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Serial Crystallography (SX) involves the processing of thousands of diffraction patterns in random orientations. To compile a complete data set, these patterns must be indexed, integrated, and merged. Herein, we introduce TORO (Torch-Powered Robust Optimization) Indexer, a robust, adaptable, and performant indexing algorithm. Developed using the PyTorch framework, TORO Indexer can operate on GPUs, CPUs, TPUs, and other accelerators supported by PyTorch, ensuring it adapts to a broad range of computational setups. On a modern NVIDIA A100 GPU, TORO achieves an indexing speed of 250 images/s.

This is the Renku project to use with Week 12 Assignment 1. You should fork, make private, link the R kernel to get a new project of your own. Then use the Jupyter notebook called week-12-assignment-1 that is in the project as the template for your work.

Code used in the causality course (401-4632-15) at ETH Zurich.