def setDummyInputData(self, xmlFile):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
if t.data.data_type == 'volume':
dataFn = pyhrf.get_data_file_name('dummySmallBOLD.nii.gz')
maskFn = pyhrf.get_data_file_name('dummySmallMask.nii.gz')
sd = FMRISessionVolumicData(bold_file=dataFn)
t.set_init_param(
'fmri_data',
FmriData.from_vol_ui(mask_file=maskFn, sessions_data=[sd]))
elif t.data.data_type == 'surface':
fn = 'real_data_surf_tiny_bold.gii'
dataFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_parcellation.gii'
maskFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_mesh.gii'
meshFn = pyhrf.get_data_file_name(fn)
sd = FMRISessionSurfacicData(bold_file=dataFn)
t.set_init_param(
'fmri_data',
FmriData.from_surf_ui(mask_file=maskFn,
mesh_file=meshFn,
sessions_data=[sd]))
else:
raise Exception('Unsupported class ... todo')
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def setDummyInputData(self, xmlFile):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
if t.data.data_type == 'volume':
dataFn = pyhrf.get_data_file_name('dummySmallBOLD.nii.gz')
maskFn = pyhrf.get_data_file_name('dummySmallMask.nii.gz')
sd = FMRISessionVolumicData(bold_file=dataFn)
t.set_init_param('fmri_data',
FmriData.from_vol_ui(mask_file=maskFn,
sessions_data=[sd]))
elif t.data.data_type == 'surface':
fn = 'real_data_surf_tiny_bold.gii'
dataFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_parcellation.gii'
maskFn = pyhrf.get_data_file_name(fn)
fn = 'real_data_surf_tiny_mesh.gii'
meshFn = pyhrf.get_data_file_name(fn)
sd = FMRISessionSurfacicData(bold_file=dataFn)
t.set_init_param('fmri_data',
FmriData.from_surf_ui(mask_file=maskFn,
mesh_file=meshFn,
sessions_data=[sd]))
else:
raise Exception('Unsupported class ... todo')
f = open(xmlFile, 'w')
f.write(xmlio.to_xml(t))
f.close()
def setDummyInputData(self, xmlFile):
f = open(xmlFile, "r")
xml = f.read()
t = xmlio.fromXML(xml)
if t.data.data_type == "volume":
dataFn = pyhrf.get_data_file_name("dummySmallBOLD.nii.gz")
maskFn = pyhrf.get_data_file_name("dummySmallMask.nii.gz")
sd = FMRISessionVolumicData(bold_file=dataFn)
t.set_init_param("fmri_data", FmriData.from_vol_ui(mask_file=maskFn, sessions_data=[sd]))
elif t.data.data_type == "surface":
fn = "real_data_surf_tiny_bold.gii"
dataFn = pyhrf.get_data_file_name(fn)
fn = "real_data_surf_tiny_parcellation.gii"
maskFn = pyhrf.get_data_file_name(fn)
fn = "real_data_surf_tiny_mesh.gii"
meshFn = pyhrf.get_data_file_name(fn)
sd = FMRISessionSurfacicData(bold_file=dataFn)
t.set_init_param("fmri_data", FmriData.from_surf_ui(mask_file=maskFn, mesh_file=meshFn, sessions_data=[sd]))
else:
raise Exception("Unsupported class ... todo")
f = open(xmlFile, "w")
f.write(xmlio.toXML(t, handler=NumpyXMLHandler()))
f.close()
def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))
def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata)
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata)
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_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
fdata = FmriData.from_vol_ui()
g, dm, cons = glm_nipy(fdata, contrasts=cons)
def test_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {
'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
fdata = FmriData.from_vol_ui()
g, dm, cons = glm_nipy(fdata, contrasts=cons)
def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
#pyhrf.verbose.set_verbosity(3)
fdata = FmriData.from_vol_ui()
