def calc_hesheng_surf(subject, atlas):
subject_fol = op.join(fmri.MMVT_DIR, subject, 'fmri')
if not (utils.both_hemi_files_exist(
op.join(subject_fol, 'fmri_hesheng_{hemi}.npy'))
and op.isfile(op.join(subject_fol, 'hesheng_minmax.pkl'))):
# Copy and rename Hesheng's files
hesheng_fnames = glob.glob(
op.join(hesheng_surf_fol.format(subject=subject),
hesheng_template.format(subject=subject)))
for fname in hesheng_fnames:
hemi = lu.get_label_hemi_invariant_name(utils.namebase(fname))
target_file = op.join(fmri.FMRI_DIR, subject,
'hesheng_{}.nii.gz'.format(hemi))
mgz_target_file = utils.change_fname_extension(target_file, 'mgz')
if not op.isfile(mgz_target_file):
shutil.copy(fname, target_file)
fu.nii_gz_to_mgz(target_file)
os.remove(target_file)
# Load Hesheng's files
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=atlas,
function='load_surf_files',
overwrite_surf_data=True,
fmri_file_template='hesheng_{hemi}.mgz'))
pu.run_on_subjects(args, fmri.main)
def create_aparc_aseg_file(subject, atlas, overwrite_aseg_file=False, print_only=False, args={}):
if not utils.both_hemi_files_exist(op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format('{hemi}', atlas))):
print('No annot file was found for {}!'.format(atlas))
print('Run python -m src.preproc.anatomy -s {} -a {} -f create_surfaces,create_annotation'.format(subject, atlas))
return False
# aparc_aseg_fname
ret = fu.create_aparc_aseg_file(
subject, atlas, SUBJECTS_DIR, overwrite_aseg_file, print_only, mmvt_args=args)
if isinstance(ret, Iterable):
ret, aparc_aseg_fname = ret
if not ret:
return False
aparc_aseg_file = utils.namebase_with_ext(aparc_aseg_fname)
utils.make_dir(op.join(MMVT_DIR, subject, 'freeview'))
blender_file = op.join(MMVT_DIR, subject, 'freeview', aparc_aseg_file)
utils.remove_file(blender_file)
shutil.copyfile(aparc_aseg_fname, blender_file)
atlas_mat_fname = utils.change_fname_extension(blender_file, 'npy')
if not op.isfile(atlas_mat_fname) or overwrite_aseg_file:
d = nib.load(blender_file)
x = d.get_data()
np.save(atlas_mat_fname, x)
return op.isfile(blender_file) and op.isfile(atlas_mat_fname)
def obj_to_ply():
fol = '/homes/5/npeled/space1/Angelique/Lionel Recon/blender/objs'
files = glob.glob(op.join(fol, '*.obj'))
for f in files:
print(f)
verts, faces = utils.read_obj_file(f)
ply_fname = utils.change_fname_extension(f, 'ply')
utils.write_ply_file(verts, faces, ply_fname, True)
def main(html_template_fname, scan_fol, patient_name, task_name, mrn, scan_date, img_prefix=None,
report_name_suffix='', output_fname=''):
if utils.get_parent_fol(html_template_fname) != scan_fol:
shutil.copy(html_template_fname, scan_fol)
if output_fname == '':
output_fname = op.join(scan_fol, '{}{}.pdf'.format(mrn, '_{}'.format(report_name_suffix) \
if report_name_suffix != '' else ''))
new_html_fname = utils.change_fname_extension(output_fname, 'html')
if img_prefix == 'auto':
img_prefix = utils.find_common_start([utils.namebase(f) for f in glob.glob(op.join(scan_fol, '*.png'))])
img_prefix = img_prefix[:-1] if img_prefix[-1] == '_' else img_prefix
img_prefix = img_prefix[:-2] if img_prefix[-2:] == '_l' else img_prefix
html = read_html(html_template_fname)
html = replace_fields(html, patient_name, task_name, mrn, scan_date, img_prefix)
create_new_html(html, new_html_fname)
create_pdf(new_html_fname, output_fname)
