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      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XKOW_oSqCxue"
      },
      "outputs": [],
      "source": [
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nds1nJ0AC_G7",
        "outputId": "98e0969e-6146-4630-adce-3f1db3243d31"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Data Formatting <a name=\"data_formatting\"/>\n"
      ],
      "metadata": {
        "id": "bumDrO0T8pa7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "from urllib.request import urlopen\n",
        "from zipfile import ZipFile\n",
        "from io import BytesIO\n",
        "\n",
        "#this zip is a bunch of zips for each station\n",
        "zf = ZipFile(\"/content/drive/MyDrive/PRSA2017_Data_20130301-20170228.zip\")\n",
        "\n",
        "#make a dataframe and unpack all the zips into it\n",
        "df = pd.DataFrame()\n",
        "for file in zf.infolist():\n",
        "    if file.filename.endswith('.csv'):\n",
        "        df = df._append(pd.read_csv(zf.open(file)))\n",
        "\n",
        "# the data format is broken into multiple columsn, so we need to fix that into a single DateTime\n",
        "df['timestamp'] = pd.to_datetime(df[[\"year\", \"month\", \"day\", \"hour\"]])\n",
        "df.drop(columns=['No'], inplace=True)\n",
        "\n",
        "#now turn both wind direction and station name into ints so we can treat them like categorical vars\n",
        "df = df.replace(df['station'].unique(), list(range(len(df['station'].unique()))))\n",
        "df = df.replace(df['wd'].unique(), list(range(len(df['wd'].unique()))))\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "H3VQX-IaDGKi",
        "outputId": "59b9ca42-384b-4f07-cfe4-a6cbfa934532"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  \\\n",
              "0  2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7  1023.0   \n",
              "1  2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1  1023.2   \n",
              "2  2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1  1023.5   \n",
              "3  2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4  1024.5   \n",
              "4  2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0  1025.2   \n",
              "\n",
              "   DEWP  RAIN   wd  WSPM  station           timestamp  \n",
              "0 -18.8   0.0  0.0   4.4        0 2013-03-01 00:00:00  \n",
              "1 -18.2   0.0  1.0   4.7        0 2013-03-01 01:00:00  \n",
              "2 -18.2   0.0  0.0   5.6        0 2013-03-01 02:00:00  \n",
              "3 -19.4   0.0  2.0   3.1        0 2013-03-01 03:00:00  \n",
              "4 -19.5   0.0  1.0   2.0        0 2013-03-01 04:00:00  "
            ],
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              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
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              "      <th>hour</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>SO2</th>\n",
              "      <th>NO2</th>\n",
              "      <th>CO</th>\n",
              "      <th>O3</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>PRES</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>RAIN</th>\n",
              "      <th>wd</th>\n",
              "      <th>WSPM</th>\n",
              "      <th>station</th>\n",
              "      <th>timestamp</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>0</td>\n",
              "      <td>2013-03-01 02:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>-1.4</td>\n",
              "      <td>1024.5</td>\n",
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              "      <td>0</td>\n",
              "      <td>2013-03-01 03:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import time\n",
        "\n",
        "def convert_to_timestamp(x):\n",
        "    \"\"\"Convert date objects to integers\"\"\"\n",
        "    return time.mktime(x.timetuple())\n",
        "\n",
        "\n",
        "def normalize(series):\n",
        "    \"\"\"Normalize the DF using min/max\"\"\"\n",
        "    scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "    dates_scaled = scaler.fit_transform(series)\n",
        "\n",
        "    return dates_scaled\n",
        "\n",
        "df['timestamp_ind'] = df['timestamp'].apply(convert_to_timestamp)\n",
        "df['timestamp_ind'] = normalize(df[['timestamp_ind']])\n",
        "pca_df = df.drop(['timestamp', 'year', 'month', 'day', 'hour'], axis='columns')\n",
        "X = pca_df.values # getting all values as a matrix of dataframe\n",
        "sc = StandardScaler() # creating a StandardScaler object\n",
        "X_std = sc.fit_transform(X) # standardizing the data\n",
        "\n",
        "pca_pm25 = PCA(n_components=5)\n",
        "principalComponents_PM25 = pca_pm25.fit_transform(X_std)\n",
        "\n",
        "pca_pm25.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUrTYChbeVfC",
        "outputId": "00d7099e-a0b3-4d82-c3ae-978b808958f2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.27993098, 0.16634779, 0.10778534, 0.08644793, 0.07649932])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca_pm25 = PCA(n_components=0.95)\n",
        "principalComponents_PM25 = pca_pm25.fit_transform(X_std)\n",
        "\n",
        "pca_pm25.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MIYHAynkvxe4",
        "outputId": "08a03a38-8d92-47f9-d630-470d9b2d58b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.27993098, 0.16634779, 0.10778534, 0.08644793, 0.07649932,\n",
              "       0.07217492, 0.05948165, 0.03871491, 0.03341706, 0.02597669,\n",
              "       0.02074129])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.cumsum(pca_pm25.explained_variance_ratio_))\n",
        "plt.xlabel('number of components')\n",
        "plt.ylabel('cumulative explained variance');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "PLRgLK7LwXYw",
        "outputId": "ba1e4d80-afa7-46af-8ef4-157378b64dd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(pca_pm25.components_, columns = pca_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "C_fvqrZizHto",
        "outputId": "3813f6d5-e142-4cf8-ad42-7e50bbc1420d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PM2.5      PM10       SO2       NO2        CO        O3      TEMP  \\\n",
              "0   0.419950  0.400414  0.337110  0.428611  0.430992 -0.244071 -0.243305   \n",
              "1   0.247370  0.248128 -0.008656  0.080670  0.094705  0.322625  0.510142   \n",
              "2   0.032918  0.056635  0.133969 -0.018992  0.066376 -0.011494 -0.187738   \n",
              "3  -0.043341 -0.108417 -0.366008  0.139395  0.027498 -0.436957  0.010785   \n",
              "4  -0.123258 -0.126997  0.253563  0.031549 -0.133827 -0.173433  0.006384   \n",
              "5  -0.108514 -0.107538  0.031010  0.064440 -0.056363 -0.112400 -0.030732   \n",
              "6  -0.166964 -0.209359  0.027373  0.087247 -0.066672 -0.227372 -0.138200   \n",
              "7  -0.001508 -0.092581  0.316681 -0.221925  0.078156  0.446654 -0.051659   \n",
              "8   0.218225  0.284427 -0.635406 -0.066165 -0.024592 -0.053345 -0.119701   \n",
              "9  -0.154160 -0.096315  0.286728  0.331914 -0.107851 -0.314601  0.345385   \n",
              "10  0.120520  0.412683  0.115323  0.168670 -0.834764  0.026756  0.000375   \n",
              "\n",
              "        PRES      DEWP      RAIN        wd      WSPM   station  timestamp_ind  \n",
              "0   0.094684 -0.103090 -0.050311 -0.000974 -0.194102  0.035957      -0.018557  \n",
              "1  -0.301575  0.544958  0.148703  0.253063 -0.085257  0.020834      -0.124801  \n",
              "2  -0.540140 -0.241816  0.598892  0.012107  0.471398 -0.041622       0.067994  \n",
              "3  -0.209201  0.283188  0.310759 -0.422888 -0.401445  0.025787       0.282811  \n",
              "4  -0.112417  0.042874  0.110163 -0.242984 -0.166436 -0.194494      -0.839153  \n",
              "5  -0.026070 -0.017484  0.090291  0.194335 -0.043572  0.935781      -0.174075  \n",
              "6  -0.039560 -0.029424  0.172003  0.782690 -0.349955 -0.272738       0.056699  \n",
              "7   0.385171 -0.046736  0.540570 -0.151953 -0.387813  0.044880       0.123903  \n",
              "8   0.421212 -0.055223  0.343314  0.133245  0.142251 -0.032870      -0.328905  \n",
              "9   0.467993  0.244069  0.236698  0.002052  0.436602 -0.055811       0.127782  \n",
              "10 -0.056100 -0.152478  0.033366 -0.044258 -0.167236  0.017724       0.134611  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9da680d4-b407-47cc-b16d-a0c1dbc91dac\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>SO2</th>\n",
              "      <th>NO2</th>\n",
              "      <th>CO</th>\n",
              "      <th>O3</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>PRES</th>\n",
              "      <th>DEWP</th>\n",
              "      <th>RAIN</th>\n",
              "      <th>wd</th>\n",
              "      <th>WSPM</th>\n",
              "      <th>station</th>\n",
              "      <th>timestamp_ind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.419950</td>\n",
              "      <td>0.400414</td>\n",
              "      <td>0.337110</td>\n",
              "      <td>0.428611</td>\n",
              "      <td>0.430992</td>\n",
              "      <td>-0.244071</td>\n",
              "      <td>-0.243305</td>\n",
              "      <td>0.094684</td>\n",
              "      <td>-0.103090</td>\n",
              "      <td>-0.050311</td>\n",
              "      <td>-0.000974</td>\n",
              "      <td>-0.194102</td>\n",
              "      <td>0.035957</td>\n",
              "      <td>-0.018557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.247370</td>\n",
              "      <td>0.248128</td>\n",
              "      <td>-0.008656</td>\n",
              "      <td>0.080670</td>\n",
              "      <td>0.094705</td>\n",
              "      <td>0.322625</td>\n",
              "      <td>0.510142</td>\n",
              "      <td>-0.301575</td>\n",
              "      <td>0.544958</td>\n",
              "      <td>0.148703</td>\n",
              "      <td>0.253063</td>\n",
              "      <td>-0.085257</td>\n",
              "      <td>0.020834</td>\n",
              "      <td>-0.124801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.032918</td>\n",
              "      <td>0.056635</td>\n",
              "      <td>0.133969</td>\n",
              "      <td>-0.018992</td>\n",
              "      <td>0.066376</td>\n",
              "      <td>-0.011494</td>\n",
              "      <td>-0.187738</td>\n",
              "      <td>-0.540140</td>\n",
              "      <td>-0.241816</td>\n",
              "      <td>0.598892</td>\n",
              "      <td>0.012107</td>\n",
              "      <td>0.471398</td>\n",
              "      <td>-0.041622</td>\n",
              "      <td>0.067994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.043341</td>\n",
              "      <td>-0.108417</td>\n",
              "      <td>-0.366008</td>\n",
              "      <td>0.139395</td>\n",
              "      <td>0.027498</td>\n",
              "      <td>-0.436957</td>\n",
              "      <td>0.010785</td>\n",
              "      <td>-0.209201</td>\n",
              "      <td>0.283188</td>\n",
              "      <td>0.310759</td>\n",
              "      <td>-0.422888</td>\n",
              "      <td>-0.401445</td>\n",
              "      <td>0.025787</td>\n",
              "      <td>0.282811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.123258</td>\n",
              "      <td>-0.126997</td>\n",
              "      <td>0.253563</td>\n",
              "      <td>0.031549</td>\n",
              "      <td>-0.133827</td>\n",
              "      <td>-0.173433</td>\n",
              "      <td>0.006384</td>\n",
              "      <td>-0.112417</td>\n",
              "      <td>0.042874</td>\n",
              "      <td>0.110163</td>\n",
              "      <td>-0.242984</td>\n",
              "      <td>-0.166436</td>\n",
              "      <td>-0.194494</td>\n",
              "      <td>-0.839153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.108514</td>\n",
              "      <td>-0.107538</td>\n",
              "      <td>0.031010</td>\n",
              "      <td>0.064440</td>\n",
              "      <td>-0.056363</td>\n",
              "      <td>-0.112400</td>\n",
              "      <td>-0.030732</td>\n",
              "      <td>-0.026070</td>\n",
              "      <td>-0.017484</td>\n",
              "      <td>0.090291</td>\n",
              "      <td>0.194335</td>\n",
              "      <td>-0.043572</td>\n",
              "      <td>0.935781</td>\n",
              "      <td>-0.174075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.166964</td>\n",
              "      <td>-0.209359</td>\n",
              "      <td>0.027373</td>\n",
              "      <td>0.087247</td>\n",
              "      <td>-0.066672</td>\n",
              "      <td>-0.227372</td>\n",
              "      <td>-0.138200</td>\n",
              "      <td>-0.039560</td>\n",
              "      <td>-0.029424</td>\n",
              "      <td>0.172003</td>\n",
              "      <td>0.782690</td>\n",
              "      <td>-0.349955</td>\n",
              "      <td>-0.272738</td>\n",
              "      <td>0.056699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.001508</td>\n",
              "      <td>-0.092581</td>\n",
              "      <td>0.316681</td>\n",
              "      <td>-0.221925</td>\n",
              "      <td>0.078156</td>\n",
              "      <td>0.446654</td>\n",
              "      <td>-0.051659</td>\n",
              "      <td>0.385171</td>\n",
              "      <td>-0.046736</td>\n",
              "      <td>0.540570</td>\n",
              "      <td>-0.151953</td>\n",
              "      <td>-0.387813</td>\n",
              "      <td>0.044880</td>\n",
              "      <td>0.123903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.218225</td>\n",
              "      <td>0.284427</td>\n",
              "      <td>-0.635406</td>\n",
              "      <td>-0.066165</td>\n",
              "      <td>-0.024592</td>\n",
              "      <td>-0.053345</td>\n",
              "      <td>-0.119701</td>\n",
              "      <td>0.421212</td>\n",
              "      <td>-0.055223</td>\n",
              "      <td>0.343314</td>\n",
              "      <td>0.133245</td>\n",
              "      <td>0.142251</td>\n",
              "      <td>-0.032870</td>\n",
              "      <td>-0.328905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-0.154160</td>\n",
              "      <td>-0.096315</td>\n",
              "      <td>0.286728</td>\n",
              "      <td>0.331914</td>\n",
              "      <td>-0.107851</td>\n",
              "      <td>-0.314601</td>\n",
              "      <td>0.345385</td>\n",
              "      <td>0.467993</td>\n",
              "      <td>0.244069</td>\n",
              "      <td>0.236698</td>\n",
              "      <td>0.002052</td>\n",
              "      <td>0.436602</td>\n",
              "      <td>-0.055811</td>\n",
              "      <td>0.127782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.120520</td>\n",
              "      <td>0.412683</td>\n",
              "      <td>0.115323</td>\n",
              "      <td>0.168670</td>\n",
              "      <td>-0.834764</td>\n",
              "      <td>0.026756</td>\n",
              "      <td>0.000375</td>\n",
              "      <td>-0.056100</td>\n",
              "      <td>-0.152478</td>\n",
              "      <td>0.033366</td>\n",
              "      <td>-0.044258</td>\n",
              "      <td>-0.167236</td>\n",
              "      <td>0.017724</td>\n",
              "      <td>0.134611</td>\n",
              "    </tr>\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "initial_feature_names = pca_df.columns\n",
        "# get the most important feature names\n",
        "most_important = [np.abs(pca_pm25.components_[i]).argmax() for i in range(pca_pm25.n_components_)]\n",
        "most_important_names = [initial_feature_names[most_important[i]] for i in range(pca_pm25.n_components_)]\n",
        "print(most_important_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAjkhDtN6HRS",
        "outputId": "28b7ffbd-80dc-4e9d-c2ec-746ef3ef6431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['CO', 'DEWP', 'RAIN', 'O3', 'timestamp_ind', 'station', 'wd', 'RAIN', 'SO2', 'PRES', 'CO']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_regression, mutual_info_regression\n",
        "\n",
        "y_s = df[['PM2.5']]\n",
        "x_s = df[['TEMP', 'PRES', 'DEWP', 'RAIN', 'WSPM', 'wd', 'station', 'timestamp_ind']]\n",
        "\n",
        "# configure to select all features\n",
        "fs = SelectKBest(score_func=f_regression, k='all')\n",
        "\n",
        "# learn relationship from training data\n",
        "fs.fit(x_s, y_s)\n",
        "\n",
        "results = pd.DataFrame({\"column\":x_s.columns, \"score\":fs.scores_})\n",
        "\n",
        "print(results.sort_values(by=[\"score\"], ascending=False))\n",
        "\n",
        "#for i in range(len(fs.scores_)):\n",
        "# print('Feature %d %s: %f' % (i, x_s.columns[i], fs.scores_[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw1yX7NJDSAx",
        "outputId": "95ae1e33-33e0-4ccc-82d4-c2e8b2d79cb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          column         score\n",
            "4           WSPM  29596.179859\n",
            "0           TEMP   7466.488882\n",
            "2           DEWP   5251.510465\n",
            "5             wd   3120.139781\n",
            "6        station    428.761398\n",
            "7  timestamp_ind    246.055690\n",
            "3           RAIN     64.926256\n",
            "1           PRES     17.824851\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_regression, mutual_info_regression\n",
        "\n",
        "y_s = df[df['station'] == 3][['PM2.5']]\n",
        "x_s = df[df['station'] == 3][['TEMP', 'PRES', 'DEWP', 'RAIN', 'WSPM', 'wd', 'timestamp_ind']]\n",
        "\n",
        "# configure to select all features\n",
        "fs = SelectKBest(score_func=f_regression, k='all')\n",
        "\n",
        "# learn relationship from training data\n",
        "fs.fit(x_s, y_s)\n",
        "\n",
        "results = pd.DataFrame({\"column\":x_s.columns, \"score\":fs.scores_})\n",
        "\n",
        "print(results.sort_values(by=[\"score\"], ascending=False))\n",
        "\n",
        "#for i in range(len(fs.scores_)):\n",
        "# print('Feature %d %s: %f' % (i, x_s.columns[i], fs.scores_[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRdg1ui4iKq_",
        "outputId": "fb07d85d-ba78-4d53-8458-e6fec5c07a86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          column        score\n",
            "4           WSPM  3218.934425\n",
            "0           TEMP   744.236239\n",
            "2           DEWP   502.256426\n",
            "5             wd   330.414167\n",
            "3           RAIN    12.062777\n",
            "6  timestamp_ind     2.697578\n",
            "1           PRES     0.008807\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "anova_df = df[['PM2.5', 'station']]\n",
        "anova_df = anova_df.rename(columns={\"PM2.5\": \"PM25\"})\n",
        "\n",
        "# get ANOVA table as R like output\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols\n",
        "\n",
        "# Ordinary Least Squares (OLS) model\n",
        "model = ols('PM25 ~ C(station)', data=anova_df).fit()\n",
        "anova_table = sm.stats.anova_lm(model, typ=2)\n",
        "anova_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "tBHtRWc6DH3P",
        "outputId": "f2b06fd4-a563-4d7b-fff6-f16a30d36e36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  sum_sq        df           F  PR(>F)\n",
              "C(station)  1.804090e+07      11.0  254.803782     0.0\n",
              "Residual    2.708258e+09  420756.0         NaN     NaN"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>sum_sq</th>\n",
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              "      <th>C(station)</th>\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59b6f0e9-83e8-43ed-b4a5-40b5e83e0fad button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59b6f0e9-83e8-43ed-b4a5-40b5e83e0fad');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bd49c5f8-17b3-4b2f-ab6d-ecbd7f5d3faa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd49c5f8-17b3-4b2f-ab6d-ecbd7f5d3faa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bd49c5f8-17b3-4b2f-ab6d-ecbd7f5d3faa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e598ebdd-6f5b-4978-afe2-ace7e0dbba12\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('anova_table')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e598ebdd-6f5b-4978-afe2-ace7e0dbba12 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('anova_table');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastdtw\n",
        "\n",
        "# Computation packages\n",
        "from scipy.spatial.distance import euclidean\n",
        "from fastdtw import fastdtw"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFbHwcaS-gC0",
        "outputId": "5ffd26ea-176e-4816-bda1-bec1290d8f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fastdtw\n",
            "  Downloading fastdtw-0.3.4.tar.gz (133 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.4/133.4 kB\u001b[0m \u001b[31m925.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fastdtw) (1.23.5)\n",
            "Building wheels for collected packages: fastdtw\n",
            "  Building wheel for fastdtw (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fastdtw: filename=fastdtw-0.3.4-cp310-cp310-linux_x86_64.whl size=512576 sha256=24d23c7c89a47f0fe0a87cb4de8a042967d8d258eacbbf52c59c3daecd4881b7\n",
