Petset (#18)

* [WIP] implementation of whole-grained Petri nets (#12)

* implementation of whole-grained Petri nets

* ENH: Some cleanup of basic Petri net implementation

Co-authored-by: Owen Lynch <root@owenlynch.org>

* Updated reaction network definition

* Catch up petset to master (#16)

* Updated catlab interface (#14)

* Updated catlab interface

* patch version bump to fix broken catlab

* Don't dev catlab for docs

* Updated petri to fix graphing

* Update examples to match new interface

* Change test versions

* Added code coverage (#15)

* Added code coverage

* Fix documentation dependency incorrect resolution

* Use newest version of catlab

* Updated Catlab API

* Working on CSet implementation of Petri nets

* Added better constructor for building petri nets

* Updated to newer cset type definitions

* Improve interface to work with both Petri and ReactionNets

* Added labelled petri nets

* Added more nice constructors for building petri nets

* Finished constructors and cleaned up duplicate code a little

* Updated comment of example code

* Added interface for Petri.jl

* Added chime toy model

* Removed unnecessary comments

* Separated time span to variable for clearer definition

* Added JSON generation

* Added final json form and cleaned up

* Added executable CHIME model using CSet implementation as well as JSON generation with code

* Cleaned up code some more

* Final code clean up for this example

* Fix typo

* Added chime model folder

* Updated README

* Updated documentation for CHIME models

* Updated README

* Updated README

* Updated types with new patch in Catlab

* Update to catlab 0.8 and petri 1.2.1

* Added looser codecov rules

* Moved open petri nets to use the cset implementation

* Updated examples

* Updated chime-cset imports

* Updated examples

* Added more testing

* Fix julia v1.0 support

* Simplified each row iterator

* Fix dimensionality mismatch in rate_eq

* Added support for labelled petri net vector field generation

* Split out vectorfield generation using multiple dispatch

* Removed duplicate function

* Moved to LabelledArrays to fix diffeq bug with dictionaries

* Fix typo and update tests

* final push on petsets

* Fixed typo in epi:

Co-authored-by: olynch <oclynch888@gmail.com>
Co-authored-by: Owen Lynch <root@owenlynch.org>

Project.toml

@@ -2,7 +2,7 @@ name = "AlgebraicPetri"
 uuid = "4f99eebe-17bf-4e98-b6a1-2c4f205a959b"
 license = "MIT"
 authors = ["Micah Halter <micah@mehalter.com>"]
-version = "0.3.2"
+version = "0.4.0"
 
 [deps]
 AutoHashEquals = "15f4f7f2-30c1-5605-9d31-71845cf9641f"

docs/Project.toml

@@ -7,4 +7,3 @@ Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Petri = "4259d249-1051-49fa-8328-3f8ab9391c33"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"

examples/covid/Project.toml

@@ -6,4 +6,3 @@ LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Petri = "4259d249-1051-49fa-8328-3f8ab9391c33"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"

examples/covid/chime/Project.toml

@@ -5,4 +5,3 @@ JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Petri = "4259d249-1051-49fa-8328-3f8ab9391c33"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"

examples/covid/chime/chime.jl

@@ -2,7 +2,6 @@ using AlgebraicPetri
 using AlgebraicPetri.Epidemiology
 using Petri: Model, Graph
 using OrdinaryDiffEq
-using StochasticDiffEq
 using Plots
 using Catlab.Theories
 using Catlab.CategoricalAlgebra.FreeDiagrams
@@ -34,8 +33,3 @@ prob = ODEProblem(vectorfield(p_sir),u0,t_span,p)
 sol = OrdinaryDiffEq.solve(prob,Tsit5())
 plot(sol)
 png("ode-chime.png")
-
-prob,cb = SDEProblem(Model(p_sir),u0,t_span,p);
-sol = solve(prob,SRA1(),callback=cb)
-plot(sol)
-png("sde-chime.png")

examples/covid/epidemiology.jl

@@ -7,7 +7,6 @@ using AlgebraicPetri.Epidemiology
 
 using Petri: Model, Graph
 using OrdinaryDiffEq
-using StochasticDiffEq
 using Plots
 
 using Catlab.Theories
@@ -56,11 +55,11 @@ seir = sei ⋅ recovery
 # here we convert the C-Set decoration to a Petri.jl model
 # to use its StochasticDifferentialEquations support
 
-p_seir = Model(decoration(F_epi(seir)));
+p_seir = decoration(F_epi(seir));
 
 display_wd(seir)
 #-
-Graph(p_seir)
+Graph(Model(p_seir))
 
 # define initial states and transition rates, then
 # create, solve, and visualize ODE problem
@@ -68,8 +67,8 @@ Graph(p_seir)
 u0 = [10.0, 1, 0, 0];
 p = [.9, .2, .5];
 
-prob,cb = SDEProblem(p_seir,u0,(0.0,15.0),p);
-sol = solve(prob,SRA1(),callback=cb)
+prob = ODEProblem(vectorfield(p_seir),u0,(0.0,15.0),p);
+sol = solve(prob,Tsit5())
 
 plot(sol)
 
@@ -81,11 +80,11 @@ seird = sei ⋅ Δ(I) ⋅ (death ⊗ recovery)
 
 # get resulting petri net and visualize model
 
-p_seird = Model(decoration(F_epi(seird)));
+p_seird = decoration(F_epi(seird));
 
 display_wd(seird)
 #-
-Graph(p_seird)
+Graph(Model(p_seird))
 
 # define initial states and transition rates, then
 # create, solve, and visualize ODE problem
@@ -93,7 +92,7 @@ Graph(p_seird)
 u0 = [10.0, 1, 0, 0, 0];
 p = [0.9, 0.2, 0.5, 0.1];
 
-prob,cb = SDEProblem(p_seird,u0,(0.0,15.0),p);
-sol = solve(prob,SRA1(),callback=cb)
+prob = ODEProblem(vectorfield(p_seird),u0,(0.0,15.0),p);
+sol = solve(prob,Tsit5())
 
 plot(sol)