Package evaluation to test DiffEqBayes on Julia 1.13.0-DEV.1290 (92af0d8cdf*) started at 2025-10-10T01:40:15.586 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.92s ################################################################################ # Installation # Installing DiffEqBayes... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [ebbdde9d] + DiffEqBayes v3.9.0 Updating `~/.julia/environments/v1.13/Manifest.toml` [47edcb42] + ADTypes v1.18.0 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.8.2 ⌅ [7a57a42e] + AbstractPPL v0.12.0 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.4.0 [0bf59076] + AdvancedHMC v0.8.2 [5b7e9947] + AdvancedMH v0.8.8 [576499cb] + AdvancedPS v0.7.0 [b5ca4192] + AdvancedVI v0.4.1 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [ec485272] + ArnoldiMethod v0.4.0 [4fba245c] + ArrayInterface v7.20.0 [4c555306] + ArrayLayouts v1.12.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [e2ed5e7c] + Bijections v0.2.2 [76274a88] + Bijectors v0.15.10 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [8e7c35d0] + BlockArrays v1.7.2 [70df07ce] + BracketingNonlinearSolve v1.5.0 [2a0fbf3d] + CPUSummary v0.2.7 [336ed68f] + CSV v0.10.15 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [0ca39b1e] + Chairmarks v1.3.1 [9e997f8a] + ChangesOfVariables v0.1.10 [fb6a15b2] + CloseOpenIntervals v0.1.13 [944b1d66] + CodecZlib v0.7.8 ⌅ [861a8166] + Combinatorics v1.0.2 [a80b9123] + CommonMark v0.9.1 [38540f10] + CommonSolve v0.2.4 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.18.1 [5224ae11] + CompatHelperLocal v0.1.27 [b152e2b5] + CompositeTypes v0.1.4 [a33af91c] + CompositionsBase v0.1.2 [2569d6c7] + ConcreteStructs v0.2.3 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.8.0 [864edb3b] + DataStructures v0.19.1 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [2b5f629d] + DiffEqBase v6.190.2 [ebbdde9d] + DiffEqBayes v3.9.0 [459566f4] + DiffEqCallbacks v4.10.0 [77a26b50] + DiffEqNoiseProcess v5.24.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.7.8 [8d63f2c5] + DispatchDoctor v0.4.26 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.122 [ced4e74d] + DistributionsAD v0.6.58 [ffbed154] + DocStringExtensions v0.9.5 [5b8099bc] + DomainSets v0.7.16 [bbc10e6e] + DynamicHMC v3.5.1 ⌅ [366bfd00] + DynamicPPL v0.36.15 [7c1d4256] + DynamicPolynomials v0.6.4 [06fc5a27] + DynamicQuantities v1.10.0 [cad2338a] + EllipticalSliceSampling v2.0.0 [4e289a0a] + EnumX v1.0.5 [f151be2c] + EnzymeCore v0.8.14 [e2ba6199] + ExprTools v0.1.10 [55351af7] + ExproniconLite v0.10.14 [7a1cc6ca] + FFTW v1.10.0 [7034ab61] + FastBroadcast v0.3.5 [9aa1b823] + FastClosures v0.3.2 [a4df4552] + FastPower v1.1.3 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.14.0 [64ca27bc] + FindFirstFunctions v1.4.2 [6a86dc24] + FiniteDiff v2.28.1 [1fa38f19] + Format v1.3.7 [f6369f11] + ForwardDiff v1.2.1 [069b7b12] + FunctionWrappers v1.1.3 [77dc65aa] + FunctionWrappersWrappers v0.1.3 [d9f16b24] + Functors v0.5.2 [46192b85] + GPUArraysCore v0.2.0 [c27321d9] + Glob v1.3.1 [86223c79] + Graphs v1.13.1 [34004b35] + HypergeometricFunctions v0.3.28 [615f187c] + IfElse v0.1.1 [3263718b] + ImplicitDiscreteSolve v1.2.0 [d25df0c9] + Inflate v0.1.5 [22cec73e] + InitialValues v0.3.1 [842dd82b] + InlineStrings v1.4.5 [18e54dd8] + IntegerMathUtils v0.1.3 [a98d9a8b] + Interpolations v0.16.2 [8197267c] + IntervalSets v0.7.11 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 ⌅ [682c06a0] + JSON v0.21.4 [ae98c720] + Jieko v0.2.1 [98e50ef6] + JuliaFormatter v2.1.6 ⌅ [70703baa] + JuliaSyntax v0.4.10 [ccbc3e58] + JumpProcesses v9.19.1 [5ab0869b] + KernelDensity v0.6.10 [5be7bae1] + LBFGSB v0.4.1 [b964fa9f] + LaTeXStrings v1.4.0 [2ee39098] + LabelledArrays v1.16.1 [23fbe1c1] + Latexify v0.16.10 [10f19ff3] + LayoutPointers v0.1.17 [1fad7336] + LazyStack v0.1.3 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 [6f1fad26] + Libtask v0.9.5 [87fe0de2] + LineSearch v0.1.4 [d3d80556] + LineSearches v7.4.0 [6fdf6af0] + LogDensityProblems v2.1.2 [996a588d] + LogDensityProblemsAD v1.13.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [c7f686f2] + MCMCChains v7.4.0 [be115224] + MCMCDiagnosticTools v0.3.15 [e80e1ace] + MLJModelInterface v1.12.0 [d8e11817] + MLStyle v0.4.17 [1914dd2f] + MacroTools v0.5.16 [d125e4d3] + ManualMemory v0.1.8 [dbb5928d] + MappedArrays v0.4.2 [bb5d69b7] + MaybeInplace v0.1.4 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [dbe65cb8] + MistyClosures v2.1.0 [961ee093] + ModelingToolkit v10.25.0 [2e0e35c7] + Moshi v0.3.7 [46d2c3a1] + MuladdMacro v0.2.4 [102ac46a] + MultivariatePolynomials v0.5.13 [d8a4904e] + MutableArithmetics v1.6.6 [d41bc354] + NLSolversBase v7.10.0 [77ba4419] + NaNMath v1.1.3 [86f7a689] + NamedArrays v0.10.5 [d9ec5142] + NamedTupleTools v0.14.3 [c020b1a1] + NaturalSort v1.0.0 [be0214bd] + NonlinearSolveBase v2.