more univariate sims

CovidAlertVaccinationModel/src/ABM/abm.jl

@@ -6,7 +6,7 @@ const household_data = read_household_data()
 default(dpi = 300)
 default(framestyle = :box)
 import LightGraphs.neighbors
-export DebugRecorder,mean_solve,plot_model, get_parameters, HeatmapRecorder
+export DebugRecorder,mean_solve,plot_model, univariate_simulations, plot_parameter_plane, get_parameters, HeatmapRecorder
 """
     bench()
 

CovidAlertVaccinationModel/src/ABM/model_setup.jl

@@ -13,13 +13,13 @@ function get_parameters()#(0.0000,0.00048,0.0005,0.16,-1.30,-1.24,-0.8,0.35,0.35
         π_base_y = -1.37,
         π_base_m = -1.46,
         π_base_o = -0.95,
-        η = 0.00,
+        η = 0.01,
         κ = 0.0,
         ω = 0.0055,
-        ω_en = 0.0005,
+        ω_en = 0.0,
         Γ = 1/7,
         ξ = 5.0,
-        notification_parameter = 0.001,
+        notification_parameter = 0.0005,
         vaccinator_prob = 0.6,
         app_user_fraction = 0.0,
         notification_threshold = 2,
@@ -33,7 +33,7 @@ function get_parameters()#(0.0000,0.00048,0.0005,0.16,-1.30,-1.24,-0.8,0.35,0.35
 end
 function get_app_parameters()
 
-    return merge(get_parameters(),(app_user_fraction = 0.5,))
+    return merge(get_parameters(),(app_user_fraction = 0.3,))
 end
 function get_u_0(nodes,vaccinator_prob)
     is_vaccinator = rand(Random.default_rng(Threads.threadid()),nodes) .< vaccinator_prob

CovidAlertVaccinationModel/src/ABM/output.jl

@@ -123,12 +123,13 @@ function record!(t,modelsol, recorder::Nothing)
 end
 
 
-function mean_solve(samples,parameter_tuple,recorder)
+function mean_solve(samples,parameter_tuple,recorder;progmeter = nothing)
     stat_recorder = recorder(Variance(), parameter_tuple.sim_length)
     output_recorder = recorder(0.0,parameter_tuple.sim_length)
     avg_populations = [0.0,0.0,0.0]
     for _ in 1:samples
         sol = abm(parameter_tuple,output_recorder)
+        isnothing(progmeter) || next!(progmeter)
         avg_populations .+= length.(sol.index_vectors)
         fit!(stat_recorder,output_recorder)
     end
@@ -136,14 +137,13 @@ function mean_solve(samples,parameter_tuple,recorder)
     return stat_recorder,avg_populations
 end
 
-function mean_solve(samples,parameter_tuple,recorder::Type{HeatmapRecorder})
+function mean_solve(samples,parameter_tuple,recorder::Type{HeatmapRecorder};progmeter = nothing)
     stat_recorder = recorder(parameter_tuple.sim_length)
     avg_populations = [0.0,0.0,0.0]
     for _ in 1:samples
-
         output_recorder = recorder(parameter_tuple.sim_length)
         sol = abm(parameter_tuple,output_recorder)
-
+        isnothing(progmeter) || next!(progmeter)
         avg_populations .+= length.(sol.index_vectors)
         fit!(stat_recorder,output_recorder)
     end

CovidAlertVaccinationModel/src/ABM/parameter_planes.jl

@@ -2,10 +2,10 @@
 
 const univariate_path = "CovidAlertVaccinationModel/plots/univariate/" 
 const bivariate_path = "CovidAlertVaccinationModel/plots/univariate/" 
-function univarate_test(variable, variable_range)
+function univarate_test(variable, variable_range,samples; progmeter = nothing)
     default_parameters = get_app_parameters() 
     parameter_range_list = [merge(default_parameters,NamedTuple{(variable,)}((value,))) for value in variable_range]
-    solve_fn(p) = mean_solve(samples, p,DebugRecorder)[1]
+    solve_fn(p) = mean_solve(samples, p,DebugRecorder;progmeter)[1]
 
     univariate_outlist = ThreadsX.map(solve_fn, parameter_range_list)
     
