Documenter 1.0 upgrade

.github/workflows/ci.yml

@@ -1,5 +1,15 @@
 name: CI
-on: [push, pull_request]
+on:
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
 jobs:
   test:
     runs-on: ubuntu-latest

README.md

@@ -9,7 +9,7 @@
 [![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)
 
-CellMLToolkit.jl is a Julia library that connects [CellML](https://cellml.org/) models to [SciML](http://github.com/SciML/), the Scientific Julia ecosystem. CellMLToolkit.jl acts as a bridge between CellML and ModelingToolkit.jl. It imports a CellML model (in XML) and emits a ModelingToolkit.jl intermediate representation (IR), which can then enter the SciML ecosystem.
+CellMLToolkit.jl is a Julia library that connects [CellML](https://cellml.org/) models to [SciML](https://github.com/SciML/), the Scientific Julia ecosystem. CellMLToolkit.jl acts as a bridge between CellML and ModelingToolkit.jl. It imports a CellML model (in XML) and emits a ModelingToolkit.jl intermediate representation (IR), which can then enter the SciML ecosystem.
 
 ## Tutorials and Documentation
 
@@ -20,11 +20,11 @@ the documentation, which contains the unreleased features.
 
 ## CellML
 
-[CellML](http://cellml.org) is an XML-based open-standard for the exchange of mathematical models. CellML originally started in 1998 by the Auckland Bioengineering Institute at the University of Auckland and affiliated research groups. Since then, its [repository](https://models.physiomeproject.org/welcome) has grown to more than a thousand models. While CellML is not domain-specific, its focus has been on biomedical models. Currently, the active categories in the repository are *Calcium Dynamics*, *Cardiovascular Circulation*, *Cell Cycle*, *Cell Migration*, *Circadian Rhythms*, *Electrophysiology*, *Endocrine*, *Excitation-Contraction Coupling*, *Gene Regulation*, *Hepatology*, *Immunology*, *Ion Transport*, *Mechanical Constitutive Laws*, *Metabolism*, *Myofilament Mechanics*, *Neurobiology*, *pH Regulation*, *PKPD*, *Protein Modules*, *Signal Transduction*, and *Synthetic Biology*. There are many software tools to import, process and run CellML models; however, these tools are not Julia-specific.
+[CellML](https://cellml.org) is an XML-based open-standard for the exchange of mathematical models. CellML originally started in 1998 by the Auckland Bioengineering Institute at the University of Auckland and affiliated research groups. Since then, its [repository](https://models.physiomeproject.org/welcome) has grown to more than a thousand models. While CellML is not domain-specific, its focus has been on biomedical models. Currently, the active categories in the repository are *Calcium Dynamics*, *Cardiovascular Circulation*, *Cell Cycle*, *Cell Migration*, *Circadian Rhythms*, *Electrophysiology*, *Endocrine*, *Excitation-Contraction Coupling*, *Gene Regulation*, *Hepatology*, *Immunology*, *Ion Transport*, *Mechanical Constitutive Laws*, *Metabolism*, *Myofilament Mechanics*, *Neurobiology*, *pH Regulation*, *PKPD*, *Protein Modules*, *Signal Transduction*, and *Synthetic Biology*. There are many software tools to import, process and run CellML models; however, these tools are not Julia-specific.
 
 ## SciML
 
-[SciML](http://github.com/SciML) is a collection of Julia libraries for open source scientific computing and machine learning. The centerpiece of SciML is [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl), which provides a rich set of ordinary differential equations (ODE) solvers. One major peripheral component of SciML is [ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl). It is a modeling framework for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. The core of ModelingToolkit.jl is an IR language to code the scientific problems of interest in a high level. Automatic code generation and differentiation allow for the generation of a usable model for the other components of SciML, such as DifferentialEquations.jl.
+[SciML](https://github.com/SciML) is a collection of Julia libraries for open source scientific computing and machine learning. The centerpiece of SciML is [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl), which provides a rich set of ordinary differential equations (ODE) solvers. One major peripheral component of SciML is [ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl). It is a modeling framework for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. The core of ModelingToolkit.jl is an IR language to code the scientific problems of interest at a high level. Automatic code generation and differentiation allow for the generation of a usable model for the other components of SciML, such as DifferentialEquations.jl.
 
 ## Installation
 
@@ -89,7 +89,7 @@ In addition to the model equations, the initial conditions and parameters are al
 
 As expected,
 
-![](figures/lorenz.png)
+![](docs/src/assets/lorenz.png)
 
 Let's look at more complicated examples. The next one is the [ten Tusscher-Noble-Noble-Panfilov human left ventricular action potential model](https://journals.physiology.org/doi/full/10.1152/ajpheart.00794.2003). This is a mid-range electrophysiology model with 17 states variables and relatively good numerical stability.
 