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 [429524aa] + Optim v1.13.2 [3bd65402] + Optimisers v0.4.6 [7f7a1694] + Optimization v4.8.0 ⌅ [bca83a33] + OptimizationBase v2.12.0 ⌃ [36348300] + OptimizationOptimJL v0.4.5 [bac558e1] + OrderedCollections v1.8.1 [bbf590c4] + OrdinaryDiffEqCore v1.35.0 [90014a1f] + PDMats v0.11.35 [65ce6f38] + PackageExtensionCompat v1.0.2 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [e409e4f3] + PoissonRandom v0.4.7 [f517fe37] + Polyester v0.7.18 [1d0040c9] + PolyesterWeave v0.2.2 [2dfb63ee] + PooledArrays v1.4.3 [85a6dd25] + PositiveFactorizations v0.2.4 [d236fae5] + PreallocationTools v0.4.34 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 ⌅ [08abe8d2] + PrettyTables v2.4.0 [27ebfcd6] + Primes v0.5.7 [33c8b6b6] + ProgressLogging v0.1.5 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [74087812] + Random123 v1.7.1 [e6cf234a] + RandomNumbers v1.6.0 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [731186ca] + RecursiveArrayTools v3.37.1 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [ae5879a3] + ResettableStacks v1.1.1 [79098fc4] + Rmath v0.8.0 [f2b01f46] + Roots v2.2.10 [7e49a35a] + RuntimeGeneratedFunctions v0.5.15 [9dfe8606] + SCCNonlinearSolve v1.6.0 [94e857df] + SIMDTypes v0.1.0 ⌅ [26aad666] + SSMProblems v0.5.2 [0bca4576] + SciMLBase v2.120.0 [19f34311] + SciMLJacobianOperators v0.1.11 [c0aeaf25] + SciMLOperators v1.7.2 [431bcebd] + SciMLPublic v1.0.0 [53ae85a6] + SciMLStructures v1.7.0 [30f210dd] + ScientificTypesBase v3.0.0 [91c51154] + SentinelArrays v1.4.8 [efcf1570] + Setfield v1.1.2 [727e6d20] + SimpleNonlinearSolve v2.9.0 [699a6c99] + SimpleTraits v0.9.5 [ce78b400] + SimpleUnPack v1.1.0 [a2af1166] + SortingAlgorithms v1.2.2 [9f842d2f] + SparseConnectivityTracer v1.1.0 [dc90abb0] + SparseInverseSubset v0.1.2 [0a514795] + SparseMatrixColorings v0.4.21 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [d0ee94f6] + StanBase v4.12.2 ⌃ [c1514b29] + StanSample v7.1.1 [aedffcd0] + Static v1.3.0 [0d7ed370] + StaticArrayInterface v1.8.0 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.3 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.6 [4c63d2b9] + StatsFuns v1.5.0 [7792a7ef] + StrideArraysCore v0.5.8 [5e0ebb24] + Strided v2.3.2 [4db3bf67] + StridedViews v0.4.1 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 ⌃ [2efcf032] + SymbolicIndexingInterface v0.3.44 ⌃ [19f23fe9] + SymbolicLimits v0.2.3 ⌅ [d1185830] + SymbolicUtils v3.32.0 [0c5d862f] + Symbolics v6.55.0 [ab02a1b2] + TableOperations v1.2.0 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [ed4db957] + TaskLocalValues v0.1.3 [02d47bb6] + TensorCast v0.4.9 [8ea1fca8] + TermInterface v2.0.0 [5d786b92] + TerminalLoggers v0.1.7 [1c621080] + TestItems v1.0.0 [8290d209] + ThreadingUtilities v0.5.5 [a759f4b9] + TimerOutputs v0.5.29 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.85 [84d833dd] + TransformVariables v0.8.17 [f9bc47f6] + TransformedLogDensities v1.1.1 [24ddb15e] + TransmuteDims v0.1.17 [410a4b4d] + Tricks v0.1.12 [781d530d] + TruncatedStacktraces v1.4.0 [9d95972d] + TupleTools v1.6.0 ⌅ [fce5fe82] + Turing v0.39.10 [5c2747f8] + URIs v1.6.1 [3a884ed6] + UnPack v1.0.2 [1986cc42] + Unitful v1.25.0 [a7c27f48] + Unityper v0.1.6 [ea10d353] + WeakRefStrings v1.4.2 [efce3f68] + WoodburyMatrices v1.0.0 [76eceee3] + WorkerUtilities v1.6.1 [700de1a5] + ZygoteRules v0.2.7 [f5851436] + FFTW_jll v3.3.11+0 [61579ee1] + Ghostscript_jll v9.55.1+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [aacddb02] + JpegTurbo_jll v3.1.3+0 [81d17ec3] + L_BFGS_B_jll v3.0.1+0 [856f044c] + MKL_jll v2025.2.0+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [1317d2d5] + oneTBB_jll v2022.0.0+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.16.0+0 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.9.9 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.46.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.67.1+0 [3f19e933] + p7zip_jll v17.6.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 9.19s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 1534.66s ################################################################################ # Testing # Testing DiffEqBayes Status `/tmp/jl_SUPHP4/Project.toml` [2b5f629d] DiffEqBase v6.190.2 [ebbdde9d] DiffEqBayes v3.9.0 [163ba53b] DiffResults v1.1.0 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.122 [ffbed154] DocStringExtensions v0.9.5 [bbc10e6e] DynamicHMC v3.5.1 [f6369f11] ForwardDiff v1.2.1 [2ee39098] LabelledArrays v1.16.1 [996a588d] LogDensityProblemsAD v1.13.1 [1914dd2f] MacroTools v0.5.16 [e1d29d7a] Missings v1.2.0 [961ee093] ModelingToolkit v10.25.0 [429524aa] Optim v1.13.2 [1dea7af3] OrdinaryDiffEq v6.102.1 [90014a1f] PDMats v0.11.35 [65888b18] ParameterizedFunctions v5.19.0 [d96e819e] Parameters v0.12.3 [731186ca] RecursiveArrayTools v3.37.1 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [1bc83da4] SafeTestsets v0.1.0 [53ae85a6] SciMLStructures v1.7.0 ⌃ [c1514b29] StanSample v7.1.1 [2913bbd2] StatsBase v0.34.6 [9672c7b4] SteadyStateDiffEq v2.7.0 [09ab397b] StructArrays v0.7.1 [84d833dd] TransformVariables v0.8.17 [f9bc47f6] TransformedLogDensities v1.1.1 ⌅ [fce5fe82] Turing v0.39.10 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [44cfe95a] Pkg v1.13.