def spherical_rois(fdpy,fsr,sq_radius=4):
R=atlantic_points()
dpr=Dpy(fdpy,'r')
T=dpr.read_tracks()
dpr.close()
center=R['BCC']
refimg=nib.load(fref)
aff=refimg.get_affine()
SR={}
for key in R:
center=R[key]
#back to world space
centerw=np.dot(aff,np.array(center+(1,)))[:3]
centerw.shape=(1,)+centerw.shape
centerw=centerw.astype(np.float32)
res= [track_roi_intersection_check(t,centerw,sq_radius) for t in T]
res= np.array(res,dtype=np.int)
ind=np.where(res>0)[0]
SR[key]={}
SR[key]['center']=center
SR[key]['centerw']=tuple(np.squeeze(centerw))
SR[key]['radiusw']=np.sqrt(sq_radius)
SR[key]['indices']=ind
save_pickle(fsr,SR)
def warp_tracks_linearly(flirt_filename,fa_filename, tracks_filename,linear_filename):
import nibabel as nib
from dipy.external.fsl import flirt2aff
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
img_fa = nib.load(fa_filename)
flirt_affine= np.loadtxt(flirt_filename)
img_ref =nib.load(fsl_ref)
#create affine matrix from flirt
mat=flirt2aff(flirt_affine,img_fa,img_ref)
#read tracks
tensor_tracks = load_whole_tract(tracks_filename)
#linear tranform for tractography
tracks_warped_linear = transform_tracks(tensor_tracks,mat)
#save tracks_warped_linear
dpr_linear = Dpy(linear_filename, 'w')
dpr_linear.write_tracks(tracks_warped_linear)
dpr_linear.close()
def skeletonize(fdpy,flsc,points=3):
dpr=Dpy(fdpy,'r')
T=dpr.read_tracks()
dpr.close()
print len(T)
Td=[downsample(t,points) for t in T]
C=local_skeleton_clustering(Td,d_thr=10.,points=points)
#Tobject=np.array(T,dtype=np.object)
#'''
#r=fvtk.ren()
skeleton=[]
for c in C:
#color=np.random.rand(3)
if C[c]['N']>0:
Ttmp=[]
for i in C[c]['indices']:
Ttmp.append(T[i])
si,s=most_similar_track_mam(Ttmp,'avg')
print si,C[c]['N']
C[c]['most']=Ttmp[si]
#fvtk.add(r,fvtk.line(Ttmp[si],color))
print len(skeleton)
#r=fos.ren()
#fos.add(r,fos.line(skeleton,color))
#fos.add(r,fos.line(T,fos.red))
#fvtk.show(r)
#'''
save_pickle(flsc,C)
def tracking_prob(dir_src, dir_out, verbose=False):
wm_name = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
wm_mask, affine = load_nifti(pjoin(dir_src, wm_name), verbose)
sh_name = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
sh, _ = load_nifti(pjoin(dir_src, sh_name), verbose)
sphere = get_sphere('symmetric724')
classifier = ThresholdTissueClassifier(wm_mask.astype('f8'), .5)
classifier = BinaryTissueClassifier(wm_mask)
max_dg = ProbabilisticDirectionGetter.from_shcoeff(sh, max_angle=par_trk_max_angle, sphere=sphere)
seeds = utils.seeds_from_mask(wm_mask, density=2, affine=affine)
streamlines = LocalTracking(max_dg, classifier, seeds, affine, step_size=par_trk_step_size)
streamlines = list(streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_trk_prob_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
save_trk(trk_out, streamlines, affine, wm_mask.shape)
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def tracking_eudx(dir_src, dir_out, verbose=False):
# Loading FA and evecs data
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, fa_name), verbose)
evecs_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
evecs, _ = load_nifti(pjoin(dir_src, evecs_name), verbose)
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices= sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox,) * 3
dims = FA.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_rec_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def test_picking_trajectories():
curves=[100*np.random.rand(10,3),100*np.random.rand(5,3),100*np.random.rand(3,3)]
curves=[100*np.array([[0,0,0],[1,0,0]]), 100*np.array([[0,1,0],[0,1,3]]),100*np.array([[0,2,0],[0,2,3]])]
'''
from nibabel import trackvis as tv
#fname='/home/eg309/Data/PROC_MR10032/subj_01/101/1312211075232351192010091419011391228126452ep2dadvdiffDSI25x25x25b4000s003a001_FA_warp.trk'
fname='/home/eg309/Data/fibers.trk'
streams,hdr=tv.read(fname)
T=[s[0] for s in streams]
curves=T[:200000]
'''
fname='/home/eg309/Data/PROC_MR10032/subj_02/101/1312211075232351192010091708112071055601107ep2dadvdiffDSI10125x25x25STs002a001_QA_native.dpy'
from dipy.io.dpy import Dpy
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(20000))
from dipy.core.track_metrics import length
curves=[t for t in T if length(t) > 20]
dpr.close()
#colors=np.random.rand(len(curves),4).astype('f4')
colors=0.5*np.ones((len(curves),4)).astype('f4')
for (i,c) in enumerate(curves):
orient=c[0]-c[-1]
orient=np.abs(orient/np.linalg.norm(orient))
colors[i,:3]=orient
c=InteractiveCurves(curves,colors=colors)
w=World()
w.add(c)
wi=Window()
wi.attach(w)
def warp_tracks(input_tracks_filename, input_flirt_fmatrix, input_fa_filename, output_filename = None, input_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'):
print 'Loading fa, flirt matrix ...'