            "  Stored in directory: /root/.cache/pip/wheels/73/c8/f7/c25448dab74c3acf4848bc25d513c736bb93910277e1528ef4\n",
            "Successfully built fastdtw\n",
            "Installing collected packages: fastdtw\n",
            "Successfully installed fastdtw-0.3.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dist_mat = pd.DataFrame()\n",
        "\n",
        "for i in df['station'].unique():\n",
        "  dist_station = []\n",
        "  x = np.array(df[df['station'] == i][\"PM2.5\"])\n",
        "  for j in df['station'].unique():\n",
        "    if(i != j):\n",
        "      y = np.array(df[df['station'] == j][\"PM2.5\"])\n",
        "      dtw_distance, warp_path = fastdtw(x, y, dist=2)\n",
        "      dist_station.append(dtw_distance/len(x))\n",
        "    else:\n",
        "      dist_station.append(-1)\n",
        "\n",
        "  dist_mat[str(i)] = dist_station\n",
        "\n",
        "\n",
        "sns.heatmap(dist_mat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "w1dG7iKaAnfx",
        "outputId": "f87bdf2d-fbc5-4bfb-8733-7f782451f74e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What this tells is that these stations are not the same. Not entirely surprising, but good to know. It means we need to account for them differently.\n",
        "\n",
        "Now let's look at the monthly and daily rolling average to get a sense of what kinds of cyclical patterns might be in the data:"
      ],
      "metadata": {
        "id": "Mf7p1vCUd8dU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['daily_avg' ] = df['PM2.5'].rolling(24).mean()"
      ],
      "metadata": {
        "id": "B7koeQTXNbfQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(figsize=(12, 4))\n",
        "\n",
        "df.groupby(df[\"timestamp\"].dt.day)[\"PM2.5\"].mean().plot(kind='bar', rot=0, ax=axs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "iky7syy1QwmK",
        "outputId": "ebf743bf-3eb4-45be-aab0-2362432a7abf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='timestamp'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(figsize=(12, 4))\n",
        "df.groupby(df[\"timestamp\"].dt.hour)[\"PM2.5\"].mean().plot(kind='bar', rot=0, ax=axs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "dtt_-pF2RAYa",
        "outputId": "bb2743b3-8d90-4bef-a196-a20ecfa5212b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='timestamp'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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84x9/+Jy+ffuqb9++HocCAAAAAOBi4Zu7pgMAAAAAcImgWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGAAxRoAAAAAAAMo1gAAAAAAGECxBgAAAADAAIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGAAxRoAAAAAAAMo1gAAAAAAGECxBgAAAADAAIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAYYKtb5+fnq2bOnIiIi9O2337qMe/PNN3XDDTcoMjJSgwcP1qZNmwwFBQAAAADAigwV6yVLlqikpKTM8HfffVczZ85UXFycUlNTFRUVpfj4eO3YscPI7AAAAAAAsByPi3V6erpeffVVTZo0qcy4RYsWaeDAgZoyZYpiYmI0d+5cRUZGavHixYbCAgAAAABgNR4X68TERI0cOVKhoaEuwzMzM3XgwAHFxcW5DB8wYIC2bdumM2fOeDpLAAAAAAAsp5onL0pLS9PevXuVlJSk3bt3u4zLyMiQpDKFOywsTEVFRcrMzFRYWJiHcaVq1S78XUBAgG+uxeaN6foim7emSTZzpuGr6Vr1Z+bN6fhimlbNxrJm7nR8MU2rZmNZM3c6vpimVbOxrJk7HV9M06rZWNbMnY4vpmnlbG4X68LCQs2fP18JCQmqU6dOmfE5OTmSpODgYJfhpY9Lx3vC399P9esHefx6TwUH16r0eVaEVXNJZPOUVbNZNZdENk9ZNZtVc0lk85RVs1k1l0Q2T1k1m1VzSWTzlFWzWTWXdGlkc7tYp6Sk6LLLLtOwYcO8EsAddrtDubkFFxwfEODvk19abm6hSkrshqbhi2zeyCWRzRMsa54hm/tY1jxDNvexrHmGbO5jWfMM2dzHsuYZsrkKDq5Vob3abhXrQ4cOafny5Vq8eLHy8vIkSQUFBc7/5+fnq169epKkvLw8NWzY8JzAuZLkHO+p4mLjvxB3lZTYTZlveayaSyKbp6yazaq5JLJ5yqrZrJpLIpunrJrNqrkksnnKqtmsmksim6esms2quaRLI5tbxTorK0tFRUUaP358mXGjRo1Sx44d9fTTT0s6e65169atneMzMjJUvXp1NW/e3GBkAAAAAACsw61i3aZNG7388ssuw/bs2aMnnnhCc+bMUWRkpJo3b65WrVopLS1N/fr1cz7PZrMpNjZWgYGB3kkOAAAAAIAFuFWsg4ODFR0dfd5x7dq1U7t27SRJkyZN0rRp09SiRQtFR0fLZrNp165dWrVqlfHEAAAAAABYiEe32yrPoEGDVFhYqNTUVC1dulShoaFKTk5Wp06dfDE7AAAAAABMY7hYR0dH68cffywzfPjw4Ro+fLjRyQMAAAAAYGm+uWs6AAAAAACXCIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGAAxRoAAAAAAAMo1gAAAAAAGECxBgAAAADAAIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGAAxRoAAAAAAAMo1gAAAAAAGECxBgAAAADAAIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABggFvF+r333tO9996rnj17KioqSkOGDNFbb70lh8Ph8rw333xTN9xwgyIjIzV48GBt2rTJq6EBAAAAALAKt4r1Sy+9pFq1amn69OlKSUlRz549NXPmTC1evNj5nHfffVczZ85UXFycUlNTFRUVpfj4eO3YscPb2QEAAAAAMF01d56ckpKiBg0aOB/HxsYqOztbK1as0H333Sd/f38tWrRIAwcO1JQpUyRJMTEx2rt3rxYvXqzU1FSvhgcAAAAAwGxu7bE+t1SXatOmjU6ePKmCggJlZmbqwIEDiouLc3nOgAEDtG3bNp05c8ZYWgAAAAAALMatPdbn89VXX6lRo0aqU6eOvvrqK0lSaGioy3PCwsJUVFSkzMxMhYWFGZpftWoX/i4gIMA312LzxnR9kc1b0ySbOdPw1XSt+jPz5nR8MU2rZmNZM3c6vpimVbOxrJk7HV9M06rZWNbMnY4vpmnVbCxr5k7HF9O0cjZDxfrLL7+UzWbTww8/LEnKycmRJAUHB7s8r/Rx6XhP+fv7qX79IEPT8ERwcK1Kn2dFWDWXRDZPWTWbVXNJZPOUVbNZNZdENk9ZNZtVc0lk85RVs1k1l0Q2T1k1m1VzSZdGNo+L9eHDh5WQkKDo6GiNGjXKK2HKY7c7lJtbcMHxAQH+Pvml5eYWqqTEbmgavsjmjVwS2TzBsuYZsrmPZc0zZHMfy5pnyOY+ljXPkM19LGueIZur4OBaFdqr7VGxzs3N1bhx4xQSEqKkpCT5+5+dUb169SRJeXl5atiwocvzzx1vRHGx8V+Iu0pK7KbMtzxWzSWRzVNWzWbVXBLZPGXVbFbNJZHNU1bNZtVcEtk8ZdVsVs0lkc1TVs1m1VzSpZHN7QPKT506pQkTJigvL0/Lli1T3bp1neNat24tScrIyHB5TUZGhqpXr67mzZsbjAsAAAAAgLW4VayLi4s1ZcoUZWRkaNmyZWrUqJHL+ObNm6tVq1ZKS0tzGW6z2RQbG6vAwEDjiQEAAAAAsBC3DgWfM2eONm3apOnTp+vkyZPasWOHc1zbtm0VGBioSZMmadq0aWrRooWio6Nls9m0a9curVq1ytvZAQAAAAAwnVvF+tNPP5UkzZ8/v8y4jz76SM2aNdOgQYNUWFio1NRULV26VKGhoUpOTlanTp28kxgAAAAAAAtxq1hv3LixQs8bPny4hg8f7lEgAAAAAAAuJr65azoAAAAAAJcIijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGAAxRoAAAAAAAMo1gAAAAAAGECxBgAAAADAAIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGAAxRoAAAAAAAMo1gAAAAAAGECxBgAAAADAAIo1AAAAAAAGUKwBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMIBiDQAAAACAARRrAAAAAAAMoFgDAAAAAGCAz4p1enq67rnnHkVFRal79+566qmndObMGV/NDgAAAAAAU1TzxURzcnJ01113qVWrVkpKStKRI0c0f/58nTp1SrNmzfLFLAEAAAAAMIVPivXrr7+u/Px8JScnKyQkRJJUUlKiOXPmaMKECWrUqJEvZgsAAAAAQKXzyaHgW7ZsUWxsrLNUS1JcXJzsdrs+/fRTX8wSAAAAAABT+DkcDoe3JxobG6thw4Zp2rRpLsOvueYaDRkypMzwinI4HLLbLxzXz0/y9/dXdt5pFZfYPZrHuaoF+Cukbg3Z7XYZ/Sl5M5s3c5HN/FxWznap/D6tnI1ljWyVlY1ljWyVlY1ljWyVlY1ljWzeyObv7yc/P7/yp2cozQXk5uYqODi4zPB69eopJyfH4+n6+fkpIKD8NxVSt4bH8zgff3/v7dj3ZjZv5pLI5gmWNc+QzX0sa54hm/tY1jxDNvexrHmGbO5jWfMM2dycjlemAgAAAADAJconxTo4OFh5eXllhufk5KhevXq+mCUAAAAAAKbwSbFu3bq1MjIyXIbl5eXp6NGjat26tS9mCQAAAACAKXxSrHv27KmtW7cqNzfXOSwtLU3+/v7q3r27L2YJAAAAAIApfHJV8JycHA0cOFChoaGaMGGCjhw5ovnz5+vGG2/UrFmzvD07AAAAAABM45NiLUnp6el6/PHH9c033ygoKEhDhgxRQkKCAgMDfTE7AAAAAABM4bNiDQAAAADApYDbbQEAAAAAYADFGgAAAAAAAyjWAAAAAAAYQLEGAAAAAMAAijUAAAAAAAZQrAEAAAAAMKCa2QEqU3p6uhITE13urT1lyhRL3Fv74MGDevHFF7Vz507t27dPrVu31oYNG8yOpffee0//+te/tHv3buXm5qply5a68847NWzYMPn5+ZmabfPmzUpNTdX+/ft18uRJNWrUSP369VN8fLzq1q1rarZz5efnKy4uTkeOHNFbb72lyMhI07KsW7dOM2bMKDN83LhxmjZtmgmJynr77be1cuVKpaenq3bt2oqMjFRycrJq1qxpWqY777xTn3/++XnHPfPMMxo4cGAlJ/rNRx99pOeff1779+9XUFCQrr76ak2bNk3Nmzc3LVOpTZs2adGiRdq3b58uu+wyDRs2TPfff78CAgIqNUdFP1/ffPNNLVu2TD///LNCQ0OVkJCg3r17m5rLZrPpvffe086dO3XkyBE99NBDGjNmjM8yVTTbyZMntWLFCm3evFkHDhxQYGCgOnTooISEBEVERJiaTZKefPJJbdmyRT///LP8/PwUGhqq0aNH+/xv1d11+b///W/df//9+vOf/+zTdX5Fcl3oc85msyksLMzUbJKUm5urRYsWKS0tTTk5OWrUqJFuv/12jR492rRsWVlZ6tu373lfGxgYqG+//da0bJJUWFioJUuWyGaz6ddff1Xjxo118803a+zYsapWzTcVoCK5zpw5o4ULF2r9+vXKzc1VeHi4HnzwQcXGxvokk1Tx7dnKXg9UNJtZ64Lyspm5LqjIz62y1wWXTLHOycnRXXfdpVatWikpKUlHjhzR/PnzderUKc2aNcvseNq3b582b96sjh07ym63yyq3F3/ppZfUtGlTTZ8+XfXr19fWrVs1c+ZMHT58WPHx8aZmy87OVocOHXTnnXcqJCRE+/btU1JSkvbt26fly5ebmu1cS5YsUUlJidkxXCxbtszly4dGjRqZmOY3KSkpSk1N1cSJExUVFaUTJ05o27Ztpv/8Zs+erZMnT7oMW7lypT744AOfbgiUZ/v27YqPj9dNN92khIQEZWdna+HChRo9erTeeecdU7+M2LFjh+677z4NHDhQU6dO1f79+/Xcc8+psLBQDz/8cKVmqcjn67vvvquZM2dq4sSJiomJkc1mU3x8vFavXq2oqCjTcqWlpSkzM1O9evXSmjVrfJLDk2w///yz1qxZo2HDhmnKlCk6ffq0li9frhEjRmjt2rU+LWIV+bnl5+dr+PDhat26tfz8/PT+++9r6tSpstvtuvHGG03NVurUqVOaN2+eLr/8cp/lcTdX586dy/x9NmvWzPRsBQUFuvPOOxUQEKBHHnlEl112mQ4cOFDmc7mys/3pT38q83fpcDg0duxYxcTEmJpNkubOnasPPvhAU6dOVVhYmHbs2KFFixapsLBQCQkJpuWaN2+e1q9frylTpig0NFTr1q3TuHHjtGbNGrVr184nuSqyPWvGeqCi2cxaF5SXzcx1QUV+bpW+LnBcIp5//nlHVFSU48SJE85hr7/+uqNNmzaOw4cPmxfs/5SUlDj//fDDDzsGDhxoYprfHDt2rMywRx991NG5c2eXzFaxZs0aR3h4uCV+pw6Hw7F//35HVFSU47XXXnOEh4c7du3aZWqetWvXOsLDw8/7ezVbenq6o23bto6PP/7Y7CgV0qdPH8e4ceNMzTBz5kxHnz59HHa73Tls27ZtjvDwcMcXX3xhYjKHY/To0Y6bb77ZZdiLL77oaNeunePo0aOVmqUin6/XX3+9Y+rUqS7DRowY4Rg7dqypuc59Tnh4uGPZsmU+y+NOtvz8fEdBQYHLsJMnTzq6devmmDt3rqnZLmTEiBGOe+65x1exHA6He9mee+45x1//+tdKWedXJNcdd9zhGD9+vE9znE9Fsj377LOOvn37OvLz8yszmkfL2meffeYIDw932Gw2X0YrN1tJSYmjY8eOjkWLFrkMf+ihhxx9+/Y1Ldfhw4cdbdq0cbz88svOYXa73TFo0CDHxIkTfZarItuzZqwHKprNrHVBednMXBd42lF8uS64ZM6x3rJli2JjYxUSEuIcFhcXJ7vdrk8//dS8YP/H39+av4oGDRqUGdamTRudPHlSBQUFJiT6Y6W/36KiInOD/J/ExESNHDlSoaGhZkexvHXr1qlZs2a69tprzY5Srq+//lpZWVk+3fNVEcXFxQoKCnI5jK30SASHyUe97NmzR927d3cZ1qNHDxUVFek///lPpWYp7/M1MzNTBw4cUFxcnMvwAQMGaNu2bTpz5owpuSr6HF8ob761a9dWrVq1XIYFBQWpRYsW+uWXX3wZzeOfSUhIiM/XDRXN9tNPP2nFihV69NFHfZqnlFW3MaSKZXvrrbc0bNgw1a5duxIS/caTn9uGDRtUp04d9enTxweJflNeNofDoeLi4jKnxtWtW9en64fycv3www8qKSlxWT/4+fmpR48e+s9//uOzz9vytmfNWg9UJJtk3t9wednMXBd42lF8uS6w7ietl2VkZKh169Yuw4KDg9WwYUNlZGSYlOri9NVXX6lRo0aqU6eO2VEkSSUlJTp9+rR2796txYsXq0+fPj4/dK0i0tLStHfvXt1///1mRylj0KBBatOmjfr27asXXnjB9EOtJWnnzp0KDw/XkiVLFBsbq/bt22vkyJHauXOn2dHK2LBhg2rXrn3Bc+sqy9ChQ5Wenq7Vq1crLy9PmZmZeuaZZ9S2bVt17tzZ1GynT58uc/2K0sfp6elmRLqg0nXA778ACwsLU1FRkTIzM82IddHJzc11nlNpBaXFIjc3V//85z/16aef6q9//avZsSRJ/+///T8NGTJEV111ldlRXHz++eeKiopSZGSk7rjjDn3xxRdmR1JWVpaOHj2q+vXra+LEiWrfvr26deumRx99VPn5+WbHc1FUVKQPPvhA1113nWrUqGFqloCAAA0dOlSrVq3Srl27lJ+fr61bt2r9+vW64447TMtVWlDPt344c+aMsrKyKi3LuduzVlsPWG1b+1zlZTNzXXC+bJW5LrhkzrHOzc1VcHBwmeH16tVTTk6OCYkuTl9++aVsNlulnyP5R3r37q0jR45Ikq655ho9/fTTJic6e8GQ+fPnKyEhwVIfig0bNtSkSZPUsWNH+fn5aePGjXruued05MgR0681cPToUX333Xfau3evZs+erVq1aun555/X6NGj9cEHH+iyyy4zNV+p4uJivffee+rTp0+l7z35vS5duig5OVkPPvig5s6dK+nst7XLli2r9AuE/V7Lli21a9cul2E7duyQJMt95pbm+f06ovSx1fJa1T/+8Q/5+fnptttuMzuKJGnbtm265557JEnVqlXTzJkz1b9/f5NTSRs3btQ333yjtLQ0s6O46Nq1q4YMGaJWrVrpl19+0Ysvvqh77rlHr7zyijp16mRarl9//VXS2YsQXX/99UpNTdWBAwf09NNPq6CgQM8884xp2X5vy5Ytys7O1qBBg8yOIuns9UFmz56t4cOHO4dNmDDB+XdhhpYtW0qSdu3a5bITpLLXD7/fnrXSesCK29qlKpLNrHXBhbJV5rrgkinWMO7w4cNKSEhQdHS0Ro0aZXYcp6VLl6qwsFD79+9XSkqKJk6cqBUrVphaLFJSUpxXQbaSa665Rtdcc43zcY8ePVSjRg2tXLlSEydO1J/+9CfTsjkcDhUUFGjhwoXOvTgdO3ZUnz59tGrVKk2ePNm0bOf69NNPdfz4cUtsOH399dd66KGHdOutt6pXr17Kzs7WkiVLNH78eL366qumXrzs9ttv19///netXLlSQ4YMcV68zOzCD99Yu3at3njjDc2fP1+NGzc2O44kqUOHDnrrrbd08uRJbdmyRYmJiQoICHApGZXt9OnTmjdvniZNmnTewxjN9MADD7g87tWrlwYNGqQlS5YoNTXVpFSS3W6XdHZP4pNPPilJio2NVbVq1fToo48qISHBEndBkKR33nlHl19+uakXtTzXggUL9PHHHysxMVGtWrXSjh07tHjxYgUHB2vs2LGmZAoPD1eXLl20YMECXXHFFWrVqpXWrVvnPDqiMu44Y9XtWeniz2bWuuCPslXmuuCSKdbBwcHKy8srMzwnJ0f16tUzIdHFJTc3V+PGjVNISIiSkpIsdb5WaQnr1KmTIiMjNWTIEH344Yem7Zk4dOiQli9frsWLFzuXudJzPQoKCpSfn6+goCBTsp1PXFycli9frj179pharIODgxUSEuJyaGRISIjatm2r/fv3m5br9zZs2KCQkBD16NHD7ChKTExUTEyMpk+f7hwWFRWlXr16af369RoxYoRp2YYOHaq9e/fqqaee0rx581S9enXFx8dr5cqVpi5n51O6DsjLy1PDhg2dw3Nzc13G4/w2b96sWbNm6b777tPNN99sdhynOnXqOG9vGBsbq5KSEs2fP19Dhw417QuelStXyt/fXwMHDnQuX0VFRbLb7crNzVXNmjUtcQtQ6ex59Ndee63ef/99U3OU/v1FR0e7DC+96va+ffssUazz8/O1adMmDR8+3BJfIO7du1fLly9XSkqK83zvrl27qri4WAsXLtTIkSNNO6Ju/vz5mjJlikaOHClJatq0qe677z4lJSW5fAb7woW2Z62wHrDytnZFspm1LigvW2WuCy6ZYt26desy51Ln5eXp6NGjljkfzKpOnTqlCRMmKC8vT2vWrLHUPaJ/LyIiQtWrV9dPP/1kWoasrCwVFRVp/PjxZcaNGjVKHTt21BtvvGFCMmu78sorL/h7O336dCWnOb9Tp07p3//+twYPHqzq1aubHUfp6ellzvNu3Lix6tevb+rfgHT2QiuPPPKIJk2apEOHDqlJkyYqLi7Ws88+q44dO5qa7fdK1wG/vxZHRkaGqlevbomNdqvasWOHJk+erJtuuskyR5VcSLt27bRy5UodP37c5xvvF5KRkaGDBw+ed49m165d9dhjj1nmUHqraN68+R9+2WCV9cOHH36oU6dOmX5Ry1KlX0i3adPGZXjbtm115swZHTlyxLRi3bx5c61du1ZZWVk6deqUQkNDtWLFCjVs2FBNmzb12Xz/aHvW7PWAlbe1K5LNrHWBJz83X64LLpli3bNnTz3//PMu51qnpaXJ39+/zJVr8Zvi4mJNmTJFGRkZWr16tWXud3whO3fuVFFRkakXL2vTpo1efvlll2F79uzRE088oTlz5ji/NbMKm82mgIAAtW3b1tQcvXv31rp167Rnzx7nhsCJEye0e/du3X333aZmK7Vx40YVFBRYZsOpSZMm+v77712GHTp0SCdOnPDpxok76tat6zwKYeHChWrWrJn+8pe/mJzKVfPmzdWqVSulpaWpX79+zuE2m02xsbGW2YNoNfv379eECRMUExOjOXPmmB2nXF999ZXq1Kmj+vXrm5Zh3LhxZfbkLF26VP/973/1xBNPqFWrVuYEO4+CggJ9/PHHpq+zAgMD1b17d23bts1l+NatWyXJZ/c9dteGDRvUokULy3xxWLoO2L17t6644grn8O+++05+fn5q0qSJWdGcSrfVTp06pbfeesunp2mUtz1r5nrAytvaFclm1rrA05+bL9cFl0yxHjlypF555RXdf//9mjBhgo4cOaKnnnpKI0eOtMQCXFhYqM2bN0s6u2F88uRJ54VNunXrZtq5WHPmzNGmTZs0ffp0nTx50nlxCenst55mbnDGx8erffv2ioiIUM2aNfXDDz/oxRdfVEREhMuHYmULDg4uc8haqXbt2pm6ETBmzBhFR0crIiJCkvTRRx/pjTfe0KhRo0zbg1OqX79+ioyM1AMPPKCEhATVqFFDS5cuVWBgoG6//XZTs5V655131KRJE1199dVmR5F09nNt3rx5SkxMVJ8+fZSdne08v//3twypbLt27dLnn3+uNm3a6NSpU9q4caPWr1+v1NTUSj9MsiKfr5MmTdK0adPUokULRUdHy2azadeuXVq1apWpufbv3+9yKsTevXuVlpamWrVq+fTWdOVlczgcGjNmjGrUqKG77rpL3333nfO1derU0ZVXXmlatl9++UULFixQ//791bRpU2dBfPPNNzV16lRVq+a7TZ/ysoWFhSksLMzlNW+//baOHDlywfVGZeTKyMjQsmXLdN1116lp06b65ZdftGLFCh09elQLFy70Wa6KZGvQoIHi4+M1cuRIPfjgg7r55pt18OBBPf3007rxxhvVokULU7NJ0vHjx7Vt2zaNGzfOZ1nczda+fXu1b99es2fP1rFjx9SiRQvt2rVLS5cu1bBhw8rcIqmycjVo0ECrVq1SnTp1dMUVV+jQoUNasWKFatSo4dOfX0W2Z81YD1Q0m1nrgvKy5eXlmbYuKC9bRkZGpa8L/Bxm3+y0EqWnp+vxxx/XN998o6CgIA0ZMkQJCQmW2BuRlZV1wVv3vPzyyz5d4f6RPn366NChQ+cd99FHH5m6Z3jp0qWy2Wz66aef5HA41LRpU1133XUaM2aMpa7ELUnbt2/XqFGj9NZbb5n67X9iYqI++eQTHT58WHa7Xa1atdLw4cN15513VsoFQ8pz/PhxPfHEE9q0aZOKiorUpUsXzZgxw6cfzBWVk5Oj7t2766677tLf/vY3s+NIOnvBt9dff12vvfaaMjMzFRQUpKioKCUkJJTZeK9se/bs0ezZs7Vv3z5JZy9EN3nyZFOuLlzRz9c333xTqamp+vnnnxUaGqqpU6eqd+/epuZKSkpScnJymfFNmzbVxo0bTcsm6YIXr+nWrZteeeUV07KFhYVp3rx52rFjh44ePaq6deuqdevWuvvuu33+pasn6/Lp06fru+++04YNG0zL1bhxY82dO1c//vijsrOzVatWLXXq1Enx8fHq0KGDz3JVJFvpz2zbtm1asGCB9u7dq3r16unGG2/0+TZcRbOtXr1ac+fOlc1mq7TP3opkK/1iZOvWrTp27JgaN26sQYMGady4cT67uGVFci1fvlyvvvqqDh8+rJCQEF1//fWaPHmyT89jruj2bGWvByqazax1QXnZDh06ZNq6oLxsNWvWrPR1wSVVrAEAAAAA8DbrXG4OAAAAAICLEMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABlCsAQAAAAAwgGINAAAAAIABFGsAAAAAAAygWAMA4EV9+vTR9OnTzY4BAAAqEcUaAAAPfP3110pKSlJubq7ZUSqssLBQSUlJ2r59u9lRAACoUijWAAB44JtvvlFycnKZYp2WlqbHH3/cpFR/rLCwUMnJyfr888/NjgIAQJVSzewAAABUJYGBgWZHAAAAlYw91gAAuCkpKUlPPfWUJKlv376KiIhQRESEsrKyypxjvW7dOkVEROjLL79UYmKiYmJi1KVLF82aNUtnzpxRbm6uHnroIXXt2lVdu3bVU089JYfD4TI/u92ul156SQMHDlRkZKT+8pe/aNasWcrJyXF53rfffqsxY8YoOjpaHTp0UJ8+fTRjxgxJUlZWlmJjYyVJycnJzsxJSUmSpB9++EHTp09X3759FRkZqe7du2vGjBk6ceJEmfceERGh//73v5o2bZquvvpqxcTE6LnnnpPD4dD//vc/3XvvvercubO6d++u5cuXu7x++/btioiIkM1m0zPPPKPu3bsrKipKEydO1P/+9z8v/HYAAKh87LEGAMBN1113nQ4cOKANGzZoxowZql+/viSpQYMGF3xNYmKiLr/8ck2aNEk7d+7UmjVrVLduXX3zzTe64oorlJCQoC1btujFF19UeHi4brrpJudrZ82apbfffltDhw7VnXfeqaysLK1evVrff/+9XnvtNVWvXl3Hjh3TmDFjVL9+fY0fP17BwcHKysrShx9+6Mz22GOP6bHHHtN1112n6667TpIUEREhSdq6dasyMzM1dOhQNWzYUPv27dMbb7yh/fv364033pCfn5/L+0lISFBYWJgefPBBbd68WSkpKQoJCdHrr7+umJgYTZs2Te+8846efPJJRUZGqmvXri6vT0lJkZ+fn8aNG6djx45p5cqVuvvuu7V+/XrVrFnT8O8IAIBK5QAAAG5btmyZIzw83JGZmekyvHfv3o6HH37Y+Xjt2rWO8PBwx+jRox12u905fMSIEY6IiAjHrFmznMOKi4sdPXv2dNxxxx3OYV988YUjPDzc8a9//ctlPlu2bHEZ/uGHHzrCw8Mdu3btumDmY8eOOcLDwx2LFi0qM66wsLDMsA0bNjjCw8MdX3zxhXPYokWLHOHh4Y6ZM2eWyR0REeF44YUXnMNzcnIcHTp0cPl5fPbZZ47w8HDHNddc48jLy3MOt9lsjvDwcMfKlSsvmB8AAKviUHAAACrBLbfc4rLXt0OHDnI4HLrlllucwwICAtS+fXtlZmY6h6Wlpalu3brq3r27jh8/7vyvXbt2ql27tvMK33Xr1pUkffzxxyoqKnI737l7iU+fPq3jx4+rY8eOkqTdu3ef9/38Pvfv309wcLBCQ0Nd3k+pm266SXXq1HE+7t+/vxo2bKjNmze7nR0AALNxKDgAAJWgSZMmLo9Li/AVV1xRZvi5504fPHhQeXl5zvOjf+/YsWOSpG7duumGG25QcnKyXnrpJXXr1k39+vXTjTfeWKELqmVnZys5OVk2m805zVJ5eXkVej81atQoczh83bp1lZ2dXeb1LVu2dHns5+enli1b6tChQ+VmBQDAaijWAABUAn//8x8kdqHhpex2uy677DItWLDgvONLi6yfn58WLVqkHTt2aNOmTfrkk0/0yCOPaMWKFVqzZo2CgoL+cD5TpkzRN998ozFjxqhNmzaqXbu27Ha7xo4dW+ZiahfKHRAQcN5pn+/1AABUJRRrAAA88PuLeflKixYttG3bNnXu3LlCF/WKiopSVFSUEhIS9M4772jatGmy2WwaPnz4BTPn5ORo27ZtmjRpkuLj453DDxw44K23UcbBgwddHjscDh08eNB5MTUAAC4mnGMNAIAHatWqJen8h0l7U1xcnEpKSrRkyZIy44qLi5WbmyvpbDn+/Z7hNm3aSJLOnDkj6bfMpa8pdaE9zStXrjQW/g/885//1MmTJ52P09LSdPToUfXs2dNn8wQAwFfYYw0AgAfatWsnSXr22Wc1YMAAVa9eXb179/b6fLp166YRI0bohRde0J49e9S9e3dVr15dBw4cUFpamv7+97+rf//+evvtt/Xaa6+pX79+atGihfLz8/XGG2+oTp06zrJas2ZNXXnllXrvvffUqlUrhYSE6M9//rPCw8PVtWtXLVu2TEVFRWrUqJE+/fRTZWVlef39lKpXr55uv/12DR061Hm7rZYtW+rWW2/12TwBAPAVijUAAB7o0KGDJk+erNdff12ffPKJ7Ha7PvroI5/Ma+7cuWrfvr1ef/11PfvsswoICFDTpk01ePBgde7cWdLZAv7tt9/KZrPp119/Vd26ddWhQwctWLBAzZs3d04rMTFRjz/+uJ544gkVFRUpPj5e4eHhevrpp/X444/r1VdflcPhUPfu3ZWamqprrrnGJ+9p4sSJ+vHHH7V06VLl5+crNjZWs2fPdu5VBwDgYuLn4IoiAACgkmzfvl2jRo3SwoUL1b9/f7PjAADgFZxjDQAAAACAARRrAAAAAAAMoFgDAAAAAGAA51gDAAAAAGAAe6wBAAAAADCAYg0AAAAAgAEUawAAAAAADKBYAwAAAABgAMUaAAAAAAADKNYAAAAAABhAsQYAAAAAwACKNQAAAAAABvx/V2w74P4Eb0cAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set(style=\"white\")\n",
        "\n",
        "#daily_average = df[df['station'] == 1].groupby(df[df['station'] == 1][\"timestamp\"].dt.day)[\"PM2.5\"].mean()\n",
        "\n",
        "fig, axs = plt.subplots(2,1, figsize=(15, 8))\n",
        "sns.lineplot(x = \"timestamp\", y = \"PM2.5\", data = df[(df['station'] == 3) & (df['year'] == 2016)], ax=axs[0])\n",
        "sns.lineplot(x = \"timestamp\", y = \"PM2.5\", data = df[(df['station'] == 6) & (df['year'] == 2016)], ax=axs[0], alpha=0.5)\n",
        "\n",
        "differencing = abs(df[(df['station'] == 3) & (df['year'] == 2016)][\"PM2.5\"] - df[(df['station'] == 6) & (df['year'] == 2016)][\"PM2.5\"])\n",
        "\n",
        "sns.lineplot(data = differencing, ax=axs[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "DWFJuneoLR4_",
        "outputId": "006a81a1-c966-49f5-e4a3-3afdb0f89f50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='PM2.5'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(differencing)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HeDjQQUtdFMt",
        "outputId": "f4ccc383-2101-47a4-fd57-b7cf7f028f39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.802026411657558"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_mat = pd.DataFrame()\n",
        "cov_mat = pd.DataFrame()\n",
        "\n",
        "for i in df['station'].unique():\n",
        "  corr_station = []\n",
        "  cov_station = []\n",
        "  for j in df['station'].unique():\n",
        "    if(i != j):\n",
        "      #corr_mat[i, j] = df[df[\"station\"] == i][\"PM2.5\"].corr(df[df[\"station\"] == j][\"PM2.5\"])\n",
        "      #print(i, j, df[df[\"station\"] == i][\"PM2.5\"].cov(df[df[\"station\"] == j][\"PM2.5\"]))\n",
        "      corr_station.append(df[df[\"station\"] == i][\"PM2.5\"].corr(df[df[\"station\"] == j][\"PM2.5\"]))\n",
        "      cov_station.append(df[df[\"station\"] == i][\"PM2.5\"].cov(df[df[\"station\"] == j][\"PM2.5\"]))\n",
        "    else:\n",
        "      corr_station.append(-1)\n",
        "      cov_station.append(-1)\n",
        "  corr_mat[str(i)] = corr_station\n",
        "  cov_mat[str(i)] = cov_station"
      ],
      "metadata": {
        "id": "JqiMiO49WsZ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.color_palette(\"flare\")\n",
        "\n",
        "sns.heatmap(cov_mat, vmin = 3000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "qXdHKgXoaxJs",
        "outputId": "e712cc92-76c2-477d-d968-5954fc0cb824"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(corr_mat, vmin = 0.7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "nFnAq2L9bGGM",
        "outputId": "eeb1be2c-ec05-4d6b-a7f7-ee33a2303733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, let's look at the ADF test. Interpreting the results, if we see a higher p-value that is an indication of non-stationarity."
      ],
      "metadata": {
        "id": "NrY2L8iFfa__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "def adf_test(timeseries, station_name):\n",
        "  print(\"Results for station\", station_name)\n",
        "  result = adfuller(timeseries, autolag=\"AIC\")\n",
        "  print('test statistic: %f' % result[0])\n",
        "  print('p-value: %f' % result[1])\n",
        "  print('observations used: %f' % result[3])\n",
        "  print('Critical Values:')\n",
        "  for key, value in result[4].items():\n",
        "    print('\\t%s: %.3f' % (key, value))\n",
        "\n",
        "for i in df['station'].unique():\n",
        "  adf_test(df[(df['station'] == i)]['PM2.5'], str(i))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFTgM0RySMMn",
        "outputId": "2eaee0c0-a28e-49a5-aa2d-61af192b0aad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for station 0\n",
            "test statistic: -19.096519\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 1\n",
            "test statistic: -21.989901\n",
            "p-value: 0.000000\n",
            "observations used: 35038.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 2\n",
            "test statistic: -19.501851\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 3\n",
            "test statistic: -19.057672\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 4\n",
            "test statistic: -19.560599\n",
            "p-value: 0.000000\n",
            "observations used: 35011.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 5\n",
            "test statistic: -18.873612\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 6\n",
            "test statistic: -19.563424\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 7\n",
            "test statistic: -19.171350\n",
            "p-value: 0.000000\n",
            "observations used: 35014.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 8\n",
            "test statistic: -19.237664\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 9\n",
            "test statistic: -19.756938\n",
            "p-value: 0.000000\n",
            "observations used: 35024.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 10\n",
            "test statistic: -18.960793\n",
            "p-value: 0.000000\n",
            "observations used: 35012.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n",
            "Results for station 11\n",
            "test statistic: -18.920628\n",
            "p-value: 0.000000\n",
            "observations used: 35021.000000\n",
            "Critical Values:\n",
            "\t1%: -3.431\n",
            "\t5%: -2.862\n",
            "\t10%: -2.567\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks like we don't have non-stationarity to worry about, our time serires don't possess strong trends.\n",
        "\n",
        "Next, let's look at the KPSS test. Interpreting the results of its p-value is the opposite of the ADF test: a higher p-value is an indication of stationarity."
