{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Statistics Quickstart\n",
    "\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 detail how to use the statistic calculations and the corresponding plots in py50. Essentially, py50 wraps three main python modules into a single package.  \n",
    "\n",
    "1) Many of the functions are used similarly, with statistics using the 'get_x()' format, where x is the name. Statistic functions are wrappers from the [Pingouin](https://pingouin-stats.org/build/html/index.html) tool. If there are issues with the calculations using py50, the Pingouin may be a be a workaround.  \n",
    "\n",
    "2) py50 wraps [Statannotation](https://github.com/trevismd/statannotations), which automatically adds statistical annotations to seaborn plots. The Pingouin statistics is also used to perform calculations. As such, users cna use Plots directly and show the output DataFrame for analysis/checking if needed.  \n",
    "\n",
    "3) I also wrapped another tool that I like using - [scikit-posthocs](https://scikit-posthocs.readthedocs.io/en/latest/). This will plot a heatmap based on matplotlib/seaborn and highlight p-values between groups. I do not use calculations from scikit-posthocs, only the plotting options. The calculations come from the Pingouin package. Its usage in py50 requires a final calculated table, the py50.stats functions, before the final figure can be made. \n",
    "\n",
    "More details on the specific plotting options are given under [009_advanced_plotting](https://github.com/tlint101/py50/blob/main/tutorials/009_advanced_plotting.ipynb) tutorial. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "abc507f1e884a9b5"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:34.235228Z",
     "start_time": "2026-05-21T04:12:33.340140Z"
    }
   },
   "source": [
    "from py50 import Stats, Plots\n",
    "import pingouin as pg\n",
    "from matplotlib import pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "# Load an example dataset comparing pain threshold as a function of hair color\n",
    "data = pg.read_dataset('anova')\n",
    "print('List of Group names in Dataset:', data['Hair color'].unique())\n",
    "\n",
    "# Initialize Stats and Plots\n",
    "stat = Stats(data)\n",
    "plot = Plots(data)\n",
    "\n",
    "# Visualize data\n",
    "plot.show(2)  # or stat.show() since same DataFrame"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:34.254937Z",
     "start_time": "2026-05-21T04:12:34.236374Z"
    }
   },
   "id": "a92b9de015d43c30",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of Group names in Dataset: ['Light Blond' 'Dark Blond' 'Light Brunette' 'Dark Brunette']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Subject   Hair color  Pain threshold\n",
       "0        1  Light Blond              62\n",
       "1        2  Light Blond              60"
      ],
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Hair color</th>\n",
       "      <th>Pain threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>60</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. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1a0e45ba58766658"
  },
  {
   "cell_type": "code",
   "source": [
    "# Test for normality\n",
    "stat.get_normality(value_col='Pain threshold', group_col='Hair color').round(3)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:34.386643Z",
     "start_time": "2026-05-21T04:12:34.292793Z"
    }
   },
   "id": "512d2139f53ac36d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    W   pval  normal\n",
       "Hair color                          \n",
       "Light Blond     0.991  0.983    True\n",
       "Dark Blond      0.940  0.664    True\n",
       "Light Brunette  0.931  0.598    True\n",
       "Dark Brunette   0.883  0.324    True"
      ],
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>pval</th>\n",
       "      <th>normal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hair color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Light Blond</th>\n",
       "      <td>0.991</td>\n",
       "      <td>0.983</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dark Blond</th>\n",
       "      <td>0.940</td>\n",
       "      <td>0.664</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Brunette</th>\n",
       "      <td>0.931</td>\n",
       "      <td>0.598</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dark Brunette</th>\n",
       "      <td>0.883</td>\n",
       "      <td>0.324</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": "stat.get_homoscedasticity(value_col='Pain threshold', group_col='Hair color').round(3)",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:35.069498Z",
     "start_time": "2026-05-21T04:12:35.046366Z"
    }
   },
   "id": "50b0303f491ff622",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            W  pval  equal_var\n",
       "levene  0.393  0.76       True"
      ],
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>W</th>\n",
       "      <th>pval</th>\n",
       "      <th>equal_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>levene</th>\n",
       "      <td>0.393</td>\n",
       "      <td>0.76</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Visualize Data Distribution\n",
    "Oftentimes it may be quicker and easier to visualize the distribution of the dataset. py50 includes a Histogram Plot and QQ Plot to visualize normality and homoscedasticity, respectively. The plots can be drawn individually, or if called as specific axes, can be combined into a single plot. Here I will draw them together in a grid. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c97fe9a2462bec7"
  },
  {
   "cell_type": "code",
   "source": [
    "# create subplot grid\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))\n",
    "plot.distribution(val_col='Pain threshold', type='histplot', ax=ax1)\n",
    "plot.distribution(val_col='Pain threshold', type='qqplot', ax=ax2)\n",
    "\n",
    "# Adding titles to subplots\n",
    "ax1.set_title('Histogram Plot')\n",
    "ax2.set_title('QQ Plot')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:35.343086Z",
     "start_time": "2026-05-21T04:12:35.116276Z"
    }
   },
   "id": "2ff811ce553b05a0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Perform ANOVA\n",
    "Several tests, parametric and non-parametric, are available in py50. They use the \"get_x\" format. Here, the One-Way ANOVA script will be demoed. The final table will be a Pandas.DataFrame object. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "18e40cccb3988dae"
  },
  {
   "cell_type": "code",
   "source": [
    "# Calculate ANOVA\n",
    "anova = stat.get_anova(value_col='Pain threshold', group_col='Hair color')\n",
    "anova"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:35.660178Z",
     "start_time": "2026-05-21T04:12:35.636434Z"
    }
   },
   "id": "7cac2c6006ddbd49",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Source  ddof1  ddof2         F     p_unc       np2 significance\n",
       "0  Hair color      3     15  6.791407  0.004114  0.575962           **"
      ],
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       "      <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>Hair color</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>6.791407</td>\n",
       "      <td>0.004114</td>\n",
       "      <td>0.575962</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": [
    "ANOVA is a type of omnibus test and determines if a set of groups are statistically different. It does not tell you which group is different. For that, a post-hoc test is needed to perform pairwise comparisons between groups. This will signify which group is statistically significant. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7153748b9c55056e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Post-Hoc Tests\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": "37b8a807a8b7828c"
  },
  {
   "cell_type": "code",
   "source": [
    "tukey = stat.get_tukey(value_col='Pain threshold', group_col='Hair color')\n",
    "tukey"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:35.769415Z",
     "start_time": "2026-05-21T04:12:35.710421Z"
    }
   },
   "id": "55fe34c6f78a1de9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               A               B  mean_A  mean_B  diff        se         T  \\\n",
       "0     Dark Blond   Dark Brunette    51.2    37.4  13.8  5.168623  2.669957   \n",
       "1     Dark Blond     Light Blond    51.2    59.2  -8.0  5.168623 -1.547801   \n",
       "2     Dark Blond  Light Brunette    51.2    42.5   8.7  5.482153  1.586968   \n",
       "3  Dark Brunette     Light Blond    37.4    59.2 -21.8  5.168623 -4.217758   \n",
       "4  Dark Brunette  Light Brunette    37.4    42.5  -5.1  5.482153 -0.930291   \n",
       "5    Light Blond  Light Brunette    59.2    42.5  16.7  5.482153  3.046249   \n",
       "\n",
       "    p_tukey    hedges significance  \n",
       "0  0.074068  1.413596         n.s.  \n",
       "1  0.435577 -0.810661         n.s.  \n",
       "2  0.414728  0.982361         n.s.  \n",
       "3  0.003708 -2.336811           **  \n",
       "4  0.789321 -0.626769         n.s.  \n",
       "5  0.036647  2.015280            *  "
      ],
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       "      <th>mean_A</th>\n",
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       "      <th>T</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>51.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.168623</td>\n",
       "      <td>2.669957</td>\n",
       "      <td>0.074068</td>\n",
       "      <td>1.413596</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>51.2</td>\n",
       "      <td>59.2</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>5.168623</td>\n",
       "      <td>-1.547801</td>\n",
       "      <td>0.435577</td>\n",
       "      <td>-0.810661</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>51.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5.482153</td>\n",
