def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata)
def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
fdata = FmriData.from_vol_ui()
glm_nipy(fdata)
def test_fir_glm(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
#pyhrf.verbose.set_verbosity(3)
fdata = FmriData.from_vol_ui()
# print 'fdata:'
# print fdata.getSummary()
glm_nipy(fdata, hrf_model='FIR', fir_delays=range(10))
def test_glm_default_real_data(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
#pyhrf.verbose.set_verbosity(3)
fdata = FmriData.from_vol_ui()
# print 'fdata:'
# print fdata.getSummary()
glm_nipy(fdata)
def test_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
fdata = FmriData.from_vol_ui()
g, dm, cons = glm_nipy(fdata, contrasts=cons)
def test_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {
'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
fdata = FmriData.from_vol_ui()
g, dm, cons = glm_nipy(fdata, contrasts=cons)
def test_glm_contrasts(self):
from pyhrf import FmriData
from pyhrf.glm import glm_nipy
cons = {'audio-video': 'audio - video',
'video-audio': 'video - audio',
}
#pyhrf.verbose.set_verbosity(3)
fdata = FmriData.from_vol_ui()
# print 'fdata:'
# print fdata.getSummary()
g, dm, cons = glm_nipy(fdata, contrasts=cons)
def analyse_roi(self, fdata):
glm = glm_nipy(fdata, contrasts=None, hrf_model='Canonical',
drift_model='Cosine', hfcut=128,
residuals_model='spherical', fit_method='ols',
fir_duration=None, fir_delays=None)
outputs = {}
ns = fdata.shape[0]
dm = glm.design_matrix.matrix
tr = fdata.tr
cdesign_matrix = xndarray(dm,
axes_names=['time','regressor'],
axes_domains={'time':np.arange(ns)*tr,
'regressor':dm.names})
outputs['design_matrix'] = cdesign_matrix
return outputs
def analyse_roi(self, fdata):
glm = glm_nipy(fdata, contrasts=None, hrf_model='Canonical',
drift_model='Cosine', hfcut=128,
residuals_model='spherical', fit_method='ols',
fir_duration=None, fir_delays=None)
outputs = {}
ns = fdata.shape[0]
dm = glm.design_matrix.matrix
tr = fdata.tr
cdesign_matrix = xndarray(dm,
axes_names=['time','regressor'],
axes_domains={'time':np.arange(ns)*tr,
'regressor':dm.names})
outputs['design_matrix'] = cdesign_matrix
return outputs
def analyse_roi(self, fdata):
glm = glm_nipy(
fdata,
contrasts=None,
hrf_model="Canonical",
drift_model="Cosine",
hfcut=128,
residuals_model="spherical",
fit_method="ols",
fir_duration=None,
fir_delays=None,
)
outputs = {}
ns = fdata.shape[0]
dm = glm.design_matrix.matrix
tr = fdata.tr
cdesign_matrix = xndarray(
dm, axes_names=["time", "regressor"], axes_domains={"time": np.arange(ns) * tr, "regressor": dm.names}
)
outputs["design_matrix"] = cdesign_matrix
return outputs
def analyse_roi(self, fdata):
pyhrf.verbose(1, 'Run GLM analysis (ROI %d) ...' %fdata.get_roi_id())
if self.rescale_factor is not None:
m = np.where(fdata.roiMask)
rescale_factor = self.rescale_factor[:,m[0],m[1],m[2]]
else:
rescale_factor = None
glm, dm, cons = glm_nipy(fdata, contrasts=self.contrasts,
hrf_model=self.hrf_model,
drift_model=self.drift_model,
hfcut=self.hfcut,
residuals_model=self.residuals_model,
fit_method=self.fit_method,
fir_delays=self.fir_delays,
rescale_results=self.rescale_results,
rescale_factor=rescale_factor)
outputs = {}
ns, nr = dm.matrix.shape
tr = fdata.tr
if rescale_factor is not None:
#same sf for all voxels