img_fa = nib.load(input_fa_filename)
flirt_affine= np.loadtxt(input_flirt_fmatrix)
img_ref =nib.load(input_ref)
#create affine matrix from flirt
mat=flirt2aff(flirt_affine,img_fa,img_ref)
#read tracks
print 'Loading tracks ...'
tensor_tracks = load_tracks(input_tracks_filename)
#linear tranform for tractography
tracks_warped_linear = transform_tracks(tensor_tracks,mat)
if output_filename == None:
filename_save = input_tracks_filename.split('.')[0]+'_linear.dpy'
else:
filename_save = os.path.abspath(output_filename)
#save tracks_warped_linear
print 'Saving warped tracks into :', filename_save
dpr_linear = Dpy(filename_save, 'w')
dpr_linear.write_tracks(tracks_warped_linear)
dpr_linear.close()
return filename_save
def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath=tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
print "Loading", self.tracpath
# Old nibabel API:
# streams, self.hdr = nib.trackvis.read(self.tracpath, points_space='voxel')
# self.T = np.array([s[0] for s in streams], dtype=np.object)
# New nibabel API
tmp = nib.streamlines.load(self.tracpath)
streams = tmp.tractogram.apply_affine(np.linalg.inv(tmp.affine)).streamlines
self.header = tmp.header
self.T = np.array(streams, dtype=np.object)
# The following code has been commented out to avoid
# misalignment between original streamlines IDs and final IDs.
# print "Removing short streamlines"
# self.T = np.array([t for t in self.T if length(t)>= 15], dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except (IOError, KeyError):
print "General information not found, recomputing buffers"
self.update_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler('Bundle Picker',
self.buffers, self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
def load_tractogram(filename, lazy_load=False):
""" Loads tractogram files (*.trk or *.tck or *.dpy)
Parameters
----------
filename : str
input trk filename
lazy_load : {False, True}, optional
If True, load streamlines in a lazy manner i.e. they will not be kept
in memory and only be loaded when needed.
Otherwise, load all streamlines in memory.
Returns
-------
streamlines : list of 2D arrays
Each 2D array represents a sequence of 3D points (points, 3).
hdr : dict
header from a trk file
"""
if 'dpy' in os.path.splitext(filename)[1].lower():
dpw = Dpy(filename, 'r')
streamlines = dpw.read_tracks()
dpw.close()
return streamlines, {}
trk_file = nib.streamlines.load(filename, lazy_load)
return trk_file.streamlines, trk_file.header
def load_tractogram(filename, lazy_load=False):
""" Loads tractogram files (*.trk or *.tck or *.dpy)
Parameters
----------
filename : str
input trk filename
lazy_load : {False, True}, optional
If True, load streamlines in a lazy manner i.e. they will not be kept
in memory and only be loaded when needed.
Otherwise, load all streamlines in memory.
Returns
-------
streamlines : list of 2D arrays
Each 2D array represents a sequence of 3D points (points, 3).
