Package evaluation to test Newtrinos on Julia 1.14.0-DEV.3141 (fbd24f51d0*) started at 2026-09-09T08:03:37.557 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 15.55s ################################################################################ # Installation # Installing Newtrinos... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [5b289081] + Newtrinos v0.1.0 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.24.0 [14f7f29c] + AMD v0.5.4 [621f4979] + AbstractFFTs v1.5.0 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.45 [79e6a3ab] + Adapt v4.7.0 [35492f91] + AdaptivePredicates v1.2.0 [2c83c9a8] + AffineMaps v0.3.5 [66dad0bd] + AliasTables v1.1.3 [27a7e980] + Animations v0.4.2 [dce04be8] + ArgCheck v2.5.0 [c7e460c6] + ArgParse v1.2.0 [4fba245c] + ArrayInterface v7.30.1 [4c555306] + ArrayLayouts v1.12.2 ⌅ [65a8f2f4] + ArraysOfArrays v0.6.6 [a9b6321e] + Atomix v1.1.3 ⌅ [6e1301d5] + AutoDiffOperators v0.3.2 [67c07d97] + Automa v1.2.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 ⌃ [c0cd4b16] + BAT v4.0.4 [ab4f0b2a] + BFloat16s v0.6.1 [198e06fe] + BangBang v0.4.9 [18cc8868] + BaseDirs v1.4.0 [9718e550] + Baselet v0.1.1 [6e4b80f9] + BenchmarkTools v1.8.0 [0e736298] + Bessels v0.2.8 [e2ed5e7c] + Bijections v0.2.2 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [fa961155] + CEnum v0.5.0 [2a0fbf3d] + CPUSummary v0.2.7 [96374032] + CRlibm v1.0.2 [336ed68f] + CSV v0.10.17 [052768ef] + CUDA v6.3.1 [bd0ed864] + CUDACore v6.3.1 [9ec180c6] + CUDATools v6.3.1 [1af6417a] + CUDA_Runtime_Discovery v2.1.1 [9e67e8f6] + CUPTI v6.3.1 [159f3aea] + Cairo v1.1.1 [13f3f980] + CairoMakie v0.15.14 [49dc2e85] + Calculus v0.5.2 [082447d4] + ChainRules v1.73.0 [d360d2e6] + ChainRulesCore v1.26.1 [9e997f8a] + ChangesOfVariables v0.1.11 [0b6fb165] + ChunkCodecCore v1.0.2 [4c0bbee4] + ChunkCodecLibZlib v1.1.0 [55437552] + ChunkCodecLibZstd v1.0.0 [fb6a15b2] + CloseOpenIntervals v0.1.13 [aaaa29a8] + Clustering v0.15.8 [da1fd8a2] + CodeTracking v3.0.2 [944b1d66] + CodecZlib v0.7.9 [6b39b394] + CodecZstd v0.8.7 [a2cac450] + ColorBrewer v0.4.2 [35d6a980] + ColorSchemes v3.31.0 [3da002f7] + ColorTypes v0.12.1 [c3611d14] + ColorVectorSpace v0.11.0 [5ae59095] + Colors v0.13.1 [861a8166] + Combinatorics v1.1.0 [38540f10] + CommonSolve v0.2.14 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.2.2 [34da2185] + Compat v4.18.1 [9db33cc3] + CompilerCaching v0.5.0 [b152e2b5] + CompositeTypes v0.1.4 [a33af91c] + CompositionsBase v0.1.2 [95dc2771] + ComputePipeline v0.1.8 [2569d6c7] + ConcreteStructs v0.2.8 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [ac509c8a] + ContentHashes v0.1.0 [d38c429a] + Contour v0.6.3 [b7a15901] + CoreMath v0.1.0 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.2.0 [f68482b8] + Cthulhu v3.0.2 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.8.2 [864edb3b] + DataStructures v0.19.6 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [927a84f5] + DelaunayTriangulation v1.6.6 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [85a47980] + Dictionaries v0.4.6 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.16.0 [a0c0ee7d] + DifferentiationInterface v0.7.21 [8d63f2c5] + DispatchDoctor v0.4.28 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.131 [ced4e74d] + DistributionsAD v0.6.58 [ffbed154] + DocStringExtensions v0.9.5 ⌅ [5b8099bc] + DomainSets v0.7.18 [497a8b3b] + DoubleFloats v1.11.2 [fdbdab4c] + ElasticArrays v1.2.12 [547eee1f] + ElasticClusterManager v2.0.0 [0bbb1fad] + EmpiricalDistributions v0.3.11 [4e289a0a] + EnumX v1.0.7 [429591f6] + ExactPredicates v2.2.9 [e2ba6199] + ExprTools v0.1.11 [55351af7] + ExproniconLite v0.10.14 [b86e33f2] + FFTA v0.3.1 [7a1cc6ca] + FFTW v1.10.0 [9aa1b823] + FastClosures v0.3.2 [5789e2e9] + FileIO v1.20.0 [8fc22ac5] + FilePaths v0.9.0 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.17.0 [6a86dc24] + FiniteDiff v2.33.0 ⌅ [53c48c17] + FixedPointNumbers v0.8.6 [1eca21be] + FoldingTrees v1.2.2 [1fa38f19] + Format v1.3.7 [f6369f11] + ForwardDiff v1.4.5 [450a3b6d] + ForwardDiffPullbacks v0.2.7 [b38be410] + FreeType v4.1.1 [663a7486] + FreeTypeAbstraction v0.10.8 [8e6b2b91] + FunctionChains v0.2.7 [a85aefff] + FunctionMaps v0.1.2 [069b7b12] + FunctionWrappers v1.1.3 [77dc65aa] + FunctionWrappersWrappers v1.13.0 [d9f16b24] + Functors v0.5.3 [0c68f7d7] + GPUArrays v11.5.14 [46192b85] + GPUArraysCore v0.2.0 [61eb1bfa] + GPUCompiler v2.5.4 [096a3bc2] + GPUToolbox v3.0.0 ⌃ [a0844989] + Gamma v1.1.0 [14197337] + GenericLinearAlgebra v0.4.1 [c145ed77] + GenericSchur v0.5.8 [5c1252a2] + GeometryBasics v0.5.12 [a2bd30eb] + Graphics v1.1.3 [3955a311] + GridLayoutBase v0.11.3 [f67ccb44] + HDF5 v0.17.3 [076d061b] + HashArrayMappedTries v0.2.0 [2182be2a] + HeterogeneousComputing v0.2.7 [34004b35] + HypergeometricFunctions v0.3.30 ⌅ [09f84164] + HypothesisTests v0.11.8 [615f187c] + IfElse v0.1.1 [2803e5a7] + ImageAxes v0.6.12 [c817782e] + ImageBase v0.1.7 [a09fc81d] + ImageCore v0.10.5 [82e4d734] + ImageIO v0.6.10 [bc367c6b] + ImageMetadata v0.9.10 [313cdc1a] + Indexing v1.1.1 [9b13fd28] + IndirectArrays v1.0.0 [d25df0c9] + Inflate v0.1.5 [22cec73e] + InitialValues v0.3.1 ⌅ [842dd82b] + InlineStrings v1.4.6 [18e54dd8] + IntegerMathUtils v0.1.4 [a98d9a8b] + Interpolations v0.16.3 [d1acc4aa] + IntervalArithmetic v1.0.11 [8197267c] + IntervalSets v0.7.14 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [f1662d9f] + Isoband v0.1.1 [c8e1da08] + IterTools v1.10.0 [42fd0dbc] + IterativeSolvers v0.9.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [033835bb] + JLD2 v0.6.6 [692b3bcd] + JLLWrappers v1.8.0 [682c06a0] + JSON v1.8.0 [ae98c720] + Jieko v0.2.1 [b835a17e] + JpegTurbo v0.1.6 [70703baa] + JuliaSyntax v1.0.2 [63c18a36] + KernelAbstractions v0.9.42 [5ab0869b] + KernelDensity v0.6.12 [ba0b0d4f] + Krylov v0.10.9 [5be7bae1] + LBFGSB v0.4.1 [929cbde3] + LLVM v9.13.1 [8b046642] + LLVMLoopInfo v1.0.0 [11f193de] + LMDB v3.0.1 [b964fa9f] + LaTeXStrings v1.4.1 [10f19ff3] + LayoutPointers v0.1.17 [8cdb02fc] + LazyModules v0.3.1 [e5afb96c] + LazyReports v0.2.4 [1d6d02ad] + LeftChildRightSiblingTrees v0.3.0 ⌃ [d3d80556] + LineSearches v7.5.1 [7a12625a] + LinearMaps v3.11.4 ⌅ [7ed4a6bd] + LinearSolve v3.87.0 ⌅ [2ab3a3ac] + LogExpFunctions v0.3.29 [aa2f6b4e] + LogarithmicNumbers v1.4.1 [e6f89c97] + LoggingExtras v1.2.0 ⌅ [2fda8390] + LsqFit v0.15.1 [be115224] + MCMCDiagnosticTools v0.3.19 ⌅ [fdae7790] + MGVI v0.4.3 [6c6e2e6c] + MIMEs v1.1.0 [7e8f7934] + MLDataDevices v1.17.10 [e80e1ace] + MLJModelInterface v1.12.1 [3da0fdf6] + MPIPreferences v0.1.12 [1914dd2f] + MacroTools v0.5.16 [ee78f7c6] + Makie v0.24.14 [d125e4d3] + ManualMemory v0.1.8 [dbb5928d] + MappedArrays v0.4.3 [0a4f8689] + MathTeXEngine v0.6.9 [a3b82374] + MatrixFactorizations v3.1.3 [fa1605e6] + MeasureBase v0.14.13 [eff96d63] + Measurements v2.14.1 [442fdcdd] + Measures v0.3.3 [c03570c3] + Memoize v0.4.4 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [568f7cb4] + MonotonicSplines v0.3.3 [e94cdb99] + MosaicViews v0.3.4 [2e0e35c7] + Moshi v0.3.12 [46d2c3a1] + MuladdMacro v0.2.7 ⌅ [d41bc354] + NLSolversBase v7.10.0 [15e1cf62] + NPZ v0.4.3 [611af6d1] + NVML v6.3.1 [5da4648a] + NVTX v1.0.3 [77ba4419] + NaNMath v1.1.4 [86f7a689] + NamedArrays v0.10.5 [b8a86587] + NearestNeighbors v0.4.29 [f09324ee] + Netpbm v1.1.1 [5b289081] + Newtrinos v0.1.0 [510215fc] + Observables v0.5.5 [6fe1bfb0] + OffsetArrays v1.17.0 [762dc654] + OneTwoMany v0.1.2 [52e1d378] + OpenEXR v0.3.3 ⌅ [429524aa] + Optim v1.13.3 ⌅ [7f7a1694] + Optimization v4.8.0 ⌅ [bca83a33] + OptimizationBase v2.14.0 ⌅ [bac558e1] + OrderedCollections v1.8.2 [afe20452] + PCHIPInterpolation v0.2.2 [90014a1f] + PDMats v0.11.41 [f57f5aa1] + PNGFiles v0.4.5 [19eb6ba3] + Packing v0.5.1 [5432bcbf] + PaddedViews v0.5.12 [43a3c2be] + PairPlots v3.0.8 [8e8a01fc] + ParallelProcessingTools v0.4.10 [d96e819e] + Parameters v0.13.1 ⌅ [69de0a69] + Parsers v2.8.8 [eebad327] + PkgVersion v0.3.3 [995b91a9] + PlotUtils v1.4.4 [f517fe37] + Polyester v0.7.19 [98d1487c] + PolyesterForwardDiff v0.1.4 [1d0040c9] + PolyesterWeave v0.2.2 [647866c9] + PolygonOps v0.1.2 [2dfb63ee] + PooledArrays v1.4.3 [85a6dd25] + PositiveFactorizations v0.2.4 [d236fae5] + PreallocationTools v1.7.1 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [54e16d92] + PrettyPrinting v0.4.3 [08abe8d2] + PrettyTables v3.4.8 [27ebfcd6] + Primes v0.5.7 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [0c0d3e7f] + PureKLU v1.4.1 [4b34888f] + QOI v1.0.2 [1fd47b50] + QuadGK v2.11.3 [be4d8f0f] + Quadmath v1.1.0 [74087812] + Random123 v1.7.1 [e6cf234a] + RandomNumbers v1.6.0 