diff --git a/algorithms/_utils_abc.jl b/algorithms/_utils_abc.jl
index 5c1d3730f111605afda7cf41f8d9e7669e933ed2..66e2b700064dc7495c170e4c2bcaa4a2972783ea 100644
--- a/algorithms/_utils_abc.jl
+++ b/algorithms/_utils_abc.jl
@@ -1,6 +1,6 @@
function _init_abc_automaton!(mat_p_old::Matrix{Float64}, vec_dist::Vector{Float64},
- pm::ParametricModel, str_var_aut::String;
+ pm::ParametricModel, sym_var_aut::VariableAutomaton;
l_obs::Union{Nothing,AbstractVector} = nothing,
func_dist::Union{Nothing,Function} = nothing)
vec_p = zeros(pm.df)
@@ -9,7 +9,7 @@ function _init_abc_automaton!(mat_p_old::Matrix{Float64}, vec_dist::Vector{Float
mat_p_old[:,i] = vec_p
if l_obs == nothing
S = volatile_simulate(pm, vec_p)
- vec_dist[i] = S[str_var_aut]
+ vec_dist[i] = S[sym_var_aut]
else
σ = simulate(pm, vec_p)
vec_dist[i] = func_dist(σ, l_obs)
@@ -45,7 +45,7 @@ function _task_worker!(d_mat_p::DArray{Float64,2}, d_vec_dist::DArray{Float64,1}
pm::ParametricModel, epsilon::Float64,
wl_old::Vector{Float64}, mat_p_old::Matrix{Float64},
mat_cov::Matrix{Float64}, tree_mat_p::Union{Nothing,KDTree},
- kernel_type::String, str_var_aut::String;
+ kernel_type::String, sym_var_aut::VariableAutomaton;
l_obs::Union{Nothing,AbstractVector} = nothing,
func_dist::Union{Nothing,Function} = nothing)
mat_p = localpart(d_mat_p)
@@ -54,7 +54,7 @@ function _task_worker!(d_mat_p::DArray{Float64,2}, d_vec_dist::DArray{Float64,1}
l_nbr_sim = zeros(Int, length(vec_dist))
Threads.@threads for i = eachindex(vec_dist)
_update_param!(mat_p, vec_dist, l_nbr_sim, wl_current, i, pm, epsilon,
- wl_old, mat_p_old, mat_cov, tree_mat_p, kernel_type, str_var_aut;
+ wl_old, mat_p_old, mat_cov, tree_mat_p, kernel_type, sym_var_aut;
l_obs = l_obs, func_dist = func_dist)
end
return sum(l_nbr_sim)
@@ -86,7 +86,7 @@ function _update_param!(mat_p::Matrix{Float64}, vec_dist::Vector{Float64},
pm::ParametricModel, epsilon::Float64,
wl_old::Vector{Float64}, mat_p_old::Matrix{Float64},
mat_cov::Matrix{Float64}, tree_mat_p::Union{Nothing,KDTree},
- kernel_type::String, str_var_aut::String;
+ kernel_type::String, sym_var_aut::VariableAutomaton;
l_obs::Union{Nothing,AbstractVector} = nothing,
func_dist::Union{Nothing,Function} = nothing)
d_weights = Categorical(wl_old)
@@ -102,7 +102,7 @@ function _update_param!(mat_p::Matrix{Float64}, vec_dist::Vector{Float64},
end
if l_obs == nothing
S = volatile_simulate(pm, vec_p_prime)
- dist_sim = S[str_var_aut]
+ dist_sim = S[sym_var_aut]
else
σ = simulate(pm, vec_p)
dist_sim = func_dist(σ, l_obs)
diff --git a/algorithms/abc_smc.jl b/algorithms/abc_smc.jl
index 26cc1d5393c40201cdbe7fe9a0e62f8c91e0fd55..679098845a71791ba324d8a9bab6615176c84131 100644
--- a/algorithms/abc_smc.jl
+++ b/algorithms/abc_smc.jl
@@ -28,7 +28,7 @@ end
function automaton_abc(pm::ParametricModel; nbr_particles::Int = 100, alpha::Float64 = 0.75, kernel_type = "mvnormal",
NT::Float64 = nbr_particles/2, duration_time::Float64 = Inf,
- bound_sim::Int = typemax(Int), str_var_aut::String = "d", verbose::Int = 0)
+ bound_sim::Int = typemax(Int), sym_var_aut::VariableAutomaton = :d, verbose::Int = 0)
@assert typeof(pm.m) <: SynchronizedModel
@assert 0 < nbr_particles
@assert 0.0 < alpha < 1.0
@@ -41,14 +41,14 @@ function automaton_abc(pm::ParametricModel; nbr_particles::Int = 100, alpha::Flo
write(file_cfg, "kernel type : $(kernel_type) \n")
close(file_cfg)
if nprocs() == 1
- return _abc_smc(pm, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, str_var_aut)
+ return _abc_smc(pm, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, sym_var_aut)
end
- return _distributed_abc_smc(pm, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, str_var_aut)
+ return _distributed_abc_smc(pm, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, sym_var_aut)
end
"""
`abc_smc(pm::ParametricModel, l_obs, func_dist; nbr_particles, alpha, kernel_type, NT
- duration_tiùe, bound_sim, str_var_aut, verbose)`
+ duration_tiùe, bound_sim, sym_var_aut, verbose)`
Run the ABC-SMC algorithm with the pm parametric model.
`func_dist(l_sim, l_obs)` is the distance function between simulations and observation,
@@ -66,7 +66,7 @@ If pm is defined on a ContinuousTimeModel, then `T1` should verify `T1 <: Trajec
function abc_smc(pm::ParametricModel, l_obs::AbstractVector, func_dist::Function;
nbr_particles::Int = 100, alpha::Float64 = 0.75, kernel_type = "mvnormal",
NT::Float64 = nbr_particles/2, duration_time::Float64 = Inf,
- bound_sim::Int = typemax(Int), str_var_aut::String = "d", verbose::Int = 0)
+ bound_sim::Int = typemax(Int), sym_var_aut::VariableAutomaton = :d, verbose::Int = 0)
@assert 0 < nbr_particles
@assert 0.0 < alpha < 1.0
@assert kernel_type in ["mvnormal", "knn_mvnormal"]
@@ -78,9 +78,9 @@ function abc_smc(pm::ParametricModel, l_obs::AbstractVector, func_dist::Function
write(file_cfg, "kernel type : $(kernel_type) \n")
close(file_cfg)
if nprocs() == 1
- return _abc_smc(pm, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, str_var_aut; l_obs = l_obs, func_dist = func_dist)
+ return _abc_smc(pm, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, sym_var_aut; l_obs = l_obs, func_dist = func_dist)
end
- return _distributed_abc_smc(pm, l_obs, dist, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, str_var_aut; l_obs = l_obs, func_dist = func_dist)
+ return _distributed_abc_smc(pm, l_obs, dist, nbr_particles, alpha, kernel_type, NT, duration_time, bound_sim, dir_res, sym_var_aut; l_obs = l_obs, func_dist = func_dist)
end
@@ -89,7 +89,7 @@ end
function _abc_smc(pm::ParametricModel, nbr_particles::Int, alpha::Float64,
kernel_type::String, NT::Float64, duration_time::Float64,
- bound_sim::Int, dir_res::String, str_var_aut::String;
+ bound_sim::Int, dir_res::String, sym_var_aut::VariableAutomaton;
l_obs::Union{Nothing,AbstractVector} = nothing, func_dist::Union{Nothing,Function} = nothing)
@info "ABC PMC with $(nworkers()) processus and $(Threads.nthreads()) threads"
begin_time = time()
@@ -102,7 +102,7 @@ function _abc_smc(pm::ParametricModel, nbr_particles::Int, alpha::Float64,
vec_dist = zeros(nbr_particles)
wl_old = zeros(nbr_particles)
@info "Step 1 : Init"
- _init_abc_automaton!(mat_p_old, vec_dist, pm, str_var_aut; l_obs = l_obs, func_dist = func_dist)
+ _init_abc_automaton!(mat_p_old, vec_dist, pm, sym_var_aut; l_obs = l_obs, func_dist = func_dist)
prior_pdf!(wl_old, pm, mat_p_old)
normalize!(wl_old, 1)
ess = effective_sample_size(wl_old)
@@ -141,7 +141,7 @@ function _abc_smc(pm::ParametricModel, nbr_particles::Int, alpha::Float64,
end
Threads.@threads for i = eachindex(vec_dist)
_update_param!(mat_p, vec_dist, l_nbr_sim, wl_current, i, pm, epsilon,
- wl_old, mat_p_old, mat_cov, tree_mat_p, kernel_type, str_var_aut;
+ wl_old, mat_p_old, mat_cov, tree_mat_p, kernel_type, sym_var_aut;
l_obs = l_obs, func_dist = func_dist)
end
normalize!(wl_current, 1)
@@ -182,7 +182,7 @@ end
function _distributed_abc_smc(pm::ParametricModel, nbr_particles::Int, alpha::Float64,
kernel_type::String, NT::Float64, duration_time::Float64, bound_sim::Int,
- dir_res::String, str_var_aut::String;
+ dir_res::String, sym_var_aut::VariableAutomaton;
l_obs::Union{Nothing,AbstractVector} = nothing, func_dist::Union{Nothing,Function} = nothing)
@info "Distributed ABC PMC with $(nworkers()) processus and $(Threads.nthreads()) threads"
begin_time = time()
@@ -195,7 +195,7 @@ function _distributed_abc_smc(pm::ParametricModel, nbr_particles::Int, alpha::Fl
vec_dist = zeros(nbr_particles)
wl_old = zeros(nbr_particles)
@info "Step 1 : Init"
- _init_abc_automaton!(mat_p_old, vec_dist, pm, str_var_aut; l_obs = l_obs, func_dist = func_dist)
+ _init_abc_automaton!(mat_p_old, vec_dist, pm, sym_var_aut; l_obs = l_obs, func_dist = func_dist)
prior_pdf!(wl_old, pm, mat_p_old)
normalize!(wl_old, 1)
ess = effective_sample_size(wl_old)
@@ -240,7 +240,7 @@ function _distributed_abc_smc(pm::ParametricModel, nbr_particles::Int, alpha::Fl
@async l_nbr_sim[t_id_w] =
remotecall_fetch(() -> _task_worker!(d_mat_p, d_vec_dist, d_wl_current,
pm, epsilon, wl_old, mat_p_old, mat_cov, tree_mat_p,
- kernel_type, str_var_aut;
+ kernel_type, sym_var_aut;
l_obs = l_obs, func_dist = func_dist), id_w)
end
wl_current = convert(Array, d_wl_current)
diff --git a/automata/automaton_F.jl b/automata/automaton_F.jl
index a38d49282b5ea5b373f5fbf30ddae3799dd48582..ec0106e215addabf463f12cb23c42d5fe3334bfd 100644
--- a/automata/automaton_F.jl
+++ b/automata/automaton_F.jl
@@ -1,6 +1,6 @@
-function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1::Float64, t2::Float64, str_obs::String)
- @assert str_obs in m.g
+function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1::Float64, t2::Float64, sym_obs::VariableModel)
+ @assert sym_obs in m.g "$(sym_obs) is not observed."
# Locations
locations = [:l0, :l1, :l2, :l3]
@@ -14,8 +14,8 @@ function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
locations_final = [:l2]
#S.n <=> S.values[A.map_var_automaton_idx[:n]]
- #P <=> xn[map_var_model_idx[constants[str_O]] with str_O = "P". On stock str_O dans constants
- # P = get_value(A, x, str_obs)
+ #P <=> xn[map_var_model_idx[constants[str_O]] with str_O = :P. On stock str_O dans constants
+ # P = get_value(A, x, sym_obs)
## Map of automaton variables
map_var_automaton_idx = Dict{VariableAutomaton,Int}(:n => 1, :d => 2, :isabs => 3)
@@ -38,7 +38,7 @@ function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
cc_aut_F_l0l1_1(A::LHA, S::StateLHA) = true
us_aut_F_l0l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1;
- S[:n] = get_value(A, x, str_obs);
+ S[:n] = get_value(A, x, sym_obs);
S[:d] = Inf;
S[:isabs] = m.isabsorbing(m.p,x))
edge1 = Edge([nothing], cc_aut_F_l0l1_1, us_aut_F_l0l1_1!)
@@ -108,9 +108,9 @@ function create_automaton_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
cc_aut_F_l3l1_1(A::LHA, S::StateLHA) = true
us_aut_F_l3l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1;
- S[:n] = get_value(A, x, str_obs);
+ S[:n] = get_value(A, x, sym_obs);
S[:isabs] = m.isabsorbing(m.p,x))
- edge1 = Edge(["ALL"], cc_aut_F_l3l1_1, us_aut_F_l3l1_1!)
+ edge1 = Edge([:ALL], cc_aut_F_l3l1_1, us_aut_F_l3l1_1!)
map_edges[:l3][:l1] = [edge1]
diff --git a/automata/automaton_G.jl b/automata/automaton_G.jl
index e2608f951a986ee1d7bb0474bb84af840440bc4b..d53fcb1edd18798cefab74631cea517f34deee7d 100644
--- a/automata/automaton_G.jl
+++ b/automata/automaton_G.jl
@@ -1,6 +1,6 @@
-function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1::Float64, t2::Float64, str_obs::String)
- @assert str_obs in m.g
+function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1::Float64, t2::Float64, sym_obs::VariableModel)
+ @assert sym_obs in m.g
# Locations
locations = [:l0, :l1, :l2, :l3, :l4]
@@ -38,7 +38,7 @@ function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
us_aut_G_l0l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1;
S[:d] = 0;
- S[:n] = get_value(A, x, str_obs);
+ S[:n] = get_value(A, x, sym_obs);
S[:in] = true;
S[:isabs] = m.isabsorbing(m.p,x))
edge1 = Edge([nothing], cc_aut_G_l0l1_1, us_aut_G_l0l1_1!)
@@ -142,9 +142,9 @@ function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
cc_aut_G_l3l1_1(A::LHA, S::StateLHA) = true
us_aut_G_l3l1_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1;
- S[:n] = get_value(A, x, str_obs);
+ S[:n] = get_value(A, x, sym_obs);
S[:isabs] = m.isabsorbing(m.p,x))
- edge1 = Edge(["ALL"], cc_aut_G_l3l1_1, us_aut_G_l3l1_1!)
+ edge1 = Edge([:ALL], cc_aut_G_l3l1_1, us_aut_G_l3l1_1!)
map_edges[:l3][:l1] = [edge1]
@@ -172,10 +172,10 @@ function create_automaton_G(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1
(S.loc = :l1;
S[:d] += S[:tprime] * min(abs(A.constants[:x1] - S[:n]), abs(A.constants[:x2] - S[:n]));
S[:tprime] = 0.0;
- S[:n] = get_value(A, x, str_obs);
+ S[:n] = get_value(A, x, sym_obs);
S[:in] = true;
S[:isabs] = m.isabsorbing(m.p,x))
- edge1 = Edge(["ALL"], cc_aut_G_l4l1_1, us_aut_G_l4l1_1!)
