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October 17, 2021 06:19
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| # This file is machine-generated - editing it directly is not advised | |
| [[Adapt]] | |
| deps = ["LinearAlgebra"] | |
| git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7" | |
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| [[Libffi_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "761a393aeccd6aa92ec3515e428c26bf99575b3b" | |
| uuid = "e9f186c6-92d2-5b65-8a66-fee21dc1b490" | |
| version = "3.2.2+0" | |
| [[Libgcrypt_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll", "Pkg"] | |
| git-tree-sha1 = "64613c82a59c120435c067c2b809fc61cf5166ae" | |
| uuid = "d4300ac3-e22c-5743-9152-c294e39db1e4" | |
| version = "1.8.7+0" | |
| [[Libglvnd_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"] | |
| git-tree-sha1 = "7739f837d6447403596a75d19ed01fd08d6f56bf" | |
| uuid = "7e76a0d4-f3c7-5321-8279-8d96eeed0f29" | |
| version = "1.3.0+3" | |
| [[Libgpg_error_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "c333716e46366857753e273ce6a69ee0945a6db9" | |
| uuid = "7add5ba3-2f88-524e-9cd5-f83b8a55f7b8" | |
| version = "1.42.0+0" | |
| [[Libiconv_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "42b62845d70a619f063a7da093d995ec8e15e778" | |
| uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" | |
| version = "1.16.1+1" | |
| [[Libmount_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "9c30530bf0effd46e15e0fdcf2b8636e78cbbd73" | |
| uuid = "4b2f31a3-9ecc-558c-b454-b3730dcb73e9" | |
| version = "2.35.0+0" | |
| [[Libtiff_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Pkg", "Zlib_jll", "Zstd_jll"] | |
| git-tree-sha1 = "340e257aada13f95f98ee352d316c3bed37c8ab9" | |
| uuid = "89763e89-9b03-5906-acba-b20f662cd828" | |
| version = "4.3.0+0" | |
| [[Libuuid_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "7f3efec06033682db852f8b3bc3c1d2b0a0ab066" | |
| uuid = "38a345b3-de98-5d2b-a5d3-14cd9215e700" | |
| version = "2.36.0+0" | |
| [[LineSearches]] | |
| deps = ["LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "Printf"] | |
| git-tree-sha1 = "f27132e551e959b3667d8c93eae90973225032dd" | |
| uuid = "d3d80556-e9d4-5f37-9878-2ab0fcc64255" | |
| version = "7.1.1" | |
| [[LinearAlgebra]] | |
| deps = ["Libdl"] | |
| uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" | |
| [[LogExpFunctions]] | |
| deps = ["ChainRulesCore", "DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] | |
| git-tree-sha1 = "34dc30f868e368f8a17b728a1238f3fcda43931a" | |
| uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" | |
| version = "0.3.3" | |
| [[Logging]] | |
| uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" | |
| [[MacroTools]] | |
| deps = ["Markdown", "Random"] | |
| git-tree-sha1 = "5a5bc6bf062f0f95e62d0fe0a2d99699fed82dd9" | |
| uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" | |
| version = "0.5.8" | |
| [[Markdown]] | |
| deps = ["Base64"] | |
| uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" | |
| [[MbedTLS]] | |
| deps = ["Dates", "MbedTLS_jll", "Random", "Sockets"] | |
| git-tree-sha1 = "1c38e51c3d08ef2278062ebceade0e46cefc96fe" | |
| uuid = "739be429-bea8-5141-9913-cc70e7f3736d" | |
| version = "1.0.3" | |
| [[MbedTLS_jll]] | |
| deps = ["Artifacts", "Libdl"] | |
| uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" | |
| [[Measures]] | |
| git-tree-sha1 = "e498ddeee6f9fdb4551ce855a46f54dbd900245f" | |
| uuid = "442fdcdd-2543-5da2-b0f3-8c86c306513e" | |
| version = "0.3.1" | |
| [[Missings]] | |
| deps = ["DataAPI"] | |
| git-tree-sha1 = "bf210ce90b6c9eed32d25dbcae1ebc565df2687f" | |
| uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28" | |
| version = "1.0.2" | |
| [[Mmap]] | |
| uuid = "a63ad114-7e13-5084-954f-fe012c677804" | |
| [[MozillaCACerts_jll]] | |
| uuid = "14a3606d-f60d-562e-9121-12d972cd8159" | |
| [[NLSolversBase]] | |
| deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"] | |
| git-tree-sha1 = "144bab5b1443545bc4e791536c9f1eacb4eed06a" | |
| uuid = "d41bc354-129a-5804-8e4c-c37616107c6c" | |
| version = "7.8.1" | |
| [[NaNMath]] | |
| git-tree-sha1 = "bfe47e760d60b82b66b61d2d44128b62e3a369fb" | |
| uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3" | |
| version = "0.3.5" | |
| [[NetworkOptions]] | |
| uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" | |
| [[OffsetArrays]] | |
| deps = ["Adapt"] | |
| git-tree-sha1 = "c0e9e582987d36d5a61e650e6e543b9e44d9914b" | |
| uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" | |
| version = "1.10.7" | |
| [[Ogg_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "7937eda4681660b4d6aeeecc2f7e1c81c8ee4e2f" | |
| uuid = "e7412a2a-1a6e-54c0-be00-318e2571c051" | |
| version = "1.3.5+0" | |
| [[OpenLibm_jll]] | |
| deps = ["Artifacts", "Libdl"] | |
| uuid = "05823500-19ac-5b8b-9628-191a04bc5112" | |
| [[OpenSSL_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "15003dcb7d8db3c6c857fda14891a539a8f2705a" | |
| uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" | |
| version = "1.1.10+0" | |
| [[OpenSpecFun_jll]] | |
| deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "13652491f6856acfd2db29360e1bbcd4565d04f1" | |
| uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e" | |
