A11 Configuring Mac for scientific work
Get your Mac computer set up for scientific computing tasks.
>Software Carpentry tutorial for Shell, Git and Nano installations on a Mac.
>Software Carpentry set up material
MacOS became popular for scientific work when it became based on Unix: it provided an easy interface and the shell, which is a great combination.
This page gets you set up for basic scientific work using Python.
The shell provides an interface to efficiently access the true power of a computer. Now we use it to install tools but it can be used for many other tasks too.
Mac comes with the bash (or zsh for 10.15 and later), so you don’t need to do anything. Just start it by TODO. To verify, try running this:
Version control (git)
Using version control is like an insurance for your projects. It is not only about tracking changes but also to improve your project visibility and make it easier to collaborate.
Git is the most popular system for version control and GitHub is one of the services that provide online storing for projects.
You install Git for MacOS by downloading the most recent “mavricks” installer from http://sourceforge.net/projects/git-osx-installer/files/
If you have Homebrew (a package manager) you can do:
brew install git
Nothing appears in Applications, since it’s a command line program. From
Verify it from the shell terminal:
Your organization might provide you access to some other repository manager than GitHub but since GitHub is a higher availablity solution, it does not hurt to create an account there. You can sign up for Github here
In software development there are some standard packages that are useful to have without the trouble of installing them separately with their dependencies.
There are very many programming languages, and you probably won’t only use Python. But, it is quite common so we mention it here. We install the Anaconda distribution of Python: it gets you all the basic things you need, and can also install R and other programming languages, too. Anaconda is large and has all the most common things people need - if you want to save space, install Miniconda instead (then you have to decide what extra packages you want).
This will get you Jupyter and many other Python things, too.
Anaconda allows you to manage your development environment which is good since you can have different environments dedicated to their designated purposes.
Same stuff from Linux page. How to use it.
To verify from the shell (see above to start the shell):
$ python3 -V Python 3.6.8 :: Anaconda custom (64-bit) $ conda info active environment : None ... base environment : /home/rkdarst/anaconda3 (writable)
Homebrew is a package manager for MacOS, which lets you install lots of packages easily. Many of these are essential to having a good environment for programming, and taking full advantage of MacOS.
To install, go to brew.sh and follow instructions. You can then, for example, use
to install many things you may need.
After installing, you can run
brew doctor to ensure everything was installed correctly.
FOR IDE (Integrated development environment): Visual Studio Code is a free editor available for Windows, macOS and Linux. It is a good alternative for both a beginner and a more advanced user as it is simple to use but highly customizable. Install and learn more here. Out of principle, we recommend you disable data collection.
For command line: You should make sure
nano is installed by typing in the shell for instance,
nano my_file.txt. You can also use vi/vim or emacs but
as those are harder to use, we do not recommend them for your first command line editor.
Nano is used through keyboard shortcuts and some of them are shown in the editor. See this
tutorial to start editing with nano.
Jupyter is an interactive way to explore data. It can be used to add code, output, titles, text and visualisations into one document. It’s already installed along with Anaconda.
Follow this to install useful extensions to your environment. Especially ipywidgets are needed if you continue to do exercises.
Other programming tools
If you wish to obtain credits from the course, you might need
to complete exercises. These libraries are pre-installed with Anaconda installation. Further information about installations can be found here: NumPy and Matplotlib