Package evaluation to test CalibrateEmulateSample on Julia 1.12.4 (0f21d93eaa*) started at 2026-01-27T13:04:42.034 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.12` Set-up completed after 7.97s ################################################################################ # Installation # Installing CalibrateEmulateSample... Resolving package versions... Installed Conda ────────────────── v1.10.3 Installed PyCall ───────────────── v1.96.4 Installed CalibrateEmulateSample ─ v0.7.0 Updating `~/.julia/environments/v1.12/Project.toml` [95e48a1f] + CalibrateEmulateSample v0.7.0 Updating `~/.julia/environments/v1.12/Manifest.toml` [47edcb42] + ADTypes v1.21.0 [14f7f29c] + AMD v0.5.3 [621f4979] + AbstractFFTs v1.5.0 [99985d1d] + AbstractGPs v0.5.24 [80f14c24] + AbstractMCMC v5.11.0 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.43 [79e6a3ab] + Adapt v4.4.0 [5b7e9947] + AdvancedMH v0.8.9 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [7d9fca2a] + Arpack v0.5.4 [4fba245c] + ArrayInterface v7.22.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 [198e06fe] + BangBang v0.4.7 [9718e550] + Baselet v0.1.1 [6e4b80f9] + BenchmarkTools v1.6.3 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.7 [95e48a1f] + CalibrateEmulateSample v0.7.0 [d360d2e6] + ChainRulesCore v1.26.0 [ae650224] + ChunkSplitters v3.1.2 [fb6a15b2] + CloseOpenIntervals v0.1.13 [523fee87] + CodecBzip2 v0.8.5 [944b1d66] + CodecZlib v0.7.8 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [8f4d0f93] + Conda v1.10.3 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [f65535da] + Convex v0.16.5 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.8.1 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.7.15 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.123 [ffbed154] + DocStringExtensions v0.9.5 [fdbdab4c] + ElasticArrays v1.2.12 [2904ab23] + ElasticPDMats v0.2.4 [aa8a2aa5] + EnsembleKalmanProcesses v2.6.1 [4e289a0a] + EnumX v1.0.6 [c87230d0] + FFMPEG v0.4.5 [b86e33f2] + FFTA v0.3.1 [7a1cc6ca] + FFTW v1.10.0 [442a2c76] + FastGaussQuadrature v1.1.0 [1a297f60] + FillArrays v1.16.0 [6a86dc24] + FiniteDiff v2.29.0 [f6369f11] + ForwardDiff v1.3.1 [069b7b12] + FunctionWrappers v1.1.3 [d9f16b24] + Functors v0.5.2 [891a1506] + GaussianProcesses v0.12.6 [e4b2fa32] + GaussianRandomFields v2.2.7 [3e5b6fbb] + HostCPUFeatures v0.1.18 [34004b35] + HypergeometricFunctions v0.3.28 [615f187c] + IfElse v0.1.1 [22cec73e] + InitialValues v0.3.1 [842dd82b] + InlineStrings v1.4.5 [18e54dd8] + IntegerMathUtils v0.1.3 ⌅ [a98d9a8b] + Interpolations v0.15.1 [8197267c] + IntervalSets v0.7.13 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v1.4.0 [0f8b85d8] + JSON3 v1.14.3 [5ab0869b] + KernelDensity v0.6.11 [ec8451be] + KernelFunctions v0.10.67 [40e66cde] + LDLFactorizations v0.10.1 [b964fa9f] + LaTeXStrings v1.4.0 [10f19ff3] + LayoutPointers v0.1.17 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 ⌃ [d3d80556] + LineSearches v7.5.1 [6fdf6af0] + LogDensityProblems v2.2.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [bdcacae8] + LoopVectorization v0.12.173 ⌅ [c7f686f2] + MCMCChains v6.0.7 [be115224] + MCMCDiagnosticTools v0.3.15 [e80e1ace] + MLJModelInterface v1.12.1 [1914dd2f] + MacroTools v0.5.16 [d125e4d3] + ManualMemory v0.1.8 [b8f27783] + MathOptInterface v1.48.0 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [46d2c3a1] + MuladdMacro v0.2.4 [d8a4904e] + MutableArithmetics v1.6.7 ⌅ [d41bc354] + NLSolversBase v7.10.0 [77ba4419] + NaNMath v1.1.3 [c020b1a1] + NaturalSort v1.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 ⌅ [429524aa] + Optim v1.13.3 [bac558e1] + OrderedCollections v1.8.1 ⌃ [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [1d0040c9] + PolyesterWeave v0.2.2 [2dfb63ee] + PooledArrays v1.4.3 [85a6dd25] + PositiveFactorizations v0.2.4 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.1 ⌅ [08abe8d2] + PrettyTables v2.4.0 [27ebfcd6] + Primes v0.5.7 [49802e3a] + ProgressBars v1.5.1 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [438e738f] + PyCall v1.96.4 [1fd47b50] + QuadGK v2.11.2 [36c3bae2] + RandomFeatures v0.3.4 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [37e2e3b7] + ReverseDiff v1.16.2 [79098fc4] + Rmath v0.9.0 [c946c3f1] + SCS v2.6.0 [94e857df] + SIMDTypes v0.1.0 [476501e8] + SLEEFPirates v0.6.43 [431bcebd] + SciMLPublic v1.0.1 [30f210dd] + ScientificTypesBase v3.0.0 [3646fa90] + ScikitLearn v0.7.