hdr : dict
header from a trk file
"""
if 'dpy' in os.path.splitext(filename)[1].lower():
dpw = Dpy(filename, 'r')
streamlines = dpw.read_tracks()
dpw.close()
return streamlines, {}
trk_file = nib.streamlines.load(filename, lazy_load)
return trk_file.streamlines, trk_file.header
def write_tracks(fdpy,scalar,indices,seed_no=10**6,a_thr=.2,compression=1):
eudx=EuDX(scalar,indices,seed_no=seed_no,a_low=a_thr)
#exi=iter(eudx)
dpw=Dpy(fdpy,'w',compression=1)
#for (i,track) in enumerate(exi):
for track in eudx:
dpw.write_track(track.astype(np.float32))
dpw.close()
def save_dpy(streamlines, filename):
''' Save tractography to a .dpy file'''
print "Save tracks as .dpy"
tracks = [track for track in streamlines]
dpw = Dpy(filename, 'w')
dpw.write_tracks(tracks)
dpw.close()
def load_cst(tracks_filename, cst_index_file, ext):
from dipy.io.dpy import Dpy
from dipy.io.pickles import load_pickle
dpr_tracks = Dpy(tracks_filename, 'r')
all_tracks=dpr_tracks.read_tracks()
dpr_tracks.close()
tracks_id = load_pickle(cst_index_file)
cst = [all_tracks[i] for i in tracks_id]
cst_ext = [all_tracks[i] for i in tracks_id]
medoid_cst = []
#len_dis = 250
if ext:
k = np.round(len(cst)*1.2)
not_cst_fil = []
min_len = min(len(i) for i in cst)
#print 'min_len of cst', min_len
min_len = min_len*2.2/3#2./3.2# - 20
for i in np.arange(len(all_tracks)):
if (i not in tracks_id) and (length(all_tracks[i]) > min_len):
not_cst_fil.append(all_tracks[i])
#for st in all_tracks:
# if (length(st)>=min_len) and (st not in cst):
# not_cst_fil.append(st)
from dipy.segment.quickbundles import QuickBundles
qb = QuickBundles(cst,200,18)
medoid_cst = qb.centroids[0]
med_notcst_dm = bundles_distances_mam([medoid_cst], not_cst_fil)
med_cst_dm = bundles_distances_mam([medoid_cst], cst)
cst_rad = med_cst_dm[0][np.argmax(med_cst_dm[0])]
len_dis = cst_rad * 2.8/2.
#print med_cst_dm
#print cst_rad
#print len_dis
#k_indices which close to the medoid
sort = np.argsort(med_notcst_dm,axis = 1)[0]
#print sort[:k+1]
while (k>0 and med_notcst_dm[0][sort[k]]>=len_dis):
k = k - 1
#print med_notcst_dm[0][sort[0:k]]
#print k
#close_indices = np.argsort(cst_dm,axis = 1)[:,0:k][0]
close_indices = sort[0:k]
for idx in close_indices:
cst_ext.append(not_cst_fil[idx])
return cst, cst_ext, medoid_cst
return cst
def roi_track_counts(fdpy,fref,fatlas,roi_no,dist_transf=True,fres=None):
dpr=Dpy(fdpy,'r')
T=dpr.read_tracks()
dpr.close()
img=nib.load(fref)
affine=img.get_affine()
zooms = img.get_header().get_zooms()
iaffine=np.linalg.inv(affine)
T2=[]
#go back to volume space
for t in T:
T2.append(np.dot(t,iaffine[:3,:3].T)+iaffine[:3,3])
del T
tcs,tes=track_counts(T2,img.get_shape(),zooms,True)
atlas_img=nib.load(fatlas)
atlas=atlas_img.get_data()
roi=atlas.copy()
roi[atlas!=roi_no]=0
if dist_transf:
roi2=distance_transform_cdt(roi)
roi[roi2!=roi2.max()]=0
I=np.array(np.where(roi==roi_no)).T
else:
I=np.array(np.where(roi==roi_no)).T
"""
if erosion_level>0:
roi2=binary_erosion(roi,cross,erosion_level)
I=np.array(np.where(roi2==True)).T
else:
roi2=distance_transform_cdt(roi)
I=np.array(np.where(roi==roi_no)).T
"""
#print I.shape
#nib.save(nib.Nifti1Image(roi2,affine),'/tmp/test.nii.gz')
Ttes=[]
for iroi in I:
try:
Ttes.append(tes[tuple(iroi)])
except KeyError:
pass
Ttes=list(set(list(chain.from_iterable(Ttes))))
T2n=np.array(T2,dtype=np.object)
res=list(T2n[Ttes])
#back to world space
res2=[]
for t in res:
res2.append(np.dot(t,affine[:3,:3].T)+affine[:3,3])
np.save(fres,np.array(res2,dtype=np.object))
def loading_full_tractograpy(self, tracpath=None):
"""
Loading full tractography and creates StreamlineLabeler to
show it all.