      ],
      "metadata": {
        "id": "IUidIPTJfOHb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import kpss\n",
        "\n",
        "\n",
        "def kpss_test(timeseries, station):\n",
        "    print(\"Results of KPSS Test for station \", station)\n",
        "    kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
        "    kpss_output = pd.Series(\n",
        "        kpsstest[0:3], index=[\"Test Statistic\", \"p-value\", \"Lags Used\"]\n",
        "    )\n",
        "    for key, value in kpsstest[3].items():\n",
        "        kpss_output[\"Critical Value (%s)\" % key] = value\n",
        "    print(kpss_output)\n",
        "\n",
        "\n",
        "for i in df['station'].unique():\n",
        "  kpss_test(df[(df['station'] == i)]['PM2.5'], str(i))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQe4oSr2ejxG",
        "outputId": "cc1f10ee-9c58-4f80-d81b-43349b9d21da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results of KPSS Test for station  0\n",
            "Test Statistic             0.239015\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  1\n",
            "Test Statistic             0.544397\n",
            "p-value                    0.031667\n",
            "Lags Used                107.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Statistic             0.276061\n",
            "p-value                    0.100000\n",
            "Lags Used                107.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  3\n",
            "Test Statistic             0.167471\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  4\n",
            "Test Statistic             0.147068\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  5\n",
            "Test Statistic             0.141487\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is smaller than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results of KPSS Test for station  6\n",
            "Test Statistic             0.26127\n",
            "p-value                    0.10000\n",
            "Lags Used                107.00000\n",
            "Critical Value (10%)       0.34700\n",
            "Critical Value (5%)        0.46300\n",
            "Critical Value (2.5%)      0.57400\n",
            "Critical Value (1%)        0.73900\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  7\n",
            "Test Statistic             0.189226\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  8\n",
            "Test Statistic             0.139856\n",
            "p-value                    0.100000\n",
            "Lags Used                105.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  9\n",
            "Test Statistic             0.200034\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  10\n",
            "Test Statistic             0.760523\n",
            "p-value                    0.010000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n",
            "Results of KPSS Test for station  11\n",
            "Test Statistic             0.194775\n",
            "p-value                    0.100000\n",
            "Lags Used                106.000000\n",
            "Critical Value (10%)       0.347000\n",
            "Critical Value (5%)        0.463000\n",
            "Critical Value (2.5%)      0.574000\n",
            "Critical Value (1%)        0.739000\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-85-140993b5e628>:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once again, we seem to have stationarity in our series. So both of our tests have passed and we can move on to looking at some approaches to forecasting that we might be able to use.\n",
        "\n",
        "Let's check seasonality:"
      ],
      "metadata": {
        "id": "EIz41i63nD4z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "\n",
        "seasonal_analysis = df[(df[\"station\"] == 0) & (df[\"year\"] == 2014)][[\"PM2.5\", \"timestamp\"]].copy()\n",
        "\n",
        "tdi = pd.DatetimeIndex(seasonal_analysis[\"timestamp\"])\n",
        "seasonal_analysis.set_index(tdi, inplace=True)\n",
        "seasonal_analysis.drop(columns='timestamp', inplace=True)\n",
        "seasonal_analysis.index.name = 'datetimeindex'"
      ],
      "metadata": {
        "id": "_ni_SqGQVuLP"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decompose_result_mult = seasonal_decompose(seasonal_analysis, model=\"additive\")\n",
        "\n",
        "trend = decompose_result_mult.trend\n",
        "seasonal = decompose_result_mult.seasonal\n",
        "residual = decompose_result_mult.resid"
      ],
      "metadata": {
        "id": "sCBlFtYSXSW3"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decompose_result_mult.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "c79l8YcBkvxE",
        "outputId": "034f73f2-57c1-416f-efe9-6f9dd57748d9"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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+36Tc+iZU/L7rFNYcKsZ7yw/j8zXHJI/JJ75DhZV4ZdE+lFYHP6niVAT21SX8szWnFEVV9SiuqsdPW0/ip60nUV4TGYsG0bxoWdD8CjuZEPpuY67PeSd//cZos2A6XBTLnTh8LWgl1TZ8tZ7bMApS1+yDsoL5/t/X+328skjZkPL499sx6ZstIQupCpSIEXYrVqzApEmTsH79eixevBh2ux3jx49HdbVU2Nxzzz04deqU+O/VV18VH3M6nZg4cSJsNhvWrl2LL774ArNmzcKzzz4b8HiCXqBYsZOD//v87aZCVUyXXWNar682NiX3qr+6eM/+tiuwATYSrZ2ZfPgXvbkS7y8/jOfn7W7Scejho5WHsWhXfpBGQ6gRyHS++VgJ/vbeWox6ZZnECiFAQGFFXdhkIRLhiVacl8nPfK90bo169S/J3/Ks28aIs2BWO1Aq86L0Xdz9xSbJ/S6ZsDtyWl8hZ+/7an+GqnoH5m45gQU7T6GgQj2Eqikx+z8kPFi0aJHk71mzZiE9PR3Z2dkYPXq0eH9sbCwyMzMVX+PPP//Enj17sGTJEmRkZGDAgAGYNm0a/vnPf+L555+HxWIJ6WcIFKXTKVAzeajCwdgFq/X6ahtLxeBfhbs+W3NUvB1oinpj0YqlkH/l7O+dJ4NfmfzT1UfwwPldG/TcbblleNnjqj82Y2Iwh0WgYfFDDqcLf/9gHQB35h7/CgcLq3DtB+vQq00iFj4yKkijbBil1TbM33kKl/drg+TY8JoXz2SOFVVj1tpjqo/7i/9VKpUiv09usQsXV6zSuqEkurbklEnHAEGyftwwNCug9/UXY8iLxnBpShQxFjs55eXuRTQ1NVVy/zfffIO0tDT06dMHU6dORU2Nt1L0unXr0LdvX2RkZIj3TZgwARUVFdi9W9naUl9fj4qKCsm/UKBcF873Pvm57S9kIhSu2HqHEysOuHsJapV9CWRCkB+ZU1wT8O5HEAQcLaoOSsJFdb26e15tF2oxBedy2nXSe441pmbe6crw2D2eCei9yvbJisjyl8jPW92FsMOhgPdD323Ff37Zhcco9i+sePLH7ZqP+5vvTyj0bm0jK98kt+qFi3sxV6Fjhh6LoEuQlj/plp4Q0Ps6/GTU8utcuGT5R6Swc7lcePTRRzFy5Ej06dNHvP/GG2/E119/jWXLlmHq1Kn46quvcPPNN4uP5+fnS0QdAPHv/Hxld9X06dORlJQk/svKcqv9YAsmvZeOj8XOj4gJhcVuxu/7xF6CWl9DQMkTss8lb4+jRzR9suooLnh9OT5YGXjbGDn/++uQ6mNq81yw3N5Pz90RlNcJk81jRJFbUoOx/7ccszfmhOT1o2TnsZM7mdQ6CjQHrK3Usv2n/RxJNCX5from+Lvmp//uGyN2RBanzRIsuraOA6DcAlIvwRSF936V7XMfv8QcK6pWXHMEQZBUmXj3r4O631MQBEz9yVt2Kj3BqnCM93ZjunQEk4gUdpMmTcKuXbswe/Zsyf333nsvJkyYgL59++Kmm27Cl19+iZ9//hmHDzd8oZ86dSrKy8vFf7m57iD+YBvC9MbY+VjsAoixC1bpED6ZQEvMNCYrVv659GT3ssrrry7a7+fIhsFEstpkpddg98qifbj07VWqVsFKDWthIIQqI7ol8+yvu3D4dDWe/imwGoJ6z3T5jr6Wy9CLlFIKRPMRZ9GOnkqJ03abj+uZofk44D1HB2SlAGicKzbU1arYXPxj9gmc//pyPPmDr0VTEKQ1VksDSFIqlHk9lD4Pv86Rxa6BTJ48GfPnz8eyZcvQvn17zWOHDRsGADh0yG19yczMREFBgeQY9rdaXJ7VakViYqLkn5tgW+z0XQGBx9h5x+lPBDYE+bfAi8dAdmvyQ+WisKlj7JSwmt0VyNU+lb/AZcb7yw9jz6kK/LTlRJBGpkywu6OcCQQirhpyNckn/o9XeuNI5+841YBXJBpKjc2B48WBBdI3N/68Av7m3LMz4/2+BytIHB3llgeNWTUakzxRq+NaZMvEzGXuNf6nrSd9jhEgNFhgyj1zysYWEnYNRhAETJ48GT///DP++usvdO7c2e9ztm3bBgBo06YNAGDEiBHYuXMnCgsLxWMWL16MxMRE9OrVK6DxBNvFqVcDyU/QQFyxwWjIvGiXdPGRW4X4XoSBCEkfi53scw3qkKz7tUKFONEFyRWrZLavCpK1DkDY+WKPFlVj7SHf2ozhhPwX+WbDcdw1a5PfQtF6v2r5bz5nc/DK+BwqrGqxBa1DweX/W40xry3HnrzgxDTWO5x+y081Fn/rjr8Z11+GJ+AVJ+JGthmSJ2ZvzEHPZxdhgZ/NDlvTtMq8NGbZk6+Z/nqkh0k4YuQIu0mTJuHrr7/Gt99+i4SEBOTn5yM/Px+1te7eo4cPH8a0adOQnZ2NY8eO4bfffsOtt96K0aNHo1+/fgCA8ePHo1evXrjllluwfft2/PHHH3jmmWcwadIkWK2+vnMtgu6K1fke8hNNSzy9MG837uKK+zYmVoJx/9fSGkDySZGfOALZvPiLHWyfot7hoqmIjmIWO+XvPBhi/83FBxr/Ih7CTNfhgteX48ZPNuBAQaX/g5sJ+Xn47593Yem+Qny57lhQXj9UO/pVB09j3BsrcK0n47YxyK+9YAmfcOOwp+zFwp3BsZS+8vt+XPD6cvy8NXSWeH+x3f6EhVY7MsAd51nsqcfJ6o02rtxJw2ChEPI2lXL0jK0x45cbGJSMI/x9JOwC5P3330d5eTnOP/98tGnTRvw3Z84cAIDFYsGSJUswfvx49OjRA48//jiuueYazJs3T3wNk8mE+fPnw2QyYcSIEbj55ptx66234sUXXwx4PFrWmcLKOvy+81SAyQP6jpOfWFpiTV5YNxgWO9/xSP/mhV0gMX2C5/jrPlyHO2dt8hGsoRh7oDBh5xKAyjrfOI0tOWVYvKfA5/5A2OqnJZAWTpeAXSfL4XQJyC+vw/vLG59EEgoOFlQ19xBUUTtli/1kJ+s9O/VU/m8IczwFvINRckeelXvpO6sa/ZrhTLBaBLLyTCyxLBT4Myj4s64prUl896DX//RuLK2isFN+zdySGuSXaydzhHraFgvl6zimIci/L6VX4o8Jh3UKiKA6dv5+nKysLKxYscLv63Ts2BELFy5s9Hi0TqSJ76zG6cp6PH95L9w+0usyzj5eiq05pbhzZGeFyURvjJ3070BOpFDE2MnhXbEBuZkEILe0BhuPlgAArj9HWmsoHK4XNtH9svUk3l9+GE9f0gP3j5HWmLvny82NqhkXYzE1+Ln3f52NxXsKcHZGApyCgEOF4SmgzCblqye3pAYWsxEZidGKjzcFavOM0nXWkHMyVMWH9bjY/HGsqBr/++sQhnZOCcKIIoeoIMfVhKrbjx78umIVLAFtkr3X2wcrvJtBa5S6xa7O7sSoV5cBAA7+9xKfbO+mQs/61xiLna8hRcli570dBssUgAiy2IUbvMWuQJaCzuqHLd4rtd5c8/5avLRgL+btyPN5PaXzU8kqKD8ukGDvxmTFltfa8YRCxpGchrqaBAiSzyavKN7UO6E4BYHFLHanPLvUGQqlA/yh1AKHx9yIVFZmLdxfUOkj6vy9b6jhS3ko9ReurLNj1KvLMOzlpc1aN0vtEvF36bhcgq74SL3Xh97voNbmxK/bTqK4uvE1C++YtQlzt5zAP+cqZwSXVNvCpqZZY+E/hznIokQr3uvNxQfwyaqGW/SSYqI0H9eaJx1OF7KPl/ncr5ZkFa0RY/cn55mQl6aSvHaINa6e8CK5mE2M1m/P0mOx47+fcLHYkbBrIPyPebiwCp+uPoo1ssDwLQoXEeBOzZaLLLXTQRAEzRNHLXPI384iUN74cz9+zPYfO6Lmfr77PO1kFx/BKlsklV73Xz/vRKenF+DXbSdRWBncnqpKsShWhR63gfLBcu+kXlTluxizws9qPP79dkz9KfA6d3JL0YoDp7ErBJ0y1NjNxWnx1mo2yZ4sqxXvC3WJBC3Ur0Pt5932+UaMeuUvnCqvVXw8v7wOV7+3BnN1XEOA/lJBLy3Yg0dmb8OmYw134QPA3OwTmoH/S/cWYNC0xXjtj+CVEhIEASUh6K+sB94lrtS7ujGoCbuc4hq8vfQgXlqwt8GbbHYdv3vjQOUDNF52xu/7sFeh+LXauca+F6Vz/1cu+7SqTn1Do6XrXC4BZRqiUA96vkV5RYVAtJePsFN4Lu8JCxNdR8KuofDZbSsPFmHa/D246ZMNkqy0WpUMtVUHi/Dp6qPca7kUT3BBENDr2T9w0ZsrRXEn35nbnC7dPfQCifmTc6JUecGSo+QSapccg2cu0846FgTpBVItE6xKQ/92g7uI7COzt+GvvYW+B3B8ue4Ybvl0g+6FROm7Yha7xjCfs9a+t/wwHv/evxWUUVxVj7lbTuC7jbmSgpsMrd0x/2kOn67CbZ9txGX/W42r31uDUpXvZPbGnKAFll85c414m7fYKS0ejTlPG0NZjQ3bc8sUH/NXtuHI6WqU1tixVOU8fP633diSU6a74K/eQqffbPAtpPx7A36zx/1Y4//1s9uK956fuM3Dp6skIl2Lf87dgUHTFvtsZgRBwIIdp0IaSlBn44SdSmhAQ1ETdhVcXK6eWEsl8cfmdZaxKkfrrPmEW3Mk76OiRrRi7AZkJYu3L/y/Fcg+rryx0JqT7v5yMwa8uLhRG0w9FmR5B55AZhd5+JJiNyiX9uPNAQm7BmJzeoUHb6mTu2XVYMV0AeCSt1cpCpfVh4pQa3fiUGGVeEG/q9ARoUZBQCqVPThYWKnYUkYPdp2LLW/pautpVfPviT39Pk+AIBGF5TKh6++C8RcA/eyvuz2C2r8bRBAERYsdK3fSGOSvOldWy25oJ2mLvL7tksTbvGu+WEGMaQcQe28f4NpabckpU+yycaK0Bk//tBMPfqOdldYQ+B00cz2Hg7B7a4l6RXrF00/hPrUYukBdpXpj8ZSsTQ+E4DfT096vvMaOsf+3AiNn/OX3WAD4frP73P/fUun3vuLAaUz6dgvGveE/ZppnS04pFu06pcvdXefg+3uGXtjVO5wSr4LSxptnxu/7cM5/l/hYgNl5oWZlbIiu8Gexkwu7wso6/J8se58P0+EFqZY176997k3QFxq9bwF3vUE12NgqNd5HHoYSiPjyzYpVHwNAMXYRT2Wd92Thf/wX5+3R9Xz+2lfbmSrVx5FbsgBld+wWhezKGz/egPNeWeZzf3mNHVfOXCOxIspZ6cdFyOC/C9Y8PM7qP6ZBEKTxR0Uy4eIvdkEpbkuJmcsOY/jLSzVdIWq7aSWLXaAixF/bKPmEvfNkuThW/r2UsjT1LlB1somOZfPxlHHV2YO9C52zyWtlYokUvEWsKZJ85OzJq9DMSNb7Hci/W4a/MhNy9GbPxgTBihwsIX2izLtpDOQ15ckGfFauw+ORWH+kWLNG384T5bj6vbW4/+stmL5QO/ZVEATJBvmZX3bpHqselOaiu2Ztxp2zvKWn/MW8frDiMIqrbXhvmdRCyp6nFhbSkILAanMr23TJ49hu+Gi9z7G8VYy/BtLi/ZcR83eq/LBZPXyBPVer1ZpP71u/I+JeX/bZlb5f/toOVnenxkLCLgj0apso3l66z+uKSdAI0rykbxu/r8vPD2yxG9sj3ec4pQQKLQsWv0gt3HkK57++DNtzyzBtvj5RqgUvztjtKB2uDgHAMu67+3O3tHevTys12R1yt7fWQpxfUYf3lqv3gv14pbJVL1rB/SG3uAHAvnz1ul+d0+Ikf8uFnNJOnGUX8+4OpUQWzW+Z+zoC7WcY6Fy1ZE8B3lt+CIIg4JNVR9D939IsdN7ayHoAN6fFLq+sFpe+swrbT0hdQqsOejczekdUUatsOQhUq+otNFyhUHYnUJSaqzcEPjMykELJ8v1I26QY8fYP2Sfw9tKD+MdH6zH5262qr7HuiNdrorRR4ZmzKRdfrT+ue3z+OFlWK3EnKlnsVsvir+t1dtKRB/4zwa9msWvIpaN2vYktFGVnP6v/p3QsIP1sesJX/IlRrU2RnmSFxiTi+Zbdkj7+4YrDuIoLMwkPWUfCLiiouU1Gd2+t+hyTwYBft53EhDdXqh7Dn0QuTxLFKYW6QbypOrekBg6nS1wwlfjfX4dE4fPgN1sC6p3nD36SYBckm/Dv0kigEATgfS7VvqhK22InN8//+2fprtvfBPfrNt/MZAZfy4lHyRWrFHB+8VurVGP5EqKlWW02hwu/bvMGIv+1zzdGi5WAuf9rbxNspTgmvR4lPdYn/rUCFVp3f7kZry7aj7WHi/HSgr0+QpJ3zzCLHf/7au16v9+Ui05PL8CGI8UBjUkLNYv5LZ9u9I5J52KgtkgFOuG/s1Rfo/LGGjdPldfi/NeXN+5FPPCLeyDlV9i5tiWnFMeKqiWhLVN/2il6EpbsVa8RGSvrobonrwLj31yBP2QbxPzyOsU+wI2xSo+c8Rcu+9/qgJ6j1yJbVS8VyEw0qVrsGvA52PXtcgmiKL1uSHvRA6Dn8r+4j7clJy+k9LjF/Q1ZKwZS67lDOrrL9rD1mX02recIgoDckhrxe/RNnpD+PV1WGYGyYlsQamUOlGoGMUxGAx6ZvQ37dVbhd7kEfLDiCGYpxCOwC2nFgdMY9eoyPPXjDs0d8xuLD2CrSpB4Y2Firtbmba/DXBNPXXw2/neDSjYXBFzWT92K6SvstC0C/i4wNWuqfCK6vH9b8XYgrtg8lQBypXE/MnubphtQbwSQ1gTMCw49EzVfAmHvqQqM/b/lksQPPajFm/JWJhYEzo+pzuHEQ99tlbhsGU/NdWcEX6/gDmoocVYdVgWF70zRLaMmaAKc8H/QmT3bUFwuAZ2eXoAR0/XFw+nDe87YA2xzs+NEGa5+by3Of325z2fXU9IpNc4i+fvSd1bhQEEV7vsqW3L/NpV5L5hWYj0hEXotdvO2S6+5xiRPqME+ekFlHZwuAWajAS//ra8o1PWIFX5uLK/1Xt96LLdysXT4tHSjFWUyBlRfEgDS4i1iTVDmvha9Axpj+XDlEYx6dZkYb6snK1Z6gJ/HmwgSdkFATdj9sVt9hxloEUunS8Ari5RjR5iF40mPe+6nrSd9UrzlqKWZ250uFFTUYe3hokbt/v72ntc8zdykVrNJIpR4BAHowyUKyJGvE4EIO6VJu1OrOJ/7ALfo5fm/a/ujd9tEXNgjXdH9oWatlVsKGGoT3bHi6kbHsmlNwOyhPXkVmKpgsdDiyplrcPh0taYrTOs95aQneAuisl00/xv9ui0P87bn+dRTC1XGmR530ZacMl2vpfb7yt28NwzNUjyuqdh2oizor8lvigKx2JmMBqw73DgLbKzOwt5q5WjkLrctOaUNbg1WZ3Ni2vw92HysRPUYrU2/FjaHtsWOFxaHCitVM07vGdUZQzu7E7XYtXesyO2Sb58SA7PJKK5RkgK8Ktcgv5rxXXnUKkPwyMOGJsmSf6JMRlV3rNacx8YvfmfM46JxarLapG8vlQo7Vj/Qn8gNkxA7EnbBQCvzR777YARa7FzrfGEnfSEfwOrngrKYlCfCv/YV4twZf+HGjzdgXQPcXQ6nCzOXHcI+LvOyd1t1wcYQoL1rll9Q1X6KwfLz5ooDvu7NIbLsU8bXstgbi9mIBQ+Pwme3n6NoOVPLblPKNAW8v8uYs6Ru+iiTEXO3eF2yfBHNzcdLcVCHZVdP9Xel9lByaweg7OoNFnzbK7ZQ8EklSu3aAGniUIKOhBy9qMXF8SjV/1JC6RxWWgwTY6LQIzNB9XX6tEtUfSwYqNW/1EM5F7qxP78St362EdtyyyRzjpYL7lR5rWQzbDIaG70g6j1fX1BJbpP/RFe/txaPzdmuWv5Gi43HSvDp6qP4u0bf3kDjXBn+kifYPCkIAsa9sRKX/W+1YlhI/6xkvHFdfwBeUZtT4vawdPBselmtdP78VZvv+N+PF3P+sn8B+IQN8WsH4J6DVd/XBcXfqKjKJrr4xbhEk2+W74vz9mD673t9ni++vudY5nXyb7ALD2VHwi4IVGukY1fUKi9SWtXJldDaKSgtJmrZeeLjKsLvvq+yxdfb3ICipw6X4FPEVE+lb0EQNIWd/ON/qJLgwOC/L6W4NbULUOtXkbtYTEZDwJ022KQXLxMmUSajJCHiw1uGiLdzSmpwkUIs5pI9BRKLoVZsmtYEq9TC654vNysc6Z+c4sAC8Qsr61FUVY/NXGKI0scorqrHiJeXin8P66IszAF3TKJWsV05L8zb7feYLmnKFl49KH33FpMRUy9VLwN0Uc9M1ceCgZ5yKjcM7aB4P1/z7s5Zm7DywGlcNXONxEugZmE5VV6LEdP/wjkvLRHvMxkaH5v03K/+f0MtnC4BhRV1uPWzjZJ+z3rrd2qhJDob4vp1OF3iteGv3Akfw6dU4spsNIhrEJs3WKx1a08mK7N48WNV807xvx9/Huhxxaq1GGTEWU2q56sgCJIamTxyi51YM9PzeGFFHT5bcxQfrjiiaihgawdL+PJ3mpLFrgWhZbFT+50DbTytdUIpCjs/rtgau9Ovays5Vrt9DSMr1ZvFpnQB6ok5EaA8uf/D0zNW/pg89kQOf7zd4fu6aheg1ljlTzEa9KXzM3KKa8QODPK4LnnmsJ5q+Hd/uRlvL/W6jrWyx7QKDTsb6BZSYvRrywI6/lR5HYa8tERiQfLpyiIIGPzSElRyk6/aZ1118DSu+3AdLlBICMgvr5NYLx76bismfbvFx0KgRHqivt959qZcyXUlCAJ+3+X73ducLrRSsJSKzwvhzn/TsRI8+aP/7iXxKrGHfBIDL1r4zaRaWR+WCMRbdQLd5PJsySlFfnldwOVk5DgFAc/+uhsrD5yWbGr81a7UU95CSdxolT1Se4zfIPiLseOPNRoMPnO9yWiEiQk3z2Ofe7KJWfyxkitW3l2Jwc+3/G+rR8D6a6MoCOrlYeo0NijsrKqXua/Zd+HPTfzCvN2KJcC01k0qUNyCqNTRI1K+awp0LvNnsZNfQP52Sk6Xy+/ugpX3YJMxT5skr5Xn27uHi7flO7qurfVZOnbnVeBlhfpTF3rKuwS6o+c/m5JbSzVWRON3WSub1ExGAzq1itU9phe5cjLyiflAgdRlr/f8mLPJXQrF3+9drmI5