arokem/gist:1f3529f967f334af74b7 Secret

## gistfile1.txt
{
 "metadata": {
  "name": "",
  "signature": "sha256:c019c22050aac730187e5b5fc6a9eddf26de3ec541601192b09f8ad08956fc21"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext memory_profiler"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from dipy.data import read_stanford_labels\n",
      "import nibabel as nib\n",
      "hardi_img, gtab, labels_img = read_stanford_labels()\n",
      "data = hardi_img.get_data()\n",
      "candidate_sl = [s[0] for s in nib.trackvis.read(\n",
      "    '/Users/arokem/source/dipy/doc/examples/lr-superiorfrontal.trk',\n",
      "                                                  points_space='voxel')[0]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dataset is already in place. If you want to fetch it again, please first remove the folder /Users/arokem/.dipy/stanford_hardi \n",
        "All files already in /Users/arokem/.dipy/stanford_hardi."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import dipy.tracking.life as life\n",
      "fiber_model = life.FiberModel(gtab)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in np.arange(0, 14):\n",
      "    this = 2 ** i\n",
      "    print(\"Number of streamlines: %s\"%this)\n",
      "    %memit fiber_fit = fiber_model.fit(data, candidate_sl[:this], affine=np.eye(4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of streamlines: 1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/memory_profiler.py:68: UserWarning: psutil module not found. memory_profiler will be slow\n",
        "  warnings.warn(\"psutil module not found. memory_profiler will be slow\")\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 353.95 MiB, increment: 2.66 MiB\n",
        "Number of streamlines: 2\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 355.89 MiB, increment: 1.93 MiB\n",
        "Number of streamlines: 4\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 355.79 MiB, increment: -0.09 MiB\n",
        "Number of streamlines: 8\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 346.62 MiB, increment: 4.06 MiB\n",
        "Number of streamlines: 16\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 297.62 MiB, increment: 9.21 MiB\n",
        "Number of streamlines: 32\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 316.05 MiB, increment: 20.67 MiB\n",
        "Number of streamlines: 64\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 347.95 MiB, increment: 37.66 MiB\n",
        "Number of streamlines: 128\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 442.94 MiB, increment: 103.92 MiB\n",
        "Number of streamlines: 256\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 546.53 MiB, increment: 156.33 MiB\n",
        "Number of streamlines: 512\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 805.21 MiB, increment: 359.52 MiB\n",
        "Number of streamlines: 1024\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 1322.86 MiB, increment: 892.62 MiB\n",
        "Number of streamlines: 2048\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 2107.50 MiB, increment: 1541.49 MiB\n",
        "Number of streamlines: 4096\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 3229.17 MiB, increment: 2586.12 MiB\n",
        "Number of streamlines: 8192\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "peak memory: 4551.69 MiB, increment: 3649.34 MiB\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = [353.95, 355.89, 355.79, 346.62, 297.62, 316.05, 347.95, \n",
      "     442.94, 546.53, 805.21, 1322.86, 2107.50, 3229.17, 4551.69]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "ax.plot(2 ** np.arange(14), m, 'o-')\n",
      "ax.set_ylabel('Memory usage (MiB)')\n",
      "ax.set_xlabel('# streamlines')\n",
      "p = np.polyfit(2 ** np.arange(14), m, 1)\n",
      "xx = np.linspace(0, 10000, 2)\n",
      "ax.plot(xx, np.polyval(p, xx), '--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[<matplotlib.lines.Line2D at 0x114b33ed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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5uKCwgBGfjqDrSV05pFKpW/RURPYhmq7I1c3sn2Y2O3z9w8ySsOqGJMr69dC3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\n9/+DTsfseIkaWgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114a35210>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "ax.set_ylabel('Memory usage (MiB)')\n",
      "ax.set_xlabel('# streamlines')\n",
      "xx = np.linspace(0, 1000000, 2)\n",
      "ax.plot(xx, np.polyval(p, xx), '--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[<matplotlib.lines.Line2D at 0x114a3eb50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9NzNbF676MzMzK+BEZUWZOROGDctm3TUzKycnKmvRu+/CqFFwyCFw4IGwySZ5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\ns4rmRGVmZhXNicrMzCra/wdFw4CoMdmsfgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114a3e650>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}