{ "metadata": { "name": "", "signature": "sha256:24648a1f9db6e93d877fab27fa44b562cd8b272bea6d163686001b593267e9ab" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DWI: signal and model \n", "\n", "This notebook displays the signal in one voxel across different b values. We also demonstrate the fit of the tensor model to the signal and the process of predicting back the signal from the model. This process is at the core of our cross-validation approach. \n", "\n", "We import the modules we will use: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import os\n", "import tempfile\n", "from IPython.display import Image, display\n", "\n", "import nibabel as ni\n", "import osmosis.model.dti as dti\n", "import osmosis.viz.maya as maya\n", "import osmosis.viz.mpl as mpl\n", "import osmosis.utils as ozu\n", "import osmosis.volume as ozv\n", "import osmosis.tensor as ozt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the data path to the installation-specific location:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import osmosis as oz\n", "import osmosis.io as oio\n", "oio.data_path = os.path.join('/biac4/wandell/data/osmosis/')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get data file-names:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "subject = 'SUB1'\n", "data_1k_1, data_1k_2 = oio.get_dwi_data(1000, subject)\n", "data_2k_1, data_2k_2 = oio.get_dwi_data(2000, subject)\n", "data_4k_1, data_4k_2 = oio.get_dwi_data(4000, subject)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We choose a voxel by its XYZ coordinates. \n", "\n", "To use only this voxel, we define a mask around that voxel, which we will pass to mask the model class instances" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# A corpus callosum voxel:\n", "vox_idx = (40, 74, 34)\n", "\n", "mask = np.zeros(ni.load(data_1k_1[0]).shape[:3])\n", "mask[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": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We initialize `TensorModel` class instances " ] }, { "cell_type": "code", "collapsed": false, "input": [ "TM_1k_1 = dti.TensorModel(*data_1k_1, mask=mask, params_file='temp')\n", "TM_1k_2 = dti.TensorModel(*data_1k_2, mask=mask, params_file='temp')\n", "TM_2k_1 = dti.TensorModel(*data_2k_1, mask=mask, params_file='temp')\n", "TM_2k_2 = dti.TensorModel(*data_2k_2, mask=mask, params_file='temp')\n", "TM_4k_1 = dti.TensorModel(*data_4k_1, mask=mask, params_file='temp')\n", "TM_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: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0009_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvals\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0009_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvecs\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0011_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvals\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0011_01_DWI_2mm150dir_2x_b1000_aligned_trilin.bvecs\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0005_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvals\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0005_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvecs\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0007_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvals\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0007_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.bvecs\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0005_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvals\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0005_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvecs\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0007_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvals\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0007_01_DWI_2mm150dir_2x_b4000_aligned_trilin.bvecs\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next