Package evaluation of CalibrateEmulateSample on Julia 1.12.0-DEV.2153 (83dcbd426d*) started at 2025-03-31T12:45:29.143


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.16s


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# Installation
#

Installing CalibrateEmulateSample...
   Resolving package versions...
   Installed Conda ────────────────── v1.10.2
   Installed PyCall ───────────────── v1.96.4
   Installed CalibrateEmulateSample ─ v0.6.1
    Updating `~/.julia/environments/v1.12/Project.toml`
  [95e48a1f] + CalibrateEmulateSample v0.6.1
    Updating `~/.julia/environments/v1.12/Manifest.toml`
  [47edcb42] + ADTypes v1.14.0
  [14f7f29c] + AMD v0.5.3
  [621f4979] + AbstractFFTs v1.5.0
⌅ [80f14c24] + AbstractMCMC v4.4.2
  [1520ce14] + AbstractTrees v0.4.5
  [79e6a3ab] + Adapt v4.3.0
⌃ [5b7e9947] + AdvancedMH v0.7.5
  [66dad0bd] + AliasTables v1.1.3
  [dce04be8] + ArgCheck v2.5.0
  [7d9fca2a] + Arpack v0.5.4
  [4fba245c] + ArrayInterface v7.18.0
  [13072b0f] + AxisAlgorithms v1.1.0
  [39de3d68] + AxisArrays v0.4.7
⌅ [198e06fe] + BangBang v0.3.40
  [9718e550] + Baselet v0.1.1
  [6e4b80f9] + BenchmarkTools v1.6.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.6
  [95e48a1f] + CalibrateEmulateSample v0.6.1
  [d360d2e6] + ChainRulesCore v1.25.1
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [523fee87] + CodecBzip2 v0.8.5
  [944b1d66] + CodecZlib v0.7.8
  [bbf7d656] + CommonSubexpressions v0.3.1
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.16.0
  [a33af91c] + CompositionsBase v0.1.2
  [8f4d0f93] + Conda v1.10.2
  [88cd18e8] + ConsoleProgressMonitor v0.1.2
  [187b0558] + ConstructionBase v1.5.8
  [f65535da] + Convex v0.16.4
  [adafc99b] + CpuId v0.3.1
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [a93c6f00] + DataFrames v1.7.0
  [864edb3b] + DataStructures v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [244e2a9f] + DefineSingletons v0.1.2
  [163ba53b] + DiffResults v1.1.0
  [b552c78f] + DiffRules v1.15.1
  [a0c0ee7d] + DifferentiationInterface v0.6.49
  [b4f34e82] + Distances v0.10.12
  [31c24e10] + Distributions v0.25.118
  [ffbed154] + DocStringExtensions v0.9.4
  [fdbdab4c] + ElasticArrays v1.2.12
  [2904ab23] + ElasticPDMats v0.2.3
  [aa8a2aa5] + EnsembleKalmanProcesses v2.3.1
  [c87230d0] + FFMPEG v0.4.2
  [7a1cc6ca] + FFTW v1.8.1
⌅ [442a2c76] + FastGaussQuadrature v0.4.9
  [1a297f60] + FillArrays v1.13.0
  [6a86dc24] + FiniteDiff v2.27.0
  [59287772] + Formatting v0.4.3
⌅ [f6369f11] + ForwardDiff v0.10.38
  [891a1506] + GaussianProcesses v0.12.5
⌃ [e4b2fa32] + GaussianRandomFields v2.1.6
  [3e5b6fbb] + HostCPUFeatures v0.1.17
  [615f187c] + IfElse v0.1.1
  [22cec73e] + InitialValues v0.3.1
  [842dd82b] + InlineStrings v1.4.3
  [a98d9a8b] + Interpolations v0.15.1
  [8197267c] + IntervalSets v0.7.10
  [3587e190] + InverseFunctions v0.1.17
  [41ab1584] + InvertedIndices v1.3.1
⌅ [92d709cd] + IrrationalConstants v0.1.1
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.0
  [682c06a0] + JSON v0.21.4
  [0f8b85d8] + JSON3 v1.14.2
  [5ab0869b] + KernelDensity v0.6.9
  [40e66cde] + LDLFactorizations v0.10.1
  [b964fa9f] + LaTeXStrings v1.4.0
  [10f19ff3] + LayoutPointers v0.1.17
  [1d6d02ad] + LeftChildRightSiblingTrees v0.2.0
  [d3d80556] + LineSearches v7.3.0
  [6fdf6af0] + LogDensityProblems v2.1.2