BgKvsaaXQF41j4t9kp/LSgVMK2rtiosGn8kqOb7OjuHTl2LQtMX4acsJFFTUYd72PCzYoa9Lg1LMmNopydcAXLK3EI/N8S1N88vWk+ikYQUMRj0sJStEVb0D136wTrGdnRytzQU73/jrZRsXh6jmalTaOBkNhgZ93g1HinH1e2tx2f98wwsCxeUScFyh7Mv9X2djzGvL8OGKw4qFcv15RuodTkk7PYbdJaDO7sTCnad8Stbc9YXUWs42I7xYU7fYub9HPjnDYPDNwt2TVyEaFwQBmP77XrEINfMmMGHHXrPO7sQjs7eJr8HX6+P3hnXcJk3tGuGvXX/C3iWoJ5todRJi51q9aLHz9L71PM6fo0q1Mz9fc0x1PGqEia4jYRcMtNwaMxbuw+Pfb8d3G6UZfmqVstWEgtbJ5HAJ4m6L4V/Y+RdLYur7h9JYEfmkkpUai0EdkgH4xkepuXPkyNu+MER3QIAXjMRqovC42kJi537LS/pI3WGJsgbcJoNBd0cO+ZjkzJClzetNrql3uFBnd6LHfxZpv7fGY06XgFqbE5uPlUAQBNUYNzXm78jDC/N2+wgtpVp7avzExRfKv9L/Uyg/syWnTJKgAwCzNOqXHeNcs1O+3x5w7bbUOIvu3TifESq/7hnnn5Xu447n0XNaaY3nj9356P3cH3hf1gJsfADdHLSKHzNLBj+Et7kSLYEkByzffxo5DailxxrRy0sj8ejtA+sSgFoF4WZ3CjheXIPpv++TdCY5XlyN2z7biF7P/qH5uuuPlODh77b63F9UWY8e/1mEB7/Zgn7P/yl5TN5i7Zz/ut3WbJ3h3ahyvBY73gIO1MjKpgztnCpa7ADgwxXe0BYmNOVZsfJNO1++SuKK5d9bdo4WVdVj4c5TkvAlf65Yp0sQhWkgsbXs49XLXbGeIfHnaCAbC02LXZjE2AUvAplQZOOxEmw8VoIrB0izQdXqqB1TiVHSOvGcLpdPaQmtFivs9fwJO83eo7KnsolmpywLqzFulmsGtVcM4AXcF7iWpZQXGccVvlO1T84H6L930yDN8RmNBjgDiLHr3S5JLGCdnqDu2uvVJlH391ZZ59DlStSajBxOAT2fdQvDxGgzKjTOHYfTBbMs2JllywbiltZCfl6q/c47PJmmVfUO3PPFZs1kH7noDNRK+eeeAnSe6i60vG/axZqZtPxGTym+EwDuG9NF8/30dN9YtEs58xrwiupXFu3DA+d3Fe/PU6iD2RD8JZOoWuwU7rM5XbrKu8jjHPW4+Zj7Lft4KWariGz2WlrnPQBJSaLL3lmty1Oj1NMZ8Jbt4SmvtWP5ft/zxekS4HIJuO8rtyVP69wVLXbcOVjncPrECg7skKwad/vdxhy8eGUfnzp2cgPGlPFnYceJcqw7Uiw5XyVhFdxQBUHADR+tx8HCKvTmivqb/GxiBUEQLXbx0drzPuPe0V1wvNi9mau3+yacCIIgaZMXSMyj1qFBjGppFGSxayIak4UGaJt4HS4B3TO8GXYJ0Wa/ws4pCH7NxoEsfps8iRbfy9q/6G31pcTIbq24OA/vWBbuPOX34uaHLs90ZY+vPliEJ37YLu5Q5a4rudtIbgU1GQ0BfUfMKpkYbUbX9HjV44Z0StFdbBiApPK5GnqTb/wtbtX16uexPGmmoQQaWP7Z6qN+M7jlvxNvwdPqEKOE0vnEk1fmXzxpWesAfaEHKw8qxzsB/jd2jcVf/K1anFhj2rLKXdd6EpdYJuQ176/1EY8f3TJYvO0SBL9eDvabTfpmiy5xAQAfqSR5KZ3jU3/aIXF18lTbHD4lc5Rgpw3vuvzXTztx+bveAsqLHxuN6CiT6ubxlWv6AfC12PFlkh66sBusZpMYKsOfrnx8N3turc2JzlMX4qCnGDjvnvZ3prsE/9nAcs7plCrW4mQbX6mwA2q43zCQNobMKqe0WQ4Pex0JuyajIa4GHn8LM3+SVtY5FFtdAcCE3hnic/wtoIHUolIj0CQRnugok1fYcXO4nsb07KJzuQTFnakgCLj50w34MfsE3vG4WMZrdAEBfLOETQaDrnR+h9OFkTP+Et1yUy/tqelqfWLC2QHXOfSHP1e+Xvq/+CdcLkH8ftXqITaGQHvF6okXk9c9/Gmr1/WrVO5FC2aFUxvl0z+5rTFam7l4P2JSj2so0Li0QNp8AdqbyaSYKE0rcFW9A9nHS4MaTC6fA/VsALTmzfG9M0Xx4nQJfue7OKsZgiBggUYikpyzuA23FpuOlWDhTnULrFYsGY+SK/agrLMKMwKoWcr+NrCd+3FZpwbeRXzfGLcV2CATf4A0KWGVZ/PxM3e9yfHXP/tUea1YOkava91k9JZrYfCeDQHSuEN/lrZpV/XxPpeJZ4W5nzpPnGG0TY7xf5AG/oRdtU6LIF8Q1t9JqBYnE4jk8Gdm18JqNipa7PTA5ny15tBruYKoBzwTH5/hl6GQBXnriI6SvyvrHdikUYSU8eA3WySv3TreqrpbnnPvcCRGRzVa2HWTWQS1z5/A/Add/rUQnacuxIGCSl3WqUCRZ5X6Q0/pjhqZhYWflAO1brHCrttySxUfP+LppfnnHvWFWt4CCwD6tU8SszD1aDa91wSzbB0sUK6p2RASoqPwqoaF9skfd+Ca99fiW5n70xDQ7CFF/p0ohVjI8SfWorjaZv7afCXHRuluBcbQO+9fq1HzDtD3WQFt0SFHLRmVeSoM3Nwr30Qw66VYMoV7mLlAeUo1NoD+NpYvLfDWmVPLBvZ5Tafgc67xcdyCIPhNnuDh400/WXUEM37fp7hxEwRg5YHTeGzONs2EtVBDwi4E9FXooKAWa6MXrXN/S06pYlq2EibP1ewWdtrHak2KeoNEGxNjZzWbvI2oPW+nt24cu1DVrBT877FSFqwMADcN6+hz39RLe2LG1X3Fv20OF5bv930uQxDclq0/90g7kCTGRKmKqTRP7F1jg3CfmnC2bDDu//iiuDcPdye2NLRX8L1fbvZbDkINrWxp3hLz5Tr/zdr1WKJ+klkM+Mw9tVqTajDrw52z1Gv9CYKAr1TGzseKpcW7rYUPX9gNv00+D3eOdAek89+BkmXuj935utuO/dezMGrtFa5Q6AjDv2ucxYRxPTPEv81Gg09iBg+zMPFB+f7GwOicFodHxnb3uV/+PegpoK62YN872h3jyOYnPUWqU2MtAYfUyDNeG8ozv+zyfxC88wY/Tj6ebdYd54i3/W26eVfsEZW6kF6Pivd7/l0W+/nn7nzNMA1+nfEnhtRcsfJaqTU2p8+5xs9VLkFqKfRn/ebf9/U/D+CDFYd94skB93V/62cb8fPWk3hzsW/SV1NBwi4ENEbMKE1ogLbl4uv1ObpN9SbuYvV3Mjtcrkb3UGzMdxEdZZTsGgH9i7C86KYe+ItXadwmowEjurbS/XqXvrMal7y9Cld7XBuMhGiz6o6axXupFfrV6lbAePqSHrioV4bkPiXrjr/6Uf44VlyDC/9Pf5Ylz+0jO6s+xmv3536TFp6VZyoD0HSNldfYUe9wSorOAtKi4oEmUhwvrlFt1cTG+PTcnZKiyzwJXHb1hn+NQ/Yz4zBlvFuIs/OupNqGh77bijcWH0C/F/70Kfki74GqBesvrTXmt64fgI3/HouzVVyHO5+fgHdv9PZ51vudyQWqlkuOkZ6gbNHmz2G9bmi1cf7z4h4AvOKGrwephlMQdPWtBYB2HkvdtxukFku9rc/k6A3lcQnuJI87Zm0S7+Pj2c4/O1287W9u5sNg8lWSbvT0k53+u3IrTAb7jcpqbOj/wp+ax6qF9sgt4FmpsT4x0u/8w3v+ChAkRgJ/65xSspSSFZJ/GbV+4U0BCbsgcVaG1/XVmISBQR1TFO9Xq/gdKEbOFSuf9L68cyj+NrCdGIdndwqqRRz1ekaDZbFjO29/Ozo2cb6yaB+25Zbp2jUN6ZiCOrtTIrbUfsNAPs/eUxXYl1+J7bK+nPFWs2rWYGK0e9FXKgEQZzFhwcOj/L7v/WO6+kxq7PfiJ2B5UeRQIheayTHqwfdam5g4WdLBS/P3qH6XJ8tq0f/FP3H2M76lYBrbUeCy/61Wfez3XfmYszlX9XHeumAyGtCKyyZmv9tv2939ct9ZehBV9Q5R4J6urMfgaYsbNOanFXoED+yQjFl3nAOj0YD0hGjJwsn/DEajAdFRJlw/xF0wXC3bUw7rz1labcNv2/N8BLYSdqdLUseRbXb4c1cpq1QJpQV79FmtxeuYfV6tvt4Mh1O/sFPqBbz08TE+569eWmtk0fMIgoBH52zTdazBYNC0oDJhV1lnx82fblA8hmXIM5F0RKGFpj8vC7Oc/ZdzuaqhVFMV8BXMgzok+9QC5TflggDs5WLuAnHFMpRCQHhPi78yLqGEhF2Q4BvY62kqroZahp68nElDeOO6/pJq47wLa9GjozD6rNZ48/oBSI1zTyJOl6BYlDOQcLdGCbsooySA9+OVR/wmTrDJaP6OU7hq5hpJXMX258aLgcE8TkHAWJnlSW3cDbFyFVZIF8H0RKuqS4Hd3z3d13LSoVUcTEYD+rX3dfUz+EQA/jO4BOn/gP5A5GDw8a1DJH9rZVXK3aY88jIxn2iEIPy6zb91SI48NjEUbNPoP6rlHiuuqsc5/10itjcKhOd/U2659eT4syVWHH+bUpNnsfp4lb7Qj1iLCXV2J85/fbluwXHTsI6SjQnb7Kw/UoJJ327Bywv34kfODc1qm53XLc3ntZwuwadrwbDO3nZ0gUxPH648gnu/Una/pydYseLJ8zG8SyrG9khXLD6dGmtRrdfpj3ZcrN73943QPFYrc12O1vnGHtLKlGeCigleuXsyIdrsdxPFxLdWwW5/8GturzaJbtHKPX5OpxTJOVVZ55BYU/1ZgOWdggBlIwP/MqZGekQaAwm7IMGfWEk6W3Epvo5KcCiLAfFXJoHn9nM7ibcnX9ANVw9qL+kPyIRdcmwUemR64zDY5O5wunwKWwKBZS02NnmCXYxOl4D/Ltzrt2abfKLeX+A+/uLemUiKiVJ0BdXUO336OaotcA3ZhfGlEQZkJcNqNuGCHuk4t2srPDNR2i+Ufd6s1Fh8c/cwyWPMwqY2tn9f2hO/P+K16PFHMWsHP4GxBVMvwbTwJWlY7LS4abhv7KMaepIqeL64cyheuKJ3oEMKGK1kDS2hMZjrrxoI53ZtJbpj5bSS1R70txEL1BtxvKgGPf6zSFcg+eizWuO3ySNx9aB2EksSXxh8wY5TPiVEtjx7EY7NmIivZdcL47PVxyR/868X6MaTJcbI+e7e4ejYKg6z7x2BT28/R/F7sjYwHhXwumJ7ZCaIyTtKaLXfUkLLpa7nu2EuUJaMJheVemKi7S4BpyvrG1UuaQ9XV5Hd5hPQeO8P4C36zPC3plnMRom4BqRJHQze29AYz11jIWEXJCxc0daGLlq3jeiouniyINzMJN+G7apj4iwyMZ6dldcV601Ll5uZmXixuwRJLJIWn8gsMowoU8NPMXe5E/dtva5otRiMnm1824oxlD6j2qTW2GxV9pvEW8349p7huHtUF6ybeiEm9m2D+Q+dJzl2pMwCwSaKf6k0j79lREdkJHrPD6XJindnxQQY73P1wPYBHQ8Ad4zshOxnxvncH2Uy4p5RnXHVgLY4p5Ny+IES8slVDv/zqNUQU6NXm8RG5GyqwwesA8C1g9W/x8aUB1KjfYr6d9ZO9lhWqnaLPDWLdY/MBIzqnoaxPdIl9+ut9wa4LWn92id7rC3e78FfnUF+jvnsdvc8dDfXFeH1P6WCwcodr3Y9z33AbRXrpTFv8GQmSufl1gm+87Tapp2nO2cx/vOx0Xj92v4AvMko/uoHChAaXTOVoWeu28cJqjq7E//62e3uZwkbekpmOV0uzNmkXjw6UMT1jBu+zenSzMj2F2NnNZt0zZf8/NrQxLJgcMYKu5kzZ6JTp06Ijo7GsGHDsHGjco9JvaTEel1gWkUU2ygIs9YJVhybMREvXNnHR1Awkch2bFUBlGbgrYhxnpOSWdDqHE5xApALOzZROl2CYvN4pRiocb0y0FbhswUaUxLHXTx8uRO15BB5dwi1ySgpxj0OeawXoBJvpfI6ja3LddMw3xZrbZJiMPOmQRJ3PmPmjd7PxxbVIZ1SsfHfY32OlYcA8ENlExc/fxkMhoCCuSdf2A3tkmMwRCUOVIm/D24vWoWmcxnFFrMR/57YC2/9Y6Bu1/6Ys1oDUA9APzZjIrb9Z7z4t954KEbrBGtI+uaeLUt44b8HOYFuHG4Y2gHLnzgfX9w5VHL/rSM64vGLzgLge+2wGFpAer0B7mzq4V1SfVznDDWL9bs3DsJXdw3T3UJQCf785b+GoyoZmUpc2CMDO58fj8fHe7PC5Ys23/JNPt92SYvD5Au6YXDHVBybMRELHxmFJ+UZ5hx3juyMjf8a6zPPDVa4RvyJ9oRoM4Zwm5yzMhJ8RK2ax0ZMYnAFr/ez2nD532ZgB+94/9jtzYZlQtfhEnw+AxN97LPYnYJiD2El9LQUS0twr8X8tTS0U6pmPKHLpT23W81Gn2tFCd4aH6dQ0qipOCNbis2ZMwdTpkzBBx98gGHDhuGtt97ChAkTsH//fqSnp/t/AQ9XD2on9rg8p7P3BE/QcHEN65yKXzztxCb2a4Mnxp8tCYyVW7jaJEWjvNYuln1Qq8umRCsu3opNKqsOustzvL/8sBhrIhcEbLL7Y3e+Yv2kf13aEx8qWEPO75HukwUWiOsYAAZ3ShXLj7jN59oX/KV920g6bahNRsw9Pq5nur4AbhU3nj+h+uWdQ3HrZ8qbhM/vOAcXnK3//ALc58ikb923+UWV30gA8Gv1+mXbScRYTCjlFnmjAfjh/hGY+I47EcBfO7Gs1FisefpCAECnpxeoHje4YwqyPRmh/KaBd8fx56beZYhZs1c8eYGPK0WkkQavwgbGQGkh/63kLdkkjwVosRvaOQWd0uIk8UmDO6bgxSv74GPPNbpkr7TU0uPjz8a9o7sgzmr2WVCzUmMx+163tWp3nm8WrZqoDtT6C7hL3nRLj8cfuwuQFm/FjZwo5MVY9/T4gGLTEqKjNIveXtbXW9pFPr98ctsQdGktjbPUmoOevbyX6mM3DM3CdxvVk2jk9GqTiF5tkwDkipZpuQdnmUp5JaPBAJcgBFw7bduzF2H2plyfXtWAb+cdBr9G9Wzj3bTwyQ/8ZlkeejBv8nmwu1z4dWsenpq7A4v3FCiWCLuwR7pPmTA1cXxZvzaYv8NthMgtcb83bz2Tez/kOAVB08JvMRt1zVN8yMPW3DK8vNB/Qohu6vU3OTgjhd0bb7yBe+65B3fccQcA4IMPPsCCBQvw2Wef4emnn9b9Ok+MP1sUdp3T4vDN3cNQUFGH4V1a4R2uITZjzr3DJWnoC3acklhkAN8dpJLpnS2cQzunqmYJAdLJlhUn5XvR3v65eyxyscjcykqi7s/HRqN7ejz25Vfix+wTkkK+fdr6XpyBxpXwPSitZqMuF+yLV/bG499vx8t/64sX5u1RPKZba/cEdP05HfDPub7ZgXL4EgE80VEmjOqeJlZUlzPaY1VSop/C5KWHe0Z1xserjuIpT4kGQCoAbhzWAU+O97UqfHTLYNzrKYtRWefwmbi6to5Hb+43szsFHJsxUVO06WHKRWfhpk/cWXT8poHPhE3mxM7wLqmiEFSCnY/MCtM6wYpebRLFWJpzu7YSH2uop/w5zwKtFET96t/74akftbMwtc6J6CgTYi0m1NicfrMbE2MCm5JjonyPZzFYG44q13nT2w1BqbSOWpgJE/BlOoXFY+POwsNju6mKh9HdW+ONxQdwwdmt8ei4s1SzkHe9MEHxfrPJiDev74/H5myX3L9+6lhJDLS8ZIVSF5J3//Kdy/Xw8t/6BiTs0hKsuGloB0QZDeiflQzAN2btPxOVwzBiLSZU1Dnw4nzl+U+N5FgL7h/TFSv2n8a6I8USS6NajOp4zuvBrzH8pojFNsu5vH9bGI0GWI0mnOJKqLwhq17QPytZsfbrhT3SseLAaR8r9H+v6isKO1ajkK3NAJASF6UZM+ivLWNybJTYm1oLPgYz+3ip5rwWKC4SdurYbDZkZ2dj6tSp4n1GoxHjxo3DunXa1b95tvznIqQlx+CjWwaj3uFCm6QYtEnyxqvwVgvGkE6pePfGgWJhU75ECoM3W0+6oCtWH5JOzoseHYXW8Vb8vPUkrh7UHoO40gcLHj5PtL4AbvMz49Fx7vp4ndPifFwb8otEafK2mI34+q5h4qLw/BW90bNNoqSu2HVD2sPhcmFQhxRxIu6skOk094FzMeP3vchKifXJgEyLt4q7c6PRoNr9gudvA9tjQu9MxFrMiiUdAKADFzu09PExeO7X3Xhywtm4UuWCfupidfdLl7Q41UVcC3mgul7+PbEXHh9/tsxV5Z2kLumTiRSFBWl870y8dFUffLzqCCb09v5O5TV2DO6UguFd3On/d53XGZ+uPipm2615+kJ8vykXu06WIz7ajJJqG+4ZJW1af8HZrUXrwcS+bdAjMwH/55mc+7VPwh0jO0EQpKEH1wxuj0OFVTivu3T3/NCF3ZEWb8XJ0lqfLNc4iwm7XpiAOrtLsoj8POlcLNlTiDFnt5ZYhdWm7u7p8Vj06Gh8sOKwT5D2qO5pYqLRFQPaYsneAow5uzW6pMWjsLIOV/RvixMlNchKjcWTHoH39V3D8MDX2WIM2Ye3DMbdX2yWdDSxmI1Y/c8LAAB/PDoai/cU+HVVyoWfltXnsn5tJKEFK548H3/tKxTf48Ur+2DJ3r8kzzk6/VLN9+eZ0DsT//1bH/Rrlyzep2axZsJObe1skxSNL+4cinbJMSiusqFDK+1Yvr7tk3D4Ze2xmo0GTY/A3wa2x/bcctGKMqF3hk+MMu+uv25Ie8mGgzGhT6ZEJADA57efoxg6wWMwGGA0uMMf2Aa9XXKMmKjVp10idp30biBf/3t/GI0G/IM7R3ihecfITpL6j2//YwD+788DuHtUZzz7q2/W86+TRkrmtyVTRquOdeZNg/DTlhOSqgHy3+i+0V3QOsGK687xlnJR6qACAB/cPBj3fy2ttfjvS3vieq4MTNtk5Xjx+8d0xWMXdfcpU3TriI54csLZqKhzYNGufGzJKcUCj5hLio3CnhcnYP2RYlzYw31NjOjSSixizZIDU+Msumq+vnFdf0z53r0p+OfFPTQ7Xtx+bifF5CQmMINBoPHCBiGYzfwigLy8PLRr1w5r167FiBHetPGnnnoKK1aswIYN0no99fX1qK/37kQqKiqQlZWF8vJyJCaqB9buOFGGK951X1STL+iGW8/tiHRPQO3WnFI8P28Pvr5rqKLbdvXBIggQMKp7a/y1r0AUgq0TrNj0b2kg+hM/bMeP2ScwsEMyfn5wpGhtefD8rnjq4h44XlyNjMRoURQcLKjERQo9UY/NmCje3nSsxKfFzaH/XqLpQmoM7y0/hFcXuRfbH+8fgb9/sA5p8RZsfuYiFFbWYeh/lyo+78kJZ2PSBd0k9/HWpvQEKwor69E+JQar/3mh4ms8/v12sa/ut/cMw7ldtU32AJBbUoOxb6zAed3SxF1ll7Q4PHhBN/x9cHtsPlYi9jacN/k8PP7DNtw7uiv+rhE03xDYZ1039ULJpqIh1NmdAZXp2Z9fifu/zsbHtw5B19ZxMBgM+GtfAVLjrBjgsTY0hL2nKhBvNeP3Xaew82QF3r5+QEAJBS6XgC7/Wii5Lzk2Cn89fn7APWH9sfLAadz62UZERxmxb9olcLoEdPW897s3DsRl/Xy7OfjD6RLw5uIDODszAWnxVozo2go3fLTep8sCf71qwV8P8x86z68Y8UdOcQ1Gv7YMALDsifNxwevLAbgFo8FgQL3DiUdnb8N53dPw75/d3RKemdgTd49q3CK3+mCRpJbaNYPa467zOqNXW//JDWwhV/r9+e+HfQY57Dd5d9khAG4Pwa0jOukad63NifJauygoT5TW4LxXlsFiMmLH8+OxZG8Bps7dib+eOF/RmutyCXho9lZ0bhWHJzRi/abN3+PTfejIy5dizOvLkFtSix/uH4FzuI2+Xh7+bit+256HdskxWP7k+YrJcLtOlkssqn89PgZdWsf7WP7l52y9w+kj3s7OSMD8h89DlMmIshobBrzoNVzIf5+Cijrc8ukG3DKiE25RyZZfdfA0+rRNkmx8f995Cg/4KZt1dPqleHruTmQmReMxT6xqbkkNRr26THJcapwFW/5zEV77Yx9mLpN2YtF7jerhQEElxs1YhNy3rvOrPQASduL9asLu+eefxwsvvODzOnq+3GCx6VgJuqTFqVp86h1OyY5C/recX7aexFNzd8DmcOGCs1vjresH+pRo+TH7BPLLa3FOp1QM7ZyqO7g1GKw9VIR2KTHo2Mpt7ft120k8Mnsbbh7eAZ1axaFr63icf3ZrxTGV19px31ebcd/orrigR2DxbIHAvuPtuWVIjInysUweKqxCnd3Z6IVUi2NF1SivtYtuG8KN3emCyWDAyoOnMTArBTEWU0hq9gmCgBUHTqN9SqxY/87hdCGvrM6vRaqhuFxCwJmz/uaDQNmXX4Foswmd0uKwLbcMidFmn7g0wH0tbs8tw8huaY2qZyl/75gokzg3NJZ6hxOfrzmGcT0z/NYwLK+xY9uJMpwXxM8TTHacKENJtQ3V9U50S4/3SdppKA6ny++mfsORYvy1vxCTL+gmMVZ8uvoops3fg18njVSdpz5aeRgvL9yHnx88V5KMEUrYHGE0GnC6sh5vLz2Ak6W1eP3a/kiJtfi9xljIhvy4OrsTlXUOWMzGBlfHUKOiogJJSUkk7JSw2WyIjY3Fjz/+iKuuukq8/7bbbkNZWRl+/fVXyfENtdgRBEEQBEEEg0CE3RkXY2exWDB48GAsXbpUFHYulwtLly7F5MmTfY63Wq2wWr2WMqaDKyqUg+sJgiAIgiCCCdMcemxxZ5ywA4ApU6bgtttuw5AhQzB06FC89dZbqK6uFrNktSgudse6ZGX59gIkCIIgCIIIFZWVlUhK0g7xOSOF3fXXX4/Tp0/j2WefRX5+PgYMGIBFixYhI8O3eK2c1FR3AGpOTo7fLzecYC7k3NzcsHYhR8o45UTiuCNxzEDkjTvSxsuI1HEDkTf2SBsvIxLHHYljBtyWusrKSrRt6z8p64wUdgAwefJkRderP4ye6v9JSUkRdVIwEhMTI2LckTJOOZE47kgcMxB544608TIiddxA5I090sbLiMRxR+KY9RqTztiWYgRBEARBEC0NEnYEQRAEQRAtBBJ2AWK1WvHcc89JMmUjgUgZd6SMU04kjjsSxwxE3rgjbbyMSB03EHljj7TxMiJx3JE45kA54+rYEQRBEARBtFTIYkcQBEEQBNFCIGFHEARBEATRQiBhRxAEQRAE0UIgYUcQBEEQBNFCIGFHEARBEATRQiBhRxAEQRAE0UIgYUcQBEEQBNFCIGFHEARBEATRQiBhRxAEQRAE0UIgYUcQBEEQBNFCIGFHEARBEATRQiBhRxAEQRAE0UIgYUcQBEEQBNFCIGFHEARBEATRQjA39wAiDZfLhby8PCQkJMBgMDT3cAiCIAiCaOEIgoDKykq0bdsWRqO2TY6EXYDk5eUhKyuruYdBEARBEMQZRm5uLtq3b695DAm7AElISADg/nITExObeTQEQRAEQbR0KioqkJWVJWoQLUjYBQhzvyYmJpKwIwiCIAiiydATAkbJE0SjWH2wCCOmL8Wfu/ObeygEQRAEccYTscJuxowZMBgMePTRR8X76urqMGnSJLRq1Qrx8fG45pprUFBQIHleTk4OJk6ciNjYWKSnp+PJJ5+Ew+Fo4tG3HF77Yx9Oldfh3q+ym3soBEEQBHHGE5HCbtOmTfjwww/Rr18/yf2PPfYY5s2bhx9++AErVqxAXl4err76avFxp9OJiRMnwmazYe3atfjiiy8wa9YsPPvss039EVoM20+UN/cQCIIgCILwEHHCrqqqCjfddBM+/vhjpKSkiPeXl5fj008/xRtvvIELL7wQgwcPxueff461a9di/fr1AIA///wTe/bswddff40BAwbgkksuwbRp0zBz5kzYbLbm+kgEQRAEQRBBIeKE3aRJkzBx4kSMGzdOcn92djbsdrvk/h49eqBDhw5Yt24dAGDdunXo27cvMjIyxGMmTJiAiooK7N69u2k+QAvC5nCJt+OtlIdDEARBEM1NRK3Gs2fPxpYtW7Bp0yafx/Lz82GxWJCcnCy5PyMjA/n5+eIxvKhjj7PHlKivr0d9fb34d0VFRWM+Qoti3vY88XZidESdSgRBEATRIokYi11ubi4eeeQRfPPNN4iOjm6y950+fTqSkpLEf1Sc2MuBwkrxdqt4azOOhCAIgiAIIIKEXXZ2NgoLCzFo0CCYzWaYzWasWLEC77zzDsxmMzIyMmCz2VBWViZ5XkFBATIzMwEAmZmZPlmy7G92jJypU6eivLxc/Jebmxv8D+dhW24ZjhdXh+z1g43Z6K2nY3e6NI4kCIIgCKIpiBhhN3bsWOzcuRPbtm0T/w0ZMgQ33XSTeDsqKgpLly4Vn7N//37k5ORgxIgRAIARI0Zg586dKCwsFI9ZvHgxEhMT0atXL8X3tVqtYjHiUBYlzimuwVUz12DMa8tD8vqhoN7uFXP1DhJ2BEEQBNHcRExgVEJCAvr06SO5Ly4uDq1atRLvv+uuuzBlyhSkpqYiMTERDz30EEaMGIHhw4cDAMaPH49evXrhlltuwauvvor8/Hw888wzmDRpEqzW5nUlbs0tFW+7XAKMRv/VpZsb3kpXZ3c240gIgiAIggAiSNjp4c0334TRaMQ111yD+vp6TJgwAe+99574uMlkwvz58/HAAw9gxIgRiIuLw2233YYXX3yxGUftxukSxNuVdQ4kxUY142j0YSNhRxAEQRBhRUQLu+XLl0v+jo6OxsyZMzFz5kzV53Ts2BELFy4M8cgaR0WdPTKEncMrRmtJ2BEEQRBEsxMxMXYtHd6tWe+IDJEkdcW6IAiCxtEEQRAEQYQaEnZhgt3pFUWRkohgk40zUsZNEARBEC0VEnZhAm/9kgumcEVe4oTPkiUIgiBCw/78Sny7IQcuF3lJCF8iOsauJSF1xUaGQLLJhF2t3YkkhH9sIEEQRCQz4a2VAIBYiwlXDWzXzKMhwg2y2IUJvCs2Uix28nFSZixBEETTsS23LGSvXWNzwEGF5yMSEnZhQiS6YuUWuxobCTuCIIimwmoOzRJeVFWPYS8vxQ0frw/J6xOhhYRdmBCJrtg6u68rtqVyqLASw15egq/WH2/uoRAEcQbDW9GsUaaQvMfvu/JRWefApmOlVO0gAiFhFyZIXLHOyBBI8rIsLdkV+/TcnSioqMd/ftnV3EMhCOIMpqLOId62mELToSi/vFa8XVXv0DiSCEdI2IUJvPs1UlyxLAvW4JlbagNwxb68cC/++eOOiMnq2ny81P9BBEEQIaasxibenrX2WEjeo6LWK+ZasiempULCLkyITFes+4KPt7qTq+XlT7Se99HKI5izORdHiqpCNj6CIIiWRnmtXbxdVGXTOLLhlFR7X5f3JjU3eWW1eGHebhwrqm7uoYQ1VO4kTHBEYFYsE3YJVjMq6xyw67S+8UkWkZB0RTEmBEGEC2WcsLukT2ZI3qO4ul68HU7r0f1fZ2PHiXJ8vuYYAODAS5fAEqIEkkiGvpEwIdIsdg6nC9UegZYabwEA2HWOmzftT/1pB95aciD4AwwifPavITQhLQRBELqo4ISd0WDA20sO4rE524Ia1lLMWQLDSdjtOFEu+fv3XaeaaSThDQm7MMEWYcKOD+BNi7cCABwuncKOs9htySnDW0sOhrVVjB9vGA+TIIgzgLIar7Crdzjx5pID+HnrSaw/Uhy09+BdseEk7OSEqtxLpBMSV+zAgQNh0Gna2LJlSyiGEHHwFrtIKArJ4jzirWZEm90p9zadsRhKSRZOlwBziDK8Gou8Pp/D6YLZRBMKQRBNDx9jxxsBCirrdD2/pNqGkup6dEtPUHzc5RJQyiVoyOuVhhO00VYmJMLuqquuEm/X1dXhvffeQ69evTBixAgAwPr167F79248+OCDoXj7iCTSYuxYZlZSTBSiPLumhrhiGbYwFktyYVfncCE+TMdKEETLRmKx42qJrjtcjL8NbO/3+ef8dwmcLgH3je6CqZf29H39Wjt4r244r0d1DsrYVSIkwu65554Tb9999914+OGHMW3aNJ9jcnNzQ/H2EQm/K9KbXdqcsF1jcmwUojyWNt2uWCVh53Ah1hK88QUTuYWxzu4UM4EJgiCakrJarzWtkqsxlxCtr0+306PaPlx5RFHYFVfVS/4OZ4tdrS18x9achNzs8MMPP+DWW2/1uf/mm2/G3LlzQ/32EYOkpVgYpZerUemJsUuINiPK6LHYNcIVG867wmqbtEBnSy7ETBBEeFPJxTfX2IJfPLi4WlpCJZznZqqxp0zIhV1MTAzWrFnjc/+aNWsQHR0d6rePGHhRFAkWOyZuoqNMiDK7LXZKE8CiXafw0vw9krjBWrvvZBTeu8Izp8MGQRDhTTVnpeNvO3VkxfJJau2SYxSPKZEJu3Baj2JkLdTU4tEFQUBuSU3QkvK+Wn8cj3+/PSLi34EmqGP36KOP4oEHHsCWLVswdOhQAMCGDRvw2Wef4T//+U+o3z5i4E+YcN4hMeo8Y4yJMiHKE28md8W6XALu/9qdHNO7XaIY/6FkPneEsZXSJ8bOHv6/D0EQLRNezPHtvvRUU+A30GpC0McVGybrkcsliBa6pJgolNfaVUXnhyuPYMbv+zDtqj64ZXjHRr83ayU5oXcGxvcOTe3AYBJyYff000+jS5cuePvtt/H1118DAHr27InPP/8c1113XajfPmKwRZrFzsZZ7EzKrtickhrx9mNztuP3nfn46NYhiuZzvfF5zUFlnV3yN1nsCIJoLqq5jSa/ydQjwHjvg9qcG66uWD5RYlzPDMzdckI1bGnG7/sAuAVZY4UdP9/LN/nhSpNEgF933XUk4vxgj7DkCa8r1igmT8gnAD5lHgD+3FOAk2W1qFWICwmntjVy+JgWgCx2BEE0H7zFjqdeR4ZojYoo5JG7YuvDZD3iRWlCtFu6NIVr9PBpb9vLcC3JJafJUvtsNhsKCwvhku0SOnTo0FRDCGsiLXmC7Z6sZnVXrFwQAcDevApli10Yf2ay2BEEES5UqQg7PZY1XthV1TvgdAkwGaViRW6x01vGKtSw8B+LyQhrFPMSaY+N/2x/7s5HQWU9bh7WQXedXQA4ctrblzZSNvUhF3YHDx7EnXfeibVr10ruFwQBBoMBTictkoD04rFFQG0edoJLXLEOqTirkAkiwL0bVIqxs4ezK1Y2kVLtJIIgmgNBEFQtdnoS0OSJYFX1DiTFSMuksJZlRgPgEsInsY1tqK1RRlhUwn/kmAxeb9K9X2UDAAZ1SEbvtkm637dWYuWMjLk/5MLu9ttvh9lsxvz589GmTZuAlPKZhN3Fx9iFr/WKoeSKle+elCx2JTW2CLTYNb0r9q0lB2AxG/Hg+d1C/l4EQUQG9Q4X1JJf9VnspHOZkrBjFsHUOCuKqurDJ8bO7hvX7U90MovdwcJK8b7q+sDEGf+dkbDzsG3bNmRnZ6NHjx6hfquIJvJi7BQsdrIZR+7CBJjFzlfwhXMaeVO7YnOKa/DWkoMAgDvO7YwYi8nPMwiCOBNQc8MCOpMnZHOXe26Tlj2p8mxkW8VZwkzYeSsxsFg3f25iJuwOFHiFXaC172q44yOhjzvQBMKuV69eKCoqCvXbRDxSV6z/k6fO7kRxtU21FlGoYe7ImCgTjEbli4xZum4a1gHpCdF4c8kBt7BTuLBOy1LsGcv2FyI9wRqQ6TxYvPHnfuwvqERRlTTmJNTCjv8uSmpsaGdpnt+YIIjwQs0NC+gzCPi4YhW8KmzeTo1ztwIKN1dstMQV6zs2fv1k3qRyrg2bkmFBC/47q48Qi13ICxS/8soreOqpp7B8+XIUFxejoqJC8o9ww7tf9VxIL8zbg5Ez/sLaQ80jmr3lToyw+HHFJsdGISPRCsBtscsv921Wvf5Iic99+/MrccfnmzDxndVBHbsequsdeOevQ/hjdwGyj5cCAFonuD9DqHdtpyu9wk7J6klENnV2J9YeLoJLR0FZguBhbkSL2Xfp1jMvyct1HC+u8TlGdMXGe4RdmFiplFyxSmFLZVw1BtZ/nC8RE7DFjo+xC5Pvwh8hF3bjxo3D+vXrMXbsWKSnpyMlJQUpKSlITk5GSkpKqN8+IhAEQZI8oGfn9d3GHADAByuPhGxcWjCLnZYrliVPJERHibu/4qp6bD9R7vN6VoWJauPRYvG2nqrqwWTnSd8xto53C7uQW+wqvcJXaUdNRDbP/bobN368Ad9vpl7ZRGCw9obJMb59YfWsGzWyuevxH7ZL/na5BFHYtQo7i50n/MfMCzvfsfFZvcwAwcfJBdpftiYCLXYhd8UuW7Ys1G8R8ThdAvjOJ/LsUjml3ImrJIiaAnaRWc0m8cJXc8UmRJvRyrP744sW8yjVYDpRViverrU7EW9tsuo82KUg7DISrdhzqimEHWex03C9EJHJHI+g+99fh/CPoVTuidAPE13JsVEorJR1iNDlivWdT/iSJ3xfbNEVGyZWKj4rVi1hD5DW4au2OTyZxN45O9CqE/x3Fi4i1x8hVwVjxozR/KeX6dOn45xzzkFCQgLS09Nx1VVXYf/+/ZJj6urqMGnSJLRq1Qrx8fG45pprUFBQIDkmJycHEydORGxsLNLT0/Hkk0/C4WjexVNuTva385q75YR4mxVqbGpYrIeWK5bFJsRaTEiNc1u7SmuUXYtKbgSWds+/VlOxJ883TCArNRZA6LNi+QlbK6aGiGxOldf6P6iZOHK6Cm95YmKJ8IHNB8kxFp/H/BkEAOXOCfw8y4RjlMkgbqTDJZmPj+tmrmglVyx/zroE93zNW+wCDaWRWuzC47vwR5OYe1atWoWbb74Z5557Lk6ePAkA+Oqrr7B6tf7YqRUrVmDSpElYv349Fi9eDLvdjvHjx6O62ls88LHHHsO8efPwww8/YMWKFcjLy8PVV18tPu50OjFx4kTYbDasXbsWX3zxBWbNmoVnn302eB+2Ach3Af52SPyJ2RylUbbllmFfvjvLKM5qVjWLMytctNmEtHjpRMSCX73Hau+8mlrYKSVzZCRGu8cSYosdL+zIFdtyYdEFx4ur8a+fd+IIV+G+uXlh3h68teQgXlqwp7mHQnDUeCxP8dFm0WrFCKSOXf/23mS0Ml7YeeabeKuZE0/hIWaUKjEofWafzhkOp1ScBSjs+Pk+XLpw+CPkwm7u3LmYMGECYmJisGXLFtTXuxet8vJyvPzyy7pfZ9GiRbj99tvRu3dv9O/fH7NmzUJOTg6ys7PF1/v000/xxhtv4MILL8TgwYPx+eefY+3atVi/fj0A4M8//8SePXvw9ddfY8CAAbjkkkswbdo0zJw5EzZb8+1M5ReOvwu0mMvSrGkGi86/ftop3k6JjRInAHnHDHYBRUeZEG81S9zGesSsRNg1cWwD28XGcqVGYqLct0Ptii3kY+zIYteiOVVeiw9WHMG3G3Jw56xNzT0ckRUHTgMAftpyMmwWdsI7H/AbaoaeDhHM1Xr+2eliRYVyTthVMGEXbdZMUGgOpLVT9cXYAe61hRd2gbqWG/Pc5iLkwu6ll17CBx98gI8//hhRUd6Az5EjR2LLli0Nft3ycncMVGpqKgAgOzsbdrsd48aNE4/p0aMHOnTogHXr1gEA1q1bh759+yIjI0M8ZsKECaioqMDu3bsV36e+vj7kmbzy4rz+JtLiaq9FpzmaEp/kYt+SYiyi9U0eJ1cvxuEZYTAYEB2lXo9NaRfFX6Dywpqhhk1wz1/eG/2zkvHZ7UPE8YfaFcvH2JGwa1nIr5EnftguJkIdU8hQbC74DU2RSikioulhrtg4i8lH2OmxJvFxz6wwMZ9FyuabBGuUpnhqDvisWLNGjF2+LMSh3uGShLTo6anLQ8JOgf3792P06NE+9yclJaGsrKxBr+lyufDoo49i5MiR6NOnDwAgPz8fFosFycnJkmMzMjKQn58vHsOLOvY4e0yJ6dOnIykpSfyXlZXVoDFrIT85XYJ2FqjEYtcMWTq8WzSZt9g5lF2xrK8fvzOccXVfybFKAa2lYWCx65eVhF8njcSFPTIQHaUsYIOJ0yVI6uaRK7ZlIa+JeKJUugjN256H4mYWUlX1DsliVlRJcXbhAivboWixc7ogCNrWNd7VyoRduZIrlnP1hqOwizJ6+pMrWBN5wwMQBFcsnzxBws5NZmYmDh065HP/6tWr0aVLlwa95qRJk7Br1y7Mnj27scPzy9SpU1FeXi7+y80NfokC5pY0cw2LtS4m3kXZ1K5Yd49f9+1nJvZElMkIq9m9u5dfMOxv9vhj484CALx6TT+fbED5c10uQTLhNGUrF0EQxFItidFeK3N0E7hiS6ptElHPx78QkY9ctHVJi5P8/dB3W/GPj9Y35ZB8KJGJz6JqstiFC9USV6w0xk4QAIefslAsyz4+2ozkWAVhV+8pUcXHTutIymgKpDF2nh6wiha7Op/n8dm+gSZASCx2YSJy/RHylMp77rkHjzzyCD777DMYDAbk5eVh3bp1eOKJJ/Cf//wn4NebPHky5s+fj5UrV6J9+/bi/ZmZmbDZbCgrK5NY7QoKCpCZmSkes3HjRsnrsaxZdowcq9UKq9Ua8DgDgYm4GItJNJXXO1yqrsvSGt5F2bSWrJJqmyjCbju3EwCoWuzE9HTP4w9e0BVXDmiLTrLFTOm5FXV2SU/EpsxGqrO7xLiSxBhe2BnFxxuDIAhwugSxeCbPaVkJg9IWmpW49rC7sPa5XdOaeSRNi7xPpVKSzsHC5k2iqJAVxS6qJGEXLmi5YgH3WqJ0P6NKdMVGca5Y7+/NbidwMXbhImb4GDs2dypZ7OThKzanS0w6YX8HQi25Yn15+umnceONN2Ls2LGoqqrC6NGjcffdd+O+++7DQw89pPt1BEHA5MmT8fPPP+Ovv/5C586dJY8PHjwYUVFRWLp0qXjf/v37kZOTgxEjRgAARowYgZ07d6KwsFA8ZvHixUhMTESvXr0a+UkbDjs5+bgWNYudIAjNmlTARGVSjDcGgwk3nxg7mcUuymSUiLqPbhmMszLiAfheoGWysihN2aOP7WBNRgPiuN8k2hwci93tn2/CiBl/obDCtwMHnzgBtMwYuzq7Ezd+vAE3frzhjOusUWt3/57MOF9YEX6iyUfYVbXMzUUkUqVhsQP8Cw/2/HirGUkKFjsWT5kWb9V0xdqdLryz9CB2nCgL/EM0ELbWuQsUu8em1GNc7M7BYr99yp3on78FQZCEO5Gwg7u8yKpVqzBp0iSUlJRg165dWL9+PU6fPo1p06YF9FqTJk3C119/jW+//RYJCQnIz89Hfn4+amvd/vSkpCTcddddmDJlCpYtW4bs7GzccccdGDFiBIYPHw4AGD9+PHr16oVbbrkF27dvxx9//IFnnnkGkyZNCrlVTgu2g7CYvYUX1U6gWrtTInKa0kUJACXV7kmAFa8EvMLO5pDGeIjCLkr5NBvfOxMvXumOkeQ7bwBSq6T7tfx/zveXH8aV765WFEyB4HXDmmEweCdPK3PFNiLGrrCyDisOnMbpynpc8vYqhcelC31zJMeEmkoubjCvrHG/VaTBFp1OrdwbHPnvHQ5U1Eo3E5Q8ET6w+SBeIcYO8G+NEpMjJMkTvLBzz7tpCVZRGClZxT5edQRvLD6AK95d04BP0TCUy51IxyYIguh2TYlzf746h1PSUiwQ74/N6ZKExoSL9dIfIRV2JpMJ48ePR2lpKSwWC3r16oWhQ4ciPj4+4Nd6//33UV5ejvPPPx9t2rQR/82ZM0c85s0338Rll12Ga665BqNHj0ZmZiZ++uknyXjmz58Pk8mEESNG4Oabb8att96KF198MSift6GwNPUok1EsqaG2oMtr9NTanX4DZoMJe/+UWK+LklnkXFyMh4O7ILS6Y3h3Xo232L2yaB+2nyjHL9tO+j1WC5Y4kShr2xMMV2wu13lDnpYPeF2xHVu5iyG3xALFvBUyryx8i/SGAubWaZ8aC4OvwSUskFtRSdiFD+zaibWYFPvF+rXYcckTrAAxsyIDMoudRh271Qe9Pcqbao4S66JynSccMoNAjc0pdnFiRfEr6xwScRaI90dePzVSLHYhj7Hr06cPjhw54uM6DRQ94iU6OhozZ87EzJkzVY/p2LEjFi5c2KixBBsWzxVlNCIhOgoVdQ5VF1ypx2IWbzWjqt4BQXDvIpi4CjXMksZb7PgJxuZwx3jwF4/W2MxidpMfi50fMcVbLhu7qVJKnACCkzxRLHNruZNRvCs8E3adWsXheHGNJOi3pcBn+p5pnQ2YSygh2oxWcVZV0eRwuhRjMJuCijqy2IUr1ZwrVdFipyE86uxO0eIUH20W5zPeiMDmn9YJVs0YO97SvPFYCS44Oz3QjxIwbN6NiTJx64ZUF7Dvx2gAkmLc8kYepxyIOJMbWJoyJKgxNEkduyeeeALz58/HqVOnQl4TrqmoqLPj952nFH38VfWOgGKH2I4oymxAnNV9santglgqN7PoAEBdgE2NG4PXYqcs7NiJz18ASjtLhliPSJbNJW895k9M8W1xXI20YJaLFjvpvodNhI1J5JALVnmMJIux6+yJRZQH2+tlT16FxDoYTlTWe38r+ffR0hHLVVhMSE9QD/8ItIzRH7vz8dB3WxsdhgB4LXZsjmmJ7vKyGhue+GE7vl5/vLmHEhA1knInviZfrWLCvLEgzmIWY7p5q5Toio23iFUa5Ba7OrtT0iVl+b5ChAK5cUPiijUri04xBtHiFa5KnSj0Ihd2gfaZbS5CJuxefPFFVFdX49JLL8X27dtxxRVXoH379khJSUFKSgqSk5ORkpISqrcPOW8uPoAHvtmCZ3+TFjZ2ugSc/9pyjJzxl+6dgSjsTEbRPF6pUr+MnaRtkqLFC7spEyjY7oe32JmMBnESsInCzj2mKJNBbDCtRJRJ2WJ3stS3yKQWfMB3Y10DLMYoSe6K5SYTeZ3BOrtTV70nuftV7nJmwfSdmCvW08Q6EAoq6nDpO6sw6tVlTeqm18uZbLHz9k82Iz1RXdgF2kLvP7/swrzteZi5zLe0VKCw8//crmkwGQ04WlSNnDAqnhwMPl51BD9mn8Azv+zC9tyy5h6ObrQ6TwDa1ijeDWsyGnw66ThdAko8pW1ax1tV+7EeLKiSVCzYcLSkgZ9Gnb/2FaDv83/gk1VHxPvEKgtRRkQZlZMn2EY4zmoWYwR947Ub4YqNkBi7kLliX3jhBdx///1YtmxZqN6iWflh8wkYrbH4dkMOXryiN277fCPirWa8cEUf0XWRW1qDrq39xxOKrliTUYxHUxMn7MJLjbMg2myC3elo0gQK0WIXJ+v9ajbCYXOKgs7bdULbRazWtobFXsVEmTwJI76fcfGeAlTW2XH1oPYo5wK+G5twIMbYqbhiAbdwjbW4L5//LT2I/1t8AABwbMZEzdeWuwXKa+1o62ntA3jLX7DsYcHTxDrGot/VvjuvXLx9qrxO8vrNCROZvHs53Cx2L8zbDZPBgH9P7ClxkQcL9tljLSa0jtew2AV4DjPXWDAWWWaxa58Sg95tE7HjRDn2nCpHB85LEI44XYLkutRiI/c9HSqsQv+s5BCOLDgIgsDVsVMud6IlPPiMWAA+8dylNTa4BMBgcK8vzLosb1W295Tb05ZgNaOy3iHJqmVM+nYLjhVV4/v7RiDOGrjMeOjbrRAE4KUFe3H3KHe921reFev57KyYPzMeVHHfD0t2k2+mAxF2YuiE57Oe8TF2bBIfM2ZMqN4ibDhQUIU1h4oBAFcOaCfeX6Zz0fJa7AxIiHb/JGoxdsWixcyKaIsJlfWOJrXYlbAYu1ipsIuJMqHG5hR3TN5SJ9pGYTVzf1mt+30yk6JxtKja52Kssztxz5ebAQDDu7SSWOwCtXbIEWPsfJInvOKq1uZdQJioA9yCT0vMyicZflIUBIGz2HnLwlTVO0RhJ4/JU4KvhZd9vDQshN3WnFJc/9F6PDCmK1rFe8+dcLLYlVbb8PmaYwCAe0d3QXpitO7n1jucMBuNmtZpgLfYmSQWu/O6paFdcgzmbHYXQA+khR5vtVBaZAOFzT2J0WZ0SYvDjhPl+HDlEXy3MRcvXdUHWamhF3g1NgeiTEbNmmxyXpi3G99uyMGvk0eid9sk1eMq6uzYdKxU/DtYcawbjhTDGmXCgBCJxHqHS0xOa0i5E+YdYCEmbE5h6wczSKTEWmA2eRMU5BUL9niE3dDOqVi6r1ASBgO4558FO04BAHaeLMfwLq0C+JRunAqeBjHGzmKSfHa704WNR8uQX1GLBKt7zuZ7kzcqxs7znslxUaisd7gTBJsx/lUvIR1dKHa84UhuqddNwVtL9E6yNgVXrJqwYwthqziLuONSEnaCIGDFgdM44Rnb0aJqvP7Hfp8CuIFSqmKxY4tggSfGR2wn5kfYia5YmWuTWeBae+KQ5HFtvJA7XlwjmVwaK3SZKyoxWrrvMRkNonm/jpscOnALnZoLnSEXMrwrtooT6emJVrGGHlvkT5TWYMhLS/DivD2qr19YUSeJiWruYreMr9fnwOZw4e2lByXfEUsGCgf4Hszs/NqfX4ldJ8vVngLAXen+nJeW4OHvtvp9j2reFZvgFY4ju6Xhlb/3E13wgVjsKmSu7ca630XLTrQZXTweh605ZVhx4DRemKfcUzuYsPP80dnbAnrel+uOw+ES8MXaY5rHfbLqqORvuTBpCCdKa3D9R+txw0frQ9bXmj8n4izKrlitcBA+4xXwCjsmmFjruDTPxsui4k3Zl+8WdsO6uPu0V9ucks1FDp/5H8QaiGxTFBMltVY6XALu/zobj83ZjlUHTwPwuGKZsGNFlz1rayAxdnWe9+TDciLBHRtSYXfWWWchNTVV819LgBdL+/Mrxdvy+Ck1HJwrNsHj/pMXCWWUcDFu3vIbvifqigOncdtnG3HfV9nYlluGC15fjneXHcK/f96pa0xyymps+Gz1UezKq/C8v9SaxSYDNj4+0FULljzhdAmSBancYxnM8AhG+cXIx2l9uvqIZHFrrCuWCXJ5jB3grcnHvnNBECRFhf31dpXvHvlFhbnT4q1mxFrMiJWJ/HnbT6G42obP1hxVXLy/Xn8cQ19eireXHhTvK9FoB/XBisN4a8mBJonD412u/IanJIxcsfnlvLBzhzj8/YO1uOx/q336T/L8vusUKuocWLDzlN9Fo5Z3xXLJE608G6UYjxU4kHOY/z7rHa6ACwrbHC6sOnhaHHulGIsVhS6tpV1ituaUBfTaDeH7TbmosTmxYOcp3c/hQ1f8WWTWHy6W/C3PAm4Ih09XA3BvKg8UKG+mCivrMOX7bX43CmqwzxgdZZRsMgGIpXO0PjsTdq2YsJO5Yk9Xuecxdl4y8eR0CZKYYvb5zunkXb/5zRq/djU01EL+OQRBEDfT0VEmSfvNas4dzEIRJDF2bM30rFGBlKtiG22JsIsAd2xIy5288MILSEpSN4m3FPjFek+eN9NXr7BjuyyLySi6YivrHPhrXwF+2ZqHZy/vJe6y2A4oNd7iE/zKs9szjt15FXh10T7x/j/3FCCvrDZg99wHK47ggxWHxb9ZjSAGy7Bipmu2SGhlxAIQmzkD7u/rqbk7cFm/NuKFyjIH5a5Y3qJZWeeQWeyUJ+qKOjteXrAX152ThUEd1BN31FyxgHtSqaxzYFtOGbq2jseJ0lrJROHPYsdcsR1bxeJ4cY2kDyfbILDPHG8143RlvSQGhqEUO6eU4VdeqzyehTtPYcbv7vPikj5tcHZmgua4Gwtvuy/gMjfDqWXas7/uEm9X1jlwsKBK/D235ZShnco1w7tf9+dXol/7ZNX3YKEKsVazJLOcuae9mYr6xYbc4nS8uFoiGv3x5bpjeGnBXlzevy3+d8NASSyW/DM3RUxkHtfr0+Zw+Z1DAOk5pbVwu1wC9nosTmN7pGPpvsKgWNj4bOSdJ8sV3bGPzt6GtYeLsfpgETb+e1zA7yGPkePDOuIs7vJXWha77SfcgrKzxyrMu2IFQRDnH7bWmGXuTpPRhOKqepRU22AwAD0yExFrcYfgVNTZRS9ORa3UCxEo9Q4n5C1v7U6vuIyOMsFkNMBgcMcg871hT3iS7iSuWK5E1/HimgZlxcZbzTAa3DF9kVDyJKTC7h//+AfS00Nf36a5OcZljPGTkl5XLLsYzSaDaLGrrHPgkdnbUFnnQGKMGS9d1ReA1BXLgkNrFcqd8JaytbId6ierjuLZy6Ut1PLKarH2cDGuHtgORoU4od9kRX/lMXYs3owtSIFa7ABg9qZcLN5TgMV7CsT7MhJVhB0noMpq7JJdotza4XC6cPOnG7D+SIn4PlpJDmp17ACv+Hr8h+24ZnB75MvKS+SW1qBve/XNDBMyQzul4nhxDfad8lp42eLEFmW2yLOdOl9PTEmcKwl8tXPwFU7sbz9RFnJhx5ezOc5dL6U1NrhcguI519QcKaoWb1fW2WHiQknkrd54+F6qO0+Wawo7tvGJjTJJMsvZYipukAJyxUp/4xOltRjSSffTRcvYvO15sJiMYvJEQrRZLLvDcAnu7yZB4doIFsXceV5SbcOBgkq0T4kR3cJKFFRIn6PG8ZIaVNY5YDUbMbBDskfYNT5Gma/rNmPhXtwyvKPkcUEQxHm4od1GmABlyQhl3LUtWuw0hN3aQ+6iwud1bw3Aa7ETBKmll52LvLvT7nT3LmeWyfYpMYixmJAQbUaNzSm12PHCrgHWUN7i7BZvgiS8JibKBIPBgCijETanC6e4NbdKIbmErR3MKs5Eor94WMA7p8Za3K7dOrvLx2KXW1KD2ZtycPu5nQPaUIWSkLliz5T4OgA4dFrZ9N6wGDvvjp1dLLkl7l0I3yc2NU7bYqd1QeVX+LqVrn5vLZ74YTu+9wRvy2ktCyRPkMWfxcgWpEBj7AAolh0QY+zkrlhuJ5hbWiNpgyRPnjh8uloUdXoQY+xitPc9DqfL57t/eeFe1ePr7N7WNhf2cG94Nhz1im42abLEiTiPWGZWHn5nqrQ4KMWzKJ2DRVX1EnHVFN0f+LqOfA0sl6AedqDElpxSiVU8WMh/x4paaS1KeV1FntPc974jV9vNVsM6B1hNyEyKhtHgLqba2ePy9Nd5Ro4gCPgx+4TkvkAKCguCICllMnfLCVEkxVvNitnYBSHsbysIAnZyrsrFe/Jx62cbccunGzWfxwvv0xqf/7An5rRberxokW9sshUgtRjGyrJABUFA56mNL4pfVe8VGQDEkh8A0DMzEYBGK0qbU7Tw9Wzj3sTFyJLBvK5atwCSCjv3xozNFe2T3VY/pdAh3rXdEIsd71ZnopNdn0aDt1sRMwrkl/vOX3yMHYPfSOm12rHrMDrK5O09K/uOn/llF2YuO4w7Z23S9ZpNQciEXTjWzwoVh1UC1JWyYstr7D7fDR9jxyxcfGFadiJX1TtEEdgqzqqZPKG1WMoX+7yyWtHyJLfuMZxcZtTQzqk+FpZY0XooLXfiz2Inz27iSYw2IybKLHk9Bj9h1NicOF5cLfmbp1hhote6sLVi7Hgq6hw+i4JW6Rkmys1GA8ac7d41F1TUi7WaWBkBZj0Ti1V7duq8ACuQWQrr7E5UKkyilQrC7gVZ8sWpEBegLam2SWKz5DFNejNji6vq8Y8P1+PSd1Y1OgmIx+kSMHjaYsl95bV2WZKH+hj5x46XVKseB3jPzViLu1fnD/efi98mnydah5k1Rq978JHZ2/DrtjzJfVrCRs7JslrF1naAN0Fq74sX486R3s5BWtbLxnK8uEZisflgxRFxnErF4Bn89VBYUYdT5bXYprBRZPNcm6QYcW4KRlWBQk7syud4+blqMRkbtD56u064x83bTpI9LR7VLHZMtFnN3gQ9s8koipVau1PiDQLcIQYmWb24PI+IapPs3ujzoUMMtdt6kT+nxuYUrxtmrQO8wvOUQlHuOIuSsPNa0/TG2fElViyeagdy8bzigDthY2cDYydDQciEncvlOiPcsID6rqRMtqjuzivHoJcW45lfdknu98bYeYtGnuJ2IUbPicwuvJgoE2IsJp/kibWHi/DdxhwAypleLBBaLuy25HhT/9UWlBLPZPvTg+fi+/tG+DwudyHV6bTYGQzeyUNediApNsqbrKBhsQO8MYX8GBhFCgtXuYoFxuUSREuNkit2VPc08XZpjU288FmV/pJqm6pQ4WsA8rW2XlqwF7U2pyjserZx777Zzr+63oEamwPHuWwzudWEX5wTrGaM6+m+9pQsdms8Lhk2qReoLNS7TpZj0jdbNBMH9PDK7/sU72eivrTGhhqbA9nHSzQXvG25ZeLCxZ+zjSWvrFbSJBzwCDvuHNOKLeMTQCpUYhoZojvNc70M7piCPu28rnu55dsfv23P87kvENG73WNh7NsuCfMfOk+832IyihubGIsJz17eC8M9WZDBFNVy/tidL/k7j5sHtTYA/PVQbXPi/NeW46qZa8QMTu9x7nM9M8naILe3EoIgYBE3bpvTJdm8yNcBm9Plc77pgS9ODAAGLnJV7OuqYrE7zWXE8t40toZIhZ1XALFrlF13TMCypDY+dIjBryGBdGBiyOf2GptDFLV8CAAbW0G5grDjkicYCdHe+/TWfuXLE1lVul2kadSjbC7CuxhLhCNfVGcuOwSnS8A3G3Ik99vEGDujOLHziym7aIo5Nyzgm65+48cbMPWnnVh/pNjnvbu0jsPr1/YH4FtiYv0Rr5WOuX15BEEQxZFaUVV5Np9eix3grWUndyUmx1jEi0lusZPv6vjPK79olSx28smWUW1ziIG7SskTL17Zx/saNXbxvbq1jkfPNolwCcCgaYsVC0zLd8SDO3oTODYcLRYDf5mrJJ77TvfnV4LXPHKrCRPeGYlW7HxhAqZd5R5nea3UeiAIgjhZPXXx2ZJxybn6vbVYsPMUnp67Q/FxPWw+ViLWZpPTJS1eHONbSw7imvfX4cOVRxSPBaTn6WGV8IeGwItiJl7Ka22SRUkrEYq3zPPt0njmbc/DV+uPey0PKgWn5ZbvQBhzltsKrFd4TZu/B5O+3QIA6Nc+Cb3bJoqPCfAV2Kw8S2EIXbELd0mFHX/Oa1ki5RZs5i5b6bGmMFg4Q2ZitCjs5POFw+nC2kNFujrJAMBy2XsA7vOenT/s3OmSFidu3Ev8ZC6/OG8POj29QJIQVSMTdj3aeONirRp9XQFvHGhavDQ2OoZrK8Z+17QEXthJS57I5zCvxY7v/OP9PhvrigUgieGL50KAWL/YUwrCLt5q8rHYxXHiLFBhF2Pxvp7cYse3BgwkMSOUkLALIXKrkEvhmvty3TF86HE3RJmM4oXP1w5iooXtTFhCAe9K4N0U+05V+AiXdskxYsKD3EW86ajX+pFTUuNjNaniKm63kk0MDOY2ZLs1ZsliO0It2OQhFxhJMVFisV95XINW27AaWRsuJhhvG9ERXTzB4GoLNdtpW7guIDyd0+LQ12NhKa+1SZJEzsrwBncv3+872TOrD8uGnHLRWeJjrKBnm6RoJHsej+X6Bu/jyugAvgsZK+jMXptZGx0uQWKR4AtaM8ugWq0ptkjI39sfZTU2ceKUL6yMBKsZaQnusZbX2vGRR9C9veSg4vGAtFuAvOVcY2AlYXq3TcTYHhkAgO825ko2D2rit9YmLW+hZLFzugQ89N1W/OeXXeJ5HKfSHSEQKxJ/zf82eSRuH9kJALDqYJFfV191vQOfrvbWc+uflSyx5MizEgHvAhYKV2ytzYk/d+eLcba8ZZyhVcZFTWzKN4TMFZuRGC3On/Lv+sOVR3DjJxvw3wXq8bI8LCkB8LYCvOuLzbjtM3dcIJtvk2KjxE15kUYZohJPSSMAEu8O32cYAB4e2x3ndm2FOfcOV+3gw5AnRjCY56C81i5a7tuneJOyvK/r/h7lc1iigitWarELXNjJ48NrbE6fjGDA3VsdgE8CG6AcYxdnNYsJh3ozW2vEdcwbYycXdvyYTgRxXmoMJOxCiJIJnuFwuvDxyiN49ldvwU+LyaDYDodNDGxn0ibJfeFFc1mxvDvI4RJ8XLGZidHixVjNtf4SBAH7C7wLd63dic5TF+