Source code for feets.extractors.ext_pair_slope_trend
#!/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 PairSlopeTrend(Extractor):
r"""
**PairSlopeTrend**
Considering the last 30 (time-sorted) measurements of source magnitude,
the fraction of increasing first differences minus the fraction of
decreasing first differences.
.. code-block:: pycon
>>> fs = feets.FeatureSpace(only=['PairSlopeTrend'])
>>> features, values = fs.extract(**lc_normal)
>>> dict(zip(features, values))
{'PairSlopeTrend': -0.16666666666666666}
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 = ["PairSlopeTrend"]
[docs] def fit(self, magnitude):
data_last = magnitude[-30:]
pst = (float(len(np.where(np.diff(data_last) > 0)[0]) -
len(np.where(np.diff(data_last) <= 0)[0])) / 30)
return {"PairSlopeTrend": pst}