       "      <td>1.586968</td>\n",
       "      <td>0.414728</td>\n",
       "      <td>0.982361</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>37.4</td>\n",
       "      <td>59.2</td>\n",
       "      <td>-21.8</td>\n",
       "      <td>5.168623</td>\n",
       "      <td>-4.217758</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>-2.336811</td>\n",
       "      <td>**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>37.4</td>\n",
       "      <td>42.5</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>5.482153</td>\n",
       "      <td>-0.930291</td>\n",
       "      <td>0.789321</td>\n",
       "      <td>-0.626769</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Light Blond</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>59.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>5.482153</td>\n",
       "      <td>3.046249</td>\n",
       "      <td>0.036647</td>\n",
       "      <td>2.015280</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "The calculated results show that only two groups ((Light Blond-Dark Brunette) and (Light Blond-Light Brunette)) with statistical significance.\n",
    "\n",
    "The output table is the same format as the Pingouin,  but with the addition of a \"significance\" column. This denotes the typical asterisks associated with pvalues. The inclusion of this column is for quick visualization of results.  "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "53d3403d5e8ac260"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Sometimes it is easy to forget the numerical values associated with the pvalues. In py50, the values will remain consistent. A reference, as type DataFrame, can be printed out as follows:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8cd4ac51c52e7a31"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:36.326503Z",
     "start_time": "2026-05-21T04:12:36.274320Z"
    }
   },
   "cell_type": "code",
   "source": "stat.get_gameshowell(value_col='Pain threshold', group_col='Hair color')",
   "id": "cfa8a22bcfe24ff4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               A               B  mean_A  mean_B  diff        se         T  \\\n",
       "0     Dark Blond   Dark Brunette    51.2    37.4  13.8  5.576737  2.474565   \n",
       "1     Dark Blond     Light Blond    51.2    59.2  -8.0  5.637375 -1.419100   \n",
       "2     Dark Blond  Light Brunette    51.2    42.5   8.7  4.965548  1.752072   \n",
       "3  Dark Brunette     Light Blond    37.4    59.2 -21.8  5.329165 -4.090697   \n",
       "4  Dark Brunette  Light Brunette    37.4    42.5  -5.1  4.612664 -1.105652   \n",
       "5    Light Blond  Light Brunette    59.2    42.5  16.7  4.685794  3.563964   \n",
       "\n",
       "         df      pval    hedges significance  \n",
       "0  7.906609  0.140085  1.413596         n.s.  \n",
       "1  7.942669  0.522844 -0.810661         n.s.  \n",
       "2  6.562510  0.371497  0.982361         n.s.  \n",
       "3  7.995416  0.014769 -2.336811            *  \n",
       "4  6.821772  0.698000 -0.626769         n.s.  \n",
       "5  6.772089  0.037775  2.015280            *  "
      ],
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>51.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.576737</td>\n",
       "      <td>2.474565</td>\n",
       "      <td>7.906609</td>\n",
       "      <td>0.140085</td>\n",
       "      <td>1.413596</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>51.2</td>\n",
       "      <td>59.2</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>5.637375</td>\n",
       "      <td>-1.419100</td>\n",
       "      <td>7.942669</td>\n",
       "      <td>0.522844</td>\n",
       "      <td>-0.810661</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>51.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.965548</td>\n",
       "      <td>1.752072</td>\n",
       "      <td>6.562510</td>\n",
       "      <td>0.371497</td>\n",
       "      <td>0.982361</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>37.4</td>\n",
       "      <td>59.2</td>\n",
       "      <td>-21.8</td>\n",
       "      <td>5.329165</td>\n",
       "      <td>-4.090697</td>\n",
       "      <td>7.995416</td>\n",
       "      <td>0.014769</td>\n",
       "      <td>-2.336811</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>37.4</td>\n",
       "      <td>42.5</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>4.612664</td>\n",
       "      <td>-1.105652</td>\n",
       "      <td>6.821772</td>\n",
       "      <td>0.698000</td>\n",
       "      <td>-0.626769</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Light Blond</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>59.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>4.685794</td>\n",
       "      <td>3.563964</td>\n",
       "      <td>6.772089</td>\n",
       "      <td>0.037775</td>\n",
       "      <td>2.015280</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
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  {
   "cell_type": "code",
   "source": "Stats.explain_significance()",
   "metadata": {
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     "end_time": "2026-05-21T04:12:36.412254Z",
     "start_time": "2026-05-21T04:12:36.363785Z"
    }
   },
   "id": "1e4de89412a42083",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       pvalue                 p_value\n",
       "0    p > 0.05  No Significance (n.s.)\n",
       "1    p ≤ 0.05                       *\n",
       "2    p ≤ 0.01                      **\n",
       "3   p ≤ 0.001                     ***\n",
       "4  p ≤ 0.0001                    ****"
      ],
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       "      <th></th>\n",
       "      <th>pvalue</th>\n",
       "      <th>p_value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p ≤ 0.01</td>\n",
       "      <td>**</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p ≤ 0.001</td>\n",
       "      <td>***</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p ≤ 0.0001</td>\n",
       "      <td>****</td>\n",
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     },
     "execution_count": 9,
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     "output_type": "execute_result"
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   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plot results\n",
    "py50 has wrapped functions from the [Statannotation](https://github.com/trevismd/statannotations) package, allowing annotations to be draw on a corresponding seaborn plot. This will allow statistical significance calculated from py50.Stats() module to be annotated on said figure. Sample plotting examples can be seen below. \n",
    "\n",
    "A reference of tests available for plotting can be obtained as follows:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "13ed0a24bfbfd879"
  },
  {
   "cell_type": "code",
   "source": "plot.list_test()",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:36.911828Z",
     "start_time": "2026-05-21T04:12:36.893979Z"
    }
   },
   "id": "13120e8afd618f01",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of tests available for plotting: 'tukey', 'gameshowell', 'pairwise-parametric', 'pairwise-rm', 'pairwise-mixed', 'pairwise-nonparametric', 'wilcoxon', 'mannu', 'kruskal''kruskal'\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "### General plotting\n",
    "Several plotting options are available. They use the same parameters. Thus, only the name of the plot needs to be changed (for example plot.box_plot() vs. plot.bar_plot()). I personally prefer options and love to play with configurations. To that end, there are a lot of options available for tweaking the plot to your liking. First we will start with a general plot and see how it works before adding modifications.  "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d2f98773d40ec18a"
  },
  {
   "cell_type": "code",
   "source": [
    "# General plotting\n",
    "plot.boxplot(test='tukey', value_col='Pain threshold', group_col='Hair color')\n",
    "plt.title('Tukey Plot', fontsize=30)\n",
    "\n",
    "# to save fig\n",
    "# plt.savefig('tukey_box_plot.png', bbox_inches='tight', dpi=300 )"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:37.104334Z",
     "start_time": "2026-05-21T04:12:36.921539Z"
    }
   },
   "id": "4b5d501164920510",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tukey Plot')"
      ]
     },
     "execution_count": 11,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "source": [
    "The plot function is essentially another wrapper for the py50.Stats() module, but with the default final output being the plot. All plot functions also accept **kwargs associated for [Statannotations](https://statannotations.readthedocs.io/en/latest/index.html) or [Seaborn](https://seaborn.pydata.org/api.html). Their usage can be found on their sites accordingly. \n",
    "\n",
    "The figure can be saved by calling plt.savefig(). The example above shows a common method. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8a58629fe6612607"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Output the Calculated DataFrame\n",
    "There are times where we may want to output the statistics results alongside the image. This can be done using the **return_df** parameter. This will cause the plot functions to **return a DataFrame and a figure** in that order. If two variables are not given, then the returned DataFrame will be printed instead. "
   ],
   "id": "38e5c0c67756e259"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:37.294376Z",
     "start_time": "2026-05-21T04:12:37.114527Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.title('Plot and DataFrame Returned!', fontsize=30)\n",
    "df, figure = plot.boxplot(test='tukey', value_col='Pain threshold', group_col='Hair color', return_df=True)\n",
    "df"
   ],
