volpath=path+'/ICBM_WMPM_tweaked_'+str(brain) +'_'+str(scan)+'.nii'

print volpath

template,voxsz,aff=pbc.loadvol(volpath)

tracks=pbc.load_approximate_tracks(path,brain,scan)

print 'template shape', template.shape

tcs,tes = tv.track_counts(tracks, template.shape, vox_sizes=(1,1,1), return_elements=True)

print 'tcs shape', tcs.shape

''' For a specific value in the ICBM atlas return the tracks that cross the region that has the same value.

'''

ind=np.where(template==value)

indices=set([])

for i in range(len(ind[0])):

try:

tmp=tes[(ind[0][i], ind[1][i], ind[2][i])]

indices=indices.union(set(tmp))

except:

pass

bundle=[tracks[i] for i in list(indices)]

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)

#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']

#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])

'''

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)

overlaps = np.zeros((len(refs),len(refs)), dtype=np.int)

for b1 in range(2,len(refs)):

ind1 = set(refs[b1])

leftover = set(refs[b1])

for b2 in range(1,b1):

ind2 = set(refs[b2])

overlaps[b1,b2]=len(ind1.intersection(ind2))

overlaps[b2,b1]=overlaps[b1,b2]

leftover=leftover.difference(ind2)

overlaps[0,b1]=len(leftover)

overlaps[b1,0]=overlaps[0,b1]

brainscan_keys = [(1,1),(1,2),(2,1),(3,1),(3,2)]

refdic = pbc.load_pickle('/home/eg01/Data/PBC/pbc2009icdm/refdic.pkl')

for bs in brainscan_keys:

refs=refdic[bs]['template_indices']

print 'Overlaps for brainscan', bs

min_ref_class = []

for track in bundle:

d = []

for ref in refs:

#d.append(pf.zhang_distances(track,ref,metric='min'))

d.append(pf.zhang_distances(track,ref,metric='avg'))

#print np.argmin(d), len(refclass), len(ref_indices), len(refs)

min_dist=np.argmin(d)

if min_dist>15:

min_ref_class.append(0)

else:

min_ref_class.append(refclass[np.argmin(d)])

assignment={}

#for c in range(1,21):

for c in range(0,9):

assignment[c] = []

for (i,bi) in enumerate(bundle_indices):

assignment[min_ref_class[i]].append(bi)

path='/home/eg01/Data/PBC/pbc2009icdm'

#refdic = pbc.load_pickle(path + '/refdic.pkl')

refdic = pbc.load_pickle(path + '/refdic_8_sc1.pkl')

#brains = [(1,1),(1,2),(2,1),(3,1),(3,2)]

brains = [(1,1),(2,1),(3,1)]

for (b,s) in brains:

print b,s

tA= refdic[(b,s)]['template_tracks']

iA= refdic[(b,s)]['template_indices']

tR = []

iR = []

cR=[]

#for bundle in range(1,21):

for bundle in range(1,9):

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):

#for bundle in range(1,9):

for bundle in range(0,9):

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):

for bundle1 in range(1,9):

print 'Remapping',

#for bundle2 in range(1,21):

for bundle2 in range(1,9):

try:

IDS[bundle2]['indices']+=relabelling[bundle1][bundle2]

except :

IDS[bundle2]['indices']=relabelling[bundle1][bundle2]

print 'Saving',b,s