Merge branch 'master' into patch-1

.github/workflows/Downstream.yml

@@ -21,7 +21,7 @@ jobs:
           - {user: SciML, repo: Catalyst.jl, group: All}
           - {user: SciML, repo: CellMLToolkit.jl, group: All}
           - {user: SciML, repo: NeuralPDE.jl, group: NNPDE}
-          - {user: SciML, repo: DataDrivenDiffEq.jl, group: Standard}
+          - {user: SciML, repo: DataDrivenDiffEq.jl, group: Core}
           - {user: SciML, repo: ModelOrderReduction.jl, group: All}
 
     steps:

Project.toml

@@ -1,10 +1,10 @@
 name = "Symbolics"
 uuid = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 authors = ["Shashi Gowda <gowda@mit.edu>"]
-version = "4.13.0"
+version = "5.1.0"
 
 [deps]
-ArrayInterfaceCore = "30b0a656-2188-435a-8636-2ec0e6a096e2"
+ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 DiffRules = "b552c78f-8df3-52c6-915a-8e097449b14b"
@@ -21,7 +21,6 @@ Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
-Metatheory = "e9d8d322-4543-424a-9be4-0cc815abe26c"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -33,25 +32,23 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
-TermInterface = "8ea1fca8-c5ef-4a55-8b96-4e9afe9c9a3c"
 TreeViews = "a2a6695c-b41b-5b7d-aed9-dbfdeacea5d7"
 
 [compat]
-ArrayInterfaceCore = "0.1.1"
+ArrayInterface = "6, 7"
 ConstructionBase = "1.1, 1.2"
 DataStructures = "0.18"
 DiffRules = "1.4"
 Distributions = "0.23, 0.24, 0.25"
 DocStringExtensions = "0.7, 0.8, 0.9"
 DomainSets = "0.5"
-Groebner = "0.1, 0.2"
+Groebner = "0.3"
 IfElse = "0.1"
 IntervalSets = "0.5, 0.6, 0.7"
 LaTeXStrings = "1.3"
 LambertW = "0.4.5"
 Latexify = "0.11, 0.12, 0.13, 0.14, 0.15"
 MacroTools = "0.5"
-Metatheory = "1.2.0"
 NaNMath = "0.3, 1"
 RecipesBase = "1.1"
 Reexport = "0.2, 1"
@@ -62,8 +59,7 @@ SciMLBase = "1.8"
 Setfield = "0.7, 0.8, 1"
 SpecialFunctions = "0.7, 0.8, 0.9, 0.10, 1.0, 2"
 StaticArrays = "1.1"
-SymbolicUtils = "0.18, 0.19"
-TermInterface = "0.2, 0.3"
+SymbolicUtils = "1.0.1"
 TreeViews = "0.3"
 julia = "1.6"
 

docs/Project.toml

@@ -15,5 +15,5 @@ Latexify = "0.15"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
 StaticArrays = "1.5"
-SymbolicUtils = "0.19"
-Symbolics = "4.13"
+SymbolicUtils = "1"
+Symbolics = "5"

docs/make.jl

@@ -33,11 +33,14 @@ makedocs(
                              #canonical="https://mtk.sciml.ai/stable/"),
     pages=[
         "Home" => "index.md",
+        "getting_started.md",
         "Tutorials" => Any[
-            "tutorials/symbolic_functions.md",
             "tutorials/auto_parallel.md",
             "tutorials/converting_to_C.md"
         ],
+        "Examples" => Any[
+            "examples/perturbation.md"
+        ],
         "Manual" => Any[
             "manual/variables.md",
             "manual/expression_manipulation.md",
@@ -46,6 +49,7 @@ makedocs(
             "manual/arrays.md",
             "manual/build_function.md",
             "manual/functions.md",
+            "manual/parsing.md",
             "manual/io.md",
             "manual/sparsity_detection.md",
             "manual/types.md",

docs/src/comparison.md

@@ -1,19 +1,19 @@
 # Comparison of Julia's Symbolics.jl vs SymPy for Symbolic Computation
 
-Symbolics.jl is a symbolic modeling language for Julia built in
+Symbolics.jl is a symbolic modeling language for Julia, built in
 Julia. Its goal is very different from Sympy: it was made to support
 symbolic-numerics, the combination of symbolic computing with numerical
 methods to allow for extreme performance computing that would not be
 possible without modifying the model. Because of this, Symbolics.jl
 excels in many areas due to purposeful design decisions:
 
 - Performance: Symbolics.jl is built in Julia, whereas SymPy was
-  built in Python. Thus the performance bar for Symbolics.jl is
+  built in Python. Thus, the performance bar for Symbolics.jl is
   much higher. Symbolics.jl started because SymPy was far too
   slow and SymEngine was far too inflexible for the projects they were
   doing. Performance is key to Symbolics.jl. If you find any
   performance issues, please file an issue.
-- `build_function`: `lambdify` is "fine" for some people, but if you're building
+- `build_function`: `lambdify` is “fine” for some people, but if you're building
   a super fast MPI-enabled Julia/C/Fortran simulation code, having a
   function that hits the Python interpreter is less than optimal. By
   default, `build_function` builds fast JIT-compiled functions due
@@ -24,25 +24,25 @@ excels in many areas due to purposeful design decisions:
 - Parallelism: Symbolics.jl has pervasive parallelism. The
   symbolic simplification via [SymbolicUtils.jl](https://github.com/JuliaSymbolics/SymbolicUtils.jl)
   has built-in parallelism, Symbolics.jl builds functions that
-  parallelizes across threads. Symbolics.jl is compatible with GPU libraries like CUDA.jl.
-- Extendability: All of Symbolics.jl and its underlying tools are written in
+  parallelize across threads. Symbolics.jl is compatible with GPU libraries like CUDA.jl.
+- Extensible: Symbolics.jl and its underlying tools are written in
   pure Julia. Want to add new or better simplification rules? Add some Julia code!
   Need to add new derivatives? Add some Julia code! You get the picture. Breaking
   down these barriers makes it easier for the user to tailor the program to their
   needs and accelerates the development of the library.
 - Deep integration with the Julia ecosystem: Symbolics.jl's integration
   with neural networks is not the only thing that's deep. Symbolics.jl
   is built with the same philosophy as other SciML packages, eschewing
-  "monorepos" for a distributed development approach that ties together
+  “monorepos” for a distributed development approach that ties together
   the work of many developers. The differentiation parts utilize tools
   from automatic differentiation libraries, all linear algebra functionality
   comes from tracing Julia Base itself, symbolic rewriting (simplification
   and substitution) comes from
   [SymbolicUtils.jl](https://github.com/JuliaSymbolics/SymbolicUtils.jl),
-  parallelism comes from Julia Base libraries and Dagger.jl, and etc.
+  parallelism comes from Julia Base libraries, Dagger.jl, etc.
   SciML Tools like
   [DataDrivenDiffEq.jl](https://datadriven.sciml.ai/dev/) can reconstruct
-  symbolic expressions from neural networks and data while
+  symbolic expressions from neural networks and data, while
   [NeuralPDE.jl](https://github.com/SciML/NeuralPDE.jl)
   can automatically solve partial differential equations from symbolic
   descriptions using physics-informed neural networks.