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1.5.0-DEV-13d1bd4689.log
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Julia Version 1.5.0-DEV.0
Commit 13d1bd4689 (2019-12-31 18:18 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
Resolving package versions...
Installed OpenSpecFun_jll ── v0.5.3+1
Installed SpecialFunctions ─ v0.9.0
Installed ForneyLab ──────── v0.10.0
Updating `~/.julia/environments/v1.5/Project.toml`
[9fc3f58a] + ForneyLab v0.10.0
Updating `~/.julia/environments/v1.5/Manifest.toml`
[9fc3f58a] + ForneyLab v0.10.0
[efe28fd5] + OpenSpecFun_jll v0.5.3+1
[276daf66] + SpecialFunctions v0.9.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8ba89e20] + Distributed
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Testing ForneyLab
Updating `/tmp/jl_AYojfR/Project.toml`
[no changes]
Updating `/tmp/jl_AYojfR/Manifest.toml`
[no changes]
Running sandbox
Status `/tmp/jl_AYojfR/Project.toml`
[9fc3f58a] ForneyLab v0.10.0
[276daf66] SpecialFunctions v0.9.0
[2a0f44e3] Base64
[b77e0a4c] InteractiveUtils
[37e2e46d] LinearAlgebra
[de0858da] Printf
[2f01184e] SparseArrays
[10745b16] Statistics
[8dfed614] Test
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = differentialEntropy(::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.Gamma}) at gamma.jl:78
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/gamma.jl:78
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = averageEnergy(::Type{ForneyLab.Gamma}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.Gamma}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.PointMass}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.PointMass}) at gamma.jl:86
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/gamma.jl:86
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = (::ForneyLab.var"#65#67"{ForneyLab.ProbabilityDistribution{ForneyLab.MatrixVariate,ForneyLab.Wishart}})(::Int64) at none:0
└ @ ForneyLab ./none:0
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = (::ForneyLab.var"#69#70"{ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.PointMass}})(::Int64) at none:0
└ @ ForneyLab ./none:0
┌ Warning: `lbeta(x::Real, w::Real)` is deprecated, use `(logabsbeta(x, w))[1]` instead.
│ caller = differentialEntropy(::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.Beta}) at beta.jl:84
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/beta.jl:84
┌ Warning: `lbeta(x::Real, w::Real)` is deprecated, use `(logabsbeta(x, w))[1]` instead.
│ caller = averageEnergy(::Type{ForneyLab.Beta}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.Beta}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.PointMass}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Univariate,ForneyLab.PointMass}) at beta.jl:92
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/beta.jl:92
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = differentialEntropy(::ForneyLab.ProbabilityDistribution{ForneyLab.Multivariate,ForneyLab.Dirichlet}) at dirichlet.jl:107
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/dirichlet.jl:107
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = averageEnergy(::Type{ForneyLab.Dirichlet}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Multivariate,ForneyLab.Dirichlet}, ::ForneyLab.ProbabilityDistribution{ForneyLab.Multivariate,ForneyLab.PointMass}) at dirichlet.jl:129
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/dirichlet.jl:129
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = differentialEntropy(::ForneyLab.ProbabilityDistribution{ForneyLab.MatrixVariate,ForneyLab.Dirichlet}) at dirichlet.jl:117
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/dirichlet.jl:117
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = averageEnergy(::Type{ForneyLab.Dirichlet}, ::ForneyLab.ProbabilityDistribution{ForneyLab.MatrixVariate,ForneyLab.Dirichlet}, ::ForneyLab.ProbabilityDistribution{ForneyLab.MatrixVariate,ForneyLab.PointMass}) at dirichlet.jl:143
└ @ ForneyLab ~/.julia/packages/ForneyLab/Hz4kD/src/factor_nodes/dirichlet.jl:143
Nonlinear integration with given inverse: Test Failed at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:69
