def rm_far_ends(ref, tracks, dist=25):
''' rm tracks with far endpoints
Parameters
----------
ref : array, shape (N,3)
xyz points of the reference track
tracks : sequence
of tracks as arrays, shape (N1,3) .. (Nm,3)
dist : float
endpoint distance threshold
Returns
-------
tracksr : sequence
reduced tracks
indices : sequence
indices of tracks
'''
indices = [
i for (i, t) in enumerate(tracks)
if tm.max_end_distances(t, ref) <= dist
]
tracksr = [tracks[i] for i in indices]
return tracksr, indices
def bundle_from_refs(brain,braind, refs, divergence_threshold=0.3, fibre_weight=0.7,far_thresh=25,zhang_thresh=15, end_thresh=10):
'''
'''
bundle = set([])
centres = []
indices = []
for ref in refs:
refd=tm.downsample(ref,3)
brain_rf, ind_fr = rm_far_tracks(refd,braind,dist=far_thresh,down=True)
brain_rf=[brain[i] for i in ind_fr]
#brain_rf,ind_fr = rm_far_tracks(ref,brain,dist=far_thresh,down=False)
heavy_weight_fibres, index, centre = refconc(brain_rf, ref, divergence_threshold, fibre_weight)
heavy_weight_fibres_z = [i for i in heavy_weight_fibres if pf.zhang_distances(ref,brain_rf[i],'avg')<zhang_thresh]
#heavy_weight_fibres_z_e = [i for i in heavy_weight_fibres_z if tm.max_end_distances(brain_rf[i],ref)>end_thresh]
hwfind = set([ind_fr[i] for i in heavy_weight_fibres_z])
bundle = bundle.union(hwfind)
bundle_med = []
for i in bundle:
minmaxdist = 0.
for ref in refs:
minmaxdist=min(minmaxdist,tm.max_end_distances(brain[i],ref))
if minmaxdist<=end_thresh:
bundle_med.append(i)
#centres.append(centre)
#indices.append(index)
#return list(bundle), centres, indices
return bundle_med
def bundle_from_refs(brain,braind, refs, divergence_threshold=0.3, fibre_weight=0.7,far_thresh=25,zhang_thresh=15, end_thresh=10):
'''
'''
bundle = set([])
# centres = []
# indices = []
for ref in refs:
refd=tm.downsample(ref,3)
brain_rf, ind_fr = rm_far_tracks(refd,braind,dist=far_thresh,down=True)
brain_rf=[brain[i] for i in ind_fr]
#brain_rf,ind_fr = rm_far_tracks(ref,brain,dist=far_thresh,down=False)
heavy_weight_fibres, index, centre = refconc(brain_rf, ref, divergence_threshold, fibre_weight)
heavy_weight_fibres_z = [i for i in heavy_weight_fibres if pf.zhang_distances(ref,brain_rf[i],'avg')<zhang_thresh]
#heavy_weight_fibres_z_e = [i for i in heavy_weight_fibres_z if tm.max_end_distances(brain_rf[i],ref)>end_thresh]
hwfind = set([ind_fr[i] for i in heavy_weight_fibres_z])
bundle = bundle.union(hwfind)
bundle_med = []
for i in bundle:
minmaxdist = 0.
for ref in refs:
minmaxdist=min(minmaxdist,tm.max_end_distances(brain[i],ref))
if minmaxdist<=end_thresh:
bundle_med.append(i)
#centres.append(centre)
#indices.append(index)
#return list(bundle), centres, indices
return bundle_med
def rm_far_ends(ref,tracks,dist=25):
''' rm tracks with far endpoints
Parameters
----------
ref : array, shape (N,3)
xyz points of the reference track
tracks : sequence
of tracks as arrays, shape (N1,3) .. (Nm,3)
dist : float
endpoint distance threshold
Returns
-------
tracksr : sequence
reduced tracks
indices : sequence
indices of tracks
'''
indices=[i for (i,t) in enumerate(tracks) if tm.max_end_distances(t,ref) <= dist]
tracksr=[tracks[i] for i in indices]
return tracksr,indices
