Merge pull request #2091 from ArnoStrouwen/links

strict links

Author: ChrisRackauckas

docs/make.jl

@@ -22,7 +22,8 @@ mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/ma
 makedocs(sitename = "ModelingToolkit.jl",
          authors = "Chris Rackauckas",
          modules = [ModelingToolkit],
-         clean = true, doctest = false,
+         clean = true, doctest = false, linkcheck = true,
+         linkcheck_ignore = ["https://epubs.siam.org/doi/10.1137/0903023"],
          strict = [
              :doctest,
              :linkcheck,

docs/src/basics/Linearization.md

@@ -59,7 +59,7 @@ Input derivatives appeared in expressions (-g_z\g_u != 0)
 
 This means that to simulate this system, some order of derivatives of the input is required. To allow `linearize` to proceed in this situation, one may pass the keyword argument `allow_input_derivatives = true`, in which case the resulting model will have twice as many inputs, ``2n_u``, where the last ``n_u`` inputs correspond to ``\dot u``.
 
-If the modeled system is actually proper (but MTK failed to find a proper realization), further numerical simplification can be applied to the resulting statespace system to obtain a proper form. Such simplification is currently available in the experimental package [ControlSystemsMTK](https://github.com/baggepinnen/ControlSystemsMTK.jl#internals-transformation-of-non-proper-models-to-proper-statespace-form).
+If the modeled system is actually proper (but MTK failed to find a proper realization), further numerical simplification can be applied to the resulting statespace system to obtain a proper form. Such simplification is currently available in the package [ControlSystemsMTK](https://juliacontrol.github.io/ControlSystemsMTK.jl/dev/#Internals:-Transformation-of-non-proper-models-to-proper-statespace-form).
 
 ## Tools for linear analysis
 

docs/src/examples/modelingtoolkitize_index_reduction.md

@@ -115,7 +115,7 @@ But that's not a satisfying answer, so what do you do about it?
 
 It turns out that higher order DAEs can be transformed into lower order DAEs.
 [If you differentiate the last equation two times and perform a substitution,
-you can arrive at the following set of equations](https://courses.seas.harvard.edu/courses/am205/g_act/dae_notes.pdf):
+you can arrive at the following set of equations](https://people.math.wisc.edu/%7Echr/am205/g_act/DAE_slides.pdf):
 
 ```math
 \begin{aligned}

docs/src/examples/perturbation.md

@@ -2,7 +2,7 @@
 
 ## Prelims
 
-In the previous tutorial, [Mixed Symbolic-Numeric Perturbation Theory](https://symbolics.juliasymbolics.org/stable/examples/perturbation), we discussed how to solve algebraic equations using **Symbolics.jl**. Here, our goal is to extend the method to differential equations. First, we import the following helper functions that were introduced in [Mixed Symbolic/Numerical Methods for Perturbation Theory - Algebraic Equations](@ref perturb_alg):
+In the previous tutorial, [Mixed Symbolic-Numeric Perturbation Theory](https://symbolics.juliasymbolics.org/stable/examples/perturbation/), we discussed how to solve algebraic equations using **Symbolics.jl**. Here, our goal is to extend the method to differential equations. First, we import the following helper functions that were introduced in [Mixed Symbolic/Numerical Methods for Perturbation Theory - Algebraic Equations](@ref perturb_alg):
 
 ```julia
 using Symbolics, SymbolicUtils

docs/src/tutorials/acausal_components.md

@@ -113,7 +113,7 @@ At the top, we start with defining the fundamental qualities of an electric
 circuit component. At every input and output pin, a circuit component has
 two values: the current at the pin and the voltage. Thus we define the `Pin`
 component (connector) to simply be the values there. Whenever two `Pin`s in a
-circuit are connected together, the system satisfies [Kirchhoff's laws](https: //en.wikipedia.org/wiki/Kirchhoff%27s_circuit_laws),
+circuit are connected together, the system satisfies [Kirchhoff's laws](https://en.wikipedia.org/wiki/Kirchhoff%27s_circuit_laws),
 i.e. that currents sum to zero and voltages across the pins are equal.
 `[connect = Flow]` informs MTK that currents ought to sum to zero, and by
 default, variables are equal in a connection.

docs/src/tutorials/parameter_identifiability.md

@@ -144,6 +144,6 @@ global_id = assess_identifiability(ode, measured_quantities = measured_quantitie
 Both parameters `b, c` are globally identifiable with probability `0.9` in this case.
 
