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)
def show_simulated_2fiber_crossings():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data=create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,47,5),snr=None)
#data=create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,92,5),snr=None)
print data.shape
#stop
#"""
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
dn.peak_thr=.4
dn.iso_thr=.05
dn.radius=np.arange(0,5,0.1)
dn.radiusn=len(dn.radius)
dn.create_qspace(bvals,bvecs,16,8)
dn.radon_params(64)
dn.precompute_interp_coords()
dn.precompute_fast_coords()
dn.precompute_equator_indices(5.)
dn.precompute_angular(None)#0.01)
dn.fit()
dn2=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=False)
dn2.peak_thr=.4
dn2.iso_thr=.05
dn2.radius=np.arange(0,5,0.1)
dn2.radiusn=len(dn.radius)
dn2.create_qspace(bvals,bvecs,16,8)
dn2.radon_params(64)
dn2.precompute_interp_coords()
dn2.precompute_fast_coords()
dn2.precompute_equator_indices(5.)
dn2.precompute_angular(None)#0.01)
dn2.fit()
#"""
eis=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eis.radius=np.arange(0,6,0.2)
eis.radius=np.arange(0,5,0.1)
eis.gaussian_weight=None
eis.set_operator('signal')#'laplap')
eis.update()
eis.fit()
eil=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eil.radius=np.arange(0,6,0.2)
eil.radius=np.arange(0,5,0.1)
eil.gaussian_weight=None
eil.set_operator('laplacian')#'laplap')
eil.update()
eil.fit()
eil2=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eil2.radius=np.arange(0,6,0.2)
eil2.radius=np.arange(0,5,0.1)
eil2.gaussian_weight=None
eil2.set_operator('laplap')
eil2.update()
eil2.fit()
ds=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=True,save_odfs=True)
gq=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,auto=False,save_odfs=True)
gq.fit()
gq2=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,auto=False,save_odfs=True)
gq2.fit()
#blobs=np.zeros((19,1,8,dn.odfn))
blobs=np.zeros((10,1,6,dn.odfn))
"""
blobs[:,0,0]=dn.odfs()
blobs[:,0,1]=dn2.odfs()
blobs[:,0,2]=eis.odfs()
blobs[:,0,3]=eil.odfs()
blobs[:,0,4]=eil2.odfs()
blobs[:,0,5]=ds.odfs()
blobs[:,0,6]=gq.odfs()
blobs[:,0,7]=gq2.odfs()
"""
blobs[:,0,0]=dn2.odfs()
blobs[:,0,1]=eil.odfs()
eo=eil2.odfs()
#eo[eo<0.05*eo.max()]=0
blobs[:,0,2]=eo
blobs[:,0,3]=ds.odfs()
blobs[:,0,4]=gq.odfs()
blobs[:,0,5]=gq2.odfs()
show_blobs(blobs,dn.odf_vertices,dn.odf_faces,scale=0.5)
#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:
ODFs=get_all_odfs(M,R,len(qg.odf_vertices))
