Package evaluation to test CalibrateEmulateSample on Julia 1.14.0-DEV.3081 (21a70e450d*) started at 2026-09-02T12:44:17.425


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# Set-up
#

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

Set-up completed after 31.41s


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# Installation
#

Installing CalibrateEmulateSample...
   Resolving package versions...
   Installed libaom_jll ─────────────────────── v3.14.1+0
   Installed libass_jll ─────────────────────── v0.17.5+0
   Installed CommonWorldInvalidations ───────── v1.2.0
   Installed HarfBuzz_jll ───────────────────── v100.14003.0+0
   Installed Libuuid_jll ────────────────────── v2.42.0+0
   Installed Bzip2_jll ──────────────────────── v1.0.9+0
   Installed MKL_jll ────────────────────────── v2025.2.0+0
   Installed OrderedCollections ─────────────── v1.8.2
   Installed Cairo_jll ──────────────────────── v1.18.7+0
   Installed Functors ───────────────────────── v0.5.3
   Installed SIMDTypes ──────────────────────── v0.1.0
   Installed Graphite2_jll ──────────────────── v1.3.16+0
   Installed Glossaries ─────────────────────── v0.1.2
   Installed Nullables ──────────────────────── v1.0.0
   Installed Manopt ─────────────────────────── v0.5.39
   Installed Optim ──────────────────────────── v1.13.3
   Installed RandomFeatures ─────────────────── v0.3.5
   Installed SimpleWeightedGraphs ───────────── v1.5.1
   Installed CondaPkg ───────────────────────── v0.2.36
   Installed Xorg_libpciaccess_jll ──────────── v0.19.0+0
   Installed UnsafePointers ─────────────────── v1.0.0
   Installed InitialValues ──────────────────── v0.3.1
   Installed ArgCheck ───────────────────────── v2.5.0
   Installed CpuId ──────────────────────────── v0.3.1
   Installed ElasticArrays ──────────────────── v1.2.12
   Installed Fontconfig_jll ─────────────────── v2.17.1+0
   Installed EnsembleKalmanProcesses ────────── v2.7.4
   Installed NaNMath ────────────────────────── v1.1.4
   Installed Opus_jll ───────────────────────── v1.6.1+0
   Installed OpenSpecFun_jll ────────────────── v0.5.6+0
   Installed Pidfile ────────────────────────── v1.3.0
   Installed Distances ──────────────────────── v0.10.12
   Installed StaticArrayInterface ───────────── v1.10.0
   Installed QuadGK ─────────────────────────── v2.11.3
   Installed ManifoldsBase ──────────────────── v2.5.0
   Installed GaussianRandomFields ───────────── v2.2.7
   Installed Tables ─────────────────────────── v1.14.0
   Installed libva_jll ──────────────────────── v2.23.0+0
   Installed Xorg_libxcb_jll ────────────────── v1.17.1+0
   Installed EnumX ──────────────────────────── v1.0.7
   Installed Ogg_jll ────────────────────────── v1.3.6+0
   Installed Arpack ─────────────────────────── v0.5.4
   Installed WoodburyMatrices ───────────────── v1.1.0
   Installed InverseFunctions ───────────────── v0.1.17
   Installed NLSolversBase ──────────────────── v7.10.0
   Installed LLVMOpenMP_jll ─────────────────── v22.1.7+0
   Installed Static ─────────────────────────── v1.4.6
   Installed SortingAlgorithms ──────────────── v1.2.3
   Installed Missings ───────────────────────── v1.2.0
   Installed Reexport ───────────────────────── v1.2.2
   Installed TableTraits ────────────────────── v1.0.1
   Installed FFMPEG ─────────────────────────── v0.4.5
   Installed LaTeXStrings ───────────────────── v1.4.1
   Installed Preferences ────────────────────── v1.5.2
   Installed Crayons ────────────────────────── v4.2.0
   Installed libdrm_jll ─────────────────────── v2.4.134+0
   Installed ElasticPDMats ──────────────────── v0.2.4
   Installed ADTypes ────────────────────────── v1.24.0
   Installed MCMCChains ─────────────────────── v7.7.0
   Installed RangeArrays ────────────────────── v0.3.2
   Installed UnPack ─────────────────────────── v1.0.2
   Installed NaturalSort ────────────────────── v1.0.0
   Installed DataStructures ─────────────────── v0.19.6
   Installed Inflate ────────────────────────── v0.1.5
   Installed FillArrays ─────────────────────── v1.17.0
   Installed MacroTools ─────────────────────── v0.5.16
   Installed ManualMemory ───────────────────── v0.1.8
   Installed DiffResults ────────────────────── v1.1.0
   Installed AbstractTrees ──────────────────── v0.4.5
   Installed Quaternions ────────────────────── v0.7.7
   Installed VectorizationBase ──────────────── v0.21.74
   Installed CodecZlib ──────────────────────── v0.7.9
   Installed ArrayInterface ─────────────────── v7.30.1
   Installed StatsBase ──────────────────────── v0.34.13
   Installed ArnoldiMethod ──────────────────── v0.4.0
   Installed micromamba_jll ─────────────────── v2.3.1+0
   Installed Xorg_libXau_jll ────────────────── v1.0.13+0
   Installed oneTBB_jll ─────────────────────── v2022.3.0+0
   Installed x264_jll ───────────────────────── v10164.0.1+0
   Installed ConsoleProgressMonitor ─────────── v0.1.2
   Installed Xorg_libXfixes_jll ─────────────── v6.0.2+0
   Installed MCMCDiagnosticTools ────────────── v0.3.19
   Installed HostCPUFeatures ────────────────── v0.1.18
   Installed AbstractGPs ────────────────────── v0.5.24
   Installed IntelOpenMP_jll ────────────────── v2025.2.0+0
   Installed DataValueInterfaces ────────────── v1.0.0
   Installed CommonSubexpressions ───────────── v0.3.1
   Installed StructUtils ────────────────────── v2.8.5
   Installed Expat_jll ──────────────────────── v2.8.3+0
   Installed Libffi_jll ─────────────────────── v3.4.7+0
   Installed LoggingExtras ──────────────────── v1.2.0
   Installed Pixman_jll ─────────────────────── v0.46.4+0
   Installed RealDot ────────────────────────── v0.1.0
   Installed LayoutPointers ─────────────────── v0.1.17
   Installed NamedDims ──────────────────────── v1.2.3
   Installed IrrationalConstants ────────────── v0.2.6
   Installed TerminalLoggers ────────────────── v0.1.8
   Installed IntegerMathUtils ───────────────── v0.1.4
   Installed SimpleTraits ───────────────────── v0.9.6
   Installed libpng_jll ─────────────────────── v1.6.58+0
   Installed LeftChildRightSiblingTrees ─────── v0.3.0
   Installed StableRNGs ─────────────────────── v1.0.4
   Installed ColorVectorSpace ───────────────── v0.11.0
   Installed AdvancedMH ─────────────────────── v0.8.10
   Installed Roots ──────────────────────────── v3.0.7
   Installed Interpolations ─────────────────── v0.16.3
   Installed FFMPEG_jll ─────────────────────── v8.1.2+0
   Installed pixi_jll ───────────────────────── v0.76.2+0
   Installed LowRankApprox ──────────────────── v0.5.5
   Installed MatrixEquations ────────────────── v2.6.5
   Installed libfdk_aac_jll ─────────────────── v2.0.4+0
   Installed Scratch ────────────────────────── v1.3.0
   Installed Setfield ───────────────────────── v1.1.2
   Installed PDMats ─────────────────────────── v0.11.36
   Installed AxisArrays ─────────────────────── v0.4.8
   Installed BenchmarkTools ─────────────────── v1.8.0
   Installed CompositionsBase ───────────────── v0.1.2
   Installed CommonSolve ────────────────────── v0.2.14
   Installed DocStringExtensions ────────────── v0.9.5
   Installed ForwardDiff ────────────────────── v1.4.5
   Installed PythonCall ─────────────────────── v0.9.35
   Installed Rmath_jll ──────────────────────── v0.5.2+0
   Installed MathOptInterface ───────────────── v1.53.0
   Installed DifferentiationInterface ───────── v0.7.21
   Installed KernelDensity ──────────────────── v0.6.12
   Installed SLEEFPirates ───────────────────── v0.6.46
   Installed AliasTables ────────────────────── v1.1.3
   Installed IteratorInterfaceExtensions ────── v1.0.0
   Installed DataAPI ────────────────────────── v1.16.0
   Installed BitTwiddlingConvenienceFunctions ─ v0.1.6
   Installed RecipesBase ────────────────────── v1.3.4
   Installed LDLFactorizations ──────────────── v0.10.2
   Installed ManifoldDiff ───────────────────── v0.4.5
   Installed FastGaussQuadrature ────────────── v1.3.0
   Installed ReverseDiff ────────────────────── v1.17.0
   Installed StaticArrays ───────────────────── v1.9.19
   Installed Colors ─────────────────────────── v0.13.1
   Installed StaticArraysCore ───────────────── v1.4.4
   Installed BangBang ───────────────────────── v0.4.9
   Installed AbstractFFTs ───────────────────── v1.5.0
   Installed ColorSchemes ───────────────────── v3.31.0
   Installed AxisAlgorithms ─────────────────── v1.1.0
   Installed SciMLPublic ────────────────────── v1.3.0
   Installed Glib_jll ───────────────────────── v2.88.3+0
   Installed ColorTypes ─────────────────────── v0.12.1
   Installed StringManipulation ─────────────── v0.5.0
   Installed OpenBLAS32_jll ─────────────────── v0.3.34+0
   Installed DiffRules ──────────────────────── v1.16.0
   Installed Requires ───────────────────────── v1.3.1
   Installed FunctionWrappers ───────────────── v1.1.3
   Installed Tullio ─────────────────────────── v0.3.9
   Installed LogExpFunctions ────────────────── v0.3.29
   Installed Parsers ────────────────────────── v2.8.7
   Installed Gamma ──────────────────────────── v1.1.0
   Installed JSON ───────────────────────────── v1.7.1
   Installed FiniteDiff ─────────────────────── v2.33.0
   Installed Distributions ──────────────────── v0.25.131
   Installed PrettyTables ───────────────────── v3.4.8
   Installed Arpack_jll ─────────────────────── v3.5.2+0
   Installed CloseOpenIntervals ─────────────── v0.1.13
   Installed StatsFuns ──────────────────────── v1.5.2
   Installed Rmath ──────────────────────────── v0.9.0
   Installed Xorg_libXdmcp_jll ──────────────── v1.1.6+0
   Installed MutableArithmetics ─────────────── v1.8.0
   Installed JLLWrappers ────────────────────── v1.8.0
   Installed ZygoteRules ────────────────────── v0.2.8
   Installed Convex ─────────────────────────── v0.16.7
   Installed ThreadingUtilities ─────────────── v0.5.6
   Installed SCS_jll ────────────────────────── v300.200.1100+0
   Installed x265_jll ───────────────────────── v4.1.0+0
   Installed ProgressBars ───────────────────── v1.5.1
   Installed IterTools ──────────────────────── v1.10.0
   Installed Primes ─────────────────────────── v0.5.7
   Installed Accessors ──────────────────────── v0.1.45
   Installed StatisticalTraits ──────────────── v3.5.0
   Installed LowRankMatrices ────────────────── v1.0.2
   Installed Adapt ──────────────────────────── v4.7.0
   Installed Xorg_xtrans_jll ────────────────── v1.6.0+0
   Installed Graphs ─────────────────────────── v1.14.0
   Installed FFTA ───────────────────────────── v0.3.1
   Installed HypergeometricFunctions ────────── v0.3.30
   Installed Libmount_jll ───────────────────── v2.42.0+0
   Installed ConstructionBase ───────────────── v1.6.0
   Installed ChunkSplitters ─────────────────── v3.2.0
   Installed LinearMaps ─────────────────────── v3.11.4
