def test_mu(self):
""" Validate estimation of mu """
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.sandbox.physio import simulate_asl_physio_rfs
simu = simulate_asl_physio_rfs(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['truebrf'], fdata, nb_its=20,
mu_prior_type='regularized',
check_fv='raise')
def test_simulate_asl_physio_rfs(self):
r = phy.simulate_asl_physio_rfs()
# let's just test the shapes of objects and the presence of some
# physio-specific simulation items:
item_names = r.keys()
self.assertIn('perf_stim_induced', item_names)
self.assertIn('primary_brf', item_names)
self.assertIn('perf_stim_induced', item_names)
self.assertEqual(r['labels_vol'].shape, (3, 1, 2, 2))
self.assertEqual(r['bold'].shape[1], 4) # flat spatial axis
def test_simulate_asl_physio_rfs(self):
r = phy.simulate_asl_physio_rfs()
# let's just test the shapes of objects and the presence of some
# physio-specific simulation items:
item_names = r.keys()
self.assertIn('perf_stim_induced', item_names)
self.assertIn('primary_brf', item_names)
self.assertIn('perf_stim_induced', item_names)
self.assertEqual(r['labels_vol'].shape, (3, 1, 2, 2))
self.assertEqual(r['bold'].shape[1], 4) # flat spatial axis
def test_mu(self):
""" Validate estimation of mu """
pyhrf.verbose.set_verbosity(2)
from pyhrf.sandbox.physio import simulate_asl_physio_rfs
simu = simulate_asl_physio_rfs(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['truebrf'], fdata, nb_its=20,
mu_prior_type='regularized',
check_fv='raise')
print 'pyhrf_view_qt3 %s/jde_mcmc_truebrf_pm.nii' %self.tmp_dir
def test_simulate_asl_physio_rfs(self):
pyhrf.verbose.set_verbosity(0)
r = phy.simulate_asl_physio_rfs()
# let's just test the shapes of objects and the presence of some
# physio-specific simulation items
item_names = r.keys()
self.assertIn("perf_stim_induced", item_names)
self.assertIn("primary_brf", item_names)
self.assertIn("perf_stim_induced", item_names)
self.assertEqual(r["labels_vol"].shape, (3, 1, 2, 2)) # cond, spatial axes
self.assertEqual(r["bold"].shape, (321, 4)) # nb scans, flat spatial axis
def test_simulate_asl_physio_rfs(self):
# pyhrf.verbose.set_verbosity(0)
pyhrf.logger.setLevel(logging.WARNING)
r = phy.simulate_asl_physio_rfs()
# let's just test the shapes of objects and the presence of some
# physio-specific simulation items:
item_names = r.keys()
self.assertIn('perf_stim_induced', item_names)
self.assertIn('primary_brf', item_names)
self.assertIn('perf_stim_induced', item_names)
# cond, spatial axes
self.assertEqual(r['labels_vol'].shape, (3, 1, 2, 2))
self.assertEqual(r['bold'].shape[1], 4) # flat spatial axis
def test_default_jde_small_simulation(self):
""" Test ASL Physio sampler on small simulation with small nb of
iterations. Estimation accuracy is not tested.
"""
pyhrf.verbose.set_verbosity(0)
sampler_params = {
jde_asl_physio.ASLPhysioSampler.P_NB_ITERATIONS : 100,
jde_asl_physio.ASLPhysioSampler.P_SMPL_HIST_PACE : 1,
jde_asl_physio.ASLPhysioSampler.P_OBS_HIST_PACE : 1,
'brf' : jde_asl_physio.PhysioBOLDResponseSampler(zero_constraint=False),
'brf_var' : jde_asl_physio.PhysioBOLDResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'prf' : jde_asl_physio.PhysioPerfResponseSampler(zero_constraint=False),
'prf_var' : jde_asl_physio.PhysioPerfResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'noise_var' : jde_asl_physio.NoiseVarianceSampler(),
'drift_var' : jde_asl_physio.DriftVarianceSampler(),
'drift_coeff' : jde_asl_physio.DriftCoeffSampler(),
'brl' : jde_asl_physio.BOLDResponseLevelSampler(),
'prl' : jde_asl_physio.PerfResponseLevelSampler(),
'bold_mixt_params' : jde_asl_physio.BOLDMixtureSampler(),
'perf_mixt_params' : jde_asl_physio.PerfMixtureSampler(),
'label' : jde_asl_physio.LabelSampler(),
'perf_baseline' : jde_asl_physio.PerfBaselineSampler(),
'perf_baseline_var' : jde_asl_physio.PerfBaselineVarianceSampler(),
'assert_final_value_close_to_true' : False,
}
sampler = jde_asl_physio.ASLPhysioSampler(sampler_params)
simu_items = phym.simulate_asl_physio_rfs(spatial_size='random_small')
simu_fdata = FmriData.from_simulation_dict(simu_items)
dt = simu_items['dt']
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=dt, driftParam=4, driftType='polynomial',
outputFile=None,outputPrefix='jde_mcmc_',
randomSeed=None)
treatment = FMRITreatment(fmri_data=simu_fdata, analyser=analyser)
treatment.run()
def test_default_jde_small_simulation(self):
""" Test ASL Physio sampler on small simulation with small nb of
iterations. Estimation accuracy is not tested.
