assocplot from R implemented in Python

assocplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "def assocplot(x, rownames, colnames, col=['k', 'r'], space=0.3):\n",
    "    if x.ndim != 2:\n",
    "        raise ValueError('\"x\" must be a 2-d contingency table')\n",
    "    if (x < 0).any() or np.isnan(x).any():\n",
    "        raise ValueError('all entries of \"x\" must be nonnegative and finite')\n",
    "    if len(col) != 2:\n",
    "        raise ValueError('incorrect \"col\": must be length 2')    \n",
    "    if len(rownames) != x.shape[0]:\n",
    "        raise ValueError('the length of \"rownames\" must be equal to the number of rows')\n",
    "    if len(colnames) != x.shape[1]:\n",
    "        raise ValueError('the length of \"colnames\" must be equal to the number of columns')\n",
    "        \n",
    "    n = x.sum() \n",
    "    if n == 0:\n",
    "        raise ValueError('at least one entry of \"x\" must be positive')\n",
    "    \n",
    "    f = x[:, ::-1]\n",
    "    colnames = colnames[::-1]\n",
    "    \n",
    "    e2 = f.sum(axis=1, keepdims=True) @ f.sum(axis=0, keepdims=True) / n\n",
    "    e = np.sqrt(e2)\n",
    "    d = (f - e2) / e\n",
    "    \n",
    "    x_w = e.max(axis=1)\n",
    "    y_h = d.max(axis=0) - d.min(axis=0)\n",
    "\n",
    "    x_delta = x_w.mean() * space\n",
    "    y_delta = y_h.mean() * space\n",
    "    \n",
    "    x_r = (x_w + x_delta).cumsum()\n",
    "    x_m = np.convolve(x_r, [0.5, 0.5], 'same')\n",
    "    \n",
    "    y_u = (y_h + y_delta).cumsum()\n",
    "    y_m = y_u - np.maximum(d, 0).max(axis=0) - y_delta / 2\n",
    "    \n",
    "    xs, ys = np.meshgrid(x_m, y_m)\n",
    "\n",
    "    ax = plt.gca()\n",
    "    \n",
    "    ax.set_xlim(0, x_w.sum() + x.shape[0] * x_delta)\n",
    "    ax.set_ylim(0, y_h.sum() + x.shape[1] * y_delta)\n",
    "    \n",
    "    ax.set_xticks(x_m)\n",
    "    ax.set_xticklabels(rownames)\n",
    "    \n",
    "    ax.set_yticks(y_m)\n",
    "    ax.set_yticklabels(colnames)\n",
    "    ax.grid(True, axis='y')\n",
    "    \n",
    "    for x, w, y, h in zip(xs.flatten(), e.T.flatten(), ys.flatten(), d.T.flatten()):\n",
    "        color = col[int(h < 0)]\n",
    "        ax.add_patch(plt.Rectangle((x - w / 2, y), w, h, color=color, alpha=0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(\n",
    "    [[ 68, 20, 15,  5],\n",
    "     [119, 84, 54, 29],\n",
    "     [ 26, 17, 14, 14],\n",
    "     [  7, 94, 10, 16]],\n",
    "    index=['Black', 'Brown', 'Red', 'Blond'],\n",
    "    columns=['Brown', 'Blue', 'Hazel', 'Green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Brown</th>\n",
       "      <th>Blue</th>\n",
       "      <th>Hazel</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>68</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>119</td>\n",
       "      <td>84</td>\n",
       "      <td>54</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red</th>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blond</th>\n",
       "      <td>7</td>\n",
       "      <td>94</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Brown  Blue  Hazel  Green\n",
       "Black     68    20     15      5\n",
       "Brown    119    84     54     29\n",
       "Red       26    17     14     14\n",
       "Blond      7    94     10     16"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.title('Relation between hair and eye color')\n",
    "plt.xlabel('Hair')\n",
    "plt.ylabel('Eye')\n",
    "assocplot(df.values, df.index, df.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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