# print 'fdata:'
# print fdata.getSummary()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
#pyhrf.verbose.set_verbosity(3)
fdata = FmriData.from_vol_ui()
# print 'fdata:'
# print fdata.getSummary()
glm_nipy(fdata)
def test_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
#pyhrf.verbose.set_verbosity(3)
fdata = FmriData.from_vol_ui()
# print 'fdata:'
# print fdata.getSummary()
g, dm, cons = glm_nipy(fdata, contrasts=cons)
def setSimulationData(self, xmlFile, simu_file):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
sd = FMRISessionSimulationData(simulation_file=simu_file)
t.set_init_param('fmri_data',
FmriData.from_simu_ui(sessions_data=[sd]))
f = open(xmlFile, 'w')
sxml = xmlio.to_xml(t)
f.write(sxml)
f.close()
def setSimulationData(self, xmlFile, simu_file):
f = open(xmlFile, 'r')
xml = f.read()
t = xmlio.from_xml(xml)
sd = FMRISessionSimulationData(simulation_file=simu_file)
t.set_init_param(
'fmri_data', FmriData.from_simu_ui(sessions_data=[sd]))
f = open(xmlFile, 'w')
sxml = xmlio.to_xml(t)
f.write(sxml)
f.close()
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_parcellation(self):
p_size = 300
np.random.seed(125437)
parcellation, _ = parcellation_for_jde(FmriData.from_vol_ui(), p_size,
output_dir=self.tmp_dir)
ms = np.mean([(parcellation == i).sum()
for i in np.unique(parcellation) if i != 0])
size_tol = 50
if np.abs(ms - p_size) > size_tol:
raise Exception('Mean size of parcellation seems too '
'large: %1.2f >%d+-%d ' % (ms, p_size, size_tol))
if 0:
print parcellation_report(parcellation)
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 setSimulationData(self, xmlFile, simu_file):
f = open(xmlFile, "r")
xml = f.read()
t = xmlio.fromXML(xml)
sd = FMRISessionSimulationData(simulation_file=simu_file)
t.set_init_param("fmri_data", FmriData.from_simu_ui(sessions_data=[sd]))
f = open(xmlFile, "w")
sxml = xmlio.toXML(t, handler=NumpyXMLHandler())
# print 'sxml:'
# print sxml
f.write(sxml)
f.close()
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 = {
'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.
"""
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.
"""
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 glm_nipy_from_files(bold_file, tr, paradigm_csv_file, output_dir,
mask_file, session=0, contrasts=None,
con_test_baseline=0.0,
hrf_model='Canonical',
drift_model='Cosine', hfcut=128,
residuals_model='spherical', fit_method='ols',
fir_delays=[0]):
"""
#TODO: handle surface data
hrf_model : Canonical | Canonical with Derivative | FIR
"""
fdata = FmriData.from_vol_files(mask_file, paradigm_csv_file,
[bold_file], tr)
g, dm, cons = glm_nipy(fdata, contrasts=contrasts, hrf_model=hrf_model,
hfcut=hfcut, drift_model=drift_model,
residuals_model=residuals_model,
fit_method=fit_method, fir_delays=fir_delays)
ns, nr = dm.matrix.shape
cdesign_matrix = xndarray(dm.matrix, axes_names=['time','regressor'],
axes_domains={'time':np.arange(ns)*tr,
'regressor':dm.names})
cdesign_matrix.save(op.join(output_dir, 'design_matrix.nii'))
beta_files = []
beta_values = dict.fromkeys(dm.names)
beta_vars = dict.fromkeys(dm.names)
beta_vars_voxels = dict.fromkeys(dm.names)
for ib, bname in enumerate(dm.names):
#beta values
beta_vol = expand_array_in_mask(g.beta[ib], fdata.roiMask>0)
beta_fn = op.join(output_dir, 'beta_%s.nii' %bname)
write_volume(beta_vol, beta_fn, fdata.meta_obj)
beta_files.append(beta_fn)
