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 print_all_overlaps():
brainscan_keys = [(1,1),(1,2),(2,1),(3,1),(3,2)]
refdic = pbc.load_pickle('/home/ian/tractarian/refdic.pkl')
for bs in brainscan_keys:
refs=refdic[bs]['template_indices']
print 'Overlaps for brainscan', bs
print measure_internal_overlaps(refs)
def make_ref_dic():
'''
Creates a dictionary containing reference fibres, names, and template values
for the original 20 tracts in the training dataset
'''
path='/home/eg01/Data/PBC/pbc2009icdm'
#name_list=['Unassigned', 'Arcuate L', 'Cingulum L','Corticospinal R', 'Forceps Major','Fornix','Inferior Occipitofrontal Fasciculus L','Superior Longitudinal Fasciculus L','Uncinate R']
name_list=['Not Assigned']+[ids[i]['bundle_name'][0] for i in range(1,9)]
#name_list=['Not Assigned']+[ids[i]['bundle_name'][0] for i in range(1,21)]
#value_list=[0,41,35,9,5,6,31,41,48]
#value_list= [[0]]+[ids[i]['value'] for i in range(1,21)]
value_list= [[0]]+[ids[i]['value'] for i in range(1,9)]
#corr = pbc.load_pickle(path+'/corr_20.pkl')
corr = pbc.load_pickle(path+'/corr_8_sc1.pkl')
#brainscan_keys = [(1,1),(1,2),(2,1),(3,1),(3,2)]
brainscan_keys = [(1,1),(2,1),(3,1)]
refdic={}
#for (i, (b,s)) in enumerate([(1,1)]):
for (i, (b,s)) in enumerate(brainscan_keys):
refdic[(b,s)] = {}
refdic[(b,s)]['reference_indices']=[[]]+[list(set(c)) for c in corr[i]]
refdic['names'] = name_list
refdic['template_values'] = value_list
template,tcs,tes,tracks = load_template_tes_and_tracks(path,b,s)
#refdic[(b,s)]['reference_tracks'] =[[]]+ [[tracks[r] for r in l] for l in refdic[(b,s)]['reference_indices'][1:]]
for (j,l) in enumerate(refdic[(b,s)]['reference_indices']):
if j==0:
refdic[(b,s)]['reference_tracks'] = [[]]
else:
refdic[(b,s)]['reference_tracks'].append( [tracks[t] for t in l])
for (j,values) in enumerate(value_list):
if j==0:
refdic[(b,s)]['template_tracks'] =[[]]
refdic[(b,s)]['template_indices'] = [[]]
else:
all_template_tracks = []
all_template_indices = []
for value in values:
#template_tracks, template_indices = tracks_and_indices_for_a_value_in_template(template,value,tes,tracks)
template_tracks, template_indices = for_a_value_in_template(template,value,tes,tracks)
all_template_tracks += template_tracks
all_template_indices += template_indices
refdic[(b,s)]['template_tracks'] += [all_template_tracks]
refdic[(b,s)]['template_indices'] += [all_template_indices]
pbc.save_pickle(path+'/refdic_8_sc1.pkl',refdic)
def relabel_them_all(no):
path='/home/eg01/Data/PBC/pbc2009icdm'
refdic = pbc.load_pickle('/home/ian/tractarian/refdic.pkl')
brains = [(1,1),(1,2),(2,1),(3,1),(3,2)]
for (b,s) in [brains[no]]:
print b,s
tA= refdic[(b,s)]['template_tracks']
iA= refdic[(b,s)]['template_indices']
tR = []
iR = []
cR=[]
for bundle in range(1,21):
tmp=refdic[(b,s)]['reference_tracks'][bundle]
tR+=tmp
iR+=refdic[(b,s)]['reference_indices'][bundle]
#print len(tmp),np.repeat(bundle,len(tmp)).astype(int)
cR+=list(np.repeat(bundle,len(tmp)).astype(int))
relabelling = {}
for bundle in range(1,21):
print 'Relabelling', bundle
#print len(tR),len(iR),len(cR)
relabelling[bundle] = map_bundle_to_refs(tA[bundle], iA[bundle], tR, iR, cR)
IDS=ids
for bundle1 in range(1,21):
print 'Remapping',
for bundle2 in range(1,21):
try:
IDS[bundle2]['indices']+=relabelling[bundle1][bundle2]
except :
IDS[bundle2]['indices']=relabelling[bundle1][bundle2]
print 'Saving',b,s
pbc.save_pickle(path+'/Relabelling_'+str(b)+'_'+str(s)+'.pkl',IDS)
def print_results():
#brains=[(1,1),(1,2),(2,1),(3,1),(3,2)]
brains=[(1,1),(2,1),(3,1)]
for (b,s) in brains:
fname=path+'/eg309_brain'+str(b)+'_scan'+str(s)+'_ch1.txt'
f=open(fname,'wt')
subm=np.zeros((250000,2)).astype(int)
ids = pbc.load_pickle(path+'/Relabelling_'+str(b)+'_'+str(s)+'.pkl')
for i in ids:
indices=ids[i]['indices']
for ind in indices:
subm[ind,0]=ind+1
subm[ind,1]=i
for i in range(subm.shape[0]):
print >> f, '%d\t%d' % (i,subm[i,1])
f.close()
import pbc from dipy.viz import fos path='/home/eg309/Data/' tracks1zshift=pbc.load_pickle(path+'t1zs.pkl') tracks2zshift=pbc.load_pickle(path+'t2zs.pkl') tracks3zshift=pbc.load_pickle(path+'t3zs.pkl') track2track=pbc.load_pickle(path+'t2t.pkl') track2track2=pbc.load_pickle(path+'t2t2.pkl') 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' 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)