def modify_left_button_callback(i_ren, obj, button):
print button.current_icon_id
print button.current_icon_name
if button.current_icon_id == 0:
new_f = _computeFib(streamlines, sphereWidget)
save_tck(new_f,
header=fib.header,
data_per_streamline=fib.tractogram.data_per_streamline,
data_per_point=fib.tractogram.data_per_point,
affine_to_rasmm=fib.tractogram.affine_to_rasmm,
out_path=os.path.join(file_select_menu.current_directory,
'%s.tck') % text.text)
if button.current_icon_id == 1:
pass
button.next_icon()
i_ren.force_render()
label_img = modularity_finetune[0]
# choose fiber according to node clusters
# index
fig, ax = plt.subplots()
slice = img.get_data()[img.shape[0] / 2, :, :]
ax.imshow(slice.T, cmap='gray', origin='lower')
color = plt.cm.spectral(np.linspace(0, 1, len(set(label_img))))
# save the fiber cluster
out_path = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100408/result/' \
'result20vs45/cc_20fib_step20_new_correct_modularity_set0-1_%s.tck'
for i in set(label_img):
l = np.argwhere(label_img == i)
stream = Ls_temp_labels == l
stream_sum = stream.sum(axis=0)
stream_temp = stream_sum > 0
fib_path_index = imgtck.streamlines[stream_temp]
mid_node_affine = apply_affine(npl.inv(img.affine), Ls_temp[stream_temp])
ax.plot(np.array(mid_node_affine)[:, 1], np.array(mid_node_affine)[:, 2],
'o', color=color[i-1])
save_tck(fib_path_index, imgtck.header, imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point, imgtck.tractogram.affine_to_rasmm,
out_path % i)
plt.show()
L_temp_n.append(imgtck[i])
elif imgtck[i][j][0] == 0:
if (j - 20) in range(len(imgtck[i])) \
and (j + 20) in range(len(imgtck[i])) \
and imgtck[i][j - 20][0] * imgtck[i][j + 20][0] < 0:
L_temp_need.append(imgtck[i])
else:
L_temp_n.append(imgtck[i])
return L_temp_need, L_temp_n
if __name__ == '__main__':
from pyfat.io.load import load_tck
from pyfat.io.save import save_tck
# load data
file = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100206/Diffusion/100k_sift_1M45006_dynamic250.tck'
imgtck = load_tck(file)
# extract CC
L_temp = extract_multi_node(imgtck)[1]
# print L_temp
# save data
out_path = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100206/result/CC_multi_node_fib.tck'
save_tck(L_temp, imgtck.header, imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point,
imgtck.tractogram.affine_to_rasmm, out_path)
print len(labels)
d = zip(labels, Ls_temp)
L_temp_0 = nibAS.ArraySequence()
L_temp_1 = nibAS.ArraySequence()
L_temp_2 = nibAS.ArraySequence()
L_temp_3 = nibAS.ArraySequence()
for k in range(len(d)):
if d[k][0] == 0:
L_temp_0.append(img_cc.streamlines[k])
if d[k][0] == 1:
L_temp_1.append(img_cc.streamlines[k])
if d[k][0] == 2:
L_temp_2.append(img_cc.streamlines[k])
if d[k][0] == 3:
L_temp_3.append(img_cc.streamlines[k])
out_put = '/home/brain/workingdir/data/dwi/hcp/preprocessed/response_dhollander/100206/result/CC_fib_only3_0.tck'
out_put1 = '/home/brain/workingdir/data/dwi/hcp/preprocessed/response_dhollander/100206/result/CC_fib_only3_1.tck'
out_put2 = '/home/brain/workingdir/data/dwi/hcp/preprocessed/response_dhollander/100206/result/CC_fib_only3_2.tck'
out_put3 = '/home/brain/workingdir/data/dwi/hcp/preprocessed/response_dhollander/100206/result/CC_fib_only3_3.tck'
save_tck(L_temp_0, img_cc.header, img_cc.tractogram.data_per_streamline, img_cc.tractogram.data_per_point,
img_cc.tractogram.affine_to_rasmm, out_put)
save_tck(L_temp_1, img_cc.header, img_cc.tractogram.data_per_streamline, img_cc.tractogram.data_per_point,
img_cc.tractogram.affine_to_rasmm, out_put1)
save_tck(L_temp_2, img_cc.header, img_cc.tractogram.data_per_streamline, img_cc.tractogram.data_per_point,
img_cc.tractogram.affine_to_rasmm, out_put2)
save_tck(L_temp_3, img_cc.header, img_cc.tractogram.data_per_streamline, img_cc.tractogram.data_per_point,
img_cc.tractogram.affine_to_rasmm, out_put3)
L_temp2 = apply_affine(npl.inv(img.affine), L_temp2)
L_temp3 = apply_affine(npl.inv(img.affine), L_temp3)
ax.plot(np.array(L_temp0)[:, 1], np.array(L_temp0)[:, 2], 'o', color='r')
ax.plot(np.array(L_temp1)[:, 1], np.array(L_temp1)[:, 2], 'o', color='b')
ax.plot(np.array(L_temp2)[:, 1], np.array(L_temp2)[:, 2], 'o', color='g')
ax.plot(np.array(L_temp3)[:, 1], np.array(L_temp3)[:, 2], 'o', color='c')
plt.show()
# save the fiber cluster
out_path = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100206/result/CC_fib_ncut_new_set0-1_0.tck'
out_path1 = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100206/result/CC_fib_ncut_new_set0-1_1.tck'
out_path2 = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100206/result/CC_fib_ncut_new_set0-1_2.tck'
out_path3 = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100206/result/CC_fib_ncut_new_set0-1_3.tck'
save_tck(L_temp_0, imgtck.header, imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point, imgtck.tractogram.affine_to_rasmm,
out_path)
save_tck(L_temp_1, imgtck.header, imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point, imgtck.tractogram.affine_to_rasmm,
out_path1)
save_tck(L_temp_2, imgtck.header, imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point, imgtck.tractogram.affine_to_rasmm,
out_path2)
save_tck(L_temp_3, imgtck.header, imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point, imgtck.tractogram.affine_to_rasmm,
out_path3)
other.append(f)
fib_total.append(remain_fib_clusters[int(node_sort[0][0])])
fib_total.append(remain_fib_clusters[int(node_sort[1][0])])
if len(other) == 0:
pass
else:
fib_total.append(other)
fib_total.append(remain_fib_clusters[int(node_sort[-1][0])])
out_path = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100408/result/' \
'result20vs45/cc_20fib_lr1.5_01_new_hierarchical_single_%s.tck'
spe_name_four = ['oc', 'ac', 'other', 'cc']
spe_name_three = ['oc', 'ac', 'cc']
for fi in range(len(fib_total)):
if len(fib_total) == 4:
save_tck(fib_total[fi], imgtck.header,
imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point,
imgtck.tractogram.affine_to_rasmm,
out_path % spe_name_four[fi])
if len(fib_total) == 3:
save_tck(fib_total[fi], imgtck.header,
imgtck.tractogram.data_per_streamline,
imgtck.tractogram.data_per_point,
imgtck.tractogram.affine_to_rasmm,
out_path % spe_name_three[fi])
plt.show()