Reference

xrayvision.visibility Module

Modules contains visibility related classes.

This contains classes to hold general visibilities and specialised classes hold visibilities from certain spacecraft or instruments

Classes

Visibility(vis, *, u, v[, offset, center])

Hold a set of related visibilities and information.

Class Inheritance Diagram

xrayvision.imaging Module

Functions

get_weights(vis[, natural, norm])	Return natural spatial frequency weight factor for each visibility.
validate_and_expand_kwarg(q[, name])	Expand a scalar or array of size one to size two by repeating.
vis_psf_image(vis, *[, shape, pixel_size, ...])	Create the point spread function for given u, v point of the visibilities.
vis_psf_map(vis, *[, shape, pixel_size, natural])	Create a map of the point spread function for given the visibilities.
vis_to_image(vis[, shape, pixel_size, natural])	Create an image by 'back projecting' the given visibilities onto the sky.
vis_to_map(vis[, shape, pixel_size, natural])	Create a map by performing a back projection of inverse transform on the visibilities.
generate_header(vis, *, shape, pixel_size)	Generate a map head given the visibilities, pixel size and shape
image_to_vis(image, *, u, v[, center, ...])	Return a Visibility created from the image and u, v sampling.
map_to_vis(amap, *, u, v)	Return a Visibility object created from the map and u, v sampling.

xrayvision.transform Module

Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT) related functions.

There are two implementations one a standard DFT dft and IDFT idft in terms of pixel space, i.e. the input has no positional information other than an arbitrary 0 origin and a length. The second takes inputs which have positional information dft_map and the inverse idft_map

Functions

dft_map(input_array, *, u, v[, center, ...])	Discrete Fourier transform in terms of coordinates returning 1-D array complex visibilities.
generate_uv(number_pixels[, center, pixel_size])	Generate the u or v coordinates given the number of pixels, center and pixel size.
generate_xy(number_pixels[, center, pixel_size])	Generate the x or y coordinates given the number of pixels, center and pixel size.
idft_map(input_vis, *, u, v, shape[, ...])	Inverse discrete Fourier transform in terms of coordinates returning a 2D real array or image.

xrayvision.clean Module

CLEAN algorithms.

The CLEAN algorithm solves the deconvolution problem by assuming a model for the true sky intensity which is a collection of point sources or in the case of multiscale clean a collection of appropriate component shapes at different scales.

Functions

clean(dirty_map, dirty_beam[, pixel, ...])	Clean the image using Hogbom's original method.
vis_clean(vis, shape, pixel[, ...])	Clean the visibilities using Hogbom's original method.
ms_clean(dirty_map, dirty_beam, pixel[, ...])	Clean the map using a multiscale clean algorithm.
vis_ms_clean(vis, shape, pixel[, scales, ...])	Clean the visibilities using a multiscale clean method.

xrayvision.mem Module

Implementation of Maximum Entropy Method

Functions

get_entropy(image, flux)	Return the entropy of an image.
get_fourier_matrix(vis[, shape, pixel_size])	Return the complex Fourier matrix used to compute the value of the visibilities.
estimate_flux(vis, shape, pixel[, maxiter, tol])	Estimate the total flux in the image by solving an optimisation problem.
get_mean_visibilities(vis, shape, pixel)	Return the mean visibilities sampling the same call in the discretisation of the (u,v) plane.
proximal_entropy(y, m, lamba, Lip[, tol])	This function computes the value of the proximity operator of the entropy function subject to positivity constraint, i.e. it solves the problem.
proximal_operator(z, f, m, lamb, Lip[, niter])	Computes the value of the proximity operator of the entropy function subject to positivity constraint and flux constraint by means of a Dykstra-like proximal algorithm (see Combettes, Pesquet, "Proximal Splitting Methods in Signal Processing", (2011)).
optimise_fb(Hv, Visib, Lip, flux, lambd, ...)	Solve the optimization problem using a forward-backward splitting algorithm
mem(vis[, percent_lambda, shape, pixel, ...])	Maximum Entropy Method for visibility based image reconstruction

xrayvision.utils Module

Functions

get_logger(name[, level])

Return a configured logger instance.

• Release History
  • Full Changelog