m9DQZrEkAqRYeqr6i3vuW5oaWBzzWYWF0vgQLqQ4touHq/ZsW00YOnT7NnzMHV1G3G53NTVbWTPnimbYHY8Zr5K/nFqjBfX78x+bJKhoxFQsOS65P9skbi8MihYkryTqtPPwltfhjUfg+W3zf54kfZS8n4h5sxb5aWpu4lQopsEzVH/SD/PtT0nEXxpRlbA2GXiZWKLVvnv2fMwO3bsZGDgAeBKWlv3smPHPQBs336neVBHo4nfK8/c9aOOUlwHrnzeeuEn7NhxcOZjkwwdDVC5Vba0dwKPNznF9WgAnv+I2aRky1cX/vOE4yX0Xi5s4a32MhwaprW3ldUVq5P+859pfYbR8KhE8KUZmbm2Swp2bdu5c1fkzfgaIBe4hoGBB9i5c5d5gA6b4nrJ9SaLVVjP5QZPPSffeWrmY5MMowE484JclXCKZO3Muu+TMHDMxLnlliz85wnHm/W9XNjG6ji+huYGCnIKuHKlBA6kE6mo7FKyxhS0FhbXbW1vA1NfkFdGbgfOvglDp6X4SjXPemor+pjx2CTDqSdNQoX00zuDxwvDZ2B4ARtOtP0YjnwPLtgJi6yL/hLOMut7ubDNxDg+KzS2NPKe2vdQkFNgyc8X1pDi2i7ufCheZWliyMqVG4C9U27dG7kd2fLcLp71rF4MeTlPTLljwrFJhvZGyCmGainCHGGhV6sGTsJLO6DyYtj4l4D04WaLWd/LhW0WFS2ivKDcksSQk8GTHOw8KBF8aUiKazslqwczjl27dlJUdA9mk8tR4EmKiu5h166d5gHtDVB2PhQts2wMIgbPetwuuLBuhmOTDB2NsPjq5G5aIuYvus5iPifUWpvYvdAgXP4guHLH+3BbW+9H6yFaW+9nx46dUmBnoFnfy4VtlFJ4q7yWFNfRCD4prtOPFNd2KvVC8LDpfbbA9u13snv3Lmpr70WpAmpr72X37l1mAUxoCDqflS2x7eAxs033fe5DsY9NMvS3mv5eaQlxjpJVoHLg5C9hbDDuw2LORh/6BrT/Crb84/gMuPThZo8Z38uF7bzV3qT3XA+NDfGV57/CyrKVXLhEAgfSjRTXdvJ4zUzUwHHLnmL79js5evQA4XCIo0cPnHsz7txrCmwpvlLPY2Yw33NRdexjswDRwuy3b10FwH+/MrrgnymSxJULq++Ctn+HR1aD/37zGpwg1mz0fX/9Gcb2fRrOex+s/d3xx0ofbnaJ+14ubFdfWc+J4An6RvqS9jM/+/hnOXD6ALtv2Y1LAgfSjhwxO6UgMSSu9gbzYb/kqtQ/d7bLKYailRB4J6k/dmJhdv3GD3K8u5o7dnxD2gSc5NJvw/VPm9f+K5+ER9bC4X+G0DAwfTY6130F37q7kN6BMFz6wKRdNqUPVwhnSPaixsdbHuerL3yVT1z8Cd679r1J+ZkitaS4ttOULZFTujipoxGqrzCFnkg9z/o5FdeJ/G5ECzOXeg/XX/AEjW/ewsDAd6RNwGkWvweuexKufRxK6uDlj8N/10PTtzh54i0mzkZ/7oOfY8uqJj62OwyFNZN+jPThCuEMyYzj6xns4a6f3sX66vX8/Q1/v+CfJ+whxbWdCpdCTgkE/aldnDR4ymybLFti26dsgymuE+i3T/R3I9om4Kt7jarSbhrevBFpE3AopaDmWrj+WbimwbwXvLSDpq/m8NH37MTtGuOK+r386a1f5oGnbua1zvOn/QjpwxXCGdZWrkWhkjJz/fGff5xT/ad46PaHKMotSsLohB2kuLaTUuOJISldnHTqcfNVFjPax7Mexvph4MSsD030dyPaJnDjhQ0APH7wOqRNwOGUMie5Nz4PV/+c4ooV/Ovv/ANv/0Md3//Eb3K0cwmf/dFbcWejpQ9XCPsV5hZSW1674MSQ77/5fX5w4Ad87qrP8a7z3pWk0Qk7SHFtt1JTXKd0cVJHI+RVQoUv+T9bJMaz3nxNoDUk0d+NaJvADRf+B68dvYjOwEFpE0gXSsF5N1P1m008pf+IkXAXS8uP8ZmfFHDf/V+UolkIh1toHF9bbxsf/9nHuXzF5fzplX+axJEJO+TYPYCs5/FC6/dZt3oDh5r3YmYnoyyYddTaLGasuc5sxS3sMbG4nqU9Z+XKDbS2zv67sX37nVTRxLvDf8XfPwq1tfeya5e0CaQVpbh6+1dA/wOM9PCf/6fS7hEJIRLgrfLy3LHn0FqjJiw8TkRYh7nrp3cR0iEevP1BclxSmqU7mbm2WyQx5L7P35WaxUmBt2HwpLSE2K1gCeSWm+Mxi4QXro31c5PnQXJKV/LZB89Km0A6UwrypbAWIl3UV9XTN9LHyeDJOX/vfc/fx1NHn+LrN32d1RWrLRidSDUpru0WKa7f/+7VqVmc1B7Z8lwWM9pLqYQTQxJeuPbqH0GwCS77N8grs2jgQgghpvItNW2Wt/3gNn526GdorRP6vjdOvcHOJ3bygfUf4O7Nd1s5RJFCKtFfgHSwdetWvW/fPruHMTdj/fDDEtj0N7DxL6x/vqfeb3aFvDU5eZxiAV642+y6d/vcZzqmOfEoPH0rbPgM+CS+SQghUu3