Source code for feets.extractors.ext_std
#!/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 Std(Extractor):
r"""
**Std** - Standard deviation of the magnitudes
The standard deviation :math:`\sigma` of the sample is defined as:
.. math::
\sigma=\frac{1}{N-1}\sum_{i} (y_{i}-\hat{y})^2
For example, a white noise time serie should have :math:`\sigma=1`
.. code-block:: pycon
>>> fs = feets.FeatureSpace(only=['Std'])
>>> features, values = fs.extract(**lc_normal)
>>> dict(zip(features, values))
{'Std': 0.99320419310116881}
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 = ["Std"]
[docs] def fit(self, magnitude):
return {"Std": np.std(magnitude)}