Fix in simulation of macro generated models. Small changes with variables...

Fix in simulation of macro generated models. Small changes with variables types of the pkg. Small change in test of macro models.

core/MarkovProcesses.jl

@@ -13,7 +13,8 @@ export Distribution, Product, Uniform, Normal
 # Common types and constructors
 export Observations, AbstractTrajectory, Trajectory, SynchronizedTrajectory
 export Model, ContinuousTimeModel, SynchronizedModel, ParametricModel
-export LHA, StateLHA, Edge
+export VariableModel, ParameterModel, Transition
+export LHA, StateLHA, Edge, Location, VariableAutomaton
 
 # Trajectory related methods
 export +, -, δ, dist_lp

core/common.jl

@@ -5,11 +5,11 @@ import Distributions: Distribution, Univariate, Continuous, UnivariateDistributi
 abstract type Model end 
 abstract type AbstractTrajectory end
 
+const VariableModel = Symbol
+const ParameterModel = Symbol
 const Transition = Union{Symbol,Nothing}
 const Location = Symbol
 const VariableAutomaton = Symbol
-const VariableModel = Symbol
-const ParameterModel = Symbol
 
 mutable struct ContinuousTimeModel <: Model
     name::String

core/model.jl

@@ -341,13 +341,20 @@ end
 Distribute over workers the computation of the mean value 
 of a LHA over `nbr_sim` simulations of the model.
 """
-function distribute_mean_value_lha(sm::SynchronizedModel, sym_var::Union{VariableAutomaton,Vector{VariableAutomaton}}, nbr_sim::Int)
+function distribute_mean_value_lha(sm::SynchronizedModel, sym_var::VariableAutomaton, nbr_sim::Int)
     sum_val = @distributed (+) for i = 1:nbr_sim 
         volatile_simulate(sm)[sym_var] 
     end
     return sum_val / nbr_sim
 end
 
+function distribute_mean_value_lha(sm::SynchronizedModel, sym_var::Vector{VariableAutomaton}, nbr_sim::Int)
+    sum_val = @distributed (+) for i = 1:nbr_sim 
+        volatile_simulate(sm)[sym_var]
+    end
+    return sum_val / nbr_sim
+end
+
 function mean_value_lha(sm::SynchronizedModel, sym_var::VariableAutomaton, nbr_sim::Int)
     sum_val = 0.0 
     for i = 1:nbr_sim 

core/network_model.jl

@@ -102,7 +102,7 @@ macro network_model(expr_network,expr_name...)
     nbr_rand = rand(1:1000)
     nbr_reactions = length(list_expr_reactions)
     basename_func = "$(replace(model_name, ' '=>'_'))_$(nbr_rand)"
-    expr_model_f! = "function $(basename_func)_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}}, xn::Vector{Int}, tn::Float64, p::Vector{Float64})\n\t"
+    expr_model_f! = "function $(basename_func)_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Transition}, xn::Vector{Int}, tn::Float64, p::Vector{Float64})\n\t"
     # Computation of nu and propensity functions in f!
     str_l_a = "l_a = ("
     str_test_isabsorbing = "@inbounds("

tests/unit/macro_network_model.jl

@@ -7,6 +7,7 @@ model_SIR = @network_model begin
 end "SIR"
 set_x0!(model_SIR, [95,5,0])
 set_param!(model_SIR, [0.012, 0.05])
+simulate(model_SIR)
 
 model_unnamed_SIR = @network_model begin
     R1: (S+I => 2I, ki*S*I)
@@ -14,6 +15,7 @@ model_unnamed_SIR = @network_model begin
 end
 set_x0!(model_unnamed_SIR, [95,5,0])
 set_param!(model_unnamed_SIR, [0.012, 0.05])
+simulate(model_unnamed_SIR)
 
 model_ER = @network_model begin
     R1: (E+S => ES, k1*E*S)
@@ -22,6 +24,7 @@ model_ER = @network_model begin
 end "ER"
 set_x0!(model_ER, [100,100,0,0])
 set_param!(model_ER, [1.0,1.0,1.0])
+simulate(model_ER)
 
 return true