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))