class pysm3.PowerLawRealization(largescale_alm, freq_ref, amplitude_modulation_temp_alm, amplitude_modulation_pol_alm, small_scale_cl, largescale_alm_pl_index, small_scale_cl_pl_index, nside, seeds=None, synalm_lmax=None, has_polarization=True, map_dist=None)[source] [edit on github]

Bases: PowerLaw

PowerLaw model with stochastic small scales

Small scale fluctuations in the templates and the spectral index are generated on the fly based on the input power spectra, then added to deterministic large scales.

largescale_alm, largescale_alm_pl_index: `pathlib.Path`

Paths to the Alm expansion of the template IQU maps and the spectral index Templates are assumed to be in logpoltens formalism, units refer to the unit of the maps when transformed back to IQU maps.

freq_ref: Quantity or string

Reference frequencies at which the intensity and polarization templates are defined. They should be a astropy Quantity object or a string (e.g. “1500 MHz”) compatible with GHz.

amplitude_modulation_temp_alm, amplitude_modulation_pol_alm: `pathlib.Path`

Paths to the Alm expansion of the modulation maps used to rescale the small scales to make them more un-uniform, they are derived from highly smoothed input emission.

small_scale_cl, small_scale_cl_pl_index: `pathlib.Path`

Paths to the power spectra of the small scale fluctuations for logpoltens iqu and the spectral index

nside: int

Resolution parameter at which this model is to be calculated.

seeds: list of ints

List of seeds used for generating the small scales, first is used for the template, the second for the spectral index. If None, it uses random seeds.

synalm_lmax: int

Lmax of Synalm for small scales generation, by default it is 3*nside-1, with a maximum of 16384.

map_dist: Map distribution

Unsupported, this class doesn’t support MPI Parallelization

Methods Summary

draw_realization([synalm_lmax, seeds])

Methods Documentation

draw_realization(synalm_lmax=None, seeds=None)[source] [edit on github]