def test_noise_var(self):
""" Validate estimation of noise variances at high SNR"""
pyhrf.verbose.set_verbosity(2)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['noise_var'], fdata, nb_its=100,
check_fv='raise')
def test_brls(self):
""" Validate estimation of BRLs at high SNR"""
pyhrf.verbose.set_verbosity(2)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['bold_response_levels'], fdata, nb_its=100,
check_fv='raise')
def test_prf(self):
""" Validate estimation of PRF """
pyhrf.verbose.set_verbosity(2)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['prf'], fdata, nb_its=100,
check_fv='raise')
def test_prf(self):
""" Validate estimation of PRF """
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['prf'], fdata, simu, nItMax=20,
estimateG=True)
def test_prls(self):
""" Validate estimation of PRLs at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['perf_response_levels'], fdata, simu,
nItMax=100, estimateC=True)
def test_brf_physio_reg(self):
""" Validate estimation of BRF at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['brf'], fdata, nb_its=100,
check_fv='raise')
def test_brf_physio_reg(self):
""" Validate estimation of BRF at high SNR"""
pyhrf.verbose.set_verbosity(2)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['brf'], fdata, nb_its=100,
check_fv='raise')
print 'pyhrf_view_qt3 %s/*brf*nii' %self.tmp_dir
def test_perf_baseline(self):
""" Validate estimation of drift at high SNR"""
pyhrf.verbose.set_verbosity(2)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['perf_baseline'], fdata, nb_its=100,
check_fv='raise')
print 'pyhrf_view_qt3 %s/*perf*nii' %self.tmp_dir
def test_prls(self):
""" Validate estimation of PRLs at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['perf_response_levels'], fdata, simu,
nItMax=100, estimateC=True)
def test_prf(self):
""" Validate estimation of PRF """
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['prf'], fdata, simu, nItMax=20,
estimateG=True)
def test_prf_var(self):
""" Validate estimation of PRF """
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['prf_var'], fdata, nb_its=20,
check_fv='raise')
def test_perf_baseline(self):
""" Validate estimation of drift at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['perf_baseline'], fdata, nb_its=100,
check_fv='raise')
def test_sigmaG(self):
""" Validate estimation of drift at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['sigma_G'], fdata, simu,
nItMax=100, estimateSigmaG=True)
print 'pyhrf_view %s/*mixt_params*perf*nii' % self.tmp_dir
def test_brf_physio_nonreg(self):
""" Validate estimation of BRF at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['brf'], fdata, nb_its=100,
check_fv='raise',
rf_prior_type='physio_stochastic_not_regularized')
def test_noise_var(self):
""" Validate estimation of noise variances at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['noise_var'], fdata, simu, nItMax=100,
estimateNoise=True)
print 'pyhrf_view %s/*noise*nii' % self.tmp_dir
def test_sigmaG(self):
""" Validate estimation of drift at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['sigma_G'], fdata, simu,
nItMax=100, estimateSigmaG=True)
print 'pyhrf_view %s/*mixt_params*perf*nii' % self.tmp_dir
def test_noise_var(self):
""" Validate estimation of noise variances at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_parameters(['noise_var'], fdata, simu, nItMax=100,
estimateNoise=True)
print 'pyhrf_view %s/*noise*nii' % self.tmp_dir
def test_prf_physio_nonreg(self):
""" Validate estimation of BRF at high SNR"""
pyhrf.verbose.set_verbosity(2)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
print simu['prf'].shape
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['prf'], fdata, nb_its=100,
check_fv='raise',
rf_prior_type='physio_stochastic_not_regularized')