os.remove(op.join(scan_fol, utils.namebase_with_ext(html_template_fname)))
def create_lut_file_for_atlas(subject, atlas):
if not utils.both_hemi_files_exist(
op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format(
'{hemi}', atlas))):
print('No annot file was found for {}!'.format(atlas))
print(
'Run python -m src.preproc.anatomy -s {} -a {} -f create_surfaces,create_annotation'
.format(subject, atlas))
return False
# Read the subcortical segmentation from the freesurfer lut
new_lut_fname = op.join(SUBJECTS_DIR, subject, 'label',
'{}ColorLUT.txt'.format(atlas))
mmvt_lut_fname = op.join(MMVT_DIR, subject, 'freeview',
'{}ColorLUT.txt'.format(atlas))
# if op.isfile(mmvt_lut_fname) and not args.overwrite_aseg_file:
# return
lut = utils.read_freesurfer_lookup_table(get_colors=True)
lut_new = [[l[0], l[1].astype(str), l[2], l[3], l[4], l[5]] for l in lut
if l[0] < 1000]
for hemi, offset in zip(['lh', 'rh'], [1000, 2000]):
if hemi == 'lh':
lut_new.append([offset, 'ctx-lh-unknown', 25, 5, 25, 0])
else:
lut_new.append([offset, 'ctx-rh-unknown', 25, 5, 25, 0])
_, ctab, names = _read_annot(
op.join(SUBJECTS_DIR, subject, 'label',
'{}.{}.annot'.format(hemi, atlas)))
names = [name.astype(str) for name in names]
for index, (label, cval) in enumerate(zip(names, ctab)):
r, g, b, a, _ = cval
lut_new.append([index + offset + 1, label, r, g, b, a])
lut_new.sort(key=lambda x: x[0])
# Add the values above 3000
for l in [l for l in lut if l[0] >= 3000]:
lut_new.append([l[0], l[1].astype(str), l[2], l[3], l[4], l[5]])
with open(new_lut_fname, 'w') as fp:
csv_writer = csv.writer(fp, delimiter='\t')
csv_writer.writerows(lut_new)
# np.savetxt(new_lut_fname, lut_new, delimiter='\t', fmt="%s")
utils.make_dir(op.join(MMVT_DIR, subject, 'freeview'))
shutil.copyfile(new_lut_fname, mmvt_lut_fname)
lut_npz_fname = utils.change_fname_extension(mmvt_lut_fname, 'npz')
x = np.genfromtxt(mmvt_lut_fname, dtype=np.str)
np.savez(lut_npz_fname, names=x[:, 1], ids=x[:, 0].astype(int))
return op.isfile(mmvt_lut_fname) and op.isfile(lut_npz_fname)
def mat_to_ply():
fol = '/homes/5/npeled/space1/Angelique/Lionel Recon/'
files = glob.glob(op.join(fol, 'Iso*.mat'))
avg_fname = op.join(fol, 'blender', 'mean_vertices.npy')
if not op.isfile(avg_fname):
vertices_avg = []
for f in files:
print(f)
m = utils.read_mat_file_into_bag(f)
vertices_avg.append(np.mean(m.vertices.T / 10, 0))
m.clear()
vertices_avg = np.mean(vertices_avg)
np.save(avg_fname, vertices_avg)
else:
vertices_avg = np.load(avg_fname)
for f in files:
print(f)
m = utils.read_mat_file_into_bag(f)
ply_fname = utils.change_fname_extension(f, 'ply')
utils.write_ply_file(m.vertices.T / 10.0 - vertices_avg , m.faces.T, ply_fname, True)
m.clear()
def plot_norm_data(x_cond,
x_baseline,
con_names,
condition,
threshold,
nodes_names,
stc_data,
stc_times,
windows_len=100,
windows_shift=10,
figures_fol='',
ax=None,
nodes_names_includes_hemi=False):
# con_norm = x_cond - x_baseline
# con_norm = x_cond - x_cond[:, :200].mean(axis=1, keepdims=True)
# baseline_std = np.std(x_baseline, axis=1, keepdims=True)
# baseline_mean = np.mean(x_baseline, axis=1, keepdims=True)
windows_num = x_cond.shape[1]
dt = (stc_times[-1] - stc_times[windows_len]) / windows_num
time = np.arange(stc_times[windows_len], stc_times[-1], dt)[:-1]
t0, t1 = np.where(time > -0.1)[0][0], np.where(time > 1)[0][0]
