Code runs in Julia but not in pyjulia

[michaelkyu]

The following code to solve an ODE using DifferentialEquations.jl does not work in pyjulia. Strangely, the code works in a native Julia terminal (v1.0.2).

I am using the latest 'master' branch of pyjulia.

from julia import Main

Main.eval("""
using DifferentialEquations

function lorenz(u,p,t)
 dx = 10.0*(u[2]-u[1])
 dy = u[1]*(28.0-u[3]) - u[2]
 dz = u[1]*u[2] - (8/3)*u[3]
 [dx,dy,dz]
end

u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz,u0,tspan)
solve(prob,Tsit5())
""")

I get the error

---------------------------------------------------------------------------
JuliaError                                Traceback (most recent call last)
<ipython-input-4-326c146306c2> in <module>
     15 prob = ODEProblem(lorenz,u0,tspan)
     16 solve(prob,Tsit5())
---> 17 """)

~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/julia/core.py in eval(self, src)
    773 
    774         if res is None:
--> 775             self.check_exception("convert(PyCall.PyObject, {})".format(src))
    776         return self._as_pyobj(res)
    777 

~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/julia/core.py in check_exception(self, src)
    750             exception = sprint(showerror, self._as_pyobj(res))
    751         raise JuliaError(u'Exception \'{}\' occurred while calling julia code:\n{}'
--> 752                          .format(exception, src))
    753 
    754     def _typeof_julia_exception_in_transit(self):

JuliaError: Exception 'MethodError: no method matching strides(::ODESolution{Float64,2,Array{Array{Float64,1},1},Nothing,Nothing,Array{Float64,1},Array{Array{Array{Float64,1},1},1},ODEProblem{Array{Float64,1},Tuple{Float64,Float64},false,Nothing,ODEFunction{false,typeof(lorenz),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Nothing,DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(lorenz),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{Array{Float64,1},1},Array{Float64,1},Array{Array{Array{Float64,1},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}}})
Closest candidates are:
  strides(!Matched::SubArray) at subarray.jl:264
  strides(!Matched::Base.CodeUnits) at strings/basic.jl:696
  strides(!Matched::PermutedDimsArray{T,N,perm,iperm,AA} where AA<:AbstractArray where iperm) where {T, N, perm} at permuteddimsarray.jl:62
  ...' occurred while calling julia code:
convert(PyCall.PyObject, 
using DifferentialEquations

function lorenz(u,p,t)
 dx = 10.0*(u[2]-u[1])
 dy = u[1]*(28.0-u[3]) - u[2]
 dz = u[1]*u[2] - (8/3)*u[3]
 [dx,dy,dz]
end

u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz,u0,tspan)
solve(prob,Tsit5())
)

[tkf]

There is no good solution for it at the moment. The right solution would be JuliaPy/PyCall.jl#617

For now, using diffeqpy would be the way to go. Maybe something like:

from diffeqpy import de
prob = Main.prob
sol = de.solve(prob, de.Tsit5)