SignalFilters 0.0.8

Filtering digital signals using a front end to scipy filters

A collection of digital signal filter front end for SciPy

A collection of signal processing tools, utilities and class for signal processing

Description

The signal processing tool box has the following topics

1. filters: Definition of three digital signal filters (all with low, high, -band-pass mode)

   • Ideal block filter

   • Butterworth filter

   • Kaiser filter

   • Phase shift removal

2. utils: Classes and function to support signal processing

   • SignalGenerator: class to generated signal with multiple harmonic components and noise for testing purposes

   • get_peaks: Extract the peaks from a power spectral density

Installation

SignalFilters can be installed via pip from PyPi

pip install SignalFilters

Examples

Using digital filters is easy. In two steps we do:

1. Define a noisy sine wave

from  numpy import linspace, sin, random, pi
from signal_filters.filters import filter_signal


A_peak = 1.0            # Amplitude at 10 m
a_noise = 0.2 * A       # Noise rms at 0.2 m
T_peak = 10             # period of 10 seconds
f_peak = 1 / T_peak     # peak frequency at 0.1 Hz
total_time = 1000       # total sampling time of 1000 seconds
f_sample = 10           # sample frequency at 10 Hz
n_points = total_time * f_sample

time = linspace(0, total_time, num=n_points, endpoint=False)
y_original = sin(2 * pi * time / T_peak)
y_noise = random.normal(scale=a_noise, size=y_original.size)
y_total = y_original + y_noise

2. Filter the noisy sine wave with a band pass filter with low and high cut-off frequency at 0.08 Hz and 0.12 Hz, respectively:

y_sine_filtered = filter_signal(y_total,
                                f_cut_low=0.08,
                                f_cut_high=.12,
                                f_sampling=f_sample)

More examples can be found at example_filtering and example_filtering_rtd.

Notes

• The SciPy packages provides most signal processing tool, such as as a Power Spectral Density (PSF) estimator.

• The filters defined in this package are a front end to the Scipy filters, making it easier to use digital filters in your code.

• For peak finding either the PeakUtils or the PyWafo package is recommended.

• The function get_peaks is a front end to the peakutils.peaks function

• This project has been set up using PyScaffold 4.5.0. For details and usage information on PyScaffold see http://pyscaffold.readthedocs.org/.