SPLAT is a python package built upon numpy, scipy, astropy and matplotlib, as well as some other common packages. SPLAT is designed to interface specifically with the SpeX Prism Library (SPL: http://www.browndwarfs.org/spexprism), an online repository of over 1500 low-resolution, near-infrared spectra of low-temperature stars and brown dwarfs. SPLAT tools allow you to search the library; read in spectra from it; perform basic spectral analyses such as classification, index measurement and spectrophotometry; perform advanced analyses such as spectral model fitting and spectral binary analysis; and plot/tabulate your results.
SPLAT is best forked from this github site, which is updated on a semi-regular basis. SPLAT has not yet reached v1.0, so bugs are rampant. Please help us knock them down by sending bug reports to aburgasser@ucsd.edu
General instructions on setting up to run SPLAT are maintained at http://bit.ly/1AQuy9G
You should copy the file .splat_access into your home directory - this is your access key
The best place to start is the code documentation, housed at http://bit.ly/1zPZgi2
Here are some examples:
- The best way to read in a spectrum is to use getSpectrum:
import splat
splist = splat.getSpectrum(shortname='0415-0935')
splist = splat.getSpectrum(young=True)
splist = splat.getSpectrum(spt=['M7','L5'],jmag=[14.,99.])
In each case, splist is a list of Spectrum objects, which is a container of various aspects of the spectrum and it source. For example, selecting the first spectrum,
sp = splist[0]
sp.wave gives the wavelengths of this spectrum, sp.flux the flux values, and sp.noise the flux uncertainty.
You can also read in your own spectrum using the loadSpectrum function
sp = splat.loadSpectrum(filename='myspectrum.fits',local=True)
Note that this file must conform to the standard of the SPL data: the first column is wavelength in microns, second column flux in f_lambda units, third column (optional) is flux uncertainty.
- To flux calibrate the spectrum, use the object's built in fluxCalibrate() method:
sp = splat.getSpectrum(shortname='0415-0935')[0]
sp.fluxCalibrate('2MASS J',14.0)
- To display the spectrum, use plotSpectrum
splat.plotSpectrum(sp)
which will pop up a window displaying flux and noise vs. wavelength. You can save this display by adding a filename:
splat.plotSpectrum(sp,file='spectrum.png')
You can also compare multiple spectra:
sp1 = splat.getSpectrum(shortname='0415-0935')[0]
sp2 = splat.getSpectrum(shortname='1217-0311')[1]
splat.plotSpectrum(sp1,sp2,colors=['k','r'])
SPLAT can compare an arbitrary number of spectra.
- To measure spectral indices, use measureIndex or measureIndexSet:
sp = splat.getSpectrum(shortname='0415-0935')[0]
value, error = splat.measureIndex(sp,[1.14,1.165],[1.21,1.235],method='integrate')
indices = splat.measureIndexSet(sp,set='burgasser')
Note that the latter is a dictionary, whose value,error pair can be accessed by the name of the index:
print indices['H2O-J'] # returns value, error
- To classify a spectrum, use the classifyByXXX methods:
sp = splat.getSpectrum(shortname='0415-0935')[0]
spt,unc = splat.classifyByIndex(sp,set='burgasser')
spt,unc = splat.classifyByStandard(sp)
- To compare a spectrum to another spectrum or a model, use compareSpectra:
sp = splat.getSpectrum(shortname='0415-0935')[0]
mdl = splat.loadModel(teff=700,logg=5.0) # currently BTSettl08 only
chi,scale = splat.compareSpectra(sp,mdl)
mdl.scale(scale)
splat.plotSpectrum(sp,mdl,colors=['k','r'])
This can be placed in a for loop or MCMC chain for best-fit parameter determination.
All of these routines have many options worth exploring, and we are continually adding additional functionality. If there are capabilities you need, please suggest them, or even better contribute your own code!
SPLAT is an experimental, collaborative project of research students in Adam Burgasser's UCSD Cool Star Lab, aimed at teaching students how to do research by building their own analysis tools. Contributors to SPLAT include Christian Aganze, Daniella Bardalez Gagliuffi, Adam Burgasser (PI), Caleb Choban, Ivanna Escala, Aishwarya Iyer, Yuhui Jin, Mike Lopez, Alex Mendez, Johnny Parra, Julian Pilate-Hutcherson, Maitrayee Sahi and Melisa Tallis.
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