| version = "0.5.5+0" | |
| [[Optim]] | |
| deps = ["Compat", "FillArrays", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"] | |
| git-tree-sha1 = "7863df65dbb2a0fa8f85fcaf0a41167640d2ebed" | |
| uuid = "429524aa-4258-5aef-a3af-852621145aeb" | |
| version = "1.4.1" | |
| [[Opus_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "51a08fb14ec28da2ec7a927c4337e4332c2a4720" | |
| uuid = "91d4177d-7536-5919-b921-800302f37372" | |
| version = "1.3.2+0" | |
| [[OrderedCollections]] | |
| git-tree-sha1 = "85f8e6578bf1f9ee0d11e7bb1b1456435479d47c" | |
| uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" | |
| version = "1.4.1" | |
| [[PCRE_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "b2a7af664e098055a7529ad1a900ded962bca488" | |
| uuid = "2f80f16e-611a-54ab-bc61-aa92de5b98fc" | |
| version = "8.44.0+0" | |
| [[PDMats]] | |
| deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] | |
| git-tree-sha1 = "4dd403333bcf0909341cfe57ec115152f937d7d8" | |
| uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" | |
| version = "0.11.1" | |
| [[Parameters]] | |
| deps = ["OrderedCollections", "UnPack"] | |
| git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe" | |
| uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a" | |
| version = "0.12.3" | |
| [[Parsers]] | |
| deps = ["Dates"] | |
| git-tree-sha1 = "98f59ff3639b3d9485a03a72f3ab35bab9465720" | |
| uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" | |
| version = "2.0.6" | |
| [[Pixman_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "b4f5d02549a10e20780a24fce72bea96b6329e29" | |
| uuid = "30392449-352a-5448-841d-b1acce4e97dc" | |
| version = "0.40.1+0" | |
| [[Pkg]] | |
| deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] | |
| uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" | |
| [[PlotThemes]] | |
| deps = ["PlotUtils", "Requires", "Statistics"] | |
| git-tree-sha1 = "a3a964ce9dc7898193536002a6dd892b1b5a6f1d" | |
| uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" | |
| version = "2.0.1" | |
| [[PlotUtils]] | |
| deps = ["ColorSchemes", "Colors", "Dates", "Printf", "Random", "Reexport", "Statistics"] | |
| git-tree-sha1 = "b084324b4af5a438cd63619fd006614b3b20b87b" | |
| uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" | |
| version = "1.0.15" | |
| [[Plots]] | |
| deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "GeometryBasics", "JSON", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "PlotThemes", "PlotUtils", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs"] | |
| git-tree-sha1 = "ba43b248a1f04a9667ca4a9f782321d9211aa68e" | |
| uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" | |
| version = "1.22.6" | |
| [[PositiveFactorizations]] | |
| deps = ["LinearAlgebra"] | |
| git-tree-sha1 = "17275485f373e6673f7e7f97051f703ed5b15b20" | |
| uuid = "85a6dd25-e78a-55b7-8502-1745935b8125" | |
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| [[Preferences]] | |
| deps = ["TOML"] | |
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| uuid = "21216c6a-2e73-6563-6e65-726566657250" | |
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| [[Printf]] | |
| deps = ["Unicode"] | |
| uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" | |
| [[ProgressMeter]] | |
| deps = ["Distributed", "Printf"] | |
| git-tree-sha1 = "afadeba63d90ff223a6a48d2009434ecee2ec9e8" | |
| uuid = "92933f4c-e287-5a05-a399-4b506db050ca" | |
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| [[PyCall]] | |
| deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Serialization", "VersionParsing"] | |
| git-tree-sha1 = "169bb8ea6b1b143c5cf57df6d34d022a7b60c6db" | |
| uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" | |
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| [[PyPlot]] | |
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| uuid = "d330b81b-6aea-500a-939a-2ce795aea3ee" | |
| version = "2.10.0" | |
| [[Qt5Base_jll]] | |
| deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "xkbcommon_jll"] | |
| git-tree-sha1 = "ad368663a5e20dbb8d6dc2fddeefe4dae0781ae8" | |
| uuid = "ea2cea3b-5b76-57ae-a6ef-0a8af62496e1" | |
| version = "5.15.3+0" | |
| [[QuadGK]] | |
| deps = ["DataStructures", "LinearAlgebra"] | |
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| uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" | |
| version = "2.4.2" | |
| [[QuantumControl]] | |
| deps = ["Krotov", "QuantumControlBase", "QuantumPropagators"] | |
| path = "/Users/goerz/.julia/dev/QuantumControl" | |
| uuid = "8a270532-f23f-47a8-83a9-b33d10cad486" | |
| version = "0.0.2" | |
| [[QuantumControlBase]] | |
| deps = ["Distributions", "LinearAlgebra", "QuantumPropagators", "Random", "SparseArrays"] | |
| path = "/Users/goerz/.julia/dev/QuantumControlBase" | |
| uuid = "f10a33bc-5a64-497c-be7b-6f86b4f0c2aa" | |
| version = "0.0.2" | |
| [[QuantumPropagators]] | |
| deps = ["LinearAlgebra", "OffsetArrays", "ProgressMeter", "Random", "SpecialFunctions", "StaticArrays"] | |
| path = "/Users/goerz/.julia/dev/QuantumPropagators" | |
| uuid = "7bf12567-5742-4b91-a078-644e72a65fc1" | |
| version = "0.0.2" | |
| [[REPL]] | |
| deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] | |
| uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" | |
| [[Random]] | |
| deps = ["Serialization"] | |
| uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" | |
| [[RecipesBase]] | |
| git-tree-sha1 = "44a75aa7a527910ee3d1751d1f0e4148698add9e" | |
| uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" | |
| version = "1.1.2" | |
| [[RecipesPipeline]] | |
| deps = ["Dates", "NaNMath", "PlotUtils", "RecipesBase"] | |
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| uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" | |
| version = "0.4.1" | |
| [[Reexport]] | |
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| uuid = "189a3867-3050-52da-a836-e630ba90ab69" | |
| version = "1.2.2" | |
| [[Requires]] | |
| deps = ["UUIDs"] | |
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| uuid = "ae029012-a4dd-5104-9daa-d747884805df" | |
| version = "1.1.3" | |
| [[Rmath]] | |
| deps = ["Random", "Rmath_jll"] | |
| git-tree-sha1 = "bf3188feca147ce108c76ad82c2792c57abe7b1f" | |
| uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa" | |
| version = "0.7.0" | |
| [[Rmath_jll]] | |
| deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] | |
| git-tree-sha1 = "68db32dff12bb6127bac73c209881191bf0efbb7" | |
| uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f" | |
| version = "0.3.0+0" | |
| [[SHA]] | |
| uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" | |
| [[Scratch]] | |
| deps = ["Dates"] | |
| git-tree-sha1 = "0b4b7f1393cff97c33891da2a0bf69c6ed241fda" | |
| uuid = "6c6a2e73-6563-6170-7368-637461726353" | |
| version = "1.1.0" | |
| [[Serialization]] | |
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| [[SharedArrays]] | |
| deps = ["Distributed", "Mmap", "Random", "Serialization"] | |
| uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383" | |
| [[Showoff]] | |
| deps = ["Dates", "Grisu"] | |
| git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de" | |
| uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f" | |
| version = "1.0.3" | |
| [[Sockets]] | |
| uuid = "6462fe0b-24de-5631-8697-dd941f90decc" | |
| [[SortingAlgorithms]] | |
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| uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" | |
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| [[SparseArrays]] | |
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| uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | |
| [[SpecialFunctions]] | |
| deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] | |
| git-tree-sha1 = "793793f1df98e3d7d554b65a107e9c9a6399a6ed" | |
| uuid = "276daf66-3868-5448-9aa4-cd146d93841b" | |
| version = "1.7.0" | |
| [[Static]] | |
| deps = ["IfElse"] | |
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| uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3" | |
| version = "0.3.3" | |
| [[StaticArrays]] | |
| deps = ["LinearAlgebra", "Random", "Statistics"] | |
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| uuid = "90137ffa-7385-5640-81b9-e52037218182" | |
| version = "1.2.13" | |
| [[Statistics]] | |
| deps = ["LinearAlgebra", "SparseArrays"] | |
| uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" | |
| [[StatsAPI]] | |
| git-tree-sha1 = "1958272568dc176a1d881acb797beb909c785510" | |
| uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0" | |
| version = "1.0.0" | |
| [[StatsBase]] | |
| deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] | |
| git-tree-sha1 = "eb35dcc66558b2dda84079b9a1be17557d32091a" | |
| uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" | |
| version = "0.33.12" | |
| [[StatsFuns]] | |
| deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"] | |
| git-tree-sha1 = "95072ef1a22b057b1e80f73c2a89ad238ae4cfff" | |
| uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c" | |
| version = "0.9.12" | |
| [[StructArrays]] | |
| deps = ["Adapt", "DataAPI", "StaticArrays", "Tables"] | |
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| uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" | |
| version = "0.6.3" | |
| [[SuiteSparse]] | |
| deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] | |
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| [deps] | |
| GRAPE = "6b52fcaf-80fe-489a-93e9-9f92080510be" | |
| LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" | |
| Optim = "429524aa-4258-5aef-a3af-852621145aeb" | |
| Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" | |
| Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" | |
| PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" | |
| QuantumControl = "8a270532-f23f-47a8-83a9-b33d10cad486" | |
| QuantumControlBase = "f10a33bc-5a64-497c-be7b-6f86b4f0c2aa" | |
| QuantumPropagators = "7bf12567-5742-4b91-a078-644e72a65fc1" | |
| Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b" | |
| SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | |
| [compat] | |
| julia = "1.6" |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Example 1: Optimization of a State-to-State Transfer in a Two-Level-System" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "$\n", | |
| "\\newcommand{tr}[0]{\\operatorname{tr}}\n", | |
| "\\newcommand{diag}[0]{\\operatorname{diag}}\n", | |
| "\\newcommand{abs}[0]{\\operatorname{abs}}\n", | |
| "\\newcommand{pop}[0]{\\operatorname{pop}}\n", | |
| "\\newcommand{aux}[0]{\\text{aux}}\n", | |
| "\\newcommand{opt}[0]{\\text{opt}}\n", | |
