ext_linear_trend.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# The MIT License (MIT)

# Copyright (c) 2017 Juan Cabral

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

""""""


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

from scipy import stats

from .core import Extractor


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


class LinearTrend(Extractor):
    r"""
    **LinearTrend**

    Slope of a linear fit to the light-curve.

    .. code-block:: pycon

        >>> fs = feets.FeatureSpace(only=['LinearTrend'])
        >>> features, values = fs.extract(**lc_normal)
        >>> dict(zip(features, values))
        {'LinearTrend': -3.2084065290292509e-06}

    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 = ["LinearTrend"]

    def fit(self, magnitude, time):
        regression_slope = stats.linregress(time, magnitude)[0]
        return {"LinearTrend": regression_slope}