I opened https://github.com/sympy/sympy/issues/2790 for this issue so it's not forgotten about. Let us know if you want to give it a shot fixing it.
Aaron Meurer On Mon, Jan 13, 2014 at 10:56 AM, Ondřej Čertík <[email protected]> wrote: > Janwillem, thanks for sending the script to the list. > That's the most efficient way to get things done. > > Ondrej > > On Mon, Jan 13, 2014 at 1:28 AM, Janwillem van Dijk > <[email protected]> wrote: >> I would be very glad if that could be done. My little project is part of a >> discussion on uncertainty evaluation in radiation dose measurements. I try >> to convince my colleagues that the traditional method developed by Gauss in >> 1800 is not reliable in our field. The method of choice is Monte Carlo >> integration. So I want a script where the traditional (called LPU) functions >> look as much as possible to the Monte Carlo method (MCM) functions as >> possible. When starting this project I also tried the *-way but thought it >> ugly and not very convincing when comparing it with the numpy/scipy based >> MCM (and old people like me reminds it of pointers and crashing computers). >> So I was very glad discovering the numpy option in lambdify which worked as >> I understood it would. So again I would be very pleased to see it working >> with 3.3. >> For your info please find the problem scripts and some background in the >> attached archive (comments very welcome). >> Cheers, Janwillem >> >> On Tuesday, 7 January 2014 11:04:30 UTC+1, Janwillem van Dijk wrote: >>> >>> I have a SymPy script with a.o. >>> >>> f_mean = lambdify([mu, sigma], mean, modules='numpy') >>> >>> >>> where mean is a function of mu and sigma and mu and sigma are both arrays >>> >>> mu = symbols('mu_0:%d' % n, real=True, bounded=True) >>> >>> sigma = symbols('sigma_0:%d' % n, positive=True, real=True, bounded=True) >>> >>> >>> Under Python 2.7.5+ SymPy 0.12.0 I can use: >>> >>> y = f_mean(x_n, ux_n) >>> >>> returning y as a numpy array of size n when x_n and ux_n are both numpy >>> arrays of size n. >>> >>> However, with Python 3.3.2+ and SymPy 0.7.4.1-git I get (for n=5): >>> >>> y = f_mean(x_n, ux_n) >>> TypeError: <lambda>() missing 10 required positional arguments: 'mu_2', >>> 'mu_3', 'mu_4', 'mu_5', 'sigma_0', 'sigma_1', 'sigma_2', 'sigma_3', >>> 'sigma_4', and 'sigma_5' >>> >>> >>> Which is similar to what I got in Python 2.7 before I added the >>> modules=numpy argument >>> >>> All this on ubuntu 13.10 >>> >>> >>> Have I missed something in the docs or did I stumble on a not yet >>> implemented feature? >>> >>> Any help very welcome.heers, >>> >>> Cheers, Janwillem >>> >>> >> -- >> You received this message because you are subscribed to the Google Groups >> "sympy" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To post to this group, send email to [email protected]. >> Visit this group at http://groups.google.com/group/sympy. >> For more options, visit https://groups.google.com/groups/opt_out. > > -- > You received this message because you are subscribed to the Google Groups > "sympy" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at http://groups.google.com/group/sympy. > For more options, visit https://groups.google.com/groups/opt_out. -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/sympy. For more options, visit https://groups.google.com/groups/opt_out.
