#!/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.
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
import pandas as pd
from six.moves import range
__all__ = [
"remove_noise",
"align"]
# =============================================================================
# FUNCTIONS
# =============================================================================
[docs]def remove_noise(time, magnitude, error, error_limit=3, std_limit=5):
"""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.
"""
data, mjd = magnitude, time
data_len = len(mjd)
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, error2):
"""Synchronizes the light-curves in the two different bands.
Returns
-------
aligned_time
aligned_magnitude
aligned_magnitude2
aligned_error
aligned_error2
"""
error = np.zeros(time.shape) if error is None else error
error2 = np.zeros(time2.shape) 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
