What customers say ...
Mike is an outstanding teacher ... I will look for his classes in the future.
Jennifer Trasti, Software/Systems Engineer, Sandia National Laboratories, Albuquerque, NM, USA more...
Very nice course, got many useful suggestions.
Dr.-Ing. Ralf Wieland, Institut für Landschaftssystemanalyse, Leibniz-Zentrum für Agrarlandschaftsforschung e.V. about the German version of the course "Python for Scientists and Engineers" more...
Good course. Very fast progress without any prior Python knowledge.
Daniel Fuchs, GIGATRONIK Ingolstadt GmbH, about the German version of the course "Python for Programmers" more...
We had a wide range of Python experience in our group and each person gained something valuable to take away....
Dr. Ryan Woodard, Chair of Entrepreneurial Risks, ETH ETH Zurich, Switzerland more...
The course "Python for Scientists and Engineers" is a very useful introduction to Python programming for scientific applications ...
Dr. Mihai Duta, Oxford Supercomputing Centre, UK more...
Module - Python-Extensions with Other Languages
Dates for Open Courses
Course only available as in-house training. Please ask us at firstname.lastname@example.org
Python can be readily connected with other languages. This way, existing libraries in other languages can be used.
Programming in Python is rather comfortable and efficient. The speed of Python programs for some tasks is considerably slower than for programs in other languages such as C/C++, C#, Java, or FORTRAN. As a solution slow program parts can be reimplemented in other languages and seamlessly incorporated in Python.
Furthermore, Python is often termed "glue languages" because of its ability to connected very different systems. The connection of libraries and programs that are implemented in other languages plays a important role for this ability.
Introduction to Example
We use a computationally intensive example throughout the course. This allows for comparison of the different extension methods.
Use of Python's C-API
Standard Python is implemented in C and offers a comprehensive API for writing extensions. The basics of this API are taught. A working extension will be developed by hand that can be used by a Python program.
Python Extensions with Pyrex/Cython
Pyrex is a special language for writing Python extensions. It has mainly Python syntax with some limitations and some additions that allow for automatic translation into C code suitable to be compiled in an Python extension. Examples are used to show how Pyrex works. The possibilities of incorporating existing C programs are also explained.
Use of DLLs with ctypes
The package ctypes allows to access DLLs or shared libraries from Python. It works on the operating systems Windows, Windows CE, Mac OS X, Linux, Solaris, FreeBSD, and OpenBSD. The language in which the DLL is implemented doesn't matter. The usage of ctypes is introduced with examples. Under Windows Microsoft's .NET compiler und the mingw (gcc) compiler are used to compile the DLL. The shared library under Linux Linux is compiled with the gcc. One focus is type conversion between Python and the DLL.
Automatic generation of Extensions with SWIG
The "Simplified Wrapper and Interface Generator" (SWIG) allows to make C/C++ libraries accessible from 13 different languages. One of them is Python. The way SWIG works is covered using examples in C as well as in C++.
Jython is an implementation of Python in Java. It allows to access Java classes directly. The course covers the basics of Jython programs. Examples for use of existing Java classes as well as self written classes are used.
IronPython is a implementation of Python in .NET. It allows Access to all .NET features and makes it a first class .NET language right next to C# and Visual Basic. The course introduces IronPython, demonstrates how to use .NET assemblies, and how access self written C# classes.
Use of FORTRAN Subroutines from Python
FORTRAN is one of the oldest programming languages but it is still in heavy use for scientific applications due to its high performance. There are many old but well proven numerical libraries that can be used from Python.
The usage of F2PY to connect FORTRAN77 as well as FORTRAN90/95 programs with Python is demonstrated. One focus are object-oriented interfaces to those libraries.
The participants can follow all steps directly on their computers. There are exercises at the end of each unit providing ample opportunity to apply the freshly learned knowledge.
Every participant receives comprehensive printed materials that cover the whole course content as wells as a CD with all source codes and used software.
The Python Academy is sponsor of PyCon Ireland 2014.
The Python Academy is sponsor of EuroSciPy 2014.
The Python Academy is sponsor of PyData London 2014.
The Python Academy is sponsor of EuroPython 2014.
The Python Academy is sponsor of PyCon 2014 Montréal.
The Python Academy is sponsor of Python BarCamp Köln 2014.
The Python Academy is sponsor of PyConDE 2013.
The Python Academy is sponsor of EuroPython 2013.
The Python Academy is sponsor of PyCon US 2013.
The Python Academy is sponsor of EuroSciPy 2013.
The Python Academy is sponsor of PyConPL 2012.
The next open cousers
Python Academy sponsors EuroPython conference 2013
Python Academy sponsors EuroSciPy conference 2013
Python Academy sponsors Python BarCamp in Cologne
Next Meeting of Leipzig Python User Group, November 12, 2013
Introduction to Django, November 11 - 13, 2013
Professional Testing with Python, November 25 - 27, 2013
Advanced Django, January 13 - 15, 2014
Python Academy sponsors PyCon US conference 2013
Python Academy founder receives PSF Community Service Award