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e6f89c97] + LoggingExtras v1.1.0
  [bdcacae8] + LoopVectorization v0.12.172
⌃ [c7f686f2] + MCMCChains v5.7.1
⌅ [be115224] + MCMCDiagnosticTools v0.2.1
  [e80e1ace] + MLJModelInterface v1.11.0
  [1914dd2f] + MacroTools v0.5.15
  [d125e4d3] + ManualMemory v0.1.8
  [b8f27783] + MathOptInterface v1.38.0
⌅ [128add7d] + MicroCollections v0.1.4
  [e1d29d7a] + Missings v1.2.0
  [d8a4904e] + MutableArithmetics v1.6.4
  [d41bc354] + NLSolversBase v7.9.0
  [77ba4419] + NaNMath v1.1.2
  [c020b1a1] + NaturalSort v1.0.0
  [6fe1bfb0] + OffsetArrays v1.16.0
  [429524aa] + Optim v1.11.0
  [bac558e1] + OrderedCollections v1.8.0
  [90014a1f] + PDMats v0.11.33
  [d96e819e] + Parameters v0.12.3
  [69de0a69] + Parsers v2.8.1
  [1d0040c9] + PolyesterWeave v0.2.2
  [2dfb63ee] + PooledArrays v1.4.3
  [85a6dd25] + PositiveFactorizations v0.2.4
  [aea7be01] + PrecompileTools v1.3.0
  [21216c6a] + Preferences v1.4.3
  [08abe8d2] + PrettyTables v2.4.0
  [49802e3a] + ProgressBars v1.5.1
  [33c8b6b6] + ProgressLogging v0.1.4
  [92933f4c] + ProgressMeter v1.10.2
  [43287f4e] + PtrArrays v1.3.0
  [438e738f] + PyCall v1.96.4
  [1fd47b50] + QuadGK v2.11.2
  [36c3bae2] + RandomFeatures v0.3.4
  [b3c3ace0] + RangeArrays v0.3.2
  [c84ed2f1] + Ratios v0.4.5
  [3cdcf5f2] + RecipesBase v1.3.4
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
⌅ [79098fc4] + Rmath v0.7.1
  [c946c3f1] + SCS v2.1.0
  [94e857df] + SIMDTypes v0.1.0
  [476501e8] + SLEEFPirates v0.6.43
  [30f210dd] + ScientificTypesBase v3.0.0
  [3646fa90] + ScikitLearn v0.7.0
  [6e75b9c4] + ScikitLearnBase v0.5.0
  [91c51154] + SentinelArrays v1.4.8
  [efcf1570] + Setfield v1.1.2
  [a2af1166] + SortingAlgorithms v1.2.1
  [276daf66] + SpecialFunctions v2.5.0
  [171d559e] + SplittablesBase v0.1.15
  [860ef19b] + StableRNGs v1.0.2
  [aedffcd0] + Static v1.2.0
  [0d7ed370] + StaticArrayInterface v1.8.0
  [90137ffa] + StaticArrays v1.9.13
  [1e83bf80] + StaticArraysCore v1.4.3
  [64bff920] + StatisticalTraits v3.4.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.0
⌅ [2913bbd2] + StatsBase v0.33.21
⌅ [4c63d2b9] + StatsFuns v0.9.18
  [892a3eda] + StringManipulation v0.4.1
  [856f2bd8] + StructTypes v1.11.0
  [9449cd9e] + TSVD v0.4.4
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.0
  [5d786b92] + TerminalLoggers v0.1.7
  [8290d209] + ThreadingUtilities v0.5.2
  [3bb67fe8] + TranscodingStreams v0.11.3
⌃ [28d57a85] + Transducers v0.4.80
  [bc48ee85] + Tullio v0.3.8
  [3a884ed6] + UnPack v1.0.2
  [3d5dd08c] + VectorizationBase v0.21.71
  [81def892] + VersionParsing v1.3.0
  [efce3f68] + WoodburyMatrices v1.0.0
⌅ [68821587] + Arpack_jll v3.5.1+1
  [6e34b625] + Bzip2_jll v1.0.9+0
  [83423d85] + Cairo_jll v1.18.4+0
  [2e619515] + Expat_jll v2.6.5+0
⌅ [b22a6f82] + FFMPEG_jll v4.4.4+1
  [f5851436] + FFTW_jll v3.3.10+3
  [a3f928ae] + Fontconfig_jll v2.15.0+0
  [d7e528f0] + FreeType2_jll v2.13.4+0
  [559328eb] + FriBidi_jll v1.0.16+0
  [78b55507] + Gettext_jll v0.21.0+0
⌃ [7746bdde] + Glib_jll v2.82.4+0
  [3b182d85] + Graphite2_jll v1.3.14+1
  [2e76f6c2] + HarfBuzz_jll v8.5.0+0
  [1d5cc7b8] + IntelOpenMP_jll v2025.0.4+0
  [c1c5ebd0] + LAME_jll v3.100.2+0
  [1d63c593] + LLVMOpenMP_jll v18.1.7+0
  [dd4b983a] + LZO_jll v2.10.3+0
⌅ [e9f186c6] + Libffi_jll v3.2.2+2