# baseline_mean = np.max(x_cond[:, :t0], axis=1, keepdims=True)
# baseline_std = np.std(x_cond[:, :t0], axis=1, keepdims=True)
# con_norm = (x_cond - baseline_mean) / baseline_std
con_norm = x_cond - x_baseline
fig_fname = op.join(figures_fol, 'ictal-baseline',
'{}-connectivity-ictal-baseline.jpg'.format(condition))
connection_fname = utils.change_fname_extension(fig_fname, 'pkl')
norm = {}
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
conn_conditions = list(product(['within', 'between'], utils.HEMIS))
colors = ['c', 'b', 'k', 'm']
lines, labels = [], []
no_ord_con_names = [con_name.split(' ')[0] for con_name in con_names]
connections = []
for conn_type, color in zip(conn_conditions, colors):
mask = epi_utils.filter_connections(
con_norm,
no_ord_con_names,
threshold,
nodes_names,
conn_type,
use_abs=False,
nodes_names_includes_hemi=nodes_names_includes_hemi)
if sum(mask) == 0:
print('{} no connections {}'.format(condition, conn_type))
continue
else:
print('{}: {} connection for {} {}'.format(condition, sum(mask),
conn_type[0],
conn_type[1]))
names = np.array(con_names)[mask]
norm[conn_type] = con_norm[mask]
# print('windows num: {} windows length: {:.2f}ms windows shift: {:2f}ms'.format(
# windows_num, (stc_times[windows_len] - stc_times[0]) * 1000, dt * 1000))
marker = '+' if conn_type[0] == 'within' else 'x'
label_title = ' '.join(
conn_type) if conn_type[0] == 'within' else '{} to {}'.format(
*conn_type)
first = True
for k in range(norm[conn_type].shape[0]):
first_sec_max = norm[conn_type][k][t0:t1].max()
if norm[conn_type][k][t0:t1].max() > 2:
# if conn_type[0] == 'between':
first_sec_max_t = norm[conn_type][k][t0:t1].argmax()
connections.append((time[first_sec_max_t + t0], label_title,
first_sec_max, names[k]))
l = ax.scatter(time, norm[conn_type][k],
color=color) #, marker=marker) # .max(0)
if first:
lines.append(l)
labels.append(label_title)
first = False
conn_type = (conn_type[0],
'right') if conn_type[1] == 'rh' else (conn_type[0],
'left')
connections = sorted(connections)
for con in connections:
print(con)
utils.save(connections, connection_fname)
if stc_data is not None:
ax2 = ax.twinx()
l = ax2.plot(stc_times[windows_len:],
stc_data[windows_len:].T,
'y--',
alpha=0.2) # stc_data[:-100].T
lines.append(l[0])
labels.append('Source normalized activity')
# ax2.set_ylim([0.5, 4.5])
# ax2.set_xlim([])
# ax2.set_yticks(range(1, 5))
ax2.set_ylabel('Source z-values', fontsize=12)
# ax.set_xticks(time)
# xticklabels = ['{}-{}'.format(t, t + windows_shift) for t in time]
# xticklabels[2] = '{}\nonset'.format(xticklabels[2])
# ax.set_xticklabels(xticklabels, rotation=30)
ax.set_ylabel('Causality: Interictals\n minus Baseline', fontsize=12)
# ax.set_yticks([0, 0.5])
ax.set_ylim(bottom=0) #, 0.7])
# ax.axvline(x=x_axis[10], color='r', linestyle='--')
plt.title('{} ictal-baseline ({} connections)'.format(
condition, x_cond.shape[0]))
# labs = [*conn_conditions, 'Source normalized activity']
# ax.legend([l1[conn_conditions[k]][0] for k in range(4)] + l2, labs, loc=0)
# ax.legend([l1[conn_conditions[0]]] + [l1[conn_conditions[1]]] + l2, labs, loc=0)
ax.legend(lines, labels, loc='upper right') #loc=0)
plt.axvline(x=0, linestyle='--', color='k')
# if ax is None:
if figures_fol != '':
plt.savefig(fig_fname, dpi=300)
print('Figure was saved in {}'.format(fig_fname))
plt.close()
else:
plt.show()