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [91c51154] + SentinelArrays v1.4.9 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms 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Arpack_jll v3.5.2+0 [6e34b625] + Bzip2_jll v1.0.9+0 [83423d85] + Cairo_jll v1.18.5+0 [2e619515] + Expat_jll v2.7.3+0 [b22a6f82] + FFMPEG_jll v8.0.1+0 [f5851436] + FFTW_jll v3.3.11+0 [a3f928ae] + Fontconfig_jll v2.17.1+0 [d7e528f0] + FreeType2_jll v2.13.4+0 [559328eb] + FriBidi_jll v1.0.17+0 [b0724c58] + GettextRuntime_jll v0.22.4+0 [7746bdde] + Glib_jll v2.86.2+0 [3b182d85] + Graphite2_jll v1.3.15+0 [2e76f6c2] + HarfBuzz_jll v8.5.1+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [c1c5ebd0] + LAME_jll v3.100.3+0 [1d63c593] + LLVMOpenMP_jll v18.1.8+0 [dd4b983a] + LZO_jll v2.10.3+0 ⌅ [e9f186c6] + Libffi_jll v3.4.7+0 [94ce4f54] + Libiconv_jll v1.18.0+0 [4b2f31a3] + Libmount_jll v2.41.2+0 [38a345b3] + Libuuid_jll v2.41.2+0 [856f044c] + MKL_jll v2025.2.0+0 [e7412a2a] + Ogg_jll v1.3.6+0 [656ef2d0] + OpenBLAS32_jll v0.3.30+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [91d4177d] + Opus_jll v1.6.0+0 ⌅ [30392449] + Pixman_jll v0.44.2+0 [f50d1b31] + Rmath_jll v0.5.1+0 [f4f2fc5b] + SCS_jll v300.200.1100+0 [4f6342f7] + Xorg_libX11_jll v1.8.12+0 [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0 [a3789734] + Xorg_libXdmcp_jll v1.1.6+0 [1082639a] + Xorg_libXext_jll v1.3.7+0 [ea2f1a96] + Xorg_libXrender_jll v0.9.12+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [a4ae2306] + libaom_jll v3.13.1+0 [0ac62f75] + libass_jll v0.17.4+0 [f638f0a6] + libfdk_aac_jll v2.0.4+0 [b53b4c65] + libpng_jll v1.6.54+0 [f27f6e37] + libvorbis_jll v1.3.8+0 [1317d2d5] + oneTBB_jll v2022.0.0+1 ⌅ [1270edf5] + x264_jll v10164.0.1+0 [dfaa095f] + x265_jll v4.1.0+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.12.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.12.1 [de0858da] + Printf v1.11.0 [9abbd945] + Profile v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.12.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.15.0+0 [e37daf67] + LibGit2_jll v1.9.0+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.11.4 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.44.0+1 [bea87d4a] + SuiteSparse_jll v7.8.3+2 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.64.0+1 [3f19e933] + p7zip_jll v17.7.0+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. To see why use `status --outdated -m` Building Conda ─────────────────→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/8f06b0cfa4c514c7b9546756dbae91fcfbc92dc9/build.log` Building PyCall ────────────────→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` Building CalibrateEmulateSample → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/f58547feedb27247426c2a1b4c3ba1a881596722/build.log` Installation completed after 160.26s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling packages... 46495.0 ms ✓ KernelDensity 104402.5 ms ✓ Optim → OptimMOIExt 35599.0 ms ✓ MCMCChains 575061.5 ms ✓ EnsembleKalmanProcesses 48739.3 ms ? AdvancedMH → AdvancedMHMCMCChainsExt 857539.2 ms ✓ RandomFeatures 407393.4 ms ✓ CalibrateEmulateSample 6 dependencies successfully precompiled in 2131 seconds. 315 already precompiled. 1 dependencies failed but may be precompilable after restarting julia 1 dependency had output during precompilation: ┌ AdvancedMH → AdvancedMHMCMCChainsExt │ WARNING: Method definition is_inplaceable_destination(SparseArrays.SparseVector{Tv, Ti} where Ti<:Integer where Tv) in module ChainRulesCoreSparseArraysExt at /home/pkgeval/.julia/packages/ChainRulesCore/Vsbj9/ext/ChainRulesCoreSparseArraysExt.jl:7 overwritten in module ChainRulesCoreSparseArraysExt on the same line (check for duplicate calls to `include`). │ ERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation. └ Precompilation completed after 2116.2s ################################################################################ # Testing # Testing CalibrateEmulateSample Status `/tmp/jl_IIb77Y/Project.toml` [99985d1d] AbstractGPs v0.5.24 [80f14c24] AbstractMCMC v5.11.0 [5b7e9947] AdvancedMH v0.8.9 [95e48a1f] CalibrateEmulateSample v0.7.0 [ae650224] ChunkSplitters v3.1.2 [8f4d0f93] Conda v1.10.3 [31c24e10] Distributions v0.25.123 [ffbed154] DocStringExtensions v0.9.5 [aa8a2aa5] EnsembleKalmanProcesses v2.6.1 [f6369f11] ForwardDiff v1.3.1 [891a1506] GaussianProcesses v0.12.6 [ec8451be] KernelFunctions v0.10.67 ⌅ [c7f686f2] MCMCChains v6.0.7 [49802e3a] ProgressBars v1.5.1 [438e738f] PyCall v1.96.4 [36c3bae2] RandomFeatures v0.3.4 [37e2e3b7] ReverseDiff v1.16.2 [3646fa90] ScikitLearn v0.7.0 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.1 ⌅ [2913bbd2] StatsBase v0.33.21 [37e2e46d] LinearAlgebra v1.12.0 [44cfe95a] Pkg v1.12.1 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_IIb77Y/Manifest.toml` [47edcb42] ADTypes v1.21.0 [14f7f29c] AMD v0.5.3 [621f4979] AbstractFFTs v1.5.0 [99985d1d] AbstractGPs v0.5.24 [80f14c24] AbstractMCMC v5.11.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.43 [79e6a3ab] Adapt v4.4.0 [5b7e9947] AdvancedMH v0.8.9 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.22.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.8 [198e06fe] BangBang v0.4.7 [9718e550] Baselet v0.1.1 [6e4b80f9] BenchmarkTools v1.6.3 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.7 [95e48a1f] CalibrateEmulateSample v0.7.0 [d360d2e6] ChainRulesCore v1.26.0 [ae650224] ChunkSplitters v3.1.2 [fb6a15b2] CloseOpenIntervals v0.1.13 [523fee87] CodecBzip2 v0.8.5 [944b1d66] CodecZlib v0.7.8 [bbf7d656] CommonSubexpressions v0.3.1 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [8f4d0f93] Conda v1.10.3 [88cd18e8] ConsoleProgressMonitor v0.1.2 [187b0558] ConstructionBase v1.6.0 [f65535da] Convex v0.16.5 [adafc99b] CpuId v0.3.1 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.1 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [a0c0ee7d] DifferentiationInterface v0.7.15 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.123 [ffbed154] DocStringExtensions v0.9.5 [fdbdab4c] ElasticArrays v1.2.12 [2904ab23] ElasticPDMats v0.2.4 [aa8a2aa5] EnsembleKalmanProcesses v2.6.1 [4e289a0a] EnumX v1.0.6 [c87230d0] FFMPEG v0.4.5 [b86e33f2] FFTA v0.3.1 [7a1cc6ca] FFTW v1.10.0 [442a2c76] FastGaussQuadrature v1.1.0 [1a297f60] FillArrays v1.16.0 [6a86dc24] FiniteDiff v2.29.0 [f6369f11] ForwardDiff v1.3.1 [069b7b12] FunctionWrappers v1.1.3 [d9f16b24] Functors v0.5.2 [891a1506] GaussianProcesses v0.12.6 [e4b2fa32] GaussianRandomFields v2.2.7 [3e5b6fbb] HostCPUFeatures v0.1.18 [34004b35] HypergeometricFunctions v0.3.28 [615f187c] IfElse v0.1.1 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.5 [18e54dd8] IntegerMathUtils v0.1.3 ⌅ [a98d9a8b] Interpolations v0.15.1 [8197267c] IntervalSets v0.7.13 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v1.4.0 [0f8b85d8] JSON3 v1.14.3 [5ab0869b] KernelDensity v0.6.11 [ec8451be] KernelFunctions v0.10.67 [40e66cde] LDLFactorizations v0.10.1 [b964fa9f] LaTeXStrings v1.4.0 [10f19ff3] LayoutPointers v0.1.17 [1d6d02ad] LeftChildRightSiblingTrees v0.2.1 ⌃ [d3d80556] LineSearches v7.5.1 [6fdf6af0] LogDensityProblems v2.2.0 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [bdcacae8] LoopVectorization v0.12.173 ⌅ [c7f686f2] MCMCChains v6.0.7 [be115224] MCMCDiagnosticTools v0.3.15 [e80e1ace] MLJModelInterface v1.12.1 [1914dd2f] MacroTools v0.5.16 [d125e4d3] ManualMemory v0.1.8 [b8f27783] MathOptInterface v1.48.0 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [46d2c3a1] MuladdMacro v0.2.4 [d8a4904e] MutableArithmetics v1.6.7 ⌅ [d41bc354] NLSolversBase v7.10.0 [77ba4419] NaNMath v1.1.3 [c020b1a1] NaturalSort v1.0.0 [6fe1bfb0] OffsetArrays v1.17.0 ⌅ [429524aa] Optim v1.13.3 [bac558e1] OrderedCollections v1.8.1 ⌃ [90014a1f] PDMats v0.11.35 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [1d0040c9] PolyesterWeave v0.2.2 [2dfb63ee] PooledArrays v1.4.3 [85a6dd25] PositiveFactorizations v0.2.4 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.1 ⌅ [08abe8d2] PrettyTables v2.4.0 [27ebfcd6] Primes