LanguageTool

Author: ArnoStrouwen

README.md

@@ -24,7 +24,7 @@ the documentation, which contains the unreleased features.
 
 ## SciML
 
-[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 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
 

docs/src/index.md

@@ -8,7 +8,7 @@ CellMLToolkit.jl is a Julia library that connects CellML models to the Scientifi
 
 ## SciML
 
-[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 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
 

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
@@ -26,7 +26,7 @@ As expected,
 
 ![](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")