Stellib

class beast.physicsmodel.stars.stellib.Stellib(*args, **kargs)

Bases: object

Basic stellar library class

Contructor

Attributes Summary

nbytes

return the number of bytes of the object

Methods Summary

genQ(self, qname, r, \*\*kwargs)	Generate a composite value from a previously calculated
genSpectrum(self, T0[, g0, Z0, weights])	Generate a composite sprectrum Does the interpolation or uses a previously calculated interpolation Works on 1 desired star or a population of stars
gen_spectral_grid_from_given_points(self, pts)	Reinterpolate a given stellar spectral library on to an Isochrone grid
get_boundaries(self[, dlogT, dlogg])	Returns the closed boundary polygon around the stellar library with given margins
get_radius(self, logl, logt)	Returns the radius of a star given its luminosity and temperature
interp(self, T0, g0, Z0, L0[, dT_max, eps])	Interpolation of the T,g grid
interpMany(self, T0, g0, Z0, L0[, dT_max, …])	run interp on a list of inputs and returns reduced results
plot_boundary(self[, ax, dlogT, dlogg])	Parameters:
points_inside(self, xypoints[, dlogT, dlogg])	Returns if a point is inside the polygon defined by the boundary of the library

Attributes Documentation

nbytes

return the number of bytes of the object

Methods Documentation

genQ(self, qname, r, **kwargs)

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: ndarray

the result from a previous interpolation

Returns:

val: ndarray

an array containing the value

genSpectrum(self, T0, g0=None, Z0=None, weights=None, **kwargs)

Generate a composite sprectrum Does the interpolation or uses a previously calculated interpolation Works on 1 desired star or a population of stars

if T0 and g0 are iterable, it calls interpMany

Parameters:

T0: float or sequence

log(Teff) of each star or a 2d-array containing the result from a previous interpolation

g0: float or sequence

log(g) of each stars

Z0: float or sequence

metallicity

weights: float or sequence

individual weights of each star

**kwargs: forwarded to interpMany

Returns:

s: ndarray

an array containing the composite spectrum

gen_spectral_grid_from_given_points(self, pts, bounds={'dlogT': 0.1, 'dlogg': 0.3})

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 grid.get_stellib_boundaries) default: {dlogT:0.1, dlogg:0.3}

Returns:

g: SpectralGrid

Spectral grid (in memory) containing the requested list of stars and associated spectra

get_boundaries(self, dlogT=0.1, dlogg=0.3, **kwargs)

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

Returns:

b: ndarray[float, ndim=2]

closed boundary edge points: [logT, logg]

Note

as computing the boundary could take time, it is saved in the object and only recomputed when parameters are updated

get_radius(self, logl, logt)

Returns the radius of a star given its luminosity and temperature

Assuming a black body, it comes:

R ^ 2 = L / ( 4 pi sigma T ^ 4 ),

with:

L, luminosity in W, pi, 3.141592… sig, Stephan constant in W * m**-2 * K**-4 T, temperature in K

Parameters:

logl: ndarray[float, ndim=1]

log luminosities from the isochrones, in Lsun

logt: ndarray[float, ndim=1]

log temperatures from the isochrones, in K

Returns:

radii: ndarray[float, ndim=1]

array of radii in m (SI units)

interp(self, T0, g0, Z0, L0, dT_max=0.1, eps=1e-06)

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

Returns:

Returns 3 to 12 star indexes and associated weights

see __interp__

TODO: compute new weights accounting for Z

interpMany(self, T0, g0, Z0, L0, dT_max=0.1, eps=1e-06, weights=None, pool=None, nthreads=1)

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: float

temperature sensitivity under which points are considered to have the same temperature

weights: ndarray(float)

luminosity weigths to apply after interpolation

pool: Pool-like object

specify a multiprocessing pool for parallel processing

nthreads: int

number of processes to use by default

Returns:

r: ndarray

Returns 3 to 12 star indexes and associated weights

see __interp__

TODO: compute new weights accounting for Z

plot_boundary(self, ax=None, dlogT=0.0, dlogg=0.0, **kwargs)

Parameters:

dlogT: float

margin in logT (see get_boundaries)

dlogg: float

margin in logg (see get_boundaries)

agg_filter: unknown

alpha: float or None

animated: [True | False]

antialiased or aa: [True | False] or None for default

axes: an :class:`~matplotlib.axes.Axes` instance

clip_box: a :class:`matplotlib.transforms.Bbox` instance

clip_on: [True | False]

clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ]

color: matplotlib color spec

contains: a callable function

edgecolor or ec: mpl color spec, or None for default, or ‘none’ for no color

facecolor or fc: mpl color spec, or None for default, or ‘none’ for no color

figure: a :class:`matplotlib.figure.Figure` instance

fill: [True | False]

gid: an id string

hatch: [ ‘/’ | ‘’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ]

label: string or anything printable with ‘%s’ conversion.

linestyle or ls: [‘solid’ | ‘dashed’ | ‘dashdot’ | ‘dotted’]

linewidth or lw: float or None for default

lod: [True | False]

path_effects: unknown

picker: [None|float|boolean|callable]

rasterized: [True | False | None]

snap: unknown

transform: :class:`~matplotlib.transforms.Transform` instance

url: a url string

visible: [True | False]

zorder: any number

.. seealso::

Patch

For additional kwargs

points_inside(self, xypoints, dlogT=0.1, dlogg=0.3)

Returns if a point is inside the polygon defined by the boundary of the library

Parameters:

xypoints: sequence

a sequence of N logg, logT pairs.

dlogT: float

margin in logT

dlogg: float

margin in logg

Returns:

r: ndarray(dtype=bool)

a boolean ndarray, True for points inside the polygon. A point on the boundary may be treated as inside or outside.