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_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_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_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_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_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_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_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_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 __init__(self,
fmri_data=FmriData.from_vol_ui(),
analyser=JDEMCMCAnalyser(),
output_dir='./',
make_outputs=True,
result_dump_file=DEFAULT_DUMP_FILE):
xmlio.XmlInitable.__init__(self)
self.analyser = analyser
self.output_dir = output_dir
if result_dump_file is None:
if self.output_dir is not None and DEFAULT_DUMP_FILE is not None:
self.result_dump_file = op.join(self.output_dir,
DEFAULT_DUMP_FILE)
else:
self.result_dump_file = None
else:
self.result_dump_file = op.join(self.output_dir, result_dump_file)
self.data = fmri_data
self.make_outputs = make_outputs
def create_treatment_surf(boldFiles,
parcelFile,
meshFile,
dt,
tr,
paradigmFile,
nbIterations=4000,
writeXmlSetup=True,
parallelize=False,
outputDir=None,
outputSuffix=None,
outputPrefix=None,
contrasts=';',
beta=.6,
estimBeta=True,
pfMethod='ps',
estimHrf=True,
hrfVar=.01,
roiIds=None,
nbClasses=2,
gzip_rdump=False,
simulation_file=None,
make_outputs=True):
if roiIds is None:
roiIds = np.array([], dtype=int)
outDump = make_outfile(DEFAULT_DUMP_FILE, outputDir, outputPrefix,
outputSuffix)
if gzip_rdump:
outDump += '.gz'
if contrasts is not None:
cons = dict(
("con_%d" % i, ce) for i, ce in enumerate(";".split(contrasts)))
else:
cons = {}
if nbClasses == 2:
sampler = BG(
**{
'nb_iterations':
nbIterations,
# level of spatial correlation = beta
'beta':
BS(
**{
'val_ini': np.array([beta]),
'do_sampling': estimBeta,
'pf_method': pfMethod,
}),
# HRF
'hrf':
HS(**{
'do_sampling': estimHrf,
}),
# HRF variance
'hrf_var':
HVS(**{
'do_sampling': False,
'val_ini': np.array([hrfVar]),
}),
# neural response levels (stimulus-induced effects)
'response_levels':
NS(**{
'contrasts': cons,
}),
})
elif nbClasses == 3:
raise NotImplementedError('3 class model not maintained')
analyser = JDEMCMCAnalyser(sampler, dt=dt)
fmri_data = FmriData.from_surf_files(paradigmFile, boldFiles, tr, meshFile,
parcelFile)
if simulation_file is not None:
f_simu = open(simulation_file)
simulation = cPickle.load(f_simu)
f_simu.close()
fmri_data.simulation = simulation
tjde = FMRITreatment(fmri_data, analyser, outputDir)
# print 'make_outputs:', make_outputs
sxml = xmlio.to_xml(tjde)
if writeXmlSetup is not None and outputDir is not None:
outSetupXml = make_outfile(DEFAULT_CFG_FILE_JDE, outputDir,
outputPrefix, outputSuffix)
logger.info("Writing XML setup to: %s", outSetupXml)
f = open(outSetupXml, 'w')
f.write(sxml)
f.close()
else:
outSetupXml = None
return tjde, outSetupXml
def parse_data_options(options):
""" Return an FmriData object corresponding to input options """
