Package evaluation to test CalibrateEmulateSample on Julia 1.14.0-DEV.2307 (a8f97b1944*) started at 2026-06-09T01:55:00.205


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...
  Activating project at `~/.julia/environments/v1.14`

Set-up completed after 15.14s


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# Installation
#

Installing CalibrateEmulateSample...
   Resolving package versions...
   Installed libaom_jll ─────────────────────── v3.13.3+0
   Installed libass_jll ─────────────────────── v0.17.4+0
   Installed Bzip2_jll ──────────────────────── v1.0.9+0
   Installed CommonWorldInvalidations ───────── v1.0.0
   Installed HarfBuzz_jll ───────────────────── v8.5.1+0
   Installed MKL_jll ────────────────────────── v2025.2.0+0
   Installed SIMDTypes ──────────────────────── v0.1.0
   Installed Libuuid_jll ────────────────────── v2.42.0+0
   Installed Cairo_jll ──────────────────────── v1.18.7+0
   Installed OrderedCollections ─────────────── v1.8.2
   Installed Functors ───────────────────────── v0.5.2
   Installed Glossaries ─────────────────────── v0.1.1
   Installed Optim ──────────────────────────── v1.13.3
   Installed Nullables ──────────────────────── v1.0.0
   Installed Manopt ─────────────────────────── v0.5.39
   Installed SimpleWeightedGraphs ───────────── v1.5.1
   Installed RandomFeatures ─────────────────── v0.3.5
   Installed InitialValues ──────────────────── v0.3.1
   Installed Graphite2_jll ──────────────────── v1.3.15+0
   Installed UnsafePointers ─────────────────── v1.0.0
   Installed CpuId ──────────────────────────── v0.3.1
   Installed Xorg_libpciaccess_jll ──────────── v0.19.0+0
   Installed ElasticArrays ──────────────────── v1.2.12
   Installed Distances ──────────────────────── v0.10.12
   Installed CondaPkg ───────────────────────── v0.2.36
   Installed ArgCheck ───────────────────────── v2.5.0
   Installed Fontconfig_jll ─────────────────── v2.17.1+0
   Installed StaticArrayInterface ───────────── v1.10.0
   Installed OpenSpecFun_jll ────────────────── v0.5.6+0
   Installed Pidfile ────────────────────────── v1.3.0
   Installed QuadGK ─────────────────────────── v2.11.3
   Installed Opus_jll ───────────────────────── v1.6.1+0
   Installed NaNMath ────────────────────────── v1.1.3
   Installed libva_jll ──────────────────────── v2.23.0+0
   Installed ManifoldsBase ──────────────────── v2.4.0
   Installed WoodburyMatrices ───────────────── v1.1.0
   Installed Arpack ─────────────────────────── v0.5.4
   Installed Tables ─────────────────────────── v1.12.1
   Installed Xorg_libxcb_jll ────────────────── v1.17.1+0
   Installed EnsembleKalmanProcesses ────────── v2.7.1
   Installed Ogg_jll ────────────────────────── v1.3.6+0
   Installed Reexport ───────────────────────── v1.2.2
   Installed NLSolversBase ──────────────────── v7.10.0
   Installed InverseFunctions ───────────────── v0.1.17
   Installed GaussianRandomFields ───────────── v2.2.7
   Installed EnumX ──────────────────────────── v1.0.7
   Installed TableTraits ────────────────────── v1.0.1
   Installed Missings ───────────────────────── v1.2.0
   Installed LaTeXStrings ───────────────────── v1.4.0
   Installed Preferences ────────────────────── v1.5.2
   Installed SortingAlgorithms ──────────────── v1.2.2
   Installed FFMPEG ─────────────────────────── v0.4.5
   Installed Static ─────────────────────────── v1.4.0
   Installed LLVMOpenMP_jll ─────────────────── v18.1.8+0
   Installed MCMCChains ─────────────────────── v7.7.0
   Installed Crayons ────────────────────────── v4.1.1
   Installed RangeArrays ────────────────────── v0.3.2
   Installed NaturalSort ────────────────────── v1.0.0
   Installed libdrm_jll ─────────────────────── v2.4.125+1
   Installed UnPack ─────────────────────────── v1.0.2
   Installed Inflate ────────────────────────── v0.1.5
   Installed DiffResults ────────────────────── v1.1.0
   Installed ManualMemory ───────────────────── v0.1.8
   Installed DataStructures ─────────────────── v0.19.5
   Installed Quaternions ────────────────────── v0.7.7
   Installed ElasticPDMats ──────────────────── v0.2.4
   Installed ADTypes ────────────────────────── v1.22.0
   Installed MacroTools ─────────────────────── v0.5.16
   Installed FillArrays ─────────────────────── v1.16.0
   Installed AbstractTrees ──────────────────── v0.4.5
   Installed x264_jll ───────────────────────── v10164.0.1+0
   Installed CodecZlib ──────────────────────── v0.7.8
   Installed ArnoldiMethod ──────────────────── v0.4.0
   Installed oneTBB_jll ─────────────────────── v2022.3.0+0
   Installed VectorizationBase ──────────────── v0.21.74
   Installed StatsBase ──────────────────────── v0.34.11
   Installed HostCPUFeatures ────────────────── v0.1.18
   Installed DataValueInterfaces ────────────── v1.0.0
   Installed IntelOpenMP_jll ────────────────── v2025.2.0+0
   Installed ConsoleProgressMonitor ─────────── v0.1.2
   Installed micromamba_jll ─────────────────── v2.3.1+0
   Installed Xorg_libXau_jll ────────────────── v1.0.13+0
   Installed Xorg_libXfixes_jll ─────────────── v6.0.2+0
   Installed ArrayInterface ─────────────────── v7.25.0
   Installed CommonSubexpressions ───────────── v0.3.1
   Installed MCMCDiagnosticTools ────────────── v0.3.18
   Installed LayoutPointers ─────────────────── v0.1.17
   Installed RealDot ────────────────────────── v0.1.0
   Installed StructUtils ────────────────────── v2.8.2
   Installed Expat_jll ──────────────────────── v2.8.1+0
   Installed Libffi_jll ─────────────────────── v3.4.7+0
   Installed Pixman_jll ─────────────────────── v0.46.4+0
   Installed LoggingExtras ──────────────────── v1.2.0
   Installed NamedDims ──────────────────────── v1.2.3
   Installed IrrationalConstants ────────────── v0.2.6
   Installed AbstractGPs ────────────────────── v0.5.24
   Installed AdvancedMH ─────────────────────── v0.8.10
   Installed IntegerMathUtils ───────────────── v0.1.3
   Installed SimpleTraits ───────────────────── v0.9.6
   Installed ColorVectorSpace ───────────────── v0.11.0
   Installed LeftChildRightSiblingTrees ─────── v0.2.1
   Installed libfdk_aac_jll ─────────────────── v2.0.4+0
   Installed TerminalLoggers ────────────────── v0.1.7
   Installed Scratch ────────────────────────── v1.3.0
   Installed libpng_jll ─────────────────────── v1.6.58+0
   Installed StableRNGs ─────────────────────── v1.0.4
   Installed FFMPEG_jll ─────────────────────── v8.1.0+0
   Installed pixi_jll ───────────────────────── v0.63.2+0
   Installed LowRankApprox ──────────────────── v0.5.5
   Installed Setfield ───────────────────────── v1.1.2
   Installed Interpolations ─────────────────── v0.16.2
   Installed BenchmarkTools ─────────────────── v1.8.0
   Installed DocStringExtensions ────────────── v0.9.5
   Installed PDMats ─────────────────────────── v0.11.36
   Installed MatrixEquations ────────────────── v2.5.8
   Installed CompositionsBase ───────────────── v0.1.2
   Installed AxisArrays ─────────────────────── v0.4.8
   Installed Rmath_jll ──────────────────────── v0.5.1+0
   Installed PythonCall ─────────────────────── v0.9.34
   Installed ForwardDiff ────────────────────── v1.4.0
   Installed KernelDensity ──────────────────── v0.6.12
   Installed BitTwiddlingConvenienceFunctions ─ v0.1.6
   Installed AliasTables ────────────────────── v1.1.3
   Installed SLEEFPirates ───────────────────── v0.6.46
   Installed DifferentiationInterface ───────── v0.7.18
   Installed LDLFactorizations ──────────────── v0.10.2
   Installed RecipesBase ────────────────────── v1.3.4
   Installed IteratorInterfaceExtensions ────── v1.0.0
   Installed DataAPI ────────────────────────── v1.16.0
   Installed ManifoldDiff ───────────────────── v0.4.5
   Installed Colors ─────────────────────────── v0.13.1
   Installed MathOptInterface ───────────────── v1.51.1
   Installed FastGaussQuadrature ────────────── v1.3.0
   Installed ReverseDiff ────────────────────── v1.16.2
   Installed StaticArraysCore ───────────────── v1.4.4
   Installed AbstractFFTs ───────────────────── v1.5.0
   Installed AxisAlgorithms ─────────────────── v1.1.0
   Installed StaticArrays ───────────────────── v1.9.18
   Installed SciMLPublic ────────────────────── v1.0.1
   Installed BangBang ───────────────────────── v0.4.9
   Installed Glib_jll ───────────────────────── v2.86.3+0
   Installed ColorTypes ─────────────────────── v0.12.1
   Installed FunctionWrappers ───────────────── v1.1.3
   Installed StringManipulation ─────────────── v0.4.4
   Installed Requires ───────────────────────── v1.3.1
   Installed Tullio ─────────────────────────── v0.3.9
   Installed LogExpFunctions ────────────────── v0.3.29
   Installed DiffRules ──────────────────────── v1.16.0
   Installed Parsers ────────────────────────── v2.8.5
   Installed JSON ───────────────────────────── v1.6.1
   Installed OpenBLAS32_jll ─────────────────── v0.3.33+1
   Installed Arpack_jll ─────────────────────── v3.5.2+0
   Installed Distributions ──────────────────── v0.25.126
   Installed CloseOpenIntervals ─────────────── v0.1.13
   Installed Rmath ──────────────────────────── v0.9.0
   Installed PrettyTables ───────────────────── v3.3.2
   Installed MutableArithmetics ─────────────── v1.8.0
   Installed StatsFuns ──────────────────────── v1.5.2
   Installed Xorg_libXdmcp_jll ──────────────── v1.1.6+0
   Installed ZygoteRules ────────────────────── v0.2.7
   Installed FiniteDiff ─────────────────────── v2.31.0
   Installed JLLWrappers ────────────────────── v1.8.0
   Installed ColorSchemes ───────────────────── v3.31.0
   Installed x265_jll ───────────────────────── v4.1.0+0
   Installed IterTools ──────────────────────── v1.10.0
   Installed ProgressBars ───────────────────── v1.5.1
   Installed Primes ─────────────────────────── v0.5.7
   Installed ThreadingUtilities ─────────────── v0.5.6
   Installed SCS_jll ────────────────────────── v300.200.1100+0
   Installed StatisticalTraits ──────────────── v3.5.0
   Installed Convex ─────────────────────────── v0.16.6
   Installed FFTA ───────────────────────────── v0.3.1
   Installed Adapt ──────────────────────────── v4.6.0
   Installed Graphs ─────────────────────────── v1.14.0
   Installed Xorg_xtrans_jll ────────────────── v1.6.0+0
   Installed Accessors ──────────────────────── v0.1.44
   Installed ConstructionBase ───────────────── v1.6.0
   Installed LowRankMatrices ────────────────── v1.0.2
   Installed ChunkSplitters ─────────────────── v3.2.0
   Installed LinearMaps ─────────────────────── v3.11.4
   Installed HypergeometricFunctions ────────── v0.3.28
   Installed TranscodingStreams ─────────────── v0.11.3
   Installed Libmount_jll ───────────────────── v2.42.0+0
   Installed LoopVectorization ──────────────── v0.12.174
   Installed ProgressLogging ────────────────── v0.1.6
   Installed Compat ─────────────────────────── v4.18.1
   Installed Xorg_libX11_jll ────────────────── v1.8.13+0
   Installed TensorCore ─────────────────────── v0.1.1
   Installed TSVD ───────────────────────────── v0.4.4
   Installed Statistics ─────────────────────── v1.11.1
   Installed StatsAPI ───────────────────────── v1.8.0
   Installed PositiveFactorizations ─────────── v0.2.4
   Installed ProgressMeter ──────────────────── v1.11.0
   Installed OffsetArrays ───────────────────── v1.17.0
   Installed PrecompileTools ────────────────── v1.3.4
   Installed Manifolds ──────────────────────── v0.11.27
   Installed CalibrateEmulateSample ─────────── v1.1.0
   Installed CodecBzip2 ─────────────────────── v0.8.5
   Installed GettextRuntime_jll ─────────────── v0.22.4+0
   Installed Ratios ─────────────────────────── v0.4.5
   Installed CPUSummary ─────────────────────── v0.2.7
   Installed MicroMamba ─────────────────────── v0.1.15
   Installed LAME_jll ───────────────────────── v3.100.3+0
   Installed LogDensityProblems ─────────────── v2.2.0
   Installed AMD ────────────────────────────── v0.5.3
   Installed FriBidi_jll ────────────────────── v1.0.17+0
   Installed Xorg_libXrender_jll ────────────── v0.9.12+0
   Installed ChainRulesCore ─────────────────── v1.26.1
   Installed SCS ────────────────────────────── v2.6.3
   Installed KernelFunctions ────────────────── v0.10.67
   Installed IfElse ─────────────────────────── v0.1.1
   Installed MuladdMacro ────────────────────── v0.2.4
   Installed SpecialFunctions ───────────────── v2.8.0
   Installed libvorbis_jll ──────────────────── v1.3.8+0
