def two_fiber_signal(bvals, bvecs, angle, w=[0.5, 0.5], SNR=0):
R0 = rotation_around_axis([0, 1, 0], 0)
R1 = rotation_around_axis([0, 1, 0], np.deg2rad(angle))
E = w[0] * single_tensor(gradients=bvecs, bvals=bvals, S0=1, evecs=R0, snr=SNR)
E += w[1] * single_tensor(gradients=bvecs, bvals=bvals, S0=1, evecs=R1, snr=SNR)
return E
for k, gamma in enumerate(angles):
print "Angle:", np.rad2deg(gamma)
# Gamma is the angle separating fibres
# ================
# Set up test data
# ================
# b is tau * |q|^2 in s/mm^2
# If b is too low, the signal does not attenuate enough to measure.
# Too high, the signal to noise ratio increases.
angles = np.array([gamma / 2., -gamma / 2])
R0 = rotation_around_axis([0, 1, 0], 0)
R1 = rotation_around_axis([0, 1, 0], angles[0] - angles[1])
# ================
# Q-Space Sampling
# ================
# Note: We sample our signal in Q-space on the low-order quadrature points
# here, but wwe could just as well have used other, random points on the
# sphere.
E = w[0] * single_tensor(gradients=xyz, bvals=b, S0=1, rotation=R0, SNR=SNR)
E += w[1] * single_tensor(gradients=xyz, bvals=b, S0=1, rotation=R1, SNR=SNR)
print "Signal mean:", E.mean()