Package: astrobase
Astrobase is a Python package for analyzing light curves and finding variable
stars. It includes implementations of several period-finding algorithms, batch
work drivers for working on large collections of light curves, and a small
web-app useful for reviewing and classifying light curves by stellar variability
type.Most functions in this package that deal with light curves usually require three
Numpy ndarrays as input:times,mags, anderrs, so they should work with
any time-series data that can be represented in this form. If you have flux time
series measurements, most functions also take amagsarefluxeskeyword argument
that makes them handle flux light curves correctly.Read the docs:https://astrobase.readthedocs.io/en/latest/Jupyter notebooks that demonstrate some of the functionality are available in
theastrobase-notebooksrepository.Aoverviewof the modules and subpackages is provided below.Installastrobasefrom the
Python Package Index (PyPI):$pipinstallnumpy# needed to set up Fortran wrappers$pipinstallastrobaseSee theinstallation instructionsbelow for details. This
package requires Python >= 3.5 as of version 0.5.0. Usepip install astrobase<0.5.0for older Python versions.Python 3.6:Python 3.7:Python 3.8:Python 3.9:Contentsastrokep:
contains functions for dealing with Kepler and K2 Mission light curves from
STScI MAST (reading the FITS files, consolidating light curves for objects
over quarters), and some basic operations (converting fluxes to mags,
decorrelation of light curves, filtering light curves, and fitting object
centroids for eclipse analysis, etc.)astrotess:
contains functions for dealing with TESS 2-minute cadence light curves from
STScI MAST (reading the FITS files, consolidating light curves for objects
over sectors), and some basic operations (converting fluxes to mags, filtering
light curves, etc.)checkplot:
contains functions to make checkplots: a grid of plots used to quickly decide
if a period search for a possibly variable object was successful. Checkplots
come in two forms:Python pickles: If you want to interactively browse through large numbers of
checkplots (e.g., as part of a large variable star classification project),
you can use thecheckplotserverwebapp that works on checkplot pickle
files. This interface allows you to review all phased light curves from all
period-finder methods applied, set and save variability tags, object type
tags, best periods and epochs, and comments for each object using a
browser-based UI (see below). The information entered can then be exported as
CSV or JSON for the next stage of a variable star classification pipeline.Thelightcurves-and-checkplotsJupyter notebook outlines how to do this. A more detailed example using light
curves of an arbitrary format is available in thelc-collection-worknotebook, which shows how to add in support for a custom LC format, add
neighbor, cross-match, and color-mag diagram info to checkplots, and visualize
these with the checkplotserver.PNG images: Alternatively, if you want to simply glance through lots of
checkplots (e.g. for an initial look at a collection of light curves), there's
acheckplot-viewerwebapp available that operates on checkplot PNG
images. Thelightcurve-workJupyter notebook goes through an example of generating these checkplot PNGs
for light curves. See thecheckplot-viewer.jsfile for more
instructions andcheckplot-viewer.pngfor a screenshot.coordutils:
functions for dealing with coordinates (conversions, distances, proper motion)fakelcs:
modules and functions to conduct an end-to-end variable star recovery
simulation.hatsurveys:
modules to read, filter, and normalize light curves from various HAT surveys.lcfit:
functions for fitting light curve models to observations, including
sinusoidal, trapezoidal and full Mandel-Agol planet transits, eclipses, and
splines.lcmath: functions for light curve operations such
as phasing, normalization, binning (in time and phase), sigma-clipping,
external parameter decorrelation (EPD), etc.lcmodels:
first order models for fast fitting (for the purposes of variable
classification) to various periodic variable types, including sinusoidal
variables, eclipsing binaries, transiting planets, and flares.lcproc:
driver functions for running an end-to-end pipeline including: (i) object
selection from a collection of light curves by position, cross-matching to
external catalogs, or light curve objectinfo keys, (ii) running variability
feature calculation and detection, (iii) running period-finding, and (iv)
object review using the checkplotserver webapp for variability
classification.periodbase: parallelized functions (usingmultiprocessing.map) to run fast period searches on light curves, including:
the generalized Lomb-Scargle algorithm from Zechmeister & Kurster
(2008;periodbase.zgls), the phase dispersion
minimization algorithm from Stellingwerf
(1978,2011;periodbase.spdm), the AoV and
AoV-multiharmonic algorithms from Schwarzenberg-Czerny
(1989,1996;periodbase.saov,periodbase.smav), the BLS algorithm from
Kovacs et al. (2002;periodbase.kblsandperiodbase.abls),
the similar TLS algorithm from Hippke & Heller
(2019;periodbase.htls),
and the ACF period-finding algorithm from McQuillan et al.
(2013a,2014;periodbase.macf).plotbase: functions to plot light curves, phased
light curves, periodograms, and download Digitized Sky Survey cutouts from the
NASA SkyView service.services: modules and functions to query various
astronomical catalogs and data services, including GAIA, SIMBAD, TRILEGAL,
NASA SkyView, and 2MASS DUST.timeutils: functions for converting from
Julian dates to Baryocentric Julian dates, and precessing coordinates between
equinoxes and due to proper motion; this will automatically download and save
the JPL ephemeridesde430.bspfrom JPL upon first import.varbase:
functions for calculating auto-correlation features, masking and pre-whitening
periodic signals in light curves, and planet transit specific tools.varclass: functions for calculating various
variability, stellar color and motion, and neighbor proximity features, along
with a Random Forest based classifier.ChangelogPlease seehttps://github.com/waqasbhatti/astrobase/blob/master/CHANGELOG.mdfor
a list of changes applicable to tagged release versions.InstallationRequirementsThis package requires the following other packages:numpyscipyastropymatplotlibPillowjplephemrequeststornadopyeeblstqdmscikit-learnFor optional functionality, some additional packages are required:forastrobase.lcdbto work, you'll needpsycopg2-binary.forlcfit.transits.mandelagol_fit_magseries, you'll needbatman-package,emcee,corner, andh5py.forlcproc.awsrun, you'll needparamiko,boto3, andawscli.forperiodbase.tls, you'll needtransitleastsquaresInstalling with pipIf you're using:64-bit Linux and Python 2.7, 3.4, 3.5, 3.6, 3.764-bit Mac OSX 10.12+ with Python 2.7 or 3.664-bit Windows with Python 2.7 and 3.6You can simply install astrobase with:(venv)$pipinstallastrobaseOtherwise, you'll need to make sure that a Fortran compiler and numpy are
installed beforehand to compile the pyeebls package that astrobase depends on:## you'll need a Fortran compiler.                              #### on Linux: dnf/yum/apt install gcc gfortran                   #### on OSX (using homebrew): brew install gcc && brew link gcc   #### make sure numpy is installed as well!                        #### this is required for the pyeebls module installation         ##(venv)$pipinstallnumpy# in a virtualenv# or use dnf/yum/apt install numpy to install systemwideOnce that's done, install astrobase.(venv)$pipinstallastrobaseOther installation methodsInstall all the optional dependencies as well:(venv)$pipinstallastrobase[all]Install the latest version (may be unstable at times):$gitclonehttps://github.com/waqasbhatti/astrobase
$cdastrobase
$pythonsetup.pyinstall
$# or use pip install . to install requirements automatically$# or use pip install -e . to install in develop mode along with requirements$# or use pip install -e .[all] to install in develop mode along with all requirementsLicenseastrobaseis provided under the MIT License. See the LICENSE file for the full
text.