[BUG]: method error when used in the debugger #218

wenpw · 2023-06-02T06:52:53Z

@MilesCranmer Thank you very much for your code which are very helpful.

I am trying to debug the code using the test_fast_cycle.jl.
There is no problem when I directly run the code.

However, if I use debug mode in Vscode and Julia extensions to check some middle variables, the following bug happens:

[EvaluateEquation.jl]

        elseif tree.r.degree == 0
            (cumulator_l, complete) = _eval_tree_array(tree.l, cX, operators, Val(turbo))
            @return_on_false complete cumulator_l
            @return_on_nonfinite_array cumulator_l
            # op(x, y), where y is a constant or variable but x is not.
            return deg2_r0_eval(tree, cumulator_l, cX, op, Val(turbo))

The Bug is

Exception has occurred: MethodError
MethodError: Cannot convert an object of type Nothing to an object of type Symbol
Closest candidates are:
convert(::Type{T}, !Matched::T) where T at Base.jl:61
Symbol(::Any...) at strings/basic.jl:229

Stacktrace:
[1] _eval_tree_array(tree::Node{Float32}, cX::Matrix{Float32}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum, #unused#::Val{false})

@ DynamicExpressions.EvaluateEquationModule C:\Users\Administrator.julia\packages\DynamicExpressions\YQrb6\src\EvaluateEquation.jl:123

And even for the example code in DynamicExpressions, using debug mode, the above similar error happens.


using DynamicExpressions

operators = OperatorEnum(; binary_operators=[+, -, *], unary_operators=[cos])

x1 = Node(; feature=1)
x2 = Node(; feature=2)

expression = x1 * cos(x2 - 3.2)

X = randn(Float64, 2, 100);
expression(X, operators) # 100-element Vector{Float64}

Thank you in advance.

Best regards

Version

0.18.0

Operating System

Windows

Interface

Other (specify below)

Relevant log output

Exception has occurred: MethodError
MethodError: Cannot `convert` an object of type Nothing to an object of type Symbol
Closest candidates are:
  convert(::Type{T}, !Matched::T) where T at Base.jl:61
  Symbol(::Any...) at strings/basic.jl:229

