Merge branch 'master' into myb/uni

Author: YingboMa

.JuliaFormatter.toml

@@ -1 +1,2 @@
 style = "sciml"
+format_markdown = true

.github/workflows/Downstream.yml

@@ -26,6 +26,9 @@ jobs:
           - {user: SciML, repo: StructuralIdentifiability.jl, group: All}
           - {user: SciML, repo: ModelingToolkitStandardLibrary.jl}
           - {user: SciML, repo: ModelOrderReduction.jl, group: All}
+          - {user: SciML, repo: MethodOfLines.jl, group: Interface}
+          - {user: SciML, repo: MethodOfLines.jl, group: 2D_Diffusion}
+          - {user: SciML, repo: MethodOfLines.jl, group: DAE}
           - {user: ai4energy, repo: Ai4EComponentLib.jl, group: Downstream}
     steps:
       - uses: actions/checkout@v2

CONTRIBUTING.md

@@ -1,3 +1,3 @@
-- This repository follows the [SciMLStyle](https://github.com/SciML/SciMLStyle) and the SciML [ColPrac](https://github.com/SciML/ColPrac).
-- Please run `using JuliaFormatter, ModelingToolkit; format(joinpath(dirname(pathof(ModelingToolkit)), ".."))` before commiting.
-- Add tests for any new features.
+  - This repository follows the [SciMLStyle](https://github.com/SciML/SciMLStyle) and the SciML [ColPrac](https://github.com/SciML/ColPrac).
+  - Please run `using JuliaFormatter, ModelingToolkit; format(joinpath(dirname(pathof(ModelingToolkit)), ".."))` before commiting.
+  - Add tests for any new features.

LICENSE.md

@@ -2,43 +2,36 @@ The ModelingToolkit.jl package is licensed under the MIT "Expat" License:
 
 > Copyright (c) 2018-22: Yingbo Ma, Christopher Rackauckas, Julia Computing, and
 > contributors
->
->
+> 
 > Permission is hereby granted, free of charge, to any person obtaining a copy
->
+> 
 > of this software and associated documentation files (the "Software"), to deal
->
+> 
 > in the Software without restriction, including without limitation the rights
->
+> 
 > to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
->
+> 
 > copies of the Software, and to permit persons to whom the Software is
->
+> 
 > furnished to do so, subject to the following conditions:
->
->
->
+> 
 > The above copyright notice and this permission notice shall be included in all
->
+> 
 > copies or substantial portions of the Software.
->
->
->
+> 
 > THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
->
+> 
 > IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
->
+> 
 > FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
->
+> 
 > AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
->
+> 
 > LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
->
+> 
 > OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
->
+> 
 > SOFTWARE.
->
->
 
 The code in `src/structural_transformation/bipartite_tearing/modia_tearing.jl`,
 which is from the [Modia.jl](https://github.com/ModiaSim/Modia.jl) project, is

NEWS.md

@@ -1,10 +1,9 @@
 # ModelingToolkit v8 Release Notes
 
-
 ### Upgrade guide
 
-- `connect` should not be overloaded by users anymore. `[connect = Flow]`
-  informs ModelingToolkit that particular variable in a connector ought to sum
-  to zero, and by default, variables are equal in a connection. Please check out
-  [acausal components tutorial](https://docs.sciml.ai/ModelingToolkit/stable/tutorials/acausal_components/)
-  for examples.
+  - `connect` should not be overloaded by users anymore. `[connect = Flow]`
+    informs ModelingToolkit that particular variable in a connector ought to sum
+    to zero, and by default, variables are equal in a connection. Please check out
+    [acausal components tutorial](https://docs.sciml.ai/ModelingToolkit/stable/tutorials/acausal_components/)
+    for examples.

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelingToolkit"
 uuid = "961ee093-0014-501f-94e3-6117800e7a78"
 authors = ["Yingbo Ma <[email protected]>", "Chris Rackauckas <[email protected]> and contributors"]
-version = "8.40.0"
+version = "8.46.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -87,6 +87,7 @@ julia = "1.6"
 [extras]
 AmplNLWriter = "7c4d4715-977e-5154-bfe0-e096adeac482"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+ControlSystemsMTK = "687d7614-c7e5-45fc-bfc3-9ee385575c88"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 Ipopt_jll = "9cc047cb-c261-5740-88fc-0cf96f7bdcc7"
@@ -107,4 +108,4 @@ Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["AmplNLWriter", "BenchmarkTools", "NonlinearSolve", "ForwardDiff", "Ipopt", "Ipopt_jll", "ModelingToolkitStandardLibrary", "Optimization", "OptimizationOptimJL", "OptimizationMOI", "OrdinaryDiffEq", "Random", "ReferenceTests", "SafeTestsets", "StableRNGs", "Statistics", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials"]
+test = ["AmplNLWriter", "BenchmarkTools", "ControlSystemsMTK", "NonlinearSolve", "ForwardDiff", "Ipopt", "Ipopt_jll", "ModelingToolkitStandardLibrary", "Optimization", "OptimizationOptimJL", "OptimizationMOI", "OrdinaryDiffEq", "Random", "ReferenceTests", "SafeTestsets", "StableRNGs", "Statistics", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials"]

