Source code for beast.observationmodel.noisemodel.splinter

splinter noise model assumes that every photometric band is independent
from the others and has a fractional flux uncertainty and no bias.

Create a noise model that has sigmas that are frac_unc times sed_flux and
zeros for the bias terms.
import numpy as np

import tables

__all__ = ["make_splinter_noise_model"]

[docs] def make_splinter_noise_model( outname, sedgrid, frac_unc=0.10, absflux_a_matrix=None, **kwargs ): """ Splinter noise model assumes that every filter is independent with any other. And assumes a fractional uncertainty at all fluxes. No ASTs are used. Parameters ---------- outname: str path and filename into which save the noise model sedgrid: SEDGrid instance sed model grid for everyone of which we will evaluate the model frac_unc: float [default = 0.10 (10%)] fractional flux uncertainy absflux_a_matrix: ndarray absolute calibration a matrix giving the fractional uncertainties including correlated terms (off diagonals) for the splinter model, only the diagonal terms are used returns ------- noisefile: str noisemodel file name """ n_models, n_filters = sedgrid.seds.shape # fill the bias vector with zeros bias = np.full((n_models, n_filters), 0.0) # fill the sigma vector with uncertainties based on the # input fraction uncertainty sigma = sedgrid.seds[:] * frac_unc # fill the completeness vector with ones compl = np.full((n_models, n_filters), 1.0) # absolute flux calibration uncertainties # off-diagnonal terms are ignored for the splinter if absflux_a_matrix is not None: if absflux_a_matrix.ndim == 1: abs_calib_2 = absflux_a_matrix[:] ** 2 else: # assumes a cov matrix abs_calib_2 = np.diag(absflux_a_matrix) noise = np.sqrt(abs_calib_2 * sedgrid.seds[:] ** 2 + sigma ** 2) else: noise = sigma # save to disk print("Writting to disk into {0:s}".format(outname)) with tables.open_file(outname, "w") as outfile: outfile.create_array(outfile.root, "bias", bias) outfile.create_array(outfile.root, "error", noise) outfile.create_array(outfile.root, "completeness", compl) return outname