   Installed ScikitLearnBase ────────────────── v0.5.0
   Installed FixedPointNumbers ──────────────── v0.8.6
   Installed PtrArrays ──────────────────────── v1.4.0
   Installed Libiconv_jll ───────────────────── v1.18.0+0
   Installed ScientificTypesBase ────────────── v3.1.0
   Installed IntervalSets ───────────────────── v0.7.14
   Installed FreeType2_jll ──────────────────── v2.14.3+1
   Installed FFTW_jll ───────────────────────── v3.3.12+0
   Installed FFTW ───────────────────────────── v1.10.0
   Installed Xorg_libXext_jll ───────────────── v1.3.8+0
   Installed AbstractMCMC ───────────────────── v5.15.1
   Installed LineSearches ───────────────────── v7.5.1
   Installed Kronecker ──────────────────────── v0.5.5
   Installed PolyesterWeave ─────────────────── v0.2.2
   Installed GaussianProcesses ──────────────── v0.12.6
   Installed MLJModelInterface ──────────────── v1.12.1
  Installing 44 artifacts
   Installed artifact Graphite2         120.2 KiB
   Installed artifact Expat             288.6 KiB
   Installed artifact libvorbis         271.0 KiB
   Installed artifact Xorg_libXrender   435.9 KiB
   Installed artifact Fontconfig        984.8 KiB
   Installed artifact Pixman            390.8 KiB
   Installed artifact Arpack            138.3 KiB
   Installed artifact libva             235.7 KiB
   Installed artifact FFTW              2.2 MiB
   Installed artifact libdrm            368.0 KiB
   Installed artifact Xorg_libpciaccess 26.2 KiB
   Installed artifact libfdk_aac        2.8 MiB
   Installed artifact Xorg_libXext      286.6 KiB
   Installed artifact HarfBuzz          1.7 MiB
   Installed artifact FreeType2         1.5 MiB
   Installed artifact FriBidi           78.9 KiB
   Installed artifact Xorg_libxcb       2.1 MiB
   Installed artifact GettextRuntime    543.0 KiB
   Installed artifact Xorg_libXfixes    59.0 KiB
   Installed artifact libass            427.8 KiB
   Installed artifact Opus              960.9 KiB
   Installed artifact Xorg_libXdmcp     67.7 KiB
   Installed artifact Glib              7.7 MiB
   Installed artifact Ogg               250.3 KiB
   Installed artifact Libuuid           3.9 MiB
   Installed artifact Cairo             2.2 MiB
   Installed artifact LAME              292.5 KiB
   Installed artifact x265              1.4 MiB
   Installed artifact Libffi            44.2 KiB
   Installed artifact OpenSpecFun       194.9 KiB
   Installed artifact LLVMOpenMP        661.6 KiB
   Installed artifact Libiconv          1.9 MiB
   Installed artifact Rmath             121.9 KiB
   Installed artifact Xorg_libXau       36.6 KiB
   Installed artifact libpng            329.4 KiB
   Installed artifact x264              2.1 MiB
   Installed artifact Xorg_libX11       4.8 MiB
   Installed artifact Xorg_xtrans       48.2 KiB
   Installed artifact FFMPEG            11.9 MiB
   Installed artifact libaom            6.4 MiB
   Installed artifact Bzip2             503.5 KiB
   Installed artifact SCS               543.6 KiB
   Installed artifact Libmount          6.9 MiB
   Installed artifact OpenBLAS32        10.2 MiB
    Updating `~/.julia/environments/v1.14/Project.toml`
  [95e48a1f] + CalibrateEmulateSample v1.1.0
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [47edcb42] + ADTypes v1.22.0
  [14f7f29c] + AMD v0.5.3
  [621f4979] + AbstractFFTs v1.5.0
  [99985d1d] + AbstractGPs v0.5.24
  [80f14c24] + AbstractMCMC v5.15.1
  [1520ce14] + AbstractTrees v0.4.5
  [7d9f7c33] + Accessors v0.1.44
  [79e6a3ab] + Adapt v4.6.0
  [5b7e9947] + AdvancedMH v0.8.10
  [66dad0bd] + AliasTables v1.1.3
  [dce04be8] + ArgCheck v2.5.0
  [ec485272] + ArnoldiMethod v0.4.0
  [7d9fca2a] + Arpack v0.5.4
  [4fba245c] + ArrayInterface v7.25.0
  [13072b0f] + AxisAlgorithms v1.1.0
  [39de3d68] + AxisArrays v0.4.8
  [198e06fe] + BangBang v0.4.9
  [6e4b80f9] + BenchmarkTools v1.8.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [95e48a1f] + CalibrateEmulateSample v1.1.0
  [d360d2e6] + ChainRulesCore v1.26.1
  [ae650224] + ChunkSplitters v3.2.0
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [523fee87] + CodecBzip2 v0.8.5
  [944b1d66] + CodecZlib v0.7.8
  [35d6a980] + ColorSchemes v3.31.0
  [3da002f7] + ColorTypes v0.12.1
  [c3611d14] + ColorVectorSpace v0.11.0
  [5ae59095] + Colors v0.13.1
  [bbf7d656] + CommonSubexpressions v0.3.1
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [a33af91c] + CompositionsBase v0.1.2
  [992eb4ea] + CondaPkg v0.2.36
  [88cd18e8] + ConsoleProgressMonitor v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [f65535da] + Convex v0.16.6
  [adafc99b] + CpuId v0.3.1
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.19.5
  [e2d170a0] + DataValueInterfaces v1.0.0
  [163ba53b] + DiffResults v1.1.0
  [b552c78f] + DiffRules v1.16.0
  [a0c0ee7d] + DifferentiationInterface v0.7.18
  [b4f34e82] + Distances v0.10.12
  [31c24e10] + Distributions v0.25.126
  [ffbed154] + DocStringExtensions v0.9.5
  [fdbdab4c] + ElasticArrays v1.2.12
  [2904ab23] + ElasticPDMats v0.2.4
  [aa8a2aa5] + EnsembleKalmanProcesses v2.7.1
  [4e289a0a] + EnumX v1.0.7
  [c87230d0] + FFMPEG v0.4.5
  [b86e33f2] + FFTA v0.3.1
  [7a1cc6ca] + FFTW v1.10.0
  [442a2c76] + FastGaussQuadrature v1.3.0
  [1a297f60] + FillArrays v1.16.0
  [6a86dc24] + FiniteDiff v2.31.0
⌅ [53c48c17] + FixedPointNumbers v0.8.6
  [f6369f11] + ForwardDiff v1.4.0
  [069b7b12] + FunctionWrappers v1.1.3
  [d9f16b24] + Functors v0.5.2
  [891a1506] + GaussianProcesses v0.12.6
  [e4b2fa32] + GaussianRandomFields v2.2.7
  [8f48dd54] + Glossaries v0.1.1
  [86223c79] + Graphs v1.14.0
  [3e5b6fbb] + HostCPUFeatures v0.1.18
  [34004b35] + HypergeometricFunctions v0.3.28
  [615f187c] + IfElse v0.1.1
  [d25df0c9] + Inflate v0.1.5
  [22cec73e] + InitialValues v0.3.1
  [18e54dd8] + IntegerMathUtils v0.1.3
  [a98d9a8b] + Interpolations v0.16.2
  [8197267c] + IntervalSets v0.7.14
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.6
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.8.0
  [682c06a0] + JSON v1.6.1
  [5ab0869b] + KernelDensity v0.6.12
⌅ [ec8451be] + KernelFunctions v0.10.67
  [2c470bb0] + Kronecker v0.5.5
  [40e66cde] + LDLFactorizations v0.10.2
  [b964fa9f] + LaTeXStrings v1.4.0
  [10f19ff3] + LayoutPointers v0.1.17
⌅ [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1
⌃ [d3d80556] + LineSearches v7.5.1
  [7a12625a] + LinearMaps v3.11.4
  [6fdf6af0] + LogDensityProblems v2.2.0
⌅ [2ab3a3ac] + LogExpFunctions v0.3.29
  [e6f89c97] + LoggingExtras v1.2.0
  [bdcacae8] + LoopVectorization v0.12.174
  [898213cb] + LowRankApprox v0.5.5
  [e65ccdef] + LowRankMatrices v1.0.2
  [c7f686f2] + MCMCChains v7.7.0
  [be115224] + MCMCDiagnosticTools v0.3.18
  [e80e1ace] + MLJModelInterface v1.12.1
  [1914dd2f] + MacroTools v0.5.16
  [af67fdf4] + ManifoldDiff v0.4.5
  [1cead3c2] + Manifolds v0.11.27
  [3362f125] + ManifoldsBase v2.4.0
  [0fc0a36d] + Manopt v0.5.39
  [d125e4d3] + ManualMemory v0.1.8
  [b8f27783] + MathOptInterface v1.51.1
  [99c1a7ee] + MatrixEquations v2.5.8
  [0b3b1443] + MicroMamba v0.1.15
  [e1d29d7a] + Missings v1.2.0
  [46d2c3a1] + MuladdMacro v0.2.4
  [d8a4904e] + MutableArithmetics v1.8.0
⌅ [d41bc354] + NLSolversBase v7.10.0
  [77ba4419] + NaNMath v1.1.3
  [356022a1] + NamedDims v1.2.3
  [c020b1a1] + NaturalSort v1.0.0
  [4d1e1d77] + Nullables v1.0.0
  [6fe1bfb0] + OffsetArrays v1.17.0
⌅ [429524aa] + Optim v1.13.3
⌅ [bac558e1] + OrderedCollections v1.8.2
⌅ [90014a1f] + PDMats v0.11.36
  [69de0a69] + Parsers v2.8.5
  [fa939f87] + Pidfile v1.3.0
  [1d0040c9] + PolyesterWeave v0.2.2
  [85a6dd25] + PositiveFactorizations v0.2.4
  [aea7be01] + PrecompileTools v1.3.4
  [21216c6a] + Preferences v1.5.2
  [08abe8d2] + PrettyTables v3.3.2
  [27ebfcd6] + Primes v0.5.7
  [49802e3a] + ProgressBars v1.5.1
  [33c8b6b6] + ProgressLogging v0.1.6
  [92933f4c] + ProgressMeter v1.11.0
  [43287f4e] + PtrArrays v1.4.0
  [6099a3de] + PythonCall v0.9.34
  [1fd47b50] + QuadGK v2.11.3
  [94ee1d12] + Quaternions v0.7.7
  [36c3bae2] + RandomFeatures v0.3.5
  [b3c3ace0] + RangeArrays v0.3.2
  [c84ed2f1] + Ratios v0.4.5
  [c1ae055f] + RealDot v0.1.0
  [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 v2.6.3
  [94e857df] + SIMDTypes v0.1.0
  [476501e8] + SLEEFPirates v0.6.46
  [431bcebd] + SciMLPublic v1.0.1
  [30f210dd] + ScientificTypesBase v3.1.0
  [6e75b9c4] + ScikitLearnBase v0.5.0
  [6c6a2e73] + Scratch v1.3.0
  [efcf1570] + Setfield v1.1.2
  [699a6c99] + SimpleTraits v0.9.6
  [47aef6b3] + SimpleWeightedGraphs v1.5.1
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.8.0
  [860ef19b] + StableRNGs v1.0.4
  [aedffcd0] + Static v1.4.0
  [0d7ed370] + StaticArrayInterface v1.10.0
  [90137ffa] + StaticArrays v1.9.18
  [1e83bf80] + StaticArraysCore v1.4.4
  [64bff920] + StatisticalTraits v3.5.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.11
⌅ [4c63d2b9] + StatsFuns v1.5.2
  [892a3eda] + StringManipulation v0.4.4
  [ec057cc2] + StructUtils v2.8.2
  [9449cd9e] + TSVD v0.4.4
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.1
  [62fd8b95] + TensorCore v0.1.1
  [5d786b92] + TerminalLoggers v0.1.7
  [8290d209] + ThreadingUtilities v0.5.6
  [3bb67fe8] + TranscodingStreams v0.11.3
  [bc48ee85] + Tullio v0.3.9
  [3a884ed6] + UnPack v1.0.2
  [e17b2a0c] + UnsafePointers v1.0.0
  [3d5dd08c] + VectorizationBase v0.21.74
  [efce3f68] + WoodburyMatrices v1.1.0
  [700de1a5] + ZygoteRules v0.2.7
⌅ [68821587] + Arpack_jll v3.5.2+0
  [6e34b625] + Bzip2_jll v1.0.9+0
  [83423d85] + Cairo_jll v1.18.7+0
  [2e619515] + Expat_jll v2.8.1+0
  [b22a6f82] + FFMPEG_jll v8.1.0+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 v1.0.17+0
⌅ [b0724c58] + GettextRuntime_jll v0.22.4+0
  [7746bdde] + Glib_jll v2.86.3+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
⌅ [e9f186c6] + Libffi_jll v3.4.7+0
  [94ce4f54] + Libiconv_jll v1.18.0+0
  [4b2f31a3] + Libmount_jll v2.42.0+0
  [38a345b3] + Libuuid_jll v2.42.0+0
  [856f044c] + MKL_jll v2025.2.0+0
  [e7412a2a] + Ogg_jll v1.3.6+0
  [656ef2d0] + OpenBLAS32_jll v0.3.33+1
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [91d4177d] + Opus_jll v1.6.1+0
  [30392449] + Pixman_jll v0.46.4+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [f4f2fc5b] + SCS_jll v300.200.1100+0
  [4f6342f7] + Xorg_libX11_jll v1.8.13+0
  [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0
  [a3789734] + Xorg_libXdmcp_jll v1.1.6+0
  [1082639a] + Xorg_libXext_jll v1.3.8+0
  [d091e8ba] + Xorg_libXfixes_jll v6.0.2+0
  [ea2f1a96] + Xorg_libXrender_jll v0.9.12+0
  [a65dc6b1] + Xorg_libpciaccess_jll v0.19.0+0
  [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0
  [c5fb5394] + Xorg_xtrans_jll v1.6.0+0
  [a4ae2306] + libaom_jll v3.13.3+0
  [0ac62f75] + libass_jll v0.17.4+0
  [8e53e030] + libdrm_jll v2.4.125+1
  [f638f0a6] + libfdk_aac_jll v2.0.4+0
  [b53b4c65] + libpng_jll v1.6.58+0
  [9a156e7d] + libva_jll v2.23.0+0
  [f27f6e37] + libvorbis_jll v1.3.8+0
  [f8abcde7] + micromamba_jll v2.3.1+0
  [1317d2d5] + oneTBB_jll v2022.3.0+0
  [4d7b5844] + pixi_jll v0.63.2+0
⌅ [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 v1.13.0
  [4af54fe1] + LazyArtifacts v1.11.0
  [b27032c2] + LibCURL v1.0.0
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.14.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] + Pkg v1.14.0
  [de0858da] + Printf v1.11.0
  [9abbd945] + Profile v1.11.0
  [3fa0cd96] + REPL v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.13.0
  [9e88b42a] + Serialization v1.11.0
  [1a1011a3] + SharedArrays v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.13.0
  [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 v1.5.2+0
  [deac9b47] + LibCURL_jll v8.20.0+1
  [e37daf67] + LibGit2_jll v1.9.4+0
  [29816b5a] + LibSSH2_jll v1.11.101+0
  [14a3606d] + MozillaCACerts_jll v2026.5.14
  [4536629a] + OpenBLAS_jll v0.3.33+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [458c3c95] + OpenSSL_jll v3.5.6+0
  [efcefdf7] + PCRE2_jll v10.47.0+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [83775a58] + Zlib_jll v1.3.2+0
  [3161d3a3] + Zstd_jll v1.5.7+1
  [8e850b90] + libblastrampoline_jll v5.15.0+0
  [8e850ede] + nghttp2_jll v1.69.0+0
  [3f19e933] + p7zip_jll v17.8.0+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`

Installation completed after 23.54s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...
Precompiling project...
   5.3 s  ✓ TestEnv
  1 dependency successfully precompiled in 5 seconds. 27 already precompiled.

