Crate light_curve_feature[−][src]
Expand description
light-curve-feature
is a part of light-curve
family that
implements extraction of numerous light curve features used in astrophysics.
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 weights arrays, all having the same length. Note
that multiple features interpret weight array as inversed squared observation errors.
use light_curve_feature::*;
// Let's find amplitude and reduced Chi-squared of the light curve
let fe = FeatureExtractor::<_, Feature<_>>::new(vec![Amplitude::default().into(), ReducedChi2::default().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::*;
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::<_, Feature<_>>::new(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);
Re-exports
pub use features::antifeatures;
pub use features::*;
Modules
Feature sctructs implements crate::FeatureEvaluator trait
Structs
$\Delta t = \mathrm{duration} / (N - 1)$ is the mean time interval between observations
A TimeSeries component
Bulk feature extractor
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
Derive Nyquist frequency from time series
Periodogram execution algorithm
Traits
The trait each feature should implement