print 'pyhrf_view_qt3 %s/*prf*nii' %self.tmp_dir
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_prf_physio_det(self):
""" Validate estimation of BRF at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
print simu['prf'].shape
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['prf'], fdata, nb_its=100,
check_fv='raise',
rf_prior_type='physio_deterministic')
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_all(self):
""" Validate estimation of full ASL model at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
np.random.seed(25430)
v = ['bold_response_levels', 'perf_response_levels', 'drift', 'drift_var',
'brf', 'brf_var', 'prf', 'labels', 'bold_mixt_params',
'perf_mixt_params', 'perf_baseline', 'perf_baseline_var']
self._test_specific_samplers(v, fdata, nb_its=500, check_fv='print')
def test_perf_baseline_var(self):
""" Validate estimation of drift at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
perf_baseline = simu['perf_baseline']
perf_baseline_mean = simu['perf_baseline_mean']
print 'perf_baseline_mean = ', perf_baseline_mean
print 'perf_baseline_mean emp = ', np.mean(perf_baseline)
perf_baseline_var = simu['perf_baseline_var']
print 'perf_baseline_var = ', perf_baseline_var
print 'perf_baseline_var emp = ', np.var(perf_baseline)
fdata = FmriData.from_simulation_dict(simu)
self._test_specific_samplers(['perf_baseline_var'], fdata, nb_its=15,
check_fv='raise')
def test_perfusion(self):
""" Validate estimation of perfusion component at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
np.random.seed(25430)
v = ['perf_response_levels', 'prf']
mem.cache(self._test_specific_parameters)(v, fdata, simu,
nItMax=100,
estimateG=True,
estimateC=True,
estimateSigmaG=True)
print 'pyhrf_view %s/*nii' % self.tmp_dir
def test_perfusion(self):
""" Validate estimation of perfusion component at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
np.random.seed(25430)
v = ['perf_response_levels', 'prf']
mem.cache(self._test_specific_parameters)(v, fdata, simu,
nItMax=100,
estimateG=True,
estimateC=True,
estimateSigmaG=True)
print 'pyhrf_view %s/*nii' % self.tmp_dir
def test_all(self):
""" Validate estimation of full ASL model at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
np.random.seed(25430)
v = ['bold_response_levels', 'perf_response_levels',
'brf', 'brf_var', 'prf', 'labels', 'bold_mixt_params',
'perf_mixt_params', 'drift_perf_baseline']
self._test_specific_parameters(v, fdata, simu,
estimateSigmaH=False, nItMax=100,
nItMin=10, estimateBeta=True,
estimateSigmaG=True, PLOT=False,
constrained=True, fast=False,
estimateH=True, estimateG=True,
estimateA=True, estimateC=True,
estimateZ=True, estimateLA=True,
estimateMP=True)
print 'pyhrf_view %s/*nii' % self.tmp_dir
def test_all(self):
""" Validate estimation of full ASL model at high SNR"""
# pyhrf.verbose.set_verbosity(2)
pyhrf.logger.setLevel(logging.INFO)
from pyhrf.jde.asl import simulate_asl
simu = simulate_asl(self.tmp_dir, spatial_size='normal')
fdata = FmriData.from_simulation_dict(simu)
np.random.seed(25430)
v = ['bold_response_levels', 'perf_response_levels',
'brf', 'brf_var', 'prf', 'labels', 'bold_mixt_params',
'perf_mixt_params', 'drift_perf_baseline']
self._test_specific_parameters(v, fdata, simu,
estimateSigmaH=False, nItMax=100,
nItMin=10, estimateBeta=True,
estimateSigmaG=True, PLOT=False,
constrained=True, fast=False,
estimateH=True, estimateG=True,
estimateA=True, estimateC=True,
estimateZ=True, estimateLA=True,
estimateMP=True)
print 'pyhrf_view %s/*nii' % self.tmp_dir
def simulate_subjects(output_dir, snr_scenario='high_snr',
spatial_size='tiny', hrf_group=None, nb_subjects=15,
vhrf=0.1, vhrf_group=0.1):
'''
Simulate daata for multiple subjects (5 subjects by default)
'''
drift_coeff_var = 1.
drift_amplitude = 10.