@@ -100,7 +100,7 @@ Let's look at more complicated examples. The next one is the [ten Tusscher-Noble
   plot(sol, idxs=12)
 ```
 
-![](figures/ten.png)
+![](docs/src/assets/ten.png)
 
 We can tell which variable to plot (`idxs=12` here) by looking at the output of `list_states(ml)` (see below).
 
@@ -166,7 +166,7 @@ For the next example, we chose a complex model to stress the ODE solvers: [the O
   plot(sol, idxs=49)  # membrane₊v
 ```
 
-![](figures/ohara_rudy.png)
+![](docs/src/assets/ohara_rudy.png)
 
 ## Multi-file Models (Import)
 
@@ -198,4 +198,4 @@ Same as before, we can plot the output as
 plot(sol, idxs=2)
 ```
 
-![](figures/noble_1962.png)
+![](docs/src/assets/noble_1962.png)

docs/Project.toml

@@ -4,4 +4,4 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 
 [compat]
 CellMLToolkit = "2.9"
-Documenter = "0.27"
+Documenter = "1"

docs/make.jl

@@ -20,16 +20,7 @@ makedocs(sitename = "CellMLToolkit.jl",
          linkcheck_ignore = [
              "https://journals.physiology.org/doi/full/10.1152/ajpheart.00794.2003",
          ],
-         strict = [
-             :doctest,
-             :linkcheck,
-             :parse_error,
-             :example_block,
-             # Other available options are
-             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-         ],
-         format = Documenter.HTML(; analytics = "UA-90474609-3",
-                                  assets = ["assets/favicon.ico"],
+         format = Documenter.HTML(;assets = ["assets/favicon.ico"],
                                   mathengine,
                                   canonical = "https://docs.sciml.ai/CellMLToolkit/stable/",
                                   prettyurls = (get(ENV, "CI", nothing) == "true")),

docs/src/index.md

@@ -4,11 +4,11 @@ CellMLToolkit.jl is a Julia library that connects CellML models to the Scientifi
 
 ## CellML
 
-[CellML](http://cellml.org) is an XML-based open-standard for the exchange of mathematical models. CellML originally started in 1998 by the Auckland Bioengineering Institute at the University of Auckland and affiliated research groups. Since then, its [repository](https://models.physiomeproject.org/welcome) has grown to more than a thousand models. While CellML is not domain-specific, its focus has been on biomedical models. Currently, the active categories in the repository are *Calcium Dynamics*, *Cardiovascular Circulation*, *Cell Cycle*, *Cell Migration*, *Circadian Rhythms*, *Electrophysiology*, *Endocrine*, *Excitation-Contraction Coupling*, *Gene Regulation*, *Hepatology*, *Immunology*, *Ion Transport*, *Mechanical Constitutive Laws*, *Metabolism*, *Myofilament Mechanics*, *Neurobiology*, *pH Regulation*, *PKPD*, *Protein Modules*, *Signal Transduction*, and *Synthetic Biology*. There are many software tools to import, process and run CellML models; however, these tools are not Julia-specific.
+[CellML](https://cellml.org) is an XML-based open-standard for the exchange of mathematical models. CellML originally started in 1998 by the Auckland Bioengineering Institute at the University of Auckland and affiliated research groups. Since then, its [repository](https://models.physiomeproject.org/welcome) has grown to more than a thousand models. While CellML is not domain-specific, its focus has been on biomedical models. Currently, the active categories in the repository are *Calcium Dynamics*, *Cardiovascular Circulation*, *Cell Cycle*, *Cell Migration*, *Circadian Rhythms*, *Electrophysiology*, *Endocrine*, *Excitation-Contraction Coupling*, *Gene Regulation*, *Hepatology*, *Immunology*, *Ion Transport*, *Mechanical Constitutive Laws*, *Metabolism*, *Myofilament Mechanics*, *Neurobiology*, *pH Regulation*, *PKPD*, *Protein Modules*, *Signal Transduction*, and *Synthetic Biology*. There are many software tools to import, process and run CellML models; however, these tools are not Julia-specific.
 
 ## SciML
 
-[SciML](http://github.com/SciML) is a collection of Julia libraries for open source scientific computing and machine learning. The centerpiece of SciML is [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl), which provides a rich set of ordinary differential equations (ODE) solvers. One major peripheral component of SciML is [ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl). It is a modeling framework for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. The core of ModelingToolkit.jl is an IR language to code the scientific problems of interest in a high level. Automatic code generation and differentiation allow for the generation of a usable model for the other components of SciML, such as DifferentialEquations.jl.
+[SciML](https://github.com/SciML) is a collection of Julia libraries for open source scientific computing and machine learning. The centerpiece of SciML is [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl), which provides a rich set of ordinary differential equations (ODE) solvers. One major peripheral component of SciML is [ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl). It is a modeling framework for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. The core of ModelingToolkit.jl is an IR language to code the scientific problems of interest at a high level. Automatic code generation and differentiation allow for the generation of a usable model for the other components of SciML, such as DifferentialEquations.jl.
 
 ## Installation
 
@@ -91,32 +91,19 @@ Pkg.status(; mode = PKGMODE_MANIFEST) # hide
 </details>
 ```
 
