[WIP] Documentation dependency fix (#36)

* Test new catlab branch to fix compose version resolution

* Fix syntax

* Fix trailing paren

* Fixed export of object that doesn't exist and updated examples

* Added second simple example

* First attempt at codecov

* update catlab dev branch

* Added code coverage badge

* Use newest version of catlab

.github/workflows/codecov.yml

@@ -0,0 +1,19 @@
+name: Code Coverage
+
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: '1.5'
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-runtest@latest
+      - uses: julia-actions/julia-processcoverage@v1
+      - uses: codecov/codecov-action@v1
+        with:
+          file: lcov.info
+          fail_ci_if_error: true

.github/workflows/docs.yml

@@ -16,7 +16,7 @@ jobs:
           sudo apt-get update
           sudo apt-get install graphviz ttf-dejavu
       - name: "Install Julia dependencies"
-        run: julia --project=docs -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate(); Pkg.update();'
+        run: julia --project=docs -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate();'
       - name: "Build and deploy docs"
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Project.toml

@@ -15,7 +15,7 @@ StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 
 [compat]
 AutoHashEquals = "^0.2.0"
-Catlab = "^0.7.0"
+Catlab = "^0.7.4"
 DiffEqBase = "^6"
 DiffEqJump = "^6"
 OrdinaryDiffEq = "^5"

README.md

@@ -2,6 +2,7 @@
 
 [![Documentation](https://github.com/mehalter/Petri.jl/workflows/Documentation/badge.svg)](https://mehalter.github.io/Petri.jl/stable/)
 ![Tests](https://github.com/mehalter/Petri.jl/workflows/Tests/badge.svg)
+[![codecov](https://codecov.io/gh/mehalter/Petri.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/mehalter/Petri.jl)
 [![DOI](https://zenodo.org/badge/203420191.svg)](https://zenodo.org/badge/latestdoi/203420191)
 
 

docs/make.jl

@@ -40,6 +40,7 @@ makedocs(
     "Petri.jl" => "index.md",
     "Examples" => Any[
       "examples/epidemiology.md",
+      "examples/lotka-volterra.md",
     ],
     "Library Reference" => "api.md",
   ]

docs/src/usage.md

@@ -1,84 +0,0 @@
-# Basic Usage
-
-We need to include our dependencies. `Petri` is the only requirement to build the models. `OrdinaryDiffEq` is required for simulating the network with and ordinary differential equation. `Plots` can be used to plot the solutions generated by `OrdinaryDiffEq`. `LabelledArrays` can be used to make things more readable, but is not necessary. Lastly, `Catlab` is required for visualizing the models as graphviz diagrams.
-
-```julia
-using Petri
-using LabelledArrays
-using OrdinaryDiffEq
-using Plots
-using Catlab.Graphics.Graphiz
-import Catlab.Graphics.Graphviz: Graph
-```
-
-The SIR model represents the epidemiological dynamics of an infectious disease that causes immunity in its victims. There are three *states:* `Suceptible ,Infected, Recovered`. These states interact through two *transitions*. Infection has the form `S+I -> 2I` where a susceptible person meets an infected person and results in two infected people. The second transition is recovery `I -> R` where an infected person recovers spontaneously.
-
-
-![The SIR model system shown as a Petri net with ODE formulas](img/sir_petri+ode.png)
-
-```julia
-# define the structure of the model
-sir = Petri.Model([:S,:I,:R],LVector(
-                                inf=(LVector(S=1,I=1), LVector(I=2)),
-                                rec=(LVector(I=1),     LVector(R=1))))
-
-# define the initial conditions
-u0 = LVector(S=100.0, I=1, R=0)
-
-# define the parameters of the model, each rate corresponds to a transition
-p = LVector(inf=0.05, rec=0.35)
-
-# evaluate the expression to create a runnable function
-f = vectorfields(sir)
-
-# this is regular OrdinaryDiffEq problem setup
-prob = ODEProblem(f,u0,(0.0,365.0),p)
-sol = OrdinaryDiffEq.solve(prob,Tsit5())
-
-# generate a graphviz visualization of the model
-graph = Graph(sir)
-
-# visualize the solution
-plt = plot(sol)
-```
-
-![A solution to the SIR model system](img/sir_sol.png)
-
-Petri Nets are a simple language for describing reaction networks, you can make increasingly complex diseases. For example the `SEIR` model has an `Exposed` phase where people have the disease, but are not infectious yet.
-
-![The SEIR model system shown as a Petri net](img/seir.png)
-
-```julia
-seir = Petri.Model([:S,:E,:I,:R],LVector(
-                                    exp=(LVector(S=1,I=1), LVector(I=1,E=1)),
-                                    inf=(LVector(E=1),     LVector(I=1)),
-                                    rec=(LVector(I=1),     LVector(R=1))))
-u0 = LVector(S=100.0, E=1, I=0, R=0)
-p = (exp=0.35, inf=0.05, rec=0.05)
-f = vectorfields(seir)
-prob = ODEProblem(f,u0,(0.0,365.0),p)
-sol = OrdinaryDiffEq.solve(prob,Tsit5())
-plt = plot(sol)
-```
-
-![A solution to the SEIR model system](img/seir_sol.png)
-
-The previous models have transitory behavior, the infection spreads and then terminates as you end up with no infected people in the population. The following `SEIRS` model has a non-trivial steady state, because recovered people lose their immunity and become susceptible again.
-
-![The SEIRS model system shown as a Petri net](img/seirs.png)
-
-```julia
-seirs = Petri.Model([:S,:E,:I,:R],LVector(
-                                    exp=(LVector(S=1,I=1), LVector(I=1,E=1)),
-                                    inf=(LVector(E=1),     LVector(I=1)),
-                                    rec=(LVector(I=1),     LVector(R=1)),
-                                    deg=(LVector(R=1),     LVector(S=1))))
-u0 = LVector(S=100.0, E=1, I=0, R=0)
-p = LVector(exp=0.35, inf=0.05, rec=0.07, deg=0.3)
-f = vectorfields(seirs)
-prob = ODEProblem(f,u0,(0.0,365.0),p)
-sol = OrdinaryDiffEq.solve(prob,Tsit5())
-plt = plot(sol)
-```
-
-![A solution to the SEIRS model system](img/seirs_sol.png)

