demo_independent_extended.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from CosinorPy import file_parser, cosinor, cosinor1\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = file_parser.generate_test_data(phase = 0, n_components = 1, name=\"test1\", noise=0.5, replicates = 3)\n",
    "df2 = file_parser.generate_test_data(phase = np.pi, n_components = 1, name=\"test2\", noise=0.5, replicates = 3)\n",
    "df = df.append(df2, ignore_index=True)\n",
    "\n",
    "df2 = file_parser.generate_test_data(phase = 0, n_components = 3, name=\"test3\", noise=0.5, replicates = 3, time_step=1)\n",
    "df = df.append(df2, ignore_index=True)\n",
    "df2 = file_parser.generate_test_data(phase = np.pi, n_components = 3, name=\"test4\", noise=0.5, replicates = 3, time_step=1)\n",
    "df = df.append(df2, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These data can be exported to either excel or csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#file_parser.export(df,os.path.join(\"test_data\",\"independent_data.xlsx\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#file_parser.export_csv(df,os.path.join(\"test_data\",\"independent_data.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = file_parser.read_excel(os.path.join(\"test_data\",\"data.xlsx\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cosinor analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify the best models and/or the best periods (possible periods can be given as an interval or as a single value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_results = cosinor.fit_group(df, n_components = [1,2,3], period=24, plot=False) #folder=\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the best fitting periods with criterium 'RSS' (```reverse=False``` means lower is better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>test</th>\n",
       "      <th>period</th>\n",
       "      <th>n_components</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>p_reject</th>\n",
       "      <th>q_reject</th>\n",
       "      <th>RSS</th>\n",
       "      <th>R2</th>\n",
       "      <th>R2_adj</th>\n",
       "      <th>log-likelihood</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>acrophase</th>\n",
       "      <th>mesor</th>\n",
       "      <th>peaks</th>\n",
       "      <th>heights</th>\n",
       "      <th>troughs</th>\n",
       "      <th>heights2</th>\n",
       "      <th>ME</th>\n",
       "      <th>resid_SE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>1.694089e-01</td>\n",
       "      <td>2.541134e-01</td>\n",
       "      <td>11.512154</td>\n",
       "      <td>0.784755</td>\n",
       "      <td>0.778776</td>\n",
       "      <td>-36.142211</td>\n",
       "      <td>1.039764</td>\n",
       "      <td>0.150947</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>[23.423423423423426]</td>\n",
       "      <td>[1.0417527895534135]</td>\n",
       "      <td>[11.411411411411413]</td>\n",
       "      <td>[-1.0376503623054327]</td>\n",
       "      <td>0.797114</td>\n",
       "      <td>0.399864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>1.021567e-01</td>\n",
       "      <td>2.043134e-01</td>\n",
       "      <td>11.387176</td>\n",
       "      <td>0.787092</td>\n",
       "      <td>0.774926</td>\n",
       "      <td>-35.732880</td>\n",
       "      <td>1.047858</td>\n",
       "      <td>0.255772</td>\n",
       "      <td>-0.020210</td>\n",
       "      <td>[23.023023023023026]</td>\n",
       "      <td>[1.0276325302218887]</td>\n",
       "      <td>[11.811811811811813]</td>\n",
       "      <td>[-1.0680687802692428]</td>\n",
       "      <td>0.804413</td>\n",
       "      <td>0.403329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>1.541825e-01</td>\n",
       "      <td>2.541134e-01</td>\n",
       "      <td>10.745646</td>\n",
       "      <td>0.799087</td>\n",
       "      <td>0.781359</td>\n",
       "      <td>-33.558362</td>\n",
       "      <td>1.074762</td>\n",
       "      <td>0.517833</td>\n",
       "      <td>0.021674</td>\n",
       "      <td>[22.022022022022025]</td>\n",
       "      <td>[1.0964102096839057]</td>\n",
       "      <td>[10.51051051051051]</td>\n",
       "      <td>[-1.0530889528437992]</td>\n",
       "      <td>0.793244</td>\n",
       "      <td>0.397523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.664535e-16</td>\n",
       "      <td>2.477270e-01</td>\n",
       "      <td>3.303027e-01</td>\n",
       "      <td>19.738163</td>\n",
       "      <td>0.631150</td>\n",
       "      <td>0.620904</td>\n",
       "      <td>-56.360359</td>\n",
       "      <td>0.932110</td>\n",
       "      <td>3.086036</td>\n",
       "      <td>-0.022534</td>\n",
       "      <td>[12.212212212212213]</td>\n",
       "      <td>[0.909543628170932]</td>\n",
       "      <td>[0.2002002002002002]</td>\n",
       "      <td>[-0.9546338325238869]</td>\n",
       "      <td>1.043747</td>\n",
       "      <td>0.523585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.441691e-15</td>\n",
       "      <td>3.754572e-15</td>\n",
       "      <td>3.050343e-01</td>\n",
       "      <td>3.660411e-01</td>\n",
       "      <td>18.871758</td>\n",
       "      <td>0.647341</td>\n",
       "      <td>0.627189</td>\n",
       "      <td>-54.677081</td>\n",
       "      <td>0.974877</td>\n",
       "      <td>-2.987501</td>\n",
       "      <td>0.044050</td>\n",
       "      <td>[11.411411411411413]</td>\n",
       "      <td>[1.0188907403269165]</td>\n",
       "      <td>[1.4014014014014016]</td>\n",
       "      <td>[-0.9307801583587405]</td>\n",
       "      <td>1.035565</td>\n",
       "      <td>0.519227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>test2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.331468e-14</td>\n",
       "      <td>2.331468e-14</td>\n",
       "      <td>3.976676e-01</td>\n",
       "      <td>4.338192e-01</td>\n",
       "      <td>18.000502</td>\n",
       "      <td>0.663622</td>\n",
       "      <td>0.633942</td>\n",
       "      <td>-52.904573</td>\n",
       "      <td>0.944199</td>\n",
       "      <td>-2.673027</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>[10.21021021021021]</td>\n",
       "      <td>[0.9524627647822834]</td>\n",
       "      <td>[2.802802802802803]</td>\n",
       "      <td>[-0.9359344148537028]</td>\n",
       "      <td>1.026675</td>\n",
       "      <td>0.514503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>test3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>2.734479e-13</td>\n",
       "      <td>1.640688e-12</td>\n",
       "      <td>66.223643</td>\n",
       "      <td>0.518810</td>\n",
       "      <td>0.512127</td>\n",
       "      <td>-149.975429</td>\n",
       "      <td>0.976145</td>\n",
       "      <td>-0.052412</td>\n",
       "      <td>-0.000464</td>\n",
       "      <td>[0.2002002002002002]</td>\n",
       "      <td>[0.9756393977843096]</td>\n",
       "      <td>[12.212212212212213]</td>\n",
       "      <td>[-0.9765331583700944]</td>\n",
       "      <td>1.340413</td>\n",
       "      <td>0.678149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>test3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>3.037484e-05</td>\n",
       "      <td>9.112452e-05</td>\n",
       "      <td>44.880162</td>\n",
       "      <td>0.673895</td>\n",
       "      <td>0.664709</td>\n",
       "      <td>-121.380866</td>\n",
       "      <td>1.121927</td>\n",
       "      <td>-0.026206</td>\n",
       "      <td>0.356556</td>\n",
       "      <td>[0.1001001001001001]</td>\n",
       "      <td>[1.4782516162182213]</td>\n",
       "      <td>[7.907907907907909, 16.416416416416418]</td>\n",
       "      <td>[-0.751810841497804, -0.7652736478040391]</td>\n",
       "      <td>1.111343</td>\n",
       "      <td>0.562190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>5.402881e-02</td>\n",
       "      <td>1.296691e-01</td>\n",
       "      <td>36.586735</td>\n",
       "      <td>0.734156</td>\n",
       "      <td>0.722763</td>\n",
       "      <td>-106.364072</td>\n",
       "      <td>1.276843</td>\n",
       "      <td>-0.052412</td>\n",
       "      <td>0.503533</td>\n",
       "      <td>[0.2002002002002002]</td>\n",
       "      <td>[1.7803386532554812]</td>\n",
       "      <td>[6.106106106106107, 12.412412412412413, 18.218...</td>\n",
       "      <td>[-0.5691918677907137, -0.7732614633253607, -0....</td>\n",
       "      <td>1.010686</td>\n",
       "      <td>0.511208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>3.985701e-14</td>\n",
       "      <td>4.782841e-13</td>\n",
       "      <td>57.213312</td>\n",
       "      <td>0.600590</td>\n",
       "      <td>0.595042</td>\n",
       "      <td>-139.225984</td>\n",
       "      <td>1.071631</td>\n",
       "      <td>-3.039913</td>\n",
       "      <td>-0.042529</td>\n",
       "      <td>[11.611611611611613]</td>\n",
       "      <td>[1.0290544729827293]</td>\n",
       "      <td>[23.623623623623626]</td>\n",
       "      <td>[-1.1141359081211701]</td>\n",
       "      <td>1.245893</td>\n",
       "      <td>0.630329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>4.640942e-06</td>\n",
       "      <td>1.856377e-05</td>\n",
       "      <td>38.957728</td>\n",
       "      <td>0.728033</td>\n",
