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)
import matplotlib.pyplot as plt
# load data
data_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100408/Structure/T1w_acpc_dc_restore_brain.nii.gz'
img = nib.load(data_path)
img_data = img.get_data()
# tck_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
# 'response_dhollander/100408/result/45006old/CC_fib_ncut_set0-1_1.tck'
# tck_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
# 'response_dhollander/100408/result/result20vs45/cc_20fib_step20_new_sample5000.tck'
tck_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100408/result/result20vs45/cc_20fib_step20_new_correct.tck'
imgtck = load_tck(tck_path)
# streamstck = imgtck.streamlines
# print streamstck
# extract cc fib
# imgtck_fib = extract_cc(imgtck)
# remove multi-node fib
# imgtck_fib = extract_multi_node(imgtck_fib)[0]
# step > 20
# imgtck_fib = extract_cc_step(imgtck_fib)[0]
# extract node according to x-value
Ls_temp = xmin_extract(imgtck)
# print len(Ls_temp)
# show node or density
# show_2d_node(img, Ls_temp)
# fiber density of volume
def fib_density_map(volume, fiber, output):
'''
fiber density map
:param volume: structure image T1w
:param fiber: tck file
:param output: density map file
:return:
'''
shape = volume.shape
affine = volume.affine
streamstck = fiber.streamlines
image_volume = ditu.density_map(streamstck,
vol_dims=shape,
voxel_size=0.625,
affine=affine)
dm_img = nib.Nifti1Image(image_volume.astype("int16"), affine)
dm_img.to_filename(output)
if __name__ == '__main__':
from pyfat.io.load import load_tck
img = nib.load("/home/brain/workingdir/data/dwi/hcp/preprocessed/"
"response_dhollander/100206/Structure/T1w_short.nii.gz")
img_cc = load_tck('/home/brain/workingdir/data/dwi/hcp/preprocessed/'
'response_dhollander/100206/result/CC_fib.tck')
output = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100206/result/CC_fib_density_map.nii.gz'
fib_density_map(img, img_cc, output)
# !/usr/bin/python
# -*- coding: utf-8 -*-
from pyfat.io.load import load_tck
from pyfat.io.save import save_tck
from pyfat.algorithm.remove_non_cc import extract_up_z
data_path = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/100206/result/CC_fib.tck'
# load tck data
img_cc = load_tck(data_path)
L_temp = extract_up_z(img_cc)
out_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100206/result/CC_fib_remove_non_cc_z-10_x-min.tck'
save_tck(L_temp, img_cc.header, img_cc.tractogram.data_per_streamline, img_cc.tractogram.data_per_point,
img_cc.tractogram.affine_to_rasmm, out_path)
# !/usr/bin/python
# -*- coding: utf-8 -*-
from pyfat.io.load import load_tck
from pyfat.io.save import save_tck
from pyfat.algorithm.node_extract import xmin_extract
from pyfat.algorithm.hierarchical_clustering import hierarchical_clust
import nibabel.streamlines.array_sequence as nibAS
from sklearn.neighbors import kneighbors_graph
input_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100206/result/CC_fib.tck'
img_cc = load_tck(input_path)
Ls_temp = xmin_extract(img_cc)
# connectivity = kneighbors_graph(Ls_temp, n_neighbors=10, mode='connectivity', include_self=True)
# connectivity = kneighbors_graph(Ls_temp, n_neighbors=10, include_self=False)
labels = hierarchical_clust(Ls_temp, 4, linkage='complete')
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])
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(imgtck.streamlines, colormap_full))
fvtk.record(ren, n_frames=1, out_path=out_path, size=(600, 600))
fvtk.show(ren)
if __name__ == '__mian__':
from pyfat.io.load import load_tck
from dipy.io.pickles import save_pickle
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import SumPointwiseEuclideanMetric
# load fiber data
data_path = '/home/brain/workingdir/data/dwi/hcp/' \
'preprocessed/response_dhollander/101006/result/CC_fib.tck'
imgtck = load_tck(data_path)
world_coords = True
if not world_coords:
from dipy.tracking.streamline import transform_streamlines
streamlines = transform_streamlines(imgtck.streamlines,
np.linalg.inv(imgtck.affine))
metric = SumPointwiseEuclideanMetric(feature=ArcLengthFeature())
qb = QuickBundles(threshold=2., metric=metric)
clusters = qb.cluster(streamlines)
# extract > 100
# print len(clusters) # 89
for c in clusters:
if len(c) < 100:
# !/usr/bin/python
# -*- coding: utf-8 -*-
from pyfat.io.load import load_tck
from pyfat.algorithm.node_extract import xmin_extract
from pyfat.algorithm.node_clustering import hiera_single_clust
source_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100408/result/result20vs45/cc_20fib_lr1.5_new_correct_sample1000.tck'
data = load_tck(source_path)
Ls_temp = xmin_extract(data)
c = hiera_single_clust(Ls_temp)
print c
alpha = 1
prefix = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/100408/result/463_clusters/clusters'
# prefix = input('Input the prefix of images:')
files = glob.glob(prefix + '_*')
num = len(files)
filename_lens = [len(x) for x in files] # length of the files
min_len = min(filename_lens) # minimal length of filenames
max_len = max(filename_lens) # maximal length of filenames
if min_len == max_len: # the last number of each filename has the same length
files = sorted(files) # sort the files in ascending order
else: # maybe the filenames are:x_0.png ... x_10.png ... x_100.png
index = [0 for x in range(num)]
for i in range(num):
filename = files[i]
start = filename.rfind('_') + 1
end = filename.rfind('.')
file_no = int(filename[start:end])
index[i] = file_no
index = sorted(index)
files = [prefix + '_' + str(x) + '.tck' for x in index]
for f in files[23:]:
streamlines = load_tck(f).streamlines
vertex = terminus2surface_map(streamlines, geo_path)
surface_roi_contour(subjects_dir, subject_id, hemi, surf, alpha, vertex,
lr_label)
# surface_streamlines_map(subjects_dir, subject_id, hemi, surf, alpha, vertex)
plt.figure('Fiber statistics')
plt.subplot(111)
plt.title('Length histogram')
plt.hist(lengths, color='burlywood')
plt.xlabel('Length')
plt.ylabel('Count')
# save length histogram
# plt.legend()
# plt.savefig('lr250_sift12_hcp_FFA_projabs-2_length_histogram.png')
plt.show()
if __name__ == '__main__':
from pyfat.io.load import load_tck, load_trk
# tck
fname_tck = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/lr250_sift12_hcp_FFA_projabs-2.tck'
imgtck = load_tck(fname_tck)
stream = imgtck.streamlines
lengths = list(fib_lengths_count(stream))
show(lengths)
# trk
fname_trk = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/lr250_sift12_hcp_FFA_projabs-2.trk'
imgtrk, hdr = load_trk(fname_trk)
stream = [s[0] for s in imgtrk]
lengths = list(fib_lengths_count(stream))
show(lengths)