   Installed TranscodingStreams ─────────────── v0.11.3
   Installed LoopVectorization ──────────────── v0.12.174
   Installed CalibrateEmulateSample ─────────── v1.1.0
   Installed Compat ─────────────────────────── v4.18.1
   Installed Statistics ─────────────────────── v1.11.4
   Installed Xorg_libX11_jll ────────────────── v1.8.13+0
   Installed ProgressLogging ────────────────── v0.1.6
   Installed PositiveFactorizations ─────────── v0.2.4
   Installed TSVD ───────────────────────────── v0.4.4
   Installed TensorCore ─────────────────────── v0.1.1
   Installed StatsAPI ───────────────────────── v1.8.0
   Installed Manifolds ──────────────────────── v0.11.29
   Installed PrecompileTools ────────────────── v1.3.4
   Installed GettextRuntime_jll ─────────────── v0.22.4+0
   Installed CodecBzip2 ─────────────────────── v0.8.5
   Installed Ratios ─────────────────────────── v0.4.5
   Installed MicroMamba ─────────────────────── v0.1.15
   Installed LogDensityProblems ─────────────── v2.2.0
   Installed ProgressMeter ──────────────────── v1.11.0
   Installed OffsetArrays ───────────────────── v1.17.0
   Installed ChainRulesCore ─────────────────── v1.26.1
   Installed SCS ────────────────────────────── v2.6.4
   Installed KernelFunctions ────────────────── v0.10.67
   Installed LAME_jll ───────────────────────── v3.100.3+0
   Installed FriBidi_jll ────────────────────── v1.0.17+0
   Installed Xorg_libXrender_jll ────────────── v0.9.12+0
   Installed AMD ────────────────────────────── v0.5.3
   Installed IfElse ─────────────────────────── v0.1.1
   Installed GaussianProcesses ──────────────── v0.12.6
   Installed MuladdMacro ────────────────────── v0.2.7
   Installed libvorbis_jll ──────────────────── v1.3.8+0
   Installed SpecialFunctions ───────────────── v2.9.0
   Installed CPUSummary ─────────────────────── v0.2.7
   Installed FixedPointNumbers ──────────────── v0.8.6
   Installed AbstractMCMC ───────────────────── v5.16.0
   Installed ScikitLearnBase ────────────────── v0.5.0
   Installed PtrArrays ──────────────────────── v1.4.0
   Installed IntervalSets ───────────────────── v0.7.14
   Installed ScientificTypesBase ────────────── v3.1.0
   Installed Xorg_libXext_jll ───────────────── v1.3.8+0
   Installed Libiconv_jll ───────────────────── v1.18.0+0
   Installed FreeType2_jll ──────────────────── v2.14.3+1
   Installed FFTW_jll ───────────────────────── v3.3.12+0
   Installed FFTW ───────────────────────────── v1.10.0
   Installed LineSearches ───────────────────── v7.5.1
   Installed Kronecker ──────────────────────── v0.5.5
   Installed PolyesterWeave ─────────────────── v0.2.2
   Installed MLJModelInterface ──────────────── v1.12.1
  Installing 44 artifacts
   Installed artifact Rmath             111.3 KiB
   Installed artifact libdrm            304.7 KiB
   Installed artifact Xorg_libXau       25.8 KiB
   Installed artifact Xorg_xtrans       39.5 KiB
   Installed artifact Pixman            343.0 KiB
   Installed artifact FFMPEG            10.2 MiB
   Installed artifact FFTW              1.8 MiB
   Installed artifact Xorg_libXext      187.3 KiB
   Installed artifact FreeType2         1.2 MiB
   Installed artifact Xorg_libxcb       1.3 MiB
   Installed artifact FriBidi           67.6 KiB
   Installed artifact Libffi            39.2 KiB
   Installed artifact Bzip2             152.8 KiB
   Installed artifact Libuuid           1.9 MiB
   Installed artifact OpenSpecFun       105.4 KiB
   Installed artifact x265              1.1 MiB
   Installed artifact Fontconfig        443.8 KiB
   Installed artifact Graphite2         109.8 KiB
   Installed artifact Glib              4.2 MiB
   Installed artifact Libiconv          1022.9 KiB
   Installed artifact libass            361.1 KiB
   Installed artifact libaom            3.9 MiB
   Installed artifact Xorg_libXrender   174.0 KiB
   Installed artifact libvorbis         226.3 KiB
   Installed artifact HarfBuzz          1.5 MiB
   Installed artifact Opus              792.2 KiB
   Installed artifact x264              908.7 KiB
   Installed artifact Xorg_libpciaccess 23.4 KiB
   Installed artifact Xorg_libXdmcp     49.9 KiB
   Installed artifact SCS               318.6 KiB
   Installed artifact Expat             255.9 KiB
   Installed artifact Cairo             1.5 MiB
   Installed artifact GettextRuntime    345.6 KiB
   Installed artifact Xorg_libX11       2.3 MiB
   Installed artifact libfdk_aac        2.4 MiB
   Installed artifact Arpack            110.5 KiB
   Installed artifact Xorg_libXfixes    41.4 KiB
   Installed artifact Ogg               213.4 KiB
   Installed artifact OpenBLAS32        5.0 MiB
   Installed artifact libva             187.1 KiB
   Installed artifact LLVMOpenMP        573.2 KiB
   Installed artifact LAME              243.9 KiB
   Installed artifact libpng            234.7 KiB
   Installed artifact Libmount          3.4 MiB
    Updating `~/.julia/environments/v1.14/Project.toml`
  [95e48a1f] + CalibrateEmulateSample v1.1.0
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [47edcb42] + ADTypes v1.24.0
  [14f7f29c] + AMD v0.5.3
  [621f4979] + AbstractFFTs v1.5.0
  [99985d1d] + AbstractGPs v0.5.24
  [80f14c24] + AbstractMCMC v5.16.0
  [1520ce14] + AbstractTrees v0.4.5
  [7d9f7c33] + Accessors v0.1.45
  [79e6a3ab] + Adapt v4.7.0
  [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 v7.30.1
  [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 v0.7.9
  [35d6a980] + ColorSchemes v3.31.0
  [3da002f7] + ColorTypes v0.12.1
  [c3611d14] + ColorVectorSpace v0.11.0
  [5ae59095] + Colors v0.13.1
  [38540f10] + CommonSolve v0.2.14
  [bbf7d656] + CommonSubexpressions v0.3.1
  [f70d9fcc] + CommonWorldInvalidations v1.2.0
  [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 v0.16.7
  [adafc99b] + CpuId v0.3.1
  [a8cc5b0e] + Crayons v4.2.0
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.19.6
  [e2d170a0] + DataValueInterfaces v1.0.0
  [163ba53b] + DiffResults v1.1.0
  [b552c78f] + DiffRules v1.16.0
  [a0c0ee7d] + DifferentiationInterface v0.7.21
  [b4f34e82] + Distances v0.10.12
  [31c24e10] + Distributions v0.25.131
  [ffbed154] + DocStringExtensions v0.9.5
  [fdbdab4c] + ElasticArrays v1.2.12
  [2904ab23] + ElasticPDMats v0.2.4
  [aa8a2aa5] + EnsembleKalmanProcesses v2.7.4
  [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 v1.17.0
  [6a86dc24] + FiniteDiff v2.33.0
⌅ [53c48c17] + FixedPointNumbers v0.8.6
  [f6369f11] + ForwardDiff v1.4.5
  [069b7b12] + FunctionWrappers v1.1.3
  [d9f16b24] + Functors v0.5.3
⌃ [a0844989] + Gamma v1.1.0
  [891a1506] + GaussianProcesses v0.12.6
  [e4b2fa32] + GaussianRandomFields v2.2.7
  [8f48dd54] + Glossaries v0.1.2
  [86223c79] + Graphs v1.14.0
  [3e5b6fbb] + HostCPUFeatures v0.1.18
  [34004b35] + HypergeometricFunctions v0.3.30
  [615f187c] + IfElse v0.1.1
  [d25df0c9] + Inflate v0.1.5
  [22cec73e] + InitialValues v0.3.1
  [18e54dd8] + IntegerMathUtils v0.1.4
  [a98d9a8b] + Interpolations v0.16.3
  [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 v1.7.1
  [5ab0869b] + KernelDensity v0.6.12
⌅ [ec8451be] + KernelFunctions v0.10.67
  [2c470bb0] + Kronecker v0.5.5
  [40e66cde] + LDLFactorizations v0.10.2
  [b964fa9f] + LaTeXStrings v1.4.1
  [10f19ff3] + LayoutPointers v0.1.17
  [1d6d02ad] + LeftChildRightSiblingTrees v0.3.0
⌃ [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 v0.3.19
  [e80e1ace] + MLJModelInterface v1.12.1
  [1914dd2f] + MacroTools v0.5.16
  [af67fdf4] + ManifoldDiff v0.4.5
  [1cead3c2] + Manifolds v0.11.29
  [3362f125] + ManifoldsBase v2.5.0
⌅ [0fc0a36d] + Manopt v0.5.39
  [d125e4d3] + ManualMemory v0.1.8
  [b8f27783] + MathOptInterface v1.53.0
  [99c1a7ee] + MatrixEquations v2.6.5
  [0b3b1443] + MicroMamba v0.1.15
  [e1d29d7a] + Missings v1.2.0
  [46d2c3a1] + MuladdMacro v0.2.7
  [d8a4904e] + MutableArithmetics v1.8.0
⌅ [d41bc354] + NLSolversBase v7.10.0
  [77ba4419] + NaNMath v1.1.4
  [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 v2.8.7
  [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 v3.4.8
  [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 v0.9.35
  [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 v1.17.0
  [79098fc4] + Rmath v0.9.0
  [f2b01f46] + Roots v3.0.7
  [c946c3f1] + SCS v2.6.4
  [94e857df] + SIMDTypes v0.1.0
  [476501e8] + SLEEFPirates v0.6.46
  [431bcebd] + SciMLPublic v1.3.0
  [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 v1.2.3
  [276daf66] + SpecialFunctions v2.9.0
  [860ef19b] + StableRNGs v1.0.4
  [aedffcd0] + Static v1.4.6
  [0d7ed370] + StaticArrayInterface v1.10.0
  [90137ffa] + StaticArrays v1.9.19
  [1e83bf80] + StaticArraysCore v1.4.4
  [64bff920] + StatisticalTraits v3.5.0
  [10745b16] + Statistics v1.11.4
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.13
⌅ [4c63d2b9] + StatsFuns v1.5.2
⌅ [892a3eda] + StringManipulation v0.5.0
  [ec057cc2] + StructUtils v2.8.5
  [9449cd9e] + TSVD v0.4.4
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.14.0
  [62fd8b95] + TensorCore v0.1.1
  [5d786b92] + TerminalLoggers v0.1.8
  [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 v0.2.8
⌅ [68821587] + Arpack_jll v3.5.2+0
  [6e34b625] + Bzip2_jll v1.0.9+0
  [83423d85] + Cairo_jll v1.18.7+0
  [2e619515] + Expat_jll v2.8.3+0
⌅ [b22a6f82] + FFMPEG_jll v8.1.2+0
  [f5851436] + FFTW_jll v3.3.12+0
  [a3f928ae] + Fontconfig_jll v2.17.1+0
  [d7e528f0] + FreeType2_jll v2.14.3+1
  [559328eb] + FriBidi_jll v1.0.17+0
⌅ [b0724c58] + GettextRuntime_jll v0.22.4+0
  [7746bdde] + Glib_jll v2.88.3+0
  [3b182d85] + Graphite2_jll v1.3.16+0
  [2e76f6c2] + HarfBuzz_jll v100.14003.0+0
  [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0
  [c1c5ebd0] + LAME_jll v3.100.3+0
  [1d63c593] + LLVMOpenMP_jll v22.1.7+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 v0.3.34+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [91d4177d] + Opus_jll v1.6.1+0
  [30392449] + Pixman_jll v0.46.4+0
  [f50d1b31] + Rmath_jll v0.5.2+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 v3.14.1+0
  [0ac62f75] + libass_jll v0.17.5+0
  [8e53e030] + libdrm_jll v2.4.134+0
  [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 v0.76.2+0
⌅ [1270edf5] + x264_jll v10164.0.1+0
  [dfaa095f] + x265_jll v4.1.0+0
  [0dad84c5] + ArgTools v1.2.0
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.12.0
  [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 v1.5.7+0
  [deac9b47] + LibCURL_jll v8.21.0+0
  [e37daf67] + LibGit2_jll v1.9.7+0
  [29816b5a] + LibSSH2_jll v1.11.104+0
  [14a3606d] + MozillaCACerts_jll v2026.8.13
  [4536629a] + OpenBLAS_jll v0.3.34+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [458c3c95] + OpenSSL_jll v3.5.8+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 v1.70.0+0
  [3f19e933] + p7zip_jll v17.8.2+0
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

