Mastering scipy.spatial | Tutorials
The heavily-used scipy library is so large that each of the major modules could fill its own tutorial syllabus. It is also production-quality software with a 1.0 release imminent. In this tutorial, my focus is to cover the scipy.spatial component of the library in great detail, from the perspective of a heavy user and active developer of the computational geometry components of scipy. From distance matrices to Voronoi diagrams and Hausdorff distances, we will explore the corners of scipy.spatial code--both long-established features and even proposed features that haven't yet made it into a stable release.
Tyler Reddy
I have a PhD in biochemistry and molecular biology and am a post-doctoral fellow in computational virology. I build computational models of viruses (like influenza A and dengue) to better understand their biophysical properties. This requires extensive use of the Python programming language to parse shapes, volumes and areas. This is accomplished by leveraging numpy and scipy to perform computational geometry calculations. I am working with the scientific Python community to improve our computational geometry capabilities--my most recent presentations were at PyData London 2015 and PyCon 2016, both of which focus on computational geometry in Python.
Room 8
Thursday, 18th May, 09:00 - 12:20
