EXAMPLES/fmri.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import plotastic as plst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Analysis: FMRI\n",
    "\n",
    "**Raw Data:**\n",
    "https://github.com/mwaskom/seaborn-data\n",
    "  \n",
    "**Original Source:**\n",
    "https://github.com/mwaskom/Waskom_CerebCortex_2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import example Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>subject</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>event</th>\n",
       "      <th>region</th>\n",
       "      <th>signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>s7</td>\n",
       "      <td>9</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>0.058897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>s8</td>\n",
       "      <td>9</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>0.170227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>s0</td>\n",
       "      <td>0</td>\n",
       "      <td>stim</td>\n",
       "      <td>frontal</td>\n",
       "      <td>-0.021452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>s1</td>\n",
       "      <td>0</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.064454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>127</td>\n",
       "      <td>s13</td>\n",
       "      <td>9</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>0.013245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 subject  timepoint event    region    signal\n",
       "0          17      s7          9  stim  parietal  0.058897\n",
       "1          36      s8          9  stim  parietal  0.170227\n",
       "2          67      s0          0  stim   frontal -0.021452\n",
       "3          84      s1          0  stim  parietal -0.064454\n",
       "4         127     s13          9  stim  parietal  0.013245"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Example Data\n",
    "DF, _dims = plst.load_dataset(\"fmri\", verbose=False)\n",
    "DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign each column to a dimension (y, x, hue, col, row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dims = dict(\n",
    "    y=\"signal\",\n",
    "    x=\"timepoint\",\n",
    "    hue=\"event\",\n",
    "    col=\"region\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize DataAnalysis Object\n",
    "- DataAnalysis will give you feedback on data\n",
    "- This iobject contains every tool you need, from plotting to statistics!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================================================================\n",
      "#! Checking data integrity...\n",
      "✅ DATA COMPLETE: All combinations of levels from selected factors are present\n",
      "   in the Dataframe, including x.\n",
      "✅ GROUPS COMPLETE: No groups with NaNs.\n",
      "✅ GROUPS EQUAL: All groups (40 total) have the same samplesize n = 14.0.\n",
      "🌳 LEVELS WELL CONNECTED: These Factors have levels that are always found\n",
      "   together: ['region', 'event'].\n",
      "   👉 Call .levels_combocount() or .levels_dendrogram() to see them all.\n",
      "✅ Subjects complete: No subjects with missing data\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "DA = plst.DataAnalysis(\n",
    "    data=DF,  # Dataframe\n",
    "    dims=dims,  # Dictionary with y, x, hue, col, row\n",
    "    subject=\"subject\",  # Data is paired by subject (optional)\n",
    "    verbose=True,  # Print out Info about the Data\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick Preview Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 580x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DA.catplot(alpha=0.3)  # Works with *kwargs of seaborn.catplot()\n",
    "plt.savefig(\"fmri1.png\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Statistics\n",
    "- No need to re-write arguments. DataAnalysis object will remember them!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>pval</th>\n",
       "      <th>normal</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>event</th>\n",
       "      <th>timepoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">frontal</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">cue</th>\n",
       "      <th>0</th>\n",
       "      <td>0.914917</td>\n",
       "      <td>0.185710</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.974828</td>\n",
       "      <td>0.933696</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.932624</td>\n",
       "      <td>0.331983</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.869613</td>\n",
       "      <td>0.041439</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.965730</td>\n",
       "      <td>0.814991</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.953819</td>\n",
       "      <td>0.621403</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.853934</td>\n",
       "      <td>0.025128</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.959380</td>\n",
       "      <td>0.712867</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.912661</td>\n",
       "      <td>0.172244</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.907248</td>\n",
       "      <td>0.143726</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">stim</th>\n",
       "      <th>0</th>\n",
       "      <td>0.898415</td>\n",
       "      <td>0.106968</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.939964</td>\n",
       "      <td>0.417863</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.945087</td>\n",
