We focus on scientific/engineering software development using RAD abilities of Python language, accompanied with free scientific libraries such as NumPy and SciPy. Our mainstream research activity is numerical optimization, including nonsmooth optimization and solving systems of nonlinear equations. OpenOpt framework - universal numerical optimization package with several own solvers (e.g. ralg) and connections to tens of other, graphical output of convergence and many other goodies FuncDesigner - tool to rapidly build functions over variables/arrays and get their derivatives via automatic differentiation. Also, you can perform integration, interpolation, solve systems of linear/nonlinear/ODE equations and numerical optimization problems coded in FuncDesigner by OpenOpt (see some examples in its doc), uncertainty analysis and interval analysis DerApproximator - tool to get (or check user-supplied) derivatives via finite-difference approximation SpaceFuncs - tool for 2D, 3D, N-dimensional geometric modeling with possibilities of parametrized calculations, numerical optimization and solving systems of geometrical equations
SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world's leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!