For an individual who has no previous coding experience, playing with Python is the most efficient way to begin in scientific programming. Even very advanced C++\/Fortran coders who know Python will confess it’s virtually always the fastest route to small programs and the fastest route to results for “easy-to-medium” tasks. It requires much less “legacy” knowledge (UNIX shell, build systems, debuggers, libraries etc.) than a systems programming language, and it has a very mature open-source suite of scientific libraries basically exceeding the utility of Matlab.<\/p>\n
So go ahead and install Python and Scipy\/Numpy <\/a>on your computer. Make some plots with matplotlib. Calculate some eigenspectra. For further reading check out Dive into Python. <\/a>There’s even whole electronic structure codes<\/a> wholly realized in Python. Recoding some Numerical Recipes<\/a> in Python is a worthwhile exercise for anyone who wants to do scientific programming.<\/p>\n In my experience most computational groups do a bad job of sharing code and collaborating on code. At the end of the day this leads to time-wasted due to reduplicated effort, and time wasted trying to find “the version that worked.”. We will use the free tools Bitbucket<\/a>, and git to share and maintain codes our group works on. There’s a quite nice free book<\/a> available about git you can easily digest in a weekend.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" For an individual who has no previous coding experience, playing with Python is the most efficient way to begin in scientific programming. Even very advanced C++\/Fortran coders who know Python will confess it’s virtually always the fastest route to small … Continue reading