tutorials/007_parametric_example.ipynb

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   "source": [
    "# Parametric Tutorial\n",
    "**NOTE** \n",
    "This notebook is not meant to teach statistics, but only demo how to run the py50 functions. There are plenty of resources available online. I particularly found introductory [tutorials by DATAtab](https://datatab.net/tutorial/get-started) helpful. \n",
    "\n",
    "The following page will show examples of parametric tests using the Stats() and Plots() modules of py50. There are many plot features available for py50, but they will not all be demoed here. Instead, to see all the plots available, take a look at the [006_statistics_quickstart](https://github.com/tlint101/py50/blob/main/tutorials/006_statistics_quickstart.ipynb). As py50 is built on top of Pingouin, additional examples with ANOVA tests can be found [here](https://github.com/raphaelvallat/pingouin/blob/main/notebooks/01_ANOVA.ipynb). "
   ],
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   "id": "initial_id",
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     "start_time": "2024-08-30T03:49:50.554188Z"
    }
   },
   "source": [
    "from py50 import Stats, Plots\n",
    "import pingouin as pg"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "## List Datasets available from Pingouin\n",
    "# pg.list_dataset()\n",
    "\n",
    "# Read Dataset\n",
    "df = pg.read_dataset('penguins')\n",
    "\n",
    "# Initialize Stats and Plots\n",
    "stats = Stats(df)\n",
    "plot = Plots(df)\n",
    "\n",
    "print('List of Group names in Dataset:', df['species'].unique())\n",
    "stats.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:50.683270Z",
     "start_time": "2024-08-30T03:49:50.673946Z"
    }
   },
   "id": "fd69c83e0c7c4b0e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of Group names in Dataset: ['Adelie' 'Chinstrap' 'Gentoo']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Biscoe            37.8           18.3              174.0   \n",
       "1  Adelie  Biscoe            37.7           18.7              180.0   \n",
       "2  Adelie  Biscoe            35.9           19.2              189.0   \n",
       "3  Adelie  Biscoe            38.2           18.1              185.0   \n",
       "4  Adelie  Biscoe            38.8           17.2              180.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3400.0  female  \n",
       "1       3600.0    male  \n",
       "2       3800.0  female  \n",
       "3       3950.0    male  \n",
       "4       3800.0    male  "
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>37.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>37.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>180.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>male</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>35.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>189.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>38.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>185.0</td>\n",
       "      <td>3950.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>38.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data Distribution\n",
    "The data distribution is important to determine patterns that may show up in the dataset. For example, it can also tell you how often a value may occur, the shape of the data, variability of a value, etc. \n",
    "\n",
    "py50 includes two tests that can help analyze the dataset. \n",
    "- Normality. This will test if the dataset follows normal distribution - i.e. a bell-shape curve.\n",
    "- Homoscedasticity. This will test for the \"spread\" or consistency of the dataset along a regression model. "
   ],
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   "id": "6a54ab3a4a9ff776"
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  {
   "cell_type": "code",
   "source": [
    "# Test for normality\n",
    "stats.get_normality(value_col='body_mass_g', group_col='species').round(3)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:50.818026Z",
     "start_time": "2024-08-30T03:49:50.811216Z"
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   "id": "45cf4ebadee59202",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               W   pval  normal\n",
       "species                        \n",
       "Adelie     0.981  0.032   False\n",
       "Chinstrap  0.984  0.561    True\n",
       "Gentoo     0.986  0.234    True"
      ],
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       "      <th>pval</th>\n",
       "      <th>normal</th>\n",
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       "      <th>species</th>\n",
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       "      <th>Adelie</th>\n",
       "      <td>0.981</td>\n",
       "      <td>0.032</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinstrap</th>\n",
       "      <td>0.984</td>\n",
       "      <td>0.561</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gentoo</th>\n",
       "      <td>0.986</td>\n",
       "      <td>0.234</td>\n",
       "      <td>True</td>\n",
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   "execution_count": 3
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  {
   "cell_type": "code",
   "source": "stats.get_homoscedasticity(value_col='body_mass_g', group_col='species').round(3)",
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     "start_time": "2024-08-30T03:49:50.838304Z"
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    {
     "data": {
      "text/plain": [
       "         W  pval  equal_var\n",
       "levene NaN   NaN      False"
      ],
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     "execution_count": 4,
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   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Perform ANOVA\n",
    "Several tests, parametric and non-parametric, are available in py50. The functions use the \"get_x\" format, where x is the name of said test. Here, the One-Way ANOVA script will be demoed. The final table will be a Pandas.DataFrame object. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1225371f424832b6"
  },
  {
   "cell_type": "code",
   "source": [
    "anova = stats.get_anova(value_col='body_mass_g', group_col='species')\n",
    "anova"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:50.930801Z",
     "start_time": "2024-08-30T03:49:50.923306Z"
    }
   },
   "id": "42b90441e1a9435c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Source  ddof1  ddof2           F         p-unc       np2 significance\n",
       "0  species      2    339  343.626275  2.892368e-82  0.669672         ****"
      ],
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       "      <th>np2</th>\n",
       "      <th>significance</th>\n",
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       "      <th>0</th>\n",
       "      <td>species</td>\n",
       "      <td>2</td>\n",
       "      <td>339</td>\n",
       "      <td>343.626275</td>\n",
       "      <td>2.892368e-82</td>\n",
       "      <td>0.669672</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ]
     },
     "execution_count": 5,
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   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Tukey\n",
    "\n",