v0.5.7 [49802e3a] ProgressBars v1.5.1 [33c8b6b6] ProgressLogging v0.1.6 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 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LLVMOpenMP_jll v18.1.8+0 [dd4b983a] LZO_jll v2.10.3+0 ⌅ [e9f186c6] Libffi_jll v3.4.7+0 [94ce4f54] Libiconv_jll v1.18.0+0 [4b2f31a3] Libmount_jll v2.41.2+0 [38a345b3] Libuuid_jll v2.41.2+0 [856f044c] MKL_jll v2025.2.0+0 [e7412a2a] Ogg_jll v1.3.6+0 [656ef2d0] OpenBLAS32_jll v0.3.30+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [91d4177d] Opus_jll v1.6.0+0 ⌅ [30392449] Pixman_jll v0.44.2+0 [f50d1b31] Rmath_jll v0.5.1+0 [f4f2fc5b] SCS_jll v300.200.1100+0 [4f6342f7] Xorg_libX11_jll v1.8.12+0 [0c0b7dd1] Xorg_libXau_jll v1.0.13+0 [a3789734] Xorg_libXdmcp_jll v1.1.6+0 [1082639a] Xorg_libXext_jll v1.3.7+0 [ea2f1a96] Xorg_libXrender_jll v0.9.12+0 [c7cfdc94] Xorg_libxcb_jll v1.17.1+0 [c5fb5394] Xorg_xtrans_jll v1.6.0+0 [a4ae2306] libaom_jll v3.13.1+0 [0ac62f75] libass_jll v0.17.4+0 [f638f0a6] libfdk_aac_jll v2.0.4+0 [b53b4c65] libpng_jll v1.6.54+0 [f27f6e37] libvorbis_jll v1.3.8+0 [1317d2d5] oneTBB_jll v2022.0.0+1 ⌅ [1270edf5] x264_jll v10164.0.1+0 [dfaa095f] x265_jll v4.1.0+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.7.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [4af54fe1] LazyArtifacts v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.12.1 [de0858da] Printf v1.11.0 [9abbd945] Profile v1.11.0 [3fa0cd96] REPL v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [1a1011a3] SharedArrays v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.12.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.15.0+0 [e37daf67] LibGit2_jll v1.9.0+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.11.4 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.44.0+1 [bea87d4a] SuiteSparse_jll v7.8.3+2 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.64.0+1 [3f19e933] p7zip_jll v17.7.0+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... Starting tests for Emulator [ Info: fit successful SVD truncated at k: 3/6 [ Info: reducing input dimension from 10 to rank(input_cov) during low rank in normalization Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 SVD truncated at k: 2/6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Completed tests for Emulator, 183 seconds elapsed Starting tests for GaussianProcess ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/GaussianProcess.jl:151 optimized hyperparameters of GP: 1 Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.4671112501723779, -0.11637219097092133] Type: Noise{Float64}, Params: [-2.7795647959619263] ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 ┌ Warning: implicit `obsdim=2` argument is deprecated and now has to be passed explicitly to specify that each column corresponds to one observation │ caller = #_#1 at finite_gp_projection.jl:36 [inlined] └ @ Core ~/.julia/packages/AbstractGPs/lWdNB/src/finite_gp_projection.jl:36 optimised GP: 1 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 1) - σ² = 0.7923560881646375 White Kernel - σ² = 0.0038521278620295635 [ Info: AbstractGP already built. Continuing... ┌ Warning: implicit `obsdim=2` argument is deprecated and now has to be passed explicitly to specify that each column corresponds to one observation │ caller = #_#1 at finite_gp_projection.jl:36 [inlined] └ @ Core ~/.julia/packages/AbstractGPs/lWdNB/src/finite_gp_projection.jl:36 ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 optimized hyperparameters of GP: 1 Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.467111250159053, -0.11637219100329507] Type: Noise{Float64}, Params: [-2.912614529741828] ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 Using user-defined kernelPyObject 1**2 * RBF(length_scale=1) Learning additive white noise [ Info: Training kernel 1, [ Info: PyObject 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1) ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/Emulator.jl:121 ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/H2455/src/GaussianProcess.jl:271 SKlearn, already trained. continuing... Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 2 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] kernel in GaussianProcess: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] created GP: 1 kernel in GaussianProcess: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] created GP: 2 optimized hyperparameters of GP: 1 Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.034010033457988934, 2.6947372695174536, 1.9374032687140703] Type: Noise{Float64}, Params: [-0.19545576083347674] optimized hyperparameters of GP: 2 Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [2.040064043166128, -0.263116528583071, 2.0697093362932244] Type: Noise{Float64}, Params: [-0.08245764941529067] optimized hyperparameters of GP: 1 Type: SEArd{Float64}, Params: [-0.070755506795137, 2.7805790822912098, 1.879857339393294] optimized hyperparameters of GP: 2 Type: SEArd{Float64}, Params: [2.0918473623519653, -0.15767169342096962, 2.145456577252389] optimised GP: 1 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 2) - σ² = 48.17337764399554 White Kernel - σ² = 0.6764400036755718 optimised GP: 2 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 2) - σ² = 62.76632305723065 White Kernel - σ² = 0.8479655247177975 Completed tests for GaussianProcess, 72 seconds elapsed Starting tests for RandomFeature ┌ Info: Shrinkage scale: 0.9045601401358063, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 2.0329223656499 [ Info: NICE-adjusted covariance condition number: 5.9859566666416 [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 40, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 70, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 90, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 100, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 20, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 0, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 30, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 0, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter learning for 1 models using 50 training points, 50 validation points and 100 features estimate cov with 520 iterations... ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== cmd: /opt/julia/bin/julia 610 running 1 of 1 signal (10): User defined signal 1 _ZN4llvm17LazyValueInfoImpl45intersectAssumeOrGuardBlockValueConstantRangeEPNS_5ValueERNS_19ValueLatticeElementEPNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl13getBlockValueEPNS_5ValueEPNS_10BasicBlockEPNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl12getEdgeValueEPNS_5ValueEPNS_10BasicBlockES4_PNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl23solveBlockValueNonLocalEPNS_5ValueEPNS_10BasicBlockE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl19solveBlockValueImplEPNS_5ValueEPNS_10BasicBlockE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl15solveBlockValueEPNS_5ValueEPNS_10BasicBlockE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl5solveEv at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm17LazyValueInfoImpl14getValueOnEdgeEPNS_5ValueEPNS_10BasicBlockES4_PNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm13LazyValueInfo17getConstantOnEdgeEPNS_5ValueEPNS_10BasicBlockES4_PNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZL10processPHIPN4llvm7PHINodeEPNS_13LazyValueInfoEPNS_13DominatorTreeERKNS_13SimplifyQueryE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZL7runImplRN4llvm8FunctionEPNS_13LazyValueInfoEPNS_13DominatorTreeERKNS_13SimplifyQueryE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm30CorrelatedValuePropagationPass3runERNS_8FunctionERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:89 run at /source/usr/include/llvm/IR/PassManager.h:543 [inlined] run at /source/usr/include/llvm/IR/PassManagerInternal.h:89 _ZN4llvm27ModuleToFunctionPassAdaptor3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:89 _ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) run at /source/src/pipeline.cpp:741 operator() at /source/src/jitlayers.cpp:1459 withModuleDo<(anonymous namespace)::sizedOptimizerT::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]:: > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined] operator() at /source/src/jitlayers.cpp:1420 [inlined] operator() at /source/src/jitlayers.cpp:1572 [inlined] addModule at /source/src/jitlayers.cpp:2031 jl_compile_codeinst_now at /source/src/jitlayers.cpp:626 jl_compile_codeinst_impl at /source/src/jitlayers.cpp:824 jl_compile_method_internal at /source/src/gf.c:3524 _jl_invoke at /source/src/gf.c:4002 [inlined] ijl_apply_generic at /source/src/gf.c:4210 GeneratedFunctionStub at ./expr.jl:1694 _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_call_staged at /source/src/method.c:662 ijl_code_for_staged at /source/src/method.c:735 call_get_staged at ./../usr/share/julia/Compiler/src/utilities.jl:103 [inlined] get_staged at ./../usr/share/julia/Compiler/src/utilities.jl:88 jfptr_get_staged_2640.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_invoke at /source/src/gf.c:4017 tojlinvoke85219.1 at /opt/julia/lib/julia/sys.so (unknown line) j_get_staged_77149.1 at /opt/julia/lib/julia/sys.so (unknown line) retrieve_code_info at ./../usr/share/julia/Compiler/src/utilities.jl:121 InferenceState at ./../usr/share/julia/Compiler/src/inferencestate.jl:602 typeinf_edge at ./../usr/share/julia/Compiler/src/typeinfer.jl:954 abstract_call_method at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:735 infercalls at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:167 abstract_call_gf_by_type at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:338 abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2783 abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2890 infercalls at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:1748 abstract_apply at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:1873 abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2649 abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2890 abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2883 [inlined] abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3043 abstract_eval_statement_expr at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3386 abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3836 [inlined] abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3793 [inlined] typeinf_local at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4343 jfptr_typeinf_local_83491.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4501 typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1254 typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1437 [inlined] typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1446 jfptr_typeinf_ext_toplevel_80948.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_apply at /source/src/julia.h:2391 [inlined] jl_type_infer at /source/src/gf.c:462 jl_compile_method_internal at /source/src/gf.c:3512 _jl_invoke at /source/src/gf.c:4002 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_apply at /source/src/julia.h:2391 [inlined] start_task at /source/src/task.c:1252 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point ============================================================== ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== cmd: /opt/julia/bin/julia 1 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 poptask at ./task.jl:1216 wait at ./task.jl:1228 #wait#398 at ./condition.jl:141 wait at ./process.jl:685 wait at ./process.jl:686 jfptr_wait_37520.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 subprocess_handler at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2553 unknown function (ip: 0x75ca5698b8c3) at (unknown file) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 #205 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2493 withenv at ./env.jl:265 #190 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2308 with_temp_env at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2161 #186 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2275 #mktempdir#21 at ./file.jl:936 unknown function (ip: 0x75ca56989d3c) at (unknown file) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 mktempdir at ./file.jl:932 mktempdir at ./file.jl:932 #sandbox#182 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2222 [inlined] sandbox