g2 is an easy to use, portable and powerful 2D graphics library. It provides a comprehensive set of functions for simultaneous generation of graphical output on different types of devices. The following devices are currently supported: Postscript, X11, FIG (xfig), PNG, and JPEG using the gd library, and Win32. g2 is written in C (ANSI) and additionally has Fortran, Perl, and Python interfaces.
LinAl was designed to bring together C++ and FORTRAN. At the same time LinAl is supposed to be easy to use, fast, and reasonably safe. The LinAl library is based on STL techniques and uses STL containers for the storage of matrix data and STL algorithms where feasible. Low level, algebraic operators, linear solvers, and eigenvalue solvers are implemented, based on calls to BLAS, LAPACK, and CGSOLX.
The ATLAS (Automatically Tuned Linear Algebra Software) project is an ongoing research effort focusing on applying empirical techniques in order to provide portable performance. It provides C and Fortran77 interfaces to a portably efficient BLAS implementation, as well as a few routines from LAPACK.
GetDP is a general finite element solver using mixed elements to discretize de Rham-type complexes in one, two, and three dimensions. The main feature of GetDP is the closeness between the input data defining discrete problems (written by the user in ASCII data files) and the symbolic mathematical expressions of these problems.
CFITSIO is a library of C and Fortran subroutines for reading and writing data files in the FITS (Flexible Image Transport System) data format. It simplifies the task of writing software that deals with FITS files by providing an easy to use set of high-level routines that insulate the programmer from the internal complexities of the FITS file format.
The National Space Science Data Center's (NSSDC) Common Data Format (CDF) is a self-describing data abstraction for the storage and manipulation of multidimensional data in a platform- and discipline-independent fashion. It consists of a scientific data management package (known as the "CDF Library") that allows programmers and application developers to manage and manipulate scalar, vector, and multi-dimensional data arrays.
ffnet is a fast and easy-to-use feed-forward neural network training solution for Python. You can use it to train, test, save, load, and use an artificial neural network with sigmoid activation functions. Any network connectivity without cycles is allowed (not only layered). Training can be performed with several optimization schemes, including genetic alorithm based optimization. There is access to exact partial derivatives of network outputs versus its inputs. Normalization of data is handled automatically by ffnet.