    "py50 comes with several post-hoc tests. Again, this can be called using the same 'get_x' phrase. As the above ANOVA test show significance between the Hair color group, a Tukey test will be performed. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "44b16c88076ddad3"
  },
  {
   "cell_type": "code",
   "source": [
    "tukey = stats.get_tukey(value_col='body_mass_g', group_col='species').round(4)\n",
    "tukey"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.042803Z",
     "start_time": "2024-08-30T03:49:50.984034Z"
    }
   },
   "id": "b7276b2e7a036ab7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           A          B    mean(A)    mean(B)      diff       se        T  \\\n",
       "0     Adelie  Chinstrap  3700.6623  3733.0882   -32.426  67.5117  -0.4803   \n",
       "1     Adelie     Gentoo  3700.6623  5076.0163 -1375.354  56.1480 -24.4952   \n",
       "2  Chinstrap     Gentoo  3733.0882  5076.0163 -1342.928  69.8569 -19.2240   \n",
       "\n",
       "   p-tukey  hedges significance  \n",
       "0   0.8807 -0.0739         n.s.  \n",
       "1   0.0000 -2.8602         ****  \n",
       "2   0.0000 -2.8753         ****  "
      ],
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       "      <th>diff</th>\n",
       "      <th>se</th>\n",
       "      <th>T</th>\n",
       "      <th>p-tukey</th>\n",
       "      <th>hedges</th>\n",
       "      <th>significance</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>3700.6623</td>\n",
       "      <td>3733.0882</td>\n",
       "      <td>-32.426</td>\n",
       "      <td>67.5117</td>\n",
       "      <td>-0.4803</td>\n",
       "      <td>0.8807</td>\n",
       "      <td>-0.0739</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Gentoo</td>\n",
       "      <td>3700.6623</td>\n",
       "      <td>5076.0163</td>\n",
       "      <td>-1375.354</td>\n",
       "      <td>56.1480</td>\n",
       "      <td>-24.4952</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.8602</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Gentoo</td>\n",
       "      <td>3733.0882</td>\n",
       "      <td>5076.0163</td>\n",
       "      <td>-1342.928</td>\n",
       "      <td>69.8569</td>\n",
       "      <td>-19.2240</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-2.8753</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "The tukey results can be plotted as follows:\n",
    "\n",
    "The parameters for all plots in py50 are created to be as similar as possible. For each plot, there is also an option to \"return_df\". This parameter will output the calculated dataframe that was used to create the plot. Users can use this to compare to the plot generated using the Stats() module to check for any inconsistencies. "
   ],
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   "id": "4b376c1151dd92d"
  },
  {
   "cell_type": "code",
   "source": [
    "title = 'Tukey Results Bar Plot'\n",
    "\n",
    "plot.barplot(test='tukey', value_col='body_mass_g', group_col='species', palette='RdYlGn', title=title, fontsize=30,\n",
    "             return_df=True)"
   ],
   "metadata": {
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     "end_time": "2024-08-30T03:49:51.325485Z",
     "start_time": "2024-08-30T03:49:51.113448Z"
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   },
   "id": "3d3385061a012bae",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(           A          B      mean(A)      mean(B)         diff         se  \\\n",
       " 0     Adelie  Chinstrap  3700.662252  3733.088235   -32.425984  67.511684   \n",
       " 1     Adelie     Gentoo  3700.662252  5076.016260 -1375.354009  56.147971   \n",
       " 2  Chinstrap     Gentoo  3733.088235  5076.016260 -1342.928025  69.856928   \n",
       " \n",
       "            T       p-tukey    hedges significance  \n",
       " 0  -0.480302  8.806666e-01 -0.073946         n.s.  \n",
       " 1 -24.495169  1.443290e-14 -2.860201         ****  \n",
       " 2 -19.223978  1.443290e-14 -2.875327         ****  ,\n",
       " <statannotations.Annotator.Annotator at 0x3109a5be0>)"
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     "execution_count": 7,
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       "<Figure size 640x480 with 1 Axes>"
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NAG5ubqKu9sLKzMwUtTZXqlQJs2bNKtS+rq6uOHnypBCeDhw4gJkzZ372QOzyhAGIyoT//vsPY8aMwZs3bwC8H2eydu3aQvXV//fff6KxJJ06dSr0P3G9evVQu3ZtPHnyBED+k7ipqamhX79+2LRpE4D3Tc7/+9//RDPgZktPT8eJEyeEZQcHhyK9sZWk2rVrY/z48Rg4cOAHP7RyT474KU30Xbp0EQIQ8P73mvNvmTvsLF68GAqFAvb29gUGm8KEr/KkQYMG+OuvvwC878J1cnLCwoUL0aBBgwL3KYv32Xr16hVWrFgBHx8fLFmypMDAX6dOHdFrorAqVaokWi7s2KcPXeZdnFxdXUVThnwKiUSC6dOnF3lMUlhYmPCeCQA9e/aEnp5eofatXLky7O3tcfDgQQBAcnIyQkNDYWtrW6S6lEfsAiOle/z4McaMGSO6EsbU1BR2dnaF2j/3VUSfOu9QzpaU8PDwfK9cKuzVYOfPn8erV68K3E9ZWrRogZkzZ+LAgQM4deoUBg0a9NFv7J/zezUxMUG1atWE5eDgYFF57dq1Rd/O37x5g59++gnt2rXDTz/9hMOHD1f4MQmDBg0StSJevXoVvXv3Ru/evbFy5UpcuHABqamppVqnVq1aiQZt5/65desWLl68KMxxVadOHWHfV69ewcXFpViuKnr16hWuXLmCX3/9FePHjxeV5Rzr8iGfM6i4NDRs2BCbN2/GhAkTinyM3OMRc09w+jG5w86tW7eKXJfyiC1ApHQBAQF51j19+hTe3t6YNm3aR/fPfWn2unXrsG7duiLVRaFQICEhIc+3qC+++AI2NjYICgoC8H5wb35dFjmbo2vXrl1ic2zkN8lf9iDVyMhIBAQEYOfOnULz9u3bt2FlZfXRbqaccv9eC9tlkZ/cTf4AsHz5cgwePFhUlpiYiOPHj+P48eMA3rfQtW/fHnZ2dmjVqtVH5xUqT8zMzLBo0aI8A7zDw8MRHh6Obdu2QSqVwsbGBh07doSdnZ3o/lHKIJVKYWhoCENDQzRv3hzDhw+Hp6cntm3bJmyzZMkSyGSyPLdbyc+DBw8QFhaGx48f49mzZ4iKikJERARevnz52XUtK12E2TeRrVy5MmrXrg0rKyu0bdtWNKi9qHL/nnJfNfcxubfPOY5SFVScdxMq99TV1aGpqSl86/X19UX37t0/ODgUgKgJuDgUNMZgwIABQgBKTk7Gn3/+Kboy7c2bN6KBoR+ar6gkSCQS6OjooEGDBvjxxx/Rr18/TJgwAc+ePUN6ejp27dqFa9euYefOnYWa+LA4f6/5/U5NTU1x8OBBuLm5iS6lzunhw4d4+PChUOc+ffpg0qRJ+U5cWR4NHjwYxsbGcHNzE2awzkkul+PSpUu4dOkSVq5cCUtLS4wcORKOjo4fbcErDerq6pg9ezYePXqEf/75B8D7LiovLy+sX78+333S09Px+++/Y9euXULX84doaGjkGURfGB8akF2cPDw8lNbSm3v+qE+dRyj39oUdX1VRKP8/iAjv3+TWrl0ruiIpIyMDc+fO/Wiff1HeHD+koG6Hj80JdOLECWEOInV19Q9etl8a6tSpg23btomm1r937x7GjRv3wQkKsxXnPDMF/U5NTEzg7e2NEydOwNnZGY0bNy7w6pXExETs2rUL3bt3L3PzAH0OOzs7/Pnnn9iyZQscHR0/GO7u37+PefPm4dtvvy3U37C0fP/996Lls2fPiubjyhYTE4MhQ4Zg6dKlBYYfTU1NNGrUCIMGDcKqVauKfLm/KgzmLWx3YEFyX0lZFkJ1aWILEBWronxoampqYt26dejWrRsyMzNx7Ngx3L59G8D7D2wfHx84OzsXuH/ubzFbtmzJd4Dy59LT00P37t2Fbq6LFy8iISFB+MDKOS7oq6++KhNN8Obm5vjll18wcuRI4c0uLCwMs2bNwsaNGz+4b+5v0CEhIdDW1i6RetarVw9Tp07F1KlTERcXh8uXL+PKlSu4dOkSIiMjRdsmJSVh0qRJCAgIgKmp6Wc/d34f1KVNXV0dnTp1QqdOnaBQKHD37l3h/K9fvy5cGZntxo0bmDFjhmiOJmVq0aIF1NXVhdeYXC7H48ePRePr5HI5nJycRJf/a2pqom3btrC1tUWDBg1Qt25dmJmZibo6y8Lfp6zK/T/6qa22b9++FS1/6CrEiki14h4VWu5v4YVtZSnslP45jRw5Et26dQPw/oNg2bJloiuBNm/ejP/++6/A/XN/Y845CLm45ZyJOjMzU5gJNSYmRrhnGFB2Bj8D7wc6Tp48WbTur7/+Es3unJ/cN58sjnEZhWFkZIQ+ffpg2bJl+Ouvv3DixAk4OTmJWt+Sk5M/Wv/ChvGivGZLkkQiQZMmTTBmzBhs2bIFly9fxq+//pon1J89e7bQtz0oaRoaGnleLznnpgHe3/YjZ/hp0KABTpw4gV9//RVOTk7o0qULateunWecV1lq6Sprct84Nef8Y4WRe96znPN3qQIGIMpX7kuRc7+ZFaQw09Tnlvsy8SZNmmDUqFHCcnp6OubOnVvgxHdWVlai5U+5xxDw/k0jJiZGmPDvQ9q0aSN6k8i+jDkwMFBojtbX1y/wTsrKMnHixDyz33p6eiI8PLzAfXJv/6m/17t3734wjKalpeG///7DnTt3PnicevXqYfr06fDx8RGtDw0NzbNtzg/Pwr5mnz17VqjtSsLLly8RFBT0wcGnUqkUHTt2xJYtW/LcCuZT/yYlRaFQ5AmSOW+LAogvEADeX6zwxRdffPTYubvKPrfbpyLJPSlp9hjFwsr5pQ14/7+mShiAKF+5596IjY396D4pKSnF9obs4uIiuuz61q1boitNcmrevLkoRP3555+FbjZPSkrCoEGD0LFjRzRv3hwODg7C7QnyI5FIRGN7Ll26hJSUFNE9cXr37l3mxh9oaGhgxYoVonp9LFi2bt1atFzY+6AB72dq7t+/P9q0aQNra2v89NNPovIBAwagRYsW6NOnT56JJwvSunVrUWtfzrmfsuV83RbmNQvkne+oNFy7dg2tWrVC27ZtMXz4cNHcUR+Se1LQ/H4HyhAcHCxqcdPV1c3TBZyzdcLAwOCD8x3llHuAfHmfAbw4NWnSRPSaDwwMLHSL2du3b3H69GlhWSqV5jvXVlm5iXNJYACifOUeW1HQVTo5bd68WbiFxefS0dHBkiVLROs2bNiAR48e5dlWKpWKupzi4uKESQs/xsvLS+gHT09PR506dQqcGTlbzju7p6am4vTp06JvXmWp+yunevXqYcqUKaJ1oaGhBXYltWzZUnQp899//42LFy9+9HkyMjKwatUqYTk5OTlPa5KpqanQ4nb79u1CzT/y9u1b0RiHmjVr5tkm57pXr1599BtxQkJCnukEPlfOVqiCWhUbNGgg+qDy9/cv1Ad77ivF8vsdKMP27dtFy127doWmpqZoXc6Wm+Tk5EJ9SQkNDc1z37eycBPYskJTU1N0tWlycrLof+9DVq1aJWoltbe3z3eMX+7egMK0lJcXDECUL0tLS1Gf/qVLlz54E8JDhw5h69atxVqH9u3bo2/fvsJyWloa5s2bl+8/4NixY0X/vJs2bfroGBF/f3/RNhKJJM9YmfxkzwmU7eeffxbelC0tLT962b4yjR8/Ps8tNNavX5/vFTkSiQSTJk0SlhUKBX788cd8Z8vOlpmZiQULFoi6p4yMjDB48GDRdrmnCJg1a9YHWzMUCgWWL18u+vDr3Llznu1y3/og95t8TgkJCXB2di72S39zDiR9/fp1vl02ubtJ79+/j9WrV3+weycmJkbUDaipqYkOHToUU62Lbvv27cLtZID3r5vvvvsuz3Y556BKTU0tsEU324ULF/Lcuw7AB1toVdGYMWNELeC///471q9fX+BrSaFQYP369di/f7+wTiqVim6QmlPugdFlbczc52AAonypqamJblSoUCgwY8YMeHh44L///kNKSgpiY2Pxzz//wMnJCa6urkhPTy+Wq3JycnV1Fc1Zc+PGjXy/sZuZmYlajBQKBTw8PITuhZiYGMjlckRHR+Off/7BmDFjMH/+fNGbxNixYwsdXnK28uS8qWFZbf3JpqGhAXd3d9G3utTUVMybNy/fN0wHBwfROb1+/RqjR4/GzJkzcfHiRSQmJgpX/Bw8eBC9e/fGoUOHhO0lEgmWLl2aZ5xX586dRZNEPnr0CH369MGvv/6K8PBwpKamIiMjA3FxcThz5gxGjRolOq6pqSmGDh2ap7729vaiN+zg4GAMHToUp06dQmxsLFJSUhAeHo6NGzeiX79+whiI4mxJyTn2JTk5GT4+PkhOTkZaWpqo1cfZ2VnUSrJ9+3Z89913CAwMRExMDDIyMpCamoqHDx9ix44dcHR0FE1OOWLEiDzjbEpD9v/R0aNHMWLECKxYsUJUPnjw4HxvmJvzywzw/otDdlhOSUlBWloanj59ipMnT8LJyQnjx4/Pd2xU7iuXVJ2pqSmWLl0qWuft7Y0hQ4bgxIkTiI2NRXp6OuLi4nDixAkMHToU3t7eou3nzJlT4CSbxsbGouWdO3ciKioKGRkZpXZhREnhZfBUoIkTJ+Kvv/4SBjanp6djx44dBbas1KpVCz///HOR72qcHwMDA7i6umL27NnCup9//hldunSBubm5aNv+/fvj5cuXWL16tdBKFBQUVKiBgX379s0zTuVDevbsiWXLlolaFzQ1NfO8yZdFVlZWGDVqlOgb+PXr17Fnz558v7kvXrwYqampwjiVrKwsHD16tMDbgWRTU1PDggUL8m2pkUgkWL16Nb799lvhQ/3ly5fw9PSEp6fnB49brVo1+Pr65nvJbvXq1TF9+nQsW7ZMWHf//v08dxTPadq0aYiIiMCBAwc++LyF1aJFC+zdu1dYzjkzef/+/bFy5UoA71sLFy5ciIULFwrh8/r167h+/fpHn6Nr166f9HotrM+9yW6XLl0KHNM1dOhQHD9+XLgtikKhwP79+0UtEfmpUaMGqlevjrCwMADvu7hTU1NLbEqG8qhPnz54+fIlVq5cKXSlhoSEfHQmfYlEghkzZuDbb78tcJsGDRpAV1dXeK+7efMmunTpAuD9e96NGzfK3JjHwmILEBWoatWq2LVrV57BsPnp2LEj9u7dm+fbQnHo378/2rVrJywnJyfnab3JNnbsWGzdurVQ0/AD728IOH/+fKxateqT7i5eqVKlPDcwtLOzy3MpcFnl4uKSJ0CuWbMmz5w7AKClpYV169Zh/vz5hT6/2rVrY+vWrRg+fHiB25iZmWHv3r1o3759oevduXNn+Pv7f/BqlREjRmDx4sUfvSlk5cqV4eHhUWDTf1H16dOnwNsc3L9/X7Q8ePBgrF+/vtAtObq6upg2bRrWrVtXpm4LUq1aNSxYsABeXl4FfhhqamrCx8en0N122a3QR44cEd1gNSMjo1AhUdWMGjXqk977LC0tsWvXrjz3WstNR0cHrq6u+Q6GTk9Pz3dcZnlRdv6DqEwyMTGBn58fLly4gBMnTiAoKEi4usbExAQtWrRA3759hYCS+/5RxWXJkiXo06ePMKPw5cuX8fvvv+fbDdKuXTscPXoU586dw7lz53Djxg3ExcXhzZs30NTUhIGBARo3bowOHTrAwcEBlStXLlKd+vXrhyNHjgjLZb37KycdHR0sXboUo0ePFtYlJydjwYIFeQa0ZhsxYgQGDhyIwMBA/O9//0NYWBhevXqFd+/eCVf9WFlZoWvXrujUqVOhPqBNTU2xdetWhIaG4sSJEwgNDcXTp0/x9u1bKBQKGBoawsTEBG3atEG3bt3QrFmzQp3fsGHD0L17dxw+fBj//PMPnjx5gsTERFSrVg1mZmbo1q0b+vbtm2celeKgrq6Obdu2YceOHQgMDERkZCTevXsHPT29fFstunfvjg4dOuDMmTM4d+4c7t27h9jYWLx79w7a2tqoXr066tSpAzs7O3Tr1k2ptwHR0NCArq4u9PT0YG5ujoYNG+Krr75C+/bt8wx6zo++vj58fX3xv//9D0ePHkVISAhiY2ORmpoKXV1d6Ovro379+rCyskKvXr2Ee1V169YNa9asEY7z+++/f1JwVhVt27bF0aNH8c8//+Ds2bO4efMm4uLi8PbtW2hpacHc3BwtWrRAjx49RF8qP2bw4MGoVasWdu3ahbCwMCQmJkJTUxPGxsblekyQRMFJFYiKZP/+/cKNLI2MjPDPP/+UqW/lRERUMHaBERVRzond+vXrx/BDRFSOMAARFcHjx4+FcQgSiQRDhgxRco2IiOhTMAARFcH69euFx3Z2doWa0p+IiMoOttkTfcTdu3dhamoKHR0dPHv2DL6+vsIl4Wpqah+8Uz0REZVNDEBEH7FmzRr8+++/+ZaNHTu20FcmERFR2cEuMKKPsLCwyHe9g4MDfvzxx9KtDBERFQu2AOUjKysLsbGxqFSpUoW+Ey4VTsuWLXHp0iVERkZCS0sLderUgaOjI3r16oW0tDTem4iIqIxQKBR49+4djI2Noab24TYezgOUjxcvXqBTp07KrgYREREVwblz51CjRo0PbsMWoHxUqlQJwPtf4Mem0yciIqKyISkpCZ06dRI+xz+EASgf2d1eenp6DEBERETlTGGGr3AQNBEREakcpQWgQ4cOwdLSMs9Po0aNAAB37tzBoEGDIJPJMHDgQISFhYn2P378OLp27QqZTIYpU6bg5cuXQplCoYCnpyfatGmDVq1aYdWqVcjKyirV8yMiIqKyS2kByMHBARcuXBB+/vnnH1hYWGDkyJFITk7GhAkTYGtri0OHDsHa2hpOTk5ITk4GAISGhmLevHlwdnbG77//jjdv3sDV1VU49vbt23H8+HF4eXlh/fr1OHbsWIF3uCYiIiLVo7QApK2tDSMjI+Hn6NGjUCgUmDFjBk6cOAEtLS3MmjUL9erVw7x581CpUiUEBgYCAHbv3g17e3v0798fjRo1wqpVq3Du3DlEREQAAPz8/ODi4gJbW1u0adMGM2bMwJ49e5R1qkRERFTGlIkxQImJifj111/x008/QSqVIiQkBDY2NsIgJolEgpYtWyI4OBgAEBISAltbW2H/mjVrwtTUFCEhIYiJicHz58/x5ZdfCuU2NjaIiopCbGxsqZ4XERERlU1lIgDt3bsXxsbG6NmzJwAgLi4OxsbGom0MDQ3x4sULAEBsbGyB5XFxcQAgKq9evToACPvnJpfLkZSUJPohIiKiikvpl8ErFAr4+/tj/PjxwrqUlBRIpVLRdlKpFHK5HACQmppaYHlqaqqwnLMMgLB/bj4+PvDy8vr8kyEiIqJyQekB6NatW4iJiUGvXr2EdVpaWnnCilwuh7a29gfLdXR0RGFHS0tLeAwAOjo6+dbByckJY8aMEZazJ1IiIiKiiknpAejff/+Fra0tqlatKqwzMTFBfHy8aLv4+HihW6ugciMjI5iYmAB4341mZmYmPAYAIyOjfOsglUrztCgRERFRxaX0MUChoaFo2bKlaJ1MJsPNmzeRfZsyhUKBGzduQCaTCeVBQUHC9s+fP8fz588hk8lgYmICU1NTUXlQUBBMTU3zjBsiooqpR48eiImJEZZ9fHywefNm0TaxsbHo0aNHnn3HjRuHGzdulHgdiUi5lN4CFB4ejr59+4rW9ezZE2vWrIG7uzuGDh2Kffv2ISUlBfb29gCAYcOGYcSIEWjRogWsrKzg7u4OOzs7mJubC+Wenp7CjdDWrFmDsWPHlu6JEVGpO3r0KO7duwdNTU2cOXMGT58+RWRkJAwMDKCmpgYXFxcsWbIE27Ztg4GBATQ1NfHLL7/A3NwcNWvWxMmTJyGXy3H9+nX4+/vDw8ND2adERCVE6S1A8fHxqFKlimidnp4efHx8EBQUBEdHR4SEhGDLli3Q1dUFAFhbW8PNzQ3e3t4YNmwYqlatKnqjGjduHBwcHODs7IwffvgB/fr1w+jRo0vztIhICZo3bw5tbW2Eh4fjypUr6NatG+zt7XHx4kWcP38eXbt2hVQqhZ2dHYKDgxEeHg6FQoEWLVqgbt26qFu3Lm7evIkLFy7k2zpERBWH0luAQkND813fvHlzHD58uMD9HB0d4ejomG+Zuro6XF1dRbNDE1HFZ2FhgbCwMEydOhWHDh2ChYUF4uLiYGFhAYVCgfT0dFSqVAl16tTBnTt3MHXqVFy7dg1Tp06Furo6Hj9+jBEjRuDUqVMwNTVV9ukQUQlSegAiIiouEokEjo6O6NGjB5o0aYLKlSujfv36mD9/PtTU1JCSkgIAqFSpElxdXdG5c2ecPHkS6urqAAA7Ozu0bdsWHTt2FOYPI6KKSaLIHmlMgqSkJNjY2CAoKAh6enrKrg4REREVwqd8fit9DBARERFRaWMAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHI4ESKVKoVCgdTUVGVXg4hIqbS1tSGRSJRdDZXGAESlRqFQYNy4cQXe/oSISFXIZDL4+voyBCkRu8Co1KSmpjL8EBEBCAkJYWu4krEFiJTi9OnT0NHRUXY1iIhKVUpKCrp3767sahAYgEhJdHR0GICIiEhp2AVGREREKocBiIiIiFQOAxARERGpHI4BolKjra0NmUwmPCYiUjV8Hyw7GICo1EgkEvj6+gqPiYhUDd8Hyw4GICpV/IcnIlXH98GygWOAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERJRHjx49EBMTIyz7+Phg8+bNom1iY2PRo0ePPPuOGzcON27cKPE6En0OToRIRESCo0eP4t69e9DU1MSZM2fw9OlTREZGwsDAAGpqanBxccGSJUuwbds2GBgYQFNTE7/88gvMzc1Rs2ZNnDx5EnK5HNevX4e/vz88PDyUfUpE+WILEBERCZo3bw5tbW2Eh4fjypUr6NatG+zt7XHx4kWcP38eXbt2hVQqhZ2dHYKDgxEeHg6FQoEWLVqgbt26qFu3Lm7evIkLFy7k2zpEVFYwABERkcDCwgJhYWGYOnUqwsLCYGFhAXV1dVhYWKB27dpIT09HpUqVUKdOHdy5cwdTp07FzZs3YWFhARMTEzx+/BgjRoxAZGQkTE1NlX06RAViFxgREQkkEgkcHR3Ro0cPNGnSBJUrV0b9+vUxf/58qKmpISUlBQBQqVIluLq6onPnzjh58iTU1dUBAHZ2dmjbti06duyI6tWrK/NUiD5IolAoFMquRFmTlJQEGxsbBAUFQU9PT9nVISIiokL4lM9vdoERERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOUoNQHK5HEuWLMGXX36Jdu3aYe3atci+Kv/OnTsYNGgQZDIZBg4ciLCwMNG+x48fR9euXSGTyTBlyhS8fPlSKFMoFPD09ESbNm3QqlUrrFq1CllZWaV6bkRERFR2KXUixGXLluHKlSvYunUr3r17h2nTpsHU1BR9+/bFhAkT0KdPH6xYsQJ79+6Fk5MT/vzzT+jq6iI0NBTz5s3DkiVL0KhRI7i7u8PV1RU+Pj4AgO3bt+P48ePw8vJCRkYGZs6cCUNDQ4wbN06Zp0tEZYBCoUBqaqqyq0EqTltbGxKJRNnVUGlKC0CJiYk4ePAgtm/fjubNmwMAxo4di5CQEGhoaEBLSwuzZs2CRCLBvHnzcP78eQQGBsLR0RG7d++Gvb09+vfvDwBYtWoVOnfujIiICJibm8PPzw8uLi6wtbUFAMyYMQO//PILAxCRilMoFBg3bhxCQ0OVXRVScTKZDL6+vgxBSqS0LrDsWRpbtWolrJswYQI8PDwQEhICGxsb4YUhkUjQsmVLBAcHAwBCQkKEcAMANWvWhKmpKUJCQhATE4Pnz5/jyy+/FMptbGwQFRWF2NjY0jk5IiqTUlNTGX6oTAgJCWFLpJIprQUoIiICtWrVwpEjR7B582akp6fD0dERkyZNQlxcHOrXry/a3tDQEOHh4QCA2NhYGBsb5yl/8eIF4uLiAEBUnn0/mhcvXuTZj4hU0+nTp6Gjo6PsapCKSUlJQffu3ZVdDYISA1BycjKePn2Kffv2wcPDA3FxcVi4cCF0dHSQkpICqVQq2l4qlUIulwN4/y2uoPLsRJ2zPPtx9v65yeVyUVlSUtLnnyARlWk6OjoMQEQqTGkBSENDA0lJSVizZg1q1aoFAIiOjsbevXthYWGRJ6zI5XJoa2sDALS0tPIt19HREYUdLS0t4TGAAt/sfHx84OXlVXwnR0RERGWa0sYAGRkZQUtLSwg/AFCnTh08f/4cJiYmiI+PF20fHx8vdF8VVG5kZAQTExMAELrCcj42MjLKty5OTk4ICgoSfs6dO/f5J0hERERlltICkEwmQ1paGh4/fiyse/ToEWrVqgWZTIabN28KcwIpFArcuHEDMplM2DcoKEjY7/nz53j+/DlkMhlMTExgamoqKg8KCoKpqWmB43+kUin09PREP0RERFRxKa0LrG7durCzs4OrqysWL16MuLg4bNmyBZMmTULPnj2xZs0auLu7Y+jQodi3bx9SUlJgb28PABg2bBhGjBiBFi1awMrKCu7u7rCzs4O5ublQ7unpiRo1agAA1qxZg7FjxyrrVImojNDW1ha+SGV3qROVJr4Gyw6JIruZRQnevn2LpUuX4s8