#!/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
# =============================================================================
"""Functions for preprocessing light curve data vectors."""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
import pandas as pd
# =============================================================================
# FUNCTIONS
# =============================================================================
[docs]
def remove_noise(time, magnitude, error, error_limit=3, std_limit=5):
"""Removes noise from the light curve data vectors.
Points within `std_limit` standard deviations from the mean and with
errors greater than `error_limit` times the error mean are considered
as noise and thus are eliminated.
Parameters
----------
time : array-like
magnitude : array-like
error : array-like
error_limit : float, default=3
std_limit : float, default=5
Returns
-------
time_clean : array-like
magnitude_clean : array-like
error_clean : array-like
"""
data, mjd = magnitude, time
data_len = len(mjd)
if data_len == 0:
return np.array([]), np.array([]), np.array([])
if data_len == 1:
return np.array([mjd[0]]), np.array([data[0]]), np.array([error[0]])
error_mean = np.mean(error)
error_tolerance = error_limit * (error_mean or 1)
data_mean = np.mean(data)
data_std = np.std(data)
mjd_out, data_out, error_out = [], [], []
for i in range(data_len):
is_not_noise = (
error[i] < error_tolerance
and (np.absolute(data[i] - data_mean) / data_std) < std_limit
)
if is_not_noise:
mjd_out.append(mjd[i])
data_out.append(data[i])
error_out.append(error[i])
data_out = np.asarray(data_out)
mjd_out = np.asarray(mjd_out)
error_out = np.asarray(error_out)
return mjd_out, data_out, error_out
[docs]
def align(time, time2, magnitude, magnitude2, error=None, error2=None):
"""Synchronizes two light curves in different bands.
Parameters
----------
time : array-like
time2 : array-like
magnitude : array-like
magnitude2 : array-like
error : array-like, optional
error2 : array-like, optional
Returns
-------
aligned_time : array-like
aligned_magnitude : array-like
aligned_magnitude2 : array-like
aligned_error : array-like
aligned_error2 : array-like
"""
error = np.zeros(len(time)) if error is None else error
error2 = np.zeros(len(time2)) if error2 is None else error2
# this asume that the first series is the short one
sserie = pd.DataFrame({"mag": magnitude, "error": error}, index=time)
lserie = pd.DataFrame({"mag": magnitude2, "error": error2}, index=time2)
# if the second serie is logest then revert
if len(time) > len(time2):
sserie, lserie = lserie, sserie
# make the merge
merged = sserie.join(lserie, how="inner", rsuffix="2")
# recreate columns
new_time = merged.index.values
new_mag, new_mag2 = merged.mag.values, merged.mag2.values
new_error, new_error2 = merged.error.values, merged.error2.values
if len(time) > len(time2):
new_mag, new_mag2 = new_mag2, new_mag
new_error, new_error2 = new_error2, new_error
return new_time, new_mag, new_mag2, new_error, new_error2
