timeseries benchmark

bench.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "source": [
    "import MDAnalysis as mda"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "from MDAnalysisTests.datafiles import PSF, DCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "dcd = mda.lib.formats.libdcd.DCDFile(DCD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "6.29 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit dcd.readframes()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [],
   "source": [
    "u = mda.Universe(PSF, DCD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.3 ms ± 249 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "xyz = np.empty((u.trajectory.n_frames, u.atoms.n_atoms, 3))\n",
    "for i, ts in enumerate(u.trajectory):\n",
    "    xyz[i] = ts.positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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