+ edge1 = Edge([:ALL], cc_aut_G_l4l1_1, us_aut_G_l4l1_1!)
map_edges[:l4][:l1] = [edge1]
diff --git a/automata/automaton_G_and_F.jl b/automata/automaton_G_and_F.jl
index f3ab84cf512c0976b777ff8562213a3be9d8c776..2e05a94df9610b5cd487bfa7ca01d3701391bcfa 100644
--- a/automata/automaton_G_and_F.jl
+++ b/automata/automaton_G_and_F.jl
@@ -1,9 +1,9 @@
-function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1::Float64, t2::Float64, str_obs_G::String,
- x3::Float64, x4::Float64, t3::Float64, t4::Float64, str_obs_F::String)
+function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float64, t1::Float64, t2::Float64, sym_obs_G::VariableModel,
+ x3::Float64, x4::Float64, t3::Float64, t4::Float64, sym_obs_F::VariableModel)
- @assert str_obs_G in m.g
- @assert str_obs_F in m.g
+ @assert sym_obs_G in m.g
+ @assert sym_obs_F in m.g
# Locations
locations = [:l0G, :l1G, :l2G, :l3G, :l4G,
:l1F, :l2F, :l3F]
@@ -49,7 +49,7 @@ function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float
us_aut_G_l0Gl1G_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1G;
S[:d] = 0;
- S[:n] = get_value(A, x, str_obs_G);
+ S[:n] = get_value(A, x, sym_obs_G);
S[:in] = true;
S[:isabs] = m.isabsorbing(m.p,x))
edge1 = Edge([nothing], cc_aut_G_l0Gl1G_1, us_aut_G_l0Gl1G_1!)
@@ -154,9 +154,9 @@ function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float
cc_aut_G_l3Gl1G_1(A::LHA, S::StateLHA) = true
us_aut_G_l3Gl1G_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1G;
- S[:n] = get_value(A, x, str_obs_G);
+ S[:n] = get_value(A, x, sym_obs_G);
S[:isabs] = m.isabsorbing(m.p,x))
- edge1 = Edge(["ALL"], cc_aut_G_l3Gl1G_1, us_aut_G_l3Gl1G_1!)
+ edge1 = Edge([:ALL], cc_aut_G_l3Gl1G_1, us_aut_G_l3Gl1G_1!)
map_edges[:l3G][:l1G] = [edge1]
tuple = (:l3G, :l2G)
@@ -184,10 +184,10 @@ function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float
(S.loc = :l1G;
S[:d] += S[:tprime] * min(abs(A.constants[:x1] - S[:n]), abs(A.constants[:x2] - S[:n]));
S[:tprime] = 0.0;
- S[:n] = get_value(A, x, str_obs_G);
+ S[:n] = get_value(A, x, sym_obs_G);
S[:in] = true;
S[:isabs] = m.isabsorbing(m.p,x))
- edge1 = Edge(["ALL"], cc_aut_G_l4Gl1G_1, us_aut_G_l4Gl1G_1!)
+ edge1 = Edge([:ALL], cc_aut_G_l4Gl1G_1, us_aut_G_l4Gl1G_1!)
map_edges[:l4G][:l1G] = [edge1]
@@ -207,7 +207,7 @@ function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float
cc_aut_F_l2Gl1F_1(A::LHA, S::StateLHA) = true
us_aut_F_l2Gl1F_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1F;
- S[:n] = get_value(A, x, str_obs_F);
+ S[:n] = get_value(A, x, sym_obs_F);
S[:dprime] = Inf;
S[:isabs] = m.isabsorbing(m.p,x))
edge1 = Edge([nothing], cc_aut_F_l2Gl1F_1, us_aut_F_l2Gl1F_1!)
@@ -280,9 +280,9 @@ function create_automaton_G_and_F(m::ContinuousTimeModel, x1::Float64, x2::Float
cc_aut_F_l3Fl1F_1(A::LHA, S::StateLHA) = true
us_aut_F_l3Fl1F_1!(A::LHA, S::StateLHA, x::Vector{Int}) =
(S.loc = :l1F;
- S[:n] = get_value(A, x, str_obs_F);
+ S[:n] = get_value(A, x, sym_obs_F);
S[:isabs] = m.isabsorbing(m.p,x))
- edge1 = Edge(["ALL"], cc_aut_F_l3Fl1F_1, us_aut_F_l3Fl1F_1!)
+ edge1 = Edge([:ALL], cc_aut_F_l3Fl1F_1, us_aut_F_l3Fl1F_1!)
map_edges[:l3F][:l1F] = [edge1]
diff --git a/bench/array_memory_order/access_trajectory.jl b/bench/array_memory_order/access_trajectory.jl
index 39be2cd79ebcba33b979bb4d67f34966f227a63f..4936299beee22d95d7b222f6991bf7503c084c08 100644
--- a/bench/array_memory_order/access_trajectory.jl
+++ b/bench/array_memory_order/access_trajectory.jl
@@ -7,7 +7,7 @@ include(get_module_path() * "/core/_tests_simulate.jl")
BenchmarkTools.DEFAULT_PARAMETERS.samples = 20000
if ARGS[1] == "SIR"
bound_time = 200.0
- l_var = ["S", "I", "R"]
+ l_var = [:S, :I, :R]
load_model("_bench_perf_test/SIR_col_buffer")
SIR_col_buffer.time_bound = bound_time
@@ -19,7 +19,7 @@ if ARGS[1] == "SIR"
model_col_buffer = SIR_col_buffer
model_row_buffer = SIR_row_buffer
elseif ARGS[1] == "ER"
- l_var = ["E","S","ES","P"]
+ l_var = [:E,:S,:ES,:P]
bound_time = 20.0
load_model("_bench_perf_test/ER_col_buffer")
@@ -42,7 +42,7 @@ function access_trajectory_col(m::Model)
n_sim = 100
for k = 1:n_sim
σ = _simulate_col_buffer(m)
- t = _get_values_col(σ, "I")
+ t = _get_values_col(σ, :I)
res += t[end-1]
end
return res
@@ -59,7 +59,7 @@ function access_trajectory_row(m::Model)
n_sim = 100
for k = 1:n_sim
σ = _simulate_row_buffer(m)
- t = _get_values_row(σ, "I")
+ t = _get_values_row(σ, :I)
res += t[end-1]
end
return res
diff --git a/bench/array_memory_order/multiple_sim.jl b/bench/array_memory_order/multiple_sim.jl
index eadcbd24bacc2e86f4007c146bf576ba5578fac5..32c75e782f6cb2cd5438131bddcaa958a0590d9b 100644
--- a/bench/array_memory_order/multiple_sim.jl
+++ b/bench/array_memory_order/multiple_sim.jl
@@ -6,7 +6,7 @@ using MarkovProcesses
include(get_module_path() * "/core/_tests_simulate.jl")
if ARGS[1] == "SIR"
- l_var = ["S", "I", "R"]
+ l_var = [:S, :I, :R]
bound_time = 200.0
load_model("_bench_perf_test/SIR_col")
@@ -23,7 +23,7 @@ if ARGS[1] == "SIR"
model_col_buffer = SIR_col_buffer
model_row_buffer = SIR_row_buffer
elseif ARGS[1] == "ER"
- l_var = ["E","S","ES","P"]
+ l_var = [:E,:S,:ES,:P]
bound_time = 20.0
load_model("_bench_perf_test/ER_col")
diff --git a/bench/array_memory_order/read_random_state_trajectory.jl b/bench/array_memory_order/read_random_state_trajectory.jl
index 0804ab8fbd36e9d001b66d9af6f65bdf359836bd..5fe0da5faa03cba0e94d53b6a05664aa66b9109d 100644
--- a/bench/array_memory_order/read_random_state_trajectory.jl
+++ b/bench/array_memory_order/read_random_state_trajectory.jl
@@ -7,7 +7,7 @@ include(get_module_path() * "/core/_tests_simulate.jl")
BenchmarkTools.DEFAULT_PARAMETERS.samples = 20000
if ARGS[1] == "SIR"
bound_time = 200.0
- l_var = ["S", "I", "R"]
+ l_var = [:S, :I, :R]
load_model("_bench_perf_test/SIR_col_buffer")
SIR_col_buffer.time_bound = bound_time
@@ -19,7 +19,7 @@ if ARGS[1] == "SIR"
model_col_buffer = SIR_col_buffer
model_row_buffer = SIR_row_buffer
elseif ARGS[1] == "ER"
- l_var = ["E","S","ES","P"]
+ l_var = [:E,:S,:ES,:P]
bound_time = 20.0
load_model("_bench_perf_test/ER_col_buffer")
diff --git a/bench/array_memory_order/read_trajectory.jl b/bench/array_memory_order/read_trajectory.jl
index db51374d94e136da2813dce3c3a1a828c92d7ddb..c1af4e574f712762f905d6462046414c2fd08cfe 100644
--- a/bench/array_memory_order/read_trajectory.jl
+++ b/bench/array_memory_order/read_trajectory.jl
@@ -7,7 +7,7 @@ include(get_module_path() * "/core/_tests_simulate.jl")
BenchmarkTools.DEFAULT_PARAMETERS.samples = 20000
if ARGS[1] == "SIR"
bound_time = 200.0
- l_var = ["S", "I", "R"]
+ l_var = [:S, :I, :R]
load_model("_bench_perf_test/SIR_col_buffer")
SIR_col_buffer.time_bound = bound_time
@@ -19,7 +19,7 @@ if ARGS[1] == "SIR"
model_col_buffer = SIR_col_buffer
model_row_buffer = SIR_row_buffer
elseif ARGS[1] == "ER"
- l_var = ["E","S","ES","P"]
+ l_var = [:E,:S,:ES,:P]
bound_time = 20.0
load_model("_bench_perf_test/ER_col_buffer")
@@ -44,7 +44,7 @@ function read_trajectory_col(m::Model)
n_read = 100000
for k = 1:n_read
for i = 1:n_states
- res += _get_value_col(σ, "I", i)
+ res += _get_value_col(σ, :I, i)
end
end
return res
@@ -63,7 +63,7 @@ function read_trajectory_row(m::Model)
n_read = 100000
for k = 1:n_read
for i = 1:n_states
- res += _get_value_row(σ, "I", i)
+ res += _get_value_row(σ, :I, i)
end
end
return res
diff --git a/bench/array_memory_order/sim.jl b/bench/array_memory_order/sim.jl
index 79fbc4fc8b377b17fbc8bcb5837d751e41be9ed2..7df223eb4da8ca1023e2766fe1ee7d3afae949cc 100644
--- a/bench/array_memory_order/sim.jl
+++ b/bench/array_memory_order/sim.jl
@@ -6,7 +6,7 @@ using MarkovProcesses
include(get_module_path() * "/core/_tests_simulate.jl")
if ARGS[1] == "SIR"
- l_var = ["S", "I", "R"]
+ l_var = [:S, :I, :R]
bound_time = 200.0
load_model("_bench_perf_test/SIR_col")
@@ -23,7 +23,7 @@ if ARGS[1] == "SIR"
model_col_buffer = SIR_col_buffer
model_row_buffer = SIR_row_buffer
elseif ARGS[1] == "ER"
- l_var = ["E","S","ES","P"]
+ l_var = [:E,:S,:ES,:P]
bound_time = 20.0
nbr_sim = 10000
diff --git a/bench/pkg/catalyst.jl b/bench/pkg/catalyst.jl
index cac5bb6cec921e7df2e965f64e1d6d3f3848a455..830c58b054643de5139af3b2bc4c21a3e4709fbd 100644
--- a/bench/pkg/catalyst.jl
+++ b/bench/pkg/catalyst.jl
@@ -5,8 +5,8 @@ using Catalyst
using DiffEqJump
load_model("ER")
-set_param!(ER, "k1", 0.2)
-set_param!(ER, "k2", 40.0)
+set_param!(ER, :k1, 0.2)
+set_param!(ER, :k2, 40.0)
ER.buffer_size = 100
ER.estim_min_states = 8000
diff --git a/bench/pygmalion/multiple_long_sim_er.jl b/bench/pygmalion/multiple_long_sim_er.jl
index 98254998cccd5abc37d1e318db810748043390ad..61d155bccb427e08d6eee3f22db230f8a9d7df8c 100644
--- a/bench/pygmalion/multiple_long_sim_er.jl
+++ b/bench/pygmalion/multiple_long_sim_er.jl
@@ -29,8 +29,8 @@ println("MarkovProcesses:")
using MarkovProcesses
MarkovProcesses.load_model("ER")
ER.time_bound = 20.0
-set_param!(ER, "k1", 0.2)
-set_param!(ER, "k2", 40.0)
+set_param!(ER, :k1, 0.2)
+set_param!(ER, :k2, 40.0)
@timev MarkovProcesses.simulate(ER)
println("Default buffer size=10")
diff --git a/bench/pygmalion/read_random_state_trajectory.jl b/bench/pygmalion/read_random_state_trajectory.jl
index a0cf950bf7b3c003c99e6d70b546d780600ac39b..3ef3575203bc5ccdd0d89705bf0c7d3c978bcccc 100644
--- a/bench/pygmalion/read_random_state_trajectory.jl
+++ b/bench/pygmalion/read_random_state_trajectory.jl
@@ -8,7 +8,7 @@ println("Pygmalion:")
str_m = "enzymatic_reaction"
str_d = "abc_er"
pygmalion.load_model(str_m)
-l_var = ["E", "S", "ES", "P"]
+l_var = [:E, :S, :ES, :P]
str_oml = "E,S,ES,P,R,time"
ll_om = split(str_oml, ",")
x0 = State(95.0, 95.0, 0.0, 0.0, 0.0, 0.0)
@@ -18,11 +18,11 @@ tml = 1:400
g_all = create_observation_function([ObserverModel(str_oml, tml)])
so = pygmalion.simulate(f, g_all, x0, u, p_true; on = nothing, full_timeline = true)
function random_trajectory_value_pyg(so::SystemObservation)
- n_states = get_number_of_observations(so, "P")
+ n_states = get_number_of_observations(so, :P)
return to_vec(so, "P", rand(1:n_states))
end
function random_trajectory_state_pyg(so::SystemObservation)
- n_states = get_number_of_observations(so, "P")
+ n_states = get_number_of_observations(so, :P)
return to_vec(so, so.oml, rand(1:n_states))
end
# Bench
@@ -37,7 +37,7 @@ ER.time_bound = 10.0
σ = MarkovProcesses.simulate(ER)
function random_trajectory_value(σ::AbstractTrajectory)
n_states = length_states(σ)
- return σ["P"][rand(1:n_states)]
+ return σ[:P][rand(1:n_states)]
end
# Bench
@timev random_trajectory_value(σ)
diff --git a/bench/pygmalion/read_trajectory_er.jl b/bench/pygmalion/read_trajectory_er.jl
index f40df1dc3273505f9a7c44a4545aad37fefef132..fdaa77057d4469b98a7f233126ae3af8e50d8c89 100644
--- a/bench/pygmalion/read_trajectory_er.jl
+++ b/bench/pygmalion/read_trajectory_er.jl
@@ -26,7 +26,7 @@ function read_trajectory_pyg_v1(so::SystemObservation)
return res
end
function read_trajectory_pyg_v2(so::SystemObservation)
- idx_P = om_findfirst("P", so.oml)
+ idx_P = om_findfirst(:P, so.oml)
n_states = length(so.otll[idx_P])
res = 0.0
for i = 1:n_states
@@ -51,13 +51,13 @@ function read_trajectory_v1(σ::AbstractTrajectory)
n_states = length_states(σ)
res = 0
for i = 1:n_states
- res += σ["P",i]
+ res += σ[:P,i]
end
return res
end
function read_trajectory_v2(σ::AbstractTrajectory)
n_states = length_states(σ)
- vals = σ["P"]
+ vals = σ[:P]
res = 0
for i = 1:n_states
res += vals[i]
diff --git a/core/_tests_simulate.jl b/core/_tests_simulate.jl
index 9279efd0cd070ecaa07bc0f1cbeae3c1d522f425..3da01d17598916be2169b50821eb8d4a90d550a8 100644