few notebook cells use 3D visualization techniques from our mayavi module. We plot an interpolation of the measured signal in each of the b values " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%gui wx\n", "# Interpolate the signal from the measurement b-vectors using radial basis functions and display in 3D:\n", "fig = maya.plot_signal_interp(TM_1k_1.bvecs[:,TM_1k_1.b_idx], TM_1k_1.relative_signal[vox_idx], cmap='hot', origin=[0,0,0], vmin=0, vmax=1)\n", "fig = maya.plot_signal_interp(TM_2k_1.bvecs[:,TM_2k_1.b_idx], TM_2k_1.relative_signal[vox_idx], cmap='hot', origin=[0,0,2], vmin=0, vmax=1, figure=fig, offset=-2)\n", "# Put it into a temporary file:\n", "\n", "fn = '%s.png'%tempfile.NamedTemporaryFile().name \n", "#orientation_axes = mlab.orientation_axes(xlabel='', ylabel='').axes\n", "fig = maya.plot_signal_interp(TM_4k_1.bvecs[:,TM_4k_1.b_idx], TM_4k_1.relative_signal[vox_idx], cmap='hot', origin=[0,0,4], colorbar=True, vmin=0, vmax=1, figure=fig, offset=-4, file_name=fn, roll=90)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/arokem/usr/lib/python2.7/site-packages/osmosis/model/base.py:383: RuntimeWarning: divide by zero encountered in divide\n", " signal_rel = self.signal/np.reshape(self.S0, (self.S0.shape + (1,)))\n", "/home/arokem/usr/lib/python2.7/site-packages/osmosis/model/base.py:383: RuntimeWarning: invalid value encountered in divide\n", " signal_rel = self.signal/np.reshape(self.S0, (self.S0.shape + (1,)))\n", "/usr/lib/python2.7/dist-packages/scipy/interpolate/rbf.py:122: RuntimeWarning: divide by zero encountered in log\n", " result = r**2 * log(r)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0009_01_DWI_2mm150dir_2x_b1000_aligned_trilin.nii.gz\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0005_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.nii.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0005_01_DWI_2mm150dir_2x_b4000_aligned_trilin.nii.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/usr/lib/python2.7/dist-packages/scipy/interpolate/rbf.py:122: RuntimeWarning: invalid value encountered in multiply\n", " result = r**2 * log(r)\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "i = Image(filename=fn)\n", "display(i)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SI0zxEoQEgZR4Pr5PbDnW5KETDEt2DTIWUSiWDPOGkwUdQ7ugneNJhkoMhpQD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\nO3p6PD+Dn3HjJokOg4ismw6vpOBiKXSMilARaYSO55eQjCP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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cells are commented out per default, but they can be useful if you want to make animations interrogating the signal from different angles: " ] }, { "cell_type": "code", "collapsed": false, "input": [ " # Make figures for a movie: \n", "\n", "#for ang in range(360):\n", "# fn = '/home/arokem/Dropbox/osmosis_paper_figures/movie_figures_sig_2k/frame%03d.png'%ang\n", "# if not os.path.exists(fn):\n", "# fig = maya.plot_signal_interp(TM_2k_1.bvecs[:,TM_2k_1.b_idx], TM_2k_1.relative_signal[vox_idx], cmap='hot', colorbar=True, vmin=0, vmax=1, elevation=ang, file_name=fn)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#for