light-curve-featurelight-curve-feature is a part of light-curve family that
implements extraction of numerous light curve features used in astrophysics.
If you are looking for Python bindings for this package, please see https://github.com/light-curve/light-curve-python
All features are available in Feature enum, and the recommended way to extract multiple features at
once is FeatureExtractor struct built from a Vec<Feature>. Data is represented by
TimeSeries struct built from time, magnitude (or flux) and weight arrays, all having the same length. Note
that multiple features interpret weight array as inversed squared observation errors.
```rust use lightcurvefeature::prelude::*;
// Let's find amplitude and reduced Chi-squared of the light curve
let fe = FeatureExtractor::fromfeatures(vec![
Amplitude::new().into(),
ReducedChi2::new().into()
]);
// Define light curve
let time = [0.0, 1.0, 2.0, 3.0, 4.0];
let magn = [-1.0, 2.0, 1.0, 3.0, 4.5];
let weights = [5.0, 10.0, 2.0, 10.0, 5.0]; // inverse squared magnitude errors
let mut ts = TimeSeries::new(&time, &magn, &weights);
// Get results and print
let result = fe.eval(&mut ts)?;
let names = fe.getnames();
println!("{:?}", names.iter().zip(result.iter()).collect::
```
There are a couple of meta-features, which transform a light curve before feature extraction. For example Bins feature accumulates data inside time-windows and extracts features from this new light curve.
```rust use lightcurvefeature::prelude::*; use ndarray::Array1;
// Define features, "raw" MaximumSlope and binned with zero offset and 1-day window let maxslope: Feature<_> = MaximumSlope::default().into(); let bins: Feature<_> = { let mut bins = Bins::new(1.0, 0.0); bins.addfeature(maxslope.clone()); bins.into() }; let fe = FeatureExtractor::fromfeatures(vec![maxslope, bins]); // Define light curve let time = [0.1, 0.2, 1.1, 2.1, 2.1]; let magn = [10.0, 10.1, 10.5, 11.0, 10.9]; // We don't need weight for MaximumSlope, this would assign unity weight let mut ts = TimeSeries::newwithout_weight(&time, &magn); // Get results and print let result = fe.eval(&mut ts)?; println!("{:?}", result);
```
The crate is configured with the following Cargo features:
- ceres-system and ceres-source - enable Ceres Solver support for non-linear fitting. The former
uses system-wide installation of Ceres, the latter builds Ceres from source and links it statically. The latter overrides the former. See ceres-solver-rs crate for details
- fftw-system, fftw-source (enabled by default) and fftw-mkl - enable FFTW support for Fourier transforms needed by Periodogram. The
first uses system-wide installation of FFTW, the second builds FFTW from source and links it statically, the last downloads and links statically Intel MKL instead of FFTW.
- gsl - enables GNU Scientific Library support for non-linear fitting.
- default - enables fftw-source feature only, has no side effects.
If you found this project useful for your research please cite Malanchev et al., 2021
bibtex
@ARTICLE{2021MNRAS.502.5147M,
author = {{Malanchev}, K.~L. and {Pruzhinskaya}, M.~V. and {Korolev}, V.~S. and {Aleo}, P.~D. and {Kornilov}, M.~V. and {Ishida}, E.~E.~O. and {Krushinsky}, V.~V. and {Mondon}, F. and {Sreejith}, S. and {Volnova}, A.~A. and {Belinski}, A.~A. and {Dodin}, A.~V. and {Tatarnikov}, A.~M. and {Zheltoukhov}, S.~G. and {(The SNAD Team)}},
title = "{Anomaly detection in the Zwicky Transient Facility DR3}",
journal = {\mnras},
keywords = {methods: data analysis, astronomical data bases: miscellaneous, stars: variables: general, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
year = 2021,
month = apr,
volume = {502},
number = {4},
pages = {5147-5175},
doi = {10.1093/mnras/stab316},
archivePrefix = {arXiv},
eprint = {2012.01419},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.5147M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}