dm.matrix[:,:rescale_factor.shape[0]] /= rescale_factor[:,0]
cdesign_matrix = xndarray(dm.matrix,
axes_names=['time','regressor'],
axes_domains={'time':np.arange(ns)*tr,
'regressor':dm.names})
outputs['design_matrix'] = cdesign_matrix
axes_names = ['time', 'voxel']
axes_domains = {'time' : np.arange(ns)*tr}
bold = xndarray(fdata.bold.astype(np.float32),
axes_names=axes_names,
axes_domains=axes_domains,
value_label='BOLD')
fit = np.dot(dm.matrix, glm.beta)
cfit = xndarray(fit, axes_names=['time','voxel'],
axes_domains={'time':np.arange(ns)*tr})
outputs['bold_fit'] = stack_cuboids([bold,cfit], 'stype', ['bold', 'fit'])
nb_cond = fdata.nbConditions
fit_cond = np.dot(dm.matrix[:,:nb_cond], glm.beta[:nb_cond,:])
fit_cond -= fit_cond.mean(0)
fit_cond += fdata.bold.mean(0)
outputs['fit_cond'] = xndarray(fit_cond, axes_names=['time','voxel'],
axes_domains={'time':np.arange(ns)*tr})
outputs['s2'] = xndarray(glm.s2, axes_names=['voxel'])
if 0:
cbeta = xndarray(glm.beta, axes_names=['reg_name','voxel'],
axes_domains={'reg_name':dm.names})
outputs['beta'] = cbeta
else:
if self.hrf_model == 'FIR':
fir = dict((d * fdata.tr, OrderedDict()) for d in self.fir_delays)
for ib, bname in enumerate(dm.names):
outputs['beta_' + bname] = xndarray(glm.beta[ib],
axes_names=['voxel'])
if self.hrf_model == 'FIR' and 'delay' in bname:
#reconstruct filter:
cond, delay = bname.split('_delay_')
delay = int(delay) * fdata.tr
fir[delay][cond] = xndarray(glm.beta[ib], axes_names=['voxel'])
if self.hrf_model == 'FIR':
chrf = tree_to_cuboid(fir, ['time', 'condition'])
outputs['hrf'] = chrf
outputs['hrf_norm'] = (chrf**2).sum('time')**.5
for cname, con in cons.iteritems():
#print 'con:'
#print dir(con)
outputs['con_effect_'+cname] = xndarray(con.effect,
axes_names=['voxel'])
#print '%%%%%%% con.variance:', con.variance.shape
ncon = con.effect / con.variance.std()
outputs['ncon_effect_'+cname] = xndarray(ncon, axes_names=['voxel'])
outputs['con_pvalue_'+cname] = xndarray(con.pvalue(self.con_bl),
axes_names=['voxel'])
roi_lab_vol = np.zeros(fdata.get_nb_vox_in_mask(), dtype=np.int32) + \
fdata.get_roi_id()
outputs['mask'] = xndarray(roi_lab_vol, axes_names=['voxel'])
# for ib, bname in enumerate(design_matrix.names):
# beta_vol = expand_array_in_mask(my_glm.beta[ib], mask_array)
# beta_image = Nifti1Image(beta_vol, affine)
# beta_file = op.join(output_dir, 'beta_%s.nii' %bname)
# save(beta_image, beta_file)
# beta_files.append(beta_file)
return outputs
def parcellation_for_jde(fmri_data, avg_parcel_size=250, output_dir=None,
method='gkm', glm_drift='Cosine', glm_hfcut=128):
"""
method: gkm, ward, ward_and_gkm
"""
if output_dir is None:
output_dir = tempfile.mkdtemp(prefix='pyhrf_JDE_parcellation_GLM',
dir=pyhrf.cfg['global']['tmp_path'])
glm_output_dir = op.join(output_dir, 'GLM_for_parcellation')
if not op.exists(glm_output_dir): os.makedirs(glm_output_dir)
pyhrf.verbose(1, 'GLM for parcellation')
# if fmri_data.data_type == 'volume':
# paradigm_file, bold_file, mask_file = fmri_data.save(glm_output_dir)
# beta_files = glm_nipy_from_files(bold_file, fmri_data.tr, paradigm_file,
# glm_output_dir, mask_file,
# drift_model=glm_drift, hfcut=glm_hfcut)
# elif fmri_data.data_type == 'surface':
# beta_files = glm_nipy(fmri_data, glm_output_dir,
# drift_model=glm_drift, hfcut=glm_hfcut)
g, dm, cons = glm_nipy(fmri_data, drift_model=glm_drift, hfcut=glm_hfcut)
