def show():
#brains=[(1,1),(1,2),(2,1),(3,1),(3,2)]
brains=[(1,1),(2,1),(3,1)]
ids=tl.emi_atlas()
#print ids.keys()
for (b,s) in brains:
ids2 = pbc.load_pickle(path+'/Relabelling_8_sc1_'+str(b)+'_'+str(s)+'.pkl')
#'/Relabelling_8_sc1_'+str(b)+'_'+str(s)+'.pkl'
print b,s, ids2.keys()
tracks = pbc.load_approximate_tracks(path,b,s)
for i in ids:
if i >0:
r=fos.ren()
color=np.array(ids[i]['color'])
indices=ids2[i]['indices']
bundle=[tracks[ind] for ind in indices]
fos.add(r,fos.line(bundle,color,opacity=0.9))
print 'Bundle_name',i,ids[i]['bundle_name']
fos.show(r,title=ids[i]['bundle_name'][0])
def show_specific_bundles(r,template,ids,tes,tracks):
for i in ids:
vs=ids[i]['value']
color=ids[i]['color']
for v in vs:
bundle,indices=for_a_value_in_template(template,v,tes,tracks)
fos.add(r,fos.line(bundle,color,opacity=0.9))
def show_rep3(C, r=None, color=fos.white):
if r == None: r = fos.ren()
for c in C:
fos.add(r, fos.line(C[c]['rep3'] / C[c]['N'], color))
fos.show(r)
return r
def show_rep3(C,r=None,color=fos.white):
if r==None: r=fos.ren()
for c in C:
fos.add(r,fos.line(C[c]['rep3']/C[c]['N'],color))
fos.show(r)
return r
def tracks_in_roi():
froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii'
wI=get_roi(froi,35,1) #4 is genu
fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(2*10**4))
print len(T)
Troi=[]
for t in T:
if track_roi_intersection_check(t,wI,.5):
Troi.append(t)
print(len(Troi))
dpr.close()
from dipy.viz import fos
r=fos.ren()
fos.add(r,fos.line(Troi,fos.red))
fos.add(r,fos.point(wI,fos.green))
fos.show(r)
def test_fos_functions():
# Create a renderer
r=fos.ren()
# Create 2 lines with 2 different colors
lines=[np.random.rand(10,3),np.random.rand(20,3)]
colors=np.random.rand(2,3)
c=fos.line(lines,colors)
fos.add(r,c)
# Create a volume and return a volumetric actor using volumetric rendering
vol=100*np.random.rand(100,100,100)
vol=vol.astype('uint8')
r = fos.ren()
v = fos.volume(vol)
fos.add(r,v)
# Remove all objects
fos.rm_all(r)
# Put some text
l=fos.label(r,text='Yes Men')
fos.add(r,l)
# Show everything
#fos.show(r)
def skeletonize():
froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii'
wI=get_roi(froi,35,1) #4 is genu
#fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
fname='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(2*10**4))
dpr.close()
print len(T)
from dipy.core.track_metrics import downsample
from dipy.core.track_performance import local_skeleton_clustering, most_similar_track_mam
Td=[downsample(t,3) for t in T]
C=local_skeleton_clustering(Td,d_thr=20.)
#Tobject=np.array(T,dtype=np.object)
from dipy.viz import fos
r=fos.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']
fos.add(r,fos.line(Ttmp[si],color))
#print len(skeleton)
#fos.add(r,fos.line(skeleton,color))
#fos.add(r,fos.line(T,fos.red))
fos.show(r)
def skeletonize_both():
from dipy.viz import fos
from dipy.core.track_metrics import downsample
from dipy.core.track_performance import local_skeleton_clustering, most_similar_track_mam
froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii'
wI=get_roi(froi,9,0) #4 is genu
fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
fname2='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
r=fos.ren()
#'''
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(2*10**4))
dpr.close()
print len(T)
Td=[downsample(t,3) for t in T if length(t)>40]
C=local_skeleton_clustering(Td,d_thr=20.)
for c in C:
#color=np.random.rand(3)
color=fos.red
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']
fos.add(r,fos.line(Ttmp[si],color))
dpr=Dpy(fname2,'r')
T=dpr.read_indexed(range(2*10**4))
dpr.close()
print len(T)
Td=[downsample(t,3) for t in T if length(t)>40]
C=local_skeleton_clustering(Td,d_thr=20.)
