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      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "VI3dohtzKPTP"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import xgboost as xgb\n",
        "from sklearn.metrics import mean_squared_error, root_mean_squared_error\n",
        "color_pal = sns.color_palette()\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('./march_hourly.csv', delimiter=\";\")\n",
        "df = df.set_index('Start date')\n",
        "df.index = pd.to_datetime(df.index)\n",
        "\n",
        "df.rename(columns={\"Germany/Luxembourg [€/MWh] Original resolutions\": \"Price\"}, inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "3Qb0vZmKKQv1",
        "outputId": "4dc8585a-8287-4906-f717-9ef2877fff56"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-6d8c505ea55e>:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  df.index = pd.to_datetime(df.index)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  End date  Price\n",
              "Start date                                       \n",
              "2024-03-17 00:00:00   Mar 17, 2024 1:00 AM  64.99\n",
              "2024-03-17 01:00:00   Mar 17, 2024 2:00 AM  60.49\n",
              "2024-03-17 02:00:00   Mar 17, 2024 3:00 AM  55.97\n",
              "2024-03-17 03:00:00   Mar 17, 2024 4:00 AM  58.75\n",
              "2024-03-17 04:00:00   Mar 17, 2024 5:00 AM  60.45\n",
              "...                                    ...    ...\n",
              "2024-03-30 19:00:00   Mar 30, 2024 8:00 PM  83.53\n",
              "2024-03-30 20:00:00   Mar 30, 2024 9:00 PM  74.11\n",
              "2024-03-30 21:00:00  Mar 30, 2024 10:00 PM  71.47\n",
              "2024-03-30 22:00:00  Mar 30, 2024 11:00 PM  70.83\n",
              "2024-03-30 23:00:00  Mar 31, 2024 12:00 AM  65.74\n",
              "\n",
              "[336 rows x 2 columns]"
            ],
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              "      <th></th>\n",
              "      <th>End date</th>\n",
              "      <th>Price</th>\n",
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              "      <th>Start date</th>\n",
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              "    <tr>\n",
              "      <th>2024-03-17 00:00:00</th>\n",
              "      <td>Mar 17, 2024 1:00 AM</td>\n",
              "      <td>64.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 01:00:00</th>\n",
              "      <td>Mar 17, 2024 2:00 AM</td>\n",
              "      <td>60.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 02:00:00</th>\n",
              "      <td>Mar 17, 2024 3:00 AM</td>\n",
              "      <td>55.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 03:00:00</th>\n",
              "      <td>Mar 17, 2024 4:00 AM</td>\n",
              "      <td>58.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 04:00:00</th>\n",
              "      <td>Mar 17, 2024 5:00 AM</td>\n",
              "      <td>60.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>2024-03-30 19:00:00</th>\n",
              "      <td>Mar 30, 2024 8:00 PM</td>\n",
              "      <td>83.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 20:00:00</th>\n",
              "      <td>Mar 30, 2024 9:00 PM</td>\n",
              "      <td>74.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 21:00:00</th>\n",
              "      <td>Mar 30, 2024 10:00 PM</td>\n",
              "      <td>71.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 22:00:00</th>\n",
              "      <td>Mar 30, 2024 11:00 PM</td>\n",
              "      <td>70.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 23:00:00</th>\n",
              "      <td>Mar 31, 2024 12:00 AM</td>\n",
              "      <td>65.74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>336 rows × 2 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 336,\n  \"fields\": [\n    {\n      \"column\": \"Start date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-03-17 00:00:00\",\n        \"max\": \"2024-03-30 23:00:00\",\n        \"num_unique_values\": 336,\n        \"samples\": [\n          \"2024-03-20 00:00:00\",\n          \"2024-03-21 14:00:00\",\n          \"2024-03-29 10:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"End date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 336,\n        \"samples\": [\n          \"Mar 20, 2024 1:00 AM\",\n          \"Mar 21, 2024 3:00 PM\",\n          \"Mar 29, 2024 11:00 AM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.08469158915915,\n        \"min\": -8.45,\n        \"max\": 174.7,\n        \"num_unique_values\": 314,\n        \"samples\": [\n          76.87,\n          72.84,\n          72.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 5))\n",
        "\n",
        "# Plot the data trend (line)\n",
        "df.plot(style='-', ax=ax, label='Trend', title='Price in [€/MWh]')\n",
        "\n",
        "# Plot the actual data points (dots)\n",
        "df.plot(style='.', ax=ax, label='Data Points')\n",
        "\n",
        "ax.legend(['Trend', 'Data Points'])\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "jjS0qTsJKkS8",
        "outputId": "a10954a4-c8ff-41f3-87ba-7a2d1c25409a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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XVGA1qa644hOYMeMkfOMbayv+s0eCsk+xDIfD2LBhA97//vfDYBjaaG/t2rW44YYbYLfbcc455+Cmm26C3W4f1s/UaDh7gIiIiIhouILbu+Hr/j0gy4AgoG7llTCcvLLayyKiUUqSRn7vsTvuuCfl31df/RlcfPHHsGrVuept48d3VnpZY4JGI0CrHRoPyrdFWdkDZJs2bcLAwMCQ8kq9Xo9LL70Uy5cvh9VqxRtvvIE77rgD27Ztw1/+8hfodLqif6bVahrusomIiIiIxrSIqw/9SnAMAGQZvu570DxnCbTWpuoujohGpUBAgxMnxIyBjpFg/vx5Q27r6OhIe7siEAjAaDSWc1kAYoEiQRi5z20mkiRAFEXYbHXDeh7LHiB77LHH0NzcjNNPPz3l9tbWVqxdu1b99+LFizFjxgxcddVV2LhxI9asWVP0z3S5/IhGpaK/n4iIiIhorAsf3pMIjilkCY4De6EbX/4LOSIae0KhICRJQjQqIxIZPdf0yb/P3XffiT/96Q/45S9/h1/+8v9h166d+NznvohPfOJT2LdvL+6443Zs3boZ0WgUCxYswvXXfyUl42z58lPxxS9eg0AggL/97VFIUhTLlp2JG274KkymRLLQW2+9gZ///CfYt28Pxo/vxH//93WQ5dgQxdH03AKx51eSJDidPvj90SFft9lMebULK2uAzOv14plnnsEll1wCjUaT8/4rV65EXV0d3n777WEFyKJRadT9wYmIiIiIKkk2t0KGAAFJQTJBhGxu4bk2EZVFNCqnvV2WZYTC1Tnu6HUihHxr9PIUDofx3e9+Ex/96Cdw1VX/A6vVhsOHD+Hqq6/E1KnT8PWvr4UoCrj//t/juuu+iAcffBR6vV79/kcffRjz5i3AN76xFgcPHsBvf/tLNDQ04otfvAYA0Nd3AjfeeA2mTZuO733vh3C73fh//+82BAJ+zJhxUkl/l1qSKbA6eK8nk7IGyDZu3IhAIIALLqh88zkiIiIiIiqeaGlE70kfQevORyEKMiCIMKy4nI36iaiiZFnGD/+wBbsPO6vy86d32vC1yxaWNEgWiUTwhS/8N845Z7V62w9+8B1YrVb8/Oe/Ufu3z549Dx/96IVYv/7vuOiiS9T7NjU14zvf+QEAYOnSM/Duuzvw7LNPqwGyhx9+CIIg4Kc//RUsFgsAoLW1Dddd98WS/Q6jUVkDZOvXr8fEiRMxb17mWttkzzzzDHw+H+bMmVPOZRERERERUR76Wk/Fb1+R0aJx44ufOhv1be3VXhIRjUUjv3f/EKefvjzl3//5z8s455zV0Gg0iEQiAID6+nqcdNJM7NixPeW+p522JOXfkydPwdNPb1D/vX37NixcuEgNjgHAokWnwWq1lfrXGFUKDpD5/X50d3cDAA4fPgyPx4OnnnoKQKyPWGNjbEfJ4XDgpZdewuc///m0j3PbbbdBEATMnz8fVqsVb775Ju68807Mnj0bq1atKvb3ISIiIiKiEglHJDhlM5wRM7yCBQ3VXhARjTmCIOBrly0cVSWWRqMRdXV1KbcNDAzg4YcfwsMPPzTk/lpt6hBDi6U+5d86nQ6hUEj9d1/fCXR2ThjyOA0NPIpnU3CArK+vD9ddd13Kbcq/77//fixZEotkPvnkk4hEIhnLK6dNm4aHHnoIDz/8MAKBANra2nDxxRfj2muvhVZb9tkBRERERESUQySpH5DHH67iSohoLBMEAQZ97r7mI0W6gJvVasPppy9LKaVUDA6m5dLU1Iz+fseQ2/v7+wt6nLGm4EhUZ2cndu7cmfN+l112GS677LKMX7/kkktwySVD//BERERERFQbkpsdewMMkBERlcuppy7G3r3vYcaMmXkNOcxm1qxT8Le/PQqPx6OWWW7e/B+4XNXp4zZS5J5zSUREREREY1I4mgiQMYOMiKh8PvvZq3Dw4EHceOM1ePrpjdi6dTOefnoDfvrT27Bx41MFPdZHP/oJyLKEm266Fs8/340nn1yPH/7we7DZ2IMsGwbIiIiIiIgoreQMMgbIiIjKp7NzAu666z7YbDb87Ge34ctfvgZ33PFrBAJ+TJs2o6DHam5uxk9/+isEgwF861u34I9/vA833ngzWlrayrT60UGQZVnOfbeRpb/fm/JhTkQ00kkeByRXL0RrG0RLY7WXQ0REY8Sj3e/h8Zf2AwDOXTIRHz1repVXRESjWTgcQl/fUTQ1dUCn01d7OTRC5HrdNDaaodHkzg9jN3wiohoX2tGN4HP3ArIMCAIMK66AvmtltZdFRERjQJgZZERENEawxJKIqIZJHkciOAYAsozgc/dB8gydSkNERFRqkaQeZF4GyIiIaBRjgIyIqIZJrt5EcEwhS7HbiYiIyiw5g4wBMiIiGs0YICMiqmGitQ0QhNQbBTF2OxERUZklZ5B5ApEqroSIiKi8GCAjIqphoqURhhVXQEIsSCbJAvTLP81G/UREVBHMICMiorGCTfqJiGqcvmslbn9RQtBxFMej9bip+VSMr/aiiIhoTIhEE2X+Hn8YsixDGJzZTERENAowg4yIaARwRE3YHWmHUzbjQK+n2sshIqIxIpxUYhmVZARC0SquhoiIqHwYICMiGgFC4cQFyoFj7iquhIiIxpLkEksA8AZYZklERKMTA2RERCNAKJLYsWcGGRERVUpyk34A8PrZqJ+IiEYnBsiIiEaAUNIO/oFeN2RZznJvIiKi0hicQeZho34iIhql2KSfiKjGybKMUDiRQeYNRNDnCqDZZqriqoiIaCxQMsgEAZBlllgSEeXj7rvvxD333AUAEAQBdXV1aGtrx/z5C3HRRR/F5MlTCn7MLVtew7Ztb+LTn76yJGs8evQILrnkQ+q/9XoDxo0bjzVrzsdHP/oJaLX5hYuUx/n+92/DWWetKujnP/HEY7jwwovQ3NxS8PrLgQEyIqIaF5VkKAljzTYjTjgDONDrYYCMiIjKTskgs5r1cHpCzCAjIsqTwWDAL395BwDA7/fivfd24x//+Csee+xvuOWWb+EDH1hT0ONt3boZf/rTH0oWIFNcddX/YMGCU+H3+9Dd/Qx++9tfweVy4eqrv5TX9zc1NeOOO+7BxIkTC/q5R48ewT333IVly1YwQEZERPlJzh6b3mmLB8jcWHhSbXyQEBHR6KVMsWywGBggI6IRSfI4ILl6IVrbIFoaK/ZzRVHE7Nlz1H+fdtpSfPjDl+CrX70et932fcyePRfjx3dWbD2ZdHZOUNd52mlLcPDgfjz66MN5B8j0en3K7zmSMUBGRFTjlP5joiBgSocVL7/dy0b9RERUEZH4Z5DdYgDgZpN+IhpRQju6EXzu3liNuCDAsOIK6LtWVm09BoMB11//FXzqUx/F+vV/x1VX/Q8A4Mkn1+Mf//gr9u3bC1mWMX36DPz3f1+Lk0+eDSC1ZHP58lMBAPPnL8Svf/2/2L9/H37/+zvx1ltvwukcQEfHOHzwgxfiYx/7BESx8LbzM2fOwubN/0F/fz8aGhrw+utbcMcdv8a77+6EyWTEsmVn4ktfuh5Wqw1A+hLLiy++AGecsRyTJk3Bgw/eD4/HjYULT8VXv/pNNDQ0YMuW13DttVcDAD73uU+rP/v5519DJBLBnXf+Bk8/vQH9/Q5YrVbMnHkyvv3t78NisRT5zOeHATIiohqnZJDpdCImtdUDAPb3uqu5JCIiGiOUDDJ7vQEAm/QT0cgheRyJ4BgAyDKCz90HbeecimaSDTZlylS0tLRi27Y31dt6eo7i3HM/iPHjOxEOh/Gvf/0TX/rSF3DvvQ9h4sRJuOCC/8Lx48ewceNTatmm2WwGABw/fgwTJ07G+99/Hurq6rB797u4++474ff7cOWVXyh4fUePHoFer4fNZsOOHe/ghhv+BwsWLML3v38b+vv7cMcdv8bevXtwxx2/h0ajyfg4zz+/CYcOHcSNN94Mp3MAv/rVz/CLX/wY3/3uDzFzZhduvPFm/OxnP8LXv/4dTJw4Wf2+Bx64B3/726P44hevwZQpU+F0DuDVV19GOBwq+HcpFANkREQ1TskgM2hFTGiN7Zr0u4Nw+0Kor9NXc2lERDTKRSKxC0u7JfZ5wyb9RDRSSK7eRHBMIUuxcssqBsgAoLW1DQ5Hn/rvz3zm8+r/S5KE005bgnfeeRtPPrkeV131P2htbUNLS+uQsk0AOPXUxTj11MUAYsO95s6dj0AggEcffTivAJkkyYhEIggEAnj22aexadMzOOusVRBFEfff/3s0Njbhxz/+hdq0v7W1HTfe+CW89NILWL78zKyPfdttP4NeH/v8OHr0CB544B5IkgSz2aIOKpg6dRq6uk5Wv+edd97G4sVLcNFFl6i3ve995+T8PUqBATIiohoXCscCZDqtBiaDFq0NJhzr9+NArwenTKnuhzsREY1eUUmCJCsBslgGmZcZZEQ0QojWtsQIXoUgxm6vMlmWIQiC+u99+/bizjt/g23b3kR/v0O9/eDB/TkfKxgM4g9/uBcbNjyJ3t4eRCKJUnifz4e6urqs3/+d73xN/X9BEHDWWatwww1fAQC8+eZWrFr1gZSJlosXL4XFUo8333w9a4Bs/vyFanAMACZPnopIJIL+fgeampozft9JJ3XhwQcfwN1334kzzliOmTNnFVUqWgwGyIiIalw4Eiux1OtiHwwT2+oRHDgB53tvQmqZW/UdMCIiGp2U7DEgESBjiSURjRSipRGGFVcg+Nx9gCwBggjDistr4tz5+PFjmDAhNvXR5/Pixhu/BLvdjmuuuQFtbR0wGPS47bYfIBTKXVb4u9/djsce+ys+85nPY+bMWaivr8dzz3XjvvvuRigUyhkg++IXr8GiRafBYDCio2McjEaj+jW3242GhqHPV2NjI9xuV9bHHdwvTKfTAUDO3+nTn74SgiDgqacexz333AW7vQEXXXQJPvOZz6cEFcuBATIiohoXjGeQ6bWxGv8lundxmf0xiHtkePdWv9koERGNTkr/MSC5xJJN+olo5NB3rYS2c05VplhmsmfPezh+/BjOO+98AMC2bW/h2LFe/OhHP8eMGSep9/N6PQBacz7eM8/8CxdeeBE++ckr1NtefPH5vNczbtz4lBLHZPX1VvT39w+53eFwoL7emvfPKIRer8dnP3sVPvvZq3Do0EE8/vg/8Pvf/y/GjRuPc8/9YFl+pqIyeWpERFS05AwyyePAjMOPQRRSm41KHkeWRyAiIipcON4DUxAAqznRg0wa3NOHiKiGiZZGaMfNqongWDAYxC9+8RPo9XpccMF/xW8LAEhkWAHAW2+9gaNHj6R8r06nS5t9FQwGodUmvjcajeLppzeUZL1z587Hc889m1K2+Z//vAyPx425c+cP67GV3zcYzJxR1tk5AVdd9T+wWm3Yv3/fsH5ePphBRkRU40JqBpkIydULAbXZbJSIiEaXSDyDTKcRYTHFLmRkGfAFIuq/iYgoPUmSsG3bWwAAv9+HPXt24x//+CuOHDmMr3/9O+joGAcAOOWUOTCZ6vCzn/0In/zkFTh+/BjuvvtOtLSkZo9NmjQF0WgUDz/8EObMmQuz2YyJEyfjtNOW4LHH/oYpU6bCZrPjr3/9C0Kh0pTDf/rTV+KLX7wSX/3qDbj44o/B4YhNsZw16xScfvqyYT32hAmToNFo8Pjjf4dGo4FWq0FX18n42te+jJkzZ2HGjJkwmUx44YVNcLtdWLjw1JL8TtkwQEZEVONCagaZBqK1DTKE1CBZjTQbJSKi0UUNkGlFaDUiDHoNgqEovIEwA2RERDkEg0FcffVnAAAmUx06OjqwaNFpuPXWn2LSpMnq/Robm/D979+G3/zmF7jlli9jwoSJ+MpXvo4//vG+lMdbtmwFPvzhS/CHP9yL/n4H5s1bgF//+n9xww1fwU9+8kP8/Oc/gdFoxHnnnY8zzzwLP/rRD4b9O3R1zcLPfvZr3Hnnb/DNb34VRqMJy5efiS996XpoNJphPbbdbscNN3wVDz54P/75zycQjUbx/POvYc6cefj3v/+FP/3pD4hGo5gwYRK+/e3v47TTlgz798lFkOXRlyPd3+9FJCLlviMR0Qiw8T8H8dDTu7B4ViuuvnA2vNueQeSF+2NllvFmo+xBRkREpXag14219/wHNrMeP79mOb7y2xfQ5wriG59ehGnjbNVeHhGNQuFwCH19R9HU1AGdTp/7G4iQ+3XT2GiGRpO7wxgzyIiIalxyBhkAmE55H778uActGjf+5/JzoG9h9hgREZVeOCmDDADMJh36XEF4OcmSiIhGITbpJyKqcck9yABAFAQEdVbsjrQjoKmv5tKIiGgUUyoytPFdd6Ws0uvnJEsiIhp9GCAjIqpxgzPIAMBkiCUAB0LRqqyJiIhGPyWDTAmQmY2xAJmHGWRERDQKMUBGRFTjQpHUDDIAMOpjwTJ/kLv4RERUHpFIrFWxUmKpZpAFGCAjIqLRhwEyIqIaFwoPzSAz6mMZZP4QA2RERFQeag8yjQAAMJtinz3MICOichuFswSpjEr1emGAjIioxoUjqU2SgUQGGUssiYioXCKDPn8sLLEkojLTaGLnuKFQsMoroZFEeb1oNMObQ8kplkRENU5p0m9I14OMJZZERFQmQ3qQqSWW/OwhovIQRQ1MJgs8nn4AgF5vgCAIVV4V1SpZlhEKBeHx9MNkskAUh5cDxgAZEVGNU5r0J2eQmZQeZMwgIyKiMlEymLXa1AAZM8iIqJys1kYAUINkRLmYTBb1dTMcDJAREdU4JYNMrx3agyzAHmRERFQmkeigEkslg4wBMiIqI0EQYLM1ob6+AdEoz3UpO41GO+zMMUXBAbL9+/fj7rvvxhtvvIFdu3Zh6tSpWL9+fcp9PvWpT+HVV18d8r1PPPEEpk2bpv7b7Xbjhz/8If71r38hHA5jxYoV+OY3v4nW1tYifhUiotFJySDT65J6kBmUKZbMICMiovJQM8g0nGJJRJUniiJEUV/tZdAYUnCAbNeuXeju7sa8efMgSVLGaQELFy7EzTffnHJbZ2dnyr+vv/567N69G2vXroXBYMAvfvELfP7zn8ejjz4KrZbJbUREQHIGWVKJpYEZZEREVF6DM8jMxvgE5WAUkaikBs6IiIhGg4KjUGeffTZWrVoFALjllluwbdu2tPezWq2YP39+xsfZunUrnn/+edx9991Yvnw5AGDKlClYs2YNNmzYgDVr1hS6NCKiUSmsZpAll1jGp1gyg4yIiMpEnaKsNOmPT7EEYo36bWZmdhAR0ehR8LZPqWo7N23aBKvVimXLlqm3TZ06FbNmzcKmTZtK8jOIiEaDYLoMsngPMj8zyIiIqEwGT7EURQHtxiCma3vg6ztWzaURERGVXNnqGF999VXMnz8f0WgU8+bNw3XXXYfTTjtN/fqePXswZcqUISNbp06dij179gzrZ2uY7k1Eo4iyg19n1CUmidXFdvGD4ah6GxERUSlJUqyVikGvgVYrIri9GzebHoYoyJCf2ojo+66E4eSVVV4lERFRdoPCThmVJUB22mmn4cILL8TkyZNx7Ngx3H333fjMZz6DBx54AAsWLAAAuFwu1NfXD/lem82WsWwzX1araVjfT0RUK2RZVpv0tzRb0GA1xv6/yQcACEUkNDSYq7Y+IiIavYT4pnO9xYB6TQD93b+HKMSCZgJk+LrvQfOcJdBam6q5TCIiopIoS4Ds2muvTfn3+973Ppx//vn47W9/i7vuuqscPzKFy+VHNJ4STkQ0koUjEpRZKH5fEIjGgmWReGmlxxdGf7+3WssjIqJRzOsLAQAi4SgcB/YAg4dzyRIcB/ZCN95YhdURERHlx2Yz5dUurCKjIuvq6rBy5Ur885//VG+zWq3o6ekZcl+n0wmbzTasnxeNSohEGCAjopHPFwir/y8A6rFNaZgcCEV4vCMiorJQpihrBEA2t8ZqVJKDZIII2dzCzyEiIqppg/d3Mqla45qpU6di7969kAetdO/evZg6dWqVVkVEVFvUixNRUJskA4ApaYrl4OMoERFRKUSSmvSLlkYYVlwBCbFGLjIEGFZcDtHSWM0lEhERlUxFAmQ+nw/PPvss5syZo9525plnwul04qWXXlJv27t3L7Zv344zzzyzEssiIqp5Sv8x3aBG/EZDLAFYRqxRPxERUakpQ2KUzyB910o83nY1bnetxn+6boC+iw36iYho9Ci4xNLv96O7uxsAcPjwYXg8Hjz11FMAgMWLF2PPnj34v//7P7z//e/H+PHjcezYMdxzzz04fvw4fvnLX6qPs2DBAixfvhxf//rXcfPNN8NgMODnP/85Zs6cidWrV5fo1yMqnORxQHL1QrS2cVeUqi4czyDT6zQpt+u1olrp4g9GYdRXpGKeiIjGkHBSBpkiarRjd6QdJwuWai2LiIioLAq+ourr68N1112Xcpvy7/vvvx/t7e0Ih8P4+c9/joGBAZhMJixYsADf/e53MXfu3JTv+8UvfoEf/vCH+Pa3v41IJILly5fjm9/8JrRaXuhRdYR2dCP43L2xqIMgwLDiCu6OUlUF4xlk+kEZZIIgwKTXwheMIBCKADBUYXVERDSaRQZlkAGAXhf7/xD7jhER0ShTcCSqs7MTO3fuzHqfu+++O6/Hqq+vx6233opbb7210GUQlZzkcSSCYwAgywg+dx+0nXOYSUZVkymDDABMBk08QMYSSyIiKj01gyw5QKaNfR6FWN5PRESjTNWa9BPVGsnVm3Z8ueTqrc6CiJC5BxkAtazSH4xUdE1ERDQ2DJ6cDCRlkIWZQUZERKMLA2REcaK1DXJ8MpNKECFa26qzICIkLkAM6QJkhtguvj/IXXwiIio9ZYplaollPIMsws8eIiIaXRggI4oTLY3omfERSHI8SCaIHF9OVadmkKUrsYxnkMV6kBEREZWWMsUyuUm/QQmQMYOMiIhGGXbDJ0rS27gQvxuQ0aJx48qPnYn6CZ3VXhKNccoFyOAm/QBg1McuUtiDjIiIyiEcjbWeSM4gU/4/yB5kREQ0yjBARpQkEI7CKZvhjJjRL5nRXu0F0ZinTAlL16TfaGAPMiIiKp9EBlmiBYXSpD/MKZZERDTKsMSSKEkwKRPH6Q1WcSVEMcqUMGaQERFRJcmynOhBllJiqTTp52cPERGNLgyQESVJLhdweUJVXAlRjJpBps3cg8zPHmRERFRiUSkx2Ttdk/4gM8iIiGiUYYCMKEkgmJxBxgAZVZ+aQaYberg2xUssA5xiSUREJZZcQpncpF/PDDIiIhqlGCAjShIIJzJxBphBRjVAuUDRZS2xZAYZERGVVjiaFCBLziDTKlMsGSAjIqLRhQEyoiTJPchc7EFGNUC5ADGkbdIfu41N+omIqNQi8Q0ajShAFJKa9CsZZCyxJCKiUYYBMqIkgTBLLKm2hLJkkCk9yNikn4iISk3JIBv8+aP0IAtHJEiyPOT7iIiIRioGyIiSpE6xZICMqi/RgyxNBlm8xNLPABkREZWYkkGW3H8MAAxJQ2PCYWaRERHR6MEAGVGS5Ewcty+sjjcnqpbEFMtsTfpZYklERKWVKYNMlzQ0JhjhBg0REY0eDJARJQkOysRx+8JVWglRTCiSJYPMwBJLIiIqj0gkVj6pG5RBJgqCmlXGDDIiIhpNGCAjShIYNJHJyUb9VGXKxUe6DDKlxDIYjkKS2AeGiIhKJxzfoNGm+fwxqI36uUFDRESjBwNkREmUDLK6eGaO08M+ZFRdQaXEMk0GmdKkH2AWGRERlVY4mj6DDEh8JoWYQUZERKMIA2REcZIsIxjPIGttMAFgo36qPmUHP90US51WhEYUAACBEPuQERFR6Sh9WLVaYcjXlKzmYJibM0RENHowQEYUl9x/rK2xDgDg9LDEkqpL2Z1Pl0EGJBr1+9mon4iISigcz2DOmkHGEksiIhpFGCAjilN2QQUALXYjAGaQUfWpTfrTZJABiT5kLLEkIqJSSmSQpQuQxXuQscSSiIhGEQbIiOKUDDKDXgOb2QCAATKqLlmWc2aQGeN9yPwssSQiohLKmkGmVXqQcXOGiIhGDwbIiOICKQEyPQA26afqUnbvgcwZZCZDPIMsyIsUIiIqnXD8MyhdD0yDWmLJDDIiIho9GCAjilNKLI16LWyWeIDMyx5kVD3BpNKVdBcoQFIPMmaQERFRCakllmkyyJTPJGaQERHRaMIAGVGcMgXQqNPAZkmUWMqyXM1l0RimlLdoRCHtBQqQ1IOMGWRERFRCymdQus8ftQcZM8iIiGgUYYCMKC6lxLIulkEWCktsfk5Vo+zMKxci6bAHGRERlUMkS4mlOsWSGWRERDSKMEBGFKc06TfqNTDoNWpmDhv1U7UoO/M6bfoG/UBSDzIGcomIqISyNek3qE36mUFGRESjBwNkRHGB+C6o0ng20aiffciKJXkciBx5B5LHUe2ljEhqBlmG/mNAIoMsEGQGGRERlU4kEmsxoU2bQRa7LRjh5gwREY0e2movgKhWBJIyyADAZjGgt9/PDLIihXZ0I7DpXgiQAUGAYcUV0HetrPayRpREiWWWDDI9M8iIiKj01CmWGmHI11hiSUREoxEzyIjigkk9yICkDDIGyAomeRwIPhcPjgGALCP43H3MJCuQUmKZNYNMmWLJDDIiIiohtUl/ugwydYolSyyJiGj0YICMKC44OINMLbFkgKxQkqsXGDz9U5Zit1Pe8gqQxV+vfmaQERFRCalN+tNOsWQGGRERjT4MkBHFBcKxDBy1B5lFySBjD7JCidY2AKklGRIEyBo9e5IVIK8Sy3gGWYBTLImIqISyZpDFe5ApGzlERESjAXuQEcUlMshibwub2QCAJZbFEC2NCJ36CWj/8yBEQYYkC/hPcAoW//0H7ElWgMQUy9wZZIEgd/GJiKh0smWQqVMs2aSfiIhGEQbIiOKGNumPZZC5WGJZFFfHEvxqIICp9QEsmz8Jp731uyE9ybSdcyBaGqu70BoWHjRZNR1TPKDrZwYZERGVkBogS7NJo9OxBxkREY0+LLEkigsMCkYoPcgGmEFWFF8gAqdsxnHDRMydXA9RYE+yQgXzySAzcIolERGVnlpima4HmZY9yIiIaPRhgIwoLlOTfrcvBEmSM34fpecNhAEAdUYdRGsb5EE9ySCI8V5llEk4krsHmVISHI5I6m4/5SZ5HOyHR0SURThLBpmymcgeZERENJqwxJIoTgmQGeIBsvo6PeyiF82iG+4TvbC1tldzeSOOLxAr+TMbtRAtjXDMugQN2/8SyyQTRBhWXM7yyhyU0pV8plgCsSwyi4n7HrmEdnQjuOlegP3wiIgyyppBppZYMoOMiIhGj4IDZPv378fdd9+NN954A7t27cLUqVOxfv169esejwf33HMPuru7sW/fPuj1esydOxc33HADZs6cqd7v0KFDOOecc4Y8/rx58/Dwww8X+esQFU8psVQyciLvbsJ3bOsgCjLkv21E6ExeRBcikUEWez41M8/E2hejmGT249orVjE4lgdlZz5bBplWI0KvFRGKSAgEI7CYdJVa3ogkeRwIbLqX/fCIiHLI1oNMKbEMsgcZVZHkcUBy9UK0tvEznIhKouAA2a5du9Dd3Y158+ZBkiTIcmrp2ZEjR/DnP/8ZH/nIR3D99dcjGAzi97//PT72sY/h0UcfxbRp01Luf+ONN2LJkiXqv81mc5G/CtHwBOJNzg16DSSPA8Hn7lX7ZgngRXShlAyyOkMsYGMz6+GUzXjTY4ZksrO+Ow/KzryyU5+JUa9BKCLBzz5kOUmu3kRwTBHvh8f3NhFRQiQaO1amm2KpfC5FohIkSYYoCkPuQ1ROoR3dCD53LyAzG5yISqfgANnZZ5+NVatWAQBuueUWbNu2LeXrnZ2d2LhxI0wmk3rb0qVLcfbZZ+PBBx/Et771rZT7T5o0CfPnzy9i6USlI0myWs5m1GkgOXtjH7jJeBFdEG9SiSUAWOp0EAUBkizD7Qujod5QzeWNCGoGmTZzBhkAtBgCaA0dR8h5Ami1VGJpI5bSDy8lSMZ+eEREQyRKLIcGv5Izm0ORqJp9T1QJyka2eq7ObHAiKpGCkzhEMfu31NXVpQTHgFhW2MSJE3Hs2LFCfxxRRQSTemgY9JrYxbLApvLD4RtUYikKAqzmWDbZgCdYtXWNJGoGWZYeZKEd3fiS8CCusW5AyzPfRWhHd6WWNyKJlka80XweJDn2/pZkAbpln+YJNRHRINma9Cffxkb9VGmSK/NGNhHRcFRku8flcmHXrl0444wzhnxt7dq1uOGGG2C323HOOefgpptugt1uH9bP06RJBSfKJuqPl1IKsYCOYGqGvPJKeJ79PUTIkCHAvPIz0Nubq7zSkUMp96s366GNn0jb6w0Y8ITgCYTV2ygzpf+L0aBN+3yppcBILQU2Tp7HgE8W7xpmY92ACS0aN45H63GZ5mQs5euRiChFJJL9M0jpfylJMj/TqaLExg74BSE1SCaI0Dd2QORrkYjSGJz7kklFAmQ/+clPIAgCLr30UvU2vV6PSy+9FMuXL4fVasUbb7yBO+64A9u2bcNf/vIX6HTFN5q2Wk2570SUxB+JfcCaDFo0NsZL1JatwbqBZjy3aStmze3CF5axr0EhlKy8tmYLGhpivQVbGuqw76gbYQnqbZRZvP0LGhvq0j5ffuceONPsoNZJTpgaJlRghSNTMCLDKZthtLbA6fDhma1HcN7yabm/kYhojJAkGVEp9vnS3GSBzTK0LYIh3v/SWGfgZzpVVoMZujVX49jjd0CEDEkWYHv/59A0gec+RDQ8ZQ+QPfroo3j44Ydx2223ob29Xb29tbUVa9euVf+9ePFizJgxA1dddRU2btyINWvWFP0zXS4/olGme1P+eo+7AQAGnQb9/d7EF4w27I60o96jTb2dcnJ5QgAAORJVn7s6faxnyZFjbj6fefDHy1RDgXDa50vS2GPbIYN2UH2iDQE+vxkNuAMAgHOXTMQf/rkTR/YfxJubujF+ylRm3hERIVHiDwBeTwBSODLkPkrz/uN9Hlj0zNqhCpt0On4a6Icp5MDxaD2ukE7CIp77EFEGNpspZ7swoMwBsu7ubnz729/Gf//3f+PDH/5wzvuvXLkSdXV1ePvtt4cVIItGJTUtnCgfXn8sEKHXaVJeO3WG2FvE7Q3zNVUgbzy4Y0h6Tq11egBAvyvI5zMPwXiZqkYU0j9fRjsMK65AYNO9ECBDggDTisshGe2Q+Pxm5PHFXpvtDSZ8dGIPFjs3QHxOhvN5TsEiIgIAfzAREBOAtJ9Bunijfn8gws90qjhJlnHEr4csxxIwtr3nwLxpbIVCROkNLrrJpGwBstdffx3XXXcd/uu//gvXXXdduX4MUUko5YBGXeq0wPp4QMftC1V8TSOZJMvwDZpiCQA2S+z5dHr5fOZDnWKpy7zboe9aiS2uVvx701Y0dU7AVV0rKrW8EUsJ3lrgwRLXBggCp2ANh+RxxCb8Wtv4vBGNEkrAS0BskyYdQ7zXU3K2GVGlBIKRlAvedw70V28xRDRqlCVAtnv3blx11VVYunQpvvvd7+b9fc888wx8Ph/mzJlTjmURZRSIZ+oY9KkBMosp1gvPE88wo/wEghEo5yx1xkQ/QZs51sPEySmWeUlMsdRkvV9dUyt2R9oR9A3tEUOpZFmG1x8P3ob6ISD9FCwGevIT2tGN4HP3xrblBGbgEY0WygRLrVaEkKGzsT6+qRgMM3uMKs8T34jViAIkScaRE14MeIKwp+mXR0SUr4IDZH6/H93d3QCAw4cPw+Px4KmnngIQ6yMmyzI++9nPwmAw4PLLL8e2bdvU77VYLJg+fToA4LbbboMgCJg/fz6sVivefPNN3HnnnZg9ezZWrVpVit+NKG9KgMyoH5xBFgvueAMRRKIStJyQmhdv/KRFrxVTRsErGWQDHmaQ5SOsZJDlmMhkVzLzGHjMKRCKQopvOZuaOxBK08NNtLZVaXUjizJFVX3+mIFHNGoonz+6LOc9SnZzKMIMMqo8pT2KzaKHxaTDgV4Pduzvx9JT2nN8JxFRZgUHyPr6+oaUTCr/vv/++wEAPT09AIArrrgi5X6LFy/GAw88AACYNm0aHnroITz88MMIBAJoa2vDxRdfjGuvvRZabUWGaxKpghkCZGajDgIAGbGgj82sr/ziRiClvLLOmPpetpsTJZayLGfclaZYplOixDJ7BpmyW+r2hRGVJGjyaEA5Vikn1DqtCIO9BcKKK+DfdC9EyJAhwLjicgZ38iS5eoc2dGAGHtGoEImPUdZm2aBRsptZYknVoHyem406nDypEQd6PdjOABkRDVPBkajOzk7s3Lkz631yfR0ALrnkElxyySWF/niisgjET+4MgwIRoijAbNLB4w/D7QsxQJYnpceTOam8EkhkkEWiEnzByJCvU0I4IsEmeNGicUMbHADMrRnva6nTQSMKiEoyXN4wGupZXpCJd1BvPH3XStz/phbHDuzHyjPnY1nXydVc3ogiWtsgQ0gtU2UGHtGokMggy7yRlcggY4klVZ5HPdfUYtbkBjz16gHs2M8+ZEQ0PEwzIEIig2xwDzIgUWapTL6j3JQMMtOgDDKdVqNOBnWyzDKr4DvdWGtfh2usGxB99GaEdnRnvK8oCLCalfJVlllmo55QmxLBWaGuAbsj7XBJ5mota0QSLY3wzP04JDl2AS3JAnZ2nM/sMaJRIKL2IMucwaxkNzODjKpB6SdqMekwo9MGjSjghDOAYwP+Kq+MiEYy1jISIbnEcuhbot6kw1EAbjbqz5uaQWYY+nzaLHr4ghE4PUGMa2ZAIh3J44D88gMQC5iuaDPr0e8OMkCWQ2K6aiJApgRyfcFIVdY0kvW3n4afdIfQpvOgN2yB1i1i0vStaOqcxEAZ0QimNOnPmkGmTrFkBhlVnlpiadLBqNdi6jgrjh0+goNvvobmhafwM4iIisIAGRGAQCh2YTy4BxkAWOpimTkeHzOe8qUEGurSlFDazHoc7fPB6eXzmYnk6i14umKsD5mbmXk5JHqWJD7+lKxGJXhG+QsEI3DKZrS0jMM5eAcrfE9D3CTDy4mWRCOaWmKZrQeZkkHGJv1UBZ5B7TzOtu1Dl3c9xB0yvDv5GURExWGJJREy9yADEiWWbpZY5s03qM9TMlu8oTwnWWam9HZKkaO3k93CEst8eNOUWCqBXGaQFU55zpp0fpzpf3pI1qPkcVRxdURUrEg8QJZtejczyKiakkssJY8Ds46u52cQEQ0bA2REyDzFEoh98AIssSyEN8MUSwDqoAMXM8gyEi2NcJx8idrbCYIIQ47pigw85kc9oU7KbmQGWfECwdixs1Xjypj1SEQjj1JimS1AZmAPMqoib1KT/myZ90REhWCJJRFyZZDFAjpulljmzZdhiiWglAICA15mOmVzovlU/HIgivntEj71kRU5e2koE0KdzCDLKtGkP6nEUu1BxiB4ofzx8vSwqRkYEACZEy2JRoNIASWWQU6xpCpI7kEmWtsAgZ9BRDR8zCAjQlIGmSFNgCyeQeZhBlne8skgq6VeWZLHgciRdyB5HCn/X03+UKy3U3/d5LwazdrNSuCxdp7XWpToQcYMslJQMshQ1wjDiivU0mAJQs6sRyKqXRG1SX8+JZbMIKPK8yS18xAtsc8gJfNeziPznogoHWaQESGpSb8uzRRL9iArWLYMMjXTqUYCOaEd3Qg+dy8gy2pyvgAAVW4yrgQe0pX9pmOvZwZZPpTgbWoPMgbIiqX0IDMaNNB3rcQbvnZs/Pdm2Ds68d9dZ1Z5dURUrIKa9DNARlWgbHgprVD0XSvxs01hSK5juPiC03HSzCnVXB4RjVDMICNCIoPMkHaKJTPICpVfBln1AzmSx4HApnvVlHwh/h+Aqjd49ccDDyZDfvsYtngGmdMbgiTJOe49diX3LFEkSiwZICuUsrlg0seeQ5O9Gbsj7egNGqu5LCIapnx6kOl1sa+FWWJJFSbJctqhO6KlEbsj7XBETdVaGhGNcAyQESGpB1maAFm9KdGDTJYZeMhHPlMsvYFI1U+q0zZ1TVbFBq/+QYGHXKxmHQTEYn3sl5dZ+hLL2P+HIxLCEWZCFMIfz3RUArncUCAaHZTPZ20+Pcg4xZIqLBCMqO3Gkj/PrcogqBpq40FEIwsDZDTmSZKsjihPO8UyfsEXicoIhHjxnIssy2qArC5NiaXZqIVWE8vTcla5Ub9obVN7JqVVxQavicBDfiWWGlFEffzEkJMsM/OmCd4aDRr1VeAL8j1eCDWQG3+dKtNBGSAjKk6t9MGMRGPRh2w9yAzaeIklNxaowpT+YwadJqUMWM2m50YhERWJATIa84JJvTOMaaZYGnQatYzAzYu+nAKhKKT4tl66DDJBEBJlllXuQyZaGrFn4oVqU1dAUANmcpWbjKt98fIssQQAu/q8Vr98tRaFwlE1KyKlJEMQ1AwopX8e5ccfTH2dKhsK4YiUcmwlotxCO7rhfejL8K//EbwPfRmhHd1VW4vSpF+rzbyJpGOTfqqSxATL1HMkpc8tM8iIqFhs0k9jnpIVJgiZm9HWm3ToCwfh8YXRamdfg2yUnhBajZDx+bSaDehzBWtikuVB63zcN6DBOTP0WLP6NDy79TBefuUtTJg2FZ/qWlq1dSn9sPItsQQAe70BB455mEGWgZI9JgrCkGzROqMWvmCEfcgKFIg/X8okUINOA61GQCQqw+MLw2DLLwOSaKxTemKqZf/xPpjazjlV2ahRm/Tn0YMsxBJLqrB07RIAwFpXGxuwRDRyMYOMxjwly8Go10AQ0u+UWuoSfcgou+TyykzPp72GJlkGghE4ZTPc1qkQLY2oa2rB7kg7jvr1VV5XYSWWQGIAwkANDECoRck7zoNfm0qAx89JlgXxD5q2KgiCOlGMZZZE+UvbE7OKfTCVJv3ZplgadCyxpOrwBFInWCpqbVI6EY08DJDRmKdOsExTXqmo5wVf3rI16FfU0iRLJYNQucBviA8R6K9yFlait1P+GWTKAIRayMyrRYkJlkN74ymTLL0MkOVNluW0r1NLfLAJj5dE+UvbE7OKfTB1QSema3tQF3VnvI/SpD8SlRGVmEVGleP1pz/XrJUWHkQ0cjFARmOe2uspSylbfbyvjtvHC75cvGoGWZYAmRLIqYETmESvr9iJvj2+tmpnYSmla4UEyJTMvGqvvVZ5lBNq09DnVBkowRLL/AXDUXWKWHIpsCX+/Lr91X9/E40UoqURrzedl+iJKYhV64MZ2tGNDx2/E9dYN2DR9p9l7IWmT8ouY5klVVIiI3xQBpk5UfEhSZw8T0SFYw8yGvOUDCJDmgmWCiUjolYv+CSPA5KrF6K1rWpN5RW+LFk6ikQGWfWfT+Xvr1zgK+n5wVAU/mCkoABVKfkGla7lw15DgcdalO21Wccm/QVTyitFQVB7EQGJknRlh5+I8rNdezL+OmBCi8aNL37qbNS3tVd8DZLHgeBz90KMl3sKyNwLLbn8MhyRYDJUdKk0hnkyfJ7X1+khCIAsxwZrKeebRET5YoCMxjy1B1m2EssaziAL7ehG8Ll7Y2cDggDDiiug71pZtfXkl0FWO9MW1Sl88UCUUa+FyaCBPxjFgCdYlQCZLMtFZZDZmEGWlVct/81cYskMsvwF1PLK1P6NSk8Y9mwkKozXH4ZTNsMZMeNE2IiGKqxBcvVCTQ1VxHuhDQ6QCfHgeCgscZIlVZSSQTa4B5koCqg36eDyheH0BBkgI6KCscSSxry8MsjiATJPjQXIlJ1e9WQ2PvVK8jiqtiZfML6rZ8icQWa3GGATvLB791V1rUByD7JEIMpmVsosq3OBHwxH1VbNBZVYmhM9yOTBFziU6EGWrsSSTfoLlmjQn/p8skk/UXE8ScefEwOBqqxBtLbFxnony9ILTa+NnTsFIyyxpMrxZul3a42fC7mYTU9ERWCAjMa8YCh3KVt9jZZYZtvprZZ8MsjsPf/BWvs6XKF9HN6Hvpyxv0klDG7SDyRN2axSJlZK6VqWCWKDKRlkUUlmcCINdcc5TQaZiRlkBfMHExlkyTjUhKg43qT3zHGnvyprEC2NMKy4Qu2FJkPI2gvNEC+vZgYZVVKmHmQAJ1kS0fCwxJLGvEA4jwBZjWaQKVOvUkbDV3HqFZB7iqXkcUD72h8AITXrLV1/k0oIpJnCZ6+vbgZZcuBBGLyTn4VWI8Ji0sHjD8PpCaG+jqUFyZTsjHQn1IkeZAyQ5cufoQyYGWRExUmeonvCWZ0MMgDQd63E9/7pR13YgSs+ugLWiRMy3lcXzyBjgIwqyZOhxBLgJEsiGh5mkNGYpwRIDLqRN8VStDSid8ZH8t7prQSljK0uQ5P+Wst6S59BVt1Jlv48JqtmwkmWmak7zmmCt0rGo5cBsrz50wSXgdotSSeqZVFJUoPOAHBioDoZZECsD2Zv0IDdkXYY7S1Z76sM6AixxJIqKFuJpRIgY4klERWDGWQ05gm+fkzX9sAqZA4qKTtUvmAEkagEraZ2YsuHbPPx2wEZLRo3zjpzAU7vmlXV9eTKIFP7m8jVz3qTZLkmA2SBeIllMQMCbBYDDh33Vi37rZYlepANDd4qjftZYpm/QIZJq2oGGSeCEuVtcHC+mhlkobCEqBT7jM71OaTXMYOMKkuS5ayf58wgI6LhqJ2rfKIqCO3oxupDv8U11g1Y8d4vM/bCMht1as9ab42VDbl8salXuyPtOBqo/oz1XD3I1P4mqH7Wm9J/DkjN1lKzsNzV6kGWvrdTPuzm2pkQWmu8/ixTLNUm/bX1/q5lOUssmUFGlDfl3EIpqne4gohK1cnKUjYKBCF7+wkAMGiVHmTMIKPKCAQj6h5rus9zq7m6fWSJaGRjgIzGLGUCpNK/S0DmCZCiKKgfwu4aC5C5fYkdsr4q7jgrfDlKLIFYf5O/Nn4et7tW4/U5N0HftbJSy0uhZI8JQqJMBEjOIKt2D7LiMshsghea4+9WfUJorVF3nLOUWPqCEU4AzVPGEst4gCwUkRBkVglRXpTNpSabEVqNAEmW4XBVd5OmzqDN2QdTySALRvhep8pQ+o8ZdBro0gwyYgYZEQ0HA2Q0ZhXaC6tW+5C5ktbT56pugEyW5ZwllgqNtQm7I+3oC5sqsbS0Akm9vpIvAhJN+oNVCZb485ismsmMwFtYa1+HZT0PVn1CaC2JRCU1IJquJEMJ8kSiMsLspZMXZdqqadDr1KjXQCPG3k+1lnFLVKuSm443WY0AqldmWcgmjRIgCzODjCpE7T9mSv/6tMY3OdmDjIiKwQAZjVlqL6xkWXph1dfoZDa3t3YyyJL7lmQqsVRY4xMWXb7qncCk6z8GJMoUQ5HUpskVW1fS7n0hJI8DUw78HeKgCaHMJEudTpnueTXqNerhgH3I8qO8N4yDnk9BENRG/bW2oUBUq9QhIiYdmu2xjaNqNeovKECmlFgyg4wqJDFwJ32lgpJB5g1EuOFFNUHyOBA58g7Px0cIBshozFJ7YeU5AbI+HtBxVzGgk05ygKnfXb2eJUCihE0jCjDosmc/WeMX0NXc4VMCUYMDZHqdRs2A669CmaUvQ+AhF8nVq5YMq6o4IbSWqNNVDVqI4tCSIUEQ1MCZj5Ms86KWWKaZtspG/USFUbJiLCYdWmyxDLLjVdr08hWRQRZkBhlVSHK2ZTpmo1bNYq61c3Yae0I7uuF96Mvwr/8RKztGCAbIaEzTd63ETwIfw+2u1XCf+72svbCUjIhaazydnKEhyTL6q9RYHkgEFuqMufuWJAKO1Xs+lQyydBcB1ZxkGVADD4WVWBaaFTmWqA36M5RkAEl9yBggy0u2aav1bNRPVJBEVowWTTalxLK6GWT5ZDEr/Ts5xZIqxZujlYcgCIlG/SyzpCpS+l2rLX1Y2TEiMEBGY5osy+gNGrA70g6TvSXrfWuxB1lUktSdNOUitZpllt5AGDbBi1nGYzkP/srJSy2WWALVnWSp9HYqNIOs0KzIscQTyF6SAQB1htjXfMHaeY/XskST/qHvH0uNlqQT1arkY1SLUmJZtR5kSvA79yaNXhu7T4ilbFQhyeXImaiN+qs0bIkIKLzfNdWGwkekEY0ihfTMspj0sAle1Dl3Q/I010TQwRPPihEATGqzYMeBgao26tfsfRFr7esgQob3ofUwrLgiY1aeEiCrZvq7P6lJ/2DVzCBT1lVoDzIglhV53xsaHD94AMuXzcOZXaeUenkjUj4n1MwgK0y2PkUMkBEVJqUHma26PcgKK7FkBhlVVq4SSyB5kmX1qiqIRGsbZAip7U9Y2VHzmEFGY1ohPbMmul/HWvs6rOp/uGZqyJUG/Za6xAl1tTLIJI8DrTsfybtBvNKDzB+MIlyl5r5ZM8jUSZaVD+Cpzc/TBO7y0dDWgd2RduxzcQ9Ekc90VTVAxib9eVEzHdO8f2q1JJ2oViV6kGnRbI+VWA54QlX5fCysSb8GNsGLBu9elg1RRXjzyAi3WVhiSdUXMdjwj8hytbJDYmXHiMCrJxrTkndJs/XMkjwOjNu9DsKg4I+2c05VD3JKeaK1Tp/Us6RKAbIsDeLTPUcmQ6yJalSS4faF0WgtrN9WKQSyNBmvag+yAspb0ulsMQMADh33lGxNpSB5HLHXg7Wt4u8b9YQ6WwYZm/TnTZblrNNWLabYxQmb9BPlx5M0ma/epINeJyIUlnDCGUBHk7miaymkB1l7/xastf8VolOG96FHsmaOE5VCrh5kAGA1x87hqjkIiujfWw7j364p2Cy0okXjhrl1HK7j8bHmMYOMxrTkpvLZ1Op0QCVAVl+nQ3M8QFatEksljThFljTi5Caq1epDFlB7fWXpQVbFEst8du/TGd9iAQAcPu6FPLj3QZVUe4qP2qQ/Ww8yZpDlLRiOqkfEdL3yLPFhCB5OECPKS3KJpSAIaKliVni+GWSSx4EJe/6ad+Y4USl4Cyqx5GcQVYfPcQzvvPoSbIIX8+bOwO5IOw559NVeFuWBATIa03x57pIWGvypFLc3dpJgNevRZI0HyKqUQSZaGvFm8xo1jRiCmDONWBl8UK0dvkQPsnQBsngGmbsaJZaZpwPmo6OpDhpRgD8YqepUU0UtTPFRMsgs2UosmUGWN+U1KgoC9NqhpxJqBpmfzyVRPgZnxSibXsdrOUBWo5uHNLp5CmnSzwAZVUFoRzcij9yMzxmexNqGdbig7RCAWE+8Wtm4pswKDpDt378f3/72t3HhhRfi5JNPxvnnn5/2fn/5y1/wgQ98AHPmzMGHPvQhPPPMM0Pu43a78fWvfx2LFy/GggULcO211+LYsWOF/xZERfLnmUEmWhqhXXZ5QcGfSkhkkCVKLPtcQUhVOvi+a5iNtQMX4Y0pl8N86U9zllmoGWTe6pRhJXqQZS+xrOSHmZRUumZKE7jLh1Yjoq2xDgBw6Li3ZGsrVi1M8ZF9DkzX9sAu+jLepy6eXeZjWWBOiQtoTdry9ESTfl6cEOUSlST1PaW8d5rt1WvUn2+T/lrdPKx1kseByJF3mGlXpPxKLOPnl5xiSRWmbAormwciZOg3Pwib4EUkKquvX6pdBQfIdu3ahe7ubkyaNAnTpk1Le5/HH38c3/rWt3Deeefhrrvuwvz58/GlL30Jr7/+esr9rr/+erzwwgtYu3YtfvrTn2Lv3r34/Oc/j0iELxyqDCWrpC5L2ZWi7pT34VbvxbjdtRr+D/6gJnpsuJNKLBvqDRAEIBKV1Ob9leYLRuCUzQg3zcgreGitq+4ky2xN+pUGr1FJrugkvmAoe+lavpQ+ZIdroA+ZaG2DVMWLqNCObnzc9XtcY92Ari0/yljeqWaQscQyp1xlwEqTfjenWBLllHzBpGzYtdRABlk+m4eRUy+ruc3DWpau3QADZvmTZBna4ACma3tgkTOf37BJP1VLpk3hiXWxzY6BGqjsoOwKvvo6++yzsWrVKgDALbfcgm3btg25z69+9St88IMfxPXXXw8AWLp0Kd5991385je/wV133QUA2Lp1K55//nncfffdWL58OQBgypQpWLNmDTZs2IA1a9YU+zsR5S3fEkuFZGzAbpcJbpjRWs6F5ckdnxBnrdNDqxFhtxjQ7w7ihCsAWzwDqpIKmXwFJAJkVetBppZYDl2vViOivk4Hty+MAU8I9XWV6RugBO0yla7la3yz0qi/+hlkvUEDHvMsxcfML0MUZMiCAGOFLqKUnTwxHnYUkHnAhtqDjLt7Oan9+zJMWq2PZ8GEwhJC4Sj0OaYEE41lSk+l2PCa2HG/yWaCTfDC1L8LkmdcRYNOyjEwn89y85yzcMtGP1o0blx/5SroG2vh7Kg2pWs3ENh0DwQIAGRAEDjkIAfftmfwHdu6WN+7v29EKMPzpZRYBsNRBEKRoqeCExVKyaxNKT8XRIRNzYA3VpnS2Wqp3gIpp4KvvkQx+7ccPHgQ+/btw3nnnZdy+5o1a/DSSy8hFIpdCG/atAlWqxXLli1T7zN16lTMmjULmzZtKnRZREXJt0m/QsmK8PhqIysiucQSQKLMskp9yHx57jor6s1KD7IqlVhmadIPVGeSZa7StXwlGvVXP4Ns42uH8HJoBtYOXITbXatxZPm3KnYBUEh5J5v05y/5dZqOUa+BRoy9fiuZgUk0EikZZMpwCwAY79yKtfZ1uDj894oONpFlWe0xmM/moUGnQUBnxe5IO9zgRV826T6PYkdJDjnIh+RxQHrpgbyGQhj1WrTqA5iu7YHrOHviUeWIlkb0zvjIkMxarTW2ydFfheFfVJiSh9P37NkDIJYNlmzatGkIh8M4ePAgpk2bhj179mDKlClDLgCnTp2qPkaxNBrOHqD8KNk6FpMO2jyydZQdKV8wktf9y03JIGuwGqDVimi1m7D7kBP97mBV1qf2UKnT5/XzlQCUxx+uynpz/f0b6g04eMwDly9UsfWFIhKA2M79cH7mpPZ6AMDRPh8EEWpWQqW5fSG8+NZRAEDEYMPugBkuwVKx51Ns7IBfEFIvSgQR+sYOiIPWUB9/f/tr5P1dy4KR+AW0MfPr1FKng9MTgj8U5fNJlEXisyj22Sl5HDC/8RAwKBBgnDyv7JlkwVBU7WNab87/3OhYyA9PIILxfK9nlPbzaDBZguA9Dq29uXILGyHC3mNph0Kke76C27vxNfNfYlnrj29E9H1XwnAyM/OoMo42LsRvB2ScMUmDj/3XGRAtjWjY+zaA2LUbz4mqI9+8g5IHyJxOJwDAarWm3K78W/m6y+VCfX39kO+32WxpyzYLYbWahvX9NHaEpdgHbUuTGQ0N5pz3b4w3zY3IQl73LzclQDahw4aGBjPGt9UD23rgDkSqsj7lJL+9pT6vnz+uLXZc8Aars95gOLbetgzrbWsyw7bnAPSO3ajXNEFrbSr7mjTHYiWRljr9sJ4Tm60OBr0GwVAU/ggwoa3yz2/E1YeXXn4FpqgbEzrHob3JjBfeOIKojMr9vRvMkFZ9Hs4Nd8V2nQURzWuugnXChCF3leJBRF8gAru9blgZfKOdoIlljtnqjRn/ljaLAU5PCIJGUxPHS6JaJYt9AAB7vQENDWb4nXvgTJP5Wic5YWoYeuwqpT5nrE+OKApob7XmdRxssplwrN+PqFAb50Y1q8EM17IrEH3u3ngWlAB58JgDQUTjxCnQWvk8DhbRTIU7Tena4Ocr4upDf/fv1UwzATJ83fegec6SipzHEYVlwCmbEW2bhKb4+WZHSz2Ao/CHJR4na9yoLMh2ufyIRqVqL4NykDwORJ090Njaq9bQdcAVL0WMSujvz92ryRCP+B/r8+R1/3IKhaNqxpYciaK/3wtLvNzp8LHKr0+WZbWPSiQUzuvni3LsfdrvClRlvcrzFwqE0v78ad43sMa+HuIOGQd2/h51K8u/A3m8L1YSadCKw35OxjebseeIC9vfOw6LvrK7VcHt3fB1/x5dsoy1dgE94y/Ca5GZAIDePm9F/959TQvxg4GLMM7gxc1Xn4uopTHtzw/HA7xRSUbvMTcMRU4RHQsc/bFpoBoBGf+WdfHn78gxFya21FVsbUQjTe+J2HtIOe5LGntsq3tQ5qtPtCFQ5mPn0fhaTHoNBgYyT/1NVhc/9zjS60J/v61saxsNDtfPwx0DF2FWYxhXf+ps7HntRTS8/Rd1A6du5WfgjhqBKp9j1iYjdk/4EKYd+EfW5yt8eE/a1gqOA3uhG2+s8JppLDrWFz+mawT1HMmojYXCe05U/xpyrLLZTDnbhQFlCJDZbLEPRrfbjZaWFvV2l8uV8nWr1Yqenp4h3+90OtX7FCsalRCJMEBWy0I7uhHYFB+BW8WmpMoUS4NOk9drxhzvx+H0hKr+Gut3xWrYNaIAnUZAJCKhIV6yeMLpr/j6guEoovGMPJ1GLOj5dHlDCIejFc3YCSWtVysOXa/kcaDr6HoISSUuvu57IIw7pawBXW88K9Cgz+81mc24pliA7ECPGwtntOT+hhKRPA74un+vnqCKgoxxu/+KxmnXAgBcnmBFX58ubwhO2QyNvhGS0Q4pw88Whdj7KSrJcHqCaLTyRDoTpa9YtmOn2aT0GKz+8ZKolimTp+uM2th7xWiHYcUV8G+KDRiRERtsku34VSqe+FpMBm3e71ulD+qAu7LH9pHI4QrAKZsxYLZDMtohTF+BtS9EMdnsxzVXrIpNBuVzmNE7mlPwwIAWF8ytw/tWLEj7fMnm1rQBZtncwueWKsIZ7zNmNiaOo8pgsn4eJ6smW3V7spKnFEydOhUAhvQR27NnD3Q6HSbE0wynTp2KvXv3Qh600r1796qPQaOT5HEkgmNAVZuSFtqkv15p0l8DTafd/thJrNWsVwNLyU36B7+3yk3JxhKEWIPufCgn1VFJrnhjdKUcFEi/XsnVm7bXRbrm7qVU6CTQbDpbYinchys8yTJTY/wmMbZR4q7wkAsls9Fs1GW9nyAI6vPORv3ZKRNgTVne6xZTbQ01IapVymZd8jFK37USf7F/Dre7VuPNeV+p2CZiMZ9B1rpEMJyyU54jZdK41ayHUzbjLW8LYG6o5tJGBIc7FmAU2mZm3KwULY0wrLhCLV6VIMBQocnZREDiPFe5zgEAmyW+kcAm/TWv5AGyCRMmYPLkyXjqqadSbn/iiSdw+umnQ6+PvTjOPPNMOJ1OvPTSS+p99u7di+3bt+PMM88s9bKohlQr8JCOciKYz6QmINZAF4g1Hq82ZfKjErQDoGa8BEJRdSpWpajBRoM270wwnVZUT8IrfWKtXOAbdBqI4tD1KmOaUwgiRGtbWdfljwfusgUe8jW+xQKb4IXm+M6KBqBFa9vQTpiCCK099txV+v3jUS8+c7/Plfv4Kvz+GWmUKXfZLqKVAJm7BjYUiGqZsummZF0qNJZG7I60wxGpXG9dXzEBsviAExeD4Tk5lQBZ/DlTzuEkWa6Jzdda53DHggsN9dkzvPVdK7H/9G/gdtdq/J/u01WpUqGxKxEgSxzTlSofpyekDkKh2lRwioLf70d3d2zU9OHDh+HxeNRg2OLFi9HY2IhrrrkGN910EyZOnIglS5bgiSeewJtvvok//OEP6uMsWLAAy5cvx9e//nXcfPPNMBgM+PnPf46ZM2di9erVJfr1qBaJ1jZIECAOarJZ7sDDYJIsqxfB+Vw4A4kDXS1c8ClBBmvS7oRBp0FnXQjGkAOOnqOwTJlYsfUUm/lkrdPBH4zA7Qujo4K9U5UMskzZbqKlEd55l6Lu9YfUXheV2IEsaQaZayvW2tdBFGR4H3q8YqXMoqURwtJPIfriAynPncnUCuBwxd8/Xn/8fW7KnkEGJLJJmUGWnT+U+3WqBMi8NXC8JKpl3gznIkr2QSWzbgvdOAQS5yHMIMvN6Ulk/wOAViPCbNTCG4jA5Q2lnNPRUP3xAFmj1ZDzvrbWNuyOtMPiGpUtt6mGKddoyRlkyns+KsWC4Xyv166Cjxh9fX247rrrUm5T/n3//fdjyZIlOP/88+H3+3HXXXfhf//3fzFlyhT8+te/xoIFC1K+7xe/+AV++MMf4tvf/jYikQiWL1+Ob37zm9BqeSAbzURLI57EmThP3gRRkCHJAvpPvhj1FU59DgSjaoiu0BLLSpeIpeNKc/AN7ejGl41/hmiUIW/ciNCZlevtVsxJNQDUm/Xo7fdX/MRaWW+2clDtzBVY+2wQHXoPbvr8ByqSnq9kthmHGSCTPA6Ir/4BEFJLmbWdcyryexysn4//HfBiujWIL37qbIiWRtQfjw0gqPT7x5emfCkT5fXrZwZZVon3T/YAmU3wwuLeA8nTwfIWogyUILJlUBC/Gm0dEtmh+WcxqxlkDJDlpJy7KRlkQOz5UwJkqFy70BEnHJHU11hDfe4AWXO87YjHH0YwFOXgHaoIOSkbNDmDTKsRYa3TweULY8AdZICshhV8BdbZ2YmdO3fmvN8ll1yCSy65JOt96uvrceutt+LWW28tdBk0gkWiEp4emIxX5GYsm6zBC/ui6NxlwrVTtld0oqUvGDt4aTUidNrCemb5gxFEohK0mspOBkzmjpdYWs3x9HyPA8Hn7lUz8wRUNiBSTFkGkNh5rnTZXSKDLPN6zUYtnLIZzqAZUaO99DXpafiCpSmxzNQHTHL1VuT1sK/HHesT0j5Z/Xn1SRlFkiSnLW0tBzU7w5T7tWmKB9GYQZZdII+L6I6BLbEMRq8M70PrqjaMhajWqT3IMgTIKvn5WMxnuRLscdZA+4lap2SQJQfIbGY9jvb5GGDMQendpNOKQ4LJ6dQZdTAZNPAHo3C4A+hoMpd7iUTwBSPqELD6QUEwm8UQC5B5QphY2cIpKkD1ru6ppCSPA5Ej71Sl0X2hjg/4EZVkBLRWrD5/FeaYevC50P0IPP5jeB/6MkI7uiuyjkIb9Cv3VVorVbtXxOAMsmwBkUooOkCmnFhXvAdZ9hJLIJbFpYRwKhUwCZSoxDJTH7BKlTLv63EDACZ3WNXblIs/GZV9/6jZGXlmkNkEL/R9746I42m15CqxlDwONL/zSKzEFqjqMBaiWufx12CJZQHnRsrneDAURTAczXHvsU05d7Oah5ZesYdbdv1q/zFD3r1uld68fc5A2dZFlEw5Xhv1Gui0qaEWu9qHjI36axkDZKOAf/uz8Dz4ZfjX/6iiAaZiHTnhAwC0N9XBIntwsfHFqlxEKSeB+fYfAwBREGpmMtvgBpDVDogUc1INJKZfVbrsLpBHDyVREBI9qQKVWV8+vZ3yUe0pTvt6YhMrJ7fXq7cpvVaAyvbxS2SQ5Q6QnRR8C2vt6zB/3/0j4nhaLbl65dXSMBaiWhaVpMT5SA1kkBXTB9Oo16gZ9cyCykySZPVvmVJiyR5ueXG4YkGuxjzKKxVN8QDZCRcDZFQZif5jQ8857ZxkOSIwQDbCSR4Hws/fl7gQGQG79D0OLwCgo6muqhdR3kBxPbPUyWxVLiVwDWrSX+2AiJKRV2hgR9khd1X4+VT6rGTLIAMSv0+lpoL6S1RiCcSmOL0+5ybc7lqNR+yfq1h5m8cfxvGB2MnopKQAGQBY4n9vTwX/3vlOsZQ8Dsw+9gSznnKQZDlRYplpyEWVA/ZEI0XyxNwhGWSmRAaZXKGpZ8UEyARBgC3e7qHSn+UjidsXgiwDAgBL0sUze7jlJzmDLF9KgMzBABlVSCKBYWiPMSWDbMDD93otY4BshBuJu/RH+2IZZB1N5qpeRKkBnQIzntSShyqXWLrTpOnru1Zi56m34HbXavzR/JmK9vspeoplfP3uipdY5m7SDyQau1csgyxYmib9ivrmVuyOtOOQr3LNQPfHyytbG0xDGuNXY9CFUmJZl6PEciQeT6shGEoMOMn0flcC9lI8YC9XOGBPNFIo5eYmgxYaMfW0XAmiRCVZ3TwpN1+Rm4cM8uSmtJKor9Ol/K0TJZZ87rJxuJQJlsa8v6fJxhJLqiw1gyxN1YK9XgmQMYOsljFANsIJ9a2Q5JG1S68GyBrrqnoR5St26qKpOiWByWRZhss7dEIKAJjszdgdaUdvMP8TiFIodoqlUmJZ6d4b+TTpBxIlo5XKIMun9LMQjfWx10F/BXdP05VXKuqrkIGplljmCIaL1jY1A1NV48fTalDeOxpRGNJfI5m+ayX+bPssbnetxlvzvsIG/URpZDs+GXQa6HWx95jbX5ljZtGbXSwTzEl5bqzm1Awo5bmrdC/Wkcbhjp3HFJNBxgAZVUrWDDKzUmLJ93otY4BshOuL1uHP3qVqkKzWd+llWcbRvkSJJRC7iNp68o243bUa65q+ULGLKCUjKFdWyWDVGLs+WCAURSQqxdeTegBWephUen3FDD0AkpsQV6lJf45R9ma1B1n5A2QppWulCpBZYyeSLl8Y4YhUksfMZd/ReIP+duuQr1W66bQsy4km/Tl6kImWRoRO/URi00EQa/p4Wi3K5oJRr8nZKLm+qS2Wwegt7DhLNFYox6dMPRKTyywrYbgDdxggy0wJgCnlqAo+d/kZTolln4sZO1QZg3tEJ2MG2chQmiswqpp9R114OTQDO8Lj0KxxQ9/YjptqeJd+wBNCIBSFIACtDXXq7Y0dHdgdOY7AQOVitr4imvQDiZKHavYgU9LwDToNDLrUAE99UoBMluW8J/0M13BLLL2BCCJRSW30W26JEsv8MsgqUWKZUrpWgh5kQCwopNOKCEck9LsDKe+7clEyyKZ0pMkgq3CJZTAcVcdtDy73TMc29xzcvDGAZo0bX7r8HOhbmD02WCGTVlsbTACAY/3+sq6JaKTyBrIH8OvrdOhzBSp2zjHcz3Ilu52GyphBZk6cV1byvG2kccQDZEpmfD6UEst+dxBRSRpSxkxUakq2b7YeZE5PCJIsQ+R7vSbxKDHCKZka006ajPci7dh+rLYbUfbEs8da7aaU0pz2JnPs6w4/JKkyjWiL7bOh7OZWM4Ms2+6EsgsdjkgIhSuTMQQUX7JaZ9RCI8Y+ICpZtpooscweiFIyDCtRYqlcmOQqXSuEIAjqbqujAjuoLm8Ifa4gBAAT27KUWFaoXMjrjz2nWo2gliplo9OKECyN2B1px4mwqdzLG5EKmbTa1sgAGVE2Hn/2zbp6dbBJ+T8fZTnR66zwdgnxMkH20cookUGWeuGs/DsSldVzKUoViUpqgLHBmn8Gmc2ih0YUIMkyBtx8bVL5uZN6DQ5mNesgIFYxUs1WPZQdA2QjnJKpMXdqE6aNtwEA3th9oppLyuqoI9GgP1mz1QitRkQkKlVsFHOxTfotVWgyPpjbO7RBvyI2bj0WcKpkEK/YXWdRENTntJLlBYFgvk36K1di6U8K2pVyB1kZia707yinffEG/W2NdWlfC5UusfSqEyx1eT+nyo7zCSeDOunkmmCZTMlYPDbgh1ShKXxEI0nOEkvlnKMCn+ehsKS+T0052g8MxjLB3FwZzt10Wo36fPP5S28gnj2m1Yhpm59nIiZtEvbVcAIBjR7ZepBpRBH1Sh8yN8ssaxUDZCOYJMvqxeiUDivmTW8CALy+u6+ay8rq6IlYgKy9KbXMSxQFtMczDZQss3Irukl/DQTI/APHMV3bg3bj0IOrIAhV6UPmU3adCww4Aomd50qWrSoZZLkCeokMsvI/l4WUrhVCmfjUX4EP46MHD2G6tgezWtN/vdLvn1wXn+m0cOpVVr4CJq02WQ3QiALCEYkng0RpJAfx06mvYFsH5b0tCsKQ9g25qBOpmUGWkZpBZhl64cwhB9klyisNBW8gNiuf6QyQUQUomxnpMsgAwG5RhnLwnKhWMUA2gvU6fAiEotBrRXQ012H+9GYAwDv7+xEMVWYceKGOOuIN+huH9kFSyiyVKZfl5lMnRxXYpF9pmFuhErHBQju6Mfv1n+Aa6wZ8dOD/ENrRPeQ+9RUOkMWayxcf3LHW6WATvIgefQeSx1Hq5aWVb4mlkkHmr0DZQ7FZeLkojfrLXWIZ2tGNpe/+AtdYN+DCY3emf23WVfb9k+8Ey2RNtliw/gQDZGlJ7j5M1/agUZs7w04jimi2x57PXkdlju1EI4lyjMrcg6xyWbeJz6DCs5iZQZZbpgyy5NsqPdF7pChmgqWikZMsqUJkWVY3CTIHyJRG/TxW1io26R/BlP