Expression: occursin("ruleSPNonlinearOutNG(nothing, messages[2], Main.ForneyLabTest.NonlinearTest.g)", algo)
Evaluated: occursin("ruleSPNonlinearOutNG(nothing, messages[2], Main.ForneyLabTest.NonlinearTest.g)", "begin\n\nfunction step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 3))\n\nmessages[1] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=3.0))\nmessages[2] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=1.0))\nmessages[3] = ruleSPNonlinearOutNG(nothing, messages[2], g)\n\nmarginals[:y] = messages[1].dist * messages[3].dist\n\nreturn marginals\n\nend\n\nend # block")
Stacktrace:
[1] macro expansion at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:69 [inlined]
[2] macro expansion at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1116 [inlined]
[3] top-level scope at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:61
Nonlinear integration with given inverse: Test Failed at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:74
Expression: occursin("ruleSPNonlinearIn1GG(messages[2], nothing, Main.ForneyLabTest.NonlinearTest.g, Main.ForneyLabTest.NonlinearTest.g_inv)", algo)
Evaluated: occursin("ruleSPNonlinearIn1GG(messages[2], nothing, Main.ForneyLabTest.NonlinearTest.g, Main.ForneyLabTest.NonlinearTest.g_inv)", "begin\n\nfunction step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 3))\n\nmessages[1] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=1.0))\nmessages[2] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=3.0))\nmessages[3] = ruleSPNonlinearIn1GG(messages[2], nothing, g, g_inv)\n\nmarginals[:x] = messages[1].dist * messages[3].dist\n\nreturn marginals\n\nend\n\nend # block")
Stacktrace:
[1] macro expansion at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:74 [inlined]
[2] macro expansion at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1116 [inlined]
[3] top-level scope at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:61
Nonlinear integration without given inverse: Test Failed at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:86
Expression: occursin("ruleSPNonlinearOutNG(nothing, messages[2], Main.ForneyLabTest.NonlinearTest.g)", algo)
Evaluated: occursin("ruleSPNonlinearOutNG(nothing, messages[2], Main.ForneyLabTest.NonlinearTest.g)", "begin\n\nfunction step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 3))\n\nmessages[1] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=3.0))\nmessages[2] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=1.0))\nmessages[3] = ruleSPNonlinearOutNG(nothing, messages[2], g)\n\nmarginals[:y] = messages[1].dist * messages[3].dist\n\nreturn marginals\n\nend\n\nend # block")
Stacktrace:
[1] macro expansion at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:86 [inlined]
[2] macro expansion at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1116 [inlined]
[3] top-level scope at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:78
Nonlinear integration without given inverse: Test Failed at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:92
Expression: occursin("ruleSPNonlinearIn1GG(messages[2], messages[1], Main.ForneyLabTest.NonlinearTest.g)", algo)
Evaluated: occursin("ruleSPNonlinearIn1GG(messages[2], messages[1], Main.ForneyLabTest.NonlinearTest.g)", "begin\n\nfunction init()\n\nmessages = Array{Message}(undef, 3)\nmessages[1] = Message(vague(GaussianMeanVariance))\n\nreturn messages\n\nend\n\nfunction step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 3))\n\nmessages[1] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=1.0))\nmessages[2] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=2.0), Message(Univariate, PointMass, m=3.0))\nmessages[3] = ruleSPNonlinearIn1GG(messages[2], messages[1], g)\n\nmarginals[:x] = messages[1].dist * messages[3].dist\n\nreturn marginals\n\nend\n\nend # block")
Stacktrace:
[1] macro expansion at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:92 [inlined]
[2] macro expansion at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1116 [inlined]
[3] top-level scope at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/factor_nodes/test_nonlinear.jl:78
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = (::ForneyLab.var"#106#107"{Float64})(::Int64) at none:0