at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2212 unknown function (ip: 0x75ca5697d639) at (unknown file) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 #test#193 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2478 test at /source/usr/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2384 [inlined] #test#170 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:538 test at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:515 unknown function (ip: 0x75ca5697d271) at (unknown file) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 #test#84 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:169 unknown function (ip: 0x75ca569771e0) at (unknown file) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 test at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:158 #test#82 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:157 test at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:157 [inlined] #test#81 at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:156 [inlined] test at /source/usr/share/julia/stdlib/v1.12/Pkg/src/API.jl:156 unknown function (ip: 0x75ca569720bf) at (unknown file) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_apply at /source/src/julia.h:2391 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:689 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:898 jl_toplevel_eval_flex at /source/src/toplevel.c:1035 jl_toplevel_eval_flex at /source/src/toplevel.c:975 ijl_toplevel_eval at /source/src/toplevel.c:1047 ijl_toplevel_eval_in at /source/src/toplevel.c:1092 eval at ./boot.jl:489 include_string at ./loading.jl:2870 _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 _include at ./loading.jl:2930 include at ./Base.jl:306 exec_options at ./client.jl:317 _start at ./client.jl:550 jfptr__start_39366.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_apply at /source/src/julia.h:2391 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1139 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x75ca72ad8249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.12/Profile/src/Profile.jl:1361 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x000075ca585fc010 Total snapshots: 315. Utilization: 0% ╎315 @Base/client.jl:550 _start() ╎ 315 @Base/client.jl:317 exec_options(opts::Base.JLOptions) ╎ 315 @Base/Base.jl:306 include(mod::Module, _path::String) ╎ 315 @Base/loading.jl:2930 _include(mapexpr::Function, mod::Module, _pat… ╎ 315 @Base/loading.jl:2870 include_string(mapexpr::typeof(identity), mo… ╎ 315 @Base/boot.jl:489 eval(m::Module, e::Any) ╎ ╎ 315 @Pkg/src/API.jl:156 kwcall(::@NamedTuple{julia_args::Cmd}, ::typ… ╎ ╎ 315 @Pkg/src/API.jl:156 #test#81 ╎ ╎ 315 @Pkg/src/API.jl:157 test ╎ ╎ 315 @Pkg/src/API.jl:157 test(pkgs::Vector{String}; kwargs::Base.P… ╎ ╎ 315 @Pkg/src/API.jl:158 kwcall(::@NamedTuple{julia_args::Cmd}, :… ╎ ╎ ╎ 315 @Pkg/src/API.jl:169 test(pkgs::Vector{PackageSpec}; io::IOC… ╎ ╎ ╎ 315 @Pkg/src/API.jl:515 kwcall(::@NamedTuple{julia_args::Cmd, … ╎ ╎ ╎ 315 @Pkg/src/API.jl:538 test(ctx::Pkg.Types.Context, pkgs::Ve… ╎ ╎ ╎ 315 @Pkg/…erations.jl:2384 test ╎ ╎ ╎ 315 @Pkg/…erations.jl:2478 test(ctx::Pkg.Types.Context, pkg… ╎ ╎ ╎ ╎ 315 @Pkg/…rations.jl:2212 kwcall(::@NamedTuple{preferences… ╎ ╎ ╎ ╎ 315 @Pkg/…rations.jl:2222 #sandbox#182 ╎ ╎ ╎ ╎ 315 @Base/file.jl:932 mktempdir(fn::Function) ╎ ╎ ╎ ╎ 315 @Base/file.jl:932 mktempdir(fn::Function, parent::S… ╎ ╎ ╎ ╎ 315 @Base/file.jl:936 mktempdir(fn::Pkg.Operations.var… ╎ ╎ ╎ ╎ ╎ 315 @Pkg/…tions.jl:2275 (::Pkg.Operations.var"#186#18… ╎ ╎ ╎ ╎ ╎ 315 @Pkg/…tions.jl:2161 with_temp_env(fn::Pkg.Operat… ╎ ╎ ╎ ╎ ╎ 315 @Pkg/…tions.jl:2308 (::Pkg.Operations.var"#190#… ╎ ╎ ╎ ╎ ╎ 315 @Base/env.jl:265 withenv(::Pkg.Operations.var"… ╎ ╎ ╎ ╎ ╎ 315 @Pkg/…ions.jl:2493 (::Pkg.Operations.var"#205… ╎ ╎ ╎ ╎ ╎ ╎ 315 @Pkg/…ons.jl:2553 subprocess_handler(cmd::Cm… ╎ ╎ ╎ ╎ ╎ ╎ 315 @Base/…ss.jl:686 wait(x::Base.Process) ╎ ╎ ╎ ╎ ╎ ╎ 315 @Base/…ss.jl:685 wait(x::Base.Process, syn… ╎ ╎ ╎ ╎ ╎ ╎ 315 @Base/…on.jl:141 wait(c::Base.GenericCond… ╎ ╎ ╎ ╎ ╎ ╎ 315 @Base/…sk.jl:1228 wait() 314╎ ╎ ╎ ╎ ╎ ╎ ╎ 315 @Base/…sk.jl:1216 