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@arokem
Created December 8, 2012 21:19
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TensorModel
{
"metadata": {
"name": "TensorModel"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "# We need to set the environment display variable by hand, so that the visualization functions work:\nimport os\nos.environ['DISPLAY'] = 'localhost:10.0'",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "import os\nimport tempfile\nfrom IPython.display import Image, display\n\nimport nibabel as ni\n\nimport osmosis as oz\nimport osmosis.model.dti as dti\nimport osmosis.model.sparse_deconvolution as ssd",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "import osmosis.viz.maya as maya\nimport osmosis.viz.mpl as mpl",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "import osmosis.utils as ozu\nimport osmosis.volume as ozv\nimport osmosis.io as oio\nimport osmosis.tensor as ozt",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": "data_1k_1, data_1k_2 = oio.get_dwi_data(1000)\ndata_2k_1, data_2k_2 = oio.get_dwi_data(2000)\ndata_4k_1, data_4k_2 = oio.get_dwi_data(4000)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "# A corpus callosum voxel:\nvox_idx = (40, 73, 33)\n\nmask = np.zeros(ni.load(data_1k_1[0]).shape[:3])\nmask[vox_idx[0]-1:vox_idx[0]+1,\n vox_idx[1]-1:vox_idx[1]+1,\n vox_idx[2]-1:vox_idx[2]+1]=1",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "TM_1k_1 = dti.TensorModel(*data_1k_1, mask=mask, params_file='temp')\nTM_1k_2 = dti.TensorModel(*data_1k_2, mask=mask, params_file='temp')\nTM_2k_1 = dti.TensorModel(*data_2k_1, mask=mask, params_file='temp')\nTM_2k_2 = dti.TensorModel(*data_2k_2, mask=mask, params_file='temp')\nTM_4k_1 = dti.TensorModel(*data_4k_1, mask=mask, params_file='temp')\nTM_4k_2 = dti.TensorModel(*data_4k_2, mask=mask, params_file='temp')",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Loading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0009_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvals\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0011_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvals\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0005_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvals\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0007_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvals\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0005_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvals\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0007_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvals\n"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Interpolate the signal from the measurement b-vectors using radial basis functions and display in 3D:\nfig = maya.plot_signal_interp(TM_1k_1.bvecs[:,TM_1k_1.b_idx], TM_1k_1.relative_signal[vox_idx], cmap='hot', vmin=0, vmax=1)\nfig = maya.plot_signal_interp(TM_2k_1.bvecs[:,TM_2k_1.b_idx], TM_2k_1.relative_signal[vox_idx], cmap='hot', vmin=0, vmax=1, figure=fig, offset=-2)\n# Put