#r=fos.ren()
for c in C:
#color=np.random.rand(3)
color=fos.yellow
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']
fos.add(r,fos.line(Ttmp[si],color))
#'''
fos.add(r,fos.point(wI,fos.green))
fos.show(r)
for c in C: print c, C[c]['rep3']/C[c]['N']
r=show_rep3(C)
print 'Merging ...'
t=time.clock()
C=merge(C,5.)
print time.clock()-t, len(C)
for c in C: print c, C[c]['rep3']/C[c]['N']
show_rep3(C,r,fos.red)
"""
#print 'Showing initial dataset.'
r=fos.ren()
#fos.add(r,fos.line(T,fos.white,opacity=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))
print ('Generate QA in %d secs' %(t3-t2))
T=tp.FACT_Delta(gqs.QA,gqs.IN,seeds_no=10000).tracks
t4=time()
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()
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()
print ('Create %d FA tracks in %d secs' %(len(T2),t6-t5))
T2=[t+np.array([100,0,0]) for t in T2]
print dname
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)
ALL_T.append((T,T2))
0,
], [6, 1.5, 0]]),
np.array([[0, 1.8, 0], [
1,
1.8,
0,
], [6, 1.8, 0]]),
np.array([[0, 0, 0], [2, 2, 0], [4, 4, 0]])
]
tracks = [t.astype(np.float32) for t in tracks]
C = pf.larch_3split(tracks, None, 0.5)
r = fos.ren()
fos.add(r, fos.line(tracks, fos.red))
#fos.show(r)
for c in C:
color = np.random.rand(3)
for i in C[c]['indices']:
fos.add(r, fos.line(tracks[i] + np.array([8., 0., 0.]), color))
fos.add(r, fos.line(tracks[i] + np.array([16., 0., 0.]), color))
fos.add(
r,
fos.line(C[c]['rep3'] / C[c]['N'] + np.array([16., 0., 0.]),
fos.white))
fos.show(r)
'''
def plot_sphere(v, key):
r = fos.ren()
fos.add(r, fos.point(v, fos.green, point_radius=0.01))
fos.show(r, title=key, size=(1000, 1000))
#tracks=[tm.downsample(t,3) for t in tracks]
#C=pf.local_skeleton_clustering(tracks,20.)
print 'Done in total of ',time.clock()-tim,'seconds.'
print 'Saving result...'
pkl.save_pickle(C_fname,C)
streams=[(i,None,None)for i in atracks]
tv.write(appr_fname,streams,hdr)
else:
print 'Loading result...'
C=pkl.load_pickle(C_fname)
skel=[]
for c in C:
skel.append(C[c]['repz'])
print 'Showing dataset after clustering...'
r=fos.ren()
fos.clear(r)
colors=np.zeros((len(skel),3))
for (i,s) in enumerate(skel):
color=np.random.rand(1,3)
colors[i]=color
fos.add(r,fos.line(skel,colors,opacity=1))
fos.show(r)
along = reference[index+1]-reference[index]
import numpy as np
normal=along/np.sqrt(np.inner(along,along))
crossings = list([])
hit_div = list([])
for k in range(len(b1)):
t = b1[k]
cross= -1
for i in range(len(t))[:-1]:
q = t[i]
r = t[i+1]
if np.inner(normal,q-p)*np.inner(normal,r-p) <= 0:
# print "Segment %d of track %d crosses the normal plane" % (i,k)
cross = i
crossings.append([k,cross])
if np.inner((r-q),normal) != 0:
alpha = np.inner((p-q),normal)/np.inner((r-q),normal)
hit = q+alpha*(r-q)
divergence = (r-q)-np.inner(r-q,normal)*normal
hit_div.append([hit,divergence])
else:
hit_div.append([hit,0])
break
# if cross<0:
# print "No crossing segment"
# if cross >= 0:
print "%d tracks cross the plane" % (len(crossings))
