In this lecture, we'll cover the following:

• How to visualise transverse shear $(q_x$ and $q_y)$ across a slab using triangulated contour plots.
• How to implement masking with NumPy masked arrays to isolate a central strip of the slab.
• How to construct and control a configurable “middle strip” using geometric bounds and strip fractions.
• How to post-process results, including extracting maximum shear values from masked data.
• How this workflow compares to previous bending moment visualisation and sets up further comparisons with other libraries.

In this lecture, we extend our visualisation workflow from bending moments to transverse shear in a slab, following largely the same logic but introducing masked arrays to control which regions of the slab are displayed. We define a flexible plotting function that allows us to switch between $q_x$ and $q_y$ shear components, and we build a triangulated contour plot based on global coordinates. A key addition is the use of NumPy masked arrays, which enables us to selectively exclude data outside a specified middle strip while still working effectively with triangulated data.

We then develop the logic for constructing this middle strip by identifying slab bounds, calculating midpoints, and defining a strip width based on a clipped fraction of the slab dimensions. Depending on whether we are examining $q_x$ or $q_y$, we generate a mask that retains only the relevant region. Finally, we compute maximum shear values from the masked dataset and incorporate them into the plot, confirming that the results are qualitatively consistent with our earlier custom implementation. The lecture concludes by positioning this work within a broader comparison between OpenSeesPy, our custom code, and upcoming analysis using the PyNite library.

Next up

In the next lecture, we will build a second benchmark model using the PyNite library, providing an additional point of comparison for our results.