{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from biokit import viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from biokit.viz import anova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "X = random.random(100)\n",
    "\n",
    "df = pd.DataFrame({'A':X, 'B': X + X/10, \n",
    "                   'C': X + random.random(100), \n",
    "                   'D': X + random.random(100)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "an = anova.ANOVA(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.276745e-01</td>\n",
       "      <td>2.641227e-23</td>\n",
       "      <td>4.452266e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.276745e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.970184e-19</td>\n",
       "      <td>2.411152e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.641227e-23</td>\n",
       "      <td>4.970184e-19</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.233366e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>4.452266e-19</td>\n",
       "      <td>2.411152e-15</td>\n",
       "      <td>4.233366e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A             B             C             D\n",
       "A  1.000000e+00  2.276745e-01  2.641227e-23  4.452266e-19\n",
       "B  2.276745e-01  1.000000e+00  4.970184e-19  2.411152e-15\n",
       "C  2.641227e-23  4.970184e-19  1.000000e+00  4.233366e-01\n",
       "D  4.452266e-19  2.411152e-15  4.233366e-01  1.000000e+00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "an.imshow_anova_pairs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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 },
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}