On Thu, Sep 30, 2021 at 11:59 AM Chris Smith <smi...@gmail.com> wrote: > > I had a brief look at the tides and spokes. My impression was that it was > well presented. > > I am curious on the large expressions for which you used subexpression > elimination and wonder if you have tried `lambdify(..., cse=True)` on them > with any favorable results.
The cse argument to lambdify is new in SymPy 1.9, which is still in pre-release (as an aside, it looks like the release note entry for https://github.com/sympy/sympy/pull/21546 wasn't very accurate). Aaron Meurer > > I look forward to spending some more time looking at what you have done. > > Best regards, > /c > > On Wednesday, September 29, 2021 at 4:23:17 PM UTC-5 blues...@gmail.com wrote: >> >> Hello, >> >> we just released the latest version of our Taylor integrator heyoka.py: >> >> https://github.com/bluescarni/heyoka.py >> >> heyoka.py is an implementation of Taylor's method for the numerical >> integration of systems of ODEs based on automatic differentiation and >> just-in-time compilation via LLVM. >> >> Current features include: >> >> - support for both double-precision and extended-precision floating-point >> types, >> - the ability to maintain machine precision accuracy over tens of billions >> of timesteps, >> - high-precision zero-cost dense output, >> - accurate and reliable event detection, >> - excellent performance, >> - batch mode integration to harness the power of modern SIMD instruction >> sets. >> >> heyoka.py needs to represent the ODEs symbolically in order to apply the >> automatic differentiation rules necessary for an efficient implementation of >> Taylor's method. For this purpose, heyoka.py uses its own expression system, >> but in recent versions we added the ability to convert heyoka.py's symbolic >> expressions to/from SymPy. Here's a simple example of interoperability >> between heyoka.py and SymPy: >> >> https://bluescarni.github.io/heyoka.py/notebooks/sympy_interop.html >> >> Here instead is a non-trivial example where the equations of motion are >> formulated via SymPy's classical mechanics module and then integrated via >> heyoka.py: >> >> https://bluescarni.github.io/heyoka.py/notebooks/tides_spokes.html >> >> This second example also shows how the common subexpression elimination >> capabilities of heyoka.py were able to drastically simplify highly-complex >> Lagrangian equations. >> >> As a long-time observer/user of SymPy, I thought that other SymPy users >> might find this project interesting. I am also looking for feedback on our >> SymPy conversions facilities, as this is my first time digging into the >> SymPy expression system internals. >> >> Thanks and kind regards, >> >> Francesco > > -- > 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 view this discussion on the web visit > https://groups.google.com/d/msgid/sympy/d607d3af-4b4d-4e10-a291-e3790df8bc05n%40googlegroups.com. -- 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 view this discussion on the web visit https://groups.google.com/d/msgid/sympy/CAKgW%3D6Ksk_YdoTfYdjqtJA9Xv%3DgVwdFzYxCCbixSfVtD3KtF1A%40mail.gmail.com.
