"""do stats report"""
stats_report_filename = os.path.join(subject_data.output_dir,
"report_stats.html")
contrasts = dict((contrast_id, contrasts[contrast_id])
for contrast_id in z_maps.keys())
glm_reporter.generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.mask,
design_matrices=[design_matrix],
subject_id=subject_data.subject_id,
anat=anat,
anat_affine=anat_affine,
cluster_th=50, # we're only interested in this 'large' clusters
start_time=stats_start_time,
# additional ``kwargs`` for more informative report
paradigm=paradigm.__dict__,
TR=TR,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
)
# shutdown main report page
base_reporter.ProgressReport().finish_dir(output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
def execute_glm(doc, out_dir, contrast_definitions=None,
outputs=None, glm_model='ar1',
):
"""Function to execute GLM for one subject --and perhaps multiple
sessions thereof
"""
stats_start_time = time.ctime()
# study_dir = os.path.join(out_dir, doc['study'])
if outputs is None:
outputs = {'maps': False,
'data': False,
'mask': True,
'model': True,
}
else:
outputs['maps'] = False
subject_id = doc['subject']
subject_output_dir = os.path.join(
out_dir, subject_id)
_export(doc, subject_output_dir, outputs=outputs)
params = load_glm_params(doc)
# instantiate GLM
fmri_glm = FMRILinearModel(params['data'],
params['design_matrices'],
doc['mask'])
# fit GLM
fmri_glm.fit(do_scaling=True, model=glm_model)
# save beta-maps to disk
beta_map_dir = os.path.join(subject_output_dir, 'beta_maps')
if not os.path.exists(beta_map_dir):
os.makedirs(beta_map_dir)
for j, glm in zip(range(len(fmri_glm.glms)), fmri_glm.glms):
# XXX save array in some compressed format
np.savetxt(os.path.join(beta_map_dir, "beta_map_%i.txt" % j),
glm.get_beta(), # array has shape (n_conditions, n_voxels)
)
# define contrasts
if contrast_definitions is not None:
params['contrasts'] = make_contrasts(params, contrast_definitions)
contrasts = sorted(params['contrasts'][0].keys())
_contrasts = {}
z_maps = {}
# compute stats maps
for index, contrast_id in enumerate(contrasts):
print ' study[%s] subject[%s] contrast [%s]: %i/%i' % (
doc['study'], doc['subject'],
contrast_id, index + 1, len(contrasts)
)
contrast = [c[contrast_id] for c in params['contrasts']]
contrast_name = contrast_id.replace(' ', '_')
z_map, t_map, c_map, var_map = fmri_glm.contrast(
contrast,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True,)
for dtype, out_map in zip(['z', 't', 'c', 'variance'],
[z_map, t_map, c_map, var_map]):
map_dir = os.path.join(subject_output_dir, '%s_maps' % dtype)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_name)
nb.save(out_map, map_path)
# collect z map
if dtype == 'z':
_contrasts[contrast_name] = contrast
z_maps[contrast_name] = map_path
# invoke a single API to handle plotting and html business for you
subject_stats_report_filename = os.path.join(
subject_output_dir, "report_stats.html")
glm_reporter.generate_subject_stats_report(
subject_stats_report_filename,
_contrasts,
z_maps,
doc['mask'],
design_matrices=list(params['design_matrices']),
subject_id=doc['subject'],
cluster_th=15, # 15 voxels
start_time=stats_start_time,
TR=doc['TR'],
n_scans=doc['n_scans'],
n_sessions=doc['n_sessions'],
model=glm_model,
)
print "Report for subject %s written to %s" % (
doc['subject'],
subject_stats_report_filename)
def execute_glm(
doc,
out_dir,
contrast_definitions=None,
outputs=None,
glm_model='ar1',
):
"""Function to execute GLM for one subject --and perhaps multiple
sessions thereof
"""
stats_start_time = time.ctime()
# study_dir = os.path.join(out_dir, doc['study'])
if outputs is None:
outputs = {
'maps': False,
'data': False,
'mask': True,
'model': True,
}
else:
outputs['maps'] = False
subject_id = doc['subject']
subject_output_dir = os.path.join(out_dir, subject_id)
_export(doc, subject_output_dir, outputs=outputs)
params = load_glm_params(doc)
# instantiate GLM
fmri_glm = FMRILinearModel(params['data'], params['design_matrices'],
doc['mask'])
# fit GLM
fmri_glm.fit(do_scaling=True, model=glm_model)
# save beta-maps to disk
beta_map_dir = os.path.join(subject_output_dir, 'beta_maps')
if not os.path.exists(beta_map_dir):
os.makedirs(beta_map_dir)
for j, glm in zip(xrange(len(fmri_glm.glms)), fmri_glm.glms):
# XXX save array in some compressed format
np.savetxt(
os.path.join(beta_map_dir, "beta_map_%i.txt" % j),
glm.get_beta(), # array has shape (n_conditions, n_voxels)
)
# define contrasts
if contrast_definitions is not None:
params['contrasts'] = make_contrasts(params, contrast_definitions)
contrasts = sorted(params['contrasts'][0].keys())
_contrasts = {}
z_maps = {}
# compute stats maps
for index, contrast_id in enumerate(contrasts):
print ' study[%s] subject[%s] contrast [%s]: %i/%i' % (
doc['study'], doc['subject'], contrast_id, index + 1,
len(contrasts))
contrast = [c[contrast_id] for c in params['contrasts']]
contrast_name = contrast_id.replace(' ', '_')
z_map, t_map, c_map, var_map = fmri_glm.contrast(
contrast,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True,
)
for dtype, out_map in zip(['z', 't', 'c', 'variance'],
[z_map, t_map, c_map, var_map]):
map_dir = os.path.join(subject_output_dir, '%s_maps' % dtype)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_name)
nb.save(out_map, map_path)
# collect z map
if dtype == 'z':
_contrasts[contrast_name] = contrast
z_maps[contrast_name] = map_path
# invoke a single API to handle plotting and html business for you
subject_stats_report_filename = os.path.join(subject_output_dir,
"report_stats.html")
glm_reporter.generate_subject_stats_report(
subject_stats_report_filename,
_contrasts,
z_maps,
doc['mask'],
design_matrices=list(params['design_matrices']),
subject_id=doc['subject'],
cluster_th=15, # 15 voxels
start_time=stats_start_time,
TR=doc['TR'],
n_scans=doc['n_scans'],
n_sessions=doc['n_sessions'],
model=glm_model,
)
print "Report for subject %s written to %s" % (
doc['subject'], subject_stats_report_filename)