beta_values[bname] = beta_vol
#normalized variance of betas
beta_vars[bname] = sp.diag(g.nvbeta)[ib] #variance: diag of cov matrix
#sig2 = g.s2 #ResMS
var_cond = sp.diag(g.nvbeta)[ib]*g.s2 #variance for all voxels, condition ib
beta_vars_voxels[bname] = var_cond
#beta_var_fn = op.join(output_dir, 'var_beta_%s.nii' %bname)
#write_volume(beta_var, beta_var_fn, fdata.meta_obj)
#beta_var_files.append(beta_var_fn)
if cons is not None:
con_files = []
pval_files = []
for cname, con in cons.iteritems():
con_vol = expand_array_in_mask(con.effect, fdata.roiMask>0)
con_fn = op.join(output_dir, 'con_effect_%s.nii' %cname)
write_volume(con_vol, con_fn, fdata.meta_obj)
con_files.append(con_fn)
pval_vol = expand_array_in_mask(con.pvalue(con_test_baseline),
fdata.roiMask>0)
pval_fn = op.join(output_dir, 'con_pvalue_%s.nii' %cname)
write_volume(pval_vol, pval_fn, fdata.meta_obj)
pval_files.append(pval_fn)
else:
con_files = None
pval_files = None
dof = g.dof
#if do_ppm:
#for
#TODO: FIR stuffs
return beta_files, beta_values, beta_vars_voxels, dof#, con_files, pval_files
def glm_nipy_from_files(bold_file,
tr,
paradigm_csv_file,
output_dir,
mask_file,
session=0,
contrasts=None,
con_test_baseline=0.0,
hrf_model='Canonical',
drift_model='Cosine',
hfcut=128,
residuals_model='spherical',
fit_method='ols',
fir_delays=[0]):
"""
#TODO: handle surface data
hrf_model : Canonical | Canonical with Derivative | FIR
"""
fdata = FmriData.from_vol_files(mask_file, paradigm_csv_file, [bold_file],
tr)
g, dm, cons = glm_nipy(fdata,
contrasts=contrasts,
hrf_model=hrf_model,
hfcut=hfcut,
drift_model=drift_model,
residuals_model=residuals_model,
fit_method=fit_method,
fir_delays=fir_delays)
ns, nr = dm.matrix.shape
cdesign_matrix = xndarray(dm.matrix,
axes_names=['time', 'regressor'],
axes_domains={
'time': np.arange(ns) * tr,
'regressor': dm.names
})
cdesign_matrix.save(op.join(output_dir, 'design_matrix.nii'))
beta_files = []
beta_values = dict.fromkeys(dm.names)
beta_vars = dict.fromkeys(dm.names)
beta_vars_voxels = dict.fromkeys(dm.names)
for ib, bname in enumerate(dm.names):
# beta values
beta_vol = expand_array_in_mask(g.beta[ib], fdata.roiMask > 0)
beta_fn = op.join(output_dir, 'beta_%s.nii' % bname)
write_volume(beta_vol, beta_fn, fdata.meta_obj)
beta_files.append(beta_fn)
beta_values[bname] = beta_vol
# normalized variance of betas
# variance: diag of cov matrix
beta_vars[bname] = sp.diag(g.nvbeta)[ib]
# sig2 = g.s2 #ResMS
# variance for all voxels, condition ib
var_cond = sp.diag(g.nvbeta)[ib] * g.s2
beta_vars_voxels[bname] = var_cond
#beta_var_fn = op.join(output_dir, 'var_beta_%s.nii' %bname)
#write_volume(beta_var, beta_var_fn, fdata.meta_obj)
# beta_var_files.append(beta_var_fn)
if cons is not None:
con_files = []
pval_files = []
for cname, con in cons.iteritems():
con_vol = expand_array_in_mask(con.effect, fdata.roiMask > 0)
con_fn = op.join(output_dir, 'con_effect_%s.nii' % cname)
write_volume(con_vol, con_fn, fdata.meta_obj)
con_files.append(con_fn)
pval_vol = expand_array_in_mask(con.pvalue(con_test_baseline),
fdata.roiMask > 0)
pval_fn = op.join(output_dir, 'con_pvalue_%s.nii' % cname)
write_volume(pval_vol, pval_fn, fdata.meta_obj)
pval_files.append(pval_fn)
else:
con_files = None
pval_files = None
dof = g.dof
# if do_ppm:
# for
# TODO: FIR stuffs
# , con_files, pval_files
return beta_files, beta_values, beta_vars_voxels, dof
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
}
