saftig 0.1.0

Implementations of different static and adaptive filtering techniques for the prediction of a correlated signal component from witness signals.

saftig – Static & Adaptive Filtering Techniques In Gravitational-wave-research

Python implementations of different static and adaptive filtering techniques for the prediction of a correlated signal component from witness signals. The main goal is to provide a unified interface for the different filtering techniques.

Features

Static:

• Wiener Filter (WF)

Adaptive

• Updating Wiener Fitler (UWF)
• Least-Mean-Squares Filter (LMS)

Non-Linear:

• Experimental non-linear LMS Filter variant (PolynomialLMS)

Minimal example

>>> import saftig as sg
>>>
>>> # generate data
>>> n_channel = 2
>>> witness, target = sg.TestDataGenerator([0.1]*n_channel).generate(int(1e5))
>>>
>>> # instantiate the filter and apply it
>>> filt = sg.LMSFilter(n_filter=128, idx_target=0, n_channel=n_channel)
>>> filt.condition(witness, target)
>>> prediction = filt.apply(witness, target) # check on the data used for conditioning
>>>
>>> # success
>>> sg.RMS(target-prediction) / sg.RMS(prediction)
0.08221177645361015

Terminology

• Witness signal w: One or multiple sensors that are used to make a prediction
• Target signal s: The goal for the prediction

Useful commands

make # run linter, testing and generate documentation
make test # run just the tests
make view # build and open documentation
make coverage # report test coverage in terminal
make cweb # full test coverage report with html

make ie # install as editable package
make testpublish # build and push to test pypi

# run an individual test
python -m unittest testing.test_wf