└ @ ForneyLab ./none:0
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = (::ForneyLab.var"#106#107"{Float64})(::Int64) at none:0
└ @ ForneyLab ./none:0
Test Summary: | Pass Fail Total
ForneyLab | 1471 4 1475
Helpers | 33 33
FactorNode | 193 193
Interface | 3 3
matches | 4 4
Univariate | 15 15
Multivariate | 12 12
MatrixVariate | 4 4
PointMass ProbabilityDistribution and Message construction | 12 12
dims | 4 4
isValid | 8 8
gaussianQuadrature | 3 3
@RV | 21 21
Edge | 6 6
disconnect! | 12 12
Variable | 3 3
associate! | 42 42
FactorGraph | 8 8
generateId | 3 3
addNode! | 4 4
@ensureVariables | 2 2
Clamp | 2 2
constant | 3 3
placeholder | 7 7
SPClamp | 9 9
SPEqualityGaussian | 11 11
SPEqualityGammaWishart | 13 13
SPEqualityBernoulli | 8 8
SPEqualityBeta | 8 8
SPEqualityCategorical | 8 8
SPEqualityDirichlet | 8 8
SPEqualityPointMass | 16 16
Addition node construction through + syntax | 4 4
Addition node construction through - syntax | 6 6
SPAdditionOutNGG | 7 7
SPAdditionIn2GGN | 5 5
SPAdditionIn1GNG | 5 5
SPAdditionOutNGP | 5 5
SPAdditionOutNPG | 5 5
SPAdditionIn1PNG | 5 5
SPAdditionIn2PGN | 5 5
SPAdditionIn1GNP | 5 5
SPAdditionIn2GPN | 5 5
SPAdditionOutNPP | 5 5
SPAdditionIn2PPN | 5 5
SPAdditionIn1PNP | 5 5
Multiplication node construction through * syntax | 4 4
SPMultiplicationOutNGP | 7 7
SPMultiplicationOutNPG | 6 6
SPMultiplicationOutNPP | 5 5
SPMultiplicationIn1GNP | 5 5
SPMultiplicationIn1PNP | 5 5
SPMultiplicationAGPN | 4 4
SPMultiplicationAPPN | 4 4
Exponential node construction through exp() syntax | 2 2
SPExponentialOutNG | 4 4
SPExponentialOutNP | 4 4
SPExponentialIn1LN | 4 4
SPExponentialIn1PN | 4 4
dims | 2 2
vague | 2 2
isProper | 5 5
== | 4 4
unsafe statistics | 16 16
convert | 4 4
SPGaussianMeanVarianceOutNPP | 6 6
SPGaussianMeanVarianceMPNP | 6 6
SPGaussianMeanVarianceOutNGP | 6 6
SPGaussianMeanVarianceMGNP | 6 6
VBGaussianMeanVarianceM | 6 6
VBGaussianMeanVarianceOut | 6 6
averageEnergy and differentialEntropy | 3 3
dims | 2 2
vague | 2 2
isProper | 5 5
== | 4 4
unsafe statistics | 16 16
convert | 4 4
SPGaussianMeanPrecisionOutNPP | 6 6
SPGaussianMeanPrecisionMPNP | 6 6
SPGaussianMeanPrecisionOutNGP | 6 6
SPGaussianMeanPrecisionMGNP | 6 6
VBGaussianMeanPrecisionM | 6 6
VBGaussianMeanPrecisionW | 5 5
VBGaussianMeanPrecisionOut | 5 5
SVBGaussianMeanPrecisionMGVD | 5 5
SVBGaussianMeanPrecisionW | 5 5
SVBGaussianMeanPrecisionOutVGD | 5 5
MGaussianMeanPrecisionGGD | 4 4
averageEnergy and differentialEntropy | 4 4
dims | 2 2
vague | 2 2
isProper | 5 5
== | 4 4
unsafe statistics | 16 16
convert | 4 4
SPGaussianWeightedMeanPrecisionOutNPP | 6 6
VBGaussianWeightedMeanPrecisionOut | 6 6
sample | 3 3
prod! | 8 8
dims | 1 1
vague | 1 1
prod! | 4 4
unsafe mean and variance | 2 2
SPGammaOutNPP | 4 4
VBGammaOut | 5 5
averageEnergy and differentialEntropy | 1 1
dims | 1 1
vague | 1 1
unsafe mean and variance | 6 6
Gamma approximatons to LogNormal | 1 1
prod! | 3 3
SPLogNormalOutNPP | 4 4
VBLogNormalOut | 5 5
averageEnergy and differentialEntropy | 1 1
dims | 1 1
vague | 1 1
isProper | 3 3
prod! | 4 4
unsafe mean and variance | 5 5
SPWishartOutNPP | 4 4
VBWishartOut | 5 5
averageEnergy and differentialEntropy | 2 2
Bernoulli ProbabilityDistribution and Message construction | 7 7
dims | 1 1
vague | 1 1
unsafe mean and variance | 2 2
prod! | 2 2
SPBernoulliOutNP | 4 4
SPBernoulliIn1PN | 4 4
SPBernoulliOutNB | 4 4
VBBernoulliOut | 6 6
VBBernoulliIn1 | 4 4
averageEnergy and differentialEntropy | 1 1
Categorical ProbabilityDistribution and Message construction | 7 7
dims | 1 1
vague | 2 2
unsafe mean and variance | 1 1
sample | 3 3
prod! | 3 3
SPCategoricalOutNP | 4 4
VBCategoricalOut | 6 6
VBCategoricalIn1 | 6 6
averageEnergy and differentialEntropy | 3 3
Contingency ProbabilityDistribution and Message construction | 4 4
dims | 1 1
vague | 2 2
differentialEntropy | 1 1
SPTransitionOutNPP | 5 5
SPTransitionIn1PNP | 4 4
SPTransitionOutNCP | 5 5
SPTransitionIn1CNP | 4 4
VBTransitionOut | 4 4
VBTransitionIn1 | 4 4
VBTransitionA | 4 4
SVBTransitionOutVCD | 4 4
SVBTransitionIn1CVD | 4 4
SVBTransitionADV | 4 4
MTransitionCCD | 3 3
averageEnergy | 2 2