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 ⌅ [731186ca] + RecursiveArrayTools v3.54.0 [189a3867] + Reexport v1.2.2 [42d2dcc6] + Referenceables v0.1.3 [05181044] + RelocatableFolders v1.0.1 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [f2b01f46] + Roots v3.0.8 [5eaf0fd0] + RoundingEmulator v0.2.1 [7e49a35a] + RuntimeGeneratedFunctions v0.5.26 [fdea26ae] + SIMD v3.7.2 [94e857df] + SIMDTypes v0.1.0 ⌅ [0bca4576] + SciMLBase v2.155.2 ⌅ [a6db7da4] + SciMLLogging v1.10.1 [c0aeaf25] + SciMLOperators v1.30.0 [431bcebd] + SciMLPublic v1.3.0 [53ae85a6] + SciMLStructures v1.10.5 [30f210dd] + ScientificTypesBase v3.1.0 [7e506255] + ScopedValues v1.6.2 [6c6a2e73] + Scratch v1.3.0 [91c51154] + SentinelArrays v1.4.10 [efcf1570] + Setfield v1.1.2 [65257c39] + ShaderAbstractions v0.5.0 [73760f76] + SignedDistanceFields v0.4.1 [699a6c99] + SimpleTraits v0.9.6 [45858cf5] + Sixel v0.1.5 [ed01d8cd] + Sobol v1.5.0 [a2af1166] + SortingAlgorithms v1.2.3 [a57abbd0] + SparseColumnPivotedQR v2.1.8 [9f842d2f] + SparseConnectivityTracer v1.2.3 [dc90abb0] + SparseInverseSubset v0.1.3 [0a514795] + SparseMatrixColorings v0.4.28 [276daf66] + SpecialFunctions v2.9.0 [03a91e81] + SplitApplyCombine v1.3.0 [171d559e] + SplittablesBase v0.1.15 [860ef19b] + StableRNGs v1.0.4 [cae243ae] + StackViews v0.1.2 [aedffcd0] + Static v1.4.6 [0d7ed370] + StaticArrayInterface v1.10.0 [90137ffa] + StaticArrays v1.9.20 [1e83bf80] + StaticArraysCore v1.4.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.5 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.13 ⌅ [4c63d2b9] + StatsFuns v1.5.3 [7792a7ef] + StrideArraysCore v0.5.9 ⌅ [892a3eda] + StringManipulation v0.5.0 [09ab397b] + StructArrays v0.7.3 [ec057cc2] + StructUtils v2.8.5 [2efcf032] + SymbolicIndexingInterface v0.3.55 [ab02a1b2] + TableOperations v1.2.0 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.14.0 [62fd8b95] + TensorCore v0.1.1 [5d786b92] + TerminalLoggers v0.1.8 [b718987f] + TextWrap v1.0.2 [8290d209] + ThreadingUtilities v0.5.6 [ac1d9e8a] + ThreadsX v0.1.12 [731e570b] + TiffImages v0.11.9 [e689c965] + Tracy v0.1.6 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.85 [410a4b4d] + Tricks v0.1.13 [981d1d27] + TriplotBase v0.1.0 [d265eb64] + TypedSyntax v1.5.4 [9d95f2ec] + TypedTables v1.4.6 [3a884ed6] + UnPack v1.0.2 [1cfade01] + UnicodeFun v0.4.1 [1986cc42] + Unitful v1.29.0 [013be700] + UnsafeAtomics v0.3.2 [136a8f8c] + ValueShapes v0.11.7 [ea10d353] + WeakRefStrings v1.4.3 [e3aaa7dc] + WebP v0.1.3 [b8c1c048] + WidthLimitedIO v1.0.1 [efce3f68] + WoodburyMatrices v1.1.0 [76eceee3] + WorkerUtilities v1.6.1 [a5390f91] + ZipFile v0.10.1 [700de1a5] + ZygoteRules v0.2.8 [182d3088] + cuBLAS v6.3.1 [533571aa] + cuFFT v6.3.1 [20fd9a0b] + cuRAND v6.3.1 [887afef0] + cuSOLVER v6.3.1 [b26da814] + cuSPARSE v6.3.1 [6e34b625] + Bzip2_jll v1.0.9+0 [4e9b3aee] + CRlibm_jll v1.0.1+0 [d1e2174e] + CUDA_Compiler_jll v0.6.2+0 [4ee394cb] + CUDA_Driver_jll v13.3.4+0 [76a88914] + CUDA_Runtime_jll v0.24.4+2 [83423d85] + Cairo_jll v1.18.7+0 [a38c48d9] + CoreMath_jll v0.1.0+0 ⌅ [5ae413db] + EarCut_jll v2.2.4+0 [2e619515] + Expat_jll v2.8.4+0 ⌅ [b22a6f82] + FFMPEG_jll v8.1.2+0 [f5851436] + FFTW_jll v3.3.12+0 [a3f928ae] + Fontconfig_jll v2.17.1+0 [d7e528f0] + FreeType2_jll v2.14.3+1 [559328eb] + FriBidi_jll 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Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 18.3s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 6.6 s ✓ LazyReports → LazyReportsStatsBaseExt WARNING: Imported binding InteractiveUtils.is_expected_union was undeclared at import time during import to Cthulhu. WARNING: Imported binding Compiler.ConstCallInfo was undeclared at import time during import to Cthulhu. WARNING: Imported binding Compiler.WorldView was undeclared at import time during import to Cthulhu. ERROR: LoadError: UndefVarError: `ConstPropResult` not defined in `Compiler` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty(x::Module, f::Symbol) @ Base Base_compiler.jl:51 [2] top-level scope @ ~/.julia/packages/Cthulhu/7jFQA/src/CthulhuCompiler.jl:32 [3] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:335 [4] top-level scope @ ~/.julia/packages/Cthulhu/7jFQA/src/Cthulhu.jl:58 [5] include(mod::Module, _path::String) @ Base Base.jl:334 [6] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Cthulhu/7jFQA/src/CthulhuCompiler.jl:32 in expression starting at /home/pkgeval/.julia/packages/Cthulhu/7jFQA/src/Cthulhu.jl:1 in expression starting at stdin:5 ✗ Cthulhu 1.7 s ✓ AutoDiffOperators 1.2 s ✓ PCHIPInterpolation → PCHIPInterpolationForwardDiffExt 7.7 s ✓ MonotonicSplines 6.1 s ✓ OptimizationBase → OptimizationMLDataDevicesExt 5.3 s ✓ LinearSolve → LinearSolveIterativeSolversExt 5.0 s ✓ HypothesisTests 3.4 s ✓ EmpiricalDistributions 5.4 s ✓ ValueShapes 8.9 s ✓ DistributionsAD 6.4 s ✓ SciMLBase → SciMLBaseDistributionsExt 2.6 s ✓ DifferentiationInterface → DifferentiationInterfacePolyesterForwardDiffExt 404.6 s ✓ Makie 20.9 s ✓ HeterogeneousComputing → HeterogeneousComputingCUDAExt 1.1 s ✓ AutoDiffOperators → AutoDiffOperatorsLinearMapsExt 1.3 s ✓ AutoDiffOperators → AutoDiffOperatorsStaticArraysExt 2.9 s ✓ MonotonicSplines → MonotonicSplinesInverseFunctionsExt 2.4 s ✓ MonotonicSplines → MonotonicSplinesFunctorsExt 2.4 s ✓ MonotonicSplines → MonotonicSplinesChainRulesCoreExt 4.4 s ✓ ValueShapes → ValueShapesZygoteRulesExt 3.6 s ✓ ValueShapes → ValueShapesChainRulesCoreExt 3.0 s ✓ ValueShapes → ValueShapesChangesOfVariablesExt 3.6 s ✓ DistributionsAD → DistributionsADForwardDiffExt 38.7 s ✓ Measurements → MeasurementsMakieExt 38.7 s ✓ DomainSets → DomainSetsMakieExt 43.7 s ✓ SciMLBase → SciMLBaseMakieExt 115.0 s ✓ PairPlots 174.3 s ✓ CairoMakie 7.9 s ✓ MGVI 14.9 s ✓ BAT 22.6 s ✓ PairPlots → PairPlotsDynamicUnitfulExt 6.4 s ✓ MGVI → MGVIOptimizationExt 5.6 s ✓ MGVI → MGVIOptimExt 9.2 s ✓ BAT → BATHDF5Ext 8.6 s ✓ BAT → BATOptimExt 10.4 s ✓ BAT → BATOptimizationExt 11.2 s ✓ BAT → BATMGVIExt [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0x5f0c68e5277a35a4, 0xc01a35eb67f8d438, 0x20c0757f15e942de, 0xe0f7ee19f8122953, 0x4fffeacbf6a87caf, 0xe491480ac63aef95, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x5f0c68e5277a35a4, 0xc01a35eb67f8d438, 0x20c0757f15e942de, 0xe0f7ee19f8122953, 0x4fffeacbf6a87caf, 0xe491480ac63aef95, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) 46.7 s ✓ Newtrinos 38 dependencies successfully precompiled in 1093 seconds. 645 already precompiled. 2 dependencies had output during precompilation: ┌ Cthulhu │ WARNING: Imported binding InteractiveUtils.is_expected_union was undeclared at import time during import to Cthulhu. │ WARNING: Imported binding Compiler.ConstCallInfo was undeclared at import time during import to Cthulhu. │ WARNING: Imported binding Compiler.WorldView was undeclared at import time during import to Cthulhu. │ ERROR: LoadError: UndefVarError: `ConstPropResult` not defined in `Compiler` │ Suggestion: check for spelling errors or missing imports. │ Stacktrace: │ [1] getproperty(x::Module, f::Symbol) │ @ Base Base_compiler.jl:51 │ [2] top-level scope │ @ ~/.julia/packages/Cthulhu/7jFQA/src/CthulhuCompiler.jl:32 │ [3] include(mapexpr::Function, mod::Module, _path::String) │ @ Base Base.jl:335 │ [4] top-level scope │ @ ~/.julia/packages/Cthulhu/7jFQA/src/Cthulhu.jl:58 │ [5] include(mod::Module, _path::String) │ @ Base Base.jl:334 │ [6] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Cthulhu/7jFQA/src/CthulhuCompiler.jl:32 │ in expression starting at /home/pkgeval/.julia/packages/Cthulhu/7jFQA/src/Cthulhu.jl:1 │ in expression starting at stdin:5 └ ┌ Newtrinos │ [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0x5f0c68e5277a35a4, 0xc01a35eb67f8d438, 0x20c0757f15e942de, 0xe0f7ee19f8122953, 0x4fffeacbf6a87caf, 0xe491480ac63aef95, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) │ [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x5f0c68e5277a35a4, 0xc01a35eb67f8d438, 0x20c0757f15e942de, 0xe0f7ee19f8122953, 0x4fffeacbf6a87caf, 0xe491480ac63aef95, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) └ ERROR: LoadError: The following 1 package failed to precompile: Cthulhu Failed to precompile Cthulhu [f68482b8-f384-11e8-15f7-abe071a5a75f] to "/home/pkgeval/.julia/compiled/v1.14/Cthulhu/jl_zWSwty" (ProcessExited(1)). in expression starting at /PkgEval.jl/scripts/precompile.jl:34 Precompilation failed after 1115.9s ################################################################################ # Testing # Testing Newtrinos Status `/tmp/jl_HLCbKw/Project.toml` [47edcb42] ADTypes v1.24.0 [7d9f7c33] Accessors v0.1.45 [c7e460c6] ArgParse v1.2.0 ⌅ [65a8f2f4] ArraysOfArrays v0.6.6 ⌅ [6e1301d5] AutoDiffOperators v0.3.2 ⌃ [c0cd4b16] BAT v4.0.4 [6e4b80f9] BenchmarkTools v1.8.0 [0e736298] Bessels v0.2.8 [e2ed5e7c] Bijections v0.2.2 [336ed68f] CSV v0.10.17 [052768ef] CUDA v6.3.1 [13f3f980] CairoMakie v0.15.14 [082447d4] ChainRules v1.73.0 [d360d2e6] ChainRulesCore v1.26.1 [35d6a980] ColorSchemes v3.31.0 [3da002f7] ColorTypes v0.12.1 [5ae59095] Colors v0.13.1 [ac509c8a] ContentHashes v0.1.0 [f68482b8] Cthulhu v3.0.2 [a93c6f00] DataFrames v1.8.2 [864edb3b] DataStructures v0.19.6 [8bb1440f] DelimitedFiles v1.9.1 [b429d917] DensityInterface v0.4.0 [a0c0ee7d] DifferentiationInterface v0.7.21 [8d63f2c5] DispatchDoctor v0.4.28 [31c24e10] Distributions v0.25.131 [5789e2e9] FileIO v1.20.0 [1a297f60] FillArrays v1.17.0 [f6369f11] ForwardDiff v1.4.5 [8e6b2b91] FunctionChains v0.2.7 [f67ccb44] HDF5 v0.17.3 [a98d9a8b] Interpolations v0.16.3 [3587e190] InverseFunctions v0.1.17 [c8e1da08] IterTools v1.10.0 [033835bb] JLD2 v0.6.6 [5ab0869b] KernelDensity v0.6.12 [b964fa9f] LaTeXStrings v1.4.1 [e5afb96c] LazyReports v0.2.4 ⌅ [2fda8390] LsqFit v0.15.1 ⌅ [fdae7790] MGVI v0.4.3 [ee78f7c6] Makie v0.24.14 [fa1605e6] MeasureBase v0.14.13 [c03570c3] Memoize v0.4.4 [568f7cb4] MonotonicSplines v0.3.3 [15e1cf62] NPZ v0.4.3 [5b289081] Newtrinos v0.1.0 ⌅ [429524aa] Optim v1.13.3 ⌅ [7f7a1694] Optimization v4.8.0 [afe20452] PCHIPInterpolation v0.2.2 [90014a1f] PDMats v0.11.41 [43a3c2be] PairPlots v3.0.8 [98d1487c] PolyesterForwardDiff v0.1.4 [85a6dd25] PositiveFactorizations v0.2.4 [92933f4c] ProgressMeter v1.11.0 [276daf66] SpecialFunctions v2.9.0 [90137ffa] StaticArrays v1.9.20 [10745b16] Statistics v1.11.5 [2913bbd2] StatsBase v0.34.13 [09ab397b] StructArrays v0.7.3 [bd369af6] Tables v1.14.0 [ac1d9e8a] ThreadsX v0.1.12 [9d95f2ec] TypedTables v1.4.6 [136a8f8c] ValueShapes v0.11.7 [ade2ca70] Dates v1.11.0 [76f85450] LibGit2 v1.11.0 [37e2e46d] LinearAlgebra v1.14.0 [56ddb016] Logging v1.11.0 [de0858da] Printf v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_HLCbKw/Manifest.toml` [47edcb42] ADTypes v1.24.0 [14f7f29c] AMD v0.5.4 [621f4979] AbstractFFTs v1.5.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.45 [79e6a3ab] Adapt v4.7.0 [35492f91] AdaptivePredicates v1.2.0 [2c83c9a8] AffineMaps v0.3.5 [66dad0bd] AliasTables v1.1.3 [27a7e980] Animations v0.4.2 [dce04be8] ArgCheck v2.5.0 [c7e460c6] ArgParse v1.2.0 [4fba245c] ArrayInterface v7.30.1 [4c555306] ArrayLayouts v1.12.2 ⌅ [65a8f2f4] ArraysOfArrays v0.6.6 [a9b6321e] Atomix v1.1.3 ⌅ [6e1301d5] AutoDiffOperators v0.3.2 [67c07d97] Automa v1.2.0 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libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.70.0+0 [3f19e933] p7zip_jll v17.8.2+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... ┌ Warning: accessing `Type.name` is deprecated without replacement. If for detection, use `Base.isType(x)`. │ caller = Truncated(d::Normal{Float64}, l::Float64, u::Float64) at truncate.jl:109 └ @ Distributions ~/.julia/packages/Distributions/RTt4y/src/truncate.jl:109 ┌ Warning: `Truncated(d::UnivariateDistribution, l::Real, u::Real)` is deprecated, use `truncated(d, l, u)` instead. │ caller = ip:0x0 └ @ Core :-1 ┌ Warning: extrapolate(itp, Linear()) is deprecated, use extrapolate(itp, Line()) instead │ caller = (::Newtrinos.xsec.var"#make_interpolation#get_scale##1")(name::String, df::DataFrames.DataFrame) at xsec.jl:215 └ @ Newtrinos.xsec ~/.julia/packages/Newtrinos/RHjks/src/physics/xsec.jl:215 ┌ Warning: `_unsetindex!(A, i)` is deprecated, use `Base.unsetindex!(A, i)` instead. │ caller = _delete!(h::OrderedCollections.OrderedDict{Symbol, Any}, index::Int64) at ordered_dict.jl:441 [inlined] └ @ OrderedCollections ~/.julia/packages/OrderedCollections/MglBA/src/ordered_dict.jl:441 ┌ Warning: `_unsetindex!(A, i)` is deprecated, use `Base.unsetindex!(A, i)` instead. │ caller = _delete!(h::OrderedCollections.OrderedDict{Symbol, Any}, index::Int64) at ordered_dict.jl:442 [inlined] └ @ OrderedCollections ~/.julia/packages/OrderedCollections/MglBA/src/ordered_dict.jl:442 ┌ Warning: `_unsetindex!(A, i)` is deprecated, use `Base.unsetindex!(A, i)` instead. │ caller = _delete!(h::OrderedCollections.OrderedDict{Symbol, Distribution}, index::Int64) at ordered_dict.jl:441 [inlined] └ @ OrderedCollections ~/.julia/packages/OrderedCollections/MglBA/src/ordered_dict.jl:441 ┌ Warning: `_unsetindex!(A, i)` is deprecated, use `Base.unsetindex!(A, i)` instead. │ caller = _delete!(h::OrderedCollections.OrderedDict{Symbol, Distribution}, index::Int64) at ordered_dict.jl:442 [inlined] └ @ OrderedCollections ~/.julia/packages/OrderedCollections/MglBA/src/ordered_dict.jl:442 ┌ Warning: accessing `Type.name` is deprecated without replacement. If for detection, use `Base.isType(x)`. │ caller = MvNormal(σ::Vector{Int64}) at mvnormal.jl:207 └ @ Distributions ~/.julia/packages/Distributions/RTt4y/src/multivariate/mvnormal.jl:207 ┌ Warning: accessing `Type.name` is deprecated without replacement. If for detection, use `Base.isType(x)`. │ caller = Truncated(d::Normal{Float64}, l::Int64, u::Int64) at truncate.jl:109 └ @ Distributions ~/.julia/packages/Distributions/RTt4y/src/truncate.jl:109 [ Info: Configuring CEvNS cross-section assets [ Info: Configuring CEvNS cross-section assets [ Info: Configuring CEvNS cross-section assets [ Info: Configuring CEvNS cross-section assets ┌ Warning: accessing `Type.name` is deprecated without replacement. If for detection, use `Base.isType(x)`. │ caller = MvNormal(μ::Vector{Float64}, σ::Float64) at mvnormal.jl:206 └ @ Distributions ~/.julia/packages/Distributions/RTt4y/src/multivariate/mvnormal.jl:206 [ Info: Poisson-based model. Rounding Asimov data to nearest integer. [ Info: Not Poisson-based model. Returning std floating-point Asimov data. [ Info: Poisson-based model. Rounding Asimov data to nearest integer. [ Info: Not Poisson-based model. Returning std floating-point Asimov data. [ Info: Not Poisson-based model. Returning std floating-point Asimov data. [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0xafde3b27711340dd, 0xc7bdf931292eb1bc, 0x215f5faec4893bca, 0xa2adc785d88073fc, 0xab13256f7106f44a, 0xb36be953684329d3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xafde3b27711340dd, 0xc7bdf931292eb1bc, 0x215f5faec4893bca, 0xa2adc785d88073fc, 0xab13256f7106f44a, 0xb36be953684329d3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xafde3b27711340dd, 0xc7bdf931292eb1bc, 0x215f5faec4893bca, 0xa2adc785d88073fc, 0xab13256f7106f44a, 0xb36be953684329d3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.5 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xafde3b27711340dd, 0xc7bdf931292eb1bc, 0x215f5faec4893bca, 0xa2adc785d88073fc, 0xab13256f7106f44a, 0xb36be953684329d3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xafde3b27711340dd, 0xc7bdf931292eb1bc, 0x215f5faec4893bca, 0xa2adc785d88073fc, 0xab13256f7106f44a, 0xb36be953684329d3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point [ Info: using cached file /tmp/jl_3H9FwK/bb17f276054c0e20cc2cc6af1c2cb589438a528c.jld2 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xafde3b27711340dd, 0xc7bdf931292eb1bc, 0x215f5faec4893bca, 0xa2adc785d88073fc, 0xab13256f7106f44a, 0xb36be953684329d3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point ┌ Warning: `quantile(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(quantile, d), X)` instead. │ caller = (::Newtrinos.var"#_generate_grid##0#_generate_grid##1"{OrderedDict{Symbol, Int64}, @NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}})(var::Symbol) at analysis_tools.jl:635 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:635 ┌ Warning: `product(xss...)` is deprecated, use `Iterators.product(xss...)` instead. │ caller = _generate_grid(vars_to_scan::OrderedDict{Symbol, Int64}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}) at analysis_tools.jl:636 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:636 ┌ Warning: `quantile(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(quantile, d), X)` instead. │ caller = (::Newtrinos.var"#_generate_grid##0#_generate_grid##1"{OrderedDict{Symbol, Int64}, @NamedTuple{x::Uniform{Float64}, y::Uniform{Float64}}})(var::Symbol) at analysis_tools.jl:635 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:635 ┌ Warning: `product(xss...)` is deprecated, use `Iterators.product(xss...)