def setUp(self):
self.tmp_dir = pyhrf.get_tmp_path()
simu = simulate_sessions(output_dir=self.tmp_dir,
snr_scenario='high_snr',
spatial_size='random_small')
self.data_simu = merge_fmri_sessions(simu)
def test_multisession_simu(self):
fd1 = FmriData.from_simu_ui()
fd2 = FmriData.from_simu_ui()
fd_msession = merge_fmri_sessions([fd1, fd2])
self.assertEqual(fd_msession.nbSessions, 2)
def setUp(self):
#pyhrf.verbose.set_verbosity(2)
np.random.seed(8652761)
# tmpDir = tempfile.mkdtemp(prefix='pyhrf_tests',
# dir=pyhrf.cfg['global']['tmp_path'])
self.tmp_outputs = True #save outputs in tmp dir
#if False then save in current dir
if not self.tmp_outputs:
self.tmp_dir_small = './JDE_MS_test_small_simu'
if not op.exists(self.tmp_dir_small): os.makedirs(self.tmp_dir_small)
self.tmp_dir_big = './JDE_MS_test_big_simu'
if not op.exists(self.tmp_dir_big): os.makedirs(self.tmp_dir_big)
else:
self.tmp_dir_small = pyhrf.get_tmp_path()
self.tmp_dir_big = pyhrf.get_tmp_path()
simu = simulate_sessions(output_dir = self.tmp_dir_small,
snr_scenario='high_snr', spatial_size='tiny')
self.data_small_simu = merge_fmri_sessions(simu)
simu = simulate_sessions(output_dir=self.tmp_dir_big,
snr_scenario='low_snr', spatial_size='normal')
self.data_simu = merge_fmri_sessions(simu)
# Parameters for multi-sessions sampler
dict_beta_single = {
BetaSampler.P_VAL_INI : np.array([0.5]),
BetaSampler.P_SAMPLE_FLAG : False,
BetaSampler.P_PARTITION_FUNCTION_METH : 'es',
BetaSampler.P_USE_TRUE_VALUE : False,
}
dict_hrf_single = {
HRF_MultiSess_Sampler.P_SAMPLE_FLAG : False,
HRF_MultiSess_Sampler.P_NORMALISE : 1., # normalise samples
HRF_MultiSess_Sampler.P_USE_TRUE_VALUE : True,
HRF_MultiSess_Sampler.P_ZERO_CONSTR : True,
#HRF_MultiSess_Sampler.P_PRIOR_TYPE : 'singleHRF',
}
dict_var_hrf_single = {
RHSampler.P_SAMPLE_FLAG : False,
RHSampler.P_VAL_INI : np.array([0.001]),
}
dict_nrl_sess_single = {
NRL_Multi_Sess_Sampler.P_SAMPLE_FLAG : False,
NRL_Multi_Sess_Sampler.P_USE_TRUE_VALUE : True,
}
dict_nrl_sess_var_single = {
Variance_GaussianNRL_Multi_Sess.P_SAMPLE_FLAG : False,
Variance_GaussianNRL_Multi_Sess.P_USE_TRUE_VALUE : True,
}
dict_nrl_bar_single = {
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG : False,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_NRLS : True,
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_LABELS : False,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_LABELS : True,
}
dict_drift_single = {
Drift_MultiSess_Sampler.P_SAMPLE_FLAG : False,
Drift_MultiSess_Sampler.P_USE_TRUE_VALUE : True,
}
dict_drift_var_single = {
ETASampler_MultiSess.P_SAMPLE_FLAG : False,
ETASampler_MultiSess.P_USE_TRUE_VALUE : True,
}
dict_noise_var_single = {
NoiseVariance_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG : False,