Stacktrace:
  [1] _eval_tree_array(tree::Node{Float32}, cX::Matrix{Float32}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum, #unused#::Val{false})
    @ DynamicExpressions.EvaluateEquationModule C:\Users\Administrator\.julia\packages\DynamicExpressions\YQrb6\src\EvaluateEquation.jl:123
  [2] eval_tree_array(tree::Node{Float32}, cX::Matrix{Float32}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum; turbo::Bool)
    @ DynamicExpressions.EvaluateEquationModule C:\Users\Administrator\.julia\packages\DynamicExpressions\YQrb6\src\EvaluateEquation.jl:65
  [3] (::DynamicExpressions.EvaluateEquationModule.var"#eval_tree_array##kw")(::NamedTuple{(:turbo,), Tuple{Bool}}, ::typeof(eval_tree_array), tree::Node{Float32}, cX::Matrix{Float32}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum)
    @ DynamicExpressions.EvaluateEquationModule C:\Users\Administrator\.julia\packages\DynamicExpressions\YQrb6\src\EvaluateEquation.jl:59
  [4] eval_tree_array(tree::Node{Float32}, X::Matrix{Float32}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}; kws::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ SymbolicRegression.InterfaceDynamicExpressionsModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\InterfaceDynamicExpressions.jl:51
  [5] eval_tree_array(tree::Node{Float32}, X::Matrix{Float32}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}})
    @ SymbolicRegression.InterfaceDynamicExpressionsModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\InterfaceDynamicExpressions.jl:50
  [6] _eval_loss(tree::Node{Float32}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}})
    @ SymbolicRegression.LossFunctionsModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\LossFunctions.jl:66
  [7] eval_loss(tree::Node{Float32}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}})
    @ SymbolicRegression.LossFunctionsModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\LossFunctions.jl:95
  [8] score_func(dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}, member::Node{Float32}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, complexity::Int64)
    @ SymbolicRegression.LossFunctionsModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\LossFunctions.jl:136
  [9] PopMember(dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}, t::Node{Float32}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, complexity::Nothing; ref::Int64, parent::Int64, deterministic::Bool)
    @ SymbolicRegression.PopMemberModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\PopMember.jl:99
 [10] (::Core.var"#Type##kw")(::NamedTuple{(:parent, :deterministic), Tuple{Int64, Bool}}, ::Type{PopMember}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}, t::Node{Float32}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, complexity::Nothing)
    @ SymbolicRegression.PopMemberModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\PopMember.jl:88
 [11] (::Core.var"#Type##kw")(::NamedTuple{(:parent, :deterministic), Tuple{Int64, Bool}}, ::Type{PopMember}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}, t::Node{Float32}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}})
    @ SymbolicRegression.PopMemberModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\PopMember.jl:88
 [12] (::SymbolicRegression.PopulationModule.var"#2#3"{Float32, Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Int64, Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}})(i::Int64)
    @ SymbolicRegression.PopulationModule none:0
 [13] iterate(g::Base.Generator{UnitRange{Int64}, SymbolicRegression.PopulationModule.var"#2#3"{Float32, Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Int64, Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}}, s::Tuple{})
    @ Base generator.jl:47
 [14] collect(itr::Base.Generator{UnitRange{Int64}, SymbolicRegression.PopulationModule.var"#2#3"{Float32, Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Int64, Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}})
    @ Base array.jl:787
 [15] Population(dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}; npop::Int64, nlength::Int64, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, nfeatures::Int64)
    @ SymbolicRegression.PopulationModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\Population.jl:40
 [16] (::Core.var"#Type##kw")(::NamedTuple{(:npop, :options, :nfeatures), Tuple{Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Int64}}, ::Type{Population}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}})
    @ SymbolicRegression.PopulationModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\Population.jl:37
 [17] (::SymbolicRegression.SearchUtilsModule.var"#6#8"{Int64, Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}})(i::Int64)
    @ SymbolicRegression.SearchUtilsModule none:0
 [18] iterate(g::Base.Generator{UnitRange{Int64}, SymbolicRegression.SearchUtilsModule.var"#6#8"{Int64, Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}}, s::Tuple{})
    @ Base generator.jl:47
 [19] collect(itr::Base.Generator{UnitRange{Int64}, SymbolicRegression.SearchUtilsModule.var"#6#8"{Int64, Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}})
    @ Base array.jl:787
 [20] (::SymbolicRegression.SearchUtilsModule.var"#5#7"{Int64, Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}})(j::Int64)
    @ SymbolicRegression.SearchUtilsModule none:0
 [21] iterate(g::Base.Generator{UnitRange{Int64}, SymbolicRegression.SearchUtilsModule.var"#5#7"{Int64, Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}}, s::Tuple{})
    @ Base generator.jl:47
 [22] collect(itr::Base.Generator{UnitRange{Int64}, SymbolicRegression.SearchUtilsModule.var"#5#7"{Int64, Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}})
    @ Base array.jl:787
 [23] init_dummy_pops(nout::Int64, npops::Int64, datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}})
    @ SymbolicRegression.SearchUtilsModule C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SearchUtils.jl:50
 [24] _EquationSearch(parallelism::Symbol, datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}; niterations::Int64, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, numprocs::Nothing, procs::Nothing, addprocs_function::Nothing, runtests::Bool, saved_state::Nothing)
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:493
 [25] (::SymbolicRegression.var"#_EquationSearch##kw")(::NamedTuple{(:niterations, :options, :numprocs, :procs, :addprocs_function, :runtests, :saved_state), Tuple{Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Nothing, Nothing, Nothing, Bool, Nothing}}, ::typeof(SymbolicRegression._EquationSearch), parallelism::Symbol, datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}})
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:411
 [26] EquationSearch(datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}}; niterations::Int64, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, parallelism::Symbol, numprocs::Nothing, procs::Nothing, addprocs_function::Nothing, runtests::Bool, saved_state::Nothing)
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:398
 [27] (::SymbolicRegression.var"#EquationSearch##kw")(::NamedTuple{(:niterations, :options, :parallelism, :numprocs, :procs, :addprocs_function, :runtests, :saved_state), Tuple{Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Symbol, Nothing, Nothing, Nothing, Bool, Nothing}}, ::typeof(EquationSearch), datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}}})
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:363
 [28] EquationSearch(X::Matrix{Float32}, y::Matrix{Float32}; niterations::Int64, weights::Nothing, varMap::Vector{String}, options::Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, parallelism::Symbol, numprocs::Nothing, procs::Nothing, addprocs_function::Nothing, runtests::Bool, saved_state::Nothing, multithreaded::Nothing, loss_type::Type{Nothing})
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:331
 [29] (::SymbolicRegression.var"#EquationSearch##kw")(::NamedTuple{(:varMap, :niterations, :options), Tuple{Vector{String}, Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}}, ::typeof(EquationSearch), X::Matrix{Float32}, y::Matrix{Float32})
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:291
 [30] EquationSearch(X::Matrix{Float32}, y::Vector{Float32}; kw::Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:varMap, :niterations, :options), Tuple{Vector{String}, Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}}})
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:356
 [31] (::SymbolicRegression.var"#EquationSearch##kw")(::NamedTuple{(:varMap, :niterations, :options), Tuple{Vector{String}, Int64, Options{Int64, Optim.Options{Float64, Nothing}, L2DistLoss, Nothing, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}}, ::typeof(EquationSearch), X::Matrix{Float32}, y::Vector{Float32})
    @ SymbolicRegression C:\Users\Administrator\.julia\packages\SymbolicRegression\5L9HL\src\SymbolicRegression.jl:353
 [32] top-level scope
    @ j:\dsdyp\ai\PySR-master\SymbolicRegression.jl-master\test\test_fast_cycle.jl:19

