#!/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
# =============================================================================
"""Linear exponential fit extractor."""
# =============================================================================
# IMPORTS
# =============================================================================
from light_curve import LinexpFit as _LinexpFit
from .light_curve_extractor import LightCurveExtractor
from ..libs import doctools
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]
class LinexpFit(LightCurveExtractor):
r"""Linexp function fit.
Four fit parameters and goodness of fit (reduced :math:`\chi^2`) of the
Linexp function developed for core-collapsed supernovae:
.. math::
f(t) = A \frac{(t-t_0)}{\tau} \times
\exp{\left(\frac{(t-t_0)}{\tau}\right)} + B.
Note, that the Linexp function is developed to be used with fluxes, not
magnitudes.
**LinexpFit_Amplitude** (:math:`A`)
Amplitude of the Linexp function
**LinexpFit_ReferenceTime** (:math:`t_0`)
Reference time of the Linexp fit
**LinexpFit_FallTime** (:math:`\tau`)
Fall time of the Linexp function
**LinexpFit_Baseline** (:math:`B`)
Baseline of the Linexp function
**LinexpFit_ReducedChi2** (reduced :math:`\chi^2`)
Linexp fit quality
Parameters
----------
algorithm : str
Non-linear least-square algorithm, supported values are:
'mcmc', 'ceres', 'mcmc-ceres', 'lmsder', 'mcmc-lmsder'.
mcmc_niter : int, optional
Number of MCMC iterations, default is 128.
ceres_niter : int, optional
Number of Ceres iterations, default is 10.
ceres_loss_reg : float, optional
Ceres loss regularization, default is to use square norm as is, if set
to a number, the loss function is regularized to discriminate outlier
residuals larger than this value. Default is None which means no
regularization.
lmsder_niter : int, optional
Number of LMSDER iterations, default is 10.
init : list or None, optional
Initial conditions, must be `None` or a `list` of `float`s or `None`s.
The length of the list must be 4, `None` values will be replaced
with some default values. It is supported by MCMC only.
bounds : list of tuples or None, optional
Boundary conditions, must be `None` or a `list` of `tuple`s of `float`s
or `None`s. The length of the list must be 4, boundary conditions must
include initial conditions, `None` values will be replaced with some
broad defaults. It is supported by MCMC only.
ln_prior : str or list of ln_prior.LnPrior1D or None, optional
Prior for MCMC, None means no prior. It is specified by a string
literal or a list of 5 `light_curve.ln_prior.LnPrior1D` objects, see
`light_curve.ln_prior` submodule for corresponding functions. Available
string literals are:
- 'no': no prior
transform : bool or None, optional
If `False` or `None` (default) output is not transformed. If `True` output
is transformed as following:
- Half-amplitude A is transformed as :math:`zp - 2.5 lg(2*A)`,
:math:`zp = 8.9`, so that the amplitude is assumed to be the
object peak flux in Jy.
- baseline flux is normalised by :math:`A: baseline -> baseline / A`
- reference time is removed
- goodness of fit is transformed as :math:`ln(reduced chi^2 + 1)` to
reduce its spread
- other parameters are not transformed
"""
features = [
"LinexpFit_Amplitude",
"LinexpFit_Baseline",
"LinexpFit_ReferenceTime",
"LinexpFit_FallTime",
"LinexpFit_ReducedChi2",
]
def __init__(
self,
algorithm="mcmc",
mcmc_niter=128,
lmsder_niter=10,
ceres_niter=10,
ceres_loss_reg=None,
init=None,
bounds=None,
ln_prior=None,
transform=None,
):
self.algorithm = algorithm
self.mcmc_niter = mcmc_niter
self.lmsder_niter = lmsder_niter
self.ceres_niter = ceres_niter
self.ceres_loss_reg = ceres_loss_reg
self.init = init
self.bounds = bounds
self.ln_prior = ln_prior
self.transform = transform
self._extract = _LinexpFit(**self.params)