| "\\newcommand{tgt}[0]{\\text{tgt}}\n", | |
| "\\newcommand{init}[0]{\\text{init}}\n", | |
| "\\newcommand{lab}[0]{\\text{lab}}\n", | |
| "\\newcommand{rwa}[0]{\\text{rwa}}\n", | |
| "\\newcommand{bra}[1]{\\langle#1\\vert}\n", | |
| "\\newcommand{ket}[1]{\\vert#1\\rangle}\n", | |
| "\\newcommand{Bra}[1]{\\left\\langle#1\\right\\vert}\n", | |
| "\\newcommand{Ket}[1]{\\left\\vert#1\\right\\rangle}\n", | |
| "\\newcommand{Braket}[2]{\\left\\langle #1\\vphantom{#2}\\mid{#2}\\vphantom{#1}\\right\\rangle}\n", | |
| "\\newcommand{op}[1]{\\hat{#1}}\n", | |
| "\\newcommand{Op}[1]{\\hat{#1}}\n", | |
| "\\newcommand{dd}[0]{\\,\\text{d}}\n", | |
| "\\newcommand{Liouville}[0]{\\mathcal{L}}\n", | |
| "\\newcommand{DynMap}[0]{\\mathcal{E}}\n", | |
| "\\newcommand{identity}[0]{\\mathbf{1}}\n", | |
| "\\newcommand{Norm}[1]{\\lVert#1\\rVert}\n", | |
| "\\newcommand{Abs}[1]{\\left\\vert#1\\right\\vert}\n", | |
| "\\newcommand{avg}[1]{\\langle#1\\rangle}\n", | |
| "\\newcommand{Avg}[1]{\\left\\langle#1\\right\\rangle}\n", | |
| "\\newcommand{AbsSq}[1]{\\left\\vert#1\\right\\vert^2}\n", | |
| "\\newcommand{Re}[0]{\\operatorname{Re}}\n", | |
| "\\newcommand{Im}[0]{\\operatorname{Im}}\n", | |
| "$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:09.999000-04:00", | |
| "start_time": "2021-10-17T06:16:00.712Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import Pkg\n", | |
| "Pkg.activate(\".\")\n", | |
| "Pkg.develop(url=\"https://github.com/JuliaQuantumControl/QuantumPropagators.jl.git#8a224f5c53b85d8eab01675584b4dc6cda0b14f4\")\n", | |
| "Pkg.develop(url=\"https://github.com/JuliaQuantumControl/QuantumControlBase.jl.git#d595083f4a305d18b62be61495b2c7e8beb8af8a\")\n", | |
| "Pkg.develop(url=\"https://github.com/JuliaQuantumControl/GRAPE.jl.git#930d1593468a37225f0fe393e19cabc2a38af713\")\n", | |
| "Pkg.develop(url=\"https://github.com/JuliaQuantumControl/QuantumControl.jl.git#7274192eb05a23f9ea8cd623cb4e9a5dc01a2041\")\n", | |
| "Pkg.instantiate()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This first example illustrates the basic use of the `GRAPE.jl` by solving a\n", | |
| "simple canonical optimization problem: the transfer of population in a two\n", | |
| "level system." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:30.616000-04:00", | |
| "start_time": "2021-10-17T06:16:28.133Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "using QuantumControl\n", | |
| "using LinearAlgebra\n", | |
| "using GRAPE # XXX" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Two-level Hamiltonian" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We consider the Hamiltonian $\\op{H}_{0} = - \\frac{\\omega}{2} \\op{\\sigma}_{z}$, representing\n", | |
| "a simple qubit with energy level splitting $\\omega$ in the basis\n", | |
| "$\\{\\ket{0},\\ket{1}\\}$. The control field $\\epsilon(t)$ is assumed to couple via\n", | |
| "the Hamiltonian $\\op{H}_{1}(t) = \\epsilon(t) \\op{\\sigma}_{x}$ to the qubit,\n", | |
| "i.e., the control field effectively drives transitions between both qubit\n", | |
| "states.\n", | |
| "\n", | |
| "A brief explanation of notation: a \"ket\", e.g. $\\ket{v}$ is a quantum physicists's notation for a vector; read this as $\\vec{v}$. The hat in $\\Op{H}$ denotes a matrix.\n", | |
| "\n", | |
| "We we will use" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:32.819000-04:00", | |
| "start_time": "2021-10-17T06:16:32.308Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ϵ(t) = 0.2 * QuantumControl.shapes.flattop(t, T = 5, t_rise = 0.3, func = :blackman);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:33.508000-04:00", | |
| "start_time": "2021-10-17T06:16:33.345Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "\"\"\"Two-level-system Hamiltonian.\"\"\"\n", | |
| "function hamiltonian(Ω = 1.0, ϵ = ϵ)\n", | |
| " σ̂_z = ComplexF64[1 0; 0 -1]\n", | |
| " σ̂_x = ComplexF64[0 1; 1 0]\n", | |
| " Ĥ₀ = -0.5 * Ω * σ̂_z\n", | |
| " Ĥ₁ = σ̂_x\n", | |
| " return (Ĥ₀, (Ĥ₁, ϵ))\n", | |
| "end;" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:34.141000-04:00", | |
| "start_time": "2021-10-17T06:16:33.956Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "H = hamiltonian();" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The control field here switches on from zero at $t=0$ to it's maximum amplitude\n", | |
| "0.2 within the time period 0.3 (the switch-on shape is half a [Blackman pulse](https://en.wikipedia.org/wiki/Window_function#Blackman_window)).\n", | |
| "It switches off again in the time period 0.3 before the\n", | |
| "final time $T=5$). We use a time grid with 500 time steps between 0 and $T$:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:35.446000-04:00", | |
| "start_time": "2021-10-17T06:16:35.198Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "tlist = collect(range(0, 5, length = 500));" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:48.199000-04:00", | |