--- a/core/_tests_simulate.jl
+++ b/core/_tests_simulate.jl
@@ -3,16 +3,16 @@ struct OldTrajectory <: AbstractTrajectory
m::ContinuousTimeModel
values::Matrix{Int}
times::Vector{Float64}
- transitions::Vector{Union{String,Nothing}}
+ transitions::Vector{Union{Symbol,Nothing}}
end
# File for benchmarking simulation and memory access of the package.
# Trajectories
-_get_values_col(σ::OldTrajectory, var::String) =
+_get_values_col(σ::OldTrajectory, var::Symbol) =
@view σ.values[(σ.m)._map_obs_var_idx[var],:]
-_get_values_row(σ::OldTrajectory, var::String) =
+_get_values_row(σ::OldTrajectory, var::Symbol) =
@view σ.values[:,(σ.m)._map_obs_var_idx[var]]
_get_state_col(σ::OldTrajectory, idx::Int) =
@@ -20,9 +20,9 @@ _get_state_col(σ::OldTrajectory, idx::Int) =
_get_state_row(σ::OldTrajectory, idx::Int) =
@view σ.values[idx,:]
-_get_value_col(σ::OldTrajectory, var::String, idx::Int) =
+_get_value_col(σ::OldTrajectory, var::Symbol, idx::Int) =
σ.values[(σ.m)._map_obs_var_idx[var],idx]
-_get_value_row(σ::OldTrajectory, var::String, idx::Int) =
+_get_value_row(σ::OldTrajectory, var::Symbol, idx::Int) =
σ.values[idx,(σ.m)._map_obs_var_idx[var]]
# Model
@@ -31,7 +31,7 @@ function _simulate_col(m::ContinuousTimeModel)
# trajectory fields
full_values = zeros(m.dim_state, 0)
times = zeros(0)
- transitions = Vector{Union{String,Nothing}}(undef,0)
+ transitions = Vector{Union{Symbol,Nothing}}(undef,0)
# values at time n
n = 0
xn = @view m.x0[:]
@@ -65,7 +65,7 @@ function _simulate_row(m::ContinuousTimeModel)
# trajectory fields
full_values = zeros(m.dim_state, 0)
times = zeros(0)
- transitions = Vector{Union{String,Nothing}}(undef,0)
+ transitions = Vector{Union{Symbol,Nothing}}(undef,0)
# values at time n
n = 0
xn = @view m.x0[:]
@@ -100,7 +100,7 @@ function _simulate_col_buffer(m::ContinuousTimeModel; buffer_size::Int = 5)
# trajectory fields
full_values = zeros(m.dim_state, 0)
times = zeros(0)
- transitions = Vector{Union{String,Nothing}}(undef,0)
+ transitions = Vector{Union{Symbol,Nothing}}(undef,0)
# values at time n
n = 0
xn = @view m.x0[:]
@@ -108,7 +108,7 @@ function _simulate_col_buffer(m::ContinuousTimeModel; buffer_size::Int = 5)
# at time n+1
mat_x = zeros(Int, m.dim_state, buffer_size)
l_t = zeros(Float64, buffer_size)
- l_tr = Vector{Union{String,Nothing}}(undef, buffer_size)
+ l_tr = Vector{Union{Symbol,Nothing}}(undef, buffer_size)
isabsorbing = m.isabsorbing(m.p,xn)::Bool
while !isabsorbing && (tn <= m.time_bound)
i = 0
@@ -140,7 +140,7 @@ function _simulate_row_buffer(m::ContinuousTimeModel; buffer_size::Int = 5)
# trajectory fields
full_values = zeros(0, m.dim_state)
times = zeros(0)
- transitions = Vector{Union{String,Nothing}}(undef,0)
+ transitions = Vector{Union{Symbol,Nothing}}(undef,0)
# values at time n
n = 0
xn = @view m.x0[:]
@@ -148,7 +148,7 @@ function _simulate_row_buffer(m::ContinuousTimeModel; buffer_size::Int = 5)
# at time n+1
mat_x = zeros(Int, buffer_size, m.dim_state)
l_t = zeros(Float64, buffer_size)
- l_tr = Vector{Union{String,Nothing}}(undef, buffer_size)
+ l_tr = Vector{Union{Symbol,Nothing}}(undef, buffer_size)
isabsorbing = m.isabsorbing(m.p,xn)::Bool
while !isabsorbing && (tn <= m.time_bound)
i = 0
@@ -181,7 +181,7 @@ function _simulate_without_view(m::ContinuousTimeModel)
full_values = Matrix{Int}(undef, 1, m.dim_state)
full_values[1,:] = m.x0
times = Float64[m.t0]
- transitions = Union{String,Nothing}[nothing]
+ transitions = Union{Symbol,Nothing}[nothing]
# values at time n
n = 0
xn = @view m.x0[:]
@@ -189,7 +189,7 @@ function _simulate_without_view(m::ContinuousTimeModel)
# at time n+1
mat_x = zeros(Int, m.buffer_size, m.dim_state)
l_t = zeros(Float64, m.buffer_size)
- l_tr = Vector{Union{String,Nothing}}(undef, m.buffer_size)
+ l_tr = Vector{Union{Symbol,Nothing}}(undef, m.buffer_size)
isabsorbing = m.isabsorbing(m.p,xn)::Bool
while !isabsorbing && (tn <= m.time_bound)
i = 0
@@ -227,7 +227,7 @@ function _simulate_27d56(m::ContinuousTimeModel)
full_values = Matrix{Int}(undef, 1, m.dim_state)
full_values[1,:] = m.x0
times = Float64[m.t0]
- transitions = Union{String,Nothing}[nothing]
+ transitions = Union{Symbol,Nothing}[nothing]
# values at time n
n = 0
xn = view(reshape(m.x0, 1, m.dim_state), 1, :) # View for type stability
@@ -235,7 +235,7 @@ function _simulate_27d56(m::ContinuousTimeModel)
# at time n+1
mat_x = zeros(Int, m.buffer_size, m.dim_state)
l_t = zeros(Float64, m.buffer_size)
- l_tr = Vector{Union{String,Nothing}}(undef, m.buffer_size)
+ l_tr = Vector{Union{Symbol,Nothing}}(undef, m.buffer_size)
isabsorbing::Bool = m.isabsorbing(m.p,xn)
# use sizehint! ?
while !isabsorbing && tn <= m.time_bound
@@ -288,7 +288,7 @@ function _simulate_d7458(m::ContinuousTimeModel)
# at time n+1
mat_x = zeros(Int, m.buffer_size, m.dim_state)
l_t = zeros(Float64, m.buffer_size)
- l_tr = Vector{Union{String,Nothing}}(undef, m.buffer_size)
+ l_tr = Vector{Union{Symbol,Nothing}}(undef, m.buffer_size)
isabsorbing::Bool = m.isabsorbing(m.p,xn)
end_idx = -1
# use sizehint! ?
diff --git a/core/common.jl b/core/common.jl
index 1cac6188991537f583efef5b19cd5b03c8ff8982..56987bf6ef79e37205d8bc9442de7281a5dc2ee0 100644
--- a/core/common.jl
+++ b/core/common.jl
@@ -5,11 +5,11 @@ import Distributions: Distribution, Univariate, Continuous, UnivariateDistributi
abstract type Model end
abstract type AbstractTrajectory end
-const Transition = Union{String,Nothing}
+const Transition = Union{Symbol,Nothing}
const Location = Symbol
const VariableAutomaton = Symbol
-const VariableModel = String
-const ParameterModel = String
+const VariableModel = Symbol
+const ParameterModel = Symbol
mutable struct ContinuousTimeModel <: Model
name::String
@@ -85,16 +85,17 @@ struct ParametricModel
end
# Constructors
-function ContinuousTimeModel(dim_state::Int, dim_params::Int, map_var_idx::Dict, map_param_idx::Dict, transitions::Vector{<:Transition},
- p::Vector{Float64}, x0::Vector{Int}, t0::Float64,
- f!::Function, isabsorbing::Function;
- g::Vector{VariableModel} = keys(map_var_idx), time_bound::Float64 = Inf,
- buffer_size::Int = 10, estim_min_states::Int = 50, name::String = "Unnamed")
+function ContinuousTimeModel(dim_state::Int, dim_params::Int, map_var_idx::Dict{VariableModel,Int},
+ map_param_idx::Dict{ParameterModel,Int}, transitions::Vector{<:Transition},
+ p::Vector{Float64}, x0::Vector{Int}, t0::Float64,
+ f!::Function, isabsorbing::Function;
+ g::Vector{VariableModel} = 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)
for i = 1:dim_obs_state
- _g_idx[i] = map_var_idx[g[i]] # = ( (g[i] = i-th obs var)::String => idx in state space )
+ _g_idx[i] = map_var_idx[g[i]] # = ( (g[i] = i-th obs var)::VariableModel => idx in state space )
_map_obs_var_idx[g[i]] = i
end
@@ -111,8 +112,8 @@ function ContinuousTimeModel(dim_state::Int, dim_params::Int, map_var_idx::Dict,
end
LHA(A::LHA, map_var::Dict{VariableModel,Int}) = LHA(A.transitions, A.locations, A.Λ,
- A.locations_init, A.locations_final, A.map_var_automaton_idx, A.flow,
- A.map_edges, A.constants, map_var)
+ A.locations_init, A.locations_final, A.map_var_automaton_idx, A.flow,
+ A.map_edges, A.constants, map_var)
Base.:*(m::ContinuousTimeModel, A::LHA) = SynchronizedModel(m, A)
Base.:*(A::LHA, m::ContinuousTimeModel) = SynchronizedModel(m, A)
diff --git a/core/lha.jl b/core/lha.jl
index 412b8809627210a82585884fb5e77f5152d166ed..4215d19c37478f9dd0c7b6db6ff93b7a26f0a9e2 100644
--- a/core/lha.jl
+++ b/core/lha.jl
@@ -1,6 +1,6 @@
length_var(A::LHA) = length(A.map_var_automaton_idx)
-get_value(A::LHA, x::Vector{Int}, var::String) = x[A.map_var_model_idx[var]]
+get_value(A::LHA, x::Vector{Int}, var::VariableModel) = x[A.map_var_model_idx[var]]
copy(S::StateLHA) = StateLHA(S.A, S.loc, S.values, S.time)
# Not overring getproperty, setproperty to avoid a conversion Symbol => String for the dict key
@@ -100,7 +100,7 @@ function _get_edge_index(edge_candidates::Vector{Edge}, nbr_candidates::Int,
end
# Synchronous detection
if !detected_event && tr_nplus1 != nothing
- if (edge.transitions[1] == "ALL") ||
+ if (edge.transitions[1] == :ALL) ||
(tr_nplus1 in edge.transitions)
ind_edge = i
bool_event = true
diff --git a/core/model.jl b/core/model.jl
index afdd0edcd3ca89eac276e7ebaf96d8e0a780cadd..88c3e103fe9efa0cb0249d0dc82125c83e4d5d4d 100644
--- a/core/model.jl
+++ b/core/model.jl
@@ -336,7 +336,7 @@ function volatile_simulate(pm::ParametricModel, p_prior::AbstractVector{Float64}
return volatile_simulate(pm.m; p = full_p)
end
"""
- `distribute_mean_value_lha(sm::SynchronizedModel, str_var::String, nbr_stim::Int)`
+ `distribute_mean_value_lha(sm::SynchronizedModel, sym_var::Symbol, nbr_stim::Int)`
Distribute over workers the computation of the mean value
of a LHA over `nbr_sim` simulations of the model.
