Package evaluation to test IntrinsicTimescales on Julia 1.14.0-DEV.2802 (918269a29b*) started at 2026-08-28T08:01:08.140 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 14.96s ################################################################################ # Installation # Installing IntrinsicTimescales... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [10d2f5c9] + IntrinsicTimescales v0.7.1 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.24.0 [14f7f29c] + AMD v0.5.3 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.16.0 ⌅ [7a57a42e] + AbstractPPL v0.13.6 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.45 [79e6a3ab] + Adapt v4.7.0 [0bf59076] + AdvancedHMC v0.8.6 [5b7e9947] + AdvancedMH v0.8.10 ⌅ [576499cb] + AdvancedPS v0.7.2 ⌅ [b5ca4192] + AdvancedVI v0.4.1 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [ec485272] + ArnoldiMethod v0.4.0 [4fba245c] + ArrayInterface v7.30.0 [a9b6321e] + Atomix v1.1.3 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 [198e06fe] + BangBang v0.4.9 [0e736298] + Bessels v0.2.8 ⌅ [76274a88] + Bijectors v0.15.16 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 ⌃ [70df07ce] + BracketingNonlinearSolve v1.12.1 [fa961155] + CEnum v0.5.0 [2a0fbf3d] + CPUSummary v0.2.7 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v1.13.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.5.7+0 [deac9b47] + LibCURL_jll v8.21.0+0 [e37daf67] + LibGit2_jll v1.9.6+0 [29816b5a] + LibSSH2_jll v1.11.103+0 [14a3606d] + MozillaCACerts_jll v2026.7.16 [4536629a] + OpenBLAS_jll v0.3.34+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.7+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.69.0+0 [3f19e933] + p7zip_jll v17.8.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 8.78s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 14.2 s ✓ SciMLBase → SciMLBaseZygoteExt 21.5 s ✓ DynamicPPL [61] signal 11 (1): Segmentation fault in expression starting at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:7 __atomic_add at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/ext/atomicity.h:53:54 [inlined] __atomic_add_dispatch at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/ext/atomicity.h:96:19 [inlined] _M_add_ref_copy at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr_base.h:139:41 [inlined] __shared_count at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr_base.h:737:4 [inlined] __shared_ptr at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr_base.h:1167:7 [inlined] shared_ptr at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr.h:129:7 [inlined] withContextDo]:: > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:50:14 [inlined] withModuleDo > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:117:6 [inlined] jl_emit_native_impl at /source/src/aotcompile.cpp:1014:32 compile_method_instance at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/jlgen.jl:840:0 (pc: 423) irgen at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/irgen.jl:4:0 (pc: 1) #emit_llvm#124 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:209:0 (pc: 23) emit_llvm at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:191:0 (pc: 1) #compile_unhooked#121 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:95:0 (pc: 11) compile_unhooked at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:80:0 [inlined] #compile#119 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:67:0 [inlined] compile at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:55:0 [inlined] #184 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:39:0 [inlined] #JuliaContext#118 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:34:0 (pc: 25) unknown function (ip: 0x7f5567456661) at (unknown file) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 JuliaContext at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:25:0 (pc: 1) unknown function (ip: 0x7f55ab772802) at (unknown file) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 macro expansion at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:38:0 [inlined] macro expansion at /home/pkgeval/.julia/packages/PrecompileTools/QUxvR/src/workloads.jl:70:0 [inlined] macro expansion at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:29:0 [inlined] macro expansion at /home/pkgeval/.julia/packages/PrecompileTools/QUxvR/src/workloads.jl:118:0 [inlined] top-level scope at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:115:0 (pc: 88) jl_invoke_oneshot at /source/src/gf.c:4613:23 ijl_eval_thunk at /source/src/toplevel.c:759:18 jl_toplevel_eval_flex at /source/src/toplevel.c:711:26 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_toplevel_eval_flex at /source/src/toplevel.c:683:27 ijl_toplevel_eval at /source/src/toplevel.c:781:12 ijl_toplevel_eval_in at /source/src/toplevel.c:826:13 eval at ./boot.jl:545:0 (pc: 1) include_string at ./loading.jl:3186:0 (pc: 208) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 _include at ./loading.jl:3248:0 (pc: 123) include at ./Base.jl:330:0 (pc: 1) IncludeInto at ./Base.jl:331:0 (pc: 2) jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 jl_apply at /source/src/julia.h:2512:12 [inlined] do_call at /source/src/interpreter.c:123:26 eval_value at /source/src/interpreter.c:259:16 eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] eval_body at /source/src/interpreter.c:760:13 jl_interpret_toplevel_thunk at /source/src/interpreter.c:962:21 ijl_eval_thunk at /source/src/toplevel.c:767:18 jl_toplevel_eval_flex at /source/src/toplevel.c:711:26 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_eval_module_expr at /source/src/toplevel.c:266:5 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:664:27 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_toplevel_eval_flex at /source/src/toplevel.c:683:27 ijl_toplevel_eval at /source/src/toplevel.c:781:12 ijl_toplevel_eval_in at /source/src/toplevel.c:826:13 eval at ./boot.jl:545:0 (pc: 1) include_string at ./loading.jl:3186:0 (pc: 208) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 _include at ./loading.jl:3248:0 (pc: 123) include at ./Base.jl:329:0 (pc: 1) include_package_for_output at ./loading.jl:3359:0 (pc: 848) jfptr_include_package_for_output_1.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 jl_apply at /source/src/julia.h:2512:12 [inlined] do_call at /source/src/interpreter.c:123:26 eval_value at /source/src/interpreter.c:259:16 eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] eval_body at /source/src/interpreter.c:760:13 jl_interpret_toplevel_thunk at /source/src/interpreter.c:962:21 ijl_eval_thunk at /source/src/toplevel.c:767:18 jl_toplevel_eval_flex at /source/src/toplevel.c:711:26 jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 jl_toplevel_eval_flex at /source/src/toplevel.c:683:27 ijl_toplevel_eval at /source/src/toplevel.c:781:12 ijl_toplevel_eval_in at /source/src/toplevel.c:826:13 eval at ./boot.jl:545:0 (pc: 1) include_string at ./loading.jl:3186:0 (pc: 208) include_string at ./loading.jl:3196:0 [inlined] __script_entry_include_string at ./client.jl:104:0 [inlined] exec_options at ./client.jl:376:0 (pc: 819) _start at ./client.jl:619:0 (pc: 295) jfptr__start_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4572:23 [inlined] ijl_apply_generic at /source/src/gf.c:4820:12 jl_apply at /source/src/julia.h:2512:12 [inlined] true_main at /source/src/jlapi.c:989:29 jl_repl_entrypoint at /source/src/jlapi.c:1156:15 main at /source/cli/loader_exe.c:117:15 unknown function (ip: 0x7f55c86f1249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S Allocations: 93966937 (Pool: 93965892; Big: 1045); GC: 26 ✗ GPUCompiler 9.3 s ✓ OptimizationBase → OptimizationZygoteExt 7.4 s ✓ NonlinearSolveBase → NonlinearSolveBaseTrackerExt 24.9 s ✓ RecursiveArrayTools → RecursiveArrayToolsReverseDiffExt ERROR: LoadError: `@check_args` requires each check to include an offending value; use `(arg, cond)` or `(arg, cond, message)`. Got: `α > zero(α) && θ > zero(θ)`. Stacktrace: [1] error(::String, ::Expr, ::String) @ Base error.jl:56 [2] (::Distributions.var"#@check_args##0#@check_args##1"{Vector{Any}})(check::Expr) @ Distributions ~/.julia/packages/Distributions/Oj6YW/src/utils.jl:66 [3] map(f::Distributions.var"#@check_args##0#@check_args##1"{Vector{Any}}, t::Tuple{Expr}) @ Base tuple.jl:359 [4] macro expansion @ ~/.julia/packages/Distributions/Oj6YW/src/utils.jl:50 [5] fl_lower(ex::Expr, mod::Module, filename::String, lineno::UInt64, world::UInt64, warn::Bool) @ Base flfrontend.jl:24 [6] include(mod::Module, _path::String) @ Base Base.jl:329 [7] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [8] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/DistributionsAD/GAQrK/ext/DistributionsADReverseDiffExt.jl:127 in expression starting at /home/pkgeval/.julia/packages/DistributionsAD/GAQrK/ext/DistributionsADReverseDiffExt.jl:1 in expression starting at stdin:5 ✗ DistributionsAD → DistributionsADReverseDiffExt 3.9 s ✓ RecursiveArrayTools → RecursiveArrayToolsTrackerExt 6.5 s ✓ SciMLBase → SciMLBaseTrackerExt 9.2 s ✓ DistributionsAD → DistributionsADTrackerExt 8.1 s ✓ DiffEqBase → DiffEqBaseTrackerExt 11.5 s ✓ DiffEqNoiseProcess 7.9 s ✓ SimpleNonlinearSolve → SimpleNonlinearSolveTrackerExt 10.8 s ✓ JumpProcesses → JumpProcessesKernelAbstractionsExt 7.9 s ✓ DynamicPPL → DynamicPPLChainRulesCoreExt 12.3 s ✓ DynamicPPL → DynamicPPLMCMCChainsExt 6.8 s ✓ DynamicPPL → DynamicPPLForwardDiffExt 6.3 s ✓ DynamicPPL → DynamicPPLEnzymeCoreExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("61eb1bfa-7361-4325-ad38-22787b887f55"), "GPUCompiler") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] _eval_import(imported::Bool, to::Module, from::Nothing, paths::Expr) @ Base module.jl:111 [10] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/src/typetree.jl:6 [11] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:330 [12] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/src/Enzyme.jl:148 [13] include(mod::Module, _path::String) @ Base Base.jl:329 [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [15] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/src/typetree.jl:6 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/src/Enzyme.jl:1 in expression starting at stdin:5 ✗ Enzyme 26.2 s ✓ DiffEqNoiseProcess → DiffEqNoiseProcessReverseDiffExt 9.1 s ✓ DiffEqNoiseProcess → DiffEqNoiseProcessOptimExt 12.5 s ✓ StochasticDiffEqCore 21.5 s ✓ Turing ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeGPUArraysCoreExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeGPUArraysCoreExt.jl:1 in expression starting at stdin:5 ✗ Enzyme → EnzymeGPUArraysCoreExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeStaticArraysExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeStaticArraysExt.jl:1 in expression starting at stdin:5 ✗ Enzyme → EnzymeStaticArraysExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeChainRulesCoreExt.jl:5 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeChainRulesCoreExt.jl:1 in expression starting at stdin:5 ✗ Enzyme → EnzymeChainRulesCoreExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeOrderedCollectionsExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeOrderedCollectionsExt.jl:1 in expression starting at stdin:5 ✗ Enzyme → EnzymeOrderedCollectionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeSpecialFunctionsExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeSpecialFunctionsExt.jl:1 in expression starting at stdin:5 ✗ Enzyme → EnzymeSpecialFunctionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeLogExpFunctionsExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeLogExpFunctionsExt.jl:1 in expression starting at stdin:5 ✗ Enzyme → EnzymeLogExpFunctionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] _eval_import(imported::Bool, to::Module, from::Nothing, paths::Expr) @ Base module.jl:111 [10] top-level scope @ ~/.julia/packages/LogDensityProblemsAD/DH6MG/ext/LogDensityProblemsADEnzymeExt.jl:9 [11] include(mod::Module, _path::String) @ Base Base.jl:329 [12] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [13] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/LogDensityProblemsAD/DH6MG/ext/LogDensityProblemsADEnzymeExt.jl:1 in expression starting at stdin:5 ✗ LogDensityProblemsAD → LogDensityProblemsADEnzymeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/QuadGK/5mgi5/ext/QuadGKEnzymeExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/QuadGK/5mgi5/ext/QuadGKEnzymeExt.jl:2 in expression starting at stdin:5 ✗ QuadGK → QuadGKEnzymeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/FastPower/soIUe/ext/FastPowerEnzymeExt.jl:7 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/FastPower/soIUe/ext/FastPowerEnzymeExt.jl:1 in expression starting at stdin:5 ✗ FastPower → FastPowerEnzymeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) @ Base module.jl:101 [11] top-level scope @ ~/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersEnzymeExt.jl:5 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersEnzymeExt.jl:1 in expression starting at stdin:5 ✗ FunctionWrappersWrappers → FunctionWrappersWrappersEnzymeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) @ Base module.jl:101 [11] top-level scope @ ~/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceEnzymeExt/DifferentiationInterfaceEnzymeExt.jl:32 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceEnzymeExt/DifferentiationInterfaceEnzymeExt.jl:1 in expression starting at stdin:5 ✗ DifferentiationInterface → DifferentiationInterfaceEnzymeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) @ Base module.jl:101 [11] top-level scope @ ~/.julia/packages/SciMLBase/S0d2P/ext/SciMLBaseEnzymeExt.jl:4 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/SciMLBase/S0d2P/ext/SciMLBaseEnzymeExt.jl:1 in expression starting at stdin:5 ✗ SciMLBase → SciMLBaseEnzymeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/OptimizationBase/mYxHK/ext/OptimizationEnzymeExt.jl:8 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/OptimizationBase/mYxHK/ext/OptimizationEnzymeExt.jl:1 in expression starting at stdin:5 ✗ OptimizationBase → OptimizationEnzymeExt 12.5 s ✓ StochasticDiffEqRODE 12.1 s ✓ StochasticDiffEqROCK 11.4 s ✓ StochasticDiffEqIIF 20.6 s ✓ StochasticDiffEqLeaping 11.7 s ✓ StochasticDiffEqHighOrder 12.2 s ✓ StochasticDiffEqLowOrder 11.7 s ✓ StochasticDiffEqMilstein 21.6 s ✓ StochasticDiffEqWeak 19.4 s ✓ StochasticDiffEqImplicit 18.7 s ✓ Turing → TuringOptimExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_using(to::Module, path::Expr, flags::UInt8) @ Base module.jl:137 [inlined] [11] top-level scope @ ~/.julia/packages/NonlinearSolveBase/txQ1o/ext/NonlinearSolveBaseEnzymeExt.jl:5 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/NonlinearSolveBase/txQ1o/ext/NonlinearSolveBaseEnzymeExt.jl:1 in expression starting at stdin:5 ✗ NonlinearSolveBase → NonlinearSolveBaseEnzymeExt 20.5 s ✓ StochasticDiffEq ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace: [1] error(s::String) @ Base error.jl:56 [2] __require_prelocked(pkg::Base.PkgId, env::String) @ Base loading.jl:2891 [3] _require_prelocked(uuidkey::Base.PkgId, env::String) @ Base loading.jl:2739 [4] macro expansion @ loading.jl:2653 [inlined] [5] macro expansion @ lock.jl:376 [inlined] [6] __require(into::Module, mod::Symbol) @ Base loading.jl:2617 [7] require(into::Module, mod::Symbol) @ Base loading.jl:2593 [inlined] [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) @ Base module.jl:36 [inlined] [9] eval_import_path_all(at::Module, path::Expr, keyword::String) @ Base module.jl:60 [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) @ Base module.jl:101 [11] top-level scope @ ~/.julia/packages/SciMLSensitivity/k5Qj5/src/SciMLSensitivity.jl:49 [12] include(mod::Module, _path::String) @ Base Base.jl:329 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) @ Base loading.jl:3359 [14] top-level scope @ stdin:5 in expression starting at /home/pkgeval/.julia/packages/SciMLSensitivity/k5Qj5/src/SciMLSensitivity.jl:1 in expression starting at stdin:5 ✗ SciMLSensitivity 45.7 s ✓ IntrinsicTimescales 32 dependencies successfully precompiled in 675 seconds. 