Precompiling package dependencies...
Precompiling project...
   4.8 s  ✓ MacroTools
   1.0 s  ✓ Glossaries
   0.5 s  ✓ Reexport
   0.6 s  ✓ TensorCore
   1.0 s  ✓ ConstructionBase
   2.0 s  ✓ IrrationalConstants
   0.5 s  ✓ DataValueInterfaces
   0.6 s  ✓ StatsAPI
   2.9 s  ✓ LinearMaps
   1.0 s  ✓ IterTools
   0.8 s  ✓ IntervalSets
   0.7 s  ✓ Inflate
   0.6 s  ✓ ArgCheck
   1.1 s  ✓ TranscodingStreams
   0.5 s  ✓ LaTeXStrings
   0.8 s  ✓ Statistics
   0.7 s  ✓ StaticArraysCore
   0.7 s  ✓ ChunkSplitters
   0.7 s  ✓ StableRNGs
   0.5 s  ✓ IfElse
   0.5 s  ✓ PtrArrays
   0.5 s  ✓ NaturalSort
   0.7 s  ✓ ManualMemory
   0.6 s  ✓ Adapt
   0.7 s  ✓ PositiveFactorizations
   0.6 s  ✓ DataAPI
   0.6 s  ✓ SciMLPublic
   0.5 s  ✓ RealDot
   0.6 s  ✓ CommonWorldInvalidations
   0.6 s  ✓ LowRankMatrices
   0.7 s  ✓ InverseFunctions
   0.5 s  ✓ CompositionsBase
   0.9 s  ✓ AbstractTrees
   1.7 s  ✓ InitialValues
   0.5 s  ✓ MuladdMacro
   0.8 s  ✓ AbstractFFTs
   0.6 s  ✓ EnumX
   2.3 s  ✓ FillArrays
   0.5 s  ✓ IntegerMathUtils
   0.5 s  ✓ UnPack
   0.6 s  ✓ UnsafePointers
   0.5 s  ✓ RangeArrays
   0.8 s  ✓ Nullables
   1.1 s  ✓ OrderedCollections
   1.1 s  ✓ FunctionWrappers
   1.1 s  ✓ ADTypes
   0.9 s  ✓ DocStringExtensions
   1.5 s  ✓ OffsetArrays
   0.4 s  ✓ SIMDTypes
   0.4 s  ✓ IteratorInterfaceExtensions
   1.9 s  ✓ Crayons
   0.8 s  ✓ ProgressLogging
   0.7 s  ✓ NaNMath
   0.9 s  ✓ Requires
   1.0 s  ✓ ProgressBars
   2.5 s  ✓ ProgressMeter
   2.0 s  ✓ WoodburyMatrices
   2.5 s  ✓ AMD
   1.1 s  ✓ Scratch
   1.4 s  ✓ LoggingExtras
   1.7 s  ✓ StructUtils
   1.6 s  ✓ CpuId
   2.6 s  ✓ PDMats
   0.9 s  ✓ Compat
   1.5 s  ✓ Preferences
   1.2 s  ✓ ScientificTypesBase
   0.8 s  ✓ Pidfile
  16.1 s  ✓ MutableArithmetics
   1.4 s  ✓ CommonSubexpressions
   2.9 s  ✓ SimpleTraits
   0.5 s  ✓ ConstructionBase → ConstructionBaseLinearAlgebraExt
   4.2 s  ✓ MatrixEquations
   2.7 s  ✓ LinearMaps → LinearMapsSparseArraysExt
   0.5 s  ✓ IntervalSets → IntervalSetsRandomExt
   0.5 s  ✓ ConstructionBase → ConstructionBaseIntervalSetsExt
   0.8 s  ✓ CodecZlib
   1.9 s  ✓ Statistics → SparseArraysExt
   4.6 s  ✓ FixedPointNumbers
   0.7 s  ✓ ScikitLearnBase
   2.3 s  ✓ NamedDims
   1.2 s  ✓ Distances
   0.6 s  ✓ LinearMaps → LinearMapsStatisticsExt
   0.5 s  ✓ IntervalSets → IntervalSetsStatisticsExt
   0.6 s  ✓ DiffResults
   0.7 s  ✓ AliasTables
   1.8 s  ✓ ThreadingUtilities
   0.8 s  ✓ ArrayInterface
   1.9 s  ✓ Adapt → AdaptSparseArraysExt
   0.6 s  ✓ ElasticArrays
   0.6 s  ✓ TSVD
   0.7 s  ✓ Missings
   0.9 s  ✓ Quaternions
   1.1 s  ✓ InverseFunctions → InverseFunctionsDatesExt
   1.7 s  ✓ InverseFunctions → InverseFunctionsTestExt
   0.5 s  ✓ CompositionsBase → CompositionsBaseInverseFunctionsExt
   0.6 s  ✓ LeftChildRightSiblingTrees
   5.3 s  ✓ AbstractFFTs → AbstractFFTsTestExt
   2.5 s  ✓ FillArrays → FillArraysSparseArraysExt
   1.0 s  ✓ FillArrays → FillArraysStatisticsExt
   1.0 s  ✓ LowRankMatrices → LowRankMatricesFillArraysExt
   1.0 s  ✓ Primes
   3.8 s  ✓ DataStructures
   0.5 s  ✓ ADTypes → ADTypesConstructionBaseExt
   1.7 s  ✓ DifferentiationInterface
   1.4 s  ✓ LogExpFunctions
   1.9 s  ✓ LogDensityProblems
   0.6 s  ✓ OffsetArrays → OffsetArraysAdaptExt
   0.4 s  ✓ TableTraits
   0.6 s  ✓ Ratios
   1.5 s  ✓ ConsoleProgressMonitor
   2.0 s  ✓ AxisAlgorithms
   2.2 s  ✓ LDLFactorizations
   1.1 s  ✓ StructUtils → StructUtilsStaticArraysCoreExt
   3.6 s  ✓ ElasticPDMats
   2.3 s  ✓ FillArrays → FillArraysPDMatsExt
   0.5 s  ✓ Compat → CompatLinearAlgebraExt
   2.0 s  ✓ PrecompileTools
   2.3 s  ✓ JLLWrappers
   8.2 s  ✓ ManifoldsBase
   1.0 s  ✓ StatisticalTraits
   2.9 s  ✓ Setfield
   1.9 s  ✓ AxisArrays
   2.8 s  ✓ ColorTypes
   0.8 s  ✓ NamedDims → AbstractFFTsExt
   2.0 s  ✓ Distances → DistancesSparseArraysExt
   0.5 s  ✓ ArrayInterface → ArrayInterfaceStaticArraysCoreExt
   2.0 s  ✓ ArrayInterface → ArrayInterfaceSparseArraysExt
   5.7 s  ✓ Accessors
   1.6 s  ✓ TerminalLoggers
   2.6 s  ✓ FFTA
   0.9 s  ✓ SortingAlgorithms
   2.3 s  ✓ QuadGK
   2.0 s  ✓ DifferentiationInterface → DifferentiationInterfaceSparseArraysExt
   0.6 s  ✓ LogExpFunctions → LogExpFunctionsInverseFunctionsExt
   1.7 s  ✓ Tables
   0.7 s  ✓ Ratios → RatiosFixedPointNumbersExt
   2.6 s  ✓ ChainRulesCore
   1.0 s  ✓ Functors
   4.3 s  ✓ StringManipulation
  18.0 s  ✓ StaticArrays
   4.1 s  ✓ RecipesBase
  10.1 s  ✓ Parsers
  17.7 s  ✓ Static
   2.4 s  ✓ Libffi_jll
   2.4 s  ✓ OpenBLAS32_jll
   2.4 s  ✓ Bzip2_jll
   2.4 s  ✓ Rmath_jll
   2.4 s  ✓ OpenSpecFun_jll
   1.5 s  ✓ Xorg_xtrans_jll
   2.4 s  ✓ Opus_jll
   5.6 s  ✓ IntelOpenMP_jll
   2.4 s  ✓ x265_jll
   2.4 s  ✓ libpng_jll
   5.7 s  ✓ micromamba_jll
   5.8 s  ✓ oneTBB_jll
   2.4 s  ✓ Arpack_jll
   2.5 s  ✓ Libmount_jll
   2.4 s  ✓ libfdk_aac_jll
   2.4 s  ✓ Libuuid_jll
   2.3 s  ✓ FriBidi_jll
   2.4 s  ✓ Xorg_libXau_jll
   2.4 s  ✓ Ogg_jll
   2.4 s  ✓ FFTW_jll
   2.4 s  ✓ LAME_jll
   2.4 s  ✓ Graphite2_jll
   2.4 s  ✓ x264_jll
   2.5 s  ✓ Xorg_libpciaccess_jll
   2.5 s  ✓ LLVMOpenMP_jll
   6.4 s  ✓ pixi_jll
   2.6 s  ✓ Libiconv_jll
   2.5 s  ✓ libaom_jll
   2.6 s  ✓ Xorg_libXdmcp_jll
   2.6 s  ✓ Expat_jll
   1.7 s  ✓ ManifoldsBase → ManifoldsBaseStatisticsExt
   1.7 s  ✓ ManifoldsBase → ManifoldsBaseQuaternionsExt
   2.2 s  ✓ ManifoldDiff
   4.1 s  ✓ MLJModelInterface
   1.4 s  ✓ ColorTypes → StyledStringsExt
  12.0 s  ✓ Colors
   6.0 s  ✓ ColorVectorSpace
   1.1 s  ✓ FiniteDiff
   2.3 s  ✓ Accessors → TestExt
   2.7 s  ✓ Accessors → IntervalSetsExt
   2.6 s  ✓ Accessors → LinearAlgebraExt
   5.8 s  ✓ StatsBase
   1.2 s  ✓ StructUtils → StructUtilsTablesExt
   2.2 s  ✓ ChainRulesCore → ChainRulesCoreSparseArraysExt
   2.4 s  ✓ ZygoteRules
   0.6 s  ✓ LinearMaps → LinearMapsChainRulesCoreExt
   0.7 s  ✓ AbstractFFTs → AbstractFFTsChainRulesCoreExt
   0.6 s  ✓ ADTypes → ADTypesChainRulesCoreExt
   0.8 s  ✓ NamedDims → ChainRulesCoreExt
   0.6 s  ✓ Distances → DistancesChainRulesCoreExt
   0.5 s  ✓ ArrayInterface → ArrayInterfaceChainRulesCoreExt
   0.7 s  ✓ DifferentiationInterface → DifferentiationInterfaceChainRulesCoreExt
   4.2 s  ✓ LogExpFunctions → LogExpFunctionsChainRulesCoreExt
  56.2 s  ✓ PrettyTables
   4.0 s  ✓ ArnoldiMethod
   2.1 s  ✓ StaticArrays → StaticArraysStatisticsExt
   2.3 s  ✓ StaticArrays → StaticArraysChainRulesCoreExt
   2.2 s  ✓ ConstructionBase → ConstructionBaseStaticArraysExt
   2.2 s  ✓ Adapt → AdaptStaticArraysExt
   2.8 s  ✓ FillArrays → FillArraysStaticArraysExt
   2.4 s  ✓ DifferentiationInterface → DifferentiationInterfaceStaticArraysExt
   2.3 s  ✓ Accessors → StaticArraysExt
   1.6 s  ✓ IntervalSets → IntervalSetsRecipesBaseExt
   8.8 s  ✓ JSON
   1.2 s  ✓ BitTwiddlingConvenienceFunctions
   3.5 s  ✓ CPUSummary
  20.4 s  ✓ StaticArrayInterface
   1.3 s  ✓ CodecBzip2
   2.6 s  ✓ FreeType2_jll
   1.9 s  ✓ Rmath
   6.5 s  ✓ SpecialFunctions
 Downloading artifact: micromamba
   8.1 s  ✓ MicroMamba
 Downloading artifact: IntelOpenMP
 Downloading artifact: oneTBB
  12.4 s  ✓ MKL_jll
   1.7 s  ✓ Arpack
   2.5 s  ✓ libvorbis_jll
   2.5 s  ✓ libdrm_jll
   2.5 s  ✓ SCS_jll
   2.6 s  ✓ Pixman_jll
   2.6 s  ✓ GettextRuntime_jll
   2.7 s  ✓ Xorg_libxcb_jll
  10.0 s  ✓ ColorSchemes
   2.2 s  ✓ FiniteDiff → FiniteDiffStaticArraysExt
   2.0 s  ✓ FiniteDiff → FiniteDiffSparseArraysExt
   0.7 s  ✓ DifferentiationInterface → DifferentiationInterfaceFiniteDiffExt
   2.0 s  ✓ BangBang
   2.3 s  ✓ PDMats → StatsBaseExt
   4.1 s  ✓ Kronecker
  12.5 s  ✓ Graphs
   6.0 s  ✓ Interpolations
  14.6 s  ✓ BenchmarkTools
   3.1 s  ✓ HostCPUFeatures
   2.0 s  ✓ PolyesterWeave
   1.4 s  ✓ StaticArrayInterface → StaticArrayInterfaceOffsetArraysExt
   2.5 s  ✓ StaticArrayInterface → StaticArrayInterfaceStaticArraysExt
   1.4 s  ✓ CloseOpenIntervals
   1.6 s  ✓ LayoutPointers
   2.7 s  ✓ Fontconfig_jll
   5.5 s  ✓ SpecialFunctions → SpecialFunctionsChainRulesCoreExt
   5.4 s  ✓ FastGaussQuadrature
   2.7 s  ✓ HypergeometricFunctions
   1.1 s  ✓ DiffRules
   2.1 s  ✓ ColorVectorSpace → SpecialFunctionsExt
 Downloading artifact: pixi
  10.9 s  ✓ CondaPkg
  12.2 s  ✓ FFTW
   2.6 s  ✓ Glib_jll
   2.4 s  ✓ Xorg_libX11_jll
  21.6 s  ✓ Manopt
   5.9 s  ✓ AbstractMCMC
   1.5 s  ✓ BangBang → BangBangChainRulesCoreExt
   1.5 s  ✓ BangBang → BangBangTablesExt
   2.5 s  ✓ BangBang → BangBangStaticArraysExt
   5.3 s  ✓ SimpleWeightedGraphs
   5.7 s  ✓ Graphs → GraphsSharedArraysExt
  22.1 s  ✓ VectorizationBase
  14.1 s  ✓ KernelFunctions
   3.6 s  ✓ StatsFuns
  18.7 s  ✓ Tullio
   8.7 s  ✓ ForwardDiff
  28.5 s  ✓ PythonCall
WARNING: Constructor for type "Array" was extended in `LowRankApprox` without explicit qualification or import.
  NOTE: Assumed "Array" refers to `Base.Array`. This behavior is deprecated and may differ in future versions.
  NOTE: This behavior may have differed in Julia versions prior to 1.12.
  Hint: If you intended to create a new generic function of the same name, use `function Array end`.
  Hint: To silence the warning, qualify `Array` as `Base.Array` in the method signature or explicitly `import Base: Array`.
   5.6 s  ✓ LowRankApprox
   8.2 s  ✓ GaussianRandomFields
   2.5 s  ✓ Xorg_libXfixes_jll
   2.6 s  ✓ Xorg_libXrender_jll
   2.6 s  ✓ Xorg_libXext_jll
   3.9 s  ✓ SLEEFPirates
   4.8 s  ✓ StatsFuns → StatsFunsChainRulesCoreExt
   1.2 s  ✓ StatsFuns → StatsFunsInverseFunctionsExt
  10.9 s  ✓ Distributions
   1.1 s  ✓ Tullio → TullioChainRulesCoreExt
   1.6 s  ✓ Tullio → TullioFillArraysExt
  29.6 s  ✓ Manifolds
   2.6 s  ✓ ForwardDiff → ForwardDiffStaticArraysExt
   1.8 s  ✓ DifferentiationInterface → DifferentiationInterfaceForwardDiffExt
 125.7 s  ✓ MathOptInterface
   4.1 s  ✓ Interpolations → InterpolationsForwardDiffExt
   2.5 s  ✓ Cairo_jll
   2.3 s  ✓ libva_jll
  47.5 s  ✓ LoopVectorization
   5.3 s  ✓ Distributions → DistributionsTestExt
   4.4 s  ✓ Distributions → DistributionsChainRulesCoreExt
   6.8 s  ✓ MCMCDiagnosticTools
   6.2 s  ✓ AdvancedMH
   7.6 s  ✓ Manifolds → ManifoldsRecipesBaseExt
  10.3 s  ✓ Manifolds → ManifoldsTestExt
   8.3 s  ✓ Manopt → ManoptManifoldsExt
  50.2 s  ✓ ReverseDiff
   2.8 s  ✓ NLSolversBase
  11.4 s  ✓ MathOptInterface → MathOptInterfaceBenchmarkToolsExt
  77.6 s  ✓ SCS
   2.4 s  ✓ HarfBuzz_jll
   3.5 s  ✓ LoopVectorization → SpecialFunctionsExt
   3.9 s  ✓ LoopVectorization → ForwardDiffExt
   5.5 s  ✓ KernelDensity
   7.3 s  ✓ AbstractGPs
   5.6 s  ✓ AdvancedMH → AdvancedMHForwardDiffExt
  19.2 s  ✓ ArrayInterface → ArrayInterfaceReverseDiffExt
  18.9 s  ✓ DifferentiationInterface → DifferentiationInterfaceReverseDiffExt
   3.0 s  ✓ LineSearches
  17.1 s  ✓ Convex
   2.3 s  ✓ libass_jll
  11.8 s  ✓ MCMCChains
   5.7 s  ✓ Manopt → ManoptLineSearchesExt
   8.9 s  ✓ Optim
   2.7 s  ✓ FFMPEG_jll
  10.6 s  ✓ AdvancedMH → AdvancedMHMCMCChainsExt
  12.5 s  ✓ Optim → OptimMOIExt
   9.6 s  ✓ GaussianProcesses
   1.3 s  ✓ FFMPEG
  24.4 s  ✓ EnsembleKalmanProcesses
  60.0 s  ✓ RandomFeatures
    CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CondaPkg/lKlVY/CondaPkg.toml
    CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/CondaPkg.toml
    CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/JksWe/CondaPkg.toml
    CondaPkg Resolving changes
             + openssl
             + python
             + scikit-learn
             + scipy
    CondaPkg Initialising pixi
             │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi
             │ init
             │ --format pixi
             └ /tmp/jl_FOwVIz/.CondaPkg
✔ Created /tmp/jl_FOwVIz/.CondaPkg/pixi.toml
    CondaPkg Wrote /tmp/jl_FOwVIz/.CondaPkg/pixi.toml
             │ [dependencies]
             │ openssl = ">=3, <3.6"
             │ scikit-learn = "=1.5.1"
             │ scipy = "=1.14.1"
             │ 
             │     [dependencies.python]
             │     version = "=3.11, >=3.10,!=3.14.0,!=3.14.1,<4"
             │     build = "*cp*"
             │     channel = "conda-forge"
             │ 
             │ [workspace]
             │ name = ".CondaPkg"
             │ description = "automatically generated by CondaPkg.jl"
             │ platforms = ["linux-64"]
             │ channel-priority = "strict"
             └ channels = ["conda-forge"]
    CondaPkg Installing packages
             │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi
             │ install
             └ --manifest-path /tmp/jl_FOwVIz/.CondaPkg/pixi.toml
✔ The default environment has been installed.
 118.6 s  ✓ CalibrateEmulateSample
  314 dependencies successfully precompiled in 1631 seconds. 42 already precompiled.
  5 dependencies had output during precompilation:
┌ MKL_jll
│  Downloading artifact: IntelOpenMP
│   Downloading artifact: oneTBB
└  
┌ CondaPkg
│  Downloading artifact: pixi
└  
┌ MicroMamba
│  Downloading artifact: micromamba
└  
┌ CalibrateEmulateSample
│  CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CondaPkg/lKlVY/CondaPkg.toml
│      CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/CondaPkg.toml
│      CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/JksWe/CondaPkg.toml
│      CondaPkg Resolving changes
│               + openssl
│               + python
│               + scikit-learn
│               + scipy
│      CondaPkg Initialising pixi
│               │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi
│               │ init
│               │ --format pixi
│               └ /tmp/jl_FOwVIz/.CondaPkg
│  ✔ Created /tmp/jl_FOwVIz/.CondaPkg/pixi.toml
│      CondaPkg Wrote /tmp/jl_FOwVIz/.CondaPkg/pixi.toml
│               │ [dependencies]
│               │ openssl = ">=3, <3.6"
│               │ scikit-learn = "=1.5.1"
│               │ scipy = "=1.14.1"
│               │ 
│               │     [dependencies.python]
│               │     version = "=3.11, >=3.10,!=3.14.0,!=3.14.1,<4"
│               │     build = "*cp*"
│               │     channel = "conda-forge"
│               │ 
│               │ [workspace]
│               │ name = ".CondaPkg"
│               │ description = "automatically generated by CondaPkg.jl"
│               │ platforms = ["linux-64"]
│               │ channel-priority = "strict"
│               └ channels = ["conda-forge"]
│      CondaPkg Installing packages
│               │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi
│               │ install
│               └ --manifest-path /tmp/jl_FOwVIz/.CondaPkg/pixi.toml
│  ✔ The default environment has been installed.
└  
┌ LowRankApprox
│  WARNING: Constructor for type "Array" was extended in `LowRankApprox` without explicit qualification or import.
│    NOTE: Assumed "Array" refers to `Base.Array`. This behavior is deprecated and may differ in future versions.
│    NOTE: This behavior may have differed in Julia versions prior to 1.12.
│    Hint: If you intended to create a new generic function of the same name, use `function Array end`.
│    Hint: To silence the warning, qualify `Array` as `Base.Array` in the method signature or explicitly `import Base: Array`.
└  