| "start_time": "2021-10-17T06:16:36.046Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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4TJ06FSkpKVi6dKmzY6Q2vIIuERHJdNauqcpNQrwdeZCPjw/+67/+C1lZWSguLobJZMLEiRMxcuRIZ8dH12mfjmESQkTk6QKtXVOVW5jqUBIiGzFiBEaMGOGsWOgm5I6pOk7HEBF5PHeYjrE7CcnMzLT7oKtWrXIoGOqa2Wy2dsZj23YiIpJHxZXcNdXuJKS4uNjmflFREYxGI+Li4gAAP//8M1QqFRISEpwbIQEAmlpMaDaaALAmhIiIrpuO8YQk5KuvvrL+vGrVKvj7++Pjjz9GUFAQAODKlSt47rnnkJqa6vwoyTrn5+0lwU+tEhwNERGJFuAG0zEOrY555513kJ2dbU1AACAoKAgrVqzAO++847TgqN313VIlSRIcDRERiSZPzdd6WhKi1+tRWVnZYXtVVRXq6+t7HRR1JCchnIohIiLg+poQ5a6OcSgJeeSRR/Dcc8/h008/xfnz53H+/Hl8+umnmDt3Lh599FFnx0hgjxAiIrLVvkRXuSMhDi3RXb9+PRYtWoTZs2ejpcVy8t7e3pg7dy7efvttpwZIFu3dUrkyhoiI2r+UXm02ornVZL2gnZI4lIT4+voiJycHb7/9Nn799VeYzWbccsst8PPzc3Z81IaNyoiI6Hr+Wh9IEmA2W0bLw/w1okPqsV41K/Pz88O4ceOcFQt1g1fQJSKi66m8JARofVB3rQV115o9JwmZNm1atys09u7d63BA1Dl5zo8XryMiIplugJyEKLMuxKEkZMKECTb3W1paUFJSghMnTuCZZ55xRlx0gzpOxxAR0Q0CfX1Qelm5DcscSkL+8pe/dLp9+fLlaGho6FVA1LlaFqYSEdEN5OJUpSYhTi2lnT17Nj766CNnHpLacIkuERHdSOkXsXNqElJYWAitVuvMQ1Iba7My1oQQEVEbpfcKcWg65saGZGazGeXl5Th69CiWLVvmlMDIFmtCiIjoRoEDLFP0Su2a6lASEhAQYLM6xsvLC3FxccjKykJaWprTgiOLVqMJ9YZWAKwJISKidh45EpKbm+vkMKg7+qZW688B2l61diEiIjei9CvpOlQTMnz4cNTU1HTYXltbi+HDh/c6KLJV2zbM5q/xhrdKeW15iYiobwR64uqYs2fPwmg0dthuMBhw4cKFXgdFtuRhNhalEhHR9eQpeo8YCdm1axd27doFAPjiiy+s93ft2oUdO3bgjTfeQExMTI8CyMnJQWxsLLRaLRISEnDgwIEu9y0vL8dTTz2FuLg4eHl5YeHChZ3ut23bNsTHx0Oj0SA+Ph47duzoUUyuhstziYioM0pfotujAoOZM2cCACRJ6tAZ1cfHBzExMXjnnXfsPl5eXh4WLlyInJwc3HHHHfjggw8wY8YM/PDDDxg2bFiH/Q0GA8LCwrBkyZIuG6YVFhYiPT0db7zxBh555BHs2LEDjz/+OA4ePIikpCT7T9aFWFfGcCSEiIiuYy1MvdoMk8kML6+uL6niiiSz2Wzu6YNiY2Nx5MgRhIaG9urJk5KSMGnSJKxbt866bfTo0Zg5cyays7O7fexdd92FCRMmYPXq1Tbb09PTodfr8fnnn1u33XfffQgKCsKWLVvsikuv10On06Gurg4BAQH2n1Afyf3nGSz/nx/wwNjBeP/pSaLDISIiF9HUYsSty3YDAL5bnoYArfgvqz35DHWoJuTMmTO9TkCam5tRVFTUYUlvWloaDh065PBxCwsLOxxz+vTp3R7TYDBAr9fb3FwJa0KIiKgzWh8VNN6Wj/I6BRan2j0ds3btWrzwwgvQarVYu3Ztt/u+/PLLNz1edXU1jEYjwsPDbbaHh4ejoqLC3rA6qKio6PExs7Oz8frrrzv8nH2NNSFERNSVQF8fVOoNqLvWgijRwfSQ3UnIX/7yFzz99NPQarVd1mMAlnoRe5KQ6/e/ntls7rCtp3p6zMWLFyMzM9N6X6/XIyrKdX6V7JZKRERdCRygRqXeoMhlunYnIWfOnOn0Z0eFhoZCpVJ1GKGoqqrqMJLRExERET0+pkajgUajcfg5+5o8HcPCVCIiupH1SrrXlNe6XVjnK7VajYSEBBQUFNhsLygoQEpKisPHTU5O7nDMPXv29OqYosnNynQD2LKdiIhsyfWCSlyma/dIyPXTFTezatUqu4+ZkZGBxMREJCcnY8OGDSgtLcW8efMAWKZJLly4gE2bNlkfU1JSAgBoaGjApUuXUFJSArVajfj4eADAggULMHXqVKxcuRIPP/wwdu7ciS+//BIHDx60O35Xw5oQIiLqipK7ptqdhBQXF9u1X0/qOdLT01FTU4OsrCyUl5djzJgxyM/PR3R0NABLc7LS0lKbx0ycONH6c1FRETZv3ozo6GicPXsWAJCSkoKtW7di6dKlWLZsGUaMGIG8vDzF9ggB2pMQTscQEdGNAhU8EuJQnxB350p9QsxmM0Yu+RytJjMKF9+NwboBQuMhIiLX8t7eU/jznp+RnhiFlY+NEx1O3/cJuV5ZWRnOnz/f28NQF642G9FqsuSJgawJISKiG+jarh/jMYWpra2tWLZsGXQ6HWJiYhAdHQ2dToelS5eipUV5w0GuTF4Zo1Z5QevDK+gSEZEtj6gJud6LL76IHTt24K233kJycjIAS6fS5cuXo7q6GuvXr3dqkJ7MujLG16fX/VOIiMj9KPkidg4lIVu2bMHWrVsxY8YM67Zx48Zh2LBheOKJJ5iEOJHcqIwrY4iIqDNKLkx1aHxfq9UiJiamw/aYmBio1axbcKYrbUlIEFfGEBFRJ+R6QSVOxziUhMyfPx9vvPEGDAaDdZvBYMCbb76JF1980WnBUXuhUaAvkzsiIupIHim/1mKEodUoOJqecWg6pri4GP/4xz8wdOhQjB8/HgBw7NgxNDc34ze/+Q0effRR677bt293TqQeqpYjIURE1A1/rTckCTCbLVMyg/xVokOym0NJSGBgIGbNmmWzzZUu+OZO5MJUjoQQEVFnvLwk6Ab4oPZqC+qutmCQv1Z0SHZzKAnZuHGjs+OgLsg1IeyWSkREXQlsS0JqFVacysYTLs46EsJGZURE1AXrMl2FFac6NBJSU1OD1157DV999RWqqqpgMpls/v3y5ctOCY5YE0JERDfX3jXVA5KQ2bNn49dff8XcuXMRHh7OJlp96Mp1zcqIiIg60941VVmt2x1KQg4ePIiDBw9aV8ZQ35GbzwSxMJWIiLogT8foFTYS4lBNyK233opr1645Oxa6gdlstk7HsDCViIi6In9GKG06xqEkJCcnB0uWLMG+fftQU1MDvV5vcyPnqDe0Wq+gy5EQIiLqik6hF7FzuE9IXV0d7r77bpvtZrMZkiTBaFRWxzZXJVc5a328oPVRTvMZIiLqX0q9iJ1DScjTTz8NtVqNzZs3szC1D13h8lwiIrJDoCetjjlx4gSKi4sRFxfn7HjoOqwHISIie1ivpKuw1TEO1YQkJiairKzM2bHQDawjIUxCiIioG9Ylup4wEvLSSy9hwYIF+I//+A+MHTsWPj62H5Ljxo1zSnCejstziYjIHtcv0TWZzPDyUkaZhENJSHp6OgDgX//1Xzv8GwtTnedKI6djiIjo5gLakhCT2bKyUk5KXJ1DSciZM2ecHQd14gqvoEtERHbQ+qgwwEeFay1G1F1tce8kJDo6GgDwww8/oLS0FM3N7YUwkiRZ/516p306RhlvJiIiEkc3wMeShCioLsShJOT06dN45JFHcPz4cUiSBLPZ0lBLXqrL6Rjn4BJdIiKyV6CvDyr0Tai9ppwVMg6tjlmwYAFiY2NRWVkJX19fnDhxAvv370diYiK+/vprJ4foubhEl4iI7KXErqkOjYQUFhZi7969CAsLg5eXF1QqFe68805kZ2fj5ZdfRnFxsbPj9Ei1rAkhIiI7KbFrqkMjIUajEQMHDgQAhIaG4uLFiwAstSI//fST86LzcFeusiaEiIjsY72InYIaljk0EjJmzBh89913GD58OJKSkvDWW29BrVZjw4YNGD58uLNj9EgtRpM1mw3240gIERF1L9hPAwCoaXTzJGTp0qVobGwEAKxYsQIPPvggUlNTERISgry8PKcG6KnkolRJ4nQMERHdXEjbF9bL7p6ETJ8+3frz8OHD8cMPP+Dy5csICgrixeycRH4TBfmqoVJI5zsiIhIn2FOSkM4EBwc761AE4HKD5U3EqRgiIrJH8EDL50VNg3KSEIcKU6nvVbdlsiFMQoiIyA6h1poQg+BI7MckxEVdbrC8iUIGMgkhIqKbk0dCLjc2W5uIujomIS5KntPjdAwREdlDHjlvMZpRb2gVHI19mIS4qBprEqIRHAkRESmB1kcFX7UKQHtdoatjEuKiLrMmhIiIekgePVdKrxAmIS5Krm5mTQgREdkrZKBl9Fwpy3SZhLgoubqZNSFERGQvefS8pkEZK2SYhLio9ukY1oQQEZF9OB1DvWY0mVHL68YQEVEPKa11O5MQF3TlajPkJd68gi4REdlLaa3bmYS4IPnNE+jrA28Vf0VERGQfTsf0UE5ODmJjY6HVapGQkIADBw50u/++ffuQkJAArVaL4cOHY/369Tb/npubC0mSOtyampr68jScqlrulsqpGCIi6oHQttUxLEy1Q15eHhYuXIglS5aguLgYqampmDFjBkpLSzvd/8yZM7j//vuRmpqK4uJivPrqq3j55Zexbds2m/0CAgJQXl5uc9Nqtf1xSk7BolQiInKE0qZjnHYVXUesWrUKc+fOxfPPPw8AWL16Nb744gusW7cO2dnZHfZfv349hg0bhtWrVwMARo8ejaNHj+LPf/4zZs2aZd1PkiRERET0yzn0BbZsJyIiR1w/HWM2myFJkuCIuidsJKS5uRlFRUVIS0uz2Z6WloZDhw51+pjCwsIO+0+fPh1Hjx5FS0uLdVtDQwOio6MxdOhQPPjggyguLu42FoPBAL1eb3MTSW5UFsxGZURE1ANyg8vmVhMam42Co7k5YUlIdXU1jEYjwsPDbbaHh4ejoqKi08dUVFR0un9rayuqq6sBALfeeityc3Oxa9cubNmyBVqtFnfccQdOnTrVZSzZ2dnQ6XTWW1RUVC/PrnfYsp2IiBzhq/aG1sfy0a6EuhDhhak3DhXdbPios/2v3z5lyhTMnj0b48ePR2pqKj755BOMGjUK7777bpfHXLx4Merq6qy3srIyR0/HKS7VW944coERERGRvcL8LZ8d1QpIQoTVhISGhkKlUnUY9aiqquow2iGLiIjodH9vb2+EhIR0+hgvLy9Mnjy525EQjUYDjcZ1PvAr6y0recIDlFNMS0REriHcX4uyy9dQqXf9JETYSIharUZCQgIKCgpsthcUFCAlJaXTxyQnJ3fYf8+ePUhMTISPT+dNvcxmM0pKSjB48GDnBN4PqtreOIMCXCcxIiIiZZC/wFbqXb81hdDpmMzMTPz1r3/FRx99hJMnT+KVV15BaWkp5s2bB8AyTTJnzhzr/vPmzcO5c+eQmZmJkydP4qOPPsKHH36IRYsWWfd5/fXX8cUXX+D06dMoKSnB3LlzUVJSYj2mqzObzajiSAgRETlIno6pqnf9kRChS3TT09NRU1ODrKwslJeXY8yYMcjPz0d0dDQAoLy83KZnSGxsLPLz8/HKK6/g/fffR2RkJNauXWuzPLe2thYvvPACKioqoNPpMHHiROzfvx+33357v5+fI65cbUGL0VLnEsaaECIi6iEljYRIZrmyk6z0ej10Oh3q6uoQEBDQr899slyPGWsOIMRPjaJl9/brcxMRkfJt//Y8Mj85hjtvCcV/Pp/U78/fk89Q4atjyJY8fCYPpxEREfWEkkZCmIS4GPlNw3oQIiJyxKC2L7FMQqjHqqxJCEdCiIio5wa1fYnVN7WiqcW1u6YyCXEx8rruQf4cCSEiop4L0LZ3Ta1y8V4hTEJcTPvyXI6EEBFRz0mS1F4XUu/aUzJMQlyMdSSENSFEROSgcH9lFKcyCXExVSxMJSKiXgoLkItTOR1DdjKZzNYluoO4RJeIiBwkj4RUcTqG7HXlajNaTW3dUpmEEBGRg+S6Qhamkt3kYbPQgWr4qPirISIixyilYRk/6VyIXMUcxuW5RETUC/JV2CuYhJC9LtZeAwBE6piEEBGR4wbrBgAAymub4MqXiGMS4kIuXLEkIUOCBgiOhIiIlGxw25fZay1GXLnaIjiarjEJcSEX2kZChgQyCSEiIsdpfVTWBQ7yF1xXxCTEhZznSAgRETmJ/IX2Qu1VwZF0jUmIC5Gz1aFBvoIjISIipZO/0J7nSAjdTHOrybo6htMxRETUW0OtIyFMQugmKuqaYDYDam8vhA5Uiw6HiIgUjiMhZLdzlxsBAMOCfSFJkuBoiIhI6aKCLVP7ZZdZE0I3cbbG8iaJCWE9CBER9V5MiB8A4GxNo8v2CmES4iLOVVtGQqLb3jRERES9MSRwAFReEppaTNaLo7oaJiEugiMhRETkTGpvL+tCh7NtX3RdDZMQF1F6mSMhRETkXNFtX2zPuWhdCJMQF2AymXGubSQkmiMhRETkJNa6EI6EUFcu1F6DodVkM3RGRETUW8PDLEnIL1UNgiPpHJMQF/BzZT0AYHioH7xV/JUQEZFzjBzkD4BJCHXjVNubY2S4v+BIiIjInYwMHwjAskzX0GoUHE1HTEJcwKlKSxIyatBAwZEQEZE7GeSvQYDWGyYzcPqS69WFMAlxAb9UWaZj5IyViIjIGSRJso6yy1P/roRJiGCtRhN+rLC8MeIiAgRHQ0RE7iYuwpKEnCxnEkI3+OVSAwytJvhrvBEdzOW5RETkXGOH6AAAJy7UCY6kIyYhgh0/b3lTxEcGwMuLF64jIiLnGhNpSUKOX6hzuWvIMAkRTM5M5UyViIjImUZFDISPSkLdtRacv3JNdDg2mIQIVlJWCwAYO5RJCBEROZ/GW4XRgy01h9+WXhEcjS0mIQI1GFpx4qIeADA5JlhwNERE5K7kz5gjZy8LjsQWkxCBis5dgdFkRlTwAESyXTsREfWR22MtScj/nmESQm2+OV0DALg9JkRwJERE5M7kkZCfKxtQ3WAQHE07JiEC7T1ZBQC4cySTECIi6jvBfmqMGWKpC/nqxyrB0bRjEiJIac1V/FRZD5WXhGlxg0SHQ0REbu6e0eEAgC9PVgqOpB2TEEHyT5QDACbHBCHQVy04GiIicndyErLv50vQN7UIjsaCSYgAZrMZeUfKAACPTBwiOBoiIvIEt0UG4JZBA9HUYsL/HLsoOhwALpCE5OTkIDY2FlqtFgkJCThw4EC3++/btw8JCQnQarUYPnw41q9f32Gfbdu2IT4+HhqNBvHx8dixY0dfhe+QfT9fwpnqRvipVXhwXKTocIiIyANIkoQnJkcBAD4+dBZGk/juqUKTkLy8PCxcuBBLlixBcXExUlNTMWPGDJSWlna6/5kzZ3D//fcjNTUVxcXFePXVV/Hyyy9j27Zt1n0KCwuRnp6OjIwMHDt2DBkZGXj88cdx+PDh/jqtbrUaTXhr908AgCdvHwY/jbfgiIiIyFP8n4QoBGi98XNlA3YUXxAdDiSzwEbySUlJmDRpEtatW2fdNnr0aMycORPZ2dkd9v/d736HXbt24eTJk9Zt8+bNw7Fjx1BYWAgASE9Ph16vx+eff27d57777kNQUBC2bNliV1x6vR46nQ51dXUICHDelW1NJjOy/v4Dcg+dhb/GG/t+Ow3BfqwHISKi/rPu61+xcveP8Nd4I+//JiM+0rlXcO/JZ6iwkZDm5mYUFRUhLS3NZntaWhoOHTrU6WMKCws77D99+nQcPXoULS0t3e7T1TEBwGAwQK/X29yc7Z09PyHlT3uRe+gsAOBPs8YxASEion73fGosJscEod7QigffPYDH1h3Cd+drhcQiLAmprq6G0WhEeHi4zfbw8HBUVFR0+piKiopO929tbUV1dXW3+3R1TADIzs6GTqez3qKiohw5pW7VNDajQt8EX7UKb80ahwfGDXb6cxAREd2Mj8oLGzISMS0uDCYzcPTcFfhrfYTEIrwgQZJsL19vNps7bLvZ/jdu7+kxFy9ejMzMTOt9vV7v9ERkdlI07rstAgnRQawDISIioYL81Nj43O04V9OIonNXEBPiKyQOYZ+GoaGhUKlUHUYoqqqqOoxkyCIiIjrd39vbGyEhId3u09UxAUCj0UCj0ThyGnZz9pwbERFRb0WH+CE6xE/Y8wubjlGr1UhISEBBQYHN9oKCAqSkpHT6mOTk5A7779mzB4mJifDx8el2n66OSURERGIInRfIzMxERkYGEhMTkZycjA0bNqC0tBTz5s0DYJkmuXDhAjZt2gTAshLmvffeQ2ZmJv7t3/4NhYWF+PDDD21WvSxYsABTp07FypUr8fDDD2Pnzp348ssvcfDgQSHnSERERJ0TmoSkp6ejpqYGWVlZKC8vx5gxY5Cfn4/o6GgAQHl5uU3PkNjYWOTn5+OVV17B+++/j8jISKxduxazZs2y7pOSkoKtW7di6dKlWLZsGUaMGIG8vDwkJSX1+/kRERFR14T2CXFVfdUnhIiIyN0pok8IEREReTYmIURERCQEkxAiIiISgl2zOiGXyfRF+3YiIiJ3Jn922lNyyiSkE/X19QDQJ+3biYiIPEF9fT10Ol23+3B1TCdMJhMuXrwIf3//btu994TcCr6srIwrbpyEr6nz8TV1Lr6ezsfX1Ln64vU0m82or69HZGQkvLy6r/rgSEgnvLy8MHTo0D45dkBAAP/jOBlfU+fja+pcfD2dj6+pczn79bzZCIiMhalEREQkBJMQIiIiEoJJSD/RaDT4wx/+0OdX6/UkfE2dj6+pc/H1dD6+ps4l+vVkYSoREREJwZEQIiIiEoJJCBEREQnBJISIiIiEYBJCREREQjAJ6Sc5OTmIjY2FVqtFQkICDhw4IDokxdq/fz8eeughREZGQpIkfPbZZ6JDUrTs7GxMnjwZ/v7+GDRoEGbOnImffvpJdFiKtm7dOowbN87aACo5ORmff/656LDcRnZ2NiRJwsKFC0WHoljLly+HJEk2t4iIiH6Pg0lIP8jLy8PChQuxZMkSFBcXIzU1FTNmzEBpaano0BSpsbER48ePx3vvvSc6FLewb98+zJ8/H9988w0KCgrQ2tqKtLQ0NDY2ig5NsYYOHYo//elPOHr0KI4ePYq7774bDz/8ML7//nvRoSnekSNHsGHDBowbN050KIp32223oby83Ho7fvx4v8fAJbr9ICkpCZMmTcK6deus20aPHo2ZM2ciOztbYGTKJ0kSduzYgZkzZ4oOxW1cunQJgwYNwr59+zB16lTR4biN4OBgvP3225g7d67oUBSroaEBkyZNQk5ODlasWIEJEyZg9erVosNSpOXLl+Ozzz5DSUmJ0Dg4EtLHmpubUVRUhLS0NJvtaWlpOHTokKCoiLpWV1cHwPKhSb1nNBqxdetWNDY2Ijk5WXQ4ijZ//nw88MADuOeee0SH4hZOnTqFyMhIxMbG4oknnsDp06f7PQZewK6PVVdXw2g0Ijw83GZ7eHg4KioqBEVF1Dmz2YzMzEzceeedGDNmjOhwFO348eNITk5GU1MTBg4ciB07diA+Pl50WIq1detWfPvttzhy5IjoUNxCUlISNm3ahFGjRqGyshIrVqxASkoKvv/+e4SEhPRbHExC+okkSTb3zWZzh21Eor344ov47rvvcPDgQdGhKF5cXBxKSkpQW1uLbdu24ZlnnsG+ffuYiDigrKwMCxYswJ49e6DVakWH4xZmzJhh/Xns2LFITk7GiBEj8PHHHyMzM7Pf4mAS0sdCQ0OhUqk6jHpUVVV1GB0hEumll17Crl27sH//fgwdOlR0OIqnVqtxyy23AAASExNx5MgRrFmzBh988IHgyJSnqKgIVVVVSEhIsG4zGo3Yv38/3nvvPRgMBqhUKoERKp+fnx/Gjh2LU6dO9evzsiakj6nVaiQkJKCgoMBme0FBAVJSUgRFRdTObDbjxRdfxPbt27F3717ExsaKDsktmc1mGAwG0WEo0m9+8xscP34cJSUl1ltiYiKefvpplJSUMAFxAoPBgJMnT2Lw4MH9+rwcCekHmZmZyMjIQGJiIpKTk7FhwwaUlpZi3rx5okNTpIaGBvzyyy/W+2fOnEFJSQmCg4MxbNgwgZEp0/z587F582bs3LkT/v7+1lE7nU6HAQMGCI5OmV599VXMmDEDUVFRqK+vx9atW/H1119j9+7dokNTJH9//w41Sn5+fggJCWHtkoMWLVqEhx56CMOGDUNVVRVWrFgBvV6PZ555pl/jYBLSD9LT01FTU4OsrCyUl5djzJgxyM/PR3R0tOjQFOno0aOYNm2a9b48f/nMM88gNzdXUFTKJS8dv+uuu2y2b9y4Ec8++2z/B+QGKisrkZGRgfLycuh0OowbNw67d+/GvffeKzo0IgDA+fPn8eSTT6K6uhphYWGYMmUKvvnmm37/XGKfECIiIhKCNSFEREQkBJMQIiIiEoJJCBEREQnBJISIiIiEYBJCREREQjAJISIiIiGYhBAREZEQTEKIyGV8/fXXkCQJtbW1okMhon7AZmVEJMxdd92FCRMmYPXq1QCA5uZmXL58GeHh4bzKNJEHYNt2InIZarUaERERosMgon7C6RgiEuLZZ5/Fvn37sGbNGkiSBEmSkJubazMdk5ubi8DAQPz9739HXFwcfH198dhjj6GxsREff/wxYmJiEBQUhJdeeglGo9F67ObmZvz2t7/FkCFD4Ofnh6SkJHz99ddiTpSIusSRECISYs2aNfj5558xZswYZGVlAQC+//77DvtdvXoVa9euxdatW1FfX49HH30Ujz76KAIDA5Gfn4/Tp09j1qxZuPPOO5Geng4AeO6553D27Fls3boVkZGR2LFjB+677z4cP34cI0eO7NfzJKKuMQkhIiF0Oh3UajV8fX2tUzA//vhjh/1aWlqwbt06jBgxAgDw2GOP4W9/+xsqKysxcOBAxMfHY9q0afjqq6+Qnp6OX3/9FVu2bMH58+cRGRkJwHLZ8t27d2Pjxo344x//2H8nSUTdYhJCRC7N19fXmoAAQHh4OGJiYjBw4ECbbVVVVQCAb7/9FmazGaNGjbI5jsFgQEhISP8ETUR2YRJCRC7Nx8fH5r4kSZ1uM5lMAACTyQSVSoWioiKoVCqb/a5PXIhIPCYhRCSMWq22KSh1hokTJ8JoNKKqqgqpqalOPTYRORdXxxCRMDExMTh8+DDOnj2L6upq62hGb4waNQpPP/005syZg+3bt+PMmTM4cuQIVq5cifz8fCdETUTOwiSEiIRZtGgRVCoV4uPjERYWhtLSUqccd+PGjZgzZw7+/d//HXFxcfiXf/kXHD58GFFRUU45PhE5BzumEhERkRAcCSEiIiIhmIQQERGREExCiIiISAgmIURERCQEkxAiIiISgkkIERERCcEkhIiIiIRgEkJERERCMAkhIiIiIZiEEBERkRBMQoiIiEgIJiFEREQkxP8HQtQ+l9CJtWcAAAAASUVORK5CYII=", | |
