# Diffeqpy, using ensembling

**URL:** <https://discourse.julialang.org/t/diffeqpy-using-ensembling/40411>\
**Category:** General Usage\
**Tags:** diffeq\
**Created:** [May 29, 2020, 12:13pm UTC](https://discourse.julialang.org/t/diffeqpy-using-ensembling/40411 "2020-05-29T12:13:00Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![Abolfazl\_Ziaeemehr](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abolfazl_ziaeemehr/32/9014_2.png) [@Abolfazl\_Ziaeemehr](https://discourse.julialang.org/u/Abolfazl_Ziaeemehr)\
**Post date:** [May 29, 2020, 12:13pm UTC](https://discourse.julialang.org/t/diffeqpy-using-ensembling/40411/1 "2020-05-29T12:13:00Z")

</div>

It is possible to use use ensembling in `diffeqpy`?  
here is my example code:

```python
import numpy as np
import pylab as pl
from time import time
from sys import exit

try:
    from diffeqpy import de
    from julia import Main
except:
    from julia.api import Julia
    jl = Julia(compiled_modules=False)
    from julia import Main
    from diffeqpy import de

np.random.seed(1)

julia_f = Main.eval("""
function f!(du, u, p, t)
    du = 1.01 * u
    return du
end
""")

prob_func = Main.eval("""
function prob_func(prob, i, repeat)
    remake(prob, u0 = rand() * prob.u0)
    end
""")

# def prob_func(prob, i, repeat):
# de.remake(prob, u0=rand() * prob.u0)

if __name__ == " __main__":

    tspan = (0.0, 10.0)
    prob = de.ODEProblem(julia_f, 0.5, tspan)
    ensemble_prob = de.EnsembleProblem(
        prob,
        prob_func=prob_func)
    
    sol = de.solve(ensemble_prob,
                   de.Tsit5(),
                   de.EnsembleThreads(),
                   trajectories=2)

```

output error:

```julia
RuntimeError Traceback (most recent call last)
~/git/workshop_julia/DiffEquations/diffeqpy/synchrony_test/test_ensemble1.py in <module>
     45 de.Tsit5(),
     46 de.EnsembleThreads(),
---> 47 trajectories=2)

RuntimeError: Julia exception: TaskFailedException:
MethodError: no method matching similar(::Float64)
Closest candidates are:
  similar(!Matched::Sundials.NVector) at /home/abolfazl/.julia/packages/Sundials/Mtc8o/src/nvector_wrapper.jl:71
  similar(!Matched::JuliaInterpreter.Compiled, !Matched::Any) at /home/abolfazl/.julia/packages/JuliaInterpreter/dEBFI/src/types.jl:7
  similar(!Matched::Array{T,1}) where T at array.jl:356

```

Is there any minimal example for using ensemble in `diffeqpy`?

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [May 29, 2020, 12:36pm UTC](https://discourse.julialang.org/t/diffeqpy-using-ensembling/40411/2 "2020-05-29T12:36:03Z")

</div>

> [@Abolfazl\_Ziaeemehr](#):
>
> ```julia
> julia_f = Main.eval("""
> function f!(du, u, p, t)
> du .= 1.01 * u
> end
> """)
> 
> prob_func = Main.eval("""
> function prob_func(prob, i, repeat)
> remake(prob, u0 = rand() * prob.u0)
> end
> """)
> 
> # def prob_func(prob, i, repeat):
> # de.remake(prob, u0=rand() * prob.u0)
> 
> if __name__ == " __main__":
> 
> tspan = (0.0, 10.0)
> prob = de.ODEProblem(julia_f, [0.5], tspan)
> ensemble_prob = de.EnsembleProblem(
> prob,
> prob_func=prob_func)
>     
> sol = de.solve(ensemble_prob,
> de.Tsit5(),
> de.EnsembleThreads(),
> trajectories=2)
> 
> ```

Should be simpler and work. Using arrays instead of scalars for ODEs is probably a lot more common too.