def test_TreatmentXML(self):
from pyhrf.ui.jde import JDEMCMCAnalyser
from pyhrf.jde.models import BOLDGibbsSampler
from pyhrf.ui.treatment import FMRITreatment
a = JDEMCMCAnalyser(sampler=BOLDGibbsSampler(nb_iterations=42))
t = FMRITreatment(analyser=a)
txml = xmlio.to_xml(t, pretty=True)
t2 = xmlio.from_xml(txml)
self.assertEqual(t2.analyser.sampler.nbIterations, 42)
def glm_analyse(fdata, contrasts, output_dir, output_prefix,
hrf_model="Canonical", fir_delays=None,
rescale_factor_file=None):
glm_analyser = GLMAnalyser(hrf_model=hrf_model, contrasts=contrasts,
outputPrefix=output_prefix, fir_delays=fir_delays,
rescale_factor_file=rescale_factor_file)
glm_analyser.set_pass_errors(False)
glm_analyser.set_gzip_outputs(True)
tt = FMRITreatment(fdata, glm_analyser, output_dir=output_dir)
tt.run()
def jde_analyse(fdata, contrasts, output_dir):
from pyhrf.jde.models import BOLDGibbsSampler as BG
from pyhrf.jde.hrf import RHSampler
from pyhrf.jde.nrl.bigaussian import NRLSampler
sampler = BG(nb_iterations=250,
hrf_var=RHSampler(do_sampling=False, val_ini=np.array([0.05])),
response_levels=NRLSampler(contrasts=contrasts))
analyser = JDEMCMCAnalyser(sampler=sampler)
analyser.set_gzip_outputs(True)
tt = FMRITreatment(fdata, analyser, output_dir=output_dir)
tt.run(parallel='local')
def test_jdevemanalyser(self):
""" Test BOLD VEM sampler on small simulation with small
nb of iterations. Estimation accuracy is not tested.
"""
jde_vem_analyser = JDEVEMAnalyser(beta=.8,
dt=.5,
hrfDuration=25.,
nItMax=2,
nItMin=2,
fast=True,
computeContrast=False,
PLOT=False,
constrained=True)
tjde_vem = FMRITreatment(fmri_data=self.data_simu,
analyser=jde_vem_analyser,
output_dir=None)
tjde_vem.run()
def test_default_jde_small_simulation(self):
""" Test JDE multi-sessions sampler on small
simulation with small nb of iterations.
Estimation accuracy is not tested.
"""
sampler = BMSS()
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=.5, driftParam=4, driftType='polynomial',
outputPrefix='jde_MS_mcmc_',
randomSeed=9778946)
treatment = FMRITreatment(fmri_data=self.data_simu,
analyser=analyser)
treatment.run()
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.
"""
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_specific_parameters(self, parameter_name, fdata, simu,
beta=.8, dt=.5, nItMax=100, nItMin=10,
hrfDuration=25., estimateSigmaH=False,
estimateBeta=False, estimateSigmaG=False,
PLOT=False, constrained=True, fast=False,
estimateH=False, estimateG=False,
estimateA=False, estimateC=False,
estimateZ=False, estimateLA=False,
estimateNoise=False, estimateMP=True):
"""
Test specific samplers.