Precompilation completed after 1653.25s


################################################################################
# Testing
#

     Testing CalibrateEmulateSample
      Status `/tmp/jl_pwIlxo/Project.toml`
  [99985d1d] AbstractGPs v0.5.24
  [80f14c24] AbstractMCMC v5.15.1
  [5b7e9947] AdvancedMH v0.8.10
  [95e48a1f] CalibrateEmulateSample v1.1.0
  [ae650224] ChunkSplitters v3.2.0
  [992eb4ea] CondaPkg v0.2.36
  [31c24e10] Distributions v0.25.126
  [ffbed154] DocStringExtensions v0.9.5
  [aa8a2aa5] EnsembleKalmanProcesses v2.7.1
  [f6369f11] ForwardDiff v1.4.0
  [891a1506] GaussianProcesses v0.12.6
⌅ [ec8451be] KernelFunctions v0.10.67
  [7a12625a] LinearMaps v3.11.4
  [898213cb] LowRankApprox v0.5.5
  [c7f686f2] MCMCChains v7.7.0
  [1cead3c2] Manifolds v0.11.27
  [0fc0a36d] Manopt v0.5.39
⌅ [90014a1f] PDMats v0.11.36
  [49802e3a] ProgressBars v1.5.1
  [6099a3de] PythonCall v0.9.34
  [36c3bae2] RandomFeatures v0.3.5
  [37e2e3b7] ReverseDiff v1.16.2
  [860ef19b] StableRNGs v1.0.4
  [10745b16] Statistics v1.11.1
  [2913bbd2] StatsBase v0.34.11
  [9449cd9e] TSVD v0.4.4
  [37e2e46d] LinearAlgebra v1.14.0
  [44cfe95a] Pkg v1.14.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_pwIlxo/Manifest.toml`
  [47edcb42] ADTypes v1.22.0
  [14f7f29c] AMD v0.5.3
  [621f4979] AbstractFFTs v1.5.0
  [99985d1d] AbstractGPs v0.5.24
  [80f14c24] AbstractMCMC v5.15.1
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.44
  [79e6a3ab] Adapt v4.6.0
  [5b7e9947] AdvancedMH v0.8.10
  [66dad0bd] AliasTables v1.1.3
  [dce04be8] ArgCheck v2.5.0
  [ec485272] ArnoldiMethod v0.4.0
  [7d9fca2a] Arpack v0.5.4
  [4fba245c] ArrayInterface v7.25.0
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.8
  [198e06fe] BangBang v0.4.9
  [6e4b80f9] BenchmarkTools v1.8.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [95e48a1f] CalibrateEmulateSample v1.1.0
  [d360d2e6] ChainRulesCore v1.26.1
  [ae650224] ChunkSplitters v3.2.0
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [523fee87] CodecBzip2 v0.8.5
  [944b1d66] CodecZlib v0.7.8
  [35d6a980] ColorSchemes v3.31.0
  [3da002f7] ColorTypes v0.12.1
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.1
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [a33af91c] CompositionsBase v0.1.2
  [992eb4ea] CondaPkg v0.2.36
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [f65535da] Convex v0.16.6
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.19.5
  [e2d170a0] DataValueInterfaces v1.0.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.16.0
  [a0c0ee7d] DifferentiationInterface v0.7.18
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.126
  [ffbed154] DocStringExtensions v0.9.5
  [fdbdab4c] ElasticArrays v1.2.12
  [2904ab23] ElasticPDMats v0.2.4
  [aa8a2aa5] EnsembleKalmanProcesses v2.7.1
  [4e289a0a] EnumX v1.0.7
  [c87230d0] FFMPEG v0.4.5
  [b86e33f2] FFTA v0.3.1
  [7a1cc6ca] FFTW v1.10.0
  [442a2c76] FastGaussQuadrature v1.3.0
  [1a297f60] FillArrays v1.16.0
  [6a86dc24] FiniteDiff v2.31.0
⌅ [53c48c17] FixedPointNumbers v0.8.6
  [f6369f11] ForwardDiff v1.4.0
  [069b7b12] FunctionWrappers v1.1.3
  [d9f16b24] Functors v0.5.2
  [891a1506] GaussianProcesses v0.12.6
  [e4b2fa32] GaussianRandomFields v2.2.7
  [8f48dd54] Glossaries v0.1.1
  [86223c79] Graphs v1.14.0
  [3e5b6fbb] HostCPUFeatures v0.1.18
  [34004b35] HypergeometricFunctions v0.3.28
  [615f187c] IfElse v0.1.1
  [d25df0c9] Inflate v0.1.5
  [22cec73e] InitialValues v0.3.1
  [18e54dd8] IntegerMathUtils v0.1.3
  [a98d9a8b] Interpolations v0.16.2
  [8197267c] IntervalSets v0.7.14
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.6
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.8.0
  [682c06a0] JSON v1.6.1
  [5ab0869b] KernelDensity v0.6.12
⌅ [ec8451be] KernelFunctions v0.10.67
  [2c470bb0] Kronecker v0.5.5
  [40e66cde] LDLFactorizations v0.10.2
  [b964fa9f] LaTeXStrings v1.4.0
  [10f19ff3] LayoutPointers v0.1.17
⌅ [1d6d02ad] LeftChildRightSiblingTrees v0.2.1
⌃ [d3d80556] LineSearches v7.5.1
  [7a12625a] LinearMaps v3.11.4
  [6fdf6af0] LogDensityProblems v2.2.0
⌅ [2ab3a3ac] LogExpFunctions v0.3.29
  [e6f89c97] LoggingExtras v1.2.0
  [bdcacae8] LoopVectorization v0.12.174
  [898213cb] LowRankApprox v0.5.5
  [e65ccdef] LowRankMatrices v1.0.2
  [c7f686f2] MCMCChains v7.7.0
  [be115224] MCMCDiagnosticTools v0.3.18
  [e80e1ace] MLJModelInterface v1.12.1
  [1914dd2f] MacroTools v0.5.16
  [af67fdf4] ManifoldDiff v0.4.5
  [1cead3c2] Manifolds v0.11.27
  [3362f125] ManifoldsBase v2.4.0
  [0fc0a36d] Manopt v0.5.39
  [d125e4d3] ManualMemory v0.1.8
  [b8f27783] MathOptInterface v1.51.1
  [99c1a7ee] MatrixEquations v2.5.8
  [0b3b1443] MicroMamba v0.1.15
  [e1d29d7a] Missings v1.2.0
  [46d2c3a1] MuladdMacro v0.2.4
  [d8a4904e] MutableArithmetics v1.8.0
⌅ [d41bc354] NLSolversBase v7.10.0
  [77ba4419] NaNMath v1.1.3
  [356022a1] NamedDims v1.2.3
  [c020b1a1] NaturalSort v1.0.0
  [4d1e1d77] Nullables v1.0.0
  [6fe1bfb0] OffsetArrays v1.17.0
⌅ [429524aa] Optim v1.13.3
⌅ [bac558e1] OrderedCollections v1.8.2
⌅ [90014a1f] PDMats v0.11.36
  [69de0a69] Parsers v2.8.5
  [fa939f87] Pidfile v1.3.0
  [1d0040c9] PolyesterWeave v0.2.2
  [85a6dd25] PositiveFactorizations v0.2.4
  [aea7be01] PrecompileTools v1.3.4
  [21216c6a] Preferences v1.5.2
  [08abe8d2] PrettyTables v3.3.2
  [27ebfcd6] Primes v0.5.7
  [49802e3a] ProgressBars v1.5.1
  [33c8b6b6] ProgressLogging v0.1.6
  [92933f4c] ProgressMeter v1.11.0
  [43287f4e] PtrArrays v1.4.0
  [6099a3de] PythonCall v0.9.34
  [1fd47b50] QuadGK v2.11.3
  [94ee1d12] Quaternions v0.7.7
  [36c3bae2] RandomFeatures v0.3.5
  [b3c3ace0] RangeArrays v0.3.2
  [c84ed2f1] Ratios v0.4.5
  [c1ae055f] RealDot v0.1.0
  [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 v2.6.3
  [94e857df] SIMDTypes v0.1.0
  [476501e8] SLEEFPirates v0.6.46
  [431bcebd] SciMLPublic v1.0.1
  [30f210dd] ScientificTypesBase v3.1.0
  [6e75b9c4] ScikitLearnBase v0.5.0
  [6c6a2e73] Scratch v1.3.0
  [efcf1570] Setfield v1.1.2
  [699a6c99] SimpleTraits v0.9.6
  [47aef6b3] SimpleWeightedGraphs v1.5.1
  [a2af1166] SortingAlgorithms v1.2.2
  [276daf66] SpecialFunctions v2.8.0
  [860ef19b] StableRNGs v1.0.4
  [aedffcd0] Static v1.4.0
  [0d7ed370] StaticArrayInterface v1.10.0
  [90137ffa] StaticArrays v1.9.18
  [1e83bf80] StaticArraysCore v1.4.4
  [64bff920] StatisticalTraits v3.5.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.11
⌅ [4c63d2b9] StatsFuns v1.5.2
  [892a3eda] StringManipulation v0.4.4
  [ec057cc2] StructUtils v2.8.2
  [9449cd9e] TSVD v0.4.4
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.12.1
  [62fd8b95] TensorCore v0.1.1
  [5d786b92] TerminalLoggers v0.1.7
  [8290d209] ThreadingUtilities v0.5.6
  [3bb67fe8] TranscodingStreams v0.11.3
  [bc48ee85] Tullio v0.3.9
  [3a884ed6] UnPack v1.0.2
  [e17b2a0c] UnsafePointers v1.0.0
  [3d5dd08c] VectorizationBase v0.21.74
  [efce3f68] WoodburyMatrices v1.1.0
  [700de1a5] ZygoteRules v0.2.7
⌅ [68821587] Arpack_jll v3.5.2+0
  [6e34b625] Bzip2_jll v1.0.9+0
  [83423d85] Cairo_jll v1.18.7+0
  [2e619515] Expat_jll v2.8.1+0
  [b22a6f82] FFMPEG_jll v8.1.0+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 v1.0.17+0
⌅ [b0724c58] GettextRuntime_jll v0.22.4+0
  [7746bdde] Glib_jll v2.86.3+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
⌅ [e9f186c6] Libffi_jll v3.4.7+0
  [94ce4f54] Libiconv_jll v1.18.0+0
  [4b2f31a3] Libmount_jll v2.42.0+0
  [38a345b3] Libuuid_jll v2.42.0+0
  [856f044c] MKL_jll v2025.2.0+0
  [e7412a2a] Ogg_jll v1.3.6+0
  [656ef2d0] OpenBLAS32_jll v0.3.33+1
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [91d4177d] Opus_jll v1.6.1+0
  [30392449] Pixman_jll v0.46.4+0
  [f50d1b31] Rmath_jll v0.5.1+0
  [f4f2fc5b] SCS_jll v300.200.1100+0
  [4f6342f7] Xorg_libX11_jll v1.8.13+0
  [0c0b7dd1] Xorg_libXau_jll v1.0.13+0
  [a3789734] Xorg_libXdmcp_jll v1.1.6+0
  [1082639a] Xorg_libXext_jll v1.3.8+0
  [d091e8ba] Xorg_libXfixes_jll v6.0.2+0
  [ea2f1a96] Xorg_libXrender_jll v0.9.12+0
  [a65dc6b1] Xorg_libpciaccess_jll v0.19.0+0
  [c7cfdc94] Xorg_libxcb_jll v1.17.1+0
  [c5fb5394] Xorg_xtrans_jll v1.6.0+0
  [a4ae2306] libaom_jll v3.13.3+0
  [0ac62f75] libass_jll v0.17.4+0
  [8e53e030] libdrm_jll v2.4.125+1
  [f638f0a6] libfdk_aac_jll v2.0.4+0
  [b53b4c65] libpng_jll v1.6.58+0
  [9a156e7d] libva_jll v2.23.0+0
  [f27f6e37] libvorbis_jll v1.3.8+0
  [f8abcde7] micromamba_jll v2.3.1+0
  [1317d2d5] oneTBB_jll v2022.3.0+0
  [4d7b5844] pixi_jll v0.63.2+0
⌅ [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 v1.13.0
  [4af54fe1] LazyArtifacts v1.11.0
  [b27032c2] LibCURL v1.0.0
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.14.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [a63ad114] Mmap v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.14.0
  [de0858da] Printf v1.11.0
  [9abbd945] Profile v1.11.0
  [3fa0cd96] REPL v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v1.13.0
  [9e88b42a] Serialization v1.11.0
  [1a1011a3] SharedArrays v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.13.0
  [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 v1.5.2+0
  [deac9b47] LibCURL_jll v8.20.0+1
  [e37daf67] LibGit2_jll v1.9.4+0
  [29816b5a] LibSSH2_jll v1.11.101+0
  [14a3606d] MozillaCACerts_jll v2026.5.14
  [4536629a] OpenBLAS_jll v0.3.33+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.6+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.2+0
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.69.0+0
  [3f19e933] p7zip_jll v17.8.0+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...
[ Info: [in test/runtest.jl], create plots? CES_TEST_PLOT_OUTPUT: false
Starting tests for Emulator
    CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CondaPkg/lKlVY/CondaPkg.toml
    CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/CondaPkg.toml
    CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/JksWe/CondaPkg.toml
    CondaPkg Resolving changes
             + openssl
             + python
             + scikit-learn
             + scipy
    CondaPkg Initialising pixi
             │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi
             │ init
             │ --format pixi
             └ /tmp/jl_pwIlxo/.CondaPkg
✔ Created /tmp/jl_pwIlxo/.CondaPkg/pixi.toml
    CondaPkg Wrote /tmp/jl_pwIlxo/.CondaPkg/pixi.toml
             │ [dependencies]
             │ openssl = ">=3, <3.6"
             │ scikit-learn = "=1.5.1"
             │ scipy = "=1.14.1"
             │ 
             │     [dependencies.python]
             │     version = "=3.11, >=3.10,!=3.14.0,!=3.14.1,<4"
             │     build = "*cp*"
             │     channel = "conda-forge"
             │ 
             │ [workspace]
             │ name = ".CondaPkg"
             │ description = "automatically generated by CondaPkg.jl"
             │ platforms = ["linux-64"]
             │ channel-priority = "strict"
             └ channels = ["conda-forge"]
    CondaPkg Installing packages
             │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi
             │ install
             └ --manifest-path /tmp/jl_pwIlxo/.CondaPkg/pixi.toml
✔ The default environment has been installed.
[ Info: fit successful
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 6, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 6, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
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
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 6, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
┌ Warning: GaussianProcess already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/GaussianProcess.jl:188
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 6, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 6, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
Completed tests for Emulator, 310 seconds elapsed
Starting tests for GaussianProcess
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: GaussianProcess already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/GaussianProcess.jl:188
optimized hyperparameters of GP: 1
Type: SumKernel{SEIso{Float64}, Noise{Float64}}
  Type: SEIso{Float64}, Params: [0.4671112501513754, -0.11637219099834126]  Type: Noise{Float64}, Params: [-2.779564795897494]
optimised GP: 1
Sum of 2 kernels:
	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
			- ARD Transform (dims: 1)
			- σ² = 0.7923560881211849
	White Kernel
			- σ² = 0.0038521278625259676
