typos ci

.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"

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

src/Hydraulic/IsothermalCompressible/components.jl

@@ -497,7 +497,7 @@ dm ────►               │  │ area
     name)
     @assert(N>=0,
         "the Tube component must be defined with 0 or more segments (i.e. N>=0), found N=$N")
-    @assert (direction == +1)||(direction == -1) "direction arument must be +/-1, found $direction"
+    @assert (direction == +1)||(direction == -1) "direction argument must be +/-1, found $direction"
 
     #TODO: How to set an assert effective_length >= length ??
     pars = @parameters begin

src/ModelingToolkitStandardLibrary.jl

@@ -8,7 +8,7 @@ Omits the check expression if the argument `J` is symbolic.
 """
 macro symcheck(ex)
     ex.args[1].head === :call ||
-        error("Expected an expresion on the form sym > val || error()")
+        error("Expected an expression on the form sym > val || error()")
     sym = ex.args[1].args[2]
     quote
         _issymbolic(x) = !(unwrap(x) isa Real)

test/Blocks/continuous.jl

@@ -7,7 +7,7 @@ using Test
 
 #=
 Testing strategy:
-The general strategy is to test systems using simple intputs where the solution
+The general strategy is to test systems using simple inputs where the solution
 is known on closed form. For algebraic systems (without differential variables),
 an integrator with a constant input is often used together with the system under test.
 =#

test/Blocks/test_analysis_points.jl

@@ -143,7 +143,7 @@ lsys = sminreal(ss(matrices...))
 
 matrices_So, _ = get_sensitivity(sys_outer, :inner_plant_output)
 lsyso = sminreal(ss(matrices_So...))
-@test lsys == lsyso || lsys == -1 * lsyso * (-1) # Output and input sensitivites are equal for SISO systems
+@test lsys == lsyso || lsys == -1 * lsyso * (-1) # Output and input sensitivities are equal for SISO systems
 
 ## A more complicated test case
 using ModelingToolkit, OrdinaryDiffEq, LinearAlgebra

test/Electrical/digital.jl

@@ -32,7 +32,7 @@ using OrdinaryDiffEq: ReturnCode.Success
     @test typeof(logic.logic) == Vector{Logic}
     @test get_logic_level(logic) == [1, 6, 2, 4]
 
-    # Predefiend logic vectors
+    # Predefined logic vectors
     @test std_ulogic.logic == [U, X, F0, F1, Z, W, L, H, DC]
     @test UX01.logic == [U, X, F0, F1]
     @test UX01Z.logic == [U, X, F0, F1, Z]