ipta_slope_analysis_method_new.ipynb

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        "<a href=\"https://colab.research.google.com/github/trewto/Enviroment_Study/blob/main/ipta_slope_analysis_method_new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "df = pd.read_excel('/content/drive/MyDrive/research/Rainfall_1981-2022.xls', header=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert day columns (1 to 31) to numeric, forcing errors to NaN\n",
        "df.loc[:, 1:31] = df.loc[:, 1:31].apply(pd.to_numeric, errors='coerce')\n",
        "#if needed remove this later// which will be proper way to handle the missing value\n",
        "#df.loc[:, 1:31] = df.loc[:, 1:31].fillna(df.loc[:, 1:31].mean(axis=1), axis=0)\n",
        "\n",
        "# Sum the columns from 1 to 31 row-wise, ignoring NaN values\n",
        "df['sum_1_to_31'] = df.loc[:, 1:31].sum(axis=1)"
      ],
      "metadata": {
        "id": "KpvULAvsOpsG"
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      "execution_count": 23,
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      "source": [
        "df.head()\n"
      ],
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              "          Stati  Year Month     1    2    3    4     5    6    7  ...   23  \\\n",
              "0  Dhaka         1981     1   0.0  0.0  0.0  0.0   0.0  0.0  0.0  ...  0.0   \n",
              "1  Dhaka         1981     2   9.0  2.0  0.0  0.0   0.0  0.0  0.0  ...  0.0   \n",
              "2  Dhaka         1981     3   0.0  0.0  0.0  0.0   0.0  0.0  0.0  ...  0.0   \n",
              "3  Dhaka         1981     4  41.0  2.0  0.0  0.0   4.0  0.0  0.0  ...  2.0   \n",
              "4  Dhaka         1981     5   0.0  0.0  0.0  0.0  20.0  8.0  0.0  ...  0.0   \n",
              "\n",
              "     24    25    26   27   28    29    30   31 sum_1_to_31  \n",
              "0   0.0   0.0   0.0  0.0  0.0   0.0   0.0  6.0        10.0  \n",
              "1   0.0   0.0   0.0  0.0  0.0   NaN   NaN  NaN        42.0  \n",
              "2  10.0   0.0  13.0  0.0  8.0   0.0   2.0  0.0       109.0  \n",
              "3   0.0   0.0   0.0  0.0  0.0   0.0   3.0  NaN       274.0  \n",
              "4   0.0  13.0   1.0  1.0  1.0  43.0  45.0  0.0       272.0  \n",
              "\n",
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      "source": [
        "print(df['Stati'].unique())"
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      "execution_count": 25,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "['Dhaka       ' nan\n",
            " '          Daily  total  Rainfall  data  in  Millimeter  ' 'Stati'\n",
            " 'Tangail     ' 'Mymensingh  ' 'Faridpur    ' 'Madaripur   '\n",
            " 'Srimangal   ' 'Sylhet      ' 'Bogra       ' 'Dinajpur    '\n",
            " 'Ishurdi     ' 'Rajshahi    ' 'Rangpur     ' 'sydpur      '\n",
            " 'chuadanga   ' 'Jessore     ' 'Khulna      ' 'Mongla      '\n",
            " 'Satkhira    ' 'Barisal     ' 'Bhola       ' 'Khepupara   '\n",
            " 'Patuakhali  ' 'Chandpur    ' 'Teknaf      ' 'Chittagong  '\n",
            " 'Comilla     ' \"Cox'sbazar  \" \"Cox's Bazar \" 'Feni        '\n",
            " 'Hatiya      ' 'Kutubdia    ' 'M.court     ' 'Rangamati   '\n",
            " 'Sandwip     ' 'Ambagan(Ctg)' 'Sitakunda   ']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_city = df[df['Stati'] == 'Faridpur    ']\n",
        "df_city = df_city[['Year', 'Month', 'sum_1_to_31']]\n",
        "df_city.head(20)"
      ],
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        "id": "HrCVgD89O6Rs",
        "outputId": "5254ce89-2bd3-4bd5-fcfd-8d8933991b20"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year Month sum_1_to_31\n",
              "1518  1981     1        10.0\n",
              "1519  1981     2        58.0\n",
              "1520  1981     3       140.0\n",
              "1521  1981     4       357.0\n",
              "1522  1981     5       263.0\n",
              "1523  1981     6       283.0\n",
              "1524  1981     7       422.0\n",
              "1525  1981     8       740.0\n",
              "1526  1981     9       199.0\n",
              "1527  1981    10        35.0\n",
              "1528  1981    11         0.0\n",
              "1529  1981    12        92.0\n",
              "1530  1982     1         0.0\n",
              "1531  1982     2        24.0\n",
              "1532  1982     3        63.0\n",
              "1533  1982     4       126.0\n",
              "1534  1982     5       217.0\n",
              "1535  1982     6       211.0\n",
              "1536  1982     7       210.0\n",
              "1537  1982     8       271.0"
            ],
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              "      <th>1527</th>\n",
              "      <td>1981</td>\n",
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              "      <th>1528</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_city",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: get all row from df_city  Where Month =1 , and devide it two part the first half and last half\n",
        "\n",
        "dataset = []\n",
        "# Filter rows where Month is 1\n",
        "for i in range(1,13):\n",
        "  df_month = df_city[df_city['Month'] == i]\n",
        "\n",
        "  # Calculate the midpoint of the filtered DataFrame\n",
        "  midpoint = len(df_month) // 2\n",
        "\n",
        "  # Split the DataFrame into two parts\n",
        "  first_half = df_month.iloc[:midpoint]\n",
        "  last_half = df_month.iloc[midpoint:]\n",
        "\n",
        "  #print(\"First Half:\")\n",
        "  #print(first_half)\n",
        "  sum_first_half = first_half['sum_1_to_31'].sum()\n",
        "\n",
        "  sum_first_half = first_half['sum_1_to_31'].mean()\n",
        "\n",
        "  std_first_half = first_half['sum_1_to_31'].std()\n",
        "  #print(f\"The sum of 'sum_1_to_31' for the first half is: {sum_first_half}\")\n",
        "\n",
        "  #print(\"\\nLast Half:\")\n",
        "  #print(last_half)\n",
        "  sum_last_half = last_half['sum_1_to_31'].sum()\n",
        "  sum_last_half = last_half['sum_1_to_31'].mean()\n",
        "  std_last_half = last_half['sum_1_to_31'].std()\n",
        "  #print(f\"The sum of 'sum_1_to_31' for the last half is: {sum_last_half}\")\n",
        "  dataset.append([i, sum_first_half, sum_last_half,std_first_half,std_last_half])\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wPhXB_s_PSjE"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: what if a want to give a collam name to dataset\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming 'dataset' is already defined as in the preceding code\n",
        "\n",
        "df_dataset = pd.DataFrame(dataset, columns=['Month', 'Sum_First_Half', 'Sum_Last_Half','std_First_Half','std_Last_Half'])\n",
        "print(df_dataset)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZ2Eitm5QAFT",
        "outputId": "314ef355-090f-49b0-f8d1-ef8695c123ab"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Month  Sum_First_Half  Sum_Last_Half  std_First_Half  std_Last_Half\n",
            "0       1        7.238095       6.714286       11.797901      11.866520\n",
            "1       2       26.428571      20.523810       22.714690      32.194128\n",
            "2       3       55.714286      26.809524       56.793611      38.488465\n",
            "3       4      139.285714      80.952381       71.342934      59.568008\n",
            "4       5      264.095238     186.380952       98.381352      82.671928\n",
            "5       6      316.952381     293.952381      148.363229     105.066872\n",
            "6       7      308.238095     401.523810       90.973021     202.693024\n",
            "7       8      315.380952     297.904762      147.613169     170.042908\n",
            "8       9      265.047619     232.476190      166.206040     188.042979\n",
            "9      10      162.571429     136.761905      119.949811      92.736134\n",
            "10     11       38.333333      36.714286       51.740055      69.524918\n",
            "11     12       13.523810      15.809524       29.066508      39.421592\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Function to calculate slope between two points\n",
        "def calculate_slope(x1, y1, x2, y2):\n",
        "    if x2 - x1 != 0:\n",
        "        return (y2 - y1) / (x2 - x1)\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "def calculate_length(x1, y1, x2, y2):\n",
        "    return ((x2 - x1)**2 + (y2 - y1)**2)**0.5\n",
        "\n",
        "\n",
        "\n",
        "# Function to calculate slopes and plot IPTA\n",
        "def plot_ipta(Month,data_first, data_last):\n",
        "    #data_first_1 will be in first collam Name Sum_First_Half and data_last one will be in second collam and merged\n",
        "    df = pd.concat([Month,data_first, data_last], axis=1)\n",
        "    df.columns = ['Month','Sum_First_Half', 'Sum_Last_Half']\n",
        "\n",
        "    # Calculate slopes and lengths\n",
        "    slopes = []\n",
        "    lengths= []\n",
        "\n",
        "\n",
        "    for i in range(len(df)):\n",
        "\n",
        "\n",
        "\n",
        "        if i ==  len(df) -1 :\n",
