Closed LilithHafner closed 10 months ago
Merging #525 (681dad8) into master (578352c) will decrease coverage by
6.53%
. The diff coverage is83.33%
.
@@ Coverage Diff @@
## master #525 +/- ##
==========================================
- Coverage 49.55% 43.03% -6.53%
==========================================
Files 53 53
Lines 3925 3925
==========================================
- Hits 1945 1689 -256
- Misses 1980 2236 +256
Files | Coverage Δ | |
---|---|---|
src/ensemble/ensemble_problems.jl | 34.61% <83.33%> (+7.34%) |
:arrow_up: |
... and 15 files with indirect coverage changes
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I see, does not fix 😅
For some reason I get init
of nothing:
>>> sol = de.solve(ensembleprob,de.Tsit5(),de.EnsembleSerial(),trajectories=10000,saveat=0.01)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\accou\.julia\packages\PythonCall\qTEA1\src\jlwrap\any.jl", line 208, in __call__
return self._jl_callmethod($(pyjl_methodnum(pyjlany_call)), args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
juliacall.JuliaError: MethodError: no method matching init(::Nothing, ::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; saveat::Float64)
Closest candidates are:
init(!Matched::SciMLBase.OptimizationProblem, ::Any, !Matched::Any...; kwargs...)
@ SciMLBase C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\solve.jl:146
init(!Matched::SciMLBase.PDEProblem, ::SciMLBase.AbstractDEAlgorithm, !Matched::Any...; kwargs...)
@ DiffEqBase C:\Users\accou\.julia\packages\DiffEqBase\xSmHR\src\solve.jl:1116
init(!Matched::SciMLBase.AbstractJumpProblem, ::Any...; kwargs...)
@ DiffEqBase C:\Users\accou\.julia\packages\DiffEqBase\xSmHR\src\solve.jl:499
...
Stacktrace:
[1] solve(::Nothing, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})
@ CommonSolve C:\Users\accou\.julia\packages\CommonSolve\JfpfI\src\CommonSolve.jl:23
[2] batch_func(i::Int64, prob::SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#k#545"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var"#637#generated_observed#555"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})
@ SciMLBase C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:100
[3] batch_func
@ C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:95 [inlined]
[4] (::SciMLBase.var"#606#607"{Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}}, SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#k#545"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var"#637#generated_observed#555"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}})(i::Int64)
@ SciMLBase C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:154
[5] responsible_map(f::Function, II::UnitRange{Int64})
@ SciMLBase C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:147
[6] solve_batch(prob::SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#k#545"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var"#637#generated_observed#555"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, ::SciMLBase.EnsembleSerial, II::UnitRange{Int64}, pmap_batch_size::Int64; kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})
@ SciMLBase C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:153
[7] solve_batch
@ C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:152 [inlined]
[8] macro expansion
@ .\timing.jl:393 [inlined]
[9] __solve(prob::SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#k#545"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var"#637#generated_observed#555"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ComposedFunction{typeof(SciMLBasePythonCallExt._pyconvert), Py}, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, ensemblealg::SciMLBase.EnsembleSerial; trajectories::Int64, batch_size::Int64, pmap_batch_size::Int64, kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})
@ SciMLBase C:\Users\accou\.julia\packages\SciMLBase\tXu8A\src\ensemble\basic_ensemble_solve.jl:64
[10] #solve#44
@ C:\Users\accou\.julia\packages\DiffEqBase\xSmHR\src\solve.jl:1053 [inlined]
[11] pyjlany_call(self::typeof(CommonSolve.solve), args_::Py, kwargs_::Py)
@ PythonCall C:\Users\accou\.julia\packages\PythonCall\qTEA1\src\jlwrap\any.jl:34
[12] _pyjl_callmethod(f::Any, self_::Ptr{PythonCall.C.PyObject}, args_::Ptr{PythonCall.C.PyObject}, nargs::Int64)
@ PythonCall C:\Users\accou\.julia\packages\PythonCall\qTEA1\src\jlwrap\base.jl:69
[13] _pyjl_callmethod(o::Ptr{PythonCall.C.PyObject}, args::Ptr{PythonCall.C.PyObject})
@ PythonCall.C C:\Users\accou\.julia\packages\PythonCall\qTEA1\src\cpython\jlwrap.jl:47
Fixup for #519 (I didn't see these entry points) Does not fix https://github.com/SciML/diffeqpy/pull/122#issuecomment-1768836371.