Gridding Data

rascil.processing_components.griddata.convolution_functions Module

Functions that define and manipulate ConvolutionFunctions.

The griddata has axes [chan, pol, z, dy, dx, y, x] where z, y, x are spatial axes in either sky or Fourier plane. The order in the WCS is reversed so the grid_WCS describes UU, VV, DUU, DVV, WW, STOKES, FREQ axes.

GridData can be used to hold the Fourier transform of an Image or gridded visibilities. In addition, the convolution function can be stored in a GridData, most probably with finer spatial sampling.

Functions

`create_convolutionfunction_from_image`(im[, ...])	Create a convolution function from an image
`copy_convolutionfunction`(cf)	Make a copy of a convolution function
`calculate_bounding_box_convolutionfunction`(cf)	Calculate bounding boxes
`apply_bounding_box_convolutionfunction`(cf[, ...])	Apply a bounding box to a convolution function
`qa_convolutionfunction`(cf[, context])	Assess the quality of a convolutionfunction
`export_convolutionfunction_to_fits`(cf[, ...])	Write a convolution function to fits

rascil.processing_components.griddata.gridding Module

Imaging is based on used of the FFT to perform Fourier transforms efficiently. Since the observed visibility data_models do not arrive naturally on grid points, the sampled points are resampled on the FFT grid using a convolution function to smear out the sample points. The resulting grid points are then FFT’ed. The result can be corrected for the griddata convolution function by division in the image plane of the transform.

This module contains functions for performing the griddata process and the inverse degridding process.

The GridData data model is used to hold the specification of the desired result.

GridData, ConvolutionFunction and Vis/BlockVis always have the same PolarisationFrame. Conversion to stokesIQUV is only done in the image plane.

Functions

`convolution_mapping_visibility`(vis, ...[, ...])	Find the mappings between visibility, griddata, and convolution function
`grid_visibility_to_griddata`(vis, griddata, cf)	Grid Visibility onto a GridData
`degrid_visibility_from_griddata`(vis, ...)	Degrid blockVisibility from a GridData
`fft_griddata_to_image`(griddata, template[, gcf])	FFT griddata after applying gcf
`fft_image_to_griddata`(im, griddata[, gcf])	Fill griddata with transform of im
`griddata_merge_weights`(gd_list)	Merge weights into one grid
`grid_visibility_weight_to_griddata`(vis, griddata)	Grid Visibility weight onto a GridData
`griddata_visibility_reweight`(vis, griddata)	Reweight visibility weight using the weights in griddata

rascil.processing_components.griddata.kernels Module

Functions that define and manipulate kernels

Functions

`create_pswf_convolutionfunction`(im[, ...])	Fill an Anti-Aliasing filter into a ConvolutionFunction
`create_box_convolutionfunction`(im[, ...])	Fill a box car function into a ConvolutionFunction
`create_awterm_convolutionfunction`(im[, ...])	Fill AW projection kernel into a GridData.
`create_vpterm_convolutionfunction`(im[, ...])	Fill voltage pattern kernel projection kernel into a GridData.

rascil.processing_components.griddata.operations Module

Functions that define and manipulate GridData.

The griddata has axes [chan, pol, z, y, x] where z, y, x are spatial axes in either sky or Fourier plane. The order in the WCS is reversed so the grid_WCS describes UU, VV, WW, STOKES, FREQ axes.

GridData can be used to hold the Fourier transform of an Image or gridded visibilities. In addition, the convolution function can be stored in a GridData, most probably with finer spatial sampling.

Functions

`create_griddata_from_image`(im[, ...])	Create a GridData from an image
`create_griddata_from_array`(data, grid_wcs, ...)	Create a griddata from an array and wcs's
`copy_griddata`(gd)	Copy griddata
`qa_griddata`(gd[, context])	Assess the quality of a griddata