#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Python module containing detrend methods.

:copyright:
    The ObsPy Development Team (devs@obspy.org)
:license:
    GNU Lesser General Public License, Version 3
    (https://www.gnu.org/copyleft/lesser.html)
"""
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
from future.builtins import *  # NOQA

import numpy as np
from scipy.interpolate import LSQUnivariateSpline


def simple(data):
    """
    Detrend signal simply by subtracting a line through the first and last
    point of the trace

    :param data: Data to detrend, type numpy.ndarray.
    :return: Detrended data.
    """
    ndat = len(data)
    x1, x2 = data[0], data[-1]
    return data - (x1 + np.arange(ndat) * (x2 - x1) / float(ndat - 1))


def _plotting_helper(data, fit, plot):
    import matplotlib.pyplot as plt
    fig, axes = plt.subplots(2, 1, figsize=(8, 5))
    plt.subplots_adjust(hspace=0)
    axes[0].plot(data, color="k", label="Original Data")
    axes[0].plot(fit, color="red", lw=2, label="Fitted Trend")
    axes[0].legend(loc="best")
    axes[0].label_outer()
    axes[0].set_yticks(axes[0].get_yticks()[1:])

    axes[1].plot(data - fit, color="k", label="Result")
    axes[1].legend(loc="best")
    axes[1].label_outer()
    axes[1].set_yticks(axes[1].get_yticks()[:-1])
    axes[1].set_xlabel("Samples")

    plt.tight_layout(h_pad=0)

    if plot is True:
        plt.show()
    else:
        plt.savefig(plot)
        plt.close(fig)


def polynomial(data, order, plot=False):
    """
    Removes a polynomial trend from the data.

    :param data: The data to detrend. Will be modified in-place.
    :type data: :class:`numpy.ndarray`
    :param order: The order of the polynomial to fit.
    :type order: int
    :param plot: If True, a plot of the operation happening will be shown.
        If a string is given that plot will be saved to the given file name.
    :type plot: bool or str

    .. note::

        In a real world application please make sure to use the convenience
        :meth:`obspy.core.trace.Trace.detrend` method.


    .. rubric:: Example

    >>> import obspy
    >>> from obspy.signal.detrend import polynomial

    Prepare some example data.

    >>> tr = obspy.read()[0].filter("highpass", freq=2)
    >>> tr.data += 6000 + 4 * tr.times() ** 2
    >>> tr.data -= 0.1 * tr.times() ** 3 + 0.00001 * tr.times() ** 5
    >>> data = tr.data

    Remove the trend.

    >>> polynomial(data, order=3, plot=True)  # doctest: +SKIP

    .. plot::

        import obspy
        from obspy.signal.detrend import polynomial

        tr = obspy.read()[0].filter("highpass", freq=2)
        tr.data += 6000 + 4 * tr.times() ** 2 - 0.1 * tr.times() ** 3 - \
            0.00001 * tr.times() ** 5

        polynomial(tr.data, order=3, plot=True)
    """
    # Convert data if it's not a floating point type.
    if not np.issubdtype(data.dtype, float):
        data = np.require(data, dtype=np.float32)

    x = np.arange(len(data))
    fit = np.polyval(np.polyfit(x, data, deg=order), x)

    if plot:
        _plotting_helper(data, fit, plot)

    data -= fit
    return data


def spline(data, order, dspline, plot=False):
    """
    Remove a trend by fitting splines.

    :param data: The data to detrend. Will be modified in-place.
    :type data: :class:`numpy.ndarray`
    :param order: The order/degree of the smoothing spline to fit.
        Must be 1 <= order <= 5.
    :type order: int
    :param dspline: The distance in samples between two spline nodes.
    :type dspline: int
    :param plot: If True, a plot of the operation happening will be shown.
        If a string is given that plot will be saved to the given file name.
    :type plot: bool or str


    .. note::

        In a real world application please make sure to use the convenience
        :meth:`obspy.core.trace.Trace.detrend` method.


    .. rubric:: Example

    >>> import obspy
    >>> from obspy.signal.detrend import spline

    Prepare some example data.

    >>> tr = obspy.read()[0].filter("highpass", freq=2)
    >>> tr.data += 6000 + 4 * tr.times() ** 2
    >>> tr.data -= 0.1 * tr.times() ** 3 + 0.00001 * tr.times() ** 5
    >>> data = tr.data

    Remove the trend.

    >>> spline(data, order=2, dspline=1000, plot=True)  # doctest: +SKIP

    .. plot::

        import obspy
        from obspy.signal.detrend import spline

        tr = obspy.read()[0].filter("highpass", freq=2)
        tr.data += 6000 + 4 * tr.times() ** 2 - 0.1 * tr.times() ** 3 - \
            0.00001 * tr.times() ** 5

        spline(tr.data, order=2, dspline=1000, plot=True)
    """
    # Convert data if it's not a floating point type.
    if not np.issubdtype(data.dtype, float):
        data = np.require(data, dtype=np.float32)

    x = np.arange(len(data))
    splknots = np.arange(dspline / 2.0, len(data) - dspline / 2.0 + 2,
                         dspline)

    spl = LSQUnivariateSpline(x=x, y=data, t=splknots, k=order)
    fit = spl(x)

    if plot:
        _plotting_helper(data, fit, plot)

    data -= fit
    return data


if __name__ == '__main__':
    import doctest
    doctest.testmod(exclude_empty=True)
