def best_smoother():
for smoo in np.linspace(3,5,10):
gqs=GeneralizedQSampling(data,bvals,bvecs,smoo,
odf_sphere=odf_sphere,
mask=None,
squared=True,
auto=False,
save_odfs=True)
gqs.peak_thr=0.5
gqs.fit()
gqs.ODF[gqs.ODF<0]=0.
odf=gqs.ODF[0,0,0]
print smoo, np.sum((direct_odf/direct_odf.max() - odf/odf.max())**2)
#mf,mevals,mevecs=example('1b')
#signal=MultiTensor(bvals,bvecs,S0=1.,mf=mf,mevals=mevals,mevecs=mevecs)
#data=signal
#data=data[None,None,None,:]
data=data[:,4:40,:,:]
#ten
ten = Tensor(100*data, bvals, bvecs)
FA = ten.fa()
#GQI
gqs=GeneralizedQSampling(data,bvals,bvecs,smooth[i],
odf_sphere=odf_sphere,
mask=None,
squared=True,
auto=False,
save_odfs=True)
gqs.peak_thr=0.5
gqs.fit()
gqs.ODF[gqs.ODF<0]=0.
#manipulate
qg=gqs
#pack_results
M,R=analyze_peaks(data,ten,qg)
if test=='train':
K=np.load('trainSF.npy')
print 'SNR',snr, 'smooth',smooth[i],\
'Missed',np.sum(np.abs(M-K)>0), \
'Success',100*(np.float(np.prod(M.shape))-np.sum(np.abs(M-K)>0))/np.float(np.prod(M.shape)),'%'
if save==True:
save_for_mat(test,typ,snr,M,R)
#show ODFs
if show==True:
