movesense_validation_stats.ipynb

movesense_validation_stats.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1jM6ruMwzxzqciupo8bJ3h9S8CrTkMEnq",
      "authorship_tag": "ABX9TyNVG8gzN8RlRJXEvm3LXSUO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alexisduque/d6e888c37c5e2097d82c7ec566d383b2/movesense_validation_stats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Tn78f3Txhq78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72162e5f-2a19-4fc7-9e23-f8fc81b85902"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Mounted at /content/drive\n",
            "Running in Colab: True\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import plotly as plty\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import numpy as np\n",
        "import os\n",
        "import pickle\n",
        "import statsmodels.stats.power as smp\n",
        "import statsmodels.stats.correlation_tools as sm\n",
        "import scipy.stats as stats\n",
        "import statsmodels as sm\n",
        "from pathlib import Path\n",
        "try:\n",
        "    import google.colab\n",
        "    IN_COLAB = True\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "    path = '/content/drive/My Drive/Colab Notebooks/'\n",
        "    os.makedirs(path, exist_ok=True)\n",
        "\n",
        "    if os.path.exists(path + 'auth_token.pickle'):\n",
        "        with open(path + 'auth_token.pickle', 'rb') as token:\n",
        "            creds = pickle.load(token)\n",
        "        drive.mount('/content/drive', force_remount=True)\n",
        "    else:\n",
        "        from google.colab import auth\n",
        "        from google.auth import default\n",
        "        auth.authenticate_user()\n",
        "        creds, _ = default()\n",
        "        drive.mount('/content/drive', force_remount=True)\n",
        "        with open(path + 'auth_token.pickle', 'wb') as token:\n",
        "            pickle.dump(creds, token)\n",
        "except ImportError:\n",
        "    IN_COLAB = False\n",
        "\n",
        "print(\"Running in Colab:\", IN_COLAB)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = \"/content/drive/MyDrive/Movesense Validation/\" if IN_COLAB else \"./data/\""
      ],
      "metadata": {
        "id": "ZUyLhL33iOql"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data = pd.read_csv(data_path+'data_all.csv', delimiter=',')\n",
        "# data[data.Sujets == 'SOUTH'].head()\n",
        "#data.loc[255, 'Exclude'] = 'OK'\n",
        "# data.loc[data['Sujets'] == 'SOUTH', 'Exclude'] = 'OK'\n",
        "# data[data.Sujets == 'SOUTH'].head()\n"
      ],
      "metadata": {
        "id": "13MI1pB1318U"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data.to_csv(data_path+'data_all.csv', index=False)"
      ],
      "metadata": {
        "id": "0hJg875gqsj2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(data_path+'data_all.csv', delimiter=',')\n",
        "#data.loc[data['Sujets'] == 'SOUTH', 'Exclude'] = 'OK'\n",
        "data = data[(data.Exclude == 'OK')]\n",
        "grouped_data = data.groupby(['Sujets', 'Localisation'])\n",
        "grouped_data.tail()"
      ],
      "metadata": {
        "id": "UDcyM-OaiWIw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "fcbb64b0-4f02-446b-cbd1-8d55eb543f1c"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Sujets      Localisation  Condition  Sensor   medF_Acc  Energy_Acc  \\\n",
              "0    AUBRO   Gastro Medialis          8  DYTRAN  39.519231   83.347116   \n",
              "2    AUBRO  Vastus Lateralis          8  DYTRAN  48.365385  124.695285   \n",
              "3    AUBRO   Gastro Medialis         10  DYTRAN  42.211538  110.151088   \n",
              "5    AUBRO  Vastus Lateralis         10  DYTRAN  45.288462  129.216480   \n",
              "6    AUBRO   Gastro Medialis         12  DYTRAN  50.769231  188.578466   \n",
              "..     ...               ...        ...     ...        ...         ...   \n",
              "278  VAUJU  Vastus Lateralis         12  DYTRAN  55.865385  417.907770   \n",
              "280  VAUJU             Talon         14  DYTRAN  94.038462  473.923343   \n",
              "281  VAUJU  Vastus Lateralis         14  DYTRAN  53.557692  392.907970   \n",
              "283  VAUJU             Talon         16  DYTRAN  89.903846  562.740408   \n",
              "284  VAUJU  Vastus Lateralis         16  DYTRAN  56.923077  594.518324   \n",
              "\n",
              "           ST       Damp    Peak_Acc  medF_Acc_Mov  ...      SDST     SDDamp  \\\n",
              "0    0.136385  17.755861   62.731254     33.250000  ...  0.011884   2.094695   \n",
              "2    0.066154  33.603771   69.386818     45.173077  ...  0.027095   9.007543   \n",
              "3    0.127769  18.867015   87.293111     42.576923  ...  0.022107   3.272941   \n",
              "5    0.098615  24.275008   68.664708     42.192308  ...  0.058121  12.600707   \n",
              "6    0.103231  22.413203  152.650587     49.500000  ...  0.020298   3.738436   \n",
              "..        ...        ...         ...           ...  ...       ...        ...   \n",
              "278  0.094462  26.976610  152.561816     47.192308  ...  0.047628  15.166181   \n",
              "280  0.034154  65.194455  217.678605     93.346154  ...  0.007543  10.444152   \n",
              "281  0.114615  19.113554  150.312816     46.134615  ...  0.049005  12.761141   \n",
              "283  0.049000  66.609222  296.352369     84.596154  ...  0.015692   7.898744   \n",
              "284  0.090846  24.502243  186.165484     49.692308  ...  0.055946  13.983492   \n",
              "\n",
              "     SDPeak_Acc  SDmedF_Acc_Mov  SDEnergy_Acc_Mov  SDST_Mov  SDDamp_Mov  \\\n",
              "0      8.247854        4.915145         20.699078  0.023919    5.146344   \n",
              "2     13.138813        5.865385         18.266657  0.029830    8.697157   \n",
              "3     11.019690        5.977027         38.086609  0.032074    5.796081   \n",
              "5     11.950839        3.016228         21.707450  0.047939    8.726940   \n",
              "6     27.471170        4.439768         23.619972  0.010832    6.340993   \n",
              "..          ...             ...               ...       ...         ...   \n",
              "278   45.211829        4.269924         99.416836  0.045540   14.812554   \n",
              "280   25.797677        4.115564         28.139577  0.011706    7.753860   \n",
              "281   34.461104        5.798802        108.076244  0.039920   14.777620   \n",
              "283   36.959917        1.623264         18.075032  0.015662    6.836665   \n",
              "284   39.885371        4.947954        155.390591  0.044856   15.862625   \n",
              "\n",
              "     SDPeak_Acc_Mov  Exclude  Comment  \n",
              "0         16.388975       OK      NaN  \n",
              "2         14.765997       OK      NaN  \n",
              "3         20.361804       OK      NaN  \n",
              "5          9.028326       OK      NaN  \n",
              "6         12.166044       OK      NaN  \n",
              "..              ...      ...      ...  \n",
              "278       23.971263       OK      NaN  \n",
              "280       22.945430       OK      NaN  \n",
              "281       22.009087       OK      NaN  \n",
              "283       11.654036       OK      NaN  \n",
              "284       15.162180       OK      NaN  \n",
              "\n",
              "[230 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de173fd4-20d1-4f97-8a77-9f085c2e0f98\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .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>Sujets</th>\n",
              "      <th>Localisation</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Sensor</th>\n",
              "      <th>medF_Acc</th>\n",
              "      <th>Energy_Acc</th>\n",
              "      <th>ST</th>\n",
              "      <th>Damp</th>\n",
              "      <th>Peak_Acc</th>\n",
              "      <th>medF_Acc_Mov</th>\n",
              "      <th>...</th>\n",
              "      <th>SDST</th>\n",
              "      <th>SDDamp</th>\n",
              "      <th>SDPeak_Acc</th>\n",
              "      <th>SDmedF_Acc_Mov</th>\n",
              "      <th>SDEnergy_Acc_Mov</th>\n",
              "      <th>SDST_Mov</th>\n",
              "      <th>SDDamp_Mov</th>\n",
              "      <th>SDPeak_Acc_Mov</th>\n",
              "      <th>Exclude</th>\n",
              "      <th>Comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AUBRO</td>\n",
              "      <td>Gastro Medialis</td>\n",
              "      <td>8</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>39.519231</td>\n",
              "      <td>83.347116</td>\n",
              "      <td>0.136385</td>\n",
              "      <td>17.755861</td>\n",
              "      <td>62.731254</td>\n",
              "      <td>33.250000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.011884</td>\n",
              "      <td>2.094695</td>\n",
              "      <td>8.247854</td>\n",
              "      <td>4.915145</td>\n",
              "      <td>20.699078</td>\n",
              "      <td>0.023919</td>\n",
              "      <td>5.146344</td>\n",
              "      <td>16.388975</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AUBRO</td>\n",
              "      <td>Vastus Lateralis</td>\n",
              "      <td>8</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>48.365385</td>\n",
              "      <td>124.695285</td>\n",
              "      <td>0.066154</td>\n",
              "      <td>33.603771</td>\n",
              "      <td>69.386818</td>\n",
              "      <td>45.173077</td>\n",
              "      <td>...</td>\n",
              "      <td>0.027095</td>\n",
              "      <td>9.007543</td>\n",
              "      <td>13.138813</td>\n",
              "      <td>5.865385</td>\n",
              "      <td>18.266657</td>\n",
              "      <td>0.029830</td>\n",
              "      <td>8.697157</td>\n",
              "      <td>14.765997</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AUBRO</td>\n",
              "      <td>Gastro Medialis</td>\n",
              "      <td>10</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>42.211538</td>\n",
              "      <td>110.151088</td>\n",
              "      <td>0.127769</td>\n",
              "      <td>18.867015</td>\n",
              "      <td>87.293111</td>\n",
              "      <td>42.576923</td>\n",
              "      <td>...</td>\n",
              "      <td>0.022107</td>\n",
              "      <td>3.272941</td>\n",
              "      <td>11.019690</td>\n",
              "      <td>5.977027</td>\n",
              "      <td>38.086609</td>\n",
              "      <td>0.032074</td>\n",
              "      <td>5.796081</td>\n",
              "      <td>20.361804</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AUBRO</td>\n",
              "      <td>Vastus Lateralis</td>\n",
              "      <td>10</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>45.288462</td>\n",
              "      <td>129.216480</td>\n",
              "      <td>0.098615</td>\n",
              "      <td>24.275008</td>\n",
              "      <td>68.664708</td>\n",
              "      <td>42.192308</td>\n",
              "      <td>...</td>\n",
              "      <td>0.058121</td>\n",
              "      <td>12.600707</td>\n",
              "      <td>11.950839</td>\n",
              "      <td>3.016228</td>\n",
              "      <td>21.707450</td>\n",
              "      <td>0.047939</td>\n",
              "      <td>8.726940</td>\n",
              "      <td>9.028326</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AUBRO</td>\n",
              "      <td>Gastro Medialis</td>\n",
              "      <td>12</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>50.769231</td>\n",
              "      <td>188.578466</td>\n",
              "      <td>0.103231</td>\n",
              "      <td>22.413203</td>\n",
              "      <td>152.650587</td>\n",
              "      <td>49.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.020298</td>\n",
              "      <td>3.738436</td>\n",
              "      <td>27.471170</td>\n",
              "      <td>4.439768</td>\n",
              "      <td>23.619972</td>\n",
              "      <td>0.010832</td>\n",
              "      <td>6.340993</td>\n",
              "      <td>12.166044</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</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>278</th>\n",
              "      <td>VAUJU</td>\n",
              "      <td>Vastus Lateralis</td>\n",
              "      <td>12</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>55.865385</td>\n",
              "      <td>417.907770</td>\n",
              "      <td>0.094462</td>\n",
              "      <td>26.976610</td>\n",
              "      <td>152.561816</td>\n",
              "      <td>47.192308</td>\n",
              "      <td>...</td>\n",
              "      <td>0.047628</td>\n",
              "      <td>15.166181</td>\n",
              "      <td>45.211829</td>\n",
              "      <td>4.269924</td>\n",
              "      <td>99.416836</td>\n",
              "      <td>0.045540</td>\n",
              "      <td>14.812554</td>\n",
              "      <td>23.971263</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>VAUJU</td>\n",
              "      <td>Talon</td>\n",
              "      <td>14</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>94.038462</td>\n",
              "      <td>473.923343</td>\n",
              "      <td>0.034154</td>\n",
              "      <td>65.194455</td>\n",
              "      <td>217.678605</td>\n",
              "      <td>93.346154</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007543</td>\n",