examples/epidemiology.jl

@@ -37,6 +37,15 @@ tspan = (0.0,40.0)
 # as is dependent on the current state of the system
 β = LVector(inf=((u,t)->((3/sum(u))/(t+1))), rec=0.25);
 
+# each transition rates can one of three options:
+#
+# - constant: `β = [.25]`
+#   - where the rate is specified by a value of type `Number`
+# - time dependent: `β = [t->((3/1000)/(t+1))]`
+#   - where `t` is the current time step
+# - state and time dependent: `β = [(u,t)->((3/sum(u))/(t+1))]`
+#   - where `u` is the current state of `u0` and `t` is the current time step
+
 # Petri.jl provides interfaces to StochasticDiffEq.jl, DiffEqJump.jl, and
 # OrdinaryDiffEq.jl Here, we call the `JumpProblem` function that returns an
 # DiffEqJump problem object that can be passed to the DiffEqJump solver which

examples/lotka-volterra.jl

@@ -0,0 +1,41 @@
+# # [Lotka-Volterra Model](@id lotka_volterra_example)
+#
+#md # [![](https://img.shields.io/badge/show-nbviewer-579ACA.svg)](@__NBVIEWER_ROOT_URL__/examples/lotka-volterra.ipynb)
+
+using Petri
+using LabelledArrays
+using Plots
+using OrdinaryDiffEq
+
+# **Step 1:** Define the states and transitions of the Petri Net
+# 
+# Here we have 2 states, wolves and rabbits, and transitions to
+# model predation between the two species in the system
+
+S  = [:rabbits, :wolves]
+Δ  = LVector(
+       birth=(LVector(rabbits=1), LVector(rabbits=2)),
+       predation=(LVector(wolves=1, rabbits=1), LVector(wolves=2)),
+       death=(LVector(wolves=1), LVector()),
+     )
+lotka = Petri.Model(S, Δ)
+
+Graph(lotka)
+
+# **Step 2:** Define the parameters and transition rates
+#
+# Once a model is defined, we can define out initial parameters `u0`, a time
+# span `tspan`, and the transition rates of the interactions `β`
+
+u0 = LVector(wolves=10.0, rabbits=100.0)
+tspan = (0.0,100.0)
+β = LVector(birth=.3, predation=.015, death=.7);
+
+# **Step 3:** Generate a solver and solve
+#
+# Finally we can generate a solver and solve the simulation
+
+prob = ODEProblem(lotka, u0, tspan, β)
+sol = OrdinaryDiffEq.solve(prob,Tsit5(),reltol=1e-8,abstol=1e-8)
+
+plot(sol)

src/Petri.jl

@@ -6,7 +6,7 @@ Provides a modeling framework for representing and solving stochastic petri nets
 """
 module Petri
 
-export Model, Problem, NullPetri, EmptyPetri, vectorfield, Graph
+export Model, NullPetri, EmptyPetri, vectorfield, Graph
 
 include("types.jl")
 include("solvers.jl")