Public_Copy_of_COMP_432_Project.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/changlmasaki/The-COVID-19-Trend-Analysis-and-Prediction-in-Canada/blob/main/Public_Copy_of_COMP_432_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# The COVID-19 trend analysis and prediction in Canada (Liu, Trinh. 2020)\n",
        "# COMP 432 Machine Learning G17\n",
        "\n",
        "##### Analysis based on:\n",
        "##### https://www.kaggle.com/mabusalah/brentoilprices\n",
        "##### https://medium.com/@josemarcialportilla/using-python-and-auto-arima-to-forecast-seasonal-time-series-90877adff03c\n",
        "##### https://github.com/ishaberry/Covid19Canada/tree/master/timeseries_canada"
      ]
    },
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        },
        "id": "vBUBiuwiTEDJ",
        "outputId": "a4b5d21d-f450-4445-dbad-c4b819393dbb"
      },
      "source": [
        "!pip install wget\n",
        "\n",
        "!pip install pmdarima\n",
        "\n",
        "!pip install pyramid-arima"
      ],
      "execution_count": null,
      "outputs": [
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            "Collecting wget\n",
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            "Successfully built wget\n",
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            "Successfully installed wget-3.2\n",
            "Collecting pmdarima\n",
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            "Installing collected packages: statsmodels, Cython, pmdarima\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "hwtOW9kFIwv6"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "import sklearn.tree        # For DecisionTreeClassifier class\n",
        "import sklearn.ensemble    # For AdaBoostClassifier class\n",
        "import sklearn.datasets    # For make_gaussian_quantiles\n",
        "import sklearn.metrics     # For accuracy_score\n",
        "import sklearn.svm             # For SVC class\n",
        "import sklearn.preprocessing   # For scale function\n",
        "import pandas as pd\n",
        "import statistics\n",
        "import pmdarima as pm\n",
        "import wget # For downloading online files\n",
        "import statsmodels.api as sm\n",
        "\n",
        "from pylab import rcParams\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from math import sqrt\n",
        "from statsmodels.tsa.stattools import adfuller # For stats and verify our model's prediction\n",
        "from numpy import log\n",
        "from fbprophet import Prophet\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJa3VV0RW6pC"
      },
      "source": [
        "Here we download all our datasets to be processed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3A_0nO3cSkxF"
      },
      "source": [
        "cases_timeseries_canada_csv = wget.download('https://raw.githubusercontent.com/ishaberry/Covid19Canada/master/timeseries_canada/cases_timeseries_canada.csv')\n",
        "mortality_timeseries_canada_csv = wget.download('https://raw.githubusercontent.com/ishaberry/Covid19Canada/master/timeseries_canada/mortality_timeseries_canada.csv')\n",
        "recovered_timeseries_canada_csv = wget.download('https://raw.githubusercontent.com/ishaberry/Covid19Canada/master/timeseries_canada/recovered_timeseries_canada.csv')\n",
        "TemperatureOct2020_csv= wget.download('https://raw.githubusercontent.com/changlmasaki/temp_wind_oct_2020/main/TemperatureOct2020.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hw1vOB3cVpx",
        "outputId": "40c056c1-8eaf-4a42-f4ec-9c942aaa0dda"
      },
      "source": [
        "# Cases of COVID-19\n",
        "startDateTrain = '2020-03-08'\n",
        "endDateTrain = '2020-09-30'\n",
        "\n",
        "df_cases = pd.read_csv('/content/cases_timeseries_canada.csv')\n",
        "df_cases['date_report'] = pd.to_datetime(df_cases['date_report'], dayfirst=True)\n",
        "df_cases = df_cases[(df_cases['date_report'] > startDateTrain)]\n",
        "df_cases_weekly = df_cases.groupby(pd.Grouper(key='date_report', freq='W')).sum()\n",
        "\n",
        "X_train_cases = df_cases_weekly.index\n",
        "y_train_cases = df_cases_weekly['cases']\n",
        "\n",
        "print(X_train_cases)\n",
        "print(y_train_cases)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DatetimeIndex(['2020-03-15', '2020-03-22', '2020-03-29', '2020-04-05',\n",
            "               '2020-04-12', '2020-04-19', '2020-04-26', '2020-05-03',\n",
            "               '2020-05-10', '2020-05-17', '2020-05-24', '2020-05-31',\n",
            "               '2020-06-07', '2020-06-14', '2020-06-21', '2020-06-28',\n",
            "               '2020-07-05', '2020-07-12', '2020-07-19', '2020-07-26',\n",
            "               '2020-08-02', '2020-08-09', '2020-08-16', '2020-08-23',\n",
            "               '2020-08-30', '2020-09-06', '2020-09-13', '2020-09-20',\n",
            "               '2020-09-27', '2020-10-04', '2020-10-11', '2020-10-18',\n",
            "               '2020-10-25', '2020-11-01', '2020-11-08', '2020-11-15',\n",
            "               '2020-11-22', '2020-11-29', '2020-12-06'],\n",
            "              dtype='datetime64[ns]', name='date_report', freq='W-SUN')\n",
            "date_report\n",
            "2020-03-15      274\n",
            "2020-03-22     1130\n",
            "2020-03-29     4848\n",
            "2020-04-05     9593\n",
            "2020-04-12     9145\n",
            "2020-04-19    10953\n",
            "2020-04-26    11836\n",
            "2020-05-03    12458\n",
            "2020-05-10     9624\n",
            "2020-05-17     8254\n",
            "2020-05-24     7809\n",
            "2020-05-31     6405\n",
            "2020-06-07     4703\n",
            "2020-06-14     3240\n",
            "2020-06-21     2584\n",
            "2020-06-28     2113\n",
            "2020-07-05     2225\n",
            "2020-07-12     1966\n",
            "2020-07-19     2891\n",
            "2020-07-26     3552\n",
            "2020-08-02     2964\n",
            "2020-08-09     2575\n",
            "2020-08-16     2637\n",
            "2020-08-23     2803\n",
            "2020-08-30     3078\n",
            "2020-09-06     3979\n",
            "2020-09-13     4834\n",
            "2020-09-20     7014\n",
            "2020-09-27     9674\n",
            "2020-10-04    13080\n",
            "2020-10-11    15874\n",
            "2020-10-18    16350\n",
            "2020-10-25    18038\n",
            "2020-11-01    20758\n",
            "2020-11-08    25855\n",
            "2020-11-15    31876\n",
            "2020-11-22    36496\n",
            "2020-11-29    39954\n",
            "2020-12-06    38895\n",
            "Freq: W-SUN, Name: cases, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyVwFPc6QYOA",
        "outputId": "af1a9d31-4320-443a-c054-f002943eb665"
      },
      "source": [
        "#Make sure we have no null values\n",
        "print(df_cases.isnull().values.any())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86R45r_jQp9n",
        "outputId": "9506a105-ea1a-47f4-99b3-07c2ce7e7936"
      },
      "source": [
        "#Let us split the date into year, month and week to explore trends more in depth later on\n",
        "df_cases['year']=df_cases['date_report'].dt.year\n",
        "df_cases['month']=df_cases['date_report'].dt.month\n",
        "df_cases['week']=df_cases['date_report'].dt.week"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: FutureWarning:\n",
            "\n",
            "Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pu6iT0IyRc13",
        "outputId": "b91681ad-37f0-4ed6-d94d-5ff5384bfac7"
      },
      "source": [
        "#Trends for January to October\n",
        "train_cases = df_cases[(df_cases['date_report'] > startDateTrain) & (df_cases['date_report'] <= endDateTrain)]\n",
        "\n",
        "print(train_cases)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    province date_report  cases  cumulative_cases  year  month  week\n",
            "44    Canada  2020-03-09     19                86  2020      3    11\n",
            "45    Canada  2020-03-10      9                95  2020      3    11\n",
            "46    Canada  2020-03-11     21               116  2020      3    11\n",
            "47    Canada  2020-03-12     40               156  2020      3    11\n",
            "48    Canada  2020-03-13     42               198  2020      3    11\n",
            "..       ...         ...    ...               ...   ...    ...   ...\n",
            "245   Canada  2020-09-26   1246            153817  2020      9    39\n",
            "246   Canada  2020-09-27   1411            155228  2020      9    39\n",
            "247   Canada  2020-09-28   2243            157471  2020      9    40\n",
            "248   Canada  2020-09-29   1669            159140  2020      9    40\n",
            "249   Canada  2020-09-30   1816            160956  2020      9    40\n",
            "\n",
            "[206 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "cmJ08Bt-hFZC",
        "outputId": "fc50c10b-7bd9-4016-bb09-8614c2e01686"
      },
      "source": [
        "#Visualize the data for each data type\n",
        "plt.figure(figsize=(16,6))\n",
        "plt.bar(X_train_cases, y_train_cases, width=5, color='#C70039')\n",
        "plt.xticks(X_train_cases, X_train_cases.strftime(\"%Y-%m-%d\"), rotation=75)\n",
        "plt.title('Weekly new cases of COVID-19 in Canada') \n",
        "plt.xlabel('week', size=10)\n",
        "plt.ylabel('number of Cases', size=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "l_ZIKFCuTVv8",
        "outputId": "441a00c2-6756-4ac1-be70-66512c6e81c3"
      },
      "source": [
        "#Weekly visualization\n",
        "plt.figure(figsize=(16,4))\n",
        "weeklyCases=train_cases.groupby([\"week\"])['cases'].sum()\n",
        "weeklyCases.plot(marker=\"o\", c='#C70039')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3349803748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jzDrgHN9zgC+BjcB64E3gHwDW2nzgceAn39djvjZ827zl22cD8O/qeWtSH9TFK1PeEg9rB45i13+X0+69h4m84AynIwmAMeAylOcX6JeAiIiI+IWM0RPwFpUc0OYtKiFj9ASHEvkH46/rKyYnJ9uUlBSnY0gN2ntlav8fbFdoMEkTRxEzpK8jmbylZawbNJrtn39P28n3Ezt0gCM55GBLEwdWXMX8HXebOHqkfupAIhEREZE/tsjVCyqrx4zhVO8PtR+ojjHGLLHWJv++vcoTO4nUlENdmUq/6xVH7nu05eVsuPoxtn/+PYnj71QBW8d40rOPql1ERETEae6E2KNqlwoqYqXOOlTxUZqZT0pUf3676F4yX/mQopUbqekRBdbrZePwp8mb8RUJT/+D5jdfUqPnk6N3qP/sg1pE1XISERERkSMTeeFZB7W5QoOJHzPCgTT+Q0Ws1FmHKkoCo5sSNbg3Rcs3kHrbi/za9UqWtriAdVc8TPakf1G8aWu15rDWkvbPl8iZ/AWtHhxKy3uurNbjS/WIHzMCV2jwQe1leTvJ/+w7BxKJiIiIHFrZ9gLyZ35NUJvmuBPi9rXHP3WTY7fO+YtApwOIHEr8mBFsuPYJKPvf0GFXaDBtxo3c94NdnLqNggVL2Pl1CgULlpA3fT4AwUktCe/dk6a9exLeuyfu5sfeG5fxwBtkvvwhzf95Ga0fvb5qb0pqzN5/ExmjJ+BJz8adEEuLO/5O7ntzWTvwPlqNvobWj16PCQhwOKmIiIgIpN35CqW5O+n201s0PqkTJemZLGs7CM+WHKej1Xma2EnqLG9pGT9F9cWUe/Hu8eBOiCV+zIhDXpmy1rJndWpFUbtgCQXfLKV8xy4AGh2XRNM+FQVt+Nk9CIwIO6IMW558h4zRbxA7/EKSJtxDxUpQ4k+8xSVsuuUFcib9i4j+p9F+2sMERoY7HUtEREQasB3zF7Pm3JG0vO9qEp7839DhtYNGs/OrFHps/oyAxo0cTFg3HGpiJxWxUmdtn/0Dvw24m46fP02zv5151Pvb8nIKf15Hwdcp7FywhF3f/4K3qBhcLhr36EjTPsmE9+5J2BndCQgNASpmRN7bkxcQ0YTy7buIvrIv7d55EOPS6Ht/Za0le+LnpN76Au6EODp9OpbQbu2cjiUiIiINUPnuIn7tdhXGHcQJv7yDK+R/t0Pt+uFXVp4xgsTX7qL5TRc7mLJuUBErfmfd5Q9WXInaOguXO6jKx/N6Stn948p9Q493L1qJLS3DBAXS5LSuBEZHsGP2f7Elnv/tFOCi3ZTRxFzVv8rnF+ftWrictZeMpnznbtpOvp/oy85xOpKIiIg0MKkjx5H50gcc9/3rhJ/R/YDXrLWsOHkY5buK6L7q/QbfiaIldsSvlO3cTf7n3xN1+TnVUsACuNxBhJ95IvGPXM/x/3md5O1z6TznBZqPvAxvYTHbP/n2wAIWoNxLxoMTq+X84ryw07rRbclkGp/UkfWXP0Ta3eOxZWVOxxIREZEGYtfC5WS+/CFxN19yUAELYIyhxcjLKP4tnR1zf3QgoX9QESt1Uv7H32KLPURf1a/GzhHQuBERfU+lzTM30y1lMhzifletM1q/uFtE02XBK8TdfAnbnnuf1f3uoDR3h9OxREREpJ7zlnjYOOwp3K1jSXjq0EvoNBvUm6AW0WSOm1mL6fyLilipk3Le/TchHeJpcvJxtXZOLTbdcLjcQSSNv5O2U0az6/9+ZXnPoRQu/c3pWCIiIlKPbRnzDntWp5L0xj0EhDU+5HYudxDNb76YnfMWU7RqUy0m9B8qYqXOKUnbxq7vlhF9Vb9anQ24snVGtdh0/RZ77fkc/3+vg4UVvW4k591/Ox1JRERE6qHCX9ez9al3ib6qH5H9Tzvs9rHDL8SEuMl8+cNaSOd/VMRKnZM7bR4A0VfW7iLPMUP6kjRxFO42cWAM7jZxJE0cpcWm67kmyV3otmQyYad2ZcM1j7Pp1hfwluo+WREREaketqyMjcOeJCAyjDYv3n5E+wTFRBJzZV9y3/03pXk7azih/1ERK3WKtZacqXMIO7M7IUkta/38MUP60iP1U071/kCP1E9VwDYQQTGRdJk/jhZ3XE7W+I9Y3edWPJl5TscSERGRemDbuA8oTFlD0vg7CYpqesT7Nb99MN49JWS/+XkNpvNPKmKlTilcsobiNWk1OqGTSGVMYCBtnr+N9u8/QmHKGpb3HMquRSucjiUiIiJ+rHj9ZjIenEjkhWfSbFDvo9o3tGs7mp7zJ7LGf6xRYr+jIlbqlNypczDBbqKO8odcpLpE//1cjl84EVewm1V/vpmsiZ85HUlERET8kPV62XD9U7iC3SS9dtcxzfXSfORgPFtyyP/4mxpI6L9UxEqd4S0tI3f6fCIv6EVgRJjTcaQBa9y9A91SJhP+lx5suvEZNg4fi/f3awiLiIiI/IHst2ax67tltHnuFtwtY47pGBH9TyOkQzyZ4z6o5nT+TUWs1Bk75/1IWc4ODSWWOiGwWTidZz9Hy/uvJvvNWaz68814tuQ4HUtERET8QMnmbNLvfpXwv/QgZtgFx3wc43LR/PbB7P5xpW5z2o+KWKkzcqfOITCqKRH9TnU6iggAJiCAhDEj6PjxkxSt3MTynkMp+P5np2OJiIhIHWatZdNNz2JLy2j75qgqLxkZc01/Apo2IXPczGpK6P9UxEqdULZzN/mff0/U5efgcgc5HUfkAM0uPpuuP75JQNMmrO59K5mvfIi11ulYIiIiUgflzfyKHV/8QPwTwwlp17rKxwtoEkrs9ReQ99G3lGRkVUNC/6ciVuqE/I+/xRZ7NJRY6qzQ45LouvgtIvqfRuptL7LhmsfJnjKbpYkDWeTqxdLEgeRMm+t0TBEREXFQae4OUm99kcYnH0fz2wdX23HjbrkUrCXr1Y+r7Zj+TEWs1Am5U+cQ0iGeJicf53QUkUMKbNqEjp+NpfWj15M7dQ4bhz2JJy0LrMWTlsWm4WNVyIqIiDRgaSPHUb5zN+0m3YcJCKi244YktqDZwLPImvg55YV7qu24/kpFrDiuJD2Tgm+XEn1VvyrfMyBS04zLReuHriMwJgJ+N6TYW1RCxugJDiUTERERJ22f/QO50+bR8v6rCe3artqP33zkZZRv30Xue7pgriJWHJc7bR4A0Vf2dTiJyJEry91ZabsnLYvdi1dhy7QouYiISENRVlDIphufodHxSbS67+oaOUdYrxNo3LMz28bNxHq9NXIOf1GlItYY809jzEpjzApjzHRjTIgxJskY86MxZr0xZqYxxu3bNtj3fL3v9cT9jnOfr/03Y4wqmQbEWkvu1DmEndGdkKSWTscROWLuhNhDvrbilOtJierPb3+7h23jZlL46/oG/8tGRESkPku/9zU8W3NpO+l+XMHuGjmHMYbmIwdTvCaNnfMX18g5/MUxF7HGmFbAbUCytbYrEABcDjwNvGitbQ9sB4b5dhkGbPe1v+jbDmPMcb79jgf6Aa8ZY6pvALnUaYVL1rBndSrRV+nahfiX+DEjcIUGH9DmCg2mzat30n7GY0Rdfg57VqeS9s+XWN79apbEDWDt4AfImvApe9ama3ZjERGReqLgu2VkT/iU5iMHE3bK8TV6rqjBfQhqHsW2cR/U6HnqusBq2L+RMaYUCAW2Ab2BK3yvvwM8ArwOXOh7DPARMN5U3AB5ITDDWlsCbDLGrAdOBhZWMZv4gdypczDuIKIG9XY6ishRiRlSceElY/QEPOnZuBNiiR8zYl979GXnAL57vr9Zys4FSyj4OoX8DxcA4G4VQ3jvnjTtk0z4X3oQnNDcmTciIiIix8y7p4SN1z9FcFJL4h8fXuPnc7mDiLv5YjY/+CZ7VqfSqEtijZ+zLjrmItZau8UY8xyQDuwB5gFLgB3W2r03g20GWvketwIyfPuWGWN2AlG+9kX7HXr/faQe85aWkTt9PpEX9CIwMtzpOCJHLWZI331F66EEJzQn5prziLnmPKy1FK/fTMHXKexcsIQd/15E7tQ5AIS0b014754VX2f3wB3XrDbegoiIiFRBxiNvUbx+M12+epmAxo1q5ZxxN17ElifeYdvLH9L29btr5Zx1zTEXscaYSCp6UZOAHcCHVAwHrjHGmOHAcICEhISaPJXUgp3zfqQsZwfRV/d3OopIrTDG0KhDPI06xBM3YiDW66VoxUYKFiyhYMES8mZ8RfbEzwFo1LUtTfcWtX8+icCIMHKmzT1kz6+IiIjUrt0pq9n23HRihl1A0z7JtXbeoJhIoq/sS+47X5Iw5kYCmzW8zqCqDCc+B9hkrc0BMMZ8AvQCIowxgb7e2NbAFt/2W4B4YLMxJhBoCuTt177X/vscwFo7EZgIkJycrBvK/Fzu1DkERjUlot+pTkcRcYRxuWh8Qnsan9CeFiMvw5aVUbh0LTsXpFCwYAnZb84i8+UPweXC3SYOT0Y2lJUD7FuXFlAhKyIiUsu8pWVsHPYUQXHNaPPcLbV+/ha3DyZn0r/IevNzWt17Va2f32lVmZ04HTjVGBPqu7e1D7AK+Aa41LfNNcDnvsezfM/xvb7AVsxsMgu43Dd7cRLQAWjY0201AGU7d5P/+fdEXX4OLneQ03FE6gQTGEiTk4+j1air6TLvJZK3z+W4716l1YPXUro1d18Bu5fWpRUREXHGtmfeo+jX9SS9dieBEWG1fv7Qbu0I792TrPEf4y1teMv6HXMRa639kYoJmpYCy33HmgjcC9zhm6ApCpjk22USEOVrvwMY5TvOSuADKgrgOcDN1toDP6lJvZP/8bfYYg/RV9XoCHQRv+YKdhN+1knEP3I91lP5LyhPenYtpxIREWnY9qxOZfNjU2g2qDfNLvqzYzlajLwMz+Zs8j/51rEMTjH+usxDcnKyTUlJcTqGHKNVf7kFz5Ycuv82g4qOfBH5I0sTB+JJyzqo3dWkEcn5c3EFVXWyeRERETkcW17OyjNvYs+aNLqvnu7oRIzW6+XnjpcRFBNB14VvOpajJhljllhrD7rhuCrDiUWOSUl6JgXfLiX6qn4qYEWOUGXr0hIYgHf3HtacO5LSnO3OBBMREWlAsl77hN0LV5A4bqTjKwkYl4sWtw9m96KV7Fq0wtEstU1FrNS63GnzAIi+UpPRiBypmCF9SZo4CnebODAGd5s42r39AO3efZBdC1ew/E/DKPxlndMxRURE6q3i1G2k3zeBpn1PqTO3xMVcex4B4Y3JfOkDp6PUKo0/k1plrSV36hzCzuhOSFJLp+OI+JVDrUvbqHMbfrtoFCtPv5F27zxA1KW9HUgnIiJSf1lr2XTj0wC0feOeOjOaMCCsMbHXX0Dmyx9S8mw2wa1jnY5UK9QTK7WqcOlv7FmdSvRV6oUVqS5N/nQc3VImE3pCO9YNeoCMh97Eer1OxxIREak3ct/9NzvnLSZh7E0Et2nhdJwDxN1yKdZryXrtE6ej1BoVsVKrcqfOwbiDiBqkniKR6uRuEc1x375KzNDz2fL4FNYOHEVZQaHTsURERPyeJzOP1H++RFivE4j7x8VOxzlISFJLml10FtlvfEZ5UbHTcWqFilipNd7SMvKmzyfygl4ERoY7HUek3nEFu2k76X7avDSS7bMXsvK04RRv2Ox0LBEREb+WeusLeAuLafvWKIyrbpZPzUcOpiy/gNz35jgdpVbUzb8FqZd2zvuR0uztdeZGeJH6yBhDi9sG02Xui5Rm5rH8T8PYMX+x07FERET8Uv4n35L/0Te0fvg6GnVOdDrOIYWd0Z3GPTqROe4D/HUJ1aOhIlZqTe7UOQRGNSWi/2lORxGp95r2SabrT5Nwt4xmTb872PbijAbxS01ERKS6lG0vYNPNzxPavQMt7h7idJw/ZIyh+cjB7Fmdys4GcPFaRazUirKdu8n//HuiLuuDyx3kdByRBiGkbSu6LpxI5IVnknbHy2wYOgZvcYnTsUREROq0nGlzWZo4kJRm/SjNzKPZoL/gCqr7i7pEDe5DUPMoMsfV/+V2VMRKrcj/+FtssUdDiUVqWUBYYzp+NIbWjwwj950vWXX2LXi25jgdS0REpE7KmTaXTcPH4knL2te29cl3yJk218FUR8YV7CbupoHs+PdC9qxJdTpOjW8BwpsAACAASURBVFIRK7Uid+ocQjrE0+SU452OItLgGJeL1g8Po+PHT1K0YiPLk4ex68eVTscSERGpczJGT8BbdOCoJW9RCRmjJziU6OjEjbgI4w4i8+UPnY5So1TESo0rSc+k4NulRF/Vr84sDC3SEDW7+Gy6LpyIK8TNqrP+Qc47XzodSUREpE7xpGcfVXtdExTbjOgh55Lzzr8pyy9wOk6NURErNS532jwAoq/s63ASEQnt1o6uP00i7IwT2HDtE6Te8RK2rMzpWCIiInWCq0mjStvdCbG1nOTYNb99MN6iYrLfmuV0lBqjIlZqlLWW3KlzCDujOyFJLZ2OIyJAUFRTOs95kea3DSLzxZmsOe/Oen21VkRE5EjkzpiPd1cRBAYc0O4KDSZ+zAiHUh29xt07EP6XHmSO/7jeXqhWESs1qnDpb+xZnUr0VeqFFalLXEGBJL70T9pOup+C735m+cnDKFq50elYIiIijihavoGNw54irNcJtH3rPtxt4sAY3G3iSJo4ipgh/vVZtvnIy/BkZJH/6X+cjlIjjL+uG5icnGxTUlKcjiGHkTpyHFmvf0rPzH8RGBnudBwRqcSuhctZO/A+yguLaf/eQzS78CynI4mIiNSasu0FLP/TMLxFJXRbMhl3i2inI1WZLS/n506XExTXjK4/vOF0nGNmjFlirU3+fbt6YqXG2LIy8qbPJ/KCXipgReqwsNO60TVlMo06J7D2olFsfmIK/nqBU0RE5GhYr5f1Vz6KJz2Ljh+NqRcFLIAJCKD5bYPY/d/l7F68yuk41U5FrNSYHfMWU5q9XWvDiviB4NaxHP+f14m+si+bH3yTdYMfIGvyv1iaOJBFrl4sTRzoF2vkiYiIHI3Nj05mx5cLafPSSMJO7+Z0nGoVO/R8AsIbs+2lD5yOUu1UxEqNyZ06h8CopkT0P83pKCJyBFyNgmn37kMkPHcL+R99w6brfYu9W4snLYtNw8eqkBURkXojf9b3bHlsMjHXnkfciIFOx6l2AWGNiRk2gPwPvsazJcfpONVKRazUiLKCQvI/+w9Rl/XB5Q5yOo6IHCFjDC3vvILA2Ej43ZBif1rsXURE5I/sWZvOhqseo3HPziS9djfGGKcj1Yjmtw7Cei2Zr33sdJRqpSJWakT+x99giz0aSizip8pydlTa7i+LvYuIiBxK+a5C1g68D+MOouPHY3A1CnY6Uo0JSWpJ5IVnkv3G55QXFTsdp9qoiJUakfvuHEI6xNPklOOdjiIix+BQi7r702LvIiIiv2etZcN1T7JnTRodZjxGcJsWTkeqcS1GDqYsbye59eiWIBWxUu1K0jMp+HYp0Vf2rbdDM0Tqu/gxI3CFHnxlutnAPzuQRkREpHpse3Ya+R99Q8LYm2ja56CVW+qlsDNPJPTEDmSO+6DerD6gIlaqXe60eQBEX+lfi0KLyP/EDOlL0sRR/1vsPT6W4HatyBr/MXkfLXA6noiIyFHb+dVPpN83gWaD+9DiriucjlNrjDG0GHkZe1ZtYudXPzkdp1pUqYg1xkQYYz4yxqwxxqw2xpxmjGlmjJlvjFnn+x7p29YYY142xqw3xvxqjOmx33Gu8W2/zhhzTVXflDjHWkvu1DmE9TqBkLatnI4jIlUQM6QvPVI/5VTvD/RI/4xuS9+m8cnHse7yh8md+ZXT8URERI5Yceo21l3+EI26tKHdpPsa3GjBqMvPISg2ksxxM52OUi2q2hP7EjDHWtsZ6A6sBkYBX1trOwBf+54D9Ac6+L6GA68DGGOaAQ8DpwAnAw/vLXzF/xQu/Y09q1OJvloTOonUN4Hhjeky5wXCTu/K+iseIXf6PKcjiYiIHJZ3TwlrL74PW1ZOx0/HEtAk1OlItc4V7CbuHxez48uF7Pktzek4VXbMRawxpilwFjAJwFrrsdbuAC4E3vFt9g5wke/xhcC7tsIiIMIY0wLoC8y31uZba7cD8wFVQH4qd+ocjDuIqEG9nY4iIjUgIKwxnb98nrAzu7P+ysfIeW+O05FEREQOyVrLxhHPULRsLe3fe5hGHeKdjuSY2BEDMe4gMl/+0OkoVVaVntgkIAeYYoxZZox5yxjTGIiz1m7zbZMJxPketwIy9tt/s6/tUO0HMcYMN8akGGNScnLq14K99YEtKyNv+nwiL+hFYGS403FEpIYENAml8+znCD/7JDZc/Tg573zpdCQREZFKZb32Cbnv/ptWD19H5IBeTsdxlDuuGdFX/JWct7+kbHuB03GqpCpFbCDQA3jdWnsSUMj/hg4DYCumv6q2KbCstROttcnW2uSYmJjqOqxUkx3zFlOavV1rw4o0AAGNG9HpX8/StE8yG4aOIXvyF05HEhEROcCuH34lbeQ4Igb0ovVD1zkdp05ofvtgvEXFLGs7iEWuXixNHEiOHy69U5UidjOw2Vr7o+/5R1QUtVm+YcL4vmf7Xt8C7N9/39rXdqh28TO5U+cQGNWUiP6nOR1FRGpBQGgInWY9Q9NzT2bjsCfJevNzpyOJiIgA4Nmaw9pLRxOc2IL2Ux/CuLQoC0DRyk3gMpTv2AXW4knLYtPwsX5XyB7z36a1NhPIMMZ08jX1AVYBs4C9MwxfA+z9VDMLuNo3S/GpwE7fsOO5wLnGmEjfhE7n+trEj5QVFJL/2X+IuqwPLneQ03FEpJa4GgXT6bOxRJx3GpuGP03WhE+djiQiIg2c11PK2kEPUL6riI6fPkVgRJjTkeqMjNETwHvgQFlvUUlFux8JrOL+twLTjDFuYCMwlIrC+ANjzDAgDRjs2/ZL4DxgPVDk2xZrbb4x5nFg76JFj1lr86uYS2pZ/sffYIs9Gkos0gC5QoLp+MlTrL10NJtuehZb7qX5zZc4HUtERBqotH++xO7/LqfDzMcJ7drO6Th1iic9+6ja66oqFbHW2p+B5Epe6lPJtha4+RDHmQxMrkoWcVbu1DmEdIinySnHOx1FRBzgCnbT8eMnWTf4AVJveR5bVkaL2y9zOpaIiDQw2W/PJuu1T2hx1xVEDT6oJGnw3AmxeNKyKm33JxocLlVWkpFFwbfLiL6yb4NbOFpE/sflDqLDB0/Q7OKzSRv5EttemO50JBERaUB2L1nDphHPEt67JwlPjXA6Tp0UP2YErtDgA9pcocHEj/GvPy8VsVJludPmgrVEX9nX6Sgi4jCXO4j2Mx6j2aDepN35ClufneZ0JBERaQBKc7az9uL7CIqLpMOMxzCBVb1rsn6KGdKXpImjcLeJA2Nwt4kjaeIoYob41+d4/e1KlVhryX13DmG9TiCkbaXL+4pIA+MKCqTD+4+wPsBF+j2vYsvLaTXqaqdjiYhIPWXLylj394cpzdrO8f/3OkExkU5HqtNihvT1u6L191TESpUULv2NPatTSZpwj9NRRKQOMYGBtJ/6ELhcZNw3AVtWTusHhjodS0RE6qH0+9+g4OsU2k6+nybJXZyOI7VARaxUSe7UORh3EFGDezsdRUTqGBMYSPt3H8QEuNj84JtQ7qX1w8OcjiUiIvVI3ocL2PbsNOJuGkjs0AFOx5FaoiJWjpktKyNv+nwiB/QiMDLc6TgiUgeZgADaTRldUcg+Mglb7qX1o9drEjgREamyohUb2DB0DE1O60qbcSOdjiO1SEWsHLMd8xZTmr2d6Kv8e0y9iNQsExBA20n3YwID2PL4FGx5OfFP3KhCVkREjlnZjl2sHXgfAWGhdPxoDC53kNORpBapiJVjljt1DoHNwok473Sno4hIHWdcLpLeuBcCAtj65LvYsnISxv5DhayIiBw16/Wy/qrHKEndRpdvxuNuGeN0JKllKmLlmJQVFJL/2X+IvW6ArnyJyBExLhdJr92FCXCx7ZlpUO4l4dlbVMiKiMhR2fL4FHZ88QOJr9xB+BndnY4jDlARK8ck/+NvsMUeoq/q53QUEfEjxuUicfydFYXs89OxZeW0efF2FbIiInJEtn/xA5sfmUT01f2Ju/kSp+OIQ1TEylHJmTaXjNET8KRlQWAAxRu2EHZqV6djiYgfMcbQ5qV/QkAAmeNmgtdLm5f+qUJWREQqte/zZ3o2AEFtmtN2wj36vdGAqYiVI5YzbS6bho/FW1RS0VBWzqbhYwH8fsFkEaldxhjavHDbAT2yiePvxLhcTkcTEZE65KDPn0BZ9nbyPvlWnz8bMGOtdTrDMUlOTrYpKSlOx2hQliYOrOiB/R13mzh6pH7qQCIR8XfWWjLue52tT79H2F96ULJhC56MbNwJscSPGaEPKCIiDZw+fzZsxpgl1trk37erJ1aO2N4hHEfaLiJyOMYY4p+6iaLVqeyY9X/72j1pWRrpISLSwO1esqbSAhb0+bOh07gtOWLuhNijahcRORLGGIp+XndQu7eohIzRExxIJCIiTtqdspo1F9zNiuTr4BD3verzZ8OmIlaOWPzjww/6j8QVGkz8mBEOJRKR+sKToZEeIiIN3e7Fq1hz/p2s+NMwdv3wK60fv4GkiffgCg0+YDt9/hQNJ5Yj5goJBmsJjI6gLG+n7lkTkWrjToitdMhYUIsoB9KIiEht2rVwOZsfnczOuT8S2Cyc+DE3EnfLpQSGNwbA1Shk3+zE+vwpoImd5AhZa1nxp2GUFxTSffX7mIAApyOJSD1S2eyTAKZxCF2+fJ7ws05yKJmIiNSUXT/8yuZHJ7Fz/k8ERkfQ4q6/0/wfFxMQ1tjpaFJHHGpiJw0nliNSsGAJhUvW0OLuK1TAiki1ixnSl6SJo3C3iQNjcLeJI+GFWwmOj2P1ObeT886XTkcUEZFqUvD9z6w65zZWnjGCwp/XkfDMzZy06SNa3XuVClg5IuqJlSOy+tzbKVq+kZM2fVQxrFhEpBaU7djF2ktHU/B1Ci3vv5r4x4drLVkRET9V8N0yNj86iYJvlhIUG0mLe4YQN2IgAY0bOR1N6igtsSPHrHDZb+yc/xPxT41QASsitSowIozO/36B1FueZ+uT71K8NoN27zxIQGiI