#!/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
# =============================================================================
# DOCS
# =============================================================================
"""Synthetic light curve dataset generators."""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
from .base import LightCurveDataset
from ..extractors.extractor import DATA_ERROR, DATA_MAGNITUDE, DATA_TIME
# =============================================================================
# CONSTANTS
# =============================================================================
DATASET_NAME = "feets-synthetic"
DATASET_DESCRIPTION = "Lightcurve created with random numbers"
DATASET_BANDS = ("B", "V")
DATASET_METADATA = None
DEFAULT_SIZE = 10000
DEFAULT_TIMEF_PARAMS = {"start": 0.0, "stop": 1.0}
# =============================================================================
# FUNCTIONS
# =============================================================================
[docs]
def create_random(
magf,
magf_params,
errf,
errf_params,
timef=np.linspace,
timef_params=None,
size=DEFAULT_SIZE,
_id=None,
name=DATASET_NAME,
description=DATASET_DESCRIPTION,
bands=DATASET_BANDS,
metadata=DATASET_METADATA,
):
"""Generate a light curve dataset with any given random generator.
Parameters
----------
magf : callable
Function to generate the magnitudes.
magf_params : dict-like
Parameters to feed the `magf` function.
errf : callable
Function to generate the magnitudes.
errf_params : dict-like
Parameters to feed the `errf` function.
timef : callable, default=numpy.linspace
Function to generate the times.
timef_params : dict-like or None, (default={"start": 0., "stop": 1.})
Parameters to feed the `timef` callable.
size : int, default=10000
Number of observations to generate for each light curve.
_id : object, optional
Id for the generated dataset.
name : str, default="feets-synthetic"
Name for the generated dataset.
description : str, default="Lightcurve created with random numbers"
Description for the generated dataset.
bands : tuple of strings, default=("B", "V")
The bands to generate.
metadata : dict-like, optional
The metadata for the generated dataset
Returns
-------
LightCurveDataset
Dataset with randomly generated light curve data vectors.
Examples
--------
>>> from numpy import random
>>> create_random(
... magf=random.normal, magf_params={"loc": 0, "scale": 1},
... errf=random.normal, errf_params={"loc": 0, "scale": 0.008})
LightCurveDataset(_id=None, name='feets-synthetic', bands=('B', 'V'))
"""
timef_params = (
DEFAULT_TIMEF_PARAMS.copy()
if timef_params is None
else timef_params.copy()
)
timef_params.update(num=size)
magf_params = magf_params.copy()
magf_params.update(size=size)
errf_params = errf_params.copy()
errf_params.update(size=size)
data = {}
for band in bands:
data[band] = {
DATA_TIME: timef(**timef_params),
DATA_MAGNITUDE: magf(**magf_params),
DATA_ERROR: errf(**errf_params),
}
return LightCurveDataset(
id=_id,
name=name,
description=description,
bands=bands,
data=data,
metadata=metadata,
)
[docs]
def create_normal(
mu=0.0, sigma=1.0, mu_err=0.0, sigma_err=1.0, seed=None, **kwargs
):
"""Generate a light curve dataset that follows a Gaussian distribution.
Both the magnitudes and the errors are drawn from Gaussian distributions.
Parameters
----------
mu : float, default=0.0
Mean of the Gaussian distribution for the magnitudes.
sigma : float, default=1.0
Standard deviation of the Gaussian distribution for the magnitudes.
mu_err : float, default=0.0
Mean of the Gaussian distribution for the magnitudes errors.
sigma_err : float, default=1.0
Standard deviation of the Gaussian distribution for the magnitudes
errors.
seed : int or array_like, optional
Random seed used to initialize the pseudo-random number generator.
kwargs : optional
extra arguments for `create_random()`.
Returns
-------
LightCurveDataset
Dataset with randomly generated light curve data vectors.
See Also
--------
create_random
Examples
--------
>>> ds = create_normal(0, 1, 0, .0008, seed=42)
>>> ds
LightCurveDataset(_id=None, name='feets-synthetic', bands=('B', 'V'))
>>> ds.data.B
LightCurve(time[10000], magnitude[10000], error[10000])
>>> ds.data.B.time
array([ 0.00000000e+00, 1.00010001e-04, 2.00020002e-04, ...,
9.99799980e-01, 9.99899990e-01, 1.00000000e+00])
"""
random = np.random.RandomState(seed)
return create_random(
magf=random.normal,
magf_params={"loc": mu, "scale": sigma},
errf=random.normal,
errf_params={"loc": mu_err, "scale": sigma_err},
**kwargs,
)
[docs]
def create_periodic(mu_err=0.0, sigma_err=1.0, seed=None, **kwargs):
"""Generate a light curve dataset with periodic variability.
The magnitude values follow a sinusoidal pattern with added Gaussian noise,
while the error values are drawn from a Gaussian distribution.
Parameters
----------
mu_err : float, default=0.0
Mean of the Gaussian distribution for the magnitude errors.
sigma_err : float, default=1.0
Standard deviation of the Gaussian distribution for the magnitude
errors.
seed : int or array_like, optional
Random seed used to initialize the pseudo-random number generator.
kwargs : optional
extra arguments for `create_random()`.
Returns
-------
LightCurveDataset
Dataset with randomly generated light curve data vectors.
Examples
--------
>>> ds = synthetic.create_periodic(bands=["Ks"])
>>> ds
LightCurveDataset(_id=None, name='feets-synthetic', bands=('Ks',))
>>> ds.data.Ks.magnitude
array([ 0.95428053, 0.73022685, 0.03005121, ..., -0.26305297,
2.57880082, 1.03376863])
"""
random = np.random.RandomState(seed)
size = kwargs.get("size", DEFAULT_SIZE)
times, mags, errors = [], [], []
for _ in kwargs.get("bands", DATASET_BANDS):
time = 100 * random.rand(size)
error = random.normal(size=size, loc=mu_err, scale=sigma_err)
mag = np.sin(2 * np.pi * time) + error * random.randn(size)
times.append(time)
errors.append(error)
mags.append(mag)
times, mags, errors = iter(times), iter(mags), iter(errors)
return create_random(
magf=lambda **k: next(mags),
magf_params={},
errf=lambda **k: next(errors),
errf_params={},
timef=lambda **k: next(times),
timef_params={},
**kwargs,
)