484 already precompiled. 18 dependencies had output during precompilation: ┌ Enzyme → EnzymeOrderedCollectionsExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeOrderedCollectionsExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeOrderedCollectionsExt.jl:1 │ in expression starting at stdin:5 └ ┌ FunctionWrappersWrappers → FunctionWrappersWrappersEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) │ @ Base module.jl:101 │ [11] top-level scope │ @ ~/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersEnzymeExt.jl:5 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/FunctionWrappersWrappers/7CPBX/ext/FunctionWrappersWrappersEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ┌ SciMLBase → SciMLBaseEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) │ @ Base module.jl:101 │ [11] top-level scope │ @ ~/.julia/packages/SciMLBase/S0d2P/ext/SciMLBaseEnzymeExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/SciMLBase/S0d2P/ext/SciMLBaseEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Enzyme → EnzymeStaticArraysExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeStaticArraysExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeStaticArraysExt.jl:1 │ in expression starting at stdin:5 └ ┌ Enzyme → EnzymeSpecialFunctionsExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeSpecialFunctionsExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeSpecialFunctionsExt.jl:1 │ in expression starting at stdin:5 └ ┌ FastPower → FastPowerEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/FastPower/soIUe/ext/FastPowerEnzymeExt.jl:7 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/FastPower/soIUe/ext/FastPowerEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ┌ DifferentiationInterface → DifferentiationInterfaceEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) │ @ Base module.jl:101 │ [11] top-level scope │ @ ~/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceEnzymeExt/DifferentiationInterfaceEnzymeExt.jl:32 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/DifferentiationInterface/seMaz/ext/DifferentiationInterfaceEnzymeExt/DifferentiationInterfaceEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ┌ GPUCompiler │ [61] signal 11 (1): Segmentation fault │ in expression starting at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:7 │ __atomic_add at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/ext/atomicity.h:53:54 [inlined] │ __atomic_add_dispatch at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/ext/atomicity.h:96:19 [inlined] │ _M_add_ref_copy at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr_base.h:139:41 [inlined] │ __shared_count at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr_base.h:737:4 [inlined] │ __shared_ptr at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr_base.h:1167:7 [inlined] │ shared_ptr at /usr/local/x86_64-linux-gnu/include/c++/9.1.0/bits/shared_ptr.h:129:7 [inlined] │ withContextDo]:: > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:50:14 [inlined] │ withModuleDo > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:117:6 [inlined] │ jl_emit_native_impl at /source/src/aotcompile.cpp:1014:32 │ compile_method_instance at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/jlgen.jl:840:0 (pc: 423) │ irgen at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/irgen.jl:4:0 (pc: 1) │ #emit_llvm#124 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:209:0 (pc: 23) │ emit_llvm at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:191:0 (pc: 1) │ #compile_unhooked#121 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:95:0 (pc: 11) │ compile_unhooked at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:80:0 [inlined] │ #compile#119 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:67:0 [inlined] │ compile at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:55:0 [inlined] │ #184 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:39:0 [inlined] │ #JuliaContext#118 at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:34:0 (pc: 25) │ unknown function (ip: 0x7f5567456661) at (unknown file) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ JuliaContext at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/driver.jl:25:0 (pc: 1) │ unknown function (ip: 0x7f55ab772802) at (unknown file) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ macro expansion at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:38:0 [inlined] │ macro expansion at /home/pkgeval/.julia/packages/PrecompileTools/QUxvR/src/workloads.jl:70:0 [inlined] │ macro expansion at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:29:0 [inlined] │ macro expansion at /home/pkgeval/.julia/packages/PrecompileTools/QUxvR/src/workloads.jl:118:0 [inlined] │ top-level scope at /home/pkgeval/.julia/packages/GPUCompiler/BSi1T/src/precompile.jl:115:0 (pc: 88) │ jl_invoke_oneshot at /source/src/gf.c:4613:23 │ ijl_eval_thunk at /source/src/toplevel.c:759:18 │ jl_toplevel_eval_flex at /source/src/toplevel.c:711:26 │ jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 │ jl_toplevel_eval_flex at /source/src/toplevel.c:683:27 │ ijl_toplevel_eval at /source/src/toplevel.c:781:12 │ ijl_toplevel_eval_in at /source/src/toplevel.c:826:13 │ eval at ./boot.jl:545:0 (pc: 1) │ include_string at ./loading.jl:3186:0 (pc: 208) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ _include at ./loading.jl:3248:0 (pc: 123) │ include at ./Base.jl:330:0 (pc: 1) │ IncludeInto at ./Base.jl:331:0 (pc: 2) │ jfptr_IncludeInto_1.1 at /opt/julia/lib/julia/sys.so (unknown line) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ jl_apply at /source/src/julia.h:2512:12 [inlined] │ do_call at /source/src/interpreter.c:123:26 │ eval_value at /source/src/interpreter.c:259:16 │ eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] │ eval_body at /source/src/interpreter.c:760:13 │ jl_interpret_toplevel_thunk at /source/src/interpreter.c:962:21 │ ijl_eval_thunk at /source/src/toplevel.c:767:18 │ jl_toplevel_eval_flex at /source/src/toplevel.c:711:26 │ jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 │ jl_eval_module_expr at /source/src/toplevel.c:266:5 [inlined] │ jl_toplevel_eval_flex at /source/src/toplevel.c:664:27 │ jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 │ jl_toplevel_eval_flex at /source/src/toplevel.c:683:27 │ ijl_toplevel_eval at /source/src/toplevel.c:781:12 │ ijl_toplevel_eval_in at /source/src/toplevel.c:826:13 │ eval at ./boot.jl:545:0 (pc: 1) │ include_string at ./loading.jl:3186:0 (pc: 208) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ _include at ./loading.jl:3248:0 (pc: 123) │ include at ./Base.jl:329:0 (pc: 1) │ include_package_for_output at ./loading.jl:3359:0 (pc: 848) │ jfptr_include_package_for_output_1.1 at /opt/julia/lib/julia/sys.so (unknown line) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ jl_apply at /source/src/julia.h:2512:12 [inlined] │ do_call at /source/src/interpreter.c:123:26 │ eval_value at /source/src/interpreter.c:259:16 │ eval_stmt_value at /source/src/interpreter.c:194:23 [inlined] │ eval_body at /source/src/interpreter.c:760:13 │ jl_interpret_toplevel_thunk at /source/src/interpreter.c:962:21 │ ijl_eval_thunk at /source/src/toplevel.c:767:18 │ jl_toplevel_eval_flex at /source/src/toplevel.c:711:26 │ jl_eval_toplevel_stmts at /source/src/toplevel.c:601:15 │ jl_toplevel_eval_flex at /source/src/toplevel.c:683:27 │ ijl_toplevel_eval at /source/src/toplevel.c:781:12 │ ijl_toplevel_eval_in at /source/src/toplevel.c:826:13 │ eval at ./boot.jl:545:0 (pc: 1) │ include_string at ./loading.jl:3186:0 (pc: 208) │ include_string at ./loading.jl:3196:0 [inlined] │ __script_entry_include_string at ./client.jl:104:0 [inlined] │ exec_options at ./client.jl:376:0 (pc: 819) │ _start at ./client.jl:619:0 (pc: 295) │ jfptr__start_0.1 at /opt/julia/lib/julia/sys.so (unknown line) │ _jl_invoke at /source/src/gf.c:4572:23 [inlined] │ ijl_apply_generic at /source/src/gf.c:4820:12 │ jl_apply at /source/src/julia.h:2512:12 [inlined] │ true_main at /source/src/jlapi.c:989:29 │ jl_repl_entrypoint at /source/src/jlapi.c:1156:15 │ main at /source/cli/loader_exe.c:117:15 │ unknown function (ip: 0x7f55c86f1249) at /lib/x86_64-linux-gnu/libc.so.6 │ __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) │ unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S │ Allocations: 93966937 (Pool: 93965892; Big: 1045); GC: 26 └ ┌ QuadGK → QuadGKEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/QuadGK/5mgi5/ext/QuadGKEnzymeExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/QuadGK/5mgi5/ext/QuadGKEnzymeExt.jl:2 │ in expression starting at stdin:5 └ ┌ DistributionsAD → DistributionsADReverseDiffExt │ ERROR: LoadError: `@check_args` requires each check to include an offending value; use `(arg, cond)` or `(arg, cond, message)`. Got: `α > zero(α) && θ > zero(θ)`. │ Stacktrace: │ [1] error(::String, ::Expr, ::String) │ @ Base error.jl:56 │ [2] (::Distributions.var"#@check_args##0#@check_args##1"{Vector{Any}})(check::Expr) │ @ Distributions ~/.julia/packages/Distributions/Oj6YW/src/utils.jl:66 │ [3] map(f::Distributions.var"#@check_args##0#@check_args##1"{Vector{Any}}, t::Tuple{Expr}) │ @ Base tuple.jl:359 │ [4] macro expansion │ @ ~/.julia/packages/Distributions/Oj6YW/src/utils.jl:50 │ [5] fl_lower(ex::Expr, mod::Module, filename::String, lineno::UInt64, world::UInt64, warn::Bool) │ @ Base flfrontend.jl:24 │ [6] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [7] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [8] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/DistributionsAD/GAQrK/ext/DistributionsADReverseDiffExt.jl:127 │ in expression starting at /home/pkgeval/.julia/packages/DistributionsAD/GAQrK/ext/DistributionsADReverseDiffExt.jl:1 │ in expression starting at stdin:5 └ ┌ Enzyme → EnzymeLogExpFunctionsExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeLogExpFunctionsExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeLogExpFunctionsExt.jl:1 │ in expression starting at stdin:5 └ ┌ SciMLSensitivity │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr}) │ @ Base module.jl:101 │ [11] top-level scope │ @ ~/.julia/packages/SciMLSensitivity/k5Qj5/src/SciMLSensitivity.jl:49 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/SciMLSensitivity/k5Qj5/src/SciMLSensitivity.jl:1 │ in expression starting at stdin:5 └ ┌ OptimizationBase → OptimizationEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/OptimizationBase/mYxHK/ext/OptimizationEnzymeExt.jl:8 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/OptimizationBase/mYxHK/ext/OptimizationEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ┌ NonlinearSolveBase → NonlinearSolveBaseEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/NonlinearSolveBase/txQ1o/ext/NonlinearSolveBaseEnzymeExt.jl:5 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/NonlinearSolveBase/txQ1o/ext/NonlinearSolveBaseEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ┌ Enzyme → EnzymeGPUArraysCoreExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeGPUArraysCoreExt.jl:4 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeGPUArraysCoreExt.jl:1 │ in expression starting at stdin:5 └ ┌ Enzyme → EnzymeChainRulesCoreExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] eval_import_path_all(at::Module, path::Expr, keyword::String) │ @ Base module.jl:60 │ [10] _eval_using(to::Module, path::Expr, flags::UInt8) │ @ Base module.jl:137 [inlined] │ [11] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/ext/EnzymeChainRulesCoreExt.jl:5 │ [12] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [14] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/ext/EnzymeChainRulesCoreExt.jl:1 │ in expression starting at stdin:5 └ ┌ Enzyme │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("61eb1bfa-7361-4325-ad38-22787b887f55"), "GPUCompiler") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] _eval_import(imported::Bool, to::Module, from::Nothing, paths::Expr) │ @ Base module.jl:111 │ [10] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/src/typetree.jl:6 │ [11] include(mapexpr::Function, mod::Module, _path::String) │ @ Base Base.jl:330 │ [12] top-level scope │ @ ~/.julia/packages/Enzyme/OvStQ/src/Enzyme.jl:148 │ [13] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [15] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/src/typetree.jl:6 │ in expression starting at /home/pkgeval/.julia/packages/Enzyme/OvStQ/src/Enzyme.jl:1 │ in expression starting at stdin:5 └ ┌ LogDensityProblemsAD → LogDensityProblemsADEnzymeExt │ ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("7da242da-08ed-463a-9acd-ee780be4f1d9"), "Enzyme") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) │ Stacktrace: │ [1] error(s::String) │ @ Base error.jl:56 │ [2] __require_prelocked(pkg::Base.PkgId, env::String) │ @ Base loading.jl:2891 │ [3] _require_prelocked(uuidkey::Base.PkgId, env::String) │ @ Base loading.jl:2739 │ [4] macro expansion │ @ loading.jl:2653 [inlined] │ [5] macro expansion │ @ lock.jl:376 [inlined] │ [6] __require(into::Module, mod::Symbol) │ @ Base loading.jl:2617 │ [7] require(into::Module, mod::Symbol) │ @ Base loading.jl:2593 [inlined] │ [8] eval_import_path(at::Module, from::Nothing, path::Expr, keyword::String) │ @ Base module.jl:36 [inlined] │ [9] _eval_import(imported::Bool, to::Module, from::Nothing, paths::Expr) │ @ Base module.jl:111 │ [10] top-level scope │ @ ~/.julia/packages/LogDensityProblemsAD/DH6MG/ext/LogDensityProblemsADEnzymeExt.jl:9 │ [11] include(mod::Module, _path::String) │ @ Base Base.jl:329 │ [12] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing) │ @ Base loading.jl:3359 │ [13] top-level scope │ @ stdin:5 │ in expression starting at /home/pkgeval/.julia/packages/LogDensityProblemsAD/DH6MG/ext/LogDensityProblemsADEnzymeExt.jl:1 │ in expression starting at stdin:5 └ ERROR: LoadError: The following 18 packages failed to precompile: Enzyme → EnzymeOrderedCollectionsExt Failed to precompile EnzymeOrderedCollectionsExt [9f48294f-d755-529c-87f6-33d8c4e64c70] to "/home/pkgeval/.julia/compiled/v1.14/EnzymeOrderedCollectionsExt/jl_Gm1fPh" (ProcessExited(1)). FunctionWrappersWrappers → FunctionWrappersWrappersEnzymeExt Failed to precompile FunctionWrappersWrappersEnzymeExt [8b5e6218-fb45-53e8-9fbf-7818f227e2e5] to "/home/pkgeval/.julia/compiled/v1.14/FunctionWrappersWrappersEnzymeExt/jl_qGK1mA" (ProcessExited(1)). SciMLBase → SciMLBaseEnzymeExt Failed to precompile SciMLBaseEnzymeExt [646a5054-6093-561c-aa01-617fdd7b7ab5] to "/home/pkgeval/.julia/compiled/v1.14/SciMLBaseEnzymeExt/jl_Ndyzw7" (ProcessExited(1)). Enzyme → EnzymeStaticArraysExt Failed to precompile EnzymeStaticArraysExt [8724f06c-f4c4-5d10-8ca5-5036e220244b] to "/home/pkgeval/.julia/compiled/v1.14/EnzymeStaticArraysExt/jl_hErPev" (ProcessExited(1)). Enzyme → EnzymeSpecialFunctionsExt Failed to precompile EnzymeSpecialFunctionsExt [bc91e8c5-4631-5c58-9d34-c5da8b408cf1] to "/home/pkgeval/.julia/compiled/v1.14/EnzymeSpecialFunctionsExt/jl_wefuCP" (ProcessExited(1)). FastPower → FastPowerEnzymeExt Failed to precompile FastPowerEnzymeExt [1988527d-e786-55ff-ad44-d3491204aacc] to "/home/pkgeval/.julia/compiled/v1.14/FastPowerEnzymeExt/jl_Nz6lsf" (ProcessExited(1)). DifferentiationInterface → DifferentiationInterfaceEnzymeExt Failed to precompile DifferentiationInterfaceEnzymeExt [b2bef6f4-9359-51c0-9eac-bc8d0bbb8621] to "/home/pkgeval/.julia/compiled/v1.14/DifferentiationInterfaceEnzymeExt/jl_QaCjQu" (ProcessExited(1)). GPUCompiler Failed to precompile GPUCompiler [61eb1bfa-7361-4325-ad38-22787b887f55] to "/home/pkgeval/.julia/compiled/v1.14/GPUCompiler/jl_1snOsp" (ProcessSignaled(11)). QuadGK → QuadGKEnzymeExt Failed to precompile QuadGKEnzymeExt [d6a8b538-ba19-559d-8edd-bc30cab2b164] to "/home/pkgeval/.julia/compiled/v1.14/QuadGKEnzymeExt/jl_p9eryJ" (ProcessExited(1)). DistributionsAD → DistributionsADReverseDiffExt Failed to precompile DistributionsADReverseDiffExt [f5c92b6a-1dba-5bad-b8d3-af339927a739] to "/home/pkgeval/.julia/compiled/v1.14/DistributionsADReverseDiffExt/jl_q32EPk" (ProcessExited(1)). Enzyme → EnzymeLogExpFunctionsExt Failed to precompile EnzymeLogExpFunctionsExt [1a9f04a6-12b6-5435-a00e-55b0a0022015] to "/home/pkgeval/.julia/compiled/v1.14/EnzymeLogExpFunctionsExt/jl_AHzZe8" (ProcessExited(1)). SciMLSensitivity Failed to precompile SciMLSensitivity [1ed8b502-d754-442c-8d5d-10ac956f44a1] to "/home/pkgeval/.julia/compiled/v1.14/SciMLSensitivity/jl_XwchmJ" (ProcessExited(1)). OptimizationBase → OptimizationEnzymeExt Failed to precompile OptimizationEnzymeExt [d1b7b352-1e8f-5e52-bcce-529fb2b58f08] to "/home/pkgeval/.julia/compiled/v1.14/OptimizationEnzymeExt/jl_8z0O1n" (ProcessExited(1)). NonlinearSolveBase → NonlinearSolveBaseEnzymeExt Failed to precompile NonlinearSolveBaseEnzymeExt [af2e4ce3-0815-55b3-8347-61d9acc1bcc9] to "/home/pkgeval/.julia/compiled/v1.14/NonlinearSolveBaseEnzymeExt/jl_kSu1xn" (ProcessExited(1)). Enzyme → EnzymeGPUArraysCoreExt Failed to precompile EnzymeGPUArraysCoreExt [6f04138c-15cb-59c5-94f1-55440044fc8e] to "/home/pkgeval/.julia/compiled/v1.14/EnzymeGPUArraysCoreExt/jl_a6xzYR" (ProcessExited(1)). Enzyme → EnzymeChainRulesCoreExt Failed to precompile EnzymeChainRulesCoreExt [dea36ce8-68cc-5ac3-8d54-6d6fec99b42c] to "/home/pkgeval/.julia/compiled/v1.14/EnzymeChainRulesCoreExt/jl_SG9763" (ProcessExited(1)). Enzyme Failed to precompile Enzyme [7da242da-08ed-463a-9acd-ee780be4f1d9] to "/home/pkgeval/.julia/compiled/v1.14/Enzyme/jl_aAmG1S" (ProcessExited(1)). LogDensityProblemsAD → LogDensityProblemsADEnzymeExt Failed to precompile LogDensityProblemsADEnzymeExt [9c7b786d-8b85-5d9b-b738-6a949d0ed336] to "/home/pkgeval/.julia/compiled/v1.14/LogDensityProblemsADEnzymeExt/jl_WS789c" (ProcessExited(1)). in expression starting at /PkgEval.jl/scripts/precompile.jl:34 Precompilation failed after 681.03s ################################################################################ # Testing # Testing IntrinsicTimescales Status `/tmp/jl_3nNPfy/Project.toml` [621f4979] AbstractFFTs v1.5.0 ⌃ [b0b7db55] ComponentArrays v0.15.44 [717857b8] DSP v0.8.6 [a0c0ee7d] DifferentiationInterface v0.7.21 ⌃ [31c24e10] Distributions v0.25.127 [7a1cc6ca] FFTW v1.10.0 [77fa7db0] FastTransformsForwardDiff v0.0.2 ⌅ [f6369f11] ForwardDiff v0.10.39 [10d2f5c9] IntrinsicTimescales v0.7.1 [5ab0869b] KernelDensity v0.6.12 ⌃ [d3d80556] LineSearches v7.5.1 [fc60dff9] LombScargle v1.0.3 [e1d29d7a] Missings v1.2.0 [b946abbf] NaNStatistics v0.6.58 ⌃ [8913a72c] NonlinearSolve v4.12.0 [67456a42] OhMyThreads v0.8.6 ⌃ [7f7a1694] Optimization v5.4.0 ⌃ [36348300] OptimizationOptimJL v0.4.8 [21216c6a] Preferences v1.5.2 [92933f4c] ProgressMeter v1.11.0 [189a3867] Reexport v1.2.2 [ff01fdde] Romberg v0.2.0 ⌅ [0bca4576] SciMLBase v2.155.2 ⌃ [1ed8b502] SciMLSensitivity v7.106.0 [53ae85a6] SciMLStructures v1.10.5 [90137ffa] StaticArrays v1.9.19 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.13 ⌅ [789caeaf] StochasticDiffEq v6.102.0 ⌅ [fce5fe82] Turing v0.41.4 [37e2e46d] LinearAlgebra v1.14.0 [56ddb016] Logging v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_3nNPfy/Manifest.toml` [47edcb42] ADTypes v1.24.0 [14f7f29c] AMD v0.5.3 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.16.0 ⌅ [7a57a42e] AbstractPPL v0.13.6 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.45 [79e6a3ab] Adapt v4.7.0 [0bf59076] AdvancedHMC v0.8.6 [5b7e9947] AdvancedMH v0.8.10 ⌅ [576499cb] AdvancedPS v0.7.2 ⌅ [b5ca4192] AdvancedVI v0.4.1 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [ec485272] ArnoldiMethod v0.4.0 [4fba245c] ArrayInterface v7.30.0 [a9b6321e] Atomix v1.1.3 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.8 [198e06fe] BangBang v0.4.9 [0e736298] Bessels v0.2.8 ⌅ [76274a88] Bijectors v0.15.16 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 ⌃ [70df07ce] BracketingNonlinearSolve v1.12.1 [fa961155] CEnum v0.5.0 [2a0fbf3d] CPUSummary v0.2.7 [49dc2e85] Calculus v0.5.2 [082447d4] ChainRules v1.73.0 [d360d2e6] ChainRulesCore v1.26.1 [0ca39b1e] Chairmarks v1.3.1 [9e997f8a] ChangesOfVariables v0.1.11 [ae650224] ChunkSplitters v3.2.0 [fb6a15b2] CloseOpenIntervals v0.1.13 [861a8166] Combinatorics v1.1.0 [38540f10] CommonSolve v0.2.14 [bbf7d656] CommonSubexpressions v0.3.1 [f70d9fcc] CommonWorldInvalidations v1.2.0 [34da2185] Compat v4.18.1 ⌃ [b0b7db55] ComponentArrays v0.15.44 [a33af91c] CompositionsBase v0.1.2 [2569d6c7] ConcreteStructs v0.2.8 [88cd18e8] ConsoleProgressMonitor v0.1.2 [187b0558] ConstructionBase v1.6.0 [adafc99b] CpuId v0.3.1 [a8cc5b0e] Crayons v4.2.0 [717857b8] DSP v0.8.6 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.19.6 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b429d917] DensityInterface v0.4.0 ⌅ [2b5f629d] DiffEqBase v6.218.0 ⌃ [459566f4] DiffEqCallbacks v4.19.2 ⌃ [77a26b50] DiffEqNoiseProcess v5.32.0 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.16.0 [a0c0ee7d] DifferentiationInterface v0.7.21 ⌃ [31c24e10] Distributions v0.25.127 [ced4e74d] DistributionsAD v0.6.58 [ffbed154] DocStringExtensions v0.9.5 ⌅ [366bfd00] DynamicPPL v0.38.10 [cad2338a] EllipticalSliceSampling v2.0.0 [4e289a0a] EnumX v1.0.7 [7da242da] Enzyme v0.13.199 [f151be2c] EnzymeCore v0.8.21 [e2ba6199] ExprTools v0.1.11 [55351af7] ExproniconLite v0.10.14 [b86e33f2] FFTA v0.3.1 [7a1cc6ca] FFTW v1.10.0 [7034ab61] FastBroadcast v1.4.0 [9aa1b823] FastClosures v0.3.2 [a4df4552] FastPower v1.5.0 [77fa7db0] FastTransformsForwardDiff v0.0.2 [1a297f60] FillArrays v1.17.0 [6a86dc24] FiniteDiff v2.33.0 ⌅ [f6369f11] ForwardDiff v0.10.39 ⌅ [f62d2435] FunctionProperties v0.1.7 [069b7b12] FunctionWrappers v1.1.3 [77dc65aa] FunctionWrappersWrappers v1.13.0 [d9f16b24] Functors v0.5.3 [46192b85] GPUArraysCore v0.2.0 ⌅ [61eb1bfa] GPUCompiler v1.23.0 ⌃ [a0844989] Gamma v1.1.0 [86223c79] Graphs v1.14.0 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.30 [7869d1d1] IRTools v0.4.20 [615f187c] IfElse v0.1.1 [d25df0c9] Inflate v0.1.5 [22cec73e] InitialValues v0.3.1 [18e54dd8] IntegerMathUtils v0.1.4 [a98d9a8b] Interpolations v0.16.3 [8197267c] IntervalSets v0.7.14 [10d2f5c9] IntrinsicTimescales v0.7.1 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.8.0 [682c06a0] JSON v1.7.1 [ae98c720] Jieko v0.2.1 ⌃ [ccbc3e58] JumpProcesses v9.29.0 [63c18a36] KernelAbstractions v0.9.42 [5ab0869b] KernelDensity v0.6.12 [ba0b0d4f] Krylov v0.10.9 [929cbde3] LLVM v9.13.1 [b964fa9f] LaTeXStrings v1.4.1 [10f19ff3] LayoutPointers v0.1.17 [1d6d02ad] LeftChildRightSiblingTrees v0.3.0 [6f1fad26] Libtask v0.9.18 ⌃ [87fe0de2] LineSearch v0.1.14 ⌃ [d3d80556] LineSearches v7.5.1 ⌅ [7ed4a6bd] LinearSolve v3.87.0 [6fdf6af0] LogDensityProblems v2.2.0 [996a588d] LogDensityProblemsAD v1.13.1 ⌅ [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [fc60dff9] LombScargle v1.0.3 [c7f686f2] MCMCChains v7.7.0 [be115224] MCMCDiagnosticTools v0.3.19 [e80e1ace] MLJModelInterface v1.12.1 [1914dd2f] MacroTools v0.5.16 [d125e4d3] ManualMemory v0.1.8 [dbb5928d] MappedArrays v0.4.3 [bb5d69b7] MaybeInplace v0.1.8 [eff96d63] Measurements v2.14.1 [e1d29d7a] Missings v1.2.0 [dbe65cb8] MistyClosures v2.1.0 [2e0e35c7] Moshi v0.3.12 [46d2c3a1] MuladdMacro v0.2.7 ⌅ [d41bc354] NLSolversBase v7.10.0 ⌃ [872c559c] NNlib v0.9.31 [77ba4419] NaNMath v1.1.4 [b946abbf] NaNStatistics v0.6.58 [86f7a689] NamedArrays v0.10.5 [c020b1a1] NaturalSort v1.0.0 ⌃ [8913a72c] NonlinearSolve v4.12.0 ⌅ [be0214bd] NonlinearSolveBase v2.30.3 ⌅ [5959db7a] NonlinearSolveFirstOrder v1.11.1 ⌃ [9a2c21bd] NonlinearSolveQuasiNewton v1.13.1 ⌃ [26075421] NonlinearSolveSpectralMethods v1.7.1 [d8793406] ObjectFile v0.5.1 [6fe1bfb0] OffsetArrays v1.17.0 [67456a42] OhMyThreads v0.8.6 ⌅ [429524aa] Optim v1.13.3 [3bd65402] Optimisers v0.4.9 ⌃ [7f7a1694] Optimization v5.4.0 ⌅ [bca83a33] OptimizationBase v4.2.0 ⌃ [36348300] OptimizationOptimJL v0.4.8 ⌅ [bac558e1] OrderedCollections v1.8.2 ⌅ [bbf590c4] OrdinaryDiffEqCore v3.33.1 ⌅ [4302a76b] OrdinaryDiffEqDifferentiation v2.9.0 ⌅ [127b3ac7] OrdinaryDiffEqNonlinearSolve v1.28.0 [90014a1f] PDMats v0.11.41 ⌅ [69de0a69] Parsers v2.8.7 [e409e4f3] PoissonRandom v0.4.13 [f517fe37] Polyester v0.7.19 [1d0040c9] PolyesterWeave v0.2.2 [f27b6e38] Polynomials