09FEROQIWGsp+HYpmx+dzK7vlhHUPIo2L9xG7I0X6f9yOWYqYuWwtj4zjYCwUOJGDHQ6iog0QK6gQJIm3EOjTgmk3TWekrRMOs16BndzTfokIlJXWWsp+DqFzY9NZtf3vxDUIpo2424nbvhFuBqpU0SqRkWs/KHijVvI+2ABLe78O4ERYU7HEZEGyhhDizv+TnD71qy/4hFWnHw9nb54lsYntHc6moiI7Mday875i9n86GR2/3c57lYxJL5yB7HXX6ARfVJtqnxjkTEmwBizzBjzhe95kjHmR2PMemPMTGOM29ce7Hu+3vd64n7HuM/X/psxRvNl1yHbnp+OCQygxcjBTkcREaHZ387k+O9fA6+Xlb1GsH32D05HEhERKorXHXMWsfL04azp+0886VkkvnonJ67/gOa3XKoCVqpVdcyOcTuwer/nTwMvWmvbA9uBYb72YcB2X/uLvu0wxhwHXA4cD/QDXjPGaPrbOqA0O5/syV8QfVU/3C1jnI4jIgJA45M60XXxWzTqlMBvf7uXbS/NxF8nKRQR8Uc50+ayNHEgi1y9WNpmIKl3v8KKU29gTf878GzNJen1uyuK139couJVakSVilhjTGvgfOAt33MD9AY+8m3yDnCR7/GFvuf4Xu/j2/5CYIa1tsRauwlYD5xclVxSPTLHf4wtKaXlXX93OoqIyAHcLWM47rtXibzwTNJGvkTqzc9hy8qcjiUiUu/tXdfbk5YF1uJJzyLzuekUb9hM0hv3cOK6D4gbMRBXsNvpqFKPVbUndhxwD+D1PY8Cdlhr936S2Ay08j1uBWQA+F7f6dt+X3sl+xzAGDPcGJNijEnJycmpYnT5I+W7i8gc/xGRF55Jo86JTscRETlIQONGdPxoDC3vvZKs1z9lzfl3UbZzt9OxRETqtYz7J+AtKjmoPaBxo4pJm9xBDqSShuaYi1hjzAAg21q7pBrz/CFr7URrbbK1NjkmRsNba1L2W/+ifPsuWt57pdNRREQOybhcJIz9B20n3U/BgiWsPP1GijdtdTqWiEi9VPB/v+BJz6r0NU9Gdi2nkYasKj2xvYC/GWNSgRlUDCN+CYgwxuyd9bg1sMX3eAsQD+B7vSmQt397JfuIA7ylZWx7YQZhZ51I2KldnY4jInJYsdcNoPO8cXi25bLilOvZtXC505FEROqNwqW/sea8O1l15k3gqrx8cCfE1nIqaciOuYi11t5nrW1trU2kYmKmBdbaIcA3wKW+za4BPvc9nuV7ju/1BbZiJo5ZwOW+2YuTgA7A4mPNJVWXN2M+nowsWt4zxOkoIiJHrOlfetJ10ZsENm3Cqr/cSu70eU5HEhHxa3vWpLJ28AMs7zmUXYtWED/2JpLevBdX6IGTNblCg4kfM8KhlNIQ1cQ6sfcCM4wxTwDLgEm+9knAVGPMeiCfisIXa+1KY8wHwCqgDLjZWlteA7nkCFhr2frMNBp1bUvEeac7HUdE5Kg06pjA8YveZO3F97H+ikcoXptBq4euo2IeQRERORLFqdvY8ugkct6dgys0hFYPDqXFHZcTGBEGgCvYTcboCXjSs3EnxBI/ZgQxQ7RKptQe46/LEiQnJ9uUlBSnY9Q722f/wG8D7qbduw8Sc1V/p+OIiBwTr6eUTTc+Tc7bXxJ1xbm0m3SflnkQv5Izba6KBKl1nsw8tox5h+w3PgOXi+Y3X0zLUVcRFBPpdDRpoIwxS6y1yb9vr4meWPFjW59+D3dCHFGX/9XpKCIix8zlDqLt5NGEdEog474JlKRuo9OnTxEU28zpaCKHtXcJk70zwHrSstg0fCyAClmpEWX5BWx95j0yX/4Qr6eM2GEDaPXgUIJb6z5XqZuqusSO1CO7Fi5n1/e/0OKOy3EF6fqGiPg3YwytRl1Nhw+foHDpb6w45QaKVm1yOpbIYaXf+9pBS5h4i0rIuH+CQ4mkvirfVcjmJ6awLOkStj4zjWYX/5kT10yn7Rv3qoCVOk1FrOyz9ZlpBESGETvsAqejiIhUm6hLe3P8f17DW+xh5WnD2THvR6cjiRzEW+Ih76MFrO5/B6VbcirdxpOeRdrd49mdshp/vR1M6gZvcQnbXpzBsraD2Pzgm4T/pQcn/PIu7d97hJD2rZ2OJ3JYuidWgIrZ5345bgitHriW+MducDqOiEi1K0nP5LcL7qFo5SaSxt9B3IiBTkcSofDX9eRM/oLc9+ZSlrcTd+tYyncVUr6z8KBtTaNgKC3DlpUT3K4VUZf1Ieqycwjt1k6Tl8kR8ZaWkfP2bLY8NgXP5myanvMnWj8xnLBTjnc6mkilDnVPrIpYAWDDsCfJmz6fk9I+0c37IlJvle8qZN3fH2bH7P8S3u9UildtwpOhiXOkdpVtLyB3+nxyJs+mcMkajDuIyIvOInbYAJr2SSZ3xlcH3BMLFUuYJE0cRWT/08j/9DvyZn7Fzq+XgNdLSOc2RF9+DlGX9aFR50Tn3pjUWdbrJW/GV2Q89CYlG7bQ5LSuxI+5kaZ/6el0NJE/pCJWDsmzJYdlSZcQO/xCksbf6XQcEZEaZcvLWT3gbgrmLDqgfW+RoEJWaoL1eilYsITsyV+Q/8l32BIPod07EDtsAFFXnEtQVNMDtj+S2YlLs/PJ+/hb8mZ+za7//AzWEnpCe18PbR9C2mlYaENnrWX7rO/JeGAie1ZsJPSE9sSPuZGI809X7734BRWxckhpd49n2wszOHHdTELatnI6johIjVuaOBBPWtZB7e42cfRI/dSBRFJfFaduI+ft2eS8/SWetEwCIsOIHtKX2OvOp/FJnartPJ6tOeR9uIC8mV+ze+EKABondybqsnOIGtyb4ITm1XYuqXsqu+jhbh5F+v0TKFy8ipAO8bR+/AaiBvXGuDQljvgPFbFSqbIdu1iWMJCI80+nw/THnI4jIlIrFrl6wSF+/7Wf/ijNLjpL68rKMfPuKSH/s+/InvQFBV+ngDE0/eufiLluAM0uPLPG/22VpG0j78NvyJvxFYVL1gDQ5PRuFT20g3rjbhFdo+eX2vX7JZkAcBnwWtzxcbR++DpirumPCdTKE+J/VMRKpbaMfZeM+ybQbemUar0iLCJSlx2qJ5YAF5R7CYgII/qKvxJz3QAa9+ikYXdyWNZaCpf+VjFJ0/vzKd+xi+DEFsRcN4CYa/o71hNavH4zeR98Td6MryhavgGMIfzPJxF1WR+aXXL2vnkwjmT4stRNS9sMxJN+8P9nAZFh9Nw6SxfkxK+piJWDeItLWJZ4CaHd29Nl7jin44iI1JrKei5cocEkvXEv7uZRZE+ZTf4n32KLPYR2a0fM0POJvrKvJr5rwA5V5JXm7iB32lxyJs+m6Nf1mBA3UZecTcywCwj/80l1aujmntWp5M78iryZX1O8Jg0CAmjauwfuNi3ImzYX756DJ5JSIVt32LIyijduZc+aNPasTmXP6orvhYtXVb6DMZzq/aF2Q4pUMxWxcpCsiZ+x6cZn6PL1yzTtfdC/DRGReu1wPU9lO3aRN+MrsqfMpnDxKkxgABEXnEHs0POJ6H+qhuY1IJVd9DDBQYR2b0/RsnXY0jIa/6kLsdcNIOrycwiMCHMw7eFZayn6dT15M78mb+ZXlGzcWul2ukfcGeVFxRT/llZRpO4rWFMpXrcZ6yndt11Qy2gadW7D7sWr8O7ec9Bx9Pcn9YGKWDmALS/nly5XEBDemK4/TdJQORGRP1C0ciM5U2aTO3UOpdnbCYprRvTV/Ykdej6NuiQ6HU9q2CGHn7sMzW8fTOzQAYR2a1f7waqBtZYfA3rBIT4Otn7sBsJO60qTU44jIKxx7YbzY0c0u3Tezv8VqGvS9vWslqRl/u+efZeLkHataNSlDSGd29CoS2LFV+c2BDZtsu9ch1qSST3p4u9UxMoB8j7+hnWXjqbDB08QNai303FERPyCt7SMHV/+l5wps9kx+7/YsnKanHo8MUPPJ+qyc/Z9qJT65ZATgdWT4ZqHKtJNUCC2rLzivbtchHZrR9jpXWlyWlfCTu9GcNtWugheiUP13De7tDcBjUP29a6W5ezY97qrUTAhnRL2FagVxWobQjrE4wp2H9E5dU+z1EcqYmUfay0rTrme8u276L5mOiYgwOlIIiJ+x5OVT+57c8iZMps9KzfhahRMs0vOJmbo+YSf3aNO3Qspx6Z8dxHbXpzJ5offqrSIrS/DNf+oJy9yQC92/7iSXf9dzu6FK9i9aCXlBYUABMVG0uT0bhU9tad3o0nPzrgaNYxJhKy1lO8qojQzj9JteXi25VKamY9nWy5Zr36Mt7C40v0Cm4XvK1AbdUnc17sa3Ka5/s8QqYSKWNln5zdLWN37VpIm3EPcjRc5HUdExK9ZaylMWU325NnkTZ9P+c7dFbPSXnse0decR0hiC0A9Jf7E6ykl+81ZbHl8CqVZ+TRO7kLRig3YYs++berbcM0j/fdpy8vZsyqVXf9dzq6FK9j93+UUr8sAKnpuQ0/qSNjp3Qg7vRtNTutKcOvY2n4rlTqa91eau5PS/YrS0m15lGbm4dnmK1h9hau36OBC1biDDrhv9cAXDaeU/596r0WOgopY2Wd1v39StGwtJ6V+0mCumIqI1AbvnhLyP/2OnCmz2fl1ClhLeJ9kQjomkPv2bM3+WsdZr5e8D74mY/QblGzcStifTyJh7E2EndpVFyH+QGnO9oqCduGKih7bxav2Ffzu+LiKIci+HtvQEzviCgqslT9PW16Od08JOe/NIe2OV7D7/fyZoECann8aQVER+xWseZRmb4fy8oOOFdC0CUEtonC3iCKo+f++V7RFE9S8Ge4W0QREhrEs6eJKh2fXl557kdqkIlYAKPxlHctPvIb4MTfS6v5rnI4jIlJvlaRnkvPOv8mZMpuSTZr9tS6z1rJz/mLSR71O0bK1hJ7QnvixNxHR71T1mh0Dr6eUol/W7eup3fXfFXgyKoo6V6NgghLi8GzYUnG/rY8JDiLulksJO/V4vEUlePf87quo+OC2PSV/uK0tLTts1qB9BWgUQS2icTdvVvF9/yK1edRRXfTXREsi1UdFrACwbsgjbJ/1f/RI/4TAyHCn44iI1HvW6+XHwDMOOfvries+ILidJshxyu6fVpE+6nUKFiwhOLEFrR+/gegrztX9idWsZHP2vp7arNc+xnoOX2Duz9UoGFdoSMX3/b9Cf//84G3S73m18oPW4MRc6rkXqR4qYoXi1G383H4wLUYOps1ztzodR0SkwTjkEi0+wUktaXruyUScezLhvXvW+XVG64M96zLIGP0G+R8uIDA6glYPXkvcjRcd0UywUjWHnu0ZTvhl6kHFqQl2V+kiz6F+/jQSQqTuO1QRq5XaG5Btz0/HuAzNR17mdBQRkQYlfsyISocXtnrsBgJCgtk5fzF5788j+43PwOWiySnH+YraU2hychdMoH5dVxfPtlw2PzaF7Ddn4Qpx0+qh62hx598JDNcaqLXFnRBbeVGZEFcj6+0e6ucvfsyIaj+XiNQO/VZsIEpzd5Az6V9EDzm3zswUKCLSUOwdRnio4YXNb74Eb2kZu39cyc55i9k5bzFbHn+bLY9OJiC8MeG9e+7rqQ35//buPTir+s7j+PtLSAi3Gqghi4SAilCpVap411m1VWxta921bl26VezUpdPr7NrVVkuvzFpbb7UXy7autk1VrL04HafI1F6srXhBamtdlV0ExISLSAjXkOS3fzwHTCRBAiHneZL3ayaT8/zOeU6+mfnxC5/n/M7vHF6b56/SrWKfPtnatImXrvsRjTfNJ+1opeYjFzDumkupqBmdd2kDTl+Hytf79yep9DideIBY+YXvseqLt3HM337M0CMn5l2OJOl1tK7fSNODj9P0wKNseOBRWpY3AjDksEM46JwTC1OPzzy2KKYeF/NCNu3btrP62z9l1dw7aF2/kTdefDbjv/zhov0wYKAo9g89JBUH74kdwNo2b+XJugsYedrRTPnFxY75HwAAD3NJREFUdXmXI0nqoZQS255fSdPCx2h6YBFNDy6mfdMWKCtjxAlHdjn1+ECFhNTeTtvGzbS+0kzbK820bmjm+X+aQ+u6DbsdW1FXw7HL87nnMLW1sfaHv+LFOd+jZeVqDppxInX/OZvhb52SSz2SpJ4zxA5gDd+Yz/JP3sSbH/4uI095S97lSJL2U/uOVjY98tddV2k3P/YMpFSYevy26ZRVjeDluxaStrbsek/HK6PtLTuyELqR1g2bCtsbmjsE002dQmphf9bWtKnrRXm6MXTqoVROHs/QKXVUTq7Lvo9n8MFVB2RF5pQSG375MCs+8x22Pr2M4ccfSd21H+Ggs3b7P5Akqcj1eoiNiPHAD4AaCg8OmJdSujkiRgN3AxOBF4CLUkqvROEv1c3AO4EtwKUppcXZuS4BrslO/ZWU0h2v9/MNsXunfUcrSya9jyHja3jzH27NuxxJ0gHQun4jTb9+nKYHFhWmHq/oZiXkskEMqiinfev2rvdnBg0dQtmokQweNZLBVSML21UjGDxqJGWj3sDgqhGd9j9/8Rx2NLy8+4/L7ufd9txKti19kdSy49V9VSN3BdqdAbdySh2Vk2opG1b5ur9zV1eaKyeOZcWV36b54aeonFzH+LmXM/ofz/TxRZJUog5EiB0LjE0pLY6IkcATwHuBS4H1KaVrI+IqYFRK6cqIeCfwcQoh9kTg5pTSiVnofRyYTiEMPwEcl1J6ZU8/3xC7d9bVL2DpB77IlPuuY9S7T8u7HEnSAZZSYlHZad1eLR17xT93CqFlVTvD6IhdAbWnj5nZm3tiU1sb25c3svXZFWx7bgXbnl3B1udWsu25lbSs7By6K+pqGDq5EHArp9QVtqfUMaSuhigr6/LnUTYI2topH3swtV+4jOpZ72JQuetXSlIp6/VH7KSUGoCGbLs5Ip4BxgHnA2dkh90B/Ba4Mmv/QSqk5kcioioLwmcAC1NK67NCFwLnAnfua20qSCnx0nX1DJ16KFXnnZJ3OZKkPhAR3T/CZEINE772sV7/mXuz+muUlVF52DgqDxsH7zi50/vbNm9l29IXs2D7asBdV/9AYfryznNUlFM5qZbty17a/WpyWztlVSOYtnT+Xl3JlSSVrl75iDIiJgJvBRYBNVnABWikMN0YCgF3ZYe3vZi1ddfe1c+5HLgcoK6urjdK79c2/OoRtjy1lMNvv4YYNCjvciRJfSSP52JWz5yxzwtHlQ0fyvBjjmD4MUd0ak8p0br2lezq7cpXA+7flnV5nramzQZYSRoA9jvERsQI4F7gUymljR3vO0kppYjotZWjUkrzgHlQmE7cW+ftr1766o+oqB3DGy8+O+9SJEl9qL88FzMiKB8zmvIxo3nD6dN2tS+eeEHXV5rrfA66JA0E+xViI6KcQoCtTyn9NGteHRFjU0oN2XThNVn7KmB8h7fXZm2reHX68c723+5PXYLmRU/T/LsnmXD9xxlUUZ53OZKkPrY/V0aLXR5XmiVJxWOf55hmqw1/H3gmpXRDh133AZdk25cAv+jQ/sEoOAloyqYdLwDOiYhRETEKOCdr035ouK6esqqRjPnwe/IuRZKkXlU9cwaHzruKigk1EEHFhJpOi0hJkvq3/bkSeyrwL8BfImJJ1vZZ4FpgfkR8CFgOXJTtu5/CysRLKTxiZxZASml9RHwZeCw77ks7F3nSvtn67HLW/+x3jPvsBykbOTzvciRJ6nX9+UqzJGnP9md14j8A3T147W1dHJ+Aj3ZzrtuA2/a1FnXW8PUfExXl/N0n3pd3KZIkSZLUq1yytp9paVjH2h/8iupZ51E+ZnTe5UiSJElSrzLE9jONN88ntbZxyBUX512KJEmSJPU6Q2w/0tq0idXf+RmjLzyTysNr8y5HkiRJknqdIbYfWfPdn9O2cTPjrvxA3qVIkiRJ0gFhiO0n2re30HDTfA56+/EMP3ZK3uVIkiRJ0gFhiC1xa+sXsHjiBTxaeQY7GtYx7DgDrCRJkqT+yxBbwtbWL2DZ5dfSsnz1rrbVt9zD2voFOVYlSZIkSQeOIbaErbz6Vtq3bO/U1r5lOyuvvjWniiRJkiTpwDLElqi2Lds6XYHtqGXFmj6uRpIkSZL6hiG2xLRt3spL1/+YJYdd2O0xFXVj+rAiSZIkSeo7htgS0bZ5Ky99rZ4lh13Iiiu+ydCjDuOQz13KoGFDOh03aNgQxs+dnVOVkiRJknRgDc67AO1Z26YtNH7rXhq+fiet6zZw0NnHU/v5DzHy1KMBGDplAiuvvpWWFWuoqBvD+LmzqZ45I+eqJUmSJOnAMMQWqbbmzTR+814arr+T1pebOGjGidR+/jJGnvyWTsdVz5xhaJUkSZI0YBhii0zrxs2s/uZPCuF1/Uaq3nEy4+bMYuRJR+VdmiRJkiTlzhBbJFqbNtF4yz003HAXba80U3XeKdTOuYwRJ0zNuzRJkiRJKhqG2Jy1bmim8Rv30HDj3bRtaGbUu09j3JxZjJh+ZN6lSZIkSVLRMcTmpHVDMw033U3jTfNpa9rEqPNPp3bOZQw/dkrepUmSJElS0TLE9rHW9RsL4fXm+bRt3MyoC/6e2jmzGD5tct6lSZIkSVLRM8T2kR0vN9F44100fuMe2pq3MPofzmDcnFkMP+aIvEuTJEmSpJJhiO1la+sXdHpu6yFXfZCWFatpvOUntG/awugLz2Tc52Yx/OhJeZcqSZIkSSXHENuL1tYvYNnl19K+ZTsALctX88JHvgbA6IveRu3nLmXYUYfnWaIkSZIklTRDbC9aefWtuwJsR+VjD2by3V/OoSJJkiRJ6l8G5V1Af9KyYk2X7TsaX+7jSiRJkiSpfyqaEBsR50bEsxGxNCKuyruefVFRN6ZH7ZIkSZKknimKEBsRZcC3gHcAU4GLI2JqvlX13Pi5sxk0bEintkHDhjB+7uycKpIkSZKk/qUoQixwArA0pfR/KaUW4C7g/Jxr6rHqmTM4dN5VVEyogQgqJtRw6LyrqJ45I+/SJEmSJKlfKJaFncYBKzu8fhE48bUHRcTlwOUAdXV1fVNZD1XPnGFolSRJkqQDpFiuxO6VlNK8lNL0lNL06urqvMuRJEmSJPWxYgmxq4DxHV7XZm2SJEmSJO1SLCH2MeCIiDg0IiqA9wP35VyTJEmSJKnIFMU9sSml1oj4GLAAKANuSyk9nXNZkiRJkqQiUxQhFiCldD9wf951SJIkSZKKV6SU8q5hn0TEWmB5D95yMLDuAJWj/sf+op6wv6gn7C/qCfuLesL+op4ohf4yIaW024q+JRtieyoiHk8pTc+7DpUG+4t6wv6inrC/qCfsL+oJ+4t6opT7S7Es7CRJkiRJ0usyxEqSJEmSSsZACrHz8i5AJcX+op6wv6gn7C/qCfuLesL+op4o2f4yYO6JlSRJkiSVvoF0JVaSJEmSVOIMsZIkSZKkktEvQ2xE3BYRayLirx3aRkfEwoh4Pvs+Ks8aVTy66S9fiIhVEbEk+3pnnjWqeETE+Ij4TUT8LSKejohPZu2OMepkD33F8UVdiojKiHg0Iv6c9ZkvZu2HRsSiiFgaEXdHREXetSpfe+grt0fEsg7jy7S8a1XxiIiyiHgyIn6ZvS7ZsaVfhljgduDc17RdBfw6pXQE8OvstQRd9xeAG1NK07Kv+/u4JhWvVuDfU0pTgZOAj0bEVBxjtLvu+go4vqhr24GzUkrHANOAcyPiJOCrFPrMJOAV4EM51qji0F1fAfh0h/FlSX4lqgh9Enimw+uSHVv6ZYhNKf0eWP+a5vOBO7LtO4D39mlRKlrd9BepSymlhpTS4my7mcIfg3E4xug19tBXpC6lgk3Zy/LsKwFnAT/J2h1ftKe+InUpImqB84DvZa+DEh5b+mWI7UZNSqkh224EavIsRiXhYxHxVDbd2Kmh2k1ETATeCizCMUZ78Jq+Ao4v6kY23W8JsAZYCPwvsCGl1Jod8iJ+GCJ27ysppZ3jy9xsfLkxIobkWKKKy03AfwDt2es3UsJjy0AKsbukwnOF/LRKe/Id4HAKU3QagOvzLUfFJiJGAPcCn0opbey4zzFGHXXRVxxf1K2UUltKaRpQC5wAvCnnklSkXttXIuIo4DMU+szxwGjgyhxLVJGIiHcBa1JKT+RdS28ZSCF2dUSMBci+r8m5HhWxlNLq7I9DO/BfFP4jIQEQEeUUQkl9SumnWbNjjHbTVV9xfNHeSCltAH4DnAxURcTgbFctsCq3wlR0OvSVc7PbGFJKaTvw3zi+qOBU4D0R8QJwF4VpxDdTwmPLQAqx9wGXZNuXAL/IsRYVuZ1hJHMB8NfujtXAkt1D8n3gmZTSDR12Ocaok+76iuOLuhMR1RFRlW0PBc6mcC/1b4ALs8McX9RdX/mfDh+mBoX7Gx1fRErpMyml2pTSROD9wIMppZmU8NgShVlv/UtE3AmcARwMrAY+D/wcmA/UAcuBi1JKLuaj7vrLGRSm+iXgBeBfO9zvqAEsIk4DHgL+wqv3lXyWwr2OjjHaZQ995WIcX9SFiDiawuIqZRQuNMxPKX0pIg6jcPVkNPAk8IHsSpsGqD30lQeBaiCAJcDsDgtASUTEGcAVKaV3lfLY0i9DrCRJkiSpfxpI04klSZIkSSXOECtJkiRJKhmGWEmSJElSyTDESpIkSZJKhiFWkiRJklQyDLGSJJW4iPhtREzPuw5JkvqCIVaSJEmSVDIMsZIk9bGI+HREfCLbvjEiHsy2z4qI+og4JyL+FBGLI+KeiBiR7T8uIn4XEU9ExIKIGPua8w6KiNsj4it9/1tJktQ3DLGSJPW9h4DTs+3pwIiIKM/angKuAd6eUjoWeBz4t2z/LcCFKaXjgNuAuR3OORioB55PKV3TN7+GJEl9b3DeBUiSNAA9ARwXEW8AtgOLKYTZ04H7gKnAwxEBUAH8CZgCHAUszNrLgIYO5/wuMD+l1DHYSpLU7xhiJUnqYymlHRGxDLgU+COFq69nApOAZcDClNLFHd8TEW8Bnk4pndzNaf8InBkR16eUth2w4iVJypnTiSVJysdDwBXA77Pt2cCTwCPAqRExCSAihkfEZOBZoDoiTs7ayyPizR3O933gfmB+RPghtSSp3zLESpKUj4eAscCfUkqrgW3AQymltRSu0N4ZEU9RmEr8ppRSC3Ah8NWI+DOwBDil4wlTSjdQCMI/jAj/xkuS+qVIKeVdgyRJkiRJe8VPaSVJkiRJJcMQK0mSJEkqGYZYSZIkSVLJMMRKkiRJkkqGIVaSJEmSVDIMsZIkSZKkkmGIlSRJkiSVjP8HgNh3ATBTaiAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "6mmykBe1VL_f",
        "outputId": "be7f6a64-fa9c-4458-8041-600ad961f8bd"
      },
      "source": [
        "#Decompose into three distinct components: trend, seasonality, and noise.\n",
        "\n",
        "#Cases\n",
        "rcParams['figure.figsize'] = 16, 6\n",
        "#trend resulting from the convolution is linear least-squares extrapolated on both ends\n",
        "# period/freq is 1\n",
        "decomposition = sm.tsa.seasonal_decompose(weeklyCases, freq=7, model='additive')\n",
        "fig = decomposition.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: FutureWarning:\n",
            "\n",
            "the 'freq'' keyword is deprecated, use 'period' instead\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWLXRUAfVhNN",
        "outputId": "d1f47532-43d1-4a7d-bc4d-47af35ef4e57"
      },
      "source": [
        "startDateTest = '2020-10-01'\n",
        "endDateTest = '2020-10-31'\n",
        "\n",
        "test_cases = df_cases[(df_cases['date_report'] >= startDateTest) & (df_cases['date_report'] <= endDateTest)]\n",
        "\n",
        "print(test_cases)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    province date_report  cases  cumulative_cases  year  month  week\n",
            "250   Canada  2020-10-01   1821            162777  2020     10    40\n",
            "251   Canada  2020-10-02   2121            164898  2020     10    40\n",
            "252   Canada  2020-10-03   1801            166699  2020     10    40\n",
            "253   Canada  2020-10-04   1609            168308  2020     10    40\n",
            "254   Canada  2020-10-05   2910            171218  2020     10    41\n",
            "255   Canada  2020-10-06   2380            173598  2020     10    41\n",
            "256   Canada  2020-10-07   1859            175457  2020     10    41\n",
            "257   Canada  2020-10-08   2476            177933  2020     10    41\n",
            "258   Canada  2020-10-09   2531            180464  2020     10    41\n",
            "259   Canada  2020-10-10   2118            182582  2020     10    41\n",
            "260   Canada  2020-10-11   1600            184182  2020     10    41\n",
            "261   Canada  2020-10-12   1732            185914  2020     10    42\n",
            "262   Canada  2020-10-13   3414            189328  2020     10    42\n",
            "263   Canada  2020-10-14   2549            191877  2020     10    42\n",
            "264   Canada  2020-10-15   2271            194148  2020     10    42\n",
            "265   Canada  2020-10-16   2566            196714  2020     10    42\n",
            "266   Canada  2020-10-17   2027            198741  2020     10    42\n",
            "267   Canada  2020-10-18   1791            200532  2020     10    42\n",
            "268   Canada  2020-10-19   3269            203801  2020     10    43\n",
            "269   Canada  2020-10-20   2326            206127  2020     10    43\n",
            "270   Canada  2020-10-21   2698            208825  2020     10    43\n",
            "271   Canada  2020-10-22   2838            211663  2020     10    43\n",
            "272   Canada  2020-10-23   2649            214312  2020     10    43\n",
            "273   Canada  2020-10-24   2212            216524  2020     10    43\n",
            "274   Canada  2020-10-25   2046            218570  2020     10    43\n",
            "275   Canada  2020-10-26   4173            222743  2020     10    44\n",
            "276   Canada  2020-10-27   2718            225461  2020     10    44\n",
            "277   Canada  2020-10-28   2667            228128  2020     10    44\n",
            "278   Canada  2020-10-29   3012            231140  2020     10    44\n",
            "279   Canada  2020-10-30   3546            234686  2020     10    44\n",
            "280   Canada  2020-10-31   2358            237044  2020     10    44\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABuuigKYW6o-",
        "outputId": "02756f5f-1aaa-4486-d10c-ea302761ac83"
      },
      "source": [
        "d={'ds':train_cases['date_report'],'y':train_cases['cases']}\n",
        "df_pred=pd.DataFrame(data=d)\n",
        "model1 = Prophet(daily_seasonality=True, weekly_seasonality=True)\n",
        "model1.fit(df_pred)\n",
        "futureCases = model1.make_future_dataframe(periods=31) # we try to predict 31 days\n",
        "forecastCases = model1.predict(futureCases)\n",
        "forecastCasesNov = forecastCases[(forecastCases['ds'] >= startDateTest) & (forecastCases['ds'] <= endDateTest)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "d6rBIFqtZSqQ",
        "outputId": "f6ec1aac-7938-4cfa-c07d-3f8091eb1088"
      },
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "model1.plot(forecastCases, xlabel = 'Date', ylabel = 'Cases')\n",
        "plt.title('Cases Prediction');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "RdeCk87OblK9",
        "outputId": "a3a1d860-6e2b-449b-a12b-cec5a6918e43"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(forecastCasesNov['ds'], forecastCasesNov['yhat'], label='Predicted Cases')\n",
        "ax.plot(test_cases['date_report'], test_cases['cases'], label='Actual Cases')\n",
        "legend = ax.legend(loc='upper left', shadow=True)\n",
        "plt.title('Prophet Model COVID-19 Cases Forecast Oct 2020 to Nov 2020')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Cases')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XPdr3+JHfNufhxuoe8N5T3Up89g+V2Pe3fusHAOnevgt9r1cZ13kGrkV8F/C032cp3fu3SAeGVvZvIK4XyUGOr2Jd7vFwBbMs8LcKPg/Vvm5c5f8jJfadUuL9PNvbdxVl/F3XQw899AjEwzcVgoiIlMMYY4Ge1rXCiIiIiEgdpC7WIiIiIiIiIihBFhEREREREQFQF2sRERERERERUAuyiIiIiIiICKAEWURERERERASAuHAHEAxt2rSx3bp1C3cYIiIiIiIiEgTp6ek7rbVtA33cOpkgd+vWjQULFoQ7DBEREREREQkCY8zGYBxXXaxFREREREREUIIsIiIiIiIiAihBFhEREREREQHq6Bjk0hw+fJjMzEwKCgrCHYrUUOPGjenRowf169cPdygiIiIiIlKHRE2CnJmZScuWLenVqxcxMWo4r62Ki4vJzs5m4cKFNGrUiJSUFIwx4Q5LRERERETqgKjJFAsKCmjfvr2S41ouJiaGDh06EBcXxxdffMHKlSvDHZKIiIiIiNQRUZUtKjmuG2JiYjDG0KhRIzZuDEp1dxERERERiULKGEMoNjaW/v3706dPH8aNG1ej8dATJkxg+vTpAEycOJHly5eXue3s2bP59ttvq3yObt26sXPnzhOW5+fnc+utt9KjRw9SU1MZMWIE3333XZWPX1PGGAoLC0N+XhERERERqZuUIIdQo0aNWLRoERkZGdSvX58pU6Yct766yd7zzz9PUlJSmeurmyCXZeLEicTHx7NmzRrS09N56aWXSk2kRUREREREahMlyGFy1llnsXbtWmbPns1ZZ53FxRdfTFJSEkVFRUyePJlBgwaRkpLCs88+C4C1lkmTJtGrVy/OO+88tm/ffvRYI0aMYMGCBQDMnDmTgQMH0q9fP84991w2bNjAlClTePLJJ+nfvz9ff/01O3bs4IorrmDQoEEMGjSI//73vwDk5uYycuRIkpOTmThxItbaE+LOzMzku+++4+GHHz7aZT0xMZExY8YAcOmll5KamkpycjLPPfccAEVFRUyYMIE+ffrQt29fnnzyyaPHGj16NKmpqZx11llHxxO/+eab9OnTh379+jFs2LBgvPwiIiIiIiIniJoq1v4e/GAZy7fuDegxkzo15/6Lkiu1bWFhITNmzGD06NEALFy4kIyMDBITE3nuuedo0aIF8+fP59ChQwwZMoSRI0fyww8/sGrVKpYvX05OTg5JSUnceOONxx13x44d3HzzzcyZM4fExETy8vKIj4/ntttuo2nTptx5550AXHvttdxxxx0MHTqUTZs2MWrUKFasWMGDDz7I0KFDue+++/joo4944YUXToh92bJl9O/fn9jY2FKv7cUXXyQ+Pp4DBw4waNAgrrjiCjZs2EBWVhYZGRkA7N69G4BbbrmFKVOm0LNnT7777jt+9rOf8eWXX/LQQw/xySefkJCQcHRbERERERGRYIvKBDlcDhw4QP/+/QHXgnzTTTfx7bffMnjwYBITEwH49NNPWbJkydHxxXv27GHNmjXMmTOHa665htjYWDp16sQ555xzwvHnzZvHsGHDjh4rPj6+1Dg+//zz48Ys7927l/z8fObMmcPbb78NwJgxY2jVqlWVr/Hpp5/mnXfeAWDz5s2sWbOGXr16sW7dOm6//XbGjBnDyJEjyc/P59tvv2XcuHFH9z106BAAQ4YMYcKECYwfP57LL7+8yjGIiIiISAWKi2Hnamh3argjEYkoUZkgV7alN9B8Y5BLatKkydGfrbU888wzjBo16rhtPv7444DFUVxczLx582jYsGGV901OTmbx4sUUFRWd0Io8e/ZsPv/8c+bOnUvjxo0ZMWIEBw8epFWrVixevJhPPvmEKVOm8MYbb/DUU0/RsmXLUl+PKVOm8N133/HRRx+RmppKeno6rVu3rvb1ioiIiEgJy9+F6TfCpPnQpme4oxGJGBqDHGFGjRrFP/7xD44cOQLA6tWr2b9/P8OGDWPatGkUFRWxbds2Zs2adcK+p59+OnPmzGH9+vUA5OXlAdCsWTP27dt3dLuRI0fyzDPPHP3dl6QOGzaM1157DYAZM2awa9euE87Ro0cP0tLSuP/++4+OUd6wYQMfffQRe/bsoVWrVjRu3JiVK1cyb948AHbu3ElxcTFXXHEFDz/8MAsXLqR58+YkJiby5ptvAu7GwOLFiwE3Nvm0007joYceom3btmzevLkGr6iIiIiInCAnA7CwaV64IxGJKEqQI8zEiRNJSkpi4MCB9OnTh1tvvZXCwkIuu+wyevbsSVJSEj/+8Y8544wzTti3bdu2PPfcc1x++eX069ePq666CoCLLrqId95552iRrqeffpoFCxaQkpJCUlLS0Wra999/P3PmzCE5OZm3336brl27lhrj888/T05ODieffDJ9+vRhwoQJtGvXjtGjR1NYWEjv3r25++67Of300wHIyspixIgR9O/fn+uuu45HH30UgFdffZUXXniBfv36kZyczHvvvQfA5MmT6du3L3369OHMM8+kX79+AX+dRURERKJabqZ7zkoPbxwiEcaUVqm4tktLS7O+qs4+6enppKamhikiCbT09HTmz59P586dGTt2bLjDEREREaldpgyF7KXQoS/c9k24oxGpMmNMurU2LdDHVQuyiIiIiEg0sRZy14GJgZzlcLgg3BGJRAwlyCIiIiIi0WRfNhzZD91HgC2CbYvDHZFIxFCCLCIiIiISTfK88ccprl6NxiGLHKMEWUREREQkmvgKdHU9HVp0hawF5W8vEkWUIIuIiIiIRJPctRBbH1p0gYSBakEW8aMEWUREREQkmuStg1bdICYWOqfB7k2QvyPcUYlEBCXIIfbuu+9ijGHlypUVbvvUU09RUFD9qoL/+te/mDRpUqnrZsyYQVpaGklJSQwYMIBf//rX1T6PiIiIiNQiuZnQ+mT3c4I3DapakUUAJcghN3XqVIYOHcrUqVMr3LamCXJZMjIymDRpEq+88grLly9nwYIFnHzyyQE/j4iIiIhEmOJi14Ic39393rEfmFiNQxbxBD1BNsbEGmN+MMZ86P2eaIz5zhiz1hgzzRhT31vewPt9rbe+m98x7vGWrzLGjAp2zMGSn5/PN998wwsvvMDrr79+dHlRURF33nknffr0ISUlhWeeeYann36arVu3cvbZZ3P22WcD0LRp06P7TJ8+nQkTJgDwwQcfcNpppzFgwADOO+88cnJyyo3jscce49577+XUU08FIDY2lp/+9KflHuurr76if//+9O/fnwEDBrBv3z4AHn/8cQYNGkRKSgr3338/APv372fMmDH069ePPn36MG3atAC8eiIiIiJSY3u3QNEhaN3D/V6/CbRLUguyiCcuBOf4H2AF0Nz7/U/Ak9ba140xU4CbgH94z7ustScbY672trvKGJMEXA0kA52Az40xp1hri6od0Yy7IXtptXcvVYe+cMEfy93kvffeY/To0Zxyyim0bt2a9PR0UlNTee6559iwYQOLFi0iLi6OvLw84uPj+ctf/sKsWbNo06ZNuccdOnQo8+bNwxjD888/z2OPPcaf//znMrfPyMgos0t1Wcd64okn+Nvf/saQIUPIz8+nYcOGfPrpp6xZs4bvv/8eay0XX3wxc+bMYceOHXTq1ImPPvoIgD179lTw4omIiIhISPgqWLf26z3YORWWveNal2PUwVSiW1A/AcaYzsAY4HnvdwOcA0z3NnkZuNT7+RLvd7z153rbXwK8bq09ZK1dD6wFBgcz7mCZOnUqV199NQBXX3310W7Wn3/+Obfeeitxce5+RXx8fJWOu2XLFkaNGkXfvn15/PHHWbZsWbVjLOtYQ4YM4Ve/+hVPP/00u3fvJi4ujk8//ZRPP/2UAQMGMHDgQFauXMmaNWvo27cvn332GXfddRdff/01LVq0qHY8IiIiIhJAvjmQ43scW5aQCgf3uK7XIlEu2C3ITwG/AZp5v7cGdltrC73ftwAJ3s8JwGYAa22hMWaPt30CMM/vmP77VE8FLb3BkJeXx5dffsnSpUsxxlBUVIQxhscff7zSx3D3C5yDBw8e/fn222/nV7/6FRdffDGzZ8/mgQceKPc4ycnJpKen069fvxPWlXWsu+++mzFjxvDxxx8zZMgQPvnkE6y13HPPPdx6660nHGfhwoV8/PHH/O53v+Pcc8/lvvvuq/R1ioiIiEiQ5GZCXCNo1vHYsoQ095y1ANqoLo1Et6C1IBtjxgLbrbUhGdBgjLnFGLPAGLNgx47IK1M/ffp0rr/+ejZu3MiGDRvYvHkziYmJfP3115x//vk8++yzFBa6+wZ5eXkANGvW7OhYX4D27duzYsUKiouLeeedd44u37NnDwkJ7p7Byy+/TEUmT57MH/7wB1avXg1AcXExU6ZMKfdYmZmZ9O3bl7vuuotBgwaxcuVKRo0axYsvvkh+fj4AWVlZbN++na1bt9K4cWOuu+46Jk+ezMKFC6v9uomIiIhIAOVmuvHH/l2p2/aC+k01DlmE4LYgDwEuNsZcCDTEjUH+P6ClMSbOa0XuDGR522cBXYAtxpg4oAWQ67fcx3+fo6y1zwHPAaSlpdmgXFENTJ06lbvuuuu4ZVdccQVTp07lmWeeYfXq1aSkpFCvXj1uvvlmJk2axC233MLo0aPp1KkTs2bN4o9//CNjx46lbdu2pKWlHU1MH3jgAcaNG0erVq0455xzWL9+fbmxpKSk8NRTT3HNNddQUFCAMYaxY8eWe6ynnnqKWbNmERMTQ3JyMhdccAENGjRgxYoVnHHGGYArIvbKK6+wdu1aJk+eTExMDPXq1eMf//hHoF9OEREREamOvExXlMtfTCx0GgBbVMlaxFgb/FzSGDMCuNNaO9YY8ybwll+RriXW2r8bY34O9LXW3uYV6brcWjveGJMMvIYbd9wJ+ALoWV6RrrS0NLtgwfEfcF9BLKkb0tPTmT9/Pp07dz6a3IuIiIhIOYoK4ZH2cObtcN4Dx6/77D6Y+3f4bRbENQhHdCJVYoxJt9amBfq44ShTdxfwK2PMWtwY4xe85S8Arb3lvwLuBrDWLgPeAJYDM4Gf16iCtYiIiIhINNq9EYoLjy/Q5ZOQBsVHAj/Ti0gtE4ppnrDWzgZmez+vo5Qq1Nbag8C4MvZ/BHgkeBGKiIiIiNRxvirVrUspxJXg9bTMSofOAW+UE6k1NNGZiIiIiEg0ODoHciktyC0SXGVrjUOWKBdVCXJxcXG4Q5AA0L+jiIiISDXkroX6zaBJ29LXJ6SqkrVEvahJkBs3bkxOTo6Sq1quuLiY7Oxsjhw5AkBMTNS8hUVERERqJs+b4smY0tcnpLptCvJCG5dIBAnJGORI0KNHDzIzM9m6dWu4Q5EaOnLkCBs3bqSgoIA2bdqEOxwRERGR2iE3s/zxxUfHIS+EnueFJiaRCBM1CXL9+vXp3bs3ubm5vPPOOxQUFIQ7JKkBay09e/ZkwIAB4Q5FREREJPIVHoI9myHlqrK36TQAMK6btRJkiVJRkyD7tG7dmhtuuIEDBw4QijmgJThiY2Np1KgRpqwuQiIiIiJyzK4NYItLL9Dl07A5tO0FWSrUJdEr6hJkcMlV06ZNwx2GiIiIiEhoHK1gXcoUT/4S0mD1DLC27LHKInWYKhyJiIiIiNR1eV6CHN+9/O0SBkJBrmtxFolCSpBFREREROq63LXQqBU0ji9/O18RL033JFFKCbKIiIiISF2Xm1lx92qAdkkQ10gJskQtJcgiIiIiInVd3jqIL6dAl09sPejYTwmyRC0lyCIiIiIiddnhAtibVX4Fa3+d02DbYig6Ety4RCKQEmQRERERkbosb517rqhAl0/CQCg8CDnLgheTSIRSgiwiIiIiUpflVXKKJ58EX6EuzYcs0UcJsoiIiIhIXXZ0DuRKdrFu2RUat4GshcGLSSRCKUEWEREREanLcjOhSTto0Kxy2xvjxiFvUQuyRB8lyCIiIiIidVleJad48peQCjtXw8E9wYlJJEIpQRYRERERqctyM6F1JQt0+SSkAha2/hCUkEQilRJkEREREZG66uBe2L+9cnMg+0sY6J41H7JEGSXIIiIiIiJ1VVUrWPs0auX22aIEWaKLEmQRERERkbqqqhWs/SWkuqfgbcwAACAASURBVKmerA1sTCIRTAmyiIiIiEhdlbfOPbdKrPq+CWmQnwN7swIbk0gEU4IsIiIiIlJX5a6F5glQv3HV901Idc8ahyxRRAmyiIiIiEhdlZtZve7VAB36QGx9zYcsUUUJsoiIiIhIXZWXWfUK1j5xDaBDX8haGNiYRCKYEmQRERERkbqoIA8O7Kp+CzK4cchbf4DiosDFJRLBlCCLiIiIiNRFudWc4slfQioc2Q87VgYmJpEIpwRZRERERKQu8s2BXN0u1gCd09yzxiFLlFCCLCIiIiJSF+VmgomBVt2qf4z47tCwpSpZS9RQgiwiIiIiUhflroUWXSCufvWPYYzrZq0EWaKEEmQRERERkbooL7Nm4499ElJh+3I4vL/mxxKJcEqQRURERETqGmshd13NKlj7dE4DWwxbF9X8WCIRTgmyiIiIiEhds38HHN5XswJdPgmp7lndrCUKKEEWEREREalrcte650B0sW7SBlqeBFmqZB02RYXw/T/d3NYSVEqQRURERETqmqNzIHcPzPESUiFrYWCOJVW34EX4+E5Y+HK4I6nzgpYgG2MaGmO+N8YsNsYsM8Y86C3/lzFmvTFmkffo7y03xpinjTFrjTFLjDED/Y71E2PMGu/xk2DFLCIiIiJSJ+RlQkwctOgamON1ToM9m2FfTmCOJ5VXkAezHnE/r58T3liiQFwQj30IOMdam2+MqQd8Y4yZ4a2bbK2dXmL7C4Ce3uM04B/AacaYeOB+IA2wQLox5n1r7a4gxi4iIiIiUnvlroVWiRAboK/7/uOQT70wMMeUypn1CBzaB93Pho1zofAQxDUId1R1VtBakK2T7/1az3vYcna5BPi3t988oKUxpiMwCvjMWpvnJcWfAaODFbeIiIiISK0XqArWPh37gYnVOORQy1nmulcPugkG3wyFB2DL/HBHVacFdQyyMSbWGLMI2I5Lcr/zVj3idaN+0hjju/2RAGz2232Lt6ys5SXPdYsxZoExZsGOHTsCfi0iIiIiIrVCcTHkrQtMBWufeo2gfTJsUYIcMtbCjLugYQsYcQ+cNARMjLpZB1lQE2RrbZG1tj/QGRhsjOkD3AOcCgwC4oG7AnSu56y1adbatLZt2wbikCIiIiIitc++ba6lMVAFunw6p8HWH1wCLsG34gPY8DWcfS80jodGLaHTAFj3Vbgjq9NCUsXaWrsbmAWMttZu87pRHwJeAgZ7m2UBXfx26+wtK2u5iIiIiIiUFMgpnvwlpMKhvZC7JrDHlRMdOQCf3gvtkiH1hmPLE4e5bu6H8sveV2okmFWs2xpjWno/NwLOB1Z644oxxhjgUiDD2+V94MdeNevTgT3W2m3AJ8BIY0wrY0wrYKS3TERERERESsrzpngKZBdrgIQ095yVHtjjyom+/Svs3gQX/PH4QmuJw6G4EDZ+G77Y6rhgtiB3BGYZY5YA83FjkD8EXjXGLAWWAm2Ah73tPwbWAWuBfwI/A7DW5gG/944xH3jIWyYiIiIiIiXlZkJcQ2h+QtmemmnTE+o30zjkYNuTBd/8BXpf7FqM/XU9HWIbwHp1sw6WoE3zZK1dAgwoZfk5ZWxvgZ+Xse5F4MWABigiIiIiUhflZkJ8d4gJcFtYTCwkDFALcrB9fj8UF8HI35+4rl4j6DJYCXIQhWQMsoiIiIiIhEielyAHQ0Iq5GS4MbISeJvmwdI3YcgvoFW30rdJHA7ZS2F/bkhDixZKkEVEJHwOF8D/9Yfl74U7EhGRuqG4CHZtCOwcyP4S0twY2OylwTl+NCsudtM6NesEQ+8oe7vuw93zBk33FAxKkEVEJHxylsGu9fDdc+GORESkbtizGYoOB75Al09CqnvWOOTAW/QqbFsE5z8E9ZuUvV2ngW4suKZ7CgolyCIiEj45XgvExm9g9+bwxiIiUhcEa4onn+YdXfEvjUMOrIN74IsHoctp0PfK8reNjYNuQ2C9WpCDQQmyiIiET3aGq7QKkDE9vLGIiNQFuevcc7C6WAMkDHRz8UrgzHkc9u+EC/4ExlS8feIwN9Z8z5bgxxZllCCLiEj45GS47npdToPF08DacEckIlK75WVC/abQtH3wzpGQ5sY5q0hUYOxcC/OmwIAfQacTJgEqXaI3DlndrANOCbKIiIRHcbFrQW7fB1LGw44VLmEWEZHqy13rKlhXphWyunzjkNXNOjA++a2bvunc+yu/T7skaNxG0z0FgRJkEREJj13r4ch+6NAHki+HmDhYMi3cUYmI1G65mcHtXg2uldPEKEEOhDWfwZpPYPhvoGm7yu8XE+O6Wa+fo95XAaYEWUREwsPXWty+DzSOh5PPh6VvuSlKRESk6oqOwO5Nwatg7dOgKbQ9VeOQa6rwMMy8xxVUG3xr1ffvPhz2bYOdawIfWxRTgiwiIuGRneFaINr1dr+njId9W2HDN+GNS0Skttq1EWxR8FuQwXWzzkpX62VNfP8c5K6BUY9CXP2q7584zD2rm3VAKUEWEZHwyMmA1j3duCuAXhe4eR2XvBHeuEREaqtgT/HkLyEVDuyCvHXBP1ddlL8DvvqT6z11ysjqHaNVIrToCutmBzS0aKcEWUREwiM7Azr0PfZ7vUaQdDGseB+OHAhfXCIitVVepnsOdhdrgM5p7jlrYfDPVRd9+RAcKYDRj1b/GMZA92Gu55WGJwWMEmQREQm9A7thzyZXoMtfyng4tBdWzwxPXCIitVluJjRs4eo6BFvb3lCvscYhV8fWH2Dhf+C026BNz5odK3EEHNwN2UsCEpooQRYRkXA4WqCr7/HLu50FzTqqm7WE3u7NbvymSG2Wu9Z1rw7mFE8+sXHQsb8qWVeVtTDjbmjc2lWurqnEs9yz5kMOGCXIIiISetleglyyBTkmFvpe6aa9KMgLfVwSvd6+BaZeHe4oRGomb11oulf7JAyEbUtcNWapnIy3YPM8OPc+19pfU806uIriKtQVMEqQRUQk9HKWQuM20LT9iev6jofiI7DsndDHJdGp6AhsXQjbl8P2leGORqR6jhyEPVtCU8Hap3MaFB061itIynd4P3x2H3TsBwOuC9xxE4fDxrm6UREgSpBFRCT0sjNc63Fp3QA79HVj29TNWkJl+3IoPOh+XvF+eGMRqa5d6wEbmgrWPgmp7lndrCvnm6dgbxaM/pPrMRUo3YdD4QHYMj9wx4xiSpBFRCS0igph+wpo36f09ca4Yl2b58GuDSENTaKU78t9y5Ng+XvhjUWkunxTPMV3D905W3SBJu2UIFfGro3w7dPQ50o46YzAHvukIWBi1M06QJQgi4hIaOWudV3yOqSUvU3fK93z0jdDE5NEt6x0aBQPp//UdRXduTbcEYlUXa43xVMou1gb41qRt6iSdYU++1/AwPkPBv7YjVq6gmkq1BUQSpBFRCS0spe655IFuvy17OruiC95w1X8FAmmrB/cl/zeF7vfl78b3nhEqiMv09V2CEThp6ronAq5a9z0fVK69V+73iln/QpadA7OOboPd1NuHcoPzvGjiBJkEREJrZylEFsf2pxS/nYp42Hnati2KDRxSXQ6lA87VrhqvC0SoPNgdbOW2ik3M7Tjj31845C3Lgz9uWuDokKYeTe06Apn3h688yQOh+JC2DQ3eOeIEkqQRUQktLIzoG0viK1X/nZJl7hEeom6WUsQbVsMtvjYl/ykSyB7iZsuR6Q2yc0Mbfdqn04D3bPGIZdu4ctu6MbI30O9RsE7T5fT3P+Z62YH7xxRQgmyiIiEVk4GtO9b8XaNWkHPkZAx3d2BFwkGX6uX70t+kq+btapZSy1yKB/ys0NboMunUUto3RO2KEE+wYFd8OXDcNJQd/MtmOo3dkmyCnXVmBJkEREJnfwdkJ9T/vhjfylXue31H74ES1a66/rYtK37vWVXlyyrm7XUJr4eD+HoYg1uPuSsdNWMKGn2H+HgbrjgT6VPaxhoicNdnY+CvOCfqw5TgiwiIqGT4yvQVYkWZHAtyA1bqJq1BE9Wuht/7C/pEteyvHtTeGISqSrfFE/h6GINbojC/u2wZ3N4zh+Jtq+A7/8JqTdU/qZwTXUf7p7XzwnN+eooJcgiIhI62Rnuuaw5kEuq19AlKys+gMP7gxeXRKf9O10S7Bt/7KNu1lLb5HlTPIWjizUc+wxpHLJjLcy8Bxo0hbPvDd15Ow2E+s3U66qGlCCLiEjoZC+F5gnQOL7y+6RcBYfzYdWM4MUl0SnLG39csgU5vrubp1vdrKW2yF0HzTpC/SbhOX/7PhDbQPMh+6z6GNbNcslxk9ahO29sHJx0puZDriElyCIiEjo5GZVvPfbpeiY07wxLpgUnJoleWelgYqBj/xPXJV8KW76HPVtCH5dIVeWuDd/4Y4C4+tAx5dhNp2hWeAg++S20PRXSbgz9+bsPdz0K9Ler2pQgi4hIaBQecvMaV3UsVkwM9L0S1n7husSKBMrWhe5LbIOmJ67r7VWcXfFBaGMSqY68zPB1r/ZJSHXz1kf7rANz/wa7NsDoP1Y8nWEwJGocck0pQRYRkdDYsRKKC6veggyum7Utgoy3Ax+XRCdrXQtyp4Glr29zsnuvqpu1RLoDu6EgN3wFunwS0uBIAWxfHt44wmnvNpjzBPQaAz3ODk8M7ZKgcRt1s64BJcgiIhIavgJdla1g7a99kps7Wd2sJVB2b3RJRcnxx/6SLoFN89yXXpFI5SvQFc4u1nDssxTNhbq+eBCKj8Coh8MXQ0wMJJ7lCnVp2q1qUYIsIiKhkZMB9RpXvxtgyjjIWgC5mYGNS6LT0QJdqWVvk3QJYGHlhyEJSaRafH8T48PcghzfHRq1cn+no9GWBbB4Kpzx8/B3d08cDvu2wc414Y2jllKCLCIioZG91HX9iomt3v59rgSM5kSWwMhKd1V32yeXvU3bXm6MsrpZSyTLzQQMtOoW3jiMcTecorFQV3ExzPgNNO0AZ/063NH4zYesbtbVoQRZRESCz1qXIFe1QJe/Fgmu29iSaeo2JjWXtdBV3a2oiE7SJbDxv5C/PTRxiVRVXia07OLmjQ+3hDTYvgIO7Qt3JKG15HV30+28B6BBs3BHA60SoUVXJcjVFLQE2RjT0BjzvTFmsTFmmTHmQW95ojHmO2PMWmPMNGNMfW95A+/3td76bn7HusdbvsoYMypYMYuISJDszYKDu6tXoMtfylWQty46WygkcIoKXbXd8rpX+yRdArZY3awlcuWuDX/3ap+EVMDC1kXhjiR0Du2Dzx9wNwdSrgp3NI4xkDgM1n8NxUXhjqbWCWYL8iHgHGttP6A/MNoYczrwJ+BJa+3JwC7gJm/7m4Bd3vInve0wxiQBVwPJwGjg78aYavbPExGRsKhJgS5/vS9y3WJVrEtqYucqV223rArW/tolueJHy94NflwiVWUt5K4LfwVrH99Np2gahzznCcjPgQv+5ApkRYruw92N6ewl4Y6k1gnav6J18r1f63kPC5wDTPeWvwxc6v18ifc73vpzjTHGW/66tfaQtXY9sBYYHKy4RUQkCHKWuufyxntWRsMW0OsCyHgLio7UPC6JTr4qu5VpQTbGtSJv+EbzcEvkKciFQ3sipwW5SWs3FjpaKllvXQTz/g79roXOaeGO5niJw9yz5kOusqDe5jDGxBpjFgHbgc+ATGC3tdY3g/gWIMH7OQHYDOCt3wO09l9eyj4iIlIbZGe4L02BGJuVchUU7ITMWTU/lkSnrIXuZktlK80mXerm4V75UXDjEqmq3AiZ4slfQhpsiYIEOW8dvHolNG0P5z8Y7mhO1KyDKzKo+ZCrLKgJsrW2yFrbH+iMa/U9NVjnMsbcYoxZYIxZsGPHjmCdRkREqiMno+bdq31OPs9NJbL0jcAcT6JPVjp0GlD57pAd+rqiN6pmLZEmd617jpQu1uBaUvdthb1bwx1J8OTvgFeugOJCuO4taNou3BGVLnEYbJoLhYfDHUmtEpKO8tba3cAs4AygpTEmzlvVGcjyfs4CugB461sAuf7LS9nH/xzPWWvTrLVpbdu2Dcp1iIhINRze71o52gcoQY6rD8mXwYoPo69SqtTckQOQs6xy3at9fN2s138FBXnBi02kqvIywcRCy67hjuSYo+OQ62gr8qF8eG0c7N0G177hpoOLVInDXb2FLfPDHUmtEswq1m2NMS29nxsB5wMrcInyld5mPwF8t2Pf937HW/+ltdZ6y6/2qlwnAj2B74MVt4iIBFjOcsDWbIqnklKugsID6vIqVZe91HWXrkqCDC5BLi6EVTOCE5dIdeRmuuErFU1XFkodUiAmrm4myEVH4M2fwLbFMO4l6BLhZZG6DQUTo3HIVRTMFuSOwCxjzBJgPvCZtfZD4C7gV8aYtbgxxi94278AtPaW/wq4G8Bauwx4A1gOzAR+bq1VvXIRkdriaIGuACbIXU5zLSZL1M1aqsj3pb0yFaz9dRrg5hVVN2uJJLmZkdW9Gtx8zO37wJY6VsnaWnj/dlj7OYx9yhWMjHSNWkLH/poPuYriKt6keqy1S4ABpSxfRylVqK21B4FxZRzrEeCRQMcoIiIhkJ0BDVoEtgugMdB3PHzzF9iXA83aB+7YUrdlpUOzTtC8Y9X2MwaSLobvnoWDe1yRL5FwstYViuo2NNyRnKhzGiye5ubgjakjs7N+8SAsngpn3wupP6l4+0jRfTh8+4zrGt6gabijqRUiaLIuERGpk3Iy3PROxgT2uCnjwRa7KZ9EKitrISRUsfXYJ+lSKD4Cq2YGNiaR6tiXDUf2R14LMrghDIf3wc7V4Y4kMOZNgW+ehLQbYdjkcEdTNYnD3PCQTXPDHUmtoQRZRESCp7jYFUQK5Phjn7a9oGM/VbOWyivIc0WNqpsgJ6RC8wRY/m5g4xKpjjzfFE+RmCB7cwLXhXHIGW/DzLvh1LFw4ROBv9kbbF1Oh9j66mZdBUqQRSqy6DX462A4sDvckYjUPrvWw+H8wE3xVFLKVbD1B9hRR1opJLi2/uCeq1qgyycmBnpfDGu/gIN7AxeXSHX4pniKj8AEufXJbmhNbR+HvH4OvHOrq3txxfO1s7t4/cYufs2HXGlKkEUqsuxd2LkKvnos3JGI1D45Ge45kAW6/PW5wlXoVCuyVMbWhe650wklUiov+VIoOgRrPg1MTCLVlZvpWgZbdA53JCeKiYGEAbW7BTl7Kbz+I4jvDtdMhXqNwh1R9SUOd9ejaeoqRQmySHmKi2HzPIipB98/CztWhTsikdolO8MlsO16B+f4zTpA9xGumrW1wTmH1B1ZC6F1z5oV2Oo8GJp2UDdrCb+8dS55i9RWzYRUN8TmcEG4I6m6XRvhlSuhflO47i1oHB/uiGomcRhgNd1TJSlBFinP9uWuWul590O9Jm4Mir6Ei1ReToZLSIJ5573veNi9ETZ/H7xzSO1nrWvNqm73ap+YGFfNes1nriqsSLjkro3M7tU+CWluzvHsJeGOpGoK8uCVK6DwAFz/dmS20FdVwkCX7CtBrhQlyCLl8VX8630RnP1byPwSVn0c3phEapPsjOAU6PLXeyzENYIl04J7Hqnd9m6F/JyaJ8gASZdA4UFY+1nNjyVSHcXFkLceWncPdyRl833WatM45MMF8Np42L0Jrnk9eL2fQi22Hpw0RIW6KkkJskh5Nn7r5stseRIMugnangoz74EjB8MdmUjkO7Ab9mwK3vhjnwbN4NQxsOxtKDwc3HNJ7eUbC1ndCtb+up4BTdrC8vdqfiyR6ti7xY2Fj+QW5GbtoUWX2jMOuagQpt/g4r3yBTjpzHBHFFjdh7teB3uywh1JxFOCLFIWa10L8klnuJL+sfVg9B9dV865fw13dCKRL2eZe+6QEvxzpYyHA7sg84vgn0tqp6x0V08iEDdsYmJdz6LVn9bO8ZXBUJAHX/8ZZv4W9uWEO5q6L9c3xdPJ4Y2jIgkDIasWtCBbCx/+ElbPdFM59b4o3BEFXuIw96xW5AopQRYpy64NsG+baynw6XG2+6P59Z91B06kItlL3XOwu1gD9DgHGrdWN2sp29aF7r1Yr2Fgjpd0CRzZr5syeevg48nwZDJ88ZAraPnXQfD9P6G4KNzR1V2+KZ4icQ5kfwlprrty/o5wR1K+WX+AH/4Dw37jegzWRe2S3f+Tmu6pQkqQRcriG39csovNyIfdf/qf3x/6mERqk5yl0LgNNG0f/HPF1nNTPq2aoflp5UTFxZD1A3QKQPdqn5OGQqN4NxVgNNo8H6ZdD8+kwoKXIPly+Olc+Pn3rtXw4zvhn2fDllrSvba2yVsH9RpDs47hjqR8vnHIkdzNev7zMOcxGHC9qzdTV8XEuFbk9XNUcLYCSpBFyrLxW2jYEtqWKNDQqhsM+R9Y+iZsnBuW0ERqBV+BLmNCc76+413hpBUfhOZ8UnvkroHD+wJToMsnNs4ViFs9M3rqUhQXuc/XC6PghfPcF+0hv4RfLoVL/wbtk1yL5vXvwJUvua7Wz58LH97hhkBI4ORmuvHHofr7Wl2d+rup/iI1QV7xAXx0J5wyGsY+FfmvZ00lDod9W4/1QJBSKUEWKcumudD1dHfHraShd0DzzjBjsrqQiZSmqBC2rwh+gS5/ndOgVaK6WcuJsha650AmyOC6WR/OdzMc1GWHC1wr21/TYNp1bvjRBY/BHcvcNIjNS7RiGgN9LodJ8+H0n0H6y/BMGiyaqparQMldG9kVrH3qN4F2SZE5DnnjXJh+k/u/48qX3E2vus43Dnnd7LCGEemUIIuUJn+7+8/Hf/yxv/qNYeRDbozlwn+HNjaR2iB3rauw2qFv6M5pDKRc5Vq19m4N3Xkl8mWluzlA2/QM7HETh7ueRnW1mnX+dvjyETe++KNfQ6NWMO5fcPtCOO1WaNC0/P0bNofRf4Bbv4L47vDubfDShZCzPCTh11lFha5gaCRXsPaXkOo+g5F0c2T7Cph6FbTsCtdMc9/rokF8d1dZXIW6yqUEWaQ0ZY0/9pd8uZtT7ouH1HVMpKScDPccyhZkcNWssZDxVmjPK5EtKx06DXDVpwMpth6cOtaNfS88FNhjh9OOVfD+7fBkH5jzuLtZfMNMmPgFJF9W9Za2Dn3hxk/g4mdgxwp49iz49H/hUH5w4q/rdm+E4sLIr2Dtk5AKB/ccq7wdbnu2wCtXQFwjuO4taNI63BGFjjHuxt76r11tBimVEmSR0mya5/5wduxf9jbGwAV/goO7YdajoYtNpDbIXgqx9aHNKaE9b+se7suYulmLT+Ehd8MmEPMflybpEji0p/ZXhrXWfWl+dTz8bTAseQMG/AgmLYBrXjs25WF1xcTAwB/DpHTofy18+7Q7z/L3I6tlsTbIW+eeI72CtU/nNPccCeOQD+xyyfGhfXDddGh1UrgjCr3uw9131+wl4Y4kYilBFinNxm/dH/S4+uVv16EvpN7gxmapy5jIMdlLoW2vij9DwZBylTu/PpMCLjkuOhzYCtb+ug+HBi1qbzfroiOwdDo8NwJeHuuSmBH3uPHFY5+ENgFupWzS2rUk3/SZqwL+xvXw6rhjSZ9UzFdgqbZ0sW57KtRrEv5xyEcOwNRr3Hvt6ldDOwQokmg+5AopQRYp6dA+d1etrPHHJZ3zO2jQDGbepbvgIj45GdA+TF8+ki8HEwtL3wjP+SWyBKtAl09cA+h1Aaz80CWbtcWhfTD3b/D0AHjrJldsbOxTcEcGjLgbmrQJ7vm7DIZbZsOoR92wpr+fAV89Vre6qgdLbiY0aB78f6NAiYl1QxzC2YJcXARvTXQ9BC979liSGI2adYA2vWp/r5cgUoIsUtLm78EWuwrWldE43iXJ6+doehkRgPwdkJ/jpngKh6Ztocc5rlVMY6wkayE0aQctOgfvHEmXuC6L6+cE7xyBsifLjf/9SxJ88luvSNHr8PP5kHYD1GsUulhi4+CMn7lq170uhFmPuES5rlcFr6m8TNe9ujZNSZQw0PXs2b8z9Oe21s3LvfJDNzSuz+WhjyHSdB/ubkwVHg53JBFJCbJISZvmujn7ugyu/D6pN0C7ZPjkXteFRySa5Sx1z6Eu0OUv5SrYs/lYwT2JXlnp7st5MJOJHue4KtnL3w3eOWpq2xJ4+xb4vxSY+1c4+Ty4+Uu44WPXAl7alIah0rwTjHvJzZ8M8J/L4M0JqkZflty1tad7tU/X091Qh8d7wOM94eWL4OPJbojahm+CmzjPeQIWvOjm7D7t1uCdpzZJHA5HCsLf7T1CRcGEXyJVtHEudEhx3aYrKzbO3ZV8eSz892kYcVfw4hOJdNleBetwju869UI35m3JNOg2JHxxSHgd3As7V0PfK4N7nnoN4ZTRsOJDGPNkZM2num0xfHafm/e0XhMYdDOcfhu06hbuyE7U4xz42Vz3/+jXT8Caz+Ds38LgWyPrNQ2nwkOuCnO/a8IdSdWccgFc9zbkLHNV0nesdPNiH953bJvGrd145aOPXu65abvq3+BKfxlmPexer/MeCMSV1A3dhrrGoHVflT9jS5TSXxsRf4WH3N20tBurvm/iWZB0KXzzpKvQ2bJL4OMTqQ1yMqBZJzf8IFzqN4HeY12L3oWPu3GiEn22LQJs8CpY+0u6BDKmw8ZvoPuI4J+vMvLWw78vhZg4lxykTnBzGUeyuAYwfLK7qTHjN64b+KLXYMxfoOtp4Y4u/HZtcMPAassUTz4xMXDyue7hYy3szXLJsi9p3rHKDY85tOfYdg1bHkuY2/U+ljg361h+4rxqBnz4S+hxrisMV5u6pAdbo5bQsZ8r1HX2PeGOJuIoQRbxt3URFB6sfIGukkb+HlZ/Ap/9L4z7V0BDE6k1spdGRnXQlPGuBXnNp9D7onBHI+HgKwoUrArW/nqe71pol78XGQnywb0w9WqXTN04s/ZMCeQTnwjXvuHGjc64C14cCQOuh/MejK55a0vyzSVc27pYl8YYVxugRWfX5d/HWlfHYvuK4xPnFe/DwpePbdegkDoujAAAIABJREFUuZcs94K2vY8l0S06w5b58OYNLgkc/283Z7kcL3G4G25xeL+7qSxHKUEW8bfpW/dc3QS5ZVcYegfM/gOk3eRalUWiSeEh16W11wXhjgQSR7jiTEumKUGOVlnp0CoxNL0Z6jWCU0a6Yo0XPuEq94ZLcZGrTJ271nVrrW3JsY8x7rPb/WyY85irur3yQ5ckD7g+vOOmw8U3xVPr7uGNI5iMcZWWm3WAHmcfW26tG6u8Y6XfY5VrmPjhlWPb1W/qbgw17wjXvgkNmob+GmqD7sPhv0+5oYU9z6t4+yiiBFnE38a50Lqnq4JbXUN+4f5Qz7wbbvlK46YkuuxYCcWF4S3Q5RMb57ppzn8eDux2XcokumT9UPkZCQIh6RJY9o4rDtdtaOjOW9Jn97meE2P+4r4E13YNmsL5D7lxpB/9Gj7w/p8d+5fI6K0SSnmZbv7oSO8qHwzGuO9nTdue2ACxPxd2+rU2H9jl5vOuyfe5uq7L6RBbH9bPVoJcQhTeehMpQ3ExbJ4HJ1Wz9dinXiMY9bAbh5n+UmBiE6ktIqFAl7++41zl1OXvhTsSCbV92bB3S2jGH/ucfD7ENQrv+23hf1y3ycG3wKCbwhdHMLTrDRM+gkunQN46eP58lwxFk9zM2jf+OBSatHbFptJudEVTL3/OddOXstVvDJ0Haz7kUihBFvHZvhwO7oGuAajm1/tiNwn9rEegIK/mxxOpLXIyXIIQHyHd/zoNcL1ClrwR7kgk1LIWuueE1NCds0FT1xKz/P3wzMG94b/w4R2uS/KoR0N//lAwBvpfA7d9477gvzUxuuZyzc2svV3mJfJ0H+7qhui76nGUIIv4+OZLrWkLMrj/wEf/yRVJmfVIzY8nUltkL4X2SeEdf+nPGDcn8sZvYPfmcEcjobR1IZhYN21fKCVdCvnZsOX70J531wZ443o3fdO4l+r+8J7mHeHiv0L2EjeNTzQ4XAD7ttaNAl0SGRKHAxY2fB3uSCJKlRNkY0yMMaZ5MIIRCauN37qpaVqeFJjjtU+CQRPd5PTZSwNzTJFIZq1rQY6E8cf+fHPgZkwPbxwSWlnp0C7JtTKGUs+RENsAlr0bunMe3AuvXe2Kc107LXrGp556IaTe4OZNXj8n3NEEX94696wWZAmUhIGuqJm6WR+nUgmyMeY1Y0xzY0wTIANYboyZHNzQRELIWteCfNIZgZ0n7+x73Px9M+525xCpy/ZmucIokTL+2Cc+EbqcBoun6XMYLax1XaxDOf7Yp2FzN9frihB1sy4uct2Md65209lEW/I06hF3ze/c5v7+1GV53hRP0fZvLMETWw9OGuLmQ5ajKtuCnGSt3QtcCswAEoHrgxaVSKjt2gD7tlV/eqeyNGoF5/6v6975/+zdd3iUVfbA8e9NhSS00CEFRHqHSJFqByyIiFgQda2/ta661nXtrmWta9m1CxaKiKAUFUURCWJCDSAQSkISkkBCgPTMzP39cScQICEJzMw75XyeJ89MZibve5SUOe+559yNc117bCG8jbcN6KqqzxWwd7OpcAv/l78DSgs8239cVY/x5oJR5T7M7rTkcdj2HYx70T8mVtdXWCRMfN/sm/vNPf59EaxyiydvmfEg/EPHkeZ760Cm1ZF4jbomyKFKqVBMgjxfa10B+PFvIBFwDvcfu2BA17EGXGcShu8fM/1DQvirHGcrQeue1sZRnR4TIChEhnUFisMDuiyoIAN0GQNBobDJzcus13wKK/7jnFh9k3vP5c3a9YezHjX/v9d9YXU07pO3A6LaQHgjqyMR/qTywlogtCnUUV0T5P8Bu4BIYJlSKh446K6ghPC4tBVmKXTL7q4/dlAwjH3JbDfy22uuP74Q3iI7xQwI8sY3b5HNzRY8G740S1L9ycE98MH58OvLVkfiPTKTzTR1d/xOr4uGTaHT2WaatbsqmmkrTMXUnydW18ewuyF+OCz8+5FeXX+TLxOshRu06gkRzWWZdRV1SpC11m9ordtrrcdpIw04y82xCeE56YkQNwSC3DTYPX4o9Loclr9mlnML4Y+8cUBXVX2uMBNg/elNwP40+GgM7P4dlv7LbAEjzATrdv2sneTcYzwcSIesNa4/9v5dMHMKNIsPjInVdREUDBP+ayaXf3Ur2G1WR+R6eamyvFq4XlCQWWa94xf/blGoh7oO6WqtlPpAKbXI+XkP4Dq3RiaEpxTmmj86ru4/PtZ5T5k/4N//w73nEcIK5UUmOfPG/uNKXcdCZCv46hb3JC2etm8bfDTWDCa68gsIaQDfPWp1VNazV8CeddDOouXVlbqONcv6N81z7XFLD8IXV4HDBlcF0MTqumgaCxe/arbY+vXfVkfjWqUHoWivVJCFe3QcaS4gy0VWoO5LrD8GvgPaOT/fCtxzoi9QSsUqpZYqpTYppTYqpe52Pv6EUipTKbXW+TGuytc8rJRKVUptUUpdUOXxMc7HUpVSD9XnP1CIWrmz/7iqJu1hxH2w+RvY8bN7zxVIyosgIxlWTzPTwj++CN4cdGRglPCMnE2A9u4EObQh3LDQLL396EJIXWJ1RCcvO8Ukx7YyuH6B2e5m5H2wdRFs/8nq6KyVuwlspdb1H1eKiDZ7jG6a57qqTOXE6r1bYNIn0OJ01xzXn/SaCH2uhF9egN0e3ovanQ5PsJZ/c+EGHSv7kH+2NAxvUdcEuYXWehbgANBa24DamrhswH1a6x7AEOB2Z+UZ4FWtdT/nx0I4XJW+EugJjAHeVkoFK6WCgbeAsUAP4KoqxxHi1KUlmjfMbfu5/1xD7zA9mose8s/lX+7ksJsrm5vmm6WkM6fAG/3hufbw/tkw/06TJFeUQHEezL4Oyg5ZHXXgODygy4uXWAO06Aw3/WCWKX4+GdbNsDqi+stIho8vNEOgblh05KLEkL9Cs46w+JHA/v1yeECXRROsq+oxHvbvhOwNrjle1YnVnaTTrUbjXoImseZiQqmfjMyprOxFSwVZuEH0aeZnRvZDBqCuTStFSqnmOCdXK6WGAAdO9AVa6z3AHuf9Q0qpzUD7E3zJeGCG1roM2KmUSgUGOZ9L1VrvcJ57hvO1m+oYuxAnlr4CYhIgJMz95wptABc8BzOuhqQPYPCt7j+nLyraBzkbTSUoJ8VUJ3M3g63EPK+CzC/zNr1NpaB1T2jdA5p2ML00O3+FaZfAt3+Dy95z7d7WonrZKRDeBJrGWR1J7Rq1gRsWmIssc28128OceZdvfJ/s+s0k9hHRcN18c8GtUkg4nP8MzLwGkj+CQTdbFqalMpOhYfTR/2+s0u0i83to09fQts+pHWvNZ2Zi9Rk3B/bE6rpo0Bgue9esslj0IEx4x+qITt3hBLmjtXEI/6SUqSJvWWD2b3fXTB4fUdcE+V5gPtBJKfUb0BK4vK4nUUp1APoDvwPDgDuUUlOBJEyVeT8meV5Z5csyOJJQ7z7m8cF1PbcQJ1R60FzZH3G/587ZdZyZOrr0WbMULLKF587tbSpKYd8WkwxXfuRuMglLpYgWJgFO+ItJglv1gJbdICyi5uN2HAGjH4Glz0CHETBQRia4XU6K+XfyhSQToEETuOZLmHsb/PBPMwn6gue8+01B6hKYMcX0WU6dB43bHf+abheaXrLK3y8R0Z6P02qZq83yam/4XoxsDh2Gw8av4ezHTj6mtET45m44bTSMed6VEfqvuCEw8u9mqXXn86DXZVZHdGryt5sKX2hDqyMR/qrjSFj7KWSvN0MOA1idEmSt9Wql1CigK6CALc69kGullIoC5gD3aK0PKqXeAZ7GVKOfBl4G/nIywR9znluAWwDi4nyggiG8Q8Yq0A4zZdpTlIKxL8A7Z8JPT8PFr3vu3FY6kAF71juTYGcynLcdtLNbI6SBSXxPP9ckwa17mo+oVid3vhH3QtpvsOgBs9SyjZcv/fVlDof59+x3tdWR1E9IOEz8wFSUV74Nhdkw4X/mcW+z+Vv48gZo2RWmzIWoltW/TimTQP13OPz8vFmKG0jKi2DvZuh+kdWRHNFjPCy411z8O5k9wvenmVUBzeJh0scysbo+Rj4AqT/Ct/dA7CBoEmN1RCcvb7tMsBbu1XGkud25LOAT5LpOsZ4ENNRabwQuBWYqpWqdfqGUCsUkx59prb8C0FrnaK3tWmsH8B5HllFnArFVvjzG+VhNjx9Fa/2u1jpBa53QsmUNbxyEOFZaotkSImZQ7a91pZZdYdCtkPyJmbbq7zbNh1d7wYyrTFU3aw0072yGlk36GO5Igkey4NZf4NK34cw7TH/dySbHYCaGX/ae2d9a+pHdq2AXlBd6f/9xdYKCTOX4vKdh41z4dCKUnrCDyPPWz4JZU6FtX7jum5qT40qte8LA6+GP9yH3T4+E6DX2rDMXPa2eYF1V94sBdXLTrMsOwRdXysTqkxUcAhPfMzMs5t7m23ug56XKBGvhXo3bQouu/rUV4kmq61qyx5x9xMOBc4APgBM2dCillPN1m7XWr1R5vG2Vl00AKkfNzgeuVEqFK6U6Ap2BVcAfQGelVEelVBhmkNf8OsYtxImlJ5q+sPAoz5971ANmY/aFD/j3vnMOO/z4lKkO37gEHs6Au9fBVZ/D2Y9CzwlmcFJQsOvPHdUSJr4P+TtMH6A//3+2UuXEcF+t0isFw+6CCe+a3wkfjTNLrr1B0kdmW6r4M+HauXVPkM56FMKi4LtHAuv7PjPZ3Fo9wbqqqFYQP6z+CbJMrHaN6NPMqq1dv5oebl9UnA+lBTKgS7jfaaMgbQXYyq2OxFJ1TZArL7ldCLyntV4A1DbRaBhwLXD2MVs6vaiU2qCUWg+cBfwNwFmdnoUZvrUYuN1ZabYBd2C2mdoMzHK+VohTYyszb6bi3Ly9U00aNoVzH4fdK2HDl9bE4AkpcyBvG5z1MMSeAeGNPHv+yn7kDbPNlGvhetkbzOC0Vj6+wUDfyXD1LMjfCR+cb/YZtlLiW2Zp6OnnwjWz6/ezE9kCRj8I23+Ebd+7L0Zvk7kamsSd2uoTd+h5Kez9s34V/SVPwNbFJrmTidWnpt81Zqn7T89A1lqro6m/PNniSXhIx5FQUQyZSVZHYqm6JsiZSqn/AZOBhUqp8Nq+Vmu9XGuttNZ9qm7ppLW+Vmvd2/n4Jc5p15Vf86zWupPWuqvWelGVxxdqrbs4n3v2ZP5DhThO1lqzV6Yn+4+P1W8KtOtvhgSVFVoXh7vYbWZASute0O1i6+IYcZ8ZjLboAdkf2R1yUsySeX8YHnP6OWbCta3EJMm7//B8DFrDLy+a6m/3S+DKz0/u/+0ZN5t/l+8eCZxqQGYytO9vdRTH63YRoGBzHRfArfkMVrxhplUH6jRyV1IKLnoNIluaqnx5sdUR1c/hPZClgizcrMNwc8F75zKrI7FUXRPkKzAV3Au01gVANPB3t0UlhCekrzC3cRYmyEFBMPZFOJQFy1+p/fW+JuVL0zc16kFrpwMHBUk/sjtlp/ju8urqtOsPN35vJl1/cjFsWey5c2tt9rpd+iz0vQou/+jkt6ALCTP91Xmp8Md7ro3TGxXtg4I079j/+FiN25qpynVZZl05sbrjKJlY7UoR0TDhv+bn4ftHrY6mfvJSTdLSNN7qSIS/a9jMzLsI8P2Q6/SOVWtd7ByydUApFQeEAgE2+UP4nbREaNHF+m2WYgeZvXxX/Mf0yvoLu81UwVr3dlZPLBbVEi7/QPqRXa2kAA6k++aArhOJPg1u/MEM1JtxtWeW5zscsPB++O11SLgRxr996hOLu5xvlmj//IJJIP1Z1hpz640JMpglvjkpsC+15tdUnVh9xScQHOq5+ALBaaPMEMikD2HLotpf7y3ytpvk+GQvlglRHx1HQcYfZleAAFXXKdaXKKW2ATuBX5y3PvSbRYhjOBym9zduiNWRGOc+AcFh8N0/rI7EdTbMNsvCRltcPa6qw3A4S/qRXSrHORKiTW9r43CHqJZw/QKz9+z8O+GXl9x3YcVug3m3m8nTZ94FF77sup+bC54zU8Z/esY1x/NWmcmAMtUPb9Td2Way6evqn5eJ1Z5x9mPm99W82+FQjtXR1E3+dlleLTzntFHgqDCFpABV17++TwNDgK1a647AucBKt0UlhLvlbjJbuVg1oOtYjdvCyPthywKzZ6Ovs9tg2YvmTYg3VI+rGi79yC6V4/x/6G8V5ErhUXD1TLPKY+kzZj9bV28VYyuHOX+BdZ+b6dPnPWV6Jl2lZVfTx7r6E//+ns9MNtPyPT0IsK6axEDMGdUvs3bYYc7NMrHaEyr3Py8vgnl/9f7VRFo790CWBFl4SOwQU7QJ4O2e6pogV2it84AgpVSQ1nopkODGuIRwr3TnVTErB3Qda8hfzbLOxQ/5/kCdDbPMUubRD7v2jb4rSD+ya2WvN9uVNWpjdSTuExxqeheH3WOWZs6aChUlrjl2RYlZUrtpHpz/rNn+zR0/M6MeND3Vix/y/oTgZGhtJlh76/LqSj3Gm5+ZY9tpfnwSti6SidWe0rIrnP8MpC6BVe9aHc2JFeaaFSBSQRaeEhYBMYMkQa6DAqVUFLAM+Ewp9ToQuAvThe9LWwGN2nnXwIuQcDOQZd9WWPU/q6M5eZW9x236QNdxVkdTPelHdp3sFLNSwNsuhLiaUnDekzDmBfhzAUy71OxNeirKDsFnk2DbD2bC7pl3uCbW6kREm+r0rl/hz2/ddx6rFKRD8T7vnGBdVfdLzO2mKtOs13x2pO9cJlZ7zhk3QecL4PvHIHez1dHUTCZYCyucNgr2rD/1v3M+6oQJslLqdKXUMGA8UIzZs3gxkAfc6f7whHADrU0FOX6o972p73IBdBkDPz8Ph7KtjubkrJ8J+3d6Z/W4qqP6kT+xOhrfZLeZN5b+ury6OkNug8s/hKzV8NFYOJBxcscp2Q/TJ5iLdRP+Bwk3uDbO6gy8AVp2h+//ARWl7j+fJ2Umm1tvryA3izdT0iuXWaevNHtddxxlqsfCc5SC8W9Cg8Zm6ydbmdURVa9yD2RZYi08qeMoQJuLqgGotgrya8BBrXWR1tqhtbZprT8B5gJPuD06Idxh/y44tMfa7Z1OZMy/wF5hrmr7GrsNlr1khuR0HWt1NLWr7Ede+ABkb7A6Gt+Tlwr2Mv8c0HUivS6DKXPgYBa8fx7kbKrf1xftM9tHZa01k4r7TnZPnMcKDjG/X/bvgpVve+acnpK1GoLDoVVPqyOpXY9LTby7lsOMa6BJrEystkpUKzMtPicFfnzK6miql5cKQaHm+0QIT2k/ADqONL3IAai2BLm11vq4d43Oxzq4JSIh3O1w/7GXDOg6VvRpMOxu08e76zero6mf9TN8o3pcqbIfuWEzmH299CPXl78P6DqRjiPhhoWgHfDRmLr/rB7Mgo/Gwb5tcNWMI5ONPaXTWab14deXfXeVSnUyV5sLNb6wDU4P5zLr6RPMpNirZWK1pbqcD2fcDIlvwvafrI7mePnbIbrjqW/5JkR9BIfCdd/4RrHDDWpLkJue4LmGrgxECI9JW2EGNLXsbnUkNRv+N3O1eNEDpirrC+wVzupxP7NM3FdU7Uf+5h7pR66P7A2mstGii9WRWKNNb7jxe4hsZZKd6qYTV7U/zSzLPphpKtCdz/VMnMc6/xmznPTHp605v6s57KYa7+3LqytFn2ZmNDjsMOljaNHZ6ojE+U9Di64w9/+8r+cyb4csrxbCw2pLkJOUUsdNjFBK3QQkuyckIdwsPdHsf+wte/NWJyzC7F2ak2Km5vqCdTPM0k1fqR5XVdmPnPKl9CPXR06K2VbHF6p27tIs3iTJbfvArOtg1XvVv27fNvhwDJQUwNT55nvOKs07mV7qtZ9B1hrr4nCVvVugosh3EmQwfefXzYdOZ1sdiQAIbQgT34fiPLPnubdcKHU4ZA9kISxQW4ZwD3CDUupnpdTLzo9fgBuBu90fnhAuVphr+nm8tf+4qu4Xm/7Yn56Bwr1WR3NildXjdv3NoDFfJP3I9Ze9AdoE4PLqY0VEm6S3ywWw8H7Ty1j1DXZ2iqkcOyrg+gUQ4wWJ3Mi/m+25FvnBtk+HB3QNsDaO+mjdw9qLJOJ4bfvAOf80U97XTLc6GuNQFthKJUEWwsNOmCBrrXO01mcCTwK7nB9Paq2Haq39qHlJBAxv7z+uSikY9xJUFMOPT1gdzYmt+wIK0nyzelxJ+pHrp3AvFOYE3oCumoRFwOTPoP+1pr933h3mwlFGMnx8oRl0csMi77mg0KCJSQZ2r4SNX1kdzanJTIbwJrIMVZy6oXeY+QKLHjwyPdpKMsFaCEvUaY2p1nqp1vo/zg8vnGAgRB2lJUJIQ9Mn6wtadIahf4U1n0JGktXRVM9W7qweD4DO51sdzamRfuS6y3FW2QNxQFdNgkPgkv/AqAdh7admr+Rpl0DDpiY59rZe0/5TzAWO7/8J5cVWR3Pyslab/Y+9uW1G+IagILj0v+aC1pybzEUuK8keyEJYQv6aiMCSvgJiEnyrZ3Lk36FRW1hwnxnq4m3WfQEF6b5dPa5K+pHrJts5wVoqyEdTynz/XPiK+X3TuJ1JjpvFWx3Z8YKCYcwLcDADVvzH6mhOTkUJ5Gw0F+