KXrd4sWN4FrNauh93PJh/vheg/e46Z1OWxPkmxUV6LnYor1qyQ2SRPSWeFZ1vFW5HkiUVR6wpSLlZnj1JNAGLxLKXVdk7ASi0wJ7ii1QxmSWHieGS3NNx+bicAwA+eAHjeLedNnnCI9RF7eOLv5GKMCVXmPovlLEK8KGELYEK0t5SFkmjhRUNMlAmPzt6KH1Qsbzz55XUYMf0v3PzJBgDSCvIduZZUqfEWcayzN3pfV96Dl7Evv0Is1wAEN+FDLCEUZ0E7blHjryG18+WtJQckf1fW+cbQKrlxVS12VqnlWwuW0GEwAL3aJEpKk2Qf13ZVHyuWxgKy8j/M6nft4PY+z2GdMtQsgseLq3H+a8s0E4jU+PcvO3HvV9kA3GJhoEK5kCINSyQTJfJuDPIwAq8rNprL5Jd+16/9sR8AMMtPoWMGKwI+7crekvlxS04Zymvs4rWZHBMlXvtaFruDBdKNFLs+5a7YtHgrvr1nOIZ1aSWKGDXBIi9OzGDz1uHTVRAE97XeiksyEF2xDqmwSxUtdswVy1nsuO9Tq6uH2rXu44qtd6Cq3pupLY5Nw2Kn5IqNt5o1a78qwbtiRYudU/rcGq68VqjjlfVCwi4EsIWf33kIgoC/9hWKf5fW2PFf2QRojTL5ZJsCXoHIT0qAN6upzuGULPSlNTYfi1RKnAUJ0WYxno09voULah/W2Vtw8tE528TbbLenZq0DvAG91fUOrDhwGvs88WLsfi3U2rMkxUSJF6JauZNrBvkuQIB0subHnxzDhJ2axU7adkcJZlErq7WL7xMdZcT1Q7LEY5R2kawjBL8Ay8uM8PX1mNuhsLIeBzyTPWvPIxcL7Bxh4p0Xpcv3e887Vm8rPcEqFuystTt9FrcCbhHNKanBL9vy8OSPO/xagjYcLUat3YnNx0tRXmsXJ7ru6fF4+MLu4nGpcV5hx/c3dSiZtQGx5h4j0CK8ALDjRBnmbMpRFV6t4iy4qFeGeD/v7lUrpLyUu6YB96ZCHoy/hrPoAO6gdLXY01hZtxEt2LiTYqJgNhnRjSsHomUpsTlcOHLaK+ymX90X3dLdz/3PZb3w4Pld8dTFPXyeJ7piVQTWZ6uP4lhxDT5aeSTg/tU/bfFuJJ+8uIdYRJdHKdu3vMaOHSfKRBcrvzECfC0oSq5YreQJrYQNBsvS7tU2yWeTdLKsVrSmJ8daREGkGS8o+37ZtV+jYLViyC1rPq/JrvtE6fca45lft3nWgazUGMnc4WOx84TxsMQa1s2BP9/4GoxqnpVT5bUY/NJiPMfVjmT4xtg5xfme/+xmmejkSYxWtthFi5Uk9Fns6iTJE8oWO34T1hDXcyggYRcC2Eluc7pES9MWWcX2rQrB3+W1dsWGyTUeC9spblICOGHHpaoD7tYwbDJhJMdGwWg0iMKGLQp7PC3QDAbgsn5tFD+PPK5CCTbu7OOluO2zjVi2/7Tkfi2iVOoJJfOuWHmMnecC6tMuUbFnYg03WbMYu1ZxVk6UKU+scsuXEike4V7OJU/EWc0Y0bUVrh7k7hWcr7CLPFnmtuLx9ec6yvpusgUWAIZ4YvCW7z8tZisz8V0qy7wrq2aLh3fcbOHid8bMspGeEI0Ert+kXLioWcT81eDiLSS5JTX4yWP5nXxhNzHgGnD/Fko9dVU28cj1JI4wN5FS3CTgLpnz/vLDcCm80KNztuGfc3f6JBzw/ZejTEbxPN910ht4X2d3IbekRlKIFoDo2ge8WYr8hq7O7sQjstZYcRaTqjU4kHInvKURAIxGg+jCVEv2KKiow7kzluIhT/uzqwe1ww1DO4iPd0uPx1MX95CUhmC0Fl2xyt89b/nny+nogS2aj4ztjluGd1R8fyVh98A32bji3TXixq+fTNjx7R5rbU4x1CIjKZr7rtXLc7C+qGrkldWisLIeRoPbmi53U58qr5XMKSwcRisZR24dZsJOXu6ER010MPJl3h4Gey12nXaV1QpkVi+HpzOQ1xXrnmeUsmL5GLuaeuXz+LsNOSirseOLdb5F1eW/QbXNIc73ElesRr/W5FiLj8UuzmpS9QCpwc4NrXIn/GfUChFqSkjYBYl4qxldPVmnZ2V4rTDsJM+RlUFQSo12OAUxVk1OeY1dnDTSxRg7b0YTP+kdK6722ZEkx7gnFLbwM7HGCirfOrwjrh2SJS70vMm+WNaKRgk1AadH2GlZ7PgLkRcy7AJKjImSJHSw4/ldI1u40+ItYkyIWo1B5rbVynQSrX61dnESivUs1uN6ui0+Su4qJYudvKH6oA7J4u0enhg4Jh4tJiOGeoSdzeGSvIfYwJtbEP/ucafxQedswWU7c9GCUKVP2B0tqla8n3GMe/wjLhFiYFaKxOLbKs6i2ixdyerAztcXrugNwP2byi1vNocLV85cg1cW7cPPXCgB4BZYzEolz7wskVmkmYCRB6KPenUZxr+1UjI+JtKvHthOTHbhF6anfvRNPEmKVd80qLkHleAtjQz2e6q5SxftypdYO+ULuRZijJ1M3D7+/XZcNXONxP17SqG2mBp2p7fo660j3IV9lYSd3MrOiqozUuMsPtdTXlmtKPKZFT3WYkICV6Ovzu6CyyXA5RIwXeZF4eM65aw/UiyeZ33bJSHOahbPT/79mTU9OTZKIoR2nSzHT1tOiOPbnluGK95djW/WS5Pr2PfKfu9khfPH4pm3+XPzk1VH8KunsDyb51mZEoY8hIR5BBiixc7hwsHCKtTYnLCYjT5ZsdIC8ZzI4+KdT5XX4pr31+K7jTmSDZw8Fl2p3EmVUvKEbN3gv5eUWEtQLHY1Sq5YTti5XILEO6Plem5KSNgFiaSYKPx7Yk/0bZeE16/tJ1pL2AkpD+5l/QJHn9Uan99+Dsb1TMd9Y7ooWjEAt4iQp6N7T1KpK1apFyE76dmEySZJtoC3S3HXdVr51AWex23ihFOsx2KnEnuXrqMSt1nB4sbGzIJdXYJ718hg40+MjpJc7Ozz8RaPYnHhtoqWOLWyFKxsgqaw8+y6i6ttPu4RLVcL+655i12bJO9EG281S8RzvNUsujoA4NK+mUiN805YQ19eKlrIsj0WYN6CxCZfvhQEE2asij+r7VQsC+bOU4kV8ddgnU8k4C1jWakxUmEXb8FVA9vhk1uH+LyG3DJTZ3eK8WS927otMvUO35IRv+/y9hY9UCiNU+LdqnLRJJah8fyuWpuRshq7JHGFWVd6tEkQz0Mm7BxOl2I5ErVrBeBb8/lfIORZ8gDQwSNs+NI4PM/9tlvy9xAuM9sfzBXL+ugC7s86d8sJbMstkwTuy5N75AiCgOX7C1FYUSden2ajQfwNlISdfDMmT8zp1jrep/K/3SmIQoF3wxoMBkmB3jqHE6NeXeZTseClBXsx5KXF+FiWrX2woBL/+Gi9GI/Huo3cPLwjlkwZjZuGua2ghZX1onBJjomSxKQ9PHsrpny/HYv3FqC63oErZ67BjhPlouWTJWP9sTsfdXanZPxy5KJj76kKvLRgLx6ZvQ2l1TZRaPPzDeAb68k8DgyWoGBzurDqoDukYHiXVuLao2ix464vl+AVUZ+vOYbs46WY+tNOSTz1odPSa1Veg5VPnpCUO+E8PQaD9JxJiolSyIo1c6E9+ix27HMlRJsVy51szZV63sgV2wLI4OIVEmOicGGPDMx76Dz0bpskLvSs/IHcfcF2mm2TonFBj3R8cts5EleVnLIau6TrBCDt9cfv0JVqJDFhx0SJaLErYxe8W2y46xy5RRTbIfL9adVQszRqtQFi8P1ieXiLHSA1gbPvs3WCFRd6Mhn5XThbvOvs3kmhVbxFLGGiZrFj2ZbtktV/CxZgf6KkRvyumaWFlROQJ4LU2b2V33mLndlkxBX92wIA7vRkNfLwIvDKAe1gMBgkO8ZbPtmAvacqRLchH2OUruA6Yy5N5gJupSJElaq5A8Dvu/JVg54BZUvR69f2d1sHuTZ0TASN7ZmO+8d0xb2ju4jjlb8GO0djLSZkJFpVS0Ys4+Ld5LWtDnFFxPNVagCy60peSkUuMvhgbSasM5NiREHIzjfeusms+YB2lnEgWbElVb7CjnUt+XZDDjo9vUBMugGULaEDNXomy0mM8QakM/F9oED5s+SXa7vs/9xTgNs/34ShLy8VBbmDay2nR9it4mIXMxOj8fwVvdGxVZz8aeI8xjrusHnWbWVnx9glYQR8K7WiKpukTzbg+xsyEWYwGNAtPUEUX4UV9ZIYOyaEiqttogV5w5ES7Djhuxn/++D2iI5yt7E6VlztE2PNI68ewFvONx8vFTfCPq5YmcVO3iqOlRSxOwUxqYO3tCslT9TIxA27HnZyn3E+t+E5WCC93ljSEpv7a+q9vcATVFyxKbEWSQtAk9HgI+zc/WOVQ3t41h0uRk6xuzoE3zdcyWL3Y7bUM9CQFmqhgIRdI+AnkSRZsL24e6+Tdg2QN7WX9/tUo6S6XrRaMMtHtJkJO5di3EuvNt66VMwVKy95wiwzbT1Cho8xYpYe0T0Zp27FSlDJfuUzIdVgu0LALTSYKO6cFi8Vdh4rgc3hEseUnmjFXed1xuQLuuH7+0d442Y8x7JF22IyIsFqFi12pTU2fLX+ODZwtdEAr7VTaYFgsMXzWHEN17/R/b7MAlZea5cspKxVm9Vs9EnMePXv/TD73uF4ZNxZkNOTq1XF3GZ8JvCRompc8vYq7njvb54hLi5eIeI9h6ye8bp/61Plddh1slx0mzD3bVaq+/xM4dwcS/d6e/k6XQLeW34If+zOdzcSV4iDurRvpnvc3ETMxK3BYMDTl/TAvy7tyY1X+hqs7ITN4YLBYBDPf1Yyot7hxJQ52/AL14EhR2ax4hePgoo62J0uXPfBOvztvTWiJYS9rtw9mShLaGItuOxOF/Z6ev12T48Xz1t2zbP4rEEdkrFkyhif70UJtkFQi03iYV0uMhO9c8g5nVIlxzz03RbxNu9ef/P6/tj74sU+i58WBoNB3Lgwd+5+mcBhp6a/kijrOBeqvBMK4LWeAt64Q7mwY0Jj1h3nYP2/xqJX20T0b5+EO0d2xvVDskTrlCjsPGKTCSODwSC6z/nuQZ/cOkSs+ckorrZJ4qf4zUeH1Fhc1CtTcnxrrjSMmBUb67XY7eVi905X1Su2pYyzmtE93X39HzldLc5lSha7aFlNPl6kLvMkT8VaTD7nMm+xY0XNeSxc8gTbqHRO887pcoudyyVI4psBt2vW4XRhx4ky8T6++PehQmVhx36/apvXYs+7W6O4c7dVnMWn8qJvjB2XFatisVt9sAg3fLweo19bhqveWyt+rsRoZWGXfbxEOlay2EU+/C68lUz0xMt272wCZD1CGUrCrgv3uv08DeWPF9eIlhI26bGLsl5msWP053ZWrGZYchwTNna4XAK3C/SOo7WY/eZ+TF6mQ4nEGLOPG+T8s1trBrgyzJzFLjk2CrPuOAfv3zQIZ2cmwGDw7rzqPBfU1pxSCIL7Yk6Ls6J1ghVPTDgbPdskim4utjDyYzcYDKLFbtXBIvznl1248ZMN4q5s0a5TohtEqY4Wg7m7ymvtooBgE1xyTJS4uDErWI3NIbrA2iRF+wTOR0eZMLxLK8Wm1IM4VxlLHHh4rDu7VJ40wgoOM5hQ4mNAvFZfqWv+tT/247L/rRZLVbDvbcpFZ+HN6/tj3kPn4e7z3G2l7v0qG9Pm74HLJWD1oSK8umg/7vsqG8eKa3yCt68e2E4S7P3a3/vhjpGdMLGvb6KOaLGTiUOWedrJY0VpJYsLnPH7PjH4m7nQ5SUQDnKu2ZJqG9YcKsLGYyXYmlMmfj/su/g/2aIeL1sM2QK3+mARquodSI2z4KyMBPGaZ3E2TPT1aJMIg8EgXvuX9JGKAJ6YALJi2SakTzuvmM9KjZG4GA8WVom/yTGPID0rIx5/G9heteSKFq1lVlVe2L1zw0C84CngrZVkU2d3SoQN4/4xXcXbvOBkvwsv7FwuQYwX7cRtwgwGA569vBde+Xs/n7CTAq6GHYP9tqx/cVZqDMb1ypBsihlsbqh3OPHifLcYvfu8zlj51AU+VjQWB11YWS+2nOMtdoe5rORtuaU4pGD57NUmUdxws2Q7i9moGGMnT7rhhR0T0WpzD+Nyj+eAh226S2tsYvZ6h1Tv950o6zxRWecQi0qzTXRVvQN7T1WqdtuQhw0wg0L7FPc8W2tziIYIqeD3niNp8VafmFv+HEqLt8JiNorGELWs7XeWeuto8r3LJa5Yz/VUXe8QRenFnmu6SseGrCkgYdcIxnO7NHlFb17Y1dqcOOqpG3WeTDC0VTCrv/b3fujVJhFf3TVUtB4ws30Cl8bNFyhWEnZd0uLw8a1D8O6NA8X4GHZhlFbbUFZrF+PW+Fg4b5C0+zWZWV/LimUwGPDmdQOQYDVjWOdUfHrbELx/02DV43l4gdI6wYohnVJxCbfwe7tPuC+a1R4XzMhuaT49a+ULozfN3/355dmuTpcguvAe+MZr3UiOVRexMRZvWRrWqYNZ6oxGg7j4sffenVchTnasI4Re/j64Pa4e2A5vXT9A/KwPXdgN+6ZdjDVPXyjZvcsDn9ulxCDKZEBZjR1/7SuAw+kSF0d2HsjjJj9edRR1dieOFnkyeJNi8LeB7dE+JVacvADg09VHseLAaUnNrY8UukbwdeEA4NohWXju8t6KCTNy0QBAUk/xizuHusccL40LZMWgY6JM+OF+d7u7goo6SamKgzKrwA+bT/i8PxOFnTg3XKzF5BMTx4TdD9luK2y/9kkwGQ1iUkRRpXsRYu2smBX19Wv748Ure2PG1f183pt/P8B//9J6hzcZpBfXMcJgMGDGNX3FvwXBa73M8cxB/MIcKPLfaLPHYvHm9f1xRf+2qu50nreXHpSUuGHcO7qL4vFsQ8knT+RX1KHe4YLZaJAU1OVhwo5Z7L71tFvkz0nmPmeil50DMRYTfpk0Ej89eK5Y24+VNfmOi8NTcosC3njEvLJasXxJZmK0Yjebk6W1onX3+ct7YcbVfTH73uEY2CEF7ZLd4mazJ4FCSZwB8OkbzhfwZuerkhGB3wTISy8BXncn74rm43jZPFhrd8LudIlu51iLSbTy19ic2HDULS7P65YmXg/iBpn7XY8VVSO/og4GA8ROKNU2p5j8wYtyfsPYKt4idvJhn4m32DGBbOWSJ3adLMcNH63HnE3u39PlEsRYZR6L2SgtUOyZV/acqoBLcIdkse8kXCx2/lMWCUXio02SCUVureKF3cFCdzuo1DiLaFpnKF1sgzumYuEjowAAS/e6RceRIvfCxC/EYlaszanYG7ZdSoykLhcASbo9i5NJjo2SWNYyEr1uhDq796LiL2glzuuehh3Pj1ct5aAGb7FTijO0mk2ohEN0X7PvRC6SAb4DhnuCEy2Sns+kVMZk7aFiXNwnUxRfF/dWt6gwkmOjJAHD/O+SmRiNgop65JfXoV97b2zJuJ4ZGNW9td/X5om1mPHG9QMk9xkMBkRHmRAdZcL6f43Fkj0FyCmp8SnqGm814+qB7TFncy4W7MgXA7wNBu/3oBQ3eek7q8Rzg1+4BnVIQd92SWJG95acUkks4fwdbldop1ax4kLZU8HyoQYb0xuLD2DOplx8e88wcQdtNHjdt2wTtS+/ErU2p1hsd9U/L0BKrAVmowEOl4DCynq0TY6BzeESrTupcRaUVNtE9xTDYjJK3FRv/2MA/rtgLz68ZTBmLjskOZbF4LHvgS2I4ibMc63u81jsennc6alxFtw6opPmd8CEnd0pwO50qVq8956qhMMlICHa7OOau3JAO4zq3ho3f7IBe05V4HhxNbqlx4vfgZ7wCDX4Tcuri/aJsZ3ndXOf10zYbcstw6qDp33O9x825+L95dJ4NcD9uZXi6gB3670dJ8pRa3fC5nDBYjaKv3lWaqxqVj0fT1xeaxeFfv/23jhUJkyY+OGTplgsWe+2iVhx4LTYj3r2Jm9Bbfm8z+iUFgeDwRv6YPZs+PJksasGgzvBYNMxt6AY1DFFvE4Brwjd6imXpRaHHWORFt7NVeiAoOTCreJa4HVWmN/Z+cc2sIM7pkgy7/m6q1V1DvHzpsRaRNFcWWfHdE8dyvPPbo2rB7VH9vFSxESZcPOnGySlX9g11b99srg2ltXYxI0CL8p5i3NavBUPj+2O1DirWA2At9i19Xik+CoLX607jnVHirHuSDH+PjgL5bV2xfhhZpWUF4FmBo+zMhLEz0pZsRHOnSM7S8oW9JbVT+Jj7PZxXQPkZnS1HR+DHc925/zkx3YmldwFxSd0KIlGsdxJjV1cvOVWG74QKTPpx1vNkgtajUBFHSDNilUWdt5J62SZd3d7/tm+IilW1rNW7n5R2jEfPl0lLnpmowHv3TTI75hTZBY9/ndh1kG2U2eTVb/20nMkWIzrlYE7z+us+N0P8JRPKa+1iy4hVtAWUM505ovX8okeRqMBP9w/AjcMdRdiPlpUjS1ciQsmdNPirZj7wAg8Oq47JugQyQx+kThZVotr3l8nWro/vf0c8bFL+ritubM35mLx3gIIglvspcVbYTIaxN+auWOPFVfD6RKQYDVjqCcGTZ6c0DpB2hz9ygHtsPHf4zCwQ4qPhetYcTVcLkFc7AZmuS0FLCHleHENSqttoouXL3/kD94KUWNzCxklt9FfnjjH87qlKf7uqXEWcePJYuuOia7Lhgs7Jnzm7cjDex6BZjEZRYGTzl2/t3y6UTznGO/KRPIjnrCCt/8x0Oe9vrl7GG4e3gFPjD9bTHJgFudjRf4/S4rYZcaO5fsL4RLc5xgvnNhmgllXlbLhmUV0T145Vh08LUmcGK2yUYu3miU1KjMSo2EyGiSbhz7tEn3EFF/HEpBef4CyOAP4LkTu9pJKvZTbKKwHfAa4UkUGeZwaCwNhmE1GcTNSWed1mSbFRImW7iV7C0XBdEnfNkj1FAJP4cKCGKygdOe0OFG4MUEdbzVLYn35xI/WCVakxlnw9CU9xO+QF3aszAtf7oSPAz1YWCmuhwnRZrEjEOAtVi+PsePXFvm609yQsGsg947uilZxVnRPj0dmYrSkawPgtdjlltaItax6t02UWIzcnRW041xYzTS+iCojRua2iTIZkJXinUzkkwLgrXNWVmMTq4jLd8osPqSgos6bYu9HgDYG3iqhVB6F/5xHPaKja+s4UYDyyCv3s4DpDBVXLOBe8FjwdO92ST7uXSXkWXF8Cy+2yB0urMK23DJs9wQN920XGmGnRSJXZ0qMr+NEKS/+rxogjbHJTIz2sYZER5lwwdnuWLHl+08rZni2TrBicMdUPDruLMW4QTXkGXlFVfUQBLfL5vyzvAvo+We3Rt92Sai1O/Gwp9DukI7e6481MWf1F1niRLeMeHTPkC6c4nM04kfvGd0ZF/fOxBvX9YfZaECd3YU7v9jEHeFetJgl7HhxDfZ6hEJWaozP59LCYjaKyTH55XUY/NJiDJ++VMxmZiz2WK1Z3UQl2G970pMgtb/APSY9mepqsHObF/98+Qf598iy/9nCzhcufuv6AXjsorNwbMZEH88C4A61eOmqvojjyv6Ue1x9x4v9h4ckc96JhTvdmbe3ySymbH5lcV28W5vB4u325VdK6tqtfPICzc0u/1rMFcifC5mJ0ZLyI1mpMT7Fh+VuZnm5EgbvilWKdVV77mX92qJNUrSPYGPIS1HxyVwMtiGrqLOLv2+bpGjRe7LaUyalW3q8ZE0SS09xGbUsbKBdcowogtkGrV/7JMkmhrfYKW1QeWHHzlu+pRhfBmp/vlfYZSRG47xuXm8Q+80sJmnmMYsjzUi0ciFA4SHsyBXbCExGAxY9Ohp2p8tHoDFh9zVXbPLiPm0kx+mp8SaP9eJP4Ngo6c/XNjlGcmIpLVZ8jJ234KXcYucN/NWqnRQs+Bg7JQEZy5UwYW5hJbeB+1jWX9X9PXjLubhfN46bDCwmI2xOF4qq6sU+pHxCjBb8xDmuZ4Zkwknz/Eaz1h6T9JuUtztqCthus6LW7q0az51D/donY+olPZASa8GVA9uiXUoMZi5zW2KevbyX4muy754lBo3s1grbcsrE4Gitsj1aKLXTA9yCk/9+DQYDbhneEU/N9Rb/nXxhN/E2+/6LqmyY+tNOfOeJrercKk6M25HTPlXd8tMmKQYf3OKOF31h3h6U19rFuD7Am6TUwSPsTpXXiu73Hpn6XdGMGIsJlXUOTHhrpXjfv37eiS/vHAqDwYAv1x0Tkw8ukCVj8bBFNK+sFiXVNtHC2JjzUGkeeHKCN2lHbvVZfagIRVX1+O/CvZK6Y7temKDYGkuN5FgLKuocosVuvSebXW0eALwWu7yyWqzzHH9JX6kFWb7RU6rrx4L46x0ubPa4TB88v6v4e6vRq00iFu50F8NmZUYSOWF3w9AO4uMAJC3hGD4WOzVhx2XFsoSWrq3jJEkaSsKuW3o81k0dq/oZ+E13p1axipvphOgoFFTUo7LOIYkrZdZ2Jtb4DieA97tnVum5W06I12qX1nE+69JArng7/5kBZUsrb21kFR34sjB8Utnx4hoYPXNMqzgL2qd6v3cmMOUWOxaDnp7gbU8XaCu9UEEWu0ZiMhoUrW7yTDpA2lUA0FfqRF6lns9MTYyRFrBtlxwjSflWctGwya6iziHuOFLl4tFzkZRw7qRQWuz4GDu5KwKQZnwd95PIwYRbQUUdFu8pEGNnWEV6/js5r3ua6JpmCRR6K/G/eX1/pMRG4a3rB2DmTVI3UpqCYG+TFK0ajxNK+Ky1kmpp4gTjvjFdcd05WbCaTXhyQg8cmzERx2ZMxKUKmauANLkAAC44O11S8V8tmN0fataPi/v4juOCHulIiomCweAOOOfFCpvkv914XFwoALcb6JxOqeJGokvrONGVp7dQLys8y7hvTBdx0W4db0WsxQSXADGmqKdCQLo/zApWzlUHi7DnVAWq6x149ldvkWG1uDTAO7/kltaILvMuaXGa7fL8IZ+z3ry+Px48v6vkvoc5kf3dxhw899tu2LiC0hec3TogUQd4RcDpShumzNkmbsT4jGA57LtZfagIdXYXMhOjfbJdk7hrIcFqVnSbW8ze+EsmEAfpqP/Hl0BhoirGYsJ9o7vgygFtMfqs1hKxpWRJTY6NkngDeI8MTwznit1zyv3dDOmYKlqoAOW51R+8sBsiK6XD8JY8sXOZ4AliKRmGvNNMvNXbu7y81o5vPUkpVrMRl/Rp4zNP8S50QFroW2l9kgg7T1UI9n2UcwX/AXcyDrPwp8VbJd8zux4tsgLFYieoBKukPZ3TJWDlgdM+HTWaErLYhYjWsh3E9md9kwq0yocw5Cc3fwIbDAb0bJOIjcfc7oF2yTG44Ox0/PdUhWL8GQBxMRQE4JCnBIS8MTTfZorV7FJy6wYLPoOuS5rv5MMmNtbfEvAVFwy2i1uw8xQW7PR2IuDjXbJSY5BbUotxPTNQVFWPshpvtXe91oy/DWyPqzwFg+Uo7bwHB1DhP5gkip027D59HhtKlMkoSZC4ZlB7HCioxL78SrSKs+COkZ0b9Lr8NfPI2O54e+lB9GqTqOj+aZ1gxZIpY1BRZ/cR4+y64nu9PjH+LNHdN+uOoTh8ugoX986ExWzE9hPlGNlVmlGsxsNjuyMt3oqXF+5FfLQZN3JWCIPB4JPNqiRK/XFe99aY5yngOv+h8/DqH/ux8sBpLNlTiMV7vRaed27wjUvjYW7nXSfLMf13dxmbkZyLqSG0kcw/7nhH+TXw2EVn4bZzO2HwS0t8nh9vNeM9ndnyPGwOuP/rbPG+28/tpCmwmOWYdcRg5ZN4eJE7sGOKauhAq3ir2GcWADql+Y9TPDszAdcObo81h4rwD+48mXppT/E2P58rbSoNBgOSYqJET4yalZB3xTJr8pBOKZiz2Zvo0ZA53MJ5U4aqCjtvi8X9Yjx5oqTFnPs+6XVsMLhjDktr3GWjWFjHb5PPQwyXVcvoLxN2fDy5vJUc4I4J7pGZgPyKOlGUso2uvFd7QXmdaOlPiYuSxB6yGEAmCudtz0OUySDG/qVzrtiiKhuueX8ttuWWYXDHFMx94FyfcTUFZ6ywmzlzJl577TXk5+ejf//++N///oehQ4cG7fV5gTDtqj4Sy9td53XGnE25eHSsb0FaOcmy3XUH2Qn89yHtRWHXu20irjsnCylxFp96eQyzyYjE6CiUcxeh3JLEzNaV9Q4xVqiLThdlQ2DmewCKBVOVml53VrPYKVgCemQmSIK637p+IHaeKMN1Q9pjzeEisep7nMXkY+7XQi1RZFiXVp6G4PVIi7fgUGGVmKnV1DBLQ2W9Q2x/pGRRDJQXr+yDqT/txCNjuyMlzoIpF50Ns8mIy/q1CSiujuesjAQM7piCdskxeHRcd1zcJxPtU2JUv+fWCVZFK6i8p/G3dw/DuZygGdktTSJwxpylvAlSIjrKhDvP64zL+rvDKhJl8XN/G9AOP209iW7p8Xjlmn6KMVv+eO7yXhjaORXje2UgIzEaI7u2wsoDp/HmkgPiMa/9vZ/YsUSNLmlxsJqNqHe4cPh0NdLirZhykf85R4tUWfa3krfCXUTaigFZydjmqQW28OFRiLeaEWs1Nah+XpfWcZISKe/fNEhSEkmJczqlomOrWDHuq7uCxYrVcwSAfhqbuntGdcG/ft4JwB1LqbQBVeI1WU1EObxr+4Ieyudht/R4Mc5MzaPAChS7BG+ywZizW+Pi3plYtDvfJzlIL7wHaEgnZRHNLHZrDxWh1u6E1eze+MXLvElKc3NiTBRKa+z4cfMJOF0CemQmiFnm/Jrp7k8rva5HdElD57Q4DO/SStUK/cukkRAE74afXY9Fsq41BZV1yKx2/xYsjr19SgxOlNaKWdRtuVqvP23xlmHKSokVy4Y5XYJ4zmcfL0WvZxch1mIWk3/YL+D9W9oWTXqM9LHRnfRbXM9IYTdnzhxMmTIFH3zwAYYNG4a33noLEyZMwP79+5Gerh6zEgj8JJIhW3z+c1kvPH1JD13Fe+VZtHKL0uX92uKDFYdhMRlxzeD2iLWY/YqIlFi3sGPxF/K4icQYs1gygoke+W4pmPx9cBY+W3NUMYAaUBZ7akKTd1uMOas1BnVIwZWypIDBHVNEC9pZ6QlYALdl7+5RXXwW6oZgMhqw4OFRsDtdMBkNOO0pu9EcJMdaRAslQ14WpSGMPqs11jx9ofh3ZlI0Xv5bX41n+MdiNkp2uIGUSuHhY0sTrGZJkedgoRRrBAD/mtgT43tnYlzPdNUyHP5Ii7filuEdxb8v7dsGH608IiZQXd6/La4dkuX3dcwmI3pkJohuy0fGdtOV2a4Fv9ioxSsynpxwNr5Yewx3j+rSIIErfa0eaJsUg9IaO3q3TfQr6gD3dTjtyj64/+tsmIwGXHeO73fGbxqvV3icceOwDuiflYSFO0/htnM76Uqw0sOwLqkY2CEZ53Zt5dPui/Hc5b0xfeFePDy2u+qmKUYmsLukuZPL7hndGUYj8JhCVxs9uLiiv2rxjMz4wDq/XNQrw5Mt6/1u1a5lJsiYZfFSSf1S72fqoWRtjY3CsifO1xy/fOPRs00iRnVPE/vess3HrpMVYlF7Fsf++e3n4LfteXjAE2qgtOZ0T49HemK0mA0sp8bmDFpCxdf5xf4P8nBGCrs33ngD99xzD+644w4AwAcffIAFCxbgs88+w9NPPx2U9zCbjJh2ZW/sOlmhGOCsR9QBbjN3WrwFRVU2/G1gO58g0RiLCUunjIFLgG5LSUqcRXSjAfDZCRkMBqTGWcQYvDFntVZ1fQaDpy/pgb7tEzFWJcOPD0gd1T0NqXEW1ewwPuD2/67rrxhUy/OPoVn4c08+rGYj7hrVMBeiEiajASaje1JpLlHHxvHno2PE9lMJ0VEhdauHA8M6t0JavBUuQcD/Xdvfb+Z5MEmLt0oKOQeDrNRYrJ16IXadLEfvtkkBfZ6rBrbDrrwKDOucqksM6uGRsd3xY/YJPDNRObmGIbeMNobUOAseUsnc1GL0Wa2xbupYmI0GRYvR8C6t0LFVLM7t2krRncfTu20SercNbgJUQnQUfn5wpOYx3dLjJeV+lIgyGcVkMAAY4QktGNwxFYM7KrtQ9fC3ge3w69Y83Du6i6rF79yuafjGEx9nMhrwxHh