"""
pyhrf.verbose.set_verbosity(0)
sampler_params = {
'nb_iterations' : 3,
'smpl_hist_pace' : 1,
'obs_hist_pace' : 1,
'brf' : jde_asl_physio.PhysioBOLDResponseSampler(zero_constraint=False),
'brf_var' : jde_asl_physio.PhysioBOLDResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'prf' : jde_asl_physio.PhysioPerfResponseSampler(zero_constraint=False),
'prf_var' : jde_asl_physio.PhysioPerfResponseVarianceSampler(val_ini=\
np.array([1e-3])),
'noise_var' : jde_asl_physio.NoiseVarianceSampler(),
'drift_var' : jde_asl_physio.DriftVarianceSampler(),
'drift' : jde_asl_physio.DriftCoeffSampler(),
'bold_response_levels' : jde_asl_physio.BOLDResponseLevelSampler(),
'perf_response_levels' : jde_asl_physio.PerfResponseLevelSampler(),
'bold_mixt_params' : jde_asl_physio.BOLDMixtureSampler(),
'perf_mixt_params' : jde_asl_physio.PerfMixtureSampler(),
'labels' : jde_asl_physio.LabelSampler(),
'perf_baseline' : jde_asl_physio.PerfBaselineSampler(),
'perf_baseline_var' : jde_asl_physio.PerfBaselineVarianceSampler(),
'check_final_value' : None,
}
sampler = jde_asl_physio.ASLPhysioSampler(**sampler_params)
simu_items = phym.simulate_asl_physio_rfs(spatial_size='random_small')
simu_fdata = FmriData.from_simulation_dict(simu_items)
dt = simu_items['dt']
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=dt, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_')
treatment = FMRITreatment(fmri_data=simu_fdata, analyser=analyser,
output_dir=None)
treatment.run()
def test_default_jde_small_simulation(self):
""" Test ASL Physio sampler on small simulation with small nb of
iterations. Estimation accuracy is not tested.
"""
sampler_params = {
'nb_iterations': 3,
'smpl_hist_pace': 1,
'obs_hist_pace': 1,
'brf': jde_asl_physio.PhysioBOLDResponseSampler(zero_constraint=False),
'brf_var': jde_asl_physio.PhysioBOLDResponseVarianceSampler(val_ini=np.array([1e-3])),
'prf': jde_asl_physio.PhysioPerfResponseSampler(zero_constraint=False),
'prf_var': jde_asl_physio.PhysioPerfResponseVarianceSampler(val_ini=np.array([1e-3])),
'noise_var': jde_asl_physio.NoiseVarianceSampler(),
'drift_var': jde_asl_physio.DriftVarianceSampler(),
'drift': jde_asl_physio.DriftCoeffSampler(),
'bold_response_levels': jde_asl_physio.BOLDResponseLevelSampler(),
'perf_response_levels': jde_asl_physio.PerfResponseLevelSampler(),
'bold_mixt_params': jde_asl_physio.BOLDMixtureSampler(),
'perf_mixt_params': jde_asl_physio.PerfMixtureSampler(),
'labels': jde_asl_physio.LabelSampler(),
'perf_baseline': jde_asl_physio.PerfBaselineSampler(),
'perf_baseline_var': jde_asl_physio.PerfBaselineVarianceSampler(),
'check_final_value': None,
}
sampler = jde_asl_physio.ASLPhysioSampler(**sampler_params)
simu_items = phym.simulate_asl_physio_rfs(spatial_size='random_small')
simu_fdata = FmriData.from_simulation_dict(simu_items)
dt = simu_items['dt']
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=dt, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_')
treatment = FMRITreatment(fmri_data=simu_fdata, analyser=analyser,
output_dir=None)
treatment.run()