def setUp(self):
np.random.seed(8652761)
tmpDir = tempfile.mkdtemp(prefix='pyhrf_tests',
dir=pyhrf.cfg['global']['tmp_path'])
self.tmp_dir = tmpDir
simu = simulate_bold(self.tmp_dir, spatial_size='random_small')
self.data_simu = FmriData.from_simulation_dict(simu)
def setUp(self):
np.random.seed(8652761)
tmpDir = tempfile.mkdtemp(prefix='pyhrf_tests',
dir=pyhrf.cfg['global']['tmp_path'])
self.tmp_dir = tmpDir
self.clean_tmp = True
simu = simulate_bold(self.tmp_dir, spatial_size='random_small')
self.data_simu = FmriData.from_simulation_dict(simu)
def test_hrf_with_var_sampler_2(self):
# estimation of HRF and its variance tested in the following situation:
# - simulated gaussian smooth HRF is not normalized
pyhrf.verbose.set_verbosity(2)
simu = simulate_bold(self.tmp_dir, spatial_size='small',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['HRFVariance','HRF'], simu_scenario=simu,
check_fv='print', nb_its=100,
hrf_prior_type='voxelwiseIID')
def test_hrf_with_var_sampler_2(self):
# estimation of HRF and its variance tested in the following situation:
# - simulated gaussian smooth HRF is not normalized
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
simu = simulate_bold(self.tmp_dir, spatial_size='small',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['HRFVariance', 'HRF'], simu_scenario=simu,
check_fv='print', nb_its=100,
hrf_prior_type='voxelwiseIID')
def test_hrf_var_sampler(self):
# estimation of HRF variance tested in the following situation:
# - simulated gaussian smooth HRF is not normalized
# -> else the simulated HRF variance is not consistent
pyhrf.verbose.set_verbosity(2)
simu = simulate_bold(self.tmp_dir, spatial_size='small',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['HRFVariance'], simu_scenario=simu,
check_fv='raise', nb_its=100,
hrf_prior_type='singleHRF')
def test_hrf_var_sampler(self):
# estimation of HRF variance tested in the following situation:
# - simulated gaussian smooth HRF is not normalized
# -> else the simulated HRF variance is not consistent
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
simu = simulate_bold(self.tmp_dir, spatial_size='small',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['HRFVariance'], simu_scenario=simu,
check_fv='raise', nb_its=100,
hrf_prior_type='singleHRF')
def test_default_jde_small_simulation(self):
""" Test ASL sampler on small simulation with small nb of iterations.
Estimation accuracy is not tested.
"""
simu = simulate_asl(spatial_size='random_small')
fdata = FmriData.from_simulation_dict(simu)
sampler = jde_asl.ASLSampler()
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=.5, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_', randomSeed=None)
treatment = FMRITreatment(fmri_data=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.
"""
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()
def test_full_sampler(self):
""" Test JDE on simulation with normal size.
Estimation accuracy is tested.
"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
simu = simulate_bold(self.tmp_dir, spatial_size='normal',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
sampler = BG(self.sampler_params_for_full_test)
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=.5, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_',
randomSeed=None)
treatment = FMRITreatment(fmri_data=simu,
analyser=analyser, output_dir=self.tmp_dir)
treatment.run()
print 'output_dir:', self.tmp_dir
def test_full_sampler(self):
""" Test JDE on simulation with normal size.
Estimation accuracy is tested.
"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
simu = simulate_bold(self.tmp_dir, spatial_size='normal',
normalize_hrf=False)
simu = FmriData.from_simulation_dict(simu)
sampler = BG(self.sampler_params_for_full_test)
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=.5, driftParam=4, driftType='polynomial',
outputPrefix='jde_mcmc_',
randomSeed=None)
treatment = FMRITreatment(fmri_data=simu,
analyser=analyser, output_dir=self.tmp_dir)
treatment.run()
print 'output_dir:', self.tmp_dir
def setUp(self):
np.random.seed(8652761)
tmpDir = tempfile.mkdtemp(prefix='pyhrf_tests',
dir=pyhrf.cfg['global']['tmp_path'])
self.tmp_dir = tmpDir
self.clean_tmp = True
bf = 'subj0_bold_session0.nii.gz'
self.boldFiles = [pyhrf.get_data_file_name(bf)]
pf = 'subj0_parcellation.nii.gz'
self.parcelFile = pyhrf.get_data_file_name(pf)
self.tr = 2.4
self.dt = .6
self.onsets = pyhrf.paradigm.onsets_loc_av
