nuf_ob = MriSenseNufft(im_size=(nx, nx), smap=smapT).to(dtype)
nuf_ob = nuf_ob.to(gpu)
adjnuf_ob = AdjMriSenseNufft(im_size=(nx, nx), smap=smapT).to(dtype)
adjnuf_ob = adjnuf_ob.to(gpu)
d2 = sio.loadmat('/Shared/lss_jcb/abdul/prashant_cardiac_data/Data/ktraj.mat')
ktraj = np.asarray(d2['ktraj'])
ktraj = ktraj.astype(np.complex64)
ktraj = np.transpose(ktraj, (2, 1, 0)) / nx
ktraj = ktraj[0:NF]
ktraj = np.reshape(ktraj, (1, NF * nintl * nx)) * 2 * np.pi
#dcf=np.abs(ktraj)
ktraj = np.stack((np.real(ktraj), np.imag(ktraj)), axis=1)
dcf = calculate_radial_dcomp_pytorch(nuf_ob, adjnuf_ob,
torch.tensor(ktraj).cuda())
dcf = torch.tensor(dcf).unsqueeze(0).unsqueeze(0)
#dcf=dcf.repeat(nbasis,nch,2,1)
dcf = dcf.repeat(1, 1, 2, 1)
ktraj = np.tile(ktraj, (nbasis, 1, 1))
ktrajT = torch.tensor(ktraj).to(dtype)
cc = torch.zeros((1, 1, 2, nx, nx)).cuda()
cc[0, 0, :, 255, 255] = 1
#cc[0,0,:,236,247]=1
tr = ktrajT[0].unsqueeze(0)
dd = nuf_ob(cc, tr.cuda())
ee1 = adjnuf_ob(dd * dcf.cuda(), tr.cuda())
dcf = dcf / ee1[0, 0, 0, 255, 255]
adjnuf_ob = adjnuf_ob.to(gpu)
#real_mat, imag_mat = precomp_sparse_mats(ktrajT, adjnuf_ob)
#interp_mats = {'real_interp_mats': real_mat, 'imag_interp_mats': imag_mat}
nufft_ob1 = MriSenseNufft(im_size=im_size, smap=smapT2).to(dtype)
nufft_ob1 = nufft_ob1.to(gpu)
nufft_ob = MriSenseNufft(im_size=im_size, smap=smapT).to(dtype)
nufft_ob = nufft_ob.to(gpu)
adjnufft_ob = AdjMriSenseNufft(im_size=im_size, smap=smapT).to(dtype)
adjnufft_ob = adjnufft_ob.to(gpu)
#nufft_ob = MriSenseNufft(smap=smapT,im_size=im_size).to(dtype)
#adjnufft_ob = AdjMriSenseNufft(smap=smapT,im_size=im_size ).to(dtype)
#At=lambda x: adjnufft_ob(x*dcfT,ktrajT,interp_mats)
#A=lambda x: nufft_ob(x,ktrajT,interp_mats)
dcfT = calculate_radial_dcomp_pytorch(nufft_ob, adjnufft_ob, ktrajT)
kdataT = nufft_ob1(atbT, ktrajT1)
kdataT = kdataT.permute(1, 2, 0, 3)
atb = torch.zeros((nbasis, im_size[0], im_size[1]))
kdataT = torch.reshape(kdataT, (1, nch, 2, NF, nintl * nkpts))
kdataT = kdataT.permute(3, 0, 1, 2, 4)
kdataT = torch.reshape(kdataT, (NF, 1 * nch * 2 * nintl * nkpts))
temp = torch.matmul(VT, kdataT)
temp = torch.reshape(temp, (nbasis, NF, 1, nch, 2, nintl * nkpts))
temp = temp.permute(0, 2, 3, 4, 1, 5)
temp = torch.reshape(temp, (nbasis, nch, 2, NF * nintl * nkpts))
atb = adjnufft_ob(temp, ktrajT)
#ktb=nufft_ob(atb,ktrajT)