"""
# load the tracks registered in MNI space
self.tracpath = tracpath
basename = os.path.basename(self.tracpath)
tracks_basename, tracks_format = os.path.splitext(basename)
if tracks_format == '.dpy':
dpr = Dpy(self.tracpath, 'r')
print "Loading", self.tracpath
self.T = dpr.read_tracks()
dpr.close()
self.T = np.array(self.T, dtype=np.object)
elif tracks_format == '.trk':
streams, self.hdr = nib.trackvis.read(self.tracpath,
points_space='voxel')
print "Loading", self.tracpath
self.T = np.array([s[0] for s in streams], dtype=np.object)
print "Removing short streamlines"
self.T = np.array([t for t in self.T if length(t) >= 15],
dtype=np.object)
tracks_directoryname = os.path.dirname(self.tracpath) + '/.temp/'
general_info_filename = tracks_directoryname + tracks_basename + '.spa'
# Check if there is the .spa file that contains all the
# computed information from the tractography anyway and try to
# load it
try:
print "Looking for general information file"
self.load_info(general_info_filename)
except (IOError, KeyError):
print "General information not found, recomputing buffers"
self.update_info(general_info_filename)
# create the interaction system for tracks,
self.streamlab = StreamlineLabeler(
'Bundle Picker',
self.buffers,
self.clusters,
vol_shape=self.dims,
affine=np.copy(self.affine),
clustering_parameter=len(self.clusters),
clustering_parameter_max=len(self.clusters),
full_dissimilarity_matrix=self.full_dissimilarity_matrix)
self.scene.add_actor(self.streamlab)
def create_save_tracks(anisotropy, indices, seeds, low_thresh, filename):
euler = EuDX(
anisotropy, ind=indices, odf_vertices=get_sphere("symmetric362").vertices, seeds=seeds, a_low=low_thresh
)
tensor_tracks_old = [track for track in euler]
tracks = [track for track in tensor_tracks_old if track.shape[0] > 1]
dpw = Dpy(filename, "w")
dpw.write_tracks(tracks)
dpw.close()
def create_save_tracks(anisotropy,indices, seeds, low_thresh,filename):
euler = EuDX(anisotropy,
ind=indices,
#odf_vertices=get_sphere('symmetric362'),
seeds=seeds, a_low=low_thresh)
#odf_vertices=get_sphere('symmetric362').vertices,
tracks = [track for track in euler]
dpw = Dpy(filename, 'w')
dpw.write_tracks(tracks)
dpw.close()
def save_tractogram(fname, streamlines, affine, vox_size=None, shape=None,
header=None, reduce_memory_usage=False,
tractogram_file=None):
""" Saves tractogram files (*.trk or *.tck or *.dpy)
Parameters
----------
fname : str
output trk filename
streamlines : list of 2D arrays, generator or ArraySequence
Each 2D array represents a sequence of 3D points (points, 3).
affine : array_like (4, 4)
The mapping from voxel coordinates to streamline points.
vox_size : array_like (3,), optional
The sizes of the voxels in the reference image (default: None)
shape : array, shape (dim,), optional
The shape of the reference image (default: None)
header : dict, optional
Metadata associated to the tractogram file(*.trk). (default: None)
reduce_memory_usage : {False, True}, optional
If True, save streamlines in a lazy manner i.e. they will not be kept
in memory. Otherwise, keep all streamlines in memory until saving.
tractogram_file : class TractogramFile, optional
Define tractogram class type (TrkFile vs TckFile)
Default is None which means auto detect format
"""
if 'dpy' in os.path.splitext(fname)[1].lower():
dpw = Dpy(fname, 'w')
dpw.write_tracks(Streamlines(streamlines))
dpw.close()
return
tractogram_file = tractogram_file or detect_format(fname)
if tractogram_file is None:
raise ValueError("Unknown format for 'fname': {}".format(fname))
if vox_size is not None and shape is not None:
if not isinstance(header, dict):
header = {}
header[Field.VOXEL_TO_RASMM] = affine.copy()
header[Field.VOXEL_SIZES] = vox_size
header[Field.DIMENSIONS] = shape
header[Field.VOXEL_ORDER] = "".join(aff2axcodes(affine))
if reduce_memory_usage and not callable(streamlines):
sg = lambda: (s for s in streamlines)
else:
sg = streamlines
tractogram_loader = LazyTractogram if reduce_memory_usage else Tractogram
tractogram = tractogram_loader(sg)
tractogram.affine_to_rasmm = affine
track_file = tractogram_file(tractogram, header=header)
nib.streamlines.save(track_file, fname)
def sums_length(dname,type='64'):
for root, dirs, files in os.walk(dname):
if root.endswith(type):
for file in files:
if file.endswith('_warp.dpy'):
fname=os.path.join(root,file)
dpr=Dpy(fname,'r')
sum=0
for i in range(dpr.track_no):
sum+=length(dpr.read_track())
dpr.close()
print fname, sum
def get_luigi_SLFI():
fseg1 = "/home/eg309/Devel/segmented_bundles/luigi_s1_for_eleftherios/S1_SLFI"
# fseg2='/home/eg309/Devel/segmented_bundles/luigi_s1_for_eleftherios/S1_SLFII'
subject = "01"
fdpyw = "data/subj_" + subject + "/101_32/DTI/tracks_gqi_3M_linear.dpy"
dpr = Dpy(fdpyw, "r")
T = dpr.read_tracks()
dpr.close()
seg_inds = load_pickle(fseg1)
T1 = [T[i] for i in seg_inds]
# seg_inds=load_pickle(fseg2)
# T2=[T[i] for i in seg_inds]
return T1
def generate_lengths():
for root, dirs, files in os.walk(dname):
for file in files:
if file.endswith('_warp.dpy'):
fname=os.path.join(root,file)
dpr=Dpy(fname,'r')
lengths=np.zeros((dpr.track_no,))
for i in range(dpr.track_no):
lengths[i]=length(dpr.read_track())
dpr.close()
fname2=fname.split('_warp.dpy')[0]+'_warp_lengths.npy'
print fname2
np.save(fname2,lengths)
def create_save_tracks(anisotropy,indices, seeds, low_thresh,filename):
#this is new features in new dipy -current 121011 0.6.0.dev
#print "Computing EuDX reconstruction."