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_SUPHP4/Manifest.toml` [47edcb42] ADTypes v1.18.0 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.8.2 ⌅ [7a57a42e] AbstractPPL v0.12.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.4.0 [0bf59076] AdvancedHMC v0.8.2 [5b7e9947] AdvancedMH v0.8.8 [576499cb] AdvancedPS v0.7.0 [b5ca4192] AdvancedVI v0.4.1 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [ec485272] ArnoldiMethod v0.4.0 [4fba245c] ArrayInterface v7.20.0 [4c555306] ArrayLayouts v1.12.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.8 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [e2ed5e7c] Bijections v0.2.2 [76274a88] Bijectors v0.15.10 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[83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.67.1+0 [3f19e933] p7zip_jll v17.6.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... WARNING: Method definition allocate_vjp(AbstractArray{T, N} where N where T, Any) in module DiffEqCallbacks at /home/pkgeval/.julia/packages/DiffEqCallbacks/8BaKL/src/functor_helpers.jl:70 overwritten in module DiffEqCallbacksFunctorsExt at /home/pkgeval/.julia/packages/DiffEqCallbacks/8BaKL/ext/DiffEqCallbacksFunctorsExt.jl:25. ┌ Warning: Environment variable CMDSTAN_HOME not set. Use set_cmdstan_home!. └ @ StanBase ~/.julia/packages/StanBase/IgV1g/src/StanBase.jl:52 ┌ Warning: Independent variable t should be defined with @independent_variables t. └ @ ModelingToolkit ~/.julia/packages/ModelingToolkit/IdXTK/src/utils.jl:121 ┌ Info: found initial stepsize └ ϵ = 0.2 ┌ Info: Starting MCMC │ total_steps = 75 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00014 │ estimated_seconds_left = 0.011 └ ϵ = 0.2 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Info: Starting MCMC │ total_steps = 25 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00011 │ estimated_seconds_left = 0.0026 └ ϵ = 0.000444 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.000178043560906864, 0.1987098610912473, 0.14676524422500278] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.031 │ estimated_seconds_left = 1.5 └ ϵ = 0.000201 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00017953349378911924, 0.2645750988484682, 0.25294026711548206] ┌ Info: Starting MCMC │ total_steps = 100 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0002 │ estimated_seconds_left = 0.02 └ ϵ = 0.646 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00016626253305295364, 0.26489628398147574, 0.21085605125927903] ┌ Info: Starting MCMC │ total_steps = 200 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0002 │ estimated_seconds_left = 0.039 └ ϵ = 0.637 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00032 │ estimated_seconds_left = 0.032 └ ϵ = 0.882 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00018507370992640405, 0.26327014742098537, 0.21265736584728848] ┌ Info: Starting MCMC │ total_steps = 400 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0001 │ estimated_seconds_left = 0.041 └ ϵ = 0.788 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00052 │ estimated_seconds_left = 0.16 └ ϵ = 0.497 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.00026 │ estimated_seconds_left = 0.051 └ ϵ = 0.735 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.00026 │ estimated_seconds_left = 0.026 └ ϵ = 1.37 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00017976998777274696, 0.29301825412979343, 0.2525597864777429] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00025 │ estimated_seconds_left = 0.012 └ ϵ = 0.843 ┌ Info: Starting MCMC └ total_steps = 1000 ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0003 └ estimated_seconds_left = 0.3 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.0011 └ estimated_seconds_left = 1.0 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.0002 └ estimated_seconds_left = 0.16 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.00029 └ estimated_seconds_left = 0.2 ┌ Info: MCMC progress │ step = 401 │ seconds_per_step = 0.00031 └ estimated_seconds_left = 0.19 ┌ Info: MCMC progress │ step = 501 │ seconds_per_step = 0.00031 └ estimated_seconds_left = 0.16 ┌ Info: MCMC progress │ step = 601 │ seconds_per_step = 0.00031 └ estimated_seconds_left = 0.12 ┌ Info: MCMC progress │ step = 701 │ seconds_per_step = 0.00032 └ estimated_seconds_left = 0.094 ┌ Info: MCMC progress │ step = 801 │ seconds_per_step = 0.00042 └ estimated_seconds_left = 0.084 ┌ Info: MCMC progress │ step = 901 │ seconds_per_step = 0.00032 └ estimated_seconds_left = 0.032 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Info: found initial stepsize └ ϵ = 0.0125 ┌ Info: Starting MCMC │ total_steps = 75 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 4.3e-5 │ estimated_seconds_left = 0.0032 └ ϵ = 0.0125 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Info: Starting MCMC │ total_steps = 25 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00018 │ estimated_seconds_left = 0.0043 └ ϵ = 0.0005 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.001963833788707167, 0.002235478924228983, 