pval_files = []
if cons is not None:
func_data = [('con_pval_%s' %cname, con.pvalue()) \
for cname, con in cons.iteritems()]
else:
reg_cst_drift = re.compile(".*constant.*|.*drift.*")
func_data = [('beta_%s' %reg_name, g.beta[ir]) \
for ir,reg_name in enumerate(dm.names) \
if not reg_cst_drift.match(reg_name)]
for name, data in func_data:
val_vol = expand_array_in_mask(data, fmri_data.roiMask>0)
val_fn = op.join(glm_output_dir, '%s.nii' %name)
write_volume(val_vol, val_fn, fmri_data.meta_obj)
pval_files.append(val_fn)
mask_file = op.join(glm_output_dir,'mask.nii')
write_volume(fmri_data.roiMask>0, mask_file, fmri_data.meta_obj)
nvox = fmri_data.get_nb_vox_in_mask()
nparcels = round_nb_parcels(nvox * 1. / avg_parcel_size)
pyhrf.verbose(1, 'Parcellation from GLM outputs, method: %s, ' \
'nb parcels: %d' %(method, nparcels))
if fmri_data.data_type == 'volume':
parcellation_file = op.join(output_dir, 'parcellation_%s_np%d.nii'
%(method, nparcels))
make_parcellation_from_files(pval_files, mask_file, parcellation_file,
nparcels, method)
parcellation,_ = read_volume(parcellation_file)
else:
mesh_file = fmri_data.data_files[-1]
parcellation_file = op.join(output_dir, 'parcellation_%s_np%d.gii'
%(method, nparcels))
make_parcellation_surf_from_files(pval_files, mesh_file,
parcellation_file, nparcels, method,
verbose=1)
parcellation,_ = read_texture(parcellation_file)
#print parcellation_file
pyhrf.verbose(1, parcellation_report(parcellation))
return parcellation, parcellation_file
def parcellation_for_jde(fmri_data,
avg_parcel_size=250,
output_dir=None,
method='gkm',
glm_drift='Cosine',
glm_hfcut=128):
"""
method: gkm, ward, ward_and_gkm
"""
if output_dir is None:
output_dir = tempfile.mkdtemp(prefix='pyhrf_JDE_parcellation_GLM',
dir=pyhrf.cfg['global']['tmp_path'])
glm_output_dir = op.join(output_dir, 'GLM_for_parcellation')
if not op.exists(glm_output_dir):
os.makedirs(glm_output_dir)
logger.info('GLM for parcellation')
g, dm, cons = glm_nipy(fmri_data, drift_model=glm_drift, hfcut=glm_hfcut)
pval_files = []
if cons is not None:
func_data = [('con_pval_%s' % cname, con.pvalue())
for cname, con in cons.iteritems()]
else:
reg_cst_drift = re.compile(".*constant.*|.*drift.*")
func_data = [('beta_%s' % reg_name, g.beta[ir])
for ir, reg_name in enumerate(dm.names)
if not reg_cst_drift.match(reg_name)]
for name, data in func_data:
val_vol = expand_array_in_mask(data, fmri_data.roiMask > 0)
val_fn = op.join(glm_output_dir, '%s.nii' % name)
write_volume(val_vol, val_fn, fmri_data.meta_obj)
pval_files.append(val_fn)
mask_file = op.join(glm_output_dir, 'mask.nii')
write_volume(fmri_data.roiMask > 0, mask_file, fmri_data.meta_obj)
nvox = fmri_data.get_nb_vox_in_mask()
nparcels = round_nb_parcels(nvox * 1. / avg_parcel_size)
logger.info('Parcellation from GLM outputs, method: %s, nb parcels: %d',
method, nparcels)
if fmri_data.data_type == 'volume':
parcellation_file = op.join(
output_dir, 'parcellation_%s_np%d.nii' % (method, nparcels))
make_parcellation_from_files(pval_files, mask_file, parcellation_file,
nparcels, method)
parcellation, _ = read_volume(parcellation_file)
else:
mesh_file = fmri_data.data_files[-1]
parcellation_file = op.join(
output_dir, 'parcellation_%s_np%d.gii' % (method, nparcels))
make_parcellation_surf_from_files(pval_files,
mesh_file,
parcellation_file,
nparcels,
