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


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# DOC
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"""Linear trend extractor."""


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# IMPORTS
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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)
[docs] @doctools.doc_inherit(LightCurveExtractor.extract) def extract(self, time, magnitude, error=None): """ Parameters ---------- time : array-like magnitude : array-like error : array-like, optional """ [linear_trend, linear_trend_sigma, reduced_chi2] = self._extract( time, magnitude, error ) return { "LinearTrend": linear_trend, "LinearTrend_Sigma": linear_trend_sigma, "LinearTrend_ReducedChi2": reduced_chi2, }