Access SPLAT's full documentation at https://splat.physics.ucsd.edu/splat.
SPLAT is a python-based spectral access and analysis package designed to interface
with the SpeX Prism Library (SPL), an online repository of over
3,000 low-resolution, near-infrared spectra, primarily
of low-temperature stars and brown dwarfs.
It is built on common python packages such as
astropy,
astroquery,
emcee,
matplotlib,
numpy,
pandas,
scipy, and others.
SPLAT tools allow you to:
- Search the SpeX Prism Library for spectral data and source information;
- Access and analyze publically-available spectra contained in it;
- Analyze your own spectral data from SpeX and other instruments;
- Perform basic spectral analyses such as type classification, gravity classification, index measurement, spectrophotometry, reddening, blended light analysis, and basic math operations;
- Access atmosphere models and perform fits to spectral data;
- Transform observables to physical parameters using evolutionary models;
- Use published empirical trends between spectral type, absolute magnitudes, colors, luminosities, effective temperatures, and others;
- Access online data repositories through wrappers to [astroquery] (https://astroquery.readthedocs.io/en/latest)
- Simulate very low mass star and brown dwarf populations by combining spatial, evolutionary, and observational properties; and
- Plot, tabulate, and publish your results.
Note: Many features in SPLAT continue to be in development. Help us improve the code by reporting bugs (and solutions!) to our github site, https://github.com/aburgasser/splat.
The optimal installation method for SPLAT is cloning from the github site https://github.com/aburgasser/splat, which is updated on a (semi-)regular basis.
git clone https://github.com/aburgasser/splat.git
cd splat
python -m setup.py install
Warning: At this time please do not install splat using
pip, as this is an outdated version of SPLAT that is no longer supported.
Once you've downloaded the code and data, you will need to add the SPLAT top-level directory to the environment variable PYTHONPATH. See the following links on how to update environment variables:
- bash or zsh (Unix/Mac): https://apple.stackexchange.com/questions/356441/how-to-add-permanent-environment-variable-in-zsh
- windows: https://www.computerhope.com/issues/ch000549.htm
SPLAT has core dependencies on the following packages:
- astropy
- astroquery
- matplotlib
- numpy
- pandas
- requests
- scipy
- corner (for model fitting only)
- emcee (for model fitting only)
- bokeh (for SPLAT web interface only)
- flask (for SPLAT web interface only)
SPLAT is organized into a series of modules based on core functionalities:
splat.core: core functionalities, including index measurement, database access and classificationsplat.citations: biblographic/bibtex routinessplat.database: access the spectral and source databases, as well as online resources through astroquerysplat.empirical: empirical conversion relationssplat.evolve: access to evolutionary modelssplat.model: access to spectral models and model-fitting routinessplat.photometry: spectrophotometry routines and filter accesssplat.plot: plotting and visualization routinessplat.simulate: population simulation routinessplat.utilities: additional routines for general analysissplat.web: SPLAT's web interface (under development)
SPLAT has been tested on both Python 2.7 and 3.0-3.11, and is best used in
ipython or jupyter notebook.
All of the necessary data is included in the github package, so you don't need to be online to run most programs.
The best way to read in a spectrum is to use getSpectrum(), which takes a number of search keywords and returns a list of Spectrum objects:
import splat
splist = splat.getSpectrum(shortname='0415-0935')
Retrieving 1 file
splist = splat.getSpectrum(name='TWA30A')
Retrieving 3 files
splist = splat.getSpectrum(opt_spt=['L2','L5'],jmag=[12,13])
Retrieving 5 files
In each case, splist is a list of Spectrum objects, each a container of various aspects of each spectrum and its source properties. For example, selecting the first spectrum,
sp = splist[0]
sp
SPEX-PRISM spectrum of 2MASSW J0036159+182110
The main elements of the Spectrum obejct are:
sp.wave: wavelength array in default units of micronsp.flux: flux array in default units of erg/cm^2/s/micronsp.noise: flux uncertainty array in default units of erg/cm^2/s/micron
A summary of the Spectrum object can be accessed using sp.info().
sp.info()
SPEX-PRISM spectrum of 2MASSW J0036159+182110
Airmass = nan
Source designation = J00361617+1821104
Median S/N = 274
SpeX Classification = L2.0
Spectrum key = 10249, Source key = 10068
If you use these data, please cite:
Burgasser, A. J. et al. (2008, Astrophysical Journal, 681, 579-593)
bibcode: 2008ApJ...681..579B
History:
SPEX-PRISM spectrum successfully loaded
You can also read in your own spectrum by passing a filename
sp = splat.Spectrum(file='PATH_TO/myspectrum.fits')
or a URL
sp = splat.Spectrum(file='http://splat.physics.ucsd.edu/splat/spexprism/spectra/spex-prism_SO0253+1625_20040908_BUR08B.txt')
Both fits and ascii (tab or csv) data formats are supported, but files should ideally conform to the following data format standard:
- column 1: wavelength, assumed in microns
- column 2: flux in flambda units
- column 3: (optional) flux uncertainty in flambda units.