       "      <td>0.487335</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.909201</td>\n",
       "      <td>0.153429</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.943336</td>\n",
       "      <td>0.462730</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.962568</td>\n",
       "      <td>0.765064</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.869722</td>\n",
       "      <td>0.041585</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.897018</td>\n",
       "      <td>0.102095</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.940434</td>\n",
       "      <td>0.423909</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.876136</td>\n",
       "      <td>0.051214</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                W      pval  normal   n\n",
       "region  event timepoint                                \n",
       "frontal cue   0          0.914917  0.185710    True  14\n",
       "              6          0.974828  0.933696    True  14\n",
       "              3          0.932624  0.331983    True  14\n",
       "              5          0.869613  0.041439   False  14\n",
       "              7          0.965730  0.814991    True  14\n",
       "              1          0.953819  0.621403    True  14\n",
       "              4          0.853934  0.025128   False  14\n",
       "              2          0.959380  0.712867    True  14\n",
       "              8          0.912661  0.172244    True  14\n",
       "              9          0.907248  0.143726    True  14\n",
       "        stim  0          0.898415  0.106968    True  14\n",
       "              6          0.939964  0.417863    True  14\n",
       "              4          0.945087  0.487335    True  14\n",
       "              1          0.909201  0.153429    True  14\n",
       "              2          0.943336  0.462730    True  14\n",
       "              3          0.962568  0.765064    True  14\n",
       "              9          0.869722  0.041585   False  14\n",
       "              5          0.897018  0.102095    True  14\n",
       "              7          0.940434  0.423909    True  14\n",
       "              8          0.876136  0.051214    True  14"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.check_normality().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Sphericity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>spher</th>\n",
       "      <th>W</th>\n",
       "      <th>chi2</th>\n",
       "      <th>dof</th>\n",
       "      <th>pval</th>\n",
       "      <th>group count</th>\n",
       "      <th>n per group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">frontal</th>\n",
       "      <th>cue</th>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>3.260856e+20</td>\n",
       "      <td>-462.715239</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>[14, 14, 14, 14, 14, 14, 14, 14, 14, 14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stim</th>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>2.456616e+17</td>\n",
       "      <td>-392.270460</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>[14, 14, 14, 14, 14, 14, 14, 14, 14, 14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">parietal</th>\n",
       "      <th>cue</th>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>1.202935e+20</td>\n",
       "      <td>-452.946123</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>[14, 14, 14, 14, 14, 14, 14, 14, 14, 14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stim</th>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>2.443175e+13</td>\n",
       "      <td>-301.989490</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>[14, 14, 14, 14, 14, 14, 14, 14, 14, 14]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  spher             W        chi2  dof  pval  group count  \\\n",
       "region   event                                                              \n",
       "frontal  cue   0   True  3.260856e+20 -462.715239   44   1.0           10   \n",
       "         stim  0   True  2.456616e+17 -392.270460   44   1.0           10   \n",
       "parietal cue   0   True  1.202935e+20 -452.946123   44   1.0           10   \n",
       "         stim  0   True  2.443175e+13 -301.989490   44   1.0           10   \n",
       "\n",
       "                                               n per group  \n",
       "region   event                                              \n",
       "frontal  cue   0  [14, 14, 14, 14, 14, 14, 14, 14, 14, 14]  \n",
       "         stim  0  [14, 14, 14, 14, 14, 14, 14, 14, 14, 14]  \n",
       "parietal cue   0  [14, 14, 14, 14, 14, 14, 14, 14, 14, 14]  \n",
       "         stim  0  [14, 14, 14, 14, 14, 14, 14, 14, 14, 14]  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.check_sphericity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repeated Measures ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>stars</th>\n",
       "      <th>p-GG-corr</th>\n",
       "      <th>ng2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">parietal</th>\n",
       "      <th>0</th>\n",
       "      <td>timepoint</td>\n",
       "      <td>1.583420</td>\n",
       "      <td>9</td>\n",
       "      <td>117</td>\n",
       "      <td>0.175936</td>\n",
       "      <td>26.205536</td>\n",
       "      <td>3.402866e-24</td>\n",
       "      <td>****</td>\n",
       "      <td>5.834631e-07</td>\n",
       "      <td>0.542320</td>\n",
       "      <td>0.222299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>event</td>\n",
