Mars surface wind plot

Surface wind vectors from a simulation of the ancient Martian climate.

Updated 2016-10-07 Thanks to @michaelaye for useful comments and corrections.

I use a Mac computer for most of my climate research, since the Mac OS X operating system provides me computational foundation I need to develop and run planetary climate models. I am not a fanatic follower of Apple and I will use Windows machines when the task demands it, e.g. CAD design on Solidworks or mapping on ArcGIS. For me, a computer OS is just another tool, like Fortran, Python, a spectrometer, or a soldering iron. I have a toolbox and I put the tools in for the job. The trick is setting up the tools right.

Since I am using the Unix underpinnings of Mac OS X, my setup requires a number of steps that the average Mac owner does not need in order to be productive. Most of these additional steps involve installing and configuring software for writing my modeling and analysis code. This is essential for my research. The rest of the additional steps are there just to make my life easier.

Once you dive into the Unix engine under the hood, you are no longer working with Mac OS X software installers. Instead, you are often in the realm of package managers, compiling your own code, and customizing the paths and configurations. Not being a computer scientist, I was intimidated at first. Fortunately, a number of people posted their own experiences in setting up their own systems1. Over time I strung together disparate instructions and suggestions that resulted in a working system for me. In the spirit of paying things forward, I am providing this description of my setup2 in case it might be useful to another scientist out there facing the same problems that I already faced.

Taming BASH by using a dotfiles system

I use a number of machines at work and home, with a roughly 50/50 split between Macs and Linux machines. I like to have the Unix environment set up the same on all of the machines. Ideally, this means using a bash shell with a custom prompt, colored ls output, and all of my standard aliases in place. To manage my environments across multiple computers, both Mac and Linux, I have created a 'dotfiles' system using a simple script and GitHub. This method is based on Michael Smalley's dotfiles setup. I built on his script and setup to create my own dotfiles system.

The code in my repository organizes my various dotfiles, including .bashrc, .bash_profile, .vimrc, and others. The repository is cloned into the home directory of any of my machines such that the path is ~/dotfiles/. The setup script creates symlinks of the dotfiles from the home directory to the files in ~/dotfiles/. The setup script is smart enough to back up my existing dotfiles into a ~/dotfiles_old/ directory thus giving me a means of reversing any changes. By hosting the code on GitHub, I can clone and setup this system on any Unix based machine that I work on. The code is "smart enough" to setup environments for different flavors of Unix (Linux or Mac OS X). Right now, my dotfiles only supports a bash environment, but a quick Google search will find other versions for csh, zsh, and other shells.

iTerm and Solarized

I use iTerm2 for my Mac terminal. After years of fighting with terminal color schemes, I have settled on a scheme created and used by many software engineers: Solarized. I am a bit indifferent to the specific colors, but the scheme overall works really well and gives me two consistent and easy to apply colors schemes, one light color scheme and one dark color scheme. Also, the color scheme is available to a number of other programs. For example, I used Solarized for my vim apps as well.


Alright, it is time to get started on configuring the Mac for scientific research. First, we need to be sure that we have XCode installed. XCode provides a number of tools that a scientific programmer will likely not need, but the Command Line tools included in XCode are critical for scientific programming. So, if you don't already have XCode, get it from the App Store.

Once you have installed XCode from the App Store, then you need to install the command line developer tools. Using the command line, enter:

xcode-select --install

This will generate a pop-up message asking to install the command line developer tools. Go ahead and install. Once that is successfully done you will then have a number of command line tools that we will be using throughout the rest of this setup.

Update: According to Michale Aye (@michaelaye) you no longer need the entire XCode software to get the commmand line tools:

Install X11

Mac OS X no longer comes with a pre-installed X-Window manager for use with the terminal and command line tools. Therefore, you need to be sure you have X11/XQuartz installed. Visit and download and install the most recent version. Just follow the instructions at the XQuartz site. You might need to fix the symlink it makes by entering the following command in the terminal:

ln -s /opt/X11 /usr/X11

Package Manager: Homebrew

To install and manage many of my tools I use Homebrew. There are other package managers for OS X, including MacPorts and Fink, but I have found Homebrew to be the most usable and useful. Needs and preferences will vary.

A fresh installation of Homebrew

To install Homebrew from scratch, run the following command:

ruby -e "$(curl -fsSL"

This will both download and install the Homebrew software. After installing, run 'brew doctor' to insure that everything was installed correctly. If everything is working well, then you can start installing packages. For example, I install HDF5, NetCF, ack, and the Silver Searcher (ag), among others. The Homebrew website provides details on how to use Homebrew. As well, typing man brew at the command line will bring up the manual page for Homebrew.

Updating Homebrew from a previous version of OS X

Since I actually had Homebrew installed for on a previous version of Mac OS X, I took the following steps to make sure everything was still working properly.