@@ -398,13 +398,13 @@ function check_consistency(m::ContinuousTimeModel)
end
# Set and get Model fields
-function set_observed_var!(am::Model, g::Vector{String})
+function set_observed_var!(am::Model, g::Vector{VariableModel})
m = get_proba_model(am)
dim_obs_state = length(g)
- _map_obs_var_idx = Dict{String}{Int}()
+ _map_obs_var_idx = Dict{VariableModel}{Int}()
_g_idx = zeros(Int, dim_obs_state)
for i = 1:dim_obs_state
- _g_idx[i] = m.map_var_idx[g[i]] # = ( (g[i] = i-th obs var)::String => idx in state space )
+ _g_idx[i] = m.map_var_idx[g[i]] # = ( (g[i] = i-th obs var)::VariableModel => idx in state space )
_map_obs_var_idx[g[i]] = i
end
m.g = g
@@ -413,7 +413,7 @@ function set_observed_var!(am::Model, g::Vector{String})
end
function observe_all!(am::Model)
m = get_proba_model(am)
- g = Vector{String}(undef, m.dim_state)
+ g = Vector{VariableModel}(undef, m.dim_state)
_g_idx = collect(1:m.dim_state)
for var in keys(m.map_var_idx)
g[m.map_var_idx[var]] = var
@@ -427,11 +427,11 @@ function set_param!(am::Model, new_p::Vector{Float64})
@assert length(new_p) == m.dim_params "New parameter vector hasn't the same dimension of parameter space"
m.p = new_p
end
-function set_param!(am::Model, name_p::String, p_i::Float64)
+function set_param!(am::Model, name_p::ParameterModel, p_i::Float64)
m = get_proba_model(am)
m.p[m.map_param_idx[name_p]] = p_i
end
-function set_param!(am::Model, l_name_p::Vector{String}, p::Vector{Float64})
+function set_param!(am::Model, l_name_p::Vector{ParameterModel}, p::Vector{Float64})
m = get_proba_model(am)
@assert length(l_name_p) == length(p) "Parameter names vector and parameter values haven't the same dimensions"
for i = eachindex(l_name_p)
@@ -447,7 +447,7 @@ set_time_bound!(am::Model, b::Float64) = (get_proba_model(am).time_bound = b)
get_param(am::Model) = get_proba_model(am).p
-function getindex(am::Model, name_p::String)
+function getindex(am::Model, name_p::ParameterModel)
m = get_proba_model(am)
m.p[m.map_param_idx[name_p]]
end
diff --git a/core/network_model.jl b/core/network_model.jl
index 2c578efdc5b23f0e886fa008ae6b58a5cefda8aa..3d70ac44e11fd3e6014b0da9cb4b8bbdb9d03216 100644
--- a/core/network_model.jl
+++ b/core/network_model.jl
@@ -4,21 +4,20 @@ using MacroTools
function get_multiplicand_and_species(expr::Expr)
@assert expr.args[1] == :*
multiplicand = reduce(*, expr.args[2:(end-1)])
- str_species = String(expr.args[end])
- return (multiplicand, str_species)
+ sym_species = expr.args[end]
+ return (multiplicand, sym_species)
end
-get_multiplicand_and_species(sym::Symbol) = (1, String(sym))
+get_multiplicand_and_species(sym::Symbol) = (1, sym)
function get_str_propensity(propensity::Expr, dict_species::Dict, dict_params::Dict)
str_propensity = ""
for op in propensity.args[2:end]
- str_op = String(op)
- if haskey(dict_species, str_op)
- str_propensity *= "xn[$(dict_species[str_op])] * "
- elseif haskey(dict_params, str_op)
- str_propensity *= "p[$(dict_params[str_op])] * "
+ if haskey(dict_species, op)
+ str_propensity *= "xn[$(dict_species[op])] * "
+ elseif haskey(dict_params, op)
+ str_propensity *= "p[$(dict_params[op])] * "
else
- str_propensity *= "$(str_op) * "
+ str_propensity *= "$(op) * "
end
end
return str_propensity[1:(end-2)]
@@ -37,9 +36,9 @@ end
macro network_model(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}()
+ transitions = Transition[]
+ dict_species = Dict{VariableModel,Int}()
+ dict_params = Dict{ParameterModel,Int}()
dim_state = 0
dim_params = 0
list_expr_reactions = Any[]
@@ -48,23 +47,23 @@ macro network_model(expr_network,expr_name...)
local isreaction = @capture(expr_reaction, TR_: (reactants_ => products_, propensity_))
if isreaction
push!(list_expr_reactions, expr_reaction)
- push!(transitions, String(TR))
+ push!(transitions, TR)
# Parsing reactants, products
for reaction_part in [reactants, products]
# If there's several species interacting / produced
if typeof(reaction_part) <: Expr && reaction_part.args[1] == :+
for operand in reaction_part.args[2:end]
- mult, str_species = get_multiplicand_and_species(operand)
- if !haskey(dict_species, str_species)
+ mult, sym_species = get_multiplicand_and_species(operand)
+ if !haskey(dict_species, sym_species)
dim_state += 1
- dict_species[str_species] = dim_state
+ dict_species[sym_species] = dim_state
end
end
else
- mult, str_species = get_multiplicand_and_species(reaction_part)
- if !haskey(dict_species, str_species)
+ mult, sym_species = get_multiplicand_and_species(reaction_part)
+ if !haskey(dict_species, sym_species)
dim_state += 1
- dict_species[str_species] = dim_state
+ dict_species[sym_species] = dim_state
end
end
end
@@ -82,19 +81,17 @@ macro network_model(expr_network,expr_name...)
@assert propensity.args[1] == :* "Only product of species/params/constants are allowed in propensity"
for operand in propensity.args[2:end]
if typeof(operand) <: Symbol
- str_op = String(operand)
# If it's not a species, it's a parameter
- if !(str_op in list_species) && !haskey(dict_params, str_op)
+ if !(operand in list_species) && !haskey(dict_params, operand)
dim_params += 1
- dict_params[str_op] = dim_params
+ dict_params[operand] = dim_params
end
end
end
elseif typeof(propensity) <: Symbol
- str_op = String(propensity)
- if !(str_op in list_species) && !haskey(dict_params, str_op)
+ if !(propensity in list_species) && !haskey(dict_params, propensity)
dim_params += 1
- dict_params[str_op] = dim_params
+ dict_params[propensity] = dim_params
end
end
if !isreaction && !(typeof(expr_reaction) <: LineNumberNode)
@@ -121,21 +118,21 @@ macro network_model(expr_network,expr_name...)
nu = l_nu[i]
if typeof(reactants) <: Expr && reactants.args[1] == :+
for operand in reactants.args[2:end]
- mult, str_species = get_multiplicand_and_species(operand)
- nu[dict_species[str_species]] -= mult
+ mult, sym_species = get_multiplicand_and_species(operand)
+ nu[dict_species[sym_species]] -= mult
end
else
- mult, str_species = get_multiplicand_and_species(reactants)
- nu[dict_species[str_species]] -= mult
+ mult, sym_species = get_multiplicand_and_species(reactants)
+ nu[dict_species[sym_species]] -= mult
end
if typeof(products) <: Expr && products.args[1] == :+
for operand in products.args[2:end]
- mult, str_species = get_multiplicand_and_species(operand)
- nu[dict_species[str_species]] += mult
+ mult, sym_species = get_multiplicand_and_species(operand)
+ nu[dict_species[sym_species]] += mult
end
else
- mult, str_species = get_multiplicand_and_species(products)
- nu[dict_species[str_species]] += mult
+ mult, sym_species = get_multiplicand_and_species(products)
+ nu[dict_species[sym_species]] += mult
end
expr_model_f! *= "nu_$i = $(Tuple(nu))\n\t"
# Anticipating the line l_a = (..)
@@ -151,7 +148,7 @@ macro network_model(expr_network,expr_name...)
expr_model_f! *= "end\n\t"
# Computation of array of transitions
expr_model_f! *= "l_nu = (" * reduce(*, ["nu_$i, " for i = 1:nbr_reactions])[1:(end-2)] * ")\n\t"
- expr_model_f! *= "l_str_R = $(Tuple(transitions))\n\t"
+ expr_model_f! *= "l_sym_R = $(Tuple(transitions))\n\t"
# Simulation of the reaction
expr_model_f! *= "u1 = rand()\n\t"
expr_model_f! *= "u2 = rand()\n\t"
@@ -172,7 +169,7 @@ macro network_model(expr_network,expr_name...)
expr_model_f! *= "@inbounds xnplus1[i] = xn[i]+nu[i]\n\t"
expr_model_f! *= "end\n\t"
expr_model_f! *= "@inbounds l_t[1] = tn + tau\n\t"
- expr_model_f! *= "@inbounds l_tr[1] = l_str_R[reaction]\n"
+ expr_model_f! *= "@inbounds l_tr[1] = l_sym_R[reaction]\n"
expr_model_f! *= "end\n"
expr_model_isabsorbing = "isabsorbing_$(basename_func)(p::Vector{Float64},xn::Vector{Int}) = $(str_test_isabsorbing) === 0.0"
model_f! = eval(Meta.parse(expr_model_f!))
diff --git a/core/plots.jl b/core/plots.jl
index 7008048c8600719513b6b2e4be907ff25a9ba831..9d4a0a04be31e070ae099b0909fb302a1347ce1c 100644
--- a/core/plots.jl
+++ b/core/plots.jl
@@ -7,11 +7,11 @@ import Plots: current, palette, display, png, close
Plot a simulated trajectory σ. var... is a tuple of stirng variables.
`plot(σ)` will plot all the variables simulated in σ
-whereas `plot(σ, "I", "R")` only plots the variables I and R of the trajectory (if it exists).
+whereas `plot(σ, :I, :R)` only plots the variables I and R of the trajectory (if it exists).
If `plot_transitions=true`, a marker that corresponds to a transition of the model will be plotted
at each break of the trajectory.
"""
-function plot(σ::AbstractTrajectory, vars::String...; plot_transitions::Bool = false, A::Union{Nothing,LHA} = nothing, filename::String = "")
+function plot(σ::AbstractTrajectory, vars::VariableModel...; plot_transitions::Bool = false, A::Union{Nothing,LHA} = nothing, filename::String = "")
# Setup
palette_tr = palette(:default)
l_tr = unique(transitions(σ))
diff --git a/core/trajectory.jl b/core/trajectory.jl
index f1c11a24867be38ea8f23472eb303ba2cb12b28b..a9a64b728d9018d18837338e81f7cdfc9d11d98b 100644
--- a/core/trajectory.jl
+++ b/core/trajectory.jl
@@ -51,11 +51,11 @@ Requires `get_obs_var(σ1) == get_obs_var(σ2)`, it is verified if they are simu
...
# Arguments
- `σ1::AbstractTrajectory` is the first trajectory. σ2 is the second.
-- `var::String` is an observed variable. Have to be contained in `get_obs_var(σ1)` and `get_obs_var(σ2)`.
+- `var::Symbol` is an observed variable. Have to be contained in `get_obs_var(σ1)` and `get_obs_var(σ2)`.
- `verbose::Bool` If `true`, launch a verbose execution of the computation.
...
"""
-function dist_lp(σ1::AbstractTrajectory, σ2::AbstractTrajectory, var::String;
+function dist_lp(σ1::AbstractTrajectory, σ2::AbstractTrajectory, var::VariableModel;
verbose::Bool = false, p::Int = 1)
if !isbounded(σ1) || !isbounded(σ2)
@warn "Lp distance computed on unbounded trajectories. Result should be wrong"
@@ -196,9 +196,9 @@ isaccepted(σ::SynchronizedTrajectory) = isaccepted(σ.state_lha_end)
issteadystate(σ::AbstractTrajectory) = @warn "Unimplemented"
# Access to trajectory values
-get_var_values(σ::AbstractTrajectory, var::String) = σ.values[σ.m._map_obs_var_idx[var]]
+get_var_values(σ::AbstractTrajectory, var::VariableModel) = σ.values[σ.m._map_obs_var_idx[var]]
get_state(σ::AbstractTrajectory, idx::Int) = [σ.values[i][idx] for i = 1:length(σ.values)] # /!\ Creates an array
-get_value(σ::AbstractTrajectory, var::String, idx::Int) = get_var_values(σ, var)[idx]
+get_value(σ::AbstractTrajectory, var::VariableModel, idx::Int) = get_var_values(σ, var)[idx]
# Operation σ@t
function get_state_from_time(σ::AbstractTrajectory, t::Float64)
@assert t >= 0.0
@@ -232,10 +232,10 @@ transitions(σ::AbstractTrajectory) = σ.transitions
# Get i-th state [i]
getindex(σ::AbstractTrajectory, idx::Int) = get_state(σ, idx)
-# Get i-th value of var ["I", idx]
-getindex(σ::AbstractTrajectory, var::String, idx::Int) = get_value(σ, var, idx)
