AMP_PARKINSONS_PRED.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QiryRxhAk3Ff"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VjqLXj2MlwM_"
      },
      "outputs": [],
      "source": [
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "vbvZLKrflyPg",
        "outputId": "4c15d851-17a4-4a07-c999-16cfddfd4939"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3935b650-3344-4bee-abf3-91608742aac9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3935b650-3344-4bee-abf3-91608742aac9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"yuvannabawa\",\"key\":\"5398f503ad9d25353f75d32795a29c63\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GoH64ipRmuW3"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VJnu-6EKmx3_"
      },
      "outputs": [],
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NNtBfJoJnJi6"
      },
      "outputs": [],
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1OyJH6ul0TB",
        "outputId": "0c2dca1f-328b-44f1-a21a-c58d7356b655"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading amp-parkinsons-disease-progression-prediction.zip to /content\n",
            "\r  0% 0.00/16.1M [00:00<?, ?B/s]\r 31% 5.00M/16.1M [00:00<00:00, 42.6MB/s]\n",
            "\r100% 16.1M/16.1M [00:00<00:00, 92.6MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions download -c amp-parkinsons-disease-progression-prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiVAiiGUnRHr",
        "outputId": "41d0d49c-b85b-4fbb-b300-65a2bf335250"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/amp-parkinsons-disease-progression-prediction.zip\n",
            "  inflating: amp_pd_peptide/__init__.py  \n",
            "  inflating: amp_pd_peptide/competition.cpython-37m-x86_64-linux-gnu.so  \n",
            "  inflating: amp_pd_peptide_310/__init__.py  \n",
            "  inflating: amp_pd_peptide_310/competition.cpython-310-x86_64-linux-gnu.so  \n",
            "  inflating: example_test_files/sample_submission.csv  \n",
            "  inflating: example_test_files/test.csv  \n",
            "  inflating: example_test_files/test_peptides.csv  \n",
            "  inflating: example_test_files/test_proteins.csv  \n",
            "  inflating: public_timeseries_testing_util.py  \n",
            "  inflating: supplemental_clinical_data.csv  \n",
            "  inflating: train_clinical_data.csv  \n",
            "  inflating: train_peptides.csv      \n",
            "  inflating: train_proteins.csv      \n"
          ]
        }
      ],
      "source": [
        "!unzip '/content/amp-parkinsons-disease-progression-prediction.zip'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-s3cFtq6UJgC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from scipy.optimize import minimize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2p2GEtxpayy4"
      },
      "outputs": [],
      "source": [
        "train_clinical_data = pd.read_csv(\"/content/train_clinical_data.csv\")\n",
        "supply_clinical = pd.read_csv(\"/content/supplemental_clinical_data.csv\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "owVTrOhwtBO_"
      },
      "outputs": [],
      "source": [
        "train_full = pd.concat([train_clinical_data, supply_clinical])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "XIS4NEbDuIYL",
        "outputId": "c3498a1a-641c-4661-d988-a53ce787564c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visit_id  patient_id  visit_month  updrs_1  updrs_2  updrs_3  updrs_4  \\\n",
              "0      55_0          55            0     10.0      6.0     15.0      NaN   \n",
              "1      55_3          55            3     10.0      7.0     25.0      NaN   \n",
              "2      55_6          55            6      8.0     10.0     34.0      NaN   \n",
              "3      55_9          55            9      8.0      9.0     30.0      0.0   \n",
              "4     55_12          55           12     10.0     10.0     41.0      0.0   \n",
              "5     55_18          55           18      7.0     13.0     38.0      0.0   \n",
              "6     55_24          55           24     16.0      9.0     49.0      0.0   \n",
              "7     55_30          55           30     14.0     13.0     49.0      0.0   \n",
              "8     55_36          55           36     17.0     18.0     51.0      0.0   \n",
              "9     55_42          55           42     12.0     20.0     41.0      0.0   \n",
              "10    55_48          55           48     17.0     16.0     52.0      0.0   \n",
              "11    55_54          55           54     12.0     18.0     51.0      0.0   \n",
              "12    55_60          55           60     23.0     21.0     56.0      0.0   \n",
              "13    942_0         942            0      3.0      2.0     20.0      NaN   \n",
              "14    942_3         942            3      7.0      2.0     17.0      NaN   \n",
              "15    942_6         942            6      8.0      2.0     21.0      NaN   \n",
              "16   942_12         942           12      5.0      2.0     25.0      0.0   \n",
              "17   942_18         942           18      6.0      5.0     18.0      0.0   \n",
              "18   942_24         942           24      2.0      3.0     23.0      NaN   \n",
              "19   942_30         942           30      4.0      4.0     19.0      0.0   \n",
              "\n",
              "   upd23b_clinical_state_on_medication  \n",
              "0                                  NaN  \n",
              "1                                  NaN  \n",
              "2                                  NaN  \n",
              "3                                   On  \n",
              "4                                   On  \n",
              "5                                   On  \n",
              "6                                   On  \n",
              "7                                   On  \n",
              "8                                   On  \n",
              "9                                   On  \n",
              "10                                  On  \n",
              "11                                  On  \n",
              "12                                  On  \n",
              "13                                 NaN  \n",
              "14                                 NaN  \n",
              "15                                 NaN  \n",
              "16                                 NaN  \n",
              "17                                 NaN  \n",
              "18                                 NaN  \n",
              "19                                 NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e826a82-4275-4218-aa39-608a7044cbe9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>visit_id</th>\n",
              "      <th>patient_id</th>\n",
              "      <th>visit_month</th>\n",
              "      <th>updrs_1</th>\n",
              "      <th>updrs_2</th>\n",
              "      <th>updrs_3</th>\n",
              "      <th>updrs_4</th>\n",
              "      <th>upd23b_clinical_state_on_medication</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>55_0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55_3</td>\n",
              "      <td>55</td>\n",
              "      <td>3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>55_6</td>\n",
              "      <td>55</td>\n",
              "      <td>6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>55_9</td>\n",
              "      <td>55</td>\n",
              "      <td>9</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55_12</td>\n",
              "      <td>55</td>\n",
              "      <td>12</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>55_18</td>\n",
              "      <td>55</td>\n",
              "      <td>18</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>55_24</td>\n",
              "      <td>55</td>\n",
              "      <td>24</td>\n",
              "      <td>16.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>55_30</td>\n",
              "      <td>55</td>\n",
              "      <td>30</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>55_36</td>\n",
              "      <td>55</td>\n",
              "      <td>36</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>55_42</td>\n",
              "      <td>55</td>\n",
              "      <td>42</td>\n",
              "      <td>12.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>55_48</td>\n",
              "      <td>55</td>\n",
              "      <td>48</td>\n",
              "      <td>17.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>55_54</td>\n",
              "      <td>55</td>\n",
              "      <td>54</td>\n",
              "      <td>12.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>55_60</td>\n",
              "      <td>55</td>\n",
              "      <td>60</td>\n",
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              "      <td>30</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e826a82-4275-4218-aa39-608a7044cbe9')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e826a82-4275-4218-aa39-608a7044cbe9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e826a82-4275-4218-aa39-608a7044cbe9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "train_clinical_data.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
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        "id": "bQqCDMr0uBnL",
        "outputId": "773fe106-f4e1-4abd-9fc0-4f4cac88d3f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  visit_id  patient_id  visit_month  updrs_1  updrs_2  updrs_3  updrs_4  \\\n",
              "0     55_0          55            0     10.0      6.0     15.0      NaN   \n",
              "1     55_3          55            3     10.0      7.0     25.0      NaN   \n",
              "2     55_6          55            6      8.0     10.0     34.0      NaN   \n",
              "3     55_9          55            9      8.0      9.0     30.0      0.0   \n",
              "4    55_12          55           12     10.0     10.0     41.0      0.0   \n",
              "5    55_18          55           18      7.0     13.0     38.0      0.0   \n",
              "6    55_24          55           24     16.0      9.0     49.0      0.0   \n",
              "7    55_30          55           30     14.0     13.0     49.0      0.0   \n",
              "8    55_36          55           36     17.0     18.0     51.0      0.0   \n",
              "9    55_42          55           42     12.0     20.0     41.0      0.0   \n",
              "\n",
              "  upd23b_clinical_state_on_medication  \n",
              "0                                 NaN  \n",
              "1                                 NaN  \n",
              "2                                 NaN  \n",
              "3                                  On  \n",
              "4                                  On  \n",
              "5                                  On  \n",
              "6                                  On  \n",
              "7                                  On  \n",
              "8                                  On  \n",
              "9                                  On  "
            ],
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              "      <td>55_24</td>\n",
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              "      <td>55_36</td>\n",
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              "      <td>17.0</td>\n",
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              "      <td>51.0</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4af1a4e1-eeb4-4881-926a-b733b569b43a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4af1a4e1-eeb4-4881-926a-b733b569b43a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train_full.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "Y2xVdTdDuEUK",
        "outputId": "ed1c68a5-86c1-4504-b463-89e722424b90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   visit_id  patient_id  visit_month upd23b_clinical_state_on_medication  \\\n",
              "13    942_0         942            0                                 NaN   \n",
              "14    942_3         942            3                                 NaN   \n",
              "15    942_6         942            6                                 NaN   \n",
              "16   942_12         942           12                                 NaN   \n",
              "17   942_18         942           18                                 NaN   \n",
              "18   942_24         942           24                                 NaN   \n",
              "19   942_30         942           30                                 NaN   \n",
              "20   942_36         942           36                                 NaN   \n",
              "21   942_42         942           42                                 NaN   \n",
              "22   942_48         942           48                                 NaN   \n",
              "23   942_54         942           54                                 NaN   \n",
              "24   942_60         942           60                                 NaN   \n",
              "25   942_72         942           72                                 Off   \n",
              "26   942_84         942           84                                 Off   \n",
              "27   942_96         942           96                                 Off   \n",
              "\n",
              "    updrs_1  updrs_2  updrs_3  updrs_4  \n",
              "13      3.0      2.0     20.0      NaN  \n",
              "14      7.0      2.0     17.0      NaN  \n",
              "15      8.0      2.0     21.0      NaN  \n",
              "16      5.0      2.0     25.0      0.0  \n",
              "17      6.0      5.0     18.0      0.0  \n",
              "18      2.0      3.0     23.0      NaN  \n",
              "19      4.0      4.0     19.0      0.0  \n",
              "20      2.0      4.0     19.0      0.0  \n",
              "21      2.0      3.0     32.0      0.0  \n",
              "22      2.0      6.0     35.0      0.0  \n",
              "23      2.0      4.0     26.0      0.0  \n",
              "24      2.0      5.0     31.0      0.0  \n",
              "25      4.0      2.0     33.0      0.0  \n",
              "26      4.0      8.0     26.0      0.0  \n",
              "27      6.0      9.0     39.0      3.0  "
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              "      <th></th>\n",
              "      <th>visit_id</th>\n",
              "      <th>patient_id</th>\n",
              "      <th>visit_month</th>\n",
              "      <th>upd23b_clinical_state_on_medication</th>\n",
              "      <th>updrs_1</th>\n",
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              "      <th>updrs_3</th>\n",
              "      <th>updrs_4</th>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>942_0</td>\n",
              "      <td>942</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <th>14</th>\n",
              "      <td>942_3</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>17.0</td>\n",
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              "      <th>15</th>\n",
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              "      <td>21.0</td>\n",
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              "      <th>16</th>\n",
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              "      <td>25.0</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>942_18</td>\n",
              "      <td>942</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>942_24</td>\n",
              "      <td>942</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>23.0</td>\n",
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              "      <th>19</th>\n",
              "      <td>942_30</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>19.0</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>942_36</td>\n",
              "      <td>942</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>19.0</td>\n",
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              "      <th>21</th>\n",
              "      <td>942_42</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>32.0</td>\n",
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              "      <th>22</th>\n",
              "      <td>942_48</td>\n",
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              "      <td>35.0</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>942_54</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>942_60</td>\n",
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              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>942_72</td>\n",
              "      <td>942</td>\n",
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              "      <td>2.0</td>\n",
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              "      <th>26</th>\n",
              "      <td>942_84</td>\n",
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              "      <th>27</th>\n",
              "      <td>942_96</td>\n",
              "      <td>942</td>\n",
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              "      <td>Off</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>39.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "trainclincal = pd.read_csv('/content/train_clinical_data.csv')[\n",
        "    ['visit_id', 'patient_id', 'visit_month', 'upd23b_clinical_state_on_medication', 'updrs_1', 'updrs_2','updrs_3', 'updrs_4']]\n",
        "\n",
        "trainclincal[trainclincal.patient_id == 942]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTFWaTfazB3Z"
      },
      "source": [
        "**What are we looking at, and what do we have?**\n",
        "\n",
        "\n",
        "*   Basically, we have patients ( patient_id ) who periodically visits ( visit_id ) a doctor and undergoes medical examination.\n",
        "\n",
        "*   For some visits (visit_id) from train_clinical_data.csv we have additional medical test data in train_proteins.csv (some measurements of proteins) and in train_peptides.csv (some measurements of peptitedes). train_peptides.csv could be joint with train_proteins.csv using key=('visit_id', 'UniProt').\n",
        "\n",
        "* supplemental_clinical_data.csv doesn't have additional data in train_proteins.csv and train_peptides.csv (patiend_ids without this information).\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_IPzbCsS1A13"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import SGD\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from keras.optimizers import Adam\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from keras import regularizers\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew\n",
        "import warnings\n",
        "from pathlib import Path\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.impute import KNNImputer\n",
        "from keras.layers import Input, Dense, concatenate\n",
        "from keras.models import Model\n",
        "import tensorflow.keras.backend as K\n",
        "import tensorflow as tf\n",
        "from keras.losses import mean_absolute_percentage_error\n",
        "import tensorflow as tf\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yvji3Z6pj3tp"
      },
      "outputs": [],
      "source": [
        "train_clinical_df = pd.read_csv('/content/train_clinical_data.csv')\n",
        "train_peptide_df = pd.read_csv('/content/train_peptides.csv')\n",
        "train_protein_df = pd.read_csv('/content/train_proteins.csv')\n",
        "supp_df = pd.read_csv('/content/supplemental_clinical_data.csv')\n",
        "supp_df.dropna(subset=['updrs_1', 'updrs_2', 'updrs_3', 'updrs_4','upd23b_clinical_state_on_medication'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvveP98Bm-2r",
        "outputId": "e5ecf42c-e508-4531-dcaf-16fa3f949075"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train_Clinical_df: \n",
            "(Rows, Columns): (2615, 8) \n",
            "Number of NAN in updrs_1, updrs_2, updrs_3, updrs_4,medication: \n",
            " 1 2 25 1038 1327\n",
            "\n",
            "Supplementary_Clinical_df: \n",
            "(Rows, Columns): (817, 8) \n",
            "Number of NAN in updrs_1, updrs_2, updrs_3, updrs_4,medication: \n",
            " 0 0 0 0 0\n"
          ]
        }
      ],
      "source": [
        "clinical_df = train_clinical_df.copy()\n",
        "nan_updrs_1 = clinical_df['updrs_1'].isna().sum()\n",
        "nan_updrs_2 = clinical_df['updrs_2'].isna().sum()\n",
        "nan_updrs_3 = clinical_df['updrs_3'].isna().sum()\n",
        "nan_updrs_4 = clinical_df['updrs_4'].isna().sum()\n",
        "nan_medical = clinical_df['upd23b_clinical_state_on_medication'].isna().sum()\n",
        "print(\"Train_Clinical_df: \\n(Rows, Columns):\", clinical_df.shape,\"\\nNumber of NAN in updrs_1, updrs_2, updrs_3, updrs_4,medication: \\n\", nan_updrs_1,nan_updrs_2, nan_updrs_3, nan_updrs_4, nan_medical)\n",
        "clinical_df = supp_df.copy()\n",
        "nan_updrs_1 = clinical_df['updrs_1'].isna().sum()\n",
        "nan_updrs_2 = clinical_df['updrs_2'].isna().sum()\n",
        "nan_updrs_3 = clinical_df['updrs_3'].isna().sum()\n",
        "nan_updrs_4 = clinical_df['updrs_4'].isna().sum()\n",
        "nan_medical = clinical_df['upd23b_clinical_state_on_medication'].isna().sum()\n",
        "print(\"\\nSupplementary_Clinical_df: \\n(Rows, Columns):\", clinical_df.shape,\"\\nNumber of NAN in updrs_1, updrs_2, updrs_3, updrs_4,medication: \\n\", nan_updrs_1,nan_updrs_2, nan_updrs_3, nan_updrs_4, nan_medical)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UZMzle2dsrmK",
        "outputId": "c46c0ab0-e4bd-4aa9-fcf7-242c9a810cc9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      visit_id  patient_id  visit_month  updrs_1  updrs_2  updrs_3  updrs_4  \\\n",
              "0         55_0          55            0     10.0      6.0     15.0      NaN   \n",
              "1         55_3          55            3     10.0      7.0     25.0      NaN   \n",
              "2         55_6          55            6      8.0     10.0     34.0      NaN   \n",
              "3         55_9          55            9      8.0      9.0     30.0      0.0   \n",
              "4        55_12          55           12     10.0     10.0     41.0      0.0   \n",
              "...        ...         ...          ...      ...      ...      ...      ...   \n",
              "2610  65043_48       65043           48      7.0      6.0     13.0      0.0   \n",
              "2611  65043_54       65043           54      4.0      8.0     11.0      1.0   \n",
              "2612  65043_60       65043           60      6.0      6.0     16.0      1.0   \n",
              "2613  65043_72       65043           72      3.0      9.0     14.0      1.0   \n",
              "2614  65043_84       65043           84      7.0      9.0     20.0      3.0   \n",
              "\n",
              "     upd23b_clinical_state_on_medication  \n",
              "0                                    NaN  \n",
              "1                                    NaN  \n",
              "2                                    NaN  \n",
              "3                                     On  \n",
              "4                                     On  \n",
              "...                                  ...  \n",
              "2610                                 Off  \n",
              "2611                                 Off  \n",
              "2612                                 Off  \n",
              "2613                                 Off  \n",
              "2614                                 Off  \n",
              "\n",
              "[2615 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th>visit_id</th>\n",
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              "      <td>55_9</td>\n",
              "      <td>55</td>\n",
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              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55_12</td>\n",
              "      <td>55</td>\n",
              "      <td>12</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>On</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>2610</th>\n",
              "      <td>65043_48</td>\n",
              "      <td>65043</td>\n",
              "      <td>48</td>\n",
              "      <td>7.0</td>\n",
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              "    <tr>\n",
              "      <th>2611</th>\n",
              "      <td>65043_54</td>\n",
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              "    <tr>\n",
              "      <th>2612</th>\n",
              "      <td>65043_60</td>\n",
              "      <td>65043</td>\n",
              "      <td>60</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Off</td>\n",
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              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>65043_72</td>\n",
              "      <td>65043</td>\n",
              "      <td>72</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Off</td>\n",
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              "    <tr>\n",
              "      <th>2614</th>\n",
              "      <td>65043_84</td>\n",
              "      <td>65043</td>\n",
              "      <td>84</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Off</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2615 rows × 8 columns</p>\n",
              "</div>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "train_clinical_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "awNqEK93sxRt",
        "outputId": "c3f8cbc2-e2db-4bbb-bca4-2e028323c2e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      visit_id  patient_id  visit_month  updrs_1  updrs_2  updrs_3  updrs_4  \\\n",
              "3        75_36          75           36      1.0      8.0     38.0      0.0   \n",
              "5        337_0         337            0      5.0      7.0      6.0      0.0   \n",
              "6       337_36         337           36      8.0      7.0      8.0      0.0   \n",
              "8       527_36         527           36      2.0     18.0     22.0      0.0   \n",
              "10      557_36         557           36      6.0      9.0     17.0      0.0   \n",
              "...        ...         ...          ...      ...      ...      ...      ...   \n",
              "2208  65006_36       65006           36     26.0     20.0     31.0      6.0   \n",
              "2213  65290_12       65290           12      4.0     14.0     13.0      0.0   \n",
              "2214  65290_24       65290           24      2.0     13.0     14.0      2.0   \n",
              "2215  65290_30       65290           30      4.0     16.0     13.0      0.0   \n",
              "2222  65530_36       65530           36      8.0      4.0     15.0      4.0   \n",
              "\n",
              "     upd23b_clinical_state_on_medication  \n",
              "3                                     On  \n",
              "5                                     On  \n",
              "6                                     On  \n",
              "8                                     On  \n",
              "10                                    On  \n",
              "...                                  ...  \n",
              "2208                                  On  \n",
              "2213                                  On  \n",
              "2214                                  On  \n",
              "2215                                  On  \n",
              "2222                                  On  \n",
              "\n",
              "[817 rows x 8 columns]"
            ],
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              "<p>817 rows × 8 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "supp_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39UnhPq-nbDX"
      },
      "source": [
        "**Data Cleaning**\n",
        "\n",
        "\n",
        "*   remove rows with missing values in \"supp_df\" in the specified columns ( the updrs_{i} columns ), just to keep accurate records\n",
        "*   Analyze the trends of the **visit_month** with the average of the 4 updr scores\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "qm4SLoVNrs9J",
        "outputId": "af1a283f-3cd1-4450-ee78-03745f6c4c9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 4 Axes>"
            ],
            "image/png": 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hlUpx6dIlbN26FR9//DGmTZsGX19fPP/881i5ciXuvPNOTJw4EX///Td+++23Vtf2iEQirF27FpMnT0a/fv3w8MMPIzAwEBcuXEBaWhp2794NNzc3jBw5Eu+++y5UKhWCg4Pxxx9/NHkHZuDAgQCAJUuW4F//+hekUikmT57c5Gzpyy+/LGyJsGDBAnh5eeGbb77B1atX8eOPP5q85UZD7bUWC9CmZ7733nt49tln0adPH8yePVv4eX755ZfQaDT49ddfW10/055WrVqFO+64A/Hx8ZgzZ46wZZi7u7tBewuaypw/q9dff93gO5QN8Xcr+T0iOzr67KHPntZQPNkHimWKZWvgt6basGFDo/2UiXlRjHesGOfXdp8+fVrvvQ4dOhSenp6YNWsWFixYAI7j8O233xq8La9YLMaSJUvw8MMPN/l8QkICDhw4YPI2vyZrx0rpAt1tAFrS3PYsX3zxBRs4cCCTyWRMLpez2NhY9uKLL7KcnBzhGLVazZYtW8YCAwOZTCZjCQkJ7Ny5c41K7zfcBoB3+PBhNm7cOCaXy5mLiwvr06cP++STT4Tnb968yaZOnco8PDyYu7s7mz59OsvJyWEA2Ouvv653rjfffJMFBwczkUikV1q/YVsYYywzM5NNmzaNeXh4MCcnJzZkyBC2a9cuvWN0S+/runr1qs1udXHw4EE2ZcoU5uPjw6RSKQsLC2OPPfYYy8rKanSs7rY9LYEFtwHYu3cvGzZsGJPJZMzNzY1NnjyZnT9/3qR2GstcP6tRo0YxAEZvceTj48Pi4uJMbr8to8+eq8L7o88eiid7RrF8VXh/FMvW7Ud88sknDAD7/fff23Qeoo9i/Krw/uw9xptrC2P1sdpwy7AjR46wuLg4JpPJWFBQEHvxxRfZ7t27G/0emtpujDHGVCoV69KlS5MxPnDgQBYQEGCeN2cEjrH2HuYTQmzV+fPnERMTg127dmHSpEnWbg4hdo3iiZCO77777kNWVhaSkpKs3RRCSCvKy8vh5eWFjz76CPPmzWvXa9tMejkhxPr++usvxMfH0wCBEDOgeCKkY2OMYf/+/fjuu++s3RRCiAEOHjyI4OBgPPbYY+1+bZrpJoQQQgghhBBCLMQmqpcTQgghhBBCCCEdEQ26CSGEEEIIIYQQC6FBNyGEEEIIIYQQYiE06CaEEEIIIYQQQiyEBt02qK6uDi+++CJCQ0MhEolw9913W7tJZpWVlQWO47Bx40ZrN4WQdkNxTUjnQfFOiH2jGCbmRoNuIzDG4OXlhXXr1gEA/v77b3Ach6ysLLNe5+uvv8aqVaswbdo0fPPNN1i0aJFZz0/q7du3D4888gi6d+8OZ2dnREVF4dFHH0Vubm6Tx9fW1uKtt95CdHQ0nJyc4O/vj0mTJuHmzZvCMRUVFXj99dcxYcIEeHl5tfihNnv2bHAc1+grOjraEm+XNIHiuuPJzc3Fyy+/jNGjR0Mul4PjOOzfv7/JYxMSEpqMwQkTJjQ6Njk5GRMmTICbmxvkcjnGjx+P1NRUvWOqqqrw6aefYvz48QgMDIRcLkf//v2xdu1aqNVqC7xbYgyK945n48aNTcYwx3HIy8vTO3bLli148MEH0a1bN3Ach4SEhCbPmZaWhunTpyMqKgrOzs7w8fHByJEj8fPPP7fDOyItoRjueIz5m/3WW28hLi4Ovr6+cHJyQrdu3bBw4UIUFBToHXfhwgW8+OKL6NevH+RyOQIDAzFp0iScOnWq0TkjIiKa/Qzp1q2b2d4n7dNthEuXLqGkpARxcXEAgGPHjsHf3x8RERFmvc6ff/6J4OBgfPjhh2Y9L2nspZdeQnFxMaZPn45u3brhypUrWLNmDXbt2oXU1FQEBAQIx6pUKkyaNAlHjx7FY489hj59+qCkpAQnTpyAQqFASEgIAKCwsBBvvPEGwsLC0Ldv32Y/OHiOjo746quv9B5zd3c3+3slTaO47ngyMjLwzjvvoFu3boiNjcWxY8daPD4kJAQrV67UeywoKEjv+5SUFAwfPhyhoaF4/fXXodFo8Nlnn2HUqFFISkpCjx49AABXrlzB008/jbFjx+LZZ5+Fm5sbdu/ejaeeegrHjx/HN998Y943S4xC8d5xvfHGG4iMjNR7zMPDQ+/7tWvXIjk5GYMHD0ZRUVGz57p27RrKy8sxa9YsBAUFoaqqCj/++CPuuusurFu3Do8//rgl3gIxAMVwx2PM3+zk5GT069cP//rXvyCXy5Geno4vv/wSv/zyC1JTU+Hi4gIA+Oqrr7B+/Xrce++9eOqpp6BQKLBu3TrExcXh999/R2JionDOjz76CBUVFXrXuXbtGl555RWMHz/efG+UEYN9++23zNXVldXV1THGGHvwwQfZXXfdZfbrjB49msXExJj9vLbi6tWrDADbsGGDtZvCDhw4wNRqdaPHALAlS5boPf7OO+8wqVTKTpw40eI5a2pqWG5uLmOMsZMnT7b4XmfNmsVcXFxMfwOkzSiuzcOW4rqsrIwVFRUxxhjbunUrA8D++uuvJo8dNWqUQb+XiRMnMk9PT1ZYWCg8lpOTw1xdXdk999wjPFZQUMDOnTvX6PUPP/wwA8AuXbpk5Lsh5kTxbh62FO8bNmxgANjJkydbPfb69evC3/yYmBg2atQog69TV1fH+vbty3r06GFqU4kZUAybhy3FsDF/s5uybds2BoB9//33wmOnTp1i5eXlescVFhYyX19fNmzYsFbP+eabbzIA7MiRIwa3ozWUXt6KiooKFBYWorCwEIcPH0ZsbCxKSkpQWFiIY8eOoVevXigsLERJSUmr56qsrMRzzz2H0NBQODo6okePHnjvvffAGANQv77ir7/+QlpampDa0NJM6alTp3D77bfDx8cHMpkMkZGReOSRR/SOee+99zB06FB4e3tDJpNh4MCB2LZtW6NzcRyH+fPnY+vWrejVqxdkMhni4+Nx9uxZAMC6devQtWtXODk5ISEhoVEqT0JCAnr37o3k5GQMHTpUaM/nn3/e6s8G0KaCTJs2DV5eXnBycsKgQYOwc+dOvWNUKhWWLVuGbt26wcnJCd7e3hg+fDj27Nmjd8yFCxeaTRHXNXLkSIhEokaPeXl5IT09XXhMo9Hg448/xtSpUzFkyBDU1dWhqqqqyXM6OjrqzZAbQq1Wo6yszKjXENNRXHfsuJbL5fDy8jKofby6urpGd7p1HTp0CImJifD29hYeCwwMxKhRo7Br1y7htT4+PoiJiWn0+qlTpwKA3ucKaR8U7x073nWVl5e3uIyDX59rCrFYjNDQUJSWlpr0emI6iuGOHcOm/M3WxWc56MbmwIED4erqqnect7c3RowYYdDf4c2bNyMyMhJDhw41uV2NmG343kHNmjWLAWj1Kzw8vMXzaDQaNmbMGMZxHHv00UfZmjVr2OTJkxkAtnDhQsYYYxUVFezbb79l0dHRLCQkhH377bfs22+/ZXl5eU2e89atW8zT05N1796drVq1in355ZdsyZIlrGfPnnrHhYSEsKeeeoqtWbOGffDBB2zIkCEMANu1a5fecQBYnz59WGhoKHv77bfZ22+/zdzd3VlYWBhbs2YN69WrF3v//ffZK6+8whwcHNjo0aP1Xj9q1CgWFBTE/Pz82Pz589nq1avZ8OHDGQC2fv164bim7q6dO3eOubu7s169erF33nmHrVmzho0cOZJxHMe2b98uHPfvf/+bcRzHHnvsMfbll1+y999/n91///3s7bffbnT+WbNmtfg7aU55eTlzcHBgjz/+uPDY2bNnGQC2fPly9thjjzEHBwcGgMXGxrI///yz2XMZMtPNcRxzdnZmAJinpyd76qmnGt2dI+ZFcd154tqQmW6pVCrEtL+/P3vllVdYbW2t3nEODg7soYceavT66dOnMwDs2LFjLbbjiy++YADY0aNHjWo/aTuK944d7/xMt6urKwPAHBwc2OTJk9nFixdbfJ0hM90VFRWsoKCAXb58mX3wwQdMLBazBx54oNU2EfOiGO7YMazLkJlujUbDCgoKWG5uLjt48CAbOnQoE4vFLD09vdXzDx06lHXv3r3FY1JSUprMeG0rGnS3Ii0tje3Zs0dIXXj//ffZnj172Msvv8wcHR3ZH3/8wfbs2cMOHz7c4nn+97//CYM2XdOmTWMcx7HLly8Ljxma7rhjxw6DUqqqqqr0vq+trWW9e/dmY8aM0XscAHN0dGRXr14VHlu3bh0DwAICAlhZWZnw+OLFixkAvWNHjRol/Ix4SqWS9evXj/n5+Qmd2KYCfezYsSw2NpbV1NQIj2k0GjZ06FDWrVs34bG+ffuySZMmtfh+2zro5lNK9u3bJzy2fft2BoB5e3uzbt26sQ0bNrANGzawbt26MQcHB3b69Okmz9XaoPvll19mL730EtuyZQv7/vvvhT8sw4YNYyqVyqT2k9ZRXHeeuG7tD/gjjzzCli5dyn788Uf2n//8h911110MALvvvvv0jouNjWXdu3cXUhoZ0/4cwsLCGAC2bdu2ZtugVCpZr169WGRkJMW1FVC8d+x437JlC5s9ezb75ptv2I4dO9grr7zCnJ2dmY+PD7t+/XqzrzNk0P3EE08IAzqRSMSmTZvGiouLW20TMS+K4Y4dw7oMGXTn5ubq3WwJCQlhW7ZsafXcBw8eZBzHsVdffbXF45577jkGgJ0/f96otreGBt0G+umnn5hUKmUVFRWMMcbmzZvHEhISDH79448/zsRisV6wMMbYsWPHGAD2ySefCI8ZGuh//fUXA8Bef/31RrMyzSkuLmYFBQVs7ty5zMPDQ+85AGzixIl6j6WmpjIAbN68eXqP8x9cugPTUaNGMYlEIvyMeGvXrtWbCWoY6EVFRYzjOPbmm2+ygoICva9ly5YxAOzmzZvCNSIiIlq9g22qAwcOMIlE0qjD/Z///Ee4g677R/zatWtMKpWymTNnNnm+1gbdTVmxYgUD9NemEMuguO74cW3K+rDHHnus0ew1/35nzZrF0tLS2NmzZ9mMGTOYVCplANi3337b6vl++eWXtrwV0kYU7x0/3nmHDh1iHMexJ554otljDBl0p6ensz179rBvvvmGTZo0iU2dOrXZGU9ieRTDHT+GDfmbrVQq2Z49e9jPP//M3njjDdavXz+9Wfym3Lp1i4WEhLCoqKgWs0nVajULDg5m/fv3N/UtNIvWdLegqqpKWEPy+++/o1+/fqiurkZhYSH+/PNPDBo0SHi+NdeuXUNQUBDkcrne4z179hSeN9aoUaNw7733YtmyZfDx8cGUKVOwYcMGKJVKveN27dqFuLg4ODk5wcvLC76+vli7di0UCkWjc4aFhel9z1fRDg0NbfLxhutngoKChMqBvO7duwNAs9s5XL58GYwxvPrqq/D19dX7ev311wEA+fn5ALTVSUtLS9G9e3fExsbihRdewJkzZ5r9GRnjwoULmDp1Knr37t2omrhMJgMADBs2TO9nERYWhuHDh+Po0aNmaQMALFq0CCKRCHv37jXbOUk9iuvOFdemeO655wBALwaffPJJ/Pvf/8bmzZsRExOD2NhYZGZm4sUXXwSARmvHeKtWrcKXX36JN998ExMnTrR844keivfOGe/Dhw/Hbbfd1ua/o9HR0UhMTMRDDz0k1G6YPHmysP6XWB7FcOeM4ZY4ODggMTERd955J1599VV8+umnmDNnDnbt2tXk8ZWVlbjzzjtRXl6On376qdm/1wBw4MABZGdnY+bMmWZvN20Z1oJ3330Xy5Yt03vM19dX+P/09HS89957AGCVD2CO47Bt2zYcP34cP//8M3bv3o1HHnkE77//Po4fPw5XV1ccOnQId911F0aOHInPPvsMgYGBkEql2LBhAzZv3tzonGKxuMlrNfe4Od63RqMBADz//PO4/fbbmzyma9euALRFzjIzM/HTTz/hjz/+wFdffYUPP/wQn3/+OR599FGT23Djxg2MHz8e7u7u+PXXXxt9IPPbB/n7+zd6rZ+fH/7++2+Tr92QTCaDt7c3iouLzXZOUo/iuvXHO0pcm4rv2DSMwRUrVuD5559HWloa3N3dERsbi3//+98A6js0ujZu3IiXXnoJTz75JF555RXLN5w0QvHe+uMdNd5DQ0ORkZFhtvMBwLRp0/DEE0/g4sWLwjaBxLIohlt/vKPGsKGGDh2KwMBAbNq0CXfeeafec7W1tbjnnntw5swZ7N69G717927xXJs2bYJIJML9999v9nbSoLsFDz30EIYPH46qqipMmTIFq1atQr9+/XDw4EG88847+Pnnnw2ughkeHo69e/eivLxcb0B34cIF4XlTxcXFIS4uDitWrMDmzZsxc+ZM/PDDD3j00Ufx448/wsnJCbt374ajo6Pwmg0bNph8vZbk5OSgsrJS7w7bxYsXAaDZPRSjoqIAAFKpVG/fvOZ4eXnh4YcfxsMPP4yKigqMHDkSS5cuNTnQi4qKMH78eCiVSuzbtw+BgYGNjomNjYVUKkV2dnaj53JycvT+ALRVeXk5CgsLzXpOUo/i2nj2GNdtceXKFQBoMgY9PT0xfPhw4fu9e/ciJCQE0dHResf99NNPePTRR3HPPffg008/tWyDSbMo3o3XUeL9ypUrZv87Wl1dDQBNzk4Sy6AYNl5HiWFj1NTUNIpLjUaDhx56CPv27cN///tfjBo1qsVzKJVK/Pjjj0hISBAm28yJ0stbEBUVhcTERMjlcnAchzlz5iAxMRG1tbXo378/xo8fj8TERIP+cU6cOBFqtRpr1qzRe/zDDz8Ex3G44447jG5fSUlJo7tb/fr1AwAhrUUsFoPjOL0tNLKysvC///3P6OsZoq6uDuvWrRO+r62txbp16+Dr64uBAwc2+Ro/Pz8kJCRg3bp1TW4tUFBQIPx/UVGR3nOurq7o2rWrXhqPMdsUVFZWYuLEicjOzsavv/6Kbt26NXmcXC7HxIkTcfToUeHDGdDeYT169CjGjRvX6rUaqqmpQXl5eaPH33zzTTDGMGHCBKPPSVpHcW08e4trQ5WVlTVKAWSMYfny5QDQ7N1+3pYtW3Dy5EksXLhQr9N38OBB/Otf/8LIkSOFu+bEOijejWdv8a57bt6vv/6K5ORkk/+O8mm0ulQqFf7zn/9AJpOhV69eJp2XGI9i2Hj2FsOGqqysbHK73h9//BElJSUYNGiQ3uNPP/00tmzZgs8++wz33HNPq+f/9ddfUVpaapHUcoBmug1y5MgRREdHw9PTEwBw9OhRo/dtmzx5MkaPHo0lS5YgKysLffv2xR9//IGffvoJCxcuRJcuXYxu1zfffIPPPvsMU6dORZcuXVBeXo4vv/wSbm5uwtrBSZMm4YMPPsCECRPwwAMPID8/H59++im6du1qkfUXQUFBeOedd5CVlYXu3btjy5YtSE1NxRdffAGpVNrs6z799FMMHz4csbGxeOyxxxAVFYVbt27h2LFjuHnzJk6fPg0A6NWrFxISEjBw4EB4eXnh1KlT2LZtG+bPny+cKzs7Gz179sSsWbOwcePGFts7c+ZMJCUl4ZFHHkF6erre3n2urq64++67he/feust7Nu3D2PGjMGCBQsAAKtXr4aXl5eQYspbs2YNSktLkZOTAwD4+eefcfPmTQDaDwF3d3fk5eWhf//+uP/++4VZst27d+PXX3/FhAkTMGXKlFZ+2qQtKK4NZ29xDUAYOKelpQEAvv32Wxw+fBgAhFTvlJQU3H///bj//vvRtWtXVFdXY8eOHThy5Agef/xxDBgwQDjfwYMH8cYbb2D8+PHw9vbG8ePHsWHDBkyYMAHPPPOMcNy1a9dw1113geM4TJs2DVu3btVrV58+fdCnT59W20/Mi+LdcPYW70OHDkX//v0xaNAguLu7IyUlBV9//TVCQ0Mb/W0+ePAgDh48CEA7iKisrBQ+K0aOHImRI0cCAJ544gmUlZVh5MiRCA4ORl5eHjZt2oQLFy7g/fffb3FNKLEMimHD2VsMA4b9zb506RISExMxY8YMREdHQyQS4dSpU/juu+8QERGh97f4o48+wmeffYb4+Hg4Ozvju+++07ve1KlTG61737RpExwdHXHvvfe22l6TmL00Wwc0YcIENmfOHMaYtsS/TCZjW7duNfo85eXlbNGiRSwoKIhJpVLWrVs3tmrVKqbRaPSOM7RiYkpKCrv//vtZWFgYc3R0ZH5+fuzOO+9kp06d0jtu/fr1rFu3bszR0ZFFR0ezDRs2sNdff501/PWjicqIfHXDVatW6T3OV2vU/Tnw7T516hSLj49nTk5OLDw8nK1Zs6bJczas6J2ZmckeeughFhAQwKRSKQsODmZ33nmn3lY8y5cvZ0OGDGEeHh5MJpOx6OhotmLFCr2KkcZsUxAeHq637YDuV1P7PSYnJ7PExETm4uLC5HI5mzJlSpPVG1s6L7+1Q0lJCXvwwQdZ165dmbOzM3N0dGQxMTHsrbfeMrgCJjEdxXXHjWv+fTf3xbty5QqbPn06i4iIYE5OTszZ2ZkNHDiQff75541+f5cvX2bjx49nPj4+ws985cqVTKlUNvkzbO7r9ddfN6j9xLwo3jtuvC9ZsoT169ePubu7M6lUysLCwtjcuXObrDLO/8xai83vv/+eJSYmMn9/fyaRSJinpydLTExkP/30U6vtIZZBMdxxY5h/3639zS4oKGCPP/44i46OZi4uLszBwYF169aNLVy4kBUUFOidr7W93XW3WWOMMYVCwZycnNg999xjUHtNwf3zRglps4SEBBQWFuLcuXPWbgohxEworgnpPCjeCbFvFMO2ixaaEUIIIYQQQgghFkKDbkIIIYQQQgghxEJo0E0IIYQQQgghhFgIrekmhBBCCCGEEEIshGa6CSGEEEIIIYQQC6FBNyGEEEIIIYQQYiESazfA0jQaDXJyciCXy8FxnLWbQ4hZMcZQXl6OoKAgiESd5x4axTXp6DpjbFNck46uM8Y1QLFNOjZD47rDD7pzcnIQGhpq7WYQYlE3btxASEiItZvRbiiuSWfRmWKb4pp0Fp0prgGKbdI5tBbXHX7QLZfLAWh/EG5ublZuDSHmVVZWhtDQUOHfeWdBcU06us4Y2xTXpKPrjHENUGyTjs3QuO7wg24+jcXNzY0CnXRYnS1di+KadBadKbYprkln0ZniGqDYJp1Da3HdeRaUEEIIIYQQQggh7YwG3YQQQgghhBBCiIXQoJsQQgghhBBCCLGQDr+mmxDSeanVaqhUKms3gxjIwcGhU22jQ0xDcW1fpFIpxGKxtZtBCCFWRYNuQkiHwxhDXl4eSktLrd0UYgSRSITIyEg4ODhYuynEBlFc2y8PDw8EBAR0ugJihBDCo0E3IaTD4Tvmfn5+cHZ2po6eHdBoNMjJyUFubi7CwsLod0Yaobi2P4wxVFVVIT8/HwAQGBho5RYRQoh10KCbENKhqNVqoWPu7e1t7eYQI/j6+iInJwd1dXWQSqXWbg6xIRTX9ksmkwEA8vPz4efnR6nmhJBOiRbPEUI6FH6tp7Ozs5VbQozFp5Wr1Wort4TYGopr+8b/3mgtPiGks6JBNyGkQ6LUU/tDvzPSGvo3Yp/o90YI6exo0E0IIYQQQgghhFgIDboJIYQQQgghhBALoUE3IYTYiISEBCxcuLDR4xs3boSHhwcAYOnSpeA4DhzHQSKRICIiAosWLUJFRQUAICsrS3ie4zjI5XLExMRg3rx5uHTpUqPz8seJRCIEBgZixowZuH79ut5xV69exQMPPICgoCA4OTkhJCQEU6ZMwYULF1p8PwsWLMDAgQPh6OiIfv36mfxzIcSeUVwTQgihQTchhNiZmJgY5ObmIisrC++88w6++OILPPfcc3rH7N27F7m5uTh9+jTeeustpKeno2/fvti3b5/ecW5ubsjNzUV2djZ+/PFHZGRkYPr06cLzKpUK48aNg0KhwPbt25GRkYEtW7YgNjbWoP2SH3nkEcyYMcMs75uQjozimhBCOi7aMowQQuyMRCJBQEAAAGDGjBnYt28fdu7ciXXr1gnHeHt7C8dERUVh8uTJGDt2LObMmYPMzExh2x6O44TjAgMDMWfOHCxYsABlZWVwc3NDWloaMjMzsW/fPoSHhwMAwsPDMWzYsFbbuXr1agBAQUEBzpw5Y74fACEdEMU1IYR0XDTTTQjp8BhjqKqta/cvxli7vD+ZTIba2toWjxGJRHjmmWdw7do1JCcnN3lMfn4+duzYAbFYLHTefX19IRKJsG3bNtrKi9gUa8V1e8U2xTUhhHQcNNNNCOnwqlVq9Hptd7tf9/wbt8PZwbIfs8nJydi8eTPGjBnT6rHR0dEAtOtDhwwZAgBQKBRwdXXVDmCqqgBo12y6uLgAAIKDg7F69Wq8+OKLWLZsGQYNGoTRo0dj5syZiIqKstC7IqR11oprwPKxTXFNCCEdC810E0KInTl79ixcXV0hk8kwZMgQxMfHY82aNa2+jp+d090zVy6XIzU1FadOncL777+PAQMGYMWKFXqvmzdvHvLy8rBp0ybEx8dj69atiImJwZ49ewAAd9xxB1xdXeHq6oqYmBgzvlNCOg+Ka0II6bhoppsQ0uHJpGKcf+N2q1zXGG5ublAoFI0eLy0thbu7u/B9jx49sHPnTkgkEgQFBcHBwcGg86enpwMAIiMjhcdEIhG6du0KAOjZsycyMzMxd+5cfPvtt3qvlcvlmDx5MiZPnozly5fj9ttvx/LlyzFu3Dh89dVXqK6uBgBIpVKj3nNnVFFbAVcHV2s3w+5ZK675axuK4rpzqKmrQXVdNTydPK3dFEKIDaJBN7FbjDG8cuQVqDQqvD3ibYg4StwgTeM4zuJp3ubQo0cP/PHHH40eT0lJQffu3YXvHRwchA61oTQaDVavXo3IyEj079+/2eNefvlldOnSBYsWLcKAAQOaPIbjOERHR+Po0aMAtKmqxHBz/piDMmUZ3h75Nvr69rV2c+wWxTXFtS35z/n/YOO5jXhh8AuY2m2qtZtDSLtQKBWYv28+EsMTMStmlrWbY9NolELsVl5lHnZm7sRvV39DenG6tZtDSJvNnTsXFy9exIIFC3DmzBlkZGTggw8+wPfff99o66DWFBUVIS8vD1euXMHOnTuRmJiIpKQkrF+/Xiim1JTQ0FBMnToVr732GgAgNTUVU6ZMwbZt23D+/HlcvnwZ69evx9dff40pU6a02IbLly8jNTUVeXl5qK6uRmpqKlJTU1stDtWRldSUIL0oHTcrbiLIJcjazSHtgOK648uvysdXZ79CuaocDmLDMhQI6QgO3DyA1IJUrDu9DnWaOms3x6bZ/i1iYlf2XtuL4ppi3NfjPotf63LpZeH/D908hBhvWnNmi95++20sXrwYzzzzDD766CMAQE1NDZ577jn88MMPUCqVuP322/HZZ5/B39/fuo21sqioKBw8eBBLlixBYmIiamtrER0dja1bt2LChAlGnSsxMREA4OzsjPDwcIwePRpffPGFQTNpixYtQnx8PJKSkhAVFYWIiAgsW7YMWVlZ4DhO+H7RokUtnufRRx/FgQMHhO/5mbirV68iIiLCqPfTUZzIPQEGhq4eXeHr7Gvt5pB2QHHd8a1OWY3qumr08e2DiZETrd0c0okxxrAhbQN6efdCXGCcxa/H98XLVeU4XXAaA/0HWvya9opj7bWnjZWUlZXB3d0dCoUCbm5u1m5Oh8YYQ9zmOFTVVWHPtD0IcAmw6PU2ntuI95PfBwD08e2DTRM3WfR6tsjW/32fPHkS9913H9zc3DB69Ghh0D137lz88ssv2LhxI9zd3TF//nyIRCIcOXLEoPO29L5rampw9epVREZGwsnJydxviVhQZ/jdvX70dWy/tB0P9XoILwx+odnjbD22LYHiuuPqyL+/tKI03L/rfjAwfDfxuxaXjHTGuAY67/u2hrTCNPzrl38hTB6GX+75xeLXm7dvHg7ePAgAmNN7DhYOXGjxa9oaQ/99U3o5MZtKVSWq6rRbkxRUFVj8eroz3WcLzqKkpsTi1ySGq6iowMyZM/Hll1/C07O+sIxCocD69evxwQcfYMyYMRg4cCA2bNiAo0eP4vjx41ZsMSGWxRjD0Rztetn4oHgrt4YQ0laMMbyb9C4YGCZFTaIaDcTq8qvyhf+2x7xqZmmm8P+Hsg9Z/Hr2jAbdxGx0B73FNcUWvx4f6Bw4MDAcyTFslpS0j3nz5mHSpElCOiQvOTkZKpVK7/Ho6GiEhYXh2LFj7d1MQtpNVlkW8irzIBVJKQWPkA5gz7U9SMlPgZPYCQsHLLR2cwhBiVLbF69Ra6vpW1KVqgrZFdkAtH3xiyUXkVeZZ9Fr2jMadBOzKVbWD7QtPejWMA0yFdpB9+jQ0QC067qJbfjhhx+QkpKClStXNnouLy8PDg4O8PDw0Hvc398feXlNf1grlUqUlZXpfRFib/hZ7gF+AyCTyKzcGkJIWyjVSnyQ/AEA4OHeD1t8SR0hhtDtfxfVFFn0WlcUVwAAXk5e6OPbBwBwOPuwRa9pz2jQTcymtKZU+H9LB3puZS6q66ohEUkws+dMAMCRnCNQa9QWvS5p3Y0bN/DMM89g06ZNZlu7t3LlSri7uwtfoaGhZjkvIe3peI52+QSllhNi/749/y2yK7LhJ/PD7JjZ1m4OIQD0++KWngDjl3l29eiKEcEjANAEWEto0E3MRje4LR3ofGp5hFsEBvgPgNxBDoVSgbOFZy16XdK65ORk5OfnY8CAAZBIJJBIJDhw4ABWr14NiUQCf39/1NbWorS0VO91t27dQkBA0zMFixcvhkKhEL5u3LjRDu+EEPNRaVRIyksCQINuQuxdYXUhvjzzJQBg4cCFcJY6W7lFhGjx6eUAUFzdPn3xLh5dMCJEO+g+nnscKrXKote1VzToJmZTqiwV/r+9Bt1dPbpCIpJgaNBQAJTWYgvGjh2Ls2fPCnu3pqamYtCgQZg5c6bw/1KpFPv27RNek5GRgevXryM+vunBiKOjI9zc3PS+CLEnZwrOoKquCp6Onoj2irZ2cwghbfDJ35+gqq4Kvb17Y1LUJGs3hxBBe9ZX0u2LR3tFw9vJG1V1VUjJT7Hode0VDbqJ2egGuqUrifMpLV08ugBAfVoLVU60Orlcjt69e+t9ubi4wNvbG71794a7uzvmzJmDZ599Fn/99ReSk5Px8MMPIz4+HnFxlt9TkhBr4NdzxwXGQcTRn15C7NWF4gvYcWkHAOClIS9RPBObotcXV1q2L6470y3iRBgePBwApZg3hz4piNnopbS04901ABgWPAwAcL7oPAqrCy16bdJ2H374Ie68807ce++9GDlyJAICArB9+3ZrN4sQi6H13ITYP8YY3kl6BwwMd0TcgX5+/azdJEL06PbFi6otV1+pSlWFnMocAPV9cT7FnCbAmiaxdgNIx6GX0mLBdSQaphEqJvIz3T4yH8R4xyCtKA2Hsw/j7q53W+z6xHj79+/X+97JyQmffvopPv30U+s0iJB2pFAqcK7oHAAadBNiz/68/idO3ToFR7EjFg5caO3mENJIe6WX85NfPjIfuDu6A9D+fRNzYlxRXMHN8psIkYdY7Pr2iGa6idk0DHTGmEWuk1ORg+q6akhFUoTK66tYC3fYKK2FEGJDkvKSoGEaRLlH0bZChNipWnUt3jv1HgBgVswsBLkGWblFhOhTqpWoqqsSvrfkoFtY5uneRXjMzcFNyP6gGkuN0aCbmI1uSksdq0NZrWX2UhYql7tHQCKqT9bg13UfyzkGlYYqJxL7k5CQgIULFzZ6fOPGjcK+5kuXLgXHceA4DhKJBBEREVi0aBEqKioAAFlZWcLzHMdBLpcjJiYG8+bNw6VLlxqdlz9OJBIhMDAQM2bMwPXr1/WOu3r1Kh544AEEBQXByckJISEhmDJlCi5cuNDsezl9+jTuv/9+hIaGQiaToWfPnvj444/b9gOyU/x6bprl7pworjuGTembcLPiJnxlvpjTe461m0NIIw3rKbXHTDefccqjGkvNo0E3MZv2CnZhX0D3rnqPx3jHwNPRE+WqcpzOP22RaxNiC2JiYpCbm4usrCy88847+OKLL/Dcc8/pHbN3717k5ubi9OnTeOutt5Ceno6+ffvqVY0HADc3N+Tm5iI7Oxs//vgjMjIyMH36dOF5lUqFcePGQaFQYPv27cjIyMCWLVsQGxvbaNs3XcnJyfDz88N3332HtLQ0LFmyBIsXL8aaNWvM+rOwdYwxHMs5BgDCLguENIXi2nYVVhdi3Zl1AIAFAxbQFmHEJrXnoPuyQr+gMY/POk3KTUJNXY3Frm+PaE03MQuVWoUKlfaOvIejB0qVpSiuKUake6TZr9Xc3TWxSIxhwcOw68ouHMo+hEEBg8x+bUJsgUQiEfY0nzFjBvbt24edO3di3bp1wjHe3t7CMVFRUZg8eTLGjh2LOXPmIDMzE2KxGADAcZxwXGBgIObMmYMFCxagrKwMbm5uSEtLQ2ZmJvbt24fw8HAAQHh4OIYNG9ZiGx955BG976OionDs2DFs374d8+fPN88Pwg7cKL+B7IpsSEQSDPKnzyTSPIpr2/Vp6qeoVFWip1dP3NXlLms3h5Am8RmnfD+8pKYEGqaxSIX9hgWNed08usHf2R+3qm7h1K1TQkVzQjPdxEz4QBdzYoS7af+AW3ymu0GgA/VpLbSWhOhhDKitbP8vC9U1aEgmk6G2trbFY0QiEZ555hlcu3YNycnJTR6Tn5+PHTt2QCwWC513X19fiEQibNu2DWq1uk3tVCgU8PLyatM57A0/y93Ptx/NjpmbteK6nWKb4to2ZBRnYPsl7e4atEUYsWX8THeUexQAQM3UKFOaf6lnRW0F8irzADSeAOM4Tpjtpr64PprpJmbBB7q7ozt8ZD4ALFPBXMM0uKq4CqBxoAPa9E0RJ8LFkovIq8yjokVES1UFvGWFojf/zgEcXCx6ieTkZGzevBljxoxp9djo6GgA2vWhQ4YMAaDtMLu6uoIxhqoqbQGWBQsWwMVF2+7g4GCsXr0aL774IpYtW4ZBgwZh9OjRmDlzJqKiogxu59GjR7Flyxb88ssvxr5Fu0bruS3IWnENWDy2Ka5tA2MMq06tgoZpMD58PAb6D7R2kwhpVqmyFADg6+wLNwc3lNWWobimGB5OHma9TqZCO8vtK/MVKpfrGh48HNsubsOhm4fw8pCXzXpte0a364hZ8DPdXk5e8HLS3vG2xEx3dnk2atQ1cBA56FUu53k4eSDWJxZAx7jDplJTQTjS2NmzZ+Hq6gqZTIYhQ4YgPj7eoDWV/I4CHMcJj8nlcqSmpuLUqVN4//33MWDAAKxYsULvdfPmzUNeXh42bdqE+Ph4bN26FTExMdizZw8A4I477oCrqytcXV0RExPT6Lrnzp3DlClT8Prrr2P8+PFteet2pU5Th6S8JAC0npu0juLa9uy/sR8nck/AQeSARQMXWbs5hLSI73d7OnoKffGiGvPv1d3cMk9eXGAcJCIJrpdfx7Wya2a/fntSa9RQa9qWDcSjmW5iFvxMt4ejh0UDnU8tj3SPhFgkbvKYEcEjcLrgNA7dPIRp3aeZvQ3tpVJVibFbx6K3T2+sHr2aUlPbQuqsnZmyxnWN4ObmBoVC0ejx0tJSuLvX303u0aMHdu7cCYlEgqCgIDg4OBh0/vT0dABAZGR9rQWRSISuXbVLNXr27InMzEzMnTsX3377rd5r5XI5Jk+ejMmTJ2P58uW4/fbbsXz5cowbNw5fffUVqqurtW9ZKtV73fnz5zF27Fg8/vjjeOWVVwxqZ0dxrvAcKlQVcHNwQ0+vntZuTsdjrbjmr20gimv7pFKrhC3CHop5iPYcJjaP74t7OmkH3VllWRaZAGtpmScAuEhdMNB/IE7knsChm4cQ3ivc7G1oLydyT+CFgy/gzqg7sfi2xW06l1VnuleuXInBgwdDLpfDz88Pd999NzIyMvSOSUhI0Nsmg+M4PPnkk1ZqMWlOw0AHLDPTzae0NHd3DaivnHg89zhq1S2vh7NlSblJqFRVIrs8mwbcbcVx2lTQ9v7SmXkyRI8ePZCSktLo8ZSUFHTv3l343sHBAV27dkVERITBHXONRoPVq1cjMjIS/fv3b/a4l19+GVu2bGmyHTyO4xAdHY3KykoA2lTVrl27omvXrkJRJgBIS0vD6NGjMWvWrEazbJ0Bv547LjCu2ZuEpA2sFddGxjbFtX3afGEzrpdfh7eTNx6NfdTazbEI6od3LHx6uaeTJ7xl3gAs1BdvZaYb6Dhbhx3OOYyy2jJU11W3+VxWHXQfOHAA8+bNw/Hjx7Fnzx6oVCqMHz9e+MDnPfbYY8jNzRW+3n33XSu1mDSHTy/XTWmxxt01AIj2ioaPzAdVdVVIyW++g2HrjuQcAQAMC265mizpOObOnYuLFy9iwYIFOHPmDDIyMvDBBx/g+++/b7R1UGuKioqQl5eHK1euYOfOnUhMTERSUhLWr18vFFNqSmhoKKZOnYrXXnsNAJCamoopU6Zg27ZtOH/+PC5fvoz169fj66+/xpQpU5o9z7lz5zB69GiMHz8ezz77LPLy8pCXl4eCggKj3oc9o/XcBKC4tkfFNcVYd7p+izAXqWVrc1gL9cM7Ft30ck9HT73HzMmQvjg/AXYy7ySqVFVmb0N7OZqt/Ttujr64VdPLf//9d73vN27cCD8/PyQnJ2PkyJHC487OzsLWF8Q2tdtMtwF310ScCMODh+N/l/+HQzcPIS4wzuztsDTGmLAmnbZb6DyioqJw8OBBLFmyBImJiaitrUV0dDS2bt2KCRMmGHWuxMREANrPz/DwcIwePRpffPGFkHLakkWLFiE+Ph5JSUmIiopCREQEli1bhqysLHAcJ3y/aFHzaxy3bduGgoICfPfdd/juu++Ex8PDw5GVlWXUe7FH5bXlOFt4FgANujs7imv781nqZyhXlSPaKxpTujR/E8LeUT+8YymtKQXwT19c9k9f3MxFjctqy5BflQ8AiPJovuhipFskgl2DkV2RjaS8JCSEJpi1He0htyIXmYpMiDiRWcYSNrWmm1/z1HDriU2bNuG7775DQEAAJk+ejFdffRXOzk2n2yqVSiiVSuH7sjLzl8onjbXHoFutUQuVy1u6uwZo01r+d/l/OJR9CC8MfsGs7WgP18uvC3v7DgkYYu3mkHY0ePBg/PHHH80+v3TpUixdurTZ5yMiIoTCSq2ZPXs2Zs+e3ejxuLg4vXN8/PHHBp1PV2vt7OiS8pKgZmqEu4Uj2DXY2s0hVkZxbT8ulVzC1otbAQAvDn6xUy0NMUc/HKC+uLUIWacW7ItfKb0CAPBz9oObg1uzx3EchxHBI/BDxg84dPOQXQ66+YzTWJ/YJqu0G8tmBt0ajQYLFy7EsGHD0Lt3b+HxBx54AOHh4QgKCsKZM2fw0ksvISMjA9u3b2/yPCtXrsSyZcvaq9nkH8I6Esf6u2sKpQJ1mjpIROb5Z5ZdkQ2lWglHsWOrndj4oHiIOTGuKq7iZvlNuyuAciRbG+gD/AbQem5C7BC/njs+kGa5CbEXjDGsOqndIiwxLBGDAwZbu0ntxlz9cID64tagYRr9vriFBt18xmlrk1+ANsX8h4wfcDj7MBhjejss2AN+iZi5lnnazKB73rx5OHfuHA4f1t/m6fHHHxf+PzY2FoGBgRg7diwyMzPRpUvjFOPFixfj2WefFb4vKytDaGjjraWIeQnrSJw84e7gDhEnEj4A+H2724pfQxLlHtXqnWe5gxz9/Poh+VYyDmcfxr+i/2WWNrQX/u4abTNEiH0SBt2UWk6I3TiUfQjHco9BKpLi2YHPtv6CDsRc/XCA+uLWUKYsg4ZpAGi3z7XUoJvvi7e0zJM3OGAwHEQOyKnMwRXFFYNeYyvqNHU4nnMcADAsyDyDbpvYp3v+/PnYtWsX/vrrL4SEtDwjedtttwEALl++3OTzjo6OcHNz0/silqebXi4WieHh6AEAKKo237Zhhqzn1mWvlRNr1bU4mXcSAK3nJsQe3Sy/ievl1yHmxLQ8hBA7odKosOrkKgDAg70eRKhb5xkkmrMfDlBf3BqKldrBtVwqh1QkhbeTZaqXGzPTLZPIMDhQmy1y6KZ99cXPFp5Fuaoc7o7uiPGOMcs5rTroZoxh/vz52LFjB/7880+9PSabk5qaCgAIDAy0cOuIoRhjeiktACxyh82Yu2tAfeXEpNwk1NTVmK0dlpaSn4Lqumr4yHzQ3bN76y8ghNiUY7naWe4+vn3g6uBq5dYQQgyx5cIWZJVlwcvJC4/HPt76CzoA6od3HLpF1ID6fnhZbRlUapXZrtNZJsD4YsZDA4eara6DVQfd8+bNw3fffYfNmzdDLpcLW09UV2v3QsvMzMSbb76J5ORkZGVlYefOnXjooYcwcuRI9OnTx5pNJzrKasugZmoA9cFuiTtsQqC7Gxbo3Ty6wd/ZHzXqGpy6dcps7bA0fj330KChdrf+hRBCqeWE2JvSmlJ8dvozAMD8/vM7zc0y6od3HHzGqYeTBwDAzdENYk47WOQLrLVVWW0Z8qv/qVzu3nzlcl38oDvlVgoqaivM0o72IPTFg823zNOqg+61a9dCoVAgISEBgYGBwteWLVsAAA4ODti7dy/Gjx+P6OhoPPfcc7j33nvx888/W7PZpAF+lttF6gIHsQMA8890G1O5nMdxnDDbbU9pLfx6bkotJ8T+qDVqnMg9AYCKqBFiL9aeXovy2nJ09+yOe7reY+3mtBvqh3cc/MDay1Hb/xZxImEizFx9cX7yy9/ZH3IHuUGvCXMLQ4RbBOpYHY7nHjdLOyytuKYY54vOAzDfem7AyoXUWtv+IjQ0FAcOHGin1hBTCeu5/0ktB1C/P6CZAv1G+Q3UamrhJHZCsNzw7XdGBI/AtovbcPDmQbw85GWbnzm+VXkLl0ougQNHHXZC7ND5ovMoqy2DXCpHb5/erb+AEGJVV0qvYEuGdpDZ2bYIo354x6FbW4nn5eSFwupCs+3VzS/zNHTyizc8eDiyyrJwKPsQEsMTzdIWSzqWcwwMDD08e8DX2dds57WJQmrEvjUX6ID5765FukdCxBn+zzYuMA4SkQQ3K27iWtk1s7TFkvjtCXr79BZShAgh9oOP4SGBQ8y2XSIhxHJWnVoFNVNjdOho3BZ4m7WbQ4hJ+Jlu3b4j3xcvqjFPUWNj13Pz+KzTwzcPt3qjxxZYIrUcoEE3MQM+0JscdFv57pqz1BmD/AcBqC+KYMv41HJz7QlICGlffBE12u6PENuXmp+Kw9mHIRFJ8Nyg56zdHEJMxk+A8enlgPknwEztiw/yHwSZRIb86nxcLLlolrZYioZphJvnw4PMu8yTBt2kzfhg5rcJAyw3023KHn/2UjmxTlMnFGAy5xoSQkj7qFRV4nT+aQC0npsQe7Dv+j4AwISICQh3C7dyawgxXcNCaoDt9MUdxA5CFomt98UzijNQVFMEmUSG/n79zXpuGnSTNuO3KeCDW/f/zZXScllh2t01ABgeor1TdTLvJKpUVWZpjyWcKzynXQvqQGtBO6uEhAQsXLiw0eMbN26Eh4cHAGDp0qXgOA4cx0EikSAiIgKLFi1CRYW2KmhWVpbwPMdxkMvliImJwbx583Dp0qVG5+WPE4lECAwMxIwZM3D9+nW9465evYoHHngAQUFBcHJyQkhICKZMmYILFy40+16KioowYcIEBAUFwdHREaGhoZg/fz7Kysra9kOyYafyTqGO1SHENaRT7fFLWkZxbbsO3jwIABgVOsrKLSGkbYRCak30xc0x6FYoFSisLgTQxgkwGy9szGec3hZwG6RiqVnPTYNu0mbCOhILzXTXaeqQpcgCYFqgR7pFItg1GCqNCkl5SW1uj6Xw6SzxgfG0FpS0KCYmBrm5ucjKysI777yDL774As89p58auXfvXuTm5uL06dN46623kJ6ejr59+2Lfvn16x7m5uSE3NxfZ2dn48ccfkZGRgenTpwvPq1QqjBs3DgqFAtu3b0dGRga2bNmC2NhYlJaWNttGkUiEKVOmYOfOnbh48SI2btyIvXv34sknnzTrz8KWCDFMW4URE1Bct6+b5TdxRXEFYk5My0GI3RNmui3UF+dTywNdAuEidTH69fyOPKkFqVAoFW1uj6Xw67ktscyTevakzYR1JE3cXauuq0aVqgrOUmeTz3+9/DpUGhVkEhmCXIOMfj3HcRgRPAI/ZPyAQzcPISE0weS2WJIlA510LBKJBAEBAQCAGTNmYN++fdi5cyfWrVsnHOPt7S0cExUVhcmTJ2Ps2LGYM2cOMjMzIRZrK/RyHCccFxgYiDlz5mDBggUoKyuDm5sb0tLSkJmZiX379iE8XJt+GR4ejmHDWv536unpiblz5wrfh4eH46mnnsKqVavM94OwMbSem7QFxXX74tNc+/n1g5uDm5VbQ0jb8Nv3Wqq+UluWeQJAkGsQunp0xeXSyziWcwwTIie0uU3mVlFbgdT8VACWWeZJM92kzZqqXu4idYGDSLtnNz8Tbio+0KPco4yqXK5L2K87+5BNVk4srSnFuaJzAKjDbgmMMVSpqtr9q73+rclkMtTW1rZ4jEgkwjPPPINr164hOTm5yWPy8/OxY8cOiMViofPu6+sLkUiEbdu2Qa1Wm9zGnJwcbN++HaNGdcw0zrzKPFxVXIWIE2FI4BBrN6dTsFZct1dsU1xbFp9aPjJkpJVbQkjbVNdVo7quGkCDCTAzbt9rahE1XbZeYykpLwl1rA5h8jCLLBGjmW7SZk2ll3McBy+ZF/Iq81BcXYxgV8P31m6ID3RT764BwOCAwXAUOyK3MheZpZno6mn6h4YlHM89Dg3ToKtHVwS4BFi7OR1OdV01btvc/lvBnHjgRJuyPAyRnJyMzZs3Y8yYMa0eGx0dDUC7PnTIEO3AUKFQwNXVVTuAqdLWPFiwYAFcXLTpY8HBwVi9ejVefPFFLFu2DIMGDcLo0aMxc+ZMREVFtXrN+++/Hz/99BOqq6sxefJkfPXVV6a+VZvGF0Hs7dObZs3aibXiGrB8bFNcW1Z1XTVO5p0EAIwMpkE3sW98bSWpSApnSf3nkjnTy9s60w1oJ8A2pG3A4ezD0DCNyRNplmLpjFPberfELjWVXq77vblmuttyd00mkWFwwGAAtnmHjd/OjKqWE0OcPXsWrq6ukMlkGDJkCOLj47FmzZpWX8fPznEcJzwml8uRmpqKU6dO4f3338eAAQOwYsUKvdfNmzcPeXl52LRpE+Lj47F161bExMRgz549AIA77rgDrq6ucHV1RUxMjN5rP/zwQ6SkpOCnn35CZmYmnn322ba+fZvED7qpajkxFcV1+0nKTYJSrUSgS2CbBhGE2IJipXZQ7enkqfc54O3kDQCoUde0uZCwOWa6+/n1g4vUBcU1xThfdL5N7TE3xlj9tr0W6ovTTDdpE6Vaiao6bSDrblMA6FQwr25bBXNz3F0DtGkth7MP43D2YTzc++E2ncucGGNCASZaz20ZMokMJx44YZXrGsPNzQ0KReMCI6WlpXB3dxe+79GjB3bu3AmJRIKgoCA4ODgYdP709HQAQGRkpPCYSCRC167aP6I9e/ZEZmYm5s6di2+//VbvtXK5HJMnT8bkyZOxfPly3H777Vi+fDnGjRuHr776CtXV2tQ2qVS/2mdAQAACAgIQHR0NLy8vjBgxAq+++ioCAwMNarM90DANjuceB0DLQ9qTteKav7ahKK5tj25que4ghRB7JCzzdPTUe1wmkcFJ7IQadQ2Ka4pNzs4pqSkRZsuj3FvPhGmOVCTF0KCh2HNtDw5lH7KpnXqulV1DdkU2pCKpMElnbjToJm3CB7qEk0Aules9Z460FpVGhayyLABtu7sGaNNaViatRMqtFFTUVsDVwbVN5zOXiyUXUVBdAJlEhgH+A6zdnA6J4ziLp3mbQ48ePfDHH380ejwlJQXdu3cXvndwcBA61IbSaDRYvXo1IiMj0b9/83tPvvzyy+jSpQsWLVqEAQOa/vfIcRyio6Nx9Kj2ZlFwsGHLRzQaDQBAqVQa1XZbd6H4AkqUJXCRuiDWN9bazek0KK4prk3BGBMy3mg9N+kImqqtBPyz1NPJCzmVOSiuKUaIPMSk8/OTX8GuwW3+zB0RPAJ7ru3B4ZuHMbfv3NZf0E74We4B/gMs9neF0stJmwhbFDh5NLpbzKe1tGXQfaPsBuo0dXCWOCPQpW130EPloYhwi0AdqxNmpWwBH+iD/AfBUexo5dYQa5o7dy4uXryIBQsW4MyZM8jIyMAHH3yA77//vtHWQa0pKipCXl4erly5gp07dyIxMRFJSUlYv369UEypKaGhoZg6dSpee+01AEBqaiqmTJmCbdu24fz587h8+TLWr1+Pr7/+GlOmTGn2PL/++is2bNiAc+fOISsrC7/88guefPJJDBs2DBEREUa9F1vHZ6oMDhgMqci8+3oS+0dxbVsul15GbmUuHMWOFpvRIqQ9NTfTDZhnAsxcGadAfUbn2cKzZllrbi7tscyTZrpJm/DrtRveXQPME+i6RdTMkQI2PHg4ssqycCj7EBLDE9t8PnM4mk2p5UQrKioKBw8exJIlS5CYmIja2lpER0dj69atmDDBuO01EhO1/76dnZ0RHh6O0aNH44svvjBoJm3RokWIj49HUlISoqKiEBERgWXLliErKwscxwnfL1q0qNlzyGQyfPnll1i0aBGUSiVCQ0Nxzz334OWXXzbqfdiD4znam3i0nps0heLatvCp5UMChhi9BIgQW9TUdmE8c1QwN0dBY56fsx+ivaJxofgCjmQfweQuk9t8zrZSqpU4lXcKgGX74jToJm0iFFFz9Gr0nDkC3Zx31wBtivl36d/h0E3t1mHWXstVpapCcr52m5fhwcOt2hZiGwYPHtxkKipv6dKlWLp0abPPR0REGLyd0ezZszF79uxGj8fFxemd4+OPPzbofLpGjx4tpKl2ZNV11UjJTwFA67lJ8yiubQc/6Oa3EiXE3vH9bEtNgGUq2l7QWNeI4BG4UHwBh7IP2cSgO/lWMmrUNfCT+aGbRzeLXYfSy0mb8HfXGhZRA8w80+1unkH3IP9BkElkKKguQEZJhlnO2RZJeUmo09Qh2DUYYfIwazeHEGKk5FvJUGlUCHQJRLhbuLWbQwhpgUKpwOmC0wBoPTfpOISZ7hbSy9tS1FiYADNTX5y/4XUk+wjUGrVZztkW/FZhQ4OHWnQyjgbdpE2Eu2strSOptp2ZbgexA24L1O7reuim9bcO4wN9ePBwq8+6E0KMx6/nHhpk2T/WhJC2O5ZzDGqmRhf3Lgh2NaxQHCG2rrlCakDbJ8CKa4qF10a6R7ZytGFifWLh5uCGstoynC08a5ZztkV77SBEg27SJoYGuqFpcbpUahWulV0DYL6UFkCb1gLYxn7dfBE1SkslxD7x+3PHBcVZuSWEkNbobhVGSEdhyfRyc1Yu50lEEqFgGR+T1pJXmYfLpZch4kQWr8tCg27SJi0Vb+Afq2N1KKstM/rc18quoY7VwUXqggCXgDa1Uxc/6D5dcBoKZeO9U9vL9bLruFF+AxJOIsy+E0LsR35VPi6XXgYHDnEBNOgmxJapNWqhQjGt5yYdiSHp5aYOuvllnuac/ALqY5CPSWvhZ7l7+/SGu6O7Ra9Fg27SJi2llzuKHeEqddU7zhiXFfXruc2ZthnoGoiuHl2hYRphlsoa+Fnu/v794SJ1sVo7CCGm4bce7OXdq8m6FoQQ23Gu6BxKlCWQS+Xo59fP2s0hxCzUGrUwgWSJ+krmXubJGxo0FBw4pBeno6CqwKznNgY/6B8eZPlixjToJm1SWlMKoOmZbqBtwW6pQAfq77BZM8VcKNxAqeWE2CXd9dydzdtvvw2O47Bw4ULhsZqaGsybNw/e3t5wdXXFvffei1u3blmvkYTo4Ou4xAfFQyqSWrk1hJiHolYBBu0STg9Hj0bP8/3wkpoSaJjG6PNbaqbbW+aN3j69AVhvtrtOUyds+Tk02PJ/x2nQTdqkpX26ARsedAfXp7WY8iHUVrXqWiTlJQGgrcIIsUcapqnfnzuoc+3PffLkSaxbtw59+vTRe3zRokX4+eefsXXrVhw4cAA5OTm45557rNRKQvTRem7SEfG1ldwd3SERNd4Jmu+Hq5kaZUrjlnoyxizaF+f7v9aaADtXeA7lqnK4Obiht3dvi1+PBt3EZBqmaXEdCdC2CuaWursGAP38+sFF6oLimmKcLzpv9vO35u/8v1FdVw0fmQ96ePZo9+sTQtrmUsklFNUUQSaRoa9vX2s3p91UVFRg5syZ+PLLL+HpWf+5r1AosH79enzwwQcYM2YMBg4ciA0bNuDo0aM4fvy4FVtMCFBQVYD04nQAdKObdCxCQeNm+uFSsRRyBzkA4yfAimqKUKosBQfObJXLdfETYMdyjkGlUZn9/K3hZ9iHBg2FWCS2+PVo0E1MVqYsE2aJm1vP6CX7Z9CtNC7Qa9W1uF52HYBl7q5JRVIhJdQaW4fpppbTNkOE2B++HsQg/0FwEDtYuTXtZ968eZg0aRISExP1Hk9OToZKpdJ7PDo6GmFhYTh2rOnaGUqlEmVlZXpfhFgCP5PW27s3vGXeVm4NIebTWsYpAHg7af/NGzvo5me5Q+QhkElkJraweTE+MfBy8kKFqgKp+almP39r2nuZJw26icn4QJdL5c2ujzJ1pjurLAtqpoar1BX+zv5ta2gzrLl1GF9Ejd8ygRAASEhI0Fsjy9u4cSM8PDwAAEuXLgXHceA4DhKJBBEREVi0aBEqKioAAFlZWcLzHMdBLpcjJiYG8+bNw6VLlxqdlz9OJBIhMDAQM2bMwPXr1/WOu3r1Kh544AEEBQXByckJISEhmDJlCi5cuGDQ+yoqKkJISAg4jkNpaanRPxdb1BnXc//www9ISUnBypUrGz2Xl5cHBwcH4d8pz9/fH3l5eU2eb+XKlXB3dxe+QkNDLdFsq6O4tj5KLScdFT/T3dR6bp6pSz35jFNLTH4BgIgTCf3g9u6Ll9SUIK0oDYDl9+fm0aCbmKylPbp5pgb6ldIrALSBbqmZYD7IzhWeM7mqoynyq/JxseQiOHCdbi0oMY+YmBjk5uYiKysL77zzDr744gs899xzesfs3bsXubm5OH36NN566y2kp6ejb9++2Ldvn95xbm5uyM3NRXZ2Nn788UdkZGRg+vTpwvMqlQrjxo2DQqHA9u3bkZGRgS1btiA2NtbgjvacOXMarf+1ZzV1NUjJTwHQedZz37hxA8888ww2bdoEJycns5xz8eLFUCgUwteNGzfMcl57RXFtGSq1SshMoUE36Wj4vjjf325KW/villjmyRMKG7dz1umxnGNgYOju2R1+zn7tck0adBOTCXfXWtgqx9SUFkuu5+b5Ofuhp1dPMDAhxaQ98NeK8Y5p8YaFvVq7di369OkDNzc3uLm5IT4+Hr/99pvwfEJCgt6MDcdxePLJJ63YYvsjkUgQEBCAkJAQzJgxAzNnzsTOnTv1jvH29kZAQACioqIwZcoU7N27F7fddhvmzJkDtVotHMdxHAICAhAYGIihQ4dizpw5SEpKElJ909LSkJmZic8++wxxcXEIDw/HsGHDsHz5csTFtb439dq1a1FaWornn3/evD8EK0rJT4FSrYSfsx+i3KOs3Zx2kZycjPz8fAwYMAASiQQSiQQHDhzA6tWrIZFI4O/vj9ra2kYDtlu3biEgIKDJczo6OgqfE/xXZ0ZxbRnJ+cmoqquCt5M3enr3tHZzCDErPuvUHme6AW22mIgT4XLpZeRVNp0VZQnWyDilQTcxGR/oXo7mv7tmyWqJuqxROZFPS22vdJb2FhISgrfffhvJyck4deoUxowZgylTpiAtLU045rHHHkNubq7w9e6771qxxfZPJpOhtra2xWNEIhGeeeYZXLt2DcnJyU0ek5+fjx07dkAsFkMs1hYV8fX1hUgkwrZt2/Q69YY4f/483njjDfznP/+BSNRx/twIVcsD4ztNTYaxY8fi7NmzSE1NFb4GDRqEmTNnCv8vlUr1ZlwzMjJw/fp1xMd3jmwAc6O4Ng8+tXxEyAiIONtvLyHGMCjrVGZ8X5wxhkyFti9uyQkwd0d3oRhpe/XFNUwjTIC1Z1+8cW15QgxkyEy3Ld9dA7SpZl+e/RJHso9ArVFbvHqhWqPu8IPuyZMn632/YsUKrF27FsePH0dMTAwAwNnZudnZL0tgjIFVV7fb9XicTGbxQVlycjI2b96MMWPGtHpsdHQ0AO360CFDhgDQVp12dXUFYwxVVVUAgAULFsDFxQUAEBwcjNWrV+PFF1/EsmXLMGjQIIwePRozZ85EVFTzs7xKpRL3338/Vq1ahbCwMFy5cqWtb9VmdMb13HK5HL1762+p4uLiAm9vb+HxOXPm4Nlnn4WXlxfc3Nzw9NNPIz4+3qCZU1NYK64By8c2xbX58GmrfB0XQjoSSy31LKopgkKpgIgTWaRyua4RwSPwd/7fOHTzEKZ3n976C9roYslFYfeR/n79LX49Hg26ickMqZjI310rVZaiTlPX5B6CDdWqa3GjXLu2z5J31wAg1icW7o7uUCgVOFt4Fv38+ln0emlFaSirLYNcKkesT6xFr2UL1Go1tm7disrKSr3Zrk2bNuG7775DQEAAJk+ejFdffRXOzs4WawerrkbGgIEWO39zeqQkg7PA+zp79ixcXV2hVqtRW1uLSZMmYc2aNa2+jjEGAHqDBblcjpSUFKhUKvz222/YtGkTVqxYofe6efPm4aGHHsL+/ftx/PhxbN26FW+99RZ27tyJcePG4Y477sChQ9qObXh4ONLS0rB48WL07NkTDz74oBnfufUVVhcioyQDAHBb4G1Wbo1t+fDDDyESiXDvvfdCqVTi9ttvx2effWax61krrgHLxDbFtfldL7uOrLIsSDhJp6m/QDqX1rbuBeoH3UXVRQafl5/8CpWHwlHsaHoDDTAiZARW/70ax3OPo1Zda/EdQfhZ7iEBQ9p19xEadBOTCcUbWkgvd3dwBwcODAylylL4yHxaPe9VxVWomRpyBzl8Zb5ma29TxCIxhgYOxW9Zv+HgzYMWH3TzgR4XFGfQDQh7dfbsWcTHx6Ompgaurq7YsWMHevXqBQB44IEHEB4ejqCgIJw5cwYvvfQSMjIysH379mbPp1QqoVQqhe876tZCbm5uUCgUjR4vLS2Fu7u78H2PHj2wc+dOSCQSBAUFwcHBsD8a6enafWojI+vvWotEInTtqr251bNnT2RmZmLu3Ln49ttv9V4rl8sxefJkTJ48GcuXL8ftt9+O5cuXY9y4cfjqq69Q/c+Mo1Sq3cngzz//xNmzZ7Ft2zYA9QMDHx8fLFmyBMuWLTOozbbmeK42tbynV89Ov/XQ/v379b53cnLCp59+ik8//dQ6DbJRFNfWw6erDvAfIOxVTEhHws9em7uQmrDM092yGacA0MOzB3xlviioLkDyrWSL3yAT1nO3c8Zpx+31E4NlV2Rjx6UduD/6fqM6kULxhhbSy8UiMTydPFFcU4yi6iKDBt26gd4e6yVHhIzAb1m/4XD2YSwYsMCi1zqccxhAx98qrEePHkhNTYVCocC2bdswa9YsHDhwAL169cLjjz8uHBcbG4vAwECMHTsWmZmZ6NKl6Q/3lStXtqkzx8lk6JHS9HpHS+Jkxu1r2aNHD/zxxx+NHk9JSUH37t2F7x0cHIQOtaE0Gg1Wr16NyMhI9O/ffDrVyy+/jC5dumDRokUYMGBAk8dwHIfo6GgcPapNsw4ODm50zI8//ih02AHg5MmTeOSRR3Do0KFmf8/2gK+CHBdkmZRpYjhrxTV/bUNRXFsPbRVG7MWpvFM4XXAaD/d+2ODaA4wxYabb3Es922uZJ6D97BkePBw7Lu/AoexDFh10V6oq8fetvwG0f1+cBt0EG85twJaMLQCA+f3nG/w6Q7Yp4J8vrik2ONjbM9AB7bpMDhzSi9ORX5Vvsa0DFEoFzhWeA9Bx13PzdDuPAwcOxMmTJ/Hxxx9j3bp1jY697TZtiu7ly5eb7bQtXrwYzz77rPB9WVmZUXv6chxnkTRvc5s7dy7WrFmDBQsW4NFHH4WjoyN++eUXfP/99/j555+NOldRURHy8vJQVVWFc+fO4aOPPkJSUhJ++eUXoZhSU0JDQzF16lS89tpr2LVrF1JTU/H666/j//7v/9CrVy84ODjgwIED+Prrr/HSSy81e56Gv8vCwkIA2lm3hns52wvGmDDo7kzruW0VxTXFdUuqVFU4mXcSQP22RITYqmXHliGrLAu9fXobvHSpuq4aSrU2C9CQ9PKy2jKo1CpIxdJWz91eBY15I0JGaAfdNw/hxcEvWuw6SblJqGN1CJWHIswtzGLXaQoNugmulV0DoC0sYAyhkFoL2xQAxt9h4wPd0uu5ed4yb/T26Y2zhWdxJPsIpnabapHrHMs9Bg3ToKtHVwS4tF8RMVug0Wj00sN1paamAgACAwObfb2joyMcHS27psgWREVF4eDBg1iyZAkSExNRW1uL6OhobN26FRMmTDDqXImJiQC0RevCw8MxevRofPHFFwbNpC1atAjx8fFISkpCVFQUIiIisGzZMmRlZYHjOOH7RYsWmfQ+7VVmaSYKqgvgKHZs1+IrxL5RXFvH8dzjUGlUCHYNRqSbZQtBEdIW1XXVen1xQwfdfL/aUewImaT57Bt3R3eIOBE0TIMSZUmrk0uMsXbZuldXXGAcJJwEWWVZuFF+A6FywydWjGGNrcJ4NOgmyK7IBlA/2DWUULyhlb2mjR50K9r37hqgrZx4tvAs/nP+PxgXPg6uDq5mvwa/nrujz5AtXrwYd9xxB8LCwlBeXo7Nmzdj//792L17NzIzM7F582ZMnDgR3t7eOHPmDBYtWoSRI0eiT58+1m66TRg8eHCTqai8pUuXYunSpc0+HxERIayzbM3s2bMxe/bsRo/HxcXpnePjjz826HwtSUhIMLhdtoqvWj7If5DFC8uQjoXiuv3x67lHhozsNFv7Eft0RXEFDNo4MqYvrtsPb+nfuIgTwdPRE0U1RSiuKW510F1QXYDy2nKIOBEi3CMMbk9byB3k6O/fHyfzTuLz059j+bDlZo9bxhgOZ/+zzNMKGae0YWEnp9aokVuZCwC4UX4DNXU1Br2uuq4a1XXadV0tpbQAxg26lWplu1Uu1zWt+zT4ynxxufQyXjz4ItQa4/YtbQ1jDEezO/ZWYbz8/Hw89NBD6NGjB8aOHYuTJ09i9+7dGDduHBwcHLB3716MHz8e0dHReO6553DvvfcanWJJiDUcy9WmllMVZEJsG2NM2CqM1nMTW6c70OZnmA3B96tb64cDOnt1V7feF+fbECYPa9cbzI/3eRxiToydmTvx9bmvzX7+6+XXkV2RDYlIgiEBQ8x+/tbQTHcnV1BdgDpNHQCAgSGrLAvRXtGtvq60phQAIBVJ4SJ1afFYYwbdVxVXoWEauDm4GVR0zVx8nX3xyZhPMPv32TiUfQjvnXoPLw1pfl2bsS6VXkJ+dT6cxE4Y6G+dLW7ay/r165t9LjQ0FAcOHGjH1hBiHrXqWpzKOwWABt2E2LqLJRdxq+oWnMROGBww2NrNIaRFugPtK6VXwBgzaJbX0IxTQGfbsJrWtw1r7/XcvLjAOLw85GWsOLECH6V8hHC3cCSGJ5rt/Pws90C/gXCWtn89EJrp7uT41HKeoXfYipX/3F1rJaUFMO3uWlePru2eDhbjE4MVw7X7mH6X/h3+m/Ffs52bTy0fFEBpqYTYo9T8VNSoa+Aj80E3j27Wbg4hpAV81fLbAm+jv7nE5unOdJerypFflW/Q6/jaSsYMug2ZALPWoBsA/hX9L9wffT8A4N+H/43zRefNdm5hmWewdZZ50qC7k8upyNH73tC1JPxMt0EpLXygK2070AFgfMR4PN3/aQDAWyfeEioVtxVfuGF48HCznI8Q0r74GI4LjKP1oYTYONoqjNgTvu/LgdP7vjXCoNuAvri3k7fea1rS3kXUGnpx8IsYFjQM1XXVeHrf07hVeavN51SqlTh1S5utZq1te2nQ3cnxM90STrvSwNBAF9aRGHB3jQ90Y2a6rTXoBoDHYh/DnVF3Qs3UeG7/c7iiuNKm81WpqpByKwVAxy+iRkhHxXfi6cYZIbattKYUZwrPANAWSSXEllWpqoS++AD/AQDqCwq3pkRp/pluxpjVJ8AkIglWjVqFLu5dkF+dj6f/fBpVqqo2nTPlVgqq66rhK/NFd8/uZmqpcWjQ3cnxgd7fX7v9jSXurpmS0mKtu2uAdu/XZUOXob9ff5SryjF/33xhZt8Up26dErYtiXCLMFs7SctstaIuaZ6t/s5ult/E5dLLEHNiGnRbma3+GyEta8/f25GcI9AwDbp5dkOga/NbURLSVkXVRVhyeAkyijNMPgc/sePl5CXU/DF2Aqy1rXv58+u+pjm3qm6hQlUBMSe2ap9V7iDHmrFr4OnoifTidCw5vAQapjH5fPzuI0ODhlotW40G3Z0cn14+MlibgmVoBXNTijdU1VUJFc+bUlNXg5vlNwFYd6YbABzEDvho9EcIdg3GjfIbWLh/IVRqlUnnErYnCBpGaantQCqVAgCqqtp2V5S0v9raWgCAWCy2ckv0HbipLf7X368/3B3drdyazoni2r7xvzf+92hJQmp5MKWWE8valL4JOzN34rPUz0w+h24qNz/hZGh9JX5CiO9nt8TQQfeVUu1NgDC3MDiIHQxqh6WEyEPw8ZiPIRVJsff6Xnzy9ycmn4vvi1vzxjlVL+/k+JnuPr594O7oDoVSYVAFc+HumpNHq9dwkbrAQeSAWk0tSmpKIHOVNXncVcVVMDB4OHoIKenW5OXkhTVj1uD/fvs/JN9KxhvH38AbQ98weuBs7cINnY1YLIaHhwfy87WFSJydnelmhx3QaDQoKCiAs7MzJBLb+tN04IZ20D0qZJSVW9J5UVzbJ8YYqqqqkJ+fDw8PD4vfUFNr1EL9hREhlFpOLCu9OB0AkFaUZvI5dFO5+QknQyuY8+nlBs10ywwbdFt7PXdD/f36Y9nQZfj34X/jq7NfIcItAlO6TjHqHHmVebhcehkcOMQFxlmopa2zrZ4NaVd1mjrkVeYBAIJdg9HFvQtS8lNwufRyq4Nufqbby7H1u2scx8FL5oW8yjwU1xQjyDWoyeN013PbSmeqq2dXrBq1CvP2zcP/Lv8Pke6ReKT3Iwa//kbZDVwvvw4JJ8FtAbdZsKVEV0BAAAAIHXRiH0QiEcLCwmwm/gGgUlWJk7dOAgBGhdKg25ooru2Xh4eH8PuzpLOFZ6FQKuDm4Ia+vn0tfj3SuV0ovgBAm5JdVF0Eb5nxE0a6g9wItwiIObFQwdzfxb/F1/JLPc05082vJ7d2xqmuyV0m46riKr48+yWWHluKEHmIUdvv8kWRY31iDZostBQadHdi+VX5UDM1pCIpfJ190cVDO+g2ZC2JMdsUANq13/yguzm2sJ67KcODh+OlwS9hZdJKfJT8EcLl4RgbPtag1/J33Pv59YOrg6slm0l0cByHwMBA+Pn5QaUybVkAaX8ODg4QiWxr1dPRnKOo09Qh3C0cke6R1m5Op0ZxbZ+kUmm7LRnhU8uHBQ2DRERdXGI5BVUFKKwuFL4/X3TepOwK3ZluB7EDQuWhyCrLQmZpZouDbpVGhbLaMgDGLfWsrqtGlaqq2X2qbaGgcVPm95+PrLIs7Lm2Bwv/WojNEzcj1C3UoNcKyzyDrVO1nEefSJ0Yn1oe5BoEEScSAsygQbcRFROB+rSWouqiZo+xdrXEljzQ8wFcVVzFDxk/YPHhxfjG9Rv09O7Z6uv41HJrB3pnJRaLbW59MLEv+2/sB0BbD9kSimvSHH7QTanlxNL41HKeKYPuSlUlcitzAdRPOHX16KoddCsyW1yWqFAqAGi3GXNzcGv1Ws4SZziKHaFUK1FcU9zkoJsxJqzp7upuWxNgIk6EFcNXIKciB2lFaZj35zx8N/G7Vt97naYOx3K1M93W3kHItqYUSLsSBt0u2nRvowbdRlQvB3S2DWthptvW1pE09NKQlzA0aCiq66ox/8/5yK9qOcVRpVbhRN4JANbbE5AQYjq1Ri3cIU8ISbBuYwghLcqrzENGSQY4cLTLALG49CLtoFvMaW8Ani86b/Q5+P62j8xHKNIZ5RGl91xz+H64h6MHxKLWb0JyHNdqijlfuVzCSRDuFm7Ym2hHMokMn4z5BP7O/riquIrn9z+POk1di685V3gO5bXlcHNwQ2+f3u3U0qbRoLsT053pBuoHu61VMFdr1MIdNkPXRrQW6NV11UJ7otyjDDpne5OIJHhv1HvafQOrtPsGtlSN/e/8v1FdVw1vJ2/08OrRji0lhJjD2cKzKK4phlwqF7ZVJITYpkPZhwAAsb6xBmfhEWIqfj03nwV1vtj0QbduhqehFcyFQbcRa5Rb64vz1wxzC4NUbPmdBkzh6+yLNWPXQCaR4VjuMbyd9HaLWxLyyzzjg+KtvuSEBt12Sq1R460Tb+GXK7+YfA5+u7AQeQgA7Wy0u6M7GBiyyrKafZ2iVgEG7T9wQyomAq0H+hXFFTAweDp6mlSIor3IHeT4ZOwn8HT0xPmi8y3uG3g4RztDNjRoKEQchRoh9kZYHxo8DFKRbXZACCFah25qB920VRhpD3x6+dSuU8GBQ15lXotLKJvSVIZnwwrmzRGWeRqYcQq03he35WWeuqK9ovH2iLfBgcOWjC3YfGFzs8cKyzxtIOOURgJ26kzhGXx/4Xu8k/SOyedomF7OcRy6uGsDraU7bPzdNTcHN4PvGnWUQAeAUHkoPhr9EaQiKfZc24M1f69p8rij2UcB0HpuQuzV/pv7AVDVckJsXa26FsdzjwOg+gvE8hRKhdCHHhgwUEjFNjbFvKm+b8MK5s0xtqAxYPhMt60u89Q1JmwMFg1cBAB49+S7wk03XaU1pThXeA6A9ddzAzTotlv8Vl8lypJWy/83h//ACJYHC48Zsq7bmC0KeIYGuj0MugFggP8ALB26FADw5dkvsTNzp97zBVUFwtqy+KB4K7SQENIWORU5uFRyCSJOhOFBtD6UEFt2Ku8Uquuq4SvzbXXLU0Laik8tD3YNhpuDG3p59wJg/KC7qUEuX8EcaKUvbmRBY6D1osb2NAEGALNjZmNq16nQMA1eOPgCLpVc0nv+WO4xMDB09eja6vZr7YEG3XZK9+6XIYXPGlKpVcI5gl2NHHS3IdCLq1ue6baHu2u8u7rchcdiHwMAvH70dSTfShaeO5qjneXu5d3LqJsThBDbcODmAQBAP99+Vt3XkxDSuoPZ9VXLOY6zcmtIR8cXUeMH26YMustry3Gr6haAxoNcvi/M75ndFGMLGgMtFzVmjNldX5zjOLwa9yoG+Q9CpaoS8/fN17uhwKeW20phRRp02yl+phuAUN7f2NdrmAaOYkchCAHjZroNXc8N6AS6srjJNSr2dneNN7//fIwLH4c6TR0W/rUQN8puAKCtwgixdwduaAfdCaEJ1m0IIaRFjDGh/gKt5ybtgV/PzWdVxHjHADCumBrf7/WT+TXa9sqQCualNaUAzJdenluZi6q6KkhEEoS5hRl8TmuTiqX4MOFDhMnDkFOZg2f+egZKtRKMMaGImq30xWnQbaf0ZrpbuBPWnOzK+srluneFDalgbkp6Of+hUKepQ7mqXO+5KlWVkOpuL3fXePy+gb28e6FUWYr5f86HQqnA0dx/1nPbQOEGQohxqlRVSMpLAgCMCqH13ITYsmtl13Cj/AYkIgniguKs3RzSCfCD7p5ePbX/9e4pFFMzdMlnS5NNhlQwL1Zqr2PKoJvvx+virxXhFmF3hUM9nDywZuwayB3kOF1wGq8deQ0ZJRkorC6ETCLDAL8B1m4iABp02y3dQbcpM9185XJ+uzCeIRXM+fRyY2a6HcWOcJW6AmicYn5FoW2/l5OXXW7zwe8b6OfshyuKK5j12ywolArIpXL08e1j7eYRQox0LOcYVBoVQuWhiHSPtHZzCCEt4Ge5B/kPgovUxcqtIR1dlaoKWYosANrBNgC4SF2MLqbWUi0jQyqYm5Je3tJMt71mnPIi3SPxQcIHkHAS/Hr1V7x88GUAwOCAwXAQO1i5dVo06LZT/DoQwLSZ7pvlNwEAwS7Beo8bUsHclIqJQPPBbk/VEpvj5+yHNWO0+wbyv4+4oDir7wlICDGeULU8ZBStDyXExvHrualqOWkPF0sugoHBV+YLH5mP8Lix67pbWj9tSAXztqSXl9SUNNru1t4KGjclLjAOS+KWAKgfG9lSxikNuu2QhmlQUFUgfF9YXQiFUmHUOXIqtTPdupXLea2t6zb3oNve767xenr3xMoRK8FB20m3he0JCCHG0TCNMHNGW4URYtsqVZVCEdMRwSOs3BrSGTRcz83jB91phWkGnaelvm9rFcwZY/Xp5SbMdNexOpTX6i/1tLcias2Z1n0a/q/X/wnf28p6bgCgaTg7VFxTjDpWBw4cfGQ+KKguwBXFFfT362/wOZpLLwfqPwCam+kuVZYCMC7QgfpBenODbnsPdAAYGzYWy4cvx5HsI5gYOdHazSGEGOlc4TkU1xTDVeqKgX4Drd0cQkgLjuccR52mDmHyMES4R1i7OaQT4CuX86nlPGGm24BiamW1Zciv1s5gNzfh1NWjK7LKsnC59DKGButP4lSqKlGnqQNg3ASYVCyFXCpHuaocRTVFcHd0B6C92cwv9bT3CTAAeG7gc5BwEjiIHRAmt52icDTotkN8arm3zBvdvbqjILsAmaWZRg26s8u1hctCXEMaPae7lqQp/KDZ2K2w+OOLavT3B+woM928u7rchbu63GXtZhBCTLD/xn4A2kwVqdi+iskQ0tlQajlpb/we3XwRNV5PL/1iai31kfl+r7+zP+QO8iaPifKIAq7X1z3SxWecyiQyOEmcjGq/l8wL5apyFFcXI8pdWyU9tzIX1XXVkIqkNjVINZVYJMazg561djMaofRyO5Rfqb075ufsJ6y/Nmav7lp1rXCHramZ7pYqmDPGhJluY/euFdLLdQqpVamqhFT3jjDTTQixb3xqOW0VRohtY4zh0M1DALT7cxNiaSq1CpdKLwFoPNPt6uBqcDE1Q2oZtVTBnC9obGzGKdD0Uk9+DBHhHkG1iCyIBt12iC+q4O/sb9C+2g3xqeUyiazJgNWtYH5VcVXvueq6aijVSgDGB7u37J+9upsIdB+Zj5DmQggh1pBbkYuMkgyIOBGGBw+3dnMIIS1IL05HQXUBZBIZBvkPsnZzSCdwufQy6jR1cHNwQ5BL40krQ4upGZLhqdu/b1jB3NTaSkDTg27hJoA7TX5ZEg267RCfXu7n7CekhhhTwZwfdAe7BjdZmVe3gnnD8/JB6ih2hEwiM6rdLQV6R0ktJ4TYL36Wu69vX7vcvpCQzoSP17jAOJvZEoh0bLr7czfVfzZ00G3ITDdfwbxCVdGogjnfjzY24xRoeaab+uKWRYNuO8QPuv2d/bVrPqCd/W5YibA52ZXa9dxNpZbzmptBF4qoOXkavZVOS4FOqeWEEGvT3SqMEGLbDmVrU8tpPXfnsOPSDsz8ZaZRmZ3m1lwRNZ45Z7pbqmDO98W9HI2rrQS0MtNNfXGLokG3HRLSy1384ebgBj+ZH4Cmiy00hS+iFuzaeLswXnMVzPkgNdc6kssKmukmhFhflaoKSblJAGjQTYitK64pxtmCswBoq7DO4ELxBbxx/A2cKTyDlw6+BJVaZZV2NLddGI8vrpZbmdtopx6eQqlAYXUhgNb7vs2t6+bTy80x061hGmEpKfXFLYsG3XZIN70cgDDb3Vy18YZ008ub01wFc92ZbmPxgV6qLBW2OhDu9rlToBNCrOd47nHUamoR7BpMHQ9CbNyR7CNgYIj2ioa/i7+1m0MsSKlWYvGhxUK/MaMkA2tPr233dqg1alwsuQig+ZluVwdXRLhFAGh+tpvv9wa6BMJF6tLiNYX+fYNJNb6QmrG7CAHa6uUAUFSt3UkouyJbqFweIm+8oxExHxp02yF+ppsfdBtbTC27ovX08uYqmLeleIOHowc4aFPSS5WlqKitQF5lHgC6u0YIsa4DNw8A0FYtN3bpDCGkffHruWmWu+P7OOVjXC69DG8nb7wa9yoAYP259ThTcMbkc2ZXZAv9T0NdK7uG6rpqyCQyhMvDmz2utRRzY2oZtTbTbUrWqbeTflFjfuwQ6R5JlcstjAbddqaitgKVqkoA2jXdAIwupsYPulua6W6ugnlbAl0sEguD9aLqIqG9vjJfqlxOCLEaDdPgwA3toJvWhxJi2zanb8burN0AKF47uuO5x/Ht+W8BAG8MewP39bgPEyMnQsM0WHJ4Carrqo0+Z3ltOab/PB0zds0Q+tOG4FPLe3j2gFgkbva41gbdxmR4NlfBnJ/pNkd6ORU0bj806LYz/Cy3q9RVSEtpLhW8KTV1NSiq0aaUtDTobq6CubA3oImVfXWDnaolEkJswfmi8yiqKYKL1AWD/QdbuzmEkCZomAbvn3ofK5NWgoFhRo8Z6Ovb19rNIhaiUCrwyuFXAADTu08XbrD8+7Z/w0/mh6yyLHyc8rHR5z1dcBrlteUorinGL1d+Mfh1fBG15tZz8wwedBvQ922ugjk/AWZSevk/rymrLYNKo6KCxu2IBt1NUCgVBt09U2vU7dAafQ3XcwP1d8tyKnNQpapq8fX8em4XqQvcHNxaPLaptHVhmwJHD+Ma/g8+2EtqSqhaIiHEJuy/sR8AMDRoKKRiqVXbQghpTKlW4qWDL2Fj2kYAwDMDnsGS25bQUpAObMWJFbhVdQth8jA8P+h54XF3R3csG7YMALApfROO5x436rynC04L///fjP822gO7OReKLwCoH1Q3R7eYGj841mVM37e5CuZCITUT+uLuju4QcdrhX2lNKU2AtSMadDdQU1eDO3fciXHbxglrhhrKrcjFnN1zMGLLCGEQ214arucGtOkl/GBWNxW8Kbqp5a39sWqqgnlpTSkA0+6u6b6OZroJIbaC/6ynquWE2B6FUoHH/3gcv2f9DolIgreGv4VHYx+lAXcHlnwrGb9d/Q1iToyVI1bCWeqs9/zw4OGY3n06AGDp0aXQMI3B507NTxX+P6MkQ28Q3hzGGM4Xa2euW5vpbqmYWmlNqZBtamjft+G6bpVahQpVBQDT+uIiTiQsES2oLhCKtNEEmOXRoLuBguoClCpLoVAqMG/fPHyU/JFQMREA9l7bi2k/T0NSXhLKa8txrvBcu7ZP2C7MWb9apzAr3cq6bv4mQUtF1BqeUzdtXVhHYuJMN5+WXlxTTDPdhBCry6vMQ3pxOjhwGBFCRZkIsSU3y2/iwV8fREp+CuRSOdYlrsPkLpOt3SxiYdfKrgEA4oLi0Me3T5PHPD/oeUg4CbIrsvVSr1ui1qhxtlC71Rw/Y/3fjP+2+rqcyhyU15ZDIpIY1Gflq5s3HHTz/d4gl6BGNxKa07CCOd8PF3NiyB3kBp2jIb4vfrbgLJRqJRzFjghxpcrllkaD7gb4tHK+yvb6c+sxZ/ccXC+7jjePvYlF+xehrLZMOL68trxd29dUejmgU0ytlQrm/Ey3IcHVVAXztqwj0X1dVlmW8CHJf6AQQkh742e5+/j2MflzjRBifmmFaXjw1weRVZaFAJcAfHPHNxgSOMTazSLtgO+Ly6XNDyqdpc7CdnG5lbkGnTdTkYlKVSVkEhkWD1kMANidtVvI4mwOv567m0c3g5YgxXjHAADSitL0r29ChmfDmW6+H66bJm4svoJ5Ul4SAG3l8paKwxHzoEF3A/zgMsg1CKtGrYKL1AUp+SmYtGMS/ntRezfskd6PYELEBADQG4C3B37Q3dxMd2vF1AzZLozXsIK5SqMS3m9bC6kl30oGoL150NrackIIsRTdrcIIIbbh4M2DeHj3wyiqKUIPzx7YNHETunl2s3azSDvhB90yiazF4/i+rKFLPflU8lifWPT17YueXj1Rq6nF/y7/r8XX8ZXLm9ufu6HmiqmZkuHZsIJ5W/bo5vGvPXXrlN41iGVZddC9cuVKDB48GHK5HH5+frj77ruRkZGhd0xNTQ3mzZsHb29vuLq64t5778WtW7cs1ibdQJ8QMQFb7twirN/wdvLGunHrsGjgIuEfbHvPdAvp5S4NBt1NVBpvijGD7oYVzBVKhfZxcCYPlBvuD0ip5YQQa6muq8aJ3BMAaD03IbZi68WtePrPp1FdV42hQUOxccLGRtl9xDxssR8OQCgK3NqgO9AlEIDhM938eu6+vn3BcRxm9JgBAPjvxf+2uC7c0MrlvOaKqfF9dGMGuQ0rmPOz8qYu8wQAL5n+tmHUF28fVh10HzhwAPPmzcPx48exZ88eqFQqjB8/HpWV9fvmLVq0CD///DO2bt2KAwcOICcnB/fcc4/F2tTw7lq4Wzi+m/gdPkr4CDum7MDQoKEAADdH7aCz3We6K5tJL/8nRftm+U1htr4p/N1AQ9du6N5h062WaGoaCh/oDc9PCCHt7XjOcSjVSgS5BFGngxArY4zh45SP8caxN6BhGtzd9W6sGbsGrg6u1m5ah2WL/XDAcjPdZwrOAAD6+fUDANwReQfkUjlulN/A8Zzmq6ALM91ehs10N1dMzZTtuRpWMOcHyqZmnAKNZ8kN2TOctJ3Emhf//fff9b7fuHEj/Pz8kJycjJEjR0KhUGD9+vXYvHkzxowZAwDYsGEDevbsiePHjyMuLs7sbeIHrLqB7ih2xNjwsXrH8etM2nPQrdKohGBrOOjmU8EVSgWyyrKavBtXpaoS0lIMmekG9CuYlypLAWirpZuqYaBTR5cQYi18avmo0FFUCZkQK1KpVXj16KvCvslP9X0KT/Z9kuLSwmyxHw4YMeh2+WfQXdn6oLukpgRZZVkAgD4+2uJszlJnTO4yGZsvbMaWjC0YGjy00esKqgpQWF0IDhy6e3Y3+D309O6JrLIsnC86j2HBw1BcUyz04SPdIw0+D6DtK2eVZeFy6WWhcjlfgdwU1Be3Dpta061QaNOXvbz+WfebnAyVSoXExEThmOjoaISFheHYsWMWaQMf6E4SpxaPs8ZMd2FVIRgYJCJJo4DRSwVvppgan1ru5uBmcMVD3bXiwt01MwY6zXQTQqxBwzS0VRghNqCstgxP7n0Sv1z5BRJOgjeHvYm5/ebSgNsKbKEfDgBVdQaml7tq08sNmenmZ7kj3CL0Jo/u63EfAGD/zf3Iq8xr9Dp+ljvSPdLgiuNAfTE1fqab75sHuwYbdR5Av4K5uWe6ncROCJYHm3wuYjibGXRrNBosXLgQw4YNQ+/evQEAeXl5cHBwgIeHh96x/v7+yMtrHBgAoFQqUVZWpvdlDEPvrvGD1vZc0y1ULpf5NVmxkA/K5gbd/IdSsKvhwaVbwZz/MGpLoLtKXSEV1Vd+pJQWQog1pBeno6C6AM4SZwwOGGzt5hDSKeVW5GLWb7OQlJcEF6kLPh37Ke7uere1m9UpmasfDpixLy41bKY7tyIXjLEWj+WLqPX17av3eBePLhjkPwgapsGPl35s9LoLxRcAGL6em9ewmFpbtsnVrWDOL/U016A70j3S5CroxDg281OeN28ezp07hx9++KFN51m5ciXc3d2Fr9DQUKNeL8x0i1uZ6f6nkFiZsv1mupvbLozHD2D5vfwaullxE4DhqeWAfgXzlFspANoW6BzHCcEe4BJAa7UIIVZx4IY2tXxo0FA4iB2s3BpCOp8LxRfw4K8P4nLpZfjJ/PDNhG+aTO8l7cNc/XDAfH1xZ0nLM8IBLgEAgBp1jbB8sjnCoNuvb6Pn+IJqP178ESqNSu85vogaP4g2FL/+O6cyByU1JSZtF8bTq6/0z/tsS9YpX9QYoNTy9mQTg+758+dj165d+OuvvxASUl/gKyAgALW1tSgtLdU7/tatWwgICGjyXIsXL4ZCoRC+bty4YVRbDJ3p5gfd7TnT3Vzlcp4lZrp109aT87XbfLUl0IH6O2yUWk4IsZb9N/YDAEaGjLRqOwjpjI5kH8Gs32YhvzofXT26YtOkTejh1cPazeq0zNkPB9qvL+4gdoCvzBeAdra7OXWaOpwtPAsA6Ofbr9HzY8PGwtvJGwXVBcLfBh6fXm7sTHfDYmptmenWrWB+seQigDbOdOsUNaa+ePux6qCbMYb58+djx44d+PPPPxEZqV9YYODAgZBKpdi3b5/wWEZGBq5fv474+Pgmz+no6Ag3Nze9L2MIhdRaSWkRZrpry1pNaTGX5iqX8/jB8Y3yG6hV1zZ6nh90GzPTDdQHJL9lWFsCHagPdkotJ4RYw63KW0gvTgcHjgbdhLSzHZd2YN6+eaiqq8JtAbfhmzu+EWYsSfuyRD8caHtf3NBBN6CzrruFYmqXSi6huq4arlLXJgeZUrEU93TTVmTfkrFFeFyhVAj1kIwddAP1+3qfLzrfpplu3Qrm5uiLO0uc4Sh2NLk9xDRWHXTPmzcP3333HTZv3gy5XI68vDzk5eWhulobbO7u7pgzZw6effZZ/PXXX0hOTsbDDz+M+Ph4y1dMFLcy6P6nkJpKo4JSrbRIWxoSZrqdm57p9nP2g6vUFWqmxrWya42e5z84DN0ujNcwINs66O7ro03tiQ9q/gOb2K+1a9eiT58+wh/a+Ph4/Pbbb8Lz1tjzkxBdB7O1BdRifWPhLfNu5WhCiDkwxvBZ6md47ehrUDM1JkdNxtrEtcIkBml/ttgPB4BqleGDbqGCeQvF1PjU8lif2GbXL0/rPg0cOJzIPYEsRRYAIKNYu2d5sGsw3B3dDW4/jy+mdjj7MEqVpeDAGV25nNdwhrwtWaccx6GPbx+4SF3Qx7ePyechxrHqoHvt2rVQKBRISEhAYGCg8LVlS/1dpg8//BB33nkn7r33XowcORIBAQHYvn27xdpUo268ZVhTnCXOEHPavarbq4I5v6a7uUE3x3H16z4UjVPM+UG3qTPdvLamlz/Z90kcnHEQw4OHt+k8xDaFhITg7bffRnJyMk6dOoUxY8ZgypQpSEtLA2CdPT8J0XXwBlUtJ6Q9aZgGK06swNrTawEAj8U+hhXDV0AqlrbySmJJttgPBwyvXg7Uz3TnVjafXs4Puvn9uZsS5BokZD799+J/ARi/P3dD/DrwlHxtTaQQeYhB76kp/BJSXlsnwNYlrsOeaXsa7SpELMeq+3Qbkpbt5OSETz/9FJ9++mk7tMjwLcM4joPcQY5SZSnKlGXNpnybU2uF1ADtAPl0wWlcKdUvplZeWy7cHDBmTTfQxN21NgY6x3FtPgexXZMnT9b7fsWKFVi7di2OHz+OkJAQq+z5SQivpq4Gx3OPA6BBNyHtQcM0WHZsGbZf2g4OHF6NfxXTu0+3drMIbLMfDhheSA0wbKY7NT8VQOPK5Q3d1+M+HLh5AD9d/gkL+i+oH3R7mzbobpiS3pZUbt2+uIvUpc0FQKViKd30amc2UUjNlhhzd03YNkxl+WJqjDEUVBUAaHnQHeXedDE1/sPI09HT6P0B+QrmvLbOdJPOQ61W44cffkBlZSXi4+OttucnIbwTuSdQo65BgEsAunt2t3ZzCOnQ1Bo1Xj3yKrZf2g4RJ8JbI96iATdpEWPM4PpKQH32ZnMz3UXVRcLuPbG+sS2ea1jQMAS7BqOstgy/Z/0uVC43ZT03oB0nhLuFC9+3pVK47oDdw9HD5PMQ66FBdwNCoBsw6G7PbcNKlaWo1WiLo7U20w003jbM1NRyQL+COQB4OHkYfQ7SuZw9exaurq5wdHTEk08+iR07dqBXr15W2/OTEN6Bm9qtwkaFjALHcVZuDSEdV52mDv8+/G/szNwJMSfGOyPfwZ1Rd1q7WcTG1ahrwKCdgTdkpjvQ5Z9Cas3MdPOp5V09urZaP0AsEmNa92kAgG/Pf4ussiwAxm8Xpkv3tW2Z6eYrmAOglHA7RYPuBgxNLwfqZ7rbY003n1ru5eTVYkoJPzjOKsvS22uwLYNuoP6DQiaRmbwehXQePXr0QGpqKk6cOIG5c+di1qxZOH/+vMnna+uen4QA2hkUftCdEJpg3cYQ0oGpNCq8dPAl/Hr1V0g4Cd4b9R4mREywdrOIHeD74YBhfXG+X1tWW4ZKVWWj51MLUgG0nlrOm9p1KiQiCS6WXISGaeAj84GPzMeg1zaFL6YGtG2mW7eCOc102ycadDdg0kx3Owy6+crlra0dD3AJgLPEGXWaOtwor98Xkb8DaGzlch4/6KbUcmIIBwcHdO3aFQMHDsTKlSvRt29ffPzxx1bb85MQQFsUJ78qHzKJDIMDBlu7OYR0SCq1Ci8ceAF/XPsDEpEEHyR8gMTwxNZfSAh0Jr/ETs1WGtflInUR+uNNzXafztfOdBs66PaWeWNc+Djhe1OLqPH4mW4RJzK5cjmPH7RTXST7RIPuBozZG1BY011r+TXdrVUu53EcJ6zr1i2m1taZ7n6+/QAAEe4RJr2edG4ajQZKpdJqe34SAtSnlscHxgt7lBJCzKdWXYtn9z+Lfdf3wUHkgI9Hf4zRYaOt3SxiR4zZLozX3LpulUaF80XaLLu+foYNugFgRo8Zwv+bup6b19e3L4YFD8MD0Q+0+e8Of+OgrYN3Yh1WrV5ui4wZdPN7dbdLenll65XLeVEeUThXdA6ZpZnC3WV+0G1s5XJejE8Mvpv4HcLkYSa9nnQeixcvxh133IGwsDCUl5dj8+bN2L9/P3bv3q2356eXlxfc3Nzw9NNPW3zPT0IA4MANSi0nxFJq6mqwcP9CHMk+AkexI1aPXo2hwUOt3SxiZ4wpaMwLdAnEheILjWa6LxZfRI26Bm4ObohwizD4fAP8BqC7Z3dcLLnY4jZjhnAQO+DzxM/bdA7ezJ4zEeMTY/CsPbEtNOhugE8vN2QdCZ/O0h4z3YamlwNotFc3Y0z4IDJ10A0YnppDOrf8/Hw89NBDyM3Nhbu7O/r06YPdu3dj3DhtutaHH34IkUiEe++9F0qlErfffjs+++wzK7eadHQFVQVIK9LuFT8iZISVW0NIx1JdV40Ffy7A8dzjkElk+GTMJ7gt8DZrN4vYIWMmv3h837bhoJtfz93Ht49Bqeo8juPwyZhPcKbgDEYE287fC6lYSkuj7BgNunVomAY1atusXs4PultLLwfqi6nx6eVltWWoUFUAAAJdAy3UQkK01q9f3+Lz1tjzk5CDNw8CAGJ9YttUFIcQoq9KVYX5f87HybyTkElk+GzsZxgUMMjazSJ2Stij24jtbYUK5pX6g25+PTe/RNIYQa5BJi/JJKQpNOjWwc9yA8YNuttjn25D13QD2vRyALiquAq1Ri2klns7eVPlcUJIp7T/5n4AwMiQkdZtCCEdSKWqEk/tfQop+Slwkbrg88TP25yOSzo3U2a6hTXdFfpruvntwoxZz02IpVAhNR262xQYUuxA2DLMiJnuw9mHsTplNdQatVFt4wfdhqSXB7kEwUnshFpNLbIrss2SWk4IIfaqpq4GJ3JPAKD13ISYS3ltOZ7Y8wRS8lMgl8rxxbgvaMBN2syUQTefxak7051flY+cyhyIOBFifWLN20hCTECDbh26gW7I2g9T1nS/eexNfHn2S6GKrqHt4q/h79L6TLdYJBYqG2aWZra5iBohhNizpLwkVNdVw9/ZHz08e1i7OYTYPYVSgcf/eBynC07DzcENX47/En18+1i7WaQDMGmm20U7011YXQilWgmgfpa7m0c3uEhdzNxKQoxHg24dQhE1cetF1ACdmW4Dq5eX1JQId+H4DwND8Ou5ZRIZXKWuBr2GTzHPVGS2ebswQgixZ3zV8lEho8BxnJVbQ4h9K60pxWN/PIZzRefg4eiB9bevR4xPjLWbRToIUwbdHo4ewvF5lXkAjN+fmxBLo0G3DmMDnd8yrEJVYVC6eHpxuvD/xgy6+e3C/J39De4w6hZTo0E3IaSzYowJmUWjQkdZuTWE2LfimmLM+WMO0ovT4eXkhfW3r2/zPsaE6KpSGb9lGMdx9cXU/llSyVcup/XcxFbQoFuHMZXLgfqZbgBCdfCWpBfVD7rTCtNQp6kz6DrGFFHj6c508x9AIa4hBr+eEEI6goySDNyqugWZREZbGBHSBoXVhZizew4ullyEt5M3vr79a3T37G7tZpEOxpSZbqB+XXduZS5q1bU4X3QegGmVywmxBBp06+AD3ZA9ugFAKpIKHwqGFFPTnemuUdfgYslFg65jzB7dPH6m+6riKs10E0I6LT61/LbA2wwqkEkIaaygqgCP7H4El0svw0/mhw0TNqCLRxdrN4t0QKZsGQbUr+vOqchBenE6VBoVPB09ESoPNXsbCTEFDbp1VNUZn9IirOtWGTDo/memm1+XfabgjEHXMKZyOS9EHgKpSIrqumrhA4z26CaEdDZ8anlCSIJ1G0KIncqrzMPDux/GVcVVBLgEYMOEDUKxVkLMzZS+OKCzbVhlrt56bqrjQWwFDbp1CIXUDJzpBuormLc2011eW47r5dcBAJO7TAZg+LpuU2a6JSIJItwjhO/9ZH40y0MI6VSKqotwtvAsANqfmxBT1NTV4NE/HsW1smsIcgnChts3IMwtzNrNIh2YyenlOmu6aT03sUU06NZhSqAbum3YheILALTpL3znz9CZbn7Qbch2Ybr4FHOAUssJIZ1PWlEaAO1noa+zr5VbQ4j9OZl3EtfKrsHLyQsbJmxAiJxqwxDLMnXQrTfTXUCVy4ntkVi7AbaEn+k2ZdDd2rZhfEGHnt49EesTCwC4Xn4dJTUl8HTybPG1utXLjcEXUwOAYDnt0U0I6VwyijMAANHeVF2ZEFOcvHUSgDZThG7ek/ZQrWrbTDdfx0jMiRHjTVvZEdtBM906TJrpdjRsppsvotbTqyfcHd2F9VCtzXbXaepQWFMIwLj0cqDBTLcL/bEkhHQufIZRtCcNugkxRXJeMgBgcMBgK7eEdBamrun2lflCwtXPJXb37G50MTZCLIkG3TpMGXQLhdRamenmi6j19O4JoD7lpbV13UXVRdAwDcScGN5O3ga3C4BeZdFgV5rpJoR0Lhkl2pnuHl49rNwSQuxPpapSWKIxyH+QlVtDOgtT08vFIrHeMsx+fv3M2SxC2owG3TqM3TIMMGxNd5WqClcVVwEAvbx7AQD6+PYB0PpMN1+53EfmA7FIbHC7ACBMHibc9aP0ckJIZ1KpqsT1Mm3xShp0E2K81PxUqJkawa7BlFpO2o2pW4YB+vWLaD03sTU06NbRppnuFqqXXyy5CAYGX5kvfGQ+AOo/DM4WnoVao272tUIRNSPXcwOAVCxFQmgCfGW+wmCfEEI6g0sll8DA4OfsBy8nL2s3hxC7czJPu557oP9AK7eEdCamznQD9eu6ARp0E9tDhdR0tKmQWgv7dOsWUeN1ce8CZ4kzquqqcLn0crMzMfxMt7GVy3kfJHwANVNDIqJfNSGk8xDWc3vRem5CTHHq1ikAtJ6btB/GmEl9cR4/0+0j86FllcTm0Ey3DiG9XGx4ejk/012ubD69nC+ipjvbLBaJhSrmZwqbTzHnB93GFlHjcRxHA25CSJukFaXh3ZPv4uDNg6jT1Fm7OQbhB909PCm1nHQ+n5/+HHf8eIdQydlYVaoqpBXSem7SvmrUNWBgAABnifHp5b29ewMAhgUNA8dxZm0bIW1Fg24dQkqL1LxbhglF1Lx66j3Or+s+nd98MTU+vdzUQTchhLTVO0nv4Nvz32LevnlI3JqIVSdXCdtx2SphuzCa6Sad0NaMrbhZcRP/u/w/k16fWpCKOlaHQJdAmjEk7aZKVSX8vzH1lXgjQ0Zi44SNeHnIy+ZsFiFmQYNuHUJKi9h81cuVaiUySzMBoNG6an69SUsz3W1Z000IIW1VU1eDs4VnAQDuju4oqinCf87/B9N+noZpO6fhm7RvUFhdaOVW6qvT1OFS6SUANOgmnU9BVQHyq7V9hz+v/2nSOU7laVPLB/kPohlD0m50M05FnPFDFI7jMNB/IFwdXM3dNELajAbdOkwp3uDu6A5AO+hmjDV6/nLJZdSxOng6ejYaOPMz3VcVV6FQKpo8P810E0Ks6VzhOdRp6uAr88Vf9/2FT8Z8gnHh4yAVSZFRkoH3Tr2HxK2JeGrvU/g963eoNCprNxnXyq5BqVbCWeKMEHmItZtDSLvi68gA2kKuN8pvGH0OWs9NrKEtRdQIsXU06NZRoza+eAM/012nqRNer+t8cX0RtYZ3iz2dPBEmDwMAYSZJF2MMtyr/KaRGM92EECv4O/9vAEB/v/6QirQ7InyQ8AH+uu8vvBr3Kvr49oGaqXEo+xBeOPACVp1c9f/s3Xd8U9X7wPFPku69KC1QOigbCoIgiLKHoIjKV3HrT1yIouDEjSK4UdyIgiI4EFAEUZYs2SCjbEpLobR0753k98dtQktXkiYd6fN+vXjRJPfenEKf5jz3nPOcBm7xpfXcHXw7WDRaIkRTZthb28Dc0e6C0gJjn0TWc4v6VJftwoRo7KQ3Uo4l+3S7ObihUSn7Z1e1bVh167kNjFPMq9ivO7s425jIy0i3EKIh7E/eD0Cvlr0qPO/t7M1tHW9j8ZjF/HHTH9zW4TYAdlzYUe9tvNyJDGU9t+zPLZojQ9Id4R0BmJ90H0w5SKmulJZuLWWmiKhXMtIt7Jkk3eVYEuwqlcpYTC2nuHIFc2PS7V910m0sppZSuZiaYWq5t7O3RQUlhBCiLrQ6rbHQ4xWBV1R7XJh3GI9f8TgAcdlx1S6XqS9SRE00V3q93lh1/LGejwHKbJW0gjSTr2HYn/vKIFnPLeqXJN3CnlmUdG/dupW7776b/v37k5CgbEexaNEitm3bZtXG1Se9Xm/RSDdUX0ytRFfCyYyTAHTx61LpPLg00n045TA6va7Ca3XdLkwIc9hjXIu6OZ15mpySHNwc3Ojg26HGY31dfAnxDAEqT2+tT3q9XvbovozEdvORnJ9MWmEaGpWGQW0G0cW/C3r0bD6/2eRrGIqo9Wkp67kbM3uMa0m6hT0zO+letmwZo0aNwtXVlf/++4+ioiIAsrKymDVrltUbWF/Kr8c2d2/A6ka6z2SeoVhXjKejZ7VTtNr7tsfVwZWckhxis2IrvCaVy0V9sde4FnVjWM/do0UPHNQOtR7fLUDZI/VwSuUaFfUltSCV9MJ01Co1kT6RDdaOxkJiu3kx3PBq59MOFwcXhrUdBsCG+A0mnV9+t4Irg2Q9d2Nlr3Ft2DJMkm5hj8xOumfOnMmXX37J119/jaOjo/H5AQMGsH//fqs2rj4ZtgsDcNY4m3VudSPdx9KVqeWd/DtVO0XLQe1AV/+uQOV13YaRbkm6ha3Za1yLujGs576iZfVTy8uLClCWy0SnRtusTbUxjHKHe4XLshwktpsbQ9Jt6FcMDRkKwM4LO8kryav1/EMphyjRlRDoGmgs9CoaH3uNaxnpFvbM7KT7xIkTDBw4sNLz3t7eZGZmWqNNDcIQ6M4aZzRqjVnnejkrI92Vku5aiqgZVLeu21C5XKaXC1uz17gWdWMY6e4d2Nuk4w0j3YdSD1W5hWJ9kCJqFVkrtr/44guioqLw8vLCy8uL/v37s2bNGuPrhYWFTJ48GX9/fzw8PBg/fjwXL160xrcgzHB50t3Opx2hXqEU64rZllD7tOM9F5X13L2Dest67kbMXj+zJekW9szspDsoKIjTp09Xen7btm1ERERYpVENoS6BXttId3VF1AwM67ovT7plermoL/Ya18JyibmJJOUl4aByMCbTtenk1wkHlQPphekk5iXauIVVk/XcFVkrttu0acPbb7/Nvn372Lt3L0OHDmXcuHEcOaIkeVOnTuWPP/5g6dKlbN68mQsXLnDLLbdY7fsQtdPr9RxNVbYp7RqgJN0qlco42m1KFXPjem7Zn7tRs9fPbNkyTNgzs5Puhx56iCeffJJdu3ahUqm4cOECixcv5plnnmHSpEm2aGO9MEwvt2Q6YlVrurU6rbHzV10RNQPDSHdMZgy5xbnG5w1Jt4x0C1uz17gWljNMLe/s39nkDpCLgwsd/JSCa4dSK2+DWB8MlctlpFthrdgeO3YsY8aMoX379nTo0IG33noLDw8Pdu7cSVZWFt988w0ffvghQ4cOpXfv3ixYsIDt27ezc+dOG353orykvCQyijJwUDnQ3re98fmhbZWke+v5rZRoS6o9v0hbZFzmJvtzN272+pktI93CntVeGecyL7zwAjqdjmHDhpGfn8/AgQNxdnbmmWee4YknnrBFG+tFfqnlxRuMI93l9uk+m32WgtICXB1cCfUKrfH8ANcAWnu0JiE3gcOph+nfqj8g1ctF/bHXuBaWM0wtr2mrsKp0D+jO0bSjRKdEc13YdbZoWrXyS/I5m30WgI6+knSDbWJbq9WydOlS8vLy6N+/P/v27aOkpIThw4cbj+nUqRNt27Zlx44d9OvXr8rrFBUVGQtAAWRnZ1d5nDlyi3N5adtLnMs9xy2Rt3Bj5I3GG+P2zjC1vL1v+wq1aaJaRBHgGkBqQSp7kvZwdeurqzz/UMohinXFBLgGEOYVVh9NFhay189sSbqFPTNrpFur1bJ161YmT55Meno60dHR7Ny5k5SUFN58801btbFeGEe6NdYZ6T6arkzx6ujb0aQ14obRbsNd5iJtEZlFmQAEuQeZ3SYhTGXPcS0sZxjp7hXYy6zzugd0BzBWQK5PJzNOokdPoGsg/q7+9f7+jY21Y/vw4cN4eHjg7OzMo48+yooVK+jSpQtJSUk4OTnh4+NT4fiWLVuSlJRU7fVmz56Nt7e38U9ISIjZbSovvTCdiWsnsvHcRk5lnOKdPe8wfOlw3tjxhnH7zsYsqyiLl7a9ZLzhZS5D0t3Fv+LsOrVKzZCQIUDNVcwNU8uvbCn7czdm9vyZLdXLhT0zK+nWaDSMHDmSjIwMnJyc6NKlC3379sXDw8NW7as3dbm7Zki6y6/pNhZRq2U9t8Hl67oNU8udNc7N5i69aBj2HNfCMllFWZzOUNYL9gzsada5hqT7aNpRSnWl1m5ajWRqeUXWju2OHTty4MABdu3axaRJk7jvvvs4evSoxe2bPn06WVlZxj/nzp2z+FqJuYnct+Y+jqYdxdfZlyd7PUmkTyQFpQUsPbmU8SvH8+XBLy2+fn1YcWoFK2NW8s7udyw6/0hqWRG1svXc5RmmmP9z7h90el2V5++9KOu5mwJ7/syWkW5hz8xe092tWzfOnDlji7Y0KGOgO1op6U43rXK5gSHpNlT9LV+5XO44C1uz17gWljmYchA9esK8wsweMQ7zDsPD0YNCbSExmTE2amHVjmdIEbXLWTO2nZyciIyMpHfv3syePZsePXrw8ccfExQURHFxcaWqyRcvXiQoqPqZWs7OzsZq6IY/lojJjOHuNXcTlx1HsHsw343+jge7P8jyG5fz7ahvjXtVL4heQJG2qJarNRxD5f0jaUdILUg161y9Xl+pcnl5fYP64u7oTkpBSpWzUIq1xcab/rKeu/Gz189sSbqFPbNon+5nnnmGVatWkZiYSHZ2doU/TZVhermrxvI13Ybp5Tq9zjjSffk0r+p09O2Is8aZrKIszmaflcrlol7Za1wLy+y/WLY/t5nruUGZymoYaavvYmoy0l2ZLWNbp9NRVFRE7969cXR0ZMOGS1OXT5w4QXx8PP3796/rt1CjwymHue+v+0jOTybCO4LvR39PuHc4oFTu7hPUhzmD59DSrSX5pfnsuLDDpu2pC0PSDUrRM3Mk5CaQXZyNo9qR9j7tK73upHFiYGtli6mqqpgfTj1MkbYIPxc/47+faLzs9TNbkm5hz8wupDZmzBgAbrzxxgojsHq9HpVKhVartV7r6lGdppdftk93Qk4CuSW5OKodifAxbesGR40jXfy78F/yfxxKPUR6QTogRdRE/bDXuBaWsbSImkFUQBS7EncRnRrNrR1utWbTqlWqKzWu25WR7kusFdvTp09n9OjRtG3blpycHJYsWcKmTZv4+++/8fb2ZuLEiUybNg0/Pz+8vLx44okn6N+/f7VF1Kxhx4UdPPnPkxSUFtA9oDufD/scHxefSsepVCqGhw5n8bHFrDu7jsEhg23WJksVa4uJzYw1Pt6asJWb299s8vmGUe4Ovh1w1DhWeczQtkNZE7eGjfEbearXUxV+HvYkKftz9wnqI7PrmgB7/cw2FDWWLcOEPTI76f7nn39s0Y4GV6BVku66bBmWV5JHqa7UWEStg28HHNVVf/hVJSogSkm6Uw4Zz5ORblEf7DWuhfmKtEXG6ae9WppXRM3AsK+3oTBkfYjPjqdIW4SrgyshnnUryGVPrBXbycnJ3HvvvSQmJuLt7U1UVBR///03I0aMAGDOnDmo1WrGjx9PUVERo0aN4vPPP7fKe1dlbdxant/6PKW6UvoH9+ejIR/V2FEf3lZJujed20SJrsSsz+b6cCbrDKX6UlSo0KNn+4XtlGhLqk2gL1fT1HKDa1pfg6PakbjsOGKzYisMChjWc8vU8qbBXj+zZaRb2DOzk+5BgwbZoh0Nri6B7uF0qXhFbnGu2UXUDHoE9oCjynpKQ6expbsk3cL27DWumzu9Xg9g1sjV0bSjlOhK8HPxo61nW4ve11BMLSYzhrySPNwd3S26jjmOpyvruTv4dkCtMnvllN2yVmx/8803Nb7u4uLCZ599xmeffWaV96vJLyd+YebOmejRMzJ0JLOvnY2TxqnGc64IvAI/Fz/SC9PZm7TXuDWnNSTmJqLVa2nj2cbiaxiWRlwReAVx2XGkF6azP3k/VwVfZdL5R1OVm/1VFVEz8HDy4Krgq9iWsI0N8RuMSXeJtoSDycp6bimi1jTY62e2JN3CnpmddANkZmbyzTffcOyYklx27dqVBx54AG9vb6s2rj4VlFge6I5qR9wc3MgvzSenOMfsImoGUQHKtmEnM04aq4vK9HJRX+wxrpuzuKw47lh9B7d2uJVpV04z+TzDeu5egb0snmbawq0FQe5BJOUlcTTtaL105KWIWvXsJbb1ej3zD89n7n9zAbi1w628dNVLJm3LqVFrGBIyhGWnlrH+7HqrJd0luhLu/vNuinXF/DX+L4tvMBnWc3f270wbzzasjFnJlvNbTEq69Xo9R9PKku4aRroBhrUdxraEbWyM38hDUQ8BEJ0WTaG2ED8XPyK8TVsSJxqevcR1eZJ0C3tm9nDA3r17adeuHXPmzCE9PZ309HQ+/PBD2rVrx/79+23RxnpRqC3bp9uC6eVwqZhadnG22UXUDFq6tyTIPQidXmdcmyhJt6gP9hrXzdnm85vJLcnl+6Pfcy7b9K2Y6rqe26C+9+uWImpVs6fY/unET8aE+6HuD/FKv1dMSrgNRoQqU+E3xG9Aq7POmtek3CSSC5LJLMo0Jr6WOJmufOZ39O3IwDZKwbMt57eYdO65nHPklOTgpHaqtY7M4JDBqFARnRZNUp6yh7phPXfvlr1lPXcTYU9xbaDT6yTpFnbN7KR76tSp3HjjjcTFxbF8+XKWL19ObGwsN9xwA0899ZQNmlg/6hrohmJqJzNOklGUgUalob1v5QqitTGMdhvImm5RH+w1rpszw407rV7L/Oj5Jp2j0+uMSbel67kNjEl3iu2Tbr1eb5xe3slXRrrLs6fYHhM+hg6+HXj2ymeZ0muK2Qli36C+eDp6klaYZtweq67O5V66oWXpDSa9Xm8c6e7g14GrW12Ng8qBuOw4k26YGdZzd/LrVOta9QDXAHoG9gSUPbsB9ibJeu6mxp7i2sCwixCAm4MUUhP2x6KR7ueffx4Hh0sz0x0cHHjuuefYu3evVRtXn4xbhlmYdHs6KiPdu5J2AdDOpx3OGmezr2PYrxuUrXcCXAMsao8Q5rDXuG7ODEk3wMrTK7mQe6HWc85kniG7OBtXB9c6jxgbiqnVx0h3akEq6YXpqFVqIn0jbf5+TYk9xba3szc/Xv8j93a916LzHTWOxsrl6+PXW6VN53POG7+OTo226BrJ+cpIuUalIdInEk8nT65oqcw02ZJQ+2j3kVQl6TZ1dt3QkKGAMuJfoivhQMoBQNZzNyX2FNcGhsEvsHzWqRCNmdlJt5eXF/Hx8ZWeP3fuHJ6enlZpVEOw1kj37sTdgPnruQ2iWlwa6fZ38cdBbdGyeyHMYq9x3VyV6kqJyYwBINw7nFJ9Kd9Gf1vrefuTlWmJUQFRda7u3NW/K2qVmov5F0nOT67TtWpjGOUO8wqTaYmXsbfYrq1gWm2GhQ4DYMPZDcZCg3VxLqfuI92GUe4wrzDjzXrDntqmTDE3Vi6voYhaeUPbKkn33qS97Liwg4LSAnycfWjn087stouGYW9xDRX74VIMU9gjs3+qJ0yYwMSJE/n55585d+4c586d46effuLBBx/kjjvusEUb60Wdk+6ybcNSClIA8yuXG3Tx72Ls7Mp6blFf7DWum6uz2Wcp0ZXg5uDGy1e9DMDyU8u5mHexxvOsNbUclH1WDZ14W492G5IWWc9dmcR2RVe3uhpXB1cu5F0wbu9ZF+VHupPykkgtSDX7GoZZKR38OhifM6zr3pO0h/yS/GrP1el1xuKtpo50t/VqS6RPJFq9lo/2fwQoU8sl0Wk67DGuZT23sHdmD6O+//77qFQq7r33XkpLSwFwdHRk0qRJvP3221ZvYH0xBLulU1oMSbeBuUXUDJw0TnT268yh1EOynlvUG3uN6+bK0Ilv79uePkF96BXYi/3J+1l4ZCHP932+2vOsVUTNICogilMZp4hOjWZY22FWuWZVDEXUpHJ5ZRLbFbk6uHJN62tYd3YdG85uqLXad23Kj3SDMsXcMIXdVMYigL6XbhqFe4fTxqMN53PPszNxp3F0+nJns8+SV5KHi8bFrMrjw9oO43TmaU5lnALgyiBZz92U2GNc55cqN5ck6Rb2yuzbmk5OTnz88cdkZGRw4MABDhw4QHp6OnPmzMHZ2fw1zI1FXe+wGaqXA6hQVfjwNJehyEkrj1YWX0MIc1gzrmfPnk2fPn3w9PQkMDCQm266iRMnTlQ4ZvDgwahUqgp/Hn30UWt+S82aoSPdwbcDKpWKR6IeAeDXk7+SVpBW5TlJeUkk5CagUWkqLHOpC+O6bhsXU5MiatWz18/suhjedjgA686uq9MUc71ez/lcZaTbUI/FklkdVc3UUKlUJlUxL19EzZzlaJcn8VJErWmxx7iWkW5h78xOurOyskhPT8fNzY3u3bvTvXt33NzcSE9PJzs72xZtrBeGQmoumrqPdId5h+HmaHnlxfu73s+D3R/kni73WHwNIcxhzbjevHkzkydPZufOnaxbt46SkhJGjhxJXl5eheMeeughEhMTjX/effdda35LzVr5kW6A/q36082/G4XaQr47+l2V5xxIPgAoHX9L9xq+nKGCeXRaNDq9zirXvFx+ST5ns88CFafnCoW9fmbXxcA2A3FUOxKXHceZrDMWXyejKIO8kjxUqBgVNgowv5haYWmh8ef38pv1hqR7a8LWam8OGIqombqe26CzX2eC3YMBpUCdJbutiIZjj3FdUCJJt7BvZifdt99+Oz/99FOl53/55Rduv/12qzSqIRjusFm6TYGhkBpYXkTNoIVbC57s9aSMdIt6Y824/uuvv7j//vvp2rUrPXr0YOHChcTHx7Nv374Kx7m5uREUFGT84+XlVc0VhbmMa0R9lSRUpVLxSA9ltPun4z+RWZhZ6RxDEbVegXVfz23Qzqcdrg6u5JXkEZsVa7Xrlncq8xR69LRwbSG7PVTBXj+z68LDyYN+wf0A2JawzeLrGKaWB7oFGusgRKdGmzV6fjrzNDq9Dj8Xv0o/v1cGXYmrgyvJ+cnG0fDLGfYGN3eavEqlMo529w7sLeu5mxh7jGsZ6Rb2zuzfsrt27WLIkCGVnh88eDC7du2ySqPqm16vvxTsjnXbMgwsX88tREOxZVxnZWUB4OfnV+H5xYsXExAQQLdu3Zg+fTr5+dUXCxKmyy7OJjEvEaDC6NWgNoPo5NeJgtICFh1bVOk8a6/nBnBQOxh/H9qqmJpxPawUUauSPX5mW4NhZLguN4MMRdRCPEPo4NMBJ7UT2cXZldZ518Tw82tYClKes8aZq4KvAqqeYq7Vac0uolbew1EPM6HjBKb0mmL2uaJh2WNcS9It7J3ZSXdRUZGxaEN5JSUlFBQUVHFG41esK0aPcmfa4unlVhzpFqK+2SqudTodTz31FAMGDKBbt27G5++8805++OEH/vnnH6ZPn86iRYu4++67a2xfdnZ2hT+iaqczTgMQ5B5UYdmLSqXi4aiHAVhybAnZxZf+DXOKc4ydf2sm3XBpirmt1nUb13NLEbUq2eNntjWEe4UDdUu6Dcl1G882OGoc6eSv/Ayac4PJuJ67mjowNa3rjsuOo6C0AFcHV8K8wsxpOgB+Ln683O9l2SqsCbLHuK7rjFMhGjuzk+6+ffsyb968Ss9/+eWX9O7d2yqNqm+GdSRgefXy8oXUDB+8QjQVtorryZMnEx0dXWka3MMPP8yoUaPo3r07d911F99//z0rVqwgJiamyuvMnj0bb29v45+QkBCL22TvLp9aXt6wtsOI9IkktySXJceWGJ8/mHIQPXpCPENo4dbCqu0xJt0y0t0g7PEz2xrCveuedJcf6YZyNQzMWNdd28/vta2vBeBQyiEyCjMqvGYootbZrzMatcaMloumzh7juq4zToVo7MzeMmzmzJkMHz6cgwcPMmyYsgXMhg0b2LNnD2vXrrV6A+tDoVYpouaodjSr+md5YV5htPNuR4RPRKXtw4Ro7GwR148//jirVq1iy5YttGnTpsZjr7pKmUJ5+vRp2rWrPOoyffp0pk2bZnycnZ0tiXc1akq61So1D3V/iOe3Ps8Px37gni734O7ozv6Lynpua49yw6VE5FTGKQpLCy2+sVkVrU5r/H6lcnnV7PEz2xpCvUIBpRhaZmEmPi4+Zl/DMNJtSLqN1fpNvMGk1+sr7DRQlSD3IDr6duRExgm2JWxjbLuxxtcsLaImmj57jGvZMkzYO7NHugcMGMCOHTsICQnhl19+4Y8//iAyMpJDhw5x7bXX2qKNNmeNQHfSOLFi3Ao+HPyhtZolRL2xZlzr9Xoef/xxVqxYwcaNGwkPD6/1nAMHDgAQHBxc5evOzs54eXlV+COqVlPSDTAqbBShXqFkFWXx03FlBoJhPbc1i6gZBLkH4e/iT6m+1DgV3FrO5pylUFuIq4OrMfERFdnjZ7Y1uDm6EeQeBCjTtC1hGOlu46HcVOzmryTdx9OPU6IrqfX8C3kXyCnJwUHtUOMe29VNMTeMdNd1r3HR9NhjXMuabmHvLBrW7dmzJ4sXL7Z2WxqMIdDrOgJzeREUIZoSa8X15MmTWbJkCb///juenp4kJSUB4O3tjaurKzExMSxZsoQxY8bg7+/PoUOHmDp1KgMHDiQqyjr7QzdXOr2O05nKmu72PlVvAaRRa3iw+4O88u8rfH/0e27teKtxZO6KltYf6VapVHRv0Z1N5zZxKOUQPQN7Wu3a5YtQyfTa6tnbZ7a1hHuFk5SXRGxWrNk/l4WlhSQXJAOXRrrberXF08mTnOIcTmecprN/zfVdDD+/7bzb4ahxrPa4gW0G8vXhr/n3wr+U6kpxUDtQqis1ni/FW5sne4trSbqFvTM56S4tLUWr1eLs7Gx87uLFi3z55Zfk5eVx4403cs0119ikkbZm2KO7uRRv0Gq1lJTUfhdeNA5OTk6o1bbZzsUWcf3FF18AShXV8hYsWMD999+Pk5MT69ev56OPPiIvL4+QkBDGjx/Pyy+/XOfvp7m7kHuBvJI8HNWOhHqHVnvc9RHX8+XBL0nITeCNHW9QpC3Cx9nHWFzK2roHKEm3uXsY10aKqFXPnj+zrSXMO4wdiTssWtedkJsAgIejB97O3oCyfKObfzd2JO7gcOrh2pPuDNPqEXQP6I6Psw+ZRZkcSD7AlUFXcibrDIXaQtwd3Y1T5YX9s+e4bk77dOt0OoqLixu6GcJEjo6OaDR1v7FvctL90EMP4eTkxFdffQVATk4Offr0obCwkODgYObMmcPvv//OmDFj6tyo+tZc7q7p9XqSkpLIzMxs6KYIM6jVasLDw3FycrL6tW0R17XtURsSEsLmzZvr1G5RNcPU8nY+7XBUVz9y5qh2ZGL3ibyx4w3+jvsbUNZz22q2jq2KqZUf6RYV2fNntrUYi6llm590ly+iVj5uugUoSXd0ajS3dbytxmucTK95KYiBRq1hQOsBrD6zmi0JW7gy6Erjeu4u/l1kj+1mxJ7jurn0xYuLi4mNjUWn0zV0U4QZfHx8CAoKqlM/yeSk+99//+XTTz81Pv7+++/RarWcOnUKb29vnn/+ed57770mGeiGkW5rFvhpjAwJd2BgIG5ubjIdvgnQ6XRcuHCBxMRE2rZta/X/M3uO6+bIUJSpuqnl5Y1rN46vDn7FxfyLgG3WcxsYCj2dzz1PemE6fi5+tZxhGhnprp7Edu0MSXdcVpzZ55bfLqw8c24wmTrSDTCw9UBWn1nN1vNbmdZ7mqznbqbsOa6bw5Zher2exMRENBoNISEhNpvFKKxHr9eTn59PcrKynKi62kOmMDnpTkhIoH37Sx25DRs2MH78eLy9lWlV9913HwsWLLC4IQ2pOdxd02q1xoTb39+/oZsjzNCiRQsuXLhAaWkpjo7Vj15awp7jujmqrYhaeU4aJx7o9gCzd88GbLOe28DLyYtw73Bis2KJTo02Foaqi9SCVNIK01Cr1LT3rf0mQ3MjsV07w97W53LOUaItqXFd9eWqS7oNFczPZJ0hvyQfN8eqE4i8kjzjNarbo7u8Aa0HoFapOZ15mgu5FziadhSQ9dzNjT3HdXPoi5eWlpKfn0+rVq1wc7Pfmwv2xtVV+ZlMTk4mMDDQ4qnmJt9icXFxoaDg0n7WO3fuNG7zY3g9NzfXokY0NGMhNY39jnQb1nBLkDc9hmnlWq3W6te257hujsxJugFuaX8LkT6RRHhH0MXPtp13a08xN4xyh3qF2nUnzVIS27Vr6dYSVwdXtHot53LPmXXu+dyKe3QbtHBrQUu3luj0OmNiXBXDrJRA10B8XXxrfT9vZ296tugJwMb4jcalFTLS3bzYc1wbdxKy4326Df04WywXFLZlyJ/qUhPL5KS7Z8+eLFq0CICtW7dy8eJFhg4danw9JiaGVq1aWdyQhmS8u2bHgW4gU8qbHlv+n9lzXDc3haWFxOfEA5g88uvi4MLSsUv5bdxvZo3yWcJWSbfsz101ie3aqVSqS+u6zSymZhzp9mhT6TXDz3pNhQON9Qj8TK9HcG0bZSuoRUcXUawrxtPRU7bKa2bsOa6bw0i3gfTFmx5r/J+ZnHS/+uqrfPzxx7Rr145Ro0Zx//33V5jXvmLFCgYMGFDnBjWE5hToQpRnz3Hd3MRkxaDT6/B19iXANcDk8xzUDvXSASifiNRWbM8UhqTFlPWwzZHEtmkMU8zNSbp1eh0JOUr18qqSXsMU85puMBnXc5swtdxgUJtBgLK/N0CXgC7SeW9m7DmupS8u7J3Ja7oHDRrEvn37WLt2LUFBQdx6660VXu/Zsyd9+/a1egPrg7GQmh1PLxeiKvYc103VnqQ95BbnMqTtELPOK18JuTF2xDv4dsBJ7URWURbncs7R1qttna4nRdRqJrFtGkuKqSXnJ1OsK8ZB5UCQe1Cl1w1Jt6HYWVXMKaJmEOkTSbB7MIl5iYBMLW+O7DmuJekW9s6ssnmdO3fmySefZMKECZUq7j388MP07NnTmm2rNxLojdvgwYN56qmnKj2/cOFCfHx8AHj99ddRqVSoVCocHBwICwtj6tSpxrVNcXFxxtdVKhWenp507dqVyZMnc+rUqUrXNRynVqsJDg5mwoQJxMfHVzguNjaWO++8k1atWuHi4kKbNm0YN24cx48fr/H7mTJlCr1798bZ2blRxIy9xnVTo9Vp+WjfRzzw9wNM+WcKZ7LOmHW+YT13Yy0q5qhxpJO/kiAfSj1Up2vll+RzNvssICPdNZHYrp0l24YZppYHewTjoK48dtHFvwsqVCTkJpBWkFbpdZ1eZ1zTbc52dyqVqkIRQimi1jzZY1zr9LpmUb28qZJ+uHVIrXok6bYXXbt2JTExkbi4ON555x3mzZvH008/XeGY9evXk5iYyMGDB5k1axbHjh2jR48ebNiwocJxXl5eJCYmkpCQwLJlyzhx4kSFO8olJSWMGDGCrKwsli9fzokTJ/j555/p3r27SfugP/DAA0yYMMEq37do+jIKM3h0/aN8E/2N8bl1cevMusapTPM78fXNlLWupjiVeQo9egJcA8yaSi/E5cqv6TZ12UP5Pbqr4unkabxuVaPd53LOUVBagJPaiVCvULPaWz7plpFuYS8MM05B+uJNmfTDa2by9HJ7Zgh2CfSmzcHBgaAgZarfhAkT2LBhAytXruSrr74yHuPv7288JiIigrFjxzJs2DAmTpxITEyMcRsAlUplPC44OJiJEycyZcoUsrOz8fLy4siRI8TExLBhwwZCQ5VOU2hoqElrqebOnQtASkoKhw7VbcRPNH3RqdFM2zSNxLxEXB1cGdxmMGvi1rDu7Doe6fGISdfQ6/UVppc3VsZiail1K6Ym67mFtbT1bIsKFTnFOaQXpuPvWvuWmjUVUTPoFtCNM1lnOJx6uNIWeYaf30jfyCpHymvSN6gvEd4ReDl50dqjtVnnCtFYGSqXg1LgUzRN0g+vmYx00zxHuvV6PfnFpQ3yxxpFlEzh6upKcXFxjceo1WqefPJJzp49y759+6o8Jjk5mRUrVqDRaIy/DFq0aIFarebXX3+1yVZeonlYdnIZ9665l8S8REK9QlkyZgkvXvUiGpWGExkniM+Or/0iQFphGhlFGahVaiJ8ImzcassZku5j6cco0Vq+7YYhaZHK5aKuXBxcaOWhVHs2tZhadduFlVdTtX5LiqgZuDi48Nu431g0ZlGjrN0ghCXK98PVquaTmth7X1z64RU16Ej3li1beO+999i3bx+JiYmsWLGCm266yfj6/fffz3fffVfhnFGjRvHXX39ZtR0F2rJ9upvR3bWCEi1dXv27Qd776BujcHOy7Y/evn37WLJkSYWtNKrTqZPScY+LizMWIMnKysLDw0P5hZiv3IGdMmUK7u7uALRu3Zq5c+fy3HPPMWPGDK688kqGDBnCXXfdRURE4016RONQpC1i9q7ZLDu1DIAhIUN465q38HTyBKBPUB92Ju5k3dl1TOw+sdbrGUa523q2bdQ3D0M8Q/B29iarKIsTGSeMBafMdTxDiqgJ6wn3DichN4HY7FiuDLqy1uNrm14O5YqppR5Br9dXSJAN8WrpTA1JtoU1NYa+eHMc/AL77otLP7yyOt1OKiws5LvvvuPzzz+vtAjeFHl5efTo0YPPPvus2mOuu+46EhMTjX9+/PHHujS5Ss012O3N4cOH8fDwwNXVlb59+9K/f38+/fTTWs8z3O0r35Hx9PTkwIED7N27lw8++IBevXrx1ltvVThv8uTJJCUlsXjxYvr378/SpUvp2rUr69Ypa3FHjx6Nh4cHHh4edO3adNbe1TWum4q8kjxe/fdVlp1cVq/vm5ibyH1r7mPZqWWoUDHliil8NOQjY8INMCJ0BADrz6436ZqG9dyNtYiagUqlMmk7pZpodVpjESqZXm6e5hLb5jJ32zDj9HLP6qeXd/DtgKPakcyiTOPIuIGh6GFjXgoimg576ItLP9w+SD+8Zibf4pg2bRolJSV88sknABQXF9O/f3+OHDmCm5sbzz33HOvWraN///4mv/no0aMZPXp0jcc4Ozsb5/TbiiHYm9NIt6ujhqNvjGqw9zaHl5cXWVlZlZ7PzMzE29vb+Lhjx46sXLkSBwcHWrVqhZOTk0nXP3bsGADh4eHG59RqNZGRkYBSKTQmJoZJkyaxaNGiCud6enoyduxYxo4dy8yZMxk1ahQzZ85kxIgRzJ8/n4IC5WfL0dHRrO+5vtgirpsCvV7PjO0zWBO3hs3nNzO+w/h6ec/tF7Yzfet0Mooy8Hb25t1r3+Xq1ldXOnZo26HM3DmT6LRoLuReME5/rU5T6sRHBUTxb8K/FhdTi8+Jp6C0AFcHV9p61m3bMXvWXGPbEuZsG5ZTnENmUSZQc9LtpHGik18nDqceJjo12jgqnl2cbdxnuynEq2hc7LUv3lyT7qbSF5d+uHWYPNK9du1aRowYYXy8ePFizp49y6lTp8jIyODWW29l5syZVm/gpk2bCAwMpGPHjkyaNIm0tMrbb5RXVFREdnZ2hT+1MRRSa07bFKhUKtycHBrkj7lT4zp27Mj+/fsrPb9//346dLjUaXFyciIyMpKwsDCTA12n0zF37lzCw8O54oorqj3uhRde4Oeff66yHQYqlYpOnTqRl5cHKFNfIiMjiYyMNBZ5aGwaKq4b2tKTS1kTtwaA9MJ0irU1rzmyRHZxNtsTtvPFwS+YtH4S1/58LY+uf5SMogy6+Hfh5xt+rjLhBghwDaB3y96AaaPdjX27sPIMI92HUiwrXmJYz93etz0atXk38JqT5hrblihfwbw2hqnlfi5+uDu613hsVbM6DFPLg92D8Xb2rvI8Iapjr33xgpLmuV1YU+mLSz/cOkwe6Y6Pj6dLl0t7Qq5du5b//e9/xm/iySefZMyYMVZt3HXXXcctt9xCeHg4MTExvPjii4wePZodO3YYF9Jfbvbs2cyYMcOs92mud9iaikmTJvHpp58yZcoUHnzwQZydnVm9ejU//vgjf/zxh1nXSktLIykpifz8fKKjo/noo4/YvXs3q1evrvZnCiAkJISbb76ZV199lVWrVnHgwAFee+017rnnHrp06YKTkxObN2/m22+/5fnnn6+xDadPnyY3N5ekpCQKCgo4cOAAgPE69akh4rqhHU07ytu7367wXEpBSp0rAZ/OOM1/Kf9xKOUQh1IOVbnPtpPaiZsib+K5vs/hrHGu8XrDQ4ez9+Je1p1dx71d7632uBJdCTGZMUDTGDkzFJiKy44juzgbLycvs84/nl62nluKqNWoOca2pQxJd0JuAkXaohpj0zBVvKZRboPy67oN6lJETQh77YtLP7xxk364dZicdKvV6gqV7nbu3Mkrr7xifOzj40NGRoZVG3f77bcbv+7evTtRUVG0a9eOTZs2MWzYsCrPmT59OtOmTTM+zs7OJiSk+mIncGmkuzlNL29KIiIi2LJlCy+99BLDhw+nuLiYTp06sXTpUq677jqzrjV8+HAA3NzcCA0NZciQIcybN884haUmU6dOpX///uzevZuIiAjCwsKYMWMGcXFxqFQq4+OpU6fWeJ0HH3yQzZs3Gx8b7uzFxsYSFhZm1vdTVw0R1w0puzibpzc9TYmuhCEhQziZcZKE3ASS85MtTrr1ej3v7X2PRUcXVXotxDOEqBZRRAVE0aNFD2Wdp8a0KU7D2w7n7d1vcyDlABfzLtLSvWWVx8Vnx1OiK8HNwa1JbCHk6+JLG482nM89T3RqNFe3qnq0vzqGImqynrtmzS2268LfxR9PR09ySnKIz46vccaIYT13TUXUDAxJ99G0o5TqSnFQO1xaCuLX+G+QicbHXvvihi3DJOlunKQfbh0mJ92dO3fmjz/+YNq0aRw5coT4+HiGDBlifP3s2bO0bFl1p9BaIiIiCAgI4PTp09UGurOzM87ONY8glafX6+UOWxPQp08f1q5dW+3rr7/+Oq+//nq1r4eFhZm8PcL999/P/fffX+n5fv36VbjGxx9/bNL1Lrdp0yaLzrOFxhDX9UWv1/Pqv69yPvc8rT1a8+aAN5mycQoJuQlczL9o8XW/P/o9i44uQoWKvkF9iWqhJNjdW3THz8XP4uu2dG9JjxY9OJhykA3xG7iz851VHld+anlT2Wqle4vuFifdxu3CpHJ5jZpTbNeVSqUi3DucQ6mHiM2KNSnprmmPboMwrzA8HD3ILcklJjOGjn4dL+0xLyPdwgKNIa5t0ReXfnjjJ/3wujM56X7uuee4/fbbWb16NUeOHGHMmDEVFrz/+eefxjLvtnL+/HnS0tIIDg622jVLdCVo9cr+bjLSLZqbxhDX9eWHYz+wIX4DjmpHPhj0Ad7O3gS6BQKQnJds0TXXnV3HB3s/AODpK5/mvq73Wa29oFQxP5hykPXx601KupuK7gHdWRO7hsMp5lUwTy1IJbUgFRUqIn1qvyvenDWn2LaGMO8wY9JdE1O2CzNQq9R0DejKrsRdHE49TDufdpzOPA3ITA1hmcYQ17boixuTbkdJuoX9MnlY5Oabb+bPP/8kKiqKqVOn8vPPP1d43c3Njccee8ysN8/NzeXAgQPGufSxsbEcOHCA+Ph4cnNzefbZZ9m5cydxcXFs2LCBcePGERkZyahR1qv0Zwh0kDtsovmxRVw3RgeSD/Dh3g8BeLbPs3QNULaOMCbd+eYn3QeSDzB963T06Lm94+3c26X6ddeWGh6qTMPad3EfaQVVF64xbJ/VFNZzGxjWdR9OPWzynW+4NMod6hWKm2PzKrhjruYS29ZirGCeHVfjcaZsF1ae4Wc9OjWa+Ox4irRFuDq4mpS0C3E5e++LSz9c2DOzdkUfNmxYtVNJXnvtNbPffO/evRWmxRjWf9x333188cUXHDp0iO+++47MzExatWrFyJEjefPNN82aslIbQ6A7qB1wVDd8OXkh6pu147qxySjM4JnNz1CqL2VU2Chu73hpfZqlSXd8djxTNk6hSFvE4DaDeaHvC2ZX5TdFa4/WdPXvypG0I2w8t5FbO9xa6ZimtF2YQSe/TjioHEgrTCMxL7HWLdEMjEXUZGq5Sew9tq0p3Kv2CuYluhKS8pIA00a6Abr5K+u6o1OjjUXUmtJSENH42HNfXJJuYc+s9lt/+fLlREVFmXXO4MGD0ev1lf4sXLgQV1dX/v77b5KTkykuLiYuLo558+ZZfa2KoYiaq0YCXYjLWRLXjYlOr+PFbS9yMf8ioV6hvN7/9QrJcUs35feJOWu6MwozmLR+EhlFGXT178o7A9+x6dZVhtHudXHrKr2WXZxNYl4iQJOabu3i4GKcDl9+O6XaGNfDytTcOmvqsW1tYd5hgJJ0Vzf7Iik3Ca1ei7PGmRauLUy6rqGY2unM0xxMOQjIem5hO021L25IupvblmGieTEr6f7qq6/43//+x5133smuXbsA2LhxI1dccQX33HMPAwYMsEkjbUnuronmzh7j2uDb6G/ZlrANZ40zHwz6AA8njwqvmzvSXVhayJSNU4jPiaeVeys+Hfapzac5jwhV9mTdnbSbzMLMCq8ZppYHuQc1uT1/o1ooHUNz1nUbKpfLSLdp7Dm2rS3EMwSNSkN+aT4pBSlVHlO+iJqpM1taurck0DUQrV7L6jOrAUm6Rd3YY1wb9umWvriwZyYn3W+//TZPPPEEcXFxrFy5kqFDhzJr1izuuusuJkyYwPnz5/niiy9s2VabMCTdUkRNNEf2GtcAe5L28Ml/nwDw0lUvVTk6Wj7prm1tsU6v46VtL3Eg5QCeTp58MfwLAlwDrN/wy4R6hdLBtwNavZZ/zv1T4bWmuJ7bwDACaOpId35JPnFZcYAk3aaw59i2BSeNk3GddnVTzA17dJu7Htvws55ZlAnITA1hOXuNa9kyTDQHJifdCxYs4Ouvv2bv3r2sWbOGgoICtm/fzunTp3nhhRfw9fW1ZTttxji9XAJdNEP2GtepBak8t+U5dHodN7a7kZsib6ryOEPSXawrJqsoq8Zrztk3h7Vn1+KgduDjIR8T4RNh7WZXyzDavT5+fYXnm+J6boOoAGWk+1j6MUp1pbUefzrzNHr0+Lv418vNjqbOXmPblsK8woDqk25zi6gZGJJug6a004BoXOw1rmXWqWgOTE664+PjGTp0KADXXnstjo6OzJgxA3d3d5s1rj7ISLdozuw1rt/a+RapBalE+kTy0lUvVTsV1EnjhK+z0kmpaV33T8d/YuGRhQC8OeBN+gT1sXqba2JIurdf2E5OcY7xeeN2YT5NrxMf5q3sYVxQWkBMZkytx0sRNfPYa2zbUm0VzA3bhdUl6Q7xDMHdUf4PhGXsNa4l6RbNgclJd1FRES4ulxJTJycn/Pz8bNKo+iRTWkRzZq9x/fSVT9MnqA8fDPqg1jXXta3r3nxuM7N3zwbgiSue4IaIG6zbWBO082lHhHcEpbpSNp/fDCjT3Q17/jbFkW7DHsZg2hRzKaJmHnuNbVsyJN21jXSbO73c8HMOsp5b1I29xrXs0y2aA7MKqb3yyitMmzaNadOmUVxczMyZM42PDX+amkKtTC9v7AYPHsxTTz1V6fmFCxfi4+MDwOuvK1WpVSoVDg4OhIWFMXXqVHJzcwGIi4szvq5SqfD09KRr165MnjyZU6dOVbqu4Ti1Wk1wcDATJkwgPj6+wnGxsbHceeedtGrVChcXF9q0acO4ceM4fvx4td/LwYMHueOOOwgJCcHV1ZXOnTvz8ccf1+0fqI7sMa7beLbh21HfmjQFvLak+50976DT6xjffjwPdX/Iqu00h6GK+fqzyhTzC7kXyCvJw1HtSKh3aIO1qy7K79ddGymiZj57jG1bqml6uV6vt3h6uZeTl/HaHfya3g0y0bjYY1zLSHfjJv1w6zB5n+6BAwdy4sQJ4+Orr76aM2fOVDjGFvvU2pqhYqJML2/6unbtyvr16yktLeXff//lgQceID8/n6+++sp4zPr16+natSv5+fkcPnyYjz/+mB49evDHH39U2PfSy8uLEydOoNfriY2N5bHHHuPWW281VgotKSlhxIgRdOzYkeXLlxMcHMz58+dZs2YNmZmZ1bZx3759BAYG8sMPPxASEsL27dt5+OGH0Wg0PP744zb7t6mOvca1OWpKuvNK8owd7am9pzbov8XI0JHMOzSPbQnbyC/JN04tj/COwFHt2GDtqgtTk26tTmssGicj3aaR2DafYaQ7MS+RgtKCCglARlEG+aX5qFDR2qO12dceFzmO+YfnM6xt1fsrC2EKe41r2TLMPkg/vGYmJ92bNm2yYTMajmGkWwK96XNwcCAoKAiACRMmsGHDBlauXFkh2P39/Y3HREREMHbsWIYNG8bEiROJiYlBo1H2W1apVMbjgoODmThxIlOmTCE7OxsvLy+OHDlCTEwMGzZsIDRUGWUMDQ2tdauOBx54oMLjiIgIduzYwfLlyxsk6bbXuDZHTXt1G9Z2+rv4N/iWXB18O9DWsy3xOfFsTdhqHI1rilPLDQxJd0xmDPkl+dUuBYjPiaegtAAXjQuhnk1zVL++SWybz9fFFx9nHzKLMjmbfbbCrArDzbdAt0CcNc5mX/vB7g/yYPcHrdZW0TzZa1znl8hST3sg/fCamTW93B5JITX75erqSnFxcY3HqNVqnnzySc6ePcu+ffuqPCY5OZkVK1ag0WiMvwxatGiBWq3m119/RavV1qmdWVlZdrEmq6mqaaTbkNgaRsAakkqlMk4xX3d2XZPeLsyghVsLWrq1RKfXcSTtSLXHGdZzd/DtgEatqa/miWaouinmhiJq5q7nFkLUTKfXyVJPOyX98IrMSrrz8vJ49dVX6datGx4eHnh6ehIVFcUbb7xBfn6+rdpoU812HYleD8V5DfOnlv2QrWHfvn0sWbLEWOWzJp06KaMZcXFxxueysrLw8PDA3d2dli1b8s8//zB58mRjhdDWrVszd+5cXn31VXx9fRk6dChvvvlmpWletdm+fTs///wzDz/8sFnnWZM9xrU5mkrSDcoUc4At57cYk9Smvv1QVAtl67CappifyJAiapZo7rFtieqKqVlaRE0Ia7O3uC7RldDZrzNhXmG1Fj61O3bcF5d+eGUmTy8vLi5m0KBBREdHM3r0aMaOHYter+fYsWO89dZbrFmzhi1btuDo2LTWFjbbfbpL8mFWq4Z57xcvgJP1t7c4fPgwHh4eaLVaiouLuf766/n0009rPU9f9oun/DooT09P9u/fT0lJCWvWrGHx4sW89dZbFc6bPHky9957L5s2bWLnzp0sXbqUWbNmsXLlSkaMGMHo0aPZunUroEx5OXKk4khedHQ048aN47XXXmPkyJF1/fYtYq9xbY6mlHR38e9CK/dWXMi7QEJuAtC0R7pB2U5p3dl1RKdGV3uMbBdmPoltyxi3DcuKq/C8pUXUhLAme4xrZ40zv4z9paGb0TDsrC8u/fCamZx0f/HFF5w/f56DBw/SsWPF0Ybjx48zePBgvvzyS5544gmrN9KWDFuGuWhkenlj5eXlRVZWVqXnMzMz8fa+tM62Y8eOrFy5EgcHB1q1aoWTk5NJ1z927BgA4eGXEiu1Wk1kZCQAnTt3JiYmhkmTJrFo0aIK53p6ejJ27FjGjh3LzJkzGTVqFDNnzmTEiBHMnz+fggJlJsXlH4BHjx5l2LBhPPzww7z88ssmtdMW7DWuzWFY051RlEGxthgnzaWfm8aWdBummH9/9HsAfJ19CXANaOBW1Y1hXfehlEPVHiPbhZlPYtsyxpHubJleLhofiWvREKQfbh0mJ93Lly/nlVdeqRTkoEwLeOmll/j111+bXKAbR7qb296Ajm7KXa6Gem8zdOzYkbVr11Z6fv/+/XTocGmUz8nJyRigptLpdMydO5fw8HCuuOKKao974YUXaNeuHVOnTqVXr15VHqNSqejUqRPbt28HlKkvVTly5AhDhw7lvvvuq3TXrr7Za1ybw9vZGye1E8W6YpLzk40jWVqdlrPZZ4HGk3QDjAgdYUy6O/h2aJKVasvr6t8VtUrNxfyLJOcnG2ceGKQWpJJSkIIKFe19mvZU+voksW0Zw5ruuKw4dHodapWyCs+QdLfxkJFu0XAkru1ME+mLSz/cOkxe03306FEGDx5c7etDhgzh6NGj1mhTvTIWUmtuI90qlTKtpCH+mJkkTJo0iZMnTzJlyhQOHTrEiRMn+PDDD/nxxx95+umnzbpWWloaSUlJnDlzhpUrVzJ8+HB2797NN998YyzOUJWQkBBuvvlmXn31VQAOHDjAuHHj+PXXXzl69CinT5/mm2++4dtvv2XcuHHVXic6OpohQ4YwcuRIpk2bRlJSEklJSaSkpJj1fViLvca1OVQqVZVTzC/kXqBEV4Kzxplg9+CGal4lUS2iCHRV2tvU13MDuDm60c6nHUCVU8xPpitbo4V6hTa/9X51ILFtmdaerXFQO1CoLeRinrKjQWFpIckFyu8GGekWDUni2s40kb649MOtw+SR7szMTPz9/at93d/fv8qpB42d7A3Y+EVERLBlyxZeeuklhg8fTnFxMZ06dWLp0qVcd911Zl1r+HCl+rObmxuhoaEMGTKEefPmmXRnburUqfTv35/du3cTERFBWFgYM2bMIC4uDpVKZXw8derUaq/x66+/kpKSwg8//MAPP/xgfD40NLRCAYn6Yq9xba5At0DO556vkHQbppeGeoUaR7saA7VKzR2d7+Dj/R8zJGRIQzfHKroHdOdUxikOpx5maNuKRVeOZyjruWVquXkkti3jqHakrWdbzmSdITYrlmCPYGP9BE9HzwbfOlA0bxLXoiFIP9w6TE66dTpdjXcg1Gp1nUu2N4SpvaeSUpBCt4BuDd0UUYM+ffpUObXF4PXXX+f111+v9vWwsDBjoYba3H///dx///2Vnu/Xr1+Fa3z88ccmXa+82tpZ3+w1rs1V1V7djW09d3kTu03kzk532s3Ib/eA7iw/tbzKCuZSRM0yEtuWC/MKU5Lu7Fiubn11hSJqTX05h2jaJK5FQ5F+eN2ZnHTr9XqGDRuGg0PVp5SWllqtUfWpd8veDd0EIRqMvca1uaqaXt6Yk26VSmU3CTdcKqZ2JPVIhXW0UK6Imq+MdJtDYtty4d7hcO7S7wDjem6pXC4amMS1EE2XyUn3a6+9Vusx48ePr1NjhBD1S+JaUWPS7dX4km57086nHa4OruSW5BKXFUeETwSgLP+Jy44DZKTbXBLblrt82zDZLkw0FhLXQjRdVk26hRBNi8S1ItC9aY102xsHtQOd/TqzP3k/h1IPGZPu0xmn0el1+Ln4Nfmt0eqbxLblwrzDgHIj3bmyXZhoHCSuhWi6TE66AXbu3Mkff/xBcXExw4YNM3vxvBCi8ZG4rrymO6Mwg4yiDEAppCZsL6pFFPuT9xOdGs1NkTcBl4qodfLrJGtpLSCxbRnDtmHJBcnkleQZR7ol6RaNgcS1EE2TyUn3r7/+yoQJE3B1dcXR0ZEPP/yQd955h2eeecaW7RNC2JDEtcIwvTwlPwW9Xm+c0hzsHmxXa6cbM0Mxy0Mph4zPGddzS+Vys0lsW87b2Rt/F3/SCtM4k3mGhBylerns0S0amsS1EE2XyfvgzJ49m4ceeoisrCwyMjKYOXMms2bNsmXbhBA2JnGtMOx7XawrJrMoU6aWNwBDMbVTGacoLC0EylUu95X13OaS2K4bwxTzXUm7KNYV46ByIMg9qGEbJZo9iWshmi6Tk+4TJ07wzDPPGLcqePrpp8nJySE5ObmWM4UQjZXEtcJR44ifix+grOuWpLv+BbsH4+/iT6m+lOPpx9HqtJzMOAlIETVLSGzXjSH2t57fCkCwRzAOarNW5AlhdRLXQjRdJifd+fn5eHl5GR87OTnh4uJCbm6uTRomhLA9W8T17Nmz6dOnD56engQGBnLTTTdx4sSJCscUFhYyefJk/P398fDwYPz48Vy8eLGaK9YPwxTzi/kXpXJ5A1CpVMbR7sOphzmXc46C0gJcNC6yrt4C8pldN4bYP5hyEJD13KJxkLgWouky67bt/Pnz8fDwMD4uLS1l4cKFBARcqio7ZcoU67VOCGFz1o7rzZs3M3nyZPr06UNpaSkvvvgiI0eO5OjRo7i7uwMwdepUVq9ezdKlS/H29ubxxx/nlltu4d9//7XeN2amQLdAjqcfl5HuBtS9RXc2nd/E4dTDtHBrAUB73/Zo1JoGblnTJJ/ZljNML9fqtYAk3aLxkLgWomkyOelu27YtX3/9dYXngoKCWLRokfGxSqWSQBeiCbFFXP/1118VHi9cuJDAwED27dvHwIEDycrK4ptvvmHJkiUMHToUgAULFtC5c2d27txJv3796vAdWc4w0n0+57xxiyBJuuuXoZja4ZTDtPZoDUgRNUvJZ3bdXB77UkRNNAYS10I0XSZPL4+LiyM2NrbGP2fOnLFlW0UzNXjwYJ566qlKzy9cuBAfHx8AXn/9dVQqFSqVCgcHB8LCwpg6dapxylVcXJzxdZVKhaenJ127dmXy5MmcOnWq0nUNx6nVaoKDg5kwYQLx8fEVjouNjeXOO++kVatWuLi40KZNG8aNG8fx48er/V7S0tK47rrraNWqFc7OzoSEhPD444+TnZ1dt38kC9VHXGdlZQHg56esmd63bx8lJSUMHz7ceEynTp1o27YtO3bsqNN71YUh6d57cS86vQ4PRw/ZG7qeGZLu87nn2XlhJyBF1Cwln9l108q9FU5qJ+NjGekWjYHEtWgI0g+3DpOTbiEau65du5KYmEhcXBzvvPMO8+bN4+mnn65wzPr160lMTOTgwYPMmjWLY8eO0aNHDzZs2FDhOC8vLxITE0lISGDZsmWcOHGCW2+91fh6SUkJI0aMICsri+XLl3PixAl+/vlnunfvTmZmZrVtVKvVjBs3jpUrV3Ly5EkWLlzI+vXrefTRR636b9FY6HQ6nnrqKQYMGEC3bkpClZSUhJOTk/EXtUHLli1JSkqq8jpFRUVkZ2dX+GNthr26o1OjAWWkS/aGrl9eTl7GPZKj05T/BxnpFg1Bo9bQ1qut8XEbTxnpFkKImkg/vGYmTy+fNm1alc97e3vToUMHbrnlFpydna3WMCHM5eDgQFCQsqXLhAkT2LBhAytXruSrr74yHuPv7288JiIigrFjxzJs2DAmTpxITEyMsSKoSqUyHhccHMzEiROZMmUK2dnZeHl5ceTIEWJiYtiwYQOhoUqRp9DQUAYMGFBjG319fZk0aZLxcWhoKI899hjvvfee9f4hzGDruJ48eTLR0dFs27bN4muAUpxtxowZdbpGbQwj3YY1nDK1vGFEtYgy7pOuQkUH3w4N26AmSj6z6y7cO5zTmacBSbpF4yBxLRoz6YfXzOSk+7///qvy+czMTE6fPs0rr7zCxo0badu2bZXHCVHfXF1dKS4urvEYtVrNk08+yc0338y+ffvo27dvpWOSk5NZsWIFGo3G+MugRYsWqNVqfv31V5566inj8+a6cOECy5cvZ9CgQRadX1e2jOvHH3+cVatWsWXLFtq0udRhDQoKori4mMzMzAqj3RcvXjT+gr3c9OnTK3Q2srOzCQmx7nRPQ9JtIEl3w+gW0I2VMSsBCPUKxc3RrYFb1DTJZ3bdGX4H+Ln44e7o3sCtEULiWjQt0g+vyOSk+59//qn2tezsbO666y5eeOEFlixZYpWGCdvS6/UUlBY0yHu7OrjafNruvn37KhTqqkmnTsqa0bi4OGOwZ2Vl4eHhgV6vJz8/H1CqgRqqb7du3Zq5c+fy3HPPMWPGDK688kqGDBnCXXfdRURERK3veccdd/D7779TUFDA2LFjmT9/vqXfap3YIq71ej1PPPEEK1asYNOmTYSHV0xee/fujaOjIxs2bGD8+PGAsvdofHw8/fv3r/Kazs7ONr97b5hebiDbhTWMqIAo49cytdxy8pldd5E+kQCyZZ1oNCSu7Ys998WlH16ZWVuGVcfLy4tXXnmlwlx70bgVlBZw1ZKrGuS9d925yyajV4cPH8bDwwOtVktxcTHXX389n376aa3n6fV6gAq/fDw9Pdm/fz8lJSWsWbOGxYsX89Zbb1U4b/Lkydx7771s2rSJnTt3snTpUmbNmsXKlSsZMWIEo0ePZuvWrYAyfeXIkSPGc+fMmcNrr73GyZMnjaO4n3/+uTX+GazG0riePHkyS5Ys4ffff8fT09O4Ttvb2xtXV1e8vb2ZOHEi06ZNw8/PDy8vL5544gn69+/fYJXLQVlP7KxxpkhbBMhId0Pp4NsBR7UjJboSOvpK0m0L8pltmqFth/Jg9wcZHDK4oZsiRK0krpsee+uLSz+8ZlZJugECAgJIT0+31uWEMPLy8jJWwC4vMzMTb29v4+OOHTuycuVKHBwcaNWqFU5OTpXOqcqxY8cAKozIqtVqIiOVUY7OnTsTExPDpEmTKmzLAcovhbFjxzJ27FhmzpzJqFGjmDlzJiNGjGD+/PkUFCh3MB0dHSucFxQURFBQEJ06dcLPz49rr72WV155heDgYJPaXF8siesvvvgCUKpdlrdgwQLuv/9+QPllp1arGT9+PEVFRYwaNarBbzqoVCoC3QI5l3MOjUoj1YobiKPGkStbXsmOxB30btm7oZtjt+Qzu3bOGmee7PVkQzdDCJNJXAtbkH64dVgt6d65cyft2rWz1uWEjbk6uLLrzl0N9t7m6NixI2vXrq30/P79++nQ4VKRJScnJ2OAmkqn0zF37lzCw8O54oorqj3uhRdeoF27dkydOpVevXpVeYxKpaJTp05s374dUKa+mNoGUCp0NzaWxLXhjmVNXFxc+Oyzz/jss88sbZpNtHBtwbmcc4R4huCocaz9BGETs66dxdnss/RqWXWsibqTz2wh7I/EddPSVPri0g+3DpOT7kOHDlX5fFZWFvv27WPWrFm89tprVmuYsC2VStVkChRNmjSJTz/9lClTpvDggw/i7OzM6tWr+fHHH/njjz/MulZaWhpJSUnk5+cTHR3NRx99xO7du1m9enWNRRhCQkK4+eabefXVV1m1ahUHDhzgtdde45577qFLly44OTmxefNmvv32W55//vlqr/Pnn39y8eJF+vTpg4eHB0eOHOHZZ59lwIABhIWFmfW9WIPEdUWGdd1h3mEN25BmLsA1QPZIryOJbSHsj8S1fWkqfXHph1uHyUl3z549UalUVY5iBQQEMG3aNB577DGrNk4IULYU2LJlCy+99BLDhw+nuLiYTp06sXTpUq677jqzrjV8+HAA3NzcCA0NZciQIcybN8+kO3NTp06lf//+7N69m4iICMLCwpgxYwZxcXGoVCrj46lTp1Z7DVdXV77++mumTp1KUVERISEh3HLLLbzwwgtmfR/WInFdkSHZ7uzXuWEbIkQdSWwLYX8krkVDkH64daj0pswFBc6ePVvl815eXvj6+lq1UdaUnZ2Nt7c3WVlZeHl5NXRzGkxhYSGxsbGEh4fj4uLS0M0RZqjp/66uP98S1xXlFuey6fwmhoQMkS2CRINqjrEtn9fC3jXHuAaJbQPpizdd1uiLmzzSbdh4XAhhPySuK/Jw8uCGiBsauhlC1JnEthD2R+JaiKZL3dANEEIIIYQQQggh7JUk3UIIIYQQQgghhI1I0i2EEEIIIYQQQtiIJN1CCCGEEEIIIYSNWJR0Z2ZmMn/+fKZPn056ejqgbJCekJBg1cYJIeqPxLUQ9kliWwj7I3EtRNNicvVyg0OHDjF8+HC8vb2Ji4vjoYcews/Pj+XLlxMfH8/3339vi3YKIWxI4loI+ySxLYT9kbgWoukxe6R72rRp3H///Zw6darCPmVjxoxhy5YtVm2cEKJ+SFwLYZ8ktoWwPxLXQjQ9Zifde/bs4ZFHHqn0fOvWrUlKSrJKo4QQ9UviWgj7JLEthP2RuBai6TE76XZ2diY7O7vS8ydPnqRFixZWaZQQon5JXAthnyS2hbA/EtdCND1mJ9033ngjb7zxBiUlJQCoVCri4+N5/vnnGT9+vNUbKMTgwYN56qmnKj2/cOFCfHx8AHj99ddRqVSoVCocHBwICwtj6tSp5ObmAhAXF2d8XaVS4enpSdeuXZk8eTKnTp2qdF3DcWq1muDgYCZMmEB8fHyF42JjY7nzzjtp1aoVLi4utGnThnHjxnH8+HGTvq+0tDTatGmDSqUiMzPT7H8Xa5K4FsI+SWwLYX8krkV9kn64dZiddH/wwQfk5uYSGBhIQUEBgwYNIjIyEk9PT9566y1btFEIk3Tt2pXExETi4uJ45513mDdvHk8//XSFY9avX09iYiIHDx5k1qxZHDt2jB49erBhw4YKx3l5eZGYmEhCQgLLli3jxIkT3HrrrcbXS0pKGDFiBFlZWSxfvpwTJ07w888/0717d5MDd+LEiURFRdX5+7YGiWsh7JO1Ynv27Nn06dMHT09PAgMDuemmmzhx4kSFYwoLC5k8eTL+/v54eHgwfvx4Ll68aO1vSYhmTz6zRWMk/fCamV293Nvbm3Xr1rFt2zYOHTpEbm4uvXr1Yvjw4bZonxAmc3BwICgoCIAJEyawYcMGVq5cyVdffWU8xt/f33hMREQEY8eOZdiwYUycOJGYmBg0Gg2g3DU2HBccHMzEiROZMmUK2dnZeHl5ceTIEWJiYtiwYQOhoaEAhIaGMmDAAJPa+sUXX5CZmcmrr77KmjVrrPZvYCmJayHsk7Vie/PmzUyePJk+ffpQWlrKiy++yMiRIzl69Cju7u4ATJ06ldWrV7N06VK8vb15/PHHueWWW/j3339t8a0J0WzJZ7ZojKQfXjOzk26Da665hmuuucaabRH1SK/Xoy8oaJD3Vrm6olKpbP4+rq6uFBcX13iMWq3mySef5Oabb2bfvn307du30jHJycmsWLECjUZj/GXQokUL1Go1v/76K0899ZTxeVMcPXqUN954g127dnHmzBnzvikbk7gWwj7VNbb/+uuvCo8XLlxIYGAg+/btY+DAgWRlZfHNN9+wZMkShg4dCsCCBQvo3LkzO3fupF+/fnVqvxCiMvnMbtrsvS8u/fCKzE66586dW+XzKpUKFxcXIiMjGThwoFnfvKh/+oICTvTq3SDv3XH/PlRubjZ9j3379lXo/NWkU6dOgLLexBDsWVlZeHh4oNfryc/PB2DKlCnGEZ3WrVszd+5cnnvuOWbMmMGVV17JkCFDuOuuu4iIiKj2vYqKirjjjjt47733aNu2baNJuiWuhbBPtortrKwsAPz8/ADld25JSUmFkbZOnTrRtm1bduzYUWXSXVRURFFRkfFxVYWhhBCVyWe2fbDnvrj0wyszO+meM2cOKSkp5Ofn4+vrC0BGRgZubm54eHiQnJxMREQE//zzDyEhIVZvsBDVOXz4MB4eHmi1WoqLi7n++uv59NNPaz1Pr9cDVLjj5+npyf79+ykpKWHNmjUsXry40jqpyZMnc++997Jp0yZ27tzJ0qVLmTVrFitXrmTEiBGMHj2arVu3AsqUlyNHjjB9+nQ6d+7M3XffbcXvvO4kroWwT7aIbZ1Ox1NPPcWAAQPo1q0bAElJSTg5ORmL6hi0bNmy2i2MZs+ezYwZMyz/5oRopuQzWzRG0g+vmdlJ96xZs5g3bx7z58+nXbt2AJw+fZpHHnmEhx9+mAEDBnD77bczdepUfv31V6s3WFiHytWVjvv3Ndh7m8PLy8s4qlJeZmYm3t7exscdO3Zk5cqVODg40KpVK5ycnEy6/rFjxwAIDw83PqdWq4mMjASgc+fOxMTEMGnSJBYtWlThXE9PT8aOHcvYsWOZOXMmo0aNYubMmYwYMYL58+dTUDZtyNHREYCNGzdy+PBhY2wYftEEBATw0ksvNVgHVOJaCPtki9iePHky0dHRbNu2rU5tmz59OtOmTTM+zs7OlgRBCBPIZ7Z9aCp9cemHW4fZSffLL7/MsmXLjEEOEBkZyfvvv8/48eM5c+YM7777rmxZ0MipVCqbT/G2lo4dO7J27dpKz+/fv58OHToYHzs5ORkD1FQ6nY65c+cSHh7OFVdcUe1xL7zwAu3atWPq1Kn06tWrymNUKhWdOnVi+/btgDL15XLLli0z/gIA2LNnDw888ABbt26tEFP1TeJaCPtk7dh+/PHHWbVqFVu2bKFNmzbG54OCgiguLiYzM7PCaPfFixeNxXAu5+zsjLOzs2XfmBDNmHxm24em0heXfrh1mJ10JyYmUlpaWun50tJS4xSyVq1akZOTU/fWCQFMmjSJTz/9lClTpvDggw/i7OzM6tWr+fHHH/njjz/MulZaWhpJSUnk5+cTHR3NRx99xO7du1m9enWNa59CQkK4+eabefXVV1m1ahUHDhzgtdde45577qFLly44OTmxefNmvv32W55//vlqr3N5QKempgLKXbzLp2XWJ4lrIeyTtWJbr9fzxBNPsGLFCjZt2lRhRAKgd+/eODo6smHDBmNH/8SJE8THx9O/f38rfTdCCJDPbFG/pB9uHWYn3UOGDOGRRx5h/vz5xjsS//33H5MmTTIulj98+HClD2QhLBUREcGWLVt46aWXGD58OMXFxXTq1ImlS5dy3XXXmXUtQ5EfNzc3QkNDGTJkCPPmzTPpztzUqVPp378/u3fvJiIigrCwMGbMmEFcXBwqlcr4eOrUqRZ9nw1J4loI+2St2J48eTJLlizh999/x9PT09ix9/b2xtXVFW9vbyZOnMi0adPw8/PDy8uLJ554gv79+0vlciGsTD6zRX2SfriV6M2UmJioHz58uF6lUumdnJz0Tk5OerVarR8xYoQ+KSlJr9fr9Rs3btT//fff5l7aJrKysvSAPisrq6Gb0qAKCgr0R48e1RcUFDR0U4SZavq/s9bPt8S1EI1LY4ttoMo/CxYsMB5TUFCgf+yxx/S+vr56Nzc3/c0336xPTEw0ua0S18LeNba4ri8S2wrpizdd1uiLq/T6shXkZjp+/DgnT54ElLn+HTt2rGP6bxvZ2dl4e3uTlZWFl5dXQzenwRQWFhIbG0t4eDguLi4N3Rxhhpr+76z98y1xLUTj0BxjW+Ja2LvmGNcgsW0gffGmyxp9cbOnlxt06tTJuK+aEMI+SFwLYZ8ktoWwPxLXQjQdFiXd58+fZ+XKlcTHx1NcXFzhtQ8//NAqDRNC1C+JayHsk8S2EPZH4lqIpsXspHvDhg3ceOONREREcPz4cbp160ZcXBx6vb7aEu5CiMZN4loI+ySxLYT9kbgWoulRm3vC9OnTeeaZZzh8+DAuLi4sW7aMc+fOMWjQIG699VZbtFEIYWMS10LYJ4ltIeyPxLUQTY/ZSfexY8e49957AXBwcKCgoAAPDw/eeOMN3nnnHas3UFiXhXXzRAOqj/8ziWsh7JPEthD2R+K6aZO+eNNjjf8zs5Nud3d349qR4OBgYmJijK8ZNhgXjY+joyMA+fn5DdwSYS5DvGk0Gpu9h8S1EPZJYlsI+yNx3TQZ+nGXr8EXjZ8hfzLkU5Ywe013v3792LZtG507d2bMmDE8/fTTHD58mOXLl9OvXz+LGyJsS6PR4OPjQ3JyMqBsSq9SqRq4VaI2Op2OlJQU3NzccHCweLOBWklcC2GfJLaFsD8S102Tg4MDbm5upKSk4OjoiFpt9tinqGd6vZ78/HySk5Px8fGp0wCY2b34Dz/8kNzcXABmzJhBbm4uP//8M+3bt5dqiY1cUFAQgDHxFk2DWq2mbdu2Nr1JInEthH2S2BbC/khcN00qlYrg4GBiY2M5e/ZsQzdHmMHHx8eYR1nKrKRbq9Vy/vx5oqKiAGV6y5dfflmnBoj6Ywj2wMBASkpKGro5wkROTk42vRsqcS2EfZLYFsL+SFw3bU5OTrRv316mmDchjo6OVlniaVbSrdFoGDlyJMeOHcPHx6fOby4ahkajsen6YNG0SFwLYZ8ktoWwPxLXTZ9arcbFxaWhmyHqmdnDZ926dePMmTO2aIsQooFIXAthnyS2hbA/EtdCND1mJ90zZ87kmWeeYdWqVSQmJpKdnV3hjxCi6ZG4FsI+SWwLYX8kroVoelR6MzceK7+2tHxhJ71ej0qlQqvVWq91VpCdnY23tzdZWVl4eXk1dHOEsCpr/XxLXAvRuDTH2Ja4FvauOcY1SGwL+2bqz7fZ1cv/+eefOjVMCNH4SFwLYZ8ktoWwPxLXQjQ9ZifdgwYNstqbb9myhffee499+/aRmJjIihUruOmmm4yv6/V6XnvtNb7++msyMzMZMGAAX3zxBe3bt7daG4QQ1o1rIUTjIbEthP2RvrgQTY9F+xBt3bqVu+++m6uvvpqEhAQAFi1axLZt28y6Tl5eHj169OCzzz6r8vV3332XuXPn8uWXX7Jr1y7c3d0ZNWoUhYWFljRbCFEDa8W1EKJxkdgWwv5IX1yIpsXspHvZsmWMGjUKV1dX9u/fT1FREQBZWVnMmjXLrGuNHj2amTNncvPNN1d6Ta/X89FHH/Hyyy8zbtw4oqKi+P7777lw4QK//fabuc0WQtTAmnEthGg8JLaFsD/SFxei6bGoevmXX37J119/jaOjo/H5AQMGsH//fqs1LDY2lqSkJIYPH258ztvbm6uuuoodO3ZUe15RUZFUcRTCTPUV10KI+iWxLYT9kb64EE2P2Un3iRMnGDhwYKXnvb29yczMtEabAEhKSgKgZcuWFZ5v2bKl8bWqzJ49G29vb+OfkJAQq7VJCHtVX3EthKhfEttC2B/piwvR9JiddAcFBXH69OlKz2/bto2IiAirNKoupk+fTlZWlvHPuXPnGrpJzcveb2HT22DeTnSigTX2uBZCWEZiWwj709jjWvriDUinhb9fgujlDd0ScRmzk+6HHnqIJ598kl27dqFSqbhw4QKLFy/mmWeeYdKkSVZrWFBQEAAXL16s8PzFixeNr1XF2dkZLy+vCn9EPSnKhdVPw6bZkHKioVsjzGDNuN6yZQtjx46lVatWqFSqSuu+7r//flQqVYU/1113nRW/GyGEQX19Zgsh6o/0xUW1Yv6BHZ/Cn882dEvEZczeMuyFF15Ap9MxbNgw8vPzGThwIM7OzjzzzDM88cQTVmtYeHg4QUFBbNiwgZ49ewLK5uO7du2SjkJjlXQY9Drl65RjENipYdsjTGbNuDZUQn3ggQe45ZZbqjzmuuuuY8GCBcbHzs7OdWq/EKJq9fWZLYSoP9IXF9W68J/yd34q5KWCe0DDtkcYmZ10q1QqXnrpJZ599llOnz5Nbm4uXbp0wcPDw+w3z83NrTA9JjY2lgMHDuDn50fbtm156qmnmDlzJu3btyc8PJxXXnmFVq1aVdg/UDQihkAHGeluYqwZ16NHj2b06NE1HuPs7FzjXXIhhHVYM7aFEI2D9MVFtSr0xY+D+zUN1xZRgdlJ9w8//MAtt9yCm5sbXbp0qdOb7927lyFDhhgfT5s2DYD77ruPhQsX8txzz5GXl8fDDz9MZmYm11xzDX/99RcuLi51el9hI4kHLn2dfKzBmiHMZ824NsWmTZsIDAzE19eXoUOHMnPmTPz9/as9vqioyLglCiCVUIUwUX3HthDC9qQvLqp1eV88TJLuxkKl15tX8apFixYUFBRw4403cvfddzNq1Cg0Go2t2ldn2dnZeHt7k5WVJWtKbO3TPpB6Uvm6RWeYvLNh29MMWOvn21ZxrVKpWLFiRYU74j/99BNubm6Eh4cTExPDiy++iIeHBzt27Kj2PV9//XVmzJhR6XmJa2GvGnts24J8Xgt71xzjGiS2603ORfigw6XHfR6C699vuPY0E6b+fJtdSC0xMZGffvoJlUrFbbfdRnBwMJMnT2b79u11arBo4gqzIfXUpcdpp0Fb0nDtEWapz7i+/fbbufHGG+nevTs33XQTq1atYs+ePWzatKnac6QSqhCWkc9sIeyPxLWoUvlRblCml4tGw+yk28HBgRtuuIHFixeTnJzMnDlziIuLY8iQIbRr184WbRRNQdIhQA9ercHRHXQlkH6moVvV5P2y5xznM/Jt/j4NGdcREREEBARUuf2JgVRCFcIy8pkthP2RuBZVMqznDuio/C1Jd52VanV8veUM+cWldb6W2Wu6y3Nzc2PUqFFkZGRw9uxZjh2TdbzNliHQW/eCrAS4sF8J9hYdG7ZdTdh/8Rm8sPwQro4a1j89iGBv13p53/qO6/Pnz5OWlkZwcLBN30eI5k4+s4WwPxLXwsjQF+8xATa8AXkpkJcG7tXXzBE1+/bfWGb9eZzfDyawcvI1qNUqi69l9kg3QH5+PosXL2bMmDG0bt2ajz76iJtvvpkjR45Y3BDRxBkCPbgntCjbKixZ7rBZqqhUy3O/HkKnh5Fdg+ol4bZWXOfm5nLgwAEOHDgAXKqEGh8fT25uLs8++yw7d+4kLi6ODRs2MG7cOCIjIxk1apQNvishhHxmC2F/JK5FJYa+eOg14N1W+VpGuy0Wm5rHB2uVWlX39AutU8INFox033777axatQo3Nzduu+02XnnlFfr371+nRgg7cOGA8nerK0DjqHwtgW6xzzae5lRyLgEeTrx6g+0rDlszrmuqhPrFF19w6NAhvvvuOzIzM2nVqhUjR47kzTfflL26hbAB+cwWwv5IXItKshMh9yKo1BDUHQI7QVa80hcPG9DQrWtydDo9zy87RFGpjmsiA7jtypA6X9PspFuj0fDLL79UWSkxOjqabt261blRookpyIT0GOXrVleArmzdg+zVbZGjF7L5fJPy7znjxm74ujvZ/D2tGdeDBw+mpk0R/v77b4vbKYQwj3xmC2F/JK5FJYZR7hadwclNWd55aq30xS20eHc8u2PTcXXUMPuW7qhUdRvlBguS7sWLF1d4nJOTw48//sj8+fPZt28fWq22zo0STUziQeVvn1Bw87u0jjvtFGhLQVOn0gHNSqlWx/PLDlGq0zOyS0vGdA+ql/eVuBbCPklsC2F/JK5FJYaku9UVyt+GpZ4pssbfXAmZBbz9p/Lv9uyojoT4uVnluhat6QbYsmUL9913H8HBwbz//vsMHTqUnTtlX+Zm6fJA924Ljm6gLYaM2IZrVxM0f1sshxOy8HJxYOZN3axyZ80cEtdC2CeJbSHsj8S1MDL2xXsqf7forPwtI91m0ev1vLTiMHnFWnq19eG+q8Osdm2zhiCTkpJYuHAh33zzDdnZ2dx2220UFRXx22+/0aWL7dedikbq8qRbrYaADsp+gcnHIKB9gzWtKTmTksucdUrBhpdv6EKgl0u9vK/EtRD2SWJbCPsjcS0q0evL9cV7KX+36KD8nXsR8tOVmaiiViv+S2DTiRScNGre/V8UmjoWTyvP5JHusWPH0rFjRw4dOsRHH33EhQsX+OSTT6zWENGEXX53DSBQ7rCZo3zBhmvbB3Br7zb18r4S10LYJ4ltIeyPxLWoUtZ5yE8FtQO07Ko85+wJ3mXFv6QvbpKUnCLeWHUUgCeHtycy0NOq1zd5pHvNmjVMmTKFSZMm0b69jFyKMvnpkHlW+Tq4x6XnDeu6pYK5SX7YdZY9cRm4OWmYdbN1CjaYQuJaCPsksS2E/ZG4FlVKPKD8HdgZHMvNkmzREbLOKX3xUKluX5vXVkaTmV9Cl2AvHh4YYfXrmzzSvW3bNnJycujduzdXXXUVn376KampqVZvkGhiDIHuFwGuvpeeNxZwsHHSnZsCOUm2fQ8bO5+RzztrlH+n56/rZLWCDaaQuBbCPklsC2F/JK5FlS5f5mlQX33xjDgozLbte9jYX9GJ/Hk4CY1axbv/i8JRY3HZs2qZfMV+/frx9ddfk5iYyCOPPMJPP/1Eq1at0Ol0rFu3jpycHKs3TjQBtQV6alkFc1vIT4cv+sOHXeD3x5XpNU2MXq9n+nKlYEOfMF/u6Rdar+8vcS2EfZLYFsL+SFyLKjVk0h23Deb2gjndYMv7UJxnu/eykcz8Yl7+7QgAjw6KoFtrb5u8j9lpvLu7Ow888ADbtm3j8OHDPP3007z99tsEBgZy44032qKNojGrLtB9QsHBFbRFl6afW9vueZCXAnot/LdICfq/XoS8pnPXd9n+BLaeSsXJQc3b46NQW7FggzkkroWwTxLbQtSgtAj+nQuHlipfNxES18KoQhG16pJuG67p3vS20g8vyoKNb8LHPWHXvCYVTzNXHyM1t4h2Ldx5Yqjtlm3Uaey8Y8eOvPvuu5w/f54ff/zRWm0STcmFA8rflwe6Wn2pcmKyDfYILMqBnV8oXw98DkKvURL8nZ/Bxz3gn9mNfqpLck4hb5YVbHhqeHvatfBo4BYpJK6FsE8S20JcZv0MWPcKLH8QPuwM616F9Ka11anEdTOXeRYKMkDjBIGXVa831FfKSYSCTOu/d/wuiNsKakcY/R74hkFeMqx5Fj69Eg78CLrGvWf85pMp/LrvPCoVvPu/KFwcNTZ7L6tMWNdoNNx0002sXLnSGpcTTUVeqlKgASAoCoA/Dl7gu+1x6PV6205r2fstFGaCf3sY/ALcvwruXqYUcyvOhc1vK8n39k+hpND6728Fr/52hKyCErq19uLha61fsKGuJK6FsE8S20IApzcoN+oBPFpCfhr8+zHMvQJ+GA/H/2z0CUN5EtfNlGGUO7ALODhTqtXx+abTbDqRDC5e4NVaed0Wo91b31f+7nkHXPUwTN4D138AHkGQGQ+/PQpfDIBjq5QR+UYmt6iUF5cfBuC+/mH0DrXttmrWXyUumg/DKLd/e3Dx4nhSNk/+9B+vrTzC7th021UwLylQkmmAa6aCWgMqFUQOh4c2wa0LlTYVpMPal+CTXrDvO9utLbfAn4cT+etIEg5qFe+O74GDDQo2CCGEEKIKeWnw2yTl6z4PwdSjMGExtBsK6OH0evjpDvgoCja/2+QLtgo7dtmM0x/3nOPdv07w+JL/yC0qLdcXt/Ks08SDcGotqNQw4CnlOQcn6PMgTPkPhs8AFx/lfX++C+YPgzObrNuGOnr3r+MkZBbQxteVZ0d1tPn7SU9fWO6yNSTvrDmOruxG1vc7z0ILw17dVk66//tBmb7i3Raibqv4mloNXW+Gx3bCjZ+CVxvIToA/psBnfSF6Geh01m2PmTLyinn192gAJg1uR5dWXg3aHiGEEKLZ0Oth5eOQexECOsLIN0HjAJ1vgHtWwBP74eop4OoH2efhn7dgTlf45V44s7lRjtiJZqxcXzy3qJSP158ElFHcFf8llOuLW3mke+sHyt/dxoN/u4qvObnBNU/Bkwfh2mfA0Q0S9sH34+C7G+H8Puu2xQK7Y9P5fodSc+rtW6JwdzZ5F22LSdItLFcu0LefTuWfEykY6oD9HZ1Emmu48iD1lPWmaGlLlOlfAAOmgMax6uM0DtDrHnhiH4yaDW7+kB4Dvz4A8wbBqXUN9sH55qqjpOYW0z7Qg8eHRjZIG4QQQohmad8COPGnsgb2f9+Ao2vF1/3bKYn4tGNw8zwIuQp0pXD0d/j+RmWt6o7PlHW05fwXn8GXm2PIL248s+qEndPrK4x0z9tyhtTcYjRlnfFFO+LQ22LWacoJOFq2jOGaadUf5+oDw15Rku++jyhrv2M3w/yh8NNdkGzjrcyqUVii5fllhwCYcGUI17QPqJf3laRbUHp2Jzlf34D+4hHzTixLunXBPZldts/0Pf1C6R3qS6lOzw8nAQcXKC1U9vCzhkM/K+vIPVqi7Xk3H60/yew/j5GWW02VREcX6P+YEvCDXwQnT0g6BIv/BwvGQPxO67TLRP+cSGb5fwmoVPDO/6JwdrBdwQYhRCOlLVVuRgoh6lfKSWWXE4Dhr0NQ9+qPdXSBHhNg4lp49F+48gFw8oC00/D3i/BBJ/jtMWXUTq/ng7UneXvNcd79y4aVooXdKvj3K3IX3WleEeD0M0rVcI0zyS7hfL3lDACzbu6Gq6OGkxdzOVLaSjnWmgnutjmAHjrdQLpHJNOXH+bbbbEUl1Yzk9QjEMa8qwyE9bhTq12YlgAAp1lJREFUmZJ+fJWy7e+KRyHDRrscVeOj9aeITc2jpZczL17fud7eV5Juwellb+CZsJUzi6eaflJOEuRcAJWaP1MDOJyQhYezA08Ma8+9/ZW9ppfsSUDvX1Z63xrTWnRa2Pqh8mW/x3lmxQk+Wn+Kr7acYfD7m/h6y5nqA97ZEwY/ryTf/R8HjTPEb4dvR8HiWyHxUN3bV4ucwhJeKivY8MCAcHq19bX5ewohGpnMc7DwelgwGnIuNnRrhGg+Sotg2UQoLYCIIXDVJNPPDeoGN8yBp4/D9R9Cy27KgMKBxTB/KHmfXEPr2KV4qouYeE247b4HYZf0hdmo172CR8xqTq7+yPQTDTNOg7oz5584Ckq0XNHWh9uuDOGmK5Rke+FJZ+WYnAtQmFX3xqbHwqFfAMi8cgp3fr2TH3fH88aqo4ycs5l1Ry8qxZSr4hsKN38Bk3ZApxtAr4ODP8InveHPZyE3ue7tq8Xh81l8vVW5OTHzpu54u1YzY9YGJOluwtYfvcjJizl1usapxAxaZ+0HoF32Lnbu+te0E8ums+j82/P2hvOAsqF8gIcz13ULIsDDiYvZRVxwClOOt0YBhyMrID0Gvasvz53tzYr/EtCoVbQP9CCnsJS3/jzGyDmbWXskqfqAd/eHUW8pRR563QcqjVII4qtrlann5o72m+Gdv45zIauQtn5uPD2yg83eRwjRSB3/E768Bs7tVBIAW+zsIISo2saZykw3Vz+46QulBoy5nD2hz0R4dBs8sBaiJoDGGff0aN5x/Jo9Lo8Tcnqx9dsu7NrxzT/hjDJj0/vwAi6kmTjaXZZ0Z/p25ec98QC8OKYzKpWKe/qFAfDbsVy0HsHK8dYYAPv3Y9BrKQ4bzIQ/ijielEOAhzMBHs7EpeXz0Pd7ufubXRxLrOF7COwEty+GBzdCxGDQlcDuecquQxvesNkN6eJSHc/+ehCtTs/YHq0Y0aWlTd6nOpJ0N1G7Y9N58Pu93PrlDlKrm1ptgp9X/oGnqsD4OGHNByRlmbDFVuIBAE47tOd8RgEtvZyZeI2y7ZWzg4bb+7QFYGumv3J8XQNdpzOOcq9yu4lfD2fioFbx2Z29+Oupgbw7PsoY8A8v2ld7wHu3hhvnwuTdShEIUIqsfXE1fDMKDv5s1a3Gdp5J44edyi/Et2/pjpuTCQUbSgqVNTP//WC1dgghGkBpEax5QamGXJipFJ98ZAtEDGrolgnRPJzZBNvnKl+P+xS8gut2PZUK2l4Ft8xjzy3/8lbJnZzVt8RFlwfu9bM+VNiPkv0/Gb9uSTpLv/+EUq0JRX8TDwKwIikQnR5GdmlJnzBl26surby4smy553kHpU9e5xu92ReU2R3A88mjOHExh0BPZ35+pB+bnh3MY4Pb4eSg5t/TaVw/dysvrjhcc47Spjfc+zvcuxJa94aSfKVA25wu8Mt9Vi9c+OXmGI4n5eDr5sjrY7vUfgIoOx3smQ9xJg5K1kCS7iZq+X5ldDmroIRZf1o2irw9JhWn+K0AFHspAXmDfgsvL/4Hra6WH/Kyu2vLkwIBmDaiA65Ol9Yn33lVW9Qq2JhWNoW6roF+8i9IPkKByo2XEvrjqFHx+V29uK5bEBq1itv6hFgW8AGR8L9v4ZGt0PlGZeT73E5Y8TB82BnWvgxpMXVqekGxlhfKCjbc0TeEqyNr+EDWlkLMRmWd2Pvt4Zd7YP2MRrXdmRDCDGkx8M0I2PWF8rj/48oImZ9MQRWiXuSnw4qyqeS9/w86XW+1S+v1et7blsrX2hv4puevSvXzTjdY7frC/p04fYquhcqM0+R2yiDQkIylzC2rQl4tnc446/SnBH80ahXPXdepwiH3lC333J5d1u+s6wDY9k9BW8whTRdWpIcS5OXCz4/0p10LDzycHXjuuk5smDaI67sHo9PDkl3xDHlvE/O2xFBUWkNB5YhB8OAGZdu+Nn3LChf+VrFwYX56nZp+8mIOn2xUaqm8fmNX/D2cqz+4KFeZQr/4VvigA6x+GnZ/Vaf3B0m6m6TCEi2rDycaHy/fn8D2mFSzrqHT6Zn15zEGqJXp1E7XTKEosAfOqhI6J/zKpxtPV3+yXm9MuncXtaVDSw/G92pT4ZBWPq4M79ySk/qy51NOWr5Vl16Pbsv7ACwoGU6hxosv7urNyK5BFQ6rU8AHR8GERTD1CAx5WdlqrCAdtn+i7PP9/Thl1FlbYnbz56w/SVxaPkFeLkwfU0XBBr0ezu+FNc8rif6im5U7iUXZSjt63qGsQRNCNC2Hf4WvBimjEa6+cMfPyvIWB6eGbpkQzYNer2wZmnMB/Nsr8WdF22PS2B2bjpNGzaSh7ZV9vqvbVUWIKhxd9x0alZ44l84E3vIeWrUzUepYdm5eXXPfPj0GinMowpnT+tbc3ieEyECPCocYlnseLCqb2ZFch6WeeWno9n4LwPsFYwn2duGnh/sRHuBe4bAQPzc+u6sXvzzSn26tvcgpKmXWn8cZOWcLf9e0/FOlUrbte3Cdsnzj8sKFH3ZWbp6d22P26LdWp+e5Xw9RotUzrFMgN/ZoVfmg0mJlCdjS/4P3ImH5Q8ryU10pBPeA0GvMes+qSNLdBG06kUxOYSlBXi7cdZUyQv3yiuiak8rLrDx4gVMJqVypLruTFj4I52ueAOBeh3V8seEIu86kVX1yTiLkXqRUr+aoPpQXRnfCQVP5R+ne/mHE61tSpHdUksZMy6oTlpz6B/WFfRTonfie6/nqnt4Mr2EdhsUBD8qUs0HPKgXX7vgJ2o8EVMrUtF/ugTnd4J9ZkHXepLYfPJfJ/LKCDW/d3A0vl3IfxsnHYcObMLcnzB8Gu75U9h939YMrJ8L/rYGnDsOIN5R1ZEKIpqE4H1Y+oRRtKs6Btlcr1Y87XtfQLROieflvERz7Q9mqaPx8cHKv/RwT6fV65qxT+lB3XtWWYG/XWs4QoqILmQW0S1wNgNMVt4O7P5qetwPwgGYNT/10oPrdecoGvw7rQnF2cuLJ4e0rHWJY7nlK11p5og4j3Tmb56IuLeCQLpwYz6v4+eH+hAVUH099w/1YOfka3vtfFC08nTmbls8ji/Zx59e1LP8EZVcBQ+HCG+ZAy+5K4cKDS+Cb4fDltbD3Wygyra7Vgn9jOXAuE09nB966uTsqVdn+xjotxG6FlVOU2aU/3QFHlis5i187GPQCPL5XWQ521cOm/lNVS5LuJui3/y4AMK5nK54f3YkWns6cSc3jq81nTDq/sETLe3+foJf6FM6qEvAMhoD20PUm8GxFC1UW16t28ORPB8jIK658gbJAP6VvQ8+IYIZ0DKzyfQZE+hPWwpMYfdkdJQummBeVaolZ/joAv+iH8s69wxjSqer3u1xNAX/0Qi0Br3GAjqPhrqXw5AFlH0L3FpCbBJvfgY+6w493wKn11Y7gF5fqeO7XQ+j0yv/VsM4tlcrF2z6CL66Bz6+Cre8r26k5ukP32+DOpfDMSbjhQwi92rJCL0KIhpN8DL4eCvu/B1Qw8Fm47w+ljoQQov6knlZmkIGyV3Crnla9/NZTqew9m4Gzg5pJg9tZ9dqiefh9w2ai1GfQoqbVgLuUJ/s9BsBIzV6cc+N5eulBdFUs+dSeV6akH9aF8/DACAI9Xap8jzuvaksMZZ8/2efN25KsTOLFi0qhM+Anl9v46ZH+tPV3q/U8tVrFrVeG8M8zg3l8SCRODmp2nFGWf05fXsvyT1AGnK58AB7dChPXK9uNObjAxcOwaqqybd+qqZAUXe0lzqbl8f5a5WbDi9d3JsjLWclj/n4J5nSF726A/d8p9VY8g5UlYA/9o2xvNmS6kh9ZifTom5is/BI2HldK6o/r2RovF0deuUEpBvDpP6eJTc2r9RoLt8eRkFnASNeyO17hA5VpHRpH6PsQAJNc/iYpu4Bnlh6sNDKcfELZ2/qwLtxYJbEqSvXEUE7plWDXm7lHYGGJlve//o5OhQcp1mvoNP4lBnVoYdY1DAG/6fKA/2Qr05cfIiXHhCJ0vmEw/DWYelRZ/x12rbLNwYk/YfF4ZaR664eQm1LhtM83nebExRzauRUyq81u+PY6+KgbrH9N+YWhdoSOY5RrPnsKxn8NHUbK1DQhmiK9Xkm05w1RdmvwaAn3/gZDX1Zu4gkh6k9pMSx/UCnMFD4Q+j9h1cvr9Xo+LBvlvuuqUFp6VZ3wCFGd7MISOLQUgMzga8GjrH8b2Akih6NGz4OOa9l0IoVvtsVWOj/lpNIXj3PuwEPXRlT7Pq18XLmqSzsu6n2UJ1JrWSt+mfMZ+fwxfwae5HNGFcJjj04hxK/2hLs8D2cHnhnVkQ3TBnFDlLL888fd8Qx+bxNfba5l+ScoOUpIH2W7sWnHYNQs8I+E4lxlxPvLATB/BBz4EUouLcfU6/W8sOwwhSU6bmlbwO15PyhrxOcNhh2fKjN3Xbyh173KzfGpR5QlKK17Ke9pZZJ0NzFrohMp1uro0NKDzsHKlOOxUcFc2z6A4lIdr/4eXeP06fS8Yj4rW699o5dSUIDwchV0e98Pjm5E6mK51vE4G44ns+DfuArXSDy2Q/mi1RVEtfGpsb3je7chTqWs606JNX0v7MISLQ8v2sfVFxYq7W4/nqt6RJl8/uXcywJ+49NKwOv18OPucwx5fxNfmhLwoKzD7DYe7l8Fk/coe3y6eCvT5jfMUNab/PoAxG3jZHwi5zYt4FvHd1mnfxj39c9B/A5ApSTtYz9WRrTv+FG5phWnvAlhjq2nUth0wvZ7Y9q1wmxY9qAypby0QFnX+eg2ZSsUIUT92zRbGc1y8YGbv7L6rLFNJ1M4cC4TF0c1jw6uPuERojo/7jzLGP0WAPz6313xxbLR7jsdN+NJPu/8dZyD5zKNL2fnF+KVeRSAPv2H4e5c843de/uHGaeYF1wwfWvcc+n53PflJv5X/DsAvqNeoI2fRy1nVS/Ez41P7+zF0kf70721N7lFpcxec5wRH27hr+haln8auPlB/8nKtO/7/oAuN4HaAc7vht8eVfrif78Eqaf5bcteupz9nlXOL/Nh8kRUm99R1og7uELXW+D2H+GZU3DjJ8rNObWm1revC0m6m5jfDiQAcNMVrY0jzCqVijfHdcPJQc3WU6msPHih2vPnbjhFTlEpvYMc8Mssm44RPvDSAW5+0PNOAGYFbQZg9ppjHD6fBcCWE8m0LlBGyAcNHllre71cHPGP6AFAQUL10z/KKyjW8tD3e0k7tZshmoPoVWqCRr9g0rm1aeN7KeCj2igB/3ZZwK85nEhGXjG5RaUUlmhrruDeogOMfhumHYdxnytbHehKlG3HFl5Pu2+78IHD5wzVHECtL4XgnjDyLZh2VEnae9+v/FsL0YCScwr5vwV7mPjdXtO2ChSVXTgA8wZB9K/K7gfDXoO7loGHactghBBWFrcNts1Rvr5xLnhVUTSpDsqv5b6nX2i103qFqE5xqY7d29YSqk6mVOOK6vKK+u2GQovOOGrzeb3NXkp1ep748T9ldBxY+tdG3CiiABeuG1R7ga+r2/lz0UXZMePM0b0mtTE+LZ/b5+1kcO5q/FS5lHqH4tvndvO+0Wr0CfPj98kDeP/WHgR6OhOfns+jP+zjjq93cuBcJln5JeQVlVJUqq1yaj2gjESHD4TbvlNmog59BbxDoCBDGcX+tDfjNo7gFcfFdFOdUT6f24+EW75WZpfeugA6jQGHGqqYW5nMeWtCLmQWsPOMUjL/8sp7YQHuPDEkkg/WneTNVccY3DEQb9eK05RjU/P4YadSzOzNnlmoNmnBLwJ8Qiq+0VWTYM98QlK2cHfkA/xw2oknftzPyieuYf6fW/lelY1WpaFl+94mtbv/VQMgDloUniUpM58gn+qnpRQUa5n43R62x6TxlfNKAFTdxoO/dddL9Qnz47fHBrDivwTe+es48en5TFq8v9JxKhU4atQ4qlU4aNTK1xoVDhoVjmrlsYOmLQ6aN+noH8PIgjUMyN+IK4XEEkyL/nfj0ft2ZWsyIRqZPw8l4q7LQYWedccuck+/0IZuUtOh18Our2DdK6AtVj7sx3+j7N0rhGgYBRmw/GFAD1fcA13GWf0tNhxL5tD5LFwdNTwySNZyC/OtOnSBawv/AQdQdb6h8mxHlQr6TYI/pnBz8So+9h5OfHo+Ly4/zEvXd+bEf1tBA4UB3XB1rH1JokqlIjiyJxxbRe75I+j1+mqXhgLEpeZxx9c7ScvKYZLrn6AHh4HTrLpUSq1W8b/ebRjdLYgvN8cwb8sZdp5J56bPKu+HrVGrcFCryvXBy/fLVWV98atwdurLlT77GVX4Jz0Ld6NW6Tnq2JVOIx5A3fUmcK9hy956IEl3E2IYwe4b7kcbb2dY95qy1VU3ZV+/hwdFsOJAAmdS8njv7+PMvKl7hfPf/es4pTo9gzu2oEvhNuXJ8qPcBgGR0OE6OPkXr7TYzMaUscSl5XPTZ//SPu0QOIG+RRdwNO3ubrsO3SnBATdVEUu27uLBsUOqPC6/uJQHFu5h55l0ujslMlK1W3nh2qdNeh9zqdUqxvduw3Xdgvhqcwzf/htHblHF/bD1euWOpFJOruYp6Afx5xfuxp3x+KuymXzzMCb0lSRGNF7rD5zhL+cXcKSU1w/Pl6TbVPnpylTy46uUx51uUKanyewVIRqOXg9/PAXZCUrl4evetsFb6JlTtn/yfVeHEVDTXr9CVEGv1/PN5pN8r1GWamp6VDN6HHUbbJiBOvs8C4dcZMTffqw6lMjRxGzu0ccA4BPZ1+T37dGrHxyD1iVn2XEmjavbVZ2AnknJ5c6vd5GUXciT3jsJKEoHr9bQ4w7zvlETuTs78PTIjkzoE8I7f51gzeFESi8b3dbq9Gh1eopKa996eB/t+YonaUEmThoVCyeNRd2ycewAJEl3E/Lbf2VTy3u2hlPr4N+PlHUJ7YaBqw/ODhreuqk7d3y9k8W74hnfqw1XtPUFYG9cOmuik1CrYProzrBiqnLR8uu5y+v3GJz8C+fon/nsf1P433fHOZOSx3gHpUK6Q5tepjdc40CBVwSO2SeJPrCbkjGDcLxsi7G8olL+b8Eedsel4+HswMLIbahi9EpnNrCKva2tyN3ZgWkjOzJtZEe0Oj0lWh2lOj0lpTpKdDpKtcpzJVo9pWWPi7XK36Va3aWvdTqKy57zdXdisJlF34SoTxcyCwi78AfBjsrsmQHxX5FTOBRPFynkV6P4XcpWYFnnQOMEI2dC34dtUnRFCGGGgz/C0d+U9Z3jvwZny9eeVmft0YscuZCNu5OGhwfKWm5hvq2nUmmRsh1/pxx0bi1QV1f7w9FV2T52y7tEnPqOZ0Z+yTt/KX3xKCelL65qZXpf3L11VwDaqFL5YPsxrm53baVjTifncufXO0nOKaJjC1emqFdDEXD1FJtPw27j68Ynd1yB/vaeaHV6SnXV97VLyvfLy/6u2F9XnusU5En7RpJwgyTdTcbxpGyOJ+XgpFFzffdg+Geu8kJpgbKWsM+DAPRv588tvVqzfH8CL66I5o/HB6BRq3jrz2MA3HZlCB09CuFiFeu5ywsfqOyLd/EwV6T8ztThN/D+2pP0cTwLeqDVFWa1371NVzh6ksDCWP4+ksQNUZemx+cWlXL/t7vZezYDT2cHfrw1CP9lytRyBj5j1vvUlUatQmMopCA3sIUdW33wAvdp1hof36beyLY9/zLo2sEN16jGTFsK2+fCxpmgL1ua878FVt+GSAhhgbQY+PNZ5eshLyp1VqxMp9Pz0XqlAO39A8Lwc3ey+nsI+/f11jOM1yhTqNXdbql5ynafB5UBtvO7eWRkGtvbB7D91EW6a+LN74u7+VHq2gKHghRijx8gKasPQd6XZqyeTs7h9nm7SM0tomNLT369+iyaNfHgFqBU964nKpWyhNNBAy6Oti1sVt+kkFoTYdibe3DHFni7OULMhksv7vuuwrEvjemMt6sjxxKzWbg9jj8PJ/FffCaujhqmjegAcUq1RFp2q359g0oF/ZXqieyax2PXhvLxhB70dlLWhJvb0dS0VLY166BO4PsdZ43P5xSWcO83u5SE28WBRQ9eRbfYBUqntt0ws5N7IYRp4vf9SXt1AiUaN2K8+6FR6QncObOhm9W46PVwbo/Smf+wk7JLgV4L3W+FR7ZIwi1EY6AtUdZxF+dC6DUw4CmbvM3fR5I4lpiNh7NDjVs0CVGdIxey2HfqPCPV+5QnoibUfIJnS+XzBlDv+pzP7+rF3OFuOOmLwMlTuflrBocgZeZoJOdYsjve+PzJizncPm8nqblFdAry5MeH+uK55xPlxf6Twcm8LcJE1STpbgJ0Oj0ry1UtJy0G0s8oU6g0TpB0SNkao4y/hzPTR3cC4MN1J5lVNsr98MAIAr1cILYs6a5uarlBt/HgHgg5F1Af+51xYaVoijKV9wzsYt430aIjAO1VCeyOTed4UjbZhSXc881u9sdn4uXiwOIHr6Kndz4cWKycU8+j3EI0F2fT8hiYsQKA0qg7yB/2NsV6DZ3z9lB6Ym0tZzcDycdhw5vwcQ/4Zjjsngd5KeDmDzd+qlQ/dW48U9aEaNY2vwsJe5UtPG/5yibb/pQf5X5gQBg+bjLKLcw3f2ssI9V7cVMVKQlzaxOmh/ebpPx9dCWehUlc75+kPG7V0/yt8FoouUGkKoEfd8dTXKrjeFI2d8zbSWpuMV2CvfjxoX74xf8NqSeUmCqbSSvqTpLuJmB3XDoXsgrxdHZgaKdAiNmovNC2P3S+Ufl6//cVzrntyhCuDPUlv1hLQmYBLTydL60/OqNsBVbt1HIDB2fo+5Dy9c7PIKGsunfLruav7Wih3F3rqElAhY4vNsVwz/xdHDiXiberI0se6qfs+b39U6UScNurIfRq895DCGGSLbv2MEytxLPrgEl06daTX9RjACj680VlKnVzk3Ue/v0YvrwGPr8Ktr4PmWfB0V0ZjbjrV3j6BPS6R9ZvC9FYnN2hxCrADR+BdxubvM2f0YmcuJiDp4sDE6+RUW5hvguZBfxx8AI3lU0tJ2qCaZ8lQd2V/rpeC7u/ujTIZslMq7Kku5vjBVJyivhk4ynumLeTtLxiurX2YslDV+Hr5ghbymKq7yPg4mX++4gqSdJdz+ZuOMX4L7ZzLj3f5HN+LxvlHt09SFnfcLpsanm7oZfWWRxaCsV5xnPUahVv3dwdB7US0NNGdMDd2QEy4yEjVtmvzpSk9soHQOOsBPnuecpzlkz59gsHtSMu+kJakcbvBy5w8HwWvm6OLHnoKrq19oa8VNj7rXL8QNtULBdCgOvBBahVepICroaA9mjUKk51epQMvQfuWafgv+9rv8jl9HpIPta0Evb8dNi7ABZcD3O6wbpXIemwMouow2hlC7BnT8Et86D9CNBIkTkhGo2CTGVauV4HPe6EbrfY5G205Ua5J14TrizxE8JMC/6NxUeXyUDNYeWJsmnjJuk3Wfl73/cQV5a0W9IXL0u6uzslAvDJxtNk5JcQ1cabxRP7KTM4Tq1TZtA6ul8aZRdWIUm3pQqzlM6ZGU4n5/DR+pPsO5vBfQt2k5FXXOs5RaVaVh9SguOmK1pDadGl6eGRwyHsWvANh+IcOLKiwrkdgzyZe8cVPD2iA7ddWbYXt+Hc1r1Mu3vlHgA9ytacxCvbG1gU6BpHCGgPwEBfpVqyn7sTSx7qR9dW3soxOz9XCsMF91TWcwshrO70+YuMKFSmkHsOetz4/DXd2/NRqbL9oH7jW1CYbfpF9XpY8zx83g9+uVd53FgV50P0MlhyO7zfAVY9BWe3AXoIHQA3zIFnTsGdP0H3/1XeP1UI0Tj8+QxkxYNvGIx512Zvs+rQBU4n5+Ll4sAD14Tb7H2EBdJjIeNs7cc1sOzCEn7cfY4bNDtQo1MK/fmbscd7+5HgHwlFWZCiLBm1qC9ethuQd1EiHuoiAHqG+LBo4lXKzSS9/tLMkSv/T7bBtDJJui2RdBg+66dMQyy3lro2c9afwrD13JmUPCZ+t4fCkpr3fv7neArZhaUEebnQL9wf4ndCSR54tFSmnKjVl0a791cenRrTPZgnhikjWcobG6aW17Keu7x+j1V8bGlxs7J13Y90LmJ0tyB+ergfnYPLEv+CTNj9tfL1wGdk+qYQNhL7zwK8VflcdGiFe9fRxueviQxgmXoEMbpgVPmpsO1D0y6o1ysjxLu/Uh6fWA1Hf7dBy+tAW6LcvV/+MLwXCb8+ACfXgK5E2aVhxBsw9Qj835/K7B7paNifC/9BQUZDt0JYy6Ff4PBSZdbeLfNtVmOhVKvj47JR7ocHRuAlWyo2DtoS2PQ2fNIb5g2CksKGblGNftodT25RKROcdypP1FZA7XJqdcVRZxdvZcDNXG5+4K5sZzv7Giduu7IN30/si7dr2c913DY4t0uZ4Xr1E+ZfX9RIkm4TpOcV88KyQxxPylY6bt9eBzlKNXES9pl0jSMXslh9KBGVCj654wq8XBzYH5/JlB//Q6urflTIMLV8XM9WqNUqOL1eeaHdsEuJac87lQ+ec7uU6Z3V0esvjXRHmJF0B3a+NPKscTZOTzFb2XlhuvN8cXdvOpTfO2/P11CUraz97ni9ZdcXQtRIr9MRGasUKrzY6Z4KRVhcnTT0bx/MrNI7lSd2fG7aCMKm2cpWWqBUDgZl1Lswy5pNt4xOCxvegA86weL/waGflZuWPqFw7TPw2E6YtA0GPGmztaCiEUjYD/MGKzddRNN3dgesnKJ8PfgFCOljs7daefACZ1Lz8HFz5L6rw2z2PsIMKSfhmxHKZ49eq9xMu3ikYdpSnK8MGFXx/kWlWvbEpfPpxlN8tfkMYapEOutOKv31rhYshehxB7j4KF8H97R8cKqsLz62VQ7v/q9HxRtJhlHuXveAZ5Bl1xfVkqTbBIt2nOWnPefY8fN7sGSCsi2FpqyQWFqMSdeYs+4kADdEtWJsj1Z8fe+VOGnUrD16kRl/HEFfxXTMrIISNhxLBmBcz9bKk4YiapHlpl97BkHHshGr/Yuqb0TqSchNAgcXaNPXpHYbXfMUoILway1f12hI1lOOV3y+OE/p4ANc+7T51RiFECY5u+8vwnXx5OmdCR/+SKXXR3RpyQZdLw44RIG2SNkiqyZbP4DN7yhfj34X7l4Gfu2U3zMbG8H2Yzs+VdqYn6rsNdr3YZi4Dp48CMNeMU61E3YuVfn85ex20Okati2ibhIPKf2w0gKIHAHXTLPZW5VqdczdcGmU21NGuRuWTgc7v4SvrlVmrrh4K0sLABJNn3VqVaunKcscvrga7c/3cWD/Lj5af5I75u0k6vW13PrlDt5fe5K0vGLuc9+tnNNuCHi0MP+9nMqtsW43xPI2V9cXP78PzmxSapoMeNLy64tqSXZjghOJmbzgsIT/y5ir3FXrcSeMLOtQpp2u9fz/4jNYfywZtQqeGq6sa74qwp85E3qiUsH3O87y1ZYzlc77KzqRYq2Oji096RzsCdmJcDEaUEHEZQFnmGJ+8Edl3XdVDKPcIVeBo4sp3/ol4QNh0nZlqxxLGQP9RMU1n/sWQkG6MlWm682WX18IUaPi7V8CsMf7Ojx9/Cu9PqxTIGqVihfzbkePSln7fG5P1Rfb8bkyigwwfAZc9Yjye+WGsmnpu79WPsQbysWjlxL/kW8plcfHvAchfWX5SnOTl6L8XZyrbLcpmqbU07DoZmVda9ur4bbvQeNgs7db8V8CcWn5+Lk7cV//MJu9jzDRH1Pgr+ehtFApJPzYTmVrW4DEg/XfnjOb4OCPymcloDn2G91/H0XI5qlciD1CUakOf3cnxnQP4vUbOnOvR1nSbe7U8vIGPgcPboD+j9d+bHXKlnpWSroNo9xRE8CnreXXF9WSpNsEQ+M/5lGHVQCcjXoKbvr80giJCUn3B2uVu+zje7WhXQsP4/PXRwXz8vXKftdvrzlunEpusOK/sqnlV7RCpVJBTFnV8ta9wP2yDnPkcPBqrSSvx/6ouiFnNil/17ZVWHVadqnbWkf/dsodtOIcyC77XkuLYPsnytfXTLXpB6gQzZk+I452GVsBUPWtepqtv4czvUN9OaoP43Srsu0I/36xcmG0PfPh7+nK14NfLJsJUyZiMETdDuhh1ZMNU828tBhWPKJsP9h+FPSfLL9bmjND0g2Q1ACdc1F3mefg+3HKrJWgKKXQoZObzd6uRKtj7kZllPuRgRHK7i+i4Rz8Cf5bBCo1jHkf7l4OXq2UadYAFw7Ua3NKC/PIXqokvt+VjmB00WzWanujUekZr9nGPy7PcvCKlex9vAOf39Wb+8PS0WTGgqMbdBxj+Rur1dDmyrrtpGHIX8on3RePwIk/AZXSFxc2IUl3LUp2zud/JUoS+0zJI/zoeocySuIfqRyQcVYp6FCNHTFpbDudiqNGxZRh7ZXtvv6ZpXQKUbafeLCsGuYzSw/y7+lUABKzCtgVq1T5vrFHK+Vixq3CqqjsrdbAFXcrX1dRUA2dFuKUDjcRg0387q1M43jp3y25LNgPLIacROWGQY87GqZdQjQDyRs/R4OO7fru9O3bv9rjRnRpCcDH+tuVDsL53RV3RvjvB1hdtqXfNVNh0HOVLzJyprL2LOkw7PrSit+Fiba8p2x54uoLN8612sh2am41s4hE45aXeunrxEMN1w5hmdwUWHQTZJ8H//ZwzwplarENLd9/nnPpBQR4OHFP/1CbvpeoReopWFW2jGDQC9D3oUu/0w17VScfq36Wp5Xtjk1n6Zwn8So4R6Lej9UtHuS+m2+g/ZN/oH/oH4gcgVqvxfvYT6g+6a18Xu4qKzTa6QZw9qj5DWzNMOs046yyJh2UZVgAXW8y7jQkrE+S7prEbMThb6VD+X7JrfyqHcS202V3zD2DlD3s9Npqiw3p9Xo+WHsCgNv7tCXE2xGWP6Ssgdz/nfG4F8d05oaoYEq0eh5dtI9jidmsPHABvR76hvvRxtdNSZqN67mHV93eK+4GVBC7ufIUuqRDSmEjZ69LdwYbQvlpLdoS2DZHeXz1FHBwarh2CWHPivPxOroEgOj/b+++w6OqtgYO/6ZPeic9oSaASJESQLpIUVHscq2IYsGK5ZNrQ6+K5eq1odhRr6h4VexYkCodaUoLkEAS0tukzUxm5nx/nGSSQIAEkkzKep8nzyQzp+wZ2MlZZ++9Vsw0vIy64256bm81ecrSQ2BNqspe+tvjanbYnf+Db6qmtSXdBuc8Xn9A6xsGE/6lfr/8aXWUqqVkbKm5gDj/pSZJBuNyKcz5ageDnvqNF37ec/IdROtSO+jOkqC7TbEWw38vUWcVBsTCdUvUUqbNyO5w8eoydRbjraO74W2UUW6PqbTCF9OhsgxH3AjWx0yvmwMpIBa8gtVKFM2cTC2nxMrsz7fxyNuLucz6FQD7znqUz+6cwFVD4ugS6oMm+iy45n9q7pAuo9V2bXoXdi5WD9L3imZtY4P4hIJ3CKCo+S7y9sNf6vth5H0ebVp7J0H38eTuhcU3oFGcfOkcwdJgdRT5rwwL+aW2qtHuruq2x5livnJfLpsPFWLSa7ljXHd1pLo8X31x3evuaZdarYYXr+hHUpdgSmwObvhgI59tUi9Sp1YnUMv4E6xF6t3d6IH1tzkwTl3nAupoVG3VpcLiz/bsNMuwWtNadv4Pig6rCY6q16QLIZqca8cXeDlLOOwKo8uwE+dN6BLqQ/dOvjhcCr8FXg5+UWo//d/0quzPilpWa9K8E48g978G4oZBZTn8+EDL1O6urICvb1Vvhva5FPqcQobYoyiKwmPf/sWnG9XfyfOXH+C3XdmnfVzRgmpPL8/c0brryIsa9nI1aVrWDrXM0bVLWqTKwP+2pJNRVEGYn4mrk2SU26N+eQSyd4J3KP8y3stV727iyz9rLcXUaGpGuzO3NeiQLqeL9f99gp/fn8uXmw+z64gFu+P4CRYdThfvrUnhnH+v5OutacwzvItB48Te/TxGX3RjTUne2mKHwPXfwg0/qPkHQL1B4KmZpkdzX4vvrRr8UiBhklqKWDQbCbrrU5YHn1wOtmIO+/ZjTuXNjOgRRs8ItcTVHweqAufqqdL1BN3qKLe6lvvaofGE+5thx2c1GxSmwu5v3T+a9Drevm4QCeG+ZFtspOSVYdRpOf/MSHWD6lJhXcecOGgeeL36uPWTumspT6VUWHOoHunO2VVTB3jYrGZdmyVEh6YoWP+YD8Dn2smM6hl+0l2qp5gv3VcC5zymPrn3RzWY7X81nPfiyadsa7VwwcugNag1sfd8fzrvomGWPaneufeNUNf9nSZFUXj6h938d/1hNBpI6qLmtLjvi+1kFFWc9vFFC6k90l2epy5pEq2bww6Lr4XD68AUoK7hDe3e7Ke1OZy8XrWW+/Yx3U44K0g0s13fquVkAeXiBfyQqj798brUuttF9lMfG7Cu2+F08fEHrzB0/0tMPPwfKpfcyQWvrqT3Y0uZ9PIq7v18G2+vOsDq5FxyS2xsOJjPBa+t4V/f76LE5uDBkLUM1CaD0Q/jlAb8jek8Aqb/CDNXwo1LT28tdlOqvhbf/2tNbDLyfs+1p4OQoPtoigKLr4eiQxDUmWcDHsWOgYRwP0b2UKc0rUmuumt+gqD757+z2ZlRjLdRx21juqlTpPb+pL5YnUThj1fq3HEP8DKwcPoQwv3VcmRjEsMI8K7qoNVJ1I43tbxawmR15Lg0C5J/Vp9z2NU/XHDqSdSaSnUCh4wt6sWxOQAG3+TZNgnRnh36A+/CvZQrJop7XolJf/KLyOqge+XeXOxnXA5RA9QX+lwKF77W8LJ+nXrC2VX1dH98EGwlp/IOGiZlFayvKj144Wunl/Sxyou/7OPdNSkAzLv4TD6ekUS/mACKKyq5Y9GfJxwdEa1IeVXQbfJXH2Vdd+vmcsLXM9XBBr0XXL0YIvu2yKkXb0rjSLGVcH8T04ZIBmePsWTCt1VLmYbfRXrI2e6cGtvTi9l1xFKzbfWSyZNkMLc7XNz3yVrGp73mfu4q/QpeMy9AcTnYk1XC11szeObHPVz73kYGP/0bV769nj1ZJQR6G/jP5HBudVTNIj3nUQiIbth7qR6Nb4FZGg1WfS2+8wtwOdTYoBnr3QuVBN1HKzgIh9aAzgj/WMyWXHU0JzHCjxE91Lp6q5Pz1DUlxwm6nS6Fl35V13LfeHYXQnxNakZxhxVCE9ULQr2XOhWmegS6SlSgF5/clMSVg2J5cFJVsoPyAjVIhfqTqNWmN0L/f6jfVydUS9+kTvH0DoVOvRv/mTSl4G6gqXXRn3QrmP091x4h2jnXejWR2VfOEYwfkNCgffrHBBLmZ6LE5mB9SqE6rfMfi+Hit9WkjfXIL7Xx0i972Xq4sO4Lox5Qa6mWHIHfnz6Nd3ICVgssmaV+P/AGSJhw2od8bVkyry9Xf7c/ceEZXDUkDqNey+v/OAt/s56th4tkfXdbYC9T//5BzU1nT5QXao0KU9VBhvTNnm5JDUWB7+9VkzdqDXDVfyFuaIuc2lrpZP7yAwDMGtsds0FGuT0m+Wd1sCq8D5zzGFvTiuq8/PmmwzU/uJOp7XInKT6atdLJzI8303Xfu0Rr8qnwjoapb4JWz/msYecZn/Lu1X2579wEzjszQl2frVHj5WlD4lh+3xguzn4Vjc2iLvFs64NF1SPd1UY94Jl2dDASdB+tMFV9DO5GkU8Xsi3qnbWEcF+GdA7GqNeSWWzlQG5ZraD7QJ1DfL/jCPuyS/E367l5VNW67x2fq499r1CTGFRnGv/jlWOa0L2TH89d1pfunaoyHB5cDopLDZgbcmftrKop5sm/QHFGTWDfZZTn69PqjWrpMFAT0SXd6tn2iCa3atUqpkyZQlSUWupuyZIldV5XFIXHHnuMyMhIvLy8GD9+PMnJyZ5pbHtXlIZm7w8AfG04n7O7NywBkVarYXyvTgD8uisbvAIhYeJxl7bkldqY9s56Xv19P5cvWMeHa1Nrkt0YvNSEZgAb34IjW0/rLdXr5zlQfBgC49XM6afpnVUHefFXdXnQP8/ryfXDO7tfiw325oXL1emM76xOUT8f0XpVr+fWmyG+am2lJFNT/fww7FoC393deta5//a4mmhWo4VL3zn57L4m9NnGw2RZrEQGmLlycGyLnVfUo/paPP5s0BncN3MTwtXr4q+3ZmCtdKrbBMar1TKcdjXwPkppVa6kA/v+4laduszJ64Jn1QGqKz4GnRHvAz8yfsds7hwVyxtXD2T5/WP4+4mJbH30XOZdciZB6ctg1zfqoNGUV49787nNqM5gDhAzBDqP9FxbOhAJuo9WVJWJPDCOvVnqVMjoQC/8zAa8jDoGdw4CqqaYB1cF1CVHwFYKqOtF/lN1sTZzVFcCvAxVgW9Vua7qzIXDZql/VA4sU8vqnMj+qqzl1UnSTia0u/qLSnHBtkVqNnPw/HruatXrbwbf2CRTQEXrUlZWRr9+/Zg/f369rz///PO8+uqrLFiwgA0bNuDj48PEiROxWq0t3NIOYPN7aBQXfzjPIOHMIRh0Df+VXz3F/Lfd2XWzxR4lt8TGtLfXsy+7FJNei8Ol8Pi3f3Pf4u1U2KsuirqfA30uU38nfXePOn20qez9qSpxpAYuXgAmv9M63EfrUnn6x90AzD43gZmjuh2zzcQzIrjx7JpSj+mF5ad1TtGMqtdz+4Sp9Z1BppeDmkCpOs9C9l9wcIVHmwPA6pdqBiKmvAJnnDjpY1OyVjqZv6JmlLshy3BEM6oOuoM6A7D1cBGgZpOPDvTCYnXw019VuRlOkEytuLySa97dwPqDBcw1foJJU6lmFe81Rd2g53kw7TN19mnyL7DoCnV2DOBt1BPobVSv73+oWu88/A6I6NMsb7lF+YSBX1XOqFH3e35AroOQoPtoRVVTVoLi2ZutBt3VCdQARnSvmWKOd3BV2n3cJbq+/DOd1Pxygn2MTK+6KGPnF4CiBsKBVWuEgrvU/EH549Xjt0dRapKoNeaOb/Vo95aF6vRy8Px67mrnPA6TnoWxD3u6JaIZTJ48maeeeoqLLz72gklRFF5++WUeeeQRLrroIvr27ctHH33EkSNHjhkRF6epsgJly0IAFjonMqVfZKN2H94tFG+jjsxiK39lWOrdJqfEyrR31pOcU0qEv5ml94zikfN7odNq+GprBpe+uZa0gqqAdOIzag6HzG2w8Z3TeGO1lOXDt1VrxofNqhnJPEWfbzrMY9+oZWduH9ONO8cdP3HTQ5N70i82sGp991ZZ391auYPu0JrMvMWH1WVbHdmal9VHbVXemHWve6wpgFpWadkT6vcTnmrxiiafbDhMbomN6EAvrhgko9we5w6647E5nO413APjg9yzEKorSgA167prJVPLK7Vx1Tvr2ZZWxGSvvzlHsxm0epj8fN0gs/s5apkvg486SPXfS9UlS9WWP63WiA+MV+uEtwcaDVz+IVzyDvQ4/eVYomEk6D5aYc1I956qke7EWkF3dTK19QfzqXS61DXKAPn7sTmc7tqOt4/pho+pairmjuPU5xtedbH415c1wf7Rsv9Wk6IZvNXyOw3V+0L1AteSriZJCIiDoC4N3785BcbC0NvUaaeiQ0lJSSErK4vx42tuIAUEBJCUlMS6deuOu5/NZsNisdT5Eifx9xI0FYWkucLY4T2UpC4hjdrdbNAxqiqPxa+7so55PafEyrS317M/p5TIADOfzRxKl1AfbhrZlf/OSCLEx8iuTAsXvLaGlftywS8cxs9Vd/79X+oMoNOhKPDDbCjLUafKjXv0tA739dZ0HvpKnXU0Y0QXHpiYiOYEd/+Nei2vTxuAv1nPtrQinl8q67tbperp5d6h6jKJwKoSUCebYdaeFaXV1A2+eIE6627/b5B97NTcFrHzfzUjiSPvh+F3tujpK+xO3qwa5b5jXHeMerk09rhaI91/H7Fgd7oI9jESF+zN5YNi0GpgY0oBB3LVWaZHj3RnFldwxVvr2J1pIcJHy8sBVRm6h9yiJvg8WucRag14U4CaePiji6ryKf0JG9S8KFzwUvuqtBOXpMYlMsrdYuQ3y9Gqg9/AePbVE3T3jvQnxMdImd2pTnepXtddcIDPN6WRUVRBuL+Ja4bW+sOe87eamK33RXXPFdVfLQGmOGHdG/W3pzpreecRYDA3/H0YvKDvlTU/d20F67lFh5eVpQZv4eF1y1aFh4e7X6vPvHnzCAgIcH/FxspIxElt/xSAz51jmNw3pv5aoidRPcX8l6PWLedY1ID7QG6ZO+DuHOrjfn1YtxC+v2uEeyT4hg828vrvybgGXK+uH7OXwk8PnsabQ71ZuWuJOnJx8YLG/X48yo87M7lv8XYUBa4ZGscj5/c6YcBdLTbYm39Xre9+d00Kv/x9/P/DwkOqg24f9QaSOwt2R17Xve71mozFZ14GPS+oer7+JUHNat/P8PUtgAKDb4Zxj7R4E/67/hB5pTZig724bGAryjDdUVmLoaIqIWdgPNuqppYPiA1Eo9EQGeDFmEQ158jiTVWj3dXLFrP/5nBOMZcvWMfB3DKiAsz8kPQ3pqID6u+AMf93/PNW19b2CoYjf8KHU9SZVIoLzry8RfMLiPZJgu6jVa3pVgJj3dPLawfdWq2G4VXJiFYn57qTgh058Bev/KYmg7pjXI+arJfVCdQSJoFX0LHnO/tu9fHPD+uf7nYqU8urVU8xB3UNixBt1Jw5cyguLnZ/paWlnXynjqw4HaUqgeIS14hGTy2vNq5nJ3RaDXuyStzTxLMtVq6qCrijqgLu+BCfY/aNDPBi8S1DmTYkDkWBf/+yj1s+2UrphH+rgfKe72HPj6f2/iyZ8MN96vejHqgpaXYKftuVzV2fbsWlwOUDY3jywj4NCrirTTgjgptG1Kzvdk+nF61Deb766FOVRDCi6uK8o67rLsuDLR+q34+4V32sHlneuRhKWjAxYOoaWHydegOg75XHTvttATvSi1iwUh3lvnNcj0blvRDNpHrGqU8YmHzdmcsHxAW6N7mqaor5/7akq0t7grqgmAPAaeefby8mvbCCziHefHldN0I2/0fdafxcdQboiUT1hxt+AJ9Oaq6D7J1qkraJ85ryHYoOSn671GYvd98Vz9RGUGJ1oNdq6BrqW2ez6inmq5Pz2GlV755npfxFfpmdbmE+XFm9HsjlVKdNQd1R59q6jlXXmVWWq2uaarOVwqGqKbenEnRH9IFeF6rT6eQOnWgFIiIiAMjOrnthl52d7X6tPiaTCX9//zpf4gR2fI4GhXXO3rj8YxkQW88NvwYI8jEyKF7d99dd2WRXjXAfzCsjOtCLz2YOqzfgrmbS65h3yZk8e8mZGHVaft2VzYWLCynoN1Pd4McH3EkoG0xR1Pqt1iJ1Hd/I+07pvQGs2pfL7Z/8icOlcGG/KJ69tC/aU5gR8OCknvSPDcRidXDHp7K+u1U5ZqS7KujuqCPdGxaAo0LtO13Hqs/FDlFnoDjtsPHtlmlHxp+w6Cq1lGrCZLhoPmhb5pK0wu5k8aY0Lnx9DRe+/gf5ZXY6h3hzyYAG1l0Wzat6annVUpDqzOUD4mr+jo3t2YkwPxP5ZXaW/p3F//7MYGulun1UxV4Swn1ZfMswIjc+q86sih4I/f7RsPOH94bpP4F/1f+HiU+Db1iTvDXRsUnQXVv11HKTP3sL1Y+ma5jPMet7qoPubWlFPLBcHdXoqsli1thufHnb8JrtU1dDSaZ6l6zHufWfU6OBs+9Rv9+wACoral5LXQ2uSjV7Y3Wm9Ma68mO4Z4dkCRetQpcuXYiIiGDZsmXu5ywWCxs2bGDYsEbkLBDHpyiwXV2/9pVrBBcNiD6lQLJa9RTzr7dmcFWdgHsocSENW9921ZA4Ft86jMgAMwfzyhi/ZSjl3tFqzokVjRxB2LJQnQGkM8HFb4HO0Mh3pFp3IJ+bP9qM3eli0hkRvHhFv1Oagg9V67v/MYAALwPb04p4TtZ3tx7uoLtqpLt6ennePvVGe0diK6kJqkfOrjuqXD3avfk9d/bmZpO7V01WZS9RSxVdvvCU+3Fj7M8pYe63fzPkmd948Msd7EgvxqjTMrV/FB/eOAS9jHK3DrXWc+eUWEkvrECjgb4xNaPUBp2Wy6uWAtz92Vbu/2I7G21qouKrYwv4323D6VS03b3MiskvNO6mTmh3uO0PuPn3mhK/Qpwm+Q1TW6313Huy1dGXxIhjR9QiA7xIDFennGfro9RdNKU8MLKTWl6g2vaqqeV9LgG96fjn7T1VTXRWng/bPql5fn9VYNLtHFmPLdqM0tJStm3bxrZt2wA1edq2bds4fPgwGo2Ge+65h6eeeopvv/2WnTt3ct111xEVFcXUqVM92u5248ifkLePCsXIT84hNTNvTtGE3uoMhJ0ZxaTUCrhjgxuXUKZ/bCDf3TmCYV1DKLDrub3oagCU9W82fKpvQYpaWxjgnMfqT4jTAFsOFTDjw03YHC7G9ezEq9MGnPa00pggb16sWt/93poUfpb13a3D0SPdfhHq1FHFpSYq7Ug2f6Culw3pXrOOu1rP89VkqxWFaqnR5lJ4CD6aChUF6rKQaZ+eVj6Gk7E7XHy/4whXvb2O8S+tYuHaVEqsDuKCvXlock/WzRnHy1cNOOGMHdHCagXd1eu5e3Tyxc9c98bMlYNj0WjU+8yhvka69T0bgH66VPyNWvjpAXXDAddAzMDGt8MrSB0hF6KJSNBdW50a3Wp25Nrlwmp7ZVp/Hp/Sm18enFQzBSV/f80G9nLY/a36/fGmllfT6dXafwBrX6upYXs667mF8JDNmzczYMAABgxQ19nOnj2bAQMG8NhjjwHw4IMPcueddzJz5kwGDx5MaWkpS5cuxWxuvguvDqVqlPtn1yDO7BpbJ8HZqYgL8XbfZIwJOrWAu1qor4mPZwxh5qiurHD153vnUDSKk8pv7j557W6XE5bcDpVlavnFobefUht2pBdxw/ubKLc7GdE9lDeuPqvJshWP7x3OzSPV9d0PyPru1qHsqDXdUCuZ2vaWb4+nOGw1idLOvge0R9Wh1upq+tS6+Sfvj6eiMBU+ngolR9SKA1d/Cab6r7FOV3phOf/+eS/Dn/2dOxZtZf3BArQaGN8rnIXTB7Pi/jHcOrobIb4nGBARnlF9LR7UuWY9dz1LpOJDfHjn2kE8f1lf1vzfOM49Z6L6QtZfsPl9yNwOJn+1TK0QrYDe0w1oVdwdPZ69e9WR7oTw+v8g9Izwp2f1KHhIN7BkqEF37BD1ub0/qutIAuMhNunk5x5wjTrNsjBVDdYj+kJhilpDs8vI03xjQrScMWPGoCjKcV/XaDQ8+eSTPPnkky3Yqg7CYUfZ+T80wJfOUVw1pGmyvD96QW++3prBvef2ICbo9Eqm6HVa/nleL/rGBPD8/25glLId/6w/yfjtdaIn3H38HdfNh8NrwegLU984pfWfuzMtXPveRkpsDoZ0Cebt6wbWJL1sIg9O6snmQ4VsPVzEHYv+5Itbh0sJIk9RlLolw6pF9FVvanekZGrbP1XLj/pHH38gYMDVak3iwhT1GqbXlKY5t7UYVr8E698Epw0C4+Dar8GncWUMT8bpUli1L5f/rj/E8r05uKr+DIX5mbhqcCxXDYkjOlBKlbZ6tWp0b91cvZ47sN5Nx/euVQkluKta8stWXDMjaswc8O3UfG0VohHkSqC2qoyJTv9YDuSoQffxRrrrqC4bVnukuzpred8rGzY13OgDQ6qSC615uWZqedzQZrsTLIRoZ5J/QVNRQLYSyF/G/kw84/jJ6RpjRI9QXryi32kH3LVd0DeKd2ddwPvm6wDw/2Me367eUv/GObvV2t6gJrUJ6tzo8+3PKeGadzdQXFHJgLhA3r9hMN7Gpr/vbNBpef0fZ6nru9OLmffT7iY/h2gga7GaFwWOM9LdQYJulxP+eEX9ftgdoDfWv53RBwbPUL9f+/rpn9fpUBPEvnoW/PGyGnB3HgnXfwf+Uad//Cp5pTbeWLGf0S8sZ/rCTSzbowbcw7uF8MbVZ7H2oXHcNyFRAu62wOV0L/V0+MexI70YqJtE7bg0mpq+7bSpsymG3NxcLRWi0STorq2qo2drw7E7XfgYdQ37JX100F2aWxM0972i4ecfMhP0ZsjcVvMHsvs5Dd9fCNGxVSWN+do5govOimvyUdymlhDux413P8lBU0/8NBVof5nDP7/eic1Ra2qrs1Kt4+u0Q/dz65ZCbKDUvDL+8c4G8svs9In2Z+H0Ifiamm+iV3Sgl3t99wd/pLL0r/axvnvVqlVMmTKFqKgoNBoNS5YsqfO6oig89thjREZG4uXlxfjx40lOTvZMY6GmXJjRDwy1/pZHVF2YZ+9S/3+1d7uWQMFBdY3qWdedeNshM0FnhLT1kLbp1M6nKGr97TeHq6X9yvMgpAdM+0wNuE/hptmxp1DYcDCfuz7dyrB5y3h+6V7SCyvwN+u58ewuLLtvNItuHsp5Z0ZKGbC2pCRT/V2v1bOvIoByuxNfk57unXxPvi/UVCcAmPxciyToE6Kh5DdRbVXTy5Mr1UzfPcL9Gpb11x10H1Qf//4KFCdEnQWhPRp+fp/QmiyJlnT1UdZzCyEaorwAZd/PAHzlHMmVg5tmanlz8/c20/n6t3Gh4wLdBo5s+oar3l5PVrFV3WDVC+raPK8guOj1RieVTCso5x/vrCenxEbPCD8+vjGJAK/mvxAb3zucmaPUqhMP/K99rO8uKyujX79+zJ8/v97Xn3/+eV599VUWLFjAhg0b8PHxYeLEiVit1hZuaZWjM5dXC+qiBuJOm5rFvD1TFFhdVac46VYwnSR48YuAMy9Xv1/3WuPPl7VTXbe96ArI2wtewXDev+H2dZA4+bSTwiqKwvK9OUx5fQ1Xvr2eb7cfodKp0C82kOcv68uGf47nsSm96RbWwCBNtC6FNbmVtmWUANAvNqDhlSUSqtZ1n3kFdB3T9O0T4jTImu5qVouatRPYUeIPlDdsajnUBN0FB8DlcicyOmkCtfoMu0NNAKG4wDccwvs0/hhCiI7nry/RuCr5y9UZr5gz6RXZdmqZa6P6wfDbYe1rPG1cyPjDvRj9wnIGG1NZ6HoBPfCQ9QZ+eXknsLNRxy63O7BWuugW5sPHM5II8jnO1Npm8MDERDanFvBnO1nfPXnyZCZPnlzva4qi8PLLL/PII49w0UUXAfDRRx8RHh7OkiVLuOqqq1qyqarjBd1aLUScqeYIyNwO4We0fNtayv5lkL0TDLWWsJ3MsFlqJZXd36nraxsyMl2SpS4B2foJoKij5Um3wsj7wCvw1Ntfy8aUAl74eQ+bUtVrNS+Djov6R3F1Ujxn1ionJdqwWjW63fW560midlxdRsE9fzXp8gUhmkrb/evfRHIsVqyVNWtI8ApiR56afSOxoUF3YBxo9VBZDqmr1JI9Gh30ubTxDQruopYQA3WUW0qFCSEaQKmaWv6lcyRXtZFR7jrGzIGAWKLJ5cmAH8BhZa7jVfS4+M45lM8qBlNQZm/0l7XSRddQHz65aShhfi2bqdig0/LaP84i0Ftd3/3Mj+13fXdKSgpZWVmMH18zOysgIICkpCTWrVtX7z42mw2LxVLnq0kdXS6stphB6mPqmqY9Z2uz5iX1ceAN4B3csH3Cz4Bu49Sb/+vfPPG29jJY8Zy6bnvrfwEFzrgY7tgEE/7VJAH3zvRirn9/I1e8tY5NqYUY9VpuHtmFPx4ax7OX9pWAuz2pVS6sOnN5/9jAxh0jMPbY7PxCtAIdfqT70W/+YtnuHK4L2cVjQIExkr8z1MQNicfJXH4MnUHNUl5wAJbPU5/rfg741vOHviEmP69mYaxOaCKEECeSuw9NxhYcipZfdSO5r18bvMtv9FGnoX56JZfZlzC5Vzm+KUdweHei95Xv8Ku5EaMdR+kS6oPeQ+s6owO9eOmKfty4cDML16YytGswk/pEeqQtzSkrS123Hh4eXuf58PBw92tHmzdvHk888UTzNaq+cmHVuo2Dta/Cgd/VKdjt8Qb34Q1w6A+1CsqwWY3bd/id6mfz58cw5iF1eUdtLhfs+AyWPamuwwWIGQwTnoa4BlRsaYD9OSW89Os+ftyp/v/RazVcMTiWO8d1JzJAkqK1S1VBd4VfLPurEhr3P07mciHamg4fdB8psuJwKSgFqWCA9QW+HKlU1581eKQb1CnmBQfU5CNwalPLq/mGwTmPnvr+QoiOZYe6pGWFqx9n9+vVrEnCmlXiJOg1Bc3u7/BN+QkA/dT5dIuP83DDTs+4nuHcMrorb608yAP/20HvyADiQpouE3xbNWfOHGbPnu3+2WKxEBvbhLM06isXVi1umJq4tCQTcvdAp15Nd95TZS9XbwT4R0PCpFO/cV9tTdVa7n5XQUB04/btOlZd3pb9F2z+AEbW/DuRskotyVSd/T0wDsbPhTMuaZKbF2kF5byyLJmv/kzHVXU/ZGr/aO4Z34P4EJ/TPr5oXcrtDp76YTd5JTZmp+2kJ/DP39X13HHB3oRKLXXRTrTRK7Om8+0dZ3Ok2Irt+59hPzj8YvEq0ZHUNZiQxnT0kO6QrCYxwugLiec1T4OFEKI2lwvXts/QoiZQu6mJanN7zOTn4cAKsJeomZYTJni6RU3i/gmJbE4tZMuhQmYt+pP/3TYMk779TIGMiFDL02VnZxMZWTOSn52dTf/+/evdx2QyYTI14wX1iaaXG8wQfzYcWKauez6doPvIVrXUUfWU9VO17nVYUTVbDg3EJkHP8yDxfAjt3rhjZe+CfT+pxzn7nsa3RaNRR8eX3AYb31bzzRQdgl8fU2t4A5j8YdT9MOQW9fM8TTklVub/vp9FGw9T6VSX+U3oHc59ExIbNwgi2hSjTsuiDeoSz6dMmaCB5Eq1hvslZzXyZpEQrViHD7o1Go1aFkyvTkO7cMwwpgyeiKaxd2tDutV83+tCMMoohhCiBaSuRluSQbHizaHQUQxo7Pq31sY/Cq5YCAeWq+u82wmDTstr0wZw/qur2ZlRzLwf9zD3wvaTwKtLly5ERESwbNkyd5BtsVjYsGEDt912m2caVZ6nPtYXdIO6DOzAMnUa9fA7Tu0cFUXwwXlq6bG7tqrrSU+Fy6UmLwMIiIPiw1Vlu9argW5ognozP/E8dRq3VqtOiy/LhYIUKExRy4JVf59blZW994WND9ir9bkMfntCnQ3wyaVwaC24HGrOmkE3qtPO65u630jF5ZUsWHWAhX+kUlGplgsc0T2U+yYkNKw+s2jT9DotD05KJNDgoNOvRQC8fvvFBIeF42+Wkl+i/ejwQbebu0xBfOMDbqjJYA6Nq80thBCno6pawvfOYVwypPup/f5qbbqPb5flEqMCvXjpiv5MX7iJhWtTGdE9lPG9w0++YytRWlrK/v373T+npKSwbds2goODiYuL45577uGpp56iR48edOnShUcffZSoqCimTp3qmQaXVQfdxwkMu41THw/9AZXWUxut3fezmkQV1ERiY0/xRtHhtep6VqMfzNqg1hjf+xPs/UFN9pa3T/3642X1JoJvhBpc20uPf0yDN4x68NTaA6A3QtItsOwJdUo5qNPez30SwhJP/bhVymwOFq5NZcHKA5RYHYCaNOvBiYkM7376wbxoO24f0x1ydsOvgDmAzrExnm6SEE1Ogu5q1dnLA09x7WDEmWAKUGtcdhnVdO0SQojjsZfh/HsJOuBbRvHmAJmK19qN7dmJW0d340hRBUO7hXi6OY2yefNmxo4d6/65ej329ddfz8KFC3nwwQcpKytj5syZFBUVMWLECJYuXYrZfPpTj0/J8UqGVQvrCX6R6kju4XXQbWz9253Inu9qvt/6MYx+8NQyJ29bpD72uVidKWf0hqSZ6ldFEez/Dfb8oD6W5da8NzTqGvDgLupXUBc1EWtwFwjudvK63Ccz6Eb46ys1AD/nsSapfWxzOFm04TDzl+8nr9QOqIlr75+YyPhendrHjUPReLUylwvRHrXqoHvu3LnHZDZNTExkz549TXuiikKwqRnLTzno9g6GOzermcylVIEQHrUvu4SFa1O5Jime3lFtp151o+3+Hp2jnFRXOGG9RhLcgjWoxal7YGIiWg1tLrgYM2YMiqIc93WNRsOTTz7Jk08+2YKtOg6XSx0thuNPL9do1NHubZ+o08wbG3Tby9X14AA6E1gy1KA4YWLjjmMrhb+XqN/3v+bY170C4czL1C+HXb1BUFmhBteBcU2ynvq4vALhtlMrq6YoCtZKFyW2SkqtDspsTv46Usxry5I5UqwmrO0c4s295yYwpW8UWm3b6g8dRYtdi9eacSpEe9Sqg26AM844g99++839s17fDE2uHuX2CTu9tdi+nZqmPUKI0/LZ2n38umE3izYcZmB8ENcOjWfymRHtKnEVgHPrJ+hQE6hNS5ILlbZCJ8FF86soVOtMA3ifYEaBO+he3vhzHPhdnVoeEAe9psD6+bDlw8YH3buWQGWZukwtdsiJt9Uboevoxrf1NBzOLyclv4xSq4NSWyUlVgelNgdlNvWx+udS61E/2xw4XfXfpInwN3P3+B5cNjAGg4fK+YmGa5FrcRnpFu1cqw+69Xq9Oytqs5G7a0K0K9f4/cnD5v/jF9cgPk47h3sOFfDk9yauGBTL1UlxxAa3g0SHxRloU9V1luv9xnNP17Y1VVmIZlU9/docqM5AO56uYwGNWhqrJEtdItZQe75XH3ueDwOvV4PufUvBkgn+jajFvrUqgVr/f7SqeuGFZXZe+GUvn248zAkmOJyURgO+Jj1+Jj0B3kYuPSuaa4bGYza0r5ug7VnLXIunqo8SdIt2qtUH3cnJyURFRWE2mxk2bBjz5s0jLq6Ja7ae7npuIUSr0tW2F3AyWbuBycYNHNJE8ZF1LJ+uHM1bqw4wJiGMa4fFMzqhU9sdddy5GA0KG1w9GTVkkEzNFKK2E5ULq80nBCL7QeY2dbS7/7SGHd9ZqSY6A3WUOyxRrf19eB1s+y+MeqBhx8k/oCZR02ihXwPP3cycLoVPNx7m37/spai8ElDXXAd4G/Az6fEx6fE1q0G0b9X3viY9fmY9vibDUT/r8TLo5PdTG9ci1+ISdIt2rlUH3UlJSSxcuJDExEQyMzN54oknGDlyJH/99Rd+fvXXbLTZbNhsNvfPFovl5CcqqhrpDpKRbiHahfP/DYOmw+YPYPtnxNuP8KjhE/7P+AXfOobyyb5zuHFvDjFB3jx6QW8mntHMd/CbmqJg37III/C1cyT3DGzjtbmFaGonKxdWW/dzqoLu3xsedKeuAWsReIdC3FD1ubOuV4PuPz+CEfepZb1OZvun6mPXsbh8I1m+O5uB8UEEensmP8OWQwU89s3f/H1EvXbqGeHH3AvPYKjMpOmwWuRaXFFqXYt3bqKWC9G6tOqge/Lkye7v+/btS1JSEvHx8SxevJgZM2bUu8+8efOOSfhwUjLSLUT7E36GGnyPnwt//Q82vYcxaweX6VZxmW4Ve+jMh5ZzeOSzIoY/PAW/1lAPtDgd9vwIxWlg9FFL/hh9ar4M3mD0hdIsjIX7sCoGSrudT0SAh7JDC9FanaxcWG3dxsHqF+HgcjUBW0OC5eqp5YmTa5KnnjEVlv6fek1xcLkazJ+IywnbqoLuAVfz3w2HeOybv4kP8eaTm5KICTr1ZTBlNgd7sizEBHnTyc900qR9OSVWnv1pD1/9mQGAv1nP7HMTuGZoPHpZc92htci1eFluVek9DQTITWTRPrXqoPtogYGBJCQk1KkTerQ5c+a4y5iAenctNvYkHVjWdAvRfpl8YeAN6ihUxhbY/D789SU9HanMM7zHU8r7FL15BvQ5BzqPUketTrfMTkMpCuTuVcsO7f5eHW1rhJ9dg7kwqVfztE2Ituxk5cJqixmi3swqy1XXdkf2PfH2Lpdavgug14U1zxu8oO+VsPFt2LLw5EF3yiqwpIM5AEePybz98noADuWXc8WCdSy6eSidQ31O3v6j7M60MGPhJneGcH+znoRwP3qE+5EQ7lv1vS9hviYcLoUP16by8m/JlNocaDRwxcBYHpiUSKivqdHnFu1fs1yLV08tD4hRkwUK0Q61qaC7tLSUAwcOcO211x53G5PJhMnUiD8UilJrpFuCbiHaLY0GYgapXxOegu2fUrDmXYLLDhJS/Bf88Rf88QrojOrrSbc07Lgul1puKPlXMAeAf5T65Rep1s/1DnYnRzqQW4qfSUsnyy7Y/Z06WpZf+8JFowb9UQPAYQV7Wc1XZTnYy1DsZVjLLBTY4H+mqbzfU6omCHGMhq7pBvUiv/NI2PeT2pdPFnRnbFFrexv9KI4czu3vrkdRYOH0IRgH3qAG3Xt/hNKcE1c12VaVQO3My1m6t4j0wgqCvA0E+Rg5mFvGFW+t45ObkugRXv8U3vr8viebOxdtpczuxM+kp7zSicXqYPOhQjYfKqyzbaC3AS+Djsyq4LxfTABPXNSH/rGBDT6f6Hia5Vpc1nOLDqBVB933338/U6ZMIT4+niNHjvD444+j0+mYNq0Jk42U56ulOgACZUqLEB2CdzAMm4XrzJsYOW8xA5VdzD0zn8Ds9eq6sp8eVAPdkbOPf4zKCtj+Gax/E/L2Hn87nQm7dzgpNn8OVngxQLsfNLUufnVG6DIael0Aiecd9yI9Na+Mr/5M56utGaRbKgC4fVg3KbcjRH3KGrGmG9Qp5vt+Utd1j7j3xNvu+Q6Aym7jueHj7Ww9XATA4s1pXDP0DIgeBBmbYdsiGHFP/ceoKFJvvAFKv3/wzpKDAFw3rDPXDI3n2vc2sCerhCvfXs/HM4ZwRlTACZukKArv/5HK0z/swqXAsK4hvHnNWXgZdRzMLWNfdknVVynJ2SUcKiinqLySIioJ8THyf5N6ctnAGEl4Jo7RItfi1UG3DH6JdqxVB93p6elMmzaN/Px8wsLCGDFiBOvXrycsrIF/RBuiOnGDXyToZSqVEB1JqK+JM3v3ZsnOYAJ9OjP37rdgxbOw8llY9gQ4bDDmobplfEqyYOM76jT1igL1OaMfnHkpoFFHwCxH1K/yPHDaMJYcJhFIrFr6WaqYWeHqz6/KYGyx5zChdw/O7R1+zLry4opKvt9xhK/+zGBLrVEqX5OeC/tHcee4Hs37AQnRVlUH3Seq0V1bt3Hq4+H16g0343GmdSuKO1h+PbMXW7OK0GrApcBrvydz2cAYzANvUIPuPz+Es++uvwzY31+rs1nCerHRFs/29A2Y9FquGxZPiK+JT28eynXvb2RnRjHT3l7PRzOSjjsCXel08fi3f7Nogzpr76rBsfxrah/3Dblekf70ivSvs4+10sn+nFKyLVYGdQ4mwKsV5LQQrVKLXIsXShI10f616qD7s88+a/6TyHpuITq0KwfH8ePOLL7emsFDk3tiHjtHvQG37Ak1+HZUwPgnIGsHrHsD/voSXGoZHQLjIOlWGHAtmGsuaivsTt5dfZB3V+7BrzKfCPKZFKdwSYIeZ2BXlhR3Z8lf+WqG4ORyliZvx6jXMi6xE1P6ReFl1PLllgx+3Z2N3eECQKuBkT3CuHRgDBN6h0uNWyFOpDHTywFCukFAHBQfhkNroce59W+XsxsKDmLHwLtZ3fAz6Vl442DuXLSVI8VWPtlwmBlDLoGlc6DgIKSuhi6jjj1O9dTyAVfzzpoUAC4bGENI1TrqIB8jn9ycxPQPNrHlUCHXvLuBD6YPZnDn4DqHKa6oZNYnf7Jmfx4aDfxzci9uGtnlpInTzAYdfaID6BN94hF0IVrmWjxVfZSgW7RjrTrobhGSuVyIDm1E91CiA73IKKrg57+zuKh/tDqtXG+Gn+fAH69Quet7DIUHanaKHQrDboeeF9RkLgZcLoWvt2bwws97ybJYAS2dY3vwf+dfWOdi+Wbg5nHqGu/vt2fy7fYMDuSWsfTvLJb+nVWnfYnhflw6MJqp/aPp5C9ZyoVokMaUDAN1NLr7ODUB2v5lxw26Hbu+Qw+sdvZBMfqy8MbBDIwP5q5zevDQVzt5c8V+pg2Jxbvv5epsmC0Ljw26c/dC+ibQ6EiJOo/fvt2DRgMzRnSps5m/2cBHNw5hxoebWH+wgOve28i71w/i7O5qcrhD+WXcuHATB3LL8DbqeOWqAZzbO7zhn5EQrYU76JYBMNF+SdAtNbqF6NB0Wg2XD4rh5d+S+XxTmhp0Awy7nUK7hqDlD2EoPEClouN37TB+DbiMCnM/olLMRBUeJirQi+hALwrL7Ty3dA9/Zaj1SKMDvXhwUiJT+kYdd51ktzBf7h7fg7vO6c7uzBK+23GEH3ZkYnM4Of/MKC45K5ozovxPOmolhKjFWQkVVcsxjhN0O10KmcUVRAd61fSvblVB94Hf693H4XRxZP0XxAHLGMK71w9iYLx6M+3SgTG8ufIAh/LL+XDtIW4763o16N79HZTlg0+tae7Vo9w9JvDWFjWnzLm9wukadmzVBB+TnoXTh3DLx1tYuS+X6Qs38dY1A/Ex6bnl480UllcSGWDm3esHnXTdtxCtksMGFrVUnYx0i/ZMgm4Z6Raiw7t8UCyvLEtm7YF8DuWXER/iQ0peGVevTaCX/T56atL40jmSLEKgAsjKPO6x/Ex6bh/bnelnd27wFHCNRkPvKH96R/nzf5N6NtG7EqKDKs9XHzVa8Aqqd5P//LqP15fvp3snX64bFs8lZ8Xg22WUuk/eXihOV8sXVXG5FJ5d9DOP2JJxKhrOv2w6w7vVlCMz6LTcfU4PZi/ezoKVB7h66Fj8I/urZQC3fwrD71A3dDpg++cAFPW8gq++VIONmaO6HvftmA063r5uIHcu2sovu7KZ+fFmACqdCn1jAnj3ukEyC0a0XU67mnCwOL3hM1OEaIMk6K60AhpZ0y1EBxYd6MWoHmGs3JfL55vSuLB/FNe8u5G8UhvmsFE8dVMSM/Q6jhRVkF5YwZGiqq/iCjKKrBwpqqDU6uCygTHcM76He12mEMIDqtdze4eA9tjs/g6ni083qjfc9+eU8tg3f/P80r1celY0D3UagFf2FnW0+6zrADUz+MNLduK953swQHGnwZzdr9cxx72ofzTzl+/nQG4Z769J4Z6B18P329SEasNmqVPYD/wOpVngHcIHOQnYnYcYEBfIwPj6bw5UM+l1zL/6LGYv3s53248AcN6ZEbx4eX+8jJLfQbRhJj8YP9fTrRCi2UnQPf0HdWqLRsruCNGRXTU4lpX7cvlsUxqfbDhMcUUlvSL9+XjGEEKrguhgH+NxEw8piiLTwIVoDU5SLmz9wQLyy+wEeRu4+5wefLT+EAdzy/hw3SGC9PHco99C5p8/EtbvGnRaDU98t4tPN6ax2KiOMAcPvLTe4+q0Gmafm8isRX/y3uoUbrjnIgIND0PePji8DuKHw7b/AlB5xmV8uFENnm8Z1bVBvzsMOi0vX9mf3pH+eBt1XDs0Xkp8CSFEGyGRJqiZinVSLkM0jbL168l97XVcFRWebopohHN6hRPiY6SgzE5xRSUD4gL57Oah7oD7ZCTgFqJ5Hcgt5aVf9/HOqoMn3vAk5cK+36EGu5P6RHLD2V1YNns0/52RxLm9w1nj6guAOW01Y59fxq3/3cLCtamEUsxg7V71AD3PP+6pJ/eJoFekPyU2B29vyIU+VQH6lg+hvAD2/gTAUv05FJVXEh/izbm9Ixr4CaiB/W1junH98M4ScAshRD0URaHws88pXLzY002pQ4JuIZpQ2YaNHL55Jnnz55Pzwr893RzRCEa9lmlD1NwOw7uF8N8ZSQR4y804IVqL9MIKXl2WzOeb00684QnKhVU6Xe4KARf0jQTUG2YjeoTyznWD+M/sGVh1vgRpSgmy7Obnv7MBeH1gJhoUiOwPgbHHPbVWq2H2uQkAfPBHKkW9/6G+sGsJbHoXnHaUiL68sN0IwE0juqCT4FkIIZpM/tvvkDV3LlmPPY5l6VJPN8dNgm4hmojt4EHS77wTKtUazoWLFlG2fr2HWyUa465zevDpzUNZOH0IPiZZfSNEa5IQrmb3Tskrw+ZwHn/DE5QL+2N/HkXllYT6GknqEnzM67Gh/pgTxgLwTN8cRiWE8ewlZzLUtk7doNeUk7ZzfK9O9IsJoKLSyWt7AiC8DzissOJZAHaHX8DhgnKCvA1cNvD4AbwQQojGKf7hB3L/8x/3z1lPPIkjP9+DLaohQbcQTcBRUEDazFtwWSx49e9PwGXqlMLMhx/BWVrm4daJhjLqtQzrFoJRL78ahWhtIvzN+Jn0OF0KKXkn+L16gpHu73eolQcm94lErztOP+82DoA+FVv46MYhXNU3AA6uUF9rQNCt0Wi4b0IiAB9vOIylerRbcaJoDcxLPxOAa4fGSxI0IYRoIuV//knmnH8CEHTNNZh69sRZWEjWE0+iKIqHWydBtxCnzWW1kn77LCrT0zHExhLzxnwi5szBEB1NZUYGOf9+wdNNFEKINk+j0dCjarR7X3bp8Td0J1Kru6bb5nDy81FTy+tVFXSTvhGsFkj+FVyVENIDwhIb1NaRPUIZ3DkIu8PFa7lngV4t6VUYew6rMxSMei3XDe/coGMJIYQ4MfuhQ6TfPgvFbsf3nHMIn/MQUfOeAb2ekl9+oeSnnzzdRAm6hTgdisvFkYfmULFtG9qAAGLfegt9cDBaHx8in34agKLPPqds7VoPt1QIIdq+hHA/AJKzS46/0XGyl6/el0eJ1UEnPxODOx87tdwtqDMEdwOXA1LXwO7v1OcbMMpdrfZo98KthVjOvAG0et6pnATApWfFNDhJoxBCiONzFBaSdsutOIuKMPfpQ/QLz6PR6TD36kXorbcCkPXkv3Dk5Xm0nRJ0C3Eacv/zMiVLl4LBQMyrr2Lq2sX9ms/QJIKuvhqAI488grP0BCMzQgghTqo66N53wqC7/unl1VnLzzsz8uSZv6tHu/f+oI50A/S6oFFtHdo1hBHdQ6l0Kjxtv4qDN+1mQUonAG4a2eUkewshhDgZl91Oxp13YU9NRR8VSeybb6D19na/HnrLTEy9euEsKiLriSc8Os1cgm4hTlHhF1+Q/847AET+60l8koYcs02n+2ZjiI3FcSSTnOeeb+kmCiFEu1ITdNdzEzNzB3xyORSmqD/XCrqtlU5+3aVmIp/S7wRTy6tVB93bPoXKMvCPhqizGt3e2RPUTOb/25rJ3KWpKAqM7xVOtzDfRh9LCCFEDUVRyHzkEco3b0br60vsggXow+rebNUYDEQ9Ow8MBkp+/Q3L9z94qLUSdAtxSkr/+IOsuU8AEDprFoFTp9a7ndbbm6hnqqaZf/EFpavXtFQThRCi3anOYH4ovwxrZVUG8/wD8L8b4a2RkPwLaHSQdCsEd3Xvt2JvLmV2J1EBZgbEBp38RF1GglYPStU5el4AmsaX9jorLohxPTvhdCms2qeOwM8c1fUkewkhhDiZvNfnY/n2O9DpiH7lZcwJCfVuZ05MJOz22wDIeuopKnNyWrKZbhJ0C9FI1n37yLj7HnA68b9wCqF3zDrh9t6DBxN03bUAZD76KM6SE0yLFEIIcVxhfiYCvAy4FDiUkgzf3gWvD4a/vlQ36HMp3LEJJj9XJ0iunlp+ft8GTC0HMPlBbFLNz42cWl5bdd1ugP6xgQzu3ICgXwghxHEVLVlC3vz5AEQ8/hi+Z599wu1DbroJ8xln4CouJuvxuR6ZZi5BtxCN4MjNJe3WW3GVluI9aBCRTz2FpgGjH53uvRdDfByOrCyyn322BVoqhBDtj0ajYWCYwj/1n9D9s5Hw54fqaHSPCXDLarjsfQjpVmefcruDZbvVkY0L+kY1/GTd1HrdeAVD3PBTbnOf6AAuHhANwF3ndG/Q3wwhhBD1K9u4kcxHHwMg5OabCLriipPuozEYiJz3DBqDgdLly7F8+21zN/MYEnQL0UCu8nLSbrsdx5FMjJ07E/P6a2iNxgbtq/XyIuqZZ0CjofjLryhdubKZWyuEEO2MrQRWPMcbeTcwU/8DOpcd4obB9KVw9RcQ2bfe3TamFFBR6SQ60Iu+MQENP1+/f0DEmTD6QdDpT6vpz1/WlzX/N5ZxPcNP6zhCCNGR2Q6mkH7nXVBZid+kSYTde2+D9zUnJBB6xx0AZD39DJXZLTvNXIJuIRpAcTrJeOBBrH/9hS4wkNi3FqALDGzUMbwHDiT4uusAyHz0MZzFxc3QUiGEaKd2LIYVz2B2lbPLFc8r4U/D9J8gftgJdztSZAWgZ4Rf40aZA6Lh1jUw9LbTaTUABp2WmCDvk28ohBCiXo6CAtJuuQVXcTFe/foR9ew8NNrGhbIhM27EfOaZuCwWsh57rEWnmUvQLUQD5Lzwb0qXLUNjNBLzxnyM8fGndJywe+7G2LkzjpwcsufJNHMhhGiwAddCt3HsHfkK59uf5suSMxqU3CzLogbd4QHm5m6hEEKIZuCy2Ui/fRaVaWkYYmKIeWM+WnPjf6dr9HqiqqeZr1xJ8ddLmr6xxyFBtxAnUbBoEQULFwIQOe8ZvM9qfNmYalovLyLnPQNaLcVLllDy+/ImaqUQQrRzeiNc+zUhSdNQ0JJWWE6F3XnS3XKqg24/CbqFEKKtUVwujjz0EBXbtqH19yf27bfQh4Sc8vFM3bsTdvddAGQ/8wyVWVlN1dQTkqBbiBMoXbmS7KfUkl9h99xDwPnnn/YxvQcMIPiGGwDIevxxnEVFp31MIYToKEJ9TYT4GFEU2J9TT73uo7hHuv1Nzd00IYQQTSz35Vco+WkpGAzEvPYapq6nX3YxePp0zP364iotJfPRlplmLkG3EMdh3b2bjHtng8tFwKWXEHLLzCY7dthdd2Ls2hVHbi5ZzzzTZMcVQoiOoEdVve592ceWYCxdvYaiJUvcP2dbbIBMLxdCiLam6H//I//ttwGIfPJJfJKGNMlxNTodUfPmoTEaKVu9muIvv2yS456IBN1C1KMyK4u0W2/DVV6O97ChRM6d26RlXrRmM1FV08wt335HybJlTXbstmBu1edZ+6tnz56ebpYQoo1ICPcDjg26XWVlpN9xB5kPzaHyiFqbW6aXCyFE21O2di2Zc58AIPT22wm8eGqTHt/UtSth99wDQPa8Z91/M5qLBN1CHMVZWqaWBsvOxtitGzGvvILGYGjy83j160fIjBsByHx8Lo7CwiY/R2t2xhlnkJmZ6f5as2aNp5skhGgjehwn6C5duxbFpo5sV2ZmYnM4yS+zAxAhI91CCNEm2JKTSb/rbnA48L/gAkLvvKNZzhN8/XV4DRiAq6yMzEcebdZp5hJ0C1GL4nCQcd9sbLt3owsJIfatt9D5+zfb+ULvuANj92448/Lca8c7Cr1eT0REhPsrNDTU000SQrQRCZ2qp5fXXdNdWis5pSMvn9wSNQA36rQEeTf9zVMhhBBNy5GbS9ott+IqLcVr0EAin3m6SWeb1qbR6dTjm0yUrV1L0eIvmuU8IEG3EG6KopD9zDzKVq5CYzIR++YbGGOim/WcWpOJqHnzQKfD8sMPWH75pVnP15okJycTFRVF165dufrqqzl8+PBxt7XZbFgsljpfQoiOq3p6eUZRBaU2BwCK00npihXubRx5uWRXTS3v5G9qtos2IYQQTcNVUUHa7bOoPHIEY3w8Ma+9htZobNZzmrp0odPsewHIee457OkZzXIeCbqFqFL40UcULloEGg1Rzz+PV9++LXJerzPPJOSmmwDImvsEjoKCFjmvJyUlJbFw4UKWLl3Km2++SUpKCiNHjqSk5NikSADz5s0jICDA/RUbG9vCLRZCtCZBPkbC/NRs5MlVU8wrtm/HWWuZjjM/vyaJmr9MLRdCiNZMcTo58uCDWHfuRBcYqJYGCwpqkXMHXXstXgMH4iovJ/ORR1BcriY/hwTdosNzlpaR8+JLZD/7HACd7r8f/4kTWrQNobNux9SjB86CArL+9a8WPbcnTJ48mcsvv5y+ffsyceJEfvzxR4qKili8eHG928+ZM4fi4mL3V1paWgu3WAjR2iRUZTBPrppiXrp8eZ3XHXn57pFuKRcmhBCtlz09g/Q776Lk19/QGAzEzH8dY3x8i51fo9US9czTaMxmytevp+jzz5v8HBJ0iw5Lcbko/uYbDk6eTP4774CiEHT11QTfOL3F26I1GomsmmZe8tNSLEuXtngbPCkwMJCEhAT2799f7+smkwl/f/86X0KIjq1Hp7rJ1Eqq1nN7D1FLyjjy8mrV6JaRbiGEaG1c5eXkvvoqB887j9Lffwedjshn5+E9cGCLt8UYH0+n2bMByH7h39jT05v0+BJ0iw6pYudODk37B0f+7yEcubkY4uKIeeMNwh952GPr/rz6nEFoVS3wrCeexJGf75F2eEJpaSkHDhwgMjLS000RQrQRiRFVQXdOKfbUVOwHDoBeT8DFFwPgyM8jR6aXCyFEq6MoCsU//MCB884n7403Uex2vIcOpcvXXxFw/vkea1fQNVfjPWgQSnk5mf98uEmnmUvQLToUR14eR/75MKmXX0HF9u1ovL0Ju282Xb//Dr9xYz2eaCf01lsxJSbiLCwk64knm7V0gSfdf//9rFy5ktTUVNauXcvFF1+MTqdj2rRpnm6aEKKNqJleXkLJ8hUA+AwZjLGzOiXRmZdPVrE60h0hQbcQQrQK1l27OHTttRy5734cWVkYoqOJfvUV4j54H3NCgkfbptFqiZz3DBpvb8o3bqRw0adNdmwJukWHoNjt5L//AQcmTab4q68ACLjoQrr99BOhN9/c7JkRG0pjNBL17DzQ6yn55RcsP/7o6SY1i/T0dKZNm0ZiYiJXXHEFISEhrF+/nrCwME83TQjRRnSvml6eWWyl6LdlAPiOGYu+6veIIy+PbEsFoGYvF0II4TmOwkIyH59LyqWXUbF5CxovL8LuvouuP3yP/4QJHh/4qmaMjaXTfeo085wXX8R+guo6jaFvkqMI0YqVrlpF9rxnsaekAGDu04eIRx7Gq39/zzbsOMy9ehF6663kvf462U/+C58hQ9wXke3FZ5995ukmCCHauAAvAxH+ZkrzCrBt3YoG8B03Fn1ICACKzYYlvxgwyEi3EEJ4iFJZSeGnn5H7+uu4qkq++p93Hp0euB9DK11WGDRtGiW//Er5hg0c+ec/if/oIzTa0xurlpFu0W7ZU1NJu+VW0mbegj0lBV1ICJFPP03nxZ+32oC7WugtMzH16oWzuJjMuU+022nmQghxOnqE+zI4ew8alxNTQgLGmBi0Xl5ovb0BMJUUAdBJgm4hhGhxZWvXcvDii8l+5hlcFgumXr2I/+/HRL/0YqsNuKFqmvnTT6P19qZi8xYK//vf0z6mBN2i3XGWlpHz739zYMqFlK5cCXo9wdOn023pTwReeslp36lqCRqDQZ1mbjBQsW0bjuxsTzdJCCFancRwP5Ky/gbUUe5qurBQAAKtJfia9PiaZGKfEEK0FHtaGml33MHhG2dg338AXVAQEU88QZf/fYH3oEGebl6DGGOi6fTgAwBYlv582knV5K+QaDfUEmDfkvPSizhz8wDwGTWS8IfmYOraxcOtazxzYiLRL72I96BB6IOCPN0cIYRodRJDzHTN3gOA37hx7uf1IaFUHjpMkK0Eq6znFkKIFuEqLyfv7bcpeP8DFLsddDqCrv4HYbNmoQsI8HTzGi3wyivRmMwEXHD+aQ/aSdAt2oWKHTvIevpprNt3AFW19uY8hN+YMZ5t2GnyP/dcTzdBCAGUrVtH7suvYOrdC9/Ro/EZOhStWaYse1pC9n6MDhtFXv6Y+/RxP1+9rjvIVoJLppYLIUSzUhQFyw8/kvPCC+7ZmT7DhxE+Zw6mHj083LpTp9FoCLx4apMcS4LuBnBVVGD58Ud8R49GHxrq6eYIQHE4cJaU4MzPJ/+99yn++msAtN7ehN5+G0HXXddqMpILIdo26959pN9xJ66yMiq2b6fo08/QmM34DB2K75jR+I4e3arXprVnwds3UAqs69SLXlYHgd7q7329e3p5KQYJukUtiqLgyMqiYts2KrZtx7Z/P76jRxF07bWtJnuyEG2By27HZbFgP3SInJf+Q8WWLQAYYmIIf+j/8D3nHOlTtUjQ3QBFX39N9pP/IvDyy4n815Oebk67oLhcuMrKcFksavBcbMFVYsFpKVEfi9XnXRYLTosFZ4kFl6VEfa64GFd5+THHDJg6lbDZ92Lo1MkD70gI0R458vJIv+02XGVleA0ciDkxgZLlK3BkZlK6YgWlK1YAYOrZE9/Ro/EdMxqvvn3R6HSebXgHoCgKtpUrANgQ0ZsR2aUM6RIMgK7WSLePBN0dmstqxfr33+4gu2L7dhw5OXW2KfvjD6y79xD5xFw0csNedBDVA1iukhL1+ttSjNNSUnXNXeua3FKC01Jccx1edW2u2Gx1jqfx8iL0lpkET5+O1iTLeo4mQXcD2PfvB8C6Z4+HW9J2KYqC/cABSpYvp3T5Ciq2bwen87SPq/X2xty7N50euB+vfv2aoKVCCKFyWa2kz7qDyiNHMMbHEzv/dXSBgYQ/+ii2fcnuoLti+3Zse/Zg27OH/LfeQhcUhO+oUfiOGY3PiBHo/Pw8/VbaJdu+fVQeOUKl3si2sB7syy5xB936ULXMYpCtBI2s6e4wFEWhMi2Niu3b1QB72zase/eCw1F3Q50Oc2IiXv37ofXzJ//ddyn++msqMzOJefUVdP7+nnkDQjQjxeGgYutWSlasoHTFSuwHDpz+QTUatH5++I4ZTafZszFERJz+MdspCbobwJ6erj6mpKAoikyVaCDFbqd882ZKlq+gdPlyKqs+x9o0BgPagAB0/v7o/PzQVj8G+KPz80fnX/Wcvz9aPz90/gE1z/n5odHLf2EhRNNTFIXMfz5MxfbtaAMCiFnwJrrAQEBd42VOTMCcmEDoLTNxFBZStnq1GoSvXoOzsJDib76h+JtvQK/He+BAfMeMwXf0aIxdOsvfkCZS+vvvAOQl9sWmN5KcXeJ+TR+qjnQH2kowy0h3u+UsLcP61073CHbF9u04CwqO2U4XFop3//549euHV79+mPv0Qevl5X7de9BAMu6+h/L160md9g9i31qAMSamJd+KEM3CabFQtmYNJctXULZqFc7i4mO20Xh7o/PzU6+1q66vdQH+aGtfh/v5o/X3U6/Na7/m69smqgK1Bh0+YnFZrdgPHcYQHY3O16febSrTM9RtS0tx5uWhDwtrySa2GYrLhSMzk7JNmyhdsZKyNWtwlZa6X9cYDHgPHYrv2DH4jhiBPjxcpp8IIVqlvNfnY/nxR9DriXnlFUxdjl8BQR8URMCFFxJw4YUolZWU/7mV0pUrKV2xAvvBg5Rv2ED5hg3kPPcchvg4/KoCcO9Bg2Qq62koWb4CANewkZAHe2sH3dXTy60l+EnQ3a4oikLBwg8pXrIEW3IyHF3Gx2DA3LtXnSBbHxV1wptdviNHEr/oE9JuuRX7gQOkXnkVsW++gVffvs38boRoWordjv3QIUr/+IPS5Sso37KlzkwPbUAAvqNG4Td2DN6DB6MLDERjMHiuwR1Ihw+6Uy+/HFvyfmLfeQffkSOOeV1RFCozMtw/21JSOnTQrbhcOHJysKcewn6o9lcqlYfT1PIAtehCQ/EdPQq/sWPxGTYMrU/9NzaEEKK1KP7ue/Lmzwcgcu7j+AxNavC+GoMBn6Qh+CQNIfzBB7AfPkzpCjUAL9u0icpDhyn48CMKPvyI2Pfexffss5vrbbRrlTk5WHeo1So6TTgHFu0hObvmJq82RE2kFmQrJUSml7cbLpuNzH8+jOWHH9zP6aMi8erXzx1km3r1OqUb+uaePem8+HPSbr0N2+7dHLrueqJeeF6qiIhWR6msxJ6ejv3QISoPHcJ+6LD7erzyyJFjbkQZu3bFd+wY/MaOxat/f5kl6iEd/lM3xMVjS96P/dAhqCfoduTm1kkUYD+Ygs+QIS3ZRI9x5Odj3bMH25692Pbtxbp3H/bUVBSr9fg7GQyYenTHd/Ro/MaMwXzmmTLtRAjRZpRv3Urmww8DEDzjRgIvu+y0jmeMiyP4umsJvu5anKVllK39g9KVKynfvBnvwYOboskdUnUCO3O/vsT3jEej2UN+mZ38UhshviZKvNR19EaXgxAqPdhS0VQcBQWk33EnFX/+CXo94Q/cj9+kyRjCmy55qiE8nPiPPybjvtmUrVxFxl13U/nAAwRPv0GWhYgWVz3wZ9uzB+uevdj27sG6b586A/cEeZE03t549e2L39gx+I4ZgzE+vuUaLY6rwwfdxs7qf0T7oUP1vl49tbyaPSWl2dvU0hSHA3tKSk2H3rMX6949OHPz6t9Bp8MQE40xPh5jfOeqx3iMneMxREbKHTQhRJtkT88gfdYdKHY7vuecQ6fZs5v0+DpfH/wnTMB/wgTJD3KaSn9fDoDf2LF4G/XEBnlzuKCcfdmlDPM1kW2HCr0ZH4cVbWEBBAV4uMXidNgOHiTtllupTEtD6+dHzKuv4DNsWLOcS+frQ+z8+WQ/8wyFiz4l5/nnsacdJuLhh+X6RjQbxenE+tdfda7FbXv34iorq3d7jdmMMS7Off1dfS1uiI9HHxYmf19aoQ7/26P67o89NbXe1ysz6ib/sqW27aDbabFg27sX6+49WPdWjWInJx8zLRwAjQZDXCzmxJ6YeiZi7tkTU9euGKKjZf2HEO2E4nCguFwdvq69s7SU9NtuxVlQgKlXL6Kff65Zy37JBdGpc1VUULZuHQC+Y8cBkBDuy+GCcpJzShjWLYQciw2HyRcfhxVnfh50Pf6afNG6la1fT/pdd+OyWDDExBD71gJM3bo16zk1ej3hjz6KITaOnOefp+jTz6g8coToF186bv4fIU6VYrdz+KabKd+48ZjXNAYDxu7dMScmqtfiCQkYu3ZF36mTzCRtYyToju8MnGikWw26DdHRVGZkYE9JbaGWnVh1ogSnxYJS6UCprERxVKJUVoKj+mf10ZGTi3XvXmx79tRZn16b1tsbU3WHTuyJuWciph49ZA22EO2Y7cABDt98Mxo0xC/6pMOW+lAcDjJmz8aWvB99WBixb74hv/tasbJ161BsNgzR0ZgSegDQI9yP33bnsDdLTaaWbbGiM/sRU5aHI+84s7ZEq1f05ZdkPj4XHA68BgwgZv7r6IODW+TcGo2GkOk3YIiJ5sgDD1K2chWHrr2W2AVvYggPb5E2iI4h+4V/U75xIxqzGe+zzsLUs+o6PLEnpq5dZKCrnZCgu3NnQA2uFbv9mEyy1eXCfEaOoOizz6lMT8dlt7fYqJDidGI/fBhbcjK2/fvVx+Rk7KmHjq072UD6qEjMPXupHbpnT8w9e2KIiZE7ZkJ0INa9+zg8fbq7vE763XcT//HHHXLEO/u55ylbtRqN2UzMG2902JsPbUVJVakw33Hj3DMGEsJ9AdzJ1LIsVrxN6rpuR16+B1opToficpH7n/+Q/867APifdx6R857xSMUT/3PPxfDRh6Tddju23btJveJKYt9agLlnzxZvi2h/LD/9ROHHHwMQ/dJL+I0b6+EWiebS4YNufacwNN7eKOXl2NMzMB01Ba16TbdX//5Yvv8BV2kplYcOYerRo9naZDt4kPx338O6Zzf2AwfrJHKrTevriy4kGI3BgEZvqHrUq4/V3xsNaP383XfMzIkJ7lqzQoiOybprF4dvnIGzqAhTz55UHjmCdfsOcp59lojHHjvl4zpyc9H6+7epUoAFixa5L3iinn8OrzP7eLhF4kQUl4vSqlJhtS9OE8LVAHtfTgmKopBtsRFoUgNxR76MdLclrooKjvzfQ5T88gsAobffTuidd3h0SYZX3750/vxz0m65BfuBAxz6x9VEv/wffEeN8lib2gtFUXAcOYItNbXDVXOwHUwh8+FHAAi5+WYJuNu5Dh90azQajPHx2Hbvxp6aWk/QrY50G2NiMHbpgnXnTmwpKc0WdFt++YXMh+bgKi+vaaPZjKlbN0w9eqhfCeqjPjxc1gUKIRqlYscODt90My6LBXPfvsS9+w4VW7eSdsutFC76FHPfvgROndro4+a98w65L74EWi3GLl0wJyZgSkjAlJCIOTHhpHVyPaF09Rqyn34GgLDZs/GfMMHDLRInY92xA2d+PlpfX7wHDnQ/3y3MF60GisoryS21kW2xgnukW4LutsKRm0varDvUcnAGA1FP/YuAiy7ydLMAMMZE0/nTRaTfdTfl69eTdtvtRDz6CEFXXeXpprVpzrw89p8zHrRaErdsRuvl5ekmtQhXeTkZd9+Fq7wc78GDCbv7Lk83STSzDh90AzVB91HruhWHg8qsLAAMMTEYu3TGunNns6zrVpxOcl9+hfx33gHAe/Bggm+4HlOPHmrismZM6COE6BjK/9xK2s034yorw2vAAGLfeRudry++o0cTOmsWefPnk/X4XMyJiZh79WrQMRVFIffVV8l/c4H6hMuF/cAB7AcOwI8/ubfT+vlhSkioCsYTMSUmYOqR4LGkRLb9+8m4915wOgmYOpWQm2/ySDtE45RUjXL7jhpZZzmY2aAjPsSHlLwy9mWVkm2xYjSrQbdTppe3CdZ9+0i79VYcRzLRBQQQ8/prra6sns7fn7i33yLz8bkUf/01WXOfwH44jU733ydL9E6RPiwMXWgozrw8bMnJePXt6+kmNTtFUch64glsyfvRhYUS/dKLkhm/A5B/YWqXDUut83xlVjY4nWgMBvSdOmHqoo6CN3XZMEdhIUfuu4+ytWo21uAbblB/gUsHFEI0kfJNmzh8y60oVXfVYxe8WSdZWOis26nYuYOyVatJv/Muunz5P3QBJy6zpCgKOc8+R8GHHwIQdt9sAi68CNu+vdj27VMTOO7dh+3gQVwlJVRs2ULFli11jmGIjcWUmIA5IVEdGU9MwBgX16w3Gh0FBaTdehuu0lK8Bg0k4sknWt0ovKhfafV67qqs5bX16OSrBt3ZJWRbbPjKSHebUbp6DRn33ourtBRjfDyxby1w59xpbTRGI5HPPI0xLpbcV16l4P33qUxPJ+r559CazZ5uXptk7tmTsjVrsO7e0yGC7qIvvqD4m29BqyX6xRfRh4V5ukmiBUhUR60M5ql1R7rdmcujotBotRi7dAXAlnKwyc5d8dffpN91J44jmWi8vIh86l8EnH9+kx1fCCHK1q0j7bbbUaxWfIYPI2b+/GOm8Gm0WqKff56Uyy6nMj2djAcfJPbNN487eqO4XGQ9+SRFn30OQPgjjxB8zdUAGMI74TtyZM22dju2lFQ1GN+7F+vefdj27sWRk0NlWhqVaWmU/raspi1mM+aePfEdNw7/Cec26cW3y2YjfdYdVKanY4iLI+a11zpk8ri2yJ6Whi05GXQ6fEeNPOb1hHA/ftmVza5MC/llNoKrRrod+TLS3ZoVfvYZWf96CpxOvAcPJua1V1t97hmNRkPobbdhiIkl85//pOSXXziUnUXs/PnoQ0M93bw2x9yrKujes/u42zjy8ijf8id+E85t0zdJK/7+m+ynngYg7N578BkyxMMtEi1Fgm5qj3QfFXRX1eg2xMSo27lHulNRFOW0O33RV1+TNXcuit2OIT6OmFdfw5yYcFrHFEKI2kpXryb9jjtRbDZ8Ro1Ug8zjJDrTBQYS8+orpE77B2UrV5H3xpuE3THrmO0Uh4PMhx9W79RrNEQ+9S8CL730uG3QGI2YE9Wp5UyZ4n7eUViojoTv21szKp6cjGK1UrFtGxXbtpH70kuYEhLwO/dc/CZMwJTQ45R/9yqKQuajj1KxdStaPz9iF7yJPijolI4lWl7Ftu0AeA8cWO8sjIQINcheuz8PRYFSr+rp5XlN8jdbNC3F6STn+RfcM2UCpk4l8sknjqki05oFTLkAQ0Q46XfciXX7DlKvvIrYt99q9jri7Y0pUc0Eb9u957jbZP3rKUp+/pnIp58m8NJLWqppTcpZXEzG3feg2O34jh1LyIwZnm6SaEESdFNTNsyRmYmrosI9AlRdLswddMfHgUaDy2LBWVCAPiTEfQzF6ST76aepzMzC1K0rxq7dqh67ovPzq3M+xW4na948ij79DADfMWOIev45dP7+zf1WhRAdSMnvy8m4+26Uykp8x40j+uX/nHRU19y7NxFPzCXzoTnkzZ+P15l98B092v26YreT8cCDlPz8M+h0RD33HAEXnNrsHH1QEPqhSfgMTao5flWZxPINGyn59VfKNmzAtm8ftn37yJs/H0N8HP4TJuA3YQLmPn0aFUjlL1iA5dvvQKcj5pWXMXXtekrtFp4RMOUCvJOG4Cwqqvf16rJhR4qtABhC1SmbSmUlLovlpMslOhqnxULRV19h6tYN7yFDWrTqgKusjIwHHnQvFwi7525CbrmlTd4Y8R48mPjPPiXtllupPHyYoi+/IvzBBzzdrDbF3EsNuq379qE4nccsL1IUhfL16wEo+eWXNhl0K4rCkTn/VGdZRUcT9ew8yQPQwUjQjTq6o/X3x2WxYD+c5h5tri4XZoiJBkBrNmOIiqIyIwN7SkqdoLts7VoKF30KQOny5XWOr+/UCWO3rpi6dsPYtQuWb7+jYvt20GgIvWMWobfdJh1PCNGkytavJ/2uu8DhwG/CBKL//UKDR5ACp06lYvt2ij79jIwHHqTLl//DGBuLy2Yj4667KV25Eo3BQPR/XsJv/PgmbbdGp8PUpQumLl0IuupKnEVFlCxfoQbga9ZQeegw+e+8S/4776KPjMTv3PH4n3suXmeddcJ14JaffiL3lVcBiHj0UXyGD2/SdouWYejUCUOnTvW+1iXUB51Wg9OlABAc7IvWzw9XSQmO/HwJumtx5OZyeMZN2PbtA9QlHT5Dh+I7ehS+o0ZhiI5utnNXZmeTdttt2HbtRmM0EvXsPPzPO6/ZztcSTF260Pnzzyh4/wPJQn0KjJ07ozGb1fK9hw+7cyhVs6em4iwuBtTlUq7ycrTe3s3WHntqKjkvvkTpH3+AwwGK+jtFAfX7qp9RFNBqMXXtile/fnj164u5b19M3bod8/eo4L33KP39d/Vv5yuvyO+jDkiCbqrKhnXujHXHDuyHUmsF3TXlwqoZu3ShMiMDW0oK3oMGuZ+3VGXp9R42FFOXrtgOHsR+4ACO3FwcOTk4cnIoX7fevb3W35/oF56vM4IkhGg6HXk6aWV2Nhmz71MD7kmT1IC7kYkZw+fMwbprF9btO0i/8y7iPnifjNmzKV+3Ho3JRMzrr+M7ckQzvYMausBAAi+eSuDFU3GWllG2ehWWX36hdOUqHJmZFH70MYUffYwuJAS/c87Bb8IEfJKGoDEY3Meo2LGDIw/NASD4+usJuurKZm+3aHkmvY7OId4cyC0DIMLfjD4kBHtJCY68PJnZUMWensHhG2+k8vBhdMHBaAwGHNnZlK5YQemKFQCYenTHZ9QofEeNxvusAXX60+mw7t5N2q234cjORhccTMz81/EeMKBJju1p+qAgOt0329PNaJM0Oh2mhASsO3Zg27v3mKC7Yvt29/eK3U7ZunX4nXNOk7fDWVRE7htvqINoDkfDdnK53LOxir74AgCtjw/mPn3w6tsXr/79AMj5z8sAhD/8MF59zmjytovWT4LuKsb4eDXorpVMrfKo6eUAxq5dKFuzBvvBmgzmLrudkt9+AyDs9tvrlLhwWizYDx7EduAgtoMHsB84iMZsptPsezHGxTX32xKiQ6rYto2sZ+YRMv0G/M49t0NVAlAqK8m4516cBQWYevZUp7CdwvvXGo3EvPIKKZdcim3PHg6cOwFXaSlab29iFrzpkeQvOl8f/CdPxn/yZFxWK2Vr11Ly8y+ULF+OMz+fosWLKVq8GK2/P35jx+I3cQLGLl1Iu30Wis2G7+jRdJJpn+1aYoSfO+gO9zejCw2B1FScksEcANuBAxy+cQaO7GwMMTHEvf8ehthYbPv2UbpyFaUrV1KxdSu25P3YkvdT8N77aH19McTF1j2QUs/BFeXEP6PmzlGsVozduqkZymtdX4mOzdyzJ9YdO7Du3oP/pEl1XnMH3Xo9OByU/P57kwbdLrudwk8Wkffmm7gsFgB8Ro0k7I471FmtGk3NF1Q9akADir0S257dVGzfTsX2HVT89ReusjLKN2ygfMOGOufxv3AKgVde0WTtFm1Lx7kSPYmjy4a5rFYcublA3aC7vrJhZWv+wFVSgr5TJ7wGDqxzXJ2/P179++PVv38ztl4IUVvBRx9j3bGDjHtnY4iOJvi6awm49DKP1YRuSTn//reaKMzXl5hXXj6tEjaGiAiiX3qJwzfeqAbcVTVqW8PvM63ZjN+4cfiNG4dSWUlZ1Rrwkt9+w5mfT/E331D8zTfu7U2JiUS9+GKzliITntejkx+QBahBt75qXbdDanVT8dffpN10E86iIozduxH33nsYwsMBMCcmYk5MJHTmzTiLiyn74w9KV66kdNVqnIWF2HYdP6t0Y/kMH0b0yy9LHhtRh3tddz0ZzKuTKAZedilFn31O6YqVKC7XaS/NVBSFkl9+Jeff/6YyLQ0AU0ICnf7vQXzPPrvBxzHGRLuXWilOJ7b9B6jYvo2KHTuwbt+Bbf9+TL16Ejl3boedgSck6HY7umxY5ZEjAGi9veuUrjDWE3RbflKnlvtNmihrs4VoBcIf/ifGLl0oXLSIyowMsuc9S+7r8wm68gqCrrkGQ0SEp5vYLCw//UTBhx8BEPXcsxjj40/7mD5Dk4h85mks335Lp/vvx9y792kfs6lpDAZ8R5yN74iziXhMzU5u+eUXSn79DUdmJrqwUGLffKND3HTp6BLCaxKXhvub3LlXOnqt7vJNm9Ta9GVlmPv0Ifadt4+buV8XEID/eefhf955KE4n1l27cRYVHrXVUYHD0YGEpvZLNT9ovLzw6ttXbn6JY5h61p/B3FVejm3vXgBCbroJyw8/4szPx7pjx2ndAK7YsYPs556nYssWAHRhoXS6+24CLr74tP5/anQ6d7WOoCvUUW1XRQUag6FDzboTx5J//SrVGcyry4bVnlpe+w+GO+hOT0ex21FcLkqXqfVl/SdPbsEWCyGORx8SQtiddxBy800UL/mGgoULsaemkv/ue+Qv/BD/8yYTMn065l69PN3UJmM7eJDMhx8BIOSmGU069S5w6lQCp05tsuM1J41Oh/egQXgPGkT4nDnY9u1DHxpaJ/GlaL+qM5hD1ZruMLVmsiO/4wbdpStXkn7X3Sg2m1oH+8030Pn6nnxH1P7kdWafZm6hEGBOSACNRs2DlJ/v/p1d8ddf4HKhDw/HGBOD78gRWH78iZLlK04adCuKgrOggMqMDCqPHFEfM45gT02hbO06QE0iGHLjdEJmzEDr0zw3ZqurIomOTYLuKtXTy515eThLS48pF1ZN36kTWm9vXOXl2NPTse3fj6u8HH1UZKuYcimEqKE1mwm66koCr7ic0hUrKXj/fco3b8by7XdYvv0O76FDCX/wgVY5etsYrrIy0u+6C1d5Od6DBxN2zz2eblKroNFoMCcmeroZogV1DvXBqNNid7qICDCjq7pwd3bQ6eXFP/zAkf97CBwOfMeMUcsGnsaSEyGai9bHB2NcHPZDh7Du2eOe3l09tbz6Gtt37FgsP/5E6e+/0+nee+ocw1laSv5bb2Pds8cdaCtWa/0n1GgIuOgiwu65u93OfhOtiwTdVXS+vuhCQnDm52NPPXRMubBq7kznu3ZhP3jQPbXcf9JkWachRCul0WrxGzcWv3Fjqdj5FwUffIDl558pX7+ewzNvocfvyxpcTqu1URSFzMcex77/APqwMKJfelGmsIkOy6DT8uRFZ5BWWE6XUB9KQ6pGujvg9PLCzxeTNXcuKAr+F1xA1LxnmiwLuRDNwdSrF/ZDh7DVDrqrkqh59VOzgPuOHAk6HbbkZOzp6e5kfIqikDnnn5T8+mvdg2o06Dt1whAVhSE62v3oNaC/OrouRAuRK7NajJ07U5Gfj/1Qar3lwtzbde2qltLZtYvSFSsBmVouRFvhdWYfol96kU4Zs0m56iqcuXmUrFyJ/7nnerppp6Rw0SIsP/wAOh3R/3kJfViYp5skhEddNaSmMkjN9PL2MdJdmZ1D0eLF2A8fRnFUgsOJ4nQe9b0DxW7HunMnAIHTriLi0Ucl54xo9cw9e1KydCnWPeoabkVRaoLuqpFuXWAg3medRfmmTZT+vpzg664FoPjLL9WA22Ag/P/+D1O3rmqQHRHRZm+qi/ZFgu5ajPHxVGzZgj01td5yYe7tunQGoPCzz1EqKjDExmKWmntCtCmG6GgCL7qI/Hffo/irr5s96HaVlVG6ejVKpQN9SDC6kBB0QUHog4JOefSpYvt2sp99DoBO992H96BBTdlkIdo8dyK1/HwURWnxGWmK04lt717KN2+mMjsb37PPxjspqdGJmmzJyeR/sJDi776DysoG7xcycyZh994jM/FEm1CdwdxWlcG8MiNDLfdnMGDuXZODxXfcODXoXqEG3baUFLKefgaATnffRfA1V7d844U4CQm6a6mdTM2eUTW9PPrYoLu6bJizoAAA/0mT5A+aEG1QwCWXkP/ue5SuWkVlTg6GTp2a9PiKolCxbRvFX32F5YcfcZWX17udLiAAXXAwupBg9MEh6CPCMfXogTkhAVP37vUmd3EUFpJ+z71QWYnfuecSPP2GJm27EO2BLlQd6aayEldxcZ1qJM1Bsdup+OtvyjdvpnzzJir+3IqrtNT9esF776MLDcV/4kT8zz8Pr/79jzsCrSgK5Rs2kv/+e5StWu1+3mvgQPzGjUNjNKLR60CvR6PTq9/rdGj0BjR6HfqICLzOkAEB0XaYeqqBte1gCi6r1b2e29yzZ51cBH5jx5Dz3HOUbdyEo7CQI/c/gFJRgXdSEsE33uiJpgtxUhJ011JdXse68y9cxcXqc0et6YaaDObV/M+TqeVCtEWmrl3xGjBALTH17beE3HRTkxzXkZ9P8TffUvTll9gPHHA/b4iPwxAZhTM/H0dBAc7CQnC5cBYX4ywuhlqlCGszxMRgSkjAlNDDHYxnP/scjsxMjPHxRM57Rm78CVEPrdGI1t8fl8WCIz+/WYJu6549lPz6G+WbN1OxffsxiZu0Pj54DTwLfXAIpcuX48zLo/CTTyj85BP0UZH4T56M/3nnYe7dG41Gg+JwYPn5Zwreex/rrl3qQTQa/M49l5Abp0vSVtFu6TuFoQsOxllQgC15PxXbtgEc83/e2Lkzxq5dsR88SNpNN2P9+2+0AQFEPfesLKMQrZYE3bW4R7qrLnx1QUH1jjDVrn1r7NzZXVtQCNH2BFxyMRVbt1L05VcEz5hxysGr4nBQumaNuq5s+QpwOAC1HIn/xIkEXnYpXoMG1Tm+4nTitFjUIDy/AGdBPo68fCrT07El78O6LxlnXh6V6elUpqdT+vvvdc6pMZuJfvXVBpf/EaIj0oeEYLdYcOTmYerWrcmOq7hc5L/7Hrkvvwwul/t5XVCQWrZusFq6zpSY6J5OrlRWUrZuHZYffqDkt2U4jmRS8N77FLz3Psb4eHxGjKB0+XIqjxwB1D4eeMnFBF9/fZ1rDyHaI41Gg7lnT8rWrsW6Z/cxSdRq8x07hoKDB7H+/TcAkU8+KVnIRasmQXctxrjYOj/Xt54bQOvtjT4qEseRTPzPk6zlQrRl/pMnk/3MPOwpKVRs3Yb3WQPqvG47cIDS5ctxVVhxVVSgWCvU760VKBVWXFYrSkUF9iMZOHNrMiSb+/Yl8JJL8D//PHR+fvWeW6PToa9a123qXn/7HAUF2PYlY0tOxrZvn/qYnIyrooLIf/0Lc6JkXxXiRPShodhTUpq0VrejsJAjDz1E2cpVAPiOHo3v2LF4Dx6EsWvX414XaAwGfEeNwnfUKFxWK6WrVqnlj5YvV5e2HToEqIF70DVXE/SPf6APCmqydgvR2pmqgu6Kbduw7lbXdtc3u8Nv3DgK3nsfgIDLLsV/4oSWbKYQjSZBdy1aLy/0ERE4srKAY8uF1RZ4yaVYvvuOwMsua6nmCSGagc7XF/+JEylesoTir7+qE3RXbN/OoRumo1RUNOxYgYEEXHQhAZdc2mTBsD44GP3QJHyGJrmfUxQFxWpF6+XVJOcQoj3ThVbX6m6aoLti2zbS752NIzMTjclE+CMPE3jZZY2+Aa81m/GfMAH/CRNwlpZRuvx3yjduxHzGGQRMnSr1tEWHVJ1MrWTpz+BwoAsNxRAddcx2Xv374z14MEplJRFz5rR0M4VoNAm6j2Ls3NkddNdXLqxa2B2zCLtjVks1SwjRjAIvvYTiJUuw/PAj4XPmoPX2xnYwhbRbbkWpqMDcpw/mM/ugNXuh9TKjMXuhNZvReJndz2l9/fA6awDaFihNotFo0EjALUSD6N21uk+vbJiiKBR8+CE5/34RHA6M8fFEv/Iy5iZYYqbz9SFgyhQCpkw57WMJ0ZZV9ydXWRmgTi2v74aWRqcj/uOPWrRtQpwOCbqPYoyPp3z9eqD+zOVCiPbHa9AgDPFxVB46jOXnX/AZPozDN83AWVSE+cwziV/4Qb35HYQQrZ8+tKZWtyM3l7INGynfsAH7oUOYz+yDz7DheA8aeMKRZafFQubDD1Py628A+E2aRORT/5J8CkI0MWOXLmiMRhS7HQCv/seu5xaiLZKg+yjVydTg+Gu6hRDti0ajIfDiS8h9+WUKP/2Ugg8+wHFEzQwe+9YCCbiFaMP0VdPLi7/7juKvvqrzWvnGjRS89z4aoxGvs87CZ/hwfIYNw9y7lzv5WcXff5Nxz71UpqWBwUD4Q/9H0D/+IflchGgGGr0eU0IC1r/+AupPoiZEW9Qm8urPnz+fzp07YzabSUpKYuPGjc12rjqZyU+wplsI0b4ETL0ItFqsO3Zg27cPXVgose+9iz442NNNE0KcBvff9cpK0Ggw9e5F8A03EPHkEwRccgn6iAgUu53y9evJfeklUi+/nOThZ5N+9z3kvPgih66aRmVaGoboaDov+oTgq6+WgFt0OC15LV69rhudDq8+fZrtPEK0pFY/0v35558ze/ZsFixYQFJSEi+//DITJ05k7969dOrUqcnP5x7p1mrRRx2buEEI0T4ZIiLwOftsylavRuvrS9w775wwr4MQom3wGjSI6Jf/AzodPoMH16nVHXTFFSiKgj0llbK1aylbu5byDRtwFhdT8vPP7u18x40jat4z6AICPPAOhPCslr4Wry7Fa0pMQOvt3eTHF8ITNIqiKJ5uxIkkJSUxePBgXn/9dQBcLhexsbHceeedPPTQQyfd32KxEBAQQHFxMf7+/ifdXlEUsp9+Bn1oKKG33nLa7ReiOTX2/3d70Vzv27prF7mvvkbIzJnHlA4ToiV1xL7dWt6z4nBQsWMnZevWYv17Fz5nD5fp5KJJtJb/443V0tfizpISsp54koCLLsJ35IjTbr8Qzamh/79b9Ui33W5ny5YtzKlVCkCr1TJ+/HjWrVtX7z42mw2bzeb+2WKxNOqcGo2GiEcePrUGCyHaNHPv3sQueNPTzRBCeJBGr8f7rAFy400IPHMtrvPzI/rfL5xag4VopVr1mu68vDycTifh4eF1ng8PDyerqqzX0ebNm0dAQID7KzY2tiWaKoQ4BS25RkwI0TKkXwvRfsi1uBBNo1UH3adizpw5FBcXu7/S0tI83SQhRD2q14g9/vjj/Pnnn/Tr14+JEyeSk5Pj6aYJIU6R9GshhFyLC3GsVh10h4aGotPpyM7OrvN8dnY2ERER9e5jMpnw9/ev8yWEaH1eeuklbr75ZqZPn07v3r1ZsGAB3t7evP/++55umhDiFEm/FqJ9kWtxIZpGqw66jUYjAwcOZNmyZe7nXC4Xy5YtY9iwYR5smRDidFSvERs/frz7uROtEbPZbFgsljpfQojWRfq1EO2PXIsL0TRaddANMHv2bN555x0+/PBDdu/ezW233UZZWRnTp0/3dNOEEKeosWvEZH2YEK2f9Gsh2ie5Fhfi9LXq7OUAV155Jbm5uTz22GNkZWXRv39/li5deswfdSFE+zVnzhxmz57t/tliscgFuhBtnPRrIdoGuRYX4vS1+qAb4I477uCOO+7wdDOEEE2ksWvETCYTJpOppZonhDgF0q+FaL/kWlyI09Pqp5cLIdofWSMmRPsj/VoIIYSoX5sY6RZCtD+zZ8/m+uuvZ9CgQQwZMoSXX35Z1ogJ0cZJvxZCCCGOJUG3EMIjZI2YEO2P9GshhBDiWBJ0CyE8RtaICdH+SL8WQggh6pI13UIIIYQQQgghRDORoFsIIYQQQgghhGgmEnQLIYQQQgghhBDNRIJuIYQQQgghhBCimUjQLYQQQgghhBBCNBMJuoUQQgghhBBCiGYiQbcQQgghhBBCCNFMJOgWQgghhBBCCCGaid7TDWhuiqIAYLFYPNwSIZpe9f/r6v/nHYX0a9HedcS+Lf1atHcdsV+D9G3RvjW0X7f7oLukpASA2NhYD7dEiOZTUlJCQECAp5vRYqRfi46iI/Vt6deio+hI/Rqkb4uO4WT9WqO089ttLpeLI0eO4Ofnh0ajOeZ1i8VCbGwsaWlp+Pv7e6CFbZN8bo3XHJ+ZoiiUlJQQFRWFVttxVoucrF+D/B89FfKZnRrp201D+nXzkM/s1Ei/bjpyLd485HNrPE/263Y/0q3VaomJiTnpdv7+/vIf9hTI59Z4Tf2ZdaS75dUa2q9B/o+eCvnMTo307dMj/bp5yWd2aqRfnz65Fm9e8rk1nif6dce5zSaEEEIIIYQQQrQwCbqFEEIIIYQQQohm0uGDbpPJxOOPP47JZPJ0U9oU+dwaTz6zliWfd+PJZ3Zq5HNrOfJZN558ZqdGPreWI5/1qZHPrfE8+Zm1+0RqQgghhBBCCCGEp3T4kW4hhBBCCCGEEKK5SNAthBBCCCGEEEI0Ewm6hRBCCCGEEEKIZtLhg+758+fTuXNnzGYzSUlJbNy40dNNajXmzp2LRqOp89WzZ0/361arlVmzZhESEoKvry+XXnop2dnZHmyxZ6xatYopU6YQFRWFRqNhyZIldV5XFIXHHnuMyMhIvLy8GD9+PMnJyXW2KSgo4Oqrr8bf35/AwEBmzJhBaWlpC76L9kX69fFJv24Y6detj/TrE5O+fXLSr1sn6dvHJ/26YdpC3+7QQffnn3/O7Nmzefzxx/nzzz/p168fEydOJCcnx9NNazXOOOMMMjMz3V9r1qxxv3bvvffy3Xff8cUXX7By5UqOHDnCJZdc4sHWekZZWRn9+vVj/vz59b7+/PPP8+qrr7JgwQI2bNiAj48PEydOxGq1ure5+uqr+fvvv/n111/5/vvvWbVqFTNnzmypt9CuSL8+OenXJyf9unWRft0w0rdPTPp16yN9++SkX59cm+jbSgc2ZMgQZdasWe6fnU6nEhUVpcybN8+DrWo9Hn/8caVfv371vlZUVKQYDAbliy++cD+3e/duBVDWrVvXQi1sfQDl66+/dv/scrmUiIgI5YUXXnA/V1RUpJhMJuXTTz9VFEVRdu3apQDKpk2b3Nv89NNPikajUTIyMlqs7e2F9OsTk37deNKvPU/69clJ324c6detg/TtE5N+3XittW932JFuu93Oli1bGD9+vPs5rVbL+PHjWbdunQdb1rokJycTFRVF165dufrqqzl8+DAAW7ZsobKyss7n17NnT+Li4uTzqyUlJYWsrKw6n1NAQABJSUnuz2ndunUEBgYyaNAg9zbjx49Hq9WyYcOGFm9zWyb9umGkX58e6dctS/p1w0nfPnXSr1ue9O2GkX59elpL3+6wQXdeXh5Op5Pw8PA6z4eHh5OVleWhVrUuSUlJLFy4kKVLl/Lmm2+SkpLCyJEjKSkpISsrC6PRSGBgYJ195POrq/qzONH/s6ysLDp16lTndb1eT3BwsHyWjST9+uSkX58+6dctS/p1w0jfPj3Sr1ue9O2Tk359+lpL39Y3yVFEuzR58mT393379iUpKYn4+HgWL16Ml5eXB1smhDhV0q+FaJ+kbwvR/ki/bj867Eh3aGgoOp3umAx/2dnZREREeKhVrVtgYCAJCQns37+fiIgI7HY7RUVFdbaRz6+u6s/iRP/PIiIijkkY4nA4KCgokM+ykaRfN57068aTft2ypF+fGunbjSP9uuVJ32486deN11r6docNuo1GIwMHDmTZsmXu51wuF8uWLWPYsGEebFnrVVpayoEDB4iMjGTgwIEYDIY6n9/evXs5fPiwfH61dOnShYiIiDqfk8ViYcOGDe7PadiwYRQVFbFlyxb3Nr///jsul4ukpKQWb3NbJv268aRfN57065Yl/frUSN9uHOnXLU/6duNJv268VtO3myQdWxv12WefKSaTSVm4cKGya9cuZebMmUpgYKCSlZXl6aa1Cvfdd5+yYsUKJSUlRfnjjz+U8ePHK6GhoUpOTo6iKIpy6623KnFxccrvv/+ubN68WRk2bJgybNgwD7e65ZWUlChbt25Vtm7dqgDKSy+9pGzdulU5dOiQoiiK8uyzzyqBgYHKN998o+zYsUO56KKLlC5duigVFRXuY0yaNEkZMGCAsmHDBmXNmjVKjx49lGnTpnnqLbVp0q9PTPp1w0i/bl2kX5+c9O2Tk37d+kjfPjHp1w3TFvp2hw66FUVRXnvtNSUuLk4xGo3KkCFDlPXr13u6Sa3GlVdeqURGRipGo1GJjo5WrrzySmX//v3u1ysqKpTbb79dCQoKUry9vZWLL75YyczM9GCLPWP58uUKcMzX9ddfryiKWqrg0UcfVcLDwxWTyaScc845yt69e+scIz8/X5k2bZri6+ur+Pv7K9OnT1dKSko88G7aB+nXxyf9umGkX7c+0q9PTPr2yUm/bp2kbx+f9OuGaQt9W6MoitI0Y+ZCCCGEEEIIIYSorcOu6RZCCCGEEEIIIZqbBN1CCCGEEEIIIUQzkaBbCCGEEEIIIYRoJhJ0CyGEEEIIIYQQzUSCbiGEEEIIIYQQoplI0C2EEEIIIYQQQjQTCbqFEEIIIYQQQohmIkG3EEIIIYQQQgjRTCTobufmzp1L//79m3zb1kqj0bBkyRJPN0OIZid9W4j2R/q1EO2P9GsBEnS3WVOmTGHSpEn1vrZ69Wo0Gg07duzg/vvvZ9myZQ065tHb3nDDDUydOvWk+91www1oNBpuvfXWY16bNWsWGo2GG264oUFtaKj28EtJiPpI35a+Ldof6dfSr0X7I/1a+nVjSNDdRs2YMYNff/2V9PT0Y1774IMPGDRoEH379sXX15eQkJAGHbMx2x4tNjaWzz77jIqKCvdzVquVRYsWERcXd0rHFKIjkr4tRPsj/VqI9kf6tWgMCbrbqAsuuICwsDAWLlxY5/nS0lK++OILZsyYARx7F2rFihUMGTIEHx8fAgMDOfvsszl06NAx286dO5cPP/yQb775Bo1Gg0ajYcWKFcdtz1lnnUVsbCxfffWV+7mvvvqKuLg4BgwYUGdbm83GXXfdRadOnTCbzYwYMYJNmzbVaaNGo2HZsmUMGjQIb29vhg8fzt69ewFYuHAhTzzxBNu3b3e3rfbnkJeXx8UXX4y3tzc9evTg22+/bejHKoTHSd+Wvi3aH+nX0q9F+yP9Wvp1Y0jQ3Ubp9Xquu+46Fi5ciKIo7ue/+OILnE4n06ZNO2Yfh8PB1KlTGT16NDt27GDdunXMnDkTjUZzzLb3338/V1xxBZMmTSIzM5PMzEyGDx9+wjbdeOONfPDBB+6f33//faZPn37Mdg8++CBffvklH374IX/++Sfdu3dn4sSJFBQU1Nnu4Ycf5sUXX2Tz5s3o9XpuvPFGAK688kruu+8+zjjjDHfbrrzySvd+TzzxBFdccQU7duzgvPPO4+qrrz7m2EK0VtK3pW+L9kf6tfRr0f5Iv5Z+3SiKaLN2796tAMry5cvdz40cOVK55ppr3D8//vjjSr9+/RRFUZT8/HwFUFasWFHv8WpvqyiKcv311ysXXXTRSdtRvV1OTo5iMpmU1NRUJTU1VTGbzUpubq5y0UUXKddff72iKIpSWlqqGAwG5ZNPPnHvb7fblaioKOX5559XFEVRli9frgDKb7/95t7mhx9+UACloqKi3rZWA5RHHnnE/XNpaakCKD/99NNJ34cQrYX07Zq2VpO+Ldo66dc1ba0m/Vq0ddKva9paTfp1/WSkuw3r2bMnw4cP5/333wdg//79rF692j2d5WjBwcHccMMNTJw4kSlTpvDKK6+QmZnZZO0JCwvj/PPPZ+HChXzwwQecf/75hIaG1tnmwIEDVFZWcvbZZ7ufMxgMDBkyhN27d9fZtm/fvu7vIyMjAcjJyTlpO2rv5+Pjg7+/f4P2E6K1kL5dP+nboi2Tfl0/6deiLZN+XT/p18eSoLuNmzFjBl9++SUlJSV88MEHdOvWjdGjRx93+w8++IB169YxfPhwPv/8cxISEli/fn2TtefGG29k4cKFfPjhh+4pKKfKYDC4v6+eduNyuRq1X/W+DdlPiNZE+vaJ96veV/q2aEukX594v+p9pV+LtkT69Yn3q963o/drCbrbuCuuuAKtVsuiRYv46KOPuPHGG+tdF1LbgAEDmDNnDmvXrqVPnz4sWrSo3u2MRiNOp7NR7Zk0aRJ2u53KykomTpx4zOvdunXDaDTyxx9/uJ+rrKxk06ZN9O7du8HnOZW2CdGWSN8Wov2Rfi1E+yP9WjSE3tMNEKfH19eXK6+8kjlz5mCxWE5Ygy8lJYW3336bCy+8kKioKPbu3UtycjLXXXddvdt37tyZn3/+mb179xISEkJAQMAxd66OptPp3FNTdDrdMa/7+Phw22238cADDxAcHExcXBzPP/885eXlx52Kc7y2paSksG3bNmJiYvDz88NkMjV4fyFaO+nb0rdF+yP9Wvq1aH+kX0u/bggZ6W4HZsyYQWFhIRMnTiQqKuq423l7e7Nnzx4uvfRSEhISmDlzJrNmzeKWW26pd/ubb76ZxMREBg0aRFhYWJ07Yifi7++Pv7//cV9/9tlnufTSS7n22ms566yz2L9/Pz///DNBQUENOj7ApZdeyqRJkxg7dixhYWF8+umnDd5XiLZC+rb0bdH+SL+Wfi3aH+nX0q9PRqMotXLcCyGEEEIIIYQQosnISLcQQgghhBBCCNFMJOgWQgghhBBCCCGaiQTdQgghhBBCCCFEM5GgWwghhBBCCCGEaCYSdAshhBBCCCGEEM1Egm4hhBBCCCGEEKKZSNAthBBCCCGEEEI0Ewm6hRBCCCGEEEKIZiJBtxBCCCGEEEII0Uwk6BZCCCGEEEIIIZqJBN1CCCGEEEIIIUQzkaBbCCGEEEIIIYRoJv8PP16dmmg8lJkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(ncols = 4,  figsize=(10, 6))\n",
