From 6184afd6471c32e5f2a02995cdb77facd8e67bba Mon Sep 17 00:00:00 2001 From: Christoph Hotter <52912924+ChristophHotter@users.noreply.github.com> Date: Tue, 27 Aug 2024 08:48:55 +0200 Subject: [PATCH] Symbolicsv3 (#216) * bump compat TermInterface to v2 * fix deprication warning * dowgrade TermInterface@v0.4 * replace `istree` with `iscall` * replace `concrete_symtype` with `symtype` * Replace similarterm by maketerm and update to new method signature * fix typo in `maketerm` * Avoid extra call to typeof in maketerm * Add missing symtype overload for averages * Remove unneeded symtype method * Start updating to Symbolics v3 * fix indexing and sums tests; conj; project.toml * replace remake function in example; update docs/Porject.toml * example superradiant laser and excitation transport: steady state and remake * adjoint; tests * v-level test_broken; delete comment * Remove ipynb checkpoints * Add jupyter checkpoints to gitignore * Make symtypes Complex{Real} instead of ComplexF64 * Properly define iv in correlation --------- Co-authored-by: oameye Co-authored-by: David Plankensteiner --- .gitignore | 2 + Project.toml | 6 +- docs/Manifest.toml | 899 +++++++++++------- docs/Project.toml | 5 +- .../examples/cavity_antiresonance_indexed.md | 9 +- .../examples/excitation-transport-chain.md | 4 +- .../cavity_antiresonance_indexed.ipynb | 71 +- .../noisy excitation transport.ipynb | 523 +++++----- .../superradiant_laser_indexed.ipynb | 203 +--- docs/src/examples/superradiant-laser.md | 5 +- .../examples/superradiant_laser_indexed.md | 5 +- src/average.jl | 17 +- src/cnumber.jl | 13 +- src/correlation.jl | 21 +- src/diffeq.jl | 13 +- src/index_average.jl | 75 +- src/index_correlation.jl | 21 +- src/index_double_sums.jl | 13 +- src/index_meanfield.jl | 46 +- src/index_scale.jl | 8 +- src/index_utils.jl | 18 +- src/indexing.jl | 25 +- src/latexify_recipes.jl | 4 +- src/qnumber.jl | 11 +- src/scale.jl | 10 +- src/spin.jl | 3 +- src/utils.jl | 14 +- test/test_average_sums.jl | 35 +- test/test_indexed_correlation.jl | 5 +- test/test_spin.jl | 4 +- test/test_v-level.jl | 14 +- 31 files changed, 1036 insertions(+), 1066 deletions(-) diff --git a/.gitignore b/.gitignore index e479d89e..e37ee48a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ docs/build /examples/ /.vscode/ +Manifest.toml +docs/src/examples/jupyter_notebooks/.ipynb_checkpoints diff --git a/Project.toml b/Project.toml index 806b2ebf..61c1bfa2 100644 --- a/Project.toml +++ b/Project.toml @@ -24,9 +24,9 @@ MacroTools = "0.5" ModelingToolkit = "9" QuantumOpticsBase = "0.4, 0.5" SciMLBase = "1, 2" -SymbolicUtils = "1, 2" -Symbolics = "5" -TermInterface = "0.4, 1" +SymbolicUtils = "3.1.2" +Symbolics = "6" +TermInterface = "2" julia = "1.6" [extras] diff --git a/docs/Manifest.toml b/docs/Manifest.toml index 45a110ec..fcad91d8 100644 --- a/docs/Manifest.toml +++ b/docs/Manifest.toml @@ -2,12 +2,17 @@ julia_version = "1.10.2" manifest_format = "2.0" -project_hash = "1f0ab8b6d1b7d39c9a90f5b6a9bcd1b9fecb1403" +project_hash = "56e5636392132101289fc242925ec6a7b0903f94" [[deps.ADTypes]] -git-tree-sha1 = "016833eb52ba2d6bea9fcb50ca295980e728ee24" +git-tree-sha1 = "99a6f5d0ce1c7c6afdb759daa30226f71c54f6b0" uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" -version = "0.2.7" +version = "1.7.1" +weakdeps = ["ChainRulesCore", "EnzymeCore"] + + [deps.ADTypes.extensions] + ADTypesChainRulesCoreExt = "ChainRulesCore" + ADTypesEnzymeCoreExt = "EnzymeCore" [[deps.ANSIColoredPrinters]] git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c" @@ -30,25 +35,52 @@ git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.5" +[[deps.Accessors]] +deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "MacroTools", "Markdown", "Test"] +git-tree-sha1 = "f61b15be1d76846c0ce31d3fcfac5380ae53db6a" +uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697" +version = "0.1.37" + + [deps.Accessors.extensions] + AccessorsAxisKeysExt = "AxisKeys" + AccessorsIntervalSetsExt = "IntervalSets" + AccessorsStaticArraysExt = "StaticArrays" + AccessorsStructArraysExt = "StructArrays" + AccessorsUnitfulExt = "Unitful" + + [deps.Accessors.weakdeps] + AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5" + IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953" + Requires = "ae029012-a4dd-5104-9daa-d747884805df" + StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" + StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" + Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" + [[deps.Adapt]] deps = ["LinearAlgebra", "Requires"] -git-tree-sha1 = "cde29ddf7e5726c9fb511f340244ea3481267608" +git-tree-sha1 = "6a55b747d1812e699320963ffde36f1ebdda4099" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.7.2" +version = "4.0.4" weakdeps = ["StaticArrays"] [deps.Adapt.extensions] AdaptStaticArraysExt = "StaticArrays" +[[deps.AliasTables]] +deps = ["PtrArrays", "Random"] +git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff" +uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8" +version = "1.1.3" + [[deps.ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" version = "1.1.1" [[deps.ArnoldiMethod]] deps = ["LinearAlgebra", "Random", "StaticArrays"] -git-tree-sha1 = "62e51b39331de8911e4a7ff6f5aaf38a5f4cc0ae" +git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6" uuid = "ec485272-7323-5ecc-a04f-4719b315124d" -version = "0.2.0" +version = "0.4.0" [[deps.Arpack]] deps = ["Arpack_jll", "Libdl", "LinearAlgebra", "Logging"] @@ -63,16 +95,20 @@ uuid = "68821587-b530-5797-8361-c406ea357684" version = "3.5.1+1" [[deps.ArrayInterface]] -deps = ["Adapt", "LinearAlgebra", "Requires", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "c5aeb516a84459e0318a02507d2261edad97eb75" +deps = ["Adapt", "LinearAlgebra"] +git-tree-sha1 = "f54c23a5d304fb87110de62bace7777d59088c34" uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9" -version = "7.7.1" +version = "7.15.0" [deps.ArrayInterface.extensions] ArrayInterfaceBandedMatricesExt = "BandedMatrices" ArrayInterfaceBlockBandedMatricesExt = "BlockBandedMatrices" ArrayInterfaceCUDAExt = "CUDA" + ArrayInterfaceCUDSSExt = "CUDSS" + ArrayInterfaceChainRulesExt = "ChainRules" ArrayInterfaceGPUArraysCoreExt = "GPUArraysCore" + ArrayInterfaceReverseDiffExt = "ReverseDiff" + ArrayInterfaceSparseArraysExt = "SparseArrays" ArrayInterfaceStaticArraysCoreExt = "StaticArraysCore" ArrayInterfaceTrackerExt = "Tracker" @@ -80,15 +116,19 @@ version = "7.7.1" BandedMatrices = "aae01518-5342-5314-be14-df237901396f" BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" + CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e" + ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2" GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527" + ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" [[deps.ArrayLayouts]] deps = ["FillArrays", "LinearAlgebra"] -git-tree-sha1 = "6404a564c24a994814106c374bec893195e19bac" +git-tree-sha1 = "0dd7edaff278e346eb0ca07a7e75c9438408a3ce" uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a" -version = "1.8.0" +version = "1.10.3" weakdeps = ["SparseArrays"] [deps.ArrayLayouts.extensions] @@ -101,20 +141,20 @@ uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.Bijections]] -git-tree-sha1 = "c9b163bd832e023571e86d0b90d9de92a9879088" +git-tree-sha1 = "95f5c7e2d177b7ba1a240b0518038b975d72a8c0" uuid = "e2ed5e7c-b2de-5872-ae92-c73ca462fb04" -version = "0.1.6" +version = "0.1.7" [[deps.BitFlags]] -git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" +git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" -version = "0.1.8" +version = "0.1.9" [[deps.BitTwiddlingConvenienceFunctions]] deps = ["Static"] -git-tree-sha1 = "0c5f81f47bbbcf4aea7b2959135713459170798b" +git-tree-sha1 = "f21cfd4950cb9f0587d5067e69405ad2acd27b87" uuid = "62783981-4cbd-42fc-bca8-16325de8dc4b" -version = "0.1.5" +version = "0.1.6" [[deps.Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -124,33 +164,27 @@ version = "1.0.8+1" [[deps.CPUSummary]] deps = ["CpuId", "IfElse", "PrecompileTools", "Static"] -git-tree-sha1 = "601f7e7b3d36f18790e2caf83a882d88e9b71ff1" +git-tree-sha1 = "5a97e67919535d6841172016c9530fd69494e5ec" uuid = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9" -version = "0.2.4" +version = "0.2.6" [[deps.CSTParser]] deps = ["Tokenize"] -git-tree-sha1 = "b544d62417a99d091c569b95109bc9d8c223e9e3" +git-tree-sha1 = "0157e592151e39fa570645e2b2debcdfb8a0f112" uuid = "00ebfdb7-1f24-5e51-bd34-a7502290713f" -version = "3.4.2" +version = "3.4.3" [[deps.Cairo_jll]] deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "a4c43f59baa34011e303e76f5c8c91bf58415aaf" +git-tree-sha1 = "a2f1c8c668c8e3cb4cca4e57a8efdb09067bb3fd" uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" -version = "1.18.0+1" - -[[deps.Calculus]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad" -uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" -version = "0.5.1" +version = "1.18.0+2" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra"] -git-tree-sha1 = "575cd02e080939a33b6df6c5853d14924c08e35b" +git-tree-sha1 = "71acdbf594aab5bbb2cec89b208c41b4c411e49f" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.23.0" +version = "1.24.0" weakdeps = ["SparseArrays"] [deps.ChainRulesCore.extensions] @@ -158,27 +192,27 @@ weakdeps = ["SparseArrays"] [[deps.CloseOpenIntervals]] deps = ["Static", "StaticArrayInterface"] -git-tree-sha1 = "70232f82ffaab9dc52585e0dd043b5e0c6b714f1" +git-tree-sha1 = "05ba0d07cd4fd8b7a39541e31a7b0254704ea581" uuid = "fb6a15b2-703c-40df-9091-08a04967cfa9" -version = "0.1.12" +version = "0.1.13" [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73" +git-tree-sha1 = "bce6804e5e6044c6daab27bb533d1295e4a2e759" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.4" +version = "0.7.6" [[deps.ColorSchemes]] deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] -git-tree-sha1 = "67c1f244b991cad9b0aa4b7540fb758c2488b129" +git-tree-sha1 = "b5278586822443594ff615963b0c09755771b3e0" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" -version = "3.24.0" +version = "3.26.0" [[deps.ColorTypes]] deps = ["FixedPointNumbers", "Random"] -git-tree-sha1 = "eb7f0f8307f71fac7c606984ea5fb2817275d6e4" +git-tree-sha1 = "b10d0b65641d57b8b4d5e234446582de5047050d" uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" -version = "0.11.4" +version = "0.11.5" [[deps.ColorVectorSpace]] deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "Requires", "Statistics", "TensorCore"] @@ -192,9 +226,9 @@ weakdeps = ["SpecialFunctions"] [[deps.Colors]] deps = ["ColorTypes", "FixedPointNumbers", "Reexport"] -git-tree-sha1 = "fc08e5930ee9a4e03f84bfb5211cb54e7769758a" +git-tree-sha1 = "362a287c3aa50601b0bc359053d5c2468f0e7ce0" uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" -version = "0.12.10" +version = "0.12.11" [[deps.Combinatorics]] git-tree-sha1 = "08c8b6831dc00bfea825826be0bc8336fc369860" @@ -218,11 +252,16 @@ git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7" uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" version = "0.3.0" +[[deps.CommonWorldInvalidations]] +git-tree-sha1 = "ae52d1c52048455e85a387fbee9be553ec2b68d0" +uuid = "f70d9fcc-98c5-4d4a-abd7-e4cdeebd8ca8" +version = "1.0.0" + [[deps.Compat]] deps = ["TOML", "UUIDs"] -git-tree-sha1 = "c955881e3c981181362ae4088b35995446298b80" +git-tree-sha1 = "8ae8d32e09f0dcf42a36b90d4e17f5dd2e4c4215" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.14.0" +version = "4.16.0" weakdeps = ["Dates", "LinearAlgebra"] [deps.Compat.extensions] @@ -234,9 +273,18 @@ uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" version = "1.1.0+0" [[deps.CompositeTypes]] -git-tree-sha1 = "02d2316b7ffceff992f3096ae48c7829a8aa0638" +git-tree-sha1 = "bce26c3dab336582805503bed209faab1c279768" uuid = "b152e2b5-7a66-4b01-a709-34e65c35f657" -version = "0.1.3" +version = "0.1.4" + +[[deps.CompositionsBase]] +git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" +uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" +version = "0.1.2" +weakdeps = ["InverseFunctions"] + + [deps.CompositionsBase.extensions] + CompositionsBaseInverseFunctionsExt = "InverseFunctions" [[deps.ConcreteStructs]] git-tree-sha1 = "f749037478283d372048690eb3b5f92a79432b34" @@ -245,15 +293,15 @@ version = "0.2.3" [[deps.ConcurrentUtilities]] deps = ["Serialization", "Sockets"] -git-tree-sha1 = "6cbbd4d241d7e6579ab354737f4dd95ca43946e1" +git-tree-sha1 = "ea32b83ca4fefa1768dc84e504cc0a94fb1ab8d1" uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" -version = "2.4.1" +version = "2.4.2" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] -git-tree-sha1 = "260fd2400ed2dab602a7c15cf10c1933c59930a2" +git-tree-sha1 = "a33b7ced222c6165f624a3f2b55945fac5a598d9" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.5.5" +version = "1.5.7" weakdeps = ["IntervalSets", "StaticArrays"] [deps.ConstructionBase.extensions] @@ -261,9 +309,9 @@ weakdeps = ["IntervalSets", "StaticArrays"] ConstructionBaseStaticArraysExt = "StaticArrays" [[deps.Contour]] -git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781" +git-tree-sha1 = "439e35b0b36e2e5881738abc8857bd92ad6ff9a8" uuid = "d38c429a-6771-53c6-b99e-75d170b6e991" -version = "0.6.2" +version = "0.6.3" [[deps.CpuId]] deps = ["Markdown"] @@ -283,9 +331,9 @@ version = "1.16.0" [[deps.DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "0f4b5d62a88d8f59003e43c25a8a90de9eb76317" +git-tree-sha1 = "1d0a14036acb104d9e89698bd408f63ab58cdc82" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.18" +version = "0.18.20" [[deps.DataValueInterfaces]] git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" @@ -296,6 +344,12 @@ version = "1.0.0" deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" +[[deps.Dbus_jll]] +deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl"] +git-tree-sha1 = "fc173b380865f70627d7dd1190dc2fce6cc105af" +uuid = "ee1fde0b-3d02-5ea6-8484-8dfef6360eab" +version = "1.14.10+0" + [[deps.DelimitedFiles]] deps = ["Mmap"] git-tree-sha1 = "9e2f36d3c96a820c678f2f1f1782582fcf685bae" @@ -303,12 +357,13 @@ uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" version = "1.9.1" [[deps.DiffEqBase]] -deps = ["ArrayInterface", "DataStructures", "DocStringExtensions", "EnumX", "EnzymeCore", "FastBroadcast", "ForwardDiff", "FunctionWrappers", "FunctionWrappersWrappers", "LinearAlgebra", "Logging", "Markdown", "MuladdMacro", "Parameters", "PreallocationTools", "PrecompileTools", "Printf", "RecursiveArrayTools", "Reexport", "SciMLBase", "SciMLOperators", "Setfield", "SparseArrays", "Static", "StaticArraysCore", "Statistics", "Tricks", "TruncatedStacktraces"] -git-tree-sha1 = "aee5798bd4f1ed1260bd1741221f9589e5ee8a90" +deps = ["ArrayInterface", "ConcreteStructs", "DataStructures", "DocStringExtensions", "EnumX", "EnzymeCore", "FastBroadcast", "FastClosures", "ForwardDiff", "FunctionWrappers", "FunctionWrappersWrappers", "LinearAlgebra", "Logging", "Markdown", "MuladdMacro", "Parameters", "PreallocationTools", "PrecompileTools", "Printf", "RecursiveArrayTools", "Reexport", "SciMLBase", "SciMLOperators", "Setfield", "Static", "StaticArraysCore", "Statistics", "Tricks", "TruncatedStacktraces"] +git-tree-sha1 = "4cb796ab54311147b9c7191d34d502693b603a22" uuid = "2b5f629d-d688-5b77-993f-72d75c75574e" -version = "6.147.3" +version = "6.154.0" [deps.DiffEqBase.extensions] + DiffEqBaseCUDAExt = "CUDA" DiffEqBaseChainRulesCoreExt = "ChainRulesCore" DiffEqBaseDistributionsExt = "Distributions" DiffEqBaseEnzymeExt = ["ChainRulesCore", "Enzyme"] @@ -317,10 +372,12 @@ version = "6.147.3" DiffEqBaseMeasurementsExt = "Measurements" DiffEqBaseMonteCarloMeasurementsExt = "MonteCarloMeasurements" DiffEqBaseReverseDiffExt = "ReverseDiff" + DiffEqBaseSparseArraysExt = "SparseArrays" DiffEqBaseTrackerExt = "Tracker" DiffEqBaseUnitfulExt = "Unitful" [deps.DiffEqBase.weakdeps] + CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" @@ -329,6 +386,7 @@ version = "6.147.3" Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7" MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca" ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" @@ -343,10 +401,10 @@ version = "2.37.0" Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4" [[deps.DiffEqNoiseProcess]] -deps = ["DiffEqBase", "Distributions", "GPUArraysCore", "LinearAlgebra", "Markdown", "Optim", "PoissonRandom", "QuadGK", "Random", "Random123", "RandomNumbers", "RecipesBase", "RecursiveArrayTools", "Requires", "ResettableStacks", "SciMLBase", "StaticArraysCore", "Statistics"] -git-tree-sha1 = "65cbbe1450ced323b4b17228ccd96349d96795a7" +deps = ["DiffEqBase", "Distributions", "GPUArraysCore", "LinearAlgebra", "Markdown", "Optim", "PoissonRandom", "QuadGK", "Random", "Random123", "RandomNumbers", "RecipesBase", "RecursiveArrayTools", "ResettableStacks", "SciMLBase", "StaticArraysCore", "Statistics"] +git-tree-sha1 = "ab1e6515ce15f01316a9825b02729fefa51726bd" uuid = "77a26b50-5914-5dd7-bc55-306e6241c503" -version = "5.21.0" +version = "5.23.0" [deps.DiffEqNoiseProcess.extensions] DiffEqNoiseProcessReverseDiffExt = "ReverseDiff" @@ -366,6 +424,42 @@ git-tree-sha1 = "23163d55f885173722d1e4cf0f6110cdbaf7e272" uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" version = "1.15.1" +[[deps.DifferentiationInterface]] +deps = ["ADTypes", "Compat", "DocStringExtensions", "FillArrays", "LinearAlgebra", "PackageExtensionCompat", "SparseArrays", "SparseMatrixColorings"] +git-tree-sha1 = "06955b4e80eff01df10552ccf0226571bde94d49" +uuid = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63" +version = "0.5.14" + + [deps.DifferentiationInterface.extensions] + DifferentiationInterfaceChainRulesCoreExt = "ChainRulesCore" + DifferentiationInterfaceDiffractorExt = "Diffractor" + DifferentiationInterfaceEnzymeExt = "Enzyme" + DifferentiationInterfaceFastDifferentiationExt = "FastDifferentiation" + DifferentiationInterfaceFiniteDiffExt = "FiniteDiff" + DifferentiationInterfaceFiniteDifferencesExt = "FiniteDifferences" + DifferentiationInterfaceForwardDiffExt = "ForwardDiff" + DifferentiationInterfacePolyesterForwardDiffExt = "PolyesterForwardDiff" + DifferentiationInterfaceReverseDiffExt = "ReverseDiff" + DifferentiationInterfaceSymbolicsExt = "Symbolics" + DifferentiationInterfaceTapirExt = "Tapir" + DifferentiationInterfaceTrackerExt = "Tracker" + DifferentiationInterfaceZygoteExt = ["Zygote", "ForwardDiff"] + + [deps.DifferentiationInterface.