There are a few built-in readers for specific data formats.
To flux calibrate a spectrum, use the Spectrum 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
sp.plot()
or the splat.plot routine plotSpectrum() :
import splat.plot as splot
splot.plotSpectrum(sp)
You can save your spectrum by adding a filename:
splot.plotSpectrum(sp,file='spectrum.pdf')
You can also compare multiple spectra:
sp1 = splat.getSpectrum(shortname='0415-0935')[0]
sp2 = splat.getSpectrum(shortname='1217-0311')[0]
splot.plotSpectrum(sp1,sp2,colors=['k','r'])
plotSpectrum() and related routines have many extras to label features, plot uncertainties,
indicate telluric absorption regions, make multi-panel and multi-page plots
of lists of spectra, plot batches of spectra, etc. Be sure to look through the splat.plot
subpackage for more details.
SPLAT's primary purpose is to allow the analysis of ultracool dwarf 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='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
You can also determine the gravity classification of a source following [Allers & Liu (2013)] (http://adsabs.harvard.edu/abs/2013ApJ...772...79A) using classifyGravity():
sp = splat.getSpectrum(young=True, lucky=True)[0]
print(splat.classifyGravity(sp)) # returned 'VL-G'
To classify a spectrum, use the various classifyByXXX methods:
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 compare a spectrum to another spectrum or a model, use compareSpectra():
import splat.model as spmod
mdl = spmod.loadModel(teff=720,logg=4.8,set='btsettl') # loads a BTSettl08 model
sp = splat.getSpectrum(shortname='0415-0935')[0]
chi,scale = splat.compareSpectra(sp,mdl)
mdl.scale(scale)
splat.plotSpectrum(sp,mdl,colors=['k','r'],legend=[sp.name,mdl.name])
You can shortcut the last three lines using the plot keyword:
chi,scale = splat.compareSpectra(sp,mdl,plot=True)
There are also codes still in development to fit models directly to spectra: modelFitGrid(), modelFitMCMC(), and modelFitEMCEE():
import splat.model as spmod
sp = splat.getSpectrum(shortname='0415-0935')[0]
sp.fluxCalibrate('2MASS J',14.49,absolute=True)
nbest = 5
result1 = splat.modelFitGrid(sp,set='btsettl')
result2 = splat.modelFitMCMC(sp,set='btsettl',initial_guess=[800,5.0,0.],nsamples=300,step_sizes=[50.,0.5,0.])
result3 = splat.modelFitEMCEE(sp,set='btsettl',initial_guess=[800,5.0,0.],nwalkers=12,nsamples=500)
The outputs of all of these fitting functions is a dictionary or list of dictionaries containing the parameters of the best-fitting models; there are also several diagnostic plots produced depending on the routine. View the model fitting page for more details.
All of these routines have many options worth exploring, and which are (increasingly) documented at https://splat.physics.ucsd.edu/splat. If there are capabilities you need, please suggest them to [email protected], or note it in the "Issues" link on our `github site https://github.com/aburgasser/splat.
If you use SPLAT tools for your research, please cite Burgasser et al. (2017, ASInC 14, 7), bibcode 2017ASInC..14....7B [NASA ADS] (https://ui.adsabs.harvard.edu/abs/2017ASInC..14....7B/abstract).
In addition, if you use data contained in SPLAT or the SpeX Prism Library, please be sure to cite the original spectral data source, which can be accessed from the Spectrum object:
sp = splat.getSpectrum(lucky=True)
sp.citation().data_reference
'2016ApJ...817..112S'
import splat.citations as spcite
spcite.shortRef(sp.data_reference)
Schneider, A. C. et al. (2016, Astrophysical Journal, 817, 112)
SPLAT is an collaborative project of research students in the [UCSD Cool Star Lab] (http://www.coolstarlab.org), aimed at developing research through the building of spectral analysis tools. Contributors to SPLAT have included Christian Aganze, Jessica Birky, Daniella Bardalez Gagliuffi, Adam Burgasser (PI), Caleb Choban, Andrew Davis, Ivanna Escala, Joshua Hazlett, Carolina Herrara Hernandez, Elizabeth Moreno Hilario, Aishwarya Iyer, Yuhui Jin, Mike Lopez, Dorsa Majidi, Diego Octavio Talavera Maya, Alex Mendez, Gretel Mercado, Niana Mohammed, Johnny Parra, Maitrayee Sahi, Adrian Suarez, Melisa Tallis, Tomoki Tamiya, Chris Theissen, and Russell van Linge.
This project has been supported by the National Aeronautics and Space Administration under Grant No. NNX15AI75G.
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