v4.1.1 [85a6dd25] PositiveFactorizations v0.2.4 ⌃ [d236fae5] PreallocationTools v1.6.0 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Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 Default method failed, trying `LineSearches.BackTracking()` ┌ Warning: Terminated early due to NaN in gradient. └ @ Optim ~/.julia/packages/Optim/gmigl/src/multivariate/optimize/optimize.jl:117 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 Accepted samples: 2 Time: 0:00:00 ( 0.29 s/it) Accepted samples: 10 Time: 0:00:00 (80.30 ms/it) Starting step 1 epsilon = 1.0 Acceptance Rate = 1.0 Current theta = [5.448366543249369;;] -------------------- epsilon = 0.0 Acceptance Rate = 1.0 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 Starting step 1 epsilon = 1.0 Accepted samples: 2 Time: 0:00:02 ( 1.06 s/it) Accepted samples: 3 Time: 0:00:04 ( 1.42 s/it) Accepted samples: 4 Time: 0:00:05 ( 1.29 s/it) Accepted samples: 5 Time: 0:00:06 ( 1.39 s/it) Accepted samples: 6 Time: 0:00:08 ( 1.45 s/it) Accepted samples: 7 Time: 0:00:09 ( 1.34 s/it) Accepted samples: 8 Time: 0:00:09 ( 1.24 s/it) Accepted samples: 9 Time: 0:00:10 ( 1.18 s/it) Accepted samples: 10 Time: 0:00:11 ( 1.14 s/it) Accepted samples: 11 Time: 0:00:12 ( 1.17 s/it) Accepted samples: 12 Time: 0:00:13 ( 1.11 s/it) Accepted samples: 13 Time: 0:00:14 ( 1.15 s/it) Accepted samples: 14 Time: 0:00:15 ( 1.10 s/it) Accepted samples: 15 Time: 0:00:16 ( 1.08 s/it) Accepted samples: 16 Time: 0:00:25 ( 1.59 s/it) Accepted samples: 17 Time: 0:00:25 ( 1.52 s/it) Accepted samples: 18 Time: 0:00:35 ( 1.95 s/it) Accepted samples: 19 Time: 0:00:35 ( 1.87 s/it) Accepted samples: 20 Time: 0:00:36 ( 1.81 s/it) Accepted samples: 21 Time: 0:00:36 ( 1.76 s/it) Accepted samples: 22 Time: 0:00:37 ( 1.70 s/it) Accepted samples: 23 Time: 0:00:38 ( 1.67 s/it) Accepted samples: 24 Time: 0:00:39 ( 1.64 s/it) Accepted samples: 25 Time: 0:00:41 ( 1.65 s/it) Accepted samples: 26 Time: 0:00:41 ( 1.60 s/it) Accepted samples: 27 Time: 0:00:48 ( 1.78 s/it) Accepted samples: 28 Time: 0:00:49 ( 1.75 s/it) Accepted samples: 29 Time: 0:00:51 ( 1.77 s/it) Accepted samples: 30 Time: 0:00:52 ( 1.75 s/it) Accepted samples: 31 Time: 0:00:53 ( 1.72 s/it) Accepted samples: 32 Time: 0:00:54 ( 1.70 s/it) Accepted samples: 33 Time: 0:01:00 ( 1.83 s/it) Accepted samples: 34 Time: 0:01:00 ( 1.79 s/it) Accepted samples: 35 Time: 0:01:01 ( 1.76 s/it) Accepted samples: 36 Time: 0:01:02 ( 1.72 s/it) Accepted samples: 37 Time: 0:01:03 ( 1.71 s/it) Accepted samples: 38 Time: 0:01:05 ( 1.72 s/it) Accepted samples: 39 Time: 0:01:09 ( 1.77 s/it) Accepted samples: 40 Time: 0:01:10 ( 1.75 s/it) Accepted samples: 41 Time: 0:01:10 ( 1.73 s/it) Accepted samples: 42 Time: 0:01:13 ( 1.74 s/it) Accepted samples: 43 Time: 0:01:13 ( 1.71 s/it) Accepted samples: 44 Time: 0:01:14 ( 1.70 s/it) Accepted samples: 45 Time: 0:01:15 ( 1.68 s/it) Accepted samples: 46 Time: 0:01:16 ( 1.65 s/it) Accepted samples: 47 Time: 0:01:16 ( 1.63 s/it) Accepted samples: 48 Time: 0:01:17 ( 1.61 s/it) Accepted samples: 49 Time: 0:01:18 ( 1.59 s/it) Accepted samples: 50 Time: 0:01:18 ( 1.58 s/it) Accepted samples: 51 Time: 0:01:19 ( 1.56 s/it) Accepted samples: 52 Time: 0:01:20 ( 1.55 s/it) Accepted samples: 53 Time: 0:01:21 ( 1.54 s/it) Accepted samples: 54 Time: 0:01:22 ( 1.53 s/it) Accepted samples: 55 Time: 0:01:23 ( 1.52 s/it) Accepted samples: 56 Time: 0:01:24 ( 1.50 s/it) Accepted samples: 57 Time: 0:01:26 ( 1.52 s/it) Accepted samples: 58 Time: 0:01:27 ( 1.52 s/it) Accepted samples: 59 Time: 0:01:28 ( 1.50 s/it) Accepted samples: 60 Time: 0:01:28 ( 1.48 s/it) Accepted samples: 61 Time: 0:01:29 ( 1.47 s/it) Accepted samples: 62 Time: 0:01:30 ( 1.46 s/it) Accepted samples: 63 Time: 0:01:32 ( 1.47 s/it) Accepted samples: 64 Time: 0:01:33 ( 1.46 s/it) Accepted samples: 65 Time: 0:01:34 ( 1.46 s/it) Accepted samples: 66 Time: 0:01:35 ( 1.44 s/it) Accepted samples: 67 Time: 0:01:40 ( 1.50 s/it) Accepted samples: 68 Time: 0:01:40 ( 1.49 s/it) Accepted samples: 69 Time: 0:01:42 ( 1.48 s/it) Accepted samples: 70 Time: 0:01:42 ( 1.47 s/it) Accepted samples: 71 Time: 0:01:43 ( 1.46 s/it) Accepted samples: 72 Time: 0:01:44 ( 1.45 s/it) Accepted samples: 73 Time: 0:01:45 ( 1.44 s/it) Accepted samples: 74 Time: 0:01:45 ( 1.43 s/it) Accepted samples: 75 Time: 0:01:47 ( 1.43 s/it) Accepted samples: 76 Time: 0:01:47 ( 1.42 s/it) Accepted samples: 77 Time: 0:01:57 ( 1.53 s/it) Accepted samples: 78 Time: 0:01:59 ( 1.53 s/it) Accepted samples: 79 Time: 0:02:00 ( 1.53 s/it) Accepted samples: 80 Time: 0:02:01 ( 1.52 s/it) Accepted samples: 81 Time: 0:02:06 ( 1.57 s/it) Accepted samples: 82 Time: 0:02:07 ( 1.55 s/it) Accepted samples: 83 Time: 0:02:09 ( 1.56 s/it) Accepted samples: 84 Time: 0:02:10 ( 1.55 s/it) Accepted samples: 85 Time: 0:02:12 ( 1.56 s/it) Accepted samples: 86 Time: 0:02:13 ( 1.55 s/it) Accepted samples: 87 Time: 0:02:13 ( 1.54 s/it) Accepted samples: 88 Time: 0:02:14 ( 1.53 s/it) Accepted samples: 89 Time: 0:02:15 ( 1.52 s/it) Accepted samples: 90 Time: 0:02:16 ( 1.51 s/it) Accepted samples: 91 Time: 0:02:16 ( 1.50 s/it) Accepted samples: 92 Time: 0:02:22 ( 1.55 s/it) Accepted samples: 93 Time: 0:02:25 ( 1.56 s/it) Accepted samples: 94 Time: 0:02:25 ( 1.55 s/it) Accepted samples: 95 Time: 0:02:26 ( 1.54 s/it) Accepted samples: 96 Time: 0:02:27 ( 1.53 s/it) Accepted samples: 97 Time: 0:02:27 ( 1.52 s/it) Accepted samples: 98 Time: 0:02:28 ( 1.52 s/it) Accepted samples: 99 Time: 0:02:30 ( 1.52 s/it) Accepted samples: 100 Time: 0:02:30 ( 1.50 s/it) Accepted samples: 100 Time: 0:02:30 ( 1.50 s/it) Acceptance Rate = 1.0 Current theta = [47.3541320081635;;] -------------------- epsilon = 0.013448121050816952 Acceptance Rate = 1.0 Starting step 1 epsilon = 1.0 Accepted samples: 2 Time: 0:00:02 ( 1.13 s/it) Accepted samples: 3 Time: 0:00:03 ( 1.25 s/it) Accepted samples: 4 Time: 0:00:04 ( 1.14 s/it) Accepted samples: 5 Time: 0:00:06 ( 1.26 s/it) Accepted samples: 6 Time: 0:00:08 ( 1.34 s/it) Accepted samples: 7 Time: 0:00:08 ( 1.25 s/it) Accepted samples: 8 Time: 0:00:09 ( 1.15 s/it) Accepted samples: 9 Time: 0:00:09 ( 1.10 s/it) Accepted samples: 10 Time: 0:00:10 ( 1.07 s/it) Accepted samples: 11 Time: 0:00:12 ( 1.10 s/it) Accepted samples: 12 Time: 0:00:13 ( 1.09 s/it) Accepted samples: 13 Time: 0:00:14 ( 1.08 s/it) Accepted samples: 14 Time: 0:00:14 ( 1.04 s/it) Accepted samples: 15 Time: 0:00:15 ( 1.01 s/it) Accepted samples: 16 Time: 0:00:26 ( 1.63 s/it) Accepted samples: 17 Time: 0:00:35 ( 2.08 s/it) Accepted samples: 18 Time: 0:00:35 ( 1.99 s/it) Accepted samples: 19 Time: 0:00:36 ( 1.92 s/it) Accepted samples: 20 Time: 0:00:36 ( 1.84 s/it) Accepted samples: 21 Time: 0:00:37 ( 1.79 s/it) Accepted samples: 22 Time: 0:00:38 ( 1.77 s/it) Accepted samples: 23 Time: 0:00:41 ( 1.81 s/it) Accepted samples: 24 Time: 0:00:48 ( 2.03 s/it) Accepted samples: 25 Time: 0:00:52 ( 2.08 s/it) Accepted samples: 26 Time: 0:00:52 ( 2.04 s/it) Accepted samples: 27 Time: 0:00:53 ( 2.00 s/it) Accepted samples: 28 Time: 0:00:59 ( 2.13 s/it) Accepted samples: 29 Time: 0:01:00 ( 2.08 s/it) Accepted samples: 30 Time: 0:01:01 ( 2.04 s/it) Accepted samples: 31 Time: 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52 Time: 0:01:27 ( 1.69 s/it) Accepted samples: 53 Time: 0:01:31 ( 1.72 s/it) Accepted samples: 54 Time: 0:01:31 ( 1.69 s/it) Accepted samples: 55 Time: 0:01:32 ( 1.68 s/it) Accepted samples: 56 Time: 0:01:37 ( 1.74 s/it) Accepted samples: 57 Time: 0:01:38 ( 1.73 s/it) Accepted samples: 58 Time: 0:01:39 ( 1.71 s/it) Accepted samples: 59 Time: 0:01:39 ( 1.69 s/it) Accepted samples: 60 Time: 0:01:40 ( 1.67 s/it) Accepted samples: 61 Time: 0:01:41 ( 1.66 s/it) Accepted samples: 62 Time: 0:01:42 ( 1.66 s/it) Accepted samples: 63 Time: 0:01:43 ( 1.64 s/it) Accepted samples: 64 Time: 0:01:44 ( 1.63 s/it) Accepted samples: 65 Time: 0:01:55 ( 1.77 s/it) Accepted samples: 66 Time: 0:01:56 ( 1.76 s/it) Accepted samples: 67 Time: 0:01:57 ( 1.75 s/it) Accepted samples: 68 Time: 0:02:02 ( 1.81 s/it) Accepted samples: 69 Time: 0:02:05 ( 