Installation completed after 22.5s


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

Precompiling PkgEval dependencies...
Precompiling project...
   5.8 s  ✓ TestEnv
  1 dependency successfully precompiled in 6 seconds. 27 already precompiled.

Precompiling package dependencies...
Precompiling project...
   3.7 s  ✓ MacroTools
   0.8 s  ✓ Glossaries
   0.5 s  ✓ Reexport
   0.6 s  ✓ TensorCore
   0.9 s  ✓ ConstructionBase
   2.1 s  ✓ IrrationalConstants
   0.6 s  ✓ DataValueInterfaces
   0.6 s  ✓ StatsAPI
   2.6 s  ✓ LinearMaps
   0.9 s  ✓ IterTools
   0.8 s  ✓ IntervalSets
   0.7 s  ✓ Inflate
   0.6 s  ✓ ArgCheck
   1.0 s  ✓ TranscodingStreams
   0.5 s  ✓ LaTeXStrings
   0.8 s  ✓ Statistics
   0.6 s  ✓ StaticArraysCore
   0.6 s  ✓ ChunkSplitters
   0.6 s  ✓ StableRNGs
   0.5 s  ✓ IfElse
   0.5 s  ✓ PtrArrays
   0.5 s  ✓ NaturalSort
   0.6 s  ✓ ManualMemory
   0.6 s  ✓ Adapt
   0.6 s  ✓ PositiveFactorizations
   0.6 s  ✓ DataAPI
   0.5 s  ✓ RealDot
   0.6 s  ✓ LowRankMatrices
   0.5 s  ✓ InverseFunctions
   0.5 s  ✓ CompositionsBase
   0.8 s  ✓ AbstractTrees
   1.4 s  ✓ InitialValues
   0.8 s  ✓ AbstractFFTs
   0.6 s  ✓ EnumX
   2.0 s  ✓ FillArrays
   0.6 s  ✓ IntegerMathUtils
   0.5 s  ✓ UnPack
   0.6 s  ✓ UnsafePointers
   0.5 s  ✓ RangeArrays
   0.7 s  ✓ Nullables
   1.0 s  ✓ OrderedCollections
   1.1 s  ✓ FunctionWrappers
   1.1 s  ✓ DocStringExtensions
   1.0 s  ✓ Gamma
   1.5 s  ✓ OffsetArrays
   0.5 s  ✓ SIMDTypes
   0.5 s  ✓ IteratorInterfaceExtensions
   1.9 s  ✓ Crayons
   0.8 s  ✓ ProgressLogging
   0.7 s  ✓ NaNMath
   0.8 s  ✓ Requires
   1.1 s  ✓ ProgressBars
   2.2 s  ✓ ProgressMeter
   1.8 s  ✓ WoodburyMatrices
   1.8 s  ✓ AMD
   1.5 s  ✓ Scratch
   1.2 s  ✓ LoggingExtras
   1.5 s  ✓ StructUtils
   1.6 s  ✓ CpuId
   2.0 s  ✓ PDMats
   0.9 s  ✓ Compat
   1.2 s  ✓ Preferences
   1.0 s  ✓ ScientificTypesBase
   0.6 s  ✓ Pidfile
  11.3 s  ✓ MutableArithmetics
   0.9 s  ✓ CommonSubexpressions
   2.9 s  ✓ SimpleTraits
   0.5 s  ✓ ConstructionBase → ConstructionBaseLinearAlgebraExt
   4.3 s  ✓ MatrixEquations
   2.0 s  ✓ LinearMaps → LinearMapsSparseArraysExt
   0.5 s  ✓ IntervalSets → IntervalSetsRandomExt
   0.5 s  ✓ ConstructionBase → ConstructionBaseIntervalSetsExt
   0.7 s  ✓ CodecZlib
   1.5 s  ✓ Statistics → SparseArraysExt
   2.2 s  ✓ FixedPointNumbers
   0.7 s  ✓ ScikitLearnBase
   1.7 s  ✓ NamedDims
   1.0 s  ✓ Distances
   0.5 s  ✓ LinearMaps → LinearMapsStatisticsExt
   0.5 s  ✓ IntervalSets → IntervalSetsStatisticsExt
   0.6 s  ✓ DiffResults
   0.7 s  ✓ AliasTables
   1.3 s  ✓ ThreadingUtilities
   0.8 s  ✓ ArrayInterface
   1.3 s  ✓ Adapt → AdaptSparseArraysExt
   0.6 s  ✓ ElasticArrays
   0.7 s  ✓ TSVD
   0.7 s  ✓ Missings
   0.8 s  ✓ Quaternions
   0.9 s  ✓ InverseFunctions → InverseFunctionsDatesExt
   1.3 s  ✓ InverseFunctions → InverseFunctionsTestExt
   0.5 s  ✓ CompositionsBase → CompositionsBaseInverseFunctionsExt
   0.6 s  ✓ LeftChildRightSiblingTrees
   3.4 s  ✓ AbstractFFTs → AbstractFFTsTestExt
   1.8 s  ✓ FillArrays → FillArraysSparseArraysExt
   0.9 s  ✓ FillArrays → FillArraysStatisticsExt
   0.9 s  ✓ LowRankMatrices → LowRankMatricesFillArraysExt
   1.0 s  ✓ Primes
   3.5 s  ✓ DataStructures
   1.3 s  ✓ LogExpFunctions
   1.4 s  ✓ LogDensityProblems
   1.7 s  ✓ HypergeometricFunctions
   0.6 s  ✓ OffsetArrays → OffsetArraysAdaptExt
   0.5 s  ✓ TableTraits
   0.8 s  ✓ Ratios
   1.6 s  ✓ ConsoleProgressMonitor
   1.5 s  ✓ AxisAlgorithms
   1.7 s  ✓ LDLFactorizations
   0.8 s  ✓ StructUtils → StructUtilsStaticArraysCoreExt
   2.5 s  ✓ ElasticPDMats
   1.8 s  ✓ FillArrays → FillArraysPDMatsExt
   0.6 s  ✓ Compat → CompatLinearAlgebraExt
   1.3 s  ✓ PrecompileTools
   1.3 s  ✓ JLLWrappers
   7.3 s  ✓ ManifoldsBase
   1.1 s  ✓ StatisticalTraits
   1.8 s  ✓ Setfield
   1.7 s  ✓ AxisArrays
   2.3 s  ✓ ColorTypes
   0.7 s  ✓ NamedDims → AbstractFFTsExt
   1.4 s  ✓ Distances → DistancesSparseArraysExt
   1.1 s  ✓ ArrayInterface → ArrayInterfaceFillArraysExt
   0.7 s  ✓ ArrayInterface → ArrayInterfaceStaticArraysCoreExt
   1.4 s  ✓ ArrayInterface → ArrayInterfaceSparseArraysExt
   4.0 s  ✓ Accessors
   1.3 s  ✓ TerminalLoggers
   0.9 s  ✓ SortingAlgorithms
   1.9 s  ✓ QuadGK
   0.6 s  ✓ LogExpFunctions → LogExpFunctionsInverseFunctionsExt
   1.7 s  ✓ Tables
   0.6 s  ✓ Ratios → RatiosFixedPointNumbersExt
   2.3 s  ✓ ChainRulesCore
   0.9 s  ✓ Functors
   6.2 s  ✓ StringManipulation
   1.6 s  ✓ CommonSolve
  15.3 s  ✓ StaticArrays
   1.5 s  ✓ SciMLPublic
   1.5 s  ✓ CommonWorldInvalidations
   1.8 s  ✓ MuladdMacro
   3.0 s  ✓ RecipesBase
   1.8 s  ✓ ADTypes
  10.7 s  ✓ Parsers
   1.6 s  ✓ Libffi_jll
   1.3 s  ✓ OpenBLAS32_jll
   1.2 s  ✓ Bzip2_jll
   1.3 s  ✓ Rmath_jll
   1.6 s  ✓ OpenSpecFun_jll
   1.1 s  ✓ Xorg_xtrans_jll
   1.2 s  ✓ Opus_jll
   4.2 s  ✓ IntelOpenMP_jll
   1.1 s  ✓ x265_jll
   1.2 s  ✓ libpng_jll
   4.1 s  ✓ micromamba_jll
   4.6 s  ✓ oneTBB_jll
   1.1 s  ✓ Arpack_jll
   1.2 s  ✓ Libmount_jll
   1.1 s  ✓ libfdk_aac_jll
   1.2 s  ✓ Libuuid_jll
   1.2 s  ✓ FriBidi_jll
   1.4 s  ✓ Xorg_libXau_jll
   1.1 s  ✓ Ogg_jll
   1.2 s  ✓ FFTW_jll
   1.1 s  ✓ LAME_jll
   1.1 s  ✓ Graphite2_jll
   1.1 s  ✓ x264_jll
   1.2 s  ✓ Xorg_libpciaccess_jll
   1.3 s  ✓ LLVMOpenMP_jll
   4.2 s  ✓ pixi_jll
   1.2 s  ✓ Libiconv_jll
   1.2 s  ✓ libaom_jll
   1.0 s  ✓ Xorg_libXdmcp_jll
   1.1 s  ✓ Expat_jll
   1.3 s  ✓ ManifoldsBase → ManifoldsBaseStatisticsExt
   1.3 s  ✓ ManifoldsBase → ManifoldsBaseQuaternionsExt
   3.4 s  ✓ MLJModelInterface
   1.0 s  ✓ ColorTypes → StyledStringsExt
   4.8 s  ✓ Colors
   1.4 s  ✓ ColorVectorSpace
   1.0 s  ✓ FiniteDiff
   1.9 s  ✓ Accessors → TestExt
   2.3 s  ✓ Accessors → IntervalSetsExt
   1.6 s  ✓ Accessors → LinearAlgebraExt
   4.3 s  ✓ StatsBase
   0.9 s  ✓ StructUtils → StructUtilsTablesExt
   1.5 s  ✓ ChainRulesCore → ChainRulesCoreSparseArraysExt
   1.5 s  ✓ ZygoteRules
   0.7 s  ✓ LinearMaps → LinearMapsChainRulesCoreExt
   0.6 s  ✓ AbstractFFTs → AbstractFFTsChainRulesCoreExt
   0.7 s  ✓ NamedDims → ChainRulesCoreExt
   0.6 s  ✓ Distances → DistancesChainRulesCoreExt
   0.6 s  ✓ ArrayInterface → ArrayInterfaceChainRulesCoreExt
   2.8 s  ✓ LogExpFunctions → LogExpFunctionsChainRulesCoreExt
  40.6 s  ✓ PrettyTables
   2.9 s  ✓ ArnoldiMethod
   1.3 s  ✓ StaticArrays → StaticArraysStatisticsExt
   1.4 s  ✓ StaticArrays → StaticArraysChainRulesCoreExt
   1.3 s  ✓ ConstructionBase → ConstructionBaseStaticArraysExt
   1.3 s  ✓ Adapt → AdaptStaticArraysExt
   1.9 s  ✓ FillArrays → FillArraysStaticArraysExt
   1.4 s  ✓ Accessors → StaticArraysExt
   1.8 s  ✓ Static
   2.1 s  ✓ FFTA
   1.2 s  ✓ IntervalSets → IntervalSetsRecipesBaseExt
   0.9 s  ✓ ADTypes → ADTypesConstructionBaseExt
   0.9 s  ✓ ADTypes → ADTypesChainRulesCoreExt
   2.1 s  ✓ DifferentiationInterface
   8.5 s  ✓ JSON
   0.9 s  ✓ CodecBzip2
   1.1 s  ✓ FreeType2_jll
   1.4 s  ✓ Rmath
   4.6 s  ✓ SpecialFunctions
 Downloading artifact: micromamba
   5.3 s  ✓ MicroMamba
 Downloading artifact: IntelOpenMP
 Downloading artifact: oneTBB
   8.9 s  ✓ MKL_jll
   1.3 s  ✓ Arpack
   1.2 s  ✓ libvorbis_jll
   1.5 s  ✓ libdrm_jll
   1.1 s  ✓ SCS_jll
   1.1 s  ✓ Pixman_jll
   1.4 s  ✓ GettextRuntime_jll
   1.3 s  ✓ Xorg_libxcb_jll
   9.2 s  ✓ ColorSchemes
   1.7 s  ✓ FiniteDiff → FiniteDiffStaticArraysExt
   1.5 s  ✓ FiniteDiff → FiniteDiffSparseArraysExt
   1.5 s  ✓ BangBang
   5.1 s  ✓ Roots
   1.6 s  ✓ PDMats → StatsBaseExt
   2.8 s  ✓ Kronecker
   9.0 s  ✓ Graphs
   4.5 s  ✓ Interpolations
   0.9 s  ✓ BitTwiddlingConvenienceFunctions
   2.5 s  ✓ CPUSummary
   2.5 s  ✓ StaticArrayInterface
   1.0 s  ✓ DifferentiationInterface → DifferentiationInterfaceFiniteDiffExt
   1.3 s  ✓ DifferentiationInterface → DifferentiationInterfaceStaticArraysExt
   1.6 s  ✓ DifferentiationInterface → DifferentiationInterfaceSparseArraysExt
   1.0 s  ✓ DifferentiationInterface → DifferentiationInterfaceChainRulesCoreExt
   1.8 s  ✓ ManifoldDiff
  10.4 s  ✓ BenchmarkTools
   1.6 s  ✓ Fontconfig_jll
   3.5 s  ✓ SpecialFunctions → SpecialFunctionsChainRulesCoreExt
   3.9 s  ✓ FastGaussQuadrature
   1.1 s  ✓ DiffRules
   1.6 s  ✓ ColorVectorSpace → SpecialFunctionsExt
   2.9 s  ✓ StatsFuns
 Downloading artifact: pixi
   9.3 s  ✓ CondaPkg
   3.8 s  ✓ FFTW
   1.8 s  ✓ Glib_jll
   1.5 s  ✓ Xorg_libX11_jll
   5.0 s  ✓ AbstractMCMC
   1.1 s  ✓ BangBang → BangBangChainRulesCoreExt
   1.1 s  ✓ BangBang → BangBangTablesExt
   1.5 s  ✓ BangBang → BangBangStaticArraysExt
   1.0 s  ✓ Roots → RootsChainRulesCoreExt
   3.5 s  ✓ SimpleWeightedGraphs
   3.7 s  ✓ Graphs → GraphsSharedArraysExt
   1.9 s  ✓ HostCPUFeatures
   1.9 s  ✓ PolyesterWeave
   1.0 s  ✓ StaticArrayInterface → StaticArrayInterfaceOffsetArraysExt
   1.5 s  ✓ StaticArrayInterface → StaticArrayInterfaceStaticArraysExt
   1.0 s  ✓ CloseOpenIntervals
   1.4 s  ✓ LayoutPointers
  19.3 s  ✓ Manopt
  10.6 s  ✓ KernelFunctions
   4.9 s  ✓ Tullio
   6.6 s  ✓ ForwardDiff
   3.0 s  ✓ StatsFuns → StatsFunsChainRulesCoreExt
   0.9 s  ✓ StatsFuns → StatsFunsInverseFunctionsExt
  19.9 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`.
   4.6 s  ✓ LowRankApprox
   5.7 s  ✓ GaussianRandomFields
   1.1 s  ✓ Xorg_libXfixes_jll
   1.1 s  ✓ Xorg_libXrender_jll
   1.1 s  ✓ Xorg_libXext_jll
  17.0 s  ✓ VectorizationBase
   0.7 s  ✓ Tullio → TullioChainRulesCoreExt
   1.0 s  ✓ Tullio → TullioFillArraysExt
  34.3 s  ✓ Manifolds
   1.9 s  ✓ ForwardDiff → ForwardDiffStaticArraysExt
   1.4 s  ✓ DifferentiationInterface → DifferentiationInterfaceForwardDiffExt
  95.1 s  ✓ MathOptInterface
   0.9 s  ✓ Roots → RootsForwardDiffExt
   2.2 s  ✓ Interpolations → InterpolationsForwardDiffExt
  10.0 s  ✓ Distributions
   1.2 s  ✓ Cairo_jll
   1.3 s  ✓ libva_jll
   2.8 s  ✓ SLEEFPirates
   5.6 s  ✓ Manifolds → ManifoldsRecipesBaseExt
   8.1 s  ✓ Manifolds → ManifoldsTestExt
   6.1 s  ✓ Manopt → ManoptManifoldsExt
  59.5 s  ✓ ReverseDiff
   2.5 s  ✓ NLSolversBase
   8.8 s  ✓ MathOptInterface → MathOptInterfaceBenchmarkToolsExt
  52.6 s  ✓ SCS
   4.5 s  ✓ Distributions → DistributionsTestExt
   3.5 s  ✓ Distributions → DistributionsChainRulesCoreExt
   5.8 s  ✓ MCMCDiagnosticTools
   5.6 s  ✓ AdvancedMH
   1.2 s  ✓ HarfBuzz_jll
  34.9 s  ✓ LoopVectorization
  19.3 s  ✓ ArrayInterface → ArrayInterfaceReverseDiffExt
  19.0 s  ✓ DifferentiationInterface → DifferentiationInterfaceReverseDiffExt
   2.8 s  ✓ LineSearches
  13.1 s  ✓ Convex
   4.7 s  ✓ KernelDensity
   6.0 s  ✓ AbstractGPs
   4.8 s  ✓ AdvancedMH → AdvancedMHForwardDiffExt
   1.1 s  ✓ libass_jll
   3.0 s  ✓ LoopVectorization → SpecialFunctionsExt
   2.9 s  ✓ LoopVectorization → ForwardDiffExt
   4.8 s  ✓ Manopt → ManoptLineSearchesExt
   6.3 s  ✓ Optim
  10.3 s  ✓ MCMCChains
   1.3 s  ✓ FFMPEG_jll
  10.0 s  ✓ Optim → OptimMOIExt
   7.4 s  ✓ GaussianProcesses
   8.2 s  ✓ AdvancedMH → AdvancedMHMCMCChainsExt
   0.9 s  ✓ FFMPEG
  17.6 s  ✓ EnsembleKalmanProcesses
  44.3 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/5WGSP/CondaPkg.toml
    CondaPkg Resolving changes
             + openssl
             + python
             + scikit-learn
             + scipy
    CondaPkg Initialising pixi
             │ /home/pkgeval/.julia/artifacts/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi
             │ init
             │ --format pixi
             └ /tmp/jl_vwMBbV/.CondaPkg
✔ Created /tmp/jl_vwMBbV/.CondaPkg/pixi.toml
    CondaPkg Wrote /tmp/jl_vwMBbV/.CondaPkg/pixi.toml
             │ [dependencies]
             │ openssl = ">=3, <3.6"
             │ scikit-learn = "=1.5.1"
             │ scipy = "=1.14.1"
             │ 
             │     [dependencies.python]
             │     version = ">=3.10,!=3.14.0,!=3.14.1,<4, =3.11"
             │     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/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi
             │ install
             └ --manifest-path /tmp/jl_vwMBbV/.CondaPkg/pixi.toml
✔ The default environment has been installed.
  97.5 s  ✓ CalibrateEmulateSample
  320 dependencies successfully precompiled in 1247 seconds. 42 already precompiled.
  5 dependencies had output during precompilation:
┌ CondaPkg
│  Downloading artifact: pixi
└  
┌ MKL_jll
│  Downloading artifact: IntelOpenMP
│   Downloading artifact: oneTBB
└  
┌ MicroMamba
│  Downloading artifact: micromamba
└  
┌ 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`.
└  
┌ 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/5WGSP/CondaPkg.toml
│      CondaPkg Resolving changes
│               + openssl
│               + python
│               + scikit-learn
│               + scipy
│      CondaPkg Initialising pixi
│               │ /home/pkgeval/.julia/artifacts/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi
│               │ init
│               │ --format pixi
│               └ /tmp/jl_vwMBbV/.CondaPkg
│  ✔ Created /tmp/jl_vwMBbV/.CondaPkg/pixi.toml
│      CondaPkg Wrote /tmp/jl_vwMBbV/.CondaPkg/pixi.toml
│               │ [dependencies]
│               │ openssl = ">=3, <3.6"
│               │ scikit-learn = "=1.5.1"
│               │ scipy = "=1.14.1"
│               │ 
│               │     [dependencies.python]
│               │     version = ">=3.10,!=3.14.0,!=3.14.1,<4, =3.11"
│               │     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/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi
│               │ install
│               └ --manifest-path /tmp/jl_vwMBbV/.CondaPkg/pixi.toml
│  ✔ The default environment has been installed.
└  

