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"""
======================
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,
single_tensor_odf,
all_tensor_evecs)
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 interetested 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('symmetric724')
sphere = sphere.subdivide(2)
odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)
from dipy.viz import fvtk
ren = fvtk.ren()
odf_actor = fvtk.sphere_funcs(odf, sphere)
odf_actor.RotateX(90)
fvtk.add(ren, odf_actor)
print('Saving illustration as multi_tensor_simulation')
fvtk.record(ren, out_path='multi_tensor_simulation.png', size=(300, 300))
"""
.. figure:: multi_tensor_simulation.png
:align: center
**Simulating a MultiTensor ODF**
"""
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