README.md

@@ -1,14 +1,13 @@
 # ModelingToolkit.jl
 
-
 [![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
 [![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/ModelingToolkit/stable/)
 
 [![codecov](https://codecov.io/gh/SciML/ModelingToolkit.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/ModelingToolkit.jl)
 [![Coverage Status](https://coveralls.io/repos/github/SciML/ModelingToolkit.jl/badge.svg?branch=master)](https://coveralls.io/github/SciML/ModelingToolkit.jl?branch=master)
 [![Build Status](https://github.com/SciML/ModelingToolkit.jl/workflows/CI/badge.svg)](https://github.com/SciML/ModelingToolkit.jl/actions?query=workflow%3ACI)
 
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
 ModelingToolkit.jl is a modeling framework for high-performance symbolic-numeric computation
@@ -121,7 +120,9 @@ plot(sol, idxs = (a, lorenz1.x, lorenz2.z))
 ![](https://user-images.githubusercontent.com/17304743/187790221-528046c3-dbdb-4853-b977-799596c147f3.png)
 
 # Citation
+
 If you use ModelingToolkit.jl in your research, please cite [this paper](https://arxiv.org/abs/2103.05244):
+
 ```
 @misc{ma2021modelingtoolkit,
       title={ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling},

docs/Project.toml

@@ -11,6 +11,8 @@ Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
+StructuralIdentifiability = "220ca800-aa68-49bb-acd8-6037fa93a544"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
@@ -27,6 +29,8 @@ Optimization = "3.9"
 OptimizationOptimJL = "0.1"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
+StochasticDiffEq = "6"
+StructuralIdentifiability = "0.4"
 SymbolicUtils = "0.19"
 Symbolics = "4.13"
 Unitful = "1.12"

docs/pages.jl

@@ -28,6 +28,7 @@ pages = [
                           "systems/JumpSystem.md",
                           "systems/NonlinearSystem.md",
                           "systems/OptimizationSystem.md",
+                          "systems/DiscreteSystem.md",
                           "systems/PDESystem.md"],
     "comparison.md",
     "internals.md",

docs/src/basics/AbstractSystem.md

@@ -10,65 +10,66 @@ model manipulation and compilation.
 ### Subtypes
 
 There are three immediate subtypes of `AbstractSystem`, classified by how many independent variables each type has:
-* `AbstractTimeIndependentSystem`: has no independent variable (eg: `NonlinearSystem`)
-* `AbstractTimeDependentSystem`: has a single independent variable (eg: `ODESystem`)
-* `AbstractMultivariateSystem`: may have multiple independent variables (eg: `PDESystem`)
+
+  - `AbstractTimeIndependentSystem`: has no independent variable (e.g.: `NonlinearSystem`)
+  - `AbstractTimeDependentSystem`: has a single independent variable (e.g.: `ODESystem`)
+  - `AbstractMultivariateSystem`: may have multiple independent variables (e.g.: `PDESystem`)
 
 ## Constructors and Naming
 
 The `AbstractSystem` interface has a consistent method for constructing systems.
-Generally it follows the order of:
+Generally, it follows the order of:
 
-1. Equations
-2. Independent Variables
-3. Dependent Variables (or States)
-4. Parameters
+ 1. Equations
+ 2. Independent Variables
+ 3. Dependent Variables (or States)
+ 4. Parameters
 
 All other pieces are handled via keyword arguments. `AbstractSystem`s share the
 same keyword arguments, which are:
 
-- `system`: This is used for specifying subsystems for hierarchical modeling with
-  reusable components. For more information, see the [components page](@ref components)
-- Defaults: Keyword arguments like `defaults` are used for specifying default
-  values which are used. If a value is not given at the `SciMLProblem` construction
-  time, its numerical value will be the default.
+  - `system`: This is used for specifying subsystems for hierarchical modeling with
+    reusable components. For more information, see the [components page](@ref components).
+  - Defaults: Keyword arguments like `defaults` are used for specifying default
+    values which are used. If a value is not given at the `SciMLProblem` construction
+    time, its numerical value will be the default.
 
 ## Composition and Accessor Functions
 
 Each `AbstractSystem` has lists of variables in context, such as distinguishing
