up

Author: TorkelE

docs/src/introduction_to_catalyst/catalyst_for_new_julia_users.md

@@ -37,7 +37,8 @@ typeof(1)
 
 Finally, we note that the first time some code is run in Julia, it has to be *compiled*. However, this is only required once per Julia session. Hence, the second time the same code is run, it runs much faster. E.g. try running this line of code first one time, and then one additional time. You will note that the second run is much faster.
 ```@example ex1
-rand(100, 100)^3.5;
+rand(100, 100)^3.5
+nothing # hide
 ```
 (This code creates a random 100x100 matrix, and takes it to the power of 3.5)
 

docs/src/inverse_problems/petab_ode_param_fitting.md

@@ -103,8 +103,10 @@ nothing # hide
 ### Fitting parameters
 We are now able to fit our model to the data. First, we create a `PEtabODEProblem`. Here, we use `petab_model` as the only input, but it is also possible to set various [numeric solver and automatic differentiation options](@ref petab_simulation_options) (such as method or tolerance).
 ```@example petab1
+petab_problem = PEtabODEProblem(petab_model; verbose=false); nothing # hide
+```
+```julia
 petab_problem = PEtabODEProblem(petab_model)
-nothing # hide
 ```
 Since no additional input is given, default options are selected by PEtab.jl (and generally, its choices are good).
 
@@ -380,11 +382,16 @@ While in our basic example, we do not provide any additional information to our
 
 Here is an example, taken from the [more detailed PEtab.jl documentation](https://sebapersson.github.io/PEtab.jl/dev/Boehm/#Creating-a-PEtabODEProblem)
 ```@example petab1
+PEtabODEProblem(petab_model, 
+                ode_solver=ODESolver(Rodas5P(), abstol=1e-8, reltol=1e-8), 
+                gradient_method=:ForwardDiff, 
+                hessian_method=:ForwardDiff, verbose=false); nothing # hide
+```
+```julia
 PEtabODEProblem(petab_model, 
                 ode_solver=ODESolver(Rodas5P(), abstol=1e-8, reltol=1e-8), 
                 gradient_method=:ForwardDiff, 
                 hessian_method=:ForwardDiff)
-nothing # hide
 ```
 where we simulate our ODE model using the `Rodas5p` method (with absolute and relative tolerance both equal `1e-8`) and use [forward automatic differentiation](https://github.com/JuliaDiff/ForwardDiff.jl) for both gradient and hessian computation. More details on available ODE solver options can be found in [the PEtab.jl documentation](https://sebapersson.github.io/PEtab.jl/dev/API_choosen/#PEtab.ODESolver).
 
@@ -411,8 +418,10 @@ This is required for the various [optimisation evaluation plots](@ref petab_plot
 ## Objective function extraction
 While PEtab.jl provides various tools for analysing the objective function generated by `PEtabODEProblem`, it is also possible to extract this function for customised analysis. Given a `PEtabODEProblem`
 ```@example petab1
+petab_problem = PEtabODEProblem(petab_model; verbose=false); nothing # hide
+```
+```julia
 petab_problem = PEtabODEProblem(petab_model)
-nothing # hide
 ```
 We can find the:
 1. Objective function as the `petab_problem.compute_cost`. It takes a single argument (`p`) and returns the objective value.