pyYeti

pyYeti has tools mostly related to structural dynamics:

• Solve matrix equations of motion in the time and frequency domains
• Shock response spectrum (SRS)
• Fatigue damage equivalent power spectral densities (PSD)
• Hurty-Craig-Bampton model checks
• Coupled loads analysis tools
• Statistics tools for computing k-factors (for tolerance bounds and intervals) and for order statistics
• A data-cursor for interacting with 2D/3D matplotlib plots
• Eigensystem Realization Algorithm
• Force limiting analysis tools
• Read/write Nastran output4 (.op4) files
• Limited capability to read Nastran output2 (.op2) files
• Rainflow cycle counting
• Resample data with the Lanczos method
• A "vectorized" writing module
• Eigensolution with the subspace iteration method
• Tools for working with the "nas2cam" Nastran DMAP
• Other miscellaneous tools

Installation

pyYeti runs on Python 3.10 or later. The dependencies are NumPy, SciPy, Matplotlib, pandas and setuptools. These are all conveniently provided by the Anaconda Python distribution: https://www.anaconda.com/distribution/.

You can install pyYeti via pip:

python -m pip install pyyeti

or:

python -m pip install --user --upgrade pyyeti

You can also install from source, doing something like this:

git clone https://github.com/twmacro/pyyeti.git
cd pyyeti
python -m build -n
cd dist
python -m pip install <filename>.whl

Note that for the C version of the rainflow cycle counter, you also need a C compiler installed. However, the speed of the pure Python version is on par with the C version if you have Numba installed. In fact, the C version may be removed in the future after making Numba a required dependency.

Documentation

pyYeti documentation is here:

http://pyyeti.readthedocs.org/

Tutorials

The documentation contains several tutorials in the documentation. These are also available (in their original form) as Jupyter notebooks:

https://github.com/twmacro/pyyeti/tree/master/docs/tutorials

License

BSD. See LICENSE.txt

Contributing to pyYeti

Contributions are much appreciated. Bug reports, documentation updates, feature requests, and code enhancements are all great ways to contribute.