pipetools

smicatools.fit_model_to_cov(model, stats, nmodes, maxiter=50, noise_fix=False, afix=None, logger=None)

Fit the model to empirical covariance.

smicatools.get_cmb(names, unit_to, bins, polar=False, calib=None, cmb_unit='uK_CMB', ref_beam=5, fixed=True)

Return a smica cmb component of known mixing matrix.

smicatools.get_gal(nmap, dim, Q, polar=False, polar2D=False)

Return a smica N dimensional galactic component

smicatools.get_gal_from(Q, filename=None, model=None, em=None)

Return a smica N dimensional galactic component of fixed mixmat read from file or model.

smicatools.get_noise(N, fixed=False)

Return a smica noise component

smicatools.is_mixmat_fixed(model)

smicatools.load_from_dir(dirname)

smicatools.plot_calib(ref, fit, efit, names, filename=None)

Plot calibration result.

smicatools.plot_cmb_powspec(cmb, ecmb, bins, polar=False, cmb_unit='uK_CMB', ref_beam=5, filename=None)

Plot the fitted CMB power spectra versus the 2013 lambdaCDM best fit.

smicatools.plot_decomp(model, stats, nmodes, ll, names, filename=None)

Plot a decomposition of the data into cmb + fg + noise for each cross spectra.

smicatools.plot_mismatch(model, stats, nmodes, ll, filename=None)

Plot spectral mismatch

smicatools.plot_mismatch_KLD(model, stats, nmodes, names, ll, filename=None)

Plot KLD decomposition of the spectral mismatch.

Not working yet.

smicatools.plot_mismatch_pair(model, stats, nmodes, names, ll, filename=None)

Plot spectral mismatch per detector pair.

smicatools.plot_noise(model, em, N, ll, names, Nfix=None, filename=None)

Plot the fitted noise versus expected noise as predicted by jackknives.

smicatools.plot_relative_diff(model, Stat, NModes, channelnames, lmean, filename=None)

Plot relative difference between model and data covariance matrices.

smicatools.save_calib(filename, ref, model, em)

Save calibration result in txt file.