from pyhrf.core import DEFAULT_BOLD_SURF_FILE, DEFAULT_BOLD_VOL_FILE, \
DEFAULT_SIMULATION_FILE
# If SPM.mat is provided, retrieve paradigm from it for all sessions.
# Leave data file pathes to unknown.
if options.spmFile is not None:
paradigm, tr = load_paradigm_from_mat(options.spmFile)
nb_sessions = len(paradigm)
if options.inputDataType == 'volume':
SessDataClass = FMRISessionVolumicData
if options.func_data_file is None:
data_fns = [DEFAULT_BOLD_VOL_FILE] * nb_sessions
else:
data_fns = options.func_data_file
if options.mask_file is None:
options.mask_file = DEFAULT_MASK_VOL_FILE
fmriDataInit = FmriData.from_vol_ui
elif options.inputDataType == 'surface':
SessDataClass = FMRISessionSurfacicData
if options.func_data_file is None:
data_fns = [DEFAULT_BOLD_SURF_FILE] * nb_sessions
else:
data_fns = options.func_data_file
if options.mask_file is None:
options.mask_file = DEFAULT_MASK_SURF_FILE
fmriDataInit = FmriData.from_surf_ui
elif options.inputDataType == 'simulation':
SessDataClass = FMRISessionSimulationData
data_fns = DEFAULT_SIMULATION_FILE
fmriDataInit = FmriData.from_simu_ui
sessions_data = []
if len(data_fns) != nb_sessions:
raise Exception('Inconsistent number of data files and sessions: '
'%d sessions in paradigm, %d data files' %
(nb_sessions, len(data_fns)))
# TODO: check nb of sessions and nb of data files
for isess, sess in enumerate(sorted(paradigm.keys())):
sessions_data.append(
SessDataClass(paradigm[sess]['onsets'],
paradigm[sess]['stimulusLength'],
data_fns[isess]))
if options.inputDataType == 'surface':
return fmriDataInit(sessions_data=sessions_data,
tr=tr,
mask_file=options.mask_file,
mesh_file=options.mesh_file)
return fmriDataInit(sessions_data=sessions_data,
tr=tr,
mask_file=options.mask_file)
# unstack & take 1st set of onsets for each condition to get only one
# session
onsets = unstack_trees(eval('pyhrf.paradigm.onsets_%s' %
options.paradigm))[0]
durations = unstack_trees(
eval('pyhrf.paradigm.durations_%s' % options.paradigm))[0]
if options.paradigm_csv is not None:
onsets, durations = load_paradigm_from_csv(options.paradigm_csv)
from pyhrf.tools import apply_to_leaves
onsets = apply_to_leaves(onsets, lambda x: x[0])
durations = apply_to_leaves(durations, lambda x: x[0])
# Set data type:
if options.inputDataType == 'volume':
if options.data_scenario == 'default':
SessDataClass = FMRISessionVolumicData
sd = SessDataClass(onsets, durations)
if options.mask_file is None:
options.mask_file = DEFAULT_MASK_VOL_FILE
elif options.data_scenario == 'small':
sd = SessDataClass(onsets, durations)
if options.mask_file is None:
options.mask_file = DEFAULT_MASK_SMALL_VOL_FILE
elif options.data_scenario == 'realistic':
sd = FMRISessionVolumicData(onsets, durations,
REALISTIC_REAL_DATA_BOLD_VOL_FILE)
if options.mask_file is None:
options.mask_file = REALISTIC_REAL_DATA_MASK_VOL_FILE
else:
raise Exception("Uknown data scenario: %s" % options.data_scenario)
if options.func_data_file is not None:
sessions_data = []
sessions_data.append(
SessDataClass(onsets, durations, options.func_data_file))
else:
sessions_data = [sd]
if hasattr(options, 'tr') and options.tr is not None:
tr = options.tr
res = FmriData.from_vol_ui(sessions_data=sessions_data,
tr=tr,
mask_file=options.mask_file)
else:
res = FmriData.from_vol_ui(sessions_data=sessions_data,
mask_file=options.mask_file)
return res
elif options.inputDataType == 'surface':
mask_fn = DEFAULT_MASK_SURF_FILE
mesh_fn = DEFAULT_MESH_FILE
if options.data_scenario == 'default':
# TODO: create a bigger surface default dataset
sd = FMRISessionSurfacicData(onsets, durations)
if options.data_scenario == 'small':
sd = FMRISessionSurfacicData(onsets, durations)
elif options.data_scenario == 'realistic':
raise NotImplementedError('Realistic surfacic dataset not yet '
'available (TODO)')
return FmriData.from_surf_ui(sessions_data=[sd],
mask_file=mask_fn,
mesh_file=mesh_fn)
elif options.inputDataType == 'simulation':
if options.data_scenario == 'default':
sd = FMRISessionSimulationData(onsets, durations)
elif options.data_scenario == 'small':
raise NotImplementedError('Small artificial dataset not yet '
'available (TODO)')
