forecast_sales_using_numpyro.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sungkim11/forecast-sales/blob/main/forecast_sales_using_numpyro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Forecast Sales with NumPyro"
      ],
      "metadata": {
        "id": "RqWvOREF5Lbf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NumPyro is probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU."
      ],
      "metadata": {
        "id": "188oZ-Lg5Tg3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Prequisites"
      ],
      "metadata": {
        "id": "t5mhxZKL5nEa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following are prerequisite for this tutorial:\n",
        "\n",
        "- Data\n",
        "- Python Package: NumPyro\n",
        "- Python Package: Jax\n",
        "- Python Package: Statsmodels"
      ],
      "metadata": {
        "id": "KWJWGZ0K5vbp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.1. Data"
      ],
      "metadata": {
        "id": "mfdzR3yw56v4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Three data sources were used for this tutorial where United Stated Census Bureau Monthly Retail Sales is used to forecast future sales and both FRED Data and OECD Data were used as Regressors data."
      ],
      "metadata": {
        "id": "S5PG6l9f6AXa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**United Stated Census Bureau Monthly Retail Sales**\n",
        "United Stated Census Bureau maintains Monthly Retail Trade Report, from January 1992 to Present. This data was picked to illustrate forecasting because it has extensive historical data with same monthly frequency. Data is available as Excel spreadsheet format at https://www.census.gov/retail/mrts/www/mrtssales92-present.xls"
      ],
      "metadata": {
        "id": "JHPHhRr76Cot"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**FRED Data: https://fred.stlouisfed.org/**\n",
        "\n",
        "- Inflation\n",
        "- CPI\n",
        "- M1\n",
        "- M2\n",
        "- Interest Rate"
      ],
      "metadata": {
        "id": "3VbAVbXl6FuQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**OECD Data: https://data.oecd.org/leadind/composite-leading-indicator-cli.htm#indicator-chart**\n",
        "\n",
        "- Composite leading indicator (CLI)\n",
        "- Business confidence index (BCI)\n",
        "- Consumer confidence index (CCI)"
      ],
      "metadata": {
        "id": "2eu-DoRo6Kmx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2. Python Packages"
      ],
      "metadata": {
        "id": "N7y_R84B6Iy5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1.2.1. Install Python Packages"
      ],
      "metadata": {
        "id": "o0qdiHYi6Xh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile requirements.txt\n",
        "\n",
        "pandas==1.3.5\n",
        "matplotlib==3.2.2\n",
        "jax==0.3.8\n",
        "numpyro==0.9.2\n",
        "statsmodels==0.10.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPbsaG63td3W",
        "outputId": "a9415d72-47b1-4831-8451-c0b133241f00"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing requirements.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qmat3AXMth-k",
        "outputId": "4daad6a2-0242-427b-d8a2-02782ae2d104"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas==1.3.5 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (1.3.5)\n",
            "Requirement already satisfied: matplotlib==3.2.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (3.2.2)\n",
            "Requirement already satisfied: jax==0.3.8 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (0.3.8)\n",
            "Collecting numpyro==0.9.2\n",
            "  Downloading numpyro-0.9.2-py3-none-any.whl (283 kB)\n",
            "\u001b[K     |████████████████████████████████| 283 kB 10.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: statsmodels==0.10.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (0.10.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5->-r requirements.txt (line 2)) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5->-r requirements.txt (line 2)) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5->-r requirements.txt (line 2)) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.2.2->-r requirements.txt (line 3)) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.2.2->-r requirements.txt (line 3)) (1.4.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.2.2->-r requirements.txt (line 3)) (3.0.8)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax==0.3.8->-r requirements.txt (line 4)) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jax==0.3.8->-r requirements.txt (line 4)) (4.2.0)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from jax==0.3.8->-r requirements.txt (line 4)) (1.4.1)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax==0.3.8->-r requirements.txt (line 4)) (1.0.0)\n",
            "Requirement already satisfied: jaxlib>=0.1.65 in /usr/local/lib/python3.7/dist-packages (from numpyro==0.9.2->-r requirements.txt (line 5)) (0.3.7+cuda11.cudnn805)\n",
            "Collecting multipledispatch\n",
            "  Downloading multipledispatch-0.6.0-py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from numpyro==0.9.2->-r requirements.txt (line 5)) (4.64.0)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels==0.10.2->-r requirements.txt (line 6)) (0.5.2)\n",
            "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib>=0.1.65->numpyro==0.9.2->-r requirements.txt (line 5)) (2.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.4.0->statsmodels==0.10.2->-r requirements.txt (line 6)) (1.15.0)\n",
            "Installing collected packages: multipledispatch, numpyro\n",
            "Successfully installed multipledispatch-0.6.0 numpyro-0.9.2\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Code"
      ],
      "metadata": {
        "id": "owoLig2m6gEw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1. Import Python Package"
      ],
      "metadata": {
        "id": "nkWBEOIF6ixa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import python packages and show its version. Showing version is important since it will enable other users to replicate your work using same python version and python packages version."
      ],
      "metadata": {
        "id": "9HKYgi3V6nH6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import platform\n",
        "\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import pandas as pd\n",
        "from IPython.display import set_matplotlib_formats\n",
        "\n",
        "import statsmodels\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "\n",
        "import jax\n",
        "from jax import numpy as jnp\n",
        "from jax import random\n",
        "\n",
        "import numpyro\n",
        "from numpyro import distributions as dist\n",
        "from numpyro.contrib.control_flow import scan\n",
        "from numpyro.diagnostics import autocorrelation, hpdi\n",
        "from numpyro.infer import MCMC, NUTS, Predictive\n",
        "\n",
        "if \"NUMPYRO_SPHINXBUILD\" in os.environ:\n",
        "    set_matplotlib_formats(\"svg\")\n",
        "\n",
        "numpyro.set_host_device_count(8)\n",
        "\n",
        "\n",
        "print('Python: ', platform.python_version())\n",
        "print('pandas: ', pd.__version__)\n",
        "print('matplotlib: ', matplotlib.__version__)\n",
        "print('Jax: ', jax.__version__)\n",
        "print('NumPyro: ', numpyro.__version__)\n",
        "print('statsmodels: ', statsmodels.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQ2zajmlk0TQ",
        "outputId": "44d0f8fc-6b7e-4d8e-f81c-65475fe13754"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python:  3.7.13\n",
            "pandas:  1.3.5\n",
            "matplotlib:  3.2.2\n",
            "Jax:  0.3.8\n",
            "NumPyro:  0.9.2\n",
            "statsmodels:  0.10.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2. Mount Storage"
      ],
      "metadata": {
        "id": "bg5A4pyZ6028"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "hZXG8Wn6ojmw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09ad8511-3600-4b58-e858-286178276f47"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.3. Prep Data"
      ],
      "metadata": {
        "id": "1BcdudrX67Pp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Import and validate dataset"
      ],
      "metadata": {
        "id": "sNtGQaV36_VP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/forecast-sales/data/mrtssales92-present.csv', index_col = None)\n",
        "retail_sales.info()"
      ],
      "metadata": {
        "id": "hyOA9YiBpNSV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4caa1a7-8e45-421a-f789-19fe0854656e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 205 entries, 0 to 204\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   month        205 non-null    object\n",
            " 1   sales_total  205 non-null    int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 3.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Resize dataset"
      ],
      "metadata": {
        "id": "Y8wuSMiU7S7D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will ONLY be using retail sales data from 01/01/2005 thru 12/31/2018."
      ],
      "metadata": {
        "id": "42_gHTZr7Z4G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales = retail_sales[:-37]\n",
        "retail_sales.info()\n",
        "retail_sales.tail(5)"
      ],
      "metadata": {
        "id": "N3egiM887yN1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "6d3130bb-03b1-444a-d4a1-c51d8b990813"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 168 entries, 0 to 167\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   month        168 non-null    object\n",
            " 1   sales_total  168 non-null    int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 2.8+ KB\n"
          ]
        },
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              "         month  sales_total\n",
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              "          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
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Convert 'month' column to datetime column."
      ],
      "metadata": {
        "id": "6ZKEFU_R7K24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales['month'] = pd.to_datetime(retail_sales.month)\n",
        "retail_sales.head(5)"
      ],
      "metadata": {
        "id": "CWUElzlmsNb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "508b6c5f-e000-4721-e1c1-afd7250abbf6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       month  sales_total\n",
              "0 2005-01-01       293261\n",
              "1 2005-02-01       295062\n",
              "2 2005-03-01       339141\n",
              "3 2005-04-01       335632\n",
              "4 2005-05-01       345348"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e66bd736-8e6d-4bba-ba81-e6e15b146bd9\">\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>month</th>\n",
              "      <th>sales_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2005-01-01</td>\n",
              "      <td>293261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2005-02-01</td>\n",
              "      <td>295062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2005-03-01</td>\n",
              "      <td>339141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2005-04-01</td>\n",
              "      <td>335632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2005-05-01</td>\n",
              "      <td>345348</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e66bd736-8e6d-4bba-ba81-e6e15b146bd9')\"\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",
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              "  </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-e66bd736-8e6d-4bba-ba81-e6e15b146bd9 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-e66bd736-8e6d-4bba-ba81-e6e15b146bd9');\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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Split datasets for Exploratory Data Analysis\n",
        "- Train\n",
        "- Test"
      ],
      "metadata": {
        "id": "yQ0P4gGr85Rq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales_train = retail_sales[:-24]\n",
        "retail_sales_test = retail_sales[-24:]\n",
        "\n",
        "print('Train: ', retail_sales_train.shape)\n",
        "print('Test: ', retail_sales_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_9RRdUh8_ft",
        "outputId": "fe534e05-8c37-4277-c0d1-c7e941c765f5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train:  (144, 2)\n",
            "Test:  (24, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.4. Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "PmoChUwi9313"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Set Plotting Parameters"
      ],
      "metadata": {
        "id": "8VvMHv8i91Ib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get current size\n",
        "fig_size = plt.rcParams[\"figure.figsize\"]\n",
        " \n",
        "# Set figure width to 12 and height to 9\n",
        "fig_size[0] = 30\n",
        "fig_size[1] = 5\n",
        "plt.rcParams[\"figure.figsize\"] = fig_size"
      ],
      "metadata": {
        "id": "OxZ4BQrL-A_A"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart Retail Sales"
      ],
      "metadata": {
        "id": "Er5bpCAQ-DhT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales_train.plot(x='month', y='sales_total', kind='line', grid=1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "Lg6ewURF-NML",
        "outputId": "383f5596-e864-41de-888e-35405dcae599"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Determine Retail Sales Data (Time-Series) has trend"
      ],
      "metadata": {
        "id": "hj6TPNS8-WG7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we need to determine if the retail sales data is non-stationary or it has trends using Dickey Fuller Test, which is a statistical hypothesis test that allows you to detect non-stationarity. The stationary time-series implies there is no predictable pattern in the long term."
      ],
      "metadata": {
        "id": "j4n4uCt_-XuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "adf, pval, usedlag, nobs, crit_vals, icbest =  adfuller(retail_sales_train.sales_total.values)\n",
        "print('ADF test statistic:', adf)\n",
        "print('ADF p-values:', pval)\n",
        "print('ADF number of lags used:', usedlag)\n",
        "print('ADF number of observations:', nobs)\n",
        "print('ADF critical values:', crit_vals)\n",
        "print('ADF best information criterion:', icbest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lkqB77T-biZ",
        "outputId": "699fb04d-1752-4da0-f866-9d6d0ec13d13"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF test statistic: -0.41114662261582896\n",
            "ADF p-values: 0.9082209893408628\n",
            "ADF number of lags used: 14\n",
            "ADF number of observations: 129\n",
            "ADF critical values: {'1%': -3.482087964046026, '5%': -2.8842185101614626, '10%': -2.578864381347275}\n",
            "ADF best information criterion: 2712.938864098956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the value, which is bigger than 0.05, you can determine time-series data is non-stationary."
      ],
      "metadata": {
        "id": "fTwlHlDx-gzE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.5. Prep Data for Modeling"
      ],
      "metadata": {
        "id": "vb5cGonHAKGm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For NumPyro, we will only need \"sales_total\" since data/time is not included for forecasting."
      ],
      "metadata": {
        "id": "N3L_LMrx6hJy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales_data = retail_sales[\"sales_total\"].values\n",
        "print(\"Length of time series:\", retail_sales.shape[0])"
      ],
      "metadata": {
        "id": "8MF5Jzu9CTph",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94b1df6d-975e-477c-dab9-98c79c505876"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of time series: 168\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.figure(figsize=(20, 5))\n",
        "#plt.plot(retail_sales[\"month\"], retail_sales_data)\n",
        "#plt.show()"
      ],
      "metadata": {
        "id": "2-mSw1qKDGx-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split dataset to train and test then convert the dataframe to Jax Numpy array."
      ],
      "metadata": {
        "id": "TcRoN5pz6uxJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train, y_test = jnp.array(retail_sales_data[:144], dtype=jnp.float32), retail_sales_data[144:]\n",
        "y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-wJ4i2rxNdm",
        "outputId": "0e295fb8-2c52-4ce5-d5d9-166a286c8c12"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((144,), (24,))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDs4HIhs6_FS",
        "outputId": "23af31fc-b1ef-4ff3-94ef-bc4503a8c462"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeviceArray([293261., 295062., 339141., 335632., 345348., 350945.,\n",
              "             351827., 355701., 333289., 336134., 343798., 405608.,\n",
              "             318546., 314051., 361993., 351667., 373560., 366615.,\n",
              "             362203., 375795., 346214., 348796., 356928., 417991.,\n",
              "             328877., 323162., 374142., 358535., 391512., 376639.,\n",
              "             372354., 388016., 353936., 368681., 377802., 426077.,\n",
              "             342639., 343893., 372907., 368920., 397956., 378507.,\n",
              "             383726., 382862., 350542., 349847., 335545., 384236.,\n",
              "             310188., 299412., 328526., 329854., 347728., 344405.,\n",
              "             348071., 353392., 324646., 338610., 339363., 400281.,\n",
              "             314557., 310925., 360679., 356333., 365605., 358604.,\n",
              "             361939., 362627., 345999., 355209., 365828., 426663.,\n",
              "             335587., 337314., 387088., 380775., 391999., 388700.,\n",
              "             384682., 394609., 375025., 379482., 391220., 451821.,\n",
              "             355184., 372401., 414149., 392949., 418608., 400975.,\n",
              "             396026., 417922., 385609., 399400., 411065., 462102.,\n",
              "             375468., 373974., 421748., 408551., 437214., 414816.,\n",
              "             422532., 435215., 396415., 415942., 424095., 477362.,\n",
              "             383488., 380454., 433052., 431598., 459441., 433458.,\n",
              "             443498., 451663., 421628., 438697., 439344., 502208.,\n",
              "             398555., 388766., 446581., 439993., 465244., 450172.,\n",
              "             459960., 458313., 432898., 446746., 446334., 518656.,\n",
              "             401911., 415093., 460910., 451121., 470116., 464721.,\n",
              "             462004., 471690., 448516., 453155., 467232., 539667.],            dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwivqIMb7Gow",
        "outputId": "a92ad2af-327f-4078-9c1b-fce1e874c4c8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([421943, 418315, 483082, 465788, 494621, 481729, 475374, 490324,\n",
              "       469710, 476014, 497748, 558215, 444286, 435373, 508383, 480373,\n",
              "       527991, 507106, 505209, 520544, 477137, 503602, 518457, 556614])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.6. Develop Forecast Model"
      ],
      "metadata": {
        "id": "IrKybsqX-qgM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Define model"
      ],
      "metadata": {
        "id": "vDh60yrQApUS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seasonal, Global Trend (SGT) model is copied from NumPyro website => https://num.pyro.ai/en/stable/tutorials/time_series_forecasting.html"
      ],
      "metadata": {
        "id": "D7ryJkgj7eVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sgt(y, seasonality, future=0):\n",
        "    # heuristically, standard derivation of Cauchy prior depends on\n",
        "    # the max value of data\n",
        "    cauchy_sd = jnp.max(y) / 150\n",
        "\n",
        "    # NB: priors' parameters are taken from\n",
        "    # https://github.com/cbergmeir/Rlgt/blob/master/Rlgt/R/rlgtcontrol.R\n",
        "    nu = numpyro.sample(\"nu\", dist.Uniform(2, 20))\n",
        "    powx = numpyro.sample(\"powx\", dist.Uniform(0, 1))\n",
        "    sigma = numpyro.sample(\"sigma\", dist.HalfCauchy(cauchy_sd))\n",
        "    offset_sigma = numpyro.sample(\n",
        "        \"offset_sigma\", dist.TruncatedCauchy(low=1e-10, loc=1e-10, scale=cauchy_sd)\n",
        "    )\n",
        "\n",
        "    coef_trend = numpyro.sample(\"coef_trend\", dist.Cauchy(0, cauchy_sd))\n",
        "    pow_trend_beta = numpyro.sample(\"pow_trend_beta\", dist.Beta(1, 1))\n",
        "    # pow_trend takes values from -0.5 to 1\n",
        "    pow_trend = 1.5 * pow_trend_beta - 0.5\n",
        "    pow_season = numpyro.sample(\"pow_season\", dist.Beta(1, 1))\n",
        "\n",
        "    level_sm = numpyro.sample(\"level_sm\", dist.Beta(1, 2))\n",
        "    s_sm = numpyro.sample(\"s_sm\", dist.Uniform(0, 1))\n",
        "    init_s = numpyro.sample(\"init_s\", dist.Cauchy(0, y[:seasonality] * 0.3))\n",
        "\n",
        "    def transition_fn(carry, t):\n",
        "        level, s, moving_sum = carry\n",
        "        season = s[0] * level**pow_season\n",
        "        exp_val = level + coef_trend * level**pow_trend + season\n",
        "        exp_val = jnp.clip(exp_val, a_min=0)\n",
        "        # use expected vale when forecasting\n",
        "        y_t = jnp.where(t >= N, exp_val, y[t])\n",
        "\n",
        "        moving_sum = (\n",
        "            moving_sum + y[t] - jnp.where(t >= seasonality, y[t - seasonality], 0.0)\n",
        "        )\n",
        "        level_p = jnp.where(t >= seasonality, moving_sum / seasonality, y_t - season)\n",
        "        level = level_sm * level_p + (1 - level_sm) * level\n",
        "        level = jnp.clip(level, a_min=0)\n",
        "\n",
        "        new_s = (s_sm * (y_t - level) / season + (1 - s_sm)) * s[0]\n",
        "        # repeat s when forecasting\n",
        "        new_s = jnp.where(t >= N, s[0], new_s)\n",
        "        s = jnp.concatenate([s[1:], new_s[None]], axis=0)\n",
        "\n",
        "        omega = sigma * exp_val**powx + offset_sigma\n",
        "        y_ = numpyro.sample(\"y\", dist.StudentT(nu, exp_val, omega))\n",
        "\n",
        "        return (level, s, moving_sum), y_\n",
        "\n",
        "    N = y.shape[0]\n",
        "    level_init = y[0]\n",
        "    s_init = jnp.concatenate([init_s[1:], init_s[:1]], axis=0)\n",
        "    moving_sum = level_init\n",
        "    with numpyro.handlers.condition(data={\"y\": y[1:]}):\n",
        "        _, ys = scan(\n",
        "            transition_fn, (level_init, s_init, moving_sum), jnp.arange(1, N + future)\n",
        "        )\n",
        "    if future > 0:\n",
        "        numpyro.deterministic(\"y_forecast\", ys[-future:])"
      ],
      "metadata": {
        "id": "gr5DwLDBPlYn"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Choose a good value for seasonality. I used seasonality = 60, but there must be a better way to determine this number."
      ],
      "metadata": {
        "id": "YAIKhzdS8Av0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Lag values sorted according to their autocorrelation values:\\n\")\n",
        "print(jnp.argsort(autocorrelation(y_train))[::-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZ3BdMT71zNi",
        "outputId": "43ecfedf-cbbe-4f6f-aa0a-677e75f4cda8"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lag values sorted according to their autocorrelation values:\n",
            "\n",
            "[  0  12   5   1   4   7   3  24   8   2   6   9  11  17  13  16  19  36\n",
            "  10  15  14  20  18  21  23  48  29  25  28  31  22  27  26  32  30  33\n",
            "  41  40  43  35  37  39  44  34  42  38  45  60  47  49  46  52  53  51\n",
            "  55  50  54  56  57  72  59  58  61  65  64  96  63  84  67  62  66  68\n",
            " 108  69  71  77  76  73  70  79  75 101 100  80  89  74 103  78  91  88\n",
            "  97  99  81  83  95  92 102  85  87  93  90  98 104 120  86  82 105  94\n",
            " 107 109 106 112 113 111 110 115 114 116 117 132 119 121 124 118 123 125\n",
            " 122 127 126 128 129 131 133 130 134 136 135 137 138 139 140 141 142 143]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Train model"
      ],
      "metadata": {
        "id": "YlicK1JWRAg-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Please play with different numbers to obtain best performance."