        "\n",
        "############## the status of \"upd23b_clinical_state_on_medication\"] is ignored\n",
        "# plot the first figure\n",
        "# read the DataFrame from a file\n",
        "df = train_clinical_df\n",
        "# group the DataFrame by visit_month and calculate the average of UPDRS scores for each group\n",
        "df_avg = df.groupby('visit_month').mean()\n",
        "# plot the line graph of the average UPDRS scores against visit_month\n",
        "ax[0].plot(df_avg.index, df_avg['updrs_1'], label='UPDRS-1')\n",
        "ax[0].plot(df_avg.index, df_avg['updrs_2'], label='UPDRS-2')\n",
        "ax[0].plot(df_avg.index, df_avg['updrs_3'], label='UPDRS-3')\n",
        "ax[0].plot(df_avg.index, df_avg['updrs_4'], label='UPDRS-4')\n",
        "ax[0].set_xlabel('Visit Month')\n",
        "ax[0].set_ylabel('Average UPDRS Score')\n",
        "ax[0].legend()\n",
        "ax[0].set_title('Medication = \"OFF\" or \"ON\", \\n # of samples: {}'.format(df.shape[0]))\n",
        "############## the status of \"upd23b_clinical_state_on_medication\"] is NaN\n",
        "# concatenate the two DataFrames vertically\n",
        "df = pd.concat([train_clinical_df, supp_df], ignore_index=True)\n",
        "# Filter df to select only the rows where upd23b_clinical_state_on_medication is 'On'\n",
        "nan_medication_df = df[df[\"upd23b_clinical_state_on_medication\"].isna()]\n",
        "# group the DataFrame by visit_month and calculate the average of UPDRS scores for each group\n",
        "df_avg = nan_medication_df.iloc[:, :-1].groupby('visit_month').mean()\n",
        "# group the DataFrame by visit_month and calculate the average of UPDRS scores for each group\n",
        "df_avg = df.groupby('visit_month').mean()\n",
        "# plot the line graph of the average UPDRS scores against visit_month\n",
        "ax[3].plot(df_avg.index, df_avg['updrs_1'], label='UPDRS-1')\n",
        "ax[3].plot(df_avg.index, df_avg['updrs_2'], label='UPDRS-2')\n",
        "ax[3].plot(df_avg.index, df_avg['updrs_3'], label='UPDRS-3')\n",
        "ax[3].plot(df_avg.index, df_avg['updrs_4'], label='UPDRS-4')\n",
        "ax[3].set_xlabel('Visit Month')\n",
        "ax[3].set_ylabel('Average UPDRS Score')\n",
        "ax[3].legend()\n",
        "ax[3].set_title('Medication = NaN, \\n # of samples: {}'.format(nan_medication_df.shape[0]))\n",
        "\n",
        "############## \"upd23b_clinical_state_on_medication\"] == \"On\"\n",
        "# Filter df to select only the rows where upd23b_clinical_state_on_medication is 'On'\n",
        "on_medication_df = df[df[\"upd23b_clinical_state_on_medication\"] == \"On\"]\n",
        "# group the DataFrame by visit_month and calculate the average of UPDRS scores for each group\n",
        "df_avg = on_medication_df.iloc[:, :-1].groupby('visit_month').mean()\n",
        "\n",
        "# plot the line graph of the average UPDRS scores against visit_month\n",
        "ax[1].plot(df_avg.index, df_avg['updrs_1'], label='UPDRS-1')\n",
        "ax[1].plot(df_avg.index, df_avg['updrs_2'], label='UPDRS-2')\n",
        "ax[1].plot(df_avg.index, df_avg['updrs_3'], label='UPDRS-3')\n",
        "ax[1].plot(df_avg.index, df_avg['updrs_4'], label='UPDRS-4')\n",
        "ax[1].set_xlabel('Visit Month')\n",
        "ax[1].legend()\n",
        "ax[1].set_ylabel('Average UPDRS Score')\n",
        "ax[1].set_title('Medication = \"ON\",\\n # of samples: {}'.format(on_medication_df.shape[0]))\n",
        "\n",
        "\n",
        "############## \"upd23b_clinical_state_on_medication\"] == \"Off\"\n",
        "# concatenate the two DataFrames vertically\n",
        "df = pd.concat([train_clinical_df, supp_df], ignore_index=True)\n",
        "# Filter df to select only the rows where upd23b_clinical_state_on_medication is 'On'\n",
        "off_medication_df = df[df[\"upd23b_clinical_state_on_medication\"] == \"Off\"]\n",
        "# group the DataFrame by visit_month and calculate the average of UPDRS scores for each group\n",
        "df_avg = off_medication_df.iloc[:, :-1].groupby('visit_month').mean()\n",
        "\n",
        "# plot the line graph of the average UPDRS scores against visit_month\n",
        "ax[2].plot(df_avg.index, df_avg['updrs_1'], label='UPDRS-1')\n",
        "ax[2].plot(df_avg.index, df_avg['updrs_2'], label='UPDRS-2')\n",
        "ax[2].plot(df_avg.index, df_avg['updrs_3'], label='UPDRS-3')\n",
        "ax[2].plot(df_avg.index, df_avg['updrs_4'], label='UPDRS-4')\n",
        "ax[2].set_xlabel('Visit Month')\n",
        "ax[2].set_ylabel('Average UPDRS Score')\n",
        "ax[2].legend()\n",
        "ax[2].set_title('Medication = \"OFF\",\\n # of samples: {}'.format(off_medication_df.shape[0]))\n",
        "\n",
        "# adjust the subplots layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Zn1lO4j4sVu2",
        "outputId": "1b42313f-9ebf-435a-d4ad-b2a63a90d0d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      visit_id  patient_id  visit_month  updrs_1  updrs_2  updrs_3  updrs_4\n",
              "0         55_0          55            0     10.0      6.0     15.0      0.0\n",
              "1         55_3          55            3     10.0      7.0     25.0      0.0\n",
              "2         55_6          55            6      8.0     10.0     34.0      0.0\n",
              "3         55_9          55            9      8.0      9.0     30.0      0.0\n",
              "4        55_12          55           12     10.0     10.0     41.0      0.0\n",
              "...        ...         ...          ...      ...      ...      ...      ...\n",
              "2610  65043_48       65043           48      7.0      6.0     13.0      0.0\n",
              "2611  65043_54       65043           54      4.0      8.0     11.0      1.0\n",
              "2612  65043_60       65043           60      6.0      6.0     16.0      1.0\n",
              "2613  65043_72       65043           72      3.0      9.0     14.0      1.0\n",
              "2614  65043_84       65043           84      7.0      9.0     20.0      3.0\n",
              "\n",
              "[2615 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>visit_id</th>\n",
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              "      <td>55_3</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>41.0</td>\n",
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              "      <th>2610</th>\n",
              "      <td>65043_48</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>54</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2612</th>\n",
              "      <td>65043_60</td>\n",
              "      <td>65043</td>\n",
              "      <td>60</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>65043_72</td>\n",
              "      <td>65043</td>\n",
              "      <td>72</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
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              "    <tr>\n",
              "      <th>2614</th>\n",
              "      <td>65043_84</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>20.0</td>\n",
              "      <td>3.0</td>\n",
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              "  </tbody>\n",
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              "<p>2615 rows × 7 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# concatenate the two train_clinical_df and supp_df vertically\n",
        "train_df = pd.concat([train_clinical_df, supp_df], ignore_index=True)\n",
        "\n",
        "# assume df is your DataFrame\n",
        "cols_to_normalize = ['updrs_1', 'updrs_2', 'updrs_3', 'updrs_4']\n",
        "# Apply log(1+x) transformation to the last 4 columns\n",
        "train_df[cols_to_normalize] = np.log1p(train_df[cols_to_normalize])\n",
        "\n",
        "# perform KNN imputation on the normalized data\n",
        "imputer = KNNImputer(n_neighbors=10)\n",
        "\n",
        "on_medication_mask = train_df[\"upd23b_clinical_state_on_medication\"] == \"On\"\n",
        "train_df.loc[on_medication_mask, cols_to_normalize] = np.maximum(imputer.fit_transform(train_df.loc[on_medication_mask, cols_to_normalize]), 0)\n",
        "off_medication_mask = train_df[\"upd23b_clinical_state_on_medication\"] == \"Off\"\n",
        "train_df.loc[off_medication_mask, cols_to_normalize] = np.maximum(imputer.fit_transform(train_df.loc[off_medication_mask, cols_to_normalize]), 0)\n",
        "nan_medication_mask = train_df[\"upd23b_clinical_state_on_medication\"].isna()\n",
        "train_df.loc[nan_medication_mask, cols_to_normalize] = np.maximum(imputer.fit_transform(train_df.loc[nan_medication_mask, cols_to_normalize]), 0)\n",
        "\n",
        "train_df[cols_to_normalize]=round(np.maximum(np.expm1(train_df[cols_to_normalize]),0))\n",
        "train_df = train_df.head(n= 2615)\n",
        "train_df = train_df.iloc[:, :-1]\n",
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cQ9eJQg6W-Fn",
        "outputId": "dd96daba-0760-44e2-8609-37e08892c00a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# create a new column with a unique identifier for each patient\n",
        "df = train_df.copy(deep=True)\n",
        "df['patient_index'] = df.groupby('patient_id').ngroup()\n",
        "\n",
        "# create a scatter plot for patient_index versus visit_month\n",
        "plt.scatter(df['patient_index'], df['visit_month'], s=1)\n",
        "\n",
        "\n",
        "plt.xlabel(\"Patient ID\")\n",
        "plt.ylabel(\"Visit Month\")\n",
        "plt.title(\"Visit Months for Each Patient\")\n",
        "\n",
        "# display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WABMAveUYBH2"
      },
      "source": [
        "\n",
        "\n",
        "*   In the figure above, the interval between visit_month is approximately 6 months until the 60th month, after which the data becomes sparse. This suggests that only a few patient_id have long-term records beyond 60 months. Therefore, we will focus only on the data from the 60 months prior to the current time period.\n",
        "*   Since the target of the prediction is from the current time point to 6, 6, 12 months, it would be helpful to include data up to 72 months to gather as much information as possible in our training dataset.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Vi1BdmEBY9en",
        "outputId": "531ea6a0-dc9b-45f1-b52d-5add9a71316c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      visit_id  patient_id  visit_month  updrs_1_plus_0_months  \\\n",
              "0         55_0          55            0                   10.0   \n",
              "1         55_3          55            3                   10.0   \n",
              "2         55_6          55            6                    8.0   \n",
              "3         55_9          55            9                    8.0   \n",
              "4        55_12          55           12                   10.0   \n",
              "...        ...         ...          ...                    ...   \n",
              "2610  65043_48       65043           48                    7.0   \n",
              "2611  65043_54       65043           54                    4.0   \n",
              "2612  65043_60       65043           60                    6.0   \n",
              "2613  65043_72       65043           72                    3.0   \n",
              "2614  65043_84       65043           84                    7.0   \n",
              "\n",
              "      updrs_2_plus_0_months  updrs_3_plus_0_months  updrs_4_plus_0_months  \n",
              "0                       6.0                   15.0                    0.0  \n",
              "1                       7.0                   25.0                    0.0  \n",
              "2                      10.0                   34.0                    0.0  \n",
              "3                       9.0                   30.0                    0.0  \n",
              "4                      10.0                   41.0                    0.0  \n",
              "...                     ...                    ...                    ...  \n",
              "2610                    6.0                   13.0                    0.0  \n",
              "2611                    8.0                   11.0                    1.0  \n",
              "2612                    6.0                   16.0                    1.0  \n",
              "2613                    9.0                   14.0                    1.0  \n",
              "2614                    9.0                   20.0                    3.0  \n",
              "\n",
              "[2615 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a769c18e-5548-45c0-9517-e4f103c2a8a7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>visit_id</th>\n",
              "      <th>patient_id</th>\n",
              "      <th>visit_month</th>\n",
              "      <th>updrs_1_plus_0_months</th>\n",
              "      <th>updrs_2_plus_0_months</th>\n",
              "      <th>updrs_3_plus_0_months</th>\n",
              "      <th>updrs_4_plus_0_months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>55_0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55_3</td>\n",
              "      <td>55</td>\n",
              "      <td>3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>55_6</td>\n",
              "      <td>55</td>\n",
              "      <td>6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>55_9</td>\n",
              "      <td>55</td>\n",
              "      <td>9</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55_12</td>\n",
              "      <td>55</td>\n",
              "      <td>12</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2610</th>\n",
              "      <td>65043_48</td>\n",
              "      <td>65043</td>\n",
              "      <td>48</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2611</th>\n",
              "      <td>65043_54</td>\n",
              "      <td>65043</td>\n",
              "      <td>54</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2612</th>\n",
              "      <td>65043_60</td>\n",
              "      <td>65043</td>\n",
              "      <td>60</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>65043_72</td>\n",
              "      <td>65043</td>\n",
              "      <td>72</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2614</th>\n",
              "      <td>65043_84</td>\n",
              "      <td>65043</td>\n",
              "      <td>84</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2615 rows × 7 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "train_df = train_df.rename(columns={\n",
        "    'updrs_1': 'updrs_1_plus_0_months',\n",
        "    'updrs_2': 'updrs_2_plus_0_months',\n",
        "    'updrs_3': 'updrs_3_plus_0_months',\n",
        "    'updrs_4': 'updrs_4_plus_0_months'\n",
        "})\n",
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3vg32vueZNbc"
      },
      "outputs": [],
      "source": [
        "# define an empty DataFrame with column names\n",
        "ts_df_updrs = pd.DataFrame(columns=['time', 'patient_id',  'visit_id',\n",
        "                                    'updrs_1_plus_0_months','updrs_2_plus_0_months','updrs_3_plus_0_months', 'updrs_4_plus_0_months',\n",
        "                                    'updrs_1_plus_6_months','updrs_2_plus_6_months','updrs_3_plus_6_months', 'updrs_4_plus_6_months',\n",
        "                                    'updrs_1_plus_12_months','updrs_2_plus_12_months','updrs_3_plus_12_months', 'updrs_4_plus_12_months',\n",
        "                                    'updrs_1_plus_24_months','updrs_2_plus_24_months','updrs_3_plus_24_months', 'updrs_4_plus_24_months'\n",
        "                                    ])\n",
        "\n",
        "for patient_id in train_df['patient_id'].unique():\n",
        "    # filter a subset of visiting records for a selected patient_id\n",
        "    p_df = train_df[train_df['patient_id']==patient_id]\n",
        "    ts_start = p_df.visit_month.min()\n",
        "    ts_end = p_df.visit_month.max()\n",
        "    ts_span = ts_end - ts_start\n",
        "    p_num = p_df.shape[0]\n",
        "    if (p_num > 1) and (ts_span >= 30) and (ts_span/(p_df.shape[0]-1) < 24):\n",
        "        # the last visit record is 72\n",
        "        if ts_end > 72:\n",
        "            ts_end = 72+1\n",
        "\n",
        "        patient_df = pd.DataFrame({'time': range(ts_start, ts_end, 6), 'patient_id': patient_id})\n",
        "        # Add the visit_id column\n",
        "        patient_df['visit_id'] = patient_df['patient_id'].astype(str) + '_' + patient_df['time'].astype(str)\n",
        "        # merge dataframes on visit_id using left join\n",
        "        merged_df = pd.merge(patient_df, p_df, on='visit_id', how='left')\n",
        "        merged_df.drop('patient_id_y', axis=1,inplace = True)\n",
        "        merged_df.rename(columns={'patient_id_x': 'patient_id'},inplace = True)\n",
        "        # forward fill missing values\n",
        "        merged_df = merged_df.fillna(method='ffill')\n",
        "        # generate new columns updrs_p6_1, updrs_p6_2, updrs_p6_3, updrs_p6_4\n",
        "        merged_df['updrs_1_plus_6_months'] = merged_df['updrs_1_plus_0_months'].shift(-1)\n",
        "        merged_df['updrs_2_plus_6_months'] = merged_df['updrs_2_plus_0_months'].shift(-1)\n",
        "        merged_df['updrs_3_plus_6_months'] = merged_df['updrs_3_plus_0_months'].shift(-1)\n",
        "        merged_df['updrs_4_plus_6_months'] = merged_df['updrs_4_plus_0_months'].shift(-1)\n",
        "        merged_df['updrs_1_plus_12_months'] = merged_df['updrs_1_plus_0_months'].shift(-2)\n",
        "        merged_df['updrs_2_plus_12_months'] = merged_df['updrs_2_plus_0_months'].shift(-2)\n",
        "        merged_df['updrs_3_plus_12_months'] = merged_df['updrs_3_plus_0_months'].shift(-2)\n",
        "        merged_df['updrs_4_plus_12_months'] = merged_df['updrs_4_plus_0_months'].shift(-2)\n",
        "        merged_df['updrs_1_plus_24_months'] = merged_df['updrs_1_plus_0_months'].shift(-4)\n",
        "        merged_df['updrs_2_plus_24_months'] = merged_df['updrs_2_plus_0_months'].shift(-4)\n",
        "        merged_df['updrs_3_plus_24_months'] = merged_df['updrs_3_plus_0_months'].shift(-4)\n",
        "        merged_df['updrs_4_plus_24_months'] = merged_df['updrs_4_plus_0_months'].shift(-4)\n",
        "\n",
        "        merged_df = merged_df.drop('visit_month', axis=1)\n",
        "        merged_df = merged_df.dropna()\n",
        "        ts_df_updrs= pd.concat([ts_df_updrs, merged_df],ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "K_HH3L22ZkvS",
        "outputId": "b25c15ed-fbdb-45c2-ab15-6c7599bd121f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     time patient_id  visit_id  updrs_1_plus_0_months  updrs_2_plus_0_months  \\\n",
              "0       0         55      55_0                   10.0                    6.0   \n",
              "1       6         55      55_6                    8.0                   10.0   \n",
              "2      12         55     55_12                   10.0                   10.0   \n",
              "3      18         55     55_18                    7.0                   13.0   \n",
              "4      24         55     55_24                   16.0                    9.0   \n",
              "...   ...        ...       ...                    ...                    ...   \n",
              "1598   24      65043  65043_24                    4.0                    8.0   \n",
              "1599   30      65043  65043_30                    3.0                    4.0   \n",
              "1600   36      65043  65043_36                    2.0                    7.0   \n",
              "1601   42      65043  65043_42                    9.0                   10.0   \n",
              "1602   48      65043  65043_48                    7.0                    6.0   \n",
              "\n",
              "      updrs_3_plus_0_months  updrs_4_plus_0_months  updrs_1_plus_6_months  \\\n",
              "0                      15.0                    0.0                    8.0   \n",
              "1                      34.0                    0.0                   10.0   \n",
              "2                      41.0                    0.0                    7.0   \n",
              "3                      38.0                    0.0                   16.0   \n",
              "4                      49.0                    0.0                   14.0   \n",
              "...                     ...                    ...                    ...   \n",
              "1598                   23.0                    0.0                    3.0   \n",
              "1599                    4.0                    0.0                    2.0   \n",
              "1600                    5.0                    0.0                    9.0   \n",
              "1601                   15.0                    0.0                    7.0   \n",
              "1602                   13.0                    0.0                    4.0   \n",
              "\n",
              "      updrs_2_plus_6_months  updrs_3_plus_6_months  updrs_4_plus_6_months  \\\n",
              "0                      10.0                   34.0                    0.0   \n",
              "1                      10.0                   41.0                    0.0   \n",
              "2                      13.0                   38.0                    0.0   \n",
              "3                       9.0                   49.0                    0.0   \n",
              "4                      13.0                   49.0                    0.0   \n",
              "...                     ...                    ...                    ...   \n",
              "1598                    4.0                    4.0                    0.0   \n",
              "1599                    7.0                    5.0                    0.0   \n",
              "1600                   10.0                   15.0                    0.0   \n",
              "1601                    6.0                   13.0                    0.0   \n",
              "1602                    8.0                   11.0                    1.0   \n",
              "\n",
              "      updrs_1_plus_12_months  updrs_2_plus_12_months  updrs_3_plus_12_months  \\\n",
              "0                       10.0                    10.0                    41.0   \n",
              "1                        7.0                    13.0                    38.0   \n",
              "2                       16.0                     9.0                    49.0   \n",
              "3                       14.0                    13.0                    49.0   \n",
              "4                       17.0                    18.0                    51.0   \n",
              "...                      ...                     ...                     ...   \n",
              "1598                     2.0                     7.0                     5.0   \n",
              "1599                     9.0                    10.0                    15.0   \n",
              "1600                     7.0                     6.0                    13.0   \n",
              "1601                     4.0                     8.0                    11.0   \n",
              "1602                     6.0                     6.0                    16.0   \n",
              "\n",
              "      updrs_4_plus_12_months  updrs_1_plus_24_months  updrs_2_plus_24_months  \\\n",
              "0                        0.0                    16.0                     9.0   \n",
              "1                        0.0                    14.0                    13.0   \n",
              "2                        0.0                    17.0                    18.0   \n",
              "3                        0.0                    12.0                    20.0   \n",
              "4                        0.0                    17.0                    16.0   \n",
              "...                      ...                     ...                     ...   \n",
              "1598                     0.0                     7.0                     6.0   \n",
              "1599                     0.0                     4.0                     8.0   \n",
              "1600                     0.0                     6.0                     6.0   \n",
              "1601                     1.0                     6.0                     6.0   \n",
              "1602                     1.0                     3.0                     9.0   \n",
              "\n",
              "      updrs_3_plus_24_months  updrs_4_plus_24_months  \n",
              "0                       49.0                     0.0  \n",
              "1                       49.0                     0.0  \n",
              "2                       51.0                     0.0  \n",
              "3                       41.0                     0.0  \n",
              "4                       52.0                     0.0  \n",
              "...                      ...                     ...  \n",
              "1598                    13.0                     0.0  \n",
              "1599                    11.0                     1.0  \n",
              "1600                    16.0                     1.0  \n",
              "1601                    16.0                     1.0  \n",
              "1602                    14.0                     1.0  \n",
              "\n",
              "[1603 rows x 19 columns]"
            ],
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              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>patient_id</th>\n",
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              "      <th>updrs_1_plus_24_months</th>\n",
              "      <th>updrs_2_plus_24_months</th>\n",
              "      <th>updrs_3_plus_24_months</th>\n",
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              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>55</td>\n",
              "      <td>55_6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>55</td>\n",
              "      <td>55_12</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18</td>\n",
              "      <td>55</td>\n",
              "      <td>55_18</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24</td>\n",
              "      <td>55</td>\n",
              "      <td>55_24</td>\n",
              "      <td>16.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>24</td>\n",
              "      <td>65043</td>\n",
              "      <td>65043_24</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599</th>\n",
              "      <td>30</td>\n",
              "      <td>65043</td>\n",
              "      <td>65043_30</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1600</th>\n",
              "      <td>36</td>\n",
              "      <td>65043</td>\n",
              "      <td>65043_36</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1601</th>\n",
              "      <td>42</td>\n",
              "      <td>65043</td>\n",
              "      <td>65043_42</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1602</th>\n",
              "      <td>48</td>\n",
              "      <td>65043</td>\n",
              "      <td>65043_48</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1603 rows × 19 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "ts_df_updrs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SoDhR0U7Zolq"
      },
      "source": [
        "**PART II  - Extracting features from Protein and Peptide Train data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-Mc1fZARZ3Hh",
        "outputId": "fc302785-316e-4d26-9010-806037058039"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         visit_id UniProt       NPX                                 Peptide  \\\n",
              "0            55_0  O00391   11254.3                           NEQEQPLGQWHLS   \n",
              "1            55_0  O00533  732430.0                             GNPEPTFSWTK   \n",
              "2            55_0  O00533  732430.0                         IEIPSSVQQVPTIIK   \n",
              "3            55_0  O00533  732430.0  KPQSAVYSTGSNGILLC(UniMod_4)EAEGEPQPTIK   \n",
              "4            55_0  O00533  732430.0                            SMEQNGPGLEYR   \n",
              "...           ...     ...       ...                                     ...   \n",
              "981829  58648_108  Q9UHG2  369437.0                         ILAGSADSEGVAAPR   \n",
              "981830  58648_108  Q9UKV8  105830.0                           SGNIPAGTTVDTK   \n",
              "981831  58648_108  Q9Y646   21257.6                             LALLVDTVGPR   \n",
              "981832  58648_108  Q9Y6R7   17953.1        AGC(UniMod_4)VAESTAVC(UniMod_4)R   \n",
              "981833  58648_108  Q9Y6R7   17953.1                          GATTSPGVYELSSR   \n",
              "\n",
              "        PeptideAbundance  \n",
              "0               11254.30  \n",
              "1              102060.00  \n",
              "2              174185.00  \n",
              "3               27278.90  \n",
              "4               30838.70  \n",
              "...                  ...  \n",
              "981829         202820.00  \n",
              "981830         105830.00  \n",
              "981831          21257.60  \n",
              "981832           5127.26  \n",
              "981833          12825.90  \n",
              "\n",
              "[981834 rows x 5 columns]"
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8bae121d-9bfb-48d1-9fa9-3f1f103f26f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8bae121d-9bfb-48d1-9fa9-3f1f103f26f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "Prot_Peptides_df = pd.merge(train_protein_df, train_peptide_df, on = ['visit_id','UniProt'], how = 'left')\n",
        "Prot_Peptides_df = Prot_Peptides_df[['visit_id','UniProt','NPX','Peptide','PeptideAbundance']]\n",
        "Prot_Peptides_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjE8llwWZ3uB"
      },
      "source": [
        "Select a subset of UniProt, which are shared in all visit_id samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAJQtqHaaCj5",
        "outputId": "ebc371b8-8564-485f-a449-86d87d2a508c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'P07602', 'P10909', 'P02787', 'P23142', 'P01011', 'P05090', 'P01042', 'P02774', 'P01023', 'P01024', 'P02768', 'P01009', 'O15240', 'P02647', 'P02765', 'P02749', 'P01834', 'P01876', 'P02649', 'Q92520', 'P06396', 'Q9UHG2', 'P02790', 'P41222', 'Q12805', 'P02751', 'P02766'} 27\n"
          ]
        }
      ],
      "source": [
        "# group by visit_id and get the set of unique UniProt values for each group\n",
        "grouped = Prot_Peptides_df.groupby('visit_id')['UniProt'].apply(set)\n",
        "\n",
        "# calculate the intersection of UniProt values across all groups\n",
        "intersection = set.intersection(*grouped)\n",
        "\n",
        "# print the intersection\n",
        "print(intersection, len(intersection))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mEJYKKkYazwr"
      },
      "source": [
        "\n",
        "\n",
        "*   There are 27 unique UniProt values\n",
        "*   Select the 27 UniProt as features and filter the Prot_Peptides_df to have the intersection.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veElux25asD2",
        "outputId": "40769bd5-3cf8-44ba-aee0-56672198aba9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         visit_id UniProt         NPX          Peptide  PeptideAbundance\n",
            "15           55_0  O15240    177775.0      AYQGVAAPFPK         107076.00\n",
            "16           55_0  O15240    177775.0     QQETAAAETETR           3095.35\n",
            "17           55_0  O15240    177775.0     THLGEALAPLSK          67603.70\n",
            "94           55_0  P01009  14415900.0          FLENEDR          41501.10\n",
            "95           55_0  P01009  14415900.0         FLENEDRR          46973.10\n",
            "...           ...     ...         ...              ...               ...\n",
            "981797  58648_108  Q92520    907400.0         SPFEQHIK          66357.20\n",
            "981826  58648_108  Q9UHG2    369437.0     AEAQEAEDQQAR           7884.17\n",
            "981827  58648_108  Q9UHG2    369437.0   ARAEAQEAEDQQAR          80542.00\n",
            "981828  58648_108  Q9UHG2    369437.0     GEAAGAVQELAR          78191.40\n",
            "981829  58648_108  Q9UHG2    369437.0  ILAGSADSEGVAAPR         202820.00\n",
            "\n",
            "[424373 rows x 5 columns]\n"
          ]
        }
      ],
      "source": [
        "# set of UniProt values to select\n",
        "uniprots_to_select = intersection\n",
        "\n",
        "# filter the dataframe to select rows with UniProt values in the set\n",
        "df_filtered = Prot_Peptides_df[Prot_Peptides_df['UniProt'].isin(uniprots_to_select)]\n",
        "# print the resulting dataframe\n",
        "print(df_filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ERcrudbayHP"
      },
      "outputs": [],
      "source": [
        "# apply log(x+1) transformation to columns\n",
        "df_filtered[['PeptideAbundance']] = df_filtered[['PeptideAbundance']].apply(lambda x: np.log1p(x))\n",
        "\n",
        "# standardization\n",
        "scaler1 = StandardScaler()\n",
        "df_filtered[['PeptideAbundance']] = scaler1.fit_transform(df_filtered[['PeptideAbundance']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cb5L8qpn1nSr"
      },
      "outputs": [],
      "source": [
        "# create a list of all peptides presented in the original data\n",
        "all_peptides = df_filtered['Peptide'].unique()\n",
        "# create a list of all visit_id in the original data\n",
        "all_visit_id = df_filtered['visit_id'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bu6vVFs1p6b"
      },
      "source": [
        "Till now, we have extracted features from Proteins and Peptides dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKxRkCRj2hzV"
      },
      "source": [
        "We need to understand the interactions between Protein and Peptide Abundance, and thus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RW-lphji6_rk",
        "outputId": "d336afbe-86d5-4c64-a7ab-9b0d6a9f3a2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['55_0', '1517_0', '1923_0', ..., '12703_108', '55096_108',\n",
              "       '58648_108'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "all_visit_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxzJW8tH7uN1"
      },
      "source": [
        "Let's take a random visit_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "id": "76MkYZwJ7Z8G",
        "outputId": "333a183a-5372-47f9-b164-fabc06507939"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visit_id = '55_12'\n",
        "p_df = df_filtered[df_filtered['visit_id'] == visit_id]\n",
        "pivoted = p_df.pivot(index='UniProt', columns='Peptide', values='PeptideAbundance')\n",
        "# iterate over each UniProt and add missing peptides as columns with a value of 0\n",
        "for prot in pivoted.index:\n",
        "    missing_peptides = set(all_peptides) - set(pivoted.columns)\n",
        "    for pep in missing_peptides:\n",
        "        pivoted.loc[prot, pep] = 0\n",
        "\n",
        "# fill any remaining missing values with 0\n",
        "pivoted.fillna(0, inplace=True)\n",
        "\n",
        "# Plot the heatmap\n",
        "sns.heatmap(pivoted)\n",
        "# Set the title of the heatmap\n",
        "plt.title(\"Peptide Abundance Matrix Heatmap\")\n",
        "\n",
        "# Show the heatmap\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDJTlIML7q6-",
        "outputId": "ba4b177a-b20a-4444-f02d-d420f2c6a024"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       visit_id         0         1         2         3         4         5  \\\n",
            "0          55_0 -0.408761 -0.191259 -0.143086 -0.097637 -0.121823 -0.008657   \n",
            "1        1517_0 -0.390323 -0.207683 -0.223584 -0.037062 -0.226242 -0.162619   \n",
            "2        1923_0 -0.375939 -0.189745 -0.240332 -0.183197 -0.170522 -0.098476   \n",
            "3        2660_0 -0.348659 -0.177171 -0.200724 -0.074223 -0.092662 -0.088853   \n",
            "4        3636_0 -0.366450 -0.188364 -0.165799 -0.269625 -0.250694 -0.115397   \n",
            "...         ...       ...       ...       ...       ...       ...       ...   \n",
            "1108   58648_96 -0.375338 -0.090774 -0.171280 -0.221629 -0.094996 -0.008812   \n",
            "1109   5645_108 -0.420115 -0.094926 -0.274239 -0.107466 -0.152648 -0.184514   \n",
            "1110  12703_108 -0.335764 -0.160216 -0.159047 -0.013911 -0.230621 -0.141327   \n",
            "1111  55096_108 -0.414612 -0.076189 -0.264506 -0.266721 -0.201371 -0.251426   \n",
            "1112  58648_108 -0.371630 -0.114047 -0.246301 -0.108698 -0.044771 -0.252302   \n",
            "\n",
            "             6         7         8  ...       260       261       262  \\\n",
            "0     0.000184  0.053236 -0.344581  ... -0.400615 -0.175182 -0.123466   \n",
            "1    -0.154679 -0.198600 -0.287681  ... -0.387953 -0.203422 -0.217354   \n",
            "2    -0.070053  0.059326 -0.333420  ... -0.354025 -0.160104 -0.195976   \n",
            "3    -0.301277 -0.341720 -0.287187  ... -0.338207 -0.161152 -0.178303   \n",
            "4    -0.031362 -0.209024 -0.080025  ... -0.353605 -0.167977 -0.143448   \n",
            "...        ...       ...       ...  ...       ...       ...       ...   \n",
            "1108 -0.347508 -0.134669 -0.286876  ... -0.367863 -0.083121 -0.155736   \n",
            "1109 -0.073119 -0.387532 -0.517021  ... -0.409266 -0.083371 -0.238014   \n",
            "1110 -0.093338 -0.162831 -0.246867  ... -0.340481 -0.167323 -0.168028   \n",
            "1111 -0.266453 -0.234944 -0.409002  ... -0.411310 -0.073283 -0.253589   \n",
            "1112 -0.060036 -0.153058 -0.095237  ... -0.363931 -0.106554 -0.228608   \n",
            "\n",
            "           263       264       265       266       267       268       269  \n",
            "0    -0.079868 -0.097212 -0.006589  0.000140  0.040056 -0.245864 -0.065914  \n",
            "1    -0.035079 -0.213155 -0.149553 -0.139102 -0.174679 -0.244141 -0.123182  \n",
            "2    -0.134858 -0.114427 -0.058647 -0.039848  0.032861 -0.171172 -0.021156  \n",
            "3    -0.061977 -0.076472 -0.072371 -0.239024 -0.254257 -0.179963 -0.149169  \n",
            "4    -0.228229 -0.198076 -0.073276 -0.018911 -0.121258 -0.043250 -0.301993  \n",
            "...        ...       ...       ...       ...       ...       ...       ...  \n",
            "1108 -0.198533 -0.081580 -0.007484 -0.289008 -0.100810 -0.204743 -0.152776  \n",
            "1109 -0.083822 -0.116039 -0.135047 -0.051076 -0.256362 -0.292887 -0.117733  \n",
            "1110 -0.014921 -0.248751 -0.156765 -0.105909 -0.187596 -0.293103 -0.189169  \n",
            "1111 -0.252386 -0.183261 -0.222095 -0.221935 -0.178112 -0.284108 -0.065443  \n",
            "1112 -0.096595 -0.039285 -0.218831 -0.049202 -0.123576 -0.074678  0.071649  \n",
            "\n",
            "[1113 rows x 271 columns]\n"
          ]
        }
      ],
      "source": [
        "pca = PCA(n_components = 10)\n",
        "\n",
        "# initialize an empty list to store the PCA features\n",
        "pca_features_list = []\n",
        "\n",
        "for visit_id in all_visit_id:\n",
        "    # Part 1: construct UniProt-Peptide Network for each visit_id\n",
        "    p_df = df_filtered[df_filtered['visit_id'] == visit_id]\n",
        "    # first, pivot the data so that the UniProt column becomes the index,\n",
        "    # the Peptide column becomes the columns, and the PeptideAbundance column becomes the values\n",