-parameters vs states. In addition, an `AbstractSystem` also can hold other
+parameters vs states. In addition, an `AbstractSystem` can also hold other
 `AbstractSystem` types. Direct accessing of the values, such as `sys.states`,
 gives the immediate list, while the accessor functions `states(sys)` gives the
 total set, which includes that of all systems held inside.
 
 The values which are common to all `AbstractSystem`s are:
 
-- `equations(sys)`: All equations that define the system and its subsystems.
-- `states(sys)`: All the states in the system and its subsystems.
-- `parameters(sys)`: All parameters of the system and its subsystems.
-- `nameof(sys)`: The name of the current-level system.
-- `get_eqs(sys)`: Equations that define the current-level system.
-- `get_states(sys)`: States that are in the current-level system.
-- `get_ps(sys)`: Parameters that are in the current-level system.
-- `get_systems(sys)`: Subsystems of the current-level system.
+  - `equations(sys)`: All equations that define the system and its subsystems.
+  - `states(sys)`: All the states in the system and its subsystems.
+  - `parameters(sys)`: All parameters of the system and its subsystems.
+  - `nameof(sys)`: The name of the current-level system.
+  - `get_eqs(sys)`: Equations that define the current-level system.
+  - `get_states(sys)`: States that are in the current-level system.
+  - `get_ps(sys)`: Parameters that are in the current-level system.
+  - `get_systems(sys)`: Subsystems of the current-level system.
 
 Optionally, a system could have:
 
-- `observed(sys)`: All observed equations of the system and its subsystems.
-- `get_observed(sys)`: Observed equations of the current-level system.
-- `get_continuous_events(sys)`: `SymbolicContinuousCallback`s of the current-level system.
-- `get_defaults(sys)`: A `Dict` that maps variables into their default values.
-- `independent_variables(sys)`: The independent variables of a system.
-- `get_noiseeqs(sys)`: Noise equations of the current-level system.
-- `get_metadata(sys)`: Any metadata about the system or its origin to be used by downstream packages.
+  - `observed(sys)`: All observed equations of the system and its subsystems.
+  - `get_observed(sys)`: Observed equations of the current-level system.
+  - `get_continuous_events(sys)`: `SymbolicContinuousCallback`s of the current-level system.
+  - `get_defaults(sys)`: A `Dict` that maps variables into their default values.
+  - `independent_variables(sys)`: The independent variables of a system.
+  - `get_noiseeqs(sys)`: Noise equations of the current-level system.
+  - `get_metadata(sys)`: Any metadata about the system or its origin to be used by downstream packages.
 
-Note that if you know a system is an `AbstractTimeDependentSystem` you could use `get_iv` to get the 
+Note that if you know a system is an `AbstractTimeDependentSystem` you could use `get_iv` to get the
 unique independent variable directly, rather than using `independent_variables(sys)[1]`, which is clunky and may cause problems if `sys` is an `AbstractMultivariateSystem` because there may be more than one independent variable. `AbstractTimeIndependentSystem`s do not have a method `get_iv`, and `independent_variables(sys)` will return a size-zero result for such. For an `AbstractMultivariateSystem`, `get_ivs` is equivalent.
 
 A system could also have caches:
 
-- `get_jac(sys)`: The Jacobian of a system.
-- `get_tgrad(sys)`: The gradient with respect to time of a system.
+  - `get_jac(sys)`: The Jacobian of a system.
+  - `get_tgrad(sys)`: The gradient with respect to time of a system.
 
 ## Transformations
 
@@ -118,7 +119,7 @@ patterns via an abstract interpretation without requiring differentiation.
 
 At the end, the system types have `DEProblem` constructors, like `ODEProblem`,
 which allow for directly generating the problem types required for numerical
-methods. The first argument is always the `AbstractSystem`, and the proceeding
+methods. The first argument is always the `AbstractSystem`, and the proceding
 arguments match the argument order of their original constructors. Whenever an
 array would normally be provided, such as `u0` the initial condition of an
 `ODEProblem`, it is instead replaced with a variable map, i.e., an array of