If you just want to use the software, the easiest way is to run from your system’s command line
pip install PyDynamic
This will download the latest version from the Python package repository and copy it into your local folder of third-party libraries. Usage in any Python environment on your computer is then possible by
or, for example, for the module containing the Fourier domain uncertainty methods:
from PyDynamic.uncertainty import propagate_DFT
Updates of the software can be installed via
pip install --upgrade PyDynamic
For collaboration we recommend using Github Desktop or any other git-compatible version control software and cloning the repository. In this way, any updates to the software will be highlighted in the version control software and can be applied very easily.
If you have downloaded this software, we would be very thankful for letting us know. You may, for instance, drop an email to one of the authors.
On the project website you can find various examples illustrating the application of the software in the examples folder. Here is just a short list to get you started.
Uncertainty propagation for the application of an FIR filter with coefficients b with which an uncertainty ub is associated. The filter input signal is x with known noise standard deviation sigma. The filter output signal is y with associated uncertainty uy.
from PyDynamic.uncertainty.propagate_filter import FIRuncFilter y, uy = FIRuncFilter(x, sigma, b, ub)
Uncertainty propagation through the application of the discrete Fourier transform (DFT). The time domain signal is x with associated squared uncertainty ux. The result of the DFT is the vector X of real and imaginary parts of the DFT applied to x and the associated uncertainty UX.
from PyDynamic.uncertainty.propagate_DFT import GUM_DFT X, UX = GUM_DFT(x, ux)
Sequential application of the Monte Carlo method for uncertainty propagation for the case of filtering a time domain signal x with an IIR filter b,a with uncertainty associated with the filter coefficients Uab and signal noise standard deviation sigma. The filter output is the signal y and the Monte Carlo method calculates point-wise uncertainties uy and coverage intervals Py corresponding to the specified percentiles.
from PyDynamic.uncertainty.propagate_MonteCarlo import SMC y, uy, Py = SMC(x, sigma, b, a, Uab, runs=1000, Perc=[0.025,0.975])