autolens.TransformerNUFFT

class autolens.TransformerNUFFT(uv_wavelengths, real_space_mask)
__init__(uv_wavelengths, real_space_mask)

Constructor.

Parameters:None (Python NoneType) –
Returns:NUFFT: the pynufft_hsa.NUFFT instance
Return type:NUFFT: the pynufft_hsa.NUFFT class
Example:
>>> 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

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, )
Return type:numpy array with the dtype of numpy.complex64