it into a temporary file:\nfn = '%s.png'%tempfile.NamedTemporaryFile().name \n#orientation_axes = mlab.orientation_axes(xlabel='', ylabel='').axes\nfig = maya.plot_signal_interp(TM_4k_1.bvecs[:,TM_4k_1.b_idx], TM_4k_1.relative_signal[vox_idx], cmap='hot', colorbar=True, vmin=0, vmax=1, figure=fig, offset=-4, file_name=fn)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Loading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0009_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvecs\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0009_01_DWI_2mm150dir_2x_b1000_aligned_trilin.nii.gz\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0005_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvecs"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0005_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.nii.gz\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0005_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvecs"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0005_01_DWI_2mm150dir_2x_b4000_aligned_trilin.nii.gz\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/lib/python2.7/dist-packages/osmosis/model/base.py:336: RuntimeWarning: divide by zero encountered in divide\n signal_rel = self.signal/np.reshape(self.S0, (self.S0.shape + (1,)))\n/usr/local/lib/python2.7/dist-packages/osmosis/model/base.py:336: RuntimeWarning: invalid value encountered in divide\n signal_rel = self.signal/np.reshape(self.S0, (self.S0.shape + (1,)))\n"
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "i = Image(filename=fn)\ndisplay(i)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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eevLdJz4pzg+//9nb7ok4kHNYcbRgusDXSA00E9PCdfA8yiVqVfq7\nGeiiv0pk4bxouN0NFx1jYd4SKqV27969evXqW2+9de3atcBdd9310Y9+9JOf/ORznvOcCy+88MTd\nNWvWfOMb3zj5SXHe+IUrh9/L0MOHJpa7GGBpdFl02ZQNyhqajmNROFg2ZYc+RW/MBRcPXb1ptN0N\nFx1jYd4SBkHg+77v+zt27Lj22mvjOH7b2962c+fOZcuWAR/60IdO3L3mmmu+8IUvHDvfsGHD026/\nOLe878tjb3zuqkOKPhvXYoXHxVVW1lis0++igaljOpgalYJqgNlqd4tFR1mYSfdKpTIyMrJu3TrA\nsqyNGzeOjY0VRfHBD35wZGTkD//wD0/cHR0dPXG+adOmZ7JU8QxYNlT/66/fBiw1eY5FI+KLh7jl\nID9o8FOfQwkzipaGsjA9TJeeZfV2N1l0klMn3b15TrqHxZmfEc8+qzztIpfLa/Q5zCkeDJnKGOxl\n9RKeO8CKXqplWjnf2cvFv/HxqzaMtru9omOcGlj2PAMrkcASp7FvYvwNl6xa38faLioWczlHExLF\ngzGlLp6zlMFemin7sqE/+cx4uxsrOomsdBcLb3CofiilAYVNtYRboCU0U15YwTJxfO48DBq1NfV2\nt1R0mFPmsHI1v0OIJ/Pd+x45mDKnUbh4ZSoVSmUsF80hs7igB9ehftlwu5spOswpPaz5drCMBWyI\nOL8sX1n3e4YOphMrDLpdHAvHQuXkGqlGK4dFQ1f9ymi7myk6zCk9LNkOSyygT3xxbE+TIxmxie5i\nuOCiLEKd6ZxFa4dXrKy3u42iw5w6JJQNR8XCWb6y/if/eNu9c0wqYovcRlnEJs2CpGtoYPVwuxso\nOo/0sMR/ol98yfDM0qt+Ms3hFF+jpTGTMz7HuleOvln2ZhDzd8qyhhltfssaugtZ1iDO7Euf27bj\n/7t+UY0wI/WGLn/V6Oh/29LuRomOdEpgHZ1nYC2SwBJn7fvfGfvWN8du/KMt7W6I6GCnBNaheQbW\nEgksIcQz6GktaxBCiGfSKYEl8+hCiHOZ9LCEEB1DelhCiI5xSmAl7WqFEEKcBelhCSE6hsxhCSE6\nhgSWEKJjyJBQCNExpIclhOgYpwTWU/1ZQiGEeCZID0sI0TFkDksI0TGkhyWE6BgSWEKIjiFDQiFE\nx5C3hEKIjiFDQiFEx5DAEkJ0DJnDEkJ0DOlhCSE6hgSWEKJjyFtCIUTHkDksIUTHkCGhEKJjSGAJ\nITqGfvIfap7HyRqNxvr16/v6+jZv3nzsyoEDB9avX79o0aKtW7eefPeJTwohxNnQiqI48cf/0rR5\nffj3T/rsTTfd1Gg0brjhhiuuuCKKImBkZGRwcPB1r3vda17zmhtuuOHE3RtvvPFxTwohxNk4pYeV\nzvM42djY2HXXXWcYxuDg4LErW7du/ZM/+ZM77rhj/fr1J9994pNCCHE2TgmsfJ7HCUqp3bt3r169\neteuXWvXrj12cWho6P3vf/+nPvWpv/3bvz1xd82aNU98UgghzsbCzGEFQeD7vu/7O3bsuPbaa+M4\nDsPwLW95y/e///3bb7+9Wq2euHvNNdecON+wYcMzXK0QoqMtTA+rUqmMjIysW7cOsCxr48aNH/7w\nh//5n//585//fFdX1+23337i7ujo6InzTZs2PZOlCiE63SmT7u+a56T7O076rBBC/GeTle5CiI5x\nSmAl7WqFEEKcBelhCSE6hnw1RwjRMaSHJYToGNLDEkJ0DNnATwjRMaSHJYToGBJYQoiOIZPuQoiO\nIT0sIUTHkB6WEKJjyFdzhBAdQ3pY4qn40ue2LWF8x99vpeChw/zu1o9ffNnwssF6u9slznOnbC/z\npnluL/MZ2V7mWemrn9u272tbnl+Z6HLwQw5M8+BhHjjCrDM0cuOWRcvrL37ZcLvbKM5PMuku5k1N\njK21J1aW8AyiAruMqkLMvsbEp995fZCw6JKrPvyFsXY3U5yHFuxnvsSzxO47x47csb1bw0lxEmqK\nfoPFFv0WvRY9BgMW4d07P/SeLe1uqTgPLcwWyeLZ46d3jfUamDEEaCFWTCWnR6em4YFdYCtqGj/Z\nsa3dLRXnoQX7mS/xLBEeGrdSVEDWIm9RhNgJ1ZxygZlDjpZjKaJ9E5/5xLZ2N1acb6SHJebHKiAm\na5G2iH3SFirASJhqEXUxO8TkCho6pZx/klGhWGiyrEHMz/6Hx1eFpAVxgq6Ra2QFe3zudHjl5bym\nlzmfnzzMV7/J0PzeOQtxZvKWUMzP4qX19P6dSU5scmxVy1TG3S6vu5xL6lg6UwbpYqbW8H92TrS5\nreK8I0NCMT9BShwQt4h9whYzTb6VsOxiLl1Bt4WRYWZUdFZ1sXqIf9m2rd3tFecVGRKK+fGW1IOA\nOMPSieGelKmL+ZUhui3yiCQgD9ESKhr1Hg4cGG93e8V5Rd4Sivn55etGWwFJQBwQtUirvOq5LC1B\nQtwiahL7qBAro8/kh1/Y1u72ivOKDAnF/OQFjYgoIAl4ICZYwfP6sXKSFuEs0RxJizyEGE/BoYkD\n4+PtbrI4f0hgiflZNFTfHxGFJCFHunlZnW6DPCJsEjaJfJImaQsirJSKxrtfPdzuJovzh3w1R8zb\nqzbf7MdMpmgrGKqgp8RNoiaRT+yTtMha5CFaTLngwMPyrlAsGOlhiXm7emR0UvFQjRctoQJZQOQ/\nllY+SYs0OD4qtFMM+PstW9rdZHGekHVYYt766/WHYVWNxTZaTJoTt0j848sdkpAsIo8oQsyUObhn\nbKzdTRbnCXlLKJ6Kl/zPm8NurIykRdwkbZEEpCcdeUgRoSVY6tT/VxTiaZA5LPFU/Mr1o70GWUjo\nkwRkAXlAFpCFx488REUQoxVk7W6tOG/IkFA8FXvHx7WUwMdL0BV5TBFRhOTH0iomj1AxKiUr6KvX\n291ecZ6QSXfxVAwN1aOQuTmac0Q+eYAWoSdoCSomD8kj8oRUEcC+sbH57r4txGnJkFA8FapgrsVM\nk9kmrSZ5gBnjJTgJRkKRkKUkBRFo0D8xMQCfku8ViqdNhoTiqcgLGi2mdQyLXKeqUVZ0Z9g5SU4j\nZ6bAhxAuBBOaQ0NvGR1td6tFxzslsOTFnzhLS4fqhwIaYJhkBkrHgQHog8MaD+vcqzFZoIENObxY\n0koshAWbw2o0GuvXr+/r69u8efMTrzzxruhot23b9opNI40W0z6TTY40mfRpRszmPGow42JUqXZh\ndjFdIrlg6Pdl7ahYCKf8LmFlnjOj/kmfvemmmxqNxg033HDFFVdEUfS4KzfeeOPj7orOtW98/AOr\nVpXgUegHV6Ok0aPTa9Ky2WvRcrBdXBfTYi7loVnMNVf9weYtg/X6MnljKJ6GU3pYflHM6zj5s2Nj\nY9ddd51hGIODg0+88sS7onMN1uv/7bbbMjgKU3C4YK/ivoy7Yu5JmYOqzQXdXLGU117A6y7kyqVM\n/Xjnu66++vdWrfqcTL2Lp0E/8yNnQSm1e/fu1atX79q1a+3atY+7smbNmsfdFZ3uwuHh5lVXOZDC\nDDwKP4U9BQczAgUaJZM+l8EKq2osr7DI4TDosLzdLRcdbWECKwgC3/d939+xY8e1114bx/HJV665\n5poT5xs2bFiQf1G03QfHxv7LyMgiMCGEI7APpjOCjCgjiJkJeXSW/TNMBUQZicYcJO1utuhoCxNY\nlUplZGRk3bp1gGVZGzduPPnK6OjoifNNmzYtyL8o2u7w+PhD27dfCCugFww4Co2CICWIaQTsn+G+\no+w6zMPTTAe4BSb81fXXy5Z+4ik7ZdJdiLN3eHz8s6tWVeEgTEEI3wANLjZYWmFRmS4PzaCR8HCT\nn8yhUgwwIYFXj4z8pUxmiflbmB6WeBZS4I2MWKBgDh4G89i6vhwVkYTMNDk8w4EZ9jZxU5bCAJRh\nETywfft/ldeFYv4ksMRTVMDXt2+/Bw7Co+BDDYZgCfQlVELMgLRFq8VMQheshpfAy+ByWApLJyZu\nl06WmCfZqkg8Rcvq9b9+5JHrV60qgwY9UIKlcBEsL6hm+Dn7FWn+/7d3N72N3AUcx7/j8Tz9Z8YP\nSbxZOw+ehXYLQlQqrdpKCPBeaAUCxKEgECVRj63EDTggsXvlNVRodyW4V9BKoD1sEKgHoFIrtKK0\npets0vU+JI2ziRPbsT0cyhtYx15rpN/nFfwO1lf/Gc/YNGG3Xv/c+vpnx7EIbOjA1/X4uzwkBUvG\nl4McuBBBADGchRqch+URQ4uPR6TQgg82N59vNJ5rNGY9WbJNl4