//4SOjg6GDx+OKVOmQCKRIDQ0FIsWLcLDhw9haWmJJUuWoEmTJsK+hw4dwvr16/H69Wt89dVXWLp0KapVqwYAyMzMxKpVq3Do0CGoq6vjm2++wU8//VTo+RaSkpJgY2MjXKpPRBVH9lse518hZeFrsOR8yue3UgNQWcUAREREVP58yuc3b4ZKREREKocBiIiIiFQOAxARERGpHAYgIiIiUjkMQERERKRyGICIiIhI5TAAERERkcphACIiIiKVwwBEREREKocBiIiIiFQOAxARERGpHAYgIiIiUjkMQERERKRyNJRdASIioooiIiIC3t7eePbsWb7lGRkZePv2LQCgcuXK0NAo+GP4iy++wJQpU2Bubl4idVV1DEBERETFxMvLC9evXy/UtgkJCR8sj4mJgZeXF1auXFkcVaNc2AVGREREKoctQERERMXE2dkZGzduxNOnT/Mtj4uLQ1ZWFgBATU0NRkZGBR7LwsICkydPLpF6EgMQERFRsTE3N4eHh0eB5cOHD0dMTAwAwMjICL/99ltpVY1yYRcYERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilaPUAPTnn3/C0tJS9OPi4gIAuHPnDgYNGgSZTIaBAwciLCxMtO/x48fRtWtXyGQyTJkyBS9fvhTKFAoFPD090aZNG7Rq1QqrVq1CVlZWqZ4bERERlV1KDUAPHjxA586dceHCBeFn2bJlSE5OxoQJE2Bra4tDhw7B2toaTk5OSE5OBgCEhoZi3rx5cHZ2xu+//443b97A1dVVOO727dtx/PhxeHl5Yf369Th27Bi2b9+urNMkIqJcshT8UqrqlP0a0FDmkz98+BANGzaEkZGRaP2BAwegpaWFWbNmQSKRYN68eTh//jwCAwPh6OiI3bt3w97eHv379wcArFq1Cp07d0ZERATMzc3h5+cHFxcX2NraAgBmzJiBX375BePGjSvtUyQionyoSdRw/M4eJLyLVXZVSlVS2hvR453X1imxNspjWMkYvZt8q9Q6KD0AtWvXLs/6kJAQ2NjYQCKRAAAkEglatmyJ4OBgODo6IiQkBN9//72wfc2aNWFqaoqQkBBIpVI8f/4cX375pVBuY2ODqKgoxMbGwtjYuORPjIiIPirhXSxikqKUXY1SlanIFD1WtfMvS5TWBaZQKPD48WNcuHABPXr0QNeuXeHp6Qm5XI64uLg8QcXQ0BAvXrwAgHyDTHZ5XFwcAIjKq1evDgDC/rnJ5XIkJSWJfoiIiKjiUloLUHR0NFJSUiCVSvHzzz8jMjISy5YtQ2pqqrA+J6lUCrlcDgBITU0tsDw1NVVYzlkGQNg/Nx8fH3h5eRXbuREREVHZprQAVKtWLVy5cgVVq1aFRCJB48aNkZWVhZkzZ6JVq1Z5wopcLoe2tjYAQEtLK99yHR0dUdjR0tISHgOAjo5OvnVxcnLCmDFjhOWkpCR06tSpeE6UiIiIyhyljgHS19cXLderVw9paWkwMjJCfHy8qCw+Pl7o1jIxMcm33MjICCYmJgCAuLg4mJmZCY8B5BlsnU0qleZpUSIiIqKKS2ljgP7991+0bt0aKSkpwrq7d+9CX18fNjY2uHnzJhQKBYD344Vu3LgBmUwGAJDJZAgKChL2e/78OZ4/fw6ZTAYTExOYmpqKyoOCgmBqasoB0ERERARAiQHI2toaWlpamD9/Ph49eoRz585h1apVGD9+PHr27Ik3b97A3d0dDx48gLu7O1JSUmBvbw8AGDZsGP744w/4+/vj3r17mDVrFuzs7GBubi6Ue3p64sqVK7hy5QrWrFmDkSNHKutUiYiIqIxRWheYnp4etm7diuXLl2PgwIGoVKkShg4divHjx0MikcDHxweLFi3C/v37YWlpiS1btkBXVxfA+/Dk5uaG9evX4/Xr1/jqq6+wdOlS4djjxo1DQkICnJ2doa6ujm+++QajR49W0pkSERFRWSNRZPczkSApKQk2NjYICgqCnp6esqtDRFQh7by2TuXmwTm34TZSX7+/MEe7qhSdpjZVco2Uw0SvFkZ9Oa3Yj/spn9+8GSpRGRIZGQlLS0ucPn0aXbt2hZWVFZycnJCYmJhn2zdv3mDq1KmwtbXFl19+iRkzZnAOKyKiQmIAIiqDNm/ejLVr12L37t24detWvveyW79+PeLi4rB37174+fnh3r172LhxoxJqS0RU/ij1Mngiyp+LiwuaN28OAOjTpw9u3bqVZ5uoqChUqlQJZmZm0NHRwS+//FLa1SQiKrfYAkRUBllYWAiP9fT0kJ6enmebkSNH4saNG2jbti0mTZqEW7duoXbt2qVYSyKi8osBiKgM0tTU/Og2bdu2xblz57Bo0SJIpVIsXLgQs2fPLoXaERGVf+wCIyqnduzYAUtLSwwYMAADBgxAQEAAXF1dlV0tIqJygS1AROVIXFyccMPfFy9ewM3NDcHBwXjy5AlOnTqFJk2aAHh//7u4uDhkZmYqs7pERGUWAxBROdK+fXucOHECAPDDDz+gZcuWmDRpEvr164fk5GSsXr0aAHDz5k20b98ez58/V2Z1iYjKLHaBEZUhZmZmuH//vmjd1KlThcc5y3R0dODu7p7vcVq3bp3nOERE9H/YAkREREQqhy1AVKoUCoUwhoVIWbS1tSGRSJRdDSJSIgYgKjUKhQLjxo1DaGiosqtCKk4mk8HX15chiEiFsQuMSk1qairDD5UJISEhbIkkUnFsASKlOH36NHR0dJRdDVIxKSkp6N69u7KrQRXYu4RU3DsdiaT4tHzLU9/IRY/Pbbhd4LH0qmuhUXczVDLULvZ6EgMQKYmOjg4DEBFVOHdPRSLh0dvCbawAUl/LCyxOfS3H3VORsB1ev5hqRzmxC4yIiIhUDluAiIiIiknjHma4dzoKSfH5jzFTZCmQnpIBANDU0YBEreCB+HrVtdGoe60SqScxABERERWbSobasBlWT9nVoEJgAKJSo62tDZlMJjwmKm18DRJRNgYgKjUSiQS+vr7CY6LSxtcgEWVjAKJSxQ8dUja+BokI4FVgREREpIIYgIiIiEjlMAARERGRymEAIiIiIpVTpEHQrq6u+a6XSCTQ1NSEkZERunfvjoYNG35W5YiIiIhKQpFagCpVqoQjR47g8ePHqFq1KqpUqYKIiAgcOnQICQkJuHXrFgYNGoSzZ88Wd32JiIiIPluRWoCePn2KSZMmwcXFRbR+8+bNCA4Oho+PD/z9/fHLL7+gc+fOxVJRIiIiouJSpBaga9euoW/fvnnW9+zZExcvXgQAfPXVV3j8+PHn1Y6IiIioBBQpAJmbm+PUqVN51v/555+oWbMmAODJkycwMDD4vNoRERERlYAidYHNnj0bkydPxoULF9CsWTMAQFhYGEJCQrB+/XrcvXsX06ZNw9ixY4u1skRERETFoUgtQO3bt0dAQABatmyJx48f49mzZ2jZsiUCAwNhZ2cHDQ0NLF++HE5OTsVdXyIiIqLPVuR7gZmbm2PatGn5ljVo0AANGjRAnz59sGXLFqFbjIiIiKgsKNGJECMjI5GRkVGST0FERET0yXg3eCo2ERER8Pb2xrNnz/Itz8jIwNu3bwEAlStXhoZG/i+/L774AlOmTIG5uXmJ1ZWIiFQbAxAVGy8vL1y/fr1Q2yYkJBRYFhMTAy8vL6xcubK4qkZERCTCe4ERERGRymELEBUbZ2dnbNy4EU+fPs23PC4uDllZWQAANTU1GBkZ5budhYUFJk+eXGL1pIqL3bBEVFglGoAkEklJHp7KGHNzc3h4eBRYPnz4cMTExAAAjIyM8Ntvv5VW1UhFsBuWiAqrRLvAFApFobedMGEC5syZIyzfuXMHgwYNgkwmw8CBAxEWFiba/vjx4+jatStkMhmmTJmCly9fip7X09MTbdq0QatWrbBq1Sqh5YGIiIioyAHozZs3SEtLAwDcu3cPvr6+uHTpkmgbPz8/1KhR46PHCggIwLlz54Tl5ORkTJgwAba2tjh06BCsra3h5OSE5ORkAEBoaCjmzZsHZ2dn/P7773jz5g1cXV2F/bdv347jx4/Dy8sL69evx7Fjx7B9+/ainioRlRPOzs5o1aoVTExM8v1RU/u/tzw1NbUCt2vVqhWcnZ2VeCZEVNKK1AV25swZzJgxAxs3bkStWrXw7bffokaNGvD29sZPP/2E7777DgBgZWX10WMlJiZi1apVom1PnDgBLS0tzJo1CxKJBPPmzcP58+cRGBgIR0dH7N69G/b29ujfvz8AYNWqVejcuTMiIiJgbm4OPz8/uLi4wNbWFgAwY8YM/PLLLxg3blxRTpeIygl2wxJRYRWpBejnn3+Gi4sL2rVrB39/f9SsWRMBAQFYu3Yttm3b9knHWrlyJfr164f69esL60JCQmBjYyOMIZJIJGjZsiWCg4OF8uxwAwA1a9aEqakpQkJCEBMTg+fPn+PLL78Uym1sbBAVFYXY2NiinC4RERFVMEUKQM+ePYO9vT0A4K+//kK3bt0AvL8FRs6xOB9z6dIlXL9+Pc8VP3FxcTA2NhatMzQ0xIsXLwAAsbGxBZbHxcUBgKi8evXqACDsT0RERKqtSF1gpqamuHLlCkxMTPD48WN06dIFAHDs2DHUrl27UMdIS0vDokWLsHDhQmhra4vKUlJSIJVKReukUinkcjkAIDU1tcDy1NRUYTlnGQBh/9zkcrmoLCkpqVDnQEREROVTkQKQi4sLZs2ahczMTNjZ2cHKygorV67Evn374OXlVahjeHl5oVmzZujQoUOeMi0trTxhRS6XC0GpoHIdHR1R2NHS0hIeA4COjk6+dfHx8Sl0vYmIiKj8K1IAcnBwQJs2bRATE4PGjRsDAAYNGoRx48YJ3U0fExAQgPj4eFhbWwP4v5By6tQp9O7dG/Hx8aLt4+PjhW4tExOTfMuNjIxgYmIC4H03mpmZmfAYQIET7zk5OWHMmDHCclJSEjp16lSo8yAiIqLyp8iXwb969UoIGP/++y927dqFs2fPFnr/Xbt24dixYzhy5AiOHDmCLl26oEuXLjhy5AhkMhlu3rwpzCOkUChw48YNyGQyAIBMJkNQUJBwrOfPn+P58+eQyWQwMTGBqampqDwoKAimpqZ5xg1lk0ql0NPTE/0QERFRxVWkFqDff/8dbm5u2L59O/T09DBp0iS0adMGf/75J6Kjo/HDDz989Bi1atUSLVeqVAnA+9sgGBoaYs2aNXB3d8fQoUOxb98+pKSkCAOvhw0bhhEjRqBFixawsrKCu7s77OzshGnrhw0bBk9PT2EOojVr1mDs2LFFOVUiIiKqgIrUAuTr64uVK1eiVatWOHjwIBo3bgxfX1+sW7cO/v7+n10pPT09+Pj4ICgoCI6OjggJCcGWLVugq6sLALC2toabmxu8vb0xbNgwVK1aVTT3x7hx4+Dg4ABnZ2f88MMP6NevH0aPHv3Z9SIiIqKKoUgtQDExMbCxsQEAnD17FkOGDAEA1KhRA+/evStSRVasWCFabt68OQ4fPlzg9o6OjnB0dMy3TF1dHa6urqLZoYmIiIiyFSkA1a1bF8eOHYOBgQGio6PRtWtXpKenY9u2bWjUqFFx15GIiIioWBUpAM2ePRs//vgjXr9+jeHDh6NevXpwc3PDn3/+ic2bNxd3HYmIiIiKVZECUNu2bXHp0iW8ffsWVatWBQBMnjwZrq6u0NTULNYKEhERERW3IgUg4P1NTB8/foysrCwA7y9Vl8vluHPnDiZMmFBsFSQiIiIqbkUKQPv374ebmxsyMjIgkUiE+XokEgmaN2/OAERERERlWpEug9+8eTMmTpyI0NBQGBoa4uzZszh+/DgaN24s3BiViIiIqKwqUgCKjY1F//79IZVK0bRpUwQHB6N+/fqYO3duscwDRERERFSSihSADAwM8PLlSwDvL4m/e/cugPf36IqJiSm+2hFRicjutibVxdcAqboijQGyt7fH7Nmz4e7ujg4dOmDWrFlo2rQpzp49CwsLi+KuIxEVM4lEArk8HFmKFGVXpVQpFHLR49S0UCXWRnnUJDqQShsouxpESlWkADRjxgxUrlwZr169wtdff42BAwdi0aJF0NfXx/Lly4u7jhWOIisLErUi34eWKoCy8BrIUqRAoSjazO3ll0L0WPXO/70sZVeAqAwoUgDS1NSEs7OzsDxt2jRMmzat2CpV0UnU1PDyz23IePlc2VUpVZnvXosex/7ursTaKI+GQU0YdOPNeYmIlKlIASg5ORn+/v549OgR5HJ5nvKcNyal/GW8fI70+AhlV6N0ZWWIHqvc+RMRUZlRpAA0ffp03Lx5E+3atYO2tnZx14mIiIioRBUpAF25cgXbtm2DtbV1cdeHiIiIqMQVaRRm3bp1kZqaWtx1ISIiIioVRWoBWrFiBZydndGnTx+YmppCLdfVLP379y+OuhERERGViCLfC+zp06fYu3cvtLS0RGUSiYQBiIiIiMq0IgWgAwcOYO3atXBwcCju+hARERGVuCKNAapWrRrq169f3HUhIiIiKhVFagFatGgR3NzcMGXKFJiZmUFdXV1UbmpqWiyVIyIiIioJRQpATk5OAIAxY8ZAIpEI6xUKBSQSiXBzVCIiIqKyqEgB6K+//irUdi9evICxsXGeq8SIiIiIlKlIAahWrVqF2s7BwQF//PEHzM3Ni/I0RERERCWiRJtmFArFxzciIiIiKmXsmyIiIiKVU6QuMCKisigyMg6/bjmBiIi4fMvj41+LHo8d45nvdubmRvh+ggPMzIxKpJ5EpHwMQERUYfj4BODmjQeF2jYrS4HY2MR8y2JjE5HlE4ClS0cXX+WIqExhFxgRERGpnBJtAco5RxARUUlzcuqFX389iYhnsfmWZ2RmIultCgBAr7IONHJN4prN/AtjfP+9fYnVk4iUr0QDkFQqZQgiolJjZmaEJUtGKrsaRFQOFKkLrHPnzvD09MSdO3c+uN3ly5dhZmZWpIoRERERlZQitQDNmTMHgYGB+Pbbb2FiYgIHBwf06tUL9erVK+76UTny/K0ce0LjEP1Wnm/5y5QM0ePpgY/z3c60shTfNjdCzcrSEqknERFRkQJQjx490KNHD6SmpuLs2bM4ffo0hg8fDhMTE/Tu3RsODg5s+VFBu0LiEBabXKhtsxRAfHJGvmXxyRnIConDrPaFm3GciIjoU33WVWDa2tro0aMHBg8ejN69e+Pp06fYsWMHevfujbFjx+Lx4/y/4RMREREpU5FagLKysnD58mUEBgbizJkzyMzMRLdu3bB582a0bt0aycnJWLRoESZNmoTAwMDirjOVUSNkRh/sAsvMUiBJngUA0JOqQV0t/wHy2V1gREREJaVIAaht27aQy+Wws7ODm5sbOnbsCKn0/8Zr6OnpoVu3bggJCSm2ilLZV7OyFDO+YrcVERGVfUUKQPPnz8fXX38NXV3dArfp2bMnevbsWeSKEREREZWUQgega9euCY9r1KiB27dvF7jtl19++Xm1IiIiIipBhQ5AI0aMEB5nT26oUCigo6MDTU1NvHnzBurq6qhSpQouXbpU/DUlIiIiKiaFDkD37t0THh84cAAHDhyAu7u7MPdPZGQk5s+fj/bt2xd/LYmIiIiKUZEug1+zZg0WL14smvjQzMwMc+fOxZYtWwp9nKdPn2LcuHGwtraGnZ0dfH19hbKIiAiMHj0aLVq0gIODAy5cuCDa9+LFi+jduzdkMhlGjhyJiIgIUfmOHTvQoUMHWFtbY+7cuUhJSSnKqRIREVEFVKQAJJFIEBMTk2f9kydPoKWlVahjZGVlYcKECahWrRoOHz6MJUuWYNOmTTh27BgUCgWmTJmC6tWr4+DBg+jXrx+cnZ0RHR0NAIiOjsaUKVPg6OiIAwcOwMDAAJMnT4ZCoQAAnDp1Cl5eXnBzc8POnTsREhKC1atXF+VUiYiIqAIq0lVgw4cPx6xZszBmzBg0atQICoUCt27dgp+fH6ZOnVqoY8THx6Nx48ZYvHgx9PT0ULt2bbRt2xZBQUGoXr06IiIisG/fPujq6qJevXq4dOkSDh48iKlTp8Lf3x/NmjXD2LFjAQAeHh746quvcPXqVbRu3Rp+fn4YNWoUOnfuDABYsmQJxo0bh5kzZ0JHR6cop0xEREQVSJECkLOzM4yMjODv7w8fHx8AQIMGDbBw4UL07du3UMcwNjbGzz//DOD9YOobN27g2rVrWLRoEUJCQtCkSRPRZfY2NjYIDg4GAISEhMDW1lYo09HRQdOmTREcHAxbW1vcunULzs7OQnmLFi2Qnp6Oe/fuwdrauiinTERERBVIkQIQAAwZMgRDhgwplkp06dIF0dHR6Ny5M3r06IHly5fD2NhYtI2hoSFevHgBAIiLiyuw/M2bN0hLSxOVa2hoQF9fX9g/N7lcDrn8/2YvTkpKKpbzIiIiorKpyAHozJkz8PX1xaNHj5CZmYk6dergu+++Q//+/T/5WOvXr0d8fDwWL14MDw8PpKSkiGaWBgCpVCqElA+Vp6amCssF7Z+bj48PvLy8PrneREREVD4VKQDt27cPK1euxHfffYcJEyYgKysLN27cwJIlS5Ceno5BgwZ90vGsrKwAAGlpaZgxYwYGDhyY56otuVwObW1tAICWllaeMCOXy1GlShVhEHZ+5QWN/3FycsKYMWOE5aSkJHTq1OmTzoGIiIjKjyIFIF9fXyxatEjU2tO1a1c0aNAAmzdvLlQAio+PR3BwMLp27Sqsq1+/PtLT02FkZIRHjx7l2T67W8vExATx8fF5yhs3bgx9fX1oaWkhPj5euEw/IyMDiYmJMDLK/wabUqk0T4sRERERVVxFugw+ISEBLVq0yLPe2toaz58/L9QxIiMj4ezsLLqcPiwsDAYGBrCxscHt27eF7iwACAoKgkwmAwDIZDIEBQUJZSkpKbhz5w5kMhnU1NRgZWUlKg8ODoaGhgYaNWr0qadKREREFVCRAlDjxo1x5MiRPOsPHz6M+vXrF+oYVlZWaNq0KebOnYsHDx7g3LlzWL16NSZOnIhWrVqhZs2acHV1RXh4OLZs2YLQ0FB88803AICBAwfixo0b2LJlC8LDw+Hq6gozMzO0bt0awPvL9Ldu3YozZ84gNDQUixcvxuDBg3kJPBEREQEoYhfYzJkzMXr0aFy5ckVolQkODsbdu3eFy+I/Rl1dHRs3bsTSpUsxZMgQ6OjoYMSIERg5ciQkEgk2btyIefPmwdHRERYWFvD29oapqSmA97NOb9iwAcuXL4e3tzesra3h7e0t3KOsV69eiIqKwsKFCyGXy9G9e3fMnDmzKKdKREREFZBEkT198id6+PAh/P398ejRI2hpaaFOnToYNmwYatasWdx1LHVJSUmwsbFBUFAQ9PT0SuQ5Yn93R3p8xMc3pApHs7o5jIfMU3Y1kJoWCoXinbKrQUogkVSCtlZzZVcDO6+tQ0xSlLKrQUpgolcLo76cVuzH/ZTP7yK1AKWnp+PkyZM4fvy4MBi5Zs2aMDQ0xKhRo4pySCIiIqJSU6QAtHTpUvz777+YMWMGmjRpgqysLISGhmL9+vVISEjA9OnTi7ueRERERMWmSAEoICAAPj4+ottRNGrUCLVq1cL06dMZgIiIiKhMK9JVYHp6etDQyJudKleunO96IiIiorKk0AEoOjpa+Bk5ciRmz56N8+fP49WrV3jz5g2uX7+O+fPnF/pu8ERERETKUujmmi5dugiXmWdfODZhwoQ865YsWYKhQ4cWdz2JiIiIik2hA9Bff/1VkvUgIiIiKjWFDkC1atUqyXoQERERlZoiDYImIiIiKs8YgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcpQagmJgYuLi4oFWrVujQoQM8PDyQlpYGAIiIiMDo0aPRokULODg44MKFC6J9L168iN69e0Mmk2HkyJGIiIgQle/YsQMdOnSAtbU15s6di5SUlFI7LyIiIirblBaAFAoFXFxckJKSgj179mDdunU4e/Ysfv75ZygUCkyZMgXVq1fHwYMH0a9fPzg7OyM6OhoAEB0djSlTpsDR0REHDhyAgYEBJk+eDIVCAQA4deoUvLy84Obmhp07dyIkJASrV69W1qkSERFRGaO0APTo0SMEBwfDw8MDDRo0gK2tLVxcXHD8+HFcvnwZERERcHNzQ7169eDk5IQWLVrg4MGDAAB/f380a9YMY8eORYMGDeDh4YGoqChcvXoVAODn54dRo0ahc+fOaN68OZYsWYKDBw+yFYiIiIgAKDEAGRkZwdfXF9WrVxetT0pKQkhICJo0aQJdXV1hvY2NDYKDgwEAISEhsLW1Fcp0dHTQtGlTBAcHIzMzE7du3RKVt2jRAunp6bh3717JnhQRERGVC0oLQFWqVEGHDh2E5aysLOzevRtt2rRBXFwcjI2NRdsbGhrixYsXAPDB8jdv3iAtLU1UrqGhAX19fWF/IiIiUm1l5iqw1atX486dO5g2bRpSUlIglUpF5VKpFHK5HAA+WJ6amiosF7R/bnK5HElJSaIfIiIiqrg0lF0B4H342blzJ9atW4eGDRtCS0sLiYmJom3kcjm0tbUBAFpaWnnCjFwuR5UqVaClpSUs5y7X0dHJ9/l9fHzg5eVVTGdDREREZZ3SW4CWLl2K7du3Y/Xq1ejRowcAwMTEBPHx8aLt4uPjhW6tgsqNjIygr68PLS0tUXlGRgYSExNhZGSUbx2cnJwQFBQk/Jw7d644T5GIiIjKGKUGIC8vL+zbtw9r165Fr169hPUymQy3b98WurMAICgoCDKZTCgPCgoSylJSUnDnzh3IZDKoqanByspKVB4cHAwNDQ00atQo33pIpVLo6emJfoiIiKjiUloAevjwITZu3Ijvv/8eNjY2iIuLE35atWqFmjVrwtXVFeHh4diyZQtCQ0PxzTffAAAGDhyIGzduYMuWLQgPD4erqyvMzMzQunVrAMDw4cOxdetWnDlzBqGhoVi8eDEGDx5cYBcYERERqRaljQH666+/kJmZiU2bNmHTpk2isvv372Pjxo2YN28eHB0dYWFhAW9vb5iamgIAzMzMsGHDBixfvhze3t6wtraGt7c3JBIJAKBXr16IiorCwoULIZfL0b17d8ycObPUz5GIiIjKJqUFoAkTJmDChAkFlltYWGD37t0Flnfq1AmdOnUq8vGJiIhIdSl9EDQRERFRaWMAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjllIkAJJfL0bt3b1y5ckVYFxERgdGjR6NFixZwcHDAhQsXRPtcvHgRvXv3hkwmw8iRIxERESEq37FjBzp06ABra2vMnTsXKSkppXIuREREVPYpPQClpaVh+vTpCA8PF9YpFApMmTIF1atXx8GDB9GvXz84OzsjOjoaABAdHY0pU6bA0dERBw4cgIGBASZPngyFQgEAOHXqFLy8vODm5oadO3ciJCQEq1evVsr5ERERUdmj1AD04MEDDB48GM+ePROtv3z5MiIiIuDm5oZ69erByckJLVq0wMGDBwEA/v7+aNasGcaOHYsGDRrAw8MDUVFRuHr1KgDAz88Po0aNQufOndG8eXMsWbIEBw8eZCsQERERAVByALp69Spat26N33//XbQ+JCQETZo0ga6urrDOxsYGwcHBQrmtra1QpqOjg6ZNmyI4OBiZmZm4deuWqLxFixZIT0/HvXv3SvaEiIiIqFzQUOaTDx8+PN/1cXFxMDY2Fq0zNDTEixcvPlr+5s0bpKWlico1NDSgr68v7J+bXC6HXC4XlpOSkop0PkRERFQ+KDUAFSQlJQVSqVS0TiqVCiHlQ+WpqanCckH75+bj4wMvL6/iqj4RERGVcWUyAGlpaSExMVG0Ti6XQ1tbWyjPHWbkcjmqVKkCLS0tYTl3uY6OTr7P5+TkhDFjxgjLSUlJ6NSp0+eeBhEREZVRSr8KLD8mJiaIj48XrYuPjxe6tQoqNzIygr6+PrS0tETlGRkZSExMhJGRUb7PJ5VKoaenJ/ohIiKiiqtMBiCZTIbbt28L3VkAEBQUBJlMJpQHBQUJZSkpKbhz5w5kMhnU1NRgZWUlKg8ODoaGhgYaNWpUeidBREREZVaZDECtWrVCzZo14erqivDwcGzZsgWhoaH45ptvAAADBw7EjRs3sGXLFoSHh8PV1RVmZmZo3bo1gPeDq7du3YozZ84gNDQUixcvxuDBgwvsAiMiIiLVUiYDkLq6OjZu3Ii4uDg4Ojri6NGj8Pb2hqmpKQDAzMwMGzZswMGDB/HNN98gMTER3t7ekEgkAIBevXrByckJCxcuxNixY9G8eXPMnDlTmadEREREZUiZGQR9//590bKFhQV2795d4PadOnX64EDlCRMmYMKECcVWPyIiIqo4ymQLEBEREVFJYgAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkchiAiIiISOUwABEREZHKYQAiIiIilcMARERERCqHAYiIiIhUDgMQERERqRwGICIiIlI5DEBERESkcip0AEpLS8PcuXNha2uL9u3bY9u2bcquEhEREZUBGsquQElatWoVwsLCsHPnTkRHR2P27NkwNTVFz549lV01IiIiUqIKG4CSk5Ph7++PX3/9FU2bNkXTpk0RHh6OPXv2MAARERGpuArbBXbv3j1kZGTA2tpaWGdjY4OQkBBkZWUpsWZERESkbBU2AMXFxaFatWqQSqXCuurVqyMtLQ2JiYnKqxgREREpXYXtAktJSRGFHwDCslwuF62Xy+WidW/fvgUAJCUllVj90nQMkKEn//iGVOFk6RiU6GursORyBbIUEmVXg5RATaJARrryX4OVJFWhr8b3QVVUSVK1RN4Hs4+pUCg+um2FDUBaWlr5Bh0A0NbWFq338fGBl5dXnmN06tSp5CpIKu5nZVeAiEip5mFliR373bt3qFy58ge3qbAByMTEBK9evUJGRgY0NN6fZlxcHLS1tVGlShXRtk5OThgzZoywnJWVhdevX0NfXx8SCb8hF6ekpCR06tQJ586dg56enrKrQyqIr0FSNr4GS45CocC7d+9gbGz80W0rbABq3LgxNDQ0EBwcDFtbWwBAUFAQrKysoKYmHvoklUrzdJflDklUvPT09PiPT0rF1yApG1+DJeNjLT/ZKuwgaB0dHfTv3x+LFy9GaGgozpw5g23btmHkyJHKrhoREREpWYVtAQIAV1dXLF68GKNGjYKenh6mTp2K7t27K7taREREpGQVOgDp6Ohg5cqVWLmy5AZa0aeRSqVwdnbO0+VIVFr4GiRl42uwbJAoCnOtGBEREVEFUmHHABEREREVhAGIiIiIVA4DEBEREakcBiAqkkOHDsHS0hL+/v4f3O7KlSuwtLQs9DG7dOnyyftRxfD69WusWLECXbp0gUwmg729PXbs2CHcvNjS0hJXrlzJd9/ier0oFArs2bPns49DFU9ycjJ+/vln9OzZE82bN0fr1q3h4uKC8PDwYjl+QkICTp48WSzHosJhAKIiCQgIwBdffIE//vijRI5vbW2NCxculMixqex59eoVBg0ahLCwMLi7u+P48eOYOnUqfHx84O7u/tH9i+v1cu3aNbi5uX32cahieffuHYYNG4aAgADMnDkTJ0+exNatW1GpUiUMHToUERERn/0cnp6eOHfuXDHUlgqrQl8GTyUjISEBly5dwvLlyzFnzhxERETA3Ny8WJ9DKpXCyMioWI9JZdeaNWsglUqxdetWaGlpAQDMzc2hra2NyZMn47vvvvvg/sX1euFFsZQfb29vJCQk4MSJE8JdAmrVqgUPDw88f/4cO3bswIIFCz7rOfjaK31sAaJPFhgYiMqVK6Nv374wNjYWtQIlJSVh+vTpsLa2Ro8ePXDr1i3Rvs+fP8fEiRMhk8nQpUsXeHl5ITMzM89z5O7SKOx+VP7I5XIEBATg22+/FcJPts6dO2PHjh2oVasWAOD69evo06cPrKys8N133yEqKgqA+PUSGRkJS0tLnD59Gl27doWVlRWcnJyQmJgIAEhPT8f8+fPRunVrWFtbY+LEiYiJiUFkZKQwU3x2d9ucOXMwZ84c9O3bF23btsWTJ0/w4MEDjBs3DtbW1rCyssLw4cPx8OFDoR4dO3aEn58fWrdujXbt2mHTpk2l8WukEpKVlYXDhw9jzJgx+d4iadWqVZg5cyaA969PR0dHNG/eHH369MGpU6eE7ebMmQMPDw/8+OOPkMlk6NSpE44cOQIA2LBhAw4fPozDhw8LwwBev36NBQsWoF27drCxscHMmTPx+vVr4XgPHz7EuHHj0LJlS3To0AFeXl5CdzEVDgMQfbKAgADY2dlBTU0NXbp0wZEjR4RvL4sWLcKjR4+we/duzJ8/H9u3bxf2UygUcHZ2hqGhIQ4fPgwPDw8cO3YMmzdv/uDzFXU/Kh+ePXuG5ORkWFlZ5SmTSCRo06aNMGGcv78/5s+fjwMHDuD169fw9PQs8LibN2/G2rVrsXv3bty6dUt4Le7ZswfXrl3Dtm3bcODAAbx79w7Lly9HzZo1sWHDBgDAhQsXYG1tDQD4448/8OOPP8LHxwdffPEFJk6ciFq1auGPP/7Avn37kJmZidWrVwvPm5CQgCNHjmDbtm1wc3ODr68v9u/fX2y/Lypdz549w8uXL4V7SuZmbGwMbW1txMXFwcnJCY6Ojjh27BjGjx+POXPm4Pr168K2e/bsQdOmTXH8+HF0794dixYtwtu3bzF27FjY29vD3t4eBw4cAAA4Ozvj7t272Lx5M7Zv346HDx9izpw5AICXL19i+PDhMDY2hr+/PxYtWoTdu3fDz8+v5H8hFQi7wOiTPH/+HDdu3MCYMWMAAN27d8fevXsRFBQES0tLnDx5En5+fmjatCkAYPLkycKYisuXLyM6Ohr+/v5QU1ND3bp1MXv2bLi6umLKlCkFPmdR96Py4c2bNwAKdwPDSZMmoXXr1gCAb775Bvv27StwWxcXFzRv3hwA0KdPH6E1MjIyElpaWqhVqxb09fWxYsUKJCYmQl1dHVWrVgUAUXealZWV8K08OTkZQ4cOxfDhw6GrqwsAGDBgAHx9fYXtMzIysHz5cjRq1AhNmzbFqFGjsG/fPgwePLjQvxMqO169egUAwmsDAC5evCh67zE1NUW3bt3Qrl07obvWwsICd+/exc6dO4XwZGlpie+//x4A8MMPP8DPzw/h4eFo2bIltLW1AQAGBga4d+8erl69isDAQNSpUwcAsHr1ajg4OODRo0e4cOECdHR0sHTpUmhoaKBevXqIi4uDt7c3Ro8eXeK/k4qCAYg+SUBAALS0tNC+fXsAQKtWrVC1alUcPnwYQ4YMQWZmJho1aiRsn/Nb/cOHD5GYmAgbGxthXVZWFlJTU4U3mfx8bL9q1aoV5ylSKdPX1wcAUfN+Qb744gvhceXKlZGWllbgthYWFsJjPT09pKenAwCGDBmCgIAAtG/fHq1atULXrl3h6OhY4HGyu98AQFdXF8OGDcORI0cQFhaGR48e4c6dO6hevbpom5z/A82aNcO2bds+em5UNmV3e2UHdeD9oPvs7qvTp09j7969ePToEc6ePSu0HALvu1uzAwwA1K5dW3icfRf4jIyMPM/56NEjVKlSRbRvvXr1ULVqVTx69AgPHz5E06ZNoaHxfx/h1tbWiIuLw5s3b/LtqqO8GIDokwQEBCA1NVUURjIzMxEYGIgBAwbk2T7nvW4yMjJQt25dbNy4Mc92H/r2X9T9qHz44osvULlyZdy+fVtosclp0qRJGDFiBABATa3wvfaampr5rm/QoAH+/vtv/PPPP/jnn3+wdu1aHD9+vMDL33OOS3r37h2++eYbVKtWDV26dEHv3r3x6NEjUcDJ+aEEvA/rEomk0PWmssXCwgL6+vq4efOm8PrU0dERArahoSGA9+9Tffr0wcSJE0X753w95PeazG/wc0H3CMvMzERmZmaesXIAhPE/HBtZeBwDRIX2+PFj3LlzB/Pnz8eRI0eEn3Xr1iEpKQmRkZHQ1NQUDXy+c+eO8LhOnTqIjo6GgYEBLCwsYGFhgcjISKxfv/6DHxBF3Y/KBw0NDTg4OGDPnj2Qy+Wisr///ht///03jI2Ni+35jhw5grNnz8Le3h4rV66Er68vgoKCkJCQ8NHX09WrVxEbGws/Pz+MHz8e7dq1Q3R0tOhD7M2bN4iMjBSWb926xTmtyjENDQ0MHDgQO3fuRFJSUp7ymJgYAO/fp54+fSq8R1lYWOCvv/7CsWPHCvU8OV97derUwZs3b/Do0SNh3YMHD5CUlIQ6deqgTp06uH37ttCqCQA3b96EgYGB0KJKH8cARIUWEBAAfX19DBkyBA0bNhR+HBwcUL9+fRw7dgz9+vXD0qVLERISgitXrsDLy0vYv3379qhVqxZmzpyJ+/fv4/r161iwYAF0dHSgrq5e4PMWdT8qP6ZOnYqkpCSMGzcOV69exbNnz+Dv7485c+Zg5MiRqF+/frE919u3b+Hu7o5Lly4hIiICx44dQ40aNVCtWjXo6OgAAMLCwvLtXtPX10dycjLOnDmDyMhI+Pv75xvcFixYgP/++w+nTp3Crl278O233xZb/an0TZ06FUZGRhg6dCgCAwMRERGB0NBQLFiwAOvXr4eNjQ2GDx+OsLAwrFu3Dk+ePMGxY8ewdu1amJqaFuo5dHR0EBUVhZiYGNSrVw8dO3bE7NmzERoaitDQUMyePRtffvklGjZsiD59+kAul2PhwoV4+PAhzpw5gw0bNmDYsGH8UvgJ2AVGhRYQEIA+ffrk2zw7bNgwuLu7459//sH69esxZswYVK1aFSNGjMDKlSsBAOrq6ti0aROWLl2KwYMHQ1dXFz179sTs2bM/+LxF3Y/KDyMjI+zduxcbNmzAjBkzkJiYiC+++AIuLi4YNmxYsT7Xt99+ixcvXgiXFTdr1gybNm2Curo6LC0t8dVXX2Ho0KFYu3Ztnn2tra0xZcoULFmyBGlpabC0tMTChQsxb948oSUAADp27CgMlJ4+fTr69OlTrOdApUtHRwe7du3Czp07sXHjRjx9+hRSqRTNmzfHhg0b0LVrVwDvrzz09PTE1q1bYWJiIkyhUBj9+vXDlClT0LdvX1y+fBkrV67EsmXLMHr0aKirq+Prr7+Gq6srgPfjh3x9feHu7o7+/fvDwMAAo0aNgpOTU4n9DioiiYKzLxERFYsrV65g5MiRuH//vrKrQkQfwS4wIiIiUjkMQERERKRy2AVGREREKoctQERERKRyGICIiIhI5TAAERERkcphACIiIiKVwwBERPT/WVpa4sqVK8quBhGVAl4FRkT0/8XFxaFq1aoF3oySiCoOBiAiIiJSOewCI6Iyy8/PD507d4aVlRUcHR1x/fp1XLlyBR07doSfnx9at26Ndu3aYdOmTaL99u3bhy5dusDa2hojRowQ3ZoiOTkZCxcuROvWrdG6dWssWLBAuPFpzi4wuVyOZcuWCdtl36PsQ3UjovKDAYiIyqQ7d+5g1apVWLRoEU6ePAlbW1v8+OOPyMrKQkJCAo4cOYJt27bBzc0Nvr6+2L9/PwDg77//hpeXFxYsWIDDhw/DxsYGI0eOxOvXrwEA8+fPR1BQEDZu3Iht27YhKCgIP//8c57nX7t2LcLCwvDrr7/Cz88PSUlJ+OGHHz5aNyIqJxRERGXQ6dOnFc2aNVPcv39foVAoFO/evVNcvHhR8b///U/RsGFDxd27d4Vtf/nlF8WAAQMUCoVCMWzYMIWfn5/oWAMGDFD4+fkpEhMTFY0bN1ZcvnxZKLt27ZqwfcOGDRWXL19WJCcnK5o2baq4d++esN3r168VjRo1Uty7d6/AuqWnp5fML4OIip2GsgMYEVF+2rdvj4YNG6JPnz5o0qQJvv76awwaNAhPnjyBrq4uGjVqJGzbrFkzbNu2DQDw8OFDrF69GmvXrhXK09LS8OTJEzx9+hSZmZlo2rSpUGZrawtbW1vRc0dERCA9PR1Dhw4Vrc/KysKTJ0/QsWPHfOumocG3VKLygv+tRFQm6ejowN/fH1evXsXZs2dx6NAh7N27F7Nnz84TNLKysiCRSAAAmZmZmDt3Ltq2bSvaRk9PD7GxsYV67szMTADAb7/9Bl1dXVGZoaFhgXU7dOgQTExMinrKRFSKOAaIiMqkmzdvwsfHB23atIGrqysCAwORlpYGDQ0NvHnzBpGRkcK2t27dgqWlJQCgTp06ePHiBSwsLISfzZs3Izg4GObm5lBXV8e9e/eEfc+cOYMBAwaInjt7u8TEROEYenp68PDwQEJCQoF1CwoKKp1fDhF9NgYgIiqTtLW14e3tDX9/f0RGRiIgIADJycnClVgLFizAf//9h1OnTmHXrl349ttvAQBjxozBzp07ceTIETx79gyrV6/GyZMnUa9ePejp6aF///5wd3dHaGgobt26hXXr1qFNmzai59bT08OgQYOwePFiXLlyBQ8ePMCsWbPw9OlTmJmZFVi37BBGRGUf5wEiojLrjz/+wMaNGxEdHQ1TU1O4uLigevXqGDlyJObMmQNvb2/o6upiwoQJ+O6774T9/Pz8sGPHDsTHx6N+/fqYOXOm0CWWlJQEd3d3nD59GpqamnBwcMCcOXMglUphaWkpXF6fkpKClStX4uTJk0hPT8eXX36J+fPnw9zcvMC69erVSym/JyL6dAxARFSuXLlyBSNHjhTN7UNE9KnYBUZEREQqhwGIiIiIVA67wIiIiEjlsAWIiIiIVA4DEBEREakcBiAiIiJSOQxAREREpHIYgIiIiEjlMAARERGRymEAIiIiIpXDAEREREQqhwGIiIiIVM7/A9OymoCy7xJ6AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Welch ANOVA\n",
    "\n",
    "Welch ANOVA is used for when the Homoscedasticity is false. In other words, if the datasets does not look consistent. The get_welch_anova() function can be tested usign the same Penguin dataset used above. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c142ea06967aee30"
  },
  {
   "cell_type": "code",
   "source": [
    "# welch = stats.get_welch_anova(data, value_col='body_mass_g', group_col='species').round(4)\n",
    "welch = stats.get_welch_anova(value_col='body_mass_g', group_col='species')\n",
    "welch"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.406546Z",
     "start_time": "2024-08-30T03:49:51.391595Z"
    }
   },
   "id": "5f36e4912b094887",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Source  ddof1       ddof2           F         p-unc       np2 significance\n",
       "0  species      2  189.478413  317.572267  3.093701e-61  0.669672         ****"
      ],
      "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>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>species</td>\n",
       "      <td>2</td>\n",
       "      <td>189.478413</td>\n",
       "      <td>317.572267</td>\n",
       "      <td>3.093701e-61</td>\n",
       "      <td>0.669672</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Games-Howell Post-Hoc Test\n",
    "\n",
    "Again, after the ANOVA test, a post-hoc test should be used to tell significance between groups. Games-Howell can be used to handle situations with inconsistent datasets - i.e. Homoscedasticity is false."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "24db12cd624cade0"
  },
  {
   "cell_type": "code",
   "source": [
    "gameshowell = stats.get_gameshowell(value_col='body_mass_g', group_col='species')\n",
    "gameshowell"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.506640Z",
     "start_time": "2024-08-30T03:49:51.457470Z"
    }
   },
   "id": "d72ecb30c0fd24f4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           A          B      mean(A)      mean(B)         diff         se  \\\n",
       "0     Adelie  Chinstrap  3700.662252  3733.088235   -32.425984  59.706437   \n",
       "1     Adelie     Gentoo  3700.662252  5076.016260 -1375.354009  58.810929   \n",
       "2  Chinstrap     Gentoo  3733.088235  5076.016260 -1342.928025  65.102843   \n",
       "\n",
       "           T          df      pval    hedges significance  \n",
       "0  -0.543090  152.454796  0.850154 -0.073946         n.s.  \n",
       "1 -23.386028  249.642554  0.000000 -2.860201         ****  \n",
       "2 -20.627794  170.404362  0.000000 -2.875327         ****  "
      ],
      "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>mean(A)</th>\n",
       "      <th>mean(B)</th>\n",
       "      <th>diff</th>\n",
       "      <th>se</th>\n",
       "      <th>T</th>\n",
       "      <th>df</th>\n",
       "      <th>pval</th>\n",
       "      <th>hedges</th>\n",
       "      <th>significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>3700.662252</td>\n",
       "      <td>3733.088235</td>\n",
       "      <td>-32.425984</td>\n",
       "      <td>59.706437</td>\n",
       "      <td>-0.543090</td>\n",
       "      <td>152.454796</td>\n",
       "      <td>0.850154</td>\n",
       "      <td>-0.073946</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Gentoo</td>\n",
       "      <td>3700.662252</td>\n",
       "      <td>5076.016260</td>\n",