-# Get the path of a variable ["I"]
-getindex(σ::AbstractTrajectory, var::String) = get_var_values(σ, var)
+# Get i-th value of var [:I, idx]
+getindex(σ::AbstractTrajectory, var::VariableModel, idx::Int) = get_value(σ, var, idx)
+# Get the path of a variable [:I]
+getindex(σ::AbstractTrajectory, var::VariableModel) = get_var_values(σ, var)
lastindex(σ::AbstractTrajectory) = length_states(σ)
# Operations
diff --git a/models/ER.jl b/models/ER.jl
index f41a80d25e0417f72143d43226d17bc9efed7ae2..f4d7312573d5de6285b3f9d54c9b8a799d4e418e 100644
--- a/models/ER.jl
+++ b/models/ER.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SVector, @SMatrix
d=4
k=3
-dict_var_ER = Dict("E" => 1, "S" => 2, "ES" => 3, "P" => 4)
-dict_p_ER = Dict("k1" => 1, "k2" => 2, "k3" => 3)
-l_tr_ER = ["R1","R2","R3"]
+dict_var_ER = Dict(:E => 1, :S => 2, :ES => 3, :P => 4)
+dict_p_ER = Dict(:k1 => 1, :k2 => 2, :k3 => 3)
+l_tr_ER = [:R1,:R2,:R3]
p_ER = [1.0, 1.0, 1.0]
x0_ER = [100, 100, 0, 0]
t0_ER = 0.0
-function ER_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}},
+function ER_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition},
xn::Vector{Int}, tn::Float64, p::Vector{Float64})
@inbounds a1 = p[1] * xn[1] * xn[2]
@inbounds a2 = p[2] * xn[3]
@@ -24,7 +24,7 @@ function ER_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{No
nu_2 = (1, 1, -1, 0)
nu_3 = (1, 0, -1, 1)
l_nu = (nu_1, nu_2, nu_3)
- l_str_R = ("R1", "R2", "R3")
+ l_str_R = (:R1, :R2, :R3)
u1 = rand()
u2 = rand()
@@ -50,7 +50,7 @@ function ER_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{No
end
isabsorbing_ER(p::Vector{Float64},xn::Vector{Int}) =
@inbounds(p[1]*xn[1]*xn[2] + (p[2]+p[3])*xn[3] === 0.0)
-g_ER = ["P"]
+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,name="ER pkg")
diff --git a/models/SIR.jl b/models/SIR.jl
index f53db51dae007fbd34476c6c1af73aeeef3717e5..1b4e953bf4fc07ab3b3dd0d19e755cf71cb0511c 100644
--- a/models/SIR.jl
+++ b/models/SIR.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SVector, @SMatrix
d=3
k=2
-dict_var = Dict("S" => 1, "I" => 2, "R" => 3)
-dict_p = Dict("ki" => 1, "kr" => 2)
-l_tr_SIR = ["R1","R2"]
+dict_var = Dict(:S => 1, :I => 2, :R => 3)
+dict_p = Dict(:ki => 1, :kr => 2)
+l_tr_SIR = [:R1,:R2]
p_SIR = [0.0012, 0.05]
x0_SIR = [95, 5, 0]
t0_SIR = 0.0
-function SIR_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}},
+function SIR_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition},
xn::Vector{Int}, tn::Float64, p::Vector{Float64})
@inbounds a1 = p[1] * xn[1] * xn[2]
@inbounds a2 = p[2] * xn[2]
@@ -23,7 +23,7 @@ function SIR_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{N
nu_1 = (-1, 1, 0)
nu_2 = (0, -1, 1)
l_nu = (nu_1, nu_2)
- l_str_R = ("R1","R2")
+ l_str_R = (:R1,:R2)
u1 = rand()
u2 = rand()
@@ -48,7 +48,7 @@ function SIR_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{N
@inbounds l_tr[1] = l_str_R[reaction]
end
isabsorbing_SIR(p::Vector{Float64}, xn::Vector{Int}) = (p[1]*xn[1]*xn[2] + p[2]*xn[2]) === 0.0
-g_SIR = ["I"]
+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, name="SIR pkg")
diff --git a/models/SIR_tauleap.jl b/models/SIR_tauleap.jl
index 3d9cb6e7e832be5b3a92f59424d6f0c01539593c..22dd7994ae4d2779426b30c2866b46d90cc398ea 100644
--- a/models/SIR_tauleap.jl
+++ b/models/SIR_tauleap.jl
@@ -4,13 +4,13 @@ import Distributions: Poisson, rand
d=3
k=3
-dict_var_SIR_tauleap = Dict("S" => 1, "I" => 2, "R" => 3)
-dict_p_SIR_tauleap = Dict("ki" => 1, "kr" => 2, "tau" => 3)
-l_tr_SIR_tauleap = ["U"]
+dict_var_SIR_tauleap = Dict(:S => 1, :I => 2, :R => 3)
+dict_p_SIR_tauleap = Dict(:ki => 1, :kr => 2, :tau => 3)
+l_tr_SIR_tauleap = [:U]
p_SIR_tauleap = [0.0012, 0.05, 5.0]
x0_SIR_tauleap = [95, 5, 0]
t0_SIR_tauleap = 0.0
-function SIR_tauleap_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}},
+function SIR_tauleap_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition},
xn::Vector{Int}, tn::Float64, p::Vector{Float64})
tau = p[3]
a1 = p[1] * xn[1] * xn[2]
@@ -30,10 +30,10 @@ function SIR_tauleap_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector
xnplus1[i] = xn[i]+ nbr_R1*nu_1[i] + nbr_R2*nu_2[i]
end
l_t[1] = tn + tau
- l_tr[1] = "U"
+ l_tr[1] = :U
end
isabsorbing_SIR_tauleap(p::Vector{Float64}, xn::Vector{Int}) = (p[1]*xn[1]*xn[2] + p[2]*xn[2]) === 0.0
-g_SIR_tauleap = ["I"]
+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,
diff --git a/models/_bench_perf_test/ER_col.jl b/models/_bench_perf_test/ER_col.jl
index 366e368d379f7b7f58f2a38a965c0e1bdb50717c..1354cd78792e9735e10684501c49919bc2bb777c 100644
--- a/models/_bench_perf_test/ER_col.jl
+++ b/models/_bench_perf_test/ER_col.jl
@@ -3,14 +3,14 @@ import StaticArrays: SVector, SMatrix, @SMatrix
d=4
k=3
-dict_var = Dict("E" => 1, "S" => 2, "ES" => 3, "P" => 4)
-dict_p = Dict("k1" => 1, "k2" => 2, "k3" => 3)
-l_tr = ["R1","R2","R3"]
+dict_var = Dict(:E => 1, :S => 2, :ES => 3, :P => 4)
+dict_p = Dict(:k1 => 1, :k2 => 2, :k3 => 3)
+l_tr = [:R1,:R2,:R3]
p = [1.0, 1.0, 1.0]
x0 = [100, 100, 0, 0]
t0 = 0.0
-function ER_col_f!(xnplus1::Vector{Int}, tnplus1::Vector{Float64}, tr::Vector{Union{Nothing,String}},
+function ER_col_f!(xnplus1::Vector{Int}, tnplus1::Vector{Float64}, tr::Vector{<:Transition},
xn::AbstractVector{Int}, tn::Float64, p::Vector{Float64})
a1 = p[1] * xn[1] * xn[2]
a2 = p[2] * xn[3]
@@ -40,11 +40,11 @@ function ER_col_f!(xnplus1::Vector{Int}, tnplus1::Vector{Float64}, tr::Vector{Un
xnplus1[i] = xn[i]+nu[i]
end
tnplus1[1] = tn + tau
- tr[1] = "R$(reaction)"
+ tr[1] = Symbol("R$(reaction)")
end
isabsorbing_ER_col(p::Vector{Float64},xn::AbstractVector{Int}) =
(p[1]*xn[1]*xn[2] + (p[2]+p[3])*xn[3]) === 0.0
-g = ["P"]
+g = [:P]
ER_col = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr,p,x0,t0,ER_col_f!,isabsorbing_ER_col; g=g)
export ER_col
diff --git a/models/_bench_perf_test/ER_col_buffer.jl b/models/_bench_perf_test/ER_col_buffer.jl
index 134c2189eefd83e3cddfbb7405ea89542b94d439..b975fca1fc280983c92b18e1a667eb102308751f 100644
--- a/models/_bench_perf_test/ER_col_buffer.jl
+++ b/models/_bench_perf_test/ER_col_buffer.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SMatrix
d=4
k=3
-dict_var = Dict("E" => 1, "S" => 2, "ES" => 3, "P" => 4)
-dict_p = Dict("k1" => 1, "k2" => 2, "k3" => 3)
-l_tr = ["R1","R2","R3"]
+dict_var = Dict(:E => 1, :S => 2, :ES => 3, :P => 4)
+dict_p = Dict(:k1 => 1, :k2 => 2, :k3 => 3)
+l_tr = [:R1,:R2,:R3]
p = [1.0, 1.0, 1.0]
x0 = [100, 100, 0, 0]
t0 = 0.0
-function ER_col_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}}, idx::Int,
+function ER_col_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition}, idx::Int,
xn::AbstractVector{Int}, tn::Float64, p::Vector{Float64})
a1 = p[1] * xn[1] * xn[2]
a2 = p[2] * xn[3]
@@ -39,11 +39,11 @@ function ER_col_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector
mat_x[i,idx] = xn[i]+nu[i]
end
l_t[idx] = tn + tau
- l_tr[idx] = "R$(reaction)"
+ l_tr[idx] = Symbol("R$(reaction)")
end
isabsorbing_ER_col_buffer(p::Vector{Float64},xn::AbstractVector{Int}) =
(p[1]*xn[1]*xn[2] + (p[2]+p[3])*xn[3]) === 0.0
-g = ["P"]
+g = [:P]
ER_col_buffer = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr,p,x0,t0,ER_col_buffer_f!,isabsorbing_ER_col_buffer; g=g)
export ER_col_buffer
diff --git a/models/_bench_perf_test/ER_row_buffer.jl b/models/_bench_perf_test/ER_row_buffer.jl
index 37fbd15c1ca64a9f4cbcd28d563bab2e2c3ba618..e8e66e2e97e6db1e2d3a400e865963ee0a5d49ac 100644
--- a/models/_bench_perf_test/ER_row_buffer.jl
+++ b/models/_bench_perf_test/ER_row_buffer.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SMatrix
d=4
k=3
-dict_var = Dict("E" => 1, "S" => 2, "ES" => 3, "P" => 4)
-dict_p = Dict("k1" => 1, "k2" => 2, "k3" => 3)
-l_tr = ["R1","R2","R3"]
+dict_var = Dict(:E => 1, :S => 2, :ES => 3, :P => 4)
+dict_p = Dict(:k1 => 1, :k2 => 2, :k3 => 3)
+l_tr = [:R1,:R2,:R3]
p = [1.0, 1.0, 1.0]
x0 = [100, 100, 0, 0]
t0 = 0.0
-function ER_row_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}}, idx::Int,
+function ER_row_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition}, idx::Int,
xn::AbstractVector{Int}, tn::Float64, p::Vector{Float64})
a1 = p[1] * xn[1] * xn[2]
a2 = p[2] * xn[3]
@@ -39,11 +39,11 @@ function ER_row_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector
mat_x[idx,i] = xn[i]+nu[i]
end
l_t[idx] = tn + tau
- l_tr[idx] = "R$(reaction)"
+ l_tr[idx] = Symbol("R$(reaction)")
end
isabsorbing_ER_row_buffer(p::Vector{Float64},xn::AbstractVector{Int}) =
(p[1]*xn[1]*xn[2] + (p[2]+p[3])*xn[3]) === 0.0
-g = ["P"]
+g = [:P]
ER_row_buffer = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr,p,x0,t0,ER_row_buffer_f!,isabsorbing_ER_row_buffer; g=g)
export ER_row_buffer
diff --git a/models/_bench_perf_test/SIR_col.jl b/models/_bench_perf_test/SIR_col.jl
index cc522164b740861bf70391ca281e59c539accf34..8bf497fdf8e7e42ffae8ae45089ef992c74f8353 100644
--- a/models/_bench_perf_test/SIR_col.jl
+++ b/models/_bench_perf_test/SIR_col.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SMatrix
d=3
k=2
-dict_var = Dict("S" => 1, "I" => 2, "R" => 3)
-dict_p = Dict("ki" => 1, "kr" => 2)
-l_tr = ["R1","R2"]
+dict_var = Dict(:S => 1, :I => 2, :R => 3)
+dict_p = Dict(:ki => 1, :kr => 2)
+l_tr = [:R1,:R2]
p = [0.0012, 0.05]
x0 = [95, 5, 0]
t0 = 0.0
-function SIR_col_f!(xnplus1::Vector{Int}, tnplus1::Vector{Float64}, tr::Vector{Union{Nothing,String}},
+function SIR_col_f!(xnplus1::Vector{Int}, tnplus1::Vector{Float64}, tr::Vector{<:Transition},
xn::AbstractVector{Int}, tn::Float64, p::Vector{Float64})
a1 = p[1] * xn[1] * xn[2]
a2 = p[2] * xn[2]
@@ -39,10 +39,10 @@ function SIR_col_f!(xnplus1::Vector{Int}, tnplus1::Vector{Float64}, tr::Vector{U
xnplus1[2] = xn[2]+nu[2]
xnplus1[3] = xn[3]+nu[3]
tnplus1[1] = tn + tau
- tr[1] = "R$(reaction)"
+ tr[1] = Symbol("R$(reaction)")
end
isabsorbing_SIR_col(p::Vector{Float64}, xn::AbstractVector{Int}) = (p[1]*xn[1]*xn[2] + p[2]*xn[2]) === 0.0
-g = ["I"]
+g = [:I]
SIR_col = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr,p,x0,t0,SIR_col_f!,isabsorbing_SIR_col; g=g)
export SIR_col
diff --git a/models/_bench_perf_test/SIR_col_buffer.jl b/models/_bench_perf_test/SIR_col_buffer.jl
index 8acc8d71ccb0e4137bb4633d0056b9105508dc08..cecf5f02e317b15dca0360db1d698b0c0126cf92 100644
--- a/models/_bench_perf_test/SIR_col_buffer.jl
+++ b/models/_bench_perf_test/SIR_col_buffer.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SMatrix
d=3
k=2
-dict_var = Dict("S" => 1, "I" => 2, "R" => 3)
-dict_p = Dict("ki" => 1, "kr" => 2)
-l_tr = ["R1","R2"]
+dict_var = Dict(:S => 1, :I => 2, :R => 3)
+dict_p = Dict(:ki => 1, :kr => 2)
+l_tr = [:R1,:R2]
p = [0.0012, 0.05]
x0 = [95, 5, 0]
t0 = 0.0
-function SIR_col_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}}, idx::Int,
+function SIR_col_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition}, idx::Int,