5jE9vqoRFFjGs2o9lmRNjVhz1bX8XMU2bW3PQ1JfjV0Wwe8jUlaFaxAFlQmWJZXAaZx1fZJvgAIHkcCD53L4R4K3cBMoLP3Qdt55yUv7Wlwn2eAsFEc/m6AssyAGCesAOfs2+A+LYM73YBhhVXlH2aqT/fJv3xYFVFepCF8i9dK4TS0Lac/QmV16aY47VZ6fePJ0d2RjrNaoklT6YHC+3oxsLt92KRVYZ8XEBoRzDne7WtwYRehw+9A37MqtA6iUYK2evAdG0P7MK4tF9X+zZWNEBW+OWBrUoDd0aSTD3IAAYYc1EnWBbQf0yhTLKs5R7NNDoEQlFEorHz4HQllkBSgIxZ9XmTPA5Irl6I1raKxDYYIBvB9h6N9R+bHJ8UJwgCLmg7hLnikxDfkOF9szKBhnz5gxH1A649TQZZR7zssmIllkU26VeCT1Ep1sy2mJLCYkmuXmBwHx9Zih000gTIlAbl5aacVGs1InTawoI7kseBxc4NEIT47yWnD6yUWqFN+isxxdJfQOlaIdQMsjJ+GOf72lQCZLH3T0R9fsslUWKZ/3PazB5kaeUboB9MnWTp4PNJlCy0oxuXuu+FaJUhb9mIkGXoOVt9BVsQ+ItsPQEkBu5EJRkub0jN2qGYSFRSs/qzZZA5GSBLS8mAL2SCpULtK8oAGZWZcpzW68SMZepKieWAhwGyfIR2dCP43L2xawyhMrENbu+MYGr/sXYrgNjFy7wTT0IcFGioVMlaLr39scwwa50ubSmB0rj/aAXKcKKSlOhBVWCAS6/TqAc9T4XLLEVrGzA440YQY7cnUTPIKtQLJNHPrfDMJ8nVq15wq+KBlXKJShLCkdiEz1w75UppXiUyyJTStaY8StcKUYkMMtHaBhm5X5s6rQaGeFCyEhkRxZRSJ2eQyWwsr8oWBM2mLd6oX/kMIKLMWbeDz9kq2bfRN4wMspSBO+xDNoTy9xMFIe05sI09yLJSM8iKKLFsYoklVYjaoN+UPnsMiGWQ2QQvjP27a+YavVYpn5PquWeFYhsMkI1QUUnCgd5YgEzJIKtGoKEQiQb9Q8srAaAt3qTf7QuXvXeWMsYcKG6n1FLBkodkoqURhhVXQJJjgQgZAgwrLh+SvaGcpFY6g6yok+o8AyulFEjq0Zc7g0yrfk9Uksq2pljp2s9i/buOp+/fVSwlg8wbiCAYLk9/QtHSiBOzLlZfmxDEtK9NoLIlQ2oD7AKa9DdajRAAhCMS+8EkyTdAP1ibkkHWzwwyIkW+AWd1cnYFNuSG2weTQZ7MlIbcVrMOYprWAiyxzK4/3oOssZgAmU3ZJAxy04vKKjHBMvM55wTX61hrX4fz3I/A+9CXS3q+P9oUuzE7XAyQjVBHTvgQikgwGTRoi5crFnvxUimO3qOYru3BVFv6oI1Rr1Uv5HvKnEWm7JIadJqi+mRUcuz6YLqZZ+JW78W43bUagQ/+IG2aqcVY2R5kagZZEeWmoqURxjNzB/1KKVESKuT8+ydfKCQHVkspU+laqXZITAatmrVVziyyA5b5WDtwEZ6svwTmS3+aMQW6voIXfEqQ2FLAa1OrEdU+JyyzTBAtjdAvvzzxXs0SBE2mllgO+CHx4oQIQP6bQ0omQigslW2DQ6F8xhUbIGOZYGbZGvQDiVJaZt+lp5ZYFlG6qwTVguFoRaoBaOxKNOhP/z6XPA607HykZqu9ak21YhsMkI1Q++L9xya11as7UWp2ESoXaMhXaEc3lr/3K1xj3YBzj/wuY7S8XW3UX94+ZEo/qWL7h1kqOHZ9sEAoiuMhE3ZH2mFrbU97n0QGWWUCeP7A8Had9V0r8UTH1bjdtRrPTrmm7LXlif5juder1YhqcMlbpj5k5d4hEQRBPUEsZx+yPlcATtmMUNP0rMedSk5l86rv9cJ6nTWzJCMtZ/tirB24CL/1fAB1H/9JXu/VJpsRGlFAOCKxKS1RnGhpRM+Mj+TMujUZNNCIsfuU+5zDN4weZEAi+FPp7PqRwOlRGvSnz4CyMYMso0hUUp+XYkos9ToNrPHzYn6mUzkpiROZMshqvdqr1oiWRrjnflz9nJRkAf4Fl5Y9tsEm/SPU0UOHMV3bg65me8rt+q6VeO5YA155dRvGT52Cy7tOr84Ck+Q73Q4AOhrN2L6vHz1lnmRZbIN+hbKjW+5S0HSUpo4mgyZjA8jEFMvKrG84fUsUE6dMxMbtXshHJXyoVAvLIN8G/QqzUYtgKKq+bkpN3SFJDpKVeIeksd6Ao30+9LvKF6DId4e3voI98jzxDLJCmvQDQJPNBBxycpLlID0OH5yyGRZbK7T1TXl9j0YU0Wwzorffj95+P5t3E8Xt0J2C3w3IOGuGDuevXpz2pF8QBNTX6TDgCcHjD6PZZirbetQSS2Nxk5RZJphZtgmWQKz0EmAGWToDniBkxLL+LVlK17Jpshnh8oXR5wpgUnt9aRdIFKdmimbIIKvE+f5o855xDtYNhNCiceN4tB6LBybj0jL/TGaQjUChHd34wOHf4hrrBrxv3+1DsrFaxo/H7kg7djtq489bSHZMe5OSQVaZAFmhDfoVlWyaO9hAjl1IALDEA3iVyiAb7q4zAMycaAcA7D3qRiBU3hR45fHzySADgDpDPCOvTBlklcj+bFDGnLvLF/BRyjcbc4xhr0YGWSFN+oGkRv0DLLFM1hOfRJluEnE2rfFG/cfYqJ9IdaTPC6dshm7crJrIuvUNMxvcyh5kGeUqsVRuD4Wlsp8DjTTK5ltDvSFt/7Z8NDIrnCogVw8y5XxfKa+Xaqjaq1YdOh77nPTapsIpm/HitqMIR8rbbqA2IiiUt3ymHnW2xJrg9zh8iETL11Q8X7GoeH71wx1KgKxCPcjMxWaQxQ98nioEyJzxDDJlTHA6lni2TKUyyIbb2BcAmm0mNNuMkGQZuw85S7W0tALxPivGPKduKpMsy5VBBsR6y/3Qc0mst9ya9L3lhkMtsSxnBpnaRDdHBlkFA8zFNOkHUidZUkJv/NhcaIBMadTfy0b9RCplM7Ajw/AiRX2F2joM97NcyYJiD7KhcmWQGfVa6HWxyzIGGFO5T/RiurYHnebizxmarEbYBC/Qu4P9nqhslN662TId9V0rsf+Mb+B212r8Rv5E2dvKjHSHjnsAAO8/bQIarQZ4AxFs3nm8rD+TAbIRJp9srIZ6A0wGLaKSXPZMrHyIlkZ451+a13Q75STxeL+/rME9tcSy2B5kFSwRG0zNILPkziALhSWEytzUF0icVBf7fCqULLKdBweGuaLs/GoGWX4BsroKBMh8wQiOhYyx3nJt6XvLDUdjmTPIJFlWg29NOUroKjWVLRyJIhSOHUcKadIPMECWSU+RAbJWTrIkSiFJsvp+Gtec/f1UqQwyf4l6kLFMcKhcGWRAcgYee7gpQju6MeM/t+Ea6wZc7r+36Il/XeG3sda+DkuP/JGTA6lsEhlkmd/nADBt+iS8F2nH7n6N2jqH0jscD5BNbK3HirnjAACbt+5E5Mg7ZQt2M0A2wuQz9UgQBDWLTHlRVdvRhgVYO3AR/qL9r6zT7ewWPQx6DSRZLuuFlC8Yb9xtKK6XgXLgq0YPMmVUeKZdSCC1qW8l1jjcsgzFzAkNAIAdB/qHvaZslB5kpnxLLOPBlXKVWAJQe4NZTLqMveWGQyl7LFcPMrc3hKgkQxAAe332E4NKXewp06oEATAW+NpstscCOn2uAMfCJ1Eu6NsKzSCL37+XJZZEAGITcsMRCVqNmLOvWKJvY2UCZCyxLL1cGWTJX2MGXkypJnxLHgemHvh76uTATfci/N4rVckmkzyOsl7cU/V41CmW2a8v64w6TGi1AADeLXNSwEjm8obgin/ujW82Y8XcDiw17MInvffCv/5HZQt2M0A2woiWRhyY8uGc2VidLbE33aHj5Z0GmS+HKwinbIbfPi1rnbUgCOhorINN8GJg95tl+/AYbg8yS4VOVtNRJiHZs2SQCYKglpRVIkA23F1nRVc8g2zfUTeCofJlvhXepD/2XJYzgyxRnlj4hKZ8KGWP5cog63Mppb8GaMTsHy3KxZ6nzFkGSg8+s1FXcN+ShnoDBAEIRyRe7MUFQ1H0x6dQFptBdrzfD4kBRyIc6UtkY4pi9uNTpUoshztwx2bWwyZ40R46iLD7RCmXNuKpAbIs7TFqOQOvGkGdUk34Tjs5EDICT/+u4tlkoR3d8D705bJe3FP15JtBBgAnTbADKH/VzEimJPq02I0w6DWwiz58zPxyarC7iKB5LpxiOQK9azgFdw8IWN1lwAfOOTVtwGl8PIPsUI1kkPWpzbtzTy9bZtqNU+0bIL4pw/uWAMOKK0pen632IBtuk/4qTrHM1oMMiAXxXN7QiAqQNdtNaLIa0ecKYPdhJ06ZUp6mlYFggU36lRLLYBkDZElNaMtBySDzB6PwByPDzvYbLN8G/UDl3j9KBlkx73OtRkRjvQF9riCOOwNZS5rHCiX7y2LSqZsE+Wq2GdGo8aERLgz09qCxvaMcSyQaMY72xTYwc5VXAoBlhJRYGg++hLX2dRAFGf6HNkI+s/TnbyNROBJVn9usJZY1OgU0tKMbgU3xTC6hPOfl6ShVMynBrSIm/qWdHKiIX2BrO+eUtVG65HEg2rsr8TxW8GdTZQRDUYQisbYe9XmcI500wY5/bT7EDLIslEQfJfFHcvWqfdhV8aB5Kd9DzCAbgY4PBOCUzRDauzK+GJQXUq2UWCoXz005Lp4ljwOnOTeUPTKs9iArukl/7CTGH4xUfBBCPj3IgESWW0VKLEvQpF8xc6IdNsGL3h1by7ZTWWwGmbesGWSxAFk+QeRiGPVa9fWuvB9LKfEez71+5f0TCksIlrFHnppBVmAwR9EUL3s64WTfLCC5vDJ7OVg60Xefw7esj+Ia6wZo//F17prTmHf0RH4N+oGkEssy9m2UZRn++ACbYj7LJY8DoefvU8/fii2HG42U7DGtRsh63lmLJaqSx5E2qFOJv6toacRbrWvy6mGc63EMK64AhAyXvUVkpRVCyRoLPP27oZlsZf7ZVDlKhq9WI+Z1fXFSvGrm8HFvVVr2jARKos/4eFwjn1ZTpcAA2Qh0fCB2sdZiz3yRomSQ9bmC6q5VNeWbQZY2DboMHx6+YTaVrzNqYRe9mK7tgafvWCmXlpPSgyyfDDJg5AXITjfuxlr7Opx28IGypZ8L/n5M1/bEJhrlQTmh9ZW1B1nsPVKuDDIgkd2lBONKSSmxzDXBEogFJrWa2AdcOUuGPIFEiWUxlEb9HAsfU2yDfnX6Mi+ciVRH1AyyPAJkFZj8GwpLqIcH07U9MEVcBX9/qcrhRiOXNwyb4MVcywnI3sw9VmutxFLyOPDOcxurFtSRZBnrDo/D2oGLcGje1Vl7GOei71oJ86U/hfGcLwIVuMBWKJ9/abPXAEiygKPOCHuSjQJKVUR9nQ5CHm09rHV6dDTFzqd2MYssrUQGWexzUrQ0wrjiCjVoLgtCUUHzXFhiOQIlAmSZL0TNRh0a6g3odwdx+LgX0zttlVpeWup0O1v2i+dSpVPnMtwMssjOTfiOLVZGIP9tI0IVKiMIhqPqDq/NXDsZZKWaYil5HJiw568QhPKln4d2dOOCY/dCtMqQ39mIUEvuv12iSX8lMsjKGSAz4tBxr9pHqpTUHmp5rF8QBNTX6dHvDsLtC+dsUF0srz9eYmkq7nXJSZapeosNkGW5cGZZCY1FsiyrJZbKBVI26mCgMgbI/NufVcsjw49shFhgGV26MjZZEMoWeEhH8jhixxVrW00dW6Rdz6nPrfehJzKWKNZSiWXonW4EnrsXEyFDjlVWqiQIEOpby76GXQcH0O8OwmSwYfqixRC1w8vrEC2NEC1LIIcDalacBAGmMlxgK9J+/ilfg4D/BKfgtE0/g1+obPkqlZ7yvs3VoD/ZzAl2HO3zYefBASw4qaVcSxuRJFnGkROpJZYAoJ+1Ev+3RcDA0UM468wFOL1rVsl/NjPIRhh/MKLuIGbLIANqpw9ZJCphIM/ysaGR4eLSqXNRMoHqisgsqWY2hDPef0yvFWEyZE/fVQNkZe5ZEolKCIVjZabDzSArdwah+rcrcCJSokl/+Z5LJUDWkEcGVrEa6w2wCV7IR0u/U+kooM8gAHQYg5iu7YHPcbyk60jmHXYGmVJiyQAZAPQ4YpszbQ2FBcjUC+ckkizgzX1u7prTmDTgCcEfjEIQ8ns/KRdcvjK1dZA8Doiv/mFY7S0Gl7FJsoANwkpIJnvWn1uqY0CtNj+XPA607nwkr+fWWqeDTfCiwbu3qsdFyeNAIHl6ZHJwTBbwZ89SvPHeibIfv1/ZHjv3W3RSC3TDDI4l03etRORDt+J212p8b+AjkKctL9ljD5a2JAwCjOf8NzTn3YLTDHvL3laGKiPQfwLTtT3oMOa/CX3ShFhbGd/+bfy7D3LCGUAwHIVWIw5p7dHcMQ67I+3YPVCeUBYzyEYY5ULNYtLlDEZ0tliwbY8Dh6s8yXLAHYSMWE12PlF1/ayVuOM/gOfYYbz/rEU4rWtmydc0nBLLamZDDCRNsMyVvqsGyMoY1AGQUsKbK2iXS9pGqiXMICz2b6c26S9TBpksy+gvIAOrWLMib+NC+3qIB2R4H/pDSXcq1SzRPAJkoR3d+Hz0gVgW3wsbEdKUZ8fUN4wm/UAsg8wmeGF29kPyTKypjIRKk2U5UWKZR8ZLMuXCOfjcfYAsQVZ2zd/6HfzbuGtOY4+SPdZqN+V14W826dSPRo8/nHWKdTGybU4VctzTd62EtnMOPMcO48ePHcJRvwH2F7bhfdN1Q7K6Qju6E6VnwzwGDCljiwcaxMYJQCRY1YyyQp7bxmOvJWWaPV6142L6qY+Afuml+PeJNuD1VzDtldvKmvUUiUp4bWdsA23JyaXPQmxs78AJ40QMeELY3+NWJwqWmmhpxM5x5+Okw+tjgbD4xr9u2mIIR96JPYfJmF09IoV2dGPW1ntxslWG7BYQ2iHl9Z6YGXo79p6PyPA+9A+eCyU5fCyW4DOuqQ4aMfVzcmJ7LKPsQK+7LD+bAbIRJp/+Y4rxzbWRQab2H6s3QMyjJhsAGtvb8fqRKKY7NTitxOuJRCWYIm5M1LphiroAFFbelS6II6EyZQT5jAlXVCqDTAk2GvSaIQewQikX0oHn7oUgy5BkAbozPl2yE4ViS3iTp1hKspz36zhf3kBEzcJrKNO0RMnjwMwj68tSvhqOSOprsyGPQRzpsvjKMcVJzSArskl/a9/m2IkLZHgfWj+mT1zcvjD8wQgExC7qC6VcOEuuXsgaPU77+w8Sk4g4yYvGmKN9+TfoBwBREGAx6eD2heH2lT5AVsr2FqKlEVZLI9ac3Yy3N/4Dp73zAPw7UgMpmRq/F3sMyLT55fvb9ys+eXEw0doWO0fM8dxKHgeMWx8EythiIl+Z1qybehpWTZbg2/3Lsh+/d+7Yg/bwQQTqGtE1yV6yx002pcOKrbtOYO9RV0qArNSluq9FZuLBAT0uOdWKpUsSz1O5N4WpMpTzWqHA81rJ44D2tT/UxHu+Fg1u0J9sUls9AODgMQ8kSYYolva6jCWWI0w+/ccUSr3uoeMeyBnq3ytBySwpJDNmQmts7QePlT4y7NsW67NxjXUD5HW3FJyGn66M4M+epdi2p6/s6eZKqWquCZYAYKmrTA+y4Y6FH0zftRLmj/8U90fPx9qBi/CO7uSSPC4Q+9s9rTur4IlISgaSLAOBYOmnLio9wSwmHfS64WXhZVLO8tX+eOmvTivmHG1dyUbO6hTLYjJFPQ7oNv+RpQ9xSvZYk81Y9GtUtDRCO24WhGgo45huorFA6auST4N+hdKHrByDTQRzAx6LLh/2tMBkSyfr8THLy2mPoaX+PEpXxi3LqMrkxSFrszTiX5r35Xxua2nIgWBuwN9Cy9KuWfAcL/vxO7SjGxNe+P9wjXUDbjL+GdF3nyvZYyebOs4KANhzJDGUIrSjG54HS1uqe+i4B07ZjPops1P+7sr1hNpWBuVpOE7lVex7t5be87VocIP+ZG0NddDrRITCknp+WkrMIBthCskgG9dcB7voRXPEDefxXthb28u9vLSUDLJ8Sq8UnUqArMTloZLHAbzywJATtkKj9cnZEE9tDwBvvopJL91a9iabA8oES3MBGWTlDpANc+BBOpr6JjTPnIfNrx7E5p3HsWhmaZrBHjrmwWO9nXhB8xF866KJqG/rzOvvrtNqoNOKCEck+ALhYQ8jGMyRlGVZLuXcqXQ4E+vPVfpbyR1Tj1piWUSvQTaWT6GcgLQV2KA/nbTNvAdl4dZqs22iUiikQb9C2XwoxyTL4wN+PO2cgq2aNnz/o1NgaOoY9vtOdh/LGEiJ1jVDkoXEuRgwrM8B0dKI4MJPQPfag7HhSRgSL6va8VuWZfzbNRnPhy7CzR/qROuESWnXkM9xsVJOOAPo9kzFW5p2/ODjU6FvaE/JeirnMK1is3GKMbUjFiDbe9SV9meXIqMnHJHQG+/f2ZkmE0bftRJ3vgq4jx3GuasWYWEZ2spQeRV7Xpt+sAkzCBXOYz2Yrj2GifWThnxNFAVMbK3H7sNOHOh1F7TZlA9mkI0wxwdiF6L5BMjk3c/jO/FMKfFvX6taw1JHng36k3U2WyAgNhHEWcJpPqXctVSyIVaf1plxl7TUnJ78SyzrKxQgU0osh9ugf7BFJ8WCYm+8dwLhSGmaEne/fgQAMGX6JNimzS3ohKeckyz7i3iPFErZqZTjVw2SLCC86BMlOeFMTLDMvf50GZilWsdgagZZESWWmRrLu4LCmGwsX+wEy3TSvQaeDJ4KV88hSB5HzTbbJioVT99xTNf2oNOc//lNfbyBu9C7o+THnx0HBgAAzR0dME06pSTH43THUOWi8a0eGX/2Li1p5sxO/WysHbgI6/QfRt1/fTvjz640tz8MfzAKl2xG04zM5x3pjov/zDHkoFyUgJG1pQ3GCSenyXq6vGzDtCqZVTO5wwoBsYCgyxsqy88+2ueFJMuoM2hhz3DurrM1YXekHSdC5ZnoTeWlZgKisONZuvf86w3nclMQsanKX5T/iGusGzDhhR+kPQ+c2BYLOO8vQx8yZpCNMPlmkFWyz08uSnZMky3/i3+DXoPWxjr0Onw4eMwN25Smkqwl314QBT2m90TGXdJSP9fKFMt8+o8oQYFAKIpIVIJWU554eLkCZFPHW2G36DHgCeGd/Q7MndY8rMcLhqN48e0eAMDK+eMK/n6zUQenJ6T+vqWkBJgayphBBiQyHx/62wt47TCw6MQEfLIEj9tXYBm1so77/rIJbx7T4BL9KTijBOsYzDuMJv2DG8ur49g3/gj+Kve0qQbX8V5M1/ZggqU0F5jKayA80ION/3we5+IliP/+D5SOmeqlLXty0Cjjeevf+LL+TxANMvD0RoRC+R1HTom+g8vsT0LcJcO7u7THn3f29wMAuiY1lOTxgKSeovFeYxIEmJbHLhpf2f4WNodmYI8wAdboACZOn4ZPdi0Z1s9777ATTtkM48QJ0LZOhWHFFfBvip0HyxBgrFLp2rH+2Hl7g9UAnTZ7ebpyXPTGhxz4AhF0PrsJS5YUtqE3XPuOxi44p8QzrAYzzHoffv5cBJLrGC764FJ0dU0t2c8ud4ZaMpNBi45mM46c8GLPURfmtpf+Zx+Ol1OPbzFnzLBXzumVQVw08ui7VuI3L0vwnziKCz6wGHO6puf9fdrOOdi36z3cvqEHfnc9FvrDahXQWCR5HAg/f5+aeJIphjEx3ofsQG/pe62X5Yr5U5/6FGbOnJn2v8cffzzrfd57771yLGlUkGQZJ5z59SCrpbpmtUl/gdP5JsRrjg8dK12ZpVuw4M+epSXts5Ftl7TUBryJKZa51Bm16rLKmUXmj/fkKnXZoSgIWHhSC2yCF/vfeG3YO+ZvvLEL46OHMMUWwcmTC/97KyWkvjJMBe0vok9fsURLIxauWA6nbMYbb+6G8703h/3c9hdRRi1aGmGeNBtO2Yx3DzqH9fPTiUqS2h+v2Cb9+q6VMF/6U5jOvxnhc74aG8deAz1tKi20oxsXD/wfrrFuwPy3flqyjC7R0gidvR1nRl9KOhFKCo4p2JODRgnJ44D00tA2D7mOI5LHgQWOJ8uSqS7Lshogm1XCABkQO4ZqPnIbfuf9ANb2X4QjDQvgC4Txxnt9AIBzls/B7kg7XjsYHnav3N2HY1lP08fb1J+9ff5XcbtrNe4xVm8z43g8QJbvcBPR0oj6qXPwiZPDWGtfh9nv3QvPg5XNpN3XE3suJ7fXZ7yPuakVuyPtOOzLXdFQCNHSiO3t55f0PD2bKR2x33HvERe8ogUP+05Xf7YkC8CSTw7rZx9W+ygNLa9UJAJkwaJ/DlXfsZAJuyPt0NsKS+oQLY2YMv9U2FrbEIpIeGbLoTKtcGTIt9pLadS/v8dd8l7rZckg+853vgOPJzWad99992HDhg04/fTT1dsWLlyIm2++OeV+nZ2d5VjSqDDgDiISlaERBTTWZ78QraW6ZkcRF89ArFH/azuPl7RR/5Z3j+Pl0Ax4G0/CNavbS9LfZsguqSxAe8anyvJhXkiJpSgIMBt18PjD8JRh6pVCCRiVOoMMAFaY9+AC+zqIJ2R4H/ozDCuuUHu/5frbJfcwihx6CzO33ItZ1thOcmSnoeCT5XKWWCplyOXOIFN0TbTjgtZDODv8DMSnZXiHmQ2VyCAr7D0+Y4INT70K7Do0UNTPzcaX9HcqJoNMIVoaIVoaYR+j49jLnY2c9kRoMPbkoFEi24l/1s+zIr8vH0f7fHB5Q9BpRUwdZxvWY6VjbmqDbdpc7Njei+ffPIpJbfWIRCWMbzbjfQvG49FN78HlDeHgMY+aEVAofzCCw/GJZ9PGJ36H6SdNxl3/PgpNj4RAKAKjvvKFM739sfL01ob8y9MljwMT9/1VnW5XySoQSZaxryd7BhmQKLfv6St9g+zNkZPw54GL8LHTbFi8eHZZf+epHVa88FYP9hx14cVtPXgxMB1O6wxYpX5s79Ph49pTMJzcxsQkvsw9kuz1sXN6BshGNv8wKmoEQcC5iyfifx/bjv9seRfvn+iDvmH4vSBHonyzSMe3mKERBfiCEfQ5A2guYsJ6JmX5pJg+fWha4Ze//GUsW7YMjY2JP7TVasX8+fPLsYRRSSmvbLIZc44zHVIeJAs4MOVCzKnwG80XiKgZRrmCeoNNaE2McC2VzTuPAQBmdk2FdtzQpn/F0nethDh+Nu5+6FnsGNDjA95pOLdkjx4TjkhqJli+wa76uniArAIZZCZDaacvSh4HGrc/nDL+2L/pHgjKQTNLUCe0oxvB5+4FZFk9vCrpssWeaCpBFl8ZA2SFvkeKJXv7cU7kGQglGi2d6EFWWIBvRqcdQOICzZrH8Il8eQMR2AQvOo1eCL4BYLiB8DE6jr3cwwrSPq/x0yK1LItTvahItTbsodjysXKWnSnZY9PH26DTlqcVw7I57Xhley9e2d6rntMtObkNOq2IrokNePO9Pry9z1F0gGzPURdkAM02Y8r5UYvdhCarEX2uAHYfcmL21NK06yjEsfi5e2tD/hdv1RwS09PnQyAUhV4noqM5c1BPDZA5SjtMCwCO9HnhlM2wTClNP7xslKDw3iMudUN/4YKTcHzAD+eJA9i2pw9LTh5GiWUeGWQN8des0o+WRiYlQFbs0LJTu1qxu/txXKB5HuEnZYTHWCsPhWhpxO7OCzDt4GOxrOkMWaRajYjxLWYc6PVgf6+npAGyijTp37JlCw4dOoQLLrigEj9u1CqkQT+QKA96Y8rlWDtwETY4ShcQypfyYWMx6WDQFxZA6WyN7bYc7fMhEh1+k3aPP4wd+wcAAItmtgz78QbT1jfh5CWnwymbsfG1gyVZczJnfIKlViPknRFjrkCj/uF+IGSS7gRRRPax7ZLHgfB7ryCw6V71e0tVslUXn4ToC5b2uZRlWS1RbKhAiSVQ2mEVQPIUzsICfBaTDuPjk2dKnUUW3bkJa+3r8AXTUyVp9F5sE9aRTrkwT1HCwODgJrUQRBjOvAID7/8ubnetxq3uixGdsqwkP4vGlsHDHoJvPFH1ARuipRHP6s8uuHxMtDTCeOYVJW1qr9hRpvLKZCdPakRDvQHaoBNyz07YBK8adDhlSux32Lan+L/Le4djZfrJ2WMK5fdSAoGVdqzAEksgfeuOSk20VBr0T2qrh0bMfJnY3qQEyEqbQRaJJqY+jmsq7WS6dMa3mKHViNCFnDA796BF78fSk9swW3ld7nUUXb7lD0bUNjPZpuzZ6xM9yEpdKkaVEY5I6kCxYlvOiP4BfEj7fEWGvtW69+rmYu3ARXip45MwX/rTjEFCZVOl1I36K5JrvH79etTV1eGcc85Juf3VV1/F/PnzEY1GMW/ePFx33XU47bTThv3zNGVqRl5tykG2tcEEbb67fPZmdC05Hc7NL8Kzvx+BcLSijf+UnllNNmP+a45ra6xDnVELXyCCYwP+oncWFW+8dwKSLGNimwXjs+zkDMfyeePwt+f3QvI4sP3llzB/Uel2vzz+WCDKZjZAp8sv2Giti2Xk+IKRgp//fPlDsXVZ6vQl/RliYwf8QzJLBpElCN7j0NqbEdzeDe+zv4cw9HJ+KEGEvrEDYgHrVd43/mC0pL+n2xdCKP6h2lLIe3sY0j23MoSCnxMgNUu0tbHw9c+caMfhE17sPuzEklPaC/reTCSPA/Vv/XlIhpxx8rxhvR+1s8/CS44mvPDim2jqnID/mV3eXT3J40DU2QONrb16gTh7M451XYyWdx5Rd/LqVn4GevvwhmYk084+C8bJ8xB19kJji2X6TJFluOsP43i/H2/tdeCM2aV5bdDYoJQGq8c4WUbwlYdjnw2CgLqVV8JwcuV35WVZxsaBSXgmeBG++qHxGD9lat7vbe3ss/C33Sbs3rEbs+d14aLZpw57PRF3H0KHt8Mm1GH21Kayfv58dGIPuo6shyjEMkMtx+zQNq/EvOnNeOhfu7Dr0ACisgxDnuc3yfYciQV1ZkywDfkdTpnSiOffOoqdBwcq8vk6mBIgG9diLujcXV55JXzd96hVIOulFfiktTFr0KoUlAvNqeOHPpfJOltj59F9rmDRf7d0evt9kGQZRr0GLQ2mjI3tS0WrFbGm5QDOCj4DUYi14dDvt2LWSSug14lwekM46vAVdQ3S0xMLHjbUG9QgWDrNtljwNBiOIiLJJa/IKERNnHeMQMkDvCx1+pyVXumEvcfSbl4r1zljiS8QgVM2I9w8Nev55pQOK956azfCh7dDDDTmfM3mezgpe4AsEongySefxNlnn426ukSq7mmnnYYLL7wQkydPxrFjx3D33XfjM5/5DB544AEsWLBgWD/Tah2dY3KdvljmyqQOGxoa8t9VaWgwY3KHFfuOurDzkAurFk8s1xKH8EdiJY3tTeaC1qyYMs6Gt/f0oc8Twryu4e0kvfFeLAK/YkFnUWvJ15VdDnTuXgfxbRnO7QKa11wN6/xVw37cyKHYDmmz3ZT3+pviO5YRoGy/sy7swnRtD9pM00r7MxrM0K25GieeuBOQJSTywFKDOo0TpyAa9cERD46lJ8TLuCRAENG85ipYJ0woaDnNjbHfLSzJJf09B5TAp0WPtpbM/T5KatBzK8kCnhDOxOdbOwruneA66oJN8GJinQ8tpgi01sJ62Cyc1Y5/bzmM9464Sva8+p174ExzklEnOWFqKOzvPtiSpbNx36Zj2HcwClOdAcYy9N4DANfr/0L/E3fELvCF0h1HinGidTF+85KEs2bocfmlZ0FrLUOZUoMZGPSefN+iCfjzv97FlndP4IMrppX+Z9Ko5XfugXPQ5kryZFRf9z1onrOkPK/lLPqcfngDEYiiBV2nL4e+wKDCyXNPwlPbPMAx4LPDPF4qx5jPGWRIegHN4RbYG94/rMfMJOLqw8k969WWCSISf4OTp7egpcGE4/1+HHb4sairsCwpSZLxXjxAtmhWx5DPkaXzxuPOf7yNfUdd0Bv1RQ9tKUZyi4uTpjQX9vm6bA0ic5bAd/wwbr7vXRzy6rD4sBtnzC18CnchDsYHY82Z3pL1M7mhwYz6Oh3cvjD8ERntraX5/N5+IHauO7G9Ho2N5dnMThZx9eHsUKLlhBB/bU6cswRzp7fgtXd6seuIG/O6Ct+k6d95HEDsWibX+Y3ZqIU3EEFUEMt6jZJNLZ13jDT+SOz1YzJo0dRU3Os2opkKT5pWHo0Tp0Brrc5rolpC0dhz0NJkyfp+mCfuxEL7OohuGc4HHi3Za7bsAbIXXngBDocD559/fsrt1157bcq/3/e+9+H888/Hb3/7W9x1113D+pkulx/REpe31YJD8Wb1FqMG/f2F1fwvPKkZ+4668Ozmg1g0o3InhAfjqdrWOl3BawaAjkYT3t4D7NjbhwXTil+3t+8Y3O+9AZtQj9mTG4paSz4kjwMT96xL6Zt14ok7EW6aOeydmEPxqUIWkzbv9es1sUuC4w5fWX7n4PZufGzg9xCtMuRnN6IHJd6Vn3Q6bJ+aqWaWhA+8lbKj+g/fAix+fgu279iPVZmCY/GMF93EOerjRC2NBT8fohw7pgy4AiV9LvceipV92C2Gsr0u04o/t/4TR/Djxw5hr1ML0yPP48J55oJ2Do+9/CTW2tdBFGQc+PVTBWdmdDbFgrjvHXbiSI+zJMMeJI09bb8en2hDYJjPsVknoMVuwvEBP154/RAWnlT6cm3J44BTOUkFSnocKcbhXhecshk++wS4o0agQq/TedMa8ed/AZt39OLQkYGKXtjSyJb2GJBMluA4sBe68ZXp+6h4a/cJAEBHYx28ngAKfSdNiJe1vXdoYFjHy8HHGFGQ4XjqfxFt6SrLMSZ8eE/anlrK3+DkSQ14feAEdr70AiabTytoDYdPeOH1h6HXirCZhp4faxCrSOh1+PDKm4exoAzH7EyUckWbRY+AL4iAr9AeU0agcRoWzJNx6MV9+OszuzBrQukHKSgiUQl74uWqbbbc5yRtjXVw+5zYubcPdlNpLinf3RebcNpmN1XknCh8eE/arB3Hgb2YNdGO197pxavbjuKcBYUHJnfui23MtzUYc/4udosB3kAE+w8PwKKvfKZjrZ13jDRH49foJkPh1+gJRtStvFKthpEgwLLyMxU976oV/c5Y5q0oSxmfT8njgO7VBwq65rbZTBDzyMIte4Bs/fr1sNvtWL58edb71dXVYeXKlfjnP/857J8ZjUqIREZfgExJ026qNxb8+y2c0YJ13XuwbU8fXJ5Q0fXRhToRb07aYDEU9TfpbLHAJngRPrQdoYHcqZPphHZ0I7jpXvy3Jd7wuceKiL08pRURx9G0J4Ehx1FojfZhPbYjPinQatbn/VwqfcFcnlDJ3xOSxwFf9+9Tptv5uu+BMK7ETVWNdghGOyQAmpNWwDzuFEiuXrzy3Cv4ELohbt2CdjmWV5aaOivAeM4XoWmbDtHSCAlQH0cq4rkwxnf6Pf5wSZ/LE/HegsW+R4bFaIeh044Pf6ANL/7tEZy192V49mUfgJAsNkjhLymljIW+Bqx1ejTbjAi7+rDv9f9g2swZw3/9GO14Qj4T52FTSoNPyWgv6m8/2Jypjfj3lsN4/d3jmFuGps/lPI4UQynvt5sr+xptb6jD+BYzPCeOYfvLL2LegvI3bKZRwmjHv3Vn4azQM4l+LskEEbK5peLH3P3x6YDjW8xF/WybOXa8POEM4J19/Zhb5MZhpY8xsrk17ZAT5W9whmE3/su+DuIRGc4HHiyoMfXOeG+xKR1WQEba57Vroh29Dh+27XFgTgUb9R89Ebuwa7GbhvVaWzl/HB5/aT+OHjyMg28AbZMml+VYeGDvAUwSjsBrakBjfe7jfVuDCbsPOXH4uAeRSGkCj8rUx/amuoq8P7O9Nk+eHAug9x46DOduHUzN4wp63g/Gy1XHNeV+v9ssehw+4UWf01+Va9haO+8Yady+WEshk0E7rL+f5qQVCFqm4a4Hn0WfZMV3JyyFZhTGNHJRMm9N+szPZzGv2Xxb/JU1ShIIBPCvf/0LH/rQh6DTced3OIKhKFzxfl4t9sJ3PMc1m9HRVAef4zh2b34Fs+fNAoCyT3dSLqwKnW6nmB54K5ad4pPhfeivBU/zUPqQKLtDYplHZaebyCbJAgJRDYxH3hnWc62Mf7YXMOnPYtLBJnhhde+B5CltP4FqTVlSHnuuqztRriHECy+V5z4eENFNW1yyn1tXpimWjgo36E/n5FYBEy0vDxmAkOt9EnIczdjsv5DXwAcaD+BU8Z8QN8vwbhn+1B5fIIwN/ZPwinARvvfRKTC3FHZSm8vcac3495bDeHNPH2RZLnmPlFqbmNkfD84ro+gr6UNth3BSZD3Ed2R4d4zNiU5UnOf909DtbsCN541Dq9yL4Ct/qfpkVOXiP9tEu1xmTrTjxFs92Hmw+ABZpY8xgyerJw8nkDwOtO9+NCULoJDztCP7D2K6tgcnt2YeMjBrUgNef2MXvPu2QfI0Vexv3xvf2G4b5nS1RqsRH53YgyWuDRBfkOF9sfTHwtCObtg33YtrrLH3SHhnXc7HT0yyLF2j/iPxoGIlGvQD2V+bbQDeb9+HNcJzkDbI8BYwVVCWZRyKT7Ac35L7d7FXeZJlOSfljgX++LVBKaogWsaNQ6BhGvqPe/HmeydwxuyOYT/mSKMEyLL1TU/3OVaqYSZlDZD9+9//hs/ny2t6pc/nw7PPPos5c+aUc0kj1nGnP9bnx+SDMeIGUPiH+4faDqNLWg/xbRmet2O3KQ1ry3XRoVz8N1kLD+pJHgfq3/xT0SdNQOWDOIM/aCVZwH+CU3Dakz+EH+kzc5LH0CtrThdIc3piAVKbJf9ASsfA1liA0SPD+9CjJf07C/WtkCCoGWSxGyvzYZru7yoAMJ59NQSTtSxBX3WKZaC0UyyVk6HGLA1cyy3TVMvwnlehm7o443P52Fs+rJaF1AyNAl8DkseB05z/HNJQfzhBbCVLQ2dtQv2U2UU9RjZdE+3Qa0U4XEEcPu5VmxWXimhphOb0yxF+4b5YQ2tZgG7Zp6uWPdXvUV6jlS1HkzwOzDyyvqSvDRobopKEfncQsmyGdepsGCyLcMRyCh766wvw6hrwg5lnVmVdh47FA2TDOGbMnNCAF97qwbsHBop+DNHSCCz5FKSXHkjJsi3n+0rftRLazjlDznGGc54W2tGN83rujbV52LsRoR3pz3Fmht6OnQtJMrwP/gOGMysTaD/WHwsctTYML0AmeRxY6t5QtmNhsZvJ7Y2JafOlEJUk9MQnWHZkmfpYahlfmx4HPig+V/DmIQC4fGFoAgOYoXWjw5g76NWQNMmyGkRLI95oPg9zjz+pDtIwLh/9k7pLxR9v0l9Xor6082c049BxL7buGpsBMm884GjOUvGW7pr7r6Fl+C9fCBbX8JJSyhoge+yxxzBu3DgsWrQo5fbXXnsN//d//4f3v//9GD9+PI4dO4Z77rkHx48fxy9/+ctyLmnECmzvVvv8eB96qqhMqlk965OaUCYp00VH7AQ1dqBvLCZAVoLgVjV2RJI/aI/0h3Ha8z9PBJFkGcFN90LQGaFpm4HwwbdSTkpif5lEIC35Azvq7sN0bQ+atPkNWZA8DrTufKRsJ1Q7Tgh4wbMUHzO/XLETbEWm3W9NWwnK8zJQDtLeQKSkWUNKELnSwYdkaXdhZCD08p8QeuXPQ16LALDz7Z14cdsJ9OuW4uNK9lkRr4FMwbnhBLH3xcsaJrcPb/JtJnqdBl2TGrB/zwHsf/M1jFs6t+Svu/62U/HTATdaNG4cj9bjs6Y5KH2oLzdZltUgbrYpXOVQjtcGjQ0D7hBkGdCIAurjWdcTJk/APnkcIn4JPQ4fOiqUoaKIRCU1kNCZR0ZJJjMn2gEA+3rcCIaiMOiLm3h3yDof/zvgxQxbEFd/8uzKfHZbhrbKKDabTfI4ENh0b0qbh3TnOJLHATG5T02ZKwmSHY9nkLU21OW4Z3blPhYWe77d3pTIICvFedGJgQAiUQl6rYjmIq4bhiPda3M4z/vA1n+p122hv2yEkOO6TckgU6pFquFtzclYN2BSzzsuRRdKV4cxuvlKHCBbMKMF61/cj217HAhHotBpqzfZtNJC4SjC8bLKXL1nlWvu8EAPbt/QA6t3F4S/fU1NStEvvgSalikFB8vKFiBzOp147rnncPnllw85YLa0tCAcDuPnP/85BgYGYDKZsGDBAnz3u9/F3Llzy7WkEasUgY60B/lkZbjocHpCqIcH7Xo36uEBUNjFVSlKACSTHX8LL8OF2hcqGsRRPmjH4R34h/Q/kRF4+nexnljy4L5Zib+xf9M9EOLBPRnAF2RAsALyCxsR0uTRH6qMJ1SyLOPvL+zF7tAMtJx8Ki6YW1fWUt3BsqXEl0udUQub4EWL6EZw4ASMDaXptxFx92G69jia9VNK8njFGPx8pvRzS/NaBIBOAGvtAt5uWwPLqv9XdLl2OUp99h2NB8g6yjcV9GzrXkyy/x3iHhnevaXPwj3uDMApm+GMxC6kt+11YHYFe+covIGIeqLSYKlsiWWtlZrSyJHc3kGMH8y0GhFTOuqx65ATuw87Kx4g6/n/2bvz8Kjqe3/g73MymWQyk8lksgEJkECAsIMWgSrgVlutrbdat7YubbW1rbZqe29tr7a2vb9e26u3C92uthX3BaV1Q9S6ACqIKIggEUIgZCEJySQzmX055/fHzJlMkkkyMzmzZd6v5+nz1GSY+ebk5Cyf81l6nQhIMnQFeQll1SvKSwphNhbAYvOgqcOKhbUJZtp22WGV9ZCrktPPKlbRsgA6Zv8b5o+zJkvHcRTEcI2TrnYQANDVrwTIJpZBluxjYaLvX2nSQRQEeLwB9Nu94SyoRCnllVPKiiCK6rYuSETw5w89uFbEGLwtHdafdbz7NlPo/NqfphJLALA5vLDKehhMlbD2OPD6m/ux3Ngb1+CmXKVkkKlRYgkAM6cUw2TQot/uxcGWPiyZXa7K+2YDpbwyTxRQGMMDINFgRoHBjC+dWwz9iw8NSUrxvvNk8P+HEk/wyQtiWkPSAmQlJSXYv39/1O/NnDkTf/vb35L10ZOOGoGOqCe/SEm46XAeeCP89MT1+Ctx30AqF03u7RsgyMEyI+0Z8ZUZvX/oJN4YmIVmw3T86N+mI9+U2oP8WNtdBIal8kX5fvjJ6OBrR3tKGtNnq/R7/vjjo0DnxyjPL8FZZ5wOTRxln2oZLSU+WcQjbw15GiiqUKLhPbgV38Gj4SmgXil9/ZXCT2Gad8G78/Eh3xuxLypfF2QsPvkigHOgmTY/oc8N/51vC2ZSSrKAwgkGO4+FJr7OTFIGmWS3oLblmaSW/ikDTrQaEV6/hI9C07BSTckeM+jyU/4EMxn7RqaLLLmfzD9nso3W3qG+pgSH26w40m7FmiXxT6WbiNZQ/7HqCsOEMm0EQcC86SZ89FEzug/uwfzy5QntK8dDmbYzq5JznIyHcv556+0PsGmPHeXtU/GfY7zeaenGljcO4HNysAdpWJRrnHQF2j3eQLg1xoQDZFGPheqV3YsGM6TTvgLsfDiuh8n5GhHlpkJ097nQaXFOPEDWG+o/lsLyyrGIBjMK1l4LVyhTUUZs56BE7tsyIYNswBkMTFx0Rh32v/IsviC9BfcLsQ9uymVOlQNkoiBg2ZwK7Nl7CG0fvodFVfFN981mkeWV8ZwrKzX2KEkpIaHrdKz+DJS7mrGkZpQhTYhorJpwr6cRGTfh29zgiXZg8WUoVrOBu90C44dPTPgGUtuwDnnVi/Cnh15D80Ahvla4GPF0qXtjTzsAYNnSOSiomRXHv1THyO2ukhgCpMpnKyd2tRoTew9uxdTtwUauMgQUthmANJ00o6XEJ4Nkt8Dz5oZwr61Yg5Tjvad7u7rvOVGiwYz8WafB+84TsY96UeFJvLZhHTB1Af744Os47tThas0CLE/wvRxuH06GJoMmq8QyFaV/Pdbgz/CJhkrs2N+JtpMO9Ns94YvoVEl3jzxtwzoI0xbid/f/C+1uPb5rWo705Voml7dxKzzbN4QGjfCGZCIsyn47PEBWXQIAaGq3pXxNSv+x6RNo0K9YXXgEl5n+AbFVhuOxhxPaV1pCAbIZGRAgA4LnnyWnn44H974Fa4cNLZ0DUR9yeA5uhW/7BlwEGRIw2EZjlKBOtOCSbm3yA+3doYcc+kIN9IUTH1SmbVgHoXoh1t//KlpdRbhBvxTzJvyugzpLT8Ff+p1xl9xOMReFA2TzZ44+KCEWHT3BEuRUZ3eORduwDs8d1ePjA4cwZ8EcXNqwYtx/Y5GNKIizP2tkDzJJlsOZr6lkC01inKLzYG7BWwn1XstVgxlk6j1IXFN0BBeZNkHsluF47PER7U4m6+9CySAbr7xyuHGTgWQJkAKIJfw1fgiN0k40mPGq5kxIspJClFg5mbZhHfRX3g3dhT+E/kv3QP+le/BK6WW4s/9ivOOdo+qax7qBjFdecRmK6xbDKuuxr6k35n/X2doGf0cjTKIDa5em9klxJGW7F57zLYxMGRMiatmEiO8LUV4b+c9iC5BqG9bh+Or/xHrbeVgvXTnhm61wUGdYzw/Jnp7sllQZq0RjIu+p1t+ImpSbCQjK6UGdfXE82pIK1Cw6BVZZj9ffb0/4fY6FGvRXmnSq3JREEz4JR1I5I0EJkNVOKcaM0E1iOrLI+gaC60h1/7FI+cZyFNQsgFXWY39z7OeAbKI0yQ4fZ2QZ7u0PwN/dDH/HwUl/jFXbYInl0ADZ7FCArKPHAYfKA1fGo0y0m+hQD8luwYyj/xi8+ZbjPw97vIHw5MGZVeoOGZmIEr0Wn2ioBADsfPejEfu+8neiXIOIQmhAzznfhv7Ku0e9xtE2rEPeJb/Cett5uLP/YnhnfjLpP8tgg/6J9R+LlF9cHr4e3tvUo9r7AsGJm1ZZD7e5Pq77i/AkSxUa9YczyDIoQAYAM+tmoMk/Bfs6YnvQ/dYRD55wrIKE2O/bjKFeiQFJht2Z2mMTAEgRn1vs78vI69NMpky4L1LpulOyW1DeuHHIcd697X44Hv0+XM//Co7Hvg9v41ZVPivTOBINkI24fxlGEAExtgAmM8iyxHbHbGx3mvGDz1Zjam3i/SKGZ9xMmb8c1iMHse9ILy5ZN1ut5UIuroA0wel2kZbOLsO2DzrwwZEefEmeEzXlMrI0xd/2IYoixlXrOoyAMX1P4oPbfSVkn3tE36zhzc8jf4ZoGX/xBkjnzKvF0RdbEeiX0d3nnNDFWq42zU5GiYYamaHJMrx8Va19cTzrlldjyzvHsf+oBV19TlQlsK8eO5Hc8kpgZHZmrGUX8VBKLMtNOiysNaOlcwAHjvalfJpRujPIFItmlWHP4R7sP2rB506ffDlkUSfzyhKc//x50qdNT0YW62APskjGIi2qSnXo6nPhSLsNS2anrq9fW6jEciIN+gF1zsOtJ+2Q5WBAKp7J2Klw1vJqCE1v4jMdO+E6MTSb8v33DmDeiH66MgRd8bg/u76sEn1FtbDa3Gg/ace8GRPLdhqPkkFWNcHyyuGWzynHjgOd2HO4B5edVa/awKCuUMA03vNuZKP+iZBkGSfCJZbqBRXVMDc0GKO9x4EBpxfFRaP345RkGTsOdKLHOwenrjgTS6ukmLJ9NHkijEX5sDl96Ld7wgGzVLG7fOG/rKLKaXCz/2dc1M4gi3pNAAA5kNVnDz28MiQQbIy8fwmcPArPOxvDmcMFZ1w9evBsGAbIsoDPL8Hq8AIIjisXxzgwx2vxrDIIAFq77bDY3AlNm4ymxZqP1xzqTTicX1sKTZ6AHqsbJ3qd4f4ESlAscPIovLs2AvJgE3HlkiHWcdWpMFrfrMh1Kf9/+GsBJJRWW6jVoL66BB+39mP/UQvOnkCALJODOskUrYlw99xLMGcC+5NcZMImz+m4WJvaARKxigymq7UvjqfSpMPi2WVoaT6OfW+9hXPOPDXu92/pVBr0J7dsKLLsYtHSBny+4RRV31/JIKsoKUSBRsTmnS34qMUSnhSWql5V6ZpgOdyiuuDPeKTdBqfbj6IxRn9no9EmyUYOy8iU81g26LUF99tozfDrq0vQ1edCU7s1ZQEyu8sX/luqLp9YxpYaD2yOZ1h5ZaTZpgCqDDtHTP8+3uvD87t6MceY+MPX6ZUG9NrcaO1OfoBs4GQX6jWdmK7y3+vCOjM0eQK6+1zo6HWiWqV+XUqAS8kIi9VUcxFKBAcKLBZI9ukJH58sJ05ghtwBS55xwj3b1GYs0qK6XI/2HgcOtfbj1HmVo772cGs/eqxu6ArysHhRPTT5sQdMTMUF4QBZqv82lfJKgy4f+cXlkCPLklVq0TKZuTwBAOpNsRy3XBCYtAkKgxlkiW1L5f5FM20+xFmn4Y8PvIYWhw7Xahch1nqyyXWFOUkpJS5ajQhDnOmG4yku0mJWtRFH2m3Y19yLM5dVq/K++5p7sdM7BwW1S/DlVaYJ38QVajWYN6MUB45asO9IL6aV64f0a5ExtDhxhAw6iMTTN2v4axNd/6JZZnzc2o8DRy04+5SahN5D+fzN0hpcIGzPyKBOMilBote37cHzHzoxo7sGt0zg/Zo7bNjumIUmqRp3XlKLPFNm9xNQa18cz+er2lBm2QSxQ4bjsUfjzppRSixrpyRvgqVCb65Ek98Gs1PdJ71Oty/c8LWspBCVpUXQakTA0YfOg3tg9nWGn4olO7OoL9QweKLNlyeqwqQLZ/4cbOnDqfPUmSKbKUSDGZrTr4F3+wOhm39hsIenIoPOY5nOMkqJJQDMrinBW/s70dTWn7L1tIeyx8pLCicc3FWjp1a4Qf+UzCmvVMgD3UMfwgW/iqoDD+MWo4CWooWoc3+U0ATrmkoD9jb1hLP5ksXbuBWf7tiA840y5KZX4J2m3jFaV6DB/JlmfNjci72HT6oWIOvqC2W8meMLTk3pez88wMjx2AsJnY+8jVuhjaj6CBwqRl6GZcvOnWFCe48DHx8fO0D21oedAIAVDZXQxhEcA4KN+o932cPB9FRSGvQXFwXvM7UN69CWV4vHn3kLLq0ZP5+3NuVryiZKBllRgTr36dF6hwfrFSJM0gSFwSb9E9+W+cXlqGxYhn2727DjQCfOOm1mTP+OAbIsoDwJNRsLVUuljrRkdnkwQNakXoBM6RNTP6cWmmnqlAQtmV2GA0ctOHL4KHzlJ+DZtgHRJutFNUkPIrFaWGfG01ubcbClD/6ABE1eYu0HnW4/XumvxS6hAj+/rA76imk5dbMmGsxYtHo1Htm3E/ubeyeUdflh6G9keu105NckNgFyspHsFlR8/BQQx3CPyEwqZ15xOPMqFZPZykuCv/ve0GeqRRkyUFyUj0Jt8DT9uao2nOF8FeKbMjyIOOYlObNIuVBPd4AMABbVlaGrrw0HjvZOugAZALhqVuEX/QOoyrfj+18+Da5n/mtoKV2On8di5fL4wwHmaKXB9dUlKBEcELq74BuYgfzi8qSvqautHfWaTlSWxXZxPh5twzr4K+fj9xteRZfPgB9VrcDot+wjtXQFA0QzKjMvg2zM6d+CjDr3R9BddDvg98T98HV6qP9ba7dDtfUON7xPWjIG8CyfU47jR4+jp3EvpMXGCb+vJMnoVgJkcVQZSHYLxF0Px3XOjvYenu0bwse6TKr6iDRvugmvv9+Oj1v7R32Nu/8kLE0foETQJ9QOYXCSpTfRZSZsIJRBZoyoUppeNx3H5GnwOyV097lQFWd2YS5xJqFJ/4h2J60fBlt7CDJkQZy0U72VJv1qJQWtXjgF/9rdhr2He2KeP8YAWRYYfBKanBuUpbPL8I9tzfioxQKfP4B8zcT+uG0ObziLQymLUcPS2WU4um0zLnfshPvV8fbwUOdWObeynEYzo6oYBl0+7C4fmjtsmDvdlND7tHYHf695xWYU1y1ScYXZo6q0CA0zTGg83o83953A589IrB/Sh83BxsOLZ6WuB06mG2sYQrS/3+FT//rqP4t6jRcorkhJCV5ZKEDWY1M3QNZjDfUfKwk+yZfsFqxxvRrOKBrxQCCJmUX94QCZOuX3E7Fwlhmvvt+G/UcHS00nkwGnD1ZZD2hLkV81G/4zIjLKeB6LmTLBUl+ogS5KuUtFz3vhjBfXYy9DXpvc3m7exq1Ytn8DlhtlyP0CvI1+VT6vyFwJoWoerK39+LDZgnNOje3m1R+QwhltM5LYqzFR407/liXA74FmWvwPlpT+b+09dkiSDFFU/xgS73ksEcuERiw3bYLok2F/9BkUTnAfttjcoYenQtSy5NGo8bOmYnupYV7ourmt2w6H2zciu8XbuBXebRvwTZ0MqVCAzm4EEN/vxGQIBqf67anPILM5ggEyJYMMAPI1IuqmFuNwmxWH2voZIBuFLMsRPcjUvfYc0u5k/jr88R0Jrp4T+Ox5n8DShrmqflamSLRJ/2hqpxSjylyELosTHl8AGs34SSKcYpkFxioVUMP0SgNKiwug8w/g2N7dE56WdeBo8N/PqDKo2vy1PN+Nyw07h/aeiEYQUbD2WuivvCc4sXOMyUa5QhQELAwFK/cfTXwK3PHuzH3qnErKVNS9+w7B1/ZR3H8zNoc33CtLzSBytos2HVKGEDVrRrJb4N62YcjUP9Ph53GT8WXcKDyakuk+SoDMavfC549tulUswv3HTMH3j9qUO1KSMos8vkA41b00Axp5N8wwIU8U4LP14uTHeyfdZMcBV+gGJXRRqFtwJv7HfTnW286D9byf5fx5LFbKNVO0oK5kt8D75obwdUSyJzEPz45R+/MWh3qofRjHdNeOHgf8ARm6Ag0qStIf+I5mzOnfEzjeVZUWIV8jwuuTcDLURF9torFqeCGUqsdoyW5B3rsPq7oPd4YmblaYdHEFDaOes+P8WVMxFVoNJYYCTDEXQQZwaFgW2YgsOEGG5834fydKpnY6SixtSonlsOEAc2pMAIDDbdZULylreP0SAlLwd692gGy44vIqNPmn4PhAcqa0ZwKHyhlkgiBg9YLg8cTr9UHyjZ+hyQBZFhir2awaBEHA5yrbcKdpE6a8/+cJj479MBSAUTszRrJ1RelLESKI0K68bEhALNygL4OeQKXTwlozSgQHrEf2JXwhNdjYN/P6lqTSqfMqsFbfjO/lPQ735l/D8dj34flg84iR9KNRgpRqB5Gz3fARzZIs4GnP6XDmjQzIRgsaDfYhTO5Nr6JYlx/sDQbAMqBeFllPqMRSCcBFDxwGSRCSllmkZI8VaPNULRtIVKFWg89VBc9Vum2/m3Rjzgd7wAzeoGiMZjT5p6DXl1lNqzNZbyhAVhYl636sbJVkSPbnKQ9YGlv64PMHYvo3x8PllYaMzsIUDWbkz16JgrXXDk4em2AmpSgK4Z5drd3J6UMmGswYWHw5JDm0bVXO/kzGPtVlCQYL423QH+2cfXDKZ+P6WUWDGf5PfCVp20tN82aYUCI40DPsAY1av5PBEst09CAbWWIJAHNqSgAATQyQjUrJHhMEoFCb3GslZUhdR2/iU2MluyXm+5V0GOxBpl6wceXCKqzSHoZBGkDA3jfu6yddiaXk8wZ/4YWmdC9FNcqNV7ReGmqQ7Bac0rdlsCHwBHraSLKM/aHSMbUzY6L3pRBQeM63kFdVn5En00yySPoIi0Mp+Y5Hn0VBAin5rcqFdQZOvkqlPLcVFxe8NRigkWV43nkyGKCJoWn6fpZXjkrpuRDo78JvtpxAowMo2/4hLlikGzr5NdpE1UgpKNEQBAFlJYU40etEj9UdV++WsZwMlVhWhEosR5QcCSLs8y7Ehl1u+HRl+GmSMouUUrVSQ0FG3EhLdgvO9LymyrkqEw1vkgwA5uJCHO+yh38XNL5w1n2U7Kho1xGSLOCE1Y+pOKj6VNgDPRrUyolPXRzP9EoDTAYt+u1efNzaj0V1459TBhv0Z8d5fLTp34mqqTTgWOcA2k7a8YmGeDq3xa6j9BQ82u/DsikSrrpkjar7lBpTTIfrCk2wTKSETvn9HNz/Mf66tQeyx4RT/BLyYyhjUhwqWITH+y/GoooAvnrZ2ow9nq/UHsa/mUYOEWp3F8Gkwt95OnuQKSWWxqKhWTuzq4MBsk6LEzand0QAjYL9mYHgBMtkXytNKwsGyE70JNZHcXhrkmQOeUqU2j3IAKAiVIUGXBXT6yddBlnA3gfrQ7dMqqfKFqVJf5JS4aOW78gSfM274o4utx49jim+VlQVusMHVbUMf1KllFLmzz4tY0+mmWJ4Sj4SyLDxByS0hw7IuZ5BNlb2knLTPtq2lSQZx4+2ol7TiSVTJ90hWBWiwYz8mvn4zFlLsEp7GOuOrofr+V8NzRgqKsU/PKcPPnUeLkUlGkpmr5qN+pX3KjcNHvOVkiMlS7bktM+hyT8FLQP54aeXauvPoAb9wOjnqmRl/6Sa8gS/WDd4A1JqVEpu1O1zN5n1WkfPuh+R8QIB73rqYNz2v+FjTDzZwGPpOdGBV19/H886lw+W3KmcHSMIAhaFHrQoD17GX9cJ1Gs6McsUW8ZZJlCzImB6hdKoP3mTLC02N6yyHi7TbNWvT5V9WI7I2hJXXzWhz1FKLKtKE8tUFQ1mzFuxEqLBDJvTh10H4zsmH27rh1XWQzdjQcZez0t2C6Y1bRq8jg5d69mOf4xnt+zCs87lkCb4d24KnWsHHF74A+q1bYhFtAxmIBikULIumUUWXbL6j0UztTwYxD5hcUKSYuw4H6KUAke2JklFtUU8ZFmGwx3qQabCFEvFmFVoUUy6DDIAk+qpsizLEeUCyQmQRXsaJcuAd+fj8L7zRMzRZW/jVpRGjGmWDusBlaPSaj9JzBVqNEHt6HEgIMnQF2qSti9mi7GmbAEYsW0jJy2e2L8L/174BESdDGx9BV4p857eZIqFlQJmGnYOntQiju0tAxpsc8zCx4Fp+NkXayH3HoN311PhDKtUlWioPclSluVwBpnSpF8R2axVj+CTXpvTh64+J2qnGFX5/EhK9nKmBMiiZv8A8DutEO2WrD8fRM8gC2575UEZjU8JJo422CjyOsIdELHixbuGHmPiyAYejfujN5D/5gO4Xhe8Hspf8UXkV81KynXLkllleHPfCRw70gL/fP+Yn+E5uBXXuDZANMqQ33sFXn3unX9qQpMs204mM0AWerCdpOEmyj78p4dewxFbAb5hWIqJzMLuTrDEMpImT8TZp1Tj6a3N2Lm7EaeZ+5BXMiWm/V0JvCj9rjLRaA9osOW/ca0GkDQC8k69BIVTZyf8d15clI88UUBAkmFzeJPWezqacImlfmSG2JyaErT3ONDUZsUpcyffBOmJSmWArKJEB02eCJ9fQo/Vhcp4ps5mwUAMr0+CPxBco16n3vaM2utwDJMzQAZk3C88UU6PHx5v8Clfskosh5fvyIjYh2IMNqZyTHPkTSLFRo2U/JZQWcb0DO9bkgrjTtmK2Lbexq1wbwv+bUgAiuX4/75ylTzQPfKJT+jY/lFLMHg0taYG2poFQM0C5M9elfLgudInrFelSZYDTh+8vuBz6PEC0VPK9LA5+9HZm5wAWf9A8II5YwJkw/7uJDmYuel77S/wZWipQDzCGWSRAbLQPmBReVLqZKb8LY4VnFCuIwo7DsI1TjawaJ4O+D0xH1cCA73wvvlA+NglQoZ/99MouPLupByXFtSWYnVBEy6Td8D1/MiyGeUBzYBXhLh95ICCXDv/KJMsT/a74fL4k3JTqwRpS5M0fR4I7sN5UxtgtXaj+YQN82sT+x36A1L4ocxEpxSuXToNnbtexhe9O+B+IbYSLqfbj9ZQsLJe5coTNY2WTKBcz4mQIb+/CeIE/s5FQUCNwYt8Zy+sJ7tgNs5UY+kxsUV5QKOorynBG3s7cLitP2XrySZOz2CJZbKJooCpZUVo7bajo8cZV4BMGSAyJNCbYQMxlPJKTZ6Agnz1+rmFs8djNHkDZCr9wiMzP9JxAaE8hTLo8qFVcUcZTnka5WveBe/Ox4d+M4ZgYzZEpXPZyBtLAZ5TrkRxHL8b9h8bKjILIXDyaDh7SZIFHKm5EKcYzOFJi4OBY4wYyMW/k9GNFdg92NICIHhzGH59GoLnapdYKhMsTcUF4/ZwmWIuwqHWfpyYQLPWsWRaBhkQ0aOu6zBcr/5laB/ALL/ZH1D6bkSUuJjTONUsG0myHL5uiiXTOZZsYOc/fxHczwQB2tMuRV5FXdRrQuV6ccfuw1g2SmA/GftmoX8Al+l3RM209bd9GC6pEWVgxIDCHDz/FBdpw33b2nscSQnKKD0Dk5VBpqibasS7jd04emIg4fc42e+CLAeHsZREyR6Kh16y41LdjriOy80dVshyMBs7k841w418MCoM9sNUTPDvydu4FTdrHg9meL76Cry+1Dz08fmlcBZU9AwyEwCgr6sT7uMHoDVPzaljxnicKcwgA4KN+lu77TjR68CyOeUx/zvRYMbu0k/jVMtLEIVgdrMuwwZiRJZXqp2MoW1YB7FYByFaYsMwkzJAJskCTtR/AQ0T/IVnQiO7ZJdXRhINZuTPOg3ed56IO9MoG6LSuU65sXz8mbfwbhtwvjQP58Xx7znBciQlIKOZNh/5s1fhgz0f4cG3+yD6zVgSkODp6RiZkj8c/05GpVyQDmbgCdCdcQ0ChSXhkeOJPjVXi5JB1qNagEwprxz/mD+1LPjksNOSnACZMkkr025aRIMZks04aj+yTLrYi4ddeYIf0Zi2VMkgG/BAluWcz94dj83hRUCSIQiAqXj8m/3xsoGDl3+DN/ved54M/v/QNeHQhyQbAVnGEhlDM/GBpB7no/ZWkSUEug4P6TcjCqF1Rb4uR88/NZUGyI5W9Dftg1SyUPVjRnhQRBIzyABg1rRg5nBzR+K9oZQJllWlugkfX8bqEznaNj6cBeWVisgHo9AUwPXML1QblqBU4ih/y6nM8FSyl/NEIWoWVHlJIc4yHsXn896Eb4s8KTK21ZTKEktg8NqvI85G/ZIk458d1XjOfTEq8gZgkY34Rd3pyVhiwpLRoH8IQYSoGf/3NOkCZD5tMe7svxhVHdNw2wTeZ7RGdql+Op2qk6wifEO6/QEIoWyY/NOvHvdnFvSleEFagwuE7cH0/Qwe05zLRIMZZXOWwtrahP3HLDjvtBkx/TtJlnG8WxkNzwyyaESDGYtWfxL44G30DXjw/p5D6Gv+GKtGPLUXgncoMv9OYqFtWAdfRQP++OBrOOE14KaS5fC0WeHzSygxaDGtTJ3JkYlS+oT1DXgQkCTkiRMbvHCyP/YAmdIvJnkZZJkZIANGKXcRhCE3J+nOAI9XtBLL0tBUM59fgt3lG9FAmYZSHiqWFhfE/Lc4WjawHC1DRCHLcG+7Hwg9GIwMPCmBqPD+meTjfNS/BQC2jmPQDsuMU3qr5fr5Z5X2ML5u2gzxYxmOQ+re7PsDEqyhKYTJ7iE1s6oYoiCg3+5F34AnoWO18oBlIv3HFIm081DK9uZMz9zyykiRmerDp0tP5O8pnZU4Sv9LQ1H0rB3Z0YfPa96MmqWai8eP4ZQAWVFhijLIQpMsO3rjC5A1tVthd/mgLyyBtbAMln43Glv6sDyD+so5QhNB9SnalqOZdAGyAl0hBmCAtc2K9h5HePJGvHx9JzKiZDDcSyOFjRq1DeuQV70If3roNTQPFOK6oiVYOM6/aWq34pX+WnxQMAU/vWQmCph+m7EW1gV/L4eO98PnDyBfM37pbk+/C25vAJo8EVPSHJDIZPkaEZ/6xHS0vb0F8957KJTCjMHsytAFFAdNxEdfVoXS+iVoPNCFbR90hAMIC2aWpj2jpsSgDTfV7R/whjPKEqVkog1v0B+N8rfY1eeEJMsQVdwW/oAEm13pQZZ5QzmilY2/Z/o0zg79PWVCBng8ApIUvjCMDILla0QY9VrYHMEbYAbIxtaXYHP04dnAo2aIRAj+tckR/3/o9wrPvgGCzpj04/xovfm0B18c0iMpuDARuotuj6un2mQj2S1Y1L15SGagmjf7/XYPZASzcaL1c1JTgTYP1RXBcqvmDhtOnRf/jW53aIJlPL2MRqPsi65twUwoGQIKxwga+QMSmk/YAABzMrj/2GjUHBymRq/gRNmUBv2jnF9Gy1LN5oxtNTndqS+xBICOXmdcmeXvHzoJAFgyuwxFBfl49f02fHjUklkBslAGmT5ZGWQxmtij7gyUJ4pYPjdYj/vOuwdjGtUt2S3h10l2C3ztH+H5dzshycN2uDSkovfF0UtDTXnFZSiasRBWWY+DLX3jvn7H/k4AwJw5tdDNUD9dndRTXa6HyaCF1y/hUIwjmzuOt6Fe04mGchmavEl32FDVujmFuFy/M9wIWQwljBWe823or7w7WAOv4sj6XLF26TQAwDsHu/BBUw8AYP7M9G8/URDCGb5qNOoPB8hM4x/zy0sKockT4PNLsKhU4qmwObwpu8lLlLZhHfRX3o2+VTfizv6L8XhLFWwOb1aMMh/O7gpeYAsYWVpQykmWMetVIeteOT5rKmcFm/oKCZzzBBF5VXNSdpxX/hYKzv4WBEEIB8UEJZsttKaCNddAUzkrp88/Y5UBqqEvIvNWzYcWo6mbGiyzPBoKNMVrMINs/IcysdA2rMN782/Fett5eLzk62M+mGjttsPrk6Av1GBqggkN6abW9ZwSXJRD4XYJQsoyPG0OJUAW/VyvtNEZIkfLs6NxpbBJPwBUluqQJwrweAMx9yeVZRl7DwevnZfPqcCiWcH9an9zL+TRenCmgT1DAmSTLoMMAM46pRr5R9/GOcd3wtU69tPjIU+ZAYRqn3CuLGC3dxY+UdAMEelrZKfGxV6i5teW4q39nTjYYgEwe9TX+fwS3m3sBgCsXjQlRaujRAmCgIV1Zrz1YScONFuwcJweTt7GrZj1zgbcZJQh+wR4G8WMzsRItwKPBdKIshwZgq44Z29I1DB3ugmVpTp4+ntQ2NeOEqF4SIP+dCozFuJkvxs9VhfmTjdN6L08/SdRrzmJqoK6cV+bJ4qoLC1CR48DnRYnyk3q3OAAwZu8EsGB+mIP4OgDMnTfFQ1mzFhiRtne3bB22LBj10GsM3dkRAZ4PJTySr0uH+KwTurm4gK0dA6EhybQ6NTu2zpa+eVgzliUG4s0lS6KBjPEopG9+QQA2lVXIn/Wiozd/1Mp2Zk6SiA7VZUfs6YZse2DjoT7kLn6TqJe04uphaNf58erfm4tHn7rJFrbAvAHpFEfrLYcaUG9phMVU2ekJJiY6bQN63BcnIknn30bAX057kjRtbZSYlk8ypAG0WCGvPIrkHc+zDY6Ubg8AQCAriB5w/QiafJEVJbqcKLXiY4eR0zHmvYeB7r7XdDkiVg0ywwBAjR5AnqsbnT1uVQpsVaD0qTfUMgAmeoWVAqojsjgGC19esRT5uCLAQCiIGNFQTOsa27FQy98CJtowi/qz0jdDxFiSWGT/uGU7IxjnQNwun0oGmVn/eijJkz1t8FXXIaGGZlxw0pjUwJk+49acNkYr1P+RoQ0NA3NVulMk5/MBEHApTUnUI/nBqfvdBgBY/qDtUpZ5UQnWXoObsV38GhwgtXWV+CVxy8LnGoOBshO9DqxaFbZhD4/knRoO+40bYIoyHA89kLGlyiet2I69r74T6w69BB8ghy1tCyT/wbDNyhRnuArF7+cZDk+d18P6jWdqCpU73cdrfxSNFaFJkQO9h/SnvbFUSdcpspo5x8GxwaFywC3boAoyJCFscsA46UEslP1YFvJIDvWOQBJkkcE2Mfi3P9GeGoiXnsFXr86x/maSgOMRfmwOX040m7FvCj3Bt7GrVj+0QacapQhWwV4GwMZfY5JFVNVFZr8U5A3IKjeOmE0A+OUWAKAccnZuOMNH4p8FnzlkjMwoy62Hsa5wBnuQZa6oM60cn04QBbLtd+eUHnlwtpSFGqD4Z85NSYcbOnDh829GRMgG8wgS2+IalLWSsm2rsHgWPiLI9OnXT1RnjJHECBjilGDXt1MdHsLcbClPwmrHZ0kyegbSE2jz2hKiwswxVwEWQY+Pt4f9TXexq2o3fnfuMn4Mm7Jfxz+Q9tSu0hKyMJaMwQAAz1d6D+yb9TSo7GahlJ0ysV3uCyHT9pUIdktmNP+3GDpKjKnbE7pFzaREsvwBCthaDB6vJ9vShImWUp2C6oOPz3iIVMmbOvRLK/OG1LaHFlaJskC9pZ9JqP/BpUblGiTm8zhEktmkI3F27gVl/Tdh5uML2PZh3fD27hV9c+ILKdSyhp1F/4wWN649IK0ly7y/BMbbcM6PKy/Futt56Fx+Q9VDcxYEuyDl6jqcj0K8vPg9gZwIo7zgGS3wP/2AxH3S+od50VBCE+YPnBsZJuW0SY2ZvI5JlVMhgIIAAKSjIFQ6WOy2aIMiBlOEAQYyivR5J+Cdgd7YUYa7EGWmgwyILJR/+Df/PCWUZFtpg4dOoZ6TSdOmzn4u1scCqztb86cvztHqN0ESyyTIK9kymBTbEXE02PJboG9uw2Pvn4cV8rCyGBaxL/JK6nC0tkC3tjbgQ+aerBktnpP6MfTb/dAkmXkiQJKRkl7Tbb5M0vRaXHio4gpF8pkMGgK4N6WnpHENDHFRVpcUNGKc/1vQHxVhmOUMmSl78Bof0sUnZqNWykokfHxqaJk+E4kgyzRn29wkmV804ySsZZ0EuwnR5zLBQCWuRfht+9oMNBvwOzjrajU2DPyb1J5ahqtCX+pkT3IxjPaDXeyr0ciJ9plCp5/YlNcXoX32gJocObjNBXfN+XT50UBM6cUo6utHd0H38fU5bH1AXb1dCT1OL+gthTvfNSFj45ZcPHaWUO+l86JjZlOkyfCaNDCavfCMuBBiSH5+5GSwTxWBhkQDMYeau1HW4896WvKJkoPslQ16QciG/UHr/1GaxklQ4Ct6lRc530vWJ2w9xV4jcH7vUWzzHjydaCztQ3uVg20pekfsGdniWXyiAYzCtcOTlGJ7B/mbdwK97ZgydiVsoC9gdlYnn8Eghw5pHtoffWyORLe2NuBvU09+Mp5c1M2NU25GC4tLogrZVpN82eW4vU97WgMNeqP/AOM3GJhPMFlBcluwacCb4w7xckuGrDRtRpfLNzBvgNxysQbp2yWyaWrSollzwQCZIkGo5ORQSYaqyBBGDq1KkO29WhG2z+mf2IdZlvaoT32NvRbHoQLmTnVcswSy2KWWI6HN9xD8fwzPmVqY5fFper7RjbpT5W1+iNYaNoM8aAMR+PYxzfJbkHA2ol/7GjH54YnCah4nFf62x49YYPD7YM+4oZ3zwkRc5P42dnOXFwYDJDZPKibmvzPU5r0j9aDTFFdEQrKnFTvgdxkkOom/QAwtawIJYIDOstJuDqM8G3bMCzgPfiwyNi5e3BwS8TDo+ryUpxtPIrP5b0J34syfBlwbZQpUywnZYAMCD5Ba5anY9MLOzGgMeHOWWeM6KckCjKW5zej6KI7wiOvAYx46jZ/Zim0+SL6Bjw43mXHzCnFKfkZBhv0p768UtEwsxQCgs39rN2dECOi0wKQdX1eKCjWDJFX3m3FW656WI1zcNN5U5BXwqfRlB5K6VBkz59MCdaGe5DZPAn3DBENZvxLcybO8b0RVzB6aiiDrN/uhcvjV+UJ5oBgwBP2VYMlixm0rUcz1v5x+So3tCd3Dgb8RnkgkE4DY5S4hEssBzxxjXTPJZkcQKfMVBWa2tjVp97DBSD4dwqkrsRSsluwqHvzmA88lcqP4KCJjYAs43OygPd8wWFkgqz+cd5sLMQUcxE6LU40tvTj1HnBKpS2lla8se19NGI5LtLvCV6LZsE5JpXMxgIcPYGUDWaJpQcZEMwgA4L3hBQkyTJc3tQHyCp63gv3ifU+9yLGyqMZccmg3O8B+LzmzcH7wQy4NnKEylWjtZtIpUkbIAOAuQ11sG7txMl+N3Yd7MLqCuuIJ4yCLAF+DzTT5oe/NnynyNfkYWGtGXsO9+CDpp6UBchS3egzGoMuH9OrDLB2d6F552uoH779BAxelPIElzViuZmw93bh6N53USLocdbpq5BfXZGGlRINytTSIXNxAQQB8AckDDi8CZVEON1+vHByOrbjYvzk4hkomTo9pp+vqDAfRr0WNocXnRZnuGHzRBxu7cdO7xz0G+fg1gumZtS2Hsto+0cprHCN0pc0U36ucAaZbuQNiqk42JPGH5Aw4PTBmKaWC5lMNJghnfYVgFPWKEZVSgZZn0u1wLPPL4WzcVJ17T7aA09f8y7kzzotNExiw4jKD1GQ8Qnt0CQBtf9eFtaa4eo7ie7GPQhMOxU9je+h+P3HcIM+WN2jPe1SaCrTO9giE4WzhlNQVi/LMmxjZDBHUsr6eqxuuL3+cLP3XObxBsK3UqkqsRzeP1AUoiSsjCV0v5dp7TRkWR7MICtM7741qfdsURBw5rJqbHzjCN7Y24GFn65GXoIpvcvqy9Hc1IKeQ3shLStJyY5jsQYPjOmYYBnpvNIWLPRuhtgZfTKY7qLbk3ZypeRQsi2UcmMJAnRnDN5MeBu3Qtq2Ad/UyZB0AgoDRgCZU45EuSsTS4c0eSJmGHzId/XC0tWJEsPMuN/jUGs/ZBkoLC1H6Zylcf3bKeYiCM4+DDR/CKl44k3CD7X1AwCmzaiGZtq8Cb1XqkXbP7Khl+JYGWSRPWn6BjwMkI3CM2M1/nuLE9MKHPj+dedl3HGCMkuFSQdBCN7gWh1emFTo9dRnD16352vElGVARHvgKcuAd+fj8Ox8HMBgUGz4/bMAeUSSgJpWFhzGhaZNELtk2B99FDoM3kOIkOF79ylor7ybf6vDlIazhpOfQeb2BuDzSwDGzyArLtKGH8id6FXngVy2U8or80QB+ZrUzD6M1lJgSMLKsJZRmvrV8DftiFp9kUnXRm5vAAEpuBZmkCXZ6YunYtO2Zlg6T2DT860ocC7H5/V7gqUWcTxhXIJGLDFtguiXYX/0WRSuTX6Nrqv/ZGhc+ZSkfs5YhqduK5PBBCC8/TSVs8Z6C8pQ2oZ18FY04E8PvoYTXgO+ZViKucDIUmTI8L75APKnZ045ElEm8TZuxc35j0PMlyG/+gq8vvjPDwdDfR7nzyyN+/NXFRzGctMWiAdkOD6aeA+JQ639AIC5000Jv0cmGd6XVIaAwgzLLhoIPTU1jPIE31xcEOpJ405ZFnu28fgkWGU9ZNGUUb9bykz5GhFlxkL0WN3osjjVCZAprVGKC1JWCj28vFwGIvoNjSOJN8OS3YKqQ08BEVkuI2RYJm+mULIPLSnoO6k8nNHmiyjQjj+FsbpcD5vDi7aTdgbIADiV/mOFmtT9zY9SBRSZsAIMbRklrbhkRHa9aDBD88mr4XvrwYzIvFayx/I1IrT5qZsIGs2kD5AZ9VpcNqMTp1lfhijJkIoEeBf9G0pr58