#!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2017 Juan Cabral
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# =============================================================================
# DOCS
# =============================================================================
"""Synthetic light curve generator.
"""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
from .base import Data
# =============================================================================
# CONSTANTS
# =============================================================================
DS_NAME = "feets-synthetic"
DESCRIPTION = "Lightcurve created with random numbers"
BANDS = ("B", "V")
METADATA = None
DEFAULT_SIZE = 10000
# =============================================================================
# FUNCTIONS
# =============================================================================
[docs]def create_random(magf, magf_params, errf, errf_params,
timef=np.linspace, timef_params=None, size=DEFAULT_SIZE,
id=None, ds_name=DS_NAME, description=DESCRIPTION,
bands=BANDS, metadata=METADATA):
"""Generate a data with any given random function.
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 obervation of the light curves
id : object (default=None)
Id of the created data.
ds_name : str (default="feets-synthetic")
Name of the dataset
description : str (default="Lightcurve created with random numbers")
Description of the data
bands : tuple of strings (default=("B", "V"))
The bands to be created
metadata : dict-like or None (default=None)
The metadata of the created data
Returns
-------
data
A Data object with a random lightcurves.
Examples
--------
.. code-block:: pycon
>>> 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})
Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
"""
timef_params = (
{"start": 0., "stop": 1.}
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] = {
"time": timef(**timef_params),
"magnitude": magf(**magf_params),
"error": errf(**errf_params)}
return Data(
id=id, ds_name=ds_name, description=description,
bands=bands, metadata=metadata, data=data)
[docs]def create_normal(mu=0., sigma=1., mu_err=0.,
sigma_err=1., seed=None, **kwargs):
"""Generate a data with magnitudes that follows a Gaussian
distribution. Also their errors are gaussian.
Parameters
----------
mu : float (default=0)
Mean of the gaussian distribution of magnitudes
sigma : float (default=1)
Standar deviation of the gaussian distribution of magnitude errors
mu_err : float (default=0)
Mean of the gaussian distribution of magnitudes
sigma_err : float (default=1)
Standar deviation of the gaussian distribution of magnitude errorrs
seed : {None, int, array_like}, optional
Random seed used to initialize the pseudo-random number generator.
Can be any integer between 0 and 2**32 - 1 inclusive, an
array (or other sequence) of such integers, or None (the default).
If seed is None, then RandomState will try to read data from
/dev/urandom (or the Windows analogue) if available or seed from
the clock otherwise.
kwargs : optional
extra arguments for create_random.
Returns
-------
data
A Data object with a random lightcurves.
Examples
--------
.. code-block:: pycon
>>> ds = create_normal(0, 1, 0, .0008, seed=42)
>>> ds
Data(id=None, ds_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., sigma_err=1., seed=None, **kwargs):
"""Generate a data with magnitudes with periodic variability
distribution; the error instead are gaussian.
Parameters
----------
mu_err : float (default=0)
Mean of the gaussian distribution of magnitudes
sigma_err : float (default=1)
Standar deviation of the gaussian distribution of magnitude errorrs
seed : {None, int, array_like}, optional
Random seed used to initialize the pseudo-random number generator.
Can be any integer between 0 and 2**32 - 1 inclusive, an
array (or other sequence) of such integers, or None (the default).
If seed is None, then RandomState will try to read data from
/dev/urandom (or the Windows analogue) if available or seed from
the clock otherwise.
kwargs : optional
extra arguments for create_random.
Returns
-------
data
A Data object with a random lightcurves.
Examples
--------
.. code-block:: pycon
>>> ds = synthetic.create_periodic(bands=["Ks"])
>>> ds
Data(id=None, ds_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 b in kwargs.get("bands", 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)