if options.data_scenario == 'realistic':
raise NotImplementedError('Realistic artificial dataset not yet '
'available (TODO)')
return FmriData.from_simu_ui(sessions_data=[sd])
def test_from_vol_ui_default(self):
fmri_data = FmriData.from_vol_ui()
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 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 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 main():
pyhrf.verbose.set_verbosity(1)
output_dir = './'
bold_file = get_data_file_name('real_data_vol_4_regions_BOLD.nii.gz')
mask_file = get_data_file_name('real_data_vol_4_regions_mask.nii.gz')
paradigm_file = get_data_file_name('paradigm_loc.csv')
contrasts = pyhrf.paradigm.default_contrasts_loc
experiments = [ ('audio', 'audio-video',
{'axial':17, 'coronal':42, 'sagittal':9}, 1,
'temporal', -1., 1.05),
('calculaudio', 'computation-sentences',
{'axial':30, 'coronal':25, 'sagittal':53}, 11,
'parietal', -1.55, 1.05),
]
parcellation_dir = get_dir(output_dir, 'parcellation')
# Load data
fdata = FmriData.from_vol_files(mask_file=mask_file,
bold_files=[bold_file],
paradigm_csv_file=paradigm_file)
glm_hcano_output_dir = get_dir(output_dir, 'glm_cano')
if 1:
# GLM with canonical HRF
print 'GLM with canonical HRF'
glm_analyse(fdata, contrasts, output_dir=glm_hcano_output_dir,
output_prefix='glm_hcano_')
if 1:
# GLM with basis set
print 'GLM with basis set'
glm_hderiv_output_dir = get_dir(output_dir, 'glm_hderivs')
glm_analyse(fdata, contrasts, hrf_model="Canonical with Derivative",
output_dir=glm_hderiv_output_dir,
output_prefix='glm_hderivs_')
parcellation_file = op.join(parcellation_dir, 'parcellation_func.nii.gz')
if 1:
# parcellation from results of GLM basis set
mask_file = op.join(glm_hderiv_output_dir, 'glm_hderivs_mask.nii.gz')
beta_files = [op.join(glm_hderiv_output_dir,'glm_hderivs_beta_%s.nii.gz'%c)\
for c in fdata.get_condition_names()]
make_parcellation_from_files(beta_files, mask_file, parcellation_file,
nparcels=20, method='ward_and_gkm')
jde_output_dir = get_dir(output_dir, 'jde')
if 1:
# JDE
print 'JDE'
fdata_parc = FmriData.from_vol_files(mask_file=parcellation_file,
bold_files=[bold_file],
paradigm_csv_file=paradigm_file)
jde_analyse(fdata_parc, contrasts, jde_output_dir)
glm_hcano_rs_output_dir = get_dir(output_dir,'glm_hcano_rescaled_on_jde')
if 1:
# GLM hcano rescaled onto JDE (provide the same results as normal
# GLM hcano but with effects resized so that there is a consistency btw
# X^m.h in JDE and the corresponding column of the design matrix in GLM
print 'GLM rescaled'
rescale_factor_file = op.join(jde_output_dir, 'scale_factor_for_glm.nii.gz')
compute_jde_glm_rescaling(jde_output_dir, glm_hcano_output_dir,
rescale_factor_file)
glm_analyse(fdata, contrasts, output_dir=glm_hcano_rs_output_dir,
output_prefix='glm_hcano_rs_',
rescale_factor_file=rescale_factor_file)
## Outputs
for condition_of_interest, contrast_of_interest, point_of_interest, \
parcel_of_interest, plot_label, ymin, ymax in experiments:
if 'temporal' in plot_label:
paradigm_tag = 'loc_av'
else:
paradigm_tag = 'loc'
paradigm_file = get_data_file_name('paradigm_%s.csv' %paradigm_tag)
fir_mask = op.join(parcellation_dir, 'mask_single_voxel_for_fir_%s.nii.gz'\
%paradigm_tag)
make_mask_from_points([point_of_interest], mask_file, fir_mask)
fdata_fir = FmriData.from_vol_files(mask_file=fir_mask,
bold_files=[bold_file],
paradigm_csv_file=paradigm_file)
glm_fir_output_dir = get_dir(output_dir, 'glm_fir_%s' %paradigm_tag)
if 1:
# GLM FIR
print 'GLM FIR'
glm_analyse(fdata_fir, contrasts={}, hrf_model="FIR",
output_dir=glm_fir_output_dir, output_prefix='glm_fir_',
fir_delays=range(11))
rfir_output_dir = get_dir(output_dir, 'rfir_%s' %paradigm_tag)
if 1:
# Regularized FIR
print 'RFIR'
rfir_analyse(fdata_fir, output_dir=rfir_output_dir)
fig_dir = get_dir(output_dir, 'figs')
plot_detection_results(fig_dir, point_of_interest, condition_of_interest,
contrast_of_interest, parcellation_file,
plot_label, jde_output_dir,
glm_hcano_rs_output_dir)
plot_estimation_results(fig_dir, point_of_interest, parcel_of_interest,
condition_of_interest, plot_label,
glm_fir_output_dir, rfir_output_dir,
jde_output_dir, ymin, ymax)