Hi Rahul, > a) Can you guys tell me briefly about the kind of problems you are > tackling with numpy and scipy?
I'm a grad student "doing" computational biology. I primarily use the NumPy/SciPy/matplotlib triumvirate as a post processing tool to analyze what the heck happened after we run some learning algorithms we develop (or canned ones, like libsvm (for example)) to look for some sense in the results. I've been working w/ analyzing interaction networks/graphs, so I also use NetworkX[1] quite a bit as well (it's also a nice package w/ responsive authors). Many of the folks (in my lab, and collaborators) like to use MATLAB, so I've found scipy's io.loadmat invaluable for making this a bit more seamless. So, in general, for me (so far) numpy/scipy are generally used to integrate various datasets together and see if things "look kosher" (before runs and after runs). > b) Have you ever felt that numpy/scipy was slow and had to switch to > C/C++/Fortran? Yes, for things like boosting, svm, graph mining, etc ... but that's no real surprise since their iterative and need to run on large datasets. You should also note that there are python interfaces to these things out there as well, but I (thus far) haven't taken much of advantage of those and usually pipe out data into the expected text input formats and pull them back in when the algo is done. > c) Do you use any form of parallel processing? Multicores? SMPs? > Clusters? If yes how did u utilize them? I'd really like to (not just for Python), but I haven't. -steve [1] NetworkX: https://networkx.lanl.gov/wiki _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
