Source code for feets.extractors.ext_percent_amplitude
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2017 Juan Cabral
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# SOFTWARE.
# =============================================================================
# FUTURE
# =============================================================================
from __future__ import unicode_literals
# =============================================================================
# DOC
# =============================================================================
__doc__ = """"""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
from .core import Extractor
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]class PercentAmplitude(Extractor):
r"""
**PercentAmplitude**
Largest percentage difference between either the max or min magnitude
and the median.
.. code-block:: pycon
>>> fs = feets.FeatureSpace(only=['PercentAmplitude'])
>>> features, values = fs.extract(**lc_normal)
>>> dict(zip(features, values))
{'PercentAmplitude': -168.991253993057}
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.
"""
data = ['magnitude']
features = ["PercentAmplitude"]
[docs] def fit(self, magnitude):
median_data = np.median(magnitude)
distance_median = np.abs(magnitude - median_data)
max_distance = np.max(distance_median)
percent_amplitude = max_distance / median_data
return {"PercentAmplitude": percent_amplitude}