#!/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
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# SOFTWARE.


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

""""""


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

import numpy as np

from .core import Extractor


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


class Beyond1Std(Extractor):
    """
    **Beyond1Std**

    Percentage of points beyond one standard deviation from the weighted mean.
    For a normal distribution, it should take a value close to 0.32:

    .. code-block:: pycon

        >>> fs = feets.FeatureSpace(only=['Beyond1Std'])
        >>> features, values = fs.extract(**lc_normal)
        >>> dict(zip(features, values))
        {'Beyond1Std': 0.317}

    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", "error"]
    features = ["Beyond1Std"]

    def fit(self, magnitude, error):
        n = len(magnitude)

        weighted_mean = np.average(magnitude, weights=1 / error ** 2)

        # Standard deviation with respect to the weighted mean

        var = sum((magnitude - weighted_mean) ** 2)
        std = np.sqrt((1.0 / (n - 1)) * var)

        count = np.sum(
            np.logical_or(
                magnitude > weighted_mean + std,
                magnitude < weighted_mean - std,
            )
        )

        return {"Beyond1Std": float(count) / n}