Merge pull request #224 from isaacsas/doc-updates

doc and figure tweaks

README.md

@@ -20,36 +20,52 @@ bifurcation functionality has not yet been updated to the new system. If you
 rely on this functionality please do not update at this time, or consider using
 [BifurcationKit.jl](https://github.com/rveltz/BifurcationKit.jl).*
 
-Catalyst.jl is a domain specific language (DSL) for high performance
-simulation and modeling of chemical reaction networks. Catalyst utilizes
+Catalyst.jl is a domain specific language (DSL) for high performance simulation
+and modeling of chemical reaction networks. Catalyst utilizes
 [ModelingToolkit](https://github.com/SciML/ModelingToolkit.jl)
-`ReactionSystem`s for auto-vectorization and parallelism in the
-generated networks, enabling large-scale simulations. This system
-can generate ModelingToolkit-based systems of ODEs, SDEs, jump processes
-and more. This allows for the easy simulation and parameter estimation
-of mass action ODE models, Chemical Langevin SDE models,
-stochastic chemical kinetics jump process models, and more.
-The generated models can then be used with solvers throughout the broader
-[SciML](https://sciml.ai) ecosystem, including higher level SciML
-packages (e.g. for sensitivity analysis, parameter estimation,
-machine learning applications, etc).
+`ReactionSystem`s, leveraging ModelingToolkit to enable large-scale simulations
+through auto-vectorization and parallelism. `ReactionSystems`s can be used to
+generate ModelingToolkit-based models, allowing the easy simulation and
+parameter estimation of mass action ODE models, Chemical Langevin SDE models,
+stochastic chemical kinetics jump process models, and more. Generated models can
+be used with solvers throughout the broader [SciML](https://sciml.ai) ecosystem,
+including higher level SciML packages (e.g. for sensitivity analysis, parameter
+estimation, machine learning applications, etc).
 
 ## Tutorials and Documentation
 
-For information on using the package,
-[see the stable documentation](https://catalyst.sciml.ai/stable/). Use the
-[in-development documentation](https://catalyst.sciml.ai/dev/) for the version of
-the documentation which contains the unreleased features.
+For information on using the package, [see the stable
+documentation](https://catalyst.sciml.ai/stable/). The [in-development
+documentation](https://catalyst.sciml.ai/dev/) describes unreleased features in
+the current master branch. 
 
 ## Features
+- DSL provides a simple and readable format for manually specifying chemical
+  reactions.
+- The [Catalyst.jl API](http://catalyst.sciml.ai/dev/api/catalyst_api) provides
+  functionality for extending networks, building networks programmatically, and
+  for composing multiple networks together.
+- `ReactionSystem`s generated by the DSL can be converted to a variety of
+  `ModelingToolkit.AbstractSystem`s, including ODE, SDE and jump process
+  representations.
+- By leveraging ModelingToolkit, users have a variety of options for generating
+  optimized system representations to use in solvers. These include construction
+  of dense or sparse Jacobians, multithreading or parallelization of generated
+  derivative functions, automatic classification of reactions into optimized
+  jump types for Gillespie type simulations, automatic construction of
+  dependency graphs for jump systems, and more.
+- Generated systems can be solved using any
+  [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl)
+  ODE/SDE/jump solver, and can be used within `EnsembleProblem`s for carrying
+  out GPU-parallelized parameter sweeps and statistical sampling. Plot recipes
+  are available for visualizing the solutions.
+- Julia `Expr`s can be obtained for all rate laws and functions determining the
+  deterministic and stochastic terms within resulting ODE, SDE or jump models.
+- [`Latexify`](https://github.com/korsbo/Latexify.jl) can be used to generate
+  LaTeX expressions corresponding to generated mathematical models.
 
-- DSL provides a simple and readable format for manually specifying chemical reactions.
-- The [Catalyst.jl API](@ref) provides functionality for extending networks, building networks programmatically, and for composing multiple networks together.
-- `ReactionSystem`s generated by the DSL can be converted to a variety of `ModelingToolkit.AbstractSystem`s, including ODE, SDE and jump process representations.
-- By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of dense or sparse Jacobians, multithreading or parallelization of generated derivative functions, automatic classification of reactions into optimized jump types for Gillespie type simulations, automatic construction of dependency graphs for jump systems, and more.
-- Generated systems can be solved using any [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl) ODE/SDE/jump solver, and can be used within `EnsembleProblem`s for carrying out GPU-parallelized parameter sweeps and statistical sampling. Plot recipes are available for visualizing the solutions.
-
-## Gillespie Simulations of Michaelis-Menten Enzyme Kinetics
+## Illustrative Examples
+#### Gillespie Simulations of Michaelis-Menten Enzyme Kinetics
 
 ```julia
 rs = @reaction_network begin
@@ -70,7 +86,7 @@ plot(jsol,lw=2,title="Gillespie: Michaelis-Menten Enzyme Kinetics")
 
 ![](https://user-images.githubusercontent.com/1814174/87864114-3bf9dd00-c932-11ea-83a0-58f38aee8bfb.png)
 
-## Adaptive SDEs for A Birth-Death Process
+#### Adaptive SDEs for A Birth-Death Process
 
