def get_mcmc_results(dataset_name, model_name):
model_defined_maps = mdt.load_volume_maps(output_base_pjoin(
dataset_name, model_name, 'samples', 'model_defined_maps'))
univariate_normal_maps = mdt.load_volume_maps(output_base_pjoin(
dataset_name, model_name, 'samples', 'univariate_normal'))
if model_name == 'Tensor':
return model_defined_maps['Tensor.FA'], model_defined_maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return model_defined_maps['FS'], model_defined_maps['FS.std']
# return univariate_normal_maps['w_stick0.w'], univariate_normal_maps['w_stick0.w.std']
elif model_name == 'BallStick_r2':
return model_defined_maps['FS'], model_defined_maps['FS.std']
# return univariate_normal_maps['w_stick0.w'], univariate_normal_maps['w_stick0.w.std']
elif model_name == 'BallStick_r3':
return model_defined_maps['FS'], model_defined_maps['FS.std']
# return univariate_normal_maps['w_stick0.w'], univariate_normal_maps['w_stick0.w.std']
elif model_name == 'NODDI':
return univariate_normal_maps['w_ic.w'], univariate_normal_maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return model_defined_maps['FR'], model_defined_maps['FR.std']
elif model_name == 'CHARMED_r2':
return model_defined_maps['FR'], model_defined_maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return univariate_normal_maps['w_in0.w'], univariate_normal_maps['w_in0.w.std']
def get_mcmc_std(results_pjoin, model_name):
model_defined_maps = mdt.load_volume_maps(
results_pjoin(model_name, 'samples', 'model_defined_maps'))
univariate_normal_maps = mdt.load_volume_maps(
results_pjoin(model_name, 'samples', 'univariate_normal'))
if model_name == 'Tensor':
return model_defined_maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return model_defined_maps['FS.std']
elif model_name == 'BallStick_r2':
return model_defined_maps['FS.std']
elif model_name == 'BallStick_r3':
return model_defined_maps['FS.std']
elif model_name == 'NODDI':
return univariate_normal_maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return model_defined_maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return univariate_normal_maps['w_in0.w.std']
def get_mean_std(protocol_name, model_name, snr, trial_ind):
model_defined_maps = mdt.load_volume_maps(
output_base_pjoin(protocol_name, model_name, 'output', str(snr),
str(trial_ind), model_name, 'samples',
'model_defined_maps'))
univariate_normal_maps = mdt.load_volume_maps(
output_base_pjoin(protocol_name, model_name, 'output', str(snr),
str(trial_ind), model_name, 'samples',
'univariate_normal'))
if model_name == 'Tensor':
return model_defined_maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return model_defined_maps['FS.std']
elif model_name == 'BallStick_r2':
return model_defined_maps['FS.std']
elif model_name == 'BallStick_r3':
return model_defined_maps['FS.std']
elif model_name == 'NODDI':
return univariate_normal_maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return model_defined_maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return univariate_normal_maps['w_in0.w.std']
def _start_maps_visualizer(self):
folder = self.selectedOutputFolder.text(
) + '/' + self._get_current_model_name()
maps = mdt.load_volume_maps(folder)
a_map = maps[list(maps.keys())[0]]
config = MapPlotConfig()
config.dimension = 2
if len(a_map.shape) > 2:
config.slice_index = a_map.shape[2] // 2
start_gui(data=SimpleDataInfo.from_paths([folder]),
config=config,
app_exec=False)
def test_lls_multishell_b6k_max(self):
known_values = {
'CHARMED_r1': {
'LogLikelihood': {
'mean': -8038.29248046875,
'std': 4703.6005859375
}
},
'CHARMED_r2': {
'LogLikelihood': {
'mean': -7001.50927734375,
'std': 4062.600341796875
}
},
'CHARMED_r3': {
'LogLikelihood': {
'mean': -6514.17529296875,
'std': 3659.7138671875
}
}
}
for model_name in ['CHARMED_r1', 'CHARMED_r2', 'CHARMED_r3']:
pjoin = mdt.make_path_joiner(
os.path.join(self._tmp_dir, self._tmp_dir_subdir,
'multishell_b6k_max'))
user_volumes = mdt.load_volume_maps(
pjoin('output', 'multishell_b6k_max_example_slices_24_38_mask',
model_name))
msg_prefix = 'b1k_b2k - {}'.format(model_name)
roi = mdt.create_roi(
user_volumes['LogLikelihood'],
pjoin('multishell_b6k_max_example_slices_24_38_mask'))
for map_name, test_values in known_values[model_name].items():
np.testing.assert_allclose(test_values['mean'],
np.mean(roi),
rtol=1e-4,
err_msg='{} - {} - mean'.format(
msg_prefix, map_name))
np.testing.assert_allclose(test_values['std'],
np.std(roi),
rtol=1e-4,
err_msg='{} - {} - std'.format(
msg_prefix, map_name))
def test_lls_multishell_b6k_max(self):
known_values = {
'CHARMED_r1': {
'LogLikelihood': {
'mean': -447.95446,
'std': 36.09088
}
},
'CHARMED_r2': {
'LogLikelihood': {
'mean': -436.13861,
'std': 25.13305
}
},
'CHARMED_r3': {
'LogLikelihood': {
'mean': -432.15878,
'std': 21.87481
}
}
}
for model_name in ['CHARMED_r1', 'CHARMED_r2', 'CHARMED_r3']:
pjoin = mdt.make_path_joiner(
