Observation Model

Basics

beast.observationmodel.observations Module

Defines a generic interface to observation catalog

Functions

gen_SimObs_from_sedgrid(sedgrid, ...[, ...])

Generate simulated observations using the physics and observation grids.

Classes

Observations(inputFile, filters[, ...])

A generic class that interfaces observation catalog in a standardized way

Class Inheritance Diagram

beast.observationmodel.phot Module

Photometric package

Defines a Filter class and associated functions to extract photometry.

This also include functions to keep libraries up to date

Note

integrations are done using trapz() Why not Simpsons? Simpsons principle is to take sequence of 3 points to make a quadratic interpolation. Which in the end, when filters have sharp edges, the error due to this “interpolation” are extremely large in comparison to the uncertainties induced by trapeze integration.

Functions

load_all_filters([interp, lamb, filterLib])	load all filters from the library
load_filters(names[, interp, lamb, filterLib])	load a limited set of filters
load_Integrationfilters(flist[, interp, lamb])	load a limited set of filters
extractPhotometry(lamb, spec, flist[, absFlux])	Extract seds from a one single spectrum
extractSEDs(g0, flist[, absFlux])	Extract seds from a grid
STmag_to_flux(v)	Convert an ST magnitude to erg/s/cm2/AA (Flambda)
STmag_from_flux(v)	Convert to ST magnitude from erg/s/cm2/AA (Flambda)
fluxToMag(flux)	Return the magnitudes from flux values
fluxErrTomag(flux, fluxerr)	Return the magnitudes and associated errors from fluxes and flux error values
magToFlux(mag)	Return the flux from magnitude values
magErrToFlux(mag, err)	Return the flux and associated errors from magnitude and mag error values
append_filter(lamb, flux, tablename, ...[, ...])	Edit the filter catalog and append a new one given by its transfer function
appendVegaFilter(filtInst[, VegaLib])	Add filter properties to the Vega library

Classes

Filter(wavelength, transmit[, name])	Class filter Define a filter by its name, wavelength and transmission
IntegrationFilter(wavelength, transmit[, name])	Class filter

Class Inheritance Diagram

beast.observationmodel.vega Module

Handle vega spec/mags/fluxes manipulations

Functions

from_Vegamag_to_Flux(lamb, vega_mag)

function decorator that transforms vega magnitudes to fluxes (without vega reference)

Classes

Vega([source])

Class that handles vega spectrum and references.

Class Inheritance Diagram

Noise Model

beast.observationmodel.noisemodel.noisemodel Module

Classes

NoiseModel(astfile, *args, **kwargs)

Initial class of noise models

Class Inheritance Diagram

beast.observationmodel.noisemodel.splinter Module

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

Method

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

Functions

make_splinter_noise_model(outname, sedgrid)

Splinter noise model assumes that every filter is independent with any other.

beast.observationmodel.noisemodel.toothpick Module

Classes

MultiFilterASTs(astfile, filters[, vega_fname])

A noise model for based Artificial Star Tests (ASTs) that are provided as one single table.

Class Inheritance Diagram

beast.observationmodel.noisemodel.trunchen Module

Trunchen version of noisemodel Goal is to compute the full n-band covariance matrix for each model

Classes

MultiFilterASTs(astfile, filters, *args, ...)

Implement a noise model where the ASTs are provided as a single table

Class Inheritance Diagram

beast.observationmodel.noisemodel.generic_noisemodel Module

Generates a generic noise model from artifical star tests (ASTs) results using the toothpick method. Using ASTs results in a noise model that includes contributions from measurement (photon) noise and crowding noise.

Toothpick assumes that all bands are independent - no covariance. This is a conservative assumption. If there is true covariance more accurate results with smaller uncertainties on fit parameters can be achieved using the trunchen method. The trunchen method requires significantly more complicated ASTs and many more of them.

Functions

make_toothpick_noise_model(outname, astfile, ...)	toothpick noise model assumes that every filter is independent with any other.
get_noisemodelcat(filename)	returns the noise model