Source code for obspy.signal.detrend

#!/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)
"""
import numpy as np
from scipy.interpolate import LSQUnivariateSpline


[docs] 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. Returns the original array which has been modified in-place if possible but it might have to return a copy in case the dtype has to be changed. """ # Convert data if it's not a floating point type. if not np.issubdtype(data.dtype, np.floating): data = np.require(data, dtype=np.float64) ndat = len(data) x1, x2 = data[0], data[-1] data -= x1 + np.arange(ndat) * (x2 - x1) / float(ndat - 1) return data
[docs] 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)
[docs] 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, np.floating): data = np.require(data, dtype=np.float64) 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
[docs] 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, np.floating): data = np.require(data, dtype=np.float64) 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)