update docs

docs/Project.toml

@@ -1,5 +1,11 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+LSODA = "7f56f5a3-f504-529b-bc02-0b1fe5e64312"
+PiecewiseDeterministicMarkovProcesses = "86206cdf-4603-54e0-bd58-22a2dcbf57aa"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
 
 [compat]
-Documenter = "0.24.3"
+Documenter = "1"
+LSODA = "0.7"

docs/make.jl

@@ -1,17 +1,27 @@
+using Pkg
+cd(@__DIR__)
+pkg" activate ."
+pkg" dev LSODA Sundials Plots"
+
 using Documenter, PiecewiseDeterministicMarkovProcesses
 
-makedocs(doctest = false,
-			sitename = "PiecewiseDeterministicMarkovProcesses.jl",
-			pages = Any[
-				"Home" => "index.md",
-				"Tutorials" => "tutorials.md",
-				"Problem Type" => "problem.md",
-				"Solver Algorithms" => "solver.md",
-				"FAQ" => "addFeatures.md",
-				"Library" => "library.md"
-			]
+makedocs(modules = [PiecewiseDeterministicMarkovProcesses],
+		authors = "Romain Veltz",
+		doctest = false,
+		pagesonly = true, # this is on Documenter#master, do not compile what is not in pages =
+# 		draft = true,
+		warnonly = true,
+		sitename = "PiecewiseDeterministicMarkovProcesses.jl",
+		pages = Any[
+			"Home" => "index.md",
+			"Tutorials" => "tutorials.md",
+			"Problem Type" => "problem.md",
+			"Solver Algorithms" => "solver.md",
+			"FAQ" => "addFeatures.md",
+			"Library" => "library.md"
+		]
 )
 
 deploydocs(
 	repo   = "github.com/rveltz/PiecewiseDeterministicMarkovProcesses.jl.git",
-)
+	push_preview=true, target="build", devbranch="main")

docs/src/tutorials.md

@@ -19,13 +19,14 @@ In between jumps, $x_c$ evolves according to
 
 We first need to load the library.  
 
-```julia
+```@example TUT1
+using Revise, Sundials, Random
 using PiecewiseDeterministicMarkovProcesses
 const PDMP = PiecewiseDeterministicMarkovProcesses
 ```
 We then define a function that encodes the dynamics in between jumps. We need to provide the vector field of the ODE. Hence, we define a function that, given the continuous state $x_c$ and the discrete state $x_d$ at time $t$, returns the vector field. In addition some parameters can be passed with the variable `parms`.
 
-```julia  
+```@example TUT1  
 function F!(ẋ, xc, xd, parms, t)
 	if mod(xd[1],2)==0
 		ẋ[1] = 10xc[1]
@@ -47,7 +48,7 @@ We implement these jumps using a 2x1 matrix `nu` of Integers, such that the jump
 The rates of these reactions are encoded in the following function.
 
 
-```julia
+```@example TUT1
 k(x) = 1 + x
 
 function R!(rate, xc, xd, parms, t, issum::Bool)
@@ -76,31 +77,27 @@ parms = [50.]
 
 We define a problem type by giving the characteristics of the process `F, R, Delta, nu`, the initial conditions, and the timespan to solve over:
 
-```julia
+```@example TUT1
 Random.seed!(8) # to get the same result as this simulation!
 problem = PDMP.PDMPProblem(F!, R!, nu, xc0, xd0, parms, (0.0, 10.0))
 ```
 
 After defining the problem, you solve it using `solve`.
 
-```julia
+```@example TUT1
 sol =  PDMP.solve(problem, CHV(CVODE_BDF()))
 ```
 
 In this case, we chose to sample `pb` with the [CHV algorithm](https://arxiv.org/abs/1504.06873) where the flow in between jumps is integrated with the solver `CVODE_BDF()` from [DifferentialEquations.jl](https://github.com/JuliaDiffEq/DifferentialEquations.jl).
 
 We can then plot the solution as follows:
 
-```
+```@example TUT1
 # plotting
 using Plots
 Plots.plot(sol.time,sol.xc[1,:],label="xc")
 ```
 
-This produces the graph:
-
-![TCP](example1.png)
-
 
 ## Basic example with the rejection method
 The previous method is useful when the total rate function varies a lot. In the case where the total rate is mostly constant in between jumps, the **rejection method** is more appropriate. 
@@ -110,7 +107,7 @@ The **rejection method** assumes some a priori knowledge of the process one want
 
 `sum(rate)(t) <= bound_rejection `
 
-```julia
+```@example TUT1
 function R2!(rate, xc, xd, parms, t, issum::Bool)
 	# rate function
 	bound_rejection = 1. + parms[1] + 15  # bound on the total rate
@@ -128,7 +125,7 @@ end
 
 We can now simulate this process as follows
 
-```julia
+```@example TUT1
 Random.seed!(8) # to get the same result as this simulation!
 problem = PDMP.PDMPProblem(F!, R2!, nu, xc0, xd0, parms, (0.0, 1.0))
 sol =  PDMP.solve(problem, Rejection(CVODE_BDF()))
@@ -138,12 +135,8 @@ In this case, we chose to sample `pb` with the Rejection algorithm where the flo
 
 We can then plot the solution as follows:
 
-```
+```@example TUT1
 # plotting
 using Plots
 Plots.plot(sol.time,sol.xc[1,:],label="xc")
-```
-
-This produces the graph:
-
-![TCP](example2.png)
+```