@@ -17,28 +17,32 @@ if !ispath(univariate_path)
     mkdir(univariate_path)
 end
 function univariate_simulations()
-    len = 20
+    len = 5
+    samples = 10
     univarate_test_list = (
         # (:I_0_fraction, range(0.0, 0.05; length = len)), 
         # (:base_transmission_probability, range(0.0002, 0.002; length = len)),
         # (:recovery_rate, range(0.1, 0.5; length = len)),
         # (:immunization_loss_prob, range(0.00, 0.05; length = len)),
         # (:π_base, range(-4.5, -3.5;  length = len)),
-        (:η, range(0.0, 0.01; length = len)),
+        (:η, range(0.0, 0.005; length = len)),
         # (:κ, range(0.5, 1.5; length = len)),
         # (:ω, range(0.0, 0.01; length = len)),
-        (:ω_en, range(0.0, 0.0005; length = len)),
+        (:ω_en, range(0.0, 0.001; length = len)),
         # (:γ, range(0.0, 0.5; length = len)),
         # (:ξ, range(1, 15; length = len)),
-        # (:notification_parameter, range(0.00, 0.05; length = len)),
-        # (:app_user_fraction, range(0.05, 0.25; length = len)),
+        (:notification_parameter, range(0.000, 0.002; length = len)),
+        (:app_user_fraction, range(0.05, 0.8; length = len)),
         (:notification_threshold, (1:len)),
         # (:immunization_delay, [7,10,14,20]),
     )
 
+
+    numsim = sum(map(t -> length(t[2]), univarate_test_list))
+    display(numsim)
+    progmeter = Progress(numsim*samples)
     plt_list = ThreadsX.map(univarate_test_list) do ur
-        out = univarate_test(ur...)
-        display("done $(ur[1])")
+        out = univarate_test(ur...,samples;progmeter)
         return out
     end
     for ((varname,_),p) in zip(univarate_test_list,plt_list)
@@ -48,10 +52,10 @@ end
 
 using AxisKeys 
 function multivariate_simulations()
-    len = 15
-    samples = 10
+    len = 10
+    samples = 25
     app_simulations = (
-        (:η, range(0.0, 0.01; length = len)),
+        (:η, range(0.0, 0.005; length = len)),
         (:ω_en, range(0.0, 0.05; length = len)),
         # (:notification_threshold, (1:len)),
     )
@@ -70,12 +74,12 @@ function run_multivariate_sims(sims,samples)
 
     default_parameters = get_app_parameters() 
     simvars = Iterators.product(sim_ranges...)
-    progmeter = Progress(length(simvars))
+    progmeter = Progress(length(simvars)*samples)
     app_output = ThreadsX.map(simvars) do vars
         vars_with_names = NamedTuple{varnames}(vars)
         parameters = merge(default_parameters,vars_with_names)
-        out,_ = mean_solve(1, parameters,HeatmapRecorder) 
-        next!(progmeter)
+        out,_ = mean_solve(samples, parameters,HeatmapRecorder;progmeter) 
+  
         return out
     end
     display(length(simvars))

CovidAlertVaccinationModel/src/ABM/plotting.jl

@@ -32,7 +32,7 @@ function plot_model(varname,univariate_series, output_list::Vector{T},infection_
             labelname = nothing
         end
         for (plt,(ts,title)) in zip(plts,ts_list(data))
-            plot!(plt, mean.(ts); ribbon = std.(ts),
+            plot!(plt, mean.(ts); #yerr = std.(ts),
                     label = labelname, line_z = i, color=:blues,
                     ylabel = "no. of people",
                     colorbar = false,