   "id": "331289111b8c7b29",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               A               B  mean_A  mean_B  diff        se         T  \\\n",
       "0     Dark Blond   Dark Brunette    51.2    37.4  13.8  5.168623  2.669957   \n",
       "1     Dark Blond     Light Blond    51.2    59.2  -8.0  5.168623 -1.547801   \n",
       "2     Dark Blond  Light Brunette    51.2    42.5   8.7  5.482153  1.586968   \n",
       "3  Dark Brunette     Light Blond    37.4    59.2 -21.8  5.168623 -4.217758   \n",
       "4  Dark Brunette  Light Brunette    37.4    42.5  -5.1  5.482153 -0.930291   \n",
       "5    Light Blond  Light Brunette    59.2    42.5  16.7  5.482153  3.046249   \n",
       "\n",
       "    p_tukey    hedges significance  \n",
       "0  0.074068  1.413596         n.s.  \n",
       "1  0.435577 -0.810661         n.s.  \n",
       "2  0.414728  0.982361         n.s.  \n",
       "3  0.003708 -2.336811           **  \n",
       "4  0.789321 -0.626769         n.s.  \n",
       "5  0.036647  2.015280            *  "
      ],
      "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>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>Dark Blond</td>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>51.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.168623</td>\n",
       "      <td>2.669957</td>\n",
       "      <td>0.074068</td>\n",
       "      <td>1.413596</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>51.2</td>\n",
       "      <td>59.2</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>5.168623</td>\n",
       "      <td>-1.547801</td>\n",
       "      <td>0.435577</td>\n",
       "      <td>-0.810661</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dark Blond</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>51.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5.482153</td>\n",
       "      <td>1.586968</td>\n",
       "      <td>0.414728</td>\n",
       "      <td>0.982361</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>Light Blond</td>\n",
       "      <td>37.4</td>\n",
       "      <td>59.2</td>\n",
       "      <td>-21.8</td>\n",
       "      <td>5.168623</td>\n",
       "      <td>-4.217758</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>-2.336811</td>\n",
       "      <td>**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dark Brunette</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>37.4</td>\n",
       "      <td>42.5</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>5.482153</td>\n",
       "      <td>-0.930291</td>\n",
       "      <td>0.789321</td>\n",
       "      <td>-0.626769</td>\n",
       "      <td>n.s.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Light Blond</td>\n",
       "      <td>Light Brunette</td>\n",
       "      <td>59.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>5.482153</td>\n",
       "      <td>3.046249</td>\n",
       "      <td>0.036647</td>\n",
       "      <td>2.015280</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plot modifications\n",
    "That looks great! But the plot looks a little busy with all of the no significance (n.s.) indicators plotted. What if these were removed and only important/relevant information was annotated to the plot?\n",
    "\n",
    "Good news! That can be achieved by including the 'pairs' parameter. This will take a list of the groups (from columns A and B) and will filter the results accordingly. The pairs can be in any order, as long as the combinations can be found on the resulting DataFrame and in the ['A'] and ['B'] columns. \n",
    "\n",
    "There is also a way to return the calculated DataFrame using the 'return_df' parameter. This is a way to check the calculated results and if they match with the pvalues calculated from the py50.Stats() module. If there are discrepancies, they can be changed manually by using the 'pvalue_order' parameter, which takes a list of string values. The two lits - pairs and pvalue_order - **must match** in length. The returned DataFrame will only contain rows matching with the pairs list. \n",
    "\n",
    "Finally, plot titles can be passed in as a variable. This will be passed as kwargs that correspond to matplotlib. Or it can be passed using the **plt.title()** as seen above.  \n",
    "\n",
    "Additional examples can be seen under the [009_advanced_plotting]() tutorial. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "abe4c4e74f53cdc5"
  },
  {
   "cell_type": "code",
   "source": [
    "pairs = [('Light Blond', 'Dark Brunette'), ('Light Blond', 'Light Brunette')]\n",
    "\n",
    "# This is optional and will overwrite the significance asterisks associated with the pairs. \n",
    "pvalue_order = ['p ≤ 0.01', 'p ≤ 0.05']\n",
    "\n",
    "# Generate title for plot\n",
    "title = 'Tukey Plot with Custom Annotations!'\n",
    "\n",
    "plot.boxplot(test='mannu', value_col='Pain threshold', group_col='Hair color', title=title, fontsize=30,\n",
    "             palette='Blues', pairs=pairs, pvalue_order=pvalue_order, return_df=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:37.457730Z",
     "start_time": "2026-05-21T04:12:37.329268Z"
    }
   },
   "id": "3ebce60bf8c0b1d3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(             A               B  U-val     p-val significance   RBC  CLES\n",
       " 1  Light Blond   Dark Brunette   24.0  0.015873            *  0.92  0.96\n",
       " 0  Light Blond  Light Brunette   19.0  0.031746            *  0.90  0.95,\n",
       " <statannotations.Annotator.Annotator at 0x1183366e0>)"
      ]
     },
     "execution_count": 13,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": [
    "The order of the groups can also be rearranged. This will also affect how the annotations are plotted.\n",
    "\n",
    "For example, let's order the groups alphabetically. This can be done with the **group_order** parameter. Let's also change the color schemes where blond hair colors are \"seagreen\" and brunette hair colors are \"royalblue\"."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d9b907bc1a2929e0"
  },
  {
   "cell_type": "code",
   "source": [
    "group_order = ['Dark Blond', 'Dark Brunette', 'Light Blond', 'Light Brunette']\n",
    "palette = ['seagreen', 'royalblue', 'darksalmon', 'lightskyblue']\n",
    "\n",
    "# Generate title for plot\n",
    "title = 'Tukey Plot with Custom Annotations!'\n",
    "\n",
    "plot.boxplot(test='tukey', value_col='Pain threshold', group_col='Hair color', palette=palette, pairs=pairs,\n",
    "             title=title, fontsize=30, pvalue_order=pvalue_order, group_order=group_order, return_df=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:37.617495Z",
     "start_time": "2026-05-21T04:12:37.477261Z"
    }
   },
   "id": "4c1961f9bd56dcbc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(               A               B  mean_A  mean_B  diff        se         T  \\\n",
       " 0  Dark Brunette     Light Blond    37.4    59.2 -21.8  5.168623 -4.217758   \n",
       " 1    Light Blond  Light Brunette    59.2    42.5  16.7  5.482153  3.046249   \n",
       " \n",
       "     p_tukey    hedges significance  \n",
       " 0  0.003708 -2.336811           **  \n",
       " 1  0.036647  2.015280            *  ,\n",
       " <statannotations.Annotator.Annotator at 0x1184583d0>)"
      ]
     },
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "The color of the plot can also be modified using the palette parameter. As seen above, the palette can be divergent color scheme, such as \"Blues\", or distinct colors can be used. Input for distinct colors must be in a list.\n",
    "\n",
    "The orientation of the plot can also be rotated from vertical to horizontal using the 'orient' parameter. \n",
    "\n",
    "The groups can also be reordered using the 'group_order' parameter. Let's place the groups in alphabetical order. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "565808597f74e719"
  },
  {
   "cell_type": "code",
   "source": [
    "# Specify color and rotate\n",
    "group_order = ['Dark Blond', 'Dark Brunette', 'Light Blond', 'Light Brunette']\n",
    "palette = ['green', 'teal', 'seagreen', 'orange']\n",
    "\n",
    "plot.boxplot(test='tukey', value_col='Pain threshold', group_col='Hair color', pairs=pairs,\n",
    "             palette=palette, group_order=group_order, orient='h')\n",
    "plt.title('Sideways Box Plot and New Group Order!', fontsize=30)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:37.774937Z",
     "start_time": "2026-05-21T04:12:37.627675Z"
    }
   },
   "id": "3a4a8dc870ddfd1a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sideways Box Plot and New Group Order!')"
      ]
     },
     "execution_count": 15,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Bar Plot\n",
    "py50 contains functions for additional plot types. Again, they will accept the same parameters as the box_plot, as well as **kwargs associated with their respective Seaborn counterparts."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ff6eb8124caede1d"
  },
  {
   "cell_type": "code",
   "source": [
    "title = 'Tukey Bar Plot'\n",
    "\n",
    "plot.barplot(test='tukey', value_col='Pain threshold', group_col='Hair color', palette='RdYlGn', hide_ns=True,\n",
    "             title=title, fontsize=30)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:37.924847Z",
     "start_time": "2026-05-21T04:12:37.785876Z"
    }
   },
   "id": "b0977f53da846026",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x118105990>"
      ]
     },
     "execution_count": 16,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Violin Plot\n",
    "There are times when the annotations may be drawn on top of the figure. During testing, I found this occurs regularly with the violin or swarm plot. A workaround is to use the 'loc' parameter. This will place the annotations outside of the plot. The downside is that this will interfere with the title of the plot. In otherwords, if this paramter is used, it is best to remove the plot title. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fdcb5a61f80f7968"