NoiseVariance_Drift_Multi_Sess_Sampler.P_USE_TRUE_VALUE : True,
}
dict_mixt_param_single = {
BiGaussMixtureParamsSampler.P_SAMPLE_FLAG : False,
BiGaussMixtureParamsSampler.P_USE_TRUE_VALUE : True,
BiGaussMixtureParamsSampler.P_HYPER_PRIOR : 'Jeffrey',
}
self.sampler_params_for_single_test = {
BMSS.P_NB_ITERATIONS : 100,
BMSS.P_SMPL_HIST_PACE : -1,
BMSS.P_OBS_HIST_PACE : -1,
# level of spatial correlation = beta
BMSS.P_BETA : BetaSampler(dict_beta_single),
# HRF
BMSS.P_HRF : HRF_MultiSess_Sampler(dict_hrf_single),
# HRF variance
BMSS.P_RH : RHSampler(dict_var_hrf_single),
# neural response levels (stimulus-induced effects) by session
BMSS.P_NRLS_SESS : NRL_Multi_Sess_Sampler(dict_nrl_sess_single),
# neural response levels by session --> variance
BMSS.P_NRLS_SESS_VAR : Variance_GaussianNRL_Multi_Sess(dict_nrl_sess_var_single),
# neural response levels mean: over sessions
BMSS.P_NRLS_BAR : NRLsBar_Drift_Multi_Sess_Sampler(dict_nrl_bar_single),
# drift
BMSS.P_DRIFT : Drift_MultiSess_Sampler(dict_drift_single),
#drift variance
BMSS.P_ETA : ETASampler_MultiSess(dict_drift_var_single),
#noise variance
BMSS.P_NOISE_VAR_SESS : NoiseVariance_Drift_Multi_Sess_Sampler(dict_noise_var_single),
#weights o fthe mixture
#parameters of the mixture
BMSS.P_MIXT_PARAM_NRLS_BAR : BiGaussMixtureParamsSampler(dict_mixt_param_single),
BMSS.P_CHECK_FINAL_VALUE : 'raise', #print or raise
}
# Parameters for multi-sessions sampler - full test
dict_beta_full = {
BetaSampler.P_VAL_INI : np.array([0.5]),
BetaSampler.P_SAMPLE_FLAG : True,
BetaSampler.P_PARTITION_FUNCTION_METH : 'es',
}
dict_hrf_full = {
HRF_MultiSess_Sampler.P_SAMPLE_FLAG : True,
HRF_MultiSess_Sampler.P_NORMALISE : 1., # normalise samples
HRF_MultiSess_Sampler.P_USE_TRUE_VALUE : False,
HRF_MultiSess_Sampler.P_ZERO_CONSTR : True,
#HRF_MultiSess_Sampler.P_PRIOR_TYPE : 'singleHRF',
}
dict_var_hrf_full = {
RHSampler.P_SAMPLE_FLAG : False,
RHSampler.P_VAL_INI : np.array([0.001]),
}
dict_nrl_sess_full = {
NRL_Multi_Sess_Sampler.P_SAMPLE_FLAG : True,
NRL_Multi_Sess_Sampler.P_USE_TRUE_VALUE : False,
}
dict_nrl_sess_var_full = {
Variance_GaussianNRL_Multi_Sess.P_SAMPLE_FLAG : True,
Variance_GaussianNRL_Multi_Sess.P_USE_TRUE_VALUE : False,
}
dict_nrl_bar_full = {
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG : True,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_NRLS : False,
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_LABELS : True,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_LABELS : False,
}
dict_drift_full = {
Drift_MultiSess_Sampler.P_SAMPLE_FLAG : True,
Drift_MultiSess_Sampler.P_USE_TRUE_VALUE : False,
}
dict_drift_var_full = {
ETASampler_MultiSess.P_SAMPLE_FLAG : True,
ETASampler_MultiSess.P_USE_TRUE_VALUE : False,