              "      <td>10.444152</td>\n",
              "      <td>25.797677</td>\n",
              "      <td>4.115564</td>\n",
              "      <td>28.139577</td>\n",
              "      <td>0.011706</td>\n",
              "      <td>7.753860</td>\n",
              "      <td>22.945430</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>VAUJU</td>\n",
              "      <td>Vastus Lateralis</td>\n",
              "      <td>14</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>53.557692</td>\n",
              "      <td>392.907970</td>\n",
              "      <td>0.114615</td>\n",
              "      <td>19.113554</td>\n",
              "      <td>150.312816</td>\n",
              "      <td>46.134615</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049005</td>\n",
              "      <td>12.761141</td>\n",
              "      <td>34.461104</td>\n",
              "      <td>5.798802</td>\n",
              "      <td>108.076244</td>\n",
              "      <td>0.039920</td>\n",
              "      <td>14.777620</td>\n",
              "      <td>22.009087</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>VAUJU</td>\n",
              "      <td>Talon</td>\n",
              "      <td>16</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>89.903846</td>\n",
              "      <td>562.740408</td>\n",
              "      <td>0.049000</td>\n",
              "      <td>66.609222</td>\n",
              "      <td>296.352369</td>\n",
              "      <td>84.596154</td>\n",
              "      <td>...</td>\n",
              "      <td>0.015692</td>\n",
              "      <td>7.898744</td>\n",
              "      <td>36.959917</td>\n",
              "      <td>1.623264</td>\n",
              "      <td>18.075032</td>\n",
              "      <td>0.015662</td>\n",
              "      <td>6.836665</td>\n",
              "      <td>11.654036</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>VAUJU</td>\n",
              "      <td>Vastus Lateralis</td>\n",
              "      <td>16</td>\n",
              "      <td>DYTRAN</td>\n",
              "      <td>56.923077</td>\n",
              "      <td>594.518324</td>\n",
              "      <td>0.090846</td>\n",
              "      <td>24.502243</td>\n",
              "      <td>186.165484</td>\n",
              "      <td>49.692308</td>\n",
              "      <td>...</td>\n",
              "      <td>0.055946</td>\n",
              "      <td>13.983492</td>\n",
              "      <td>39.885371</td>\n",
              "      <td>4.947954</td>\n",
              "      <td>155.390591</td>\n",
              "      <td>0.044856</td>\n",
              "      <td>15.862625</td>\n",
              "      <td>15.162180</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>230 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de173fd4-20d1-4f97-8a77-9f085c2e0f98')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-de173fd4-20d1-4f97-8a77-9f085c2e0f98 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-de173fd4-20d1-4f97-8a77-9f085c2e0f98');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d8de1d29-4d35-4638-9249-60c51fd5dfec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d8de1d29-4d35-4638-9249-60c51fd5dfec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d8de1d29-4d35-4638-9249-60c51fd5dfec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sujets = ['GASRO_03022025', 'SOUTH', 'VAUJU', 'PERMA', 'MILLI', 'DESTH',\n",
        "    'BOUJE', 'LOIRO', 'NAOAZ', 'HENLA', 'POUZA', 'DIGGU', 'FILME', 'AUBRO',\n",
        "    'MAURA', 'RIBJE', 'LEFRO', 'BERSI', 'GOLMA']\n",
        "conditions = [8, 10, 12, 14, 16]\n",
        "localisation = ['Talon', 'Vastus Lateralis', 'Gastro Medialis']\n",
        "params = ['medF_Acc', 'Energy_Acc', 'ST', 'Damp', 'Peak_Acc']\n",
        "params_mov = ['medF_Acc_Mov', 'Energy_Acc_Mov', 'ST_Mov', 'Damp_Mov', 'Peak_Acc_Mov']\n",
        "params_name = ['Median Freq.', 'TMV', 'Setting Time', 'Damping', 'Peak Acceleration']\n",
        "params_sd_mov = ['SDmedF_Acc_Mov', 'SDEnergy_Acc_Mov', 'SDST_Mov', 'SDDamp_Mov', 'SDPeak_Acc_Mov']\n",
        "params_sd_dt = ['SDmedF_Acc', 'SDEnergy_Acc', 'SDST', 'SDDamp', 'SDPeak_Acc']"
      ],
      "metadata": {
        "id": "TFzXX_NAiZna"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def bland_altman_plot(data1, data2, title=None, data1_name=None, data2_name=None):\n",
        "    \"\"\"\n",
        "    Creates a Bland-Altman plot to compare two datasets.\n",
        "\n",
        "    Args:\n",
        "        data1 (array-like): The first dataset.\n",
        "        data2 (array-like): The second dataset.\n",
        "        title (str, optional): The title of the plot. Defaults to None.\n",
        "    \"\"\"\n",
        "    data1 = np.asarray(np.float64(data1.values))\n",
        "    data2 = np.asarray(np.float64(data2.values))\n",
        "\n",
        "    diff = data1 - data2\n",
        "    mean = np.nanmean(diff)\n",
        "    std = np.nanstd(data1 - data2, ddof=1)\n",
        "\n",
        "    plt.scatter(data1, diff, alpha=0.5)\n",
        "    plt.axhline(mean, color='gray', linestyle='--', label='Mean')\n",
        "    plt.axhline(mean + 1.96 * std, color='red', linestyle='--', label='Upper Limit')\n",
        "    plt.axhline(mean - 1.96 * std, color='red', linestyle='--', label='Lower Limit')\n",
        "\n",
        "    plt.xlabel(f'{data1_name}')\n",
        "    plt.ylabel(f'Difference ({data1_name} - {data2_name})',)\n",
        "    plt.title(title or 'Bland-Altman Plot')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Assuming 'data' is your DataFrame:"
      ],
      "metadata": {
        "id": "KWH72Cb6jcNz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bland_altman_plot(data['medF_Acc'], data['medF_Acc_Mov'], title='Bland-Altman Plot for medF_Acc vs. medF_Acc_Mov')"
      ],
      "metadata": {
        "id": "I3Wh5v0jj8Nr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "47334c1c-84cd-44c6-daab-e9866a40ae00"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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YbDZcddVVMY6Rp5xyCqZPn47//ve/A36TrIN6mMsvvzzy//Az6f+sLBYLpk2bFtOuL7/8MmbMmIHp06ejq6sr8hc2F77//vsAgDfffBMAcM0118Rcd7D3Nbqvhf9S5fnnn8eCBQtQV1cHADAajTjllFNi3m9FUfCvf/0Lp512WlyfovA7/M9//hNz587FWWedlbBMPKZOnYp58+bhpZdeihyTZRmvvPIKTjvttMi7brFY4PF40rrPRMycORNHHXVU5PMRRxwBINTPJ06cOOB4+Nna7Xa89957OP/88+FyuSLPtbu7G8uWLUNDQ0PEzPrmm2/iyCOPxMKFCyPnKykpSWhirampGfBcb7rpppTuq7u7G+vWrcOFF14YOXbOOeeAYRisXbs2cuydd96By+XCzTffPMCpOPzMvvnmG+zZswfXXnvtAN+6wZ7rYMyaNQuHHHIIXnzxRQAhd4IzzjgDOp1uQNnwWH7ttdeCZQ+KAj//+c9hMpki/ZthGJx33nl488034Xa7I+VeeuklVFZW4phjjkmrrqMJdW7OExYuXBgzOF544YU49NBDsXLlSpx66qkQBCHu73w+H+69916sXr0aLS0tMT4tDodjQPnoQQkACgoKAISEAAARAWjKlCkx5UpKSiJlgdAA2983qLCwEIIgYP369ejs7MTChQvR2NgY+X7JkiV48cUXcd9998V0xFTpfw9msxkAUFVVNeB4+L7CvPHGG7jnnnvw7bffxviKxBuI+l8HCLVX/3Mm4oorrsB5550HlmVhsVgidvZEdHZ2wuv1Ytq0aQO+mzFjBhRFQXNzM2bNmpXU9Yci/KzjXW/69On45JNPYo7xPI8JEyakdI14z0qj0aC4uHjA8e7u7sjnhoYGbN++HSUlJXHPG3bW3rdvH1iWjRFQgfj3FKZ/X0uV3t5evPnmm1i5cmXM+71o0SL885//xK5duzB16lR0dnbC6XQOuWOsqakJ55xzTlp1ueCCC3DrrbeipaUFlZWV+OCDD2Cz2WK211911VVYu3YtfvSjH6GyshInnngizj//fJx00klpXRNIrQ8CB8eXxsZGEEJw22234bbbbot7bpvNhsrKSuzbty8iOEWT6Nnq9fph71h86aWXIIoiDj300Jhne8QRR+D555/H1VdfDSD0zIDBdwMmUyYdli9fjgcffBDXXXcdPv30U9x6661xyyXq34IgoLa2NvI9EHqPHn74YfznP//B8uXL4Xa78eabb+IXv/hF2kLaaEIFnzyFZVksWbIEjzzyCBoaGhJOdr/61a+wevVqXHvttTjqqKNgNpvBMAx+/OMfx3UC5Dgu7nmiBaZkaG5uHrAl+P3338dxxx0XWfWef/75cX/74YcfxnXKTJZE9xDvePR9ffzxxzj99NNx7LHH4oknnkB5eTlUKhVWr14d1/F6uG01ZcqUMbV1XK1WpyywxmvDZNpVURTMmTMHDz30UNyy/SfYkeTll19GIBDAgw8+GNcp+/nnn8edd945InW54IILcMstt+Dll1/Gtddei7Vr18JsNscINVarFd9++y3WrVuHt956C2+99RZWr16Niy++GH//+9/Tum4qfRA4+GzDY9INN9yAZcuWxS0b1qKNBuGxa9GiRXG/3717d4w2djS48MILccstt+DnP/85ioqKcOKJJw77nEceeSRqamqwdu1aLF++HK+//jp8Pl/exqeigk8eI0kSAMSoH/vzyiuvYMWKFTEDsN/vTzsqb3V1NYDQiju6g3d2dsZoOsrKygaozufOnRuJKXHBBRfE7CoJc8011+D5558fVPDJ1grjn//8JzQaDdatWxejeVm9enVWrpcqJSUl0Ol02Llz54DvduzYAZZlIxN+Jtoo/Kx37twZMSGF2blzZ+T70WDy5MnYsmULTjjhhEHvtbq6GoqioKmpKWZlG68NM8Xzzz+P2bNn44477hjw3VNPPYUXXngBd955J0pKSmAymVBfXz/o+SZPnjxkmURMmjQJCxcuxEsvvYSVK1fi1VdfxZlnnjlAsygIAk477TScdtppUBQFV111FZ566incdtttIypohMcUlUo15KKguroaDQ0NA45n69nu2bMHn376KVauXInFixfHfKcoCn7605/ihRdewO9+97uIhrG+vj5h+0WXyeQCaOLEiVi0aBE++OADXHnllQl3/kb37+ixPBgMYs+ePQPqdP755+ORRx6B0+nESy+9hJqaGhx55JEZq/dIQn188hRRFLF+/XoIgoAZM2YkLMdx3AANxKOPPpr29sOlS5dCpVLh0UcfjTnvww8/HFNOo9Fg6dKlMX8FBQV47bXX4PF4cPXVV+Pcc88d8Hfqqafin//854AtydHo9fqspFPgOA4Mw8S0zd69e/Gvf/0r49dKB47jcOKJJ+Lf//53TDiCjo4OvPDCCzjmmGNgMpkAIBJTZDjttGDBAlitVjz55JMxz+Ott97C9u3bccopp6R97uFy/vnno6WlBU8//fSA73w+HzweDwBEAsv9+c9/jinT/33NFM3Nzfjoo49w/vnnx32/L7nkEjQ2NuLzzz8Hy7I488wz8frrr2Pz5s0DzhXuX+eccw62bNmC1157LWGZwbjggguwadMm/O1vf0NXV9eAVXq0CREIaZMPOeQQAIg8d1EUsWPHDrS1tSXXEGlitVpx3HHH4amnnop7rWjz+cknn4xNmzbhiy++iPk+E36C8Qif96abbhrwXM8//3wsXrw4UubEE0+E0WjEvffeC7/fH3Oe8DObP38+Jk2ahIcffnhAP01Vw96fe+65B3fccQd+9atfJSyzdOlSCIKAP//5zzHX++tf/wqHwzGgf19wwQUIBAL4+9//jrfffjuhxj4foBqfPOGtt96KhFa32Wx44YUX0NDQgJtvvjky2cXj1FNPxbPPPguz2YyZM2fis88+w7vvvouioqK06lFSUoIbbrgB9957L0499VScfPLJ+Oabb/DWW28N8MuIx/PPP4+ioiIcffTRcb8//fTT8fTTT+O///1vQiffww47DKtWrcI999yDuro6WK3WARqJdDjllFPw0EMP4aSTTsLy5cths9nw+OOPo66uDt99992wz58J7rnnHrzzzjs45phjcNVVV4HneTz11FMIBAK4//77I+XmzZsHjuNw3333weFwQK1WRxzIk0WlUuG+++7DJZdcgsWLF+PCCy9ER0cHHnnkEdTU1OC6667Lxi0mxU9/+lOsXbsWv/zlL/H+++9j0aJFkGUZO3bswNq1a7Fu3TosWLAA8+bNw4UXXognnngCDocDRx99NDZs2BDjn5FJXnjhBRBCcPrpp8f9/uSTTwbP83j++edxxBFH4H//93+xfv16LF68GFdccQVmzJiBtrY2vPzyy/jkk09gsVhw44034pVXXsF5552HSy+9FIcddhjsdjv+85//4Mknn4wJVheP888/HzfccANuuOEGFBYWDljJX3755bDb7Tj++OMxYcIE7Nu3D48++ijmzZsXWVS1tLRgxowZWLFiRdbTxTz++OM45phjMGfOHPz85z9HbW0tOjo68Nlnn+HAgQPYsmULgJAA8uyzz+Kkk07Cr3/9a+j1evzlL39BdXV1Vvrr888/j3nz5iU0o55++un41a9+ha+//hrz58/Hn/70J1x++eU4/PDDsXz5chQUFGDLli3wer34+9//DpZlsWrVKpx22mmYN28eLrnkEpSXl2PHjh3YunUr1q1bl3ZdFy9ePEAr1Z+SkhLccsstuPPOO3HSSSfh9NNPx86dO/HEE0/g8MMPHxDscf78+airq8Nvf/tbBAKBvDVzAaBxfHKdeFtsNRoNmTdvHlm1atWAbY/ot0W3p6eHXHLJJaS4uJgYDAaybNkysmPHDlJdXR2zlTPedmZCCHn//fcHbDeVZZnceeedpLy8nGi1WnLccceR+vr6AefsT0dHB+F5PiaORX+8Xi/R6XTkrLPOiqlX9NbU9vZ2csoppxCj0UgARLakJ7qH8Nbp/vFY4sW1+Otf/0qmTJlC1Go1mT59Olm9enXk99EAiBu/Yqg2ICRxHJ949H+ehBDy9ddfk2XLlhGDwUB0Oh1ZsmRJzNbhME8//TSpra0lHMcNubU9UdsRQshLL71EDj30UKJWq0lhYSG56KKLyIEDB2LKxGvLwUjlmRAS2o48a9asmGPBYJDcd999ZNasWUStVpOCggJy2GGHkTvvvJM4HI5IOZ/PR6655hpSVFRE9Ho9Oe2000hzc3PC7ezx2iBZ5syZQyZOnDhomeOOO45YrVYiiiIhhJB9+/aRiy++mJSUlBC1Wk1qa2vJ1VdfTQKBQOQ33d3dZOXKlaSyspIIgkAmTJhAVqxYMWTMqDDhWEiXX375gO9eeeUVcuKJJxKr1UoEQSATJ04kv/jFL2Li/4Tf2WS2f1dXV5NTTjllwPF4fSZRX2hqaiIXX3wxKSsrIyqVilRWVpJTTz2VvPLKKzHlvvvuO7J48WKi0WhIZWUlufvuu8lf//rXuNvZ+78/qfDVV18RAOS2225LWGbv3r0EALnuuusix/7zn/+Qo48+mmi1WmIymcjChQvJiy++GPO7Tz75hPzwhz8kRqOR6PV6csghhwwZEiOaZMeTRH3rscceI9OnTycqlYqUlpaSK6+8csD2+jC//e1vCQBSV1eXdP1yEYaQYerUKBQKhUKhUPIE6uNDoVAoFApl3EB9fCgUCiUJHA4HfD7foGXKyspGqDaUTNLe3j7o91qtNhJzaKQIBoOw2+2DljGbzTFBVynJQU1dFAqFkgQ/+9nPhoxrQ4fT/GSo8A8j4dTdnw8++GDIeGarV6+OSShMSQ4q+FAoFEoSbNu2Da2trYOWGUsBKccT77777qDfV1RUYObMmSNUmxA9PT346quvBi0za9YslJeXj1CNxg5U8KFQKBQKhTJuoM7NFAqFQqFQxg3UubkfiqKgtbUVRqMxL5OvUSgUCoUyHiGEwOVyoaKiYtC8gVTw6Udra+uoJjikUCgUCoWSPs3NzZgwYULC76ng0w+j0Qgg1HCDpYKgUCgUCoWSOzidTlRVVUXm8URQwacfYfOWyWSigg+FQqFQKHnGUG4q1LmZQqFQKBTKuIEKPhQKhUKhUMYNVPChUCgUCoUybqCCD4VCoVAolHEDFXwoFAqFQqGMG6jgQ6FQKBQKZdxABR8KhUKhUCjjBir4UCgUCoVCGTdQwYdCoVAoFMq4gUZuplDGCIpC0NLrgycoQS/wqLRowbI00S6FQqFEQwUfCmUM0GhzYV19B5o63fBLMjQ8h8klBiybXYo66+B5aygUCmU8QQUfCiXPabS5sHrjXtg9QZSbNdAJWniDEupbHWh1+HDJohoq/FAoFEof1MeHQsljFIVgXX0H7J4gplgNMGpU4FgGRo0KU6wG2D1BrN/aAUUho11VCoVCyQmo4EOh5DEtvT40dbpRbtYMyEjMMAzKzRo02txo6fWNUg0pFAolt6CCD4WSx3iCEvySDJ0Q32qtFTgEJBmeoDTCNaNQKJTchAo+FEoeoxd4aHgO3gSCjS8oQ81z0CcQjCgUCmW8QQUfCiWPqbRoMbnEgDaHH4TE+vEQQtDm8KPOakClRTtKNaRQKJTcggo+FEoew7IMls0uRaFeQIPNDZdfhKQocPlFNNjcKNQLOHFWKY3nQ6FQKH1QwYdCyXPqrEZcsqgGsyvM6PWK2NvlQa9XxJxKM93KTqFQKP2ghn8KZQxQZzWi9jgDjdxMoVAoQ0AFHwpljMCyDKoKdaNdDQqFQslpqKmLQqFQKBTKuIEKPhQKhUKhUMYNVPChUCgUCoUybqA+PhQKZcyjKIQ6flMoFAB5pPG59957cfjhh8NoNMJqteLMM8/Ezp07Y8r4/X5cffXVKCoqgsFgwDnnnIOOjo5RqjGFQskFGm0urPqgCX96Zxf+vKEBf3pnF1Z90IRGm2u0q0ahUEaBvBF8PvzwQ1x99dXYtGkT3nnnHYiiiBNPPBEejydS5rrrrsPrr7+Ol19+GR9++CFaW1tx9tlnj2KtKRTKaNJoc2H1xr2ob3XAolOhttgAi06F+lYHVm/cS4UfCmUcwpD+ce7zhM7OTlitVnz44Yc49thj4XA4UFJSghdeeAHnnnsuAGDHjh2YMWMGPvvsMxx55JFJndfpdMJsNsPhcMBkMmXzFigUShZRFIJVHzShvtWBKVZDTPZ6QggabG7MqTTjl4snU7MXhTIGSHb+zhuNT38cDgcAoLCwEADw1VdfQRRFLF26NFJm+vTpmDhxIj777LOE5wkEAnA6nTF/FAol/2np9aGp041ysyZG6AEAhmFQbtag0eZGS69vlGpIoVBGg7wUfBRFwbXXXotFixZh9uzZAID29nYIggCLxRJTtrS0FO3t7QnPde+998JsNkf+qqqqsll1CoUyQniCEvySDF2CzPRagUNAkuFJkNmeQqGMTfJS8Ln66qtRX1+Pf/zjH8M+1y233AKHwxH5a25uzkANKRTKaKMXeGh4Dt4Ego0vKEPNc9AnEIwoFMrYJO96/MqVK/HGG2/go48+woQJEyLHy8rKEAwG0dvbG6P16ejoQFlZWcLzqdVqqNXqbFaZQqGMApUWLSaXGFDf6oBBzQ/w8Wlz+DGn0oxKi3YUa0mhUEaavNH4EEKwcuVKvPbaa3jvvfcwadKkmO8PO+wwqFQqbNiwIXJs586d2L9/P4466qiRri6FQhllWJbBstmlKNQLaLC54fKLkBQFLr+IBpsbhXoBJ84qpY7NFMo4I280PldffTVeeOEF/Pvf/4bRaIz47ZjNZmi1WpjNZlx22WW4/vrrUVhYCJPJhF/96lc46qijkt7RRaFQxhZ1ViMuWVSDdfUdaOp0o8Pph5rnMKfSjBNnlaLOahztKlIolBEmb7az99+VEWb16tX42c9+BiAUwPA3v/kNXnzxRQQCASxbtgxPPPHEoKau/tDt7BTK2INGbqZQxj7Jzt95I/iMFFTwoVAoFAol/xjzcXwoFAqFQqFQUoUKPhQKhUKhUMYNVPChUCgUCoUybqCCD4VCoVAolHEDFXwoFAqFQqGMG6jgQ6FQKBQKZdxABR8KhUKhUCjjBir4UCgUCoVCGTdQwYdCoVAoFMq4gQo+FAqFQqFQxg1U8KFQKBQKhTJuoIIPhUKhUCiUcQM/2hWgUCgUCs0gT6GMFFTwoVAolFGm0ebCuvoONHW64ZdkaHgOk0sMWDa7FHVW42hXj0IZU1DBh0KhUEaRRpsLqzfuhd0TRLlZA52ghTcoob7VgVaHD5csqqHCD4WSQaiPD4VCoYwSikKwrr4Ddk8QU6wGGDUqcCwDo0aFKVYD7J4g1m/tgKKQ0a4qhTJmoIIPhUKhjBItvT40dbpRbtaAYWL9eRiGQblZg0abGy29vlGqIYUy9qCCD4VCoYwSnqAEvyRDJ8T3OtAKHAKSDE9QGuGaUShjFyr4UCgUyiihF3hoeA7eBIKNLyhDzXPQJxCMKBRK6lDBh0KhUEaJSosWk0sMaHP4QUisHw8hBG0OP+qsBlRatKNUQwpl7EEFHwqFQhklWJbBstmlKNQLaLC54fKLkBQFLr+IBpsbhXoBJ84qpfF8KJQMQgUfCoVCGUXqrEZcsqgGsyvM6PWK2NvlQa9XxJxKM93KTqFkAWo4plAolFGmzmpE7XEGGrmZQhkBqOBDoVAoOQDLMqgq1I12NSiUMQ8VfBLh8QAcN/A4xwEaTWy5RLAsoNWmV9brBUiCoGUMA+h06ZX1+QBFSVwPvT69sn4/IMuZKavTheoNAIEAIA2ylTeVslptqJ0BIBgERDEzZTWag+9KKmVFMVQ+EWo1wPOpl5WkUFskQhAAlSr1srIcenaJUKlC5VMtqyihdy0TZXk+1BZAqE94vZkpm0q/p2NE/LJ0jEi9LB0jQv9PZYxIBkKJweFwEADEERomBv6dfHLsD3S6+OUAQhYvji1bXJy47IIFsWWrqxOXnTkztuzMmYnLVlfHll2wIHHZ4uLYsosXJy6r08WWPfnkxGX7v2bnnjt4Wbf7YNkVKwYva7MdLHvVVYOX3bPnYNkbbhi8bH39wbJ33DF42S++OFj2/vsHL/v++wfLPvbY4GXfeONg2dWrBy+7du3BsmvXDl529eqDZd94Y/Cyjz12sOz77w9e9v77D5b94ovBy95xx8Gy9fWDl73hhoNl9+wZvOxVVx0sa7MNXnbFioNl3e7By557LolhsLJ0jAj90THi4B8dI0J/WR4jIvO3w0EGgzo3UyiUjNNsH0RzQaFQKKMIQwgho12JXMLpdMJsNsPR2gqTyTSwAFVjxy9L1diplx2jauxGmwtrPmqCq9eNMrMaOoGHNyih3RFAgV6Fnx5ZjcmVhdTUBdAxIt2ydIwIkadjRMplkxwjIvO3wxF//u6DCj79SLbhKBTKQBSFYNUHTahvdWCK1RCTf4oQggabG3Mqzfjl4sl0xxKFQskoyc7f1NRFoVAyBk26SaFQch0q+FAolIxBk25SKJRch25np1AoGSM66aZRo4r5jhACmzMAvyjD6ROhKGREzV2KQmiAQAqFQgUfCoWSOcJJN+tbHTCo+Yi5y+4JoLHDjX12L4waHi9+vh9f7unBstmlI5KSodHmwrr6DjR1uuGXZGh4DpNLDGldfygBigpYFEpuQwUfCoWSMcJJN1sdPjTYQr4+flHGV/t60OsTYdEJOGxiATQqFvWtDrQ6fFnPR9Voc2H1xr2we4IoN2ugE7TwBqW0rj+UAJVJAYtCoWQHKvhQKJSMEk66ua6+A402F7a2OeH2S6gt1qPOakShPrRF1aDm0WBzY/3WDtQWG7KiFVEUgnX1HbB7gjG7zIwaVcrXH0qAOn66Fe/tsGVEwKJQKNmDCj4UCiXjhJNubt5nx1Mf7kahXoVyszZmp1f/XV7ZyFOVyi6zwa4/lAC1q8OFNZ/uhV7gMbV0eAIWhULJLnRXF4VCyQosy8CkVUGtYlFq0g4QPIDs7/LK1C6zoQQoo4bH3i4PTBqebuOnUHIcKvhQKJSsEb3LKx6+oAw1z0GfQDDJlesPJUBxLANRVhJqc+g2fgold6CCD4VCiUFRCJrtXuxod6LZ7oWipB/cPbzLq83hR/8g8YQQtDn8qLMaUGnRJjjD8MjU9YcSoGSFQMWxCdsq2wIehUJJHtoLKRRKhEzvSoq3y0srcPAFZbQ5/CjUCzhxVmnW/F4ydf1E2/SBkADl8kuoKdbD6ZdQRsiA79scfsypNGdNwKNQKMlDc3X1g+bqooxXBu5aCiUXDQsIw9mVFC1QBaSQ9qPOasCJs0Y+jk+61+/fPv0FqP67uvp/T3d1USjZhSYpTRMq+FByiZEKhjcSyUVHO7BfJq4/lAA12gIehTKeSXb+pqYuCiVHGa7ZKZWJPlPbvgeDZZmsbFkfyeuHt+knatehvqdQKKMPFXwolBxkuNGGUxWaDu5aiu+DohU4dDj9dFcShhagUhWwRlsTRqGMN6jgQ6HkGMONNpyO0DRYclGA7krKFjTFBYUy8tDt7BRKjpGM2amhw4XN++wDtpz3F5qMGhU4loFBzaPUqMa+bg9e3nwAkqTEnHe0t52PR8ICan2rAxadCrXFBlh0KtS3OrB641402lyjXUUKZUxCl28USo4xlNnJJ8rY1ubEUx82Qa3iYrQEap4bIDTZPUE02tzo8QbhF2Xs6/aCEILzD6+KaBVS2fZNTTPDJ5M5xCgUSmpQwYdCyTEGMzvZPUF8va8HLr+EIr0aVpMmxoy1eFpJjNBk9wTxbXMvfEEJBo0KejWHbncA29qcWL1xb4zZKzq5aFOnGx1OP9Q8hzmV5ri7lqhpJn1GwpmcQqHEhwo+FEqOkShYHiEEjTYXen0iaov1KOubNKO1BJv32qHmWHiDEgxqHo02N3xBCYV6AQzDICDJ0Kh41JUY0OEKDNAqDLYrKdp3qMykhkHh4fSL+GJvN1p6vbj0mElU+EkS6kxOoYweVPChUHKMRGYnm9OPfd1eFOhUqOsXayesJeh0BlBi1KC5x4tSoxo93iAMGhUYhgEhBG6/BKtJA5NW1SfMDNQqxNuVFG2aKdIL2NEeMp1JsgKeZdDm8EOr4vDbU2ZS00wSUGdyCmX0oM7NFEoOEjY7za4wo9crYm+XB3aPCKOGx/yJBSjUqwf8RitwCMoKFtQUoFAvoLHTDb8og2OBgCTD7glCK3CYXKIHwzApJc4Mm2a0KhZbDjjQ6fJDo+JQoBegEXgERAXv7bBhY1NXNppjzEGdySmU0YMuJyiUYZBNR9/+ZienT8SLn++HRsXFLR/WEswoN6G2RI+1Xx7Avm4vut0BaFQ8rCYNJpfoI0JTsloFRSFo6nTD5vLBHZDhDUoo6jOdAYCaZ1BiFHCgx4cN2zuwaHJx3ml90nmOw3n2o53DjEIZz1DBh0JJk5Fw9I02OykKwZd7ehImyoxOhMmyDG5aNg0AwbY2J+pKDDBpVTH+Qskkzgzf43cHerGrww1vUIZBrYJekKGNEpgkhUCv5tHm8OedQ246zzETzz4ZZ3IKhZJ5qOBDGRdkWjPTaHPhb5/sQUuvDwU6AcV6NTgWSUdWTqeOyWoJAKDZ7oUnKOHYqSVwByR0uAJgWSa2vE7AnAlm7LK54l4v2pm5wqJBm0ODBpsb/qCIdqeCMpMGWoGP+A6VGNXgGCYlh9zR3hqfTrDH4UbVjoamuKBQRh4q+FDGPJnWzCgKwQub9mPzvh6wAFp6fOA5FgU6AZNL9Oj2BFOOwZJsHYfSEgDAqg+aYs5j0alQbuLQ6xUj5cvNGoAAr33dEvd68eLMTC4xYr/dB0IIAqKCbk8QJQwDd0CCVuBQYdYAYJJ2yB3trfHpxNLJRvyd0c5hRqGMN6jgQxnTZHJ1HmZjUxfe32mDQoACvQAVx0CUCTpdfrgDEqZY9SnFYEm1jom0BLu73HHP0+bwo0An4Kz5lSgxqtHlCuDN79vQ4xUTXi9eIMSqQi1qinXY3xcp2ukToVFxKDVpUFusQ7dHHNJ0ls3nkirpxNKh8XcolPyH7uqijFkSpW8walSYYjXA3qeZCad7SPacG7Z3wCfKKDEKUPMsWIaBmmdRqBfgC0po6fXDL0pJmXzSrWNYSzC9zBSZYAc7T483iO8POFBXbMCWZgd6vOKg13MFxL44MwfXRgzDYE6lBRVmLUxaFYwaHjPKTZhWakC3R0zaITcbzyUdDsbSib/+i7frLZ3fUCiU3IIKPpQxSyqr81TO2ebwQ6/mIfWbmBmGgUHDo8sdgKwgKZNPpuqY7Hm+bu5JqpzbL0XizERTqBdw6MQClBjU4FgWnoAEh0/CnEpz0lqabDyXdIiOpROPeLve0vkNhULJLWjvpIxZshEd1xOUwDIMSgxqdLkDEPShtUNQUiATAoDA7RdRYdEmZfLJVB2TPU+3J5hUOYOajxs9GgAKdCqUGNVYUFOIM+ZVwKhRpeSQmytRixNFyAYS73pL5zcUCiW3oIIPZcySjei4eoGHVsVBa9HAE5TQ7vAjqCgQJQWSQiDKCgSOw/RyY1KCQKbqmOx5ivRC3HKEELj8Ul805tDW9MF2kBUZ1DhvwYS0/HByJWpxOrF0aPwdCiX/oaYuypgllei4ikLQbPdiR7sTzX3Ou4Od0ycqqCnSISAp8PglyDIBxwACy8KsVWFLswONNldG65iJ88yvKhhQzu4J4su9PfisqQuf7e7GgR4vXt/SCgADokf3esWUzFrZvOdMEC9C9lD3mM5vKBRK7kA1PpQxS7Kr891d7qS3VYfP2dLrxeZ9PdCoWFiNegRlBX5Rhkmjwtwqc9Jb2jOlQUj2PDzPxpTTqljs7Aj59DAgKDYImFZqxNZWJ9ocflyyqAZXHjc5o3FmRltr0j92UG2xAVemEEtHUQjUPIclM0pw+KQCGNQ8jBoVyk0atDn92NHupPF4KJQchiH9l1xDsH37dvzjH//Axx9/jH379sHr9aKkpASHHnooli1bhnPOOQdq9cA8QvmC0+mE2WyGw+GAyWQa7epQMkB0vJiAFDKj1FkNkbg3sduq+cgW8EK9kHAF/0lDJ/73ze0ISApYBuDY0K6ucEoIl19Er1fEdT+cmvSW9kR1TEWDkOx5Gm0uvP19O96sb4fdE4BZq0KRQR2pPyEEDTY35lSa8cvFk7MygWfqntO9ZjqxgxL9fnq5ETvaXKMWk4hCoSQ/fyct+Hz99de46aab8Mknn2DRokVYuHAhKioqoNVqYbfbUV9fj48//hhOpxM33XQTrr322rwUgKjgMzaJFyEYCAX7q291xASjAzDkxL+j3YlH3m1AiUENmRAIHAuj5qCzq6Qo2Nvlwa9OmILpZcm9R5mKYpzsefZ3e/A/b26HVsWC51gIPAs1x0XuI1XhLR1GMnLzwNhByQm5Q/2+weZGm8OPcpMGU0oNKZ+XQqFkhmTn76RNXeeccw5uvPFGvPLKK7BYLAnLffbZZ3jkkUfw4IMP4tZbb02p0kPx0Ucf4Y9//CO++uortLW14bXXXsOZZ54Z+Z4QgjvuuANPP/00ent7sWjRIqxatQpTpkzJaD0o+Ue86LjNdm/awejCTs48x6BAIwy4njcgQVII2h3+pCf0TEXwTfY8XlGGLyjD4RXR6xMhKQp4lkWhTsBkqx4mrSrru6tGKmrxcCMuJ/q9Qc1DkhS4/CJKDEJkp9dwIjlTKJTskrTgs2vXLqhUA3dg9Oeoo47CUUcdBVEUh1WxeHg8HsydOxeXXnopzj777AHf33///fjzn/+Mv//975g0aRJuu+02LFu2DNu2bYNGo8l4fSj5zXC2VQ+2rbnb7ccXe3rAcyxe+nI/tCo+J80eXa4Amu1eEEJg0QtQcTxEWYHN5YcrEApwOFZi0gw34nKi37v8Enp8Ior0Anq8Ilx+CSatKunzUiiUkSfpES2R0OP3++MKFckISanyox/9CD/60Y/ifkcIwcMPP4zf/e53OOOMMwAAzzzzDEpLS/Gvf/0LP/7xjzNeH0p+M5xt1YkcdNt6ffhybw8A4PBKEyosuhFPxZAMikLw7f5eqHgWshIy1TEMAzXPQdCz6HYHUN/ixBnzKsdETBqXX4TdG4TAsyAEMWZJYOjYQYmE5KCsQFIUmLUqOHwigrIS8/1IxSSiUCjJk9Z2dkVRcPfdd6OyshIGgwG7d+8GANx2223461//mtEKJsuePXvQ3t6OpUuXRo6ZzWYcccQR+Oyzz0alTpTcZrjbqvtva97T6cGOdhe0AofFU0swoUAHT0BCQFJQalSj2x0YkVQMydDS68PuLg9mV5igE3jYPUEEJBkKIX2TOSDKBIdUmfPeRNNoc+Ff37SiyebGpt2hLfub9/bA7glEygwVOyhRxGaBY8GzLHxBGTzLQuBih1QayZlCyT3SEnzuuecerFmzBvfffz8E4aB/w+zZs/F///d/GatcKrS3twMASktLY46XlpZGvotHIBCA0+mM+aOMD8Jam0K9gAabGy5/yM/F5RfRYHMnta26zmrElcdNxnU/nIrzD69CVYEOiyYXgWGYUGyc3d34fHc3Nu2xo9MVwNf7e7KeiiEZwhqMCosO86osKDFq4BcV9HiD8IsKyswaVBVqUWLMvw0K0YQdkvfbPbAa1eAYFmqegc3lx7fNvbB7AkkJuYmEZKOGR4FWhW5PEAW6UP6yMCMdk4hCoSRHWsuQZ555Bn/5y19wwgkn4Je//GXk+Ny5c7Fjx46MVW4kuPfee3HnnXeOdjUoo0RYaxPeotzh9EPNc5hTaU56W3XYQdcTlMBxDAISwfctDviCEgwaFVQaHqJM0OsV0ekOYHu7M+v+HkPtlorWYBTqBRxeUwCXX0JQVvq0FgQOn5TXmopoh+SppUaUGDX4trkXnoAEvZqH2y9iW5sTJQYNigyDC7mDxR7ieRYmjQo8y8IdkGgkZwolx0lrVGtpaUFdXd2A44qiZMWpORnKysoAAB0dHSgvL48c7+jowLx58xL+7pZbbsH1118f+ex0OlFVVZW1elJyjzqrEbUpBLBLhF7goeZY7Gx3wtcnUIT9SNQ8A6Lh0OmSsXmvHUunZ28yTCZWTTzn7LBTbvRW/nzWVPR3SC7UC5hXZUGjzY0ebxAyAWzOAA6rLsD5C6qGFHITCclH1RZhWtnBOD7pCM8UCmXkSEvwmTlzJj7++GNUV1fHHH/llVdw6KGHZqRiqTJp0iSUlZVhw4YNEUHH6XTi888/x5VXXpnwd2q1Oi/jDVEySya2VVdatCgxavD5HjusRvWAuECegIxyiwadzkDWdvkMjDWjjetcPdrRk0eCeA7J0dotnyihw+nHmYdWJi2cDCYkL5lmHbGYRBQKJX3SEnxuv/12rFixAi0tLVAUBa+++ip27tyJZ555Bm+88Uam6xjB7XajsbEx8nnPnj349ttvUVhYiIkTJ+Laa6/FPffcgylTpkS2s1dUVMTE+qFQsgXLMlhQU4C369vg8osAA6g4FqKswO0PmUCmlRrh8IlZ2eWTaqyaZM18IxlkMJMk2rUX1m4xDFCgU8OoTm0HaiIheaRiElEolOGRluBzxhln4PXXX8ddd90FvV6P22+/HfPnz8frr7+OH/7wh5muY4TNmzdjyZIlkc9hE9WKFSuwZs0a3HTTTfB4PLjiiivQ29uLY445Bm+//TaN4UMZMWaUmzC1zIgeTxCeoAxPQALHsrCaNJhcooeKY+EXlaz4zqQTq2YoM99wUzyMJoPFWgo7Hue7OY9CoaROyrm6xjo0ZQVlOCgKwaoPmvB9iwNlJjVE5WA6CwBZzX+1o92JP29oQG2xAVycc6eaRiNsNut2B2DU8OBYFrKiwOWXUGRQ50xMosHob/rrb87Lh3ugUCjJkfGUFfEIBoOw2WxQlNigXRMnThzOaSk5TL6aPUaKaN+ZDlcgMtm6A1JavjOptPdwAjLGu+66+g7s7/aGBKZuLyRZAc+xKNCp4AnIWL+1AzWFerT1BejLxfchE7v2KBTK2CItwaehoQGXXnopPv3005jjhBAwDANZljNSOUpukc9mj5EkU5Ntqu2dSdNOS68P3zT3wObyQ1ZIzLb8TlcAHMvg44ZO9HqD6HIHc/p9yNSuvUxDFxEUyuiQluDzs5/9DDzP44033kB5efkAfwLK2CPZ3UKUEMOdbNNp70zu1HIFROy3eyHLBEWG2G35gl5Am8OPdqcTLANMKzPl/PuQa47HdBFBoYweaQk+3377Lb766itMnz490/Wh5CDDzWyd6rXGyio43cl2OO2dKW2T2y/BF5QH5LQKExBlBCUFZSZtxKxGM5InB11EUCijS9pxfLq6ujJdF0qOMtzM1slCV8EhhtvemTDtGNQ8tCoOAVEeYDYLSAo8QQlqnoVaFZv1hmYkH5yRXERQKJT4pJWr67777sNNN92EDz74AN3d3TTX1RjnYCC4+HKyVuAQkORhxaYJr4LrWx2w6FSoLTbAolOhvtWB1Rv3otHmSvvc+UYm2jusbZpeZkJVoS7lSdSoUWFikQ4qno1JYBqQZNg9QTAMA4tOgJrn0qrfeCUVoZZCoWSHtDQ+4QzoJ5xwQsxx6tw8NsnkbqF40FVwLNlu72SotGhxaFUBApICSVLQ4xMjMYlKDAICkoIigxCTlHMk65evxIsmHY1W4NDRt0uOQqFkh7RGpvfffz/T9aDkMNkOBDdSprR8IRcC70U7Sne7g5hQqAPHMpAVApdPhEwAnWrg8EEDAx4knr9aLgi1FMp4J63etXjx4kzXg5LDZDuvU7ZXwZl2mM62A3am2zvd+vZ3lPYGJah5DodUWXBemRHv7bCN2TxfwyWRv9oPZ1lHXailUMY7aS8rent78de//hXbt28HAMyaNQuXXnopzGZzxipHyR2yGQiu/yqYEAKXX0JQViBwLACS9io40w7TwzlfIgEk3vHRigXUn8EcpauLdDQwYByG2rV1/HTrmE4OS6HkOmmlrNi8eTOWLVsGrVaLhQsXAgC+/PJL+Hw+rF+/HvPnz894RUcKmrJicLKhPTnQ48XfPtmLPd1uTLBosKfLB7s3CElRwDEMCAGOmVKM350yMyUtx6dNXXjhi/3wBCTUFuuhV6vgDUpppysYOKHxSZ8vkQAyvdyIHW2uhILJcNp7OPUdinC9XAERTp8IX1AGwzCoLdZjQkHqztS5TCrPIJyypL7VEeOvBoQ0OuGUJUtnlOKdbaH3ISCFzFt1VkNWhMaxFCKCQhmMrKasuO6663D66afj6aefBs+HTiFJEi6//HJce+21+Oijj9KrNSUjZHOgy2QguGhhoMsdQFOnG9/u74VezaHYqIYAHg6vCIUQ2FwB7O5yJzUpNNpceLu+HW9+34ZuTxAFWhWCEkGd1YBCvZCWw/RwHLATaQA27e7Ga9+2oNyswRSrIWE8l3RjAb1d344DPV5UWrQgBGCZzDiM939uXe4gAIJigxrFBnVEcKstzr1oyamSqsYsWX+10+ZW4MrjJme9fWiICAplIGkJPps3b44RegCA53ncdNNNWLBgQcYqR0mdfBno+gsD5WYNulwBOHwivEEZvV4ROoFHZYEWtcV6dHuCSU3U4fMe6PEiICkoNanBMiw6XX64AxLmVVlQqBdScphWFILN++z4en8PCvUDHVIHc8BOJDAZ1DwkRYHLL6LEIET8PTK1k+3Tpi68+X0bApKCll4feJZFoU7AZKsehXr1oPUdbDKOfm5aFYtuTxCegAQGBN0MUGwQUN/qwPZ2J6wGNXp9Yk6/h4ORTqDBVPzVsh1NmgZKpFDik5bgYzKZsH///gGRm5ubm2E00o40WuTLQBdPGHD6RCgAJhfr0euTYNGpcGiVBSatCgzDQODZIQWV6PNWWrRo6fVBzXNgmVCahW5PEFtbHZheZgTHMvCL0pAO02FB8uv9dmxtdcKsVeFAjz+iPQqTyAE7rAEoM2li/JYIIejxiijSC+jxinD5JZi0IaFquDvZGm0uvPDFfnR7gig1qaHmOYiyApvLD1dAxLy+du1f36GE5uj2rSvRY/O+XgREGaUmNQDA7gmi3RnApCIdPmroglbgsGhyESrUufkeDka6Gr5c2bVFQ0RQKIlJq/ddcMEFuOyyy/DAAw/g6KOPBgBs3LgRN954Iy688MKMVpCSHJkY6EbKFyCeOSAoK5AUBUaNgAKWgV9UwDBM5PtkdnZFn5cQgGdZiLICNc/BLyrwBCR0ugLodgfAcyzUPIdOVwDTy+KfL1qQLNKrYdaqwLHMAO0RkHhC8wQldLkDaO31odcnQlIU8CwLNc/CG5RQYlTD6RMRlJWY36W7ky38HngCEgq0KrAMC5ZhoOY5CPpQMMKmTg+mlRpi6puM0KzmuUj7ugMyerxBGDSqg1osDQ+7OwC/KINlwtFRGXBs5jRZI0W6IRbCoQi+b3GgzEQgKgQCx0biHY3Uri0aIoJCSUxags8DDzwAhmFw8cUXQ5JCA7NKpcKVV16JP/zhDxmtICU5hjvQpWsiS0dYimcOEDg2IqioOBbugBQjDCSzUo4+L8sAhTohlF1cIOhwBhCUZDAMoFfz8AYkSAyDt75vR7lZM+Ae+wuSAHCgRw2by48CnQo9XhFNnW4U6AoAJJ7QbI4Adnd6IBOCAp0KBVoBoqKgxxOE0ydCxTFQcVzf7rWDpKsZCL8HtcV6BCWCTpcfgv5gklGBZ9Ha44WiEPxgSgkqLdqkhebjppdE2rfHG4QkK1BFBTBUcSx6pSACkgKzTgVvUI55hvHew1x1vE03xALLMphebsT6be347kAvVHxI6DSqeejUPCYW6kZk1xYNlEihJCYtwUcQBDzyyCO499570dTUBACYPHkydDq6chgthjPQpWsiS1dYimcOMGr4iKCiV/PgWTYiDCQb36T/eSdb9XD6RbT0+iDLBGoVC1Em8ARCZqW5E8wJfYfiCZKTrXq4AiJ6vCIEnkWXO4A2hw/ugBx3G/KuDif+8lETHL4gFBJK7KkTeBTqVbAa1XD6JXQ4A5hTYY6JgDyceC7h96BCrUWd1QB3QILdEwTPsnD5RbiDEvyiDFEh6HaHHMajNTmDCc0Lagoi7StwLHgu1J5qPvQbUVbAgEFI1GFinmGY6Pcwl/3R0jVZNdpceG+HDSaNCjzLwB2QEJAUtPn9KDGqcfx064jcW66Y3CiUXGRYb71Op8OcOXMyVRfKMEh3oEvXRJZIWPq+xYFdHS6cMrccM8pMcVfw8SITMwzTJ6gE0e70Y0KBFlohNFknG9+k/3kL9WpMKTWg3emHwhB4gjK0Kg4VFm2fj44aAs/F1YTFEyQL9WrMq7KgyeZBlycAp0+E3SPisOqCAduQG20uPP5+E3Z3eSJCTlAOOTMHJAlFejW0Kg6STCARAndASimeSyJNSfR7UKgXMK/Kgu9berG3ywtRVkJmJ7UKcyrMaHP6sXrjXiyeVpKU0GxQ85H2rSvRo0AnRDRKQCije6FegNsvwuEVUVmgHSDQ2ZwhU9i2Vic27+1Bjzc3/dHSiZ4d3ZcOnWgBgIhfl4pl0O70Y2e7C0umWbOu8cmF6N8USq6SkuBz6aWXDlmGYRj89a9/TbtClPRId6BLx0SWSFgSZQUObxD77F7saHdiZrkJdVbjgBV8osjEKo5FgV4NjmVRoBOwr9ubUlC8eOcNCXscCDioeQ6zK0JJO4fyHUokSBbq1SioEdDm8MPuCeAXi2uxoLowZiILt0+3OwCdwKFAL0CvVsHuCcAnyvAGFRAEMbnEAFlRMKvChF6vmHQQwME0JbXFhpj3oECngobnYNbyMKh5uAMSKixaTC0LnbvB5sbmvXaoOXZIodmoUUXat7HTg3KzGk6/iA5nAACBQcNjYpEOW1ucUIiC2mJ9pJ3tngAaO9zYZ/fCoOHw9Ee7ISsECycVRGn9cscPKJ3o2fH6UthhPXzOkfKryXa0dQoln0lJ8Onp6Un4nSzLePfddxEIBKjgMwqkO9ClYyKLN8DbPQF829wLX1CGRaeCrBDwHJNwBZ8oMvFRtUVYOqMUWoFLy++j/3l7vCEzk9Wkxsxyc8xOLCCxJmwwQRIA3AEJh1UXDhB6+rdPlzsIUSbQChwqVFoEJQU+UYYoE1QVaKEQ4JJFk8AyTFL3m4xZMvo9MKg52L1B6NU8ApICk1aFuihhtdysQaczgBKjBvvtniEdclmWiWnfIr0AQgAGBEV6NRgwOGZKMWyuALo9QQg8C78o46t9Pej1ibDoBEwrNWJrqwOyrGDLAQfmVTEo1Id2huWS422q0bNzza8mm9HWKZR8JiXB57XXXot7/N///jduvfVWqNVq3H777RmpGCV10hno0jGR9R/gCSFosnngC4Z8XQiAHm8QAs9hilmbcAU/WDqE4bZD+Lwuv4h/fdOK/XYPCnSx9zeYJmw4K+Zw+0wq0qNA549xMFarOKh4Fj2eANqdfhw9uRhVBaHJPdwOzT1eMAC8ojwgtUUyZslfLp4ceQ++3t8Dp0+EWauC1aTB5BJ9RMgAwpOxgsoCDT7f043vDjgg8CwEng055AohLU70vfZ/bloVN6C+u7vcWFffgUabC1vbnHD7Q9Gz66xGKISAAVBsVKPXK6Kp04MCnTCkFm40SOUdzUW/mmz1MQolnxlWD9y4cSNuvvlmfP3111i5ciVuvvlmFBQUZKpulDRIdaBLx0TWf4B3+SXYvUEYNKHfByU54tg61Ao+W0Hcos8r8CxWb9ybsgCT7oo53D4+UY5xMDZoeKg4Fp6ABE8g5G9UZzXg06YufLu/F7u7PINGQk7WCbml1xd5Dzbvs+OpD5tQpFejLM7vfEEZAUnB1/t7YdJGO+TKaPNLCR1yh3pusdffjUK9CuVmbSRmE8+xkJS+LfCeYEwco1xzvE32Hc2kX004lcvuLg8AYFKxHlVppgLJdqBECiXfSGtk2bZtG/7f//t/ePvtt3HxxRfjxRdfxIQJEzJdN0qapDLQpaPZ6D/Ah2PwqDgehBC4/RKsJk3EVDLaK/jhqPzTWTFHt88UqwHzqixotIXMbm6/BIdPhF7NgxDguU370Gz3QsWzmFioTRgJudXhS9oJOdzOLMtgQXUhvpzYg/pWB/qHKyKEoLXXj4CogGdlHFplAdDfITeQkkNuf6drg4aHWsWi1KSN0lDxEcfokFlUiWx7z2fH20z51TTaXHjh8/3YtLsbDq8IwgAWrYAjJxVi+ZETqYmKQhkmKQk+zc3NuP322/Hcc8/h1FNPxXfffYcZM2Zkq26UESJVwaD/AG9Q8yEflYCEoKRAK4R2/4QnulxYwQ9H5Z/qijneBHjoRDM6XaF8ZJJCUFWoRVWhDtvbnFAUBb6ggm/2O6DmWVRaQpqZcCTkwyZa0NjpSdoJObqdh5qM1SoWksKiwqIdtkNuPKfrYoOAoKTE1JlhmIgmrNMVBM8x4BgmpR18ucpw/WoabS48/G4DtjT3gmOAIqMABgx6vSLe2d4BmzuAa5dOocIPhTIMUsrOrtOFdsOsXLkSixYtSlju9NNPz0jlRoPxnJ091WBy4Yku2o+jukiHOqsx4kQcnZH6ih/Uoq1PIzEefA2iBYGAJEPgWHR5ggCAQ6sscPklfLDLBp8owx+U4fJLUHEMig1qFOrVYFnALyo4qrYIDAP0eIIoMWrQ3OMdNPP3LxdPjpsoNbou4WzgdaUG/OubFtQWG8DFeRaSomBvlwe/OmEKppcl7g+JMsG39vrR5vDBpFHh0ImWmDp3uwP4Yq8dKpZBdZEeGlX2MpSPNOkEZlQUgic+aMR/vm2FrCgoMqgj7UUIQbcnCI5lcMbcClx5XN2Y7juU7JKrgUOHS1ays/v9fgDAH//4R/zxj3+MW4ZhGMiynMppKTlCqpqNaC3K9jYn/vt9GwKiDBXHQFKUGBX/tDIjnvpoN5o63fCJEhQFKDdrsXSmFUdPLh4Tna4//bVMTp+IFz/fj4I+R+dOdwDd7iBYNpTWgWMZKCRsaiKwGtURM5BFp0KHU8GCmgJ4glJa/krxNF4tvT68zbcPyyF3MKfrqaV85N53dbhQYdFG6tztCeKwiRYsnFQEjcChSC9gflUBeJ5NeK18IR2/mpZeH75vcUAmBEatKkZIDCWw5eHyS/jugGPUd7xR8pdcDhw6UqQk+CiKMnQhSs4wElJ9eICvKtShtkQfV8U/rcyI93bYIhm9ezwiOt0BfHugFx/usmHJdCuWHzE2fReiJ8Ad7U4EZAU6ge/zr/GBkNDWcQYICT4KgcCHUnd0ewIwa1QQODYigMwoNyVs56E0JdF1Cb8broCIYoOA/XYfppam55A7VCyoKVYD9tu9mFioR5c7EKlzhVkDAuDjhq7IAPzlnp5xNQBH4wlK8AYlAAQqbqDwFz7mTSK5LoUSj3xJZJ1tcmPbBCXjjIZUH0+rUG7S4KmPdvcl+hSw5YADvj7tQoFOhS5XAJ80dMEvKrj0mNzrdJkUHqN3wxECeAMS9GoVApIMDc+CZRlISmirt8CxcPklVJi1MKg5NHZ6YmLpDGeLcv93Iygp6HQF4AlKmGI1pOyQm0z8GjXP4sxDK2DUqOAJhpLFvvV9e85Gbh4N9EIofADARJLrRiP2OYDrVPyQ/nJj1ZRBSZ9MJLIeK1DBZwwymlJ9fxV/s92Lpk43ykxq7Gh3w9eXSiHc6Sx6Ab6gjJZeb851ukwLj9G7vQp0KkgKQbFBBZtLgV+UwZDQqj4gK0CfAGTRCWjs9AwQQNLdopzo3QiICpw+EfvtXqh5NiWH3GTj1xg1qkhi0g3bbOjx0gE4mkqLFnMqzdjT6YHLJ0IwsDE+Pi6/BI5lcMiEwTVw1JRBicdwE1mPJajgM8bINak+rA0wKDx6vEEYNLG+CyqOhYdIKNAJQ3a6kVzFJis8plKn6B1WB3q8IAA4lkWhXoDNGQDPMdAKocSfCkOg5hjwHJOxSLuDvRuHTrRgV4cL1UV6nDEvpJmJdy/x7jfV+DV0AI4PyzI4aXYZdrS7sKW5Fx1OP8w6FQAGDq8IhRDMrbJg2eyyYUX2psLP+CTXIouPJlTwGWPk2qQS1gY4/SIkWYFKE/vKhRJnsjBpVOj2BBJ2upFcxSYrPCqE4J2ttpTqFN7u/Pb37eh0tYcmN60K08tMKDWroRN48Aywp9uLySUGXLKoBhPSDFzXn6HejQqLFp2uQEQz05/BnkEq8WvG0gCcaWG8zmrEtUunROL4dLtDuwDNWgFH1RbiwkF84XJt0UPJLXIxsvhoMfbvcJyRa5NKWBvwxd5u8CwDUSZQ8wfV9+FghxyLhJ1upFexyQiPX+/vwc4OF4KSEqmTJyDhi73d2NrmwIULJ2JRgt1qdVYjrlpiwNyJFrz4xX54AhKsxlBy1qCkoNsvoapQh4uOnIiJRfqM3ddw3o1knkGy8WvGygCcLWG8zmrE706ZmXLk5lxb9FByi0xGFs93hj2yXHXVVbjrrrtQXFycifpQhkmuTSph805LrxdtDn9fLBoBkhISerQCh9piHdqdgbidbjRWsZ6gBJ8owyDz6HIHIgk7w9fWqDjst3thNaoxf2JBX7DBAJpsHnR7AnD4ROzt8uLk2WU4aU5Z3EmQZRn8YEoJgpKCNRv3YtNuO0RZgYpjUVOkx3kLJmRck5Xuu5FKjrArk3C6HgsDcLaFcZZlMLFIn5Lgm2uLHkpukanI4mOBYc9+zz33HG644QYq+OQIozmpJFL711mNuPSYSdDwHN7facOBHh/0ah4lRjUqzBp0e8SEnS7dVexwTBBdrgD2dXuwq8MFhgF4lkWBToWyvsB8Pd4AfEE5UqfozPShFA0cPAEJX+6zo83pTzgJNtpceG+HDXo1hyNrC8GxLGRFgcsv4b0dtkgwyEyR7ruR6jMYSpuQSwNwuoEGc9GklGuLHkruMdzI4mOFYfeAFAI/U0aA0ZpUhlL711mN+N2pM7FkhhUbtnegtdeHoKTA4ZNQZzXgnMMq43a6VFexikLwaVMX3t1mQ2uvFwFZAc+ymFxiwLkLKjG1dPBo3I02F978vg2SQiDJBCVGAe6AhJ0dLmxvc8Ko4eGXCBSFgGeZmMz0BToVRJlAVggkRUGFWQO7Jxh3EoyePKeWGuNGYc705MmyDH44sxS7Olz4en8Pys1alBjV8IuDvxvZ0CTkwgCcrqkqV01KY0GTRsk+w0nfM1agov8YZCQmleiVcpcrgDe/b0OPVxxU7R8275QaNXjlq2Y0dbohEwKb0493ttrAMsyAuqWyig0nd3x/hw1OvwiFABzDQM2z2N7mxOd7unHNCVNwwozShPe0rr4DPV4RC2sKseWAAzZXAJ6ADEUJCTSeoAyLVoDLL+Kb5l7MKDfB7g3lm2pz+OETQwlbCQF2drhRU6SLOwm29PrQaHPBoObQ7QnGmNOyNXk22lx4Z1sHPEEJNlcA+7q90AocJhbqMH9iQcJ3I/oZGNR8JIlpuM7pahJGcwAejqkqV01KuaRJo+Q26YbDGCsMW/BxuVyZqAclw2RzUoleKftEGfu6PZAUgoU1hRHhJJHav9Hmwt8/C0041UX6SE6nRBNOsqtYX1DGmk/3YvNeOwKSDJYJBQNUFAUMAxQZBLQ7/fjzhgZUFegwtWzgpBa9kjdqVJg7wYwPd3UiIMlQsSGBRFGAySU6uPwS9nR70WRzwxsI+QRJSigKs6wAWjUPh0/Ezg43ivTCgElwe7sTW9ucACEISAoYhkGBTsCsChOKDOqMT57RE/3EQh2mlRphc/nR5vBDr+axdKY14UQffgab9nRDkhT0+ERISkiTVqBVgedZHFVblJYmYTQG4OGaqnLZpJQLmjQKJdehGp8xTDYmlf4rZYPMY1eHC5JMsOWAA/OqLJEEpf01F5UWbcoTTjKr2KUzrXhnawdaer0ACBQAkkKgF1gADHyiDHdARplRjU5XAK983YybT5oxpElHxbHQCRxUnAaegIiApCAoyWi0eWDSqqBTcWhz+BGUFBAG0PBsSBPCc7Aa1NCoWHQ4AyAE0KoORuFttLnw3y1t6OlLWKoQAkkh6HIH0Orw4YhJRSg2CBmbPBNN9BUWHcrNWjTY3Hh3mw11Jca4Ez3LMphebsRr37bA5RdRpBdg1qrgC8rY3e2BSaPCtLL4v81FhmuqKjdpUGwQsK3NiboSA0xRebVywaRETRkUyuDkfzZAyojRfwI1alSQCQHDACVGAb6ghKZOd4zfl1bgEJBkeIJSShNONOFV7OwKM3q9IvZ2edDrFTGn0oxLFtVAq+LR1OlGoU5AQFYgSgrUPNtnNgIEPpTrigAQeAZNca4BxK7kASAohxKtuvwigjIBz7HQqDho+5yXOZbp0/AQiJICWSHQq3mUmULCWQgCBqEozNFt2O0JgmUYeIMyeJaFXggJOW6/hM93d2NXhwt1VkNGJs/B2h0ADGoOX+3rweZ9dijKQJ89RSHY0eZCuUmD2mI9FAI4fCFTYm2xHmUmDXa2uwb8VlEImu1e7Gh3otnujXvu0eCggBtfqIx+Z/vTaHPhqY92Y3eXB/vtXqzf1oGNjV3odPnh8otosLlzwqQUXvRMLzOhqjAzcaAolLEC1fhQkibeBCpwLHiWhaQQGDQ87J4gXH4JJm3IBBCt9g9POFqVBk6fGOMnwjDMoOadwVaxO9qd8EsyivVqMAiZuNRRbzbHMAgSBQEppI1RCOJeo79ZTcUy8IoyRJlAp2LhlxTo1TwMfUEYO5wBCDyLWqsenoAESSYo0AlQ8SwCkgy3X4JBw6NIr4ZXlCNt2GhzwReUoFaxUEiflohhwbEMNCoWDp+IbncQS2daMzJhJfJJsXuCaLS50e0JwOkT8dSHTfhy4sAkoeHnPqXUENfHxx2QBmhIhhPjJtsRutM1VfU3F1qNGuxsd6LN4UenO4CppcZBfaUoFEpuQAUfStLEm0CNGh6FOgE2lx8WnQqSoiDYl0yxv9q/pW8n16bd3fAE5YifSKFOwGSrHiqOHdS8k8h0F57IOJZBoU5AlzsAWQHCOR5lQsAygF+UUWxUw6JVxb1Gf7OaXuBASMhPyC8SqHgWhbqQWSOk1SLgWRblJi306pDZy+YKQPSG4vGUGjUoM6sBMJHreYISenxBuAMSCvVqKFoV7B4RPlGGSBSAAdR8yCG7tdcPrWr4E3+8id7uCfZtwZcg8CzMWhWK9Oq4vlbRz51hmIhQG6a/wDocx+GRiNCdzu6neOZCowYoNhTD6RPR2OnG5BIDrvhBLXieKtIplFxmWD20sbER69atg88XMhvQre1jm/6mICBkopps1UMrcOh0hfxZWAZx1f4+MZSV+0CvD2o+5MyrUbGwufz4Zn8PGmzupMw7/U0o5SYNJpcY0O70Y0a5AXp1SAshygoUQuDvM3MZ1KHs13VWIxRCBphgFIVAzXM4dmoxLFoV9nd7oSgImaoYBoV6AWpVyAxi9wRh0PAoM2tQbtGg0x0Mvf+hfwABFKKg0x2MuSe9wINjWAQkBSqOgVbgUWHRoKpAi8oCbchZmGPR5vDjr5/sxp/e2YVVHzSh0Zb+JoLwRN/m8IMQAkIIGm2hhLEFOhWCkoIigxplZg2mWA2RLfjhdon33KOJ1pDEM4dyLAOjRhX33NGEBab6VgcsOhVqiw2w6FSob3Vg9ca9w2qDaMICbqFeQIPNDZc/5Kw9mKkqkbmQYRiYdQKmlhrR6QqgzenPSB0pFEr2SEvj093djQsuuADvvfceGIZBQ0MDamtrcdlll6GgoAAPPvhgputJyQESrZQL9WrMnWDGF3t6wHMsut0BaFR8zE4SRQnltTJpVKFt4QEZDMNAxbHQq3m0O/3gWRZLZ8ROOIpC0NzjxZ6+0P0sA3zX7MDuLk+MRmB6uRGtDh/sniDmVJrw3QEnPAEJCiHgWAZFWgE6NQ+Tlke3O4BH3m0Y8PsdbS5809yD/d1e+EQZAAHHAsVGLXQCj4CkoNcbBMeysJo0KDOFtDkzyk34pLE74vhrEUKOv3u6vQMcf8NtuL3NCVFSIPChNBUyIQiKCmyuADiWhUXLYVKRATzHDDsacH9NVmgLfchM1+MVoRU4TC7RR55nf+feVDQkwwk4OZJBAVPd/ZSrW9gpFErqpCX4XHfddeB5Hvv378eMGTMixy+44AJcf/31VPAZowy2w6rbI+LwmkKcNKcMJUb1AN+MaD8RUQ5pHHq8IZMPz7KotGigFXi0OnzQChwqLVrs7nLjhU37sWmPHb2+ICRZQVAiMGtVOKzagtpiQ4wJ5fjpVuxoc6Gp043ppUbstXvg8ksRM5fdE4TTJ8LplTCl1BAxwWza3Y3Xvm2BWcvDE5AgKwqMGh4BUYGPUWD3BKHmOcwoM0Kn5iFwLAxqDo2dHsyuMKPTGUC5SYMSg4AerwiHTwTPsqgt1oNnWexsd2HJtJC/DssyOHdBJT7f043mHi9UHAtRViDJBD5RBgOgQM+h1KyFpc+slomJP3qi/3p/D5w+EWatClaTBpNL9CjUq0EIgcsvwSdK6PEG4AqIQz73/vFh0hUQRiMoYCq7n3J5CzuFQkmNtHrp+vXrsW7dOkyYMCHm+JQpU7Bv376MVGy0CQaDCAaDA46zLAue52PKJYJhGKhUqrTKiqKY0HSYrbIAIAjCoGUnWtT46cJKvLvNht12f2SlPLvcgBOml6C2xBApK0li5P/REyILBQuqjHD7JQQVBd6AhDaHH82dTvz1o10oMeph0Qlo6nJjj80FjgFKdSrYnD54JQlOr4StBwj0PIMCoxZTrAY02NzY0dqLS46qRrvLj53tLrxV3waHV0SBXoBRzWNrmxstzkBI2BBlsCoGeh6QpSC8Xj/EIAOB51CsFwCGg0ENiIqCYFBEt9MDDU9weHUB/KKEpo6QSWRWuQ7/2dJx0PHXG0RQliCwLAxhx9/2XuztdGBCgQ48z2NqqQnnHjYBj7yzEy6vHyqeAQeAhwKGZSCLEop1LEJGs1D8oDKTEHOe/vA8D5YNWa4VRYEkDdQ8TLSocdnRVTis2oSnP96LIr0apSYBDCGwO71o6nSj1yfCL8qQFYLXvtwH/vCJmFpuQZ3ViBVHTcT679uwu8uNDik00c8uM+CEGVZMtKghy3JEQPAERBiFgZZ0r1+EmiXQcLFaI4fHB38wAK1RBSL3q3uU47s7IA7aj6L7JyEEoigOWbaqUBcpG/3ORlNqUMVovaDIMfVv73FjVoUZJToOkiSN6zEiUVlJkqAoSkbKqlQHQwhkq6wsy5BlOSNlo/tntssqCsGBXi/2dXsBADVFOlRadBAE1ZBjRBiO48BxXM6UHaovR5dNhrQEH4/HA51u4OBrt9uhVqvTOWXO8eCDD0Kj0Qw4PmXKFCxfvjzy+YEHHkj4QKqrq/Gzn/0s8vmRRx6B1+uNW7aiogI///nPI58ff/xxOByOuGVLSkpw1VVXRT4//fTT6OzsjFvWbDbj2muvjXxes2YNWltb45bVaLS48cYbIyve559/PqEgq1KpcN0vr42slD966zW89Elj3LIAcOmvboysmLm9n0OyNwMAhL4/C4AZANAK4JAz8HFjJ2zOAI7kdqNCsQFh14nwG+sGUA8oC84EJ2hRbtagpX4T/vjJPyLXLOr7CyMbj0SZSQdPQIZz9zfgPXsBANP6/iADCADwAE1FRyOgMqBAJ8AS3IdaeT/QCfj7mjkc+/nDekCsXARJUxaKwNzVAKH1+0gVAcAM4Nldof+vWLECEydWw+mTcKTJgRLH9oMVPDjuA42AxB8LVWFl6CvHAZh3fxE5T3/OPfdczJo1CwCwfft2vPLKK/ELAjj99NMxf2Ih6lsdKO61w7fjI/DhNohC+gZ4tmMuVpxxAuqsRgj+HnR/+hLMUWW6dgEvbQz9f+nSpTjqqKMxucSAHbv3AS2fxL2+GcCeMhdqliwBAHR2duKff1uFUgDe3QPLCxXTIZbNhprnQAJe3HvvwwnvbcGCBTjllFMAAF6vFw888EDCsnPnzsWZZ54JIDSB33vvvQnLzpw5E8sWnxTRepU1vRHzfSmArj3AfRvH9hih0+lw4403Rj4PNUbceuutkc9r165FQ0ND3LIAcMcdd0T+/9prr2Hbtm0Jy95yyy0RQemNN97Ali1bEpa94YYboNeHkr2uW7cOmzdvTlj217/+NSwWCwBgw4YN+OyzzxKWvfLKK2G1WgEAH3/8MT788MOEZS+//HJUVob68qZNm/Duu+8mLLtixQrU1NQAAL766iu89dZbCcteeOGFmDp1KgDg+++/x7///e8BZcK98JgTT8UJRx0GYOgx4owzzsC8efMAhHx5X3zxxYRlf/SjH2HhwoUAgP379+Pvf/97wrJLly7FokWLAABtbW34v//7v4RlFy9ejOOOOw5AaIxYtWpVwrJHHXUUTjzxxITf9yct5+Yf/OAHeOaZZyKfQxFtFdx///1Y0jeYUfIPryin5EgbHSckXnyYaMJ+Ig02N+yexKvaEEwk31XitVkIjz+0GtIKHOQhnOu9QRmEAHo1B7+YeBUVxteXjsMnDV623RHAxqYufL67G/vs8SetaMJmnRLT4IsEX/DgdcM75TIBwxx07g0FfUyMNygndEaOR9gsZtYOvqYa6n2JhhCgzeFHndWAMtPAxchIER1PikLJNTqGcKz/73dtGdsgkO8wJI2tWPX19TjhhBMwf/58vPfeezj99NOxdetW2O12bNy4EZMnT85GXUcEp9MJs9mMzs5OmEwDE1rmgqmLEMDmkSLaFqueR6J5JNF5d3e68eymfejxBFFq0kRSR7S7JRTqBVyyqAbVBZqMqrE3bO/AvW/tgMcXQJFeBZZhcKDHC1FWoFFxOGpyEWqKDOj2SviooQsOnwiBIZhYGPIXOdDjA88x4BgGXjHka7FoSimKjRq4/CJ63X6sPH4yvEEZT3zQiElFBjh9Ipo63aEdN+4gVBwHncCBZwiOqSuCwLL4fK8dshJyLK60aGFQ8/CKBO2uAAKiAo4hKDOpIcoKKi0aVBbo8NMjqwEAz3y2D5v3ORCUFRQb1ZBlBR5/EFqBx5xKE+yeIGZVmHHJ0TVod/nhlwGbK4iXvtyP2iI9OIb0PVOCr/f1oNMdgFmngsMn4vBJxSg2akEIwa52J6oK1DhtbgUMah4V5lhflGRMXWHCauFGmwsvfbEf6+tbwbGARsWjQKdCbbEeBfqQUOYKynD4ZFz3w6motGiSVk3vandEzGKBPrPY5JKQWay2xBBXjd3/nQz7EbW7gig0aHDJohpMLjEkZb6KPm+myyoKwd5OB7xBGTqBG/AscmGMoKau4ZfNJ1OXohA8vmEX/vt9C2SFoFAvRIKmEgB2dwAMy+GMQ6tw5XGTAZBRN19lw9QVnr8dDkfc+TtMWqau2bNnY9euXXjsscdgNBrhdrtx9tln4+qrr0Z5eXk6p8w5BEGI6YiDlUvlnMkSPRBFM9w4JyqVCopCsGGnHXafgilllkjnNalUMOoOZgb/5eLJSTvSRg/08YiO/iv1OQF7AxKChIVZJ4BjWXR5FdSUcBB4AhXHhvJtEUAEC62KgyCEoukKHAsZPDhOBYHnYnYW1ZSY0dLrg0ZQo80VRIPNA19QgkGrhlkC3AEJroAEFgy8IlBUqIFZr8HuTg8MWjV8MgMdw8HuDaWiYFkmtKNLBkrNesypsqCx04MNO+0ghKDHr2BhbSihaa9XhEHDw6TXoNMVxBf7nVhQXYDplQVYvelA5JnJCkGz3QetikNVYUgFzwCoLTPD2dwLm1sEz3FgWTayxdrpEyER4MmP9w35zFmWTepdq7Macdb8CWjo9ISiTau4SDDJMDo1C5srCE9QSvq8ADC1zIw6qykpx2GGYSAIAqZXFuKSH6gi77fNEwjttJoQGxQw2TqEz5vpsizLoLbUklRZYOTHiFwpGw47Ef38hxonwiRbLptlU/EbGe2yLb0+1Le5IBIWRq0KYFlEi6N6HQOXX8J3Lb2RDQLJv+/J9/tslU2lfyZD2lsQzGYzfvvb32asIpShGU5guGiGs+U43Yi68aL/2j1BfN/i6JtsEYn6bNTwsBrV6HYHQ8EH++pVqFchIIVSSAg8h2KjAIAMiL1SadGitliP/3zXClkhKNILYBgGRQYBQVmBJyBB4Dm0Onyw6FTg2dBgYdaGUka0O3xw+SUQABz68n5xHGqL9WBZFuVmDb5r6QUIUFmghVGjwrwqBk02D+zeIGRFAc8xULEM5lZZ8N4OW8wz8wQk7On04Mu9PdAJPIoMIe1KvLAAQZnA6RNh0qgwsVA3ZFLXVDFqVCjUCdAJXMZ3K6WTK47mmRobjEQgSspBPEEJHjGkPVFxA/uKigttlvAG46diGW+kLfj09vbiiy++gM1mG6A6vPjii4ddMUosmYxzks6W43QGsmhBqd3hh0+UUBEV/deo4WFzBdDZF/VZVhQEZBnwA4V6FVgWkCWgyx1AoQFgmFCn5nkWejUHNc/B4ZMGxF5hWQbzJlrwz68PgBCCoByKpMyyod1BLMNA4Bkc6PHBohVw1OQiTCs7GMenweaCvy/Du8wwYFggKMvY3ekBwwAmrSrkLwQSyfdUqFejoEaIpHPgGAadLj+2tzkHPDOTVoWFkwrw4a4ufLHXjkWTi6BT8wPCAhTpBfzrm1bwLIOppcasxLZJJ4pxthmNjO2UzJGpBRolefQCD70qNBaJMoGajx0PRFkBwEAn0JALQJqCz+uvv46LLroIbrcbJpNpQCRTKvhknkzGOUk1Jkk6A1l/QSmueYdhUGc1wB2Q0OkKQiEKtrU60e0OoscbhKwQ8CwDh1+Cyy9BI3AoMaixbFYRjp9hjRsvKEyxUY2qQh2CkoJen9iXVJTFhAJdSHPDMNjT7cGFR0zEsVNKwLIMlkyzoqXXh4922fDohgYwACwGNfQCB0khsLn8cAVETLEaoBM4gCCmDaPTObj8oSSebQ4/Ki3aAc+syKDB4TUF2NHuQmuvHzzHDAig12z3ossdQEWc32cqtk0qMXoolKEY6UCUlBCVFi3mVJqxp9sT0oj3abmB0ALG5RPBsSwOqbSM6CImV0lL8PnNb36DSy+9FP/7v/8bd1s7JfNkMnJsKqv8SDZxdwBlJg0CkgJCQuaocPyc/gNZPEEpsXlHwNwJZnzc0AWXX4Y36IUoEwgciwKTCn1ZIKATeJQY1bh0UQ2OqSsZctDUCzyKDWqY+wSR/glRnb4gdKqQ7byl1xcRniotWvR4RJh0AmSFwNjXPhwLCPqQ+am+xYnT5laAAbC1zZmwDSssWrQ7/QmzgJdbtPCLCs4/vAplZs0AIW6kogWnGsU4mmwnFKXkF6MRiHK8E+6DU8uMmNCsxa52FzqcoU0SAIHDG1qEza0wYtlsuogB0hR8WlpacM0111ChZwTJZOTYVFb5zXYvvmnuQY8niL3dXkiyAp5jUaATUGc1DBjIEq34BjPvdLkDMGt5lBgFiDJBr1eE1SiAZVkQQmD3BGHS8FBzLDY2dqPYoIZRoxowyUZPwjpVyCdna5szph4A0O0O4Iu9dqhYBi992Qyt6qDZTs1z2N3lwewKExpsnkhOrkiEZQVQCMG8iRaUmzVoc/oTtuEJM6x49euWQZ+Zpu/agyVfzcQzH0pASce3hvpxUPpDU3uMLP37IMswKDVp0OsV0e0OAAAsWhWOqC3C8iMm0n7ZR1qCz7Jly7B582bU1tZmuj6UBGTaFyPZVf72did2dbig6nMAVml4iDJBp8sPd0DC7EoTAtJBh7nBVnyJzDvVRXpICkGBToXvW5wo0AuR7ZwMw8CgCeXycvklbGtzYmeHK5TRPWqSjTcJW3ShBJnRgklbrw9f7u0BAMyuKUCFRRdjtls8rQR+SUZtsQF6tWpAao0yswZqnkWJUT1kG9YWG7Cl2ZH2M8vUM09WQEnFt4b6cVDiQVN7jByJ+iDPsqgs0OLI2iJU9G30mFCgo5qeKNJ6+0455RTceOON2LZtG+bMmTNgS+Ppp5+ekcpRDpINX4yhVvmKQrB5jx2STFCg46DmQ8KImmcg6AXYPUHs6nBhYoEuMpANteLrb97Rqjjs7nRja5sDfpGDJCtQaWJfS0kmsLuDEDUEPAeUmTTQCVxMjq7+O6e8wVAaDI5lUN63Amp3+LC3O+RntHBSAYoMoWB4Yf+DXR1uvL/DBn9QRofTh3KzFofXFEQcloW+nREOnxS536HaMJ1nFq2dOaQqtD0/3WeeDQGF+nFQEpGLzvJjkcH64NTSUB/s9Yr48eETaR+MQ1qCTzhs+l133TXgO4ZhBg24REmf4fhiJGKwVX5Lrw+drgDKzRo4fCLUPBfpYAzDQK/m0dbrx4LqwshAlsyKL2zeCUgy3tjShu9aerHH5sF+zgtvUAbPsRHfHEIIujwBKITAqOFAwPTFmzkorKzZuBd6NZdw51ORQY0Vi2qwp8uDF7/YjwqzNuKEHKbHG0Sny4+trQ7wLIMd7S5UF+lQZzWiUC9E6tJgcw8YuAdrw1SfWVzNlVaFcnNIeEvV/yYbAgr146AkgjrLjwy0Dw6PtASfwSJfUrLLSMY58QQlBGQF08qM+L7FMcDfxe0XwXMMDqsuiFw/2RWfT5Tw90/3we4JosKshcMroq3XC1khaO31QcUy0Kl5BEQZbr8Eg5pHUCYoNalh7NMIMQwDo4bH1lYHjqwtTDgANHW6wTIMyswacGxIYIvG7gng2+ZeeAMSOJbBjHIjdnd5sLsr5OMzv7oAWhWXNc1amETamTaHHwU6Fc6eX4niQXay9SdbgyP146AMRjYWaJRYaB8cHtTQmoeMVJyTsPZGo+Iwr8oSCdAX3hpu0Qso0AmYUW6KMc/MrTKjpdebcMW3dEYp3tkaq4mYUmqAJyhBIUH0+CQc6PWh3KSB0x8KU67iQjEoJpfoYyZxjmUgygo4Nn7auegBIJ42ihCCJpsHvqAMg0aFgKSg1KRFkUGNxg439tm9+HpfD2aWm7KiWQu3m8sv4l/ftKLbHUioudrS3IvT5lbAE5RidqIlIluDI/XjoAwFDUSZXWgfHB5pt8qHH36IBx54ANu3h7JLz5w5EzfeeCN+8IMfZKxylNElWnszxWrAgqgAfSqWQbszgEMmhLQ3qz5oGuBYXG7i4ppn1Dw3QBNRqFdHhCv0eOHwhmL5aAUOBjUPq0mDmeUmFOpjE3vKSii9hZxACxk9AITv5/uWXpSZCESFICDK6PYEoFdz8AQkWE2ayJb3wycJmFCog90TwIVHTMSC6sKMDtzRZi27N4gmmxtWoxolRk3EvAaEtDNaFYv/ft+O71oc4FgmqR1U2RocqR8HJRloIMrska99MFfCX6Ql+Dz33HO45JJLcPbZZ+Oaa64BAGzcuBEnnHAC1qxZg+XLl2e0kpTRIZ69XqfmwARDgfmKDAKmlRkjJquB5hkBZ82vHBBocEe7M64mIhz9uM5qwO4uN86YW4ECg4C3vm9HlzsASz+/HEIIXH4JNUX6UIoJQgYdAFiWwfRyI9Zva8d3BxwQ+FA+m15vECa1ChZ9aKdYtB+T1aSGNyjBoOEz2mH7m7UEnsXerpBD4rfNvZhXZYkIP3ZPEDs73LB7AphWakBlgS4pB+VsDY7Uj4NCGV3ysQ/mUviLtLKzz5gxA1dccQWuu+66mOMPPfQQnn766YgWKB9JNrtrLpNpqTr6hQ1n2q6zGkImq20dEY1Q/4k17AjcP9lps92LP72zCxadKq4mwuUXsd/uRW2xAV3uALrcATTbvVBxLGZXmlBu0cZ08P67uvoPAGHBICxs7Ld74Q1IcAckeAISuj1B6NU8jp5cjEnF+gR10aPLHcxIh1UUglUfNMW0m9Mn4rPd3VDzDDwBGVaTBguqCwAAX+7tQWuvDxoVi6MnF0ccswdr4+hnFy1gJWqbdEj0XlA/DgplZMiXPjjQf5GPLJCHOw5Fk9Xs7Lt378Zpp5024Pjpp5+OW2+9NZ1TUjJENqTqRPb6dJ1nh9JEhLOR8yyDCosWFRYtig0C6lud+GZ/L7rcQRQb1DE+N9VFukGdKaN3OB1aZQEAuPwSApKM7W1OtPb60enyo6ZIFxPqvaHDDadfBM+yqLBkZjt4vHYzangU6gTYXH7o1XwkYSsA9HgCYEBQZDjo2D1UG0c/u2w5mlI/DgpldEm1D46GqSkXw1+kJfhUVVVhw4YNqKurizn+7rvvoqqqKiMVo6RONoPKxbPXp+s8O5iatrXXH8lGHu3kW1WoR6VFi+9aHKgtNuCSRTUxQbmGGgDiCRshzUkoyKE3KGNftxcTCrSwmjR9dfHB6Rdh0qowtTRzHTZeuzEMg8lWPVwBEW6/CJkAPlGCX1TQ4xNRbBAGOHYnauP+g1ttsQFXZklAoX4cFMrokmwfHC1TUy5uvU87V9c111yDb7/9FkcffTSAkI/PmjVr8Mgjj2S0gpTkGA2pejjOs4k0EROLtJAUBRMLdQM6CcuymFxiQK9XBMMwA+5jsAFgMCGtUK/GYdUF2LyvB3aPCG9fvcMRpePVZTgdNlG7hR28t7U6YXMF0OH0Q+A4FOsFTCs1DnDsBuInlM0VOzqFQskNRjPSei5uvU9L8LnyyitRVlaGBx98EGvXrgUQ8vt56aWXcMYZZ2S0gunw+OOP449//CPa29sxd+5cPProo1i4cOFoVyurjIZUPVzn2XhaGldAxGPvNSZM7DncLdiegAiAiURiNqg5uAMyvEEZk4r0uOSYGlh0QqgufhGPvZ/5ugzWbgU6ASVGDQ6rKcCZh1ZCL/B4fUsrtrY6h3TepmkkKBRKf0bb1JSLW+/TvtJZZ52Fs846K5N1yQgvvfQSrr/+ejz55JM44ogj8PDDD2PZsmXYuXMnrFbraFcva4yGVJ2JnQX9tTTNdu+wOkkiG3alRQuLVoVPmrrAApAUApkQyDIByzLwizIK9Wps3tODk+aUoapQN+y6pNtuRQYB5y+oiggpJ80uQ5sjcTLUE2eVAkDO2dEp6THaW35H+/qUzDLapqZc3Ho/5qIbPfTQQ/j5z3+OSy65BADw5JNP4r///S/+9re/4eabbx7l2mWP0ZKqM+08G91J9EJIGxOtnRmskwxm5gEAmzsAX1AGxwBqFQu7S4RPlMGxLMpMakwvM2BrmxNtTj8uWVSD2mJD1jpsKu2WTNlmuzfn7OipMF4n2/737RMlvLPVNmqmSmoqHXv0XxSHw4CEx1WtwMYkms40ubj1PqVZcNKkSQMG1f4wDIOmpqZhVSpdgsEgvvrqK9xyyy2RYyzLYunSpfjss8/i/iYQCCAQCEQ+O53OrNczG4ymVJ3J3T3hTrK93Yl1WzsgEwKAAGDAMQymlhnjdpLBzDwtvV5oVBxkhWDx1GI0dXrQYHMjICnQqligLwP8hIKQQBDWjvxysSGrHTaVdhuqbC7a0ZNlvE62/e87KCnodAVg0qgwpdQw4qZKaiodm0QvikWZoNHmRo83CElWwHMs9AKHAr2QVVNTrqUxSelOr7322oTf7d27F0899VSMEDHSdHV1QZZllJaWxhwvLS3Fjh074v7m3nvvxZ133jkS1csqoy1VZ2V3D4OQzAPm4Oc4DGXD3nKgF12uABZOKoJJqwLPsbA5AyjSC9CqOBAAflGByy/B1JcQNKwdyXaHTaXdBiubi3b0ZBivk+2A+EoqDTbt7ka70w9ZIRBlAo5lRsxUOdp+IJTsEV4Ub9rdjR5vEH4xlJ5HpeEhSgoO9PogKQS+YHaTi+dS+IuURsFf//rXA47Z7XbcfffdWLVqFY444gjcd999GavcSHDLLbfg+uuvj3x2Op15uyU/16TqdAgPwLJCsGxm6QBTV2OnZ8AAPJQNu0AnoLHDDVkJxeoUZQKGDW1nZxkGCiHwBEOqX2CgdiSXOmwictGOPhTjdbKNd99OnwhPUEaZSQ1PQEZTpxsFugIwDDMipsrR9gOhZA+WZfDDWVas39aOTncAZSY1VFwox6E7IKHEIMCkVeHd7R2os2a3r+VK+Iu0l38+nw8PPfQQHnjgAVRXV+PVV1/FySefnMm6pUxxcTE4jkNHR0fM8Y6ODpSVlcX9jVqthlo9cJtwvpIPk/RgpDMAD2XmMWp4gAlFYS7QCxA4FjwbyjCv5jmIsgKeZSFwoUSn8bQjudJhEzHaGr90GK+Tbbz7DsoKJEWBkRfAMEwkgGU4Sne2TZWD9SFCCERZQafbj6ZOd16NJ5QQWhWPEqMaPMvAE5ThDQbBsSysJg0ml+ih