        "    pivoted = p_df.pivot(index='UniProt', columns='Peptide', values='PeptideAbundance')\n",
        "    # iterate over each UniProt and add missing peptides as columns with a value of 0\n",
        "    for prot in pivoted.index:\n",
        "        missing_peptides = set(all_peptides) - set(pivoted.columns)\n",
        "        for pep in missing_peptides:\n",
        "            pivoted.loc[prot, pep] = 0\n",
        "\n",
        "    # fill any remaining missing values with 0\n",
        "    pivoted.fillna(0, inplace=True)\n",
        "\n",
        "    # Part II: Sum up all networks of all visit_id\n",
        "    if visit_id == all_visit_id[0]:\n",
        "        avg_network = pivoted\n",
        "    else:\n",
        "        avg_network = avg_network.add(pivoted, fill_value=0)\n",
        "\n",
        "    # Part III: PCA feature extraction\n",
        "    # reset the index so that UniProt is a column again\n",
        "    pivoted = pivoted.reset_index()\n",
        "    # rename the index column to 'UniProt'\n",
        "    pivoted.index.name = 'UniProt'\n",
        "    # save the pivoted DataFrame to a new variable called `new_df`\n",
        "    new_df = pivoted.copy(deep=True)\n",
        "    # fit and transform the data\n",
        "    df1_pca = pca.fit_transform(new_df.iloc[:,1:])\n",
        "    # flatten the array to one dimension\n",
        "    df1_pca = np.array(df1_pca).flatten()\n",
        "    # store the PCA features along with the visit_id in the list\n",
        "    pca_features_list.append({'visit_id': visit_id, 'pca_features': df1_pca})\n",
        "\n",
        "# calculate the average network\n",
        "avg_network = avg_network / len(all_visit_id)\n",
        "# construct a dataframe from the PCA features list\n",
        "df_pca_features = pd.DataFrame(pca_features_list)\n",
        "# split the pca_features column into multiple columns\n",
        "PP_features = pd.concat([df_pca_features.drop('pca_features', axis=1), df_pca_features['pca_features'].apply(pd.Series)], axis=1)\n",
        "\n",
        "# display the resulting dataframe\n",
        "print(PP_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWvIlhBul4YX"
      },
      "source": [
        "**To create a training DataFrame for a deep learning model, we join the PP_features and ts_df_updrs DataFrames, and then remove any rows with NaN values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pUrcPhLxmUF-"
      },
      "outputs": [],
      "source": [
        "PP_features1 = PP_features.merge(ts_df_updrs, on='visit_id')\n",
        "\n",
        "target_list = ['updrs_1_plus_0_months', 'updrs_2_plus_0_months',\n",
        "       'updrs_3_plus_0_months', 'updrs_4_plus_0_months',\n",
        "       'updrs_1_plus_6_months', 'updrs_2_plus_6_months',\n",
        "       'updrs_3_plus_6_months', 'updrs_4_plus_6_months',\n",
        "       'updrs_1_plus_12_months', 'updrs_2_plus_12_months',\n",
        "       'updrs_3_plus_12_months', 'updrs_4_plus_12_months',\n",
        "       'updrs_1_plus_24_months', 'updrs_2_plus_24_months',\n",
        "       'updrs_3_plus_24_months', 'updrs_4_plus_24_months']\n",
        "info_list =['visit_id','time', 'patient_id']\n",
        "cols_to_scale = PP_features1.columns.difference(target_list+info_list)\n",
        "\n",
        "#train_fea_target.columns[:-16]\n",
        "train_fea_target = PP_features1.copy(deep=True)\n",
        "# perform z-score normalization on the training data\n",
        "scaler_features = StandardScaler()\n",
        "# fit and transform the data\n",
        "scaler_features.fit(train_fea_target[cols_to_scale])\n",
        "#train_fea_target[cols_to_scale] = scaler.transform(train_fea_target[cols_to_scale])\n",
        "train_fea_target[cols_to_scale] = scaler_features.transform(train_fea_target[cols_to_scale])\n",
        "\n",
        "# Define the ratios for train, validation, and test sets\n",
        "train_ratio = 0.85\n",
        "val_ratio = 0.05\n",
        "test_ratio = 0.10\n",
        "\n",
        "# Get a list of unique patient IDs in the DataFrame\n",
        "patient_ids = train_fea_target['patient_id'].unique()\n",
        "\n",
        "# Shuffle the list of patient IDs\n",
        "np.random.shuffle(patient_ids)\n",
        "\n",
        "# Calculate the number of patients for each set\n",
        "num_patients = len(patient_ids)\n",
        "num_train_patients = int(num_patients * train_ratio)\n",
        "num_val_patients = int(num_patients * val_ratio)\n",
        "num_test_patients = num_patients - num_train_patients - num_val_patients\n",
        "\n",
        "# Split the patient IDs into train, validation, and test sets\n",
        "train_patients = patient_ids[:num_train_patients]\n",
        "val_patients = patient_ids[num_train_patients:num_train_patients+num_val_patients]\n",
        "test_patients = patient_ids[num_train_patients+num_val_patients:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6d5I8CVmsqt",
        "outputId": "5d430e0e-3086-4e37-8a98-b2a46c75fed4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set: 618 rows\n",
            "Validation set: 31 rows\n",
            "Testing set: 77 rows\n"
          ]
        }
      ],
      "source": [
        "train_df = train_fea_target[train_fea_target['patient_id'].isin(train_patients)].drop(columns= ['visit_id','time', 'patient_id'])\n",
        "val_df = train_fea_target[train_fea_target['patient_id'].isin(val_patients)].drop(columns= ['visit_id','time', 'patient_id'])\n",
        "test_df = train_fea_target[train_fea_target['patient_id'].isin(test_patients)].drop(columns= ['visit_id','time', 'patient_id'])\n",
        "\n",
        "# Print the number of rows in each set\n",
        "print(f'Training set: {len(train_df)} rows')\n",
        "print(f'Validation set: {len(val_df)} rows')\n",
        "print(f'Testing set: {len(test_df)} rows')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.to_csv(\"file1_train.csv\")"
      ],
      "metadata": {
        "id": "wQjnUL1Wx4Ez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_df.to_csv(\"file2_val.csv\")"
      ],
      "metadata": {
        "id": "I9oAORLDx8on"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.to_csv(\"file3_test.csv\")"
      ],
      "metadata": {
        "id": "jSKTBocMyIK_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J08QuQHLnXya"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "X_train = train_df.iloc[:, :-16].astype(float)  # select all columns except the last 16\n",
        "y_train = train_df.iloc[:, -16:].astype(int)   # select only the last 16 columns\n",
        "X_val = val_df.iloc[:, :-16].astype(float)  # select all columns except the last 16\n",
        "y_val = val_df.iloc[:, -16:].astype(int)   # select only the last 16 columns\n",
        "X_test = test_df.iloc[:, :-16].astype(float)   # select all columns except the last 16\n",
        "y_test = test_df.iloc[:, -16:].astype(int)  # select only the last 16 columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CIvjE-GBnUNu"
      },
      "outputs": [],
      "source": [
        "def smape_loss(y_true, y_pred):\n",
        "    y_true = K.cast(y_true,dtype='float32') + 1\n",
        "    y_pred = y_pred + 1\n",
        "    numer = K.abs(y_pred - y_true)\n",
        "    denom = K.abs(y_true) + K.abs(y_pred)\n",
        "    smape = 100 * numer / denom\n",
        "    return smape\n",
        "\n",
        "\n",
        "def smape(y_true, y_pred):\n",
        "    \"\"\"\n",
        "    Calculates the Symmetric Mean Absolute Percentage Error (SMAPE) between y_true and y_pred.\n",
        "    y_true -- array of true values\n",
        "    y_pred -- array of predicted values\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    y_true = y_true +1\n",
        "    y_pred = y_pred +1\n",
        "    numerator = np.abs(y_pred - y_true)\n",
        "    denominator = np.abs(y_pred) + np.abs(y_true)\n",
        "\n",
        "    smape = 100 * np.mean(numerator / denominator)\n",
        "    return smape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZ-k9Z6M90b-"
      },
      "source": [
        "First, tried to create a normal deep learning model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7DdM7DGdngfw",
        "outputId": "13b16a96-e7e0-4478-ba2b-99cbf4b2165e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36.17241287231445\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36.66276931762695\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36.218807220458984\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.916786193847656\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.8466682434082\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Pq6++mlq1ajFp0qQS57Ozs5k+fTrXXXcdQ4cOpU6dOgQFBdGmTRumTp162muePLS0efNmLr30UgICAmjZsiWzZs065TNPP/00TZs2JSgoiIYNG/L8889TWGhtyzBp0iReeuklVq1ahWEYGIZRXO/JQ0tr1qzhiiuuIDAwkKioKO69916ys7OL37/jjju47rrreOutt4iNjSUqKoqRI0cW30tERLybj7sLqFJM0wob5yAIiPQt4nBuAakHCmhQMwjDME5t6B8GWfvg0DZrNZPNXvJ93yBrKOoMfHx8uP3225k0aRLPPvts8b2mT5+Ow+Hg1ltvZfr06Tz99NOEhYXx448/ctttt9GoUSO6dOlyxus7nU5uuOEGateuzdKlS8nIyCgxn+aY0NBQJk2aRFxcHGvWrOGee+4hNDSUp556iptuuom1a9fyyy+/MHv2bADCw8NPuUZOTg59+vQhMTGRpKQk0tLSuPvuu3nwwQdLBLW5c+cSGxvL3Llz2bJlCzfddBPt27fnnnvuOeP3IyIi1ZuCzIkKc+G1uHP+WF3X64L8315rYvBZuOuuu3jzzTeZN28ePXv2BKxhpUGDBlGvXj1GjRpV3Pahhx5i5syZfPHFF2cVZGbPns2GDRuYOXMmcXHW38Vrr712yryW5557rvi4fv36jBo1imnTpvHUU08RGBhISEgIPj4+xMTElHmvKVOmkJeXx7///W+Cg63vffz48QwcOJDXX3+d2rVrA1CjRg3Gjx+P3W6nefPmDBgwgDlz5ijIiIiIhpY8UfPmzenevTuffvopAFu2bGH+/PmMGDECh8PByy+/TJs2bYiMjCQkJISZM2eSkpJyVtdev3498fHxxSEGIDEx8ZR2n3/+OT169CAmJoaQkBCee+65s77Hifdq165dcYgB6NGjB06nk40bNxafa9WqFXb78R6s2NhY0tL0bB4REVGPTEm+QVbPyHlwOE02pWVT5HASEx5ArZBSJrSaJqRvg4Jsa2l2jXol730ORowYwUMPPcQ//vEPPvvsMxo1asRll13G66+/zt///nfGjRtHmzZtCA4O5tFHH6WgoPyeY7N48WKGDRvGSy+9RJ8+fQgPD2fatGm8/fbb5XaPE/n6+pb42jAMnHpasoiIoCBTkmGc9fDOyexA7Shfdh8+SlqeQY3wQHzspXR41WwCBzaAI996vox/6Hndb8iQITzyyCNMmTKFf//739x///0YhsHChQu59tprufXWWwFrzsumTZto2bLlWV23RYsW7Nq1i3379hEbGwvAkiVLSrRZtGgR9erV49lnny0+t3PnzhJt/Pz8cDgcZ7zXpEmTyMnJKe6VWbhwITabjWbNmp1VvSIi4t00tFSOagT5EeBrx+E0SSvrIXm+gRBU0zrO2HPey7FDQkK46aabGD16NPv27eOOO+4AoEmTJsyaNYtFixaxfv16/vKXv7B///6zvm7v3r1p2rQpw4cPZ9WqVcyfP79EYDl2j5SUFKZNm8bWrVt57733+Prrr0u0qV+/Ptu3b2flypUcPHiQ/PxT/z6GDRtGQEAAw4cPZ+3atcydO5eHHnqI2267rXh+jIiIyOkoyJQjwzCIdT0k71B2AXmFZfRIhMaCYYeio5B76LzvN2LECA4fPkyfPn2K57Q899xzdOjQgT59+tCzZ09iYmK47rrrzvqaNpuNr7/+mqNHj9KlSxfuvvtuXn311RJtrrnmGh577DEefPBB2rdvz6JFi3j++edLtBk0aBB9+/bl8ssvp1atWqUuAQ8KCmLmzJmkp6fTuXNnBg8eTK9evRg/fvy5/2WIiIhXMsxSt2+uPjIzMwkPDycjI4OwsLAS7+Xl5bF9+3YaNGhAQEBAud1zx8EcMvMKCQvwpX7NMoaqstMgcw/YfCC6hfWnlJuK+tmKiEjlON3v7xOpR6YCxIQHYGCQmVdIdl4ZD24Lrgl2f3AWQdbZD/2IiIjIcQoyFSDA105kiB8A+zLyKLXTy7BBuOvpMzkHoKiMLQ5ERESkTAoyFaR2qD92m8HRQgeHc8volQkIs574i2lN/BUREZFzoiBTQXzsNqJDrWfJ7M/Mw+EsYypSWB3AgPxMyMusvAJFRESqAQUZKH3opxxEhfjj52Oj0OHkYHZZy7EDrPkyYE3+rd5zrytNNZ/DLiIiLl4dZI49MTY399w2ijxbNsMgNsxaMXMgK5/CojKeRhsa41qOnQc5ByukFm9z7Gd68lOBRUSkevHqNb92u52IiIjifXuCgsrYufoC+BkmAYaDo4UOdh90EhsRWHpD/5qQnQrpe8EIBLtX/2jOm2ma5ObmkpaWRkRERIk9mkREpPrx+t+Wx3ZnrshNCAuKnKRl5XMAyAy1hptOYZqQnQGOAjiYA4E1KqwebxAREXHanbdFRKR68PogYxgGsbGxREdHU1hYxuqicjDjx3X8tiGNdnXDeXtI+9J7fnYdhG+fAOwwdApENaqweqozX19f9cSIiHgJrw8yx9jt9gr95XdfrxZ8sWI/P61P57ptGVzVqpTegiaXQt12sOEH+O05uPUrayNLERERKZVXT/atTHUiArn7kgYAjPl5AwVlTfy96mWw+8HW32DTzEqsUERExPMoyFSi+3s2pmaIH9sP5jB56c7SG0U2hG4PWMcz/w+KCiqvQBEREQ+jIFOJQvx9ePzKZgCMm72ZI7llhJRLR0FwNKRvhWUfVmKFIiIinkVBppIN6VSXZrVDyThayPu/bSm9kX8o9HrBOp73BmQfqLwCRUREPIhbg8yLL76IYRglXs2bNy9+Py8vj5EjRxIVFUVISAiDBg1i/37P3inax27j2QEtAPj34h1sP5hTesP2wyC2vbV1wW8vV16BIiIiHsTtPTKtWrVi3759xa8FCxYUv/fYY4/x/fffM336dObNm8fevXu54YYb3Fht+bi0aS16NqtFocNk7M/rS29ks0Hfsdbx8n/DvtWVV6CIiIiHcHuQ8fHxISYmpvhVs6a171BGRgaffPIJ77zzDldccQUdO3bks88+Y9GiRSxZssTNVV+4Z/u3wG4zmPnnfpZsO1R6o3qJ0OoGwIRfRmsfJhERkZO4Pchs3ryZuLg4GjZsyLBhw0hJSQEgOTmZwsJCevfuXdy2efPmJCQksHjxYneVW26a1A5laJd4AF79cT3OsnbHvvJv4BMAOxfAum8rsUIREZGqz61BpmvXrkyaNIlffvmFCRMmsH37di655BKysrJITU3Fz8+PiIiIEp+pXbs2qampZV4zPz+fzMzMEq+q6tHeTQnx92HNngy+Wbmn9EYR8dDjEet41vNQmFd5BYqIiFRxbg0y/fr148Ybb6Rt27b06dOHn376iSNHjvDFF1+c9zXHjBlDeHh48Ss+Pr4cKy5fNUP8GXl5YwDe+GUjRwscpTfs8QiExsGRFFg8vhIrFBERqdrcPrR0ooiICJo2bcqWLVuIiYmhoKCAI0eOlGizf//+024GOHr0aDIyMopfu3btquCqL8ydPepTJyKQ1Mw8Ppq/rfRGfsHWEBPA/Hcgc1/lFSgiIlKFVakgk52dzdatW4mNjaVjx474+voyZ86c4vc3btxISkoKiYmJZV7D39+fsLCwEq+qLMDXzjP9rCXnE37fyv7MMoaO2gyGul2gMAfmvFSJFYqIiFRdbg0yo0aNYt68eezYsYNFixZx/fXXY7fbGTp0KOHh4YwYMYLHH3+cuXPnkpyczJ133kliYiLdunVzZ9nl7uq2sVyUEMHRQgdv/7qx9EaGAf1cy7FXTYXdyZVXoIiISBXl1iCze/duhg4dSrNmzRgyZAhRUVEsWbKEWrVqAfDuu+9y9dVXM2jQIC699FJiYmL46quv3FlyhTAMg+cGtARgevJu1u0tY4JynY7Qbqh1/MvTWo4tIiJezzDN6v3bMDMzk/DwcDIyMqr8MNODU5bzw+p9dG8UxeS7u2IYxqmNMvfB+x2tIaYbPoK2Qyq/UBERkQp2tr+/q9QcGW/3dN/m+PnYWLT1EL9tSCu9UVgsXPK4dTzrr1BQxhYHIiIiXkBBpgqJjwzirh4NAHj1p/UUOpylN0x8ECISIGsvLPx7JVYoIiJStSjIVDEPXN6IqGA/th3IYcrSlNIb+QbAVa9Yxwv/bj1fRkRExAspyFQxYQG+PHZlUwDGzd5ExtHC0hu2uAbqXQxFedYQk4iIiBdSkKmCbu4cT5PoEA7nFvKPuVtKb2QY0HcMYMCfX8HORZVao4iISFWgIFMF+dht/N+AFgBMWriDnYfKmNAb2xY6DreOf3kGnGXMqREREammFGSqqJ5Na3FJk5oUOJy8/suGshte/hz4h8G+VbBycuUVKCIiUgUoyFRRhmHw7IAW2Az4aU0qSTvSS28YUgsue8o6nvM3yKu6u32LiIiUNwWZKqx5TBg3dU4A4JUf1uF0lvHswi5/gchGkJMG89+qxApFRETcS0Gminv8yqYE+9lZtTuD71fvLb2Rjx/0ec06XjIB0svYRVtERKSaUZCp4mqF+vPA5Y0BeP3nDeQVOkpv2LQPNLoCHAXw6/OVWKGIiIj7KMh4gBEXNyAuPIC9GXl8smB76Y0MA/qMAcMOG36Abb9Xao0iIiLuoCDjAQJ87TzdrzkAH8zdQlpWXukNo5tD57ut419Gg6OokioUERFxDwUZDzGwbRzt4iPIKXDw7qxNZTfs+QwE1oC0dbB8UqXVJyIi4g4KMh7CZjN43vWQvM+TdrEhtYxl1kGR0PP/rOPfXoWjhyupQhERkcqnIONBOtWPpH+bGJwmvPrjekyzjOXYne6CWi3gaDr8/nrlFikiIlKJFGQ8zNN9m+NntzF/80F+33Sg9EZ2H+jrWo6d9BEcOM1QlIiIiAdTkPEw9aKCuaNHfcDqlSlylLG/UqMroGk/cBbBzP+rvAJFREQqkYKMBxp5eWNqBPmyJS2bqUm7ym7Y51Ww+cKWWbDp18orUEREpJIoyHig8EBfHruyKQDvztpEZl5h6Q2jGkG3+6zjmf8HjjLaiYiIeCgFGQ81tEsCjWoFk55TwAdzt5bd8NInIagmHNoMyz6qvAJFREQqgYKMh/K12/i//tZy7E8XbGdXem7pDQPCoZdry4Lfx0LOwUqqUEREpOIpyHiwK5pH06NxFAUOJ6//sqHshhfdBjFtID8D5r5aeQWKiIhUMAUZD2YYBs/2b4lhwA+r95G8s4yH39ns0Nf1PJnkSZC6ttJqFBERqUgKMh6uZVwYQzrGA/DyD+vKfkhe/R7Q8lownfDLM1BWOxEREQ+iIFMNPHFVU4L87KzcdYTvV+8ru+GVL4PdH3bMt3bIFhER8XAKMtVAdFgA91/WCIDXf95AXqGj9IY16kH3h6zjX5+DovxKqlBERKRiKMhUE3df0pCYsAD2HDnKZwt3lN3w4scgJAYO74AlH1RWeSIiIhVCQaaaCPSz81TfZgD8Y+4WDmaX0dviHwJXvmQd/+8tyEqtpApFRETKn4JMNXJd+zq0qRNOdn4R7846zUaRbYZAnY5QkA1zXq68AkVERMqZgkw1YrMZPDfAekje1GUpbNqfVVbD48uxV06GPcsrqUIREZHypSBTzXRtGEXfVjE4TWt37DLFd7Z6ZjDhl9Faji0iIh5JQaYaeqZfc3ztBvM2HWDepgNlN+z9IvgGwa4lsO7bSqtPRESkvCjIVEP1awZze2J9AF79cR1FDmfpDcPrQOJI61grmERExAMpyFRTD1/RhIggXzbtz+aLP3aX3bDz3WDzgV1LYd+qyitQRESkHCjIVFPhQb480qsJAO/M2khWXmHpDUNjoMU11vGyjyqpOhERkfKhIFON3dqtHg1rBnMwu4AJv28tu2GXe6w/18yA3PTKKU5ERKQcKMhUY752G6P7W8uxP16wnd2Hc0tvmJAItVtD0VFrObaIiIiHUJCp5nq3iKZbw0gKipy8OXNj6Y0Mw5orA5D0CTjLmBwsIiJSxSjIVHOGYfDcgJYYBny7ci8rUg6X3rDtEPAPh8PbYeucyi1SRETkPCnIeIHWdcIZ1KEuAK/8uB6ztIff+QXDRcOs42UTK7E6ERGR86cg4yVGXdWMQF87yTsP89OaMjaKPDa8tHkWpG+rvOJERETOk4KMl4gJD+AvlzUEYOwv68kvcpzaKKoRNOoFmNZcGRERkSpOQcaL3HtpQ2qH+bMr/Sj/WrSj9EbHlmKv+C8UlLHKSUREpIpQkPEiQX4+jLqqGQDvz9nCoez8Uxs1uQoiEiDvCKz9snILFBEROUcKMl5mUIe6tIoLIyu/iL/P2XxqA5sdOo2wjpdN1K7YIiJSpSnIeBmbzeDZAdZD8iYvTWFLWtapjTrcDj4BkLoadidVcoUiIiJnT0HGC3VvVJMrW9bG4TR57acNpzYIioTWg6xjLcUWEZEqTEHGS43u1xwfm8FvG9JYsPngqQ2OLcX+8xvITqvU2kRERM6WgoyXalgrhNsS6wHwyo/rcDhPmgtTpwPU6QTOQlj+LzdUKCIicmYKMl7skV5NCAvwYUNqFlOWpZza4NhS7D8+A0dR5RYnIiJyFhRkvFhEkB+PX9kUgLE/rWdX+knPjWl5HQTVhMw9sPGnyi9QRETkDBRkvNztifXpXL8GOQUOnvlqdcl9mHwDrBVMoEm/IiJSJSnIeDmbzeDNwe0I8LWxcMshJi89aYip011g2GDHfEgrZYWTiIiIGynICPVrBvNUn+YAjDl5iCkiHpr1t46TPnZDdSIiImVTkBEA7uheny71I8kpcPD0l6txnriK6dhS7FVTIS/TPQWKiIiUQkFGAGuI6Y3BbQnwtbFo6yEmn7iKqWFPiGoCBdmw+nO31SgiInIyBRkpVr9mME/3LWWIyTCOL8Ve9pH2XxIRkSpDQUZKGJ5Yny4NIsktcPDUjBOGmNoNBb8QOLgRtv/PvUWKiIi4KMhICdYqprYE+tpZvO0Qk5futN4ICIO2N1nHSR+5r0AREZETKMjIKepFBfN032YAjPl5w/EhpmPDSxt+hIzdbqpORETkuCoTZMaOHYthGDz66KPF53r27IlhGCVe9913n/uK9CK3J9anq2uI6ckZq6whpugWUP8SMJ3WtgUiIiJuViWCTFJSEh9++CFt27Y95b177rmHffv2Fb/eeOMNN1TofY49KC/Q186Sben899gQ07Gl2MmToCjfbfWJiIhAFQgy2dnZDBs2jI8++ogaNWqc8n5QUBAxMTHFr7CwMDdU6Z0SooJ4pt+xVUwbSDmUC80HQGgc5B6Edd+6uUIREfF2bg8yI0eOZMCAAfTu3bvU9ydPnkzNmjVp3bo1o0ePJjc3t9R2x+Tn55OZmVniJefvtm716NYwkqOFriEmwwc63Wm9uUyTfkVExL3cGmSmTZvG8uXLGTNmTKnv33LLLfz3v/9l7ty5jB49mv/85z/ceuutp73mmDFjCA8PL37Fx8dXROlew2YzeGNQO4L87Czdns5/luyEDsPB5gu7l8Hele4uUUREvJhhmu55utmuXbvo1KkTs2bNKp4b07NnT9q3b8+4ceNK/cxvv/1Gr1692LJlC40aNSq1TX5+Pvn5x+duZGZmEh8fT0ZGhoalLsC/F+/ghW//JNDXzi+PXkK9uQ/D2hlw0a1w7T/cXZ6IiFQzmZmZhIeHn/H3t9t6ZJKTk0lLS6NDhw74+Pjg4+PDvHnzeO+99/Dx8cHhcJzyma5duwKwZcuWMq/r7+9PWFhYiZdcuFu7njjEtBpnJ9ek3zUzIDfdvcWJiIjXcluQ6dWrF2vWrGHlypXFr06dOjFs2DBWrlyJ3W4/5TMrV64EIDY2tpKrlWOrmIL87Czbns6/d9eGmDZQlAcr/uvu8kRExEu5LciEhobSunXrEq/g4GCioqJo3bo1W7du5eWXXyY5OZkdO3bw3Xffcfvtt3PppZeWukxbKl58ZBCjXauYXp+5iUMtb7fe+OMTcJ7agyYiIlLR3L5qqSx+fn7Mnj2bq666iubNm/PEE08waNAgvv/+e3eX5tWGda1HYsMojhY6eGRdE8yAcDi8A7bMdndpIiLihdw22beynO1kITl7u9Jz6TPuf+QWOPih6c+0TvkPNL4Sbp3h7tJERKSaqPKTfcVzxUcGMbp/CwAe397ROrllFhza6saqRETEGynIyHkZ1iWB7o2i2FQYzXK/ztbJPz51b1EiIuJ1FGTkvNhsBq8PakuQn533s3taJ1f8BwpO/+RlERGR8qQgI+ft2BDTPGc7UsxoyMuANdPdXZaIiHgRBRm5IMO6JNCtUS3+U2TtlWUmfQTVe/64iIhUIQoyckGODTH9aO9FnumLkboGdi11d1kiIuIlFGTkgsVHBvFA/8586+gBQPb8CW6uSEREvIWCjJSLYV0TWB03GAD/zT/gyEx1c0UiIuINFGSkXBiGwf1DB7HCbIovRaz4Zpy7SxIRES+gICPlpm6NIHLb3Wkdb/2cbamH3VyRiIhUdwoyUq66D7yLDFsEMUY6X06diMOpFUwiIlJxFGSkXBm+ARgdhwNwcfo3fLZwu5srEhGR6kxBRspd2MX34sRGon0dX8+czbYD2e4uSUREqikFGSl/4XUxWgwA4GZm8uSM1RpiEhGRCqEgIxXC6HwPAIPs89m0c4+GmEREpEIoyEjFaHAp1GxGkJHPDfb5vDlzI1s1xCQiIuVMQUYqhmFAF6tX5t7A38gvcvDk9FUaYhIRkXKlICMVp+1N4BdCnaJd9PLfwPKUI3y6QENMIiJSfhRkpOIEhEG7oQC8FLMQgLd+1RCTiIiUHwUZqVid7wagTtrvXN/QJL/IqSEmEREpNwoyUrGim0ODSzFMJ3+rs5RQfx+WpxzhkwXb3F2ZiIhUAwoyUvFcS7FD/5zCC/0aAfDWr5vYkqYhJhERuTAKMlLxmvWHsDqQe5DBgX9wWdNaFBQ5eXKGhphEROTCKMhIxbP7QCdrV2xj2UeMHdSGUH8fVqQc4eP5GmISEZHzpyAjlaPDcLD5wp4/iM1ez/NXtwTg7Vmb2JKW5ebiRETEUynISOUIiYZW11vHSR9zY6e69GxmDTGNmq69mERE5PycU5B54IEHyM4+PkFz6tSp5OTkFH995MgR+vfvX37VSfXietIva7/EOHqYMTe0ITTAh5W7jvCRhphEROQ8nFOQ+fDDD8nNzS3++i9/+Qv79+8v/jo/P5+ZM2eWX3VSvdTtDLHtoCgPVvyH2PDA4iGmdzTEJCIi5+Gcgoxpmqf9WuS0DKN4KTZJH4PTwY0d63K5a4jpiemrKXI43VujiIh4FM2RkcrVehAERMCRFNg8C8MwGHNDW0IDfFi16wgfzddeTCIicvYUZKRy+QVBh9us42UTAYgJD+AF1xDTu7M2sXm/hphEROTs+JzrB1544QWCgoIAKCgo4NVXXyU8PBygxPwZkTJ1GgGLxsPWOXBoK0Q1YnDHuvy8NpXfNqQxavoqvry/Oz525WwRETk9wzyHiS49e/bEMIwztps7d+4FFVWeMjMzCQ8PJyMjg7CwMHeXI8dMHgKbZ0K3kdD3NQBSM/K48t15ZOUV8VTfZjzQs7GbixQREXc529/f5xRkPJGCTBW1eRZMHgz+4fDEevALBmBG8m5GTV+Fn93GDw9fTNPaoW4uVERE3OFsf3+fc999ZmYms2bN4scff+TAgQMXVKR4sUa9oEYDyM+ANdOLTw/qUIcrmkdT4HAyavoqrWISEZHTOqcgs3LlSpo3b06fPn0YOHAgjRs31nNj5PzYbND5but42Ufg6hi0VjG1ISzAh9W7M/jwf3pQnoiIlO2cgszTTz9NgwYNWLhwIcnJyfTq1YsHH3ywomqT6u6iYeATCPvXQsqS4tO1wwL468BWAPx99mY2pmoVk4iIlO6cgkxycjLvv/8+iYmJXHTRRXz66ads3bqVzMzMiqpPqrPAGtD2Rus46aMSb93QoQ69XENMT87QEJOIiJTunIJMeno6devWLf46IiKC4OBgDh06VO6FiZc49qTfdd9CVmrxacMweE1DTCIicgbnPNl33bp1rF69uvhlmibr168vcU7krMW2hfhu4CyC5H+VeKt2WAAvXmMNMY2bvUlDTCIicopzWn5ts9kwDKPUPZaOnTcMA4fDUa5FXggtv/YAa2bAlyMgJAYeWwt23+K3TNPknn//wez1abSpE85XD3THVw/KExGp9s729/c5Pdl3+3btgyMVoMU1EBwN2amw4QdodX3xW4Zh8Nr1bUja8T/W7Mngw3lbefCKJm4sVkREqpJz+k/bevXqnfGVlaXufzlHPn7Q8Q7reNlHp7wdHRbAi9dYezH9fc5mNqRqcrmIiFjKpY8+KyuLiRMn0qVLF9q1a1celxRv0+lOMOywcyHs//OUt69rX4feLWpT6DAZNX0VhVrFJCIiXGCQ+d///sfw4cOJjY3lrbfe4oorrmDJkiVn/qDIycLioMXV1nHSx6e8bQ0xtSY80Je1ezL55+9bK7lAERGpis45yKSmpjJ27FiaNGnCjTfeSFhYGPn5+XzzzTeMHTuWzp07V0Sd4g2OLcVe9TnkZZzydnRYAC+5VjG999tm1u/TEJOIiLc7pyAzcOBAmjVrxurVqxk3bhx79+7l/fffr6jaxNvUvxhqtYDCHFg5tdQm17aP48qWGmISERHLOQWZn3/+mREjRvDSSy8xYMAA7HZ7RdUl3sgwoItr/6Wkj8B5akgxDINXXUNMf+7NZIKGmEREvNo5BZkFCxaQlZVFx44d6dq1K+PHj+fgwYMVVZt4o7Y3gV8oHNoC238vtUl06PEhpvc1xCQi4tXOKch069aNjz76iH379vGXv/yFadOmERcXh9PpZNasWVp6LRfOPxTa32IdLzt10u8xGmISERE4z1VLwcHB3HXXXSxYsIA1a9bwxBNPMHbsWKKjo7nmmmvKu0bxNp1dw0ubfoYjKaU2OTbEFBGkISYREW92wc+RadasGW+88Qa7d+9m2rRpGIZRHnWJN6vVFBpcBqYT/vi0zGYaYhIRkXPaouCuu+46Y5uoqKjzLkakWJd7Yfs8ayPJy54B34BSm13TLo4fV+/j13X7GTV9Fd+M7KG9mEREvMg5/Ys/adIk5s6dy5EjRzh8+HCpryNHjlRQqeJVmvaFsLpwNB3+/LrMZoZh8MoJQ0yv/LCu1E1NRUSkejqnHpn777+fqVOnsn37du68805uvfVWIiMjK6o28WZ2H+h8F8z5m7UUu/3QMptGhwYw5vo23D95Of9avJMgfx+e6tNMw5wiIl7gnHpk/vGPf7Bv3z6eeuopvv/+e+Lj4xkyZAgzZ87UfwVL+eswHOx+sCfZep1GvzaxvHxdawAm/L6V9+ZsqYwKRUTEzc55MoG/vz9Dhw5l1qxZrFu3jlatWvHAAw9Qv359srOzK6JG8VbBNaHVDdbxaZZiH3Nbt3o8N6AFAO/O3sSH87SSSUSkurugWZE2mw3DMDBNE4fDUV41iRzXxbX/0tovIefQGZvffUlDnuzTDIAxP29g0sLtFVmdiIi42TkHmfz8fKZOncqVV15J06ZNWbNmDePHjyclJYWQkJCKqFG8WZ2OENseHPmw4t9n9ZGRlzfm4SsaA/Di9+uYuqz0Z9GIiIjnO6cg88ADDxAbG8vYsWO5+uqr2bVrF9OnT6d///7YbFryKhXAMKyl2ABJn4Lz7Hr+HruyKfde2hCA//t6DV+v2F1RFYqIiBudU/r45z//SVhYGA0bNmTevHnce++93HDDDae8zsfYsWMxDINHH320+FxeXh4jR44kKiqKkJAQBg0axP79+8/r+uLBWt8AgTUgIwU2zTyrjxiGweh+zRmeWA/ThCe+WMWPq/dVcKEiIlLZzinI3H777Vx++eVEREQQHh5e5utcJSUl8eGHH9K2bdsS5x977DG+//57pk+fzrx589i7d+95ByXxYL6B0OF26zjpo7P+mGEY/HVgK27uHI/ThEemrWDWOgVhEZHqxDDdvG46OzubDh068MEHH/DKK6/Qvn17xo0bR0ZGBrVq1WLKlCkMHjwYgA0bNtCiRQsWL15Mt27dzur6mZmZhIeHk5GRQVhYWEV+K1KRDu+Av7cHTHgwGWo2PuuPOpwmT3yxkm9W7sXPbuOj4Z24rGmtiqpURETKwdn+/nb7xJaRI0cyYMAAevfuXeJ8cnIyhYWFJc43b96chIQEFi9eXOb18vPzyczMLPGSaqBGfWjaxzpOOvNS7BPZbQZv3diO/m1iKHA4uffff7Bo68Hyr1FERCqdW4PMtGnTWL58OWPGjDnlvdTUVPz8/IiIiChxvnbt2qSmppZ5zTFjxpQY5oqPjy/vssVdji3FXjkF8s/tmUU+dhvjbrqI3i2iyS9ycve//uCPHekVUKSIiFQmtwWZXbt28cgjjzB58mQCAkrfEPB8jB49moyMjOLXrl27yu3a4mYNr4DIhpCfAWu+OOeP+/nYGH9LBy5pUpPcAgd3fpbE6t1Hyr9OERGpNG4LMsnJyaSlpdGhQwd8fHzw8fFh3rx5vPfee/j4+FC7dm0KCgpO2YRy//79xMTElHldf39/wsLCSrykmrDZoLOrV2bZx3Ae07sCfO1MvK0TXRtEkpVfxG2fLGPdXg0/ioh4KrcFmV69erFmzRpWrlxZ/OrUqRPDhg0rPvb19WXOnDnFn9m4cSMpKSkkJia6q2xxt/a3gG8QpP0JOxed1yUC/ex8ckdnOiREkHG0kNs+Wcrm/VnlXKiIiFQGtwWZ0NBQWrduXeIVHBxMVFQUrVu3Jjw8nBEjRvD4448zd+5ckpOTufPOO0lMTDzrFUtSDQVGQNsh1vE5LMU+WYi/D5/d2YXWdcI4lFPAsI+XsuNgTvnUKCIilcbtq5ZO59133+Xqq69m0KBBXHrppcTExPDVV1+5uyxxt2PDS+u/h8zzf8hdeKAv/7mrK81jQknLyueWj5awKz23nIoUEZHK4PbnyFQ0PUemmvq0L6QshsuegctHX9ClDmTlc/PExWw9kENCZBBf/CWRmPDym4AuIiLnzmOeIyNyXo4txU7+DIoKLuhStUL9mXx3NxIig0hJz+WWj5dwICu/HIoUEZGKpiAjnqn5QAipDdn7YcP3F3y5mPAAptzTlToRgWw7kMOtHy8lPefCApKIiFQ8BRnxTD5+0PFO63jZuT3ptyx1awQx+e6uRIf6s3F/Frd9spSMo4Xlcm0REakYCjLiuTreATYfSFkEqWvL5ZL1awYz5Z6uRAX78efeTO74bBnZ+UXlcm0RESl/CjLiucJiofnV1vEFLMU+WePoUP57d1cignxZkXKEuz5LIrdAYUZEpCpSkBHP1uVe68/VX8DRI+V22RaxYfznrq6E+vuwbEc69/47mbxCR7ldX0REyoeCjHi2et0huiUU5lqbSZajNnXDmXRXZ4L87CzYcpAHJi+noMhZrvcQEZELoyAjns0wji/FTvoYnOUbNDrWi+ST4Z3x97Hx24Y0Hp66giKHwoyISFWhICOer80Q8A+D9K2w9bdyv3xioyg+ur0TfnYbv/yZyuNfrMLhrNbPkRQR8RgKMuL5/EOg/TDr+MfHITe93G9xadNafDCsAz42g+9W7eWZL1fjVJgREXE7BRmpHi57CiLqwZGd8OXd4Cz/ibm9W9bmvaEXYTNgevJuXvhuLdV8hw8RkSpPQUaqh6BIuHkK+AbB1jnw2ysVcpv+bWJ5Z0h7DAP+uySFV39crzAjIuJGCjJSfcS0hmvet44XvAN/flMht7nuojqMvaENAB8v2M7bv26qkPuIiMiZKchI9dJmMCQ+aB1/8wCkra+Q29zUOYG/XdsKgPFztzD+t80Vch8RETk9BRmpfnq/BA0uhcIcmHZLuT4o70S3J9bn//o3B+CtXzfx8fxtFXIfEREpm4KMVD92Hxg8CcITIH0bfHVvuT9f5ph7L23E41c2BeCVH9fzn8U7KuQ+IiJSOgUZqZ6Co+Cm/4BPAGyeCfPGVtitHrqiMQ/0bATA89/+yRdJuyrsXiIiUpKCjFRfce1h4N+t43mvw4YfK+Q2hmHwZJ9m3NWjAQBPf7Wab1fuqZB7iYhISQoyUr21uxm63mcdf/UXOFAxK4wMw+D5q1swrGsCpgmPf7GKn9fsq5B7iYjIcQoyUv1d9QrU6wEFWdbk37zMCrmNYRi8fG1rBnesi8Np8tDUFcxZv79C7iUiIhYFGan+7L5w4yQIqwOHNsM391fY5F+bzeD1QW0Z2C6OIqfJ/f9dzvzNByrkXiIioiAj3iIk2pr8a/eHDT/A/Lcr7FZ2m8E7Q9rRp1VtChxO7vn3HyzZdqjC7ici4s0UZMR71OkIA1wBZu6rsGlmhd3K127jvaEX0bNZLfIKnYyYlETyzsMVdj8REW+lICPepcNt0GkEYMKX98ChrRV2K38fO/+8tSPdG0WRU+Dgjs+WsXZPRoXdT0TEGynIiPfpOxbiu0J+hjX5Nz+7wm4V4Gvn4+Gd6Fy/Bll5Rdz6yVI2pFbMZGMREW+kICPex8cPhvwbQmLgwAb49gGowB2sg/x8+PSOzrSLj+BIbiG3fryULWkVF55ERLyJgox4p9AYa/KvzRfWfQsLx1Xs7QJ8+fedXWgZG8bB7AKGfbyEnYdyKvSeIiLeQEFGvFd8F+j/hnU8+yXYMrtCbxce5Mt/RnShSXQI+zPzueWjpew5crRC7ykiUt0pyIh363gndLgdMGHGCEjfXqG3iwrxZ/I9XWlQM5g9R45yy0dL2J+ZV6H3FBGpzhRkxLsZBvR/C+p0grwj8PmtUFCxQz7RoQFMvrsrdWsEsvNQLrd8tISD2fkVek8RkepKQUbEx9+aLxMcDfvXwncPV+jkX4C4iECm3tON2PAAth7I4daPl3Ikt6BC7ykiUh0pyIgAhMXBkH+BzQfWzoDF/6jwW8ZHBjH57q7UCvVnQ2oWt32yjMy8wgq/r4hIdaIgI3JMve7QZ4x1POt52Davwm/ZsFYIk+/uSmSwH2v2ZHDHp8vIzi+q8PuKiFQXCjIiJ+pyD7S7BUwnTL8DjqRU+C2b1g7lPyO6EBbgw/KUI4yYlMTRAkeF31dEpDpQkBE5kWHA1e9AbHs4mg7ThkFhxS+RbhUXzr9HdCXE34el29MZOH4Bi7YcrPD7ioh4OgUZkZP5BsJN/4WgKEhdDT88VuGTfwHax0cw6c7ORAX7sSUtm1s+XspDU1eQmqHl2SIiZVGQESlNRDzcOAkMO6yaCssmVsptO9WP5LcnenJ7Yj1sBny/ai+93v6dj/63jUKHs1JqEBHxJAoyImVpcClc9bJ1/Mto2LGgUm4bHuTL365tzXcPXsxFCRHkFDh49af19P/7fBZt1XCTiMiJFGRETqfbA9DmRjAd8MVwyNhdabduXSecL+/rzhuD2hIZ7MfmtGxu+WgpD09doacBi4i4KMiInI5hwMD3oHYbyD0In98GhZUXImw2gyGd4/nticu4rVs9DAO+W7WXK976nY/na7hJRERBRuRM/ILg5v9CYA3Yuxx+eqJSJv+eKCLIj5eva813Iy+mfbw13PTKj+sZ8N58lmw7VKm1iIhUJQoyImejRn0Y/CkYNljxX/jjU7eU0aZuOF/d353XB7WhRpAvm/Znc/PEJTw6bQVpGm4SES+kICNythpdAb3+ah3//DSkLHFLGTabwU2dE5g7qie3dkvAMOCblXu54u15fLJgO0UabhIRL6IgI3IuejwCLa8DZyF8cTtk7nNbKRFBfrxyXRu+HdmDdvERZOcX8fIP6xjw3gKWarhJRLyEgozIuTAMuPYfEN0SsvdbYaYo360lta0bwdf3d2fsDdZw08b9Wdw0cQmPfb6StCwNN4lI9aYgI3Ku/EOsJ/8GhMPuZdYwk5vZbAY3d0ngtyd6cktXa7jp6xV76PXWPD7VcJOIVGMKMiLnI6oRDPoEMCD5M0j+l7srAqBGsB+vXe8abqobTlZ+EX/7YR1Xv7+AZdvT3V2eiEi5U5AROV9NroQrnrWOfxoFu/9wbz0naFs3gq8f6MGYG9oQEeTLhtQshny4mMc13CQi1YyCjMiFuPgJaH41OAqsh+Vl7Xd3RcVsNoOhXRKY+0RPhnaxhpu+cg03fbZQw00iUj0oyIhcCJsNrv8n1GwGWXth+nAoKnB3VSXUCPZjzA1t+PqBHrR1DTe99L013JS0Q8NNIuLZFGRELpR/KNw8GfzDIGUx/PqsuysqVft4a7jpteuPDzfd+M/FPPHFKg5kuXfllYjI+VKQESkPNZvA9R9ax8smwsop7q2nDHabwS1drdVNQ7vEYxjw5fLdXPH270zScJOIeCAFGZHy0rw/XPaMdfz9o7B3hVvLOZ3IYD/G3NCWr+7vTps64WTlFfHi9+sYOH4hf2i4SUQ8iIKMSHm67Glo2hcc+TDtVsg56O6KTuuihBp8M7IHr1zXmvBAX9bvy2TwPxczavoqDmZruElEqj4FGZHyZLPBDRMhqjFk7obpd4CjyN1VnZbdZnBrt3rMHdWTmzvHAzAjeTeXv/U7/1q0Q8NNIlKlKciIlLeAcLhpMviFwI75MOsFd1d0ViKD/Rg7qC1fPdCd1nXCyMor4q/f/ck14xeSvFPDTSJSNSnIiFSE6OZw3QTreMk/YPV099ZzDjok1ODbkRfz8nWtCQvwYd2+TAZNWMyTGm4SkSpIQUakorS8Bi55wjr+7iHYt9q99ZwDu83gNtdw002drOGm6cm7ueKt3/nP4h04nKabKxQRsRimaVbrf5EyMzMJDw8nIyODsLAwd5cj3sbpgClDYMtsiEiAe+dBUKS7qzpnyTsP88K3a/lzbyYAreLC+Nu1relYr4abKxOR6upsf3+rR0akItnsMOhjqFEfjqTAjDur/OTf0nSsV4PvHryYl69tRViAD3/uzWTQhEU8NWMVhzTcJCJu5NYgM2HCBNq2bUtYWBhhYWEkJiby888/F7/fs2dPDMMo8brvvvvcWLHIeQisATdPAd8g2PY7/PY3d1d0Xuw2g9sS6/PbqJ7c2LEuAF/8Ya1u+s+SnRpuEhG3cOvQ0vfff4/dbqdJkyaYpsm//vUv3nzzTVasWEGrVq3o2bMnTZs25W9/O/4Pf1BQ0DkNEWloSaqMtV/CjLus48GfQesb3FvPBUreeZjnv1nLun3WcFPrOmG8fG1rLkrQcJOIXLiz/f1d5ebIREZG8uabbzJixAh69uxJ+/btGTdu3HlfT0FGqpRfn4dF71m9M3fPhtqt3F3RBXE4TSYv3cmbMzeSlWcNmd3UKZ6n+jYjKsTfzdWJiCfzuDkyDoeDadOmkZOTQ2JiYvH5yZMnU7NmTVq3bs3o0aPJzc11Y5UiF6jXX6FhTyjMhWnD4Ohhd1d0Qew2g9sT6zN3VE8Gu4abPv9jF1e8PY//arhJRCqB23tk1qxZQ2JiInl5eYSEhDBlyhT69+8PwMSJE6lXrx5xcXGsXr2ap59+mi5duvDVV1+Veb38/Hzy849PPszMzCQ+Pl49MlJ15KbDxMusyb+Nr4RbPrcmBVcDf+xI5/lv/2S9a7gpITKImzrHc2OnukSHBri5OhHxJB4ztFRQUEBKSgoZGRnMmDGDjz/+mHnz5tGyZctT2v7222/06tWLLVu20KhRo1Kv9+KLL/LSSy+dcl5BRqqUfavgk6ugKA8uGQW9nnd3ReWmyOFk8tIU3v51I5mu4SYfm0HvFrW5uUs8lzSphd1muLlKEanqPCbInKx37940atSIDz/88JT3cnJyCAkJ4ZdffqFPnz6lfl49MuIxVn0OX99rHd/0X2gx0L31lLOjBQ5+XLOPqctSSN55fAitTkQgN3WOZ0ineGLC1UsjIqU72yDjU4k1nRWn01kiiJxo5cqVAMTGxpb5eX9/f/z9NclQPEC7m2DfSljyAXx9H9RsCrWaubuqchPoZ2dwx7oM7liXTfuzmLosha+W72HPkaO8M2sT42Zv4ormtRnaJZ6ezaLVSyMi58WtPTKjR4+mX79+JCQkkJWVxZQpU3j99deZOXMmDRs2LJ4vExUVxerVq3nssceoW7cu8+bNO+t7aNWSVGmOQvjP9dbmklGN4Z7frE0nq6m8Qge/rE1lyrIUlm0/vhFlbHgAQzrFM6RzPHUiAt1YoYhUFR4xtDRixAjmzJnDvn37CA8Pp23btjz99NNceeWV7Nq1i1tvvZW1a9eSk5NDfHw8119/Pc8995yeIyPVS/YBmNgTMndD037Ww/NsVWZBYYXZkpbN50kpzEjezeHcQgAMA3o2rcXNXRK4onk0vvbq//cgIqXziCBTGRRkxCPsWQ6f9gVHPvQcDT2fcXdFlSa/yMGvf+5n6rIUFm09VHw+OtSfGzvV5ebOCcRHBrmxQhFxBwUZFwUZ8RgrJsO3D1jH/d+CjneCvcpNY6tQOw7mMC1pFzOSd3Ewu6D4/CVNajK0SwK9W9TGz0e9NCLeQEHGRUFGPMqPoyDpI+s4shFc/n/Q6gavGGo6UUGRkznr9zNlWQrzNx8sPl8zxI9BHa1emgY1g91YoYhUNAUZFwUZ8SiOIlj2Icx/G3JdwyzRLeHyZ6H5AGsSiZfZlZ7L50m7+OKPXaRlHV/RmNgwiqFdE+jTqjb+PtXjgYIicpyCjIuCjHik/CxY8k9Y9D7kZ1jn4jrAFc9Boyu8MtAUOZz8tiGNqctS+H3TAY79y1UjyJdBHepyc5cEGkeHuLdIESk3CjIuCjLi0Y4etsLMkn9CYY51rl4PuOJ5qJd4+s9WY3uOHOULVy/Nvoy84vNd6kcytGs8/VrHEuCrXhoRT6Yg46IgI9VC9gFY8A4kfWKtbAJo1MvqoanTwb21uZHDaTJvUxpTlu5i7sa04k0qwwN9uf6iOgztkkCzmFA3Vyki50NBxkVBRqqVjD3wvzdhxX/Aae1jRPOrrTk0tU/dn8ybpGbkMf2PXUxL2sWeI0eLz3dIiGBolwSubhtHoJ96aUQ8hYKMi4KMVEvp2+D312H154AJGNBmsPUMmqjSN1T1Fg6nyYItB5m6NIXZ6/dT5OqlCQ3w4br2Vi9Nyzj9WyBS1SnIuCjISLWWtgF+fw3WfWt9bdjhomFw6VMQEe/e2qqAtKw8ZiTvZtqyXaSk5xafb1c3nKFdEhjYLo5gf+96Vo+Ip1CQcVGQEa+wdyXMfRU2/2p9bfeDTnfBJU9ASLRbS6sKnE6TxdsOMWVZCr/+mUqhw/pnL9jPzjXt63BLlwTa1K2+e1yJeCIFGRcFGfEqKUvgt1esTSgBfIOgy73Q4xEIinRvbVXEoex8vly+m6nLdrH9YE7x+VZxYQztksC17eMIDfB1Y4UiAgoyxRRkxOuYJmyfB3Nehj1/WOf8wyDxQeh2PwTo/wcApmmydHs6U5el8PPaVAqKnAAE+toZ2C6WoV0SaB8fgeGFz+wRqQoUZFwUZMRrmSZs+sXqodm/1joXGAkXPwad7wY/bcR4zOGcAr5asYepy1LYkpZdfL55TGjxXJrIYD83VijifRRkXBRkxOs5nbDua5j7GhzaYp0LiYFLR0GH28HH3731VSGmaZK88zBTlqXw4+p95Lt6acAaerq4SU0ublyTzvUj9cA9kQqmIOOiICPi4iiylmvPGwtHUqxz4Qlw2VPQbqjX7bR9Jhm5hXyzcg9f/LGLP/dmlnjPz8dG5/o1uLhxLS5uXJNWcWHYbBqCEilPCjIuCjIiJykqgOX/gv+9Bdmp1rmoxtYzaLxwp+2zkZaVx6Ith1iw5SALNh8kNTOvxPs1gnzp3qhmcY9NfKSG7UQulIKMi4KMSBkKcuGPT2D+O3A03TpXu7X1lOBm/bxyY8qzYZomWw/ksGDzARZsOcSSbYfIzi8q0aZeVBA9GtfkksY1SWwURUSQ5teInCsFGRcFGZEzyM+CJRNcO227hlDqdLT2cWp4uQLNGRQ6nKzefYT5mw+ycMtBVqQcKX6aMFh/fW3rhNOjsdVj07FeDfx9NL9G5EwUZFwUZETOUm46LHoPln4Iha6n4Na7GHo9Dwnd3FubB8nOL2LptkPFwWbzCaugAAJ8bXRpEMXFjaO4uHEtmseEan6NSCkUZFwUZETOUXaaNdz0xyfgKLDONb4SrngW4i5yb20eKDUjj4VbDlrza7Yc5EBWfon3o4L9rN4aV49NXESgmyoVqVoUZFwUZETOU8ZumPcGrPgvmA7rXIuB1hya6Bburc1DmabJpv3ZrknDB1i6PZ3cAkeJNg1rBnNxk5r0cM2vCdNThsVLKci4KMiIXKBDW+H3sbBmOsU7bbcdAj2fgciG7q7OoxUUOVmRcri4x2bV7gwcJ8yvsRnQLj6CSxpbweaihBr4+WhVmXgHBRkXBRmRcpK23tqYcv331teGHS661XoOTXhd99ZWTWTmFbJ46yEr2Gw+yLYT9oICCPKz07VBJBc3sZ5f07R2iLZQkGpLQcZFQUaknO1dYW17sGW29bXd37XT9uPaabuc7TlylIWbrd6ahVsOciinoMT7tUL9rbk1rh6bmPAAN1UqUv4UZFwUZEQqyM7FVqDZucD62jcIut4HPR6GwBrura0acjpNNqRmsWCL9fyaZdsPkVfoLNGmSXSI9fyaJjXp2jCKEH89rVk8l4KMi4KMSAUyTdg219ppe+9y65x/OHR37bTtH+re+qqxvEIHy4/Nr9l8kNV7MjjxX3Mfm8FFCRHFK6LaxUfga9f8GvEcCjIuCjIilcA0YeNP8NurkPandS4oytrDqWFPSEgE/xC3lljdHcktYPHWQ8XLvHceyi3xfoi/D10bRJLYKIpuDaNoGav9oaRqU5BxUZARqUROJ/z5lbXTdvrW4+dtPlC3CzS8zAo2dTqCXcuKK9Ku9NziULNoy0EO5xaWeD8iyNcKNg2jSGykicNS9SjIuCjIiLiBowg2/ABb58C234/vtn2MXwjU62GFmoaXQXRLbYVQgZxOkz/3ZrJ420EWbz3Esu3p5Jz0/JqoYD+6NYyiW6MoEhtG0ahWsIKNuJWCjIuCjEgVkL7dCjTb58G2ecc3qTwmONoKNA1cPTYR8e6o0msUOpys2ZPB4q3WppdJO9JPmTgcHepPt4ZRdG8URWKjKBIigxRspFIpyLgoyIhUMU4n7F9jBZptv8PORVB0tGSbyIZWoGlwGTS4FIIi3VGp1ygocrJq9xEWbz3E4q2HSE45TEFRyWATFx5Q3FuT2CiKujWC3FSteAsFGRcFGZEqrigfdidZoWbbPNiTfHxLBAAMiG13fH5NQiL4aj+iipRX6GBFyhEWbzvEkq2HWLHrMIWOkr8q4iMDi0NNYkM9w0bKn4KMi4KMiIfJy4SdC13B5nc4sKHk+3Y/iO/qml/TE2Lbg13PS6lIRwsc/LEz3eqx2XaI1SdtpQDQoGawK9RYq6Jqhfq7qVqpLhRkXBRkRDxc5j7Y/j/X/JrfIXNPyff9w6H+xccnDtdsqonDFSw7v4ikHekscQWbtXsyOCnX0CQ6pESwqRHs555ixWMpyLgoyIhUI6YJh7Yc763ZMR/yMkq2CY09Pr+m4WUQFueGQr1LxtFCkrans8gVbNbvyzylTfOYUBIbRdG9UU26NIgkPFDL7+X0FGRcFGREqjGnA/atPD5xOGUJOPJLtqnZ7Pj8mvoXQ0C4Gwr1LodzCli6/VDxUNSm/dkl3rcZ0CouvLjHpnODSG2nIKdQkHFRkBHxIoVHYdfS4xOH964ATvgnzrBBXIfjwaZuF/DVJNWKdjA7nyXbjgebbQdK7upttxm0qXM82HSqX4MgPwUbb6cg46IgI+LFctNhx4Lj82sObSn5vk+AtQrq2PyamLZgs7ujUq+yPzOPJdsOsWiLFWxS0ktup+BrN2gfH2HNr2kURYeEGgT46ufibRRkXBRkRKRYxu7jw1Db50H2/pLvB9awnltz7MF8kQ01cbgS7DlytPgZNku2HWLPkZLPFfLzsdEhIYLEhjVJbBRF+/gI/Hy0AWZ1pyDjoiAjIqUyTWtp97Fgs2MBFGSVbBMeb/XU1L8U6veA8LpuKdWbmKbJrvSjxdspLNp6iLSskvOewgJ8uKZ9HIM7xtOubrieOFxNKci4KMiIyFlxFMHe5cfn1+xaCs6SGy0SkQD1LoZ63a2XemwqnGmabDuYUzy/ZsnWQxzKKSh+v0l0CIM71uX6i+oQHab5TtWJgoyLgoyInJeCHEhZ7OqtWQj7Vp30xGGspd71ulsbYNbrAbWaKdhUMKfTZPG2Q8xI3s3Pa/cV7xFltxlc1rQWgzvWpVeLaPx9NKfG0ynIuCjIiEi5yM+yeml2LrKCzZ7kU3tsgqJcwcbVa1O7lSYPV6DMvEJ+Wr2P6cm7Sd55uPh8RJAv17azhp5a1wnT0JOHUpBxUZARkQpReNTaI2rnImtLhV1Jp25+GRBurYo6Fm5i24JdD4KrCNsOZDMjeTdfLd9DamZe8fnmMaEM7liXa9vX0bYJHkZBxkVBRkQqRVGB9dyanQuscJOyBApKPggO32BI6Ho82NTpAD765VqeHE6ThVsOMj15NzP/TC3exdvHZtCzWTSDO9bliubRWvXkARRkXBRkRMQtHEWQuvp4j83ORZB3pGQbnwCo2/n45OG6XcAvyC3lVkcZRwv5ftVeZiTvZuWuI8XnI4P9uLZ9HDd2jKdlnH4vVFUKMi4KMiJSJTidkLbOFWxcvTY5B0q2sflC3EXWUu96PaxdvgP071Z52JKWxXTX0NOBE5Zzt4wNY3DHulx3UR0itbFllaIg46IgIyJVkmnCwc3He2t2Ljx1Z2/DZj1tuL5r8nBCIgRFuqfeaqLI4WT+5oPMSN7NrHX7KXBYQ0++doMrmkdzY8d4LmtWC1+7hp7cTUHGRUFGRDyCacKRndaKqGO9Nod3nNouupWrx8a17DskutJLrS4O5xTw/Wpr6Gn17uO7qNcM8eO69nW4sVM8zWJC3Vihd1OQcVGQERGPlbHHepbNDtdQ1MGNp7aJamKFmmO9Nnr68HnZkJrJl8m7+XrFHg5mH3/gXps64dzYqS7XtIsjIkhDT5VJQcZFQUZEqo3sA5Cy6Hivzf61lNjdGyCintVTc6zXpkYDPaTvHBQ6nMzbeIDpybuYsz6NIqf19+tnt3Fly9oM7liXS5rUxEdDTxVOQcZFQUZEqq2jh61l3jsXnubpw3HHV0XFtoOwOAiprQf1nYVD2fl8t2ov0//Yzbp9mcXno0P9ub5DHW7sWJfG0Rp6qigKMi4KMiLiNc7m6cMAhh1CY6xQExYHYXVOPQ6N1cP7TvDn3gxmJO/m25V7ST9hr6d28RHc2LEuA9vGER6kv6/ypCDjoiAjIl6r8Cjs/sO1MmohHNoGWftO7bUplWFNJC4r6ITFWb09vt61UWNBkZO5G9OY/sdu5m5Mw3Fs6MnHRp9WMQzuWJeLG9fEbtNw3oVSkHFRkBEROYHTAdlpkLnXWu5d4k/XcdY+cBSc+Vpg7S9VZthx/ekXXLHfk5scyMrn25V7mP7Hbjbuzyo+HxMWwA0d6jCoY10a1QpxY4WeTUHGRUFGROQcOZ2Qe6jsoHPs+OS9pcoSEF52r86xY/8wj52UbJoma/dkMiN5F9+u2suR3OPDeR0SIrixUzwD2sYSFqChp3OhIOOiICMiUgFM05psXFrAOfG4IOvM1wLwCzl90AmrA4E1qnzYyS9yMGd9GjOSd/P7xjRcI08E+Nro2yqGwR3j6d4oCpuGns5IQcZFQUZExI3yMq2hqrKCTuYeKxCdDZ8AK9SEx0Nkw+OvqEZQoz74Blbot3Ku0jLz+HrFHqYn72ZL2vENROPCAxjUsS5XtYwhKsSP8EBfgvzsGFU8pFU2BRkXBRkRkSquIPeksFNK6Dl5X6rShNVxhZsGENnohLDTwK3zdEzTZNXuDGYk7+K7lXvJzCs6pY2v3SA80JewQF/CA32JcP1Z/AryKz6OCCr5XoBv9VxKryDjoiAjIlINFOVbYSdjDxxJgfRtkL7V+vPQNsjPOP3nQ2JcvTcNS/bmRDYE/8p7FkxeoYNZ6/YzI3k3a/dkkHG0sPihe+fL38d2Ssg5Hoj8CA/0ITzIOg47KSD5+VTdB/spyLgoyIiIVHPH5uukb4NDrnBT/Np65qGr4OiTwk0Da7gqsqE1UblCSzfJLXCQcbSQI7mFZBy1XplHCzlytKD464yjRRzJLSDzaOEJ5wq5wAxEkJ+9ZM9PKb0+YYG+RJzYI+Q6V9FLzD0iyEyYMIEJEyawY8cOAFq1asULL7xAv379AMjLy+OJJ55g2rRp5Ofn06dPHz744ANq16591vdQkBER8XK56XB4O6RvPzXs5B48/WeDok4KOY2Ohx0370TudJpkFxSRkVsy3JwxFOUWljq8da5C/X1cIceXF65uSdeGUeXwXR3nEUHm+++/x26306RJE0zT5F//+hdvvvkmK1asoFWrVtx///38+OOPTJo0ifDwcB588EFsNhsLFy4863soyIiISJnyMlwB51i42X485GTvP/1nAyKOTzY+ebgqKKpKr7ByOE2y8koPPicGnoziEFRU3BuUnX9qCJpyd1e6N65ZrjV6RJApTWRkJG+++SaDBw+mVq1aTJkyhcGDBwOwYcMGWrRoweLFi+nWrdtZXU9BRkREzkt+Vslgc+Ira9/pP+sf7pp0fFLAiWoEwbWqdMg5k0KHs8QQ15GjhVwUH1Huu4Of7e9vn3K96wVwOBxMnz6dnJwcEhMTSU5OprCwkN69exe3ad68OQkJCacNMvn5+eTn5xd/nZmZWWo7ERGR0/IPhdi21utkBTlweEcp83K2Q+Zua/LxvpXW62R+IVbICatr3cM/xDrnH3bCcYj1nt+J74daf/qUb2A4V752G1Eh/kSF+Lu1jmPcHmTWrFlDYmIieXl5hISE8PXXX9OyZUtWrlyJn58fERERJdrXrl2b1NTUMq83ZswYXnrppQquWkREvJpfMNRuZb1OVnj0eMg58XVoG2TsgoJsSF1jvc6H3f8cwo+rzbHjY4HoWCjyDfTo3iGoAkGmWbNmrFy5koyMDGbMmMHw4cOZN2/eeV9v9OjRPP7448VfZ2ZmEh8fXx6lioiInJlvIES3sF4nK8qHwzutOTlZqVaoyc92/ZllvYrPZVl/HjtXlGddw5EPufnWNhIXyrC7As7J4aeU3qBj4ae0EBVcE3zc00Pj9iDj5+dH48aNAejYsSNJSUn8/e9/56abbqKgoIAjR46U6JXZv38/MTExZV7P398ff/+q0d0lIiJSgo8/1Gpqvc6Vo/CEwHNC+Clx7qTwU+a5bMC0dkLPy7BeF2Lo59Cs74Vd4zy5PciczOl0kp+fT8eOHfH19WXOnDkMGjQIgI0bN5KSkkJiYqKbqxQREalkdl9rv6nAGhd+LacTCnNLBqHSAtFZhaQsq2fGTdwaZEaPHk2/fv1ISEggKyuLKVOm8PvvvzNz5kzCw8MZMWIEjz/+OJGRkYSFhfHQQw+RmJh41iuWREREpBQ2m2sIqRwCiJsXP7s1yKSlpXH77bezb98+wsPDadu2LTNnzuTKK68E4N1338VmszFo0KASD8QTERGRKsLNk4Wr3HNkypueIyMiIuJ5zvb3d9XdLUpERETkDBRkRERExGMpyIiIiIjHUpARERERj6UgIyIiIh5LQUZEREQ8loKMiIiIeCwFGREREfFYCjIiIiLisRRkRERExGMpyIiIiIjHUpARERERj+XW3a8rw7E9MTMzM91ciYiIiJytY7+3z7S3dbUPMllZWQDEx8e7uRIRERE5V1lZWYSHh5f5vmGeKep4OKfTyd69ewkNDcUwjHK7bmZmJvHx8ezateu024tL5dHPpGrRz6Nq0c+jatHP48xM0yQrK4u4uDhstrJnwlT7HhmbzUbdunUr7PphYWH6H2EVo59J1aKfR9Win0fVop/H6Z2uJ+YYTfYVERERj6UgIyIiIh5LQeY8+fv789e//hV/f393lyIu+plULfp5VC36eVQt+nmUn2o/2VdERESqL/XIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgsx5+sc//kH9+vUJCAiga9euLFu2zN0leaUxY8bQuXNnQkNDiY6O5rrrrmPjxo3uLktcxo4di2EYPProo+4uxWvt2bOHW2+9laioKAIDA2nTpg1//PGHu8vyWg6Hg+eff54GDRoQGBhIo0aNePnll8+4n5CUTUHmPHz++ec8/vjj/PWvf2X58uW0a9eOPn36kJaW5u7SvM68efMYOXIkS5YsYdasWRQWFnLVVVeRk5Pj7tK8XlJSEh9++CFt27Z1dyle6/Dhw/To0QNfX19+/vln1q1bx9tvv02NGjXcXZrXev3115kwYQLjx49n/fr1vP7667zxxhu8//777i7NY2n59Xno2rUrnTt3Zvz48YC1n1N8fDwPPfQQzzzzjJur824HDhwgOjqaefPmcemll7q7HK+VnZ1Nhw4d+OCDD3jllVdo374948aNc3dZXueZZ55h4cKFzJ8/392liMvVV19N7dq1+eSTT4rPDRo0iMDAQP773/+6sTLPpR6Zc1RQUEBycjK9e/cuPmez2ejduzeLFy92Y2UCkJGRAUBkZKSbK/FuI0eOZMCAASX+fyKV77vvvqNTp07ceOONREdHc9FFF/HRRx+5uyyv1r17d+bMmcOmTZsAWLVqFQsWLKBfv35ursxzVftNI8vbwYMHcTgc1K5du8T52rVrs2HDBjdVJWD1jD366KP06NGD1q1bu7scrzVt2jSWL19OUlKSu0vxetu2bWPChAk8/vjj/N///R9JSUk8/PDD+Pn5MXz4cHeX55WeeeYZMjMzad68OXa7HYfDwauvvsqwYcPcXZrHUpCRamPkyJGsXbuWBQsWuLsUr7Vr1y4eeeQRZs2aRUBAgLvL8XpOp5NOnTrx2muvAXDRRRexdu1a/vnPfyrIuMkXX3zB5MmTmTJlCq1atWLlypU8+uijxMXF6WdynhRkzlHNmjWx2+3s37+/xPn9+/cTExPjpqrkwQcf5IcffuB///sfdevWdXc5Xis5OZm0tDQ6dOhQfM7hcPC///2P8ePHk5+fj91ud2OF3iU2NpaWLVuWONeiRQu+/PJLN1UkTz75JM888ww333wzAG3atGHnzp2MGTNGQeY8aY7MOfLz86Njx47MmTOn+JzT6WTOnDkkJia6sTLvZJomDz74IF9//TW//fYbDRo0cHdJXq1Xr16sWbOGlStXFr86derEsGHDWLlypUJMJevRo8cpjyPYtGkT9erVc1NFkpubi81W8lev3W7H6XS6qSLPpx6Z8/D4448zfPhwOnXqRJcuXRg3bhw5OTnceeed7i7N64wcOZIpU6bw7bffEhoaSmpqKgDh4eEEBga6uTrvExoaesr8pODgYKKiojRvyQ0ee+wxunfvzmuvvcaQIUNYtmwZEydOZOLEie4uzWsNHDiQV199lYSEBFq1asWKFSt45513uOuuu9xdmucy5by8//77ZkJCgunn52d26dLFXLJkibtL8kpAqa/PPvvM3aWJy2WXXWY+8sgj7i7Da33//fdm69atTX9/f7N58+bmxIkT3V2SV8vMzDQfeeQRMyEhwQwICDAbNmxoPvvss2Z+fr67S/NYeo6MiIiIeCzNkRERERGPpSAjIiIiHktBRkRERDyWgoyIiIh4LAUZERER8VgKMiIiIuKxFGRERETEYynIiIjXMQyDb775xt1liEg5UJARkUp1xx13YBjGKa++ffu6uzQR8UDaa0lEKl3fvn357LPPSpzz9/d3UzUi4snUIyMilc7f35+YmJgSrxo1agDWsM+ECRPo168fgYGBNGzYkBkzZpT4/Jo1a7jiiisIDAwkKiqKe++9l+zs7BJtPv30U1q1aoW/vz+xsbE8+OCDJd4/ePAg119/PUFBQTRp0oTvvvuuYr9pEakQCjIiUuU8//zzDBo0iFWrVjFs2DBuvvlm1q9fD0BOTg59+vShRo0aJCUlMX36dGbPnl0iqEyYMIGRI0dy7733smbNGr777jsaN25c4h4vvfQSQ4YMYfXq1fTv359hw4aRnp5eqd+niJQDd+9aKSLeZfjw4abdbjeDg4NLvF599VXTNK0dze+7774Sn+natat5//33m6ZpmhMnTjRr1KhhZmdnF7//448/mjabzUxNTTVN0zTj4uLMZ599tswaAPO5554r/jo7O9sEzJ9//rncvk8RqRyaIyMile7yyy9nwoQJJc5FRkYWHycmJpZ4LzExkZUrVwKwfv162rVrR3BwcPH7PXr0wOl0snHjRgzDYO/evfTq1eu0NbRt27b4ODg4mLCwMNLS0s73WxIRN1GQEZFKFxwcfMpQT3kJDAw8q3a+vr4lvjYMA6fTWREliUgF0hwZEalylixZcsrXLVq0AKBFixasWrWKnJyc4vcXLlyIzWajWbNmhIaGUr9+febMmVOpNYuIe6hHRkQqXX5+PqmpqSXO+fj4ULNmTQCmT59Op06duPjii5k8eTLLli3jk08+AWDYsGH89a9/Zfjw4bz44oscOHCAhx56iNtuu43atWsD8OKLL3LfffcRHR1Nv379yMrKYuHChTz00EOV+42KSIVTkBGRSvfLL78QGxtb4lyzZs3YsGEDYK0omjZtGg888ACxsbFMnTqVli1bAhAUFMTMmTN55JFH6Ny5M0FBQQwaNIh33nmn+FrDhw8nLy+Pd999l1GjRlGzZk0GDx5ced+giFQawzRN091FiIgcYxgGX3/9Ndddd527SxERD6A5MiIiIuKxFGRERETEY2mOjIhUKRrtFpFzoR4ZERER8VgKMiIiIuKxFGRERETEYynIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgoyIiIh4LAUZERER8Vj/DxngQR3EWHVPAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "best_model = None\n",
        "best_test_score = float('inf')  # Set to infinity initially\n",
        "\n",
        "for _ in range(5):\n",
        "    # Create a new model for each iteration\n",
        "    model = Sequential()\n",
        "    model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dense(16, activation='linear'))\n",
        "\n",
        "    # Create Adam optimizer with a learning rate of 0.001\n",
        "    adam = Adam(learning_rate=0.0001)\n",
        "\n",
        "    # Compile model with Adam optimizer and a learning rate of 0.0001\n",
        "    model.compile(loss=smape_loss, optimizer='adam')\n",
        "\n",
        "    # Fit the model\n",
        "    history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, verbose=0, batch_size=32)\n",
        "\n",
        "    # Evaluate the model on the test set\n",
        "    test_score = model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "    # Check if the current model has a better test score than the previous best model\n",
        "    if test_score < best_test_score:\n",
        "        best_model = model\n",
        "        best_test_score = test_score\n",
        "\n",
        "    print(test_score)\n",
        "    # Plot training and validation loss over epochs for each iteration\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('Model Loss')\n",
        "    plt.ylabel('MAPE')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "# Use the best model for predictions\n",
        "model = best_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B2CpMkIcnmsE"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/content/amp_pd_peptide_310')\n",
        "import amp_pd_peptide_310\n",
        "env = amp_pd_peptide_310.make_env()\n",
        "iter_test = env.iter_test()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "paqdw5Fbnx-F",
        "outputId": "8dc34d87-0717-40f4-e4e1-fb2098bc23a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This version of the API is not optimized and should not be used to estimate the runtime of your code on the hidden test set.\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n"
          ]
        }
      ],
      "source": [
        "# Create a new DataFrame\n",
        "hist_df = pd.DataFrame(columns = ['patient_id']+ target_list)\n",
        "\n",
        "for (test, test_peptides, test_proteins, sample_submission) in iter_test:\n",
        "    # Convert patient_id in test DataFrame to string\n",
        "    test['patient_id'] = test['patient_id'].astype(str)\n",
        "\n",
        "    Prot_Peptides_df2 = pd.merge(test_proteins, test_peptides, on = ['visit_id','UniProt'], how = 'left')\n",
        "    Prot_Peptides_df2 = Prot_Peptides_df2[['visit_id','UniProt','Peptide','PeptideAbundance']]\n",
        "    df_filtered2 = Prot_Peptides_df2[Prot_Peptides_df2['UniProt'].isin(list(avg_network.index))]\n",
        "\n",
        "    # apply log(x+1) transformation to columns\n",
        "    df_filtered2[['PeptideAbundance']] = df_filtered2[['PeptideAbundance']].apply(lambda x: np.log1p(x))\n",
        "    # standardization\n",
        "    df_filtered2[['PeptideAbundance']] = scaler1.fit_transform(df_filtered2[['PeptideAbundance']])\n",
        "\n",
        "    pca_features_list2 = []\n",
        "\n",
        "    for visit_id in df_filtered2['visit_id'].unique():\n",
        "        # Part 1: construct UniProt-Peptide Network for each visit_id\n",
        "        p_df = df_filtered2[df_filtered2['visit_id'] == visit_id]\n",
        "        # Create a new empty DataFrame using the structure of avg_network\n",
        "        new_df = pd.DataFrame(index=avg_network.index, columns=avg_network.columns)\n",
        "        # Fill in the new DataFrame with values from df_filtered2 or avg_network\n",
        "        for prot in avg_network.index:\n",
        "            if prot in p_df['UniProt'].values:\n",
        "                for peptide in avg_network.columns:\n",
        "                    if peptide in p_df[p_df['UniProt'] == prot]['Peptide'].values:\n",
