dti-mem-profile-newer.ipynb

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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
   },
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   "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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   },
   "outputs": [
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     "text": [
      "Dataset is already in place. If you want to fetch it again please first remove the folder /Users/arokem/.dipy/cenir_multib \n"
     ]
    }
   ],
   "source": [
    "img, gtab = dpd.read_cenir_multib()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "data = img.get_data()"
   ]
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     "execution_count": 6,
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   "source": [
    "data.shape"
   ]
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   "cell_type": "code",
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   "source": [
    "S0 = np.mean(data[..., ~gtab.b0s_mask], -1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 5015.63 MiB, increment: 3870.77 MiB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arokem/source/dipy/dipy/reconst/dti.py:1759: RuntimeWarning: invalid value encountered in log\n",
      "  def decompose_tensor(tensor, min_diffusivity=0):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 2797.88 MiB, increment: 709.55 MiB\n",
      "peak memory: 5947.23 MiB, increment: 3873.91 MiB\n",
      "peak memory: 6081.25 MiB, increment: 3869.66 MiB\n",
      "peak memory: 5679.02 MiB, increment: 3866.48 MiB\n"
     ]
    }
   ],
   "source": [
    "for step in [1, 10, 100, 1000, 10000]:\n",
    "    tenmodel = dti.TensorModel(gtab, step=step)\n",
    "    tenfit = tenmodel.fit(data)\n",
    "    %memit pred = tenfit.predict(gtab, S0)    "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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