4tgx2dcSkbLgI8synn76adx5553QaDT485//jJ/85CdD+v6MBIIg4LDDDsOGDRtw5plnAgAURcGGDRuwcuXK0a3cCJLrk/RghAdgv8hhe1tPjC26QCegplg3wBFvKDMPzzIwawXYvUFUFepioiSrdAzc/oPJSXNVO5IM+abxy2e/pOEQ776jhXEVx8IdOKiBBLJvqkzUh+yeIBptbthcfviDMl78Yj+2tjjHvP/VWMMTlCDwLI6sLYI3eFCLHk7ILCnKmOxriUipF61duxa/+93v0Nvbi9/+9re48sorIQjC0D8cQa6//nqsWLECCxYswMKFC/Hwww/D4/FEdnlRchu9wCMoKfhqnx2yQg7aomWCTpcfdk8AVYW6mAlgKDNPuzOAo2oL4ROViDakpliHbk8A+3t8sOgEVBfp4A5IOasdSZZ80vjlq1/ScIl33/1TlkRrIEdCGI/Xh+yeIL5t7oU3KEGWFVQV6VBh1o55/6uxSPid84lyRIsYzVjta4lI6S5//OMfQ6vV4sILL8S+ffsSbg9/6KGHMlK5dLjgggvQ2dmJ22+/He3t7Zg3bx7efvvtAQ7PlNyk3KRBQFTQ6xMxsUALlg0N/mqegUqnwv4eH0olBeUmTeQ3yZh5LjxiIgDEJPSrKtTBKipQq1g4fSICopKz2pFUyBeNXz76JWWCePcdTlni9AfR7vRjQoEWWoGFyy+O2OaE6D5UZlJjV4ezL2ceYNSqMMVqgEmrglEzdv2vxirjta8lIiXB59hjjx1yu3oumLxWrlw5rkxbY4k2px9qFYsCnQo9XhEGDQ8VFzIBuP0SLFoVBJ5Fq8MHhmFi8lElY+bprw0pN2nQ1qfizWXtyFgkFb+ksRTnJ9F9qzgWBXo1ODZk1t3X7R1RU2W0qfS7A7040OODRsWhtM8PJJwyJdr/6kCPN6Yf5vNzGcvkow9gNmEIIWS0K5FLJJvWnpIddrQ78ecNDbBoVdjb5YXdG4SsKOBYFoV6ATVFOhzo8aG22ACnXxwQ96W2OD/MPJSDRMezCUghlXud1RCZ7MdqnJ9E9710Rim0AjeqkZs/aujEXz/ZjUlFBlh0qsiCNhz8zifK2N3pxowyE1wBaUw9l7HMUH0t30l2/h4fBj1K3hC2RWtUHBbUFMREGDVqeBzo8aLZ7gXDAJNLRj7IGyXzDOaXNJbj/KTjjzUSmi+WZTC5xIASgwY8x0SEHrsngCabB3ZvEE5fEA6fhE53AIdWWVBbbBgzz2W45LJ2Mp98ALNJ0oLPH/7wB1xzzTXQ6Yb2Hfj888/R1dWFU045ZViVo4w/om3RYZ+CMIqioL7FCRXP4pBKc8T/Z6zHfRkPxPNLGg9xflLxxxpJzVd/n5Aeb8jR2ReUoRc4+EUFKo4BA6DB5oFerUKhXhgzzyVd8kE7mS8+gNmETbbgtm3bUF1djauuugpvvfUWOjs7I99JkoTvvvsOTzzxBI4++mhccMEFMBpz4yFT8ouwLbpQL6DB5obLL0JSFLj8Ir5rcUCUCWZXmCJCT5j+cV8o+U8qcX7GOmHNV32rAxadCrXFIRNUfasDqzfuRaPNNejvFYWg2e7FjnYnmu1eKMrgHg7R/XBXhxvbWp3wBiTo1Tx6fRIUApSZNSg2qOELSmjqdCPsNWFQc/hqXw8277MPeZ2xxHCfEWXkSFrj88wzz2DLli147LHHsHz5cjidTnAcB7VaDa/XCwA49NBDcfnll+NnP/sZNBrNEGekUOKTKB7NpGI9CAEqLPFXK2M17st4ZbzG+enPcDVf6Wohwv1w7ZfN2NrqAMcyCEgKLDoVCAiMmpDvj0HDw+4JotnuRbszgG5PAE6fiKc+bMKXE3tyStuRLcaDdnIskZKPz9y5c/H000/jqaeewnfffYd9+/bB5/OhuLgY8+bNQ3FxcbbqSRlnxLNFK4TgkXcbxl3cl/HKeI3z05/hRLgero9UndWIM+dXYpfNhTKTNpTbjhBs2mOPRD5XcSzsniC+b3UChEDgWZi1KhTp1ePG52e8RiHPV9IaMViWxbx58zBv3rwMV4dCOUh/W7SiEBqLYhxBY4+ESEfzpSgEzT1ePLdpHw70eIflE2dUq1CoU0MncDBqVCCERIItCnoWQUmGNyiDAQOrUUCPV4TVpEGZWYMyYFxoO6h2Mr8Y20ulHCGXvfzziVyMRUGfbfaI97w1Kg6drgDaHD4UGdRYOmPsxx5JVfMVNm19d6AX37c6oFVxECWCydb4sXiG0kLEE0AnW/VwBUR0uwPwiwoIAfQChx6vCK3AYXKJPiKojgdtB9VO5hf0KWSZfPDyH01SFRxyKR9Vrj7bTAljuSDURT/vb5p7sN/uhS8oQyeEQh68s60DLIsx3ZdS0XxFm7a0AgetioNezcHm8sMVEDGvyhIRfpLVQsQTQE19kZzrW5wACBgAMiGRpJfha6RynXyGaifzCyr4ZJGxHIMkEwzH6XK0Y1Hk6rPNlDCWS0JdndUIZRbBLpsLVqMa5WYNrEYNfKI86u09EiSr6QQQ42Dr8ktQcSxYhkWhnoPdE0RTpwcFulAG+FS0EIkWHGfMq0S5RYP/fteKIr0aZXF8XMaDtiMXtdGUxIzdN3GUoV7+gzNcwWE0Y1Hk6rPNlDCW6nkG0wxlQmukKATvbLUhKCmYP7HgYHtzbFLtnQuaq+GSjKaz2e6NcbA1angU6AR0ukITb3j3lcsvwajhU9ZCJFpwAEBbrx/1rQ6U9fvNeNJ25JI2mjI4wxJ8Ghsb0dTUhGOPPRZarRaEkJzI1ZULUC//xOSq4JAsufhsM9WmqZ5nMM0QgIxojYa7qylXNFfDZShNZ38HW4ZhUGc1wB2QYPcEoVPzEGUZPd5QItTBtBCJhMVECw6q7Qi1mZrnsGRGCQ6fVACDmodRoxoVQXssCPvZJC3Bp7u7GxdccAHee+89MAyDhoYG1NbW4rLLLkNBQQEefPDBTNcz76Be/onJhuAwkh09F59tpto0lfMEJDmhZmh7uxMAICtk2KbAdNs7V82Rw2EwTWc8B9tCvYB5VRY02tywufzwiwp8ooy5EywJtRDpCIvjXdsxWJuNtMAxloT9bJGW4HPdddeB53ns378fM2bMiBy/4IILcP3111PBB9TLfzAyLTiMdEfPxWebqTZN9jyugIj3t3fG1QzpBQ7rtnYADLBsZumwU4uk0975rlVMh0QOtoV6AQuqLfiuxYFJRQacNKcUJq0Kap6DopCY+x+OsJgLvnejQS4J2LlUl1wmrZF5/fr1WLduHSZMmBBzfMqUKdi3b19GKpbvUC//xGRScBiNjp6LzzZTbZrsedx+KaFmyB2QIRMCkND/TdqD6UXS0eil0965aI7MNkM52Bo1KrAMsPbLA3EXCJkQFsdbHqhU2yybmunxKOynS1qCj8fjiZus1G63Q61Wx/nF+IN6+ScmU4LDaHX0XHy2mWrTZM9jUPMJNUNBWQFAADB9/48lVY1eOu2di+bIkSCRyancrIHNFUCb059wgaDmuXEnLA6XVE3D2dRMj0dhP13SEnx+8IMf4JlnnsHdd98NINSoiqLg/vvvx5IlSzJawXxmvNu9E5EpwWE0O3quPdvoNt3V4YJRw4NjGcgKgcsvocigTqpNk302ap5LqBkSOBYAE/X/WNIxBaba3rlojhwp+pucdCoO//m2FW0O/6ALhOOml6QtLI5XZ9pkBezt7U58uLMzq5rp8Srsp0Navf7+++/HCSecgM2bNyMYDOKmm27C1q1bYbfbsXHjxkzXMa8Zr3bvociE4DDaHT3Xnm2d1Yjjp1uxZuNebG11QpQVqDgWNcV6nDfdmvTAmsyzGSx9iEHNgWMYgAn9P5rhmAJTae9cNEeOJNEmp2a7F7u7PEMuEBbUFKQlLI5nZ9pkBGyBY7F5jz3rmunxLOynSlotMHv2bOzatQuPPfYYjEYj3G43zj77bFx99dUoLy/PdB3znvFm906W4QoOudDRc+nZNtpceG+HDXo1j6NqiyI+BU6/hPd22FBdpEtJ+Bns2QylGZpaFrpOY6cno6bAZNs7F82Ro0WyCwSDmk9ZWBzvzrTJCNgTC3Ww9ZkYs6mZHu/CfiqkPSOYzWb89re/zWRdKOOQ4QgOo9nRc021H+3vNLXUENMWZYSktaoc6tkMpRkCMKqmwFwzR44WyS4QjBpVSsIidaZNTsA+rLoA//q2BboEC7BMaaapsJ88aQk+q1evhsFgwHnnnRdz/OWXX4bX68WKFSsyUjkKZTBGq6Pnomp/tPydhtIMjbYpMNfMkaNBKgsElmWSFhapM22IoQRsNc/h7fr2EdFMU2E/OdJq6XvvvRdPPfXUgONWqxVXXHEFFXwoI8ZId/RcVe2Ppr/TYJqhkTYFJtLEjeWJdyhSXSAkKyyOto9dLjFYmw3mD5cNzTQV9ocmLcFn//79mDRp0oDj1dXV2L9//7ArRaGkwkh19FxW7eeCv9Nok4uauFwh1QVCMsIifediSdRmo6GZHu/C/lCk9UZarVZ89913qKmpiTm+ZcsWFBUVZaJeFEpKjERHz2XV/nh3bMxVTVwukekFwnh/51KBmqByi7QEnwsvvBDXXHMNjEYjjj32WADAhx9+iF//+tf48Y9/nNEKUijpkmkH5FxW7Y9nx8Zc1sSNJtk2+43ndy4dqAkqd0hL8Ln77ruxd+9enHDCCeD50CkURcHFF1+M//3f/81oBSmUdMiG2SPXVfvjdVWZy5q40WKkzH7j9Z1LF2qCyg3SGqEFQcBLL72Eu+++G1u2bIFWq8WcOXNQXV2d6fpRKCmTLbNHPqj2x+OqMpc1caPBSJv9xuM7R8lvhrU0nTp1KqZOnZqpulAowyabZo98Ue2Pt1VlrmviRpLRzF83nt45Sn6T1kggyzLWrFmDDRs2wGazQVFiExG+9957GakchZIq2TZ7UNV+7pGsJq7cpEGz3TumtRLU7EehDE1ags+vf/1rrFmzBqeccgpmz549oINRKKPFSJg9qGo/t0hGEzetzIinPto95re6Z+P9H84mgVyLcJ4K+Vx3yuCkJfj84x//wNq1a3HyySdnuj4UyrAYKbMHVe3nFoNp4qaVGfHeDtu42Oqe6fd/OE7S+RxXKZ/rThmatJ2b6+rqMl0Xyjgk06uqfHBApmSHeJq4cpMGT320e9xsdc/k+z8cJ+l8jquUz3WnJEdags9vfvMbPPLII3jssceomYuSNtlYVeWLA3KyUHV7avTXxDXbvePK5yVT7/9wnKTzOa5SPtedkjxpCT6ffPIJ3n//fbz11luYNWsWVKpYleqrr76akcpRxi7ZXFWNFQdkqm4fPuNxq3sm3v/hOEnns4N1PtedkjxpCT4WiwVnnXVWputCGSeMxKoq3x2Qqbo9M4zXre7Dff+HIzDms7CZz3WnJE9avX316tWZrgdlHDFSq6p8dUDOpmA43kxn49nnazjv/3AExnwWNvvXnRACl19CUFYgcCwAkrN1pyRP2k9PkiR88MEHaGpqwvLly2E0GtHa2gqTyQSDwZDJOlLGGHRVNTjZEgzHo+lsrPl8jRTDERjzWdiMrntQUtDU6UGPNwhJVsCzDBQAP6grzsm6U5InLcFn3759OOmkk7B//34EAgH88Ic/hNFoxH333YdAIIAnn3wy0/WkjCHycUU4kpqSbAiG+Wg6y1SbjxWfr5FkOAJjPgub4bpvb3fiw12dYBkGFp0KAs/A6RUhE6DDFcDuLjd9b/KYtAMYLliwAFu2bEFRUVHk+FlnnYWf//znGascZWySbyvCkdaUZFowzMedKplu83z3+RoNhiMw5rOwWVtsgNWohlbFgWEAb1ACx7KoKNChtliHbo+Yc/2FkhppCT4ff/wxPv30UwiCEHO8pqYGLS0tGakYZeySTyvC0dCUZFowzLedKtlq83z1+RpNhiMw5quw2dLrQ69XxKK6YgCI+PcYNaG+KPBcTvUXSuqkJfgoigJZlgccP3DgAIzG3JXkKcNnPJkfhtKU7Opw4eXNB3DGvAoYNaqMDeqZFgzzyacqH7VTY53hCIz5KGyG+0uFWgsuzjuWS/2Fkh5pCT4nnngiHn74YfzlL38BEFo1ut1u3HHHHTSNxRhmvJkfBtOU9HhFdLoC2NrqxM4OFwp1QkbNX+kIhomE0nzyqco37RRl7JFP/YWSHmk9uQceeAAnnXQSZs6cCb/fj+XLl6OhoQHFxcV48cUXM11HSg4wHs0PiTQldk8Q3zb3whuQwLFAmUkDncBl3PyVimA4mFBaW2zIG5+qfNJOUcYm+eaDSEmdtASfqqoqbNmyBS+99BK2bNkCt9uNyy67DBdddBG0WvoyjDXGq/kh3sqPEIJGmxu+oASDhkNAYqFVcVlri2QEw2SE0nzxqaKrbcpok08+iJT0SHn0EEUR06dPxxtvvIGLLroIF110UTbqRckhxqv5Id7Kz+WX0OMNwqDm4Q5IsJo0MGpC3Wg02iJZofSXiyfnvE8VQFfblNwgH3wQKemTsuCjUqng9/uzURdKjjJezQ/xVn4+UYJflCFKMnRqHpNL9DGT80i3RSpCaa77VAH5t9oeb5GwxxP50F8o6ZGWvvjqq6/Gfffdh//7v/8Dz1OV81hnPJsf+q/8erwByApBkUmNmeUmFOrVMeVHui1SFUpz2acqTL6stsdjJOzxRj70F0rqpDU6f/nll9iwYQPWr1+POXPmQK/Xx3xPs7PnN/1XseUmzbg2P0Sv/FwBEf/6ugX77T4U6GLjWI1GW4xVoTTXV9v5GAmbQqGESDs7+znnnJPpulBygESr2OnlxrwxP2SD6JWfcDiL1Rv35kRbjGWfmFxdbY9XZ39K+lCTaG5Bs7NTIgy1ij1+uhU72lw5bX4YCXLJFJNvPjFjgfHq7E9JD2oSzT1odnYKgORWsTvbXfjFsbVo6/MZGc8rl1wyxeSSIDYeGK/O/pTUoSbR3IRmZ6cASH4V2+b001VsH7lkisklQWysk0t+VdSEkrtQk2juQrOzUwDQVexYIJcEsbFMrvhVURNKbkNNorkLzc5OAZBbq1gKJZfJBb8qakLJfehiMndh0/kRzc4+9givYtscfhBCYr4Lr2LrrIa83B1EoWSasF/V7Aozer0i9nZ50OsVMafSnHWho78JxahRgWMZGDUqTLEaYPcEsX5rBxSFDH0yStaIXkzGgy4mRw+anZ0CIDdWsfkM9bUYf4yWXxU1oeQHuWISpQwkLcHnwQcfxLJly2h29jEG3R2UHtTXYvwyGn5VuWxCyeYCIN8WF3QxmbukJfhMmDCBZmcfo9DdQalBfS0oI02u+uNlcwGQr4sLupjMTRjS36EjAfPnz8eGDRtQUFCAu+66CzfccAN0urGnRnU6nTCbzXA4HDCZTKNdHUoOoygEqz5oQn2rI2a7KhBSZTfY3JhTacYvF0+mgiMlY+TiezdwAcDDG5Qimo3hLACyee6RIt+0VflKsvN30s7N27dvh8fjAQDceeedcLvdw68lhZLHpOJrQaFkirAJpVAvoMHmhssvQlIUuPwiGmzuETehZNPZeqw4codNotPLTKgq1FGhZ5RJWhc6b948XHLJJTjmmGNACMEDDzyQMELz7bffnrEKUii5Si77WlDGNrlkQsmmszV15KZkg6QFnzVr1uCOO+7AG2+8AYZh8NZbb4HnB/6cYRgq+FDGBbnqa0EZH+SKP142FwB0cZFdxqsJLukRedq0afjHP/4BAGBZFhs2bIDVas1axSiUXIduV6WMNrkQrTubCwC6uMge+eowngmS9vGZP38+enp6AAB33HEHTURKGffkmq8FhTIaZDP4KQ2smh3CDuP1rQ5YdCrUFhtg0alQ3+rA6o170WhzjXYVs0rSu7q0Wi0aGhowYcIEcByHtra2EdX4/M///A/++9//4ttvv4UgCOjt7R1QZv/+/bjyyivx/vvvw2AwYMWKFbj33nvjmuQSEfEK78s0PwCOAzSag5/7HL7jwrJA9Pb+VMp6vUCiR8MwQPSOulTK+nyAoiSuh16fXlm/H4gTzTutsjpdqN4AEAgA0iBq7FTKarWhdgaAYBAQxYyUbXSKWLe9C/+/vXsPi7LM+wD+fRiY4TAwyBkRBARBPB92Fc2gtDztW+3WaoUJaWUpqSklHtFcxUprMXdte1uxLcvD1vq6dlAjwaTyjEoZikGYgrgpIMeBmfv9g2VkQCYGBwZ8vp/rmutinuc3M7+5eZj58dz3c98XrpajrroajhAI9nTCmD5e6NX0Pyd7+/pjCKh/Tq225RxUKqDh2DUntq6uvi1aolQCdnbmx+p09b+7ltjZ1cebG6vX1x9rloi1ta1vC6D+b6Ky0jKx5vzdy/Az4kLxDbz37U+4XlELH43KMF9NQY3NzSuvXOza9Blxq+cuk5QoLKupf+5hvghxM1H4dILPCKO/e3Ni2+EzQq8XeCstBzk//Qe9vJwgodGZaghcKK5An57ueGZMeP0/bV3oM6LVV2WLVhoxYoQYO3asWLFihZAkSbz44oti5cqVt7y1h+XLl4vXX39dzJ8/X2g0mmb76+rqRL9+/cTYsWPFyZMnxaeffio8PDzEokWLzHqd0tJSAUCU1n9MNL9NnGj8AEfHW8cBQkRFGcd6eLQcO2yYcWzPni3HRkQYx0ZEtBzbs6dx7LBhLcd6eBjHRkW1HOvoaBw7cWLLsU0Ps0ceMR1bXn4zNjbWdGxx8c3YWbNMx+bl3YxNSDAdm519MzYpyXTskSNCp9OLgl8qxJVlL5uOPXDg5vNu3Gg6ds+em7GpqaZjd+y4Gbtjh+nY1NSbsXv2mI7duPFm7IEDpmNfffVm7JEjpmOTkm7GZmebjk1IuBmbl2c6dtasm7HFxaZjY2NvxpaXm4595BFhxFQsPyOEAES1yl785cvz4vyVsvpYC35GLNzy9c3n7iKfEQavvmo6tp0/Iwp+qRBb5pnO4YNnV4qCXyrqn7cLfUYYvr9LS4UpXWZw88qVKw153Mq+ffvw/fff44svvoC3tzcGDRqEVatWYeHChVixYkWzBVWJLMkw1sLZ/teDiWTATmHTbnMJxd8bgu5+HuxGboMKbR20OhNn9ADU6fV39IDxVnd1NWZjY4OioiKrDG7esmUL5s2b16yra/ny5di9ezeysrIM2/Ly8hAcHIwTJ05g8ODBrXp+dnWxq8vs2E50GhsAu7rY1cXPiLbGyuAz4uK1SqR8/j3c7QTUquYDxstravGLFpg7oW/9P3Nd6DOitV1dbRoKrzd1oFtJUVERvL29jbY13C8qKmrxcTU1Nahp9EstKyur/8HJyfgPsSWtiWlLrDmzYpsTa86SIubE2ptxpsOcWJXq5peTJWOVypt/VNaKtbO7+YFhyVhb25sfhpaMVShafwybE2tj0z6xktQ+sUDniOVnRD1+RpgV6+fqgCAf1/qZvzUOza5GLSjTGV+N2tU+I1qh1YXP7t27MWHCBNjZ2WH37t0mYx944IFWPWdiYiJeeeUVkzFnz55FeHh4a9M0W3JysqEbjYiI6E7GxVPN6Opq3L1lY9PyVfCSJEFn6hRlI1evXsUvv/xiMiY4ONhofI6lu7pudcbH39+fa3UREdEdq/E8PjV19fMhhXipu/TiqRbv6mrcvWWpri5PT094enpa5LkiIyOxevVqFBcXG8Ye7d+/Hy4uLoiIiGjxcSqVCqrWnvokIiK6A3SWmb+toctMd1lQUIBr166hoKAAOp3OcGYnJCQEarUa999/PyIiIvDEE0/g1VdfRVFREZYuXYrZs2ezsCEi6uLkurxCe+oMM39bg9lXden1emzZsgUff/wx8vPzIUkSgoKC8Mgjj+CJJ55otpCcpcTFxeHdd99ttv3AgQOIjo4GAPz000947rnnkJ6eDicnJ8TGxmLt2rVtm8CQXV1ERJ3C7SyvwIJJPlr7/W1W4SOEwP/8z//g008/xcCBAxEeHg4hBM6ePYszZ87ggQcewK5duyyRv9Ww8CEi6jwalle4VqGFr8YejkpbVGrrDANxnxwV2GLxI+f1qOSoXS5n37JlCw4ePIi0tDTcc889Rvu+/PJLPPTQQ/jHP/6BadOmtS1rIiKi/9LrBfZmX8G1Ci1CvdSGHgVnezuoVbY4X1yOfd9dQbCHutlZnOYFkwMqtXXIvlyKy6VVJgsmurO1epFSAPjwww+xePHiZkUPANx7771ITEzE1q1bLZYcERHJ16WSKly4Wn/JddNhFJIkwVdjj9ziclwqMZ7crmnB5GxvB4WNBGd7O4R6qXGtQot9312BXm+6w0OvF7h4rRI/FJXh4rXKX42nrsGsMz6nT5/Gq6++2uL+CRMmYMOGDbedFBERUYW2DtV1Ojgqbz1RooNSgStl1c2WVzCnYGppcC+7ye5cZp3xuXbtWrPZkRvz9vbG9evXbzspIiIiJ6Ut7G0VqGxh3agqbf38M05K4//hbxZMt/7f3kGpQE2drsX1qBq6ybIvl8LV0Q7BHmq4Otoh+3IpUjPzkVt84/beGFmVWYWPTqczeYWUQqFAnal1UIiIiFrJz9UBvTzVKCytRtPrcIQQKCytRoiX+ubyCv/V1oIJsFw3GXVeZnV1CSEQFxfX4rw4NaYWMiMiIjJDW5dXaCiYsi+XQq2ybbYeVWFptfF6VI1YopuMOjezCp/Y2NhfjeEVXUREZCkhXs54clSgYbzNlbJqqGwV6O+naXF5hdtZj6qt44qo6zCr8ElNTW2vPIiIiG6pLcsrtKVgAoy7yZztm692bqqbjLoG/uaIiKjTa8vyCm0pmG6nm4y6BhY+RER0xzK3YLqdbjLqGsy6qouIiOhO19BN1q+7BiWVtcj/TwVKKmvR30/DGZ/vADzjQ0RE1ERbusmoa2DhQ0REdAttGVdEnR+7uoiIiEg2WPgQERGRbLDwISIiItlg4UNERESywcKHiIiIZIOFDxEREckGCx8iIiKSDRY+REREJBssfIiIiEg2WPgQERGRbLDwISIiItlg4UNERESywcKHiIiIZIOFDxEREckGCx8iIiKSDRY+REREJBssfIiIiEg2bK2dQFek0+lQW1tr7TTISpRKJWxs+D8DEVFXxMLHDEIIFBUVoaSkxNqpkBXZ2NggKCgISqXS2qkQEZGZWPiYoaHo8fLygqOjIyRJsnZK1MH0ej0uX76MwsJCBAQE8BggIupiWPi0kk6nMxQ97u7u1k6HrMjT0xOXL19GXV0d7OzsrJ0OERGZgQMVWqlhTI+jo6OVMyFra+ji0ul0Vs6EiIjMxcLHTOzaIB4DRERdFwsfIiIikg0WPkRERCQbLHxkIC4uDpIk4dlnn222b/bs2ZAkCXFxcR2fGBERUQdj4SMT/v7+2LZtG6qqqgzbqqur8cEHHyAgIMCKmREREXUcFj4WoNVqW7zV1dW1OrbpbNAtxbXFkCFD4O/vj48//tiw7eOPP0ZAQAAGDx5s2KbX65GcnIygoCA4ODhg4MCB+Oc//2nYr9PpMGPGDMP+sLAwpKSkGL1WXFwcHnroIaxbtw6+vr5wd3fH7NmzOds1ERFZHefxsYDk5OQW94WGhuLxxx833F+3bl2LBUDPnj2NupxSUlJQWVnZLC4pKalNeU6fPh2pqamIiYkBAGzevBlPPvkk0tPTDTHJycl4//338dZbbyE0NBQHDx7E1KlT4enpiaioKOj1evTo0QM7d+6Eu7s7vv76azzzzDPw9fXF5MmTDc9z4MAB+Pr64sCBA8jNzcWUKVMwaNAgPP30023KnYiIyBJY+MjI1KlTsWjRIvz0008AgMzMTGzbts1Q+NTU1GDNmjX44osvEBkZCQAIDg7GoUOH8Le//Q1RUVGws7PDypUrDc8ZFBSEb775Bjt27DAqfLp164aNGzdCoVAgPDwckyZNQlpaGgsfIiKyKhY+FrBo0aIW9zVdzDIhIaHF2Kbzw8ydO/f2EmvC09MTkyZNwpYtWyCEwKRJk+Dh4WHYn5ubi8rKStx3331Gj9NqtUbdYX/5y1+wefNmFBQUoKqqClqtFoMGDTJ6TN++faFQKAz3fX19cebMGYu+HyIiInOx8LEAcxarbK/Y1po+fTri4+MB1BcwjZWXlwMAPvnkE/j5+RntU6lUAIBt27YhISEB69evR2RkJJydnfHaa6/h8OHDRvFNl3KQJAl6vd6i74WIiMhcLHxkZvz48dBqtZAkCePGjTPaFxERAZVKhYKCAkRFRd3y8ZmZmRg5ciRmzZpl2HbhwoV2zZmIiMhSWPjIjEKhwNmzZw0/N+bs7IyEhAS88MIL0Ov1uOuuu1BaWorMzEy4uLggNjYWoaGh+Mc//oG9e/ciKCgI7733Ho4ePYqgoCBrvB0iIiKzsPCRIRcXlxb3rVq1Cp6enkhOTsaPP/4IV1dXDBkyBIsXLwYAzJw5EydPnsSUKVMgSRIee+wxzJo1C5999llHpU9ERNRmkhBCWDuJzqSsrAwajQalpaVGBUJ1dTXy8vIQFBQEe3t7K2ZI1sZjgYio82np+7spTmBIREREssHCh4iIiGSDhQ8RERHJBgsfIiIikg0WPkRERCQbLHyIiIhINlj4EBERkWyw8CEiIiLZYOFDXdaKFSuarQpvrvz8fEiShKysLIvkREREnRsLHxmIjo7GvHnzmm3fsmULXF1dOzyf1vq1oiQhIQFpaWm39Rr+/v4oLCxEv379AADp6emQJAklJSW39bxERNQ5ca0usjqdTgdJkmBjY14drlaroVarb+u1FQoFfHx8bus5iIio6+AZHzKIi4vDQw89hJUrV8LT0xMuLi549tlnodVqDTHR0dGIj49HfHw8NBoNPDw8sGzZMjRe8q2mpgYJCQnw8/ODk5MThg8fjvT0dMP+hjNNu3fvRkREBFQqFQoKCszOt2lXV0P+a9asgbe3N1xdXfHyyy+jrq4OL774Itzc3NCjRw+kpqYaHtP4rFJ+fj7uueceAEC3bt0gSRLi4uLMzouIiDovnvGxhIqKlvcpFEDjhSxNxdrYAA4Ovx7r5GRefmZIS0uDvb090tPTkZ+fjyeffBLu7u5YvXq1Iebdd9/FjBkzcOTIERw7dgzPPPMMAgIC8PTTTwMA4uPj8f3332Pbtm3o3r07/vWvf2H8+PE4c+YMQkNDAQCVlZV45ZVX8M4778Dd3R1eXl4Wyf/LL79Ejx49cPDgQWRmZmLGjBn4+uuvcffdd+Pw4cPYvn07Zs6cifvuuw89evQweqy/vz8++ugjPPzww8jJyYGLiwscGv8+iIioy+MZH0tQq1u+PfywcayXV8uxEyYYxwYG3jquHSmVSmzevBl9+/bFpEmT8PLLL2PDhg3Q6/WGGH9/f7zxxhsICwtDTEwMnn/+ebzxxhsAgIKCAqSmpmLnzp0YPXo0evXqhYSEBNx1111GZ1pqa2vx17/+FSNHjkRYWBgcHR0tkr+bmxs2bNiAsLAwTJ8+HWFhYaisrMTixYsRGhqKRYsWQalU4tChQ80eq1Ao4ObmBgDw8vKCj48PNBqNRfIiIqLOgYUPGRk4cKBRERIZGYny8nJcvHjRsG3EiBGQJMko5vz589DpdDhz5gx0Oh169+5tGIOjVquRkZGBCxcuGB6jVCoxYMAAi+fft29fo7FC3t7e6N+/v+G+QqGAu7s7iouLLf7aRETU+bGryxLKy1vep1AY3zf1hdt0cG9+fptTaszFxQWlpaXNtpeUlFj8jEZ5eTkUCgWOHz8ORZP33nggsoODg1HxZCl2dnZG9yVJuuW2xmewiIjak14vcKmkChXaOjgpbeHn6gAbG8t//lHrdInCJz8/H6tWrcKXX36JoqIidO/eHVOnTsWSJUugVCoNcadPn8bs2bNx9OhReHp64vnnn8dLL73U/gmaM+amvWJNCAsLw759+5ptP3HiBHr37m207dSpU6iqqjKMbfn222+hVqvh7+9viDl8+LDRY7799luEhoZCoVBg8ODB0Ol0KC4uxujRoy2Sf0dqOJ50Op2VMyGiO0Fu8Q3szb6CC1fLUV2ng72tAr081RjXzxshXs7WTk+WukTh88MPP0Cv1+Nvf/sbQkJCkJ2djaeffhoVFRVYt24dAKCsrAz3338/xo4di7feegtnzpzB9OnT4erqimeeecbK78C6nnvuOWzcuBFz5szBU089BZVKhU8++QQffvgh/v3vfxvFarVazJgxA0uXLkV+fj6SkpIQHx9v1H1UUFCA+fPnY+bMmThx4gTefPNNrF+/HgDQu3dvxMTEYNq0aVi/fj0GDx6Mq1evIi0tDQMGDMCkSZPMzj8nJ6fZtr59+5r9PK3Rs2dPSJKEPXv2YOLEiXBwcLjtS+aJSJ5yi28gNTMf1yq08NXYw1HpgEptHbIvl+JyaRWeHBXI4scKukThM378eIwfP95wPzg4GDk5Odi0aZOh8Nm6dSu0Wi02b94MpVKJvn37IisrC6+//rrsC5/g4GAcPHgQS5YswdixY6HVahEeHo6dO3catSsAjBkzBqGhobj77rtRU1ODxx57DCtWrDCKmTZtGqqqqvDb3/4WCoUCc+fONWrj1NRU/OlPf8KCBQtw6dIleHh4YMSIEfjd737XpvwfffTRZtsajzmyJD8/P6xcuRKJiYl48sknMW3aNGzZsqVdXouI7lx6vcDe7Cu4VqFFqJfa0LXvbG8HtcoW54vLse+7Kwj2ULPbq4NJovEELF3I0qVL8fnnn+PYsWMA6r+My8rKsGvXLkPMgQMHcO+99+LatWvo1q3bLZ+npqYGNTU1hvtlZWXw9/dHaWkpXFxcDNurq6uRl5eHoKAg2De+PP0OEhcXh5KSEqM2bCo6OhqDBg3Cn//85w7Lq7ORw7FARLfn4rVKvLH/HFwd7eBsb9ds/43qWpRU1uKF+3rD380yV7XKXVlZGTQaTbPv76a65FVdubm5ePPNNzFz5kzDtqKiInh7exvFNdwvKipq8bmSk5Oh0WgMt8ZjWYiIiNqiQluH6jodHJW37lhxUCpQU6dDhbaugzMjqxY+iYmJkCTJ5O2HH34wesylS5cwfvx4/PGPfzRMmHc7Fi1ahNLSUsOtvbpQiIhIPpyUtrC3VaCyhcKmSquDylYBpxYKI2o/Vm3xBQsW/OqSAMHBwYafL1++jHvuuQcjR47E22+/bRTn4+ODK1euGG1ruG9qLSaVSgWVSmVm5nem1oxlabz0BBER3ZqfqwN6eaqRfbkUapWt0fQdQggUllajv58Gfq6cHb6jWbXw8fT0hKenZ6tiL126hHvuuQdDhw5FampqswUtIyMjsWTJEtTW1hrmbdm/fz/CwsJaHN9DRETUHmxsJIzr543LpVU4X1wOX409HJQKVGl1KCythpuTEvf39ebAZivoEmN8Ll26hOjoaAQEBGDdunW4evUqioqKjMbuPP7441AqlZgxYwa+++47bN++HSkpKZg/f74VMyciIrkK8XLGk6MC0a+7BiWVtcj/TwVKKmvR30/DS9mtqEt0Lu7fvx+5ubnIzc1ttrBkw0VpGo0G+/btw+zZszF06FB4eHhg+fLlsr+UnYiIrCfEyxnB0WrO3NyJdNnL2dtLS5fD8RJmasBjgYio87mjL2cnIiIiagsWPkRERCQbLHyoy5IkyeQs060RFxeHhx56yCL5EBFR59clBjfT7WnNUhSd0a/lXVhYeNtTFaSkpKDxMDcuyUFEdGdj4UNWp9VqoVQqzX6cqYkpW0uj0dz2cxARUdfBri5CRkYGfvvb30KlUsHX1xeJiYmoq6ufZn3Pnj1wdXWFTqcDAGRlZUGSJCQmJhoe/9RTT2Hq1KmG+4cOHcLo0aPh4OAAf39/zJkzBxUVFYb9gYGBWLVqFaZNmwYXF5c2TznQuKsrPz8fkiRhx44dhtf+zW9+g3PnzuHo0aMYNmwY1Go1JkyYgKtXrxqeo3FXV1xcHDIyMpCSkmJYMiU/P79NuRERUefEwscSKipavlVXtz62qqp1sRZ06dIlTJw4Eb/5zW9w6tQpbNq0CX//+9/xpz/9CQAwevRo3LhxAydPngRQXyR5eHgYLV2RkZGB6OhoAMCFCxcwfvx4PPzwwzh9+jS2b9+OQ4cOIT4+3uh1161bh4EDB+LkyZNYtmyZxd5PUlISli5dihMnTsDW1haPP/44XnrpJaSkpOCrr75Cbm4uli9ffsvHpqSkIDIyEk8//TQKCwtRWFjIRWuJiO4w7OqyBLW65X0TJwKffHLzvpcXUFl569ioKKDxWliBgcB//tM8zoJTL/31r3+Fv78/Nm7cCEmSEB4ejsuXL2PhwoVYvnw5NBoNBg0ahPT0dAwbNgzp6el44YUXsHLlSpSXl6O0tBS5ubmIiooCUL/afUxMDObNmwcACA0NxYYNGxAVFYVNmzYZ5r259957sWDBAou9jwYJCQkYN24cAGDu3Ll47LHHkJaWhlGjRgEAZsyY0eKaZBqNBkqlEo6OjhbpRiOiW9PrBSf0I6th4SNzZ8+eRWRkpNECeqNGjUJ5eTl+/vlnBAQEICoqCunp6ViwYAG++uorJCcnY8eOHTh06BCuXbuG7t27IzQ0FABw6tQpnD59Glu3bjU8nxACer0eeXl56NOnDwBg2LBh7fJ+BgwYYPjZ29sbANC/f3+jbcXFxe3y2kT063KLb2Bv9hVcuFqO6jod7G0V6OWpxrh+3lzCgToECx9LKC9veZ9CYXzf1Jduk4VX0UnGl0RHR2Pz5s04deoU7OzsEB4ejujoaKSnp+P69euGsz0AUF5ejpkzZ2LOnDnNnicgIMDws5OTU7vk2rBALQBDMdd0m16vb5fXJiLTcotvIDUzH9cqtPDV2MNR6YBKbR2yL5ficmkV16+iDsHCxxLM+RJvr9g26tOnDz766CMIIQyFQmZmJpydnQ3rojWM83njjTcMRU50dDTWrl2L69evG3VZDRkyBN9//z1CQkLaPff2oFQqDQO5ichy9HqBvdlXcK1Ci1AvteHzxtneDmqVLc4Xl2Pfd1cQ7KFmtxe1KxY+MlFaWoqsrCyjbe7u7pg1axb+/Oc/4/nnn0d8fDxycnKQlJSE+fPnw+a/Z6C6deuGAQMGYOvWrdi4cSMA4O6778bkyZNRW1trdMZn4cKFGDFiBOLj4/HUU0/ByckJ33//Pfbv3294rCXybq9Bx4GBgTh8+DDy8/OhVqvh5uZmaAciartLJVW4cLUcvhp7o651oP5MrK/GHrnF5bhUUgV/N0crZUlywMJHJtLT0zF48GCjbTNmzMA777yDTz/9FC+++CIGDhwINzc3zJgxA0uXLjWKjYqKQlZWluHqLTc3N0RERODKlSsICwszxA0YMAAZGRlYsmQJRo8eDSEEevXqhSlTplg87/aQkJCA2NhYREREoKqqCnl5eQgMDGyX1yKSkwptHarrdHBUOtxyv4NSgStl1ajQ1nVwZiQ3XJ29Ca7OTr+GxwKR+S5eq8Qb+8/B1dEOzvZ2zfbfqK5FSWUtXrivN8/4UJtwdXYiIuo0/Fwd0MtTjcLSajT9f1sIgcLSaoR4qeHneuszQkSWwsKHiIjanY2NhHH9vOHmpMT54nLcqK5FnV6PG9W1OF9cDjcnJe7v682BzdTuWPgQEVGHCPFyxpOjAtGvuwYllbXI/08FSipr0d9Pw0vZqcNwcDMREXWYEC9nBEerOXMzWQ0LHyIi6lA2NhIHMJPVsKvLTLwIjngMEBF1XSx8Wqlh2YPKlhYYJdnQarUAAEXT5UiIiKjTY1dXKykUCri6uhoWuHR0dGw2+yjd+fR6Pa5evQpHR0fY2vLPh4ioq+Entxl8fHwAgKt7y5yNjQ0CAgJY+BIRdUEsfMwgSRJ8fX3h5eWF2tpaa6dDVqJUKrl+FxFRF8XCpw0UCgXHdxAREXVB/LeViIiIZIOFDxEREckGCx8iIiKSDY7xaaJhcrqysjIrZ0JERESt1fC9/WuTzLLwaeLGjRsAAH9/fytnQkREROa6ceMGNBpNi/slwfn3jej1ely+fBnOzs6tmqelrKwM/v7+uHjxIlxcXDogw66F7WMa28c0to9pbB/T2D6m3WntI4TAjRs30L17d5NTjvCMTxM2Njbo0aOH2Y9zcXG5Iw6c9sL2MY3tYxrbxzS2j2lsH9PupPYxdaanAQc3ExERkWyw8CEiIiLZYOFzm1QqFZKSkqBSqaydSqfE9jGN7WMa28c0to9pbB/T5No+HNxMREREssEzPkRERCQbLHyIiIhINlj4EBERkWyw8CEiIiLZYOHTSps2bcKAAQMMEz1FRkbis88+M+yvrq7G7Nmz4e7uDrVajYcffhhXrlyxYsbWs3btWkiShHnz5hm2yb19VqxYAUmSjG7h4eGG/XJvn0uXLmHq1Klwd3eHg4MD+vfvj2PHjhn2CyGwfPly+Pr6wsHBAWPHjsX58+etmHHHCQwMbHbsSJKE2bNnA+Cxo9PpsGzZMgQFBcHBwQG9evXCqlWrjNZrkvPxA9Qv4TBv3jz07NkTDg4OGDlyJI4ePWrYL7v2EdQqu3fvFp988ok4d+6cyMnJEYsXLxZ2dnYiOztbCCHEs88+K/z9/UVaWpo4duyYGDFihBg5cqSVs+54R44cEYGBgWLAgAFi7ty5hu1yb5+kpCTRt29fUVhYaLhdvXrVsF/O7XPt2jXRs2dPERcXJw4fPix+/PFHsXfvXpGbm2uIWbt2rdBoNGLXrl3i1KlT4oEHHhBBQUGiqqrKipl3jOLiYqPjZv/+/QKAOHDggBBC3seOEEKsXr1auLu7iz179oi8vDyxc+dOoVarRUpKiiFGzsePEEJMnjxZREREiIyMDHH+/HmRlJQkXFxcxM8//yyEkF/7sPC5Dd26dRPvvPOOKCkpEXZ2dmLnzp2GfWfPnhUAxDfffGPFDDvWjRs3RGhoqNi/f7+IiooyFD5sn/rCZ+DAgbfcJ/f2Wbhwobjrrrta3K/X64WPj4947bXXDNtKSkqESqUSH374YUek2KnMnTtX9OrVS+j1etkfO0IIMWnSJDF9+nSjbX/4wx9ETEyMEILHT2VlpVAoFGLPnj1G24cMGSKWLFkiy/ZhV1cb6HQ6bNu2DRUVFYiMjMTx48dRW1uLsWPHGmLCw8MREBCAb775xoqZdqzZs2dj0qRJRu0AgO3zX+fPn0f37t0RHByMmJgYFBQUAGD77N69G8OGDcMf//hHeHl5YfDgwfjf//1fw/68vDwUFRUZtY9Go8Hw4cNl0T6NabVavP/++5g+fTokSZL9sQMAI0eORFpaGs6dOwcAOHXqFA4dOoQJEyYA4PFTV1cHnU4He3t7o+0ODg44dOiQLNuHi5Sa4cyZM4iMjER1dTXUajX+9a9/ISIiAllZWVAqlXB1dTWK9/b2RlFRkXWS7WDbtm3DiRMnjPqNGxQVFcm+fYYPH44tW7YgLCwMhYWFWLlyJUaPHo3s7GzZt8+PP/6ITZs2Yf78+Vi8eDGOHj2KOXPmQKlUIjY21tAG3t7eRo+TS/s0tmvXLpSUlCAuLg4A/7YAIDExEWVlZQgPD4dCoYBOp8Pq1asRExMDALI/fpydnREZGYlVq1ahT58+8Pb2xocffohvvvkGISEhsmwfFj5mCAsLQ1ZWFkpLS/HPf/4TsbGxyMjIsHZaVnfx4kXMnTsX+/fvb/ZfBdVr+O8TAAYMGIDhw4ejZ8+e2LFjBxwcHKyYmfXp9XoMGzYMa9asAQAMHjwY2dnZeOuttxAbG2vl7DqXv//975gwYQK6d+9u7VQ6jR07dmDr1q344IMP0LdvX2RlZWHevHno3r07j5//eu+99zB9+nT4+flBoVBgyJAheOyxx3D8+HFrp2YV7Ooyg1KpREhICIYOHYrk5GQMHDgQKSkp8PHxgVarRUlJiVH8lStX4OPjY51kO9Dx48dRXFyMIUOGwNbWFra2tsjIyMCGDRtga2sLb29vWbfPrbi6uqJ3797Izc2V/fHj6+uLiIgIo219+vQxdAU2tEHTK5Xk0j4NfvrpJ3zxxRd46qmnDNvkfuwAwIsvvojExEQ8+uij6N+/P5544gm88MILSE5OBsDjBwB69eqFjIwMlJeX4+LFizhy5Ahqa2sRHBwsy/Zh4XMb9Ho9ampqMHToUNjZ2SEtLc2wLycnBwUFBYiMjLRihh1jzJgxOHPmDLKysgy3YcOGISYmxvCznNvnVsrLy3HhwgX4+vrK/vgZNWoUcnJyjLadO3cOPXv2BAAEBQXBx8fHqH3Kyspw+PBhWbRPg9TUVHh5eWHSpEmGbXI/dgCgsrISNjbGX2UKhQJ6vR4Aj5/GnJyc4Ovri+vXr2Pv3r148MEH5dk+1h5d3VUkJiaKjIwMkZeXJ06fPi0SExOFJEli3759Qoj6S0oDAgLEl19+KY4dOyYiIyNFZGSklbO2nsZXdQnB9lmwYIFIT08XeXl5IjMzU4wdO1Z4eHiI4uJiIYS82+fIkSPC1tZWrF69Wpw/f15s3bpVODo6ivfff98Qs3btWuHq6ir+7//+T5w+fVo8+OCDd/Tltk3pdDoREBAgFi5c2GyfnI8dIYSIjY0Vfn5+hsvZP/74Y+Hh4SFeeuklQ4zcj5/PP/9cfPbZZ+LHH38U+/btEwMHDhTDhw8XWq1WCCG/9mHh00rTp08XPXv2FEqlUnh6eooxY8YYih4hhKiqqhKzZs0S3bp1E46OjuL3v/+9KCwstGLG1tW08JF7+0yZMkX4+voKpVIp/Pz8xJQpU4zmqZF7+/z73/8W/fr1EyqVSoSHh4u3337baL9erxfLli0T3t7eQqVSiTFjxoicnBwrZdvx9u7dKwDc8j3L/dgpKysTc+fOFQEBAcLe3l4EBweLJUuWiJqaGkOM3I+f7du3i+DgYKFUKoWPj4+YPXu2KCkpMeyXW/tIQjSa3pKIiIjoDsYxPkRERCQbLHyIiIhINlj4EBERkWyw8CEiIiLZYOFDREREssHCh4iIiGSDhQ8RERHJBgsfIiIikg0WPkTUZcTFxUGSJKxdu9Zo+65duyBJkpWyIqKuhIUPEXUp9vb2eOWVV3D9+nVrp0JEXRALHyLqUsaOHQsfHx8kJye3GPPRRx+hb9++UKlUCAwMxPr16432BwYGYs2aNZg+fTqcnZ0REBCAt99+2yjm4sWLmDx5MlxdXeHm5oYHH3wQ+fn57fGWiKgDsfAhoi5FoVBgzZo1ePPNN/Hzzz8323/8+HFMnjwZjz76KM6cOYMVK1Zg2bJl2LJli1Hc+vXrMWzYMJw8eRKzZs3Cc889h5ycHABAbW0txo0bB2dnZ3z11VfIzMyEWq3G+PHjodVqO+JtElE7YeFDRF3O73//ewwaNAhJSUnN9r3++usYM2YMli1bht69eyMuLg7x8fF47bXXjOImTpyIWbNmISQkBAsXLoSHhwcOHDgAANi+fTv0ej3eeecd9O/fH3369EFqaioKCgqQnp7eEW+RiNoJCx8i6pJeeeUVvPvuuzh79qzR9rNnz2LUqFFG20aNGoXz589Dp9MZtg0YMMDwsyRJ8PHxQXFxMQDg1KlTyM3NhbOzM9RqNdRqNdzc3FBdXY0LFy6047siovZma+0EiIja4u6778a4ceOwaNEixMXFmf14Ozs7o/uSJEGv1wMAysvLMXToUGzdurXZ4zw9PduULxF1Dix8iKjLWrt2LQYNGoSwsDDDtj59+iAzM9MoLjMzE71794ZCoWjV8w4ZMgTbt2+Hl5cXXFxcLJozEVkXu7qIqMvq378/YmJisGHDBsO2BQsWIC0tDatWrcK5c+fw7rvvYuPGjUhISGj188bExMDDwwMPPvggvvrqK+Tl5SE9PR1z5sy55YBqIuo6WPgQUZf28ssvG7qogPqzNTt27MC2bdvQr18/LF++HC+//LJZ3WGOjo44ePAgAgIC8Ic//AF9+vTBjBkzUF1dzTNARF2cJIQQ1k6CiIiIqCPwjA8RERHJBgsfIiIikg0WPkRERCQbLHyIiIhINlj4EBERkWyw8CEiIiLZYOFDREREssHCh4iIiGSDhQ8RERHJBgsfIiIikg0WPkRERCQbLHyIiIhINv4fB1RGee9DYwQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "def bland_altman_plot_plotly(data1, data2, title=None, data1_name=None, data2_name=None):\n",
        "    \"\"\"\n",
        "    Crée un diagramme de Bland-Altman avec Plotly.\n",
        "\n",
        "    Args:\n",
        "        data1 (array-like): Le premier ensemble de données.\n",
        "        data2 (array-like): Le deuxième ensemble de données.\n",
        "        title (str): Le titre du diagramme.\n",
        "    \"\"\"\n",
        "    data1 = np.asarray(np.float64(data1.values))\n",
        "    data2 = np.asarray(np.float64(data2.values))\n",
        "    diff = data1 - data2\n",
        "    mean_diff = np.nanmean(diff)\n",
        "    std_diff = np.nanstd(diff, ddof=1)\n",
        "\n",
        "    upper_limit = mean_diff + 1.96 * std_diff\n",
        "    lower_limit = mean_diff - 1.96 * std_diff\n",
        "\n",
        "    fig = go.Figure()\n",
        "    fig.add_trace(go.Scatter(\n",
        "        x=data1,\n",
        "        y=diff,\n",
        "        mode='markers',\n",
        "        name='Data Points',\n",
        "        marker=dict(color='blue', opacity=0.5),\n",
        "    ))\n",
        "    fig.add_trace(go.Scatter(\n",
        "        x=[min(data1), max(data1)],\n",
        "        y=[mean_diff, mean_diff],\n",
        "        mode='lines',\n",
        "        name='Mean Difference',\n",
        "        line=dict(color='gray', dash='dash'),\n",
        "    ))\n",
        "    fig.add_trace(go.Scatter(\n",
        "        x=[min(data1), max(data1)],\n",
        "        y=[upper_limit, upper_limit],\n",
        "        mode='lines',\n",
        "        name='Upper Limit (95% CI)',\n",
        "        line=dict(color='red', dash='dash'),\n",
        "    ))\n",
        "    fig.add_trace(go.Scatter(\n",
        "        x=[min(data1), max(data1)],\n",
        "        y=[lower_limit, lower_limit],\n",
        "        mode='lines',\n",
        "        name='Lower Limit (95% CI)',\n",
        "        line=dict(color='red', dash='dash'),\n",
        "    ))\n",
        "\n",
        "    fig.update_layout(\n",
        "        title=title,\n",
        "        xaxis_title=f'{data1_name}',\n",
        "        yaxis_title=f'Difference ({data1_name} - {data2_name})',\n",
        "    )\n",
        "\n",
        "    fig.show()"
      ],
      "metadata": {
        "id": "OUovdL3nj8m2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_power(correlation, n, alpha=0.05):\n",
        "  \"\"\"Calculates the statistical power of a correlation.\n",
        "\n",
        "  Args:\n",
        "      correlation (float): The correlation coefficient.\n",
        "      n (int): The sample size.\n",
        "      alpha (float, optional): The significance level. Defaults to 0.05.\n",
        "\n",
        "  Returns:\n",
        "      float: The statistical power.\n",
        "  \"\"\"\n",
        "  power_analysis = smp.TTestIndPower()\n",
        "  effect_size = power_analysis.solve_power(\n",
        "      effect_size=correlation,\n",
        "      nobs1=n,\n",
        "      alpha=alpha,\n",
        "      power=None,  # We want to solve for power\n",
        "      ratio=1,  # Assuming equal group sizes\n",
        "      alternative='larger'  # For a two-sided test\n",
        "  )\n",
        "  return effect_size"
      ],
      "metadata": {
        "id": "1VJ3Ngxap1R5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def spearman(data, params, title=None):\n",
        "  correlations = {}\n",
        "  p_values = {}\n",
        "  powers = {}\n",
        "  alpha = 0.05\n",
        "  for i in range(len(params)):\n",
        "      param_dt = params[i]\n",
        "      param_mov = params_mov[i]\n",
        "      data_not_nan = data.dropna(subset=[param_dt, param_mov], inplace=False)\n",
        "      # Calculate Spearman correlation with Pandas\n",
        "      correlation = data[param_dt].corr(data[param_mov], method='spearman')\n",
        "\n",
        "      x = data_not_nan[param_dt]\n",
        "      y = data_not_nan[param_mov]\n",
        "      # Calculate Spearman correlation with Scipy\n",
        "      correlation, p_value = stats.spearmanr(x, y)\n",
        "\n",
        "      # Store correlation in dictionary\n",
        "      correlations[f'{param_dt} vs {param_mov} - {title}'] = correlation\n",
        "      p_values[f'{param_dt} vs {param_mov} - {title}'] = p_value\n",
        "      nbobs = len(x)\n",
        "      print(f\"{nbobs} observations\")\n",
        "      power_analysis = smp.TTestIndPower()\n",
        "      power = power_analysis.solve_power(\n",
        "          effect_size=correlation,\n",
        "          nobs1=nbobs,\n",
        "          alpha=alpha,\n",
        "          power=None,  # We want to solve for power\n",
        "          ratio=1,  # Assuming equal group sizes\n",
        "          alternative='larger'  # For a two-sided test\n",
        "      )\n",
        "      print(\"{:.2f} power\".format(power))\n",
        "      powers[f'{param_dt} vs {param_mov} - {title}'] = power\n",
        "      # plt.scatter(data[param_dt], data[param_mov])\n",
        "      # plt.plot(np.sort(data[param_dt]), np.sort(data[param_mov]), color='red')\n",
        "      # plt.title(\"Spearman correlation coefficient: {:.2f}\".format(correlation))\n",
        "      # plt.xlabel(\"x\")\n",
        "      # plt.ylabel(\"y\")\n",
        "      # plt.show()\n",
        "      #fig = px.scatter(data, x=param_dt, y=param_mov, trendline=\"ols\",\n",
        "                      #  title=f\"Spearman correlation coefficient: {correlation:.2f}\")\n",
        "      fig = go.Figure()\n",
        "\n",
        "      fig.add_trace(go.Scatter(\n",
        "          x=data[param_dt],\n",
        "          y=data[param_mov],\n",
        "          mode='markers',\n",
        "          name='Data Points'\n",
        "      ))\n",
        "\n",
        "      fig.add_trace(go.Scatter(\n",
        "          x=np.sort(data[param_dt]),\n",
        "          y=np.sort(data[param_mov]),\n",