       "      <td>0.720372</td>\n",
       "      <td>-110.979240</td>\n",
       "      <td>1.209467</td>\n",
       "      <td>-2.122698</td>\n",
       "      <td>-0.364881</td>\n",
       "      <td>[8.108108108108109, 15.715715715715717]</td>\n",
       "      <td>[0.8444914344124669, 0.633276109824282]</td>\n",
       "      <td>[23.923923923923926]</td>\n",
       "      <td>[-1.5743345573465604]</td>\n",
       "      <td>1.035423</td>\n",
       "      <td>0.523784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>6.918695e-01</td>\n",
       "      <td>6.918695e-01</td>\n",
       "      <td>27.473154</td>\n",
       "      <td>0.808208</td>\n",
       "      <td>0.799988</td>\n",
       "      <td>-85.308054</td>\n",
       "      <td>1.431859</td>\n",
       "      <td>-3.039913</td>\n",
       "      <td>-0.497420</td>\n",
       "      <td>[5.805805805805806, 11.611611611611613, 18.318...</td>\n",
       "      <td>[0.5042794404234394, 0.9343248864017997, 0.410...</td>\n",
       "      <td>[23.923923923923926]</td>\n",
       "      <td>[-1.92895629375945]</td>\n",
       "      <td>0.875808</td>\n",
       "      <td>0.442986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test  period  n_components             p             q      p_reject  \\\n",
       "0   test1    24.0           1.0  1.110223e-16  1.480297e-16  1.694089e-01   \n",
       "1   test1    24.0           2.0  1.110223e-16  1.480297e-16  1.021567e-01   \n",
       "2   test1    24.0           3.0  1.110223e-16  1.480297e-16  1.541825e-01   \n",
       "3   test2    24.0           1.0  2.220446e-16  2.664535e-16  2.477270e-01   \n",
       "4   test2    24.0           2.0  3.441691e-15  3.754572e-15  3.050343e-01   \n",
       "5   test2    24.0           3.0  2.331468e-14  2.331468e-14  3.976676e-01   \n",
       "6   test3    24.0           1.0  1.110223e-16  1.480297e-16  2.734479e-13   \n",
       "7   test3    24.0           2.0  1.110223e-16  1.480297e-16  3.037484e-05   \n",
       "8   test3    24.0           3.0  1.110223e-16  1.480297e-16  5.402881e-02   \n",
       "9   test4    24.0           1.0  1.110223e-16  1.480297e-16  3.985701e-14   \n",
       "10  test4    24.0           2.0  1.110223e-16  1.480297e-16  4.640942e-06   \n",
       "11  test4    24.0           3.0  1.110223e-16  1.480297e-16  6.918695e-01   \n",
       "\n",
       "        q_reject        RSS        R2    R2_adj  log-likelihood  amplitude  \\\n",
       "0   2.541134e-01  11.512154  0.784755  0.778776      -36.142211   1.039764   \n",
       "1   2.043134e-01  11.387176  0.787092  0.774926      -35.732880   1.047858   \n",
       "2   2.541134e-01  10.745646  0.799087  0.781359      -33.558362   1.074762   \n",
       "3   3.303027e-01  19.738163  0.631150  0.620904      -56.360359   0.932110   \n",
       "4   3.660411e-01  18.871758  0.647341  0.627189      -54.677081   0.974877   \n",
       "5   4.338192e-01  18.000502  0.663622  0.633942      -52.904573   0.944199   \n",
       "6   1.640688e-12  66.223643  0.518810  0.512127     -149.975429   0.976145   \n",
       "7   9.112452e-05  44.880162  0.673895  0.664709     -121.380866   1.121927   \n",
       "8   1.296691e-01  36.586735  0.734156  0.722763     -106.364072   1.276843   \n",
       "9   4.782841e-13  57.213312  0.600590  0.595042     -139.225984   1.071631   \n",
       "10  1.856377e-05  38.957728  0.728033  0.720372     -110.979240   1.209467   \n",
       "11  6.918695e-01  27.473154  0.808208  0.799988      -85.308054   1.431859   \n",
       "\n",
       "    acrophase     mesor                                              peaks  \\\n",
       "0    0.150947  0.002031                               [23.423423423423426]   \n",
       "1    0.255772 -0.020210                               [23.023023023023026]   \n",
       "2    0.517833  0.021674                               [22.022022022022025]   \n",
       "3    3.086036 -0.022534                               [12.212212212212213]   \n",
       "4   -2.987501  0.044050                               [11.411411411411413]   \n",
       "5   -2.673027  0.008264                                [10.21021021021021]   \n",
       "6   -0.052412 -0.000464                               [0.2002002002002002]   \n",
       "7   -0.026206  0.356556                               [0.1001001001001001]   \n",
       "8   -0.052412  0.503533                               [0.2002002002002002]   \n",
       "9   -3.039913 -0.042529                               [11.611611611611613]   \n",
       "10  -2.122698 -0.364881            [8.108108108108109, 15.715715715715717]   \n",
       "11  -3.039913 -0.497420  [5.805805805805806, 11.611611611611613, 18.318...   \n",
       "\n",
       "                                              heights  \\\n",
       "0                                [1.0417527895534135]   \n",
       "1                                [1.0276325302218887]   \n",
       "2                                [1.0964102096839057]   \n",
       "3                                 [0.909543628170932]   \n",
       "4                                [1.0188907403269165]   \n",
       "5                                [0.9524627647822834]   \n",
       "6                                [0.9756393977843096]   \n",
       "7                                [1.4782516162182213]   \n",
       "8                                [1.7803386532554812]   \n",
       "9                                [1.0290544729827293]   \n",
       "10            [0.8444914344124669, 0.633276109824282]   \n",
       "11  [0.5042794404234394, 0.9343248864017997, 0.410...   \n",
       "\n",
       "                                              troughs  \\\n",
       "0                                [11.411411411411413]   \n",
       "1                                [11.811811811811813]   \n",
       "2                                 [10.51051051051051]   \n",
       "3                                [0.2002002002002002]   \n",
       "4                                [1.4014014014014016]   \n",
       "5                                 [2.802802802802803]   \n",
       "6                                [12.212212212212213]   \n",
       "7             [7.907907907907909, 16.416416416416418]   \n",
       "8   [6.106106106106107, 12.412412412412413, 18.218...   \n",
       "9                                [23.623623623623626]   \n",
       "10                               [23.923923923923926]   \n",
       "11                               [23.923923923923926]   \n",
       "\n",
       "                                             heights2        ME  resid_SE  \n",
       "0                               [-1.0376503623054327]  0.797114  0.399864  \n",
       "1                               [-1.0680687802692428]  0.804413  0.403329  \n",
       "2                               [-1.0530889528437992]  0.793244  0.397523  \n",
       "3                               [-0.9546338325238869]  1.043747  0.523585  \n",
       "4                               [-0.9307801583587405]  1.035565  0.519227  \n",
       "5                               [-0.9359344148537028]  1.026675  0.514503  \n",
       "6                               [-0.9765331583700944]  1.340413  0.678149  \n",
       "7           [-0.751810841497804, -0.7652736478040391]  1.111343  0.562190  \n",
       "8   [-0.5691918677907137, -0.7732614633253607, -0....  1.010686  0.511208  \n",
       "9                               [-1.1141359081211701]  1.245893  0.630329  \n",
       "10                              [-1.5743345573465604]  1.035423  0.523784  \n",
       "11                                [-1.92895629375945]  0.875808  0.442986  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_best_fits = cosinor.get_best_fits(df_results, n_components = [1,2,3], criterium='RSS', reverse = False)\n",
    "df_best_fits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or get the best models (the best fitting periods and the best fitting models - in dependence on the number of components; by default the criterium is p-value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_best_models = cosinor.get_best_models(df, df_results, n_components = [1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and plot these models (together with qq-plots and phase diagrams)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cosinor.plot_df_models(df, df_best_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the results or store the results as a csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>period</th>\n",
       "      <th>n_components</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>p_reject</th>\n",
       "      <th>q_reject</th>\n",
       "      <th>RSS</th>\n",
       "      <th>R2</th>\n",
       "      <th>R2_adj</th>\n",
       "      <th>log-likelihood</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>acrophase</th>\n",
       "      <th>mesor</th>\n",
       "      <th>peaks</th>\n",
       "      <th>heights</th>\n",
       "      <th>troughs</th>\n",
       "      <th>heights2</th>\n",
       "      <th>ME</th>\n",
       "      <th>resid_SE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.169409</td>\n",
       "      <td>0.254113</td>\n",
       "      <td>11.512154</td>\n",
       "      <td>0.784755</td>\n",
       "      <td>0.778776</td>\n",