        "          x1, y1 = df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i]\n",
        "          x2, y2 = df['Sum_First_Half'].iloc[0], df['Sum_Last_Half'].iloc[0]\n",
        "          slope = calculate_slope(x1, y1, x2, y2)\n",
        "          length  = calculate_length(x1, y1, x2, y2)\n",
        "\n",
        "          slopes.append(slope)\n",
        "          lengths.append(length)\n",
        "\n",
        "\n",
        "\n",
        "        else:\n",
        "          x1, y1 = df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i]\n",
        "          x2, y2 = df['Sum_First_Half'].iloc[i + 1], df['Sum_Last_Half'].iloc[i + 1]\n",
        "          slope = calculate_slope(x1, y1, x2, y2)\n",
        "          length  = calculate_length(x1, y1, x2, y2)\n",
        "          slopes.append(slope)\n",
        "          lengths.append(length)\n",
        "\n",
        "    # Add None for the first slope (no previous point)\n",
        "    #slopes = [None] + slopes\n",
        "    slopes = slopes\n",
        "\n",
        "    # Add slopes to the DataFrame\n",
        "    df['Slopes'] = slopes\n",
        "    df['Length'] = lengths\n",
        "\n",
        "    # Plot IPTA\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(df['Sum_First_Half'], df['Sum_Last_Half'], marker='o', linestyle='--', color='b', label='Rainfall Data')\n",
        "\n",
        "    # Plot month names beside each point\n",
        "    for i in range(len(df)):\n",
        "        plt.text(df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i],\n",
        "                 f\"{df['Month'].iloc[i]}\", fontsize=9, ha='right')\n",
        "\n",
        "    # Plot slopes\n",
        "    #for i in range(len(df) - 1):\n",
        "    #    plt.text((df['Sum_First_Half'].iloc[i] + df['Sum_First_Half'].iloc[i + 1]) / 2,\n",
        "    #              (df['Sum_Last_Half'].iloc[i] + df['Sum_Last_Half'].iloc[i + 1]) / 2,\n",
        "     #             f\"Slope: {round(slopes[i + 1], 2)}\", fontsize=9, color='green')\n",
        "\n",
        "    # Plot 45-degree reference line\n",
        "    plt.plot([df['Sum_First_Half'].min(), df['Sum_First_Half'].max()],\n",
        "             [df['Sum_First_Half'].min(), df['Sum_First_Half'].max()],\n",
        "             'r--', label='45-degree line')\n",
        "\n",
        "    plt.title('IPTA Polygon Plot')\n",
        "    plt.xlabel('First Half  ')\n",
        "    plt.ylabel('Last Half  ')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    print(df)\n",
        "\n",
        "# Example usage\n",
        "# Assuming df_dataset is your DataFrame\n",
        "print( \"Plot Rainfall Mean\")\n",
        "plot_ipta(df_dataset['Month'],df_dataset['Sum_First_Half'],df_dataset['Sum_Last_Half'])\n",
        "\n",
        "\n",
        "\n",
        "print( \"Plot Rainfall std\")\n",
        "plot_ipta(df_dataset['Month'],df_dataset['std_First_Half'],df_dataset['std_Last_Half'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WSY78RGfQwdB",
        "outputId": "bef4cf0d-1e32-440c-96e0-bfc62ae41706"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Plot Rainfall Mean\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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sWZKhQ4cSGxubqvvMnz+fMWPGpOo5L730Ejlz5mTPnj0sX748Vc9NqZUrV7ren5eXF7lz56ZatWoMHTqUEydOpPp+NpuNhQsXpn2gIiIiIh7i119/ZefOnXTr1s3qUDIFJVLpbP58aN8ejh1L3v7XX6Y9PZOpZs2aceLECQ4cOMCECRP48MMPeemll1J1j9DQUHLlypWq5+zfv59atWpRvHhx8ubNm6rnptaePXs4fvw4GzduZNiwYSxbtow777yT7du3p+vrioiIiGQ306dPp3nz5oSHh1sdSqagROoWXbp0/a+kQZ/ERHj2WbjWPr5Jbc8+m3ya3/XueSv8/f0JCwujaNGitGnThkaNGhEZGek6f/bsWR599FEKFy5MYGAglStX5rPPPkt2j/9O7StRogRjx47liSeeIFeuXBQrVoyPPvrIdd5ms7Fp0yZefvllbDYbo0aNAmDYsGGUK1eOwMBASpUqxYsvvojdbr+1N3aFAgUKEBYWRrly5ejYsSNr164lf/789O7d23XNxo0bady4Mfny5SN37tzUrVuXzZs3J3tPAA899BA2m41SpUoBJiFs3bo1BQsWJCgoiBo1arBs2bLbjllEREQkq7l06RKff/45Tz75ZJZdspLWlEjdoqCgq7+Cg70oUiSE9u1NqfQ1a64eibqS02nOr1njbitR4tr3vl1//PEH69atw8/Pz9UWGxtL9erV+f777/njjz/o1asXnTt35tdff73hvd566y3uuecefv/9d/r06UPv3r3Zs2cPACdOnOCOO+7gueee48SJEwwePBiAXLlyMXPmTHbu3MmkSZOYOnUqEyZMuP039h85cuTg6aefZu3atZw+fRqAixcv0rVrV37++Wc2bNhA2bJladGiBRcvXgRMogUwY8YMTpw4wS+//AKYOcEtWrRg+fLl/P777zRr1oxWrVpx5MiRNI9bREREJDObN28egYGBxMe3zPAlK5lVpkmkxo8fj81mSzb6ERsbS9++fcmbNy9BQUG0a9eOU6dOJXvekSNHaNmyJYGBgRQoUIAhQ4aQkJCQwdFfW0qX6tzCkp4UWbRoEUFBQQQEBFC5cmVOnz7NkCFDXOcLFy7M4MGDqVq1KqVKleKZZ56hWbNmfPnllze8b4sWLejTpw9lypRh2LBh5MuXj59++gmAsLAwfHx8CAoKIiwsjKB/s8ARI0Zw//33U6JECVq1asXgwYNv+jq3qkKFCgAcOnQIgAYNGvD4449ToUIFKlasyEcffURMTAyrVq0CIH/+/ACEhIQQFhbmOr7rrrt46qmnuPPOOylbtixjxoyhdOnSfPvtt+kSt4iIiEhmNW3aNGrW7EqHDj6WLFnJjHysDgDMiMCHH354VSnFgQMH8v333zNv3jxy585Nv379aNu2LWvXrgUgMTGRli1bEhYWxrp16zhx4gRdunTB19eXsWPHpmvM0dFXtzkcDqKiosiTJxiwUahQyu515XX/fvZPE/Xr12fKlClcunSJCRMm4OPjQ7t27VznExMTGTt2LF9++SV//fUX8fHxxMXFERgYeMP7XtlPNpuNsLAw1+jP9XzxxRe888477N+/n+joaBISEggODr69N3gdzn/nTSZtonzq1ClGjBjBypUrOX36NImJicTExNx0ZCk6OpqXX36Z77//nhMnTpCQkMDly5c1IiUiIiLZyp49e1i3bh0FCsy47pIVmw0GDIDWrcHbO8NDtITlI1LR0dF06tSJqVOnkidPHlf7hQsXmD59Om+//TYNGjSgevXqzJgxg3Xr1rFhwwYAli5dys6dO/n000+pWrUqzZs3Z8yYMUyePJn4+Ph0jTtnzut/BQSYa2rXhiJFzC/WtdhsULSoue5m9721GHNSpkwZ7rrrLj7++GN++eUXpk+f7jr/xhtvMGnSJIYNG8ZPP/3Eli1baNq06U1/dr6+vv95HzYcDsd1r1+/fj2dOnWiRYsWLFq0iN9//50XXngh3fpo165dgHvtU9euXdmyZQuTJk1i3bp1bNmyhbx589709YcMGcKCBQsYO3Ysa9asYcuWLVSuXDndf7dEREREMpPp06dTuXJtTp0qe91rnE44ejT5khVPZ/mIVN++fWnZsiWNGjXilVdecbVv2rQJu91Oo0aNXG0VKlSgWLFirF+/npo1a7J+/XoqV65MwYIFXdc0bdqU3r17s2PHDqpVq3bN14yLiyMuLs51HBUVBYDdbr+qAILdbsfpdOJwOG6YLIB7JCTpepsNJkyARx6xYbOB0+nOqGw2c+3bbzux2eAmt041p9PpiiPJ888/z+DBg+nYsSM5cuTg559/5sEHH+Sxxx4DzIjan3/+ScWKFZM977/3+e/xza5Zu3YtxYsXZ/jw4a7zSdPukq5J+tldeXyt10mS1P7ffrl8+TIfffQRderUIW/evDgcDtauXct7771Hs2bNADh69Ch///13svv7+vpit9txOByuWNatW0fXrl1p3bo1YJL+Q4cO3TCulEp6Hbvdjnd2+bNNBkv6t5wWRU3EOurHrE996BnUj57hVvvx1VdfpXJlG1263Pzao0cTsNuvMWyVhaT052NpIvX555+zefNm12L/K508eRI/Pz9CQkKStRcsWJCTJ0+6rrkyiUo6n3TuesaNG8fo0aOval+6dOlV09p8fHwICwsjOjo6xSMRSUUMABo1glmzfHn++RwcP+5OpMLDnYwbd5lGjez8m8elKbvdTkJCgitJBJNkDh06lLfffptnnnmG4sWL88033xAZGUlISAjvv/8+J0+epGzZsq7nJSQkEB8f7zp2OBzExsYmu29iYiJxcXGutv8eh4eHc+TIEWbMmMHdd9/N0qVLWbBgAU6n03VNXFwciYmJyZLa/8Z/pZiYGAAOHDhArly5iI6OZsuWLbzzzjucOXOGmTNnup5bqlQpZs2aRYUKFbh48SIjR44kR44cyd5HsWLF+PHHH6lSpQr+/v6EhIRQokQJvvrqK+rXrw/A2LFjcTgcyX4etyo+Pp7Lly+zevXqTLOmz1NdWalSsi71Y9anPvQM6kfPcCv9ePhwXqBWCq7bwOLFZ28hqswj6XPmzViWSB09epRnn32WyMhIApLmwmWQ4cOHM2jQINdxVFQURYsWpUmTJlet24mNjeXo0aOuog034nQ6uXjxIrly5XKtzwHo1Ak6doQ1axycOGHWRNWuDd7eOYAcafrekvj6+uLj43PV++nXrx8TJkxgwIABjB49mmPHjtG+fXsCAwPp2bMnbdq04cKFC67n+fj44Ofn5zr28vIiICAg2X29vb3x9/d3tf33uGPHjvz+++8MGzaMuLg4WrRowYsvvsjo0aNd1/j7++Pt7e06vl78SZIS3ho1amCz2QgKCqJUqVI0adKEgQMHEhYW5rr2448/5umnn6ZevXoULVqUV155haFDhyZ7H2+99RaDBw9m9uzZFC5cmC1btjBx4kR69uxJ06ZNyZcvH0OHDuXy5cvJfh63KjY2lhw5clCnTp0M//3PLux2O5GRkTRu3Piq6aiSdagfsz71oWdQP3qG2+nHpk3hgw+cHD+efJZVEpvNSeHCMHjwfVl+jVRK/2BuczqvtWQs/S1cuJCHHnoo2bSmxMREbDYbXl5eLFmyhEaNGvHPP/8kG5UqXrw4AwYMYODAgYwcOZJvv/2WLVu2uM4fPHiQUqVKsXnz5utO7fuvqKgocufOnSyBSBIbG8vBgwcpWbLkTT/wJhWbCA4OxsvL8uVncosyoh9T83slt8Zut7N48WJatGih/+lnYerHrE996BnUj57hdvtx/nxTnQ+S75OaNH7w1VfQtm0aBGqxG+UGV7Ls037Dhg3Zvn07W7ZscX3dc889dOrUyfXY19eX5cuXu56zZ88ejhw5QkREBAARERFs3749WcW4yMhIgoODqVSpUoa/JxERERERT9W2rUmWChdO3l6kiOckUalh2dS+XLlyceeddyZry5kzJ3nz5nW19+jRg0GDBhEaGkpwcDDPPPMMERER1KxZE4AmTZpQqVIlOnfuzOuvv87JkycZMWIEffv2xd/fP8Pfk4iIiIiIJ2vb1pQ4X7PG7B+VOzc0b559Sp5fKVPPP5swYQL/+9//aNeuHXXq1CEsLIz5V+z05e3tzaJFi/D29iYiIoLHH3+cLl268PLLL1sYtYiIiIiI5/L2hv37oXt3mDMneyZRkAnKn19p5cqVyY4DAgKYPHkykydPvu5zihcvzuLFi9M5MhERERERSRIaCnY7fP45zJwJ2XEyWKYekRIRERERkcynQgX34z17rIvDSkqkREREREQkVUqVcj9etsy6OKykREpERERERFLlyql8V5QwyFaUSImIiIiIyC1bu9bqCKyhREpERERERFLtrrusjsBaSqTkts2cOZOQkBCrw7hlK1euxGazcf78ecC8n+LFi1sblIiIiEgm9/TT7scXL1oXh1WUSGUD48ePx2azMWDAgGTt9erVw2azJft6+sp/EdlUhw4d+O2336wOQ0RERCRTe/ppCA83j3fssDYWK2SqfaQk7W3cuJEPP/yQKlWqXPN8z549k21gHBgYmFGhpZrdbsfX1zfdXydHjhzkz58/3V9HREREJKv75BPIly95OfTsQiNSt+rSpet/xcam/NrLl1N27S2Ijo6mU6dOTJ06lTx58lzzmsDAQMLCwlxfwcHBN73vzJkzKVasGIGBgTz00EOcPXv2qmu++eYb7r77bgICAihVqhSjR48mISHBdX737t3UqlWLgIAAKlWqxLJly7DZbCxcuBCAQ4cOYbPZ+OKLL6hbty4BAQHMmTMHgGnTplGxYkUCAgKoUKEC77//frLXPnr0KI888gghISGEhobSunVrDh06lMKf2tVT+0aNGkXVqlX55JNPKFGiBLlz56Zjx45cvGIM2+FwMG7cOEqWLEmOHDm46667+Oqrr1L8miIiIiJZUb16kDs3XPExL9tQInWrgoKu+vIKDiakSBFs7dsnv7ZAgWteT1AQNG+e/NoSJa593S3o27cvLVu2pFGjRte9Zs6cOeTLl48777yT4cOHExMTc8N7/vLLL/To0YN+/fqxZcsW6tevzyuvvJLsmjVr1tClSxeeffZZdu7cyYcffsjMmTN59dVXAUhMTKRNmzYEBgbyyy+/8NFHH/HCCy9c8/Wef/55nn32WXbt2kXTpk2ZM2cOI0eO5NVXX2XXrl2MHTuWF198kVmzZgFm1Kpp06bkypWLNWvWsHbtWoKCgmjWrBnx8fGp+fEls3//fhYuXMiiRYtYtGgRq1atYvz48a7z48aNY/bs2XzwwQfs2LGDgQMH8vjjj7Nq1apbfk0RERGRzK56dfPxdc0aqyPJeJra56E+//xzNm/ezMaNG697zWOPPUbx4sUJDw9n27ZtDBs2jD179jD/BpsBTJo0iWbNmjF06FAAypUrx7p16/jxxx9d14wePZrnn3+erl27AlCqVCnGjBnD0KFDeemll4iMjGT//v2sXLmSsLAwAF599VUaN2581esNGDCAtm3buo5feukl3nrrLVdbyZIlXcla165d+eKLL3A4HEybNg2bzQbAjBkzCAkJYeXKlTRp0iSlP8JkHA4HM2fOJFeuXAB07tyZ5cuX8+qrrxIXF8fYsWNZtmwZERERrvf8888/8+GHH1K3bt1bek0RERGRzC5vXvO9eXNwOKyNJaMpkbpV0dFXNTkcDqKiogjOkwfblSdOn77+fbz+MyiYiilo13P06FGeffZZIiMjCQgIuO51vXr1cj2uXLkyhQoVomHDhuzfv5/SpUtzxx13cPjwYQBq167NDz/8wK5du3jooYeS3SciIiJZIrV161bWrl3rGoECMwoVGxtLTEwMe/bsoWjRoq4kCuDee++9Zoz33HOP6/GlS5fYv38/PXr0oGfPnq72hIQEcufO7Xrtffv2uRKeJLGxsezfv/+6P4ubKVGiRLJ7FipUiNP/9uu+ffuIiYm5KhGMj4+nWrVqt/yaIiIiIpldhQqwfDk4neYjb4ECVkeUcZRI3aqcOa9uczggMRH+m7xc69rU3DeVNm3axOnTp7n77rtdbYmJiaxevZr33nuPuLg4vL29r3refffdB5jEoHTp0ixevBi73Q6YAgwpFR0dzejRo5ONJCW5UWJ3LTmv+HlE/5u8Tp061RVrkqT3Ex0dTfXq1V3rqa50OwUk/lvkwmaz4fj3zy5JcX3//fcULlw42XX+V277LSIiIuJhqlZ1P/7jD2jQwLJQMpwSKQ/UsGFDtm/fnqyte/fuVKhQgWHDhl0ziQLYsmULYEZbgGvupVSxYkV++eWXZG0bNmxIdnz33XezZ88eypQpc83XKV++PEePHuXUqVMULFgQ4IZTEJMULFiQ8PBwDhw4QKdOna55zd13380XX3xBgQIFUlQ4Iy1UqlQJf39/jhw5oml8IiIikq2UK+d+rERKsrxcuXJx5513JmvLmTMnefPmdbXv37+fuXPn0qJFC/Lmzcu2bdsYOHAgderUuW6pdID+/fvzwAMP8Oabb9K6dWuWLFmSbFofwMiRI/nf//5HsWLFaN++PV5eXmzdupU//viDV155hcaNG1O6dGm6du3K66+/zsWLFxkxYgSAa13T9YwePZr+/fuTO3dumjVrRlxcHL/99hv//PMPgwYNolOnTrzxxhu0bt2al19+mSJFinD48GHmz5/P0KFDKVKkyK38SG8oV65cDB48mIEDB+JwOKhVqxYXLlxg7dq1BAcHu9aKiYiIiHiaKxOpzZuti8MKqtqXTfn5+bFs2TKaNGlChQoVeO6552jXrh3ffffdDZ9Xs2ZNpk6dyqRJk7jrrrtYunSpKwlK0rRpUxYtWsTSpUupUaMGNWvWZMKECa4RLm9vbxYuXEh0dDQ1atTgySefdFXtu9nUvyeffJJp06YxY8YMKleuTN26dZk5cyYlS5YETDn31atXU6xYMdq2bUvFihXp0aMHsbGx6TpCNWbMGF588UXGjRtHxYoVadasGd9//70rLhERERFP9O/kIgC++ca6OKxgczqdTquDsFpUVBS5c+fmwoULV33Yjo2N5eDBg5QsWfKmH/JdxSaCg/H6bxEJuaG1a9dSq1Yt1/osK2VEP6bm90pujd1uZ/HixbRo0SJDNnKW9KF+zPrUh55B/egZ0qsfW7aExYvNY4cDbjLBKNO7UW5wJU3tE0ssWLCAoKAgypYty759+3j22Wd54IEHLE+iRERERCR1Fi409dLsdvjrL0iHlRSZkhIpscTFixcZNmwYR44cIV++fDRq1Ii33nrL6rBEREREJJV8fWHTJihZEoKCrI4m4yiREkt06dKFLl26WB2GiIiIiNwmpxNCQ2HvXshOW2hqIY+IiIiIiNyyjRvNdL6WLa2OJGMpkUoh1eSQtKTfJxEREfEUZcua7ydOwCOPWBtLRlIidRNJm9fGx8dbHIl4kpiYGABVPhIREZEsL08e9+N580zRiexAa6RuwsfHh8DAQM6cOYOvr+8Ny2E7HA7i4+OJjY1V+fMsLD370el0EhMTw+nTpwkJCXEl6iIiIiJZWc2asGGDebx3L1SqZG08GUGJ1E3YbDYKFSrEwYMHOXz48A2vdTqdXL58mRw5cmDL6gX0s7GM6MeQkBDCwsLS5d4iIiIiGa1CBXci9ccfSqTkX35+fpQtW/am0/vsdjurV6+mTp06mrKVhaV3P/r6+mokSkRERDxKuXLux3/8kT3WSimRSiEvLy8CAgJueI23tzcJCQkEBAQokcrC1I8iIiIiqZNUcAJg+3br4shIWsgjIiIiIiK3pXp1uPtu8/iPP6yNJaMokRIRERERkdtSsiT88IN5HBsL2aHgtab2iYiIiIjIbStQAM6ehdBQqyPJGBqREhERERGR23bmjJnWt3ev1ZFkDCVSIiIiIiJy20aOhLp1YdYsqyPJGEqkRERERETktiWVQH/1Vejc2dpYMoISKRERERERuW1XlkBfscK6ODKKEikREREREbltV27Ke/w4nDtnXSwZQYmUiIiIiIjctpIlwdvbfezp+0kpkRIRERERkdvm6wulSrmPlUiJiIiIiIikwJXrpJRIiYiIiIiIpEDPnlCrlnns6YmUj9UBiIiIiIiIZ2jTxkzvq1sXQkKsjiZ9KZESEREREZE0U7myqdhns1kdSfrS1D4REREREUkTDgesXw+zZkFCgtXRpC+NSImIiIiISJpp2BBiY6F2bVMS3ctDh24sfVtTpkyhSpUqBAcHExwcTEREBD/88IPrfL169bDZbMm+nn766WT3OHLkCC1btiQwMJACBQowZMgQEjw9/RURERERyYS8vNyV+8qUgR49rI0nPVk6IlWkSBHGjx9P2bJlcTqdzJo1i9atW/P7779zxx13ANCzZ09efvll13MCAwNdjxMTE2nZsiVhYWGsW7eOEydO0KVLF3x9fRk7dmyGvx8RERERkeyuXDnYvt083rbN2ljSk6WJVKtWrZIdv/rqq0yZMoUNGza4EqnAwEDCwsKu+fylS5eyc+dOli1bRsGCBalatSpjxoxh2LBhjBo1Cj8/v3R/DyIiIiIi4nblXlI7d0JiInh7WxdPesk0a6QSExOZN28ely5dIiIiwtU+Z84cPv30U8LCwmjVqhUvvviia1Rq/fr1VK5cmYIFC7qub9q0Kb1792bHjh1Uq1btmq8VFxdHXFyc6zgqKgoAu92O3W6/5feQ9NzbuYdYT/3oGdSPnkH9mPWpDz2D+tEzZFQ/li5tIynNiI2F3bvtlCuXri+ZplL687E8kdq+fTsRERHExsYSFBTEggULqFSpEgCPPfYYxYsXJzw8nG3btjFs2DD27NnD/PnzATh58mSyJApwHZ88efK6rzlu3DhGjx59VfvSpUuTTR28VZGRkbd9D7Ge+tEzqB89g/ox61Mfegb1o2dI7378++9QoLbr+JNPfici4kS6vmZaiomJSdF1lidS5cuXZ8uWLVy4cIGvvvqKrl27smrVKipVqkSvXr1c11WuXJlChQrRsGFD9u/fT+nSpW/5NYcPH86gQYNcx1FRURQtWpQmTZoQHBx8y/e12+1ERkbSuHFjfH19b/k+Yi31o2dQP3oG9WPWpz70DOpHz5BR/XjPPTB8uPvY3786LVo40u310lrSbLWbsTyR8vPzo0yZMgBUr16djRs3MmnSJD788MOrrr3vvvsA2LdvH6VLlyYsLIxff/012TWnTp0CuO66KgB/f3/8/f2vavf19U2TX6q0uo9YS/3oGdSPnkH9mPWpDz2D+tEzpHc/hofDlCmwcCEsWQI7d3rj65t1Fkml9GeT6aq6OxyOZOuXrrRlyxYAChUqBEBERATbt2/n9OnTrmsiIyMJDg52TQ8UEREREZGMY7PB00/D0KFmdKpCBasjSh+WjkgNHz6c5s2bU6xYMS5evMjcuXNZuXIlS5YsYf/+/cydO5cWLVqQN29etm3bxsCBA6lTpw5VqlQBoEmTJlSqVInOnTvz+uuvc/LkSUaMGEHfvn2vOeIkIiIiIiIZo0ED2LjR6ijSj6WJ1OnTp+nSpQsnTpwgd+7cVKlShSVLltC4cWOOHj3KsmXLmDhxIpcuXaJo0aK0a9eOESNGuJ7v7e3NokWL6N27NxEREeTMmZOuXbsm23dKREREREQy1tGjsHIl5MoFbdpYHU36sDSRmj59+nXPFS1alFWrVt30HsWLF2fx4sVpGZaIiIiIiNyGtWuhSxeoXRtatDBl0G+jplumlOnWSImIiIiISNaWtCnvmjWQMye8+qq18aQHJVIiIiIiIpKmkhIpgIQE+OMP62JJL0qkREREREQkTQUHw5W7ESmREhERERERSYFy5dyPjxyBFO5zm2UokRIRERERkTR35fQ+8LxRKSVSIiIiIiKS5q4ckQIlUiIiIiIiIjfVrh0sXgyPPGKOPS2RsnQfKRERERER8UylS5uvhATw9oaaNa2OKG0pkRIRERERkXTTqpX58jSa2iciIiIiIuni++/h5Zdh926rI0l7GpESEREREZF0MWkSREZCoULg42P2lypQwOqo0oZGpEREREREJF0kVe7r1cuUQ58719p40pISKRERERERSRf/LYG+fbs1caQHJVIiIiIiIpIuPHkvKSVSIiIiIiKSLsqWTX68Ywc4HNbEktaUSImIiIiISLooXhx8fd3Hly7BoUOWhZOmlEiJiIiIiEi68PExm/JeyVOm9ymREhERERGRdDN7ttlHqkMHc+wpiZT2kRIRERERkXRTo4b53qGDKT5Rv7618aQVJVIiIiIiIpLuHnrIfHkKJVIiIiIiIpJuzpyBjz6Cf/6BN9+0Opq0o0RKRERERETSTXw8jBgB3t7Qr59ZL1WtGhQsaHVkt0fFJkREREREJN2Eh0NgICQmQvXq0Lw5/PST1VHdPiVSIiIiIiKSbmw2U2QCwN/ffN++3bp40ooSKRERERERSVf/TaQ8oQS6EikREREREUlXZcua70qkREREREREUihpRMrX13w/cAAuXfr35NmzZgFVFqNESkRERERE0lVSInXhgrta397lR2DAAChWDL7+2rLYbpUSKRERERERSVdVq8KOHfDnn9Cg7BFm0pUqbUvDpEkQEwPff291iKmmREpERERERNJVQABUqmS+d+8Gnb3n4pWYAA0awNKlMHOm1SGmmjbkFRERERGR9OF0wg8/wIYN8PLLADTuUQxiJ8K990KNGtbGdxuUSImIiIiISNpKSIAvv4TXXoNt2wBYFdaB2ZvuoH59eLxvX4sDvH1KpEREREREJG3ExMCMGfDmm3DokGkLCoKnnmLz4bx8/LEp0Fe0qNmU9/HHISTEyoBvnRIpERERERG5fZs2QfPmcOaMOc6fH559Fvr0gTx5KPylaf7zT+jeHQ4ehDvvhHr1LIv4tqjYhIiIiIiI3JqEBPfjihXN9xIlYPJkOHwYXngB8uQB3CXQ9+41CRRk7Y15NSIlIiIiIiKps3s3vPGGGYXavBm8vCAwEH76CcqXB5+r04wyZcz3v/+GwoXN4+3bMzDmNKYRKRERERERSZlff4W2bU0t848/hq1bYfVq9/k77rhmEgVmqVR4uHns72++Z+URKSVSIiIiIiJyfU6n2eupQQO47z5YsMC0tW4N69alapFT0vS+KxMppzPtQ84ISqREREREROT61q6Fpk3NtD0fH+jWDXbsgIULISIiVbdKSqRy5TK3ioqCo0fTPOIMoURKRERERETcYmPNFL4kDzwAderAgAFw4IApb16p0i3deuxYuHQJRowwS6kg607vU7EJERERERGBCxdgyhSYONEkU0eOQHAw2GywcqX5fgv++usv+vbty5o1a7DZbDRo0IDJkyczYUJ+AgOhatW0fBMZRyNSIiIiIiLZ2YkTMGyY2SV3+HA4dcokUHv3uq+5xSQKoG/fvgAcPnyYgwcPEhsbS//+/Wnc2Ax25cx5u2/AGpYmUlOmTKFKlSoEBwcTHBxMREQEP/zwg+t8bGwsffv2JW/evAQFBdGuXTtOnTqV7B5HjhyhZcuWBAYGUqBAAYYMGULClfXsRURERETkaseOQa9eZt+n11+HixdN1b1Zs2D/fqhePU1e5sCBAzzyyCMEBQURFJSLS5c68O232zl5Mk1ubxlLE6kiRYowfvx4Nm3axG+//UaDBg1o3bo1O3bsAGDgwIF89913zJs3j1WrVnH8+HHatm3ren5iYiItW7YkPj6edevWMWvWLGbOnMnIkSOteksiIiIiIllDfDxMn26+338/fPstbNsGXbqAr2+avcygQYOYN28eFy5c4MKF82zY8BkxMa145x0YMgSefTb5vr5ZhaVrpFq1apXs+NVXX2XKlCls2LCBIkWKMH36dObOnUuDBg0AmDFjBhUrVmTDhg3UrFmTpUuXsnPnTpYtW0bBggWpWrUqY8aMYdiwYYwaNQo/Pz8r3paIiIiISObidGJbsYKy8+ZBixamrVQpMxJ1331Qq1a6vfQDDzzA1KlTyZMnz7+hlAcOM27cO0BuYCRlyjzNM8+kWwjpItMUm0hMTGTevHlcunSJiIgINm3ahN1up1GjRq5rKlSoQLFixVi/fj01a9Zk/fr1VK5cmYIFC7quadq0Kb1792bHjh1Uq1btmq8VFxdHXFyc6zgqKgoAu92O3W6/5feQ9NzbuYdYT/3oGdSPnkH9mPWpDz2D+jELS0zE9s03eL3xBj6bNlHRZiN2yBB31b3+/c33dOpbh8NB48aNadeuHU8//QNduiwHugBFgah/v07Rv7+TggUTeegh6zeVSunvueWJ1Pbt24mIiCA2NpagoCAWLFhApUqV2LJlC35+foSEhCS7vmDBgpz8d0LlyZMnkyVRSeeTzl3PuHHjGD169FXtS5cuJTAw8DbfEURGRt72PcR66kfPoH70DOrHrE996BnUj1mHl91OkZUrKbtgAUHHjwOQ4OfHkUaN2Lt5M7GHDmVIHFFRURw+fJgKFSoxcKAvMBZ4ARgO/APkA/IATvr2jcfHJxJv7wwJ7bpiYmJSdJ3liVT58uXZsmULFy5c4KuvvqJr166sWrUqXV9z+PDhDBo0yHUcFRVF0aJFadKkCcHBwbd8X7vdTmRkJI0bN8Y3DeeVSsZSP3oG9aNnUD9mfepDz6B+zGJ+/x2fhx7C9m8C5cyTB0fv3sT36sX2LVsyvB9ffvllVqz4k7NnY4DfAD9MGnInUBt4ByjE338HEhTUkvr1rR2VSpqtdjOWJ1J+fn6UKVMGgOrVq7Nx40YmTZpEhw4diI+P5/z588lGpU6dOkVYWBgAYWFh/HrlZmH/nk86dz3+/v74+/tf1e7r65smv1RpdR+xlvrRM6gfPYP6MetTH3oG9WMm5nCA17915O64w+wDVbgwDBqErWdPvHPlwtduhy1bMrwfv/nmGzp2HAiU+bdlK7AQqAs8DTwOLAfg0Ud9mDoVrqgvl+FS+rPJdPtIORwO4uLiqF69Or6+vixfvtx1bs+ePRw5coSIiAgAIiIi2L59O6dPn3ZdExkZSXBwMJVucbdlEREREZEs48AB6NPHVN1z/juSkzMnLF9uzg0aBLlyWRpiqVKVeOedJcDBf1smAS2BIGA08BNwCYBz56B9e5g/34pIU8fSRGr48OGsXr2aQ4cOsX37doYPH87KlSvp1KkTuXPnpkePHgwaNIiffvqJTZs20b17dyIiIqhZsyYATZo0oVKlSnTu3JmtW7eyZMkSRowYQd++fa854iQiIiIi4hG2boXHHoOyZWHKFPjlF1i92n2+alWwuIJ1YqIJrXhxyJ8fihQJAYpd5+rk0/kGDDDPz8wsTaROnz5Nly5dKF++PA0bNmTjxo0sWbKExo0bAzBhwgT+97//0a5dO+rUqUNYWBjzr0hPvb29WbRoEd7e3kRERPD444/TpUsXXn75ZavekoiIiIhI+nA6YdUqaN7cJEqffWam9DVrBj/9BHXqWB2hy2+/Qc2aZrDs9Gl4/32YNAmgF/Au8BdwGXgZaIgZnTKcTjh6FNassSDwVLB0jdT06dNveD4gIIDJkyczefLk615TvHhxFi9enNahiYiIiIhkLmvWQL165rGXFzzyCAwbZpKqTOKff+CFF+CDD0xCFBwMr7wCvXuDjw98+eXzdOlyjtjYu/59Rn3gk2ve68SJDAv7llhebEJERERERK4hPh527YK7/k06ateGe++F6tXhueegdGlr4/uPzz6DZ5+FM2fMcadO8OabcGUNuIcf9iY09C0aNXrrpvcrVCidAk0jSqRERERERDKT6GiYNg3eegtiYuDwYQgKApsN1q3D8o2WruPYMZNEVaxopvIlDZ79V9265u1ER1/7vM0GRYqYvDEzUyIlIiIiIpIZ/P03vPuu+frnH9MWFga7d8M995jjTJREXbwIf/0FFSqY4wEDTIHAJ564fp0LhwP69btxEgUwcWKmeqvXlOnKn4uIiIiIZCsnTpg5ccWKwcsvmySqbFn46CM4eNCdRGUSTifMm2dGnh56yMxABPD1haefvn4SlZAAXbvChx+aJV59+piRpysVKQJffWXtPlIppREpERERERErXbxoRqGcTrP+6fnnTYaSCYdk9u41I0pLl5rjUqXMzMOyZW/8vLg4ePRRWLDAFJ349FPo0AHeecfU0DhxwqyJql07U77ta1IiJSIiIiKSkdauhQ0bTMEIgHLlYMwYUy+8QQP3/LZM5PJlGDcOXnvNjED5+Zl87/nnIUeOmz9/9WpYuNA8b948ePBB0+7tff21VJmdEikRERERkfTmcMDixTB+vEmkvLygTRt35b0XXrA0vBs5eRLuv9/MMgRo2tQMoN1sFOpKjRubmYolSkCjRukSZoZTIiUiIiIikl7sdvjiCzOU88cfps3PzywW8ve3NrYUKljQ5Hvx8aYIRLt2KRs0O3fOTOlLKmP+5JPpGmaGUyIlIiIiIpIeNm82VRMOHzbHuXKZagwDBkB4uKWh3Uh8vBlxeuIJyJPHJE2zZpnNdYOCUnaPU6fMKFRiIqxaBfnypW/MVlAiJSIiIiKSVpxO93BNmTJw/jwUKGCSp969ISTEwuBubsUK6NvXVFzfv9/sBwWpy/uOHjXT9/7801RvP3NGiZSIiIiIiFzL0aMwYQJs2gQrV5pkKjjYlLerXDllFRksdOIEDB4Mc+ea4wIFzLqo1Nq/Hxo2NINwxYrB8uUmn/RESqRERERERG7Vrl3w+uumnndCgmn7+WdTxxvg3nutiy0FEhLMqNOLL0JUlMn/+vSBV15J/eDZzp1mJOrECVOIYtkyk0x5Km3IKyIiIiKSWhs2mL2eKlWCmTNNRlKvHvzwA9SqZXV0KfbKK2Yv4KgoqFEDNm6E995LfRK1dSvUrWuSqDvvNOXOPTmJAo1IiYiIiIikzqpVyTc/atMGhg0z+0BlMX37msG0IUNMVb1b3Qw3b15TiKJECfjxR3Ps6ZRIiYiIiIjcSEIC7N0LFSua49q14a674O67TQaS1J7JORwQGVmMr77yZvZsM40vf37Ys+fWE6gkRYqYpWF58pilYdmBEikRERERkWu5fNlM23vzTbh40VRQyJHDbKa7cSP4+lodYYpt2QK9e3uzYUM1AB5/3GysC7eeRC1aBDEx8Mgj5rh48duPMytRIiUiIiIicqXz52HKFLP77OnTpi1fPtixA+65xxxnkSQqKgpGjjT7QjkcXgQEJDBmjI2GDW9vCOrLL6FTJ/O4WLEsOavxtimREhEREREBkzS9+SZ88IEZgQIzzDJ4sNmdNjDQ2vhSwemEzz+HQYPg5EnT1r69g+bNl9O5cwN8fG49kZoxw6yncjjgscegevU0CjqLUdU+EREREREwO8e+8YZJou68Ez75xKyN6tcvSyVRAPHxpqT5yZOmFPnSpTB3biJ588be1n3fe8/klA6HSaZmz84yg3NpTiNSIiIiIpI9bdwIv/5qStcB3HEHjBhh5qm1aGGqMWQhMTHg5wc+PuDvb/aH+vVXUw/D3x/s9tu7//jxMHy4eTxgALz9dpb7EaUpjUiJiIiISPbhdJqdYhs1MpvlDhhgikgkGTMGWrbMUhmC0wnffGOKB77/vru9SROTF/r73/5r/PCDO4l68UUlUaARKRERERHJDhITYf58M6yyebNp8/Exi3yycEZw4AD07w/ff2+Op00zMxG90ni4pFkz6NkTypSBoUPT9t5ZlRIpEREREfFsv/9uanTv22eOAwPNAp9Bg7Jsze64OHj9dRg7FmJjzTqlwYPhhRfSLolKTDRffn4m1/zwwyydc6Y5JVIiIiIi4nmcTven/hIlTNWF0FB45hkzZJMvn6Xh3Y7166FrV1MHA6BBA5g8GSpUSLvXsNuhSxeTpH35pUnUlEQlp0RKRERERDzHqVMwaZIZhVq82Hz6z5PHPK5WDYKCrI7wtuXMaab0hYWZtUodO6ZtkhMbCx06wLffmtmPv/0GERFpd39PoURKRERERLK+/fvNHlAzZph5b2CGbu6/3zyuXdu62G6T3Q7r1kHduua4ShX46iuoXx9y507b17p0Cdq0MfU4/P3h66+VRF2PqvaJiIiISNa1ZQs8+iiUK2c20o2LM+XLFy4037O4NWvg7ruhYUP44w93e5s2aZ9EXbgATZuaJCpnTlOpr2XLtH0NT6IRKRERERHJmlatgnr13MfNm8Pzz5vRpyy+oOf0aVMdb9Ysc5w3Lxw5YvYJTg9//20q823aZBK0H37QSNTNaERKRERERLIGh8NdeQ+gVi1TYeHRR83I1OLFUKdOlk6iEhNhyhQoX96dRPXsCXv2mD2C08v+/bBzp6nB8dNPSqJSQiNSIiIiIpK5xcfDp5+aet8XLsDBgxAQAN7epqhEQIDVEaYJp9NMrVu+3BxXq2Y22M2IGYr33QeLFkGhQmZjX7k5jUiJiIiISOZ08aIpS1eqFPToYYZlYmOTLxbykCQKzEDa//4HwcHwzjvw66/pm0Tt3Qtbt7qPGzRQEpUaGpESERERkczl779NCfP33oPz501beDgMHAi9eplMwwM4nfDJJ1CkiEliwGxx1bGjKW2env74Axo1MrMl16wxUwkldZRIiYiIiEjmcuwYvPKKeVyunKm68Pjjph63h/jjD+jbF1avhjJlYPt2M7jm45P+SdSmTTZatoRz50wp9Tx50vf1PJUSKRERERGx1vbtsHEjPPGEOa5aFZ57zuwB1bq1WQvlIaKjYfRomDgREhIgMBCefBK8MmjBzc6doXTu7M3Fi3DvvaY6X2hoxry2p1EiJSIiIiIZz+mEn3+G8eNNtT0/P1N/OzzcnH/zTWvjS2NOJ8yfDwMGmAE3gIceMglVsWIZE8OyZTZGjYogPt5G3brw3XeQK1fGvLYnUiIlIiIiIhnH4TDl4V57DdatM202mxl5iouzNrZ0tGYNtG9vHpcqBe++m77lzP9r9Wpo08ab+HgbTZs6mD/fi8DAjHt9T6RESkREREQyxpYt0KmT2bAIzChUt24weDCULWtlZOnC6XRvaVW7thmBqlzZ7BmcI0fGxnL33XD33U7gBF99lZ/AQBXvvl1KpEREREQkYxQpAocOmflkffrAs8+ajYs80OLFMGqU+Z4vn0movv7aur2Cg4Jg0aJEVq36DX//5tYE4WGUioqIiIhI2jt71lRVaNvW3ZYvH3zzDRw5YtZGeWASdeSIecstW5r6GePGuc9ldBL1zjvu4odgqsZ7ezszNggPphEpEREREUk7R46YTXSnToWYGNP2yy9w333mcaNG1sWWjuLjYcIEePll87a9vU1hiZdesiaesWPhhRfM47p1zdRCSVtKpERERETk9u3cCa+/DnPmmLreANWqmQVB99xjbWzpbOVKM1Nx1y5zXLs2TJ5s1kNlNKcT/u//zIAfmEHBWrUyPo7sQImUiIiIiNyelSuhfn33cYMGMGwYNG5s3aKgDPT55yaJyp/fVG3v3Nmat+1wmGVn771njt9802zHJenD0jVS48aNo0aNGuTKlYsCBQrQpk0b9uzZk+yaevXqYbPZkn09/fTTya45cuQILVu2JDAwkAIFCjBkyBASkv4SIiIiIiJpy+k0U/iS1Kplanq3bWum8S1fDk2aeGwSlZBgloAlGTvWJCx79kCXLta87YQE6NHDJFE2G3zwgZKo9GbpiNSqVavo27cvNWrUICEhgf/7v/+jSZMm7Ny5k5w5c7qu69mzJy+//LLrOPCKoveJiYm0bNmSsLAw1q1bx4kTJ+jSpQu+vr6MHTs2Q9+PiIiIiEdLSIB588y8sX/+gf37TQlzHx/Ytg2u+PzmqTZsgN69oUAB+PFHk7SEhlq/f/CKFTBzplmbNXMmPP64tfFkB5YmUj/++GOy45kzZ1KgQAE2bdpEnTp1XO2BgYGEhYVd8x5Lly5l586dLFu2jIIFC1K1alXGjBnDsGHDGDVqFH5+fun6HkREREQ8XkwMJRcvxmfAAFO+HEw97W3b3OufPDyJOnvWLPeaNs0ch4SYH0XJklZG5dakiSl2UaxY8kKJkn4y1RqpCxcuABAaGpqsfc6cOXz66aeEhYXRqlUrXnzxRdeo1Pr166lcuTIFCxZ0Xd+0aVN69+7Njh07qFat2lWvExcXR9wVO2dHRUUBYLfbsdvttxx/0nNv5x5iPfWjZ1A/egb1Y9anPszi/vkHrw8+wOfdd6ny998AOPPnx9GvH46nn4Y8ecDD+9bhgFmzbPzf/3lz9qyZs9eli4OxYxMpUMDatx8dDXFxkDevOe7b13y/Xkz695gyKf352JxOZ6YoJu9wOHjwwQc5f/48P//8s6v9o48+onjx4oSHh7Nt2zaGDRvGvffey/z58wHo1asXhw8fZsmSJa7nxMTEkDNnThYvXkzz5ldvODZq1ChGjx59VfvcuXOTTRsUERERyc5C9u2j7uDBAFwqUIB9bdpwpGFDHP7+FkeWMc6eDeD112uwZ4/5I3+xYlE8/fRWKlU6Z3FkEB3tw5gxEdjtXowZs5acOVUfIK3ExMTw2GOPceHCBYKDg697XaYZkerbty9//PFHsiQKTKKUpHLlyhQqVIiGDRuyf/9+SpcufUuvNXz4cAYNGuQ6joqKomjRojRp0uSGP6ybsdvtREZG0rhxY3x9fW/5PmIt9aNnUD96BvVj1qc+zGL27MG2aRPOxx5zNSXu20fCvfeyPHduGjVrRqVs1I9xcfDaaz4EBTkZOdJB37458PWtaXVYnDkDLVr4sGePjZAQJ2XLNqFq1Zs/T/8eUyZpttrNZIpEql+/fixatIjVq1dTpEiRG15737+bue3bt4/SpUsTFhbGr7/+muyaU6dOAVx3XZW/vz/+1/hLiq+vb5r8UqXVfcRa6kfPoH70DOrHrE99mMlt3GgKSCxYYIpHNGsGScsmpkzBYbfjXLzY4/vR6YRvv4UWLcDX13x9/rkpa16kiDfgbXWIHD9u9jTetcsUvIiMtFGlSur6xNP78Xal9Gdjaflzp9NJv379WLBgAStWrKBkClbrbdmyBYBChQoBEBERwfbt2zl9+rTrmsjISIKDg6lUqVK6xC0iIiKS5TmdsHQpNGwI994L8+ebtqZNzeKbbGb3bpOgtGkD77zjbq9WDW7yd/4Mc+iQ2ex31y4T0+rVUKWK1VFlX5aOSPXt25e5c+fyzTffkCtXLk6ePAlA7ty5yZEjB/v372fu3Lm0aNGCvHnzsm3bNgYOHEidOnWo8u9vTZMmTahUqRKdO3fm9ddf5+TJk4wYMYK+fftec9RJREREJNvbuhW6d4fffzfHPj7QqRMMHQrZ7A/RMTHwyiumfLndDgEBVkd0bX/+aXLeY8fMll3Ll0OJElZHlb1ZmkhNmTIFMJvuXmnGjBl069YNPz8/li1bxsSJE7l06RJFixalXbt2jBgxwnWtt7c3ixYtonfv3kRERJAzZ066du2abN8pEREREblCWBjs3AmBgdCzJwwaZOpmZzPffgv9+8Phw+a4ZUt4993MU9L8St7epoJgxYqwbBmEh1sdkViaSN2sYGDRokVZtWrVTe9TvHhxFi9enFZhiYiIiHiOCxdgyhTYvh3mzDFtBQvC119DzZru2tnZzEsvQdLf3YsVM9P5HnzQbLCbGZUubTbdDQ01a7bEepaukRIRERGRdHLihNlBtlgxGD4c5s6F335zn2/ZMtsmUQAdOpgBueHDzeBc69aZL4lavRoWLXIfly+vJCozyRRV+0REREQkjezbB2+8ATNnQny8abvjDhg2DO66y9LQrBQZCVu2wJAh5rhSJTh61IzwZEZLlsBDD5npfKtXm3ogkrkokRIRERHxFCtXmooEDoc5vv9+MyrVsiV4Zc+JSH/9ZZaAffml+RE0boxrz6XMmkQtWGBGzOx203WqzJc5Zc9/USIiIiKewOmEf6seAyZxKlLEfPpeswbWroVWrbJkErV//36aN29Onjx5KFy4MK+//nqqnm+3w9tvQ4UK7iTqmWcyZyGJK336KTz8sIn/4YdNVfrMWkkwu9OIlIiIiEhWk5gI33xjNtE9cwb27jUlzP38YNs2yJ3b6ghvS2JiIg8++CBt2rTh22+/5cCBAzRu3JgiRYrw2GOP3fT5P/8MffqY+hpgampMmeIeicqsPvwQevc2+XG3bjBtmqnWJ5lT1vvzhIiIiEh2FRcH06ebBT7t2sHGjWZEassW9zVZPIkC2LNnD3v27OGll17C19eX8uXL06NHDz766KObPjc62gzCbd9upu5Nm2YG5jJ7ErV0KTz9tEmi+vUz3awkKnPTiJSIiIhIZhcVBR99BBMmwPHjpi0kxHzifuYZKFDA0vDSmuPfNV5XbpXjcDjYtm3bda53z14MCoJXXzV7DY8bB/nypXu4aaJhQ3j0UVNkcdy4zFdBUK6mESkRERGRzG7HDlNu7vhxsxPrm2/CkSMwZozHJVEA5cuXp0SJEowcOZK4uDh27NjBxx9/zIULUXz2mampkZhort20yUzd+/579/P79IGpUzN/EuV0ut+Htzd88omSqKxEI1IiIiIimc3BgyZDaN/eHEdEQPfuUKsWdOoE/v7WxpfOfH19+eabbxg4cCCFCxcmV64iXLjQHYfjQ5KWSIWHQ+XKZkqc0wkjRkCLFlknCXE4oG9fuHgRZs82I2qaype1KJESERERySy2bYPXXoMvvjDJUr167mGVjz+2NLSMdscdd7B06VLmzzf5pNM5DKjrOn/8uHuWY6dOZuusrJJEJSTAE0+YESibDZ56CmrXtjoqSS0lUiIiIiJWcjpNqfLx4+GHH9zttWvD+fOZf35aOtm2bRslSpSmf39fnM5FwMfA8quuy58fZs3KOqM58fHw2GPw9dfu6XxKorImrZESERERscr27fDAA1C3rkmivLzMTqybN8OPP0KZMlZHaJkvv/ySokWL8ddfeYA3gYXA1TvTnjlj8tCs4PJlaNPGJFF+fub7o49aHZXcKo1IiYiIiFglb16zFsrf36yBGjwYSpe2OqpMYdCgV9i//xU+//zm1544kf7x3K6LF01Z9lWrIDAQFi6Exo2tjkpuhxIpERERkYwQHW02Ndq+3WwSBKZiwty5ZlQqLMza+DKJXbtg0iRTgOHy5ZQ9p1Ch9I0pLWzfDuvXQ3CwqTBYq5bVEcntUiIlIiIikp7+/hvefdd8/fOPaXvmGfcOse3aWRZaZuFwmOp7EyfCkiXu9rvuMlXez583S8n+y2aDIkWyxhqj++83U/nCwuCee6yORtKC1kiJiIiIpIfDh6F/f7PD6ssvmySqTBmzsW6FClZHl6mMHw/Nm5skymYz64hWrjSb6k6bZq75b0W+pOOJEzNvoYljx2DPHvfx//6nJMqTpCiReuedd4iNjQXgyJEjyXaZFhEREZH/+Okns9bp3XfN/LTq1WHePNi9G3r2hIAAqyO01NGj8Oef7uNHH4U8eWDgQNi3DxYsMPU3bDZo2xa++goKF05+jyJFTHvbthkbe0rt329Gyho2NNuCiedJUSI1aNAgoqKiAChZsiRnzpxJ16BEREREspyzZ92P778fChSARo1g2TLYuNFshpRZh04ygNNp1gh16AAlS8LQoe5zJUuaghFvvw2lSl393LZt4dAhk5/OnWu+HzyYeZOoXbugTh0Tc44cphijeJ4UrZEKDw/n66+/pkWLFjidTo4dO+YaofqvYsWKpWmAIiIiIpmW0wmLF5u5aSdPmhEnb29ThW/7dlOVL5uLjzcjRxMnmnwyyaVLYLeDr6859ve/8X28vc3+xJndli2mGt/ff8Mdd0BkZNYohiGpl6JEasSIETzzzDP069cPm81GjRo1rrrG6XRis9lITExM8yBFREREMpWEBPjiC3jtNZMwgdkYaMsWM40PlERhihOOHAnHj5tjf3/o1AmefRaqXL0lVJa3YYNZ63X+PNx9t1nzlU33U84WUpRI9erVi0cffZTDhw9TpUoVli1bRl79x0FERESym5gY+PhjePNNU0wCICgIeveGAQNMOfNszul0F4KIizNJVFgY9OkDTz1lZjx6ol9+MTM5L10y1ey//x5y57Y6KklPKS5/nitXLu68805mzJjBAw88gP/Nxl9FREREPM1vv5nS5QD585vkqXdvUykhG3M44IcfzPS9Rx+FJ54w7V26mH2THnnEDNh5svLloWxZMwK1cCHkzGl1RJLeUr2PVNeuXdMjDhEREZHM59gxU4O7VStzXLs2dOxoKgl062YqCWRj0dEwa5bZQHfvXtP299/uRCooCB5/3Lr4MlJIiKkrkjNnti/KmG2kKJHKkycPtv8W77+Oc+fO3VZAIiIiIpbbtQveeAM+/dQs7DlyxIw62Wzw2WdWR2e5Q4fgvffMHk8XLpi23LnhySehXz9LQ8tQs2aZ7cEGDDDHWvmSvaQokZo4cWI6hyEiIiKSCfzyi6nAt3Chu+3++80wSzafvnelPn3MVD4w09mefRa6djUjUNnF++9D377mcbVqZt8ryV5SlEhpOp+IiIh4tB07zFDKypXutjZtYNgwqFnTqqgyhbg4mDvXhtPpnq/27LOmdPmAAaZKXXbbJ+n1182vBpifRZ061sYj1kj1GqkrxcbGEh8fn6wtODj4tgISERERyXC5csHPP4OPD3TuDEOGQMWKVkdlqdOn4YMPYMoUOHnSh/btS9K5sznXtKn5ym6cTnjpJRgzxhy/8IJ5nMIVMOJhUp1IXbp0iWHDhvHll19y9sodvP+lfaREREQkU4uNhZkzzSjUu++atmLFzIKX2rWhaFFLw7Pa1q2meMTcuWY0CiA83EmePLHWBmYxpxOeew4mTDDH48bB889bG5NYK9UDsUOHDmXFihVMmTIFf39/pk2bxujRowkPD2f27NnpEaOIiIjI7Tt/3nz6LVHClCx/7z2TTCV57LFsnUQ5nfDgg1C1KsyYYZKoGjVMQrV3bwItWx60OkRLRUa6k6h331USJbcwIvXdd98xe/Zs6tWrR/fu3alduzZlypShePHizJkzh06dOqVHnCIiIiK35sQJs8HRlClw8aJpK1YMBg82SVU2dumSe78jm83kkd7e0K6dWf9Us6Zpt9stDTNTaNIERo82P6Pu3a2ORjKDVCdS586do1SpUoBZD5VU7rxWrVr07t07baMTERERuR0rV5rFPElruu+4w1QJ6NgRfH0tDc1KBw6YUZWPP4YlS9z1NP7v/8xISzYemEsmLs58JZUAGDnS2ngkc0n11L5SpUpx8KAZ2q1QoQJffvklYEaqQkJC0jQ4ERERkVSLinI/vu8+s8HRAw/Ad9/Btm2mmEQ2TKKcTli1Ch56CMqUMYN0UVHw+efuawoXVhKVJCbGTHVs0cKM3In8V6pHpLp3787WrVupW7cuzz//PK1ateK9997Dbrfz9ttvp0eMIiIiIjfmdMLy5fDaa3DsmFn75OUFOXKY6gmFClkdoWUSEmDOHJM4bdnibm/a1Ezfa9LEosAysago+N//YM0aM/Vx506zXkzkSqlOpAYOHOh63KhRI3bv3s2mTZsoU6YMVapUSdPgRERERG4oMREWLDCb6G7aZNq8vWHzZrjnHnOcjZMoMGucRo2CQ4dMXtmlC/TvD5UqWR1Z5nT2LDRrBr/9ZgYzf/hBSZRc223tIwVQvHhxihcvnhaxiIiIiKRMXBzMng1vvAF795q2HDngySdNjeps/Nnk99/N2qe33gI/P5NXjhxp9oXq2RNCQ62OMPM6eRIaN4Y//oB8+WDpUqhWzeqoJLNKcSL1zjvvpOi6/v3733IwIiIiIiny88/Qq5d5nCcPPPMM9OsH+fNbG5dFEhPh22/N9L3Vq01bRISp6A6qMpcSR45Ao0YmLy9UCJYt06id3FiKE6kJSYXz/3X06FEKFSqEj4/7FjabTYmUiIiIpL1Tp8wCn6ZNzXGDBtC6NdSrZ0ahgoKsjM4yFy6Y0ad33jFT9wB8fODhh02BQkm5S5fgn3/MYOby5VC6tNURSWaX4kQqqVJfkly5crFq1SpXKXQRERGRNLd/P7z5ptkhNiDADBsEB5uFPwsXWh2dpU6ehLJlITraHIeGwlNPQZ8+UKSItbFlRRUrmlGo0FBVLpSUue01UiIiIiJpbssWU4Hvyy/B4TBtVaua7CFpU59sxumEPXugQgVzHBZm6mmcPm2q73XqBIGBloaY5WzebEb16tc3x3fdZW08krUokRIREZHMY9cuGDjQ7BKbpFkzs0tsnTpmJCqbiY2FuXPN+qc//zSDcgUKmHNffWVGULLhj+W2rVsHzZub9WWrVkH16lZHJFlNqjfkFREREUk3AQFmfpWXFzz6qClB98MPULdutssWTpww1faKFYMePWD7dlOB77ff3NfkzZvtfixpYtkyU50vKsokUOXKWR2RZEUpHpGKunKXcExhiejo6Kvag7PpcLuIiIikUny82Sl2505TxhygZEmYOtUkTtl0HfbRo/DCC/D552C3m7ZixUxRwiefNEUK5dZ99x20b29+/Zo1g6+/1pRIuTUpHpEKCQkhT548rq/o6GiqVavmOk46nxrjxo2jRo0a5MqViwIFCtCmTRv27NmT7JrY2Fj69u1L3rx5CQoKol27dpw6dSrZNUeOHKFly5YEBgZSoEABhgwZQkJCQqpiERERkQwSHQ0TJphE6YknzIZHV/7/v3v3bJtEAfj7m6VhdjvUqgXz5pmaG0OGKIm6XV98AW3bmiTqoYdMvRIlUXKrUjwi9dNPP6X5i69atYq+fftSo0YNEhIS+L//+z+aNGnCzp07yZkzJwADBw7k+++/Z968eeTOnZt+/frRtm1b1q5dC0BiYiItW7YkLCyMdevWceLECbp06YKvry9jx45N85hFRETkFp05A+++C