lib.MapMaking.Qmap_plotter module

lib.MapMaking.Qmap_plotter.Cl2BK(ell, Cl)

Convert C_ell to the bandpower-like quantity 100 * ell * C_ell / (2*pi).

Formula

—

result = 100 * ell * C_ell / (2*pi)

Parameters

–

ellarray_like

Multipole moments (1D). Should match or broadcast with Cl.

Clarray_like

C_ell values. Can be scalar, 1D or broadcastable to ell. Typical shape in this module: (n_nus, n_nus, n_bins).

Returns

—

ndarray

Transformed bandpower values with shape equal to the broadcasted inputs.

lib.MapMaking.Qmap_plotter.Dl2Cl(ell, Dl)

Convert angular power spectrum from D_ell to C_ell.

Formula

—

C_ell = D_ell * 2*pi / (ell * (ell + 1))

Parameters

–

ellarray_like

Multipole moments (1D). Values must be > 0 (division by zero otherwise).

Dlarray_like

D_ell values. Can be scalar, 1D (same length as ell) or broadcastable to ell. Typical shape in this module: (n_nus, n_nus, n_bins).

Returns

—

ndarray

C_ell values with the same shape as the broadcast result of Dl and ell.

Notes

• • Uses elementwise broadcasting; ensure ell and Dl shapes are compatible.

  • No guard is performed for ell == 0; caller must avoid or mask such entries.

class lib.MapMaking.Qmap_plotter.Plots

Bases: object

Instance for plotting results of Monte-Carlo Markov Chain (i.e emcee).

Methods

get_convergence(chain, job_id)	chain assumed to be not flat with shape (nsamples, nwalkers, nparams)
get_triangle(chain, names, labels, job_id)	Make triangle plot of each estimated parameters
make_list_free_parameter()	Make few list :

get_Dl_plot

get_Dl_plot(ell, Dl, Dl_noise, nus, job_id, figsize=(10, 10), model=None)

get_convergence(chain, job_id)

chain assumed to be not flat with shape (nsamples, nwalkers, nparams)

get_triangle(chain, names, labels, job_id)

Make triangle plot of each estimated parameters

make_list_free_parameter()

Make few list :

• fp : list of value of free parameters

• fp_name : list of name for each values

• fp_latex : list of name in LateX for each values

class lib.MapMaking.Qmap_plotter.PlotsCMM(preset, dogif=True)

Bases: object

Instance to produce plots on the convergence.

Methods

display_maps(input_maps, reconstructed_maps, ...)	Display input / output / residual maps at a given iteration.
plot_gain_iteration(gain[, figsize, ki])	Method to plot convergence of reconstructed gains.
plot_sed(nus_in, A_in, nus_out, A_out[, ...])	Plots the Spectral Energy Distribution (SED) and saves the plot as a svg file.

plot_beta_iteration
plot_rms_iteration

display_maps(input_maps, reconstructed_maps, seenpix, ki=0, reso=15, view='gnomview')

Display input / output / residual maps at a given iteration.

plot_beta_iteration(beta, figsize=(8, 6), truth=None, ki=0, errors=None)

plot_gain_iteration(gain, figsize=(8, 6), ki=0)

Method to plot convergence of reconstructed gains.

Arguments :

—

• gain : Array containing gain number (1 per detectors). It has the shape (Niteration, Ndet, 2) for Two Bands design and (Niteration, Ndet) for Wide Band design

• alpha : Transparency for curves.

• figsize : Tuple to control size of plots.

plot_rms_iteration(rms, figsize=(8, 6), ki=0)

plot_sed(nus_in, A_in, nus_out, A_out, figsize=(8, 6), ki=0, gif=False)

Plots the Spectral Energy Distribution (SED) and saves the plot as a svg file.

Parameters: nus (array-like): Array of frequency values. A (array-like): Array of amplitude values. figsize (tuple, optional): Size of the figure. Defaults to (8, 6). truth (array-like, optional): Array of true values for comparison. Defaults to None. ki (int, optional): Iteration index for file naming. Defaults to 0.

Returns: None

class lib.MapMaking.Qmap_plotter.PlotsFMM(seenpix)

Bases: object

Methods

plot_FMM_mollview
plot_FMM_old
plot_frequency_maps

plot_FMM_mollview(m_in, m_out, nus, job_id, figsize=(10, 8), istk=1, nsig=3, fwhm=0)

plot_FMM_old(m_in, m_out, center, seenpix, nus, job_id, figsize=(10, 8), istk=1, nsig=3, name='signal')

plot_frequency_maps(m_in, m_out, center, nus, reso=15, nsig=3, filename=None)

lib.MapMaking.Qmap_plotter.plot_cross_spectrum(nus, ell, Dl, Dl_err, ymodel, Dl2=None, Dl2_err=None, label_model='CMB + Dust', nbins=None, nrec=2, mode='Dl', figsize=None, title=None, name=None, dpi=300)

Plot the upper-triangle matrix of cross-angular power spectra D_ell (and optional model).

The function arranges a len(nus) x len(nus) grid and fills only the upper triangle (including diagonal) with small subplots labelled by the frequency pair nus[i] x nus[j]. It draws data errorbars, an optional second series (Dl - noise if Dl_noise provided), and a model line (from ymodel) either in D_ell units or transformed to the “100 * ell * C_ell / (2*pi)” units depending on mode.

Parameters

–

nusarray_like

1D array of frequency identifiers (used for subplot annotations). Length = n_nus.

ellarray_like

1D array of multipole moments. Length >= nbins (if nbins provided). Must be > 0 to avoid division-by-zero in conversions.

Dlndarray

Data D_ell values, expected shape (n_nus, n_nus, n_ell) or broadcastable to that.

Dl_noisendarray or None

Errors on Dl with same shape as Dl (or broadcastable). If provided, an additional series Dl - Dl_noise will be plotted where applicable. Errors are absolute-valued (the function applies np.abs).

ymodelndarray or None

Model values for plotting. Expected shape (n_nus, n_nus, n_ell) (or broadcastable). If None, no model line is drawn.

label_modelstr, optional

Legend label for the model line (default: “CMB + Dust”).

nbinsint or None, optional

Number of ell bins to plot. If None (default) uses len(ell).

nrecint, optional

Number of “recon” channels used to choose subplot background color and styling. Default is 2.

mode{“Dl”, …}, optional

If “Dl” the data are plotted in D_ell units. Otherwise the model/data are transformed via _Dl2Cl and _Cl2BK before plotting (matching original behaviour).

ft_nusint, optional

Font size for the subplot frequency annotations (default: 10).

figsizetuple, optional

Matplotlib figure size (default: (10, 8)).

titlestr or None, optional

Suptitle appended to the fixed prefix “Angular Cross-Power Spectra”. If None, only the prefix is used.

namestr or None, optional

If provided, the figure is saved to this filename as a PDF.

Side effects

• Creates a matplotlib figure, shows it with plt.show() and optionally saves it.

• Does not return the figure (returns None). If you need the figure object, modify the

function to return fig after creation.

Notes

• • The function preserves exact plotting order, labels and colours of the original code.

  • The caller must ensure shapes of nus, ell, Dl, Dl_noise, and ymodel are compatible.