def load_tractogram(filename,
reference,
to_space=Space.RASMM,
shifted_origin=False,
bbox_valid_check=True,
trk_header_check=True):
""" Load the stateful tractogram from any format (trk, tck, fib, dpy)
Parameters
----------
filename : string
Filename with valid extension
reference : Nifti or Trk filename, Nifti1Image or TrkFile, Nifti1Header or
trk.header (dict), or 'same' if the input is a trk file.
Reference that provides the spatial attribute.
Typically a nifti-related object from the native diffusion used for
streamlines generation
space : string
Space in which the streamlines will be transformed after loading
(vox, voxmm or rasmm)
shifted_origin : bool
Information on the position of the origin,
False is Trackvis standard, default (center of the voxel)
True is NIFTI standard (corner of the voxel)
Returns
-------
output : StatefulTractogram
The tractogram to load (must have been saved properly)
"""
_, extension = os.path.splitext(filename)
if extension not in ['.trk', '.tck', '.vtk', '.fib', '.dpy']:
logging.error('Output filename is not one of the supported format')
return False
if to_space not in Space:
logging.error('Space MUST be one of the 3 choices (Enum)')
return False
if reference == 'same':
if extension == '.trk':
reference = filename
else:
logging.error('Reference must be provided, "same" is only ' +
'available for Trk file.')
return False
if trk_header_check and extension == '.trk':
if not is_header_compatible(filename, reference):
logging.error('Trk file header does not match the provided ' +
'reference')
return False
timer = time.time()
data_per_point = None
data_per_streamline = None
if extension in ['.trk', '.tck']:
tractogram_obj = nib.streamlines.load(filename).tractogram
streamlines = tractogram_obj.streamlines
if extension == '.trk':
data_per_point = tractogram_obj.data_per_point
data_per_streamline = tractogram_obj.data_per_streamline
elif extension in ['.vtk', '.fib']:
streamlines = load_vtk_streamlines(filename)
elif extension in ['.dpy']:
dpy_obj = Dpy(filename, mode='r')
streamlines = list(dpy_obj.read_tracks())
dpy_obj.close()
logging.debug('Load %s with %s streamlines in %s seconds', filename,
len(streamlines), round(time.time() - timer, 3))
sft = StatefulTractogram(streamlines,
reference,
Space.RASMM,
shifted_origin=shifted_origin,
data_per_point=data_per_point,
data_per_streamline=data_per_streamline)
if to_space == Space.VOX:
sft.to_vox()
elif to_space == Space.VOXMM:
sft.to_voxmm()
if bbox_valid_check and not sft.is_bbox_in_vox_valid():
raise ValueError('Bounding box is not valid in voxel space, cannot ' +
'load a valid file if some coordinates are invalid')
return sft
# Save Streamlines
save_trk("my_streamlines.trk", streamlines=streamlines, affine=np.eye(4))
"""
2. We also work on our HDF5 based file format which can read/write massive
datasets (as big as the size of your free disk space). With `Dpy` we can
support
* direct indexing from the disk
* memory usage always low
* extensions to include different arrays in the same file
Here is a simple example.
"""
from dipy.io.dpy import Dpy
dpw = Dpy('fornix.dpy', 'w')
"""
Write many streamlines at once.
"""
dpw.write_tracks(streamlines)
"""
Write one track
"""
dpw.write_track(streamlines[0])
"""
or one track each time.
"""
for t in streamlines:
def warp_displacements_tracks(fdpy, ffa, fmat, finv, fdis, fdisa, fref, fdpyw):
""" Warp tracks from native space to the FMRIB58/MNI space
We use here the fsl displacements. Have a look at create_displacements to
see an example of how to use these displacements.