Extra Info

No response

The text was updated successfully, but these errors were encountered:

MilesCranmer · 2023-06-02T13:00:59Z

Do you know what debug mode is changing in the code? (Does it remove those error checks?) Or is there some way I could reproduce the bug?

MilesCranmer · 2023-06-02T13:03:20Z

It seems like it might be a bug in the VSCode debugger. There are no symbol types in the evaluation code, so that error must be from the debugger trying to print some diagnostics. Maybe post this on the Julia vscode GitHub issues as well?

wenpw · 2023-06-02T13:15:34Z

Do you know what debug mode is changing in the code? (Does it remove those error checks?) Or is there some way I could reproduce the bug?

Thank you for the reply.

Using your DynamicExpression.jl code, you could debug the following example code (https://github.com/SymbolicML/DynamicExpressions.jl) in Linux or Windows using vscode + julia extension by pressing F5, you could reproduce similar errors:

using DynamicExpressions

operators = OperatorEnum(; binary_operators=[+, -, *], unary_operators=[cos])

x1 = Node(; feature=1)
x2 = Node(; feature=2)

expression = x1 * cos(x2 - 3.2)

X = randn(Float64, 2, 100);
expression(X, operators) # 100-element Vector{Float64}

wenpw · 2023-06-02T13:19:04Z

It seems like it might be a bug in the VSCode debugger. There are no symbol types in the evaluation code, so that error must be from the debugger trying to print some diagnostics. Maybe post this on the Julia vscode GitHub issues as well?

May I ask what debug mode or IDE do you use in writing these codes? I could make a comparion between vscode and others, and pin down the problems.

MilesCranmer · 2023-06-02T14:11:57Z

I use vscode as well but I haven’t seen this issue before. What is your Julia version?

wenpw · 2023-06-02T14:27:46Z

I use vscode as well but I haven’t seen this issue before. What is your Julia version?

It is quite strange. I used julia of Version 1.9.0 (2023-05-07).

MilesCranmer · 2023-06-02T14:48:17Z

Oh, wait, I was just able to reproduce it. I think I just haven't ran the code in the Julia debugger before. This is very strange. What variable is the Symbol and what is the Nothing?

wenpw · 2023-06-03T00:29:46Z

Oh, wait, I was just able to reproduce it. I think I just haven't ran the code in the Julia debugger before. This is very strange. What variable is the Symbol and what is the Nothing?

It is great that the error could be reproduced. It's indeed very strange.

zzccchen · 2023-11-22T09:46:29Z

Dear MilesCranmer and wenpw. I encountered the same problem when using VScode Debugger or Debugger.jl [REPL] to debug code. How do you usually debug code? My development environment is Ubuntu22.04, Julia 1.9.4. Have you solved this problem?

MilesCranmer · 2023-11-22T14:34:59Z

Unfortunately I don't know what the issue is. Not sure if @wenpw has solved it either?

I usually use VSCode but I tend to not use the Julia debugger. However I think it's important that others can use it so I'm interested to solve this.

It seems unrelated to SymbolicRegression.jl though. If you post an issue on https://github.com/julia-vscode/julia-vscode and/or https://github.com/JuliaDebug/Debugger.jl they might know what it is from? I'm happy to help lobby there for support.

wenpw · 2023-11-22T14:38:54Z

@MilesCranmer @zzccchen Sorry that it is still not solved. And I have tested several versions of SymbolicRegression.jl, they all have the same debug error.

zzccchen · 2023-11-23T07:34:48Z

Sorry to hear this, thank you for your replies @MilesCranmer @wenpw

MilesCranmer · 2023-11-23T11:49:05Z

To help narrow it down, maybe see if the same issue shows up when using DynamicExpressions.jl alone?

wenpw · 2023-11-23T12:43:05Z

@MilesCranmer Yes, the same issue shows up when using DynamicExpressions.jl alone.