B1x/kc09/jpaeFi5ZdglfuPoL3LjmxrhtIkNjQ1z8rYvp7O/kzd97k0XFi1I8YrEQSqlXtNZbY90nM9d2yymGouWp2UgmNAynnpKWEKfwbIDBdhjpXdjPGToNL94D5ReZkzThSCnNsRdCpNxHLvoI73ziHb5167fo6Ovgpj03ceW/XsnjLY/HnMne+fhODpw+wHd+4ztSWGcYKa6dIJIYYrkzz0NoQFpCnGIOiSFxaQ0v/rYp0C9/CNz5yRmbSKqU5tgLIWyT687lY1s+xuF7D/PP7/9n2nrbuP7B67n6e1fz9NGnxx/3xJEnuO+F+/i9rb/H+9a9z8YRCytIce0EHi8E/aZQslJHIyg3LL7a2ucRiUlGcd38AJx4BDZ/Eco3JmdcIulSmmMvhLBdnjuP3936uxy+9zD333w/h7sOc/X3rua6f7uOXxz+BXf99C7qq+r5yo1fsXuowgJSXDuBxwujAXN530rtjVC9TRa7OUXJanDlzr+4DjbBq38AS64D76eSOzaRVCnNsRdCOEZBTgG/f8nv0/zJZr723q9x8PRB3vf999HR1yG7MGYwKa6doNQkhhC0sDVkuAu690GNtIQ4hisHStfNr7gOj8GvPwIqFy77Lih5KTtZdPfMyWT3zEwmPfZiosLcQj617VO0fKqFr9/0dR68/UEuXnax3cMSFpFPZCeIxPFZ2nd96glAy2JGpym70CSGvP6XMNKT+Pcd/CJ0vQAX/7NZECscLeGscpERpMdexFOUW8QnL/0kd2y8w+6hCAtJce0ERSvAlW9tcd3eCLllUCVnyo7i+3tYdgsc/Fv4r1Xw5udnTw858xIc+DzU/ibUyRt0Okg4q1xkhLn02MsMtxCZR3KuneJnF0JxHVz938n/2VrDI6ugwgfv+Unyf75YuJ434M3PwfGfmJ0bN/wxeD8JuaWTHzfWD7/wQWgQ3vcm5JXbMlwhRHwulxuthzCFddQoShUQDofGb4nOcJtC/EpgL0VF98iJlxBpQHKu00E0McQKwSbob4Wl0hLiWBWb4D3/CTe9AovfDW/8hTkheuvLMNp37nGv/rHZBGjb96SwFsKhEu2xlxQZITKTFNdO4fFCXwuERpL/szsazFdZzOh8lVvgqkfgvS9B5SWw/8/gkdXw9leg7T+g6Zuw/tNQc63dIxVCxJFoj72kyAiRmaS4dopSL+gQ9B9J/s/uaDQtJyVrkv+zhTWqLoZrfg43Pm/aeV77DOz9X1B+IVwks1pCOFmiPfaSIiNEZpLi2imsSgwZ7YNTT5qWEKWS+7OF9aq3wbW/guufhdUfhSv+HdwFdo9KCDGL7dvv5OjRA4TDIY4ePRCzh1pSZITITDl2D0BEWFVcv/ppGA3CqruS+3NFai2+0vwRQmSMaMG9c+e9tLW9zcqVG9i1SxYzCpHupLh2irxyKFic3EWNxx+B5m/Bhj+BRZcn7+cKIYRIiu3b75RiWogMI20hTlJan/DM9azZqIOn4MWPQflFsOkLFgxWCCGEEEJMJTPXTuLxmtnmWUzNRm1t3cuOHfcAkcuMWpvCejQA1z0J7nyLBy6EEEIIIUBmrp2l1AvDnbNugz1rNmrzt+Dko7D5S1B+gdWjFkIIIYQQEVJcO0mCixpnzEYNHIZX/hCWXGd2+BNCCCGEECkjxbWTjBfXh2Z8WLxs1FV16+H5j4ArDy77Lig5vEIIIYQQqWRZ9aWUKlBKvaSUel0pdVAp9fkYj/moUqpTKbU/8udjE+67Syl1OPInO3LkSlaDypk1MSReNupP/mYDdL0Il3wTipanYsRCCCGEEGICKxc0DgPXaq37lFK5wF6l1C+01i9Medy/a61/f+INSqlK4K+BrYAGXlFKPaK1nrkZOd25ck2BfeYFCA3F3SwkVjbqN3fdzSbX56F2O9T+71SOWgghhBBCRFg2c62Nvsg/cyN/dILf/l6gUWvdHSmoG4GbLBim8yz/AJx6Ah5ZC4f+H4SGYz5s0u5fTS9yk+dBKDwPtv7fFA9YCCGEEEJEWdqUq5RyK6X2A6cxxfKLMR72QaXUG0qpHymlVkRuWwYcm/CY45HbMt/mL8G1j0NJHez7BPz3OmjaDaGR+N/z6h9BsAku+zezGY0QQgghhLCFpcW11jqktd4MLAcuUUptnPKQ/wbqtNabMLPT35vrcyildiil9iml9nV2di580HZTCmquheufhWt+ZWajX/odeNQLzd+B8Ojkx594FJr+BTb8ESy52pYhCyGEEEIIIyVxElrrs5jVdzdNub1Lax3te/g28K7I308AKyY8dHnktlg/e7fWeqvWeuuiRYuSO3A7KQVLb4Qbn4erfw751fDiPfDoBmj5NwiPwdBpc1v5Jtj0t3aPWAghhBAi61mZFrJIKVUe+XshcAPwzpTHLJ3wz9uAtyN//xVwo1KqQilVAdwYuS37KAXn3QzvfQne8wjklsILd8HPLoBnboeRs3D5Q7ILoxBCCCGEA1iZFrIU+J5Syo0p4n+otX5UKfUFYJ/W+hHgk0qp24AxoBv4KIDWulsp9TfAy5Gf9QWtdbeFY3U+pWD5rbDsFjj+U3jzc3Dm1+D7Ryi/0O7RCSGEEEIIQGmdaICH823dulXv27fP7mGkhg5D79tQdr4pvIUQQgghREoopV7RWm+NdZ+VM9fCSsoF5RfYPQohhBBCCDGB7I8thBBCCCFEkkhxLYQQQgghRJJIcS2EEEIIIUSSSHEthBBCCCFEkkhxLYQQQgghRJJIcS2EEEIIIUSSSHEthBBCCCFEkkhxLYQQQgghRJJIcS2EEEIIIUSSSHEthBBCCCFEkkhxLYQQQgghRJJIcS2EEEIIIUSSSHEthBBCCCFEkkhxLYQQQgghRJJIcS2EEEIIIUSSKK213WNIGqVUJ9Bqw1NXA2dseF5hDzne2UWOd3aR45095Fhnl2Qf71qt9aJYd2RUcW0XpdQ+rfX/b+/+QiUv6ziOvz/sahYl/REsdMOgBTuUbJZlf2wzCLYitAjLm6SbCJKK2gupi7rxooiI6B+BkkFYQUlLZCqysGJrrC5q1lJJUBabGwjZtpSZ3y5+z8Jxz5k958w8Z2bOzvsFw8z5zczD9zmfMzPf85vnN/P6Wdeh6TDvxWLei8W8F4dZL5Zp5u2yEEmSJKkTm2tJkiSpE5vrPr4z6wI0Vea9WMx7sZj34jDrxTK1vF1zLUmSJHXinmtJkiSpE5trSZIkqROb61Uk2ZFkf5LfJvlNkk+27S9OcleSP7TzF7XtSfK1JI8meTjJpW37lUkeXHb6d5KrZzk3rdQr73bdl9oYR9ptMqt5aXWd8/5ikkfa6YOzmpNGGyPvi5McTPKfJHtPGWtPkt+1v4UbZjEfjdY565uTHEvyyCzmorX1ynvUOJOwuV7d08BnqmoJuBz4eJIl4Abg7qraCdzdfgZ4F7CznT4KfAugqvZX1a6q2gW8AzgB3DnVmWg9uuSd5M3AW4BLgFcDlwG7pzgPrU+vvN8DXArsAt4I7E1y7jQnonXZaN5PAJ8Avrx8kCTbgG8w/D0sAde2cTQ/umTdfBfYs+kVaxK98h41zthsrldRVUer6nC7/E/gCHABcBVwS7vZLcDJvdBXAd+rwX3AC5O87JRhPwDcXlUnNn0C2pCOeRdwDnA28BzgLODxqU1E69Ix7yXgQFU9XVX/Ah7GF+O5s9G8q+pYVR0C/nvKUG8AHq2qP1bVU8AP2hiaEx2zpqoOMDRjmlO98j7NOGOzuV5DkouA1wK/As6vqqPtqr8B57fLFwCPLbvbX1gZzIeAWzetUHUxSd5VdRDYDxxtpzuq6sgUytaYJnx8PwTsSfK8JOcBVwI7plC2xrTOvEdZz/O85sSEWWuL6ZX3KeOMbfskdz7TJXk+8GPgU1X15PLls1VVSdb1OYZtL9drgDs2pVB1MWneSV4JvAq4sG26K8kVVXXPZtWs8U2ad1XdmeQy4JfA34GDwP82sWRNoNfzueafWS+Wjr3as8aZpCb3XI+Q5CyGX/L3q+onbfPjJ5d7tPNjbftfefYeqwvbtpOuAW6rqhVvPWk+dMr7fcB9VXW8qo4DtwNvmkb92phej++qurEdV/FOIMDvp1G/NmaDeY+y1vO85kCnrLVF9Mp7xDhjs7leRYZ/e24CjlTVV5ZdtQ+4rl2+Dvjpsu0fzuBy4B/L3pIAuBaXhMytjnn/GdidZHt7oO5mWLulOdIr7yTbkrykjXkJw4GsHrA8Z8bIe5RDwM4kr0hyNsNSv32969X4OmatLaBX3qcZZ/za/IbGlZK8FbgH+DXwTNv8WYY1OD8CXg78Cbimqp5owXyd4WCmE8BHqur+NtZFwL3Ajqp6Bs2dXnm3TxP4JvA2hoMbf1FVn57qZLSmjnmfAxxu938S+FhVPTi9mWg9xsj7pcD9wLnt9seBpfZ287uBrwLbgJur6sapTkan1TnrW4G3A+cxHJj++aq6aYrT0Rp65c2wY2TFOFX187Frs7mWJEmS+nBZiCRJktSJzbUkSZLUic21JEmS1InNtSRJktSJzbUkSZLUic21JC2wJF9IsnfWdUjSmcLmWpIkSerEz7mWpAWT5HMM31x2DHgMeIDhCxWoqm/PsDRJ2vK2z7oASdL0JHkdw1d372J4DTgMPGBTLUl92FxL0mK5Aritqk4AJNk343ok6YzimmtJkiSpE5trSVosB4Crkzw3yQuA9wIkuT7J9bMtTZK2PpeFSNICqarDSX4IPMRwQOOhdtXFwL0zK0ySzhB+WogkiSQ/A95fVU/NuhZJ2spsriVJkqROXHMtSZIkdWJzLUmSJHVicy1JkiR1YnMtSZIkdWJzLUmSJHVicy1JkiR18n+0PHjoPM9d3wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HV8In3lnpVke",
        "outputId": "f3ca213d-31d7-471a-d2ee-5bfd840ee41e"
      },
      "source": [
