radar_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xml.etree.ElementTree as et\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "hsa = et.parse(\"hsa00140.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = hsa.getroot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "for child in root[1]:\n",
    "    pathway = child.attrib['name'].replace('TITLE:', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'HSD17B1, 17-beta-HSD, 20-alpha-HSD, E2DH, EDH17B2, EDHB17, HSD17, SDR28C1...', 'fgcolor': '#000000', 'bgcolor': '#BFFFBF', 'type': 'rectangle', 'x': '899', 'y': '1122', 'width': '46', 'height': '17'}\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(root)):\n",
    "    for child in root[i]:\n",
    "        try:\n",
    "            print(child.attrib)\n",
    "        except:\n",
    "            pass\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'HSD17B1, 17-beta-HSD, 20-alpha-HSD, E2DH, EDH17B2, EDHB17, HSD17, SDR28C1...', 'fgcolor': '#000000', 'bgcolor': '#BFFFBF', 'type': 'rectangle', 'x': '899', 'y': '1122', 'width': '46', 'height': '17'}\n"
     ]
    }
   ],
   "source": [
    "for child in root[0]:\n",
    "    print(child.attrib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes = [gene for gene in root[0].attrib['name'].split(' ')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hsa:3292', 'hsa:3294', 'hsa:51144', 'hsa:51478', 'hsa:7923', 'hsa:8630']\n"
     ]
    }
   ],
   "source": [
    "print(genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr = [0, 1, 0, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The number of FixedLocator locations (7), usually from a call to set_ticks, does not match the number of ticklabels (6).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/jz/6x71ckj13c31lbpnxzk_tm640000gn/T/ipykernel_3210/2548373637.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_loc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpathway\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pathway Analysis'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthetagrids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdegrees\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_loc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenes\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      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/work/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mthetagrids\u001b[0;34m(angles, labels, fmt, **kwargs)\u001b[0m\n\u001b[1;32m   2014\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2015\u001b[0m         lines, labels = ax.set_thetagrids(angles,\n\u001b[0;32m-> 2016\u001b[0;31m                                           labels=labels, fmt=fmt, **kwargs)\n\u001b[0m\u001b[1;32m   2017\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/work/lib/python3.7/site-packages/matplotlib/projections/polar.py\u001b[0m in \u001b[0;36mset_thetagrids\u001b[0;34m(self, angles, labels, fmt, **kwargs)\u001b[0m\n\u001b[1;32m   1334\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mangles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1336\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\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   1337\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmticker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFormatStrFormatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/work/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/work/lib/python3.7/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    469\u001b[0m                 \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 471\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/work/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_set_ticklabels\u001b[0;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[1;32m   1793\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1795\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1797\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/work/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_ticklabels\u001b[0;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[1;32m   1715\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticklabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticklabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1716\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1717\u001b[0;31m                     \u001b[0;34m\"The number of FixedLocator locations\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1718\u001b[0m                     \u001b[0;34mf\" ({len(locator.locs)}), usually from a call to\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1719\u001b[0m                     \u001b[0;34m\" set_ticks, does not match\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (7), usually from a call to set_ticks, does not match the number of ticklabels (6)."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(x))\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(polar=True)\n",
    "plt.plot(label_loc, x, label=pathway)\n",
    "plt.title('Pathway Analysis', size=20)\n",
    "lines, labels = plt.thetagrids(np.degrees(label_loc), labels=genes)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f1da6f0aa5a6d94d582cdd7738d3c63a6660a5e0e2aa51fad15a9d019dea5976"
  },
  "kernelspec": {
   "display_name": "Python 3.7.12 64-bit ('work': conda)",
   "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": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}