You could try to debug the following test_tree_constrcution.jl at vscode.

https://github.com/SymbolicML/DynamicExpressions.jl/blob/master/test/test_tree_construction.jl

The same error will appear.

MilesCranmer · 2023-11-23T13:01:10Z

I wonder if it has to do with the Node type itself? The Node type has a field which can either be a number or a nothing... https://github.com/SymbolicML/DynamicExpressions.jl/blob/46388518281b0be12479afcb3a3b8bdabc361ccd/src/Equation.jl#L57

wenpw · 2023-11-23T13:34:37Z

I wonder why debug mode and directly run the code has so large difference?
You may also try to debug the following code. Another error will come up :
https://github.com/SymbolicML/DynamicExpressions.jl/blob/master/test/test_custom_operators.jl

MilesCranmer · 2023-11-23T14:07:22Z

Hm...

Can you try to keep reducing it? So that it is the minimal code possible that still gives an error?

wenpw · 2023-11-23T14:50:41Z

I could try but I am not sure if I could find the minimal code that still gives an error.

MilesCranmer · 2023-12-22T21:11:22Z

Hey @wenpw,

Have you tried https://github.com/JuliaDebug/Infiltrator.jl? It seems to be a much more robust debugger for Julia than the built-in one in VSCode. I've been trying it and it works quite well for debugging SymbolicRegression.jl!

Cheers,
Miles

wenpw · 2023-12-25T00:38:13Z

@MilesCranmer Thank you very much. This would be of great help.

wenpw added the bug Something isn't working label Jun 2, 2023

wenpw assigned MilesCranmer Jun 2, 2023

MilesCranmer changed the title ~~[BUG]: test_fast_cycle.jl : method error~~ [BUG]: method error when used in the debugger Jun 13, 2023

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

[BUG]: method error when used in the debugger #218

[BUG]: method error when used in the debugger #218

wenpw commented Jun 2, 2023 •

edited

Loading

MilesCranmer commented Jun 2, 2023

MilesCranmer commented Jun 2, 2023

wenpw commented Jun 2, 2023 •

edited

Loading

wenpw commented Jun 2, 2023

MilesCranmer commented Jun 2, 2023

wenpw commented Jun 2, 2023

MilesCranmer commented Jun 2, 2023

wenpw commented Jun 3, 2023

zzccchen commented Nov 22, 2023

MilesCranmer commented Nov 22, 2023

wenpw commented Nov 22, 2023 •

edited

Loading

zzccchen commented Nov 23, 2023

MilesCranmer commented Nov 23, 2023

wenpw commented Nov 23, 2023

MilesCranmer commented Nov 23, 2023

wenpw commented Nov 23, 2023

MilesCranmer commented Nov 23, 2023

wenpw commented Nov 23, 2023

MilesCranmer commented Dec 22, 2023

wenpw commented Dec 25, 2023

[BUG]: method error when used in the debugger #218

[BUG]: method error when used in the debugger #218

Comments

wenpw commented Jun 2, 2023 • edited Loading

Version

Operating System

Interface

Relevant log output

Extra Info

MilesCranmer commented Jun 2, 2023

MilesCranmer commented Jun 2, 2023

wenpw commented Jun 2, 2023 • edited Loading

wenpw commented Jun 2, 2023

MilesCranmer commented Jun 2, 2023

wenpw commented Jun 2, 2023

MilesCranmer commented Jun 2, 2023

wenpw commented Jun 3, 2023

zzccchen commented Nov 22, 2023

MilesCranmer commented Nov 22, 2023

wenpw commented Nov 22, 2023 • edited Loading

zzccchen commented Nov 23, 2023

MilesCranmer commented Nov 23, 2023

wenpw commented Nov 23, 2023

MilesCranmer commented Nov 23, 2023

wenpw commented Nov 23, 2023

MilesCranmer commented Nov 23, 2023

wenpw commented Nov 23, 2023

MilesCranmer commented Dec 22, 2023

wenpw commented Dec 25, 2023

wenpw commented Jun 2, 2023 •

edited

Loading

wenpw commented Jun 2, 2023 •

edited

Loading

wenpw commented Nov 22, 2023 •

edited

Loading