Today I found myself needing to scale a large number of geometric shapes corresponding to volcanic fields in the Western United States. Shapely (used in previous posts) makes this easy with the shapely.affinity.scale() method (an affine transformation preserves lines and points, and the scale() method is a simplified transformation without adding rotation or skew).
So I put together a test script using the scale() method (on my github here) that takes the scaling factor as input. Since I’ll be using this with geometric coordinates and will be testing sensitivity of measurements to different sized shapes, I added the option to scale the shape by a a sequences of distances (angular or linear). The code is fairly straight forward and I don’t have time for a detailed post, so for now, here’s a trippy plot showing a squiggly-circle shape scaled by different amounts:
To generate the above plot (assuming you’ve cloned my learning_shapefiles repo and are in the directory containing affine_expansion.py).:
python affine_expansion.py -f ../data/expansion_test_data.csv -exp 0.25,.5,.75,1 .25,1.5,1.75,2