sigpropy - A Python package for signal processing
Joseph Vantassel, The University of Texas at Austin
Table of Contents
About sigpropy
sigpropy is a Python package for digital signal processing. It includes two main class definitions, TimeSeries and FourierTransform. These classes include methods to perform common signal processing techniques (e.g., trimming and resampling) and properties to make using them readable and intuitive.
This package and the classes therein are being used in several other
Python projects, some of which have been released publicly and others are
still in the development stage, so if you do not see a feature you would like
it may very well be under development and released in the near future. To be
notified of future releases, you can either watch the repository on
GitHub or
Subscribe to releases on the
Python Package Index (PyPI).
TimeSeries
A simple example:
import sigpropy
import matplotlib.pyplot as plt
import numpy as np
dt = 0.002
time = np.arange(0, 1, dt)
s1 = 1*np.sin(2*np.pi*10*time)
s2 = 2*np.sin(2*np.pi*20*time)
s3 = 5*np.sin(2*np.pi*30*time)
amplitude = s1 + s2 + s3
tseries = sigpropy.TimeSeries(amplitude, dt)
fseries = sigpropy.FourierTransform.from_timeseries(tseries)
plt.plot(tseries.time, tseries.amplitude)
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.show()
FourierTransform
A simple example:
import sigpropy
import matplotlib.pyplot as plt
import numpy as np
dt=0.002
time = np.arange(0, 1, dt)
s1 = 1*np.sin(2*np.pi*10*time)
s2 = 2*np.sin(2*np.pi*20*time)
s3 = 5*np.sin(2*np.pi*30*time)
amplitude = s1 + s2 + s3
tseries = sigpropy.TimeSeries(amplitude, dt)
fseries = sigpropy.FourierTransform.from_timeseries(tseries)
plt.plot(fseries.frequency, fseries.mag)
plt.xscale("log")
plt.xlabel("Frequency (Hz)")
plt.ylabel("|FFT Amplitude|")
plt.show()
Special Thanks To
- Albert Kottke for his suggestions to speed up the Konno and Ohmachi smoothing. For a standalone implementation of Konno and Ohmachi smoothing see his project pykooh.