mem-profile-dti.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import dipy.core.gradients as dpg\n",
    "import dipy.data as dpd\n",
    "import dipy.reconst.dti as dti\n",
    "import nibabel as nib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "%load_ext memory_profiler"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   },
   "outputs": [],
   "source": [
    "img = nib.load('/Users/arokem/.dipy/stanford_hardi/HARDI150.nii')\n",
    "gtab = dpg.gradient_table('/Users/arokem/.dipy/stanford_hardi/HARDI150.bval', \n",
    "                          '/Users/arokem/.dipy/stanford_hardi/HARDI150.bvec')\n",
    "data = img.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 919.16 MiB, increment: 836.98 MiB\n",
      "peak memory: 996.80 MiB, increment: 614.00 MiB\n",
      "peak memory: 985.32 MiB, increment: 502.35 MiB\n",
      "peak memory: 1025.04 MiB, increment: 538.19 MiB\n",
      "peak memory: 1054.46 MiB, increment: 541.49 MiB\n"
     ]
    }
   ],
   "source": [
    "for step in [1, 10, 100, 1000, 10000]:\n",
    "    tenmodel = dti.TensorModel(gtab, step=step)\n",
    "    %memit tenfit = tenmodel.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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