r = fos.ren()
fos.add(r,fos.points(np.array([h[0] for h in hit_div])))
fos.show()
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)
def warp_tracks():
dn='/home/eg309/Data/TEST_MR10032/subj_03/101/'
ffa=dn+'1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_bet_FA.nii.gz'
finvw=dn+'1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_warps_in_bet_FA.nii.gz'
fqadpy=dn+'1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
flaff=dn+'1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_affine_transf.mat'
fref ='/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
fdis =dn+'1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_nonlin_displacements.nii.gz'
fdis2 =dn+'1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_nonlin_displacements_withaff.nii.gz'
#read some tracks
dpr=Dpy(fqadpy,'r')
T=dpr.read_indexed(range(150))
dpr.close()
#from fa index to ref index
res=flirt2aff_files(flaff,ffa,fref)
#load the reference img
imgref=ni.load(fref)
refaff=imgref.get_affine()
#load the invwarp displacements
imginvw=ni.load(finvw)
invwdata=imginvw.get_data()
invwaff = imginvw.get_affine()
#load the forward displacements
imgdis=ni.load(fdis)
disdata=imgdis.get_data()
#load the forward displacements + affine
imgdis2=ni.load(fdis2)
disdata2=imgdis2.get_data()
#from their difference create the affine
disaff=imgdis2.get_data()-disdata
shift=np.array([disaff[...,0].mean(),disaff[...,1].mean(),disaff[...,2].mean()])
shape=ni.load(ffa).get_data().shape
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)
disdata0=affine_transform(disdata[...,0],res[:3,:3],res[:3,3],shape,order=1)
disdata1=affine_transform(disdata[...,1],res[:3,:3],res[:3,3],shape,order=1)
disdata2=affine_transform(disdata[...,2],res[:3,:3],res[:3,3],shape,order=1)
#print disgrad0.shape,disgrad1.shape,disgrad2.shape
#disdiff=np.empty(invwdata.shape)
#disdiff[...,0]=disgrad0
#disdiff[...,1]=disgrad1
#disdiff[...,2]=disgrad2
#ni.save(ni.Nifti1Image(disdiff,invwaff),'/tmp/disdiff.nii.gz')
di=disdata0
dj=disdata1
dk=disdata2
d2i=invwdata[:,:,:,0] + disaff0
d2j=invwdata[:,:,:,1] + disaff1
d2k=invwdata[:,:,:,2] + disaff2
#di=disgrad0
#dj=disgrad1
#dk=disgrad2
imgfa=ni.load(ffa)
fadata=imgfa.get_data()
faaff =imgfa.get_affine()
Tw=[]
Tw2=[]
Tw3=[]
froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii'
roiI=get_roi(froi,3,1) #3 is GCC
roiI2=get_roi(froi,4,1) #4 is BCC
roiI3=get_roi(froi,5,1) #4 is SCC
roiI=np.vstack((roiI,roiI2,roiI3))
for t in T:
if np.min(t[:,2])>=0:#to be removed
mci=mc(di,t.T,order=1) #interpolations for i displacement
mcj=mc(dj,t.T,order=1) #interpolations for j displacement
mck=mc(dk,t.T,order=1) #interpolations for k displacement
D=np.vstack((mci,mcj,mck)).T
WI=np.dot(t,res[:3,:3].T)+res[:3,3]+D#+ shift
W=np.dot(WI,refaff[:3,:3].T)+refaff[:3,3]
mc2i=mc(d2i,t.T,order=1) #interpolations for i displacement
mc2j=mc(d2j,t.T,order=1) #interpolations for j displacement
mc2k=mc(d2k,t.T,order=1) #interpolations for k displacement