[ Info: AbstractGP already built. Continuing...
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: [0.46711125015097044, -0.11637219099977898]  Type: Noise{Float64}, Params: [-2.9126145296277137]
Using user-defined kernel1**2 * RBF(length_scale=1)
Learning additive white noise
[ Info: Training kernel 1, 
[ Info: 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1)
┌ Warning: GaussianProcess already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/GaussianProcess.jl:334
SKlearn, already trained. continuing...
Using user-defined kernel1**2 * RBF(length_scale=1)
Learning additive white noise
[ Info: Training kernel 1, 
[ Info: 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1)
┌ Warning: `SKLJL` is deprecated, use `SKLPy` instead.
│   caller = top-level scope at runtests.jl:20
└ @ Core ~/.julia/packages/CalibrateEmulateSample/yapkx/test/GaussianProcess/runtests.jl:20
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
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
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
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: SEArd{Float64}, Params: [-0.08095883666729817, 0.6591588380894285, 2.0163237790280433]
optimized hyperparameters of GP: 2
Type: SEArd{Float64}, Params: [0.48546387823260384, 0.08009132351645844, 2.348678772768728]
optimized hyperparameters of GP: 1
Type: SumKernel{SEArd{Float64}, Noise{Float64}}
  Type: SEArd{Float64}, Params: [-0.06076669570339724, 0.6629187475773616, 2.0713964932483395]  Type: Noise{Float64}, Params: [-0.22010761514599403]
optimized hyperparameters of GP: 2
Type: SumKernel{SEArd{Float64}, Noise{Float64}}
  Type: SEArd{Float64}, Params: [0.4806217980287883, 0.07991481116088309, 2.344620786302477]  Type: Noise{Float64}, Params: [-0.09161176738899532]
┌ Warning: `transform_to_real` keyword is deprecated. Please use the `encode` and `add_obs_noise_cov` keywords instead.
│                                              
│ Recommended usage for users is now set by default as:
│  - `encode=nothing`, `add_obs_noise_cov=false`
│ This behaviour takes in non-encoded inputs, and returns non-encoded outputs. It gives only the uncertainty from the Machine Learning Tool (not inflated by observational noise)
│                
│ This simulation will continue with the old behavior:
│  - `transform_to_real=true` replaced with `encode=nothing, add_obs_noise_cov=true`
│  - `transform_to_real=false` replaced with `encode="out", add_obs_noise_cov=true`
│     
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:600
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
optimised GP: 1
Sum of 2 kernels:
	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
			- ARD Transform (dims: 2)
			- σ² = 62.9784740649691
	White Kernel
			- σ² = 0.6438978198523074
optimised GP: 2
Sum of 2 kernels:
	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
			- ARD Transform (dims: 2)
			- σ² = 108.7706538727328
	White Kernel
			- σ² = 0.8325820238965255
Completed tests for GaussianProcess, 107 seconds elapsed
Starting tests for RandomFeature
┌ Info: Shrinkage scale: 0.9489742380811493, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 1.2992337546168757
[ Info: NICE-adjusted covariance condition number: 2.6727300567766656
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning for 1 models using 50 training points, 50 validation points and 100 features
estimating covariances with 520 iterations...
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning using 50 training points, 50 validation points and 100 features
estimating covariances with 520 iterations...
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: hyperparameter learning for 1 models using 40 training points, 10 validation points and 100 features
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
┌ Warning: ScalarRandomFeatureInterface already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/ScalarRandomFeature.jl:356
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 50, while the space dimension is 1, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
┌ Warning: VectorRandomFeatureInterface already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/VectorRandomFeature.jl:383
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("n_features_opt" => 100, "n_cross_val_sets" => 2, "n_iteration" => 10, "cov_sample_multiplier" => 10.0, "inflation" => 0.0001, "n_ensemble" => 30, "train_fraction" => 0.8, "overfit" => 1.0, "cov_correction" => "nice", "scheduler" => DataMisfitController (T=1000.0, "stop"), "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "verbose" => true, "multithread" => "ensemble", "accelerator" => NesterovAccelerator (θ_prev=1.0))
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("n_features_opt" => 100, "n_cross_val_sets" => 2, "n_iteration" => 10, "cov_sample_multiplier" => 10.0, "inflation" => 0.0001, "n_ensemble" => 70, "train_fraction" => 0.8, "overfit" => 1.0, "cov_correction" => "shrinkage", "scheduler" => DataMisfitController (T=1000.0, "stop"), "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "verbose" => true, "multithread" => "ensemble", "accelerator" => NesterovAccelerator (θ_prev=1.0))
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("n_features_opt" => 100, "n_cross_val_sets" => 2, "n_iteration" => 10, "cov_sample_multiplier" => 10.0, "inflation" => 0.0001, "n_ensemble" => 100, "train_fraction" => 0.8, "overfit" => 1.0, "cov_correction" => "nice", "scheduler" => DataMisfitController (T=1000.0, "stop"), "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "verbose" => true, "multithread" => "ensemble", "accelerator" => NesterovAccelerator (θ_prev=1.0))
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning for 2 models using 80 training points, 20 validation points and 100 features
estimating covariances with 220 iterations...
estimating covariances with 220 iterations...
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning for 2 models using 80 training points, 20 validation points and 100 features
[ Info: training model 1 / 2
estimating covariances with 220 iterations...
estimate cov with 220 iterations...
[ Info: NICE-adjusted covariance condition number: 1.7595461268552332e6
estimate cov with 220 iterations...
[ Info: NICE-adjusted covariance condition number: 2.1254520941892434e6
┌ Info: Initializing ensemble Kalman process of type TransformInversion
│ Number of ensemble members: 30
│ Localization: NoLocalization
│ Failure handler: SampleSuccGauss
│ Scheduler: DataMisfitController
└ Accelerator: NesterovAccelerator
[ Info: Iteration 0 (prior)
[ Info: Covariance trace: 0.0037326842632157504
[ Info: Iteration 1 (T=0.03966085685980313)
┌ Info: Covariance-weighted error: 8.012825031226559
│ Covariance trace: 0.00322449052211775
└ Covariance trace ratio (current/previous): 0.8638530062384152
[ Info: Iteration 2 (T=0.09352142503461608)
┌ Info: Covariance-weighted error: 8.30290816246196
│ Covariance trace: 0.002330725590962019
└ Covariance trace ratio (current/previous): 0.722819798965099
[ Info: Iteration 3 (T=0.13977171761628018)
┌ Info: Covariance-weighted error: 8.884070665896806
│ Covariance trace: 0.0015897032362542506
└ Covariance trace ratio (current/previous): 0.6820636639588672
[ Info: Iteration 4 (T=0.18699699543854073)
┌ Info: Covariance-weighted error: 7.099950373417509
│ Covariance trace: 0.0009162006247591396
└ Covariance trace ratio (current/previous): 0.5763343773004727
[ Info: Iteration 5 (T=0.2938141245805024)
┌ Info: Covariance-weighted error: 5.822949930781018
│ Covariance trace: 0.0006966258951512971
└ Covariance trace ratio (current/previous): 0.760342087012256
[ Info: Iteration 6 (T=0.7924693336762474)
┌ Info: Covariance-weighted error: 4.950527920295337
│ Covariance trace: 0.0004323229124124553
└ Covariance trace ratio (current/previous): 0.6205955239699509
[ Info: Iteration 7 (T=1.291898847939041)
┌ Info: Covariance-weighted error: 4.825176444601657
│ Covariance trace: 0.0002568543471433955
└ Covariance trace ratio (current/previous): 0.5941261491557149
[ Info: Iteration 8 (T=2.5013768167520007)
┌ Info: Covariance-weighted error: 4.639106385437546
│ Covariance trace: 0.00016225631724482075
└ Covariance trace ratio (current/previous): 0.631705552385442
[ Info: Iteration 9 (T=3.5575834688257904)
┌ Info: Covariance-weighted error: 4.622896968456796
│ Covariance trace: 0.00011050684386959908
└ Covariance trace ratio (current/previous): 0.6810634294309825
[ Info: Iteration 10 (T=4.6175524653923725)
┌ Info: Covariance-weighted error: 4.622430049494031
│ Covariance trace: 8.523059321469272e-5
└ Covariance trace ratio (current/previous): 0.7712698166935888
[ Info: EKI Optimization result:
2×4 Matrix{Any}:
 "name"             "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "input_cholesky"  3                             (-0.097498, 0.151198)    (-0.1, 0.1)
nothing
[ Info: training model 2 / 2
estimating covariances with 220 iterations...
estimate cov with 220 iterations...
[ Info: NICE-adjusted covariance condition number: 327536.0052302047
estimate cov with 220 iterations...
[ Info: NICE-adjusted covariance condition number: 83034.42184393445
┌ Info: Initializing ensemble Kalman process of type TransformInversion
│ Number of ensemble members: 30
│ Localization: NoLocalization
│ Failure handler: SampleSuccGauss
│ Scheduler: DataMisfitController
└ Accelerator: NesterovAccelerator
[ Info: Iteration 0 (prior)
[ Info: Covariance trace: 0.00269964860789342
[ Info: Iteration 1 (T=0.09408256311489652)
┌ Info: Covariance-weighted error: 16.407857139058986
│ Covariance trace: 0.0020230415531278497
└ Covariance trace ratio (current/previous): 0.7493721765168773
[ Info: Iteration 2 (T=0.38815602933119475)
┌ Info: Covariance-weighted error: 16.44074211015101
│ Covariance trace: 0.001095127289462521
└ Covariance trace ratio (current/previous): 0.5413271357522692
[ Info: Iteration 3 (T=0.5981146436546678)
┌ Info: Covariance-weighted error: 16.19424714371061
│ Covariance trace: 0.0005209667477891416
└ Covariance trace ratio (current/previous): 0.4757134196197663
[ Info: Iteration 4 (T=1.2928990245062066)
┌ Info: Covariance-weighted error: 16.116285504375693
│ Covariance trace: 0.0003391448021569398
└ Covariance trace ratio (current/previous): 0.6509912649822457
[ Info: Iteration 5 (T=1.7380663658438422)
┌ Info: Covariance-weighted error: 16.284414434200208
│ Covariance trace: 0.00025459657935683394
└ Covariance trace ratio (current/previous): 0.750701699503031
[ Info: Iteration 6 (T=2.155450468618092)
┌ Info: Covariance-weighted error: 16.167497909795287
│ Covariance trace: 0.00024445994693972253
└ Covariance trace ratio (current/previous): 0.9601855121434909
[ Info: Iteration 7 (T=2.586756593274099)
┌ Info: Covariance-weighted error: 16.176610652494617
│ Covariance trace: 0.00023102162169362657
└ Covariance trace ratio (current/previous): 0.9450285193369141
[ Info: Iteration 8 (T=3.256056894758008)
┌ Info: Covariance-weighted error: 16.165868478344752
│ Covariance trace: 0.00023793949369687746
└ Covariance trace ratio (current/previous): 1.0299446950139808
[ Info: Iteration 9 (T=3.9567357836143175)
┌ Info: Covariance-weighted error: 16.15035970029809
│ Covariance trace: 0.00017806386950471818
└ Covariance trace ratio (current/previous): 0.7483577725502025
[ Info: Iteration 10 (T=4.9146989635200375)
┌ Info: Covariance-weighted error: 16.158724293892703
│ Covariance trace: 0.0001357210699777322
└ Covariance trace ratio (current/previous): 0.7622044289795465
[ Info: EKI Optimization result:
2×4 Matrix{Any}:
 "name"             "number of hyperparameters"  "optimized value range"    "99% prior mass"
 "input_cholesky"  3                             (-0.0377143, -0.00227601)  (-0.1, 0.1)
nothing
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features
RF output structure matrix is not positive definite, correcting for use as a regularizer
estimating covariances with 220 iterations...
approx_σ2 not posdef
approx_σ2 not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
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blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
blockcovmat not posdef
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features
RF output structure matrix is not positive definite, correcting for use as a regularizer
estimating covariances with 420 iterations...