       "      <td>-36.142211</td>\n",
       "      <td>1.039764</td>\n",
       "      <td>0.150947</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>[23.423423423423426]</td>\n",
       "      <td>[1.0417527895534135]</td>\n",
       "      <td>[11.411411411411413]</td>\n",
       "      <td>[-1.0376503623054327]</td>\n",
       "      <td>0.797114</td>\n",
       "      <td>0.399864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.664535e-16</td>\n",
       "      <td>0.247727</td>\n",
       "      <td>0.330303</td>\n",
       "      <td>19.738163</td>\n",
       "      <td>0.631150</td>\n",
       "      <td>0.620904</td>\n",
       "      <td>-56.360359</td>\n",
       "      <td>0.932110</td>\n",
       "      <td>3.086036</td>\n",
       "      <td>-0.022534</td>\n",
       "      <td>[12.212212212212213]</td>\n",
       "      <td>[0.909543628170932]</td>\n",
       "      <td>[0.2002002002002002]</td>\n",
       "      <td>[-0.9546338325238869]</td>\n",
       "      <td>1.043747</td>\n",
       "      <td>0.523585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.054029</td>\n",
       "      <td>0.129669</td>\n",
       "      <td>36.586735</td>\n",
       "      <td>0.734156</td>\n",
       "      <td>0.722763</td>\n",
       "      <td>-106.364072</td>\n",
       "      <td>1.276843</td>\n",
       "      <td>-0.052412</td>\n",
       "      <td>0.503533</td>\n",
       "      <td>[0.2002002002002002]</td>\n",
       "      <td>[1.7803386532554812]</td>\n",
       "      <td>[6.106106106106107, 12.412412412412413, 18.218...</td>\n",
       "      <td>[-0.5691918677907137, -0.7732614633253607, -0....</td>\n",
       "      <td>1.010686</td>\n",
       "      <td>0.511208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.691870</td>\n",
       "      <td>0.691870</td>\n",
       "      <td>27.473154</td>\n",
       "      <td>0.808208</td>\n",
       "      <td>0.799988</td>\n",
       "      <td>-85.308054</td>\n",
       "      <td>1.431859</td>\n",
       "      <td>-3.039913</td>\n",
       "      <td>-0.497420</td>\n",
       "      <td>[5.805805805805806, 11.611611611611613, 18.318...</td>\n",
       "      <td>[0.5042794404234394, 0.9343248864017997, 0.410...</td>\n",
       "      <td>[23.923923923923926]</td>\n",
       "      <td>[-1.92895629375945]</td>\n",
       "      <td>0.875808</td>\n",
       "      <td>0.442986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test  period  n_components             p             q  p_reject  \\\n",
       "0  test1    24.0           1.0  1.110223e-16  1.480297e-16  0.169409   \n",
       "1  test2    24.0           1.0  2.220446e-16  2.664535e-16  0.247727   \n",
       "2  test3    24.0           3.0  1.110223e-16  1.480297e-16  0.054029   \n",
       "3  test4    24.0           3.0  1.110223e-16  1.480297e-16  0.691870   \n",
       "\n",
       "   q_reject        RSS        R2    R2_adj  log-likelihood  amplitude  \\\n",
       "0  0.254113  11.512154  0.784755  0.778776      -36.142211   1.039764   \n",
       "1  0.330303  19.738163  0.631150  0.620904      -56.360359   0.932110   \n",
       "2  0.129669  36.586735  0.734156  0.722763     -106.364072   1.276843   \n",
       "3  0.691870  27.473154  0.808208  0.799988      -85.308054   1.431859   \n",
       "\n",
       "   acrophase     mesor                                              peaks  \\\n",
       "0   0.150947  0.002031                               [23.423423423423426]   \n",
       "1   3.086036 -0.022534                               [12.212212212212213]   \n",
       "2  -0.052412  0.503533                               [0.2002002002002002]   \n",
       "3  -3.039913 -0.497420  [5.805805805805806, 11.611611611611613, 18.318...   \n",
       "\n",
       "                                             heights  \\\n",
       "0                               [1.0417527895534135]   \n",
       "1                                [0.909543628170932]   \n",
       "2                               [1.7803386532554812]   \n",
       "3  [0.5042794404234394, 0.9343248864017997, 0.410...   \n",
       "\n",
       "                                             troughs  \\\n",
       "0                               [11.411411411411413]   \n",
       "1                               [0.2002002002002002]   \n",
       "2  [6.106106106106107, 12.412412412412413, 18.218...   \n",
       "3                               [23.923923923923926]   \n",
       "\n",
       "                                            heights2        ME  resid_SE  \n",
       "0                              [-1.0376503623054327]  0.797114  0.399864  \n",
       "1                              [-0.9546338325238869]  1.043747  0.523585  \n",
       "2  [-0.5691918677907137, -0.7732614633253607, -0....  1.010686  0.511208  \n",
       "3                                [-1.92895629375945]  0.875808  0.442986  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_best_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended analyses using a multi-component cosinor model\n",
    "Analyse the best models with sampling of confidence intervals of regression coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000000e+00</td>\n",
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       "      <th>1</th>\n",
       "      <td>test2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.664535e-16</td>\n",
       "      <td>0.247727</td>\n",
       "      <td>0.330303</td>\n",
       "      <td>0.932110</td>\n",
       "      <td>3.086036</td>\n",
       "      <td>[0.7477555373266069, 1.116463727216325]</td>\n",
       "      <td>1.998401e-15</td>\n",
       "      <td>1.998401e-15</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.022534</td>\n",
       "      <td>[-0.14321112798958668, 0.09814243212987028]</td>\n",
       "      <td>7.108034e-01</td>\n",
       "      <td>0.947738</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.054029</td>\n",
       "      <td>0.129669</td>\n",
       "      <td>1.276843</td>\n",
       "      <td>-0.052412</td>\n",
       "      <td>[1.0312880512906726, 1.522398829331372]</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[-0.18344301447387856, 0.07861843477451913]</td>\n",
       "      <td>0.430386</td>\n",
       "      <td>0.430386</td>\n",
       "      <td>0.503533</td>\n",
       "      <td>[0.29250900096025956, 0.7145566853326186]</td>\n",
       "      <td>5.715751e-06</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.691870</td>\n",
       "      <td>0.691870</td>\n",
       "      <td>1.431859</td>\n",
       "      <td>-3.039913</td>\n",
       "      <td>[1.215615625013616, 1.6481016097794448]</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>0.042027</td>\n",
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       "      <td>-0.497420</td>\n",
       "      <td>[-0.684624149247697, -0.31021563886491177]</td>\n",
       "      <td>5.443855e-07</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test  period  n_components             p             q  p_reject  \\\n",
       "0  test1    24.0           1.0  1.110223e-16  1.480297e-16  0.169409   \n",
       "1  test2    24.0           1.0  2.220446e-16  2.664535e-16  0.247727   \n",
       "2  test3    24.0           3.0  1.110223e-16  1.480297e-16  0.054029   \n",
       "3  test4    24.0           3.0  1.110223e-16  1.480297e-16  0.691870   \n",
       "\n",
       "   q_reject  amplitude  acrophase                             CI(amplitude)  \\\n",
       "0  0.254113   1.039764   0.150947  [0.8900871253692448, 1.1894408358928692]   \n",
       "1  0.330303   0.932110   3.086036   [0.7477555373266069, 1.116463727216325]   \n",
       "2  0.129669   1.276843  -0.052412   [1.0312880512906726, 1.522398829331372]   \n",
       "3  0.691870   1.431859  -3.039913   [1.215615625013616, 1.6481016097794448]   \n",
       "\n",
       "   p(amplitude)  q(amplitude)                                 CI(acrophase)  \\\n",
       "0  0.000000e+00  0.000000e+00  [-0.006289474781962845, 0.30818426431611545]   \n",
       "1  1.998401e-15  1.998401e-15      [2.8763864669504136, 3.2956847857478513]   \n",
       "2  0.000000e+00  0.000000e+00   [-0.18344301447387856, 0.07861843477451913]   \n",
       "3  0.000000e+00  0.000000e+00    [-5.968711568081509, -0.11111405448132139]   \n",
       "\n",
       "   p(acrophase)  q(acrophase)     mesor  \\\n",
       "0      0.059631      0.079508  0.002031   \n",
       "1      0.000000      0.000000 -0.022534   \n",
       "2      0.430386      0.430386  0.503533   \n",
       "3      0.042027      0.079508 -0.497420   \n",
       "\n",
       "                                     CI(mesor)      p(mesor)  q(mesor)  \n",
       "0  [-0.09007351737659364, 0.09413481482621422]  9.650655e-01  0.965066  \n",
       "1  [-0.14321112798958668, 0.09814243212987028]  7.108034e-01  0.947738  \n",
       "2    [0.29250900096025956, 0.7145566853326186]  5.715751e-06  0.000011  \n",
       "3   [-0.684624149247697, -0.31021563886491177]  5.443855e-07  0.000002  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_extended = cosinor.analyse_best_models(df, df_best_models, analysis=\"CI\")\n",
    "df_results_extended"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or using bootstrap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.022534</td>\n",
