def trk_fixer(trkpath, trk_newpath, verbose=False):
if verbose:
t1 = time()
txt = f'Beginning to load {trkpath}'
trkdata = load_trk_spe(trkpath, 'same')
if verbose:
t2 = time()
txt = f'It took {str(t2-t1)} seconds to load {trkpath}'
print(txt)
if hasattr(trkdata, 'space_attribute'):
header = trkdata.space_attribute
elif hasattr(trkdata, 'space_attributes'):
header = trkdata.space_attributes
remove, keep = trkdata.remove_invalid_streamlines()
if verbose:
t3 = time()
duration = t3 - t2
txt = f"it took {duration} to do the test on {trkpath}, and {np.size(remove)} streamlines were removed. saving it to {trk_newpath}"
print(txt)
if np.size(remove)==0:
shutil.copy(trkpath, trk_newpath)
else:
new_streamlines = trkdata.streamlines
save_trk_header(trk_newpath, new_streamlines, header, verbose=verbose)
if verbose:
t4 = time()
txt = f'It took {str(t4-t3)} seconds to save {trk_newpath}'
print(txt)
def trk_fixer_old(trkpath, trk_newpath, verbose = False):
if verbose:
t1 = time()
txt = f'Beginning to load {trkpath}'
trkdata = load_trk_spe(trkpath, 'same')
trk_streamlines = trkdata.streamlines
if hasattr(trkdata, 'space_attribute'):
header = trkdata.space_attribute
elif hasattr(trkdata, 'space_attributes'):
header = trkdata.space_attributes
voxdim = trkdata.dimensions
if verbose:
t2 = time()
txt = f'It took {str(t2-t1)} seconds to load {trkpath}'
print(txt)
vverbose=True
orig_size = np.shape(trk_streamlines)[0]
trk_streamlines[:] = (x for x in trk_streamlines if streamline_checker(x, voxdim,vverbose))
"""
for i, streamline in enumerate(trk_streamlines):
mymin = np.min(trk_streamlines, axis=0)
mymax = np.max(trk_streamlines, axis=0)
np.asarray(list(product(*zip(mymin, mymax))))
bbox_min = np.min(trk_streamlines, axis=0)
bbox_max = np.max(trk_streamlines, axis=0)
bbox_corners = np.asarray(list(product(*zip(bbox_min, bbox_max))))
if np.any(bbox_corners[:, 0] > voxdim[0]) or \
np.any(bbox_corners[:, 1] > voxdim[1]) or \
np.any(bbox_corners[:, 2] > voxdim[2]):
np.pop()
"""
new_size = np.shape(trk_streamlines)[0]
cut_streamlines = orig_size - new_size
if verbose:
t3 = time()
duration = t3 - t2
txt = f"it took {duration} to do the test on {trkpath}, and {str(cut_streamlines)} streamlines were removed. saving it to {trk_newpath}"
print(txt)
#np.asarray(list(product(*zip(bbox_min, bbox_max))))
if cut_streamlines!=0:
save_trk_header(trk_newpath, trk_streamlines, header, verbose = verbose)
else:
shutil.copy(trkpath, trk_newpath)
if verbose:
t4 = time()
txt = f'It took {str(t4-t3)} seconds to save {trk_newpath}'
print(txt)
def trktotck(trk_path, overwrite=False):
import warnings
try:
tractogram = load_trk(trk_path, 'same')
except:
tractogram = load_trk_spe(trk_path, 'same')
if nib.streamlines.detect_format(tractogram) is not nib.streamlines.TrkFile:
warnings.warn("Skipping non TRK file: '{}'".format(tractogram))
output_filename = tractogram[:-4] + '.tck'
if os.path.isfile(output_filename) and not overwrite:
warnings.warn("Skipping existing file: '{}'. Set overwrite to true".format(output_filename))
trk = nib.streamlines.load(tractogram)
nib.streamlines.save(trk.tractogram, output_filename)
trk_folder, group_str + '_MDT' + ratio_str + '_' +
index_to_struct[target_tuple[0]] + '_to_' +
index_to_struct[target_tuple[1]] + '_streamlines.trk')
if os.path.exists(fa_path):
with open(fa_path, 'rb') as f:
fa_lines = pickle.load(f)
if os.path.exists(md_path):
with open(md_path, 'rb') as f:
md_lines = pickle.load(f)
# '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/Centroids_MDT_non_inclusive_symmetric_100/APOE4_MDT_ratio_100_ctx-lh-inferiorparietal_left_to_ctx-lh-inferiortemporal_left_streamlines.trk'
if os.path.exists(trk_path):
try:
streamlines_data = load_trk(trk_path, 'same')
except:
streamlines_data = load_trk_spe(trk_path, 'same')
streamlines = streamlines_data.streamlines
if 'fa_lines' in locals():
cutoff = np.percentile(fa_lines, 100 - top_percentile)
select_streams = fa_lines > cutoff
fa_lines = list(compress(fa_lines, select_streams))
streamlines = list(compress(streamlines, select_streams))
streamlines = nib.streamlines.ArraySequence(streamlines)
if np.shape(streamlines)[0] != np.shape(fa_lines)[0]:
raise Exception(
'Inconsistency between streamlines and fa lines')
else:
txt = f'Cannot find {fa_path}, could not select streamlines based on fa'
warnings.warn(txt)
fa_lines = [None]