git-bump automates the best practices for doing releases for code bases stored in Git:

This plugin allows rsyslog to send syslog messages via SNMPv1 and SNMPv2c traps. By default, SNMPv2c is preferred.

Test2::Tools::Command tests that commands given particular arguments result in particular outputs by way of the exit status word, standard output, and standard error. Various parameters to the command function alter exactly how this is done, in addition to variables that can be set.

The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. Different methods are available: - dynamic programming methods based on Monte Carlo with regressions (global, local, kernel and sparse regressors), for underlying states following some uncontrolled Stochastic Differential Equations; - dynamic programming with a representation of uncertainties with a tree: transition problems are here solved by some discretizations of the commands, resolution of LP with cut representation of the Bellman values; - Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled Stochastic Differential Equations; - Stochastic Dual Dynamic Programming methods to deal with stochastic stock management problems in high dimension. Uncertainties can be given by Monte Carlo and can be represented by a state with a finite number of values (tree); - Some branching nesting methods to solve very high dimensional non linear PDEs and some appearing in HJB problems. Besides some methods are provided to solve by Monte Carlo some problems where the underlying stochastic state is controlled. For each method, a framework is provided to optimize the problem and then simulate it out of the sample using the optimal commands previously computed. Parallelization methods based on OpenMP and MPI are provided in this framework permitting to solve high dimensional problems on clusters. The library should be flexible enough to be used at different levels depending on the user's willingness.

The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data.