ang in range(360):\n", "# fn = '/home/arokem/Dropbox/osmosis_paper_figures/movie_figures_sig_2k_w_pts/frame%03d.png'%ang\n", "# if not os.path.exists(fn):\n", "# fig = maya.plot_signal_interp(TM_2k_1.bvecs[:,TM_2k_1.b_idx], TM_2k_1.relative_signal[vox_idx], cmap='hot', colorbar=True, vmin=0, vmax=1, elevation=ang, file_name=fn, points=True,scale_points=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the model prediction" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = maya.plot_signal_interp(TM_1k_1.bvecs[:,TM_1k_1.b_idx], TM_1k_1.fit[vox_idx]/TM_1k_1.S0[vox_idx], origin=[0,0,0], cmap='hot', vmin=0, vmax=1)\n", "fig = maya.plot_signal_interp(TM_2k_1.bvecs[:,TM_2k_1.b_idx], TM_2k_1.fit[vox_idx]/TM_1k_1.S0[vox_idx], origin=[0,0,2], cmap='hot', figure=fig, offset=-2, vmin=0, vmax=1)\n", "# Put it into a temporary file:\n", "fn = '%s.png'%tempfile.NamedTemporaryFile().name \n", "#orientation_axes = mlab.orientation_axes(xlabel='', ylabel='').axes\n", "fig = maya.plot_signal_interp(TM_4k_1.bvecs[:,TM_4k_1.b_idx], TM_4k_1.fit[vox_idx]/TM_4k_1.S0[vox_idx], origin=[0,0,4], cmap='hot', colorbar=True, figure=fig, offset=-4, file_name=fn, roll=90, vmin=0, vmax=1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Predicting signal from TensorModel\n", "Fitting TensorModel params using dipy\n", "Predicting signal from TensorModel" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fitting TensorModel params using dipy\n", "Predicting signal from TensorModel" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fitting TensorModel params using dipy\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "i = Image(filename=fn)\n", "display(i)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TWQ/jI488+sAD9yxOYXVGWDfOMsJ61+KKsG7/zMG/v+3g5ImxqRPjSyL0KHRJxBIViVgi\ndrYKx5gBtEUKJBZNi0mD0xqnUpxM8NIrNv/qruHlA4NXvDDk9YdvG/6HOz6zphfn92B5F5ZU0FNG\nrYSuGNUYlQgVFVQlEAkoAQkI48MrACLYyjpbWWgDbZAGcyUWDY1GiqkUUwmmNaYSnE5wcgoPT2L8\nFN7yx+9/+74DC9kKT8Vdd438x//4a6tWxcuXx0uXKmerWk11dYmQBiKkgSbU120UufqRVspKqV0n\noJRGyranhLACxsvJz7gJt5m4dFa3gjXeY9aEpnZToOF7DI2brGOtsTBGGSO1kcYorUUIsmSaijRV\nSaLSVLRastUSzaZsNDA15eOsM2fsqVP65Mn0+PHmxz52x2WXXbVQLf90dArrQ7MU1rsXXlj/5foD\nRz53UD88viLGshhLFPoi1BSqEhWJkkIkUJaIJCIBBT/CxdnKCSsVSC0aFlMaZwxOpTiVYiLFw02c\nf9nmt+07cGmhhgLPip2/PvSzo6MX9eH8HqzowrIu9FV8laoao6JQlihFKEnEwqeB0kJaf3QJ+MAK\noaPdANp1sTtn5aKtlkXLoGEwnaKh0dCYSnG6hccbeHQKD5/Bpb+x+1d3DF9+5dDCtknG5z//mY98\n5NoLLyz190dLl0a9vaq3V7qBC11dLgcU5bIJYxf8ppSNIq8qpYwQWkorhEsDjRBGQENAwMJk4vHx\nlA37foaz9g9ms52tbk8nNOEpY/1dk6Xk7q5LD61KU+mcpbWzlUpTkSQySUSzKZtN2WiIqSkxOWnr\ndbjE8NFHk/7+S2+77W8WsP2fkk5hfWCWwnrvggnrLVuGHvnG6JoKVkZYVsLSGL0RepWPqsrCH2NK\nQIqgqjAzK/t2U1ceRjvOmgbqGqdTb67HWhhrYOkrN//evgOvfn5p6w1XD03+cHTNUpzfgxXd6O/C\nkgp6y+guoap8oSqWiNF2vbAQFiILC7JDS/uDxAZPmezWhgwRSC0Si6ZBy6AZtskEpxM80cDPpvHT\nSUzV1uy/+eCrNw0tdPPgc5/7zMc+9u8HBkr9/dGSJVFvb9TbK7u7RXe3qlYRBi7Y0Bto4hhRZFzR\nSint4qkQVRkhtICGsAKuBAUA0G1bhaQvtKr14VUWUgEhyDLt8CpznTF+xwYNul+4gdBWGC20cc5y\ntlJayySRSSJbLdVqyUZDuGLW5KQ4c8acPm1PntTj49Mf+cgdl1++uIKs4tWw/ve1g5VHxy+q4NUr\nsCzGkgi9EWoKNYkuhYqLBaxPW6Qb1ZL9sfsuBQxghLeVttACKZAAXRZdEjWFKYOlJSwro7+M48dG\n3/tb/3bbn7z/P+w/sHCfey7ZuEr8mxVYtxora+jvwtKqr6x3x6golEL6HMGLXjpPmfahZY0/kIz2\nVXa/5STlAi6/L2AMYH2AFjnfCVjl35LrcPzp5PgH3zn87v9y8LIFddZdd438xV/8+zVrKsuXx0uW\nRL290lXZu7pEV5eoVq2LqkLFyob6upOU9pGUSAFjbepbzdWdLKAhMLMxfRpofJBlXCRlcsmge1km\nLBEqhiEl9BV7F22F6r37j7UVxhqjpTHGWg1Ia8N3K6wQSko3+kFEkYhjlEqiWhW9vdG3vnXX4hbW\n4l5xdFOP+OUuvLyMC5ZjZYylEXojdEt0CVQEyhIlgQiQNkhKA76/pX3yaROSGgEIASmggJIALAQg\nJWKgLFGWKCl0KYx87PqyxO8vvjrLrLhyw+Dy1vhr1mBVH1bUsMypqoxahK4YZRmqVK4ZQ/ZnNYz/\n7QdV5Wyl0xBSGX+2NzlbGWcrCw1oAW1DNdlCApFFCagIdEfQZRiLtD7+t//fwZUXDg4uXO/h29/+\n6xdfXO3vd7aKsnGhXV2iUrGuNzCOM1vpUFBPhTDIzoNWA9o5ySnJR0S6vZ+9JH8agB/FHixncqFW\nezSWP/Vmj5tc2NUWlnD/odUWxhpjjDEwWllrjFHWRuGdSSGklEIpFUXCfbRqVX30ox98+9v3L9S3\n8JQU46o5r66Jl9Xwa/04v4yVJSyPsSRCt0RVoCwQWUQIqjL+U5gQSM88eKw/Oxnhz/w6C7jcvgAA\nBURAGegWWKZgyoDA//zo9S2LP3r3gQVsimfDZavES1dizSpc0IvlNSwp+xywO0I1QkkgFlAW0kCE\nA8botqeMzu2EpeOM9hWrtqdCa2epd+7ICdoKO663SzltSdRiLK/i21/9zG++aXihhPWSl1Re+tKu\nZcvivr64VlPd3bKrS4XZNk5V7ewvjFHQwUMpoK1NrdWwxlrrM2gTnjc+yLL5/TToKS8s40tUHXFW\nqHO1a1+5fet+266YL0IyIfxXoK31wS+0NtZNQrRWWWsBBVghpBBCSiEl3Cyi3t7oG984snHjIhqT\ntdhTwn939VDjgdEty7G6gpWlkAMGVcUhxRDGl3g7zv8mVwaG3/d9K+EUlNnKChgJLaGBRCABtIQR\nEEAJ6BZYonBhGXd8+PrXXDV06aIpDJ87v/Ji8cqLcNFSnF/D8m4sqfohCxWJskQEyEz0maQ0rPae\nMs5Nqd9xj7i1Ll25KltPrq2qpzzhiyAsASOQCl/JkQbKoiTQFWFZFfvfPvy1b4/Nfyu94Q1bVq+u\n9PeX+vpKtZrq6pLVqqpU8gOsbBRZpVwCaFxIJUQKpIAxJrHWSKGtdkOkcqpykspUle0Y2DT3uzUh\nJQzC8o9nAxtsO5JqbyJ3C68tm/2ws3dh/RvRMMZHXJExyk1LtNY6bWXOKpelUrOsaz/HLGphXblE\nrK9h/RJcVMHKEpYodIucqgxsOGZMLm3xeUqoqmjbPv/7zD8IS2fnIgErg7MEUoFUwgikEqmAFZAW\nJaCmcH4F998zUjhhXf1LYu35uLAP59WwvMvbqqJQFlAANIxGamFCQmM0bAqjYTS081Rmq9RPss2E\nlVWvTDYvNzir41jKDh7l5dVubeGzdQXEAhWJxsnxb90zMv+VrIce+uaLXtTV2xt3d6tqVVWrbgJz\nNl7BZtV0QAuhXZXKGC8sgUQI46QjMk84N6XtW6tzZ4V8op15KtvJClhZb2z4Dc+wFbKmlmElZcC4\nX7UIibnIzGVTWG2M1kbr2M3vsQG3LIQQVqmO+YyLgcWbEm5dLi5bgoEqVlWwMkJfhCpQslAWViOx\n0OGn4I4rf2iZ0K3u8hQdhGV9T0o7lg62QiYs6Q8nLWAUjIBWSAVaAikAgciiLPCJD13/B+86sNDN\nMws2v0i8fBUu7MOKGpZW0FNGV4SShHL5SBA6NIQ7ooKhdBr05G7dI6GdjUaa1dqNz8Hbfe0h3XOe\ngmifGEzW2hJGQEskAkl2UGnAoqIw/6f23/7tq1esKPf2Rs5WlYp0E5hdWV0pNzzdhEJVaq22VmfC\nCnORc6rKhBW2/L47K8ysDBofqLa1le86zLLCGbZyFpPtdkZIGjpyw+AsP6wH0MZo3dLaam2NUcZY\nl5o4pMTx4+OvfvUiqrsv0gjr6hXiFX24qIKVMZZF6FEoG0jAGqQaqQ5BtYbV/sIhJkUaLiLiO9c1\ntPH9gPl8sCNyhutKBNxXq2BkOJwUjEQq0ZJohUTGatQU7jkysmkRj8zOc/WrBv/NSpzXg6Vd6Cmj\nGqEsoSxEpqosYdGwKZAFUyl02naWTtvt7AMr186ul1CHYkru5A+0m9ftWNfOElBBWC4ND85K4QM9\nJfDQ8bFLr5i/hhofH/vRj+4bHOzq7o4qFVUuy1JJhEnLJhueDhgnKWsTY1zFSguRmtQIa0UoVPly\nlvFN6q2Uhlvjzwptc+XDrqyGZdqxVbt0lY0ztUFM7ryL9qkX4ZQAd1aW4TwhOuIsN9ZEG6t1ojW0\ntsZEbvFSY7K5oYuLxdhLeNkSsakfF1bRH6EWoQTA1ac00nBcZVGVP6400nC9IzdM0WkrtTAWSTjV\nZeGVw7r+QQACwmlLwSp/UFkFo5BKJBItiaZEA0jCTK9CcPtnD/a0xleuQF8F3THKEgqAgQFSA2Ph\nisXuQDIaNvGqMho6CZ7Kdpyq0lC3CgUsbdrJILLyStZGwq895+YbQrn+dN/CVnlhpRKJQEsgMUjD\ncTufvOtd1y5ZUgq2EmGVBT+xRggTVGWs1cYkQGptYk0qhBEwue7BrPjelhRS/6PNfr1+J+0QVttT\nOgRZeVvNUJWFlT68ctjs1KuCs866dVGtse2uj9QiNVanic7O/X5OojDGDAysmdev4ZlYdBHWW14/\n9LI+nF9BX4SqQGRhDJoWxkBkh1bSzk10ijRt2yoxaBkkQAtIgDRsWVdyhsvUlfW3ykACIoVQENKb\nyyhohUQiUWgINAVaBonBYp74lufP3/27lw6gt4JqhFhAGliLxEAIaNNOAG3i0xOddAZWCbRr29Rf\nlCXNnGX8DGedFdrdKKGwuWtS+aWdpL9uFSSE8icGN8rLSN/IWqClwremkRpctHpwPtvqH//x3l/+\n5e4QWAk3GVBKhBl/rp/NGJNqnWa2MiIVJpcDpiFcTUIC6KyUbSGwMmn7vJvv2bChDjsjwgLOCl0R\nbOUmx4YTgzsr+OBLhag22MpmBUT44Ycp3KxPm6ZpWEjLurnTaWrtPJ83nolFV8OaeGB0sA89EiVA\nWpgUiYHWaOW+fuOOn3AIOWE1LZpAE2iF21ansPJftsgdNVH78EFkEaVQAkLDRuGwVEgsmgINgaZF\n76rFdc55Ou67e+TCPvSWUVGIBaSF0UgBAWgDaSGzJCUJ5wAnKRdSJb5t26eE1OfjqW4LK7V+aJXt\ntJU/fNwAN7daVuS1JRSEAgyshI1CpKaQGLQkWvCTpdN5/DUePz7W1xdXKqpUkm6xPSlttt6Ltb6y\nbozW2gAJ0LImNbD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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following is a visualization of the ADC surface of the tensor model in the same coordinate frame" ] }, { "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", "\n", "fig = maya.plot_tensor_3d(ten_1k, mode='ADC', cmap='hot', origin=[0,0,0], colorbar=True, vmin=0, vmax=2.5)\n", "\n", "ten_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", "\n", "fig = maya.plot_tensor_3d(ten_2k, mode='ADC', cmap='hot', origin=[0,0,2], vmin=0, vmax=2.5, figure=fig, offset=-2.5)\n", "\n", "\n", "ten_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", "\n", "fn = '%s.png'%tempfile.NamedTemporaryFile().name \n", "fig = maya.plot_tensor_3d(ten_4k, mode='ADC', cmap='hot', origin=[0,0,4], vmin=0, vmax=2.5, figure=fig, offset=-5, file_name=fn, roll=90)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "i = Image(filename=fn, width=1280, height=1024)\n", "display(i)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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FPgpGzbeMeTnyY5h/lJAmaIgmGFCnMP3DGwqFU94cJsaKxTmGDuSDl1k0OKhA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\nrpxFxPSjpM4yRJaaAJDkKWm0BUxoqw5FK8BM2qf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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also display the ellipsoid mean diffusion distance implied by the model" ] }, { "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", "\n", "fig = maya.plot_tensor_3d(ten_1k, mode='ellipse', cmap='Greens', colorbar=True)\n", "ten_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", "\n", "fig = maya.plot_tensor_3d(ten_2k, mode='ellipse', cmap='Greens', offset=-2.5, figure=fig)\n", "\n", "\n", "ten_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", "\n", "fn = '%s.png'%tempfile.NamedTemporaryFile().name \n", "fig = maya.plot_tensor_3d(ten_4k, mode='ellipse', cmap='Greens', offset=-5, figure=fig, file_name=fn, roll=90)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/arokem/usr/lib/python2.7/site-packages/osmosis/tensor.py:248: RuntimeWarning: invalid value encountered in sqrt\n", " dist = np.diag(1 / np.sqrt(sphADC))\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "i = Image(filename=fn, width=1280, height=1024)\n", "display(i)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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xrERjmNqZKpnlfVV16vqhB60qnFhYSTgJiGZRVL1DfCqxzAEys4QrQNGNcBYk\n7kFURAZKWWmmk4H+duGGgzKWN8jySozZzxK1PpSpwiKe0ntPwa0Ua1AhUrOxJhtGWGOOKLESgk0E\nLVEzeIlclVWSeo3idMVW2bvS9hUMXhwsdwYAZP1cuMsqMyICcimqIO85xLTaULEXSs8ZxVqRuULH\nGe9cVVWf+5zZWBML6zg6KVDMpTVUwoCBlUo5TMeSnmpbV7labCmsYReJeGvXeXnKsY2k5p7Ngo8i\ny7UOZNM9prH81NTU7gesAelkwjysMQcDMKeKq+UjJZc8iaN8VarvtLk1qK3UcJjC0pylbaw0ETgv\nAQEqpaQkVhZZaZADDq2yEFO+VHr6Oe+rulM/+IClsSYTVhKON6ZnphkAWEWessoZ9vss68E8lEGr\nflSe/VCFhfmaQdOfOUYY8tocZnlXwlmO83/IjKFSbKurUCOWpIWpMWBuRlp57+qqMhtrUmGENeZg\nKPKZHJ2jknjSLKGGkM2QE7LkybWjlml5tjC9TFE/QlR7gbMKpZa+E0Zq4sxZyBg7ZKFDJFeaWcUi\nwoFSURgr3P3LFhVOKiyHNd5gYSxmhqIl1bDfDgMkNSZkI8Vgq7yLp6RKMtd9acZRcU8+WfwCpaNU\nlYiDsQbhIqc7NuhiUS/MyZylxJeOY0Ufq9OZskWFEwnrODrmYOZBkZXpCiHMzQG0KEy78qhqOhFS\niMlBT+NcSCqrXV5hkCIx3FQ1fIk7SYBhqv1EWDlG5tCBNNSGFOQTUVyfQ9J4Ri+Szre8SHwVOjc8\n9dRTz/vDNRxzKBWW6qH9XLZRvWlDwvTMTKwDWYusLKLkq+4Imis9TVwFZ8Uj7bnC8m+aKgnLAlTO\nSjJL9lp2WTopyTPdCFCxkiwpLCQVKge+rAp9VVUPPWQ21gTCPKzxBrcVVmAu7cILNwTGYlXJiXme\nbHqZAgRt3Stg8RXzqw8KrKSo5NTIoYica1JsnaaPRyYiZWSlu4OlJjOpLowEJpGsqvIPP/zwwj5K\nwzig7NZgfDVumJmeCQQQ6SgMAVLGYQjyZF9BQSXtMDACt14i75dfBspNDPOBoeUW5+uyFR8v4sxl\nAKLFdMGpjKx8Z7Ckp7Ll5VBuTREKw+qbT3/zOX+KhrGBme5jDw4qS1QVczK2EmeJixVpQjnpactE\nVTpi0NI/AEN4sJhg1LZWy+rK/Dj0JRT9qfWxmLcAACAASURBVJnJFKeA7Lwrp30e693VdW3x0clD\nmXRXXR+fyzaqN21IYBa+yjIL0rBdbUFZARZ6Kq33yxepyrI1BalfUA0QhzyTZxnTrrhhOFh4ouLS\npLREE+oYasiLJspyRcv3YGNZfHTyYB7WeGNmZoaJYxxLee7zEYzM8EX+UNwERWSepSl7GwyyIrB4\n0XKYpyW5OC1UipGzGELD00BNnPP5qlpVnJWy9YG2yg4z2a2XW1T4qvJf//rXn/MHaRgPFAqrQlzQ\npq995pln1q9ff8IJJ1x//fXhyN69e88555wXv/jFt9xyyxvf+Mbwd/Gpp57S46P3g04oGLLCit6V\nWFjpqRZrpCsldcqJ5FRbBTXZOMwP49ZeMXmolwYBlMJLJb/ULKVe7IPyXPLahMGiOAt2hFSILnX4\ni+OovLyvHv6s+e6ThkVTWFu2bDn11FPf/e53r1mz5rbbbgOA17zmNTfeeONJJ530a7/2ay9+8Yu/\n973vvfjFL+52u5/+9KfD+Pm+dwPA7MwsURNpicV5FwqKpCW1XdIrhZbK+iweUPmH9rfjYfZTAWzv\nMSqzXuYH46xlZqz49qTwg8RNIMUjZl6L04jx7NThL1KZTBVW/pvf+MaCPkzDsY9F87B27ty5ceNG\n7/2qVasAYG5u7vHHH/+pn/qpBx54YOXKlf/yL/9yyimnXH311ffdd18aH9UfdHJBRGK7awMKIOuv\nPHmYd2NsC9TqnuEmVX6xYYOW1Cp2CxNdjiSSSt6WZONTUCv77ZmYEpUp2k3cleeL4iDcsrAy333i\nsDgKi4j27Nlz+umn79ix44wzzgCAqampq666as2aNf1+/2Mf+1hd1zMzM694xSt+7/d+7xOf+EQY\nb926dWpqahF+iKWNpiFtvBfElGiMY+ghnpc4TLKmz85Vw5DMLOVoIeTlzSlsVRSXAMCA8lZDDkuf\nlyL1mBhLnKtcKubJw3RCsLEgziKGe4BF333tBWsX/MMZjlW41s6CtoRDhw4dPHjw4MGD99577yWX\nXDI3NwcAf/RHf3TWWWf98R//8Z/92Z+tXLnyq1/96mmnnfa7v/u7aWxstSi4cO1a1lSkJvxSAL7g\njVQqFlT1QyhLTT/Oc3LWU8yBlcI70AmKwZPjU5xOSIEZIShNT0mOQdJoedpQliI6oazK+6e+bj7p\nRKEVHP0RFdaKFSs2b968evXqiy++uK7ryy677JOf/OTrX//6X/iFX7j66qubprnoootOP/30T3zi\nEzt27EjjxXj/BogVobjrrOpARV5xUlDrK1UJll57AWZgVYJlPcUAiCyeVnvmMJ6vA2DFkeBtDeQu\nNLupOcFQR6ayTywtdVDMq7yaxzvn/cMPme8+UUD9b+a3/vubFnTxx3/tvyz2+zEsGP/+9/79fffd\n9+KXvOj441csX75s2bJlU1NTU1OduiOY6tR1XVWVr3xVeRlU6YgPjaRiRjw+oHSZirkBlXhy6GI1\nJqtjIJnxxd+85P7nOcsIYmJiZiJmCmAi4oYaImoakkHTb5qm6febpt/0A3r9fq/f6/V73V6v18uD\nbq/b7R4+fPjwoSNHDh8+9IND3//+we985ztPf/PpEfxWDC8MyqT7AjfDsYDwf3xOVZ7y1aEQTSoZ\nyu0vSWZJWn4w7V4ESrX1Huo+9QKcQ14IMFS1YftdiYcFADr7IAyYxJTYWCpEWlSF3nnp/u6cd526\n3r3bfPfJweKUhIYRYtWqVaEq1E574qH0f/1WNFTlHzjRVeFzCTguCITcQVQ9FRgl3iIHEw9F2ywT\nH0rmK72FovrLBnzR0UHVhulgTjzklYb5UadJnXO+qp588snn+Qkbjh0YYY09pKBixVcAbUOKNSlF\n70oIRLnvee4wB7NAGClOAoIsaAaVXQ9UlW6Rg0qRZcMsaq42S6r3OZ9DX27Rc8/tGoolO9571brB\nWT55kmCLn8ceq6ZXERFnxgJtwANEBtLEASB0lXiKWXMZ6OsG6kQVqk8LrqXAhNDlQX3nWBWqDVsl\nJLBmrlJhpdZa+YtwFhYiK3rzLi8q9OFmOg88+MAifMqGYwOmsMYeM9MzITsaC8NEVZmKtKkFqVRL\nz+e0lpBOeCadiwyhGIxXCnukLqLILPVgvg8hJzGFsoQwu1xSIeZMQ2CxRGqQ30VirkhVar7QFWZW\nOCmXhN5572xF4SShICxvhDWGmJ6eDkUhMyX3HRIpJMGVOs4UrRmE3lToFGIINW7CVsJNkC4FkLvi\nxN5XHIKkmbLkiwwwU2pkMdUUJ1IbJqs+h7Oy8hPbPUqsohoMe6DuVeG9919/yghrcmDtZcYes7Oz\nTb8RzoJCGiUCSkn4HMGSMjDRE+dHXcGpok8G3B6LNEvfIZWHUkjmQfzuKVyqLbVijJBYTBEZZIkV\n6CtFKyS1BdLvL8QyqsrbDSkmBtZeZhLQb/rBxoKCdYQ4ZDcRTXjgrFsS5aQThYEg1XMx6ImY2sPH\ndTWq2sNcCIL6TrIXmVH1l+cUiI8ngNSJAPkNFvlS1eMh2a4p9CCZhzxR6KuqOvDkgUX8tA0jhCms\nScAFF1ygBFaRxooKSNRPKgqVgy67QhfaY1JVYwpBpNPkTD1WtWQScbILrWcleREqQU6zkAB5oIIR\nafZQu1ipLU2IVeRJw5DG8s75qnrqSZsonBBYcHQSsGrVqshXOduQiAWEsloRLcVKyn5P7KZP1pJL\nisuCp5LPP4ybAPJVAQOcJY/Rg9dsheK7B4iYKkVWOqhdrdAky3nvn3zKolgTApslnAQ0TaOjWJkg\n9FfIDNEy3UWHAYA6MZGVMEac7UMpAqFo15CqtvBvSFnpoAlIpgQyNGcl9yrNG8q3UX8kW/UgaM7K\nRaLcrAKdd+a7TwzKWUK71eB4Iiks5jJAmr+C4h/WE4WlxwRKiyURpfSTziygeOLhNjhCYYw48Jry\nAuJhJZkWNZRkGpI0a5eEKUKfkqlCUpq8JJ8FgPGmq6Eq/Lr1bJgUlAprVO/C8PywauUqapqSrVKh\npqWSdpcgudrynL6yoC3gEK+SEKg22xUyW2XR1RJZIt9QfZdcDzJgKjMBgFuWfDsHr4LvuipMDbTi\nem3nvm6tRycFlnSfBKxdu7Yh4rANCKyssbJ7JIeiY5VqwqIYBM1inPkl8hgUBwsN1dryi6t5yQBU\nqk9MKwjMFQIQKVAayFJJrfwvMCksCTZArApjyxnvLdkwITAPaxLAzE3TFHRVuOzCR9kiLwmo4Kbs\naglvRTkFzCrGPjDtko60PC0eVhVq0x0LG6tQWIrFGFKIPnWD4KKvQzFbCFIWhrt+OVsCPRkwwpoE\nzM7ONv1+6C3FlCYMWwJJSyTI/nrWSFzwVuKL9Fx8+OEzxMp3l6tkMFR/ac4SY6zwsDhqKw60JfWj\nfBM1dZjCDiFTmro2OPNnJwJWEk4Izj//Amqa0nnXjpaioSS5tAJLokqP1ZFCsoG+uFBqxQlFtTh8\ny1OX2DoSC0MAKOrI0KamTDlAimelpdLR1oLEV5YdnQy01hLaX6FxxcqVp+z58h4uTKyCdFKRB5mZ\n0lNZbmkKE4VFwC7HFIJMwsQV7WIQw3mYFRZIiSe7edMMlSx2TkcgmVnMoEx3VU6GI3JH1hwuTf57\noKynrCScCFhJOCFYuXIlxTRWpiwqRRYMUtKA8irUkHjv0YhiYnaMIYeQGu6134k+pnhKF32tDUrm\n4jZnQVysEyrBJMQSi6kONroHfKwMwyKdBx6029ZPAsq1hBZfH1usXLkqpUejmZVYSxd4Ykm1i77E\nVi1dBuJsAQAAxeXMqOOjARwbzOQgqVZYMC9hwVC2ygGIxFm6Ekz8lIahMAw8lUOkANHJwsr7F/DT\nNxwt2OLnCcGqVauapk/UsG7mp2cNByx4XRcWR7LCSsUgMTtgAKbYYEaXgakUhMxbAmErzrTFBdvw\nIJFFqsKCswbNL1DiC0AXoQEoJaFDdE9adnQiYIufJwTT09P9fp+ky0wGDSWmFn+JCivrQQJmENIb\ntN7jgAiYgeKZYczEELbw6hSuJUgDJqD2d8zEJAMVJU2zlgFciCxhK2TI95GG5GY5h975Xbt2HeVf\nimHR0TLdTTaPK2ZnZ08++eRDhw/HAOkQIQUDXJMfiRmZiBELTovKiIPEYi2sOBZdOpqFkOrCcABU\naZiUFIB6J5GeqC2v9IbBugJQuzJdqCcNGYSihLZikZj6JR+VX4XhBYTFGiYHp5yyUhRWSGMR607v\nQ0vClrxiapMFpBXVREE0hb6mQSVFFRZEVvkKSW2V2oqBidX56hxNW1pqzZuKQOaYJs0OV54uhOTA\nQ7gZ9IEDlmwYe7Tay+CCtlG9acNQrFy5smlaVWG5InpIQSfPDtjtakwku4GhSNeJkvmKqq6o9SIx\n5UqQ9VMUXyoQHw9WiMM5C4rCUApGlXgAWVqY/kFLNz/7Fzv2sFjD5GDlypWpVzIVvDWYzSJmJ/IK\nhXEQgYkJwxEgACfBK2J2ACo+lepBXQKy3AsMc99RTskI8ecLAkrEB1qs0XzlIZQevEwRMgCkLsx6\niWGKOThTWBMBKwknB+eff36/aYiaZ2MqrZXkntHazCovy4t8SFvp7VeTeckopsSAb8kuoSRO2irp\ntZKtaF6GgtzmoVRhaSmPuO+oFVbo8m6ENQFotZcxV3KMMT093e/1wiJoUVgl9bRZBkmaU6UjgWuQ\nidg5BgAZMBDH1TKBDcJBEVMxeZXHYoSn+o3zrja58lizVagrSZEXqIGs2mGRWFFbSV8HhtDnBrON\n5Zytf54ElIRl0yjjjNnZ2XPXrHli7xNNwVdtP0vVg4qqAANBIIcBOyaCQFXEgFmMR+pACjknlj5+\nEKLvqDhKnsgThRCTVwNs1fa8WPv0Q814HXBPIksv31GQhdAv0CdvOGooc1iAC9pG9aYN82HlKaeE\ndskyRajW6lB7pY4mMq3FgpWeazqp72R+MKWrWFpwZWokzpOJaRxSV3GLuilVlymxJfUg/1C2GrIJ\nZwEkdkQ9i4SIaAprAmBJ94nCKacE3104q7iF/VB5FbNXyMyQisSQa4/pdmYCcBT+uDFwrAoZGZkx\nxNoR5NZdqSAM3MGltoqvkOwyFWFnYcnWrKKc0DK21NqdKLikSFTklarCEB/1prDGHjZLOFE4ZeUp\nTdNvck3IKoxVjJAJGTEmRRVVAYabRzsGYnLgiMExATsSngKhKoyBTkyrYlBuWK9W+sXJwbCnZgnz\nMh3lYbXZigq2aqstSN8k05YKN8jTprAmBrb4eaKw9oK1/V6fMmOFhEOiq8hZDrTbHtiKkJEgyCtk\nMbCIyXHirMxTEMgudnGPOirprLAqRmINAJmzmCNBgUisQmcN01Y0yFZypJg0FKe/dLLUZh7WBKC1\nltAtaBvVmzbMh5mZmW63m/hK8gY69S7EFY2qYUulQRlY0d4iWf0XTahgNqXT0pm5W0SxqxrOK0tr\nYNxmq6FOFg2TWmrtDkDQbqX7HlYU2nLCcUerJDQOGnusOe+8vXufyPJK2Eq5REFShTm+lpMVB8nA\nSlVhLCGTdcWMgMyY2rokXYO5uQwPE1mQ3gxwOR5kqxgTG2ArFhZjbWNFbcVJ22W2AgAId1Y9er8J\nwwsA87AmDeedd97jex7XRSFFiZXlFrJzFMVUJK9kYwERg2MXIu9xEN0uLDgrDBChuDuEuO9xwq5t\nYEG03QfYSu1kthrUVqyZS21MoNhKv4f0iIj79+8/Wr8HwwsC87AmDaecckrTb3K4IRWH2ctyIqkQ\nGVF8q0BMUhwScmYrl4x2bV0xIkKovQI9FG37AGDQwAoH02yhGOd5olBPGgpbDfru1DpZnQCQWi0D\ngBjwiCnccFR+CYYXCovWD+uZZ55Zv379CSeccP3114cjN910U/gn8rKXvQwA3vjGN77yla9k5u3b\nt5944ombNm06aj/kksLaC9b2ej3SPpZaWBjdpTxtKK6WWsMcuylkEyotveHCiiosKp3J0uuZ9VM8\n/GT1TbP5JZYWBa9Ns5VwWataTDQHkbO44E7p5zea34phkdBaS7gwytLXbtmy5dRTT92xY8fWrVvD\nkZtuuomZr7rqqte//vUAsG3bts985jO9Xm/v3r3bt2+/5557ut3uUfs5lw5mZmbm5ub65USh/EeK\nslSRmC3ztPyvtNKh3C05K3JTbsBAwjgpL1oMOIVIof0iOVyqPHuhKuL58w2DcQdOrbIwNfYzvhp7\nLFpJuHPnzi1btnjvV61alQ5+9rOfve+++/bs2fPVr3714MGD69ev73Q611xzzfT09KWXXtrpdH70\nN26YHxs2bPgf9/11o+JYxVIdYmR2QkshNRpjDUwY+QBjSQilgcUYykYpJ9P6GEBWXfuK9skpOyqV\nWjar5H+6Khyo9TI9Ka+9rBPLcEPhnSmZJSFSw/ii7Dj6owZViGjPnj2nn376jh07zjjjjHCwaZo3\nv/nN73nPe44//vjf+q3f2r59+6tf/eput+u937p16+te97q5ubmpqann+xMYBnDKyaf0lY2lCsPg\nYCWjHR0jEaKDxFbECExIiOgg2u3OMXC44VaLsyQxmr12CLUXp/iosrEyc0laVPx3RWG6WNUgKQ8H\n3HfKl0o4K3671MIBIoUeMNN9zLE4awkPHTp08ODBgwcP3nvvvZdccsnc3BwAvPe9733pS1/6qle9\nam5u7ktf+tLJJ588Ozu7fv363bt3V1U1PT1tCusFwvnnn9/v9Skv0VFzhSpKyrn6K20m8bu08VSe\npmvDspqDlpmlS0LZZRp2fvki0vc9XZKWMdKA8qIBkVV4+WJmYew1YxhjLM5awhUrVmzevHn16tUX\nX3xxXdeXXXbZBz/4wVtuuWX37t2IODU19Za3vGX9+vU333xzp9PZtGnTSSedtG3bNnMUXiCsXbu2\n2+02TdMUjEViY+VZQieZBmJARiJCB8jOxRPSIAmrENcKSfdUBobpQoi39xJvIeWhUpoh7ipJVZSE\n8yIFSrW2InHfB+cW5SaG4eOQohWsIhx/ICvVfMc/fHBBF2/+md9Z7PdjWBz8xv/1G0/s/fKLXrxi\n+XHLp5Yvm1rW6Ux16qm606mrTuVrX9WVr7yvvK+dq5yvvKuc985V3nlEj+gdenRh4BBd6IyOEKZb\n0rxLbI4lN6pBQBcHqiDk9iMpD6tVEuZZARk34ZGoIeq3H5t+0/SIek3Tp6bXUJ+aXtPv9vvdfneu\n1zvc6x7pHjl05PDBwwf/9w/e9Du/+5b//JaR/mYMzwutBn72B2hCcPLJJz/2+GNiY6lMFlNcHkiU\nPaxoukcDCxiRHTI5RpIBxwR8kFoQBqotg5qxifF3nb3ilHtPu7lYaxtYpbZS/lQOTORHHXYvnSzp\nC5GCYAiw/4B5WOMNa5E8mThvzXn9fq9w3pn0SDVvEIcrJ6do6EBbXYODJh0hajjc0JWawp/iBogg\nHWz5Vi1vK53A0QtL6YricZCqko2lyREA4v2qDWMNW0s4mbjgggu6c72mX9pYnNYWIrFDoiyXKNhY\noQ+y8rCIKPxxSpJKG1gsdytNA9VDr1yRw+lYqxjMVaGaPUy+VcYwhcXcSpbq+KiSWsDCV0ZZ4w0r\nCScTs7Oz55577pef2NN23qPOciSFXjbdCckxMGOqCokAQ3kIkL32VBhy5ilCEeu66WcYFpEoNX8H\nKdvwbFWhuudFnFJMfUo5tTANz7bXQqfpwnwXe/sHPuZozRKawpocnHzyTz762COUMu+60zsRUxBZ\nLNoq5bDAERI6DE3b2YU+DVpbFWqraM8ALcIatpYw2VYQuSpOEapo1oC2avlZWmfF2jYfp0HOEtoq\n1+oYxhB2I9WJxZo15/X64b5fZV2Yo1gyG0dq3R+11vqJKJvfwGrKLV3V6IFKYzXFtfJqQE1rXQ6o\nNyBKquVhsQqUKs5SuiyxVUo5GMYZ1tN9YnHB+Rf0ut1+M4/zTkTk0rocIEBCIEIXpZPu1oCMQNIa\nvaW2AAbu/QDRNULlZXG6p7x65CSqymTWc9RWcd1icrJyVSgRrcLTylLPMLaw9jITi9nZ2XNece6X\n9+4p1uiUZhYSIkV+iUxECC4QVlqag6knMhCLAc/ACPHWFCnZIGNStpGSNnl1TiSnvExHWe/heSWR\nxMIS313mB9MsJ+SqkFPWQVWXkPws46sxR7mW0DysycKaNWsefeyRWBXqucJY5RESIgMQACESAQE6\nBAJERAdivQMgY/KtKMRDJeZOGG5oX5CXQiYpeRATC5Sk0mMtulQOKyqp5LITybrCfCQ1zxIWy5yX\n3oJhnGE5rEnGeWvO6/V6TZOLwnaXrPbKQTmoBxzPb3TeKrwU/5CtmWcg1lV4WW5AjVOyHcJpLT+L\nk7aSdYhcrChMR1gdBFFwo/6NGJ4nWiWhKayJwtq1a+fm5oKLNbCy0MWSkIO8CjoL0DkgQnToCCjN\nFSJSNrBklpDjwkGSP3wE4GLQIaXLVRkIAFIqivCBRC35SHgg2VGreGSxdLyVTnxOAq46nMUDCIWn\nYcxhpvuE49xz1nz5K3uaonNDw+SYiMO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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In what follows, we compare the signal in two separate measurements" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(1,3,squeeze=True)\n", "axes[0].set_ylabel(r'$\\frac{S}{S_0}$')\n", "for 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", " \n", "fig.set_size_inches([10,10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0011_01_DWI_2mm150dir_2x_b1000_aligned_trilin.nii.gz\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0007_01_DTI_2mm_150dir_2x_b2000_aligned_trilin.nii.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/FP/0007_01_DWI_2mm150dir_2x_b4000_aligned_trilin.nii.gz" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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9+hyx50qy4o/5ypPze2DmVWrVakKl8mva87s1IlCYOl0xVqpUK0UZnwsXLlCv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\n+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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In comparison, here is the model prediction relative to the signal in the other measurement. Relative RMSE is defined to be the ratio between the noise in these two displays of the data. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(1,3,squeeze=True)\n", "axes[0].set_ylabel(r'Predicted $\\frac{S}{S_0}$')\n", "for 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", " \n", "fig.set_size_inches([10,10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Predicting signal from TensorModel\n", "Fitting TensorModel params using dipy\n", "Predicting signal from TensorModel" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fitting TensorModel params using dipy\n", "Predicting signal from TensorModel" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fitting TensorModel params using dipy\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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oAbQHMB/Am/D0LIYiRYrggw9Go0EDIwyGYjAYPkH+/I+g1RaD0egNH58d2LDhhww8HcGL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