On Sun, Mar 20, 2011 at 11:49 AM, <josef.p...@gmail.com> wrote: > On Sun, Mar 20, 2011 at 11:44 AM, <josef.p...@gmail.com> wrote: >> On Sun, Mar 20, 2011 at 11:08 AM, Ben Smith <b...@wbpsystems.com> wrote: >>> >>> So, in addition to my computer science work, I'm a PhD student in econ. >>> Right now, the class is using GAUSS for almost everything. This sort of >>> pisses me off because it means people are building libraries of code that >>> become valueless when they graduate (because right now we get GAUSS >>> licenses for free, but it is absurdly expensive later) -- particularly when >>> this is the only language they know.
this looks interesting on this topic: http://www.vwl.uni-mannheim.de/gaudecker/teaching.htm Josef >>> >>> So, I had this idea of building some command line tools to do the same >>> things using the most basic pieces of NumPy (arrays, dot products, >>> transpose and inverse -- that's it). And it is going great. My problem >>> however is that I'd like to be able to share these tools but I know I'm >>> opening up a big can of worms where I have to go around building numpy on >>> 75 peoples computers. What I'd like to do is limit myself to just the >>> functions that are implemented in python, package it with py2exe and hand >>> that to anyone that needs it. So, my question, if anyone knows, what's >>> implemented in python and what depends on the c libraries? Is this even >>> possible? >> >> I think you can package also numpy with py2exe. > > I should have explained this first: > all basic numpy array calculations are in C, extra packages in scipy > are often in fortran. > numpy.linalg uses C, but scipy.linalg uses the fortran libraries that > are the same (LAPACK,..) or similar versions as in GAUSS. numpy.random > is in C, scipy.special for distribution functions is in C and fortran. > > Josef > >> >> Overall I think restricting to pure python is a very bad idea if you >> want to compete with Gauss. >> Even for a minimal translation of Gauss programs I need at least numpy >> and scipy, and statsmodels for the econometrics specific parts. linear >> algebra, optimization and special functions for distributions look >> like a minimum to me, and some scipy.signal for time series analysis, >> and more random numbers than in python`s standard library. >> >> Pure python will be slow for this and I doubt you will get anyone to >> switch from Gauss to pure python. >> Also, I haven`t seen yet a pure python matrix inverse, or linalg solver. >> >> If they want to write their own python programs for analysis and use >> python later on, then they are much better of getting a full python >> distribution, EPD, pythonxy or similar. Binary distributions are >> available and just one click or one command installs. >> And, for example, using Spyder would be a lot nicer and easier for >> writing scripts, that are equivalent to Gauss scripts, than using >> commandline tools. >> >> I fully agree with the objective of getting python/numpy/scipy tools >> to get economists, econometricians to switch from gauss or matlab, but >> to make it competitive we need enough supporting functions and we need >> the speed that some Monte Carlo simulations don`t take days instead of >> hours. >> >> I hope you are successful with getting economists or econ students to >> use python. >> >> Josef >> >>> Thanks! >>> >>> Ben >>> >>> -- >>> Ben Smith >>> Founder / CSA >>> WBP SYSTEMS >>> http://www.wbpsystems.com >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >> > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