  },
  {
   "cell_type": "code",
   "source": [
    "plot.violinplot(test='tukey', value_col='Pain threshold', group_col='Hair color', palette='PuBuGn', pairs=pairs,\n",
    "                loc='outside')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:38.106713Z",
     "start_time": "2026-05-21T04:12:37.952755Z"
    }
   },
   "id": "df7d492afda959af",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statannotations.Annotator.Annotator at 0x118668460>"
      ]
     },
     "execution_count": 17,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Swarm Plot\n",
    "There is also a swarm plot available. Again, it takes all the same parameters as above and with its respective Seaborn **kwargs. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "edf6bd1c17dc1cc3"
  },
  {
   "cell_type": "code",
   "source": [
    "plot.swarmplot(test='tukey', value_col='Pain threshold', group_col='Hair color', palette='coolwarm', pairs=pairs,\n",
    "               orient='h')\n",
    "plt.title('Tukey Swarm Plot', fontsize=30)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:38.260301Z",
     "start_time": "2026-05-21T04:12:38.131228Z"
    }
   },
   "id": "a42b8dcb414cb715",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tukey Swarm Plot')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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3di/sVGgaOantrGlOCgp16u+n59Lp0TSFiJqVi6uwpuqKENIqI//8iEWdPg7lh4odEDCxdFyPdpBV06lm+1cYUGDyOu9r5n6dZilaVamgb/86VVi0gQ07S01UqiB6za2qXKnZ1uss7+93d9BBBxU5l1qs1KypfoR6Ll0071os/baiVTw0t58qiV5z5eLFi6PeV4FN87V5IVdVO/UvU0jz92/yhzl/yFMo03uoEZQa2OGFb1W8Chq0oPCnYO+f6kNNvXov1SyofnGtW7fONzihoImGkTgi/0aLW2XX/urv+1pRmrcrIyI3kKAiKxOxVsX8AyBidc4554RP4aRKl87z6h9Rqc7xhc1NFnmgL86pqIrLf+YLBRDvLAnqj+avSsVz0ISqWKpmqjrlnYGhpNSs7Z+6xZsvsKizUHjNsf5KnPo9+QeqaPoS/5kkvHnz/M2warotaFSp3nd/qNN+oe2r06ZpmhZ1oI82xUsinoILVugpyPzzPMZC4d3/GaRzLyMxEeyABBU5VUUsU2lISc5bGjkdiOZR88/EryqgmmQLmrQ1co6wWM4TG3mQUTAr7FRY/gqV/6Di9SnTCE2vCqb+W4U1NxZX5Fkr/NM+lIR/Go3C+pr5R66qz5y2v3/KCn+1zuOfwkKP0TbRAA5PQdtFo2b98+upyTjWec4i97lEGlGKv0O/v8pW3HnoIu9f0CnfUP4IdkCCiqyq6ETcRVH1p7ihqiBXXnllnmqQZqx/5plnot5XzX3+cKiBArGeL1XVHs2RpQ79Wo6mD/GfHSFaJdPfd079zfS6/ec31WmudnYUoV+PHj3yXFdfuZIOGFHVwx+EIvvcFXQWCo121XsbOc1JJH+VTwNNdED25hjUsgqa5iSyghPrHGd6n/3BUWIJ6Cj7L4r+kdAaxBPLmVA8EydOLHL/oP9jYiDYAQnK31wnmjKjKGoyLahpr7gU1CJPJaT52qL1CVOI8jd96lRX3mTCRdFpt7wpOlQZVB8uzQ9XVHOsdxBRHzQ1lXqjZUtj7jpVMP0nl1f19MEHHyzRstQ/0Hu9ooEKhU0J4h+RrMDsn58uWrDz36Z9wX/uXW8alWgiq2yxNqdrpG/kVC6xhnqULTWn+911110xvVfqc+v/+1LFPNr5iyNHWhPwywfBDkhQmrTXP2eXKlNqbizsdEHjx4+P6zocfPDBec67qUqamueifWCfd955rl+YR8HOf0L5aDTy0n8fhbXLLrusyPXy5rTz6CTm3qARbbeipmcpiWuvvTbP9VdeecX1PSvOYBX1zdNj/AfIos704B8dq+f0+jlpIIO/P51HVVZ/07H/TB6FTUqs5flpXytsUIRe97333usmZY5UWMUV5UfN8P7JwDU/oQZFFTaljUZtR4561mOijYqNHFBRkv6+2HkEOyBB6YNT52j0V1Q0GlMz/Wsgg6oxap797LPP7OKLL3bnLtXBNrLSt7O0XH8/MDXtvfDCC1EDhb/Cp/XVup511lluGhL1oVN1YNWqVW6ddaaLIUOG5KkUKRzGGsr8VTktM9rt8aSAFXkKMIUavUc6D6y/ChdJ75NCkAYjeAdRvb+qmBTVZOwPY/5+ltGqddGaY/0K63eoPlP+ba/n0inTXnrpJTcpsQKlDtQaJa1Rz2ruLqhpvrBtgfKjL04a/e7v5qGBNer+oL9pdRHQ36jmqNMXyeuuu86dScXfCqCJsgv6G9Oob3+XDH050GTa2nfUdaEkI/ZRfEx3AiQwzROmwQFenyx9MKrCVVAlTBUgVa/UZy1eVDVUuLvpppvCt+k5FDgiBxUo5GgCUzXPeVU9NeH4m3EKosqgDiSx0gFGozj9YUfTtvgrjPGmM0Xo+fynAtOpmrRt9Nxdu3Z1wVojEPX6tS0UhFQZ8QdY9XdS5a6wcOZvBtZZCCKbO4sKdq+99lq+faNt27aFPtftt9/uTjLvTbGi9df0LLoURvuCwrr3nvsnYUZiUYBXl40rrrgiXFHTvqW/paJoLsb77ruv0Puon6xXJdb+o/MiezSqPJZ9HjuHih2QwDQRrL5Jx9KRXYMPXn755RKfqqkwCmw69ZVH4Say2uavuqlJ2N8nrTD6lq9l6YARORK4MKo6aE67yMEGJTnlVKzUh0h964YOHerW20+hWyNWVb3TGTtUzdLPCnb+7aQwrAOcf9qWokSes1fVvmh9nDw6eEZ2ZI/l3LA6KOugH62Jt6DRvQp9anb3n8NV24GRsYlL+87rr78e87mg9UVlxIgRrr9mUf1fBw8eXOB0OvqCg9JHxQ5IcOrsriY/TTKrJk1Vv7wRsvqdTv6uKpUXvKKdkzQe1Myq/mBeNUcj6nTKq8gO2aJ10WTBaubRRc23GlCh5hhVthS+2rVr5ybKVTNQZEiKlUbHKjyVdjNspLPPPtttC/UR1HuiAQ2FdRRXyNLoV1VSFeiKO2JXoUz96zzadgr9BdH2VbD2H0hjnf5F1T41oWm7qn+VlqGmOQU1TRCtiZm1bH3ZUNXU61elao5CoajZXY+NJUyifOgLhiYenz17tht8pL9ndWlQFU9fsPTZomqx3kO9t0UFOo8eo9CoL3dqztWE3vrb0PyH8T5DDaJLCvG1CkAFpeZGr2O3qkxqDizsHKilRVO2qDKnU39pNKnCr8KcAqv6Hmqev9KsJAKAh4odgArLO7WYV70rj1AnqmTprAy6AEB5oo8dgApJE+p6Z4BQdczfSRsAKiuCHYAKadSoUeGf1Qlcc9sBQGVHUyyAhDdv3jw3jYjmyFq+fLkbdapBC94IUU2YCgAg2AGoADTFyNSpU6P+TtOrFHa+VQCoTGiKBZDw/HOk+WmqlKuvvrrM1wcAEhVNsZWQ5hTSPGiaRDJyElMgEXXr1s3NiaXpRDSfVuvWrd2cdf369XPnJeXcpACCLBQKudMRagL6aOfp9WMeu0pIE9j27t27vFcDAAAUgyZ81ykGC0PFrhLyTveiHUTzbwEAgMSlSdBVkCnodG1+BLtKyGt+Vagj2AEAUDHE0n2KwRMAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQFT4YNemTRubMWNG1N899thjNmDAgJiWM3jwYHcpyLp16+z9998vdD38l3/84x82ZMgQ27ZtW/g+ffr0sTfeeMNKQ1HrDwAAgi/VAuy8886LOdgV5YEHHrBQKGTHHHNMgfdRkOzatavl5uba77//bkOHDrX77rvPhg8fHpd1AFCwLdtDNvn7HJu7PNdSks32bZ1sR3RLsWpVij5pdhDk5Ibss5k59v3CXNuRabZXsyQ7snuqNamX2K8/MytkH/2YYzMX51p2jlnbFsl2ZPcUq1ez+Os9c3GOTf0519ZuDtku9ZPs0M4pbnkonvTMv96TWYtzLSfXrN3uyXZU9xSrUyOx9yX8JdB7fM2aNa1evXpxWZZCXVHq1q1rjRs3tqZNm1qXLl3s4osvLrTKByA+snNCNv6DLPtxUa5lZJltzzD7en6uvfBxdqXZxG9/lWOfzsy1zdvNsrLN5v0WsnHvZ7nAm8he/jTbvpqba9vSzb13Cnjj389yga84flyUYxO+yLE/NoRcQFyxNmQvfZJtC1fmltq6B9WLH2fb1/Ny3d+R3pOfFuXaMx9kub+zeNOxdceOHYVeYjn+BmU94iHQFTtV0L755ht74YUX3PUvv/zS7r33Xlu2bJntv//+1rJlS9dUes8997jfb9261a655hr75JNPrH79+nbdddfZ8ccf75bz5ptvuvtoefp9LKpXr17g71TVe+aZZ+zll1+2NWvWWOfOnV3TrZpxRf+r2vf000/b0qVLbd9993Xr3qJFC/f77777zkaOHGmLFy+2ww47rNDny8zMdBePXicQJHOWqUqT//Zlq0O2dHWutWoa6O+wtnlbyB18I+nA/N3CXDusc4olopVrc23hqvwHyw1bzWYtybX99ol9vb+YlZPvNh2Hv/g5x/ZuHuz3P56W/5lrS1fnf0/09zV7aa512TN++5KC0vnnn2+zZs0q9H46Po4bN86SkkqnYhhKkPWIl0qzt//222926aWXuqbUt956yzp16mQvvfRSnvtMmTLFOnToYO+++6673y233GJbtmxxTbq6rsvrr78e0/OtX7/eBcoTTjgh6u+feOIJF+z0HAqNzZs3twsuuMC2b98evo8C5a233ur65W3YsMEeeeSR8LJVDTzwwAPda9lrr73sgw8+KHBdnnrqKevevXv40rt37xi3GlAxrNkYKtHvgmLN5pDlFvAy/0zg1//npkLet0J+F+3AvGZTActJ4NefiArbX4rznsQqlpA0c+ZMS09Pj/tzJ+J6xEOgK3Z+EyZMcFWvyy67zF2/6qqr7KuvvspzH/WPU7gS3U/BSxUxpfRq1aq52xs0aFDgc1x44YWWkpISLumqGfj222/Pdz/9/sUXX7Rrr73WDj/8cHfbiBEj7Mgjj7R33nnHzjjjDHfbueeeaz179nQ/n3nmmeEgquZdrccNN9zgdsYrrrjCPv/88wLXSyFQy/JX7Ah3CJKm9ZNK9LugaFw3yZKTLGq4a5rAfewKW7firLc+B9WXMFooaVIJ3v94Kmy7x7u/pt43VcAKCks6jh511FFxfc5EXo94qTTB7pdffnFVOj/1g9u06e+veV4zp9SuXdv9n5GREfNzqGlUIVDBTRU2hTcFskmTJlnDhg3zjLDduHGju68nLS3NOnbsaIsWLQrfpqZiT61atSwrK8v9/Ouvv1rbtm3zfMPQa9POF02VKlXcBQgqde5uUi8334G99S5JtnuT4DdMqFN7t72T7bsFeZtja1Uz22+fxH39uzZMtja7JdkvK/K+bw1rm3VqXbz17r1vsutj56ew27tTYjZDJ6oWTZJtj2ZJtvj3vO9J47pmHVrGf1/ScaywbktlJSlB1iMeKk2w8yppfpHXdZ9IxeksqUETXhhr1aqVa9Y94IADXIXt//7v/8L3q1q1atTH5+TkuL53/rBXkMj10n0LCnZA0KWmJNl5fVPdSL65y/43KnaPZOvTpfIc1I//R4rVq5Vk3y/MsR0ZZnvvmmyHd0uxWtUTu2J1xqGp9unMHNdH0I2K3T3ZjuyaYmmpxVvvznukWGpykutT50bFNkiyQ/dNsT13Tdxgm6j+r0+qffJTjs1ckms5OWbtWybbEV1T3N8ZEl+lCXZ77723ff/993lumzNnTp4qXVFpvrgjYpKTk91jFNj8VA1s1KiR/fTTT67yJqrGaX0OOuigmF6Lml61XC+Mzps3z/XTAyorBZgTD0y1Ew+0Sikl+a8go0tFogB3VPdUO6r7zi+rQ6tkd8HOqZKWZEf3SLWje7AlK6JABDuNZIlsMu3RI+8eedppp9n48eNt7Nixri/bhx9+6EaW7r777jE9h0q0CxcutNWrV7vKXDRq1tUIV9FoW/XRU/jSxMSRBg4caKNGjbImTZq4Kp9Gv+o1HHvssUWuS79+/dzAijvvvNNVAjVKV6GVYAcAQOUWiGCnyYMjTZ48Oc91hR4FKU0Zov9VGdPAhcKaO/369+9vgwYNcqNcv/7666gjaDSIwR8E1WdOgS1aVVAjbTWI4bbbbnP/a+CGRtEWNjjDP1+eOnpqYIbWSyFW/1eUOXYAAEDpSApVkjSwYMECy87Otvbt24dvu+iii9ygA38gqwwUJDXtiap8GpQBAECiUb/xXr16uZ+nTp1aboMbdiTAehTnuF1pOiMsX77cTfkxbdo0W7lypZv+ZPr06a5ZFgAAIAgC0RQbiyOOOML1kdOEv5pupHXr1vbwww+HBy8AAABUdJUm2InOPKELAABAEFWaplgAAICgI9gBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAVHhg12fPn2sTZs27tK2bVvr2rWrnXHGGTZ16tSdWu7gwYPdJRYDBgwIr4MuWofzzz/fli1bVqLlFdcbb7zhtgMAAKjcUi0AbrnlFjv22GMtNzfXNm3aZG+99ZZdfPHFNm7cODvwwAPLZB3OO+88dwmFQm4dxowZY5dddpm9++67lpSUVCbrgMotKztkqzZk26btOVY1NcmaNUi1OtVTSrSsNZuy7c9NORYys0Z1Uqxp3RT24xK+JyvXZ9vmHX+9J7s2SLPa1RP/+3RGVq6tXJdtWzNyrXqVZGveINVqVE389Y5Fpv5O1mfZ5h25Vi0t2XZtkGq1qgXjtQGBCXa1a9e2xo0bu5+bNm1qN954o61Zs8buvvtumzRpUpmsQ40aNcLr0KRJE1edO/jgg+2XX35xlUSgtAPEj0vSbUemothf/tiYY+13q2KN6xbvz3zh75m2an12+Pq6LTm2bkuKdWhRNa7rHHQKED8uTrf0rL/fk9V6T1pUsUZ1Evejd3tGrv20JN2ycv66vsFy7Y8N2bZvq6pWt0bJvigkUmD9cXGGZWR770mu/bEx2zq2qGoNalfs1wZ4Avs15fTTT7cFCxaEm0N//fVX1zyqZtJOnTrZWWedZYsWLXK/mzFjhmvKHDZsmHXv3t3Gjh2bZ1nr16+3vn372s033+wqcrGoXr16ob//9NNP7aSTTrJ9993XVRsnT56cp2l39OjRbn31ez23v2l59erVdsEFF1iXLl3cMpYvX16sbYPgWbE+K0+o8yxanRXzPusd1P2hzrN2c45t3Pa/Iz1i8tvarDyhTnRtcTHfk7K2bE1WONR5ckNmS1ZnWUX329psX6j7i96Kxaszy22dgHgLbLDbc889w4FOTbSXXHKJNW/e3N5++2175ZVXLCcnx+6///7w/VeuXGmZmZmuv9pxxx0Xvn3Hjh126aWXuuWNHDkypuYoLUdNsV6fu0jTp0+3K664wvr37+/W59RTT7VrrrnGZs+eHb6PHt+vXz/XlKuK32233eZeh1x11VXu5wkTJtiFF15o//73v4tcn61bt+a5IFg2bftr34iUkRWy9CiBryCFhbeNBTwHCnhPtkffXgrgquYlqoLeZ72eRA6kO7N/b8tI7PcExbd8+fIya7FLNInbHhCH5lnZtm2bpaenuwEVqtKpyVRU6VIfPD9VwVq2bBm+rvCnwFWlShV75JFHLCWl4FL9U089Zc8884z7Wc+nD8BRo0ZFDYIvvfSSq8INHDjQXW/durXNmjXLPf6hhx5yt/Xu3dtOPvlk97OCpUKgmpc3b95sP/74o6v47brrrrb33nu7QPjBBx8Uum6PP/54sbYfKpa01OhfOHRrakrsfTyrFLCcv35XolWrtNIK+LjQR0JKcuL2u9X7nJkd/fVU9P7C7u8kI3+A09uRGtgyR+UyefJkmzhxonXs2NEdi48//nirbAL7Ue1VpWrVquXC3JlnnukGVSgELV682ObOnWuNGjXK85jddtstz/X333/fsrOz7eijj3bhrjAKjmpCle3bt9vnn39u1157rT399NPWs2fPPPdVE7Du76cmYu2MnlatWoV/1msQrYsqkPXq1XOhzqOm5cKCnQaSnHvuuXm2jYIjgqNZ/VTXXBqpcd2UAkNfNA1qpVjVtCRX6fNLSdayAvtxUSo0UGL91ox8t2sgSnHCdllrVj/N9bPMf3vFf/81UGLjtvyvbZd6qZacwGEbsTvqqKNs4cKFrpix11572ccff1zo8TGIKv5fagE0aEFU0VLV7pRTTrH69eu7vnRqalW48ypsnqpV83YOb9asmQ0fPtxV8r766qtCR9jWrVs3T7WvXbt29u2339rLL7+cL9hFPo+oadVrapW0tLR89/GaQSKbQ6Ld10+htKhgiopNgWzvZmm25M8sy/5fvmtcR7cV733Xwa3T7lVt/soM25r+135Wo0qS7dO8iqUlcBhJRA1rp9heu6TZ0jV/vydN6qbYXsV8T8oj/GRm59qKddmWk/tXhbFZvVRr1aTwz5mKoHGdVNujaciW6z3Ra/vfe7LHLhX/teEvn332metSpVaxHj16uKBX2QQ22Kn61aFDB2vRooVrtvzzzz9de3tq6l8v+csvvyyyv4gGUijMnXbaaTZixAh75513igxRflq+mnMjqel15syZeW5T86puL8o+++zjplPRoBAvSM6bNy/mdUKwK0SqPGzPCLkqnSpvJVGzWrJ137O6G0ihPxFdR8k0b5hmu9RPtR0ZIauSllRoU3ciadWkiu3WMM31z9R+VJyqb6Jr0SjNhdeK9p4gNj179rRXX33Vli5d6o793mwVlUkggt2WLVtc/zMFqQ0bNtjrr79u7733Xrgip6ZLNY9+9NFHrt1dgxfUz81r4izK1Vdf7dL/s88+axdddFHU+2j5WgevyfSTTz5xz+MfoOFR3zr199OgBzWJ6hvGlClTbPz48UWuiwZxaMfV3H0aULFixQp78cUXrWbNmjG9FgSbKm61qsfnQBWUecvKW0oc35OypObiirjeQX5PULSqVau6i7pa7bfffpVykwUi2N11113uoo69DRo0sPbt29tzzz0XflPVf23QoEGuWTUjI8ONVB06dKjdeuutbuqQoigYXnnllfbAAw+4jphqoo2kEOkFSVX1VE3Tc/hH2Ho6d+5s9913nz322GMu+KlSp8EZkU22BXn44YddqFM/PfW1U98+lZ4BAEDllhSq6OPXUWwaPKFm5u+//z7mqiUAAGVJ04316tXL/ay5XIuaHzbI67G1GMdt2loAAAACgmAHAAAQEAQ7AACAgCDYAQAABATBDgAAICAIdgAAAAFBsAMAAAgIgh0AAEBAEOwAAAACgmAHAAAQEAQ7AACAgCDYAQAABATBDgAAICAIdgAAAAFBsAMAAAgIgh0AAEBAEOwAAAACgmAHAAAQEAQ7AACAgCDYAQAABATBDgAAICAIdgAAAAFBsAMAAAgIgh0AAEBAEOwAAAACgmAHAABQWYPduHHj7I8//iidtQEAAEDZBbsxY8ZYVlZWyZ8RAAAAiRHsjjvuOBs9erQtXbrUMjMzS2etAAAAUGypxX3AF198YatWrbI333wz6u/nzZtX/LUAAABA2Qe7e+65Z+efFQAAAOUf7Pbff3/3v5piFy1aZLm5uda6dWvba6+94r92AAAAKL1gt3nzZrv55pvt448/trp161pOTo5t27bNevToYU888YTVrl27uIsEAABAeQyeGDlypJvu5L333rMZM2bYd999Z5MmTbLt27fb3XffHY91AgAAQFkEu08++cRuv/1222OPPcK3qRl26NChrooHAACAChLsqlatasnJ+R+WlJTkmmUBAABQQYJdnz59bPjw4bZ8+fLwbRpIoSba3r17x3v9AAAAUFqDJ2644QYbNGiQ9e3b1+rUqRMeUNGrVy+77bbbirs4AAAAlFewU5h74YUXbP78+bZ48WLXNKvpTvx97gAAAJCgwU5nmogW8Lp06ZLvPrvuums81w8AAADxDHbqV6fBEZ5QKOT+12362f8/pxQDAABI4GDHNCYAAAABCXbNmzfPd9u0adPynFLswAMPtLS0tNJYRwAAAJTG4AmddeKyyy6zJUuWuECnueuWLVvm+tY9++yz1rRp0+IuEgAAAOUxj53msGvYsKF99tln9sYbb9jbb79tn376qQt2d955ZzzWCQAAAGUR7L7++ms3l13dunXDt9WvX9+uv/561zwLAACAChLsFOg2bdqU73ZNUkwfOwAAgAoU7Pr162dDhgyx6dOn29atW91FlTqddeLYY48tnbUEAABA/AdPXHXVVbZu3To7//zzw/PZpaSk2Kmnnmo33nhjcRcHAACA8gp2VapUsXvuucduueUWW7p0qbu+++67W40aNeK1TgAAACiLptiNGze6qt3zzz9v++67r7Vt29aOOeYYu+aaa2zLli0lWQcAAACUR7AbNmyYa4pVmPOMGTPG1q5dayNHjozHOgEAAFR4y5cvt0mTJiV2U6wGSrz66qu25557hm9r166dDR061M4+++x4rx8AAECFMnnyZJs4caJ17NjR0tPT7fjjj0/cil21atXc2ScirV+/3lJTi50TAQAAAuWoo45y3dWefPJJ++KLL+zoo48us+cudhI7+eST3cAJ9anr0KGDu23+/Pn26KOPWv/+/UtjHQEAACqMz/53dq6+fftajx49XNBL6OlONM2JRsZqIIV35okBAwbYRRddVBrrCAAAUGH07NnTdVvT7CFTpkyxxo0bJ26w05x11113nbuo+VVnm6hdu3bprB0AAEAF89tvv9mCBQssNzfXVqxYUabPXew+dn4NGjQg1AEAAPgkJye7wRMPPfSQy0pjx461ChHsAAAAkL910zs7l0JeWWIYKwAAQBzt2LHDTXGi7mofffSRDR8+3BI22I0bN86OO+4422WXXUpnjQAAACqwtm3buosCXq9evcr0uYtdH9RZJrKyskpnbQAAAAKievXqVrdu3cQOdqrWjR492g3hzczMLJ21AgAAQOk3xWoG5VWrVtmbb74Z9ffz5s0r/loAAACg7IOdJiYGAABAAILd/vvvXzprAgAAgNIPdocffri9/vrr7tRhffr0saSkpALv+/HHH+/cGgEAAKD0gt3ll19uNWvWdD9fccUVJXsmAAAAlH+wO+mkk6L+HIlpUAAAACpQH7u1a9faU089Zb/++qvl5OS423TaDIW6RYsW2bffflsa6wkAAIB4z2N3yy232NSpU61Tp072ww8/WOfOnd0JbmfNmkUzLQAAQEWq2Kki98wzz1jXrl1t2rRpduihh1r37t1t7Nixbo67c845p3TWFAAAAPGt2KnZtWnTpu7nvfbay+bOnet+PuaYY+znn38u7uIAAABQXsGuffv29vbbb7uf27Vr56p2smLFinitEwAAAMqiKfa6666zSy65xJ3Ytn///jZu3Dg7/vjj3WnGTjjhhJKsAwAAAMoj2Kk/3aeffmrp6eluwuKJEyfaRx99ZPXq1XPNsQAAAKggwU5q1arlLqL+dmeffXa81wsAAAClEeyKOo2YH6cUAwAASOBgF3kaMY2Mvf322+3KK6+0hg0blta6AQAAoDRPKeYZMWKE9e3b11q0aFGc5wMAAECiTHcCAACAxESwAwAACAiCHQAAQGXqY/fWW2/luy03N9emTJliDRo0yHP7iSeeGL+1AwAAQHyD3ahRo/LdptGwL774Yp7bNCUKwQ4AACCBg90nn3xS+msCAACAnUIfOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAo12DXpk0bmzFjRtTfPfbYYzZgwICYljN48GB3Kci6devs/fffL3Q9/Jd//OMfNmTIENu2bZuVl99++80+//zz8PV58+bZDz/8UG7rAwAAEl/CVuzOO+88F+7i4YEHHsgTkqLRc3355Zf2xRdf2JgxY2zWrFl23333WXm55ZZb3Dp4Bg0aZEuXLi239UF+uRnbLfOPpZa5eqnlZqYnxCbK3rLBMn5fbFnrVlooJyfqfXJ2bLXMP5ZY5p/LLTcrs8zXsSLQtstau9JtS21TAKgoUi1B1axZM27LCoVCRd6nbt261rhxY/dz06ZN7eKLL7bhw4e7CxAp889llrF8Xvh6xopfrFrLDpbWaLdy2VihUK6lL5pp2RtXh29LSltg1ffZz1Kq1/at5wLL/GOx7/p8q75HF0ut16TM1zlR5WzfYjsWfmehrIzwban1m1q1PTpbUlLCfhcGACdhP6Uim2JVTTv++ONt3333tQsuuMBGjBiRp/l169atds0111jnzp3t0EMPtUmTJoWX8+abb7pLnz59Yn7+6tWr57mux95///128MEH24knnmhff/21a7YtqElYz3vdddfZsGHDrFu3btazZ097+umn84TNJ554wi1vv/32s0suucRWrVoVXs4333xjjz/+uNsGuqxcudJuvvnm8PIXLFjgbtf26Nu3r7300kvF3MLYmUqdP9T97w219GVzLNcXBspS1toVeUKdW6WsDEtfOjt8PWfrhjyhzsnNtR1LfrZQbvTqXmWUvvTnPKFOsjesdhU8APGlY+GOHTsKvCBAFbvI/maXXnqpuxx99NEutI0ePdoFLM+UKVPshhtusGuvvdb+85//uKZMBTw16S5atMjdZ+jQoTE93/r16+2FF16wE044Ic/tet7x48e7HXHTpk1FLufDDz+0s846y4VKrZ+C4RFHHGGtW7e2F1980S3vwQcftEaNGtkzzzzj1lW33Xrrra7ZtWvXrq5yKP3793e/P/nkky09Pd0uvPBCO+mkk1zAXbx4sd12222uyunfJp7MzEx38YdglJwO8lGFQpa98U+r0rhFmW/e7PXR1yl32ybLzdxhyVWqW1ZB652TZTmb11G1c6F9h+Vu3xx9G2/4o1zeWyCodCw9//zz83Q7QiUJdhMmTHCVqcsuu8xdv+qqq+yrr77Kcx+FIFXyRPdTUFLgUQWvWrVq7vYGDRoU+BwKSikpKeFvD/Xq1bPbb789z30U9LwqXUGDPvy0jJtuusktV+umit3s2bNdsBs3bpyr5h1wwAHuvnfccYer3k2dOtVVB9PS0qxGjRpuGaJl1K5d2120PRo2bGhXX321+12rVq1cRe/555+PGuyeeuopV/1DgCWV+Jf/u0sM96n02EZAvCXF8NnjP44jIMHul19+sU6dOuW5rUuXLnmqZi1a/P1NWuFHMjJibxYbOXKk23kU7DZs2OAqameeeaaroClESfPmzYu13rvttpsLZB5V1LKzs91o2z/++MM1HScn/90arkpcLAMkFFjnz5/vwqwnJycnz3P5qep37rnn5qnY9e7du1ivBX9Lrb+L66tmFtF3MynZUus1Lbd1UtUtUnLNepZc5a8PxLT6u1jW6vz7V1JqmqXU/msfr+ySq1a35Jp1XaUzUlqDXcplnYAghzoVOXTsK4xCXSwBEBUo2HmVNL/I69FCTSyDJjwaMNGyZctwBaxDhw6umqZpUv7v//7P3V61atXw/aPtZAptqal/b1JV3aKtk0KYPProo656FzmIoyh6HvXZi7VpuUqVKu6C+B38q7ZsbxnL57rmVycpyaq16mjJaeWznTVoI2fLeste/3v4tqQq1dw6eVJq1bMqu+5lmat+/fuBySlWrXVnS/J9wajsqrXqZDsWaPDE3web1AbNLLVh8b7YASiajqWRfdpRCYLd3nvvbd9//32e2+bMmZOnSlfUjlOckCeqpPlDWCQvtKn6VatWLffzihUrXCgsSp06dVwVcM2aNa4foKgPnPoHqr+BvxIXjcLgxx9/nKci+Pbbb9vPP//s5t9D6VNfq9S6jV2fOrXQqVKXnPZ38C+XD8c9OltO01ZukERSWlW3TpGBreque1lag10te9MaS0pJ+es+qfm/gFRmKdVrWc1Oh7jBKBpEkVKrvqXULPoLFwAkgnL/mq5Ok5o7zn+JHAlz2mmn2U8//WRjx461JUuWuHnmvvvuu5hLs/o2oD5oq1cX0HnczDXrKmjpouZQ9XlTqCtoJK3CpsrDWhcN7lA5ee7cuTG/7oEDB9ojjzxin3zyiXs+BTJNQLzHHnu436t/nW7X5MredTXBbty40fX1U+laFTsNDNEcfXfeeWe4yRhlQ02cVZrsblUa716uoc5PAaRK01aW1qBZgVW45Go1rErTlq7KR6iLTttO21DbklAHoCIp94qdJg+ONHny5DzX1bdt1KhRdu+997r/DzroIDv88MOjNnVGoxGlmuBXgUjTlEQLhFdccUWeINixY0c32KGgqqCqdBqR+vDDD7sRtEceeaSdffbZrn9eLFSZU187hTNV/fR8GnHrNcWeeuqpbmSvBl1oVK36+2lbKexpIITW7a677nKDJTTAQs/tjaAFAACVU1KouG2U5UBztqlfWfv27cO3XXTRRW5AhT+QITYKkt27d3fN214zMgAAyE+tiL169XI/a+aK8ugTWJzjdrk3xcZi+fLlblTntGnTXJOqpvuYPn26q5IBAAAgQZpiY6FJfRcuXOgm7lWfMw0eUBNo27Zty3vVAAAAEkaFCHbinXkCAAAAFbgpFgAAAEUj2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAAAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAIiHINdn369LE2bdq4S9u2ba1r1652xhln2NSpU3dquYMHD3aXWAwYMCC8DrpoHc4//3xbtmyZlZetW7faW2+9Fb6+bt06e//99y3RhLIyLXvlEsvZuG6nl5Xz50rL/n2ZhUK5VtpCGTsse+Viy92y0RJF7ub1bp1CmenlvSoohtxtW/7al7ZvZbsBSAip5b0Ct9xyix177LGWm5trmzZtcoHm4osvtnHjxtmBBx5YJutw3nnnuUsoFHLrMGbMGLvsssvs3XfftaSkJCtrzz33nM2YMcNOPPFEd/2BBx5w63bMMcdYosj4/gvb8dnbFkrf7q6n7tnRavYfaMnVaxZrOTlrfrdtbz1jOX+ucNeT6zawGv0GWFrrtqWy3jumvmfp0yebZWWYJSVZWvvuVrPfAEtKq2LlIZSRbtsm/duyfpmpa2ZVq1n1Xv2s2gFHlMv6IDah3Bzb8eFrlvHTNLPcHLOUVKva/RCrfsQ/LSmJhhAA5afcP4Fq165tjRs3tqZNm9o+++xjN954o/Xr18/uvvvuMluHGjVquHVo0qSJ7b333q7a9+uvv9ovv/xi5UEhrrDr5S1r6S+2/YOXw6FOshfNtu2Tni/2wXHrq4+HQ53kblpvWyeMsdytmy3eMmZ/Y+lfTPor1LkVCFnWnO9sx0cTrbxoO2b98tNfoc6tZLpbn8wFCnpIVOnTPrSMH774K9RJTrZlfPOJZcz4uLxXDUAJ6Di7Y8eOAi8VSbkHu2hOP/10W7BgQbg5VCFLzaNqJu3UqZOdddZZtmjRIvc7VbbUpDts2DDr3r27jR07Ns+y1q9fb3379rWbb7455oBUvXr1qE27J5xwgvXs2dOWLl3qmm313J433njDrYd/nf7zn/9Yr169rEuXLnbDDTdYZmZm+P5TpkxxlcrOnTvbKaecYt988014OY8//ri7rud47LHH7M0333QXb/mbN292y+vWrZsdfPDBNmLECEtPL7gJT8+r5l3/ZWdk/PRl1Nuzfv3Zcrdsink52YvnuSCXf0EZljnnr+0RT5k/Rl/vjJ+/tlB2lpW1UPoOy5z7fdTfZf44rczXB7HLKGhfKuB2AIkrFAq5jKHjdbTLUUcdZRVJQga7PffcMxzo1ER7ySWXWPPmze3tt9+2V155xXJycuz+++8P33/lypUuvCgUHXfcceHblbIvvfRSt7yRI0fG1Kyq5agp1utz59FzX3311fbUU09Zq1atilzOn3/+aR9++KFrUlY4mzx5crjf3Pz58+2mm25y6/bOO++4wHjhhRe6IKuwp2Zhhdgvv/zS/awmWF1ef/119/hbb73VtmzZYi+//LI9+eST9vPPP9sdd9xR4LponRV6vUvv3r1tZ4QK6k8UClkofVvMy8ndvqX4z7ETCuwHlZXp+guWNfX1C1d8irFtUP5CO7aW2X4LoPQlxZAPVIipVq1awr8d5d7HrqDmWdm2bZurRGlAhap0ajKVk046yQUmvwsuuMBatmwZvq7wd80111iVKlXskUcesZSUlEKDzzPPPON+1vMpvY8aNSrPG61KoVcxi0VWVpYNGTLENe0qICr1K4CddtppNn78ePf/8ccf7+57zjnn2LfffuuCmiqDep1paWmueVi8HalBgwa2fPly++ijj1xFz9tOqtipP56qkt5tfuqzeO6554avq2K3M+EurVVby14yP9/tSbXrWXLDXWJfTss2rp+bAmGk1Fbx72OX1qqNZaz9Pd/tKbvsXuy+gfGQVKe+JTdoYrnr/8z3u9LqY4j4SG3ZxnU/KIv9FkDpSkpKcpmisJYv71hcHv3uAxHsvKbCWrVquZBz5plnumrX7NmzbfHixTZ37lxr1KhRnsfstttuea5rFGl2drYdffTRLtwVRsFRo2Nl+/bt9vnnn9u1115rTz/9tGt6FVUMi8sfNPVatD6iZmSt36uvvponCKpZtSh6rKqYhxxySJ7bdZsqfh07dsz3GL3+orZBcVTtdohlzvnWjWQNS0q2GkecYknJsReBNVCiWs++lv7VB3luT2vTpVSCTbUD+1rWglluBGpYaprr8F4e9AFR48hTXZ9Cf+VOYa/q/oeXyzohNtUP629bVy5yzemepOo1rXrvv1sMAFQcSUlJ+bphVVQJGey8QQuqdqlqpz5o9evXdxUzNbUq3HkVNk/VqlXzXG/WrJkNHz7cVfK++uqrQkfY1q1bN08Ia9euXbiC5gW7yOVHUoUwUmSY8vr46b5qevVGvXpiKfHqsarKTZyYv8O/BqCUhaRq1a32Oddbxsxplr1soSXVqmNVu/ay1F1alOgAmdpiTxcUQ9nZltams1Vpv1+prHdy7XpW+/ybLeOHqZbz+zJLrtfQqnbrZSnFqDLGW9peHa2O1unHLy138wZL3W0Pq9L1YEuu9ld1GokpteluVueCIW4ARc7aPyylcTOr2q23JdepV96rBqCSS8hgp9DSoUMHa9GihX366aeuv9qkSZMsNfWv1VXfs6IGQqgvmcKcmjzVVKm+bGrejJWWHy2sebQshU7Pb7/9FvOyW7dubStWrMgTJu+77z53+6mnnpqv1Kvr3uvVfdS/Trftvvvu4SCspmONJC6r9v+kqtWsmqpKcagsKdzoUhaSa9Sy6gcnzrQxktKkudXoe3p5rwaKSRXn6ofl/XIGAFbZB08opKxZs8aFNwWUO++80957773wBMP16tVzzaPqV6YwNGHCBHvppZfyjDAtjAY8aGTss88+W+B9tHytgy6///67W/706dMLnTdOfe5efPFFN0L2448/dgM3YjVw4ED3Gp9//nnXZ07z1uniDcpQOVjbQ6/Xu64BIqtXr3YDQdRf7/rrr7dZs2bZnDlzXN86vYY6derEvA4AACB4yj3Y3XXXXa5vmfqMqYP/kiVLXMjZf//93e81OnTQoEGuWVWjRxWghg4d6s7GoKBTFAXDK6+80kaPHu1CWzRq1tU66HLkkUe6aUr0HP4RtpFuu+0227hxo7uPOl3qOWKl6U9UodPzaBTsa6+9Zg8++KD16NHD/V7roD5zms9Pr7N///5uu+j1q3Knx6pPoQKitpmqeA899FDMzw8AAIIpKZRos9+iTAanqKn6+++/d4M6AABAMI7b5V6xAwAAQHwQ7AAAAAKCYAcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAIitbxXAGXPO4ucTlECAAASm3e8juUssAS7Smjbtm3u/969e5f3qgAAgGIcv2vXrl3ofZJCscQ/BEpubq79+eefVrNmTUtKStrpbxEKiJ9//nmRJybGzmN7lx22ddlie7O9g2zrTh4rFdUU6po0aWLJyYX3oqNiVwlpp9hll13iukztqAS7ssP2ZlsHFfs22zvIau3EsbKoSp2HwRMAAAABQbADAAAICIIddkqVKlXs8ssvd/+j9LG9yw7bumyxvdneQValDI+VDJ4AAAAICCp2AAAAAUGwAwAACAiCHQAAQEAQ7BCzZcuW2fnnn29du3a1Qw891MaNGxf+3W+//WYDBw60Ll262LHHHmtffvklWzZOLrroIhs8eHD4+ty5c+3UU0+1zp072z//+U+bPXs223onTZkyxdq0aZPncuWVV7K9S0lmZqYNHz7cevToYQceeKA99NBD4VMlsX/H1xtvvJFv39albdu2bO9S8Pvvv9vFF19s3bp1sz59+thzzz0X/l1Z7dsEO8R8tgoFjPr169ubb77pPpRHjx5tkyZNch/IgwYNskaNGtnEiROtf//+bvTPqlWr2Lo76b///a+bqdyzfft29z7st99+7gNbIVsfIrodJffrr7/aYYcd5r6QeJeRI0eyvUuJtu1XX31l48ePtwcffNBee+01e/XVV9nepcD7ou1dPvvsM2vZsqWdc845bO9ScPXVV1uNGjXc5/Mtt9xijzzyiPviWKaf3TqlGFCU1atXh6666qrQli1bwrcNGjQoNGzYsNBXX30V6tKlS2jbtm3h3/3rX/8KjRo1ig27EzZs2BA65JBDQv/85z9DN910k7ttwoQJoT59+oRyc3Pddf1/5JFHhiZOnMi23gnXXXdd6MEHH8x3O9u7dPbr9u3bh2bMmBG+7amnngoNHjyY7V0GxowZEzriiCNCGRkZbO8427hxY2ifffYJ/fLLL+HbLr/88tDw4cPLdFtTsUNMdH46ffPQqVBUofv+++/t22+/tf33399mzpxp7du3d99SPN27d7effvqJrbsT7r33Xlf93GuvvcK3aVtr23rn+NX/KvmzrXfOokWLrFWrVvluZ3vHnz479Dmizw6PKhl3330327uUbdy40Z5++mm77rrr3Hxq7N/xVa1aNatevbqryGVlZdnixYvthx9+sHbt2pXptibYodjUb+Css85ypeS+ffvamjVrXPDza9iwof3xxx9s3RKaPn26fffdd3bZZZfluZ1tHX/6orJkyRLXTKX9+YgjjrAHHnjA9QNje8ef+uM2b97c3nrrLTv66KPt8MMPtyeeeMJ192B7l66