Timer.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import timeit\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rmgpy.molecule import Molecule\n",
    "from rmgpy.molecule.resonance import generateAromaticResonanceStructures, generateKekuleStructure\n",
    "mol = Molecule(SMILES='C1=CC2=CC=C3C=CC4=CC=C5C=CC6=CC=C1C1=C2C3=C4C5=C61')\n",
    "arom = generateAromaticResonanceStructures(mol)[0]\n",
    "\n",
    "\n",
    "#out = generateKekuleStructure(arom)\n",
    "#out[0]\n",
    "\n",
    "arom.kekulize()\n",
    "arom"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "setup = \"\"\"\\\n",
    "from rmgpy.molecule import Molecule\n",
    "from rmgpy.molecule.resonance import generateAromaticResonanceStructures, generateKekuleStructure\n",
    "mol = Molecule(SMILES='C1=CC2=CC=C3C=CC4=CC=C5C=CC6=CC=C1C1=C2C3=C4C5=C61')\n",
    "arom = generateAromaticResonanceStructures(mol)[0]\n",
    "\"\"\"\n",
    "\n",
    "test1 = \"\"\"\\\n",
    "out = generateKekuleStructure(arom)\n",
    "\"\"\"\n",
    "\n",
    "test2 = \"\"\"\\\n",
    "arom.kekulize()\n",
    "\"\"\"\n",
    "\n",
    "test3 = \"\"\"\\\n",
    "arom2 = arom.copy(deep=True)\n",
    "arom2.kekulize()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = timeit.repeat(test1, setup, repeat=10, number=1000)\n",
    "result = np.array(result)\n",
    "print np.mean(result), np.std(result)\n",
    "\n",
    "result = timeit.repeat(test2, setup, repeat=10, number=1000)\n",
    "result = np.array(result)\n",
    "print np.mean(result), np.std(result)\n",
    "\n",
    "result = timeit.repeat(test3, setup, repeat=10, number=1000)\n",
    "result = np.array(result)\n",
    "print np.mean(result), np.std(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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