{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# imshow demonstration\n",
    "- This notebook is part of biokit \n",
    " - https://github.com/biokit/biokit\n",
    " - https://pypi.python.org/pypi/biokit\n",
    "\n",
    "- imshow with pandas dataframe : imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from biokit.viz import Imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imshow\n",
    "- we overwrite the pylab.imshow to change the default behaviour (no interpolation, hot cmap)\n",
    "- input can be a dataframe, in which case, X and Y ticks are filled automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = numpy.random.randn(10,10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f294c52dd90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(data) # this is the default behaviour in matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "im = Imshow(data)\n",
    "im.plot(cmap='jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "X  1  3\n",
       "Y  2  4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'A':[1,2], 'B':[3,4]}, index=['X','Y'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# by default xticks are rotated by 90 degrees since names could be long \n",
    "# but in this example names are short, so we reset the rotation.\n",
    "Imshow(df).plot(rotation_x=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}