Source code for feets.extractors.ext_stetson
#!/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
# =============================================================================
"""Stetson variability index extractors."""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
from .ext_slotted_a_length import start_conditions
from .extractor import Extractor
from ..libs import doctools
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]
class StetsonJ(Extractor):
r"""Stetson J variability index extractor.
**StetsonJ**
Stetson J is a robust version of the variability index. It is calculated
based on two simultaneous light curves of a same star and is defined as:
.. math::
J = \sum_{k=1}^n sgn(P_k) \sqrt{|P_k|}
with :math:`P_k = \delta_{i_k} \delta_{j_k}`
For a Gaussian magnitude distribution, :math:`J` should take a value close
to zero.
Notes
-----
This feature is based on the Welch/Stetson variability index :math:`I`
(Stetson, 1996) defined by the equation:
.. math::
I = \sqrt{\frac{1}{n(n-1)}} \sum_{i=1}^n {
(\frac{b_i-\hat{b}}{\sigma_{b,i}})
(\frac{v_i - \hat{v}}{\sigma_{v,i}})}
where :math:`b_i` and :math:`v_i` are the apparent magnitudes obtained for
the candidate star in two observations closely spaced in time on some
occasion :math:`i`, :math:`\sigma_{b, i}` and :math:`\sigma_{v, i}` are the
standard errors of those magnitudes, :math:`\hat{b}` and \hat{v} are the
weighted mean magnitudes in the two filters, and :math:`n` is the number of
observation pairs.
Since a given frame pair may include data from two filters which did not
have equal numbers of observations overall, the "relative error" is
calculated as follows:
.. math::
\delta = \sqrt{\frac{n}{n-1}} \frac{v-\hat{v}}{\sigma_v}
allowing all residuals to be compared on an equal basis.
References
----------
.. [richards2011machine] Richards, J. W., Starr, D. L., Butler, N. R.,
Bloom, J. S., Brewer, J. M., Crellin-Quick, A., ... &
Rischard, M. (2011). On machine-learned classification of variable stars
with sparse and noisy time-series data.
The Astrophysical Journal, 733(1), 10. Doi:10.1088/0004-637X/733/1/10.
Examples
--------
>>> fs = feets.FeatureSpace(only=['StetsonJ'])
>>> features = fs.extract(**lc_normal)
>>> features[0]
{'StetsonJ': np.float64(0.01823276018663087)}
"""
features = ["StetsonJ"]
[docs]
@doctools.doc_inherit(StetsonJ, warn_class=False)
class StetsonKAC(Extractor):
r"""Stetson K to slotted autocorrelation extractor.
**StetsonK_AC**
Stetson K applied to the slotted autocorrelation function of the
light-curve.
Parameters
----------
T : int, optional, default: `1`
:math:`tau` - slot size in days.
Examples
--------
>>> fs = feets.FeatureSpace(only=['SlottedALength','StetsonK_AC'])
>>> features = fs.extract(**lc_normal)
>>> features[0]
{'SlottedALength': np.int64(1),
'StetsonK_AC': np.float64(0.6440898442951952)}
"""
features = ["StetsonK_AC"]
def __init__(self, T=1):
self.T = T
[docs]
@doctools.doc_inherit(StetsonJ, warn_class=False)
class StetsonL(Extractor):
r"""Stetson L variability index extractor.
**StetsonL**
Stetson L variability index describes the synchronous variability of
different bands and is defined as:
.. math::
L = \frac{JK}{0.798}
Again, for a Gaussian magnitude distribution, :math:`L` should take a value
close to zero.
Examples
--------
>>> fs = feets.FeatureSpace(only=['StetsonL'])
>>> features = fs.extract(**lc_normal)
>>> features[0]
{'StetsonL': np.float64(0.0015499030048823923)}
"""
features = ["StetsonL"]