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + Diffractor = "9f5e2b26-1114-432f-b630-d3fe2085c51c" + Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" + FastDifferentiation = "eb9bf01b-bf85-4b60-bf87-ee5de06c00be" + FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41" + FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000" + ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" + PolyesterForwardDiff = "98d1487c-24ca-40b6-b7ab-df2af84e126b" + ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7" + Tapir = "07d77754-e150-4737-8c94-cd238a1fb45b" + Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" + Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" + [[deps.Distances]] deps = ["LinearAlgebra", "Statistics", "StatsAPI"] git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0" @@ -382,10 +476,10 @@ deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" [[deps.Distributions]] -deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] -git-tree-sha1 = "7c302d7a5fec5214eb8a5a4c466dcf7a51fcf169" +deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] +git-tree-sha1 = "0e0a1264b0942f1f3abb2b30891f2a590cc652ac" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.107" +version = "0.25.110" [deps.Distributions.extensions] DistributionsChainRulesCoreExt = "ChainRulesCore" @@ -405,32 +499,32 @@ version = "0.9.3" [[deps.Documenter]] deps = ["ANSIColoredPrinters", "AbstractTrees", "Base64", "CodecZlib", "Dates", "DocStringExtensions", "Downloads", "Git", "IOCapture", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "MarkdownAST", "Pkg", "PrecompileTools", "REPL", "RegistryInstances", "SHA", "TOML", "Test", "Unicode"] -git-tree-sha1 = "4a40af50e8b24333b9ec6892546d9ca5724228eb" +git-tree-sha1 = "76deb8c15f37a3853f13ea2226b8f2577652de05" uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4" -version = "1.3.0" +version = "1.5.0" [[deps.DomainSets]] deps = ["CompositeTypes", "IntervalSets", "LinearAlgebra", "Random", "StaticArrays"] -git-tree-sha1 = "9fd332fb3b276a080e3ebccf0dcd98f4a10bf6a6" +git-tree-sha1 = "490392af2c7d63183bfa2c8aaa6ab981c5ba7561" uuid = "5b8099bc-c8ec-5219-889f-1d9e522a28bf" -version = "0.7.10" +version = "0.7.14" + + [deps.DomainSets.extensions] + DomainSetsMakieExt = "Makie" + + [deps.DomainSets.weakdeps] + Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" [[deps.Downloads]] deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"] uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6" version = "1.6.0" -[[deps.DualNumbers]] -deps = ["Calculus", "NaNMath", "SpecialFunctions"] -git-tree-sha1 = "5837a837389fccf076445fce071c8ddaea35a566" -uuid = "fa6b7ba4-c1ee-5f82-b5fc-ecf0adba8f74" -version = "0.6.8" - [[deps.DynamicPolynomials]] deps = ["Future", "LinearAlgebra", "MultivariatePolynomials", "MutableArithmetics", "Pkg", "Reexport", "Test"] -git-tree-sha1 = "0bb0a6f812213ecc8fbbcf472f4a993036858971" +git-tree-sha1 = "30a1848c4f4fc35d1d4bbbd125650f6a11b5bc6c" uuid = "7c1d4256-1411-5781-91ec-d7bc3513ac07" -version = "0.5.5" +version = "0.5.7" [[deps.DynamicQuantities]] deps = ["Compat", "PackageExtensionCompat", "Tricks"] @@ -456,9 +550,9 @@ uuid = "4e289a0a-7415-4d19-859d-a7e5c4648b56" version = "1.0.4" [[deps.EnzymeCore]] -git-tree-sha1 = "1bc328eec34ffd80357f84a84bb30e4374e9bd60" +git-tree-sha1 = "8f205a601760f4798a10f138c3940f0451d95188" uuid = "f151be2c-9106-41f4-ab19-57ee4f262869" -version = "0.6.6" +version = "0.7.8" weakdeps = ["Adapt"] [deps.EnzymeCore.extensions] @@ -478,9 +572,9 @@ version = "0.1.10" [[deps.Expat_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "4558ab818dcceaab612d1bb8c19cee87eda2b83c" +git-tree-sha1 = "1c6317308b9dc757616f0b5cb379db10494443a7" uuid = "2e619515-83b5-522b-bb60-26c02a35a201" -version = "2.5.0+0" +version = "2.6.2+0" [[deps.ExponentialUtilities]] deps = ["Adapt", "ArrayInterface", "GPUArraysCore", "GenericSchur", "LinearAlgebra", "PrecompileTools", "Printf", "SparseArrays", "libblastrampoline_jll"] @@ -493,6 +587,12 @@ git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec" uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04" version = "0.1.10" +[[deps.Expronicon]] +deps = ["MLStyle", "Pkg", "TOML"] +git-tree-sha1 = "fc3951d4d398b5515f91d7fe5d45fc31dccb3c9b" +uuid = "6b7a57c9-7cc1-4fdf-b7f5-e857abae3636" +version = "0.8.5" + [[deps.FFMPEG]] deps = ["FFMPEG_jll"] git-tree-sha1 = "b57e3acbe22f8484b4b5ff66a7499717fe1a9cc8" @@ -519,9 +619,9 @@ version = "3.3.10+0" [[deps.FastBroadcast]] deps = ["ArrayInterface", "LinearAlgebra", "Polyester", "Static", "StaticArrayInterface", "StrideArraysCore"] -git-tree-sha1 = "a6e756a880fc419c8b41592010aebe6a5ce09136" +git-tree-sha1 = "ab1b34570bcdf272899062e1a56285a53ecaae08" uuid = "7034ab61-46d4-4ed7-9d0f-46aef9175898" -version = "0.2.8" +version = "0.3.5" [[deps.FastClosures]] git-tree-sha1 = "acebe244d53ee1b461970f8910c235b259e772ef" @@ -542,18 +642,18 @@ version = "1.0.2" [[deps.FastLapackInterface]] deps = ["LinearAlgebra"] -git-tree-sha1 = "0a59c7d1002f3131de53dc4568a47d15a44daef7" +git-tree-sha1 = "cbf5edddb61a43669710cbc2241bc08b36d9e660" uuid = "29a986be-02c6-4525-aec4-84b980013641" -version = "2.0.2" +version = "2.0.4" [[deps.FileWatching]] uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" [[deps.FillArrays]] -deps = ["LinearAlgebra", "Random"] -git-tree-sha1 = "5b93957f6dcd33fc343044af3d48c215be2562f1" +deps = ["LinearAlgebra"] +git-tree-sha1 = "fd0002c0b5362d7eb952450ad5eb742443340d6e" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" -version = "1.9.3" +version = "1.12.0" weakdeps = ["PDMats", "SparseArrays", "Statistics"] [deps.FillArrays.extensions] @@ -562,15 +662,15 @@ weakdeps = ["PDMats", "SparseArrays", "Statistics"] FillArraysStatisticsExt = "Statistics" [[deps.FindFirstFunctions]] -git-tree-sha1 = "e90fef90f7d75e6a5b435b0fd65609759f99717a" +git-tree-sha1 = "fa0ba2042021409deb144f868abafde0b06be8f0" uuid = "64ca27bc-2ba2-4a57-88aa-44e436879224" -version = "1.2.0" +version = "1.3.0" [[deps.FiniteDiff]] -deps = ["ArrayInterface", "LinearAlgebra", "Requires", "Setfield", "SparseArrays"] -git-tree-sha1 = "bc0c5092d6caaea112d3c8e3b238d61563c58d5f" +deps = ["ArrayInterface", "LinearAlgebra", "Setfield", "SparseArrays"] +git-tree-sha1 = "f9219347ebf700e77ca1d48ef84e4a82a6701882" uuid = "6a86dc24-6348-571c-b903-95158fe2bd41" -version = "2.23.0" +version = "2.24.0" [deps.FiniteDiff.extensions] FiniteDiffBandedMatricesExt = "BandedMatrices" @@ -584,15 +684,15 @@ version = "2.23.0" [[deps.FixedPointNumbers]] deps = ["Statistics"] -git-tree-sha1 = "335bfdceacc84c5cdf16aadc768aa5ddfc5383cc" +git-tree-sha1 = "05882d6995ae5c12bb5f36dd2ed3f61c98cbb172" uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" -version = "0.8.4" +version = "0.8.5" [[deps.Fontconfig_jll]] -deps = ["Artifacts", "Bzip2_jll", "Expat_jll", "FreeType2_jll", "JLLWrappers", "Libdl", "Libuuid_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "21efd19106a55620a188615da6d3d06cd7f6ee03" +deps = ["Artifacts", "Bzip2_jll", "Expat_jll", "FreeType2_jll", "JLLWrappers", "Libdl", "Libuuid_jll", "Zlib_jll"] +git-tree-sha1 = "db16beca600632c95fc8aca29890d83788dd8b23" uuid = "a3f928ae-7b40-5064-980b-68af3947d34b" -version = "2.13.93+0" +version = "2.13.96+0" [[deps.Format]] git-tree-sha1 = "9c68794ef81b08086aeb32eeaf33531668d5f5fc" @@ -611,15 +711,15 @@ weakdeps = ["StaticArrays"] [[deps.FreeType2_jll]] deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Zlib_jll"] -git-tree-sha1 = "d8db6a5a2fe1381c1ea4ef2cab7c69c2de7f9ea0" +git-tree-sha1 = "5c1d8ae0efc6c2e7b1fc502cbe25def8f661b7bc" uuid = "d7e528f0-a631-5988-bf34-fe36492bcfd7" -version = "2.13.1+0" +version = "2.13.2+0" [[deps.FriBidi_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "aa31987c2ba8704e23c6c8ba8a4f769d5d7e4f91" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1ed150b39aebcc805c26b93a8d0122c940f64ce2" uuid = "559328eb-81f9-559d-9380-de523a88c83c" -version = "1.0.10+0" +version = "1.0.14+0" [[deps.FunctionWrappers]] git-tree-sha1 = "d62485945ce5ae9c0c48f124a84998d755bae00e" @@ -634,37 +734,37 @@ version = "0.1.3" [[deps.Functors]] deps = ["LinearAlgebra"] -git-tree-sha1 = "8ae30e786837ce0a24f5e2186938bf3251ab94b2" +git-tree-sha1 = "64d8e93700c7a3f28f717d265382d52fac9fa1c1" uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196" -version = "0.4.8" +version = "0.4.12" [[deps.Future]] deps = ["Random"] uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820" [[deps.GLFW_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] -git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll", "libdecor_jll", "xkbcommon_jll"] +git-tree-sha1 = "532f9126ad901533af1d4f5c198867227a7bb077" uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89" -version = "3.3.9+0" +version = "3.4.0+1" [[deps.GPUArraysCore]] deps = ["Adapt"] -git-tree-sha1 = "2d6ca471a6c7b536127afccfa7564b5b39227fe0" +git-tree-sha1 = "ec632f177c0d990e64d955ccc1b8c04c485a0950" uuid = "46192b85-c4d5-4398-a991-12ede77f4527" -version = "0.1.5" +version = "0.1.6" [[deps.GR]] -deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"] -git-tree-sha1 = "3437ade7073682993e092ca570ad68a2aba26983" +deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Preferences", "Printf", "Qt6Wayland_jll", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "p7zip_jll"] +git-tree-sha1 = "629693584cef594c3f6f99e76e7a7ad17e60e8d5" uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -version = "0.73.3" +version = "0.73.7" [[deps.GR_jll]] deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "a96d5c713e6aa28c242b0d25c1347e258d6541ab" +git-tree-sha1 = "a8863b69c2a0859f2c2c87ebdc4c6712e88bdf0d" uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" -version = "0.73.3+0" +version = "0.73.7+0" [[deps.GSL]] deps = ["GSL_jll", "Libdl", "Markdown"] @@ -698,15 +798,15 @@ version = "1.3.1" [[deps.Git_jll]] deps = ["Artifacts", "Expat_jll", "JLLWrappers", "LibCURL_jll", "Libdl", "Libiconv_jll", "OpenSSL_jll", "PCRE2_jll", "Zlib_jll"] -git-tree-sha1 = "12945451c5d0e2d0dca0724c3a8d6448b46bbdf9" +git-tree-sha1 = "d18fb8a1f3609361ebda9bf029b60fd0f120c809" uuid = "f8c6e375-362e-5223-8a59-34ff63f689eb" -version = "2.44.0+1" +version = "2.44.0+2" [[deps.Glib_jll]] deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE2_jll", "Zlib_jll"] -git-tree-sha1 = "359a1ba2e320790ddbe4ee8b4d54a305c0ea2aff" +git-tree-sha1 = "7c82e6a6cd34e9d935e9aa4051b66c6ff3af59ba" uuid = "7746bdde-850d-59dc-9ae8-88ece973131d" -version = "2.80.0+0" +version = "2.80.2+0" [[deps.Glob]] git-tree-sha1 = "97285bbd5230dd766e9ef6749b80fc617126d496" @@ -721,9 +821,9 @@ version = "1.3.14+0" [[deps.Graphs]] deps = ["ArnoldiMethod", "Compat", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"] -git-tree-sha1 = "899050ace26649433ef1af25bc17a815b3db52b7" +git-tree-sha1 = "ebd18c326fa6cee1efb7da9a3b45cf69da2ed4d9" uuid = "86223c79-3864-5bf0-83f7-82e725a168b6" -version = "1.9.0" +version = "1.11.2" [[deps.Grisu]] git-tree-sha1 = "53bb909d1151e57e2484c3d1b53e19552b887fb2" @@ -732,9 +832,9 @@ version = "1.0.2" [[deps.HTTP]] deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "8e59b47b9dc525b70550ca082ce85bcd7f5477cd" +git-tree-sha1 = "d1d712be3164d61d1fb98e7ce9bcbc6cc06b45ed" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.10.5" +version = "1.10.8" [[deps.HalfIntegers]] git-tree-sha1 = "9c3149243abb5bc0bad0431d6c4fcac0f4443c7c" @@ -749,21 +849,21 @@ version = "2.8.1+1" [[deps.HostCPUFeatures]] deps = ["BitTwiddlingConvenienceFunctions", "IfElse", "Libdl", "Static"] -git-tree-sha1 = "eb8fed28f4994600e29beef49744639d985a04b2" +git-tree-sha1 = "8e070b599339d622e9a081d17230d74a5c473293" uuid = "3e5b6fbb-0976-4d2c-9146-d79de83f2fb0" -version = "0.1.16" +version = "0.1.17" [[deps.HypergeometricFunctions]] -deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] -git-tree-sha1 = "f218fe3736ddf977e0e772bc9a586b2383da2685" +deps = ["LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] +git-tree-sha1 = "7c4195be1649ae622304031ed46a2f4df989f1eb" uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" -version = "0.3.23" +version = "0.3.24" [[deps.IOCapture]] deps = ["Logging", "Random"] -git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c" +git-tree-sha1 = "b6d6bfdd7ce25b0f9b2f6b3dd56b2673a66c8770" uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" -version = "0.2.4" +version = "0.2.5" [[deps.IfElse]] git-tree-sha1 = "debdd00ffef04665ccbb3e150747a77560e8fad1" @@ -771,9 +871,9 @@ uuid = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173" version = "0.1.1" [[deps.Inflate]] -git-tree-sha1 = "ea8031dea4aff6bd41f1df8f2fdfb25b33626381" +git-tree-sha1 = "d1b1b796e47d94588b3757fe84fbf65a5ec4a80d" uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9" -version = "0.1.4" +version = "0.1.5" [[deps.IntegerMathUtils]] git-tree-sha1 = "b8ffb903da9f7b8cf695a8bead8e01814aa24b30" @@ -782,9 +882,9 @@ version = "0.1.2" [[deps.IntelOpenMP_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "5fdf2fe6724d8caabf43b557b84ce53f3b7e2f6b" +git-tree-sha1 = "14eb2b542e748570b56446f4c50fbfb2306ebc45" uuid = "1d5cc7b8-4909-519e-a0f8-d0f5ad9712d0" -version = "2024.0.2+0" +version = "2024.2.0+0" [[deps.InteractiveUtils]] deps = ["Markdown"] @@ -801,6 +901,16 @@ weakdeps = ["Random", "RecipesBase", "Statistics"] IntervalSetsRecipesBaseExt = "RecipesBase" IntervalSetsStatisticsExt = "Statistics" +[[deps.InverseFunctions]] +git-tree-sha1 = "2787db24f4e03daf859c6509ff87764e4182f7d1" +uuid = "3587e190-3f89-42d0-90ee-14403ec27112" +version = "0.1.16" +weakdeps = ["Dates", "Test"] + + [deps.InverseFunctions.extensions] + InverseFunctionsDatesExt = "Dates" + InverseFunctionsTestExt = "Test" + [[deps.IrrationalConstants]] git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" @@ -837,37 +947,34 @@ version = "0.21.4" [[deps.JpegTurbo_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "3336abae9a713d2210bb57ab484b1e065edd7d23" +git-tree-sha1 = "c84a835e1a09b289ffcd2271bf2a337bbdda6637" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" -version = "3.0.2+0" +version = "3.0.3+0" [[deps.JuliaFormatter]] deps = ["CSTParser", "CommonMark", "DataStructures", "Glob", "Pkg", "PrecompileTools", "Tokenize"] -git-tree-sha1 = "e07d6fd7db543b11cd90ed764efec53f39851f09" +git-tree-sha1 = "4b0b04fe63e02eed776181e14ebc1f48d787ceec" uuid = "98e50ef6-434e-11e9-1051-2b60c6c9e899" -version = "1.0.54" +version = "1.0.59" [[deps.JumpProcesses]] -deps = ["ArrayInterface", "DataStructures", "DiffEqBase", "DocStringExtensions", "FunctionWrappers", "Graphs", "LinearAlgebra", "Markdown", "PoissonRandom", "Random", "RandomNumbers", "RecursiveArrayTools", "Reexport", "SciMLBase", "StaticArrays", "UnPack"] -git-tree-sha1 = "c451feb97251965a9fe40bacd62551a72cc5902c" +deps = ["ArrayInterface", "DataStructures", "DiffEqBase", "DocStringExtensions", "FunctionWrappers", "Graphs", "LinearAlgebra", "Markdown", "PoissonRandom", "Random", "RandomNumbers", "RecursiveArrayTools", "Reexport", "SciMLBase", "StaticArrays", "SymbolicIndexingInterface", "UnPack"] +git-tree-sha1 = "f8235862db5cf813a52859ff82205d0f22f09a64" uuid = "ccbc3e58-028d-4f4c-8cd5-9ae44345cda5" -version = "9.10.1" +version = "9.13.3" weakdeps = ["FastBroadcast"] - [deps.JumpProcesses.extensions] - JumpProcessFastBroadcastExt = "FastBroadcast" - [[deps.KLU]] deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse_jll"] -git-tree-sha1 = "884c2968c2e8e7e6bf5956af88cb46aa745c854b" +git-tree-sha1 = "07649c499349dad9f08dde4243a4c597064663e9" uuid = "ef3ab10e-7fda-4108-b977-705223b18434" -version = "0.4.1" +version = "0.6.0" [[deps.Krylov]] deps = ["LinearAlgebra", "Printf", "SparseArrays"] -git-tree-sha1 = "8a6837ec02fe5fb3def1abc907bb802ef11a0729" +git-tree-sha1 = "267dad6b4b7b5d529c76d40ff48d33f7e94cb834" uuid = "ba0b0d4f-ebba-5204-a429-3ac8c609bfb7" -version = "0.9.5" +version = "0.9.6" [[deps.KrylovKit]] deps = ["ChainRulesCore", "GPUArraysCore", "LinearAlgebra", "Printf"] @@ -876,10 +983,10 @@ uuid = "0b1a1467-8014-51b9-945f-bf0ae24f4b77" version = "0.6.1" [[deps.LAME_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f6250b16881adf048549549fba48b1161acdac8c" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "170b660facf5df5de098d866564877e119141cbd" uuid = "c1c5ebd0-6772-5130-a774-d5fcae4a789d" -version = "3.100.1+0" +version = "3.100.2+0" [[deps.LERC_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -889,9 +996,9 @@ version = "3.0.0+1" [[deps.LLVMOpenMP_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713" +git-tree-sha1 = "e16271d212accd09d52ee0ae98956b8a05c4b626" uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" -version = "15.0.7+0" +version = "17.0.6+0" [[deps.LRUCache]] git-tree-sha1 = "b3cc6698599b10e652832c2f23db3cab99d51b59" @@ -903,10 +1010,10 @@ weakdeps = ["Serialization"] SerializationExt = ["Serialization"] [[deps.LZO_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "e5b909bcf985c5e2605737d2ce278ed791b89be6" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "70c5da094887fd2cae843b8db33920bac4b6f07d" uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac" -version = "2.10.1+0" +version = "2.10.2+0" [[deps.LaTeXStrings]] git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec" @@ -915,9 +1022,9 @@ version = "1.3.1" [[deps.LabelledArrays]] deps = ["ArrayInterface", "ChainRulesCore", "ForwardDiff", "LinearAlgebra", "MacroTools", "PreallocationTools", "RecursiveArrayTools", "StaticArrays"] -git-tree-sha1 = "d1f981fba6eb3ec393eede4821bca3f2b7592cd4" +git-tree-sha1 = "e459fda6b68ea8684b3fcd513d2fd1e5130c4402" uuid = "2ee39098-c373-598a-b85f-a56591580800" -version = "1.15.1" +version = "1.16.0" [[deps.LambertW]] git-tree-sha1 = "c5ffc834de5d61d00d2b0e18c96267cffc21f648" @@ -926,23 +1033,25 @@ version = "0.4.6" [[deps.Latexify]] deps = ["Format", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Requires"] -git-tree-sha1 = "cad560042a7cc108f5a4c24ea1431a9221f22c1b" +git-tree-sha1 = "ce5f5621cac23a86011836badfedf664a612cee4" uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" -version = "0.16.2" +version = "0.16.5" [deps.Latexify.extensions] DataFramesExt = "DataFrames" + SparseArraysExt = "SparseArrays" SymEngineExt = "SymEngine" [deps.Latexify.weakdeps] DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" + SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" SymEngine = "123dc426-2d89-5057-bbad-38513e3affd8" [[deps.LayoutPointers]] deps = ["ArrayInterface", "LinearAlgebra", "ManualMemory", "SIMDTypes", "Static", "StaticArrayInterface"] -git-tree-sha1 = "62edfee3211981241b57ff1cedf4d74d79519277" +git-tree-sha1 = "a9eaadb366f5493a5654e843864c13d8b107548c" uuid = "10f19ff3-798f-405d-979b-55457f8fc047" -version = "0.1.15" +version = "0.1.17" [[deps.LazilyInitializedFields]] git-tree-sha1 = "8f7f3cabab0fd1800699663533b6d5cb3fc0e612" @@ -950,15 +1059,23 @@ uuid = "0e77f7df-68c5-4e49-93ce-4cd80f5598bf" version = "1.2.2" [[deps.LazyArrays]] -deps = ["ArrayLayouts", "FillArrays", "LinearAlgebra", "MacroTools", "MatrixFactorizations", "SparseArrays"] -git-tree-sha1 = "af45931c321aafdb96a6e0b26e81124e1b390e4e" +deps = ["ArrayLayouts", "FillArrays", "LinearAlgebra", "MacroTools", "SparseArrays"] +git-tree-sha1 = "507b423197fdd9e77b74aa2532c0a05eb7eb4004" uuid = "5078a376-72f3-5289-bfd5-ec5146d43c02" -version = "1.9.0" -weakdeps = ["StaticArrays"] +version = "2.2.0" [deps.LazyArrays.extensions] + LazyArraysBandedMatricesExt = "BandedMatrices" + LazyArraysBlockArraysExt = "BlockArrays" + LazyArraysBlockBandedMatricesExt = "BlockBandedMatrices" LazyArraysStaticArraysExt = "StaticArrays" + [deps.LazyArrays.weakdeps] + BandedMatrices = "aae01518-5342-5314-be14-df237901396f" + BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e" + BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0" + StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" + [[deps.LazyArtifacts]] deps = ["Artifacts", "Pkg"] uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3" @@ -1003,10 +1120,10 @@ uuid = "e9f186c6-92d2-5b65-8a66-fee21dc1b490" version = "3.2.2+1" [[deps.Libgcrypt_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll", "Pkg"] -git-tree-sha1 = "64613c82a59c120435c067c2b809fc61cf5166ae" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll"] +git-tree-sha1 = "9fd170c4bbfd8b935fdc5f8b7aa33532c991a673" uuid = "d4300ac3-e22c-5743-9152-c294e39db1e4" -version = "1.8.7+0" +version = "1.8.11+0" [[deps.Libglvnd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"] @@ -1015,10 +1132,10 @@ uuid = "7e76a0d4-f3c7-5321-8279-8d96eeed0f29" version = "1.6.0+0" [[deps.Libgpg_error_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "c333716e46366857753e273ce6a69ee0945a6db9" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "fbb1f2bef882392312feb1ede3615ddc1e9b99ed" uuid = "7add5ba3-2f88-524e-9cd5-f83b8a55f7b8" -version = "1.42.0+0" +version = "1.49.0+0" [[deps.Libiconv_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] @@ -1028,9 +1145,9 @@ version = "1.17.0+0" [[deps.Libmount_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "dae976433497a2f841baadea93d27e68f1a12a97" +git-tree-sha1 = "0c4f9c4f1a50d8f35048fa0532dabbadf702f81e" uuid = "4b2f31a3-9ecc-558c-b454-b3730dcb73e9" -version = "2.39.3+0" +version = "2.40.1+0" [[deps.Libtiff_jll]] deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "LERC_jll", "Libdl", "XZ_jll", "Zlib_jll", "Zstd_jll"] @@ -1040,15 +1157,15 @@ version = "4.5.1+1" [[deps.Libuuid_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "0a04a1318df1bf510beb2562cf90fb0c386f58c4" +git-tree-sha1 = "5ee6203157c120d79034c748a2acba45b82b8807" uuid = "38a345b3-de98-5d2b-a5d3-14cd9215e700" -version = "2.39.3+1" +version = "2.40.1+0" [[deps.LineSearches]] deps = ["LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "Printf"] -git-tree-sha1 = "7bbea35cec17305fc70a0e5b4641477dc0789d9d" +git-tree-sha1 = "e4c3be53733db1051cc15ecf573b1042b3a712a1" uuid = "d3d80556-e9d4-5f37-9878-2ab0fcc64255" -version = "7.2.0" +version = "7.3.0" [[deps.LinearAlgebra]] deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"] @@ -1056,9 +1173,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" [[deps.LinearMaps]] deps = ["LinearAlgebra"] -git-tree-sha1 = "9948d6f8208acfebc3e8cf4681362b2124339e7e" +git-tree-sha1 = "ee79c3208e55786de58f8dcccca098ced79f743f" uuid = "7a12625a-238d-50fd-b39a-03d52299707e" -version = "3.11.2" +version = "3.11.3" weakdeps = ["ChainRulesCore", "SparseArrays", "Statistics"] [deps.LinearMaps.extensions] @@ -1067,15 +1184,16 @@ weakdeps = ["ChainRulesCore", "SparseArrays", "Statistics"] LinearMapsStatisticsExt = "Statistics" [[deps.LinearSolve]] -deps = ["ArrayInterface", "ConcreteStructs", "DocStringExtensions", "EnumX", "FastLapackInterface", "GPUArraysCore", "InteractiveUtils", "KLU", "Krylov", "Libdl", "LinearAlgebra", "MKL_jll", "PrecompileTools", "Preferences", "RecursiveFactorization", "Reexport", "SciMLBase", "SciMLOperators", "Setfield", "SparseArrays", "Sparspak", "StaticArraysCore", "UnPack"] -git-tree-sha1 = "6f8e084deabe3189416c4e505b1c53e1b590cae8" +deps = ["ArrayInterface", "ChainRulesCore", "ConcreteStructs", "DocStringExtensions", "EnumX", "FastLapackInterface", "GPUArraysCore", "InteractiveUtils", "KLU", "Krylov", "LazyArrays", "Libdl", "LinearAlgebra", "MKL_jll", "Markdown", "PrecompileTools", "Preferences", "RecursiveFactorization", "Reexport", "SciMLBase", "SciMLOperators", "Setfield", "SparseArrays", "Sparspak", "StaticArraysCore", "UnPack"] +git-tree-sha1 = "7648cc20100504f4b453917aacc8520e9c0ecfb3" uuid = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae" -version = "2.22.1" +version = "2.30.1" [deps.LinearSolve.extensions] LinearSolveBandedMatricesExt = "BandedMatrices" LinearSolveBlockDiagonalsExt = "BlockDiagonals" LinearSolveCUDAExt = "CUDA" + LinearSolveCUDSSExt = "CUDSS" LinearSolveEnzymeExt = ["Enzyme", "EnzymeCore"] LinearSolveFastAlmostBandedMatricesExt = ["FastAlmostBandedMatrices"] LinearSolveHYPREExt = "HYPRE" @@ -1090,6 +1208,7 @@ version = "2.22.1" BandedMatrices = "aae01518-5342-5314-be14-df237901396f" BlockDiagonals = "0a1fb500-61f7-11e9-3c65-f5ef3456f9f0" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" + CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869" FastAlmostBandedMatrices = "9d29842c-ecb8-4973-b1e9-a27b1157504e" @@ -1103,9 +1222,9 @@ version = "2.22.1" [[deps.LogExpFunctions]] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "18144f3e9cbe9b15b070288eef858f71b291ce37" +git-tree-sha1 = "a2d09619db4e765091ee5c6ffe8872849de0feea" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.27" +version = "0.3.28" [deps.LogExpFunctions.extensions] LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" @@ -1128,9 +1247,9 @@ version = "1.0.3" [[deps.LoopVectorization]] deps = ["ArrayInterface", "CPUSummary", "CloseOpenIntervals", "DocStringExtensions", "HostCPUFeatures", "IfElse", "LayoutPointers", "LinearAlgebra", "OffsetArrays", "PolyesterWeave", "PrecompileTools", "SIMDTypes", "SLEEFPirates", "Static", "StaticArrayInterface", "ThreadingUtilities", "UnPack", "VectorizationBase"] -git-tree-sha1 = "a13f3be5d84b9c95465d743c82af0b094ef9c2e2" +git-tree-sha1 = "8084c25a250e00ae427a379a5b607e7aed96a2dd" uuid = "bdcacae8-1622-11e9-2a5c-532679323890" -version = "0.12.169" +version = "0.12.171" weakdeps = ["ChainRulesCore", "ForwardDiff", "SpecialFunctions"] [deps.LoopVectorization.extensions] @@ -1138,10 +1257,10 @@ weakdeps = ["ChainRulesCore", "ForwardDiff", "SpecialFunctions"] SpecialFunctionsExt = "SpecialFunctions" [[deps.MKL_jll]] -deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl"] -git-tree-sha1 = "72dc3cf284559eb8f53aa593fe62cb33f83ed0c0" +deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "oneTBB_jll"] +git-tree-sha1 = "f046ccd0c6db2832a9f639e2c669c6fe867e5f4f" uuid = "856f044c-d86e-5d09-b602-aeab76dc8ba7" -version = "2024.0.0+0" +version = "2024.2.0+0" [[deps.MLStyle]] git-tree-sha1 = "bc38dff0548128765760c79eb7388a4b37fae2c8" @@ -1169,17 +1288,11 @@ git-tree-sha1 = "465a70f0fc7d443a00dcdc3267a497397b8a3899" uuid = "d0879d2d-cac2-40c8-9cee-1863dc0c7391" version = "0.1.2" -[[deps.MatrixFactorizations]] -deps = ["ArrayLayouts", "LinearAlgebra", "Printf", "Random"] -git-tree-sha1 = "78f6e33434939b0ac9ba1df81e6d005ee85a7396" -uuid = "a3b82374-2e81-5b9e-98ce-41277c0e4c87" -version = "2.1.0" - [[deps.MaybeInplace]] deps = ["ArrayInterface", "LinearAlgebra", "MacroTools", "SparseArrays"] -git-tree-sha1 = "b1f2f92feb0bc201e91c155ef575bcc7d9cc3526" +git-tree-sha1 = "1b9e613f2ca3b6cdcbfe36381e17ca2b66d4b3a1" uuid = "bb5d69b7-63fc-4a16-80bd-7e42200c7bdb" -version = "0.1.2" +version = "0.1.3" [[deps.MbedTLS]] deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"] @@ -1199,18 +1312,18 @@ version = "0.3.2" [[deps.Missings]] deps = ["DataAPI"] -git-tree-sha1 = "f66bdc5de519e8f8ae43bdc598782d35a25b1272" +git-tree-sha1 = "ec4f7fbeab05d7747bdf98eb74d130a2a2ed298d" uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28" -version = "1.1.0" +version = "1.2.0" [[deps.Mmap]] uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.ModelingToolkit]] -deps = ["AbstractTrees", "ArrayInterface", "Combinatorics", "Compat", "ConstructionBase", "DataStructures", "DiffEqBase", "DiffEqCallbacks", "DiffRules", "Distributed", "Distributions", "DocStringExtensions", "DomainSets", "DynamicQuantities", "ExprTools", "FindFirstFunctions", "ForwardDiff", "FunctionWrappersWrappers", "Graphs", "InteractiveUtils", "JuliaFormatter", "JumpProcesses", "LabelledArrays", "Latexify", "Libdl", "LinearAlgebra", "MLStyle", "NaNMath", "OrdinaryDiffEq", "PrecompileTools", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLBase", "SciMLStructures", "Serialization", "Setfield", "SimpleNonlinearSolve", "SparseArrays", "SpecialFunctions", "StaticArrays", "SymbolicIndexingInterface", "SymbolicUtils", "Symbolics", "URIs", "UnPack", "Unitful"] -git-tree-sha1 = "ef2002247f9d9c1df72f5502243e1b7493337de6" +deps = ["AbstractTrees", "ArrayInterface", "Combinatorics", "Compat", "ConstructionBase", "DataStructures", "DiffEqBase", "DiffEqCallbacks", "DiffEqNoiseProcess", "DiffRules", "Distributed", "Distributions", "DocStringExtensions", "DomainSets", "DynamicQuantities", "ExprTools", "Expronicon", "FindFirstFunctions", "ForwardDiff", "FunctionWrappersWrappers", "Graphs", "InteractiveUtils", "JuliaFormatter", "JumpProcesses", "LabelledArrays", "Latexify", "Libdl", "LinearAlgebra", "MLStyle", "NaNMath", "NonlinearSolve", "OrderedCollections", "PrecompileTools", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLBase", "SciMLStructures", "Serialization", "Setfield", "SimpleNonlinearSolve", "SparseArrays", "SpecialFunctions", "StaticArrays", "SymbolicIndexingInterface", "SymbolicUtils", "Symbolics", "URIs", "UnPack", "Unitful"] +git-tree-sha1 = "b9b53cd8c18afe4bac1718112e5287abbd01cc30" uuid = "961ee093-0014-501f-94e3-6117800e7a78" -version = "9.7.1" +version = "9.33.1" [deps.ModelingToolkit.extensions] MTKBifurcationKitExt = "BifurcationKit" @@ -1231,15 +1344,15 @@ version = "0.2.4" [[deps.MultivariatePolynomials]] deps = ["ChainRulesCore", "DataStructures", "LinearAlgebra", "MutableArithmetics"] -git-tree-sha1 = "769c9175942d91ed9b83fa929eee4fe6a1d128ad" +git-tree-sha1 = "5c1d1d9361e1417e5a065e1f84dc3686cbdaea21" uuid = "102ac46a-7ee4-5c85-9060-abc95bfdeaa3" -version = "0.5.4" +version = "0.5.6" [[deps.MutableArithmetics]] deps = ["LinearAlgebra", "SparseArrays", "Test"] -git-tree-sha1 = "2d106538aebe1c165e16d277914e10c550e9d9b7" +git-tree-sha1 = "d0a6b1096b584a2b88efb70a92f8cb8c881eb38a" uuid = "d8a4904e-b15c-11e9-3269-09a3773c0cb0" -version = "1.4.2" +version = "1.4.6" [[deps.NLSolversBase]] deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"] @@ -1264,10 +1377,10 @@ uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" version = "1.2.0" [[deps.NonlinearSolve]] -deps = ["ADTypes", "ArrayInterface", "ConcreteStructs", "DiffEqBase", "FastBroadcast", "FastClosures", "FiniteDiff", "ForwardDiff", "LazyArrays", "LineSearches", "LinearAlgebra", "LinearSolve", "MaybeInplace", "PrecompileTools", "Preferences", "Printf", "RecursiveArrayTools", "Reexport", "SciMLBase", "SimpleNonlinearSolve", "SparseArrays", "SparseDiffTools", "StaticArraysCore", "TimerOutputs"] -git-tree-sha1 = "1638addfc31707aea26333ff822afcf9d2e6f7de" +deps = ["ADTypes", "ArrayInterface", "ConcreteStructs", "DiffEqBase", "FastBroadcast", "FastClosures", "FiniteDiff", "ForwardDiff", "LazyArrays", "LineSearches", "LinearAlgebra", "LinearSolve", "MaybeInplace", "PrecompileTools", "Preferences", "Printf", "RecursiveArrayTools", "Reexport", "SciMLBase", "SimpleNonlinearSolve", "SparseArrays", "SparseDiffTools", "StaticArraysCore", "SymbolicIndexingInterface", "TimerOutputs"] +git-tree-sha1 = "bcd8812e751326ff1d4b2dd50764b93df51f143b" uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec" -version = "3.8.3" +version = "3.14.0" [deps.NonlinearSolve.extensions] NonlinearSolveBandedMatricesExt = "BandedMatrices" @@ -1296,9 +1409,9 @@ version = "3.8.3" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [[deps.OffsetArrays]] -git-tree-sha1 = "6a731f2b5c03157418a20c12195eb4b74c8f8621" +git-tree-sha1 = "1a27764e945a152f7ca7efa04de513d473e9542e" uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" -version = "1.13.0" +version = "1.14.1" weakdeps = ["Adapt"] [deps.OffsetArrays.extensions] @@ -1322,15 +1435,15 @@ version = "0.8.1+2" [[deps.OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] -git-tree-sha1 = "af81a32750ebc831ee28bdaaba6e1067decef51e" +git-tree-sha1 = "38cb508d080d21dc1128f7fb04f20387ed4c0af4" uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" -version = "1.4.2" +version = "1.4.3" [[deps.OpenSSL_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "3da7367955dcc5c54c1ba4d402ccdc09a1a3e046" +git-tree-sha1 = "a028ee3cb5641cccc4c24e90c36b0a4f7707bdf5" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "3.0.13+1" +version = "3.0.14+0" [[deps.OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] @@ -1339,10 +1452,10 @@ uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e" version = "0.5.5+0" [[deps.Optim]] -deps = ["Compat", "FillArrays", "ForwardDiff", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "PackageExtensionCompat", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"] -git-tree-sha1 = "d1223e69af90b6d26cea5b6f3b289b3148ba702c" +deps = ["Compat", "FillArrays", "ForwardDiff", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"] +git-tree-sha1 = "d9b79c4eed437421ac4285148fcadf42e0700e89" uuid = "429524aa-4258-5aef-a3af-852621145aeb" -version = "1.9.3" +version = "1.9.4" [deps.Optim.extensions] OptimMOIExt = "MathOptInterface" @@ -1362,10 +1475,10 @@ uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" version = "1.6.3" [[deps.OrdinaryDiffEq]] -deps = ["ADTypes", "Adapt", "ArrayInterface", "DataStructures", "DiffEqBase", "DocStringExtensions", "ExponentialUtilities", "FastBroadcast", "FastClosures", "FillArrays", "FiniteDiff", "ForwardDiff", "FunctionWrappersWrappers", "IfElse", "InteractiveUtils", "LineSearches", "LinearAlgebra", "LinearSolve", "Logging", "MacroTools", "MuladdMacro", "NonlinearSolve", "Polyester", "PreallocationTools", "PrecompileTools", "Preferences", "RecursiveArrayTools", "Reexport", "SciMLBase", "SciMLOperators", "SimpleNonlinearSolve", "SimpleUnPack", "SparseArrays", "SparseDiffTools", "StaticArrayInterface", "StaticArrays", "TruncatedStacktraces"] -git-tree-sha1 = "91079af18db922354197eeae2a17b177079e24c1" +deps = ["ADTypes", "Adapt", "ArrayInterface", "DataStructures", "DiffEqBase", "DocStringExtensions", "EnumX", "ExponentialUtilities", "FastBroadcast", "FastClosures", "FillArrays", "FiniteDiff", "ForwardDiff", "FunctionWrappersWrappers", "IfElse", "InteractiveUtils", "LineSearches", "LinearAlgebra", "LinearSolve", "Logging", "MacroTools", "MuladdMacro", "NonlinearSolve", "Polyester", "PreallocationTools", "PrecompileTools", "Preferences", "RecursiveArrayTools", "Reexport", "SciMLBase", "SciMLOperators", "SciMLStructures", "SimpleNonlinearSolve", "SimpleUnPack", "SparseArrays", "SparseDiffTools", "Static", "StaticArrayInterface", "StaticArrays", "TruncatedStacktraces"] +git-tree-sha1 = "a8b2d333cd90562b58b977b4033739360b37fb1f" uuid = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" -version = "6.74.1" +version = "6.87.0" [[deps.PCRE2_jll]] deps = ["Artifacts", "Libdl"] @@ -1384,6 +1497,12 @@ uuid = "65ce6f38-6b18-4e1d-a461-8949797d7930" version = "1.0.2" weakdeps = ["Requires", "TOML"] +[[deps.Pango_jll]] +deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "FriBidi_jll", "Glib_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl"] +git-tree-sha1 = "cb5a2ab6763464ae0f19c86c56c63d4a2b0f5bda" +uuid = "36c8627f-9965-5494-a995-c6b170f724f3" +version = "1.52.2+0" + [[deps.Parameters]] deps = ["OrderedCollections", "UnPack"] git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe" @@ -1403,9 +1522,9 @@ version = "1.3.0" [[deps.Pixman_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"] -git-tree-sha1 = "64779bc4c9784fee475689a1752ef4d5747c5e87" +git-tree-sha1 = "35621f10a7531bc8fa58f74610b1bfb70a3cfc6b" uuid = "30392449-352a-5448-841d-b1acce4e97dc" -version = "0.42.2+0" +version = "0.43.4+0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] @@ -1414,9 +1533,9 @@ version = "1.10.0" [[deps.PlotThemes]] deps = ["PlotUtils", "Statistics"] -git-tree-sha1 = "1f03a2d339f42dca4a4da149c7e15e9b896ad899" +git-tree-sha1 = "6e55c6841ce3411ccb3457ee52fc48cb698d6fb0" uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" -version = "3.1.0" +version = "3.2.0" [[deps.PlotUtils]] deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"] @@ -1425,10 +1544,10 @@ uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" version = "1.4.1" [[deps.Plots]] -deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] -git-tree-sha1 = "3bdfa4fa528ef21287ef659a89d686e8a1bcb1a9" +deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "TOML", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] +git-tree-sha1 = "082f0c4b70c202c37784ce4bfbc33c9f437685bf" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -version = "1.40.3" +version = "1.40.5" [deps.Plots.extensions] FileIOExt = "FileIO" @@ -1451,16 +1570,16 @@ uuid = "e409e4f3-bfea-5376-8464-e040bb5c01ab" version = "0.4.4" [[deps.Polyester]] -deps = ["ArrayInterface", "BitTwiddlingConvenienceFunctions", "CPUSummary", "IfElse", "ManualMemory", "PolyesterWeave", "Requires", "Static", "StaticArrayInterface", "StrideArraysCore", "ThreadingUtilities"] -git-tree-sha1 = "09f59c6dda37c7f73efddc5bdf6f92bc940eb484" +deps = ["ArrayInterface", "BitTwiddlingConvenienceFunctions", "CPUSummary", "IfElse", "ManualMemory", "PolyesterWeave", "Static", "StaticArrayInterface", "StrideArraysCore", "ThreadingUtilities"] +git-tree-sha1 = "6d38fea02d983051776a856b7df75b30cf9a3c1f" uuid = "f517fe37-dbe3-4b94-8317-1923a5111588" -version = "0.7.12" +version = "0.7.16" [[deps.PolyesterWeave]] deps = ["BitTwiddlingConvenienceFunctions", "CPUSummary", "IfElse", "Static", "ThreadingUtilities"] -git-tree-sha1 = "240d7170f5ffdb285f9427b92333c3463bf65bf6" +git-tree-sha1 = "645bed98cd47f72f67316fd42fc47dee771aefcd" uuid = "1d0040c9-8b98-4ee7-8388-3f51789ca0ad" -version = "0.2.1" +version = "0.2.2" [[deps.PositiveFactorizations]] deps = ["LinearAlgebra"] @@ -1470,9 +1589,9 @@ version = "0.2.4" [[deps.PreallocationTools]] deps = ["Adapt", "ArrayInterface", "ForwardDiff"] -git-tree-sha1 = "b6665214f2d0739f2d09a17474dd443b9139784a" +git-tree-sha1 = "6c62ce45f268f3f958821a1e5192cf91c75ae89c" uuid = "d236fae5-4411-538c-8e31-a6e3d9e00b46" -version = "0.4.20" +version = "0.4.24" [deps.PreallocationTools.extensions] PreallocationToolsReverseDiffExt = "ReverseDiff" @@ -1502,35 +1621,58 @@ version = "0.5.6" deps = ["Unicode"] uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" +[[deps.PtrArrays]] +git-tree-sha1 = "f011fbb92c4d401059b2212c05c0601b70f8b759" +uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d" +version = "1.2.0" + [[deps.Qt6Base_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Vulkan_Loader_jll", "Xorg_libSM_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"] -git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b" +git-tree-sha1 = "492601870742dcd38f233b23c3ec629628c1d724" uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56" -version = "6.5.3+1" +version = "6.7.1+1" + +[[deps.Qt6Declarative_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Qt6Base_jll", "Qt6ShaderTools_jll"] +git-tree-sha1 = "e5dd466bf2569fe08c91a2cc29c1003f4797ac3b" +uuid = "629bc702-f1f5-5709-abd5-49b8460ea067" +version = "6.7.1+2" + +[[deps.Qt6ShaderTools_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Qt6Base_jll"] +git-tree-sha1 = "1a180aeced866700d4bebc3120ea1451201f16bc" +uuid = "ce943373-25bb-56aa-8eca-768745ed7b5a" +version = "6.7.1+1" + +[[deps.Qt6Wayland_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Qt6Base_jll", "Qt6Declarative_jll"] +git-tree-sha1 = "729927532d48cf79f49070341e1d918a65aba6b0" +uuid = "e99dba38-086e-5de3-a5b1-6e4c66e897c3" +version = "6.7.1+1" [[deps.QuadGK]] deps = ["DataStructures", "LinearAlgebra"] -git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e" +git-tree-sha1 = "e237232771fdafbae3db5c31275303e056afaa9f" uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" -version = "2.9.4" +version = "2.10.1" [[deps.QuantumInterface]] deps = ["LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "5e4f1f1a93d2f7f8f837af9122342e43b1fe5199" +git-tree-sha1 = "87d036956ea801cffed598b2fc765453493a3d41" uuid = "5717a53b-5d69-4fa3-b976-0bf2f97ca1e5" -version = "0.3.4" +version = "0.3.5" [[deps.QuantumOptics]] deps = ["Arpack", "DiffEqBase", "DiffEqCallbacks", "FFTW", "ForwardDiff", "IterativeSolvers", "KrylovKit", "LinearAlgebra", "LinearMaps", "OrdinaryDiffEq", "QuantumOpticsBase", "Random", "RecursiveArrayTools", "Reexport", "SparseArrays", "StochasticDiffEq", "WignerSymbols"] -git-tree-sha1 = "8d6a3672d982c3236ce435846468c169c386df3b" +git-tree-sha1 = "460f4576de23a01aeb013dd504502c972df5ab77" uuid = "6e0679c1-51ea-5a7c-ac74-d61b76210b0c" -version = "1.0.15" +version = "1.1.1" [[deps.QuantumOpticsBase]] deps = ["Adapt", "FFTW", "FastExpm", "FastGaussQuadrature", "FillArrays", "LRUCache", "LinearAlgebra", "QuantumInterface", "Random", "RandomMatrices", "SparseArrays", "Strided", "UnsafeArrays"] -git-tree-sha1 = "abce3a922e9230a04f91c9f4f67cc7c53907621d" +git-tree-sha1 = "bab5dd0a189d38193d982092980f03eb45867233" uuid = "4f57444f-1401-5e15-980d-4471b28d5678" -version = "0.4.21" +version = "0.4.22" [[deps.REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] @@ -1548,15 +1690,15 @@ version = "1.7.0" [[deps.RandomMatrices]] deps = ["Combinatorics", "Distributions", "FastGaussQuadrature", "GSL", "LinearAlgebra", "Random", "SpecialFunctions", "Test"] -git-tree-sha1 = "ce39acdf57c11919b957afa1974fe774b8f94fcc" +git-tree-sha1 = "359d601ae45b05ea9a53d303dfbf477fcaca4195" uuid = "2576dda1-a324-5b11-aa66-c48ed7e3c618" -version = "0.5.4" +version = "0.5.5" [[deps.RandomNumbers]] -deps = ["Random", "Requires"] -git-tree-sha1 = "043da614cc7e95c703498a491e2c21f58a2b8111" +deps = ["Random"] +git-tree-sha1 = "c6ec94d2aaba1ab2ff983052cf6a606ca5985902" uuid = "e6cf234a-135c-5ec9-84dd-332b85af5143" -version = "1.5.3" +version = "1.6.0" [[deps.RationalRoots]] git-tree-sha1 = "e5f5db699187a4810fda9181b34250deeedafd81" @@ -1576,10 +1718,10 @@ uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" version = "0.6.12" [[deps.RecursiveArrayTools]] -deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "SparseArrays", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] -git-tree-sha1 = "d8f131090f2e44b145084928856a561c83f43b27" +deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] +git-tree-sha1 = "b034171b93aebc81b3e1890a036d13a9c4a9e3e0" uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "3.13.0" +version = "3.27.0" [deps.RecursiveArrayTools.extensions] RecursiveArrayToolsFastBroadcastExt = "FastBroadcast" @@ -1587,6 +1729,7 @@ version = "3.13.0" RecursiveArrayToolsMeasurementsExt = "Measurements" RecursiveArrayToolsMonteCarloMeasurementsExt = "MonteCarloMeasurements" RecursiveArrayToolsReverseDiffExt = ["ReverseDiff", "Zygote"] + RecursiveArrayToolsSparseArraysExt = ["SparseArrays"] RecursiveArrayToolsTrackerExt = "Tracker" RecursiveArrayToolsZygoteExt = "Zygote" @@ -1596,14 +1739,15 @@ version = "3.13.0" Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7" MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca" ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [[deps.RecursiveFactorization]] deps = ["LinearAlgebra", "LoopVectorization", "Polyester", "PrecompileTools", "StrideArraysCore", "TriangularSolve"] -git-tree-sha1 = "8bc86c78c7d8e2a5fe559e3721c0f9c9e303b2ed" +git-tree-sha1 = "6db1a75507051bc18bfa131fbc7c3f169cc4b2f6" uuid = "f2c3362d-daeb-58d1-803e-2bc74f2840b4" -version = "0.2.21" +version = "0.2.23" [[deps.Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" @@ -1641,16 +1785,16 @@ uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa" version = "0.7.1" [[deps.Rmath_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "6ed52fdd3382cf21947b15e8870ac0ddbff736da" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "e60724fd3beea548353984dc61c943ecddb0e29a" uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f" -version = "0.4.0+0" +version = "0.4.3+0" [[deps.RuntimeGeneratedFunctions]] deps = ["ExprTools", "SHA", "Serialization"] -git-tree-sha1 = "6aacc5eefe8415f47b3e34214c1d79d2674a0ba2" +git-tree-sha1 = "04c968137612c4a5629fa531334bb81ad5680f00" uuid = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47" -version = "0.5.12" +version = "0.5.13" [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" @@ -1663,15 +1807,15 @@ version = "0.1.0" [[deps.SLEEFPirates]] deps = ["IfElse", "Static", "VectorizationBase"] -git-tree-sha1 = "3aac6d68c5e57449f5b9b865c9ba50ac2970c4cf" +git-tree-sha1 = "456f610ca2fbd1c14f5fcf31c6bfadc55e7d66e0" uuid = "476501e8-09a2-5ece-8869-fb82de89a1fa" -version = "0.6.42" +version = "0.6.43" [[deps.SciMLBase]] -deps = ["ADTypes", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "Printf", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "SciMLStructures", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] -git-tree-sha1 = "d15c65e25615272e1b1c5edb1d307484c7942824" +deps = ["ADTypes", "Accessors", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "Expronicon", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "Printf", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "SciMLStructures", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] +git-tree-sha1 = "0bd2d45a5d7ed2eaaba745fd2a4c3d2e0eab7844" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "2.31.0" +version = "2.50.1" [deps.SciMLBase.extensions] SciMLBaseChainRulesCoreExt = "ChainRulesCore" @@ -1693,15 +1837,17 @@ version = "2.31.0" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [[deps.SciMLOperators]] -deps = ["ArrayInterface", "DocStringExtensions", "LinearAlgebra", "MacroTools", "Setfield", "SparseArrays", "StaticArraysCore"] -git-tree-sha1 = "10499f619ef6e890f3f4a38914481cc868689cd5" +deps = ["ArrayInterface", "DocStringExtensions", "LinearAlgebra", "MacroTools", "Setfield", "StaticArraysCore"] +git-tree-sha1 = "23b02c588ac9a17ecb276cc62ab37f3e4fe37b32" uuid = "c0aeaf25-5076-4817-a8d5-81caf7dfa961" -version = "0.3.8" +version = "0.3.9" +weakdeps = ["SparseArrays"] [[deps.SciMLStructures]] -git-tree-sha1 = "5833c10ce83d690c124beedfe5f621b50b02ba4d" +deps = ["ArrayInterface"] +git-tree-sha1 = "20ad3e7c137156c50c93c888d0f2bc5b7883c729" uuid = "53ae85a6-f571-4167-b2af-e1d143709226" -version = "1.1.0" +version = "1.4.2" [[deps.Scratch]] deps = ["Dates"] @@ -1734,24 +1880,20 @@ uuid = "777ac1f9-54b0-4bf8-805c-2214025038e7" version = "1.1.0" [[deps.SimpleNonlinearSolve]] -deps = ["ADTypes", "ArrayInterface", "ConcreteStructs", "DiffEqBase", "DiffResults", "FastClosures", "FiniteDiff", "ForwardDiff", "LinearAlgebra", "MaybeInplace", "PrecompileTools", "Reexport", "SciMLBase", "StaticArraysCore"] -git-tree-sha1 = "a535ae5083708f59e75d5bb3042c36d1be9bc778" +deps = ["ADTypes", "ArrayInterface", "ConcreteStructs", "DiffEqBase", "DiffResults", "DifferentiationInterface", "FastClosures", "FiniteDiff", "ForwardDiff", "LinearAlgebra", "MaybeInplace", "PrecompileTools", "Reexport", "SciMLBase", "Setfield", "StaticArraysCore"] +git-tree-sha1 = "4d7a7c177bcb4c6dc465f09db91bfdb28c578919" uuid = "727e6d20-b764-4bd8-a329-72de5adea6c7" -version = "1.6.0" +version = "1.12.0" [deps.SimpleNonlinearSolve.extensions] SimpleNonlinearSolveChainRulesCoreExt = "ChainRulesCore" - SimpleNonlinearSolvePolyesterForwardDiffExt = "PolyesterForwardDiff" SimpleNonlinearSolveReverseDiffExt = "ReverseDiff" - SimpleNonlinearSolveStaticArraysExt = "StaticArrays" SimpleNonlinearSolveTrackerExt = "Tracker" SimpleNonlinearSolveZygoteExt = "Zygote" [deps.SimpleNonlinearSolve.weakdeps] ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" - PolyesterForwardDiff = "98d1487c-24ca-40b6-b7ab-df2af84e126b" ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" - StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1782,9 +1924,9 @@ version = "1.10.0" [[deps.SparseDiffTools]] deps = ["ADTypes", "Adapt", "ArrayInterface", "Compat", "DataStructures", "FiniteDiff", "ForwardDiff", "Graphs", "LinearAlgebra", "PackageExtensionCompat", "Random", "Reexport", "SciMLOperators", "Setfield", "SparseArrays", "StaticArrayInterface", "StaticArrays", "Tricks", "UnPack", "VertexSafeGraphs"] -git-tree-sha1 = "a616ac46c38da60ac05cecf52064d44732edd05e" +git-tree-sha1 = "c9e5d7ee75cf6a1ca3a22c9a6a4ef451792cf62b" uuid = "47a9eef4-7e08-11e9-0b38-333d64bd3804" -version = "2.17.0" +version = "2.20.0" [deps.SparseDiffTools.extensions] SparseDiffToolsEnzymeExt = "Enzyme" @@ -1800,6 +1942,12 @@ version = "2.17.0" Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" +[[deps.SparseMatrixColorings]] +deps = ["ADTypes", "Compat", "DataStructures", "DocStringExtensions", "LinearAlgebra", "Random", "SparseArrays"] +git-tree-sha1 = "996dff77d814c45c3f2342fa0113e4ad31e712e8" +uuid = "0a514795-09f3-496d-8182-132a7b665d35" +version = "0.4.0" + [[deps.Sparspak]] deps = ["Libdl", "LinearAlgebra", "Logging", "OffsetArrays", "Printf", "SparseArrays", "Test"] git-tree-sha1 = "342cf4b449c299d8d1ceaf00b7a49f4fbc7940e7" @@ -1808,25 +1956,25 @@ version = "0.3.9" [[deps.SpecialFunctions]] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] -git-tree-sha1 = "e2cfc4012a19088254b3950b85c3c1d8882d864d" +git-tree-sha1 = "2f5d4697f21388cbe1ff299430dd169ef97d7e14" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "2.3.1" +version = "2.4.0" weakdeps = ["ChainRulesCore"] [deps.SpecialFunctions.extensions] SpecialFunctionsChainRulesCoreExt = "ChainRulesCore" [[deps.Static]] -deps = ["IfElse"] -git-tree-sha1 = "d2fdac9ff3906e27f7a618d47b676941baa6c80c" +deps = ["CommonWorldInvalidations", "IfElse", "PrecompileTools"] +git-tree-sha1 = "87d51a3ee9a4b0d2fe054bdd3fc2436258db2603" uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3" -version = "0.8.10" +version = "1.1.1" [[deps.StaticArrayInterface]] -deps = ["ArrayInterface", "Compat", "IfElse", "LinearAlgebra", "PrecompileTools", "Requires", "SparseArrays", "Static", "SuiteSparse"] -git-tree-sha1 = "5d66818a39bb04bf328e92bc933ec5b4ee88e436" +deps = ["ArrayInterface", "Compat", "IfElse", "LinearAlgebra", "PrecompileTools", "Static"] +git-tree-sha1 = "96381d50f1ce85f2663584c8e886a6ca97e60554" uuid = "0d7ed370-da01-4f52-bd93-41d350b8b718" -version = "1.5.0" +version = "1.8.0" weakdeps = ["OffsetArrays", "StaticArrays"] [deps.StaticArrayInterface.extensions] @@ -1835,9 +1983,9 @@ weakdeps = ["OffsetArrays", "StaticArrays"] [[deps.StaticArrays]] deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"] -git-tree-sha1 = "bf074c045d3d5ffd956fa0a461da38a44685d6b2" +git-tree-sha1 = "eeafab08ae20c62c44c8399ccb9354a04b80db50" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.9.3" +version = "1.9.7" weakdeps = ["ChainRulesCore", "Statistics"] [deps.StaticArrays.extensions] @@ -1845,9 +1993,9 @@ weakdeps = ["ChainRulesCore", "Statistics"] StaticArraysStatisticsExt = "Statistics" [[deps.StaticArraysCore]] -git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" +git-tree-sha1 = "192954ef1208c7019899fbf8049e717f92959682" uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c" -version = "1.4.2" +version = "1.4.3" [[deps.Statistics]] deps = ["LinearAlgebra", "SparseArrays"] @@ -1862,24 +2010,21 @@ version = "1.7.0" [[deps.StatsBase]] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] -git-tree-sha1 = "1d77abd07f617c4868c33d4f5b9e1dbb2643c9cf" +git-tree-sha1 = "5cf7606d6cef84b543b483848d4ae08ad9832b21" uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -version = "0.34.2" +version = "0.34.3" [[deps.StatsFuns]] deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"] git-tree-sha1 = "cef0472124fab0695b58ca35a77c6fb942fdab8a" uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c" version = "1.3.1" +weakdeps = ["ChainRulesCore", "InverseFunctions"] [deps.StatsFuns.extensions] StatsFunsChainRulesCoreExt = "ChainRulesCore" StatsFunsInverseFunctionsExt = "InverseFunctions" - [deps.StatsFuns.weakdeps] - ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" - InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" - [[deps.SteadyStateDiffEq]] deps = ["DiffEqBase", "DiffEqCallbacks", "LinearAlgebra", "NLsolve", "Reexport", "SciMLBase"] git-tree-sha1 = "2ca69f4be3294e4cd987d83d6019037d420d9fc1" @@ -1888,27 +2033,27 @@ version = "1.16.1" [[deps.StochasticDiffEq]] deps = ["Adapt", "ArrayInterface", "DataStructures", "DiffEqBase", "DiffEqNoiseProcess", "DocStringExtensions", "FiniteDiff", "ForwardDiff", "JumpProcesses", "LevyArea", "LinearAlgebra", "Logging", "MuladdMacro", "NLsolve", "OrdinaryDiffEq", "Random", "RandomNumbers", "RecursiveArrayTools", "Reexport", "SciMLBase", "SciMLOperators", "SparseArrays", "SparseDiffTools", "StaticArrays", "UnPack"] -git-tree-sha1 = "97e5d0b7e5ec2e68eec6626af97c59e9f6b6c3d0" +git-tree-sha1 = "1e013d271e025fc676f5b944f9537e09238b73c5" uuid = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0" -version = "6.65.1" +version = "6.68.0" [[deps.StrideArraysCore]] -deps = ["ArrayInterface", "CloseOpenIntervals", "IfElse", "LayoutPointers", "ManualMemory", "SIMDTypes", "Static", "StaticArrayInterface", "ThreadingUtilities"] -git-tree-sha1 = "d6415f66f3d89c615929af907fdc6a3e17af0d8c" +deps = ["ArrayInterface", "CloseOpenIntervals", "IfElse", "LayoutPointers", "LinearAlgebra", "ManualMemory", "SIMDTypes", "Static", "StaticArrayInterface", "ThreadingUtilities"] +git-tree-sha1 = "f35f6ab602df8413a50c4a25ca14de821e8605fb" uuid = "7792a7ef-975c-4747-a70f-980b88e8d1da" -version = "0.5.2" +version = "0.5.7" [[deps.Strided]] deps = ["LinearAlgebra", "StridedViews", "TupleTools"] -git-tree-sha1 = "40c69be0e1b72ee2f42923b7d1ff13e0b04e675c" +git-tree-sha1 = "d85461cb47420b6dcecf51de8142fc60484dd0b3" uuid = "5e0ebb24-38b0-5f93-81fe-25c709ecae67" -version = "2.0.4" +version = "2.1.1" [[deps.StridedViews]] deps = ["LinearAlgebra", "PackageExtensionCompat"] -git-tree-sha1 = "5b765c4e401693ab08981989f74a36a010aa1d8e" +git-tree-sha1 = "2917996ce0fa6b8a3a85240a5e9ff930e2aeaa43" uuid = "4db3bf67-4bd7-4b4e-b153-31dc3fb37143" -version = "0.2.2" +version = "0.3.1" [deps.StridedViews.extensions] StridedViewsCUDAExt = "CUDA" @@ -1926,30 +2071,38 @@ uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" version = "7.2.1+1" [[deps.SymbolicIndexingInterface]] -deps = ["MacroTools", "RuntimeGeneratedFunctions"] -git-tree-sha1 = "f7b1fc9fc2bc938436b7684c243be7d317919056" +deps = ["Accessors", "ArrayInterface", "RuntimeGeneratedFunctions", "StaticArraysCore"] +git-tree-sha1 = "c9fce29fb41a10677e24f74421ebe31220b81ad0" uuid = "2efcf032-c050-4f8e-a9bb-153293bab1f5" -version = "0.3.11" +version = "0.3.28" + +[[deps.SymbolicLimits]] +deps = ["SymbolicUtils"] +git-tree-sha1 = "fabf4650afe966a2ba646cabd924c3fd43577fc3" +uuid = "19f23fe9-fdab-4a78-91af-e7b7767979c3" +version = "0.2.2" [[deps.SymbolicUtils]] -deps = ["AbstractTrees", "Bijections", "ChainRulesCore", "Combinatorics", "ConstructionBase", "DataStructures", "DocStringExtensions", "DynamicPolynomials", "IfElse", "LabelledArrays", "LinearAlgebra", "MultivariatePolynomials", "NaNMath", "Setfield", "SparseArrays", "SpecialFunctions", "StaticArrays", "SymbolicIndexingInterface", "TimerOutputs", "Unityper"] -git-tree-sha1 = "669e43e90df46fcee4aa859b587da7a7948272ac" +deps = ["AbstractTrees", "Bijections", "ChainRulesCore", "Combinatorics", "ConstructionBase", "DataStructures", "DocStringExtensions", "DynamicPolynomials", "IfElse", "LabelledArrays", "LinearAlgebra", "MultivariatePolynomials", "NaNMath", "Setfield", "SparseArrays", "SpecialFunctions", "StaticArrays", "SymbolicIndexingInterface", "TermInterface", "TimerOutputs", "Unityper"] +git-tree-sha1 = "d00729521f49d96afd3fcddb437141eb71222886" uuid = "d1185830-fcd6-423d-90d6-eec64667417b" -version = "1.5.1" +version = "3.2.0" [[deps.Symbolics]] -deps = ["ArrayInterface", "Bijections", "ConstructionBase", "DataStructures", "DiffRules", "Distributions", "DocStringExtensions", "DomainSets", "DynamicPolynomials", "ForwardDiff", "IfElse", "LaTeXStrings", "LambertW", "Latexify", "Libdl", "LinearAlgebra", "LogExpFunctions", "MacroTools", "Markdown", "NaNMath", "PrecompileTools", "RecipesBase", "Reexport", "Requires", "RuntimeGeneratedFunctions", "SciMLBase", "Setfield", "SparseArrays", "SpecialFunctions", "StaticArrays", "SymbolicIndexingInterface", "SymbolicUtils"] -git-tree-sha1 = "d8b8dd08b7ec073cc7dc64cfa247fa0c4b5d5297" +deps = ["ADTypes", "ArrayInterface", "Bijections", "CommonWorldInvalidations", "ConstructionBase", "DataStructures", "DiffRules", "Distributions", "DocStringExtensions", "DomainSets", "DynamicPolynomials", "IfElse", "LaTeXStrings", "LambertW", "Latexify", "Libdl", "LinearAlgebra", "LogExpFunctions", "MacroTools", "Markdown", "NaNMath", "PrecompileTools", "RecipesBase", "Reexport", "RuntimeGeneratedFunctions", "SciMLBase", "Setfield", "SparseArrays", "SpecialFunctions", "StaticArraysCore", "SymbolicIndexingInterface", "SymbolicLimits", "SymbolicUtils", "TermInterface"] +git-tree-sha1 = "4224422f6a5452b1accaec15a65a5ce3c2ca4600" uuid = "0c5d862f-8b57-4792-8d23-62f2024744c7" -version = "5.25.2" +version = "6.2.0" [deps.Symbolics.extensions] + SymbolicsForwardDiffExt = "ForwardDiff" SymbolicsGroebnerExt = "Groebner" SymbolicsLuxCoreExt = "LuxCore" - SymbolicsPreallocationToolsExt = "PreallocationTools" + SymbolicsPreallocationToolsExt = ["PreallocationTools", "ForwardDiff"] SymbolicsSymPyExt = "SymPy" [deps.Symbolics.weakdeps] + ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" Groebner = "0b43b601-686d-58a3-8a1c-6623616c7cd4" LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623" PreallocationTools = "d236fae5-4411-538c-8e31-a6e3d9e00b46" @@ -1967,10 +2120,10 @@ uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" version = "1.0.1" [[deps.Tables]] -deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"] -git-tree-sha1 = "cb76cf677714c095e535e3501ac7954732aeea2d" +deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "OrderedCollections", "TableTraits"] +git-tree-sha1 = "598cd7c1f68d1e205689b1c2fe65a9f85846f297" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.11.1" +version = "1.12.0" [[deps.Tar]] deps = ["ArgTools", "SHA"] @@ -1983,6 +2136,11 @@ git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6" uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50" version = "0.1.1" +[[deps.TermInterface]] +git-tree-sha1 = "d673e0aca9e46a2f63720201f55cc7b3e7169b16" +uuid = "8ea1fca8-c5ef-4a55-8b96-4e9afe9c9a3c" +version = "2.0.0" + [[deps.Test]] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" @@ -1995,34 +2153,30 @@ version = "0.5.2" [[deps.TimerOutputs]] deps = ["ExprTools", "Printf"] -git-tree-sha1 = "f548a9e9c490030e545f72074a41edfd0e5bcdd7" +git-tree-sha1 = "5a13ae8a41237cff5ecf34f73eb1b8f42fff6531" uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f" -version = "0.5.23" +version = "0.5.24" [[deps.Tokenize]] -git-tree-sha1 = "5b5a892ba7704c0977013bd0f9c30f5d962181e0" +git-tree-sha1 = "468b4685af4abe0e9fd4d7bf495a6554a6276e75" uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624" -version = "0.5.28" +version = "0.5.29" [[deps.TranscodingStreams]] -git-tree-sha1 = "71509f04d045ec714c4748c785a59045c3736349" +git-tree-sha1 = "e84b3a11b9bece70d14cce63406bbc79ed3464d2" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.10.7" -weakdeps = ["Random", "Test"] - - [deps.TranscodingStreams.extensions] - TestExt = ["Test", "Random"] +version = "0.11.2" [[deps.TriangularSolve]] deps = ["CloseOpenIntervals", "IfElse", "LayoutPointers", "LinearAlgebra", "LoopVectorization", "Polyester", "Static", "VectorizationBase"] -git-tree-sha1 = "7ee8ed8904e7dd5d31bb46294ef5644d9e2e44e4" +git-tree-sha1 = "be986ad9dac14888ba338c2554dcfec6939e1393" uuid = "d5829a12-d9aa-46ab-831f-fb7c9ab06edf" -version = "0.1.21" +version = "0.2.1" [[deps.Tricks]] -git-tree-sha1 = "eae1bb484cd63b36999ee58be2de6c178105112f" +git-tree-sha1 = "7822b97e99a1672bfb1b49b668a6d46d58d8cbcb" uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775" -version = "0.1.8" +version = "0.1.9" [[deps.TruncatedStacktraces]] deps = ["InteractiveUtils", "MacroTools", "Preferences"] @@ -2060,23 +2214,20 @@ version = "0.4.1" [[deps.Unitful]] deps = ["Dates", "LinearAlgebra", "Random"] -git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa" +git-tree-sha1 = "d95fe458f26209c66a187b1114df96fd70839efd" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" -version = "1.19.0" +version = "1.21.0" +weakdeps = ["ConstructionBase", "InverseFunctions"] [deps.Unitful.extensions] ConstructionBaseUnitfulExt = "ConstructionBase" InverseFunctionsUnitfulExt = "InverseFunctions" - [deps.Unitful.weakdeps] - ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9" - InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" - [[deps.UnitfulLatexify]] deps = ["LaTeXStrings", "Latexify", "Unitful"] -git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd" +git-tree-sha1 = "975c354fcd5f7e1ddcc1f1a23e6e091d99e99bc8" uuid = "45397f5d-5981-4c77-b2b3-fc36d6e9b728" -version = "1.6.3" +version = "1.6.4" [[deps.Unityper]] deps = ["ConstructionBase"] @@ -2085,9 +2236,9 @@ uuid = "a7c27f48-0311-42f6-a7f8-2c11e75eb415" version = "0.1.6" [[deps.UnsafeArrays]] -git-tree-sha1 = "e7f1c67ba99ac6df440de191fa4d5cbfcbdddcd1" +git-tree-sha1 = "da0c9ca60d3371a4bc86b4e65c45db17086fb3ac" uuid = "c4a57d5a-5b31-53a6-b365-19f8c011fbd6" -version = "1.0.5" +version = "1.0.6" [[deps.Unzip]] git-tree-sha1 = "ca0969166a028236229f63514992fc073799bb78" @@ -2096,9 +2247,9 @@ version = "0.2.0" [[deps.VectorizationBase]] deps = ["ArrayInterface", "CPUSummary", "HostCPUFeatures", "IfElse", "LayoutPointers", "Libdl", "LinearAlgebra", "SIMDTypes", "Static", "StaticArrayInterface"] -git-tree-sha1 = "7209df901e6ed7489fe9b7aa3e46fb788e15db85" +git-tree-sha1 = "e7f5b81c65eb858bed630fe006837b935518aca5" uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f" -version = "0.21.65" +version = "0.21.70" [[deps.VertexSafeGraphs]] deps = ["Graphs"] @@ -2132,15 +2283,15 @@ version = "2.0.0" [[deps.XML2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"] -git-tree-sha1 = "532e22cf7be8462035d092ff21fada7527e2c488" +git-tree-sha1 = "d9717ce3518dc68a99e6b96300813760d887a01d" uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a" -version = "2.12.6+0" +version = "2.13.1+0" [[deps.XSLT_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"] -git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "XML2_jll", "Zlib_jll"] +git-tree-sha1 = "a54ee957f4c86b526460a720dbc882fa5edcbefc" uuid = "aed1982a-8fda-507f-9586-7b0439959a61" -version = "1.1.34+0" +version = "1.1.41+0" [[deps.XZ_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] @@ -2149,16 +2300,16 @@ uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800" version = "5.4.6+0" [[deps.Xorg_libICE_jll]] -deps = ["Libdl", "Pkg"] -git-tree-sha1 = "e5becd4411063bdcac16be8b66fc2f9f6f1e8fe5" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "326b4fea307b0b39892b3e85fa451692eda8d46c" uuid = "f67eecfb-183a-506d-b269-f58e52b52d7c" -version = "1.0.10+1" +version = "1.1.1+0" [[deps.Xorg_libSM_jll]] -deps = ["Libdl", "Pkg", "Xorg_libICE_jll"] -git-tree-sha1 = "4a9d9e4c180e1e8119b5ffc224a7b59d3a7f7e18" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libICE_jll"] +git-tree-sha1 = "3796722887072218eabafb494a13c963209754ce" uuid = "c834827a-8449-5923-a945-d239c165b7dd" -version = "1.2.3+0" +version = "1.2.4+0" [[deps.Xorg_libX11_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"] @@ -2185,10 +2336,10 @@ uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05" version = "1.1.4+0" [[deps.Xorg_libXext_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] -git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] +git-tree-sha1 = "d2d1a5c49fae4ba39983f63de6afcbea47194e85" uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3" -version = "1.3.4+4" +version = "1.3.6+0" [[deps.Xorg_libXfixes_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] @@ -2215,10 +2366,10 @@ uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484" version = "1.5.2+4" [[deps.Xorg_libXrender_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] -git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] +git-tree-sha1 = "47e45cd78224c53109495b3e324df0c37bb61fbe" uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa" -version = "0.9.10+4" +version = "0.9.11+0" [[deps.Xorg_libpthread_stubs_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] @@ -2228,9 +2379,9 @@ version = "0.1.1+0" [[deps.Xorg_libxcb_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] -git-tree-sha1 = "b4bfde5d5b652e22b9c790ad00af08b6d042b97d" +git-tree-sha1 = "bcd466676fef0878338c61e655629fa7bbc69d8e" uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b" -version = "1.15.0+0" +version = "1.17.0+0" [[deps.Xorg_libxkbfile_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] @@ -2322,10 +2473,10 @@ uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70" version = "3.1.1+0" [[deps.libaom_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d" uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" -version = "3.4.0+0" +version = "3.9.0+0" [[deps.libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] @@ -2338,6 +2489,12 @@ deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" version = "5.8.0+1" +[[deps.libdecor_jll]] +deps = ["Artifacts", "Dbus_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pango_jll", "Wayland_jll", "xkbcommon_jll"] +git-tree-sha1 = "9bf7903af251d2050b467f76bdbe57ce541f7f4f" +uuid = "1183f4f0-6f2a-5f1a-908b-139f9cdfea6f" +version = "0.2.2+0" + [[deps.libevdev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22" @@ -2364,9 +2521,9 @@ version = "1.6.43+1" [[deps.libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] -git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" +git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" -version = "1.3.7+1" +version = "1.3.7+2" [[deps.mtdev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -2379,6 +2536,12 @@ deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" version = "1.52.0+1" +[[deps.oneTBB_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493" +uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e" +version = "2021.12.0+0" + [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" diff --git a/docs/Project.toml b/docs/Project.toml index 65a42a6a..a160259f 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -3,6 +3,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" +NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec" OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" QuantumOptics = "6e0679c1-51ea-5a7c-ac74-d61b76210b0c" @@ -23,5 +24,5 @@ QuantumOptics = "1" QuantumOpticsBase = "0.4.21" SteadyStateDiffEq = "1" StochasticDiffEq = "6" -SymbolicUtils = "1" -Symbolics = "5" +SymbolicUtils = "3.1.2" +Symbolics = "6" diff --git a/docs/src/examples/cavity_antiresonance_indexed.md b/docs/src/examples/cavity_antiresonance_indexed.md index bd929ea9..3be890b7 100644 --- a/docs/src/examples/cavity_antiresonance_indexed.md +++ b/docs/src/examples/cavity_antiresonance_indexed.md @@ -144,7 +144,7 @@ nothing # hide n_ls = zeros(length(Δ_ls)) # definitions for fast replacement of numerical parameter -prob = ODEProblem(sys,u0,(0.0, 20Γ_), ps.=>p0) +prob = ODEProblem(sys,u0,(0.0, 20), ps.=>p0) prob_ss = SteadyStateProblem(prob) for i=1:length(Δ_ls) @@ -152,10 +152,9 @@ for i=1:length(Δ_ls) Δa_i = Δc_i + Ωij(1,2) # cavity on resonace with the shifted collective emitter p0_ = [Δc_i; η_; Δa_i; κ_; gi_; Γij_; Ωij_] - # create new SteadyStateProblem - prob_ss_ = remake(prob_ss, p=(ps.=>p0_)) - sol_ss = solve(prob_ss_, DynamicSS(Tsit5(); abstol=1e-6, reltol=1e-6), - reltol=1e-12, abstol=1e-12, maxiters=1e7) + # create (remake) new SteadyStateProblem + prob_ss_ = SteadyStateProblem(sys,u0,ps.=>p0_) + sol_ss = solve(prob_ss_, SSRootfind()) n_ls[i] = abs2(sol_ss[a]) end nothing #hide diff --git a/docs/src/examples/excitation-transport-chain.md b/docs/src/examples/excitation-transport-chain.md index 8d693357..34bc3e80 100644 --- a/docs/src/examples/excitation-transport-chain.md +++ b/docs/src/examples/excitation-transport-chain.md @@ -112,10 +112,10 @@ function prob_func(prob,i,repeat) x_ = x0 .+ s.*randn(N) p_ = [γ => 1.0; Δ => 0.0; Ω => 2.0; J0 => 1.25; x .=> x_;] # Return new ODEProblem - return remake(prob, p=p_) + return ODEProblem(sys,u0,(0.0,15.0),p_) end -trajectories = 50 +trajectories = 20 eprob = EnsembleProblem(prob,prob_func=prob_func) sim = solve(eprob,RK4(),trajectories=trajectories) nothing # hide diff --git a/docs/src/examples/jupyter_notebooks/cavity_antiresonance_indexed.ipynb b/docs/src/examples/jupyter_notebooks/cavity_antiresonance_indexed.ipynb index f3e8977f..49b53d2e 100644 --- a/docs/src/examples/jupyter_notebooks/cavity_antiresonance_indexed.ipynb +++ b/docs/src/examples/jupyter_notebooks/cavity_antiresonance_indexed.ipynb @@ -58,19 +58,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(ℋ(cavity) ⊗ ℋ(atom), :j, N, 2)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Hilbert space\n", "hc = FockSpace(:cavity)\n", @@ -98,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -125,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -150,28 +140,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{align}\n", - "\\frac{d}{dt} \\langle a\\rangle =& -1 i \\left( \\eta + \\underset{i}{\\overset{N}{\\sum}} {g}_{i} \\langle {\\sigma}_{i}^{{12}}\\rangle \\right) -1 i \\langle a\\rangle {\\Delta}c -0.5 \\langle a\\rangle \\kappa \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{k}^{{12}}\\rangle =& -0.5 \\underset{j{\\ne}i,k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{j}^{{12}}\\rangle {\\Gamma}_{k,j} + \\underset{j{\\ne}i,k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{k}^{{22}}\\rangle \\langle {\\sigma}_{j}^{{12}}\\rangle {\\Gamma}_{k,j} -1 i \\underset{j{\\ne}k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{j}^{{12}}\\rangle {\\Omega}_{k,j} + 2 i \\underset{j{\\ne}k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{k}^{{22}}\\rangle \\langle {\\sigma}_{j}^{{12}}\\rangle {\\Omega}_{k,j} -1 i {g}_{k} \\langle a\\rangle -0.5 \\langle {\\sigma}_{k}^{{12}}\\rangle {\\Gamma}_{k,k} -1 i \\langle {\\sigma}_{k}^{{12}}\\rangle {\\Delta}a + 2 i {g}_{k} \\langle {\\sigma}_{k}^{{22}}\\rangle \\langle a\\rangle \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{k}^{{22}}\\rangle =& -0.5 \\left( \\underset{i{\\ne}k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{i}^{{21}}\\rangle \\langle {\\sigma}_{k}^{{12}}\\rangle {\\Gamma}_{i,k} + \\underset{j{\\ne}i,k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{k}^{{21}}\\rangle \\langle {\\sigma}_{j}^{{12}}\\rangle {\\Gamma}_{k,j} \\right) + 1 i \\underset{i{\\ne}k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{i}^{{21}}\\rangle \\langle {\\sigma}_{k}^{{12}}\\rangle {\\Omega}_{i,k} -1 i \\underset{j{\\ne}k}{\\overset{N}{\\sum}} \\langle {\\sigma}_{k}^{{21}}\\rangle \\langle {\\sigma}_{j}^{{12}}\\rangle {\\Omega}_{k,j} -1.0 \\langle {\\sigma}_{k}^{{22}}\\rangle {\\Gamma}_{k,k} -1 i {g}_{k} \\langle {\\sigma}_{k}^{{21}}\\rangle \\langle a\\rangle + 1 i {g}_{k} \\langle a^\\dagger\\rangle \\langle {\\sigma}_{k}^{{12}}\\rangle \n", - "\\end{align}\n" - ], - "text/plain": [ - "∂ₜ(⟨a⟩) = (0 - 1im)*(η + var\"∑(i=1:N)gi*⟨σ12i⟩\") + (0 - 1im)*⟨a⟩*Δc - 0.5⟨a⟩*κ\n", - "∂ₜ(⟨σ12k⟩) = -0.5var\"∑(j=1:N)(j≠i,k)⟨σ12j⟩*Γkj\" + var\"∑(j=1:N)(j≠i,k)⟨σ22k⟩*⟨σ12j⟩*Γkj\" + (0 - 1im)*var\"∑(j=1:N)(j≠k)⟨σ12j⟩*Ωkj\" + (0 + 2im)*var\"∑(j=1:N)(j≠k)⟨σ22k⟩*⟨σ12j⟩*Ωkj\" + (0 - 1im)*gk*⟨a⟩ - 0.5⟨σ12k⟩*Γkk + (0 - 1im)*⟨σ12k⟩*Δa + (0 + 2im)*gk*⟨σ22k⟩*⟨a⟩\n", - "∂ₜ(⟨σ22k⟩) = -0.5(var\"∑(i=1:N)(i≠k)⟨σ21i⟩*⟨σ12k⟩*Γik\" + var\"∑(j=1:N)(j≠i,k)⟨σ21k⟩*⟨σ12j⟩*Γkj\") + (0 + 1im)*var\"∑(i=1:N)(i≠k)⟨σ21i⟩*⟨σ12k⟩*Ωik\" + (0 - 1im)*var\"∑(j=1:N)(j≠k)⟨σ21k⟩*⟨σ12j⟩*Ωkj\" - ⟨σ22k⟩*Γkk + (0 - 1im)*gk*⟨σ21k⟩*⟨a⟩ + (0 + 1im)*gk*⟨a′⟩*⟨σ12k⟩\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eqs = meanfield(a,H,J;rates=rates,order=1)\n", "complete!(eqs)" @@ -186,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +219,7 @@ "n_ls = zeros(length(Δ_ls))\n", "\n", "# definitions for fast replacement of numerical parameter \n", - "prob = ODEProblem(sys,u0,(0.0, 20Γ_), ps.=>p0)\n", + "prob = ODEProblem(sys,u0,(0.0, 20), ps.=>p0)\n", "prob_ss = SteadyStateProblem(prob)\n", "\n", "for i=1:length(Δ_ls)\n", @@ -256,10 +227,9 @@ " Δa_ = Δc_ + Ωij(1,2) # cavity on resonace with the shifted collective emitter\n", " p0_ = [Δc_; η_; Δa_; κ_; gi_; Γij_; Ωij_]\n", " \n", - " # create new ODEProblem\n", - " prob_ss_ = remake(prob_ss, p=(ps.=>p0_) )\n", - " sol_ss = solve(prob_ss_, DynamicSS(Tsit5(); abstol=1e-6, reltol=1e-6),\n", - " reltol=1e-12, abstol=1e-12, maxiters=1e7)\n", + " # create (remake) new SteadyStateProblem\n", + " prob_ss_ = SteadyStateProblem(sys,u0,ps.=>p0_)\n", + " sol_ss = solve(prob_ss_, SSRootfind())\n", " n_ls[i] = abs2(sol_ss[a])\n", "end" ] @@ -273,20 +243,9 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"/home/christoph/git/QuantumCumulants.jl/docs/src/examples/jupyter_notebooks/cavity_antiresonance_indexed.svg\"" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "T = n_ls ./ maximum(n_ls)\n", "plot(Δ_ls, T, xlabel=\"Δ/Γ\", ylabel=\"T\", legend=false)\n", diff --git a/docs/src/examples/jupyter_notebooks/noisy excitation transport.ipynb b/docs/src/examples/jupyter_notebooks/noisy excitation transport.ipynb index 075ffd18..dfa3a027 100644 --- a/docs/src/examples/jupyter_notebooks/noisy excitation transport.ipynb +++ b/docs/src/examples/jupyter_notebooks/noisy excitation transport.ipynb @@ -122,93 +122,93 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ], "text/html": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "execution_count": 3, @@ -220,7 +220,10 @@ "# Define parameters without noise\n", "d = 0.75\n", "x0 = [d*(k-1) for k=1:N]\n", - "p = [γ => 1.0; Δ => 0.0; Ω => 2.0; J0 => 1.25; x .=> x0;]\n", + "ps = [γ; Δ; Ω; J0; x...]\n", + "p0 = [1.0; 0.0im; 2.0; 1.25; x0;]\n", + "p = ps.=>p0\n", + "#p = [γ => 1.0; Δ => 0.0; Ω => 2.0; J0 => 1.25; x .=> x0;]\n", "\n", "# Create ODEProblem\n", "u0 = zeros(ComplexF64, length(eqs)) # initial state -- all atoms in the ground state\n", @@ -255,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -265,10 +268,10 @@ " x_ = x0 .+ s.*randn(N)\n", " p_ = [γ => 1.0; Δ => 0.0; Ω => 2.0; J0 => 1.25; x .=> x_;]\n", " # Return new ODEProblem\n", - " return remake(prob, p=p_)\n", + " return ODEProblem(sys,u0,(0.0,15.0),p_)\n", "end\n", "\n", - "trajectories = 50\n", + "trajectories = 20\n", "eprob = EnsembleProblem(prob,prob_func=prob_func)\n", "sim = solve(eprob,RK4(),trajectories=trajectories)\n", "nothing #hide" @@ -283,110 +286,110 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/src/examples/jupyter_notebooks/superradiant_laser_indexed.ipynb b/docs/src/examples/jupyter_notebooks/superradiant_laser_indexed.ipynb index ea8b16ca..2a1859cc 100644 --- a/docs/src/examples/jupyter_notebooks/superradiant_laser_indexed.ipynb +++ b/docs/src/examples/jupyter_notebooks/superradiant_laser_indexed.ipynb @@ -36,17 +36,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling QuantumCumulants [35bcea6d-e19f-57db-af74-8011de6c7255]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "using QuantumCumulants\n", "using OrdinaryDiffEq, SteadyStateDiffEq, ModelingToolkit\n", @@ -63,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -87,20 +79,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(ℋ(cavity) ⊗ ℋ(atom), :j, N, 2)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "@cnumbers N Δ κ Γ R ν\n", "g(i) = IndexedVariable(:g, i) \n", @@ -118,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -140,27 +121,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{align}\n", - "\\frac{d}{dt} \\langle a^\\dagger a\\rangle =& 1 i \\underset{i}{\\overset{N}{\\sum}} {g}_{i} \\langle a {\\sigma}_{i}^{{21}}\\rangle -1 i \\underset{i}{\\overset{N}{\\sum}} {g}_{i} \\langle a^\\dagger {\\sigma}_{i}^{{12}}\\rangle -1.0 \\langle a^\\dagger a\\rangle \\kappa \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{j}^{{22}}\\rangle =& R -1.0 R \\langle {\\sigma}_{j}^{{22}}\\rangle + 1 i {g}_{j} \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle -1 i {g}_{j} \\langle a {\\sigma}_{j}^{{21}}\\rangle -1.0 \\langle {\\sigma}_{j}^{{22}}\\rangle \\Gamma\n", - "\\end{align}\n" - ], - "text/plain": [ - "∂ₜ(⟨a′*a⟩) = (0 + 1im)*var\"∑(i=1:N)gi*⟨a*σ21i⟩\" + (0 - 1im)*var\"∑(i=1:N)gi*⟨a′*σ12i⟩\" - ⟨a′*a⟩*κ\n", - "∂ₜ(⟨σ22j⟩) = R - R*⟨σ22j⟩ + (0 + 1im)*gj*⟨a′*σ12j⟩ + (0 - 1im)*gj*⟨a*σ21j⟩ - ⟨σ22j⟩*Γ\n" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Derive equations\n", "ops = [a'*a, σ(2,2,j)]\n", @@ -176,31 +139,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{align}\n", - "\\frac{d}{dt} \\langle a^\\dagger a\\rangle =& 1 i \\underset{i}{\\overset{N}{\\sum}} {g}_{i} \\langle a {\\sigma}_{i}^{{21}}\\rangle -1 i \\underset{i}{\\overset{N}{\\sum}} {g}_{i} \\langle a^\\dagger {\\sigma}_{i}^{{12}}\\rangle -1.0 \\langle a^\\dagger a\\rangle \\kappa \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{j}^{{22}}\\rangle =& R -1.0 R \\langle {\\sigma}_{j}^{{22}}\\rangle + 1 i {g}_{j} \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle -1 i {g}_{j} \\langle a {\\sigma}_{j}^{{21}}\\rangle -1.0 \\langle {\\sigma}_{j}^{{22}}\\rangle \\Gamma \\\\\n", - "\\frac{d}{dt} \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle =& 1 i \\underset{i{\\ne}j}{\\overset{N}{\\sum}} {g}_{i} \\langle {\\sigma}_{i}^{{21}} {\\sigma}_{j}^{{12}}\\rangle -0.5 R \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle + 1 i {g}_{j} \\langle {\\sigma}_{j}^{{22}}\\rangle -1 i {g}_{j} \\langle a^\\dagger a\\rangle -0.5 \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle \\left( \\Gamma + \\kappa + \\nu \\right) -1 i \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle \\Delta + 2 i {g}_{j} \\langle {\\sigma}_{j}^{{22}}\\rangle \\langle a^\\dagger a\\rangle \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{j}^{{12}} {\\sigma}_{k}^{{21}}\\rangle =& -1.0 R \\langle {\\sigma}_{j}^{{12}} {\\sigma}_{k}^{{21}}\\rangle -1 i {g}_{j} \\langle a {\\sigma}_{k}^{{21}}\\rangle + 1 i {g}_{k} \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle + \\langle {\\sigma}_{j}^{{12}} {\\sigma}_{k}^{{21}}\\rangle \\left( -1.0 \\Gamma -1.0 \\nu \\right) + 2 i {g}_{j} \\langle a {\\sigma}_{k}^{{21}}\\rangle \\langle {\\sigma}_{j}^{{22}}\\rangle -2 i {g}_{k} \\langle a^\\dagger {\\sigma}_{j}^{{12}}\\rangle \\langle {\\sigma}_{k}^{{22}}\\rangle \n", - "\\end{align}\n" - ], - "text/plain": [ - "∂ₜ(⟨a′*a⟩) = (0 + 1im)*var\"∑(i=1:N)gi*⟨a*σ21i⟩\" + (0 - 1im)*var\"∑(i=1:N)gi*⟨a′*σ12i⟩\" - ⟨a′*a⟩*κ\n", - "∂ₜ(⟨σ22j⟩) = R - R*⟨σ22j⟩ + (0 + 1im)*gj*⟨a′*σ12j⟩ + (0 - 1im)*gj*⟨a*σ21j⟩ - ⟨σ22j⟩*Γ\n", - "∂ₜ(⟨a′*σ12j⟩) = (0 + 1im)*var\"∑(i=1:N)(i≠j)gi*⟨σ21i*σ12j⟩\" - 0.5R*⟨a′*σ12j⟩ + (0 + 1im)*gj*⟨σ22j⟩ + (0 - 1im)*gj*⟨a′*a⟩ - 0.5⟨a′*σ12j⟩*(Γ + κ + ν) + (0 - 1im)*⟨a′*σ12j⟩*Δ + (0 + 2im)*gj*⟨σ22j⟩*⟨a′*a⟩\n", - "∂ₜ(⟨σ12j*σ21k⟩) = -R*⟨σ12j*σ21k⟩ + (0 - 1im)*gj*⟨a*σ21k⟩ + (0 + 1im)*gk*⟨a′*σ12j⟩ + ⟨σ12j*σ21k⟩*(-Γ - ν) + (0 + 2im)*gj*⟨a*σ21k⟩*⟨σ22j⟩ + (0 - 2im)*gk*⟨a′*σ12j⟩*⟨σ22k⟩\n" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# custom filter function\n", "φ(x::Average) = φ(x.arguments[1])\n", @@ -226,31 +167,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{align}\n", - "\\frac{d}{dt} \\langle a^\\dagger a\\rangle =& -1.0 \\langle a^\\dagger a\\rangle \\kappa + 1 i N g_{1} \\langle a {\\sigma}_{1}^{{21}}\\rangle -1 i N g_{1} \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{1}^{{22}}\\rangle =& R -1.0 R \\langle {\\sigma}_{1}^{{22}}\\rangle -1 i g_{1} \\langle a {\\sigma}_{1}^{{21}}\\rangle + 1 i g_{1} \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle -1.0 \\langle {\\sigma}_{1}^{{22}}\\rangle \\Gamma \\\\\n", - "\\frac{d}{dt} \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle =& -0.5 R \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle + 1 i g_{1} \\langle {\\sigma}_{1}^{{22}}\\rangle -1 i g_{1} \\langle a^\\dagger a\\rangle -0.5 \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle \\left( \\Gamma + \\kappa + \\nu \\right) -1 i \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle \\Delta + 1 i \\left( -1 + N \\right) g_{1} \\langle {\\sigma}_{1}^{{12}} {\\sigma}_{2}^{{21}}\\rangle + 2 i g_{1} \\langle {\\sigma}_{1}^{{22}}\\rangle \\langle a^\\dagger a\\rangle \\\\\n", - "\\frac{d}{dt} \\langle {\\sigma}_{1}^{{12}} {\\sigma}_{2}^{{21}}\\rangle =& -1.0 R \\langle {\\sigma}_{1}^{{12}} {\\sigma}_{2}^{{21}}\\rangle -1 i g_{1} \\langle a {\\sigma}_{1}^{{21}}\\rangle + 1 i g_{1} \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle + \\langle {\\sigma}_{1}^{{12}} {\\sigma}_{2}^{{21}}\\rangle \\left( -1.0 \\Gamma -1.0 \\nu \\right) + 2 i g_{1} \\langle {\\sigma}_{1}^{{22}}\\rangle \\langle a {\\sigma}_{1}^{{21}}\\rangle -2 i g_{1} \\langle {\\sigma}_{1}^{{22}}\\rangle \\langle a^\\dagger {\\sigma}_{1}^{{12}}\\rangle \n", - "\\end{align}\n" - ], - "text/plain": [ - "∂ₜ(⟨a′*a⟩) = -⟨a′*a⟩*κ + (0 + 1im)*N*g_1*⟨a*σ211⟩ + (0 - 1im)*N*g_1*⟨a′*σ121⟩\n", - "∂ₜ(⟨σ221⟩) = R - R*⟨σ221⟩ + (0 - 1im)*g_1*⟨a*σ211⟩ + (0 + 1im)*g_1*⟨a′*σ121⟩ - ⟨σ221⟩*Γ\n", - "∂ₜ(⟨a′*σ121⟩) = -0.5R*⟨a′*σ121⟩ + (0 + 1im)*g_1*⟨σ221⟩ + (0 - 1im)*g_1*⟨a′*a⟩ - 0.5⟨a′*σ121⟩*(Γ + κ + ν) + (0 - 1im)*⟨a′*σ121⟩*Δ + (0 + 1im)*(-1 + N)*g_1*⟨σ121*σ212⟩ + (0 + 2im)*g_1*⟨σ221⟩*⟨a′*a⟩\n", - "∂ₜ(⟨σ121*σ212⟩) = -R*⟨σ121*σ212⟩ + (0 - 1im)*g_1*⟨a*σ211⟩ + (0 + 1im)*g_1*⟨a′*σ121⟩ + ⟨σ121*σ212⟩*(-Γ - ν) + (0 + 2im)*g_1*⟨σ221⟩*⟨a*σ211⟩ + (0 - 2im)*g_1*⟨σ221⟩*⟨a′*σ121⟩\n" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "eqs_sc = scale(eqs_c)" ] @@ -264,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -305,20 +224,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"/home/christoph/git/QuantumCumulants.jl/docs/src/examples/jupyter_notebooks/superradiant_laser_indexed.svg\"" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Solve the numeric problem\n", "sol = solve(prob,Tsit5(),maxiters=1e7)\n", @@ -350,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -369,27 +277,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{align}\n", - "\\frac{d}{d\\tau} \\langle a^\\dagger a_0\\rangle =& -1 i \\langle a^\\dagger a_0\\rangle \\Delta -0.5 \\langle a^\\dagger a_0\\rangle \\kappa + 1 i N g_{1} \\langle {\\sigma}_{1}^{{21}} a_0\\rangle \\\\\n", - "\\frac{d}{d\\tau} \\langle {\\sigma}_{1}^{{21}} a_0\\rangle =& -0.5 R \\langle {\\sigma}_{1}^{{21}} a_0\\rangle + 1 i g_{1} \\langle a^\\dagger a_0\\rangle -0.5 \\langle {\\sigma}_{1}^{{21}} a_0\\rangle \\left( \\Gamma + \\nu \\right) -2 i g_{1} \\langle {\\sigma}_{1}^{{22}}\\rangle \\langle a^\\dagger a_0\\rangle \n", - "\\end{align}\n" - ], - "text/plain": [ - "∂ₜ(⟨a′*a_0⟩) = (0 - 1im)*⟨a′*a_0⟩*Δ - 0.5⟨a′*a_0⟩*κ + (0 + 1im)*N*g_1*⟨σ211*a_0⟩\n", - "∂ₜ(⟨σ211*a_0⟩) = -0.5R*⟨σ211*a_0⟩ + (0 + 1im)*g_1*⟨a′*a_0⟩ - 0.5⟨σ211*a_0⟩*(Γ + ν) + (0 - 2im)*g_1*⟨σ221⟩*⟨a′*a_0⟩\n" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "corr_sc.de" ] @@ -403,13 +293,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "prob_ss = SteadyStateProblem(prob)\n", - "sol_ss = solve(prob_ss, DynamicSS(Tsit5(); abstol=1e-6, reltol=1e-6), \n", - " reltol=1e-12, abstol=1e-12, maxiters=1e7)\n", + "prob_ss = SteadyStateProblem(sys,sol.u[end],ps.=>p0)\n", + "sol_ss = solve(prob_ss, SSRootfind())\n", "nothing # hide" ] }, @@ -422,20 +311,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ω = [-10:0.01:10;]Γ_\n", "spec = S(ω,sol_ss.u,p0)\n", @@ -445,20 +323,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"/home/christoph/git/QuantumCumulants.jl/docs/src/examples/jupyter_notebooks/spectrum_superradiant_laser_indexed.svg\"" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot(ω, spec_n, xlabel=\"ω/Γ\", legend=false, size=(500,300))\n", "savefig(\"spectrum_superradiant_laser_indexed.svg\") # hide" diff --git a/docs/src/examples/superradiant-laser.md b/docs/src/examples/superradiant-laser.md index 098e1234..b6b6dcd4 100644 --- a/docs/src/examples/superradiant-laser.md +++ b/docs/src/examples/superradiant-laser.md @@ -192,9 +192,8 @@ To ensure we are in the steady state we use a steady solver to calculate it. To ```@example superradiant-laser -prob_ss = SteadyStateProblem(prob) -sol_ss = solve(prob_ss, DynamicSS(Tsit5(); abstol=1e-6, reltol=1e-6), - reltol=1e-12, abstol=1e-12, maxiters=1e7) +prob_ss = SteadyStateProblem(sys,sol.u[end],ps.=>p0) +sol_ss = solve(prob_ss, SSRootfind()) nothing # hide ``` diff --git a/docs/src/examples/superradiant_laser_indexed.md b/docs/src/examples/superradiant_laser_indexed.md index e7d2752e..1486ad8f 100644 --- a/docs/src/examples/superradiant_laser_indexed.md +++ b/docs/src/examples/superradiant_laser_indexed.md @@ -216,9 +216,8 @@ To ensure we are in the steady state we use a steady solver to calculate it. To ```@example superradiant_laser_indexed -prob_ss = SteadyStateProblem(prob) -sol_ss = solve(prob_ss, DynamicSS(Tsit5(); abstol=1e-6, reltol=1e-6), - reltol=1e-12, abstol=1e-12, maxiters=1e7) +prob_ss = SteadyStateProblem(sys,sol.u[end],ps.=>p0) +sol_ss = solve(prob_ss, SSRootfind()) nothing # hide ``` diff --git a/src/average.jl b/src/average.jl index 421b5d1f..6f2e0141 100644 --- a/src/average.jl +++ b/src/average.jl @@ -18,18 +18,21 @@ end # Type promotion -- average(::QNumber)::Number SymbolicUtils.promote_symtype(::typeof(sym_average), ::Type{<:QNumber}) = AvgSym +# needs a specific symtype overload, otherwise we build the wrong expressions with maketerm +SymbolicUtils.symtype(::T) where T <: Average = QuantumCumulants.AvgSym + # Direct construction of average symbolic expression function _average(operator) return SymbolicUtils.Term{AvgSym}(sym_average, [operator]) end # ensure that BasicSymbolic{<:AvgSym} are only single averages -function *(a::Average,b::Average) +function *(a::Average,b::Average) if isequal(a,b) return SymbolicUtils.Mul(CNumber,1,Dict(a=>2)) end return SymbolicUtils.Mul(CNumber,1,Dict(a=>1,b=>1)) end -function +(a::Average,b::Average) +function +(a::Average,b::Average) if isequal(a,b) return SymbolicUtils.Add(CNumber,0,Dict(a=>2)) end @@ -37,7 +40,7 @@ function +(a::Average,b::Average) end function acts_on(s::SymbolicUtils.Symbolic) - if SymbolicUtils.istree(s) + if SymbolicUtils.iscall(s) f = SymbolicUtils.operation(s) if f === sym_average return acts_on(SymbolicUtils.arguments(s)[1]) @@ -82,7 +85,7 @@ average(x::SNuN) = x average(x,order;kwargs...) = cumulant_expansion(average(x),order;kwargs...) function undo_average(t) - if SymbolicUtils.istree(t) + if SymbolicUtils.iscall(t) f = SymbolicUtils.operation(t) if isequal(f,sym_average) # "===" results in false sometimes in Symbolics version > 5 return SymbolicUtils.arguments(t)[1] @@ -133,7 +136,7 @@ Optional arguments *kwargs...: Further keyword arguments being passed to simplification. """ function cumulant_expansion(x::SymbolicUtils.Symbolic,order::Integer;simplify=true,kwargs...) - if SymbolicUtils.istree(x) + if SymbolicUtils.iscall(x) get_order(x) <= order && return x f = SymbolicUtils.operation(x) if f===sym_average @@ -158,7 +161,7 @@ end cumulant_expansion(x::Number,order;kwargs...) = x function cumulant_expansion(x::SymbolicUtils.Symbolic,order;mix_choice=maximum,simplify=true,kwargs...) - if SymbolicUtils.istree(x) + if SymbolicUtils.iscall(x) f = SymbolicUtils.operation(x) args = SymbolicUtils.arguments(x) cumulants = [cumulant_expansion(arg,order;simplify=simplify,mix_choice=mix_choice) for arg in args] @@ -319,7 +322,7 @@ julia> get_order(1) """ get_order(avg::Average) = get_order(SymbolicUtils.arguments(avg)[1]) function get_order(t::SymbolicUtils.Symbolic) - if SymbolicUtils.istree(t) + if SymbolicUtils.iscall(t) return maximum(map(get_order, SymbolicUtils.arguments(t))) else return 0 diff --git a/src/cnumber.jl b/src/cnumber.jl index f537eaec..d1cc2a19 100644 --- a/src/cnumber.jl +++ b/src/cnumber.jl @@ -13,7 +13,7 @@ a parameter. """ struct Parameter <: CNumber function Parameter(name; metadata=source_metadata(:Parameter, name)) - s = SymbolicUtils.Sym{Parameter}(name) + s = SymbolicUtils.Sym{Complex{Real}}(name) s = SymbolicUtils.setmetadata(s, MTK.VariableSource, (:Parameter, name)) return SymbolicUtils.setmetadata(s,MTK.MTKVariableTypeCtx,MTK.PARAMETER) end @@ -26,9 +26,9 @@ Base.promote_rule(::Type{<:CNumber},::Type{<:Number}) = CNumber Base.one(::Type{Parameter}) = 1 Base.zero(::Type{Parameter}) = 0 Base.adjoint(x::SymbolicUtils.Symbolic{<:CNumber}) = conj(x) +# Base.adjoint(x::SymbolicUtils.BasicSymbolic{Complex{Real}}) = _conj(x) +Base.adjoint(s::SymbolicUtils.Symbolic{<:Number}) = conj(s) # type piracy; TODO:delete after SU.jl PR is merged -# TODO: this doesn't work with just setting Complex for some reason; am I doing this right? -MTK.concrete_symtype(::Symbolics.BasicSymbolic{T}) where T <: CNumber = ComplexF64 """ @cnumbers(ps...) @@ -117,7 +117,7 @@ a real parameter. """ struct RealParameter <: RNumber function RealParameter(name; metadata=source_metadata(:RealParameter, name)) - s = SymbolicUtils.Sym{RealParameter}(name) + s = SymbolicUtils.Sym{Real}(name) s = SymbolicUtils.setmetadata(s, MTK.VariableSource, (:RealParameter, name)) return SymbolicUtils.setmetadata(s,MTK.MTKVariableTypeCtx,MTK.PARAMETER) end @@ -132,8 +132,7 @@ Base.zero(::Type{RealParameter}) = 0 Base.adjoint(x::SymbolicUtils.Symbolic{<:RNumber}) = x Base.adjoint(x::RNumber) = x Base.conj(x::RNumber) = x - -MTK.concrete_symtype(::Symbolics.BasicSymbolic{T}) where T<:RNumber = Real +# Base.adjoint(x::SymbolicUtils.Symbolic{<:Real}) = conj(x) """ @rnumbers(ps...) @@ -230,4 +229,4 @@ end const AbstractQCParameter = Union{CNumber, RNumber} -# TODO: real IndexedVariables \ No newline at end of file +# TODO: real IndexedVariables diff --git a/src/correlation.jl b/src/correlation.jl index 72d032d1..8fb5355a 100644 --- a/src/correlation.jl +++ b/src/correlation.jl @@ -28,9 +28,16 @@ system of equations. function CorrelationFunction(op1,op2,de0::AbstractMeanfieldEquations; steady_state=false, add_subscript=0, filter_func=nothing, mix_choice=maximum, - iv=SymbolicUtils.Sym{Real}(:τ), + iv=nothing, order=nothing, simplify=true, kwargs...) + if iv === nothing + iv_ = SymbolicUtils.Sym{Real}(:τ) + iv_ = SymbolicUtils.setmetadata(iv_, MTK.VariableSource, (:parameters, :τ)) + iv_ = SymbolicUtils.setmetadata(iv_, MTK.MTKVariableTypeCtx,MTK.PARAMETER) + else + iv_ = iv + end h1 = hilbert(op1) h2 = _new_hilbert(hilbert(op2), acts_on(op2)) h = h1⊗h2 @@ -62,7 +69,7 @@ function CorrelationFunction(op1,op2,de0::AbstractMeanfieldEquations; op_ = op1_*op2_ @assert get_order(op_) <= order_ - de = meanfield(op_,H,J;Jdagger=Jd,rates=de0.rates,iv=iv,order=order_) + de = meanfield(op_,H,J;Jdagger=Jd,rates=de0.rates,iv=iv_,order=order_) _complete_corr!(de, length(h.spaces), lhs_new, order_, steady_state; filter_func=filter_func, mix_choice=mix_choice, @@ -392,7 +399,7 @@ function _new_operator(t::Transition, h, aon=t.aon; add_subscript=nothing) end _new_operator(x::Number, h, aon=nothing; kwargs...) = x function _new_operator(t, h, aon=nothing; kwargs...) - if SymbolicUtils.istree(t) + if SymbolicUtils.iscall(t) args = [] if isnothing(aon) for arg in SymbolicUtils.arguments(t) @@ -552,16 +559,16 @@ function _build_spec_func(ω, lhs, rhs, a1, a0, steady_vals, ps=[]) return A, b, c, Afunc, bfunc, cfunc end -function _substitute_vars(t::SymbolicUtils.Symbolic) - if SymbolicUtils.istree(t) +function _substitute_vars(t::T) where T <: SymbolicUtils.Symbolic + if SymbolicUtils.iscall(t) f = SymbolicUtils.operation(t) if f === sym_average sym = Symbol(string(t)) - return SymbolicUtils.setmetadata(SymbolicUtils.Sym{Complex}(sym), + return SymbolicUtils.setmetadata(SymbolicUtils.Sym{Complex{Real}}(sym), Symbolics.VariableSource, (:_substitute_vars, sym)) else args = [_substitute_vars(arg) for arg∈SymbolicUtils.arguments(t)] - return SymbolicUtils.similarterm(t, f, args) + return SymbolicUtils.maketerm(T, f, args, TermInterface.metadata(t)) end else return t diff --git a/src/diffeq.jl b/src/diffeq.jl index 257398b1..225f035d 100644 --- a/src/diffeq.jl +++ b/src/diffeq.jl @@ -34,8 +34,8 @@ function MTK.equations(me::AbstractMeanfieldEquations) end # Substitute conjugate variables -function substitute_conj(t,vs′,vs′hash) - if SymbolicUtils.istree(t) +function substitute_conj(t::T,vs′,vs′hash) where T + if SymbolicUtils.iscall(t) if t isa Average if hash(t)∈vs′hash t′ = _conj(t) @@ -46,7 +46,7 @@ function substitute_conj(t,vs′,vs′hash) else _f = x->substitute_conj(x,vs′,vs′hash) args = map(_f, SymbolicUtils.arguments(t)) - return SymbolicUtils.similarterm(t, SymbolicUtils.operation(t), args) + return SymbolicUtils.maketerm(T, SymbolicUtils.operation(t), args, TermInterface.metadata(t)) end else return t @@ -103,8 +103,8 @@ function MTK.equations(me::AbstractIndexedMeanfieldEquations) end # Substitute conjugate variables for indexed equations -function substitute_conj_ind(t,vs′,vs′hash) - if SymbolicUtils.istree(t) +function substitute_conj_ind(t::T,vs′,vs′hash) where T + if SymbolicUtils.iscall(t) if t isa Average if hash(t)∈vs′hash t′ = _inconj(t) @@ -115,7 +115,8 @@ function substitute_conj_ind(t,vs′,vs′hash) else _f = x->substitute_conj_ind(x,vs′,vs′hash) args = map(_f, SymbolicUtils.arguments(t)) - return SymbolicUtils.similarterm(t, SymbolicUtils.operation(t), args) + f = SymbolicUtils.operation(t) + return SymbolicUtils.maketerm(T, f, args, TermInterface.metadata(t)) end else return t diff --git a/src/index_average.jl b/src/index_average.jl index a31fef5a..a0cd17f3 100644 --- a/src/index_average.jl +++ b/src/index_average.jl @@ -60,7 +60,7 @@ function IndexedAverageSum(term::symbolics_terms,sum_index::Index,non_equal_indi return (sum_index.range - length(non_equal_indices)) * term end prefact = 1.0 - if istree(term) + if iscall(term) op = operation(term) args = arguments(term) # move numbers outside of sum @@ -73,7 +73,7 @@ function IndexedAverageSum(term::symbolics_terms,sum_index::Index,non_equal_indi else term = *(args_nc...) end - end + end end if op === + return sum(IndexedAverageSum(arg,sum_index,non_equal_indices;metadata=metadata) for arg in arguments(term)) @@ -117,7 +117,7 @@ struct IndexedAverageDoubleSum <: CNumber end end function IndexedAverageDoubleSum(term::symbolics_terms,sum_index::Index,non_equal_indices) - if istree(term) + if iscall(term) op = operation(term) args = arguments(term) param = 1.0 @@ -161,7 +161,7 @@ struct NumberedOperator <:QSym end function NumberedOperator(op,numb::Int64) (op isa SNuN) && return op - if SymbolicUtils.istree(op) + if SymbolicUtils.iscall(op) f = SymbolicUtils.operation(op) args = [NumberedOperator(arg,numb) for arg in SymbolicUtils.arguments(op)] isempty(args) && return 0 @@ -243,10 +243,10 @@ struct SpecialIndexedAverage <: CNumber #An average-Term with special condition, end end function SpecialIndexedAverage(term::symbolics_terms,indexMapping) - if istree(term) + if iscall(term) op = operation(term) args = arguments(term) - if op === * + if op === * prefac = 1 if args[1] isa Number prefac = args[1] @@ -335,10 +335,10 @@ end SymbolicUtils._iszero(a::IndexedAverageSum) = SymbolicUtils._iszero(a.term) SymbolicUtils._isone(a::IndexedAverageSum) = SymbolicUtils._isone(a.term) -SymbolicUtils.istree(a::IndexedAverageSum) = false -SymbolicUtils.istree(a::BasicSymbolic{SpecialIndexedAverage}) = false -SymbolicUtils.istree(a::IndexedAverageDoubleSum) = false -SymbolicUtils.istree(a::BasicSymbolic{IndexedAverageDoubleSum}) = false +SymbolicUtils.iscall(a::IndexedAverageSum) = false +SymbolicUtils.iscall(a::BasicSymbolic{SpecialIndexedAverage}) = false +SymbolicUtils.iscall(a::IndexedAverageDoubleSum) = false +SymbolicUtils.iscall(a::BasicSymbolic{IndexedAverageDoubleSum}) = false average(x::NumberedOperator) = _average(x) @@ -392,7 +392,7 @@ function Base.hash(a::NumberedOperator,h::UInt) end Base.isequal(x::NumberedOperator,y::NumberedOperator) = isequal(x.op,y.op) && isequal(x.numb,y.numb) Base.isless(a::IndexedAverageSum,b::IndexedAverageSum) = a.sum_index < b.sum_index -function Base.isequal(a::BasicSymbolic{IndexedAverageSum},b::BasicSymbolic{IndexedAverageSum}) +function Base.isequal(a::BasicSymbolic{IndexedAverageSum},b::BasicSymbolic{IndexedAverageSum}) a_meta = SymbolicUtils.metadata(a)[IndexedAverageSum] b_meta = SymbolicUtils.metadata(b)[IndexedAverageSum] return isequal(a_meta,b_meta) @@ -403,7 +403,7 @@ function Base.isequal(a::IndexedAverageSum, b::IndexedAverageSum) isequal(a.non_equal_indices,b.non_equal_indices) || return false return true end -function Base.isequal(a::BasicSymbolic{SpecialIndexedAverage},b::BasicSymbolic{SpecialIndexedAverage}) +function Base.isequal(a::BasicSymbolic{SpecialIndexedAverage},b::BasicSymbolic{SpecialIndexedAverage}) a_meta = SymbolicUtils.metadata(a)[SpecialIndexedAverage] b_meta = SymbolicUtils.metadata(b)[SpecialIndexedAverage] return isequal(a_meta.term,b_meta.term) && isequal(a_meta.indexMapping,b_meta.indexMapping) @@ -475,7 +475,7 @@ function insert_index(sum::BasicSymbolic{IndexedAverageDoubleSum}, ind::Index,va return IndexedAverageDoubleSum(inner,meta.sum_index,meta.non_equal_indices) end function insert_index(term::BasicSymbolic{<:CNumber},ind::Index,value::Int64) - if istree(term) + if iscall(term) op = operation(term) if op === * return prod(insert_index(arg,ind,value) for arg in arguments(term)) @@ -511,7 +511,7 @@ function insert_index(term_::BasicSymbolic{DoubleIndexedVariable},ind::Index,val return term_ end function insert_index(term::BasicSymbolic{DoubleNumberedVariable},ind::Index,value::Int64) - if istree(term) + if iscall(term) op = operation(term) if op === * return prod(insert_index(arg,ind,value) for arg in arguments(term)) @@ -596,7 +596,7 @@ function insert_indices_lhs(term::Average,map::Dict{Index,Int64};kwargs...) inorder!(lhs) end return lhs -end +end """ evalME(me::MeanfieldEquations;limits::Dict{SymbolicUtils.BasicSymbolic,Int64}=Dict{SymbolicUtils.BasicSymbolic,Int64}()) @@ -651,7 +651,7 @@ function evalME(me::AbstractMeanfieldEquations;limits=Dict{SymbolicUtils.BasicSy dict = Dict{Index,Int}(inds .=> arr[j]) eq_lhs = insert_indices_lhs(eq.lhs,dict) if (eq_lhs ∉ states) && (_inconj(eq_lhs) ∉ states) - eq_rhs = insert_indices(eq,dict;limits=limits,h=h,kwargs...) + eq_rhs = insert_indices(eq,dict;limits=limits,h=h,kwargs...) states[counter] = eq_lhs if SymbolicUtils._iszero(eq_rhs) newEqs[counter] = Symbolics.Equation(eq_lhs,0) @@ -662,7 +662,7 @@ function evalME(me::AbstractMeanfieldEquations;limits=Dict{SymbolicUtils.BasicSy end end end - end + end states = states[1:(counter-1)] operats = undo_average.(states) newEqs = newEqs[1:(counter-1)] @@ -684,7 +684,7 @@ function evalME(me::AbstractMeanfieldEquations;limits=Dict{SymbolicUtils.BasicSy counter = counter + 1 else ranges = get_range.(inds) - counter = counter + prod(ranges) + counter = counter + prod(ranges) end end return substitute(counter,limits) @@ -701,8 +701,9 @@ function eval_term(sum_::BasicSymbolic{IndexedAverageSum};limits=Dict{SymbolicUt if meta.sum_index.range in keys(limits) rangeEval = limits[meta.sum_index.range] else - if meta.sum_index.range isa BasicSymbolic{<:CNumber} - if istree(meta.sum_index.range) + if meta.sum_index.range isa BasicSymbolic{<:Complex{<:Real}} + # if meta.sum_index.range isa BasicSymbolic{<:CNumber} + if iscall(meta.sum_index.range) args = arguments(meta.sum_index.range) args_ = Vector{Any}(nothing,length(args)) for i=1:length(args) @@ -742,7 +743,7 @@ function eval_term(sum::BasicSymbolic{IndexedAverageDoubleSum};kwargs...) return eval_term(IndexedAverageDoubleSum(eval_term(meta.innerSum;kwargs...),meta.sum_index,meta.non_equal_indices);kwargs...) end function eval_term(term::BasicSymbolic{<:CNumber};kwargs...) - if istree(term) + if iscall(term) op = operation(term) if op === + return sum(eval_term(arg;kwargs...) for arg in arguments(term)) @@ -759,7 +760,7 @@ function eval_term(term::BasicSymbolic{<:CNumber};kwargs...) args = arguments(term) return eval_term(args[1];kwargs...)