       "      <td>[-0.13829682636569632, 0.09424902928163095]</td>\n",
       "      <td>7.115382e-01</td>\n",
       "      <td>9.487177e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.054029</td>\n",
       "      <td>0.129669</td>\n",
       "      <td>1.276843</td>\n",
       "      <td>-0.052412</td>\n",
       "      <td>[1.1584351236082178, 1.4246289937138772]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[-0.12853720669699026, 0.01791804669014714]</td>\n",
       "      <td>0.141011</td>\n",
       "      <td>0.141011</td>\n",
       "      <td>0.503533</td>\n",
       "      <td>[0.37176654697324674, 0.6122579991759062]</td>\n",
       "      <td>3.803624e-13</td>\n",
       "      <td>1.521450e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.480297e-16</td>\n",
       "      <td>0.691870</td>\n",
       "      <td>0.691870</td>\n",
       "      <td>1.431859</td>\n",
       "      <td>-3.039913</td>\n",
       "      <td>[1.2838305177439708, 1.592075823245072]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[-3.2361239114059686, -2.8208991646454704]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.497420</td>\n",
       "      <td>[-0.6273238619005227, -0.36273958375447357]</td>\n",
       "      <td>1.638267e-11</td>\n",
       "      <td>3.276535e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test  period  n_components             p             q  p_reject  \\\n",
       "0  test1    24.0           1.0  1.110223e-16  1.480297e-16  0.169409   \n",
       "1  test2    24.0           1.0  2.220446e-16  2.664535e-16  0.247727   \n",
       "2  test3    24.0           3.0  1.110223e-16  1.480297e-16  0.054029   \n",
       "3  test4    24.0           3.0  1.110223e-16  1.480297e-16  0.691870   \n",
       "\n",
       "   q_reject  amplitude  acrophase                             CI(amplitude)  \\\n",
       "0  0.254113   1.039764   0.150947   [0.927318631611974, 1.1613261481205317]   \n",
       "1  0.330303   0.932110   3.086036  [0.7864291622433335, 1.0889448340292234]   \n",
       "2  0.129669   1.276843  -0.052412  [1.1584351236082178, 1.4246289937138772]   \n",
       "3  0.691870   1.431859  -3.039913   [1.2838305177439708, 1.592075823245072]   \n",
       "\n",
       "   p(amplitude)  q(amplitude)                                CI(acrophase)  \\\n",
       "0           0.0           0.0  [0.0024547584064777417, 0.2868545142383439]   \n",
       "1           0.0           0.0     [2.8975864662158095, 3.2914286443926413]   \n",
       "2           0.0           0.0  [-0.12853720669699026, 0.01791804669014714]   \n",
       "3           0.0           0.0   [-3.2361239114059686, -2.8208991646454704]   \n",
       "\n",
       "   p(acrophase)  q(acrophase)     mesor  \\\n",
       "0      0.049958      0.066611  0.002031   \n",
       "1      0.000000      0.000000 -0.022534   \n",
       "2      0.141011      0.141011  0.503533   \n",
       "3      0.000000      0.000000 -0.497420   \n",
       "\n",
       "                                     CI(mesor)      p(mesor)      q(mesor)  \n",
       "0   [-0.08789048416691528, 0.0919306058443238]  9.649974e-01  9.649974e-01  \n",
       "1  [-0.13829682636569632, 0.09424902928163095]  7.115382e-01  9.487177e-01  \n",
       "2    [0.37176654697324674, 0.6122579991759062]  3.803624e-13  1.521450e-12  \n",
       "3  [-0.6273238619005227, -0.36273958375447357]  1.638267e-11  3.276535e-11  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_extended = cosinor.analyse_best_models(df, df_best_models, analysis=\"bootstrap\")\n",
    "df_results_extended"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cosinor1 analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 1-component model can be used, but statistics is different in the background..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WCXSDHwJqa2tVz5zQyo0fGxtLRUWFXvQqhPgrk5pMpp9t27x5MwsXLmTz5s0qaKY8sbGx2Gw2vbdykHS/2Z8Q48+cUDu26r/xAbKzs1XTw1/0ymazkZSUpJoeXYmqqqpWJ2rHjBnzk5/dAaPRSGVlJT179lRblYhDN/idxB9bVbtImpZufP9CmYSEhG49pxEK3G431dXVxMfHt7g9NTU14Ad8RUUFmzdvZsyYMaSmpoZCTUWJjY2lurqatLS0bj2nEQz63dhJysrKWhxqK43/xtfCDawvlAkd1dXVGAyGkKb6RnoYSJ8rCh7dw+8E/oyBhIQEtVXRHLGxsZSXlxMfH98t1yWEAq/XS0VFRcj7DWhpNBgssbGxVFZWkpKS0q1WHXcW3cPvBN2pSFqgGI1GvXRyJ/GXBQ71A1NLo8FgaTpXpNNxdIMfJG63m5qaGtUna7VMTEyM6llDkYrP56OiokIzqb5KZIEFKkMvqhY4usEPknDEVrsaJpOJ2tpaPYUuCGpra/F6vZoJVygR9w9Uhj5XFDh6PCIIwhVb7YpER0frKXQB4i+joIVkAD9KxP2DkaHPFQVGSDx8IcQ6IUSJEOLrVrYLIcTTQogDQoh9QohzQiFXLcIVW+2KxMbGUlNTg9vtVluViMHhcOByuTSVcqhE3D8YGf65Ir00d8cIVUjneWBkG9tHASc1vCYBOSGSqzglJSUsWrRIn4zsIEIIDAaDnkIXAOXl5Zry7rWOXm6h44TE4EspdwJtzbRcC2yQ9XwGJAsheoVCttLk5uayaNEitm7dqrYqEYM/hU4vt9A+bZVR0GkZk8mkl1voIErF8LOA/CbvCxo+K2q+oxBiEvWjAPr166eIch1FSsmoUaOw2+0RncOsNHq5hY7TVhkFndYxGo1UVVWRmZmptiqaRqksnZaC3S22rpFS5kopB0spB2dkZIRZrcBwOBzEx8czbdq0sMYyg0mB00rxtNYwm82Ul5frHYvawF9GQU/1DRx/uQV9rqhtlDL4BUDfJu/7AMcUkh0yWitR2xbBGOJgUuC0vlw+KioKt9utp9C1gZ7qGzz+uSJ/QoVOyyg1dnwDmCaE2AQMAaqllD8L52gZp9OJ3W4PuIxCMFUsg0lPi4Tl8rGxsZSVlREXF6cbtWZ4vd5u2w85VPhLcycnJ2tm/YLWCInBF0L8HbgYSBdCFADzASOAlHIVkAdcARwA7MCEUMhVkmDLKARjiIOpgGg2m7nuuuvw+Xz4fD5NVqk0Go1YrVbq6up0w9aMmpoaTXdMczqdWK1WPB4P6enpHboXlK7Kqc8VtU9IDL6U8uZ2tkvg7lDIUoP2StS2RTDGuyN4PB7ef/993n77bf79739TWFjYuM1oNHLqqacydOhQrrvuOk455ZSQyw8Wo9FIeXk5ffr0UVsVzaC1MgpQn6DwxRdf8Morr/D555+zf//+xm1RUVH079+fiy++mFGjRvGb3/ymxRGbGj0a/OUWEhMT9VFkCwgtT6INHjxY7t69W201KCsro7KyUhMra71eLxs3bmT58uUUFRWRlpbG+eefz+mnn05KSgoej4eioiJ2797Nnj178Hq9XHTRRfz5z3/mrLPOUlt9oN6bHThwoJ562IDVauXYsWOaqLoqpWTbtm0sWrSI//73v8TFxTFkyBDOOuss0tLSMBgMFBUV8fXXX7Nr1y7q6ur45S9/ycyZM7n88st/YmTVqrtvtVrJysoKykHrCggh9kgpB7e4TTf4beP1evnxxx81EXf+v//7P2bOnMl3333Heeedx+TJk7nssstaHV5XVFSwceNG1q1bR1lZGbfddhv333+/6jeCw+EgLi5OL7dAvYE9dOgQUVFRqq+sPXbsGH/605/46KOPOPHEE5k6dSrXXHMNFoulRePtcDh47bXXyMnJ4eDBgwwfPpxHHnmErKwsxXRuSS+3242Ukv79+6t+z6pBWwZfmwFDDaGFMgpSSp5//nmuueYaKisrWb16Na+++iojR45sM5aamprKPffcw8cff8wdd9zBCy+8wJVXXskPP/ygoPY/R0+h+x9aKaPw/vvvc/nll/PFF1/w8MMPs337dm666SYsFgvQchaY2Wzmlltu4cMPP+SBBx7gX//6FyNGjGDnzp2K6d2SXnpp7tbRDX4b+Hw+ysvLVY2ter1e5s6dy1/+8hcuvPBCtm3bxlVXXRXQAyghIYGHH36YzZs3U1VVxRVXXMGOHTvCp3Q76OUW/ocWOqY999xz3H777fTu3Zt3332XiRMn/uwBNGbMGObNm9di8kF0dDRTpkzhvffeo0ePHowdO5bnnntOkbUhremll1toGT2k0wY1NTUUFRWpFlt1uVzcfffd5OXlcffddzNnzpxOZ3EUFRUxfvx49u/fz4oVK7jyyitDpG1gSCmpra3lxBNP7LYpdHV1dRw5ckS160tKyZIlS3jqqacYMWIEK1as6LRzU1tbyz333MO7777LxRdfzI4dO5g3b55ik7ZNsVqt9O/fv9stZNNDOkEgpaSsrEw1797r9TJ9+nTy8vJ48MEHuf/++0OSsterVy9efvllzjzzTKZMmUJeXl4ItA0c/wilOy+UqaioUDWU8+STT/LUU09x8803k5ubG5JrPS4ujtWrV/OHP/yBHTt2MHToUNXWhvhLc+v8D93gt4LdbsflcqlS10RKyezZs3nrrbd44IEHmDx5ckjPn5SUxN///nfOOuss7r77bnbt2hXS83cUi8VCeXl5t+xY5M9rV8P7rKioYMKECTz++OPccMMNLF68OKTXeXR0NE899RQ33ngjO3fu5O233w7ZuQPBbDZTXV2Ny+VSRb4W0Q1+C/gbUITC4wkmjvnMM8/w97//nRkzZjBlypRO69ASFouFDRs2MGDAAO644w5VJnINBgM+nw+r1aq4bLVRsx/ywoULee+99zj55JN54oknwrLYy2AwsGTJEi655BLuv/9+tm3bFnIZHSE6OpqqqipVZGsRTRv84uJiysrKFJfrcDhwOp0hGW4HWuPmvffeY9GiRVx33XXMmjWr0/LbIiUlhY0bN2IymZgwYYIqk6j+omrdyctXs0jad999x5tvvkmvXr144YUXwvrQMRqNrF69mjPOOIPp06dz4MCBsMlqDbPZTFVVlZ4R1oCmDX5hYSHPPfec4nLLysoCLpLWGm1lNzTn0KFDTJs2jTPPPJMlS5YokgqalZVFbm4u+fn5TJ8+XXHD2x2LqlVVVREVFaV4qq/NZuOuu+4iISGBN998M6B8+WAzbiwWC2vWrMFoNDJx4kTFR3NCCIQQekZYA5o2+FlZWUyYoGzZHYfDgcPhCFmqXEfbtvkzcoxGY8gm0DrKkCFDWLBgAdu3b2fNmjWKyfVjNpspLS3tFqWTPR6PamUU/vKXv3DkyBFWrFhBr16B9R/qTDXWrKwsVq9ezcGDB5k3b17Ax3cW/8Ixj8ejuGylae8e0rTB79mzJ+np6YrKLC8vVyVzYvHixXz11VcsWbJE0ZWKfsaPH8+IESN49NFH+eabbxSVHR0djcvlwm63KypXDdTy7rdu3crWrVu59957+c1vfhPw8YGMVFviggsuYMaMGWzdupU33ngjqHMEi9/L7w4ZYTabrc3tmjb4SlNXV4fNZlM8trpz505ycnIYN24cV1xxhSoNUIQQPP744yQnJzNt2jTFVymazWZKSkq6tJevlnd/5MgR5s6dy5AhQ5gxY0ZQ5whFE/N7772Xs88+mzlz5vyk2J8S+OeKunKbTX+ySVvoBr8JwTQ46Sw2m40//elPDBo0iPnz5wPqNUBJTU3lySefZP/+/Tz22GNBnycYjEZjl/fyq6urG71NpZBSMmvWLAwGA8uXL1e1fWJ0dDTLly/H7XYzc+ZMRR/u/kykrhzLr62tbTcFVW+e2YDfu1d61eOiRYs4duwYr732WqPnp2YDlIsuuohx48axbt06rrvuOkUrbMbGxlJaWorFYulyRa88Hg/l5eWNtWmUYtOmTXzyySc89thjqoQKmzNw4EDmz5/P7Nmz2bJli6KLsvxeflJSUpdb3d3RVHK9tEIDhYWFOJ1ORcM5u3fv5ve//z233357Y91wpWirdG1NTQ3Dhg0jNTWVvLw8Rec0rFYrffr00UQp6lBSUVFBeXm5on9XcXExw4YN4/TTT2fLli2KN1dp7Rrz+Xxcf/317N+/n48++kjReTqbzUZ6erqi5ZqVwGazUVhYSEJCAllZWXpphbYIZ+y+tdi6y+Vi1qxZ9O7dmzlz5oRcbnu0FQJKTExk4cKFfPvtt+Tm5iqql9/L17IjEigej0eVMh0PPPAALpeLJUuWqNJJq7VrzGAwsHjxYmpra1mwYIGiOvlXd3elWL6UkpKSkg7ZLz2kQ33efbi82Na6/qxbt479+/ezfv16VerTtxcCGjVqFCNHjmTp0qVcc8019O3bt8X9Qo2/DaLdbu8yXn5VVVVjhVCl2LlzJ3l5ecyePZuBAwcqJrcpbV1jJ510EtOmTWPZsmXceOONDB06VBGdmsbyu4qXb7PZcLvdHTL43T6k43A4OHr0aNhi9y0Na0tLS7nwwgs599xzeeGFF8IiNxQUFhYydOhQLrvsMlavXq2YXLfbjdfrZeDAgREfy/d4PBw8eFDRBjoul4vhw4fj8Xj44IMPVC+/3BpOp5NLLrkEo9HItm3bFAsd+nw+7HY7J5xwgqqT2KHA30AnOjq68W/RQzqt4J/oCGdmTkvpbIsWLcLhcPDQQw+FTW4oyMrKYtq0abz11lt8+umnisk1Go243e4uUWOnoqICg8Gg6INr3bp1HDhwgAULFmjW2AOYTCbmz5/PDz/8oKjjYzAYMBgMXaLGjr+xfEcfXCEx+EKIkUKI74UQB4QQPwtICyFuF0KUCiH2NrzuDIXczhLqVbUdYd++fWzatImJEydy4oknKiY3WKZMmUJWVhYPPvigonFP/+rbSK6x43K5qKysVDR2X1JSwrJly7jkkku47LLLFJMbLMOHD+fCCy/kiSeeCGujlOaYzWYqKioiusaOz+cLuMhjpw2+ECIKWAGMAk4DbhZCnNbCrpullGc1vJRfv98MKSXHjx9XNCtHSskDDzxAWloa9957r2JyO4PZbOaBBx7g22+/5e9//7ticqOjo/F6vRHt5ZeXlxMdHa2od7906VLq6uo0P3r0I4TgoYceoqamhscff1xRuQaDIaK7YlVXV+P1egNKMQ2Fh38ecEBKeVBK6QI2AdeG4LxhxWq1Kt5L9K233mL37t3MmTOHxMRExeS2RCArc6+66iqGDBnCE088oejCKIvFQklJSURmVNTV1VFdXa2od//jjz/y0ksvceutt0bE6NHPKaecwrhx49i4cSM//vijYnL99fKdTqdiMkOFP/Mr0HUdoTD4WUB+k/cFDZ8153ohxD4hxFYhRKspH0KISUKI3UKI3e0tEw6WYIZCncXtdrNo0SJOPvlkRo8erZjc1ghkZa4Qgrlz51JSUqJocTV/RkWkdS3yzw0pHT9/7LHHiI2NZebMmYrKDQUzZ87EZDKxZMkSxWQKITAajZSUlCgmM1RUVlYGlfkVCoPf0ni1eerPm8AAKeWvgPeB9a2dTEqZK6UcLKUcnJGREQL1fk5VVRVer1fRGfotW7Zw6NAh5syZo4lVfoEWwzr33HO5/PLLWblypaKxVn/edCR1LbLb7dTW1ipq8Pfs2UNeXh5TpkxRvOBgKMjIyGDSpEm8+eab7Nu3TzG5sbGx1NbWRlR5bpfLFXRNplAY/AKgqcfeBzjWdAcpZbmU0j9uehb4dQjkBoXb7W5cvq8UDoeDpUuX8utf/5rhw4crJrctgimGNXv2bGw2GytWrAijZj9FCEF0dHS7RaG0gs/n4/jx44peX1JK/va3v5Gens6kSZPa3b+zhfbCxejRozGbzfz1r39VVK7ZbOb48eMRkyBQWloa9NxQKAz+F8BJQoiBQogY4CbgJ/VPhRBNi29fA3wXArlBUVZWRnR0tKKLYNavX09xcTFz586N6Lxyo9FIZmYm69at49ixY+0fECLMZnPjYiytU11dHVCaXCj44IMP+Oyzz5g5c2aHFvGFotBeOMjLy8PhcLBr1y7+9a9/KSbXnwYcCYsooL0AACAASURBVIXVamtrsVqtQYejO231pJQeYBrwT+oN+RYp5TdCiIeFENc07HaPEOIbIcRXwD3A7Z2VGwx2u13xibSamhqWL1/OsGHDgqpDriUWLFhAcXExbrebpUuXKirbbDZTXFysaS/M7XZTUlKiqHdfXl7O7NmzycrKYuzYsR06prO17cPFmDFjmDNnDpmZmTz22GOKlteIi4ujtLRU02ma/tFjZ+xXSNxcKWWelPIXUsoTpZSPNHz2oJTyjYbf50opT5dSnimlHCal/G8o5AaCz+ejuLhY8Xomzz77LFVVVcyePVtRueFg/vz5XHrppfzhD3/g5Zdf5ujRo+0eE6rwgdFoxOPxaHqxTElJieKjx0ceeYSioiLOPvvsDmechaK2fThITU1l+vTpzJo1iy+//JL3339fMdkGg4GoqChVemh3FP/cY2cyC7vNStuqqio8Ho+iaZg1NTWsWbOGUaNG8ctf/lIxuR0hGEN84oknsmHDBubOnYvBYOCZZ55p95hQhg8sFgulpaWanMC12WydGmoHg5SS//u//yM5OZmHH35YMbnh5oYbbqBv3748+eSTinr5/jRNLU7gOp3OkMw9dguD7/+ylC7GtW7dOmpqaoLuMhROOmOIe/XqxS233MLmzZspKChoc99Qhg8MBgNGo5Hjx49rqpqm1+uluLhY8Vr327dv59tvv2XevHlkZmYqKjucGI1Gpk+fzt69e9mxY4eisi0WC8XFxZpa++FfJGo0Gjs9B9jli6dJKTl69Cg+n0/RNDmbzcaQIUMYPHgw69e3moWqGm3Vw+8IhYWF/Pa3v+Wmm25SvDuW1WqlZ8+eJCUlKSq3NY4fP47ValU8M+fKK6+koqKCjz/+WJU+zOHE5XLxu9/9jszMTN544w1Fkx1qa2tJTEzUzEO0srKSkpKSDhd47NbF0yorK6mrq1N8Eczzzz9PVVWVZksodDaOm5WVxU033cSmTZsU708aFxdHcXGxJkI7NpuNyspKVbz7r776ihkzZnQ5Yw8QExPDtGnT+M9//sPHH3+sqGyLxUJlZaUmQjtOp5OSkpKQRSe6tMGvq6ujtLRU8Xrzdrud1atXM2zYMM4++2xFZSvJtGnTAFi5cqWicv2hnaKiIlWzdjweD0VFRYobeyklS5cupW/fvtxwww2KylaSMWPG0LNnT5YtW6ZoCE8IgcVioaioCI/Ho5jc5vh8PoqKioiJiQlZIkCXNfher5djx45hMpkUz33fsGEDFRUVmvXuQ0WfPn0YPXo0L730EkVFRYrKjo2Nxel0qrZ4SEpJUVERBoNB8ZrqO3bs4KuvvuKee+7pkt69H5PJxLRp0/j888/ZtWuXorL9C5vUnC8qLy/H7XaHNDrRJQ2+lLIxZzucte5bwuFwsGrVKi688EIGD24xjNalmD59Oj6fj5ycnBa3h3NVZ1xcHGVlZdhstpCfuz3Ky8ux2+2Kp/lC/YiqZ8+eXdq793PzzTeTmZnJsmXLFJdtNpsbQ3ZKY7Vaw9L0vksa/MrKSmw2m+JDbajPfiktLdVkZk446Nu3L9dddx0vvfRSi0Y9nKs6hRDExcVx7NgxRSseWq1WysrKVGlNuXfvXnbt2sVdd92luDOjBrGxsUyZMoVPP/2UPXv2KC4/Pj6e48ePK+pUOJ1Ojh07FpYuaV3O4FutVkpKSlS5Gb1eL7m5uZxzzjmcf/75istXi6lTp+JwOHj++ed/ti3cqzqjoqIwGo0UFhYqEm+tq6ujqKhI0ZaFTcnJySEhIaHDq2ojidZGg2PHjiU5OVnxuSKodyri4+MpLCykrq4u7PI8Hg8FBQWYTKawFFnsUgbfbrdTWFio2s2Yl5fHkSNHyM7OjuiaOYHyi1/8guHDh7Nu3bqf1btRYlWnyWRCSklhYWFY86ddLldYb8b2OHz4MHl5edx2221h68GsJq2NBuPi4hg/fjz//Oc/OXDggOJ6RUVFYTKZKCgoCGtmmNfrbVzXEq7RW5cx+A6Hg4KCAiwWiyo3o5SSlStXMnDgQEaMGKG4fLW5++67qaysZNOmTarIN5vNuN1ujh07Fhaj73K5yM/PbxxRqEFubi7R0dHccccdqsgPN22NBu+44w5MJlOrc0Xhxp8pU1BQEJZ6O35j7/V6wzov1CUMfm1tLUePHiU2Nla1LvS7du1i3759TJ48WRP17pXm3HPP5bzzzmPVqlWqFaCyWCw4nU4KCgpCGt5xOp3k5+cjhFCtKXh5eTmbN2/m+uuvp2fPnqroEG7aGg2mp6czZswYXnnlFcUzwvz426EePXo0pHNG/jCO2+0OexJAxBv86upq8vPzMZvNqhl7gFWrVpGent4tMidaY+rUqRQWFvLGG2+0v3OYsFgseDyekN2UtbW1HDlyhKioKEX7Hzfnueeeo66ujilTpqimg9pMnjwZr9eraNe15sTGxmIwGDh69GhIynU7nU6OHj2Kx+NRJMkkYg2+f9FLUVER8fHxqhr77777jg8++IAJEyaokqanFS699FJOPvlkcnJyVK11YzabEUJw+PBhampqgtLF5/NRVlZGfn4+sbGxqmbE2O12nnvuOS6//HIGDRqkmh5q079/f66++mo2btyoau16k8mEyWTi6NGjlJeXB7X4T0pJdXU1R44cQQihmN2IOIPv8/moqanh8OHDjTUvlCxH2xKrVq3CbDYzfvx4VfVQG4PBQHZ2duMDUE1MJlPjasmCgoIOZ1hIKamtreXw4cNUVFSQkJCgqjMBsGnTJqqqqpg6daqqemiBqVOnYrPZ2LBhg6p6REdHk5CQQHl5OUeOHMFut3fYsairq6OgoKBxlbaSYcKIKJ7m8/lwuVzY7XYqKysbJza0ECsvLCzkggsuYPz48V2qRG2wuN1uLrjgAvr168crr7yitjpA/Q3mj4+mpqZiMpl+0iJOSonb7cbhcDT2zzWbzZpYxerxeBqLiL3++utqq6MJbrnlFr799ls+++wzVcNsfvzXjslkIjU1tfHaaXp9eTyexpXhdrudmJiYsOneVvE0dV2XdnA6nRw4cACv14sQAiEEsbGxmjD0ftauXYuUkrvuukttVTSB0Whk8uTJzJ8/n927d2titXFsbCyxsbG4XC6OHTuGlLKx4QXUG1UpZeP1pQUj4uftt98mPz+fBQsWqK2KZpg6dSpjxozh5ZdfZty4cWqrg9FobGzQ4y/F4O/FDPUZOD6fDyEEMTExJCYmqqarpj38X/3qVzIvL0/1kE1rVFdXc+655zJ8+HBFG3trHbvdzrnnnsuQIUNYt26d2uq0iJSycQiu1etLSsnIkSNxOBzs2LFDs3oqjb80dHV1NTt37tSUA9gUf2xf6f9bRJdH1tpF3nQ14MaNG6mtrSU7O1tttTSFxWJhwoQJvPfee6oslOkIQggMBoPmrq+mfPzxx3z99ddkZ2drWk+lEUIwdepUDh8+zLvvvqu2Oq2ixetLW9pEAP7VgC+++CJr1qxh6NChnHHGGWqrpTkmTJiAyWRi9erVaqsSseTk5NCjRw/+8Ic/qK2K5hg1ahQDBgxg5cqVmup+pnVCYvCFECOFEN8LIQ4IIea0sN0khNjcsP3fQogBoZCrBv7VgBaLhZKSEt27b4W0tDRGjx7N1q1bOX78uNrqRBxff/01O3fuZOLEiaot9tIyUVFRTJo0ib179/LZZ5+prU7E0GmDL4SIAlYAo4DTgJuFEKc1220iUCmlHAQsAxZ1Vq5apKamMnnyZDZs2MDpp5/OhRdeqLZKmmXSpEl4PB7NxvG1TE5ODnFxcZqYlNQqo0ePJi0tTbVyC5FIKDz884ADUsqDUkoXsAm4ttk+1wL+xq5bgUtFBFcXe//99zlw4EC3K5IWKAMHDuSKK65gw4YNqtSsj1Ty8/N58803ufXWWzXTt1eLmM1mJkyYwPbt2/n+++/VViciCIXBzwLym7wvaPisxX2klB6gGkhr6WRCiElCiN1CiN1qdTNqj5ycHPr06cNVV12ltiqKEkwzk+zsbGpqanjxxRfDqFnX4tlnn0UIwZ133qm2Kppn/PjxmM1mVq1apbYqEUEoDH5LLm7zWZSO7FP/oZS5UsrBUsrB4SypGyy7d+/m888/56677tLEwhwlCaaZyVlnncUFF1zAs88+q4mm41qnoqKCl156ieuuu47evXurrU5EcPrpp/Pqq6+qVlStJcLZ6a0zhMLgFwB9m7zvAxxrbR8hRDSQBGjrm+ggq1atIjk5mZtvvlltVRQn2GYm2dnZFBUVdbuVosHc9OvXr8fhcHTrImmBsHnzZnbv3o3X62Xt2rVqq9NIODu9dYZQrLT9AjhJCDEQKARuAm5pts8bwHjgU+AG4AMZgblUP/74I++++y7Tp08nLi5ObXUUx1++NlCGDRvGKaecwqpVq7jhhhu6zbyH/6YHOvS9ORwO1q1bx6WXXsopp5wSbvW6BH7nY8+ePbzwwgvcc889qq5k9ePXK1yd3oKl0x5+Q0x+GvBP4Dtgi5TyGyHEw0KIaxp2WwukCSEOAPcBP0vdjARWr15NTExMl21AES6EEGRnZ/Pf//6XDz/8UG11FCPQEdGWLVuoqKjQU30DwO+EzJgxA5vNxsaNG9VWCVCm01swaL60glZW0pWWljJkyBBuuOEGFi9erLY6EYe/qFr//v3ZunWr2upoDq/Xy9ChQ0lJSeHNN9/sNqOgUDJmzBgOHDjArl27uvXahYgtrVBaWqqZSY+1a9ficrmYPHmy2qpEJEajkTvvvJNPP/2UL7/8Um11NMc777zD4cOH9VTfTpCdnU1xcTH/+Mc/1FZFs2ja4BcXF2ti0sNff3vUqFGceOKJaqsTsYwdO5bExER9oUwz/P2QBwwYwMiRI9VWJ2K56KKLOPXUU1m1alVQTUm6A5o2+D179gzrpEdHsyhefPFFqqurNRdblVJSV1eHzWb72au2tla13rKtER8fz2233UZeXh6HDh1SWx3N8Mknn/DVV1+RnZ2tucqPLpeL2tparFZr47VltVqpra3VXJqtf65o//79bN++XW11NImmDX5GRkZYJz06kjrlcrnIzc3lN7/5Deecc07YdAkEn8/XaNRjY2Pp1asX/fr1Y8CAAfTv358+ffqQnp6OlBKr1YrD4VBb5UYmTpyI0WgkNzdXbVU0w8qVK8nIyNBMP2QpJXa7HavVSnR0ND169Gi8vgYMGEC/fv3o0aMH0dHRjcZfK3OB11xzDb1799ZHka2g6QYo4aYjqVP/+Mc/KC4uZsmSJUqp1Sb+VmoZGRkkJia26hFaLBZSUlIau+xUV1er3psVoEePHtx4441s2bKFP/7xj6Snp6uqj9p8/fXXfPTRR8ydO1cTjVccDgcej4e0tDSSkpJaXVxoNptJSkrC7XZTXV1NeXk5RqNR9b/BaDQyadIkHnroIfbs2cOvf/1rVfXRGpr28MNNe6lTPp+PnJwcTj31VIYNG6awdj/F6/VSU1NDQkICAwcOJCUlpUPDf5PJRK9evejfvz8+n4/a2loFtG2bSZMm4XQ6ee6559RWRXVWrlxJfHy86kXS/KPBmJgYBg4cSHp6eodWkhuNRtLT0xk4cCBGoxGr1aq6t3/LLbeQlJSkl1togW5t8Ntj+/bt7N+/X/XMCZfLhcPhICsri8zMzKCaapvNZvr37098fDw1NTWq3pSDBg1ixIgRPP/885p4AKnFkSNHePPNNxk3bpyqRdK8Xi9Wq5WMjAz69OkT1CgwJiaGPn36kJGRgdVqxev1hkHTjhEXF8dtt93GO++8w8GDB1XTQ4voBr8NVq5cSVZWFtdcc037O4cJp9OJx+OhX79+JCQkdOpcUVFR9OzZkx49emC1WlXNZMjOzqaqqopNmzappoParFq1iujoaFWLpHk8Hux2O1lZWaSmpnbKsRFCkJqaSlZWFrW1tXg8nhBqGhh33HEHRqNRb8DTDN3gt8IXX3zB559/zqRJk1QrkuZ0OvH5fPTr1y9ksVH/TdmrVy9sNptqRn/w4MGcd9555Obm/sQwaLXoVKgpKytjy5YtXH/99fTs2VMVHTweD3V1dfTt27fTzkRTEhIS6NevX+N8gBr454pefvllSktLVdFBi+gGvxVycnJITk7mllualwVSBpfLhdfrpW/fvmGZaE1KSiIzMxObzaZaeCc7O5uCggLeeuutxs+0WnQq1Kxduxan06lakTSv14vD4aBPnz5YLJaQn99isdC3b1/sdrtq4Z1Jkybhcrn0uaIm6Aa/BX744Qf++c9/MmHChLDcDO3h8XhwuVxhM/Z+UlJSGmOuanDZZZcxaNCgn/QlDbYiZyRhs9lYv349I0eOZNCgQYrL90/e9+7dO6zXt8ViaQzvqOFU+OeK1q9f/5O5ou4yimwJ3eC3wDPPPENsbCwTJkxQXLbP58Nut9OnTx9F6oGkpqaSnJysSkcqg8FAdnY233zzDR9//HGjPlosOhVK/Av5pk6dqop8m81GZmZmSMM4rZGQkEBmZqZqTkVLc0XdZRTZErrBb8aRI0d47bXXGDduHGlpLTblCiv+m1GpkYUQgh49emAymairq1NEZlOuu+46MjMzWblypeKy1aCurq7NhXzh9j5ra2tJSUkhJSUlLOdvieTkZJKSklTJyPLPFa1atapxZXB3GEW2hm7wm/HMM88QHR2tShkFu91OUlISycnJiso1GAz07t0br9er+CSbyWTizjvv5OOPP2bv3r2KylaDTZs2UVxczL333tvi9nB6n06nE6PRSEZGRsjP3RZ+pyI6Ohqn0xnSc3fkAXnPPfdw7NgxtmzZAnTtUeQXX3zR5nbd4DehoKCALVu2cPPNN5OZmamobLfbjcFgoEePHqrk/BuNRnr16tW4kldJbrvtNpKTk1m6dGlQx0dKTNbpdPLMM89w3nnn8dvf/rbFfcLlffp8PlwuF71798ZgUP62j4qKonfv3rhcrpBmhnXkAXnxxRdz9tlns3z5ctxud8RcL8GwbNmyNrd3a4Pf/B+/YsUKhBCKx1allDgcDnr16qVq8az4+HhSUlKw2+2Ky508eTLbt2/nq6++Cvj4SInJbtmyhaKiImbOnNnqQz1c3mdtbS09e/ZUtbSGyWSiZ8+eIZ0v6sgDUgjBvffeS0FBAVu3bo2Y6yVQdu/ezUcffdTmPt26lk7TFnS///3v2bRpE6NHjyYrK0tRPWpra0lPT8dsNisqtyXS09MbK20quf5gwoQJrF69mqVLl7J+/fqAjtVqO7mmuFwuli9fzjnnnMOFF16oqGyHw0F8fLwmWv8lJiZis9lwOBwhud472nbz0ksv5cwzz+Tpp59urJev5eslGJYtW0ZqamqbI5du7eE39Q5ycnLwer1MmzZNUR1cLhdGo1Ez8cSoqCh69eqFw+FQNLSTkJDApEmTeP/999m3b19Ax0ZCTHbr1q0UFhZy3333KRqy83q9+Hw+1UKFzfHH871er6L5+X4v/+jRo+zYsUPz10ug7Nmzp/HvaotubfD9hsLj8fDiiy9yww030K9fP8Xk++vZ9+rVS5W4amuYzWZSU1NDEtoJJF56xx13dCqWr1XcbjdPP/00Z511FhdffLGismtra8nMzFRttXhLGI1GMjMzFc/aGT58OGeccQZPP/20qmUfwoHfux8/fnyb+3XKygghUoUQ24QQPzT8bDHXSwjhFULsbXi90RmZ4cA/mTN9+nRF5drtdtLS0lQvKdsSaWlpGAyGTt8YgcRLExISuOuuu9i2bVvAXr6WeeWVV8jPz+fee+9t18sO5YRiXV0dcXFxiuTbB0piYiIWi0XRVGAhBDNnzuTw4cO89tpriskNN//5z3/48MMPmTJlCnFxcW3u21m3cg6wXUp5ErC94X1LOKSUZzW81KtE1gIFBQW88MIL3HTTTQwcOFAxuR6PB4PBoNlhZVRUFD169Oi0lx9o1klX8/KdTidLly7lzDPP5LLLLmt3/1BNKEopcbvdZGZmaiKU0xwhBJmZmbhcLkVDhyNGjOD0009n2bJlHe7YpfWsnqVLl5KSksLtt9/e7r6dNfjXAv4ZtvXA7zt5PsV54oknMBgMreZFhwuHw0GPHj0019KuKQkJCcTFxXWqY1ag8fXExEQmTZrEtm3b2s0pjgReeOEFCgsLmTNnTocMb6jSMmtra0lLS1O94U1bmEwm0tPTFc0KE0IwZ84cjhw5wksvvdShY5577jkWLlyoyZo8u3bt4sMPP2Tq1KntevfQeYOfKaUsAmj42aOV/WKFELuFEJ8JIdp8KAghJjXsuzvcT9QffviBrVu3Mn78eHr37h1WWU2pq6vDYrEQHx+vmMxg6dGjBx6PR1Ev7K677iIjI4O//e1vqjfT6AxWq5WnnnqK3/3udwwdOrRDx4RiAtrj8RAVFaXoatpgSUlJQQih6ATusGHDOP/883nyyScjuh+DlJK//e1v9OzZs8NlYNo1+EKI94UQX7fwujYA3fpJKQcDtwBPCiFObG1HKWWulHKwlHJwuMMdixcvxmw2Kxq79w+1tZI10R4mk4m0tDRFvTCLxcJ9993H559/zrZt2xSTG2pyc3OpqKhg7ty5isqNhNGjn1CFDgOhsrKSU045hdLSUtasWdPu/hMmTGDevHmq1NZqi3feeYcvv/ySWbNmdTjFtV2DL6W8TEp5Rguv14HjQoheAA0/S1o5x7GGnweBHcDZHfuTwsdXX31FXl4ekydPVjSO7nA4SE5OVqQwWqjwe4pKemE333wzJ5xwAo8++qiq3ZOCpaysjFWrVnHqqacqmvnldDqJjY2NiNGjn4SEBEwmU4dj6p1l8+bNPP/88/ziF7/oUGxei2m/Ho+HRx99lJNOOokbbrihw8d1NqTzBuDPAxoPvN58ByFEihDC1PB7OvBb4NtOyu0UUkoefvhhUlNTmTRpkmJyfT4fPp9PlaJsnUENL8xoNDJnzhz279/P1q1bFZMbKp5++mkcDgffffedois6nU5nxIwe/fgncJXK2PHPkyxatAibzcYzzzyjiNxQsmnTJg4ePMjcuXMDannaWYP/GDBcCPEDMLzhPUKIwUII/1jpVGC3EOIr4EPgMSmlqgY/Ly+Pzz77jFmzZimasma320lPTw+qJ63aJCQkYDQaFc1fvuKKKzj77LNZsmRJpyaOlebAgQOsX7+e66+/XtGqjA6Hg8TERE2s2A4Us9lMQkKCIv9nv8d+3nnnMXr0aJ577jkOHToUdrmhora2lqVLlzJ48GAuv/zygI7tlMGXUpZLKS+VUp7U8LOi4fPdUso7G37fJaX8pZTyzIafazsjs7M4nU4WLlzIKaecomg3K6/Xi8FgULVZdWfwF3ZT0vAKIXjggQcoKiqKqPLJCxYswGw2K7pq2z83lJ6erpjMUJOenq54gsDs2bMxGo08/PDDisnsLE8//TTHjx/ngQceCHgkp53lnQqxdu1ajh49yvz58xX1tO12OxkZGRExkdYacXFxisZaAYYMGcK1117LypUryc/PV0xusGzfvp0PPviAmTNn8v777ytWpMvhcJCamqrpNMz2MJlMJCcnK+pUZGZmMmPGDN577712C49pgUOHDpGbm8v111/P4MGDAz5eaDnt7Ve/+pV89913Q3a+4uJiLrroIs4///yAC3R1Bo/Hg8fjYcCAAZoqoRAMdrudo0ePKlqI69ixYwwdOpRhw4bx7LPPKia3KRUVFWzevJkxY8a0Onnncrm49NJLgXrDb7PZ2j0mFEgpsdlsnHDCCZoqoRAMbrebQ4cOERcXp9g8hNPp5JJLLiEmJob33ntP09/h7bffzq5du/j4449bLeGelZW1pyEr8mdEtvUJkAcffBCPx8OCBQsUletwOEhPT494Yw/1KZMWiyXkjSzaonfv3syYMYO8vDx27twZknMGunqyIytgV69ezcGDB5k/fz4xMTGKZHdUVFTw9NNPA2jaUHUUo9FISkqKol6+yWTiwQcfZP/+/axdq2rEuU0++OADtm3bxsyZM4Pu1xH5FqiDbN++nbfffpt77rmHAQMGKCbX4/FgNBo1Wc8kWDIyMhQN6wBMmjSJAQMGMHfu3JAYg0BLGLS3AvbgwYMsW7aMK664okMlFELFpk2bWLx4cWPJ365ASkoKPp9P0Vj+5ZdfzvDhw3n88cc5evSoYnI7it1u5/777+fEE09k4sSJQZ+nW4R0HA4Hw4YNIzY2lvfee0/ROKfVaqV3795dyuAD5Ofn43a7FS389sknnzB69GimTp3KX/7yl06dqyMhmo4ipWTMmDHs27ePHTt20LNnz06dLxDy8/N55513mDZtWkRP2DanrKyMqqoqxXo7AxQWFjJs2DDOPfdcNm7cqKnU1vnz57NmzRpeffVVhgwZ0ua+3T6ks2TJEvLz83nssccUNfb+JiKRtAimo6Snpyvu5f/2t79l7NixrFq1KqjOWE0JZbjl5Zdf5pNPPuH+++/vtLEPJNTk8/lISUlh3rx5XcrYQ33jc6W9/KysLObMmcOOHTs0VU1zz549rF27lttuu61dY98eXd7g79q1i9zcXMaNG8f555+vqOy6urqIWwTTUcxmM3FxcYqWtwX4y1/+Qo8ePfjjH/+o+AOnJQoLC3nooYcYPHgwt956a6fPF0ioyZ+ZE4nrOtojOjqa1NRUxddfjB8/nrPPPpsHH3yQ4uJiRWW3hNPpZNasWfTs2ZP777+/0+fr0ga/pqaGe++9l/79+/Pggw8qKtsfu+9IBbtIJT09HbfbrajMpKQkHn30Ub777jsWL16sqOzmeL1eZsyYgdvt5sknnwzJpHxHq2VKKZFSkpyc3GmZWkUNLz8qKoonn3wSh8PBzJkzQ9pwPRgeeeQRvv/+exYtWhSSsHCXNvj+p/Ty5csVjQVCvfeVkZHRJb17P2azWfGMHaifYBs3bhw5OTns2LFDUdlNWb16NZ9++ikLFy4MWS+FjoaaurJ3pzUOOAAAFmdJREFU70ctL3/QoEEsWLCAnTt3dqi4WrjYtm0ba9eu5Y477mhM9+0sXdbgb9myhZdffpnp06dzzjnnKCrb7913xdh9c9LT0xU3+FA/iXXyySczY8YMSkparNkXVvbu3cvixYu54oorGD16tKKypZR4vd4u7d37SUpKUtzLBxg7diwjR47k0Ucf5euvv1ZUNtSvGbrvvvs47bTTOp2g0JQuafC//vpr5s6dywUXXMDMmTMVl+/Pu+/K3r0fs9mM2WxWPJ5uNpvJycnBZrNx9913KxpaKi0t5c477yQzM5NFixYp/n92OBykpKR0ae/ej9FoVHz1LdSX9ViyZAlpaWlMnDhR0W5XLpeLqVOn4nA4yMnJCWkmXJcz+GVlZdx1110kJyeTk5Oj+E3h9XqJjo7uFt69n4yMDMUnbwFOPvlkHnvsMXbt2sX8+fMDOjbYtnVut5spU6ZQWVnJ2rVrFS+Z6/fuI6G5SahITk7G6/Uq7uWnpqaydu1aSktLmTx5ckBORbDXl5SSefPm8e9//5vHH3+cQYMGBap2m3Qpg2+32xk/fjwlJSWsWbNGlVS1rrSqtqOYzWZMJpPiE7gAN954I9nZ2axfvz6gVZLB9I71+Xzcd999fPbZZzz++OOcccYZwajcKerq6khOTu4Sq2o7SkxMDElJSao4FWeeeSaLFi1i165dzJ49u8MPnWB7E+fm5vLiiy8yffp0fv/70HeM7TJjQpfLRXZ2Nvv27WPt2rWcfbbyPVZ8Ph8Gg6FbefdQP/zNyMigsLBQFUM0d+5cDh06xIMPPkhiYiI33nhju8f4s2A6WrpYSslf//pXXn31VWbPns11113XKZ2DxePxdIvYfXNSUlKoqqpSpfTzjTfeyNGjR1m6dCnp6ekdSo8M9PqC+lXTDz/8MFdeeSV//vOfg9a3LbqEwXc6nUyaNIn333+fxx57LOAa0aHC4XCQlpYW0RUxgyUuLq6xXr7SYbSoqChWrFjB+PHjue+++4iJieHaa9vuwOnPhukIUkoWLlxIbm4ud9xxh6ItMZvicDgau0N1N0wmEwkJCdTV1Sm6utvPfffdR1lZGStWrGhshN7W3E0g1xfAK6+8wqxZs7j44otZvnx52CIEER93qK6u5vbbb2809uPGjVNFD39etJJVJLWEEIL09HRVht0AsbGxrFu3jnPPPZe7776bdevWheS8Ho+HOXPmsGrVKsaPH8+CBQtUm4x3u92aarOnNKmpqaqEDaH++l64cCFjx47lmWeeYd68eSFrvZmbm8s999zD+eefz5o1a8L6QI9og3/gwAGuvvpqPv30U5YuXaqasYf6+YOunhfdHvHx8RgMBtUWq8TFxfHiiy8ycuRIHnjgAebOndupB1BZWRk33XQTGzduZNq0aTzyyCOqzc04nU4sFosq3q1WUCsjzE9UVBSLFi0iOzub559/nrFjx3Yqe8fhcDBr1iwWLFjAlVdeyQsvvBD2kFVEGnyfz8eaNWsYMWIElZWVbNq0SbE2ci3Nvksp8fl8EdvNKlQYDAbS0tIU7X3bHLPZzOrVq8nOzmbDhg1cffXV7Nu3L6BzSCnJy8tjxIgRfPnllyxbtoy5c+eqmmbrdDq7XL2cYFBr3YcfIQTz5s3j8ccf5/PPP2f48OEEU+Dxyy+/5Oqrr+all15i2rRpIU+/bI2IMvg+n4+8vDwuv/xy5s+fzwUXXMB7772naI2clmbf6+rqSEpK6laZE63hD2mpWYU1KiqKefPmsWHDBkpKSrjiiiu47777+OGHH9o8TkrJp59+yq233spdd91Famoqr7/+uuILq5rjdrsxmUwR2as21JjNZsV7K7fEzTffzOuvv05ycjITJ07k1ltv5bPPPmv3uv/++++59957ueqqqygvL2fjxo3MnTtXsXk/TZdHHjRokHz88cc5fPgwe/fu5Z133qGkpIQTTjiBP/7xj1x77bWKe10tldWtqalh4MCB3XIyrSXKysqorKwMqo5QKMsWQ/3/5umnn2bt2rW4XC7OPfdcLrzwQn75y1825rIXFRWxd+9ePvjgA3744QfS0tK4++67mThxoiZCdF21xHawWK1WioqKNJEN53a7WbNmDStWrKCyspKTTz6ZYcOGcdZZZ9GzZ0+EEFRUVLBv3z527tzJnj17MJlMTJw4kRkzZoTlb2irPHKnDL4Q4kbgIeBU4Dwp5e5W9hsJPAVEAWuklI918PyNysXGxnLJJZdw7bXXMmrUKM1kwjidToxGI3369FFbFc3gdrs5ePAg8fHxAT+Qc3JyWLhwIfPmzQsoy6E9ysrK2LJlC6+//jrffvvtz+YZYmJiGDJkCFdddRXXX3+9Zrxpr9eLy+Vi4MCB3WptR1v4fD4OHTpETEyMZuyAw+Fg69atvPnmm3z++ec/m1w2GAycfvrpXHvttWFveRlOg38q4ANWA39qyeALIaKA/cBwoAD4ArhZSvlte+c/4YQT5MKFC+nXrx/9+vXThLfVHKvVSr9+/TRjILRCcXExtbW1AX8vwXj4gR5jtVrZv38/NTU1SCnp1asX/fv3V7zAXkew2WxkZmZ2+/mh5lRWVlJaWqoJL785drudw4cPU1xcjBCCpKQkTjrpJMVGaG0Z/E5ZUCnld0B7Xtx5wAEp5cGGfTcB1wLtGvz4+HguvvjizqgYVvyx1e6cOdEaKSkpVFdXB3xcoPnL8L95FaBDxyYkJPDrX/86YN2UxufzIYTQpFFTm8TERMrKyhoXO2oJi8XCaaedxmmnnaa2Kj9DCZc5C8hv8r4AaLVtixBiEjAJ6jvQaJm6ujqysrK6RZG0QDGZTI2lk8M9txHMqsZIoK6ujpSUFM2ELbREVFRU4+pbLY7MtEq7j0YhxPtCiK9beLW9lLHJKVr4rNU4kpQyV0o5WEo5WMuLTPxF0rpyg5POkpaWpkjOdCjbFWoFPdW3fZKSklQpqhbJtOvhSykv66SMAqBvk/d9gGOdPKfqOBwOMjMzde++DcxmMzExMY29fXU6jp7q2z5Go5GkpKSg5oq6K0oEv74AThJCDBRCxAA3AW8oIDds6LHVjqF2uYVIxu12d8siaYGSkpKiek5+JNEpgy+EuE4IUQD8BnhbCPHPhs97CyHyAKSUHmAa8E/gO2CLlPKbzqmtLnpstePExcURHR0dsroj3QGn00l8fLy+rqMDNJ0r0hLB1sMPN50y+FLK16SUfaSUJillppRyRMPnx6SUVzTZL09K+Qsp5YlSykc6q7Sa6LHVwDAYDKr0JY1kXC4XaWlpaqsRMSg1VxQIwdbDDzfaS2zXOHV1dSQmJuqx1QDQcgqd1tBTfQNHi3NFWs0c0+++AHG73d2qvVwo8KfQhcvL1+rwORjq6uq6TT/kUKHFuSKtZo7pBj8A9Nhq8CQlJeHz+cKSQqfV4XOg+FN9/7+9s4uRpKri+P/fNd3TPd090z3V1dM9Ayua8AAPy5oQgsEHIIQgElEjWY0mhJjw4gMmygaVxGjCgyFRX3whysqDX0RdIWKUFTHoC7gIBMiyEQkoGdhlP2CZ7urv40NXrcMy7PRsV3Xdqjq/ZNPdtb3b50zfOXXuuf97rurKd46uFU2GlnR2QK/XQ6PRiNqMWBKmhM7U6fNO6XQ6qNfrWvY6D/y1ojDaLQTd0C9KdGRNiNZWp6dSqYQioTN1+rwT/GZuKvU9fxYXF0Ey8AN4kjKDBBIU8MOu42ptdXry+byRErpJmMX4UqnvdIS1VrR3717cfffdsZ9BAgkK+GHehbW2GhwmSugmIczxpVLf4AhjrSgJM0gfo2v4b731Fk6ePDnRDzrMOq7fRkFrq9NTKBQwPz9vlIRuEsIcX9pGITi03cK5MTqCvfnmmxNnVGHdhbWNQrCYIqHbaYkmzCxvMBhoG4UAqVar7zuARBljdMBvNBqR181c14Vt21pbDZBisYhsNhuphM6UhbhOp6NS34CZn59HqVSKPKkwEaNLOo7jRFo382ur/sHcSjCQhG3bOHr0aGQzJ1OknP1+H81mM1Ibkoht23jttddUVXcWRmf4UeO6LqrVqpFHK8adUqmETCYTuIRuUkxYiOv1eigUClprDoFCoYB8Ph9LgUCYaMA/B8PhUGurIWFZVuqbqnW7XW2SFiKO48RSAhwmGvA/ANd1sbi4iFwuF7UpiWVxcREiksoTi/r9PnK5nEp9Q2RhYQHZbFb75W8iMQE/6I0x/X4/Ebpbk5mbm8Py8jLa7XbUpmxL0ONLN/KFD0k4jpPqWeTZJCbgB6m6UOXE7FhaWopFlh/k+BoMBshmsyr1nQHaVO29JGY1MkjVhSonZkc2m0WlUsHp06eNLm8EOb5c10Wz2dTsfgZkMhnUarVIFWEmkZgMPyjVRbfbVeXEjKlUKhgOh0Zn+UGNL79Nhwaf2VEul5HJZFKR5W/3O5SYgB8U3W4XjuNEbUaqyOVyWFpaSsVGGdd1UavVtE3HDMlkMrBtOxXjazsfddRtQnXR0VGtVhOvpvCPeCyXy1GbkjrCap1sGtu1lJgq4JO8heSLJEckLz/H+14l+TzJZ0kemuYzw0Sz++iYn5/H4uJiohUVfpsOze5nj2VZsG07Foqw88V13W07rk478l4A8FkAT0zw3mtEZI+IfOCNIUp6vR7m5+c1u4+Q5eXlxGb5fhM+bdMRHUnP8geDwbZrTFMFfBE5LCJHpvk/gmJanXS320W9XlflRIQkOcvXJnzRY1kWarVaIrN8f6PodlLyWc0tBcCjJJ8mefu53kjydpKHSB7aSfCeRifd6/WQz+c1uzeAJGb5w+FQs3tDWFxcjLSHUxiICPr9/kRtOrbV4ZP8M4CtTu7+log8NKFNV4nIOsk6gIMkXxKRLctAInIfgPsAYPfu3RPr9KbRSXc6HezatUuzewOYn59P3AEWruuiXq9rdm8Afi0/jMPOo6LT6aBSqUzUBmbbgC8i101rkIise4/HSB4AcAUmq/tPjK+T3indbhcLCwtGb/pJG8vLy3jnnXcgIrG/CQ+HQ1iWpdm9QSwtLeHEiRNnvps4IyIT1e59Qi/pkCySLPvPAVyP8WKvEagyxzxyuVwoh1FHQbvdhuM4qswxiEwmA8dxElHL91u4T9rkcVpZ5mdIvg7gYwAeIfkn7/oqyT94b1sB8HeSzwF4CsAjIvLHaT43KFzXRblcTkzpIElUq1Xjd99uh/bMMZdyuRz7Tpr+AU3VanXifzNVLx0ROQDgwBbX1wHc6D1/BcBl03xOGPhToVqtFrUpyhZks1nYto1Tp06hWCxGbc554bou1tbWNLs3ED/LX19fj+1GuHa7Ddu2kc1mJ/43qR2JruuiUqloR0zDOH78OO69914cP34c1WoVJGPZA8VXfsX1ZpUG/I64cTwVy1d+7fSAplQG/NFohNFopP3uDWT//v3Yt28f9u/fD8uyYltr7XQ6uq/DcEhiZWUllqdi+WtDO110Tkx75J3gN7DayVRImQ233Xbbex7L5TJOnDiBfr8fm+/L3+Kua0PmUygUUCqV4LpubL4v/3fhfJRfqcvwB4MBMpmMnlVrKLVaDXfeeeeZtZVMJoOVlZXYKHZEBMPhUM+qjRGO42AwGMRGIOC6LhqNxnnNHlMX8NvtNur1ui6kxYhisYhyuRyLoN9qtVCr1fQs5BiRy+Vg2zZarVbUpmyLryw8331DqYp6nU4HxWJRZXIxJA5Z2GAwgGVZOnuMIdVqFZZlGS3THI1GGA6HU+0bSk3A9/tNrKys6EJaDMnlcnAcx+gsrN1uo9Fo6OwxhliWhUajYfQsst1uTz17TM3IbLVacBxHp9oxplKpYG5ubttDHqLAn2qrDDO++LN/E4N+r9fD3Nzc1LPHVAT8Xq935rBsJb5kMhk0m024rmtUaWc4HGI4HKJer0dtijIl9Xr9zPdpCiKCTqeDZrM59ewx8QE/yB+WEj2FQsG4k4v8Uk5cZKPKB5PNZtFoNIwqHbZaLdi2HYhsNPER0FdN5PP5qE1RAmJ5eRmWZRmxQ9J1XZRKpdhuz1feT7lcNkYV5pdygpL5Jjrgdzod5HI53VGbMCzLwurqKrrdbqSlHV81pEKAZOHvwB2NRpGqdkajETqdDlZXVwOrTiQ24A+HQ/T7/UB/WIo55PN5OI6DjY2NSD5fRNBut9FsNjE3l8oN64lmbm4Oq6uraLfbkSUVrVYLKysrgVYnEhkJRQStVgurq6uqykkw1Wo1MlXFxsYGarWaqnISTLFYRK1WiySp8EuFQQtNEhnwNzY2YNu21lUTDskzW8xn2QDLdV0Ui0Vtn5ACbNvGwsLCTEUC3W73PWM7SBIX8NvtNkqlkva5TwmWZWFtbQ39fn8m9dZOpwOSaDabWrdPAf53nclkZpJUDAYD9Pt9rK2thXL8Ik3SM58NyXcBHInajoioATgetRERkWbfgXT7n2bfgWD8/5CIbNl/wfTVpiMicnnURkQByUPqezpJs/9p9h0I3//ElXQURVGUrdGAryiKkhJMD/j3RW1AhKjv6SXN/qfZdyBk/41etFUURVGCw/QMX1EURQkIDfiKoigpwciAT/IGkkdIvkzyrqjtCRuS95M8RvKFTdeWSR4k+S/vsRqljWFB8kKSj5M8TPJFknd41xPvP8k8yadIPuf5/h3v+odJPun5/iuSie0PQtIi+QzJ33uv0+T7qySfJ/ksyUPetVDHvXEBn6QF4EcAPgHgUgBfIHlptFaFzk8B3HDWtbsAPCYiFwN4zHudRAYAviYilwC4EsBXvO87Df53AVwrIpcB2APgBpJXAvgegB94vp8C8OUIbQybOwAc3vQ6Tb4DwDUismeT9j7UcW9cwAdwBYCXReQVEekB+CWAmyO2KVRE5AkAJ8+6fDOAB7znDwD49EyNmhEi8oaI/NN7/i7Gv/xrSIH/MsbvzJX1/giAawH82rueSN8BgOQFAD4J4MfeayIlvp+DUMe9iQF/DcB/N71+3buWNlZE5A1gHBQBJP78PJIXAfgogCeREv+9ksazAI4BOAjg3wDeFhG/MVCSx/8PAewDMPJe20iP78D45v4oyadJ3u5dC3Xcm9haYauOVKodTTgkSwB+A+CrInI6LY3JRGQIYA/JCoADAC7Z6m2ztSp8SN4E4JiIPE3yav/yFm9NnO+buEpE1knWARwk+VLYH2hihv86gAs3vb4AwHpEtkTJUZJNAPAej0VsT2iQzGIc7H8mIr/1LqfGfwAQkbcB/BXjdYwKST8ZS+r4vwrAp0i+inHZ9lqMM/40+A4AEJF17/EYxjf7KxDyuDcx4P8DwMXean0OwOcBPByxTVHwMIBbvee3AngoQltCw6vb/gTAYRH5/qa/Srz/JB0vswfJAoDrMF7DeBzA57y3JdJ3EfmGiFwgIhdh/Dv+FxH5IlLgOwCQLJIs+88BXA/gBYQ87o3caUvyRozv9haA+0XknohNChWSvwBwNcatUY8C+DaA3wF4EMAuAP8BcIuInL2wG3tIfhzA3wA8j//Xcr+JcR0/0f6T3I3xwpyFcfL1oIh8l+RHMM56lwE8A+BLIjK7E15mjFfS+bqI3JQW3z0/D3gv5wD8XETuIWkjxHFvZMBXFEVRgsfEko6iKIoSAhrwFUVRUoIGfEVRlJSgAV9RFCUlaMBXFEVJCRrwFUVRUoIGfEVRlJTwPz1UCvrdNq4tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_results = cosinor1.fit_group(df, period=[24])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the pairs to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = ([\"test1\", \"test2\"],[\"test3\", \"test4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison using cosinor1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compare_cosinor1 = cosinor1.test_cosinor_pairs(df, pairs, period=24, folder='paper' )\n",