0.0005774369923938305, 0.11545402288284687, 0.09032299779186877] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.038 │ estimated_seconds_left = 1.8 └ ϵ = 0.000153 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0016829745600242417, 0.003987493041587754, 0.0005825190354008754, 0.1731787828548423, 0.24368707314330978] ┌ Info: Starting MCMC │ total_steps = 100 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.001 │ estimated_seconds_left = 0.1 └ ϵ = 0.153 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0015949291725898388, 0.0031769902885496974, 0.0005244255891408753, 0.2658933088117449, 0.229951988197474] ┌ Info: Starting MCMC │ total_steps = 200 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00054 │ estimated_seconds_left = 0.11 └ ϵ = 0.193 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.0016 │ estimated_seconds_left = 0.16 └ ϵ = 0.307 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0017144300019073396, 0.0033152553508214856, 0.000538583090973767, 0.32115272794598115, 0.22894865139479004] ┌ Info: Starting MCMC │ total_steps = 400 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00022 │ estimated_seconds_left = 0.09 └ ϵ = 0.227 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.0014 │ estimated_seconds_left = 0.43 └ ϵ = 0.191 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.0013 │ estimated_seconds_left = 0.26 └ ϵ = 0.235 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.001 │ estimated_seconds_left = 0.1 └ ϵ = 0.27 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.001572182864008381, 0.003039160350922354, 0.0005355966932106432, 0.2509701915425726, 0.23716642413174482] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0012 │ estimated_seconds_left = 0.06 └ ϵ = 0.212 ┌ Info: Starting MCMC └ total_steps = 1000 ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00038 └ estimated_seconds_left = 0.38 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.0015 └ estimated_seconds_left = 1.3 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.0014 └ estimated_seconds_left = 1.1 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.0017 └ estimated_seconds_left = 1.2 ┌ Info: MCMC progress │ step = 401 │ seconds_per_step = 0.0015 └ estimated_seconds_left = 0.89 ┌ Info: MCMC progress │ step = 501 │ seconds_per_step = 0.0014 └ estimated_seconds_left = 0.72 ┌ Info: MCMC progress │ step = 601 │ seconds_per_step = 0.0017 └ estimated_seconds_left = 0.68 ┌ Info: MCMC progress │ step = 701 │ seconds_per_step = 0.0014 └ estimated_seconds_left = 0.43 ┌ Info: MCMC progress │ step = 801 │ seconds_per_step = 0.0016 └ estimated_seconds_left = 0.33 ┌ Info: MCMC progress │ step = 901 │ seconds_per_step = 0.0016 └ estimated_seconds_left = 0.16 ┌ Info: found initial stepsize └ ϵ = 0.2 ┌ Info: Starting MCMC │ total_steps = 75 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00022 │ estimated_seconds_left = 0.016 └ ϵ = 0.2 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Info: Starting MCMC │ total_steps = 25 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 4.3e-5 │ estimated_seconds_left = 0.001 └ ϵ = 0.00244 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00031287514766666904, 0.30790252020305214] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.032 │ estimated_seconds_left = 1.5 └ ϵ = 0.0004 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00014408627760474478, 0.31945937546997916] ┌ Info: Starting MCMC │ total_steps = 100 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00012 │ estimated_seconds_left = 0.011 └ ϵ = 0.487 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00021283729675333345, 0.2027029707063702] ┌ Info: Starting MCMC │ total_steps = 200 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00012 │ estimated_seconds_left = 0.025 └ ϵ = 0.781 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00027 │ estimated_seconds_left = 0.027 └ ϵ = 1.44 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00018195042644291926, 0.24635250886600532] ┌ Info: Starting MCMC │ total_steps = 400 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 4.9e-5 │ estimated_seconds_left = 0.019 └ ϵ = 0.814 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00034 │ estimated_seconds_left = 0.1 └ ϵ = 0.514 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.00022 │ estimated_seconds_left = 0.044 └ ϵ = 0.922 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.00022 │ estimated_seconds_left = 0.021 └ ϵ = 0.916 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.00017018973380050021, 0.26862063730031227] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00011 │ estimated_seconds_left = 0.0054 └ ϵ = 0.953 ┌ Info: Starting MCMC └ total_steps = 1000 ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00023 └ estimated_seconds_left = 0.23 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00029 └ estimated_seconds_left = 0.26 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.00027 └ estimated_seconds_left = 0.22 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.00026 └ estimated_seconds_left = 