        "                        new_df.loc[prot, peptide] = p_df[(p_df['UniProt'] == prot) & (p_df['Peptide'] == peptide)]['PeptideAbundance'].values[0]\n",
        "                    else:\n",
        "                        new_df.loc[prot, peptide] = 0 #avg_network.loc[prot, peptide]\n",
        "            else:\n",
        "                new_df.loc[prot, :] = avg_network.loc[prot, :]\n",
        "\n",
        "        # Fill any remaining missing values with 0\n",
        "        new_df.fillna(0, inplace=True)\n",
        "\n",
        "        # Part II: PCA feature extraction\n",
        "        # fit and transform the data\n",
        "        df1_pca = pca.fit_transform(new_df) #.iloc[:,1:])\n",
        "        # flatten the array to one dimension\n",
        "        df1_pca = np.array(df1_pca).flatten()\n",
        "        # store the PCA features along with the visit_id in the list\n",
        "        pca_features_list2.append({'visit_id': visit_id, 'pca_features': df1_pca})\n",
        "\n",
        "    # construct a dataframe from the PCA features list\n",
        "    df_pca_features2 = pd.DataFrame(pca_features_list2)\n",
        "    # split the pca_features column into multiple columns\n",
        "    PP_features2 = pd.concat([df_pca_features2.drop('pca_features', axis=1), df_pca_features2['pca_features'].apply(pd.Series)], axis=1)\n",
        "\n",
        "    col_fea=PP_features2.columns[1:]\n",
        "    X_test_sub = scaler_features.transform(PP_features2[col_fea])\n",
        "    # model.predict(tf.convert_to_tensor(X_test_sub)).astype(int)\n",
        "\n",
        "    test_result = np.maximum(model.predict(tf.convert_to_tensor(X_test_sub)),0)\n",
        "    # Create a new DataFrame with the visit_id and predicted target variables\n",
        "    result_df = pd.DataFrame({'visit_id': PP_features2['visit_id']})\n",
        "    for i in range(16):\n",
        "        result_df[target_list[i]]=test_result[:,i]\n",
        "\n",
        "    # Create a new DataFrame to store the predictions\n",
        "    predictions = pd.DataFrame()\n",
        "    # Generate a prediction_id for each combination of visit_id and column name\n",
        "    for col in result_df.columns[1:]:\n",
        "        predictions = predictions.append(result_df[['visit_id', col]].rename(columns={col: 'rating'}).assign(prediction_id=lambda x: x['visit_id'] + '_' + col))\n",
        "\n",
        "    for i, row in predictions.iterrows():\n",
        "        prediction_id = row['prediction_id']\n",
        "        rating = row['rating']\n",
        "        sample_submission.loc[sample_submission['prediction_id'] == prediction_id, 'rating'] = np.round(rating).astype(int)\n",
        "\n",
        "    #####\n",
        "    # Add the new rows to the DataFrame\n",
        "    result_df['patient_id'] = result_df['visit_id'].str.split('_').str[0]\n",
        "    result_df = result_df.drop('visit_id', axis=1)\n",
        "\n",
        "    # Check if patient_id exists in hist_df\n",
        "    existing_ids = hist_df['patient_id'].unique()\n",
        "    new_ids = result_df[~result_df['patient_id'].isin(existing_ids)]\n",
        "\n",
        "    if not new_ids.empty:\n",
        "        hist_df = pd.concat([hist_df, new_ids], ignore_index=True)\n",
        "    else:\n",
        "        for idx, row in result_df.iterrows():\n",
        "            pid = row['patient_id']\n",
        "            hist_df.loc[hist_df['patient_id'] == pid, result_df.columns] = row.values\n",
        "\n",
        "    # For the 'patient_id' values that exist in the DataFrame 'test' but not in the DataFrame 'test_proteins'.\n",
        "    patient_id_difference = set(test['patient_id'].unique()) - set(test_proteins['patient_id'].unique())\n",
        "    for patient_id in patient_id_difference:\n",
        "        if patient_id in hist_df['patient_id'].unique():\n",
        "            prediction_pat_id = patient_id +'_' + str(test.loc[test['patient_id']== patient_id,'visit_month'].values[-1])\n",
        "            for i in range(16):\n",
        "                prediction_id = prediction_pat_id  + '_' +target_list[i]\n",
        "                rating = hist_df.loc[hist_df['patient_id'] == patient_id].iloc[0, i+1]\n",
        "                sample_submission.loc[sample_submission['prediction_id'] == prediction_id, 'rating'] = int(rating)\n",
        "\n",
        "        else:\n",
        "            # Create a dictionary with the values for the new row\n",
        "            new_row = {\n",
        "                'patient_id': patient_id,\n",
        "                'updrs_1_plus_0_months': 6,\n",
        "                'updrs_2_plus_0_months': 5,\n",
        "                'updrs_3_plus_0_months': 16,\n",
        "                'updrs_4_plus_0_months': 1,\n",
        "                'updrs_1_plus_6_months': 6,\n",
        "                'updrs_2_plus_6_months': 6,\n",
        "                'updrs_3_plus_6_months': 17,\n",
        "                'updrs_4_plus_6_months': 1,\n",
        "                'updrs_1_plus_12_months': 7,\n",
        "                'updrs_2_plus_12_months': 6,\n",
        "                'updrs_3_plus_12_months': 17,\n",
        "                'updrs_4_plus_12_months': 1,\n",
        "                'updrs_1_plus_24_months': 7,\n",
        "                'updrs_2_plus_24_months': 6,\n",
        "                'updrs_3_plus_24_months': 18,\n",
        "                'updrs_4_plus_24_months': 1\n",
        "            }\n",
        "            # Add the new row to the DataFrame\n",
        "            hist_df = hist_df.append(new_row, ignore_index=True)\n",
        "            prediction_pat_id = patient_id +'_' + str(test.loc[test['patient_id']== patient_id,'visit_month'].values[-1])\n",
        "            for i in range(16):\n",
        "                prediction_id = prediction_pat_id + '_' + target_list[i]\n",
        "                rating = hist_df.loc[hist_df['patient_id'] == patient_id].iloc[0, i+1]\n",
        "                sample_submission.loc[sample_submission['prediction_id'] == prediction_id, 'rating'] = int(rating)\n",
        "\n",
        "\n",
        "    env.predict(sample_submission[['prediction_id', 'rating']])   # register your predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwu2JhAiugD8"
      },
      "source": [
        "Now, will move on with creating a LSTM code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "dTjKkajA-DK1",
        "outputId": "650b057e-cd43-4f88-a064-07df403acb8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3         4         5         6    \\\n",
              "0   -1.070981 -0.832562  0.274421  0.904836  0.198627  0.434785  0.450358   \n",
              "1   -0.393061 -1.099010 -0.768318  1.585669 -0.812551 -0.118806 -0.142321   \n",
              "2    0.135788 -0.808014 -0.985265 -0.056820 -0.272959  0.111831  0.181551   \n",
              "3    1.138790 -0.604026 -0.472201  1.167997  0.481021  0.146430 -0.703365   \n",
              "4    0.484688 -0.785599 -0.019798 -1.028234 -1.049334  0.050987  0.329625   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
              "720 -0.344445 -1.594382  0.071181 -0.176860  0.616223 -0.967517 -0.504772   \n",
              "721  0.075789  0.516015  0.824781 -0.677777  0.881474 -0.019499  0.070241   \n",
              "722 -0.097267 -0.750012  1.453772 -0.397995  0.247929  0.115104  0.374870   \n",
              "723  0.583571  0.155035  0.944211  0.100722  1.186114 -0.168422  0.140956   \n",
              "725 -0.198111  0.269482  0.204808 -0.028804 -0.369852  0.017327  0.214054   \n",
              "\n",
              "          7         8         9    ...       260       261       262  \\\n",
              "0    0.622780 -0.485146 -0.001225  ... -0.951450 -0.702069  0.458833   \n",
              "1   -0.254820 -0.350414 -0.090066  ... -0.457999 -1.189446 -0.863953   \n",
              "2    0.644003 -0.458719  0.102250  ...  0.864274 -0.441831 -0.562749   \n",
              "3   -0.753565 -0.349245 -0.331167  ...  1.480737 -0.459923 -0.313767   \n",
              "4   -0.291147  0.141293 -0.915532  ...  0.880624 -0.577709  0.177308   \n",
              "..        ...       ...       ...  ...       ...       ...       ...   \n",
              "720  0.203735 -0.441049 -0.383239  ...  0.151684 -1.234329  0.496508   \n",
              "721 -0.480763  0.173247 -0.165292  ...  0.149164  0.562145  0.870381   \n",
              "722  0.463257 -0.307090  0.083221  ... -0.117850 -0.797726  1.499123   \n",
              "723  0.411739  0.034982 -0.034997  ... -0.209459 -0.386757  0.579132   \n",
              "725 -0.014932 -0.357156 -0.129479  ... -0.219096  0.269879  0.192491   \n",
              "\n",
              "          263       264       265       266       267       268       269  \n",
              "0    1.073082  0.415710  1.450283  1.394464  1.114270 -0.425426  0.176814  \n",
              "1    1.625718 -1.213595 -0.357480 -0.340994 -0.392094 -0.419392  0.022529  \n",
              "2    0.394585  0.173797  0.792017  0.896062  1.063797 -0.163797  0.297396  \n",
              "3    1.293831  0.707167  0.618480 -1.586376 -0.950335 -0.194592 -0.047483  \n",
              "4   -0.757491 -1.001693  0.607040  1.157023 -0.017344  0.284281 -0.459203  \n",
              "..        ...       ...       ...       ...       ...       ...       ...  \n",
              "720  0.327449  1.035475 -1.701216  0.190417  0.703374  0.156807  0.184982  \n",
              "721 -0.680518  1.134383  0.011112  0.305423 -0.766043  0.245385 -0.089452  \n",
              "722 -0.445063  0.263440  0.410342  1.174090  0.880173 -0.395957  0.218358  \n",
              "723 -0.630844  1.389266 -1.694359 -0.306326  0.738838 -0.414413 -0.438134  \n",
              "725 -0.065389 -0.607018  0.078003  0.694451  0.010959 -0.467748 -0.062399  \n",
              "\n",
              "[621 rows x 270 columns]"
            ],
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
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              "      <th>261</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.070981</td>\n",
              "      <td>-0.832562</td>\n",
              "      <td>0.274421</td>\n",
              "      <td>0.904836</td>\n",
              "      <td>0.198627</td>\n",
              "      <td>0.434785</td>\n",
              "      <td>0.450358</td>\n",
              "      <td>0.622780</td>\n",
              "      <td>-0.485146</td>\n",
              "      <td>-0.001225</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.951450</td>\n",
              "      <td>-0.702069</td>\n",
              "      <td>0.458833</td>\n",
              "      <td>1.073082</td>\n",
              "      <td>0.415710</td>\n",
              "      <td>1.450283</td>\n",
              "      <td>1.394464</td>\n",
              "      <td>1.114270</td>\n",
              "      <td>-0.425426</td>\n",
              "      <td>0.176814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.393061</td>\n",
              "      <td>-1.099010</td>\n",
              "      <td>-0.768318</td>\n",
              "      <td>1.585669</td>\n",
              "      <td>-0.812551</td>\n",
              "      <td>-0.118806</td>\n",
              "      <td>-0.142321</td>\n",
              "      <td>-0.254820</td>\n",
              "      <td>-0.350414</td>\n",
              "      <td>-0.090066</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.457999</td>\n",
              "      <td>-1.189446</td>\n",
              "      <td>-0.863953</td>\n",
              "      <td>1.625718</td>\n",
              "      <td>-1.213595</td>\n",
              "      <td>-0.357480</td>\n",
              "      <td>-0.340994</td>\n",
              "      <td>-0.392094</td>\n",
              "      <td>-0.419392</td>\n",
              "      <td>0.022529</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.135788</td>\n",
              "      <td>-0.808014</td>\n",
              "      <td>-0.985265</td>\n",
              "      <td>-0.056820</td>\n",
              "      <td>-0.272959</td>\n",
              "      <td>0.111831</td>\n",
              "      <td>0.181551</td>\n",
              "      <td>0.644003</td>\n",
              "      <td>-0.458719</td>\n",
              "      <td>0.102250</td>\n",
              "      <td>...</td>\n",
              "      <td>0.864274</td>\n",
              "      <td>-0.441831</td>\n",
              "      <td>-0.562749</td>\n",
              "      <td>0.394585</td>\n",
              "      <td>0.173797</td>\n",
              "      <td>0.792017</td>\n",
              "      <td>0.896062</td>\n",
              "      <td>1.063797</td>\n",
              "      <td>-0.163797</td>\n",
              "      <td>0.297396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.138790</td>\n",
              "      <td>-0.604026</td>\n",
              "      <td>-0.472201</td>\n",
              "      <td>1.167997</td>\n",
              "      <td>0.481021</td>\n",
              "      <td>0.146430</td>\n",
              "      <td>-0.703365</td>\n",
              "      <td>-0.753565</td>\n",
              "      <td>-0.349245</td>\n",
              "      <td>-0.331167</td>\n",
              "      <td>...</td>\n",
              "      <td>1.480737</td>\n",
              "      <td>-0.459923</td>\n",
              "      <td>-0.313767</td>\n",
              "      <td>1.293831</td>\n",
              "      <td>0.707167</td>\n",
              "      <td>0.618480</td>\n",
              "      <td>-1.586376</td>\n",
              "      <td>-0.950335</td>\n",
              "      <td>-0.194592</td>\n",
              "      <td>-0.047483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.484688</td>\n",
              "      <td>-0.785599</td>\n",
              "      <td>-0.019798</td>\n",
              "      <td>-1.028234</td>\n",
              "      <td>-1.049334</td>\n",
              "      <td>0.050987</td>\n",
              "      <td>0.329625</td>\n",
              "      <td>-0.291147</td>\n",
              "      <td>0.141293</td>\n",
              "      <td>-0.915532</td>\n",
              "      <td>...</td>\n",
              "      <td>0.880624</td>\n",
              "      <td>-0.577709</td>\n",
              "      <td>0.177308</td>\n",
              "      <td>-0.757491</td>\n",
              "      <td>-1.001693</td>\n",
              "      <td>0.607040</td>\n",
              "      <td>1.157023</td>\n",
              "      <td>-0.017344</td>\n",
              "      <td>0.284281</td>\n",
              "      <td>-0.459203</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720</th>\n",
              "      <td>-0.344445</td>\n",
              "      <td>-1.594382</td>\n",
              "      <td>0.071181</td>\n",
              "      <td>-0.176860</td>\n",
              "      <td>0.616223</td>\n",
              "      <td>-0.967517</td>\n",
              "      <td>-0.504772</td>\n",
              "      <td>0.203735</td>\n",
              "      <td>-0.441049</td>\n",
              "      <td>-0.383239</td>\n",
              "      <td>...</td>\n",
              "      <td>0.151684</td>\n",
              "      <td>-1.234329</td>\n",
              "      <td>0.496508</td>\n",
              "      <td>0.327449</td>\n",
              "      <td>1.035475</td>\n",
              "      <td>-1.701216</td>\n",
              "      <td>0.190417</td>\n",
              "      <td>0.703374</td>\n",
              "      <td>0.156807</td>\n",
              "      <td>0.184982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>0.075789</td>\n",
              "      <td>0.516015</td>\n",
              "      <td>0.824781</td>\n",
              "      <td>-0.677777</td>\n",
              "      <td>0.881474</td>\n",
              "      <td>-0.019499</td>\n",
              "      <td>0.070241</td>\n",
              "      <td>-0.480763</td>\n",
              "      <td>0.173247</td>\n",
              "      <td>-0.165292</td>\n",
              "      <td>...</td>\n",
              "      <td>0.149164</td>\n",
              "      <td>0.562145</td>\n",
              "      <td>0.870381</td>\n",
              "      <td>-0.680518</td>\n",
              "      <td>1.134383</td>\n",
              "      <td>0.011112</td>\n",
              "      <td>0.305423</td>\n",
              "      <td>-0.766043</td>\n",
              "      <td>0.245385</td>\n",
              "      <td>-0.089452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>722</th>\n",
              "      <td>-0.097267</td>\n",
              "      <td>-0.750012</td>\n",
              "      <td>1.453772</td>\n",
              "      <td>-0.397995</td>\n",
              "      <td>0.247929</td>\n",
              "      <td>0.115104</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.463257</td>\n",
              "      <td>-0.307090</td>\n",
              "      <td>0.083221</td>\n",
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              "      <td>-0.117850</td>\n",
              "      <td>-0.797726</td>\n",
              "      <td>1.499123</td>\n",
              "      <td>-0.445063</td>\n",
              "      <td>0.263440</td>\n",
              "      <td>0.410342</td>\n",
              "      <td>1.174090</td>\n",
              "      <td>0.880173</td>\n",
              "      <td>-0.395957</td>\n",
              "      <td>0.218358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>723</th>\n",
              "      <td>0.583571</td>\n",
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              "      <td>-0.386757</td>\n",
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              "      <td>1.389266</td>\n",
              "      <td>-1.694359</td>\n",
              "      <td>-0.306326</td>\n",
              "      <td>0.738838</td>\n",
              "      <td>-0.414413</td>\n",
              "      <td>-0.438134</td>\n",
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              "    <tr>\n",
              "      <th>725</th>\n",
              "      <td>-0.198111</td>\n",
              "      <td>0.269482</td>\n",
              "      <td>0.204808</td>\n",
              "      <td>-0.028804</td>\n",
              "      <td>-0.369852</td>\n",
              "      <td>0.017327</td>\n",
              "      <td>0.214054</td>\n",
              "      <td>-0.014932</td>\n",
              "      <td>-0.357156</td>\n",
              "      <td>-0.129479</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.219096</td>\n",
              "      <td>0.269879</td>\n",
              "      <td>0.192491</td>\n",
              "      <td>-0.065389</td>\n",
              "      <td>-0.607018</td>\n",
              "      <td>0.078003</td>\n",
              "      <td>0.694451</td>\n",
              "      <td>0.010959</td>\n",
              "      <td>-0.467748</td>\n",
              "      <td>-0.062399</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>621 rows × 270 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "_7PhZXje-D6f",
        "outputId": "b77c790f-0f20-46ac-b9c4-e07e0eb7934b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     updrs_1_plus_0_months  updrs_2_plus_0_months  updrs_3_plus_0_months  \\\n",
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              "723                     18                     18                     49   \n",
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              "     updrs_4_plus_0_months  updrs_1_plus_6_months  updrs_2_plus_6_months  \\\n",
              "0                        0                      8                     10   \n",
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              "\n",
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              "725                     11                      1                       6   \n",
              "\n",
              "     updrs_2_plus_12_months  updrs_3_plus_12_months  updrs_4_plus_12_months  \\\n",
              "0                        10                      41                       0   \n",
              "1                        11                      32                       5   \n",
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              "725                       6                      16                       1   \n",
              "\n",
              "     updrs_1_plus_24_months  updrs_2_plus_24_months  updrs_3_plus_24_months  \\\n",
              "0                        16                       9                      49   \n",
              "1                        19                      11                      28   \n",
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              "\n",
              "     updrs_4_plus_24_months  \n",
              "0                         0  \n",
              "1                         3  \n",
              "2                         0  \n",
              "3                         0  \n",
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              "..                      ...  \n",
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              "[621 rows x 16 columns]"
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              "<p>621 rows × 16 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d353487e-9264-4993-99d5-a35ea9eecf6d')\"\n",
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          "metadata": {},
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    {
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      "execution_count": null,
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              "      <td>-0.644328</td>\n",
              "      <td>-0.440414</td>\n",
              "      <td>...</td>\n",
              "      <td>0.393726</td>\n",
              "      <td>-1.114846</td>\n",
              "      <td>0.496829</td>\n",
              "      <td>-1.456849</td>\n",
              "      <td>0.350344</td>\n",
              "      <td>0.144424</td>\n",
              "      <td>0.572316</td>\n",
              "      <td>-0.322491</td>\n",
              "      <td>-0.897627</td>\n",
              "      <td>-0.498053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>0.801461</td>\n",
              "      <td>0.092451</td>\n",
              "      <td>-0.109855</td>\n",
              "      <td>0.957162</td>\n",
              "      <td>0.562590</td>\n",
              "      <td>0.065446</td>\n",
              "      <td>-0.052928</td>\n",
              "      <td>0.037973</td>\n",
              "      <td>0.296463</td>\n",
              "      <td>0.021713</td>\n",
              "      <td>...</td>\n",
              "      <td>0.071252</td>\n",
              "      <td>-0.379669</td>\n",
              "      <td>-0.713268</td>\n",
              "      <td>0.563368</td>\n",
              "      <td>0.181346</td>\n",
              "      <td>-0.431729</td>\n",
              "      <td>-1.003574</td>\n",
              "      <td>-0.439683</td>\n",
              "      <td>0.346868</td>\n",
              "      <td>-0.119999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>-0.673206</td>\n",
              "      <td>-0.896124</td>\n",
              "      <td>-0.129297</td>\n",
              "      <td>0.428272</td>\n",
              "      <td>1.140881</td>\n",
              "      <td>-0.211912</td>\n",
              "      <td>-0.135108</td>\n",
              "      <td>-0.192179</td>\n",
              "      <td>0.249032</td>\n",
              "      <td>0.223013</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.133529</td>\n",
              "      <td>-0.541766</td>\n",
              "      <td>0.240967</td>\n",
              "      <td>0.782178</td>\n",
              "      <td>1.542714</td>\n",
              "      <td>-0.083832</td>\n",
              "      <td>0.183552</td>\n",
              "      <td>0.080736</td>\n",
              "      <td>0.371237</td>\n",
              "      <td>0.384928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>0.908478</td>\n",
              "      <td>-0.321950</td>\n",
              "      <td>1.324295</td>\n",
              "      <td>-1.201270</td>\n",
              "      <td>0.145382</td>\n",
              "      <td>-0.174099</td>\n",
              "      <td>0.094532</td>\n",
              "      <td>-0.264070</td>\n",
              "      <td>-0.100650</td>\n",
              "      <td>-0.151006</td>\n",
              "      <td>...</td>\n",
              "      <td>0.991489</td>\n",
              "      <td>-0.337203</td>\n",
              "      <td>1.362278</td>\n",
              "      <td>-1.292308</td>\n",
              "      <td>0.133584</td>\n",
              "      <td>-0.512023</td>\n",
              "      <td>0.374115</td>\n",
              "      <td>-0.390888</td>\n",
              "      <td>-0.095054</td>\n",
              "      <td>-0.065316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>-0.024455</td>\n",
              "      <td>1.271095</td>\n",
              "      <td>0.569457</td>\n",
              "      <td>1.936713</td>\n",
              "      <td>-0.418507</td>\n",
              "      <td>0.194764</td>\n",
              "      <td>0.040266</td>\n",
              "      <td>-0.396411</td>\n",
              "      <td>0.081599</td>\n",
              "      <td>-0.014006</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.439531</td>\n",
              "      <td>1.164952</td>\n",
              "      <td>0.344629</td>\n",
              "      <td>1.979021</td>\n",
              "      <td>-1.120432</td>\n",
              "      <td>0.448391</td>\n",
              "      <td>-0.140252</td>\n",
              "      <td>-1.134509</td>\n",
              "      <td>-0.030261</td>\n",
              "      <td>-0.064143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>-0.461066</td>\n",
              "      <td>-1.400927</td>\n",
              "      <td>-0.281206</td>\n",
              "      <td>0.103146</td>\n",
              "      <td>-0.589080</td>\n",
              "      <td>0.351185</td>\n",
              "      <td>0.294373</td>\n",
              "      <td>0.792465</td>\n",
              "      <td>-0.506987</td>\n",
              "      <td>0.011946</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.345046</td>\n",
              "      <td>-1.375670</td>\n",
              "      <td>-0.203097</td>\n",
              "      <td>0.205753</td>\n",
              "      <td>-0.685064</td>\n",
              "      <td>1.217688</td>\n",
              "      <td>0.999705</td>\n",
              "      <td>1.380549</td>\n",
              "      <td>-0.468032</td>\n",
              "      <td>0.184993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>0.778263</td>\n",
              "      <td>2.193631</td>\n",
              "      <td>3.328220</td>\n",
              "      <td>-0.663654</td>\n",
              "      <td>0.175252</td>\n",
              "      <td>-0.073410</td>\n",
              "      <td>-0.435414</td>\n",
              "      <td>-0.261376</td>\n",
              "      <td>-0.010319</td>\n",
              "      <td>-0.105562</td>\n",
              "      <td>...</td>\n",
              "      <td>0.945557</td>\n",
              "      <td>2.245093</td>\n",
              "      <td>3.420441</td>\n",
              "      <td>-0.665962</td>\n",
              "      <td>0.186005</td>\n",
              "      <td>-0.182601</td>\n",
              "      <td>-1.179533</td>\n",
              "      <td>-0.383665</td>\n",
              "      <td>0.019782</td>\n",
              "      <td>-0.011278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>0.062980</td>\n",
              "      <td>-0.412798</td>\n",
              "      <td>1.687208</td>\n",
              "      <td>-1.172883</td>\n",
              "      <td>-0.355533</td>\n",
              "      <td>-0.098351</td>\n",
              "      <td>0.107039</td>\n",
              "      <td>-0.174442</td>\n",
              "      <td>-0.170319</td>\n",
              "      <td>0.119703</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.142948</td>\n",
              "      <td>-0.586045</td>\n",
              "      <td>1.707855</td>\n",
              "      <td>-1.515332</td>\n",
              "      <td>-0.833573</td>\n",
              "      <td>-0.560570</td>\n",
              "      <td>0.194341</td>\n",
              "      <td>-0.501063</td>\n",
              "      <td>-0.383123</td>\n",
              "      <td>0.233768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>0.672186</td>\n",
              "      <td>0.285980</td>\n",
              "      <td>-0.450993</td>\n",
              "      <td>0.180315</td>\n",
              "      <td>0.316283</td>\n",
              "      <td>0.186780</td>\n",
              "      <td>0.275180</td>\n",
              "      <td>0.412027</td>\n",
              "      <td>-0.012603</td>\n",
              "      <td>0.013654</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.597192</td>\n",
              "      <td>-0.458475</td>\n",
              "      <td>-1.630747</td>\n",
              "      <td>-1.031931</td>\n",
              "      <td>-1.231999</td>\n",
              "      <td>-0.875310</td>\n",
              "      <td>-0.270794</td>\n",
              "      <td>0.672431</td>\n",
              "      <td>-1.476114</td>\n",
              "      <td>-1.376499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>0.154295</td>\n",
              "      <td>-1.415619</td>\n",
              "      <td>-0.953515</td>\n",
              "      <td>-0.013409</td>\n",
              "      <td>1.317614</td>\n",
              "      <td>-1.080784</td>\n",
              "      <td>0.338930</td>\n",
              "      <td>1.432108</td>\n",
              "      <td>-2.908014</td>\n",
              "      <td>5.243511</td>\n",
              "      <td>...</td>\n",
              "      <td>1.468193</td>\n",
              "      <td>-0.447465</td>\n",
              "      <td>0.144386</td>\n",
              "      <td>1.002467</td>\n",
              "      <td>1.744455</td>\n",
              "      <td>-0.572084</td>\n",
              "      <td>1.304492</td>\n",
              "      <td>1.273295</td>\n",
              "      <td>-0.223359</td>\n",
              "      <td>-0.014941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>-0.962523</td>\n",
              "      <td>-0.192271</td>\n",
              "      <td>-1.041557</td>\n",
              "      <td>1.757911</td>\n",
              "      <td>1.901739</td>\n",
              "      <td>-0.352425</td>\n",
              "      <td>0.243753</td>\n",
              "      <td>-0.204422</td>\n",
              "      <td>0.045984</td>\n",
              "      <td>0.027867</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994666</td>\n",
              "      <td>-0.194254</td>\n",
              "      <td>-1.092171</td>\n",
              "      <td>1.816353</td>\n",
              "      <td>2.465643</td>\n",
              "      <td>-1.031053</td>\n",
              "      <td>0.819307</td>\n",
              "      <td>-0.255969</td>\n",
              "      <td>0.093413</td>\n",
              "      <td>0.156170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>-0.616841</td>\n",
              "      <td>-0.876511</td>\n",
              "      <td>0.014781</td>\n",
              "      <td>0.009815</td>\n",
              "      <td>0.220291</td>\n",
              "      <td>0.173360</td>\n",
              "      <td>0.300331</td>\n",
              "      <td>0.589317</td>\n",
              "      <td>0.645945</td>\n",
              "      <td>-0.488573</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.769729</td>\n",
              "      <td>-1.010112</td>\n",
              "      <td>-0.082869</td>\n",
              "      <td>-0.107567</td>\n",
              "      <td>0.126159</td>\n",
              "      <td>0.523548</td>\n",
              "      <td>0.916201</td>\n",
              "      <td>1.132967</td>\n",
              "      <td>0.891338</td>\n",
              "      <td>-0.680893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>0.445759</td>\n",
              "      <td>0.435212</td>\n",
              "      <td>0.583252</td>\n",
              "      <td>-0.904547</td>\n",
              "      <td>0.293133</td>\n",
              "      <td>-0.419054</td>\n",
              "      <td>0.035571</td>\n",
              "      <td>-0.262624</td>\n",
              "      <td>-0.220010</td>\n",
              "      <td>-0.154380</td>\n",
              "      <td>...</td>\n",
              "      <td>1.049723</td>\n",
              "      <td>0.691306</td>\n",
              "      <td>0.819195</td>\n",
              "      <td>-0.509305</td>\n",
              "      <td>0.618016</td>\n",
              "      <td>-0.689547</td>\n",
              "      <td>0.564487</td>\n",
              "      <td>0.014889</td>\n",
              "      <td>0.016390</td>\n",
              "      <td>0.139775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>-0.804655</td>\n",
              "      <td>-1.555037</td>\n",
              "      <td>0.350777</td>\n",
              "      <td>-1.490930</td>\n",
              "      <td>0.887096</td>\n",
              "      <td>0.438926</td>\n",
              "      <td>-0.428246</td>\n",
              "      <td>0.264913</td>\n",
              "      <td>0.831769</td>\n",
              "      <td>-0.516161</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175025</td>\n",
              "      <td>-0.885465</td>\n",
              "      <td>0.852711</td>\n",
              "      <td>-0.502822</td>\n",
              "      <td>1.523755</td>\n",
              "      <td>1.500188</td>\n",
              "      <td>0.453273</td>\n",
              "      <td>0.735452</td>\n",
              "      <td>0.627636</td>\n",
              "      <td>0.118590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>1.162096</td>\n",
              "      <td>-0.146220</td>\n",
              "      <td>-0.936698</td>\n",
              "      <td>0.900291</td>\n",
              "      <td>1.257855</td>\n",
              "      <td>-0.351863</td>\n",
              "      <td>0.313228</td>\n",
              "      <td>-0.374714</td>\n",
              "      <td>-0.800693</td>\n",
              "      <td>-0.466879</td>\n",
              "      <td>...</td>\n",
              "      <td>2.270111</td>\n",
              "      <td>0.386239</td>\n",
              "      <td>-0.217112</td>\n",
              "      <td>1.348147</td>\n",
              "      <td>1.683445</td>\n",
              "      <td>-0.022374</td>\n",
              "      <td>1.176517</td>\n",
              "      <td>0.082523</td>\n",
              "      <td>-0.223686</td>\n",
              "      <td>0.164983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>-1.578320</td>\n",
              "      <td>0.727516</td>\n",
              "      <td>-1.931606</td>\n",
              "      <td>-0.903548</td>\n",
              "      <td>-0.443400</td>\n",
              "      <td>-0.151963</td>\n",
              "      <td>0.144036</td>\n",
              "      <td>0.878721</td>\n",
              "      <td>-1.419509</td>\n",
              "      <td>-2.087494</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.516672</td>\n",
              "      <td>0.796491</td>\n",
              "      <td>-1.876276</td>\n",
              "      <td>-0.759292</td>\n",
              "      <td>-0.265959</td>\n",
              "      <td>-0.007848</td>\n",
              "      <td>0.735432</td>\n",
              "      <td>1.401532</td>\n",
              "      <td>-1.009753</td>\n",
              "      <td>0.280394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>-0.833394</td>\n",
              "      <td>-0.429466</td>\n",
              "      <td>-0.234751</td>\n",
              "      <td>0.073440</td>\n",
              "      <td>1.213640</td>\n",
              "      <td>-0.357164</td>\n",
              "      <td>-0.506638</td>\n",
              "      <td>-0.694547</td>\n",
              "      <td>-0.383079</td>\n",
              "      <td>-0.390196</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.707744</td>\n",
              "      <td>-0.302579</td>\n",
              "      <td>-0.099798</td>\n",
              "      <td>0.222455</td>\n",
              "      <td>1.581875</td>\n",
              "      <td>-0.853624</td>\n",
              "      <td>-1.074844</td>\n",
              "      <td>-0.859585</td>\n",
              "      <td>-0.224894</td>\n",
              "      <td>-0.077528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>-0.327112</td>\n",
              "      <td>-0.574987</td>\n",
              "      <td>0.495731</td>\n",
              "      <td>-0.929859</td>\n",
              "      <td>0.285701</td>\n",
              "      <td>0.098501</td>\n",
              "      <td>-0.418589</td>\n",
              "      <td>0.320261</td>\n",
              "      <td>-0.108130</td>\n",
              "      <td>-0.149728</td>\n",
              "      <td>...</td>\n",
              "      <td>0.237801</td>\n",
              "      <td>-0.260376</td>\n",
              "      <td>0.740360</td>\n",
              "      <td>-0.530208</td>\n",
              "      <td>0.657668</td>\n",
              "      <td>0.651872</td>\n",
              "      <td>-0.463356</td>\n",
              "      <td>0.696359</td>\n",
              "      <td>0.072406</td>\n",
              "      <td>0.081931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>-1.319198</td>\n",
              "      <td>-1.016517</td>\n",
              "      <td>-0.779983</td>\n",
              "      <td>0.218254</td>\n",
              "      <td>-0.072532</td>\n",
              "      <td>0.161008</td>\n",
              "      <td>0.221426</td>\n",
              "      <td>0.081225</td>\n",
              "      <td>-0.012353</td>\n",
              "      <td>0.145859</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.382922</td>\n",
              "      <td>-1.063170</td>\n",
              "      <td>-0.832905</td>\n",
              "      <td>0.238683</td>\n",
              "      <td>-0.125360</td>\n",
              "      <td>0.601516</td>\n",
              "      <td>0.756755</td>\n",
              "      <td>0.226608</td>\n",
              "      <td>0.013806</td>\n",
              "      <td>0.302550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>430</th>\n",
              "      <td>0.815598</td>\n",
              "      <td>-0.316241</td>\n",
              "      <td>1.965770</td>\n",
              "      <td>0.126822</td>\n",
              "      <td>0.204060</td>\n",
              "      <td>0.087348</td>\n",
              "      <td>-0.097575</td>\n",
              "      <td>0.382826</td>\n",
              "      <td>-0.150216</td>\n",
              "      <td>0.150262</td>\n",
              "      <td>...</td>\n",
              "      <td>0.418828</td>\n",
              "      <td>-0.628501</td>\n",
              "      <td>2.003907</td>\n",
              "      <td>-0.223720</td>\n",
              "      <td>-0.145718</td>\n",
              "      <td>-0.000413</td>\n",
              "      <td>-0.696271</td>\n",
              "      <td>0.701121</td>\n",
              "      <td>-0.438852</td>\n",
              "      <td>0.279257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>436</th>\n",
              "      <td>-0.800256</td>\n",
              "      <td>-0.792608</td>\n",
              "      <td>0.770315</td>\n",
              "      <td>1.599709</td>\n",
              "      <td>-0.879528</td>\n",
              "      <td>-0.011150</td>\n",
              "      <td>0.411670</td>\n",
              "      <td>0.695216</td>\n",
              "      <td>-0.049452</td>\n",
              "      <td>-0.114461</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.670088</td>\n",
              "      <td>-0.668304</td>\n",
              "      <td>0.920137</td>\n",
              "      <td>1.682409</td>\n",
              "      <td>-0.970935</td>\n",
              "      <td>0.212853</td>\n",
              "      <td>1.300317</td>\n",
              "      <td>1.217235</td>\n",
              "      <td>0.031028</td>\n",
              "      <td>0.027805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>441</th>\n",
              "      <td>-0.223260</td>\n",
              "      <td>-0.986595</td>\n",
              "      <td>-0.478172</td>\n",
              "      <td>0.390653</td>\n",
              "      <td>1.174643</td>\n",
              "      <td>-0.357376</td>\n",
              "      <td>-0.940728</td>\n",
              "      <td>-0.554544</td>\n",
              "      <td>-0.314348</td>\n",
              "      <td>-0.130177</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.065637</td>\n",
              "      <td>-0.941946</td>\n",
              "      <td>-0.425645</td>\n",
              "      <td>0.461072</td>\n",
              "      <td>1.518609</td>\n",
              "      <td>-1.021560</td>\n",
              "      <td>-2.509752</td>\n",
              "      <td>-0.752841</td>\n",
              "      <td>-0.206921</td>\n",
              "      <td>0.153030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>-0.012373</td>\n",
              "      <td>1.252146</td>\n",
              "      <td>-0.380345</td>\n",