xn::AbstractVector{Int}, tn::Float64, p::Vector{Float64})
a1 = p[1] * xn[1] * xn[2]
a2 = p[2] * xn[2]
@@ -39,10 +39,10 @@ function SIR_col_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vecto
mat_x[i,idx] = xn[i]+nu[i]
end
l_t[idx] = tn + tau
- l_tr[idx] = "R$(reaction)"
+ l_tr[idx] = Symbol("R$(reaction)")
end
isabsorbing_SIR_col_buffer(p::Vector{Float64}, xn::AbstractVector{Int}) = (p[1]*xn[1]*xn[2] + p[2]*xn[2]) === 0.0
-g = ["I"]
+g = [:I]
SIR_col_buffer = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr,p,x0,t0,SIR_col_buffer_f!,isabsorbing_SIR_col_buffer; g=g)
export SIR_col_buffer
diff --git a/models/_bench_perf_test/SIR_row_buffer.jl b/models/_bench_perf_test/SIR_row_buffer.jl
index 930f1c9f8a96f8a7fca4ae364591dbea1de3f709..d8cae699db7b54c26fdc86969b79a549100f5f12 100644
--- a/models/_bench_perf_test/SIR_row_buffer.jl
+++ b/models/_bench_perf_test/SIR_row_buffer.jl
@@ -3,13 +3,13 @@ import StaticArrays: SVector, SMatrix, @SMatrix
d=3
k=2
-dict_var = Dict("S" => 1, "I" => 2, "R" => 3)
-dict_p = Dict("ki" => 1, "kr" => 2)
-l_tr = ["R1","R2"]
+dict_var = Dict(:S => 1, :I => 2, :R => 3)
+dict_p = Dict(:ki => 1, :kr => 2)
+l_tr = [:R1,:R2]
p = [0.0012, 0.05]
x0 = [95, 5, 0]
t0 = 0.0
-function SIR_row_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}}, idx::Int,
+function SIR_row_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition}, idx::Int,
xn::AbstractVector{Int}, tn::Float64, p::Vector{Float64})
a1 = p[1] * xn[1] * xn[2]
a2 = p[2] * xn[2]
@@ -39,10 +39,10 @@ function SIR_row_buffer_f!(mat_x::Matrix{Int}, l_t::Vector{Float64}, l_tr::Vecto
mat_x[idx,i] = xn[i]+nu[i]
end
l_t[idx] = tn + tau
- l_tr[idx] = "R$(reaction)"
+ l_tr[idx] = Symbol("R$(reaction)")
end
isabsorbing_SIR_row_buffer(p::Vector{Float64}, xn::AbstractVector{Int}) = (p[1]*xn[1]*xn[2] + p[2]*xn[2]) === 0.0
-g = ["I"]
+g = [:I]
SIR_row_buffer = ContinuousTimeModel(d,k,dict_var,dict_p,l_tr,p,x0,t0,SIR_row_buffer_f!,isabsorbing_SIR_row_buffer; g=g)
export SIR_row_buffer
diff --git a/models/poisson.jl b/models/poisson.jl
index db080272085cd79e5f9648ecaf8bcdc4d23d8cf3..0f107b227cb8d38361eea2603dcff05f835cc01b 100644
--- a/models/poisson.jl
+++ b/models/poisson.jl
@@ -4,23 +4,23 @@ import Distributions: Poisson, rand
d=1
k=1
-dict_var_poisson = Dict("N" => 1)
-dict_p_poisson = Dict("λ" => 1)
-l_tr_poisson = ["R"]
+dict_var_poisson = Dict(:N => 1)
+dict_p_poisson = Dict(:λ => 1)
+l_tr_poisson = [:R]
p_poisson = [5.0]
x0_poisson = [0]
t0_poisson = 0.0
-function poisson_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{Union{Nothing,String}},
+function poisson_f!(xnplus1::Vector{Int}, l_t::Vector{Float64}, l_tr::Vector{<:Transition},
xn::Vector{Int}, tn::Float64, p::Vector{Float64})
u1 = rand()
tau = (-log(u1)/p[1])
xnplus1[1] += 1
l_t[1] = tn + tau
- l_tr[1] = "U"
+ l_tr[1] = :R
end
isabsorbing_poisson(p::Vector{Float64}, xn::Vector{Int}) = p[1] === 0.0
-g_poisson = ["N"]
+g_poisson = [:N]
poisson = ContinuousTimeModel(d,k,dict_var_poisson,dict_p_poisson,l_tr_poisson,
p_poisson,x0_poisson,t0_poisson,
diff --git a/tests/automata/absorbing_x0_p.jl b/tests/automata/absorbing_x0_p.jl
index c670842fbf399148124fc1687f1dd27b11688933..2fd9843c40f9a9e2822d983dca26814a4b058d60 100644
--- a/tests/automata/absorbing_x0_p.jl
+++ b/tests/automata/absorbing_x0_p.jl
@@ -6,12 +6,12 @@ observe_all!(ER)
load_automaton("automaton_F")
load_automaton("automaton_G")
load_automaton("automaton_G_and_F")
-A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, "P")
-A_G_R5 = create_automaton_G(ER, 50.0, 100.0, 0.0, 0.8, "E")
+A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, :P)
+A_G_R5 = create_automaton_G(ER, 50.0, 100.0, 0.0, 0.8, :E)
x1, x2, t1, t2 = 50.0, 100.0, 0.0, 0.8
x3, x4, t3, t4 = 30.0, 100.0, 0.8, 0.9
-A_G_F_R6 = create_automaton_G_and_F(ER, x1, x2, t1, t2, "E",
- x3, x4, t3, t4, "P")
+A_G_F_R6 = create_automaton_G_and_F(ER, x1, x2, t1, t2, :E,
+ x3, x4, t3, t4, :P)
set_param!(ER, [0.0,0.0,0.0])
test_all = true
diff --git a/tests/automata/accept_R5.jl b/tests/automata/accept_R5.jl
index 08edfa1bf8054a397c2b77f6284db76255fc1a58..c71977941f85f59ac2d6fd0955fc7dba7365d2da 100644
--- a/tests/automata/accept_R5.jl
+++ b/tests/automata/accept_R5.jl
@@ -10,9 +10,9 @@ load_automaton("automaton_G")
width = 0.5
level = 0.95
x1, x2, t1, t2 = 50.0, 100.0, 0.0, 0.8
-A_G = create_automaton_G(model, x1, x2, t1, t2, "P")
-set_param!(ER, "k1", 0.2)
-set_param!(ER, "k2", 40.0)
+A_G = create_automaton_G(model, x1, x2, t1, t2, :P)
+set_param!(ER, :k1, 0.2)
+set_param!(ER, :k2, 40.0)
test_all = true
sync_ER = ER*A_G
diff --git a/tests/automata/read_trajectory_last_state_F.jl b/tests/automata/read_trajectory_last_state_F.jl
index 6076e49c822da2e8a58478c0197de4b691fc3c72..9699f90c3f312ca58b79dff7e3b76879d79e9d02 100644
--- a/tests/automata/read_trajectory_last_state_F.jl
+++ b/tests/automata/read_trajectory_last_state_F.jl
@@ -7,7 +7,7 @@ SIR.time_bound = 120.0
observe_all!(SIR)
x1, x2, t1, t2 = 0.0, Inf, 100.0, 120.0
-A_F = create_automaton_F(SIR, x1, x2, t1, t2, "I") # <: LHA
+A_F = create_automaton_F(SIR, x1, x2, t1, t2, :I) # <: LHA
function test_last_state(A::LHA, m::ContinuousTimeModel)
σ = simulate(m)
@@ -16,7 +16,7 @@ function test_last_state(A::LHA, m::ContinuousTimeModel)
if !test
@show Send
@show get_state_from_time(σ, Send.time)
- error("tkt")
+ error("Ouch")
end
return test
end
diff --git a/tests/automata/read_trajectory_last_state_G.jl b/tests/automata/read_trajectory_last_state_G.jl
index 93881442f7e98933c47cc1300be49cac2c494199..a50b59adc6c5ba8795e168e4bb24523563363f55 100644
--- a/tests/automata/read_trajectory_last_state_G.jl
+++ b/tests/automata/read_trajectory_last_state_G.jl
@@ -7,7 +7,7 @@ observe_all!(ER)
ER.time_bound = 2.0
x1, x2, t1, t2 = 0.0, Inf, 0.0, 2.0
-A_G = create_automaton_G(ER, x1, x2, t1, t2, "P") # <: LHA
+A_G = create_automaton_G(ER, x1, x2, t1, t2, :P) # <: LHA
function test_last_state(A::LHA, m::ContinuousTimeModel)
σ = simulate(m)
diff --git a/tests/automata/sync_simulate_last_state_F.jl b/tests/automata/sync_simulate_last_state_F.jl
index 96fd11e8b822df3560c6c8630ad8fb6a1308a049..4d95a8a31ff4c07a5b550ffe9d1657e19f958bc2 100644
--- a/tests/automata/sync_simulate_last_state_F.jl
+++ b/tests/automata/sync_simulate_last_state_F.jl
@@ -6,7 +6,7 @@ load_automaton("automaton_F")
SIR.time_bound = 120.0
x1, x2, t1, t2 = 0.0, Inf, 100.0, 120.0
-A_F = create_automaton_F(SIR, x1, x2, t1, t2, "I") # <: LHA
+A_F = create_automaton_F(SIR, x1, x2, t1, t2, :I) # <: LHA
sync_SIR = A_F * SIR
function test_last_state(A::LHA, m::SynchronizedModel)
diff --git a/tests/automata/sync_simulate_last_state_G.jl b/tests/automata/sync_simulate_last_state_G.jl
index e7e9ba78774507d084c7219914ad31565fee3a15..96b1cf9920ed652a568cddad834c73afbecfc2df 100644
--- a/tests/automata/sync_simulate_last_state_G.jl
+++ b/tests/automata/sync_simulate_last_state_G.jl
@@ -6,7 +6,7 @@ load_automaton("automaton_G")
ER.time_bound = 2.0
x1, x2, t1, t2 = 0.0, Inf, 0.0, 2.0
-A_G = create_automaton_G(ER, x1, x2, t1, t2, "P") # <: LHA
+A_G = create_automaton_G(ER, x1, x2, t1, t2, :P) # <: LHA
sync_ER = A_G * ER
function test_last_state(A::LHA, m::SynchronizedModel)
diff --git a/tests/automaton_abc/R1.jl b/tests/automaton_abc/R1.jl
index f980407cef9a04bdcacbc509fee3c475c64b71a1..cde4a9f19dfb9a9ae2dc84dcebbde5b579f558f0 100644
--- a/tests/automaton_abc/R1.jl
+++ b/tests/automaton_abc/R1.jl
@@ -3,9 +3,9 @@ using MarkovProcesses
load_model("ER")
load_automaton("automaton_F")
-A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, "P")
+A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, :P)
sync_ER = A_F_R1*ER
-pm_sync_ER = ParametricModel(sync_ER, ("k3", Uniform(0.0, 100.0)))
+pm_sync_ER = ParametricModel(sync_ER, (:k3, Uniform(0.0, 100.0)))
nbr_pa = 502
r = automaton_abc(pm_sync_ER; nbr_particles = nbr_pa)
diff --git a/tests/automaton_abc/R2.jl b/tests/automaton_abc/R2.jl
index 143182bfeeeff6ad2cc4cbd351fa1e89ec5b976c..ac624b37f349561e8edc10dacac3165876652a6d 100644
--- a/tests/automaton_abc/R2.jl
+++ b/tests/automaton_abc/R2.jl
@@ -5,9 +5,9 @@ using Plots
@everywhere load_model("ER")
@everywhere load_automaton("automaton_F")
-A_F_R2 = create_automaton_F(ER, 50.0, 75.0, 0.05, 0.075, "P")
+A_F_R2 = create_automaton_F(ER, 50.0, 75.0, 0.05, 0.075, :P)
sync_ER = A_F_R2*ER
-pm_sync_ER = ParametricModel(sync_ER, ("k3", Uniform(0.0, 100.0)))
+pm_sync_ER = ParametricModel(sync_ER, (:k3, Uniform(0.0, 100.0)))
@timev r = automaton_abc(pm_sync_ER)
@show r.nbr_sim
diff --git a/tests/automaton_abc/R3.jl b/tests/automaton_abc/R3.jl
index 530ad6a92887faf34b3790add2ae5da4b002f78c..bc41c684a53242cd64077865669fd549e9ec6a95 100644
--- a/tests/automaton_abc/R3.jl
+++ b/tests/automaton_abc/R3.jl
@@ -4,9 +4,9 @@ using Plots
load_model("ER")
load_automaton("automaton_F")
-A_F_R3 = create_automaton_F(ER, 25.0, 50.0, 0.05, 0.075, "P")
+A_F_R3 = create_automaton_F(ER, 25.0, 50.0, 0.05, 0.075, :P)
sync_ER = A_F_R3*ER
-pm_sync_ER = ParametricModel(sync_ER, ("k3", Uniform(0.0, 100.0)))
+pm_sync_ER = ParametricModel(sync_ER, (:k3, Uniform(0.0, 100.0)))
r = automaton_abc(pm_sync_ER; nbr_particles = 1000)
diff --git a/tests/automaton_abc/distributed_R1.jl b/tests/automaton_abc/distributed_R1.jl
index 583a80cdd006452e3b5dcb92098a3371ef86a958..f10b7affc0cf981118958238bd8eeced531fd3d9 100644
--- a/tests/automaton_abc/distributed_R1.jl
+++ b/tests/automaton_abc/distributed_R1.jl
@@ -14,9 +14,9 @@ path_module = get_module_path() * "/core"
load_model("ER")
load_automaton("automaton_F")
end
-A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, "P")
+A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, :P)
sync_ER = A_F_R1*ER
-pm_sync_ER = ParametricModel(sync_ER, ("k3", Uniform(0.0, 100.0)))
+pm_sync_ER = ParametricModel(sync_ER, (:k3, Uniform(0.0, 100.0)))
nbr_pa = 404
r = automaton_abc(pm_sync_ER; nbr_particles = nbr_pa)
diff --git a/tests/cosmos/distance_F/ER_1D.jl b/tests/cosmos/distance_F/ER_1D.jl
index e1917f742d0ebac5cedba54ecbee9e069bf86b48..0a2e736d13809e0168e8b3f722b38f7c531b9834 100644
--- a/tests/cosmos/distance_F/ER_1D.jl
+++ b/tests/cosmos/distance_F/ER_1D.jl
@@ -5,9 +5,9 @@
absolute_path = get_module_path() * "/tests/cosmos/"
# Values x1, x2 t1, t2
dict_exp = Dict(
- "R1" => [50, 75, 0.025, 0.05],
- "R2" => [50, 75, 0.05, 0.075],
- "R3" => [25, 50, 0.05, 0.075]
+ :R1 => [50, 75, 0.025, 0.05],
+ :R2 => [50, 75, 0.05, 0.075],
+ :R3 => [25, 50, 0.05, 0.075]
)
str_model = "ER"
if str_model == "ER"
@@ -19,9 +19,9 @@
test_all = true
width = 0.2
level = 0.95
- l_exp = ["R1", "R2", "R3"]
+ l_exp = [:R1, :R2, :R3]
end
-#l_exp = ["R2"]
+#l_exp = [:R2]
for exp in l_exp
if !(exp in keys(dict_exp))
println("Unrecognized experiment: <<$exp>>")
@@ -29,7 +29,7 @@ for exp in l_exp
end
val_exp = dict_exp[exp]