        "          mode='lines',\n",
        "          name='Trendline',\n",
        "          line=dict(color='red')\n",
        "      ))\n",
        "\n",
        "      fig.update_layout(\n",
        "          title=\"Spearman correlation coefficient: {:.2f} - Power {:.2f}\".format(correlation, power),\n",
        "          xaxis_title=\"x\",\n",
        "          yaxis_title=\"y\"\n",
        "      )\n",
        "\n",
        "      fig.show()\n",
        "  # Print correlations\n",
        "  for key, value in correlations.items():\n",
        "      print(f\"Spearman correlation for {key}: {value} - p-value: {p_values[key]} - power: {powers[key]}\")\n",
        "  return correlations"
      ],
      "metadata": {
        "id": "u_pivj3erpfv"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "  data_filtered = data[(data.Exclude == 'OK')]\n",
        "  print(f\"Data for all conditions and all subjets\")\n",
        "  print(data_filtered.describe())\n",
        "  spearman(data_filtered, params, title=f\"All Condition\")\n",
        "  for i in range(len(params)):\n",
        "    param_dt = params[i]\n",
        "    param_mov = params_mov[i]\n",
        "    param_name = params_name[i]\n",
        "    bland_altman_plot_plotly(data_filtered[param_dt], data_filtered[param_mov],\n",
        "                             title=f\"Bland-Altman Plot for {param_name} All conditions\",\n",
        "                             data1_name=f\"{param_name}\", data2_name=f\"{param_name} Mov.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RjMcmSARw8CJ",
        "outputId": "8936acb2-d18e-4440-aa6c-6f03cf75e013"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data for all conditions and all subjets\n",
            "        Condition    medF_Acc  Energy_Acc          ST        Damp    Peak_Acc  \\\n",
            "count  230.000000  230.000000  230.000000  230.000000  228.000000  230.000000   \n",
            "mean    11.982609   55.762124  282.445760    0.094714   30.584092  175.456474   \n",
            "std      2.834542   13.696908  140.650259    0.031183   11.169519   96.876824   \n",
            "min      8.000000   31.346154   57.241160    0.033923   10.516410   42.236010   \n",
            "25%     10.000000   45.456731  176.744957    0.072635   22.357574  102.788508   \n",
            "50%     12.000000   53.028846  257.882996    0.089462   29.083469  153.453846   \n",
            "75%     14.000000   64.471154  363.213081    0.110981   37.909410  222.825142   \n",
            "max     16.000000   94.038462  821.984673    0.220000   66.609222  440.223729   \n",
            "\n",
            "       medF_Acc_Mov  Energy_Acc_Mov      ST_Mov    Damp_Mov  ...  SDmedF_Acc  \\\n",
            "count    230.000000      230.000000  230.000000  220.000000  ...  230.000000   \n",
            "mean      52.740803      260.821803    0.100396   29.123119  ...    3.667628   \n",
            "std       16.599221      140.247312    0.031873   10.424528  ...    1.665517   \n",
            "min       28.153846       60.313383    0.032000   10.711680  ...    1.264706   \n",
            "25%       41.759615      155.612008    0.081096   21.776924  ...    2.531240   \n",
            "50%       46.903846      234.580140    0.097038   26.766763  ...    3.275588   \n",
            "75%       59.692308      335.362819    0.118846   35.607518  ...    4.339988   \n",
            "max      104.884615      767.035378    0.201308   63.090829  ...   12.335081   \n",
            "\n",
            "       SDEnergy_Acc        SDST      SDDamp  SDPeak_Acc  SDmedF_Acc_Mov  \\\n",
            "count    230.000000  230.000000  228.000000  230.000000      230.000000   \n",
            "mean      44.828610    0.019019    6.199328   31.855731        3.892586   \n",
            "std       30.027348    0.011057    3.234411   22.201885        1.424991   \n",
            "min        5.829362    0.002501    1.757951    5.577772        1.250000   \n",
            "25%       23.460511    0.010809    3.714015   17.149451        2.960930   \n",
            "50%       36.351917    0.017397    5.318646   25.811546        3.656968   \n",
            "75%       56.537020    0.024769    7.861480   41.998551        4.615106   \n",
            "max      206.067712    0.058121   20.370289  182.752892        9.773697   \n",
            "\n",
            "       SDEnergy_Acc_Mov    SDST_Mov  SDDamp_Mov  SDPeak_Acc_Mov  \n",
            "count        230.000000  230.000000  220.000000      230.000000  \n",
            "mean          41.303029    0.024975    7.364517       19.176359  \n",
            "std           23.676003    0.013394    3.649673        8.483122  \n",
            "min            6.328363    0.002252    1.757576        4.126767  \n",
            "25%           24.217191    0.014572    4.428992       12.818244  \n",
            "50%           37.407601    0.023149    6.548208       17.849254  \n",
            "75%           50.500391    0.033805    9.649359       23.462314  \n",
            "max          155.390591    0.067361   20.987482       48.625251  \n",
            "\n",
            "[8 rows x 21 columns]\n",
            "230 observations\n",
            "1.00 power\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "Spearman correlation for medF_Acc vs medF_Acc_Mov - All Condition: 0.8805522328911471 - p-value: 6.982769706120558e-76 - power: 1.0\n",
            "Spearman correlation for Energy_Acc vs Energy_Acc_Mov - All Condition: 0.9087045062998523 - p-value: 1.8159872057723319e-88 - power: 1.0\n",
            "Spearman correlation for ST vs ST_Mov - All Condition: 0.7324609485767308 - p-value: 6.107565647126178e-40 - power: 0.999999999999998\n",
            "Spearman correlation for Damp vs Damp_Mov - All Condition: 0.7389183274033513 - p-value: 6.698454553983697e-39 - power: 0.9999999999999939\n",
            "Spearman correlation for Peak_Acc vs Peak_Acc_Mov - All Condition: 0.9085257180957355 - p-value: 2.2460615189675505e-88 - power: 1.0\n"
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    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "for cond in conditions:\n",
        "  data_filtered = data[(data.Condition == cond) & (data.Exclude == 'OK')]\n",
        "  print(f\"Data for condition {cond} km/h\")\n",
        "  print(data_filtered.describe())\n",
        "  spearman(data_filtered, params, title=f\"Condition {cond} km/h\")\n",
        "  for i in range(len(params)):\n",
        "    param_dt = params[i]\n",
        "    param_mov = params_mov[i]\n",
        "    param_name = params_name[i]\n",
        "    bland_altman_plot_plotly(data_filtered[param_dt], data_filtered[param_mov],\n",
        "                             title=f\"Bland-Altman Plot for {param_name} at {cond} km/h\",\n",
        "                             data1_name=f\"{param_name}\", data2_name=f\"{param_name} Mov.\")"
      ],
      "metadata": {
        "id": "VV5qrvAXhykd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "outputId": "c585fe86-c378-4cd4-a3de-9f40cc1b2e12",
        "cellView": "form"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data for condition 8 km/h\n",
            "       Condition   medF_Acc  Energy_Acc         ST       Damp    Peak_Acc  \\\n",
            "count       46.0  46.000000   46.000000  46.000000  45.000000   46.000000   \n",
            "mean         8.0  49.751254  156.197910   0.111413  25.939989   98.957202   \n",
            "std          0.0  15.140268   69.520734   0.038450  11.100650   44.627938   \n",
            "min          8.0  31.346154   57.241160   0.058769  10.516410   42.236010   \n",
            "25%          8.0  39.759615  110.297330   0.079365  17.292530   60.245639   \n",
            "50%          8.0  44.663462  140.020115   0.110000  24.613946   85.560574   \n",
            "75%          8.0  52.956731  204.814357   0.132788  29.848940  127.417213   \n",
            "max          8.0  90.192308  404.277897   0.220000  57.502660  188.131355   \n",
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            "       medF_Acc_Mov  Energy_Acc_Mov     ST_Mov   Damp_Mov  ...  SDmedF_Acc  \\\n",
            "count     46.000000       46.000000  46.000000  43.000000  ...   46.000000   \n",
            "mean      47.936455      155.783695   0.115343  25.674965  ...    3.743160   \n",
            "std       18.818153       81.478696   0.037217  11.191224  ...    1.718021   \n",
            "min       28.153846       60.313383   0.050769  11.348555  ...    1.442308   \n",
            "25%       35.221154       98.405854   0.088519  18.159515  ...    2.601416   \n",
            "50%       41.951923      130.741835   0.111462  21.841740  ...    3.370589   \n",
            "75%       46.759615      184.680839   0.136808  31.897978  ...    4.247753   \n",
            "max       99.788462      385.026769   0.201308  61.276755  ...   10.101189   \n",
            "\n",
            "       SDEnergy_Acc       SDST     SDDamp  SDPeak_Acc  SDmedF_Acc_Mov  \\\n",
            "count     46.000000  46.000000  45.000000   46.000000       46.000000   \n",
            "mean      28.009199   0.020391   5.871623   21.000479        4.026797   \n",
            "std       14.305224   0.010108   3.072603   11.792061        1.659383   \n",
            "min        6.024294   0.005422   1.757951    5.845558        1.250000   \n",
            "25%       18.984460   0.011980   3.210324   10.484060        2.941091   \n",
            "50%       23.412689   0.020670   5.449429   17.930119        3.739492   \n",
            "75%       36.834173   0.026603   7.652281   28.905084        4.651804   \n",
            "max       66.196946   0.055139  13.982141   48.624561        9.633157   \n",
            "\n",
            "       SDEnergy_Acc_Mov   SDST_Mov  SDDamp_Mov  SDPeak_Acc_Mov  \n",
            "count         46.000000  46.000000   43.000000       46.000000  \n",
            "mean          28.530456   0.029406    7.021904       18.072092  \n",
            "std           15.745758   0.015106    3.308747        8.335720  \n",
            "min            7.449775   0.006467    2.444294        5.757993  \n",
            "25%           16.997415   0.017754    4.198673       11.298536  \n",
            "50%           26.625293   0.028677    6.330847       16.262553  \n",
            "75%           37.476363   0.037438    9.465789       25.590256  \n",
            "max           68.347209   0.067361   16.125308       36.007252  \n",
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            "Spearman correlation for medF_Acc vs medF_Acc_Mov - Condition 8 km/h: 0.8590815198850272 - p-value: 2.1992795406879973e-14 - power: 0.9927355655050261\n",
            "Spearman correlation for Energy_Acc vs Energy_Acc_Mov - Condition 8 km/h: 0.8987357385137218 - p-value: 2.3342634385520875e-17 - power: 0.995763655416969\n",
            "Spearman correlation for ST vs ST_Mov - Condition 8 km/h: 0.8434686089805415 - p-value: 1.8756139691960498e-13 - power: 0.9910966422344003\n",
            "Spearman correlation for Damp vs Damp_Mov - Condition 8 km/h: 0.8342111660319261 - p-value: 6.8014270730704315e-12 - power: 0.9840702952291681\n",
            "Spearman correlation for Peak_Acc vs Peak_Acc_Mov - Condition 8 km/h: 0.9026827012025901 - p-value: 1.0146857994038073e-17 - power: 0.995992200206035\n"
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            "Data for condition 10 km/h\n",
            "       Condition   medF_Acc  Energy_Acc         ST       Damp    Peak_Acc  \\\n",
            "count       47.0  47.000000   47.000000  47.000000  46.000000   47.000000   \n",
            "mean        10.0  54.134615  213.632858   0.099702  28.900694  137.002990   \n",
            "std          0.0  14.358775   88.425843   0.031586  10.244115   70.058560   \n",
            "min         10.0  38.269231   85.206054   0.043308  11.702386   49.910960   \n",
            "25%         10.0  43.269231  150.089304   0.079962  22.020997   79.731964   \n",
            "50%         10.0  49.423077  199.514611   0.093308  27.993558  123.852997   \n",
            "75%         10.0  59.519231  265.098994   0.109269  35.366249  184.938395   \n",
            "max         10.0  89.711538  437.846412   0.211308  54.669658  350.880957   \n",
            "\n",
            "       medF_Acc_Mov  Energy_Acc_Mov     ST_Mov   Damp_Mov  ...  SDmedF_Acc  \\\n",
            "count     47.000000       47.000000  47.000000  44.000000  ...   47.000000   \n",
            "mean      51.259411      192.296471   0.104735  28.209127  ...    3.844852   \n",
            "std       18.793534       97.463512   0.029802  10.030500  ...    2.024992   \n",
            "min       29.884615       74.960533   0.050692  10.808336  ...    1.514232   \n",
            "25%       39.211538      129.586164   0.084077  21.544960  ...    2.545133   \n",
            "50%       44.019231      162.907152   0.099769  26.785093  ...    3.302993   \n",
            "75%       58.346154      229.776164   0.120115  33.977315  ...    4.523777   \n",
            "max      104.884615      456.384376   0.188000  63.090829  ...   12.335081   \n",
            "\n",
            "       SDEnergy_Acc       SDST     SDDamp  SDPeak_Acc  SDmedF_Acc_Mov  \\\n",
            "count     47.000000  47.000000  46.000000   47.000000       47.000000   \n",
            "mean      36.095916   0.019800   6.261256   26.305653        3.849110   \n",
            "std       18.868289   0.010652   3.180743   16.383472        1.360586   \n",
            "min        5.829362   0.005084   2.086765    5.577772        1.501971   \n",
            "25%       22.716830   0.013360   4.200745   13.975329        3.026158   \n",
            "50%       32.845613   0.018170   5.489221   22.295027        3.750000   \n",
            "75%       49.354961   0.021896   7.774659   34.033327        4.439388   \n",
            "max       85.164410   0.058121  17.300450   83.503533        7.723494   \n",
            "\n",
