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 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
import numpy as np
import itertools
from dipy.core import performance as pf
from dipy.core import track_learning as tl
from dipy.core import track_metrics as tm
from dipy.viz import fos
import pbc
import cPickle
import cProfile as profile
import pstats
path='/home/eg01/Data/PBC/pbc2009icdm'
G,hdr,R=pbc.load_training_set(path)
tracks=pbc.load_approximate_tracks(path,1,1)
#tracks=[t for (i,t) in enumerate(tracks) if i%25==0]
def test(bundle_list, divergence_threshold_list=[0.25], fibre_weight_list=[0.8],index_lists=False):
comments = open('/home/ian/tractarian/commentary.txt','w')
#reduced_hits = []
#for b in [1,2,3,4,5,6,7,8]:
for b in bundle_list:
#print 'Starting ...'
refindex = G[b]['indices'].index([R[b]])
ref = G[b]['tracks'][refindex]