estimate cov with 420 iterations...
┌ Info: Shrinkage scale: 0.010886160335888698, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 1946.0758380518419
approx_σ2 not posdef
estimate cov with 420 iterations...
┌ Info: Shrinkage scale: 0.022935784339604717, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 507.2671179313066
approx_σ2 not posdef
┌ Info: Initializing ensemble Kalman process of type TransformInversion
│ Number of ensemble members: 70
│ Localization: NoLocalization
│ Failure handler: SampleSuccGauss
│ Scheduler: DataMisfitController
└ Accelerator: NesterovAccelerator
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 0 (prior)
[ Info: Covariance trace: 10.81173932420858
[ Info: Iteration 1 (T=0.0888948354038598)
┌ Info: Covariance-weighted error: 0.9038362633621855
│ Covariance trace: 4.2888650865274505
└ Covariance trace ratio (current/previous): 0.3966859501434933
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 2 (T=0.2892698730031424)
┌ Info: Covariance-weighted error: 0.6931430500174744
│ Covariance trace: 2.5548278340326736
└ Covariance trace ratio (current/previous): 0.5956885522135255
blockcovmat not posdef
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[ Info: Iteration 3 (T=0.486278278508969)
┌ Info: Covariance-weighted error: 0.6829119391147896
│ Covariance trace: 1.8393843504835312
└ Covariance trace ratio (current/previous): 0.719964111076773
blockcovmat not posdef
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[ Info: Iteration 4 (T=1.15578616427771)
┌ Info: Covariance-weighted error: 0.6699894963137825
│ Covariance trace: 0.922207508753108
└ Covariance trace ratio (current/previous): 0.5013674866325144
blockcovmat not posdef
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[ Info: Iteration 5 (T=1.903971118421804)
┌ Info: Covariance-weighted error: 0.6491717318044343
│ Covariance trace: 0.532595097849217
└ Covariance trace ratio (current/previous): 0.5775219706997664
blockcovmat not posdef
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[ Info: Iteration 6 (T=2.691968536198245)
┌ Info: Covariance-weighted error: 0.677920757878767
│ Covariance trace: 0.26805452973182164
└ Covariance trace ratio (current/previous): 0.5032989053303502
blockcovmat not posdef
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[ Info: Iteration 7 (T=3.5835450948534726)
┌ Info: Covariance-weighted error: 0.6584473288953145
│ Covariance trace: 0.1897772811371853
└ Covariance trace ratio (current/previous): 0.707980131233187
blockcovmat not posdef
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[ Info: Iteration 8 (T=4.508400570564147)
┌ Info: Covariance-weighted error: 0.6622855631609373
│ Covariance trace: 0.12428895376353591
└ Covariance trace ratio (current/previous): 0.6549200885309896
blockcovmat not posdef
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[ Info: Iteration 9 (T=5.31773071210299)
┌ Info: Covariance-weighted error: 0.6709824185052756
│ Covariance trace: 0.0942465501441125
└ Covariance trace ratio (current/previous): 0.7582858113313904
blockcovmat not posdef
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[ Info: Iteration 10 (T=6.226526740363855)
┌ Info: Covariance-weighted error: 0.6584383821852924
│ Covariance trace: 0.07143609378102594
└ Covariance trace ratio (current/previous): 0.7579703837625137
[ Info: EKI Optimization result:
5×4 Matrix{Any}:
 "name"                      "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "input_lowrank_Kchol"      1                             (0.904955, 0.904955)     (-3.0, 3.0)
 "input_lowrank_U"          2                             (-0.111724, 0.360351)    (-0.67082, 0.67082)
 "output_lowrank_diagonal"  2                             (0.0292405, 0.382038)    (0.000554271, 90.2094)
 "output_lowrank_U"         2                             (0.0388663, 0.130027)    (-0.67082, 0.67082)
nothing
[ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features
RF output structure matrix is not positive definite, correcting for use as a regularizer
estimating covariances with 420 iterations...
estimate cov with 420 iterations...
┌ Info: Shrinkage scale: 0.012202704455658804, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 1978.0078168163598
approx_σ2 not posdef
estimate cov with 420 iterations...
┌ Info: Shrinkage scale: 0.007790085689333379, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 3922.063028568476
approx_σ2 not posdef
┌ Info: Initializing ensemble Kalman process of type TransformInversion
│ Number of ensemble members: 70
│ Localization: NoLocalization
│ Failure handler: SampleSuccGauss
│ Scheduler: DataMisfitController
└ Accelerator: NesterovAccelerator
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 0 (prior)
[ Info: Covariance trace: 10.81173932420858
[ Info: Iteration 1 (T=0.08179005573127657)
┌ Info: Covariance-weighted error: 1.137167082023677
│ Covariance trace: 4.128725974889632
└ Covariance trace ratio (current/previous): 0.38187435444776185
blockcovmat not posdef
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[ Info: Iteration 2 (T=0.21152446951784143)
┌ Info: Covariance-weighted error: 0.9808918378018956
│ Covariance trace: 2.0179947346021265
└ Covariance trace ratio (current/previous): 0.48876935569841756
blockcovmat not posdef
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[ Info: Iteration 3 (T=0.36469346985770523)
┌ Info: Covariance-weighted error: 0.8810633082970637
│ Covariance trace: 1.1432487463204566
└ Covariance trace ratio (current/previous): 0.5665271205704423
blockcovmat not posdef
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[ Info: Iteration 4 (T=0.6595727206726993)
┌ Info: Covariance-weighted error: 0.804258299672009
│ Covariance trace: 0.7723891422544894
└ Covariance trace ratio (current/previous): 0.6756090000014625
blockcovmat not posdef
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[ Info: Iteration 5 (T=1.2265292565835437)
┌ Info: Covariance-weighted error: 0.7420553497738726
│ Covariance trace: 0.4296757034232258
└ Covariance trace ratio (current/previous): 0.5562943339274115
blockcovmat not posdef
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[ Info: Iteration 6 (T=1.7195955390920117)
┌ Info: Covariance-weighted error: 0.7798730120982842
│ Covariance trace: 0.23553168749690093
└ Covariance trace ratio (current/previous): 0.5481615218650258
blockcovmat not posdef
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[ Info: Iteration 7 (T=2.3683785770947168)
┌ Info: Covariance-weighted error: 0.7484046426637994
│ Covariance trace: 0.14360919332797775
└ Covariance trace ratio (current/previous): 0.6097234510319013
blockcovmat not posdef
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[ Info: Iteration 8 (T=3.1090251074216204)
┌ Info: Covariance-weighted error: 0.7374776122098141
│ Covariance trace: 0.08903423491436553
└ Covariance trace ratio (current/previous): 0.6199758723734854
blockcovmat not posdef
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[ Info: Iteration 9 (T=3.6818011109560613)
┌ Info: Covariance-weighted error: 0.7395576602284742
│ Covariance trace: 0.06611635704316954
└ Covariance trace ratio (current/previous): 0.7425947682569659
blockcovmat not posdef
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[ Info: Iteration 10 (T=4.391568317574324)
┌ Info: Covariance-weighted error: 0.7332462770837211
│ Covariance trace: 0.05247081245857978
└ Covariance trace ratio (current/previous): 0.7936131814449465
[ Info: EKI Optimization result:
5×4 Matrix{Any}:
 "name"                      "number of hyperparameters"  "optimized value range"   "99% prior mass"
 "input_lowrank_Kchol"      1                             (-0.0124662, -0.0124662)  (-3.0, 3.0)
 "input_lowrank_U"          2                             (-0.062379, 0.0675486)    (-0.67082, 0.67082)
 "output_lowrank_diagonal"  2                             (0.466884, 0.671894)      (0.000554271, 90.2094)
 "output_lowrank_U"         2                             (-0.107324, 0.532344)     (-0.67082, 0.67082)
nothing
[ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features
RF output structure matrix is not positive definite, correcting for use as a regularizer
estimating covariances with 420 iterations...
estimate cov with 420 iterations...
[ Info: NICE-adjusted covariance condition number: 190675.94854227826
approx_σ2 not posdef
estimate cov with 420 iterations...
[ Info: NICE-adjusted covariance condition number: 39764.30653946844
approx_σ2 not posdef
┌ Info: Initializing ensemble Kalman process of type TransformInversion
│ Number of ensemble members: 100
│ Localization: NoLocalization
│ Failure handler: SampleSuccGauss
│ Scheduler: DataMisfitController
└ Accelerator: NesterovAccelerator
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 0 (prior)
[ Info: Covariance trace: 17.20122191075149
[ Info: Iteration 1 (T=0.0991934380359418)
┌ Info: Covariance-weighted error: 0.620008257760921
│ Covariance trace: 6.01532941353617
└ Covariance trace ratio (current/previous): 0.34970361086826834
blockcovmat not posdef
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[ Info: Iteration 2 (T=0.2421415186798828)
┌ Info: Covariance-weighted error: 0.5240707917923444
│ Covariance trace: 2.349660314823679
└ Covariance trace ratio (current/previous): 0.3906120767943793
blockcovmat not posdef
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[ Info: Iteration 3 (T=0.5039168212907181)
┌ Info: Covariance-weighted error: 0.4833793954379734
│ Covariance trace: 0.8042276918762737
└ Covariance trace ratio (current/previous): 0.3422740243781254
blockcovmat not posdef
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[ Info: Iteration 4 (T=0.8143559735879045)
┌ Info: Covariance-weighted error: 0.47932513825184087
│ Covariance trace: 0.28262300751949465
└ Covariance trace ratio (current/previous): 0.35142163142894456
blockcovmat not posdef
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[ Info: Iteration 5 (T=1.2136613564342116)
┌ Info: Covariance-weighted error: 0.44910296447381254
│ Covariance trace: 0.1312825788571562
└ Covariance trace ratio (current/previous): 0.4645148319996582
blockcovmat not posdef
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[ Info: Iteration 6 (T=1.7445758277227417)
┌ Info: Covariance-weighted error: 0.4137687006907609
│ Covariance trace: 0.09013376923755076
└ Covariance trace ratio (current/previous): 0.6865630613154091
blockcovmat not posdef
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[ Info: Iteration 7 (T=2.2989372070204186)
┌ Info: Covariance-weighted error: 0.4001193873717742
│ Covariance trace: 0.08072052037964116
└ Covariance trace ratio (current/previous): 0.8955635724819113
blockcovmat not posdef
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[ Info: Iteration 8 (T=2.9569048093209322)
┌ Info: Covariance-weighted error: 0.40353285573764164
│ Covariance trace: 0.0750755121094201
└ Covariance trace ratio (current/previous): 0.9300672463003
blockcovmat not posdef
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[ Info: Iteration 9 (T=3.5888478658284506)
┌ Info: Covariance-weighted error: 0.4068504524352893
│ Covariance trace: 0.07225738736263067
└ Covariance trace ratio (current/previous): 0.9624627968879905
blockcovmat not posdef
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[ Info: Iteration 10 (T=4.253563344521469)
┌ Info: Covariance-weighted error: 0.4023422978769496
│ Covariance trace: 0.06696283454296151
└ Covariance trace ratio (current/previous): 0.9267264841296028
[ Info: EKI Optimization result:
3×4 Matrix{Any}:
 "name"                    "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "full_lowrank_diagonal"  4                             (0.479127, 0.719249)     (0.000391928, 63.7877)
 "full_lowrank_U"         8                             (-0.0344562, 0.118605)   (-0.237171, 0.237171)
nothing
[ Info: [0.04262738363385231, 0.01988292065866329, 0.13036232210536644, 0.03820777872555277, 0.08767609548238016, 0.08838472626919779]
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 100, while the space dimension is 2, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features
RF output structure matrix is not positive definite, correcting for use as a regularizer
estimating covariances with 420 iterations...

