PyDynamic.uncertainty.rst

Evaluation of uncertainties

The evaluation of uncertainties is a fundamental part of the measurement analysis in metrology. The analysis of dynamic measurements typically involves methods from signal processing, such as digital filtering, the discrete Fourier transform (DFT), or simple tasks like interpolation. For most of these tasks, methods are readily available, for instance, as part of scipy.signal. This module of PyDynamic provides the corresponding methods for the evaluation of uncertainties.

The package consists of the following modules:

• PyDynamic.uncertainty.propagate_DFT: Uncertainty evaluation for the DFT
• PyDynamic.uncertainty.propagate_convolution: Uncertainty evaluation for convolutions
• PyDynamic.uncertainty.propagate_filter: Uncertainty evaluation for digital filtering
• PyDynamic.uncertainty.propagate_MonteCarlo: Monte Carlo methods for digital filtering
• PyDynamic.uncertainty.interpolate: Uncertainty evaluation for interpolation

Uncertainty evaluation for convolutions

PyDynamic.uncertainty.propagate_convolution

Uncertainty evaluation for the DFT

PyDynamic.uncertainty.propagate_DFT

Uncertainty evaluation for digital filtering

PyDynamic.uncertainty.propagate_filter

Monte Carlo methods for digital filtering

PyDynamic.uncertainty.propagate_MonteCarlo

Uncertainty evaluation for interpolation

PyDynamic.uncertainty.interpolation