OWI-Lab/py_fatigue: Zenodo registration

<p><strong>py-Fatigue toolbox for Fatigue assessment</strong></p> <p>It provides:</p> <ul> <li>a powerful cycle-counting implementation based on the ASTM E1049-85 rainflow method that retrieves the main class of the package:&nbsp;<code>CycleCount</code></li> <li>capability of storing the&nbsp;<code>CycleCount</code>&nbsp;results in a sparse format for storage and memory efficiency</li> <li>easy applicability of multiple mean stress effect correction models</li> <li>implementation of low-frequency fatigue recovery when &quot;summing&quot; multiple&nbsp;<code>CycleCount</code>&nbsp;instances</li> <li>fatigue analysis through the combination of SN curves and multiple damage accumulation models</li> <li>crack propagation analysis through the combination of the Paris&#39; law and multiple crack geometries</li> <li>and more...</li> </ul> <p>Py-Fatigue is heavily based on&nbsp;<a href="https://numba.pydata.org/"><code>numba</code></a>,&nbsp;<a href="https://numpy.org/"><code>numpy</code></a>&nbsp;and&nbsp;<a href="https://pandas.pydata.org/"><code>pandas</code></a>, for the analytical part, and&nbsp;<a href="https://matplotlib.org/"><code>matplotlib</code></a>&nbsp;as well as&nbsp;<a href="https://plotly.com/python/"><code>plotly</code></a>&nbsp;for the plotting part.</p> <p>Therefore, it is highly recommended to have a look at the documentation of these packages as well.</p>