#!/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 numpy as np
from .core import Extractor
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]class MaxSlope(Extractor):
"""
**MaxSlope**
Maximum absolute magnitude slope between two consecutive observations.
Examining successive (time-sorted) magnitudes, the maximal first difference
(value of delta magnitude over delta time)
.. code-block:: pycon
>>> fs = feets.FeatureSpace(only=['MaxSlope'])
>>> features, values = fs.extract(**lc_normal)
>>> dict(zip(features, values))
{'MaxSlope': 5.4943105823904741}
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', 'time']
features = ["MaxSlope"]
params = {"timesort": True}
[docs] def fit(self, magnitude, time, timesort):
if timesort:
sort = np.argsort(time)
time, magnitude = time[sort], magnitude[sort]
slope = np.abs(magnitude[1:] - magnitude[:-1]) / (time[1:] - time[:-1])
return {"MaxSlope": np.max(slope)}