def render_tracks(self):
""" Renders the tracks in a separate window using DiPy Fos """
from cviewer.libs.dipy.viz import fos
if not self.data_loaded:
self.load_trackfile()
# copy tracks for visualization
T = [i[0] for i in self.streams]
r=fos.ren()
fos.add(r,fos.line(T,fos.white,opacity=0.1))
fos.show(r)
def render_tracks(self):
""" Renders the tracks in a separate window using DiPy Fos """
from cviewer.libs.dipy.viz import fos
if not self.loaded:
self.load()
# copy tracks for visualization
T = [i[0] for i in self.obj.data[0]]
r = fos.ren()
fos.add(r, fos.line(T, fos.white, opacity=0.8))
fos.show(r)
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)
print 'Some statistics about the clusters'
lens=[len(C[c]['indices']) for c in C]
print 'max ',max(lens), 'min ',min(lens)
t3=time.clock()
print ('Generate QA in %d secs' %(t3-t2))
T=tp.FACT_Delta(gqs.QA,gqs.IN,seeds_no=10000).tracks
t4=time.clock()
print ('Create %d QA tracks in %d secs' %(len(T),t4-t3))
#calculate single tensor
ten=dt.Tensor(data,bvals,gradients,thresh=50)
t5=time.clock()
print('Create FA in %d secs' %(t5-t4))
T2=tp.FACT_Delta(ten.FA,ten.IN,seeds_no=10000,qa_thr=0.2).tracks
t6=time.clock()
print ('Create %d FA tracks in %d secs' %(len(T2),t6-t5))
T2=[t+np.array([100,0,0]) for t in T2]
print('Red tracks propagated based on QA')
print('Green tracks propagated based on FA')
r=fos.ren()
fos.add(r,fos.line(T,fos.red))
fos.add(r,fos.line(T2,fos.green))
fos.show(r)