I started with the command:

brew list

which told me what packages I have installed. Many of these packages were installed in support of others, but I generally know which ones I intentionally installed. For these, I tried running each package. If the command worked, then I was all set and left things along. If a particular package did not run, then I needed to remove it and reinstall, using the following commands:

brew remove <package>
brew install <package>

These instructions are based on step 5 from myobie's Gist.

Installing NetCDF Operators (NCO) using homebrew-science

The baseic Homebrew database does not include formulas for all of the scientific software that I need. Instead, we need to use an additional Homebrew database, 'homebrew-science'. From homebrew-science we have instructions for installing software from this alternative database. First, we need to tell brew to use this alternative database. This is done by 'tapping' the database. The command to do this for homebrew-science is:

brew tap homebrew/science

Now that homebrew-science is 'tapped' we can start install software from that database. The command is similar to any Homebrew install command:

brew install <formula>.

If the formula conflicts with one from the master database or another tap, you can install with this version of the install command:

brew install homebrew/science/<formula>.

You can also install via URL:

brew install<formula>.rb

To get the NetCDF Operators, I then entered the following command:

brew install nco

That's it. I now have NetCDF Operators like ncks and nccat installed along with NCView (brew install ncview) for viewing NetCDF files. Since most climate models output the simulations results as NetCDF files, I am now ready to inspect the climate simulation output of almost any model.

Installing GrADS using homebrew-science

The GrADS tool is useful for plotting climate data and can read in NetCDF files. Though I primarily use Python for plotting, GrADS has its place in my scientific workflow. In order to get GrADS, we will need to access an alternative Homebrew database. Similar to homebrew-science we need to tap homebrew-binary to get GrADS:

brew tap homebrew/binary

I want a copy of GrADS, so I type at the command line

brew install grads

If the formula conflicts with one from mxcl/master or another tap, you can

brew install homebrew/binary/<formula>

You can also install via URL:

brew install<formula>.rb

Again, that's all there is to it. Now I have a copy of GrADS on my machine.

Installing the Anaconda Python System

To keep things simple, I use the Anaconda system for installing Python, SciPy, Numpy, Matplotlib, and almost any other Python package3. You can download either a commmand line or package installer. Once you run the installer, double check that you have a working version of Anaconda by typing:

conda --version

I installed the Anaconda system on top of my previous Python installations. This worked smoothly. The only thing I had to do was fix the PATH and PYTHONPATH variables. The Anaconda installer tried to do this automatically, but failed since I use a dotfiles system. Therefore, I had to manually remove old Python paths in my PATH and my PYTHONPATH. As well, I added the Anaconda path by adding the following line:

export PATH="/Users/user_name/anaconda/bin:$PATH"

Once this was successfully done, I had NumPy, SciPy, Matplotlib, and other packages automatically installed. I need to add a few more packages, like xarray, Cartopy, and netcdf4. This was easily done by typing at the command line:

conda install <package-name>

Cartopy required a slightly different command, since it's part of the SciTools suite:

conda install <package-name>
conda install -c scitools cartopy

Anaconda installed any dependencies and handled any conflicts. In this way, Anaconda is like Homebrew or pip4, but for Python. Right now, it looks like any package I might need will likely be available through th Anaconda system.

One quick note: I chose to install the complete Anaconda system5, which is free. The complete system automatically installs various standard packages, like NumPy, Matplotlib, and Jupyter. You can also chose to install Miniconda, a strip-down version that does not automatically install packages. With Miniconda you get more control of what packages are installed on your system.

In conclusion . . .

At this point, my Mac is ready for some scientific heavy lifting. I can compile scientific code, inspect and analyze climate model output, and manage my data with the tools that I have now installed and configured. Of course, this means that the fun has only begun, because it's time to do some science!

  1. For example, see Hacker Codex's instructions for configuring Mavericks and installing Homebrew Python and other tools. As well, both Nathan and Lowin Data have good instructions for various configurations. Finally, Damian Irving discuss installing software for climate research. His rave reviews of Anaconda were the final straw to convince me to switch to using Anaconda to manage my Python environment.  

  2. These instructions apply to Mac OS X Yosemite and El Capitan. These instructions may or may not work with earlier versions of Mac OS X. 

  3. I previously used a more customized method for installing Python and the various packages. I originally did that because older Python systems like Enthought (now Canopy) did not play well with my Fortran compiled NetCDF and other tools. With the introduction of Anaconda, these previous problems no longer exist. Thus, I have been able to completely streamline my Python software management by using Anaconda. Damian Irving provides a succinct argument for using Anaconda. On top of this, I can use Anaconda's environment management to switch between Python 2 and Python 3. This flexibility has allowed me to finally make the switch to Python 3 as my primary version of Python for scientific programming. 

  4. pip was installed as part of the Anaconda installation. 

  5. I've been told that my terminology is a little off when it comes to Anaconda vs conda. @michaelaye is right, but it doesn't affect geting things up and running. The instructions that I followed and that I shared here get me a Python installation that works and the flexibility to have multiple environments. Once again, I have a toolbox and I put the tools in for the job. I do not care how the tools are made, as long as they work.