bar_chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%env:LC_ALL=\"en_US.UTF-8\"\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "means_women = (49.48, 51.96, 53.53, 51.40, 50.20, 52.32) # 51.40\n",
    "std_women = (5.37, 4.35, 5.48, 5.71, 5.20, 5.30)\n",
    "\n",
    "fig = plt.figure(figsize=(8.5, 5))\n",
    "\n",
    "#fig, ax = plt.subplots()\n",
    "\n",
    "plt.ylim([35,60])\n",
    "\n",
    "index = np.arange(6)\n",
    "bar_width = 0.5\n",
    "error_config = {'ecolor': '0.5', \"linewidth\": \"1\"}\n",
    "opacity = 1\n",
    "colors = ['deepskyblue','deepskyblue','lightsalmon','lightsalmon', 'lime', 'lime']\n",
    "colors_outer = ['blue','blue','red','red', 'darkgreen', 'darkgreen']\n",
    "rects2 = plt.bar(index + bar_width, means_women, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color=colors,\n",
    "                 yerr=std_women,\n",
    "                 error_kw=error_config,\n",
    "                 align=\"center\",\n",
    "                 linewidth=1,\n",
    "                 edgecolor=colors_outer)\n",
    "\n",
    "plt.ylabel('UAS')\n",
    "plt.xticks(index + bar_width, ('MULTI', 'SINGLE', 'PROJ1', 'PROJ2', 'selection', 'reparsing'))\n",
    "\n",
    "plt.axhline(53.53, linestyle=\"--\", color=\"0.5\")\n",
    "plt.axhline(51.40, linestyle=\"--\", color=\"0.5\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.rcParams.update({'font.size': 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8mJn3R8QM4I0Uj7U9QvGuzDcyM2v6TAf2pAhCjUaRllC8X3NMxbokSZIkjSFVQ80aindT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\nruZKjZmZmZmZdbX/B2j3lqrrPd7VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112c38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "means_rank = (71.34, 16.35, 5.06, 2.77, 1.82, 0.38, 2.27, 28.66)\n",
    "ci_rank = (11.09, 9.30, 5.17, 2.94, 2.01, 0.43, np.nan, np.nan)\n",
    "\n",
    "fig = plt.figure(figsize=(8.5, 5))\n",
    "\n",
    "#fig, ax = plt.subplots()\n",
    "\n",
    "plt.ylim([0,85])\n",
    "plt.xlim([0,8])\n",
    "\n",
    "index = np.arange(8)\n",
    "bar_width = 0.5\n",
    "error_config = {'ecolor': '0.5', \"linewidth\": \"1\"}\n",
    "opacity = 1\n",
    "colors = ['deepskyblue', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon', 'lightsalmon']\n",
    "colors_outer = ['blue', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red']\n",
    "rects2 = plt.bar(index + bar_width, means_rank, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color=colors,\n",
    "                 yerr=ci_rank,\n",
    "                 error_kw=error_config,\n",
    "                 align=\"center\",\n",
    "                 linewidth=1,\n",
    "                 edgecolor=colors_outer)\n",
    "\n",
    "plt.ylabel('best for % sentences')\n",
    "plt.xlabel('test set rank of best source')\n",
    "plt.xticks(index + bar_width, (1,2,3,4,5,6,\"7-25\", \"2-25\"))\n",
    "\n",
    "plt.axhline(28.66, color=\"0.5\", linestyle=\"--\")\n",
    "plt.axvline(6, color=\"black\", linestyle=\"-\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.rcParams.update({'font.size': 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}