euler = EuDX(anisotropy,
ind=indices,
odf_vertices=get_sphere('symmetric362').vertices,
seeds=seeds, a_low=low_thresh)
euler = EuDX(anisotropy, ind=indices, seeds=seeds, a_low=low_thresh)
tracks = [track for track in euler]
dpw = Dpy(filename, 'w')
dpw.write_tracks(tracks)
dpw.close()
def load_data(figure, data_id):
if figure == "small_dataset":
filename = "ALS_Data/" + str(data_id) + "/DIFF2DEPI_EKJ_64dirs_14/DTI/tracks_dti_10K.dpy"
elif figure == "median_dataset":
filename = "ALS_Data/" + str(data_id) + "/DIFF2DEPI_EKJ_64dirs_14/DTI/tracks_dti_1M.dpy"
elif figure == "big_dataset":
filename = "ALS_Data/" + str(data_id) + "/DIFF2DEPI_EKJ_64dirs_14/DTI/tracks_dti_3M.dpy"
print "Loading tracks."
dpr = Dpy(filename, "r")
tracks = dpr.read_tracks()
dpr.close()
tracks = np.array(tracks, dtype=np.object)
return tracks
def load_whole_tract(tracks_filename):
from dipy.io.pickles import load_pickle
if (tracks_filename[-3:]=='dpy'):
from dipy.io.dpy import Dpy
dpr_tracks = Dpy(tracks_filename, 'r')
all_tracks=dpr_tracks.read_tracks()
dpr_tracks.close()
else:
import nibabel as nib
streams,hdr=nib.trackvis.read(tracks_filename,points_space='voxel')
all_tracks = np.array([s[0] for s in streams], dtype=np.object)
all_tracks = np.array(all_tracks,dtype=np.object)
return all_tracks
def load_data(figure, data_id):
if figure=='small_dataset':
filename = 'ALS_Data/'+ str(data_id) + '/DIFF2DEPI_EKJ_64dirs_14/DTI/tracks_dti_10K.dpy'
elif figure=='median_dataset':
filename = 'ALS_Data/' + str(data_id) + '/DIFF2DEPI_EKJ_64dirs_14/DTI/tracks_dti_1M.dpy'
elif figure=='big_dataset':
filename = 'ALS_Data/' + str(data_id) + '/DIFF2DEPI_EKJ_64dirs_14/DTI/tracks_dti_3M.dpy'
print "Loading tracks."
dpr = Dpy(filename, 'r')
tracks = dpr.read_tracks()
dpr.close()
tracks = np.array(tracks, dtype=np.object)
return tracks
def create_dataset_from_tractography(size1, size2, same=True):
if same: assert(size2 >= size1)
filename = 'data/tracks_dti_10K_linear.dpy'
print "Loading", filename
dpr = Dpy(filename, 'r')
tractography = dpr.read_tracks()
dpr.close()
print len(tractography), "streamlines"
print "Removing streamlines that are too short"
tractography = filter(lambda x: len(x) > 20, tractography) # remove too short streamlines
print len(tractography), "streamlines"
tractography = np.array(tractography, dtype=np.object)
print "Creating two simulated tractographies of sizes", size1, "and", size2
if same:
ids = fft(tractography, k=max([size1, size2]), distance=bundles_distances_mam)
tractography1 = tractography[ids[:size1]]
else:
# ids1 = np.random.permutation(len(tractography))[:size1]
# ids1 = sff(tractography, k=size1, distance=bundles_distances_mam)
ids1 = fft(tractography, k=size1, distance=bundles_distances_mam)
tractography1 = tractography[ids1[:size1]]
if same:
tractography2 = tractography[ids[:size2]]
else:
# ids2 = np.random.permutation(len(tractography))[:size2]
# ids2 = sff(tractography, k=size2, distance=bundles_distances_mam)
ids2 = fft(tractography, k=size2, distance=bundles_distances_mam)
tractography2 = tractography[ids2]
print "Done."
print "Computing the distance matrices for each tractography."
dm1 = bundles_distances_mam(tractography1, tractography1)
dm2 = bundles_distances_mam(tractography2, tractography2)
print("Computing similarity matrices.")