}
dict_noise_var_full = {
NoiseVariance_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG : True,
NoiseVariance_Drift_Multi_Sess_Sampler.P_USE_TRUE_VALUE : False,
}
dict_mixt_param_full = {
BiGaussMixtureParamsSampler.P_SAMPLE_FLAG : True,
BiGaussMixtureParamsSampler.P_USE_TRUE_VALUE : False,
BiGaussMixtureParamsSampler.P_HYPER_PRIOR : 'Jeffrey',
}
self.sampler_params_for_full_test = {
BMSS.P_NB_ITERATIONS : 400,
BMSS.P_SMPL_HIST_PACE : -1,
BMSS.P_OBS_HIST_PACE : -1,
# level of spatial correlation = beta
BMSS.P_BETA : BetaSampler(dict_beta_full),
# HRF
BMSS.P_HRF : HRF_MultiSess_Sampler(dict_hrf_full),
# HRF variance
BMSS.P_RH : RHSampler(dict_var_hrf_full),
# neural response levels (stimulus-induced effects) by session
BMSS.P_NRLS_SESS : NRL_Multi_Sess_Sampler(dict_nrl_sess_full),
# neural response levels by session --> variance
BMSS.P_NRLS_SESS_VAR : Variance_GaussianNRL_Multi_Sess(dict_nrl_sess_var_full),
# neural response levels mean: over sessions
BMSS.P_NRLS_BAR : NRLsBar_Drift_Multi_Sess_Sampler(dict_nrl_bar_full),
# drift
BMSS.P_DRIFT : Drift_MultiSess_Sampler(dict_drift_full),
#drift variance
BMSS.P_ETA : ETASampler_MultiSess(dict_drift_var_full),
#noise variance
BMSS.P_NOISE_VAR_SESS : NoiseVariance_Drift_Multi_Sess_Sampler(dict_noise_var_full),
#weights o fthe mixture
#parameters of the mixture
BMSS.P_MIXT_PARAM_NRLS_BAR : BiGaussMixtureParamsSampler(dict_mixt_param_full),
BMSS.P_CHECK_FINAL_VALUE : 'raise', #print or raise
}
def setUp(self):
self.tmp_dir = pyhrf.get_tmp_path()
simu = simulate_sessions(output_dir=self.tmp_dir,
snr_scenario='high_snr',
spatial_size='random_small')
self.data_simu = merge_fmri_sessions(simu)
def test_multisession_simu(self):
fd1 = FmriData.from_simu_ui()
fd2 = FmriData.from_simu_ui()
fd_msession = merge_fmri_sessions([fd1, fd2])
self.assertEqual(fd_msession.nbSessions, 2)
def setUp(self):
np.random.seed(8652761)
self.tmp_outputs = True #save outputs in tmp dir
#if False then save in current dir
if not self.tmp_outputs:
self.tmp_dir_small = './JDE_MS_test_small_simu'
if not op.exists(self.tmp_dir_small):
os.makedirs(self.tmp_dir_small)
self.tmp_dir_big = './JDE_MS_test_big_simu'
if not op.exists(self.tmp_dir_big): os.makedirs(self.tmp_dir_big)
else:
self.tmp_dir_small = pyhrf.get_tmp_path()
self.tmp_dir_big = pyhrf.get_tmp_path()
simu = simulate_sessions(output_dir=self.tmp_dir_small,
snr_scenario='high_snr',
spatial_size='tiny')
self.data_small_simu = merge_fmri_sessions(simu)
simu = simulate_sessions(output_dir=self.tmp_dir_big,
snr_scenario='low_snr',
spatial_size='normal')
self.data_simu = merge_fmri_sessions(simu)
# Parameters for multi-sessions sampler
dict_beta_single = {
BetaSampler.P_VAL_INI: np.array([0.5]),