++9Z+pMg9mwZ+BA8z0b+ucfmD4dtm6FTz4xbQUKwOTJpvDBPfdYG58n+eknM1vU6YTHHzeFIH1ULUBuQ4p/ferWrZvmL/7jjz8mO545cyYFChRg06ZN1KlThwsXLjB9+nTmzp1LgwYNAJgxYwYVK1Zkw4YN1KxZk6VLl7Jz506WLVtGwYIFqVq1KmPGjGHYsGGMGjUKPz+/NI9bREREUse2ahU8+CBcvmwaypaFoUOhc2czBJPN7Nlj9n6aORNiYkzboEFQrZp53KOHZaF5rFq1oFUrk7O//75ZhidyOzJVHn7hwgUAQkNDAdi0aRN2u51GjRq5rqlQoQLFihVj/fr11KxZk/Xr11O5cmUKFizouqZp06b07t2bHTt2UC3pv0hXiIuLIy4uznWctM7LbrdjT5qMfAuSnns79xDrqR89g/rRM6gfs7iYGOy+vgDEV6mCd44cOCtVwjFkCM7WrU3lBHAvBPJwTicsX27j3Xe9+OEH96f4O+900r9/IqVLOzPtjyIr/1t0Ot0FOebOBV9fU6kvMdHauKyQlfsxI6X055NpEimHw8GAAQN44IEHuPPOOwE4efIkfn5+hISEJLu2YMGCnDx50nXNlUlU0vmkc9cybtw4Ro8efVX70qVLCUyDibKRkZG3fQ+xnvrRM6gfPYP6MWsJ3bmTsvPnk+Pvv1k5YQLYbESuX0+O8eO5nD+/+VR7ZXnzbGLDhkKMH38vADabk3vuOUmrVgeoXPlvbDYz9Syzy0r/Fp1O+PTTikRH+/L009tU3fAKWakfrRCTNEx8E5kmkerbty9//PEHP//8c7q/1vDhwxk0aJDrOCoqiqJFi9KkSZPbqjpot9uJjIykcePG+P77FzjJetSPnkH96BnUj1mIw4Ft8WK83ngDr/XrAXB6edEkf36W/v13tuzDv/6CQ4dsPPCAE4BGjWD+fCdNmjjo08dBmTL5gHzWBplCWe3fosMBzz3nxddfm1HP554rSr16Toujsl5W60er/Lcq+fVkikSqX79+LFq0iNWrV1OkSBFXe1hYGPHx8Zw/fz7ZqNSpU6cICwtzXfPrr78mu19SVb+ka/7L398f/2vMx/b19U2TX6q0uo9YS/3oGdSPnkH9mInZ7aZO92uvwY4dps3PD7p1wzZ4MD4lSsDixdmqD3/91WyeO28eFC4M+/aZoga+vqbSu7e3N+BtdZi3JCv0Y2IiPPWUKSYBZj1U48aZ4iNvppEV+tFKKf3ZpHqZ3RNPPMHFixevar906RJPPPFEqu7ldDrp168fCxYsYMWKFZQsWTLZ+erVq+Pr68vy5ctdbXv27OHIkSNEREQAEBERwfbt2zl9+rTrmsjISIKDg6lUqVKq4hEREZFU+ukn6NLFJFG5csGwYXDoEHz4oSkokU3Y7aa0dkQE3HcffPYZJCRAiRKmWGES76yZP2UZdjt06mSSKC8vmDULeve2OirxVKlOpGbNmsXlpIo7V7h8+TKzZ89O1b369u3Lp59+yty5c8mVKxcnT57k5MmTrvvnzp2bHj16MGjQIH766Sc2bdpE9+7diYiIoGbNmgA0adKESpUq0blzZ7Zu3cqSJUsYMWIEffv2veaok4iIiNyGs2dh5Ur3cePG0KQJjBsHR47A+PHZrpT5d9+Zba86doQNG8yAXNeusHmz+VFlsx+HZWJjoV07k9D6+pq9uLp0sToq8WQpHueMiorC6XTidDq5ePEiAQEBrnOJiYksXryYAgUKpOrFp0yZAkC9evWStc+YMYNu3boBMGHCBLy8vGjXrh1xcXE0bdqU999/33Wtt7c3ixYtonfv3kRERJAzZ066du3Kyy+/nKpYRERE5AaOHoW334apU0258sOHISgo2xaOSEx0jy7lzw/Hjpn9n/r0gaefhv/UwZIMsHEjLF4MAQEwfz40b251ROLpUpxIhYSEYLPZsNlslCtX7qrzNpvtmpXwbsTpvPmiv4CAACZPnszkyZOve03x4sVZvHhxql5bREREUmDXLnj9dfj0UzNXDcyUvb/+gvLlrY0tgzkcsHSpWf9UpozZVxigZk1YuBCaNcuWW2KlqW7dujF37txk+4BGRka6lnTcSO3aMGeOSWL/8zd6kXSR4kTqp59+wul00qBBA77++mvXXk8Afn5+FC9enPDw8HQJUkRERDLYnj1mvdM337jb6tc3bU2akJ1qSV+6BJ98ApMmwe7dpm3dOpNfJu2a0rq1dfF5mj59+jBx4sQUXfv33xAdbdaiAXTokG5hiVwlxYlU3bp1ATh48CDFihXDlo3+AyoiIpLt2Gzw7bfme5s2JoG67z6ro8pQR4/C5Mnw0Ufwzz+mLVcuePJJ6NfPnUSJNU6cMCXlL1+GNWtMhUSRjJTqYhO7du1i7dq1ruPJkydTtWpVHnvsMf5J+q+MiIiIZB0JCabM3MiR7rZy5czctZ07zYKTbJZEAXzwganq/s8/ULq0GZE6dswsFStVyuroPNfs2bMJDQ3ljjvu4K233sLhcFx1zeHDZirfzp0QH29GpUQyWqoTqSFDhrg2qdq+fTuDBg2iRYsWHDx4MNkmtyIiIpLJXb5sNtkpVw4eewxefRUOHHCf79MHKlSwLr4MFB8Pc+eaKXtJ+vQxIx7ffGNmOvbvD8HB1sWYHfTv3589e/Zw5swZpk+fzqRJk5gwYRIrV5pcf+VKs2yvVi3Yvx9KljSjUdlsuZ5kEqnenezgwYOu/Zm+/vprWrVqxdixY9m8eTMtWrRI8wBFREQkjf3zj0mgJk1yb3KULx88+yxcsQY6O/j7bzN1b/JkOH4cmjaFH3805woXhshIa+PLbu6++27X45o1a9Ks2fP83//NJj5+oKvdy8sU/qhQAZYt05Q+sU6qEyk/Pz9iYmIAWLZsGV3+LdAfGhrqGqkSERGRTGrlSmjVyj0XqnhxGDIEunfPVot+/vjD5JGffmr2HwIICzMjHU5ntqqlkWnNnw9Tp149eSpppt/QoUqixFqpntpXq1YtBg0axJgxY/j1119p2bIlAH/++SdFihRJ8wBFRETkNsXFuR/ffbfZAKlyZZNF7N0LfftmqySqXz/z9qdNM0nU3XebqnyHD8OIEUqirPTll18SFRVFQoKT3r1/A8YD7a66zmaDl14y+3mJWCXVidR7772Hj48PX331FVOmTKHwv38K+OGHH2jWrFmaBygiIiK3aONGaNfOPcwCZpHPxo2wdSt06gS+vtbGmAGio81ysCR3322mh7VrZ9bX/PYbPP44XLF1kVjkvffeo1ixYuTKlYvTpzsBfYDnrrrO6TRVFdesyfAQRVxSPbWvWLFiLFq06Kr2CRMmpElAIiIichucTrOw57XXYMUKd/vvv5sMAsyGutnAoUOm8OC0aTB2rCkeAaauRoMG7r2HJPP46afVLF1qCkj+9tvNrz9xIv1jErmeVCdSV4qNjSU+Pj5ZW7DK2YiIiGS8xET4+msYP94kTQA+PiZrGDoU7rjD2vgyiNMJa9fCxImwYIF7Pc3337sTqYAAJVGZzdGj8PHH5uvIkZQ/r1Ch9ItJ5GZSnUhdunSJYcOG8eWXX3L27NmrzidqsqqIiEjGW7IEOnQwjwMDoWdPGDQIihWzNq4MNHeu2eNp0yZ3W6NGMGAANG9uWVhyEzExULEiXLpkjvPkMVMtv/wSTp92z0q9ks0GRYqYvaRErJLqNVJDhw5lxYoVTJkyBX9/f6ZNm8bo0aMJDw9n9uzZ6RGjiIiI/NeFC7B+vfu4WTPzqXLUKPMn/YkTs1USBeaD96ZNZsTpySdh+3Yzy7FlS7MmSjKHAwfMlMskgYHQti3Uqwdz5pgy9O+8Yyr0w9XFP5KOJ040dVNErJLqEanvvvuO2bNnU69ePbp3707t2rUpU6YMxYsXZ86cOXTq1Ck94hQRERGAkydN3e733wd/f1NqLkcOkymsWpVtSs5t22Z+DCNGmE1ZwcxgvPde6NXLbIslmUdcHCxcCFOnwvLlpq1OHahSxTz++GMzE/VKbdvCV1+Z7c2OHXO3Fylikqi2bTMicpHrS3Uide7cOUqVKgWY9VDnzp0DTFn03r17p210IiIiYuzbB2++CTNnusuZV6pkEqkKFcyxhydRiYlmrdPEifDTT6YtJATeess8vv9+8yWZx+7dptjHrFlm82Mwv6ZNmkBCgvu6/yZRSdq2hdatTXW+EyfMmqjatTUSJZlDqhOpUqVKcfDgQYoVK0aFChX48ssvuffee/nuu+8ICQlJhxBFRESysX37zLDLvHnuygkREfD88/C//2WLOWsXL8KMGWa61/79ps3b25Qvf+QRa2OT6/vpJ1MdMUl4ODzxBPTokbpiH97eZtqfSGaT6kSqe/fubN26lbp16/L888/TqlUr3nvvPex2O2+//XZ6xCgiIpJ9xcfDF1+Yxy1amASqVi2PH31KkphoCg4ePWqO8+QxU/f69oWiRa2NTZLbvh3++sss1wPza1q4sKm637OnKfhxvZEnkawo1b/OAwcOdD1u1KgRu3fvZtOmTZQpU4YqSRNdRUREJPUcDrOQZNcueOEF01apkilF16AB3HWXpeFlBKcTfvkF7rvP5Ire3vDww7B4sVkr07kz5MxpdZSSJDra5PlTp5p+K1bMFJPw9jZ7Pf/5pykmIeKJbns+QPHixWnbti2hoaH06tUrLWISERHJXuLiYPp0kzS1awcvvWTWPiUZONDjk6i4OLOO5u67zczFVavc58aMgR074OmnlURlFps2wVNPmel6Tz5pkigfH6hRA86fd1+nJEo8WZpNrD579izTp09Pq9uJiIh4vosXTaWEUqXMp9E9e0z1hOHDIVcuq6PLEKdOwejRZiSjWzfYssUUIdyzx31NYGC2WAqWZYwaBffcAx99ZH6Fy5SB114zlfW++gry5rU6QpGMoZmqIiIiVli1Ctq0cf/5PjzcbKDbq1e2SKKioqB/f/jsM7MMDExZ62eeMTllaKi18YnhdJrtyq6sJ9asGYwbZwZPe/Y0hSCyyZI9kWSUSImIiGSUhAT3avu77jKVFMqXNxsgdepk9oXKJoKCzAf0+HgzlW/AAHjoIbOuRqx39ix88olZ+7RzJ/Ts6UXLlubcffeZUuRKdiW7UyIlIiKS3rZtg9dfN+ueVq82f74PCTGZRMWKHj9v7dIlHyZN8mLePDMQFxBg3vJ770FwsPlgLtZzOmHlSpM8zZ/v3q4sRw7w83NfZ7MpiRKBVCRSbW+yffT5K1cWioiIZHdOJ/z8M4wfb0rOJdm6FapWNY/vuMOS0DLKvn0wcaIX06c3JTbW7KD62WfQvbs537ixhcHJVRo2dG90DFCtmpm699hjEBjoSPZrLCKpSKRy58590/NdunS57YBERESyNIcDFi0yCdT69abNy8vU8B461J1EeSin03wYnzjR/BicTpNAVazoZOBAGx06WBufGA4HLF8O9eu7Z5s+8ABs3GhmmfbsCdWru6+3262JUyQzS3EiNWPGjPSMQ0RExDMsXgytW5vH/v6mFN3gwaa0WTawb58Z2UjSvLmD++7bwPDhNfDz0wIoq/31F8yYAdOmmZmm334LrVqZc889B8OGmfVrInJzWiMlIiJyOy5dgt273X++b97c1IZu1MjsIBsWZm186ezECTOD8eGHzXHZstC2rXnb/ftDqVKJLF58RlXdLJSQAD/8YNY+ff+9GY0Cs0zv1Cn3dVdW5hORm1MiJSIiciv+/ttUS3j3XbMS/+BBU0XB2xt+/dXj60Fv2mSm733xhZnOd//9ULiwOffVV+63rylh1jp92mxy/Ndf7rbatc3UvfbtTSEJEbk1SqRERERS48gRs4nutGkQE2PaypSBQ4egQgVz7KFJVEICLFwIkyaZUagktWrBuXPuRMpD336WEB9vikTec485LlDAfMXFQdeuZo+upF9TEbk9SqRERERS4sABGD0a5s41GQWYP/U//7yZy+btbW186WzjRjOCceSIOfb1hQ4dzOzFpA/tYp0//zS5/axZEB1tplwGB5tzX31lktxstE2ZSIZQIiUiIpISFy/C7NnmccOGJoFq2NCjh1/i4twfvsuUMbMZ8+WD3r3NV6FC1saX3cXGmv2epk41+z8lKVTILNu7915zXKqUJeGJeDwlUiIiIv/ldJrqe7t3m1JmAHfdBa++ajY/qlHD2vjSkdMJy5aZ9U/nzrkruOfJA5GRZm8hraux3tKl8Oijpo/AVNhv3tysfWrZ0l3SXETSj/6ZiYiIJElIMNUTXnsNtm8389c6dnQv/vm//7M2vnR0+TJ8+qlZ/7Rjh2mz2UwumbSm5v77rYsvu4uJgTNnoHhxc1ypEpw/D0WLQo8e8MQT5rGIZBwlUiIiIjEx8PHH8OabZnMdMJvpPP20qcjnwY4fh8mT4cMP4exZ05Yzp/lg/swzppy5WOf3383UvTlzICICfvzRtBcpAr/8YkYIPXx5nkimpURKRESyt9WroV07swAIIH9+U0GhTx8zn83DbdgAY8eaxyVKmOTpiSe0p5CVLl6Ezz6Djz4yZeaT7N9vcv7AQHOsIh8i1lIiJSIi2Y/DYRaVANxxh/l0WqIEDBkC3bt77CIgu90UJ7Db4fHHTVvr1qb6XocO8OCDGt2w2uuvw8svm32ewcwubdvWrH2qX9/9aysi1lMiJSIi2cfu3fDGG2b63rJlpi1vXlizBqpU8dgV+ufOmelh770Hx45BeDg88oiZtejtDZ9/bnWE2dc//5hkKSjIHOfNa5Ko8uVN8tSlixkkFZHMxzP/jyEiInKlX3+F8ePNbrJOp2nbutVU4gOzH5QH2rXLFI+YPdsUkwCzOWvPnmbjVg9f/pVpOZ0md5861ezx9PrrZkolmJHBcuXMJsceXFlfxCMokRIREc/kdJoa0a+9Bj/95G5v3RqGDXMnUR7qrbdg8GD3cdWqMGCAKUKojVmtceaM2TB32jTYs8fd/vPP7kQqKAhq17YmPhFJHSVSIiLimb77ziRNYKbsPf64WQNVqZK1caWTS5fMV4EC5rhhQ7Oe5sEHTQJVp45GOKzidELnzvDll2Z9GpjKiB07mtHBpI1zRSRrUSIlIiKe4fJl2LvXrHUCaNEC7rzTbKA7cKDHbrJz9KgpX/7RR/DQQzB9ummvWhWOHHFvgSUZ69w5CA01j202U8/EbjeV9nr2NElUcLC1MYrI7VEiJSIiWdv58zBlCkycaBb97N9vvvv4wJYtHlmGzuk0ZcsnToSvv4bERNP+yy9mT+GkmhlKojJWYiIsWWKS2u+/N7VNSpc250aPhhdfNPs+iYhnUBFNERHJmo4fh6FDoVgx+L//g9OnzVy2/fvd13hgEvXtt1CzJtx/v5kqlphoymJ/842pn+GhhQcztSNHYNQoKFkSWrY0fZGQ4N48F6ByZSVRIp7G0kRq9erVtGrVivDwcGw2GwsXLkx2vlu3bthstmRfzZo1S3bNuXPn6NSpE8HBwYSEhNCjRw+io6Mz8F2IiEiGOnwYevUyn1rfeMPsXnrHHfDJJ7BvH1SsaHWE6WrrVlOE0N/fbJy7dSusWKE9oKxw9KiZQVqihBlxOnrUTOcbMAD++AP69rU6QhFJT5b+3erSpUvcddddPPHEE7Rt2/aa1zRr1owZM2a4jv3/U2qoU6dOnDhxgsjISOx2O927d6dXr17MnTs3XWMXERGLnD5t6kaDqRE9bJj5NOuBO5X+8YcpX96mjRnpAHjqKff3pMISknFiYiAw0DzOmxfWrTNTLevXN2ufHnoIAgKsjVFEMoaliVTz5s1p3rz5Da/x9/cnLCzsmud27drFjz/+yMaNG7nnnnsAePfdd2nRogVvvvkm4eHhaR6ziIhkIKeTfFu34nX4MPTvb9pq1DCLTZo0MYmUh3E44IcfzPqnpD2D9+1zJ1IFCpi3LxknLs5sQTZ1Kpw4YRJcm80kVLNmmUKQZctaHaWIZLRMP5N65cqVFChQgDx58tCgQQNeeeUV8ubNC8D69esJCQlxJVEAjRo1wsvLi19++YWHHnromveMi4sjLi7OdRwVFQWA3W7HnlSX9BYkPfd27iHWUz96BvVjFpeYiO2bb/B6/XUe2LwZp78/9rZtoWBBcz4pk/Cg/o2Ohk8+8eLdd73Yt8/UKffyctK6tZNnnnFgtzstjvDWZOV/i7t3w4wZXnzyiRd//236xGZz8vvvCVSubK5p0cJ8z4JvL1Wycj+Km/oxZVL688nUiVSzZs1o27YtJUuWZP/+/fzf//0fzZs3Z/369Xh7e3Py5EkK/Gdeg4+PD6GhoZw8efK69x03bhyjR4++qn3p0qUEJo3X34bIyMjbvodYT/3oGdSPWYuX3U6RlSspu2ABQcePA5Dg58eRRo3Ys3w58SEh1gaYjkaOvJ9t2/IDEBhop3Hjw7RocYCCBS8TFQWLF1sc4G3KSv8Wd+zIy5w5Fdi5M5+rLTT0Mg0bHqFRo8McPXqZo0ctDNBCWakf5frUjzcWExOTousydSLVsWNH1+PKlStTpUoVSpcuzcqVK2nYsOEt33f48OEMGjTIdRwVFUXRokVp0qQJwbexqYPdbicyMpLGjRvj6+t7y/cRa6kfPYP6Meux/fwz3p06YTtxAgBnnjwkPPUUyypWpG779hT1oH50OmHdOht33ukkd27TduGCjTFjzOhT584QFFQcKG5pnGkhq/xbvLJsvL+/jRde8MHLy0nz5k569HDQrJkPPj6lgFKWxmmVrNKPcmPqx5RJmq12M5k6kfqvUqVKkS9fPvbt20fDhg0JCwvj9OnTya5JSEjg3Llz111XBWbd1X+LVgD4+vqmyS9VWt1HrKV+9Azqx0zO6TSLTcAsNPnnH7P50XPPYevZE/z9iV+82GP6MT7elCyfOBE2bYI334TnnjPnOnWCxx8HLy9vwPPK72XGPoyOhs8/N2ufatWCt94y7U2awNtvwyOP2Chc2IZ2i3HLjP0oqad+vLGU/myyVCJ17Ngxzp49S6FChQCIiIjg/PnzbNq0ierVqwOwYsUKHA4H9913n5WhiojIjRw4YLKII0dg0SLTVrCgqeNdvbrZUBc8ZuHJmTPw4YcweTIkzTwPCIAr/+ip0uUZw+k0SezUqTB3rkmmwJQuf/110w9eXjBwoLVxikjmZ2kiFR0dzb59+1zHBw8eZMuWLYSGhhIaGsro0aNp164dYWFh7N+/n6FDh1KmTBmaNm0KQMWKFWnWrBk9e/bkgw8+wG63069fPzp27KiKfSIimdGWLfDaa2ZYxuEwbdu341q5HxFhWWjpwemE3r1h5kxT+Q0gPBz69TOlsvPlu+HTJY198okZadqyxd1Wtqzpi65dlcyKSOpYmkj99ttv1K9f33WctG6pa9euTJkyhW3btjFr1izOnz9