ei.fit()
ds=DiffusionSpectrum(dat,bvals,bvecs,
odf_sphere='symmetric642',
mask=None,
half_sphere_grads=True,
auto=True,
save_odfs=True)
ten=Tensor(dat,bvals,bvecs)
FA=ten.fa()
#gq=GeneralizedQSampling(dat,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
#gq2=GeneralizedQSampling(dat,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
#show_blobs(ds.ODF,ds.odf_vertices,ds.odf_faces,FA.squeeze(),size=1.5,scale=1.)
show_blobs(ei.ODF,ei.odf_vertices,ei.odf_faces,FA.squeeze(),size=1.5,scale=1.)
"""
#dti reconstruction
ten=Tensor(data,bvals,bvecs2,mask=mask)
#generate tensor tracks with random seeds
eu=EuDX(ten.fa(),ten.ind(),seeds=10000,a_low=0.2)
tenT=[e for e in eu]
#dsi reconstruction
ds=DiffusionSpectrum(data,bvals,bvecs2,mask=mask)
#generate dsi tracks with random seeds
eu_fa=EuDX(ten.fa(),ds.ind()[...,0],seeds=10000,a_low=0.2)
dsTfa=[e for e in eu_fa]
eu_gfa=EuDX(ds.gfa(),ds.ind()[...,0],seeds=10000,a_low=0.2)
dsTgfa=[e for e in eu_gfa]
eu_qa=EuDX(ds.qa(),ds.ind(),seeds=10000,a_low=0.001)
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))
show_blobs(ODFs[:,:,0,:][:,:,None,:],qg.odf_vertices,qg.odf_faces,size=1.5,scale=1.,norm=True)
#ds=DiffusionSpectrum(dat,bvals,bvecs2,odf_sphere='symmetric642',mask=None,auto=True,save_odfs=True)
ten=Tensor(dat,bvals,bvecs)
FA=ten.fa()
#gq=GeneralizedQSampling(dat,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
gq2=GeneralizedQSampling(dat,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
#remove the minimum ODF
#res=dn.ODF-np.min(dn.ODF,axis=-1)[:,:,:,None]
#show_blobs(dn.ODF,dn.odf_vertices,dn.odf_faces,FA.squeeze(),size=1.5,scale=1.)
#show_blobs(ei.ODF,ei.odf_vertices,ei.odf_faces,FA.squeeze(),size=1.5,scale=1.)
#show_blobs(ds.ODF,ds.odf_vertices,ds.odf_faces,FA.squeeze(),size=1.5,scale=1.)
#show_blobs(gq.ODF,gq.odf_vertices,gq.odf_faces,FA.squeeze(),size=1.5,scale=1.)
show_blobs(gq2.ODF,gq2.odf_vertices,gq2.odf_faces,FA.squeeze(),size=1.5,scale=1.)
#show_blobs(dn2.ODF,dn.odf_vertices,dn.odf_faces)
#show_blobs(ds.ODF,dn.odf_vertices,dn.odf_faces)
#show_blobs(gq.ODF,dn.odf_vertices,dn.odf_faces)
#sodfs=botox_weighting(dn.ODF,0.1,0.1,dn.odf_vertices)
"""
#dti reconstruction
ten=Tensor(data,bvals,bvecs2,mask=mask)
#generate tensor tracks with random seeds
eu=EuDX(ten.fa(),ten.ind(),seeds=10000,a_low=0.2)
tenT=[e for e in eu]
#dsi reconstruction
ds=DiffusionSpectrum(data,bvals,bvecs2,mask=mask)
#generate dsi tracks with random seeds
eu_fa=EuDX(ten.fa(),ds.ind()[...,0],seeds=10000,a_low=0.2)
print 'ds',t2-t1,'secs.'
gq=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',mask=famask,squared=False,save_odfs=save_odfs)
t3=time()
print 'gq',t3-t2,'secs.'
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=False,save_odfs=save_odfs,fast=True)
ei.radius=np.arange(0,5,0.4)
ei.gaussian_weight=0.05
ei.set_operator('laplacian')
ei.update()
ei.fit()
t4=time()
print 'ei',t4-t3,'secs.'
if visual:
#print 'Showing data'
#show_blobs(ds.ODF[:,:,0,:][:,:,None,:],ds.odf_vertices,ds.odf_faces,size=1.5,scale=1.)
show_blobs(ds.ODF[:20,20:40,6,:][:,:,None,:],ds.odf_vertices,ds.odf_faces,size=1.5,scale=1.,norm=True)
show_blobs(ei.ODF[:20,20:40,6,:][:,:,None,:],ds.odf_vertices,ds.odf_faces,size=1.5,scale=1.,norm=True)
show_blobs(gq.ODF[:20,20:40,6,:][:,:,None,:],ds.odf_vertices,ds.odf_faces,size=1.5,scale=1.,norm=True)
#create tensors
tensors = Tensor(data, bvals, bvecs, thresh=50)
FA = tensors.fa()
#MASK=np.zeros(FA.shape)
#MASK=(FA>.5)&(FA<.8)
#cleanup background
ds.PK[FA<.2]=np.zeros(5)
ei.PK[FA<.2]=np.zeros(5)
gq.PK[FA<.2]=np.zeros(5)
"""!Random seeds everywhere
#create random seeds
x,y,z,g=ei.PK.shape
t1=time()
#"""
dn=DiffusionNabla(dat,bvals,bvecs,odf_sphere='symmetric362',
auto=True,save_odfs=True,fast=True)
"""
dn.radius=np.arange(0,6,.2)
dn.radiusn=len(dn.radius)
dn.create_qspace(bvals,bvecs,16,8)
dn.peak_thr=.4
dn.iso_thr=.7
dn.radon_params(64)
dn.precompute_interp_coords()
dn.fit()
"""
#from dipy.reconst.dsi import DiffusionSpectrum
#mask=dat[...,0]>0
#dn=DiffusionSpectrum(dat,bvals,bvecs,save_odfs=True)
t2=time()
print t2-t1,'.sec'
show_blobs(dn.odfs(),dn.odf_vertices,dn.odf_faces)
#r=fvtk.ren()
#fvtk.add(r,fvtk.volume(dat[...,0]))
#fvtk.show(r)