-```@raw html
-You can also download the 
-<a href="
-```
-
-```@eval
-using TOML
-version = TOML.parse(read("../../Project.toml", String))["version"]
-name = TOML.parse(read("../../Project.toml", String))["name"]
-link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
-       "/assets/Manifest.toml"
-```
-
-```@raw html
-">manifest</a> file and the
-<a href="
-```
-
 ```@eval
 using TOML
+using Markdown
 version = TOML.parse(read("../../Project.toml", String))["version"]
 name = TOML.parse(read("../../Project.toml", String))["name"]
-link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
-       "/assets/Project.toml"
-```
-
-```@raw html
-">project</a> file.
-```
+link_manifest = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+                "/assets/Manifest.toml"
+link_project = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+               "/assets/Project.toml"
+Markdown.parse("""You can also download the
+[manifest]($link_manifest)
+file and the
+[project]($link_project)
+file.
+""")
+```

docs/src/tutorial.md

@@ -1,6 +1,6 @@
 # Tutorial
 
-The models directory contains few CellML model examples. Let's start with a simple one, the famous Lorenz equations!
+The models directory contains a few CellML model examples. Let's start with a simple one, the famous Lorenz equations!
 
 ```Julia
   using CellMLToolkit
@@ -11,7 +11,7 @@ The models directory contains few CellML model examples. Let's start with a simp
   prob = ODEProblem(ml, tspan)
 ```
 
-Now, `ml` points to a `CellModel` struct that contains the details of the model and `prob` is an `ODEProblem` ready for integration. We can solve and visualize `prob` as
+Now, `ml` points to a `CellModel` struct that contains the details of the model, and `prob` is an `ODEProblem` ready for integration. We can solve and visualize `prob` as
 
 ```Julia
   using DifferentialEquations, Plots
@@ -24,9 +24,9 @@ Now, `ml` points to a `CellModel` struct that contains the details of the model
 
 As expected,
 
-![](../../../figures/lorenz.png)
+![](assets/lorenz.png)
 
-Let's look at more complicated examples. The next one is the [ten Tusscher-Noble-Noble-Panfilov human left ventricular action potential model](https://journals.physiology.org/doi/full/10.1152/ajpheart.00794.2003). This is a mid-range electrophysiology model with 17 states variables and relatively good numerical stability.
+Let's look at more complicated examples. The next one is the [ten Tusscher-Noble-Noble-Panfilov human left ventricular action potential model](https://journals.physiology.org/doi/full/10.1152/ajpheart.00794.2003). This is a mid-range electrophysiology model with 17 state variables and relatively good numerical stability.
 
 ```Julia
   ml = CellModel("models/tentusscher_noble_noble_panfilov_2004_a.cellml.xml")
@@ -37,7 +37,7 @@ Let's look at more complicated examples. The next one is the [ten Tusscher-Noble
   plot(sol.t, V)
 ```
 
-![](../../../figures/ten.png)
+![](assets/ten.png)
 
 We can also enhance the model by asking ModelingToolkit.jl to generate a Jacobian by passing `jac=true` to the `ODEProblem` constructor.
 
@@ -56,7 +56,7 @@ The rest remains the same. For the last example, we chose a complex model to str
   plot(sol.t, V)
 ```
 
-![](../../../figures/ohara_rudy.png)
+![](assets/ohara_rudy.png)
 
 ## Changing Parameters
 
@@ -105,6 +105,6 @@ V = map(x -> x[1], sol.u)
 plot(sol.t, V)
 ```
 
-![](../../../figures/ten_400.png)
+![](assets/ten_400.png)
 
 `ODEProblem` also accepts a `u0` parameter to change the initial conditions (remember `u0 = list_initial_conditions(ml)`).