BetaSampler.P_SAMPLE_FLAG: False,
BetaSampler.P_PARTITION_FUNCTION_METH: 'es',
BetaSampler.P_USE_TRUE_VALUE: False,
}
dict_hrf_single = {
HRF_MultiSess_Sampler.P_SAMPLE_FLAG: False,
HRF_MultiSess_Sampler.P_NORMALISE: 1., # normalise samples
HRF_MultiSess_Sampler.P_USE_TRUE_VALUE: True,
HRF_MultiSess_Sampler.P_ZERO_CONSTR: True,
#HRF_MultiSess_Sampler.P_PRIOR_TYPE : 'singleHRF',
}
dict_var_hrf_single = {
RHSampler.P_SAMPLE_FLAG: False,
RHSampler.P_VAL_INI: np.array([0.001]),
}
dict_nrl_sess_single = {
NRL_Multi_Sess_Sampler.P_SAMPLE_FLAG: False,
NRL_Multi_Sess_Sampler.P_USE_TRUE_VALUE: True,
}
dict_nrl_sess_var_single = {
Variance_GaussianNRL_Multi_Sess.P_SAMPLE_FLAG: False,
Variance_GaussianNRL_Multi_Sess.P_USE_TRUE_VALUE: True,
}
dict_nrl_bar_single = {
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG: False,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_NRLS: True,
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_LABELS: False,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_LABELS: True,
}
dict_drift_single = {
Drift_MultiSess_Sampler.P_SAMPLE_FLAG: False,
Drift_MultiSess_Sampler.P_USE_TRUE_VALUE: True,
}
dict_drift_var_single = {
ETASampler_MultiSess.P_SAMPLE_FLAG: False,
ETASampler_MultiSess.P_USE_TRUE_VALUE: True,
}
dict_noise_var_single = {
NoiseVariance_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG: False,
NoiseVariance_Drift_Multi_Sess_Sampler.P_USE_TRUE_VALUE: True,
}
dict_mixt_param_single = {
BiGaussMixtureParamsSampler.P_SAMPLE_FLAG: False,
BiGaussMixtureParamsSampler.P_USE_TRUE_VALUE: True,
BiGaussMixtureParamsSampler.P_HYPER_PRIOR: 'Jeffrey',
}
self.sampler_params_for_single_test = {
BMSS.P_NB_ITERATIONS:
100,
BMSS.P_SMPL_HIST_PACE:
-1,
BMSS.P_OBS_HIST_PACE:
-1,
# level of spatial correlation = beta
BMSS.P_BETA:
BetaSampler(dict_beta_single),
# HRF
BMSS.P_HRF:
HRF_MultiSess_Sampler(dict_hrf_single),
# HRF variance
BMSS.P_RH:
RHSampler(dict_var_hrf_single),
# neural response levels (stimulus-induced effects) by session
BMSS.P_NRLS_SESS:
NRL_Multi_Sess_Sampler(dict_nrl_sess_single),
# neural response levels by session --> variance
BMSS.P_NRLS_SESS_VAR:
Variance_GaussianNRL_Multi_Sess(dict_nrl_sess_var_single),
# neural response levels mean: over sessions
BMSS.P_NRLS_BAR:
NRLsBar_Drift_Multi_Sess_Sampler(dict_nrl_bar_single),
# drift
BMSS.P_DRIFT:
Drift_MultiSess_Sampler(dict_drift_single),
#drift variance
BMSS.P_ETA:
ETASampler_MultiSess(dict_drift_var_single),
#noise variance
BMSS.P_NOISE_VAR_SESS:
NoiseVariance_Drift_Multi_Sess_Sampler(dict_noise_var_single),
#weights o fthe mixture
#parameters of the mixture
BMSS.P_MIXT_PARAM_NRLS_BAR:
BiGaussMixtureParamsSampler(dict_mixt_param_single),
BMSS.P_CHECK_FINAL_VALUE:
'raise', #print or raise