Beta ProbabilityDistribution and Message construction | 7 7
dims | 1 1
vague | 1 1
unsafe mean and variance | 4 4
prod! | 4 4
SPBetaOutNPP | 4 4
VBBetaOut | 4 4
averageEnergy and differentialEntropy | 1 1
Dirichlet ProbabilityDistribution and Message construction | 9 9
dims | 2 2
vague | 2 2
unsafe mean and variance | 5 5
prod! | 8 8
SPDirichletOutNP | 5 5
VBDirichletOut | 5 5
averageEnergy and differentialEntropy | 4 4
VBGaussianMixtureM | 14 14
VBGaussianMixtureW | 14 14
VBGaussianMixtureZBer | 6 6
VBGaussianMixtureZCat | 6 6
VBGaussianMixtureOut | 8 8
averageEnergy | 4 4
mapToBernoulliParameterRange | 8 8
SPSigmoidBinNG | 5 5
EPSigmoidRealGB | 7 7
EPSigmoidRealGC | 7 7
EPSigmoidRealGP | 6 6
sigmaPointsAndWeights | 6 6
SPNonlinearOutNG | 5 5
SPNonlinearIn1GG | 7 7
Nonlinear integration with given inverse | 1 2 3
Nonlinear integration without given inverse | 3 2 5
SPDotProductOutNPG | 4 4
SPDotProductOutNGP | 4 4
SPDotProductIn1GNP | 4 4
SPDotProductIn2GPN | 4 4
Poisson ProbabilityDistribution construction | 4 4
Poisson Message construction | 4 4
dims | 1 1
slug | 1 1
vague | 1 1
isProper | 3 3
unsafe mean and variance | 2 2
SPPoissonOutNP | 4 4
SPPoissonLPN | 4 4
VBPoissonOut | 4 4
VBPoissonL | 5 5
averageEnergy and differentialEntropy | 3 3
Poisson node construction | 8 8
Parameter estimation | 1 1
LinkedList | 4 4
DependencyGraph | 23 23
Message | 10 10
matches | 13 13
ScheduleEntry | 1 1
summaryPropagationSchedule | 6 6
flatten | 4 4
MarginalScheduleEntry | 1 1
marginalSchedule | 8 8
@SumProductRule | 1 1
inferUpdateRule! | 4 4
sumProductSchedule | 4 4
RecognitionFactorization | 3 3
RecognitionFactor | 20 20
hasCollider() | 6 6
Cluster | 4 4
@marginalRule | 1 1
inferMarginalRule | 1 1
marginalSchedule | 13 13
@naiveVariationalRule | 1 1
inferUpdateRule! | 1 1
variationalSchedule | 7 7
@structuredVariationalRule | 3 3
inferUpdateRule! | 3 3
variationalSchedule | 14 14
@expectationPropagationRule | 1 1
inferUpdateRule! | 1 1
expectationPropagationSchedule | 5 5
variationalExpectationPropagationSchedule | 5 5
Julia messagePassingAlgorithm | 7 7
Julia algorithm execution | 1 1
@composite | 1 1
Composite node construction | 10 10
Custom SPStateTransitionX | 4 4
Composite node scheduling and algorithm compilation | 14 14
Composite node algorithm execution | 1 1
ERROR: LoadError: Some tests did not pass: 1471 passed, 4 failed, 0 errored, 0 broken.
in expression starting at /home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/runtests.jl:9
err = ProcessFailedException(Base.Process[Process(`/opt/julia/bin/julia -Cnative -J/opt/julia/lib/julia/sys.so -g1 --code-coverage=none --color=no --compiled-modules=yes --check-bounds=yes --inline=yes --startup-file=no --track-allocation=none --eval 'append!(empty!(Base.DEPOT_PATH), ["/home/pkgeval/.julia", "/opt/julia/local/share/julia", "/opt/julia/share/julia", "/usr/local/share/julia"])
append!(empty!(Base.DL_LOAD_PATH), String[])
cd("/home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test")
append!(empty!(ARGS), String[])
include("/home/pkgeval/.julia/packages/ForneyLab/Hz4kD/test/runtests.jl")
'`, ProcessExited(1))])
ERROR: Package ForneyLab errored during testing
Stacktrace:
[1] pkgerror(::String, ::Vararg{String,N} where N) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/Types.jl:54
[2] test(::Pkg.Types.Context, ::Array{Pkg.Types.PackageSpec,1}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/Operations.jl:1471
[3] test(::Pkg.Types.Context, ::Array{Pkg.Types.PackageSpec,1}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:313
[4] test(::Pkg.Types.Context, ::Array{Pkg.Types.PackageSpec,1}) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:300
[5] #test#66 at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:294 [inlined]
[6] test at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:294 [inlined]
[7] #test#65 at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:293 [inlined]
[8] test at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:293 [inlined]
[9] test(::String; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:292
[10] test(::String) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Pkg/src/API.jl:292
[11] top-level scope at none:12