"""
logger.info('_test_specific_parameters %s', str(parameter_name))
output_dir = self.tmp_dir
# JDE analysis
jde_vem_analyser = JDEVEMAnalyser(beta=beta, dt=dt,
hrfDuration=hrfDuration,
estimateSigmaH=estimateSigmaH,
nItMax=nItMax, nItMin=nItMin,
estimateBeta=estimateBeta,
estimateSigmaG=estimateSigmaG,
PLOT=PLOT,
constrained=constrained, fast=fast,
fmri_data=fdata,
simulation=simu,
estimateH=estimateH,
estimateG=estimateG,
estimateA=estimateA,
estimateC=estimateC,
estimateLabels=estimateZ,
estimateLA=estimateLA,
estimateMixtParam=estimateMP,
estimateNoise=estimateNoise)
tjde_vem = FMRITreatment(fmri_data=fdata, analyser=jde_vem_analyser,
output_dir=output_dir)
outputs = tjde_vem.run()
print 'out_dir:', output_dir
return outputs
def main():
"""Run when calling the script"""
start_time = time.time()
if not os.path.isdir(config["output_dir"]):
try:
os.makedirs(config["output_dir"])
except OSError as e:
print("Ouput directory could not be created.\n"
"Error was: {}".format(e.strerror))
sys.exit(1)
bold_data = FmriData.from_vol_files(
mask_file=config["parcels_file"], paradigm_csv_file=config["onsets_file"],
bold_files=config["bold_data_file"], tr=config["tr"]
)
compute_contrasts, contrasts_def = load_contrasts_definitions(config["def_contrasts_file"])
jde_vem_analyser = JDEVEMAnalyser(
hrfDuration=config["hrf_duration"], sigmaH=config["sigma_h"], fast=True,
computeContrast=compute_contrasts, nbClasses=2, PLOT=False,
nItMax=config["nb_iter_max"], nItMin=config["nb_iter_min"], scale=False,
beta=config["beta"], estimateSigmaH=True, estimateHRF=config["estimate_hrf"],
TrueHrfFlag=False, HrfFilename='hrf.nii', estimateDrifts=True,
hyper_prior_sigma_H=config["hrf_hyperprior"], dt=config["dt"], estimateBeta=True,
contrasts=contrasts_def, simulation=False, estimateLabels=True,
LabelsFilename=None, MFapprox=False, estimateMixtParam=True,
constrained=False, InitVar=0.5, InitMean=2.0, MiniVemFlag=False, NbItMiniVem=5,
zero_constraint=config["zero_constraint"], drifts_type=config["drifts_type"]
)
processing_jde_vem = FMRITreatment(
fmri_data=bold_data, analyser=jde_vem_analyser,
output_dir=config["output_dir"], make_outputs=True
)
if not config["parallel"]:
processing_jde_vem.run()
else:
processing_jde_vem.run(parallel="local")
if config["save_processing_config"]:
# Let's canonicalize all paths
config_save = dict(config)
for file_nb, bold_file in enumerate(config_save["bold_data_file"]):
config_save["bold_data_file"][file_nb] = os.path.abspath(bold_file)
config_save["parcels_file"] = os.path.abspath(config_save["parcels_file"])
config_save["onsets_file"] = os.path.abspath(config_save["onsets_file"])
if config_save["def_contrasts_file"]:
config_save["def_contrasts_file"] = os.path.abspath(config_save["def_contrasts_file"])
config_save["output_dir"] = os.path.abspath(config_save["output_dir"])
config_save_filename = "{}_processing.json".format(
datetime.datetime.today()
).replace(" ", "_")
config_save_path = os.path.join(config["output_dir"], config_save_filename)
with open(config_save_path, 'w') as json_file:
json.dump(config_save, json_file, sort_keys=True, indent=4)
print("")
print("Total computation took: {} seconds".format(format_duration(time.time() - start_time)))
def _test_specific_samplers(self, sampler_names, fdata,
nb_its=None, use_true_val=None,
save_history=False, check_fv=None,
normalize_brf=1., normalize_prf=1.,
normalize_mu=1., prf_prior_type='regularized',
brf_prior_type='regularized',
mu_prior_type='regularized'):
"""
Test specific samplers.