3Mg2fDKUm7DMTo7ED3h6yl8qSW24YmoWft57EpAu6yZ/aYJ67vDcmvLUSTpeAG4d2EC1sRzzx2GzD0j0jAY+P9yYGdWwVhxuGdsDBgkpke7ofPTLOfU7GWsyS73/2vcOREmuBAEGsjyr+77GD8o0y1B5jh6w5VITlO4/D61jXxiDI+3C0cGw2G2JjY/Hjjz/iqquuEu+/7bbbUFZWhl9//VVyfH19PerrvTFgFRUVyMrKQnl5ORITG7cL1cuWnFJsOlqCW0Z0VHRLBsqnq49imqclTsdWsfjzsdE+2WyzN+bg3WWH8PfB7XHPqC6Kk2JTsXx/Iab+tBP/urSnYtsbHofThWd+2YV+7ZNxoyzQnThzEAT3hBosy0ok43IJ9D2cAbz710GsOHAaA7KScd+Yrn43tcHC6RLwzC878cPmE3h4bHexdEpuSQ2ufn8tSqtt+OnBc32SHwB3z9ybPtmAkmobxvZIVxSwgiA0yI2sxZacUtTUOzGyWyu8teQgvt2Yg4RoM64fkqUpYhm7TpYjOsqIblzDgUW78rElpxQX9crw6dkcDMrKypGSkqxLe5xxwi4vLw/t2rXD2rVrMWLECPH+p556CitWrMCGDRskxz///PN44YUXfF6nKYVdKNiXX4GSahsGdUhpUosGQRAE0fJQEmDV9Q7U2p2aIrOq3oGjp6vRq21ig+NzzwQqKiqQlJSkS3tQuRM/TJ06FeXl5eK/3Fy9xtDwpkdmIs7tmkaijiAIgmg0SlauOKvZr+Uw3mpGX0+/ZSI4nHExdmlpaTCZTCgoKJDcX1BQgMxM39gYq9UKq9V7YjIDZ0VFhc+xBEEQBEEQwYZpDj1O1jNO2FksFgwePBhLly4VY+xcLheWLl2KyZMn+31+cbE7MyUrKziByARBEARBEHqorKxEUpJ2Es8ZJ+wAYMqUKbjtttswZMgQDB06FG+99Raqq6vFLFktUlPdQZE5OTl+v9xwgiV95ObmhnVsYKSMU04kjjsSxwxE3rgjbbyMSB03EHljj7TxMiJx3JE4ZsBtqausrETbttoJhMAZKuyuv/56nD59Gs8++yzy8/MxYMAALFq0CBkZ6g21GUZP+6ukpKSIOikYiYmJETHuSBmnnEgcdySOGYi8cUfaeBmROm4g8sYeaeNlROK4I3HMeo1JZ6SwA4DJkyfrcr0SBEEQBEFECpQVSxAEQRAE0UIgYRcgVqsVzz33nCRTNhKIlHFHyjjlROK4I3HMQOSNO9LGy4jUcQORN/ZIGy8jEscdiWMOlDOuQDFBEARBEERLhSx2BEEQBEEQLQQSdgRBEARBEC0EEnYEQRAEQRAtBBJ2BEEQBEEQLQQSdgRBEARBEC0EEnYEQRAEQRAtBBJ2BEEQBEEQLQQSdgRBEARBEC0EEnYEQRAEQRAtBBJ2BEEQBEEQLQQSdgRBEARBEC0EEnYEQRAEQRAtBBJ2BEEQBEEQLQQSdgRBEARBEC0Ec3MPINJwuVzIy8tDQkICDAZDcw+HIAiCIIgWjiAIqKysRNu2bWE0atvkSNgFSF5eHrKyspp7GARBEARBnGHk5uaiffv2mse0KGG3cuVKvPbaa8jOzsapU6fw888/46qrrhIfFwQBzz33HD7++GOUlZVh5MiReP/999G9e3fd75GQkADA/eUmJiYG+yMQBEEQBEFIqKioQFZWlqhBtGhRwq66uhr9+/fHnXfeiauvvtrn8VdffRXvvPMOvvjiC3Tu3Bn/+c9/MGHCBOzZswfR0dG63oO5X+PjE5CYmAiXSwAAGI0Gv7cNBkAQ4Pc24P6bv633+XrGEerbjfmckfSZ9X5O+m0D/56a+zPTOUzncGM+Zzh85jPlHD5T1liGnhCwFiXsLrnkElxyySWKjwmCgLfeegvPPPMMrrzySgDAl19+iYyMDPzyyy/4xz/+EdB79XvhTxitsT73m4wGOD0/RJTJALvTfdtiNsLmcAEAoqOMqLO7b8daTKixOQEAcRYTqv3c5o/nX8dqNqLe8/r8+/LjCSZGA8Be1mw0wOHS/pwxUSbU2n0/g57PyT+X/5wWkxE2p8tnDPzYGgt/QUs+J/fe/JiC9Tn5747/TvnfNpifk4d/XT3nMD/uQM9h/rn866v9tvzvEczP2ZS/bXOew2rzEz+mUMxPaudwqOanQOfhUP+2/HiCeQ6Hen7ib6vOT01wDvOEYo3ljwnXNfbx87XdrzwGQQjWKRZeGAwGiSv2yJEj6Nq1K7Zu3YoBAwaIx40ZMwYDBgzA22+/rfg69fX1qK+vF/9m5tCsR79XFHYEQRAEQRDBxFVfg9y3rkN5ebnfMLAzptxJfn4+ACAjI0Nyf0ZGhviYEtOnT0dSUpL4jxInCIIgCIIIV84YYddQpk6divLycvFfbm5ucw+JIAiCIAhCkTNG2GVmZgIACgoKJPcXFBSIjylhtVqRmJgo+UcQBEEQBBGOnDHCrnPnzsjMzMTSpUvF+yoqKrBhwwaMGDGiGUdGEARBEAQRHFpUVmxVVRUOHTok/n306FFs27YNqamp6NChAx599FG89NJL6N69u1jupG3btpJadwRBEARBEJFKixJ2mzdvxgUXXCD+PWXKFADAbbfdhlmzZuGpp55CdXU17r33XpSVleG8887DokWLdNewIwiCIAiCCGdabLmTUFFRUeHOjqVyJwRBEARBNAFU7oQgCIIgCOIMhIQdQRAEQRBEC4GEHUEQBEEQRAuBhB1BEARBEEQLodmyYt955x3dxz788MMhHAlBEARBEETLoNmE3ZtvvqnrOIPBQMKOIAiCIAhCB80m7I4ePdpcb00QBEEQBNEioRg7giAIgiCIFkLYdJ44ceIEfvvtN+Tk5MBms0kee+ONN5ppVARBEARBEJFDWAi7pUuX4oorrkCXLl2wb98+9OnTB8eOHYMgCBg0aFBzD48gCIIgCCIiCAtX7NSpU/HEE09g586diI6Oxty5c5Gbm4sxY8bg2muvbe7hEQRBEARBRARhIez27t2LW2+9FQBgNptRW1uL+Ph4vPjii3jllVeaeXQEQRAEQRCRQVgIu7i4ODGurk2bNjh8+LD4WFFRUXMNiyAIgiAIIqIIixi74cOHY/Xq1ejZsycuvfRSPP7449i5cyd++uknDB8+vLmHRxAEQRAEERGEhbB74403UFVVBQB44YUXUFVVhTlz5qB79+6UEUsQBEEQBKGTsBB2Xbp0EW/HxcXhgw8+aMbREARBEARBRCZhIewYNpsNhYWFcLlckvs7dOjQTCMiCIIgCIKIHMJC2B04cAB33XUX1q5dK7lfEAQYDAY4nc5mGhlBEARBEETkEBbC7o477oDZbMb8+fPRpk0bGAyG5h4SQRAEQRBExBEWwm7btm3Izs5Gjx49mnsoBEEQBEEQEUtY1LHr1asX1asjCIIgCIJoJGEh7F555RU89dRTWL58OYqLi1FRUSH5RxAEQRAEQfgnLFyx48aNAwCMHTtWcj8lTxAEQRAEQegnLITdsmXLmnsIBEEQBEEQEU9YCLsxY8Y09xAIgiAIgiAinrAQdgBQVlaGTz/9FHv37gUA9O7dG3feeSeSkpKaeWQEQRAEQRCRQVgkT2zevBldu3bFm2++iZKSEpSUlOCNN95A165dsWXLluYeHkEQBEEQREQQFha7xx57DFdccQU+/vhjmM3uITkcDtx999149NFHsXLlymYeIUEQBEEQRPgTFsJu8+bNElEHAGazGU899RSGDBnSjCMjCIIgCIKIHMLCFZuYmIicnByf+3Nzc5GQkNAMIyIIgiAIgog8wkLYXX/99bjrrrswZ84c5ObmIjc3F7Nnz8bdd9+NG264obmHRxAEQRAEERGEhSv29ddfh8FgwK233gqHwwEAiIqKwgMPPIAZM2Y08+gIgiAIgiAig7Cw2FksFrz99tsoLS3Ftm3bsG3bNpSUlODNN9+E1WoNyXvOnDkTnTp1QnR0NIYNG4aNGzeG5H0IgiAIgiCairAQdozY2Fj07dsXHTt2xJ9//inWtAs2c+bMwZQpU/Dcc89hy5Yt6N+/PyZMmIDCwsKQvB9BEARBEERTEBbC7rrrrsO7774LAKitrcWQIUNw3XXXoV+/fpg7d27Q3++NN97APffcgzvuuAO9evXCBx98gNjYWHz22WdBfy+CIAiCIIimIiyE3cqVKzFq1CgAwM8//wxBEFBWVoZ33nkHL730UlDfy2azITs7G+PGjRPvMxqNGDduHNatW+dzfH19PSoqKiT/CIIgCIIgwpGwEHbl5eVITU0FACxatAjXXHMNYmNjMXHiRBw8eDCo71VUVASn04mMjAzJ/RkZGcjPz/c5fvr06UhKShL/ZWVlBXU8BEEQBEEQwSIshF1WVhbWrVuH6upqLFq0COPHjwcAlJaWIjo6ulnHNnXqVJSXl4v/cnNzm3U8BEEQBEEQaoRFuZNHH30UN910E+Lj49GxY0ecf/75ANwu2r59+wb1vdLS0mAymVBQUCC5v6CgAJmZmT7HW63WkGXmEgRBEARBBJOwsNg9+OCDWL9+PT777DOsXr0aRqN7WF26dAl6jJ3FYsHgwYOxdOlS8T6Xy4WlS5dixIgRQX0vgiAIgiCIpiQsLHYAMHjwYAwePFhy38SJE0PyXlOmTMFtt92GIUOGYOjQoXjrrbdQXV2NO+64IyTvRxAEQRAE0RSEjbA7ceIEfvvtN+Tk5MBms0kee+ONN4L6Xtdffz1Onz6NZ599Fvn5+RgwYAAWLVrkk1BBEARBEAQRSYSFsFu6dCmuuOIKdOnSBfv27UOfPn1w7NgxCIKAQYMGheQ9J0+ejMmTJ4fktQmCIAiCIJqDsIixmzp1Kp544gns3LkT0dHRmDt3LnJzczFmzBhce+21zT08giAIgiCIiCAshN3evXtx6623AgDMZjNqa2sRHx+PF198Ea+88kozj44gCIIgCCIyCAthFxcXJ8bVtWnTBocPHxYfK/r/9u47rql7/QP4JwlJ2GHJUEHRUpXixAFata04qtdRtUOv1not2hZb169Ve21d7aXL6u3S1lZrh9Vat6VcceECqSIi4gJBrYAICGGP5Pz+oIlJyDgnBJITn/frxUsJ35zznJOTc57zXaeoyFphEUIIIYTwik30sYuIiMDJkyfRrVs3jB49GosWLcLFixexa9cuREREWDs8QgghhBBesInE7tNPP0VFRQUAYOXKlaioqMD27dsREhJi8RGxhBBCCCH2yiYSu06dOqn/7+Ligg0bNlgxGkIIIYQQfrKJPnYAUFpaim+//RZLly5FSUkJACA1NRV37tyxcmSEEEIIIfxgEzV26enpiIqKgkwmQ25uLqKjo+Hl5YVdu3bh1q1b+OGHH6wdIiGEEEKIzbOJGruFCxfipZdewvXr1+Ho6Kh+ffTo0Th+/LgVIyOEEEII4Q+bSOz+/PNPzJkzp8nr7dq1Q0FBgRUiIoQQQgjhH5tI7KRSKeRyeZPXr127hjZt2lghIkIIIYQQ/rGJxG7cuHFYtWoV6uvrAQACgQC3bt3C4sWLMWnSJCtHRwghhBDCDzaR2K1ZswYVFRXw9fVFdXU1hg4dis6dO8PV1RXvv/++tcMjhBBCCOEFmxgVK5PJkJCQgJMnTyI9PR0VFRUIDw/HsGHDrB0aIYQQQghvWLXGLikpCQcOHFD//vjjj8PFxQVfffUVpkyZgtmzZ6O2ttaKERJCCCGE8IdVE7tVq1bh0qVL6t8vXryI6OhoDB8+HEuWLMH+/fsRGxtrxQgJIYQQQvjDqoldWlqaVnPrtm3b0L9/f2zcuBELFy7EZ599hl9//dWKERJCCCGE8IdVE7v79+/Dz89P/XtiYiKefvpp9e/9+vXD7du3rREaIYQQQgjvWDWx8/PzQ05ODgCgrq4OqampiIiIUP+9vLwcYrHYWuERQgghhPCKVRO70aNHY8mSJThx4gSWLl0KZ2dnDB48WP339PR0dO7c2YoREkIIIYTwh1WnO1m9ejUmTpyIoUOHwtXVFVu2bIFEIlH/fdOmTRgxYoQVIySEEEII4Q+rJnY+Pj44fvw4ysrK4OrqCpFIpPX3HTt2wNXV1UrREUIIIYTwi81MUKyPl5dXK0dCCCGEEMJfNvFIMUIIIYQQ0nyU2BFCCCGE2AlK7AghhBBC7AQldoQQQgghdoISO0IIIYQQO0GJHSGEEEKInaDEjhBCCCHETlBiRwghhBBiJyixI4QQQgixEwKGYRhrB8EncrkcMpkMmbn5EDu5wttFgqp6BarrGuDlIkVtgwKVtY3/r2tQoqK2Hh7OEiiUDOTVjf9nGAZl1fWQOYshgAClVXWQOYkhFApwv7IObo5iiEUC3K+qg4vUAVIHEUoqa+EscYCTWITiyjo4ioVwkTiguLIOUgch3Bwb/y8RCeHuKEZxZS0chELInMQoqaqDUAB4OElwv6oOAODpLEFpdR2UDODlLEFZdT0alEp4uUhQXtOAOoUS3i4SVNQ2oKZe2WQ7a+oVqNLZZk9nCRr+3k5PFwmUysbt9HCWgAGDsqp6re10dxLDQdi4na5SMSQOQvV2OopFKKmsg5NEBGeNbXaVPthON0cHlFTWwUEohLtT4/9FQoFZ21le04DaBiV8XLW3udrAdtYrGJTXNG6n1mersZ0CQeNn6+4khkjY+H9Xqf7P1kni0GQ7iyoMf7aq7SypqoPg7+28X1UHBo3bWVpdB4WSgZeLBPLqBtQrH2ynsc/W20WKGp1juLymHl4u+j9bmbMYAIx+toaOYc3PVnc7NT/b5h7Dqs9Wdzt1P9uKmgaD22nsszV0DDdup/7Pls0xbOqz9XaRQl5Tb9FjWPez1T0/WeIY1vfZmtrOsup61sew7vlJdR5uzvnJVo5hH1cJKusUqK5TsP5s9W6nxmer2s4SnWvPg/Ow6WO4uLIOYjOOYbbnYX3XWNV26l5jTR3DfL3GFtwrQZ+Q9igrK4O7u7vRPIUSO45UiR2bnUsIIYQQ0lxccg+beFYsn6jyYLlcbuVICCGEEPIwUOUcbOriKLHjqLi4GAAQGBho5UgIIYQQ8jApLy+HTCYzWoYSO468vLwAALdu3TK5c22JXC5HYGAgbt++bdNNyHyJUxcf4+ZjzAD/4uZbvCp8jRvgX+x8i1eFj3HzMWagsaauvLwcbdu2NVmWEjuOhMLGgcQymYxXB4WKu7s7L+LmS5y6+Bg3H2MG+Bc33+JV4WvcAP9i51u8KnyMm48xs61MoulOCCGEEELsBCV2hBBCCCF2ghI7jqRSKZYvXw6pVGrtUDjhS9x8iVMXH+PmY8wA/+LmW7wqfI0b4F/sfItXhY9x8zFmrmgeO0IIIYQQO0E1doQQQgghdoISO0IIIYQQO0GJHSGEEEKInaDEjhBCCCHETlBiRwghhBBiJyixI4QQQgixE5TYEUIIIYTYCUrsCCGEEELsBCV2hBBCCCF2ghI7QgghhBA7QYkdIYQQQoidoMSOEEIIIcROUGJHCCGEEGInKLEjhBBCCLETDtYOgG+USiXy8vLg5uYGgUBg7XAIIYQQYucYhkF5eTnatm0LodB4nRwldhzl5eUhMDDQ2mEQQggh5CFz+/ZttG/f3mgZSuw4cnNzA9C4c93d3a0cDSGktSVkFmDh9gtgdF5X1d9/+nxPDA/1b+2wCCF2TC6XIzAwUJ2DGEOJHUeq5ld3d3dK7Ah5yCiUDD45+icEUmfo64ghAPDJ0duY0D8EIiF11SCEWBabLmA0eIIQQlhKySlBflmNwb8zAPLLapCSU9J6QRFCiAZK7AghhKXCcsNJnTnlCCHE0iixI4QQlnzdHC1ajhBCLI0SO0IIYal/sBcCZI56+9cBjX3sAmSO6B/s1ZphEUKIGiV2hBDCkkgowPKxoQDQJLlT/b58bCgNnCCEWA0ldoQQwsGosACsn9YH/jLt5lZ/mSPWT+uDUWEBVoqMEEJouhNCCOFsVFgAhof6IyWnBIXlNfB1a2x+pZo6Qoi1UWJHCCFmEAkFiOzsbe0wCCFECzXFEkIIIYTYCUrsCCGEEELsBDXFEqtRKBnqo0QIIYRYECV2xCriM/Kxcn+m1uOZAmSOWD42lEYVEkKIEXUNSvyYlIubJVXo4OWM6ZEdIXGgBjjSSMAwDGPtIPhELpdDJpOhrKwM7u7u1g6Hl+Iz8vHqT6nQd+AJAJoyghBCDIiNy8TGEzlQapxAhQIgenAwlo4OtV5gpEVxyT0oxSdmUSgZJGUXY2/aHSRlF0OhZHd/oFAyWPTrBb1JHdD4EPWV+zNZL48QQh4WsXGZ+Pq4dlIHAEoG+Pp4DmLjMq0TGLEp1BRrJlVi8zD2D2tOM+r8bamorFMYLZNfVoOUnBKaSoIQQv5W16DExhM5RstsPJGDRSO6UrPsQ443n35sbCz69esHNzc3+Pr6YsKECbh69apWmSeeeAICgUDr55VXXtEqc+vWLYwZMwbOzs7w9fXFm2++iYaGBs7xjFibiCkbkzFvWxqmbEzG4x8eQXxGfrO2kQ9UzaiaSR0AFJTV4NWfUo3ug7oGJfanF7BaT0FZdbPiJIQQe/JjUm6TmjpdSqaxHHm48SaxS0xMRExMDJKTk5GQkID6+nqMGDEClZWVWuWio6ORn5+v/vnoo4/Uf1MoFBgzZgzq6upw+vRpbNmyBd9//z3effddzvHclddq/c4mseE7hZLByv2ZeptRVa8Za0bdcjqX9bpKKus4x0cIIfbqZkmVRcsR+8Wbptj4+Hit37///nv4+vri3LlzGDJkiPp1Z2dn+Pv7613GwYMHkZmZiUOHDsHPzw+9evXC6tWrsXjxYqxYsQISicTs+Bg0dvxfuT8Tw0P97bJZNiWnpElNnSYGxptRU3KKWa/Ly1VqToiEEGKXOng5W7QcsV+8qbHTVVZWBgDw8vLSev3nn3+Gj48PwsLCsHTpUlRVPbh7SUpKQvfu3eHn56d+beTIkZDL5bh06ZLe9dTW1kIul2v9GKKZ2NgKcwc56FNYbjipY1OuykTfOk2+lNgRQoja9MiOMFVfIBQ0liMPN97U2GlSKpWYP38+Bg0ahLCwMPXrU6dORYcOHdC2bVukp6dj8eLFuHr1Knbt2gUAKCgo0ErqAKh/LyjQ3/crNjYWK1eu5BQf2wRIH0tO2mvpueJ83RybVa57exlOZbOstbO/Ck9CCDGbxEGI6MHB+Pq44QEU0YODaeAE4WdiFxMTg4yMDJw8eVLr9dmzZ6v/3717dwQEBGDYsGHIzs5G586dzVrX0qVLsXDhQvXvcrkcgYGBRt/DNgHSZclEzNBccaq+gObMFdc/2AsBMkcUlNUYnIPOX9aYjOozOKQNNiTeYLWuQrn5yTFpRE/2IMS+qOapo3nsiDG8S+zmzp2LAwcO4Pjx42jfvr3RsgMGDAAAZGVloXPnzvD390dKSopWmbt37wKAwX55UqkUUim7ZkFTiY0xlkzETA1yMLcvoEgowPKxoXj1p1SDZZaPDTW4zIhO3nB0EKCmwXRzcFEFDZ5oDnqyByH2aenoUCwa0dVmnzxBN5TWZxtHAgsMw2Du3LnYvXs3jhw5guDgYJPvSUtLAwAEBDReyCIjI3Hx4kUUFhaqyyQkJMDd3R2hodzudHQPU9XvxhIbQ5o72lQXl0EOXI0KC8DsIcFN+noIBcDsIcFGkwaRUIAnuviyWs+5m7bTT5FvmjMlDSHE9kkchJg1uBNWjQ/DrMGdbCapi8/Ix+MfHrH6VGCW7FvOR7ypsYuJicHWrVuxd+9euLm5qfvEyWQyODk5ITs7G1u3bsXo0aPh7e2N9PR0LFiwAEOGDEGPHj0AACNGjEBoaCimT5+Ojz76CAUFBVi2bBliYmJY18qp+LpLcU9jxhP/ZtSGNHe0qa7mDnIwJj4jX28fD9XM572DPI3ug86+rsCluybXczKrCAolQ3d6HLVUbS0hxLbYWs1YS3T/MTeOFfsuoUBjSjJ/dylWjHvsoWmt4E1it379egCNkxBr2rx5M1566SVIJBIcOnQI69atQ2VlJQIDAzFp0iQsW7ZMXVYkEuHAgQN49dVXERkZCRcXF8yYMQOrVq3iHM/BBUNxpbjBIl8qSydizR3kYIhCyWDJrotGyyzdddFo0uDhJGa1ropaBT19wgyWvkkghNgeW0tebOWGMj4jH6/o6SpUIK/FKz+lYsND8hxy3iR2DGO8KjUwMBCJiYkml9OhQwfExcU1Ox6RUGCxC6OlEzHVIAdjF/gAM/oCJt8oRmlVvdEy96vqkXyjGIMe8dH7dx8O05g0Z3Sxpdna3bEhLVlbSwixPltMXmzhhpJNxcMSExUP5q7X1q4NvEns7FmvQA84OghR06A0WMbDWcw6ERMJBRjXM8DosPhxPQM4H3xJLKcqSco2nNj5y5xYr8/c0cWWxqeBCC1VW0sIsT42ycvinemt3tXCFm4ok7NNVzyUVtUjObsYg0L0X5+4aqw5zUSBxiwO/u6OWDHOutcG1j0udSfpNfZD2IuNy0S3d+ONJnUAt2ndFEoG+y4Y76y670I+5w6ljN6Kdm7l7lfWGvybJnNqFFsC3wYiqGprjbGVfUsI4YZN8lJW3YDPD19vpYgateYNZV2DEt+duIF392bguxM3UPf3tfP0jSJW72dbzhRVzWmBztRcBfIavGLlawPrGjsPDw8IBOzSC4WC/RMGHmaxcZlGa9U03a+qZ12NbapaHDCvWpxt/zhD5eIz8vHa1vOslvHOGO6jiy3NVvqNcNFStbWWpFAySM4uRtKNIgCNXRoiOnnbxD60xWYVQlSSWCYl6w5fR9cAt1arNWruHKdsxcZl4pvjOVrreO/3y5g9JBiFcnaVBnn3q5sVA2CZ/uYtiXVid/ToUfX/c3NzsWTJErz00kuIjIwE0Pi4ri1btiA2NtbyUdqhugYl66ROxdLV3Vyrxdn2j9NXjs0XQZOni/nP7bUUtv1Gvj+VAx83qU0kAgolg+1n/zJa5tezf+GtUd2sEmd8Rj6W7LqoVevwxdEseDiL8cHE7lZtvuBTkzt5OGXfq2Rd1tBNZ0vcvGjOcSoAtBKv5kwFpslQRQiDxhkZerZ3Z7UcLt2BDLFEf/OWxDqxGzp0qPr/q1atwqeffoopU6aoXxs3bhy6d++Ob775BjNmzLBslDZINU+OuV+O709xS+oAy1d3c60WZ/uF0FeOTROCJt3qbWtgm/iu/v2y+v/W7l9hyyccQ52+gca+L6/8lIpZgzoiKtS/1RNkW5mqgRBDGq857JsR9bXKsB1NW9eg5DwB8qiwAKyf1qfJzVFzpgLTjOcbExUhF/5i1w3My4Vdy5Mxp7LYfQ6nsopsO7HTlJSUhA0bNjR5vW/fvnj55ZebHRQfjFibiHu1IvXvpu7sdZuf/sex/Z1Lv6iWqhbvH+wFD2ex0cTB0CAPrv0aSioaTzzWbBozpz+Iqn+FtYbVW2KAS0tQKBms2HfJZLnvTuXiu1O58HQWY2Lvdq2S5PGxyZ3vqMmbu5ScEpRWN3B6j+bNKdvRtMaaO009smxUWACGh/pb/LPdcjqHZQ9v07jMzKCie7z+VVLF6n137rMrZ2lmJXaBgYHYuHEjPvroI63Xv/32W5PPUbUXd+W1EEqd1b8bu7PX1/zE1Qv9GvcrmxOiqlrcUO0Ig+ZXixtSb2AQyB2O/Ro8nCXYfyEPb+++iPKaByez1mwaC+/gCaEAMGfS8kU7LlgpEWAbbOvOxJ58o1irlsCU+1X16iSvpefnsoWpGh4m1ORtHnNGlKpuTtlOBXLu5n1sPGG4uROAyeSOzVRgXBP7P3PvG10eF77u3G7Y9R2vYpbDTq31aEyzEru1a9di0qRJ+OOPP9TPY01JScH169exc+dOiwbIF4bu7I01P3Gx9tB1bD6VCwiglSA254TI9cuVklNiMjmtrFPgiyNZmBcVovW60sQ8hLq+TszGtcKKJq/nG0igW6JD/rmb981K6gCgslaB01lFGPxoG7PXb47ITj744mg2q3LGWLJGJT4jH4t3ppv1XqDl5+die8EsKDO/07Wh/dnc/cy3mi9q8jYf15omoaDx5hRgPxWIvqRO09fHc7BoRNdmPcLMnMTeRSLS+7o5TmcVQSgQsPquGDpe641PYqHmbMG4uTArsRs9ejSuXbuG9evX48qVKwCAsWPH4pVXXnloauz00b2zZ9v8xFZpddMvpr5Ex9R6BWi8O9Odf8fUl4vtBfDbE1mY+9QjUCgZdT+N7MJyVu9V0ZfUqTDQTqBbqkN+c+dc+vzI9VZP7CI6e7NqLo8wckdtyRoVQydGc7TE5KIA+yb3ZXsy4CQRmbUP9M11Nb5XAPZdyDd7P8dn5GP53gzcLX9QK+DnJsHK8WGtcqPH9f2N5yX7bPI2p08aZxy/REqm8eY0srM369G0bGw+lYM5Qzub9V5zE/v2Hs0f8KDy5bFsfHksm1X3KUNdNNjqH2ydGn6zJygODAzEf/7zH0vGYjcKyqqRlF2MU1n3ODU/mUuV6DzV1Q/nbt7H0t3pRtfLQFXrp33xLyhr7B+2ICoEHX1cmpyc2V4Ay2uVGPjBIRTK61qswU+VQJdV15nskK+q6eF64WrunEspufcRn5HfKjUQmjWWkZ288UdGgcGyH0zsbnC7LVmjUtegxNu7Myx2DFh6clGV8A6eTUby6VNZp+Bcc2i4X1ON3hF+bPezoeXeLa9Tf4fnPhXCOkFqbjLPplP+F0euGx0UZajJu6VqJS213Ni4TGw8kaNVu/9+3GVED9buk8Y2+TMUVxHLOUA1Pbg5tVyifPBSgdHEzljtNNu+rADUy0jIvIsD6ZafEy7/7+ud5oAtzfUWldeanDbMlGkRHSwRKmesE7v09HSEhYVBKBQiPd14s0qPHj2aHRifrf79MkoqW7dtPb+sBhGxh5u1XtUXbu2hB5Nbao7yDO/gCRepCJW1pucpvCtv+e0vKKvGB39cMVlu5f5MKJXA6t+5Xbia08dOc93m1ECoTo4FZdUoqayDl6sU/u76Lz5c+nBGdWsDF7ED5m87j8raBvi5O6JPkCcCPJwQ3sHT7EEEuvH+VVqN3879pdU/0hKSbhQhorO3RS/0527e55R8sq055DrFD8Cu5kqhZPDGtjSjy1l76Dp+SbnNaoR2XHo+XtuqJ/lsZpKp2YSuiokN3Q7/LdEfz1L9d9//PVNv86WS0e6TxnZAgrEE2ZxO/6qb034dPTm/1xD53y1H+hK4hMwCg5+XzEnCqi/ri98l40zOfTQ058TLgaovr4ezGAzDoIzjABVj0m6XWqVfroAx9RDWvwmFQhQUFMDX1xdCoRACgUDv81sFAoFdT1Asl8shk8kQOP9XrcET9m7OkOAmzUbW1rO9O+sh7vqoLpm6zdiad2yaU5mY65foCPWXW3XXnlNcCQGA3oGNSZVmcqLvoqPiJBaio7cL+nb0xNujQ5F4rdAifTgBwNNZjPssksOfZw3QqjXTd/FtKU+H+SPtdqnBC705tSJXC8rx1THT/RI1ae4DQzUUp7KK8M9vz5i9rT/O7I/BXZo25a85eAWfH2EXrwAwmpjFpedh7i/nDd68qEbQn1z8FABobWd4B0+cyS5G9I9njT45R+bkACexA+spjJY+3QVCgRAns4qQeO2e3phgYrs0j4NATyd09XdHSVUdfN0c8d3JGzh0udDg9ho6H6g+W1UXk2NXC3Eiy/godKEAmBHZEZtP5xosM+fv5M5Uf+zegTKcv11mdH2aPJ3FOLtsOERCATYez8b7caZvgtno5u+KeVGPNule4OEk1ttdCGjcr/8a1BHfncq1SAx88d8XemF8r3YWWZYq9ygrK4O7u/E5+1gndjdv3kRQUBAEAgFu3rxptGyHDtapfmwND2tiZ88C/r5w6bvbtIR/DeqId8c+pveuXcXLRYz3xodh1/k7Bi86+khEAtQpWneEq8xJjA8nNfZd3H8hD6//wu5pIi1FdaGPCvXF4cuFWkmKUIAmTWL6+qZxFfNkZ7w5siviM/Lx7p4MFGqMfvN1lWDVhDBcvFOGL1kMZDFmjk6NjkLJ4DEWjyDUpDq+dfu7fXEkC2sPXWO1jMl92uFkVrHWhZxN83VL0rddgOFaNLacxSIsGP4o/GWO+E/cZa3zgbNYiCq2Pec5yFgxEo9/