def prf2visual_angle(prf_mtx, img_size, out_dir, base_name):
"""Generate retinotopic mapping based on voxels' pRF parameters.
`prf_mtx` is a #voxel x pRF-features matrix, pRF features can be 2 columns
(row, col) of image or 3 columns which adding a third pRF size parameters.
"""
feature_size = prf_mtx.shape[1]
pos_mtx = prf_mtx[:, :2]
# eccentricity
ecc = retinotopy.coord2ecc(pos_mtx, img_size, 20)
vol = ecc.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(out_dir, base_name+'_ecc.nii.gz'))
# angle
angle = retinotopy.coord2angle(pos_mtx, img_size)
vol = angle.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(out_dir, base_name+'_angle.nii.gz'))
# pRF size
if feature_size > 2:
size_angle = retinotopy.get_prf_size(prf_mtx, 55, 20)
vol = size_angle.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(out_dir, base_name+'_size.nii.gz'))
def retinotopic_mapping(prf_dir, roi):
"""Get eccentricity and angle based on pRF center for each voxel."""
roi_dir = os.path.join(prf_dir, roi)
# load selected model index
sel_models = np.load(os.path.join(roi_dir, 'reg_sel_model.npy'))
# output variables
ecc = np.zeros_like(sel_models)
angle = np.zeros_like(sel_models)
coords = np.zeros((sel_models.shape[0], 2))
for i in range(sel_models.shape[0]):
print 'Voxel %s' % (i + 1)
model_idx = int(sel_models[i])
xi = (model_idx % 2500) / 50
yi = (model_idx % 2500) % 50
# col
x0 = np.arange(5, 500, 10)[xi]
# row
y0 = np.arange(5, 500, 10)[yi]
coords[i] = [y0, x0]
# get ecc and angle
ecc = retinotopy.coord2ecc(coords, 500, 20)
angle = retinotopy.coord2angle(coords, 500)
np.save(os.path.join(roi_dir, 'ecc.npy'), ecc)
np.save(os.path.join(roi_dir, 'angle.npy'), angle)
def retinotopic_mapping(corr_file, data_dir, vxl_idx=None, figout=False):
"""Make the retinotopic mapping using activation map from CNN."""
if figout:
fig_dir = os.path.join(data_dir, 'fig')
check_path(fig_dir)
# load the cross-correlation matrix from file
corr_mtx = np.load(corr_file, mmap_mode='r')
# set voxel index
if not isinstance(vxl_idx, np.ndarray):
vxl_idx = np.arange(corr_mtx.shape[0])
elif len(vxl_idx) != corr_mtx.shape[0]:
print 'mismatch on voxel number!'
return
else:
print 'voxel index loaded.'
pos_mtx = np.zeros((73728, 2))
pos_mtx[:] = np.nan
for i in range(len(vxl_idx)):
print 'Iter %s of %s' % (i + 1, len(vxl_idx)),
tmp = corr_mtx[i, :]
tmp = np.nan_to_num(np.array(tmp))
# significant threshold for one-tail test
tmp[tmp <= 0.019257] = 0
if np.sum(tmp):
tmp = tmp.reshape(96, 27, 27)
mmtx = np.max(tmp, axis=0)
print mmtx.min(), mmtx.max()
# get indices of n maximum values
max_n = 20
row_idx, col_idx = np.unravel_index(
np.argsort(mmtx.ravel())[-1 * max_n:], mmtx.shape)
nmtx = np.zeros(mmtx.shape)
nmtx[row_idx, col_idx] = mmtx[row_idx, col_idx]
if figout:
fig_file = os.path.join(fig_dir,
'v' + str(vxl_idx[i]) + '.png')
imsave(fig_file, nmtx)
# center of mass
x, y = ndimage.measurements.center_of_mass(nmtx)