Precompilation completed after 1267.0s


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

     Testing CalibrateEmulateSample
      Status `/tmp/jl_unsET9/Project.toml`
  [99985d1d] AbstractGPs v0.5.24
  [80f14c24] AbstractMCMC v5.16.0
  [5b7e9947] AdvancedMH v0.8.10
  [95e48a1f] CalibrateEmulateSample v1.1.0
  [ae650224] ChunkSplitters v3.2.0
  [992eb4ea] CondaPkg v0.2.36
  [31c24e10] Distributions v0.25.131
  [ffbed154] DocStringExtensions v0.9.5
  [aa8a2aa5] EnsembleKalmanProcesses v2.7.4
  [f6369f11] ForwardDiff v1.4.5
  [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 v0.11.29
⌅ [0fc0a36d] Manopt v0.5.39
⌅ [90014a1f] PDMats v0.11.36
  [49802e3a] ProgressBars v1.5.1
  [6099a3de] PythonCall v0.9.35
  [36c3bae2] RandomFeatures v0.3.5
  [37e2e3b7] ReverseDiff v1.17.0
  [860ef19b] StableRNGs v1.0.4
  [10745b16] Statistics v1.11.4
  [2913bbd2] StatsBase v0.34.13
  [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_unsET9/Manifest.toml`
  [47edcb42] ADTypes v1.24.0
  [14f7f29c] AMD v0.5.3
  [621f4979] AbstractFFTs v1.5.0
  [99985d1d] AbstractGPs v0.5.24
  [80f14c24] AbstractMCMC v5.16.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.45
  [79e6a3ab] Adapt v4.7.0
  [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 v7.30.1
  [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 v0.7.9
  [35d6a980] ColorSchemes v3.31.0
  [3da002f7] ColorTypes v0.12.1
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.1
  [38540f10] CommonSolve v0.2.14
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.2.0
  [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 v0.16.7
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.2.0
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.19.6
  [e2d170a0] DataValueInterfaces v1.0.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.16.0
  [a0c0ee7d] DifferentiationInterface v0.7.21
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.131
  [ffbed154] DocStringExtensions v0.9.5
  [fdbdab4c] ElasticArrays v1.2.12
  [2904ab23] ElasticPDMats v0.2.4
  [aa8a2aa5] EnsembleKalmanProcesses v2.7.4
  [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 v1.17.0
  [6a86dc24] FiniteDiff v2.33.0
⌅ [53c48c17] FixedPointNumbers v0.8.6
  [f6369f11] ForwardDiff v1.4.5
  [069b7b12] FunctionWrappers v1.1.3
  [d9f16b24] Functors v0.5.3
⌃ [a0844989] Gamma v1.1.0
  [891a1506] GaussianProcesses v0.12.6
  [e4b2fa32] GaussianRandomFields v2.2.7
  [8f48dd54] Glossaries v0.1.2
  [86223c79] Graphs v1.14.0
  [3e5b6fbb] HostCPUFeatures v0.1.18
  [34004b35] HypergeometricFunctions v0.3.30
  [615f187c] IfElse v0.1.1
  [d25df0c9] Inflate v0.1.5
  [22cec73e] InitialValues v0.3.1
  [18e54dd8] IntegerMathUtils v0.1.4
  [a98d9a8b] Interpolations v0.16.3
  [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 v1.7.1
  [5ab0869b] KernelDensity v0.6.12
⌅ [ec8451be] KernelFunctions v0.10.67
  [2c470bb0] Kronecker v0.5.5
  [40e66cde] LDLFactorizations v0.10.2
  [b964fa9f] LaTeXStrings v1.4.1
  [10f19ff3] LayoutPointers v0.1.17
  [1d6d02ad] LeftChildRightSiblingTrees v0.3.0
⌃ [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 v0.3.19
  [e80e1ace] MLJModelInterface v1.12.1
  [1914dd2f] MacroTools v0.5.16
  [af67fdf4] ManifoldDiff v0.4.5
  [1cead3c2] Manifolds v0.11.29
  [3362f125] ManifoldsBase v2.5.0
⌅ [0fc0a36d] Manopt v0.5.39
  [d125e4d3] ManualMemory v0.1.8
  [b8f27783] MathOptInterface v1.53.0
  [99c1a7ee] MatrixEquations v2.6.5
  [0b3b1443] MicroMamba v0.1.15
  [e1d29d7a] Missings v1.2.0
  [46d2c3a1] MuladdMacro v0.2.7
  [d8a4904e] MutableArithmetics v1.8.0
⌅ [d41bc354] NLSolversBase v7.10.0
  [77ba4419] NaNMath v1.1.4
  [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 v2.8.7
  [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 v3.4.8
  [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 v0.9.35
  [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 v1.17.0
  [79098fc4] Rmath v0.9.0
  [f2b01f46] Roots v3.0.7
  [c946c3f1] SCS v2.6.4
  [94e857df] SIMDTypes v0.1.0
  [476501e8] SLEEFPirates v0.6.46
  [431bcebd] SciMLPublic v1.3.0
  [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 v1.2.3
  [276daf66] SpecialFunctions v2.9.0
  [860ef19b] StableRNGs v1.0.4
  [aedffcd0] Static v1.4.6
  [0d7ed370] StaticArrayInterface v1.10.0
  [90137ffa] StaticArrays v1.9.19
  [1e83bf80] StaticArraysCore v1.4.4
  [64bff920] StatisticalTraits v3.5.0
  [10745b16] Statistics v1.11.4
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.13
⌅ [4c63d2b9] StatsFuns v1.5.2
⌅ [892a3eda] StringManipulation v0.5.0
  [ec057cc2] StructUtils v2.8.5
  [9449cd9e] TSVD v0.4.4
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.14.0
  [62fd8b95] TensorCore v0.1.1
  [5d786b92] TerminalLoggers v0.1.8
  [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 v0.2.8
⌅ [68821587] Arpack_jll v3.5.2+0
  [6e34b625] Bzip2_jll v1.0.9+0
  [83423d85] Cairo_jll v1.18.7+0
  [2e619515] Expat_jll v2.8.3+0
⌅ [b22a6f82] FFMPEG_jll v8.1.2+0
  [f5851436] FFTW_jll v3.3.12+0
  [a3f928ae] Fontconfig_jll v2.17.1+0
  [d7e528f0] FreeType2_jll v2.14.3+1
  [559328eb] FriBidi_jll v1.0.17+0
⌅ [b0724c58] GettextRuntime_jll v0.22.4+0
  [7746bdde] Glib_jll v2.88.3+0
  [3b182d85] Graphite2_jll v1.3.16+0
  [2e76f6c2] HarfBuzz_jll v100.14003.0+0
  [1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
  [c1c5ebd0] LAME_jll v3.100.3+0
  [1d63c593] LLVMOpenMP_jll v22.1.7+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 v0.3.34+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [91d4177d] Opus_jll v1.6.1+0
  [30392449] Pixman_jll v0.46.4+0
  [f50d1b31] Rmath_jll v0.5.2+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 v3.14.1+0
  [0ac62f75] libass_jll v0.17.5+0
  [8e53e030] libdrm_jll v2.4.134+0
  [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 v0.76.2+0
⌅ [1270edf5] x264_jll v10164.0.1+0
  [dfaa095f] x265_jll v4.1.0+0
  [0dad84c5] ArgTools v1.2.0
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.12.0
  [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 v1.5.7+0
  [deac9b47] LibCURL_jll v8.21.0+0
  [e37daf67] LibGit2_jll v1.9.7+0
  [29816b5a] LibSSH2_jll v1.11.104+0
  [14a3606d] MozillaCACerts_jll v2026.8.13
  [4536629a] OpenBLAS_jll v0.3.34+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.8+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 v1.70.0+0
  [3f19e933] p7zip_jll v17.8.2+0
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
[ 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/5WGSP/CondaPkg.toml
    CondaPkg Resolving changes
             + openssl
             + python
             + scikit-learn
             + scipy
    CondaPkg Initialising pixi
             │ /home/pkgeval/.julia/artifacts/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi
             │ init
             │ --format pixi
             └ /tmp/jl_unsET9/.CondaPkg
✔ Created /tmp/jl_unsET9/.CondaPkg/pixi.toml
    CondaPkg Wrote /tmp/jl_unsET9/.CondaPkg/pixi.toml
             │ [dependencies]
             │ openssl = ">=3, <3.6"
             │ scikit-learn = "=1.5.1"
             │ scipy = "=1.14.1"
             │ 
             │     [dependencies.python]
             │     version = ">=3.10,!=3.14.0,!=3.14.1,<4, =3.11"
             │     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/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi
             │ install
             └ --manifest-path /tmp/jl_unsET9/.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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
Completed tests for Emulator, 234 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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, 80 seconds elapsed
Starting tests for RandomFeature
┌ Info: Shrinkage scale: 0.9664592005973369, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 1.1827110268032643
[ Info: NICE-adjusted covariance condition number: 2.227924099258789
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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/HV5q2/src/Observations.jl:225
[ 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: 1.2034588941674724e6
estimate cov with 220 iterations...
[ Info: NICE-adjusted covariance condition number: 1.2229699742756148e6
┌ 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=0.0627204030642035)
┌ Info: Covariance-weighted error: 7.4410803734855175
│ Covariance trace: 0.003144157959830327
└ Covariance trace ratio (current/previous): 0.8423316139580471
[ Info: Iteration 2 (T=0.1368825177864162)
┌ Info: Covariance-weighted error: 7.737772404716599
│ Covariance trace: 0.0020546330945691617
└ Covariance trace ratio (current/previous): 0.6534764222469405
[ Info: Iteration 3 (T=0.21559561260894233)
┌ Info: Covariance-weighted error: 7.581018873065422
│ Covariance trace: 0.0013191397669502988
└ Covariance trace ratio (current/previous): 0.6420317916795314
[ Info: Iteration 4 (T=0.3188250368289436)
┌ Info: Covariance-weighted error: 6.469501983143765
│ Covariance trace: 0.0007769231923196398
└ Covariance trace ratio (current/previous): 0.5889619976477534
[ Info: Iteration 5 (T=0.5994667281669005)
┌ Info: Covariance-weighted error: 5.579898448718662
│ Covariance trace: 0.0004732210786984316
└ Covariance trace ratio (current/previous): 0.6090963474594541
[ Info: Iteration 6 (T=1.3014719549226996)
┌ Info: Covariance-weighted error: 5.465707942156009
│ Covariance trace: 0.0002561284916734101
└ Covariance trace ratio (current/previous): 0.5412448920869657
[ Info: Iteration 7 (T=1.9585715886241473)
┌ Info: Covariance-weighted error: 5.41511766171311
│ Covariance trace: 0.0001467603322672271
└ Covariance trace ratio (current/previous): 0.5729949499502048
[ Info: Iteration 8 (T=3.177771893688427)
┌ Info: Covariance-weighted error: 5.253566308016129
│ Covariance trace: 9.217321579564259e-5
└ Covariance trace ratio (current/previous): 0.6280526513650153
[ Info: Iteration 9 (T=4.236749656936188)
┌ Info: Covariance-weighted error: 5.141519391046575
│ Covariance trace: 5.713830257133948e-5
└ Covariance trace ratio (current/previous): 0.6199013680722708
[ Info: Iteration 10 (T=4.950757781150405)
┌ Info: Covariance-weighted error: 5.161318696920576
│ Covariance trace: 3.972903561252247e-5
└ Covariance trace ratio (current/previous): 0.695313543186187
[ Info: EKI Optimization result:
2×4 Matrix{Any}:
 "name"             "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "input_cholesky"  3                             (0.00465923, 0.189756)   (-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: 382145.3096354059
estimate cov with 220 iterations...
[ Info: NICE-adjusted covariance condition number: 67306.75331307476
┌ 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=0.10712806916854845)
┌ Info: Covariance-weighted error: 21.348033937161734
│ Covariance trace: 0.001956089446194355
└ Covariance trace ratio (current/previous): 0.7245718722336697
[ Info: Iteration 2 (T=0.2508588973876942)
┌ Info: Covariance-weighted error: 21.47012283578515
│ Covariance trace: 0.0009384767960541216
└ Covariance trace ratio (current/previous): 0.47977192345675357
[ Info: Iteration 3 (T=0.3495638983492605)
┌ Info: Covariance-weighted error: 21.719454060896833
│ Covariance trace: 0.0003454289005215813
└ Covariance trace ratio (current/previous): 0.36807399178536593
[ Info: Iteration 4 (T=0.5078604283164563)
┌ Info: Covariance-weighted error: 19.876471674877692
│ Covariance trace: 0.00029055462642205545
└ Covariance trace ratio (current/previous): 0.8411416241760076
[ Info: Iteration 5 (T=0.7548328182449084)
┌ Info: Covariance-weighted error: 18.953288432731714
│ Covariance trace: 0.00037931286679517375
└ Covariance trace ratio (current/previous): 1.3054786683870914
[ Info: Iteration 6 (T=0.9514556322541399)
┌ Info: Covariance-weighted error: 18.133673858974372
│ Covariance trace: 0.0004593127215033885
└ Covariance trace ratio (current/previous): 1.2109073055816324
[ Info: Iteration 7 (T=1.1090162320273635)
┌ Info: Covariance-weighted error: 16.89780334336445
│ Covariance trace: 0.0005050066998225186
└ Covariance trace ratio (current/previous): 1.0994833719596704
[ Info: Iteration 8 (T=1.235221935452303)
┌ Info: Covariance-weighted error: 15.04811688010772
│ Covariance trace: 0.0003285647876191194
└ Covariance trace ratio (current/previous): 0.6506147101307594
[ Info: Iteration 9 (T=1.3697000481997694)
┌ Info: Covariance-weighted error: 12.228467416589957
│ Covariance trace: 0.00017346658638989635
└ Covariance trace ratio (current/previous): 0.5279524554255741
[ Info: Iteration 10 (T=1.4777718737597332)
┌ Info: Covariance-weighted error: 8.76024210506462
│ Covariance trace: 0.0001883237226945947
└ Covariance trace ratio (current/previous): 1.0856484041906742
[ Info: EKI Optimization result:
2×4 Matrix{Any}:
 "name"             "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "input_cholesky"  3                             (-0.338063, 0.378746)    (-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/HV5q2/src/Observations.jl:225
[ 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
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
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/HV5q2/src/Observations.jl:225
[ 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: 0.01261205205591326, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 1500.5617037817333
approx_σ2 not posdef
estimate cov with 420 iterations...
┌ Info: Shrinkage scale: 0.01680389282742542, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 903.5715012375555
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=0.08634447790457236)
┌ Info: Covariance-weighted error: 0.9211906207848315
│ Covariance trace: 4.159957521620793
└ Covariance trace ratio (current/previous): 0.38476302441978294
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 2 (T=0.2718553039489958)
┌ Info: Covariance-weighted error: 0.7318187758817939
│ Covariance trace: 2.52178025717077
└ Covariance trace ratio (current/previous): 0.6062033672373269
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 3 (T=0.4975054841189813)
┌ Info: Covariance-weighted error: 0.7357597248367672
│ Covariance trace: 1.7993745744535534
└ Covariance trace ratio (current/previous): 0.7135334529394339
blockcovmat not posdef
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[ Info: Iteration 4 (T=1.2367674163865392)
┌ Info: Covariance-weighted error: 0.7200940947357803
│ Covariance trace: 0.9425650363494188
└ Covariance trace ratio (current/previous): 0.5238292514140162
blockcovmat not posdef
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[ Info: Iteration 5 (T=1.8531985413921959)
┌ Info: Covariance-weighted error: 0.702177432613265
│ Covariance trace: 0.34912919420626987
└ Covariance trace ratio (current/previous): 0.3704032939291459
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 6 (T=2.8473483823246486)
┌ Info: Covariance-weighted error: 0.6912430543383219
│ Covariance trace: 0.23434313873998633
└ Covariance trace ratio (current/previous): 0.671221836010464
blockcovmat not posdef
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[ Info: Iteration 7 (T=3.860590476703055)
┌ Info: Covariance-weighted error: 0.6929216608847629
│ Covariance trace: 0.20881099479850204
└ Covariance trace ratio (current/previous): 0.8910480414371624
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 8 (T=4.916632473942585)
┌ Info: Covariance-weighted error: 0.7037794283532953
│ Covariance trace: 0.18344430050204605
└ Covariance trace ratio (current/previous): 0.8785183973624843
blockcovmat not posdef
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[ Info: Iteration 9 (T=5.748562424120144)
┌ Info: Covariance-weighted error: 0.7163430468823561
│ Covariance trace: 0.17853189478610415
└ Covariance trace ratio (current/previous): 0.9732212682405628
blockcovmat not posdef
blockcovmat not posdef
[ Info: Iteration 10 (T=6.626116753584808)
┌ Info: Covariance-weighted error: 0.7079254729298231
│ Covariance trace: 0.169371239551978
└ Covariance trace ratio (current/previous): 0.94868897098134
[ Info: EKI Optimization result:
5×4 Matrix{Any}:
 "name"                      "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "input_lowrank_Kchol"      1                             (0.17193, 0.17193)       (-3.0, 3.0)
 "input_lowrank_U"          2                             (-0.35918, 0.356852)     (-0.67082, 0.67082)
 "output_lowrank_diagonal"  2                             (0.0187746, 0.298696)    (0.000554271, 90.2094)
 "output_lowrank_U"         2                             (-0.0469708, 0.052479)   (-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: 0.008917166988053747, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 3495.8225516423818
approx_σ2 not posdef
estimate cov with 420 iterations...
┌ Info: Shrinkage scale: 0.008228068830266, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 3592.805300719597
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=0.08243966915349125)
┌ Info: Covariance-weighted error: 1.19389421679042
│ Covariance trace: 3.951069954085936
└ Covariance trace ratio (current/previous): 0.36544258380694494
blockcovmat not posdef
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[ Info: Iteration 2 (T=0.21139284785810064)
┌ Info: Covariance-weighted error: 1.0188723910938167
│ Covariance trace: 1.7166794190971792
└ Covariance trace ratio (current/previous): 0.43448469377817583
blockcovmat not posdef
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[ Info: Iteration 3 (T=0.4042705316111953)
┌ Info: Covariance-weighted error: 0.8956789342707638
│ Covariance trace: 0.9646869619317789
└ Covariance trace ratio (current/previous): 0.5619493955598993
blockcovmat not posdef
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[ Info: Iteration 4 (T=0.6875336314731898)
┌ Info: Covariance-weighted error: 0.8493209852290265
│ Covariance trace: 0.6126024831743548
└ Covariance trace ratio (current/previous): 0.6350272236992015
blockcovmat not posdef
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[ Info: Iteration 5 (T=1.1473470967279167)
┌ Info: Covariance-weighted error: 0.8381902261684524
│ Covariance trace: 0.3717968828067949
└ Covariance trace ratio (current/previous): 0.606913770378852
blockcovmat not posdef
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[ Info: Iteration 6 (T=1.7849152743014716)
┌ Info: Covariance-weighted error: 0.7910715660118651
│ Covariance trace: 0.20879603457179507
└ Covariance trace ratio (current/previous): 0.5615862967853241
blockcovmat not posdef
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[ Info: Iteration 7 (T=2.4913541666309476)
┌ Info: Covariance-weighted error: 0.7929374698391168
│ Covariance trace: 0.11663666019583033
└ Covariance trace ratio (current/previous): 0.5586153033750481
blockcovmat not posdef
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[ Info: Iteration 8 (T=3.327878587399561)
┌ Info: Covariance-weighted error: 0.7825515962765479
│ Covariance trace: 0.0690496105388067
└ Covariance trace ratio (current/previous): 0.5920060675852169
blockcovmat not posdef
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[ Info: Iteration 9 (T=3.8926273273863665)
┌ Info: Covariance-weighted error: 0.7884213788476447
│ Covariance trace: 0.049972745539322225
└ Covariance trace ratio (current/previous): 0.7237223374523879
blockcovmat not posdef
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[ Info: Iteration 10 (T=4.550923412559973)
┌ Info: Covariance-weighted error: 0.7849649090882442
│ Covariance trace: 0.041327447329633574
└ Covariance trace ratio (current/previous): 0.8269997352279574
[ Info: EKI Optimization result:
5×4 Matrix{Any}:
 "name"                      "number of hyperparameters"  "optimized value range"   "99% prior mass"
 "input_lowrank_Kchol"      1                             (-0.460472, -0.460472)    (-3.0, 3.0)
 "input_lowrank_U"          2                             (-0.0328102, 0.0127081)   (-0.67082, 0.67082)
 "output_lowrank_diagonal"  2                             (0.674556, 0.739546)      (0.000554271, 90.2094)
 "output_lowrank_U"         2                             (-0.00409597, 0.0476487)  (-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: 27407.45836968604
approx_σ2 not posdef
estimate cov with 420 iterations...
[ Info: NICE-adjusted covariance condition number: 805988.0892574078
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=0.0959295954309872)
┌ Info: Covariance-weighted error: 0.6654706263956843
│ Covariance trace: 6.216130886399874
└ Covariance trace ratio (current/previous): 0.3613772857912222
blockcovmat not posdef
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[ Info: Iteration 2 (T=0.229480102058025)
┌ Info: Covariance-weighted error: 0.5818898335168268
│ Covariance trace: 2.4455222208904988
└ Covariance trace ratio (current/previous): 0.3934154968070249
blockcovmat not posdef
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[ Info: Iteration 3 (T=0.47989118114381524)
┌ Info: Covariance-weighted error: 0.5542282470992234
│ Covariance trace: 0.7287616281137396
└ Covariance trace ratio (current/previous): 0.297998366928914
blockcovmat not posdef
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[ Info: Iteration 4 (T=0.825761959070141)
┌ Info: Covariance-weighted error: 0.515540061622348
│ Covariance trace: 0.2731152499563879
└ Covariance trace ratio (current/previous): 0.3747662327711938
blockcovmat not posdef
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[ Info: Iteration 5 (T=1.2416871067444575)
┌ Info: Covariance-weighted error: 0.5098329610852204
│ Covariance trace: 0.14009980277347706
└ Covariance trace ratio (current/previous): 0.5129695350071032
blockcovmat not posdef
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[ Info: Iteration 6 (T=1.6499844093049718)
┌ Info: Covariance-weighted error: 0.4765388645625165
│ Covariance trace: 0.09707662455680997
└ Covariance trace ratio (current/previous): 0.6929105011929967
blockcovmat not posdef
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[ Info: Iteration 7 (T=2.166935772496702)
┌ Info: Covariance-weighted error: 0.45174670245215537
│ Covariance trace: 0.08001326553716573
└ Covariance trace ratio (current/previous): 0.8242279323416458
blockcovmat not posdef
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[ Info: Iteration 8 (T=2.7308727155389807)
┌ Info: Covariance-weighted error: 0.4337291274162112
│ Covariance trace: 0.07078597709061828
└ Covariance trace ratio (current/previous): 0.8846780170187977
blockcovmat not posdef
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[ Info: Iteration 9 (T=3.419332213238483)
┌ Info: Covariance-weighted error: 0.436129989220179
│ Covariance trace: 0.06499630869244431
└ Covariance trace ratio (current/previous): 0.9182088227621384
blockcovmat not posdef
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[ Info: Iteration 10 (T=4.120943322241145)
┌ Info: Covariance-weighted error: 0.43536677746516284
│ Covariance trace: 0.06076042517575249
└ Covariance trace ratio (current/previous): 0.9348288602551942
[ Info: EKI Optimization result:
3×4 Matrix{Any}:
 "name"                    "number of hyperparameters"  "optimized value range"  "99% prior mass"
 "full_lowrank_diagonal"  4                             (0.461179, 0.903503)     (0.000391928, 63.7877)
 "full_lowrank_U"         8                             (-0.0888385, 0.124778)   (-0.237171, 0.237171)
nothing
[ Info: [0.03576924072593014, 0.012470280951997556, 0.02483737759977858, 0.026941460976778443, 0.07115354889696537, 0.07150172735962676]
[ 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/HV5q2/src/Observations.jl:225
[ 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...

