Source code for feets.extractors.ext_eta
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017-2024, Cabral, Juan
# Copyright (c) 2025, QuatroPe; Clariá, Felipe
# License: MIT
# Full Text:
# https://github.com/quatrope/feets/blob/master/LICENSE
# =============================================================================
# DOC
# =============================================================================
"""Eta extractor."""
# =============================================================================
# IMPORTS
# =============================================================================
from light_curve import Eta as _Eta
from .light_curve_extractor import LightCurveExtractor
from ..libs import doctools
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# EXTRACTOR CLASS
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[docs]
class Eta(LightCurveExtractor):
r"""Von Neummann Eta.
**Eta** (:math:`\eta`)
.. math::
\eta = \frac1{(N - 1)\,\sigma_m^2} \sum_{i=0}^{N-2}(m_{i+1} - m_i)^2
where :math:`N` is the number of observations,
:math:`\sigma_m = \sqrt{\sum_i (m_i - \langle m \rangle)^2 / (N-1)}` is the
magnitude standard deviation.
Parameters
----------
transform : str or bool or None, optional
Transformer to apply to the feature values. If str, must be one of:
- 'default' - use default transformer for the feature, it same as
giving True. The default for this feature is 'identity'
- 'arcsinh' - Hyperbolic arcsine feature transformer
- 'clipped_lg' - Decimal logarithm of a value clipped to a minimum
value
- 'identity' - Identity feature transformer
- 'lg' - Decimal logarithm feature transformer
- 'ln1p' - :math:`ln(1+x)` feature transformer
- 'sqrt' - Square root feature transformer
If bool, must be True to use default transformer or False to disable.
If None, no transformation is applied.
References
----------
.. [kim2014epoch] Kim, D. W., Protopapas, P., Bailer-Jones, C. A.,
Byun, Y. I., Chang, S. W., Marquette, J. B., & Shin, M. S. (2014).
The EPOCH Project: I. Periodic Variable Stars in the EROS-2 LMC
Database. arXiv preprint Doi:10.1051/0004-6361/201323252.
"""
features = ["Eta"]
def __init__(self, transform=None):
self.transform = transform
self._extract = _Eta(**self.params)
