diff --git a/core/biochemical_network.jl b/core/biochemical_network.jl
index c2592c4a9ef031506cab2fd2aaa6868d8774e670..0866b412ee7e70530cfae8615de7030abfbdda6c 100644
--- a/core/biochemical_network.jl
+++ b/core/biochemical_network.jl
@@ -35,7 +35,8 @@ function get_str_propensity(propensity::Symbol, dict_species::Dict, dict_params:
return str_propensity
end
-macro biochemical_network(expr_name,expr_network)
+macro biochemical_network(expr_network,expr_name...)
+ model_name = isempty(expr_name) ? "Unnamed macro generated" : expr_name[1]
transitions = String[]
dict_species = Dict{String,Int}()
dict_params = Dict{String,Int}()
@@ -103,7 +104,7 @@ macro biochemical_network(expr_name,expr_network)
# Let's write some lines that creates the function f! (step of a simulation) for this biochemical network
nbr_rand = rand(1:1000)
nbr_reactions = length(list_expr_reactions)
- basename_func = "$(replace(expr_name, ' '=>'_'))_$(nbr_rand)"
+ basename_func = "$(replace(model_name, ' '=>'_'))_$(nbr_rand)"
expr_model_f! = "function $(basename_func)_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}}, xn::Vector{Int}, tn::Float64, p::Vector{Float64})\n\t"
# Computation of nu and propensity functions in f!
str_l_a = "l_a = ("
@@ -178,6 +179,7 @@ macro biochemical_network(expr_name,expr_network)
model_f! = eval(Meta.parse(expr_model_f!))
model_isabsorbing = eval(Meta.parse(expr_model_isabsorbing))
return :(ContinuousTimeModel($dim_state, $dim_params, $dict_species, $dict_params, $transitions,
- $(zeros(dim_params)), $(zeros(Int, dim_state)), 0.0, $model_f!, $model_isabsorbing; g = $list_species))
+ $(zeros(dim_params)), $(zeros(Int, dim_state)), 0.0, $model_f!, $model_isabsorbing;
+ g = $list_species, name=$model_name))
end
diff --git a/core/common.jl b/core/common.jl
index e283f8330e438d2c63827436463205b358c51183..718d60936ccaf328acfa63455c343efb1ba679cf 100644
--- a/core/common.jl
+++ b/core/common.jl
@@ -10,6 +10,7 @@ const Location = String
const VariableAutomaton = String
mutable struct ContinuousTimeModel <: Model
+ name::String
dim_state::Int # state space dim
dim_params::Int # parameter space dim
map_var_idx::Dict{String,Int} # maps variable str to index in the state space
@@ -85,7 +86,8 @@ end
function ContinuousTimeModel(dim_state::Int, dim_params::Int, map_var_idx::Dict, map_param_idx::Dict, transitions::Vector{String},
p::Vector{Float64}, x0::Vector{Int}, t0::Float64,
f!::Function, isabsorbing::Function;
- g::Vector{String} = keys(map_var_idx), time_bound::Float64 = Inf, buffer_size::Int = 10, estim_min_states::Int = 50)
+ g::Vector{String} = keys(map_var_idx), time_bound::Float64 = Inf,
+ buffer_size::Int = 10, estim_min_states::Int = 50, name::String = "Unnamed")
dim_obs_state = length(g)
_map_obs_var_idx = Dict()
_g_idx = Vector{Int}(undef, dim_obs_state)
@@ -100,7 +102,8 @@ function ContinuousTimeModel(dim_state::Int, dim_params::Int, map_var_idx::Dict,
if length(methods(isabsorbing)) >= 2
@warn "You have possibly redefined a function Model.isabsorbing used in a previously instantiated model."
end
- new_model = ContinuousTimeModel(dim_state, dim_params, map_var_idx, _map_obs_var_idx, map_param_idx, transitions, p, x0, t0, f!, g, _g_idx, isabsorbing, time_bound, buffer_size, estim_min_states)
+ new_model = ContinuousTimeModel(name, dim_state, dim_params, map_var_idx, _map_obs_var_idx, map_param_idx, transitions,
+ p, x0, t0, f!, g, _g_idx, isabsorbing, time_bound, buffer_size, estim_min_states)
@assert check_consistency(new_model)
return new_model
end
diff --git a/core/model.jl b/core/model.jl
index 26136ba705e92e7eee7c74187f893d18cdaecab8..13809b739633b01e032150bf402dc3656142e653 100644
--- a/core/model.jl
+++ b/core/model.jl
@@ -364,6 +364,23 @@ function distribute_prob_accept_lha(sm::SynchronizedModel, nbr_sim::Int)
return sum_val / nbr_sim
end
+function Base.show(io::IO, m::ContinuousTimeModel)
+ print(io, "$(m.name) model\n")
+ print(io, "- variables :\n")
+ for (var, idx) in m.map_var_idx
+ print(io, "* $var (idx = $idx in state space)\n")
