#!/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
# =============================================================================
"""Bazin fit extractor."""
# =============================================================================
# IMPORTS
# =============================================================================
from light_curve import BazinFit as _BazinFit
from .light_curve_extractor import LightCurveExtractor
from ..libs import doctools
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]
class BazinFit(LightCurveExtractor):
r"""Bazin function fit.
Five fit parameters and goodness of fit (reduced :math:`\chi^2`) of
the Bazin function developed for core-collapsed supernovae:
.. math::
f(t) = A \frac{
\mathrm{e}^{ -(t-t_0)/\tau_\mathrm{fall} }
}{
1 + \mathrm{e}^{ -(t - t_0)/\tau_\mathrm{rise} }
} + B.
Note, that the Bazin function is developed to be used with fluxes,
not magnitudes. Also note a typo in the Eq. (1) of the original
paper ([bazin2009supernova]_), the minus sign is missed in the "rise"
exponent.
**BazinFit_Amplitude** (:math:`A`)
Half amplitude of the Bazin function.
**BazinFit_Baseline** (:math:`B`)
Baseline of the Bazin function.
**BazinFit_ReferenceTime** (:math:`t_0`)
Reference time of the Bazin fit.
**BazinFit_RiseTime** (:math:`\tau_\mathrm{rise}`)
Rise time of the Bazin function.
**BazinFit_FallTime** (:math:`\tau_\mathrm{fall}`)
Fall time of the Bazin function.
**BazinFit_ReducedChi2** (reduced :math:`\chi^2`)
Bazin 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 5, `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 5, 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 : str or 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
References
----------
.. [bazin2009supernova] Bazin, G., Palanque-Delabrouille, N., Rich, J.,
Ruhlmann-Kleider, V., Aubourg, E., Le Guillou, L., ... & Walker, E. S.
(2009). The core-collapse rate from the Supernova Legacy Survey.
Astronomy & Astrophysics, 499(3), 653-660.
"""
features = [
"BazinFit_Amplitude",
"BazinFit_Baseline",
"BazinFit_ReferenceTime",
"BazinFit_RiseTime",
"BazinFit_FallTime",
"BazinFit_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 = _BazinFit(**self.params)
