Scientific Tools for Python
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!
check the url:-
http://www.scipy.org/
Python and Scientific Computing...
Python and Scientific Computing
Cookbook/OptimizationDemo1
SciPy optimization on Ubuntu Linux
NumPy and SciPy are two of many open-source packages for scientific computing that use the Python programming language. This website, together with other subdomains of the scipy.org domain, serves as a portal for all scientific computing with Python, not just NumPy and SciPy. The index under Topical Software in the navigation bar lists these domains and other destinations for scientific software using Python.
Good places to start to learn more about SciPy:
* More about NumPy / SciPy
* Index of Python packages for scientific computing
* Projects using NumPy / SciPy
* scikits add-on toolkits for numpy/scipy
* Download NumPy / SciPy
* Install SciPy
* Documentation
* Cookbook
Cookbook/OptimizationDemo1
SciPy optimization on Ubuntu Linux
NumPy and SciPy are two of many open-source packages for scientific computing that use the Python programming language. This website, together with other subdomains of the scipy.org domain, serves as a portal for all scientific computing with Python, not just NumPy and SciPy. The index under Topical Software in the navigation bar lists these domains and other destinations for scientific software using Python.
Good places to start to learn more about SciPy:
* More about NumPy / SciPy
* Index of Python packages for scientific computing
* Projects using NumPy / SciPy
* scikits add-on toolkits for numpy/scipy
* Download NumPy / SciPy
* Install SciPy
* Documentation
* Cookbook
Python 3.1 final was released on June 27th, 2009.
Python 3.1
Python 3.1 is a continuation of the work started by Python 3.0, the new backwards-incompatible series of Python. Improvements in this release include:
* An ordered dictionary type
* Various optimizations to the int type
* New unittest features including test skipping and new assert methods.
* A much faster io module
* Tile support for Tkinter
* A pure Python reference implementation of the import statement
* New syntax for nested with statements
Python 3.1 is a continuation of the work started by Python 3.0, the new backwards-incompatible series of Python. Improvements in this release include:
* An ordered dictionary type
* Various optimizations to the int type
* New unittest features including test skipping and new assert methods.
* A much faster io module
* Tile support for Tkinter
* A pure Python reference implementation of the import statement
* New syntax for nested with statements