x1, x2, t1, t2 = val_exp[1], val_exp[2], val_exp[3], val_exp[4]
- A_F = create_automaton_F(model, x1, x2, t1, t2, "P")
+ A_F = create_automaton_F(model, x1, x2, t1, t2, :P)
l_k3 = 0:10:100
nb_param = length(l_k3)
l_dist_cosmos = zeros(nb_param)
@@ -50,7 +50,7 @@ for exp in l_exp
nb_accepted = dict_values["Accepted paths"][1]
nb_sim = convert(Int, nb_sim)
# MarkovProcesses estimation
- set_param!(ER, "k3", convert(Float64, k3))
+ set_param!(ER, :k3, convert(Float64, k3))
sync_ER = ER*A_F
l_dist_pkg[i] = distribute_mean_value_lha(sync_ER, :d, nb_sim)
nb_accepts_pkg = distribute_prob_accept_lha(sync_ER, nb_sim)
diff --git a/tests/cosmos/distance_G/ER_R5.jl b/tests/cosmos/distance_G/ER_R5.jl
index 4820cfcc54ce9340f3fc36b4ae599e1bca16574b..5386f2d41a7dfda3fbd3e85d77b0227a54dc5475 100644
--- a/tests/cosmos/distance_G/ER_R5.jl
+++ b/tests/cosmos/distance_G/ER_R5.jl
@@ -13,7 +13,7 @@
width = 0.2
level = 0.95
x1, x2, t1, t2 = 50.0, 100.0, 0.0, 0.8
- A_G = create_automaton_G(model, x1, x2, t1, t2, "E")
+ A_G = create_automaton_G(model, x1, x2, t1, t2, :E)
l_k1 = 0.0:0.5:1.5
#l_k1 = 0.2:0.2
l_k2 = 0:40:100
@@ -44,8 +44,8 @@ for i = 1:nb_k1
nb_accepted = dict_values["Accepted paths"][1]
nb_sim = convert(Int, nb_sim)
# MarkovProcesses estimation
- set_param!(ER, "k1", convert(Float64, k1))
- set_param!(ER, "k2", convert(Float64, k2))
+ set_param!(ER, :k1, convert(Float64, k1))
+ set_param!(ER, :k2, convert(Float64, k2))
sync_ER = ER*A_G
mat_dist_pkg[i,j] = distribute_mean_value_lha(sync_ER, :d, nb_sim)
nb_accepts_pkg = distribute_prob_accept_lha(sync_ER, nb_sim)
diff --git a/tests/cosmos/distance_G_F/ER_R6.jl b/tests/cosmos/distance_G_F/ER_R6.jl
index da19cf0a48c10c773c8c40f355fe858281be9cc1..4246eb41cb1507aed90e0fe9931d91b605a03912 100644
--- a/tests/cosmos/distance_G_F/ER_R6.jl
+++ b/tests/cosmos/distance_G_F/ER_R6.jl
@@ -15,8 +15,8 @@
level = 0.95
x1, x2, t1, t2 = 50.0, 100.0, 0.0, 0.8
x3, x4, t3, t4 = 30.0, 100.0, 0.8, 0.9
- A_G_F = create_automaton_G_and_F(model, x1, x2, t1, t2, "E",
- x3, x4, t3, t4, "P")
+ A_G_F = create_automaton_G_and_F(model, x1, x2, t1, t2, :E,
+ x3, x4, t3, t4, :P)
l_k1 = 0.0:0.5:1.5
l_k2 = 0:40:100
end
@@ -48,8 +48,8 @@ for i = 1:nb_k1
nb_accepted = dict_values["Accepted paths"][1]
nb_sim = convert(Int, nb_sim)
# MarkovProcesses estimation
- set_param!(ER, "k1", convert(Float64, k1))
- set_param!(ER, "k2", convert(Float64, k2))
+ set_param!(ER, :k1, convert(Float64, k1))
+ set_param!(ER, :k2, convert(Float64, k2))
sync_ER = ER*A_G_F
mat_dist_pkg[i,j], mat_dist_prime_pkg[i,j] = distribute_mean_value_lha(sync_ER, [:d,:dprime], nb_sim)
nb_accepts_pkg = distribute_prob_accept_lha(sync_ER, nb_sim)
diff --git a/tests/dist_lp/dist_sim_sir.jl b/tests/dist_lp/dist_sim_sir.jl
index 61008a3eb7390e41426b9d9e80b02b3699d1f545..728a4ec3003fe764d5dba1ce1bea8865dcb1beee 100644
--- a/tests/dist_lp/dist_sim_sir.jl
+++ b/tests/dist_lp/dist_sim_sir.jl
@@ -12,11 +12,11 @@ for p = 1:2
let σ1, σ2, test, test2
σ1 = simulate(SIR)
σ2 = simulate(SIR)
- d = dist_lp(σ1, σ2, "I"; p = p)
+ d = dist_lp(σ1, σ2, :I; p = p)
d2 = dist_lp(σ1, σ2; p = p)
- f_x(t::Float64) = MarkovProcesses._f_step(σ1["I"], times(σ1), t)
- f_y(t::Float64) = MarkovProcesses._f_step(σ2["I"], times(σ2), t)
+ f_x(t::Float64) = MarkovProcesses._f_step(σ1[:I], times(σ1), t)
+ f_y(t::Float64) = MarkovProcesses._f_step(σ2[:I], times(σ2), t)
diff_f(t) = abs(f_x(t) - f_y(t))^p
int_riemann = MarkovProcesses._riemann_sum(diff_f, SIR.t0, SIR.time_bound, 1E-3)
res_int_riemann = int_riemann^(1/p)
diff --git a/tests/simulation/sim_er.jl b/tests/simulation/sim_er.jl
index 21e18e22659b79879c4345c516a02a1776c4cd1a..96857f4db14fa2e2a5a0305e95e1010aa9a18912 100644
--- a/tests/simulation/sim_er.jl
+++ b/tests/simulation/sim_er.jl
@@ -6,7 +6,7 @@ load_model("ER")
σ = simulate(ER)
plt.figure()
-plt.step(times(σ), σ["P"], "ro--", marker="x", where="post", linewidth=1.0)
+plt.step(times(σ), σ[:P], "ro--", marker="x", where="post", linewidth=1.0)
plt.savefig(get_module_path() * "/tests/simulation/res_pics/sim_er.png", dpi=480)
plt.close()
diff --git a/tests/simulation/sim_er_row_buffer_bounded.jl b/tests/simulation/sim_er_row_buffer_bounded.jl
index d684462f6f2e6246dffaf69edbfae167324f4b91..940fdce7c5579a2ea1cdb4534a7bb4a09edb4907 100644
--- a/tests/simulation/sim_er_row_buffer_bounded.jl
+++ b/tests/simulation/sim_er_row_buffer_bounded.jl
@@ -8,7 +8,7 @@ ER_row_buffer.time_bound = 10.0
σ = _simulate_row_buffer(ER_row_buffer)
plt.figure()
-plt.step(times(σ), _get_values_row(σ,"P"), "ro--", marker="x", where="post", linewidth=1.0)
+plt.step(times(σ), _get_values_row(σ,:P), "ro--", marker="x", where="post", linewidth=1.0)
plt.savefig(get_module_path() * "/tests/simulation/res_pics/sim_er_row_buffer_bounded.png")
plt.close()
diff --git a/tests/simulation/sim_pm_er.jl b/tests/simulation/sim_pm_er.jl
index c208806949a9ef3b03a59acb34697737f823e115..b5e3e5a075c75a2fe2962caff77407bf2171b1e1 100644
--- a/tests/simulation/sim_pm_er.jl
+++ b/tests/simulation/sim_pm_er.jl
@@ -4,7 +4,7 @@ using MarkovProcesses
load_plots()
load_model("ER")
-pm_ER = ParametricModel(ER, ("k1", Uniform(0.0,100.0)), ("k2", Uniform(0.0,100.0)))
+pm_ER = ParametricModel(ER, (:k1, Uniform(0.0,100.0)), (:k2, Uniform(0.0,100.0)))
prior_p = [0.2, 40.0]
diff --git a/tests/simulation/sim_pm_sync_er.jl b/tests/simulation/sim_pm_sync_er.jl
index 226dcb3ef97c2545521956d53069719831eb492c..56e92b64c777c1cb3652b3b80b77d7bb34e9a365 100644
--- a/tests/simulation/sim_pm_sync_er.jl
+++ b/tests/simulation/sim_pm_sync_er.jl
@@ -5,8 +5,8 @@ load_plots()
load_model("ER")
load_automaton("automaton_F")
-A_F = create_automaton_F(ER, 0.0, 100.0, 7.0, 8.0, "P")
-pm_sync_ER = ParametricModel(ER*A_F, ("k1", Uniform(0.0,100.0)), ("k2", Uniform(0.0,100.0)))
+A_F = create_automaton_F(ER, 0.0, 100.0, 7.0, 8.0, :P)
+pm_sync_ER = ParametricModel(ER*A_F, (:k1, Uniform(0.0,100.0)), (:k2, Uniform(0.0,100.0)))
prior_p = [0.2, 40.0]
diff --git a/tests/simulation/sim_sir.jl b/tests/simulation/sim_sir.jl
index 74fd911d82c083e54d1d85450cd11da3a0f1ca6a..9ee9755b93e5bc32185ad46ff084df8dab8702b7 100644
--- a/tests/simulation/sim_sir.jl
+++ b/tests/simulation/sim_sir.jl
@@ -6,7 +6,7 @@ load_model("SIR")
σ = simulate(SIR)
plt.figure()
-plt.step(times(σ), σ["I"], "ro--", marker="x", where="post", linewidth=1.0)
+plt.step(times(σ), σ[:I], "ro--", marker="x", where="post", linewidth=1.0)
plt.savefig(get_module_path() * "/tests/simulation/res_pics/sim_sir.png")
plt.close()
diff --git a/tests/simulation/sim_sir_bounded.jl b/tests/simulation/sim_sir_bounded.jl
index 469e7844bc6bd1ac8969b0c694ecb76b052c9ba4..19a3a121e22b95ea04a0e126eefdad5dc5f7bcc7 100644
--- a/tests/simulation/sim_sir_bounded.jl
+++ b/tests/simulation/sim_sir_bounded.jl
@@ -7,7 +7,7 @@ SIR.time_bound = 100.0
σ = simulate(SIR)
plt.figure()
-plt.step(times(σ), σ["I"], "ro--", marker="x", where="post", linewidth=1.0)
+plt.step(times(σ), σ[:I], "ro--", marker="x", where="post", linewidth=1.0)
plt.savefig(get_module_path() * "/tests/simulation/res_pics/sim_sir_bounded.png")
plt.close()
diff --git a/tests/simulation/sim_sir_col_buffer_bounded.jl b/tests/simulation/sim_sir_col_buffer_bounded.jl
index 524487b03ba84e6756e7a7379d938b56e55424d5..472195d2dffeaecbf16ba650407699bbff819a1c 100644
--- a/tests/simulation/sim_sir_col_buffer_bounded.jl
+++ b/tests/simulation/sim_sir_col_buffer_bounded.jl
@@ -8,7 +8,7 @@ SIR_col_buffer.time_bound = 100.0
σ = _simulate_col_buffer(SIR_col_buffer)
plt.figure()
-plt.step(times(σ), _get_values_col(σ,"I"), "ro--", marker="x", where="post", linewidth=1.0)
+plt.step(times(σ), _get_values_col(σ,:I), "ro--", marker="x", where="post", linewidth=1.0)
plt.savefig(get_module_path() * "/tests/simulation/res_pics/sim_sir_col_buffer_bounded.png")
plt.close()
diff --git a/tests/simulation/sim_sir_row_buffer_bounded.jl b/tests/simulation/sim_sir_row_buffer_bounded.jl
index b49c589841824c71223ce8f1692fd81699db4468..034862d0801dcc3d20239fcc178f48bcec1a1616 100644
--- a/tests/simulation/sim_sir_row_buffer_bounded.jl
+++ b/tests/simulation/sim_sir_row_buffer_bounded.jl
@@ -8,7 +8,7 @@ SIR_row_buffer.time_bound = 100.0
σ = _simulate_row_buffer(SIR_row_buffer)
plt.figure()
-plt.step(times(σ), _get_values_row(σ,"I"), "ro--", marker="x", where="post", linewidth=1.0)
+plt.step(times(σ), _get_values_row(σ,:I), "ro--", marker="x", where="post", linewidth=1.0)
plt.savefig(get_module_path() * "/tests/simulation/res_pics/sim_sir_row_buffer_bounded.png")
plt.close()
diff --git a/tests/unit/change_obs_var_sir.jl b/tests/unit/change_obs_var_sir.jl
index 125b01eb5012765dfa5a2ea1f668e3280e3b7f00..f6d301eae4660ba2c4afbdf21402d05850f6c7c0 100644
--- a/tests/unit/change_obs_var_sir.jl
+++ b/tests/unit/change_obs_var_sir.jl
@@ -4,12 +4,12 @@ using MarkovProcesses
load_model("SIR")
σ = simulate(SIR)
-set_observed_var!(SIR, ["I", "R"])
+set_observed_var!(SIR, [:I, :R])
-d1 = Dict("S" => 1, "I" => 2, "R" => 3)
-d2 = Dict("I" => 1, "R" => 2)
+d1 = Dict(:S => 1, :I => 2, :R => 3)
+d2 = Dict(:I => 1, :R => 2)
-bool_test = SIR.g == ["I", "R"] && SIR._g_idx == [2,3] &&
+bool_test = SIR.g == [:I, :R] && SIR._g_idx == [2,3] &&
SIR.map_var_idx == d1 &&
SIR._map_obs_var_idx == d2
diff --git a/tests/unit/change_obs_var_sir_2.jl b/tests/unit/change_obs_var_sir_2.jl
index e85288954f7d2145f1f71ed25f6898c9924771ec..8f12ffc9e5730d6aa7bba053fc202beaac62bda8 100644
--- a/tests/unit/change_obs_var_sir_2.jl
+++ b/tests/unit/change_obs_var_sir_2.jl
@@ -4,12 +4,12 @@ using MarkovProcesses
load_model("SIR")
σ = simulate(SIR)
-set_observed_var!(SIR, ["R", "S"])
+set_observed_var!(SIR, [:R, :S])
-d1 = Dict("S" => 1, "I" => 2, "R" => 3)
-d2 = Dict("R" => 1, "S" => 2)
+d1 = Dict(:S => 1, :I => 2, :R => 3)
+d2 = Dict(:R => 1, :S => 2)
-bool_test = SIR.g == ["R", "S"] && SIR._g_idx == [3,1] &&
+bool_test = SIR.g == [:R, :S] && SIR._g_idx == [3,1] &&
SIR.map_var_idx == d1 &&
SIR._map_obs_var_idx == d2
diff --git a/tests/unit/check_trajectory_consistency.jl b/tests/unit/check_trajectory_consistency.jl
index 5ccecf599a97bcd0618e6ff153b55e5d342eb610..3757a264b3d59ace4cab46d33a744e1761c88933 100644
--- a/tests/unit/check_trajectory_consistency.jl
+++ b/tests/unit/check_trajectory_consistency.jl
@@ -47,9 +47,9 @@ for i = 1:nb_sim
end
new_SIR = deepcopy(SIR)
-sync_SIR = new_SIR * create_automaton_F(new_SIR, 0.0, Inf, 100.0, 110.0, "I")
+sync_SIR = new_SIR * create_automaton_F(new_SIR, 0.0, Inf, 100.0, 110.0, :I)
new_ER = deepcopy(ER)
-sync_ER = new_ER * create_automaton_F(new_ER, 0.0, 100.0, 4.0, 5.0, "P")
+sync_ER = new_ER * create_automaton_F(new_ER, 0.0, 100.0, 4.0, 5.0, :P)
for i = 1:nb_sim
local σ = simulate(sync_SIR)
local σ2 = simulate(sync_ER)
diff --git a/tests/unit/create_automata.jl b/tests/unit/create_automata.jl
