def test_C2_watson_gamma_equals_gamma_watson():
scheme = wu_minn_hcp_acquisition_scheme()
cylinder = cylinder_models.C2CylinderStejskalTannerApproximation()
watsoncyl = distribute_models.SD1WatsonDistributed([cylinder])
gammawatsoncyl = distribute_models.DD1GammaDistributed(
[watsoncyl],
target_parameter='C2CylinderStejskalTannerApproximation_1_diameter')
param_name = 'C2CylinderStejskalTannerApproximation_1_lambda_par'
params1 = {
'SD1WatsonDistributed_1_' + param_name: 1.7e-9,
'DD1Gamma_1_alpha': 2.,
'DD1Gamma_1_beta': 4e-6,
'SD1WatsonDistributed_1_SD1Watson_1_odi': 0.4,
'SD1WatsonDistributed_1_SD1Watson_1_mu': [0., 0.]
}
gammacyl = distribute_models.DD1GammaDistributed([cylinder])
watsongammacyl = distribute_models.SD1WatsonDistributed([gammacyl])
params2 = {
'DD1GammaDistributed_1_' + param_name: 1.7e-9,
'DD1GammaDistributed_1_DD1Gamma_1_alpha': 2.,
'DD1GammaDistributed_1_DD1Gamma_1_beta': 4e-6,
'SD1Watson_1_odi': 0.4,
'SD1Watson_1_mu': [0., 0.]
}
assert_array_almost_equal(watsongammacyl(scheme, **params2),
gammawatsoncyl(scheme, **params1), 5)
def test_C4_watson_gamma_equals_gamma_watson():
scheme = wu_minn_hcp_acquisition_scheme()
cylinder = cylinder_models.C4CylinderGaussianPhaseApproximation()
watsoncyl = distribute_models.SD1WatsonDistributed([cylinder])
gammawatsoncyl = distribute_models.DD1GammaDistributed(
[watsoncyl],
target_parameter='C4CylinderGaussianPhaseApproximation_1_diameter')
param = 'SD1WatsonDistributed_1_C4CylinderGaussianPhaseApproximation'
param += '_1_lambda_par'
params1 = {
param: 1.7e-9,
'DD1Gamma_1_alpha': 2.,
'DD1Gamma_1_beta': 4e-6,
'SD1WatsonDistributed_1_SD1Watson_1_odi': 0.4,
'SD1WatsonDistributed_1_SD1Watson_1_mu': [0., 0.]
}
gammacyl = distribute_models.DD1GammaDistributed([cylinder])
watsongammacyl = distribute_models.SD1WatsonDistributed(
[gammacyl],
target_parameter='C4CylinderGaussianPhaseApproximation_1_mu')
param = 'DD1GammaDistributed_1_C4CylinderGaussianPhaseApproximation'
param += '_1_lambda_par'
params2 = {
param: 1.7e-9,
'DD1GammaDistributed_1_DD1Gamma_1_alpha': 2.,
'DD1GammaDistributed_1_DD1Gamma_1_beta': 4e-6,
'SD1Watson_1_odi': 0.4,
'SD1Watson_1_mu': [0., 0.]
}
assert_array_almost_equal(watsongammacyl(scheme, **params2),
gammawatsoncyl(scheme, **params1), 5)
def main():
# Plot Save Path
base_plot_path = r'/nfs/masi/nathv/py_src_code_2020/dmipy_model_pictures'
base_plot_path = os.path.normpath(base_plot_path)
# Method Saving Paths
base_save_path = r'/nfs/masi/nathv/miccai_2020/micro_methods_hcp_mini'
base_save_path = os.path.normpath(base_save_path)
# Create base saving path for Method
# TODO The Method name can be made an argument later on
method_name = 'AXCALIBER'
# Base HCP Data Path
base_data_path = r'/nfs/HCP/data'
base_data_path = os.path.normpath(base_data_path)
# Subject ID's list
subj_ID_List = ['125525', '118225', '116726', '115825', '115017', '114823']
# TODO When needed loop here over the ID list
for subj_ID in subj_ID_List:
# Subject Save Path
subj_save_path = os.path.join(base_save_path, subj_ID)
if os.path.exists(subj_save_path) == False:
os.mkdir(subj_save_path)
# TODO For later the subject data, bval and bvec reading part can be put inside a function
subj_data_path = os.path.join(base_data_path, subj_ID_List[0], 'T1w',
'Diffusion')
# Read the Nifti file, bvals and bvecs
subj_bvals = np.loadtxt(os.path.join(subj_data_path, 'bvals'))
subj_bvecs = np.loadtxt(os.path.join(subj_data_path, 'bvecs'))
all_bvals = subj_bvals * 1e6
all_bvecs = np.transpose(subj_bvecs)
subj_Acq_Scheme = acquisition_scheme_from_bvalues(all_bvals,
all_bvecs,
delta=10.6 * 1e-3,
Delta=43.1 * 1e-3,
TE=89.5 * 1e-3)
print(subj_Acq_Scheme.print_acquisition_info)
print('Loading the Nifti Data ...')