` instead. │ caller = _generate_grid(vars_to_scan::OrderedDict{Symbol, Int64}, priors::@NamedTuple{x::Uniform{Float64}, y::Uniform{Float64}}) at analysis_tools.jl:636 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:636 ┌ Warning: `quantile(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(quantile, d), X)` instead. │ caller = (::Newtrinos.var"#_generate_grid##0#_generate_grid##1"{OrderedDict{Symbol, Int64}, NamedTupleDist{(:a, :b), Tuple{Uniform{Float64}, Uniform{Float64}}, Tuple{ValueAccessor{ScalarShape{Real}}, ValueAccessor{ScalarShape{Real}}}, NamedTuple}})(var::Symbol) at analysis_tools.jl:635 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:635 ┌ Warning: `product(xss...)` is deprecated, use `Iterators.product(xss...)` instead. │ caller = _generate_grid(vars_to_scan::OrderedDict{Symbol, Int64}, priors::NamedTupleDist{(:a, :b), Tuple{Uniform{Float64}, Uniform{Float64}}, Tuple{ValueAccessor{ScalarShape{Real}}, ValueAccessor{ScalarShape{Real}}}, NamedTuple}) at analysis_tools.jl:636 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:636 Progress: 40%|████████████████▍ | ETA: 0:00:00 Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 scan: Error During Test at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:491 Got exception outside of a @test GitError(Code:ENOTFOUND, Class:Repository, could not find repository at '/home/pkgeval/.julia/packages/Newtrinos/RHjks') Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/error.jl:124 [inlined] [2] LibGit2.GitRepo(path::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/repository.jl:11 [3] with(f::LibGit2.var"#head##0#head##1", ::Core.TypeEgal{LibGit2.GitRepo}, args::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/types.jl:1236 [inlined] [4] head(pkg::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/LibGit2.jl:65 [inlined] [5] add_meta!(meta::Dict{String, Any}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:933 [6] scan(likelihood::Likelihood{Base.Splat{var"#133#134"}, Vector{Float64}}, priors::NamedTupleDist{(:a, :b), Tuple{Uniform{Float64}, Uniform{Float64}}, Tuple{ValueAccessor{ScalarShape{Real}}, ValueAccessor{ScalarShape{Real}}}, NamedTuple}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}; gradient_map::Bool) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:872 [7] scan(likelihood::Likelihood{Base.Splat{var"#133#134"}, Vector{Float64}}, priors::NamedTupleDist{(:a, :b), Tuple{Uniform{Float64}, Uniform{Float64}}, Tuple{ValueAccessor{ScalarShape{Real}}, ValueAccessor{ScalarShape{Real}}}, NamedTuple}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:832 [8] top-level scope @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:31 [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [10] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:492 [inlined] [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [12] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:498 [inlined] [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0xf9f926a61b7be742, 0xcdc28e362a2ffa36, 0x49552dc873d25b12, 0xf8ef2e4b79da0b31, 0x15298adc82ec4eb8, 0xd4b3f9ad6dbd1f70, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xf9f926a61b7be742, 0xcdc28e362a2ffa36, 0x49552dc873d25b12, 0xf8ef2e4b79da0b31, 0x15298adc82ec4eb8, 0xd4b3f9ad6dbd1f70, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xf9f926a61b7be742, 0xcdc28e362a2ffa36, 0x49552dc873d25b12, 0xf8ef2e4b79da0b31, 0x15298adc82ec4eb8, 0xd4b3f9ad6dbd1f70, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 Progress: 67%|███████████████████████████▍ | ETA: 0:00:02[ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xf9f926a61b7be742, 0xcdc28e362a2ffa36, 0x49552dc873d25b12, 0xf8ef2e4b79da0b31, 0x15298adc82ec4eb8, 0xd4b3f9ad6dbd1f70, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 profile with nuisance optimization: Error During Test at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:532 Got exception outside of a @test GitError(Code:ENOTFOUND, Class:Repository, could not find repository at '/home/pkgeval/.julia/packages/Newtrinos/RHjks') Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/error.jl:124 [inlined] [2] LibGit2.GitRepo(path::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/repository.jl:11 [3] with(f::LibGit2.var"#head##0#head##1", ::Core.TypeEgal{LibGit2.GitRepo}, args::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/types.jl:1236 [inlined] [4] head(pkg::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/LibGit2.jl:65 [inlined] [5] add_meta!(meta::Dict{String, Any}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:933 [6] profile(likelihood::Likelihood{Base.Splat{var"#135#136"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}; cache_dir::Nothing, map_func::Nothing) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:802 [7] profile(likelihood::Likelihood{Base.Splat{var"#135#136"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:784 [8] top-level scope @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:31 [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [10] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:530 [inlined] [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [12] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:532 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [14] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:533 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [16] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:539 [inlined] ┌ Warning: `quantile(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(quantile, d), X)` instead. │ caller = (::Newtrinos.var"#_generate_grid##0#_generate_grid##1"{OrderedDict{Symbol, Int64}, @NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}, c::Uniform{Float64}}})(var::Symbol) at analysis_tools.jl:635 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:635 ┌ Warning: `product(xss...)` is deprecated, use `Iterators.product(xss...)` instead. │ caller = _generate_grid(vars_to_scan::OrderedDict{Symbol, Int64}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}, c::Uniform{Float64}}) at analysis_tools.jl:636 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:636 [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: -3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: -3.0 Progress: 10%|███▉ | ETA: 0:00:34[ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: -3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: -2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: -2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: -2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: -1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: -1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: -1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 0.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 0.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 0.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x1391851ef0d0c530, 0x8748db90d47a5af7, 0xce3b1a7ba7fda9f8, 0x4d7913e6f9ead0a8, 0xee69deadd9b8e112, 0x94a4796e8b5301e8, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 3.0 Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 profile 2D grid dimensions: Error During Test at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:549 Got exception outside of a @test GitError(Code:ENOTFOUND, Class:Repository, could not find repository at '/home/pkgeval/.julia/packages/Newtrinos/RHjks') Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/error.jl:124 [inlined] [2] LibGit2.GitRepo(path::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/repository.jl:11 [3] with(f::LibGit2.var"#head##0#head##1", ::Core.TypeEgal{LibGit2.GitRepo}, args::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/types.jl:1236 [inlined] [4] head(pkg::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/LibGit2.jl:65 [inlined] [5] add_meta!