QyvkqSxPX6HNRg2WIlx5kcQZ6u\ny45L22GQp2BRAwd+c+HCL3SkktNRsGR8W81m99ZmxaHqUQ04YyiFeIZ+wL7Prk/HJ++zYFODTTi4\nevXnapacgoIl40shsFkIWIyoRixGzEdEIW4IAWlAzuAbSoaqA3AD+levbus5LBmX7mHJ+FaT5MCm\naKj5FG3cHHmw0v//vmM/ZZQSDSjlWbAwQ8wIA79tNC6qWTIWnbDkVF57/XLfpmw4G1ONWYxZiCnH\nlCKKMcWIUkQ5Zt4w59KBv8PBpv5aVcakYMmpvPjy+jvl+r97fApdm9TB8YgCIkMxolxgvsB8zFzI\nXMAW1CAFXRXKeHRJKKf16z9ufP/8uWci5gyJz2rAakDJx3cp5PFGDD12UsrHzO3z9Iijen1Zj7nL\nWBQsOa0+OCfc3aOyxzWLVsAzZZ4u8aUySy6Rz4LH/IjCAWGeoK+/L5HxKVhyWstJkgcDPjybcuuI\nN7vc6NA44Tl4okzgEgdEHj0HV8GSU9A9LJmAH6ytfQ06YOAcvJByeMRf7rNxj3fb3B+AjecQONyE\nfd10l3EpWDIBP7p0KYZ3IIUiPJbn2TxmyH8f8O6nvN+mPcDKYWzeBmPxJ732LGPRJaFMQB924GX4\nGFbyFB3OuYQO+xaHPVodBikW+DY5CyfH7q3mrCdLJumEJRMwnyQ/vHmzBf+wOHJwPIoeKy6f91l0\n8MBK8XI4OYopHzj8628bs54smaQTlkxGOUl+lqbdc8ntnc3QwXgEDiUP2yPw8PLs2Xgwb7M54Mv6\n3MlYdMKSSVq7vrHdYz+H5RD6lH0qhopP7JLC8QDPoWRj27MeKtmkYMkkLSbJq9eu3xhynMd2MT6F\ngNgnZ7M/gAGei59npBOWjEXBkgmbrye7bbYH9PLYLq6L7XCU8lGH3gjPo+jROp71SskmBUsmbCVJ\nfvr65bfu8skJRzmGNscWn3TpdFjyCXxCn5PbzVnPlExSsGTynvxG48yQN27z4RF3Ttju8V6bm11q\nISbABJzxZz1RsknBksmrJcm3fnWx0ON3H/KHbf68zXs7PBFRi4kCTMDhPT3sLuNQsGQqvvmT9a5F\nJcd/7tLa5fGQC1WWSxQiAkMx5I3fX5n1RskeBUumolpPvvfLi7ZNxWepwFfP8oUKiyUKMSYkMuy1\nmrPeKNmjYMm0vPDj9a6F4+C75F3iiLkChQImxBje/+fGrAdK9ihYMi3V1eS4Uh/kGOXp2gw9TIG4\nQBgTGB7cb856oGSPgiVTdPnNjQdD+hYdi46NFRIUMQWCCKMvCuXhKVgyRdWV5NuvXTwYcZDShr6D\nE+HHeCGON+txkkEKlkzXiy+tt45pD2mP6OTAxw1xDOh1Qnl4CpZM19Jqcuny9dYRO33aKX0Xy2B5\n9NJZL5MMUrBk6p56vrE1qm8dcq/HIYw8hg7nv9KY9S7JHgVLHoW33m5uDeof7XGny/6I3R5ffKox\n61GSPVaa6mguj8jad5InlzYrc5QeW/vuK1dmPUeyR8GSR+rO7eZfr115ae3SrIdIJilYIpIZuocl\nIpmhYIlIZihYIpIZCpaIZIaCJSKZoWCJSGb8D+FO/0uHNI0zAAAAAElFTkSuQmCC\n",
"text": "<IPython.core.display.Image at 0x850a510>"
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": "ten_1k = ozt.tensor_from_eigs(TM_1k_1.model_params[vox_idx][3:].reshape(3,3), \n TM_1k_1.model_params[vox_idx][:3], \n TM_1k_1.bvecs[:, TM_1k_1.b_idx], TM_1k_1.bvals[TM_1k_1.b_idx])\n\nfig = maya.plot_tensor_3d(ten_1k, mode='ADC', cmap='hot', colorbar=True, vmin=0, vmax=2.5)\nten_2k = ozt.tensor_from_eigs(TM_2k_1.model_params[vox_idx][3:].reshape(3,3), \n TM_2k_1.model_params[vox_idx][:3], \n TM_2k_1.bvecs[:, TM_2k_1.b_idx], TM_2k_1.bvals[TM_2k_1.b_idx])\n\nfig = maya.plot_tensor_3d(ten_2k, mode='ADC', cmap='hot', vmin=0, vmax=2.5, figure=fig, offset=-2.5)\n\n\nten_4k = ozt.tensor_from_eigs(TM_4k_1.model_params[vox_idx][3:].reshape(3,3), \n TM_4k_1.model_params[vox_idx][:3], \n TM_4k_1.bvecs[:, TM_4k_1.b_idx], TM_4k_1.bvals[TM_4k_1.b_idx])\n\nfn = '%s.png'%tempfile.NamedTemporaryFile().name \nfig = maya.plot_tensor_3d(ten_4k, mode='ADC', cmap='hot', vmin=0, vmax=2.5, figure=fig, offset=-5, file_name=fn)\n\n",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Fitting TensorModel params using dipy\nFitting TensorModel params using dipy"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/lib/python2.7/dist-packages/dipy/reconst/dti.py:690: DeprecationWarning: This implementation of DTI will be deprecated in a future release, consider using TensorModel\n