ac8STZLRB3PRQ+yKdiOpK3cSsu7d8QnDjxwSvwlqSnaV609EsBgHbVlciftYIntSxnKKtCxbxlaNx3Aq/vacfc6SYErJ1sdkwUI7Wm5ykBsoY4A2SS3YJTLFvGHbgRK5fHj9bu4ISqOTWmuP99ptI2rMP27lK8s2s/TFNr8O2Gtele0hBjlVgCwZKboycGUnLDlK08vgAApP3CmrJHlbkoGCDrc2HejPgfTgxnSUODfmCwvNzXvAveUNbYuJJ8Mxx1cEaUNWRSJm+mUHpPKwHXZIp1gqWiulyPgy196GAfMgDB9hhAaidYjtZzdXjCSuTf9mjVF0WLzsIvtvuQ5+jBpZ9fjTlz65K+/tEo/cfSXV4JAKnJBUwjyW7BStvLEXW6MgoPPBNXOeBY05GSYbQbLsluScrnjSUcpY4kiAyOTSJnn1IDANjd2A2rw4s3jvggySN/57yIIRpJjfPDgDP4NBZA3DdpY/WQSERTuxWyDFSadCm/yUu2xUvmoMk/Be+3BWALPTXPBLIswz5OD5hSY+pKbrKVNxQgiyULgggAqkrVbdRvSeNwLdFgRv6s06I0IhIiUspEaFdeBt2FP4T+yruT+uA96v3DsLWwT2B0ymCWvgEvJGmcIOME2cLl/bEFyKaFJlm2c5IlgMESy1QGyIBgUFx/5d2q/C0Xl1ehyT8Frfb0ljXalUz6NJdXAjmQQaZGA7pUT0fKpHHlmTw5jtQxc0oxZk8zoufECWz+50t4tw04mr8KVxh2croQ0Tii9n+BENf54ePj/QCCT2ZL4uwvpfb5SSmvnDO9JKF/n8kqTTrMrCpGS9cA9h7uwdql09K9JADBEg0p9Psb7SYllU2bs5WSQVbADDKKkdKov9viUuX9whMs0zRca9TMkjQMt8mktWSbEoMWghCckGh1eJP6sGowgyy2oERNuQEAJ1kqlABZKidYKtTqyVtdrse+I71p/5063EqDfgbIkk6Nm4fhB3lJFnBy3iWoT9KBPdMaCmfq5DhSzyXVJzDVtQmiQ8YFJgF7y86H4TP38HdONI5o54edxZ/Cp+MprzyeWHll5OcPGbgxgYD2ZOs/NtwnGirQ0jWAdxu7MyZAptygFGrzRm3yG+5Jk4KSm2zFABnFq8ocDJB1qpRBpgSwlYB2Oox2zZ6O67hMWks2yRNFmAwF6BvwwDLgTmqATJm6Whzjw7lp5cG/mb4BD5xuP4oyoBwundIZIFOLMp20I81ZgQ5mkKWOWhlQykH+5dfew0sH3VjqnIX6JK55t+nTOLXvpYxomqesiSe0yUmyW1DdvCmikaqMU/q2ADg3aZONiCYT5fzQdfwYfv1cO2z9epzS70KFSRfTv29U+o8l2ANH27AOrrIG/Omh19DlM+BW86mYnsD7+PwBHD1hAzCJA2TzKvH01mYcPNYHu8uXERdiY02wVCg33OxBNjqvLziJTZuiSWKU/arMwWN0d58LkixDnGCT7cESy/SWp2fSNXsmrSWbmI3BAFmfzQMk8VmOUmIZaw+yosJ8lBYH19bR40B9zeTLNo+HM00llmqqVspm05xBppRY6nXp35Y5cRWhVp2uaDBj+uJTYZX12NvUGy6JSIZX+mtxZ//F6Fh+Q9L7BFBuS3WPPaLJSDSYMXXBKaipnQ5ZBv61uy2mf9ff3Qm9tRkmwYF5M0wJf35JRRWMsxbDKuvxxt72hN7j6IkB+AMySvRaVMYY3Ms2VeYiTK80oBh2HN69My29PYcLN+gf4wbFbCxAieBAmesY/AO9qVpaVvF42YOM4lNeUog8UYDPL6FfheBzuMQyjRlkNDmk6qGIf8CCek0nyjSxZ1FWlysBFXuylpU1JkOAbGqZHgKCASolozAdAvbgvmjWqFPyPhHZ+9uMk1pPMObNMKFQmwebw4tjJwYwa5r6I24HnF50WZwA9KhedCrEDHjCTZNXqnvsEU1m562YjgNHLfjgw8P4fL0HuvJpo557vI1bIW7bgJuMwdJIbYsRmMDDkDOXTcPuxm7sPNCJy86sjztQcKy5BfWaTkybVpeyceXpcGFlK+b6nod4UIajUUDBmvRMiVYoGWRjZbPp23fiTtMmiIIM52OvoHBteteciTjFkuKVJ4ooLylEV58LXRbnhJrre32BcAZEaZozyCj7paKs3tu4FRed3ADRKEP+6BV4y2M7r0wr12P/UQsb9QNweYLnHV1B9p53CvLzUG4qxMl+N9p7HDDG2QtXDd7GrTi7ZQPOMcqQj74Cb2N6r3FyIoNMTZo8EYtmlQEA9jb1JOUzjrQHS1ymlhVlRPkHTW5KGTKE0OEgA0p6ibLVojozPm1uwY+KNkJ6+W44Hvs+vI1bh7xGslvgbXon3DcMAEQVphU3zCxFZakOWq8NB3a+HdN7SXYL/B0H4flgM1Z+/FvcZHwZF/feO2LNk4Vkt2Bex/PhydaQ0zclWjEwzgRLyW6B980HwmtO52TrTMYeZJSIwT5kE8ta6BvwoERwYEFhNwp9NjWWRjmsNMkZZJLdAs/2DRAR/3mlulyPEsEBubMx589D4R5kGdBYfiKqQ8MXOtJQZqnsi0IC+2KyJCWDbNOmTfjRj3404uvXX389fvCDH4T/e+PGjfjrX/+Kjo4O1NXV4ZZbbsFZZ52VjCWpall9GXY3dmPv4R5cvHaW6u/f1G4FANRX53ZdN6UOBzEQqUN29OF8bIMwLACjqVkM0WCGt3FrODA2IkdrgtOKRUHAZdNPYDaeg9gow/Hx2NlR3sat8GzfAMgyZAw+MVMuTpQ1TyZqTLZWm5J1MlqJZSZNts5kDJBRIoKTLHtDlRuJc3/0xmCW5+MvpT0zlbKbOdSYvy9JGWQTOa/Mdn0Y3NddMhyP/SOn93WnWymxzO7zTnWFHnubetB+MvVls5l4jZPUEsu//vWvKC4uDv93VdVgydYLL7yAO+64AzfccANWrVqFzZs348Ybb8QjjzyCZcuWJXNZE7ZkdjkEARjo6ULvob0onTZD1V8gA2SUDmykSjRxYwVgAAzJGhthgqXNkt2C+rbnRg3ODX9t5FrUDtZlqkwsKR+vSX8mrjkTKU36C/JZHEGxi2zUnyjJbkHpRxtjOvYSxUIp901WBlmi5xXJbkHxh4+HB3vl+r4+GaZYAoOTLNPRqD8Tr3GS+ttcuHAhzObofyy///3v8dnPfhY333wzAGDVqlU4dOgQ/vjHP+K+++5L5rImzKDLx+er2nCm53WIb8hwCOr1MPEHpPAUsVyfDEJElG2inehlCBCNVehubYF+jODYREub48mO6m1vQeFoawmtZzIGYJSScte2YGmJDAGFaS4pD5dY6qJnkClrdm9/AIIsQZIFFJxxdU7ejIzFywwySkAwgwzo6ks8gywTM1Mpuyk9yPrtHgQkCXmiuoF/0WCGdNpXgJ0PB8v3Y7wGycRsn3SaLAEyZfBCR48DsiyntA+taDCj4IzQdZkgQxbEtF+XpeW32draimPHjuHf//3fh3z9ggsuwK9//Wt4vV5otalvEBcryW7BWd7Xk/Kk6HiXHT6/BH2hBlNCfRGIiCg7KMEMz/YHgheNsoAt3k/gjGNH8eT2VnxNFAb7XwEABBSe8y3kVdVP+PwRLTgnyQJae72YjoPhgFf38WN46NUWfEM7fC3KkiZ3H0Jtwzr8q70Ee/d8hPqGelzWcFpa1zNeDzIguOa86kVY/8CraHHo8K3iZZibqgVmiXCTfk6xpDhUmXUoERwwDnTCPzAHmuKyuN9DNFZBhjA0SDZJHzJQahiLtMgTBQQkGVa7d0IDJEZjnXIaftPvxEy9C9+99tyYzvmZmO2TThpPP+o1vdDLAwCy95ppalkRBAFwuP2wOrwwGVI7aMRb+0nc+awNFXkD+MH150FbUp7Szx8uqQGyCy+8EH19fZg2bRouu+wyXHfddcjLy0NzczMAoK6ubsjrZ8+eDZ/Ph9bWVsyePTvhz83LS256vc/RHfVJkeA4CY1pYr/Q5lD22JzpJuTzKSgRUdbRLDoLhbVL4e07gZc2b8dnsAPi2+/ia6KADzEHS4UmQJYAQUTRuq+iYN4qdT7YVA553dfg3Hp/MDgHAe966rDi7d/CJcjhs5YewDe0Ag6Ic7EYh8NrKVx5GTRVdcgrmfx9CEunTEGT34JChxYaTXpL8gZcwRJLU3HBmGvRlFagcMZCWD/qwqHWfiyom9y/o3h5/cESS12BJu2/U8oepq53h0yI1Z/5NRQsiLMixFSOrYVnY63rtXA2TtG6r0I7wXsCym2lxQXosbphdXhRmYSkCbvbB6usR7++KvZ9NXSdYX/j7+EsbH2O7uuej7biZs3jwSmgr7yCQCLHjgyh0YioLC1Cl8WJTosT5SZdSj/f7grui4F8E4rKKpP2ObEmxiUlQFZRUYGbbroJS5cuhSAIeO211/Db3/4WXV1d+MlPfgKrNdhjy2g0Dvl3yn8r30+U0ZjcX6o/bxbsUaLn5hl10Bj1E3rvlu5gc7wlcypQWjqx9yIiojQp1cNfUoS10q/DvTpEQcZSoQnTrv1vyD438kunQmOMP1thTKdfAP/ilfD1nYAPGqx4+PaIKVWDREHGYhxO7loy2KzppQCAXpsnredaWZZhD2WQ1UwtGXctn1gwBbs+6sLhdhuvEYYJSMH9vKy0iNuGYuK39aJv6/1DJsQ6t96P8sUr4z4e7vTMxWv9JvznJbWYs3BeTh1PKTkqzUXosbrhkZCUY5pf7gUAlJviPGaefgH+3FyEQ/s/xrp1p+DfTl+h+toyXfDY8XdVjh2Zom6aEV0WJ/ocvpSfQ492B3uflZUUZsT5OykBsjVr1mDNmjXh/z7jjDNQUFCABx54ADfccEMyPnIIm82FQEBK4icUomjd1+DYen+4H4ht0WUoDRQCfYk3t5NlGW3NLajX9GJG0Wz0TeC9iIgovXztzVF7ddgs/civng93ABM6Z4yuEDDWwdf+UTg4FlVK1pKZdJpguLC7z4neXjtEMXX9NiK5vX7oAgOYoRlAoL8bfeLYT05nVgSzCA4e60VXtw1aZpqHOULTQH0eP6+fKCajHaMtx48ivzr2kjZJltHd50RA1kMzdR4GJng/QAQAxlDZfesJK/pmmlR//47uAQCATpsX9zFTKDKhyT8Fs2zx/9vJQK1jRyapLClEieBA36G96K3TprSSoO1EMDmquCg/qftTSYkOYgz9/FLWg+z888/H3//+dxw8eBAlJcHm8wMDA6ioqAi/xmYLlhcq309UICDB709mgAzIm7sGhmkL8fKr7+GlRjfMh8tx++rAhJraWfb8K5yqiddfgdOfu2NziYiynayvjNqrQ9ZXJP0cNernR0rhWjKNQZcPTZ4AfyB4Y1tektpyAoVt72vh8i7PE68A4wz8KTcWorS4AH0DHnzc0of5tSyzVHi8wR5kmjwhJ/dpip9ax+i+AQ8CkgxREFCs03D/I1UofaBO9ruSsk/12YITMo1F+XG/vzE0VMYy4MnJ/V3WV0btO5jN11QNvgM4y7QJYpcM60OPqTaAMBYWmxsAUFKkTer2G+1yeLi0NGmYNWsWAIR7kSmam5uRn5+P6dOnp2NZcRMNZqw+dx1cmmJYOk/g4Ds7IdktCb2XZLdA8+4jgw2TQ43/E30/IiJKL6VhP4TQqTbFze9HfD4EhAstJ3kj/vGIghAOip3sd6dlDZLdAs3u+M77giCgYYYJAHDweF8KVpk9PJxiSXFSjpFy6LgoQUjouNhrDR5DzMYC1acNUu4yFxegRHBA23M4KfeDVkcwQFaij38wXokh+G9sdo+qa8oWosEMx9IrIMmT45pKslswrWlT2uIQ/fbBXqyZIGUZZJs3b0ZeXh4WLFiAiooK1NbWYsuWLTj33HOHvGb16tUZPcFyuBK9FtfO6cH8Ey9A3CfD8aGQUMSVI6KJiCYfbcM6aGoWB4/lxtQ3vx/++QDStpZMU2HSodPixMl+F+bPLE355yd63m+YWYodB7rQ2NKf3AVmGa+fATKKn7ZhHY4JM/DUczsgGyrwnwlkTPRYXQCAsiRMGqTcVevYF8wwtslwPPaU6hk9NkcwKGE0xH/frWS39YfeIxdZqlbg1/1ezC/z4borzszqa6p0xyGs9sSDtcmQlADZ17/+daxcuRLz5s0DALz66qt48skncfXVV4dLKm+66Sb84Ac/wIwZM7By5Ups3rwZ+/btw8MPP5yMJSWNZLdgYdcL4SbMSsRVU7M4vENJdsu4NySisQoShKH9YnJ4bC4R0WQhGsxpvXAa/vnZfBGnpnJT8Gb2ZL8rLZ8vGquilmiMd96fPyMYzLN0noCzZT8Ky6bl/O9UlmV4vMGyDPZlo3hVTJuGJv8UiFYB/oAETV58WWC9ofKgshIGyEgdkt2Cyo+fgjDG/eVEWUPBrZKiBDLIQoEMqz13A2Qutx9WWQ9LYUnWn4NFY9WIcnM5hXEIJdCqBF7TLSkBsrq6Ojz99NPo7OyEJEmora3Fj3/8Y1x11VXh11x44YVwuVy47777cO+996Kurg5/+MMfsHz58mQsKWkkW1fUJn3+E4ch6o0InDwK766NwdcIo2eXebVGPOlYjcuKdoRHRGdzqiYREVEmqwiXWKYpQGYwo2P2xZiqlDXEeN4vN+lwrukYPitsR+AlGY4xri1yhT8gQwpdizGDjOJlMmihzRfh9UnosboxxVwU179XSizLGSAjlaQio2cwgyz+oIRSYunxBeDy+KErSFlRWsZwuIODYYoK89O8kolTys092x8I7meygDe0Z+Gzsgyp42DSqw76B4IZZJM6QHb77bfH9LpLL70Ul156aTKWkDLRIq6SDLhf/wsEyJAR7vgyZvT/aIcNOzz1OFEwEz/+txksfyEiIkqiClN6e5ABQGfZKfi/3cDqGSK++LnVMZ33JbsFF4rbB2+ekpBZkG2U/mMAoM1nDyiKjyAIqCotQmu3HV0WZ9wBsp5QgIwllqSWaPeXalYW+fwSHG4/gMTK2gq1GhRo8+DxBmBzeHMyQOYMbb+iwsnxsystOSztx3H35g7McrTC+dgPgtcaSXwQJ8vyYDZjAuW+ycCriAka3gRZCYgpF64jZlqGov/DNbUHx5tWVldDM21+zl7kEhERpUJFmkssAcDrk4IlGkUzYz7vj5VZkKu8oQBZnijEXR5HBABVpcGAeZfFGfe/ZYklqU25v5RCd5JyggMkRqNkj+WJAvQJBnhMocBaf4426nd6JleADAjud+XzluELZ8zC5fqdIx7EJaNpv8vjhy80udKUIQGyyfMbTaPIJsiyywb3q38e/cWjRP+b2m0AgPrqkmQtk4iIiEKUDDK7y5e2EhFfqLF8PH2zkp1ZkI04wZImqiqUNdbVF1/AXJZlllhSUmgb1uGV9hJ8sOcjzJ43G5c3rFTtvSMzdgRhRDpHTEr0WnT1ucLvlWuUDLyiSZg9d8o0Ce49qWna3xfqY6cv1CBfkxnncD5mU4loMEMzbT7yquYEL1yjkGQBh6svHLFjSbKMI6EMsjk1pmQvlYiIKOfpCjQw6IK9Q9KVRebxBZ+aFsRxUZjszIJsFA6QaTPj4pqyT1WpEiCLL4NswOmD1x/8azSzxJJUVlkdHCBxyKLusS3cfyyBBv2KEmWSZY426neGepDpJ0EPsuHySqaMiGfIECC7bKpnkYUnWGZI/zGAGWSqG97kDoII7WlfxEc2Ix58uw+i34wlwybknOhxwOnxQ5svoqZSn8bVExER5Y4Kkw52lw8n+92YUVWc8s9XSgPz4+ybpW1Yhz0DVXh16/vQmKbgP3K4QT8AeLzxZ+IRRaoyKyWW8QXLlfLKEoOW5b2kupqK4H1he48dkiRDFBPL9hrO6ggFJRLoP6ZQ+kUp75VrJlsPskgjm/YH20e5X/2z6v3IlBLdieyLapt8v9EMEFlyqTTbX+QPQN7zNvoGPHjv45NYuWCwFELpPzZrqhF5Ik+uREREqVBhKsTRE7a0ZZB5Q303tJr4z/0LF83GX18/gUCPjC6LM1wilovCmXhs0E8JUjLILDY3fP5AzKU+PeHySl3S1ka5q6q0CFpNcMJqd78r7gESo1GjKboycdCaoxlkjkkcIAMG4xmO1o+B7fcmbTCQsv9kygRLgCWWSaOUXCo7Tr4mD2edUgMAeHvXQfjaPwqnKCoBsvoa9h8jIiJKlfAkS2t6AmS+CfTOMujy0TCzFACw++NuVdeVbbzsQUYTVFyUD11BHmQA3XH0IVP6j7FBPyWDKAqoDmWRtXXbVXtfJUBm1CcelFAyfqy52qR/EpdYKkSDGboSE8QkDgbqDwfIMieDjAGyFDpreTU+WdiEr3kegPuFX8Px2Pfhbdwa0aDflN4FEhER5ZBwgCxdPciUDLIEAzunzqsAAOz++KRqa8pGbNJPEyUIQkQfsgQCZOw/RklSU2EAALSqGCCzhYISapRY9udok/7JOMUymvBgoEgqDgZSSiyZQZajDLIdlxXtgCgMpii6tz8Ad1/wwnZ2tTGNqyMiIsotgwEyd1o+P9yDLIESSwA4ZU4FBAFo6RxAT5qCfJmAGWSkhsFJlrE36u8JZZ9ygiUlS01lMEDWdlLFDDLnxANkJn3ulljKsjzYg2wSTrGMNHwwkKTyYKDBJv3MIMtJkq1rsH43RJAlLNO2YF6ZPKlTNImIiDJNhSl4U9trdUGS5HFerT4lsKNNsHeWUa/FvOkmlAgOHH7vHdWnS2ULpQdZotuRCACqSuNv1K806WeJJSXL9CRmkBlVyCCzu3zwByRV1pUtPL4AAqFrhly4f9c2rEPTituw3nYe/q69RrUG/cBgBmImZZBN7pBnhgmnKMqDF+GyDFys3w1Zfg/eRo2qOxwRERGNzlxciDxRgD8go9/ugTnFZVK+UIllQYwNwaP5dNlx1A48A/GIDEezutOlsgVLLEkN4RJLS2wZZLIsDwbIWGJJSaJkkPVY3XB5/NCpkLGkRpN+vS4feaKAgCTD5vCm/PyZTkr2WJ4o5MyDmZra6Wjyt0NzEvAHJFWm9sqyHFFiyQyynKSkKEIIbnZZHizpFRCcCJGrT3+JiIhSTRQFlJUUokRwwHpkX8rPwYOZT4kFdiS7BXUtzwxp3ZCL1xKecCYeA2SUuHhLLJ0eP1ye4L7HDDJKFoMuH6XFwewaNcos3V5/+JhpLEo8KCEKQjgDrT/HyiydERMsheH9uSapSpMORQUa+AMS2k86VHlPtzcAb+g6qCSDMsgYIEsxbcM66K+8G9pVV4zod6fmRAgiIiIa3xlFR3CnaRPK3/1TeHhOqnj9E+tBFq11Qy5eSzCDjNRQZQ6WWPbbvfB4A+O+XmnQX1yUz32Pkkpp1K/GJEtbKHtMmy+iUDux/VbJ+sm1SZaDDfonf3mlQhAE1E4tBgAc7bSp8p5K9piuIC+jjqEMkKWBaDAjf9ZpSZ0IQURERGOT7Basdb2atgyscIllgheGyZ4ulS28oWBGwQRv9ii36QvzYdAFb3hjySLr4QRLSpHpoTLLVhUyd8LllXrthLOfSpRG/Tk2ydLh9gEA9JN8guVwtVOCAwWPnRhQ5f2UzMNM6j8GMECWNsPLLSGIqk6EICIiorGlOwNrok36lWsJOUnTpbKFZ4KBRiKF0qi/u2/8Rv1KBhknWFKy1VTqAaiTQaZMnVSCWxOhZJD151oGWY5MsByudkowg+yYShlk4QmWExgWkQy59VvNMNqGddDULIZk64JorMq5C1oiIqJ0Eo1VkCEMDZKlMANL6UGWaIklELyWeN9Wide27UHptBp8q2GtWsvLGhMNNBIpKkuL0HPiBNzHD0Cq0Yx5be6wdKNe04lqfUkKV0i5aHqFASWCA1pLN/wDtdAUlyX8XpEZZBOl9CDLtQyyyB5kuaRuajCDrP2kAz5/APkTGDAEZG4GWW79VjOQaDAzMEZERJQGosGME3MuxpRDm4JllinM5pYkGf7AxJr0K8qmTUWT/wTM/bmZQaX0i2IGGU3UMuEgLjM9D/GYDEfL6FNhvY1bcVbLBpxjlCEfewXextybHkupU3ZyN+40Bc9TzsdeQuHaxPc3JZhlVCFApgQ2rDnWpH+wxDJ3epABgNlYgOKifAw4fTjebcfsaRN7ODA4wTKzAmR81EZEREQ5y197Ou7svxiPixcFh+ik6CZX6T8GAAUTfAo7JTR9z2LzxNRcfLLhFEtSg2S3YF7H8+P2JJTsFni2b4AYyjzlJHpKJsluge+tB8L75UT3N5uKGWQlSpN+R46WWOZYBpkgCKr2IQtnMxoyq8SSATIiIiLKWUWFGlhlPRo9lSnN6Pb4BwNZ+RMsDSwu0oabBcfSXHyy4RRLUkOsPQklWxcgc3ospYba+5sSIDOqEJRQMn/6cyyDbHCKZW4FyACgLjTJ8tiJifch6x9gBhkRERFRRtGHptYpJROpovTN0uSJECc4SQwAppYFmzh3WnIvQOZlgIxUoPQkHCJKT8JYX0ekBrWnFSvZXqpkkIXew+bwQhoexJvEcrVJPxAxybJz4hlk/Q6lBxkzyIiIiIgygiH0BNjrk+Dzp6480etTJi+qcymmlFl29uZegMyj8rak3CQazBBWXQVJDgUjRulJKBrMOFF/8bivI1KD2tOK1exBprxHQJJhd6X2IVM65WoPMgConVqMEsGBIusRuPq6J/ZmDgvqNZ0wiZl13ZJ7YU8iIiKikMICDQQhWMHicPthMqQmC0npQaZW36wpZaEAWQ5mkLHEktRStOhM/OBFByryBnDjNedAWxE9S2efOB9/6b8Yn5qnxWfOXcHgGCWVtmEd3ugyYfe7BzB73mxc3rAyofeRZVnVHmSaPBEGXT7sLh9sdi+MRZmVCZQsuVxiWdS2MzwwwrfxFeQlODDCuf913Fa0Mdhb78VX4B1lIEo68FEbERER5SxREMJPgR0pfAIebiyvUTeD7ESOZZBJsjwYbNQyQEYTkyeK8GlL0OSfAodoGPV1rd12WGU9dDMXMThGKZFnKEOTfwp6A/qE38Pp8UMv2VGv6YQRdlXWpZTH9edQo/5cbdIfHlAywYERkt0C/9sPjjsQJV0YICMiIqKcpjS4d4QuelPBGyrnzJ/gBEtFuMSyzwk5h3rBKP3HAGaQkTr0uvGPB61dweDCjMrRg2hEatIVBI9vrgn0y3TufwN3mjbhJuPL8Dz5H/A2bp3wukoMBSgRHPC3H8yYAEeyKSWWRTlWYqnWwIhYB6KkCwNkRERElNPCjfpTmEGmdg+yylIdREGAxxsYMlFMslvg75i8Ny5K/zFAvWw8ym3jZZRaHV5YHV4IAGoqGCCj1CgqCO6XTk9ivTIluwUF7z+qetbOMjTiTtMmzP7or3A89n1Vgm6ZzB+QwudvfY5lkKk1MCLTB53wSoKIiIhymnJDbE/hJEslg0ytHmSaPBHlpkIAQGevI/gZjVvheOz7cD3/q0l74xIuVc0XIagwDZRICZiP1nS8tTs4va3SXIQClvVSiigZZEr/q3glI2tHsluwwvpSxpbKJYMzIrNUp82xAJkyMEIIhpAkWUDBGfEPjBANZjRP/3zGDjrJrd8qERER0TDhkipXCkssQ0+g1cx6mmIuQnefC50WJ+aVA57tGwbLIUI3LpqaxRlzEaoGr5cN+kld45Vcs7yS0kFXENwvXQkGyJSsnSFBsglm7YwVdJtM55lISnmlrkADUcy9hzLahnXA1AW456+voNtfjDunr0IioxkOFSzGA/0anL9Qh3PPPCWj9hdmkBEREVFOC5dUpTKDLJT5lK9iYCfcqN/iVK1XSKbjBEtS23gl163dwQDZdAbIKIWUhvAJB8gMZhyceqGqWTuZXiqXDEoGX66VV0bSllTAqq+DVdajo8eR0Hv0OzywynpIFXMzKjgGMEBGREREOS49TfpDPcjUzCArCzXqtzhz5saFATJSm2GcgPnxUIBsRhUDZJQ6RaEMMp9fCk/ujdfhgkW4s/9i7J5+FfRX3h3MBpoA0WBG4Zprw0E3GUJGlcolQ3iCZUHuBsgAYGroeqMjwcnZ1lCv1BJDIvlnycUAGREREeW09DTpV7cHGQBMVSZZ9jrR69fhCcfqSX/j4knCdqTcNlYPMq8vgBOhHn/TK4tTui7KbYUR/a4SzSKzu3ywynr4ytXL2tHOX4d/lF2P9bbz8E7DzRMOumW6wQmWuR0gm1auB4Dw8TBe/XYPAMBkKFBtTWrJ7d8sERER5TwlY8SZ0ib9oR5kKk2xBIApZcEL1l6rG8+8eRQ7PPX4yDsVFXkDOGvtcqxumK/aZ2WKwQwyPvMldYyVUdre44AsAwZdPkwZmPlAk5coCtAV5MHlCcDl8cOoj3//G3AGz3HFoSCwWqbNqMG2JjcM3QI+peo7Zx6XWymxVHcbZpupZUqALLEMMmXadiYeR3k1QURERDlNadJvT2WJpdKDTKNe5pOxKB+6Ag1kAG/v7wQAGMor0eSfgpO+QtU+J5Moww5YYklqGSujtDWivJJTUynVlEb9iU6yVPZpQ5G6wZ3Z1SUAgKZ2K+ThvS8nGSVwrsvxDLKp5UqJZfwZZB5fIJwFWaLPvAwyBsiIiIgopxUVpqPEUgnsqHcpJggCppiLUCI4UK/pxKk1eThlTgWAwX4fk004g0zLABmpY6weZMe7BgAAM1heSWkw0QDZgBIgUzmDbEZVMTR5IuwuH7r7XKq+d6Zhk/6gqeZgBpnV7o07+94aKq/U5ovQFWTeuTu3f7NERESU89LTpD/UO0vFDDIA+GRhEz5hegmiIEN2CjjmugiAMdzvY7JJRi83ym1KRqnDNfJ4wAmWlE5KY3hXAucqWZbDffXULrHM14ionVqMpjYrmtqtqAr1w5yMnOEeZLldYllUqIHJoEW/3YuOXifqQ1mEsQiXV+oLMjITlxlkRERElNOUkiqXx4+AlNh0sHgpGWT5KmaQSXYLVliDwTEAECCjtuUZlAgO2ByTPINM5UAj5S7leOAcdjyQZBm2k12o13RihjF12aZEiolkkHl9g9Mv1S6xBID6aYNllpOZg1Msw8J9yHriK7NUHthl4gRLgAEyIiIiynGRpRLOFGWRKRlkagZ2JFsXBAzt/yJARkXeQPiJbULva7fA33EQkt0y0SWqzuMNDTvQ8pKW1DHa8aBvz79wW9FG3GR8GcaXfgJv49Z0LI9ymDI5MZEplgOu4DlAkyckpWdjZB+yyczpZomlYnCSZXyN+q3hBv2Z138MSFKJ5Ysvvohnn30WBw4cgM1mw8yZM3HVVVfhkksuCafRXXXVVdi1a9eIf7t582bMnj07GcsiIiIiGiFPFMPTwRxuP4qLkv9UMxlTLEVjFSAIQESTZFkQcTJQDIfDA1mW4y5n8DZuhWfbBgAyIAgoWHMttA3rVFvzRA1OsWQGGakj2vFAsluQv/tRCKHsTMgyPNsfgKZmMUSDOb0LppwRziBL4EGOPaL/WDLK2uqrjQCAjpMOON3+cDBvslG2fa6XWALAtLLEGvX3OzI7gywpe+6GDRtQXV2N2267DaWlpXj77bdxxx13oLOzEzfeeGP4daeccgp++MMfDvm3NTU1yVgSERER0aj0hfnBG+IUNepPRu8s0WBGwZpr4dn+ACBLgCAi//SrYX1WAgIynB5/XKPpJbsFnu0bAGRuUMDLABklwfDjQbTsTMgSJFtXxvwt0OQX7kGWQAaZ3akEyJITlCgxFKC8pBA9VjeaT1ixqK4sKZ+Tbo5wD7LJGQCMh1Ji2RFvieVAMIOsNJcyyP785z/DbB48WaxevRr9/f24//778e1vfxuiGHxaajQasWzZsmQsgYiIiChm+sJ89FjdUSfXJYPSg0yrUbc0UNuwDpqaxcEbd2MVRIMZ+pe3weH2o9/ujS9AZusako0GIOOCAswgo2TQ64LHAyXrRjRWQYYwNEgmiMGsTaIUmUiATJlgWZyE/mOK+poS+Gy96P14L6SK5RlznlCTi1Msw6aGSix7rW54fYGYH/hZMzyDLCkNGyKDY4r58+fDbrfD6YyvRpWIiIgo2caaXJcM4SmWSQjsiAYzNNPmh29OSkJPaa1xTrJUggJDZFhQgBlklAyG8GTbUIDMYMY7xvMgyaG/B0FEwZprJmUAgDLXRJr0D2aQJS9Atkp7GHeaNuGU4w/B8dj3J12fPkmWWWIZwViUD32hBjKATkvsMR6lB1lJLmWQRfPee++hqqoKBsPgWORdu3Zh2bJlCAQCWLp0Kb73ve9hxYoVE/6svDw2aiUiIqLYGUJ9x1xePzQqZ3VFo0wT0xVqkv55JoMWHT0ODLh88X2WqRzNMy5CXcszEAUZkizAcOZXoTWVJ2+xcfKmcDtS7hg8HgTC+9Vu/1xs7jfgG2dVYOGSBgbHKOWU6ZPuiP0yVo5QUM2o1yblWCnZLahteWZEn77C2qWT5m/F4faFc0iTtR2zzbRyPQ63WdHV58Ks0KCG8ShTLMtLClO6DWNtvZeSANnu3buxefPmIf3GVqxYgYsuugi1tbXo7u7G3/72N3z1q1/FQw89hOXLl0/o84xG3USXTERERDmkrCR47RCAgNJSfdI/TwnsVJQZkv55lWY9PjrWB28AcX/Wm+56PNB/MSryBnAyUIz/O+VT0CcxAyFefil4u1Jm1qfk90a5wWwKHQ/kweNB34AHVlmPqkWnomz65Ljhp+xSWR5MNPH4pbiPd77QsbIiScdKl7UZ1ih9+ookK3Sl01X/vHTwhrKktPl5qKwoTvNqMkNdtQnd7R3wdzaieKkJGuPYvee8vuDwEwCorSkNP4zIJEkPkHV2duKWW27BypUrcfXVV4e//t3vfnfI684880xceOGF+NOf/oT77rtvQp9ps7kQCEgTeg8iIiLKHcpDzJ4+J/r64ms4Gy9ZluHxBksDXU4P+pL8AFWnDZYfnjg5ENfP5g9I2H+kB15ZD7tkQECWcfDIScyO8SlxKjhDfXW8bl/Sf2+UO/JDmQY9fQ709TkQkCT02twAAK0A7muUFgFfMLAw4PDGvQ/29gWDOxpRTsr+K+WZRkxRhiDCKZbAPUn+Xjq6bACC/cd4DAhq8HyI803PQDwk4/jh+1G07msoWDD6pOuTfS4AQH6eCK/biz5Pavq+AkBJiS7cC38sSQ2Q2Ww2XH/99TCZTFi/fv2YCyoqKsK6devw0ksvTfhzAwEJfj8DZERERBQbnTZ4STTg9Cb9GkLpmwUAeYKQ9M8rDmV89Q144vqsj4/3weuTUFyUj2llenzc2o/2k3bMrMqcJ+fu0LbUiMnfjpQ7lF5PA04f/H4JFpsbsgzkiQKKCjXc1ygttJrgww6n2xf3PmhzBPs+FRUkaf8tNKFgzbVwb38AgixBkgV4T7kSxYUmSEn8e5HsliFDaZJpINQ7S5esbZhlJLsFs44PLat1br0fwrSFo/4ueq3BBw0lBi0CARkYnnWYRMNnDo0maQEyt9uNb37zmxgYGMATTzyB4uLMuZgiIiIiiqQ06Vca8CaTN+LCOj8F/TdMoUlR8TbpP9jSBwCYP7MURQUafNzaH1cj3lQIN+nXskk/qUdpZO4IZSgq2WOlxQUQY21kQ6SywSmWAciyDCGOfVGZyFqsS15JmzJF+anndmDHcQkr+mfiSyp/RmRAzN/2ITzbNwQjH4KAgjXXQtswevbSRCnDEYo4wRJAcNK1EKWsdqxJ10r/MVOGNugHkhQg8/v9uPnmm9Hc3IxHHnkEVVXjTztyOp144403sHjx4mQsiYiIiGhU+sKhN8TJpAR18kQBmhQMFgpPsQxlEMTqo1CAbEGtGe7QjUFnb+YEyIKlqsFgI6dYkprCx4PQFEuLLXhTZzYWpm1NREqATJJleHwBFGpjv5UfcCV/iiUQnPg6f+UqbGn5AG992IlL1s5W7QGGt3FrOCCmhGXCIcLQUABNzeKkZZI53H6UCA7MFp2Q7JZJM3wgUaKxKmpZ7ViTrpUAWYkh83qPKZISIPvZz36G119/Hbfddhvsdjv27t0b/t6CBQuwb98+/PWvf8WnPvUpVFdXo7u7G/fffz9OnjyJ3/3ud8lYEhEREdGo9KEnwvYUZpBp81MzvUnJIOu3xx4gc3n8ONoR7LeyYGYp2nuC/VY6LS71F5ggf0CGFLowL0jRtqTcoGSUOlzB44FlIJhBZjZmbtYDTX7afBGiIECSZbg8sQfIZFmG3ZmaABkALKwzo8JUCK+1Fwd2vo1lp4xechcryW4ZzBZDRGAs0jjZSxNlaH8Hd5qehWiX4Xjs6aRnrGU60WBGwZpr4dq2ASJkyBBQuOaaMbe/8qAu5zLI3nrrLQDAXXfdNeJ7r776KioqKuDz+fCb3/wG/f390Ol0WL58OX72s59hyZIlyVgSERER0aj0utRnkCn9ZJKtRB8MkLk8fnh9AWhjyLY61NqPgCSjwlSIcpMOgdBNSXefE5IsZ0SZmdc/2Mstlp+JKFZKBplSlhbOICtmBhmljyAEe+DZXT443T6UFscWZHB7AwiEplgaipIfIBMFAZfP6MTs1ucgNspwfDzx8kfJ1jV+E6lxspcmQrJbUN/27JB+W8nOWMsG2oZ1eL3ThPd2H0DN7Fm4umHVmK8fLLHMsQyy1157bdzX/O1vf0vGRxMRERHFLbKkKtkBoFRnkOkKNMjXiPD5JVgdXlSYdOP+m6NHWlCv6UR9TT0AoLykEHmiAG+oYXl5yfjvkWzKJNBUlapS7lAC5k6PH5IkwxLqQVbGDDJKM11BHuwuH1yewPgvDlHKK7UaMSXl6JLdgjltzwEqBpNEYxVkCMN6XgmQgJizlyYikX5buaJm5nQ8sbMX1q7xBxcomeyZnEHGqwkiIiLKeUqJpSwD7jhuPBIRziBLUdaTIAjhLDJrDGWW3satOKvlD7jJ+DI+0/FneBu3Ik8UUVkaDIplSqN+j9Kgn9ljpDJ9RBNup8cfziArZQ8ySrOigsHgbazC5ZUpyB4DQtleowSTEiUazGiecREkOfTwShBRsPZaHDntNqy3nYc/i19JarmjbKgY/GxFEjPWsknd1OAwxpP9bticY19jWLOgBxkDZERERJTztPl50IYmSiqNuZPF6wtlkKVggqWiJNyHbOxJlpLdAneonwgACAg++ZfsFkwxFwHInEb9ynbkBEtSmyZPRGFov3K4fIM9yGIsaSNKFl1BcL90emI/T9ldwaBFKvqPARHN2yNIsoAeuwR/x0FIdktC7/u2azbu7L8YH9RdA/2Vd0PbsA5z583CkcAUfNwjhDM9YyHZLXGtpd2pLwSTIgAAO89JREFUxROOVUMDdEnMWMsmRYX5mFoWvD5QepeOJpxBps/cYylnlBIREREhWFblHfDA4fahAskrIVR6Z6WqBxkweDE63iTLgLVz1DKSKWVFwOHMyyBLZaCRcoe+MB9ubwB9Ax4MhDJwykqYQUbppQtNsoynxFLppVecqgBZqHm7Z/sDwfMHBLzrqcOK1++GS5ABIf6eZLIs41CbFTZZj8qG5RANJgDBoN+sqUYc6bBh/1EL1i6dNu57eRu3wr1tQ/BcF+NajrTbsNM7B/K0Bfj62nKIxioGxyLMmmrEiV4nmjtsWFpfHvU1/oAU3hdNGfywgVcURERERBgsq1Im1yVLOIMshaWBSgaZ1TF2BtnudmHUMpJwBlmGBchYYknJoEyybD1pBxDcz4oKmFtA6VVUqATIEimxTF1Zm7ZhHfRX3g3dhT+E+JnbsKLwKMRhPcniySTr7nfB5vBCkyeibqpxyPcWzSoDAHzY3Dvu+yhZ0uEHQTGupandCgCYWlMDzbT5DI4NM2ta8HfSfGL0DDKlxYMmTxhSxp5pGCAjIiIiwtBG/ckUziBLUZN+ACgJNcTtH6UHmWS3wHZkH57fcQxPOFZBxsgykqlmPYAMCpCFmvRrWWJJSaAcD1q7ggEys7EAQgZMb6XcpmSQOd2xB8iUJv2pKrFUiAYzNNPmQ6eRwmX7YXH2JDvU2g8g2O8qf1jW8KJZwWDVR8cs8AfGbhS/e/f+UbOkx3IkFCCbXV0S85pzyaxpwe1ytMMGaZRpo0qLhxK9NqOPpZkbuiMiIiJKIWVyncM1CXuQhZr026KUWHobt8KzfQMEWcZ/FAp4JW8ddJf/D4SBk0PKSKrMwbJTi80DjzeQ9t5fHl8AJYIDtYIDkr2OT/RJVUow4Xj3AADAzAb9lAGKChLIIEtxieVw4Z5kkYGTOBvcH24LBqjm1JhGfK9uihEGXT7sLh+aO2yYO33kayS7Bfs/OIgXdp3AvBJhMJsthrVY7R70WN0QMJgpRUNVV+iRrxHh9PjRZXFiapl+xGuyYYIlwAwyIiIiIgCDJZb2OJ7MJ2IwgyyFPchGadIv2S3wbN8QvnERBRnnyVuRJ4gjykiKi7ThbdTVl/4sMuOJXbjTtAmfczwNx2Pfh7dxa7qXRJOIEjDv6HEAYIN+ygxKgCyTp1gOp/QkUzKTJQhxN7g/HMogmzt9ZAaXKApYWGdGieBA+/73RpRLehu3wv7o91F34D7cWrIFbcWLIMWxlqb2YNlgdYU+nMFHQ2nyRMycEpxm2TxKo35nXzfqNZ2Yqht/mnY68TdMREREhHRkkKWwB5nSpH9YiaVk6xr6VB+AIMuQbF1RbximlBXhSLsNnRYnZlQVJ2/B45DsFsw89k8Iw3raaGoWM5OMVKEEg/2B4D7GDDLKBLoEMsjSVWIZSduwDo7SefjzQ6+hJ1CMO2esRqwd0awOL7r6XBAweonjmqIjuML0D4itMhyPPRxuvK88BFLKKkVBxgzHAbSechP++UYj/EVluGPe2jE//0gHyytjMWuqEU1tVjSfsOH0xVOHfM/buBVL923AMqMMuU+At9EX15CGVGIGGREREREimvQnuweZLx09yEIllk4vJGkwIBYufYk0RrnJFHMRSgQH3McPxNVgWW2SrSuhPjJEsVJ6kCnMRmaQUfrpEskgy4AAGQCUVk2Bu7Qe/bIeB47Gfv5QsseqK/Qj/i6B4AOTmuZ/RB0CEO0hEGQJsyoLcFyYhmO2fBwP9RkcTbj/2DQGyMYSbtQ/LINseJBSQPxDGlKJATIiIiIiDGaQxdP8OBHhEssU9iAzFmnDLWAGnINZZKLBDPuSKwcnV0Y05Y9mGRpxp2kTlrc8mNayRtFYNThIQBFnTxuisShTLBXMIKNMEJ5iGcd5yh465qc7QAYAi2cHzy37Y5g4qWg91op6TScWT41+zhzrgYmnoCzqZGateSoW1QWnX+45fHLUz/YHJBzrDPYhnF3N/mNjmTXNiBLBgULLYXj6B7fpaEHKTH2gxQAZERERESKmWCa7xNIfKrFMYQ8yURRgLFL6kA0ts9yLebiz/2JsNnwR+ivvHrXsQbJbMKf9uahP6VNNNJjxuvbsmAN7RPEyDM8gYw8yygDxZpBJsgy7K/ja4qJYixqTZ3EoKLX/qGXUaYeRvI1bcfbxP+Am48v4dNufoj6UiZYJLUOAaKzCq40OPOFYFe45FnmuWD6nHACw53DPqJ9/vMsOn1+CvlCDKeaiWH/MnGTsCPYF/bbhZXie/I/w7yreTPV0Yw8yIiIiIkSWWCY5g8yX+gAZEJpk6eyDp/UAJMPccDDpo2N9sMp6mOfMGTPANNZT+nQEpt52z8YbVhO+9+kpmFE/m8ExUpVeN7zEkhlklH7xTrF0efzhQJRBl/5b//qaEhRq8zDg9KGlcwB1U0fPylJK88RhpXnDe00qQwA82x8InpNkAc8GzsBnbG407d6FVt80NK24DUurpCGTmZfWl0MUBLR223Gy34UKk27EGlqPHke9phPlU2dAGB7koTDJboHnzQ3hB2jDf1feU78MzbuPBL+f4Q+00v9XQkRERJQB9IX5KBEcqPD0QLKPHSyaiHAPshSWWALAivzDOMP0L4h7ZDj2CihYcy2E+jXh/i7za0vH/Pfhp8CRT/3T9BRYlmX0DXjgl/Uw1C6CaBh5Y0M0EZEBMoMuHwUpDmgTRaMLPchxewOQJBmiOHbQRuk/VqDNQ34KB8OMRpMnYkGtGe8fOokPm3vHDJB5LCdGLc0bfn7WNqyDpmYxvJYT+O2WEzC7j0D73H/iep0MSSegsPBaaKYNzY426PIxd3oJTrS248j7u1B22tDAm7dxK5Yf2IBTjDJkqwBvYyBjG8un21hllKLBjCO6xXis/2IsqQzgmkvXZmxwDGCJJREREREAoPjEO7jTtAlfzd+c1P5ag036U3ezItktWOP614jyyKNHWuD1SzDqtagu14/5HspTeqWsURaEtD0FHnD5wtMFTSx9oyQwFA7mEbC8kjKFkkEGAC7v+FlkdmcwQFacAf3HFItmmVEiONB3+IOoJfqS3QLHsf149PWWqP3DRnsoIxrMKJyxEFeeOweX63eGz3ciZHjfjN4O4Dzzcdxp2oSFTfcPOe9nW2P5dBuvjLKlcwBWWY+8afMzOjgGMIOMiIiICJLdgvzdjwDDAkjDSznUEO5BlsIMstHKI9ubmwEIWFBbGlP5iLZhHe7bDVg723HuWafgtIaG5Cx4HP0DHgCAUa+FJo/Pe0l9RRE9yFheSZlCkyciXyPC55fgdPujTnWMNJAhEywjLZUbsdy0CaJXhuPRZ1Cw9tpwZpa3cSs82zYAkPFFWcD7/tk4Nf9I8PwVY2ledaETLmH8zDPJbsGs489AiHLeHy8jioaKVubq/8SXUBzaVsqgg9opxelcZkwYICMiIqKcl8qL4bQ06Q9NfRwSJBNEfNCdB0DC/Jljl1dG0pdV4b22ABY409fw2RIKkJUys4eSJF8jolLrhlHqx7SizL+po9xRVKCB1e+NqQ+ZkkFmKMqMAJlkt0Cz++HBh1ERvaoAwLN9AxA6T4mCjFO1R1B00R2A3zOkf9hYYm0HMFZfTRRXQoIQ7n822nvQIKXM9cGN2/BBdx4u1S7EJxFsiZBNATI+ciMiIqKcl8opS+noQSYazChcey3k0CQvSRYgnfZlfNgZDNYtmBl7ELCqNNjvq7vPpf5CY9QXCpCx9I2Sxdu4FT/Sb8RNxpdx3ijT84jSQRdHo36lB1mmlFiO9TAq2vcEWQb8HmjiKM1TspkghM6xo2SeRZ1+KQSnX+5tD+AJ+ypOSo6TaDBDN3MhrLIeTe02AEB3vwsujx+aPBHTxmnlkAmYQUZEREQ5T7mgdm0LTsySIaAwSRfD6ehBBgSf7spTFuC+x7biiK0AtXvzMDuvAzBWoqwk9hKyylCArCutATI3AGaQUXKEp+eNMpGNKJ2KQv3xnDFMXB5weQGMnMqaLtGyuyRZgMMv4sSxFkyVgSFzBxJ8UKVkM0m2rlEzz6KVBb6mOQufLzLhmTffRbt3DqYvW4lPNRTEnL1GQH21CS+hFU1t/QCAYyeC2WPTKw1Z0RKBATIiIiIiBC+oX2kvwQd7PkJ9Qz0uazgtKZ8zWGKZ+gvFAlMFVp61FtrN/8Dl7p0QjcFgoLcxP+bpXJWlRQCA7j5nMpc6pj4bSywpedh/iDKZkkHmjCGDLGDrRb2mE+WasmQvKyYjglIQ8K6nDiu23IVqyJCAwXYAE8zaEg3mcf+tEkizdbXhrmfb0O0pRP/TO6HrP4aqwlKs++RCaMbp80ZD1deUAADaTzrgdPvQopRXTs388kqAATIiIiKisJKKKjT5LdDak3NBLMsyvD6lSX9qM8gUp1TnYa5hZ7i3SrzZMZWmYAaZw+2H3eVLS/Nn9iCjZIq1hxFROsRaYult3IrzOzcEH4QcegXeKdfG/CAkmSKzuywOGSte+5/w+UjJHis859vIq6pPSUBaNJhhMpixdnUJ2t7egostgw+PNMeKgAzYZtmkRK9FpUmH7n4XjnTYcKwzWGpZW5UdAbLMz3EjIiIiSpHKJPfXCkgypNBNdzoyyABAHuge2ngYGGxMHIMCbR5KDMEG/Sf701Nm2RcOkHG6IKkv1h5GROlQFEMGWbhMeNiDEMluSckaxyMazNBMmw+zXgiXMg+SIeiKU/73du58PS7X7xxRWp0p2yybKFlkh9v60dKlZJAZ07mkmDGDjIiIiChECZD12tzwByTV+2Uo/ceA9GWQqZEdU2XSwWr3oqvPiboUX/TKsswm/ZR0sfQwIkqHohgyyLKlTDiTsjVFx8mRwboM3GbZoL66BG/v78SO/V1weQLI14iYVl6U7mXFhBlkRERERCElei20+SJkGei1ulV/f6X/mCAAmjxhnFcnhxrZMYN9yFKfQebyBOAJBRpNDJBREilZLrw5pkyiKwg+XBkrQCYaq8JTi8MysEw4k7I1UznNerJTMsh6bcHrqBmVBuSJ2RF6YgYZERERUYggCKgw6dB+0oGuPheqzOo+8YycYCkMvxBPoYlmxyS7FHUsygRLfaEGBSmeBEpElG5FoabxY02xFA1mWOZfitKPNgazojK4TDhTsjWHDxDI5G2W6aaV61FUoAmXAc+ckh39xwAGyIiIiIiGqAwFyJLRX2uwQX/6n6TGMuFrNOkNkLH/GBHlLl1BHkoEB0wOOyR79ajH8Y/yF+KV/gDWzMrDv52/KqMDPRM5H6kpU4J12U4UBMyuLsHxo8dRkTeAOaXT0r2kmDFARkRERBQhmcEfjz+UQZam/mNqqQqXWDpT/tnKBEuzkeWVRJR7Knvfx52mTRCdMhyP/QMFa6JPp2zttsMq61FQM4uBnjhkSrAu251R1IS5puchCjLkPa/AW5wZU1THk/7Hl0REREQZpNIUDJAlI4PMp2SQpWmCpVoqQtvI5vSN2QcnGQYzyBggI6LcItktqPz4qcFm8vLokxaPh6YHTq/MnvI2mhwkuwXzOp7Pyomg2X11RkRERKSyilAGWVcSsqO8kySDrKhQg+KiYB+cVJdZKj3ISg0MkBFRbpFsXRAQfdJiJK8vgE5L8Bw2vdKQquURAYh9P81EDJARERERRVAmNJ7sd0OS5XFeHR/vJMkgAyJKUZOQaTcWCzPIiChHxTppsb3HAVkGiovyYTJoU7hCouyeCJr9V2dEREREKiozFiBPFOAPSOgPBWPUEs4gmwTTFytN6elDpvxOStmDjIhyjDJpUUIw+CBBiDppcbC80pDWicmUm5T9FEIo3JRFE0HZpJ+IiIgoQp4oosxYiO5+F072u2A2qjctMZOmWE5UVbgUNdUllpxiSUS5S9uwDk1SDf65+R34i8pwx7y1I17T2m0HAMxg/zFKk2ydCJr9V2dEREREKqtMUvDH65tEGWRJnPY5Go83AIc7OBTAzBJLIspRs+fU4pg8Fcds+eFeY5GOhwJk7D9G6SQazNBMm581wTGAATIiIiKiEZRG/WpPsvT4J08GmdKrLZUlln32YPZYoTYPugIWQhBRbirIz8OcGhMA4KNjfUO+J8ky2pQAWRUDZETxyP6rMyIiIiKVVZqSkx3lm0w9yEp1KBEcKHcfh7vvZEo+s88WmmDJ7DEiynEL64JZOQeOWoZ8vaffBbc3AE2egCnmonQsjShrMUBGRERENEyyAmSTaYqltuVt3GnahJuML8O78T/gbdya9M9UJliyvJKIct3C2mCA7ODxPvgDUvjrSv+x6nIDNHnZf64hSqW0/sUcOXIEX/3qV7Fs2TKcfvrp+PWvfw2v15vOJREREREN9tfqd0GWZdXeN9yDTJPdGWSS3QLP9g0QheC2ESDDs/0BSHbLOP9yYpQG/SYGyIgox02vMsCgy4fHG0Bzhy389eNdLK8kSlTaAmRWqxXXXHMNfD4f1q9fj1tuuQVPPvkk7rrrrnQtiYiIiAgAUBHKIHN5/OGm8GrwTJIMMsnWBQwPHMpS8OtJxAmWRERBoiBgQW0pgKFllq1s0E+UsLR1N3388cfhcDjwhz/8ASaTCQAQCATws5/9DN/85jdRVVWVrqURERFRjtPm52GGwQetuxc9HR0wzJ4JyW6Z8LjyfE8/6jWd0EvZfZ0jGqsAQRgSJJNkAd32ACo7Dga/DwzZXpHbL9Hveaw9qNd0oqpgSup/aCKiDLOwzoyPG4/CemQfpFNMEA1m9HefQL2mF7XGunQvjyjrCLKadQNx+PKXv4ySkhL86U9/Cn/NZrPhtNNOwy9/+UtcfPHFCb93X58Dfr80/guJiIiIovA2boVr2waIkCFDgKdmBQra3oUAGRAEFKy5FtqGdRN6z8K18b9HJvE2boVn+wPBzDEIeNddhxUFRyEKMpSLSwGADAGuaZ+ArmM3BKj3vWzffkREE9W351WIux4OHXcF9FcuR0nXnmD5e4LnKqLJyGzWIy+GnnxpyyBrbm7GJZdcMuRrRqMRFRUVaG5untB7x/KDExEREUUT7q+Fwf5aBa27IAihF8jBfluFtUtjziST7Ba4tw19z3jfI9NoFp2FwtqlCFi74AqIWPH8LyN+vkECZOja3w1vP7W+l+3bj4hoIiS7BZrdDwMRvSBNXe9P6FxFNFkJwvivAdIYILPZbDAajSO+XlJSAqvVOqH3Nhp1E/r3RERElLtc1mZYhyXYj7iwkiUUSVboSqeP+35+Wy8+2PUGSjGyZ1es75GxSvXA9OlwHfsQJ4b/fBHGujBN9HuTYvsRESVI7XMVEaUxQJZMNpsLgQBLLImIiCh+Up5pRH+tEa8BcKKlHSViyZhP5j0fbYXjjb+jFDJkedjNiyDCKZbA3edQbe3pEss2U90k2n5ERPGK6bjL4yQRAKCkRAdRzOASS6PRiIGBgRFft1qtKCkpmdB7BwISe5ARERFRYgpNKFhzbbi/FgQRmvrV8DftCPbbkkPlftvvhfXN0Xu8SHYLHG/8Pdi3DCODYwVrroFUaII0Ga5Zhm+zcEGkPGL7qfW9SbX9iIjiNc65isdJokGxPr9LW4Bs1qxZI3qNDQwM4OTJk5g1a1aaVkVEREQEaBvWQVOzeOg0xRWXINB1GK5X/xIOeik9XjQ1i0dkkkm2rsHXRb73qiuRP2vFpOsJM3ybARix/dT+HhFRLhvtXMXjJFFi0hYgW7t2Lf7yl78M6UW2ZcsWiKKI008/PV3LIiIiIgIAiAbzkJsL0WCGZDOODHrJUvBmZNiNiGisggQh3LgeACCIkzI4poi2zZL5PSKiXBftGMnjJFFi0jbu8YorroBer8d3vvMdvPnmm3j66afx61//GldccQWqqqrStSwiIiKiUYnGqhFdkGUI4eymSL6CEmx0roYkK2MYg+UuvHEhIiIiyjyCLKeym+pQR44cwS9+8Qvs2bMHer0eF110EW655RZotdoJvW9fn4M9yIiIiCgpvI1bwz1fJFnAJs8ncdk3roW+MH/I6w4cteCeJ/ZiptGHH180A3klLHchIiIiSjWzWY+8vAxu0g8As2fPxoYNG9K5BCIiIqK4KD1fAtYu/ObFDjQ6BZS914bPn1435HUHW/oAADUzpyO/en46lkpEREREMUpbiSURERFRthINZuRXz8eZpy8CALyzuxHOlv2Q7Jbwaw62BP///JmlaVkjEREREcUurRlkRERERNnsE/MqcZ55M86XtyHwkgyHIKBgzbXw134SxzoHAADzZ7KskoiIiCjTMUBGRERElChnHy7ANghCqKWrLMOz/QEc9U6DLANTzEUoLS5I7xqJiIiIaFwssSQiIiJKkGTrgoBh845kCSeOHQMAzK9leSURERFRNmCAjIiIiChBorEKEIQhX5MhYG9X8BJr/gwGyIiIiIiyAQNkRERERAkSDWYUrLkWEIKXVJIsYIv3EwhYu2ASHGhgg34iIiKirCDIsiyP/7Ls0tfngN8vpXsZRERElCMkuwXevk68/OI2rJPfgSjIkCBAt/ZaaBvWpXt5RERERDnLbNYjL2/8/DBmkBERERFNkGgwQ1s6BWciGBwDABHBhv2S3ZLm1RERERHReBggIyIiIlLBaA37JVtXehZERERERDFjgIyIiIhIBdEa9kMQg18nIiIioozGABkRERGRCoY37IcgomDNNRAN5rSui4iIiIjGxyb9RERERCqS7BZIti6IxioGx4iIiIjSLNYm/ZoUrIWIiIgoZ4gGMwNjRERERFmGJZZERERERERERJTTGCAjIiIiIiIiIqKcxgAZERERERERERHlNAbIiIiIiIiIiIgopzFARkREREREREREOY0BMiIiIiIiIiIiymkMkBERERERERERUU5jgIyIiIiIiIiIiHIaA2RERERERERERJTTGCAjIiIiIiIiIqKcxgAZERERERERERHlNEGWZTndi1BbICClewlERERERERERJRmoihAEIRxXzcpA2RERERERERERESxYoklERERERERERHlNAbIiIiIiIiIiIgopzFARkREREREREREOY0BMiIiIiIiIiIiymkMkBERERERERERUU5jgIyIiIiIiIiIiHIaA2RERERERERERJTTGCAjIiIiIiIiIqKcxgAZERERERERERHlNAbIiIiIiIiIiIgopzFARkREREREREREOU2T7gVQ+rS0tOBvf/sbPvjgAxw+fBizZs3C888/P+Q1LpcLf/rTn7B582b09PRgypQp+MIXvoDrrrsOGg13n0ixbE+v14vf/e53eOaZZ2Cz2TB37lx8//vfx+rVq9O06szz4osv4tlnn8WBAwdgs9kwc+ZMXHXVVbjkkksgCEL4dRs3bsRf//pXdHR0oK6uDrfccgvOOuusNK48M8WyPTdv3owXX3wRH3zwAbq6uvAf//Ef+PrXv57mlWee8bal3W7H/fffj61bt+LYsWPQarVYsmQJbrnlFsybNy/dy88oseyXv/rVr7Bt2zZ0dHRAEATU1dXha1/7Gj772c+mefWZJ9bjpuJf//oXvvOd72DOnDkjzlO5LpZtedVVV2HXrl0j/u3mzZsxe/bsVC85Y8W6X9psNvz+97/Hli1bYLVaUVVVhS996Uv42te+lsbVZ5bxtmVbWxvOOeecqP9Wq9Xiww8/TPGKM1ss+ybvgWITy7bk/U9stm7divvuuw9NTU2w2+2oqqrCueeeixtvvBHFxcXh17322mv47W9/i6NHj2LatGn4xje+gUsuuSSNK59c+Nedww4fPoytW7di6dKlkCQJsiyPeM3Pf/5zvPzyy7j11lsxe/Zs7N27F7///e/hcrlwyy23pGHVmSuW7fnLX/4SzzzzDG6++WbU1dVh06ZNuP766/HEE09g4cKFaVh15tmwYQOqq6tx2223obS0FG+//TbuuOMOdHZ24sYbbwQAvPDCC7jjjjtwww03YNWqVdi8eTNuvPFGPPLII1i2bFl6f4AME8v23LJlC1pbW3HmmWfiiSeeSPOKM9d427KjowNPPPEELrnkEtx8883weDz4+9//jssvvxxPP/00b5wjxLJfOhwOXHrppZg1axYEQcBLL72EW2+9FZIk4XOf+1yaf4LMEsv2VLjdbvzyl79EeXl5mlab2WLdlqeccgp++MMfDvm3NTU1qV5uRotlWzqdTlx11VXIy8vDj3/8Y5SVleHYsWOw2+1pXn1mGW9bVlZWjjh/y7KM6667DqtWrUrTqjNXLPsm74FiE8u25P1PbPr7+7FkyRJcddVVMJlMOHz4MNavX4/Dhw/j73//OwBg9+7duPHGG/HFL34RP/7xj7Fz507853/+J/R6PT7zmc+k+SeYJGTKWYFAIPz/f/jDH8qf/exnR3x/6dKl8u9///shX/+P//gP+ZxzzknJGrPJeNuzs7NTnj9/vvzggw+GvyZJknzhhRfKN9xwQ8rWmel6e3tHfO3222+XTznllPA2Pu+88+Rbb711yGsuv/xy+brrrkvJGrNJLNszct+dO3eu/Ne//jVl68sm421Lh8MhO53OId+32+3yaaedJv/85z9P1TKzQiz7ZTSXX365/NWvfjWZS8tK8WzP3/72t/KXv/zlqOcpim1bfuUrX5G/8Y1vpHppWSeWbfmb3/xGPuecc2SHw5Hq5WWVRI6ZO3fulOfOnStv3rw52cvLOuNtT94DxW68bcn7n4l54okn5Llz58qdnZ2yLMvy1772Nfnyyy8f8ppbb71VPv/889OxvEkp63qQtbS04Cc/+QkuuugiLFiwABdeeOGQ77e1tWHevHlR/7d48eI0rTozieLYv35ZluH3+4ekdAJAcXFx1OyoXDfe9mxsbEQgEMDpp58e/pogCDjjjDPw5ptvwuv1JnuJWcFsNo/42vz582G32+F0OtHa2opjx47h/PPPH/KaCy64ADt27OB2HGa87QmMv+9S0HjbsqioCDqdbsj39Xo9ZsyYge7u7lQtMyvEsl9GYzKZ4PP5krm0rBTr9jx+/Djuv/9+3H777alcXlZJdN+kkWLZlk899RQuueQSFBUVpXp5WSWR/fL555+HwWDA2WefnezlZZ3xtifvgWI33rbk/c/EmEwmAIDP54PX68U777wzIlPsggsuwJEjR9DW1paGFU4+WXdXpJSxzZw5M2q5ipJiHPm/xx9/HAaDAWvXrk3DirNXXl4eLr74Yjz88MPYt28fHA4H3n77bTzzzDP4yle+ku7lZR3lBKDVaod8XavVwuv18qA2hvfeew9VVVUwGAxobm4GANTV1Q15zezZs+Hz+dDa2pqOJWaVyO1JEzPetrTZbOGehDS2aNtSuUmx2Wz45z//ibfeegtf/vKX07jK7BFte/6///f/cNFFF6GhoSGNK8s+0bblrl27sGzZMixevBhf+cpX8O6776Zxhdkjclu2tbXh5MmTKC0txQ033IBFixbhtNNOw+233w6Hw5HupWa8sc4/Pp8PL7/8Mj71qU+hoKAgDavLPpHbk/dAExO5LXn/E79AIACPx4MDBw7gj3/8I84++2zU1NTg+PHj8Pl8I64plZiIco9EE5N1PcjOPvtsnHvuuQCA2267Dfv37x/yfa1WO6IH0TvvvAO73T4i24zG99Of/hQ//elPcemll4a/9s1vfhNf/epX07iq7DRz5kwAwL59+4b0Kdm7dy8AwGq1pmNZGW/37t3YvHlzuNeLsp2MRuOQ1yn/ze04tuHbkxIXy7b8n//5HwiCgCuvvDKFK8s+o23LHTt2hM83Go0Gd9xxB3tsxCDa9nzttdewZ88ebNmyJY0ryz7RtuWKFStw0UUXoba2Ft3d3fjb3/6Gr371q3jooYewfPnyNK42sw3flj09PQCCAznOO+883HfffTh27BjuueceOJ1O/O///m86l5vRxjv/bNu2Df39/bz3iVG07cl7oMQM35a8/4nfWWedha6uLgDAmjVrcM899wDgPVCqZF2ALJFSIKYYJ+7uu+/GG2+8gf/6r/9CbW0t9u7diz/+8Y8wGo247rrr0r28rDJ37lx84hOfwN13342pU6eitrYWmzZtCj91jjZpLNd1dnbilltuwcqVK3H11VenezlZj9tTPbFsy6effhpPPvkk7rrrLkyZMiXFK8weY23LJUuW4KmnnoLdbse2bdvwX//1X8jLyxtyw0JDRdueHo8Hv/zlL3HTTTdFLYeh6EbbN7/73e8Oed2ZZ56JCy+8EH/6059w3333pXqZWSHatpQkCUAwI/xXv/oVAGD16tXQaDS4/fbbccstt2D69OlpW3OmiuX889xzz6G8vJxTAmMw2vbkPVD8om1L3v/E795774XL5UJTUxP+/Oc/44YbbsD999+f7mXljKwLkMWLKcaJO3ToEP7+97/jz3/+czi4uGLFCvj9fvzud7/DFVdcwRKtON111124+eabccUVVwAAqqur8e1vfxvr169HRUVFmleXWWw2G66//nqYTCasX78+HBwvKSkBAAwMDAzZZjabbcj3aajRtifFL5ZtuXXrVvzkJz/Bt7/9bXzhC19Iwyqzw3jb0mAwhPuHrl69GoFAAHfddRcuvvhi5OXlpWPJGW207fnAAw9AFEV89rOfDR8rfT4fJEmCzWZDYWHhiPKXXBfPMbOoqAjr1q3DSy+9lMIVZo/xzucrV64c8npl6uLhw4cZIBsmlv3S4XDg9ddfx6WXXsrj5DhG2568B4rfWPsm73/io7RBWL58ORYvXoyLLroIr7zyCurr6wEE74Ei8R5IXZM+QMYU48Q1NTUBCDZajLRgwQJ4vV50dXXx5BCn6dOn4+mnn0ZbWxvcbjfq6upw//33o6KiAtXV1eleXsZwu9345je/iYGBATzxxBNDmqQqdffNzc1DavCbm5uRn5/Pi+koxtqeFJ9YtuXevXvxve99D//2b/+G733ve2lYZXZIZL9cuHAhHnjgAVgsFl5UDzPW9mxubkZLS0vUbJIVK1bgzjvvZBlwBB4z1TPWtpw+ffqYgVmPx5OKJWaNWPfLV155BW63G5/73OdSvMLsMtb25D1QfMbbN3n/k7h58+YhPz8fx48fx9lnn438/Hw0NzdjzZo14dcovcfY71Ydkz5AxhTjxCkHrAMHDmDq1Knhr+/fvx+CIGDatGnpWlrWU2rw3W43nnrqKZYLRfD7/bj55pvR3NyMRx55BFVVVUO+P336dNTW1mLLli3hfoQAsHnzZqxevZpZEMOMtz0pdrFsy6amJnzzm9/EqlWr8LOf/SwNq8wOie6X7733HgwGA0pLS5O8wuwy3va8/vrrR2Qy3nvvvTh69Cj++7//G7W1tSlcbWZLZN90Op144403OC19mPG2pVarxemnn44dO3YM+frbb78NIBgQp6B49svnn38eM2bMwNKlS1O4wuwy3vbkPVDs4tk3ef8Tvw8++AA+nw81NTXQarVYuXIlXnrpJVxzzTXh12zevBmzZ88e0uONEjepA2RMMR6by+XC1q1bAQDt7e2w2+3h5r2nnXYaFi1ahEWLFuGnP/0pent7MWPGDOzbtw/33nsvLrnkEuh0unQuP+OMtz3NZjMefvhhGAwGTJ06Fe3t7bj//vtRUFCA66+/Pp1Lzyg/+9nP8Prrr+O2226D3W4PN/EEgk/utFotbrrpJvzgBz/AjBkzsHLlSmzevBn79u3Dww8/nL6FZ6hYtmdTU1P4aSkQLC3YsmULdDod1q1bl4ZVZ6bxtuXAwAC+/vWvo6CgANdcc82QITIGgyGcGk/jb8vm5mbcfffd+MxnPoPq6upwAGLjxo249dZbodFM6suXuI23PWfPnj1i8vc//vEPdHV1jShvy3Xjbct9+/bhr3/9Kz71qU+huroa3d3duP/++3Hy5En87ne/S9/CM1As558bb7wRV1xxBb7//e/jC1/4AlpaWnDPPffgc5/7HGbMmJG+xWeYWLYlAFgsFuzYsYPXleMYb3vyHih2seybvP+JzY033ohFixZh3rx5KCwsRGNjI/72t79h3rx54aSAb33rW7j66qtx55134vzzz8c777yD559/Hr/5zW/SvPrJQ5BlWU73IhKlTLF8/vnno37/n//8J374wx/iySef5FOUKNra2nDOOedE/d6DDz6IlStXhi/43n77bfT29mLKlCm48MILcf3116OwsDDFK85ssWzPv//973j00UfR2dkJk8mE8847D9/73vdYMx7h7LPPRnt7e9Tvvfrqq+GnIxs3bsR9992Hjo4O1NXV4dZbb8VZZ52VyqVmhVi25/r16/GHP/xhxPerq6vx2muvJXuJWWO8bdne3j5qw+TTTjsNDz30UDKXl1XG25aFhYX45S9/ib179+LkyZMoLi7GrFmzcO211w7JHKWgWI+bkca7hspV423LQCCAn//85/j444/R398PnU6H5cuX48Ybb8SSJUtSvNrMFut+uWPHDtx99904dOgQSkpK8LnPfQ633HILM8IjxLotH3nkEfz85z8PZ5RQdLFsT94DxSaWbcn7n9jce++92Lx5M44fPw5ZllFdXY1PfepT+PrXvz6kpPfVV1/Fb3/7Wxw9ehTTpk3DN77xDXzxi19M48onl0kdILvuuuvQ0tKCV155JcUrIyIiIiIiIiKibJF1NQqxlLEBTDEmIiIiIiIiIqLYZF2ArLe3d8RUMOW/lTI2AHjxxRfh9/s5wYWIiIiIiIiIiMaU1SWWREREREREREREEyWmewFERERERERERETpxAAZERERERERERHlNAbIiIiIiIiIiIgopzFARkREREREREREOS2jA2Tr16/HvHnzsGbNGkiSNOL7V1xxBebNm4fbbrstDasjIiIiIiIiIqLJIKMDZACQn5+Pvr4+vPvuu0O+3t7ejr1796KoqChNKyMiIiIiIiIioskgKwJka9euxQsvvDDk6y+88ALmzJmDGTNmTPgz3G73hN+DiIiIiIiIiIiyU8YHyADgwgsvxEsvvQSfzxf+2vPPP48LL7xwyOuOHDmCW265BevWrcPSpUtxwQUX4O9///uQ8sy2tjbMmzcPmzZtwu23346VK1fi0ksvTdnPQkREREREREREmSUrAmRnnXUWvF4v3nrrLQBAU1MTPv74Y1xwwQVDXtfd3Y26ujr89Kc/xb333ovLLrsMf/zjH/GnP/1pxHv+7//+L2RZxj333IN///d/T8nPQUREREREREREmUeT7gXEQqfT4eyzz8YLL7yAM888E88//zyWL1+O6dOnD3nd6tWrsXr1agCALMs49dRT4Xa78fDDD+PGG28c8tqGhgb8v//3/1L2MxARERERERERUWbKigAZECyz/P73vw+3243NmzfjqquuGvEaj8eD//u//8Nzzz2HEydODCnJdDgc0Ov14f8+88wzU7FsIiIiIiIiIiLKcFkTIDvjjDOQn5+P3/3ud2hra8P5558/4jX/8z//g40bN+I73/kOFi1ahOLiYrz66qv485//DI/HMyRAVlZWlsrlExERERERERFRhsqaAFl+fj7OO+88bNiwAatXr0Z5efmI12zZsgWXX345vvGNb4S/tnXr1qjvJwhC0tZKRERERERERETZI2sCZABw6aWXore3F5dddlnU73s8HuTn54f/OxAI4IUXXkjV8oiIiIiIiIiIKAtlVYBsyZIlUSdSKj75yU9i48aNqK+vR2lpKR599FF4vd4UrpCIiIiIiIiIiLKNmO4FqOmOO+7AihUr8Itf/AL/+Z//iblz5+KGG25I97KIiIiIiIiIiCiDCbIsy+leBBERERERERERUbpMqgwyIiIiIiIiIiKieDFARkREREREREREOY0BMiIiIiIiIiIiymkMkBERERERERERUU7LmADZiy++iG9961tYu3Ytli1bhosuughPPfUUhs8Q2LhxIz796U9j8eLF+PznP4/XX399yPf37duHH/3oR/jUpz6FpUuX4rzzzsM999wDp9M56md3dnZi+fLlmDdvHiwWS1J+PiIiIiIiIiIiykyadC9AsWHDBlRXV+O2225DaWkp3n77bdxxxx3o7OzEjTfeCAB44YUXcMcdd+CGG27AqlWrsHnzZtx444145JFHsGzZMgDBQFtLSwuuu+461NbWoqmpCb///e/xwQcf4MEHH4z62XfddReKiorGDKIREREREREREdHkJMjDU7TSxGKxwGw2D/naHXfcgc2bN+Pdd9+FKIr49Kc/jUWLFuGee+4Jv+aKK65AcXEx7rvvvlHf57nnnsMPfvADPP3001i0aNGQ7+3YsQM333wzvvnNb+JXv/oVduzYMeLfExERERERERHR5JUxJZbRglLz58+H3W6H0+lEa2srjh07hvPPP3/Iay644ALs2LEDXq931PdZsGABAKC7u3vI130+H37xi1/gpptugslkUuknISIiIiIiIiKibJIxAbJo3nvvPVRVVcFgMKC5uRkAUFdXN+Q1s2fPhs/nQ2tr65jvAwCzZs0a8vUHH3wQeXl5uPLKK1VeORERERERERERZYuMDZDt3r0bmzdvxte+9jUAgNVqBQAYjcYhr1P+W/n+cBaLBevXr8c555yD2tra8Ne7urrwxz/+ET/+8Y+Rl5eXhJ+AiIiIiIiIiIiyQUYGyDo7O3HLLbdg5cqVuPrqqxN+H5/Ph1tvvRUAcOeddw753q9//WucfvrpWL169USWSkREREREREREWS5jplgqbDYbrr/+ephMJqxfvx6iGIzhlZSUAAAGBgZQUVEx5PWR31fIsowf//jH2LdvHx599FFUVlaGv7dnzx689NJLePLJJ8P/3uVyAQAcDgd0Oh10Ol3yfkgiIiIiIiIiIsoYGRUgc7vd+OY3v4mBgQE88cQTKC4uDn9P6R/W3Nw8pJdYc3Mz8vPzMX369CHv9atf/Qovvvgi7rvvPjQ0NAz53tGjR+Hz+fCFL3xhxBrOPfdcXHDBBfjNb36j5o9GREREREREREQZKmMCZH6/HzfffDOam5vxyCOPoKqqasj3p0+fjtraWmzZsgXnnntu+OubN2/G6tWrodVqw1+79957sWHDBtx9991RSyjXrFmDBx98cMjXtm/fjvvuuw9//OMfh/QqIyIiIiIiIiKiyS1jAmQ/+9nP8Prrr+O2226D3W7H3r17w99bsGABtFotbrrpJvzgBz/AjBkzsHLlSmzevBn79u3Dww8/HH7tc889h3vuuQef//znUVNTM+R9ZsyYAbPZjIqKiiFlmgDQ3t4OADjllFNgNpuT+rMSEREREREREVHmyJgA2VtvvQUAuOuuu0Z879VXX0VNTQ0uvPBCuFwu3Hfffbj33ntRV1eHP/zhD1i+fPmI93n22Wfx7LPPDnmf//7v/8bFF1+cxJ+CiIiIiIiIiIiyjSDLspzuRRAREREREREREaWLmO4FEBERERERERERpRMDZERERERERERElNMYICMiIiIiIiIiopzGABkREREREREREeU0BsiIiIiIiIiIiCinMUBGREREREREREQ5jQEyIiIiIiIiIiLKaQyQERERERERERFRTmOAjIiIiIhw1VVX4aqrrkr3MoiIiIjSggEyIiIiogR9/PHH+O53v4uzzjoLixcvxpo1a/DVr34VDz300JDX/eUvf8G//vWvpKzh/fffx/r162Gz2ZLy/uNxuVxYv3493nnnnbR8PhEREZEaGCAjIiIiSsD777+PSy65BI2Njbj00kvxk5/8BJdeeilEUcSDDz445LX/93//l7QA2Z49e/CHP/whrQGyP/zhD9i1a1daPp+IiIhIDZp0L4CIiIgoG/3lL39BcXExnnrqKRiNxiHf6+3tTfrnO51OFBUVJf1ziIiIiHIBM8iIiIiIEnD8+HHU19ePCI4BQFlZWfj/z5s3D06nE//4xz8wb948zJs3D7fddhsAoL29HXfeeSc+/elPY8mSJVi5ciW++93voq2tbcj7bdq0CfPmzcOuXbtw5513YvXq1Vi3bh3Wr1+PX//61wCAc845J/z+w//9cE888QTOPfdcLFmyBF/84hexe/fuEa/xer343e9+h4svvhinnnoqli1bhi996UvYuXNn+DVtbW1YvXo1AOAPf/hD+PPXr18ffs2RI0fw3e9+F6eddhoWL16Miy++GK+++up4m5eIiIgopZhBRkRERJSA6upq7NmzB4cOHcLcuXNHfd2vf/1r3H777ViyZAkuu+wyAMCMGTMAAB9++CH27NmDz372s5gyZQra29vx2GOP4eqrr8YLL7wAnU435L1+9rOfwWw24zvf+Q6cTifWrl2LY8eO4fnnn8ePfvQjlJaWAgDMZvOo69m4cSN+8pOfYPny5bjmmmvQ2tqKb33rWygpKcHUqVPDr7Pb7di4cSMuvPBCXHrppXA4HHjqqadw3XXXYePGjZg/fz7MZvP/b+9uQqJc/zCOXyZjIimioGBOiUwSKWMlGPamRRIKgbrQdGNkLkLD2oQSmaD0YouyBkLIDNyMgmPWYjRyIb1Ji5ggQmjR2xAZOg4WNTqNcxbR0Bzn5Mv/dOLPfD/Lm+uZ5/fM8uJ+7kctLS1qaWlRYWGhCgsLJX0vBSXp5cuXqqysVHJysmpraxUTEyO73a66ujpdvXo1kAcAAPjTIvx+v/9PDwEAAPD/5uHDh6qtrZUkmc1m5eTkKC8vT9u2bZPBYAjKbtmyRfv379f58+eD1j0ej6Kjo4PWHA6HKioqdOHCBZWUlEj6voOsqalJOTk56unpUWRkZCDf1dWl9vZ2jYyMKDU19Zcze71e5efnKykpSX19fYqKipIk9fX16fTp08rNzQ18YMDn88nn8wUykjQzM6OioiLl5+fr7NmzkiSXy6W8vDzV19fr2LFjQfc7dOiQpqam1N/fH/gdv9+vyspKTU9Pa3h4+JfzAgAA/Fd4xRIAAGAFduzYIavVqr1792p8fFzXr19XTU2Ndu/eveRXCH8ux7xer6anp7Vu3TrFxcXpxYsXC/Ll5eVB5dhyPX/+XFNTUzp48GBQ8VVaWqrY2NigbGRkZCAzPz8vt9utb9++KSsrK+Rsf+d2uzU2NqaioiJ9/vxZLpdLLpdL09PT2rlzp16/fq2JiYkVPwsAAMC/iVcsAQAAVshsNstisWhubk7j4+O6d++ebt68qYaGBt26dUsmk+mX13s8HnV2dspms2liYkI/b+z/9OnTgvxiO8QW8/79e0nS+vXrg9YNBoOMRuOC/MDAgG7cuKFXr17J6/Uua463b9/K7/ero6NDHR0dITNTU1NKTk5eziMAAAD8FhRkAAAA/6OoqCiZzWaZzWalpaWpqalJQ0NDqq+v/+V1ra2tstlsqq6u1ubNmxUbG6uIiAidOHFCoU7BWL169e96hAUGBwfV2Nioffv2qaamRomJiYqMjFRnZ6fevXu36PXz8/OSpMOHD2vXrl0hMz/OYgMAAPjTKMgAAAD+RVlZWZKkjx8/LpodHh5WSUlJ4KuWkjQ7Oxty99g/iYiIWHI2JSVFkvTmzZvA1yel7693Op1Obdy4MWg2o9Eoi8USdI8rV64s6f4/dqQZDAZt3759yTMCAAD8CZxBBgAAsAJjY2Mhd3mNjo5KktLT0wNrMTExmpmZWZANdZ5YT0+PfD7fkuf48aXLpZRqWVlZSkhIkNVq1dzcXGB9YGBgwXw/Zvv5GZ89eyaHwxHy/n+/PjExUbm5uert7Q1ZFrpcrkXnBQAA+K+wgwwAAGAF2tra9PXrVxUWFio9PV1er1dPnz6V3W7X2rVrVVZWFshmZmbq8ePH6u7uVlJSklJTU5Wdna2CggINDg5qzZo1MplMcjgcevTokeLj45c8R2ZmpiTp0qVLKi4ulsFg0J49exQTE7MgazAYdPz4cTU3N6u6ulrFxcVyOp2y2WwLziArKCjQ3bt3VVdXp4KCAjmdTlmtVplMJn358iWQi46Olslkkt1uV1pamuLj47VhwwZlZGTozJkzqqqq0oEDB1ReXi6j0ajJyUk5HA59+PBBt2/fXua/DgAA8HtQkAEAAKzAyZMnNTQ0pNHRUfX29srr9SolJUVVVVU6evSo4uLiAtnGxkY1Nzfr8uXL8ng8Ki0tVXZ2tk6dOqVVq1bpzp07mp2d1datW9Xd3a0jR44seQ6z2ayGhgZZrVbdv39f8/PzGhkZCVmQSVJFRYV8Pp+6urrU3t6ujIwMXbt2bcFB+mVlZZqcnFRvb68ePHggk8mkixcvamhoSE+ePAnKtrW1qbW1VefOnZPX61V9fb0yMjJkMpnU398vi8WigYEBud1uJSQkaNOmTaqrq1vGvw0AAPB7RfhDvRsAAAAAAAAAhAnOIAMAAAAAAEBYoyADAAAAAABAWKMgAwAAAAAAQFijIAMAAAAAAEBYoyADAAAAAABAWKMgAwAAAAAAQFijIAMAAAAAAEBYoyADAAAAAABAWKMgAwAAAAAAQFijIAMAAAAAAEBYoyADAAAAAABAWKMgAwAAAAAAQFj7Cw8efTEhV0XiAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SPLIT_DATE = '29-03-2024'\n",