lmap1, lmap2, lmap3 = 'random_small', 'random_small', 'random_small'
if snr_scenario == 'low_snr': #low snr
vars_noise = np.zeros(nb_subjects) + 1.5
conditions = [
Condition(name='audio', m_act=3., v_act=.3, v_inact=.3,
label_map=lmap1),
Condition(name='video', m_act=2.5, v_act=.3, v_inact=.3,
label_map=lmap2),
Condition(name='damier', m_act=2, v_act=.3, v_inact=.3,
label_map=lmap3),
]
else: #high snr
vars_noise = np.zeros(nb_subjects) + .2
conditions = [
Condition(name='audio', m_act=13., v_act=.2, v_inact=.1,
label_map=lmap1),
#Condition(name='video', m_act=11.5, v_act=.2, v_inact=.1,
#label_map=lmap2),
#Condition(name='damier', m_act=10, v_act=.2, v_inact=.1,
#label_map=lmap3),
]
vars_hrfs = np.zeros(nb_subjects) + vhrf
# Common variable across subjects:
labels_vol = sim.create_labels_vol(conditions)
labels = sim.flatten_labels_vol(labels_vol)
# use smooth multivariate gaussian prior:
if hrf_group is None:# simulate according to gaussian prior
var_hrf_group = 0.1
hrf_group = sim.create_gsmooth_hrf(dt=0.6, hrf_var=var_hrf_group,
normalize_hrf=False)
n = (hrf_group**2).sum()**.5
hrf_group /= n
var_hrf_group /= n**2
simu_subjects = []
simus = []
for isubj in xrange(nb_subjects):
if output_dir is not None:
out_dir = op.join(output_dir, 'subject_%d' %isubj)
if not op.exists(out_dir): os.makedirs(out_dir)
else:
out_dir = None
s = simulate_single_subject(out_dir, conditions, vars_hrfs[isubj],
labels, labels_vol, vars_noise[isubj],
drift_coeff_var,
drift_amplitude, hrf_group, dt=0.6, dsf=4,
var_hrf_group=vhrf_group)
if 0:
print 'simu subj %d:' %isubj
print 'vhs:', s['var_subject_hrf']
print 'hg:', s['hrf_group']
print 'vhg:', s['var_hrf_group']
simus.append(s)
simu_subjects.append(FmriData.from_simulation_dict(s))
simu_subjects = FmriGroupData(simu_subjects)
return simu_subjects
def simulate_subjects(output_dir,
snr_scenario='high_snr',
spatial_size='tiny',
hrf_group=None,
nb_subjects=15,
vhrf=0.1,
vhrf_group=0.1):
'''
Simulate daata for multiple subjects (5 subjects by default)
'''
drift_coeff_var = 1.
drift_amplitude = 10.
lmap1, lmap2, lmap3 = 'random_small', 'random_small', 'random_small'
if snr_scenario == 'low_snr': # low snr
vars_noise = np.zeros(nb_subjects) + 1.5
conditions = [
Condition(name='audio',
m_act=3.,
v_act=.3,
v_inact=.3,
label_map=lmap1),
Condition(name='video',
m_act=2.5,
v_act=.3,
v_inact=.3,
label_map=lmap2),
Condition(name='damier',
m_act=2,
v_act=.3,
v_inact=.3,
label_map=lmap3),
]
else: # high snr
vars_noise = np.zeros(nb_subjects) + .2
conditions = [
Condition(name='audio',
m_act=13.,
v_act=.2,
v_inact=.1,
label_map=lmap1),
# Condition(name='video', m_act=11.5, v_act=.2, v_inact=.1,
# label_map=lmap2),
# Condition(name='damier', m_act=10, v_act=.2, v_inact=.1,
# label_map=lmap3),
]
vars_hrfs = np.zeros(nb_subjects) + vhrf
# Common variable across subjects:
labels_vol = sim.create_labels_vol(conditions)
labels = sim.flatten_labels_vol(labels_vol)
# use smooth multivariate gaussian prior:
if hrf_group is None: # simulate according to gaussian prior
var_hrf_group = 0.1
hrf_group = sim.create_gsmooth_hrf(dt=0.6,
hrf_var=var_hrf_group,
normalize_hrf=False)
n = (hrf_group**2).sum()**.5
hrf_group /= n
var_hrf_group /= n**2
simu_subjects = []
simus = []
for isubj in xrange(nb_subjects):
if output_dir is not None:
out_dir = op.join(output_dir, 'subject_%d' % isubj)
if not op.exists(out_dir):
os.makedirs(out_dir)
else:
out_dir = None
s = simulate_single_subject(out_dir,
conditions,
vars_hrfs[isubj],
labels,
labels_vol,
vars_noise[isubj],
drift_coeff_var,
drift_amplitude,
hrf_group,
dt=0.6,
dsf=4,
var_hrf_group=vhrf_group)
if 0:
print 'simu subj %d:' % isubj
print 'vhs:', s['var_subject_hrf']
print 'hg:', s['hrf_group']
print 'vhg:', s['var_hrf_group']
simus.append(s)
simu_subjects.append(FmriData.from_simulation_dict(s))
simu_subjects = FmriGroupData(simu_subjects)
return simu_subjects