       "      <td>-1375.354009</td>\n",
       "      <td>58.810929</td>\n",
       "      <td>-23.386028</td>\n",
       "      <td>249.642554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.860201</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinstrap</td>\n",
       "      <td>Gentoo</td>\n",
       "      <td>3733.088235</td>\n",
       "      <td>5076.016260</td>\n",
       "      <td>-1342.928025</td>\n",
       "      <td>65.102843</td>\n",
       "      <td>-20.627794</td>\n",
       "      <td>170.404362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.875327</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": "The post-hoc results can be plotted using py50. Her there are specific pairs denoted for plotting. This would remove the pairs that have no significance (n.s.). If the return_df=True, then the returned table will only contain the pairs annotated on the plot. ",
   "metadata": {
    "collapsed": false
   },
   "id": "3e75861c6b7dc5cb"
  },
  {
   "cell_type": "code",
   "source": [
    "title = 'Games-Howell Results Bar Plot'\n",
    "pairs = [('Gentoo', 'Chinstrap'), ('Adelie', 'Gentoo')]\n",
    "\n",
    "plot.barplot(test='gameshowell', value_col='body_mass_g', group_col='species', palette='RdYlGn', title=title,\n",
    "             fontsize=30, return_df=True, pairs=pairs)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.669611Z",
     "start_time": "2024-08-30T03:49:51.525877Z"
    }
   },
   "id": "213702e86ffbb96f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(           A       B      mean(A)     mean(B)         diff         se  \\\n",
       " 1  Chinstrap  Gentoo  3733.088235  5076.01626 -1342.928025  65.102843   \n",
       " 0     Adelie  Gentoo  3700.662252  5076.01626 -1375.354009  58.810929   \n",
       " \n",
       "            T          df  pval    hedges significance  \n",
       " 1 -20.627794  170.404362   0.0 -2.875327         ****  \n",
       " 0 -23.386028  249.642554   0.0 -2.860201         ****  ,\n",
       " <statannotations.Annotator.Annotator at 0x311220bb0>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Repeated Measures\n",
    "\n",
    "Repeated measures is used to analyze within-subjects or within-groups factors. Can be used when the subjects are measured under different conditions or time points. Examples for the get_rm_anova() function uses the 'rm_anova' dataset from pingouin as follows:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1efb20e99c35d2a6"
  },
  {
   "cell_type": "code",
   "source": [
    "repeated_m = pg.read_dataset('rm_anova')\n",
    "repeated_m.dropna(subset=['Gender'], inplace=True)\n",
    "\n",
    "rm_stats = Stats(repeated_m)\n",
    "rm_plots = Plots(repeated_m)\n",
    "rm_stats.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.698431Z",
     "start_time": "2024-08-30T03:49:51.690276Z"
    }
   },
   "id": "1e041f456070b179",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Subject  Gender Region Education  DesireToKill Disgustingness  \\\n",
       "0        1  Female  North      some          10.0           High   \n",
       "1        1  Female  North      some           9.0           High   \n",
       "2        1  Female  North      some           6.0            Low   \n",
       "3        1  Female  North      some           6.0            Low   \n",
       "4        2  Female  North   advance          10.0           High   \n",
       "\n",
       "  Frighteningness  \n",
       "0            High  \n",
       "1             Low  \n",
       "2            High  \n",
       "3             Low  \n",
       "4            High  "
      ],
      "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>Subject</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Region</th>\n",
       "      <th>Education</th>\n",
       "      <th>DesireToKill</th>\n",
       "      <th>Disgustingness</th>\n",
       "      <th>Frighteningness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>North</td>\n",
       "      <td>some</td>\n",
       "      <td>10.0</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>North</td>\n",
       "      <td>some</td>\n",
       "      <td>9.0</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>North</td>\n",
       "      <td>some</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>North</td>\n",
       "      <td>some</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>North</td>\n",
       "      <td>advance</td>\n",
       "      <td>10.0</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "source": [
    "repeated_measures = rm_stats.get_rm_anova(value_col='DesireToKill', within_subject_col='Disgustingness',\n",
    "                                          subject_col='Subject')\n",
    "repeated_measures"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.769682Z",
     "start_time": "2024-08-30T03:49:51.758006Z"
    }
   },
   "id": "7233aba8e341327f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           Source  ddof1  ddof2          F     p-unc       ng2  eps  \\\n",
       "0  Disgustingness      1     91  11.876684  0.000862  0.025827  1.0   \n",
       "\n",
       "  significance  \n",
       "0          ***  "
      ],
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>ng2</th>\n",
       "      <th>eps</th>\n",
       "      <th>significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disgustingness</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>11.876684</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>0.025827</td>\n",
       "      <td>1.0</td>\n",
       "      <td>***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Pairwise tests for repeated measures\n",
    "\n",
    "The guidelines found on [Pingouin](https://pingouin-stats.org/build/html/guidelines.html#id5) suggest pairwise_tests(). py50 warps this function in the get_pairwise_rm() function. The results can also be used to plot hte results. \n",
    "\n",
    "**NOTE:** Depending on the data, it may also be feasible to use the Tukey test. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e8448ead0e99305a"
  },
  {
   "cell_type": "code",
   "source": [
    "pairwise_test = rm_stats.get_pairwise_rm(value_col='DesireToKill', within_subject_col='Disgustingness',\n",
    "                                         subject_col='Subject')\n",
    "pairwise_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.865676Z",
     "start_time": "2024-08-30T03:49:51.850632Z"
    }
   },
   "id": "513cc3a8bc7c1a02",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         Contrast     A    B  Paired  Parametric         T   dof alternative  \\\n",
       "0  Disgustingness  High  Low    True        True  3.446257  91.0   two-sided   \n",
       "\n",
       "      p-unc    BF10    hedges significance  \n",
       "0  0.000862  26.262  0.322536          ***  "
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Contrast</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>Paired</th>\n",
       "      <th>Parametric</th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>hedges</th>\n",
       "      <th>significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disgustingness</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3.446257</td>\n",
       "      <td>91.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>26.262</td>\n",
       "      <td>0.322536</td>\n",
       "      <td>***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "source": [