sigma2 = np.mean([np.median(dm1), np.median(dm2)]) ** 2.0
print("sigma2 = %f" % sigma2)
A = np.exp(-dm1 * dm1 / sigma2)
B = np.exp(-dm2 * dm2 / sigma2)
# Note: the optimization works even using distance instead of similarity:
# A = dm1
# B = dm2
return A, B
def tracks_to_fmrib58(subj,fnames,data_type='fa'):
#affine transformation matrix (from flirt before nonlinear registration)
faff =dname+fnames[subj]+'_affine_transf.mat'
#nonlinear displacements
fdis =dname+fnames[subj]+'_nonlin_displacements.nii.gz'#look at fa_to_fmrib58
if data_type=='qa':
#fqa_warp_dpy
fqa_warp =dname+fnames[subj]+'_QA_warp.dpy'
fwarp=fqa_warp
if data_type=='fa':
ffa_warp =dname+fnames[subj]+'_FA_warp.dpy'
fwarp=ffa_warp
#fa
ffa =dname+fnames[subj]+'_bet_FA.nii.gz'
ref_fname = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
print faff
print ffa
print fdis
im2im = flirt2aff_files(faff, ffa, ref_fname)
dimg=ni.load(fdis)
daff=dimg.get_affine()
ddata=dimg.get_data()
di=ddata[:,:,:,0]#copy i
dj=ddata[:,:,:,1]#copy j
dk=ddata[:,:,:,2]#copy k
#WARP TRACKS IN BLOCKS
print fwarp
dprw=Dpy(fwarp,'r+')
rows=len(dprw.f.root.streamlines.tracks)
blocks=np.round(np.linspace(0,rows,20)).astype(int)#lets work in blocks
print rows
for i in range(len(blocks)-1):
print blocks[i],blocks[i+1]
caboodle=dprw.f.root.streamlines.tracks[blocks[i]:blocks[i+1]]
ntrack=np.dot(caboodle,im2im[:3,:3].T)+im2im[:3,3] #from image vox space to mni image vox
mci=mc(di,ntrack.T,order=1) #mapping for i
mcj=mc(dj,ntrack.T,order=1) #mapping for j
mck=mc(dk,ntrack.T,order=1) #mapping for k
wtrack=ntrack+np.vstack((mci,mcj,mck)).T
caboodlew=np.dot(wtrack,daff[:3,:3].T)+daff[:3,3]
dprw.f.root.streamlines.tracks[blocks[i]:blocks[i+1]]=caboodlew.astype('f4')
dprw.close()
def load_tracks(method="pmt"):
from nibabel import trackvis as tv
dname = "/home/eg309/Data/orbital_phantoms/dwi_dir/subject1/"
if method == "pmt":
fname = "/home/eg309/Data/orbital_phantoms/dwi_dir/workflow/tractography/_subject_id_subject1/cam2trk_pico_twoten/data_fit_pdfs_tracked.trk"
streams, hdr = tv.read(fname, points_space="voxel")
tracks = [s[0] for s in streams]
if method == "dti":
fname = dname + "dti_tracks.dpy"
if method == "dsi":
fname = dname + "dsi_tracks.dpy"
if method == "gqs":
fname = dname + "gqi_tracks.dpy"
if method == "eit":
fname = dname + "eit_tracks.dpy"
if method in ["dti", "dsi", "gqs", "eit"]:
dpr_linear = Dpy(fname, "r")
tracks = dpr_linear.read_tracks()
dpr_linear.close()
if method != "pmt":
tracks = [t - np.array([96 / 2.0, 96 / 2.0, 55 / 2.0]) for t in tracks if track_range(t, 100 / 2.5, 150 / 2.5)]
tracks = [t for t in tracks if track_range(t, 100 / 2.5, 150 / 2.5)]
print "final no of tracks ", len(tracks)
qb = QuickBundles(tracks, 25.0 / 2.5, 18)
# from dipy.viz import fvtk
# r=fvtk.ren()
# fvtk.add(r,fvtk.line(qb.virtuals(),fvtk.red))
# fvtk.show(r)
# show_tracks(tracks)#qb.exemplars()[0])
# qb.remove_small_clusters(40)
del tracks
# load
tl = TrackLabeler(qb, qb.downsampled_tracks(), vol_shape=None, tracks_line_width=3.0, tracks_alpha=1)
# return tracks
w = World()
w.add(tl)
# create window
wi = Window(caption="Fos", bgcolor=(1.0, 1.0, 1.0, 1.0), width=1600, height=900)
wi.attach(w)
# create window manager
wm = WindowManager()
wm.add(wi)
wm.run()
def create_save_tracks(anisotropy, indices, vertices, seeds, low_thresh, filename):
from dipy.tracking.eudx import EuDX
