The Assimilation Monitoring Project is a highly scalable discovery-driven monitoring system. It integrates continuous discovery of servers, services, service dependencies, switch connections, and lots of other things into the monitoring process. The discovery is "stealthy" and will never set off any network security alarms. Adding servers doesn't measurably increase monitoring load, and the system is expected to easily scale into the 100K server range. The discovery work is distributed among all the nanoprobes (agents), which run scripts that spit out JSON. The central system (CMA) stores these strings and runs optional plugins to create graph nodes.
The minfx project is a Python package for numerical optimization. It provides a large collection of standard minimization algorithms, including the line search methods (steepest descent, back-and-forth coordinate descent, quasi-Newton BFGS, Newton, and Newton-CG), the trust-region methods (Cauchy point, dogleg, CG-Steihaug, and exact trust region), the conjugate gradient methods (Fletcher-Reeves, Polak-Ribiere, Polak-Ribiere +, and Hestenes-Stiefel), the miscellaneous methods (Grid search, Simplex, and Levenberg-Marquardt), and the augmented function constraint algorithms (logarithmic barrier and method of multipliers).
Bmrblib is a Python API abstracting the Biological Magnetic Resonance Data Bank (BioMagResBank or BMRB) NMR-STAR format. It allows the writing of NMR-STAR files for BMRB data deposition and the reading and easy extraction of data from files residing in the BMRB data bank, all without knowledge of the Self-Defining Text Archive and Retrieval (STAR) format.