Source code for feets.extractors.ext_linear_trend
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017-2024, Cabral, Juan
# Copyright (c) 2025, QuatroPe; ClariĆ”, Felipe
# License: MIT
# Full Text:
# https://github.com/quatrope/feets/blob/master/LICENSE
# =============================================================================
# DOC
# =============================================================================
"""Linear trend extractor."""
# =============================================================================
# IMPORTS
# =============================================================================
from light_curve import LinearTrend as _LinearTrend
from .light_curve_extractor import LightCurveExtractor
from ..libs import doctools
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# EXTRACTOR CLASS
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[docs]
class LinearTrend(LightCurveExtractor):
r"""Linear trend extractor.
The slope, its error and noise level of the light curve in the linear fit
Least squares fit of the linear stochastic model with constant Gaussian
noise :math:`\Sigma` assuming observation errors to be zero:
.. math::
m_i = c + \mathrm{slope} t_i + \Sigma \varepsilon_i,
where :math:`c` is a constant, :math:`\{\varepsilon_i\}` are standard
distributed random variables.
:math:`\mathrm{slope}`, :math:`\sigma_\mathrm{slope}` and :math:`\Sigma`
are returned.
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.
"""
features = [
"LinearTrend",
"LinearTrend_Sigma",
"LinearTrend_ReducedChi2",
]
def __init__(self, transform=None):
self.transform = transform
self._extract = _LinearTrend(**self.params)