iEcIUm7eGSN8yFl5+ftzaWvO0Q0gAatbM2DiECjCTIInCUHjQ9k77Qf1xVeCMzdXbPWlg9zepojmYrh2X/NsNxOp9ndTSuI/3ItctJMW/aIqKtjsQ7nXEj/N8KuGmJSZK9VYdh0GM8/PYaHMi0Opr6y94ADptvDegS3q/HeOg3BZa/Yna+sEredrO8WlZHCOFR8hMnAkfGKrNnabyPJcgAvSZC/HD48Unv2oJi3edwwI+qx5WkH7l22Sne00/rrVp1N72+3u68p83qlCVPWB1J/WWuNreSIAtXG/s8NI2Hr26F0gPWxJC/HZqfZs25hQhgkiCLwJGWCCoYYgZZHUn9KQXjXjRV8J+esToa43D1OAFOt2g/V3eSfuSa2cpg3xbpP/YXzeLhzDtgwyzY/YfV0dRPZrJZydC4rdWRCH8T3shs/XQwExbc7/nz222Qv1MGdAlhAUmQReBITzTbOIRHWR3JyWndEwbdYvZFzlprdTRmD9UDu/2velyV9CNXb++fZlmr9B/7j+H3QlQbWPyg2XvVV2Qm+9b2TsK3xCSYoXsbZsH62Z4994HdZmu45qd79rxCCEmQRYCwlZkp0HE+sL3TiYx+CCJbwMK/W/sm1lZutrKJOQNOP8e6ODxB+pGPJwO6/E94FJz7hEk418+0Opq6KdlvlqBKgizcacR9EDsY5t9phnZ5quVGJlgLYRlJkEVgyFoD9jLf7D+uqmFTOPdJ00+9foZ1caz91Fk9fsh/q8eVpB/5eDkpZru0aOmN8yt9Jpte3iVPQFmh1dHULmuNuZX+Y+FOwSEw6RMziPLnf8Hr/eD3/5kLxe6Ut8PcyhJrITxOEmQRGCqnUPraBOvq9L3K9FH/8E9rBofYymDZyyaGTn5ePa4k/chHy94ArXuYbYKE/wgKgjHPQ2E2LH/V6mhql5lsbn1lX3vhuxq3hcnT4aafzPC9RQ/Amwlm2bW7VnPlpUJYFES1cs/xhRA1kgRZBIb0RGjRxSxP9nVBQTDuJSjaB0v/5fnzr/nU7JsaCNXjqqQf2dDaVJBlQJd/ih0EvSeZfZH3p1kdzYllroHmnc3KGiE8IWYgXPcNTJkD4Y3hq5vg3ZGwbYnrL5zmbzfLqwPp76wQXkISZOH/HHZI/90/qseV2vWDhBtg1buQs9Fz57WVmd7j2MHQ6WzPnddbDL/P/HcHcj/ywSzT+yn9x/7r3CdABZlVKt5Ka8hMkuXVwvOUMjs33LoMLnvf7C7x2UT45GIz68RVKvdAFkJ4nCTIwv/lboKyAxDv4wO6jnX2Y9CgsUnWPLXkd/U0s+VFoFWPKwUFwYR3ISI6cPuRKy8MSAXZfzWJgeH3wKavYddvVkdTvYNZUJgjA7qEdYKCoM8kuCMJxr5kpvu/fw7MnAJ7t57asW3lUJAmA7qEsIgkyML/pa80t/5UQQaTpJ3zOKQth5Q57j+frQx+fQVih5ipzoEqqiVMDOB+5JzKBLmntXEI9zrzLmgc49z2yW51NMfLWm1upYIsrBYSBoNvgbvWwOhHYPtSeHuImXp9MOvkjlmQBtohWzwJYRFJkP2dwwH2CqujsFbaCmjcHprGWR2J6w2YagbUfP8P91czV0+DQ1mBWz2uqsMwOOvRwOxHzk6BZh3M6gXhv8Ii4LwnzYqBNZ9aHc3xMpMhKFRWMgjvEd4IRj8Id62FQTfD2i/gjf7ww+OmLaU+8pxbPMkSayEsIQmyPyspgI/GwptnwIEMq6OxhtZmQFfcUP9M6oKCYdy/4dAeWPaS+85TUWqqx3FD4bTR7juPLxl+75F+5Nw/rY7Gc2RAV+DoNdGsGPnpadNn6U0yk80qhtAGVkcixNGiWsLYF+DOJOgxHn57HV7vC8tfg4qSuh1D9kAWwlKSIPur4nyYdol5E1G0Dz65BA5lWx2V5+3fZZJHX9//+ERiz4B+UyDx7VPve6qJVI+PV9mPHBYBC+8PjKXW5UWmsiEDugKDUjDmX1C0170X4OrL4YCstbK8Wni3Zh3gsnfhtl/NtohLHoc3Bpi/p3bbib82LxUaNjOtVEIIj5ME2R8V7oWPLzJVrSs/g2u/MsnxJ5eY5wJJeqK59bf+42Od+wSERpi9GV2dqFWUwvJXIO5M6DjKtcf2dVEtzbC0Xb96pg/carmbAS0V5EDSfgD0uwZWvnNk2afV8lKh7KAkyMI3tOkNU76E6xdA43amN/mdobD5m5r/XssEayEsJQmyvzm4Bz4eZwYIXT0Tulxg9rW8ZhYUpMP0S011OVCkrYAGTaFld6sjca+olnD2o7Bjqfmj60qrPzFVeKkeV2/g9dC2r2f6wK2Wvd7ctpEEOaCc808ICYfvH7M6EiMz2dzKBGvhSzoMh5uWwGRu1Vm2AAAgAElEQVRnT//MKfDBebBr+fGvzd8hy6uFsJAkyP7kQIZJjg9mmU3sO1WZNNxhOFz1BezbBtMnQOkB6+L0pPREiBtilsP6u4QboVVP+O4RKC92zTEre4/jh0HHka45pr8JCoZxL5uLCL+8YHU07pWdAuGNoWm81ZEIT2rUBkbcC1sWmAm9VstaDWFR0KKL1ZEIUT9KQfeL4f8S4ZL/wIFM+PhC+PTyI1voVZTAgd1SQRbCQgGQNQSI/bvMQK6ifXDtXDNl91idzoLJ0yFno/ll7O/VrsJcsxTP35dXVwoOgXEvmT+sy191zTGTP4bCbKke1yb2DOg/xSxD9eeBXZUDuuR7IfAMud1cGFn8cO39k+6WmQzt+puLU0L4ouAQswvFXavh3CchYxX8dwTMuRl2/GxeIxVkISwjCbI/2JcKH40zU0anzjNLqmvS5QKY9JF5g/H5ZNdVGr1RZf9x/JnWxuFJHYZB70lmamb+jlM7VkWJSbTjh0v1uC7OfRLCImHR3/1zYJfDYS6uyfLqwBTaAM5/BvZuhuSPrIvDVm4qbe36WxeDEK4S2hCG3wN3r4Nhd8Pm+fDFleY5SZCFsIwkyL4u90+zrNpWBtd/W7eerO4Xm8mK6Ykw4yqzjNYfpSVCSEOzT3AgOe9pCA41lZ5TUbV6LGoX2cIM7Nq5DDbOtToa1yvYBeWFMqArkHW/GDqMgKXP1X9fV1fJSQF7uQzoEv6lYTOz7/hda2DAdeZ9S4uuVkclRMByW4KslIpVSi1VSm1SSm1USt3tfDxaKfWDUmqb87aZ83GllHpDKZWqlFqvlBpQ5VjXOV+/TSl1nbti9jnZG0xyjDLTEeuz9Urvy2H8W2Ypz6yp5qq8v0lfATEJEBJmdSSe1bgtjHoQti6GLYtP7hiV1eMOI6DjCNfG588S/gJt+sB3j0JZodXRuFZ2irmVCnLgqtz2qbQA5t1htv3ytMMDuiRBFn6ocTu45A249RezhaAQwhLurCDbgPu01j2AIcDtSqkewEPAj1rrzsCPzs8BxgKdnR+3AO+ASaiBx4HBwCDg8cqkOqBlrjZbOYU0gBsWQqtu9T9Gv6vhotdg23fw5Q1gr3B9nFYpPWguIARK//GxBt9mBtgsfujkVggkfQSFOVI9rq+gYLjwZbNntDftG+sKOSmggqBVD6sjEVZq09u0E/y5AN47x317r9ckczVEtoQmMZ49rxBCiIDhtgRZa71Ha73aef8QsBloD4wHPnG+7BPgUuf98cA0bawEmiql2gIXAD9orfO11vuBH4Ax7orbJ6T/DtPGQ4PGJjk+lT6VhBtgzAvw57cw91Zw2F0Xp5UyVoF2QHyAJsghYTD2Rdi/ExL/U7+vLS8+Uj3uMNw98fmz2EFm39jEt8zUeH+RvQGan2565kRgG3YXXPsVFOXCu6Nhw5eeO3fWalM9lkFxQggh3MQjPchKqQ5Af+B3oLXWeo/zqWygtfN+e2B3lS/LcD5W0+PHnuMWpVSSUipp7969Lo3fq+xabrZpimwJNyyCZh1O/ZhDboPznoKUOWbZnMNx6se0WloiqGCIOcHAMn/X6SzoMR6WvWz2wK6r5I/MG9/Rp9jDHMjOfRJCI2ChHw3syk6pXxuH8G+dzobblpvviTk3wrf3mlkY7lR6EPZukeXVQggh3MrtCbJSKgqYA9yjtT5Y9TmttQZc8u5Ra/2u1jpBa53QsmVLVxzS+6T+aLZnahJjKseuXGI27G4461FY9zks+Jvvv6lPT4S2fSA8yupIrHX+s+b2u0fr9vryYlj+mplaXd1WYaJuolrC2Y/CjqWwaZ7V0Zy6kgI4kC4DusTRGrczwyHPvBOSPoAPLzBbDrrLnrWAhnZ1GEYphBBCnCS3JshKqVBMcvyZ1vor58M5zqXTOG9znY9nArFVvjzG+VhNjweWLYvN6P/mncxArkZtXH+OkX+HEfeZ6cWLH/LdJNlWBhlJEBdA2zvVpGksjLzPbB2x/afaX5/0oVSPXSXhRpNQfveoNcOMXClno7mVCrI4VnCo2f5p8meQtwP+NxK2LHLPuTJXm9u67NYghBBCnCR3TrFWwAfAZq31K1Wemg9UTqK+DphX5fGpzmnWQ4ADzqXY3wHnK6WaOYdzne98LHBsmg8zp5jhONd9Y6pT7qCU2aZmyO3w+39hyeO+mSRnrQF7WeD2Hx9r6J3QrCMsevDE08rLi+C316DjqMDaO9pdgkNg3L/hYAYs+7fV0ZyaHOcEa6kgi5p0v8hM3m0aby7m/vBPsNtce47MZPO7LCLatccVQgghqnBnBXkYcC1wtlJqrfNjHPA8cJ5SahtwrvNzgIXADiAVeA/4K4DWOh94GvjD+fGU87HAsOFLmH09tOsP1813/xsDpeCCZ03167fX4efna/8ab5O2wtwG6gTrY4U2gLEvwL6t8Ps7Nb8u6UMo2ivVY1eKHwp9roQV/4F9qVZHc/KyN0BEc/esXBH+I7oj3PgDDLzB/P345GI4mOW642euluqxEEIItwtx14G11suBmsZMnlPN6zVwew3H+hD40HXR+Yg1n8G820017+qZEN7IM+dVylS+7GXwy/NmIvKI+zxzbldITzRbHEW2sDoS79HlAugyFn55EXpPMr2DVZUXmd7j00ZL5d3VznsKtiyERQ/AlDm+OX03e4OpHvti7MKzQhvAxa+Zv1vf3A3/HQET3zdDA0/FoRyzGqP9X10TpxBCCFEDj0yxFich6SOY91eTsFzzpeeS40pBQXDxG9D7CvjxKbNljS9w2M02WFI9Pt6Y58xe1z/88/jn/vgAivdJ9dgdGrWGsx6B7T+a7dR8jd0GuZul/1jUT58r4OalZuXB9Anw8wuntkNCVmX/sUywFkII4V6SIHujlf+Fb++BzhfAVTMgLMKaOIKC4dJ3zFZB3z0Cq96zJo76yN0EZQekh7Y60aeZaeUbZsOu3448Xl5klkOedhbEDbEuPn92xs3QqicsfthMCvclealmNYkkyKK+WnWDm38yq1Z+fg4+mwhF+07uWJnJZuu+Nn1cG6MQQghxDEmQvc3y12Dxg9DtIpj8qVmuZqXgEJj4AXQdBwvvh9XTrY2nNmmJ5lYqyNUb/jdoEmf2560coPPH+1I9drfgEBj3EhzYDctfqf313kQGdIlTER4Fl70LF71mLsz9d4RZ5VNfmavNoEqrLhgLIYQIGJIgewutzRK0JY9Dr4kw6WPT++sNgkNNPKefC/PvhPWzrI6oZukroHF7aBpndSTeKSzCLLXO3WgS47JCUz3udDbEDbY6Ov/WYZhpWfjtdcjbbnU0dZe9AYJCTV+/ECdDKUi4AW783vxd+3gcrHiz7rskaG0qyDKgSwghhAdIguwNtDZ9vj8/B32vhsveM0mpNwkJNxXtDsNh7q2w8WurIzqe1qaCHDdUhgmdSLeLTEK89Fn4+V9QnAejH7E6qsBw/tMQHG623PKVLdRyUqBlN++5YCd8V7t+cMsv0GUMfP+o2b6wpKD2r8vfAaUFkiALIYTwCEmQraY1fPeoWXY58HoY/5bp/fVGoQ1NT3TMIJhzI2xZZHVER9u/EwqzZQpzbZSCsS9CRQkkvmlWBsSeYXVUgaFRGxj9EKT+YCZb+4LsFGgjy6uFizRsai62nv8sbF0M746CPetO/DVZa8ytDOgSQgjhAZIgW8nhgAX3wcq3YPBtpkcryMv/ScKj4JrZ0LYvzJoKqUusjuiIw/3HMqCrVi06w9DbAQWjHrI6msAy+FZo2R0WP2QuUnizwr3mopP0HwtXUgrOvAOuXwC2cnj/PLNzQ02rKjKTIaSh+bkRQggh3MzLszE/5rDDN3dC0gdmsvCY531nWXCDxmY/15ZdYcY1sHOZ1REZ6YnQoKlZDipqd87jcNcaqR57WnCoGdhVkA7LX7U6mhPL2WBupYIs3CFuCNz2q+nP//YemHubmap/rMxkc1E2OMTzMQohhAg4kiBbwW4zfbxrPoVRD8K5T/pOclypYTO4dh406wifTz5SvbVSurP/2Nur8N4iKAiiO1odRWDqOAJ6XW6m1ufvsDqammVXTrCWLZ6Em0S2gGu+NHMQ1s+E986BvVuOPG+vgD3rZXm1EEIIj5FMwtO0hq9uMnvRnvNPOOsR30uOK0U2h+vmm6nRn02CjGTrYinMNfu1Sv+x8BXnP2OqyYu9eHutnBRo1M78rAvhLkHBMPpBuPYrKNoL754FG740z+VuBluJDOgSQgjhMZIge5pSZoLwBc/BiPusjubURbUySXJkc/h0Qu3DVtwlXfqPhY9p3NasINm6GLYstjqa6smALuFJnc42S67b9DaDIL+998jvdkmQhRBCeIgkyFYYMNU5IMlPNG4H130D4Y3hw7Fmf0u7zbMxpCWaIS5t+3r2vEKciiH/By26wqIHvG9gl60M9m2RAV3Csxq3g+u/hTOdMzoWP2xaeppJO4gQQgjPkARZuEbTOPjLYrNP8vePwrujISPJc+dPXwExCbJXq/Athwd2pcFvr1sdzdH2bgGHTSrIwvOCQ00LwpWfQ1gUdBjhu61IQgghfI4kyMJ1msTA1TPhiulQnAfvn2uWyJUUuPe8pQche4MZ0CWErzltFPScYCZa799ldTRHZDsnWMuALmGVbhfC31Lg0nesjkQIIUQAkQRZuJZS0OMSuGOV2ds5+SN48wwzcKWmPS5PVcYq0A4Z0CV81/nPggr2roFdOSmmbaF5J6sjEYGsQWMIj7I6CiGEEAFEEmThHuGNYOzzcPNSaNLeDFyZPgHytrv+XGmJJrmIGeT6YwvhCU3aw6gHYMtC2Pq91dEY2RugdQ8zYVgIIYQQIkBIgizcq10/uOlHGPuS6Ul+eyj88qIZAOQq6YnQto9UGYRvG/JXaNHFObCr1NpYtDYVZBnQJYQQQogAIwmycL+gYBh8C9zxB3QbB0ufhXeGwc5fT/3YtjKTeMv2TsLXhYTB2Bdh/05Y8R9rYnA4TAX7s8uhZL+5wCWEEEIIEUAkQRae07gtTPoYrpkD9nL45CKYexsU7Tv5Y2atAXuZ9B8L/9DpLOgxHn59GQrSPXfe0gOQ+Da8ORA+n2T2Px79CPS/1nMxCCGEEEJ4AUmQhed1Phf+uhJG3GeGd/1nICR/YqpX9ZW2wtzKBGvhLy54zgy788TArtw/zaT5l7vDdw9DZEuY+AHcswFGP2i22xFCCCGECCAhVgcgAlRYBJzzT+h9BSy4F765C9Z+Dhe9agYD1VV6ounbjGzhvliF8KQmMTDyfvjxKdi2xFxQciWHHbYuht//Bzt/geBw6DXRtEG06+/acwkhhBBC+BipIAtrteoG1y+A8W/Bvq3wvxHww+NQXlz71zrskP67VI+F/xl6B0R3gkV/d91Au+J8+O0NeKMfzLga8lLh7Mfg3k0w4R1JjoUQQgghkARZeAOloP8UuCMJ+lwJv70Gbw+Grd+d+OtyN0HZAYiXAV3Cz4SEw7gXIX/HqQ/sytkI8++CV3rAD49Bk1iY9Ancvd5UqmX1hRBCCCHEYbLEWniPyOZw6VvQ72r49m/w+RXQ/RIY+wI0bnf869MSza1UkIU/Ov1c6H4xLPs39JkMTWPr/rV2G2xZAL+/C2nLIaQh9JkEg26BNr3dF7MQQgghhI+TCrLwPh2GwW3LzfLPbd/Dm2fAynfMm/6q0ldA4/bQNM6aOIVwtwv+ZW6/e6Rury/KMxOwX+8Ls6aaSdjnPWWWUV/yH0mOhRBCCCFqIRVk4Z1Cwszyz16XwYL7YfFDsO4LM8Sr/UDQ2lSQOww3S7SF8EdNY2HkffDTM7D9J+h0dvWvy1oLq941U+HtZdBxlFmi3WWM2YdcCCGEEELUiSTIwrtFnwZT5sDGuSZJfu8cGHSz6VkuzJb9j4X/O9M54X3hA/B/K8zFIwB7BWyeb5ZR714JoRHQ/xqzjLpVd2tjFkIIIYTwUZIgC++nlKkkn36OqaSteg+SPzbPxcmALuHnQsJh7Ivw2eWw8i3od435/k/6EA7tgWYdzd7J/a6Bhk2tjlYIIYQQwqcprbXVMbhcQkKCTkpKsjoM4S6ZyfDNPVB2EO5cA0HSSi8CwBdXQ+oSQIO9HDqdA4NvhdPPk58BIYQQQgQcpVSy1jrB1ceVCrLwPe0Hwq3LwGGTxEAEjjH/glkZEDvEtBm06Gx1REIIIYQQfkcSZOGblILgUKujEMJzmsWbC0NCCCGEEMJtpPwmhBBCCCGEEEIgCbIQQgghhBBCCAFIgiyEEEIIIYQQQgBuTJCVUh8qpXKVUilVHntCKZWplFrr/BhX5bmHlVKpSqktSqkLqjw+xvlYqlLqIXfFK4QQQgghhBAisLmzgvwxMKaax1/VWvdzfiwEUEr1AK4Eejq/5m2lVLBSKhh4CxgL9ACucr5WCCGEEEIIIYRwKbdNsdZaL1NKdajjy8cDM7TWZcBOpVQqMMj5XKrWegeAUmqG87WbXByuEEIIIYQQQogAZ0UP8h1KqfXOJdjNnI+1B3ZXeU2G87GaHhdCCCGEEEIIIVzK0wnyO0AnoB+wB3jZVQdWSt2ilEpSSiXt3bvXVYcVQgghhBBCCBEgPJoga61ztNZ2rbUDeI8jy6gzgdgqL41xPlbT49Ud+12tdYLWOqFly5auD14IIYQQQgghhF/zaIKslGpb5dMJQOWE6/nAlUqpcKVUR6AzsAr4A+islOqolArDDPKa78mYhRBCCCGEEEIEBrcN6VJKfQGMBloopTKAx4HRSql+gAZ2AbcCaK03KqVmYYZv2YDbtdZ253HuAL4DgoEPtdYb3RWzEEIIIYQQQojApbTWVsfgcgkJCTopKcnqMIQQQgghhBBCuIFSKllrneDq41oxxVoIIYQQQgghhPA6kiALIYQQQgghhBBIgiyEEEIIIYQQQgCSIAshhBBCCCGEEIAkyEIIIYQQQgghBCAJshBCCCGEEEIIAUiCLIQQQgghhBBCAJIgCyGEEEIIIYQQgCTIQgghhBBCCCEEIAmyEEIIIYQQQggBSIIshBBCCCGEEEIAkiALIYQQQgghhBCAJMhCCCGEEEIIIQQgCbIQQgghhBBCCAFIgiyEEEIIIYQQQgCSIAshhBBCCCGEEIAkyEIIIYQQQgghBCAJshBCCCGEEEIIAUiCLIQQQgghhBBCAJIgCyGEEEIIIYQQgCTIQgghhBBCCCEEIAmyEEIIIYQQQggBSIIshBBCCCGEEEIAkiALIYQQQgghhBCAJMhCCCGEEEIIIQQgCbIQQgghhBABp7jcxort+yitsFsdihBeJcTqAIQQQgghhBCes253AffMXMvOfUU0jQhlckIsU4bEExsdYXVoQlhOKshCCCGEEEIEALtD89bSVCa+s4LSCjsvTOzNmZ2a8/7ynYx8aSk3fLSKpX/m4nBoq0MVwjJSQRZCCCGEEMLPZewv5t5Z61i1M58L+7TluUt70yQilMlnxJF9oJTPV6Xzxap0bvj4D+KiI5gyJI4rEmJpGhFmdehCeJTS2v+uECUkJOikpCSrwxBCCCGED0jLK+LhrzaQX1TOlWfEMnFgDI0ahFodlhAuM29tJv/4OgWHQ/PU+F5cNqA9SqnjXlduc/DdxmymJ6axalc+4SFBXNK3HVOHdqB3TBMLIheiZkqpZK11gsuPKwmyEEIIIQKR1po5qzN5fF4KQUGKji0iWZ9xgIiwYCb0b8/UoR3o2qaR1WEKcdIOllbw+LyNzF2TyYC4prw2uT9xzevWZ/xn9kGmJabx9ZpMisvt9IttytSh8Yzr3ZYGocFujlyI2kmCXA+SIAshhBDiRA4UV/DI3A0s2LCHQR2jeXVyP9o3bcj6jAKmJaYxf10W5TYHgzpGM3VoPBf0bENosIxuEb4jaVc+98xcS1ZBCXed05k7zjqdkJP4Hj5YWsGc5Aymr0xjx94ioiPDmHxGLNcMjiOmmQz1EtaRBLkeJEEWQoiTl19UzvTENLq1bcQ53Vqd1BsqIbxZ4vY87p21lr2Hyrj3/C7cOrITwUFHLzfdX1TOrKTdfPp7GrvzS2jVKJyrBsVx9eA4WjduYFHkQtTOZnfwxk+pvPnTNto3a8hrk/sxMD76lI+rtea31DymJe5iyeYcAM7u1pqpQ+MZfnoLgoKOX7IthDtJglwPkiALIcTJWbZ1L/fNXsfeQ2UAtGvSgGuGxDP5jFhaRIVbHJ0Qp6bc5uCVH7byv2Xb6dA8ktev7EefmKYn/Bq7Q/PL1lymJabxy9a9BCnFBT1bc+2QDgw5LbraPk4hrJKWV8TdM9aydncBlw1oz5OX9HRLP31mQQmf/57GjFW7ySsqp2OLSKYMiefygTE0aSj9+3XlcGhKKuwUldsoKbdTfPjDRnG5vcpj5vMKu4MzO7VgcMdouSCBJMj1IgmyEELUT2mFnRcXb+HD33bSuVUUr1zRj8yCYqYlprFiex5hwUGM692Ga4d2YEBcU0kKhM9JzS3knplrSMk8yFWDYnnsoh5EhNVvM4+0vCI+XZnGrKQMDpRU0KV1FNcOiWfCgBiiwmVjEGEdrTVfJmfwxPyNBAcpnp3Qm4v7tnP7ectsdhZtyGZa4i5WpxfQMDSYS/u349ohHejRrrHbz+9J5TYHhWU2Cktt5rbMRmFZBYVldorLnAlthZ2isirJbYWdknIbRWVH7ldNgksrHCcVS1x0BJcPjGHiwBjaN23o4v9S3+FzCbJS6kPgIiBXa93L+Vg0MBPoAOwCrtBa71fmndbrwDigGLhea73a+TXXAf9wHvYZrfUntZ1bEmThSmU2O/sKywP6F5Dwb1uyD3H3jDX8mX2I64bG8/C47kcNYEnNPcT0xDTmrM6ksMxGz3aNmTo0nkv6tqdhmAxqEd5Na83nq9J5+ttNNAwN5l+X9WFMrzandMyScjvfrMti2spdpGQeJCo8hMsGtGfq0HhObyVDvYRnFRSX8+jcFBZs2MPgjtG84uyn97SUzANMT0xj3rpMSiscJMQ349qh8Yzt1ZawEGtadewOfTiZLSqzcaj0yP3CUhuHnLdF5dU/V1SZCJfaKLfXLZkNCVI0DAsmIiyYiLAQ520wDcNCiAh1Ph5unmtY+bnz+ciwYOfXhjgfCyYyLOTw8Wx2zeKNe5idlMGK7XkoBcM6tWBSQgwX9GwTcMPTfDFBHgkUAtOqJMgvAvla6+eVUg8BzbTWDyqlxgF3YhLkwcDrWuvBzoQ6CUgANJAMDNRa7z/RuSVBFq6yMesAd89YS2puIf3jjkxvDA8JrF9Awj9prflkxS6eW/QnjRuE8NLlfTmrW6saX19UZmPumkymJ6axJecQTRqGMmlgDFOGxNOhRaQHIxeibvIKy3hwzgaWbM5hROcW/HtSX5f2D2utWbO7gOmJaSxYv4dyu4OhpzVn6tB4zuvRWvr3XUxrsxy1sjrXMCw44Fs/Vmzfx70z17GvsOZ+ek87UFzB7OTdfLoyjV15xbSICueqQbFMGhhLRHgwZTYHZRV2c3vsfZudsooq920O5+f2ap8vraj+daUVDorKbJRU2OsUc8PQYKIahBAVfuQjMjyERg2qvx/l/Nzcr5oIh3jsYsDu/GLmrM7gy+QMMvaX0KhBCBf3bcekgTH0iw2MlV4+lyADKKU6AN9WSZC3AKO11nuUUm2Bn7XWXZVS/3Pe/6Lq6yo/tNa3Oh8/6nU1kQRZnCqHQ/P+8h289N0WoiPDuPKMOL5Zl8WOfUU0r5zeOCReqsrCZ+UeKuXvs9fzy9a9nN2tFS9e3qfObzS11qzamc+0lWl8l5KNzaEZ1aUlU4fGM7prK8vfnAkB8MvWvdw/ex0Hiit4YExX/jKso1t79vYVljHzj918/ns6mQUltGncgKsHx3HloFhaNQqcoV5aa8psDkrKa+urtFFUpceypMI8V1R25P6xxyipsFP1bWsgV8/KbQ5e/mEL7y7bQcfmkbx+ZX+v26fY4dD8mrqPaSt28dOWXE4l5QgLDiI8JIjw0CDCQ4IJDwkiLCSI8FBz33wEO5839xs1CCEyLISoBiE0qkxsqybBlQlvWLBPX8xyODQrd+bxZVIGC1P2UFrh4PRWUUwaGMOEAe39+vePvyTIBVrrps77CtivtW6qlPoWeF5rvdz53I/Ag5gEuYHW+hnn448BJVrrf5/ovJIgi1ORfaCU+2av5bfUPMb0bMO/LutNs8gwHA7Nb9v3MS0xjR+d0xvP6W6mNw7rJNMbhe9YsimHB+asp6jMxj8u7M6UIfEnfaU552ApX6xK5/Pf08k9VEZMs4ZMGRLPFQmxREeGuThyIWpXWmHnhcV/8tFvu+jcKorXr+zv0V5Iu0Pz4+Ycpq9M49dt+wgNVozp1ZapQ+NJiG/mlVWdyqS22mWozs8PLzU9rgfTuUS1zCxJLS63Y3fU/b1lkOL4ZahhwVU+nMtLQ4OJCK/yutBgMvaXMGd14FXPju6nj+Oxi7rXu5/e03bnF/PTn7kEKY5LZI9NfI99PiwkSC681tGh0goWrN/DrKTdrE4vIDhIMbpLSyYlxHB2t9aWLXV3F79LkJ2f79daN3NFgqyUugW4BSAuLm5gWlqa2/67hP9atGEPD321gQq7gycu7smkhJhq/8hmFpTw2co0Zv5hpjee5pzeOFGmNwovVlJu59mFm/h0ZTrd2zbmjSv70bm1a/olK+wOvt+Yw7TEXfy+M5+wkCAu7tOOqUPj6Rt74inBQrjKn9kHufuLtWzJqb6f3tN27C1k+so0vkzO4FCpjW5tGjF1aAfG92tHZB2HetkdmvITLDc9vMT0BMtSS8rtJtGt0l9ZNcEtKrNRYa/9/aBSmKWl1VXjnJ9HhoWY/srQKsntcb2YR/oqw0OCTimZDaTqmdaaz35P55kFpp/+hYl9OL/nqfXTC/+VmlvIl8kZfLU6g9xDZURHhjG+XzsmDYz1mwFq/pIgyxJr4ZWKymw8+c1GZiVl0DemCa9d2fohkw8AABgJSURBVJ+OdeipLLPZWbhhD9MS01hzeHqjGdTSva1//PIR/iEl8wB3z1jD9r1F3DLyNO47v4vbeum3ZB9i+spdzF2dSVG5nb4xTbh2aAcu6tM2oJZA1oXWmnK747itPKpu73HUElPn80VV7pdUmNd2bdOISQmx9I1p4tfVs+o4HJqPV+zi+cV166f3tOJyG1+vyWJa4i7+zD5Eo/AQEjo0w+bQxyS8x/dj1iVxPRGloIFzuelRy0qdiW5Ug2N6KqssSa18rvJ+w9Bgr/7e8ufqmemnX8+Szblu6acX/stmd/Drtn3MTt7Nkk25lNsd9GrfmMsHxDC+X3ua+fBqL39JkF8C8qoM6YrWWj+glLoQuIMjQ7re0FoPcg7pSgYGOA+5GjOkK/9E55UEWdTH2t0F3DNjDen5xfx19OncfW5nQk+iFyUl8wDTEncxb20WZTYHZ3RoxrVDOzCmZxuf/qMsfJvDoXnv1x38+3vTT//ypH4M79zCI+c+VFrBV6szmb4yjdTcQppFhHLFGbFMGRxPbHSER2JwNYdDU1xhd1bgzPYeR9+vcFbl7BSWVVBcVk2SW+FMhJ3bftRnOapSOKeeHl2NCwsOYl1GAaUVDjq3imJSQgyX9vev6llNcg+Wcv+X61m2dS/ndGvFC/Xop/c0rTVJafv5dGUa23IKaRB6/HLS2pabHv/8MX2Yx/RkhgYrr05q3cWfqmc/b8nl/tnrOVhSwYNju3HDmR2krUuclP1F5cxbm8ns5Aw2Zh0kLDiI83q05vKEGEZ2bulzS9l9LkFWSn2BqQC3AHKAx4GvgVlAHJCG2eYp39mP/CYwBrPN0w1a6yTncf4CPOI87LNa649qO7ckyKIu7A7N20tTee3HbbRp3IBXJ/djUMfoUz5uQXE5XyZnMH1lGmnO6Y1XD4rlqsFxtG0iQ72E5+w5UMJ9s9axYvvR/fSeprUmcXse01em8f2mHBxac3bXVlw7NJ6RnVu67Y2e1poKuz6uOldcbj/cY3lskltUVvmc836ZjcJSc79yOWpdhAUHERkeTGR4yFHbe0SGH7lfufQ0MvzIVh+VS08jjtnqo3KJaoPQmpejHnRWz2ZXqZ6d1bUllw+M5exurfzyQt0Pm3J4sLKf/qIeTBkcF5DJoKhZ1erZD5tyqLBrerVvzKSBsYzv146mEd5bPSutsPP8oj/5eMUuurQ2/fSyOk24yqasg8xO3s3XazLZX1xB68bhXDYghkkDYzitZZTV4dWJzyXIVpIEWdRmd34x985ayx+79jO+XzueGt/L5b3DDodm2ba9TE9M46ctuQQpxXnOoV5DOzWXN3EnqcLuOGoKatXpqJUVOg2M6tKSlo28s4rkCQs37OHhOvTTe9qeAyV88Xs6n6/azb7CMuKbR3DN4DjaN42o0jtZny0/jrymtJrX1udPXFBlf2WDUCLDg53LUUOJqrwf7rzfwNyPDK9ctuq8Hx7qXJIabPlWcJXVszmrM9jrrJ5d2q89kxJi/OINdnG5jWcWbObz39Pp0bYxb1zVT/YfFrXKr6yeJWWwaY93V8+q9tNff2YHHhrbTVpUhFuU2xz89GcOs5My+HnrXuwOzcD4ZkwaGMOFfdrSqIH3ztaRBLkeJEEWJzJvbSb/mJsCwNOX9uLS/u3dfs7d+cV8+nsas/7Yzf7iCk5vFcW1Q+K5bEB7r/7Fcyoq7I7jpqHW1GN51GPO7T3M8lPbca+raz+eqZ61cvaetTqpZfO+qLDMxpPzNzI7uX799J5WbnOweGM20xN38ceumre2DwlS1S4brdeS02NeHxEW7OyxdCbCzvsnqs76KpvdwbJte5mdlMGSzb5VPatJSuYB7pqxhh0e6KcX/mtj1gFmJ2Uwb633VM/sDs3+4nLmrc3ihcV/0rhBKC9N6sNZXb2nn174t9yDpcxdY5Zgp+YW8tDYbtw2qpPVYdVIEuR6kARZVOdgaQWPfZ3CvLVZJMQ349XJ/TzeB1laYefb9XuYnriLdRkHiAwLZsKA9kwd2oEuLpomfCocDk1ReTXbeJTWPP20ummoh0ptlNkcdT5vWEgQkWFHTzxtGOpcelq5vUeYc4uP0OqXnh5erhoWQmGZjXnrMvlqdSZ7D5XRPDKMS/ub6lm3Nr5fPavJmvT93DNzLen5xdx+Cv30npaeV0ypzX5cwhsWHOTTe1N6m2qrZz1bM2lgDCO8rHpWHbuzn/5lZz/9K1f0Y9jpnumnF/6rzGbnp825zE7O4OctuTg0LqueVSa8eYXl5BWVkV9UTn5ROfsKy8l3fm7um4/9xeWHV72c270Vz0/03n564d+01qzdXUBsdIRXfw9KglwPkiAfYbM7CFIq4Ic5/LErn3tmrCX7YCn3nNOZ/xvdyfI33ut2FzAtMY1v1mdRbnMwuGM0U4d24PyerQkNDjq8L2Wdl5k6l6WW1mN5ammF/chel6U2isrtdYo9LCToyITTKhNPK7f9OHz/mImpkVX6KivvNwwNdtu/RXXVs97tmzApIYZL+vpm9aw6NruDt3/ezusu7qcX/isl8wBfJmfw9dpMCooraNO4AZcNaM/lXtp7llVQwr2z1rJyRz5je7XhuQnW9NML/5Z7sJSv1mQyO2k32/cW0TA0mLG92nB5QgxDOjbHoTX7iyvILzIJb54zuc0rMgmvSYSdjxWWUVBSUWObR9OIUJpHhtE8MpzoyDCio8JoERlGdGQY8S0iGd2lpd+taBHC1SRBrgdfS5Arp6JW7aE8dhlq0VHPme09ik7Uh+kcRFNuc9AiqrL3LJaubayvUnpShd3BGz9u462lqcRGR/Da5H70j2tmdVhHyS8qZ1bSbj5dmUbG/hLCQ4LQmCWopyos5MTLUBuEOvspw47Zz7Km+85E1xeH/dRUPbsiIZbhp7fw+upZTXbnF/O3mWtJStvPJX3b8fSlru+nF/6rzGbnx825zE7azS9b9+LQkBDfjEkJMVzYpx1Rddyr150WrN/Dw1+tx+bQXtVPL/yX1po1uwuYnbSbb9btobDMRkRYMCUV9hoT3mYRoUQ7E97mUWHO+2E0jwo/fD86yjzfLCLU8ov0QvgDSZDrwdsT5Ae+XMdvqXmHt/oorahfIhQWHHRkGWqVJaaHt/wIdd4PDyYiNITNew7y45+metYnpgmTBsZwSd/2NInw7zfRO/cVcc/MtazbXcAVCTH88+KeXvFmryZ2h+bnLbms2J5HaHBQvfsqj319WHBQwK8cqMmx1bO2TSqrZ7Fe2a9bk7lrMnjs640oPNdPL/xXzsFSvlqdyezk3eyorJ71bsOkgbEM7hjt8t8nDofmQEnFUZW4fUXl5DuXn+YVlbPnQCnJafvpG9uU1yb386mfT+EfSsrtLN64hzXpBTSNcCa6kWE0jzpS/ZWEVwhrSIJcD96eIL/9cyqpOYX/3969B1lZ3gcc//5g2UWWiyygRRZEiRoxgrpErXWqaatWWu9sx9hbxtpOTY1pUzvV2pk4qZkYM6bRMW1imYyJ7ZgKJm20VZvYOLkYraCgYrwAGlmCIjcR5Lbs0z/Oy3JcdpfdZdn3vC/fz8yZfc97O895fmffc37nuZxKAlu/9+c9qhPd3sZYDmRM4YatO/mPbND9z9dspr5uGOfPPJLWgreedSelxIJFbdzy0DJGDB/GFy4/mbknT867WKpB3bWefXT6eFpbpjJ31uSa/ULl3W2V8fTfW5rfeHqVV0qJZ9/cxMLFe1vPpjYdxrzTpnJFyxSax3f/WuvoSGzatosNW3d0jqtcnyW867OEd0PVWMyN7+/q8Tegx46s62x5O+f4SVx77oxCjKeXJA0dE+R+qPUEOW89tZ61tkxlesG/nd+4dSd/990XeOTFtzhrxgTu+L3Z/vaw+qRr69mo+uFc+JHJtM5p5oxjmmqmS+fTK9fzmQeW1tR4epXXntazB55p42cr1xMBZ82YwIxJo/eZeGjD1p30kO8y7rARnS1vTVm30+5a4iaMrmf8qPpCDuOQJA0tE+R+MEHumx3tu/nBS2tZsHgVP8paz06f3sS8Oc38zsmTaazR1rOe/HT5Oj7zwBI2bN3J31xwAtecfaxdjNVv3bWeTWsaxbyWZq5oaWbK4UPzhUtKic3b2jsTkHVbdrL4FxuY/5PXmVaj4+lVbqs2vM/CxW1897nVbN6+a++4yi4Jb1NjPROrxl2Ob6y39VeSNOhMkPvBBLn/3np3O995ro2Fi9pYua7Sejb35Mm0tjRzeg21nnVnR/tu7vifV7nnRyuZMamRO688lY9MGZd3sVQCe1rPFixq48kVldazX5sxkdY5zVxw0q8wckTff3t1T8K7Lkt4O2c/3bJj76ynVWMxN2zdSXs3zXGtLc189uLaHk8vSZJ0sJkg94MJ8sBVWs82smBRGw8/X2k9O3rCKOadVmk9O2qIWs/6avna97j+/iW8tGYzf3DmNG6eO5PD6vuetEh9tWrD+zz4bBsLF7fRtnEbY0bWcdHso7hk9lEMGxZ7E93sZz66/uzHxh4SXoAxDXWds542NTZkM5/u2/30iLENHDFm5BA/c0mSpNpjgtwPJsiD4/2d7Tz64lssWLR37NnZH5rIvJb+t54NtpQS//r0m9z68EuMbqjji1fM4rdmHplbeXTo6OhIPLVyPQsWt/HIi2u6nYV+zMi6bruedtcNdXzjCBrq/FJHkiSpP0yQ+8EEefDtGXu2cHEbqzdtY+zIOi4+5ShaW6Yyq3ncAXfBTinx3o72zta3fbqeVi3v6Z66c3cH5xw/iS+1zrJVTbnYvH0XT61Yz2H1wzt//9KEV5Ik6eAzQe4HE+SDp7r17L9fWMOO9g6OP3I0rS1TufTUKUwa0wB0TXj3jqtc3zn2ckfVcuW2c3f3vwfdWD+cptFVXU8b62kaXc/MyWO5ePZRNT0+WpIkSdLgM0HuBxPkobF5+y4eXrqGBYtX8dybm6gbFsyYNJpN23ayceuu/Sa8Exp76Ho6up6JjQ3ZPvW5duWWJEmSVHsOVoLsNKgasLEjR3DVGdO46oxpLF/7HgsWt7Fi7RZmN47bd6KhxobOZRNeSZIkSbXIBFmD4kNHjOGmC0/MuxiSJEmSNGDD8i6AJEmSJEm1wARZkiRJkiRMkCVJkiRJAkyQJUmSJEkCTJAlSZIkSQJMkCVJkiRJAkyQJUmSJEkCTJAlSZIkSQJMkCVJkiRJAkyQJUmSJEkCTJAlSZIkSQJMkCVJkiRJAkyQJUmSJEkCIFJKeZdh0EXEO8Av8i7HfkwE1uVdCPWZ8SoW41U8xqxYjFexGK9iMV7FY8wO3EDq8OiU0qTBLkgpE+QiiIhFKaU5eZdDfWO8isV4FY8xKxbjVSzGq1iMV/EYswNXS3VoF2tJkiRJkjBBliRJkiQJMEHO0z15F0D9YryKxXgVjzErFuNVLMarWIxX8RizA1czdegYZEmSJEmSsAVZkiRJkiTABLlTREyNiB9GxEsRsSwiPp2tb4qI70fEa9nf8dn634+I5yPihYh4MiJmV53rtyPilYhYHhE39vKYj0bEpoh4uMv6YyLi6ez4f4+I+h6Ob8kef3lE3BURka1vzZ5DR0TUxGxwg61k8bolIlZHxJLsNncw6qiWlCxesyPiZ9m2hyJi7GDUUa0paMw+HxGrImJLl/V/npVrSUT8JCJmHkjd1KKSxesfq66Hr0bEpgOpm1pUtHhFxKiI+K+IeDkr721V2349Ip6NiPaImDcY9VNrShavT0TEO1X/Y9cMRh3VkpLF6+iIeDwr3xMR0TwYddQXNVaP12XHpoiY2Mvx3dZ3v69TKSVvlW7mk4HTsuUxwKvATOB24MZs/Y3AF7Pls4Dx2fKFwNPZ8nBgBXAsUA8sBWb28Ji/CVwEPNxl/QPAldny14Brezj+/4AzgQAeAS7M1p8InAA8AczJu26N137jdQtwQ951arz6HK9ngHOy5auBf8i7fo1Z535nZuXe0mX92Krli4FH865f49VzvLrs8yngG3nX76EeL2AU8LFsuR74MXuvidOBWcC3gHl5163x2m+8PgHcnXedGq8+x2sB8MfZ8m8A9x2i9XgqlWvNG8DEXsrcbX3Tz+tU7i/iWr0B/wmcB7wCTK56obzSzb7jgdXZ8q8Cj1Vtuwm4qZfHObf6RUDlw/g6oK6783V50b5cdf/jwNe77PMEJU2QyxQvDoEEuWTxepe98zdMBV7Kuz6N2T7n6C3h+jjwSN71abz6HK8ngfPyrk/jtc957gT+tMu6eylpglymeHEIJMgli9cyYGrV+TYfavXYZdsb9JAg96W++3qdsot1NyJiOpVvKp4Gjkwprck2vQUc2c0hf0KlhQlgCrCqaltbtq6vJgCbUkrt+zl+SrZtoI9TGiWJ13VZt5Rv7OmqUlYliNcy4JJsuZVKklxqBYlZryLiLyJiBZVvvq/v7/FFUoZ4QaVrIXAM8L8DOb4oihaviDicSgvP4/14nNIoSbyuyD5zLIyIUr+HlSBeS4HLs+XLgDERMaEfZRgUOddjXw3a+48JchcRMRp4EPjLlNLm6m2p8tVD6rL/x6i8CP52yAqpTiWJ1z8DM4BTgDXAHfkW5+ApSbyuBj4ZEYupdDnamXN5DqqSxIyU0ldTSjOolOvv8y7PwVKWeGWuBBamlHbnXZCDpWjxiog64H7grpTSyjzKkKeSxOshYHpKaRbwfeCbeZRtKJQkXjcA50TEc8A5wGpgSK+JRavHwWCCXCUiRlB5AfxbSuk72eq3I2Jytn0ysLZq/1nAfOCSlNL6bPVqPtii1AysjogzqiZEuLiXYqwHDs/+SaqPH151/Oeyx6keqN+crTtklCVeKaW3U0q7U0odwL8Ap/e3LoqgRPF6OaV0fkqphcob2Yr+1kVRFCxmffVt4NJ+7F8YJYzXlVT+x0qpoPG6B3gtpfSVgT3r4ipLvFJK61NKO7K784GWvtdCcZQoXr9MKV2eUjoVuDlbN2QTF9ZIPfZWvsey4+fTQ30P5Ly5jwuolRuVfuvfAr7SZf2X+OBA9Nuz5WnAcuCsLvvXASupdAvbMxD9pF4e91z2HYi+gA8OMP9kD8d2nURobpftT1DSMchlihfZOI5s+a+Ab+ddv8ar13gdkf0dlj2nq/OuX2O2zzm6TtJ1XNXyRcCivOvXePUcr2zdh6mMNYu869Z4de53K5UPysN62H4vJR2DXKZ48cHPHJcBT+Vdv8ar13hN3LMO+DzwuUOxHqu2vUHvk3T1Wt99vU7l/iKulRtwNpUuAs8DS7LbXCr92R8HXgN+ADRl+88HNlbtu6jqXHOpzPS2Ari5l8f8MfAOsI1KP/kLsvXHUvlwvjwLdEMPx88BXswe5272Thx0WXa+HcDb9GFCgKLdShav+4AXsufyParevMpyK1m8Pp09/qvAbZT3A3wRY3Z7dlxH9veWbP2dVMaOLwF+SC9vzEW9lSle2bZbgNvyrlfj1Xlsc1ben1eV4Zps20ez822l0oKzLO/6NV69xusLVK6HS6lcDz+cd/0ar17jNS8r76tZObu9nh4C9Xh9dr8d+CUwv4fju61v+nmd2vOBT5IkSZKkQ5pjkCVJkiRJwgRZkiRJkiTABFmSJEmSJMAEWZIkSZIkwARZkiRJkiTABFmSpJoVEbsjYklELIuIpRHx1xHR63t3REyPiKuGqoySJJWJCbIkSbVrW0rplJTSScB5wIXAZ/dzzHTABFmSpAHwd5AlSapREbElpTS66v6xwDPAROBo4D6gMdt8XUrpyYh4CjgReB34JnAXcBtwLtAAfDWl9PUhexKSJBWICbIkSTWqa4KcrdsEnAC8B3SklLZHxHHA/SmlORFxLnBDSul3s/3/DDgipXRrRDQAPwVaU0qvD+mTkSSpAOryLoAkSRqQEcDdEXEKsBs4vof9zgdmRcS87P444DgqLcySJKmKCbIkSQWRdbHeDaylMhb5bWA2lTlFtvd0GPCplNJjQ1JISZIKzEm6JEkqgIiYBHwNuDtVxkeNA9aklDqAPwSGZ7u+B4ypOvQx4NqIGJGd5/iIaESSJO3DFmRJkmrXYRGxhEp36nYqk3J9Odv2T8CDEfFHwKPA1mz988DuiFgK3AvcSWVm62cjIoB3gEuH6glIklQkTtIlSZIkSRJ2sZYkSZIkCTBBliRJkiQJMEGWJEmSJAkwQZYkSZIkCTBBliRJkiQJMEGWJEmSJAkwQZYkSZIkCTBBliRJkiQJgP8Hv5oHXfvNaOEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgZjOocMBFcs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQa911D2F79C"