  [d4300ac3] + Libgcrypt_jll v1.11.0+0
  [7add5ba3] + Libgpg_error_jll v1.51.1+0
  [94ce4f54] + Libiconv_jll v1.18.0+0
  [4b2f31a3] + Libmount_jll v2.40.3+0
  [38a345b3] + Libuuid_jll v2.40.3+0
  [856f044c] + MKL_jll v2025.0.1+1
  [e7412a2a] + Ogg_jll v1.3.5+1
  [656ef2d0] + OpenBLAS32_jll v0.3.29+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [91d4177d] + Opus_jll v1.3.3+0
  [30392449] + Pixman_jll v0.44.2+0
⌅ [f50d1b31] + Rmath_jll v0.4.3+0
  [f4f2fc5b] + SCS_jll v3.2.7+0
  [02c8fc9c] + XML2_jll v2.13.6+1
  [aed1982a] + XSLT_jll v1.1.42+0
  [4f6342f7] + Xorg_libX11_jll v1.8.6+3
  [0c0b7dd1] + Xorg_libXau_jll v1.0.12+0
  [a3789734] + Xorg_libXdmcp_jll v1.1.5+0
  [1082639a] + Xorg_libXext_jll v1.3.6+3
  [ea2f1a96] + Xorg_libXrender_jll v0.9.11+1
  [14d82f49] + Xorg_libpthread_stubs_jll v0.1.2+0
  [c7cfdc94] + Xorg_libxcb_jll v1.17.0+3
  [c5fb5394] + Xorg_xtrans_jll v1.5.1+0
  [a4ae2306] + libaom_jll v3.11.0+0
  [0ac62f75] + libass_jll v0.15.2+0
  [f638f0a6] + libfdk_aac_jll v2.0.3+0
  [b53b4c65] + libpng_jll v1.6.47+0
  [f27f6e37] + libvorbis_jll v1.3.7+2
  [1317d2d5] + oneTBB_jll v2022.0.0+0
⌅ [1270edf5] + x264_jll v2021.5.5+0
⌅ [dfaa095f] + x265_jll v3.5.0+0
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
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  [7b1f6079] + FileWatching v1.11.0
  [9fa8497b] + Future v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [4af54fe1] + LazyArtifacts v1.11.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.12.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
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  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
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  [f489334b] + StyledStrings v1.11.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] + LibCURL_jll v8.11.1+1
  [e37daf67] + LibGit2_jll v1.9.0+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2024.12.31
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [05823500] + OpenLibm_jll v0.8.5+0
  [458c3c95] + OpenSSL_jll v3.0.16+0
  [efcefdf7] + PCRE2_jll v10.44.0+1
  [bea87d4a] + SuiteSparse_jll v7.8.3+2
  [83775a58] + Zlib_jll v1.3.1+2
  [8e850b90] + libblastrampoline_jll v5.12.0+0
  [8e850ede] + nghttp2_jll v1.64.0+1
  [3f19e933] + p7zip_jll v17.5.0+2
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
    Building Conda ─────────────────→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b19db3927f0db4151cb86d073689f2428e524576/build.log`
    Building PyCall ────────────────→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log`
    Building CalibrateEmulateSample → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b128c35dc0bc7c14a4595c0d2bf18dfeae33daa2/build.log`

Installation completed after 116.68s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 2044.2s


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# Testing
#

     Testing CalibrateEmulateSample
      Status `/tmp/jl_qziQC9/Project.toml`
⌅ [80f14c24] AbstractMCMC v4.4.2
⌃ [5b7e9947] AdvancedMH v0.7.5
  [95e48a1f] CalibrateEmulateSample v0.6.1
  [8f4d0f93] Conda v1.10.2
  [31c24e10] Distributions v0.25.118
  [ffbed154] DocStringExtensions v0.9.4
  [aa8a2aa5] EnsembleKalmanProcesses v2.3.1
  [891a1506] GaussianProcesses v0.12.5
⌃ [c7f686f2] MCMCChains v5.7.1
  [49802e3a] ProgressBars v1.5.1