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34.8%┣███████████████                            ┫ 146/420 [00:06<00:11, 26it/s]

36.4%┣███████████████▋                           ┫ 153/420 [00:06<00:10, 27it/s]

40.5%┣█████████████████▍                         ┫ 170/420 [00:06<00:08, 30it/s]

44.5%┣███████████████████▏                       ┫ 187/420 [00:06<00:07, 32it/s]

48.6%┣████████████████████▉                      ┫ 204/420 [00:06<00:06, 35it/s]

52.6%┣██████████████████████▋                    ┫ 221/420 [00:06<00:05, 38it/s]

56.7%┣████████████████████████▍                  ┫ 238/420 [00:06<00:05, 40it/s]

60.5%┣██████████████████████████                 ┫ 254/420 [00:06<00:04, 42it/s]

62.4%┣██████████████████████████▉                ┫ 262/420 [00:06<00:04, 43it/s]

66.2%┣████████████████████████████▌              ┫ 278/420 [00:06<00:03, 46it/s]

70.0%┣██████████████████████████████             ┫ 294/420 [00:06<00:03, 48it/s]

74.0%┣███████████████████████████████▉           ┫ 311/420 [00:06<00:02, 50it/s]

77.9%┣█████████████████████████████████▌         ┫ 327/420 [00:06<00:02, 52it/s]

82.1%┣███████████████████████████████████▎       ┫ 345/420 [00:06<00:01, 55it/s]

86.2%┣█████████████████████████████████████      ┫ 362/420 [00:06<00:01, 57it/s]

89.3%┣██████████████████████████████████████▍    ┫ 375/420 [00:06<00:01, 59it/s]

93.3%┣████████████████████████████████████████▏  ┫ 392/420 [00:06<00:00, 61it/s]

97.4%┣█████████████████████████████████████████▉ ┫ 409/420 [00:06<00:00, 63it/s]

100.0%┣██████████████████████████████████████████┫ 420/420 [00:07<00:00, 64it/s]

100.0%┣██████████████████████████████████████████┫ 420/420 [00:07<00:00, 64it/s]
approx_σ2 not posdef

0.0%┣                                              ┫ 0/420 [00:00<00:00, -0s/it]

2.9%┣█▎                                          ┫ 12/420 [00:00<00:02, 211it/s]

7.1%┣███▏                                        ┫ 30/420 [00:00<00:01, 276it/s]

11.0%┣████▊                                      ┫ 46/420 [00:00<00:01, 285it/s]

15.0%┣██████▌                                    ┫ 63/420 [00:00<00:01, 295it/s]

18.3%┣███████▉                                   ┫ 77/420 [00:00<00:01, 290it/s]

22.1%┣█████████▌                                 ┫ 93/420 [00:00<00:01, 292it/s]

26.2%┣███████████                               ┫ 110/420 [00:00<00:01, 297it/s]

29.8%┣████████████▌                             ┫ 125/420 [00:00<00:01, 295it/s]

33.3%┣██████████████                            ┫ 140/420 [00:00<00:01, 294it/s]

37.6%┣███████████████▉                          ┫ 158/420 [00:01<00:01, 299it/s]

41.7%┣█████████████████▌                        ┫ 175/420 [00:01<00:01, 301it/s]

45.0%┣███████████████████                       ┫ 189/420 [00:01<00:01, 298it/s]

49.0%┣████████████████████▋                     ┫ 206/420 [00:01<00:01, 301it/s]

53.6%┣██████████████████████▌                   ┫ 225/420 [00:01<00:01, 305it/s]

57.4%┣████████████████████████                  ┫ 241/420 [00:01<00:01, 305it/s]

61.9%┣██████████████████████████                ┫ 260/420 [00:01<00:01, 309it/s]

65.7%┣███████████████████████████▋              ┫ 276/420 [00:01<00:00, 309it/s]

69.3%┣█████████████████████████████             ┫ 291/420 [00:01<00:00, 308it/s]

73.6%┣███████████████████████████████           ┫ 309/420 [00:01<00:00, 310it/s]

77.9%┣████████████████████████████████▊         ┫ 327/420 [00:01<00:00, 311it/s]

81.9%┣██████████████████████████████████▍       ┫ 344/420 [00:01<00:00, 311it/s]

86.2%┣████████████████████████████████████▏     ┫ 362/420 [00:01<00:00, 312it/s]

90.2%┣██████████████████████████████████████    ┫ 379/420 [00:01<00:00, 313it/s]

94.5%┣███████████████████████████████████████▊  ┫ 397/420 [00:01<00:00, 314it/s]

97.9%┣█████████████████████████████████████████ ┫ 411/420 [00:01<00:00, 312it/s]

100.0%┣█████████████████████████████████████████┫ 420/420 [00:01<00:00, 308it/s]

100.0%┣█████████████████████████████████████████┫ 420/420 [00:01<00:00, 307it/s]
approx_σ2 not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

1.4%┣▋                                          ┫ 1/70 [00:00<Inf:Inf, InfGs/it]

24.3%┣███████████                                  ┫ 17/70 [00:01<00:02, 30it/s]

38.6%┣█████████████████▍                           ┫ 27/70 [00:01<00:01, 44it/s]

62.9%┣████████████████████████████▎                ┫ 44/70 [00:01<00:00, 67it/s]

85.7%┣██████████████████████████████████████▋      ┫ 60/70 [00:01<00:00, 86it/s]

100.0%┣████████████████████████████████████████████┫ 70/70 [00:01<00:00, 96it/s]

100.0%┣████████████████████████████████████████████┫ 70/70 [00:01<00:00, 96it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 312it/s]

45.7%┣████████████████████▏                       ┫ 32/70 [00:00<00:00, 294it/s]

58.6%┣█████████████████████████▊                  ┫ 41/70 [00:00<00:00, 255it/s]

82.9%┣████████████████████████████████████▌       ┫ 58/70 [00:00<00:00, 275it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 281it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 281it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 308it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 315it/s]

72.9%┣████████████████████████████████            ┫ 51/70 [00:00<00:00, 315it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 301it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 303it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 302it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 315it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 321it/s]

65.7%┣█████████████████████████████               ┫ 46/70 [00:00<00:00, 293it/s]

90.0%┣███████████████████████████████████████▋    ┫ 63/70 [00:00<00:00, 302it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 305it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 304it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

27.1%┣████████████                                ┫ 19/70 [00:00<00:00, 343it/s]

50.0%┣██████████████████████                      ┫ 35/70 [00:00<00:00, 327it/s]

75.7%┣█████████████████████████████████▎          ┫ 53/70 [00:00<00:00, 335it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 333it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 332it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

20.0%┣████████▉                                   ┫ 14/70 [00:00<00:00, 255it/s]

44.3%┣███████████████████▌                        ┫ 31/70 [00:00<00:00, 289it/s]

68.6%┣██████████████████████████████▏             ┫ 48/70 [00:00<00:00, 304it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 309it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 300it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 300it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 313it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 318it/s]

70.0%┣██████████████████████████████▉             ┫ 49/70 [00:00<00:00, 307it/s]

94.3%┣█████████████████████████████████████████▌  ┫ 66/70 [00:00<00:00, 311it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 310it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 309it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

22.9%┣██████████                                  ┫ 16/70 [00:00<00:00, 298it/s]

45.7%┣████████████████████▏                       ┫ 32/70 [00:00<00:00, 296it/s]

71.4%┣███████████████████████████████▍            ┫ 50/70 [00:00<00:00, 312it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 319it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 317it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 316it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

20.0%┣████████▉                                   ┫ 14/70 [00:00<00:00, 256it/s]

45.7%┣████████████████████▏                       ┫ 32/70 [00:00<00:00, 299it/s]

71.4%┣███████████████████████████████▍            ┫ 50/70 [00:00<00:00, 315it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 315it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 313it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 312it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 312it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 324it/s]

72.9%┣████████████████████████████████            ┫ 51/70 [00:00<00:00, 321it/s]

94.3%┣█████████████████████████████████████████▌  ┫ 66/70 [00:00<00:00, 311it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 312it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 312it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 315it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 301it/s]

72.9%┣████████████████████████████████            ┫ 51/70 [00:00<00:00, 311it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 316it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 316it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 316it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 312it/s]

44.3%┣███████████████████▌                        ┫ 31/70 [00:00<00:00, 291it/s]

71.4%┣███████████████████████████████▍            ┫ 50/70 [00:00<00:00, 315it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 322it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 322it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 322it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

25.7%┣███████████▎                                ┫ 18/70 [00:00<00:00, 326it/s]

51.4%┣██████████████████████▋                     ┫ 36/70 [00:00<00:00, 342it/s]

75.7%┣█████████████████████████████████▎          ┫ 53/70 [00:00<00:00, 339it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 327it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 328it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 327it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 318it/s]

50.0%┣██████████████████████                      ┫ 35/70 [00:00<00:00, 332it/s]

71.4%┣███████████████████████████████▍            ┫ 50/70 [00:00<00:00, 319it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 325it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 325it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 325it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

25.7%┣███████████▎                                ┫ 18/70 [00:00<00:00, 334it/s]

47.1%┣████████████████████▊                       ┫ 33/70 [00:00<00:00, 316it/s]

72.9%┣████████████████████████████████            ┫ 51/70 [00:00<00:00, 325it/s]

98.6%┣███████████████████████████████████████████▍┫ 69/70 [00:00<00:00, 333it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 333it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 333it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

21.4%┣█████████▍                                  ┫ 15/70 [00:00<00:00, 270it/s]

45.7%┣████████████████████▏                       ┫ 32/70 [00:00<00:00, 300it/s]