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

0.2%┣                                          ┫ 1/420 [00:04<Inf:Inf, InfGs/it]

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34.5%┣██████████████▉                            ┫ 145/420 [00:05<00:09, 31it/s]

38.3%┣████████████████▌                          ┫ 161/420 [00:05<00:08, 34it/s]

39.3%┣█████████████████                          ┫ 165/420 [00:05<00:07, 34it/s]

44.3%┣███████████████████                        ┫ 186/420 [00:05<00:06, 38it/s]

49.3%┣█████████████████████▏                     ┫ 207/420 [00:05<00:05, 42it/s]

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60.0%┣█████████████████████████▉                 ┫ 252/420 [00:05<00:03, 50it/s]

65.2%┣████████████████████████████               ┫ 274/420 [00:05<00:03, 54it/s]

69.3%┣█████████████████████████████▉             ┫ 291/420 [00:05<00:02, 57it/s]

74.5%┣████████████████████████████████           ┫ 313/420 [00:05<00:02, 60it/s]

79.5%┣██████████████████████████████████▏        ┫ 334/420 [00:05<00:01, 64it/s]

84.3%┣████████████████████████████████████▎      ┫ 354/420 [00:05<00:01, 67it/s]

89.5%┣██████████████████████████████████████▌    ┫ 376/420 [00:05<00:01, 70it/s]

95.0%┣████████████████████████████████████████▉  ┫ 399/420 [00:05<00:00, 74it/s]

99.8%┣███████████████████████████████████████████┫ 419/420 [00:05<00:00, 77it/s]

100.0%┣██████████████████████████████████████████┫ 420/420 [00:05<00:00, 77it/s]

100.0%┣██████████████████████████████████████████┫ 420/420 [00:05<00:00, 77it/s]
approx_σ2 not posdef

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

5.7%┣██▌                                         ┫ 24/420 [00:00<00:01, 446it/s]

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27.1%┣███████████▍                              ┫ 114/420 [00:00<00:01, 440it/s]

32.4%┣█████████████▋                            ┫ 136/420 [00:00<00:01, 437it/s]

37.9%┣████████████████                          ┫ 159/420 [00:00<00:01, 437it/s]

42.9%┣██████████████████                        ┫ 180/420 [00:00<00:01, 434it/s]

48.6%┣████████████████████▍                     ┫ 204/420 [00:00<00:00, 438it/s]

53.1%┣██████████████████████▎                   ┫ 223/420 [00:01<00:00, 432it/s]

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67.9%┣████████████████████████████▌             ┫ 285/420 [00:01<00:00, 424it/s]

72.6%┣██████████████████████████████▌           ┫ 305/420 [00:01<00:00, 421it/s]

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93.6%┣███████████████████████████████████████▎  ┫ 393/420 [00:01<00:00, 421it/s]

98.6%┣█████████████████████████████████████████▍┫ 414/420 [00:01<00:00, 420it/s]

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

100.0%┣█████████████████████████████████████████┫ 420/420 [00:01<00:00, 419it/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]

18.6%┣████████▍                                    ┫ 13/70 [00:00<00:02, 27it/s]

50.0%┣██████████████████████▌                      ┫ 35/70 [00:00<00:01, 70it/s]

82.9%┣████████████████████████████████████▌       ┫ 58/70 [00:01<00:00, 106it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:01<00:00, 121it/s]
blockcovmat not posdef

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

32.9%┣██████████████▌                             ┫ 23/70 [00:00<00:00, 419it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 338it/s]
blockcovmat not posdef

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

31.4%┣█████████████▉                              ┫ 22/70 [00:00<00:00, 396it/s]

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95.7%┣██████████████████████████████████████████▏ ┫ 67/70 [00:00<00:00, 418it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 417it/s]
blockcovmat not posdef

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

25.7%┣███████████▎                                ┫ 18/70 [00:00<00:00, 324it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 369it/s]
blockcovmat not posdef

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

31.4%┣█████████████▉                              ┫ 22/70 [00:00<00:00, 412it/s]

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90.0%┣███████████████████████████████████████▋    ┫ 63/70 [00:00<00:00, 400it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 399it/s]
blockcovmat not posdef

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

31.4%┣█████████████▉                              ┫ 22/70 [00:00<00:00, 402it/s]

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88.6%┣███████████████████████████████████████     ┫ 62/70 [00:00<00:00, 393it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 380it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 378it/s]
blockcovmat not posdef