XX37ZfVZruwvbO76qVq1qQ4cOdd0K1I/umGOOsUMOOcT1qyvLbZ0a9yUi8EaNGmVr166122+/3X3L3rFjR77ZtHVdB0YUX0ZGhg0bNsx9QOgboB/bOv7UF9TbrqpKr1ixwvUBS09PZ3uXAvUp0kCsV155xX1+6ICnfV2VDvbv0v0CM2HCBLvgggvCt7G9S6f6r/665557ri1cuNBGjBhhPXv2LNNtTbBDsXXq1CkcQK6//no3ukc7rZ921shQgtg8/vjj1rFjR+vVq1fUb4SRHwRs652j6tGMGTOsbt26rnlEzSaqHt1www2uuZDtHV+pqam2detWN2hC294L16omqVM/27t0/Pzzz7Z69Wrr169f+DY+T+Lf0vL666+7AW86/ulYqW2ugYYtWrQos32bpljERBW6jz76KM9t6vulfgSNGzd2v4+8f2TZGbGPhNW2VlO3Lhp5rIt+btq0Kdu6FNSrVy/c90X23HNP98WFfTv+tE0VKLxQJ61bt3bTRLB/l56pU6e6EZn6AuNhe8eXpi/RlxN/WFP/c31xKcttTbBDTNQ8pSlM9O3DvxM3aNDAdQidM2eOa7ryd5BWHwMU3wsvvOCCnPog6aI+jbroZ23TH3/8MTznl/5X51y29c4d8A444IA8Ved58+a5sKd9m+0dX9pXFZrVr9GjTuYKeuzfpWfWrFmus37ke8H+HT8Kaepm4K/Mad/ebbfdynRbE+wQE5WUO3To4Obl0ZxfKjXff//9dskll7jmqmbNmtnNN9/s+hSMHTvWfYiccsopbN0S0AFO3/q8S82aNd1FP6vT8+bNm+3OO+9074P+VyBRJ12UjCqhqiANGTLEfQhr377vvvtcXyS2d/ztscceboJzfV7Mnz/fBWt9Zpx55pls71Kkz2b/CHth/44vfQFPS0tznyX64vLJJ5/YmDFjbMCAAWW7reM+gQoC648//nBz13Xr1i100EEHhUaPHh2ek2fp0qWhs88+O9SxY8dQv379QtOmTSvv1Q0MzWHnzWMnM2fODJ144omhTp06hU455ZTQnDlzynX9gmDBggWhgQMHuvkYtW8/9thj4X2b7R1/mzdvDt1www1ue/fs2ZPtXQb0efHFF1/ku539O74WLlzoPkt0nNR8gc8++2yZf5Yk6Z/4x0UAAACUNZpiAQAAAoJgBwAAEBAEOwAAgIAg2AEAAAQEwQ4AACAgCHYAAAABQbADAAAICIIdAABAQBDsAKCI0wS1adMmfNGp9XR6oOeeey7m7abHzZgxo0Tbefr06bZo0SL38xtvvOHWpyzs7HPpsVpGQeee1jbR/wDiKzXOywOAwNE5ko899lj3c3Z2tn399dd26623Wr169ezEE08s8vFffvml1a1bt0TPPXDgQHv++edtzz33LNHjAVQuVOwAoAi1a9e2xo0bu0uzZs3spJNOsp49e9rkyZNj2nZ6XJUqVdjOAEodwQ4ASiA1NdXS0tLcz1u3brWbb77Zhb2OHTu6ptqPPvooalOsmihfeuklO+2006xTp07Wv39/mz17dtTn8JpCzznnHHvsscfczzq9t34+4IADbL/99rN77703fP/Bgwe7ywknnODWZenSpbZ582a74YYbrFu3bnbwwQfbiBEjLD09PfyYhx56yN2+77772oABA2zhwoXh3xX2XKKm1mOOOcY99uSTT7Zvv/026uvIyspyz6tlHHLIIfb555+zzwGlhGAHAMWgkKJK3bRp0+zwww93t9155522ZMkSe+aZZ+zdd991AUZNtZmZmVGXobB00UUX2TvvvOOqgSNHjox6v9dffz18//POO8/9vGrVKvdcr7zyit1xxx327LPP2hdffBF+zNtvv21XX321PfXUU9aqVSu3Hlu2bLGXX37ZnnzySfv555/d42TKlCn26quv2iOPPOLWu1GjRi6gegp7LoU6hbWLL77Y3nrrLTvwwAPda1q9enXU1/vpp5/a6NGj7dFHH3VNywBKB8EOAIowbNgw69q1q7uoOnXTTTfZv/71L1cZkx49erjg065dOxemFMI2btxo69ati7o8NeUeccQR1rp1azv33HMLrNg1aNDA/a/+eTVr1nQ/q0qoIKjHqt9f27Ztbf78+eHHqAqoSp/Wc/ny5a5yeP/997uqoW5TGHvzzTdd2Fu5cqVb3q677mq777673Xbbba7i5ynsuV544QVX4VMfwz322MOuv/5622effezFF1/M8xpU9ZswYYJdeeWVbjtpG6rPIoDSweAJACiCQslRRx3lfq5atarrM5eSkhL+vcKNAtRrr71mixcvtjlz5rjbc3Jyoi5P4c9Tq1YtVwWMVcOGDa1GjRrh66r4+SuDzZs3D/+s0bS5ubmu+dNPty1btsz69evngpgqj126dHFh85RTTonpubTsQYMG5VmuluGN4PVs2LDB1q9f70KvP3wCKB0EOwAoggJOy5YtC/z9jTfeaD/++KPrL3fmmWe64Hf66acXeH+vb15J+AOlvyrmUfD0KFgqjE2cODHfY5o2bWrVqlWz999/3zUrq6l0/PjxLpyqabWo5/I/j//5FBqj8a/jzrx+AIWjKRYAdoIGTqh/2sMPP+wqe0ceeaRt2rQpX5gpD2pCVZNrUlKSC6a6aODEfffd5ypvn332mWsmPfTQQ2348OGuf54GXCxYsCCmZc+cOTPPbbqu2/3q16/v+u6pb59n7ty5cXyVAPyo2AHATtA0JtWrV3cDKtQnToMNvMEJBQ2eKA41hWqkavv27Yv9WM1916tXL9f/bciQIa4Cp3506rNXp04dV11TyFOFUU2l//3vf91rUVNxZJNqtPn1NDBDz9G5c2dXFVT/u3vuuSfP/RQqzz77bBs1apRrJlYF8e677y72awEQG4IdAOxksNPgBE0FogEFu+22m1166aVupOm8efN2emJhDVBQ+NJACA1eKC49VgMgFMQ0RYuCnkKeaJCFqowKWmvWrHGDIDRyNpbJlDWYYu3atS6w6bEKhhoVHO31XnLJJbZjxw675pprXLhU3zwv/AKIr6RQebcVAAAAIC7oYwcAABAQBDsAAICAINgBAAAEBMEOAAAgIAh2AAAAAUGwAwAACAiCHQAAQEAQ7AAAAAKCYAcAABAQBDsAAICAINgBAABYMPw/vKAs7a5JfY0AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:38.416301Z",
     "start_time": "2026-05-21T04:12:38.261340Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plot.stripplot(test='tukey', value_col='Pain threshold', group_col='Hair color', palette='coolwarm', pairs=pairs,\n",
    "               orient='h')\n",
    "plt.title('Tukey Strip Plot', fontsize=30)"
   ],
   "id": "ec2f84545e236593",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tukey Strip Plot')"
      ]
     },
     "execution_count": 19,
     "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",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "source": [
    "### scikit-posthocs\n",
    "Finally, a heatmap of pvalues can be generated. This script is a wrapper for the [scikit-posthoc](https://scikit-posthocs.readthedocs.io/en/latest/) tool. This is a good alternative to show a \"clean\" plot without annotations and have the pvalues represented on the side. It also offers a quick way to visualize the relationship between the groups. \n",
    "\n",
    "In order to create the pvalue heatmap, a matrix of the pvalues must be created. This is done using the 'get_p_matrix' from the Stats() module, as seen below. The output will be a DataFrame. "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ada2d21644493933"
  },
  {
   "cell_type": "code",
   "source": [
    "# Replace all string values with a new value\n",
    "matrix_df = Stats.get_p_matrix(data=tukey, test='tukey', group_col1='A', group_col2='B')\n",
    "matrix_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:38.439185Z",
     "start_time": "2026-05-21T04:12:38.422859Z"
    }
   },
   "id": "91cdcbdd4916f62a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                Dark Blond  Dark Brunette  Light Blond  Light Brunette\n",
       "Dark Blond        1.000000       0.074068     0.435577        0.414728\n",
       "Dark Brunette     0.074068       1.000000     0.003708        0.789321\n",
       "Light Blond       0.435577       0.003708     1.000000        0.036647\n",
       "Light Brunette    0.414728       0.789321     0.036647        1.000000"
      ],
      "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>Dark Blond</th>\n",
       "      <th>Dark Brunette</th>\n",
       "      <th>Light Blond</th>\n",
       "      <th>Light Brunette</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dark Blond</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074068</td>\n",
       "      <td>0.435577</td>\n",
       "      <td>0.414728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dark Brunette</th>\n",
       "      <td>0.074068</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>0.789321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Blond</th>\n",
       "      <td>0.435577</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Brunette</th>\n",
       "      <td>0.414728</td>\n",
       "      <td>0.789321</td>\n",
       "      <td>0.036647</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "source": [
    "Once the pvalues are created in a matrix, the resulting DataFrame can be \"fed\" into the 'p_matrix' function in the Plots() module. There are many paramters for modifications available as the **kwargs, which can be found on the [scikist-posthocs documentation page](https://scikit-posthocs.readthedocs.io/en/latest/)."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3f3fcda4b3f1c4ce"
  },
  {
   "cell_type": "code",
   "source": [
    "# matrix_plot = Plots.p_matrix(matrix_df, title='Plot P Value Matrix', title_fontsize=20, linewidth=1, linecolor='0.5')\n",
    "title = 'Plot P Value Matrix'\n",
    "matrix = Plots(matrix_df)\n",
    "matrix.p_matrix(title=title, titlesize=20, linewidth=1, linecolor='0.5')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:38.574869Z",
     "start_time": "2026-05-21T04:12:38.492262Z"
    }
   },
   "id": "f0795859c24284dd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Axes: title={'center': 'Plot P Value Matrix'}>,\n",
       " <matplotlib.colorbar.Colorbar at 0x1183e3820>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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Xuo5L4kykNxuAsroq6tEl1GylitIUKOpv0so6WrrSn75UePNnahiEt3IOAPgjoERMU9erClOUrdOHvTJACo6UsVOwoCpnrb8caGohrbusbJoyecqEKUC9kAUVaQlsvPGf+nuTG88n6vLUpO4aP6g5MRVc6lgo8NHzaLWhxx57zAWI/mMzg6FgVVXaCgQ1nlVdrDqmyhYrA6jflxwVF2mybU1ppABMmWadNz2HqqYV4GtKHxW8hJOCK28MpwLWtDy/2qcvMArOtOSixpHqeGpsq76MaLyi/m5VeYsmOPfPWHrZXFWK69joGOixKY0jDYYWA/CqyhWoe1lQfwr+vXOsZS2TG98KIHZlij8fMxEDAAAgapGhBAAAQEgIKAEAABASAkoAAACEhIASAAAAISGgBAAAQEgIKAEAABASAkoAAACE5P8t1nuBpbQqBgAASJ8GDBhwoZuAdIAMJQAAAKIjQ+nhm070889G9/9X4HWkET0GvzfTd53zHf0437F7vhHbyFACAAAgJASUAAAACAkBJQAAAEJCQAkAAICQEFACAAAgJASUAAAACAkBJQAAAEJCQAkAAICQEFACAAAgJASUAAAACAkBJQAAACK/lnfNmjUtU6ZMQT3h4sWLQ2sRAAAAoi+g7Natm+/6jh077M0337SHHnrIrr/+esuWLZtt3LjR3nrrLXv44Ycj2VYAAABk1ICyUaNGvuv333+/DR482OrWrevbVqtWLbvuuuvslVdesc6dO0empQAAAIiOMZTbt2+3UqVKJdlevHhx2717d7jaBQAAgGgNKG+66SZ74YUXbO/evb5tO3futEGDBtltt90W7vYBAAAg2gJKBZNHjhyxGjVq2M0332xVq1a12rVrW9asWe3555+PTCsBAACQscdQ+rv00kvt3XfftR9//NF++uknt61kyZJ2zTXXRKJ9AAAAiLaA0nPJJZdY+fLlLT4+3t3es2eP+1m0aNHwtQ4AAADRF1AuW7bMnnnmGfvtt98SbFdgqbkqN23aFM72AQAAINoCSo2TvOGGG+z111+33LlzR6ZVAAAAyDDSHFD+/vvvNnHiRDdNEAAAAJDmKu9KlSrZqlWrOHIAAAA4twxl5cqVbeDAgfb555/blVde6ZZe9Ne1a9e0PiUAAABiKaD86quvrFy5crZ//3538aeiHAAAAMSWNAeU06ZNi0xLAAAAEDvzUG7cuNEmTZpkP//8s505c8ZKlChhzZs3typVqoS/hQAAAIiuopxFixZZ06ZN3byT999/v7uoq7tt27b26aefRqaVAAAAiJ4M5auvvmq9e/e21q1bJ9g+ZcoUGzVqlN11113hbB8AAACiLUO5c+dOu/POO5Ns17bt27eHq10AAACI1oDymmuusaVLlybZ/sUXX1ixYsXC1S4AAABEa5d3t27d3GXdunVWvnx5t23t2rW2YMECGzp0aCTaCAAAgGjKUKpre8KECXbixAl75513bPbs2a5AZ/r06VavXr3ItBIAAADRNW1QtWrV3AUAAAAIKqDs169f0EdqyJAhHFUAAIAYkuYubwAAACDNGUqyjgAAAAjLGMqtW7e6n6VKlXI/v/nmG1eYc/bsWatbty5FOQAAADEoqIByx44d1rlzZ9u2bZu7XaZMGevatav16NHDqlat6rb16dPHDh8+7JZlBAAAQOwIKqAcOHCglS5d2i2veNFFF9nYsWOte/fuLqDs0KGD2+ftt9+2adOmEVACAADEmKCKctasWeMylAULFrTcuXO7YFJuv/123z61atWyX3/9NXItBQAAQMYNKI8ePWp58+b13c6ePbvlyJHD4uLifNuyZs1qp06dikwrAQAAkPGLcjJlyhS2X3ry5El3AQAAQAwFlJMmTbJcuXL5bisbOXXqVF/mUlnMYI0bN85Gjx6dYFuzZs2CfjwAAAAyWEBZuXJlW79+fYJtFSpUsM2bNyfYVqlSpaB+aceOHa1NmzYJtg0fPjyoxwIAACADBpSq3g4njcHUBQAAABkfSy8CAAAgJASUAAAACAkBJQAAANJPQLl3795wPh0AAACiMaB88sknLT4+PsG2M2fO2Pjx461u3brhbBsAAACiMaBcvXq1W3rRWxVn+fLlVr9+fTdPZa9evSLRRgAAAETDxOae6dOnW6dOnax9+/ZWoEABW7BggTVt2tR69OiRYHlGAAAAxIY0Zyjz589vb775pls1Z/78+TZx4kQbMGAAwSQAAECMCipD2a9fvyTbLr74YsuSJYs9++yzVrFiRd/2IUOGhLeFAAAAiK4ub4+CyXvvvTe8rQEAAEB0BpT+WUd1cd9zzz1WpEiRSLYLAAAA0TqGcuzYsXb69OnItAYAAADRH1Cqm/v111+3X375xU6ePBmZVgEAACB6x1AuXbrU9uzZY3PmzAl4/6ZNm8LRLgAAAERrQPniiy9GpiUAAACIjYCySpUqyd63b9++UNsDAACAaA8of/75Zxs2bJht27bNreEtWttb4ykPHDhgGzdujEQ7AQAAEC1FOU8//bQLHNu1a2d//vmntW3b1urUqWOHDx+2wYMHR6aVAAAAiJ4M5fr16+29996z6667zubOnWtXX321NW/e3EqUKGEzZ860Ro0aRaalAAAAiI4MZdasWd2yi6Jg0qvqrl69um3ZsiX8LQQAAEB0BZQVKlSwSZMm2fHjx61cuXK2ZMkSN4Zyw