Commonly used phylogenetic methods, Monash HPC access and basic bioinformatics

Python Resources

Get Your Machine Python Ready

Source

  1. Install Xcode and Xcode CLT.

  2. Install homebrew: go to their webpage, and follow the instructions.

  3. Install Python 3. I don't normally bother with Python 2. $ brew install python3

  4. Install pip. $ curl -O http://python-distribute.org/distribute_setup.py $ python distribute_setup.py $ curl -O https://raw.github.com/pypa/pip/master/contrib/get-pip.py $ python get-pip.py

  5. Download and install Anaconda, if only just for the conda environment manager.

  6. Make a Github account, and install git: brew install git

  7. Set a PYTHONPATH.

  8. IDE: Jupyter or Jupyter lab (for development work), Sublime Text for porting to a script.

Really Useful Links

  • Anatomy of matplotlib - a comprehensive Matplotlib tutorial.
  • Python PEP8 Style Guide - Write all your python code according to this format, forever. The strength of Python is actually in readibility and accessibility, not raw computing power (C or FORTRAN has Python beat in these respects). The idea is that development time spent writing and debugging is much more than computational run time, so writing modular, systematic, and well-commentated code will overall save more man-hours than just going for raw power. If code is written and commentated poorly, then this advantage is lost.