        "train = df.loc[df.index < SPLIT_DATE]\n",
        "test = df.loc[df.index >= SPLIT_DATE]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 5))\n",
        "train.plot(ax=ax, label='Training Set', title='Data Train/Test Split')\n",
        "test.plot(ax=ax, label='Test Set')\n",
        "ax.axvline(SPLIT_DATE, color='black', ls='--')\n",
        "ax.legend(['Training Set', 'Test Set'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "38VXVrCMKlUj",
        "outputId": "605119e3-4ffe-4ab1-8b55-a4cc4fdcaff4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_features(df):\n",
        "    df = df.copy()\n",
        "    df['hour'] = df.index.hour\n",
        "    df['dayofweek'] = df.index.dayofweek\n",
        "    df['dayofyear'] = df.index.dayofyear\n",
        "    df['weekofyear'] = df.index.isocalendar().week\n",
        "    return df\n",
        "\n",
        "df = create_features(df)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "koMedRa2OwoT",
        "outputId": "1b8f9046-2521-47d9-e171-9a6febb69a52"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  End date  Price  hour  dayofweek  dayofyear  \\\n",
              "Start date                                                                      \n",
              "2024-03-17 00:00:00   Mar 17, 2024 1:00 AM  64.99     0          6         77   \n",
              "2024-03-17 01:00:00   Mar 17, 2024 2:00 AM  60.49     1          6         77   \n",
              "2024-03-17 02:00:00   Mar 17, 2024 3:00 AM  55.97     2          6         77   \n",
              "2024-03-17 03:00:00   Mar 17, 2024 4:00 AM  58.75     3          6         77   \n",
              "2024-03-17 04:00:00   Mar 17, 2024 5:00 AM  60.45     4          6         77   \n",
              "...                                    ...    ...   ...        ...        ...   \n",
              "2024-03-30 19:00:00   Mar 30, 2024 8:00 PM  83.53    19          5         90   \n",
              "2024-03-30 20:00:00   Mar 30, 2024 9:00 PM  74.11    20          5         90   \n",
              "2024-03-30 21:00:00  Mar 30, 2024 10:00 PM  71.47    21          5         90   \n",
              "2024-03-30 22:00:00  Mar 30, 2024 11:00 PM  70.83    22          5         90   \n",
              "2024-03-30 23:00:00  Mar 31, 2024 12:00 AM  65.74    23          5         90   \n",
              "\n",
              "                     weekofyear  \n",
              "Start date                       \n",
              "2024-03-17 00:00:00          11  \n",
              "2024-03-17 01:00:00          11  \n",
              "2024-03-17 02:00:00          11  \n",
              "2024-03-17 03:00:00          11  \n",
              "2024-03-17 04:00:00          11  \n",
              "...                         ...  \n",
              "2024-03-30 19:00:00          13  \n",
              "2024-03-30 20:00:00          13  \n",
              "2024-03-30 21:00:00          13  \n",
              "2024-03-30 22:00:00          13  \n",
              "2024-03-30 23:00:00          13  \n",
              "\n",
              "[336 rows x 6 columns]"
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              "    <tr>\n",
              "      <th>Start date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-03-17 00:00:00</th>\n",
              "      <td>Mar 17, 2024 1:00 AM</td>\n",
              "      <td>64.99</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>77</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 01:00:00</th>\n",
              "      <td>Mar 17, 2024 2:00 AM</td>\n",
              "      <td>60.49</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>77</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 02:00:00</th>\n",
              "      <td>Mar 17, 2024 3:00 AM</td>\n",
              "      <td>55.97</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>77</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 03:00:00</th>\n",
              "      <td>Mar 17, 2024 4:00 AM</td>\n",
              "      <td>58.75</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>77</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-17 04:00:00</th>\n",
              "      <td>Mar 17, 2024 5:00 AM</td>\n",
              "      <td>60.45</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>77</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2024-03-30 19:00:00</th>\n",
              "      <td>Mar 30, 2024 8:00 PM</td>\n",
              "      <td>83.53</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>2024-03-30 20:00:00</th>\n",
              "      <td>Mar 30, 2024 9:00 PM</td>\n",
              "      <td>74.11</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 21:00:00</th>\n",
              "      <td>Mar 30, 2024 10:00 PM</td>\n",
              "      <td>71.47</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 22:00:00</th>\n",
              "      <td>Mar 30, 2024 11:00 PM</td>\n",
              "      <td>70.83</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 23:00:00</th>\n",
              "      <td>Mar 31, 2024 12:00 AM</td>\n",
              "      <td>65.74</td>\n",
              "      <td>23</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>336 rows × 6 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 336,\n  \"fields\": [\n    {\n      \"column\": \"Start date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-03-17 00:00:00\",\n        \"max\": \"2024-03-30 23:00:00\",\n        \"num_unique_values\": 336,\n        \"samples\": [\n          \"2024-03-20 00:00:00\",\n          \"2024-03-21 14:00:00\",\n          \"2024-03-29 10:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"End date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 336,\n        \"samples\": [\n          \"Mar 20, 2024 1:00 AM\",\n          \"Mar 21, 2024 3:00 PM\",\n          \"Mar 29, 2024 11:00 AM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.08469158915915,\n        \"min\": -8.45,\n        \"max\": 174.7,\n        \"num_unique_values\": 314,\n        \"samples\": [\n          76.87,\n          72.84,\n          72.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofweek\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofyear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          86,\n          88,\n          77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekofyear\",\n      \"properties\": {\n        \"dtype\": \"UInt32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11,\n          12,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TARGET = 'Price'\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "sns.boxplot(data=df, x='hour', y=TARGET)\n",
        "ax.set_title('Price by Hour')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "mI6X76FpPK_l",
        "outputId": "95499f73-330c-49ff-9cf6-d01f822b7911"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# process Train and Test Splits\n",
        "train = create_features(train)\n",
        "test = create_features(test)\n",
        "\n",
        "FEATURES = ['hour', 'dayofyear', 'dayofweek']\n",
        "\n",
        "X_train = train[FEATURES]\n",
        "y_train = train[TARGET]\n",
        "\n",
        "X_test = test[FEATURES]\n",
        "y_test = test[TARGET]"
      ],
      "metadata": {
        "id": "kCYvjTq_PPnY"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg = xgb.XGBRegressor(base_score=0.5, booster='gbtree',\n",
        "                       n_estimators=1000,\n",
        "                       early_stopping_rounds=50,\n",
        "                       objective='reg:linear',\n",
        "                       max_depth=3,\n",
        "                       learning_rate=0.01)\n",
        "reg.fit(X_train, y_train,\n",
        "        eval_set=[(X_train, y_train), (X_test, y_test)],\n",
        "        verbose=25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "RP7emC3WP4n0",
        "outputId": "accb2502-a14d-4b6e-d578-d31ef9d322bb"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-rmse:72.69784\tvalidation_1-rmse:52.90988\n",
            "[100]\tvalidation_0-rmse:31.48947\tvalidation_1-rmse:34.05339\n",
            "[200]\tvalidation_0-rmse:17.20606\tvalidation_1-rmse:29.22036\n",
            "[300]\tvalidation_0-rmse:12.43413\tvalidation_1-rmse:28.11362\n",
            "[353]\tvalidation_0-rmse:11.20481\tvalidation_1-rmse:28.35648\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [13:29:24] WARNING: /workspace/src/objective/regression_obj.cu:227: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=50,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
              "             num_parallel_tree=None, objective='reg:linear', ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=50,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
              "             num_parallel_tree=None, objective=&#x27;reg:linear&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=50,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
              "             num_parallel_tree=None, objective=&#x27;reg:linear&#x27;, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fi = pd.DataFrame(data=reg.feature_importances_,\n",
        "             index=reg.feature_names_in_,\n",
        "             columns=['importance'])\n",
        "fi.sort_values('importance').plot(kind='barh', title='Feature Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "MZUfXX57QC8z",
        "outputId": "237aa4dd-2952-4c83-8655-8ccbe30727cd"
      },
      "execution_count": 9,
      "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": [
        "test['prediction'] = reg.predict(X_test)\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5sxwOpKuQHJ1",
        "outputId": "9e7e0211-02b7-4f0c-8ceb-7a597a0a9850"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  End date   Price  hour  dayofweek  \\\n",
              "Start date                                                            \n",
              "2024-03-29 00:00:00   Mar 29, 2024 1:00 AM   20.60     0          4   \n",
              "2024-03-29 01:00:00   Mar 29, 2024 2:00 AM   18.83     1          4   \n",
              "2024-03-29 02:00:00   Mar 29, 2024 3:00 AM   12.43     2          4   \n",
              "2024-03-29 03:00:00   Mar 29, 2024 4:00 AM   10.24     3          4   \n",
              "2024-03-29 04:00:00   Mar 29, 2024 5:00 AM   12.59     4          4   \n",
              "2024-03-29 05:00:00   Mar 29, 2024 6:00 AM   14.32     5          4   \n",
              "2024-03-29 06:00:00   Mar 29, 2024 7:00 AM   22.02     6          4   \n",
              "2024-03-29 07:00:00   Mar 29, 2024 8:00 AM   22.39     7          4   \n",
              "2024-03-29 08:00:00   Mar 29, 2024 9:00 AM   15.94     8          4   \n",
              "2024-03-29 09:00:00  Mar 29, 2024 10:00 AM   13.28     9          4   \n",
              "2024-03-29 10:00:00  Mar 29, 2024 11:00 AM   16.88    10          4   \n",
              "2024-03-29 11:00:00  Mar 29, 2024 12:00 PM   18.83    11          4   \n",
              "2024-03-29 12:00:00   Mar 29, 2024 1:00 PM   13.00    12          4   \n",
              "2024-03-29 13:00:00   Mar 29, 2024 2:00 PM    9.86    13          4   \n",
              "2024-03-29 14:00:00   Mar 29, 2024 3:00 PM   13.44    14          4   \n",
              "2024-03-29 15:00:00   Mar 29, 2024 4:00 PM   21.58    15          4   \n",
              "2024-03-29 16:00:00   Mar 29, 2024 5:00 PM   49.50    16          4   \n",
              "2024-03-29 17:00:00   Mar 29, 2024 6:00 PM   79.93    17          4   \n",
              "2024-03-29 18:00:00   Mar 29, 2024 7:00 PM  101.97    18          4   \n",
              "2024-03-29 19:00:00   Mar 29, 2024 8:00 PM  105.45    19          4   \n",
              "2024-03-29 20:00:00   Mar 29, 2024 9:00 PM   87.40    20          4   \n",
              "2024-03-29 21:00:00  Mar 29, 2024 10:00 PM   79.93    21          4   \n",
              "2024-03-29 22:00:00  Mar 29, 2024 11:00 PM   75.89    22          4   \n",
              "2024-03-29 23:00:00  Mar 30, 2024 12:00 AM   70.00    23          4   \n",
              "2024-03-30 00:00:00   Mar 30, 2024 1:00 AM   69.30     0          5   \n",
              "2024-03-30 01:00:00   Mar 30, 2024 2:00 AM   58.16     1          5   \n",
              "2024-03-30 02:00:00   Mar 30, 2024 3:00 AM   58.88     2          5   \n",
              "2024-03-30 03:00:00   Mar 30, 2024 4:00 AM   58.95     3          5   \n",
              "2024-03-30 04:00:00   Mar 30, 2024 5:00 AM   59.23     4          5   \n",
              "2024-03-30 05:00:00   Mar 30, 2024 6:00 AM   65.05     5          5   \n",
              "2024-03-30 06:00:00   Mar 30, 2024 7:00 AM   58.57     6          5   \n",
              "2024-03-30 07:00:00   Mar 30, 2024 8:00 AM   47.79     7          5   \n",
              "2024-03-30 08:00:00   Mar 30, 2024 9:00 AM   45.23     8          5   \n",
              "2024-03-30 09:00:00  Mar 30, 2024 10:00 AM   43.38     9          5   \n",
              "2024-03-30 10:00:00  Mar 30, 2024 11:00 AM   34.49    10          5   \n",
              "2024-03-30 11:00:00  Mar 30, 2024 12:00 PM   23.21    11          5   \n",
              "2024-03-30 12:00:00   Mar 30, 2024 1:00 PM   14.76    12          5   \n",
              "2024-03-30 13:00:00   Mar 30, 2024 2:00 PM    6.85    13          5   \n",
              "2024-03-30 14:00:00   Mar 30, 2024 3:00 PM   15.87    14          5   \n",
              "2024-03-30 15:00:00   Mar 30, 2024 4:00 PM   34.20    15          5   \n",
              "2024-03-30 16:00:00   Mar 30, 2024 5:00 PM   52.52    16          5   \n",
              "2024-03-30 17:00:00   Mar 30, 2024 6:00 PM   77.75    17          5   \n",
              "2024-03-30 18:00:00   Mar 30, 2024 7:00 PM   88.11    18          5   \n",
              "2024-03-30 19:00:00   Mar 30, 2024 8:00 PM   83.53    19          5   \n",
              "2024-03-30 20:00:00   Mar 30, 2024 9:00 PM   74.11    20          5   \n",
              "2024-03-30 21:00:00  Mar 30, 2024 10:00 PM   71.47    21          5   \n",
              "2024-03-30 22:00:00  Mar 30, 2024 11:00 PM   70.83    22          5   \n",
              "2024-03-30 23:00:00  Mar 31, 2024 12:00 AM   65.74    23          5   \n",
              "\n",
              "                     dayofyear  weekofyear  prediction  \n",
              "Start date                                              \n",
              "2024-03-29 00:00:00         89          13   57.368252  \n",
              "2024-03-29 01:00:00         89          13   57.368252  \n",
              "2024-03-29 02:00:00         89          13   57.368252  \n",
              "2024-03-29 03:00:00         89          13   57.368252  \n",
              "2024-03-29 04:00:00         89          13   57.368252  \n",
              "2024-03-29 05:00:00         89          13   57.368252  \n",
              "2024-03-29 06:00:00         89          13   60.386349  \n",
              "2024-03-29 07:00:00         89          13   59.782932  \n",
              "2024-03-29 08:00:00         89          13   58.351234  \n",
              "2024-03-29 09:00:00         89          13   43.027107  \n",
              "2024-03-29 10:00:00         89          13   32.538853  \n",
