Plotting

This module contains a few helpful plotting routines for plotting spherical maps and simple MCMC stats and samples.

plotting.plot_map(f, title=None, cbar=True, cmap='turbo', vmin=None, vmax=None, cbar_label='', oversample=True, centre0=False, coasts=False, mask=None)

Plots a single MW sampled spherical map.

Parameters

  • f (array) – MW sampled image. Shape \((L, 2L - 1)\).

  • title (string) – Figure title

  • cbar (bool) – if True, plot the colour bar

  • cmap (string) – Name of a matplotlib colour map

  • vmin (float) – Minimum value. If None, will default to lowest value in f.

  • vmax (float) – Maximum value. If None, will default to highest value in f.

  • cbar_label (string) – Label for the colour bar. Requires cbar=True

  • oversample (bool) – if True, oversamples f to bandlimit \(L=256\) so the image is not pixelated

  • centre0 (bool) – if True, forces the colour map to be centred at 0. Overrides vmin,vmax.

  • coasts (bool) – if True, plots coastlines.

  • mask (boolarray) – A binary MW mask. Mask is applied after any oversampling, so should be a higher resolution than f

Returns

matplotlib figure

plotting.plot_wavelet_maps(f, L, B, J_min, dirs=1, spin=0, same_scale=True, **map_args)

Plots the scaling and wavelet maps of spherical map f.

Parameters

  • f (array) – MW sampled image. Shape \((L, 2L - 1)\).

  • L (int) – angular bandlimit

  • B (float) – wavelet scale parameter

  • J_min (int) – minimum wavelet scale

  • dirs (int) – azimuthal bandlimit for directional wavelets

  • spin (int) – spin number of spherical signal

  • same_scale (bool) – if True, wavelet maps are plotted on same colour scale

  • **map_args – optional arguments for plot_map()

Returns

List of figures

plotting.plot_evolution(logposteriors, L2s, L1s, figsize=(10, 8))

Plot the evolution of the MCMC chain.

Parameters

  • logposteriors – array of log posterior probabilities of the saved MCMC samples. Plot shows the negative log posterior.

  • L2s – array of log gaussian data fidelities (L2 error norms)

  • L1s – array of log Laplacian priors (L1 norms)

  • figsize (tuple) – Figure size

Returns

matplotlib figure

plotting.plot_chain_sample(X, figsize=(10, 8))

Plots the real and imaginary parts of an MCMC sample

Parameters

  • X – MCMC sample

  • figsize (tuple) – Figure size

Returns

matplotlib figure