dKRZMyfocnQQInZSD/i7OyLuYj5+TDZ+3WbLw8mB87QrQOM5rqTSctvHB7o3wc3RIokdaUSJnX1aEPUo1h261mIXquGhvkjIZJ+w8UFUtzY4dLlpjYotih7cEUMe8cXnR68jxQJTJzzTqy1GhvkbrWEJaeOC6xyeFGCIZnKXlF2MKRuTOS9DVfunSui+O3kDcgs3k1uDZm24Qsngua9P49zNUusGZYaWTpIdHQSoaaBLfWuz5IT1LZ7YbdmyBT4+PhgzZgwA4K233sI333yD0NBQ/PLLL1RjR3hH6iBELYdaEPJwe6qLD05cL0Z9K/UD+vTZngjwcEJ+aTUW7rhg1jKe7OqDU1kl6oem24O1z/fCM73bYf+FPCz69Tzq7LcXEOExS0zV1OKJXZcuXbB+/Xo89dRTSEpKwrBhw7Bu3TocOHAADg4O2LVrl9nB2zpK7Agh1uIsFqCqnmpeVPoEynD9XqXFB+kQYkma/R3NxSWxM2u6k9u3b+ORRx4BAOzZsweTJ0/G7NmzMWjQIDzxxBPmLJIQQogJlNRpS+UwmIAQa2nt53Obldi5urqiuLgYQUFBOHjwIBYuXAgAcHR0RHW1+bOzE0IIIYTYG3NHyAv+/vEUs6+VNiuxGz58OF5++WX07t0b165dw+jRowEAly5dQseOHc1ZJCGEEEII0cD8/XOPw3NnzXrmyZdffonIyEjcu3cPO3fuhLd346ikc+fOYcqUKeYsstV9+eWX6NixIxwdHTFgwACkpKRYOyRCCCGEkGZ5KKc72b59O1588UVs2LABAwYMwLp167Bjxw5cvXoVvr6+Rt9LgycIIYQQ0pqUtVW4ve45VoMnzH5K8YkTJzBt2jQMHDgQd+7cAQD8+OOPOHnypLmLbDWffvopoqOjMXPmTISGhmLDhg1wdnbGpk2brB0aIYQQQojZzErsdu7ciZEjR8LJyQmpqamorW18rl1ZWRn+85//WDRAS6urq8O5c+cQFRWlfk0oFCIqKgpJSUlNytfW1kIul2v9EEIIIYTYIrMSu/feew8bNmzAxo0bIRaL1a8PGjQIqamWeW5lSykqKoJCoYCfn5/W635+figoKGhSPjY2FjKZTP0TGBjYWqESQgghhHBiVmJ39epVDBkypMnrMpkMpaWlzY3JpixduhRlZWXqn9u3b1s7JEIIIYQQvcya7sTf3x9ZWVlNpjY5efIkOnXqZIm4WoyPjw9EIhHu3r2r9frdu3fh7+/fpLxUKoVUKm2t8AghhBBCzGZWjV10dDTmzZuHM2fOQCAQIC8vDz///DMWLVqEV1991dIxWpREIkF4eDgOHz6sfk2pVOLw4cOIjIy0YmSEEEIIIc1jVo3dkiVLoFQqMWzYMFRVVWHIkCGQSqV488038fLLL1s6RotbuHAhZsyYgb59+6J///5Yt24dKisrMXPmTGuHRgghhBBiNrNq7AQCAf7973+jpKQEGRkZSE5Oxr179yCTyRAcHGzpGC3u+eefxyeffIJ3330XvXr1QlpaGuLj45sMqCCEEEII4RNOiV1tbS2WLl2Kvn37YtCgQYiLi0NoaCguXbqELl264L///S8WLFjQUrFa1Ny5c3Hz5k3U1tbizJkzGDBggLVDIoQQQgjRIgDgImWfrnFqin333Xfx9ddfIyoqCqdPn8azzz6LmTNnIjk5GWvWrMGzzz4LkUjENWZCCCEm+Dg7oKiK/YPACSHW18ZVgj+XDW/2cuRyOWQfsivLqcZux44d+OGHH/Dbb7/h4MGDUCgUaGhowIULF/DCCy9QUkeIAW+OfBTezmZ1abVJjg5CXF41Cltm9MPATl4I9nFGZLAnJA5mP8ymWZ4I8bbKelvTc/2D8NXU3qzL924vwztjuuHyqlHwcBabfgMPdfNzgauUrjvEdq17nv131lI4XWn++usvhIeHAwDCwsIglUqxYMECCASCFgmOtI7owR3xxKN+SLpRhAYlg4qaBuQWV+FkVpG1Q2txLhIROrdxQfqdlnuiiIezGK8MfQQ92npg+uaUFltPa1r3Qi84SUQY2s0XQ7tpP1+5pKIOYz47jnx5bbPXs2ZyDxRW1GDHn7dwu7QW9QrtR1v7u0uxYtxjGBUWgNi4THx9PKfJMmY93gFtZc64WVKFDl7OmB7ZESKhAMnZxZixOQUNSn48LntQ5zYYFOKDfqdy8OfNUpPly2sbMGtw4/RTH0zsjld+av7k8QIALw/uiH+PeQz35LV45quTKK6ogwBKGKtM3DCtDx5/pA0WbD+PS/lyFFfUobZB2axYhAJg7+tDIHEQGvzszdW7vTsea+eBYB8XPN8vCNv/vIWbJVW4XlCOM7klaIlDpk+QB1JvlVp+wTYkvIMH6hqUuNiC51tzdPVzxa371aiqU1h0uSIhENG59W86BQzDsD5ERSIRCgoK0KZNGwCAm5sb0tPTeTFgwlLkcnnjEyjm/wqh1Nna4TThIhagsp79Wee/z/fC+N7tmryelF2MKRuTLRmazYn++wIFAHHpeXht6/kWWc+GaX0wKiwACiWD7iv+Z/GTR2ubPywE84c/arKcQsng9a3nEJdx12RZfYaH+mLji/2aLDMlpwSF5TXwdXNE/2AviIQPbizrGpT4MSlXK4kzVosYn5HPKeHxchHjmV7t8FvqXyirbr1mUQ9nMc4tGw6RUIDZP5zFwUx2+3TOkGAsHR0KoHFbV+zLRIG8Rv13PzcJ/NwckZ5n+kI7vJsvvvxnuMH9GZ+Rj+V7M3C3vE5r+SvHh2FUWIBWWX2fY3J2Mf753RlW26W7bUDjZz/juzNIyikx+J6Zg4LwU/LtJjcImpwlIlxcMVLruNKke4ydyi7CkSv3WMetj6ezGGeXDcdH8Zex8UROiySOluQkFkLiINT6DrhKRfjgme7Ym56HhMzCJu/R/D6P++IE0v+yneRufI8AfPpCb5y+XoQd525hX3rTp1CZY3CIN36cFWGRZalyj7KyMri7uxstyymxEwqFePrpp9UT9u7fvx9PPfUUXFxctMrt2rXLjLD5wZqJnZejEE+FBiAzvxyX8+XQ/OCEAiB6cOOJju3FSjOx0aVQMgh9Jx61iubdVas4S0RWTWgyVoxU33UbuuAfSMvD3G2WTe5mDuqI5WMf7GOuiYQ+/Tt64vb9auSXPbhAezg5oK5Biap68z+vxaMexdfHc1BaVW+wjIeTA869M8LgRU+fl7f8iUOXm57oDdGsFWoNaw5ewedHsk2We3tUV8wa0km97QczCjD7p3MtHR6ABzcHALDxeDbej7vC6n1CAXBl9dPqY11fQqVQMuj6zh9Gkwnd5RhiKvE29d7w9xKMHn+qWFTnOn1i4zLxzfEcrfOjAMDsIezOj5r7mq3oH/7Um8xEdWuDpOwSVJo49301tQ9G92hcp76bkzUHr5hdI/mP7gE4cDHfrPca8vHkHpjYp73Bz7q6ToH/xGUit7gKHb2d8fboUDhJtJvMK2oasGD7edy6X41AD0ecvlFitWvE+J4B+O+UPurfLVUDfHnVqCbbba4WS+zYzvO2efNmtovkHdXODX9nL4rqHnxg/u5S9O3ohQPphr9A6yb3wN4LfyEltxTVDUpOd2ULokIw96kQ9RfHVM1EfEY+3tpxAfLapl8UgQCYbeTEqLJo+3nsPJ/HPkg9Yp7ojIGdfbBoxwWtmoLWpHtnb4ylm3R+iY5ApE5VvKGaDX93KS7cKTe5zFeGdMKbo7o2OakCwBdHsrD20DWzYn0xsgMGdva2+EUPAN7/PRPfnsyBqbNNZCdPbPlXRKv21WNTk6qvFmdv2h3M25bWorFpNjWr1DUo0WXZH2B7+nhnTDd1k6whpo77sT38se6FPpwSenOYSrom92mH/0zsYfL4YHN+ZFu7yJahZMbUNrE9P524dg/TN3HryiESAtfeG40P/sjExhO5nN5rDJtjiitL3PSa67UnOuOtUV21XouNyzRaexrk5YRbJdUGl6mvxaE5WiyxIw92bsn9Ulwpbmhyt9LY3HEJBRr9i/SdnHecvY03f0s3uT4PJzE+mNTdrJONQskgObsYJ7IKcfEvOZylIvTv6I0ZA403T6nUNSjx6LI/OK9XxUUiQvqKkUjJKbFasy6XpE4lLj0f/95zEfdN1ByYompe0Xcx1FezsXzvRfx0xvSziKcNCMR7z/Qw+PfGi9Yl3C3n1sdNdbLW12QXIHPE8rGhZl/0gAcX252pd4zWOFuDObU4bLsreDqLtY4lX1cxhjzqC2epA9p5OEIAAW4UV+B6QQWkDkK4SB0w8jF/tPN0NljjxeUG5MXIDlg1PsxkOVMXMkscA2ywPYc2V3NqF7nS953ychHjvfFhGN2jLatl7D5/Bwu2p3Far2ZT4P4LeXj9F8u0SHwyuQcm9w20yLI0mXvuAhpbE0rN7B7x88sDMOgRnyavq85Z2UUVuCevhZ+7I4J9XNQ3CoZqai2d1AGU2LUoNjuXzQnjnT0X8WPyLZPrGxLijR8s1EZvjubUYKn6BR1Iz2vxmg1N7T2dMHNgR5P9q4xRfYbHrxVifeINs5ah2S+KjWV7LuInFsfEtIggvDehu9Eymsegl5MEM75P4dTU1tIXPa594VoD11ochZLB4x8eQUFZjd7aMwEAf5kjEt98Eudu3rf4vpzyzWkk3bhvshyX2pW6BiXe3pWO31LvNPmbKuL1ZtbactGaSVdrae42bTx+A+/HXea0Tt2mQGOJkwBAV39XXC6oMLncaQOC8N4zxs9B5tLdTz8l5+L3i6b7vPUN8sBZMwafGLsBZ4NNs7MlcEns7Gf+BRsiEgqaNL/pYtsMe/x6MeIz8lv8RGqIqgZFt88KG6VV9UjJKYGvm6PF4+rR3l1v51tL3SmpPsP+wV745c/bJvv96KPaflPHgkqwt4vpQizL6R6D0YODjSbo0YODtRIrNsdwc0gchBZvymmuUWEBGB7qz/riKxIKsHxsKF79KRUCoEmfLgBYPjYUEgdhi+zLLf+KYNU3bnpkR9bLFAkFOJVdrPdvDBq3a+X+TAwP9W/RRKuljz9raO423a+qM11Iw/BQ3yYJhuYxnne/Cml/lQIQoKN3481VzM9nWSV2d+WGmyCbS3c/9Q/2wqlsw30vVTdQ7b2czUrsYid2b9ax7CQRYbWJG+3WZt1b5IeYgEOatHJ/JhRWHCa1dHQorr73NN4Z0w1DH23D6b2F5TXoH+yFAJkjuH51hoR4w99dqvWav7sUG6b1wb65g3F51ShMjwjC4BAfTI8IwuVVoyxe/S0SCvDBRPO/tIXl7PsVTo/sCFPnF64XapWlo0MxZ0hwk+ULBeY1V9sr1UVlfK92iOzsbfKEPyosAOun9YG/TPvmxV/m2OI1WxIHIaIHG5+RQDdhNyUlp0RrUI4uBkB+WQ1SjIw8JS2DS+4RFuBu8FyoOsYn9Q3E6gndsXpCGGYN7gSJgxAuUnbzHbItZwmqc7C+zde8gZrUpz2n5QbIHM3uM2zrqMbOStyc2H8xVCdSa97BShyEeGlQML49ya1Z1tfNUatmg4tuATJsnjnAYA1Ka90pjQoLwIZpfZr0+2GDS22l6kLNpWaNi6WjQ7FoRFebawLlO641fZakSsh1+8aZ22eR7Y0IlxsWYhmRnXzwxVHTo7cB4N//MO9GbVKf9tiTZnrAHNckqrlUN1Ar92dq3Xj4a/T7VCgZk7MvODoIETupB/zd7aN53xBK7KxEwLH+SnUitWbfE1N387o8nMXq0ZqjwgLw5dTeiNl6nnVdZaG8xmaaZHQv3j4uUizacQF35cb7V6m2ny1LX6h12WITqD2w5nFqyYSd7Y1IS3SvIMZFdPaGk1iIahNTGgWYcd5RGfiIj8nkyEUiwkA9Aw1amqkbKJFQgE+f62l0ENS6F3rZZQ2dLkrsrETGocYOaDyRxmfkN7ljaa2RagD3u/SX/p7hX8XNUcypn15bTydO62tpuhfvFeNM968yJ+mmmjXClaUSdlW3CVMDQsxNHEjzSMUik4ndO2PMO+8A7JKjNc/1tFpNl6kbKFXriu4AkZYYVW3LKLGzEnkN+8747o4OuF9Zh5itqU1OtgVlNXj1p9RWGanG9S69X0ftk//nR7jNrzawU+vfFXLBpnnAXFSzRqyB7YAQe23CsmUpOSWsBnF5ukiatR6+J0fW7BphKyixsxIuTbGRnb2w+vdMvXfQrTlSTXU3z7Y5tqjywUkhPiMfKbmlrNfl4Sy2yjP2uKKTCLE3LXnDQszHdoJ3S0wEz/fzmq104bEWSuysJLKzN744msWqrJPYgfVItZY8mFV382xnB1fV8CmUDFbuz+S0rg+aOQS9NT3sJxFif/h+YbdHJRXsBm6xLWcKndf4ixI7K4no5A1niRBVdaaf7dnOg11fs9YYqTYqLABfTe2Nub+cNzh/lm4/HK6DLsQiAYaH+lsgWkKIuejCblu8WDaxsi1H7Bf1xrYSkVCATyb3NFnOw1nMegRSa41UG92jLf77Qm+9f9PXD4drwlmvaHwUGiGEkEb+MnY3+GzLEfvFi8QuNzcXs2bNQnBwMJycnNC5c2csX74cdXV1WmUEAkGTn+Rk7Wc57tixA127doWjoyO6d++OuLi41t4ctdE92mLOEOMTjH4wsTsiOnkbneBXgOYNcecqPiMf7/+uv2lV38Ss5iScSTeKzI6PEELsjaqPszGteR0gtosXid2VK1egVCrx9ddf49KlS1i7di02bNiAt99+u0nZQ4cOIT8/X/0THh6u/tvp06cxZcoUzJo1C+fPn8eECRMwYcIEZGRktObmaFk6OhRfTe0DT2ft6U9UT1gYFRag7tsGoEly19oj1VQPSzc0Ue87Y7o16VzN5oSky4oP2iCEEJujug4Yu8GnEcsEAAQMw/DyEvrxxx9j/fr1uHGj8QHtubm5CA4Oxvnz59GrVy+973n++edRWVmJAwcOqF+LiIhAr169sGHDBlbr5fIgXi7YTDxs7XnsFEoG4e8ZfmYfYPjB96qEkK1/j+6G6CE03QchhGiy9nXAmqw5Qb+1cck9eDt4oqysDF5eTaucx40bh5qaGjz66KN46623MG7cOPXfkpKSsHDhQq3yI0eOxJ49e1o6XJPYdFS29ki15Oxik/MolVbVIzm7GINCtPsFjgoLwMyBHbD59E1W66IOwIQQ0pS1rwPWEp+R3+SxjnyZW6+18TKxy8rKwueff45PPvlE/ZqrqyvWrFmDQYMGQSgUYufOnZgwYQL27NmjTu4KCgrg5+entSw/Pz8UFBQYXFdtbS1qax8cSHK53MJbw401R6qx7feWdKOoSWIHACMeC2Cd2JVW1ZkuRAghD6GHbcSyoRafAnktXvkpVd1tiTSyah+7JUuW6B3woPlz5coVrffcuXMHo0aNwrPPPovo6Gj16z4+Pli4cCEGDBiAfv364YMPPsC0adPw8ccfNyvG2NhYyGQy9U9gYGCzlsdvbO8I9ZfrH+wFRwd2y/Bwpho7Qgh52CmUDJbsumi0zJJdF6GgjtlqVq2xW7RoEV566SWjZTp1etDPKi8vD08++SQGDhyIb775xuTyBwwYgISEBPXv/v7+uHv3rlaZu3fvwt/f8JxpS5cu1Wq+lcvlD21yx3ZSZUN3kgmZBahpYPfloxo7QgghzekC9LCyamLXpk0btGnThlXZO3fu4Mknn0R4eDg2b94ModB0ZWNaWhoCAh5Uz0ZGRuLw4cOYP3+++rWEhARERkYaXIZUKoVUKmUVo72L6OQND2ex0S+Zp7MYEZ2aJnZcnz5BNXaEEEKa2wXoYcSLPnZ37tzBE088gQ4dOuCTTz7BvXv31H9T1bZt2bIFEokEvXs3Tpy7a9cubNq0Cd9++6267Lx58zB06FCsWbMGY8aMwbZt23D27FlWtX+ksV/HBxO7Gx3dGmvgUWBcnz5BNXaEEEKa2wXoYcSLxC4hIQFZWVnIyspC+/bttf6mOVvL6tWrcfPmTTg4OKBr167Yvn07Jk+erP77wIEDsXXrVixbtgxvv/02QkJCsGfPHoSFhbXatvDdqLAAbJjWByv2ZWo9bNrUcHuuT5/wcqVaUkIIedg1twvQw4i389hZS0vNY8c3XOcTSsouxpSNyQb/ruuX6Aj6ohJCyEOOzfypns5inNUzf6o94ZJ78OLJE8T2qIbbj+/VDpGdvU1+obg8fYIei0MIIQR40AXIGENdgB5WvGiKtSWqCk5rz2fHR//3ZCAWbr8AY1XEAgD/92QIKivKWyssQgghNmxgkAvWTAhBbNwVFJY/mFfWz12KJU93xcAgF7u/Jqu2j00jKzXFcnTjxg107tzZ2mEQQggh5CFz+/btJmMNdFGNHUeqx5jdunULMpnMytGwp5p/7/bt2zbdN5AvceriY9x8jBngX9x8i1eFr3ED/Iudb/Gq8DFuPsYMNNbUlZeXo23btibLUmLHkWr+PJlMxquDQsXd3Z0XcfMlTl18jJuPMQP8i5tv8arwNW6Af7HzLV4VPsbNx5jZVibR4AlCCCGEEDtBiR0hhBBCiJ2gxI4jqVSK5cuX8+4xY3yJmy9x6uJj3HyMGeBf3HyLV4WvcQP8i51v8arwMW4+xswVjYolhBBCCLETVGNHCCGEEGInKLEjhBBCCLETlNgRQgghhNgJSuwIIYQQQuyEXSR2sbGx6NevH9zc3ODr64sJEybg6tWrWmVqamoQExMDb29vuLq6YtKkSbh7965WmTfeeAPh4eGQSqXo1auX0XVmZWXBzc0NHh4erGL88ssv0bFjRzg6OmLAgAFISUnRit3R0RESiQRisRgCgQClpaU2E/u4ceMglUohEAggFosxdOhQ9f4tKSnB66+/jpCQEDg4OEAkEkEikWDcuHFW37+ax4WTkxNcXFwglUrRpk0bjB8/HleuXLH6vtWN+7XXXtN7LDMMg6effhoCgQC//vqrTcWsu68lEgkEAoHWzyuvvMKLfb1t2zY89dRTcHFxgbu7Ox5//HHMmTPHZmLW3Neurq5N9rPq5+eff7apfa17jPj4+CAwMBA+Pj5wcXFBnz59sHPnTps6RgIDAxEaGqp1fCQkJOCZZ55BmzZt4O7ujkmTJmHmzJlWiff48eMYO3Ys2rZtC4FAgD179gBoej3s0qUL2rRpAycnJ0RFReH69etW3c/64taNuX///hg0aBC8vb0hEAiQlpZmczFrqq+vx+LFi9G9e3e4uLigbdu2ePHFF5GXl2dy2S3BLhK7xMRExMTEIDk5GQkJCaivr8eIESNQWVmpLrNgwQLs378fO3bsQGJiIvLy8jBx4sQmy/rXv/6F559/3uj66uvrMWXKFAwePJhVfNu3b8fChQuxfPlypKamomfPnhg5ciQKCwvVsc+bNw+vvvqq+jm0thT7gQMHMG3aNOzduxfjxo3D6dOnERUVhcrKSuTl5SEvLw+PPPIIfH198dFHH8Hf3x+nTp2y+v5NSEhQHxf/93//hx49esDHxwd79uwBwzAYMWIE5s+fb1PHxcaNGzF9+vQmx/KHH34IgUAAANi0aZNNxay7r3v06IHAwEC0a9cO2dnZyM/Px0cffWT149jUvi4sLMTUqVPxxBNPICUlBX/++SecnZ0RFxdnMzFr7uvTp0/j0KFDGDZsmHpfr1y5Eq6urjh69KhN7WvdYyQkJATl5eUQi8U4c+YMJk6ciOeeew7Tp0+3mbgBIDc3F7///jsSEhJQU1OD0aNHQ6FQ4MiRIzh16hRSU1OxdetWbN++vdXjraysRM+ePfHll19qva55PZwyZQpu3LgBgUCAo0ePwsXFBSNHjsQbb7xhtf2sL27da3hdXR0uXbqEVatWqctY8/xhaF+rVFVVITU1Fe+88w5SU1Oxa9cuXL16FePGjWO1fItj7FBhYSEDgElMTGQYhmFKS0sZsVjM7NixQ13m8uXLDAAmKSmpyfuXL1/O9OzZ0+Dy33rrLWbatGnM5s2bGZlMZjKe/v37MzExMerfFQoF07ZtWyY2NrZJ2d27dzMAmAMHDths7H5+fkb376+//sqIxWKb27+ax8WFCxcYADa3b3XjVsXs4+PD5OfnMwAYBwcHm4556NChTHR0NO++g3369OFdzAyjfVz36tWLmTZtms3H7eLiwnzxxRda+9vDw4MRiUQ2G/f27dsZAExcXBzDMA+ODwBMQkJCq8erCQCze/durdeUSiXj7+/PLF++XL2fS0tLGYlEYtX9bCpuhnlwTG/bto0BwBw/ftyqxzSbmHWlpKQwAJibN29yWr4l2EWNna6ysjIAgJeXFwDg3LlzqK+vR1RUlLpM165dERQUhKSkJE7LPnLkCHbs2GEwc9dVV1eHc+fOaa1bKBQiKipK77pVNXWenp42G3tkZCQAw/u3rKwMMpnM5vav6rhwdHTE5s2b4e/vb3P7VjfugoICAMA777wDf39/AEBDQ4NNxwwAv/32G4DGu+elS5fi1KlTNr2vCwsL1bU0b7zxBvz8/DB48GCbjllFdVzn5+cjLS0N/fv3t/m4Bw4cqD5GPDw8sG3bNlRXV0OhUNhs3Pfv3wcA+Pn5AXhw3hMKhTh58mSrxstGTk4OCgoK0KdPHwCN52uZTIYuXbpYbT+zpTqmVc2kly9fttoxba6ysjIIBALW3QAsye4SO6VSifnz52PQoEEICwsD0HhxlEgkTXawn5+f+sLJRnFxMV566SV8//33rB8eXFRUBIVCoT4ZGFu3UqnEF198AQAIDQ21ydiVSiXS09Ph6uqqd/8WFRVh9erVmD17tk3tX6VSiWeeeQZCoRADBgzAH3/8gbfeesum9q3uupVKJf7xj3/Az88Pb7zxhrqMg4ODzcYMAC+88AJCQkLQu3dvrFy5Ej/++COWL19u0/s6KysLQOO+nTdvHuLj49GuXTsAwL1792wyZkD7fJeYmIhu3brB29vbpvc1AGzbtg0ZGRkAgPDwcMyZMwfz58+32biVSiV27twJkUiEH374AVVVVbh58yaEQiGUSiXy8/NbNV42VDF8/vnnWtdDZ2dnCIVCq+xnNjSP6S5duqjXZa1jwxw1NTVYvHgxpkyZ0qLrMcTuEruYmBhkZGRg27ZtFl92dHQ0pk6diiFDhuj9+4kTJ+Dq6qr++fnnnzktPyYmBjk5OZYItQlLxR4TE4OioiI88sgjTf4ml8sxZswYhIaGYsWKFVaL0VDcZWVlOHbsGBITE/Hoo4/is88+4xxja8Y9ZswY5OXlITEx0SJxamrJfX3hwgXcvXsX+/btwz//+U/88MMPOHv2LBgLPOSmpeL++OOPAQBz5szBzJkz0bt3b0yfPh0CgQCbNm2yyZiBB+e7zZs3Y+vWrZg1a1azYtXUknEPHToUcrkcv/zyC86ePYuFCxfis88+s9ljJCYmBteuXcOWLVuwf/9+uLq6qvd1nz59IBSafylt6evKlStXeHU95PM1HGjsv/fcc8+BYRisX7++uSGbxcEqa20hc+fOxYEDB3D8+HG0b99e/bq/vz/q6upQWlqqlfHfvXtX3bzFxpEjR7Bv3z588sknAACGYaBUKuHg4IBvvvkGU6ZMQVpamrq8n58fpFIpRCJRk9E7uutWxb527VpMnTrVJmNXxThs2DAoFIomMUZFRcHd3R27d++GWCy2mf2reVwEBwcDACIiIuDu7m4z+1Z33XPnzsXJkyfR0NCAxx57TKtMQ0MDHn/8cXXzj63EDOj/Dg4YMABA4wnPVvf1mTNnADQeFyr+/v5gGEZdm2dLMQPa+/rkyZOoqqrCiy++iIsXL9rscQ0A06dPR0ZGBuLj4zFy5EgAQM+ePbF3716cP3/e5uIuLCzUOn/885//RFFREU6fPo3x48fj9u3beOGFF1o1XjY2bNig/lfzelhVVQWlUmmV/WyK7vkjNzcXAODt7W21Y5oLVVJ38+ZNHDlyxCq1dYCdJHYMw+D111/H7t27cezYMfXFWyU8PBxisRiHDx/GpEmTAABXr17FrVu31P3F2EhKStJKaPbu3YsPP/wQp0+fRrt27eDk5KS3Jis8PByHDx/GhAkTADRWNR8+fBhz585tEvudO3dsLvZDhw7h0KFD2L17N44cOYJhw4Zh7ty56jIhISEQCASorKzEsWPH4OjoaBP7NyYmBnPnztV7XDAMA4FAAJFIZFPHxaFDhxAUFISzZ8/if//7X5MTQ/fu3SESibSSf2vHbGpfq06UDg4ONruvjx07hieffFJrmqTw8HAIBAKt9Vk7ZkP7eubMmRg3bhzatGljE+cMY3EfPHgQABAUFKT1Pg8PDwiFQpuJW6FQqJtfz549q3VM+/j4YOjQoXBwcMC9e/fUox9bM15DVNeUw4cPw8fHB1euXMGYMWMANLaqXL161arnPWMxG7qGd+vWzarHNBuqpO769es4evQovL29zVqOJdhFYhcTE4OtW7di7969cHNzU7e5y2QyODk5QSaTYdasWVi4cCG8vLzg7u6O119/HZGRkVp36FlZWaioqEBBQQGqq6vVF6TQ0FBIJBJ069ZNa71nz56FUChU910wZOHChZgxYwb69u2L/v37Y926daisrMTMmTPVsW/atAk3b97EhQsX1Mv28fFBUFCQ1WOfOnUqpFIpNmzYgPfeew/l5eUYM2YMqqurUV9fj2effRaenp4oLS3FH3/8AVdXVyxbtgwRERFW3b9Xr17Fnj17sH79emzevBlDhw6Ft7c3ysvLsXbtWjg7O2Ps2LE2dVwUFRWhoqIC+/btQ6dOndRlVccyAERFReHDDz9Et27dbCJmzX391VdfYePGjRg2bBi8vLyQm5uLJUuWYMiQIQgNDbXZfe3u7o45c+bg448/RteuXTFgwABs2bIFQqEQZ86cwdGjR20iZs19rTrfJSUlITExUT23lq2e71Rx79y5Ey+//DJmzpyJ5cuXIygoCPHx8Th27Biefvppm4l78uTJqKysxK5du9TXlW3btqFXr14IDAxEUlISRCIRnJyckJeXh4qKilaNt6KiQqs2OScnB2lpaVizZg3279+PvXv3IiEhAatWrYKPjw9CQ0Px/vvvo127doiKirLaftYX9/PPP4///e9/2LdvH9zc3HD58mXcuXMH5eXlAIC8vDyMHz8e8+bNs5mY09LS4OXlhaCgINTX12Py5MlITU3FgQMHoFAo1HmIl5cXJBKJ0eVbXKuOwW0hAPT+bN68WV2murqaee211xhPT0/G2dmZeeaZZ5j8/Hyt5QwdOlTvcnJycvSul8tQ6c8//5wJCgpiJBIJ079/fyY5Odlo7JrbYO3YjcV29OhRg39PSUlptRj17V9DcXl6ejJTp05lrly5YvV9qxu3qWMZALN9+3abitnYvvb19WXefPNNpqysjDf7WhVfZGQkc+jQIZuK2di+/u6779Tvs7V9bSxuiUTC9OjRg/nhhx9sKm5D8bq7uzNisZgJCQlhPvjgA+bVV1+1SrzGzr36fhwcHJhhw4YxV69etep+5hq35k/fvn1tKuYZM2YwDMMwOTk5BmM+evSoyeVbmoBhLNBblRBCCCGEWJ3djYolhBBCCHlYUWJHCCGEEGInKLEjhBBCCLETlNgRQgghhNgJSuwIIYQQQuwEJXaEEEIIIXaCEjtCCCGEEDtBiR0hhBBCiJ2gxI4QQgghxE5QYkcIIYQQYicosSOEEEIIsROU2BFCCCGE2In/Bw4SjQv40klQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(seasonal.max())\n",
        "print(seasonal.min())\n",
        "print(seasonal.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPfYBrTMjx-e",
        "outputId": "698f9dca-ae88-417b-d639-a16892c88c38"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.28236607142857\n",
            "-8.78365956959707\n",
            "7.218984415827502e-17\n"
          ]
        }
      ]
    }
  ]
}