        "1 - np.sum(np.power(df['y'] - df['y^s'], 2)) / np.sum(np.power(df['y'] - np.mean(df['y']), 2))\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9573670610356475"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hoI5ai4hpXHo"
      },
      "source": [
        "\"\"\"коэф. детерминации \"\"\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yXn8ycNN8rs"
      },
      "source": [
        "дарбин уотсон"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVQL6Mz6N-mU"
      },
      "source": [
        "e=df['s']"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGLol7zSPnVj",
        "outputId": "25f1920b-3b08-4f3b-b44d-d8b49c28e296"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 48 entries, 2007-01-01 to 2010-12-01\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   y       48 non-null     float64\n",
            " 1   t       48 non-null     int64  \n",
            " 2   y^      48 non-null     float64\n",
            " 3   s       48 non-null     float64\n",
            " 4   y^s     48 non-null     float64\n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 2.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrSeAxd_TuhU",
        "outputId": "f7886ae8-b706-408f-ca8b-bd28d6d508ff"
      },
      "source": [
        "e2=np.multiply(e,e)\n",
        "e2"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2007-01-01    0.000982\n",
              "2007-02-01    0.066487\n",
              "2007-03-01    0.344606\n",
              "2007-04-01    0.385905\n",
              "2007-05-01    0.305692\n",
              "2007-06-01    0.129295\n",
              "2007-07-01    0.000452\n",
              "2007-08-01    0.132905\n",
              "2007-09-01    0.331062\n",
              "2007-10-01    0.373563\n",
              "2007-11-01    0.283042\n",
              "2007-12-01    0.081416\n",
              "2008-01-01    0.000982\n",
              "2008-02-01    0.066487\n",
              "2008-03-01    0.344606\n",
              "2008-04-01    0.385905\n",
              "2008-05-01    0.305692\n",
              "2008-06-01    0.129295\n",
              "2008-07-01    0.000452\n",
              "2008-08-01    0.132905\n",
              "2008-09-01    0.331062\n",
              "2008-10-01    0.373563\n",
              "2008-11-01    0.283042\n",
              "2008-12-01    0.081416\n",
              "2009-01-01    0.000982\n",
              "2009-02-01    0.066487\n",
              "2009-03-01    0.344606\n",
              "2009-04-01    0.385905\n",
              "2009-05-01    0.305692\n",
              "2009-06-01    0.129295\n",
              "2009-07-01    0.000452\n",