Parameters
------------
fdpy : filename of the .dpy file with the tractography
ffa : filename of nifti to be warped
fmat : filename of .mat (flirt)
fdis : filename of displacements (fnirtfileutils)
fdisa : filename of displacements (fnirtfileutils + affine)
finv : filename of invwarp displacements (invwarp)
fref : filename of reference volume e.g. (FMRIB58_FA_1mm.nii.gz)
fdpyw : filename of the warped tractography
See also
-----------
dipy.external.fsl.create_displacements
"""
# read the tracks from the image space
dpr = Dpy(fdpy, 'r')
T = dpr.read_tracks()
dpr.close()
# copy them in a new file
dpw = Dpy(fdpyw, 'w', compression=1)
dpw.write_tracks(T)
dpw.close()
# from fa index to ref index
res = flirt2aff_files(fmat, ffa, fref)
# load the reference img
imgref = nib.load(fref)
refaff = imgref.get_affine()
# load the invwarp displacements
imginvw = nib.load(finv)
invwdata = imginvw.get_data()
invwaff = imginvw.get_affine()
# load the forward displacements
imgdis = nib.load(fdis)
disdata = imgdis.get_data()
# load the forward displacements + affine
imgdis2 = nib.load(fdisa)
disdata2 = imgdis2.get_data()
# from their difference create the affine
disaff = disdata2 - disdata
del disdata
del disdata2
shape = nib.load(ffa).get_data().shape
# transform the displacements affine back to image space
disaff0 = affine_transform(disaff[..., 0], res[:3, :3], res[:3, 3], shape, order=1)
disaff1 = affine_transform(disaff[..., 1], res[:3, :3], res[:3, 3], shape, order=1)
disaff2 = affine_transform(disaff[..., 2], res[:3, :3], res[:3, 3], shape, order=1)
# remove the transformed affine from the invwarp displacements
di = invwdata[:, :, :, 0] + disaff0
dj = invwdata[:, :, :, 1] + disaff1
dk = invwdata[:, :, :, 2] + disaff2
dprw = Dpy(fdpyw, 'r+')
rows = len(dprw.f.root.streamlines.tracks)
blocks = np.round(np.linspace(0, rows, 10)).astype(int) # lets work in blocks
# print rows
for i in range(len(blocks) - 1):
# print blocks[i],blocks[i+1]
# copy a lot of tracks together
caboodle = dprw.f.root.streamlines.tracks[blocks[i]:blocks[i + 1]]
mci = mc(di, caboodle.T, order=1) # interpolations for i displacement
mcj = mc(dj, caboodle.T, order=1) # interpolations for j displacement
mck = mc(dk, caboodle.T, order=1) # interpolations for k displacement
D = np.vstack((mci, mcj, mck)).T
# go back to mni image space
WI2 = np.dot(caboodle, res[:3, :3].T) + res[:3, 3] + D
# and then to mni world space
caboodlew = np.dot(WI2, refaff[:3, :3].T) + refaff[:3, 3]
# write back
dprw.f.root.streamlines.tracks[blocks[i]:blocks[i + 1]] = caboodlew.astype('f4')
dprw.close()
def save_tractogram(sft, filename, bbox_valid_check=True):
""" Save the stateful tractogram in any format (trk, tck, vtk, fib, dpy)
Parameters
----------
sft : StatefulTractogram
The stateful tractogram to save
filename : string
Filename with valid extension
Returns
-------
output : bool
Did the saving work properly
"""
_, extension = os.path.splitext(filename)
if extension not in ['.trk', '.tck', '.vtk', '.fib', '.dpy']:
TypeError('Output filename is not one of the supported format')
if bbox_valid_check and not sft.is_bbox_in_vox_valid():
raise ValueError('Bounding box is not valid in voxel space, cannot ' +
'save a valid file if some coordinates are invalid')
old_space = deepcopy(sft.space)
old_shift = deepcopy(sft.shifted_origin)
sft.to_rasmm()
sft.to_center()
timer = time.time()
if extension in ['.trk', '.tck']:
tractogram_type = detect_format(filename)
header = create_tractogram_header(tractogram_type,
*sft.space_attribute)
new_tractogram = Tractogram(sft.streamlines, affine_to_rasmm=np.eye(4))
