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.