/eval_term(args[2];kwargs...) end - + if length(arguments(term)) == 1 # exp, sin, cos, ln, ... return op(eval_term(arguments(term)[1];kwargs...)) end @@ -771,7 +772,7 @@ function eval_term(x;kwargs...) inorder!(x) return x end -function evalEq(eq::Symbolics.Equation;kwargs...) +function evalEq(eq::Symbolics.Equation;kwargs...) rhs_ = eval_term(eq.rhs;kwargs...) if SymbolicUtils._iszero(rhs_) return Symbolics.Equation(eq.lhs,0) @@ -889,7 +890,7 @@ function containsIndexedOps(term::Average) end containsIndex(term::Average,ind::Index) = ind ∈ get_indices(term) -function SymbolicUtils.simplify(sym::BasicSymbolic{SpecialIndexedAverage}) +function SymbolicUtils.simplify(sym::BasicSymbolic{SpecialIndexedAverage}) meta = SymbolicUtils.metadata(sym)[SpecialIndexedAverage] SpecialIndexedAverage(SymbolicUtils.simplify(meta.term),meta.indexMapping) end @@ -952,8 +953,8 @@ end getAvrgs(sum::BasicSymbolic{SpecialIndexedAverage}) = getAvrgs(SymbolicUtils.metadata(sum)[SpecialIndexedAverage].term) getAvrgs(sum::BasicSymbolic{IndexedAverageSum}) = getAvrgs(SymbolicUtils.metadata(sum)[IndexedAverageSum].term) getAvrgs(Dsum::BasicSymbolic{IndexedAverageDoubleSum}) = getAvrgs(SymbolicUtils.metadata(Dsum)[IndexedAverageDoubleSum].innerSum) -function getAvrgs(term::BasicSymbolic{<:CNumber}) - if istree(term) +function getAvrgs(term::BasicSymbolic{<:CNumber}) + if iscall(term) return vcat(filter(x->!=(x,nothing),[getAvrgs(arg) for arg in arguments(term)])...) else return nothing @@ -1006,11 +1007,11 @@ function _to_expression(x::NumberedOperator) x.op isa Destroy && return :(NumberedDestroy($(x.op.name),$(x.numb))) x.op isa Create && return :(dagger(NumberedDestroy($(x.op.name),$(x.numb)))) end -function _to_expression(x::BasicSymbolic{IndexedAverageSum}) +function _to_expression(x::BasicSymbolic{IndexedAverageSum}) meta = SymbolicUtils.metadata(x)[IndexedAverageSum] return :( IndexedAverageSum($(_to_expression(meta.term)),$(meta.sum_index.name),$(meta.sum_index.range),$(writeNEIs(meta.non_equal_indices))) ) end -function _to_expression(x::BasicSymbolic{SpecialIndexedAverage}) +function _to_expression(x::BasicSymbolic{SpecialIndexedAverage}) meta = SymbolicUtils.metadata(x)[SpecialIndexedAverage] return _to_expression(meta.term) end @@ -1036,9 +1037,9 @@ end #simplify functions not "really" needed, they are nice to have, since equation creation of Symbolics sometimes does not simplify certain terms #function to reduce multiplication of numbers with a sum into just a sum of multiplication function simplifyMultiplication(term::BasicSymbolic{<:CNumber}) - if istree(term) && operation(term) === * + if iscall(term) && operation(term) === * args = arguments(term) - ind = findfirst(x-> (istree(x) && operation(x) === +),args) + ind = findfirst(x-> (iscall(x) && operation(x) === +),args) (ind === nothing) && return term #no add-terms were found inside the multiplication args_ = arguments(args[ind]) # arguments of the addition @@ -1053,39 +1054,39 @@ end simplifyMultiplication(x) = x -function +(a::BasicSymbolic{SpecialIndexedAverage},b::BasicSymbolic{SpecialIndexedAverage}) +function +(a::BasicSymbolic{SpecialIndexedAverage},b::BasicSymbolic{SpecialIndexedAverage}) if isequal(a,b) return SymbolicUtils.Add(CNumber,0,Dict(a=>2)) end return SymbolicUtils.Add(CNumber,0,Dict(a=>1,b=>1)) end -function +(a::BasicSymbolic{IndexedAverageDoubleSum},b::BasicSymbolic{IndexedAverageDoubleSum}) +function +(a::BasicSymbolic{IndexedAverageDoubleSum},b::BasicSymbolic{IndexedAverageDoubleSum}) if isequal(a,b) return SymbolicUtils.Add(CNumber,0,Dict(a=>2)) end return SymbolicUtils.Add(CNumber,0,Dict(a=>1,b=>1)) end -function +(a::BasicSymbolic{IndexedAverageSum},b::BasicSymbolic{IndexedAverageSum}) +function +(a::BasicSymbolic{IndexedAverageSum},b::BasicSymbolic{IndexedAverageSum}) if isequal(a,b) return SymbolicUtils.Add(CNumber,0,Dict(a=>2)) end return SymbolicUtils.Add(CNumber,0,Dict(a=>1,b=>1)) end -function *(a::BasicSymbolic{SpecialIndexedAverage},b::BasicSymbolic{SpecialIndexedAverage}) +function *(a::BasicSymbolic{SpecialIndexedAverage},b::BasicSymbolic{SpecialIndexedAverage}) if isequal(a,b) return SymbolicUtils.Mul(CNumber,1,Dict(a=>2)) end return SymbolicUtils.Mul(CNumber,1,Dict(a=>1,b=>1)) end -function +(a::BasicSymbolic{IndexedVariable},b::BasicSymbolic{IndexedVariable}) +function +(a::BasicSymbolic{IndexedVariable},b::BasicSymbolic{IndexedVariable}) if isequal(a,b) return SymbolicUtils.Add(CNumber,0,Dict(a=>2)) end return SymbolicUtils.Add(CNumber,0,Dict(a=>1,b=>1)) end -function +(a::BasicSymbolic{DoubleIndexedVariable},b::BasicSymbolic{DoubleIndexedVariable}) +function +(a::BasicSymbolic{DoubleIndexedVariable},b::BasicSymbolic{DoubleIndexedVariable}) if isequal(a,b) return SymbolicUtils.Add(CNumber,0,Dict(a=>2)) end return SymbolicUtils.Add(CNumber,0,Dict(a=>1,b=>1)) -end \ No newline at end of file +end diff --git a/src/index_correlation.jl b/src/index_correlation.jl index eaa1b9cf..cbfec388 100644 --- a/src/index_correlation.jl +++ b/src/index_correlation.jl @@ -131,12 +131,19 @@ when the original system `de0` contains any types of [`Index`](@ref) entities. See also: [`CorrelationFunction`](@ref) """ function IndexedCorrelationFunction(op1,op2,de0::AbstractMeanfieldEquations; - steady_state=false, add_subscript=0, - filter_func=nothing, mix_choice=maximum, - iv=SymbolicUtils.Sym{Real}(:τ), - order=nothing, - extra_indices::Vector=[:i,:j,:k,:l,:m,:n,:p,:q,:r,:s,:t], - simplify=true, kwargs...) + steady_state=false, add_subscript=0, + filter_func=nothing, mix_choice=maximum, + iv=nothing, + order=nothing, + extra_indices::Vector=[:i,:j,:k,:l,:m,:n,:p,:q,:r,:s,:t], + simplify=true, kwargs...) + if iv === nothing + iv_ = SymbolicUtils.Sym{Real}(:τ) + iv_ = SymbolicUtils.setmetadata(iv_, MTK.VariableSource, (:parameters, :τ)) + iv_ = SymbolicUtils.setmetadata(iv_, MTK.MTKVariableTypeCtx,MTK.PARAMETER) + else + iv_ = iv + end h1 = hilbert(op1) h2 = _new_hilbert(hilbert(op2), acts_on(op2)) h = h1⊗h2 @@ -188,7 +195,7 @@ function IndexedCorrelationFunction(op1,op2,de0::AbstractMeanfieldEquations; IndexedMeanfieldEquations(eqs,eqs_op,lhs_new,ops,H,J,Jd,rates_,de0.iv,varmap,order_) end - de = indexed_meanfield([op_],H,J;Jdagger=Jd,rates=rates_,iv=iv,order=order_) + de = indexed_meanfield([op_],H,J;Jdagger=Jd,rates=rates_,iv=iv_,order=order_) indexed_complete_corr!(de, length(h.spaces), lhs_new, order_, steady_state, de0_; filter_func=filter_func, mix_choice=mix_choice, diff --git a/src/index_double_sums.jl b/src/index_double_sums.jl index e5a91c2e..5e2f2f72 100644 --- a/src/index_double_sums.jl +++ b/src/index_double_sums.jl @@ -71,7 +71,7 @@ function DoubleSum(innerSum::SingleSum,sum_index::Index,NEI::Vector;metadata=NO_ end end function DoubleSum(innerSum::IndexedAdd,sum_index::Index,NEI::Vector;metadata=NO_METADATA) - if istree(innerSum) + if iscall(innerSum) op = operation(innerSum) if op === + sums = [DoubleSum(arg,sum_index,NEI;metadata=metadata) for arg in arguments(innerSum)] @@ -120,7 +120,7 @@ function *(elem::IndexedObSym,sum::DoubleSum) NEI = copy(sum.NEI) if elem.ind != sum.sum_index && elem.ind ∉ NEI if (sum.sum_index.aon != sum.innerSum.sum_index.aon) # indices for different ops - if isequal(elem.ind.aon,sum.sum_index.aon) + if isequal(elem.ind.aon,sum.sum_index.aon) push!(NEI,elem.ind) addterm = SingleSum(elem*change_index(sum.innerSum.term,sum.sum_index,elem.ind),sum.innerSum.sum_index,sum.innerSum.non_equal_indices) return DoubleSum(elem*sum.innerSum,sum.sum_index,NEI) + addterm @@ -131,7 +131,7 @@ function *(elem::IndexedObSym,sum::DoubleSum) ds_term = DoubleSum(SingleSum(elem*sum.innerSum.term,sum.innerSum.sum_index,[sum.innerSum.non_equal_indices...,elem.ind]),sum.sum_index,NEI_) new_non_equal_indices1 = replace(sum.NEI, sum.innerSum.sum_index => elem.ind) ss_term1 = SingleSum(elem*change_index(sum.innerSum.term,sum.innerSum.sum_index,elem.ind),sum.sum_index,new_non_equal_indices1) - new_non_equal_indices2 = replace(sum.innerSum.non_equal_indices, sum.sum_index => elem.ind) + new_non_equal_indices2 = replace(sum.innerSum.non_equal_indices, sum.sum_index => elem.ind) ss_term2 = SingleSum(elem*change_index(sum.innerSum.term,sum.sum_index,elem.ind),sum.innerSum.sum_index,new_non_equal_indices2) return ds_term + ss_term1 + ss_term2 end @@ -141,7 +141,7 @@ function *(sum::DoubleSum,elem::IndexedObSym) NEI = copy(sum.NEI) if elem.ind != sum.sum_index && elem.ind ∉ NEI if (sum.sum_index.aon != sum.innerSum.sum_index.aon) # indices for different ops - if isequal(elem.ind.aon,sum.sum_index.aon) + if isequal(elem.ind.aon,sum.sum_index.aon) push!(NEI,elem.ind) addterm = SingleSum(change_index(sum.innerSum.term,sum.sum_index,elem.ind)*elem,sum.innerSum.sum_index,sum.innerSum.non_equal_indices) return DoubleSum(sum.innerSum*elem,sum.sum_index,NEI) + addterm @@ -152,7 +152,7 @@ function *(sum::DoubleSum,elem::IndexedObSym) ds_term = DoubleSum(SingleSum(sum.innerSum.term*elem,sum.innerSum.sum_index,[sum.innerSum.non_equal_indices...,elem.ind]),sum.sum_index,NEI_) new_non_equal_indices1 = replace(sum.NEI, sum.innerSum.sum_index => elem.ind) ss_term1 = SingleSum(change_index(sum.innerSum.term,sum.innerSum.sum_index,elem.ind)*elem,sum.sum_index,new_non_equal_indices1) - new_non_equal_indices2 = replace(sum.innerSum.non_equal_indices, sum.sum_index => elem.ind) + new_non_equal_indices2 = replace(sum.innerSum.non_equal_indices, sum.sum_index => elem.ind) ss_term2 = SingleSum(change_index(sum.innerSum.term,sum.sum_index,elem.ind)*elem,sum.innerSum.sum_index,new_non_equal_indices2) return ds_term + ss_term1 + ss_term2 end #with else it does not work? @@ -163,7 +163,7 @@ end *(sum::DoubleSum,x) = DoubleSum(sum.innerSum*x,sum.sum_index,sum.NEI) *(x,sum::DoubleSum) = DoubleSum(x*sum.innerSum,sum.sum_index,sum.NEI) -SymbolicUtils.istree(a::DoubleSum) = false +SymbolicUtils.iscall(a::DoubleSum) = false SymbolicUtils.arguments(a::DoubleSum) = SymbolicUtils.arguments(a.innerSum) checkInnerSums(sum1::DoubleSum, sum2::DoubleSum) = ((sum1.innerSum + sum2.innerSum) == 0) reorder(dsum::DoubleSum,indexMapping::Vector{Tuple{Index,Index}}) = DoubleSum(reorder(dsum.innerSum,indexMapping),dsum.sum_index,dsum.NEI) @@ -195,4 +195,3 @@ function *(sum1::SingleSum,sum2::SingleSum; ind=nothing) end end - diff --git a/src/index_meanfield.jl b/src/index_meanfield.jl index d62640b3..65f5df4a 100644 --- a/src/index_meanfield.jl +++ b/src/index_meanfield.jl @@ -83,7 +83,7 @@ function indexed_meanfield(a::Vector,H,J;Jdagger::Vector=adjoint.(J),rates=ones( end end rhs[i] = reorder((rhs_+rhs_diss),mapping) - end + end catch err println("could not calculate meanfield-equations for operator $(a[i])") rethrow(err) @@ -176,7 +176,7 @@ function indexed_master_lindblad(a_,J,Jdagger,rates) push_or_append_nz_args!(args, c1) push_or_append_nz_args!(args, c2) end - else + else error("Unknown rates type!") end end @@ -262,7 +262,7 @@ function indexed_complete!(de::AbstractMeanfieldEquations; order_ = order end - if order isa Vector + if order isa Vector order_max = maximum(order) else if order === nothing @@ -276,11 +276,11 @@ function indexed_complete!(de::AbstractMeanfieldEquations; num_ind_hilbert = length(unique([ind.aon for ind in get_indices([de.hamiltonian,de.jumps, de.jumps_dagger])])) if order_max*num_ind_hilbert > num_inds - error("Too few extra_indices provided! Please make sure that for higher orders of cumulant expansion, + error("Too few extra_indices provided! Please make sure that for higher orders of cumulant expansion, you also use the extra_indices argument to provide additional indices for calculation. The Number of extra_indices provided should be at least $(order_max - num_inds).") end - + maximum(order_) >= order_lhs || error("Cannot form cumulant expansion of derivative; you may want to use a higher order!") if order_ != de.order @@ -298,7 +298,7 @@ function indexed_complete!(de::AbstractMeanfieldEquations; if containsMultiple(allInds) && extra_indices[1] isa Symbol dic = split_inds(allInds) filter!(x->!isempty(last(x)),dic) - dic2 = Dict{Int,Any}(i => Any[] for i in keys(dic)) + dic2 = Dict{Int,Any}(i => Any[] for i in keys(dic)) for k in keys(dic) dic2[k] = filter(x->isequal(x.aon,k),indices_lhs) ind = allInds[findfirst(x->isequal(x.aon,k),allInds)] @@ -347,7 +347,7 @@ function indexed_complete!(de::AbstractMeanfieldEquations; end - #at this point extras is a list of extra_indices, sorted by their priority + #at this point extras is a list of extra_indices, sorted by their priority # (meaning that indices that were used in the ops of in indexed_meanfield come first) vhash = map(hash, vs) @@ -412,7 +412,7 @@ function indexed_complete!(de::AbstractMeanfieldEquations; filter!(x -> (x isa Average),missed) isnothing(filter_func) || filter!(filter_func, missed) # User-defined filter - + filter!(x -> filterComplete(x,de.states,scaling;kwargs...), missed) missed = inorder!.(missed) @@ -433,13 +433,13 @@ function indexed_complete!(de::AbstractMeanfieldEquations; end end end - + missed = inorder!.(missed) missed = unique(missed) missed = filter!(x -> filterComplete(x,de.states,scaling;kwargs...), missed) missed = inorder!.(missed) missed = elimRed!(missed;scaling=scaling,kwargs...) - + end if !isnothing(filter_func) @@ -448,7 +448,7 @@ function indexed_complete!(de::AbstractMeanfieldEquations; missed = find_missing(de.equations, vhash, vs′hash; get_adjoints=false) missed = find_missing_sums(missed,de;extra_indices=extras,checking=false,kwargs...) #checkin dissabled, since there might be some terms, that are redundant, but also zero -> set them to zero aswell forsa fety missed = find_missing_Dsums(missed,de;extra_indices=extras,checking=false,kwargs...) - missed = find_missing_sums(missed,de;checking=false,kwargs...) + missed = find_missing_sums(missed,de;checking=false,kwargs...) missed = find_missing_Dsums(missed,de;checking=false,kwargs...) missed = inorder!.(missed) #this one might not be right (?) -> not even needed i think @@ -507,7 +507,7 @@ function get_all_indices(me::AbstractMeanfieldEquations) end end for ind in get_indices(me.hamiltonian) - if ind ∉ inds + if ind ∉ inds push!(inds,ind) end end @@ -581,7 +581,7 @@ function find_missing_sums(missed,de::AbstractMeanfieldEquations;extra_indices:: extras_filtered = filterExtras(meta.sum_index,extras) #all the extraIndices, that have the same specific hilbertspace as the sum checkAndChange(missed_,sum,extras_filtered,de.states,checking,scaling;kwargs...) end - end + end return inorder!.(missed_) end #this function might not be so fast @@ -614,7 +614,7 @@ function checkAndChange(missed_,sum,extras,states,checking,scaling;kwargs...) if changed_ === nothing #if avrg consists of none indexed operators, or only of operators that already have the desired indices -> push along the whole average changed_ = avr end - + if changed_ isa Average changed = inorder!(changed_) # this can be done, since terms inside the sum commute anyway if !(changed isa Average) @@ -651,8 +651,8 @@ function checkAndChangeDsum(missed_,sum,extras,states,checking,scaling;kwargs... end if innerInd in avrg_inds filtered = filterExtras(innerInd,extras) - for i = 1:length(filtered) - if filtered[i] ∉ avrg_inds + for i = 1:length(filtered) + if filtered[i] ∉ avrg_inds changed_ = change_index(avr,innerInd,filtered[i]) break end @@ -683,7 +683,7 @@ function filterExtras(wanted,extras) return filter(x -> isequal(x.aon,wanted.aon),extras) end -function find_missing!(missed,missed_hashes,term::BasicSymbolic{SpecialIndexedAverage},vhash,vshash;kwargs...) +function find_missing!(missed,missed_hashes,term::BasicSymbolic{SpecialIndexedAverage},vhash,vshash;kwargs...) meta = SymbolicUtils.metadata(term)[SpecialIndexedAverage] return find_missing!(missed,missed_hashes,meta.term,vhash,vshash;kwargs...) end @@ -694,7 +694,7 @@ function checkIfSum(term) sums = Any[] if term isa BasicSymbolic{IndexedAverageSum} return copy([term]) - elseif istree(term) + elseif iscall(term) args = copy(arguments(term)) for arg in args sums = vcat(sums,checkIfSum(arg)) @@ -704,7 +704,7 @@ function checkIfSum(term) end function getDSums(term) Dsums = Any[] - if istree(term) + if iscall(term) for arg in arguments(term) Dsums = vcat(Dsums,getDSums(arg)) end @@ -757,7 +757,7 @@ function elimRed!(missed::Vector;scaling::Bool=false,kwargs...) end complete(eqs::IndexedMeanfieldEquations;kwargs...) = indexed_complete(eqs;kwargs...) -complete!(eqs::IndexedMeanfieldEquations;kwargs...) = indexed_complete!(eqs;kwargs...) +complete!(eqs::IndexedMeanfieldEquations;kwargs...) = indexed_complete!(eqs;kwargs...) """ evaluate(eqs::IndexedMeanfieldEquations;limits) @@ -794,7 +794,7 @@ function evaluate(eqs::IndexedMeanfieldEquations;limits=nothing,h=nothing,kwargs end end h = h_ - end + end if !=(limits,nothing) && limits isa Pair limits_ = Dict{BasicSymbolic,Int64}(first(limits) => last(limits)); limits = limits_ @@ -804,7 +804,7 @@ function evaluate(eqs::IndexedMeanfieldEquations;limits=nothing,h=nothing,kwargs end return subst_reds_eval(evalME(eqs;limits=limits,h=h,kwargs...);limits=limits,h=h,kwargs...) end -function evaluate(term;limits=nothing,kwargs...) +function evaluate(term;limits=nothing,kwargs...) if !=(limits,nothing) && limits isa Pair limits_ = Dict{BasicSymbolic,Int64}(first(limits) => last(limits)); limits = limits_ @@ -813,4 +813,4 @@ function evaluate(term;limits=nothing,kwargs...) limits = Dict{BasicSymbolic,Int64}(); end return eval_term(term;limits=limits) -end \ No newline at end of file +end diff --git a/src/index_scale.jl b/src/index_scale.jl index 9319af2d..c50a12e6 100644 --- a/src/index_scale.jl +++ b/src/index_scale.jl @@ -62,7 +62,7 @@ function scale_term(pow::SymbolicUtils.Pow; kwargs...) return scale_term(args[1]; kwargs...)^(args[2]) end function scale_term(add::BasicSymbolic{<:CNumber}; h=nothing, kwargs...) - if istree(add) + if iscall(add) op = operation(add) if op === + args = arguments(add) @@ -71,7 +71,7 @@ function scale_term(add::BasicSymbolic{<:CNumber}; h=nothing, kwargs...) adds[i] = scale_term(args[i]; h=h, kwargs...) end return sum(adds) - elseif op === * + elseif op === * mul = add mults = [] for arg in arguments(mul) @@ -238,7 +238,7 @@ function split_sums(term::SymbolicUtils.BasicSymbolic,ind::Index,amount::Union{< if term isa Average return term end - if SymbolicUtils.istree(term) + if SymbolicUtils.iscall(term) args = [] op = SymbolicUtils.operation(term) for i = 1:length(arguments(term)) @@ -296,6 +296,6 @@ function split_sums(me::AbstractMeanfieldEquations,ind::Index,amount) end split_sums(x,ind,amount) = x -function scale(args...; kwargs...) +function scale(args...; kwargs...) return subst_reds_scale(scale_term(args...; kwargs...);kwargs...) end diff --git a/src/index_utils.jl b/src/index_utils.jl index 1b398be1..7483e114 100644 --- a/src/index_utils.jl +++ b/src/index_utils.jl @@ -7,7 +7,7 @@ function get_indices(term::QMul) end get_indices(a::IndexedOperator) = [a.ind] get_indices(vec::Vector) = unique(vcat(get_indices.