poptask(W::Base.Intru… [610] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/CalibrateEmulateSample/H2455/test/RandomFeature/runtests.jl:14 unknown function (ip: 0x7cafe07b1043) at /lib/x86_64-linux-gnu/libc.so.6 unknown function (ip: 0x7cafe07b3e5c) at /lib/x86_64-linux-gnu/libc.so.6 malloc at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) operator new at /workspace/srcdir/gcc-14.2.0/libstdc++-v3/libsupc++/new_op.cc:50 _ZN4llvm13LiveIntervals15computeVirtRegsEv at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm13LiveIntervals20runOnMachineFunctionERNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm19MachineFunctionPass13runOnFunctionERNS_8FunctionE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm13FPPassManager13runOnFunctionERNS_8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm13FPPassManager11runOnModuleERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm6legacy15PassManagerImpl3runERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) _ZN4llvm3orc14SimpleCompilerclERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.18.1jl (unknown line) operator() at /source/src/jitlayers.cpp:1561 addModule at /source/src/jitlayers.cpp:2044 jl_compile_codeinst_now at /source/src/jitlayers.cpp:626 jl_compile_codeinst_impl at /source/src/jitlayers.cpp:824 jl_compile_method_internal at /source/src/gf.c:3524 _jl_invoke at /source/src/gf.c:4002 [inlined] ijl_apply_generic at /source/src/gf.c:4210 GeneratedFunctionStub at ./expr.jl:1694 _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_call_staged at /source/src/method.c:662 ijl_code_for_staged at /source/src/method.c:735 call_get_staged at ./../usr/share/julia/Compiler/src/utilities.jl:103 [inlined] get_staged at ./../usr/share/julia/Compiler/src/utilities.jl:88 jfptr_get_staged_2640.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_invoke at /source/src/gf.c:4017 tojlinvoke85219.1 at /opt/julia/lib/julia/sys.so (unknown line) j_get_staged_77149.1 at /opt/julia/lib/julia/sys.so (unknown line) retrieve_code_info at ./../usr/share/julia/Compiler/src/utilities.jl:121 InferenceState at ./../usr/share/julia/Compiler/src/inferencestate.jl:602 typeinf_edge at ./../usr/share/julia/Compiler/src/typeinfer.jl:954 abstract_call_method at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:735 infercalls at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:167 abstract_call_gf_by_type at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:338 abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2783 abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2890 infercalls at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:1748 abstract_apply at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:1873 abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2649 abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2890 abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2883 [inlined] abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3043 abstract_eval_statement_expr at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3386 abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3836 [inlined] abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3793 [inlined] typeinf_local at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4343 jfptr_typeinf_local_83491.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4501 typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1254 typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1437 [inlined] typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1446 jfptr_typeinf_ext_toplevel_80948.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4010 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_apply at /source/src/julia.h:2391 [inlined] jl_type_infer at /source/src/gf.c:462 jl_compile_method_internal at /source/src/gf.c:3512 _jl_invoke at /source/src/gf.c:4002 [inlined] ijl_apply_generic at /source/src/gf.c:4210 jl_apply at /source/src/julia.h:2391 [inlined] start_task at /source/src/task.c:1252 unknown function (ip: (nil)) at (unknown file) Allocations: 188304727 (Pool: 188302814; Big: 1913); GC: 93 PkgEval terminated after 2725.77s: test duration exceeded the time limit