# CompositeStellib¶

class beast.physicsmodel.stars.stellib.CompositeStellib(osllist, *args, **kwargs)[source]

Generates an object from the union of multiple individual libraries

Attributes Summary

 source wavelength return a common wavelength sampling to all libraries.

Methods Summary

 genQ(qname, r, **kwargs) Generate a composite value from a previously calculated genSpectrum(T0[, g0, Z0, weights]) Generate a composite sprectrum gen_spectral_grid_from_given_points(pts[, …]) Reinterpolate a given stellar spectral library on to an Isochrone grid get_boundaries([dlogT, dlogg]) Returns the closed boundary polygon around the stellar library with given margins interp(T0, g0, Z0, L0[, dT_max, eps, bounds]) Interpolation of the T,g grid interpMany(T0, g0, Z0, L0[, dT_max, eps, …]) run interp on a list of inputs and returns reduced results which_osl(xypoints[, dlogT, dlogg]) Returns the library indice that contains each point in xypoints

Attributes Documentation

source
wavelength

return a common wavelength sampling to all libraries. This can be used to reinterpolate any spectrum onto a common definition

Methods Documentation

genQ(qname, r, **kwargs)[source]
Generate a composite value from a previously calculated
interpolation Works on 1 desired star or a population of stars
Parameters: qname: str quantity name from self.grid r: (osl, r) tuple osl: is the library index starting from 1. 0 means no coverage. r: is the result from interp call on the corresponding library. q: float value (from weighted sum)
genSpectrum(T0, g0=None, Z0=None, weights=None, **kwargs)[source]
Generate a composite sprectrum
Does the interpolation or uses a previously calculated interpolation Works on 1 desired star or a population of stars
Parameters: T0: ndarray(float) log(Teff) to obtain g0: ndarray(float) log(g) to obtain Z0: ndarray(float) metallicity values weights: ndarray(float) individual weights of each star **kwargs forwarded to interp(Many) s: ndarray an array containing the composite spectrum reinterpolated onto self.wavelength Note if T0 and g0 are iterable, it calls interpMany
gen_spectral_grid_from_given_points(pts, bounds={'dlogT': 0.1, 'dlogg': 0.3})[source]

Reinterpolate a given stellar spectral library on to an Isochrone grid

Parameters: pts: dict like structure of points dictionary like or named data structure of points to interpolate at. pts must contain: logg surface gravity in log-scale logT log of effective temperatures (in Kelvins) logL log of luminosity in Lsun units Z metallicity bounds: dict sensitivity to extrapolation (see :func: Stellib.get_boundaries) default: {dlogT:0.1, dlogg:0.3} g: SpectralGrid Spectral grid (in memory) containing the requested list of stars and associated spectra
get_boundaries(dlogT=0.1, dlogg=0.3, **kwargs)[source]

Returns the closed boundary polygon around the stellar library with given margins

Parameters: s: Stellib Stellar library object dlogT: float margin in logT dlogg: float margin in logg b: ndarray[float, ndim=2] (closed) boundary points: [logg, Teff] (or [Teff, logg] is swap is True) Note as computing the boundary could take time, it is saved in the object and only recomputed when parameters are updated
interp(T0, g0, Z0, L0, dT_max=0.1, eps=1e-06, bounds={})[source]

Interpolation of the T,g grid

Interpolate on the grid and returns star indices and associated weights, and Z. 3 to 12 stars are returned. It calls _interp_, but reduce the output to the relevant stars.

Parameters: T0: double log(Teff) to obtain g0: double log(g) to obtain T: double log(Teff) of the grid g: double log(g) of the grid dT_max: float If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2 (resp. i1) is not used. (see below for namings) eps: foat temperature sensitivity under which points are considered to have the same temperature bounds: dict sensitivity to extrapolation (see :func: Stellib.get_boundaries) default: {dlogT:0.1, dlogg:0.3} (osl, r): tuple osl: is the library index starting from 1. 0 means no coverage. r: is the result from interp call on the corresponding library. a 3 to 12 star indexes and associated weights
interpMany(T0, g0, Z0, L0, dT_max=0.1, eps=1e-06, weights=None, bounds={}, pool=None, nthreads=1)[source]

run interp on a list of inputs and returns reduced results

Interpolation of the T,g grid at Z0 metallicity

Interpolate on the grid and returns star indices and associated weights, and Z. 3 to 12 stars are returned. It calls _interp_, but reduce the output to the relevant stars.

Parameters: T0: ndarray(float) log(Teff) to obtain g0: ndarray(float) log(g) to obtain Z0: ndarray(float) metallicity values L0: ndarray(float) luminosity values dT_max: float If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2 (resp. i1) is not used. (see below for namings) eps: foat temperature sensitivity under which points are considered to have the same temperature weights: ndarray(float) luminosity weigths to apply after interpolation bounds: dict sensitivity to extrapolation (see :func: Stellib.get_boundaries) default: {dlogT:0.1, dlogg:0.3} pool: Pool-like object specify a multiprocessing pool for parallel processing nthreads: int number of processes to use by default (osl, r): tuple osl: is the library index starting from 1. 0 means no coverage. r: is the result from interp call on the corresponding library. A 3 to 12 star indexes and associated weights
which_osl(xypoints, dlogT=0.0, dlogg=0.0)[source]

Returns the library indice that contains each point in xypoints

The decision is made from a two step search:
• first, each point is checked against the strict boundary of each library (i.e., dlogT = 0, dlogg = 0).
• second, if points are not found in strict mode, the boundary is relaxed and a new search is made.
Each point is associated to the first library matching the above
conditions.
Parameters: xypoints: sequence a sequence of N logg, logT pairs. dlogT: float margin in logT dlogg: float margin in logg res: ndarray(dtype=int) a ndarray, 0 meaning no library covers the point, and 1, … n, for the n-th library