eu = EuDX(FA, peak_indices, odf_vertices=vertices, seeds=seeds, a_low=low_thresh)
tensor_tracks_old = [streamline for streamline in eu]
# euler = EuDX(anisotropy,
# ind=indices,
# odf_vertices=get_sphere('symmetric362').vertices,
# seeds=seeds, a_low=low_thresh)
# tensor_tracks_old = [track for track in euler]
# print len(tensor_tracks_old)
tracks = [track for track in tensor_tracks_old if track.shape[0] > 1]
dpw = Dpy(filename, "w")
dpw.write_tracks(tracks)
dpw.close()
def runStream(csd_peaks, roi_file, roi_label=1, ang_thr=45., a_low=0.2, step_size=0.1, seeds_per_voxel=30, out_name=None):
img = nib.load(roi_file)
affine = img.get_affine()
mask_data = img.get_data()
p = np.asarray(np.where(mask_data == roi_label))
p = p.transpose()
# seed_points = None
# for i in p:
# points = np.random.uniform(size=[seeds_per_voxel,3]) + (i-0.5)
# if seed_points is None:
# seed_points = points
# else:
# seed_points = np.concatenate([seed_points, points], axis=0)
import dipy.tracking.utils as utils
seeds = utils.seeds_from_mask(mask_data==1, density=seeds_per_voxel)
print '# of seeds: ',len(seeds)
sphere = get_sphere('symmetric724')
print "seed eudx tractography"
eu = EuDX(csd_peaks.peak_values,
csd_peaks.peak_indices,
odf_vertices=sphere.vertices,
step_sz=step_size,
seeds=seeds,
ang_thr=ang_thr,
a_low=a_low)
csa_streamlines_mult_peaks = [streamline for streamline in eu]
out_file = 'tracts.dipy'
if out_name:
out_file = out_name+'_'+out_file
from dipy.io.trackvis import save_trk
save_trk(out_file, csa_streamlines_mult_peaks, affine,
mask.shape)
dpw = Dpy(out_file, 'w')
dpw.write_tracks(csa_streamlines_mult_peaks)
print 'write tracts to %s' % out_file
return (csa_streamlines_mult_peaks, out_file)
def see_tracks(fdpy,N=2000):
dpr=Dpy(fdpy,'r')
#T=dpr.read_tracksi(range(N))
T=dpr.read_tracks()
dpr.close()
T=[downsample(t,5) for t in T]
r=fvtk.ren()
colors=np.ones((len(T),3)).astype('f4')
for (i,c) in enumerate(T):
orient=c[0]-c[-1]
orient=np.abs(orient/np.linalg.norm(orient))
colors[i,:3]=orient
fvtk.add(r,fvtk.line(T,colors,opacity=0.5))
#fos.add(r,fos.sphere((0,0,0),10))
fvtk.show(r)
def see_spherical_intersections(fdpy,fsr):
dpr=Dpy(fdpy,'r')
T=dpr.read_tracks()
dpr.close()
SR=load_pickle(fsr)
r=fvtk.ren()
for key in SR:
ind=SR[key]['indices']
intersT=[T[i] for i in ind]
fvtk.add(r,fvtk.line(intersT,np.random.rand(3)))
centerw=SR[key]['centerw']
radius=SR[key]['radiusw']
fvtk.add(r,fvtk.sphere(position=centerw,radius=radius))
fvtk.show(r)
def save_tractogram(fname,
streamlines,
affine,
vox_size=None,
shape=None,
header=None,
reduce_memory_usage=False,
tractogram_file=None):
""" Saves tractogram files (*.trk or *.tck or *.dpy)
Parameters
----------
fname : str
output trk filename
streamlines : list of 2D arrays, generator or ArraySequence
Each 2D array represents a sequence of 3D points (points, 3).
affine : array_like (4, 4)
The mapping from voxel coordinates to streamline points.
vox_size : array_like (3,), optional
The sizes of the voxels in the reference image (default: None)
shape : array, shape (dim,), optional
The shape of the reference image (default: None)
header : dict, optional
Metadata associated to the tractogram file(*.trk). (default: None)
reduce_memory_usage : {False, True}, optional
If True, save streamlines in a lazy manner i.e. they will not be kept
in memory. Otherwise, keep all streamlines in memory until saving.