    "title = 'Pairwise T-Test Results Bar Plot'\n",
    "\n",
    "rm_plots.boxplot(test='pairwise-rm', value_col='DesireToKill', group_col='Disgustingness', subject_col='Subject',\n",
    "                 palette='RdYlGn', title=title, fontsize=30, return_df=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:51.995264Z",
     "start_time": "2024-08-30T03:49:51.910748Z"
    }
   },
   "id": "ba15bf6cd980f049",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(         Contrast     A    B  Paired  Parametric         T        dof  \\\n",
       " 0  Disgustingness  High  Low   False        True  2.595165  360.18469   \n",
       " \n",
       "   alternative     p-unc   BF10    hedges significance  \n",
       " 0   two-sided  0.009841  2.899  0.271888           **  ,\n",
       " <statannotations.Annotator.Annotator at 0x3112b4cd0>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Mixed ANOVA\n",
    "Mixed ANOVA can be used to analyze data with mixed design - both within and between group factors. The function uses the py50 standard as seen in the above examples. The example here will use the 'mixed_anova' dataset from Pingouin.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4e28273ab914da32"
  },
  {
   "cell_type": "code",
   "source": [
    "data = pg.read_dataset('mixed_anova')\n",
    "mixed_stats = Stats(data)\n",
    "mixed_plot = Plots(data)\n",
    "\n",
    "mixed_stats.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:52.058596Z",
     "start_time": "2024-08-30T03:49:52.053084Z"
    }
   },
   "id": "8bf97ef742a1d944",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Scores    Time    Group  Subject\n",
       "0  5.971435  August  Control        0\n",
       "1  4.309024  August  Control        1\n",
       "2  6.932707  August  Control        2\n",
       "3  5.187348  August  Control        3\n",
       "4  4.779411  August  Control        4"
      ],
      "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>Scores</th>\n",
       "      <th>Time</th>\n",
       "      <th>Group</th>\n",
       "      <th>Subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.971435</td>\n",
       "      <td>August</td>\n",
       "      <td>Control</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.309024</td>\n",
       "      <td>August</td>\n",
       "      <td>Control</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.932707</td>\n",
       "      <td>August</td>\n",
       "      <td>Control</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.187348</td>\n",
       "      <td>August</td>\n",
       "      <td>Control</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.779411</td>\n",
       "      <td>August</td>\n",
       "      <td>Control</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "source": [
    "mixed = mixed_stats.get_mixed_anova(value_col='Scores', group_col='Group', within_subject_col='Time',\n",
    "                                    subject_col='Subject')\n",
    "mixed"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:52.149954Z",
     "start_time": "2024-08-30T03:49:52.126691Z"
    }
   },
   "id": "d7d72676d393d952",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        Source        SS  DF1  DF2        MS         F     p-unc       np2  \\\n",
       "0        Group  5.459963    1   58  5.459963  5.051709  0.028420  0.080120   \n",
       "1         Time  7.628428    2  116  3.814214  4.027394  0.020369  0.064929   \n",
       "2  Interaction  5.167192    2  116  2.583596  2.727996  0.069545  0.044922   \n",
       "\n",
       "        eps significance  \n",
       "0       NaN            *  \n",
       "1  0.998751            *  \n",
       "2       NaN         n.s.  "
      ],
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "      <th>significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Group</td>\n",
       "      <td>5.459963</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.459963</td>\n",
       "      <td>5.051709</td>\n",
       "      <td>0.028420</td>\n",
       "      <td>0.080120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
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       "    <tr>\n",
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       "      <td>Time</td>\n",
       "      <td>7.628428</td>\n",
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       "      <td>3.814214</td>\n",
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       "      <td>0.020369</td>\n",
       "      <td>0.064929</td>\n",
       "      <td>0.998751</td>\n",
       "      <td>*</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>5.167192</td>\n",
       "      <td>2</td>\n",
       "      <td>116</td>\n",
       "      <td>2.583596</td>\n",
       "      <td>2.727996</td>\n",
       "      <td>0.069545</td>\n",
       "      <td>0.044922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "The guidelines found on [Pingouin](https://pingouin-stats.org/build/html/guidelines.html#id5) suggest pairwise_tests(). That can also be used. But depending on the dataset, it may be more prudent to use the tukey test. That is easy to use, as we have seen above. The Tukey test will be performed and the corresponding barplot() will be generated. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4252585cc643003a"
  },
  {
   "cell_type": "code",
   "source": [
    "pairwise_test = mixed_stats.get_tukey(value_col='Scores', group_col='Group')\n",
    "pairwise_test"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:52.281946Z",
     "start_time": "2024-08-30T03:49:52.258321Z"
    }
   },
   "id": "c08a58df20977302",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         A           B   mean(A)  mean(B)      diff        se         T  \\\n",
       "0  Control  Meditation  5.567851  5.91618 -0.348328  0.152115 -2.289903   \n",
       "\n",
       "   p-tukey    hedges significance  \n",
       "0   0.0232 -0.339918            *  "
      ],
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>mean(A)</th>\n",
       "      <th>mean(B)</th>\n",
       "      <th>diff</th>\n",
       "      <th>se</th>\n",
       "      <th>T</th>\n",
       "      <th>p-tukey</th>\n",
       "      <th>hedges</th>\n",
       "      <th>significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>5.567851</td>\n",
       "      <td>5.91618</td>\n",
       "      <td>-0.348328</td>\n",
       "      <td>0.152115</td>\n",
       "      <td>-2.289903</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>-0.339918</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "source": [
    "title = 'Tukey on Mixed ANOVA Results Bar Plot'\n",
    "\n",
    "mixed_plot.barplot(test='tukey', value_col='Scores', group_col='Group', palette='RdYlGn', title=title, fontsize=30,\n",
    "                   return_df=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-30T03:49:52.496266Z",
     "start_time": "2024-08-30T03:49:52.391480Z"
    }
   },
   "id": "49298054eb53bd95",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(         A           B   mean(A)  mean(B)      diff        se         T  \\\n",
       " 0  Control  Meditation  5.567851  5.91618 -0.348328  0.152115 -2.289903   \n",
       " \n",
       "    p-tukey    hedges significance  \n",
       " 0   0.0232 -0.339918            *  ,\n",
       " <statannotations.Annotator.Annotator at 0x310cd9b20>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}