Guy, We're working on the same problem for sympy.physics.mechanics. Matthew Rocklin added support for matrix conversions in the theano code that is in SymPy and I used that, but found that theano was slower that lambdify for most of my cases (I only have two cores, so I'm not taking advantage of the Theano parallel stuff). I think writing specific code gen for ode integration is going to be the best bet. I'm happy to collaborate on this with you.
Jason moorepants.info +01 530-601-9791 On Mon, Aug 5, 2013 at 3:13 PM, Guy Parsey <guy.par...@gmail.com> wrote: > Hello Everyone, > Thank you in advance for reading through my problem and for any input you > may have. I must say that I still feel like a novice programmer and my > problems may be easily solvable from a different mindset. My present > project entails time-integration of extremely stiff and non-linear ODEs > with regards to chemical kinetics (one derivative equation for each > variable species) and energy equations. Initially we were planning on using > the sympy.lambdify function to create callable functions for the main > function along with the jacobian and passing said functions to > scipy.integrate.odeint, however this method only works for easier test > cases (fewer species and/or no energy equations) before being limited by > either the list recursion limit or segfaulting due to the limited stack > size. I know that both of these limits can be edited, but that fact that I > am reaching them makes me feel as though I am doing something extremely > inefficiently. Outside of the documentation of SymPy and Theano, I have > also been heavily using the BlogPost by Matthew Rocklin > http://matthewrocklin.com/blog/work/2013/03/19/SymPy-Theano-part-1/ . > > Presently I am trying to use the mapping between Theano and SymPy > (sympy.printing.theanocode theano_function) to make my callable functions > and take advantage of the optimization routines. I have two major problems > and a few questions: > > 1st major problem: Though piecewise functions exist in SymPy > (sympy.functions.elementary.piecewise) there is no counterpart in Theano. > Looking at the source of the inspiration for theanocode ( > https://github.com/nouiz/theano_sympy/ graph_translation.py) I see > that some of the SymPy equivalents were defined as lambda functions. Is > there an equivalent way to add Theano conditional expressions wrapped into > a function to add to the mapping dictionary in theanocode.py? > > 2nd major problem: Similar to the problem above in that I am not sure that > the Theano counterpart is; some of the terms that I use are interpolated > functions (with one ODE variable as input) that we have wrapped > symbolically while providing a numerical implementation (so that symbolic > derivatives can be made, resulting in their own interpolations). Is it > possible to recreate the interpolation function as a Theano operation for > use within the system of ODEs? > > Remain questions: > I presently have to flatten my input to theano_function to a list of > expressions and then wrap to return to a form (Jacobian is a matrix not a > vector); is it possible to have a matrix of different expressions as an > input to theano_function with a vector output? > > I know that a huge amount of Theano speed up is due to parallelization of > matrix operations (which I do not have), should I be focusing on SymPy > Autowrap/Ufuncify or my own code generation instead of trying to get Theano > to play nicely? > > Stupid questions: > Does sympy.printing.theanocode.theano_function automatically optimize the > compiled graph? > > Minor comment: > Perhaps unnecessary for most uses of the theano_function, but I needed to > modify function inputs so as to be able to use the keyword argument > 'on_unused_input=ignore' as opposed to 'raise' so that I did not need to > have all symbols in all equations. This may be avoided by having the unused > symbols somehow (I don't know how) included in each expression. > > Thank you again for your time in reading my problems and any potential > help you may think of. I can attach code if necessary, I just didn't want > to make my post more confusing. > Have an excellent day. > Sincerely, > Guy Parsey > > -- > 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 sympy+unsubscr...@googlegroups.com. > To post to this group, send email to sympy@googlegroups.com. > 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 sympy+unsubscr...@googlegroups.com. To post to this group, send email to sympy@googlegroups.com. Visit this group at http://groups.google.com/group/sympy. For more options, visit https://groups.google.com/groups/opt_out.
