SDE error with de.jit #151
Description
Describe the bug 🐞
I still get the error when jitting the SDE problem even after #149.
However, the error seems to be caused by de.jit32, not de.jit.
Minimal Reproducible Example 👇
import matplotlib.pyplot as plt
from diffeqpy import de
def f(du,u,p,t):
x, y, z = u
sigma, rho, beta = p
du[0] = sigma * (y - x)
du[1] = x * (rho - z) - y
du[2] = x * y - beta * z
def g(du,u,p,t):
du[0] = 0.3*u[0]
du[1] = 0.3*u[1]
du[2] = 0.3*u[2]
u0 = [1.0,0.0,0.0]
tspan = (0., 100.)
p = [10.0,28.0,2.66]
prob = de.jit(de.SDEProblem(f, g, u0, tspan, p))
sol = de.solve(prob)
# Now let's draw a phase plot
us = de.stack(sol.u)
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(us[0,:],us[1,:],us[2,:])
plt.show()Error & Stacktrace
---------------------------------------------------------------------------
JuliaError Traceback (most recent call last)
Cell In[2], line 16
14 tspan = (0., 100.)
15 p = [10.0,28.0,2.66]
---> 16 prob = de.jit(de.SDEProblem(f, g, u0, tspan, p))
17 sol = de.solve(prob)
19 # Now let's draw a phase plot
File [~/.julia/packages/PythonCall/Nr75f/src/JlWrap/any.jl:258](http://localhost:8970/~/.julia/packages/PythonCall/Nr75f/src/JlWrap/any.jl#line=257), in __call__(self, *args, **kwargs)
256 return ValueBase.__dir__(self) + self._jl_callmethod($(pyjl_methodnum(pyjlany_dir)))
257 def __call__(self, *args, **kwargs):
--> 258 return self._jl_callmethod($(pyjl_methodnum(pyjlany_call)), args, kwargs)
259 def __bool__(self):
260 return True
JuliaError: UndefVarError: `remake` not defined
Stacktrace:
[1] jit(x::SciMLBase.SDEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, PyList{Any}, Nothing, SciMLBase.SDEFunction{true, SciMLBase.FullSpecialize, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing}, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, @Kwargs{}, Nothing})
@ Main ./none:3
[2] pyjlany_call(self::typeof(jit), args_::Py, kwargs_::Py)
@ PythonCall.JlWrap [~/.julia/packages/PythonCall/Nr75f/src/JlWrap/any.jl:43](http://localhost:8970/~/.julia/packages/PythonCall/Nr75f/src/JlWrap/any.jl#line=42)
[3] _pyjl_callmethod(f::Any, self_::Ptr{PythonCall.C.PyObject}, args_::Ptr{PythonCall.C.PyObject}, nargs::Int64)
@ PythonCall.JlWrap [~/.julia/packages/PythonCall/Nr75f/src/JlWrap/base.jl:73](http://localhost:8970/~/.julia/packages/PythonCall/Nr75f/src/JlWrap/base.jl#line=72)
[4] _pyjl_callmethod(o::Ptr{PythonCall.C.PyObject}, args::Ptr{PythonCall.C.PyObject})
@ PythonCall.JlWrap.Cjl [~/.julia/packages/PythonCall/Nr75f/src/JlWrap/C.jl:63](http://localhost:8970/~/.julia/packages/PythonCall/Nr75f/src/JlWrap/C.jl#line=62)
┌ Warning: Using arrays or dicts to store parameters of different types can hurt performance.
│ Consider using tuples instead.
└ @ SciMLBase [~/.julia/packages/SciMLBase/NtgCQ/src/performance_warnings.jl:33](http://localhost:8970/~/.julia/packages/SciMLBase/NtgCQ/src/performance_warnings.jl#line=32)When I define de.jit_ in the way fixed in #149, the jitted SDE works. Therefore, I'm sure that the change made in #149 definitely resolves the issue I have encountered in #148.
from juliacall import Main
de.jit_ = Main.seval("jit(x) = typeof(x).name.wrapper(ModelingToolkit.complete(ModelingToolkit.modelingtoolkitize(x); split = false), [], x.tspan)")
prob = de.jit_(de.SDEProblem(f, g, u0, tspan, p))
sol = de.solve(prob)
# Now let's draw a phase plot
us = de.stack(sol.u)
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(us[0,:],us[1,:],us[2,:])
plt.show()
Environment (please complete the following information):
- OS: macOS Sequoia version 15.1
- Chip: Apple M1 Max
- Python version: 3.12.7
- diffeqpy version: development (installed with pip install git+https://github.com/SciML/diffeqpy)
- Julia version: 1.10.6
(@diffeqpy) pkg> status
Status `~/.julia/environments/diffeqpy/Project.toml`
[0c46a032] DifferentialEquations v7.15.0
[961ee093] ModelingToolkit v9.52.0
[1dea7af3] OrdinaryDiffEq v6.90.1
[6099a3de] PythonCall v0.9.23