SciPy
From OpenOpt
SciPy (Scientific Python) is a mathematical library written in Python + C + Fortran code, well-known for Python language programmers, based on NumPy and done by about the same programmers.
Available subpackages:
- constants: Physical constants and conversion factors
- cluster: Vector Quantization / Kmeans
- fftpack: Discrete Fourier Transform algorithms
- integrate: Integration routines
- interpolate: Interpolation Tools
- io: Data input and output
- lib: Python wrappers to external libraries
- linalg: Linear algebra routines
- misc: Miscellaneous utilities
- optimize: Optimization Tools
- sandbox: Experimental code
- signal: Signal Processing Tools
- sparse: Sparse Matrix Support
- special: Special Functions
- stats: Statistical Functions
- weave: Allows the inclusion of C/C++ within Python code
Additional functionality
SciPy's core feature set is extended by many other dedicated software tools. For example,
- Plotting. The currently recommended 2-D plotting package is Matplotlib, however, there are many other plotting packages such as HippoDraw, Chaco, and Biggles. Other popular graphics tools include Python Imaging Library and MayaVi (for 3D visualization).
- Optimization. While SciPy has its own optimization package, OpenOpt has access to more optimization solvers and can involve Automatic differentiation.
- Advanced Data Analysis. Via RPy, SciPy can interface to the R (programming language) statistical package for advanced data analysis.
- Database. SciPy can interface with PyTables, a hierarchical database package designed to efficiently manage large amounts of data using HDF5.
- Interactive shell. IPython is an interactive environment that offers debugging and coding features similar to what MATLAB offers.
- Symbolic Mathematics. There are several Python libraries--such as PyDSTool Symbolic and SymPy--that offer symbolic mathematics.
SciPy homepage: http://scipy.org
Wikipedia entry for SciPy: http://en.wikipedia.org/wiki/SciPy