 [^1]: > R. Munoz-Tamayo, L. Puillet, J.B. Daniel, D. Sauvant, O. Martin, M. Taghipoor, P. Blavy [*Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?*](https://doi.org/10.1017/S1751731117002774), Animal, Vol 12 (4), 701-712, 2018. The model is the ODE system (3) in Supplementary Material 2, initial conditions are assumed to be unknown.
-[^2]: > Moate P.J., Boston R.C., Jenkins T.C. and Lean I.J., [*Kinetics of Ruminal Lipolysis of Triacylglycerol and Biohydrogenationof Long-Chain Fatty Acids: New Insights from Old Data*](doi:10.3168/jds.2007-0398), Journal of Dairy Science 91, 731–742, 2008
+[^2]: > Moate P.J., Boston R.C., Jenkins T.C. and Lean I.J., [*Kinetics of Ruminal Lipolysis of Triacylglycerol and Biohydrogenationof Long-Chain Fatty Acids: New Insights from Old Data*](https://doi.org/10.3168/jds.2007-0398), Journal of Dairy Science 91, 731–742, 2008
 [^3]: > Goodwin, B.C. [*Oscillatory behavior in enzymatic control processes*](https://doi.org/10.1016/0065-2571(65)90067-1), Advances in Enzyme Regulation, Vol 3 (C), 425-437, 1965
 [^4]: > Dong, R., Goodbrake, C., Harrington, H. A., & Pogudin, G. [*Computing input-output projections of dynamical models with applications to structural identifiability*](https://arxiv.org/pdf/2111.00991). arXiv preprint arXiv:2111.00991.

src/clock.jl

@@ -14,6 +14,7 @@ Symbolics.option_to_metadata_type(::Val{:timedomain}) = TimeDomain
 
 """
     is_continuous_domain(x)
+
 true if `x` contains only continuous-domain signals.
 See also [`has_continuous_domain`](@ref)
 """
@@ -33,6 +34,7 @@ get_time_domain(x::Num) = get_time_domain(value(x))
 
 """
     has_time_domain(x)
+
 Determine if variable `x` has a time-domain attributed to it.
 """
 function has_time_domain(x::Symbolic)

src/structural_transformation/pantelides.jl

@@ -76,18 +76,18 @@ Computes which variables are the "highest-differentiated" for purposes of
 pantelides. Ordinarily this is relatively straightforward. However, in our
 case, there are two complicating  conditions:
 
-  1. We allow variables in the structure graph that don't appear in the
-     system at all. What we are interested in is the highest-differentiated
-     variable that actually appears in the system.
+ 1. We allow variables in the structure graph that don't appear in the
+    system at all. What we are interested in is the highest-differentiated
+    variable that actually appears in the system.
 
-  2. We have an alias graph. The alias graph implicitly contributes an
-     alias equation, so it doesn't actually whitelist any additional variables,
-     but it may change which variable is considered the highest differentiated one.
-     Consider the following situation:
+ 2. We have an alias graph. The alias graph implicitly contributes an
+    alias equation, so it doesn't actually whitelist any additional variables,
+    but it may change which variable is considered the highest differentiated one.
+    Consider the following situation:
 
-       Vars: x, y
-       Eqs: 0 = f(x)
-       Alias: ẋ = ẏ
+    Vars: x, y
+    Eqs: 0 = f(x)
+    Alias: ẋ = ẏ
 
     In the absence of the alias, we would consider `x` to be the highest
     differentiated variable. However, because of the alias (and because there

src/systems/discrete_system/discrete_system.jl

@@ -302,9 +302,9 @@ end
 
 """
 ```julia
-SciMLBase.DiscreteFunction{iip}(sys::DiscreteSystem, dvs=states(sys),
-                                ps=parameters(sys);
-                                version=nothing,
+SciMLBase.DiscreteFunction{iip}(sys::DiscreteSystem, dvs = states(sys),
+                                ps = parameters(sys);
+                                version = nothing,
                                 kwargs...) where {iip}
 ```
 
@@ -370,10 +370,10 @@ end
 
 """
 ```julia
-    DiscreteFunctionExpr{iip}(sys::DiscreteSystem, dvs = states(sys),
-                                     ps = parameters(sys);
-                                     version = nothing,
-                                     kwargs...) where {iip}
+DiscreteFunctionExpr{iip}(sys::DiscreteSystem, dvs = states(sys),
+                          ps = parameters(sys);
+                          version = nothing,
+                          kwargs...) where {iip}
 ```
 
 Create a Julia expression for an `DiscreteFunction` from the [`DiscreteSystem`](@ref).