Automatic search of optimal filter cutoff frequency based on residual analysis
The 'optimal' cutoff frequency (in the sense that a filter with such cutoff frequency removes as much noise as possible without considerably affecting the signal) is found by performing a residual analysis of the difference between filtered and unfiltered signals over a range of cutoff frequencies.
The optimal cutoff frequency is the one where the residual starts to change very little because it is considered that from this point, it's being filtered mostly noise and minimally signal, ideally.
pip install optcutfreqOr
conda install -c duartexyz optcutfreq>>> y = np.cumsum(np.random.randn(1000))
>>> # optimal cutoff frequency based on residual analysis and plot:
>>> fc_opt = optcutfreq(y, freq=1000, show=True)
>>> # sane analysis but specifying the frequency limits and plot:
>>> optcutfreq(y, freq=1000, fclim=[200,400], show=True)
>>> # It's not always possible to find an optimal cutoff frequency
>>> # or the one found can be wrong (run this example many times):
>>> y = np.random.randn(100)
>>> optcutfreq(y, freq=100, show=True)Here is a suggestion to cite this GitHub repository:
Marcos Duarte. (2021). optcutfreq: A Python module for automatic search of optimal filter cutoff frequency based on residual analysis (Version v0.0.8). Zenodo. http://doi.org/10.5281/zenodo.4599114
And a possible BibTeX entry:
@software{marcos_duarte_2021_4599114,
author = {Marcos Duarte},
title = {{optcutfreq: A Python module for automatic search of optimal filter cutoff frequency based on residual analysis}},
month = mar,
year = 2021,
publisher = {Zenodo},
version = {v0.0.8},
doi = {10.5281/zenodo.4599114},
url = {https://doi.org/10.5281/zenodo.4599114}
}The non-software content of this project is licensed under a Creative Commons Attribution 4.0 International License, and the software code is licensed under the MIT license.