warnings.warn(\"This implementation of DTI will be deprecated in a future release, consider using TensorModel\", DeprecationWarning)\n/usr/local/lib/python2.7/dist-packages/dipy/reconst/dti.py:690: DeprecationWarning: This implementation of DTI will be deprecated in a future release, consider using TensorModel\n warnings.warn(\"This implementation of DTI will be deprecated in a future release, consider using TensorModel\", DeprecationWarning)\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nFitting TensorModel params using dipy"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/lib/python2.7/dist-packages/dipy/reconst/dti.py:690: DeprecationWarning: This implementation of DTI will be deprecated in a future release, consider using TensorModel\n warnings.warn(\"This implementation of DTI will be deprecated in a future release, consider using TensorModel\", DeprecationWarning)\n"
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "i = Image(filename=fn)\ndisplay(i)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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4MLFQHxGSJZpLuPhav778xbtHLzhngzxBc0QUEeYgXAgXoYvQQY4jxyxmSbNz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\nCCHeQMEihHgDBYsQ4g0ULEKIN/wNFsRORNtQA68AAAAASUVORK5CYII=\n",
"text": "<IPython.core.display.Image at 0x8c631d0>"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "ten_1k = ozt.tensor_from_eigs(TM_1k_1.model_params[vox_idx][3:].reshape(3,3), \n TM_1k_1.model_params[vox_idx][:3], \n TM_1k_1.bvecs[:, TM_1k_1.b_idx], TM_1k_1.bvals[TM_1k_1.b_idx])\n\nfig = maya.plot_tensor_3d(ten_1k, mode='ellipse', cmap='Greens', colorbar=True)\nten_2k = ozt.tensor_from_eigs(TM_2k_1.model_params[vox_idx][3:].reshape(3,3), \n TM_2k_1.model_params[vox_idx][:3], \n TM_2k_1.bvecs[:, TM_2k_1.b_idx], TM_2k_1.bvals[TM_2k_1.b_idx])\n\nfig = maya.plot_tensor_3d(ten_2k, mode='ellipse', cmap='Greens', offset=-2.5, figure=fig)\n\n\nten_4k = ozt.tensor_from_eigs(TM_4k_1.model_params[vox_idx][3:].reshape(3,3), \n TM_4k_1.model_params[vox_idx][:3], \n TM_4k_1.bvecs[:, TM_4k_1.b_idx], TM_4k_1.bvals[TM_4k_1.b_idx])\n\nfn = '%s.png'%tempfile.NamedTemporaryFile().name \nfig = maya.plot_tensor_3d(ten_4k, mode='ellipse', cmap='Greens', offset=-5, figure=fig, file_name=fn)\n",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/lib/python2.7/dist-packages/osmosis/tensor.py:135: RuntimeWarning: invalid value encountered in sqrt\n dist = np.diag(1 / np.sqrt(sphADC))\n"
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "i = Image(filename=fn)\ndisplay(i)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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ZYSXZij6r2RcQaTTp3litNIi5qrajOwA0pVidZFZnSSALcLRXUbZySJhT7xK6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\nmQ1DFlYmk9kwZGFlMpkNQxZWJpPZMPwfwoQoFcdIfpEAAAAASUVORK5CYII=\n",
"text": "<IPython.core.display.Image at 0x8cb5bd0>"