              "2024-03-29 11:00:00         89          13   27.192064  \n",
              "2024-03-29 12:00:00         89          13   27.192064  \n",
              "2024-03-29 13:00:00         89          13   27.192064  \n",
              "2024-03-29 14:00:00         89          13   27.192064  \n",
              "2024-03-29 15:00:00         89          13   27.255150  \n",
              "2024-03-29 16:00:00         89          13   35.694706  \n",
              "2024-03-29 17:00:00         89          13   58.785023  \n",
              "2024-03-29 18:00:00         89          13   73.137619  \n",
              "2024-03-29 19:00:00         89          13   73.137619  \n",
              "2024-03-29 20:00:00         89          13   57.176914  \n",
              "2024-03-29 21:00:00         89          13   46.287575  \n",
              "2024-03-29 22:00:00         89          13   43.425484  \n",
              "2024-03-29 23:00:00         89          13   39.911880  \n",
              "2024-03-30 00:00:00         90          13   37.001072  \n",
              "2024-03-30 01:00:00         90          13   37.001072  \n",
              "2024-03-30 02:00:00         90          13   37.001072  \n",
              "2024-03-30 03:00:00         90          13   37.001072  \n",
              "2024-03-30 04:00:00         90          13   37.001072  \n",
              "2024-03-30 05:00:00         90          13   37.001072  \n",
              "2024-03-30 06:00:00         90          13   40.019161  \n",
              "2024-03-30 07:00:00         90          13   38.840900  \n",
              "2024-03-30 08:00:00         90          13   31.977642  \n",
              "2024-03-30 09:00:00         90          13   14.094271  \n",
              "2024-03-30 10:00:00         90          13   10.331126  \n",
              "2024-03-30 11:00:00         90          13    5.440592  \n",
              "2024-03-30 12:00:00         90          13    5.440592  \n",
              "2024-03-30 13:00:00         90          13    5.440592  \n",
              "2024-03-30 14:00:00         90          13    5.440592  \n",
              "2024-03-30 15:00:00         90          13    5.503677  \n",
              "2024-03-30 16:00:00         90          13   15.583175  \n",
              "2024-03-30 17:00:00         90          13   46.398651  \n",
              "2024-03-30 18:00:00         90          13   60.852177  \n",
              "2024-03-30 19:00:00         90          13   60.852177  \n",
              "2024-03-30 20:00:00         90          13   47.265545  \n",
              "2024-03-30 21:00:00         90          13   44.244343  \n",
              "2024-03-30 22:00:00         90          13   41.382252  \n",
              "2024-03-30 23:00:00         90          13   37.725586  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14d08f85-c3c6-4bbc-bc0f-554e93f169c2\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>End date</th>\n",
              "      <th>Price</th>\n",
              "      <th>hour</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Start date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-03-29 00:00:00</th>\n",
              "      <td>Mar 29, 2024 1:00 AM</td>\n",
              "      <td>20.60</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.368252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 01:00:00</th>\n",
              "      <td>Mar 29, 2024 2:00 AM</td>\n",
              "      <td>18.83</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.368252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 02:00:00</th>\n",
              "      <td>Mar 29, 2024 3:00 AM</td>\n",
              "      <td>12.43</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.368252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 03:00:00</th>\n",
              "      <td>Mar 29, 2024 4:00 AM</td>\n",
              "      <td>10.24</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.368252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 04:00:00</th>\n",
              "      <td>Mar 29, 2024 5:00 AM</td>\n",
              "      <td>12.59</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.368252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 05:00:00</th>\n",
              "      <td>Mar 29, 2024 6:00 AM</td>\n",
              "      <td>14.32</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.368252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 06:00:00</th>\n",
              "      <td>Mar 29, 2024 7:00 AM</td>\n",
              "      <td>22.02</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>60.386349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 07:00:00</th>\n",
              "      <td>Mar 29, 2024 8:00 AM</td>\n",
              "      <td>22.39</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>59.782932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 08:00:00</th>\n",
              "      <td>Mar 29, 2024 9:00 AM</td>\n",
              "      <td>15.94</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>58.351234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 09:00:00</th>\n",
              "      <td>Mar 29, 2024 10:00 AM</td>\n",
              "      <td>13.28</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>43.027107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 10:00:00</th>\n",
              "      <td>Mar 29, 2024 11:00 AM</td>\n",
              "      <td>16.88</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>32.538853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 11:00:00</th>\n",
              "      <td>Mar 29, 2024 12:00 PM</td>\n",
              "      <td>18.83</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>27.192064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 12:00:00</th>\n",
              "      <td>Mar 29, 2024 1:00 PM</td>\n",
              "      <td>13.00</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>27.192064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 13:00:00</th>\n",
              "      <td>Mar 29, 2024 2:00 PM</td>\n",
              "      <td>9.86</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>27.192064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 14:00:00</th>\n",
              "      <td>Mar 29, 2024 3:00 PM</td>\n",
              "      <td>13.44</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>27.192064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 15:00:00</th>\n",
              "      <td>Mar 29, 2024 4:00 PM</td>\n",
              "      <td>21.58</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>27.255150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 16:00:00</th>\n",
              "      <td>Mar 29, 2024 5:00 PM</td>\n",
              "      <td>49.50</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>35.694706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 17:00:00</th>\n",
              "      <td>Mar 29, 2024 6:00 PM</td>\n",
              "      <td>79.93</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>58.785023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 18:00:00</th>\n",
              "      <td>Mar 29, 2024 7:00 PM</td>\n",
              "      <td>101.97</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>73.137619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 19:00:00</th>\n",
              "      <td>Mar 29, 2024 8:00 PM</td>\n",
              "      <td>105.45</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>73.137619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 20:00:00</th>\n",
              "      <td>Mar 29, 2024 9:00 PM</td>\n",
              "      <td>87.40</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>57.176914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 21:00:00</th>\n",
              "      <td>Mar 29, 2024 10:00 PM</td>\n",
              "      <td>79.93</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>46.287575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 22:00:00</th>\n",
              "      <td>Mar 29, 2024 11:00 PM</td>\n",
              "      <td>75.89</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>43.425484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-29 23:00:00</th>\n",
              "      <td>Mar 30, 2024 12:00 AM</td>\n",
              "      <td>70.00</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>39.911880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 00:00:00</th>\n",
              "      <td>Mar 30, 2024 1:00 AM</td>\n",
              "      <td>69.30</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.001072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 01:00:00</th>\n",
              "      <td>Mar 30, 2024 2:00 AM</td>\n",
              "      <td>58.16</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.001072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 02:00:00</th>\n",
              "      <td>Mar 30, 2024 3:00 AM</td>\n",
              "      <td>58.88</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.001072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 03:00:00</th>\n",
              "      <td>Mar 30, 2024 4:00 AM</td>\n",
              "      <td>58.95</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.001072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 04:00:00</th>\n",
              "      <td>Mar 30, 2024 5:00 AM</td>\n",
              "      <td>59.23</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.001072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 05:00:00</th>\n",
              "      <td>Mar 30, 2024 6:00 AM</td>\n",
              "      <td>65.05</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.001072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 06:00:00</th>\n",
              "      <td>Mar 30, 2024 7:00 AM</td>\n",
              "      <td>58.57</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>40.019161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 07:00:00</th>\n",
              "      <td>Mar 30, 2024 8:00 AM</td>\n",
              "      <td>47.79</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>38.840900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 08:00:00</th>\n",
              "      <td>Mar 30, 2024 9:00 AM</td>\n",
              "      <td>45.23</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>31.977642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 09:00:00</th>\n",
              "      <td>Mar 30, 2024 10:00 AM</td>\n",
              "      <td>43.38</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>14.094271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 10:00:00</th>\n",
              "      <td>Mar 30, 2024 11:00 AM</td>\n",
              "      <td>34.49</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>10.331126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 11:00:00</th>\n",
              "      <td>Mar 30, 2024 12:00 PM</td>\n",
              "      <td>23.21</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>5.440592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 12:00:00</th>\n",
              "      <td>Mar 30, 2024 1:00 PM</td>\n",
              "      <td>14.76</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>5.440592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 13:00:00</th>\n",
              "      <td>Mar 30, 2024 2:00 PM</td>\n",
              "      <td>6.85</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>5.440592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 14:00:00</th>\n",
              "      <td>Mar 30, 2024 3:00 PM</td>\n",
              "      <td>15.87</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>5.440592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 15:00:00</th>\n",
              "      <td>Mar 30, 2024 4:00 PM</td>\n",
              "      <td>34.20</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>5.503677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 16:00:00</th>\n",
              "      <td>Mar 30, 2024 5:00 PM</td>\n",
              "      <td>52.52</td>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>15.583175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 17:00:00</th>\n",
              "      <td>Mar 30, 2024 6:00 PM</td>\n",
              "      <td>77.75</td>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>46.398651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 18:00:00</th>\n",
              "      <td>Mar 30, 2024 7:00 PM</td>\n",
              "      <td>88.11</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>60.852177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 19:00:00</th>\n",
              "      <td>Mar 30, 2024 8:00 PM</td>\n",
              "      <td>83.53</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>60.852177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 20:00:00</th>\n",
              "      <td>Mar 30, 2024 9:00 PM</td>\n",
              "      <td>74.11</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>47.265545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 21:00:00</th>\n",
              "      <td>Mar 30, 2024 10:00 PM</td>\n",
              "      <td>71.47</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>44.244343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 22:00:00</th>\n",
              "      <td>Mar 30, 2024 11:00 PM</td>\n",
              "      <td>70.83</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>41.382252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30 23:00:00</th>\n",
              "      <td>Mar 31, 2024 12:00 AM</td>\n",
              "      <td>65.74</td>\n",
              "      <td>23</td>\n",
              "      <td>5</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>37.725586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14d08f85-c3c6-4bbc-bc0f-554e93f169c2')\"\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "        document.querySelector('#df-14d08f85-c3c6-4bbc-bc0f-554e93f169c2 button.colab-df-convert');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-5d4db4eb-2d17-4758-a2ca-cdfabc1f5865 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7a6caef5-f992-4bac-b489-d554c7e8dd50\">\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",
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              "        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('test')\"\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7a6caef5-f992-4bac-b489-d554c7e8dd50 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Start date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-03-29 00:00:00\",\n        \"max\": \"2024-03-30 23:00:00\",\n        \"num_unique_values\": 48,\n        \"samples\": [\n          \"2024-03-30 03:00:00\",\n          \"2024-03-30 16:00:00\",\n          \"2024-03-30 02:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"End date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 48,\n        \"samples\": [\n          \"Mar 30, 2024 4:00 AM\",\n          \"Mar 30, 2024 5:00 PM\",\n          \"Mar 30, 2024 3:00 AM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.961703902011728,\n        \"min\": 6.85,\n        \"max\": 105.45,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          77.75,\n          58.95,\n          59.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofweek\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofyear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          90,\n          89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekofyear\",\n      \"properties\": {\n        \"dtype\": \"UInt32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          44.24434280395508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.merge(test[['prediction']], how='left', left_index=True, right_index=True)"
      ],
      "metadata": {
        "id": "WcUwa2OLRSS6"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df[[TARGET]].plot(figsize=(15, 5))\n",
        "df['prediction'].plot(ax=ax, style='-')\n",
        "plt.legend(['Actual Data', 'Model Predictions'])\n",
        "ax.set_title('Actual Data and Prediction')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "Hk6J5KxXQLBE",
        "outputId": "04aa1e29-e66d-4043-a152-ebca7b0fd819"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test error fot the entire test set\n",
        "score = root_mean_squared_error(test[TARGET], test['prediction'])\n",
        "print(f'RMSE Score on Test set: {score:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OP-zpnvgQYTH",
        "outputId": "c3d8a933-066b-4288-e3b9-283a24412bbd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE Score on Test set: 28.09\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#days and (absolute) error realtion\n",
        "test['error'] = np.abs(test[TARGET] - test['prediction'])\n",
        "test['date'] = test.index.date\n",
        "test.groupby(['date'])['error'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "HafaW1PER050",
        "outputId": "7f117f1a-bfa5-4f65-ec43-fea3d4f7eebd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2024-03-29    29.191774\n",
              "2024-03-30    22.464188\n",
              "Name: error, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>error</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-03-29</th>\n",
              "      <td>29.191774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-30</th>\n",
              "      <td>22.464188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NKiKcqqlSLaF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}