if extension == '.trk':
new_tractogram.data_per_point = sft.data_per_point
new_tractogram.data_per_streamline = sft.data_per_streamline
fileobj = tractogram_type(new_tractogram, header=header)
nib.streamlines.save(fileobj, filename)
elif extension in ['.vtk', '.fib']:
save_vtk_streamlines(sft.streamlines, filename, binary=True)
elif extension in ['.dpy']:
dpy_obj = Dpy(filename, mode='w')
dpy_obj.write_tracks(sft.streamlines)
dpy_obj.close()
logging.debug('Save %s with %s streamlines in %s seconds', filename,
len(sft), round(time.time() - timer, 3))
if old_space == Space.VOX:
sft.to_vox()
elif old_space == Space.VOXMM:
sft.to_voxmm()
if old_shift:
sft.to_corner()
return True
def dpy(workingdir,
input,
compressed=None,
restored=None,
tol_error=0.01,
force=False,
coords_only=False,
verbose=False):
if not input.endswith('tck') and not input.endswith('trk'):
# we need to convert
print('Invalid format')
return None
if not compressed:
compressed = input + '_compressed' + str(tol_error) + '.dpy'
if not restored:
restored = input + '_restored' + str(tol_error) + '.tck'
original = os.path.join(workingdir, input)
#
# compression
#
c_time = -1
if not os.path.exists(os.path.join(workingdir, compressed)) or force:
# compress again!
t0 = time.time()
loaded_original = nib.streamlines.load(original, lazy_load=False)
original_streamlines = loaded_original.streamlines
# parameters from Presseau15 are 0.01 and inf
c_streamlines = compress_streamlines(original_streamlines,
tol_error=tol_error,
max_segment_length=np.inf)
# write dpy file
# set compression to highest but it does have any effect
dpw = Dpy(os.path.join(workingdir, compressed),
mode='w',
compression=9)
for c_s in c_streamlines:
dpw.write_track(c_s)
dpw.close()
c_time = time.time() - t0
if verbose:
print('compression done.')
#
# restoring
#
d_time = -1
if not os.path.exists(os.path.join(workingdir, restored)) or force:
# restore again!
t0 = time.time()
restored_data = Dpy(os.path.join(workingdir, compressed), mode='r')
restored_streamlines = restored_data.read_tracks()
restored_data.close()
d_time = time.time() - t0
with open(os.path.join(workingdir, restored), 'w') as f:
f.write('restoredok.')
if verbose:
print('restoring done.')
#
# calculate errors
#
stats = compressed + '_stats' + str(tol_error) + '.p'
if not os.path.exists(os.path.join(workingdir, stats)) or force:
statsdata = Runner.error_per_streamlines(original_streamlines,
restored_streamlines)
sizestatsdata = Runner.sizestats(
os.path.join(workingdir, original),
os.path.join(workingdir, compressed))
statsdata = [
c_time, d_time, sizestatsdata, statsdata[0], statsdata[1]
]
with open(os.path.join(workingdir, stats), 'wb') as f:
pickle.dump(statsdata, f)
else:
with open(os.path.join(workingdir, stats), 'rb') as f:
statsdata = pickle.load(f)
[c_time, d_time, sizestatsdata, (min_e, max_e, mean_e, std_e), (end_min_e, end_max_e, end_mean_e, end_std_e)] = \
statsdata
if verbose:
print('Times', c_time, d_time)
print('Size Stats', sizestatsdata)
print('Error', min_e, max_e, mean_e, std_e)
print('Endpoint Error', end_min_e, end_max_e, end_mean_e,
end_std_e)
return statsdata
def load_tractogram(filename,
reference,
to_space=Space.RASMM,
to_origin=Origin.NIFTI,
bbox_valid_check=True,
trk_header_check=True):
""" Load the stateful tractogram from any format (trk, tck, vtk, fib, dpy)
Parameters
----------
filename : string
Filename with valid extension
reference : Nifti or Trk filename, Nifti1Image or TrkFile, Nifti1Header or
trk.header (dict), or 'same' if the input is a trk file.