+ end
+ print(io, "- parameters :\n")
+ for (param, idx) in m.map_param_idx
+ print(io, "* $param (idx = $idx in parameter space)\n")
+ end
+ print(io, "- transitions : $(m.transitions)\n")
+ print(io, "- observed variables :\n")
+ for i in eachindex(m.g)
+ print(io, "* $(m.g[i]) (idx = $i in observed state space)\n")
+ end
+end
+
isbounded(m::Model) = get_proba_model(m).time_bound < Inf
function check_consistency(m::ContinuousTimeModel)
@assert m.dim_state == length(m.map_var_idx)
diff --git a/models/ER.jl b/models/ER.jl
index 11e703b2b6f81c79fc4c81b7ac27fd190ce586b5..2af5b5f409ec0155b6aa095a34d530e105335b4c 100644
--- a/models/ER.jl
+++ b/models/ER.jl
@@ -52,7 +52,7 @@ isabsorbing_ER(p::Vector{Float64},xn::Vector{Int}) =
(p[1]*xn[1]*xn[2] + (p[2]+p[3])*xn[3]) === 0.0
g_ER = ["P"]
-ER = ContinuousTimeModel(d,k,dict_var_ER,dict_p_ER,l_tr_ER,p_ER,x0_ER,t0_ER,ER_f!,isabsorbing_ER; g=g_ER)
+ER = ContinuousTimeModel(d,k,dict_var_ER,dict_p_ER,l_tr_ER,p_ER,x0_ER,t0_ER,ER_f!,isabsorbing_ER; g=g_ER,name="ER pkg")
function create_ER(new_p::Vector{Float64})
ER_new = deepcopy(ER)
diff --git a/models/SIR.jl b/models/SIR.jl
index 93fbbb920ef26eedc60d3abcf03c1d3dd7dedf10..592835f1d44553fa8b858ba686ee59fbbdfe6876 100644
--- a/models/SIR.jl
+++ b/models/SIR.jl
@@ -50,7 +50,7 @@ end
isabsorbing_SIR(p::Vector{Float64}, xn::Vector{Int}) = (p[1]*xn[1]*xn[2] + p[2]*xn[2]) === 0.0
g_SIR = ["I"]
-SIR = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr_SIR,p_SIR,x0_SIR,t0_SIR,SIR_f!,isabsorbing_SIR; g=g_SIR)
+SIR = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr_SIR,p_SIR,x0_SIR,t0_SIR,SIR_f!,isabsorbing_SIR; g=g_SIR, name="SIR pkg")
function create_SIR(new_p::Vector{Float64})
SIR_new = deepcopy(SIR)
diff --git a/models/SIR_tauleap.jl b/models/SIR_tauleap.jl
index 6dac452d92feb7e4f0478010da969f0d6787abf8..3d9cb6e7e832be5b3a92f59424d6f0c01539593c 100644
--- a/models/SIR_tauleap.jl
+++ b/models/SIR_tauleap.jl
@@ -37,7 +37,7 @@ g_SIR_tauleap = ["I"]
SIR_tauleap = ContinuousTimeModel(d,k,dict_var_SIR_tauleap,dict_p_SIR_tauleap,l_tr_SIR_tauleap,
p_SIR_tauleap,x0_SIR_tauleap,t0_SIR_tauleap,
- SIR_tauleap_f!,isabsorbing_SIR_tauleap; g=g_SIR_tauleap)
+ SIR_tauleap_f!,isabsorbing_SIR_tauleap; g=g_SIR_tauleap, name="SIR tauleap pkg")
function create_SIR_tauleap(new_p::Vector{Float64})
SIR_tauleap_new = deepcopy(SIR_tauleap)
@assert length(SIR_tauleap_new.p) == length(new_p)
diff --git a/models/poisson.jl b/models/poisson.jl
index f5991aecc075af058d83a5c09492153f69761870..db080272085cd79e5f9648ecaf8bcdc4d23d8cf3 100644
--- a/models/poisson.jl
+++ b/models/poisson.jl
@@ -24,7 +24,8 @@ g_poisson = ["N"]
poisson = ContinuousTimeModel(d,k,dict_var_poisson,dict_p_poisson,l_tr_poisson,
p_poisson,x0_poisson,t0_poisson,
- poisson_f!,isabsorbing_poisson; g=g_poisson, time_bound=1.0)
+ poisson_f!,isabsorbing_poisson;
+ g=g_poisson, time_bound=1.0, name="Poisson process pkg")
function create_poisson(new_p::Vector{Float64})
poisson_new = deepcopy(poisson)
@assert length(poisson_new.p) == length(new_p)
diff --git a/tests/unit/macro_biochemical_network.jl b/tests/unit/macro_biochemical_network.jl
index 5fab6c55eddbb7f9d7a8b1f3c0d7b6f76772dbe0..13295975103277cbb929fe7322bd0167ccaf6d55 100644
--- a/tests/unit/macro_biochemical_network.jl
+++ b/tests/unit/macro_biochemical_network.jl
@@ -1,18 +1,25 @@
using MarkovProcesses
-model_SIR = @biochemical_network "SIR" begin
+model_SIR = @biochemical_network begin
R1: (S+I => 2I, ki*S*I)
R2: (I => R, kr*I)
-end
+end "SIR"
set_x0!(model_SIR, [95,5,0])
set_param!(model_SIR, [0.012, 0.05])
-model_ER = @biochemical_network "ER" begin
+model_unnamed_SIR = @biochemical_network begin
+ R1: (S+I => 2I, ki*S*I)
+ R2: (I => R, kr*I)
+end
+set_x0!(model_unnamed_SIR, [95,5,0])
+set_param!(model_unnamed_SIR, [0.012, 0.05])
+
+model_ER = @biochemical_network begin
R1: (E+S => ES, k1*E*S)
R2: (ES => E+S, k2*ES)
R3: (ES => E+P, k3*ES)
-end
+end "ER"
set_x0!(model_ER, [100,100,0,0])
set_param!(model_ER, [1.0,1.0,1.0])