# 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
```python
>>> 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
```bash
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
```
Raw data
{
"_id": null,
"home_page": null,
"name": "saftig",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "prediction, filtering, Wiener filter, LeastMeanSquares, noise cancellation",
"author": null,
"author_email": "Tim Kuhlbusch <kuhlbusch@physik.rwth-aachen.de>",
"download_url": "https://files.pythonhosted.org/packages/91/d2/75b7ab10c5ca1993e0ff89f17430a15a406b8fcce9ad0d38d0a6301dd3cf/saftig-0.1.1.tar.gz",
"platform": null,
"description": "# saftig \u2013 Static & Adaptive Filtering Techniques In Gravitational-wave-research\n\n\n\n\n\nPython implementations of different static and adaptive filtering techniques for the prediction of a correlated signal component from witness signals.\nThe main goal is to provide a unified interface for the different filtering techniques.\n\n## Features\n\nStatic:\n* Wiener Filter (WF)\n\nAdaptive\n* Updating Wiener Fitler (UWF)\n* Least-Mean-Squares Filter (LMS)\n\nNon-Linear:\n* Experimental non-linear LMS Filter variant (PolynomialLMS)\n\n## Minimal example\n\n```python\n>>> import saftig as sg\n>>>\n>>> # generate data\n>>> n_channel = 2\n>>> witness, target = sg.TestDataGenerator([0.1]*n_channel).generate(int(1e5))\n>>>\n>>> # instantiate the filter and apply it\n>>> filt = sg.LMSFilter(n_filter=128, idx_target=0, n_channel=n_channel)\n>>> filt.condition(witness, target)\n>>> prediction = filt.apply(witness, target) # check on the data used for conditioning\n>>>\n>>> # success\n>>> sg.RMS(target-prediction) / sg.RMS(prediction)\n0.08221177645361015\n```\n\n## Terminology\n\n* Witness signal w: One or multiple sensors that are used to make a prediction\n* Target signal s: The goal for the prediction\n\n## Useful commands\n```bash\nmake # run linter, testing and generate documentation\nmake test # run just the tests\nmake view # build and open documentation\nmake coverage # report test coverage in terminal\nmake cweb # full test coverage report with html\n\nmake ie # install as editable package\nmake testpublish # build and push to test pypi\n\n# run an individual test\npython -m unittest testing.test_wf\n```\n\n",
"bugtrack_url": null,
"license": null,
"summary": "Implementations of different static and adaptive filtering techniques for the prediction of a correlated signal component from witness signals.",
"version": "0.1.1",
"project_urls": {
"Repository": "https://github.com/timbk/saftig"
},
"split_keywords": [
"prediction",
" filtering",
" wiener filter",
" leastmeansquares",
" noise cancellation"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "91d275b7ab10c5ca1993e0ff89f17430a15a406b8fcce9ad0d38d0a6301dd3cf",
"md5": "420eda15ad46eb2dcf8e66e799de2989",
"sha256": "415b07e3a8661148149bdea327aeb18fcd42d42494d3e342710d6a1cc9b47cd6"
},
"downloads": -1,
"filename": "saftig-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "420eda15ad46eb2dcf8e66e799de2989",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 38838,
"upload_time": "2025-07-27T20:58:28",
"upload_time_iso_8601": "2025-07-27T20:58:28.124303Z",
"url": "https://files.pythonhosted.org/packages/91/d2/75b7ab10c5ca1993e0ff89f17430a15a406b8fcce9ad0d38d0a6301dd3cf/saftig-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-27 20:58:28",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "timbk",
"github_project": "saftig",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "saftig"
}
JSON
