def save_frmi_color_per_subject(out_file, threshold=2):
# subjects_folders = utils.get_subfolders(SPM_ROOT)
good_subjects = ['pp002', 'pp003', 'pp004', 'pp005', 'pp006']
subjects_folders = [os.path.join(SPM_ROOT, sub) for sub in good_subjects]
subjects_colors = utils.get_spaced_colors(len(subjects_folders))
# subjects_colors = utils.arr_to_colors(range(len(subjects_folders)), colors_map='Set1')[:, :3]
for hemi in ['rh', 'lh']:
first = True
all_colors = []
for sub_id, (subject_fol, subject_color) in enumerate(
zip(subjects_folders, subjects_colors)):
subject = utils.namebase(subject_fol)
print(hemi, subject)
# if subject not in good_subjects:
# continue
fs_hemi_map = os.path.join(
subject_fol,
FS_HEMI_MAP_TEMPLATE.format(subject=subject, hemi=hemi))
old, brain = get_hemi_data(FS_SUBJECT, hemi, fs_hemi_map, 'pial')
x = old.mlab_data
brain.close()
# x = nib.load(fs_hemi_map).get_data().squeeze()
# plt.hist(x, bins=50)
# plt.show()
subject_colors = np.ones((len(x), 3))
print(sum(x > threshold))
# print(np.unique(x[np.where(x)]))
subject_colors[x > threshold, :] = subject_color
all_colors.append(subject_colors)
all_colors = np.array(all_colors).mean(0)
all_colors = np.hstack((np.ones(
(all_colors.shape[0], 1)) * 10, all_colors))
np.save(out_file.format(hemi=hemi), all_colors)
def save_frmi_color_per_subject(out_file, threshold=2):
# subjects_folders = utils.get_subfolders(SPM_ROOT)
good_subjects = ["pp002", "pp003", "pp004", "pp005", "pp006"]
subjects_folders = [os.path.join(SPM_ROOT, sub) for sub in good_subjects]
subjects_colors = utils.get_spaced_colors(len(subjects_folders))
# subjects_colors = utils.arr_to_colors(range(len(subjects_folders)), colors_map='Set1')[:, :3]
for hemi in ["rh", "lh"]:
first = True
all_colors = []
for sub_id, (subject_fol, subject_color) in enumerate(zip(subjects_folders, subjects_colors)):
subject = utils.namebase(subject_fol)
print(hemi, subject)
# if subject not in good_subjects:
# continue
fs_hemi_map = os.path.join(subject_fol, FS_HEMI_MAP_TEMPLATE.format(subject=subject, hemi=hemi))
old, brain = get_hemi_data(FS_SUBJECT, hemi, fs_hemi_map, "pial")
x = old.mlab_data
brain.close()
# x = nib.load(fs_hemi_map).get_data().squeeze()
# plt.hist(x, bins=50)
# plt.show()
subject_colors = np.ones((len(x), 3))
print(sum(x > threshold))
# print(np.unique(x[np.where(x)]))
subject_colors[x > threshold, :] = subject_color
all_colors.append(subject_colors)
all_colors = np.array(all_colors).mean(0)
all_colors = np.hstack((np.ones((all_colors.shape[0], 1)) * 10, all_colors))
np.save(out_file.format(hemi=hemi), all_colors)
def check_colors():
subjects_folders = utils.get_subfolders(SPM_ROOT)
good_subjects = ['pp002', 'pp003', 'pp004', 'pp005', 'pp006']
subjects_folders = [os.path.join(SPM_ROOT, sub) for sub in good_subjects]
subjects_colors = utils.get_spaced_colors(len(subjects_folders))
# subjects_colors = utils.arr_to_colors(range(len(subjects_folders)), colors_map='Set1')
plt.figure()
for subject_fol, color in zip(subjects_folders, subjects_colors):
subject = utils.namebase(subject_fol)
plt.scatter([0], [0], label='{} {}'.format(subject, color), c=color)
plt.legend()
plt.show()
def check_colors():
subjects_folders = utils.get_subfolders(SPM_ROOT)
good_subjects = ["pp002", "pp003", "pp004", "pp005", "pp006"]
subjects_folders = [os.path.join(SPM_ROOT, sub) for sub in good_subjects]
subjects_colors = utils.get_spaced_colors(len(subjects_folders))
# subjects_colors = utils.arr_to_colors(range(len(subjects_folders)), colors_map='Set1')
plt.figure()
for subject_fol, color in zip(subjects_folders, subjects_colors):
subject = utils.namebase(subject_fol)
plt.scatter([0], [0], label="{} {}".format(subject, color), c=color)
plt.legend()
plt.show()
def plot_3dscatter(points, labels, n_classes=None):
plt.switch_backend('agg')
fig = plt.figure()
ax = fig.gca(projection='3d')
points -= points.min(axis=0)
points = points / points.max()
xs = points[:, 0]
ys = points[:, 1]
zs = points[:, 2]
if n_classes is None:
n_classes = len(np.unique(labels))
colors = np.array(get_spaced_colors(n_classes))
for cl in range(n_classes):
i = np.where(labels == cl)
if len(i[0]) == 0:
continue
ax.scatter(xs[i], ys[i], zs[i], c=colors[cl], s=50, label=cl)
#ax.scatter(xs, ys, zs, c=colors[labels], s=50)
plt.axis('off')
plt.axis('equal')
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_zlim(0, 1)
ax.view_init(-30., 70.)
ax.legend()
#plt.show()
#print('ax.azim {}'.format(ax.azim))
#print('ax.elev {}'.format(ax.elev))
return fig