tractogram_file : class TractogramFile, optional
Define tractogram class type (TrkFile vs TckFile)
Default is None which means auto detect format
"""
if 'dpy' in os.path.splitext(fname)[1].lower():
dpw = Dpy(fname, 'w')
dpw.write_tracks(Streamlines(streamlines))
dpw.close()
return
tractogram_file = tractogram_file or detect_format(fname)
if tractogram_file is None:
raise ValueError("Unknown format for 'fname': {}".format(fname))
if vox_size is not None and shape is not None:
if not isinstance(header, dict):
header = {}
header[Field.VOXEL_TO_RASMM] = affine.copy()
header[Field.VOXEL_SIZES] = vox_size
header[Field.DIMENSIONS] = shape
header[Field.VOXEL_ORDER] = "".join(aff2axcodes(affine))
if reduce_memory_usage and not callable(streamlines):
sg = lambda: (s for s in streamlines)
else:
sg = streamlines
tractogram_loader = LazyTractogram if reduce_memory_usage else Tractogram
tractogram = tractogram_loader(sg)
tractogram.affine_to_rasmm = affine
track_file = tractogram_file(tractogram, header=header)
nib.streamlines.save(track_file, fname)
def compute_tracking(src_dti_dir, out_trk_dir, subj_name):
# Loading FA and evecs data
src_fa_file = os.path.join(src_dti_dir, subj_name + par_fa_suffix)
fa_img = nib.load(src_fa_file)
FA = fa_img.get_data()
src_evecs_file = os.path.join(src_dti_dir, subj_name + par_evecs_suffix)
evecs_img = nib.load(src_evecs_file)
evecs = evecs_img.get_data()
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = dti.quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices=sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = fa_img.get_header().get_zooms()[:3]
dims = FA.shape[:3]
seed = par_eudx_seeds
seed = "_%d%s" % (seed / 10**6 if seed > 10**5 else seed / 10**3,
'K' if seed < 1000000 else 'M')
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
strm = ((sl, None, None) for sl in streamlines
if length(sl) > par_trk_min and length(sl) < par_trk_max)
out_trk_file = os.path.join(out_trk_dir, subj_name + seed + par_trk_suffix)
nib.trackvis.write(out_trk_file, strm, hdr, points_space='voxel')
tracks = [track for track in streamlines]
out_dipy_file = os.path.join(out_trk_dir,
subj_name + seed + par_dipy_suffix)
dpw = Dpy(out_dipy_file, 'w')
dpw.write_tracks(tracks)
dpw.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
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. Please use the function ' +
'remove_invalid_streamlines to discard invalid ' +
'streamlines or set bbox_valid_check to False')
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_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))
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 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, 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.
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)
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,
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.' +
'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
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 test_dpy():
fname = 'test.bin'
dpw = Dpy(fname, 'w')
A = np.ones((5, 3))
B = 2 * A.copy()
C = 3 * A.copy()
dpw.write_track(A)
dpw.write_track(B)
dpw.write_track(C)
dpw.write_tracks([C, B, A])
dpw.close()
dpr = Dpy(fname, 'r')
assert_equal(dpr.version() == '0.0.1', True)
T = dpr.read_tracksi([0, 1, 2, 0, 0, 2])
T2 = dpr.read_tracks()
assert_equal(len(T2), 6)
dpr.close()
assert_array_equal(A, T[0])
assert_array_equal(C, T[5])
"""
del tracks_np
tracks2 = list(np.load('fornix.npy'))
"""
huge datasets use dipy.io.dpy
* direct indexing from the disk
* memory usage always low
* extendable
"""
from dipy.io.dpy import Dpy
dpw = Dpy('fornix.dpy', 'w')
"""
write many tracks at once
"""
dpw.write_tracks(tracks2)
"""
write one track
"""
dpw.write_track(tracks2[0] * 6)
"""
or one track each time
"""
for t in tracks:
#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]]
def test_dpy():
fname = 'test.bin'
with InTemporaryDirectory():
dpw = Dpy(fname, 'w')
A = np.ones((5, 3))
B = 2 * A.copy()
C = 3 * A.copy()
dpw.write_track(A)
dpw.write_track(B)
dpw.write_track(C)
dpw.write_tracks(Streamlines([C, B, A]))
all_tracks = np.ascontiguousarray(np.vstack([A, B, C, C, B, A]))
npt.assert_array_equal(all_tracks, dpw.tracks[:])
dpw.close()
dpr = Dpy(fname, 'r')
npt.assert_equal(dpr.version() == u'0.0.1', True)
T = dpr.read_tracksi([0, 1, 2, 0, 0, 2])
T2 = dpr.read_tracks()
npt.assert_equal(len(T2), 6)
dpr.close()
npt.assert_array_equal(A, T[0])
npt.assert_array_equal(C, T[5])
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
np.save('fornix.npy', streamlines_np)
streamlines2 = list(np.load('fornix.npy'))
"""
3. We also work on our HDF5 based file format which can read/write massive datasets
(as big as the size of you 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(streamlines2)
"""
Write one track
"""
dpw.write_track(streamlines2[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.affine
# load the invwarp displacements
imginvw = nib.load(finv)
invwdata = imginvw.get_data()
# 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()