DOLFIN is the C++ interface of the FEniCS project for the Automation of Computational Mathematical Modeling (ACMM), providing a consistent PSE (Problem Solving Environment) for solving ordinary and partial differential equations. Key features include a simple, consistent and intuitive object-oriented API; automatic and efficient evaluation of variational forms through FFC; automatic and efficient assembly of linear systems; and support for general families of finite elements.
Python-mcrypt is a comprehensive Python interface to the mcrypt library, which provides a uniform interface to several symmetric encryption algorithms. It is intended to have a simple interface to access encryption algorithms in ofb, cbc, cfb, ecb, and stream modes. It supports algorithms like DES, 3DES, RIJNDAEL, Twofish, IDEA, GOST, CAST-256, ARCFOUR, SERPENT, SAFER+, and more.
RPy is a very simple, yet robust, Python interface to the R Programming Language. It can manage all kinds of R objects and can execute arbitrary R functions (including the graphic functions). All the errors from the R language are converted to Python exceptions. Any module that later were installed on the R system can easily be used from within Python, without introducing any changes.
Sand Kit contains libraries designed for neural networks training and calculations, and a Linux kernel module for the SAND board neuro accelerator. The calculation library supports Multi Layer Perceptron (MLP), Radial Basic Function (RBF), and Kohonen networks in hardware acceleration mode and MLP and RBF networks in software emulation mode. It provides a C++ class library and simple C routines for neural network calculations. It has a flexible configuration which includes setting the maximum permitted memory usage, calculation and memory usage optimization options, input and output data sources, and debugging options. The configuration can be changed simply by editing the configuration file and/or with the built-in Gtk+ user interface. The learning library provides a flexible way to write new learning algorithms and methods using a powerful modules library. It contains several common learning routines, including an enhanced random search and resilient backpropogation algorithms. It is integrated with the calculation library to use software as well as hardware modes. All methods have separate configuration files. Support for interface plugins is provided, and all learning routines have built-in Gtk+ interface plugins.