Meta — Extractor and Bins

light_curve.Extractor

Combine multiple feature extractors into a single callable.

Pass any number of feature objects; the result behaves like a single feature whose output is the concatenation of all individual outputs. Use :meth:__call__ for a single light curve or :meth:many for batch processing. Especially efficient for cheap features because it avoids repeated passes over the data and reduces Python–Rust call overhead.

Parameters:

Name	Type	Description	Default
*features	feature objects	Any mix of Rust-backed or pure-Python feature instances.	required

Attributes:

Name	Type	Description
names	list of str	Concatenated feature names from all sub-features.
descriptions	list of str	Concatenated descriptions from all sub-features.

Examples:

>>> import numpy as np
>>> from light_curve import Extractor, Amplitude, StandardDeviation
>>> ext = Extractor(Amplitude(), StandardDeviation())
>>> t = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
>>> m = np.array([15.1, 14.9, 15.2, 15.0, 14.8])
>>> ext(t, m)
array([...])

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light_curve.Bins

Bases: _FeatureEvaluator

Sampled time series meta-feature

Binning time series to bins with width \(\mathrm{window}\) with respect to some \(\mathrm{offset}\). \(j-th\) bin interval is \([j \cdot \mathrm{window} + \mathrm{offset}; (j + 1) \cdot \mathrm{window} + \mathrm{offset})\). Binned time series is defined by

\[ t_j^* = (j + \frac12) \cdot \mathrm{window} + \mathrm{offset}, \]

\[ m_j^* = \frac{\sum{m_i / \delta_i^2}}{\sum{\delta_i^{-2}}}, \]

\[ \delta_j^* = \frac{N_j}{\sum{\delta_i^{-2}}}, \]

where \(N_j\) is a number of sampling observations and all sums are over observations inside considering bin. Bins takes any other feature evaluators to extract features from sample time series

• Depends on: time, magnitude, magnitude error
• Minimum number of observations: as required by sub-features, but at least 1
• Number of features: as provided by sub-features

Parameters:

Name	Type	Description	Default
features	iterable	Features to extract from binned time-series	required
window	positive float	Width of binning interval in units of time	required
offset	float	Zero time moment	required
transform	None	Not supported, apply transformations to individual features	None
bands	list of str or None	Passband names for multiband mode. If given, each single-band feature in features is evaluated independently per passband; multiband features (e.g. color features) are passed through unchanged.	required

Attributes:

Name	Type	Description
names	list of str	Feature names
descriptions	list of str	Feature descriptions
bands	numpy.ndarray of str or None	Passband names for multiband mode, or None for single-band mode

Methods:

Name	Description
__call__	Extract features and return them as a numpy array
many	Extract features from multiple light curves in parallel