MEM (‘xrayvision.mem’)

The mem submodule contains the Maximum Entropy methods.

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, shape, pixel_size, *[, ...])	Maximum Entropy Method visibility based image reconstruction