D2=np.vstack((mc2i,mc2j,mc2k)).T
WI2=np.dot(t,res[:3,:3].T)+res[:3,3]+D2 #+ shift
W2=np.dot(WI2,refaff[:3,:3].T)+refaff[:3,3]
WI3=np.dot(t,res[:3,:3].T)+res[:3,3]
W3=np.dot(WI3,refaff[:3,:3].T)+refaff[:3,3]
Tw.append(W)
Tw2.append(W2)
Tw3.append(W3)
from dipy.viz import fos
r=fos.ren()
fos.add(r,fos.line(Tw,fos.red))
fos.add(r,fos.line(Tw2,fos.green))
fos.add(r,fos.line(Tw3,fos.yellow))
fos.add(r,fos.sphere((0,0,0),10,color=fos.blue))
fos.add(r,fos.point(roiI,fos.blue))
fos.show(r)
def plot_sphere(v,key):
r = fos.ren()
fos.add(r,fos.point(v,fos.green, point_radius= 0.01))
fos.show(r, title=key, size=(1000,1000))
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)
def showline(myline):
from dipy.viz import fos
r = fos.ren()
fos.add(r,fos.line(myline,fos.blue,opacity=0.5))
fos.show(r)
np.array([[0,0.2,0],[1,0.2,0],[2,0.2,0]]),
np.array([[2,0.2,0],[1,0.2,0],[0,0.2,0]]),
np.array([[0,0,0],[0,1,0],[0,2,0]]),
np.array([[0.2,0,0],[0.2,1,0],[0.2,2,0]]),
np.array([[-0.2,0,0],[-0.2,1,0],[-0.2,2,0]]),
np.array([[0,1.5,0],[1,1.5,0,],[6,1.5,0]]),
np.array([[0,1.8,0],[1,1.8,0,],[6,1.8,0]]),
np.array([[0,0,0],[2,2,0],[4,4,0]])]
tracks=[t.astype(np.float32) for t in tracks]
C=pf.larch_3split(tracks,None,0.5)
r=fos.ren()
fos.add(r,fos.line(tracks,fos.red))
#fos.show(r)
for c in C:
color=np.random.rand(3)
for i in C[c]['indices']:
fos.add(r,fos.line(tracks[i]+np.array([8.,0.,0.]),color))
fos.add(r,fos.line(tracks[i]+np.array([16.,0.,0.]),color))
fos.add(r,fos.line(C[c]['rep3']/C[c]['N']+np.array([16.,0.,0.]),fos.white))
fos.show(r)
"""
I=np.array(np.where(roidata==no)).T
wI=np.dot(roiaff[:3,:3],I.T).T+roiaff[:3,3]
wI=wI.astype('f4')
return wI
from dipy.viz import fos
r=fos.ren()
#fos.add(r,fos.point(wI,fos.blue))
#fos.add(r,fos.point(wI2,fos.yellow))
#fos.add(r,fos.point(wI3,fos.green))
#fos.add(r,fos.point(wrefI,fos.cyan))
#fos.add(r,fos.point(wrefI,fos.yellow))
fos.add(r,fos.point(get_roi(froi,3),fos.blue))
fos.add(r,fos.point(get_roi(froi,4),fos.yellow))
fos.add(r,fos.point(get_roi(froi,5),fos.green))
fos.add(r,fos.line(Tfinal,fos.red))
fos.show(r)
print roiaff
print roiaff2
print roiaff3
print daff
I=np.array(np.where(roidata==no)).T
wI=np.dot(roiaff[:3,:3],I.T).T+roiaff[:3,3]
wI=wI.astype('f4')
return wI
from dipy.viz import fos
r=fos.ren()
#fos.add(r,fos.point(wI,fos.blue))
#fos.add(r,fos.point(wI2,fos.yellow))
#fos.add(r,fos.point(wI3,fos.green))
#fos.add(r,fos.point(wrefI,fos.cyan))
#fos.add(r,fos.point(wrefI,fos.yellow))
fos.add(r,fos.point(get_roi(froi,3),fos.blue))
fos.add(r,fos.point(get_roi(froi,4),fos.yellow))
fos.add(r,fos.point(get_roi(froi,5),fos.green))
fos.add(r,fos.line(Tfinal,fos.red))
fos.show(r)
print roiaff
print roiaff2
print roiaff3
print daff
for c in C:
print c, C[c]['rep3'] / C[c]['N']
r = show_rep3(C)
print 'Merging ...'
t = time.clock()
C = merge(C, 5.)
print time.clock() - t, len(C)
for c in C:
print c, C[c]['rep3'] / C[c]['N']
show_rep3(C, r, fos.red)
"""
#print 'Showing initial dataset.'
