Update README.md

README is updated to reflect changes in the OrdinaryDiffEq interface.

Author: shahriariravanian

README.md

@@ -1,6 +1,22 @@
 # CellMLToolkit.jl
 
-CellMLToolkit.jl is a Julia library that connects [CellML](http://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.
+[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
+[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/CellMLToolkit/stable/)
+
+[![codecov](https://codecov.io/gh/SciML/CellMLToolkit.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/CellMLToolkit.jl)
+[![Build Status](https://github.com/SciML/CellMLToolkit.jl/workflows/CI/badge.svg)](https://github.com/SciML/CellMLToolkit.jl/actions?query=workflow%3ACI)
+
+[![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.
+
+## Tutorials and Documentation
+
+For information on using the package,
+[see the stable documentation](https://docs.sciml.ai/CellMLToolkit/stable/). Use the
+[in-development documentation](https://docs.sciml.ai/CellMLToolkit/dev/) for the version of
+the documentation, which contains the unreleased features.
 
 ## CellML
 
@@ -10,12 +26,13 @@ CellMLToolkit.jl is a Julia library that connects [CellML](http://cellml.org) mo
 
 [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.
 
-## Install
+## Installation
 
-To install, run
+To install CellMLToolkit.jl, use the Julia package manager:
 
 ```julia
-  ] add CellMLToolkit
+using Pkg
+Pkg.add("CellMLToolkit")
 ```
 
 ## Simple Example
@@ -25,8 +42,8 @@ To install, run
 
   ml = CellModel("models/lorenz.cellml.xml")
   prob = ODEProblem(ml, (0,100.0))
-  sol = solve(prob, RK4())		# fourth-order Runge-Kutta method
-  plot(sol, idxs=(1,3))			# idxs keyword has superceded vars keyword
+  sol = solve(prob, RK4())    # fourth-order Runge-Kutta method
+  plot(sol, idxs=(1,3))       # idxs keyword has superceded vars keyword
 ```
 
 Note that `model` is a directory of the CellMLToolkit package. You can find its path as
@@ -38,8 +55,8 @@ Note that `model` is a directory of the CellMLToolkit package. You can find its
 and then
 
 ```julia
-  model_path = joinpath(model_root, "lorenz.cellml.xml")
-  ml = CellModel(model_path)
+model_path = joinpath(model_root, "lorenz.cellml.xml")
+ml = CellModel(model_path)
 ```
 
 ## Tutorial
@@ -59,14 +76,15 @@ The next step is to convert `ml` into an `ODEProblem`, ready to be solved.
 ```Julia
   prob = ODEProblem(ml, (0,100.0))
 ```
+
 Here, `(0,100.0)` is the `tspan` parameter, describing the integration range of the independent variable.
 In addition to the model equations, the initial conditions and parameters are also read from the XML file(s) and are available as `prob.u0` and `prob.ps`, respectively. We can solve and visualize `prob` as
 
 ```Julia
   using DifferentialEquations, Plots
 
-  sol = solve(prob, RK4())		# fourth-order Runge-Kutta method
-  plot(sol, idxs=(1,3))			# idxs keyword has superceded vars keyword
+  sol = solve(prob, RK4())    # fourth-order Runge-Kutta method
+  plot(sol, idxs=(1,3))       # idxs keyword has superceded vars keyword
 ```
 
 As expected,
@@ -78,13 +96,13 @@ Let's look at more complicated examples. The next one is the [ten Tusscher-Noble
 ```Julia
   ml = CellModel("models/tentusscher_noble_noble_panfilov_2004_a.cellml.xml")
   prob = ODEProblem(ml, (0, 10000.0))
-  sol = solve(prob, TRBDF2(), dtmax=1.0)	# it is a stiff system requiring an implcit solver (TRBDF2 instead of RK4)
+  sol = solve(prob, TRBDF2(), dtmax=1.0)    # it is a stiff system requiring an implcit solver (TRBDF2 instead of RK4)
   plot(sol, idxs=12)
 ```
 
 ![](figures/ten.png)
 
-We can tell which variable to plot (idxs=12, here) by looking at the output of `list_states(ml)` (see below).
+We can tell which variable to plot (`idxs=12` here) by looking at the output of `list_states(ml)` (see below).
 
 Let's see how we can modify the initial values and parameters. We will use the Beeler-Reuter model with 8 state variables as an example:
 
@@ -128,7 +146,7 @@ Assume we want to change `IstimPeriod`. We can easily do this with the help of `
 ```Julia
   params = list_params(ml)
   update_list!(params, :stimulus_protocol₊IstimPeriod, 250.0)
-  prob = ODEProblem(ml, (0, 10000.0); p=last.(params))	
+  prob = ODEProblem(ml, (0, 10000.0); p=last.(params))  # note that you need to pass last.(params) and not params itself to ODEProblem
 ```
 
 The rest is the same as before.
@@ -157,9 +175,9 @@ For the next example, we chose a complex model to stress the ODE solvers: [the O
 CellML specification allows for models spanning multiple XML files. In these models, the top level CellML XML file imports components from other CellML files, which in turn may import from other files. CellMLToolkit supports this functionality. It assumes that *the top-level file and all the imported files reside in the same directory*. `models/noble_1962` contained one such example:
 
 ```julia
-  ml = CellModel("models/noble_1962/Noble_1962.cellml")
-  prob = ODEProblem(ml, tspan)
-  sol = solve(prob, TRBDF2(), dtmax=0.5)
+ml = CellModel("models/noble_1962/Noble_1962.cellml")
+prob = ODEProblem(ml, tspan)
+sol = solve(prob, TRBDF2(), dtmax = 0.5)
 ```
 
 Note that the syntax is exactly the same as before. However, the list of the imported files are printed during `CellModel` generation:
@@ -179,7 +197,7 @@ Note that the syntax is exactly the same as before. However, the list of the imp
 Same as before, we can plot the output as
 
 ```julia
-  plot(sol, idxs=2)
+plot(sol, idxs = 2)
 ```
 
 ![](figures/noble_1962.png)