(meta::Dict{String, Any}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:933 [6] profile(likelihood::Likelihood{Base.Splat{var"#141#142"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}, c::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64, c::Float64}; cache_dir::Nothing, map_func::Nothing) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:802 [7] profile(likelihood::Likelihood{Base.Splat{var"#141#142"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}, c::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64, c::Float64}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:784 [8] top-level scope @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:31 [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [10] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:530 [inlined] [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [12] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:532 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [14] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:550 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [16] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:556 [inlined] [ Info: Reusing cache dir `/tmp/jl_nXBUpB` [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0x077ff14b5bcdad44, 0x3ab6ddfd0b4ff061, 0x4e72c4b9b38f9166, 0x6669fd37726f3ade, 0x6da5691d9c27ce1f, 0x4556c3ca0f4e79d4, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x077ff14b5bcdad44, 0x3ab6ddfd0b4ff061, 0x4e72c4b9b38f9166, 0x6669fd37726f3ade, 0x6da5691d9c27ce1f, 0x4556c3ca0f4e79d4, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x077ff14b5bcdad44, 0x3ab6ddfd0b4ff061, 0x4e72c4b9b38f9166, 0x6669fd37726f3ade, 0x6da5691d9c27ce1f, 0x4556c3ca0f4e79d4, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 Progress: 67%|███████████████████████████▍ | ETA: 0:00:01[ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x077ff14b5bcdad44, 0x3ab6ddfd0b4ff061, 0x4e72c4b9b38f9166, 0x6669fd37726f3ade, 0x6da5691d9c27ce1f, 0x4556c3ca0f4e79d4, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 profile with cached dir: Error During Test at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:568 Got exception outside of a @test GitError(Code:ENOTFOUND, Class:Repository, could not find repository at '/home/pkgeval/.julia/packages/Newtrinos/RHjks') Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/error.jl:124 [inlined] [2] LibGit2.GitRepo(path::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/repository.jl:11 [3] with(f::LibGit2.var"#head##0#head##1", ::Core.TypeEgal{LibGit2.GitRepo}, args::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/types.jl:1236 [inlined] [4] head(pkg::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/LibGit2.jl:65 [inlined] [5] add_meta!(meta::Dict{String, Any}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:933 [6] profile(likelihood::Likelihood{Base.Splat{var"#149#150"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}; cache_dir::String, map_func::Nothing) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:802 [7] (::var"#151#152"{@NamedTuple{a::Float64, b::Float64}, @NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}, Likelihood{Base.Splat{var"#149#150"}, Vector{Float64}}})(cache_dir::String) @ Main ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:576 [8] mktempdir(fn::var"#151#152"{@NamedTuple{a::Float64, b::Float64}, @NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}}, Likelihood{Base.Splat{var"#149#150"}, Vector{Float64}}}, parent::String; prefix::String) @ Base.Filesystem file.jl:949 [9] mktempdir(fn::Function, parent::String) @ Base.Filesystem file.jl:945 [10] top-level scope @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:31 [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [12] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:530 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [14] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:532 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [16] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:569 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [18] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:574 [inlined] ┌ Warning: `quantile(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(quantile, d), X)` instead. │ caller = (::Newtrinos.var"#_generate_grid##0#_generate_grid##1"{OrderedDict{Symbol, Int64}, @NamedTuple{a::Float64, b::Uniform{Float64}}})(var::Symbol) at analysis_tools.jl:635 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:635 ┌ Warning: `product(xss...)` is deprecated, use `Iterators.product(xss...)` instead. │ caller = _generate_grid(vars_to_scan::OrderedDict{Symbol, Int64}, priors::@NamedTuple{a::Float64, b::Uniform{Float64}}) at analysis_tools.jl:636 └ @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:636 Progress: 40%|████████████████▍ | ETA: 0:00:00 Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 profile fallback to scan: Error During Test at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:587 Got exception outside of a @test GitError(Code:ENOTFOUND, Class:Repository, could not find repository at '/home/pkgeval/.julia/packages/Newtrinos/RHjks') Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/error.jl:124 [inlined] [2] LibGit2.GitRepo(path::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/repository.jl:11 [3] with(f::LibGit2.var"#head##0#head##1", ::Core.TypeEgal{LibGit2.GitRepo}, args::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/types.jl:1236 [inlined] [4] head(pkg::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/LibGit2.jl:65 [inlined] [5] add_meta!(meta::Dict{String, Any}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:933 [6] scan(likelihood::Likelihood{Base.Splat{var"#153#154"}, Vector{Float64}}, priors::@NamedTuple{a::Float64, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}; gradient_map::Bool) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:872 [7] scan(likelihood::Likelihood{Base.Splat{var"#153#154"}, Vector{Float64}}, priors::@NamedTuple{a::Float64, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:832 [inlined] [8] profile(likelihood::Likelihood{Base.Splat{var"#153#154"}, Vector{Float64}}, priors::@NamedTuple{a::Float64, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}; cache_dir::Nothing, map_func::Nothing) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:788 [9] profile(likelihood::Likelihood{Base.Splat{var"#153#154"}, Vector{Float64}}, priors::@NamedTuple{a::Float64, b::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:784 [10] top-level scope @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:31 [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [12] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:530 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [14] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:589 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [16] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:596 [inlined] [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: -3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: -3.0 Progress: 10%|███▉ | ETA: 0:00:21[ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: -3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: -2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: -2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: -2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: -1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: -1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: -1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 0.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 0.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 0.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 1.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 2.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: -3.0 b: 3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 0.0 b: 3.0 [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xd9f4a57898fd78f6, 0x8489c8a7999fd1b5, 0x2b4651df3a582e6e, 0x055fe4c2afe78332, 0x6ff6e22ccea57d67, 0x0acb34077cad0bb3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoPolyesterForwardDiff()) [ Info: Running Optimization for point a: 3.0 b: 3.0 Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 bestfit with results from profiling: Error During Test at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:677 Got exception outside of a @test GitError(Code:ENOTFOUND, Class:Repository, could not find repository at '/home/pkgeval/.julia/packages/Newtrinos/RHjks') Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/error.jl:124 [inlined] [2] LibGit2.GitRepo(path::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/repository.jl:11 [3] with(f::LibGit2.var"#head##0#head##1", ::Core.TypeEgal{LibGit2.GitRepo}, args::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/types.jl:1236 [inlined] [4] head(pkg::String) @ LibGit2 /opt/julia/share/julia/stdlib/v1.14/LibGit2/src/LibGit2.jl:65 [inlined] [5] add_meta!