Images

rascil.processing_components.image.deconvolution Module

Image deconvolution functions

The standard deconvolution algorithms are provided:

hogbom: Hogbom CLEAN See: Hogbom CLEAN A&A Suppl, 15, 417, (1974)

msclean: MultiScale CLEAN See: Cornwell, T.J., Multiscale CLEAN (IEEE Journal of Selected Topics in Sig Proc, 2008 vol. 2 pp. 793-801)

mfsmsclean: MultiScale Multi-Frequency See: U. Rau and T. J. Cornwell, “A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry,” A&A 532, A71 (2011).

For example to make dirty image and PSF, deconvolve, and then restore:

model = create_image_from_visibility(vt, cellsize=0.001, npixel=256)
dirty, sumwt = invert_visibility(vt, model, context="2d")
psf, sumwt = invert_visibility(vt, model, context="2d", dopsf=True)

comp, residual = deconvolve_cube(dirty, psf, niter=1000, threshold=0.001, fracthresh=0.01, window_shape='quarter',
                             gain=0.7, algorithm='msclean', scales=[0, 3, 10, 30])

restored = restore_cube(comp, psf, residual)

All functions return an image holding clean components and residual image

Functions

`deconvolve_list`(dirty_list, psf_list[, ...])	Clean using a variety of algorithms
`restore_list`(model_list[, psf_list, ...])	Restore the model image to the residuals
`deconvolve_cube`(dirty, psf[, sensitivity, ...])	Clean using a variety of algorithms
`restore_cube`(model[, psf, residual, clean_beam])	Restore the model image to the residuals
`fit_psf`(psf, **kwargs)	Fit a two dimensional Gaussian to a PSF using astropy.modeling

rascil.processing_components.image.gather_scatter Module

Functions that perform gather/scatter operations on Images.

Functions

`image_gather_channels`(image_list[, im, ...])	Gather a list of subimages back into an image
`image_scatter_channels`(im[, subimages])	Scatter an image into a list of subimages using the channels
`image_gather_facets`(image_list, im[, ...])	Gather a list of subimages back into an image using the image_raster_iterator
`image_scatter_facets`(im[, facets, overlap, ...])	Scatter an image into a list of subimages using the image_raster_iterator

rascil.processing_components.image.gradients Module

Image operations visible to the Execution Framework as Components

Functions

image_gradients(im)

Calculate image first order gradients numerically

rascil.processing_components.image.iterators Module

Functions that define and manipulate images. Images are just data and a World Coordinate System.

Functions

`image_channel_iter`(im[, subimages])	Create a image_channel_iter generator, returning images
`image_null_iter`(im[, facets, overlap])	One time iterator
`image_raster_iter`(im[, facets, overlap, ...])	Create an image_raster_iter generator, returning a list of subimages, optionally with overlaps

rascil.processing_components.image.operations Module

Image operations visible to the Execution Framework as Components

Functions

`add_image`(im1, im2)	Add two images
`average_image_over_frequency`(im)	Integrate image across frequency
`convert_polimage_to_stokes`(im[, complex_image])	Convert a polarisation image to stokes IQUV (complex)
`convert_stokes_to_polimage`(im, ...)	Convert a stokes image in IQUV to polarisation_frame
`create_empty_image_like`(im)	Create an empty image like another in shape and wcs
`create_image`([npixel, cellsize, ...])	Create an empty image consistent with the inputs.
`create_image_from_array`(data, wcs, ...[, ...])	Create an image from an array and optional wcs
`create_w_term_like`(im, w[, phasecentre, ...])	Create an image with a w term phase term in it:
`create_window`(template, window_type, **kwargs)	Create a window image using one of a number of methods
`export_image_to_fits`(im[, fitsfile])	Write an image to fits
`fft_image_to_griddata`(im)	WCS-aware FFT of a canonical image
`image_add_ra_dec_grid`(im)	Add ra, dec coordinates
`image_is_canonical`(im)	Is this Image canonical format?
`import_image_from_fits`(fitsfile[, fixpol])	Read an Image from fits
`pad_image`(im, shape)	Pad an image to desired shape, adding equally to all edges
`sub_image`(im, shape)	Subsection an image to desired shape, cutting equally from all edges
`polarisation_frame_from_wcs`(wcs, shape)	Convert wcs to polarisation_frame
`qa_image`(im[, context])	Assess the quality of an image
`remove_continuum_image`(im[, degree, mask])	Fit and remove continuum visibility in place
`reproject_image`(im, newwcs[, shape])	Re-project an image to a new coordinate system
`show_components`(im, comps[, npixels, fig, ...])	Show components against an image
`show_image`(im[, fig, title, pol, chan, cm, ...])	Show an Image with coordinates using matplotlib, optionally with components
`smooth_image`(model[, width, normalise])	Smooth an image with a 2D Gaussian kernel
`scale_and_rotate_image`(im[, angle, scale, order])	Scale and then rotate and image in x, y axes
`apply_voltage_pattern_to_image`(im, vp[, ...])	Apply a voltage pattern to an image