pos_mtx[vxl_idx[i], :] = [x, y]
else:
print ' '
#receptive_field_file = os.path.join(data_dir, 'receptive_field_pos.npy')
#np.save(receptive_field_file, pos_mtx)
#pos_mtx = np.load(receptive_field_file)
# eccentricity
dist = retinotopy.coord2ecc(pos_mtx, (27, 27))
# convert distance into degree
# 0-4 degree -> d < 5.5
# 4-8 degree -> d < 11
# 8-12 degree -> d < 16.5
# 12-16 degree -> d < 22
# else > 16 degree
ecc = np.zeros(dist.shape)
for i in range(len(dist)):
if np.isnan(dist[i]):
ecc[i] = np.nan
elif dist[i] < 2.7:
ecc[i] = 1
elif dist[i] < 5.4:
ecc[i] = 2
elif dist[i] < 8.1:
ecc[i] = 3
elif dist[i] < 10.8:
ecc[i] = 4
else:
ecc[i] = 5
#dist_vec = np.nan_to_num(ecc)
#vol = dist_vec.reshape(18, 64, 64)
vol = ecc.reshape(18, 64, 64)
vutil.save2nifti(
vol, os.path.join(data_dir, 'train_max' + str(max_n) + '_ecc.nii.gz'))
# angle
angle_vec = retinotopy.coord2angle(pos_mtx, (27, 27))
#angle_vec = np.nan_to_num(angle_vec)
vol = angle_vec.reshape(18, 64, 64)
vutil.save2nifti(
vol, os.path.join(data_dir,
'train_max' + str(max_n) + '_angle.nii.gz'))
def ridge_retinotopic_mapping(corr_file, vxl_idx=None, top_n=None):
"""Make the retinotopic mapping using activation map from CNN."""
data_dir = os.path.dirname(corr_file)
# load the cross-correlation matrix from file
corr_mtx = np.load(corr_file, mmap_mode='r')
# corr_mtx.shape = (3025, vxl_num)
if not isinstance(vxl_idx, np.ndarray):
vxl_idx = np.arange(corr_mtx.shape[1])
if not top_n:
top_n = 20
pos_mtx = np.zeros((73728, 2))
pos_mtx[:] = np.nan
for i in range(len(vxl_idx)):
print 'Iter %s of %s' % (i, len(vxl_idx)),
tmp = corr_mtx[:, i]
tmp = np.nan_to_num(np.array(tmp))
# significant threshold
# one-tail test
#tmp[tmp <= 0.17419] = 0
if np.sum(tmp):
tmp = tmp.reshape(55, 55)
print tmp.min(), tmp.max()
# get indices of n maximum values
row, col = np.unravel_index(
np.argsort(tmp.ravel())[-1 * top_n:], tmp.shape)
mtx = np.zeros(tmp.shape)
mtx[row, col] = tmp[row, col]
# center of mass
x, y = ndimage.measurements.center_of_mass(mtx)
pos_mtx[vxl_idx[i], :] = [x, y]
else:
print ' '
#receptive_field_file = os.path.join(data_dir, 'receptive_field_pos.npy')
#np.save(receptive_field_file, pos_mtx)
#pos_mtx = np.load(receptive_field_file)
# eccentricity
dist = retinotopy.coord2ecc(pos_mtx, (55, 55))
# convert distance into degree
# 0-4 degree -> d < 5.5
# 4-8 degree -> d < 11
# 8-12 degree -> d < 16.5
# 12-16 degree -> d < 22
# else > 16 degree
ecc = np.zeros(dist.shape)
for i in range(len(dist)):
if np.isnan(dist[i]):
ecc[i] = np.nan
elif dist[i] < 5.445:
ecc[i] = 1
elif dist[i] < 10.91:
ecc[i] = 2
elif dist[i] < 16.39:
ecc[i] = 3
elif dist[i] < 21.92:
ecc[i] = 4
else:
ecc[i] = 5
vol = ecc.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(data_dir, 'ecc_max%s.nii.gz' % (top_n)))
# angle
angle_vec = retinotopy.coord2angle(pos_mtx, (55, 55))
vol = angle_vec.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(data_dir,
'angle_max%s.nii.gz' % (top_n)))