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, axes = plt.subplots(1,3,squeeze=True)\naxes[0].set_ylabel(r'$\\frac{S}{S_0}$')\nfor ax, models in zip(axes,([TM_1k_1,TM_1k_2],[TM_2k_1, TM_2k_2],[TM_4k_1, TM_4k_2])):\n ax.scatter(models[0].relative_signal[vox_idx], models[1].relative_signal[vox_idx])\n ax.grid()\n ax.set_xlim([0,1])\n ax.set_ylim([0,1])\n ax.set_aspect('equal')\n ax.set_xlabel(r'$\\frac{S}{S_0}$')\n \nfig.set_size_inches([10,10])",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Loading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0011_01_DWI_2mm150dir_2x_b1000_aligned_trilin.nii.gz\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0007_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.nii.gz"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0007_01_DWI_2mm150dir_2x_b4000_aligned_trilin.nii.gz"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n"
},
{
"output_type": "display_data",
"png": 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LFrB3795pyvTq1YtVqlRhbGwsIyMjWaRIEV64cCFNGZk0R9bcuXOHBw8eTFmH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}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "fig, axes = plt.subplots(1,3,squeeze=True)\naxes[0].set_ylabel(r'Predicted $\\frac{S}{S_0}$')\nfor ax, models in zip(axes,([TM_1k_1,TM_1k_2],[TM_2k_1, TM_2k_2],[TM_4k_1, TM_4k_2])):\n ax.scatter(models[0].relative_signal[vox_idx], models[1].fit[vox_idx]/models[1].S0[vox_idx])\n ax.grid()\n ax.set_xlim([0,1])\n ax.set_ylim([0,1])\n ax.set_aspect('equal')\n ax.set_xlabel(r'Measured $\\frac{S}{S_0}$')\n \nfig.set_size_inches([10,10])",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Predicting signal from TensorModel\nFitting TensorModel params using dipy\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0011_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvecs\nPredicting signal from TensorModel"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/lib/python2.7/dist-packages/dipy/reconst/dti.py:690: DeprecationWarning: This implementation of DTI will be deprecated in a future release, consider using TensorModel\n warnings.warn(\"This implementation of DTI will be deprecated in a future release, consider using TensorModel\", DeprecationWarning)\n/usr/local/lib/python2.7/dist-packages/dipy/reconst/dti.py:690: DeprecationWarning: This implementation of DTI will be deprecated in a future release, consider using TensorModel\n warnings.warn(\"This implementation of DTI will be deprecated in a future release, consider using TensorModel\", DeprecationWarning)\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nFitting TensorModel params using dipy\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0007_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvecs\nPredicting signal from TensorModel"
},
{
