Having trouble figuring out which line the error is in

Manoj_Gopalkrishnan · May 21, 2020, 8:17am

When I get an error message like this:

MethodError: Cannot `convert` an object of type Float64 to an object of type Array{Float64,1}
Closest candidates are:
  convert(::Type{Array{Float64,1}}, !Matched::Sundials.NVector) at C:\Users\Manoj\.julia\packages\Sundials\LzbrB\src\nvector_wrapper.jl:58
  convert(::Type{Array{Float64,1}}, !Matched::Ptr{Sundials._generic_N_Vector}) at C:\Users\Manoj\.julia\packages\Sundials\LzbrB\src\nvector_wrapper.jl:79
  convert(::Type{Array{T,N}}, !Matched::StaticArrays.SizedArray{S,T,N,M} where M) where {T, S, N} at C:\Users\Manoj\.julia\packages\StaticArrays\mlIi1\src\SizedArray.jl:72
  ...

Stacktrace:
 [1] setproperty!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Array{Float64,1},Nothing,Float64,Array{Float64,1},Float64,Float64,Float64,Array{Array{Float64,1},1},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,Array{Float64,1},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,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}},DiffEqBase.DEStats},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Array{Float64,1},Array{Float64,1},Array{Float64,1}},Array{Float64,1},Float64,Nothing,OrdinaryDiffEq.DefaultInit}, ::Symbol, ::Float64) at .\Base.jl:34
 [2] initialize!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Array{Float64,1},Nothing,Float64,Array{Float64,1},Float64,Float64,Float64,Array{Array{Float64,1},1},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,Array{Float64,1},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,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}},DiffEqBase.DEStats},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Array{Float64,1},Array{Float64,1},Array{Float64,1}},Array{Float64,1},Float64,Nothing,OrdinaryDiffEq.DefaultInit}, ::OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}) at C:\Users\Manoj\.julia\packages\OrdinaryDiffEq\wVdhu\src\perform_step\low_order_rk_perform_step.jl:565
 [3] __init(::ODEProblem{Array{Float64,1},Tuple{Float64,Float64},false,Array{Float64,1},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5, ::Array{Array{Float64,1},1}, ::Array{Float64,1}, ::Array{Any,1}, ::Type{Val{true}}; saveat::Array{Float64,1}, tstops::Array{Float64,1}, d_discontinuities::Array{Float64,1}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Bool, callback::Nothing, dense::Bool, calck::Bool, dt::Float64, dtmin::Nothing, dtmax::Float64, force_dtmin::Bool, adaptive::Bool, gamma::Rational{Int64}, abstol::Nothing, reltol::Nothing, qmin::Rational{Int64}, qmax::Int64, qsteady_min::Int64, qsteady_max::Int64, qoldinit::Rational{Int64}, fullnormalize::Bool, failfactor::Int64, beta1::Nothing, beta2::Nothing, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(LinearAlgebra.opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::Bool, timeseries_errors::Bool, dense_errors::Bool, advance_to_tstop::Bool, stop_at_next_tstop::Bool, initialize_save::Bool, progress::Bool, progress_steps::Int64, progress_name::String, progress_message::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), userdata::Nothing, allow_extrapolation::Bool, initialize_integrator::Bool, alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEq.DefaultInit, kwargs::Base.Iterators.Pairs{Symbol,Any,Tuple{Symbol,Symbol},NamedTuple{(:default_set, :alg_hints),Tuple{Bool,Array{Symbol,1}}}}) at C:\Users\Manoj\.julia\packages\OrdinaryDiffEq\wVdhu\src\solve.jl:402
 [4] #__solve#357 at C:\Users\Manoj\.julia\packages\OrdinaryDiffEq\wVdhu\src\solve.jl:4 [inlined]
 [5] __solve(::ODEProblem{Array{Float64,1},Tuple{Float64,Float64},false,Array{Float64,1},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Nothing; default_set::Bool, kwargs::Base.Iterators.Pairs{Symbol,Any,Tuple{Symbol,Symbol},NamedTuple{(:save_everystep, :alg_hints),Tuple{Bool,Array{Symbol,1}}}}) at C:\Users\Manoj\.julia\packages\DifferentialEquations\ddyFO\src\default_solve.jl:7
 [6] solve_call(::ODEProblem{Array{Float64,1},Tuple{Float64,Float64},false,Array{Float64,1},ODEFunction{false,typeof(line),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Nothing; merge_callbacks::Bool, kwargs::Base.Iterators.Pairs{Symbol,Any,Tuple{Symbol,Symbol},NamedTuple{(:save_everystep, :alg_hints),Tuple{Bool,Array{Symbol,1}}}}) at C:\Users\Manoj\.julia\packages\DiffEqBase\A6T7i\src\solve.jl:60
 [7] #solve#449 at C:\Users\Manoj\.julia\packages\DiffEqBase\A6T7i\src\solve.jl:84 [inlined]
 [8] test_f(::Array{Float64,1}, ::Array{Float64,1}) at .\In[129]:12
 [9] macro expansion at .\In[129]:24 [inlined]
 [10] ##evaluator#1406(::DynamicPPL.Model{var"###evaluator#1406",(:ys, :T),Tuple{Array{Float64,1},Type{Array{Float64,1}}},(),DynamicPPL.ModelGen{var"###generator#1407",(:ys, :T),(:T,),Tuple{Type{Array{Float64,1}}}}}, ::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{DynamicPPL.VarName,Int64},Array{Distribution,1},Array{DynamicPPL.VarName,1},Array{Real,1},Array{Set{DynamicPPL.Selector},1}},Float64}, ::DynamicPPL.SampleFromPrior, ::DynamicPPL.DefaultContext) at C:\Users\Manoj\.julia\packages\DynamicPPL\h0R38\src\compiler.jl:355
 [11] evaluate_singlethreaded at C:\Users\Manoj\.julia\packages\DynamicPPL\h0R38\src\model.jl:147 [inlined]
 [12] Model at C:\Users\Manoj\.julia\packages\DynamicPPL\h0R38\src\model.jl:136 [inlined]
 [13] VarInfo at C:\Users\Manoj\.julia\packages\DynamicPPL\h0R38\src\varinfo.jl:110 [inlined]
 [14] VarInfo at C:\Users\Manoj\.julia\packages\DynamicPPL\h0R38\src\varinfo.jl:109 [inlined]
 [15] DynamicPPL.Sampler(::NUTS{Turing.Core.ForwardDiffAD{40},(),AdvancedHMC.DiagEuclideanMetric}, ::DynamicPPL.Model{var"###evaluator#1406",(:ys, :T),Tuple{Array{Float64,1},Type{Array{Float64,1}}},(),DynamicPPL.ModelGen{var"###generator#1407",(:ys, :T),(:T,),Tuple{Type{Array{Float64,1}}}}}, ::DynamicPPL.Selector) at C:\Users\Manoj\.julia\packages\Turing\d4vqQ\src\inference\hmc.jl:378
 [16] Sampler at C:\Users\Manoj\.julia\packages\Turing\d4vqQ\src\inference\hmc.jl:376 [inlined]
 [17] sample(::Random._GLOBAL_RNG, ::DynamicPPL.Model{var"###evaluator#1406",(:ys, :T),Tuple{Array{Float64,1},Type{Array{Float64,1}}},(),DynamicPPL.ModelGen{var"###generator#1407",(:ys, :T),(:T,),Tuple{Type{Array{Float64,1}}}}}, ::NUTS{Turing.Core.ForwardDiffAD{40},(),AdvancedHMC.DiagEuclideanMetric}, ::Int64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\Manoj\.julia\packages\Turing\d4vqQ\src\inference\Inference.jl:161
 [18] sample at C:\Users\Manoj\.julia\packages\Turing\d4vqQ\src\inference\Inference.jl:161 [inlined]
 [19] #sample#1 at C:\Users\Manoj\.julia\packages\Turing\d4vqQ\src\inference\Inference.jl:151 [inlined]
 [20] sample(::DynamicPPL.Model{var"###evaluator#1406",(:ys, :T),Tuple{Array{Float64,1},Type{Array{Float64,1}}},(),DynamicPPL.ModelGen{var"###generator#1407",(:ys, :T),(:T,),Tuple{Type{Array{Float64,1}}}}}, ::NUTS{Turing.Core.ForwardDiffAD{40},(),AdvancedHMC.DiagEuclideanMetric}, ::Int64) at C:\Users\Manoj\.julia\packages\Turing\d4vqQ\src\inference\Inference.jl:151
 [21] top-level scope at In[135]:3

