Inspired by Armin Ronacher's "Beautiful Native Libraries".
In this example we imagine we are on a desert island and wish to compute pi by throwing darts:
This example is implemented in 3 different languages (C++, Fortran, Python) and we demonstrate how to call this functionality across languages.
These 3 implementations are combined in an example Python package that we call
At the same time we demonstrate how to automatically test the interface and the
We do not discuss memory allocation strategies. For this have a look at this demo.
Lower-level learning goals
- Approximate pi using the Monte Carlo method
- Calling Fortran libraries from C(++)
- Calling C(++) libraries from Fortran
- Calling Fortran/C(++) libraries from Python using Python CFFI
- Automatically testing Fortran/C(++) libraries on Linux and Mac OS X using pytest and Travis CI
- Hiding CMake infrastructure behind a simple
Higher-level learning goals
- Automatically test dynamic Fortran/C(++) libraries
- Write tests without recompiling the code
- Speed up your Python code
- Provide a Python API to your compiled library and leverage Python tools
Installing Python dependencies
virtualenv venv source venv/bin/activate pip install -r requirements.txt
How to configure and build the compiled libraries
mkdir build cd build cmake .. make
How to test this demo
PI_LIBRARY_DIR=build/lib PI_INCLUDE_DIR=island pytest -vv test.py
Installing with pip
This example comes with a full-fledged setup script which configures
and builds the code under the hood and makes it possible to install the demo
virtualenv venv source venv/bin/activate pip install git+https://github.com/bast/python-cffi-demo.git python -c 'import island; print(island.approximate_pi_c(100))'
C(++) calling Fortran and vice versa
$ cd build $ ./bin/pi_cpp.x pi computed by c = 3.141664 pi computed by fortran = 3.141636 $ ./bin/pi_fortran.x pi computed by fortran = 3.1416358947753906 pi computed by c = 3.1416640000000000
Timing the libraries through a Python interface
Default is 2M points but feel free to experiment by increasing the number
of points in
$ PI_LIBRARY_DIR=build/lib PI_INCLUDE_DIR=island python test.py num points: 2000000 python pi=3.14163 time spent: 1.749 sec c pi=3.14190 time spent: 0.041 sec fortran pi=3.14225 time spent: 0.126 sec
How you can contribute
Feel free to improve the C++, Fortran, and Python codes.
If you know intuitive examples that we can use to demonstrate memory allocation strategies, please suggest these.