PeHg4TZo0YcyYMcmm5c2ZM4d+/frRsGFDvLy8aNeuHe+8806GvxcREbkOpxNWrTIJ1I8/utubNYPnn4c777QutnRw5WxFmw3OnzdJVI0aZpSjfXvQjJqM4fy30GFSf/z+u0mi/P2hXTuTQNWt6z4vIpIaliZS9erVw+m8fjnXJUuW3PQeoaGh2nxXRCQz+/Zbs6MsmDlTHTrA0KFQtWq6vux7773HzJkz2b59O82bN2fhwoWuc1FRUTz99NMsWrSIHDly0K9fP168zc2ZLl6EGTPg3XfNbMXy5U37yJHw7LNQs6Y+sGeUs2fN6NO0afDOO9CggWl/+mkoXhw6d4bQUGtjFJGsL0utkRIRkSwgPh4OHnRnEs2bm/lTjRrB4MFQKmOqnoWHhzNixAiWLVvGsWPHkp175plnOHfuHEeOHOH06dM0atSI4sWL06VLl1S/zoEDJnn6+GP3mqcPPoAJE8zjSpVu951ISjidsHKlWfs0f757KuWMGe5Eqlw58yUikhaUSImISNqIjjafYt9+29SR3rvXfPfzg5073bWlM0jbtm0B2LJlS7JEKiYmhs8//5y1a9cSEhJCSEgIzzzzDNOnT09xIuV0wurVpvreN9+4p5CVLw8DBpgRD8kY8fGmH6ZNM79ySapVM1P3HnvMstBExMMpkRIRkdtz5owZknnvPVO+HCAszHyqrVjRHGdwEnUje/bsIT4+nqpXTC2sWrUqY8eOZeVKOHECChWC2rWvX3wgNtassTl71hw3bWoSqCZNzOxFyTi+vmY0cO9eCAoyZeR79jTFH0VE0lPm+T+biIhkLceOmXrR06bB5cumrWxZGDLEDMkEBFgb33VER0eTM2dOfK5I7n7/PYTz5y9yRf0jihSBSZOgbVs4dQo++wz69zeJUo4cJnE6dsy0afpexvjrL5M0zZ8P69aZfrDZYNQouHTJLL8LCrI6ShHJLpRIiYjIrTl61IxEgfnz//PPw0MPZfoa0kFBQcTExJCQkICPjw/z58OwYReAXMmu++svU2Gvbl3zoT0+3gyw/bsDByNGZHzs2VFCAixebGaNLl7srpr/9ddmA2OAjh2ti09Esi8lUiIikjI//wy7d8OTT5rjiAh47jlTTKJBgyxTkq58+fL4+vqydetWqlatzrPPAmwBKie7Lmnd08qV5ntERKYdZPNIp06Z2aIffwzHj7vb69QxU/fatbMuNhERUCIlIiI34nCYYYDx42HtWjOXqk0b906yb75paXg3kpCQ4PpyOBzExsbi5eVFYGAgHTp04MUXX6RPn884duw08C4w5rr3mjwZ+vTJsNAFUwHxlVfM43z5oFs3k8MnFYMUEbGaEikREbma3Q6ff27WQP3xh2nz8zNzqRISrI0thV555RVGjx7tOs6RIwd169Zl5cqVvPfeezz11FM8/HARIAfQD7h+xb48edI93Gxt716z1O7CBVM6Hsxyu6FDzazR1q3NJroiIpmJEikREUnu559N6bMjR8xxrlzQu7eprlCokKWhpcaoUaMYNWrUVe1OJ/z5ZzB5836W4mKCWehtZxmxsaZoxNSp7umTPj4wejQULGiOX3vNsvBERG5KiZSIiJjsImmNU6lScPIkFChgkqfevSEkxMro0sTJkzBnDsyc6R5kA1Mbw+Fwr4m6ks1mqvfVrp1hYXq8PXtgyhT45BM4d860eXlBs2Zm7VNoqLXxiYiklBIpEZHs7OhRs4Hu0aPw1VemLTwcliyB++4za6I8wNy50KULJCaa44AAU2CwWzezFueRR0zSdGUylZRXTpyY6QsRZilLlpiy8gBFi0KPHvDEE+axiEhWokRKRCQ72rXLrH/69FP3mqcdO+COO8zjevUsCy0t/P67SYSS9tyNiDCjTjVrmuSpQ4fkg2xffQXPPmv2hUpSpIhJotq2zbi4Pc3vv8OHH3oRGFiYFi1M2+OPw5o1Jnlq0kRJqohkXUqkRESykw0bTAW+b75xt9Wvb/aAyuK7yp454566t3UrtGoF335rzpUsCYcOQbFi135u27amoMGaNXDihFkTVbu2PuTfiqgos3nx1KmwaROAN+XKlXKtdwoNhXnzrIxQRCRtKJESEckuvvnGlC4HM1zTpg0MG2am8GVRdrupzj5zJixa5B5c8/MzNTIcDrP+Bq6fRCXx9s7yA3GW+uUX+OgjU+wxJsa0+flB69YO7rxzF07nvdYGKCKSxpRIiYh4qoQEU3mvVClz3KwZlChhNs8dMgQqVLA0vLTw4IPw44/u43vuge7doWNHFS3IaCNHwtKl5nGFCqZwRJcukDt3IosX/51V9msWEUkxJVIiIp7m8mUzRPPGG2aYZfdu893f3zzOohvynD1rikZ07uxe3/Tgg7B5s2nr1g3uvNPKCLMHp9NMgZw2DcaONWvJAPr2hbAwk0A98IC7WIfdbl2sIiLpSYmUiIinOH8e3n/flEQ7fdq05ctndjtNGn3KYklUQgL89ltBZs3yZtEi86Hczw+eesqc794dnnwSfH2tjTM7OHMGZs82a5/27DFtZcvCiy+axw8+aL5ERLILJVIiIlndyZOmhPkHH8DFi6ateHEYPNiURgsMtDa+W7BzpxlU++QTH06erOlqr1bN5IZJAgIyPrbsxOGAFStM8rRggXt0KWdOM32yZUtr4xMRsZISKRGRrG7PHjOND8zctuefNxsjZdFhmjNnoEqVpD2fbOTOHUe3bj507+7NXXdZHV32cvGiqWaYVDzinnvM1L1HHzXFPEREsjMlUiIiWc3GjSZ5evxxc1ynDvTpAy1amK8stKo/MREiI+G332DECNOWPz/873/mcefOCTidS2jdujm+vqpFnp4SE03hjuXLzQAnQO7cJnGy2833pH25REREiZSISNbgdMKyZWYPqBUrICjIzKvKk8ckTpMnWx1hquzZY6buzZ4Nx4+bt9C5s5mRCDB/vilbbrc7WbzYaWmsnu7IEZg+HT7+2L0hcadOUL26eTxxomWhiYhkakqkREQys8REk1WMH2/K0wH4+JgdZC9fNolUFnHhAnzxhUmg1q93t+fNaz64X7n5bdLeT5I+7Hb47juz9mnJEpOng+mLLl2Sr0MTEZFrUyIlIpJZrVsHXbvCvn3mODDQzK8aNOjmu8tmQgsWuKvteXtD8+am6l7LllmumGCWt2QJtGvnPq5fH3r1goceUl+IiKSUEikRkcyqSBE4dMjsLPvMM9CvX5YZKti3D2bNgtKlzf5OAO3bw5Qp8PDDZnlXWJilIWYbcXGwcKH53qWLaWvWDO6+Gxo3NuXjy5SxNEQRkSxJiZSISGZw8qTZ/+mvv8zCITCjTosWmd1Ng4KsjS8FLl6EL780U/d+/tm0Va5sBtVsNvMWfvnF0hCzld27zdS92bPh778hPBwee8zMDPXxMQU+slBdEhGRTEeJlIiIlfbtgzffNNlHXJxpGz4cKlY0j5s2tSy0lFq9GqZNg6+/dpfJ9vIyoXfrZtbf6AN7xrh8GebNMwlUUjILULiw2VIsLs4kUaA+ERG5XUqkRESs8Pvv8Npr5lOvw2HaatY0e0CVL29tbKn03nvmbYAJvXt3M3WvcGFr48qOhg41/QFmHVrLlmZZXbNm7gRKRETShv6zKiKS0RYuNKv6kzRvbhKo2rUz9TBBdLQZdZoxAz780J3v9epllnF16wb33Zep34JHiY6Gzz+HGjVwbVTctauZDdqjh0lolcyKiKQfJVIiIunN4TBrn4oWNcdNm0KhQqZU2tCh7k/BmZDTCWvWmORp3jy4dMm0z5wJ48aZx40amS9Jf06nWds0dSp89plJpp54wuwDBWbvp/37VT5eRCQjKJESEUkv8fHw6afw+utmmGbHDvMJN0cO2LsXcua0OsLrungRJkwwlfcOHHC3ly1rRp46d7YstGzp/HmYM8ckUFu3utvLloVq1dzHNptGBEVEMooSKRGRtHbxInz0kclE/vrLtIWEwJ9/QoUK5jgTJlFXFoXw9TXhnz8PuXJBhw4mgbr/fn1Qz2hOJ0REmCp8YPZ5at/erH2qU0f9ISJiFSVSIiJp5cwZeOcds9r//HnTFh5uNtDt1ctkJJmM02n2/Z05EzZvdpfEDgiAV16B3LnNcq5MmPd5rLNnzbS9p54yCa3NBh07wldfmeTp8cfNmjQREbGWEikRkbSydavJPgDKlTPrnx5/3AwhZDJHj8Inn5gEau9ed/v69WbUCaBvX0tCy5acTli50kzd+/prMys0PBzatjXnhw+HkSM1+iQikpkokRIRuVXbtpn5Vo88Yo4bNjQr/1u2hNatTf3pTGb9enjpJVi2zHx4BzPa9PDDpspbzZrWxpfdnDplktlp08yWYkmqVTOjgkn8/DI8NBERuQklUiIiqeF0mp1Ox4+HxYshONhU4cud2wwXJJVPyyScToiNNfUtwGzIGhlpHteta5Kndu0gKMi6GLOrI0egdGlISDDHuXLBY4+Z6XvVq1sbm4iI3JwSKRGRlHA4zAY948ebYR0wFfiaNTM1qHPntja+/zh+3D11r0kTmDTJtNepY4oItmsHpUpZGmK2c+wYbNzo3kKsWDEz8uTtbZKnRx5RQisikpUokRIRuZkNG8wOpzt3mmN/f1PCbvBgKFPG0tCuFBsL335rkqclS0zuBxATYyrweXmZryFDLA0zW0lIgO+/N2uffvjBTNE7fhzy5DHnly/PlDVIREQkBZRIiYjcTIECZi1UcDD06QPPPgthYVZHlcyoUaZg4D//uNtq1TL53sMPa4PWjHbwoJnl+fHHcOKEu/2++8y6qKRESkmUiEjWpURKRORKZ8+a8uV//WX2ggIzB27+fKhXL9NM4Tt5EvLnd9ezuHTJJFFFikDXruarbFlrY8yu5s6FTp3cx/nzm/548kkoX966uEREJG0pkRIRAbPy/+23zRysmBhTOGLwYFPGHEwVPovFx8N335mpez/8YGpdNGlizvXpYx43aJApiwV6tD//NIlstWrmuH59s/9TvXpm7VPr1qq6JyLiiZRIiUj2tnOnqb4wZ467fFq1avD886akmsWcTtiyBWbMMCMdZ8+6z/38szuRKlnSfEnGiI01+z1NnQqrVpkEdvlyc65QITOgmT+/tTGKiEj6UiIlItnXN99Amzbu4wYNTALVqFGm2Pn077/N1lTbtrnbwsOhSxczVaxCBetiy67++MMkT5984l6P5uUFgYFmxDBp5ElJlIiI51MiJSLZh9NpVvonFYpo1Mh84q1dG4YNg3vvtTQ8ux127ICqVc1x3rymzc/P5Hvdu0Pjxpq6Z5W+feH9993HxYqZYo7du0PRotbFJSIi1rC0jtPq1atp1aoV4eHh2Gw2Fi5cmOy80+lk5MiRFCpUiBw5ctCoUSP27t2b7Jpz587RqVMngoODCQkJoUePHkRHR2fguxCRTC8hwUzdu+suk4k4naY9Z07Yu9fM0bIwidq2DQYOhMKFzT5PMTGm3WYz0/lOnIAvvjBbVimJyjibN8O5c+7je+8FHx+zD9TixXDgAIwcqSRKRCS7sjSRunTpEnfddReTJ0++5vnXX3+dd955hw8++IBffvmFnDlz0rRpU2JjY13XdOrUiR07dhAZGcmiRYtYvXo1vXr1yqi3ICKZmHdcHF5TppjydY8/Dtu3w6FDsGeP+yKLqvD9/Te8+y7cfbfJ7yZOhDNnzBSx3bvd11WtCqGhloSYLUVFwQcfQPXq5mvmTPe5Rx6Bo0dNAcfmzZXUiohkd5ZO7WvevDnNmze/5jmn08nEiRMZMWIErf+tljV79mwKFizIwoUL6dixI7t27eLHH39k48aN3HPPPQC8++67tGjRgjfffJPw8PBr3jsuLo64uDjXcVRUFAB2ux273X7L7yfpubdzD7Ge+tED/PMPzsmTaTxxIt7//vt25s+Po18/HE8/bTbxsbB/v/jCxhNPeGO3m3VYvr5O/vc/J126OGja1ImPj6XhZSoZ8e/R6YRffrExfboX8+bZiIkx/eLn5+TkSQd2u9nZ2MfHPd1SUk7/TfUM6kfPoH5MmZT+fGxOZ9IcF2vZbDYWLFhAm38Xfh84cIDSpUvz+++/UzVpwQBQt25dqlatyqRJk/j444957rnn+OeKHSgTEhIICAhg3rx5PPTQQ9d8rVGjRjF69Oir2ufOnUtgYGCavi8RyXgFNm0iYswYAC4VKMC+Nm040rAhDn9/S+I5ciQXDgeUKHERgBMnAunduzGlSp2nYcMj1K59jOBg/U/NComJNoYMqcOBAyGutiJFLtKkySHq1TtGcHC8dcGJiIglYmJieOyxx7hw4QLBwcHXvS7TFps4efIkAAULFkzWXrBgQde5kydPUqBAgWTnfXx8CA0NdV1zLcOHD2fQoEGu46ioKIoWLUqTJk1u+MO6GbvdTmRkJI0bN8bX1/eW7yPWUj9mQXv2YPvzT5ytWpnj5s1J2L2bLUWLUumll6iUIweVMjikf/6BL77wYvZsG7/95sX//udg/vxE1/m6de2UKZMTqPjvl1xLWv97TConn7TnE8Dcud6cOOGkfXsnPXo4iIgIwGarAKgsYlrQf1M9g/rRM6gfUyZpttrNZNpEKj35+/vjf42/TPv6+qbJL1Va3UespX7MAjZuhPHjYcECM13v8GEICgLAPmcOfy1ezF05cmRYPyYkQGSkWVezcKEphw1mSlhAgBfe3l54/bsytaJyp1S53X+PZ87ArFkwbZpZIrdrl7t8/Ntvm/aQEBsWLx32aPpvqmdQP3oG9eONpfRnk2kTqbB/yxOfOnWKQoUKudpPnTrlmuoXFhbG6dOnkz0vISGBc+fOuZ4vIh7G6TTZymuvwYoV7vZateD8eVciZYXWrU01tyRVqpjS2I89Bv8ZPJcM4HCYTXKnTjWJbdKU95w5Td2RpESqWDHLQhQRkSws0/7prWTJkoSFhbE8aat4zDDbL7/8QkREBAARERGcP3+eTZs2ua5ZsWIFDoeD++67L8NjFpF09uuvppRa06YmifLxMTvT7thhNtctUiTDQjl/Hj78EC5ccLe1aGGKEfTvb0pnb90KAwYoibLCzp1Qpgw0aQLz5pkkqkYN+OgjU07+4YetjlBERLI6S0ekoqOj2bdvn+v44MGDbNmyhdDQUIoVK8aAAQN45ZVXKFu2LCVLluTFF18kPDzcVZCiYsWKNGvWjJ49e/LBBx9gt9vp168fHTt2vG7FPhHJwkJCzAKXwEDo2RMGDcrQ4YTERDPCMXOmmU0YGwteXiYUgCeeMI/9/DIsJPlXQgIcOQKlSpnjUqVMkps7t6l837OnKTMvIiKSVixNpH777Tfq16/vOk4qANG1a1dmzpzJ0KFDuXTpEr169eL8+fPUqlWLH3/8kYCAANdz5syZQ79+/WjYsCFeXl60a9eOd955J8Pfi4iksQsXYMoUM3wwaZJpK1cOPvsMGjUyQz8ZZO9ekzzNng3Hjrnb77gDrqxPkyNHhoUk/zp8GKZPh48/hoAA+PNPk9wGBMCPP5o+UjFWERFJD5YmUvXq1eNG1ddtNhsvv/wyL7/88nWvCQ0NZe7cuekRnohYISlxmjLF7I7q5WXmypUubc536JCh4fz9tykMkfhvwb08ecyap27dzCxDmy1DwxHMNL3vvjNrn5YsMcvmwOTWBw+6f1Vq1LAuRhER8XyZttiEiGQz+/bBG2+YoZ+kcnd33AFDh2bY9D2HA1auNMUAhw0zbfnymbVPiYkmeXrwQbBoOyoBvvzS5NWnTrnbGjQwU/ceekh9IyIiGUeJlIhY75tvoG1bk8kA3H8/PP88tGyJq154OjpwwJTGnjXLTBWz2eDRR93524IF4O2d7mHINcTFQXS0uwxtwYImiSpY0FRE7NHDFJUQERHJaEqkRCTj/X97dx4XZbn/f/w1g4CiIu6AIu5bLrmkkam57xu5Zie1smNpx7WTnUqx/P4y26yT1TlW0mamHs29cgltUUtyr0g9GEcFTQ1BEVnm/v1xxYwkKigwML6fj8c8mPu6l7nuuRicj9d1fS7LgjNnXPOcOnUyk43atTMB1J13FngVzp2DZctg4ULYutVVXq6cCaIupSCq8P30kxm69/77Jbj99oYMHWrKO3SAtWuhWzfQEigiIuJOCqREpPBkZpoFfebMMb1PO3ea7h9/fzO0rxATSCxZYnozwFShWzfTwzFggJJGuEtKiklVvmABfPNNVqmN/fsrOudB2WxmqKWIiIi7KZASkYJ38SJ88AHMnWtS4IErxVqDBma7AIOoI0dMxr1ateAvfzFlQ4bAP/9pft53X6EuQVWsXbhwgaZNm3Lq1CkSExPz7bozZ5ocI1nrcnl5mZGd99+fQWZmFDaboicRESlaFEiJSMFJSjKr1r7yisnGBybt3fjx8OijBbpS7fnz8OWX1Zk3z4uoKFPWvLkrkCpbFnbtKrCX91gzZswgNDSUU6dO3dB1kpNNWvKsYZMXLpggqlYt01M4ZgwEB0N6usW6dflQcRERkXymQEpECk5UlMm6B1CtmllAd+xYE8UUkG+/NWsKLVlSguTkVoAZDta5s8m653AUSv4KjxQdHc1nn33GSy+9xNCsSUt5YFkmI+KCBWY5sP/8B3r0MPvGjzfDK7t0UfuIiEjxoEBKRPJPbCzExEDPnma7b18YONDkDB85Enx8CrwKL70Ey5cD2Kha9TzjxpVkzBgvQkML/KU9WkZGBmPHjmX+/Pk4srIr5lJiInz4oQmg9u51la9e7QqkQkNRG4mISLGiQEpEbtyePfD88yaDQ4UKZlKSn5/pWlixokBe8sIFc+mFC+GNN6BePVP+17+azHv33ptBUtJG+vTpjbe30u7dqBdeeIEWLVrQoUMHov4YKxkVZUZsBgVB+/aXZzc8fx4eftgkkEhNNWW+vmZe2tix5hwREZHiSoGUiFwfy4KvvjIZ+Navd5W3bAmnT5tAqgBecvt2s2bv4sVmChaY9Z9mzzbPu3c3D82tyT+HDh3irbfeYtcfk8q2bjXvfadOrmOqVzfJIvr0cS2K6+cH0dEmiGrSBB56CO6910yTExERKe4USIlI3u3caZJFbN9utu12GDrUzIdq0SLfX+7cOXj9dRNAxcS4ykNDzbynUaPy/SXlEl9//TUnTpygfv36pKVBcnI6kAxUAtYCbTl6FO6+22SyP34cSpc2c9NefRXKlIG2bc22iIiIp1AgJSJ55+dngihfX5Nebdo0qFMnX1/CslxfvEuUMB1fZ8+aNZ4GDzYv27GjEhMUhqFDh9K1a1cyM+H22yE5eRvwILAbyJ55MSnJLJiblYuia9dCrqyIiEghUSAlIld37pzJEpCQYOZBATRubLqHevSAwMB8eynLMp1dkZHwww8mA5/NZpaciogwvR2DB5ufUnj8/Pzw8/MjKsr8GkBlwAbkvPhWAWa1FxERKTIUSIlIzk6dMivW/vOf8PvvplvokUdcqdXycTxdQoLJ6hYZCQcOuMp37DA9IACTJuXby0keXLgAmzebjPVZS4HBXUDiFc9xHSciIuK5FEiJSHa//mpyiL/9tvkWDSYl3mOPQdWq+fpS330HzzwDn30GmZmmrGRJCA83Q/duuy1fX05y6cQJMzxv1SrYsAFSUqB3b/MrkBtBQQVbPxERkaJAgZSIuKxaZaKYrKimVSuYPh0GDbo8t/V1sCxIS3NldTt3znxhBwgLM8HT0KEmfbkUvhdeMGtw7dhh2ipLSAg0bGjSlVevDseOZd+fxWYz+5XWXEREbgYKpERudklJrklHHTqYRBJt25oAqnPnfEm1dvIkfPSRWfOpSxd45RVTftddJm354MHQoMENv4zkQVoa7NplmjrL+vWuRIytW5t1lPv1g+bNXb8Gr75q2stmyx5MZe2fNy9fYm4REZEiT4GUyM3IsmDdOpMKLy3NfHu22SAgwOQXz4exWWlp5iUiI02vU0aGKf/9dzNy0G43jyefvOGXklw6c8a0yerVZjhlcrKZz5Q1YnPSJBg+HPr2heDgnK8RHg7LlsHEiXD0qKu8enUTRIWHF/RdiIiIFA0KpERuJunp8MknMHcu7Ntnynx84OBBqF/fbOdDEDV7tum5OHXKVdamjVnzafhwpSwvTHFxsHSpCZ6+/to1ahNMdr1ffnEFUv375+6a4eEwYIBZjzk+3vzKtG+vnigREbm5KJASuRmkpMC778KLL5pkEmBWSX34YdMNcaXuh1w6dQrKl3d9kf79d1MWGAh/+YsJoBo3vqGXkFzKzDS9gaVKme0vvzTLfGVp0sQ1ZK9Nm+sPar28zNBMERGRm5UCKZGbwfr18Oij5nmVKmZc1sMPm+jnOqWnm+FhkZGmt2PdOtfiq488YuZCde9usqZLwUpOhi++MLlC1q4109uygqfevU279OtnHrVqubeuIiIinkJfcUQ80dGjZsxW585me+BAs3jugAGmeyiru+I67N9vgqcPPzRpsrN8+aUrkKpTxzyk4MTFmQB29Wrz3qelufZd2gtVubJJYS4iIiL5S4GUiCf56SeTw/rDD6FiRYiNNQszeXmZ7qMbcOoU9OwJ0dGusipV4N57TWzWtOmNVV1yLzXVpCPPWuYLzFJfWUP22rVzX91ERERuFgqkRDzB9u3w/PPw6aeusoYNTd7xGjWu65IZGSaB3y23mO2KFeH8eTNUr29fs+ZTr17g7X3j1ZecXbgAmzaZIXtHjpjhe2Bi4x49THDbv795KH28iIhI4VIgJVKc7doFU6ZAVJSrbOBAePxxuP3267rkTz+ZoXsffGACp4QEMxLQZjNloaFmuJgUjBMnYM0aEzxt2JC91+nIEahZ0zxftkxZ8kRERNxJgZRIceblZYKoEiVMerzHHoNGjfJ8mcREkxV94ULYscNVXqmSCaxatjTbrVvnS63lCmbNMo9LF7oNCXEN2bs0uaKCKBEREfdSICVSXFy4YLqK4uPhmWdMWbNm8K9/mTF2ISHXddklS2DUKDPvBswX9D59zLynPn3MMlOSv9LSYMsWkyjioYdMSnIwPy3LBKxZwVPz5qY3UERERIoWBVIiRV1iIrzxhlnh9uRJMynpr3+FatXM/oceytPlfvnFpC7PmvvUsqUJopo0MfOeRo50LdAq+efMGZMifvVqk/cjKcmUV6zoCqR694Zjx254WS8REREpBAqkRIqq48fhlVdMj1NysimrUcPktc7j+k9JSabnaeFC+PZbM41qxQqzr25d+PFHk5tCPR/579gxuOce+OYbs1hulqpVTdKOSxe1LVXqhjLTi4iISCFSICVSFK1aBUOGuBYHatLEJJAYNizXafIcDrOe0MKFsHy5K2mB3e7an/X8OqZVSQ4yM2HbNtP71L+/KatSBfbsMfuaNjXD9fr3h9tuc73/IiIiUvwokBIpKlJSwM/PPG/XziSQaNMGpk83Y77y2F00YIDJ/palUSMzdO/eeyEoKB/rfZNLTobPPzdD9tauhdOnoVYtEzDZbCbu/fhj0+NXq5a7aysiIiL5RYGUiDtZllkoaM4c0/u0daspr1jRjLcLDc3VZc6dM+mww8PB39+UdesGX30FI0aYAOq22zR0Lz8tWgTvv296/bI6DsGMurzjDhMXly5tynr1ck8dRUREpOAokBJxh8xMM0lpzhyIjjZlXl5w6JCZtATXDKIcDhN3RUaaIOr8ebOI7oMPmv0PPmjyUJQsWXC3cbNwOEwztWhhOgrBzHn6/HPzvF4918K4d9zhOkZEREQ8l/65FylMFy+abowXXoCDB01ZqVIwdqxZWDcXPVBHjsB775lHbKyrvF697IkKskYJyvVJSTGdhatXm0dCgklZ3qGD2X/ffWZx3P79oUEDt1ZVRERE3ECBlEhhWrnSla68QgWYMAEefdSsfJsLp06ZDqus7G9ly8Lw4WbNp7AwDd27UWfOmMQcq1bBxo2uBB0AZcqYIDYrkGrb1jxERETk5qRASqQgnThhep7uvNNsh4ebb+KDBpmxd2XKXPFUyzLDx77/HiZPNmWVKkH37mYdqNGjzWXU83T9LCv7XKZDh0znYJYaNVxZ9jp2BF9f99RTREREih4FUiIF4fBhePFFk3u8UiX473/Bx8dMntmy5aqnxsWZ0X+RkeYydjsMHepaf3flylxnQJccpKWZJli92vQ8dekC77xj9rVubRJDhIWZ4KlZM/XyiYiISM4USInkp1274PnnYelSk6EAICQE4uOvOv8pJcXknoiMNPNyLMuUlyljgqj0dNexCqLy7vRpWL/eBE6ff24WKM6yebN5v202E7SuW+e+eoqIiEjxoUBKJD/s2WMWzM1K4wama2P6dGjf/prdGosXwwMPuLbvusukLA8Pv+roP8mlu+6C/ftd24GB0Lev6XXq0kW9TiIiIpJ3CqRE8kN6ugmi7HaT/eHvf4fmzXM89Ngx+OAD00E1YoQpGzwYXn4Zhgwx2eC0cGveZWTAtm2m12nTpuwjKHv3Nk2TNd+pdWuzLSIiInK9FEiJ5FVaGnz0kcmH/cQTpqx1a5g3z3xTr137slNSU83cpoULYcMGM+qvRQtXIOXvn73HRHInKQm++MIET2vXmqx7WaKiXN1M/+//mRGXIiIiIvlFgZRIbp07BwsWwEsvmW4lX1+4/36oWtXsnzjxslO+/x7efdcM3UtMdJW3b2+y7jkc6hm5XosXm967S+ePlS8PffqYePaOOyy+/tqUe3m5p44iIiLiuYr0V7iIiAhsNlu2R8OGDZ37U1NTGT9+PBUrVqRMmTLcfffdnDhxwo01Fo/0228wY4bJhT1ligmigoJg9mxX3uwreO45eOstE0SFhMBTT5ls6Fu3mhhMQdS1ORzw3Xfw9NMmYUSWZs1MEFW/PkydaobynTxphk0OHWp6+UREREQKSpHvkbrlllvYuHGjc7tECVeVJ0+ezNq1a1m6dCnlypVjwoQJhIeH880337ijquKJ1qwx38qzVmatX9/Mf7r33myLCl28aNJpR0aaEX5165rysWPNOk+jR0PnzgqccislxcxzWrXKNEFCgikfOtTk8ABo1Ah++QXq1XNfPUVEROTmVeQDqRIlShAYGHhZ+dmzZ3nnnXdYtGgRnTt3BmDhwoU0atSI7du3c/vttxd2VcVTXLzoen7bbaZLpHVrk4Fv4EDnODHLgh9+MMHTokWu+TktWsCzz5rnvXq5vvjLtaWnm8QbGza4YleAsmWhZ0+TjCOLzaYgSkRERNynyAdSBw8eJDg4mJIlSxIWFsZzzz1HjRo1iI6OJj09na5duzqPbdiwITVq1GDbtm1XDaQuXrzIxUu+LCf9sahMeno66ZdOuMijrHNv5BriPrZvvsH+wgvYU1Jg0iTTjhUqQHS0+cZus4HDwflkB2+/bee99+zs3+9KaFCtmsXIkQ5GjHCgX4FrsyzYtw/27bMxcqTlLD92zIsLF+zUqGHRt6+Dvn0tOnSw8PEx+3P73urz6BnUjsWf2tAzqB09g9oxd3L7/tgsy7KufZh7rF+/nnPnztGgQQPi4+OZNWsWx44dY//+/axevZoxY8ZkC4gA2rRpQ6dOnXj+Kim6IiIimDVr1mXlixYtws/PL9/vQ4owh4OqO3dSb/lyKv78MwCW3c7G+fNJCQrK8ZSLF+2MGdOTlBRvvL0zuf32eDp3jqNZs9+U1OAa0tNtHDhQie++C+T77wP57Tc/SpRw8N576yldOgOAAwcqULp0BqGhSVrfSURERApdSkoK99xzD2fPnsX/KpOui3SPVK9LxkQ1a9aMtm3bEhoaypIlSyhVqtR1X/eJJ55gypQpzu2kpCRCQkLo3r37Vd+sa0lPT2fDhg1069YNb2/v676OFIL0dGyLF+P14ovYfvoJAMvHB8eoUaQ9+igp//0v3bp148ABbz74wM4PP9jYvDnT+cU+NtZOmTKZDBnioHz5qkBV991LMbB5s40FC+x88YWN5GRXdFSypEWXLtC6dXfn2lm9e+fPa+rz6BnUjsWf2tAzqB09g9oxd7JGq11LkQ6k/iwgIID69etz6NAhunXrRlpaGomJiQQEBDiPOXHiRI5zqi7l6+uL7yWJArJ4e3vnyy9Vfl1HCtDSpfDAA+a5vz88/DC2iRPxCgoiMT6d1a/CzJml2LPH9aX/hx/sZI0YffzxrFJ1QeUkJgaqVDHpyMFkKvzPf8zzwEDo29csjNuliw0/PxsFmUBUn0fPoHYs/tSGnkHt6BnUjleX2/emWOUQO3fuHIcPHyYoKIhWrVrh7e3Npk2bnPtjYmKIi4sjLCzMjbWUIun0aZNDO8vgwSaRxJw5EBcHc+YQfTyI8HAIDS3BO+80Zc8eGz4+JsHB2rUm34TkLCPDpHSfNg0aNICGDWHJEtf+AQNM6vcdO0z2+AULzFpPGkkrIiIixVWR7pGaNm0a/fr1IzQ0lOPHjzNz5ky8vLwYMWIE5cqV44EHHmDKlClUqFABf39/Hn30UcLCwpSxT1zi4uDll80398qVTdeItzf4+MCOHaRn2Mj6T4fff4cVKwBs1KmTyN/+VpaRI72oWNGdN1B0XbhgUpOvWgXr1rmyFoJ5i48edW2HhLgyGYqIiIh4giLdI3X06FFGjBhBgwYNGDp0KBUrVmT79u1UrlwZgFdeeYW+ffty991306FDBwIDA1m+fLmbay3X8vrrr9O6dWt8fX0ZOHBgtn1PP/00TZs2pUSJEkyaNOn6X+TAARg1CurUgVdfNQsTlS8Px49z5gzMnw+tb7MxfbrrlM6dzbq70dHpvPTSFh5+2KEg6k9SUlzPU1NhxAj48EMTRFWoYJbXWrIETp1S4CQiIiKerUj3SC1evPiq+0uWLMn8+fOZP39+IdVI8kNwcDBPPfUUGzdu5Oil3RZA3bp1mTt3LgsWLLi+i+/fD08+abpJsnTqROZj0/nc0Y3Ix2ysXAlpaWbXiRPwwgtmoVy7HWbNMum1//e/67w5D+NwwM6d5u1cvRrKlIGs9a7Ll4f77oOKFc0wvTvugBJF+i+KiIiISP7R1x4pdOHh4QDs3r37skBq1KhRAHzyySfXd/HkZPOt32aDQYPg8ceZs7kNrz0A8fGuw269FUaPhnvuMQGUuKSkwKZN5m1cswYSElz7vL0hMRGy8ru8+647aigiIiLifgqkpPjKyDDjyE6cgMmTTVlYGBdmPIfvsIHYGzcE4MTHJoiqVAlGjjQB1K23uq3WRd6YMdkTRZQtCz17mix7vXq5gigRERGRm5kCKSkWMjPhq69MQFStwgXuPLgQ+0svwJEjUKoUmff8hY27KxEZCStWTGddR+jc2Jz7yCPQsaNZn8jHx513UXRYFuzda4brrVpl5jnVr2/29e5tsuv172+G7HXsqPdNRERE5M8USEmRt3w5TJwI547+ziO8QRdexc5vAGRUqMzmJhP5WwsfYi4ZuvfFFyZ5BEC9euZxs0tLg6goV/AUF+fat3o1TJ1qno8caeY+2Ww5XkZEREREUCAlRdzy5WbJp17WWhYznLKcAyCWmrzINBaeGcOFrWYxogoVzJyn0aOhZUs3VroI2rnTBJbJya6yUqWga1fT89S3r6tcCSNERERErk1fmaTQZWRkOB8Oh4PU1FTsdjs+Pj6kp6eTmZlJZmYmGWnp/O1vqViWF7togQ9p7KUpc5jOEoaS+cevb+/eZl5Pv37g6+vmmysCYmJMj1PFinD//aascWOTjTAw0LxP/fpBly5aEFdERETkeimQkkI3e/ZsZs2a5dwuVaoUHTt2JCoqirFjx/Lee+9dcvQbwCjiiaQV0RzgFiD7mLPHHoO77iqMmhdNGRkmJXnWkL2DB01506auQMrPz8yJqlNHWQpFRERE8oMCKSl0ERERREREZC+0LPjiCyLj4oj8o8iBjbocIpbaABygSY7XuzSt+c1m4kT44AP4/XdXmbc3dOpkhuw5HK7ASfPERERERPKPAilxr8xMWLYMnn8edu0CIJ0SfMRI5vJ3ZxB1NUFBBV3JouHXX00SjQcfdCWC+P1386hQAfr0McFT9+7g7+/euoqIiIh4Og3yEbfK+OBjGD7cBFF+fvw+ehJ1OMy0ipH0mNSYqlWvnD3OZoOQEGjfvnDr/OijjxISEoK/vz/VqlVj0qRJpKWl5fvrOBzw3Xfw1FPQvDnUrAkPPQS7d7uOmToVtm41S2m9/75JzKEgSkRERKTgKZCSwnX2LOzeTUyMmdtU87Eh/OrfFCIiIC6O8gtfYeHGGhw7Bq+8Am+8YU77czCVtT1vHnh5FeYNwCOPPMLPP/9MUlISe/bsYc+ePcydOzffrr9vH4wdC9WqQdu28H//Z+Y32e3QoQOkprqObd7cBJLKtCciIiJSuPT1SwpHQgLpL87DeuNNTlpVaJz6Mw68AF861thDzHSbM+Nely6u08LDzci/iRPh6FFXefXqJogKDy/MmzAaNWrkfG5ZFna7nYNZGR6uw/HjpvepenWz/dtv8Pbb5nnZstCrl8my16uXycQnIiIiIu6nHikpWIcOwbhxZITUxPul5/G5kMTZVB9CbMfo2xdWroRDh21XTVseHg5HjsCXX8KiReZnbKx7gqgsc+bMoUyZMlSpUoU9e/Zwxx2P8vHHZsHbzMyrn2tZsGcPPPsstGljep5efNG1v317mDLFzIc6dQo++QTuvVdBlIiIiEhRoh4pKRDnd/5EyTkReK1YBg4HJYBvCeOdyk9Qc3wfvn7A7uyByQ0vr6KV4nz69OlMnz6d1177iaee+ohx4wKd+6pXh1dfzR7oORywYYMrRfn//pf9esePu557e8NLLxXwDYiIiIjIDVEgJfnGsmDnTliwAOI+/I3PLiwxO3r3JumR6SR73cm/u9kKfU5TQVm+HCZNaoRlNQdGAxsBOHbMJH346CMYMcIca7OZbHtZwxNLlYJu3cyQvT59bp7MgyIiIiKeQoGU3LDE05lsm76SXesTePLYI3+UtmfZLTMZvCgcmjXDH+jhzkrms8xMM2/LsgDSAdccKVNmhuOFh4Ovrwmk7r/frHnVvz907mwWyRURERGR4kmBlOTKxYsXmTBhAhs3buTUqVNUq1aNaRMnY/+wBHdum0sv6xfupAz/9BlO58EVGDvWRseOEXCF1OXF1cmT51i1aimBgYM4erQcsB+YTU5hosMBCxfCuHFme9aswqypiIiIiBQkBVKSKxkZGQQFBbFy5Uaa1qzMjiefpNf4h/nEsqgPJNkD+LnrBH58y075Wu6u7Y3ZuxdWr67NV1/ZOXnSzF86ftz0JiUm2mjTZhE//jgNuAhUAe4Gco6SypUrxIqLiIiISKFRICXX5HDAtm2liYl5hog261hWsjW3n02kE7DFryz1H4wg9NmxtPEv6+6qXlFsrJm/FR+fPTDKev755yaDHkBUlJ133ml6hSuV5sknN+DvD506Xft1NfdJRERExDMpkJIrio83Q9PefdvB4ViTKT+EJpB+jtR69fju1Cmaj3+DbY2Hc+QHk7a7MBNJnD4NMTE5B0bx8fD++9CypTl25UqYPPnK17o0a17Tphbt2h2jVatAqlf3IigIgoPNIygI/P1d6z4dO+aaE3Upm83sb98+f+9ZRERERIoGBVJymSNHTCKFuDV7meZ4nudJ5X7//3DvvfDggzWwZW6jx5RXOBV3nIjZQ53n5ZT2O6/S0q7ca3T8uFlvqVkzc+yHH8KkSVe+VlycK5CqXx/atXMFQ5cGRsHBULOm67xOnSwuXNhJ79698fbOOTL08jL3OniwCZouDaZsf8wLmzevcANLERERESk8CqSKoNdff53IyEj27dtHr169+PTTTwE4efIkkydPZsuWLSQlJVGnTh1mzZpF//79b/g1L1402eWwLCr9+BWPrJlDD8d6ACybjd7bj1CqUU0sy6Jnz3fY+tUvmHTfrjWds9J+L1t2eTCVlgYJCTkHSI89Bo0bm+PeessEcVdy+LArkKpRA2rVyjkwCg6GFi1c5/XubR75KTzc3OvEia605mACynnz3LtgsIiIiIgULAVSRVBwcDBPPfUUGzdu5Ogl39DPnTtHixYteP755wkODmbt2rUMHz6c77//nsZZkUgepKWZBWIXLIDfTzvY8dRqmDOHMtu30wOw7HZsQ4Zge/xxZxD1yCPj+fLLHcAmIHsmhaxemTFjTGDUsKHZ/te/XJnrctKvnyuQCgoCH5+cA6OgIGjVynXeoEHm4U7h4TBgAHz1lQkOg4IKf4ijiIiIiBQ+BVJFUPgfXRm7d+/OFkjVrl2badOmObf79etHgwYN2L59e54CqV9+gbffhvfeg5MnTdlf+BAGjjIbvr4wejS2adOgbl3neRMmTOCLL74hPX0zUP6K109Kgk8+gZkzzXbFiuant/flwVFwMDRqdOm9Q2qqa3hcceDlBXfd5e5aiIiIiEhhUiBVjJ08eZKffvqJW25pRlTUtXtEoqIgIgK2bIHSnCOUX/EKuoUxY+CBe4bAoNlw991mrFpgYLZz9+79lTfeeANvb18g9JI99wJvXfZal75+797w229QoQLY7ZcdesXzRERERESKKgVSxVRaWhrDhw/n9tuHMnhw68vm6GQlfcjMdAUnZ8/C/i2niOB1Jnv/E1vVKpQ6fIASPnagFPz0U7ZI5uxZk+1uyRL44otQnnrKokuX3KX9vvNO13M/P/MQEREREfEUCqSKobS0NAYPHkxSkh/R0Qsu23/smOlYqlcPRoyAWbOAX3+l38aXOe79Nj7pKZAOlKwACUdN1gYALy+Sksy8qSVL4LPPzDyqLPv2mR4tpf0WERERkZudAqliJi0tjSFDhnDxYhoJCSsBn8uOyQpwDh6Ere8cxDoyG9uiRdgzMszRLVvC9Ommy+qSHiiHwySIiI93XatRIxg2DIYMcSWEUNpvEREREbnZXWPGirhDRkYGqampZGRk4HA4SE1NJS0tjfT0dIYOHcr58+eZOvVTjh3zvea1nnngV2zvvw8ZGdClC2zYADt3cr73EJb8x4tx41zBkN0OvXpBgwYwYwbs3w8//miSRlyayyIr7Xe1atlfq3r1nFOfi4iIiIh4GvVIFUGzZ89m1qxZzu1SpUrRsWNHZs2axcqVKylZsiT9+1e65Ix//PGw6M06AkngXR4A4GiDLvD3v8OQIaQ0bs26dbBkGKxZAxcumLPvvx/atDHPX38dSpa8dtY8pf0WERERkZuZAik3udKiuwARERFERETkeJ71R/dRVJQr6UMJ0hnGhzzO8zRlP2fxZxmDSaIcQcE2fmj4PHPnmrlPKSmua9WubYbtVa3qKitVKvf3oLTfIiIiInKzUiDlJldadDe32reHusEp9Dz+LlN5kZr8CkASZfkXf8ULByEh5ritW826TgA1a8LQoebRsmXxWq9JRERERKSoUCDlJldadDe3vDZ8xv5zf8GXUwCcoArzmMSbPMxZAgB4e57pNerQAZ580gzFa91awZOIiIiIyI1SIFWcWJYrCmrQAJ/zv3MmoBazzj/Gv9NHk4prXF6dOq6kD15eMHu2G+orIiIiIuKhFEgVIZmZV0je8PPPMHeuyQ7x8cfm4Fq1GBW6hUX/bUvmH80YEGAS802cCO3aue02REREREQ8ngKpImL5chMAXTrKr2/lHbxV+3mCv/sUm2XhwIZj9hxK1AkFoGL/dlRebNZ4GjoU7rjDpDAXEREREZGCpa/dRcDx42aBWxNEWXTnczbTidW/3U61HSuwWRYrGMgdfEtUbKjzvGeeMee89hrceaeCKBERERGRwqIeKTfJyMggIyODtLQM9u1zYFmpgJ37+Jj3GA1AOiX4kHuZy985VakRgwdDcLDrGmXLuqXqIiIiIiI3PQVSbvLnRXehFNCRZazlGWbwH+7mFSZzlBAANi42859ERERERMT9NBjMTSIiIrAsi0WLLCDrEUUKpanDYabysjOIAjh50l01FRERERGRP1Mg5WZBQZeXZebQUZjTcSIiIiIi4h4KpNysfXuoXv3Ki+TabBASYo4TEREREZGiwWMCqfnz51OzZk1KlixJ27Zt+e6779xdpVzx8oJXXzXP/xxMZW3Pm/fHelIiIiIiIlIkeEQg9cknnzBlyhRmzpzJDz/8QPPmzenRowcni8nEovBwWLYMqlXLXl69uikPD3dPvUREREREJGceEUi9/PLLjB07ljFjxtC4cWPeeust/Pz8ePfdd91dtVwLD4cjR+DLL2HRIvMzNlZBlIiIiIhIUVTs05+npaURHR3NE0884Syz2+107dqVbdu25XjOxYsXuXjxonM7KSkJgPT0dNLT06+7Llnn3sg12rVzPXc4zEMKV360o7if2tEzqB2LP7WhZ1A7ega1Y+7k9v2xWZZlFXBdCtTx48epVq0a3377LWFhYc7yv//972zZsoUdO3Zcdk5ERMSf1nAyFi1ahJ+fX4HWV0REREREiq6UlBTuuecezp49i7+//xWPK/Y9UtfjiSeeYMqUKc7tpKQkQkJC6N69+1XfrGtJT09nw4YNdOvWDW9v7/yoqriB2tEzqB09g9qx+FMbega1o2dQO+ZO1mi1ayn2gVSlSpXw8vLixIkT2cpPnDhBYGBgjuf4+vri6+t7Wbm3t3e+/FLl13XEvdSOnkHt6BnUjsWf2tAzqB09g9rx6nL73hT7ZBM+Pj60atWKTZs2OcscDgebNm3KNtRPREREREQkvxT7HimAKVOmMGrUKFq3bk2bNm2YN28e58+fZ8yYMe6umoiIiIiIeCCPCKSGDRvGb7/9xowZM0hISODWW2/ls88+o2rVqu6umoiIiIiIeCCPCKQAJkyYwIQJE9xdDRERERERuQkU+zlSIiIiIiIihU2BlIiIiIiISB4pkBIREREREckjBVIiIiIiIiJ5pEBKREREREQkjxRIiYiIiIiI5JHHpD+/EZZlAZCUlHRD10lPTyclJYWkpCS8vb3zo2riBmpHz6B29Axqx+JPbegZ1I6eQe2YO1kxQVaMcCUKpIDk5GQAQkJC3FwTEREREREpCpKTkylXrtwV99usa4VaNwGHw8Hx48cpW7YsNpvtuq+TlJRESEgI//vf//D398/HGkphUjt6BrWjZ1A7Fn9qQ8+gdvQMasfcsSyL5ORkgoODsduvPBNKPVKA3W6nevXq+XY9f39//XJ6ALWjZ1A7ega1Y/GnNvQMakfPoHa8tqv1RGVRsgkREREREZE8UiAlIiIiIiKSRwqk8pGvry8zZ87E19fX3VWRG6B29AxqR8+gdiz+1IaeQe3oGdSO+UvJJkRERERERPJIPVIiIiIiIiJ5pEBKREREREQkjxRIiYiIiIiI5JECKRERERERkTxSIJWP5s+fT82aNSlZsiRt27blu+++c3eV5AoiIiKw2WzZHg0bNnTuT01NZfz48VSsWJEyZcpw9913c+LECTfWWAC2bt1Kv379CA4Oxmaz8emnn2bbb1kWM2bMICgoiFKlStG1a1cOHjyY7ZgzZ84wcuRI/P39CQgI4IEHHuDcuXOFeBdyrXYcPXr0ZZ/Pnj17ZjtG7ehezz33HLfddhtly5alSpUqDBw4kJiYmGzH5ObvaFxcHH369MHPz48qVarw2GOPkZGRUZi3clPLTTveddddl30ex40bl+0YtaN7vfnmmzRr1sy5yG5YWBjr16937tdnseAokMonn3zyCVOmTGHmzJn88MMPNG/enB49enDy5El3V02u4JZbbiE+Pt75+Prrr537Jk+ezOrVq1m6dClbtmzh+PHjhIeHu7G2AnD+/HmaN2/O/Pnzc9w/d+5cXnvtNd566y127NhB6dKl6dGjB6mpqc5jRo4cyYEDB9iwYQNr1qxh69atPPTQQ4V1C8K12xGgZ8+e2T6fH3/8cbb9akf32rJlC+PHj2f79u1s2LCB9PR0unfvzvnz553HXOvvaGZmJn369CEtLY1vv/2W9957j8jISGbMmOGOW7op5aYdAcaOHZvt8zh37lznPrWj+1WvXp05c+YQHR3Nzp076dy5MwMGDODAgQOAPosFypJ80aZNG2v8+PHO7czMTCs4ONh67rnn3FgruZKZM2dazZs3z3FfYmKi5e3tbS1dutRZ9tNPP1mAtW3btkKqoVwLYK1YscK57XA4rMDAQOuFF15wliUmJlq+vr7Wxx9/bFmWZf34448WYH3//ffOY9avX2/ZbDbr2LFjhVZ3cflzO1qWZY0aNcoaMGDAFc9ROxY9J0+etABry5YtlmXl7u/ounXrLLvdbiUkJDiPefPNNy1/f3/r4sWLhXsDYlnW5e1oWZbVsWNHa+LEiVc8R+1YNJUvX956++239VksYOqRygdpaWlER0fTtWtXZ5ndbqdr165s27bNjTWTqzl48CDBwcHUrl2bkSNHEhcXB0B0dDTp6enZ2rNhw4bUqFFD7VmExcbGkpCQkK3dypUrR9u2bZ3ttm3bNgICAmjdurXzmK5du2K329mxY0eh11muLCoqiipVqtCgQQMefvhhTp8+7dyndix6zp49C0CFChWA3P0d3bZtG02bNqVq1arOY3r06EFSUpLzf9KlcP25HbN89NFHVKpUiSZNmvDEE0+QkpLi3Kd2LFoyMzNZvHgx58+fJywsTJ/FAlbC3RXwBKdOnSIzMzPbLyBA1apV+fnnn91UK7matm3bEhkZSYMGDYiPj2fWrFm0b9+e/fv3k5CQgI+PDwEBAdnOqVq1KgkJCe6psFxTVtvk9DnM2peQkECVKlWy7S9RogQVKlRQ2xYhPXv2JDw8nFq1anH48GH+8Y9/0KtXL7Zt24aXl5fasYhxOBxMmjSJdu3a0aRJE4Bc/R1NSEjI8fOatU8KV07tCHDPPfcQGhpKcHAwe/fu5fHHHycmJobly5cDaseiYt++fYSFhZGamkqZMmVYsWIFjRs3Zvfu3fosFiAFUnJT6tWrl/N5s2bNaNu2LaGhoSxZsoRSpUq5sWYiMnz4cOfzpk2b0qxZM+rUqUNUVBRdunRxY80kJ+PHj2f//v3Z5plK8XOldrx07mHTpk0JCgqiS5cuHD58mDp16hR2NeUKGjRowO7duzl79izLli1j1KhRbNmyxd3V8nga2pcPKlWqhJeX12UZUE6cOEFgYKCbaiV5ERAQQP369Tl06BCBgYGkpaWRmJiY7Ri1Z9GW1TZX+xwGBgZelgAmIyODM2fOqG2LsNq1a1OpUiUOHToEqB2LkgkTJrBmzRq+/PJLqlev7izPzd/RwMDAHD+vWfuk8FypHXPStm1bgGyfR7Wj+/n4+FC3bl1atWrFc889R/PmzXn11Vf1WSxgCqTygY+PD61atWLTpk3OMofDwaZNmwgLC3NjzSS3zp07x+HDhwkKCqJVq1Z4e3tna8+YmBji4uLUnkVYrVq1CAwMzNZuSUlJ7Nixw9luYWFhJCYmEh0d7Txm8+bNOBwO55cDKXqOHj3K6dOnCQoKAtSORYFlWUyYMIEVK1awefNmatWqlW1/bv6OhoWFsW/fvmxB8YYNG/D396dx48aFcyM3uWu1Y052794NkO3zqHYsehwOBxcvXtRnsaC5O9uFp1i8eLHl6+trRUZGWj/++KP10EMPWQEBAdkyoEjRMXXqVCsqKsqKjY21vvnmG6tr165WpUqVrJMnT1qWZVnjxo2zatSoYW3evNnauXOnFRYWZoWFhbm51pKcnGzt2rXL2rVrlwVYL7/8srVr1y7r119/tSzLsubMmWMFBARYK1eutPbu3WsNGDDAqlWrlnXhwgXnNXr27Gm1aNHC2rFjh/X1119b9erVs0aMGOGuW7opXa0dk5OTrWnTplnbtm2zYmNjrY0bN1otW7a06tWrZ6WmpjqvoXZ0r4cfftgqV66cFRUVZcXHxzsfKSkpzmOu9Xc0IyPDatKkidW9e3dr9+7d1meffWZVrlzZeuKJJ9xxSzela7XjoUOHrGeeecbauXOnFRsba61cudKqXbu21aFDB+c11I7uN336dGvLli1WbGystXfvXmv69OmWzWazvvjiC8uy9FksSAqk8tE///lPq0aNGpaPj4/Vpk0ba/v27e6uklzBsGHDrKCgIMvHx8eqVq2aNWzYMOvQoUPO/RcuXLAeeeQRq3z58pafn581aNAgKz4+3o01FsuyrC+//NICLnuMGjXKsiyTAv3pp5+2qlatavn6+lpdunSxYmJisl3j9OnT1ogRI6wyZcpY/v7+1pgxY6zk5GQ33M3N62rtmJKSYnXv3t2qXLmy5e3tbYWGhlpjx4697D+l1I7ulVP7AdbChQudx+Tm7+iRI0esXr16WaVKlbIqVapkTZ061UpPTy/ku7l5Xasd4+LirA4dOlgVKlSwfH19rbp161qPPfaYdfbs2WzXUTu61/3332+FhoZaPj4+VuXKla0uXbo4gyjL0mexINksy7IKr/9LRERERESk+NMcKRERERERkTxSICUiIiIiIpJHCqRERERERETySIGUiIiIiIhIHimQEhERERERySMFUiIiIiIiInmkQEpERERERCSPFEiJiIiIiIjkkQIpEREp0u666y4mTZrk7mrkyZEjR7DZbOzevdtZ9s0339C0aVO8vb0ZOHCg2+omIiL5Q4GUiIi43ejRo7HZbJc9Dh06xPLly3n22Wdv6Po2m41PP/30uo8bPXr0DQc/U6ZM4dZbbyU2NpbIyMgbupaIiLhfCXdXQEREBKBnz54sXLgwW1nlypXx8vK66nlpaWn4+PgUZNXyxeHDhxk3bhzVq1d3d1VERCQfqEdKRESKBF9fXwIDA7M9vLy8LhvaV7NmTZ599lnuu+8+/P39eeihh0hLS2PChAkEBQVRsmRJQkNDee6555zHAwwaNAibzebcvhGfffYZd955JwEBAVSsWJG+ffty+PDhHI/NGuZ3+vRp7r//fmw2m3qkREQ8gAIpEREpdl588UWaN2/Orl27ePrpp3nttddYtWoVS5YsISYmho8++sgZMH3//fcALFy4kPj4eOf2jTh//jxTpkxh586dbNq0CbvdzqBBg3A4HJcdGxISQnx8PP7+/sybN4/4+HiGDRt2w3UQERH30tA+EREpEtasWUOZMmWc27169WLp0qU5Htu5c2emTp3q3I6Li6NevXrceeed2Gw2QkNDnfsqV64MQEBAAIGBgdesx4gRIy4bTnjx4kX69Onj3L777ruz7X/33XepXLkyP/74I02aNMm2z8vLi8DAQGw2G+XKlctVHUREpOhTICUiIkVCp06dePPNN53bpUuXvuKxrVu3zrY9evRounXrRoMGDejZsyd9+/ale/fu11WPV155ha5du2Yre/zxx8nMzHRuHzx4kBkzZrBjxw5OnTrl7ImKi4u7LJASERHPpEBKRESKhNKlS1O3bt1cH3upli1bEhsby/r169m4cSNDhw6la9euLFu2LM/1CAwMvKweZcuWJTEx0bndr18/QkNDWbBgAcHBwTgcDpo0aUJaWlqeX09ERIonBVIiIuIR/P39GTZsGMOGDWPw4MH07NmTM2fOUKFCBby9vbP1KN2I06dPExMTw4IFC2jfvj0AX3/9db5cW0REig8FUiIiUuy9/PLLBAUF0aJFC+x2O0uXLiUwMJCAgADAZO7btGkT7dq1w9fXl/Lly1/3a5UvX56KFSvy73//m6CgIOLi4pg+fXo+3YmIiBQXytonIiLFXtmyZZk7dy6tW7fmtttu48iRI6xbtw673fwz99JLL7FhwwZCQkJo0aLFDb2W3W5n8eLFREdH06RJEyZPnswLL7yQH7chIiLFiM2yLMvdlRARERERESlO1CMlIiIiIiKSRwqkRERERERE8kiBlIiIiIiISB4pkBIREREREckjBVIiIiIiIiJ5pEBKREREREQkjxRIiYiIiIiI5JECKRERERERkTxSICUiIiIiIpJHCqRERERERETySIGUiIiIiIhIHv1/X3gngpN16LgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Month  Sum_First_Half  Sum_Last_Half     Slopes      Length\n",
            "0       1        7.238095       6.714286   0.719603   23.642701\n",
            "1       2       26.428571      20.523810   0.214634   29.952684\n",
            "2       3       55.714286      26.809524   0.647863   99.577270\n",
            "3       4      139.285714      80.952381   0.844716  163.378704\n",
            "4       5      264.095238     186.380952   2.035135  119.856121\n",
            "5       6      316.952381     293.952381 -12.344262  107.923820\n",
            "6       7      308.238095     401.523810 -14.506667  103.864948\n",
            "7       8      315.380952     297.904762   1.299905   82.549030\n",
            "8       9      265.047619     232.476190   0.934015  140.223372\n",
            "9      10      162.571429     136.761905   0.805289  159.513731\n",
            "10     11       38.333333      36.714286   0.842610   32.442588\n",
            "11     12       13.523810      15.809524   1.446970   11.055929\n",
            "Plot Rainfall std\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Month  Sum_First_Half  Sum_Last_Half    Slopes      Length\n",
            "0       1       11.797901      11.866520  1.862050   23.073534\n",
            "1       2       22.714690      32.194128  0.184699   34.655325\n",
            "2       3       56.793611      38.488465  1.448833   25.613082\n",
            "3       4       71.342934      59.568008  0.854485   35.564971\n",
            "4       5       98.381352      82.671928  0.448061   54.769713\n",
            "5       6      148.363229     105.066872 -1.701094  113.245315\n",
            "6       7       90.973021     202.693024 -0.576448   65.376880\n",
            "7       8      147.613169     170.042908  0.968117   25.878512\n",
            "8       9      166.206040     188.042979  2.060411  105.938819\n",
            "9      10      119.949811      92.736134  0.340292   72.050894\n",
            "10     11       51.740055      69.524918  1.327685   37.686868\n",
            "11     12       29.066508      39.421592  1.595674   32.519022\n"
          ]
        }
      ]
    }
  ]
}