autolens.TransformerNUFFT¶

Constructor.

Parameters: None (Python NoneType) – NUFFT: the pynufft_hsa.NUFFT instance NUFFT: the pynufft_hsa.NUFFT class
>>> from pynufft import NUFFT_cpu
>>> NufftObj = NUFFT_cpu()

Methods

 __init__(uv_wavelengths, real_space_mask) Constructor. adjoint(y) Adjoint NUFFT on CPU adjoint_lop(y) Adjoint NUFFT on CPU :param y: The input numpy array, with the size of (M,) or (M, batch) :type: numpy array with the dtype of numpy.complex64 :return: x: The output numpy array, with the size of Nd or Nd + (batch, ) :rtype: numpy array with the dtype of numpy.complex64 adjoint_many2one(y) Assume y.shape = self.multi_M apply_columns(cols) Apply subset of columns of operator cond([uselobpcg]) Condition number of linear operator. conj() Complex conjugate operator div(y[, niter]) Solve the linear problem $$\mathbf{y}=\mathbf{A}\mathbf{x}$$. dot(x) Matrix-matrix or matrix-vector multiplication. eigs([neigs, symmetric, niter, uselobpcg]) Most significant eigenvalues of linear operator. forward(x) Forward NUFFT on CPU forward_lop(x) Forward NUFFT on CPU :param x: The input numpy array, with the size of Nd or Nd + (batch,) :type: numpy array with the dtype of numpy.complex64 :return: y: The output numpy array, with the size of (M,) or (M, batch) :rtype: numpy array with the dtype of numpy.complex64 forward_one2many(x) Assume x.shape = self.Nd image_from(visibilities) initialize_plan([ratio, interp_kernel]) k2vec(k) k2xx(k) Private: the inverse FFT and image cropping (which is the reverse of k2xx_one2one(k) Private: the inverse FFT and image cropping k2y(k) Private: interpolation by the Sparse Matrix-Vector Multiplication k2y2k(k) Private: the integrated interpolation-gridding by the Sparse matmat(X) Matrix-matrix multiplication. matvec(x) Matrix-vector multiplication. plan(om, Nd, Kd, Jd[, ft_axes]) Plan the NUFFT_cpu object with the geometry provided. reset_sense() rmatmat(X) Matrix-matrix multiplication. rmatvec(x) Adjoint matrix-vector multiplication. selfadjoint(x) selfadjoint NUFFT on CPU selfadjoint2(x) selfadjoint_one2many2one(x) set_sense(coil_profile) solve(y[, solver]) Solve NUFFT_cpu. todense([backend]) Return dense matrix. toimag([forw, adj]) Imag operator toreal([forw, adj]) Real operator tosparse() Return sparse matrix. transform_mapping_matrix(mapping_matrix) transpose() Transpose this linear operator. vec2k(k_vec) Sorting the vector to k-spectrum Kd array vec2y(k_vec) gridding: visibilities_from(image) … x2xx(x) Private: Scaling on CPU Inplace multiplication of self.x_Nd by the scaling factor self.sn. xx2k(xx) Private: oversampled FFT on CPU xx2k_one2one(xx) Private: oversampled FFT on CPU xx2x(xx) Private: rescaling, which is identical to the _x2xx() method y2k(y) Private: gridding by the Sparse Matrix-Vector Multiplication y2vec(y) regridding non-uniform data (unsorted vector)

Attributes

 H Hermitian adjoint. T Transpose this linear operator. ndim
visibilities_from(image)

forward_lop(x)

Forward NUFFT on CPU :param x: The input numpy array, with the size of Nd or Nd + (batch,) :type: numpy array with the dtype of numpy.complex64 :return: y: The output numpy array, with the size of (M,) or (M, batch) :rtype: numpy array with the dtype of numpy.complex64