How to access Python for doing scientific computing

 

 

 

Much of the material in this document is taken from Appendix H.1 in the book A Primer on Scientific Programming with Python, 4th edition, by the same author, published by Springer, 2014.

Recommendations for INF1100/MAT-INF1100L students

What is the recommended way to access Python if you are a new student at the University of Oslo and attend the INF1100 or MAT-INF1100L course? The various alternatives are listed below and ranked according to what is our recommendation.

  1. Install Ubuntu in a virtual machine, VMWare Fusion/Player or VirtualBox, on your computer. This is the most flexible solution for the future, but requires some work.
  2. Use a Vagrant box with Ubuntu in a terminal window on your Windows or Mac machine. This also requires some work and is quite similar to running a full Ubuntu virtual machine, but some people get confused by the mix of Ubuntu, Vagrant, and the host operating system (Mac/Windows).
  3. Use computers at campus, either at terminal rooms or through remote login from you laptop. This is the simplest procedure, but remote login requires a fast Internet connection.
  4. Install Anaconda on your Windows or Mac laptop. This is easy and may sound as a very attractive solution, but has the disadvantage that you learn to work in an environment that is quite different from the Linux computing platform used at the University of Oslo. Many later courses have support for Linux only, and it may require significant Mac/Windows competence to install the software correctly along with Anaconda.
  5. Use the Wakari or SageMathCloud web services. This is a very easy, but you will end up writing IPython notebooks and not get used to and understand how Python programs are stored in files and how programs are run. These web services may not be sufficient in future courses.