 ```julia
 using Catalyst, Plots, StochasticDiffEq, DiffEqJump

docs/src/index.md

@@ -1,35 +1,51 @@
 # Catalyst.jl for Reaction Models
 
-Catalyst.jl is a domain specific language (DSL) for high performance
-simulation and modeling of chemical reaction networks. Catalyst utilizes
-[`ModelingToolkit.ReactionSystem`](@ref)s for auto-vectorization and
-parallelism in the generated networks, enabling large-scale simulations.
-This system can generate ModelingToolkit-based systems of ODEs, SDEs,
-jump processes and more. This allows for the easy simulation and
-parameter estimation of mass action ODE models, Chemical Langevin SDE
-models, stochastic chemical kinetics jump process models, and more.
-The generated models can then be used with solvers throughout the broader
-[SciML](https://sciml.ai) ecosystem, including higher level SciML
-packages (e.g. for sensitivity analysis, parameter estimation,
-machine learning applications, etc).
+Catalyst.jl is a domain specific language (DSL) for high performance simulation
+and modeling of chemical reaction networks. Catalyst utilizes
+[ModelingToolkit](https://github.com/SciML/ModelingToolkit.jl)
+`ReactionSystem`s, leveraging ModelingToolkit to enable large-scale simulations
+through auto-vectorization and parallelism. `ReactionSystems`s can be used to
+generate ModelingToolkit-based models, allowing the easy simulation and
+parameter estimation of mass action ODE models, Chemical Langevin SDE models,
+stochastic chemical kinetics jump process models, and more. Generated models can
+be used with solvers throughout the broader [SciML](https://sciml.ai) ecosystem,
+including higher level SciML packages (e.g. for sensitivity analysis, parameter
+estimation, machine learning applications, etc).
 
 ## Features
-- DSL provides a simple and readable format for manually specifying chemical reactions.
-- [Catalyst.jl API](@ref) provides functionality for extending networks, building networks programmatically, and for composing multiple networks together.
-- `ReactionSystem`s generated by the DSL can be converted to a variety of `ModelingToolkit.AbstractSystem`s, including ODE, SDE and jump process representations.
-- By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of dense or sparse Jacobians, multithreading or parallelization of generated derivative functions, automatic classification of reactions into optimized jump types for Gillespie type simulations, automatic construction of dependency graphs for jump systems, and more.
-- Generated systems can be solved using any [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl) ODE/SDE/jump solver, and can be used within `EnsembleProblem`s for carrying out GPU-parallelized parameter sweeps and statistical sampling. Plot recipes are available for visualizing the solutions.
+- DSL provides a simple and readable format for manually specifying chemical
+  reactions.
+- The [Catalyst.jl API](http://catalyst.sciml.ai/dev/api/catalyst_api) provides
+  functionality for extending networks, building networks programmatically, and
+  for composing multiple networks together.
+- `ReactionSystem`s generated by the DSL can be converted to a variety of
+  `ModelingToolkit.AbstractSystem`s, including ODE, SDE and jump process
+  representations.
+- By leveraging ModelingToolkit, users have a variety of options for generating
+  optimized system representations to use in solvers. These include construction
+  of dense or sparse Jacobians, multithreading or parallelization of generated
+  derivative functions, automatic classification of reactions into optimized
+  jump types for Gillespie type simulations, automatic construction of
+  dependency graphs for jump systems, and more.
+- Generated systems can be solved using any
+  [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl)
+  ODE/SDE/jump solver, and can be used within `EnsembleProblem`s for carrying
+  out GPU-parallelized parameter sweeps and statistical sampling. Plot recipes
+  are available for visualizing the solutions.
+- Julia `Expr`s can be obtained for all rate laws and functions determining the
+  deterministic and stochastic terms within resulting ODE, SDE or jump models.
+- [`Latexify`](https://github.com/korsbo/Latexify.jl) can be used to generate
+  LaTeX expressions corresponding to generated mathematical models.
 
 ## Installation
-
 Catalyst can be installed through the Julia package manager:
 
 ```julia
 ]add Catalyst
 using Catalyst
 ```
 
-## An Illustrative Example
+## Illustrative Example
 Here is a simple example of generating and solving an SIR ODE model:
 
 ```julia
@@ -47,6 +63,6 @@ sol   = solve(op, Tsit5())       # use Tsit5 ODE solver
 which we can plot as
 ```julia
 using Plots
-plot(sol)
+plot(sol, lw=2)
 ```
 ![SIR Solution](assets/SIR.svg)

docs/src/tutorials/models.md

@@ -35,7 +35,7 @@ to be provided.
 We can then plot the solution using the solution plotting receipe:
 ```julia
 using Plots
-plot(sol)
+plot(sol, lw=2)
 ```
 ![models1](../assets/models1.svg)
 
@@ -80,7 +80,7 @@ sol = solve(jump_prob, SSAStepper())
 Here we used Gillespie's `Direct` method as the underlying stochastic simulation
 algorithm. We get
 ```julia
-plot(sol)
+plot(sol, lw=2)
 ```
 ![models2](../assets/models2.svg)