def merge_fmri_sessions(fmri_data_sets):
"""
fmri_data_sets: list of FmriData objects.
Each FmriData object is assumed to contain only one session
"""
from pyhrf.tools import apply_to_leaves
all_onsets = stack_trees([fd.paradigm.stimOnsets for fd in fmri_data_sets])
all_onsets = apply_to_leaves(all_onsets, lambda l: [e[0] for e in l])
durations = stack_trees([fd.paradigm.stimDurations for fd in fmri_data_sets])
durations = apply_to_leaves(durations, lambda x: [e[0] for e in x])
bold = np.concatenate([fd.bold for fd in fmri_data_sets])
ss = []
lastScan = 0
for fd in fmri_data_sets:
nscans = fd.bold.shape[0]
ss.append(np.arange(lastScan,lastScan+nscans,dtype=int))
lastScan += nscans
if fd.simulation is not None:
simu = [fd.simulation[0] for fd in fmri_data_sets]
else:
simu=None
return FmriData(all_onsets, bold, fmri_data_sets[0].tr, ss,
fmri_data_sets[0].roiMask, fmri_data_sets[0].graphs,
durations, fmri_data_sets[0].meta_obj, simu,
backgroundLabel=fmri_data_sets[0].backgroundLabel,
data_files=fmri_data_sets[0].data_files,
data_type=fmri_data_sets[0].data_type,
edge_lengths=fmri_data_sets[0].edge_lengths,
mask_loaded_from_file=fmri_data_sets[0].mask_loaded_from_file)
def merge_fmri_sessions(fmri_data_sets):
"""
fmri_data_sets: list of FmriData objects.
Each FmriData object is assumed to contain only one session
"""
from pyhrf.tools import apply_to_leaves
all_onsets = stack_trees([fd.paradigm.stimOnsets for fd in fmri_data_sets])
all_onsets = apply_to_leaves(all_onsets, lambda l: [e[0] for e in l])
durations = stack_trees(
[fd.paradigm.stimDurations for fd in fmri_data_sets])
durations = apply_to_leaves(durations, lambda x: [e[0] for e in x])
bold = np.concatenate([fd.bold for fd in fmri_data_sets])
session_scan = []
last_scan = 0
for fmri_data in fmri_data_sets:
nscans = fmri_data.bold.shape[0]
session_scan.append(np.arange(last_scan, last_scan + nscans,
dtype=int))
last_scan += nscans
# FIXME: define fmri_data instead of loop indice
if fmri_data.simulation is not None:
simu = [fmri_data.simulation[0] for fmri_data in fmri_data_sets]
else:
simu = None
return FmriData(
all_onsets,
bold,
fmri_data_sets[0].tr,
session_scan,
fmri_data_sets[0].roiMask,
fmri_data_sets[0].graphs,
durations,
fmri_data_sets[0].meta_obj,
simu,
backgroundLabel=fmri_data_sets[0].backgroundLabel,
data_files=fmri_data_sets[0].data_files,
data_type=fmri_data_sets[0].data_type,
edge_lengths=fmri_data_sets[0].edge_lengths,
mask_loaded_from_file=fmri_data_sets[0].mask_loaded_from_file)
def evaluate(self):
'''
Evaluate the mathematical expression given as a string in the
expression member variable.
'''
variables = apply_to_leaves(self.variables, self.call_if_func)
try:
result = eval(self.expression, variables, self.functions)
except NameError, err:
undef_var = re.findall("name '(.*)' is not defined", err.args[0])[0]
raise NameError("Variable '%s' is not defined in expression '%s'" \
%(undef_var, self.expression), undef_var)
def evaluate(self):
'''
Evaluate the mathematical expression given as a string in the
expression member variable.
'''
variables = apply_to_leaves(self.variables, self.call_if_func)
try:
result = eval(self.expression, variables, self.functions)
except NameError, err:
undef_var = re.findall("name '(.*)' is not defined",
err.args[0])[0]
raise NameError("Variable '%s' is not defined in expression '%s'" \
%(undef_var, self.expression), undef_var)
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])
]),
('LangForeignRep', [np.array([12.031, 84.031, 132.031, 180.031])]),
('LangMat', [np.array([24.031, 48.031, 96.031, 144.031])]),
('LangMatRep', [np.array([36.031, 60.031, 108.031, 156.031])]),
])
durations_language_sess4 = dict([
('LangForeign', [np.array([3.292, 2.53, 2.945, 2.775])]),
('LangForeignRep', [np.array([3.292, 2.53, 2.945, 2.775])]),
('LangMat', [np.array([2.819, 2.771, 2.736, 2.492])]),
('LangMatRep', [np.array([2.819, 2.771, 2.736, 2.492])]),
])
builtin_paradigms.extend('language_sess%d' % i for i in range(1, 5))
durations_loc = apply_to_leaves(onsets_loc, lambda x: 2. + np.zeros_like(x))
default_contrasts_loc = OrderedDict({
"left_click":
"clicGaudio + clicGvideo",
"right_click":
"clicDaudio + clicDvideo",
"checkerboard_H-V":
"damier_H - damier_V",
"checkerboard_V-H":
"damier_V - damier_H",
"left_click-right_click":
"clicGaudio-clicDaudio + clicGvideo-clicDvideo",
"right_click-left_click":
"clicDaudio-clicGaudio + clicDvideo-clicGvideo",
"computation-sentences":
#onsets_language_sess4 = OrderedDict([
#('LangMat', np.array([ 0.036, 48.036, 72.036, 120.036])),
#('LangMatRep', np.array([ 12.036, 60.036, 84.036, 132.036])),
#('LangForeign', np.array([ 24.036, 96.036, 144.036, 168.036 ])),#2
#('LangForeignRep', np.array([ 36.036, 108.036, 156.036, 180.036]))
#])
#durations_language_sess4 = OrderedDict([
#('LangMat', np.array([ 2.831, 2.327, 2.587, 2.936])),
#('LangMatRep', np.array([ 2.831, 2.327, 2.587, 2.936])),
#('LangForeign', np.array([ 2.771, 2.753, 2.715, 2.774])),#2
#('LangForeignRep', np.array([ 2.771, 2.753, 2.715, 2.774]))
#])
durations_loc = apply_to_leaves(onsets_loc, lambda x: 2. + np.zeros_like(x))
default_contrasts_loc = {
"left_click" : "clicGaudio + clicGvideo",
"right_click" : "clicDaudio + clicDvideo",
"checkerboard_H-V" : "damier_H - damier_V",
"checkerboard_V-H" : "damier_V - damier_H",
"left_click-right_click" : "clicGaudio-clicDaudio + clicGvideo-clicDvideo",
"right_click-left_click" : "clicDaudio-clicGaudio + clicDvideo-clicGvideo",
"computation-sentences" : "calculaudio-phraseaudio + calculvideo-phrasevideo",
"sentences-computation" : "phraseaudio-calculaudio + phrasevideo-calculvideo",
"video_computation-sentences" : "calculvideo-phrasevideo",
"audio_computation-sentences" : "calculaudio-phraseaudio",
"motor-cognitive" : "clicGaudio + clicDaudio + clicGvideo + clicDvideo -" \
"calculaudio - phraseaudio - calculvideo - phrasevideo",
"cognitive-motor" : "calculaudio + phraseaudio + calculvideo + phrasevideo -"\
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])
