Source code for africanus.gridding.wgridder.im2vis

# -*- coding: utf-8 -*-

    from ducc0.wgridder import dirty2ms
except ImportError as e:
    ducc_import_error = e
    ducc_import_error = None

import numpy as np
from import DocstringTemplate
from africanus.util.requirements import requires_optional

@requires_optional('ducc0.wgridder', ducc_import_error)
def _model_internal(uvw, freq, image, freq_bin_idx, freq_bin_counts, cell,
                    weights, flag, celly, epsilon, nthreads, do_wstacking):
    # adjust for chunking
    # need a copy here if using multiple row chunks
    freq_bin_idx2 = freq_bin_idx - freq_bin_idx.min()
    nband, nx, ny = image.shape
    nrow = uvw.shape[0]
    nchan = freq.size
    vis = np.zeros((nrow, nchan), dtype=np.result_type(image, np.complex64))
    for i in range(nband):
        ind = slice(freq_bin_idx2[i], freq_bin_idx2[i] + freq_bin_counts[i])
        if weights is not None:
            wgt = weights[:, ind]
            wgt = None
        if flag is not None:
            mask = flag[:, ind]
            mask = None
        vis[:, ind] = dirty2ms(uvw=uvw, freq=freq[ind], dirty=image[i],
                               wgt=wgt, pixsize_x=cell, pixsize_y=celly,
                               nu=0, nv=0, epsilon=epsilon, mask=mask,
                               nthreads=nthreads, do_wstacking=do_wstacking)
    return vis

[docs]@requires_optional('ducc0.wgridder', ducc_import_error) def model(uvw, freq, image, freq_bin_idx, freq_bin_counts, cell, weights=None, flag=None, celly=None, epsilon=1e-5, nthreads=1, do_wstacking=True): if celly is None: celly = cell if not nthreads: import multiprocessing nthreads = multiprocessing.cpu_count() return _model_internal(uvw, freq, image, freq_bin_idx, freq_bin_counts, cell, weights, flag, celly, epsilon, nthreads, do_wstacking)
MODEL_DOCS = DocstringTemplate( r""" Compute image to visibility mapping using ducc degridder i.e. .. math:: V = Rx where :math:`R` is an implicit degridding operator, :math:`V` denotes visibilities of shape :code:`(row, chan)` and :math:`x` is the image of shape :code:`(band, nx, ny)`. The number of imaging bands :code:`(band)` has to be less than or equal to the number of channels :code:`(chan)` at which the data were obtained. The mapping from :code:`(chan)` to :code:`(band)` is described by :code:`freq_bin_idx` and :code:`freq_bin_counts` as described below. There is an option to provide weights during degridding to cater for self adjoint gridding and degridding operators. In this case :code:`weights` should actually be the square root of what is typically referred to as imaging weights. In this case the degridder computes the whitened model visibilities i.e. .. math:: V = \Sigma^{-\frac{1}{2}} R x where :math:`\Sigma` refers to the inverse of the weights (i.e. the data covariance matrix when using natural weighting). Parameters ---------- uvw : $(array_type) uvw coordinates at which visibilities were obtained with shape :code:`(row, 3)`. freq : $(array_type) Observational frequencies of shape :code:`(chan,)`. model : $(array_type) Model image to degrid of shape :code:`(nband, nx, ny)`. freq_bin_idx : $(array_type) Starting indices of frequency bins for each imaging band of shape :code:`(band,)`. freq_bin_counts : $(array_type) The number of channels in each imaging band of shape :code:`(band,)`. cell : float The cell size of a pixel along the :math:`x` direction in radians. weights : $(array_type), optional Imaging weights of shape :code:`(row, chan)`. flag: $(array_type), optional Flags of shape :code:`(row,chan)`. Will only process visibilities for which flag!=0 celly : float, optional The cell size of a pixel along the :math:`y` direction in radians. By default same as cell size along :math:`x` direction. epsilon : float, optional The precision of the gridder with respect to the direct Fourier transform. By deafult, this is set to :code:`1e-5` for single precision and :code:`1e-7` for double precision. nthreads : int, optional The number of threads to use. Defaults to one. If set to zero will use all available cores. do_wstacking : bool, optional Whether to correct for the w-term or not. Defaults to True Returns ------- vis : $(array_type) Visibilities corresponding to :code:`model` of shape :code:`(row,chan)`. """) try: model.__doc__ = MODEL_DOCS.substitute( array_type=":class:`numpy.ndarray`") except AttributeError: pass