(vec)...)) -function get_indices(a::BasicSymbolic{DoubleIndexedVariable}) +function get_indices(a::BasicSymbolic{DoubleIndexedVariable}) meta = SymbolicUtils.metadata(a)[DoubleIndexedVariable] return unique([meta.ind1,meta.ind2]) end @@ -17,7 +17,7 @@ const Sums = Union{SingleSum,DoubleSum} get_indices(x::SingleSum) = get_indices(x.term) get_indices(x::DoubleSum) = get_indices(x.innerSum.term) get_indices(x::Number) = [] -get_indices(term) = istree(term) ? get_indices(arguments(term)) : [] +get_indices(term) = iscall(term) ? get_indices(arguments(term)) : [] #Usability functions: Σ(a,b) = DoubleSum(a,b) #Double-Sum here, because if variable a is not a single sum it will create a single sum anyway @@ -76,11 +76,11 @@ function _inconj(v::Average) adj_arg = inadjoint(arg) return _average(adj_arg) end -function _inconj(v::SymbolicUtils.BasicSymbolic) - if SymbolicUtils.istree(v) +function _inconj(v::T) where T <: SymbolicUtils.BasicSymbolic + if SymbolicUtils.iscall(v) f = SymbolicUtils.operation(v) args = map(_inconj, SymbolicUtils.arguments(v)) - return SymbolicUtils.similarterm(v, f, args) + return SymbolicUtils.maketerm(T, f, args, TermInterface.metadata(v)) else return conj(v) end @@ -109,11 +109,11 @@ function inorder!(q::QMul) sort!(q.args_nc, by=acts_on) return merge_commutators(q.arg_c,q.args_nc) end -function inorder!(v::SymbolicUtils.BasicSymbolic) - if SymbolicUtils.istree(v) +function inorder!(v::T) where T <: SymbolicUtils.BasicSymbolic + if SymbolicUtils.iscall(v) f = SymbolicUtils.operation(v) args = map(inorder!, SymbolicUtils.arguments(v)) - return SymbolicUtils.similarterm(v, f, args) + return SymbolicUtils.maketerm(T, f, args, TermInterface.metadata(v)) end return v end @@ -233,7 +233,7 @@ function isscaleequal(a::IndexedOperator,b::IndexedOperator;h=nothing,kwargs...) end end function isscaleequal(t1,t2;kwargs...) - if SymbolicUtils.istree(t1) && SymbolicUtils.istree(t2) + if SymbolicUtils.iscall(t1) && SymbolicUtils.iscall(t2) args1 = arguments(t1) args2 = arguments(t2) isequal(operation(t1),operation(t2)) || return false diff --git a/src/indexing.jl b/src/indexing.jl index 6fcdfac8..0319f653 100644 --- a/src/indexing.jl +++ b/src/indexing.jl @@ -166,7 +166,7 @@ const IndexedAdd = Union{QAdd, BasicSymbolic{<:CNumber}} const IndexedObSym = Union{IndexedOperator,BasicSymbolic{IndexedVariable},BasicSymbolic{DoubleIndexedVariable}} function SpecialIndexedTerm(term::IndexedAdd,indexMapping) - if istree(term) + if iscall(term) op = operation(term) args = arguments(term) return op([reorder(arg,indexMapping) for arg in args]...) @@ -181,7 +181,7 @@ function SingleSum(term::IndexedObSym,sum_index::Index,non_equal_indices;metadat return SingleSum(term,sum_index,non_equal_indices,metadata) end function SingleSum(term::IndexedAdd, sum_index, non_equal_indices;metadata=NO_METADATA) - if istree(term) + if iscall(term) op = operation(term) args = arguments(term) if op === + @@ -274,7 +274,14 @@ SingleSum(ops::QMul,ind::Index;metadata=NO_METADATA) = SingleSum(ops,ind,Index[] SingleSum(ops::QAdd,ind::Index;metadata=NO_METADATA) = SingleSum(ops,ind,Index[];metadata=metadata) SingleSum(op::QNumber,ind::Index;metadata=NO_METADATA) = SingleSum(op,ind,Index[];metadata=metadata) SingleSum(ops::Number,ind::Index,NEI::Vector;metadata=NO_METADATA) = (ind.range - length(NEI))*ops -SingleSum(term, sum_index, non_equal_indices;metadata=NO_METADATA) = (sum_index.range - length(non_equal_indices)) * term +# SingleSum(term, sum_index, non_equal_indices;metadata=NO_METADATA) = (sum_index.range - length(non_equal_indices)) * term +function SingleSum(term, sum_index, non_equal_indices;metadata=NO_METADATA) + if (sum_index ∉ get_indices(term)) # saver way to avoid wrong simplification + return (sum_index.range - length(non_equal_indices)) * term + else + return SingleSum(term,sum_index,non_equal_indices,metadata) + end +end function IndexedOperator(op::QMul,ind::Index) arg_c = op.arg_c @@ -645,8 +652,8 @@ function change_index(op::BasicSymbolic{DoubleIndexedVariable},from::Index,to::I end end end -function change_index(term::BasicSymbolic{<:CNumber},from::Index,to::Index) - if istree(term) +function change_index(term::BasicSymbolic{<:CNumber},from::Index,to::Index) + if iscall(term) op = operation(term) if op === + args = arguments(term) @@ -678,11 +685,11 @@ function change_index(term::BasicSymbolic{<:CNumber},from::Index,to::Index) return term end # issue 196: TODO:test -function change_index(S::SingleSum, i::Index, j::Index) +function change_index(S::SingleSum, i::Index, j::Index) (j ∈ S.non_equal_indices) && error("Index $(j) is in the non-equal index list.") if S.sum_index == i return SingleSum(change_index(S.term,i,j), j, replace(S.non_equal_indices, i=>j), S.metadata) - end + end return S end change_index(x,from::Index,to::Index) = x @@ -693,7 +700,7 @@ getIndName(op::IndexedOperator) = op.ind.name getIndName(ind::Index) = ind.name getIndName(x) = Symbol() -SymbolicUtils.istree(a::SingleSum) = false +SymbolicUtils.iscall(a::SingleSum) = false SymbolicUtils.arguments(a::SingleSum) = SymbolicUtils.arguments(a.term) SymbolicUtils.arguments(a::IndexedOperator) = [a] @@ -897,4 +904,4 @@ SymbolicUtils._iszero(x::SpecialIndexedTerm) = SymbolicUtils._iszero(x.term) get_range(i::Index) = i.range get_aon(i::Index) = i.aon --(a::BasicSymbolic{IndexedVariable}) = -1*a \ No newline at end of file +-(a::BasicSymbolic{IndexedVariable}) = -1*a diff --git a/src/latexify_recipes.jl b/src/latexify_recipes.jl index 4b7749b3..b34b1401 100644 --- a/src/latexify_recipes.jl +++ b/src/latexify_recipes.jl @@ -208,7 +208,7 @@ _to_expression(t::QAdd) = :( +($(_to_expression.(t.arguments)...)) ) _to_expression(p::Parameter) = p.name function _to_expression(s::SymbolicUtils.Symbolic) - if SymbolicUtils.istree(s) + if SymbolicUtils.iscall(s) f = SymbolicUtils.operation(s) fsym = if isequal(f,sym_average) # "===" results in false for symbolics version 5 :AVG @@ -224,4 +224,4 @@ function _to_expression(s::SymbolicUtils.Symbolic) end end -_to_expression(p::RealParameter) = p.name \ No newline at end of file +_to_expression(p::RealParameter) = p.name diff --git a/src/qnumber.jl b/src/qnumber.jl index baef438a..31ae3264 100644 --- a/src/qnumber.jl +++ b/src/qnumber.jl @@ -42,9 +42,10 @@ Base.isless(a::QSym, b::QSym) = a.name < b.name ## Interface for SymbolicUtils TermInterface.head(::QNumber) = :call -SymbolicUtils.istree(::QSym) = false -SymbolicUtils.istree(::QTerm) = true -SymbolicUtils.istree(::Type{T}) where {T<:QTerm} = true +SymbolicUtils.iscall(::QSym) = false +SymbolicUtils.iscall(::QTerm) = true +SymbolicUtils.iscall(::Type{T}) where {T<:QTerm} = true +TermInterface.metadata(x::QNumber) = x.metadata # Symbolic type promotion SymbolicUtils.promote_symtype(f, Ts::Type{<:QNumber}...) = promote_type(Ts...) @@ -108,7 +109,7 @@ Base.hash(q::QMul, h::UInt) = hash(QMul, hash(q.arg_c, SymbolicUtils.hashvec(q.a SymbolicUtils.operation(::QMul) = (*) SymbolicUtils.arguments(a::QMul) = vcat(a.arg_c, a.args_nc) -function SymbolicUtils.similarterm(::QMul, ::typeof(*), args, symtype=nothing; metadata=NO_METADATA, exprhead=nothing) +function SymbolicUtils.maketerm(::Type{<:QMul}, ::typeof(*), args, metadata) args_c = filter(x->!(x isa QNumber), args) args_nc = filter(x->x isa QNumber, args) arg_c = *(args_c...) @@ -233,7 +234,7 @@ end SymbolicUtils.operation(::QAdd) = (+) SymbolicUtils.arguments(a::QAdd) = a.arguments -SymbolicUtils.similarterm(::QAdd, ::typeof(+), args; metadata=NO_METADATA, exprhead=nothing) = QAdd(args; metadata) +SymbolicUtils.maketerm(::Type{<:QAdd}, ::typeof(+), args, metadata) = QAdd(args; metadata) SymbolicUtils.metadata(q::QAdd) = q.metadata diff --git a/src/scale.jl b/src/scale.jl index 7a4f85ab..3a05680f 100644 --- a/src/scale.jl +++ b/src/scale.jl @@ -5,7 +5,7 @@ Function, that evaluates a given [`MeanfieldEquations`](@ref) or [`CorrelationFunction`](@ref) entity and returns again equations, where indices have been inserted and sums evaluated, regarding the same relations, as done when calculating with oparators using a [`ClusterSpace`](@ref). For this it is considered that all entities in the given -(sub)system are acting on the system equivalently. +(sub)system are acting on the system equivalently. # Arguments *`me::IndexedMeanfieldEquations`: A [`MeanfieldEquations`](@ref) entity, which shall be scaled. @@ -29,7 +29,7 @@ function scale(eqs::IndexedMeanfieldEquations;h=nothing,kwargs...) end end h = h_ - end + end return subst_reds_scale(scaleME(eqs;h=h,kwargs...);h=h,kwargs...) end function scale(he::AbstractMeanfieldEquations; kwargs...) @@ -250,8 +250,8 @@ function substitute_redundants(he::MeanfieldEquations,scale_aons::Vector{<:Vecto return MeanfieldEquations(lhs, rhs, he.hamiltonian, he.jumps, he.jumps_dagger, he.rates) end -function substitute_redundants(t::SymbolicUtils.Symbolic,scale_aons::Vector{<:Vector},names) - if SymbolicUtils.istree(t) +function substitute_redundants(t::T,scale_aons::Vector{<:Vector},names) where T <: SymbolicUtils.Symbolic + if SymbolicUtils.iscall(t) f = SymbolicUtils.operation(t) if f === sym_average op = deepcopy(SymbolicUtils.arguments(t)[1]) @@ -264,7 +264,7 @@ function substitute_redundants(t::SymbolicUtils.Symbolic,scale_aons::Vector{<:Ve for arg in SymbolicUtils.arguments(t) push!(args, substitute_redundants(arg,scale_aons,names)) end - return SymbolicUtils.similarterm(t, f, args) + return SymbolicUtils.maketerm(T, f, args, TermInterface.metadata(t)) end else return t diff --git a/src/spin.jl b/src/spin.jl index 6ae344ce..1c9bc567 100644 --- a/src/spin.jl +++ b/src/spin.jl @@ -158,7 +158,6 @@ function Spin(hilbert::HilbertSpace,name,axis::Symbol; kwargs...) Spin(hilbert,name,3; kwargs...) end end -# CallablePauli not possible, due to acts_on convenience (same number of arguments) Base.hash(s::Spin, h::UInt) = hash(s.hilbert, hash(s.name, hash(s.axis, hash(s.aon, h)))) Base.adjoint(s::Spin) = s @@ -191,4 +190,4 @@ function Base.:*(si::Spin,sj::Spin) else return QMul(1, [sj,si]) end -end +end \ No newline at end of file diff --git a/src/utils.jl b/src/utils.jl index 2c83b482..5c5fb1f4 100644 --- a/src/utils.jl +++ b/src/utils.jl @@ -36,7 +36,7 @@ function find_missing(me::AbstractMeanfieldEquations; vs_adj=nothing, get_adjoin end function find_missing!(missed, missed_hashes, r::SymbolicUtils.Symbolic, vhash, vs′hash; get_adjoints=true) - if SymbolicUtils.istree(r) + if SymbolicUtils.iscall(r) for arg∈SymbolicUtils.arguments(r) find_missing!(missed, missed_hashes, arg, vhash, vs′hash; get_adjoints=get_adjoints) end @@ -326,7 +326,7 @@ end _to_numeric(op::Destroy, b::QuantumOpticsBase.FockBasis; kwargs...) = QuantumOpticsBase.destroy(b) _to_numeric(op::Create, b::QuantumOpticsBase.FockBasis; kwargs...) = QuantumOpticsBase.create(b) -function _to_numeric(op::Pauli, b::QuantumOpticsBase.SpinBasis; kwargs...) +function _to_numeric(op::Pauli, b::QuantumOpticsBase.SpinBasis; kwargs...) (b.spinnumber ≠ 1/2) && error("The SpinBasis needs to be Spin-1/2!") axis = op.axis if axis == 1 # σx @@ -497,7 +497,7 @@ for T ∈ [:AbstractTimeseriesSolution,:AbstractNoTimeSolution] t = MTK.get_iv(ode_func.sys) vars = SciMLBase.getsyms(sol) var = make_var(avg, t) - + if any(isequal(var), vars) # sucess, we found the symbol return getindex(sol, var) @@ -523,11 +523,11 @@ function _conj(v::Average) return _average(adj_arg) end end -function _conj(v::SymbolicUtils.Symbolic) - if SymbolicUtils.istree(v) +function _conj(v::T) where T <: SymbolicUtils.Symbolic + if SymbolicUtils.iscall(v) f = SymbolicUtils.operation(v) args = map(_conj, SymbolicUtils.arguments(v)) - return SymbolicUtils.similarterm(v, f, args) + return SymbolicUtils.maketerm(T, f, args, TermInterface.metadata(v)) else return conj(v) end @@ -548,7 +548,7 @@ for linear combinations of operators, which is not possible with `sol[op]`. """ get_solution(sol, op::QNumber) = sol[op] function get_solution(sol, x) - if length(sol[1]) == 1 #SteadyStateProblem + if length(sol[:,1]) == 1 #SteadyStateProblem return x else return x*ones(length(sol)) diff --git a/test/test_average_sums.jl b/test/test_average_sums.jl index 5b45a142..5614bea8 100644 --- a/test/test_average_sums.jl +++ b/test/test_average_sums.jl @@ -52,7 +52,8 @@ g_ = insert_index(g(ind(:j)),ind(:j),1) @test gamma isa SymbolicUtils.BasicSymbolic{qc.DoubleNumberedVariable} gamma_ = insert_index(gamma,ind(:j),2) -@test gamma_ isa SymbolicUtils.BasicSymbolic{Parameter} +# @test gamma_ isa SymbolicUtils.BasicSymbolic{Parameter} +@test gamma_ isa SymbolicUtils.BasicSymbolic{Complex{Real}} @test !isequal(gamma,gamma_) @test !isequal(gamma,g_) @@ -74,10 +75,19 @@ pind = Index(h,:p,5,ha) avrgTerm = average(Σ(σ(2,1,ind(:i))*σ(1,2,ind(:j)),ind(:i))) @test avrgTerm isa SymbolicUtils.BasicSymbolic && operation(avrgTerm) === + -ADsum1 = qc.IndexedAverageDoubleSum(avrgTerm,ind(:j),[ind(:i)]) +# ADsum1 = qc.IndexedAverageDoubleSum(avrgTerm,ind(:j),[ind(:i)]) +# @test ADsum1 isa SymbolicUtils.BasicSymbolic && operation(ADsum1) === + +# @test SymbolicUtils.metadata(arguments(ADsum1)[it_2])[qc.IndexedAverageDoubleSum] isa qc.IndexedAverageDoubleSum +# @test SymbolicUtils.metadata(arguments(ADsum1)[2])[qc.IndexedAverageSum] isa qc.IndexedAverageSum + +# executing it line by line gives a different order of the arguments?? arguments(ADsum1) +ADsum1 = simplify(qc.IndexedAverageDoubleSum(avrgTerm,ind(:j),[ind(:i)])) +it_1 = findfirst(x->typeof((x))==SymbolicUtils.BasicSymbolic{IndexedAverageSum}, arguments(ADsum1)) +it_2 = findfirst(x->typeof((x))==SymbolicUtils.BasicSymbolic{IndexedAverageDoubleSum}, arguments(ADsum1)) @test ADsum1 isa SymbolicUtils.BasicSymbolic && operation(ADsum1) === + -@test SymbolicUtils.metadata(arguments(ADsum1)[1])[qc.IndexedAverageDoubleSum] isa qc.IndexedAverageDoubleSum -@test SymbolicUtils.metadata(arguments(ADsum1)[2])[qc.IndexedAverageSum] isa qc.IndexedAverageSum +@test SymbolicUtils.metadata(arguments(ADsum1)[it_2])[qc.IndexedAverageDoubleSum] isa qc.IndexedAverageDoubleSum +@test SymbolicUtils.metadata(arguments(ADsum1)[it_1])[qc.IndexedAverageSum] isa qc.IndexedAverageSum + @test isequal(qc.SpecialIndexedAverage(average(σ(1,2,ind(:i))),[(ind(:i),ind(:j))])+qc.SpecialIndexedAverage(average(σ(2,1,ind(:j))),[(ind(:i),ind(:j))]), qc.SpecialIndexedAverage(average(σ(1,2,ind(:i))) + average(σ(2,1,ind(:j))),[(ind(:i),ind(:j))])) @@ -85,21 +95,21 @@ qc.SpecialIndexedAverage(average(σ(1,2,ind(:i))) + average(σ(2,1,ind(:j))),[(i @test qc.SpecialIndexedAverage(average(0),[(ind(:i),ind(:j))]) == 0 @test SymbolicUtils.metadata(qc.SpecialIndexedAverage(average(σ(2,1,ind(:i))),[(ind(:i),ind(:j))]))[qc.SpecialIndexedAverage] isa qc.SpecialIndexedAverage -@test qc.undo_average(arguments(ADsum1)[1]) isa qc.DoubleSum -@test isequal(Σ(Σ(σ(2,1,ind(:i))*σ(1,2,ind(:j)),ind(:i)),ind(:j),[ind(:i)]),qc.undo_average(ADsum1)) +@test qc.undo_average(arguments(ADsum1)[it_2]) isa qc.DoubleSum +@test isequal(simplify(Σ(Σ(σ(2,1,ind(:i))*σ(1,2,ind(:j)),ind(:i)),ind(:j),[ind(:i)])),simplify(qc.undo_average(ADsum1))) @test σ(1,2,ind(:i))*σ(2,1,ind(:j))*σn(2,2,3) isa qc.QMul @test σn(2,2,3)*σ(1,2,ind(:i))*σ(2,1,ind(:j)) isa qc.QMul -# @test SymbolicUtils.istree(sum_A.metadata) == false +# @test SymbolicUtils.iscall(sum_A.metadata) == false @test qc.IndexedAverageSum(1) == 1 specAvrg = qc.SpecialIndexedAverage(average(σ(2,1,ind(:i))*σ(1,2,ind(:j))),[(ind(:i),ind(:j))]) @test isequal("(i≠1)",qc.writeNeqs([(ind(:i),1)])) -@test (isequal(SymbolicUtils.arguments(SymbolicUtils.arguments(ADsum1)[1]),SymbolicUtils.arguments(avrgTerm)[1]) || isequal(SymbolicUtils.arguments(SymbolicUtils.arguments(ADsum1)[1]),SymbolicUtils.arguments(avrgTerm)[2]) ) -# SymbolicUtilsv1.4.0 argument order changed -@test isequal(SymbolicUtils.arguments(SymbolicUtils.arguments(SymbolicUtils.arguments(ADsum1)[1])),SymbolicUtils.arguments(average(σ(2,1,ind(:i))*σ(1,2,ind(:j))))) +@test (isequal(SymbolicUtils.arguments(SymbolicUtils.arguments(ADsum1)[it_2]),SymbolicUtils.arguments(avrgTerm)[1]) || isequal(SymbolicUtils.arguments(SymbolicUtils.arguments(ADsum1)[it_2]),SymbolicUtils.arguments(avrgTerm)[2]) ) +# SymbolicUtilsv1.4.0 argument order changed +@test isequal(SymbolicUtils.arguments(SymbolicUtils.arguments(SymbolicUtils.arguments(ADsum1)[it_2])),SymbolicUtils.arguments(average(σ(2,1,ind(:i))*σ(1,2,ind(:j))))) @test isequal(SymbolicUtils.arguments(specAvrg),SymbolicUtils.arguments(average(σ(2,1,ind(:i))*σ(1,2,ind(:j))))) @test isequal(qc.insert_index(σ(1,2,ind(:j))*σn(1,2,2),ind(:j),1),qc.insert_index(qc.insert_index(σ(1,2,ind(:i))*σ(1,2,ind(:j)),ind(:i),2),ind(:j),1)) @@ -112,14 +122,14 @@ push!(dict,(qc.SingleNumberedVariable(:g,2) => 2)) @test isequal(qc.getAvrgs(specAvrg),average(σ(2,1,ind(:i))*σ(1,2,ind(:j)))) @test (isequal(qc.getAvrgs(SymbolicUtils.arguments(avrgTerm)[1]),average(σ(2,1,ind(:i))*σ(1,2,ind(:j)))) || isequal(qc.getAvrgs(SymbolicUtils.arguments(avrgTerm)[2]),average(σ(2,1,ind(:i))*σ(1,2,ind(:j))))) -# SymbolicUtilsv1.4.0 argument order changed +# SymbolicUtilsv1.4.0 argument order changed @test isequal(qc.IndexedAverageSum(g(ind(:i)),ind(:i),[]),average(Σ(g(ind(:i)),ind(:i),[]))) @test qc.IndexedAverageSum(g(ind(:i)),ind(:i),[]) isa SymbolicUtils.BasicSymbolic{qc.IndexedAverageSum} @test ind(:i) ∈ qc.get_indices(g(ind(:i))) -@cnumbers N_ +@cnumbers N_ ind2(i) = Index(h,i,N_,ha) N_n = 10 @@ -140,4 +150,3 @@ eva = qc.eval_term(sum3_B;limits=map2) @test !(qc.containsIndexedOps(average(a'*a))) end - diff --git a/test/test_indexed_correlation.jl b/test/test_indexed_correlation.jl index 69cb7c01..ef10b7a5 100644 --- a/test/test_indexed_correlation.jl +++ b/test/test_indexed_correlation.jl @@ -104,7 +104,10 @@ split2 = split_sums(avrgSum,k,M) @test isequal(M,arguments(split2)[1]) @test isequal(N/M,arguments(split2)[2].metadata[qc.IndexedAverageSum].sum_index.range) -avrgSum2 = arguments(arguments(eqs_c[3].rhs)[1])[2] +# avrgSum2 = arguments(arguments(eqs_c[3].rhs)[1])[2] # order of arguments changed! +args_ = (arguments(eqs_c[3].rhs)) +it_ = findfirst(x->typeof(arguments(x)[2]) == SymbolicUtils.BasicSymbolic{IndexedAverageSum}, args_) +avrgSum2 = arguments(arguments(eqs_c[3].rhs)[1it_])[2] _split0 = split_sums(avrgSum2,l,15) diff --git a/test/test_spin.jl b/test/test_spin.jl index 7eb55e98..17d71e44 100644 --- a/test/test_spin.jl +++ b/test/test_spin.jl @@ -118,6 +118,7 @@ n_ = sol_[a2'a2][end] s1zs2z_ = sol_[σ1(3)*σ2(3)][end] s1xs2y_ = sol_[σ1(1)*σ2(2)][end] +@test isequal(simplify(σ1(1) + a2), simplify(a2 + σ1(1))) @test isapprox(s1y, s1y_; atol=1e-5) @test isapprox(s2x, s2x_; atol=1e-5) @test isapprox(s1z, s1z_; atol=1e-5) @@ -145,7 +146,8 @@ S(1)≠S(2) @test isequal(S(:x)*S(:y), 1*S(:x)*S(:y)) @test !isequal(S(:x)*S(:y), S(:x)*S(:z)) @test isequal(S(1)*S(1), (S(1))^2) -@test isequal(simplify(S(1) + S(2)), simplify(S(2) + S(1))) +@test isequal(average(S(1) + S(2)), average(S(2) + S(1))) +@test isequal(simplify(S(2) + S(2)), 2S(2)) # error @test isequal(2*S(1)*S(2)*S(3), S(1)*S(2)*S(3)*2) diff --git a/test/test_v-level.jl b/test/test_v-level.jl index 8b15e0f1..2e15d320 100644 --- a/test/test_v-level.jl +++ b/test/test_v-level.jl @@ -22,14 +22,14 @@ h = hf⊗ha @test isequal(κ', κ) @test isequal(conj(Γ3), Γ3) @test isequal(adjoint(κ), κ) -# -@test typeof(Δ2*(Γ3 + 1)) == SymbolicUtils.BasicSymbolic{CNumber} -@test typeof(κ*(Γ3 + 1)) == SymbolicUtils.BasicSymbolic{RNumber} -@test typeof(1im*κ*(Γ3 + 1)) == SymbolicUtils.BasicSymbolic{Complex{RNumber}} -@test isequal((Δ2*(Γ3 + 1))', (conj(Δ2)*(Γ3 + 1))) + +@test typeof(Δ2*(Γ3 + 1)) == SymbolicUtils.BasicSymbolic{Complex{Real}} +@test typeof(κ*(Γ3 + 1)) == SymbolicUtils.BasicSymbolic{Real} +@test typeof(1im*κ*(Γ3 + 1)) == SymbolicUtils.BasicSymbolic{Complex{Real}} +@test isequal((Δ2*(Γ3 + 1))', (conj(Δ2*(Γ3 + 1)))) @test isequal(κ*(Γ3 + 1)', κ*(Γ3 + 1)) -@test isequal((1im*κ*(Γ3 + 1))', -1im*κ*(Γ3 + 1)) -@test isequal(simplify(exp(1im*κ)*(exp(1im*κ))'), 1) +@test isequal((1im*κ*(Γ3 + 1))', conj(1im*κ*(Γ3 + 1))) +@test_broken isequal(simplify(exp(1im*κ)*(exp(1im*κ))'), 1) # Operators @qnumbers a::Destroy(h) σ::Transition(h)