r=fos.ren()
#fos.add(r,fos.line(T,fos.white,opacity=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.add(r,fos.line(tracks1,fos.red,opacity=0.01))
#fos.add(r,fos.line(tracks2,fos.cyan,opacity=0.01))
tracks1zshift=[t+np.array([-70,0,0]) for t in tracks1z]
tracks2zshift=[t+np.array([70,0,0]) for t in tracks2z]
tracks3zshift=[t+np.array([210,0,0]) for t in tracks3z]
import pbc
pbc.save_pickle('t1zs.pkl',tracks1zshift)
pbc.save_pickle('t2zs.pkl',tracks2zshift)
pbc.save_pickle('t3zs.pkl',tracks3zshift)
pbc.save_pickle('t2t.pkl',track2track)
pbc.save_pickle('t2t2.pkl',track2track2)
'''
r=fos.ren()
fos.add(r,fos.line(tracks1zshift,fos.red,opacity=0.02))
fos.add(r,fos.line(tracks2zshift,fos.cyan,opacity=0.02))
fos.add(r,fos.line(tracks3zshift,fos.blue,opacity=0.02))
print 'Show track to track correspondence br1 br2'
for i in track2track:
fos.add(r,fos.line(tracks1zshift[i[1]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks1zshift[i[1]][0],(4,4,4),fos.white)
fos.add(r,fos.line(tracks2zshift[i[2]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks2zshift[i[2]][0],(4,4,4),fos.white)
print 'Show track to track correspondence br1_FACT and br2_RK2'
print 'third brain'
print track2track2[:,2].T
print 'Fos loading'
r=fos.ren()
#fos.add(r,fos.line(tracks1,fos.red,opacity=0.01))
#fos.add(r,fos.line(tracks2,fos.cyan,opacity=0.01))
tracks1zshift=[t+np.array([-70,0,0]) for t in tracks1z]
tracks2zshift=[t+np.array([70,0,0]) for t in tracks2z]
tracks3zshift=[t+np.array([210,0,0]) for t in tracks3z]
fos.add(r,fos.line(tracks1zshift,fos.red,opacity=0.02))
fos.add(r,fos.line(tracks2zshift,fos.cyan,opacity=0.02))
fos.add(r,fos.line(tracks3zshift,fos.blue,opacity=0.02))
print 'Show track to track correspondence br1 br2'
for i in track2track:
fos.add(r,fos.line(tracks1zshift[i[1]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks1zshift[i[1]][0],(4,4,4),fos.white)
fos.add(r,fos.line(tracks2zshift[i[2]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks2zshift[i[2]][0],(4,4,4),fos.white)
print 'Show track to track correspondence br1_FACT and br2_RK2'
for i in track2track2:
fos.add(r,fos.line(tracks3zshift[i[2]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks3zshift[i[2]][0],(4,4,4),fos.white)
for c in C:
print c, C[c]['rep3'] / C[c]['N']
r = show_rep3(C)
print 'Merging ...'
t = time.clock()
C = merge(C, 5.)
print time.clock() - t, len(C)
for c in C:
print c, C[c]['rep3'] / C[c]['N']
show_rep3(C, r, fos.red)
'''
#print 'Showing initial dataset.'
r=fos.ren()
#fos.add(r,fos.line(T,fos.white,opacity=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))
#C=pf.local_skeleton_clustering(tracks,20.)
print 'Done in total of ', time.clock() - tim, 'seconds.'
print 'Saving result...'
pkl.save_pickle(C_fname, C)
streams = [(i, None, None) for i in atracks]
tv.write(appr_fname, streams, hdr)
else:
print 'Loading result...'
C = pkl.load_pickle(C_fname)
skel = []
for c in C:
skel.append(C[c]['repz'])
print 'Showing dataset after clustering...'
r = fos.ren()
fos.clear(r)
colors = np.zeros((len(skel), 3))
for (i, s) in enumerate(skel):
color = np.random.rand(1, 3)
colors[i] = color
fos.add(r, fos.line(skel, colors, opacity=1))
fos.show(r)
path = '/home/eg01/Data/PBC/pbc2009icdm' import pbc from dipy.viz import fos tract = 2 G,hdr = pbc.load_training_set(path) r = fos.ren() fos.add(r,fos.line(G[tract]['tracks'],fos.blue,opacity=0.1)) fos.show(r)