| "text/plain": [ | |
| "Figure(PyObject <Figure size 600x300 with 1 Axes>)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "┌ Warning: PyPlot is using tkagg backend, which is known to cause crashes on MacOS (#410); use the MPLBACKEND environment variable to request a different backend.\n", | |
| "└ @ PyPlot /Users/goerz/.julia/packages/PyPlot/XaELc/src/init.jl:198\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "using PyPlot\n", | |
| "matplotlib.use(\"Agg\")\n", | |
| "\n", | |
| "function plot_control(pulse::Vector, tlist)\n", | |
| " fig, ax = matplotlib.pyplot.subplots(figsize = (6, 3))\n", | |
| " ax.plot(tlist, pulse)\n", | |
| " ax.set_xlabel(\"time\")\n", | |
| " ax.set_ylabel(\"amplitude\")\n", | |
| " return fig\n", | |
| "end\n", | |
| "\n", | |
| "plot_control(ϵ::T, tlist) where {T<:Function} = plot_control([ϵ(t) for t in tlist], tlist)\n", | |
| "\n", | |
| "plot_control(H[2][2], tlist);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Optimization target" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The `GRAPE` package requires the goal of the optimization to be described by a\n", | |
| "list of `Objective` instances. In this example, there is only a single\n", | |
| "objective: the state-to-state transfer from initial state $\\ket{\\Psi_{\\init}} =\n", | |
| "\\ket{0}$ to the target state $\\ket{\\Psi_{\\tgt}} = \\ket{1}$, under the dynamics\n", | |
| "of the Hamiltonian $\\op{H}(t)$:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:51.652000-04:00", | |
| "start_time": "2021-10-17T06:16:51.460Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "function ket(label)\n", | |
| " result = Dict(\"0\" => Vector{ComplexF64}([1, 0]), \"1\" => Vector{ComplexF64}([0, 1]))\n", | |
| " return result[string(label)]\n", | |
| "end;" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:52.779000-04:00", | |
| "start_time": "2021-10-17T06:16:52.410Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "objectives = [Objective(initial_state = ket(0), generator = H, target_state = ket(1))];" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:16:53.444000-04:00", | |
| "start_time": "2021-10-17T06:16:53.358Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "problem = ControlProblem(\n", | |
| " objectives = objectives,\n", | |
| " tlist = tlist,\n", | |
| " pulse_options=Dict(),\n", | |
| " iter_stop = 50,\n", | |
| " J_T = QuantumControl.functionals.J_T_sm,\n", | |
| " gradient=QuantumControl.functionals.grad_J_T_sm!,\n", | |
| " check_convergence = res -> begin\n", | |
| " ((res.J_T < 1e-3) && (res.converged = true) && (res.message = \"J_T < 10⁻³\"))\n", | |
| " end,\n", | |
| ");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Simulate dynamics under the guess field" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Before running the optimization procedure, we first simulate the dynamics under the\n", | |
| "guess field $\\epsilon_{0}(t)$. The following solves equation of motion for the\n", | |
| "defined objective, which contains the initial state $\\ket{\\Psi_{\\init}}$ and\n", | |
| "the Hamiltonian $\\op{H}(t)$ defining its evolution." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:04.254000-04:00", | |
| "start_time": "2021-10-17T06:16:54.736Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "2×500 Matrix{Float64}:\n", | |
| " 1.0 1.0 1.0 1.0 … 0.951457 0.951459 0.951459\n", | |
| " 0.0 7.73456e-40 2.03206e-11 2.96638e-10 0.0485427 0.048541 0.048541" | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "guess_dynamics = propagate(\n", | |
| " objectives[1],\n", | |
| " problem.tlist;\n", | |
| " storage = true,\n", | |
| " observables = (Ψ -> abs.(Ψ) .^ 2,),\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:05.948000-04:00", | |
| "start_time": "2021-10-17T06:17:05.620Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "Figure(PyObject <Figure size 600x300 with 1 Axes>)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "function plot_population(pop0::Vector, pop1::Vector, tlist)\n", | |
| " fig, ax = matplotlib.pyplot.subplots(figsize = (6, 3))\n", | |
| " ax.plot(tlist, pop0, label = \"0\")\n", | |
| " ax.plot(tlist, pop1, label = \"1\")\n", | |
| " ax.legend()\n", | |
| " ax.set_xlabel(\"time\")\n", | |
| " ax.set_ylabel(\"population\")\n", | |
| " return fig\n", | |
| "end\n", | |
| "\n", | |
| "plot_population(guess_dynamics[1,:], guess_dynamics[2,:], tlist);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Optimize" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "In the following we optimize the guess field $\\epsilon_{0}(t)$ such\n", | |