0.18 ┌ Info: MCMC progress │ step = 401 │ seconds_per_step = 0.00029 └ estimated_seconds_left = 0.17 ┌ Info: MCMC progress │ step = 501 │ seconds_per_step = 0.00026 └ estimated_seconds_left = 0.13 ┌ Info: MCMC progress │ step = 601 │ seconds_per_step = 0.0003 └ estimated_seconds_left = 0.12 ┌ Info: MCMC progress │ step = 701 │ seconds_per_step = 0.00026 └ estimated_seconds_left = 0.079 ┌ Info: MCMC progress │ step = 801 │ seconds_per_step = 0.00029 └ estimated_seconds_left = 0.058 ┌ Info: MCMC progress │ step = 901 │ seconds_per_step = 0.0003 └ estimated_seconds_left = 0.03 ┌ Warning: First function call produced NaNs. Exiting. Double check that none of the initial conditions, parameters, or timespan values are NaN. └ @ OrdinaryDiffEqCore ~/.julia/packages/OrdinaryDiffEqCore/Y97Yg/src/initdt.jl:132 ┌ Warning: At t=0.0, dt was forced below floating point epsilon 5.0e-324, and step error estimate = NaN. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of ForwardDiff.Dual{ForwardDiff.Tag{Base.Fix1{typeof(LogDensityProblems.logdensity), TransformedLogDensities.TransformedLogDensity{TransformVariables.TransformTuple{@NamedTuple{parameters::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}, σ::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}}}, DiffEqBayes.DynamicHMCPosterior{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, Main.var"##DynamicHMC#140".LotkaVolterraTest1{Main.var"##DynamicHMC#140".var"###ParameterizedDiffEqFunction#175", Main.var"##DynamicHMC#140".var"###ParameterizedTGradFunction#176", Main.var"##DynamicHMC#140".var"###ParameterizedJacobianFunction#177", Nothing, Nothing, ModelingToolkit.System}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Matrix{Float64}, Vector{Float64}, Vector{Distributions.Normal{Float64}}, Vector{Distributions.Normal{Float64}}, Tuple{}, Vector{Int64}, typeof(identity)}}}, Float64}, Float64, 3}). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:657 ┌ Warning: At t=2.4144670028756457e-270, dt was forced below floating point epsilon 4.2030456845295373e-286, and step error estimate = NaN. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of ForwardDiff.Dual{ForwardDiff.Tag{Base.Fix1{typeof(LogDensityProblems.logdensity), TransformedLogDensities.TransformedLogDensity{TransformVariables.TransformTuple{@NamedTuple{parameters::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}, σ::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}}}, DiffEqBayes.DynamicHMCPosterior{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, Main.var"##DynamicHMC#140".LotkaVolterraTest1{Main.var"##DynamicHMC#140".var"###ParameterizedDiffEqFunction#175", Main.var"##DynamicHMC#140".var"###ParameterizedTGradFunction#176", Main.var"##DynamicHMC#140".var"###ParameterizedJacobianFunction#177", Nothing, Nothing, ModelingToolkit.System}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Matrix{Float64}, Vector{Float64}, Vector{Distributions.Normal{Float64}}, Vector{Distributions.Normal{Float64}}, Tuple{}, Vector{Int64}, typeof(identity)}}}, Float64}, Float64, 3}). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:657 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Info: found initial stepsize └ ϵ = 0.00156 ┌ Info: Starting MCMC │ total_steps = 75 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00012 │ estimated_seconds_left = 0.0089 └ ϵ = 0.00156 ┌ Info: Starting MCMC │ total_steps = 25 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00024 │ estimated_seconds_left = 0.0058 └ ϵ = 0.000365 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0006197082896850707, 0.0002062156643268218, 0.1522549401598988] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.068 │ estimated_seconds_left = 3.4 └ ϵ = 0.000211 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0006992973106959023, 0.00024242536492156519, 0.22611760126193786] ┌ Info: Starting MCMC │ total_steps = 100 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00029 │ estimated_seconds_left = 0.029 └ ϵ = 0.538 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0009526818019554763, 0.00034990172578590247, 0.24045893962974518] ┌ Info: Starting MCMC │ total_steps = 200 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 4.6e-5 │ estimated_seconds_left = 0.0091 └ ϵ = 0.454 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00073 │ estimated_seconds_left = 0.073 └ ϵ = 0.228 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.000924363769169007, 0.0003283274459620776, 0.24172281508047083] ┌ Info: Starting MCMC │ total_steps = 400 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00011 │ estimated_seconds_left = 0.046 └ ϵ = 0.389 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00075 │ estimated_seconds_left = 0.23 └ ϵ = 0.55 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.00057 │ estimated_seconds_left = 0.11 └ ϵ = 0.322 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.00053 │ estimated_seconds_left = 0.052 └ ϵ = 0.347 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0008866714759955867, 0.0003071798168808363, 