    "compare_cosinor1 = cosinor1.test_cosinor_pairs(df, pairs, period=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>test</th>\n",
       "      <th>q(d_amplitude)</th>\n",
       "      <th>q(d_acrophase)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test1 vs. test2</td>\n",
       "      <td>0.372013</td>\n",
       "      <td>1.164985e-153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test3 vs. test4</td>\n",
       "      <td>0.372013</td>\n",
       "      <td>9.622936e-174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              test  q(d_amplitude)  q(d_acrophase)\n",
       "0  test1 vs. test2        0.372013   1.164985e-153\n",
       "1  test3 vs. test4        0.372013   9.622936e-174"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_cosinor1[[\"test\", \"q(d_amplitude)\", \"q(d_acrophase)\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison using a multi-component cosinor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use an 1-component cosinor for the first pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cosinor_lm1 = cosinor.compare_pairs_limo(df, pairs[:1], n_components = 1, period = 24)#, folder = 'results\\\\test_limo_tester\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>test</th>\n",
       "      <th>period</th>\n",
       "      <th>n_components</th>\n",
       "      <th>d_amplitude</th>\n",
       "      <th>d_acrophase</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>p params</th>\n",
       "      <th>q params</th>\n",
       "      <th>p(F test)</th>\n",
       "      <th>q(F test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test1 vs. test2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.107655</td>\n",
       "      <td>2.943474</td>\n",
       "      <td>7.112734e-37</td>\n",
       "      <td>7.112734e-37</td>\n",
       "      <td>5.180330e-40</td>\n",
       "      <td>5.180330e-40</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              test period n_components  d_amplitude  d_acrophase  \\\n",
       "0  test1 vs. test2     24            1    -0.107655     2.943474   \n",
       "\n",
       "              p             q      p params      q params     p(F test)  \\\n",
       "0  7.112734e-37  7.112734e-37  5.180330e-40  5.180330e-40  1.110223e-16   \n",
       "\n",
       "      q(F test)  \n",
       "0  1.110223e-16  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cosinor_lm1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a 3-component cosinor for second pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cosinor_lm2 = cosinor.compare_pairs_limo(df, pairs[1:], n_components = 3, period = 24)#, folder = 'results\\\\test_limo_tester\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>test</th>\n",
       "      <th>period</th>\n",
       "      <th>n_components</th>\n",
       "      <th>d_amplitude</th>\n",
       "      <th>d_acrophase</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>p params</th>\n",
       "      <th>q params</th>\n",
       "      <th>p(F test)</th>\n",
       "      <th>q(F test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test3 vs. test4</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0.155029</td>\n",
       "      <td>-2.981211</td>\n",
       "      <td>6.414126e-82</td>\n",
       "      <td>6.414126e-82</td>\n",
       "      <td>4.208287e-74</td>\n",
       "      <td>4.208287e-74</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              test period n_components  d_amplitude  d_acrophase  \\\n",
       "0  test3 vs. test4     24            3     0.155029    -2.981211   \n",
       "\n",
       "              p             q      p params      q params     p(F test)  \\\n",
       "0  6.414126e-82  6.414126e-82  4.208287e-74  4.208287e-74  1.110223e-16   \n",
       "\n",
       "      q(F test)  \n",
       "0  1.110223e-16  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cosinor_lm2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended analyses using a multi-component cosinor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get more informative results we can use an extended comparison using bootstrapping or sampling of regression coefficients confidence intervals.\n",
    "\n",
    "#### This can be performed on the basis of the best models for each test...\n",
    "\n",
    "You can do the analysis with the sampling of confidence intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>period1</th>\n",
       "      <th>n_components1</th>\n",
       "      <th>period2</th>\n",
       "      <th>n_components2</th>\n",
       "      <th>d_amplitude</th>\n",
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       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>q1</th>\n",
       "      <th>...</th>\n",
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       "      <th>p(d_amplitude)</th>\n",
       "      <th>q(d_amplitude)</th>\n",
       "      <th>CI(d_acrophase)</th>\n",
       "      <th>p(d_acrophase)</th>\n",
       "      <th>q(d_acrophase)</th>\n",
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       "      <th>CI(d_mesor)</th>\n",
       "      <th>p(d_mesor)</th>\n",
       "      <th>q(d_mesor)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test1 vs. test2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.107654</td>\n",
       "      <td>2.935088</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.4389948453060355, 0.22368614858685332]</td>\n",
       "      <td>0.521764</td>\n",
       "      <td>0.521764</td>\n",
       "      <td>[2.568202202634298, 3.301974260529814]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.024565</td>\n",
       "      <td>[-0.23718741026769274, 0.18805741695835576]</td>\n",
       "      <td>0.819690</td>\n",
       "      <td>0.819690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test3 vs. test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.155015</td>\n",
       "      <td>-2.987501</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.29375226243153363, 0.60378261660255]</td>\n",
       "      <td>0.497093</td>\n",
       "      <td>0.521764</td>\n",
       "      <td>[-4.661549059230501, -1.3134519836329697]</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>-1.000953</td>\n",
       "      <td>[-1.3971241276347097, -0.6047813467707776]</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              test  period1  n_components1  period2  n_components2  \\\n",
       "0  test1 vs. test2     24.0            1.0     24.0            1.0   \n",
       "1  test3 vs. test4     24.0            3.0     24.0            3.0   \n",
       "\n",
       "   d_amplitude  d_acrophase            p1            p2            q1  ...  \\\n",
       "0    -0.107654     2.935088  1.110223e-16  2.220446e-16  1.110223e-16  ...   \n",
       "1     0.155015    -2.987501  1.110223e-16  1.110223e-16  1.110223e-16  ...   \n",
       "\n",
       "                              CI(d_amplitude) p(d_amplitude)  q(d_amplitude)  \\\n",
       "0  [-0.4389948453060355, 0.22368614858685332]       0.521764        0.521764   \n",
       "1    [-0.29375226243153363, 0.60378261660255]       0.497093        0.521764   \n",
       "\n",
       "                             CI(d_acrophase) p(d_acrophase)  q(d_acrophase)  \\\n",
       "0     [2.568202202634298, 3.301974260529814]       0.000000        0.000000   \n",
       "1  [-4.661549059230501, -1.3134519836329697]       0.000517        0.000517   \n",
       "\n",
       "    d_mesor                                  CI(d_mesor) p(d_mesor)  \\\n",
       "0 -0.024565  [-0.23718741026769274, 0.18805741695835576]   0.819690   \n",
       "1 -1.000953   [-1.3971241276347097, -0.6047813467707776]   0.000001   \n",
       "\n",
       "   q(d_mesor)  \n",
       "0    0.819690  \n",
       "1    0.000002  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cosinor_lm = cosinor.compare_pairs_best_models(df, df_best_models, pairs, analysis=\"CI\")\n",
    "df_cosinor_lm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or using bootstrap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>period1</th>\n",
       "      <th>n_components1</th>\n",
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       "      <th>d_acrophase</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>q1</th>\n",
       "      <th>...</th>\n",