method,
verbose=1)
parcellation, _ = read_texture(parcellation_file)
logger.info(parcellation_report(parcellation))
return parcellation, parcellation_file
def analyse_roi(self, fdata):
logger.info('Run GLM analysis (ROI %d) ...', fdata.get_roi_id())
if self.rescale_factor is not None:
m = np.where(fdata.roiMask)
rescale_factor = self.rescale_factor[:, m[0], m[1], m[2]]
else:
rescale_factor = None
glm, dm, cons = glm_nipy(fdata,
contrasts=self.contrasts,
hrf_model=self.hrf_model,
drift_model=self.drift_model,
hfcut=self.hfcut,
residuals_model=self.residuals_model,
fit_method=self.fit_method,
fir_delays=self.fir_delays,
rescale_results=self.rescale_results,
rescale_factor=rescale_factor)
outputs = {}
ns, nr = dm.matrix.shape
tr = fdata.tr
if rescale_factor is not None:
# same sf for all voxels
dm.matrix[:, :rescale_factor.shape[0]] /= rescale_factor[:, 0]
cdesign_matrix = xndarray(dm.matrix,
axes_names=['time', 'regressor'],
axes_domains={
'time': np.arange(ns) * tr,
'regressor': dm.names
})
outputs['design_matrix'] = cdesign_matrix
if self.output_fit:
axes_names = ['time', 'voxel']
axes_domains = {'time': np.arange(ns) * tr}
bold = xndarray(fdata.bold.astype(np.float32),
axes_names=axes_names,
axes_domains=axes_domains,
value_label='BOLD')
fit = np.dot(dm.matrix, glm.beta)
cfit = xndarray(fit,
axes_names=['time', 'voxel'],
axes_domains={'time': np.arange(ns) * tr})
outputs['bold_fit'] = stack_cuboids([bold, cfit], 'stype',
['bold', 'fit'])
nb_cond = fdata.nbConditions
fit_cond = np.dot(dm.matrix[:, :nb_cond], glm.beta[:nb_cond, :])
fit_cond -= fit_cond.mean(0)
fit_cond += fdata.bold.mean(0)
outputs['fit_cond'] = xndarray(
fit_cond,
axes_names=['time', 'voxel'],
axes_domains={'time': np.arange(ns) * tr})
s2 = np.atleast_1d(glm.s2)
outputs['s2'] = xndarray(s2, axes_names=['voxel'])
if 0:
cbeta = xndarray(glm.beta,
axes_names=['reg_name', 'voxel'],
axes_domains={'reg_name': dm.names})
outputs['beta'] = cbeta
else:
if self.hrf_model == 'FIR':
fir = dict(
(d * fdata.tr, OrderedDict()) for d in self.fir_delays)
for ib, bname in enumerate(dm.names):
outputs['beta_' + bname] = xndarray(glm.beta[ib],
axes_names=['voxel'])
if self.hrf_model == 'FIR' and 'delay' in bname:
# reconstruct filter:
cond, delay = bname.split('_delay_')
delay = int(delay) * fdata.tr
fir[delay][cond] = xndarray(glm.beta[ib],
axes_names=['voxel'])
if self.hrf_model == 'FIR':
chrf = tree_to_xndarray(fir, ['time', 'condition'])
outputs['hrf'] = chrf
outputs['hrf_norm'] = (chrf**2).sum('time')**.5
for cname, con in cons.iteritems():
# print 'con:'
# print dir(con)
outputs['con_effect_' + cname] = xndarray(con.effect,
axes_names=['voxel'])
# print '%%%%%%% con.variance:', con.variance.shape
ncon = con.effect / con.variance.std()
outputs['ncon_effect_' + cname] = xndarray(
ncon, axes_names=['voxel'])
outputs['con_pvalue_' + cname] = xndarray(con.pvalue(
self.con_bl),
axes_names=['voxel'])
roi_lab_vol = np.zeros(fdata.get_nb_vox_in_mask(), dtype=np.int32) + \
fdata.get_roi_id()
outputs['mask'] = xndarray(roi_lab_vol, axes_names=['voxel'])
# for ib, bname in enumerate(design_matrix.names):
# beta_vol = expand_array_in_mask(my_glm.beta[ib], mask_array)
# beta_image = Nifti1Image(beta_vol, affine)
# beta_file = op.join(output_dir, 'beta_%s.nii' %bname)
# save(beta_image, beta_file)
# beta_files.append(beta_file)
return outputs