how do I know which function has the error, which variable, which assignment? I am using IJulia in a Jupyter notebook.

Note: my question is not how to solve this particular error. I am sure I will figure it out with trial and error. I just find I am very inefficient at this, and recall my C programming days when the compiler would tell me the line number where the type mismatch happened, and I could swoop in and fix things faster. Here all I know is that “somewhere in my code, there is a type mismatch” and my current debugging style is really primitive, commenting out huge chunks of code to try to localize the problem. I’m sure there are better ways, and would love to hear about them.

ettersi · May 21, 2020, 8:53am

I usually scan the stack trace for lines which list my own files rather than those of Julia or a package. In your case, that would be line [21], which states that the error originates from line 3 in In[135]. Combined with line [20] and the error message, this suggest that on In[135]:3], you called sample with a Float64 argument which should have been Vector{Float64}.

Manoj_Gopalkrishnan · May 21, 2020, 9:42am

Thanks! Wasn’t very useful here. That sample was calling a Turing model that was calling an ODE function and somehow the parameters were supposed to be Float64 but I was passing Array of Float64 instead.

It is not at all obvious to me how I could have deduced that, or the line number where that happened, from the error message. Is it because the function was in a different cell of my Jupyter notebook? Or is there some other way? Would moving to Atom or some debugger help me in localizing errors? Open to any suggestions on this, I am new to Julia and keep making these silly type errors that take me hours to locate and correct, really affecting productivity. If they could be located more precisely, I would save a lot of time.