  [438e738f] PyCall v1.96.4
  [36c3bae2] RandomFeatures v0.3.4
  [3646fa90] ScikitLearn v0.7.0
  [860ef19b] StableRNGs v1.0.2
  [10745b16] Statistics v1.11.1
⌅ [2913bbd2] StatsBase v0.33.21
  [37e2e46d] LinearAlgebra v1.12.0
  [44cfe95a] Pkg v1.12.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_qziQC9/Manifest.toml`
  [47edcb42] ADTypes v1.14.0
  [14f7f29c] AMD v0.5.3
  [621f4979] AbstractFFTs v1.5.0
⌅ [80f14c24] AbstractMCMC v4.4.2
  [1520ce14] AbstractTrees v0.4.5
  [79e6a3ab] Adapt v4.3.0
⌃ [5b7e9947] AdvancedMH v0.7.5
  [66dad0bd] AliasTables v1.1.3
  [dce04be8] ArgCheck v2.5.0
  [7d9fca2a] Arpack v0.5.4
  [4fba245c] ArrayInterface v7.18.0
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.7
⌅ [198e06fe] BangBang v0.3.40
  [9718e550] Baselet v0.1.1
  [6e4b80f9] BenchmarkTools v1.6.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.6
  [95e48a1f] CalibrateEmulateSample v0.6.1
  [d360d2e6] ChainRulesCore v1.25.1
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [523fee87] CodecBzip2 v0.8.5
  [944b1d66] CodecZlib v0.7.8
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.16.0
  [a33af91c] CompositionsBase v0.1.2
  [8f4d0f93] Conda v1.10.2
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.5.8
  [f65535da] Convex v0.16.4
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.7.0
  [864edb3b] DataStructures v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [244e2a9f] DefineSingletons v0.1.2
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [a0c0ee7d] DifferentiationInterface v0.6.49
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.118
  [ffbed154] DocStringExtensions v0.9.4
  [fdbdab4c] ElasticArrays v1.2.12
  [2904ab23] ElasticPDMats v0.2.3
  [aa8a2aa5] EnsembleKalmanProcesses v2.3.1
  [c87230d0] FFMPEG v0.4.2
  [7a1cc6ca] FFTW v1.8.1
⌅ [442a2c76] FastGaussQuadrature v0.4.9
  [1a297f60] FillArrays v1.13.0
  [6a86dc24] FiniteDiff v2.27.0
  [59287772] Formatting v0.4.3
⌅ [f6369f11] ForwardDiff v0.10.38
  [891a1506] GaussianProcesses v0.12.5
⌃ [e4b2fa32] GaussianRandomFields v2.1.6
  [3e5b6fbb] HostCPUFeatures v0.1.17
  [615f187c] IfElse v0.1.1
  [22cec73e] InitialValues v0.3.1
  [842dd82b] InlineStrings v1.4.3
  [a98d9a8b] Interpolations v0.15.1
  [8197267c] IntervalSets v0.7.10
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
⌅ [92d709cd] IrrationalConstants v0.1.1
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.7.0
  [682c06a0] JSON v0.21.4
  [0f8b85d8] JSON3 v1.14.2
  [5ab0869b] KernelDensity v0.6.9
  [40e66cde] LDLFactorizations v0.10.1
  [b964fa9f] LaTeXStrings v1.4.0
  [10f19ff3] LayoutPointers v0.1.17
  [1d6d02ad] LeftChildRightSiblingTrees v0.2.0
  [d3d80556] LineSearches v7.3.0
  [6fdf6af0] LogDensityProblems v2.1.2
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e6f89c97] LoggingExtras v1.1.0
  [bdcacae8] LoopVectorization v0.12.172
⌃ [c7f686f2] MCMCChains v5.7.1
⌅ [be115224] MCMCDiagnosticTools v0.2.1
  [e80e1ace] MLJModelInterface v1.11.0
  [1914dd2f] MacroTools v0.5.15
  [d125e4d3] ManualMemory v0.1.8
  [b8f27783] MathOptInterface v1.38.0
⌅ [128add7d] MicroCollections v0.1.4
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        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
Starting tests for Emulator
[ Info: fit successful
WARNING: llvmcall with integer pointers is deprecated.
