{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python2.7/site-packages/ipykernel_launcher.py:10: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pSiteFile = 'pSite_growth.csv'\n",
    "#PhosphoSite/PhosphoSitePlus human tyrosine phosphorylation sites\n",
    "df = pd.DataFrame.from_csv(pSiteFile)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-ea6b652b473d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mkeySubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Modification'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxRow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midxCol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeySubset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeySubset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Number'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxRow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midxCol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m#axes[idxRow,idxCol].get_xaxis().get_major_formatter().set_useOffset(False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows = 3\n",
    "ncols = 2\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols)\n",
    "fig.tight_layout()\n",
    "fig.autofmt_xdate()\n",
    "idxCol = 0\n",
    "idxRow = 0\n",
    "#keys = ('Phosphoserine', 'Phosphothreonine', 'Phosphotyrosine','N6-acetyllysine', 'Ubiquitination', 'Sumoylation')\n",
    "for key in keys:\n",
    "    keySubset = df[df['Modification']==key]\n",
    "    axes[idxRow, idxCol].bar(keySubset['Year'], keySubset['Number'])\n",
    "    axes[idxRow,idxCol].set_title(key)\n",
    "    #axes[idxRow,idxCol].get_xaxis().get_major_formatter().set_useOffset(False)\n",
    "    axes[idxRow,idxCol].get_xaxis().get_major_formatter().set_scientific(False)\n",
    "    # Tell matplotlib to interpret the x-axis values as dates\n",
    "    #axes[idxRow,idxCol].xaxis_date()\n",
    "    idxCol = idxCol + 1\n",
    "    if idxCol % (nrows-1) == 0:\n",
    "        idxCol = 0\n",
    "        idxRow += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'AxesSubplot' object has no attribute '__getitem__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-855aeb80bd14>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mkeySubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Modification'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxRow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midxCol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeySubset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeySubset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Number'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxRow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midxCol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m#axes[idxRow,idxCol].get_xaxis().get_major_formatter().set_useOffset(False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'AxesSubplot' object has no attribute '__getitem__'"
     ]
    },
    {
     "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": [
    "nrows = 1\n",
    "ncols = 1\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols)\n",
    "fig.tight_layout()\n",
    "fig.autofmt_xdate()\n",
    "idxCol = 0\n",
    "idxRow = 0\n",
    "#keys = ('Phosphoserine', 'Phosphothreonine', 'Phosphotyrosine','N6-acetyllysine', 'Ubiquitination', 'Sumoylation')\n",
    "keys = ['Phosphotyrosine']\n",
    "for key in keys:\n",
    "    keySubset = df[df['Modification']==key]\n",
    "    axes[idxRow, idxCol].bar(keySubset['Year'], keySubset['Number'])\n",
    "    axes[idxRow,idxCol].set_title(key)\n",
    "    #axes[idxRow,idxCol].get_xaxis().get_major_formatter().set_useOffset(False)\n",
    "    axes[idxRow,idxCol].get_xaxis().get_major_formatter().set_scientific(False)\n",
    "    # Tell matplotlib to interpret the x-axis values as dates\n",
    "    #axes[idxRow,idxCol].xaxis_date()\n",
    "    idxCol = idxCol + 1\n",
    "    if idxCol % (nrows-1) == 0:\n",
    "        idxCol = 0\n",
    "        idxRow += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "keySubset = keySubset.sort_values('Year')\n",
    "keySubset = keySubset[keySubset.Year !=2018]\n",
    "f = keySubset.plot.bar(x='Year', y='Number', legend=False, rot=45, fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = f.get_figure()\n",
    "fig.savefig('pTyr_growth_to2017.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}