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

30.0%┣█████████████▏                              ┫ 21/70 [00:00<00:00, 393it/s]

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87.1%┣██████████████████████████████████████▍     ┫ 61/70 [00:00<00:00, 385it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 386it/s]
blockcovmat not posdef

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

28.6%┣████████████▋                               ┫ 20/70 [00:00<00:00, 374it/s]

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85.7%┣█████████████████████████████████████▊      ┫ 60/70 [00:00<00:00, 381it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 380it/s]
blockcovmat not posdef

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

28.6%┣████████████▋                               ┫ 20/70 [00:00<00:00, 372it/s]

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87.1%┣██████████████████████████████████████▍     ┫ 61/70 [00:00<00:00, 382it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 389it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 387it/s]
blockcovmat not posdef

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

28.6%┣████████████▋                               ┫ 20/70 [00:00<00:00, 369it/s]

61.4%┣███████████████████████████                 ┫ 43/70 [00:00<00:00, 407it/s]

90.0%┣███████████████████████████████████████▋    ┫ 63/70 [00:00<00:00, 398it/s]

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100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 400it/s]
blockcovmat not posdef

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

30.0%┣█████████████▏                              ┫ 21/70 [00:00<00:00, 388it/s]

62.9%┣███████████████████████████▋                ┫ 44/70 [00:00<00:00, 419it/s]

91.4%┣████████████████████████████████████████▎   ┫ 64/70 [00:00<00:00, 408it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 412it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 410it/s]
blockcovmat not posdef

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

32.9%┣██████████████▌                             ┫ 23/70 [00:00<00:00, 424it/s]

61.4%┣███████████████████████████                 ┫ 43/70 [00:00<00:00, 405it/s]

91.4%┣████████████████████████████████████████▎   ┫ 64/70 [00:00<00:00, 407it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 406it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 404it/s]
blockcovmat not posdef

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

27.1%┣████████████                                ┫ 19/70 [00:00<00:00, 344it/s]

58.6%┣█████████████████████████▊                  ┫ 41/70 [00:00<00:00, 388it/s]

85.7%┣█████████████████████████████████████▊      ┫ 60/70 [00:00<00:00, 382it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 389it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 388it/s]
blockcovmat not posdef

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

35.7%┣███████████████▊                            ┫ 25/70 [00:00<00:00, 463it/s]

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98.6%┣███████████████████████████████████████████▍┫ 69/70 [00:00<00:00, 437it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 434it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 433it/s]
blockcovmat not posdef

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

31.4%┣█████████████▉                              ┫ 22/70 [00:00<00:00, 409it/s]

61.4%┣███████████████████████████                 ┫ 43/70 [00:00<00:00, 404it/s]

94.3%┣█████████████████████████████████████████▌  ┫ 66/70 [00:00<00:00, 420it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 420it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 419it/s]
blockcovmat not posdef

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

30.0%┣█████████████▏                              ┫ 21/70 [00:00<00:00, 383it/s]

64.3%┣████████████████████████████▎               ┫ 45/70 [00:00<00:00, 421it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 410it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 410it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 409it/s]
blockcovmat not posdef

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

30.0%┣█████████████▏                              ┫ 21/70 [00:00<00:00, 391it/s]

60.0%┣██████████████████████████▍                 ┫ 42/70 [00:00<00:00, 402it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 416it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 416it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 414it/s]
blockcovmat not posdef

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

28.6%┣████████████▋                               ┫ 20/70 [00:00<00:00, 373it/s]

62.9%┣███████████████████████████▋                ┫ 44/70 [00:00<00:00, 415it/s]

92.9%┣████████████████████████████████████████▉   ┫ 65/70 [00:00<00:00, 410it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 410it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 408it/s]
blockcovmat not posdef

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

28.6%┣████████████▋                               ┫ 20/70 [00:00<00:00, 363it/s]

50.0%┣██████████████████████                      ┫ 35/70 [00:00<00:00, 328it/s]

77.1%┣██████████████████████████████████          ┫ 54/70 [00:00<00:00, 338it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 340it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 338it/s]
blockcovmat not posdef

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

28.6%┣████████████▋                               ┫ 20/70 [00:00<00:00, 375it/s]

55.7%┣████████████████████████▌                   ┫ 39/70 [00:00<00:00, 373it/s]

90.0%┣███████████████████████████████████████▋    ┫ 63/70 [00:00<00:00, 401it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 404it/s]

100.0%┣███████████████████████████████████████████┫ 70/70 [00:00<00:00, 402it/s]
blockcovmat not posdef
Completed tests for RandomFeature, 388 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 = 0.3156267311155079 
│  Emulator STD = 0.23448539092155232
└  (These numbers should be similar sized to indicate no overfitting)
┌ Info: gpjl_1d 
│  per-point - Emulator RMSE = 0.20024482573071362 
│  Emulator STD = 0.23974737449832012
└  (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)
┌ Info: gpjl-svd_1d 
│  per-point - Emulator RMSE = 0.1974712233704437 
│  Emulator STD = 0.2400373194495227
└  (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/HV5q2/src/Observations.jl:225
[ 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, -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
optimized hyperparameters of GP: 1
Type: SEArd{Float64}, Params: [1.639066704401936, 1.6439292190344692, 1.7664555587688304, 1.2686882519363163, 2.1927427290036876, 3.7271738539647274]
┌ Info: gp_mv 
│  per-point - Emulator RMSE = 0.09972100944320987 
│  Emulator STD = 0.1065925120730421
└  (These numbers should be similar sized to indicate no overfitting)
[ 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...
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
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
blockcovmat not posdef
[ Info: Random Features already trained. continuing...
┌ Info: test 
│  per-point - Emulator RMSE = 0.24222683425623556 
│  Emulator STD = 0.21296550486633997
└  (These numbers should be similar sized to indicate no overfitting)

1D-1D GP/RF & RW Metropolis: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:380
  Got exception outside of a @test
  TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}}
  Stacktrace:
    [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool)
      @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
    [2] DataContainer(data::Matrix{Vector{Float64}})
      @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
    [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String)
      @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822
    [4] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603
    [5] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined]
    [6] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined]
    [7] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined]
    [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::RWMHSampling{GradFreeProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool)
      @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260
    [9] top-level scope
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [10] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [11] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:383 [inlined]
   [12] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [13] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:383 [inlined]
0 starting params: encoded_param_1: -0.288 encoded_param_2: -0.288
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Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:05
Sampling 100%|██████████████████████████████████████████| Time: 0:00:10
1 stepsize: 0.025 acc rate: 0.898
	params: encoded_param_1: -0.466 encoded_param_2: -0.521
Set sampler to new stepsize: 0.05
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:00:01
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Sampling  96%|████████████████████████████████████████▏ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▍ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▌ |  ETA: 0:00:00
Sampling  97%|████████████████████████████████████████▊ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
2 stepsize: 0.05 acc rate: 0.818
	params: encoded_param_1: -0.263 encoded_param_2: -0.518
Set sampler to new stepsize: 0.1
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:00:00
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Sampling  96%|████████████████████████████████████████▏ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▍ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▌ |  ETA: 0:00:00
Sampling  97%|████████████████████████████████████████▊ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
3 stepsize: 0.1 acc rate: 0.645
	params: encoded_param_1: -0.799 encoded_param_2: -0.571
Set sampler to new stepsize: 0.2
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:00:19
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Sampling  74%|███████████████████████████████▏          |  ETA: 0:00:00
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Sampling  84%|███████████████████████████████████▏      |  ETA: 0:00:00
Sampling  84%|███████████████████████████████████▎      |  ETA: 0:00:00
Sampling  84%|███████████████████████████████████▌      |  ETA: 0:00:00
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Sampling  86%|███████████████████████████████████▉      |  ETA: 0:00:00
Sampling  86%|████████████████████████████████████▏     |  ETA: 0:00:00
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Sampling  89%|█████████████████████████████████████▍    |  ETA: 0:00:00
Sampling  90%|█████████████████████████████████████▋    |  ETA: 0:00:00
Sampling  90%|█████████████████████████████████████▊    |  ETA: 0:00:00
Sampling  90%|██████████████████████████████████████    |  ETA: 0:00:00
Sampling  91%|██████████████████████████████████████▎   |  ETA: 0:00:00
Sampling  92%|██████████████████████████████████████▍   |  ETA: 0:00:00
Sampling  92%|██████████████████████████████████████▋   |  ETA: 0:00:00
Sampling  92%|██████████████████████████████████████▉   |  ETA: 0:00:00
Sampling  93%|███████████████████████████████████████   |  ETA: 0:00:00
Sampling  94%|███████████████████████████████████████▎  |  ETA: 0:00:00
Sampling  94%|███████████████████████████████████████▌  |  ETA: 0:00:00
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Sampling  95%|███████████████████████████████████████▉  |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▏ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▍ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▌ |  ETA: 0:00:00
Sampling  97%|████████████████████████████████████████▊ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
4 stepsize: 0.2 acc rate: 0.443
	params: encoded_param_1: -0.76 encoded_param_2: -0.532
Set sampler to new stepsize: 0.4
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:00:00
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Sampling  78%|█████████████████████████████████         |  ETA: 0:00:00
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Sampling  84%|███████████████████████████████████▏      |  ETA: 0:00:00
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Sampling  94%|███████████████████████████████████████▎  |  ETA: 0:00:00
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Sampling  95%|███████████████████████████████████████▉  |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▏ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▍ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▌ |  ETA: 0:00:00
Sampling  97%|████████████████████████████████████████▊ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
5 stepsize: 0.4 acc rate: 0.227
	params: encoded_param_1: -0.775 encoded_param_2: -0.473
Returning optimized stepsize: 0.4
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:02:44
Sampling   1%|▍                                         |  ETA: 0:01:23
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[ Info: ESJD [VRFI, 2d]= [0.008519934901412231, 0.0018824434888172151, 0.27625041865556593, 0.33619999999997113]
[ Info: Posterior mean: [1.6215803765120818; 1.5796552373963042;;] ≈ [1.5707963267948966, 1.5707963267948966]

1D-1D pCN: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:492
  Got exception outside of a @test
  TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}}
  Stacktrace:
    [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool)
      @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
    [2] DataContainer(data::Matrix{Vector{Float64}})
      @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
    [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String)
      @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822
    [4] MCMCWrapper(mcmc_alg::pCNMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603
    [5] MCMCWrapper(mcmc_alg::pCNMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined]
    [6] MCMCWrapper(mcmc_alg::pCNMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined]
    [7] MCMCWrapper(mcmc_alg::pCNMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined]
    [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::pCNMHSampling{GradFreeProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool)
      @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260
    [9] top-level scope
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [10] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [11] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:494 [inlined]
   [12] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [13] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:494 [inlined]
[ Info: Input dimension: 5
0 starting params: encoded_param_1: -0.286 encoded_param_2: 0.278 encoded_param_3: -0.196 encoded_param_4: -0.375 encoded_param_5: 0.112
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1 stepsize: 0.25 acc rate: 0.349
	params: encoded_param_1: 1.83 encoded_param_2: -1.04 encoded_param_3: 1.91 encoded_param_4: 1.34 encoded_param_5: -0.645
Returning optimized stepsize: 0.25
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Sampling 100%|██████████████████████████████████████████| Time: 0:00:04
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[ Info: ESJD (ND-1D) [GPJL,RW] = [0.007301935151046658, 0.00802224386081033, 0.008711154250206625, 0.006099224480332243, 0.010743451851680318, 0.36412217174167644, 0.4391499999999598]
[ Info: Mean of posterior sample norm: 9.793677392844852 ≈ 10.0
[ Info: Mean of G sample norm: 4.59008152200229 ≈ 5.0
0 starting params: encoded_param_1: -0.286 encoded_param_2: 0.278 encoded_param_3: -0.196 encoded_param_4: -0.375 encoded_param_5: 0.112
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:02:23
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Sampling  95%|███████████████████████████████████████▉  |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▏ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▍ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▌ |  ETA: 0:00:00
Sampling  97%|████████████████████████████████████████▊ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:01
Sampling 100%|██████████████████████████████████████████| Time: 0:00:01
1 stepsize: 0.025 acc rate: 0.446
	params: encoded_param_1: 1.27 encoded_param_2: -1.41 encoded_param_3: 0.488 encoded_param_4: 2.08 encoded_param_5: -1.52
Set sampler to new stepsize: 0.0125
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:00:01
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Sampling  96%|████████████████████████████████████████▏ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▍ |  ETA: 0:00:00
Sampling  96%|████████████████████████████████████████▌ |  ETA: 0:00:00
Sampling  97%|████████████████████████████████████████▊ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████ |  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▏|  ETA: 0:00:00
Sampling  98%|█████████████████████████████████████████▍|  ETA: 0:00:00
Sampling  99%|█████████████████████████████████████████▋|  ETA: 0:00:00
Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00
2 stepsize: 0.0125 acc rate: 0.535
	params: encoded_param_1: 1.83 encoded_param_2: -1.18 encoded_param_3: 1.32 encoded_param_4: 1.44 encoded_param_5: -0.814
Returning optimized stepsize: 0.0125
Sampling   0%|                                          |  ETA: N/A
Sampling   0%|▎                                         |  ETA: 0:02:05
Sampling   1%|▍                                         |  ETA: 0:01:04
Sampling   2%|▋                                         |  ETA: 0:00:44
Sampling   2%|▉                                         |  ETA: 0:00:34
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Sampling 100%|█████████████████████████████████████████▊|  ETA: 0:00:00
Sampling 100%|██████████████████████████████████████████| Time: 0:00:05
Sampling 100%|██████████████████████████████████████████| Time: 0:00:05
[ Info: ESJD (ND-1D) [GPJL,RW] = [0.002531214387911754, 0.0027346406775694966, 0.0029291665757605933, 0.0023451805104328133, 0.0035704488770896066, 0.3692916651874981, 0.49044999999995414]
[ Info: Mean of posterior sample norm: 9.854068616036425 ≈ 10.0
[ Info: Mean of G sample norm: 4.7093594112128745 ≈ 5.0
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: MinMaxScaling
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.8
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 500, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info:     truncating at 8/10 retaining 83.57065490184196% of the variance of the structure matrix
[ Info: Injecting nullspace noise: 0.20000000000184537 > 0.15
[ Info: Injecting nullspace noise: 0.20000000000184537 > 0.0
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: MinMaxScaling