"ename": "MemoryError",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-49557f12c804>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'Predicted $\\frac{S}{S_0}$'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mTM_1k_1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTM_1k_2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mTM_2k_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTM_2k_2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mTM_4k_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTM_4k_2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelative_signal\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvox_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvox_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mS0\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvox_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/osmosis/descriptors.pyc\u001b[0m in \u001b[0;36m__get__\u001b[1;34m(self, obj, type)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;31m# Errors in the following line are errors in setting a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[1;31m# OneTimeProperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 144\u001b[0m \u001b[0msetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/osmosis/model/dti.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[0madc_flat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_adc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[0mfit_flat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madc_flat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 264\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mozu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mii\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfit_flat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/osmosis/utils.pyc\u001b[0m in \u001b[0;36mnans\u001b[1;34m(shape)\u001b[0m\n\u001b[0;32m 723\u001b[0m \u001b[0mLike\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbut\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0man\u001b[0m \u001b[0marray\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mnans\u001b[0m \u001b[0minstead\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 724\u001b[0m \"\"\"\n\u001b[1;32m--> 725\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 726\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 727\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mMemoryError\u001b[0m: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nFitting TensorModel params using dipy\nLoading from file: /usr/local/lib/python2.7/dist-packages/osmosis/data/0007_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvecs\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/lib/python2.7/dist-packages/dipy/reconst/dti.py:690: DeprecationWarning: This implementation of DTI will be deprecated in a future release, consider using TensorModel\n warnings.warn(\"This implementation of DTI will be deprecated in a future release, consider using TensorModel\", DeprecationWarning)\n"
},
{
"output_type": "display_data",
"png": 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JLooVnbVz506jtKoiODjYZfVEKjoiwi23DGPp0p+wWiNwOLaxbNl/mTjx8bw2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\nOySf30jFcSWyHM9E+tH9R6tUKhEXF2f8mUwmg7e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}
],
"prompt_number": 15
}
],
"metadata": {}
}
]
}
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