Crate light_curve_feature
source · [−]Expand description
light-curve-feature
light-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.
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<_>>());
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.
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);
Citation
If you found this project useful for your research please cite Malanchev et al., 2021
@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}
}
Re-exports
pub use features::*;
Modules
Structs
$\Delta t = \mathrm{duration} / (N - 1)$ is the mean time interval between observations
A TimeSeries
component
Bulk feature extractor
User-defined Nyquist frequency
LMSDER GSL non-linear least-squares wrapper
MCMC sampler for non-linear least squares
$\Delta t$ is the median time interval between observations
Direct periodogram executor
“Fast” (FFT-based) periodogram executor
$\Delta t$ is the $q$th quantile of time intervals between subsequent observations
Iterator over sin(kx), cos(kx) pairs
Time series object to be put into Feature
Enums
Optimization algorithm for non-linear least squares
Error returned from crate::FeatureEvaluator
All features are available as variants of this enum
Natural logarithm of prior for non-linear curve-fit problem
Natural logarithm of prior for a single parameter of the curve-fit problem
Derive Nyquist frequency from time series
Periodogram execution algorithm
Traits
The trait each feature should implement