(meta::Dict{String, Any}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:933 [6] profile(likelihood::Likelihood{Base.Splat{var"#165#166"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}, c::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64, c::Float64}; cache_dir::Nothing, map_func::Nothing) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:802 [7] profile(likelihood::Likelihood{Base.Splat{var"#165#166"}, Vector{Float64}}, priors::@NamedTuple{a::Uniform{Float64}, b::Uniform{Float64}, c::Uniform{Float64}}, vars_to_scan::OrderedDict{Symbol, Int64}, params::@NamedTuple{a::Float64, b::Float64, c::Float64}) @ Newtrinos ~/.julia/packages/Newtrinos/RHjks/src/analysis/analysis_tools.jl:784 [8] top-level scope @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:31 [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [10] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:631 [inlined] [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [12] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:678 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2252 [inlined] [14] macro expansion @ ~/.julia/packages/Newtrinos/RHjks/test/test_analysis.jl:683 [inlined] [ Info: Loading dayabay data [ Info: Loading kamland data ┌ Warning: `pdf(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(pdf, d), X)` instead. │ caller = get_assets(physics::@NamedTuple{osc::Newtrinos.osc.Osc}, datadir::String) at kamland.jl:84 └ @ Newtrinos.kamland ~/.julia/packages/Newtrinos/RHjks/src/experiments/kamland/kamland_7years/kamland.jl:84 [ Info: Loading minos data [ Info: Loading deepcore data [ Info: Loading Super-K Data [ Info: Flux is not fully configured yet. [ Info: Loading and binning CsI data [ Info: Configuring Flux [ Info: Configuring CEvNS cross-section assets [ Info: Configured COHERENT CsI module. [ Info: Loading coherent lAr data [ Info: Configuring CEvNS cross-section assets ╔══════════════════════════════════════════════════════════════════════════════════════╗ ║ Likelihood Regression Summary ║ ╠══════════════╦══════════════════╦══════════════════╦══════════════╦═════════════════╣ ║ Experiment ║ Reference ║ Actual ║ Rel. Diff ║ Status ║ ╠══════════════╬══════════════════╬══════════════════╬══════════════╬═════════════════╣ ║ dayabay ║ -168.900033 ║ -168.900033 ║ 1.18e-15 ║ OK ║ ║ kamland ║ -63.111404 ║ -63.111404 ║ 0.00e+00 ║ OK ║ ║ minos ║ -268.336328 ║ -268.336328 ║ 0.00e+00 ║ OK ║ ║ deepcore ║ -950.706345 ║ -950.706345 ║ 9.55e-14 ║ OK ║ ║ super_k ║ -3706.345109 ║ -3706.345109 ║ 1.21e-14 ║ OK ║ ║ orca ║ -1164.250608 ║ -1164.250608 ║ 0.00e+00 ║ OK ║ ║ coherent_csi ║ -574.341603 ║ -574.341603 ║ 0.00e+00 ║ OK ║ ║ coherent_lAr ║ -1754.994694 ║ -1754.994694 ║ 0.00e+00 ║ OK ║ ╚══════════════╩══════════════════╩══════════════════╩══════════════╩═════════════════╝ [ Info: Loading dayabay data [ Info: Loading dayabay data ┌ Warning: `ccdf(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(ccdf, d), X)` instead. │ caller = plot!(ax::Axis, result::NewtrinosResult) at plotting.jl:59 └ @ Newtrinos.plotting ~/.julia/packages/Newtrinos/RHjks/src/utils/plotting.jl:59 ┌ Warning: `quantile(d::UnivariateDistribution, X::AbstractArray{<:Real})` is deprecated, use `map(Base.Fix1(quantile, d), X)` instead. │ caller = plot!(ax::Axis, result::NewtrinosResult; max_llh::Float64, levels::Vector{Float64}, label::Nothing, color::Symbol, linestyle::Symbol, cmap::Symbol, filled::Bool, edge::Bool, transform_x::typeof(identity), transform_y::typeof(identity)) at plotting.jl:65 └ @ Newtrinos.plotting ~/.julia/packages/Newtrinos/RHjks/src/utils/plotting.jl:65 [ Info: Using transform algorithm PriorSubstitution() Progress: 4%|█▋ | ETA: 0:00:18 Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 [ Info: Using transform algorithm PriorSubstitution() ┌ Warning: accessing `Type.name` is deprecated without replacement. If for detection, use `Base.isType(x)`. │ caller = MvNormal(μ::Vector{Float64}, σ::Vector{Float64}) at mvnormal.jl:205 └ @ Distributions ~/.julia/packages/Distributions/RTt4y/src/multivariate/mvnormal.jl:205 [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Finding modes [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0x88967d82cf56d25f, 0x5f98a8b3e48d756a, 0xfe07fcaac860ef63, 0xdc689325a98f81f4, 0x5e100bfe89cc757f, 0xf03fb9c355f1f576, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x88967d82cf56d25f, 0x5f98a8b3e48d756a, 0xfe07fcaac860ef63, 0xdc689325a98f81f4, 0x5e100bfe89cc757f, 0xf03fb9c355f1f576, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x88967d82cf56d25f, 0x5f98a8b3e48d756a, 0xfe07fcaac860ef63, 0xdc689325a98f81f4, 0x5e100bfe89cc757f, 0xf03fb9c355f1f576, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Finding modes [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0x5e67bc4251479e38, 0xf222a02c0c56d559, 0xbc7cf59704680b04, 0x66c5252c853f8812, 0x2f93eded9eabb757, 0x336a6b3d7ae9d4e3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x5e67bc4251479e38, 0xf222a02c0c56d559, 0xbc7cf59704680b04, 0x66c5252c853f8812, 0x2f93eded9eabb757, 0x336a6b3d7ae9d4e3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0x5e67bc4251479e38, 0xf222a02c0c56d559, 0xbc7cf59704680b04, 0x66c5252c853f8812, 0x2f93eded9eabb757, 0x336a6b3d7ae9d4e3, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: Generating initial samples Progress: 4%|█▋ | ETA: 0:00:14 Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Finding modes [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0xed510f7e3ca1d391, 0x13c9ed40b313ee8a, 0x9168c1b4101b06df, 0x94127a8ade7b6942, 0xa73ee1f7b16591ba, 0xaca8dd28d2c63df2, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xed510f7e3ca1d391, 0x13c9ed40b313ee8a, 0x9168c1b4101b06df, 0x94127a8ade7b6942, 0xa73ee1f7b16591ba, 0xaca8dd28d2c63df2, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xed510f7e3ca1d391, 0x13c9ed40b313ee8a, 0x9168c1b4101b06df, 0x94127a8ade7b6942, 0xa73ee1f7b16591ba, 0xaca8dd28d2c63df2, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 63.232701244452144 [ Info: Efficiency = 0.6323270124445215 [ Info: Generating 44 new samples [ Info: Effective sample size = 116.7092433902147 [ Info: Efficiency = 0.810480856876491 [ Info: Generating 46 new samples [ Info: Effective sample size = 164.7995248475397 [ Info: Efficiency = 0.8673659202502089 [ Info: Generating 45 new samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 63.232701244452144 [ Info: Efficiency = 0.6323270124445215 [ Info: Generating 44 new samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 50.00000000000002 [ Info: Efficiency = 1.0000000000000004 [ Info: Generating 7 new samples [ Info: Effective sample size = 53.873679658875105 [ Info: Efficiency = 0.945152274717107 [ Info: Generating 3 new samples [ Info: Effective sample size = 56.660816236491634 [ Info: Efficiency = 0.9443469372748605 [ Info: Generating 1 new samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 63.232701244452144 [ Info: Efficiency = 0.6323270124445215 [ Info: Generating 44 new samples [ Info: Effective sample size = 117.21077819109243 [ Info: Efficiency = 0.8139637374381419 [ Info: Generating 46 new samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Finding modes [ Info: Setting new default BAT context BATContext{Float64}(Random123.Philox4x{UInt64, 10}(0xf29c4cf7d0232778, 0x29230126b46cfd39, 0x07784ff0de463305, 0xfa35961d83a00a10, 0x5e27880046d93d79, 0xad5d83d6c42191fb, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), HeterogeneousComputing.CPUnit(), ADTypes.NoAutoDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xf29c4cf7d0232778, 0x29230126b46cfd39, 0x07784ff0de463305, 0xfa35961d83a00a10, 0x5e27880046d93d79, 0xad5d83d6c42191fb, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: (cuinit = HeterogeneousComputing.CPUnit(), precision = Float64, rng = Random123.Philox4x{UInt64, 10}(0xf29c4cf7d0232778, 0x29230126b46cfd39, 0x07784ff0de463305, 0xfa35961d83a00a10, 0x5e27880046d93d79, 0xad5d83d6c42191fb, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0x0000000000000000, 0), ad = ADTypes.AutoForwardDiff()) [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 106.76134547204244 [ Info: Efficiency = 0.7736329382032061 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 106.81903108528121 [ Info: Efficiency = 0.7740509498933421 [ Info: Effective sample size = 162.0402893092114 [ Info: Efficiency = 0.8665256112792054 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 107.27234046548604 [ Info: Efficiency = 0.7773358004745365 [ Info: Effective sample size = 160.66086177122625 [ Info: Efficiency = 0.8591489934290174 [ Info: Effective sample size = 211.83618620731488 [ Info: Efficiency = 0.9014305796055953 [ Info: Effective sample size = 251.37684390033846 [ Info: Efficiency = 0.9074976314091641 [ Info: Effective sample size = 301.07941932761895 [ Info: Efficiency = 0.9292574670605523 [ Info: Effective sample size = 350.62967696079767 [ Info: Efficiency = 0.9450934688970287 [ Info: Effective sample size = 398.8986005673345 [ Info: Efficiency = 0.9543028721706567 [ Info: Effective sample size = 447.26738872884556 [ Info: Efficiency = 0.9618653521050442 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 143.00710656057953 [ Info: Efficiency = 0.8171834660604544 [ Info: Effective sample size = 196.31730537179305 [ Info: Efficiency = 0.8248626276125759 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 112.63886032616853 [ Info: Efficiency = 0.7714990433299214 [ Info: Effective sample size = 182.50554041600827 [ Info: Efficiency = 0.8774304827692705 [ Info: Effective sample size = 250.01814742759288 [ Info: Efficiency = 0.9259931386207144 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 121.59059176729338 [ Info: Efficiency = 0.7895492971902167 [ Info: Effective sample size = 191.0233268351682 [ Info: Efficiency = 0.8682878492507645 [ Info: Effective sample size = 246.507786442067 [ Info: Efficiency = 0.886718656266428 [ Info: Effective sample size = 309.6831689678396 [ Info: Efficiency = 0.9108328499054106 [ Info: Effective sample size = 379.224847753979 [ Info: Efficiency = 0.9340513491477316 [ Info: Effective sample size = 447.8514358561036 [ Info: Efficiency = 0.9488377878307279 [ Info: Effective sample size = 516.0019122106202 [ Info: Efficiency = 0.9591113609862828 [ Info: Effective sample size = 582.2687950580724 [ Info: Efficiency = 0.9656198923019443 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 107.43568363979034 [ Info: Efficiency = 0.7785194466651474 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 108.64298108899466 [ Info: Efficiency = 0.7872679789057584 [ Info: Effective sample size = 163.5231537344364 [ Info: Efficiency = 0.874455367563831 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 108.83676968527143 [ Info: Efficiency = 0.7886722440961698 [ Info: Effective sample size = 161.67711125730295 [ Info: Efficiency = 0.8645834826593741 [ Info: Effective sample size = 211.37403481418278 [ Info: Efficiency = 0.9033078410862512 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 107.7741729393091 [ Info: Efficiency = 0.7809722676761529 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 108.05260633603247 [ Info: Efficiency = 0.7829899009857425 [ Info: Effective sample size = 162.94799120877616 [ Info: Efficiency = 0.8713796321324928 [ Info: Effective sample size = 211.4243253300402 [ Info: Efficiency = 0.9035227578206846 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 106.95769006374168 [ Info: Efficiency = 0.7750557250995774 [ Info: Effective sample size = 160.95612515533725 [ Info: Efficiency = 0.8607279420071511 [ Info: Effective sample size = 211.8689831281114 [ Info: Efficiency = 0.9015701409706869 [ Info: Effective sample size = 255.48661318016636 [ Info: Efficiency = 0.9124521899291655 [ Info: Effective sample size = 302.31036532908547 [ Info: Efficiency = 0.9273324089849247 [ Info: Effective sample size = 351.7125760996458 [ Info: Efficiency = 0.9429291584440905 [ Info: Effective sample size = 400.6397919331296 [ Info: Efficiency = 0.9539042665074514 [ Info: Effective sample size = 448.19737528800414 [ Info: Efficiency = 0.9597374203169253 [ Info: Effective sample size = 496.40187132365975 [ Info: Efficiency = 0.9657623955713225 [ Info: Effective sample size = 544.3896873546114 [ Info: Efficiency = 0.9703915995625871 [ Info: Effective sample size = 592.4777894574939 [ Info: Efficiency = 0.9744700484498255 [ Info: Effective sample size = 640.5030827933534 [ Info: Efficiency = 0.9778673019745854 [ Info: Effective sample size = 688.2847790299502 [ Info: Efficiency = 0.9804626481908122 [ Info: Effective sample size = 735.9708140035916 [ Info: Efficiency = 0.9826045580822318 [ Info: Effective sample size = 783.6899515985957 [ Info: Efficiency = 0.9845351150736127 [ Info: Effective sample size = 831.2902032483538 [ Info: Efficiency = 0.9861093751463271 [ Info: Effective sample size = 877.7078631815552 [ Info: Efficiency = 0.98729793383752 [ Info: Effective sample size = 924.1261565884585 [ Info: Efficiency = 0.988370220950223 [ Info: Effective sample size = 970.2409942320118 [ Info: Efficiency = 0.9890326138960365 [ Info: Effective sample size = 1016.3914690895824 [ Info: Efficiency = 0.9896703691232546 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Effective sample size = 109.15648281754167 [ Info: Efficiency = 0.7909890059242151 [ Info: Effective sample size = 163.3951409516317 [ Info: Efficiency = 0.8737708072279771 [ Info: Effective sample size = 208.36493712743984 [ Info: Efficiency = 0.8981247289975856 [ Info: Effective sample size = 254.70925231670938 [ Info: Efficiency = 0.9195279867029219 [ Info: Effective sample size = 303.6472663814754 [ Info: Efficiency = 0.9371829209304796 [ Info: Effective sample size = 352.6511362010141 [ Info: Efficiency = 0.9505421460943776 [ Info: Effective sample size = 401.40687652906377 [ Info: Efficiency = 0.9603035323661813 [ Info: Effective sample size = 449.77371139384024 [ Info: Efficiency = 0.9672552933200865 [ Info: Effective sample size = 496.8039623927945 [ Info: Efficiency = 0.9722191044868778 [ Info: Effective sample size = 543.7973346746838 [ Info: Efficiency = 0.9762968306547286 [ Info: Effective sample size = 590.6650258668934 [ Info: Efficiency = 0.9795439898290106 [ Info: Effective sample size = 636.6434654472392 [ Info: Efficiency = 0.9809606555427415 [ Info: Effective sample size = 683.3174275799911 [ Info: Efficiency = 0.9831905432805628 [ Info: Effective sample size = 730.0298109831889 [ Info: Efficiency = 0.9851954264280552 [ Info: Effective sample size = 776.5504386498842 [ Info: Efficiency = 0.9867222854509329 [ Info: Effective sample size = 822.8986504984686 [ Info: Efficiency = 0.9878735300101663 [ Info: Effective sample size = 869.2783320827104 [ Info: Efficiency = 0.9889400820053589 [ Info: Effective sample size = 915.4171112687643 [ Info: Efficiency = 0.989640120290556 [ Info: Effective sample size = 961.5697843761812 [ Info: Efficiency = 0.9902881404492083 [ Info: Effective sample size = 1007.9540707472687 [ Info: Efficiency = 0.9911052809707657 [ Info: Effective sample size = 1054.0652729026383 [ Info: Efficiency = 0.9915948004728489 [ Info: Effective sample size = 1100.3255752568439 [ Info: Efficiency = 0.9921781562279927 [ Info: Effective sample size = 1146.5944532084875 [ Info: Efficiency = 0.9927224703103787 [ Info: Effective sample size = 1192.8822233445535 [ Info: Efficiency = 0.993240818771485 [ Info: Effective sample size = 1238.6739559398936 [ Info: Efficiency = 0.9933231402886076 [ Info: Effective sample size = 1284.8361445237615 [ Info: Efficiency = 0.9936861133207746 [ Info: Effective sample size = 1330.8700503358373 [ Info: Efficiency = 0.9939283422971152 [ Info: Effective sample size = 1377.142561804064 [ Info: Efficiency = 0.9943267594253169 [ Info: Effective sample size = 1423.366088582754 [ Info: Efficiency = 0.9946653309453207 [ Info: Effective sample size = 1469.5210057507893 [ Info: Efficiency = 0.994936361374942 [ Info: Effective sample size = 1515.6880290967451 [ Info: Efficiency = 0.9951989685467795 [ Info: Effective sample size = 1561.8731299530875 [ Info: Efficiency = 0.9954576991415471 [ Info: Effective sample size = 1608.078375764422 [ Info: Efficiency = 0.9957141645600136 [ Info: Effective sample size = 1654.2822962257944 [ Info: Efficiency = 0.9959556268668238 [ Info: Effective sample size = 1700.2427180628345 [ Info: Efficiency = 0.9960414282734824 [ Info: Effective sample size = 1746.4025629371101 [ Info: Efficiency = 0.9962364876994353 [ Info: Effective sample size = 1792.5107641025963 [ Info: Efficiency = 0.996392864981988 [ Info: Effective sample size = 1838.6430072927635 [ Info: Efficiency = 0.9965544754974328 [ Info: Effective sample size = 1884.7648772475247 [ Info: Efficiency = 0.9967027378358142 [ Info: Effective sample size = 1930.9114069132622 [ Info: Efficiency = 0.9968566891653393 [ Info: Effective sample size = 1977.0526757724829 [ Info: Efficiency = 0.9970008450693307 [ Info: Effective sample size = 2023.1046913894702 [ Info: Efficiency = 0.9970944757956974 [ Info: Effective sample size = 2069.227119601708 [ Info: Efficiency = 0.9972178889646787 [ Info: Effective sample size = 2115.347857853598 [ Info: Efficiency = 0.9973351522176321 [ Info: Effective sample size = 2161.415963690943 [ Info: Efficiency = 0.997423148911372 [ Info: Effective sample size = 2207.4963799535226 [ Info: Efficiency = 0.9975130501371544 [ Info: Effective sample size = 2253.5499844643796 [ Info: Efficiency = 0.9975874211883043 [ Info: Effective sample size = 2299.6328773533523 [ Info: Efficiency = 0.997671530305142 [ Info: Effective sample size = 2345.7314655823648 [ Info: Efficiency = 0.9977590240673606 [ Info: Effective sample size = 2391.8395375316527 [ Info: Efficiency = 0.9978471162001055 [ Info: Effective sample size = 2437.929845640845 [ Info: Efficiency = 0.9979246195828265 [ Info: Effective sample size = 2483.7993134649464 [ Info: Efficiency = 0.9979105317255711 [ Info: Effective sample size = 2529.865773304028 [ Info: Efficiency = 0.9979746640252577 [ Info: Effective sample size = 2575.9563297460786 [ Info: Efficiency = 0.9980458464727154 [ Info: Effective sample size = 2622.053102246855 [ Info: Efficiency = 0.9981169022637438 [ Info: Effective sample size = 2668.12586000712 [ Info: Efficiency = 0.9981765282480808 [ Info: Effective sample size = 2714.11769070507 [ Info: Efficiency = 0.9982043731905369 [ Info: Effective sample size = 2760.2097212882327 [ Info: Efficiency = 0.9982675303031583 [ Info: Effective sample size = 2806.209764769968 [ Info: Efficiency = 0.9982958963962888 [ Info: Effective sample size = 2852.30369339925 [ Info: Efficiency = 0.9983562105002625 [ Info: Effective sample size = 2898.3943353125455 [ Info: Efficiency = 0.9984134809895093 [ Info: Effective sample size = 2944.4594757562177 [ Info: Efficiency = 0.9984603173130613 [ Info: Effective sample size = 2990.4655121945734 [ Info: Efficiency = 0.9984859806993568 [ Info: Effective sample size = 3036.5442156765234 [ Info: Efficiency = 0.9985347634582451 [ Info: Effective sample size = 3082.593215929254 [ Info: Efficiency = 0.998572470336655 [ Info: Effective sample size = 3128.662978420453 [ Info: Efficiency = 0.9986156969104542 [ Info: Effective sample size = 3174.7350440306404 [ Info: Efficiency = 0.9986583969898208 [ Info: Effective sample size = 3220.727736079726 [ Info: Efficiency = 0.9986752670014655 [ Info: Effective sample size = 3266.773247930379 [ Info: Efficiency = 0.9987078104342338 [ Info: Effective sample size = 3312.8410355549086 [ Info: Efficiency = 0.9987461668842051 [ Info: Effective sample size = 3358.9046812100773 [ Info: Efficiency = 0.9987822424056132 [ Info: Effective sample size = 3404.938137076385 [ Info: Efficiency = 0.9988084884354312 [ Info: Effective sample size = 3450.9822062447797 [ Info: Efficiency = 0.9988371074514558 [ Info: Effective sample size = 3496.9653328000863 [ Info: Efficiency = 0.9988475672093934 [ Info: Effective sample size = 3542.995132292393 [ Info: Efficiency = 0.9988709140942749 [ Info: Effective sample size = 3589.0584875226514 [ Info: Efficiency = 0.9989030023720155 [ Info: Effective sample size = 3635.112805582928 [ Info: Efficiency = 0.9989317959832174 [ Info: Effective sample size = 3681.1598029497636 [ Info: Efficiency = 0.9989578841111977 [ Info: Effective sample size = 3727.209230953431 [ Info: Efficiency = 0.9989839804217183 [ Info: Effective sample size = 3773.233389784924 [ Info: Efficiency = 0.9990027508035276 [ Info: Effective sample size = 3819.2787809076817 [ Info: Efficiency = 0.9990266233083133 [ Info: Effective sample size = 3865.3352283797853 [ Info: Efficiency = 0.9990527858309086 [ Info: Effective sample size = 3911.3894355943253 [ Info: Efficiency = 0.9990777613267753 [ Info: Effective sample size = 3957.4191915925735 [ Info: Efficiency = 0.9990959837396045 [ Info: Effective sample size = 4003.4715398318763 [ Info: Efficiency = 0.9991194259625347 [ Info: Effective sample size = 4049.514579987311 [ Info: Efficiency = 0.9991400394737999 [ Info: Effective sample size = 4095.5310502250686 [ Info: Efficiency = 0.9991537082764256 [ Info: Effective sample size = 4141.574840720187 [ Info: Efficiency = 0.9991736648299607 [ Info: Effective sample size = 4187.619112701848 [ Info: Efficiency = 0.9991932981870313 [ Info: Effective sample size = 4233.653722787674 [ Info: Efficiency = 0.9992102248731825 [ Info: Effective sample size = 4279.699705651877 [ Info: Efficiency = 0.9992294432995276 [ Info: Effective sample size = 4325.744331763553 [ Info: Efficiency = 0.9992479398853207 [ Info: Effective sample size = 4371.779090190263 [ Info: Efficiency = 0.9992637920434886 [ Info: Effective sample size = 4417.80661259014 [ Info: Efficiency = 0.9992776775820266 [ Info: Effective sample size = 4463.849178377402 [ Info: Efficiency = 0.999294644812492 [ Info: Effective sample size = 4509.886774738951 [ Info: Efficiency = 0.9993101650208179 [ Info: Effective sample size = 4555.891695475213 [ Info: Efficiency = 0.9993182047543788 [ Info: Effective sample size = 4601.919453845119 [ Info: Efficiency = 0.9993310431802648 [ Info: Effective sample size = 4647.805591572525 [ Info: Efficiency = 0.9993131781493282 [ Info: Effective sample size = 4693.805919392557 [ Info: Efficiency = 0.9993199743224521 [ Info: Effective sample size = 4739.838480129655 [ Info: Efficiency = 0.9993334345624405 [ Info: Effective sample size = 4785.87450045184 [ Info: Efficiency = 0.999347358624314 [ Info: Effective sample size = 4831.899358980758 [ Info: Efficiency = 0.9993587091997431 [ Info: Effective sample size = 4877.938894827689 [ Info: Efficiency = 0.9993728528636937 [ Info: Effective sample size = 4923.976523133783 [ Info: Efficiency = 0.9993863452676646 [ Info: Effective sample size = 4969.9986473367035 [ Info: Efficiency = 0.9993964704075414 [ Info: Effective sample size = 5016.027539119281 [ Info: Efficiency = 0.999407758342156 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 65.08847732359415 [ Info: Efficiency = 0.6508847732359415 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Effective sample size = 108.35151421665292 [ Info: Efficiency = 0.7851559001206733 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Effective sample size = 163.9165985591449 [ Info: Efficiency = 0.8765593505836625 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Using transform algorithm PriorSubstitution() [ Info: Generating initial samples Progress: 2%|▉ | ETA: 0:00:27 Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Generating initial samples [ Info: Using transform algorithm BAT.SampleTransformation() [ Info: Using transform algorithm PriorSubstitution() [ Info: Effective sample size = 37.2852060146765 [ Info: Efficiency = 0.372852060146765 [ Info: Effective sample size = 94.7234047751336 [ Info: Efficiency = 0.6487904436652987 [ Info: Effective sample size = 106.54645586314297 [ Info: Efficiency = 0.6659153491446436 [ Info: Using transform algorithm BAT.SampleTransformation() Test Summary: | Pass Error Total Time Newtrinos.jl | 1609 6 1615 10m18.7s physics | 757 757 2m57.2s analysis | 852 6 858 7m21.5s Helpers | 9 9 0.9s Analysis Tools | 151 6 157 1m55.4s NewtrinosResult | 3 3 0.1s sort_nt | 6 6 0.6s safe_merge | 8 8 1.2s Wrapper and Base.getproperty | 12 12 1.7s get_params and get_priors | 19 19 1.7s conditional priors | 7 7 0.8s get_observed, get_fwd_model, and generate_likelihood | 12 12 2.8s correlated_priors_vars | 7 7 1.3s generate_toy_data | 5 5 0.9s generate_asimov_data | 8 8 0.6s find_mle | 8 8 20.5s find_mle catches ArgumentError | 3 3 2.8s find_mle_cached | 6 6 54.4s _generate_grid | 12 12 0.9s generate_scanpoints | 15 15 1.5s assemble_profile_results | 10 10 1.6s scan | 1 1 5.0s profile scans | 4 4 13.4s profile | 3 3 12.4s profile with nuisance optimization | 1 1 4.6s profile 2D grid dimensions | 1 1 4.7s profile with cached dir | 1 1 3.1s profile fallback to scan | 1 1 1.0s bestfit | 10 1 11 3.4s bestfit 1D | 4 4 0.3s bestfit 2D | 3 3 0.3s bestfit with single point | 2 2 0.2s bestfit ties: returns first maximum | 1 1 0.0s bestfit with results from profiling | 1 1 2.7s ForwardDiff Compatibility | 14 14 4.7s Likelihood Regression | 8 8 2m12.6s CLI Common | 12 12 1m59.8s Molewhacker | 658 658 1m02.6s RNG of the outermost testset: Random.Xoshiro(0x9a7b53a73f24bdca, 0x013e4546a780f1d7, 0x11d933ebf14be810, 0xa18e2a25f81582fa, 0x62574e20b4b9b075) ERROR: LoadError: Some tests did not pass: 1609 passed, 0 failed, 6 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/Newtrinos/RHjks/test/runtests.jl:4 Testing failed after 679.69s ERROR: LoadError: Package Newtrinos errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3298 [3] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:587 [4] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172 [5] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [6] test(pkg::String; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [inlined] [7] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:223 in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 PkgEval failed after 1871.37s: package fails to precompile