index e6549f0f794a2c327fcd720512255adcd71746dc..5b3eb03939929eb5a508df4bf3700847d2424bba 100644
--- a/tests/unit/create_automata.jl
+++ b/tests/unit/create_automata.jl
@@ -7,10 +7,10 @@ load_automaton("automaton_F")
load_automaton("automaton_G")
t1, t2, x1, x2 = 100.0, 120.0, 1.0, 100.0
-A_F = create_automaton_F(SIR, x1, x2, t1, t2, "I")
+A_F = create_automaton_F(SIR, x1, x2, t1, t2, :I)
t1, t2, x1, x2 = 0.0, 0.8, 50.0, 100.0
-A_G = create_automaton_G(ER, x1, x2, t1, t2, "P")
+A_G = create_automaton_G(ER, x1, x2, t1, t2, :P)
return true
diff --git a/tests/unit/density_pm.jl b/tests/unit/density_pm.jl
index bdabfae48efed91e4d123caacc93615ca252b835..47ba210734799d584988655c9a125dfbd41d63e1 100644
--- a/tests/unit/density_pm.jl
+++ b/tests/unit/density_pm.jl
@@ -6,11 +6,11 @@ load_model("SIR")
tol = 0.0
dist = Uniform(0.0, 1.0)
-pm = ParametricModel(SIR, ("kr", dist))
+pm = ParametricModel(SIR, (:kr, dist))
test1 = !insupport(pm, [2.0])
dist = Normal()
-pm = ParametricModel(SIR, ("kr", dist))
+pm = ParametricModel(SIR, (:kr, dist))
test2 = isapprox(prior_pdf(pm, [0.05]), pdf(dist, 0.05); atol = tol)
@@ -21,7 +21,7 @@ test3 = isapprox(_prior_pdf.(mat_u), pdf.(dist, vec_u); atol = tol)
dist, dist2 = Normal(), Normal(1.0, 2.0)
prod_dist = product_distribution([dist, dist2])
-pm = ParametricModel(SIR, ("ki", dist), ("kr", dist2))
+pm = ParametricModel(SIR, (:ki, dist), (:kr, dist2))
mat_u = rand(2,10)
vec_u = [mat_u[:,i] for i = 1:10]
vec_res = zeros(10)
diff --git a/tests/unit/dist_lp_var.jl b/tests/unit/dist_lp_var.jl
index 0ddf14e099cb5ee39dd2ef3c362d81be85fbf29e..82bcc3a727262409571e3406d0bfdc0429bbab41 100644
--- a/tests/unit/dist_lp_var.jl
+++ b/tests/unit/dist_lp_var.jl
@@ -5,7 +5,7 @@ load_model("SIR")
SIR.time_bound = 100.0
σ1, σ2 = simulate(SIR), simulate(SIR)
-dist_lp(σ1, σ2, "I")
+dist_lp(σ1, σ2, :I)
return true
diff --git a/tests/unit/draw_pm.jl b/tests/unit/draw_pm.jl
index c4c8cbd0b5e46ba4aece35f74b184f05fffa2019..0a9e6f635a962b40bcad38bf5539db690ecc2298 100644
--- a/tests/unit/draw_pm.jl
+++ b/tests/unit/draw_pm.jl
@@ -2,16 +2,16 @@
using MarkovProcesses
load_model("ER")
-k1 = ER["k1"]
+k1 = ER[:k1]
dist_mv_unif = Product(Uniform.([2.5,6.0], [3.5,7.0]))
-pm = ParametricModel(ER, ["k3","k2"], dist_mv_unif)
+pm = ParametricModel(ER, [:k3,:k2], dist_mv_unif)
draw_model!(pm)
-test1 = 2.5 <= ER["k3"] <= 3.5 && 6.0 <= ER["k2"] <= 7.0 && pm.df == 2
+test1 = 2.5 <= ER[:k3] <= 3.5 && 6.0 <= ER[:k2] <= 7.0 && pm.df == 2
p_drawn = copy(ER.p)
p_drawn_bis = copy((pm.m).p)
draw_model!(pm)
-test2 = 2.5 <= ER["k3"] <= 3.5 && 6.0 <= ER["k2"] <= 7.0 && pm.df == 2 && p_drawn == p_drawn_bis && ER.p != p_drawn
+test2 = 2.5 <= ER[:k3] <= 3.5 && 6.0 <= ER[:k2] <= 7.0 && pm.df == 2 && p_drawn == p_drawn_bis && ER.p != p_drawn
vec_p = zeros(2)
draw!(vec_p, pm)
diff --git a/tests/unit/getindex_access_trajectory.jl b/tests/unit/getindex_access_trajectory.jl
index e5b0aaa6b36872324e70055069e7a33c6afe6f64..ff75f21c1efdbf1c56e4c7f67c876ef3ddf76618 100644
--- a/tests/unit/getindex_access_trajectory.jl
+++ b/tests/unit/getindex_access_trajectory.jl
@@ -3,8 +3,8 @@ using MarkovProcesses
load_model("SIR")
σ = simulate(SIR)
-σ["I"]
-σ["I",2]
+σ[:I]
+σ[:I,2]
σ[3]
return true
diff --git a/tests/unit/length_obs_var.jl b/tests/unit/length_obs_var.jl
index 1f3253948fe3b0886b66b65627771bbfa15b7497..67254137bffde64212787ee13e6a2a82d13fa220 100644
--- a/tests/unit/length_obs_var.jl
+++ b/tests/unit/length_obs_var.jl
@@ -5,7 +5,7 @@ load_model("SIR")
σ = simulate(SIR)
test_1 = length_obs_var(σ) == 1
-set_observed_var!(SIR, ["I", "R"])
+set_observed_var!(SIR, [:I, :R])
σ = simulate(SIR)
test_2 = length_obs_var(σ) == 2
@@ -13,7 +13,7 @@ load_model("ER")
σ = simulate(ER)
test_3 = length_obs_var(σ) == 1
-set_observed_var!(ER, ["P", "S", "ES"])
+set_observed_var!(ER, [:P, :S, :ES])
σ = simulate(ER)
test_4 = length_obs_var(σ) == 3
diff --git a/tests/unit/long_sim_er.jl b/tests/unit/long_sim_er.jl
index ea5342ddd6efbd0578a1b055d6b3503bfe356295..6fc0134282a6b6ef15a2affb44da336120ca9349 100644
--- a/tests/unit/long_sim_er.jl
+++ b/tests/unit/long_sim_er.jl
@@ -1,8 +1,8 @@
using MarkovProcesses
load_model("ER")
-set_param!(ER, "k1", 0.2)
-set_param!(ER, "k2", 40.0)
+set_param!(ER, :k1, 0.2)
+set_param!(ER, :k2, 40.0)
return true
diff --git a/tests/unit/model_prior.jl b/tests/unit/model_prior.jl
index f5b4b9ae026df50893a7fcf4fdc6c4e0d0a6bbae..dd246066cdcac0807afbf6241c19c53b6423e01b 100644
--- a/tests/unit/model_prior.jl
+++ b/tests/unit/model_prior.jl
@@ -5,17 +5,17 @@ load_model("ER")
test_all = true
load_model("ER")
-pm1 = ParametricModel(ER, ("k2", Uniform(2.0, 4.0)))
+pm1 = ParametricModel(ER, (:k2, Uniform(2.0, 4.0)))
draw_model!(pm1)
-test_all = test_all && 2.0 <= ER["k2"] <= 4.0 && pm1.df == 1
+test_all = test_all && 2.0 <= ER[:k2] <= 4.0 && pm1.df == 1
-pm2 = ParametricModel(ER, ["k3","k2"], Product(Uniform.([2.5,6.0], [3.5,7.0])))
+pm2 = ParametricModel(ER, [:k3,:k2], Product(Uniform.([2.5,6.0], [3.5,7.0])))
draw_model!(pm2)
-test_all = test_all && 2.5 <= ER["k3"] <= 3.5 && 6.0 <= ER["k2"] <= 7.0 && pm2.df == 2
+test_all = test_all && 2.5 <= ER[:k3] <= 3.5 && 6.0 <= ER[:k2] <= 7.0 && pm2.df == 2
-pm3 = ParametricModel(ER, ("k3", Uniform(10.0, 11.0)), ("k2", Uniform(13.0, 14.0)))
+pm3 = ParametricModel(ER, (:k3, Uniform(10.0, 11.0)), (:k2, Uniform(13.0, 14.0)))
draw_model!(pm3)
-test_all = test_all && 10.0 <= ER["k3"] <= 11.0 && 13.0 <= ER["k2"] <= 14.0 && pm3.df == 2
+test_all = test_all && 10.0 <= ER[:k3] <= 11.0 && 13.0 <= ER[:k2] <= 14.0 && pm3.df == 2
return test_all
diff --git a/tests/unit/models_exps_er_1d.jl b/tests/unit/models_exps_er_1d.jl
index 704516bce433d3ad8e5449b949b2a47b13661844..c644d998c2da18f089bf42d2002c3918c3b494cf 100644
--- a/tests/unit/models_exps_er_1d.jl
+++ b/tests/unit/models_exps_er_1d.jl
@@ -6,10 +6,10 @@ observe_all!(ER)
load_automaton("automaton_F")
load_automaton("automaton_G")
-A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, "P")
-A_F_R2 = create_automaton_F(ER, 50.0, 75.0, 0.05, 0.075, "P")
-A_F_R3 = create_automaton_F(ER, 25.0, 50.0, 0.05, 0.075, "P")
-A_G_R5 = create_automaton_G(ER, 50.0, 100.0, 0.0, 0.8, "E")
+A_F_R1 = create_automaton_F(ER, 50.0, 75.0, 0.025, 0.05, :P)
+A_F_R2 = create_automaton_F(ER, 50.0, 75.0, 0.05, 0.075, :P)
+A_F_R3 = create_automaton_F(ER, 25.0, 50.0, 0.05, 0.075, :P)
+A_G_R5 = create_automaton_G(ER, 50.0, 100.0, 0.0, 0.8, :E)
return true
diff --git a/tests/unit/models_exps_er_2d.jl b/tests/unit/models_exps_er_2d.jl
index 09e04a8820c5fef3b315af3601851daa0d0898a7..37198e9a89e3dd69b5bd838643ee3f49d7dab04e 100644
--- a/tests/unit/models_exps_er_2d.jl
+++ b/tests/unit/models_exps_er_2d.jl
@@ -8,10 +8,10 @@ load_automaton("automaton_G")
load_automaton("automaton_G_and_F")
x1, x2, t1, t2 = 50.0, 100.0, 0.0, 0.8
-A_G_R5 = create_automaton_G(ER, x1, x2, t1, t2, "E")
+A_G_R5 = create_automaton_G(ER, x1, x2, t1, t2, :E)
x3, x4, t3, t4 = 30.0, 100.0, 0.8, 0.9
-A_G_F_R6 = create_automaton_G_and_F(ER, x1, x2, t1, t2, "E",
- x3, x4, t3, t4, "P")
+A_G_F_R6 = create_automaton_G_and_F(ER, x1, x2, t1, t2, :E,
+ x3, x4, t3, t4, :P)
return true
diff --git a/tests/unit/observe_all.jl b/tests/unit/observe_all.jl
index ff3b5f3ca4fa37b40ff20b9f7ad9188108b2a045..c6aaab9849c4770a44cceec93b7c2d7a77fb854f 100644
--- a/tests/unit/observe_all.jl
+++ b/tests/unit/observe_all.jl
@@ -7,5 +7,5 @@ load_model("SIR")
observe_all!(ER)
observe_all!(SIR)
-return (ER.g == ["E", "S", "ES", "P"] && SIR.g == ["S", "I", "R"])
+return (ER.g == [:E, :S, :ES, :P] && SIR.g == [:S, :I, :R])
diff --git a/tests/unit/set_param.jl b/tests/unit/set_param.jl
index f99c2f9463c100c220183fc5ec5586d262852ce6..b2d31a6ce3e0030b6b3e0a63601883e6bff97eed 100644
--- a/tests/unit/set_param.jl
+++ b/tests/unit/set_param.jl
@@ -11,19 +11,19 @@ set_param!(ER, new_param_ER)
test_all = test_all && (ER.p == new_param_ER)
k2 = 4.0
-set_param!(ER, "k2", k2)
+set_param!(ER, :k2, k2)
test_all = test_all && (ER.p == [1.0, 4.0, 1.5])
k1 = 0.5
-set_param!(ER, "k1", k1)
+set_param!(ER, :k1, k1)
test_all = test_all && (ER.p == [0.5, 4.0, 1.5])
k3 = 10.0
-set_param!(ER, "k3", 10.0)
+set_param!(ER, :k3, 10.0)
test_all = test_all && (ER.p == [0.5, 4.0, 10.0])
l_k = [20.0, 10.0]
-l_name = ["k3", "k2"]
+l_name = [:k3, :k2]
set_param!(ER, l_name, l_k)
test_all = test_all && (ER.p == [0.5, 10.0, 20.0])
@@ -32,24 +32,24 @@ set_param!(SIR, new_param_SIR)
test_all = test_all && (SIR.p == new_param_SIR)
kr = 0.06
-set_param!(SIR, "kr", kr)
+set_param!(SIR, :kr, kr)
test_all = test_all && (SIR.p == [0.0013, 0.06])
ki = 0.011
-set_param!(SIR, "ki", ki)
+set_param!(SIR, :ki, ki)
test_all = test_all && (SIR.p == [0.011, 0.06])
-l_name = ["kr", "ki"]
+l_name = [:kr, :ki]
l_p = [0.03, 0.015]
set_param!(SIR, l_name, l_p)
test_all = test_all && (SIR.p == [0.015, 0.03])
-l_name = ["ki", "kr"]
+l_name = [:ki, :kr]
l_p = [0.03, 0.015]
set_param!(SIR, l_name, l_p)
test_all = test_all && (SIR.p == [0.03, 0.015])
-l_name = ["kr"]
+l_name = [:kr]
l_p = [0.02]
set_param!(SIR, l_name, l_p)
test_all = test_all && (SIR.p == [0.03, 0.02])
diff --git a/tests/unit/simulate_er.jl b/tests/unit/simulate_er.jl
index 32340d1d9fa954c7acc3983dffa70de5fe9265f7..50036b209a0238e1e881ac18d3c2960d291cfaa1 100644
--- a/tests/unit/simulate_er.jl
+++ b/tests/unit/simulate_er.jl
@@ -5,10 +5,10 @@ load_model("ER")
σ = simulate(ER)
-d1 = Dict("E" => 1, "S"=> 2, "ES" => 3, "P" => 4)
-d2 = Dict("P" => 1)
+d1 = Dict(:E => 1, :S => 2, :ES => 3, :P => 4)
+d2 = Dict(:P => 1)
-bool_test = ER.g == ["P"] && ER._g_idx == [4] &&
+bool_test = ER.g == [:P] && ER._g_idx == [4] &&
ER.map_var_idx == d1 &&
ER._map_obs_var_idx == d2
diff --git a/tests/unit/simulate_sir.jl b/tests/unit/simulate_sir.jl
index 6a1a38a5093cf90c05a707342b5dc0c933a8eb1c..4745217a46419a4b561b521eb8720176a3bf437f 100644
--- a/tests/unit/simulate_sir.jl
+++ b/tests/unit/simulate_sir.jl
@@ -5,10 +5,10 @@ load_model("SIR")
σ = simulate(SIR)
-d1 = Dict("S" => 1, "I"=> 2, "R" => 3)
-d2 = Dict("I" => 1)
+d1 = Dict(:S => 1, :I=> 2, :R => 3)
+d2 = Dict(:I => 1)
-bool_test = SIR.g == ["I"] && SIR._g_idx == [2] &&
+bool_test = SIR.g == [:I] && SIR._g_idx == [2] &&
SIR.map_var_idx == d1 &&
SIR._map_obs_var_idx == d2
diff --git a/tests/unit/simulate_sir_bounded.jl b/tests/unit/simulate_sir_bounded.jl
index c872c02612cae7273e842c3592e73ca069efe316..17b3b2768c1c19083d1481c4737b212ded62157b 100644
--- a/tests/unit/simulate_sir_bounded.jl
+++ b/tests/unit/simulate_sir_bounded.jl
@@ -6,10 +6,10 @@ SIR.time_bound = 100.0
σ = simulate(SIR)
-d1 = Dict("S" => 1, "I"=> 2, "R" => 3)
-d2 = Dict("I" => 1)
+d1 = Dict(:S => 1, :I=> 2, :R => 3)
+d2 = Dict(:I => 1)
-bool_test = SIR.g == ["I"] && SIR._g_idx == [2] &&
+bool_test = SIR.g == [:I] && SIR._g_idx == [2] &&
SIR.map_var_idx == d1 &&
SIR._map_obs_var_idx == d2 && times(σ)[end] <= SIR.time_bound