The easiest way to install the most recent version of Brian if you already have a version of Python 2.5-2.7 including the easy_install script is to simply run the following in a shell:
This will download and install Brian and all its required packages (NumPy, SciPy, etc.).
Similarly, you can use the pip utility:
pip install brian
Note that there are some optimisations you can make after installation, see the section below on Optimisations.
If you use a Debian-based Linux distribution (in addition to Debian itself, this includes for example Ubuntu or Linux Mint), you can install Brian directly from your favourite package manager (e.g. Synaptic or the Ubuntu Software Centre), thanks to the packages provided by the NeuroDebian team.
The package is called python-brian, the documentation and tutorials can be found in python-brian-doc. To install these packages from the command-line use:
sudo apt-get install python-brian python-brian-doc
Note that in contrast to the procedure described above for easy_install / pip, you will not necessarily get the most recent version of Brian this way. On the other hand, you do not have to take care of future updates yourself, as the Brian package gets updated with the standard update process. Additionally, the Brian package already includes all the compiled C code mentioned in the Optimisations section. Another way to install Brian which combines these advantages with up-to-date versions is to directly add the NeuroDebian repository to your software sources.
Installing Brian requires the following components:
Fortunately, Python packages are very quick and easy to install, so the whole process shouldn’t take very long.
We also recommend using the following for writing programs in Python (see details below):
Finally, if you want to use the (optional) automatic C++ code generation features of Brian, you should have the gcc compiler installed (on Cygwin if you are running on Windows).
Mac users: The Enthought Python Distribution (EPD ) is free for academics and contains all the libraries necessary to run Brian. Otherwise, the Scipy Superpack for Intel OS X also includes versions of Numpy, Scipy, Pylab and IPython.
Windows users: the Python(x,y) distribution includes all the packages (including Eclipse and IPython) above except Brian (which is available as an optional plugin).
Another option is the Anaconda distribution, which also includes all the packages above except Brian and Eclipse.
On Windows, Python packages (including Brian) are generally installed simply by running an .exe file. On other operating systems, you can download the source release (typically a compressed archive .tar.gz or .zip that you need to unzip) and then install the package by typing the following in your shell:
python setup.py install
Eclipse is an Integrated Development Environment (IDE) for any programming language. PyDev is a plugin for Eclipse with features specifically for Python development. The combination of these two is excellent for Python development (it’s what we use for writing Brian).
To install Eclipse, go to their web page and download any of the base language IDEs. It doesn’t matter which one, but Python is not one of the base languages so you have to choose an alternative language. Probably the most useful is the C++ one or the Java one. The C++ one can be downloaded here.
Having downloaded and installed Eclipse, you should download and install the PyDev plugin from their web site. The best way to do this is directly from within the Eclipse IDE. Follow the instructions on the PyDev manual page.
IPython is an interactive shell for Python. It has features for SciPy and PyLab built in, so it is a good choice for scientific work. Download from their page. If you are using Windows, you will also need to download PyReadline from the same page.
The default for Brian is to use the gcc compiler which will be installed already on most unix or linux distributions. If you are using Windows, you can install cygwin (make sure to include the gcc package). Alternatively, some but not all versions of Microsoft Visual C++ should be compatible, but this is untested so far. See the documentation for the SciPy Weave package for more information on this. Mac users should have XCode installed so as to have access to gcc and hence take advantage of brian compiled code. See also the section on Compiled code.
You can test whether Brian has installed properly by running Python and typing the following two lines:
from brian import * brian_sample_run()
A sample network should run and produce a raster plot.
After a successful installation, there are some optimisations you can make to your Brian installation to get it running faster using compiled C code. We do not include these as standard because they do not work on all computers, and we want Brian to install without problems on all computers. Note that including all the optimisations can result in significant speed increases (around 30%).
These optimisations are described in detail in the section on Compiled code.