1.82 s/it) Accepted samples: 70 Time: 0:02:08 ( 1.84 s/it) Accepted samples: 71 Time: 0:02:09 ( 1.82 s/it) Accepted samples: 72 Time: 0:02:09 ( 1.80 s/it) Accepted samples: 73 Time: 0:02:10 ( 1.79 s/it) Accepted samples: 74 Time: 0:02:11 ( 1.77 s/it) Accepted samples: 75 Time: 0:02:11 ( 1.75 s/it) Accepted samples: 76 Time: 0:02:19 ( 1.84 s/it) Accepted samples: 77 Time: 0:02:20 ( 1.83 s/it) Accepted samples: 78 Time: 0:02:21 ( 1.82 s/it) Accepted samples: 79 Time: 0:02:22 ( 1.80 s/it) Accepted samples: 80 Time: 0:02:22 ( 1.78 s/it) Accepted samples: 81 Time: 0:02:24 ( 1.78 s/it) Accepted samples: 82 Time: 0:02:24 ( 1.76 s/it)Acceptance Rate = 0.82 Current theta = [56.124847943564305;;] -------------------- epsilon = 0.14999251477646203 Acceptance Rate = 0.82 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`. │ caller = fit_vi(model::OneTimescaleModel; n_samples::Int64, n_iterations::Int64, n_elbo_samples::Int64, optimizer::AutoForwardDiff{nothing, Nothing}) at turing_backend.jl:95 └ @ IntrinsicTimescales.TuringBackend ~/.julia/packages/IntrinsicTimescales/42U57/src/core/turing_backend.jl:95 Optimizing 40%|████████████▍ | ETA: 0:01:32 (30.66 s/it) iteration: 2 elbo: -1256.7384257100423     Optimizing 60%|██████████████████▋ | ETA: 0:00:43 (21.59 s/it) iteration: 3 elbo: -404.5007487410346     Optimizing 80%|████████████████████████▊ | ETA: 0:00:17 (16.99 s/it) iteration: 4 elbo: -1228.0424005997718     Optimizing 100%|███████████████████████████████| Time: 0:01:10 (14.13 s/it) iteration: 5 elbo: -16631.868644019705 Optimizing 20%|██████▎ | ETA: 0:00:37 ( 4.58 s/it) iteration: 2 elbo: -1170.4419246787377     Optimizing 30%|█████████▎ | ETA: 0:00:33 ( 4.76 s/it) iteration: 3 elbo: -54811.30540368087     Optimizing 40%|████████████▍ | ETA: 0:00:28 ( 4.67 s/it) iteration: 4 elbo: -757.9296362020934     Optimizing 50%|███████████████▌ | ETA: 0:00:24 ( 4.75 s/it) iteration: 5 elbo: -1785.261423921504     Optimizing 60%|██████████████████▋ | ETA: 0:00:19 ( 4.71 s/it) iteration: 6 elbo: -1469.538892932543     Optimizing 70%|█████████████████████▊ | ETA: 0:00:14 ( 4.68 s/it) iteration: 7 elbo: -271.01601041313035     Optimizing 80%|████████████████████████▊ | ETA: 0:00:09 ( 4.74 s/it) iteration: 8 elbo: -973.4945875686001     Optimizing 90%|███████████████████████████▉ | ETA: 0:00:05 ( 4.74 s/it) iteration: 9 elbo: -121709.62260415981     Optimizing 100%|███████████████████████████████| Time: 0:00:47 ( 4.71 s/it) iteration: 10 elbo: -674.3725961094955 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 Starting step 1 epsilon = 1.0 Accepted samples: 2 Time: 0:00:00 ( 0.16 s/it) Accepted samples: 3 Time: 0:00:00 ( 0.16 s/it) Accepted samples: 4 Time: 0:00:00 ( 0.15 s/it) Accepted samples: 5 Time: 0:00:01 ( 0.27 s/it) Accepted samples: 6 Time: 0:00:01 ( 0.24 s/it) Accepted samples: 7 Time: 0:00:01 ( 0.22 s/it) Accepted samples: 8 Time: 0:00:01 ( 0.21 s/it) Accepted samples: 9 Time: 0:00:01 ( 0.22 s/it) Accepted samples: 10 Time: 0:00:02 ( 0.21 s/it) Accepted samples: 11 Time: 0:00:02 ( 0.20 s/it) Accepted samples: 12 Time: 0:00:02 ( 0.19 s/it) Accepted samples: 13 Time: 0:00:02 ( 0.19 s/it) Accepted samples: 14 Time: 0:00:02 ( 0.18 s/it) Accepted samples: 15 Time: 0:00:03 ( 0.22 s/it) Accepted samples: 16 Time: 0:00:03 ( 0.21 s/it) Accepted samples: 17 Time: 0:00:03 ( 0.22 s/it) Accepted samples: 19 Time: 0:00:03 ( 0.21 s/it) Accepted samples: 20 Time: 0:00:04 ( 0.20 s/it) Accepted samples: 21 Time: 0:00:04 ( 0.20 s/it) Accepted samples: 22 Time: 0:00:04 ( 0.20 s/it) Accepted samples: 23 Time: 0:00:04 ( 0.20 s/it) Accepted samples: 24 Time: 0:00:04 ( 0.20 s/it) Accepted samples: 25 Time: 0:00:05 ( 0.22 s/it) Accepted samples: 26 Time: 0:00:05 ( 0.22 s/it) Accepted samples: 27 Time: 0:00:05 ( 0.21 s/it) Accepted samples: 28 Time: 0:00:05 ( 0.21 s/it) Accepted samples: 29 Time: 0:00:06 ( 0.21 s/it) Accepted samples: 30 Time: 0:00:06 ( 0.21 s/it) Accepted samples: 31 Time: 0:00:06 ( 0.20 s/it) Accepted samples: 32 Time: 0:00:06 ( 0.20 s/it) Accepted samples: 33 Time: 0:00:06 ( 0.20 s/it) Accepted samples: 34 Time: 0:00:06 ( 0.20 s/it) Accepted samples: 35 Time: 0:00:06 ( 0.20 s/it) Accepted samples: 36 Time: 0:00:07 ( 0.20 s/it) Accepted samples: 37 Time: 0:00:07 ( 0.20 s/it) Accepted samples: 38 Time: 0:00:08 ( 0.21 s/it) Accepted samples: 40 Time: 0:00:08 ( 0.21 s/it) Accepted samples: 41 Time: 0:00:08 ( 0.21 s/it) Accepted samples: 42 Time: 0:00:08 ( 0.20 s/it) Accepted samples: 44 Time: 0:00:09 ( 0.20 s/it) Accepted samples: 45 Time: 0:00:09 ( 0.20 s/it) Accepted samples: 46 Time: 0:00:09 ( 0.20 s/it) Accepted samples: 47 Time: 0:00:09 ( 0.20 s/it) Accepted samples: 48 Time: 0:00:09 ( 0.20 s/it) Accepted samples: 49 Time: 0:00:10 ( 0.22 s/it) Accepted samples: 50 Time: 0:00:10 ( 0.22 s/it) Accepted samples: 51 Time: 0:00:10 ( 0.22 s/it) Accepted samples: 52 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 53 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 54 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 55 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 56 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 57 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 58 Time: 0:00:11 ( 0.21 s/it) Accepted samples: 59 Time: 0:00:12 ( 0.20 s/it) Accepted samples: 60 Time: 0:00:12 ( 0.20 s/it) Accepted samples: 61 Time: 0:00:12 ( 0.20 s/it) Accepted samples: 62 Time: 0:00:12 ( 0.20 s/it) Accepted samples: 63 Time: 0:00:13 ( 0.21 s/it) Accepted samples: 64 Time: 0:00:13 ( 0.21 s/it) Accepted samples: 65 Time: 0:00:13 ( 0.21 s/it) Accepted samples: 66 Time: 0:00:13 ( 0.21 s/it) Accepted samples: 67 Time: 0:00:14 ( 0.21 s/it) Accepted samples: 68 Time: 0:00:14 ( 0.21 s/it) Accepted samples: 69 Time: 0:00:14 ( 0.21 s/it) Accepted samples: 70 Time: 0:00:14 ( 0.21 s/it) Accepted samples: 71 Time: 0:00:14 ( 0.21 s/it) Accepted samples: 72 Time: 0:00:15 ( 0.22 s/it) Accepted samples: 73 Time: 0:00:16 ( 0.22 s/it) Accepted samples: 74 Time: 0:00:16 ( 0.22 s/it) Accepted samples: 75 Time: 0:00:16 ( 0.22 s/it)Acceptance Rate = 0.75 Current theta = [453.9267191297781 0.05030748302199332 0.5397481586946684] -------------------- epsilon = 0.6235879396189463 Acceptance Rate = 0.75 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`. │ caller = fit_vi(model::OneTimescaleAndOscModel; n_samples::Int64, n_iterations::Int64, n_elbo_samples::Int64, optimizer::AutoForwardDiff{nothing, Nothing}) at turing_backend.jl:95 └ @ IntrinsicTimescales.TuringBackend ~/.julia/packages/IntrinsicTimescales/42U57/src/core/turing_backend.jl:95 Optimizing 67%|████████████████████▋ | ETA: 0:00:28 (28.18 s/it) iteration: 2 elbo: -5295.084447462663     Optimizing 100%|███████████████████████████████| Time: 0:01:00 (20.02 s/it) iteration: 3 elbo: -12291.411335317169 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 Optimizing 67%|████████████████████▋ | ETA: 0:00:08 ( 8.15 s/it) iteration: 2 elbo: -873.3448379669521     Optimizing 100%|███████████████████████████████| Time: 0:00:25 ( 8.36 s/it) iteration: 3 elbo: -1659.3336137644853 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `tau` returned a `StalledSuccess`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 Starting step 1 epsilon = 1.0 Accepted samples: 2 Time: 0:00:03 ( 1.59 s/it) Accepted samples: 3 Time: 0:00:04 ( 1.61 s/it) Accepted samples: 4 Time: 0:00:05 ( 1.46 s/it) Accepted samples: 5 Time: 0:00:07 ( 1.56 s/it) Accepted samples: 6 Time: 0:00:09 ( 1.62 s/it) Accepted samples: 7 Time: 0:00:10 ( 1.52 s/it) Accepted samples: 8 Time: 0:00:11 ( 1.41 s/it) Accepted samples: 9 Time: 0:00:12 ( 1.35 s/it) Accepted samples: 10 Time: 0:00:13 ( 1.30 s/it) Accepted samples: 11 Time: 0:00:14 ( 1.33 s/it) Accepted samples: 12 Time: 0:00:15 ( 1.27 s/it) Accepted samples: 13 Time: 0:00:17 ( 1.32 s/it) Accepted samples: 14 Time: 0:00:17 ( 1.28 s/it) Accepted samples: 15 Time: 0:00:18 ( 1.25 s/it) Accepted samples: 16 Time: 0:00:28 ( 1.77 s/it) Accepted samples: 17 Time: 0:00:28 ( 1.70 s/it) Accepted samples: 18 Time: 0:00:37 ( 2.10 s/it) Accepted