def signal(self, S0=1):
"""
The signal generated by the ODF in each bvec direction
"""
# Initialize to zero:
signal = np.zeros(self.bvecs.shape[-1])
# Decompose the response function to its eigen-decomposition:
evals, evecs = self.response_function.decompose
# We generate the signal for each one of the fibers in the ODF:
for odf_idx, odf_vec in enumerate(self.odf.bvecs):
this_response = ozt.rotate_to_vector(odf_vec,
evals,
evecs,
self.bvecs,
self.bvals)
this_signal = (self.odf.weights[odf_idx] *
this_response.predicted_signal(S0))
signal += this_signal
if self.iso:
# The iso component has the diffusivity of the tensor:
md = np.mean(np.diag(self.Q))
iso_signal = self.iso * ozt.stejskal_tanner(S0, self.bvals, md)
signal += iso_signal
signal /= np.sum(np.hstack([self.odf.weights, self.iso]))
else:
signal /= np.sum(self.odf.weights)
return signal
def adc(self):
"""
The ADC in each bvec direction, given the ODF
"""
# Initialize to zero:
adc = np.zeros(self.bvecs.shape[-1])
# Decompose the response function to its eigen-decomposition:
evals, evecs = self.response_function.decompose
# We generate the signal for each one of the fibers in the ODF:
for odf_idx, odf_vec in enumerate(self.odf.bvecs):
this_response = ozt.rotate_to_vector(odf_vec,
evals,
evecs,
self.bvecs,
self.bvals)
this_adc = self.odf.weights[odf_idx] * this_response.ADC
adc += this_adc
return adc / np.sum(self.odf.weights)