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])
import pbc
import numpy as np
import pbc1109
from pbc1109 import track_volumes as tv
from dipy.viz import fos
from dipy.core import track_metrics as tm
from dipy.core import track_learning as tl
from dipy.core import performance as pf
from itertools import combinations
#---------------------------------------------------------------------------------------------------------------------------------------------
path='/home/eg01/Data/PBC/pbc2009icdm'
#---------------------------------------------------------------------------------------------------------------------------------------------
ids=tl.emi_atlas()
#---------------------------------------------------------------------------------------------------------------------------------------------
def load_template_tes_and_tracks(path,brain,scan):
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
return template,tcs,tes,tracks
#---------------------------------------------------------------------------------------------------------------------------------------------
def for_a_value_in_template(template,value,tes,tracks):