       "      <td>0.770449</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.770449</td>\n",
       "      <td>85.316794</td>\n",
       "      <td>4.483881e-07</td>\n",
       "      <td>****</td>\n",
       "      <td>4.483881e-07</td>\n",
       "      <td>0.365706</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>timepoint * event</td>\n",
       "      <td>0.623532</td>\n",
       "      <td>9</td>\n",
       "      <td>117</td>\n",
       "      <td>0.069281</td>\n",
       "      <td>29.541730</td>\n",
       "      <td>3.262477e-26</td>\n",
       "      <td>****</td>\n",
       "      <td>3.521208e-06</td>\n",
       "      <td>0.318157</td>\n",
       "      <td>0.171882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">frontal</th>\n",
       "      <th>0</th>\n",
       "      <td>timepoint</td>\n",
       "      <td>0.686264</td>\n",
       "      <td>9</td>\n",
       "      <td>117</td>\n",
       "      <td>0.076252</td>\n",
       "      <td>15.988779</td>\n",
       "      <td>8.285677e-17</td>\n",
       "      <td>****</td>\n",
       "      <td>8.940660e-05</td>\n",
       "      <td>0.394411</td>\n",
       "      <td>0.190812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>event</td>\n",
       "      <td>0.240461</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.240461</td>\n",
       "      <td>23.441963</td>\n",
       "      <td>3.218963e-04</td>\n",
       "      <td>***</td>\n",
       "      <td>3.218963e-04</td>\n",
       "      <td>0.185803</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>timepoint * event</td>\n",
       "      <td>0.242941</td>\n",
       "      <td>9</td>\n",
       "      <td>117</td>\n",
       "      <td>0.026993</td>\n",
       "      <td>13.031063</td>\n",
       "      <td>3.235739e-14</td>\n",
       "      <td>****</td>\n",
       "      <td>1.566020e-04</td>\n",
       "      <td>0.187360</td>\n",
       "      <td>0.213142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Source        SS  ddof1  ddof2        MS          F  \\\n",
       "region                                                                       \n",
       "parietal 0          timepoint  1.583420      9    117  0.175936  26.205536   \n",
       "         1              event  0.770449      1     13  0.770449  85.316794   \n",
       "         2  timepoint * event  0.623532      9    117  0.069281  29.541730   \n",
       "frontal  0          timepoint  0.686264      9    117  0.076252  15.988779   \n",
       "         1              event  0.240461      1     13  0.240461  23.441963   \n",
       "         2  timepoint * event  0.242941      9    117  0.026993  13.031063   \n",
       "\n",
       "                   p-unc stars     p-GG-corr       ng2       eps  \n",
       "region                                                            \n",
       "parietal 0  3.402866e-24  ****  5.834631e-07  0.542320  0.222299  \n",
       "         1  4.483881e-07  ****  4.483881e-07  0.365706  1.000000  \n",
       "         2  3.262477e-26  ****  3.521208e-06  0.318157  0.171882  \n",
       "frontal  0  8.285677e-17  ****  8.940660e-05  0.394411  0.190812  \n",
       "         1  3.218963e-04   ***  3.218963e-04  0.185803  1.000000  \n",
       "         2  3.235739e-14  ****  1.566020e-04  0.187360  0.213142  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.omnibus_rm_anova()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Post-hoc t-tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>timepoint</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>mean(A)</th>\n",
       "      <th>std(A)</th>\n",
       "      <th>mean(B)</th>\n",
       "      <th>std(B)</th>\n",
       "      <th>Paired</th>\n",
       "      <th>Parametric</th>\n",
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       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>BF10</th>\n",
       "      <th>hedges</th>\n",
       "      <th>**p-unc</th>\n",
       "      <th>Sign.</th>\n",
       "      <th>pairs</th>\n",
       "      <th>cross</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>event</th>\n",
       "      <th>Contrast</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">parietal</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">-</th>\n",
       "      <th>timepoint</th>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.024080</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>-0.034378</td>\n",
       "      <td>0.020787</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4.289549</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000880</td>\n",
       "      <td>43.681</td>\n",
       "      <td>0.498734</td>\n",
       "      <td>***</td>\n",
       "      <td>signif.</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timepoint</th>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.024080</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>-0.017773</td>\n",
       "      <td>0.032772</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.755546</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.463392</td>\n",
       "      <td>0.346</td>\n",
       "      <td>-0.227782</td>\n",
       "      <td>ns</td>\n",
       "      <td>False</td>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timepoint</th>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.024080</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>0.041580</td>\n",