              "      <td>-0.773069</td>\n",
              "      <td>0.382484</td>\n",
              "      <td>-0.363397</td>\n",
              "      <td>-0.000529</td>\n",
              "      <td>-0.101994</td>\n",
              "      <td>-0.064596</td>\n",
              "      <td>-0.134299</td>\n",
              "      <td>...</td>\n",
              "      <td>0.581722</td>\n",
              "      <td>1.420337</td>\n",
              "      <td>-0.046792</td>\n",
              "      <td>-0.375725</td>\n",
              "      <td>0.740152</td>\n",
              "      <td>-0.499281</td>\n",
              "      <td>0.512213</td>\n",
              "      <td>0.221041</td>\n",
              "      <td>0.134251</td>\n",
              "      <td>0.120858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>-0.224950</td>\n",
              "      <td>-1.343358</td>\n",
              "      <td>-1.074452</td>\n",
              "      <td>0.399023</td>\n",
              "      <td>0.179065</td>\n",
              "      <td>0.332758</td>\n",
              "      <td>-1.011068</td>\n",
              "      <td>-1.161329</td>\n",
              "      <td>-0.213553</td>\n",
              "      <td>0.995350</td>\n",
              "      <td>...</td>\n",
              "      <td>1.059720</td>\n",
              "      <td>-0.326522</td>\n",
              "      <td>0.128001</td>\n",
              "      <td>1.498723</td>\n",
              "      <td>1.304685</td>\n",
              "      <td>1.415888</td>\n",
              "      <td>0.343663</td>\n",
              "      <td>0.322912</td>\n",
              "      <td>0.364134</td>\n",
              "      <td>0.439028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>-1.985969</td>\n",
              "      <td>0.003838</td>\n",
              "      <td>1.644144</td>\n",
              "      <td>0.984833</td>\n",
              "      <td>-0.591114</td>\n",
              "      <td>0.381489</td>\n",
              "      <td>-0.090017</td>\n",
              "      <td>0.351299</td>\n",
              "      <td>0.216892</td>\n",
              "      <td>0.209959</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.122767</td>\n",
              "      <td>-0.012832</td>\n",
              "      <td>1.693880</td>\n",
              "      <td>0.989421</td>\n",
              "      <td>-0.943480</td>\n",
              "      <td>1.254754</td>\n",
              "      <td>-0.252248</td>\n",
              "      <td>0.673211</td>\n",
              "      <td>0.280302</td>\n",
              "      <td>0.388825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>518</th>\n",
              "      <td>-0.421393</td>\n",
              "      <td>-0.147090</td>\n",
              "      <td>-0.886847</td>\n",
              "      <td>-0.424909</td>\n",
              "      <td>-0.496212</td>\n",
              "      <td>-0.245741</td>\n",
              "      <td>0.106144</td>\n",
              "      <td>-0.676547</td>\n",
              "      <td>-0.379133</td>\n",
              "      <td>-0.397177</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.107210</td>\n",
              "      <td>0.043806</td>\n",
              "      <td>-0.657150</td>\n",
              "      <td>-0.152540</td>\n",
              "      <td>-0.309860</td>\n",
              "      <td>-0.237907</td>\n",
              "      <td>0.684257</td>\n",
              "      <td>-0.506122</td>\n",
              "      <td>-0.059395</td>\n",
              "      <td>0.059070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>522</th>\n",
              "      <td>-0.392201</td>\n",
              "      <td>1.581655</td>\n",
              "      <td>-0.668535</td>\n",
              "      <td>-0.715722</td>\n",
              "      <td>-0.022845</td>\n",
              "      <td>0.434222</td>\n",
              "      <td>-0.128570</td>\n",
              "      <td>0.401275</td>\n",
              "      <td>-0.290013</td>\n",
              "      <td>-0.034709</td>\n",
              "      <td>...</td>\n",
              "      <td>0.214362</td>\n",
              "      <td>1.722304</td>\n",
              "      <td>-0.260708</td>\n",
              "      <td>-0.261805</td>\n",
              "      <td>0.408374</td>\n",
              "      <td>1.465601</td>\n",
              "      <td>0.271573</td>\n",
              "      <td>0.792564</td>\n",
              "      <td>-0.052896</td>\n",
              "      <td>0.194460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>-0.431289</td>\n",
              "      <td>-0.961414</td>\n",
              "      <td>0.247211</td>\n",
              "      <td>0.530652</td>\n",
              "      <td>1.370263</td>\n",
              "      <td>0.608513</td>\n",
              "      <td>0.096912</td>\n",
              "      <td>0.642539</td>\n",
              "      <td>-0.512275</td>\n",
              "      <td>-0.099531</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.508909</td>\n",
              "      <td>-1.046861</td>\n",
              "      <td>0.225265</td>\n",
              "      <td>0.519074</td>\n",
              "      <td>1.770810</td>\n",
              "      <td>2.002464</td>\n",
              "      <td>0.322723</td>\n",
              "      <td>1.216267</td>\n",
              "      <td>-0.702463</td>\n",
              "      <td>-0.026146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>563</th>\n",
              "      <td>-0.708379</td>\n",
              "      <td>1.546141</td>\n",
              "      <td>-1.409229</td>\n",
              "      <td>1.163428</td>\n",
              "      <td>0.399564</td>\n",
              "      <td>-0.493108</td>\n",
              "      <td>-0.386344</td>\n",
              "      <td>-0.535171</td>\n",
              "      <td>-0.122809</td>\n",
              "      <td>-0.347173</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.532284</td>\n",
              "      <td>1.608232</td>\n",
              "      <td>-1.345733</td>\n",
              "      <td>1.243604</td>\n",
              "      <td>0.533450</td>\n",
              "      <td>-1.392143</td>\n",
              "      <td>-0.890879</td>\n",
              "      <td>-0.745617</td>\n",
              "      <td>-0.058131</td>\n",
              "      <td>-0.223233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>-0.069322</td>\n",
              "      <td>-1.432529</td>\n",
              "      <td>-0.820246</td>\n",
              "      <td>0.186673</td>\n",
              "      <td>0.684506</td>\n",
              "      <td>0.220949</td>\n",
              "      <td>1.147264</td>\n",
              "      <td>-0.987194</td>\n",
              "      <td>0.183544</td>\n",
              "      <td>-1.205528</td>\n",
              "      <td>...</td>\n",
              "      <td>1.200032</td>\n",
              "      <td>-0.506417</td>\n",
              "      <td>0.243996</td>\n",
              "      <td>1.229909</td>\n",
              "      <td>1.440678</td>\n",
              "      <td>1.254851</td>\n",
              "      <td>2.077681</td>\n",
              "      <td>0.098232</td>\n",
              "      <td>0.394678</td>\n",
              "      <td>0.050274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>0.495486</td>\n",
              "      <td>-0.560788</td>\n",
              "      <td>2.090418</td>\n",
              "      <td>-0.653841</td>\n",
              "      <td>0.013527</td>\n",
              "      <td>0.216450</td>\n",
              "      <td>0.068723</td>\n",
              "      <td>0.211817</td>\n",
              "      <td>-0.359723</td>\n",
              "      <td>-0.293746</td>\n",
              "      <td>...</td>\n",
              "      <td>1.069847</td>\n",
              "      <td>-0.239008</td>\n",
              "      <td>2.165710</td>\n",
              "      <td>-0.243091</td>\n",
              "      <td>0.355306</td>\n",
              "      <td>0.948036</td>\n",
              "      <td>0.585937</td>\n",
              "      <td>0.547080</td>\n",
              "      <td>-0.172158</td>\n",
              "      <td>-0.035767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>-1.073858</td>\n",
              "      <td>0.724110</td>\n",
              "      <td>1.672551</td>\n",
              "      <td>-1.628064</td>\n",
              "      <td>-0.039826</td>\n",
              "      <td>-0.130763</td>\n",
              "      <td>-0.035340</td>\n",
              "      <td>0.383273</td>\n",
              "      <td>0.395309</td>\n",
              "      <td>-0.339782</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.037449</td>\n",
              "      <td>0.784919</td>\n",
              "      <td>1.737875</td>\n",
              "      <td>-1.621297</td>\n",
              "      <td>0.003989</td>\n",
              "      <td>-0.218938</td>\n",
              "      <td>0.147392</td>\n",
              "      <td>0.751602</td>\n",
              "      <td>0.507126</td>\n",
              "      <td>-0.238402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>-0.897388</td>\n",
              "      <td>-0.460320</td>\n",
              "      <td>-0.464072</td>\n",
              "      <td>0.057362</td>\n",
              "      <td>1.076878</td>\n",
              "      <td>-0.112291</td>\n",
              "      <td>0.316881</td>\n",
              "      <td>0.489042</td>\n",
              "      <td>-0.321658</td>\n",
              "      <td>-0.215276</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.765783</td>\n",
              "      <td>-0.338034</td>\n",
              "      <td>-0.329789</td>\n",
              "      <td>0.235090</td>\n",
              "      <td>1.439916</td>\n",
              "      <td>-0.030669</td>\n",
              "      <td>1.078764</td>\n",
              "      <td>0.908350</td>\n",
              "      <td>-0.228456</td>\n",
              "      <td>-0.039202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>689</th>\n",
              "      <td>-1.254453</td>\n",
              "      <td>-2.178762</td>\n",
              "      <td>-2.728623</td>\n",
              "      <td>-3.716205</td>\n",
              "      <td>-11.357556</td>\n",
              "      <td>13.499553</td>\n",
              "      <td>1.632417</td>\n",
              "      <td>0.941890</td>\n",
              "      <td>1.117550</td>\n",
              "      <td>0.344258</td>\n",
              "      <td>...</td>\n",
              "      <td>1.039412</td>\n",
              "      <td>-0.091762</td>\n",
              "      <td>0.005227</td>\n",
              "      <td>0.616969</td>\n",
              "      <td>0.692177</td>\n",
              "      <td>0.194401</td>\n",
              "      <td>0.653565</td>\n",
              "      <td>0.560927</td>\n",
              "      <td>0.025447</td>\n",
              "      <td>0.224621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>-0.595081</td>\n",
              "      <td>-0.368895</td>\n",
              "      <td>0.719811</td>\n",
              "      <td>-0.064879</td>\n",
              "      <td>0.427504</td>\n",
              "      <td>0.258114</td>\n",
              "      <td>-0.295046</td>\n",
              "      <td>0.280757</td>\n",
              "      <td>-0.335971</td>\n",
              "      <td>-0.117040</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.456563</td>\n",
              "      <td>-0.228331</td>\n",
              "      <td>0.858129</td>\n",
              "      <td>0.103320</td>\n",
              "      <td>0.656617</td>\n",
              "      <td>0.950651</td>\n",
              "      <td>-0.503921</td>\n",
              "      <td>0.598331</td>\n",
              "      <td>-0.273366</td>\n",
              "      <td>0.047165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>724</th>\n",
              "      <td>-0.350881</td>\n",
              "      <td>-0.802910</td>\n",
              "      <td>-1.057180</td>\n",
              "      <td>-0.890072</td>\n",
              "      <td>0.966278</td>\n",
              "      <td>-0.956337</td>\n",
              "      <td>-0.560835</td>\n",
              "      <td>-0.583774</td>\n",
              "      <td>-0.541362</td>\n",
              "      <td>-1.610060</td>\n",
              "      <td>...</td>\n",
              "      <td>0.881851</td>\n",
              "      <td>-0.000141</td>\n",
              "      <td>-0.104251</td>\n",
              "      <td>0.209996</td>\n",
              "      <td>1.514722</td>\n",
              "      <td>-0.532851</td>\n",
              "      <td>0.683054</td>\n",
              "      <td>0.514757</td>\n",
              "      <td>0.289239</td>\n",
              "      <td>0.161666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37 rows × 270 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f346af6a-f06b-4a2b-abfe-56c44b191d28')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f346af6a-f06b-4a2b-abfe-56c44b191d28 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f346af6a-f06b-4a2b-abfe-56c44b191d28');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "X_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EwPtcguv-cRC",
        "outputId": "0546c171-1ef8-466c-d468-2b39ea9aaf88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     updrs_1_plus_0_months  updrs_2_plus_0_months  updrs_3_plus_0_months  \\\n",
              "34                       5                     15                     22   \n",
              "62                       2                      4                     10   \n",
              "71                      11                      8                     28   \n",
              "80                       2                      3                      7   \n",
              "86                       6                      6                     26   \n",
              "142                      2                      8                     47   \n",
              "151                      6                      0                      1   \n",
              "157                      2                      2                     15   \n",
              "176                      3                      5                     20   \n",
              "185                      5                      1                     13   \n",
              "212                      7                     20                     33   \n",
              "246                      9                      7                     34   \n",
              "285                      6                      0                      1   \n",
              "301                      1                      7                     27   \n",
              "305                     11                      7                     20   \n",
              "336                      5                      8                     10   \n",
              "347                      5                      0                      0   \n",
              "368                      5                     12                     27   \n",
              "374                      2                      4                      7   \n",
              "383                      4                      7                     26   \n",
              "430                      1                     13                     61   \n",
              "436                      5                      0                      0   \n",
              "441                      1                      4                     21   \n",
              "456                      1                      9                     27   \n",
              "462                      9                      9                     18   \n",
              "499                      5                      0                      0   \n",
              "518                      1                      2                     12   \n",
              "522                      6                      3                     12   \n",
              "526                      3                      9                     28   \n",
              "563                      3                      5                     20   \n",
              "578                     12                     12                     26   \n",
              "591                     13                     12                      7   \n",
              "611                      4                      7                     22   \n",
              "638                      6                      0                      2   \n",
              "689                      4                      6                     15   \n",
              "709                      5                      0                      1   \n",
              "724                     11                     17                     46   \n",
              "\n",
              "     updrs_4_plus_0_months  updrs_1_plus_6_months  updrs_2_plus_6_months  \\\n",
              "34                       0                      7                     20   \n",
              "62                       0                      5                      6   \n",
              "71                       6                     11                      8   \n",
              "80                       0                      7                      2   \n",
              "86                       0                      9                      7   \n",
              "142                      0                      2                     10   \n",
              "151                      0                      6                      0   \n",
              "157                      0                      2                      4   \n",
              "176                      0                      1                      7   \n",
              "185                      0                     11                      7   \n",
              "212                      0                      5                      8   \n",
              "246                      0                      4                      7   \n",
              "285                      0                      5                      0   \n",
              "301                      0                      1                      9   \n",
              "305                      0                      9                      9   \n",
              "336                      7                      6                     12   \n",
              "347                      0                     10                      0   \n",
              "368                      7                      1                      1   \n",
              "374                      0                      7                      6   \n",
              "383                      0                      7                     13   \n",
              "430                      0                      2                     14   \n",
              "436                      0                      5                      0   \n",
              "441                      0                      0                      5   \n",
              "456                      0                      1                     10   \n",
              "462                      0                      3                      6   \n",
              "499                      0                      9                      0   \n",
              "518                      0                      0                     11   \n",
              "522                      0                      7                      4   \n",
              "526                      0                      4                      8   \n",
              "563                      0                      7                      3   \n",
              "578                      0                     12                     18   \n",
              "591                      5                     21                     19   \n",
              "611                      0                      5                      7   \n",
              "638                      0                      6                      0   \n",
              "689                      5                      1                      6   \n",
              "709                      0                      5                      0   \n",
              "724                      1                      9                     14   \n",
              "\n",
              "     updrs_3_plus_6_months  updrs_4_plus_6_months  updrs_1_plus_12_months  \\\n",
              "34                      33                      0                       5   \n",
              "62                       7                      0                       5   \n",
              "71                      28                      6                       5   \n",
              "80                      12                      0                       2   \n",
              "86                      34                      0                       4   \n",
              "142                     50                      0                       1   \n",
              "151                      1                      0                       5   \n",
              "157                     16                      0                       1   \n",
              "176                     27                      0                       1   \n",
              "185                     20                      0                       9   \n",
              "212                     10                      7                       6   \n",
              "246                     26                      0                       7   \n",
              "285                      0                      0                       5   \n",
              "301                     27                      0                       1   \n",
              "305                     18                      0                       3   \n",
              "336                     23                      4                       6   \n",
              "347                      2                      0                       5   \n",
              "368                     15                      0                       1   \n",
              "374                     16                      0                       6   \n",
              "383                     29                      0                       3   \n",
              "430                     62                      0                       5   \n",
              "436                      0                      0                       8   \n",
              "441                     15                      0                       3   \n",
              "456                     33                      0                       3   \n",
              "462                     29                      0                      12   \n",
              "499                      0                      0                       9   \n",
              "518                     11                      0                       4   \n",
              "522                     19                      0                       4   \n",
              "526                     24                      0                       4   \n",
              "563                     19                      0                       7   \n",
              "578                     48                      0                      11   \n",
              "591                      4                      8                      14   \n",
              "611                     13                      0                       4   \n",
              "638                      2                      0                       5   \n",
              "689                      6                      5                       1   \n",
              "709                      1                      0                       4   \n",
              "724                     57                      3                      14   \n",
              "\n",
              "     updrs_2_plus_12_months  updrs_3_plus_12_months  updrs_4_plus_12_months  \\\n",
              "34                        8                      10                       7   \n",
              "62                        9                      10                       0   \n",
              "71                       12                      27                       7   \n",
              "80                        4                       7                       0   \n",
              "86                        7                      26                       0   \n",
              "142                      13                      61                       0   \n",
              "151                       0                       0                       0   \n",
              "157                       4                      21                       0   \n",
              "176                       9                      27                       0   \n",
              "185                       9                      18                       0   \n",
              "212                      12                      23                       4   \n",
              "246                      13                      29                       0   \n",
              "285                       0                       0                       0   \n",
              "301                      10                      33                       0   \n",
              "305                       6                      29                       0   \n",
              "336                      14                       7                       3   \n",
              "347                       0                       0                       0   \n",
              "368                       2                      12                       0   \n",
              "374                       3                      12                       0   \n",
              "383                       9                      28                       0   \n",
              "430                      17                      50                       0   \n",
              "436                       1                       1                       0   \n",
              "441                       5                      20                       0   \n",
              "456                      10                      33                       0   \n",
              "462                      12                      26                       0   \n",
              "499                       0                       0                       0   \n",
              "518                       1                      11                       0   \n",
              "522                       5                      17                       1   \n",
              "526                       7                      22                       0   \n",
              "563                       8                      22                       0   \n",
              "578                      18                      44                       1   \n",
              "591                      20                       4                      10   \n",
              "611                       6                      15                       5   \n",
              "638                       0                       1                       0   \n",
              "689                       5                      28                       5   \n",
              "709                       1                       0                       0   \n",
              "724                      13                      45                       3   \n",
              "\n",
              "     updrs_1_plus_24_months  updrs_2_plus_24_months  updrs_3_plus_24_months  \\\n",
              "34                        6                      14                       7   \n",
              "62                        9                       9                      13   \n",
              "71                        1                       2                      12   \n",
              "80                        6                       3                      12   \n",
              "86                        3                       9                      28   \n",
              "142                       5                      17                      50   \n",
              "151                       8                       1                       1   \n",
              "157                       3                       5                      20   \n",
              "176                       3                      10                      33   \n",
              "185                      12                      12                      26   \n",
              "212                      11                      14                      14   \n",
              "246                       4                       8                      24   \n",
              "285                       8                       1                       1   \n",
              "301                       2                       7                      29   \n",
              "305                      12                      18                      48   \n",
              "336                      13                      12                       7   \n",
              "347                       9                       0                       0   \n",
              "368                       4                       1                      11   \n",
              "374                       4                       5                      17   \n",
              "383                       4                       7                      22   \n",
              "430                       6                      16                      65   \n",
              "436                       6                       0                       2   \n",
              "441                       7                       8                      22   \n",
              "456                       4                      14                      51   \n",
              "462                      11                      18                      44   \n",
              "499                       9                       0                       0   \n",
              "518                       2                       3                      24   \n",
              "522                       7                       7                      25   \n",
              "526                       4                       6                      15   \n",
              "563                       6                       9                      22   \n",
              "578                      11                      17                      46   \n",
              "591                      17                      22                      10   \n",
              "611                       1                       5                      28   \n",
              "638                       4                       1                       0   \n",
              "689                       6                       6                      37   \n",
              "709                       4                       1                       0   \n",
              "724                      10                      13                      53   \n",
              "\n",
              "     updrs_4_plus_24_months  \n",
              "34                        3  \n",
              "62                        1  \n",
              "71                        0  \n",
              "80                        0  \n",
              "86                        0  \n",
              "142                       0  \n",
              "151                       0  \n",
              "157                       0  \n",
              "176                       0  \n",
              "185                       0  \n",
              "212                       7  \n",
              "246                       0  \n",
              "285                       0  \n",
              "301                       0  \n",
              "305                       0  \n",
              "336                       5  \n",
              "347                       0  \n",
              "368                       0  \n",
              "374                       1  \n",
              "383                       0  \n",
              "430                       2  \n",
              "436                       0  \n",
              "441                       0  \n",
              "456                       0  \n",
              "462                       1  \n",
              "499                       0  \n",
              "518                       2  \n",
              "522                       0  \n",
              "526                       5  \n",
              "563                       0  \n",
              "578                       1  \n",
              "591                       7  \n",
              "611                       5  \n",
              "638                       0  \n",
              "689                       7  \n",
              "709                       0  \n",
              "724                       4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-86fc3858-852e-4ecb-a7e9-87e2cb4bfd98\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>updrs_1_plus_0_months</th>\n",
              "      <th>updrs_2_plus_0_months</th>\n",
              "      <th>updrs_3_plus_0_months</th>\n",
              "      <th>updrs_4_plus_0_months</th>\n",
              "      <th>updrs_1_plus_6_months</th>\n",
              "      <th>updrs_2_plus_6_months</th>\n",
              "      <th>updrs_3_plus_6_months</th>\n",
              "      <th>updrs_4_plus_6_months</th>\n",
              "      <th>updrs_1_plus_12_months</th>\n",
              "      <th>updrs_2_plus_12_months</th>\n",
              "      <th>updrs_3_plus_12_months</th>\n",
              "      <th>updrs_4_plus_12_months</th>\n",
              "      <th>updrs_1_plus_24_months</th>\n",
              "      <th>updrs_2_plus_24_months</th>\n",
              "      <th>updrs_3_plus_24_months</th>\n",
              "      <th>updrs_4_plus_24_months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>430</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>65</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>436</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>441</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>518</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>522</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>563</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>17</td>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>689</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>37</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>724</th>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "      <td>57</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>45</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>53</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86fc3858-852e-4ecb-a7e9-87e2cb4bfd98')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86fc3858-852e-4ecb-a7e9-87e2cb4bfd98 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86fc3858-852e-4ecb-a7e9-87e2cb4bfd98');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "y_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hrfJ03gC-dXv"
      },
      "outputs": [],
      "source": [
        "best_model = None\n",
        "best_test_score = float('inf')  # Set to infinity initially\n",
        "\n",
        "for _ in range(5):\n",
        "    # Create a new model for each iteration\n",
        "    model = Sequential()\n",
        "    model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0001)))\n",
        "    model.add(Dense(16, activation='linear'))\n",
        "\n",
        "    # Create Adam optimizer with a learning rate of 0.001\n",
        "    adam = Adam(learning_rate=0.0001)\n",
        "\n",
        "    # Compile model with Adam optimizer and a learning rate of 0.0001\n",
        "    model.compile(loss=smape_loss, optimizer='adam')\n",
        "\n",
        "    # Fit the model\n",
        "    history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, verbose=0, batch_size=32)\n",
        "\n",
        "    # Evaluate the model on the test set\n",
        "    test_score = model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "    # Check if the current model has a better test score than the previous best model\n",
        "    if test_score < best_test_score:\n",
        "        best_model = model\n",
        "        best_test_score = test_score\n",
        "\n",
        "    print(test_score)\n",
        "    # Plot training and validation loss over epochs for each iteration\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('Model Loss')\n",
        "    plt.ylabel('MAPE')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "# Use the best model for predictions\n",
        "model = best_model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import LSTM"
      ],
      "metadata": {
        "id": "7bkKr5iB1kuw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# design network\n",
        "model = Sequential()\n",
        "model.add(LSTM(128, activation='relu', return_sequences = True, input_shape=(X_train.shape[1])))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(100, activation='relu', return_sequences = True, name = \"LSTM_2\"))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(50, activation='relu', return_sequences = True, name = \"LSTM_3\"))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(32, activation='relu',name = \"LSTM_3\"))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(50, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mae', optimizer='adam', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "aNzsfcLNvlbk",
        "outputId": "5fd8d869-27d9-4582-cc71-8047dff224d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-05f3186be55c>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# design network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_sequences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_sequences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"LSTM_2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/layers/rnn/lstm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, units, activation, recurrent_activation, use_bias, kernel_initializer, recurrent_initializer, bias_initializer, unit_forget_bias, kernel_regularizer, recurrent_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, recurrent_constraint, bias_constraint, dropout, recurrent_dropout, return_sequences, return_state, go_backwards, stateful, time_major, unroll, **kwargs)\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mcell_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m         )\n\u001b[0;32m--> 561\u001b[0;31m         super().__init__(\n\u001b[0m\u001b[1;32m    562\u001b[0m             \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0mreturn_sequences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_sequences\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/layers/rnn/dropout_rnn_cell_mixin.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_non_trackable_mask_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__internal__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtracking\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_automatic_dependency_tracking\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/layers/rnn/base_rnn.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, cell, return_sequences, return_state, go_backwards, stateful, unroll, time_major, **kwargs)\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_shape\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_sequences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_sequences\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/trackable/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, seed, force_generator, rng_type, **kwargs)\u001b[0m\n\u001b[1;32m   3788\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0;32mclass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3789\u001b[0m         \"\"\"\n\u001b[0;32m-> 3790\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3791\u001b[0m         self._random_generator = backend.RandomGenerator(\n\u001b[1;32m   3792\u001b[0m             \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_generator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrng_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrng_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/trackable/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, trainable, name, dtype, dynamic, **kwargs)\u001b[0m\n\u001b[1;32m    451\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m                     \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m                 \u001b[0mbatch_input_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_shape\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_input_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_input_shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lwZdD1u91bWN"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}