Reference that provides the spatial attribute.
Typically a nifti-related object from the native diffusion used for
streamlines generation
to_space : Enum (dipy.io.stateful_tractogram.Space)
Space to which the streamlines will be transformed after loading
to_origin : Enum (dipy.io.stateful_tractogram.Origin)
Origin to which the streamlines will be transformed after loading
NIFTI standard, default (center of the voxel)
TRACKVIS standard (corner of the voxel)
bbox_valid_check : bool
Verification for negative voxel coordinates or values above the
volume dimensions. Default is True, to enforce valid file.
trk_header_check : bool
Verification that the reference has the same header as the spatial
attributes as the input tractogram when a Trk is loaded
Returns
-------
output : StatefulTractogram
The tractogram to load (must have been saved properly)
"""
_, extension = os.path.splitext(filename)
if extension not in ['.trk', '.tck', '.vtk', '.fib', '.dpy']:
logging.error('Output filename is not one of the supported format.')
return False
if to_space not in Space:
logging.error('Space MUST be one of the 3 choices (Enum).')
return False
if reference == 'same':
if extension == '.trk':
reference = filename
else:
logging.error('Reference must be provided, "same" is only '
'available for Trk file.')
return False
if trk_header_check and extension == '.trk':
if not is_header_compatible(filename, reference):
logging.error('Trk file header does not match the provided '
'reference.')
return False
timer = time.time()
data_per_point = None
data_per_streamline = None
if extension in ['.trk', '.tck']:
tractogram_obj = nib.streamlines.load(filename).tractogram
streamlines = tractogram_obj.streamlines
if extension == '.trk':
data_per_point = tractogram_obj.data_per_point
data_per_streamline = tractogram_obj.data_per_streamline
elif extension in ['.vtk', '.fib']:
streamlines = load_vtk_streamlines(filename)
elif extension in ['.dpy']:
dpy_obj = Dpy(filename, mode='r')
streamlines = list(dpy_obj.read_tracks())
dpy_obj.close()
logging.debug('Load %s with %s streamlines in %s seconds.', filename,
len(streamlines), round(time.time() - timer, 3))
sft = StatefulTractogram(streamlines,
reference,
Space.RASMM,
origin=Origin.NIFTI,
data_per_point=data_per_point,
data_per_streamline=data_per_streamline)
sft.to_space(to_space)
sft.to_origin(to_origin)
if bbox_valid_check and not sft.is_bbox_in_vox_valid():
raise ValueError('Bounding box is not valid in voxel space, cannot '
'load a valid file if some coordinates are invalid.\n'
'Please set bbox_valid_check to False and then use '
'the function remove_invalid_streamlines to discard '
'invalid streamlines.')
return sft
def save_tractogram(sft, filename, bbox_valid_check=True):
""" Save the stateful tractogram in any format (trk, tck, vtk, fib, dpy)
Parameters
----------
sft : StatefulTractogram
The stateful tractogram to save
filename : string
Filename with valid extension
bbox_valid_check : bool
Verification for negative voxel coordinates or values above the
volume dimensions. Default is True, to enforce valid file.