      },
      "source": [
        "# Cases\n",
        "observedCases=test_cases['cases'].iloc[1:]\n",
        "predictedCases=forecastCasesNov['yhat'].iloc[1:]\n",
        "\n",
        "observedCases.reset_index(drop=True, inplace=True)\n",
        "predictedCases.reset_index(drop=True, inplace=True)\n",
        "\n",
        "pred_err_cases=[]\n",
        "pred_err_recovered=[]\n",
        "pred_err_mortality=[]\n",
        "\n",
        "# Loop through each observeds vs predicted values\n",
        "for count in range(len(observedCases)-1):\n",
        "    err = predictedCases[count] - observedCases[count]\n",
        "    pred_err_cases.append(err)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBOlgkuOGtt6",
        "outputId": "8cd85bbf-8568-4351-9abe-30ce4b9d1ad3"
      },
      "source": [
        "#Mean relative to absolute error\n",
        "maeCasesProphet = statistics.mean(map(abs,pred_err_cases))\n",
        "print('Mean Absolute Error Cases = %.3f' % maeCasesProphet)\n",
        "\n",
        "true_y_arr = test_cases['cases'].to_numpy()\n",
        "pred_y_arr = predictedCases.to_numpy()\n",
        "err_arr = []\n",
        "\n",
        "for i in range(len(true_y_arr)-1):\n",
        "  err_arr.append(np.abs(true_y_arr[i] - pred_y_arr[i])/true_y_arr[i])\n",
        "\n",
        "prophet_acc = (1 - np.average(err_arr))*100\n",
        "\n",
        "print('New Cases Accuracy %.2f%%' % (prophet_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error Cases = 1530.088\n",
            "New Cases Accuracy 40.84%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpMfK9wxKeKq",
        "outputId": "0b8ca892-952d-4a99-ebf8-ef7912468e9c"
      },
      "source": [
        "# Convert into time series for a 2nd model\n",
        "seriesCases=pd.Series(data=train_cases['cases'].to_numpy(), index=train_cases['date_report'])\n",
        "\n",
        "resultCases = adfuller(seriesCases)\n",
        "\n",
        "# The more negative it is the stronger the rejection of the hypothesis\n",
        "print('ADF Statistic Cases: %f' % resultCases[0])\n",
        "print('p-value: %f \\n' % resultCases[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF Statistic Cases: -1.396015\n",
            "p-value: 0.584185 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5gY5Nm5Q7GP"
      },
      "source": [
        "def getAccuracy(obj1, obj2):\n",
        "  true_y_arr = obj1['cases'].to_numpy()\n",
        "  pred_y_arr = obj2['Prediction'].to_numpy()\n",
        "  err_arr = []\n",
        "\n",
        "  for i in range(len(true_y_arr)):\n",
        "    err_arr.append(np.abs(true_y_arr[i] - pred_y_arr[i])/true_y_arr[i])\n",
        "\n",
        "  acc = (1 - np.average(err_arr))*100\n",
        "\n",
        "  return acc\n",
        "\n",
        "def plotARIMA(obj1, obj2, title):\n",
        "  fig, ax = plt.subplots()\n",
        "  if title.__contains__(\"Weekly\"):\n",
        "    ax.plot(obj1.index, obj1['Prediction'], label='Predicted Cases')\n",
        "    ax.plot(obj2.index, obj2['cases'], label='Actual Cases')\n",
        "  elif title.__contains__(\"Manual\"):\n",
        "    ax.plot(obj1.index, obj1, label='Predicted Cases')\n",
        "    ax.plot(obj2['date_report'], obj2['cases'], label='Actual Cases')\n",
        "  else:\n",
        "    ax.plot(obj1['date_report'], obj1['Prediction'], label='Predicted Cases')\n",
        "    ax.plot(obj2['date_report'], obj2['cases'], label='Actual Cases')\n",
        "  legend = ax.legend(loc='upper left', shadow=True)\n",
        "  plt.title(title)\n",
        "  plt.xlabel('Date')\n",
        "  plt.ylabel('Cases')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89a3N0k9qMzd",
        "outputId": "410afddd-c61c-494d-f52e-281d0b43373e"
      },
      "source": [
        "from pyramid.arima import auto_arima\n",
        "# AUTO ARIMA CASES\n",
        "\n",
        "# THIS TAKES A FEW MINUTE TO RUN\n",
        "\n",
        "# Create model\n",
        "stepwise_model_cases = auto_arima(df_cases['cases'], start_p=1, start_q=1,\n",
        "                           max_p=3, max_q=3, m=12,\n",
        "                           start_P=0, seasonal=True,\n",
        "                           d=1, D=1, trace=True,\n",
        "                           error_action='ignore',  \n",
        "                           suppress_warnings=True, \n",
        "                           stepwise=True)\n",
        "print(stepwise_model_cases.aic())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning:\n",
            "\n",
            "The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning:\n",
            "\n",
            "sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/pyramid/__init__.py:68: UserWarning:\n",
            "\n",
            "\n",
            "    The 'pyramid' package will be migrating to a new namespace beginning in \n",
            "    version 1.0.0: 'pmdarima'. This is due to a package name collision with the\n",
            "    Pyramid web framework. For more information, see Issue #34:\n",
            "    \n",
            "        https://github.com/tgsmith61591/pyramid/issues/34\n",
            "        \n",
            "    The package will subsequently be installable via the name 'pmdarima'; the\n",
            "    only functional change to the user will be the import name. All imports\n",
            "    from 'pyramid' will change to 'pmdarima'.\n",
            "    \n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=4009.984, BIC=4027.768, Fit time=2.385 seconds\n",
            "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 1, 0, 12); AIC=4367.832, BIC=4374.946, Fit time=0.050 seconds\n",
            "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 0, 12); AIC=4112.901, BIC=4127.128, Fit time=1.060 seconds\n",
            "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=4013.874, BIC=4028.101, Fit time=2.128 seconds\n",
            "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=4000.969, BIC=4022.310, Fit time=3.108 seconds\n",
            "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 0, 12); AIC=4025.540, BIC=4043.324, Fit time=1.900 seconds\n",
            "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=3995.003, BIC=4019.900, Fit time=8.049 seconds\n",
            "Fit ARIMA: order=(0, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=4010.804, BIC=4032.145, Fit time=6.852 seconds\n",
            "Fit ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=3971.557, BIC=4000.012, Fit time=7.379 seconds\n",
            "Fit ARIMA: order=(2, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=4016.601, BIC=4041.499, Fit time=5.565 seconds\n",
            "Fit ARIMA: order=(2, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=4013.462, BIC=4045.474, Fit time=7.311 seconds\n",
            "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=4073.502, BIC=4094.843, Fit time=5.496 seconds\n",
            "Fit ARIMA: order=(3, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=4002.632, BIC=4038.200, Fit time=8.693 seconds\n",
            "Fit ARIMA: order=(2, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=3969.053, BIC=3993.951, Fit time=5.177 seconds\n",
            "Fit ARIMA: order=(2, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=3967.337, BIC=3988.678, Fit time=3.084 seconds\n",
            "Fit ARIMA: order=(3, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=3985.995, BIC=4010.893, Fit time=3.472 seconds\n",
            "Fit ARIMA: order=(2, 1, 0) seasonal_order=(0, 1, 1, 12); AIC=4012.684, BIC=4030.469, Fit time=2.009 seconds\n",
            "Fit ARIMA: order=(2, 1, 2) seasonal_order=(0, 1, 1, 12); AIC=3970.348, BIC=3995.246, Fit time=3.369 seconds\n",
            "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 1, 1, 12); AIC=4115.054, BIC=4129.282, Fit time=1.537 seconds\n",
            "Fit ARIMA: order=(3, 1, 2) seasonal_order=(0, 1, 1, 12); AIC=3991.359, BIC=4019.813, Fit time=4.040 seconds\n",
            "Fit ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=3969.005, BIC=3993.902, Fit time=3.668 seconds\n",
            "Fit ARIMA: order=(2, 1, 1) seasonal_order=(0, 1, 0, 12); AIC=4097.920, BIC=4115.704, Fit time=0.274 seconds\n",
            "Total fit time: 86.621 seconds\n",
            "3967.33661560723\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u4ma_c3xsZTg",
        "outputId": "de54cf6d-b171-481b-d524-2ffb68cfd1b4"
      },
      "source": [
        "stepwise_model_cases.fit(train_cases['cases'])\n",
        "\n",
        "forecast_aarima_cases = stepwise_model_cases.predict(n_periods=31)\n",
        "\n",
        "# Create our forecast\n",
        "forecast_aarima_cases = pd.DataFrame(forecast_aarima_cases, index = test_cases.index, columns=['Prediction'])\n",
        "new_df = pd.concat([test_cases, forecast_aarima_cases], axis=1)\n",
        "print(new_df)\n",
        "\n",
        "plotARIMA(new_df, test_cases, 'Auto ARIMA Model Daily New COVID-19 Cases Forecast')\n",
        "\n",
        "maeCasesDaily=mean_absolute_error(test_cases['cases'], new_df['Prediction'])\n",
        "print('Mean Absolute Error Cases= %.3f' % maeCasesDaily)\n",
        "\n",
        "auto_arima_daily_acc = getAccuracy(test_cases,new_df)\n",
        "\n",
        "print('Accuracy %.2f%%' % (auto_arima_daily_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    province date_report  cases  ...  month  week   Prediction\n",
            "250   Canada  2020-10-01   1821  ...     10    40  1879.027153\n",
            "251   Canada  2020-10-02   2121  ...     10    40  1895.295940\n",
            "252   Canada  2020-10-03   1801  ...     10    40  1908.641789\n",
            "253   Canada  2020-10-04   1609  ...     10    40  1910.358117\n",
            "254   Canada  2020-10-05   2910  ...     10    41  1892.157950\n",
            "255   Canada  2020-10-06   2380  ...     10    41  1979.893918\n",
            "256   Canada  2020-10-07   1859  ...     10    41  1918.507490\n",
            "257   Canada  2020-10-08   2476  ...     10    41  1946.898601\n",
            "258   Canada  2020-10-09   2531  ...     10    41  1922.932456\n",
            "259   Canada  2020-10-10   2118  ...     10    41  2004.947391\n",
            "260   Canada  2020-10-11   1600  ...     10    41  2017.514179\n",
            "261   Canada  2020-10-12   1732  ...     10    42  1954.230633\n",
            "262   Canada  2020-10-13   3414  ...     10    42  2005.580701\n",
            "263   Canada  2020-10-14   2549  ...     10    42  2080.372536\n",
            "264   Canada  2020-10-15   2271  ...     10    42  2078.032378\n",
            "265   Canada  2020-10-16   2566  ...     10    42  2070.840821\n",
            "266   Canada  2020-10-17   2027  ...     10    42  2060.457819\n",
            "267   Canada  2020-10-18   1791  ...     10    42  2148.701774\n",
            "268   Canada  2020-10-19   3269  ...     10    43  2085.981117\n",
            "269   Canada  2020-10-20   2326  ...     10    43  2115.521953\n",
            "270   Canada  2020-10-21   2698  ...     10    43  2092.348810\n",
            "271   Canada  2020-10-22   2838  ...     10    43  2174.640202\n",
            "272   Canada  2020-10-23   2649  ...     10    43  2187.746891\n",
            "273   Canada  2020-10-24   2212  ...     10    43  2125.047574\n",
            "274   Canada  2020-10-25   2046  ...     10    43  2176.899699\n",
            "275   Canada  2020-10-26   4173  ...     10    44  2252.209643\n",
            "276   Canada  2020-10-27   2718  ...     10    44  2250.403265\n",
            "277   Canada  2020-10-28   2667  ...     10    44  2243.736157\n",
            "278   Canada  2020-10-29   3012  ...     10    44  2233.876706\n",
            "279   Canada  2020-10-30   3546  ...     10    44  2322.646897\n",
            "280   Canada  2020-10-31   2358  ...     10    44  2260.451812\n",
            "\n",
            "[31 rows x 8 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RERERkTZIGWMSZWZmUlhYyMiRI7ngggtaNB76yiuv5JlnngFg+vTpLF68uN59Z82axbvvvtvkawwcOJDS0tJD1ldWVnLNNdcwePBgxo4dy+TJk5k7d26Tz99SZkZVVVXSrysiIiIiIulJCXISderUiQ8//JBFixbRvn17ZsyYccD25iZ7Dz30EMOHD693e3MT5PpMnz6d7Oxsli1bxoIFC/j9739fZyItIiIiIiLSmihBDslJJ53E8uXLmTVrFieddBJnnXUWw4cPp7q6mltuuYXx48czevRoHnjgAQCcc9x4443k5+fzxS9+kc2bN9eea/LkycyfPx+AV155hWOPPZaCggJOPfVUVq9ezYwZM7jrrrsoLCzk7bffpqSkhPPPP5/x48czfvx43nnnHQC2bNnC6aefzogRI5g+fTrOuUPiXrFiBXPnzuXHP/5xbZf1QYMGccYZZwBwzjnnMHbsWEaMGMGDDz4IQHV1NVdeeSUjR45k1KhR3HXXXbXnmjZtGmPHjuWkk06qHU/89NNPM3LkSAoKCjj55JMT8e0XERERERE5RJupYh3tzr99yuLiiriec3i/7tz+lREx7VtVVcXLL7/MtGnTAFi4cCGLFi1i0KBBPPjgg/To0YP333+fPXv2MHHiRE4//XQ++OADPv/8cxYvXsymTZsYPnw43/zmNw84b0lJCVdddRWzZ89m0KBBlJWVkZ2dzbXXXkvXrl353ve+B8Cll17Kd77zHSZNmsTatWuZOnUqn332GXfeeSeTJk3ihz/8IS+++CIPP/zwIbF/+umnFBYWkpmZWee9PfLII2RnZ7Nr1y7Gjx/P+eefz+rVqykqKmLRokUAbNu2DYCrr76aGTNmMGTIEObOncv111/PG2+8wY9+9CNeffVVDj/88Np9RUREREREEq1NJshh2bVrF4WFhYBvQf7Wt77Fu+++y3HHHcegQYMAeO211/j4449rxxeXl5ezbNkyZs+ezSWXXEJmZib9+vVjypQph5x/zpw5nHzyybXnys7OrjOO119//YAxyxUVFVRWVjJ79myeffZZAM444wx69erV5Hu85557eO655wBYt24dy5YtIz8/n5UrV3LTTTdxxhlncPrpp1NZWcm7777LBRdcUHvsnj17AJg4cSJXXnklF154Ieedd16TYxARERGRRtTUwJZlkJsfdiQiKaVNJsixtvTGW2QM8sG6dOlS+9g5x7333svUqVMP2Oell16KWxw1NTXMmTOHjh07NvnYESNG8NFHH1FdXX1IK/KsWbN4/fXXee+99+jcuTOTJ09m9+7d9OrVi48++ohXX32VGTNm8NRTT3H33XfTs2fPOr8fM2bMYO7cubz44ouMHTuWBQsW0Lt372bfr4iIiIgcZMnf4Kkr4P8thOyjwo5GJGVoDHKKmTp1Kvfffz/79u0DYOnSpezYsYOTTz6ZJ598kurqajZs2MCbb755yLETJkxg9uzZrFq1CoCysjIAunXrxvbt22v3O/3007n33ntrn0eS1JNPPpknnngCgJdffpmtW7ceco3Bgwczbtw4br/99toxyqtXr+bFF1+kvLycXr160blzZ5YsWcKcOXMAKC0tpaamhvPPP58f//jHLFy4kO7duzNo0CCefvppwH8w8NFHHwF+bPLxxx/Pj370I3Jzc1m3bl0LvqMiIiIicojSZYCDjYvCjkQkpShBTjHTp09n+PDhHHvssYwcOZJrrrmGqqoqzj33XIYMGcLw4cO5/PLLOeGEEw45Njc3lwcffJDzzjuPgoICLrroIgC+8pWv8Nxzz9UW6brnnnuYP38+o0ePZvjw4bXVtG+//XZmz57NiBEjePbZZxkwYECdMT700ENs2rSJo48+mpEjR3LllVdy2GGHMW3aNKqqqjjmmGP4/ve/z4QJEwAoKipi8uTJFBYW8vWvf52f/exnADz++OM8/PDDFBQUMGLECJ5//nkAbrnlFkaNGsXIkSM58cQTKSgoiPv3WURERKRNqyj2y5LPw41DJMVYXZWKW7tx48a5SFXniAULFjB27NiQIpJ4W7BgAe+//z79+/fnzDPPDDscERERkdbliYtg6Ssw6gI4/6GwoxFpMjNb4JwbF+/zqgVZRERERKStqSjyS7UgixxACbKIiIiISFsT6WJdusxXtBYRQAmyiIiIiEjbsm837NwCPQZA1S4oXxt2RCIpQwmyiIiIiEhbEulePfgUvyxZGl4sIilGCbKIiIiISFsS6V5dmyAvCS8WkRSjBFlEREREpC2JJMh9RkKXXChVoS6RCCXISfbXv/4VM2PJksY/qbv77rvZuXNns6/16KOPcuONN9a57eWXX2bcuHEMHz6cMWPG8N3vfrfZ1xERERGRViTSxbp7P8jJVxdrkShKkJNs5syZTJo0iZkzZza6b0sT5PosWrSIG2+8kT/96U8sXryY+fPnc/TRR8f9OiIiIiKSgiqKoGNPaN8FcvP9VE/OhR2VSEpQgpxElZWV/Otf/+Lhhx/mz3/+c+366upqvve97zFy5EhGjx7Nvffeyz333ENxcTGnnHIKp5zix4d07dq19phnnnmGK6+8EoC//e1vHH/88YwZM4YvfvGLbNq0qcE4fvGLX/Cf//mfDBs2DIDMzEyuu+66Bs/11ltvUVhYSGFhIWPGjGH79u0A/PKXv2T8+PGMHj2a22+/HYAdO3ZwxhlnUFBQwMiRI3nyySfj8N0TERERkbioKIbuh/vHufmwpxwqG37/KNJWZIUdQChe/j5s/CS+5+w7Cr708wZ3ef7555k2bRpDhw6ld+/eLFiwgLFjx/Lggw+yevVqPvzwQ7KysigrKyM7O5tf//rXvPnmm+Tk5DR43kmTJjFnzhzMjIceeohf/OIX/O///m+9+y9atKjeLtX1netXv/oVv/3tb5k4cSKVlZV07NiR1157jWXLljFv3jycc5x11lnMnj2bkpIS+vXrx4svvghAeXl5I988EREREUmaiiLfvRogZ6hflnwO3fqGF5NIimibCXJIZs6cybe//W0ALr74YmbOnMnYsWN5/fXXufbaa8nK8j+O7OzsJp13/fr1XHTRRWzYsIG9e/cyaNCgZsdY37kmTpzIzTffzNe+9jXOO+88+vfvz2uvvcZrr73GmDFjAN9CvmzZMk466SS++93vcuutt3LmmWdy0kknNTseEREREYmz8iLIK/SPc32PQkqXwlFfCC8mkRTRNhPkRlp6E6GsrIw33niDTz75BDOjuroaM+OXv/xlzOcws9rHu3fvrn180003cfPNN3PWWWcxa9Ys7rjjjgbPM2LECBYsWEBBQcEh2+o71/e//33OOOMMXnrpJSZOnMirr76Kc44f/OAHXHPNNYecZ+HChbz00kvcdtttnHrqqfzwhz+M+T5FREREJEH27YadpdCjv3/erS906K6pnkQCGoOcJM888wyXXXYZa9asYfXq1axbt45Bgwbx9ttvc9ppp/HAAw9QVVUF+GQaoFu3brVjfQH69OnDZ599Rk1NDc8991zt+vLycg4/3I8jeeyxxxqN5ZZbbuGnP/0pS5f6ioU1NTXMmDGjwXOtWLGCUaNGceuttzJ+/HiWLFnC1KlTeeSRR6isrASgqKiIzZs3U1xcTOfOnfn617/OLbfcwsKFC5v9fRMRERGRONq+wS8jXazNfDfrEk31JAJJSJDNLNPMPjCzvwfPB5nZXDNbbmZPmln7YH2H4PnyYPvAqHP8IFj/uZlNTXTMiTBz5kzOPffcA9adf/75zJw5k+nTpzNgwABGjx5NQUEBTzzxBABXX30106ZNqy3S9fOf/5wzzzyTE088kby8vNrz3HHHHVxwwQWMHTu20fHKAKNHj+buu+/mkksu4ZhjjmHkyJGsXLmywXPdfffdtUXE2rVrx5e+9CVOP/10Lr30Uk444QRGjRrFV7/6VbZv384nn3zCcccdR2FhIXfeeSe33XZbi79/IiIiIhIHkTmQIwky+G7WpZrqSQTAXIJLupvZzcA4oLtz7kwzewp41jn3ZzObAXzknLvfzK4HRjvnrjWzi4FznXMXmdlwYCZwHNAPeB0Y6pyrru+a48aNc/Pnzz9gXaQglqSHBQsW8P7779O/f3/OPPPMsMMRERERaR0+fgqevQpumOcrWAO883/wjx/CrauhU69QwxOJlZktcM6Ni/d5E9qCbGb9gTOAh4LnBkwBngl2eQw4J3h8dvCcYPupwf5nA392zu1xzq0CluOTZRERERERaYqKIr+MbkHOCRLlErUiiyS6i/XdwL8DNcHz3sA251xV8Hw9EEzCxuHAOoBge3mwf+36Oo6pZWZXm9l8M5tfUlIS7/sQEREREWn9KoqhQw/o0G3/ukhLcqnGIYskLEE2szOBzc65BYm6RjTn3IPOuXHOuXG5ubnJuKSIiIiISOtSUXxg6zFAzwGQ1VGFukRI7DRPE4GzzOzLQEegO/B/QE8zywpaifsDQT8PioAjgPVmlgX0ALZErY+IPqZJampqyMhQ4e7WrqampvGdRERERORQ5euhx0GdMTMyofcQJcgiJLAF2Tn3A+dcf+fcQOBi4A3n3NeAN4GvBrtdATwfPH4heE6w/Q3nK4i9AFwcVLkeBAwB5jU1ns6dO7Np0yYlV61cTU0NGzduZN++fQD6wENERESkKepqQQbIHaou1iIktgW5PrcCfzazHwMfAA8H6x8G/mhmy4EyfFKNc+7ToPL1YqAKuKGhCtb1GTx4MCtWrKC4uDge9yAh2rdvH2vWrGHnzp0xTWslIiIiIkDVXtixGbofUs7HT/W06C+wdwe075L82ERSRFISZOfcLGBW8HgldVShds7tBi6o5/ifAD9pSQzt27fnmGOOYcuWLTz33HPs3LmzJaeTkDnnGDJkCGPGjAk7FBEREZHWYfsGv6yrBTlnqF+WLoN+hcmLSSTFhNGCHKrevXvzjW98g127dpHoOaAlcTIzM+nUqRN+JjARERERaVTtFE91tSBHKlkvVYIsbVqbS5DBJ1ddu3YNOwwRERERkeSpCIYa1pUgZw8Gy1ShLmnzVOFIRERERKQtqG1BrqOLdVZ7yD4KSpYkNyaRFKMEWURERESkLagohvbdoGP3urfn5vsu1iJtmBJkEREREZG2oK45kKPl5kPZSl/tWqSNUoIsIiIiItIW1DcHckROPtRU+SRZpI1SgiwiIiIi0hY0liDnRqZ6UqEuabuUIIuIiIiIpLvqfVC5qe4K1hGRuZBLNA5Z2i4lyCIiIiIi6W77BsA1nCC37wI9BqiStbRpSpBFRERERNJdQ3MgR8sdqi7W0qYpQRYRERERSXcNzYEcLScfSpdDTU3iYxJJQUqQRURERETSXW0LciMJcm4+VO2C8rWJj0kkBSlBFhERERFJd+VF0L4rdOzR8H65+X5Zom7W0jYpQRYRERERSXcVRb712Kzh/WorWStBlrZJCbKIiIiISLprbA7kiM7Z0OUwFeqSNksJsoiIiIhIuqsobryCdURuvlqQpc1SgiwiIiIiks6qq6ByY+wJcs5QKFkKziU2LpEUpARZRERERCSdVW4EVxNbF2uA3GGwpxwqNyU2LpEUpARZRERERCSd1U7xFGsX60ihriWJiUckhSlBFhERERFJZxVFfhlrC3JOZKqnpYmJRySFKUEWEREREUln5UGC3CPGFuRufaFDD1WyljZJCbKIiIiISDqrKIZ2naFjz9j2N/PdrFXJWtogJcgiIiIiIumsosh3rzaL/ZgcTfUkbZMSZBERERGRdFZRHPv444jcobBjM+zampiYRFKUEmQRERERkXRWUQTd+zftmNxhfqlCXdLGKEEWEREREUlX1VWwfWPTW5BzNNWTtE1KkEVERERE0tWOzeCqm54g9xwAWR2hVC3I0rYoQRYRERERSVcVxX7ZPcYpniIyMiFniAp1SZujBFlEREREJF2Vr/fLWOdAjqZK1tIGKUEWEREREUlXzW1BBsjNh/K1sHdHfGMSSWFKkEVERERE0lVFkR9L3KlX04/NzffL0mXxjUkkhSlBFhERERFJVxXFvvXYrOnH5gQJsrpZSxuiBFlEREREJF1VFDW9gnVE9lFgmVCqBFnaDiXIIiIiIiLpKtKC3BxZ7TsVAGAAACAASURBVKH3YLUgS5uiBFlEREREJB3VVMP2Dc1vQQbIGaoEWdoUJcgiIiIiIumocjPUVDVviqeI3HwoWwlVe+MXl0gKU4IsIiIiIpKOWjLFU0ROPrhqnySLtAFKkEVERERE0lFFkV+2pIt17VRP6mYtbYMSZBERERGRdBSXFuQhfqlxyNJGKEEWEREREUlHFeshswN07t38c7TvAj0GKEGWNkMJsoiIiIhIOqoo9t2rzVp2ntx8dbGWNkMJsoiIiIhIOmrJHMjRcvOhdJmfNkokzSlBFhERERFJRxVFLSvQFZEzFKp2w7a1LT+XSIpTgiwiIiIikm5qaqBiQ8vmQI7IHeaXpUtbfi6RFKcEWUREREQk3ewogZp9cepiPdQvS5a0/FwiKU4JsoiIiIhIuonHHMgRnXpBl8OgRC3Ikv6UIIuIiIiIpJvaOZDjkCCDKlmHbdta+OXRsGp22JGkPSXIIiIiIiLpprYFuX98zpeb7+dCdi4+55OmmTPDd5tf/s+wI0l7SpBFRERERNJNRRFktofOveNzvpx82FMB2zfG53wSu90VsPAP/vGGj8KNpQ1IWIJsZh3NbJ6ZfWRmn5rZncH6R81slZl9GHwVBuvNzO4xs+Vm9rGZHRt1rivMbFnwdUWiYhYRERERSQsVxdAtDzLi9HY/UqhL3ayT78PHYe92yCuADR+qFT/BEtmCvAeY4pwrAAqBaWY2Idh2i3OuMPj6MFj3JWBI8HU1cD+AmWUDtwPHA8cBt5tZrwTGLSIiIiLSulUUx6eCdURkqicV6kqummqYOwOOOB6OvQJ2bYXydWFHldYSliA7rzJ42i74aujjjrOBPwTHzQF6mlkeMBX4h3OuzDm3FfgHMC1RcYuIiIiItHrl6+MzB3JE1z7QoYemekq2z1+GrathwnWQV+jXFX/Y4CHSMgkdg2xmmWb2IbAZn+TODTb9JOhGfZeZdQjWHQ5EfxyyPlhX3/qDr3W1mc03s/klJSVxvxcRERERkVahpga2b4hfBWsAM9/NulQtyEk1537ocQQM+wr0GQGW6btZS8IkNEF2zlU75wqB/sBxZjYS+AEwDBgPZAO3xulaDzrnxjnnxuXm5sbjlCIiIiIirc/OLVC9N75drGF/JWtJjg0fwZp/wXFXQ2YWtOsIhx2jQl0JlpQq1s65bcCbwDTn3IagG/Ue4Pf4ccUARcARUYf1D9bVt15ERERERA5WO8VTHFuQwVey3rEZdpbF97xStzn3Q7sucOzl+9flFfou1irUlTCJrGKda2Y9g8edgNOAJcG4YszMgHOARcEhLwCXB9WsJwDlzrkNwKvA6WbWKyjOdXqwTkREREREDlabICegBRnUzToZtm+ET56BMV+DTj33r+9XCDtLfRE2SYisBJ47D3jMzDLxifhTzrm/m9kbZpYLGPAhcG2w/0vAl4HlwE7gGwDOuTIz+2/g/WC/Hznn9LGViIiIiEhdIslTohLkks9hwISG95WWef9hqKmC4689cH1egV9u+DC+RdikVsISZOfcx8CYOtZPqWd/B9xQz7ZHgEfiGqCIiIiISDqqKIKMLOgS57o8PQZAVieNQ060fbtg/sOQ/yXoPfjAbX1GgmX4btbDzggnvjSXlDHIIiIiIiKSJBXF0K0fZMT5rX5GBuQcDaVKkBPq46d8obUJ1x26rX1nPxZchboSRgmyiIiIiEg6KS9KXPfb3GFQojHICeOcL87VZxQMPKnuffoVaqqnBFKCLCIiIiKSTiqK4l/BOiInH8rXwp7KxJy/rVv5JpR85luPzereJ68QKjf5Ql4Sd0qQRUQkPNVV8NQVsH5+2JGIiKQH53wX60QlyLlD/XLLssScv62bc78fOz7qq/XvEynUVaxW5ERQgiwiIuHZugoW/xXm/z7sSERE0sPOMqjeE/8K1hE5kUrW6mYddyVLYdlrMH46ZHWof7++owBTN+sEUYIsIiLhKVvllyvf9K0eIiLSMhXr/TJRCXL2Ub5Ctgp1xd/cGZDZHsZ9q+H9OnSFnCEq1JUgSpBFRCQ8ZSv9sqIIStUaISLSYomaAzkiq71PkjXVU3ztLIOPZsKoC6FrDNNz5RWqi3WCKEEWEZHwlK2EjHb+8Yo3w41FRCQdVBT5ZaLGIAPkDFWCHG8LH4N9O+GE62Pbv18hbC+Gys2JjasNUoIsIiLhKVsJhx3jWyNWvBF2NCIirV9Fse8C3fWwxF0jd5j/+121N3HXaEuq98HcB2HQF6DPiNiOiRTqUjfruFOCLCIi4dm6yifHg6fA6n/pzZaISEuVF0G3PMjITNw1cvPBVUPZisRdoy1Z/LxvDZ4QY+sxQN/Rfqlu1nGnBFlERMJRXQVb1/gE+ahTYN8OWD8v7KhERFq3RM6BHJETTPWkbtYt5xzMuQ+yB8OQ02M/rmN3f4wqWcedEmQREQlHxXqo2QfZg2DQSWCZGocsItJSiZwDOSJnKGAqrhgP6+ZB0QKYcB1kNDE161eoLtYJoARZRETCEalgnX0UdOwB/cdpHLKISEs4FyTICapgHdG+M/Q8AkqWJPY6bcGc+/z/wIJLmn5sXgGUr4MdW+IfVxumBFlERMIRmQM5+yi/HDwFij/wU12IiEjT7doKVbsSnyAD5ORDiVqQW2TbWvjsBTj2Cj+3cVPlFfqlulnHlRJkEREJR9lKyOoEXfv650edAjhY9VaoYYmItFrJmOIpIjcftiyDmurEXytdzXsQMDju6uYdX1vJWglyPClBFhGRcJStgl4D94+5OnwsdOiuccgiIs1VUeyXyWhBzs2Hqt2wbU3ir5WO9lTCgj/A8LN8d/Xm6NTT/x/VOOS4UoIsIiLhiEzxFJGZBYNO9gmyc+HFJSLSWkVakHskqYs1qJt1c334BOwphwk3tOw8eYWa6inOlCCLiEjy1dT4FuTsQQeuH3wKlK/dX8BLRERiV17kZwTo2ifx18oNpnoq1VRPTVZTA3Pvh8PHwRHjW3auvALfir9ra3xiEyXIIiISgsqNvpBMdAsyBOOQUTVrEZHmqCiGbn0hIzPx1+rUyyfiakFuumWv+g+CT7i+5efqFynUpW7W8aIEWUREkq92iqeDWpCzj4KeR2ocsiRfTTVUV4UdhUjLVBQlp0BXRM5QTfXUHO/9Frr3h2PObvm5IpWs1c06bpQgi4hI8h08xVOEme9mvWo2VO9LflzSdj17FTx1WdhRiLRMMuZAjpabD6VLVTeiKTZ8DKvfhuOu8rU3WqpzNvQYoBbkOFKCLCIiyVe2EjLa+U/QDzZ4CuzdDkULkh+XtF1r58Dyf8K+3WFHItI8zgUtyMlMkIfBngrYvjF512zt5s6Adp1h7BXxO2e/Ak31FEdKkEVEJPnKVkLPAXV/ej7oZLAMjUOW5Nmz3ScW1Xtg/fthRyPSPLu3wb6dye9iDepmHavtm+CTp6HwUj+GO17yCvz/1d3l8TtnG6YEWUREkq9s5aHdqyM69YJ+x2ocsiRP6bL9j9e8E14cIi1ROwdyEhPk3GCqp1IV6orJ/Eegei8cf118z5s3xi83fBzf87ZRSpBFRCS5nIOtq+tPkMGPQy6aD7u2JS0sacMib+479oTV/wo3FpHmKo/MgVzH0JVE6doHOvaAEk311Kh9u+H9h2DIVMg5Or7nzivwS41DjgslyCIiklw7t/gxaw0lyEedAq7GFzIRSbTSpZCRBaMvgnXzNA5ZWqeKIEFOZguyGeTkK0GOxaJnYGdpfKZ2OljXXD/2XOOQ40IJsoiIJFd9UzxF6z8e2nfVOGRJjpLP/Qc2R03245CL5ocdkUjTVRT7+g1d+yT3urlDoVQJcoOcg/fug8NGwKAvJOYaeYWa6ilOlCCLiEhy1TfFU7Ss9jBwksYhS3KULvXFho48ATBYrXHI0gpVFPvkOLNdcq+bOwx2lMDOsuRetzVZNRs2fwoTrvOt7omQVwBblvuig9IiSpBFRCS5ylb6Vo6eAxreb/AU2Lpqf0ItkgjV+/xrMmeoLxDXd5S69kvrVLE+uVM8ReSoUFej5twHnXNg1AWJu0a/QsDBxk8Sd402QgmyiIgkV9lKX0Qmq0PD+x11il+uVCuyJFDZKqip2l+Nd+BJfqqnqj3hxiXSVBXFyR1/HJGrqZ4atGUFLH0Fxn8L2nVM3HXyCv1ShbpaTAmyiIgkV9lK6NXA+OOInCHQvb+6WUtiRcZO5gzxy4EToWo3FC0ILyaRpnLOV7EOowW5xwDI6gQlakGu05z7IbM9jPtWYq/TrQ907atxyHGgBFlERJJr66qGxx9HmMHgybDqLaipTnhY0kZFuoXmBK1gAyLjkDXdk7Qieypg345wWpAzMvwHTCrUdahdW+HDx2HkV30Cm2j9ClXJOg6UIIuISPLs2uaneYolQQY/Dnl3ORR/kNi4pO0qWepb3Tp08887Z0PfkRqHLK1L7RzIIbQggx+ioKmeDrXwD7Bvpy/OlQx5Bf5Dv707knO9NKUEWUREkmdrpIJ1DF2sAQZNBkzTPUnilH6+v3t1xJGT/HzIGocsrUVFsV+G0cUafKGu8nWwpzKc66ei6iqY+6Cva5A3OjnXzCsEVwMbFyXnemlKCbKIiCRP7RzIMbYgd+ntPxHXOGRJBOegdNn+KrwRAycF45AXhhOXSFNVBC3IYXSxhv1F7rYsC+f6qeizF3xl8QnXJ++a/VSoKx6UIIuISPJEpmzqNTD2YwafAuvnaW5Hib+KYthbub8Kb8SRJ6JxyNKqVBQDBt3ywrl+JEFWN+v95tznC1IOnZq8a3bLgy65GofcQkqQRUQkecpW+X/g7bvEfszgKX4aHiUrEm+1FawPSpA7Z0MfjUOWVqRiPXTtA5ntwrl+9lGQkaUEOWLd+366uAnXQUZm8q5r5rtZq5J1iyhBFhGR5Il1iqdoRxwP7TprHLLEX2nQHfTgLtbgp3taNw+q9iY3JpHmCGsO5IjMdpA9eH9V+LZuzn3QoTsUXpr8a+cV+Dmp9+1K/rXThBJkERFJnrKVsY8/jsjqAEdO1Dhkib+Sz6FjD+h62KHbBk6Cql1QrHHI0gqEnSCDH6pQsiTcGFJB+XpY/Dwce/n+6vjJ1K8QXDVs+jT5104TSpBFRCQ59u6Ayo2xV7CONvgUX/xl27r4xyVtV+lS373a7NBtR070S3Wzltagoji8CtYROfl+GE1b73Ux70HAwfHXhHP9vAK/1DjkZlOCLCIiybF1tV82tQUZ/DhkgJVqRZY4Kl1ad/dqiBqHrLHvkuJ2V8CeivDmQI7IHeZbLstWhBtHmPbugAWPwjFfgZ4DwomhxxHQKVvjkFtACbKIiCRH7RRPzWhBzh3mi3tpHLLEy65tULnp0ArW0Y7UOGRpBcKeAzki8rvUlgt1ffgE7C6HCTeEF4OZ72atFuRmU4IsIiLJUTvFUzMSZDM46hRYOQtqquMalrRRkWJCB1ewjjZwEuzbCcUfJCcmkeYIew7kiN5DAGu7CXJNDcy5H/odC0ccF24seQWw+TOo2hNuHK2UEmQREUmOspXQuTd06tm84wefAru2woaP4huXtE2xJMgahyytQaq0ILfv7LsVl7bRBHn5P3z38hNuqLuuQTLlFfrpEVWoq1mUIIuISHI0p4J1tKMm+6XGIUs8lHwOme2h18D69+nSGw4boXHIktoiLcjd8sKNAyA3H0ra6FRP7/0WuvWD4WeHHUlUoS59oNwcSpBFRCQ5ylY1r3t1RNfDoM8oTfck8VG6FHofDRmZDe83cCKsmwvV+5ITl0hTVRRBl8Mgq33YkfgeGaVL295QmE2fwqq34Lir/JzQYes1EDr21DjkZlKCLCIiiVe1ByrWt6wFGXw367VzfKVQkZaITPHUGI1DllSXCnMgR+TmQ/Ue2LYm7EiSa859kNUJxl4ZdiSemW9FViXrZlGCLCIiibdtLbia+CTINftg9TvxiUvapn27/bRjufVM8RRN45Al1VUUQ4/+YUfh5Q7zy7bUzbqyBD5+Ggov8dPDpYq8Ati8WFX4myFhCbKZdTSzeWb2kZl9amZ3BusHmdlcM1tuZk+aWftgfYfg+fJg+8Coc/0gWP+5mU1NVMwiIpIgLZniKdqAEyCzg8YhS8uUrfAf2MTSgtwlB3KP0ThkSV3lRanTghz5nSpZEm4cyTT/Ed9qfvx1YUdyoH6FUL0XSj4LO5JWJ5EtyHuAKc65AqAQmGZmE4D/Ae5yzh0NbAW+Fez/LWBrsP6uYD/MbDhwMTACmAbcZ2aNDBgSEZGUUpsgt7AFuV0nOPJEzYcsLRNLBetoAyfBWo1DlhS0ZzvsKU+dBLlTT+jaZ//vWLqr2gPvPwRHn9bwnOphyCv0SxXqarKEJcjOqwyetgu+HDAFeCZY/xhwTvD47OA5wfZTzcyC9X92zu1xzq0ClgMhTy4mIiJNUrYKOnT30zy11OBTfOtEZGoTkaYqWQqYL9IVi4GTYN8OjeeT1FOxwS/DnuIpWm5+25kL+a1fwI7NcOJNYUdyqF6D/P9d/d1qsoSOQTazTDP7ENgM/ANYAWxzzlUFu6wHIr/RhwPrAILt5UDv6PV1HCMiIq1B2UrfvToec0MOnuKXqmYtzVX6OfQ8ws/bGguNQ5ZUFZniKZUS5JwgQXYu7EgSa/18+NevofBrcNQXwo7mUBkZfhyyKlk3WZMTZDPLMLPusezrnKt2zhUC/fGtvsOaer0mxHW1mc03s/klJSWJuoyIiDRH2cqWTfEU7bAR0CVX45Cl+UqX+jfxseqa64sPaRyypJraBDlFuliDb0Heux22bwg7ksTZtwueu9bPezztZ2FHU7+8Ati4SMNDmiimBNnMnjCz7mbWBVgELDazW2K9iHNuG/AmcALQ08yygk39geA3myLgiOB6WUAPYEv0+jqOib7Gg865cc65cbm5ubGGJiIiiVZd5af8aOn444iMDDjqFN+CXFMTn3NK21FTA6XLY6tgHW3gJM2HLKknMtSkW164cUSL/G6lczfrf/4ItiyDs38DHXuEHU398gp9AbF0/lkkQKwtyMOdcxX48cIvA4OAyxo6wMxyzaxn8LgTcBrwGT5R/mqw2xXA88HjF4LnBNvfcM65YP3FQZXrQcAQYF6McYuISNgq1kNNVfwSZPDjkHeWwqZF8TuntA3la6FqF+QMadpxAyfB3koVvJHUUlEEnXOgXcewI9kv0jsjXQt1rXrbz3s8/ir/vyiV5RX4pf5uNUmsCXI7M2uHT5BfcM7twxfcakge8KaZfQy8D/zDOfd34FbgZjNbjh9j/HCw/8NA72D9zcD3AZxznwJPAYuBV4AbnHPVsd6giIiELF4VrKMdFbwpUTdraarSZX7ZlC7WAEdO8kuNQ5ZUUlEMPVJo/DFA18N8q2o6TvW0Zzs8f70fMnTanWFH07jeR0P7rhqH3ERZje8CwAPAauAjYLaZHQlUNHSAc+5jYEwd61dSRxVq59xu4IJ6zvUT4CcxxioiIqkkXnMgR+ue5+emXfEGTPx2/M4r6S/S1bCpXay75vqkevU7MOk78Y9LpDnKi6DXkWFHcSAzP2a/JA1bkF+7Dbatg2++Au27hB1N4zIyoO9oVbJuophakJ1z9zjnDnfOfTmYvmkNkOJ9CkREJCWUrYKsTtC1b3zPO3gKrHnPF0sRiVXp5366sc7ZTT924CRY+54fVy+SCiqKUqtAV0TOUP+7lk6W/QMWPAoT/x8MmBB2NLHLK4CNn0CNOuDGKtYiXX3M7GEzezl4Ppz944VFRETqV7bKtx5nxHlmwcGn+OIja96N73klvZUua3r36giNQ5ZUsncH7N6Wmglybj7sKIGdZWFHEh87y+D5G33Ppcn/EXY0TdOv0NddSNcx4QkQ67uVR4FXgchv4FLg3xIRkIiIpJl4TvEU7cgTIbO9xiFL05R8DrlDm3fsQI1DlhRSEUyj1L1/uHHUJSfNKlm/fKsvDHnu/alVEC0WKtTVZLEmyDnOuaeAGgDnXBWgdnoREWlYTQ1sXRXf8ccR7bvAEcf76Z5EYrGjFHaV+e6fzdH1MH/smnfiG5dIc1Ss98tUbUGG9Ohmvfh5+OQpOPkW6HdIeaXUlzPUD3PSOOSYxZog7zCz3gSVq81sAlCesKhEUsm6ef6TQ823KtJ0lRuhand8K1hHGzzFT/W0fVNizi/pJdLFsLldrMG3Iq/ROGRJAZE5kFMxQe5xBLTr3PpbkCtL4O/f8fMJn/TdsKNpnoxM6DtKlaybINYE+Wb8fMSDzewd4A/ATQmLSiSVfPAnmDsDlr8ediQirU8ipniKFpmDcuWsxJxf0kttBetmtiBDMA55O2xUd0UJWUWRX6ZigpyR4acYas0JsnPw93+DPZVw7gzIbBd2RM3XrxA2fKzGnhjFWsV6IfAF4ETgGmBEMI2TSPrb9KlfzvltuHGItEaJmOIpWt8C6JStccgSm9KlvlWrJWM2a+dD/ld8YhJpropiX5G9XaewI6lb7rDWXRjq4ydhyd9hym1w2DFhR9MyeYWwbwdsWR52JK1CrFWsLwA6Oec+Bc4BnjSzYxMamUgqqKmGzYuhQ3ffQhVJlkUkNmUrIaNd4orIZGTAUZP9OGTnEnMNSR+lS32rVksqqnfrA72H+PmQRcJUnqJTPEXkDoXydb4FtrUpL4KX/h2OmAAn3BB2NC2nQl1NEut/iP9yzm03s0nAqcDDwP2JC0skRWxdDft2+sIM7TrDe/eFHZFI61K2CnodCZlZibvG4Cl+rPPmzxJ3DUkPJUv3Fw9qCc2HLKmgohi6Hx52FPWLjPVvba3IzsELN0LNPjjnPj+Gt7XLHQZZHTUOOUaxJsiRitVnAL9zzr0ItE9MSCIpZNMivxw4CQou8VUMKzeHG5NIa1K2MnHjjyMi45BXvJHY60jrtncHlK9tfgXraAMnwZ4K2KjRZhKiilRvQR7ml60tQV7we///5LQfQe/BYUcTH5lZ0GeEWpBjFGuCXGRmDwAXAS+ZWYcmHCvSem36FCzDjz2ZcD1U74X3Hwo7KpHWwbmgBTlB448jevT3SY/GIUtDImPv4pUgg6Z7kvDs2+WnLEvlFuTsQZCR1boKdZWthFdvg6NOgfHTw44mvvIKfYKsQl2NijXJvRB4FZjqnNsGZAO3JCwqkVSx6VM/Xq1dJ8g5GoZOg/cfhn27w45MJPXtKPXVfhPdggz+zczqd/S7KfUrCVqx4tHFultf/79BhbokLLVTPKVwgpzZDrIHt54EuaYa/nqD71J99m/ALOyI4qtfoe/5snVV2JGkvFirWO90zj0LlJvZAKAdsCShkYmkgo2f+C4pEROuh52lvqu1iDQs8k84GQny4ClQtQvWzU38taR1Kv3c9wiK1+tx4CRY865/Uy3epsWw9NWwo2gbUnmKp2i5+f53rzWYcz+sfRe+9D++Z1K6qS3UpXHIjYm1ivVZZrYMWAW8FSxfTmRgIqHbXQHb1hyYIA86GfqM8sW6VDFXpGGJngM52sCJviufxiFLfUqX+u7+WR3ic76BJ2kccsSubb7i74yJ8MSF8PfvQNXesKNKb62hBRl8gly2Cqr2hB1Jw0o+h3/+CPK/7GvOpKPcYyCzPRQrQW5MrF2s/xuYACx1zg0CvgjMSVhUIqkgUhG3z8j968zghOuh5DO9ERdpTNlK32LXc0Dir9WhG/Q/TuOQpX4lS+Mz/jjiyIl+2Zane6qpgQ8eh9+Mg/d/B+O+CSfeBPMfgce+Ats3hR1h+ipf75ep3oKckw+uGrasCDuS+lVXwXPXQPsucObd6de1OiKrPRw2XIW6YhBrgrzPObcFyDCzDOfcm8C4BMYlEr5IBevoBBlg5PnQtQ+899vkxyTSmpSt8t3UspI06cHgKf4f/47S5FxPWo/qKl+kKzeOCXL3PD++sq2OQy7+EB6ZCs9f71vmr54FZ/wvnP5j+Ooj/nfxwclQtCDkQNNURTF06gXtO4cdScMiv3Op3M36X7+G4g/gzLv8POfprF9QqEu9IBsUa4K8zcy6ArOBx83s/4AdiQtLJAVs+hQ69Dh0HEpWBxh/Faz4J2zWUHyReiVjiqdokemeVs5K3jWlddi2xs9pmhOHAl3R2uI45J1lvgv1g5N9nYFz7odvvrp/fCP4D5Kn/8NPLfPIl+DDJ0ILN22l+hzIEb2HALa/SF6q2fARvPU/MPKrMOKcsKNJvLxC2L0Ntq4OO5KU1mCCbGZHm9lE4GxgJ/Ad4BVgC3BT4sMTCdGmRX78cV1dbcZ900+4Pue+5Mcl0lqUrUz8FE/R+o2Bjj1ghbpZy0EiVXTj2cUagnHI5b6gY7qrqYEFj8K9Y/3y+GvgxvlQeClk1PF2su8ouGoWDDge/nodvHwrVO9LctBpLNXnQI5o39kPsylJwQaFqj3w3LXQuTd8+ZdhR5MctYW61M26IY21IN8NVDjndjjnapxzVc65x4DngDsSHp1IWGpqfDXO6AJd0br0hoKL4aM/qzunSF12bfNzdCazBTkjEwZ9wY9DVvcxiRbp3hnPLtbgi8NB+s+HvH4BPHQq/O3bkDsMrnnbV/rt1LPh47r0hq8/BxNugLkz4I/n6n9mvFQUtY4WZAgqWadgC/Ksn8HmxXDWvdA5O+xokqPPCF/QUpWsG9RYgtzHOXfIx6LBuoEJiUgkFZSv9fO31pcgg5/yqXqPL0YiIgdK5hRP0QZP8W8cU/HNmISndBl07et7GMRT937+NZ6u45B3lMILN/nkuKIIzvsdfOMl6Duy8WMjMrNg2k/h3Adg3Tx48BS1XrXUvt2wc0srS5CXpdZQhHXz4J3/gzGXwdCpYUeTPFkd4LBj9DvYiMYS5IY+GuwUz0BEUsqmT/2y76j698nNh6NPg3m/S/3pC0SSLZlTPEWLjENWN2uJVvI55AxJzLkHTvItyKn05r+laqr9/7Z7x/rxwyfc4LtTj76w+RV+Cy6Gb77iKxo/PBU+eSa+Mbcl2yNTcjJXLgAAIABJREFUPLWCLtbgx/5X70mdca97d/iu1d37w9Sfhh1N8uUV+iJ76mlVr8YS5PlmdtXBK81sOqCyhJK+Nn0KmO9K1pATrocdm/WPXuRgkQS518DkXrfXQJ+Uaxo2iXDO9yjIjXOBrogjJ8Hu8v0zH7R2a+fCg1+Al77nPyS+9h2Y+hPo2L3l5z78WF/tut8Y+Mu34LX/Sq8PFpIlMgdyj1bUggyp07Pn9TuhbAWc89v4vK5bm36FfghU+bqwI0lZjSXI/wZ8w8xmmdn/Bl9vAd8Cvp348ERCsvETyB4EHbo2vN9Rp/g55ebcp0/iRKKVrYJueeFMQXLUKb7La9Xe5F9bUk/lJthTEf8K1hED02Q+5MrN8Nx18MjpsGMLfPX3cMXf4LBGPihuqq6HweXP+9kg3r0HHv+qr4wtsSsv8svW0sU6UhyvJAWmelr5Fsx7AI6/FgadHHY04cgr9Et1s65Xgwmyc26Tc+5E4E5gdfB1p3PuBOfcxsSHJxKSTZ82PP44wsyPRd60CFa9lfi4RFqLslXJ714dMXgK7NsB6+eFc31JLbUVrBPUxbpHf1+tvbWOQ66ugjn3++7UnzwNk74DN74PI89rfnfqxmS1hzN+BV+5x3/ffnfK/qFN0riKIEHulhduHLHq1NPXAAi7BXl3BTx/g5+//NTbw40lTH1GgGX6btZSp5jmQXbOvemcuzf4Ur81SW97d/juoX1iLEIy6gLokgvvaconkVplK30vjDAMOsn/89c4ZIH9b8oT1cUaosYh1yTuGomw+h144GR45fvQfxxc/x588Y7Ge0/Fy9gr4MoXfdGph06Dxc8n57qtXUWxLziXrJ9TPOQODX+qp1f/w3+4cO4D4fRuShXtOvkhhGpBrldMCbJIm7J5CeBiT5DbdYTx02HZq75Ko0hbt3cHVG5M7hzI0Tr28G/2VypBFnyC3L5bYlvbBk6C3dtgcytpBa3YAH+5Ch79su9+fuEf4evPJq6VvSFHHOfHJfcZDk9dDv/879b3QUOyVRT7AlOtSe4wKFka3nC0pa/CB/+fvfMOb6u8/vjnSrK893a8EtvZie3E2YPshD3DpuwNpWW07EKBlraMH6vMQguEAGEljJAFhOyQHTvTcYZ3vO14S7q/P17JdrYdS7qy9H6eR8+Vr67uPU5kSd/3nPM9H8G4P0DCCG1icCXiMsSoJ9keeEKkQJZIjsVmtNKZEmsbWTeD3lv0Iksknk6lRiOeOtJnMhRukr2NknYHa0eVCwMk2fqQXbzM2twKq1+D17Ngxzcw8SG4ez0MvMCx/z6nIyhWZJIzr4MVL8DcK4XxmeTE1Bb0HAdrGxF9xfjMtW9C8TZR2u8sGirFuLKoQTDpYedd15WJzYD6snbDN8lRSIEskRxLaTYYAyAkqfPPCYiEobNhy1z5hVwi0WoGckdSpgAq7P9VuxgkroEjHaxthCQIB3VXFsh5y+HNcbD4cSHo71oLUx53nVJTgzdc8Bqc+yLsWwbvThEZR8nx1Bb1PIHceyIERMOiR+DtCfB8AnxwDix5EnYsEFUNjuKHB8Xc6IvfEq8zCcSmi60ssz4hBq0DkEhcjtIc4Uyt6+L60ei7YfPHsOF9mPigY2KTSHoCbTOQNSqxBug1HLyDxLinQRdpF4dEW5pqoa643UXXkSSPh13fi/Lgrn5+OJK6EtFjnPO1WPi96lPod7bWUZ0YRREtS1HWcut3p8Cl77puvFpgahaZv57iYG0jsh88sFvMQi7YAIUboOA34d9iaRXHBMVD/HCIHyFusemiX7Y7ZH8F2V/C5Mchdmi3fw23IWYwKDpRZt3/HK2jcTmkQJZIOqKqIoM86JKuPzd6oCjrXP8ujP29cOmUSDyRyjzwCxe9wFqhN4iMxb6fxd+1luWjEu2w+UI4QyAnjReLpId3iC+froDFLMqVD++ESY/AuPu6LzicQdJY0Zf86TUi/smPwYQHXWvhQSvqrJnWnjIDuSOKIhZOw3qLqjsQBm0l24VYtolmm1mbziD8YOJHCF+J+BGiMqmz7+d1pfD9AxA3TLizS9ox+ov3RZlBPiFSIEskHaktFH1PZ/rlZsw9MOdSsVKffoV9Y5NIegpajnjqSJ9JsOs7IdjDU7SORqIF5dYRT44usYYO85BXuo5AXv8OFG2Gy96HwZdqHU3XCI6Hm36Eb/8APz8nvshf/BZ4B2odmba0zUDuYSXWJ8PLR5hmdTTOOnJYZJltonnrXPjtXfGYbyj0sorl+OGiWsg39Pjzqip8ex+0NgjXar2UPMcRmwF5v2gdhUsiXy0SSUdscxg762B9LKlTIaIfrHkdhl4us1YSz6RyPySN0ToKax8yosxaCmTPpHwP6LxEf7CjCUkUJcwHVsDoOxx/vdNRnS8coVOnn1lVlCvg5StEcVwGLHoM3psGV37i2X/PNlOlnlZi3RUCokTZr63012IWZnsFv1lF80b4ZSlgdWCO6GsVzVbhHDUQtn0GexbCzL+JEVOS44lNh22fijaMwBito3EppECWSDpSsl1sowac2fMVBUbfCd/9QczETB5vv9gkkp6AqRlq8iH0aq0jEVnskERRZj3yVq2jkWhB2R7xOtB7Oed6yRNgtwv0IauqMCZCFaZXPXmx1va5GjUA5t0I706GS9+HtGlaR6YNtW6WQe4MOr1oY4seKGZng/AXKNpsFc0bIHcJbP1EPOblB6pFmNGNulO7uF2duAyxLd4qBfIxyGYOiaQjpTniC3V3eifTrwTfMFjzhv3ikkh6ClUHAdU1SqwVRWSRD6wQ420knkf5budmj5LHQWMVlO103jVPxI75sOdHmPwohHZhIoMr02cS3PYzBCfCnMtg5cueOcO1tgi8g2WpuU8Q9DlLmKJe/Sk8uBfu2wqX/geGXS9eLxf9W/atn4qYIYACRVu0jsTlkK8aiaQjpTlnXl5tw8sXRtwMuxdCxT77xCWR9BRcYcRTR/pMhuZaUZIn8SxMLaLcP8IJ/cc2XGEecmM1LPwzxAx1v+xZaDLcvAgGXQxLn4Kvb/c8kVxb6FnZ486iKOL1MeQyOPt5uPoz57RW9GS8AyE8VRp1nQApkCUSG61NULG3+wIZYMStoqRv3VvdP5dE0pNoG/HkIgK590QxymLfz1pHInE2lXmgmp3jYG0jNElUIR1Y4bxrHsuyp6H+MFzwqnsaExn9henYhAetfaY/ah2Rc5ECWWJP4jLEqCfJUUiBLJHYKNslelaiB3X/XIHRMPgyMfKjsar755NIegqVeWL+sF+Y1pEI/MIgLlMYdUk8izYHaycb9CSNhwOrRB+yszm0Fja8LzLHcZnOv76zUBQxtio8FZY+LUycPIXaIimQJfYjNl0suhwp0zoSl0IKZInERmm22Nojgwww5i4xXmDj/+xzPomkJ1CZJ2ZcupIpUJ/JosS6sVrrSCTOpHyP2IanOfe6yeOhsVIsujoTU4sYaxOcIHqP3R29AaY+Kfq9t36qdTTOwdQiRiAFx2sdicRdiO1g1CVpQwpkicRGaQ4YfMWXe3sQM0SUd65/RxoESTwHV5mB3JGUKaLUVsuyV4nzKdsDQfHgHeDc69qmFzi7D3nVK0KUn/ui839nrRhwgZiD+/PfRJuUu1NXDKgygyyxH7FDxbZ4s7ZxuBhSIEskNkqzxRgJnd5+5xx9tyhd2THffueUSFwVswmqD0KonRaZ7EX8CPDyl33Inkb5Hm3mn4YmCadlZy7IlOfCr/8S5lV9ZzrvulqjKDDtaagtgN/e1Toax9M2A1kKZImd8AkWi9oyg3wUUiBLJCBcMEuyIcZO5dU20maIHqk1r3ue06bE86jJB4vJ9TLIBiP0niD7kD0JiwXK9zrXwbojyePg4CrnvO+rKnz3BzD4wKx/OP56rkbvCZA6DX59wf3bKNpmIPfSNg6JexGbAUVSIHdECmSJBOBIqegZs1f/sQ2dDkbfKYbZH1pr33NLJK6GqzlYd6TPZDGCqnK/1pFInEFtIbTWQ4ST+49tJI+Hhgrn9CFv+URkq6c/LQwiPZFpT0FTjSgzd2faMshSIEvsSGw61ByChkqtI3EZpECWSEBkj8E+DtbHkn4V+IbC2jfsf26JxJVwtRnIHUmZIrZ5sszaI7AZdEVqlUF2Uh9yfTksfgwSRsOw6x17LVcmZggMmQ1r34TaYq2jcRy1hWAMBJ8grSORuBNxNqMuOe7JhhTIEgm0O1hHDbT/uY3+MPxG2PW9zF5J3JvK/cLoLjBG60iOJyJNZF3ctQ/ZbJJtHB2xCWStSqxDkoSbtKP7kBc9Cs1H4PxXRMWSJzPlMdHisfx5rSNxHHIGssQRxKaLbZEUyDY8/N1UIrFSmiO+PDtqduvIW0HRwbq3HXN+icQVcMURTzYUBVImw/7l7jczta4EXhsGix/XOhLXoWw3+ISAf4Q211cUSBon5iE7auEidxls+wwm3A9R/R1zjZ5EaDKMuBk2fST6z90ROQNZ4gh8Q8WinjTqakMKZIkEhEC2d/9xR4LiYPClsPkj0SclkbgjrjjiqSN9Jou/vyI3GmdhaoHPrxfu4b+9J3vIbJTvFeXVWi7WJI+HhnIh1u1NSwN890dhAjn+fvufv6cy8SHw8oNlf9U6EsdQWwTBsv9Y4gDiMmSJdQekQJZITC1Qvtsx/ccdGX0XtByBTR869joSiRZYLKIH2V5zxB1Bn8nWSo633KccedEjkL8WzvozmJpg4wdaR+QalO+GCA1GPHWkrQ/ZAWXWy/8hFkXOfwW8fOx//p6KfwSMvRd2LoD837SOxr6YW0W1iDTokjiC2HSoOgCNVVpH4hJIgSyRlO8WfUuOFshxGZA0XpRZm02OvZanI/99nU9dsRBorjYDuSP+4XDWw7B9Hqx8Wetous/mj0XWeOy9MPlR6DMJ1r8nvkh7Mg2VUF+mvUAOTYagePsbdZVsh9WvQea17SJc0s6Yu8E/EpY+5T4LYSDEMaossZY4hlibUdc2beNwEaRAlkhKc8TWkSXWNsbcJWbF7vrW8dfyFFQVyvbAxv/B13fCKxnwXAzkr9c6Ms/ClUc8deSsP8Hgy2DZ07BjgdbRnDmFG+G7+6H3WTD1KbFv1J1QVwQ75msamubY+k+1crC2oSj2n4dsMcO394mewenP2Oec7oZ3gKioOLgScpdqHY39kCOeJI4kVjpZd8RhAllRlARFUX5WFGWHoig5iqLcZ93/lKIohYqibLHezunwnEcURclVFGW3oigzO+yfZd2XqyjKw46KWeKhlGaD3lv0cjmavrNEhm2NHPl0xphaROncqldh7tXwrxR4YwR8+3vYu0hUAviFwcI/i7JfiXNw5RFPHVEUuPANiB8JX90GhZu0jqjrHCmDz66DgGi47APQG8T+tBni33/dW9rGpzXl1p5frTPIIDK89WXtrtrd5bf3xOLIrOcdZyrpDgy7XnzWLn3KfT4HagvFVgpkiSPwDxfO+9KoCwCDA89tAh5QVXWToiiBwEZFUZZYH3tZVdUXOh6sKMpA4EpgEBAHLFUUxfbp9gYwHSgAflMUZYGqqjscGLvEkyjNEQ6gekf+OVjR6UUv8sKHRIYzYaTjr9nTaaoRgvjQGji0Fgo3iFJeEGKg7yxIHA2JY8Qih6LAlrnwzR2w/XNIv1Lb+D2FyjzQeUFwvNaRnB4vH7hyDrw7FeZeBbf+1HOMb8ytMO8GaKiAmxeLLzU2dDoYdQcs/BMUbID4LM3C1JSy3WLRMyRR60iO7kPubka7pkCYT6VMhSGXdT82d8ZghCmPw5c3i5aK9Cu0jqj7tAlkWWItcRCx6XLUkxWHZZBVVS1WVXWT9X4dsBM41TeQC4FPVVVtVlV1P5ALjLTeclVVzVNVtQX41HqsRGIfHO1gfSwZV4NPsMwin4yaQtj+BXz/ILw5Hp5PgjmXip7R1nrIugku/xAe2AO/3wwX/RuG/U7MubU51g69AuIyYenT0FKv7e/jKVTmQWiSWATqCQREwdWfidfH3CvELNmewJInReno+a+0z67sSMbV4B0Ea990fmyuQvle8X7gCq/F0N4QGCfGPXUHVYUfHhIl1ue95Jqj1FyNQZeIv5GfngVTs9bRdJ/aIvDyF98fJBJHEJsBlfvktBWc1IOsKEoykAmss+66R1GUbYqivK8oSqh1Xy8gv8PTCqz7Trb/2GvcpijKBkVRNpSVldn5N5C4LUfK4Eip4w26OuIdAMNvEC6b1Yecd11XxGKB0h3w23/gy1vh5SHw8kCx6r91rnAknfQI/G4+PHwIbvsFZv0dBl4IgdEnP69OBzP/LvoxV73qrN/Gs6nMc/3y6mOJHgizPxCLZF/d5vqlmFs/g7X/Fr3GJ6uM8A6EzOtgxzftPYuehis4WNtQFJFFPrCye33IO7+F3T/A5EeE+Zfk9Oh0MO0pqDkEG97XOpruU1sossdycUTiKOKsfcgl27WNwwVwuEBWFCUA+BL4g6qqtcCbQAqQARQDL9rjOqqqvqOqapaqqlmRkZH2OKXEEyjNFltnCmSAkbcBinC09iRam+DgGljxEsy5HP6ZDG+Oge/vh/3LodcwmPUPuG05/Pkg/O4bmPRn4c7rHdC1ayWNgUEXw6pXRFZa4jhUFSoP9DyBDJA2XfRz7v4elj2ldTQnp3ir6LNPGg8zTmPONOo2UC2iX9XTaG2EqoOuI5DB2od8uN08rKs01Yiy+ZghMPpu+8bm7qRMEZ8fv/4Lmmq1jqZ7yBnIEkdjq0qSZdYO7UFGURQvhDieo6rqVwCqqpZ2ePxd4Dvrj4VAQoenx1v3cYr9Ekn3cKaDdUeC42HQRWIm8qSHRdbHnak+JBymC9aDuUXsi+gHAy8SvcOJo0VWxN4r49Oehl0/CMfiS96x77kl7dSXQ0uda494OhUjbxMmSqteEX3sw36ndURHU18Bn14LfuEi4633OvXxocnQ7xzY8AFMfAi8fJ0SpktQsQ9QIdLFBDJY+5DPIK5lfxWVTlfOcY5Xhrsx7Sl4Z5IYjTXlMY2D6QY1hZAyWesoJO5MQJRoCZFGXQ51sVaA/wA7VVV9qcP+2A6HXQxYU3gsAK5UFMVbUZTeQBqwHvgNSFMUpbeiKEaEkVcPns0hcSlKcyAgRpTyOpvRd0NzrZhl6u6sfk2I41F3wJVz4aE8uGc9XPAqZFwFYb0dUzYWmiRmYm77DAo22v/8EkFPGfF0MhRFVC6kTIHv/gj7V2gdUTtmE3xxoxBIV3wkvsB0htF3QmMlbPvcsfG5Gm0O1hqPeOpIWB8IjBXjnrpK/nrRgjLydug13P6xeQJxmaIfec3rUFd6+uNdEbMJjpRIgy6J44nLkKOecGyJ9TjgOmDKMSOd/qkoynZFUbYBk4E/AqiqmgN8DuwAfgTuVlXVrKqqCbgHWIQw+vrceqxE0n1Ks51fXm0jfjgkjBZmOhazNjE4g9Ym8SV9wPmiNLT/OUc77zqaCfeDfxQsesR+s0glR9NTRjydCr1BjEwKS4HPrrVmIl2AZU+L9oPzXuqaQEoaB9FDxPuLJ73uy/YACoSnaB1JO2fah2xuFTOPg+J6dubTFZjyuKhe+vWfWkdyZhwpFW0TcsSTxNHEpot2kJ5iXOkgHOlivVJVVUVV1aGqqmZYbz+oqnqdqqpDrPsvUFW1uMNznlNVNUVV1X6qqi7ssP8HVVX7Wh97zlExSzwMswnKdmknkEFkN6sPwq7vtYvB0ez+AZqqIeMaba7vHQhTn4D8dZDzlTYxuDuVeaDoXGOsTnfwDRHO1jo9fHI5NFRqG0/2l7D6Vci6GTKv7dpzFUVkkct2CoHtKZTvEZUjrlZWnjxeiJyK3M4/Z/WrcHgHnPui+7fhOJrwFGGOufG/rrP41RVshntSIEscTWwGoHq8UZdTXKwlEpekYq9YUXZ2/3FH+p8LIUnCmdZd2TJHfKj3maRdDBnXCIObJX8RJj4S+1KZJ/rqDUatI+k+Yb3hijmib/7z34ksnhaUZMP8eyBhlDAROxMGXwp+EZ418ql8j2uVV9tIniC2BzpZvl+xD375Bwy4APqd7bi4PImJfwK9UYx96mnUFoitLLGWOBqbUZeHl1lLgSzxXNoMujTMIOv0oi/30BoodMMe2ZpCyF0mZrNqOZNUp4eZf4OafDl/2hH0xBFPpyJpDFzwmhAz39/v/BLlhkr47Boxz/jyD8984cHLB0bcDHsW9cysWVexmNtnILsaYX2E30Vn5iGrquiFN3jD2T20JNgVCYyGMfeISqLCTVpH0zXaMshSIEscTFAsBER7vFGXFMgSz6U0G3Re2o8DybxWfBFe44ZZ5K1zAVUIZK3pPRH6nydGTNWVaB2Ne1G5370EMog5wxMeFE7za1533nUtZvjqVrG4dMVHEBjTvfNl3Qw6g2eMlKs+BOZmiHTBDHJX+pC3firK4qf9RXxZldiPsfcKN/ilT2kdSdeoLQIvP/AN1ToSiScQm+Hxo56kQJZ4LqU54ouU1mWhPkFirMyOb6CmQNtY7ImqivLqpPGuI56m/1WU1f90mjmyks7TWCXckl3l/9ieTH4MBl4Ii58Q48Kcwc/PQe5SOOefkDCy++cLjBal1lvmiHm67kz5HrF1xRJrsPYhl5w6m19fAYseFaX1w29yXmyegk+QGH22fzns+0nraDpPTYHIHjti2oNEciyx6WIiQEuD1pFohhTIEs+lNEfb8uqOjLpdOFSud6NZvYfWiNLbTI3MuU5EeIr4t948x+NXR+1GpdXBuqfOQD4VOh1c9JYYE/PlLVC8zbHX27EAVrwoFsyG32i/846+A1qOuP9IuTLbiCcXLLGGzvUhL34Mmuvg/FfE609if7JuEoaCS/4CFovW0XSO2iJZXi1xHnEZ4jtpafbpj3VT5LuvxDNpqITaQtcRyCGJwoxl43/dx1p/8xwwBogMnCsx8SHwC4NFj3nW+BtH0dNnIJ8Oox9cNVc4XM+90nHl+Yd3wTd3Qq8sOOcF+2aK4jIhcYwos3bnkXLle8A/Uvx9uyLhKaK372TzkPf9LNpSxt0HUQOcG5snYfCGyY9DybaeM9mgtkg6WEuch82oy4MTCVIgSzwTVzDoOpYxd4sSyC2faB1J92k+Ajlfw6CLweivdTRH4xsCkx+Fgyth57daR9Pzsc1ADk3WNAyHEhgDV30KjdVCJNu77KypBj69WowmuvxD8QXe3oy6Q4yU273w9Mf2VFzVwdrGqfqQWxuFMVdYiljEkziWIbPFnPCfngFTi9bRnBqLGeqKpUCWOI+gXmICggcbdUmBLPFM2gSyhiOejiVhJMSPgHVv9vwsz45voLW+67NbncWwGyByACx5AkzNWkfTs6ncD4GxItPqzsQOhUvfEyvq39xhv9JMiwW+uk2I19n/g2AHfQnufx4EJ8C6txxzfq1RVVFi7arl1TaSxwuxY6u8sLH8n2Kx6byXhfu4xLHodMIEreoAbPqf1tGcmiOloJplibXEeSiKKLP24FFPUiBLPJPSbLE6FhCtdSRHM+Zu8cWpp2d5Nn8M4anCaMYV0Rtg1t/ElyN3FQzOwt1GPJ2K/ufAjGdgx3xhpmUPlv8D9vwIM/8OyePsc84ToTfAyFtF/6uje6m1oL4cmqpd08G6I0njxfbAyvZ9pTmw+lUxr73PWdrE5YmkThN94cv/4dqtTW0jnmQGWeJEYtPh8E5obdI6Ek2QAlnimdgMulzNEbL/+aIf2ZljZexNxT5h0JVxjev9+3YkZQqkzYRfX4AjZVpH03OpzIMwNzToOhlj7hEmWiteEON4usOuH2D585B+tRCvjmbY78SoGHcc+VRuM+jSeGzf6YhIA/+odoFsMcOC34NPMMx4VtvYPA1FgWlPQX0ZrHlD62hOTm2h2MoMssSZxGaIygVbxaWHIQWyxPOwmMWqmCuVV9vQG2D0XUJgFmzQOpozY8scUHSQfpXWkZyeGc9Ca4P9soGeRku9KP/zlAwyiC/V574k5movuBcOrjmz85Tvha9vF6v0573knMUk31Dxd7n9c/dbFCrrIQL52D7kDe9D4QZRQeCq5mLuTHyWMMhc/arr/k3YMsjB8drGIfEsbEZdxZu1jUMjpECWeB6VeWBqdC2Dro5kXgvewbD6Na0j6ToWM2yZK0rXgmK1jub0RPaFEbeIHjQPXSXtFu484ulU6L2EmVZIInx2Tfu/Q2dpqhWmXHovuOJjYc7lLEbdIWaBb/zAedd0BuV7wcu/Z4iI5PFQVyTcrJc+DX0mw9DLtY7Kc5n6pDBJW/GC1pGcmJoCMPiIBS6JxFmEJIrF4D6TtY5EE6RAlngetrluriqQvQMh60bYuUD0yPYk9v0svvhluNDs49Nx1p/BOwgWPSrHPnUVdx/xdCp8Q+Hqz8Wi0CdXCIfrzmCxiHFOFftg9n/FlxBnEtkXUqfDb++5vntvVyi3GnS5cluHjWRrH/Jn14Gl1XkVBJITE5EGw66D3/7T9cUuZ2CbgSxfIxJnoigw4mYxns4DkQJZ4nmU5oCih8j+WkdyckbdLsqU176pdSRdY/NH4BsG/c7WOpLO4xcGkx6BvF9gzyKto+lZ2EY8eVIPckfCU0QGuHIfzLsBzKbTP2flS7DrO2H21Xuiw0M8IaPvEKXxOV9rc31HULbH9curbUT0FfOaGyth0sOeucDkapz1MOgMrtluI2cgSzpgtsiFfGcgBbLE8yjNESvGrjxKIygOBl8Gmz6Cxiqto+kcDZWw+wdRKuiIOa6OZMTNEJ4Gix9zr6yao6nME27wPsFaR6IdvSfAef8HeT/Dwj+dugph7xL46Vkxg3X0Xc6L8VhSpop5wWv/7R5VE81HoLZAZMd7Aooixm71Gi5M3yTaExQLo++E7fNcb/arFMgeT6vZwuKcEm79cAP9Hl/IlBd/4cn52fyYXUJNY6vW4bklBq0DkEicTkk2JIzQOorTM/Ye2PYpbPwvjP+j1tGcnu1BS+GdAAAgAElEQVRfiN5GV519fCr0XjDzOfjkclF6OkZD8dKT8DQH65Mx7Dqo2AurXhFjhkbdfvwxFfvgy5uFOeD5r2pbLqkoIsbv74f8dZA4WrtY7EHFXrGNcPERTx0572WxOKGTeQqXYdx9wjRt6dNw3VdaRyOwmEXbknSw9kh2ldQyb0MB32wupKK+hchAb64amUhBVQNfbCzgwzUH0SkwJD6E8anhjEuNYFhiKD5eeq1D7/FIgSzxLJpqoOYQZN2gdSSnJ2YI9JkkRrKMvhsMRq0jOjWbP4KYoSLunkjaDDH6afnzkH6ldJTtDJX7IWms1lG4BlOfEiL4x4eFaVnfGe2PNR+Bz64VbRNXfgxGP83CbCP9Slj2V5FF7ukCuWyP2PaUEmsQixSyp9S18A2BiQ/C4schb7lrzKSuLwOLSQpkD6K6oYUFW4uYt6GA7YU1eOkVpvaPZnZWPGf1jcSgF4tqLSYLW/KrWZlbzurcct5anscbP+/D26BjZO8wxqVGMD41goGxQeh08r2mq0iBLPEsSneIrSuOeDoRY+6FOZdC9peQ4cJjk4q3Qck2OPufWkdy5igKzHgO3hoHv/wdzvmX1hG5NqZm4a4q+ycFOh1c8g68Pwu+uAluXiSMAFUVFtwDZbvgmi8gNFnrSAVGfxh+vXDLrz7kfLMwe1K+R/hKyNeipLuMuBXWvgVLn4Jbf9J+EaNtBrIssXZnzBaVX/eW8cWGApbsKKXFbGFgbBB/OX8gF2b0Isz/+ASJ0SqER/YO4/7pfalramX9/kpW5pazKrec5xfuAiDUz4uxKRGMTQ1nfGoEiWF+KFq/rnsAUiBLPIs2B+seIpBTp0LUQFjzusj4uOqb2pY5oDeK3sqeTPRAGH6jcDMdcYsol5WcmKqDgCpFSUeM/nDVp/DuFPjkSrh1GWydK8ywpj0l/p5diRG3wurXYf27wjSsp1K+W7wOXb3KRuL6ePnA5Edh/l2wYz4MukjbeNpmIEuB7I7sKzvCFxsL+GpTAaW1zYT6eXH1qERmZ8UzKK5r3h6BPl5MHRDN1AHRAByubWL1voo2wfz99mIA4kN9GZ8awbjUCMamhBMe0MM8Y5yEFMgSz6I0B3xCek65kqLAmLth/t3CBChlitYRHY+pBbZ9Dv3OcY+y5MmPin7qRY/BtV9oHY3rYhvx5GkzkE9HcC+4+lN4/2z477lQkQsDL4Rxf9A6suMJSYAB54s54JMeFgK/J9KTHKwlrk/6laKyYtlfof+5wqNCK2pkBtndqGtq5bttxczbkM+mQ9XodQqT+kby9AXxTOkfjdFgH1+CqCAfLsrsxUWZvVBVlbzyelZ1EMuf/pYPwIDYoLb+5ZG9w/AzSmkIUiBLPI3SbJE9dtVM7IkYMlt8UK9+3TUF8p6FYlxJ5nVaR2If/CPgrIdEH9repZA2TeuIXBNPnoF8OuIyRbn159dB5AC48N+u+54z+i7Y8Y3IdI+4Retouo65VbwW+5+jdSQSd0Gnh2l/gblXCm+NrJu0i6W2UFRn+YVrF4Ok21gsKmvzKpi3sYCF2cU0tVpIjQrgkbP7c3FmL6KCHDtVRVEUUiIDSIkM4HdjkjGZLWQX1bIqt5yVe8v53+qDvLtiP156hczE0LYMc3p8cFvPs6chBbLEc7BYRA9yT3NZNnjDyNvgp2dE/NEDtY7oaDZ/DIFxkDJZ60jsx8jbhZvp4seEUZpevlUeR9V+8A52j6oBRzDwArhpEYSlgHeA1tGcnISRQtCvexuG39TzXJWrDoCltWc5WEtcn76zIGE0/PI8DL1Cu+qKWquDtasusElOSX5lA/M2FvDlxgIKqxsJ9DFw6bB4ZmclkB4frFkvsEGvIyMhhIyEEO6enEpji5kNB9v7l19euoeXluwh0NvAE+cP5PKsBE3i1BL5rU/iOVQfgNZ6YZzT08i6CVa8KHqRL/q31tG0U1sMuUvFGCqdG40VMBhh+jPw2TWw8QMYeavWEbkelXkQliy/uJ2KnuAOrSgii/zVrbDvp55XMVG2W2xlibXEnigKTH8a3p8Ja98U7tZaUFsEQfF2P21NQytLd5byY04JGw5UogI6RUGniGyjcszPOp3tZwVFaX9M/Nx+v+145QTHW89h1OswGsTN27o16vVH/Xzs1qjXt9/v8Ji37TEv3VHnNegUzcRnQ4uJhdtLmLcxn7V5lSgKjE+N4E+z+jFzUIxLjmDyNeqZkBbJhLRIACrrW1izr4JV+8rpHdFDW2+6iRTIEs+hNEdsY3qIQVdH/MJE5nvDBzD1SQiM0Toiwda5oFog4xqtI7E//c+F5Anw899gyGXgG6p1RK5FZR7EpmsdhcQeDLwIFj8B697seQK53DbiKU3bOCTuR+Jo4a2x6hWxSK1FtUxtgchk24HDdU0szillUU4Ja/ZVYLKoxAb7MGNgDL5GPRZVtd5AVVUsFo7+2XrfoqqoKscfr57geOs5zBaVVrOKWVVpMVnEzSy2zbafrfvsgU4Bb4MeX6MeXy+x9TvuvgFfow4/owFfL+s+6zF+RgN+Rj0+1v3HPubjpTtKgKuqyoaDVczbkM/324qpbzGTHO7HgzP6csmweOJCfO3yezmLMH8j5w6N5dyhsVqHohlSIEs8h9IcQBE9gT2R0XcKt9l1b4v+KK1RVeFenTgGwlO0jsb+KArM/Bu8PRF+fQFmPqd1RK6D2SRGAw26WOtIJPbAYBT9xz8/KwyvIntQNrZ8j2jx8AnSOhKJOzL1SXhzrKjgcvZngMUiqrS6YSqaX9nAopwSFuWUsOFgFaoKyeF+3DKhD2cPjmGohmW+J8JiUYVwNreL5qMFtJnmVgvNJ3zcTIvZQnOreH6zyUJji5mGFjNNrWYaWkw0tJiprG+hsErsb7Tub2rtmjBXFITYtgpuk1mlpLYJP6Oec4fEMjsrgRHJoS71byvpGlIgSzyHku1CyBn9tI7kzAjrAwPOE72xEx/U3nE2f71w6B3/R23jcCSxQ2HYdWJRIusm91wIOBNq8sFikgZd7kTWjfDrv2DdW3DeS1pH03nKdsvsscRxRA2A9Kth/Tsw6nbnzgtvKBf99V10sM49XMeP2SX8mFNCdmEtIJyK/zC1L7MGx9A3OsBlhZtOp+Cj0zu9DNliUWkyWUVzm3AW4tkmshtbze33W0xtxzS2mDFZVCb2jeTswTH4e0tp5Q7I/0WJ51CaIwRPT2bs72Hnt7B5Doy6TdtYNn8EXv6iPNOdmfw4ZH8lSlCv+kTraFwDOeLJ/fCPgKGzRdvE1Cd6RkuBqkL5Xsi4SutIJO7M5Edg+zz46Tm45G3nXbfWOuLpNDOQVVUlp6iWH7NLWJhdzL6yegCGJYbw6Dn9mTkohqRwz+wj7Sw6nWItq5aySCKQrwSJZ9B8RLjuZlytdSTdI2EkxI+EtW/AiJu1M8ZqqYecr0WJrSs79NqDwGiY8AAsexrylkOfs7SOSHvkiCf3ZNSdwpV+04cw7j6tozk9dcXQUicNuiSOJTgeRt8hepH1Bpj1D+d87rXNQD6+xNpsUdl0qEpkirNLKKxuRK9TGNU7jOvHJjNjYAwxwY4dHSSRuDNSIEs8g8M7xTa6Bxp0HcvYe+Dz38Gu72DghdrEsGM+tByBTDc05zoRo+8SbtaLHoXbf3Uvx+4zoeoAGHxdxyxOYh9iBgtjunXvwOi7XX+8mXSwljiLKU+AzgArXoIDq+DS9yA+y7HXrC0SW2uJdavZwpp9FfyYU8LinFLKjzRj1OuYkBbBfdPSmDYgmjB/o2Njkkg8BBf/9JNI7ERpttj2xBFPx9L/PAhNhtWvayeQN88R2cPEMdpc39l4+cD0v8K8G0Rp+fAbtI5IWyrzxP+/i/axSbrB6Dvh06vFAtwgF2+fKN8rtpFyBrI70mKycKTZxJEmE3XNrdQ1iftHmk3UNbVS12zCqNcxuk84A2OD0Okc+H6k9xKGXSlT4evb4T8z4Kw/i+oiRy0k1Rai6rxYcsDEjzu2sHRHKbVNJvyMeib3j2LWoBgm9Ysk0MfLMdeXSDwYKZAlnkFpNhgDnWuw4Sh0epHdWfgQHFoHiaOce/3KPDi4Uqyoe5JAGniRWBD46VkYdIlnu+ZW5kF4qtZRSBxB31liAW7tmz1AIO8G72AIiNY6EkkHWs2WNiFb29Tadl/8bBO57YK3roMIbhfAJppNnXcWDvXzYmxqBOOtt4QwB5lxJo+DO1bCDw/CL3+Dfcvg4rchzH5+DIfrmlizr4LYbdnEmkO57ePNBPt6MX1gDLMGxzAhLcIlZ+lKJO6EFMgSz6A0R2SP3UXQZV4DPz8Ha15zvkDe8gkoOkj3MGMc29indyeLkR/Tn9Y6Im2wWKByP6RN1zoSiSPQ6WHk7bDoESjcBL2GaR3RybE5WLvL+3oPpqi6kW+3FrFgaxE5RbWnPV6vUwj0MRDgLW6BPgaiAn3oE2EgwEf8HGh9LMDHq/1nHwOBPl5tz6ltbGXVvnJW7q1gZW4Z328rBiAp3I9xVrE8NiWcED87lh77hogS67SZ8P398NYEOOef4jOxi6/FFpOFncW1bDpUxeZD1Ww6VEVBVSMAX/kWYQ6I5eNrRzGqTxheep39fgeJRHJKpECWuD+qKgTykNlaR2I/jP7CpGvFS+3lrs7AYoYtcyFlymmdNd2SXsPEl6C1/xZjcUKTtY7I+dQVg7lZGnS5M5nXws9/EyOfLnlH62hOTvleSJ2qdRQeS1V9Cz9kFzN/SxHr91cCkJ4Qwu+nphHqZxOxXm1CONDHKnC9vfDx0tll1JCPl56LM+O5ODMeVVXZV3aElXvLWZlbzoItRXyy7hCKAkN6BTMuNYIJqREMSwq1TwZ26GyxQP3V7fDNnbBnEZz3MviFnfQpJTVNbD5U1SaItxfWtGXKY4J8GJYUwg1jkxmWFErm1w0ovbJITovofqwSiaRLSIEscX9q8qG5VhjQuBMjb4PVr8Gaf8O5Lzjnmnm/QG0BzHjGOddzRaY+KUzKljwJl3+odTTOR454cn98gkSVym//Eb33rmjG1lQDR0qkQZeTqW82sXRnKfO3FPHrnjJMFpXUqAAemN6X89PjSI7QbpyQoiikRgWSGhXIDeN602q2sK2gmhV7y1mVW867v+bx5i/78DboGNk7jPGpEYxLjehe/3JIItzwHaz6P7GolL8eLn4L+pxFs8lMdmEtm61iePOhKopqmgAwGnQM6RXMdaOThBhODCE22Lf9vKoqTLoGeuBCtETiAkiBLHF/SnPE1h0crDsSGCOy4lvmwORHT7lqbTe2zAGfEOh3juOv5aoExcH4P4oS94OrIWms1hE5FzniyTMYeRuse1uI5CmPOf3y+8vr+XJjAV9vLsSiqu3lsqnhRAX6QNkecaAUyA6nxWTh1z1lzN9axNIdpTS2mokL9uHmCb25ML0XA2ID7ZINtjdeeh3Dk8IYnhTGH6b15UiziXV5FazMLWfl3nL+vnAXAGH+RsamhLcJ5i73L+v0qOPvpyxqLH7f3oH/hxfyje8lPF57EfVmkanuFeLL8OQwbkkIYVhSKANiA/E2nCKL3VAB5pY2B2uJROJcpECWuD8lVgfrqAHaxuEIxtwjROuG92Hig469VmMV7PwOhl8vXJ09mTH3wMb/wo8Pw62/gM6DesMq80DnJWaDStyX8BRh2LXhfeHU64S/+bqmVn7YXswXGwv47UAVOgXGp0US6G1g6c5SvthYAED/mEDuDFnHhUBDcAoOsmPyaCwWlXX7K1mwtZAftpdQ09hKqJ8Xlw7vxQXpvchKCnWsa7QDCPA2MHVANFMHCFO30tomVlqzyytzy/nO2r+cfFT/cgTBfse7RDe1mtleWMOmg+29w4frmvHlSf5i/IQrG79kbPA29o57mb5DRhAV1MW/nxrxWj/RDGSJROJ4pECWuD+l2aJX1DtQ60jsT/RAMXZi/Tsw9l4weDvuWtu/EL2nGR4y+/hUGP1g2tPw1S2wda7nzIMGqNov/p48fRa0JzD6TvhwIWR/IfqSHYDForI2r4J5GwtYmF1MU6uFPpH+/GlWPy7JjCcmWAgLs0VlR1GtyP7lllGat5VmxcCw1/cwNLFSuBenRTC0VzAGaWZ0RqiqSnZhLfO3FPLdtmJKapvwM+qZMTCaCzN6MT4twq2MoqKDfLh0eDyXDhf9y3sPH2kTzN9sLmTOukPoOvQv947wJ6dIGGrtKKrFZFEBSAzzY2xKOJmJoQxLDKV/7MWw90eiF9xD9M+XgeEZGHlr1wy82mYgS4EskWiBoqqq1jHYnaysLHXDhg1ah+EarPk3VB+CWX/3XKfP17LEnMwr52gdiWPY9zN8dBFc+IbDvsQC8M4kMJvgjhWe+1rqiKrCe9PESv+9G8E7QOuInMNb4yEwFq6Zp3UkEkejqvDmWFD0dv+7P1ghSqi/3FRIYXUjgT4Gzk+P47Lh8WQmhJy2ZNc85wqaDu/j1f4fsSq3nJyiWlQVAn0MjOkTzvg0US7bJ8Lf5cp/zRaVktomWkwWIgO9CfDWNleRV3aE+VuK+HZrEXnl9XjpFc7qG8WFGXFMHRCFn9HzcimtZgtb8tv7l7fkV2O2qPh66UlPCGZYYiiZiaJ3OCLgJAvTdaUw/27IXQKp08VndGAnR5Ktf1eMknpgT+efI5F4IIqibFRVNcve5/W8dz1PYue3YlQHiIzP6Ds0DUcTWhqgch8MvlTrSBxHn0miv3r16yK764gvg6U5ULQZZj0vxbEN29in92eIcuux92gdkeNRVTHiKWmc1pFInIGiiCzygnvhwEroPaFbpzvSbGoroV6/vxJFgfGpEfxpVj9mDorpkrOwvmIv/r0G88jZonWmsr6F1fuEmFmxt5zFO0oBiAv2EeWyaaJcNjLQgVU2VswWldLaJgqqGimoaiC/UmwLqhopqG6guLqpLfsI4G/UExXkQ2SgN9FBPkQFeotbkDfRgT5EBXkTGehDkI/BbmK/pKaJ77YVMX9LEdsLa8R/de9wbpvYh1mDY+w7FqkH4qXXMSI5jBHJYdw/vS91Ta2U1DTRO8K/8xUKgdFiIfG392Dx4/DmGCGS+519+ufWFoHOAP6R3ftFJBLJGSEFsrtSvhe+vhN6DQe/CFjyBCSNgdh0rSNzLmW7QLWIGcjuiqKInthv7oDcZZA2zf7X2DxH9J0Oudz+5+7JJI6CpPGw9k0YdTvoj+9Vcyvqy6DliDTo8iSGzIYlfxEjn85AINt6WedtzGfh9hIaW830ifDnoZn9uGRYr6OdezuLqVmU+ndY+AzzN3Le0DjOGxqHqqocqmxoy/4t3lHKvA79yxOs2eWRvcPOKDtqsaiU1rUL4ILKxjbxm1/ZSFF141ECGCA6yJv4UD+GJYaSkO5HfKgvRoOOw3XNHK5t5nBdE4drm9leUE1pbTONrebjrutt0LUL6CBvoqziOSrw6H2hfl4nFNLVDS0szC5h/pZC1u2vRFVhaHwwj587gPOGxrWVs0uOR4yrOoP3d0UR5dXJE+DLW2DulTD8Rpj5nBjXeDJqCyEwzrP8LSQSF0IKZHek+Qh8di0YjGIMjcFXlEXOuxFuX+6evbgno83B2o0FMogvisuehjWv2V8gm1pg22di1ds/3L7ndgfG3gtzr4Ccb8RcTHemcr/YSoHsOXj5QtZNsOJF8f8f1rnxXvmVDXyxsYAvNxVQUNVIoLeBizJFCfWwxNDuZUIr9omFz5M4WCuKQlK4P0nh/lw7OgmzRSW7sIaVuUIw/2/1Qd5dsR+jXsewpJA29+Kh8SHodQoWi0rZkea2rG9+pTX7axXEhdWNtJqPFsCRgd7Eh/qSkRDCeUNjiQ8VIjghzI/YYJ8uZcdVVeVIs+k48Xy4ronDdc2U1jaxq6SOFXvKqWs2Hfd8o15HZKA3kdZMdHSQD8U1jSzfU0arWaVPhD/3TU3jgvQ4+kR6SGuI1kT1h1uXwU/PiGqvAyvg0vcgLvPEx9cWyf5jiURDpEB2N1RVlMOV74Hrvm53mr30Xfjf+fDDQ2JGn6dQmg1efu4/s9VgFBnMpU9B8TaIHWq/c+9dBA3lju1v7smkzRBf1Fe/CkMuc+8SdDeYgVxZ30J9swmdTkGngF5RUBQFvfVnnU5BryjoFAWdDnSKYj0Gl+tldRojbhFzXte/C7P+dtLD6ptNLMwu4YuN+azNay+hfmhmP2YMjMHXaCdjt3LriKfIzo140usU0hNCSE8I4e7JqTS2mPntQGVbOfYLi/fwwuI9BPkYCA/wprCqkRaz5ahzRAQYiQ/1Y3CvYGYNjm0Tv/GhvvQK8e2SAD4diqK0ZSxTTiNgG1pMVvEsBHSpVUiXWfftL69n3f5K/Ix6bhibzIUZvRgUF+S5r2UtMXjDjGdFP/LXdwgPi8mPwrg/HG96WFt4cvEskUgcjhTI7sbaf0POVzDtKdGbaiN5PJz1Z/jl79D7LMi4Spv4nE1pDkQN9IwypeE3wPJ/wZo34JK37XfezXMgIEa4ZUuOR6cTWeQF98L+X6HPWVpH5Dgq80DRQUii1pF0iQPl9fyYU8KinBI2H6o+4/PoFKzCuV1ci/sdBLbS/rOigNGgw9ugt25tNz3eXsf8bLvvJe4bj33M69jzHL/f10uP3hGjd4JiYdDFsPkjmPzIUVVIqqqyfn8l8zYW8MP2YhpazCSH+/HgjL5cPCyeXiFnUEJ9OmwCOTztjJ7ua9QzsW8kE/tG8ghQcaSZ1fsqWJVbTl2TiRkDo4m3it+EUF96hfjZT9zbGT+jgeQIA8kRpyjXlbgWfc6CO1fBd3+EZX8VrVEXv9X+vqqqIoPc/1xt45RIPBgpkN2JA6tg8RPQ/zyxInksEx+C/Svg+wcgfgREpDo/RmeiqiKDPPBCrSNxDr6hMOx38Nu7MPVJCO7V/XPWlcLexUIA6uXbxUkZcjksewZWv+beArlqPwQniIoFF0ZVVXYU17Iop5RF2SXsLq0DxLiWB6b3JTbEF4tFxayqWFQVi0XFogpzJYttn+1n22Oqiqqq1mPA0nb/FMdYVFrMFppN1lurmbomExWmFppN5qP22+53F2+DDj+jHj+jwbrV42vU42804Gv9ueNjtvu2Y9ruexvw9RLH+Hsb8B51B8r2ebDlExh1O/mVDXy1qZAvNxVwqLKBAG8DF1hdqIcndbOE+nSU7YbgRDFuzQ6EB3hzfnoc56fLklaJk/ALg9n/FWMCf3gI3hwP574o2nQaKsHUBEF2+AyXSCRnhPzG6y7UFsO8G0R/2EVvnrjMU6eHS96Bt8bBFzfCLUsdOzdXa+qKobFKODx7CqPvgPVvi9v0v3b/fNs+BdUsy6tPh5cPjLoNfnoWSneI+dTuSGVep3tQnY3ForLpUBWLckr4MaeE/MpGdApkJYfx5HkDmTEomvhQ+wgqR6GqKq1m9YTiucX2s8lMc2v7fdv+plYzja1mGlvMNLTdTDS0iH0ltU1tj9W3mGhsMR9nJHUqFAW+MvYlfOFLXPtLCoeqmlEUGJsSzh+npzFrUKzzsqzlezpdXi2RuCyKAhlXQ+IY+Oo2+OoW0dI07HrxuOxBlkg0Qwpkd8DUAvOuh5Z6uP5b8Ak6+bHBvYSAnnulcCY9+3nnxelsPMWgqyOhyTDgAtjwX1Ex0B1DNlUV5dUJoyDizEoZPYqsm2HFS6LE/aI3tI7GMVTmiVJbF6HFZGFtXgU/5pSwZEcpZXXNeOkVxqdGcPekVKYNjD75jFIXRFEUjAYFo0GHM6wUW0yWNhF9rKDuKKRtj+WVXEdm3hNcG76b5qwZXDysl/MXHSwWMaUhuXsjpyQSlyGsN9y4UBjhLf8H7PxO7A+K1zYuicSDkQLZHVj8OOSvg8s+EE6Jp6Pf2TDqTlj3JvSeCP3PcXyMWlCyXWyj3DSbdzLG3gs7voHNH4sZpmdKwQYo3w3nv2q/2NwZvzCRad/wAUx9AgJjtI7IvjRWiZvGDtaNLWaW7yljUU4JS3eWUtdkws+oZ3K/KGYMimZy/yiCzmQciwdiNOgwGoyEdFbjmvvAK29xm9cimHq3Q2M7KTX5YGqUi3YS90JvgEl/hpQp8NWtUH2ox3k9SCTuhBTIPZ1tn4ty2jH3wOBLOv+86U/DwVUw/y6IXWWfflVXozRH9Ev6hmgdiXOJzxIlW2v+DSNuPfPe4S0fCwdwF8oYujyj74Tf3oN1b8O0v2gdjX3RcMRTTUMry3aVsiinhOV7ymhqtRDi58XMQTHMGhTD+LQIu7oIS06C3kvMdF36lHatBOV7xTayn/OvLZE4moQRcMdKqD4IAZFaRyOReCwOs/ZVFCVBUZSfFUXZoShKjqIo91n3hymKskRRlL3Wbah1v6IoyquKouQqirJNUZRhHc51vfX4vYqiXO+omHscJdmw4PeQNE64VncFg7cwiDC3iuH15uNnKfZ4SnM8q/+4I2PugZpDsHPBmT2/pQG2fykMzk5Vsi85mrA+MOB82PAfMY/cnXDyiKfDtU18vPYg1/1nHcOfXcL9n29la34NV2Ql8Mkto9jw2DRemJ3OtIHRUhw7k2HXg8EXljwpPj+cTflusY2QAlnipngHeFZrmETigjgyg2wCHlBVdZOiKIHARkVRlgA3AMtUVX1eUZSHgYeBPwNnA2nW2yjgTWCUoihhwF+ALEC1nmeBqqpVDozd9Wmshs+uFdnR2f8VK/tdJTxFuCZ+fTv8+i8xvsNdMDULIxdPHZPQ72wISxGuyoMu7vps3p3fQkudNOc6E8b+HnbMt5a439G2W1VVmk0WjjSbONJkElvr/foWE3XWffXNR98/Yv25vtlEk8nM4LhgxqdFMCE1ksRwJ/Z/2jLIockOu8ShioY2k61Nh6pQVegd4c8tE/owc1A06fEh6BwxxkjSefzCYMYz8MOD8MVNcNn7Z/b5c6aU7QbfMPAPd941JRKJROJROEwgq6paDBRb79cpirIT6AVcCEyyHgK/RfYAACAASURBVPY/4BeEQL4Q+FBVVRVYqyhKiKIosdZjl6iqWglgFdmzgLmOit3lsVjEkPmafLjhBwiIOvNzpV8Jeb/Ar/+E3hPEvGR3oGy3cF/21FVYnR7G3CVGeh1aA0lju/b8LR8LIZQ0ziHhuQuHKhr4efdhKutb2sVss47few8mZPFL3LimH7UtapsQ7oxrsE4Bf28Dgd4GAnwM4r6PgbgQHxRFYdPBKhZmlwCQGOZnFcsRjE2JINjPgUKlMg8C4+w2WgegprGVrfnVbDhQyZKdh9lZXAvAoLgg/jitL7MGx5AWFeDYkUGSrjPyVpE9XvQIfHkzXPof54nk8r2yvFoikUgkDsUpPciKoiQDmcA6INoqngFKgGjr/V5AfoenFVj3nWy/57LiRdizEM7+FySO6v75znkB8tfDl7eK3hd3WJkvzRZbTy2xBki/Gn56Dla/3jWBXHUA9v8Kkx/reubZAzhc28R324pZsLWILfnVbfv9jHoCrKL2C+NFPNb8LBd6b2R33DQCfAxtjwV4H3PzOfq+r5f+lIJQVVX2ldWzcm8ZK3PLmb+5kE/WHUKnwJD4ECakRjAhLYLMxFCMBjt20VTt71b/sdmisqe0js2Hqtl8qIrN+dXkHhZl6IoCWUmhPH7uAGYOiiEhzLXHMUkQC3CosOhRQIFL33OOSC7fDf3Pc/x1JBKJROKxOFwgK4oSAHwJ/EFV1dqOX/xUVVUVRen8IMZTX+c24DaAxEQ3dv7LXQo/PwdDrxCr+PbAOwBmfwDvTROmXVd92vOFUWkOGHw0d9zVFKMfjLhFlM+X50JEaueet2UuoED6VQ4NrydR09DKjzlCFK/ZV4FFhYGxQTxydn/OGRJLXIgv+o6lv5aJ8Ppcbjd8D5c/YNe/J0VRSI0KIDUqgBvG9abVbGFLfjUr9pazcm8Zby7fx+s/5+Jn1DO6TzjjrYI5tbuZ2Mo8SJve6cPL6prZkm8Vw4eq2VpQTUOLGYAwfyOZCSFclBFHZmIoQ+ODCZTO0z2PMXeLcXCLrYtpl7x35qaAnaG+AhoqIELOQJZIJBKJ43CoQFYUxQshjueoqvqVdXepoiixqqoWW0uoD1v3FwIJHZ4eb91XSHtJtm3/L8deS1XVd4B3ALKysuwiul2OqoPCUCt6EJz3f/YVsbHpMONZWPgnWPdW98YDuQKl2RA1wLFf1uxMq9nCz7sOs7O4jszEEEYkh+Fr7Kb50MhbYdUrsPYNOO/l0x9vscCWT6DPJAhJON3Rbk1ji5llu0qZv6WI5bvLaDFbSA73454paVyQHkdqVMDJn6zTwdh74Ls/wsHVkOy4UnUvvY4RyWGMSA7j/ul9qW1qZc2+ClbuLWdlbjk/7RJvsdFB3oxPjWRCWgTjUiOIDOzCfODmI3Ck9KQLTi0mCzuKa9vE8Ob8KvIrGwEw6BQGxgUxe3g8mYmhZCaGkBjmJ8um3YWx9wCqGDeIApe867j33fI9YitLrCUSiUTiQBymHhTx7ec/wE5VVV/q8NAC4Hrgeet2fof99yiK8inCpKvGKqIXAX+zuV0DMwA3cpPqJK2N8Pl1QsBc/qFd+wDbGHmb6Ede/IQYExSXYf9rOIvSHOg7U+soOkVOUQ1fbixk/pZCKupb2vYb9ToyE0MYlxrBuNRwhsaH4KXvYslsQBQMvVyI3smPgX/EqY8/8Ktwv3a3EUWdpNVsYeXecuZvKWTJjlLqW8xEB3lz3ZgkLsyIY0iv4M4Lu/Sr4KdnhVGaAwXysQT5iPFHMweJOcz5lQ2szBViedmuUr7cVABA/5hAJqRFMD4tkpGnW4ypah/xpKoqRTVN7WL4UBXZRbW0mCwAxAb7kJkYwu9GJ5OZGMLgXsHSZdrdGXsvqBbhbK0ocPE7jhHJbQ7WMoMskUgkEsfhyPTaOOA6YLuiKFus+x5FCOPPFUW5GTgIXG597AfgHCAXaABuBFBVtVJRlGeA36zH/dVm2OUxqCp8/yAUb4WrPhPu045AUeDCN+Ct8cKd9Pbl4B3omGs5kiOHob7MpfuPy480M39LEV9sLGBncS1GvY5pA6O4bHg8w5PC2HSoitW55azKreClJXt4aQkEeBsY2TuMsSnhjEuNoF90YOccfcfcA5s/gt/+A5P+fOpjN88Bn2CP6vGzWFR+O1DJgq1F/LC9mKqGVoJ9vbggI47z0+MY1Tv86PLpzuLlKxadfvk7lO2BSG2+1CeE+XHVyESuGpmIxaKSU1TLitwyVu4t53+rD/Luiv0Y9TqykkPb3LEHxQW1vbYaWkwU7NxGX+DZNU0s+GYZh+uaAfA26BgaH8wNY5PJTAghIzGE2GBfTX5PicaMu098Vi39C6DAxW/bXySX7REjpoI9u7pFIpFIJI5FEabR7kVWVpa6YcMGrcOwHxv/C9/eBxP/BFMec/z1DqyC/50HQy6HS952/PXsTe4y+PgSuP5b6D1R62jaaDFZ+GnXYb7YWMAvuw9jsqikxwdz2fB4zk+PI8TPeMLnVda3sDavglW55azeV8H+8noAwv2NjLGK5XEpEace+TNnNhRthj9kg5fPiY9prIYX+4nRTue+2N1f16VRVSEUF2wt4tutRRTXNOHrpWf6wGguSI9jYt9I+xhc1ZfDy4OEZ8AFr3b/fHamscXM+gOVrNxbxoq95ewqqQMg1M+L4UlhFNc0squkjluUBTziNZdz/D6hX1IvMhNDyEwIpX9sYNerGiTuzcqXYelTMGS2EMk6O1YPfHwZHCkRZpISiUQi8XgURdmoqmqWvc/bcxo0PZXCjfDDQ5AyFSY97JxrJo+Dsx6GX/4melEzephZU2mO2LpABtkmxL7YWMD8LYVUNbQSFejNzRN6c9mweNKiT5+hD/M3cs6QWM4ZEgtAYXUjq61ieVVuOd9tE6bw8aG+jEuJYGxqOGNTjukxHXsv/O982PYZDL/+xBfK+QpMTZBxTbd+5/pmE4XVjRRWNVJg3RZVN1JZ30J4gJHoIB+iAr2JDvKx3sR9Z5Th5pUdYcHWIhZsLSKvrB6DTuGsvpE8fHZ/pg+Mxs9o57dE/wjIuFpk5qc83r2RbA7A16jnrL6RnNU3EoDDdU2szq1gxd5yNh2qoleIL3dNSuHKEjOWkgh++JOHzhWXdJ7xfxTl1sv+isgkv2U/kVy+G+JH2udcEolEIpGcBCmQXZn6cvjsdxAYI0Zo2HMl/nRMfFCM+vn+AYjPgog05127u5TmiHmtfmGahXC4ron5m0UJ9e7SOowGHTMGRnPp8HgmpEZg6EbWrVeIL7OzEpidlWAd+XOEVblCLC/MLuazDWIqWr/owLYM86jeowmKGQprXofM64SJ1LFs/hiiBkJc5kmvraoqVQ2tFFY1UljdQEFVY5sYLqwWt+qG1qOeY9ApxIb4EObvzaHKBkprm2i29qt2JNjXq00sRwW2C+foIG+irGI6KtC7yxnLkpomvttWxPwtRWwvrEFRYFTvMG4Z34ezB8cQ6n/izL3dGH03bPgA1r/rnAqQbhAV6MNFmb24KPP/27vz+Kiq+//jr09WEpaEXTYBAUVERMQFcMEdcUGFtti6UqWKYN36rX67yNdqa/Wn/apYrYh1aesGtlXE+hUUNwQFRSUiyKaAkLCFsGU/vz/ODSSBxAQyc2cm7+fjMY+ZuXOXT84nmZlP7rnnVJtJ7+n1jXtEeKmfk27x3a3f+p2/dOfCRw/886t4J+Svhv6XNkyMIiIiNVCBHKvKy/x1wDs2wE//L/rFXlIyjJwMjw6BqVfB1bMgpR6j3oYpN8eP9B1lRaVlzFqcx7QFa5i9dANl5Y6jD87mrgv7cn6/jmRlNvw0Nn7Kn+b0bNecKwZ3o6zcsWjtVj5YvpE5yzbx3Eff8tScVSQnGde3Ppubt93H4nen0X3IxVXP2OZ9BWsXUH7W3azfWsjafH/Wd68CeMsudpWUVYkhMy2ZTtkZdGqZQf8u2XRqmUGn7Aw6t8ygU3YmbZunV7mG1zlHwa5ScrcVkltQyPqtheRtKyK3oDC4FbE8byN524ooLd/7EpA2zdKqFNDtKs5CNw/OSGelk2zGGzm5vPLZWuat3IxzcGSnLH597uGc168jB2XV0M08Etr0hN7nwseT/dm1SAywF2mbV9ZvLm2Rk28FnB+oDoML/3xgRfKmZX5/IV3LLyIijYcK5Fj11l2w8h0/aFZYo0m36Oj/8//cj/zopOf8MZw46qOsBDZ8BT1Pj8rhnHN8vmYr0z5Zw78XfsfWXSUc1KIJY08+hJEDOtc+FVAEJCcZR3XJ5qgu2Ywb2pPCkrJgwK9NfLisKesKppA/6wGOmtmMgd1a0rdjFhu2FzH0m4cYTjKDp7ch75W3quyzZWYqnVpm0KNtU07u1bZaAZxBdmZqvabsMTOyMlPJykzl0Fq6mJeXOzbtKCa3oJC8bb5wriig8woKWV9QyBdrC9i0o4iahlI4pG1Tfn66n5bpkLbRzUUVgyfAV9Nh4d8bbv7yaCkphK1rdAZZ6u/kX/gzyW/fDZYEIybtf5FcMcVTG03xJCIikaUCORYtng7vPwDHXOUHTArTYcPghHEw98/Q/RToPRzwZ0u3FZayvbCUbYWlbCsqYVthKYUlZTgH5c5RHtw754Jle57veVz1vmI5+AJpn9vg71OSjJaZabRqmkbLzDRaNk2j/c7ldCgvwbU/gkjOsppXUMg/P13L1AVr+DpvO+kpSZx9xEGMOqYzQ3q22b9RjyOgSWoyg3u0YXCPNnD2YRS+cwMd3p7ILYcX8vK6Yp54fyUdmiXz29JZ5DQbzMi+/XefDe6cnUHH7AyapofzNpGUZLRtnh5cS51V43olZeVs3F60u4DOKyhkW1EpJ/fyozHHxHy7XY6HzsfCh4/AwDHRvVziQOV/AzgVyLJ/TvkvXyTP/r3vbn3Bw/v3+79hiS+yIzWLg4iISEAFcqzZuAz+eS10HBCRM7Zl5Y7tRaVsLyplW2FJpQLXP99T9JYEy0op3HUOdyS/Qdvnx3JJ0n0sK8qmuGzva0gjyQySzDCCe4PSckdZtS64I5Le58E0OPeFLWx8dWZQOKfuLqL3FNOpVZ63appGZlpyrcVUYUkZMxfnMnXBGt5duoFyB8d0bckfLj6Sc/t1oEWThu9C3dCaHD8GPrifsSmvMfbGJ3DOYUteh+fzyT7/eo46rHfYIdZbanISHbIyYnt6ITN/FvnFy+Gr16DPBWFHVHeb98yBLLJfhv4ScH7KMyqK5HqOw7BxKbTsFj+X+oiISNxSgRxLirbDC5dCShr86NkavwiUlTsKdpWQv6uE/J3F5O8q8c93BrddxWzdWcLWYJ2CXb7w3VZYwo7isn3us7Ik83PuNm+SSvMmKTRvksIT7X/NxHXjeCTjUV4Y+AjNMzKCdVKqrNskNZnkJCMpKGjBnwmseF5R6FYpdpMqlu1Zx6j2fB+Fq3OOgsJStuwoZvPOYrbsKKbz/JmUrUjl1CFD2LSrnM07itmys5iludvZEjzex2WtAKSlJNEys1Lh3DSNVsGZ6Y3bi5j+2XcUFJbSMasJ44b25OIBncLttrs/mmT5UaznPgqn34Fld/GDczVtBz3PDDu6xNb7PP8Ff87DcVYgr/D3LbuHG4fEt6G3+dGt3/kjGHB+PYvkjUvVvVpERKJCBXKICkvKyK8oZHcUcfDsCbTfsIQZ/R9h8dzt5O/8gvxdJZWK3WLyd/pitzbN0lPIykglKyOV7MxUerRtFhS6qTRrkkKLakXtnmX+eY1nUj8vpfvL13Bbxqtw6n9HqFXqzsx2/5zdaOoXLvgW2vfmF8P3PcVTebmjoLBkd+G8ZUfJ7uJ69/0O/4+HxesK2LLD/wMiPSWJc/p2YNQxnRl0SGuSYqQL9X45/lpfIM97DIbcCF+/4bvRJ+vtIKKSkmHQeJhxK3w7Dw4+PuyI6mbzCkjPCnVUeEkQQ2/33a3fvRcwOP+huhXJZaV+kK6eZ0Q8RBEREX0jDsF1f1vAW1/lVZnqZkzy6/w2dQb3lIzmsblZJCetIDso/rIyU2nTLI2e7ZpVKXyzM4PXM9IqPU6t9zQ4ddbvh7D8bXj3Puh2EnQ/KTLHORC5OX7u5hokJRnZmWlkZ9Z9ap+ycke5c5Fr12jL7gJHXAgLnoa0plBeGv617o1F/x/7AYvmPAQH/z3saOpm8wpo1d13Exc5EGbBP1ed/xwxg/Me/P4iOf8bKCuGtjqDLCIikacCOQSDe7Tm4FaZfiTfjFS67/icQe/9g61dh/GT8x/g+qZpNEtPiY3Bhaobfh+s+Qhevgau/QCatg47oj12bIJt6xp8iqfkJCM5okN+hWDQeFg0zX9J7XysvnhGS1pTOPZqePf/wabl8THg0JaV0CGkkfQl8ZjBqb/y3a3fu98PvHXun2ovkjWCtYiIRFGCnBKLL5cN6sbtww9n3NCe/OTwNAYvuBlr1Z2s0ZPp0ropzZvUb9qcqEpvBqP+Cjs3wb+uo8b5dcKQu8jfhzAHctzpNAC6nui/pPb/SdjRNC7HjYXkVD+idawrK4H8b/0ZZJGGYgan/QZOvBkWPAWv3QzltQz8uGGJv2/TKyrhiYhI46YCOUylxfDSFVC8A370N2jSIuyI6qZDPzjrbn/t6txHw45mj9wcf9/+yHDjiBen/RoOHgx9R4YdSePSrB0cNdrPibxjY9jR1G7rat8FXyNYS0Mzg9N/CyfeBAv+CjNuqblI3vg1NGsPGdnRjVFERBolFchhevM3sHoejJgE7Q4PO5r6Oe4aOOxcePO38N2nYUfj5eb40ZibtQ07kvjQdRCMeT1+/jGTSAaNh9JC+HhK2JHUrmIEaxXIEglmcPodMOTnMP9JP4DdvnolbVwCbQ6NfnwiItIoqUAOy+cv+VGEB42HvheHHU39mfnCvll7mDoGiraFHZHvYq3u1RIP2h4Ghw6Djx6Hkl1hR1OzijmQNcWTRIoZnPE/MPgGmD9l7yLZOdiwVAWyiIhEjQrkMKxfBK9MgK5D4IyJYUez/zJbwcjJsGUVTL853OuRy0ohb7EKZIkfgyfAzo3w2fNhR1KzzSshJQOaHxR2JJLIzODMO/3fxMdPwIxf7Pk82Z4HRVs1kKCIiESNCuRocw6m3+ivpRr1Vz9YTzzrOtjPbfnFi/DZc+HFsXk5lBVB+33PfywSc7oOgY5Hw4eTah+gKEybV/ju1bE6aKAkDjM483e+V9XHk+H1X/rPy40VA3TpDLKIiESHpnmKNjMY9aQfnKd5+7CjaRgn3QIr34XXbvFTBoUx0mjFCNYHqUCWOGHmz5hNHQNL/wO9h4cd0d62rITWPcOOQhoLMzjrLl8Yz33EP6/4/VOBLCIiUaIzyGHIPthPs5MokpLh4smQmgHP/xi2fBP9GHJzIClFX6Ikvhw+ArIOhjkPhR3J3srLfRdrTfEk0WQGZ98NJ4zz43TMvgfSmkOLjmFHJiIijYQKZGkYLTrAD5+F7bkw+VRY9X50j5+b44vjlPToHlfkQCSnwKBx8O2HsPrjsKOpatt3/rIFjWAt0WYGZ/8ejr/OX6ffppe6+YuISNSoQJaG020IXPM2ZLaBZ0b4wVaiZb1GsJY4dfSl0CQLPnw47Eiq0hRPEiYzGPYHP1fyCePCjkZERBoRFcjSsFr3gKtnQo/T/TXJ02+C0uLIHnPXFihYowJZ4lN6cxg4Bha/uqcojQUVUzypQJawmPkxLvr9IOxIRESkEVGBLA2vSQu45Dk48SaY/yQ8e6EflCxScr/09+2PjNwxRCLpuJ+BJcPcR8OOZI/NKyApFVp0CjsSERERkahRgSyRkZTs53geOQXWLoDHT4X1X0TmWLk5/l5nkCVetegA/X4In/4Ndm4OOxpv8wpo2c3/LYuIiIg0EiqQJbKOHAVXvQ7lpTDlLPjy3w1/jNxFkNEKmh/U8PsWiZZB46FkJ8yfEnYk3uaV6l4tIiIijY4KZIm8TgNg7Gxo3xdevBze/oOfQqah5AYDdGmUU4ln7ftAzzNg3uNQUhhuLM75OZBVIIuIiEgjowJZoqN5e7hyOvS/FN65B168DIq2H/h+y8sgb7EvvkXi3eAJsCMPvngxvBhKCuH9B6B4uwpkERERaXRUIEv0pKTDiEkw7B5YMsN3ud6y6sD2uWWV75aq648lEXQ/BQ46EuZMatheFnXhHCyaBpOOhVl3wqHD/HXRIiIiIo2ICmSJLjM44Tq4dBoUrPWDd618b//3l7vI3x+kM8iSAMxg8A2wcQksezN6x139sf+H1dQxfk7my/8NP34BMrKjF4OIiIhIDFCBLOHocRpc8xY0beungfposj+DVV+5OWBJ0LZ3w8coEoYjLvJTK815OPLH2vKNL4qnnAH538AFk+Bn78AhQyN/bBEREZEYpAJZwtO6B1w90w9MNONWmH4jlBbXbx+5OdC6J6RmRCZGkWhLTvW9LFa9B2s/icwxCgtg5kTfnfqrGXDyf8GET2DAZZrWSURERBo1FcgSriYtYPQ/4MSbYcFT8MwI2L6h7tuv/0LXH0viGXAFpLeADyc17H7LSmH+k/DQ0fD+n/zZ6gnz4bRfQXqzhj2WiIiISBxSgSzhS0qGM+6AkVPgu09g8qmw7vPv366wwHcLVYEsiaZJCzjmCsj5l+8G3RCWzYTHToTpN0GbQ+Gat+Hiv0BW54bZv4iIiEgCUIEssePIUXDV637qpifPhpx/1r5+3mJ/3/7IyMcmEm3HX+sH7Zr32IHtJ/dLePZi+NtIKC2EHz4LV83w85OLiIiISBUqkCW2dBoAY2f7eY1fuhLeurvm6W4qRrDWGWRJRFmdoe9IWPA07NpS/+2358GrN8JjQ2DtfDjrbrh+HvS5wBfeIiIiIrIXFcgSe5q3hyunQ/9L4d174cXLoGjb3uvl5kB6lrqISuIaNB5Kdvjr8+uqpBDeewAeGgCfPgvHjYUbFsLg8X4uchERERGpkQpkiU0p6TBiEgy7B5bM8HO0bl5ZdZ3cRf7ssc6GSaLq0M9PuTT3se8f4d05+GKqH5l61v9A95Ng3Fw454+Q2Soa0YqIiIjEPRXIErvM/HQ3l06Dgu/84F0r3/WvlZf7ayvVvVoS3eAJsH09LJpa8zqrP4IpZ8K0n0JGFlz+ClzyHLTpFb04RURERBKACmSJfT1Og2vegqbt4JkL4aPJfvTq4m1wUN+woxOJrB6nQ7s+MOdhf5a4si2r/LX6U86E/NUw4hEY+w4cckoYkYqIiIjEvZSwAxCpk9Y94OqZ8PI1MONW6BiMwNteBbIkODN/Fvlf18HyWdDzDCjcCu/dD3MfBUuGU34Jg2/QXMYiIiIiB0gFssSPJi1g9D/grbvg/QcAg7a9w45KJPL6joJZd8IHD/qzxm//HnZugqMugdN+A1mdwo5QREREJCGoQJb4kpQMZ9wBHY+G/G91xkwah5Q0OP5nMHOivw6/6xA4+27/dyAiIiIiDUYFssSnPheEHYFIdA38KWxaBocOg97nafR2ERERkQhQgSwiEg+atPCDcImIiIhIxGgUaxERERERERFUIIuIiIiIiIgAKpBFREREREREABXIIiIiIiIiIoAKZBERERERERFABbKIiIiIiIgIEMEC2cyeNLM8M1tUadlEM1trZguD2/BKr91uZsvMbImZnV1p+bBg2TIzuy1S8YqIiIiIiEjjFskzyE8Bw/ax/E/Ouf7BbQaAmfUBRgNHBNv82cySzSwZeAQ4B+gDXBKsKyIiIiIiItKgUiK1Y+fcu2bWrY6rjwCed84VASvNbBlwXPDaMufcCgAzez5Y98sGDldEREREREQauTCuQR5vZp8HXbBbBss6AasrrbMmWFbT8r2Y2Vgzm29m8zds2BCJuEVERERERCSBRbtAfhToAfQH1gH3N9SOnXOPO+cGOucGtm3btqF2KyIiIiIiIo1ExLpY74tzLrfisZlNBqYHT9cCXSqt2jlYRi3LRURERERERBpMVM8gm1mHSk8vAipGuH4FGG1m6WbWHegFfAR8DPQys+5mloYfyOuVaMYsIiIiIiIijUPEziCb2XPAUKCNma0B7gCGmll/wAGrgJ8BOOdyzOxF/OBbpcD1zrmyYD/jgTeAZOBJ51zO9x17wYIFG83smwb/oRpWG2Bj2EFInSlf8UX5ij/KWXxRvuKL8hVflK/4o5wduP1pw66RCMScc5HYr3wPM5vvnBsYdhxSN8pXfFG+4o9yFl+Ur/iifMUX5Sv+KGcHLpbaMIxRrEVERERERERijgpkEREREREREVQgh+nxsAOQelG+4ovyFX+Us/iifMUX5Su+KF/xRzk7cDHThroGWURERERERASdQRYREREREREBVCDvZmZdzOxtM/vSzHLM7OfB8lZm9qaZfR3ctwyW/8TMPjezL8xsjpkdVWlfw8xsiZktM7Pbajnmf8ws38ymV1ve3czmBdu/EMwBva/tjwmOv8zMHjIzC5b/IPgZys0sJkaDa2gJlq+JZrbWzBYGt+EN0UaxJMHydZSZfRi89qqZtWiINoo1cZqzu81stZltr7b82iCuhWb2vpn1OZC2iUUJlq8/VXo/XGpm+QfSNrEo3vJlZplm9pqZfRXEe0+l1042s0/MrNTMRjVE+8SaBMvXlWa2odLf2NUN0UaxJMHy1dXMZgXxzTazzg3RRnURY+04PtjWmVmbWrbfZ3vX+33KOaeb72beARgQPG4OLAX6APcCtwXLbwP+GDweDLQMHp8DzAseJwPLgUOANOAzoE8NxzwdOB+YXm35i8Do4PFjwHU1bP8RcAJgwOvAOcHyw4HDgNnAwLDbVvn63nxNBG4Nu02Vrzrn62PglODxGOB3YbevcrZ7vROCuLdXW96i0uMLgP+E3b7KV835qrbOBODJsNu3secLyARODR6nAe+x5z2xG9APeAYYFXbbKl/fm68rgUlht6nyVed8vQRcETw+DXi2kbbj0fj3mlVA8RQihAAABmdJREFUm1pi3md7U8/3qdB/iWP1BvwbOBNYAnSo9IuyZB/rtgTWBo8HAW9Ueu124PZajjO08i8B/sv4RiBlX/ur9kv7VaXnlwB/qbbObBK0QE6kfNEICuQEy9dW9ozf0AX4Muz2VM722kdtBdclwOtht6fyVed8zQHODLs9la+99vMgcE21ZU+RoAVyIuWLRlAgJ1i+coAulfZX0Njasdprq6ihQK5Le9f1fUpdrPfBzLrh/1MxD2jvnFsXvLQeaL+PTX6KP8ME0AlYXem1NcGyumoN5DvnSr9n+07Ba/t7nISRIPkaH3RLebKiq0qiSoB85QAjgsc/wBfJCS1OclYrM7vezJbj//N9Q323jyeJkC/wXQuB7sBb+7N9vIi3fJlZNv4Mz6x6HCdhJEi+RgbfOaaaWUJ/hiVAvj4DLg4eXwQ0N7PW9YihQYTcjnXVYJ8/KpCrMbNmwDTgRudcQeXXnP/Xg6u2/qn4X4JfRi1I2S1B8vUo0APoD6wD7g83nMhJkHyNAcaZ2QJ8l6PikOOJqATJGc65R5xzPfBx/TrseCIlUfIVGA1Mdc6VhR1IpMRbvswsBXgOeMg5tyKMGMKUIPl6FejmnOsHvAk8HUZs0ZAg+boVOMXMPgVOAdYCUX1PjLd2bAgqkCsxs1T8L8DfnXMvB4tzzaxD8HoHIK/S+v2AJ4ARzrlNweK1VD2j1BlYa2bHVxoQ4YJawtgEZAd/JJW3T660/Z3BcSpfqN85WNZoJEq+nHO5zrky51w5MBk4rr5tEQ8SKF9fOefOcs4dg/8gW17ftogXcZazunoeuLAe68eNBMzXaPzfWEKK03w9DnztnPvf/fup41ei5Ms5t8k5VxQ8fQI4pu6tED8SKF/fOecuds4dDfwqWBa1gQtjpB1ri++NYPsnqKG992e/oV8XECs3fL/1Z4D/rbb8PqpeiH5v8PhgYBkwuNr6KcAKfLewigvRj6jluEPZ+0L0l6h6gfm4GratPojQ8GqvzyZBr0FOpHwRXMcRPL4JeD7s9lW+as1Xu+A+KfiZxoTdvsrZXvuoPkhXr0qPzwfmh92+ylfN+QqW9cZfa2Zht63ytXu9u/BflJNqeP0pEvQa5ETKF1W/c1wEzA27fZWvWvPVpmIZcDdwZ2Nsx0qvraL2Qbpqbe+6vk+F/kscKzfgRHwXgc+BhcFtOL4/+yzga2Am0CpY/wlgS6V151fa13D8SG/LgV/Vcsz3gA3ALnw/+bOD5Yfgv5wvCxKdXsP2A4FFwXEmsWfgoIuC/RUBudRhQIB4uyVYvp4Fvgh+lleo9OGVKLcEy9fPg+MvBe4hcb/Ax2PO7g22Kw/uJwbLH8RfO74QeJtaPpjj9ZZI+QpemwjcE3a7Kl+7t+0cxLu4UgxXB68dG+xvB/4MTk7Y7at81ZqvP+DfDz/Dvx/2Drt9la9a8zUqiHdpEOc+308bQTveEDwvBb4Dnqhh+322N/V8n6r4wiciIiIiIiLSqOkaZBERERERERFUIIuIiIiIiIgAKpBFREREREREABXIIiIiIiIiIoAKZBERERERERFABbKIiEjMMrMyM1toZjlm9pmZ3WJmtX52m1k3M/txtGIUERFJJCqQRUREYtcu51x/59wRwJnAOcAd37NNN0AFsoiIyH7QPMgiIiIxysy2O+eaVXp+CPAx0AboCjwLNA1eHu+cm2Nmc4HDgZXA08BDwD3AUCAdeMQ595eo/RAiIiJxRAWyiIhIjKpeIAfL8oHDgG1AuXOu0Mx6Ac855waa2VDgVufcecH6Y4F2zrm7zCwd+AD4gXNuZVR/GBERkTiQEnYAIiIisl9SgUlm1h8oAw6tYb2zgH5mNip4ngX0wp9hFhERkUpUIIuIiMSJoIt1GZCHvxY5FzgKP6ZIYU2bAROcc29EJUgREZE4pkG6RERE4oCZtQUeAyY5f31UFrDOOVcOXAYkB6tuA5pX2vQN4DozSw32c6iZNUVERET2ojPIIiIisSvDzBbiu1OX4gfleiB47c/ANDO7HPgPsCNY/jlQZmafAU8BD+JHtv7EzAzYAFwYrR9AREQknmiQLhERERERERHUxVpEREREREQEUIEsIiIiIiIiAqhAFhEREREREQFUIIuIiIiIiIgAKpBFREREREREABXIIiIiIiIiIoAKZBERERERERFABbKIiIiIiIgIAP8fV6la30I6oJAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error Cases= 492.541\n",