data_start_time = time.time()
subj_babel_object = nib.load(
os.path.join(subj_data_path, 'data.nii.gz'))
subj_data = subj_babel_object.get_fdata()
axial_slice_data = subj_data[58:60, 68:70, 60:62, :]
mask_babel_object = nib.load(
os.path.join(subj_data_path, 'nodif_brain_mask.nii.gz'))
mask_data = mask_babel_object.get_fdata()
axial_mask_slice_data = mask_data[58:60, 68:70, 60:62]
data_end_time = time.time()
data_time = np.int(np.round(data_end_time - data_start_time))
print('Data Loaded ... Time Taken: {}'.format(data_end_time -
data_start_time))
print('The Data Dimensions are: {}'.format(subj_data.shape))
#### AxCaliber Begin ####
ball = gaussian_models.G1Ball()
cylinder = cylinder_models.C4CylinderGaussianPhaseApproximation()
gamma_cylinder = distribute_models.DD1GammaDistributed(
models=[cylinder])
axcaliber_gamma = modeling_framework.MultiCompartmentModel(
models=[ball, gamma_cylinder])
axcaliber_gamma.set_fixed_parameter(
'DD1GammaDistributed_1_C4CylinderGaussianPhaseApproximation_1_lambda_par',
1.7e-9)
axcaliber_gamma.set_fixed_parameter(
'DD1GammaDistributed_1_C4CylinderGaussianPhaseApproximation_1_mu',
[0, 0])
print('Fitting the AxCaliber Model ...')
fit_start_time = time.time()
mcdmi_fit = axcaliber_gamma.fit(subj_Acq_Scheme,
axial_slice_data,
mask=axial_mask_slice_data,
solver='mix',
maxiter=100,
use_parallel_processing=True,
number_of_processors=32)
fit_end_time = time.time()
print('Model Fitting Completed ... Time Taken to fit: {}'.format(
fit_end_time - fit_start_time))
fit_time = np.int(np.round(fit_end_time - fit_start_time))
fitted_parameters = mcdmi_fit.fitted_parameters
# Get List of Estimated Parameter Names
para_Names_list = []
for key, value in fitted_parameters.items():
para_Names_list.append(key)
### Nifti Saving Part
# Create a directory per subject
subj_method_save_path = os.path.join(subj_save_path, method_name)
if os.path.exists(subj_method_save_path) == False:
os.mkdir(subj_method_save_path)
# Retrieve the affine from already Read Nifti file to form the header
affine = subj_babel_object.affine
# Loop over fitted parameters name list
for each_fitted_parameter in para_Names_list:
new_img = nib.Nifti1Image(fitted_parameters[each_fitted_parameter],
affine)
# Form the file path
f_name = each_fitted_parameter + '.nii.gz'
param_file_path = os.path.join(subj_method_save_path, f_name)
nib.save(new_img, param_file_path)
print('debug here')
return None
bvalues = np.loadtxt(allBvalsNames[iMask]) # given in s/mm^2
bvalues_SI = bvalues * 1e6
acq_scheme = acquisition_scheme_from_bvalues(bvalues_SI, gradient_directions_normalized, delta, Delta)
# gtab_dipy = gradient_table(bvalues, gradient_directions, big_delta=Delta, small_delta=delta, atol=3e-2)
# acq_scheme = gtab_dipy2mipy(gtab_dipy)
acq_scheme.print_acquisition_info
dwi_nii = nib.load(allDwiNames[iMask])
dwi = dwi_nii.get_data()
mask = nib.load(allMaskNames[iMask]).get_data()
ball = gaussian_models.G1Ball()
cylinder = cylinder_models.C4CylinderGaussianPhaseApproximation()
gamma_cylinder = distribute_models.DD1GammaDistributed(models=[cylinder])
axcaliber_gamma = MultiCompartmentModel(models=[ball, gamma_cylinder])
print axcaliber_gamma.parameter_cardinality
axcaliber_gamma.set_fixed_parameter('DD1GammaDistributed_1_C4CylinderGaussianPhaseApproximation_1_lambda_par', 1.7e-9)
axcaliber_gamma.set_fixed_parameter('DD1GammaDistributed_1_C4CylinderGaussianPhaseApproximation_1_mu', [0, 0])
axcaliber_gamma_fit = axcaliber_gamma.fit(acq_scheme, dwi, mask = mask, solver='mix', maxiter=100)
fitted_parameters = axcaliber_gamma_fit.fitted_parameters
hdr = dwi_nii.header
# create output folder
dir_sub = os.path.join(directory, "%03d" % iMask)