| "that the intended state-to-state transfer $\\ket{\\Psi_{\\init}} \\rightarrow\n", | |
| "\\ket{\\Psi_{\\tgt}}$ is solved.\n", | |
| "\n", | |
| "**The control parameters are the values of the control function $\\epsilon(t)$ discretized on the intervals of the time grid (`tlist`). The GRAPE algorithm calculates the gradient vector for these control parameters and feeds them to LBFGS for optimization.**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:23.888000-04:00", | |
| "start_time": "2021-10-17T06:17:07.789Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Iter Function value Gradient norm \n", | |
| " 0 9.514590e-01 3.910143e-03\n", | |
| " * time: 0.02400994300842285\n", | |
| " 1 9.184734e-01 5.483692e-03\n", | |
| " * time: 2.6363608837127686\n", | |
| " 2 9.137205e-01 5.620519e-03\n", | |
| " * time: 2.6885340213775635\n", | |
| " 3 2.023652e-02 2.815586e-03\n", | |
| " * time: 2.7280919551849365\n", | |
| " 4 8.817667e-04 7.593890e-05\n", | |
| " * time: 2.807523012161255\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Note: set extended_trace=true to see the \"step width\"\n", | |
| "opt_result = optimize_grape(problem, show_trace=true, extended_trace=false, info_hook=(args...) -> nothing);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:25.092000-04:00", | |
| "start_time": "2021-10-17T06:17:24.781Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "GRAPE Optimization Result\n", | |
| "-------------------------\n", | |
| "- Started at 2021-10-17T02:17:15.365\n", | |
| "- Number of objectives: 1\n", | |
| "- Number of iterations: 4\n", | |
| "- Reason for termination: J_T < 10⁻³\n", | |
| "- Ended at 2021-10-17T02:17:23.887 (8522 milliseconds)" | |
| ] | |
| }, | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "opt_result" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can plot the optimized field:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:26.742000-04:00", | |
| "start_time": "2021-10-17T06:17:26.580Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "Figure(PyObject <Figure size 600x300 with 1 Axes>)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plot_control(opt_result.optimized_controls[1], tlist);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Simulate the dynamics under the optimized field" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Having obtained the optimized control field, we can simulate the dynamics to\n", | |
| "verify that the optimized field indeed drives the initial state\n", | |
| "$\\ket{\\Psi_{\\init}} = \\ket{0}$ to the desired target state\n", | |
| "$\\ket{\\Psi_{\\tgt}} = \\ket{1}$." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:28.890000-04:00", | |
| "start_time": "2021-10-17T06:17:28.135Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "2×500 Matrix{Float64}:\n", | |
| " 1.0 0.80614 0.399254 0.0774892 … 0.599465 0.193522 0.000881767\n", | |
| " 0.0 0.19386 0.600746 0.922511 0.400535 0.806478 0.999118" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "opt_dynamics = propagate(\n", | |
| " objectives[1],\n", | |
| " problem.tlist;\n", | |
| " controls_map = IdDict(ϵ => opt_result.optimized_controls[1]),\n", | |
| " storage = true,\n", | |
| " observables = (Ψ -> abs.(Ψ) .^ 2,),\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2021-10-17T02:17:29.692000-04:00", | |
| "start_time": "2021-10-17T06:17:29.476Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "Figure(PyObject <Figure size 600x300 with 1 Axes>)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plot_population(opt_dynamics[1,:], opt_dynamics[2,:], tlist);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "---\n", | |
| "\n", | |
| "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "hide_input": false, | |
| "kernelspec": { | |
| "display_name": "Julia 1.6.1", | |
| "language": "julia", | |
| "name": "julia-1.6" | |
| }, | |
| "language_info": { | |
| "file_extension": ".jl", | |
| "mimetype": "application/julia", | |
| "name": "julia", | |
| "version": "1.6.1" | |
| }, | |
| "toc": { | |
| "base_numbering": 1, | |
| "nav_menu": {}, | |
| "number_sections": true, | |
| "sideBar": true, | |
| "skip_h1_title": false, | |
| "title_cell": "Table of Contents", | |
| "title_sidebar": "Contents", | |
| "toc_cell": false, | |
| "toc_position": {}, | |
| "toc_section_display": true, | |
| "toc_window_display": false | |
| }, | |
| "varInspector": { | |
| "cols": { | |
| "lenName": 16, | |
| "lenType": 16, | |
| "lenVar": 40 | |
| }, | |
| "kernels_config": { | |
| "python": { | |
| "delete_cmd_postfix": "", | |
| "delete_cmd_prefix": "del ", | |
| "library": "var_list.py", | |
| "varRefreshCmd": "print(var_dic_list())" | |
| }, | |
| "r": { | |
| "delete_cmd_postfix": ") ", | |
| "delete_cmd_prefix": "rm(", | |
| "library": "var_list.r", | |
| "varRefreshCmd": "cat(var_dic_list()) " | |
| } | |
| }, | |
| "types_to_exclude": [ | |
| "module", | |
| "function", | |
| "builtin_function_or_method", | |
| "instance", | |
| "_Feature" | |
| ], | |
| "window_display": false | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 3 | |
| } |
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