Returns
-------
output : bool
True if the saving operation was successful
"""
_, extension = os.path.splitext(filename)
if extension not in ['.trk', '.tck', '.vtk', '.fib', '.dpy']:
raise TypeError('Output filename is not one of the supported format.')
if bbox_valid_check and not sft.is_bbox_in_vox_valid():
raise ValueError('Bounding box is not valid in voxel space, cannot '
'load a valid file if some coordinates are invalid.\n'
'Please set bbox_valid_check to False and then use '
'the function remove_invalid_streamlines to discard '
'invalid streamlines.')
old_space = deepcopy(sft.space)
old_origin = deepcopy(sft.origin)
sft.to_rasmm()
sft.to_center()
timer = time.time()
if extension in ['.trk', '.tck']:
tractogram_type = detect_format(filename)
header = create_tractogram_header(tractogram_type,
*sft.space_attributes)
new_tractogram = Tractogram(sft.streamlines, affine_to_rasmm=np.eye(4))
if extension == '.trk':
new_tractogram.data_per_point = sft.data_per_point
new_tractogram.data_per_streamline = sft.data_per_streamline
fileobj = tractogram_type(new_tractogram, header=header)
nib.streamlines.save(fileobj, filename)
elif extension in ['.vtk', '.fib']:
save_vtk_streamlines(sft.streamlines, filename, binary=True)
elif extension in ['.dpy']:
dpy_obj = Dpy(filename, mode='w')
dpy_obj.write_tracks(sft.streamlines)
dpy_obj.close()
logging.debug('Save %s with %s streamlines in %s seconds.', filename,
len(sft), round(time.time() - timer, 3))
sft.to_space(old_space)
sft.to_origin(old_origin)
return True
self.b.pack(side=Tkinter.BOTTOM)
def ok(self):
self.value = self.s.get()
self.parent.destroy()
if __name__ == '__main__':
#load T1 volume registered in MNI space
#img = nib.load('data/subj_05/MPRAGE_32/T1_flirt_out.nii.gz')
#data = img.get_data()
#affine = img.get_affine()
#load the tracks registered in MNI space
fdpyw = 'data/subj_05/101_32/DTI/tracks_gqi_1M_linear.dpy'
dpr = Dpy(fdpyw, 'r')
T = dpr.read_tracks()
dpr.close()
#load initial QuickBundles with threshold 30mm
fpkl = 'data/subj_05/101_32/DTI/qb_gqi_1M_linear_30.pkl'
#qb=QuickBundles(T,30.,12)
#save_pickle(fpkl,qb)
qb = load_pickle(fpkl)
#create the interaction system for tracks
tl = TrackLabeler('Bundle Picker',
qb,
qb.downsampled_tracks(),
vol_shape=(182, 218, 182),
tracks_alpha=1)
#add a interactive slicing/masking tool
#d101='/home/eg309/Data/TEST_MR10032/subj_10/101/'
d101='/home/eg309/Data/PROC_MR10032/subj_10/101/'
ffa=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_bet_FA.nii.gz'
fdis=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_nonlin_displacements.nii.gz'
ffareg=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_bet_FA_reg.nii.gz'
flirtaff=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_affine_transf.mat'
ftrack=d101+'1312211075232351192010092912092080924175865ep2dadvdiffDSI10125x25x25STs005a001_QA_native.dpy'
froi='/home/eg309/Data/PROC_MR10032/NIFTI_ROIs/AnatomicalROIs/ROI01_GCC.nii'
froi2='/home/eg309/Data/PROC_MR10032/NIFTI_ROIs/AnatomicalROIs/ROI02_BCC.nii'
#froi3='/home/eg309/Data/PROC_MR10032/NIFTI_ROIs/AnatomicalROIs/ROI03_SCC.nii'
froi3='/home/eg309/Downloads/SCC_analyze.nii'
ref_fname = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
dpr=Dpy(ftrack,'r')
print dpr.track_no
T=dpr.read_indexed([0,1,2,3,2000,1000000])
for t in T:
print t.shape
dpr.close()
track=T[4]
im2im = flirt2aff_files(flirtaff, ffa, ref_fname) #ref_name to be replaced by ffareg
print im2im
from dipy.core.track_metrics import length
print len(track)
print length(track)
#ntrack=np.dot(im2im[:3,:3],track.T)+im2im[:3,[3]]
