@@ -23,6 +23,15 @@ In [1]: from graph_tool.all import *
In [2]:
```
If you want to use interactive visualization from within docker, you have first to enable local connections to the X server:
```
xhost +local:
```
and then run the container with
```
docker run -ti --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix tiagopeixoto/graph-tool ipython
```
# Native installation
Python modules are usually very easy to install, typically requiring nothing more that `pip install <package>` for basically any operating system. For `graph-tool`, however, the situation is different. This is because, in reality, `graph-tool` is a C++ library wrapped in Python, and it has many C++ dependencies such as [Boost](http://www.boost.org/), [CGAL](http://www.cgal.org/) and [expat](http://expat.sourceforge.net/), which are not installable via Python-only package management systems such as [pip](http://www.pip-installer.org/). Because the module lives between the C++ and Python worlds, its installation is done more like a C++ library rather than a typical python module. This means it inherits some of the complexities common of the C++ world that some Python users do not expect.