            "       SDEnergy_Acc_Mov   SDST_Mov  SDDamp_Mov  SDPeak_Acc_Mov  \n",
            "count         47.000000  47.000000   44.000000       47.000000  \n",
            "mean          31.943478   0.027466    7.400085       18.574821  \n",
            "std           16.512706   0.014022    2.949535        9.085475  \n",
            "min            6.328363   0.005852    2.224384        4.126767  \n",
            "25%           21.659089   0.015900    5.500915       12.151597  \n",
            "50%           27.982931   0.025178    6.810657       16.299408  \n",
            "75%           41.587926   0.035861    9.345846       24.607314  \n",
            "max           80.623611   0.060802   13.188389       48.625251  \n",
            "\n",
            "[8 rows x 21 columns]\n",
            "47 observations\n",
            "0.99 power\n"
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            "Spearman correlation for medF_Acc vs medF_Acc_Mov - Condition 10 km/h: 0.8533094384069649 - p-value: 2.5781314521683434e-14 - power: 0.9930731885863271\n",
            "Spearman correlation for Energy_Acc vs Energy_Acc_Mov - Condition 10 km/h: 0.8418131359851989 - p-value: 1.2397730907792622e-13 - power: 0.9919297786284479\n",
            "Spearman correlation for ST vs ST_Mov - Condition 10 km/h: 0.7367082023374326 - p-value: 3.5632192550390625e-09 - power: 0.9712852564239967\n",
            "Spearman correlation for Damp vs Damp_Mov - Condition 10 km/h: 0.8147085472666866 - p-value: 2.975046015777181e-11 - power: 0.982228522890879\n",
            "Spearman correlation for Peak_Acc vs Peak_Acc_Mov - Condition 10 km/h: 0.8747687326549491 - p-value: 9.291727320886774e-16 - power: 0.9948305275309209\n"
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            "Data for condition 12 km/h\n",
            "       Condition   medF_Acc  Energy_Acc         ST       Damp    Peak_Acc  \\\n",
            "count       46.0  46.000000   46.000000  46.000000  46.000000   46.000000   \n",
            "mean        12.0  56.091137  276.819632   0.092221  30.998139  168.733130   \n",
            "std          0.0  12.711770  103.475643   0.027655  10.426483   75.075307   \n",
            "min         12.0  40.769231  117.553601   0.037769  14.576684   67.490622   \n",
            "25%         12.0  46.778846  195.641854   0.071923  22.967880  111.632456   \n",
            "50%         12.0  53.653846  257.212507   0.088231  30.724885  152.938148   \n",
            "75%         12.0  59.735577  347.984342   0.107923  37.798898  215.218230   \n",
            "max         12.0  89.615385  577.752406   0.174923  58.014210  389.080502   \n",
            "\n",
            "       medF_Acc_Mov  Energy_Acc_Mov     ST_Mov   Damp_Mov  ...  SDmedF_Acc  \\\n",
            "count     46.000000       46.000000  46.000000  44.000000  ...   46.000000   \n",
            "mean      53.243729      256.652575   0.097513  29.549472  ...    3.620402   \n",
            "std       15.760635      112.515243   0.028760   9.646675  ...    1.523459   \n",
            "min       33.442308      108.128511   0.045231  10.711680  ...    1.665433   \n",
            "25%       42.264423      185.539506   0.075654  21.816106  ...    2.633219   \n",
            "50%       48.682692      216.785434   0.095000  27.133947  ...    3.224377   \n",
            "75%       58.562500      311.457026   0.112865  35.761663  ...    4.190973   \n",
            "max       94.019231      637.492943   0.194769  57.100829  ...    8.546882   \n",
            "\n",
            "       SDEnergy_Acc       SDST     SDDamp  SDPeak_Acc  SDmedF_Acc_Mov  \\\n",
            "count     46.000000  46.000000  46.000000   46.000000       46.000000   \n",
            "mean      48.306431   0.021224   6.380777   34.574350        4.054002   \n",
            "std       37.521879   0.011339   3.308933   27.657456        1.557262   \n",
            "min       14.119734   0.002501   2.087019    8.422374        2.028368   \n",
            "25%       25.224378   0.012002   3.792751   17.742789        2.988172   \n",
            "50%       36.895670   0.020309   5.503386   26.939724        3.752464   \n",
            "75%       55.062886   0.031481   9.183695   45.170184        4.887476   \n",
            "max      206.067712   0.047628  15.166181  182.752892        9.773697   \n",
            "\n",
            "       SDEnergy_Acc_Mov   SDST_Mov  SDDamp_Mov  SDPeak_Acc_Mov  \n",
            "count         46.000000  46.000000   44.000000       46.000000  \n",
            "mean          45.179471   0.025846    8.024166       20.570564  \n",
            "std           23.238297   0.012465    3.901915        8.833083  \n",
            "min           12.690198   0.003542    1.757576        9.036355  \n",
            "25%           25.697136   0.015536    4.575654       12.995214  \n",
            "50%           39.191182   0.025457    7.958922       19.410907  \n",
            "75%           61.575689   0.035364   10.382890       23.892736  \n",
            "max          101.593917   0.048191   16.806252       47.025181  \n",
            "\n",
            "[8 rows x 21 columns]\n",
            "46 observations\n",
            "0.99 power\n"
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            "Spearman correlation for medF_Acc vs medF_Acc_Mov - Condition 12 km/h: 0.8577067801425526 - p-value: 2.683033645885643e-14 - power: 0.992602778271171\n",
            "Spearman correlation for Energy_Acc vs Energy_Acc_Mov - Condition 12 km/h: 0.8738205365402405 - p-value: 2.2657540906638646e-15 - power: 0.9940324585088611\n",
            "Spearman correlation for ST vs ST_Mov - Condition 12 km/h: 0.7204045764934908 - p-value: 1.657061269812807e-08 - power: 0.9627832555148399\n",
            "Spearman correlation for Damp vs Damp_Mov - Condition 12 km/h: 0.7272727272727272 - p-value: 2.2516936121062113e-08 - power: 0.9590122948773145\n",
            "Spearman correlation for Peak_Acc vs Peak_Acc_Mov - Condition 12 km/h: 0.8193031144002467 - p-value: 3.39324907650408e-12 - power: 0.9879210383628232\n"
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            "Data for condition 14 km/h\n",
            "       Condition   medF_Acc  Energy_Acc         ST       Damp    Peak_Acc  \\\n",
            "count       45.0  45.000000   45.000000  45.000000  45.000000   45.000000   \n",
            "mean        14.0  58.876068  345.770034   0.088190  33.061118  216.217215   \n",
            "std          0.0  12.535332  126.446062   0.026257  12.297829   94.598572   \n",
            "min         14.0  42.788462  185.010852   0.033923  14.052007   89.933733   \n",
            "25%         14.0  48.269231  255.975956   0.069462  22.597257  150.609132   \n",
            "50%         14.0  55.769231  311.401637   0.085231  32.626391  187.988689   \n",
            "75%         14.0  65.480769  426.469823   0.103923  39.751025  278.838972   \n",
            "max         14.0  94.038462  821.984673   0.149000  65.194455  403.635272   \n",
            "\n",
            "       medF_Acc_Mov  Energy_Acc_Mov     ST_Mov   Damp_Mov  ...  SDmedF_Acc  \\\n",
            "count     45.000000       45.000000  45.000000  44.000000  ...   45.000000   \n",
            "mean      54.702991      323.690512   0.095619  30.031178  ...    3.633184   \n",
            "std       15.029252      139.087068   0.030281  10.677289  ...    1.699333   \n",
            "min       30.750000       79.981435   0.037308  12.925588  ...    1.264706   \n",
            "25%       44.596154      231.193791   0.081462  23.343067  ...    2.531240   \n",
            "50%       52.096154      270.787606   0.093846  27.357618  ...    3.159938   \n",
            "75%       59.788462      398.695955   0.106538  35.312434  ...    4.305503   \n",
            "max       93.346154      767.035378   0.192462  61.015610  ...    9.020030   \n",
            "\n",
            "       SDEnergy_Acc       SDST     SDDamp  SDPeak_Acc  SDmedF_Acc_Mov  \\\n",
            "count     45.000000  45.000000  45.000000   45.000000       45.000000   \n",
            "mean      52.502948   0.017926   6.399255   38.583670        3.831517   \n",
            "std       28.336068   0.011201   3.300373   23.641235        1.210715   \n",
            "min       16.066637   0.005659   2.389568    7.752957        1.426192   \n",
            "25%       31.496599   0.010171   3.939003   22.494008        3.017761   \n",
            "50%       48.854733   0.014272   4.921750   29.080916        3.624630   \n",
            "75%       65.102725   0.021769   8.490143   51.306096        4.469861   \n",
            "max      161.882541   0.054930  14.177076  135.950665        7.169751   \n",
            "\n",
            "       SDEnergy_Acc_Mov   SDST_Mov  SDDamp_Mov  SDPeak_Acc_Mov  \n",
            "count         45.000000  45.000000   44.000000       45.000000  \n",
            "mean          48.722072   0.022331    7.212645       20.460375  \n",
            "std           24.114274   0.012268    3.989943        9.076986  \n",
            "min           11.643119   0.002252    2.058451        5.187849  \n",
            "25%           31.449835   0.013710    4.219323       15.263400  \n",
            "50%           44.974928   0.019114    6.314556       19.046959  \n",
            "75%           59.321582   0.031025    9.165209       23.440338  \n",
            "max          108.076244   0.051197   17.441780       43.519193  \n",
            "\n",
            "[8 rows x 21 columns]\n",
            "45 observations\n",
            "0.98 power\n"
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            "Spearman correlation for medF_Acc vs medF_Acc_Mov - Condition 14 km/h: 0.7919082831024803 - p-value: 9.272675434661354e-11 - power: 0.9813467034390784\n",
            "Spearman correlation for Energy_Acc vs Energy_Acc_Mov - Condition 14 km/h: 0.8465085638998682 - p-value: 2.391508246183647e-13 - power: 0.9903414198598116\n",
            "Spearman correlation for ST vs ST_Mov - Condition 14 km/h: 0.5587140554432551 - p-value: 6.641038340778695e-05 - power: 0.8376624016113253\n",
            "Spearman correlation for Damp vs Damp_Mov - Condition 14 km/h: 0.6978153629316419 - p-value: 1.4045751082924427e-07 - power: 0.9454527023080667\n",
            "Spearman correlation for Peak_Acc vs Peak_Acc_Mov - Condition 14 km/h: 0.8173913043478259 - p-value: 7.352411776796967e-12 - power: 0.9861787061712417\n"
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            "Data for condition 16 km/h\n",
            "       Condition   medF_Acc  Energy_Acc         ST       Damp    Peak_Acc  \\\n",
            "count       46.0  46.000000   46.000000  46.000000  46.000000   46.000000   \n",
            "mean        16.0  60.060619  422.680913   0.081794  33.973409  258.093883   \n",
            "std          0.0  11.449539  126.967593   0.021918  10.237290  100.745512   \n",
            "min         16.0  42.980769  241.773542   0.043538  17.309777  102.308565   \n",
            "25%         16.0  50.889423  324.181531   0.062635  26.298184  180.782622   \n",
            "50%         16.0  57.548077  392.440433   0.079962  34.282258  241.689212   \n",
            "75%         16.0  66.442308  516.000105   0.097327  39.914169  355.351936   \n",
            "max         16.0  89.903846  741.117965   0.142462  66.609222  440.223729   \n",
            "\n",
            "       medF_Acc_Mov  Energy_Acc_Mov     ST_Mov   Damp_Mov  ...  SDmedF_Acc  \\\n",
            "count     46.000000       46.000000  46.000000  45.000000  ...   46.000000   \n",
            "mean      56.636288      378.542151   0.088570  32.006947  ...    3.491941   \n",
            "std       13.123643      133.611518   0.027031   9.934062  ...    1.324706   \n",
            "min       39.019231      213.367184   0.032000  18.814726  ...    1.709268   \n",
            "25%       47.072115      279.205583   0.067788  25.146794  ...    2.525753   \n",
            "50%       53.394231      352.188988   0.090692  28.805366  ...    3.122371   \n",
            "75%       60.533654      461.141861   0.109269  36.787224  ...    4.410508   \n",
            "max       88.538462      695.399904   0.145846  61.091209  ...    6.647841   \n",
            "\n",
            "       SDEnergy_Acc       SDST     SDDamp  SDPeak_Acc  SDmedF_Acc_Mov  \\\n",
            "count     46.000000  46.000000  46.000000   46.000000       46.000000   \n",
            "mean      59.585231   0.015714   6.080951   39.081417        3.701119   \n",
            "std       34.119277   0.011527   3.409563   22.907576        1.319493   \n",
            "min       16.349624   0.002783   2.680953   10.596503        1.538462   \n",
            "25%       31.799834   0.007193   3.669407   23.875507        2.851281   \n",
            "50%       48.759764   0.013316   5.021071   31.616754        3.458841   \n",
            "75%       83.427936   0.020428   7.298507   53.532953        4.504785   \n",
            "max      164.463623   0.055946  20.370289  124.996946        7.941283   \n",
            "\n",
            "       SDEnergy_Acc_Mov   SDST_Mov  SDDamp_Mov  SDPeak_Acc_Mov  \n",
            "count         46.000000  46.000000   45.000000       46.000000  \n",
            "mean          52.504420   0.019712    7.160634       18.244934  \n",
            "std           27.575704   0.010889    4.036358        6.900583  \n",
            "min           18.075032   0.003638    2.055306        5.815001  \n",
            "25%           37.118119   0.011084    4.454906       13.645257  \n",
            "50%           45.378213   0.017264    6.257826       17.689096  \n",
            "75%           62.533727   0.024635    8.665788       21.495908  \n",
            "max          155.390591   0.044885   20.987482       43.220044  \n",
            "\n",
            "[8 rows x 21 columns]\n",
            "46 observations\n",
            "0.99 power\n"
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