}
# Parameters for multi-sessions sampler - full test
dict_beta_full = {
BetaSampler.P_VAL_INI: np.array([0.5]),
BetaSampler.P_SAMPLE_FLAG: True,
BetaSampler.P_PARTITION_FUNCTION_METH: 'es',
}
dict_hrf_full = {
HRF_MultiSess_Sampler.P_SAMPLE_FLAG: True,
HRF_MultiSess_Sampler.P_NORMALISE: 1., # normalise samples
HRF_MultiSess_Sampler.P_USE_TRUE_VALUE: False,
HRF_MultiSess_Sampler.P_ZERO_CONSTR: True,
#HRF_MultiSess_Sampler.P_PRIOR_TYPE : 'singleHRF',
}
dict_var_hrf_full = {
RHSampler.P_SAMPLE_FLAG: False,
RHSampler.P_VAL_INI: np.array([0.001]),
}
dict_nrl_sess_full = {
NRL_Multi_Sess_Sampler.P_SAMPLE_FLAG: True,
NRL_Multi_Sess_Sampler.P_USE_TRUE_VALUE: False,
}
dict_nrl_sess_var_full = {
Variance_GaussianNRL_Multi_Sess.P_SAMPLE_FLAG: True,
Variance_GaussianNRL_Multi_Sess.P_USE_TRUE_VALUE: False,
}
dict_nrl_bar_full = {
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG: True,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_NRLS: False,
NRLsBar_Drift_Multi_Sess_Sampler.P_SAMPLE_LABELS: True,
NRLsBar_Drift_Multi_Sess_Sampler.P_USE_TRUE_LABELS: False,
}
dict_drift_full = {
Drift_MultiSess_Sampler.P_SAMPLE_FLAG: True,
Drift_MultiSess_Sampler.P_USE_TRUE_VALUE: False,
}
dict_drift_var_full = {
ETASampler_MultiSess.P_SAMPLE_FLAG: True,
ETASampler_MultiSess.P_USE_TRUE_VALUE: False,
}
dict_noise_var_full = {
NoiseVariance_Drift_Multi_Sess_Sampler.P_SAMPLE_FLAG: True,
NoiseVariance_Drift_Multi_Sess_Sampler.P_USE_TRUE_VALUE: False,
}
dict_mixt_param_full = {
BiGaussMixtureParamsSampler.P_SAMPLE_FLAG: True,
BiGaussMixtureParamsSampler.P_USE_TRUE_VALUE: False,
BiGaussMixtureParamsSampler.P_HYPER_PRIOR: 'Jeffrey',
}
self.sampler_params_for_full_test = {
BMSS.P_NB_ITERATIONS:
400,
BMSS.P_SMPL_HIST_PACE:
-1,
BMSS.P_OBS_HIST_PACE:
-1,
# level of spatial correlation = beta
BMSS.P_BETA:
BetaSampler(dict_beta_full),
# HRF
BMSS.P_HRF:
HRF_MultiSess_Sampler(dict_hrf_full),
# HRF variance
BMSS.P_RH:
RHSampler(dict_var_hrf_full),
# neural response levels (stimulus-induced effects) by session
BMSS.P_NRLS_SESS:
NRL_Multi_Sess_Sampler(dict_nrl_sess_full),
# neural response levels by session --> variance
BMSS.P_NRLS_SESS_VAR:
Variance_GaussianNRL_Multi_Sess(dict_nrl_sess_var_full),
# neural response levels mean: over sessions
BMSS.P_NRLS_BAR:
NRLsBar_Drift_Multi_Sess_Sampler(dict_nrl_bar_full),
# drift
BMSS.P_DRIFT:
Drift_MultiSess_Sampler(dict_drift_full),
#drift variance
BMSS.P_ETA:
ETASampler_MultiSess(dict_drift_var_full),
#noise variance
BMSS.P_NOISE_VAR_SESS:
NoiseVariance_Drift_Multi_Sess_Sampler(dict_noise_var_full),
#weights o fthe mixture
#parameters of the mixture
BMSS.P_MIXT_PARAM_NRLS_BAR:
BiGaussMixtureParamsSampler(dict_mixt_param_full),
BMSS.P_CHECK_FINAL_VALUE:
'raise', #print or raise
}
