""" ====================== MultiTensor Simulation ====================== In this example we show how someone can simulate the signal and the ODF of a single voxel using a MultiTensor. """ import numpy as np from dipy.sims.voxel import multi_tensor, multi_tensor_odf from dipy.data import get_sphere """ For the simulation we will need a GradientTable with the b-values and b-vectors Here we use the one we created in :ref:example_gradients_spheres. """ from gradients_spheres import gtab """ In mevals we save the eigenvalues of each tensor. """ mevals = np.array([[0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]]) """ In angles we save in polar coordinates (:math:\theta, \phi) the principal axis of each tensor. """ angles = [(0, 0), (60, 0)] """ In fractions we save the percentage of the contribution of each tensor. """ fractions = [50, 50] """ The function multi_tensor will return the simulated signal and an array with the principal axes of the tensors in cartesian coordinates. """ signal, sticks = multi_tensor(gtab, mevals, S0=100, angles=angles, fractions=fractions, snr=None) """ We can also add Rician noise with a specific SNR. """ signal_noisy, sticks = multi_tensor(gtab, mevals, S0=100, angles=angles, fractions=fractions, snr=20) import matplotlib.pyplot as plt plt.plot(signal, label='noiseless') plt.plot(signal_noisy, label='with noise') plt.legend() #plt.show() plt.savefig('simulated_signal.png') """ .. figure:: simulated_signal.png :align: center **Simulated MultiTensor signal** """ """ For the ODF simulation we will need a sphere. Because we are interested in a simulation of only a single voxel, we can use a sphere with very high resolution. We generate that by subdividing the triangles of one of DIPY_'s cached spheres, which we can read in the following way. """ sphere = get_sphere('repulsion724') sphere = sphere.subdivide(2) odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions) from dipy.viz import window, actor # Enables/disables interactive visualization interactive = False scene = window.Scene() odf_actor = actor.odf_slicer(odf[None, None, None, :], sphere=sphere, colormap='plasma') odf_actor.RotateX(90) scene.add(odf_actor) print('Saving illustration as multi_tensor_simulation') window.record(scene, out_path='multi_tensor_simulation.png', size=(300, 300)) if interactive: window.show(scene) """ .. figure:: multi_tensor_simulation.png :align: center Simulating a MultiTensor ODF. .. include:: ../links_names.inc """