              "2009-08-01    0.132905\n",
              "2009-09-01    0.331062\n",
              "2009-10-01    0.373563\n",
              "2009-11-01    0.283042\n",
              "2009-12-01    0.081416\n",
              "2010-01-01    0.000982\n",
              "2010-02-01    0.066487\n",
              "2010-03-01    0.344606\n",
              "2010-04-01    0.385905\n",
              "2010-05-01    0.305692\n",
              "2010-06-01    0.129295\n",
              "2010-07-01    0.000452\n",
              "2010-08-01    0.132905\n",
              "2010-09-01    0.331062\n",
              "2010-10-01    0.373563\n",
              "2010-11-01    0.283042\n",
              "2010-12-01    0.081416\n",
              "Name: s, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obx8dsq5W-BI",
        "outputId": "f73d1de7-94d9-46c9-a759-b767a3f82f22"
      },
      "source": [
        "e2sum=sum(e2)\n",
        "e2sum"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.741623232231143"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_ZaJLSvVoQ2"
      },
      "source": [
        "e2square = []\n",
        "for i in range(1,len(e2)):\n",
        "    x = (e2[i] - e2[i-1])**2\n",
        "    e2square.append(x)"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wj4s4vrnVRdx",
        "outputId": "4798f040-4a83-432c-e9b3-26dc42754488"
      },
      "source": [
        "e2square"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.004290892585033088,\n",
              " 0.07735034523119011,\n",
              " 0.0017056574619962988,\n",
              " 0.006434165218192279,\n",
              " 0.03111604026848085,\n",
              " 0.01660050864404139,\n",
              " 0.017543771527735355,\n",
              " 0.03926614797254567,\n",
              " 0.0018063714505922315,\n",
              " 0.0081941335158859,\n",
              " 0.04065285484039333,\n",
              " 0.0064696980679828184,\n",
              " 0.004290892585033088,\n",
              " 0.07735034523119011,\n",
              " 0.0017056574619962988,\n",
              " 0.006434165218192279,\n",
              " 0.03111604026848085,\n",
              " 0.01660050864404139,\n",
              " 0.017543771527735355,\n",
              " 0.03926614797254567,\n",
              " 0.0018063714505922315,\n",
              " 0.0081941335158859,\n",
              " 0.04065285484039333,\n",
              " 0.0064696980679828184,\n",
              " 0.004290892585033088,\n",
              " 0.07735034523119011,\n",
              " 0.0017056574619962988,\n",