71.4%┣███████████████████████████████▍            ┫ 50/70 [00:00<00:00, 318it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 310it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 312it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 312it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 312it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 324it/s]

71.4%┣███████████████████████████████▍            ┫ 50/70 [00:00<00:00, 317it/s]

97.1%┣██████████████████████████████████████████▊ ┫ 68/70 [00:00<00:00, 324it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 324it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 324it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 310it/s]

44.3%┣███████████████████▌                        ┫ 31/70 [00:00<00:00, 293it/s]

68.6%┣██████████████████████████████▏             ┫ 48/70 [00:00<00:00, 306it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 312it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 313it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 313it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

20.0%┣████████▉                                   ┫ 14/70 [00:00<00:00, 253it/s]

44.3%┣███████████████████▌                        ┫ 31/70 [00:00<00:00, 291it/s]

68.6%┣██████████████████████████████▏             ┫ 48/70 [00:00<00:00, 304it/s]

88.6%┣███████████████████████████████████████     ┫ 62/70 [00:00<00:00, 296it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 298it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 297it/s]
blockcovmat not posdef

0.0%┣                                               ┫ 0/70 [00:00<00:00, -0s/it]

24.3%┣██████████▊                                 ┫ 17/70 [00:00<00:00, 305it/s]

48.6%┣█████████████████████▍                      ┫ 34/70 [00:00<00:00, 321it/s]

70.0%┣██████████████████████████████▉             ┫ 49/70 [00:00<00:00, 312it/s]

94.3%┣█████████████████████████████████████████▌  ┫ 66/70 [00:00<00:00, 316it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 317it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 316it/s]
blockcovmat not posdef
Completed tests for RandomFeature, 513 seconds elapsed
Starting tests for MarkovChainMonteCarlo
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, 0.0]
kernel in GaussianProcess:
Type: SEArd{Float64}, Params: [-0.0, 0.0]
created GP: 1
optimized hyperparameters of GP: 1
Type: SEArd{Float64}, Params: [-0.7344956715967699, 1.6909811297504154]
optimised GP: 1
Sum of 2 kernels:
	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
			- ARD Transform (dims: 1)
			- σ² = 29.428460770481188
	White Kernel
			- σ² = 2.061153622438558e-9
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: QuartileScaling
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95
[ Info:     truncating at 1/1 retaining 100.0% of the variance of the structure matrix
Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0]
kernel in GaussianProcess:
Type: SEIso{Float64}, Params: [0.0, 0.0]
created GP: 1
optimized hyperparameters of GP: 1
Type: SEIso{Float64}, Params: [-0.8279716521015293, 2.768606386766634]
[ Info: hyperparameter learning for 1 models using 64 training points, 16 validation points and 100 features
estimating covariances with 180 iterations...
[ Info: Random Features already trained. continuing...
┌ Info: srfi_1d 
│  per-point - Emulator RMSE = 0.30946815204475625 
│  Emulator STD = 0.23454207282758657
└  (These numbers should be similar sized to indicate no overfitting)
┌ Info: gpjl_1d 
│  per-point - Emulator RMSE = 0.20024482573071362 
│  Emulator STD = 0.23974737449832298
└  (These numbers should be similar sized to indicate no overfitting)
┌ Info: agpjl_1d 
│  per-point - Emulator RMSE = 0.20024481767058971 
│  Emulator STD = 0.2397473781137044
└  (These numbers should be similar sized to indicate no overfitting)

======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile.
--trace-compile is enabled during profile collection.
======================================================================================

cmd: /opt/julia/bin/julia 1315 running 1 of 1

signal (10): User defined signal 1
_ZN4llvm15SmallVectorImplISt5tupleIJPNS_17MachineBasicBlockEPS3_S4_EEEaSEOS6_.isra.0 at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm25ReversePostOrderTraversalIPNS_17MachineBasicBlockENS_11GraphTraitsIS2_EEE10InitializeERKS2_ at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm13LoopTraversal8traverseERNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm19ReachingDefAnalysis4initEv at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm19ReachingDefAnalysis20runOnMachineFunctionERNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm19MachineFunctionPass13runOnFunctionERNS_8FunctionE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm13FPPassManager13runOnFunctionERNS_8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm13FPPassManager11runOnModuleERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm6legacy15PassManagerImpl3runERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
operator() at /source/src/jitlayers.cpp:1414:23
compileModule at /source/src/jitlayers.cpp:2390:79
materialize at /source/src/jitlayers.cpp:906:36
_ZN4llvm3orc19MaterializationTask3runEv at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
dispatch at /source/src/julia-task-dispatcher.h:353:11
_ZN4llvm3orc16ExecutionSession22dispatchOutstandingMUsEv at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession17OL_completeLookupESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EESt10shared_ptrINS0_23AsynchronousSymbolQueryEESt8functionIFvRKNS_8DenseMapIPNS0_8JITDylibENS_8DenseSetINS0_15SymbolStringPtrENS_12DenseMapInfoISF_vEEEENSG_ISD_vEENS_6detail12DenseMapPairISD_SI_EEEEEE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm3orc25InProgressFullLookupState8completeESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession19OL_applyQueryPhase1ESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EENS_5ErrorE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession6lookupENS0_10LookupKindERKSt6vectorISt4pairIPNS0_8JITDylibENS0_19JITDylibLookupFlagsEESaIS8_EENS0_15SymbolLookupSetENS0_11SymbolStateENS_15unique_functionIFvNS_8ExpectedINS_8DenseMapINS0_15SymbolStringPtrENS0_17ExecutorSymbolDefENS_12DenseMapInfoISI_vEENS_6detail12DenseMapPairISI_SJ_EEEEEEEEESt8functionIFvRKNSH_IS6_NS_8DenseSetISI_SL_EENSK_IS6_vEENSN_IS6_SV_EEEEEE at /opt/julia/bin/../lib/julia/libLLVM.so.21.1jl (unknown line)
publishCIs at /source/src/jitlayers.cpp:2085:14
jl_compile_codeinst_impl at /source/src/jitlayers.cpp:510:39
jl_compile_method_internal at /source/src/gf.c:3677:27
_jl_invoke at /source/src/gf.c:4130:16 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
* at ./operators.jl:653:0 [inlined]
_decode_structure_matrix at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/elementwise_scaler.jl:269:0 (pc: 5)
unknown function (ip: 0x7f641fc66b3d) at (unknown file)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
decode_with_schedule at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:923:0 (pc: 222)
decode_structure_matrix at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:962:0 [inlined]
decode_structure_matrix at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:298:0 (pc: 2)
unknown function (ip: 0x7f63e2b14b25) at (unknown file)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
#predict#76 at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:376:0 (pc: 276)
predict at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:325:0 [inlined]
#validate_emulator#48 at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:46:0 (pc: 34)
validate_emulator at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:34:0 (pc: 5)
unknown function (ip: 0x7f641fc56b56) at (unknown file)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
jl_apply at /source/src/julia.h:2388:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:243:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:706:13
eval_body at /source/src/interpreter.c:563:21
eval_body at /source/src/interpreter.c:571:21
eval_body at /source/src/interpreter.c:571:21
eval_body at /source/src/interpreter.c:571:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:897:21
ijl_eval_thunk at /source/src/toplevel.c:768:18
jl_toplevel_eval_flex at /source/src/toplevel.c:712:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:602:15
jl_toplevel_eval_flex at /source/src/toplevel.c:684:27
ijl_toplevel_eval at /source/src/toplevel.c:782:12
ijl_toplevel_eval_in at /source/src/toplevel.c:827:13
eval at ./boot.jl:521:0 (pc: 1)
include_string at ./loading.jl:3132:0 (pc: 207)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
_include at ./loading.jl:3192:0 (pc: 122)
include at ./Base.jl:327:0 (pc: 1)
IncludeInto at ./Base.jl:328:0 [inlined]
macro expansion at ./timing.jl:505:0 [inlined]
include_test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:15:0 (pc: 6)
unknown function (ip: 0x7f64213061f2) at (unknown file)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
jl_apply at /source/src/julia.h:2388:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:243:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:706:13
eval_body at /source/src/interpreter.c:563:21
eval_body at /source/src/interpreter.c:571:21
eval_body at /source/src/interpreter.c:571:21
eval_body at /source/src/interpreter.c:571:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:897:21
ijl_eval_thunk at /source/src/toplevel.c:768:18
jl_toplevel_eval_flex at /source/src/toplevel.c:712:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:602:15
jl_toplevel_eval_flex at /source/src/toplevel.c:684:27
ijl_toplevel_eval at /source/src/toplevel.c:782:12
ijl_toplevel_eval_in at /source/src/toplevel.c:827:13
eval at ./boot.jl:521:0 (pc: 1)
include_string at ./loading.jl:3132:0 (pc: 207)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
_include at ./loading.jl:3192:0 (pc: 122)
include at ./Base.jl:327:0 (pc: 1)
IncludeInto at ./Base.jl:328:0 (pc: 2)
jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
jl_apply at /source/src/julia.h:2388:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:243:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:706:13
jl_interpret_toplevel_thunk at /source/src/interpreter.c:897:21
ijl_eval_thunk at /source/src/toplevel.c:768:18
jl_toplevel_eval_flex at /source/src/toplevel.c:712:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:602:15
jl_toplevel_eval_flex at /source/src/toplevel.c:684:27
ijl_toplevel_eval at /source/src/toplevel.c:782:12
ijl_toplevel_eval_in at /source/src/toplevel.c:827:13
eval at ./boot.jl:521:0 (pc: 1)
exec_options at ./client.jl:321:0 (pc: 425)
_start at ./client.jl:596:0 (pc: 294)
jfptr__start_0.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
jl_apply at /source/src/julia.h:2388:12 [inlined]
true_main at /source/src/jlapi.c:971:29
jl_repl_entrypoint at /source/src/jlapi.c:1138:15
main at /source/cli/loader_exe.c:58:15
unknown function (ip: 0x7f643b8a9249) 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.
Disabling --trace-compile
==============================================================

┌ Info: gpjl-svd_1d 
│  per-point - Emulator RMSE = 0.1974712233704437 
│  Emulator STD = 0.2400373194495221
└  (These numbers should be similar sized to indicate no overfitting)
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 500, while the space dimension is 5, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
Using default squared exponential kernel, learning length scale and variance parameters┌ 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/v1.14/Profile/src/Profile.jl:1361

Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x00007f64213fc010 Total snapshots: 2. Utilization: 100%
 ╎2 @Base/client.jl:596  _start()
 ╎ 2 @Base/client.jl:321  exec_options(opts::Base.JLOptions)
 ╎  2 @Base/boot.jl:521  eval(m::Module, e::Any)
 ╎   2 @Base/Base.jl:328  (::Base.IncludeInto)(fname::String)
 ╎    2 @Base/Base.jl:327  include(mapexpr::Function, mod::Module, _path::Strin…
 ╎     2 @Base/loading.jl:3192  _include(mapexpr::Function, mod::Module, _path:…
 ╎    ╎ 2 @Base/loading.jl:3132  include_string(mapexpr::typeof(identity), mod:…
 ╎    ╎  2 @Base/boot.jl:521  eval(m::Module, e::Any)
 ╎    ╎   2 @CalibrateEmulateSample/…:15  include_test(_module::String)
 ╎    ╎    2 @Base/timing.jl:505  macro expansion
 ╎    ╎     2 @Base/Base.jl:328  (::Base.IncludeInto)(fname::String)
 ╎    ╎    ╎ 2 @Base/Base.jl:327  include(mapexpr::Function, mod::Module, _path…
 ╎    ╎    ╎  2 @Base/loading.jl:3192  _include(mapexpr::Function, mod::Module,…
 ╎    ╎    ╎   2 @Base/loading.jl:3132  include_string(mapexpr::typeof(identity…
 ╎    ╎    ╎    2 @Base/boot.jl:521  eval(m::Module, e::Any)
 ╎    ╎    ╎     2 @CalibrateEmulateSample/…:34  kwcall(::@NamedTuple{exp_name:…
 ╎    ╎    ╎    ╎ 2 @CalibrateEmulateSample/…:46  validate_emulator(em::Emulato…
 ╎    ╎    ╎    ╎  2 @CalibrateEmulateSample/…:325  kwcall(::@NamedTuple{add_ob…
1╎    ╎    ╎    ╎   2 @CalibrateEmulateSample/…:376  predict(emulator::Emulator…

Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0]
kernel in GaussianProcess:
Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0]
created GP: 1

======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile.
--trace-compile is enabled during profile collection.
======================================================================================

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:1404:0
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430:0
ijl_task_get_next at /source/src/scheduler.c:524:34
wait at ./task.jl:1248:0 (pc: 107)
wait_forever at ./task.jl:1170:0 (pc: 4)
jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4138:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4364:12
jl_apply at /source/src/julia.h:2388:12 [inlined]
start_task at /source/src/task.c:1275:19
unknown function (ip: (nil)) at (unknown file)


==============================================================
Profile collected. A report will print at the next yield point.
Disabling --trace-compile
==============================================================

┌ 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/v1.14/Profile/src/Profile.jl:1361
Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x000076ac75cd51e0 Total snapshots: 444. Utilization: 0%
   ╎444 @Base/task.jl:1170  wait_forever()
443╎ 444 @Base/task.jl:1248  wait()

optimized hyperparameters of GP: 1
Type: SEArd{Float64}, Params: [1.6390667044763825, 1.6439292190982882, 1.7664555588197455, 1.268688251992631, 2.192742729098899, 3.7271738541307533]
┌ Info: gp_mv 
│  per-point - Emulator RMSE = 0.09972100944363241 
│  Emulator STD = 0.10659251207287128
└  (These numbers should be similar sized to indicate no overfitting)

[1315] signal 15: Terminated
in expression starting at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:297
ijl_get_nth_field at /source/src/datatype.c:1855:1
ijl_get_nth_field_checked at /source/src/datatype.c:1906:21
jl_f_getfield at /source/src/builtins.c:1088:9
abstract_call_gf_by_type at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:137:0 (pc: 15)
abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3030:0 (pc: 1662)
abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3138:0 (pc: 189)
abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3131:0 [inlined]
abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3282:0 (pc: 152)
abstract_eval_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3300:0 [inlined]
abstract_eval_statement_expr at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3670:0 (pc: 11)

PkgEval terminated after 2728.99s: test duration exceeded the time limit