os.path.join(self._tmp_dir, self._tmp_dir_subdir,
'multishell_b6k_max'))
user_volumes = mdt.load_volume_maps(
pjoin('output', 'multishell_b6k_max_example_slices_24_38_mask',
model_name))
msg_prefix = 'b1k_b2k - {}'.format(model_name)
roi = mdt.create_roi(
user_volumes['LogLikelihood'],
pjoin('multishell_b6k_max_example_slices_24_38_mask'))
for map_name, test_values in known_values[model_name].items():
np.testing.assert_allclose(test_values['mean'],
np.mean(roi),
rtol=1e-4,
err_msg='{} - {} - mean'.format(
msg_prefix, map_name))
np.testing.assert_allclose(test_values['std'],
np.std(roi),
rtol=1e-4,
err_msg='{} - {} - std'.format(
msg_prefix, map_name))
def get_mle_results(dataset_name, model_name):
maps = mdt.load_volume_maps(output_base_pjoin(dataset_name, model_name))
if model_name == 'Tensor':
return maps['Tensor.FA'], maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return maps['w_stick0.w'], maps['w_stick0.w.std']
elif model_name == 'BallStick_r2':
return maps['FS'], maps['FS.std']
elif model_name == 'NODDI':
return maps['w_ic.w'], maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return maps['FR'], maps['FR.std']
elif model_name == 'CHARMED_r2':
return maps['FR'], maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return maps['w_in0.w'], maps['w_in0.w.std']
def get_mle_std(results_pjoin, model_name):
maps = mdt.load_volume_maps(results_pjoin(model_name))
if model_name == 'Tensor':
return maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return maps['w_stick0.w.std']
elif model_name == 'BallStick_r2':
return maps['FS.std']
elif model_name == 'BallStick_r3':
return maps['FS.std']
elif model_name == 'NODDI':
return maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return maps['w_in0.w.std']
def test_lls_b1k_b2k(self):
known_values = {
'BallStick_r1': {
'LogLikelihood': {
'mean': -327297.53125,
'std': 359791.5625
}
},
'Tensor': {
'LogLikelihood': {
'mean': -5891.28515625,
'std': 7691.93408203125
}
},
'NODDI': {
'LogLikelihood': {
'mean': -19352.4609375,
'std': 14489.8623046875
}
}
}
for model_name in ['BallStick_r1', 'Tensor', 'NODDI']:
pjoin = mdt.make_path_joiner(
os.path.join(self._tmp_dir, self._tmp_dir_subdir, 'b1k_b2k'))
user_volumes = mdt.load_volume_maps(
pjoin('output', 'b1k_b2k_example_slices_24_38_mask',
model_name))
msg_prefix = 'b1k_b2k - {}'.format(model_name)
roi = mdt.create_roi(user_volumes['LogLikelihood'],
pjoin('b1k_b2k_example_slices_24_38_mask'))
for map_name, test_values in known_values[model_name].items():
np.testing.assert_allclose(test_values['mean'],
np.mean(roi),
rtol=1e-4,
err_msg='{} - {} - mean'.format(
msg_prefix, map_name))
np.testing.assert_allclose(test_values['std'],
np.std(roi),
rtol=1e-4,
err_msg='{} - {} - std'.format(
msg_prefix, map_name))
def test_lls_b1k_b2k(self):
known_values = {
'BallStick_r1': {
'LogLikelihood': {
'mean': -1215.52355,
'std': 924.27117
}
},
'Tensor': {
'LogLikelihood': {
'mean': -182.73164,
'std': 20.02311
}
},
'NODDI': {
'LogLikelihood': {
'mean': -451.16198,
'std': 37.77514
}
}
}
for model_name in ['BallStick_r1', 'Tensor', 'NODDI']:
pjoin = mdt.make_path_joiner(
os.path.join(self._tmp_dir, self._tmp_dir_subdir, 'b1k_b2k'))
user_volumes = mdt.load_volume_maps(
pjoin('output', 'b1k_b2k_example_slices_24_38_mask',
model_name))
msg_prefix = 'b1k_b2k - {}'.format(model_name)
roi = mdt.create_roi(user_volumes['LogLikelihood'],
pjoin('b1k_b2k_example_slices_24_38_mask'))
for map_name, test_values in known_values[model_name].items():
np.testing.assert_allclose(test_values['mean'],
np.mean(roi),
rtol=1e-4,
err_msg='{} - {} - mean'.format(
msg_prefix, map_name))
np.testing.assert_allclose(test_values['std'],
np.std(roi),
rtol=1e-4,
err_msg='{} - {} - std'.format(
msg_prefix, map_name))
def get_mode_std(protocol_name, model_name, snr, trial_ind):
maps = mdt.load_volume_maps(
output_base_pjoin(protocol_name, model_name, 'output', str(snr),
str(trial_ind), model_name))
if model_name == 'Tensor':
return maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return maps['w_stick0.w.std']
elif model_name == 'BallStick_r2':
return maps['FS.std']
elif model_name == 'BallStick_r3':
return maps['FS.std']
elif model_name == 'NODDI':
return maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return maps['w_in0.w.std']
def get_mle_std(output_dir, model_name):
maps = mdt.load_volume_maps(output_dir)
if model_name == 'Tensor':
return maps['Tensor.FA.std']
elif model_name == 'BallStick_r1':
return maps['w_stick0.w.std']
elif model_name == 'BallStick_r2':
# return maps['FS.std']
return maps['w_stick0.w.std']
elif model_name == 'BallStick_r3':
# return maps['FS.std']
return maps['w_stick0.w.std']
elif model_name == 'NODDI':
return maps['w_ic.w.std']
elif model_name == 'CHARMED_r1':
return maps['FR.std']
elif model_name == 'CHARMED_r2':
return maps['FR.std']
elif model_name == 'CHARMED_r3':
return maps['FR.std']
elif model_name == 'BinghamNODDI_r1':
return maps['w_in0.w.std']