       "      <td>0.056211</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-4.031294</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>28.944</td>\n",
       "      <td>-1.517100</td>\n",
       "      <td>**</td>\n",
       "      <td>signif.</td>\n",
       "      <td>(0, 3)</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timepoint</th>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.024080</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>0.119216</td>\n",
       "      <td>0.072481</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-6.565685</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>1298.443</td>\n",
       "      <td>-2.623299</td>\n",
       "      <td>****</td>\n",
       "      <td>signif.</td>\n",
       "      <td>(0, 4)</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timepoint</th>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.024080</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>0.168279</td>\n",
       "      <td>0.076726</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-8.059735</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9016.247</td>\n",
       "      <td>-3.338521</td>\n",
       "      <td>****</td>\n",
       "      <td>signif.</td>\n",
       "      <td>(0, 5)</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">frontal</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">stim</th>\n",
       "      <th>event * timepoint</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.171782</td>\n",
       "      <td>0.113811</td>\n",
       "      <td>0.024913</td>\n",
       "      <td>0.083956</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.202021</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>180.476</td>\n",
       "      <td>1.425848</td>\n",
       "      <td>***</td>\n",
       "      <td>signif.</td>\n",
       "      <td>((8, stim), (6, stim))</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event * timepoint</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.171782</td>\n",
       "      <td>0.113811</td>\n",
       "      <td>-0.042044</td>\n",
       "      <td>0.070980</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.978460</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>568.484</td>\n",
       "      <td>2.188817</td>\n",
       "      <td>****</td>\n",
       "      <td>signif.</td>\n",
       "      <td>((9, stim), (6, stim))</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event * timepoint</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.109996</td>\n",
       "      <td>0.098997</td>\n",
       "      <td>0.024913</td>\n",
       "      <td>0.083956</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.899680</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>507.441</td>\n",
       "      <td>0.899980</td>\n",
       "      <td>****</td>\n",
       "      <td>signif.</td>\n",
       "      <td>((8, stim), (7, stim))</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event * timepoint</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0.109996</td>\n",
       "      <td>0.098997</td>\n",
       "      <td>-0.042044</td>\n",
       "      <td>0.070980</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.295658</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>892.198</td>\n",
       "      <td>1.713727</td>\n",
       "      <td>****</td>\n",
       "      <td>signif.</td>\n",
       "      <td>((9, stim), (7, stim))</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event * timepoint</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0.024913</td>\n",
       "      <td>0.083956</td>\n",
       "      <td>-0.042044</td>\n",
       "      <td>0.070980</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.073903</td>\n",
       "      <td>13.0</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>651.786</td>\n",
       "      <td>0.836222</td>\n",
       "      <td>****</td>\n",
       "      <td>signif.</td>\n",
       "      <td>((9, stim), (8, stim))</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 timepoint  A  B   mean(A)    std(A)  \\\n",
       "region   event Contrast                                                \n",
       "parietal -     timepoint                 -  0  1 -0.024080  0.019279   \n",
       "               timepoint                 -  0  2 -0.024080  0.019279   \n",
       "               timepoint                 -  0  3 -0.024080  0.019279   \n",
       "               timepoint                 -  0  4 -0.024080  0.019279   \n",
       "               timepoint                 -  0  5 -0.024080  0.019279   \n",
       "...                                    ... .. ..       ...       ...   \n",
       "frontal  stim  event * timepoint       NaN  6  8  0.171782  0.113811   \n",
       "               event * timepoint       NaN  6  9  0.171782  0.113811   \n",
       "               event * timepoint       NaN  7  8  0.109996  0.098997   \n",
       "               event * timepoint       NaN  7  9  0.109996  0.098997   \n",
       "               event * timepoint       NaN  8  9  0.024913  0.083956   \n",
       "\n",
       "                                   mean(B)    std(B) Paired  Parametric  \\\n",
       "region   event Contrast                                                   \n",
       "parietal -     timepoint         -0.034378  0.020787   True        True   \n",
       "               timepoint         -0.017773  0.032772   True        True   \n",
       "               timepoint          0.041580  0.056211   True        True   \n",
       "               timepoint          0.119216  0.072481   True        True   \n",
