## Create and add in Rician noise
noiseR = np.random.random(E.shape)
noiseI = np.random.random(E.shape)
noise = noiseR + 1j * noiseI
tau = (10.0**(-sig_noise / 10.0) * norm(E, 2))
noise = noise * (tau / norm(noise)) # Normalize to get desired SNR
E = E + noise # Add noise to signal
E = abs(E) # Measured amplitude signal
# Drop data
np.random.shuffle(mask)
E[mask] = np.nan
fit = sk.fit(E)
xyz = fit.directions
if len(xyz) > 1:
w = np.dot(xyz[0], xyz[1])
else:
w = 1
recovered_angle[n, kk] = np.rad2deg(np.arccos(np.abs(w)))
# End MC simulation
np.savetxt('_recovered_angle.npy', recovered_angle)
np.set_printoptions(precision=2, suppress=True)
"""
Remove most of the background using dipy's mask module.
"""
# Instantiate model
from dipy.reconst.multi_voxel import multi_voxel_fit
SparseKernelModel.fit = multi_voxel_fit(SparseKernelModel.fit)
#from dipy.segment.mask import median_otsu
#maskdata, mask = median_otsu(data, 3, 1, True,
# vol_idx=range(10, 50), dilate=2)
# Select segment of data to work with
data_small = data[13:43, 44:74, 28:29]
fit = model.fit(data_small)
sphere = get_sphere('symmetric724')
odfs = fit.odf(sphere)
# It is common for some models to produce negative values, we can remove those
# using ``np.clip``
odfs = np.clip(odfs, 0, np.max(odfs, -1)[..., None])
# Visualie
from dipy.viz import fvtk
r = fvtk.ren()
fvtk.add(r, fvtk.sphere_funcs(odfs, sphere, colormap='jet'))
fn = model.__class__.__name__ + '_odfs.png'
