IPython notebook for comparison of dipy and matlab implementations of life

life_for_dipy_test

{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "import matplotlib.pyplot as plt\nimport numpy as np\n%matplotlib inline\nimport nibabel as nib\nimport dipy.data as dpd\nimport dipy.core.gradients as grad\nimport dipy.reconst.dti as dti\nimport dipy.tracking.eudx as edx\nimport dipy.tracking.life as life\nimport scipy.io as sio\nimport dipy.tracking.streamline as dts\nimport dipy.core.ndindex as nd\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "fdata, fbval, fbvec = dpd.get_data('small_25')\ngtab = grad.gradient_table(fbval, fbvec)\nni_data = nib.load(fdata)\ndata = ni_data.get_data()\ndtmodel = dti.TensorModel(gtab)\ndtfit = dtmodel.fit(data)\nsphere = dpd.get_sphere()\npeak_idx = dti.quantize_evecs(dtfit.evecs, sphere.vertices)\neu = edx.EuDX(dtfit.fa.astype('f8'), peak_idx,\n              seeds=list(nd.ndindex(data.shape[:-1])), \nodf_vertices=sphere.vertices, a_low=0)\ntensor_streamlines = [streamline for streamline in eu]\nlife_model = life.FiberModel(gtab)\nlife_fit = life_model.fit(data, tensor_streamlines)", "outputs": [{"output_type": "stream", "name": "stderr", "text": "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/dipy/reconst/dti.py:1705: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n  if odf_vertices == None:\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "tensor_for_mat = [np.floor(t) for t in tensor_streamlines]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "fib_dict = dict(fibers=tensor_for_mat)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "sio.savemat('dipy_fibers.mat', fib_dict)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "data.shape", "outputs": [{"execution_count": 6, "output_type": "execute_result", "data": {"text/plain": "(10, 8, 2, 26)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "#life_fit.vox_coords", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "idx = np.argsort(life_fit.b0_signal)[0]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "life_fit.data[idx]", "outputs": [{"execution_count": 9, "output_type": "execute_result", "data": {"text/plain": "array([162,  33,  44,  24,  63,  35,  44,  79,  99,  30,  76,  24,  19,\n        81,  29,  64,  78,  35, 104,  38,  32,  58,  94, 106,  87,  63], dtype=uint8)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "#life_fit.beta", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "life_fit.streamline[0]", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "array([[ 0.        ,  0.        ,  0.        ],\n       [ 0.42694336,  0.08724971,  0.24516699],\n       [ 0.85246557,  0.18221058,  0.48993886],\n       [ 1.25993264,  0.27377641,  0.76486844]])"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 12, "cell_type": "code", "source": "vista_eye_affine = np.eye(4)\nvista_eye_affine[:3, 3] = vista_eye_affine[:3, 3] - 1.5 ", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "vista_eye_affine", "outputs": [{"execution_count": 13, "output_type": "execute_result", "data": {"text/plain": "array([[ 1. ,  0. ,  0. , -1.5],\n       [ 0. ,  1. ,  0. , -1.5],\n       [ 0. ,  0. ,  1. , -1.5],\n       [ 0. ,  0. ,  0. ,  1. ]])"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 14, "cell_type": "code", "source": "small_data = nib.load('small_25.nii.gz').get_data()\nnib.Nifti1Image(small_data, vista_eye_affine).to_filename('./small_25_eye.nii')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 15, "cell_type": "code", "source": "#np.corrcoef(sio.loadmat('matlab_weights.mat')['w'].squeeze(), life_fit.beta)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 16, "cell_type": "code", "source": "model_error = life_fit.predict() - life_fit.data\nmodel_rmse = np.sqrt(np.mean(model_error[:, 1:] ** 2, -1))", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "matlab_rmse = sio.loadmat('matlab_rmse.mat')['rmse'].squeeze()\nmatlab_weights = sio.loadmat('matlab_weights.mat')['w'].squeeze()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 18, "cell_type": "code", "source": "np.median(matlab_rmse), np.median(model_rmse)", "outputs": [{"execution_count": 18, "output_type": "execute_result", "data": {"text/plain": "(11.344136984012376, 11.046015886138505)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 19, "cell_type": "code", "source": "np.mean(matlab_rmse), np.mean(model_rmse)", "outputs": [{"execution_count": 19, "output_type": "execute_result", "data": {"text/plain": "(12.305177539805802, 10.98778075033761)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 24, "cell_type": "code", "source": "np.save('/Users/arokem/source/dipy/dipy/data/life_matlab_rmse', matlab_rmse)\nnp.save('/Users/arokem/source/dipy/dipy/data/life_matlab_weights', matlab_weights)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 25, "cell_type": "code", "source": "plt.scatter(matlab_rmse, model_rmse)\nplt.plot([0, 30], [0, 30], '--')", "outputs": [{"execution_count": 25, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x107c9fc50>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x107ae8f50>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 26, "cell_type": "code", "source": "plt.scatter(matlab_weights, life_fit.beta)\n#plt.plot([0, 30], [0, 30], '--')", "outputs": [{"execution_count": 26, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.collections.PathCollection at 0x107f28e50>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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