      ],
      "metadata": {
        "id": "z_xh338iDayY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "kernel = NUTS(sgt)\n",
        "mcmc = MCMC(kernel, num_warmup=5000, num_samples=5000, num_chains=2)\n",
        "mcmc.run(random.PRNGKey(0), y_train, seasonality=60)\n",
        "mcmc.print_summary()\n",
        "samples = mcmc.get_samples()"
      ],
      "metadata": {
        "id": "xHCPM0zjL0dS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "54b5270ba97949909286c1e7118197de",
            "dbf83f7f09554c38a595f5ffe6627f6d",
            "18c97e74b4454ebf9a43d25577b2fc68",
            "996b197a5e824b6eb61101ba4e5a99f3",
            "b9c255c0f9c34291822f010a24ade4b9",
            "e3f98ce8d2f6450c86754e55e4f20ab7",
            "211e968d40fa4dfa8ec31f1fb6a23cf1",
            "e618ba9ec7664833996b9dffb4f33844",
            "a4deae55bace47c9a2c0f8b81684cb4f",
            "170a287b4935472f9a8fe24e2066a117",
            "3254756f729f49ebb88d14e2440cc2fc",
            "4e9e8a08adb64291b35ded56050ccfa8",
            "d3655fb762424d478fd8885c67b13226",
            "ae79d71f33b943a5aed1e50838186f5d",
            "4339d31333944ff3836912502d9dfdbd",
            "44382acacf9440518f430a713463593d",
            "fd0e451e53df403e8c4094ff9eadbcb3",
            "d30c387da3bc4268b0acada1c778997d",
            "287b2b2cb8e94abb94701db80bb7017a",
            "8fbad82f5d0d4b209903269c692ee12a",
            "9ff7d4d1b224452cba00a33db8b644e2",
            "36e1d0180a2b49f89546e72491973704"
          ]
        },
        "outputId": "d9feb58b-649a-4811-dcfe-08d068b8e353"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54b5270ba97949909286c1e7118197de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4e9e8a08adb64291b35ded56050ccfa8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
            "      coef_trend  12926.60  32879.82   4290.40    430.55  25116.68    419.20      1.01\n",
            "       init_s[0]  11332.11  10102.36  11844.45  -3934.94  25927.21   1534.14      1.00\n",
            "       init_s[1]  -9946.26  10418.48  -9306.38 -24772.83   4429.24   2756.61      1.00\n",
            "       init_s[2]  30041.11  12943.60  31360.71  16694.78  46954.26    824.94      1.00\n",
            "       init_s[3]  20422.90  37279.10  27374.57   7928.28  50533.45    108.59      1.02\n",
            "       init_s[4]  19287.04  51993.78  34807.20  -7492.88  58370.48    239.78      1.01\n",
            "       init_s[5] -103690.29  94060.34 -127945.58 -215036.39  49156.58    214.33      1.01\n",
            "       init_s[6]   9668.70  54390.01  27314.97 -62886.11  56876.00    198.86      1.01\n",
            "       init_s[7]  21940.93  35280.89  29969.52   2876.12  51406.94    385.59      1.01\n",
            "       init_s[8]   7680.70  13813.34   8967.25  -6892.89  24473.25    599.17      1.00\n",
            "       init_s[9]  11481.58   9899.00  11961.75  -3417.28  26120.76   2036.82      1.00\n",
            "      init_s[10]  17385.94  11296.97  18354.51   1570.91  33633.59    785.80      1.00\n",
            "      init_s[11]  75889.15  13267.30  77293.19  62041.39  93514.05    698.38      1.01\n",
            "      init_s[12]  -9662.13  10313.83  -9393.84 -25173.09   4701.03   1841.56      1.00\n",
            "      init_s[13] -13327.18   9744.29 -12543.70 -27244.48   1474.81   2062.60      1.00\n",
            "      init_s[14]  31401.42  11161.78  32043.54  17335.59  47477.98   2140.24      1.00\n",
            "      init_s[15]  21144.09  10316.10  21977.21   5092.82  34673.06    667.67      1.00\n",
            "      init_s[16]  39110.31  14720.72  41518.18  23324.66  56537.29    684.68      1.00\n",
            "      init_s[17]  33548.34  11316.84  34849.87  19767.08  49237.71    870.53      1.01\n",
            "      init_s[18]  28556.36  10094.39  29004.60  15349.10  44028.32    228.79      1.01\n",
            "      init_s[19]  42402.00  10006.55  42523.99  28208.53  58661.01    561.43      1.01\n",
            "      init_s[20]  14205.31   9701.55  13407.23   1398.13  29746.60   2324.18      1.00\n",
            "      init_s[21]  16560.45   9850.78  16019.38   1665.85  31343.25   3490.72      1.00\n",
            "      init_s[22]  23482.85  10090.22  23630.10   8762.09  38094.81    566.17      1.00\n",
            "      init_s[23]  81553.54  10575.80  81857.59  66267.77  96485.48   2261.73      1.00\n",
            "      init_s[24]  -6754.70   9579.12  -6391.38 -21057.62   7448.44   2103.76      1.00\n",
            "      init_s[25] -10623.98  10617.36 -11156.45 -25695.05   3791.26    564.99      1.01\n",
            "      init_s[26]  35693.53  10370.63  35769.78  21189.75  49996.23    554.82      1.00\n",
            "      init_s[27]  18403.35  12747.83  20214.00   4080.27  34858.52    593.39      1.01\n",
            "      init_s[28]  49377.94  11003.42  49952.19  34624.69  66072.78   2100.45      1.00\n",
            "      init_s[29]  35090.80  10291.60  35881.75  20702.38  50555.45   2334.22      1.00\n",
            "      init_s[30]  31645.53   9640.49  32179.89  16465.16  45795.29   3499.68      1.00\n",
            "      init_s[31]  46933.11   9673.10  46155.40  33144.00  62554.06   1603.21      1.00\n",
            "      init_s[32]  14325.49  10730.15  13105.91    -50.34  30080.33   1406.71      1.00\n",
            "      init_s[33]  30834.62  13813.08  28212.05   7072.00  48267.30     33.34      1.06\n",
            "      init_s[34]  37930.26  11837.98  36866.39  21140.95  54045.77   1166.21      1.00\n",
            "      init_s[35]  84878.38  11962.98  84070.30  67374.76  99486.94   1946.79      1.00\n",
            "      init_s[36]   3047.26  11066.44   2239.62 -13089.97  18272.54   2492.25      1.00\n",
            "      init_s[37]   5067.89  13906.38   3143.71 -13872.68  20463.24   1053.02      1.00\n",
            "      init_s[38]  38624.41  25359.13  32403.58  13575.28  63295.57    717.83      1.00\n",
            "      init_s[39]  28297.00  12510.79  26601.87  10540.98  42452.07   1731.13      1.00\n",
            "      init_s[40]  53586.01  12783.92  53300.43  27803.77  67449.69     87.87      1.02\n",
            "      init_s[41]  38015.36  17317.26  34502.63  12294.10  61202.73     44.20      1.05\n",
            "      init_s[42]  39917.33  15173.26  38349.37  20979.16  55432.50    265.05      1.01\n",
            "      init_s[43]  40805.23  20018.57  37213.64  19823.91  57702.28    783.65      1.00\n",
            "      init_s[44]   6648.32  11988.41   5789.86  -9774.37  22782.87   2756.68      1.00\n",
            "      init_s[45] 875890.25 113312.23 872816.62 704045.69 1077130.12   1002.70      1.00\n",
            "      init_s[46]  41981.04  10531.85  41055.91  28991.51  60951.52     91.28      1.03\n",
            "      init_s[47]  88462.55  10792.08  88233.96  73158.41 103342.90   2225.10      1.00\n",
            "      init_s[48]  16633.84  13125.48  15595.28   -345.02  30282.27    199.85      1.02\n",
            "      init_s[49]   5995.90  16437.68   3784.72 -10531.78  21120.82    477.43      1.00\n",
            "      init_s[50]  40099.06  25586.00  34520.04  16086.05  56377.85    476.13      1.00\n",
            "      init_s[51]  35237.05  10879.00  34200.61  16947.95  47335.57    397.38      1.02\n",
            "      init_s[52]  50409.12  10030.36  50345.66  35918.25  65258.42   2418.08      1.00\n",
            "      init_s[53]  44864.64  10151.18  45551.66  30468.23  59368.05   1225.54      1.00\n",
            "      init_s[54]  49231.01  10028.94  48382.59  35753.17  63926.09   1811.15      1.00\n",
            "      init_s[55]  53729.89   9176.00  53833.96  40341.76  67446.33   3814.05      1.00\n",
            "      init_s[56]  25164.50  10210.97  25401.23  10860.02  39073.70   1147.27      1.00\n",
            "      init_s[57]  37306.81   9652.58  36857.05  22686.13  50603.87   1912.35      1.00\n",
            "      init_s[58]  36929.86   9904.71  36916.63  23348.60  51462.52   2418.10      1.00\n",
            "      init_s[59]  96224.59  11052.56  96419.56  79754.45 112349.51    851.01      1.01\n",
            "        level_sm      0.06      0.01      0.06      0.05      0.07   1319.70      1.00\n",
            "              nu      2.24      0.22      2.18      2.00      2.52   3103.00      1.00\n",
            "    offset_sigma   2450.63   1581.03   2143.31      1.54   4759.66   2468.65      1.00\n",
            "      pow_season      0.00      0.00      0.00      0.00      0.00   3670.02      1.00\n",
            "  pow_trend_beta      0.38      0.06      0.38      0.29      0.48    140.88      1.02\n",
            "            powx      0.09      0.08      0.07      0.00      0.20   2435.40      1.00\n",
            "            s_sm      0.98      0.02      0.99      0.96      1.00   1248.97      1.00\n",
            "           sigma   1404.05   1140.65   1202.35      0.07   2996.16   3917.44      1.00\n",
            "\n",
            "Number of divergences: 123\n",
            "CPU times: user 21min 44s, sys: 1.03 s, total: 21min 45s\n",
            "Wall time: 12min 20s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Predict Retail Sales with model"
      ],
      "metadata": {
        "id": "tW4M6sYIQ6fj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictive = Predictive(sgt, samples, return_sites=[\"y_forecast\"])\n",
        "forecast_marginal = predictive(random.PRNGKey(1), y_train, seasonality=60, future=24)[\n",
        "    \"y_forecast\"\n",
        "]"
      ],
      "metadata": {
        "id": "XWWTKdVNMxiI"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.7. Validate Forecast Model Using NumPyro"
      ],
      "metadata": {
        "id": "1VbmNrsNjF9j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = jnp.mean(forecast_marginal, axis=0)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHqQm9jCnJyk",
        "outputId": "6bb5ec90-427e-42cd-cf3d-0ca649f1716b"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeviceArray([432648.22, 450141.03, 492334.6 , 471545.7 , 497817.62,\n",
              "             480987.53, 476760.3 , 499753.97, 467696.94, 482233.44,\n",
              "             494952.22, 546892.6 , 461033.72, 460759.34, 510162.  ,\n",
              "             497517.16, 527354.6 , 506190.38, 514950.34, 528746.75,\n",
              "             491016.44, 512842.75, 521375.53, 575445.  ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate both smape and rmse."
      ],
      "metadata": {
        "id": "2v1Ms6xSEG-Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sMAPE = jnp.mean(jnp.abs(y_pred - y_test) / (y_pred + y_test)) * 200\n",
        "msqrt = jnp.sqrt(jnp.mean((y_pred - y_test) ** 2))\n",
        "print(\"sMAPE: {:.2f}, rmse: {:.2f}\".format(sMAPE, msqrt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRuddK23ECN9",
        "outputId": "677e1d0f-019d-4255-b789-7d1543629681"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sMAPE: 1.91, rmse: 12167.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, plot both actual retail sales and forecasted retail sales."
      ],
      "metadata": {
        "id": "HRR6lQZVETpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 5))\n",
        "plt.plot(retail_sales[\"month\"], retail_sales_data)\n",
