def test_approx_ei_traj():
segs=100
t=np.linspace(0,1.75*2*np.pi,segs)
x =t
y=5*np.sin(5*t)
z=np.zeros(x.shape)
xyz=np.vstack((x,y,z)).T
xyza=pf.approximate_ei_trajectory(xyz)
yield assert_equal, len(xyza), 27
def test_approx_ei_traj():
segs = 100
t = np.linspace(0, 1.75 * 2 * np.pi, segs)
x = t
y = 5 * np.sin(5 * t)
z = np.zeros(x.shape)
xyz = np.vstack((x, y, z)).T
xyza = pf.approximate_ei_trajectory(xyz)
yield assert_equal, len(xyza), 27
def load_reduce_translate(fname, reduction=1):
""" Loads a trackvis file using DIPY io, only keeping a ratio of
1/reduction tracks. Performs trajectory approximation to reduce the
track lengths.
Parameter
---------
fname : str
The Trackvis file
reduction : int, optional
The reduction factor (keep only 1 line per set of reduction lines)
Returns
-------
tracks : list
A list of tracks
"""
lines, hdr = tv.read(fname)
print 'loaded,',
sys.stdout.flush()
#ras = tv.get_affine(hdr)
ras = tv.aff_from_hdr(hdr)
if not ras[:3,-1].any():
# dot(ras[:3,:3],md_vox) + t = (0,0,0) --> t = -dot(ras[:3,:3],md_vox)
md_vox = hdr['dim']/2
t = -np.dot(ras[:3,:3], md_vox)
ras[:3,-1] = t
tracks = [l[0] for l in lines[::reduction]]
del lines
ras_cmap = ni_api.AffineTransform.from_params('ijk', 'xyz', ras)
flat_tracks, breaks = flatten_tracks(tracks)
del tracks
flat_tracks_xyz = ras_cmap(flat_tracks)
tracks = recombine_flattened_tracks(flat_tracks_xyz, breaks)
print 'translated,',
sys.stdout.flush()
tracks_reduced = [tp.approximate_ei_trajectory(line, alpha=np.pi/4)
for line in tracks ]
print 'reduced,'
sys.stdout.flush()
return tracks_reduced
print 'Copying tracks...'
T=[i[0] for i in streams]
print 'Representing tracks using only 3 pts...'
tracks=[tm.downsample(t,3) for t in T]
print 'Deleting unnecessary data...'
del streams,hdr
print 'Hidden Structure Clustering...'
now=time.clock()
C=pf.local_skeleton_clustering(tracks,d_thr=20)
print 'Done in', time.clock()-now,'s.'
print 'Reducing the number of points...'
T=[pf.approximate_ei_trajectory(t) for t in T]
print 'Showing initial dataset.'
r=fos.ren()
fos.add(r,fos.line(T,fos.white,opacity=0.1))
fos.show(r)
print 'Showing dataset after clustering.'
fos.clear(r)
colors=np.zeros((len(T),3))
for c in C:
color=np.random.rand(1,3)
for i in C[c]['indices']:
colors[i]=color
fos.add(r,fos.line(T,colors,opacity=1))
fos.show(r)
for i in np.where(to_be_deleted>0)[0]: del C[k[i]]
return C
def most(C):
for c in C:
pass'pf.most_similar_track_zhang()
T=pkl.load_pickle(fname)
print 'Reducing the number of points...'
T=[pf.approximate_ei_trajectory(t) for t in T]
print 'Reducing further to tracks with 3 pts...'
T2=[tm.downsample(t,3) for t in T]
print 'LARCH ...'
print 'Splitting ...'
t=time.clock()
C=pf.larch_3split(T2,None,5.)
print time.clock()-t, len(C)
for c in C: print c, C[c]['rep3']/C[c]['N']
r=show_rep3(C)