Use actual pointers instead, replacing i32 or i64 with i8* or ptr
in initialize_task(Any) at /home/pkgeval/.julia/packages/ThreadingUtilities/3z3g0/src/ThreadingUtilities.jl
SVD truncated at k: 4/6
[ Info: reducing input dimension from 10 to rank(input_cov) during low rank in normalization
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
SVD truncated at k: 3/6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 2
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 3
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 4
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 5
kernel in GaussianProcess:
Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}}
  Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0]  Type: GaussianProcesses.Noise{Float64}, Params: [0.0]
created GP: 6
Completed tests for Emulator, 97 seconds elapsed
Starting tests for GaussianProcess
┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov.
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120
Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: SumKernel{SEIso{Float64}, Noise{Float64}}
  Type: SEIso{Float64}, Params: [0.0, 0.0]  Type: Noise{Float64}, Params: [0.0]
created GP: 1
┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov.
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120
┌ Warning: GaussianProcess already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/GaussianProcess.jl:117
optimized hyperparameters of GP: 1
Type: SumKernel{SEIso{Float64}, Noise{Float64}}
  Type: SEIso{Float64}, Params: [0.4671112501723779, -0.11637219097092133]  Type: Noise{Float64}, Params: [-2.7795647959619263]
┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov.
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120
Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: SumKernel{SEIso{Float64}, Noise{Float64}}
  Type: SEIso{Float64}, Params: [0.0, 0.0]  Type: Noise{Float64}, Params: [0.0]
created GP: 1
optimized hyperparameters of GP: 1
Type: SumKernel{SEIso{Float64}, Noise{Float64}}
  Type: SEIso{Float64}, Params: [0.467111250159053, -0.11637219100329507]  Type: Noise{Float64}, Params: [-2.912614529741828]
┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov.
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120
Using user-defined kernelPyObject 1**2 * RBF(length_scale=1)
Learning additive white noise
[ Info: Training kernel 1, 
[ Info: PyObject 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1)
┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov.
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120
┌ Warning: GaussianProcess already built. skipping...
└ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/GaussianProcess.jl:236
SKlearn, already trained. continuing...
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0]
Learning additive white noise
kernel in GaussianProcess:
Type: SumKernel{SEArd{Float64}, Noise{Float64}}
  Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0]  Type: Noise{Float64}, Params: [0.0]
created GP: 1
kernel in GaussianProcess:
Type: SumKernel{SEArd{Float64}, Noise{Float64}}
  Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0]  Type: Noise{Float64}, Params: [0.0]
created GP: 2
Using default squared exponential kernel, learning length scale and variance parameters
Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0]
kernel in GaussianProcess:
Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0]
created GP: 1
kernel in GaussianProcess:
Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0]
created GP: 2
optimized hyperparameters of GP: 1
Type: SumKernel{SEArd{Float64}, Noise{Float64}}
  Type: SEArd{Float64}, Params: [-0.034010033457988934, 2.6947372695174536, 1.9374032687140703]  Type: Noise{Float64}, Params: [-0.19545576083347674]
optimized hyperparameters of GP: 2
Type: SumKernel{SEArd{Float64}, Noise{Float64}}
  Type: SEArd{Float64}, Params: [2.040064043166128, -0.263116528583071, 2.0697093362932244]  Type: Noise{Float64}, Params: [-0.08245764941529067]
optimized hyperparameters of GP: 1
Type: SEArd{Float64}, Params: [-0.070755506795137, 2.7805790822912098, 1.879857339393294]
optimized hyperparameters of GP: 2
Type: SEArd{Float64}, Params: [2.0918473623519653, -0.15767169342096962, 2.145456577252389]
Completed tests for GaussianProcess, 30 seconds elapsed
Starting tests for RandomFeature
┌ Info: Shrinkage scale: 1.0, (0 = none, 1 = revert to scaled Identity)
└  shrinkage covariance condition number: 1.0
[ Info: NICE-adjusted covariance condition number: 4.7972631296280595
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 40, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10)
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 70, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10)
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 90, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0)
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 100, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0)
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 20, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 0, "n_iteration" => 10)
[ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 30, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 0, "n_iteration" => 10, "tikhonov" => 0)
[ Info: hyperparameter learning for 1 models using 50 training points, 50 validation points and 100 features
estimate cov with 520 iterations...