              " 0.006434165218192279,\n",
              " 0.03111604026848085,\n",
              " 0.01660050864404139,\n",
              " 0.017543771527735355,\n",
              " 0.03926614797254567,\n",
              " 0.0018063714505922315,\n",
              " 0.0081941335158859,\n",
              " 0.04065285484039333,\n",
              " 0.0064696980679828184,\n",
              " 0.004290892585033088,\n",
              " 0.07735034523119011,\n",
              " 0.0017056574619962988,\n",
              " 0.006434165218192279,\n",
              " 0.03111604026848085,\n",
              " 0.01660050864404139,\n",
              " 0.017543771527735355,\n",
              " 0.03926614797254567,\n",
              " 0.0018063714505922315,\n",
              " 0.0081941335158859,\n",
              " 0.04065285484039333]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PTIsOlMWd44",
        "outputId": "07838082-cb12-490d-f49d-d531164c4c5e"
      },
      "source": [
        "e2squaresum=sum(e2square)\n",
        "e2squaresum"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9992526490682946"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMPyi0H_WtuQ",
        "outputId": "e7eed763-6cbd-4f6a-aac8-32eae0a2518e"
      },
      "source": [
        "re=e2squaresum/e2sum\n",
        "re"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10257557957714546"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYq-W8Dka7DU"
      },
      "source": [
        " коэффициент автокорреляции первого порядка"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhntjxfSXbTz",
        "outputId": "9f592b9d-1df9-4cab-e888-33f266e0fddd"
      },
      "source": [
        "DW=2*(1-re)\n",
        "DW"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.794848840845709"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLFwQJFiXtM4"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rl_eXxWYcVi2",
        "outputId": "66145e4b-2b09-4d61-a7f4-c2716fbd7861",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 48 entries, 2007-01-01 to 2010-12-01\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   y       48 non-null     float64\n",
            " 1   t       48 non-null     int64  \n",
            " 2   y^      48 non-null     float64\n",
            " 3   s       48 non-null     float64\n",
            " 4   y^s     48 non-null     float64\n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 2.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWeT0QnmdTIq"
      },
      "source": [
        "1.50 и 1.59"
      ]
    }
  ]
}