task_list = [
'archi_standard', 'archi_spatial', 'archi_social', 'archi_emotional',
'hcp_language', 'hcp_social', 'hcp_gambling', 'hcp_motor', 'hcp_emotion',
'hcp_relational', 'hcp_wm', 'rsvp_language'
]
# BIDS conversion of task names
# Load dictionary file
with open(os.path.join('bids_postprocessed.json'), 'r') as f:
task_dic = json.load(f)
TASKS = [task_dic[tkey] for tkey in task_list]
TASKS = flatten(TASKS)
df = data_parser(derivatives=SMOOTH_DERIVATIVES,
subject_list=SUBJECTS,
conditions=CONTRASTS,
task_list=TASKS)
# Mask of the grey matter across subjects
_package_directory = os.path.dirname(
os.path.abspath(ibc_public.utils_data.__file__))
mask_gm = os.path.join(_package_directory, '../ibc_data',
'gm_mask_1_5mm.nii.gz')
data_dir = SMOOTH_DERIVATIVES
masker = NiftiMasker(mask_img=mask_gm, memory=write_dir).fit()
def append_correlation(imgs, masker, correlations=[]):
X = masker.transform(imgs)
corr_matrix = np.triu(np.corrcoef(X), 1)
display_mode=display_mode,
cut_coords=[cut],
black_bg=True,
annotate=False,
dim=0, # title=subject,
colorbar=False,
view_type='filled_contours',
linewidths=2.)
axes.axis('off')
fig.savefig(os.path.join(write_dir, 'snapshot_%s.pdf' % name),
facecolor='k',
dpi=300)
# plt.close(fig)
db = data_parser(derivatives=SMOOTH_DERIVATIVES, conditions=CONTRASTS)
# db = db[db.task.isin(task_list)]
mask_gm = nib.load(os.path.join(DERIVATIVES, 'group', 'anat',
'gm_mask.nii.gz'))
masker = NiftiMasker(mask_img=mask_gm, memory=mem).fit()
write_dir = 'output'
if not os.path.exists(write_dir):
os.mkdir(write_dir)
"""
task_contrast = [('archi_social', 'false_belief-mechanistic_video'),
('archi_social', 'false_belief-mechanistic_audio'),
('archi_social', 'triangle_mental-random'),
('hcp_social', 'mental-random')]
plot_contrasts(db, task_contrast, masker, write_dir, cut=-50, display_mode='x',
name='social')