self.durations = None
self.nbIt = 3
self.pfMethod = 'es'
simu = simulate_bold(self.tmp_dir, spatial_size='random_small')
self.data_simu = FmriData.from_simulation_dict(simu)
self.sampler_params_for_single_test = {
'nb_iterations': 100,
'smpl_hist_pace': 1,
'obs_hist_pace': 1,
# level of spatial correlation = beta
'beta': BS({
'do_sampling': False,
'use_true_value': False,
'val_ini': np.array([0.6]),
}),
# HRF
'hrf': HS({
'do_sampling': False,
'use_true_value': True,
'prior_type': 'singleHRF',
}),
# HRF variance
'hrf_var': HVS({
'use_true_value': True,
'do_sampling': False,
}),
# neural response levels (stimulus-induced effects)
'nrl': NS({
'use_true_nrls': True,
'use_true_labels': True,
'do_sampling': False,
'do_label_sampling': False,
}),
'mixt_params': BGMS({
'do_sampling': False,
'use_true_value': True,
}),
'noise_var': NoiseVarianceSampler({
'do_sampling': False,
'use_true_value': True,
}),
'check_final_value_close_to_true': 'raise', # print or raise
}
self.sampler_params_for_full_test = {
'nb_iterations': 500,
'smpl_hist_pace': 1,
'obs_hist_pace': 1,
# level of spatial correlation = beta
'beta': BS({
'do_sampling': True,
'use_true_value': False,
'val_ini': np.array([0.6]),
}),
# HRF
'hrf': HS({
'do_sampling': True,
'use_true_value': False,
'normalise': 1.,
}),
# HRF variance
'hrf_var': HVS({
'use_true_value': False,
'do_sampling': True,
}),
# neural response levels (stimulus-induced effects)
'nrl': NS({
'use_true_nrls': False,
'use_true_labels': False,
'do_sampling': True,
'do_label_sampling': True,
}),
'mixt_params': BGMS({
'do_sampling': True,
'use_true_value': False,
}),
'noise_var': NoiseVarianceSampler({
'do_sampling': True,
'use_true_value': False,
}),
'check_final_value_close_to_true': 'print', # print or raise
}
def setUp(self):
np.random.seed(8652761)
tmpDir = tempfile.mkdtemp(prefix='pyhrf_tests',
dir=pyhrf.cfg['global']['tmp_path'])
self.tmp_dir = tmpDir
self.clean_tmp = True
bf = 'subj0_bold_session0.nii.gz'
self.boldFiles = [pyhrf.get_data_file_name(bf)]
pf = 'subj0_parcellation.nii.gz'
self.parcelFile = pyhrf.get_data_file_name(pf)
self.tr = 2.4
self.dt = .6
self.onsets = pyhrf.onsets_loc_av
self.durations = None
self.nbIt = 3
self.pfMethod = 'es'
simu = simulate_bold(self.tmp_dir, spatial_size='random_small')
self.data_simu = FmriData.from_simulation_dict(simu)
self.sampler_params_for_single_test = {
BG.P_NB_ITERATIONS: 100,
BG.P_SMPL_HIST_PACE: 1,
BG.P_OBS_HIST_PACE: 1,
# level of spatial correlation = beta
BG.P_BETA: BS({
BS.P_SAMPLE_FLAG: False,
BS.P_USE_TRUE_VALUE: False,
BS.P_VAL_INI: np.array([0.6]),
}),
# HRF
BG.P_HRF: HRFARSampler({
HS.P_SAMPLE_FLAG: False,
HS.P_USE_TRUE_VALUE: True,
HS.P_PRIOR_TYPE: 'singleHRF',
}),
# HRF variance
BG.P_RH: HVS({
HVS.P_USE_TRUE_VALUE: True,
HVS.P_SAMPLE_FLAG: False,
}),
# neural response levels (stimulus-induced effects)
BG.P_NRLS: NRLARSampler({
NS.P_USE_TRUE_NRLS: True,
NS.P_USE_TRUE_LABELS: True,
NS.P_SAMPLE_FLAG: False,
NS.P_SAMPLE_LABELS: False,
}),
BG.P_MIXT_PARAM: BGMS({
BGMS.P_SAMPLE_FLAG: False,
BGMS.P_USE_TRUE_VALUE: True,
}),
BG.P_NOISE_VAR: NoiseVarianceARSampler({
NoiseVarianceARSampler.P_SAMPLE_FLAG: False,
NoiseVarianceARSampler.P_USE_TRUE_VALUE: True,
}),
BG.P_NOISE_ARP: NoiseARParamsSampler({
NoiseARParamsSampler.P_SAMPLE_FLAG: False,
NoiseARParamsSampler.P_USE_TRUE_VALUE: True,
}),
BG.P_CHECK_FINAL_VALUE: 'raise', # print or raise
}
self.sampler_params_for_full_test = {
BG.P_NB_ITERATIONS: 500,
BG.P_SMPL_HIST_PACE: 1,
BG.P_OBS_HIST_PACE: 1,
# level of spatial correlation = beta
BG.P_BETA: BS({
BS.P_SAMPLE_FLAG: True,
BS.P_USE_TRUE_VALUE: False,
BS.P_VAL_INI: np.array([0.6]),
}),
# HRF
BG.P_HRF: HRFARSampler({
HS.P_SAMPLE_FLAG: True,
HS.P_USE_TRUE_VALUE: False,
HS.P_NORMALISE: 1.,
}),
# HRF variance
BG.P_RH: HVS({
HVS.P_USE_TRUE_VALUE: False,
HVS.P_SAMPLE_FLAG: True,
}),
# neural response levels (stimulus-induced effects)
BG.P_NRLS: NRLARSampler({
NS.P_USE_TRUE_NRLS: False,
NS.P_USE_TRUE_LABELS: False,
NS.P_SAMPLE_FLAG: True,
NS.P_SAMPLE_LABELS: True,
}),
BG.P_MIXT_PARAM: BGMS({
BGMS.P_SAMPLE_FLAG: True,
BGMS.P_USE_TRUE_VALUE: False,
}),
BG.P_NOISE_VAR: NoiseVarianceARSampler({
NoiseVarianceARSampler.P_SAMPLE_FLAG: True,
NoiseVarianceARSampler.P_USE_TRUE_VALUE: False,
}),
BG.P_NOISE_ARP: NoiseARParamsSampler({
NoiseARParamsSampler.P_SAMPLE_FLAG: True,
NoiseARParamsSampler.P_USE_TRUE_VALUE: False,
}),
BG.P_CHECK_FINAL_VALUE: 'print', # print or raise
}