[918] signal 11 (1): Segmentation fault
in expression starting at /home/pkgeval/.julia/packages/CalibrateEmulateSample/wGWPe/test/RandomFeature/runtests.jl:14
_PyInterpreterState_GET at /usr/local/src/conda/python-3.12.9/Include/internal/pycore_pystate.h:133 [inlined]
dict_dealloc at /usr/local/src/conda/python-3.12.9/Objects/dictobject.c:2353
_Py_Dealloc at /usr/local/src/conda/python-3.12.9/Objects/object.c:2640 [inlined]
Py_DECREF at /usr/local/src/conda/python-3.12.9/Include/object.h:705 [inlined]
Py_XDECREF at /usr/local/src/conda/python-3.12.9/Include/object.h:798 [inlined]
subtype_dealloc at /usr/local/src/conda/python-3.12.9/Objects/typeobject.c:2026
_Py_Dealloc at /usr/local/src/conda/python-3.12.9/Objects/object.c:2640 [inlined]
Py_DECREF at /usr/local/src/conda/python-3.12.9/Include/object.h:705 [inlined]
Py_XDECREF at /usr/local/src/conda/python-3.12.9/Include/object.h:798 [inlined]
_PyObject_FreeInstanceAttributes at /usr/local/src/conda/python-3.12.9/Objects/dictobject.c:5576 [inlined]
subtype_dealloc at /usr/local/src/conda/python-3.12.9/Objects/typeobject.c:2023
pydecref_ at /home/pkgeval/.julia/packages/PyCall/1gn3u/src/PyCall.jl:118 [inlined]
pydecref at /home/pkgeval/.julia/packages/PyCall/1gn3u/src/PyCall.jl:123
unknown function (ip: 0x7ef0540c38d5) at (unknown file)
_jl_invoke at /source/src/gf.c:3462 [inlined]
ijl_apply_generic at /source/src/gf.c:3662
run_finalizer at /source/src/gc-common.c:180
jl_gc_run_finalizers_in_list at /source/src/gc-common.c:270
run_finalizers at /source/src/gc-common.c:316
finish_nocycle at ./../usr/share/julia/Compiler/src/typeinfer.jl:210
jfptr_finish_nocycle_120092.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3462 [inlined]
ijl_apply_generic at /source/src/gf.c:3662
typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4360
typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1219
typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1311 [inlined]
typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1319
jfptr_typeinf_ext_toplevel_120002.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3462 [inlined]
ijl_apply_generic at /source/src/gf.c:3662
jl_apply at /source/src/julia.h:2347 [inlined]
jl_type_infer at /source/src/gf.c:453
jl_compile_method_internal at /source/src/gf.c:2959
_jl_invoke at /source/src/gf.c:3454 [inlined]
ijl_apply_generic at /source/src/gf.c:3662
jl_apply at /source/src/julia.h:2347 [inlined]
start_task at /source/src/task.c:1249
Allocations: 176472446 (Pool: 176470638; Big: 1808); GC: 86

Testing failed after 218.0s

ERROR: LoadError: Package CalibrateEmulateSample errored during testing (received signal: 11)
Stacktrace:
  [1] pkgerror(msg::String)
    @ Pkg.Types /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Types.jl:68
  [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool)
    @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2365
  [3] test
    @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/Operations.jl:2220 [inlined]
  [4] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:486
  [5] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:164
  [6] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:152
  [7] test
    @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:152 [inlined]
  [8] #test#81
    @ /opt/julia/share/julia/stdlib/v1.12/Pkg/src/API.jl:151 [inlined]
  [9] top-level scope
    @ /PkgEval.jl/scripts/evaluate.jl:219
 [10] include(mod::Module, _path::String)
    @ Base ./Base.jl:303
 [11] exec_options(opts::Base.JLOptions)
    @ Base ./client.jl:328
 [12] _start()
    @ Base ./client.jl:560
in expression starting at /PkgEval.jl/scripts/evaluate.jl:210

PkgEval crashed after 2465.95s: GC corruption was detected