# `light-curve-feature`
`light-curve-feature` is a part of [`light-curve`](https://github.com/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>
[](https://docs.rs/light-curve-feature)
[](https://results.pre-commit.ci/latest/github/light-curve/light-curve-feature/master)
All features are available in [Feature](crate::Feature) enum, and the recommended way to extract multiple features at
once is [FeatureExtractor](crate::FeatureExtractor) struct built from a `Vec<Feature>`. Data is represented by
[TimeSeries](crate::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 light_curve_feature::prelude::*;
// Let's find amplitude and reduced Chi-squared of the light curve
let fe = FeatureExtractor::from_features(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.get_names();
println!("{:?}", names.iter().zip(result.iter()).collect::<Vec<_>>());
# Ok::<(), EvaluatorError>(())
```
There are a couple of meta-features, which transform a light curve before feature extraction. For example
[Bins](crate::Bins) feature accumulates data inside time-windows and extracts features from this new light curve.
```rust
use light_curve_feature::prelude::*;
use ndarray::Array1;
// Define features, "raw" MaximumSlope and binned with zero offset and 1-day window
let max_slope: Feature<_> = MaximumSlope::default().into();
let bins: Feature<_> = {
let mut bins = Bins::new(1.0, 0.0);
bins.add_feature(max_slope.clone());
bins.into()
};
let fe = FeatureExtractor::from_features(vec![max_slope, 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::new_without_weight(&time, &magn);
// Get results and print
let result = fe.eval(&mut ts)?;
println!("{:?}", result);
# Ok::<(), EvaluatorError>(())
```
### Citation
If you found this project useful for your research please cite [Malanchev et al., 2021](https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.5147M/abstract)
```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}
}
```