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       "      <th>0</th>\n",
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       "      <td>24.0</td>\n",
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       "      <td>2.220446e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
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       "      <td>[-0.373679039895789, 0.1583703431766068]</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>[-0.23324098162777995, 0.18411098831844297]</td>\n",
       "      <td>8.178536e-01</td>\n",
       "      <td>8.178536e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test3 vs. test4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.155015</td>\n",
       "      <td>-2.987501</td>\n",
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       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
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       "      <td>[-3.3166090224947156, -2.658392020368755]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000953</td>\n",
       "      <td>[-1.2609866674823134, -0.7409188069231739]</td>\n",
       "      <td>6.379341e-13</td>\n",
       "      <td>1.275868e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              test  period1  n_components1  period2  n_components2  \\\n",
       "0  test1 vs. test2     24.0            1.0     24.0            1.0   \n",
       "1  test3 vs. test4     24.0            3.0     24.0            3.0   \n",
       "\n",
       "   d_amplitude  d_acrophase            p1            p2            q1  ...  \\\n",
       "0    -0.107654     2.935088  1.110223e-16  2.220446e-16  1.110223e-16  ...   \n",
       "1     0.155015    -2.987501  1.110223e-16  1.110223e-16  1.110223e-16  ...   \n",
       "\n",
       "                             CI(d_amplitude) p(d_amplitude)  q(d_amplitude)  \\\n",
       "0   [-0.373679039895789, 0.1583703431766068]       0.428984        0.428984   \n",
       "1  [-0.1374788345750444, 0.4475091887460607]       0.299815        0.428984   \n",
       "\n",
       "                             CI(d_acrophase) p(d_acrophase)  q(d_acrophase)  \\\n",
       "0   [2.6019913879916596, 3.2681850751724526]            0.0             0.0   \n",
       "1  [-3.3166090224947156, -2.658392020368755]            0.0             0.0   \n",
       "\n",
       "    d_mesor                                  CI(d_mesor)    p(d_mesor)  \\\n",
       "0 -0.024565  [-0.23324098162777995, 0.18411098831844297]  8.178536e-01   \n",
       "1 -1.000953   [-1.2609866674823134, -0.7409188069231739]  6.379341e-13   \n",
       "\n",
       "     q(d_mesor)  \n",
       "0  8.178536e-01  \n",
       "1  1.275868e-12  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cosinor_lm = cosinor.compare_pairs_best_models(df, df_best_models, pairs, analysis=\"bootstrap\")\n",
    "df_cosinor_lm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can also choose to use the same type of a cosinor model for each comparison (namely, the same number of components and period)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using sampling of confidence intervals..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cosinor_lm = cosinor.compare_pairs(df, pairs, n_components = 1, period=24, analysis=\"CI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>test1 vs. test2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>[2.5944083475591375, 3.2757681156049747]</td>\n",
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       "      <td>0.819761</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test3 vs. test4</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0.095486</td>\n",
       "      <td>-2.987501</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
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       "      <td>[-0.23219517823000402, 0.42316719489238874]</td>\n",
       "      <td>0.566725</td>\n",
       "      <td>0.566725</td>\n",
       "      <td>[-3.328180405454652, -2.6468206374088186]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.042064</td>\n",
       "      <td>[-0.25526249362159925, 0.17113385765549527]</td>\n",
       "      <td>0.698054</td>\n",
       "      <td>0.819761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              test period n_components  d_amplitude  d_acrophase  \\\n",
       "0  test1 vs. test2     24            1    -0.107654     2.935088   \n",
       "1  test3 vs. test4     24            1     0.095486    -2.987501   \n",
       "\n",
       "             p1            p2            q1            q2  \\\n",
       "0  1.110223e-16  2.220446e-16  1.110223e-16  2.220446e-16   \n",
       "1  1.110223e-16  1.110223e-16  1.110223e-16  2.220446e-16   \n",
       "\n",
       "                               CI(d_amplitude)  p(d_amplitude)  \\\n",
       "0    [-0.4377342152871977, 0.2224255185680155]        0.520178   \n",
       "1  [-0.23219517823000402, 0.42316719489238874]        0.566725   \n",
       "\n",
       "   q(d_amplitude)                            CI(d_acrophase)  p(d_acrophase)  \\\n",
       "0        0.566725   [2.5944083475591375, 3.2757681156049747]             0.0   \n",
       "1        0.566725  [-3.328180405454652, -2.6468206374088186]             0.0   \n",
       "\n",
       "   q(d_acrophase)   d_mesor                                  CI(d_mesor)  \\\n",
       "0             0.0 -0.024565   [-0.2372726765344102, 0.18814268322507322]   \n",
       "1             0.0 -0.042064  [-0.25526249362159925, 0.17113385765549527]   \n",
       "\n",
       "   p(d_mesor)  q(d_mesor)  \n",
       "0    0.819761    0.819761  \n",
       "1    0.698054    0.819761  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cosinor_lm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... or bootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cosinor_lm = cosinor.compare_pairs(df, pairs, n_components = 1, period=24, analysis=\"bootstrap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>period</th>\n",
       "      <th>n_components</th>\n",
       "      <th>d_amplitude</th>\n",
       "      <th>d_acrophase</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>CI(d_amplitude)</th>\n",
       "      <th>p(d_amplitude)</th>\n",
       "      <th>q(d_amplitude)</th>\n",
       "      <th>CI(d_acrophase)</th>\n",
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       "      <th>CI(d_mesor)</th>\n",
       "      <th>p(d_mesor)</th>\n",
       "      <th>q(d_mesor)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test1 vs. test2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.107654</td>\n",
       "      <td>2.935088</td>\n",
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       "      <td>1.110223e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
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       "      <td>0.418366</td>\n",
       "      <td>0.512704</td>\n",
       "      <td>[2.610130136009095, 3.2600463271550173]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.024565</td>\n",
       "      <td>[-0.2312230088103398, 0.18209301550100282]</td>\n",
       "      <td>0.816107</td>\n",
       "      <td>0.816107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test3 vs. test4</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0.095486</td>\n",
       "      <td>-2.987501</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>[-0.1900487538779586, 0.38102077054034333]</td>\n",
       "      <td>0.512704</td>\n",
       "      <td>0.512704</td>\n",
       "      <td>[-3.2905084554600585, -2.6844925874034122]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.042064</td>\n",
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       "      <td>0.696571</td>\n",
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      ],
      "text/plain": [
       "              test period n_components  d_amplitude  d_acrophase  \\\n",
       "0  test1 vs. test2     24            1    -0.107654     2.935088   \n",
       "1  test3 vs. test4     24            1     0.095486    -2.987501   \n",
       "\n",
       "             p1            p2            q1            q2  \\\n",
       "0  1.110223e-16  2.220446e-16  1.110223e-16  2.220446e-16   \n",
       "1  1.110223e-16  1.110223e-16  1.110223e-16  2.220446e-16   \n",
       "\n",
       "                              CI(d_amplitude)  p(d_amplitude)  q(d_amplitude)  \\\n",
       "0  [-0.3676409352948059, 0.15233223857562372]        0.418366        0.512704   \n",
       "1  [-0.1900487538779586, 0.38102077054034333]        0.512704        0.512704   \n",
       "\n",
       "                              CI(d_acrophase)  p(d_acrophase)  q(d_acrophase)  \\\n",
       "0     [2.610130136009095, 3.2600463271550173]             0.0             0.0   \n",
       "1  [-3.2905084554600585, -2.6844925874034122]             0.0             0.0   \n",
       "\n",
       "    d_mesor                                 CI(d_mesor)  p(d_mesor)  \\\n",
       "0 -0.024565  [-0.2312230088103398, 0.18209301550100282]    0.816107   \n",
       "1 -0.042064   [-0.2532785125024304, 0.1691498765363264]    0.696571   \n",
       "\n",
       "   q(d_mesor)  \n",
       "0    0.816107  \n",
       "1    0.816107  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cosinor_lm"
   ]
  }
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