Source code for feets.extractors.ext_color

#!/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
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# The above copyright notice and this permission notice shall be included in
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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
# =============================================================================

import numpy as np

from .core import Extractor


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

class Color(Extractor):
    """
    **Color**

    The color is defined as the difference between the average magnitude of
    two different bands observations.

    .. code-block:: pycon

        >>> fs = feets.FeatureSpace(only=['Color'])
        >>> features, values = fs.extract(**lc)
        >>> dict(zip(features, values))
        {'Color': -0.33325502453332145}

    References
    ----------

    .. [kim2011quasi] Kim, D. W., Protopapas, P., Byun, Y. I., Alcock, C.,
       Khardon, R., & Trichas, M. (2011). Quasi-stellar object selection
       algorithm using time variability and machine learning: Selection of
       1620 quasi-stellar object candidates from MACHO Large Magellanic Cloud
       database. The Astrophysical Journal, 735(2), 68.
       Doi:10.1088/0004-637X/735/2/68.

    """
    data = ['magnitude', 'magnitude2']
    features = ["Color"]

    def fit(self, magnitude, magnitude2):
        return {"Color": np.mean(magnitude) - np.mean(magnitude2)}