Test the encode-decode for posterior samples: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:556
  Got exception outside of a @test
  MethodError: no method matching +(::Vector{Float64}, ::Float64)
  For element-wise addition, use broadcasting with dot syntax: array .+ scalar
  The function `+` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    +(::Any, ::Any, !Matched::Any, !Matched::Any...)
     @ Base operators.jl:668
    +(!Matched::Bool, ::T) where T<:AbstractFloat
     @ Base bool.jl:155
    +(!Matched::Complex{Bool}, ::Real)
     @ Base complex.jl:316
    ...
  Stacktrace:
    [1] (::Base.Splat{typeof(+)})(args::Tuple{Vector{Float64}, Float64})
      @ Base operators.jl:1381
    [2] iterate(::Base.Generator{Base.Iterators.Zip{Tuple{Matrix{Vector{Float64}}, Matrix{Float64}}}, Base.Splat{typeof(+)}})
      @ Base generator.jl:49 [inlined]
    [3] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{Matrix{Vector{Float64}}, Matrix{Float64}}}, Base.Splat{typeof(+)}})
      @ Base array.jl:839 [inlined]
    [4] map(f::typeof(+), it::Matrix{Vector{Float64}}, iters::Matrix{Float64})
      @ Base abstractarray.jl:3645 [inlined]
    [5] _broadcast_preserving_zero_d(::typeof(+), ::Matrix{Vector{Float64}}, ::Matrix{Float64})
      @ Base arraymath.jl:13 [inlined]
    [6] +(::Matrix{Vector{Float64}}, ::Matrix{Float64})
      @ Base arraymath.jl:49
    [7] create_noise_injector(encoder_schedule::Vector{Any}, prior::ParameterDistribution{Parameterized, Constraint{BoundedAbove}, String}, noise_injector_threshold::Float64, noise_injector_scaling::Float64)
      @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:1185
    [8] top-level scope
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
    [9] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [10] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:559 [inlined]
   [11] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [12] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:640 [inlined]

Autodiff MCMC variants: Test Failed at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657
  Expression: mcmc_test_template(prior, σ2_y, em_1; bad_mcmc_params...)
    Expected: ArgumentError
      Thrown: TypeError
      TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}}
      Stacktrace:
        [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool)
          @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
        [2] DataContainer(data::Matrix{Vector{Float64}})
          @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
        [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String)
          @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822
        [4] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603
        [5] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined]
        [6] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined]
        [7] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined]
        [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::BarkerSampling{GradFreeProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool)
          @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260
        [9] top-level scope
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
       [10] macro expansion
          @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
       [11] macro expansion
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
       [12] macro expansion
          @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
       [13] macro expansion
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 [inlined]
       [14] macro expansion
          @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:980 [inlined]
       [15] macro expansion
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 [inlined]
  Stacktrace:
   [1] top-level scope
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [2] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [3] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [4] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [5] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 [inlined]

Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:658
  Test threw exception
  Expression: contains(thrown.value.msg, "autodiff_gradient")
  FieldError: type String has no field `msg`; String has no fields at all.
  Stacktrace:
   [1] getproperty(x::String, f::Symbol)
     @ Base Base_compiler.jl:58
   [2] top-level scope
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [3] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [4] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:658 [inlined]
   [7] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined]

Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:659
  Test threw exception
  Expression: contains(thrown.value.msg, "GradFreeProtocol")
  FieldError: type String has no field `msg`; String has no fields at all.
  Stacktrace:
   [1] getproperty(x::String, f::Symbol)
     @ Base Base_compiler.jl:58
   [2] top-level scope
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [3] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [4] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:659 [inlined]
   [7] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined]

Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:660
  Test threw exception
  Expression: contains(thrown.value.msg, "ForwardDiffProtocol")
  FieldError: type String has no field `msg`; String has no fields at all.
  Stacktrace:
   [1] getproperty(x::String, f::Symbol)
     @ Base Base_compiler.jl:58
   [2] top-level scope
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [3] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [4] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:660 [inlined]
   [7] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined]

Autodiff MCMC variants: Test Failed at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666
  Expression: mcmc_test_template(prior, σ2_y, em_1; bad_mcmc_params...)
    Expected: ArgumentError
      Thrown: TypeError
      TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}}
      Stacktrace:
        [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool)
          @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
        [2] DataContainer(data::Matrix{Vector{Float64}})
          @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
        [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String)
          @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822
        [4] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603
        [5] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined]
        [6] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined]
        [7] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
          @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined]
        [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::BarkerSampling{ForwardDiffProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool)
          @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260
        [9] top-level scope
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
       [10] macro expansion
          @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
       [11] macro expansion
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
       [12] macro expansion
          @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
       [13] macro expansion
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 [inlined]
       [14] macro expansion
          @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:980 [inlined]
       [15] macro expansion
          @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 [inlined]
  Stacktrace:
   [1] top-level scope
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [2] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [3] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [4] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [5] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 [inlined]

Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:667
  Test threw exception
  Expression: contains(thrown.value.msg, "does not implement the required emulator interface")
  FieldError: type String has no field `msg`; String has no fields at all.
  Stacktrace:
   [1] getproperty(x::String, f::Symbol)
     @ Base Base_compiler.jl:58
   [2] top-level scope
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [3] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [4] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:667 [inlined]
   [7] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined]
[ Info: testing algorithm: RWMHSampling{GradFreeProtocol}

Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:646
  Got exception outside of a @test
  TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}}
  Stacktrace:
    [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool)
      @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
    [2] DataContainer(data::Matrix{Vector{Float64}})
      @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/DataContainers.jl:30
    [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String)
      @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822
    [4] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603
    [5] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined]
    [6] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined]
    [7] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}})
      @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined]
    [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::RWMHSampling{GradFreeProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool)
      @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260
    [9] top-level scope
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303
   [10] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [11] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined]
   [12] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined]
   [13] macro expansion
      @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:676 [inlined]
Completed tests for MarkovChainMonteCarlo, 167 seconds elapsed
Starting tests for Utilities
┌ Warning: For 2 parameters, the recommended minimum ensemble size (`N_ens`) is 20. Got `N_ens` = 10`.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/EnsembleKalmanProcess.jl:262
[ Info: extracting iterations 1:1 from EnsembleKalmanProcess
[ Info: extracting iterations [1, 2] from EnsembleKalmanProcess
[ Info: extracting iterations [1, 2] from EnsembleKalmanProcess
[ Info: extracting iterations [1, 2] from EnsembleKalmanProcess
[ Info: extracting iterations [1, 2] from EnsembleKalmanProcess
┌ Info: Detected fewer `samples_out` (1) than `samples_in` (2) and `dt` (2). Input-output structure vectors will be created from 1 samples.
│ This commonly occurs when samples are built from `get_u(ekp), get_g(ekp)`.
│ The final interation of output samples, (e.g., from evaluating `g=forward_map_ensemble(get_ϕ_final(ekp)`) can be provided by `encoder_kwargs_from(ekp, prior; final_samples_out=g)`
└ 
┌ Warning: Detected that observation covariances vary for different observations.
│ Encoder kwarg `:obs_noise_cov` will be set to the FIRST of these covariances for the purpose of data processing.
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:142
┌ Warning: Comparing equality of linear maps with size (3050, 3050) and (50, 50). Was this intended?
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:430
┌ Warning: Comparing equality of linear maps with size (3050, 3050) and (50, 50). Was this intended?
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:430
[ Info: Initialize encoding of data: "in" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.85
┌ Warning: Structure vectors do not contain key `:dt`. 
│  Continuing, assuming all vectors come from the prior `:dt=>0`.
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:109
┌ Info: Constructing a likelihood-informed subspace using, 
│  iterations:[1], 
└  α: [0] 
┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125
┌ Info: Structure vectors either not provided, else do not contain keys `:samples_in, :samples_out`. 
└  Continuing using input-output pairs as structure vectors
[ Info:     truncating at 2/10 retaining 97.07119036719803% of the information
[ Info: Initialize encoding of data: "out" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.85
┌ Warning: Structure vectors do not contain key `:dt`. 
│  Continuing, assuming all vectors come from the prior `:dt=>0`.
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:109
┌ Info: Constructing a likelihood-informed subspace using, 
│  iterations:[1], 
└  α: [0] 
┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125
┌ Info: Structure vectors either not provided, else do not contain keys `:samples_in, :samples_out`. 
└  Continuing using input-output pairs as structure vectors
┌ Warning: Using LikelihoodInformed on output data with α≠0 or with obs_noise_cov≠I triggers a manifold optimization process that may take some time. If α=0, consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:300
[ Info:       increasing k from 1 to 2
[ Info:       increasing k from 2 to 4
[ Info:       increasing k from 4 to 8
[ Info:       increasing k from 8 to 16
[ Info:       increasing k from 16 to 32
[ Info:     truncating at 32/50 retaining 98.07999426926906% of the KL divergence reduction
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: QuartileScaling
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: QuartileScaling
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: MinMaxScaling
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: MinMaxScaling
[ 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 120, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ 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 120, while the space dimension is 50, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=combined
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 120, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 120, while the space dimension is 50, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info: Initialize encoding of data: "in" with CanonicalCorrelation:
[ Info: Initialize encoding of data: "out" with CanonicalCorrelation:
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95
[ Info:     truncating at 6/10 retaining 96.3172690603363% of the variance of the structure matrix
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95
[ Info:     truncating at 25/50 retaining 95.4332051678535% of the variance of the structure matrix
[ Info: Initialize encoding of data: "in" with CanonicalCorrelation: retain_var=0.95
[ Info:     truncating at 9/10 retaining 95.02188092356373% of the variance in the joint space
[ Info: Initialize encoding of data: "out" with CanonicalCorrelation: retain_var=0.95
[ Info:     truncating at 9/9 retaining 100.0% of the variance in the joint space
[ Info: Initialize encoding of data: "in" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.99
┌ Info: Constructing a likelihood-informed subspace using, 
│  iterations:[1], 
└  α: [0.0] 
┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125
[ Info:     truncating at 4/10 retaining 99.76383264279497% of the information
[ Info: Initialize encoding of data: "out" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.99
┌ Info: Constructing a likelihood-informed subspace using, 
│  iterations:[1], 
└  α: [0.0] 
┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125
┌ Warning: Using LikelihoodInformed on output data with α≠0 or with obs_noise_cov≠I triggers a manifold optimization process that may take some time. If α=0, consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:300
[ Info:       increasing k from 1 to 2
[ Info:       increasing k from 2 to 4
[ Info:       increasing k from 4 to 8
[ Info:       increasing k from 8 to 16
[ Info:       increasing k from 16 to 32
[ Info:       increasing k from 32 to 49
[ Info:     any truncation loses <98.83862456443372% information, setting encoder = I
[ Info: Initialize encoding of data: "in" with LikelihoodInformed: iters=1:2, grad_type=localsl, retain_info=0.99
┌ Info: Constructing a likelihood-informed subspace using, 
│  iterations:1:2, 
└  α: [0.0, 0.5] 
┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125
[ Info:     truncating at 10/10 retaining 99.99999999999997% of the information
[ Info: Initialize encoding of data: "out" with LikelihoodInformed: iters=1:2, grad_type=localsl, retain_info=0.99
┌ Info: Constructing a likelihood-informed subspace using, 
│  iterations:1:2, 
└  α: [0.0, 0.5] 
┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125
┌ Warning: Using LikelihoodInformed on output data with α≠0 or with obs_noise_cov≠I triggers a manifold optimization process that may take some time. If α=0, consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed
└ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:300
[ Info:       increasing k from 1 to 2
[ Info:       increasing k from 2 to 4
[ Info:       increasing k from 4 to 8
[ Info:       increasing k from 8 to 16
[ Info:       increasing k from 16 to 32
[ Info:       increasing k from 32 to 49
[ Info:     truncating at 49/50 retaining 99.89543419573863% of the KL divergence reduction
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95
[ Info:     truncating at 6/10 retaining 96.3172690603363% of the variance of the structure matrix
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95
[ Info:     truncating at 25/50 retaining 95.4332051678535% of the variance of the structure matrix
[ Info: Initialize encoding of data: "in" with CanonicalCorrelation:
[ Info: Initialize encoding of data: "out" with CanonicalCorrelation:
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 120, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info:     truncating at 8/10 retaining 99.64145742343696% of the variance of the structure matrix
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 120, while the space dimension is 50, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info:     truncating at 34/50 retaining 99.08168482305003% of the variance of the structure matrix
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 120, while the space dimension is 8, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info:     truncating at 8/8 retaining 100.0% of the variance of the structure matrix
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99
┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. 
│  here # samples is 120, while the space dimension is 34, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info:     truncating at 34/34 retaining 100.0% of the variance of the structure matrix
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "in" with CanonicalCorrelation:
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=bad_value
[ 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 120, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info: Initialize encoding of data: "bad" with CanonicalCorrelation:
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling
[ Info: Initialize encoding of data: "in" with ElementwiseScaler: QuartileScaling
[ 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 150, while the space dimension is 10, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ 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 150, while the space dimension is 50, and representation will be inefficient.
└ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/HV5q2/src/Observations.jl:225
[ Info: Initialize encoding of data: "out" with ElementwiseScaler: MinMaxScaling
[ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat
[ Info: Initialize encoding of data: "in" with CanonicalCorrelation:

