We will use a custom Anaconda installation of python as opposed to whatever python is already installed on the linux system. This will allow us to add packages and keep a consistent python experience across machines/operating systems.
Before we get started, you must modify your ~/.mycshrc file after logging into any UNC linux machine (the ~/ indicates this file is in your home directory, and please notice the filename begins with a "." so you can't see it with "ls", although you can with "ls .*"):
setenv QT_XKB_CONFIG_ROOT /usr/share/X11/xkb
This environment variable will now be read anytime you log in or open a new terminal window. Next open a new terminal and type
unc_anaconda
This will load you into a bash subshell (differences with tcsh are negligible for general use) with anaconda python as the default python. You should see your terminal prompt change to (anaconda_bash)user@machine:~$ or similar. Once in this subshell you can make your own anaconda environment by typing
conda create --name astro scikit-learn matplotlib astropy pymc spyder pandas
This will create an anaconda environment named astro in your home directory (~/.conda/envs/astro), with the packages that you need for our purposes. To activate this environment type
source activate astro
You are now your own anaconda environment, and your terminal prompt should start with (astro)(anaconda_bash).... To install a few more packages you may use for other tutorials type the following
conda install -c OpenAstronomy healpy
pip install git+https://github.com/astroML/astroML
conda install nb_conda_kernels
conda install -c astropy emcee=2.2.1
Note that the above steps are a one time setup. If you ever want to use the anaconda environment you have set up all you have to do is type
unc_anaconda
source activate astro