webbpsf_ext.spectra.source_spectrum

class webbpsf_ext.spectra.source_spectrum(name, sptype, mag_val, bp, votable_file, Teff=None, metallicity=None, log_g=None, Av=None, **kwargs)[source]

Bases: object

Model source spectrum

The class ingests spectral information of a given target and generates pysynphot.spectrum model fit to the known photometric SED. Two model routines can fit. The first is a very simple scale factor that is applied to the input spectrum, while the second takes the input spectrum and adds an IR excess modeled as a modified blackbody function.

Parameters

  • name (string) – Source name.

  • sptype (string) – Assumed stellar spectral type. Not relevant if Teff, metallicity, and log_g are specified.

  • mag_val (float) – Magnitude of input bandpass for initial scaling of spectrum.

  • bp (pysynphot.obsbandpass) – Bandpass to apply initial mag_val scaling.

  • votable_file (string) – VOTable name that holds the source’s photometry. The user can find the relevant data at http://vizier.u-strasbg.fr/vizier/sed/ and click download data.

Keyword Arguments

  • Teff (float) – Effective temperature ranging from 3500K to 30000K.

  • metallicity (float) – Metallicity [Fe/H] value ranging from -2.5 to 0.5.

  • log_g (float) – Surface gravity (log g) from 0 to 5.

  • catname (str) – Catalog name, including ‘bosz’, ‘ck04models’, and ‘phoenix’. Default is ‘bosz’, which comes from BOSZ_spectrum().

  • res (str) – Spectral resolution to use (200 or 2000 or 20000).

  • interpolate (bool) – Interpolate spectrum using a weighted average of grid points surrounding the desired input parameters.

Example

Generate a source spectrum and fit photometric data

>>> import webbpsf_ext
>>> from webbpsf_ext.spectra import source_spectrum
>>>
>>> name = 'HR8799'
>>> vot = 'votables/{}.vot'.format(name)
>>> bp_k = webbpsf_ext.bp_2mass('k')
>>>
>>> # Read in stellar spectrum model and normalize to Ks = 5.24
>>> src = source_spectrum(name, 'F0V', 5.24, bp_k, vot,
>>>                       Teff=7430, metallicity=-0.47, log_g=4.35)
>>> # Fit model to photometry from 0.1 - 30 micons
>>> # Saves pysynphot spectral object at src.sp_model
>>> src.fit_SED(wlim=[0.1,30])
>>> sp_sci = src.sp_model
__init__(name, sptype, mag_val, bp, votable_file, Teff=None, metallicity=None, log_g=None, Av=None, **kwargs)[source]

Methods

__init__(name, sptype, mag_val, bp, votable_file)

bb_jy(wave, T)

Blackbody function (Jy)

fit_SED([x0, robust, use_err, IR_excess, ...])

Fit a model function to photometry

func_resid(x[, IR_excess, wlim, use_err])

Calculate model residuals

model_IRexcess(x[, sp])

Model for stellar spectrum with IR excess

model_scale(x[, sp])

Simple model to scale stellar spectrum

plot_SED([ax, return_figax, xr, yr, units])

bb_jy(wave, T)[source]

Blackbody function (Jy)

For a given wavelength set (in um) and a Temperature (K), return the blackbody curve in units of Jy.

Parameters

  • wave (array_like) – Wavelength array in microns

  • T (float) – Temperature of blackbody (K)

fit_SED(x0=None, robust=True, use_err=True, IR_excess=False, wlim=[0.3, 10], verbose=True)[source]

Fit a model function to photometry

Use scipy.optimize.least_squares() to find the best fit model to the observed photometric data. If no parameters passed, then defaults are set.

Keyword Arguments

  • x0 (array_like) – Initial guess of independent variables.

  • robust (bool) – Perform an outlier-resistant fit.

  • use_err (bool) – Should we use the uncertainties in the SED photometry for weighting?

  • IR_excess (bool) – Include IR excess in model fit? This is a simple modified blackbody.

  • wlim (array_like) – Min and max limits for wavelengths to consider (microns).

  • verbose (bool) – Print out best-fit model parameters. Defalt is True.

func_resid(x, IR_excess=False, wlim=[0.1, 30], use_err=True)[source]

Calculate model residuals

Parameters

  • x (array_like) – Model parameters for either model_scale or model_IRexcess. See these two functions for more details.

  • IR_excess (bool) – Include IR excess in model fit? This is a simple modified blackbody.

  • wlim (array_like) – Min and max limits for wavelengths to consider (microns).

  • use_err (bool) – Should we use the uncertainties in the SED photometry for weighting?

model_IRexcess(x, sp=None)[source]

Model for stellar spectrum with IR excess

Model of a stellar spectrum plus IR excess, where the excess is a modified blackbody. The final model follows the form:

\[x_0 BB(\lambda, x_1) \lambda^{x_2}\]
model_scale(x, sp=None)[source]

Simple model to scale stellar spectrum