        "t_future = retail_sales[\"month\"][144:]\n",
        "hpd_low, hpd_high = hpdi(forecast_marginal)\n",
        "plt.plot(t_future, y_pred, lw=2)\n",
        "plt.fill_between(t_future, hpd_low, hpd_high, alpha=0.3)\n",
        "plt.title(\"Forecasting retail sales with SGT model (90% HPDI)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "-Uwb71hb-2xT",
        "outputId": "d922e8a2-3929-41b4-ba40-bd7753d4d65a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": 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29PSwc+fOhdyWiIiIiIiIiIgsgJkdmW3fQsviPgb81Dl3AbAD2AP8DfAz59wW4Gf+7wCvBLb4/24APuVPohV4D/A84ErgPWbW4p/zKeDtZedd62+f7RoiIiIiIiIiIvIsMG9wycyagBcCnwdwzmWcc1HgtcBN/mE3Aa/zX78W+LLz3Ac0m1k3cA1wu3NuxM8+uh241t8Xcc7d57yl6748baxK1xARERERERERkWeBhWQubQQGgS+a2SNm9jkzqwe6nHO9/jF9QJf/eg1wrOz84/62ubYfr7CdOa4hIiIiIiIiIiLPAgsJLlUBlwGfcs5dCkwwrTzNzzhyp356C7uGmd1gZjvNbOfg4ODpnIaIiIiIiIiIiJRZSHDpOHDcOXe///u38YJN/X5JG/7PAX//CWBd2flr/W1zbV9bYTtzXGMK59x/OOcud85d3tFRsXG5iIiIiIiIiIicBvMGl5xzfcAxM9vqb3oZsBv4IXC9v+164Af+6x8CbzHPVcCYX9p2K3C1mbX4jbyvBm7198XM7Cp/pbm3TBur0jVERERERERERORZoGqBx70L+KqZVQMHgd/HC0x908z+ADgCvME/9mbgVcB+IOEfi3NuxMz+EXjQP+79zrkR//WfAl8CaoFb/H8AH5rlGiIiIiIiIiIi8ixgXiujc8fll1/udu7cudzTEBERERERERE5Z5jZQ865yyvtW0jPJRERERERERERkYoUXBIRERERERERkSVTcElERERERERE5DQoFM6tVkSzUXBJREREREREROQ0SGbzyz2FM0LBJRERERERERGRUyybL5DLK3NJRERERERERESWYKVkLYGCSyIiIiIiIiIip1wyo+CSiIiIiIiIiIgskYJLIiIiIiIiIiKyZCqLExERERERERGRJUsoc0lERERERERERJYqpcwlERERERERERFZCuecgksiIiIiIiIiIrI0qWwB55Z7FmeOgksiIiIiIiIiIqdQIpNb7imcUVXLPQERERERERERkXNJMptn9cFvUz2QgUuug+Z1yz2l00rBJRERERERERGRUyiVzbPm4NepHXkcNl51zgeXVBYnIiIiIiIiInIKJTJ5aieOeb+09CzrXM4EBZdERERERERERE6hTDxKdXoUVxWGhq7lns5pp+CSiIiIiIiIiMipFD0MQKFpAwTO/dDLuX+HIiIiIiIiIiJnSDZfIBQ7CkChecMyz+bMUHBJREREREREROQUSWbz1MaPA1Bo7lneyZwhCi6JiIiIiIiIiJwiyUyeugk/c6lJmUsiIiIiIiIiIrIIyUye2ri3UpzK4kREREREREREZFESmTy1E8Xg0sZlns2ZoeCSiIiIiIiIiMgpkkxnCE+cAKDQtG6ZZ3NmKLgkIiIiIiIiIotWKLjlnsKzkhs7QcDlSIc7IVS33NM5IxRcEhEREREREZFFG0/llnsKzzrOOQLRQwAkG9Yu82zOHAWXRERERERERGTRxpLZ5Z7Cs04ymyfsN/NO1q+MkjhQcElEREREREREFimXLxBPK3NpuvKV4hIN65d5NmeOgksiIiIiIiIisii/PDDMu772MMlMfrmn8qySzE6uFJesXwe2zBM6QxRcEhEREREREZFFuWf/EAcGJzg5llzuqTyrpMqCS4/Gm/nwT58inTv3A3AKLomIiIiIiIjIopwY9YJK6rs0VaKsLO6+aCO3PtlHdfDcD71ULfcEREREREREROTscjKq4FIlmfgo1Zko+WCYJ6Nhtq2ux+zcr41bUPjMzA6b2RNm9qiZ7fS3vdfMTvjbHjWzV5Ud/24z229me83smrLt1/rb9pvZ35Rt32hm9/vbv2Fm1f72Gv/3/f7+nlN14yIiIiIiIiKyNP2xFADRicwyz+RZZvQwAIn6dRyPpti2OrK88zlDFpOb9RLn3CXOucvLtn3E33aJc+5mADPbBrwRuAi4FvikmQXNLAh8AnglsA14k38swIf9sTYDo8Af+Nv/ABj1t3/EP05ERERERERElolzjsF4GoCRhDKXirL5AtXjXkncaPVqcgXHtm4Fl5bqtcDXnXNp59whYD9wpf9vv3PuoHMuA3wdeK15+WEvBb7tn38T8LqysW7yX38beJmthHwyERERERERkWepwfE02bwDYFSZSyXl/ZZOBroAuGh103JO6YxZaHDJAbeZ2UNmdkPZ9nea2eNm9gUza/G3rQGOlR1z3N822/Y2IOqcy03bPmUsf/+Yf7yIiIiIiIiILINDQxOl16MJBZeKUtk8tfGjABzKdRAOBdjYXr/MszozFhpceoFz7jK8krZ3mNkLgU8B5wGXAL3Av56eKc7PzG4ws51mtnNwcHC5piEiIiIiIiJyzjs8PBlcUkPvSclMntoJL6dmd6qVrV2NBAMro/hqQcEl59wJ/+cA8D3gSudcv3Mu75wrAJ/FK3sDOAGsKzt9rb9ttu3DQLOZVU3bPmUsf3+Tf/z0+f2Hc+5y59zlHR0dC7klEREREREREVmCYyNJfi3wOF8O/wvE+5d7Os8aibLg0sPjzSumJA4WEFwys3ozayy+Bq4GnjSz7rLDXg886b/+IfBGf6W3jcAW4AHgQWCLvzJcNV7T7x865xzwc+A6//zrgR+UjXW9//o64L/940VERERERERkGRwfTfL7VbfzQh7h/Ni9yz2dZ41kOk144iQA+7NtPGfNygkuVc1/CF3A9/w+2lXAfznnfmpm/2lml+D1YzoM/BGAc26XmX0T2A3kgHc45/IAZvZO4FYgCHzBObfLv8ZfA183sw8AjwCf97d/HvhPM9sPjOAFpERERERERERkmfSOJdkY7AcHtZmR5Z7Os4YbO0HA5RgPdZBOVbNt9cpYKQ4WEFxyzh0EdlTY/uY5zrkRuLHC9puBm2e5xpUVtqeA355vjiIiIiIiIiJyZgyOTbDW9QFQnxs949fP5ApUVy20hfTsTkSTrG4KcyoWpXfOEYgeAqC/ahUBgwtWNT7jcc8Wz/zdEBEREREREZEVwTlHYKKfEN6C743LEFzaPxB/xmPk8gUODMSZyORPwYwgmc0THvf6LR0pdLKutY5wKHhKxj4bKLgkIiIiIiIiIgsyMpGhI3ui9HuzGyNfOHOtkQsFR18s+YxXqTs6kiCTKzCeOjWr3ZWvFPd0po0LVq2ckjhQcElERERERERkinQuz2d/cZBsvrDcU3nWOTQ0wQabXCGu3caIJTNn7PpjySyFAvTHUkseI5MrcGQkAUAsmTsl80pmJ4NLe9PtXNSt4JKIiIiIiIjIinXPviFuvHkPDx5Ss+rpjgxPDy7FGIqfueDSSMK7Vn8sxVIXkz88PEE+7517KjOX6uJecOmo62T7upWzUhwouCQiIiIiIiIyRX8sDfCMS6/ORcdGklOCS63EGI4vPYtosaJ+cCmdLTCaWPz7k8rmOT6aKP0+nsotOUhVrjxz6ZjrZMfa5mc85tlEwSURERERERGRMsWSq9gpymo5l5yIJukpCy4FzREfHTgj1y4U3JSAX9/Y4oNaBwcnKJRVO+YLjsQzbOqdyOQYjw4RyoyRshoKdR201Fc/ozHPNgouiYiIiIiIiJQZGPcyl05VP55zSW80yYaAF1xKVrcCkBrtOyPXLvZbKhoYT1FYRDPxiXSO3rHkjO3jqaW/z8lMnoePRKkaOwrACbrY1Nmw5PHOVgouiYiIiIiIiJQZ8DOXVBY3UybWRz0pstVNjDdsAiA3fmYyl0YTU3s75fKOoYn0gs8/MBinUgXcUjPUUtk8Dx8dJZXNU+v3WzqY62BrV+OSxjubKbgkIiIiIiIiUqaUuaSyuCnSuTwNfl+hRMN68uE2AArxwTNy/Uo9lvrHFhZcGktmGYhVPnYpTb3TuTwPHxkl6ZfU1ca9zKWjrpMLV9hKcaDgkoiIiIiIiMgUpZ5LylyaYjyVpSV9HIBk/XpytV5wKZA4/cGlQsFVfD+G4mly+UKFM6baPxCfdV9skWVxmVyBh49Ep/RqKjbzPuo62b52Za0UBwouiYiIiIiIiJTkC46heDFzST2Xyh0fTbLeb+adaNhA1s9cqk4Nn/ZrjyWz5Cv0V8oXHIPxubOXBsfTjE5kZt2fzzsSmYW919l8gYePjjKRnnp8MbjUF+hmU0f9gsY6lyi4JCIiIiIiIuIbjqcpxjCUuTTV4aEEG/zgUrJxPTR0AlCTGTnt157eb6lc7xyrxsVSWZ48OTZjuxWybH34faw++C3vuAU0b3fO8cjRKPEKQcdiz6V803pqQ1XzjnWuUXBJRERERERExNfv9+WpDgbU0HuaY6MJeoqZS/XrqY50AVCXHT3t1y72W5oY6aPve39LbKh3ct9EhnQuP+OciXSOR45GyednZjytOfAN1u3/Klse+xA4t6C+S9FEtmLA0Qo5womTAIQ7NhEI2ILv61yh4JKIiIiIiIiIb2Dcy4LZ2F6vht7T9EZTpcyldGQD1U1e5lIkf3qDS+X9lrqe+CS/l/0WHTv/ubTfOWY0605l8zxyNEo2N7MfUzAbZ+PuTwAQyo5TO3FsQSWQxUbv04UTJwm4PL2ulZ5VrQu+r3OJgksiIiIiIiIivmLm0uauhgWVSq0kYyMDtFicXFUdwcZVBBu94FJTIVqxH9JCxdNzP+dYarLf0uroQwBsj92Fy00Ge4pN2MHri/TI0Sip7MxsJoANe79ATXqyT1Tj6JMLylwanCW4VN7MeyWuFAcKLomIiIiIiIiUFDOXzutoIJnNk6mQ+bISZfMFQuNHAEjWr6O2poqgXxbX4sae0XPafTI2Z0PtYklcMDPOmvQBAJqIk917e+mYaCJLMpMnX3A8eiw6o+F2UXVqiPVPfwGAkc6rAIiM7iI3T1PvsWR21mBVsd/SMdfF+V0Ns45xLlNwSURERERERMTXH0vTVl9Ne0M1gErjfMlsnoifoZNs2EBddZDquiayFqLeUqST40seO57OsvtkDOcqZz+N+Cu9NQ8/TJDJIFbbwR9MOa53LMljx6OMJWZ/zzbu+jhVuQSDq1/KsS1vBqBxdBcA43OUxg2Oz940vJi5FA2vJlIbmvW4c5mCSyIiIiIiIiK+wfEUHY01RMJekEArxnkS6Rwt6RPe64b11IaChKqCTASbARgf7lvSuKlsnkLByzw6NpKcsb+831LT4IMA3F//UgB2JH5JIRUrHXtoaIKR+OyrytWOH2bNwW/iLMD+i/+SWMtzAC9zab6m3tN7Ok0ZN34UgEzjesKh4KzHncsUXBIRERERERHx9cfSdEXCRGq95eQX0uh5JeiPpVnrvBXakg3rqa0OEgoGSIS8BtbxkZNLGjeZmSw1OzAYn1GaVt5vKdL/AAB7u17J8cilhC1LdtePSsfOkvhUsvmJjxBwOU72/CYTTVv4zr4CiVALocwY4cSJWd/reDpHIlO5JK6l/5d0nPgZALm2C6mrVnBJREREREREVoDZ+tGI13Ops7GGplplLpU7OpxgfWAAgIRfFgeQrmkDIBUdWNK4ibI+RvmCY9e08rhiv6VALklL9EkKzkituoKx814LQPexnyzoOpHhx+g6fgv5YA0HL/ozDg9N8P3HetnDRm//6K5Z3+uBWOWSuPqxp9nxy3cScDk+m3sV4XXbCQVXZphlZd61iIiIiIjICnXfwWEuef9t9I7NLEFa6fIFx+C4n7nkl8WNKbgEwPFogh7zSt9SjesJV3nBpWytF1zKxfqXNG5yWkbQWCLL0ZFE6ffRhFfm1jTyGEGXY7fbQGtrO0PrryVHkO3ph8mNz3Nt59jy+L8AcGzLW0jXreLHT3hZWI/megBoHHmSXN7NmA/AQIVV4moSfVz6iz+kKhvn4YYX8sHc73B+Z+OC7/tco+CSiIiIiIjICvJUb4xs3nFiVMGl6YYn0hQcdEVqSo2Z1dDbMzA8QpdFyQdCEFlDIGAAuLp272d8aZlLlYI5BwbjTKRzFAqu1Jy72e+39BAX0lQbIlvTyom2X6XKCuSf+O6c12jru4uWwQfIVjdx+II/4thogkePRWmqDfFAej0AjdHdADP6LiUzeeLTyuWC2TiX3HMD4WQfI62X8bbY27l4TQutfhP4lUjBJRERERERkRWkmIWhjJyZik2bOxrDZQ29VUJYKDgYPQxAqlZ+/SsAACAASURBVH4t4ZrJIEqgsdP7mRxa0tjTeyx514PdvbEp/ZZa/ODSvtrtmHmBrdjm1wGwsfeWOSafZ/Pj/wrAoQv/mFx1hJ883ks4FOC3LlvDLlcsi3sSnJvRd2lg2ipxVshy8b1/TmP0KSYaevj/295DNBPkNTtWr9h+S6DgkoiIiIiIyIrS7wdQlJEzUzGQ0BWpIRwKEAraWfucookM8VPUWyuZzRMePwJM7bcEEGrs8n6mhpc8diVjiSy7T3orwVk+Q9PwowD0NV1aOmZwzctIW5ht+adIDx6oOM7Gpz5D49heknWrOb759+gdS/LQkVFesrWT87saOe7aSQQjVKdHqUn0zshcmlIS5xwXPPQe2vvuJlPTyr2/8mm+vzfFjrVN9LTXr9iV4kDBJRERERERkRWlGEBRRs5MxcBbZySMmdFUGzrjDb1z+cIpGWdgPE3U71f0TCUyeZqSxwBINmygtiyIEm72gkvh9OKDS9l8gVx+9iXeiiu0RUZ3Ecyn2FdYQ33LqtL+QlUdJ1e9DIDAk9+ZcX7H8ds478mP4jCeeu57KQRruPmJPkJVAa7e1kVrfTU1VUEOhbZ414numpK5lMrmS2V5AD17Ps2aQ98mHwzz6As+w4+OhUlk8rx6x2qAKc9lpVFwSUREREREZAUpln6pLG6mUllcQw0AkXDojD+nA4MTZHLPPMDUH0sRTZyauSczeVozJ7zXDeumZC4FI15ZXF12dNHjJir0W6qkeWgnAA8ULqArEp6yL7rZWzXuwqGfQtkqcw3Rp7jogb8CYP/Ff8lw94sZGE9x/6FhXnR+B43hEAEz1jTX8qTrAaBxZBfZXIGUn001WJa1VJUZY9Puj+Mwnrzq3+hrvIjbd/dzydpmetrqAahVWZyIiIiIiIisBP2lzCUFl6brH0/RWl9NdZX3VbmxNjSjB8/pNjienrJa2lKMJbKks4XSSmvP1Fgyy+q8t7paomHDlCBK0C+La8xHFx0US2byVGViNA3unPO4YjPv+wsXsCoSpqV+sufTSNfzmQg20eOOkzj2GOCV6O2450+oyiXo3fAajlzwdgBueaKPgBnXbPPm3NFYw5qW2lJT78jok8BkyWh5SVzHyZ8RKGQZ7Xweg2tezs/2DPhZS90AmClzSURERERERFaAdC5fymY5W3sJnU4DsTSdjTWl3yPhqjMahEtkcqSyeY6PJp5ReVwxgJjOFiquxrYY2XyB3b0xNpi3GlyyYf2UIEpVo7daXGNhjExucYG4ZDbPlsc+xBU//x1WH/hG5YMKeZqHHgLgwcIFdEVqOL+rgWBxtbpAiP511wIQfuo7WD7D9l++i9rECcZat7Pn8hvBjOF4ml8eHObXtrTTXFeNGWzubKCnrW5yxbjRXeAc46kcmVxhSllh57GfAtC/9pUkMjlu293PJeua2eBnLYVDwVKj8ZVIwSUREREREZEVolj2BSqLq2RgPEVnWdlVU23ojAbhhuNeMCOXd5yIJpc8Tvn7HE0+s+ylff1xhkZjrLYhCgTIRdZRFZwMJVRXhxm3BoIUyI4vbsW4RCZH3bCXLbT5iX+lKh2dcUzD2F5C2XH6g6tI1a2isTZEYzhER1kQcHiTt2rc9ugdbH34vbQM7SRV28Vjz/8khaB33E939QFw7UVez6bOxjD1NVVs7YpwzHWSqmqkJj1MTbKf8VSOwXi6VGVXlYnR1v8/OAswuPYV3LFngGQ2z2u2ry7NYSU38wYFl0RERERERFaM8jIfNfSeaSCWpqs8c+kUNPSevpT9XEYmJgNBR0cSFAqzN7uezVgiW+oZBDA6sfT5jyWynIwmKYweIWiORG03tbW1U46pDgaIBZoByI8PLGr8ZDpHbfyoN04mynlPfnTGMS1+ydwjdqFXElfnlcSV914aa7uUaPUqVjHM2kPfJheo4bHnf5JMrdcPKprIcPe+IX51Uxttfj+tnvY6AC5aHQGM4zVeU+/G0V3Ektkp/ZbaT/63VxLXfgXRQDO37+7n0nXNrG+rKx1Tt4L7LYGCSyIiIiIiIivGQMwLdKxuCitzaZp8wTEYT9MZKS+LCxFL5nBu8UGeoqPDCytxc84xUlaGlc4W6I0tPDBVND2YtdQV45xz7OmLAVA1dhiAVOOGGX2FAgFjvKoFgML44KKukYsPEi4kSLsQeYKsPfh1GqJ7phzTPOT1W7o7s5VVTWGa60IAtDdUE/J7Y2EBhnpeXTrnL1I38I47HTf98jAPHBrhR4/3UnCOV13s9UfqaKyhMeyN09NeR0NNFU8HNgHeynSZXIHh+GRwqeu4VxI3sO5abt/d72Ut7ZjMWoKV3W8JFFwSERERERFZMYqZS5u7GtVzaZrhiTT5gpuSEROprSKTL5Be4uptzu/fM1gWqJjNWDJLPj81iHVkaGLRga3+2NRrJTJ50rnF9106NpIk7jczr5vwsotSjRsqroiWDLUCkI8vPHMpX3AEokcA2OfW8KPqX8dcga0P/+Pkqm/O0eI3874nez5dkcngkpnRVRYI7NvyO8QjW3hoy/9D5PL/xbqWOnYeGeU/7j7IXU8P8ryNbaVSuo0d9aXzIrXVrGkO83B2A+D3XWJyCsHMOG19d+MwDne8lDv2DHDZ+mbWtU5mLcHKXikOFhhcMrPDZvaEmT1qZjv9ba1mdruZ7fN/tvjbzcz+j5ntN7PHzeyysnGu94/fZ2bXl21/rj/+fv9cm+saIiIiIiIisnj9sRRVAaOnrU6rxU1T7FPU2Ti15xIsfWW9VLZAvuDoG5s/A2l4YmaGUSKTn1KeNZ+x5NSSuKJiE/eFSmXzHBiKl35vTh7z5lO/vmL5V7rGDy6N9y/4Gslsnjq/JO6I6+R98deQrmmjZWgnXUd/DEDd+EGq0yNMVLdzxHWxurm2lHEEsKosEJiqX8N91/6E0Uv/hJde0Mk7XrKZj/6vS/jfr7qAN16xjuueuxaAtoZqImVjBAPGhrZ67plYB3iZS+U6en9OoJAl2nEFPzlUIJnN8+ppWUs1oUAp6LVSLSZz6SXOuUucc5f7v/8N8DPn3BbgZ/7vAK8Etvj/bgA+BV6gCHgP8DzgSuA9ZcGiTwFvLzvv2nmuISIiIiIiMkOh4PjaA0crfsEWL6ulo7GG5toQ4+ncknr6nKuK5WTTy+Jg6c3PJzJe5s/IRIbMPNlPIxWCSwCHhxMLvt7ALGV0iw0u7euPT8miasueBCDZuL5i+Veuts37GVt45lIiM9lv6WSgm9FCHb9Y/w4Azn/swwSz8VLW0sG6HYCxdVXjlDGa66rnbKQdDBib2ht4+YVdpUDhxvb6Gcdt7mxgb7adbFU9NakBqpOT99F57BYA+tdewwOHRrioO8K6lsmspaqgcen6FmqqlLm0VK8FbvJf3wS8rmz7l53nPqDZzLqBa4DbnXMjzrlR4HbgWn9fxDl3n/Py/b48baxK1xAREREREZnhkWOjvPu7T3Dn3sU1Fl4piquhRWpDOAfjaTX1LipmLk0ti/Mzl5ZYQjjhP1/n5m7snc0XZs2OiiWzU/r/zGVgliyn0UX0XRqOp+kvC1IVnGN1oReAREPlsrhCndc4200svOdSKlMoBZespYdgwPhu/oWMte6gJjXAxt2fotlv5v148CKqAsaWzoYZ46xqqpmxbTYt9dU0+w3By120OoIjQF/dVmCyNC6YjZdK4vY0v4T+8TQXr20qnRcMGJesa6ahpmrBczhXLTS45IDbzOwhM7vB39blnOv1X/cBXf7rNcCxsnOP+9vm2n68wva5riEiIiIiIjLD8VFv+faRZ7BC1rlsIJams7FmMmii0riSYq+ijobyzCUvaLDUlfUm0pMZdP1zNOceTWSYq7XSQrKXxpJZkpnJ663f+3nW7/2iP48c2QU0FS8UHHv7xqdsiyVSrMUL1mYa11fM0Ak0dHg/E0PzXqMokc0RjnshgnTjes7rqGd3f5y9l/09DmP9vi/R1vcLAO7Lb6UrEi6t9FauPBg4n00VspYALl7jBYwOVm0GJkvj2nvvJFjIEG1/Lg+Petfe1h0BwAyes6apYrBqJVpocOkFzrnL8Ere3mFmLyzf6WccndZ8yrmuYWY3mNlOM9s5OLi47vQiIiIiInLu6PV720STS1sh61w3MJ6iK1LzjMu9zkUD4yla66uprpr8mvyMM5cyk0GpaKJyPySYWhIXyKWYHmkancjM+14NlmVG1caPcv5jH+b8x/6JUGoY5xZWGnd4eIJEZuoc08PHqLY88eoOaupmZg4BVDd5mUuh1PC81yhKZPKlzKV0wwYu7I5wdDjBybptnNz4WwQKWaozUbLVTTw40Ul3U7gU7CvXGA5Rv4DMoea6EC31lQNBa1rqaKkL8URhozemH1zq8kviBtZey+6TMVrqQnQ3ecGsC7sjpQbhssDgknPuhP9zAPgeXs+kfr+kDf9nMe/0BLCu7PS1/ra5tq+tsJ05rjF9fv/hnLvcOXd5R0fHQm5JRERERETOQb1RL3NpsT1mVoJ0Ls9oIktXY3iyUbVWjCvp97O6yj3Tht4TZWWHzk2W3k03EveCS01Dj/CS713Cxt2fmHHM4aGJOa9Vvkpcx/HbS6+bhx8GYGyegGsqm+dIhQwpGzkIQLyucr8lgNrmVQCE0yML7uOVScSpTQ+ScUFc0xq2dUdwwFN9MfZf/JdkQ15/pZG25zIYz7GhrQ5/7a8ZVjXNn71UqddSUTBgrGut477UZFPvYHailDnVt+Zq9vTFuGh1E2bGeZ0NrG6uXdB9rhTzBpfMrN7MGouvgauBJ4EfAsUV364HfuC//iHwFn/VuKuAMb+07VbgajNr8Rt5Xw3c6u+LmdlV/ipxb5k2VqVriIiIiIiIzHAi6mcuLaLHzEpRXHWsM1JDpPaZlXudiwb9flTlGv1MmaVkeKWyeXL5qYGWvgqlcclMvpQttOrIDzBXYMPezxPMTC1PGxxPs/PwCOMVAoKx1NSSuM4Tt5ZeNw15waXReQKuh4cnyFcIDCUG9gOQa9pQcaU4gMZ2b/W0+twImQWU3znnsOhhAI67DiJ1YXra6qkNBdndGyMbbmPfjnfjMPZ1XE3eOTZ3Ns463qp5SuOa60IVS+rKndfRwAPjLeSq6ggn+1h96DsE82mi7ZexN9FAIpNnW3eE9W11cwaqVqqFZC51AfeY2WPAA8BPnHM/BT4EvMLM9gEv938HuBk4COwHPgv8KYBzbgT4R+BB/9/7/W34x3zOP+cAcIu/fbZriIiIiIiIzNA75mUuzfdFeiUqZrZ0RsKlsjj1XJpUKXOppipIOBQgllp8EG56eRl4zzuRmTrW8MRkxlFb//8AUJWbYM2hb804P5rI8sChEfb1j5MrC+KUrxJXk+ijefjR0u/NQw8BMJ7KVgwegRfgOuln/ZXL5gvYyCEAUo2Vm3kDtDS3kXZV1BSSpJPxiseUS2ULhMe9krijrotIuIpgwFsNbk+vF1Q7uek6fv6bj3Ff/UsB2NY9e3CptjpIU12o4r6OxhouWdc875wuWNVIJm+MNHpNvXv2fAqA/rWvZFdvDAMuWd9Usam4wLyFic65g8COCtuHgZdV2O6Ad8wy1heAL1TYvhN4zkKvISIiIiIiUknxC/KYgkszFAMQnY01pS/iKovzFAqOwXiarsjM7JZIOLSkINzELCvx9cfSbGyf/Cpe7LdUGz9KXfwIBWcEzLFu35c5tuUtuMDUr+3OwZHhBH2xFFu7GumMhKeUxHWeuA2A4a5fpbX/XiKjuwjkUhSqwowls7RW6Dt0YDBOoULC0d6jJ3mT3Q1AvPkCWmYpi6sLhxgiQjcj5GIDEGmqeFxRIpMr9Vs64jrp9MsPt3VHePRYlMHxNB2NNRSqwvSNRQHYvnbuANGqSHjG331Pez2bFxgMKjb1PlpzPp08Qk3a6x81sPYadv8iyvq2OjZ3Ns5amrfSLbSht4iIiIiIyLNaMpMvZSwtZun1laK4TH1XJExDdRVmauhdNDyRIV9wdDbOLK9qqg0tKQg3kclRnRykZ/enCGYns3n6xiazjJxzpeBSa5+XtXRb4XIOum5qEyfp8ANFlaSzBR4/PsaDh0emlsQd90riTvb8FvHmrQQKWSKjTwCV/y7i6dysK9n17P40XRYl2rKdoe4XUVddOT8lFAwQNS/4U4hXbJU8RTI72cy7N9Bdyoi60M9O2t0bKx3bF0sRCVfN2oy7qDNSQzHuEwwYz1nTtODAEsDFa5swYA/nlbZF2y4lWtXBgcE4F3VH6Krw+RCPgksiIiIiInJOOOmXxNVVB4kqaDJDfyxFVcBorasmELAlZ+Sciwb8ldYqZi7VhpYUhJtI59mw97NsfvIjbNr18bLtuVLfpFgyV+rL1NZ/DwD32iV8Kf9KANY//aV5r1OerVOdGqJ5cCeFQIih1S8h2v5cAJoHdwKVG90fGIhPX5wOgODoAV4V/w4AT1/291ggQDg0ewghGvCDS+Pzr+CezOSpnTgGwHD1aratjhAMGqsiYVrqQuwpCy71x1Ksb6ubd8yaqiAt9dXUhAJctqFlQU2+yzWGQ3RGangou760bWDttTzVF6Pg4JL1LbOW3omCSyIiIiIico7o9Zt5X7CqkWgig6v0jXkFG/BLjQIBL70jUlu1pF5C56LiKm4dFTJTIuGqJTU+n0jnaIjuBWDV0R9DYTK7qFjGNuJnElkhR0v/vQAMdj6f+NbrGHUNNA8/StPQIwu+ZseJOzAcw13P53+OpdlZOB+Y7LsUS2anrOY2lsyWGr1Pt/aBD1BtefZ0vYZY2w5qQ8E5S8LiVV5wyU3Mn7mUyOSp8zOXxsJraa2rZktnA2bGhd0R9vTGKPh/v32xFOd1LCwDaVN7PVdubC2t8rdYG9vreXC8jWwogrMA/WuvYXdvjJqqAM8/r21JY64UCi6JiIiIiMg5oZi5dGF3hGzeVWyovJL1x6auhhYJLy0j51w0X+bSYsvisvkCmVyB+vEDANSkBmgdvK+0v1iGNuI3846MPEYoF+dAoZvWNZt52faNfNdeAcC6p7+44OsWS+KOdL6cL997hI/vbwegafgRcAXyBcd4WUDxwGDl5tttJ3/OlrH/YZxa+q/4awDCszTzLkqEWr0XEwvIXEqnCU+c8M5rWEdVMMDaljpaG6rZ1h1hIpPn6EiCRCbHeCrH1lWzN/Mu11xXTU3V3POcy/ldjfSOZ9n5/E/zyK99jnT9anafjLG1q5G1rfNnT61kCi6JiIiIiMg54WQ0iRlc0B0BUGncNIPjU1dDa6pVWVxRfylzaWZwaSnPaSKdI5iNE05OZvGsOvLD0utkJs9wPF0K7rX1eSVxdxcuZuuqRmpCQca3/z4ZF6Tz+G2EJ47Pe82qdJSWgfspWBVfiV5EJl/gQKaFsepVhLLj1I/tAyb7Lo1OZBiJz+zBZPkMWx65EYCbW68nV9cBeOWmc0nXeMElSwzNO1c3dpyAy9HvWog0TgaOtnVHeI7fWHtPb6zUn2rzAjOXnqlt3REKDp4KbWNk1QsYHE/TP55mx7rmJWdDrRQKLomIiIiIyDmhN5qio6GGjgYvQDA6oabe5fpjqSmZOZHw0hpVn4sGxlO01IUqZr14zym3qDLLiUye+piXtZTxgy6dx28jkEuWjtnbP15aoa2132vmvbPqUlb52WUXbd3KXaFfI0CB7qe+PO81O07+NwGXY7DtSm7en+bKnlYaw1U8bhcA0Dz0MDAZXJota2n901+iYeIo+wpriF38+6XttbOsFFeUC3tZUsF5gkvpXJ6a2BEADruu0t8rQDgU5MqNraxprmV3b4w+P8Nr0xkKLu3wV6Q7Oeq9T8XG4i/e2nFGrn82U3BJRERERETOCSfHknQ319LsN91VydekdM5bSa98NbRIbZWeka8/lqYrUrkBdKS2inzBMbGIMstEOlcqiRvpvIqx1u1U5SboOPmzsmO88aoyYzQNP06WIGOdzyv1NTIz4pfeAMDqg9+csuJcJcWSuNu5kmyhwGsuWc3zNrZyx8RGYLLv0lgyy8B4qmJz7+pkPxv3fBKAj1a9jZ7O5tK+2nkyl1ydF1wKJOYui0tm8tTGvWbeRwudU0o1AVY31/LcDS3s649zbDRJ0Iz1Z6gk7bzOBoJmnIj6waWTMVrqQlzR03JGrn82U3BJRERERETOCSejSdY0h2mp85Ysr7Ts+kpVbNpcnrnklXupoTdMNjuvJBL2gpWLKY2Lp3OlzKWJyHn0bXgNMLU0rqi1/16MAjvzW9nQ3TllX+PGy9ldvZ1alyCy52uzXi+YjdPWfw8O4xO9F3DVxjZWRcJctbGNB/JTm3rn8o6nescrjrPlsX+hKpfg1vzlFDa9ZEoD7/kylwKN3tyDieE5j0tm89T6zbyPuC66K6zq9hvbu8kVHPcfHGZday3VVWcmdFFdFWBtSy0noknyBceevhg71jYTqa0+I9c/mym4JCIiIiIiZz3nHCejKbqbJjOXKmVmrFTFnkJTMpfCIZLZPJlcYbmm9awxEEvNkbnkB5cWUUKYyOSpjx0E4I7BZo50X0vBgrT13U0oNTLl2Nb+qf2WpovueDsAa5/+MlaoHAxs772TQCHLvvDF9BUi/Pr2bsxgQ1sdY42bmaCO2sQJahJ9ABXf86ahh+g++kOyVs0Hcr/LlT2tU/bXVVfNec+hiBdcqk4Pz/mZKl8p7qjrYnVT7Yxjnr+5nWDAiKVyC14p7lQ5r7OBE9Ekh4cnSGTyPH9L+xm9/tlKwSURERERETnrjSWzJLN5VjfXlhrvRpW5VDLor4bWWd5zaQlBk3NRoeBmNDsvV/w8LTTLK19wJDN56vzMpe8eq+f2IwVGVr2AgMvTdewnkwc7V2rm/XBost9SuUTPKxisXkNXoZ+Jx39Q8ZrFkrhvTFxaylpqrgvREA7xvPM6eTC/BZjMXprBOc5/9IMAfLP69WQjG1jbMhn0qa4KEAxY5XN9dS1dANRkRsnmZn9WyUye2oli5lIna1pmBpfqa6p47nqvFG1TR/2c1z3VLuyOMBTP8PDRUQy49qJVZ/T6ZysFl0RERERE5KxX7JGyuilMOBSkNhRU5lKZYuZSeXbOZNBkZT+nkUSGXMHNnrkUXlwPr4lMDstnqJs4SoEAh1w3P987wMl1Xmlcd1lpXN34IWoTJxmlEde1fUoZWokFGNj2NgCu2vthEg99HcqaiwdyCdp77wLg5tzl/Pr2bsDLUmupD3HVxlZ2FrzSuCa/qfd0bb130jTyBKnqVm4cu4Yre1qnzGW+leIAmhsbiLp6AhTIjs/e1DuZyU32XHJdrKpQFgde9hLAxvYzm7l08Rpvtcm79w2xsb2edWeo39PZTsElERERERE56/VGvcyc7mYvC6KlLkR0hQdNyvXHUlQFjNa6yd4xkVqvzGmlN/Xu91ckmy1zqficFhqES6Tz1MUPY67AYFU3GUIMxTP8zF1OrqqeppHHqB0/DFDKWvpF/mLO724CoCE8s/ysf9N1DLVeRqdFec2Bf+C8n1xH4+huANr77iaYT/FoYTMbNm5hVSSMmZel1lxbTVtDDX3NlwCzZC45x6Zd/w7AHa1vIkF4RgPr8Dz9lgDa62sYdl5gJjc+MOtx2fFBqnITTFg9+XAzoWDlsMTVF3VRUxXgsg3NFfefLhes8u4hkcnzK+e1ndFrn80UXBIRERERkbNe75ifudTsZUE01VWrLK5MsWF1oKy0qZS5lFrZTb0H/Gbn01ctKyo19F5g+WB5M+9jwbW0N1TTVl/NT5+OMbDmFQCsOvojYLLf0i/y29na5fVbOq+joVSyWFSoquXRl/0Xj192I1FrZmPiCa64/fVs3fn3dB/6LgC3FK7gNy5eDUBzXTU1VcFS/7GmzVeRdUEaok/NWHWure8umkafJF3TxifjL2JdSy3d0/ogLShzqT7EEF6ArDBeecW4XL5AKHoYgL5gNy31lQN64JWn7Xn/taVgz5myrrWOGr+B+Cu2dZ3Ra5/NFFwSERERETlLZHIF3vCZe/nlgdlLTlaqE9EUoaDR7n9Zba4NqSyuTH8sNSMzZymroJ2LBubJXGoMFzOXFhaES2Qmg0sH3Gpa66t5ydZO9vaP80TbNYBXGmf5DK0D9wPwWPWldEVqCAS8rLuN7RX6DFmAgc2/zcOvuYPbIteRdwHWHfwGHb0/B6B39dWlErPiqoDhUJDa6iA7Nnazy/UQoEDT8GOTYzrHpl0fB2D3xreyeyjHlRunNvKGyUDkXCLhEEPFzKX4AJlcgVy+QKEwWcKXyE72WzpGF231c6/CFpinz9PpEAwYPe311FQF+NXz1Mx7oRRcEhERERE5SxwZnuCBQyM8cGhk/oNXmN6xJN1NtaUvoy31IUaVuVQyOJ6ekZlTzI5Z6WVxA8WV9CKVg0tVwQANNVULfk7lmUt7c6tprq3mBVvaqQ4G+Ep/D+lwB3XxI6x/+osE80n2sY7WVRswM5pqQ1QFA3Q01pSCWtMVaiIErv0gHz3/S9yV3w7Ag4XzufLS5wJ4JXFlqwI214Woq67iaMOO/8veecfHUd/p/5ntTdvVu2S5YLlibNPBELBDh4QWypF+gQsJuSTk7pJLcuSX5C6XQtqRcoRwSSgJAUIChGaacQMbd7mo76ruanvf/f7+mJnVStquYkn7eb9eelmanZ0ZjXbXO88+z/MBAOhH9iRvswy+CYPzACJKM34ZvAgAcNakKXEVeiUsuswOIxGVXAqX4FzyjtjxxvERbO8YwavHhvHykSG8emwIe7udUAuT4rriFbBmEPRON7dvbsRd5zZBISPJJF/oTBEEQRAEQRDEAqHXGQAAjPlJNJmM3RVEtSG1rFpR8qJJKumcSwaaFgcAGPKGYNLIoZRljn7pVbK8zlMiwRCKxqH1dgIADoQrUW1UQaeU4exWC3Z2u9Bb80EAQMsR3jH0Wmw1llXxkThzSkysOceUtHXrNuGdzQ/hXuJpKwAAIABJREFUyuh38KuaB5KuJaNGMUEUMQo9W4m6TQAAhX03f0NK19JT6hvwtw4vzm21wJoiJMllkuSx5YNHxnc1KcKOKbclEvyXWOZ9MlqOqrL0UcTTza0bG3DvJUtP92EsKEhcIgiCIAiCIIgFQo+DF5ecFPeagt0VQo1xvCfGpOFjcSxlqlapEo7FMRaITpmGppRJoJBK8o57LVaGPeEJTp906NXyvOKDwWgciXgCGm8XAKAjXoMlFTpIJMCW5RWIxhmeZecBAKRx3jH1ZmJVUsCx6MZjYhVlqrTl3qlsaLbgruuvwnXnrk4uq5zkwDIJvUv6tnP57XoOgUtEYR56Cwbn+3Bzenxj8GxsXVmFO89pmnDfZZVlWUW3yfhlvOtJnkZcEtEIsbjORDmqjPNTXJJIOKjz6JkixiFxiSAIgiAIgiAWCKJzyekPn+YjmV/EEwyDnlCyzBvgo0CxBIMvXNrCCcBH4oCpogPHcdCr8497zSeCkXjy95ouQ95wxkiciF4tz8u55A/HoPLbII2HEFCUwwsNGswaWHVK1BrVWFFdhv/rNsCrXwIAiECBDkU7KsuUUMgkyR4skbTdS5MQo3TA1EgcAGgUMihkEiS0FRiU1ULFQpCPHEHjwQcBAD+PfhA3bFqKD51ZBwk33nFkLVMm3VD5Elby4pIylFlcEp1LPYlKVGUoUScWHiQuEQRBEARBEMQCYVxcWnhiwGwy4g0jnmATJlyJUSAq9QaGxE6hNO6cfEWT+UQklsBtv96Fa37yFuKJ6TvTRjyh3M4llRzuPBxe/kgcWi/ftzSqbgQA1BrVSRHlkuWVGAvG8K6enxq3FyvQVGUFx3Ewpym3rtSroFVmdy+lYtIq0vYEmYTng9u6HgBg3PPfsIy9jzFWBu25n8ZFyyomrC+TclheQBxOJKayAMjsXJLEAlCGRhDnZBiEGeXztHOJKBwSlwiCIAiCIAhigdDj8AOgzqXJ2N1BAPxFvIhR6BMicQkY8QrT0NK4c/Sq/OJeM0EsnsDtv96Fb/31CMKxeNHb+fbzR/Fuzxjs7hDe6x2b1jElEgzD3vAUV9dk9GpZXufJn1LmPSjnxaVKvQoWnRJSKYfVdQaUlynxX66LcLj5o/hG+Ja0kbhU8nEviUyOPooYhWhcTOhdWh3YCQA4seQfsKKxZsr6bZVlUMkLj4XFNfx0NUUG55LoWnIpa5CAhMSlRQSJSwRBEARBEASxAEgkGPrGeBHF6Y9Ql1AKdhd/XqonxOIE51KQhLhsziVDnl1CM8GbJ0bx5olR/PLNLlz70x04OewteBvPHbDj4be7cdOGeiikErxwaHBaxzQWiCCWYFPKziejV+UfixPFpR5pHQBe1JNKOJTrlJBwHLYsq8Dh0Ti+G7sZHawhpcw7vbhUqVdCo8wt9EgkyPh7iOKS27ohuSwsN8C/+q4p65p1iglCbSFwOt4BpQinn2gp9i2NynlBy5rHFDpiYUDiEkEQBEEQBEEsAAY9IURiCTRaNIjEE/BHind+LDYGXLwzJzUWJ5YYj5FzCcPeEKQSDpY04gUfi5ubXqon9vbBrFXgf247E0OeEK788Vv4w+7evIXSk8M+fPmPB7C+wYj/uLYd57VZ8cKhwWkJraLwlsnxI6JXy+ELx5DIEcMLROLQevhJcadYLYzq8Sl04j7OXWKBUibBGydGYVDLUVmmhE4ly1iczXFcXu4lk0YBuTT9Jb5OKYNMyiFQ1oyIkp/o1rfso4jLdRPWk0o4rKjS59xXJtQ6E8JMBlnMD0ksOPV2Hy8uDUiqIeW4ZFyPWPiQuEQQBEEQBEEQCwCxb2ltvREAReNSsbmC0Cll0KdM1jKITo0AnachT5h3zUi4KbfpVXNT6O30R/Dy0SFct64WW9ur8MK952NDoxlfeeog7v79e3DnEAH94Rj+8f/ehVIuxU8/sh4KmQRb26tgcwVxyOYp+rheOjIEAGir1GVdz6CWgzHAm6Ug3h+OIR5PJDuXOqJVE6KIFq0CMikHjUKGc1v5+NjyqjJwHAdrhkicSJVeBU2O6WXZBDKO43g3H8ehc+VnMVR3Ofrabp+y3pIK3bSmpJl1CjjAi1OKNL1LYiyuD5UwaeWQpnlMEgsTEpcIgiAIgiAIYgHQ65goLjlIXEoy4A6i2qAClzLpyqjmL9bnyrkUiSVw2692YXvH8JzsrxCydQqJsbjZjlk+vc+GaJzhwxvEqJgKv/3oRty/bTn+fngI2370Bv5+eBCxeGLKfRlj+Jc/H8SpER9+fMu6pEPtAysqIZVweP7QQFHH5PCF8cs3O7GtvQpLKrKXV4vCZbYIoc0VhCLsgDziRkyuw6lQGapS3HQSCZeMJm5ZUQGZhMOqWgMAwKzNHg/jOA5NWdxLEgly9heJPWT9Sz6Cg+f8eIprSaOUos5UXBxOxKRRwMEEcSlN75JGcC51JypQTpG4RQWJSwRBEARBEASxAOhx+iGVcGgXLkbJuTSO3RVCzaSOGIVMAq1COmeF3nu6nXjr5Cge+OvRGZlgNpMMe0KoyOBq0avliCUYgtHZjVk++W4/VtUasDwlciWRcPj0ha340z+eA4VMgk8++i7O/s6r+PbfjuLE0Hgf0//t7MEz++247wNLce4Sa3K5SavA5hZz0dG4n20/hUAkhi9ctjTnunpBmMnk8orFE7C5gsm+JX9ZK9yhGGoME897lfBzlV6F//rQamxqNkMq4ZLCTzZqjGqsbTBCp5o6PS5bJE5E7F3KRItVN0GgLQaDWo5Rxr9GpetdUgudSydj5Rkfk8TChMQlgiAIgiAIYt4w4A7CnyV2Usr0OAKoNaqThb3kXBpnwB1EjXHqhapRo4BrjmJxomPp5LAPfztYnJNmthjyhDIWPetV2UWTmeCQzY2jAx7cKLiWJrOm3oi/f/5CPHT7mVhTZ8Sv3urCB37wBq756dv40csn8M3njmDL8gp85qIlU+67dWUVOkf9ODnsK+iYbK4gHn2nBx86sy6nawkYP0+ZSr0H3CHE4wwaQVzylrXAE4xOEVBMGjkUMv4yvEwlB8dxMGkVaSOL6bDqlNjUbMbKWv2EaW5VhtxCjV6VOYamU8ny2kYuDGo5HBDEpdDohNu4RAwqvx0AcCJspklxi4ypkidBEARBEARBnAYYY7jmJ2/j+vV1uH/b8tN9OPOOPmcAjRYNTEIpMzmXeELROEZ9EdQYpsZ5jBo5XHM0CW17xwjOabVgxBvGj189gStWVectGMwm4VgcY4Foxj4eg+CY8QRjqDYUvv1jgx60VZRl7c55cm8fFDIJrl5Tm3EdhUyCy1dW4fKVVRjxhvHMfhv++G4/fvDycdSZ1Pj+jWvSns/LV1bha88exvOHBtFWmVskEvnhS8cBDrj30tyuJWDieZoMYwx9Qiea1suXeTvUzWDgHUqpcByHSr0quT6AtEXr2eA4DtUGNSrLVOgbC6DXGchr6ppEwkGvlmHMP/U50VKeuzA8HwwaOfYJsbiariehc3cgIVUiLlVBkohBwmIIqSsx7JbQpLhFBolLBEEQBEEQxLzA6Y9g2BtG31gg98olSI8zgA+uqkaZUga5lIOTiqoBAINuYVJcmtHppjlyLvWPBXBi2IebzqpHhV6Fz/5hH54/NIgrVldnvd/LR4bgDESwvsGEFqt2VsSoES8/DS2jc0nNXxIW41w6NeLD1h++iY+e24yvXXVG2nVC0Tie3m/HZWdUJkvWc1FepsTHz2/Bx85rxrFBL8xaBV9GnYYKvQrrG0x44dAgPntJW17bPznsxZ/e68dHz21GbZrHTTrE85Suc2nUF0FAmN4oxuIG5PUAgCrD1PNeqVdOFJdylHlnQiLh0GjRot6kyfuxY9QopohLZSpZsgtquhjUcnSzKn5fjv0wOvZPWcerbUJsjJFzaZFB4hJBEARBEAQxL+h2+AEAo8LFMDGOOxiFKxBFo1nDx2g0Cjh9cycuMcaw7UdvYmt7FT6Xp9NjrrC7+XHnk7ttAN5FYXdNHYc+02zvGAEAXLSsHM1WHX708nE8+MoJbGuvynjRv+PUKD7x6F6IVUF6lQxrG0xY32DEugYTNjaZpzW1S2RYeD7ldi4VLi4dG+B7kf737S6c1WTCtlVTxbSXjw7BHYzixg31BW+f4zisqNbnXG9bexUe+OtR9DoCaLBocq7/vRePQ6OQ4TMXT43ZZULsXEoXi+tNEYpEcalHUg8gkla0MWoUUMmlCEXjUCuk0Cimd1leiCiZrtuptSL7pLxC0Kvk+GP8AixrbsTmSgZJPARpPARJjP+XS0RxwHoF0J8gcWmRQeISQRAEQRAEMS/oHuUv0KhLaCqiy6FRuHA2axVz6lzqHPXj2KAXkVhi/olLLt65NLnQG+AvpOciFre9YwR1JjVay/lC5M9e0oZ7H9uPFw8PphVcHL4wPvfYfjRbtHjwlnU4YvdgX98Y3utx4UevnABjwKZmMx7/1NnTPrZhD39+Ml3I5+oSykbnCN9z1F6rx5f+eAArqvVTJpo9ubcf1QbVhCLumebylby49MLhAXzygtas6+7vc+GFw4P4/KVLYS4gjqZTyMBxU0U4byiajKhKoz6ogoOISxToiVsB2DP2GFXqlehxBAo6hpnAqFGA45AUNQ0a+YzG0zQKKZhEjt2qc1Hfmr5jq3vQA+A4rEU6toj5CRV6EwRBEARBEPOCHsG55PCRc2kyPQ5eXGow8xfuZq1iTjuXdpzki3k7R/3Jv9N8YUBwJqW7iBdjcYlZnN4WjsWx49QoLlpWnpy0deXqGrRYtfjRKyem7DuRYPjCk+/DFYziJ7euR3utATeeVY9vX78aL37+Ahz498vwifObsavLiQH39F1XuZxLuaagZaNz1I9qgwoP3b4BUimHf/zdewilTJ0bcAfxxokR3LC+Lmsn03SpN2vQXqvH84cGs67HGMN3nz8Gi1aBj53fXNA+JBIOepUcntDEzqU+5/jfSOxbCpQ1wxVOQC7lYM4Q56sUHq/FRuKKRSrhUKYady+1ls+cawng3WZlKlkyJpgO8bGWKapJLExIXCIIgiAIgiDmBd2CgDIWiCIWT5zmo5lf9Dh5QUeM/Ji0CjjnUlw65UCZkg89iBGw+YLdHYRVp5gwOUvEqJEjwQDvLE4g3NM1hkAkjouWViSXSSUc7tmyBMcGvXjp6NCE9X/1Vie2d4zgq1eswBk1UyNfZSo5PixEyF47Nv1zPeQJQSrhMpZG61Vil1Dh56hzxIeWci1qjWr84Ka1ODrgwb8/czh5+1Pv2cAY8KEz0ztYZpKtK6uwr9eV7OBKx1snR/FOpwP3bFkCnbLwEI9eLZsgwkViCQx6UsQlIRIXKGuBLxxDRZkqY2RNr5JDq5RlFJ9mE5PQfWXSymfFOaVXyRGIZH48eQWBjgq9FxckLhEEQRAEQRDzgu4URwyVVU+k1xGARatIXhBb5jAWl0gwvNPpwNb2KjRbtdjeMTwn+80XuyuE6jST4gAkS6Bns9R7e8cwFFIJzllimbD86jU1aLJo8OArJ8CEDNK+3jH85wsd2LqyCrdtbsy4zbYKHepMarx6bPrnesgTRrlOmVHkkEkl0CqkBcfiGGPoHPGjxco7Xy5eVoG7L27F43v78Md3+8EYw5N7+7Cx2TwlKjcbbG3n44cvHk7vXmKM4T9f6ECtUY1bNzUUtQ+9Sj4hFmdzBZFI0cE1grjk17fAHYiiUp9dPFlaqYNMOveX5GKx+ky7lkT0ahkC4czOJX84BrmUS/Z9EYuDvB/JHMdJOY7bx3Hcc8LPv+E4rovjuP3C11phOcdx3IMcx53kOO4Ax3HrU7ZxJ8dxJ4SvO1OWn8lx3EHhPg9ygp+U4zgzx3EvCeu/xHGcaeZ+dYIgCIIgCGK+wBhD16g/2cHhmMOy6oVAr3NiUbFJo4A7ODcOryMDHrgCUZyzxIILl5ZjxynHhOjT6cbuCqI6Q6+NWF7sCsxe79L24yPY1GKeUsosk0pwz5Y2HLZ78PLRYbiDUfzTH/ahUq/Cd29YnYzQpYPjOGxZXoG3T45O+1wPe8M5RQ69Wl5wLG7EF4Y3HJswwv7zly7F5hYz/u3pg/i/Xb3odgTw4TlwLQHAkgodllTo8EKaaFw0nsB///04DtrcuO8DS6GUFVeUzsfi+POUSDD0T5psqU2KS61wBiIZ+5ZELKfJuWPSKGDRZZ7AN12MagX8WZxLvnAM5Tpl1ucAsfAoRCa9F8DRScu+yBhbK3yJMwa3AWgTvj4J4OcALxQB+HcAmwBsBPDvKWLRzwF8IuV+W4Xl9wN4hTHWBuAV4WeCIAiCIIgFybf/dhTv9Y6d7sOYl7gCUXhDMaxv4N8ekrg0kR5HAI3mcXHJrFWAMcxJWfWOU3zf0jmtVly8vALhWALvdDqK3t6+3jE8sadvpg4PA+5Q2jJvgI/9ALN3nvqcAZwc9uHCpeVpb792bQ0aLRr86JXj+MpTBzDgDuHBW9YlnSPZuHh5BYLROHZO41wDfKF3eY4x8wa1vOBpcZ0jvNOwJcX9IpNK8OAt61CmkuOrTx+CViHFB9MUms8W29qrsKvLMaG37f0+F6768Vv4yWsncfWaGly7rrbo7evVsmR8cMgbQjg6UdwVO5f8+lY4feknxc0H5FJJXlP4isWokWftXPKGYrBS39KiIy9xieO4OgBXAPhVHqtfA+C3jGcnACPHcdUALgfwEmPMyRgbA/ASgK3CbXrG2E7G+0V/C+DalG09Inz/SMpygiAIgiCIBYU7GMVDb3Ti+YMDp/tQ5iViJO7MRkFc8lOpt0gklsCAO4iGSeISgDkp9d5xyoHWci0q9SpsajZDJZfg9SJ7lw70u3Dbr3bhy08dmJGomicUhS8cQ40x/UW8QT27sbjtx/nzcNGyirS3y6QS3H3xEhyyefC3g4P458uWJR/juTi7xQKVXILXphmNy8u5lOLIyZekuDQp8lZRpsKPb1kHCQdctaYG2iK6jYrl8pVVSDDg5aNDCERieOC5I7juZ29jLBDBL24/Ew/esm5axeIG9fh56nVMdC1x8QjUvl4wcHCqGuCPxHM6l04n6TrKZopc4pI7GEU59S0tOvJ1Lv0QwJcATPbdfkuIvv2A4zjx0VELIPWjiH5hWbbl/WmWA0AlY0x8BzYIoDLP4yUIgiAIgphX2IWJVmOzGM9ZyIjT0MQL71FyLiWxuYJIMKDBMn4RL4pLs13qHY0nsLvLiXNa+THyKrkU57Ra8VoRvUunRnz4h4f3QMJxYAzY1eWc9vGJz6uMziXN7MbiXu8YRp1JjdbyzJ1C162rxbLKMly6ogKfuqAl722r5FKc22rFqx3Dyc6mQonEEnD6czto+FhcYYXenSM+KGUS1KY595tbLPj75y/A169eWdA2p8vKGj3qzWo8/HY3Lv/hG/jVW124ZWMDXrrvQly2smra29er+PigOxhNllKLmEb2QMLiCJQ1YSzCX2ZXZZjQt9gxqPlC70yPW2cggnJyLi06copLHMddCWCYMfbupJu+AmA5gLMAmAF8eeYPbxzB1ZT20clx3Cc5jtvLcdzekZH5Nb2CIAiCIAgCAGxjgrg0hxO+FhJdo35wHNBea4BMwk2ItZQ6PYKrq3FS5xIw++LS+30uBCJxnJtSVn3RsnL0OALoGvVnuedEBt0h3PHr3ZBwwJ8+cw6UMsm0414AMODiJ4NlKvQWC4PHZsG5FI7FseOUAxcvq8jaHSOXSvDsP52LX96xIWOpdia2rKhAnzOIk8O+oo5xRHge5e5ckhUeixv1o9mqzfg7Lakom1V3TDo4jsPWlVU4NuiFXCLB45/cjG9dtwp61cwUR+vVvCPHn8blVX/yUQDAQOO1CApxuYoc532xolfxUxrDsamdcIkEgysQpUlxi5B8PIrnAria47gPAlAB0HMc93+MsduE28Mcxz0M4J+Fn20A6lPuXycsswG4aNLy7cLyujTrA8AQx3HVjLEBIT6X9iMSxtgvAPwCADZs2FCcrE8QBEEQBDGL2N28uERT0NLT4/CjxqCGSi6FWaugzqUUep28qyu1c8kiFJ/P9uNpxykHOI53oohctLQCwGG8dmwYzec159yGKxDB7b/eBXcwisc+uRlLK8twZqMJuzqn71yyJZ1L6R0iMqkEZUpZUc6lRILh5aNDuGBpeVqRZE/XGAKROC5alr5vKZViC6QvFuJ2rx4bRltlWcb1Okd8+NN7/dAoZChTyaBT8l9DHl58q8zhoJk8BS0fOkd8OKNm9np7iuXui5egrbIMV6+pmXFxS6/iL5994YmuJbWvF1b7a0hI5LC13IjAGB8JK2XnEsBPhZv8N/BHYognGDmXFiE5xSXG2FfAu5TAcdxFAP6ZMXZbiujDge9COiTc5VkA93Ac9xj48m63sN6LAP5fSon3ZQC+whhzchzn4ThuM4BdAO4A8OOUbd0J4DvCv89M/1cmCIIgCIKYe8i5lJ1uRwBNVl48seiU1LmUQo8jAJVcMuFizCjEvZyzLMK9fXIUK2v0E6ZKNVg0aCnXYvvxEXw0h7gUiMTw0d/sQY8jgN989Cy01xoAAJuaLfjhK8fhCkSmNbFqwB2EVMJljX0ZtfKiOpf2dDvxyUffxVlNJvzyjg1TjvO1jmEopBKc3WrJsIXpU2NUY3lVGV49NoxPXdiadh3GGO7/00Hs7s4s1tWZ0ju7RAxqObxh/qI/n06iSCyBvrEgrlxdk3PducaoUeDGDfW5VywCsYh9ciSu7uTvwYFhoP4KRFUWeIK8Ky+XqLdYEcWlQDSOyc+OkODqInFp8TGddrXfcRxXDoADsB/Ap4XlfwPwQQAnAQQA3AUAgoj0HwD2COt9kzEmvgJ+BsBvAKgBPC98Abyo9ATHcR8D0APgxmkcL0EQBEEQxGnDRp1LWelx+LFNmCpl1SmocymFXmcADWbNhOiVUiaFTimbVedSMBLHvl4X/uHcpim3XbysAo/u7EEwEodakd4dEo0n8JnfvYf9fS787CPrk71NALC5xQz2MrC7yzmtLhy7K4QqvSqrIGJUK4qaFtcnCMLv9ozhhp/vwG/u2oj6FPfY9o5hbGoxQ6OY3cLqLcsr8NAbnXAHo8mL9lTeODGK3d1OfPOalbjprHr4QjF4QzH4wvy/cimX1fUE8HEvAPCFYnlNsut1+hFPMLRk6ZpajIjxulRxSRILoKbrjwCAvrY7APBF82VK2ZyWmc8nUp1LUikHpUwClVwKlUwKr+D6oljc4iPfQm8AAGNsO2PsSuH7LYyxVYyxdsbYbYwxn7CcMcbuZoy1CrfvTbn//zLGlghfD6cs3ytsp5Uxdo/QrwTGmIMxdgljrI0xdmmKGEUQBEEQBLGgEMUldzCKWHxqD0Up4w5EMRaIoknoFLKSc2kCvY4AGsxTL+LNWsWsOuH29jgRiSdwThpnzkXLyhGJJfBO52jG+//bnw9he8cIvnXdKmxtnziOfk29EUqZZNql3nZXMGMkTsSokRcl6opl4b+5ayNGvGFc//MdOGRzAwD6nAGcGvFnnBI3k2xZXoF4guGN41O7ZRlj+N6LHagzqXHzWQ1QyqSw6JRosmrRXmvA2a0WbGgy59yHGPdy5ynCnRInxZXrCvhNFj6iCOcNj5+n6p5nIY964LKsg9fcDoDvQqucx5PiZptGqxYyCYddXU6cv8SKc1qtWN9gwhk1+mTJNzmXFh8FiUsEQRAEQRBEcdhdQYjGk2JcFIuZbqGwukmYhmahzqUkjDH0OgMTyrxFTFoFHLMoLu045YBMwuGsNOLExmYz1HIpXjuWfpjOM/tteHxvH+6+uBW3bGyYcrtKLsW6BuO0S70H3KGMZd4iRo0C7iIcXgPuIKw6BS5YWo4//eM5UEgluPGhd7C9YxjbBaEnn76l6bKuwQSjRo7Xjk2tn33x8CAO2ty495I2KGTFX9qJThNPmqLqdHQmxaUSdy4xhvoTfJF3X9vtAACO46ddlmrfEgDUGtV44Np2vHliFA/89eiE20a8/AcHJC4tPkhcIgiCIAiCmGUisQSGvWG0WPkLMepdmkhSXBLOj0WnRCASRyBS2Gj0xciIL4xgNI4G81RxyayRz8oUNJEdpxxYW29MG+1RyqQ4d4kFr3UMTxk33ucM4N/+fAhnNprw+UuXZtz+5hYLjgx48nbLTCaRYBhwB1Gdw7lk0siLEnRtrhBqjLxw1VZZhqc+cw6aLFp87JG9eOj1U6g3q5PP6dlEKuFw0dJybD8+gnhi/FzHEwzf+/txtJZrcd262mntQ3Tk5Fvq3Tnig1WnnLEpbAsFvVoo9BbEJdPILug8JxBWVWC49jIAgFohxbAnVLKT4kRu3tiAj5/XjN/s6MajO3uSy0d9YajkEmgzxGmJhQuJSwRBEARBELPMoDsExpAsM57t8fELjR4HPw1NFFDESWjkXuIjcQBfoj0Zs1aJMf/suODcwSgO9rtwzhJrxnUuWlaB/rFgMiIFALF4Avc+tg8A8MOb1kImzXy5sanZAsaAPXlG4xhj8Iai6B71Y2+3E0/vtyEaZ6g15nAuqeVwB6MThJl8sLuCqElxRVXqVXji02fjnFYL+seCuGhpxYQerNnk4uUVcPojeL/flVz2zH4bTg77cN8HlmU9z/kgOpfyFfo6R/0l51oCUh1evLhUf+K3AID+1pvBpPzrllouxbA3XNLOJZGvfHAFtiyvwNefPYy3TvAR2hFvGOVlyjl77hBzR2k2jBEEQRAEQcwh/S5eIGivMeCZ/XYq9Z5Et8OPaoMqObLaKohLo77whALlUkQU3hrTOZe08lnrptrd5USCIW3fkogYCdveMYwlFXz3zoOvnsR7vS48eMu6nH+7dQ1GKGQS7Ox04NIzKjOu926PE597fD+GPGFEYlP7ypZX6bPux6hRgDHAG4rmPZmOMQa7K4gL2ibG3nRKGf73H87CH3b34vJpFJEXyoVLyyHhgNctcV2QAAAgAElEQVSODWN9gwmRWAI/fPkEVtbosa19+sehLzgW58PWGdjvQkMtl0Im4eALR6Hy21BufxUJiRy2lpuS60TjDLEEQ1UJdy6JSCUcfnTzWnzo5+/gM797F3+++1yM+iJU5r1IIXGJIAiCIAhilrG7QgDGnUuzGWVaiHSP+pN9SwBg0fIXHnPpXHrugB0qmTSryHE66HEGwHFAnSl951Iomsg6sa1Y3j45CpVcgnUNxozr1Jk0aKvQYXvHCD5+fgt2dznxk1dP4Ib1dbh6Te4R9Sq5FOvqjTlLvX/22in4QjHcdW4TrFolLDoFLDolLFoFKvWqnN0tRmH62Vggf3HJHYwiEImnLQuXSyW44+ymvLYzUxg1CpzZaMIrR4fxhcuW4Ym9feh1BvDwXWdBkmVSXr4UUug95o9gLBBFi7W0yrwBgOM46NVyeEMx1J38PTiWwGD9lYiox0VIMc5bUUbiEgCUqeT41Z0bcO1P38bHH9mLaDyBM6qzC8LEwoRicQRBEARBELOMTRhpvrKWf0NNsbiJ9DgCaLKOiyfJWNwcTYxjjOHrzx7B1545hESB0anZps8ZQI1Bnbas2aLlz5NzFsTKd045cFaTGUpZdtHq4uUV2N3lxIA7iM89tg/1Zg2+cc3KvPezqcWCw3Z3RsdMryOAVzuGcfvmRnxl2wp84oIWXL++DhcuLUd7rSGvUmBRXHIVcJ7E6Y65IndzycXLK3BkwIPuUT9+/OoJbGg04aKlM1MorlPKIOEATzB3z1nnqA9A6ZV5i+hVMoQDPtR0PQlgvMhbRHwsk3NpnHqzBg/dfiZsY0H0jwWpzHuRQuISQRAEQRDELGN38W+m9So5NAopFXqn4AlF4fBH0JjGuTQ6R86lUyM+jPrCsLtD2J/SaVMMf3nfji3f2z5jZeQ9Dn/aMm8AMAkuHOcMn6cRbxgdQ16c05q5b0nkoqXliMQTuPkXOzHsDeNHN6+DLk0BeCY2t5iRYMDe7vTupUd3dkPKcbh1U2Pe25yM6FZyFRBHFd2G1fNIXNqyvAIAcPfv38OQJ4wvXr5sxnprREdOPrG4U8lJcaXnXAL43qXVrpegiLjgNq+Gx7Jmwu1u4XFGnUsT2dBkxnduWAUAqCbhbVFC4hJBEARBEMQsY3MFkw4Ik0YxK06ThYpYWN2UUlitVkihVUjnLBb3zikHAEDCAX89MFD0dtzBKL7+7GF0jvpxyOaZkWPrdQbQmKbMGwDM03QuDXtC8IWnimDvdPLnI1vfksiGJjO0Cil6HAHcd9lSrK3PHKNLx/oGExRSCXZ2ThWXgpE4Ht/Th8vbq6blAjEKfUKuYP7nacDNO5fSxeJOF8sqy1BrVOOw3YPz26zY1JL771MIepU8r1hc54gfcimHetP8Ed7mEr1Khg94nwYw1bWkUUox4g1Dwo13xxHjXL++Do99cjNu39x0ug+FmAWoc4kgCIIgCGKWsbuCWCF0TJi08oIcFIudrlHeBdE0aaS7Raecs1jczk4nqg0qnFGtx/MHB/CvH1xRVI/Nj14+AYfgSjtsd2Njs3lax+ULxzDqi6SdFAeMi0vFOOHG/BGc993XEE0k0GzRYmWtAe01erTXGvDq0SGUqWTJjrBsKGQSfOjMOtjdIXzqgtaCj0Mll2JtvRE7BUErlaf32+AJxXDnNPuNTEU4l2yuIBRSCaza+RPf4TgOW5ZX4NGdPfji5ctmfPsGtRyevMQlHxrMmmlPqFuorMdRNMe7EVZZMVS3bcJttUY1Bj0hWHXKkj0/udg8w6IoMX8gcYkgCIIgCGIWYYzB5gomi6JNGgV1LqXQ4+DFpcnRL4tOMSfOJcYYdnY6cOHScpzXZsUrx4axv9+F9Q2mgrZzfMiLR97pxi0bG/DSkSEctk/fudTn5F1dmWJxorjkKOLx1DnqRySewLVraxCIxPFezxj+8r49efsHzqiENE+B7RvXtBe8/1Q2t5jxk9dOwhuKokzFu4wYY3hkRzdWVOtxVlNhf4vJ6NVycBwKmtJod4VQbVTNSFn2THLvpW24ZEUFVtcV5hDLB71aBk8on84lf8lG4gDAKvVjCGb4W24Ck467k6RSDjVGNQY9YVRSJI4oQUhcIgiCIAiCmEUc/gjCsQRqhFiPWatAryAaEEC3I4BKvRIaxcS3pRatEv1js3+eTgz74PBHsLnFgkvPqIRCKsFfDwwUJC7xheCHoVPK8MXLl8HuCuKQzT3tY+sRIoON5vTFyXqVHFIJV5RzSTy392xZgiUVZQD4ovnDdjeODXhx0bKZKYrOh00tFjz46kns7R7DxUKv0O4uJ44NevGd61dNu1dIKuH4yFcB8UG7K4gaw/yLfVl1Sly0rGJWtm1QyzHk8WVdJxZPoMfhxyUrZucYFgL9VZdgy6lG/Hj5RFG12qCCXCrBsCeUdrojQSx2yKtHEARBEAQxi4iT4mqFiw1yLk2kx+GfUOYtYtUpinLkFIrYt3R2qwV6lRznt1nx/MGBgqbGvXBoEDtOOfCFy5bCrFVgZY0eJ4d9CMfi0zq2Xqfg6soQi5NIOJg08qLOk+iKqjWOb9usVeD8tnJ84oIWtFWWFXHExbG+wQS5lJsQjfvtOz0wqOW4Zm3tjOzDqJEX6FwKomYelXnPBXpV7lhc/1gQ0ThDq7V0nUt6lRz+mARhbqI7SXQYDnpCqDLMnzglQcwVJC4RBEEQBEHMIuJIc7EY2KRRwBuKIRpPnM7Dmjd0jQbQnEZcsuh4Ea4QkacYdnY6UGtUo04oJ75idXVBU+OCkTge+OtRLK8qw60bGwAAK2sMiCUYjg9md4HkotcZgFEjh0EopE6HSaMo0rkUhFWnhFohnc4hzghqhdC71MWXeg+6Q3jh8CBuOqt+xo7PqJbDlUefEABE4wkMeUKonUdl3nOBXp270LtzlH9Mt5Snd9OVAnrh+RiIjIvH1jLefRmKxuEKRFFZVlqPHYIASFwiCKIE6B8L4NjgzEztIQgiM199+hB+v6v3dB/GvMMuiEt1gkPErBUmV1Gpt1BYHUajdaozx6pTIp5geU2vKpZEgu9b2txiSUavUqNx+fDz10/B5gri61evTBb4ttfy5e2H7dOLxvU4Ahn7lkTM2uKmD/aNBVBvnj/OnE3NFhyyueELx/C7XT1IMIbbNjXO2PaNGgVceZ6nIU8ICQZUl5hzyaCWIxxLIBTN7LjrHOHddKXcuaRX8RHeQGS8n0qcnDfs4YcQVE5juiFBLFRIXCIIYtHzH88dwT2/33e6D4MgFjWMMTz1Xj9ePTZ0ug9l3tE/FoRWIYVezV+QmMQJX0WOj19MiGXeTWmdS3ysZDYnxh0f9mIsEMXmlvGpboVE4/qcAfzP66dw1ZqaCROQ6k0alCllODRNcanXmae4VFQsLoj6edQLs7nFgniCYcfJUfxhdy8uWV6RMQ5YDEZN/lMaB9whACjBWBz/GuXNUup9asQPo0aeLJMvRSY7l7RKWfL1atDDP3aqqNCbKEFIXCIIYtFzYtiHQeGNIkEQs4MnFIM/Ep+TjpyFht0VRK1JnXTGiGPRqXcppbA6jYhgFS5eR2dxYpzYtzR5NHa+0bgH/noEUo7Dv3xw+YTlEgmHFTX6aU2McwejsI0F056bVEzawmNx8QSD3RWcV86l9Y1GyKUcvv38MYz6Irjj7KYZ3b5Jo8hb0BXdhqUYiwOQ1S3YOeJDi7V0I3EAkjFVUVxKFUFFcYmmxRGlCIlLBEEsamLxBHodAfjCsaw2b4IgpodYWk2CyVRsk4qBRXGpmJ6cxUZ3Ps6lWRSXdnY6UGdSo36SOyifaNwbx0fw4uEh3LNlCarTTBVbWaPHsQEv4kV0RoWicXzikb3gOODSFZVZ17VoedGkkG6qAXcQsQSbVxOtNAoZVtcZ0TXqR0u5Fuctsc7o9g1qObyhGGJ5dJ2JPWnp/q6LGVFc8oQyi0unRvwlHYkDeHcjwMfi5DIJqlOEpGFyLhElDIlLBEEsavrG+DfQADDinb1oBUGUOuIn/c5ZFAIWKnZXELUp4pI5GYujzqXuUT/Ky5TQKmVTbrPo+PNUbCzuS398Hy8cyiwOJRIMu7qcOHuSawnIHY07PuTFZx/bhxarFh87rznt9ttrDAhG4+gaLazUOxZP4J7f78OeHid+cNNarGswZV3fpFEgwbK7TSbT5+Sfr/MpFgcgGU+8Y3MjJBJuRrdt0ojCSebIl4jdFYRRI0/7uFzMiKJJpolxnlAUo75wSZd5A3zEEgDePDGKUDQ+4bE66A5BJZckY9AEUUqQuEQQxKIm9U39qI/EJYKYLcRP+r3h2LTHry8mApEYxgLRCc4l8cKEOpeAbkcATRliXyaNAhxXXCzOG4riib39+Nc/H8rowjg66IErEJ0SiRPJFI3rdQRw2692QSGV4Dd3bYRKnn6a2Uqh1PuQLf9oHGMM9z91EC8fHcI3r16JK1fX5LyPKFYWUurdP8bHEedTLA4ArltXhytWV+NDG+pnfNtGTf5dZ3ZXCDUl5loCxuNemYTKZJm3tbSdS1adEvdcvAQ9jgA+9ei7+Pgje/F+H/86MegJoVKvSsagCaKUIHGJIIhFjfhGCJjd3g6CKHVE5xIAjPnJkSOSnBRnGr9QVcml0CikcxohDERi8zKG1+PwozFNJA4ApBIOZo2iqA8GxC4nhz+Cn752Mu06Ozv5sfebW9OLS+micUOeEG779S5E4gk8+rFNWQunW8t1UMgkBU2M+87zx/DHd/tx7yVtuD3PzqGkE66Av2/fWBASbv4VVi+p0OGnt66HbhYcQ6Kom0+pt31SlLVUEN02mdxdnSP8B3atJe5cAoA7zm7Ew3edhfs+sBR7up245qdv447/3Y0jdg/1LRElC4lLBLHA2d/nwk9ePXG6D2Pe0jnqh+hWJucSQcwethRxaTaney00+oUuqskXqiZN4SXM0+FfnjqIq37yVl59M9lwBSL49vNHEYxM350WiMQw5AmjOUs5sEWngGMa4tKaOgMefqsbvcLPqbxzyoEGs2ZCZDGVydG4MX8Et/96Fxy+MH5z10YsqyrLegxyqQTLq8ryLvV+6PVTeOiNTty+uRGfu7Qtr/sA4+JSIWX6/c4Aqg1qyKWlcykgOpdceTiX+J600hMIcsXiOkf8kEq4GZ3it2DhgBXVenz2kja8ff8W3L9tOY7Y3egc9aPaUHqPHYIASFwiiAXPk3v78L2/H0d0mhcMi5WuET9WVPPRhGIuUAiCyA+bKwiVnH9bQaXe49hdfLnrZAHDrM1/ctV0icYTeOXoMPrHgnjpyNC0tvXYnj489HonXusYnvZxZZsUJ2LRKosq9BaLwn948zpIJRy+88LRCbfHEwy7uxxp+5ZSEaNxb58axT88vBvdjgB+eecGrK035nUcK2sMOGz3gLHsZdtP7u3Dt58/hitWV+PrV68sKFJjKsq5FJjgpisFjOr8nEveUBTeUKwknUsquRRKmSSzuDTqQ71JDaUsfRS0lNCr5MkYoU4pw6cvbMWbX9qC/7xhNe65eMlpPjqCOD2QuEQQCxwxckHdHenpGvVjeZUeZSoZxeIIYhaxu4I4QxBySVwax+YKQCrhUFGmnLDcpFXAOUeF3vv7XPCGY5BwwCPvdE9rW3953w4A2NPtnPZx9WSZFCdi0SkKcuSkbruiTIlmqxafvrAVfzs4iN1d48d8dMADTyiGza3mrNsRo3Eff2QvDtk9+Omt63FOa/5TzFbW6OEORpMOtnQcHfDg/qcO4rwlVnz/xjWQFlhkbdYU7lzqcwbn1aS4ucCUZ+fSgJsXhEtRXAL4iXGZeso6aVJcknRda2qFFDeeVY+2yuyuRoJYrJC4RBALHDGKMpujmhcq/nAMg54QWsq1KNcpMULOJYKYFSKxBIa9YayqNQCg16NU7K4QqvQqyCbFj8wa+bRicW+fHMUhW35dPq93jEAq4fDpC1uxs9OJY4P5F0yn0jXqT0a83u0ZK2obqXTn4Vyy6pRFRZq7RwNJ0eqTF7SgSq/Cfzx3JDn5bWenAwAylnmL6FVyXLDUikg8gf/+8Bp84IzKgo5jZQ0vuGaLxj2+pw9SCYef3LquKEeIWiGFWi7N+/EUjsUx5A3NuzLv2aZMJYOEyz1VT3xfVVuCsTgA0Ktkac9RnzOAUyM+LCXhhCCIDJC4RBALGMYYbMKnoeQUmErXKP+peLNVy1+geElcIojZYNAdAmN8/4RUwtHrUQq2sWDaTh/jNDuXvvjk+/jSHw/kte7rx0ewvsGIT5zfAqVMgt++01PUPp8TXEvXr6/FYbsHgUjuke7Z6HH4YdUpUCb0vKTDolXAGyp8AmG3w58UrdQKKb60dRkO2tz48z4bAL5vqcmiQXUeE8H+33Wr8OSnzsa162oLOgYAWF6lh4QDjmQo9Y7EEnhmvw0fOKMy2QlUDGatIu9pcXYX/3ytLzHnkkTCwaCW54zFiY7wUnUuGdRyeIJTn9vfef4YpBIOd57TeBqOiiCIhQCJSwSxgPGEYvALpapUVj0VUVxqKdfCWlbcxCGCIHIjftJfb9bApJEXFWNarNhcQdSm6bYxaxXwhmOIxArvy/OGorC7Qzgy4MGJIW/WdUd9YRy0uXHh0nKYtApcvaYGf37PltO9kY6/HLDjrCYTrlpdg3iCYb8wertYukYzT4oTsej4OGEhgmUgEsOwN4ymlKLwa9fWYnWdAf/1Ygd84Rh2dzlxdoYpcZOp0KuwoSl7fC4TaoUUSyp0GZ1Lr3UMYywQxQ3rCxeuUjFrFXmfoz4n7xirN5eWuAQIom4OEc7uCgpR1hJ1LqWJxe3tduKvBwfwqQta8xJkCYIoTUhcIogFjC2lw4GcAlMRxaUmi+BcoqgOQcwKtpRP+vmLXBJyASAWT2DQE0rrXBJLmF3Bwl+XTgz7kt8/K7iJMvHmiREAwIVLKwAAd57ThGA0jif39hW0z+NDXhwf8uGqNTVY32ACALzbXXw0LhSN48SQL2skDuA7l4DCopbpisIlEg5fvfIMDHpC+Nxj++ENx3JG4maKlTUGHMrgXHrqvX5YdUpc0FY+rX2YtPk74frGRHGp9EQCo0aeU1gVo6yFdl8tFgzqiecokWD4j+eOoFKvxKcubDmNR0YQxHyHxCWCWMDYXSQuZaNzxIdaoxoquRRWnRLuYLQolwBBENkRX4uqDaqCHBSLnWFvGPEESxuvEUuYx/yFO4hEt1KLVYtn9tuzTiJ7vWMEVp0i2f3TXmvAmY0mPLqzJ9k/lA/PvW+HhAO2tVfDoJFjaaUOe6fRu/STV0/C4Y/g+nV1WdezCuJSIc7TTEXhZzWZccWqarx8lJ+YN3fikh5DnvCU32HMH8Grx4Zx7dqaKZ1chWLWyPOOxfU5g5BLS9OZY1TLczqXbK4gakq0bwnge8ZSp8U9874N7/e78cXLl0OjkJ3GIyMIYr5D4hJBLGDsbv6CTi7lyJWThq5RP1rK+YsLqxCtcJCjgiBmHNtYEFadEiq5FBatksQlgWQxcJpYnEnD9wwVc66OD/mgkkvw6Qtb0esMYF+GeFoiwfDGiVFc0FYOSYoL446zG9HjCOB1wdWUC8YY/nJgAJtbLCgXpt6d2WjGe71jBQlUIscGPfif10/hhvV1OK8t++S15Gt3Af/HZSsKv3/bciikErRYtajUz42AcEaGUu9n37cjGme4fn12gS0fzFolnHmeo76xAGqN6pJ05pg0ipydSwPuYMn2LQGAXi2DJxQDYwzBSBz/+UIHVtUacH0RnWMEQZQWJC4RxALG5gpCIZWgyaKlGMokGGPoHPWjWejcEKMVo1666CWImcbuHu8VIufSOPYsU6fEWFwuF0U6jg950Vquw7ZVVVDIJHh2f/po3EGbG05/BBcumxi52tZejfIyJR7Z0Z3X/g7bPega9eOqNTXJZRsaTfCGYjg+nL3zaTLxBMOX/3QQBrUc/3bFipzrW4r4YCBbUXi9WYPv37QG/5rHvmeKlTX8FMXDk6Jxf3qvHyuq9UnxaTqYtXL4I3GEormLz/udgZLsWwIAgyZ7oXc8wTDoDpW0uGRQyxFPMPgjcfzijU4MuEP46pVnTBCoCYIg0kHiEjGvCURieO3Y8Ok+jHmLbYy3blt1Shr9PYlRXwTeUCwpLomfflOpN0HMPPxENF5AMWsVcAWjiBfhaFls9I9lnjplnoa4dGKIHwdeppLj0hUVeO6AHbH41Mjv68dHwHHAeUsmuoMUMglu3diA7R0j6Ba66bLx3IEByCQctq6sSi7b0MT3Lu0tsHfpkR3deL/Pha9ddUZSYMuGViGFUiYp6P+4XEXhV66uwSUrKvPe3nQxqOWoN6tx2DbuXDox5MWBfve0i7xFkh1eOVw5AP+4rCuxSXEiRrUCvnAM0TTPF4B/jxCNp4+ylgp6QZQ9PuTF/7x+Ctvaq7CxubhCe4IgSgsSl4h5zZ/es+Gu3+zBgDuYe+USxO7irdtmHTkFJjM+KU4HACgncYkgZgXGGN9RIkwQsugUYKw40WSxYXcFYdLI0/aUGIVYXL4lzCKeUBSDnhDaKvnXtqvX1GLUF8GOU44p675+fASraw1J908qH9nUAJmEw6M7e7LujzGG5w7Yce4S6wQxqMGsgVWnxN5uZ97H3ucM4Ht/78DFy8pxdYoLKhscxxU8kKHHEchZFD7XrKw2THAu/ek9G6QSDtesnRlxySL8bXI5vPzhGBz+SEmWeQOAScs/7zKVetuyuA1LBb2aP0dff/Yw4gmGr2ybO5cfQRALm7zFJY7jpBzH7eM47jnh52aO43ZxHHeS47jHOY5TCMuVws8nhdubUrbxFWF5B8dxl6cs3yosO8lx3P0py9PugygdxHG5wx4SBNJhd/HWbatWQaLJJLpG+WlKLaJzqUwshaULXoKYSZz+CMKxxIRYnLi81LG5gmn7lgBAKZNCq5DCWWCh94kh/rVtaUUZAODi5eUoU8nw9H7bhPXcgSj29Y7hwqXpp5BV6FXYtqoaT+ztgz8cy7i//X0u9I8FceXq6gnLOY7DWU2mvEu9GWP416cPAQAeuG4VOC7/iI1Fp8g7FheKxjHgDk0p8z7drKzRo9sRgDfEu/r+vK8fFy4tT3ZYTRdTngXxopuuvkSdSwZBOHFlEL/trsxuw1JBPEcH+t2469wmNMwzoZYgiPlLIc6lewEcTfn5uwB+wBhbAmAMwMeE5R8DMCYs/4GwHjiOOwPAzQBWAtgK4GeCYCUF8FMA2wCcAeAWYd1s+yBKBPETJCphnkoklsCQlx9xbdYq4QnFaBJaCp0jfihkkuQbRI1CBo1CSiIcQcwwtkkXY6K4RFFdwV1qyHyRatIqCnZ4iZPillby4pJSJsUH26vx4qHBCX07b50cRYJhSt9SKnee3QhvKDZFmErluQMDUEgluCwlEidyZqMJ/WNBDHlCOY/7mf12vHF8BF+6fBlqC7xwt2gVeT+eep2Zy7xPJ+21fO/S0QEv3j45iiFPGDfMQJG3iNgrmGtinPihXV0G0XOxI4pwmeKD45MvS/P8AOOxOItWgbu3LDnNR0MQxEIiL3GJ47g6AFcA+JXwMwdgC4A/Cqs8AuBa4ftrhJ8h3H6JsP41AB5jjIUZY10ATgLYKHydZIx1MsYiAB4DcE2OfRAlwoDwnzy5TaYy5AmBMaDWqE6+qaQYyjido340WTQTpuHw0QoSlwhiJhkvrRZicVreiVHqziXGGN9FleUivpjy8+NDPqjl0gniwDVra+CPxPHK0fGOwtePD0OvkmFNnTHjts5sNGFljR4Pv90Nb2jqxXYiwfDXAwO4YGl50s2QyoYmvoclV++S0x/BN587gnUNRtx+dlOuX3EKFp0Sjjxfu8UOqfnoXAL4Uu+n3uuHXiXDJSsqZmz7omjizHGe+sZ4calUC72TcdSM4lIIOqUMetXUKGupUGNUQSGT4MtblyeFJoIgiHzI17n0QwBfAiDaIiwAXIwx0UfdD0AMjdcC6AMA4Xa3sH5y+aT7ZFqebR9EiWB38Z+G0ifgU0l1C1jIKTCFrpRJcSJWHcUHieL5+rOH8eU/HjjdhzHvEGM2tcbJsbjSfq55gjH4I/GsLh1+LHqBzqVhL5ZU6CZMbtrUYkFFmRLPCA4kxhhePz6C89vKIZNmfqvHcRz+acsSdI74sPWHb+KdSb1Ne3vGMOgJ4ao11Wnvv7JGD5Vcgr092XuXHnjuCLyhKL57w+oJgn++WHQKjPoiYCx3SXyPgxdP5pu4VKHnh2/s6nTihcODuHJNDVRy6Yxt36hRgOMAZ45C7z5nEGq5NPm+odQYdy5ljsXVGFUFxTYXGxadEu9/7TLceFb96T4UgiAWGDnFJY7jrgQwzBh7dw6Opyg4jvskx3F7OY7bOzIycroPh5ghonE+9gUg708sS4nxXgBVsqy11J0CIrF4Aj0OP5qtugnLrTolRr1ze44yTaQhFh47To3mvIguReyuENRyadIRIP7rKPHXo34XL3JkE5fMWkXOGNNkTgz50FYx8bVNKuFw1ZoabO8YgTsQRceQF0OecMa+pVS2tlfjyU+fDbmUwy2/3Ilv/uVIMl733AE7VHIJLs0wWU0ulWBNnRHvZulderfHiaf22fDpC1uTUb5CsWqViMQT8GbphhLpdvhh0shh0Mw/x8XKGj1eODyIUDQxo5E4gH8MGNXynKJu/1gA9WZ1yYonhqRzKYO45A6WdN+SiFoxc8InQRClQz7OpXMBXM1xXDf4yNoWAD8CYOQ4TvSM1gEQA/s2APUAINxuAOBIXT7pPpmWO7LsYwKMsV8wxjYwxjaUl+d+I0UsDAbdfOwLoIuUdNhSRlyb85wSUyrYXEFE4wwt5ZOcS2VzG4vb2elA+7+/mOxIIRYuYsSJIrpTsQul1eLFqlwqgUEtL3mxW3TeZrtQNWrkOQuYU3EHxUlxU0Waa9bWIBJP4IXDA3i9g/+g7YI8xCUAOPAJ3mgAACAASURBVLPRjL/dez7uOLsR//t2F6548E3s6x3D3w4OYMvyCmiVmSNCG5pMOGz3IBCZKvwwxvDdFzpg1Snxjxe15vlbTkWMfufjzu12+NE4z1xLIu21fDSu2arF+obMccViMWkVOR9PfWPBki3zBoAypQyNFg0e2dGTdlKjOCiFIAiCKJyc4hJj7CuMsTrGWBP4Qu5XGWMfAfAagA8Jq90J4Bnh+2eFnyHc/irjfczPArhZmCbXDKANwG4AewC0CZPhFMI+nhXuk2kfRAkgOnMAGh+fDrs7CKtOAZVcCmsBb7xLgU6hc6NlSixOCWcggtgcuYlePjKEcCyBP+zuy70yMa/xhPiIkzsYpeL8SdhcUz/pt2gVJf+hgE3otsnauaRRwBeOIRyLZ1wnlZPDYpm3bsptq2oNaLZq8fQ+O14/PoLlVWWoMuQ/Tl2jkOGb17Tj0Y9thD8cx3U/24FRXwRXrq7Jer8NTWbEEwz7+1xTbnvzxCh2dznx2UuWQKMovsNGdOfm42LuHg2gaZ6VeYusrOFLvW9YXzsrziGzJnuHF2MM/c5AyZZ5A3wU9MGb12HEG8Z9T+xHIjEetQxG4nD6IwUXzhMEQRA8hUyLm8yXAdzHcdxJ8P1IvxaW/xqARVh+H4D7AYAxdhjAEwCOAHgBwN2MsbjQqXQPgBfBT6N7Qlg32z6IEsDu5sWlFquWRJM02FI+XdOr5JBKOHIuCXSN8OJSus4lxnJP05kpdnbx/SV/3tdPgsQCR3QKAlScPxm7KzjlYsysVcC5AF+3Y/EEjs+Q09DmCkIpk2TttjFps0+umszxIR8ApI2XcRyHq9fUYGeXA7u7nFmnxGXj/LZyvPj5C3D9+losqdDh4mXZS6fXN5jAccC7k0q9GWP4rxc7UGdS4+azGoo6FhHxHOZyDoZjcdjdwXnrXLpgaTnuPLsRt25qnJXt5yqIdwej8IZjJVvmLbKm3oh/u3IFXusYwc9fP5VcLr7vrDHmL8oSBEEQ4xQkLjHGtjPGrhS+72SMbWSMLWGMfZgxFhaWh4Sflwi3d6bc/1uMsVbG2DLG2PMpy//GGFsq3PatlOVp90GUBmKkoL3WQKJJGmxjgeQFnUTCwZTjE8tSonPUB4NanowLiliFT7/nonfJHYzisN2D9Q1GjAWiePno0Kzvk5g9yEmZnmAkDoc/gtpJF2PFTEGbD3zt2cO47AdvZO0Qyhe7K4RaY/ZuG/E1Kl/B8viQF2q5NKOz4uq1NWAMiCVYXn1LmTCo5fj+jWvx8n0X5uxeMajlWFpRhj2TztmLhwdx0ObG5y5dCoVsOp9ljr9253ov0OcMgjGgyTo/xROdUoZvXNM+5f+mmSJXh1efk38dqyvhWJzI7ZsbcdWaGvz33zuSRfbi63y1gZxLBEEQxTC9/+0JYhaxuYIwaxWoM6nh9Oc3JaZUYIxN6QWwCtN0iPFJcZMv6pLi0hyIA3u7nWAM+MJly1ClV+GJvRSNW8jYJohL9DwTGf+kf1IsTje3sbhQNA5/HmXP2Xhmvw2/39ULAHhiz/Sfr/1p4oKTEcvP8xXiTgz50FY5cVJcKq3lOqyqNUCjkGJDo7mwA54GZzaZsK9nDHEhYhRPMHzv78expEKH69ZNf9CvOc+JqD0O3rU6X51Lsw3fuZT5/VKfENWsN5N4wnEcvn39KjRZtfinP+zDsDeUFJcoFkcQBFEcJC4R8xZxHKxFp0Q0zuAJTe/CYTHhCkQRjMYnXLjMtVPg5LAP9z2+P22J6+mma8Q/pW8JwHg31Rw44XZ2OqCQSXBmowkfOrMObxwfwYA7mPuOxLwk1blE0yvHyXQxZtYqMBaITOgzmS3iCYZbfrkTGx54GV99+hA6R3wFb+PUiA//8tRBbGg04fp1tXjugH3ar23p4oKTSTqX8iz1PjHsRVtF9olrD1zbjh/ctHbabqFC2NBogjccS0YK/7zPhpPDPnzhA0shzSCEFYJCxpfE53rudTt48aSpRMUli1aBWCLz+6X+pLhEziWAd5L9/CNnwheO4rN/2Ic+ZxAcB1TqKRZHEARRDCQuEfMWuyuIGoM6payaLuhEbMkLuvE3QBadck7P0dP7bHhqnw2/fadnzvaZD4FIDHZ3aMqkOICfFgfMTSxuZ6cT6+qNUMml+PCGOiQY8NR7aQdeEgsAmytIxflpSJ1amYpZq0Q8weAJ5T8JrVgefrsL+3pd2NBkwuN7+nDJ91/Hxx/Zgx2nRvNyvIaicdz9u/egkEnw41vX4ZZNDfBH4vjbwcGij+nVY0MY8YZxRo0+63pmDf+YyqcHzh2MYsgTRluaMu9U1tQbcfnKqvwPdgYQXVJ7e8YQjsXxg5eOY1WtAVvbZ+44LDoFRnN8gNLj8KNMJYNJcISVGiaNKFamP099ziAMajn0qtI8P+lYVlWGb127Cjs7nXj47S5UlCnnVJglCIJYTNCrJzFvEWNfFq3YtUAXdCLj4tL4p49zPZ1pXx/fr/GLNzqnHUeZSbpH+U9mm61TL8DKlDIoZJJZj8V5QlEctruxucUCgI9obG4x44m9fXPi5CBmHpsriKWVZVBIZ//xs5Cwu4KQcJgylUwsYJ7t16ReRwDf+3sHLllegd9+dCPeuv9i/NOWNuzrdeHWX+7CFQ++hT/v60/GtdLxjb8cxrFBL75/01pUG9TY0GhCs1VbdJTVH47hq08fxtJKHW7ZmL3I2iiIAa48ztOJocyT4k439WY1KsqUeLfbicf39MHmCuKfL182oxPRrNrcH6BkikT/f/buO76t8twD+O/VtC1ZtiXZjuMdO3s4TpwdCJsww54to0ChQIGyae/tLd20BQoUaNnQQmnYZa8ECNlOnMTZ8d5Lw0OSJUt67x86Ujy0Ldmy9Xw/n3xwjo6Ojszxid9Hz4gX7kw4X8HKRkN8T4rz5eLFObhiSS5MNkfAUlZCCCG+UXCJxKSe/gH0We1CWRxlLg3nLkUZPNFEo5Cht98+JlPJHE6OvY3dKMlJgd5kG1X2ks3uxI2v7sR3Rzsjcm61Xd4nxQGuHgvpSjk6o3wtldfp4eTAsmnHe55cVpaLep0ZO+r0UX1tEh3uEifqbTZUk9GCTFUCpOKhv054FrlhBpeCCcJyzvHz9yohEYnw2wvngTGGjOQE3H36DGx+8BQ8cvF82J1O/Ow/e3HuU9/j+2NdI47xfkUz/r2jEbeeVOSZisYYwyWLc7CjVo864X4Sir98cQQt3Rb84aIFATMgZBIRlHJJUJlL7klxgcrixgNjDGUFadheq8dTG6qwtFCNE6drI/oaGqUsiJ5L5rjttwQM+rnz8X1q1JuRS828vfrV+XOxKC8VZflp430qhBAyYVFwicSk48GTxKBHEMeTFqMFCVLRkIkzauXoFnOhqOroQ5/VjmtWFGDNjHQ891112NlL71U04atDHXh/T2RKxtz9VrwFl4CxaXy+rUYPmViERXnHf0k9a14WkuUSauw9AdnsTnT0WpGdlugqP6XplR6++goF24DZl0v+vgVXv7DNZ3kPALy1qwnfV3XhgbNmjZjulCAV4/Ilefj8rhPx1JWl6O0fwA9e3I7rX97h6QtU1dGHn79XiaUFatx9+owhz794UQ5EDHh7V1NI572n0YhXttThh8vzsTjIRWqaQur3fbodbe9Fksz3pLjxtjhfjdbufnT2WnFfhLOWgMBN4m12J5oMZhRo4jd44i9ziXOOJoOFmnn7kCAV452frMQvzpkz3qdCCCETFgWXSEwaHFxKG+UiZTJqFqYQDf7l/Xj5YPQXvhUNrpK40rxU3HXadBjMA3h1a13Ix3E4OZ79plo4pjEi51bbZcLUlASf47O1Sjm6eqP7Pdpeo8PCPFe/JbdEmRjnLZyKTypb0TsGfWhI5LR194NzIditlFFZ3CDNPiaiaUYR7LbaHahoNGJzlQ4XPrMZ1V4adHf09uN3Hx/CkoI0XO2n9IwxhvNKpuKru9fg52fPQnm9AWv/+h1+/l4lbn9jNxKkYjx5ZSkkwzKvpqQk4MQZ6Xh7l/+SusEGHE48+M4+ZCYn4L4zZwb9ftVJMujNge8JrmbevifFjTd3xsfJM9OxpCDyk+o0CjkMZhvsDu/Zuc1GC5w8fifFAfD8vuTt566z1wqr3UnNvP2I13JKQgiJFAoukZjUbOwH4JpAJBWLkJokpWyBQZqN/SM+vdaMYbPhigYjUhKlKNQqUJqXhpNmpuP572rQF2L20ieVrajTmVGal4raLlNQn94HUtNlQqGXZt5uWqU8qsGB3v4BVDZ3Y3nhyMXVZWW56B9w4sO9rV6fyznH7gYD9jUZ0dVnDaoZcaR8f6wLf/+2esxebyJpHjQRTauUU6Bb4HBytHX3ew0uHS+LC/1nrclgAefANSvy0dtvx4VPb8aWqqElbQ//9yAsNgf+cNGCoIItCVIxfnxiEb6972Rcs6IA63c24nBbLx6/fOGIflFul5Xloq2nH99XjSyn8+b5TTU43NaLX6+bi+QQGia7x8cHcqy9D9MzY68kzm1edgp+ekoxHj5/XlSOr1XKwDlg8BGIq9O5ShjjOXNJIRNDJhF5vZ4aheb7VBZHCCEkWii4RGJSi9ECqdjVHwcQmlXTgs7DPUlvMM0oe5yEoqLRgNK8VM+nfHedNsOVvbSlLuhjcM7x9MYqFKUrPJ/y72kcXfYS5xw1nX2Y5qWZt5s22VVaEa3G2uV1Bjg5PM28ByvJScGMTKXX0jij2Yaf/Gs3LnpmC87/22aU/fYrzPzfz3DSnzfiyue24Z71e3GwpScq5wwA/9pWjye+OjamAa2JomVQcMnd94W+T0BXnxUDDo5sLw2C5RIxlHJJWA29G4Rx8usWTsX7t61CpioB17y0A2/uaAAAfHGgDR9XtuKOU4tRnBFac2u1QoZfnT8XX929Bm/ctAxrZqT73PfU2RlITZIGVcpa12XCE18dw1nzpuCMECe1pSXJYAjQc6nbPICOXmtMNvN2E4sY7jljJvKiFNzRKP1n59YL/bHiOXPJ3Vfw7V1NeOzLo557FwA0GVw/V9TQmxBCSLRQcInEpBajBVNSEjyfSGuinG0ykVjtDnQK/V8Gc5fFjcUktGMdfSjNPd5PZGFuKk6emY7nNwWfvbTxSAcOt/Xi1pOKsTA3FSJ2vNwuXHqTDT39dp/9lgDX98nh5DBaolOatq1GB5lYhNK8kf1WGGO4rCwXexqNnr4vgKuM7qwnNuGrQ+2478yZeO6Hi/Gr8+bg+pUFmJedApvDiQ/3tUQ1s6i2ywTLgAO9MTT5L1a4M5empCRAq5DD5nDS9wmuDCMAyE71nvmjVsjCCnY36F2L4Dy1ArnqJLxz60qsLNbiwXcr8fCHB/C/H+zHrCnJuHlNUdjnXqBVYGWR/4bTcokYFyzMxpcH2mH0E/xxNxaXSUR4+Py5IZ9LWlLgzKWjHa77RSw28x4rmgAl8nU6MxQyMbRKmdfH48Wjl5Vgfk4KntpwDKsf2YAbXy3HxiMdqNe5g0uUuUQIISQ6JON9AoR4MzwzR6uUeSblxLtWoWRweCmKKlECiYhFPXNpX2M3OHf1WxrsztNm4IKnN+PVLXW47eRiv8fgnONvG6qQnZqI8xdOhVQswqwpKlSMMnPJMynOX1lc8vEg3OCG6JGyrVaPktwUnz2fLizNxh8/PYy3yhvxwNpZePLrY/jbxirkqZPw7q0rsSAn1evzbn19F3bVjy745ovTyVErlJR09FihCqGkJx60GC1IT5YjQSqGNlkYMNBL36fjGV3eF6vhBpfqdWYkDQoSqBKkeOnaMvz6o4N4eXMdRAx47odlIybURcOlZTl4ZUsdPtjTgmtXFnjd561dTdhSrcPvL5yPDJX3QJs/aoUUJpsD/QOOIX3aBnMHo6fHcOZStLkzl3x9gFKnM6FAq4j7vjnLp2mwfJoGjXoz/r2jAevLG/HVoXaImKss3Ne/TYQQQshoUeYSiUktw3oKaRRy6ChzCcDgZudDFzGMMajHoHywosEAxoCFw4JLC3NTccqsDDy/qSZgw+rttXrsbjDiljXTPAvE0rxU7GkwjqpcrUYILhX5K4tTHg8ORFpv/wD2N3d7LYlz0yjlOG12Jt7d3YzLn9uGJzdU4cLSHHx0xwk+A0sAUJavRrPRgtZui899wtXSbYHN7mqS29HTH/HjT3SDm1Yfb5xPZbrNPu5FbuGWMzfoTchTJw0JEkjEIvx63Tw8emkJHrl4AUpyff+sRNLcqSmYO1XlszRua7UOv/v4EJYWqHHFktywXsPdhNnop6n3sfY+KGJ4UtxY8Ny7fVxT9TozCuK4JG64XHUS7l87C1sePBV/u6oUK4o0OHdB1nifFiGEkEmMgksk5tgdTrT1DG0Sq1HKYDAP+JwSE08GNxceTq3wP6o5EioajShOV3rN2rjz1OkwBtF76emNVdAq5bi07PhirDQvDb1Wu9fJUMGq6TRBKmZee8C4uft4dUXh+1Reb4DDyf0GlwDg8iW50JlsONrWiyeuWIhHLyuBUu4/kbSswFVmV14X+ewld8YXALT3UnBpuGajxVP6dbxxPgW7W4wWJCdIfDavHk1ZXJ6PiVYXL84Zct8YC5cuzsGBlh4caOn2bOvqs+Lu/+zBlc9vgypRgkcuCa6xuDfqJNc15a/v0rGOXhRnJsd1Vo4qQQqJiHn92bM7nGjUm5Efx828fZFJRDh3wVS8fuNy/CqMsk1CCCEkWBRcIjGno9cKh5MPCy65AgL6AE1P40Gz0QLG4HXCkVYpD2s6U7A456hoMIwoiXMryU3FqbMy8PymWp+L731NRmw61oUbTygcUgLiPmZFQ3ilcR29/Xi/ohmzs1QQ+1nkad3BpShkLm2v0UMqZljkpd/SYGtmpOOPF83HJ3eegHULs4M69uwsFRKl4qiUxg0JLvVQ0GQwzjlajBZPMNcdnOykAQNDvi/eqJWu4FIozc85536DS+Nh3cJsyMQivFXeBKeT443tDTj10W/x4b4W3H5yMb782Rq/fd4CSXUHl/wE4o6292FGiM3LJxuRyHd2bouxH3Ynp8wlQgghZBxRcInEHG9lX9oAjTxj2RNfHcPXh9ojdrwWowXpSjnkkpF9E6KduVSvM8NgHvDarNrtZ6fPgMlqx2mPfYt/72iAY1iZ2zMbq6FKkODqZXlDthdqFEhJlKKiMfTgidXuwM3/3IVuywD+eNECv/umJLo+/Y5G4/NtNTqU5KQG7GkhEjFcsTQPuSEsoKViERbmpqK8Xj/a0xyhptMEhUyMJJkY7VQWN4TeZEP/gNMT7E5TUOaSW5PBf3BJo5DB5nAG3eQfcH240D/gjKkMlDSFDKfPycT7e5pxyd+34OfvVWLWlGR8eucJuPfMmT77JAXL3fvN14cnRrMNnb1WzMiM32bebhqlHEfae3GkrXdIJnOdzj0pLnauG0IIISTeUHCJxBxvZV+eEcQTLLi0pboLj391FP/4tiZix2wx9o9o5u2mUcqgD/N7ZDDZcKjV/6h7d+DHV+YSAMzLTsF/b1+N4gwlHnq3Ehc+s9kzBe5Yey8+O9CG61YWjCilEYkYFuamhpy5xDnHL97bj4oGIx67rARzpqr87i8SMWiUsogHl/qsdlQG6Lc0WmUFaTjU2gtThCeV1XaZUJiuwBRVAjqikNE1kbUMa6AvFYuQmiSdcPeiaGgxWvyWoKqF/lShlMZ5JsXFWAbKpWU5MJoHUKcz49FLS/Dmj5ejOEKT29IUrnuhr8wl9zCL4jhu5u02OysZexqNOPOv32Herz7HRc9sxv99sB/v7m4C4JoCSAghhJDxQcElEnNau12LuaxhPZcAQBfFkq/BmgxmXPncNmyt1oV9DKeT4w+fHAbgCspYbI6InJu/BZ1GIUOv1Q6rPfTXenLDMax7erPfzJXd9UYoZOKA47DnTFVh/c0r8MQVC9HW3Y8Ln9mC+9/ei0e/OIpEqRjXrSr0+rzSvFQcae8N2BB8sJc21+HtXU2489TpOGt+cM1KtUq5z6aw/phtduxpNHot8ymv0wfVb2k0FuenweHk2DPKqXrD1XaZUKhVIj1ZTg29h/Ea7FZEPjg50fT2D6Cn3+4z0A0MGh0fQnDJPS49lsriAFcp64vXlmHDPWtw8eKciPY+ShPK4vQm7/c996Q4ylwC/nJJCb66+0Q8fnkJrlqaD7GI4a1dTXh/TwtSEqXIEKaBEkIIIWTs+e8gS8g4aDFakJIoHdLgWKtwjyAem2yBt3c1YWuNDrsaDHj6qkU4fU5myMf4cF8LKpu7cX7JVPx3bwt2Nxiwqlg7qvPinKPZaMFpPs7H05vKZENWSmhThWo6TbDZnXhhUw1+cc4cr/tUNBpQkpvqt6eRG2MM6xZm49TZmXjq62N48fta2J0cN6wu9JSBDLcoLw2cA/uauoP6Xn13tBO/+/gg1s6dgjtPnR5wfzdXcCn04MDTG6vw9MZqlOWn4YGzZmFJgdrz2PZaPSQihkX50ZtitSg/DYwBO+v0o76W3Kx2B5oMZlxQmg0GRDxwNdF5Cy5plfK4z1wantHljafcK4TvVYPOBBHzPrBgPDHGcOrs0P8dCIZULEKyXOKzofeh1h4o5RJM9dJnL96IRAzFGckozkjGhaWubQ4nR01nHyRiUVw3PCeEEELGG2UukZjTMmjst5sqUeJzSkw0fFLZivnZKZg9JRm3/GuXJ+U+WFa7A3/67AjmZKnw2wvnQSxio8qCctOZbLDanT4XGepR9KZqFMpRXt/e4LU8w2Jz4FBrr9+SOG+UcgkeOns2PrvrRNyypgi3nVzsc1/3eHF3GZ0/tV0m3P7GbszITMajl5WENKlJq5SH1dB7S7UO2amJaNCbcenft+KGV3bicJurlHBbjQ4lualIkkUvZq9KkGJmZnJEm3o36s1wcmCaVoFMlRztPf0hNWCe7FqMFiRKxUhNOl7GqVXK0TVGWZSxqsXP1Eo3T3ApxLK4qamJkEni69eTNIXMa3DJbLPjw70tWDMjnQInPohFDNMzk0fVVJ0QQgghoxdfv72RCaHZ2O8Z++3GmKtPzlhkC1R19OJoex8uXpSN129ajmWFaty9fi9e2Vwb9DFe21KPZqMFPz97NlQJUszPTsHWmtEHl443O/ddFgeEVoYCuD75bTJYcMqsDJhtDry8pW7EPpXN3XA4ecBJaL4UZyjx4FmzfGYtAa5m28UZyoB9l3r6B3DjqzshEYvw/DVlUMhDC+holTJ09YU2xcpss6OyqRvrFk7Ft/edjPvXzsSOOj3OemIT7nqzAvuaurF8mjrwgUaprCANFQ3GEY3Sw1XT6WqEW6hVIFOVAKvdiR5LZHs6TWTuMtTBC/uxuhfFsqYggkvHy5lDKIuLsUlxYyVNIfMahHt3dzN6+u24flXB2J8UIYQQQkgIKLhEYo63zCUA0CjkY9Jz6ZPKNgDA2nlZUMoleOm6JThjTiZ+9eFB/PWrowEDEkazDU9tOIY1M9KxerqrdGlFkQZ7G40w20a3aG82CAs6Xz2XPI3PQ/s+tff0w+Zw4tTZGThjTiZe2Vw7ou+RO5toYW70yr4AoDQ3FRU++hq5PfD2PtTrzHjm6kUhTVxz0yrlsDmc6OkP/v/HngYj7E6OJYVqJMrEuPWkYmy6/2T8+IRp+HR/W9T7LbmV5avRZ7V7MqZGq7bLFVwq0CqQoXIFdTt6qe+SW7OX+5FWKUe3ZQA2u9PHsya/FqMFUjHz2+MmSSZBglQEfQj37QadOS4nfqmTpCMylzjneGVLHeZnp2BxfnhBfUIIIYSQsULBJRJT+qx2dFsGvAeXhGyTaPukshVl+WmYIpSeJUjFeObqRbhkcQ7++tUxPPzhQTj9ZI08vbEKvVY7Hjxrlmfb8mka2J0c5XWjK2fy1v9lsHDKUIBBE5rUSbjt5GL09Nvx+vaGIftUNBiRr0nyBLCipTQvDXqTzXNOw+1pNOLT/W2489TpYQdztMmu71MofZe21+ohYhiyyEtNkuGhs2fjm/tOwmOXlWBVUWT6IPnjfv1IlcbVdpmgUciQkihFphAoaO+J75KvwVqMlhGZlO6MnFB/ziaTZoMFU1ISApajuj4UCO771Ge1Q2eyIU8df+VNaUkyGIY19N50rAtVHX24flUBlcQRQgghJOZRcInElFYheJLlpaeQVhn9zKWazj4cbusdMXVMIhbhTxcvwI9WFeKVLXW48vltOCZM8BmsUW/Gq1vqccmiHMzOUnm2l+WnQSJioy6NazH2I0kmRkqi1OvjqgQJpGIWclnc4OBSSW4qTpiuxQubatE/4Jo6xznH7gYDSqOctQTA09PJV2ncU18fQ2qSFNev9j5xLhhaIUAWSt+lHbV6zM5SQZUw8nuflZKIixblhNT3KVw5aYnIVMlHHah0q+kyeXqVuDOX/E0MjCf9Aw509dlGBHM1ngEDYxOEM1ntaNB5D7aOF1fQLXDTbbWPci9v3O8xHjOXvPVcenlzLbRKOc5ZENwUTEIIIYSQ8UTBJRJT/GXmqBWykKYOhePT/a6SuLPnTxnxmEjE8L/nzsYfL5rvCkA9sQmPfHZ4SKnbX744ApEIuPuMGUOeq5BLsCAnBdtGHVxyLeh8fYrNGINaIQu5LK5Jb4aIHe/ldNvJxejqs2J9eSMAoLW7Hx29VpSG2W8pFDMyk5EkE3tt6r2/uRtfH+7ADasKh0wTDJU7uBRsEM5md6Ki0YClhdHvqRQIYwxl+eqIZi55gkvuzCUqiwPgu8dZehiZb+Gy2h246oXtOO2xb/HZ/taov14w9jd3Y2+TEbOmqALuG1JwSe8q0YzHnktqhQxmm8MT0K/p7MPGI534wfI8yCXicT47QgghhJDAKLhEYoq/8dYapQwmmwMWmyNqr//xvlYsyktFVor3T+QZY7hiaR6+vmcNLijNxrPfVOP0x77Dlwfbsa/JiA/2tOCG1YVen7+iSIN9lwixSQAAIABJREFUTd3os4bfd8lb/5fhNAp5WGVxU1MTIRW7bgnLCtVYnJ+Gf3xbgwGH05NFFOqkuHCIRQwlOa6+S8M9teEYkhMkuHaUzW09mUtBBgcqm7vRP+DEshgILgGu0rhmo8UT/AhXb/8AOnutKEx3BZcUcgmS5RJ0UFkcAN/3I3fm0lg09f71hwext9GIHHUibn19N97eFdrkykiz2By4880KqBUy3HXa9ID7axTBNz/3ZFDGY+ZSkitg6c5eenVLHWRiEa5elj+ep0UIIYQQEjQKLpGY0mK0QCzy3iRW617QhVEaZ7U7UNUxsoxtsLouEw629uDs+YFLELRKOf5yaQnW37wCSrkEN71Wjqtf2A61QoZb1hR5fc6KaVo4nBzldfqQz9/NV7PzwcLpTdUwbEITYwy3n1yMZqMF71c0o6LBALlEFFSmQiSU5qXiYEuP51N8ADjc1oPPD7Tj+lWFXkvTQqFWyCBiwZfF7RT+n5UVxEZwqazAlUFWPsrspbou12J+2qAR3hkqOTX0FjQbXd+fEWVxnilo0Q3CvbOrCa9vb8DNJ07DRz9djVXFWtz71l68HGBypdXuwNZqHT7Y04wXNtXgD58ewj3r9+Kal3bg0r9vQb3OFPY5/fHTQ6juNOHRSxciNcn35Ee3UDKX6nVmpCVJR/3zPRGpFa73rDfZ0NM/gLd3NeHckiyk+2mYTgghhBASSyi4RGJKi9GCKaoESMQjL03Pgi6MbIH1Oxtx2mPf4dujnT73+UQoORneb8mfpYVqfHTHavzi7NkAgAfXzkKyj4XR4vw0SMXh913qH3BAZ7KNaC48nCaExZxbg96C3LSh2QInzUzHnCwVnv22GuX1BszPToFMMja3jNK8NNidHPubuz3bntpQBaVcgh9FYCS3WOQqH+wM8lraUatHUbrCk/E03uZkqZAkE2PXKAKVAFDT1QcAKNQqPdsyVQnU0FvQbOyHiMHT3N9NKZdALhGFnblkttkDTo480NKNn79XieXT1LjvzJlIkknwwrVlWDt3Ch7+8CCe/PrYiImKPf0DePabaqx+ZCOufH4b7nxzD3778SG8/H0dtlZ3odtsw96mbjz7TXVY573xSAde3VqPG1YXeiZhBqJWymAZCC7jdHiQO564A3VG8wDW72yEyebAj1aF31eOEEIIIWSshd+0hJAoaOm2YKqP4IlGGX7m0tF21yL6nvV78emdJ3j9NPjTyjaU5KYG1aR2MKlYhJtOnIYbTyj0O9EnUSbGwtxUbKsOL7jk6UeV5v/81Ap5SD2XzDY7uvqsI0pRGGO47eRi3PbGbgDATSeM3ULHXX63u8GAsgI1qjp68UllK36ypiiobIlgaJXyoMriHE6OnXV6nBtDTXUlYhEW5qaOOnOptssExoY2UM5Ilo/6uJNFi9GCTFWCp1zUjTEGrVKOzjB7Lv30jQrsrNPjrtNm4Icr8kccv9s8gJ/8azdSk6R46spFnmC7XCLG364qxYPvVuKxL4+ixzKAX5wzG+09Vry8uRavb29An9WOE6Zr8fsL56NQq0C6Ug5VosRzb/rFe5V4a1cT7jljZkhZMbo+K+5/ex9mZibjvjNnBv08jeJ4lleOzH/gqF5nRskYDA2IRe5Jn119Vry6tQ5LCtIwLztlfE+KEEIIISQElLlEYkqLsd9n2ZfG88t36NkCdToTpqgS0NM/gHvf2gunc+gn/g06Myqbu3GOl0bewQpmVPSKaRpUNnejt38g4L7DeZoL++gH5ebuTTW4pMyfRr3ruLleMgbWzpuCaUI/nkVj0MzbTauUI0+d5On19PTGaiRKxbjxhGkRfY1ggktH2nrR22+PiWbeg5Xlp+FQa8+oenjVdpkwNSURCdLjDYMzVQno6LGOyIqJR80G32WoGmXwvYSGq2zuht3J8euPDuLsJzZh07HjGZVOJ8fd6/egtduCZ65ePCIA5J5ced3KArzwfS0ueHozTvjTBjy/qQanzMrARz9djX/esAynz8lEcYYSKUnSIfemH60uhM3uxD+31Qd9vpxzPPRuJbrNA/jrFQuHXC+BqIVy5kDZlHaHE81GC/LjNHPJ3XPp7V1NaNRbcD1lLRFCCCFkgqHgEokZTidHa7f/xRwQXllcvc6MJYVq/M85s/Ht0U68NKxnyafukrh50c1OWT5NAyc/3sMnFL4mVw13PFMguO9To7uJrpdFnVjE8LPTZkAhE495v6HSvFRUNBhR22XCB3ua8YPl+Z5P9yNBq5QFFVzaUevKNFtaqInYa0fC4gI1nBzY0zCy8XmwartMnuChW4YqATaHE0Zz6AHQyabF3/1IIQsri9Jic6Cj14qfrCnC89eUwWp34ocv7sBNr5WjQWfG0xur8PXhDvzPOXOwON97QFckYvi/8+bgrtOmo7rThKuW5uHb+07Gk1eWBsx2KUpX4rTZGfjXtvqgA9DryxvxxcF23L92JmZnhdZ3TR3k/ajF2A+Hk8dxWZyrnHrTsS5kpybijDmZ43xGhBBCCCGhoeASGbVIZTh09Vkx4OA+F3NJMgmSZOKQSr4A1xj5JoMZBZok/HB5Pk6bnYlHPjs8pJ/PJ5WtWJCT4jV7J5IW5adBJhZha4ilcU4nx8bDnZCI2Ij+L8O5F3P6UCc0+Xjv55VMxd7/O2PMG8uW5qairacfv/xgv6v0MIJZS4CrzLKrN/D3aEedHtmpiSGXS0ZbaV4qGAPK68Pru8Q5R22nCYXaocGlTJXr/3NHkM3OJyunk6PV2O/z/7s2yOtnuEbD8Ylop8/JxJd3n4j7187E5qounPbYt3jsq6O4YOFUXLPC/5QwxhjuOm0GKn91Bh5eNy+ke9eNJ0yD3mTDO7sDT56r6zLh4Q8PYmWRJqweQJog70f1eleT8XicFAe4yquTE1ydCn64It9r30FCCCGEkFhGv72QsNgdTqzf2YhVf9zg6ckzWp6eQn4aVmuUsqAzcgYf18mBfI0CjDH86ZIFUCtkuOPNCphtdjQZzNjb1B31rCUASJCKsTAvFdtqgg8IcM7xu08O4bMDbbjrtOkj+rMMF2pvqga9GUq5BGlJvic0jcdCp1Qow9t0rAtXLcuLeHBLq5TDMuCAyU9ZGeccO2oNMVcSBwCqBClmZiZjV5j9kbr6bOi12r0El1w/f+098T0xrqvPCpvD6fN+pFHKoTOFXj5Yr3MFl/I1ru+7XCLGrScVY+O9J+G8kqlYVaTF7y+aH1SZLRBcOe5wywrVmJ+dghc31Y4oER5swOHEz9bvgUTE8OhlJRCJQn8ttZBxGqgszh3kzo/T4BLg+mAgQSrCFUtyx/tUCCGEEEJCRsElEhKnk+ODPc04/fHvcP87+9DbP4DPD7SHPJ3MmxajazHrr+xLowiuT85gdcLY7QJh0aJWyPD4ZQtR22XCw/89iE8r2wAAZ4+i31IoVkzT4EBLN7otwZUdPfNNNV78vhbXrSzAbScXB9zfUxYXZOZSo96MXHVSWIvUaJqdpYJMIoJMIsIta4oifnyt8ngDXV9qu0zo6rPGZHAJAMoK0rC73gC7wxnyc2u7XD8Xw4NLGUIQbyIGl0xWe8QyKZsDlKFqlTIMODh6+kPreVUv3I+G9xbKVCXg0ctK8K8blyFJFt1ZG4wx3HhCIWq6TNhwuMPnfr/7+BAqGoz4w0ULkBWg15svyXIJpGIW8EOBBp0ZMokImcn+MzMns3MXZOGu02ZEbGgBIYQQQshYChhcYowlMMZ2MMb2MsYOMMYeFra/whirZYztEf4sFLYzxtiTjLEqxtg+xtiiQce6ljF2TPhz7aDtixljlcJznmTCKpcxpmaMfSns/yVjbOw6CpMhOOf4/EAbznpiE+58cw/kEhGev6YMb9y0HA6n67HRCqankDaMJrr1wiLanSkAACuLtfjJmiL8p7wRz35bjblTVUMej6YVRULfpdrA2Uv/3tGAP39+BOsWTsUvz50TVABIE2SmgFuD3ozcABPoxoNMIsLlZbm445RiTzZNJGmFIIq/4NIO4f/RkjHuNxWsJQVqmGwOHG7rDfm5tV2uCYrTtMoh2zOExf1EKouzO5z461dHseDhL/BeRXNEjukOdvuazqhVBr5+vGnUm5Esl3h67IyXs+dnYWpKAp7fVOP18Q/2NOOVLXX40apCnDOKSYmMMagVMugDZFLW61z3oXCyoyaL+86cFZVAOiGEEELIWAjm41ErgFM4532MMSmA7xljnwqP3cc5f3vY/mcBmC78WQbgWQDLGGNqAP8HoAwAB7CLMfZfzrlB2OcmANsBfAJgLYBPATwI4GvO+R8ZYw8Kf38g/LdLAuntH0BVRx+ajRY0GyxoMljQbLSgprMPdTozCrUKPHllKc6dnwWRiIFzjgJNEj6pbMWVS/NG9drNRguS5RKoEnwvujQKOSoH9UoKRp3ODIVM7MlUcfvZ6TOwpVqHPY1G3LB67CbzLMxNhUwiwtYaHU7z07T1s/2t+MV7lVgzIx1/viT4khSlXAKZWISuIMriOOdo0JuxZkZ60Oc/ln5zwbyoHTvdExzwHYTbUaeHRiFDUfrYBB5D5W74vKveEPLY8pouE6RiNiJ4kigTQ5UgmTCZS416M+76zx7sqjdAImL44kA7LlqUM+rjNhtdZVrBDBgoCuHHp15vRp5m/DMFpWIRrl9ViN99cgiVTd2Yn3P8+jnc1oMH36nE0gI1Hjp71qhfS62QB1UWN1YBfkIIIYQQEnkBg0vcVWPQJ/xVKvzxV3ewDsBrwvO2McZSGWNZAE4C8CXnXA8AjLEvAaxljH0DQMU53yZsfw3ABXAFl9YJzwOAVwF8AwouRc2nla1CqdvxMg9VggTZaUkozkjGrScV46JF2UP67zDGcPb8LPzjuxroTbZRTfNqMfqezOTmHv/NOQ96cVavM3n6LQ0mFYvw1JWl+M1HB3HJ4tEvRoOVIBVjcV6a36beW6q6cMe/92Bhbiqe/cEiyCTBV7AyxqBRyoJq6N3Za4XV7ozLJrrBZJ7sqNVjSYF63AMBvmSnJmKKKgHPfVeD8noDMpLlyEiWIz1ZjozkBJTmpUIh936br+10/VyIvQQtM1UJ6OiJ/cyl9yua8b/v7wcAPHHFQnx/rAtfHGyHw8m9vq9QtBj7/Qa7NQqht1mImUsNOjNmZSWP6twi5fKluXji62N44fsaPHFFKQCg2zKAm/+5C8kJEvzt6tKAPd6C4Zqs5/t+5A5yx2r5KSGEEEIICSyoxg6MMTGAXQCKATzNOd/OGPsJgN8xxn4J4GsAD3LOrQCyATQOenqTsM3f9iYv2wEgk3PeKnzdBsBrmgdj7McAfgwAeXmjy56JR1a7A7//+BBe3VqPktxU3H5yMXLVrulYyX6yiNzOWZCFZ76pxucH2kaVveQa++2//EmjlMPu5Oix2JESZFlJvZ/FXK46Cc9dUxbyuY7W8mka/PXrozCabUP6a3DOsa1Gj5teK0eBNgkvXbckrP4r6gCLOTf35KpoT8mLRe7ME18Tv1qMrsy9cCZkjRXGGO44dTreq2jCviYjOnqssAwaL78wNxXv3brSa3CstmvkpDi3TFUC2ntjN3Opp38A//v+fnywpwVLCtLw2GULkatOAufAW7uacKi1J+RMruGaDBafJXEAoE0Wrp8Q+s05nByNBjNOnxsbY+ZVCVJcsSQXL2+pwwNrZ2GKKgF3/2cPmg0W/Ofm5Z4SydFSK2See403epMNfVa7z4mVhBBCCCEk9gW1auWcOwAsZIylAniPMTYPwENwBXxkAJ6DK6Po19E6Uc45Z4x5zZjinD8nnAPKysoi0801TtTrTLj9jQpUNnfjhtWFeGDtrJCyZABgTpYqIqVxLcZ+lOSk+t3H04TZZA0quGR3ONFoMOPMeWPTrDtYK4o0ePwrYHutHmfOnYJGvRkf7GnGexXNqO40ISctEa/9aFnYjV1dk6wCL3rdE5ricVEnFYuQmiT1mbm0s87VbynWsymuWpaHq5a5fu445zDZHOjo6cfnB9rxyGeH8dn+Npw1f2jPHIeTo15nximzMrweM0Mlx/YaU9TPHQAMJhs2HunA6XMyAwaznU6OT/a34g+fHEZbTz/uPn0Gbj2pyJNNuaJIAwDYXNU16uBSoExKdZI7OBl85lJbTz8GHBz56tgp/7p+dSFe3lKHV7bUIVkuwdeHO/Dw+XOxOD9y171a4T+TkibFEUIIIYRMfCGlRHDOjYyxjQDWcs7/Imy2MsZeBnCv8PdmAIPn6OYI25pxvMTNvf0bYXuOl/0BoJ0xlsU5bxVK63yPtSEh+7SyFfe/vQ+MAc/9cDHOmBteACYSpXH9Aw7oTbbAZXGeUpTg+py0drsWcwUxtmgpyU1BglSEFzfV4nmhpAlwBTJuWD0N5yzIQkpi+A1/NQoZajr7Au7XoLOAMVd5VTzSKn1PH9xeq0eyXILZWaoxPqvwMcaglEugTFfixycq8M7uJjz65VGcMXfKkDKxFqMFNofTZ+ZSRnICOnr74XTyqDdY/vMXR/DG9gYkyyW4Ymkurl1ZgJy0oT+vTifHFwfb8PiXx3CkvRfTM5R465YVWJQ3dMZDpioBxRlKbKnW4eZRNkZu6bZgUb7vYLdELEJakhS6IHqbuXkmxcXQ/Sg7NRFnz8/Ca1vrYLU7cWFpNq5ZkR/R19AoZOi12mG1OyCXiEc8Hs9BbkIIIYSQySKYaXHpQsYSGGOJAE4HcFgI9kCY7HYBgP3CU/4L4BphatxyAN1CadvnAM5gjKUJU9/OAPC58FgPY2y5cKxrAHww6FjuqXLXDtpORsHp5PjVfw/gJ6/vxrQMJT6+44SwA0tuZ8/PGtXUuOOT4gKVxbmb6Aa3oKvTjZwUFwvkEjGWFWqwo06Pnv4B3L92Jr5/4GSsv3kFrlqWN6rAEuBazAUzLa5Bb8YUVQISpCMXfPFAq5T5DC7tqNVjcUHaqHv3jBexiOGe02egqqMP7w+boFYjTFD0XRYnx4CDw2AObTJjqPqsdnxQ0YyTZqbj5FkZeGlzHdb8+Rvc9sZuVDQYwDnHFwfacO5T3+OWf+3GgNOJJ68sxWd3nTgisOS2skiDHbV62OzOsM/LZLXDaB5Adqr/YIdGKQ9pemWDLjaDKDedUIj+ASdmZibj9xfOj3iPMbVw3zaYBrw+Xq+L3/JcQgghhJDJIpjMpSwArwp9l0QA1nPOP2KMbWCMpQNgAPYAuEXY/xMAZwOoAmAGcD0AcM71jLHfANgp7Pdrd3NvALcCeAVAIlyNvN3T6P4IYD1j7AYA9QAuC/eNkuOe/bYar2ypw/WrCvDQWbNDLoPzZu7U0ZXGucd+T00J3NAbCL7PSZ2waCmIseASADx6WQkMJhuKM5RRWcyZbQ5YbA4kynwHjhr1ZuSmxe+CTqOU42BLz5BtTifHsY4+VHX04aJF2T6eOTGsnTcF87JVePyrozivZKrnZ71WyGor9DEFL1PlCvJ29FqhERqfR8MHe5phsjlw56nTUZqXhgfPmoVXt9ThjR0N+HhfK9KT5ejstaJAk4THLy/B+SXZAYN9K4s0eG1rPfY2GbGkILzSrmCD3f6Ck9406M2QiBiyUiLTyyhSFuSk4sVryzA/O8Xv/SJcGiGbVWeyYoqX9x7vQW5CCCGEkMkgmGlx+wCUetl+io/9OYDbfDz2EoCXvGwvBzBi5jjnXAfg1EDnSIK3o1aPR784gvNKpuKX586JWFBjNKVxTifHW7tcvd4DZRi5+5wEm7lU32VCglSEjOToLZDDpVXKPRPLIm3wYi5H5jt41KA3Y1WxNirnMBGkK+Vo7+nHP76txtH2Phzr6EVVRx/MNldT7JVFE/t7wxjDvWfMxHUv78R/yhvxw+WucqfaLhOUcgnSfVx/mSrX9vae/qiVBXLO8fq2BszOUmFhrqv8bGpqIh46ezZ+eup0vFXeiG+OdOLcM7NwYenQKZX+LJ+mAWPAlipd2MGlZiG4FKhcVKOU49Cw4KQ/9XozctISg34vY+nU2dFrMq4Wypl9ZS416Mwxl81FCCGEEEJCE3u/4ZKo0ZtsuOPfFchTJ+H3F86LeLaMuzTuixBK45xOjp+/V4kP9rTg7tNneP1UezCJ0IQ52FKUOp0Z+WpF1PvGxBp3byp/pXH9Aw609fTH9aIuV50Es82BP3x6GJuOdUKVIMXlS3Lxh4vm48PbV3uCHhPZmhnpWFqgxlNfH4NFCJrVCJPifN0D3FPCOnqCz8oJ1d6mbhxs7cHVy/JGnIdSLsH1qwrx6o+W4tKy3JCCMalJMsydqsLm6q6wz80TXPIzLQ4AtIoQM5d05rgs/VIPCnZ7U683IS+G+lARQgghhJDQhT7jnExITifHvW/thd5kw7u3rgw4lSkcc6eqkK9JwseVrbgiiNI4zjn+778H8ObORtx+cjHuOHV6UK+jUciCbqJbr/M9bn0yU3t6U/kOLrkX0Hma+GzmDQA/WJ6HpQVq5KmTgpo+OBExxnDvmTNx2T+24rWtdbh5TRFqu0w+exYBQHry8cylaHl9Wz2SZGKsWzg14sdeVaTFS5trA5aF+tJitEAiYp4gmy9apRw9/XbY7M6gyosb9GaU5GYF3G+ycWdSegt29w840N5jRX4cBt0IIYQQQiYTylyaBJxOju01OljtDp/7vPh9LTYc7sD/nDt71CO6fWGM4Zz5WdhSrQvYTJpzjt9+fAj/3FaPm0+chnvOmBH06wTbRNfp5KjXm1EQh8ElrXuqnp//DzShydVYfX5OyqQNLLktLVRjzYx0PPttNbr6rGg2Wvz+XCRIxUhNkqK9NzrBpW7LAD7c14J1C7OjEuheUaTBgINjZ50+4L6ccxjNNhxp68V3Rzvx9q4mfF+lw5SUhID9ndz9qIIJdnebB9BtGUC+Ov7uRymJUohFzOu/C43u+xBlLhFCCCGETGiUuTQJfHO0Az96pRwZyXLcsLoQVy3LG7Jgq2gw4JHPDmPt3CmenivRcvb8LDzzTTW+ONDmM3uJc45HPjuCF7+vxfWrCvDgWbNCKtHTKmU42t4XcL+2nn7Y7M6YGvs9VtRBTNVzL+risUwnHt17xkyc97fv8csP9oNzYFqAoGtmckLUyuLe292E/gEnrl4WevP/YCwtVEMiYthSrcOJM9K97uN0ctz6+m5sONLhdbLcJYtzAr6OZlCGYFaAYQT1eteEvnj8eROJGNKSpF6D3fUxOkGPEEIIIYSEhoJLk8Ch1l4AQFG6En/49DCe3liFa1YU4LpVBZCKRLj9jQpMSUnAI5csiHifpeGCKY17/Ktj+Pu31bh6WV5YTcU1Cjl0fbqA+9XpXIu5WJwUF20KmRhyichvBlmDzowEqchnU2cyuczPScFZ86bgk0pXT7RA5aIZKjnaeyMfXOKc440dDSjJSYlaFmWSTILSvFRs8dN36ZP9rfjsQBsuWpSNuVNTkKmSI1OVgMzkBGSo5EFNLnM35A+m75I7UzAeg92Aq++SXsg4HXA4cbClB+X1BnxS2Qog8DAHQgghhBAS2yi4NAnUdJqQqZLj3z9ejr2NRvz922o8/U0Vnt9Ugzx1Etp7+vH2T1YiJTH6pT/uqXHPDZsaxznH9lo9/rWtHh/ta8VlZTn4zbrwmoprlDIYzAOwO5x+G/26PxGPx8UcYwwahQxdfsoHG/Rm5KYlRT3gSGLH3afPwOcH2uDkCFgumqlKQFVH+E2xfSmvN+Boex8euXh+xI892MoiLZ7acAzd5oERZY92hxOPfXkUMzKV+PMlJQHL33zRCplL/n7O3OI9Q0etkKGi0YArn9uGPY1GWAZcZdw5aYm4flUB0iZ5aSohhBBCyGRHwaVJoKarD9O0SgBASW4qnv3BYlR39uEf31bj/YoW/OKc2WM69eqc+Vl4ViiNO3V2Jt7Z3YT/7GxEbZcJyXIJbllThPvOnBn2BDd3nxO92ea34W6dzgSZWBSwXGWy0ijl0PvpBdOgp/Hf8WZ6ZjIuX5KLbTX6gMHmTJUcHb1WOJ08otMW39jegGS5BOeVRL6R92ArizR44utj2Farw5lzpwx57P09LajpNOHvP1gUdmAJGNRzKZjMJZ0ZWqUcCnl8/rM7PSMZO+sMyEi24/IluSgrSENZvjrghFBCCCGEEDIxxOdvuZMI5xzVHX0jFmpF6Ur86ZIS/P7C+SGN8Y4Ed2ncnz4/gv95fz/sTo6y/DTcdnIxzpmfFdb0psG0iuN9TvwFl+q7zMhVJ45q8TiRqRUyn2VxnHM0GSxYPk0zxmdFxttvL5jvt/m/W0ZyAhxODp3J5pkeN1oGk81VMrskF0my6P7zU5qXhgSpCFurhwaXbHYn/vrVUczLVo0IOoVKIRMjQSry2zjfzRXMjc9ANwD8et1c/OKc2UGVGxJCCCGEkImHgksTnM5kQ0+/HdPSlV4fH+vAEuAqyfrBsnw8t6kG160swOVLcjE9Mzlixz+eLeB/QVenM8VlvyU3jUKGqg7vjc8N5gH0We2UuRSHxCIWVGAnU+X6Oevo7Y9YcOmd3U2w2Z24KkqNvAeTSURYUqDG5qqhpX3ryxvRZLDgNxeEV5Y7mKv8VI6uIHpTNejNWFqoHtXrTWSMMQosEUIIIYRMYhRcmuBqOl1Nq6elx1YQ5aYTp+GmE6dF5dieCU1+Sr4456jXmbGySBuVc5gINErfmUvu5sIUXCK+ZKhcWYEdPVbMjUAFG+ccb2xvwOL8NMyaohr9AYOwqliLP356GB29/chITkD/gANPbTiGsvw0nORjilyotEoZugJkLlntDrR0W+jnjRBCCCGETFpjn9ZCIqqm05WZUqT1nrk0GWkV7glNvhd0nb1WWAYcKNDG72JOrZDDMuCA2WYf8ZgnuBSHzc5JcDKF4FJ7T3/Yx3A4Oao7+/DxvlY8/OFB1HSZcJWPKZLRsLLIVfa5tdo1XfJf2+rR3mPFPWfMjFgje41SHrDnUrPBAs4pmEsIIYTqiMd/AAASEElEQVQQQiYvylya4Gq6TJBJRMhOi59eHqpECSQi5ndBV+eZFBdbGV1jyZ3htbveiNXTh2ZwNQrBpZw4um5IaNKF8tP2nsAlX4PtazLita31ONLWi6PtvbDanQAAEQNWTNPgnAVZET9XX+ZOTYEqQYItVTqcNjsTz35TjVXFGqwoilyvMa1ShoMtPX73qdfH7+RKQgghhBASHyi4NMHVdPahUKOIq6bVjDFolDK/PZfqdK5ywYI4XsydNCPdNeb7lR34xdmzce3KAk+2hntyVbSbKpOJSyYRQa2Qob03tMylv3xxFDtr9SgrSMM1K/Ixc4oKs6YkozhDOeY9d8QihuXTNNhc3YVXttRBZ7LhnjNmRvQ1NEo5dCYrOOc+s6EadJQpSAghhBBCJjdaWU5w1Z0mzJoSuWbZE4VGIffbc6leZ4JExJCdGr+ZORmqBHz80xNw9/o9+NWHB7Gz3oBHLl4ApVwS95OrSHAykuXoCKEszuHkqKg34MJF2fj9hfOjeGbBW1WsxRcH2/G3DVU4dVYGFuWlRfT4GoUMAw6OHosdKUlSr/vU68xIlIo92WCEEEIIIYRMNtRzaQKz2Z1o0Jtjrpn3WNAoZX57LtXpzMhJSxyXaXmxJCVJiuevKcMDa2fh08pWnP+373GkrReNBjP1fyEBZaoS0BHEJDS3I2296LXaUZYf2QDOaLj7LlkGHLj7jBkRP757kl6Xn2C3K5ibFLE+T4QQQgghhMSa+F55T3ANejMcTo5pcdTM202rDJy5FM/9lgYTiRh+clIR3rhpOXosdqx7+nu0GGlyFQksUyUPqaH3rno9AGBJgTpapxSy4gwl8tRJOL9kKuZOTYn48TXuAQN+gnANehOVxBFCCCGEkEmNgksTmGdSXEb8BZc0Ct89lzjnqO8yx3W/JW+WT9PgkztWoyQnFU4OFMZhxhsJTaYqAZ29VjicPKj9y+sNyEiWx1SjeMYYPvzpavzl0pKoHN/dOF9n8n0/cmcuEUIIIYQQMllRz6UJrKbL1bQ6Psvi5DDbHDDb7COaUutNNvRa7ZS55EWGKgGv37gMm6q6sLpYG/gJJK5lqBLg5ICuz4oMVULA/cvrDCgrSIu58q+URO+9kCJBK/RR8jW9srPXiv4BJ02KI4QQQgghkxplLk1g1R190CrlUCVEb+EUqzQKIVvAS/ZSnTCZqUBLizlvJGIRTp6ZAWmc96MigWUI/YTaewL3XWrttqDZaEFZfuyUxI2FtCQpGAM6fWRS1uuFSXGUuUQIIYQQQiYxWl1OYDVdprjMWgL8l6LU61wZXZS5RMjoZArZSh29gfsuldcZAABlBbHTzHssSMQipCXJfGYu1esouEQIIYQQQiY/Ci5NYDWdfSiK0+CSu8Tk5+9WYle9YchjdTozRAwx1feFkIkoUxV85tKuegMSpWLMzlJF+7Rijlbpuwdcg959P6LgEiGEEEIImbwouDRBGUw2GMwDKEqPv2beAFCckYynr1oEncmKi5/dgvvf3uvJHKjXmTA1NRFyiXicz5KQiU2rlIMxBDUxbmedHgtzU+Oy3FKj8D29skFnQlZKImSS+Pu+EEIIIYSQ+EG/7U5QNV2uSXHxWhYHAOcsyMLX95yEm0+chnd3N+OUR7/Fv7bVo6bThAIqiSNk1KRiETQKecCyuD6rHYdae7Akzkri3DRKGbr89FyikjhCCCGEEDLZUXBpgqruFCbFaeMzc8lNKZfgobNn49M7T8CcLBX+5/39qGzupslMhERIRrI8YFncngYjnBxYXBBfzbzd0pPlaDZY8NrWOvQPOIY81qg30/2IEEIIIYRMehRcmqCqO/sgFTPqKySYnpmMN25ahievLMWMTCVOnJE+3qdEyKSQqQqcuVRerwdjQGle6hidVWy5fmUhFuSk4JcfHMCJf9qIFzbVwGyzo89qR1efDXkUXCKEEEIIIZOcZLxPgISnptOEfI0Ckjjsb+ILYwznl0zF+SVTx/tUCJk0MlUJ2N/S43ef8joDZk1RQZUgHaOzii15miS8dcsKbK3R4W8bqvDbjw/hmW+qcda8KQCAfDWV6RJCCCGEkMmNgksTlGtSXHyXxBFCoi9DlYCuPivsDqfXYLbd4URFgwEXLcoZh7OLHYwxrCzSYmWRFrvq9XhqQxVe394AANRziRBCCCGETHoUXJqA7A4nGvRmnDF3ynifCiFkkivQJIFz4MuD7ThrftaIxw+39cJkc6AsTpt5e7M4X41Xrl+KfU1G7K43YO5U1XifEiGEEEIIIVFFNVUTUKPBggEHxzQtlVoQQqLrvJKpmJetwi/e3++191J5nR4AUBanzbz9WZCTiutWFUIkYuN9KoQQQgghhEQVBZcmoJrOPgDANCqLI4REmVQswuOXLYTJasdD71SCcz7k8fJ6A7JSEpCdSsMFCCGEEEIIiVcUXJqAqoXgUlE6ZS4RQqJvemYyHlg7C18f7sCbOxuHPLar3oDF+VQSRwghhBBCSDyj4NIEVNNpglohQ2qSbLxPhRASJ65bWYBVxRr85qODqNeZAADNRgtau/uxhEriCCGEEEIIiWsBg0uMsQTG2A7G2F7G2AHG2MPC9kLG2HbGWBVj7D+MMZmwXS78vUp4vGDQsR4Sth9hjJ05aPtaYVsVY+zBQdu9vka8q+k0UdYSIWRMiUQMf76kBGIRw93r98Lh5J5+S5S5RAghhBBCSHwLJnPJCuAUznkJgIUA1jLGlgN4BMDjnPNiAAYANwj73wDAIGx/XNgPjLE5AK4AMBfAWgDPMMbEjDExgKcBnAVgDoArhX3h5zXiWk1XH6Zpqd8SIWRsTU1NxG/WzcOuegP+/m01yusMUMjEmDUlebxPjRBCCCGEEDKOAgaXuEuf8Fep8IcDOAXA28L2VwFcIHy9Tvg7hMdPZYwxYfubnHMr57wWQBWApcKfKs55DefcBuBNAOuE5/h6jbjVbRlAV58N0yhziRAyDtYtnIpzFmThr18dxecH2rAoPw0SMVVYE0IIIYQQEs+CWhEIGUZ7AHQA+BJANQAj59wu7NIEIFv4OhtAIwAIj3cD0AzePuw5vrZr/LxG3KJJcYSQ8cQYw+8umIe0JBk6eq1UEkcIIYQQQggJLrjEOXdwzhcCyIEr02hWVM8qRIyxHzPGyhlj5Z2dneN9OlFV3elqpEuZS4SQ8ZKaJMOfLy2BTCzCmhnp4306hBBCCCGEkHEWUi0D59wIYCOAFQBSGWMS4aEcAM3C180AcgFAeDwFgG7w9mHP8bVd5+c1hp/Xc5zzMs55WXr65F7o1HT2QSJiyFMnjfepEELi2JoZ6dj/8JkozaPMJUIIIYQQQuJdMNPi0hljqcLXiQBOB3AIriDTJcJu1wL4QPj6v8LfITy+gXPOhe1XCNPkCgFMB7ADwE4A04XJcDK4mn7/V3iOr9eIWzWdJuRpkiClHieEkHEmk9B9iBBCCCGEEAJIAu+CLACvClPdRADWc84/YowdBPAmY+y3ACoAvCjs/yKAfzLGqgDo4QoWgXN+gDG2HsBBAHYAt3HOHQDAGLsdwOcAxABe4pwfEI71gI/XiCsWmwMVjQbsrDVge60Oi/PV431KhBBCCCGEEEIIIQAA5koQmjzKysp4eXn5eJ/GqPQPOLC1WocddXrsqNVjX5MRAw4OxoDZU1S498wZOGVW5nifJiGEEEIIIYQQQuIEY2wX57zM22PBZC6RMdZnteP6V3ZCImKYn5OCH60uxLJCNRbnq5GSKB3v0yOEEEIIIYQQQgjxoOBSDNIq5Xj7lhWYM1WFJBn9LyKEEEIIIYQQQkjsoshFjCoroL5KhBBCCCGEEEIIiX006ocQQgghhBBCCCGEhI2CS4QQQgghhBBCCCEkbBRcIoQQQgghhBBCCCFho+ASIYQQQgghhBBCCAkbBZcIIYQQQgghhBBCSNgouEQIIYQQQgghhBBCwkbBJUIIIYQQQgghhBASNgouEUIIIYQQQgghhJCwUXCJEEIIIYQQQgghhISNgkuEEEIIIYQQQgghJGyMcz7e5xBRjLFOAPXjfR5kzGgBdI33SZAJja4hEgl0HZFIoOuIRAJdRyQS6DoikUDX0eSTzzlP9/bApAsukfjCGCvnnJeN93mQiYuuIRIJdB2RSKDriEQCXUckEug6IpFA11F8obI4QgghhBBCCCGEEBI2Ci4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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.8. Validate Forecast Model"
      ],
      "metadata": {
        "id": "QQXhoBqWHGYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_df = pd.DataFrame(y_pred, columns = ['prediction']).astype(int)\n",
        "y_pred_df['numrow'] = range(1, len(y_pred_df) + 1)\n",
        "retail_sales_test['numrow'] = range(1, len(retail_sales_test) + 1)\n",
        "retail_sales_validate = retail_sales_test.merge(y_pred_df, on='numrow', how='left')\n",
        "retail_sales_validate = retail_sales_validate.drop('numrow', 1)\n",
        "retail_sales_validate.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "FcgPDWSBHNTM",
        "outputId": "af7e99aa-a7db-4895-e12b-e3d52320620f"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       month  sales_total  prediction\n",
              "0 2017-01-01       421943      432648\n",
              "1 2017-02-01       418315      450141\n",
              "2 2017-03-01       483082      492334\n",
              "3 2017-04-01       465788      471545\n",
              "4 2017-05-01       494621      497817"
            ],
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retail_sales_validate['AE'] = (retail_sales_validate['sales_total'] - retail_sales_validate['prediction']).abs()\n",
        "retail_sales_validate['APE'] = (retail_sales_validate['sales_total'] - retail_sales_validate['prediction']).abs()/retail_sales_validate['sales_total']\n",
        "retail_sales_validate.loc['MAPE'] = pd.Series(retail_sales_validate['APE'].mean(), index = ['APE'])\n",
        "retail_sales_validate.loc['Min APE'] = pd.Series(retail_sales_validate['APE'].min(), index = ['APE'])\n",
        "retail_sales_validate.loc['Max APE'] = pd.Series(retail_sales_validate['APE'].max(), index = ['APE'])\n",
        "retail_sales_validate.tail(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "oeSNZMmSIRpL",
        "outputId": "3432ccb3-3283-456d-fb34-bcf4d822cb6d"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        month  sales_total  prediction  AE       APE\n",
              "MAPE      NaT          NaN         NaN NaN  0.019438\n",
              "Min APE   NaT          NaN         NaN NaN  0.001206\n",
              "Max APE   NaT          NaN         NaN NaN  0.076081"
            ],
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          "metadata": {},
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    }
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}