Feature extractors

light_curve provides 40+ hand-crafted feature extractors for astrophysical light curves. All share a common interface: callable objects with .names and .descriptions attributes.

import light_curve as lc
import numpy as np

t = np.array([0.0, 1.2, 3.5, 7.1, 9.0])
m = np.array([15.1, 15.3, 14.9, 15.0, 15.2])
err = np.array([0.05, 0.05, 0.05, 0.05, 0.05])

ext = lc.Extractor(lc.Amplitude(), lc.BeyondNStd(nstd=1), lc.LinearFit())
result = ext(t, m, err)
print(dict(zip(ext.names, result)))

Use Extractor to combine multiple features into a single callable, or call each class directly for a single feature. The .many() method processes a list of light curves in batch with reduced Python–Rust overhead.

See the API reference for full signatures, parameters, and equations.

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Feature table

Statistical

Feature	Description	Outputs
Amplitude	Half peak-to-peak amplitude	1
AndersonDarlingNormal	Unbiased Anderson–Darling normality test statistic	1
BeyondNStd	Fraction of observations beyond \(n\,\sigma_m\) from \(\langle m \rangle\)	1
Cusum	Range of cumulative sums	1
ExcessVariance	Measure of intrinsic variability amplitude	1
InterPercentileRange	\(Q(1-p) - Q(p)\) inter-percentile range	1
Kurtosis	Excess kurtosis of magnitude	1
Mean	Mean magnitude	1
MeanVariance	Standard deviation to mean ratio	1
Median	Median magnitude	1
MedianAbsoluteDeviation	Median of \(\lvert m_i - \mathrm{median}(m) \rvert\)	1
MedianBufferRangePercentage	Fraction of points within \(q \times \mathrm{amplitude}\) of median	1
OtsuSplit	Otsu thresholding: bimodality measure (subset means, std devs, fraction)	4
PercentAmplitude	Maximum deviation of magnitude from its median	1
PercentDifferenceMagnitudePercentile	Ratio of inter-percentile range to median	1
ReducedChi2	Reduced \(\chi^2\) of magnitude measurements	1
Roms	Robust median statistic (experimental)	1
Skew	Skewness \(G_1\) of magnitude	1
StandardDeviation	Standard deviation \(\sigma_m\) of magnitude	1
StetsonK	Stetson \(K\) light-curve shape coefficient	1
WeightedMean	Inverse-variance weighted mean magnitude	1

Variability & trend

Feature	Description	Outputs
Eta	Von Neumann \(\eta\)	1
EtaE	\(\eta^e\), adapted for unevenly sampled time series	1
LinearFit	Slope, its error, and reduced \(\chi^2\) of the weighted linear fit	3
LinearTrend	Slope, its error, and noise level of the unweighted linear fit	3
MaximumSlope	Maximum slope between consecutive observations	1

Time sampling

Feature	Description	Outputs
Duration	Total time span of the light curve	1
MaximumTimeInterval	Maximum gap between consecutive observations	1
MinimumTimeInterval	Minimum gap between consecutive observations	1
ObservationCount	Number of observations	1
TimeMean	Mean observation time	1
TimeStandardDeviation	Standard deviation of observation times	1

Periodogram

Feature	Description	Outputs
Periodogram	Lomb–Scargle periodogram: period and power of the strongest peaks	≥2

Parametric fits (transients, flux only)

Feature	Description	Outputs
BazinFit	Bazin function — 5-parameter rising/falling exponential fit for core-collapse SNe	6
LinexpFit	Linexp function — linear-times-exponential fit for core-collapse SNe	5
VillarFit	Villar function — 7-parameter Gaussian+plateau fit for SN classification	8

Detection-based (experimental)

Feature	Description	Outputs
FluxNNotDetBeforeFd	Number of non-detections (flux) before the first detection	1
MagnitudeNNotDetBeforeFd	Number of non-detections (magnitude) before the first detection	1

Multiband (experimental)

Feature	Description	Outputs
ColorOfMedian	Difference between median magnitudes of two bands	1
RainbowFit	Multiband blackbody fit (Russeil+23) — bolometric flux + temperature evolution	8

Meta

Feature	Description
Extractor	Combine multiple feature extractors into a single callable
Bins	Bin a time series, then apply any set of features to each bin