#!/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.
# =============================================================================
# FUTURE
# =============================================================================
from __future__ import unicode_literals
# =============================================================================
# DOC
# =============================================================================
__doc__ = """"""
# =============================================================================
# IMPORTS
# =============================================================================
import math
import numpy as np
from .core import Extractor
# =============================================================================
# CONSTANTS
# =============================================================================
COMMON_DOC = r"""
In order to caracterize the sorted magnitudes distribution we use percentiles.
If :math:`F_{5, 95}` is the difference between 95% and 5% magnitude values,
we calculate the following:
- flux_percentile_ratio_mid20: ratio :math:`F_{40, 60}/F_{5, 95}`
- flux_percentile_ratio_mid35: ratio :math:`F_{32.5, 67.5}/F_{5, 95}`
- flux_percentile_ratio_mid50: ratio :math:`F_{25, 75}/F_{5, 95}`
- flux_percentile_ratio_mid65: ratio :math:`F_{17.5, 82.5}/F_{5, 95}`
- flux_percentile_ratio_mid80: ratio :math:`F_{10, 90}/F_{5, 95}`
For the first feature for example, in the case of a normal distribution, this
is equivalente to calculate:
.. math::
\frac{erf^{-1}(2 \cdot 0.6-1)-erf^{-1}(2 \cdot 0.4-1)}
{erf^{-1}(2 \cdot 0.95-1)-erf^{-1}(2 \cdot 0.05-1)}
So, the expected values for each of the flux percentile features are:
- flux_percentile_ratio_mid20 = 0.154
- flux_percentile_ratio_mid35 = 0.275
- flux_percentile_ratio_mid50 = 0.410
- flux_percentile_ratio_mid65 = 0.568
- flux_percentile_ratio_mid80 = 0.779
References
----------
.. [richards2011machine] Richards, J. W., Starr, D. L., Butler, N. R.,
Bloom, J. S., Brewer, J. M., Crellin-Quick, A., ... &
Rischard, M. (2011). On machine-learned classification of variable stars
with sparse and noisy time-series data.
The Astrophysical Journal, 733(1), 10. Doi:10.1088/0004-637X/733/1/10.
"""
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]class FluxPercentileRatioMid20(Extractor):
__doc__ = COMMON_DOC
data = ['magnitude']
features = ["FluxPercentileRatioMid20"]
[docs] def fit(self, magnitude):
sorted_data = np.sort(magnitude)
lc_length = len(sorted_data)
F_60_index = int(math.ceil(0.60 * lc_length))
F_40_index = int(math.ceil(0.40 * lc_length))
F_5_index = int(math.ceil(0.05 * lc_length))
F_95_index = int(math.ceil(0.95 * lc_length))
F_40_60 = sorted_data[F_60_index] - sorted_data[F_40_index]
F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
F_mid20 = F_40_60 / F_5_95
return {"FluxPercentileRatioMid20": F_mid20}
[docs]class FluxPercentileRatioMid35(Extractor):
__doc__ = COMMON_DOC
data = ['magnitude']
features = ["FluxPercentileRatioMid35"]
[docs] def fit(self, magnitude):
sorted_data = np.sort(magnitude)
lc_length = len(sorted_data)
F_325_index = int(math.ceil(0.325 * lc_length))
F_675_index = int(math.ceil(0.675 * lc_length))
F_5_index = int(math.ceil(0.05 * lc_length))
F_95_index = int(math.ceil(0.95 * lc_length))
F_325_675 = sorted_data[F_675_index] - sorted_data[F_325_index]
F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
F_mid35 = F_325_675 / F_5_95
return {"FluxPercentileRatioMid35": F_mid35}
[docs]class FluxPercentileRatioMid50(Extractor):
__doc__ = COMMON_DOC
data = ['magnitude']
features = ["FluxPercentileRatioMid50"]
[docs] def fit(self, magnitude):
sorted_data = np.sort(magnitude)
lc_length = len(sorted_data)
F_25_index = int(math.ceil(0.25 * lc_length))
F_75_index = int(math.ceil(0.75 * lc_length))
F_5_index = int(math.ceil(0.05 * lc_length))
F_95_index = int(math.ceil(0.95 * lc_length))
F_25_75 = sorted_data[F_75_index] - sorted_data[F_25_index]
F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
F_mid50 = F_25_75 / F_5_95
return {"FluxPercentileRatioMid50": F_mid50}
[docs]class FluxPercentileRatioMid65(Extractor):
__doc__ = COMMON_DOC
data = ['magnitude']
features = ["FluxPercentileRatioMid65"]
[docs] def fit(self, magnitude):
sorted_data = np.sort(magnitude)
lc_length = len(sorted_data)
F_175_index = int(math.ceil(0.175 * lc_length))
F_825_index = int(math.ceil(0.825 * lc_length))
F_5_index = int(math.ceil(0.05 * lc_length))
F_95_index = int(math.ceil(0.95 * lc_length))
F_175_825 = sorted_data[F_825_index] - sorted_data[F_175_index]
F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
F_mid65 = F_175_825 / F_5_95
return {"FluxPercentileRatioMid65": F_mid65}
[docs]class FluxPercentileRatioMid80(Extractor):
__doc__ = COMMON_DOC
data = ['magnitude']
features = ["FluxPercentileRatioMid80"]
[docs] def fit(self, magnitude):
sorted_data = np.sort(magnitude)
lc_length = len(sorted_data)
F_10_index = int(math.ceil(0.10 * lc_length))
F_90_index = int(math.ceil(0.90 * lc_length))
F_5_index = int(math.ceil(0.05 * lc_length))
F_95_index = int(math.ceil(0.95 * lc_length))
F_10_90 = sorted_data[F_90_index] - sorted_data[F_10_index]
F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
F_mid80 = F_10_90 / F_5_95
return {"FluxPercentileRatioMid80": F_mid80}