       "               timepoint          0.168279  0.076726   True        True   \n",
       "...                                    ...       ...    ...         ...   \n",
       "frontal  stim  event * timepoint  0.024913  0.083956   True        True   \n",
       "               event * timepoint -0.042044  0.070980   True        True   \n",
       "               event * timepoint  0.024913  0.083956   True        True   \n",
       "               event * timepoint -0.042044  0.070980   True        True   \n",
       "               event * timepoint -0.042044  0.070980   True        True   \n",
       "\n",
       "                                         T   dof alternative     p-unc  \\\n",
       "region   event Contrast                                                  \n",
       "parietal -     timepoint          4.289549  13.0   two-sided  0.000880   \n",
       "               timepoint         -0.755546  13.0   two-sided  0.463392   \n",
       "               timepoint         -4.031294  13.0   two-sided  0.001426   \n",
       "               timepoint         -6.565685  13.0   two-sided  0.000018   \n",
       "               timepoint         -8.059735  13.0   two-sided  0.000002   \n",
       "...                                    ...   ...         ...       ...   \n",
       "frontal  stim  event * timepoint  5.202021  13.0   two-sided  0.000170   \n",
       "               event * timepoint  5.978460  13.0   two-sided  0.000046   \n",
       "               event * timepoint  5.899680  13.0   two-sided  0.000052   \n",
       "               event * timepoint  6.295658  13.0   two-sided  0.000028   \n",
       "               event * timepoint  6.073903  13.0   two-sided  0.000039   \n",
       "\n",
       "                                      BF10    hedges **p-unc    Sign.  \\\n",
       "region   event Contrast                                                 \n",
       "parietal -     timepoint            43.681  0.498734     ***  signif.   \n",
       "               timepoint             0.346 -0.227782      ns    False   \n",
       "               timepoint            28.944 -1.517100      **  signif.   \n",
       "               timepoint          1298.443 -2.623299    ****  signif.   \n",
       "               timepoint          9016.247 -3.338521    ****  signif.   \n",
       "...                                    ...       ...     ...      ...   \n",
       "frontal  stim  event * timepoint   180.476  1.425848     ***  signif.   \n",
       "               event * timepoint   568.484  2.188817    ****  signif.   \n",
       "               event * timepoint   507.441  0.899980    ****  signif.   \n",
       "               event * timepoint   892.198  1.713727    ****  signif.   \n",
       "               event * timepoint   651.786  0.836222    ****  signif.   \n",
       "\n",
       "                                                   pairs cross  \n",
       "region   event Contrast                                         \n",
       "parietal -     timepoint                          (0, 1)     x  \n",
       "               timepoint                          (0, 2)     x  \n",
       "               timepoint                          (0, 3)     x  \n",
       "               timepoint                          (0, 4)     x  \n",
       "               timepoint                          (0, 5)     x  \n",
       "...                                                  ...   ...  \n",
       "frontal  stim  event * timepoint  ((8, stim), (6, stim))     x  \n",
       "               event * timepoint  ((9, stim), (6, stim))     x  \n",
       "               event * timepoint  ((8, stim), (7, stim))     x  \n",
       "               event * timepoint  ((9, stim), (7, stim))     x  \n",
       "               event * timepoint  ((9, stim), (8, stim))     x  \n",
       "\n",
       "[292 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.test_pairwise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save Results\n",
    "Output is one excel file containing results of all performed tests (normality, anova,\n",
    "t-tests, etc.) in different sheets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "DA.save_statistics(\n",
    "    fname=\"fmri\",\n",
    "    overwrite=True,  # Overwrite existing files\n",
    "    # overwrite = \"day\"  # Adds the current date to filename (daily overwrite)\n",
    "    # overwrite = False  # Adds a number to filename, file never overwritten\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use matplotlib styles (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcdefaults()  # Reset rc to default\n",
    "plt.style.use(\"ggplot\")  # Set styles as you're used to'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chain multiple commands for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 740x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    DA.plot_box_strip().annotate_pairwise(  # Use a pre-built plotting function to initialize and draw the plot  # Place results calculated previously (DA.test_pairwise()) on the plot\n",
    "        include=\"__HUE\"  # Only annotate significant pairs across each hue, not within hue\n",
    "    )\n",
    ")\n",
    "\n",
    "# Saving the plot requires\n",
    "plt.savefig(\n",
    "    fname=\"fmri2.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches=\"tight\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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