Skip to main content
https://gitlab.com/pyFBS/pyFBS/-/raw/master/docs/logo/logo-big.png

pyFBS

pyFBS is a Python package for Frequency Based Substructuring, Transfer Path Analysis and also, as a new addition, multi-reference modal identification. It enables the user to use state-of-the-art dynamic substructuring methodologies in an intuitive manner.

With the package also basic and application examples are provided, together with real datasets so you can directly try out the capabilities of the pyFBS.

pypi docs codequality MIT

Features

  • 3D display

  • FRF synthetization

  • Virtual Point Transformation

  • System Equivalent Model Mixing

  • Singular Vector Transformation

  • Experimental Modal Analysis

For more information on features, basic and application examples check out the documentation

Citation

If you use pyFBS in your research, please consider citing our published paper in the Journal of Open Source Software.

License

Licensed under the MIT license.

Metadata

Release files for pyFBS 1.0.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyFBS 1.0.7
File Size Uploaded
pyfbs-1.0.7.tar.gz 4.6 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for pyFBS 1.0.7
File Interpreter ABI Platform
pyfbs-1.0.7-py3-none-any.whl Python 3 none any Details

Total release size: 4.7 MB

Release files / pyfbs-1.0.7.tar.gz

Download URL pyfbs-1.0.7.tar.gz
Size 4.6 MB
Tags Source
SHA-256 checksum
How to use checksums
43efb39e3c6b5a94e6136f63fdcc334acf10b9060c57cb9f9c32a4b7958b1904
BLAKE2b-256 checksum
How to use checksums
fbd0f25dd4022529f0b045e9a3ea4669e66fef7f4231ce46eaa3ce446fcdbccc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.14.8

Release files / pyfbs-1.0.7-py3-none-any.whl

Download URL pyfbs-1.0.7-py3-none-any.whl
Size 139.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
2d3eb7f47208e0dbec2ab1489acdab674fb0dc67df20b1d7d5f35570e7a294c7
BLAKE2b-256 checksum
How to use checksums
d01039a14e470558dd6b1b00075358597c4e039582779edbb6679fc4b42aa64d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.14.8
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page