SciPy
| Developer(s) | community project sponsored and supported by Enthought |
|---|---|
| Stable release | 0.6.0
/ Sept. 20, 2007 |
| Repository | |
| Operating system | Cross-platform (list) |
| Type | Technical computing |
| License | BSD |
| Website | www.scipy.org
|
SciPy is an open source library of algorithms and mathematical tools for the Python programming language.
SciPy contains modules for optimization, integration, special functions, signal and image processing, genetic algorithms, ODE solvers, and other tasks common in science and engineering. It has a similar audience to applications as MATLAB and Scilab.
SciPy is currently distributed under the BSD license and its development is sponsored by Enthought.
Data structures
The basic data structure in SciPy is a multidimensional array provided by the NumPy module. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code. [1]
Modules
Overview
Available subpackages:
- 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.[2] 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 packages and solvers.
- Advanced Data Analysis. Via RPy, SciPy can interface to the R 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.
See also
External links
- SciPy Course Outline by Dave Kuhlman