MI + Spearman Scores Histogram.ipynb

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   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "mutual=np.genfromtxt('norm_MI_scores.txt',dtype=float)\n",
    "spearman=np.genfromtxt('spearman_coefficients.txt',dtype=None)\n",
    "spearman[40] = 0\n",
    "\n",
    "mpl.rcParams['axes.linewidth'] = 1.0\n",
    "plt.rc('font', **{'family':'sans-serif','sans-serif':['Times New Roman']})\n",
    "\n",
    "subxticks=[]\n",
    "subxticks.append('D0')\n",
    "for i in range(1,len(mutual)-1):\n",
    "    subxticks.append('')\n",
    "subxticks.append('D50')\n",
    "\n",
    "plt.figure(0)\n",
    "\n",
    "x=np.arange(0,51,1)\n",
    "xticks=[]\n",
    "y=np.arange(0,8,1)\n",
    "yticks=np.arange(0,15,1)\n",
    "\n",
    "ml = AutoMinorLocator(4)\n",
    "plt.tick_params(which='both', width=1)\n",
    "plt.tick_params(which='major', length=6)\n",
    "plt.tick_params(which='minor', length=4)\n",
    "plt.xticks(x,subxticks)\n",
    "plt.yticks(y,yticks)\n",
    "plt.axis([-.5, len(subxticks)-.5, 0,1])\n",
    "plt.ylabel('Score')\n",
    "plt.tick_params(axis='both', which='major')\n",
    "plt.axes().yaxis.set_minor_locator(ml)\n",
    "plt.tick_params(axis='x',which='both',bottom=False)\n",
    "plt.title('Normalized Mutual Information Score')\n",
    "\n",
    "plt.scatter(x,mutual,s=20,c='red')\n",
    "\n",
    "##\n",
    "\n",
    "plt.figure(1)\n",
    "\n",
    "x=np.arange(0,51,1)\n",
    "xticks=[]\n",
    "y=np.arange(-1,8,1)\n",
    "yticks=np.arange(-1,15,1)\n",
    "\n",
    "ml = AutoMinorLocator(4)\n",
    "plt.tick_params(which='both', width=1)\n",
    "plt.tick_params(which='major', length=6)\n",
    "plt.tick_params(which='minor', length=4)\n",
    "plt.xticks(x,subxticks)\n",
    "plt.yticks(y,yticks)\n",
    "plt.axis([-.5, len(subxticks)-.5, -1,1])\n",
    "plt.ylabel('Score')\n",
    "plt.tick_params(axis='both', which='major')\n",
    "plt.axes().yaxis.set_minor_locator(ml)\n",
    "plt.tick_params(axis='x',which='both',bottom=False)\n",
    "plt.title('Spearman Coefficient')\n",
    "plt.plot([-10,100],[0,0],'--k',linewidth=1.0)\n",
    "\n",
    "plt.scatter(x,spearman,s=20,c='blue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}