I updated my Macbook Pro to Mountain Lion (hereafter ML) and I wanted to see if
my previous Python installation tutorial is valid also for ML. It turns out that most of the commands still work, but some of them need a little fine-tuning.
Please use this guide if you want to get a
working scientific Python environment (Python + Scipy + iPython + Numpy + matplotlib) under Mac OS X Mountain Lion. The installation will be based on the Python binary that comes by default with OS X (i.e. Xcode).
Here are the steps:
- Install the requirements: Xcode which includes Python (via App Store), gfortran (via Macports), virtualenv, additional libraries required by matplotlib
- Create a python environment with virtualenv
- Install Numpy, Scipy, matplotlib, ipython with pip. Install readline with easy_install
- Create an alias in .profile or .bash_profile (depending on your shell) to run ipython
After these steps are completed, you will get a working Python environment for scientific analysis, visualization and statistics with Mac OS X Mountain Lion.
1. How to get the requirements working
Xcode + Python
Launch the
App Store and download
Xcode. After it is installed, open it and go to Xcode's preferences. There, go to the
Downloads tab, look for
Command Line Tools and click
Install. That should install the default OS X Python binary (these instructions were inspired by
this post).
gfortran
In my case, I installed gfortran by installing
MacPorts and installing GCC which comes with gfortran:
sudo port install gcc44
To make gfortran visible to the system I created an alias in /usr/local/bin:
cd /usr/local/bin/
sudo ln -s /opt/local/bin/gfortran-mp-4.4 gfortran
virtualenv
I went to
web page that hosts virtualenv and downloaded virtualenv.py. You will use virtualenv.py below.
Additional libraries required by matplotlib (optional)
I use the graphical backend TkAgg,
which requires the following additional libraries for matplotlib to work: tk, freetype, libpng. I installed them using macports:
sudo port install tk
sudo port install freetype
sudo port install libpng
2. Create a python environment with virtualenv
Create a directory stdpy (in my example) somewhere and issue the command
/usr/bin/python virtualenv.py stdpy
to create an isolated python environment based on the python provided by default with Mac OS X. This avoids trouble with mixing libraries. Activate the environment by running
source stdpy/bin/activate
You should now see a (stdpy) showing up in your terminal.
3. Install Numpy, Scipy, matplotlib, ipython with pip and readline with easy_install
After activating the python environment, let's proceed and install the additional modules with pip and easy_install:
pip install numpy
pip install git+https://github.com/scipy/scipy#egg=scipy-dev
pip install git+https://github.com/matplotlib/matplotlib.git#egg=matplotlib-dev
easy_install readline
pip install ipython
The reason why issuing simply the commands "pip install scipy" and "pip install matplotlib" do not work is explained
in this blog post.
You may need to install additional libraries in order to get matplotlib compiled, depending on the kind of graphical backend that you choose. In my case, I use TkAgg which depends on Tk, freetype and libpng libraries which I installed via macports.
4. Create an alias in .profile or .bash_profile (depending on your shell) to run python
In my case I use Bash and I added the following line to the file .bash_profile in my home directory:
alias ipy='source ~/stdpy/bin/activate && ipython --pylab'
Now, when I open the terminal and issue the command
ipy
it will automatically activate the python environment and run ipython.
Let me know if these instructions work well for you.
Changelog:
- Aug. 18 2012: added matplotlib compilation requirements
- Sep. 1st 2012: made explanation about matplotlib dependencies clearer (hopefully)
