Source code for feets.extractors.ext_skew

#!/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
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.


# =============================================================================
# FUTURE
# =============================================================================

from __future__ import unicode_literals


# =============================================================================
# DOC
# =============================================================================

__doc__ = """"""


# =============================================================================
# IMPORTS
# =============================================================================

from scipy import stats

from .core import Extractor


# =============================================================================
# EXTRACTOR CLASS
# =============================================================================

class Skew(Extractor):
    r"""

    **Skew**

    The skewness of a sample is defined as follow:

    .. math::

        Skewness = \frac{N}{(N-1)(N-2)}
            \sum_{i=1}^N (\frac{m_i-\hat{m}}{\sigma})^3

    Example:

    For a normal distribution it should be equal to zero:

    .. code-block:: pycon

        >>> fs = feets.FeatureSpace(only=['Skew'])
        >>> features, values = fs.extract(**lc_normal)
        >>> dict(zip(features, values))
        {'Skew': -0.00023325826785278685}

    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 = ["Skew"]

    def fit(self, magnitude):
        return {"Skew": stats.skew(magnitude)}