julia struct based ABM

.gitignore

@@ -27,3 +27,7 @@ Manifest.toml
 .DS_Store
 .vscode
 .ipynb_checkpoints
+
+cache/
+
+figures/SIRSDiscretetrajectory.png

Project.toml

@@ -8,6 +8,7 @@ version = "0.0.1"
 AlgebraicPetri = "4f99eebe-17bf-4e98-b6a1-2c4f205a959b"
 AlgebraicRewriting = "725a01d3-f174-5bbd-84e1-b9417bad95d9"
 Catlab = "134e5e36-593f-5add-ad60-77f754baafbe"
+DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Fleck = "5bb9b785-358c-4fee-af0f-b94a146244a8"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

docs/literate/game_of_life.jl

@@ -0,0 +1,41 @@
+# # Petri Net rewriting
+#
+# First we want to load our package with `using`
+
+using AlgebraicABMs
+using Catlab, AlgebraicPetri # for declaring model building blocks 
+using Distributions # for defining hazard rates
+
+# ## Schema 
+# 
+# We create a regular grid
+
+@present SchLife(FreeSchema) begin 
+  Cell::Ob 
+  (N,E,W,S)::Hom(Cell,Cell)
+  Life::Ob
+  live::Hom(Life,Cell)
+end
+
+# We define a network of cells
+
+@present SchLifeGraph <: SchSymmetricGraph begin 
+  Life::Ob
+  live::Hom(Life,V)
+end
+
+@acset_type Life(SchLifeGraph) <: AbstractSymmetricGraph
+
+function living_neighbors(n::Int; alive=false)
+  X = Life(1)
+  alive && add_part!(X, :Live, live=1)
+  for _ in 1:n
+    v = add_part!(X, :V)
+    add_part!(X, :Life, live=v)
+    add_edge!(X, v, 1)
+  end
+  X
+end
+
+living_neighbors(2)
+

docs/literate/petri_example.jl

@@ -22,7 +22,7 @@ to_graphviz(sir_pn)
 
 # A "state of the world" in this model is just a finite set of Susceptible, Infected, and Recovered people. Thus we can specify a model with 3 integers.
 
-init = PetriNetCSet(sir_pn, S=100, I=5) # Initial state
+init = PetriNetCSet(sir_pn, S=100, I=5); # Initial state
 
 
 # We declare parameters to specify the random waiting times
@@ -46,15 +46,15 @@ clockdists[:wane] = (t) -> Exponential(wane)
 
 ## the Weibull clock (non-Markov)
 α, θ = weibullpar(30, 5)
-clockdists[:rec] = (t) -> Weibull(α, θ)
+clockdists[:rec] = (t) -> Weibull(α, θ);
 
 # We make a reporting function that extracts information from each time step.
 count(acs::ACSet) = 
-  NamedTuple(Dict([o => nparts(acs, o) for o in ob(acset_schema(acs))]))
+  NamedTuple(Dict([o => nparts(acs, o) for o in ob(acset_schema(acs))]));
 
 
 # We simulate a model
-sirout = run!(sir_pn, clockdists, init; save=count, maxevent=20)
+sirout = run!(sir_pn, clockdists, init; save=count, maxevent=2000)
 X = first.(sirout)
 SIR = [getindex.(last.(sirout), x) for x in 1:3]
 f = Figure();

docs/src/index.md

@@ -4,4 +4,7 @@
 CurrentModule = AlgebraicABMs
 ```
 
-`AlgebraicABMs.jl` is a Julia library for creating agent-based models.
+`AlgebraicABMs.jl` is a Julia library for creating agent-based models. We ultimate want to provide capabilities on par with software like AnyLogic, NetLogo, and Agents.jl - all while offering a mostly declarative interface such that: 
+- The model's logic is transparent (rather than hidden away in complicated interactions of code blocks)
+- Models can be built compositionally (we can naturally 'glue' models together at a high level, without worrying about implementation details and edge-cases)
+- Models can be _migrated_ at a high level, whether interpersonally (collaboration with others who have a different ontology/vocabulary) or intrapersonally (one updates one's own model of the of world and wishes to reuse one's old model under new assumptions, without having to manually refactor code and dig into implementation details).