def __init__(self, stim_onsets=unstack_trees(DEFAULT_ONSETS)[0],
stim_durations=unstack_trees(DEFAULT_STIM_DURATIONS)[0],
func_data_file=DEFAULT_BOLD):
Initable.__init__(self)
self.onsets = stim_onsets
self.durations = stim_durations
self.func_data_file = func_data_file
def to_nipy_paradigm(self):
p = self.join_sessions()
sorted_conds = sorted(p.stimOnsets.keys())
onsets = unstack_trees(p.stimOnsets)
durations = unstack_trees(p.stimDurations)
cond_ids = [
np.hstack([[c] * len(o) for c, o in sorted(sess_ons.items())])
for sess_ons in onsets
]
# print 'cond_ids:', cond_ids
onsets = [
np.hstack([sess_ons[c] for c in sorted_conds])
for sess_ons in onsets
]
durations = [
np.hstack([sess_dur[c] for c in sorted_conds])
for sess_dur in durations
]
# print 'onsets:'
# print onsets
# print 'durations'
# print durations
# print '[(dur>0.).any() for dur in durations]:'
# print [(dur>0.).any() for dur in durations]
if any([(dur > 0.).any() for dur in durations]):
# Block paradigm
if len(onsets) > 1:
dd = [('session%02d' % i, BlockParadigm(d[0], d[1], d[2]))
for i, d in enumerate(zip(cond_ids, onsets, durations))]
return dict(dd)
else:
return BlockParadigm(cond_ids[0],
onsets[0],
durations[0],
amplitude=None)
else:
if len(onsets) > 1:
dd = [('session%02d' % i, EventRelatedParadigm(d[0], d[1]))
for i, d in enumerate(zip(cond_ids, onsets))]
return dict(dd)
else:
return EventRelatedParadigm(cond_ids[0],
onsets[0],
amplitude=None)
def to_nipy_paradigm(self):
p = self.join_sessions()
sorted_conds = sorted(p.stimOnsets.keys())
onsets = unstack_trees(p.stimOnsets)
durations = unstack_trees(p.stimDurations)
cond_ids = [ np.hstack([[c]*len(o) \
for c,o in sorted(sess_ons.items())]) \
for sess_ons in onsets ]
#print 'cond_ids:', cond_ids
onsets = [ np.hstack([sess_ons[c] for c in sorted_conds]) \
for sess_ons in onsets ]
durations = [ np.hstack([sess_dur[c] for c in sorted_conds]) \
for sess_dur in durations ]
# print 'onsets:'
# print onsets
# print 'durations'
# print durations
# print '[(dur>0.).any() for dur in durations]:'
# print [(dur>0.).any() for dur in durations]
if any([(dur>0.).any() for dur in durations]):
# Block paradigm
if len(onsets) > 1:
dd = [('session%02d'%i, BlockParadigm(d[0], d[1], d[2])) \
for i,d in enumerate(zip(cond_ids, onsets, durations))]
return dict(dd)
else:
return BlockParadigm(cond_ids[0], onsets[0], durations[0],
amplitude=None)
else:
if len(onsets) > 1:
dd = [('session%02d'%i, EventRelatedParadigm(d[0], d[1])) \
for i,d in enumerate(zip(cond_ids,onsets))]
return dict(dd)
else:
return EventRelatedParadigm(cond_ids[0], onsets[0],
amplitude=None)
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)))
# print 'paradigm:', paradigm.keys()
# print 'data_fns:', len(data_fns)
# print data_fns
#TODO: check nb of sessions and nb of data files
for isess, sess in enumerate(sorted(paradigm.keys())):
#print len(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]
#print options.paradigm
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)
#print 'sd!!:', sd
#print 'mask:', options.mask_file
if options.func_data_file is not None:
#print 'options.func_data_file:', options.func_data_file
sessions_data = []
sessions_data.append(SessDataClass(onsets, durations, options.func_data_file))
else:
sessions_data = [sd]
#print options
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 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])