======================================================================================
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 2203 running 1 of 1

signal (10): User defined signal 1
_ZNK4llvm8CallBase13getArgOperandEj at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm23ObjectSizeOffsetVisitor13visitCallBaseERNS_8CallBaseE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm11InstVisitorINS_23ObjectSizeOffsetVisitorENS_10OffsetSpanEE5visitERNS_11InstructionE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm23ObjectSizeOffsetVisitor12computeValueEPNS_5ValueE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm23ObjectSizeOffsetVisitor11computeImplEPNS_5ValueE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm23ObjectSizeOffsetVisitor7computeEPNS_5ValueE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm13getObjectSizeEPKNS_5ValueERmRKNS_10DataLayoutEPKNS_17TargetLibraryInfoENS_14ObjectSizeOptsE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN12_GLOBAL__N_18DSEState11isOverwriteEPKN4llvm11InstructionES4_RKNS1_14MemoryLocationES7_RlS8_ at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN12_GLOBAL__N_18DSEState15getDomMemoryDefEPN4llvm9MemoryDefEPNS1_12MemoryAccessERKNS1_14MemoryLocationEPKNS1_5ValueERjSC_bSC_b at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZL19eliminateDeadStoresRN4llvm8FunctionERNS_9AAResultsERNS_9MemorySSAERNS_13DominatorTreeERNS_17PostDominatorTreeERKNS_17TargetLibraryInfoERKNS_8LoopInfoE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm7DSEPass3runERNS_8FunctionERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91:41
_ZN4llvm11PassManagerINS_8FunctionENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91:41
_ZN4llvm27ModuleToFunctionPassAdaptor3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91:41
_ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
run at /source/src/pipeline.cpp:990:12
operator() at /source/src/jitlayers.cpp:1461:17
operator() at /source/src/jitlayers.cpp:1599:12 [inlined]
optimizeModule at /source/src/jitlayers.cpp:2689:18
operator() at /source/src/jitlayers.cpp:1051:35 [inlined]
CallImpl<JLMaterializationUnit::materialize(std::unique_ptr<llvm::orc::MaterializationResponsibility>)::<lambda()> > at /source/usr/include/llvm/ADT/FunctionExtras.h:212:49
operator() at /source/usr/include/llvm/ADT/FunctionExtras.h:366:62 [inlined]
operator() at /source/src/objcache.cpp:340:24 [inlined]
get at /source/src/objcache.cpp:381:30
materialize at /source/src/jitlayers.cpp:1061:37
_ZN4llvm3orc19MaterializationTask3runEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
dispatch at /source/src/julia-task-dispatcher.h:377:13 [inlined]
dispatch at /source/src/julia-task-dispatcher.h:366:6
_ZN4llvm3orc16ExecutionSession12dispatchTaskESt10unique_ptrINS0_4TaskESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession22dispatchOutstandingMUsEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
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_ZN4llvm3orc25InProgressFullLookupState8completeESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession19OL_applyQueryPhase1ESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EENS_5ErrorE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
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publishCIs at /source/src/jitlayers.cpp:2247:14
jl_compile_codeinst_impl at /source/src/jitlayers.cpp:521:39
jl_compile_method_very_internal at /source/src/gf.c:4105:27
_jl_invoke at /source/src/gf.c:4582:16 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
* at ./operators.jl:668:0 [inlined]
_encode_structure_matrix at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/canonical_correlation.jl:182:0 (pc: 20)
unknown function (ip: 0x7b824d63f051) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
#49 at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:730:0 (pc: 86)
iterate at ./generator.jl:49:0 [inlined]
Dict at ./dict.jl:93:0 (pc: 177)
unknown function (ip: 0x7b8327634ad4) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
#initialize_and_encode_with_schedule!#44 at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:730:0 (pc: 727)
initialize_and_encode_with_schedule! at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:661:0 (pc: 53)
unknown function (ip: 0x7b8252995fbd) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:259:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:829:21
eval_body at /source/src/interpreter.c:704:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21
ijl_eval_thunk at /source/src/toplevel.c:772:18
jl_toplevel_eval_flex at /source/src/toplevel.c:716:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15
jl_toplevel_eval_flex at /source/src/toplevel.c:688:27
ijl_toplevel_eval at /source/src/toplevel.c:786:12
ijl_toplevel_eval_in at /source/src/toplevel.c:831:13
eval at ./boot.jl:618:0 (pc: 1)
include_string at ./loading.jl:3258:0 (pc: 140)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
_include at ./loading.jl:3320:0 (pc: 123)
include at ./Base.jl:335:0 (pc: 1)
IncludeInto at ./Base.jl:336:0 [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: 0x7b8321506fc2) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:259:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:829:21
eval_body at /source/src/interpreter.c:704:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21
ijl_eval_thunk at /source/src/toplevel.c:772:18
jl_toplevel_eval_flex at /source/src/toplevel.c:716:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15
jl_toplevel_eval_flex at /source/src/toplevel.c:688:27
ijl_toplevel_eval at /source/src/toplevel.c:786:12
ijl_toplevel_eval_in at /source/src/toplevel.c:831:13
eval at ./boot.jl:618:0 (pc: 1)
include_string at ./loading.jl:3258:0 (pc: 140)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
_include at ./loading.jl:3320:0 (pc: 123)
include at ./Base.jl:335:0 (pc: 1)
IncludeInto at ./Base.jl:336:0 (pc: 2)
jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:259:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:829:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21
ijl_eval_thunk at /source/src/toplevel.c:772:18
jl_toplevel_eval_flex at /source/src/toplevel.c:716:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15
jl_toplevel_eval_flex at /source/src/toplevel.c:688:27
ijl_toplevel_eval at /source/src/toplevel.c:786:12
ijl_toplevel_eval_in at /source/src/toplevel.c:831:13
eval at ./boot.jl:618:0 (pc: 1)
__script_entry_eval at ./client.jl:106:0 [inlined]
exec_options at ./client.jl:350:0 (pc: 426)
_start at ./client.jl:695:0 (pc: 217)
jfptr__start_0.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
true_main at /source/src/jlapi.c:989:29
jl_repl_entrypoint at /source/src/jlapi.c:1156:15
main at /source/cli/loader_exe.c:117:15
unknown function (ip: 0x7b833f699249) 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)

#= 1085.5 ms =# precompile(Tuple{typeof(Base.:(*)), Array{Float64, 2}, LinearMaps.CompositeMap{Float64, Tuple{LinearMaps.TransposeMap{Float64, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##4#initialize_processor!##5"{Array{Any, 1}, Array{Any, 1}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##6#initialize_processor!##7"{Array{Any, 1}, Array{Any, 1}}, false}}, LinearMaps.TransposeMap{Float64, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##4#initialize_processor!##5"{Array{Any, 1}, Array{Any, 1}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##6#initialize_processor!##7"{Array{Any, 1}, Array{Any, 1}}, false}}, LinearMaps.TransposeMap{Float64, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##26#initialize_processor!##27"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.QuartileScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##28#initialize_processor!##29"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.QuartileScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, false}}, LinearMaps.TransposeMap{Float64, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##26#initialize_processor!##27"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.ZScoreScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##28#initialize_processor!##29"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.ZScoreScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, false}}, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#21#22"{Array{Any, 1}, Array{Any, 1}, Array{Any, 1}, Array{Any, 1}, Array{Any, 1}}, CalibrateEmulateSample.Utilities.var"#25#26"{Array{Any, 1}, Array{Any, 1}, Array{Any, 1}, Array{Any, 1}, Array{Any, 1}}, false}, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##26#initialize_processor!##27"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.ZScoreScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##28#initialize_processor!##29"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.ZScoreScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, false}, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##26#initialize_processor!##27"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.QuartileScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##28#initialize_processor!##29"{CalibrateEmulateSample.Utilities.ElementwiseScaler{CalibrateEmulateSample.Utilities.QuartileScaling, Array{Float64, 1}, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T, Array{T, 1} where T}}, false}, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##4#initialize_processor!##5"{Array{Any, 1}, Array{Any, 1}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##6#initialize_processor!##7"{Array{Any, 1}, Array{Any, 1}}, false}, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##4#initialize_processor!##5"{Array{Any, 1}, Array{Any, 1}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##6#initialize_processor!##7"{Array{Any, 1}, Array{Any, 1}}, false}}}})
#=  111.0 ms =# precompile(Tuple{typeof(LinearMaps.check_dim_mul), LinearMaps.WrappedMap{Float64, Array{Float64, 2}}, LinearMaps.FunctionMap{Float64, CalibrateEmulateSample.Utilities.var"#initialize_processor!##4#initialize_processor!##5"{Array{Any, 1}, Array{Any, 1}}, CalibrateEmulateSample.Utilities.var"#initialize_processor!##6#initialize_processor!##7"{Array{Any, 1}, Array{Any, 1}}, false}})

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


======================================================================================
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:573:34
wait at ./task.jl:1652:0 (pc: 108)
wait_forever at ./task.jl:1528:0 (pc: 4)
jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
start_task at /source/src/task.c:1278:23
unknown function (ip: (nil)) at (unknown file)


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

Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x0000702b30f5f190 Total snapshots: 363. Utilization: 0%
   ╎363 @Base/task.jl:1528  wait_forever()
362╎ 363 @Base/task.jl:?  wait()


[1] signal 15: Terminated
in expression starting at /PkgEval.jl/scripts/evaluate.jl:214
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:573:34
wait at ./task.jl:1652:0 (pc: 108)

[2203] signal 15: Terminated
in expression starting at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/Utilities/runtests.jl:109
_ZL20MemOperandsHaveAliasRKN4llvm16MachineFrameInfoEPNS_14BatchAAResultsEbPKNS_17MachineMemOperandES7_ at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZNK4llvm12MachineInstr8mayAliasEPNS_14BatchAAResultsERKS0_b at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm17ScheduleDAGInstrs20addChainDependenciesEPNS_5SUnitERNS0_12Value2SUsMapE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm17ScheduleDAGInstrs15buildSchedGraphEPNS_9AAResultsEPNS_18RegPressureTrackerEPNS_13PressureDiffsEPNS_13LiveIntervalsEb at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm17ScheduleDAGMILive23buildDAGWithRegPressureEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm17ScheduleDAGMILive8scheduleEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm11impl_detail20MachineSchedulerBase15scheduleRegionsERNS_17ScheduleDAGInstrsEb at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm11impl_detail20MachineSchedulerImpl3runERNS_15MachineFunctionERKNS_13TargetMachineERKNS1_16RequiredAnalysesE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN12_GLOBAL__N_122MachineSchedulerLegacy20runOnMachineFunctionERN4llvm15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm19MachineFunctionPass13runOnFunctionERNS_8FunctionE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm13FPPassManager13runOnFunctionERNS_8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm13FPPassManager11runOnModuleERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm6legacy15PassManagerImpl3runERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
operator() at /source/src/jitlayers.cpp:1578:23
compileModule at /source/src/jitlayers.cpp:2702:79
operator() at /source/src/jitlayers.cpp:1052:53 [inlined]
CallImpl<JLMaterializationUnit::materialize(std::unique_ptr<llvm::orc::MaterializationResponsibility>)::<lambda()> > at /source/usr/include/llvm/ADT/FunctionExtras.h:212:49
operator() at /source/usr/include/llvm/ADT/FunctionExtras.h:366:62 [inlined]
operator() at /source/src/objcache.cpp:340:24 [inlined]
get at /source/src/objcache.cpp:381:30
materialize at /source/src/jitlayers.cpp:1061:37
_ZN4llvm3orc19MaterializationTask3runEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
dispatch at /source/src/julia-task-dispatcher.h:377:13 [inlined]
dispatch at /source/src/julia-task-dispatcher.h:366:6
_ZN4llvm3orc16ExecutionSession12dispatchTaskESt10unique_ptrINS0_4TaskESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession22dispatchOutstandingMUsEv at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (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.22.1jl (unknown line)
_ZN4llvm3orc25InProgressFullLookupState8completeESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (unknown line)
_ZN4llvm3orc16ExecutionSession19OL_applyQueryPhase1ESt10unique_ptrINS0_21InProgressLookupStateESt14default_deleteIS3_EENS_5ErrorE at /opt/julia/bin/../lib/julia/libLLVM.so.22.1jl (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.22.1jl (unknown line)
publishCIs at /source/src/jitlayers.cpp:2247:14
jl_compile_codeinst_impl at /source/src/jitlayers.cpp:521:39
jl_compile_method_very_internal at /source/src/gf.c:4105:27
_jl_invoke at /source/src/gf.c:4582:16 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
* at ./operators.jl:668:0 [inlined]
_decode_structure_matrix at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/elementwise_scaler.jl:269:0 (pc: 4)
unknown function (ip: 0x7b824d6a8b76) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
decode_with_schedule at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:867:0 (pc: 167)
unknown function (ip: 0x7b824d64c319) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:259:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:829:21
eval_body at /source/src/interpreter.c:704:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21
ijl_eval_thunk at /source/src/toplevel.c:772:18
jl_toplevel_eval_flex at /source/src/toplevel.c:716:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15
jl_toplevel_eval_flex at /source/src/toplevel.c:688:27
ijl_toplevel_eval at /source/src/toplevel.c:786:12
ijl_toplevel_eval_in at /source/src/toplevel.c:831:13
eval at ./boot.jl:618:0 (pc: 1)
include_string at ./loading.jl:3258:0 (pc: 140)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
_include at ./loading.jl:3320:0 (pc: 123)
include at ./Base.jl:335:0 (pc: 1)
IncludeInto at ./Base.jl:336:0 [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: 0x7b8321506fc2) at (unknown file)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:259:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:829:21
eval_body at /source/src/interpreter.c:704:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
eval_body at /source/src/interpreter.c:712:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21
ijl_eval_thunk at /source/src/toplevel.c:772:18
jl_toplevel_eval_flex at /source/src/toplevel.c:716:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15
jl_toplevel_eval_flex at /source/src/toplevel.c:688:27
ijl_toplevel_eval at /source/src/toplevel.c:786:12
ijl_toplevel_eval_in at /source/src/toplevel.c:831:13
eval at ./boot.jl:618:0 (pc: 1)
include_string at ./loading.jl:3258:0 (pc: 140)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
_include at ./loading.jl:3320:0 (pc: 123)
include at ./Base.jl:335:0 (pc: 1)
IncludeInto at ./Base.jl:336:0 (pc: 2)
jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
do_call at /source/src/interpreter.c:123:26
eval_value at /source/src/interpreter.c:259:16
eval_stmt_value at /source/src/interpreter.c:194:23 [inlined]
eval_body at /source/src/interpreter.c:829:21
jl_interpret_toplevel_thunk at /source/src/interpreter.c:1052:21
ijl_eval_thunk at /source/src/toplevel.c:772:18
jl_toplevel_eval_flex at /source/src/toplevel.c:716:26
jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15
jl_toplevel_eval_flex at /source/src/toplevel.c:688:27
ijl_toplevel_eval at /source/src/toplevel.c:786:12
ijl_toplevel_eval_in at /source/src/toplevel.c:831:13
eval at ./boot.jl:618:0 (pc: 1)
__script_entry_eval at ./client.jl:106:0 [inlined]
exec_options at ./client.jl:350:0 (pc: 426)
_start at ./client.jl:695:0 (pc: 217)
jfptr__start_0.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4590:23 [inlined]
ijl_apply_generic at /source/src/gf.c:4838:12
jl_apply at /source/src/julia.h:2533:12 [inlined]
true_main at /source/src/jlapi.c:989:29
jl_repl_entrypoint at /source/src/jlapi.c:1156:15
main at /source/cli/loader_exe.c:117:15
unknown function (ip: 0x7b833f699249) 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)
Allocations: 526430633 (Pool: 526425866; Big: 4767); GC: 4645
wait_safe_interrupt at ./park.jl:231:0 (pc: 6)
#wait#428 at ./condition.jl:390:0 (pc: 117)
wait at ./condition.jl:325:0 [inlined]
_trywait at ./asyncevent.jl:204:0 (pc: 38)
#_trywait#722 at ./asyncevent.jl:175:0 [inlined]
_trywait at ./asyncevent.jl:175:0 [inlined]
profile_printing_listener at ./Base.jl:366:0 (pc: 23)
#start_profile_listener##0 at ./Base.jl:386:0 (pc: 2)
jfptr_YY.start_profile_listenerYY.YY.0_0.1 at /opt/julia/lib/julia/sys.so (unknown line)
start_task at /source/src/task.c:1275:23
unknown function (ip: (nil)) at (unknown file)
Allocations: 27775052 (Pool: 27773707; Big: 1345); GC: 46

PkgEval terminated after 2730.59s: test duration exceeded the time limit