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Most scientific computing is not "serious". My past couple papers, for example, have been mostly Python analysis of geospatial data. I use Python because it's e
by capnrefsmmat 11y ago
Most scientific computing is not "serious". My past couple papers, for example, have been mostly Python analysis of geospatial data. I use Python because it's easy to integrate with Postgres/PostGIS, and the numerical code provided by Numpy is fast enough. (I drop down into Cython when needed.) The algorithms I need to run aren't hugely intensive -- at most, something might take an hour to run, and most of my simulations take just a minute or two.
I don't need raw speed. I need development speed so I can easily iron out bugs and try new methods. My colleagues develop in R for the same reason.
- polemic 11y ago> "Most scientific computing is not "serious"" Stopped reading after this.
- bigger_cheese 11y agoI think it depends what you count as scientific computing. I'm an engineer at an industrial plant and like you for offline non intensive stuff when I have to knock up a prototype fast I use SAS mostly because it has good integration with databases and I can crank out code very fast using it. My housemate is a Math/Stats person he works in finance and uses R for much the same reasons. Maybe Python is useful but it would have to offer me something compelling to make me switch over.