"""
if use_true_val is None:
use_true_val = dict((n, False) for n in sampler_names)
logger.info('_test_specific_samplers %s ...', str(sampler_names))
params = deepcopy(self.sampler_params_for_single_test)
# Loop over given samplers to enable them
for var_name in sampler_names:
var_class = params[var_name].__class__
use_tval = use_true_val[var_name]
# special case for HRF -> normalization and prior type
if var_class == jaslh.PhysioBOLDResponseSampler:
params[var_name] = \
jaslh.PhysioBOLDResponseSampler(do_sampling=True,
use_true_value=use_tval,
normalise=normalize_brf,
zero_constraint=False,
prior_type=brf_prior_type)
elif var_class == jaslh.PhysioPerfResponseSampler:
params[var_name] = \
jaslh.PhysioPerfResponseSampler(do_sampling=True,
use_true_value=use_tval,
normalise=normalize_brf,
zero_constraint=False,
prior_type=prf_prior_type)
elif var_class == jaslh.PhysioTrueBOLDResponseSampler:
params[var_name] = \
jaslh.PhysioTrueBOLDResponseSampler(do_sampling=True,
use_true_value=use_tval,
normalise=normalize_mu,
zero_constraint=False,
prior_type=mu_prior_type)
else:
params[var_name] = var_class(do_sampling=True,
use_true_value=use_tval)
if nb_its is not None:
params['nb_iterations'] = nb_its
if save_history:
params['smpl_hist_pace'] = 1
params['obs_hist_pace'] = 1
if check_fv is not None:
params['check_final_value'] = check_fv
sampler = jaslh.ASLPhysioSampler(**params)
output_dir = self.tmp_dir
analyser = JDEMCMCAnalyser(sampler=sampler, osfMax=4, dtMin=.4,
dt=fdata.simulation[0]['dt'], driftParam=4,
driftType='polynomial',
outputPrefix='jde_mcmc_',
pass_error=False)
treatment = FMRITreatment(fmri_data=fdata, analyser=analyser,
output_dir=output_dir)
outputs = treatment.run()
print 'out_dir:', output_dir
return outputs
def _test_specific_samplers(self,
sampler_names,
simu,
nb_its=None,
use_true_val=None,
save_history=False,
check_fv=None,
normalize_hrf=1.,
hrf_prior_type='singleHRF',
normalize_hrf_group=1.,
hrf_group_prior_type='singleHRF',
reg_hgroup=True):
"""
Test specific samplers.
"""
if use_true_val is None:
use_true_val = dict((n, False) for n in sampler_names)
logger.info('_test_specific_samplers %s ...', str(sampler_names))
params = deepcopy(self.sampler_params_for_single_test)
# Loop over given samplers to enable them
for var_name in sampler_names:
var_class = params[var_name].__class__
use_tval = use_true_val[var_name]
# special case for HRF -> normalization and prior type
if var_class == jms.HRF_Sampler:
params[var_name] = jms.HRF_Sampler(do_sampling=True,
use_true_value=use_tval,
normalise=normalize_hrf,
prior_type=hrf_prior_type,
zero_contraint=False)
elif var_class == jms.HRF_Group_Sampler:
ptype = hrf_group_prior_type
nhg = normalize_hrf_group
shg = jms.HRF_Group_Sampler(do_sampling=True,
use_true_value=use_tval,
normalise=nhg,
prior_type=ptype,
zero_contraint=False,
regularise=reg_hgroup)
params[var_name] = shg
else:
params[var_name] = var_class(do_sampling=True,
use_true_value=use_tval)
if nb_its is not None:
params['nb_iterations'] = nb_its
if save_history:
params['smpl_hist_pace'] = 1
params['obs_hist_pace'] = 1
if check_fv is not None:
params['check_final_value'] = check_fv
sampler = BMSS(**params)
output_dir = self.simu_dir
analyser = JDEMCMCAnalyser(sampler=sampler,
osfMax=4,
dtMin=.4,
dt=.6,
driftParam=4,
driftType='polynomial',
outputPrefix='jde_mcmc_',
randomSeed=5421087,
pass_error=False)
treatment = FMRITreatment(fmri_data=simu,
analyser=analyser,
output_dir=output_dir)
outputs = treatment.run()
return outputs
def rfir_analyse(fdata, output_dir):
analyser = RFIRAnalyser(RFIREstim(nb_its_max=150))
analyser.set_gzip_outputs(True)
tt = FMRITreatment(fdata, analyser, output_dir=output_dir)
tt.run()