4YNliNHjsi0EgAAANHT5d2vXz/r1KmTFS9e3B588EGbOnWqq/w+evSode7cOTKtBAAAQPQElNdee60tXLjQZShz5sxps2bNshUrVtgll1xiN954Y2RaCQAAgOgJKHfu3OmmDDpy5Ijlzp3bSpYsaTVq1IhM6wAAABA9AeXy5cttyJAh9uOPP7oxk55MmTJZ2bJlrW/fvlapUqVItRMAAAAZuShn2bJl1r59eytTpoxNmzbNvvnmG/vhhx/s22+/tSlTprhq7zZt2tiaNWsi32IAAABkvAzlmDFjrHXr1tanT58E2/PmzWtVq1Z1F11//fXXbfz48ZFqKwAAADJqhnLz5s2pTljepEkTll0EAACIQUEFlKroVgYyJfny5XNLMgIAACC2BBVQqggnc+aUd1Vxjn+xDgAAAGJD0FXen3zyiZsmKDl///13uNoEAACAaAsoixYtapMnT051vyJFioSjTQAAAIi2gFLrdQMAAADnPIYSAAAASA4BJQAAAEJCQAkAAICQEFACAACAgBIAAAAXDhlKAAAAhISAEgAAACEhoAQAAEBICCgBAAAQkkzx8fHxlg4MHDjwQjcBAACk0YABAzhmIEMJAACA0NDlDQAAgJBktXSm/78aX+gmIMIGvzfTd52ukujnP5xlWtxXF7QtiLyWR27xXef9PLbezxHbyFACAAAgJASUAAAAuDAB5eHDh23jxo128uRJdx0AAACxKc0B5YkTJ+ypp56yKlWqWOPGjW3v3r3Wt29fa9eunf3111+RaSUAAACiJ6B8+eWXbdu2bTZnzhzLkSOH29atWzc7ePCgDRo0KBJtBAAAQDQFlAsXLrT+/ftb6dKlfdt0/fnnn7elS5eGu30AAACItoDyyJEjljNnziTbz549a2fOnAlXuwAAABCtAWXNmjVtxIgRCQpxdu7c6bq777jjjnC3DwAAANEWUD7zzDOWOXNmV5Rz7Ngxe+CBB6x27dqWJ08eV6wDAACA2JLmlXIuvvhiGzVqlO3YscN+/vlnO336tJUoUcKuueaayLQQAAAA0ZWhrFWrlh06dMiuuOIKq1Gjht11110umNT0QdWqVYtMKwEAAJCxM5Tz58+3L774wl3fvXu3Pffcc74pgzzaniVLlsi0EgAAABk7Q6nxkv7i4+OT7FOyZEl77bXXwtcyAAAARE+GMn/+/DZkyBB3vVixYta2bVvLlStXkv0OHDgQ/hYCAAAguopyxowZY82aNUsSUKrL+95777U1a9aEs30AAADJ0rLPR48ejegRUsyTN29ezkKoAeXcuXNt9uzZvu7uLl26WLZs2RLss2/fPitUqFAwTwcAABCWYHLMqFF2KsILqyjmUexDUBliQPnPf/7Tdu3a5a6vWLHCbrzxRouLi0sSvWs/AACA80GZSQWT91WtYgXyXByR37H/f3/bf79d4X4XAWWIAaWCx65du/rGUNarVy9JlTcAAMCFoGCySP58HPyMNA9lo0aNXPf2Sy+9ZJ07d3bXZ86caatWrYpMCwEAABBdAeXKlSutQYMGrgjnyy+/tBMnTrgVcx5++GFbuHBhZFoJAACA6AkoX375ZevVq5eNHDnSsmb9vx7zxx9/3Hr37u22AQAAILakOaDcunWr3XHHHQGXZNT63gAAAIgtaQ4oVZSzfv36JNs///xzdx8AAABiS5onNu/Ro4f17dvXBZVnzpxxc1RqSqGPPvrIhg4dGplWAgAAIHoylJpr8u2337b9+/e79bsXL15sJ0+edNs0nRAAAABiS5ozlFKmTBmykQAAADi3gPLUqVOum1td3qdPn3ZLMfobMmRIWp8SAAAAsdTl3b9/fxs8eLAdPHgwSTAJAACA2JPmDOWiRYtszJgxdsstt0SmRQAAABlA6dKl7d5777Xhw4cn2D579mwbPXq0LVmyxNe7O3bsWNfDu3fvXitYsKDdfffd1q1bN8udO7fFZIby4osvtssuuywyrQEAAMhA5s2bZ8uXL09xn2HDhrnVBAcNGmTz5893wwO/+uortyhMtEhzQNmpUyfX5f3TTz+5MZQAAACxSnNwP/fcc27Gm+TMmTPHHn30UatWrZpdfvnl7uezzz5rn332me3bt89iMqCcMGGCW89bKd7rr7/errvuugQXAACAWKH5uffu3WuTJk1Kdp9MmTLZN998Y2fPnvVtq1ChgpvDO1++fBaTYyhffPHFyLQEAAAgg9EwwO7du9uIESNcsq148eJJ9mnVqpWNHDnSPv30U7d8dfXq1e3WW2+1a6+91qJFmgPKKlWqRKYlAAAAGVDLli1dIY6GBKr4JrEuXbq4QHP69On2/vvv27vvvmtxcXFu5pwHHnjAYjKgrFmzpkvdJkcr5wAAAMSKLFmyuDGRzZo1c1nIQBo0aOAumnZx2bJl9tZbb7mAUpXi5cqVs5gLKFXi7k+FOTt37nSRuQacAgAAxJqKFSu6bKOylO3bt/dt37x5s5suqG/fvu62xkzWr1/fTRtUu3ZtN7YyJgPKRo0aBdxevnx5mzx5sjVp0iQc7QIAAMhQevfubXXq1ElQoHPmzBl74403XHbyH//4h2979uzZ7aKLLrL8+fNbTFZ5J0cDS7UcIwAAQCzKly+fCyp3797t21a2bFmrUaOGde7c2T788EPbtWuXrV271gYMGOCmGlKWMhqkOUOpKYMSO3LkiE2bNs1KliwZrnYBAABkOI0bN7ZZs2YlmF/ylVdeccU6Wj1nz549litXLlflrXGU0bJSTtZzqWRKLFu2bG5OSs0ADwAAEAu2bNmSZFumTJlcFbe/nDlzWs+ePd0lWqU5oFywYIFdeeWVkWkNAAAAon8MpUriN2zYEJnWAAAAIPozlAULFrT9+/eH9Es1CDWlNS8BAAAQxQGlSt5VqaQxk1oQXWXv/oYMGZLqc4wbN84NTE2c+QQAAEAMBJSiuZRC0bFjR2vTpk2CbcOHDw/pOQEAAJBBAspgMpCpUVYzcWYTAAAAMRBQbt261f0sVaqU+6nlgt555x07e/as1a1b1+rVqxeZVgIAACBjB5Q7duxw4ya3bdvmbpcpU8a6du1qPXr0sKpVq7ptffr0scOHD1vTpk0j22IAAABkvIBy4MCBVrp0aZsyZYpbd1KzvXfv3t0FlB06dHD7vP322261HAJKAACA2BLUPJRr1qxxGUpNGaQlghRMyu233+7bp1atWvbrr79GrqUAAADIuAHl0aNHLW/evL7bKqjJkSOHxcXF+bZlzZrVTp06FZlWAgAAIOMX5WhtSgAAgPRmfubvLUfmyMwecyIzC7GENaCcNGmS5cqVy3db2cipU6f6MpfKYgIAACD2BBVQVq5c2davX59gW4UKFWzz5s0JtlWqVCm8rQMAAEB0BJSq3gYAAADOuSgHAAAASA4BJQAAAEJCQAkAAID0E1Du3bs3nE8HAACAaAwon3zySYuPj0+w7cyZMzZ+/HirW7duONsGAACAaAwoV69e7ZZe9FbFWb58udWvX9/NU9mrV69ItBEAAADRMLG5Z/r06dapUydr3769FShQwBYsWGBNmza1Hj16JFieEQAAALEhzRnK/Pnz25tvvulWzZk/f75NnDjRBgwYQDAJAAAQo4LKUPbr1y/JtosvvtiyZMlizz77rFWsWNG3fciQIeFtIQAAAKKzylvB5L333psgmAQAAEB4fP311y7WKl++vLVq1cp27tyZ7L4qmB42bJjdfPPNVqVKFRs6dKidPXvWd/+UKVOsdOnSCS4vvfTS+c1Q+mcd1cV9zz33WJEiRcLWCAAAAPw/e/bssS5duli3bt3stttuszFjxljnzp3tgw8+sEyZMllib7zxhs2bN89Gjx5tp0+ftj59+rhal3bt2rn7t23bZs2aNXPP4cmZM6ddsAzl2LFjXUMBAABwbk6ePGmzZs1KdoacGTNmWLly5axt27ZWsmRJl9zbvXu3rVixIuD+U6dOdbPwVKpUyWUpe/fubW+//bbv/p9++snKlCljhQoV8l1y58594QJKpV5ff/11++WXX9zBAAAAiDW7du1y3cYffvihyyAqkBs0aFCqSbfDhw/bhAkTrFatWjZixAi78cYbA+63bt0695z+2cSyZcva2rVrAy4s89tvv1nlypV922666SYXgO7bt8/d/vnnn+2qq66ydDNt0NKlS10ads6cOQHv37RpUzjaBQAAkO6pi3nEiBEukHz88cctLi7OevbsGTDo0zjG999/34oWLeqmW9Q83tmzZw/4vH/88YddeumlCbapC/v3338PuK/471+wYEH3U/tnzpzZDh065GI3FVrnyJHDGjdu7LKfgbrPz0tA+eKLL4blFwMAAGR0GqtY6f/PJD766KOuMEbBYuJATV3QW7ZscfffeeedqT7vsWPHkgSbuh2od/j48eO++/33Fe2v7KQXkKqXWck/ZVNVYN26dWu7IAGlKoeS46VVAQAAYoH/bDflypWzAwcO2MGDB9283f4eeughe/XVV+3pp5+2li1b2oMPPpjiHN7KIiYOHnU7T548Sfb1Dx71OO+6f1f5N998Y/ny5XPb1FWvdr7zzjsXLqBUlKvoWtVCWsPbK1VXw9W4jRs3hqVhAAAA6V22bNl8171pegJ1I9erV8/q1KljCxcudDPmqMj5gQcesIcfftiKFy+eZP/LLrvM/vzzzwTbdPu6664LuK/X9X355Zf7rouKb8QLJj3XXHON64a/YEU5iqwVOKoMXX+Y+t91gDTIdPDgwWFrGAAAQHrnXzuyYcMGN44xcfDm0VhGxUwzZ8601157zbZv3+6mBQpEc0+uWrUqQRe4knbaHiig1LhM//11XdvUHlWM33333S4B6N/uq6++2sIlzRnK9evX23vvveci5Llz57rGNG/e3EqUKOEOUKNGjcLWOAAAgPRMybRBgwbZ33//7bq0W7RoEdTjqlWr5i6qzg5E2ctJkybZ+PHj3ZhLzUOp7GPVqlXd/UeOHLETJ074utbVpa4e5MKFC7vbw4cPd0k/qV69upt2SBOZaz8Fvqo0f/755y9chjJr1qxu2UVRMOlF5mqsBpsCAADECnVld+zY0R577DFr0qSJdejQIU2PT26hGAWPo0aNcnNVqiJbVdoKKr3u9MmTJ7vtHvUcqy1du3Z1xUH33Xefb3xksWLFXGC6Zs0aa9CggQs2VSSk/S9YhrJChQouYn7iiSfc4NOPPvrI2rRp46JdbyAoAABALNDqgR07dozIc99xxx3uEoi6yv27y1WxrSmBdAlElejqYY6UNAeUaminTp3cAFJVKGlmdlV+Hz16NMFyPgAAAIgNaQ4or732WlehpDmPVIquVKyWAbrkkkuSne0dAAAA0SvNAeXOnTvdlEEaDKo1ILW+ZI0aNSLTOgAAgHRIYxypHTmHgHL58uWuQujHH39MUHauwaGaMLNv374J1pwEAABAbAiqynvZsmXWvn17K1OmjE2bNs3Ntv7DDz/Yt99+69alVLW3CnNUPQQAAIDYElSGUmXqKj3XepX+tGSQ5kPSRde1PqTK0gEAABA7gspQbt68OdUJyzX3EssuAgAAxJ6gAkpVdKe0gLlomSEtyQgAAIDYElRAqSIcrT+ZEhXn+BfrAAAAIDYEXeX9ySefuGmCkqM1LAEAABB7ggooixYt6taMPNf1KAEAABDjAeWSJUsi3xIAAIBzMOGLRXYgy5GIHLv8Z+LsHmMlwLCvlBNpg9+beaGbgPNo4MCBHO8Y0vLILRe6CTiPeD8HYkdQRTkAAABAcggoAQAAEF1d3v3/1fhCNwHnsRtsWtxXHO8Y6uYeMGDABW0Lzu8wlhIPFeOQR7nt7+y+0E1AOkGGEgAAACEhoAQAAEBICCgBAAAQEgJKAAAAhISAEgAAACEhoAQAAEBICCgBAAAQEgJKAAAAhISAEgAAIB36+uuv7d5777Xy5ctbq1atbOfOncnuGx8fb8OGDbObb77ZqlSpYkOHDrWzZ88m2e/QoUNWvXp127VrV1jbSkAJAACQzuzZs8e6dOli999/v82cOdPy589vnTt3doFjIG+88YbNmzfPRo8ebSNHjrQPP/zQbfP3119/2SOPPGL79+8Pe3sJKAEAAM6zkydP2qxZs6xXr14B758xY4aVK1fO2rZtayVLlrQhQ4bY7t27bcWKFQH3nzp1qnXv3t0qVarkspS9e/e2t99+23f/d99954LTo0ePXri1vPv16xf0E+oPBgAAiGbqMq5Vq5brZh46dKgdO3bMGjZsaH379rWsWZMPrw4fPmzvvPOOCwCVbezYsWPA/datW+eCQ0/OnDmtbNmytnbtWqtatWqCfffu3Wu//fabVa5c2bftpptucgHovn377NJLL7Vly5bZAw88YPfcc4/Vrl3bLkhA6U8HbP78+Xb99de7S7Zs2Wzjxo22evVqdyABAABihbqYR4wYYadPn7bHH3/c4uLirGfPnkn2U9A3ZcoUe//9961o0aLWo0cPq1+/vmXPnj3g8/7xxx8uEPRXoEAB+/333wPuK/77FyxY0P3U/tqu3yfhHjuZpoDSP+uoBnXt2tVd/E2cONGWL18e/hYCAACkU3369PFlEh999FGXsVSslClTpgT7qQt6y5Yt7v4777wzqARe4mBTt9VVntjx48d99/vvK4H2j4Q0j6H8/PPPXcVRYkr7qn8eAAAgVlSsWNF3XWMeDxw4YAcPHkyy30MPPWT58uWzp59+2saNG+cKZFKSI0eOJMGgbqvrO7FAwaN3PdD+6SKgLFGihBtE6k9jADTws3Tp0uFsGwAAQLqmoX8eb5qexNlJqVevnn3yySf21FNP2aJFi6xGjRo2aNCgZKcCuuyyy+zPP/9MsE23CxUqFHBf/65v/+uB9o+ENI+h7N+/vys5X7hwoS+A/OGHH1y6Vd3eAAAAsWLTpk1u3kfZsGGDG6+oTGQgmTNntjp16riLhgkqburWrZvNnTs3yb6ae3LVqlUJusBVs5J4yKEXUGpcpva//PLL3TZd17bE4zDTTUCpcQIKJhVl//TTT25b+/btXdVQnjx5ItFGAACAdGnw4MEu0/j333/bq6++ai1atAjqcdWqVXMXVWcHoorsSZMm2fjx492YyzFjxrhg0avwPnLkiJ04ccLNT+l1qWt8ZuHChd3t4cOHuymHzpc0B5Sixjdv3jz8rQEAAMhA1JXdsWNH192toK5Dhw5penyRIkUCblfwOGrUKHvhhRdcMFmhQgX30+tOnzx5ss2ZM8eWLFnibrdr185NWK4MZpYsWaxx48bWunVrS7cBpeY0euWVV2z9+vWuRD7xjO2LFy8OZ/sAAADSLfXQdkxmLslQ3XHHHe4SiLrKdfEoiNS84anNHa5AVdXmFzyg1BxLql5ShjJ37txhbxAAAAAyljQHlN9//71LsV577bWRaREAAACiO6C86qqr3BxLAAAAsSpSXccxE1D++9//dnMotWnTxq688soE8y+J/zqSAAAAiH7nNIZSBg4cmOQ+VR5pPiYAAADEjjQHlJs3b45MSwAAAJAhndM8lFoV54MPPnATm585c8auvvpqNw/TJZdcEv4WAgAAIF1L81reW7dutdq1a9vrr79ue/bscRctcl63bl3btm1bZFoJAACA6MlQaomhW265xZ5//nnLmvX/Hq4JzlWoo9ncNXM7AAAAYkeaM5Rr1651ld5eMCm6rm1r1qwJd/sAAAAQbQFloUKFbMeOHUm2a1tcXFy42gUAAIBo7fJ+8MEHXff2o48+ajfccIPbtm7dOhs5cqQ1adIkEm0EAABI1q4tf9lvJ/6KyBE6muO02ZUc/LAHlO3atbNjx47ZsGHD7K+//u/kFSxY0Fq3bm1t27ZN69MBAAAg1gJKTV7erVs3d9m/f7/lyJHDcufOHZnWAQAAIDoCyrlz5wb9hA0bNgylPQAAAIjGgFLjI4PNXhJQAgAAxJagAsolS5ZEviUAAACInaUXPQcOHLBPPvnE4uPjrWbNmla0aNHwtQwAAADRE1AePXrUXn75Zfv444/d7fvuu89atmzpphBSxbcCSt0/ceJEq1y5cqTbDAAAgIw2sfmQIUNs9erVNmDAAHdda3Y3bdrUqlevbt9++62tXLnSBZnBjrUEAABAjGUoFy9ebGPHjvVNZH7jjTe6YLJFixaWLVs2t01zUDZq1CiyrQUAAEDGzFBqrGThwoV9t/Pnz285c+a0fPny+bZpLsrjx49HppUAAADI+Gt5Z8mSJeA0QQAAAIhtQVd5r1mzxvLmzeu7rUKc77//3n7//Xd321uGMRgnT550FwAAAMRQQNm1a9ck23r16nVOGctx48bZ6NGjE2xr1qxZsE0BAACIel9//bW98MILtnPnTitfvrwNHjzYihcvHnBfJfqGDx9uM2fOtLNnz1rjxo2td+/eljnz/3VGHzx40J555hlbtmyZG7L46KOPuoLqxL777jt74oknXP1M2APKzZs3Wzh17NjR2rRpk2CbDgIAAADM9uzZY126dLFu3brZbbfdZmPGjLHOnTvbBx98EDCB98Ybb9i8efNcwu706dPWp08fK1CggLVr187d369fP1fr8t5779m6devsqaeeshIlSvgKrmXLli0u0MyRI0fkxlCGU/bs2V0Rj/8FAAAgVpw8edJmzZqVpLfXM2PGDCtXrpybRadkyZJu2sbdu3fbihUrAu4/depU6969u1WqVMluvvlml518++233X07duywzz77zAYNGmSlSpWyJk2aWIMGDWz69Om+x7/77rtufnEFoefiggSUAAAAGdmuXbusdOnS9uGHH7oMogI5BWzKDqbk8OHDNmHCBKtVq5aNGDHCTcUYiLKIek6PZtcpW7asrV27Nsm+e/futd9++y3B4jI33XSTC0D37dvnnqtIkSJ2+eWXJ7hf9TGepUuX2ksvvWStW7e28770IgAAQCxTF/OIESNcIPn4449bXFyc9ezZM2DQN2XKFHv//ffdUtU9evSw+vXru17bQP744w+79NJLE2xT9tArhk68r/jvX7BgQfdT+yf3XGqT57XXXnM/Z8+ebeeCgBIAAOAcaaxipf8/k6jxh8OGDXPBYuJxjuqC1hhF3X/nnXem+rxa2jpxsKnbgWbJ8eYB99/fu6790/Jc5yrNXd5K0R46dCjJdkW51apVC1e7AAAA0r2KFSv6rmvMoxaDUUV1Yg899JCrrn766afdbDepTbeowpjEAZ9uq+s7Mf/g0X9f0f7JPddFF11k4RJUhnL+/Pn2xRdfuOvqj3/uueeSVABpe6DJzwEAAKKVtwS1aLoeCVSFXa9ePatTp44tXLjQJk6c6Ja0fuCBB+zhhx8OOBXQZZddZn/++WeCbbp93XXXBdxX1LXtjZP0usELFSqU7HPpvvOaoaxSpUqSuY4SUwWS1/8OAAAQCzZt2uS7vmHDBjdW0X9pan+aE1JBpeaKVMy0fft2Ny1QIJp3ctWqVb7b6rbeuHGj256YAkaNy/TfX9e1Te1R4Y8Sf/7jL3V/cgVBEctQau1ulatLsWLFXAl7rly5wtYIAACAjEiTjQ8aNMj+/vtve/XVV61FixZBPU7DBHVRdXYgyl5OmjTJxo8f78Zcah5KZR+rVq3q7j9y5IidOHHCxWhel7rGZxYuXNg3v7fiNVEG9NZbb3XjPfv372/r1693c1a+9dZbYToK51CUoxVzVPKuZRfV/544W+lfsg4AABDN1JXdsWNH192toK5Dhw5perym8wlEweOoUaPcSjkKJitUqOB+et3pkydPtjlz5tiSJUvcbU1gvn//fhenaQiiVsrxnwJo6NChLphs2rSp6+rW8/pPan7eA8qPPvrInnzySRcVJ6Y/0j/1CwAAEM3uueceF1BGwh133OEugair3L+7XEGkVsPRJRBNE6Rxm6m5//773SXiAaXSqc2bN3fL/7DCDQAAANI8bZBK4Zs1a0YwCQAAgHMLKGvWrGmLFi1K68MAAACihsY4aqJy/+UMY1lQXd7+/fGnTp1yAzs1j9IVV1zhSuD9edXgAAAAiA1pHkOpcZMNGzaMTGsAAAAQnQElWUcAAACELUOZXDm6pgzS8kOa26h27dpWqlSptD41AAAAYqEoJy4uzubOneuWC8qbN6/lyZPHdu7cabNnz3YTamr29SZNmthnn30WmRYDAAAgY2cof/31V+vUqZN17949wXZNlrl27VobN26czZgxwy0/pKWCAAAAEN3SnKFcuXKlNWjQIMl2LXb+9ddfu+u33HKLy2ACAAAg+qU5Q6kFxhcsWJBkmSHNTemtR/nLL7/4FisHAACIpILZc2fI547pgPKJJ55wyy4uW7bMypUr57Zt2LDB1q1bZyNHjnRreffs2dPatm0bifYCAAA4uXLlcgXBDxS5KaJHRL9DvwthDChvvfVW++ijj9w4ya1bt7rFyCtWrGgvvfSSFS1a1H788Ud74YUXrFatWml9agAAgKCpOLhLly529OjRiB41BZP6XQhjQOl1ez/22GMB7ytZsqS7AAAARJoCPYK9DBJQtmrVykaPHu2mCGrZsqWbczI5U6dODWf7AAAAEA0BZZUqVdz4AalatWqk2wQAAIBoCyi7du0a8Lq/48eP2x9//BG+lgEAACA656FMaX5KLbkIAACA2BK2gBIAAACxiYASAAAAISGgBAAAQOSLcjQ+MjVbtmwJrSUAAACI3oBSc08GI6X5KQEAABDDAeXmzZsj3xIAAABkSIyhBAAAQEgIKAEAABASAkoAAACEhIASAAAAISGgBAAAQEgIKAEAABASAkoAAACEhIASAAAAISGgBAAAQEgyxcfHx1s6MHDgwAvdBAAAkEYDBgzgmIEMJQAAAEJDlzcAAABCktXSmf7/anyhm4AIG/zeTN91zndsne8SDxW7oG1B5G1/Z7fvOl2h0Y/havCQoQQAAEBICCgBAABw/gPKDz74wO6//36rVKmS7dy50wYPHmzjx48PrSUAAACIjYBy+vTpNnToUBdQnjp1ym0rV66cTZo0yUaPHh2JNgIAACCaAspp06bZoEGDrEWLFpY58/89/L777nNB5owZMyLRRgAAAERTQLlnzx675pprkmwvXry4HTp0KFztAgAAQLQGlOXLl7e5c+cm2KbFdiZPnmw33HBDONsGAACAaJyH8qmnnrIOHTrY559/bidPnnRzUP3yyy92/PhxmzBhQmRaCQAAgOgJKEuVKmULFiywDz/80H766Sc7c+aM1apVyxo0aGBxcXGRaSUAAACiJ6Ds16+f9e/f3xo3TriizV9//WXdu3e3kSNHhrN9AAAAiIaAcs2aNfbrr7+66xo/WbZsWcudO3eCfX7++WdbtmxZZFoJAACAjB1Q5syZ00aNGuWKb3SZOHGib8ogyZQpk+XKlct69+4dybYCAAAgowaUZcqUscWLF7vrLVu2dBOY582bN9JtAwAAQDROG6RspC6JHThwwK2eAwAAgNgSVIZy6dKl9v3337vrK1eutLFjx7oubn8aY7l79+7ItBIAAAAZO6AsUaKEGzfpjaFcvXq1ZcuWLckYysGDB0eyrQAAAMioAaWWVZw6dWqCaYMSV3kDAAAgNqV5DOWQIUNc1bdWypkyZYr973//s3Xr1tnff/8dmRYCAAAguiY2/+2336xt27ZuInNdtEqOusM1V+WkSZOsdOnSkWkpAAAAoiNDqbW7K1WqZF9++aVlz57dbfvPf/5j1atXt0GDBkWijQAAAIimgHLVqlUuQ5klSxbfNhXodO7c2TZs2BDu9gEAACDaAsqLLrrI9u/fn2T79u3bKdQBAACIQWkOKB988EF75plnXFGOF0jOmjXLnn76aWvcuHEk2ggAAIBoKsrp0qWL5cmTx5599lk7duyYdejQwQoUKGCtW7e2du3aRaaVAAAAiJ6A0lvPW5ejR4/amTNn7OKLLw5/ywAAABC9AaWWWVQBzqlTp5Lc17Bhw3C0CwAAANEaUGrOyWHDhlnevHktLi4uwX1agpGAEgAAILakOaCcPHmy9enTh/GSAAAAOLcq7xMnTljt2rXT+jAAAABEqTQHlPXr17fp06dbfHx8ZFoEAACA6O7yPnz4sM2cOdPmzZtnl19+uVslx9/UqVPD2T4AAABEW0B51VVX2SOPPBKZ1gAAACD6A8quXbtGpiUAAACIjYCyX79+Kd4/ZMiQUNoDAACAaC/KSez06dNuPe+PP/7Y8ufPH55WAQAAIHozlMllIDXh+datW8PRJgAAAMRShtJTp04dW7RoUbieDgAAALEUUB49etTef/99y5cvXzieDgAAANHc5V2mTBm3ZndiOXLksEGDBoWrXQAAAIjWgPLNN99MEFDquiY3v/baay137tzhbh8AAACiLaB86aWXXGFO6dKlI9MiAAAARPcYyn379lmWLFki0xoAAABEf4ayYcOG1r59e2vQoIEVK1bMjZ1MfH9qTp486S4AAACIwYBSE5hnzpzZ5s2bl+Q+jacMJqAcN26cjR49OsG2Zs2apbUpAAAAyIgB5ZIlS0L+pR07drQ2bdok2DZ8+PCQnxcAAAAZIKD0d+DAAfvkk08sPj7eatasaUWLFg3qcdmzZ3cXAAAAxEhAeezYMRs6dKjr7pb77rvPWrZsaQ8++KC7TwHlyy+/7JZfrFy5cqTbDAAAgIxW5f3CCy/Y6tWrbcCAAW7KoG3btlnTpk2tevXq9u2339rKlStdkDly5MjItxgAAAAZL0O5ePFiGzt2rN1www3u9o033uiCyRYtWrhJzaVt27bWqFGjyLYWAAAAGTNDqbGShQsX9t3Onz+/5cyZM8Ha3Vol5/jx45FpJQAAADL+xOaBJjMPtKY3AAAAYkvQVd5r1qyxvHnz+m6rEOf777+333//3d3+66+/ItNCAAAAREdA2bVr1yTbevXqleA2GUsAAIDYE1RAuXnz5si3BAAAANE9hhIAAAAIhIASAAAAISGgBAAAQEgIKAEAAHB+A8patWrZoUOHkmzfu3evVatWLbTWAAAAIDqrvOfPn29ffPGFu75792577rnnLEeOHAn20fZAk58DAAAgugWVoaxSpUqC25rUPLGSJUvaa6+9Fr6WAQAAIHoylFq7e8iQIe56sWLFrG3btpYrV65Itw0AAADRtFKO/4o5hw8fdssunjx5Mkm2snLlyuFsHwAAAKItoPzoo4/sySeftBMnTiS5T0svbtq0KVxtAwAAQDQGlMOGDbPmzZtb586dLXfu3JFpFQAAAKJ32qCDBw9as2bNCCYBAABwbgFlzZo1bdGiRWl9GAAAAGK5y7tfv36+66dOnbKhQ4fawoUL7YorrrDMmRPGpF41OAAAAGJDmsdQatxkw4YNI9MaAAAARGdASdYRAAAAYctQ+nd/J54yKFu2bFaoUCGrXbu2lSpVKq1PDQAAgFgoyomLi7O5c+fa9u3bLW/evJYnTx7buXOnzZ492/bv32/r16+3Jk2a2GeffRaZFgMAACBjZyh//fVX69Spk3Xv3j3B9rFjx9ratWtt3LhxNmPGDHv11VftzjvvDGdbAQAAEA0ZypUrV1qDBg2SbK9Tp459/fXX7vott9ziMpgAAACIfmkOKIsXL24LFixIsl1zUxYpUsRd/+WXXyx//vzhaSEAAACiq8v7iSeecMsuLlu2zMqVK+e2bdiwwdatW2cjR450a3n37NnT2rZtG4n2AgAAIKNnKG+99Vb76KOPrEKFCq5be8eOHVaxYkWbP3++1ahRw7JmzWovvPCCdezYMTItBgAAQMbOUHrd3o899ljA+0qWLOkuAAAAiA1BBZStWrWy0aNHuymCWrZs6eacTM7UqVPD2T4AAABEQ0BZpUoVN2m5VK1aNdJtAgAAQLQFlF27dg143d/x48ftjz/+CF/LAAAAEJ1FOSnNT6klFwEAABBbwhZQAgAAIDYRUAIAACAkBJQAAACIfFGOxkemZsuWLaG1BAAAANEbUGruyWCkND8lAAAAYjig3Lx5c+RbAgAAgAwpU3x8fLylAwMHDrzQTQAAAGk0YMAAjhnST0AZi06ePGnjxo2zjh07Wvbs2S90cxBhnO/YwvmOLZxvxDqqvC/wG5DWSNdPRD/Od2zhfMcWzjdiHQElAAAAQkJACQAAgJAQUAIAACAkBJQXkApxunbtSkFOjOB8xxbOd2zhfCPWUeUNAACAkJChBAAAQEgIKAEAABASAkoAAACEhIASAAAAIYnZgLJmzZpWunRpdylTpoxVqFDBHnzwQfvyyy9Det6+ffu6SzBatmzpa4MuakO7du3s119/PafnS6vZs2e74xANMsr5PN8OHz5sc+fO9d3ev3+/ffLJJxbtdPy//fbbgPeNGjXKnatwnP/Ujqf/60GXm2++2Z566ik7cuRIgteu/hcjIZLvH+dLRjqX59vOnTvtiy++8N3etGmTrV69+oK1B7EtZgNKefLJJ23ZsmXuH/K9996zihUrunW1v/766/PWhrZt27o2KPB5//337ZJLLrHOnTsbS6ynHeczqSlTptisWbN8t4cNG5bgAygW6X9OgUg4BHM89bv0P7506VIbO3asff/99zZ06NCw/P5YF+vnUu95aoOnS5cu9ssvv1yw9iC2xXRAefHFF1uhQoXssssus1KlStnjjz9u99xzjw0ZMuS8tSFXrlyuDZdeeqmVLFnSfYPetm2bbdmy5by1IVpwPpNK/MWELypmcXFx7otbOARzPPPmzet7n7nxxhvdl9ZYyBKfD5xLIP2I6YAykH/961+2detWXzelgjt1W6r78vrrr7dmzZrZTz/95O5TN4y6qwYMGGA33XSTjR8/PsFzHThwwO6++27r169f0B/kOXPmTPH+zz77zBo1amQ33HCD1atXzxYuXOi7T10/r7/+umuv7tfv9u/y3bt3r7Vv3959qOk5duzYYdEuvZ1Pr9utQYMGVq1aNZdNSNyl5z8UwWvT9OnT7bbbbnPnrk+fPnby5Enf/osWLXKvhfLly1vjxo1txYoVvucZPXq0u63foezKnDlz3MV7/v/973/u+ZSdv/XWW+3555+348ePWzRL3E2qjFP9+vXd/4z+P3QM/LtGNWygZ8+e7vjWqFHDPvzwQ9/zJD6eof6Pnz171iZOnGi1atVy7VE7/b9c6jz+97//tXvvvdfKlSvnXr/q9vR899131rBhQ/fYRx991I4dO2bRLL2dSz325Zdfdv9LOg/ffPONO2fJdb3r9/bq1cu95+h/UO8JEyZM8O2r95kxY8a456tUqZI98sgjtmfPHt/z6H9b/+M6Brrs3r3bvT95z6/3Pm33Pg/efvvtNB5hIHgElIlcc801vsBDb+76By5WrJh7E3/33XftzJkz7g3Do39gfbjrw1tv8h69kXfq1Mk936BBgyxTpkypngw9j7pRvDE6iS1fvty6detm9913n2tPkyZN3Jvjhg0bfPvo8cqyzps3z40lfPrpp93fIfqA0fUZM2bYv//9b3vzzTct2qXH86nf3aNHDxs3bpxdddVVqT7Pvn37bMGCBS7Q0AeQvkR44yI3b95sTzzxhGvbBx984AJVnVsF0Aoy1SWo4FkftLpet25dd5k5c6Z7fP/+/e3vv/+2d955x1577TVbv369PffccxYrFIzp2OmY6JjqS0biD10F7GXLlnX/U9pP3Yw6ZoGOZ2r0pWTatGnuPAWi4GHy5Mnudyi40WtVgdHRo0d9++g1oPOm1+jBgwftlVde8T23sp/Vq1d3f8u1115r8+fPt1iRXs6lgtRJkybZiy++GNT7hP63c+TI4c63vuyq63379u3uvrfeess93/Dhw92wrAIFCri2njp1yr0G9L/tdfvrUrhwYfc36T59MdR7gb4c671B7xP6H/cfUw2EU9awPluUdJuKBlrrH1KFHcoCqGtalNnTB7s/veFfeeWVvtsKUhToaSkuvdlnyZIl2d+noEIfIKLfp2+kI0eODPhGpDdHfcts3bq1u12iRAk3fkaP/89//uO23XHHHXb//fe763pzVfD5xx9/uEzUmjVrXIazaNGirntdgWi0f+Ckx/OpD7q0ZEH04aHB/zpnCkyVqVTg17RpU/fBpZ/KykirVq1s5cqVLkBUlkJ/Z7Zs2VyXq1x00UXuZ/78+V2G+tNPP3VZDu84KaOjzIqyHN62aKYvV8reaNyy96Ur8RhqfWjrNSHaT+f3559/dlku/+OZHH2o6zWj14K+mKi7/dlnn02yn+5XAPHYY4+5DKV3Pv75z3+6gECvXWnTpo3LZMlDDz3kC5rUja52KOOs15u+fMbSeNn0ci4VYHpfIJMrJvKn51Cwp+dV25Sh1Huz3t/13qTsZdWqVd2++rKnbKV6nvQeov9t/Y97Qzj0HPq/1UXHQwGovryKvrzqC/PUqVPd/zgQbgSUiahLRHLnzu3+UfWGrW90+gfXG8/GjRutYMGCCR5z+eWXJ7itN/bTp09bnTp1Ul2nWx8SXpeNshD6ANAHit5UvA8Nj7pmvQ8V/zdI/6IL/4yX/gZRW5Sh05uOgkn/wCbaA8r0eD6VdUor/wBXf4va470m1D5lL/wDUH3opEaPVdb29ttvT7Bd25ThVJdqtFN3sv4P/GlYwV9//eW7Xbx4cd91L8g+ceJE0L9DGW0FLApClFFU0KjXoTJP+sD3rzI+dOiQ29ejgEHnwRuWEei1oPMt+h9Xr0TiLy/R3u2d3s5lWv+/9X7j/yVV40L1/60vwb///rv7Mps58//rTNQX1WAKb/T+ph4MfUb4fzlO6QsxEAoCykS88UrKBukfWmPS8uXL574NqgtU/6ReBsqj7gp/RYoUsYEDB7pvm/qGrC6olAbs+39AXHfddb4MU+KAMvHv8T78vS5t7wMoMW+8X+Jxf4H2jTbp8XwGOo/+9KafWOJA1juX2ldZk8QZBy/bktrv0Yeq/xcSjwpIYoGXbfKX+HagD+C0FDfpWHqvCX3hU5erMk76ItCiRQvffsm9LnSeUvsfT65d2jdWAsr0ci79z2OgniYFi1mzZk31Pdt7H3j11VddtjLx+0xq9Hv0nvPMM88E/fcBoWAMZSL6cNWbhL7JqitQ49fURaBgQoGEBkSn9gakMSvaV12R6rLyMgjB8n8z8ac3lXXr1iXYpm7sxG82gaiKXd/U/edE1Jxl0S49n0//DxT/uez8iyxSo3O/a9cu9yHnXZSt1LQmgT7Q/G/rsRo/pm3eY5X90DQo/kU/0UxfNH744YcE2xLfTkkwY+QSU7Yp0GtCwb2y5WvXrvVt02tN7Qnmf1x/izLu/s8bC//j6fFcJg4WvZ4S0f9rMPLkyeOynhqy5P1/6sutxnx7YyxToteM9lMG1Hu8Xlsa9wlEQkwHlPow1T+rggxlsgYPHmwff/yxr0JOXcTqttQ4M70JaEyKxisF+2GrsSsauP3GG28ku4+eX23Q5bfffnPPr+IbDQ5PTGMnNYBbxTTq8tAcgxpkri6X1KiYRN9WNWBb3SD6m9RdE00y2vn0qJtO50LndPHixWma5FqvCf2NCpI1JlKvCV28oQ+qQtXx8D7EdFvjqFTxr9eExmP27t3bjcXVh6/GTupv0IdZRqe/SYG1/yVxtk5fEvQhq4p+ffiqiEqV0sEGF/7HMzn6Iue9JnSONQ5OAUigcbQ6nxpzu2TJEtfNraI6dcmqwCo1KsbT36fXvTLvGn+3atUqiwYZ8Vx6Qa56C9QWfVHUOVHQHyy9HjRuW68H/T6NpdbE5VdffbW7X8N4tF3DJbzbOvcaOqGxnPqCqAylXksafqPXhv8wCyCcYrrL+4UXXnAXveFoIPY//vEP92Gs6RlEY080Uay6O/WmroHW+udUBV1KbzoeBTDdu3d3VXsqmtC3y8TU3ep1uerbrL5F6nf4Vxh7NHZH2SNV8+lbqr6B6s0mcdd4ckaMGOE+oDTOT2MpNdYvUit0XAgZ7Xx6dE70QaF9FFzqd+gDKBgaI+a9JvTziiuucBWhlStXdveroEPV7Ao29KGkIi0dA33YaEoTPUbjwvTBpW44BZhqSzTQeUrMf5otb7ybAriXXnrJ/bzllltcQUyww0ESH89AwYuKY/yDFo2J1Jha//F8HlXsKpul14R+6jWrjFJKhSL+3aAKWFQkonbpNaCf0TD3aEY8l94YV/Vq6L1X51H/j82bN3fjL4Ohqm/1Xug9RK8H/T4V4nld3prpQ0kC9bioSlzJBR0rBZmaTkht03uihsTo/Uu/WzMBAJGQKT4a3m0A4Bxonj6NNdOXD0+HDh1cYO8fPCD941wCF1ZMd3kDiG0aJqBpeL766ivX3alhEBqioEwSMhbOJXBhkaEEENO0upQKmTQOTcNINOTgrrvuutDNwjngXAIXDgElAAAAQkKXNwAAAEJCQAkAAICQEFACAAAgJASUAAAACAkBJQAAAEJCQAkAAICQEFACAAAgJASUAAAAsFD8f8DycbgcOrChAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Confidence Interval Plot\n",
    "\n",
    "There are times when a project contains mutliple variabls to compare. This can result in an \"ugly\" image. For example, cell 11 appears to be the limit annotattions available. While this could possibly be remedied by hiding the n.s. labels using the \"hide_ns\" argument, another option is available. This is where the **ci_plot** method comes in.\n",
    "\n",
    "This plot was included after reading a post by [Pat Walters](https://practicalcheminformatics.blogspot.com/2025/03/even-more-thoughts-on-ml-method.html). This plot utilizes the Tukey's Honest Significant Difference (HSD) Test from [statsmodels](https://www.statsmodels.org/dev/index.html). The **ci_plot** wraps the function and includes default naming scheme. For instance, the title will include a specific name and the results of the ANOVA test (rounded to 3 sigfigs).\n",
    "\n",
    "Plot is read as:\n",
    "- Blue line denotes the highest mean\n",
    "- Gray line denotes equivalency to the blue line\n",
    "- Red line indicates no significance to the blue line."
   ],
   "id": "cda57d40948f746e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-21T04:12:38.945973Z",
     "start_time": "2026-05-21T04:12:38.760903Z"
    }
   },
   "cell_type": "code",
   "source": [
    "title = \"Tukey HSD Confidence Intervals\"\n",
    "plot.ci_plot(data=data, value_col='Pain threshold', group_col=\"Hair color\", title=title)"
   ],
   "id": "3e60f69f85064f31",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 22
  }
 ],
 "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
}