SPLAT: The SpeX Prism Library Analysis Toolkit

SPLAT is a python-based spectral access and analysis package designed to interface 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. It is built on common python packages such as numpy, scipy, astropy and matplotlib.

SPLAT tools will allow you to:
  • search the SPL for data and source information;
  • access the publically-available (published) spectra contained in it;
  • compare your near-infrared spectrum to these data;
  • make use of published empirical trends in absolute magnitudes and effective temperatures;
  • perform basic spectral analyses such as spectral classification, gravity classification, index measurement, spectrophotometry, reddening, robust comparison statistics, basic math operations;
  • perform advanced analyses such as MCMC spectral model fitting, spectral binary analysis, and optimized index definition;
  • transform observable to physical parameters using evolutionary models; and
  • plot/tabulate/publish your results.

Note that many of these features are currently under development.

Installation and Dependencies

SPLAT is best forked from the github site http://github.org/aburgasser/splat, which is updated on a regular basis. SPLAT has not yet reached v1.0, so bugs are common. Please help us squish them by sending bug reports to aburgasser@ucsd.edu or start an issue on the github site.

Instructions on setting up and using SPLAT are maintained at http://www.browndwarfs.org/splat.

You should copy the file .splat_access into your home directory - this is your access key if you have priveleged access to unpublished data.

Using SPLAT

SPLAT is best used in the ipython or ipython notebook; all of the necessary data is included in the github install, so you won’t need to be online to run anything.

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 the container of various aspects of the spectrum and it source properties. 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 by passing a filename

>>> sp = splat.Spectrum(filename='PATH_TO/myspectrum.fits')

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 the Spectrum object’s plot function or plotSpectrum :
>>> sp.plot()
>>> splat.plotSpectrum(sp)

which will pop up a window displaying flux 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')[0]
>>> splat.plotSpectrum(sp1,sp2,colors=['black','red'])

You can add several extras to this to label features, plot uncertainties, indicate telluric absorption regions, make multi-panel and multi-page plots of lists of spectra, etc. Be sure to look through the plotting subpackage for more details.

SPLAT can analyze and compare an arbitrary number of spectra.

>>> 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='testi')

The last line returns a dictionary, whose value,error pair can be accessed by the name of the index:

>>> print indices['sH2O-J']             # returns value, error

>>> sp = splat.getSpectrum(young=True, lucky=True)[0]
>>> print splat.classifyGravity(sp)   # returned 'VL-G'

>>> sp = splat.getSpectrum(shortname='0415-0935')[0]
>>> spt,unc = splat.classifyByIndex(sp,set='burgasser')
>>> spt,unc = splat.classifyByStandard(sp,spt=['T5','T9'])
>>> result = splat.classifyByTemplate(sp,spt=['T6','T9'],nbest=5)

The last line returns a dictionary containing the best 5 template matches to the Spectrum 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)                    # loads a BTSettl08 model by default
>>> chi,scale = splat.compareSpectra(sp,mdl)
>>> mdl.scale(scale)
>>> splat.plotSpectrum(sp,mdl,colors=['black','red'],legend=[sp.name,mdl.name])

# There is also a basic Markov Chain Monte Carlo code to compare models to spectra called modelFitMCMC:

>>> sp = splat.getSpectrum(shortname='0415-0935')[0]
>>> sp.fluxCalibrate('2MASS J',14.49,absolute=True)
>>> table = splat.modelFitMCMC(sp,initial_guess=[800,5.0,0.],nsamples=300,step_sizes=[50.,0.5,0.])

All of these routines have many options worth exploring, and which are (increasingly) documented on this website. If there are capabilities you need, please suggest them to aburgasser@ucsd.edu, or note it in the “Issues” link on our github site.

Acknowledgements

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 have included Christian Aganze, Daniella Bardalez Gagliuffi, Adam Burgasser (PI), Caleb Choban, Ivanna Escala, Aishwarya Iyer, Yuhui Jin, Mike Lopez, Alex Mendez, Gretel Mercado, Johnny Parra, Maitrayee Sahi, Adrian Suarez, Melisa Tallis, Tomoki Tamiya and Chris Theissen.

This project is supported by the National Aeronautics and Space Administration under Grant No. NNX15AI75G.

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