            "Accuracy 82.11%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JoQJghdyj76V",
        "outputId": "ec303d12-9490-4bfd-f3e8-cc4b19c47ed3"
      },
      "source": [
        "# AUTO ARIMA CASES WEEKLY BASE\n",
        "\n",
        "# WE REMOVE THE SEASONAL NOW SINCE WE ARE TAKING PREDICTION ON WEEKLY BASIS INSTEAD OF WEEKLY\n",
        "# Create model\n",
        "stepwise_model_cases = auto_arima(df_cases_weekly['cases'], start_p=1, start_q=1,\n",
        "                           max_p=3, max_q=3, m=12,\n",
        "                           start_P=0, seasonal=False,\n",
        "                           d=1, D=1, trace=True,\n",
        "                           error_action='ignore',  \n",
        "                           suppress_warnings=True, \n",
        "                           stepwise=True)\n",
        "print(stepwise_model_cases.aic())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fit ARIMA: order=(1, 1, 1); AIC=673.341, BIC=679.891, Fit time=0.121 seconds\n",
            "Fit ARIMA: order=(0, 1, 0); AIC=691.954, BIC=695.229, Fit time=0.003 seconds\n",
            "Fit ARIMA: order=(1, 1, 0); AIC=671.475, BIC=676.388, Fit time=0.027 seconds\n",
            "Fit ARIMA: order=(0, 1, 1); AIC=677.992, BIC=682.905, Fit time=0.017 seconds\n",
            "Fit ARIMA: order=(2, 1, 0); AIC=673.317, BIC=679.867, Fit time=0.104 seconds\n",
            "Fit ARIMA: order=(2, 1, 1); AIC=675.287, BIC=683.475, Fit time=0.147 seconds\n",
            "Total fit time: 0.421 seconds\n",
            "671.4753217750579\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91P_bO6zlgbu"
      },
      "source": [
        "# Create train/test data based on date\n",
        "\n",
        "train_cases_weekly = df_cases_weekly[(df_cases_weekly.index > startDateTrain) & (df_cases_weekly.index <= endDateTrain)]\n",
        "\n",
        "test_cases_weekly = df_cases_weekly[(df_cases_weekly.index >= startDateTest) & (df_cases_weekly.index <= endDateTest)]\n",
        "\n",
        "stepwise_model_cases.fit(train_cases_weekly['cases'])\n",
        "\n",
        "forecast_aarima_cases = stepwise_model_cases.predict(n_periods=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "IZZr-n_qmADl",
        "outputId": "20a7ad01-32d0-4d18-a830-8c9c38266eee"
      },
      "source": [
        "# Create our forecast\n",
        "forecast_aarima_cases = pd.DataFrame(forecast_aarima_cases, index = test_cases_weekly.index,columns=['Prediction'])\n",
        "new_df = pd.concat([test_cases_weekly, forecast_aarima_cases], axis=1)\n",
        "print(new_df)\n",
        "\n",
        "plotARIMA(new_df, test_cases_weekly, 'Auto ARIMA Model Weekly New COVID-19 Cases Forecast')\n",
        "\n",
        "maeCasesWeekly=mean_absolute_error(test_cases_weekly['cases'], new_df['Prediction'])\n",
        "print('Mean Absolute Error Cases= %.3f' % maeCasesWeekly)\n",
        "\n",
        "true_y_arr = test_cases_weekly['cases'].to_numpy()\n",
        "pred_y_arr = new_df['Prediction'].to_numpy()\n",
        "err_arr = []\n",
        "\n",
        "auto_arima_weekly_acc = getAccuracy(test_cases_weekly,new_df)\n",
        "\n",
        "print('Accuracy %.2f%%' % (auto_arima_weekly_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             cases  cumulative_cases    Prediction\n",
            "date_report                                       \n",
            "2020-10-04   13080           1140249  11294.496109\n",
            "2020-10-11   15874           1245434  12362.770390\n",
            "2020-10-18   16350           1357254  13137.684589\n",
            "2020-10-25   18038           1479822  13756.755369\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error Cases= 3197.573\n",
            "Accuracy 80.21%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAhe9PfuOyL3",
        "outputId": "e73c7bc9-6b54-44ed-b2c8-a094a4b3c9fc"
      },
      "source": [
        "# fit model Autoregressive Integrated Moving Average\n",
        "# Order of differencing required to make the series stationary\n",
        "# transparams=True transform the parameters to ensure stationarity\n",
        "# https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.ARIMA.html\n",
        "###\n",
        "# classpmdarima.arima.ARIMA(order, seasonal_order=(0, 0, 0, 0), start_params=None, method='lbfgs', maxiter=50, suppress_warnings=False, out_of_sample_size=0, scoring='mse', scoring_args=None, trend=None, with_intercept=True, **sarimax_kwargs)\n",
        "# season_order was not working with our version of ARIMA and so it had to be ommitted making the model not as acurate as desired\n",
        "###\n",
        "modelCases = ARIMA(seriesCases, order=(4, 0, 0)).fit(transparams=True, method='mle')\n",
        "print(modelCases.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/arima_model.py:472: FutureWarning:\n",
            "\n",
            "\n",
            "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
            "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
            "between arima and model) and\n",
            "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
            "\n",
            "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
            "is both well tested and maintained.\n",
            "\n",
            "To silence this warning and continue using ARMA and ARIMA until they are\n",
            "removed, use:\n",
            "\n",
            "import warnings\n",
            "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
            "                        FutureWarning)\n",
            "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
            "                        FutureWarning)\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
            "\n",
            "No frequency information was provided, so inferred frequency D will be used.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                  206\n",
            "Model:                     ARMA(4, 0)   Log Likelihood               -1410.123\n",
            "Method:                           mle   S.D. of innovations            225.796\n",
            "Date:                Sun, 06 Dec 2020   AIC                           2832.246\n",
            "Time:                        04:45:29   BIC                           2852.213\n",
            "Sample:                    03-09-2020   HQIC                          2840.322\n",
            "                         - 09-30-2020                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const        846.6245    449.786      1.882      0.060     -34.939    1728.189\n",
            "ar.L1.y        0.3198      0.069      4.668      0.000       0.186       0.454\n",
            "ar.L2.y        0.1864      0.069      2.705      0.007       0.051       0.321\n",
            "ar.L3.y        0.2929      0.071      4.149      0.000       0.155       0.431\n",
            "ar.L4.y        0.1752      0.070      2.491      0.013       0.037       0.313\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1            1.0112           -0.0000j            1.0112           -0.0000\n",
            "AR.2           -0.2674           -1.5988j            1.6210           -0.2764\n",
            "AR.3           -0.2674           +1.5988j            1.6210            0.2764\n",
            "AR.4           -2.1477           -0.0000j            2.1477           -0.5000\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBMn4jwBQD-t"
      },
      "source": [
        "# startDateTest = '2020-10-01'\n",
        "# endDateTest = '2020-10-31'\n",
        "\n",
        "ARIMA_Predict_Cases = modelCases.predict(start=startDateTest, end=endDateTest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tptiHfLKQfZZ",
        "outputId": "95d72d2b-e05f-43fa-c6ae-a2edc80c3cf4"
      },
      "source": [
        "# Root-mean-squared error and std dev\n",
        "mseProphetCases = mean_squared_error(test_cases['cases'], forecastCasesNov['yhat'])\n",
        "mseARIMACases = mean_squared_error(test_cases['cases'], ARIMA_Predict_Cases)\n",
        "rmseProphetCases = sqrt(mseProphetCases)\n",
        "rmseARIMACases = sqrt(mseARIMACases)\n",
        "\n",
        "print('CASES DATA')\n",
        "print('The MSE of ARIMA %.3f' % mseARIMACases)\n",
        "print('The RMSE of ARIMA %.3f' % rmseARIMACases)\n",
        "print('The MSE of Prophet %.3f' % mseProphetCases)\n",
        "print('The RMSE of Prophet %.3f' % rmseProphetCases)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CASES DATA\n",
            "The MSE of ARIMA 1029753.375\n",
            "The RMSE of ARIMA 1014.768\n",
            "The MSE of Prophet 2556101.307\n",
            "The RMSE of Prophet 1598.781\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "id": "vYgikoJjUDAa",
        "outputId": "783f24e7-c227-47e7-e1bd-733145348036"
      },
      "source": [
        "# Plot prediction vs actual\\\n",
        "print(ARIMA_Predict_Cases)\n",
        "\n",
        "plotARIMA(ARIMA_Predict_Cases, test_cases, 'Manual ARIMA Model New COVID-19 Cases Forecast')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-10-01    1817.730337\n",
            "2020-10-02    1823.371698\n",
            "2020-10-03    1767.968733\n",
            "2020-10-04    1777.567551\n",
            "2020-10-05    1772.266038\n",
            "2020-10-06    1757.123301\n",
            "2020-10-07    1744.395709\n",
            "2020-10-08    1737.632486\n",
            "2020-10-09    1727.733829\n",
            "2020-10-10    1716.927027\n",
            "2020-10-11    1707.415283\n",
            "2020-10-12    1698.275302\n",
            "2020-10-13    1688.680232\n",
            "2020-10-14    1679.229053\n",
            "2020-10-15    1670.074864\n",
            "2020-10-16    1660.974339\n",
            "2020-10-17    1651.908725\n",
            "2020-10-18    1642.976467\n",
            "2020-10-19    1634.161116\n",
            "2020-10-20    1625.427636\n",
            "2020-10-21    1616.787297\n",
            "2020-10-22    1608.249609\n",
            "2020-10-23    1599.806569\n",
            "2020-10-24    1591.454551\n",
            "2020-10-25    1583.195666\n",
            "2020-10-26    1575.029235\n",
            "2020-10-27    1566.952969\n",
            "2020-10-28    1558.965993\n",
            "2020-10-29    1551.067771\n",
            "2020-10-30    1543.257181\n",
            "2020-10-31    1535.533105\n",
            "Freq: D, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ir-HNngqZvCl",
        "outputId": "a5e3d332-1e4c-4beb-d966-917efdca2e5d"
      },
      "source": [
        "print('AUTO ARIMA models WITH daily and weekly seasonability\\n')\n",
        "\n",
        "print('Mean Absolute Error DAILY Cases= %.3f' % maeCasesDaily)\n",
        "print('New Cases DAILY Accuracy %.2f%%' % (auto_arima_daily_acc))\n",
        "\n",
        "print('\\nMean Absolute Error WEEKLY Cases= %.3f' % maeCasesWeekly)\n",
        "print('New Cases WEEKLY Accuracy %.2f%%' % (auto_arima_weekly_acc))\n",
        "\n",
        "print('\\n\\n\\nMANUAL ARIMA models WITHOUT daily and weekly seasonability\\n')\n",
        "\n",
        "true_y_arr = test_cases['cases'].to_numpy()\n",
        "pred_y_arr = ARIMA_Predict_Cases.to_numpy()\n",
        "err_arr = []\n",
        "\n",
        "for i in range(len(true_y_arr)):\n",
        "  err_arr.append(np.abs(true_y_arr[i] - pred_y_arr[i])/true_y_arr[i])\n",
        "\n",
        "manual_arima_daily_acc = (1 - np.average(err_arr))*100\n",
        "\n",
        "print('New Cases DAILY Accuracy %.2f%%' % (manual_arima_daily_acc))\n",
        "\n",
        "maeCasesDailyManual=mean_absolute_error(test_cases['cases'], ARIMA_Predict_Cases)\n",
        "print('Mean Absolute Error DAILY Cases= %.3f' % maeCasesDailyManual)\n",
        "\n",
        "\n",
        "print('\\n\\n\\nProphet model\\n')\n",
        "\n",
        "\n",
        "maeCasesProphet=mean_absolute_error(test_cases['cases'], forecastCasesNov['yhat'])\n",
        "print('New Cases Daily Accuracy %.2f%%' % (prophet_acc))\n",
        "print('Mean Absolute Error Cases = %.3f' % maeCasesProphet)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUTO ARIMA models WITH daily and weekly seasonability\n",
            "\n",
            "Mean Absolute Error DAILY Cases= 402.478\n",
            "New Cases DAILY Accuracy 83.26%\n",
            "\n",
            "Mean Absolute Error WEEKLY Cases= 3197.573\n",
            "New Cases WEEKLY Accuracy 80.21%\n",
            "\n",
            "\n",
            "\n",
            "MANUAL ARIMA models WITHOUT daily and weekly seasonability\n",
            "\n",
            "New Cases DAILY Accuracy 70.96%\n",
            "Mean Absolute Error DAILY Cases= 806.704\n",
            "\n",
            "\n",
            "\n",
            "Prophet model\n",
            "\n",
            "New Cases Daily Accuracy 40.84%\n",
            "Mean Absolute Error Cases = 1507.683\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "pyKUQINs5wPn",
        "outputId": "3629bd2e-ef8e-470c-a948-01aef02555e2"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "X2 = df_cases['week'].values.reshape(-1,1)\n",
        "y2 = df_cases['cases'].values.reshape(-1,1)\n",
        "ir2 = LinearRegression()\n",
        "ir2.fit(X2, y2)\n",
        "predictions = ir2.predict(X2)\n",
        "print(\"Y = {:.5} + {:.5}X\".format(ir2.intercept_[0], ir2.coef_[0][0]))\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.scatter(X2,y2,c='dodgerblue')\n",
        "plt.plot(X2,predictions,c='aqua',linewidth=2)\n",
        "plt.xlabel(\"week\")\n",
        "plt.ylabel(\"cases\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y = -1128.3 + 88.346X\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "7vGlHvU5U0F4",
        "outputId": "46f74667-ebe5-463e-e767-8b056363a572"
      },
      "source": [
        "forecastCasesNov['week']=forecastCasesNov['ds'].dt.week\n",
        "X3 = forecastCasesNov['week'].values.reshape(-1,1)\n",
        "y3 = forecastCasesNov['yhat'].values.reshape(-1,1)\n",
        "\n",
        "ir3 = LinearRegression()\n",
        "ir3.fit(X3, y3)\n",
        "predictions = ir3.predict(X3)\n",
        "print(\"Y = {:.5} + {:.5}X\".format(ir3.intercept_[0], ir3.coef_[0][0]))\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.scatter(X3,y3,c='darkviolet')\n",
        "plt.plot(X3,predictions,c='plum',linewidth=2)\n",
        "plt.xlabel(\"forecast_week_nov\")\n",
        "plt.ylabel(\"forecast_cases_nov\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y = -952.4 + 45.08X\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning:\n",
            "\n",
            "Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMbfbauy_e5e",
        "outputId": "df482275-5e78-46ab-998c-d1083ebd53b6"
      },
      "source": [
        "# Temperature and wind oct 2020\n",
        "startDateTrain = '2020-09-30'\n",
        "endDateTrain = '2020-10-31'\n",
        "\n",
        "df_temp = pd.read_csv('/content/TemperatureOct2020.csv')\n",
        "df_temp['Date'] = pd.to_datetime(df_temp['Date'], dayfirst=True)\n",
        "\n",
        "df_temp = df_temp[(df_temp['Date']> startDateTrain)]\n",
        "df_temp_daily = df_temp.groupby(pd.Grouper(key='Date', freq='D')).sum()\n",
        "\n",
        "X_train_temp = df_temp_daily.index\n",
        "y_train_temp = df_temp_daily['TemperatureAvg']\n",
        "y_train_wind= df_temp_daily['WindSpeedAvg']\n",
        "\n",
        "print(X_train_temp)\n",
        "print(y_train_temp)\n",
        "print(y_train_wind)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DatetimeIndex(['2020-10-01', '2020-10-02', '2020-10-03', '2020-10-04',\n",
            "               '2020-10-05', '2020-10-06', '2020-10-07', '2020-10-08',\n",
            "               '2020-10-09', '2020-10-10', '2020-10-11', '2020-10-12',\n",
            "               '2020-10-13', '2020-10-14', '2020-10-15', '2020-10-16',\n",
            "               '2020-10-17', '2020-10-18', '2020-10-19', '2020-10-20',\n",
            "               '2020-10-21', '2020-10-22', '2020-10-23', '2020-10-24',\n",
            "               '2020-10-25', '2020-10-26', '2020-10-27', '2020-10-28',\n",
            "               '2020-10-29', '2020-10-30', '2020-10-31'],\n",
            "              dtype='datetime64[ns]', name='Date', freq='D')\n",
            "Date\n",
            "2020-10-01    57.0\n",
            "2020-10-02    51.9\n",
            "2020-10-03    49.3\n",
            "2020-10-04    51.4\n",
            "2020-10-05    54.7\n",
            "2020-10-06    57.1\n",
            "2020-10-07    56.2\n",
            "2020-10-08    44.7\n",
            "2020-10-09    47.2\n",
            "2020-10-10    61.9\n",
            "2020-10-11    44.3\n",
            "2020-10-12    48.1\n",
            "2020-10-13    50.2\n",
            "2020-10-14    50.8\n",
            "2020-10-15    57.0\n",
            "2020-10-16    50.2\n",
            "2020-10-17    47.4\n",
            "2020-10-18    51.9\n",
            "2020-10-19    50.7\n",
            "2020-10-20    47.3\n",
            "2020-10-21    49.2\n",
            "2020-10-22    50.7\n",
            "2020-10-23    60.2\n",
            "2020-10-24    53.6\n",
            "2020-10-25    38.7\n",
            "2020-10-26    37.8\n",
            "2020-10-27    35.7\n",
            "2020-10-28    38.8\n",
            "2020-10-29    42.1\n",
            "2020-10-30    29.7\n",
            "2020-10-31    32.8\n",
            "Freq: D, Name: TemperatureAvg, dtype: float64\n",
            "Date\n",
            "2020-10-01    12.1\n",
            "2020-10-02     4.1\n",
            "2020-10-03     4.7\n",
            "2020-10-04     5.1\n",
            "2020-10-05     6.4\n",
            "2020-10-06     9.4\n",
            "2020-10-07    14.8\n",
            "2020-10-08    14.0\n",
            "2020-10-09    10.0\n",
            "2020-10-10    19.0\n",
            "2020-10-11     7.0\n",
            "2020-10-12    10.8\n",
            "2020-10-13    10.8\n",
            "2020-10-14     7.8\n",
            "2020-10-15    14.3\n",
            "2020-10-16     9.6\n",
            "2020-10-17     6.8\n",
            "2020-10-18    13.5\n",
            "2020-10-19     7.3\n",
            "2020-10-20     9.0\n",
            "2020-10-21    10.9\n",
            "2020-10-22     4.3\n",
            "2020-10-23    10.5\n",
            "2020-10-24    14.7\n",
            "2020-10-25     9.2\n",
            "2020-10-26     7.8\n",
            "2020-10-27     7.2\n",
            "2020-10-28     7.1\n",
            "2020-10-29    10.1\n",
            "2020-10-30    11.8\n",
            "2020-10-31     6.6\n",
            "Freq: D, Name: WindSpeedAvg, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "ox9IwSeNMTFi",
        "outputId": "1e122058-7e76-41cb-9aa0-e255b3d1975a"
      },
      "source": [
        "#Visualize the data for reach data type\n",
        "plt.figure(figsize=(16,6))\n",
        "plt.bar(X_train_temp, y_train_temp, width=0.7, color='wheat')\n",
        "plt.xticks(X_train_temp, X_train_temp.strftime(\"%Y-%m-%d\"), rotation=75)\n",
        "plt.title('Average Temperature in Oct 2020') \n",
        "plt.xlabel('week', size=10)\n",
        "plt.ylabel('Average Temperature(Fahrenheit)', size=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "KCm0M4xwNF5_",
        "outputId": "b127f6cb-c2a5-425f-fdef-12d7ade3a5db"
      },
      "source": [
        "#Visualize the data for reach data type\n",
        "plt.figure(figsize=(16,6))\n",
        "plt.bar(X_train_temp, y_train_wind, width=0.7, color='lightseagreen')\n",
        "plt.xticks(X_train_temp, X_train_temp.strftime(\"%Y-%m-%d\"), rotation=75)\n",
        "plt.title('Average Wind Speed in Oct 2020') \n",
        "plt.xlabel('week', size=10)\n",
        "plt.ylabel('Average Wind Speed(km/h)', size=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}