Welcome to SpecMatch Synth’s documentation!

Contents:

Installation Instructions

1. Install dependencies: pandas, astropy
2. Download specmatch-synth from git repo
3. Run python setup.py install from within the main repo directory

Quickstart Tutorial

API

Contents:

Model Library Grid

Module to define the Library class

This module defines the Library object used for specmatch-synth

class smsyn.library.Library(header, model_table, wav, model_spectra, wavlim=None)

The Library object

This object handles reading the model grid and associating the models with stellar parameters

Args:

header (dict): a dictionary containing metadata that describes

the model library. ‘model_name’ and ‘model_reference’ are the only required keys. (e.g. {‘model_name’: ‘coelho05’, ‘model_reference’: ‘Coelho et al. (2005)’})

model_table (DataFrame): Pandas DataFrame with the following

columns: teff, logg, fe, model_index. The model_index column should give the index to the model spectrum in the model_spectra array that is associated with the given parameters.

wav (array): 1-d vector containng the wavelength scale for the

model spectra

model_spectra (array): array containing all model spectra

ordered so that the they can be referenced by the indicies contained in the model_table.

wavlim (2-element iterable): (optional) list, tuple, or other

2-element itarable that contains the upper and lower wavelengths limits to be read into memory

select_model(pars)

Select a model spectrum

Grab a model spectrum from the library that corresponds to a given set of stellar parameters.

Args:

pars (3-element iterable): A 3-element tuple containing teff, logg,

and fe

Returns:

array: model spectrum flux resampled at the new wavelengths

synth(wav, teff, logg, fe, vsini, psf, rotation='rot', interp_kw=None)

Synthesize a model spectrum

For a given set of wavelengths teff, logg, fe, vsini, psf, compute a model spectrum by:

1. Determine the 8 coelho models surounding the (teff,logg,fe)
2. Perform trilinear interpolation
3. Resample onto new wavelength scale
4. Broaden with rot-macro turbulence
5. Broaden with PSF (assume gaussian)

Args:

wav (array): wavelengths where the model will be calculated teff (float): effective temp (K) logg (float): surface gravity (logg) fe (float): metalicity [Fe/H] (dex) vsini (float): rotational velocity (km/s) psf (float): sigma for instrumental profile (pixels)

Returns:

array: synthesized model calculated at the wavelengths specified

in the wav argument

to_hdf(outfile)

Save model library

Save a model library as an h5 file

Args:

outfile (string): path to output h5 file

smsyn.library.read_hdf(filename, wavlim=None)

Read model library grid

Read in a model library grid from an h5 file and initialze a Library object.

Args:

filename (string): path to h5 file that contains the grid

of stellar atmosphere models

wavlim (2-element iterable): upper and lower wavelength limits

(in Angstroms) to load into RAM

Returns:

Library object

smsyn.library.trilinear_interp(c, v0, v1, vi)

Trilinear interpolation

Perform trilinear interpolation as described here. http://en.wikipedia.org/wiki/Trilinear_interpolation

Args:

c (8 x n array): where C each row of C corresponds to the value at one

corner

v0 (length 3 array): with the origin v1 (length 3 array): with coordinates on the diagonal vi (length 3 array): specifying the interpolated coordinates

Returns:

interpolated value of c at vi

Spectrum Object

Defining Spectrum Class

class smsyn.io.spectrum.Spectrum

A light superclass on top of numpy record array that stores header information and and read and write to fits objects.

Args:

wav (array): wavelengths corresponding to each pixel in the

flux array

flux (array): continuum-normalized flux as a function of

rest wavelength

uflux (array): relative flux uncertainty header (dict): dictionary containing metadata associated with the

observed spectrum. Similar to a header from a fits file. Required keys: object, observation

to_fits(outfile, clobber=True)

Save to FITS

Save a Spectrum object as a mutli-extension fits file.

Args:

outfile (string): name of output file name clobber (bool): if true, will overwrite existing file

smsyn.io.spectrum.read_fits(filename)

Read spectrum from fits file

Read in a spectrum as saved by the Spectrum.to_fits method into a Spectrum object

Args:

filename (string): path to fits file

Returns:

Spectrum object

Match Object

This module defines the Match class that is used in fitting routines.

class smsyn.match.Match(*args, **kwargs)

masked_nresid(params, **kwargs)

Masked normalized residuals

Return the normalized residuals with masked wavelengths excluded

Args:

params (lmfit.Parameters): see params in self.model

Returns:

array: normalized residuals where self.wavmask == 1

model(params, wav=None, **kwargs)

Calculate model

Return the model for a given set of parameters

Args:

params (lmfit.Parameters): Parameters object containing at least

teff, logg, fe, vsini, psf, and spline coefficients

wav (array): (optional) array of wavelengths at which to

calculate the model. Useful for generating a more finely sampled model for plotting

**kwargs: extra keyword arguments passed to lib.synth

nresid(params, **kwargs)

Normalized residuals

Args:

params (lmfit.Parameters): see params in self.model

Returns:

array: model minus data divided by errors

resid(params, **kwargs)

Residuals

Return the residuals

Args:

params (lmfit.Parameters): see params in self.model

Returns:

array: model minus data

spline(params, wav)

Continuum model

Unpacks the params object and returns a spline evaluated at specified wavelengths.

Args:

params (lmfit.Parameters): See params in self.model wav: array of wavelengths at which to calculate the continuum model.

Returns:

array: spline

Indices and tables for Python code

• Index
• Module Index
• Search Page