samples: 19 Time: 0:00:38 ( 2.03 s/it) Accepted samples: 20 Time: 0:00:39 ( 1.97 s/it) Accepted samples: 21 Time: 0:00:40 ( 1.91 s/it) Accepted samples: 22 Time: 0:00:40 ( 1.85 s/it) Accepted samples: 23 Time: 0:00:41 ( 1.81 s/it) Accepted samples: 24 Time: 0:00:43 ( 1.80 s/it) Accepted samples: 25 Time: 0:00:45 ( 1.82 s/it) Accepted samples: 26 Time: 0:00:46 ( 1.77 s/it) Accepted samples: 27 Time: 0:00:52 ( 1.96 s/it) Accepted samples: 28 Time: 0:00:53 ( 1.93 s/it) Accepted samples: 29 Time: 0:00:56 ( 1.95 s/it) Accepted samples: 30 Time: 0:00:57 ( 1.92 s/it) Accepted samples: 31 Time: 0:00:58 ( 1.89 s/it) Accepted samples: 32 Time: 0:00:59 ( 1.87 s/it) Accepted samples: 33 Time: 0:01:05 ( 1.99 s/it) Accepted samples: 34 Time: 0:01:07 ( 1.97 s/it) Accepted samples: 35 Time: 0:01:07 ( 1.94 s/it) Accepted samples: 36 Time: 0:01:08 ( 1.90 s/it) Accepted samples: 37 Time: 0:01:09 ( 1.89 s/it) Accepted samples: 38 Time: 0:01:12 ( 1.90 s/it) Accepted samples: 39 Time: 0:01:15 ( 1.95 s/it) Accepted samples: 40 Time: 0:01:17 ( 1.93 s/it) Accepted samples: 41 Time: 0:01:17 ( 1.90 s/it) Accepted samples: 42 Time: 0:01:20 ( 1.92 s/it) Accepted samples: 43 Time: 0:01:21 ( 1.89 s/it) Accepted samples: 44 Time: 0:01:22 ( 1.87 s/it) Accepted samples: 45 Time: 0:01:23 ( 1.85 s/it) Accepted samples: 46 Time: 0:01:24 ( 1.83 s/it) Accepted samples: 47 Time: 0:01:24 ( 1.80 s/it) Accepted samples: 48 Time: 0:01:25 ( 1.79 s/it) Accepted samples: 49 Time: 0:01:26 ( 1.77 s/it) Accepted samples: 50 Time: 0:01:27 ( 1.75 s/it) Accepted samples: 51 Time: 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72 Time: 0:01:57 ( 1.63 s/it) Accepted samples: 73 Time: 0:01:58 ( 1.62 s/it) Accepted samples: 74 Time: 0:01:59 ( 1.61 s/it) Accepted samples: 75 Time: 0:02:01 ( 1.62 s/it) Accepted samples: 76 Time: 0:02:01 ( 1.60 s/it) Accepted samples: 77 Time: 0:02:11 ( 1.71 s/it) Accepted samples: 78 Time: 0:02:13 ( 1.71 s/it) Accepted samples: 79 Time: 0:02:14 ( 1.70 s/it) Accepted samples: 80 Time: 0:02:15 ( 1.69 s/it) Accepted samples: 81 Time: 0:02:21 ( 1.74 s/it) Accepted samples: 82 Time: 0:02:21 ( 1.73 s/it) Accepted samples: 83 Time: 0:02:24 ( 1.74 s/it) Accepted samples: 84 Time: 0:02:25 ( 1.73 s/it) Accepted samples: 85 Time: 0:02:27 ( 1.74 s/it) Accepted samples: 86 Time: 0:02:28 ( 1.73 s/it) Accepted samples: 87 Time: 0:02:29 ( 1.72 s/it) Accepted samples: 88 Time: 0:02:30 ( 1.71 s/it) Accepted samples: 89 Time: 0:02:31 ( 1.70 s/it) Accepted samples: 90 Time: 0:02:32 ( 1.69 s/it) Accepted samples: 91 Time: 0:02:32 ( 1.68 s/it) Accepted samples: 92 Time: 0:02:38 ( 1.72 s/it) Accepted samples: 93 Time: 0:02:41 ( 1.73 s/it) Accepted samples: 94 Time: 0:02:41 ( 1.72 s/it) Accepted samples: 95 Time: 0:02:43 ( 1.72 s/it) Accepted samples: 96 Time: 0:02:44 ( 1.71 s/it) Accepted samples: 97 Time: 0:02:44 ( 1.70 s/it) Accepted samples: 98 Time: 0:02:45 ( 1.69 s/it) Accepted samples: 99 Time: 0:02:47 ( 1.69 s/it) Accepted samples: 100 Time: 0:02:47 ( 1.68 s/it) Accepted samples: 100 Time: 0:02:47 ( 1.68 s/it) Acceptance Rate = 1.0 Current theta = [47.3541320081635;;] -------------------- epsilon = 0.04897690905717058 Acceptance Rate = 1.0 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`. │ caller = fit_vi(model::OneTimescaleWithMissingModel; n_samples::Int64, n_iterations::Int64, n_elbo_samples::Int64, optimizer::AutoForwardDiff{nothing, Nothing}) at turing_backend.jl:95 └ @ IntrinsicTimescales.TuringBackend ~/.julia/packages/IntrinsicTimescales/42U57/src/core/turing_backend.jl:95 Optimizing 40%|████████████▍ | ETA: 0:00:29 ( 9.54 s/it) iteration: 2 elbo: -1600.0220393304219     Optimizing 60%|██████████████████▋ | ETA: 0:00:18 ( 8.95 s/it) iteration: 3 elbo: -593.9096430655275     Optimizing 80%|████████████████████████▊ | ETA: 0:00:08 ( 8.44 s/it) iteration: 4 elbo: -990.1335495843914     Optimizing 100%|███████████████████████████████| Time: 0:00:40 ( 8.15 s/it) iteration: 5 elbo: -8414.426436156387 Starting step 1 epsilon = 1.0 Accepted samples: 2 Time: 0:00:01 ( 0.96 s/it) Accepted samples: 3 Time: 0:00:03 ( 1.12 s/it) Accepted samples: 4 Time: 0:00:04 ( 1.07 s/it) Accepted samples: 5 Time: 0:00:05 ( 1.01 s/it) Accepted samples: 6 Time: 0:00:05 ( 0.97 s/it) Accepted samples: 7 Time: 0:00:07 ( 1.03 s/it) Accepted samples: 8 Time: 0:00:07 ( 1.00 s/it) Accepted samples: 9 Time: 0:00:08 ( 0.97 s/it) Accepted samples: 10 Time: 0:00:09 ( 0.96 s/it) Accepted samples: 11 Time: 0:00:11 ( 1.00 s/it) Accepted samples: 12 Time: 0:00:11 ( 0.98 s/it) Accepted samples: 13 Time: 0:00:12 ( 0.96 s/it) Accepted samples: 14 Time: 0:00:13 ( 0.95 s/it) Accepted samples: 15 Time: 0:00:14 ( 0.98 s/it) Accepted samples: 16 Time: 0:00:15 ( 0.97 s/it) Accepted samples: 17 Time: 0:00:16 ( 0.96 s/it) Accepted samples: 18 Time: 0:00:17 ( 0.95 s/it) Accepted samples: 19 Time: 0:00:18 ( 0.97 s/it) Accepted samples: 20 Time: 0:00:19 ( 0.96 s/it) Accepted samples: 21 Time: 0:00:19 ( 0.95 s/it) Accepted samples: 22 Time: 0:00:20 ( 0.94 s/it) Accepted samples: 23 Time: 0:00:21 ( 0.94 s/it) Accepted samples: 24 Time: 0:00:22 ( 0.96 s/it) Accepted samples: 25 Time: 0:00:23 ( 0.95 s/it) Accepted samples: 26 Time: 0:00:24 ( 0.95 s/it) 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89 Time: 0:01:22 ( 0.92 s/it) Accepted samples: 90 Time: 0:01:22 ( 0.92 s/it) Accepted samples: 91 Time: 0:01:23 ( 0.92 s/it) Accepted samples: 92 Time: 0:01:25 ( 0.92 s/it) Accepted samples: 93 Time: 0:01:25 ( 0.92 s/it) Accepted samples: 94 Time: 0:01:26 ( 0.92 s/it) Accepted samples: 95 Time: 0:01:27 ( 0.92 s/it) Accepted samples: 96 Time: 0:01:28 ( 0.92 s/it) Accepted samples: 97 Time: 0:01:29 ( 0.92 s/it) Accepted samples: 98 Time: 0:01:30 ( 0.92 s/it) Accepted samples: 99 Time: 0:01:31 ( 0.92 s/it) Accepted samples: 100 Time: 0:01:31 ( 0.92 s/it) Accepted samples: 100 Time: 0:01:31 ( 0.92 s/it) Acceptance Rate = 1.0 Current theta = [586.4957480436209 0.05342141832383249 0.49462384739297494] -------------------- epsilon = 0.31900637404842874 Acceptance Rate = 1.0 ┌ Warning: Solver for `tau` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: Solver for `knee` returned a `MaxIters`. This may or may not be a problem. We recommend plotting your autocorrelation function or power spectrum, along with the obtained fit to visually check the result. │ Other recommendations: The default solver for `IntrinsicTimescales.jl` is `LevenbergMarquardt`. You can try one of the alternative solvers in from `NonlinearSolve.jl` or change `solver_kwargs`: https://docs.sciml.ai/NonlinearSolve/stable/solvers/nonlinear_least_squares_solvers/ │ For details about the return code, see the documentation of NonlinearSolve.jl: https://docs.sciml.ai/NonlinearSolve/stable/basics/nonlinear_solution │ For example code to change the solver or solver_kwargs, see `IntrinsicTimescales.jl` documentation: https://duodenum96.github.io/IntrinsicTimescales.jl/stable/acw │ └ @ IntrinsicTimescales.Utils ~/.julia/packages/IntrinsicTimescales/42U57/src/utils/utils.jl:38 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: ADVI functionality is experimental. Proceed with caution. └ @ IntrinsicTimescales.Models ~/.julia/packages/IntrinsicTimescales/42U57/src/core/model.jl:243 ┌ Warning: This specialization along with the type `ADVI` will be deprecated in future releases. Please refer to the new interface for `vi`. │ caller = fit_vi(model::OneTimescaleAndOscWithMissingModel; n_samples::Int64, n_iterations::Int64, n_elbo_samples::Int64, optimizer::AutoForwardDiff{nothing, Nothing}) at turing_backend.jl:95 └ @ IntrinsicTimescales.TuringBackend ~/.julia/packages/IntrinsicTimescales/42U57/src/core/turing_backend.jl:95 Optimizing 67%|████████████████████▋ | ETA: 0:00:06 ( 6.43 s/it) iteration: 2 elbo: -37.57824846731592     Optimizing 100%|███████████████████████████████| Time: 0:00:16 ( 5.51 s/it) iteration: 3 elbo: -139.23276657199298 Test Summary: | Pass Total Time IntrinsicTimescales.jl | 529 529 25m15.9s Testing IntrinsicTimescales tests passed Testing completed after 1553.02s PkgEval succeeded after 2337.54s