0.2862476387516118] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00013 │ estimated_seconds_left = 0.0064 └ ϵ = 0.418 ┌ Info: Starting MCMC └ total_steps = 1000 ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00057 └ estimated_seconds_left = 0.57 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.00065 └ estimated_seconds_left = 0.59 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.00058 └ estimated_seconds_left = 0.46 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.00099 └ estimated_seconds_left = 0.69 ┌ Info: MCMC progress │ step = 401 │ seconds_per_step = 0.00051 └ estimated_seconds_left = 0.31 ┌ Info: MCMC progress │ step = 501 │ seconds_per_step = 0.00056 └ estimated_seconds_left = 0.28 ┌ Info: MCMC progress │ step = 601 │ seconds_per_step = 0.00047 └ estimated_seconds_left = 0.19 ┌ Info: MCMC progress │ step = 701 │ seconds_per_step = 0.00052 └ estimated_seconds_left = 0.15 ┌ Info: MCMC progress │ step = 801 │ seconds_per_step = 0.00054 └ estimated_seconds_left = 0.11 ┌ Info: MCMC progress │ step = 901 │ seconds_per_step = 0.00052 └ estimated_seconds_left = 0.052 ┌ Warning: Assignment to `bayesian_result` in soft scope is ambiguous because a global variable by the same name exists: `bayesian_result` will be treated as a new local. Disambiguate by using `local bayesian_result` to suppress this warning or `global bayesian_result` to assign to the existing global variable. └ @ ~/.julia/packages/DiffEqBayes/CpDUh/test/dynamicHMC.jl:69 ┌ Warning: Independent variable t should be defined with @independent_variables t. └ @ ModelingToolkit ~/.julia/packages/ModelingToolkit/IdXTK/src/utils.jl:121 ┌ Info: Starting MCMC │ total_steps = 75 └ tuning = "stepsize" ┌ Warning: First function call produced NaNs. Exiting. Double check that none of the initial conditions, parameters, or timespan values are NaN. └ @ OrdinaryDiffEqCore ~/.julia/packages/OrdinaryDiffEqCore/Y97Yg/src/initdt.jl:132 ┌ Warning: At t=0.0, dt was forced below floating point epsilon 5.0e-324, and step error estimate = NaN. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of ForwardDiff.Dual{ForwardDiff.Tag{Base.Fix1{typeof(LogDensityProblems.logdensity), TransformedLogDensities.TransformedLogDensity{TransformVariables.TransformTuple{@NamedTuple{parameters::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}, σ::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}}}, DiffEqBayes.DynamicHMCPosterior{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, Main.var"##DynamicHMC#140".LotkaVolterraTest4{Main.var"##DynamicHMC#140".var"###ParameterizedDiffEqFunction#182", Main.var"##DynamicHMC#140".var"###ParameterizedTGradFunction#183", Main.var"##DynamicHMC#140".var"###ParameterizedJacobianFunction#184", Nothing, Nothing, ModelingToolkit.System}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Matrix{Float64}, Vector{Float64}, @NamedTuple{a::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}, b::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}, c::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}, d::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}}, Vector{Distributions.Normal{Float64}}, Tuple{}, Nothing, typeof(identity)}}}, Float64}, Float64, 6}). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:657 ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.026 │ estimated_seconds_left = 1.9 └ ϵ = 1.0 ┌ Warning: First function call produced NaNs. Exiting. Double check that none of the initial conditions, parameters, or timespan values are NaN. └ @ OrdinaryDiffEqCore ~/.julia/packages/OrdinaryDiffEqCore/Y97Yg/src/initdt.jl:132 ┌ Warning: At t=0.0, dt was forced below floating point epsilon 5.0e-324, and step error estimate = NaN. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of ForwardDiff.Dual{ForwardDiff.Tag{Base.Fix1{typeof(LogDensityProblems.logdensity), TransformedLogDensities.TransformedLogDensity{TransformVariables.TransformTuple{@NamedTuple{parameters::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}, σ::TransformVariables.ArrayTransformation{TransformVariables.TVExp, 1}}}, DiffEqBayes.DynamicHMCPosterior{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, Main.var"##DynamicHMC#140".LotkaVolterraTest4{Main.var"##DynamicHMC#140".var"###ParameterizedDiffEqFunction#182", Main.var"##DynamicHMC#140".var"###ParameterizedTGradFunction#183", Main.var"##DynamicHMC#140".var"###ParameterizedJacobianFunction#184", Nothing, Nothing, ModelingToolkit.System}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Matrix{Float64}, Vector{Float64}, @NamedTuple{a::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}, b::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}, c::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}, d::Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}}, Vector{Distributions.Normal{Float64}}, Tuple{}, Nothing, typeof(identity)}}}, Float64}, Float64, 6}). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:657 ┌ Warning: Interrupted. Larger maxiters is needed. If you are using an integrator for non-stiff ODEs or an automatic switching algorithm (the default), you may want to consider using a method for stiff equations. See the solver pages for more details (e.g. https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/#Stiff-Problems). └ @ SciMLBase ~/.julia/packages/SciMLBase/YE7xF/src/integrator_interface.jl:623 ┌ Info: Starting MCMC │ total_steps = 25 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.00013 │ estimated_seconds_left = 0.0032 └ ϵ = 0.00071 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0014943882940735222, 0.001781016483881825, 0.0017654930320675274, 0.0024874384181546357, 0.24447267531763014, 0.10680011242648024] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.041 │ estimated_seconds_left = 2.0 └ ϵ = 0.000144 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0011909868996535437, 0.0014788821445851027, 0.0015475263458394088, 0.002172575137594642, 0.30719006041547403, 0.27652823019406714] ┌ Info: Starting MCMC │ total_steps = 100 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0012 │ estimated_seconds_left = 0.12 └ ϵ = 0.0942 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0008953382462428687, 0.0013129660162863478, 0.0013776293082013303, 0.001642620863581176, 0.24535441088672436, 0.25678319799655397] ┌ Info: Starting MCMC │ total_steps = 200 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0026 │ estimated_seconds_left = 0.52 └ ϵ = 0.0906 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.0051 │ estimated_seconds_left = 0.51 └ ϵ = 0.00537 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.001090043083315937, 0.0015022085574099325, 0.0015200756010220516, 0.0018982805114274688, 0.23748642051470809, 0.230643464829681] ┌ Info: Starting MCMC │ total_steps = 400 └ tuning = "stepsize and LinearAlgebra.Diagonal metric" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0026 │ estimated_seconds_left = 1.0 └ ϵ = 0.158 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.0029 │ estimated_seconds_left = 0.88 └ ϵ = 0.17 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.0023 │ estimated_seconds_left = 0.47 └ ϵ = 0.0646 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.0019 │ estimated_seconds_left = 0.19 └ ϵ = 0.124 ┌ Info: adaptation finished └ adapted_kinetic_energy = Gaussian kinetic energy (Diagonal), √diag(M⁻¹): [0.0012998592715511945, 0.0017999242052858587, 0.0017249291328280756, 0.002398340685022243, 0.3219589561204174, 0.25878216314913477] ┌ Info: Starting MCMC │ total_steps = 50 └ tuning = "stepsize" ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0011 │ estimated_seconds_left = 0.055 └ ϵ = 0.157 ┌ Info: Starting MCMC └ total_steps = 1000 ┌ Info: MCMC progress │ step = 1 │ seconds_per_step = 0.0013 └ estimated_seconds_left = 1.3 ┌ Info: MCMC progress │ step = 101 │ seconds_per_step = 0.003 └ estimated_seconds_left = 2.7 ┌ Info: MCMC progress │ step = 201 │ seconds_per_step = 0.0024 └ estimated_seconds_left = 1.9 ┌ Info: MCMC progress │ step = 301 │ seconds_per_step = 0.0028 └ estimated_seconds_left = 1.9 ┌ Info: MCMC progress │ step = 401 │ seconds_per_step = 0.0027 └ estimated_seconds_left = 1.6 ┌ Info: MCMC progress │ step = 501 │ seconds_per_step = 0.0027 └ estimated_seconds_left = 1.4 ┌ Info: MCMC progress │ step = 601 │ seconds_per_step = 0.0029 └ estimated_seconds_left = 1.2 ┌ Info: MCMC progress │ step = 701 │ seconds_per_step = 0.0031 └ estimated_seconds_left = 0.91 ┌ Info: MCMC progress │ step = 801 │ seconds_per_step = 0.0032 └ estimated_seconds_left = 0.63 ┌ Info: MCMC progress │ step = 901 │ seconds_per_step = 0.0029 └ estimated_seconds_left = 0.29 Test Summary: | Pass Broken Total Time DynamicHMC | 8 2 10 7m29.6s 450.932077 seconds (226.70 M allocations: 13.597 GiB, 1.82% gc time, 50.60% compilation time: 12% of which was recompilation) One parameter case ┌ Warning: Independent variable t should be defined with @independent_variables t. └ @ ModelingToolkit ~/.julia/packages/ModelingToolkit/IdXTK/src/utils.jl:121 ┌ Info: Found initial step size └ ϵ = 0.00625 bayesian_result = MCMC chain (500×14×1 Array{Float64, 3}) ┌ Warning: Using a `Bool` for keyword argument `autodiff` is deprecated. Please use an `ADType` specifier. │ caller = _process_AD_choice(ad_alg::Bool, ::Val{0}, ::Val{:forward}) at misc_utils.jl:158 └ @ Core ~/.julia/packages/OrdinaryDiffEqCore/Y97Yg/src/misc_utils.jl:158 ┌ Info: Found initial step size └ ϵ = 0.0125 ┌ Info: Found initial step size └ ϵ = 0.0125 Multithreaded case ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/z4BsN/src/sample.jl:432 ┌ Info: Found initial step size └ ϵ = 0.025390625 ┌ Info: Found initial step size └ ϵ = 0.00625 ┌ Info: Found initial step size └ ϵ = 0.0125 ┌ Info: Found initial step size └ ϵ = 0.0125 ┌ Info: Found initial step size └ ϵ = 0.0125 Four parameter case ┌ Warning: Independent variable t should be defined with @independent_variables t. └ @ ModelingToolkit ~/.julia/packages/ModelingToolkit/IdXTK/src/utils.jl:121 ┌ Info: Found initial step size └ ϵ = 0.025 bayesian_result = MCMC chain (500×17×1 Array{Float64, 3}) Steady state problem ┌ Warning: The `alias_u0` keyword argument is deprecated. Please use a NonlinearAliasSpecifier, e.g. `alias = NonlinearAliasSpecifier(alias_u0 = true)`. └ @ NonlinearSolveBase ~/.julia/packages/NonlinearSolveBase/2E600/src/solve.jl:57 ┌ Warning: `alias_u0` keyword argument is deprecated, to set `alias_u0`, │ please use an ODEAliasSpecifier, e.g. `solve(prob, alias = ODEAliasSpecifier(alias_u0 = true)) │ caller = ip:0x0 └ @ Core :-1 ┌ Warning: `alias_u0` keyword argument is deprecated, to set `alias_u0`, │ please use an ODEAliasSpecifier, e.g. `solve(prob, alias = ODEAliasSpecifier(alias_u0 = true)) │ caller = __solve(::SciMLBase.SteadyStateProblem{Vector{Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.FullSpecialize, typeof(Main.var"##Turing#141".f), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}}, ::SteadyStateDiffEq.DynamicSS{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Float64}; abstol::Float64, reltol::Float64, odesolve_kwargs::@NamedTuple{}, save_idxs::Nothing, termination_condition::NonlinearSolveBase.NormTerminationMode{typeof(NonlinearSolveBase.Linf_NORM)}, kwargs::Base.Pairs{Symbol, Bool, Nothing, @NamedTuple{alias_u0::Bool}}) at solve.jl:56 └ @ Core ~/.julia/packages/SteadyStateDiffEq/ovRt6/src/solve.jl:56 Turing: Error During Test at /home/pkgeval/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:30 Got exception outside of a @test LoadError: MethodError: no method matching turing_inference(::SciMLBase.SteadyStateProblem{Vector{Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.FullSpecialize, typeof(Main.var"##Turing#141".f), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}}, ::SteadyStateDiffEq.DynamicSS{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Float64}, ::Nothing, ::Vector{Float64}, ::Vector{Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Float64}}; syms::Vector{Symbol}, solve_kwargs::Dict{Symbol, Float64}, sample_args::@NamedTuple{num_samples::Int64}) The function `turing_inference` exists, but no method is defined for this combination of argument types. Closest candidates are: turing_inference(!Matched::SciMLBase.AbstractDEProblem, ::Any, ::Any, ::Any, ::Any; likelihood_dist_priors, likelihood, syms, sample_u0, progress, solve_kwargs, sample_args, sample_kwargs) @ DiffEqBayes ~/.julia/packages/DiffEqBayes/CpDUh/src/turing_inference.jl:1 Stacktrace: [1] top-level scope @ ~/.julia/packages/DiffEqBayes/CpDUh/test/turing.jl:110 [2] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [3] top-level scope @ ~/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:11 [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1954 [inlined] [5] macro expansion @ ~/.julia/packages/DiffEqBayes/CpDUh/test/runtests.jl:11 [inlined] [6] eval(m::Module, e::Any) @ Core ./boot.jl:489 [7] macro expansion @ ~/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:28 [inlined] [8] macro expansion @ ./timing.jl:689 [inlined] [9] top-level scope @ ~/.julia/packages/DiffEqBayes/CpDUh/test/runtests.jl:10 [10] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [11] top-level scope @ none:6 [12] eval(m::Module, e::Any) @ Core ./boot.jl:489 [13] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [14] _start() @ Base ./client.jl:577 in expression starting at /home/pkgeval/.julia/packages/DiffEqBayes/CpDUh/test/turing.jl:110 Test Summary: | Pass Error Total Time Turing | 13 1 14 7m48.5s RNG of the outermost testset: Random.Xoshiro(0x862a1d0228a6762d, 0x711e583ba345da68, 0xde3d253f4d825fb8, 0x681b04619ebefb48, 0x0973fe9ced3cc2f5) ERROR: LoadError: Some tests did not pass: 13 passed, 0 failed, 1 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/DiffEqBayes/CpDUh/test/runtests.jl:6 Testing failed after 931.71s ERROR: LoadError: Package DiffEqBayes errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.13/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/v1.13/Pkg/src/Operations.jl:2674 [3] test @ /opt/julia/share/julia/stdlib/v1.13/Pkg/src/Operations.jl:2523 [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/v1.13/Pkg/src/API.jl:548 [5] kwcall(::@NamedTuple{julia_args::Cmd, io::IOContext{IO}}, ::typeof(Pkg.API.test), ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.13/Pkg/src/API.jl:525 [6] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.13/Pkg/src/API.jl:172 [7] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkgs::Vector{PackageSpec}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.13/Pkg/src/API.jl:161 [8] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.13/Pkg/src/API.jl:160 [9] test @ /opt/julia/share/julia/stdlib/v1.13/Pkg/src/API.jl:160 [inlined] [10] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkg::String) @ Pkg.API /opt/julia/share/julia/stdlib/v1.13/Pkg/src/API.jl:159 [11] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:219 [12] include(mod::Module, _path::String) @ Base ./Base.jl:309 [13] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [14] _start() @ Base ./client.jl:577 in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 PkgEval failed after 2515.7s: package tests unexpectedly errored