Merge pull request #243 from ArnoStrouwen/qa

typos + Aqua ci

Author: ChrisRackauckas

.github/dependabot.yml

@@ -5,3 +5,6 @@ updates:
     directory: "/" # Location of package manifests
     schedule:
       interval: "weekly"
+    ignore:
+      - dependency-name: "crate-ci/typos"
+        update-types: ["version-update:semver-patch"]

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v3
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

@@ -0,0 +1,4 @@
+[default.extend-words]
+Nd = "Nd"
+nin = "nin"
+coul = "coul"

Project.toml

@@ -12,14 +12,22 @@ ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
+Aqua = "0.8"
 ChainRulesCore = "1"
+ControlSystemsBase = "1"
+DataInterpolations = "4"
 DiffEqBase = "6"
 IfElse = "0.1"
+LinearAlgebra = "1"
 ModelingToolkit = "8.67"
+OrdinaryDiffEq = "6"
+SafeTestsets = "0.1"
 Symbolics = "4.9, 5"
+Test = "1"
 julia = "1.6"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -28,4 +36,4 @@ SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["LinearAlgebra", "OrdinaryDiffEq", "SafeTestsets", "Test", "ControlSystemsBase", "DataInterpolations"]
+test = ["Aqua", "LinearAlgebra", "OrdinaryDiffEq", "SafeTestsets", "Test", "ControlSystemsBase", "DataInterpolations"]

README.md

@@ -32,7 +32,7 @@ the documentation, which contains the unreleased features.
 
 ## Libraries
 
-The following are the constituant libraries of the ModelingToolkit Standard Library.
+The following are the constituent libraries of the ModelingToolkit Standard Library.
 
   - [Basic Blocks](https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/API/blocks/)
   - [Mechanical Components](https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/API/mechanical/)

docs/src/index.md

@@ -21,7 +21,7 @@ Pkg.add("ModelingToolkitStandardLibrary")
 
 ## Libraries
 
-The following are the constituant libraries of the ModelingToolkit Standard Library.
+The following are the constituent libraries of the ModelingToolkit Standard Library.
 
   - [Basic Blocks](https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/API/blocks/)
   - [Mechanical Components](https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/API/mechanical/)

docs/src/tutorials/input_component.md

@@ -47,7 +47,7 @@ prob = ODEProblem(sys, [], (0, time[end]))
 sol = solve(prob, ImplicitEuler())
 ```
 
-If we want to run a new data set, this requires building a new `LinearInterpolation` and `ODESystem` followed by running `structural_simplify`, all of which takes time.  Therefore, to run serveral pieces of data it's better to re-use an `ODESystem`.  The next couple methods will demonstrate how to do this.
+If we want to run a new data set, this requires building a new `LinearInterpolation` and `ODESystem` followed by running `structural_simplify`, all of which takes time.  Therefore, to run several pieces of data it's better to re-use an `ODESystem`.  The next couple methods will demonstrate how to do this.
 
 ## Custom Component with External Data
 
@@ -105,7 +105,7 @@ Additional code could be added to resolve this issue, for example by using a `Re
 
 ## `SampledData` Component
 
-To resolve the issues presented above, the `ModelingToolkitStandardLibrary.Blocks.SampledData` component can be used which allows for a resusable `ODESystem` and self contained data which ensures a solution which remains valid for it's lifetime.  Now it's possible to also parallize the call to `solve()`.
+To resolve the issues presented above, the `ModelingToolkitStandardLibrary.Blocks.SampledData` component can be used which allows for a resusable `ODESystem` and self contained data which ensures a solution which remains valid for it's lifetime.  Now it's possible to also parallelize the call to `solve()`.
 
 ```julia
 function System(; name)
@@ -146,4 +146,4 @@ sol2 = Ref{ODESolution}()
 end
 ```
 
-Note, in the above example, we can build the system with an empty `SampledData` component, only setting the expected data type: `@named src = SampledData(Float64)`.  It's also possible to initialize the component with real sampled data: `@named src = SampledData(data, dt)`.  Additionally note that before running an `ODEProblem` using the `SampledData` component, one must be careful about the parameter vector Type.  The `SampledData` component contains a `buffer` parameter of type `Parameter`, therefore we must generate the problem using `tofloat=false`.  This will initially give a parameter vector of type `Vector{Any}` with a mix of numbers and `Parameter` type.  We can convert the vector to a uniform `Parameter` type by running `p = Parameter.(p)`.  This will wrap all the single values in a `Parameter` which will be mathmatically equivalent to a `Number`.
+Note, in the above example, we can build the system with an empty `SampledData` component, only setting the expected data type: `@named src = SampledData(Float64)`.  It's also possible to initialize the component with real sampled data: `@named src = SampledData(data, dt)`.  Additionally note that before running an `ODEProblem` using the `SampledData` component, one must be careful about the parameter vector Type.  The `SampledData` component contains a `buffer` parameter of type `Parameter`, therefore we must generate the problem using `tofloat=false`.  This will initially give a parameter vector of type `Vector{Any}` with a mix of numbers and `Parameter` type.  We can convert the vector to a uniform `Parameter` type by running `p = Parameter.(p)`.  This will wrap all the single values in a `Parameter` which will be mathematically equivalent to a `Number`.

src/Blocks/analysis_points.jl

@@ -52,7 +52,7 @@ eqs = [connect(P.output, C.input)
        connect(C.output, :plant_input, P.input)]
 sys = ODESystem(eqs, t, systems = [P, C], name = :feedback_system)
 
-matrices_S, _ = get_sensitivity(sys, :plant_input) # Compute the matrices of a state-space representation of the (input) sensitivity funciton.
+matrices_S, _ = get_sensitivity(sys, :plant_input) # Compute the matrices of a state-space representation of the (input) sensitivity function.
 matrices_T, _ = get_comp_sensitivity(sys, :plant_input)
 ```
 

src/Blocks/continuous.jl

@@ -572,7 +572,7 @@ To set the initial state, it's recommended to set the initial condition for `x`,
 
 # Parameters:
 - `b`: Numerator polynomial coefficients, e.g., `2s + 3` is specified as `[2, 3]`
-- `a`: Denomenator polynomial coefficients, e.g., `s² + 2ωs + ω^2` is specified as `[1, 2ω, ω^2]`
+- `a`: Denominator polynomial coefficients, e.g., `s² + 2ωs + ω^2` is specified as `[1, 2ω, ω^2]`
 
 # Connectors:
   - `input`

src/Electrical/Digital/logic.jl

@@ -1,10 +1,10 @@
-@enum Logic Uninitialized=1 ForcingUnknown ForcingZero ForcingOne HighImpedence WeakUnknown WeakZero WeakOne DontCare
+@enum Logic Uninitialized=1 ForcingUnknown ForcingZero ForcingOne HighImpedance WeakUnknown WeakZero WeakOne DontCare
 
 const U = Uninitialized
 const X = ForcingUnknown
 const F0 = ForcingZero
 const F1 = ForcingOne
-const Z = HighImpedence
+const Z = HighImpedance
 const W = WeakUnknown
 const L = WeakZero
 const H = WeakOne