record vac degrees

.vscode/settings.json

 {
-    "julia.environmentPath": "/home/peterj/.julia/environments/v1.6"
 }
\ No newline at end of file

CovidAlertVaccinationModel/abm_timeseries.jl

@@ -3,10 +3,10 @@ using OnlineStats
 using Plots
 pgfplotsx()
 using CovidAlertVaccinationModel:vaccination_data,ymo_vac,ymo_attack_rate
-const samples = 10
+const samples = 25
 
 function solve_and_plot_parameters()
-    p =  CovidAlertVaccinationModel.get_app_parameters()
+    p =  CovidAlertVaccinationModel.get_parameters()
     display(p)
     out,avg_populations = mean_solve(samples, p, record_degrees = true)
     p = plot_model(nothing,[nothing],[out],p)

CovidAlertVaccinationModel/src/ABM/output.jl

@@ -22,6 +22,7 @@ struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
     record_degrees_flag::Bool
     avg_weighted_degree_of_vaccinators::Vector{StatAccumulator}
     avg_weighted_degree::Vector{StatAccumulator}
+    avg_weighted_degree_of_vaccinator_belief::Vector{StatAccumulator}
 
     function Recorder(val::T,sim_length; record_degrees = false) where T
     
@@ -45,6 +46,8 @@ struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
     
         avg_weighted_degree_of_vaccinators = [Variance() for _ in 1:3]
         avg_weighted_degree = [Variance() for _ in 1:3]
+
+        avg_weighted_degree_of_vaccinator_belief = [Variance() for _ in 1:sim_length]
     
         return new{T,typeof(state_totals),typeof(total_vaccinators),typeof(daily_immunized_by_age),eltype(avg_weighted_degree)}(
             state_totals,
@@ -61,7 +64,8 @@ struct Recorder{ElType,ArrT1,ArrT2,ArrT3,StatAccumulator}
             daily_unvac_by_age,
             record_degrees,
             avg_weighted_degree_of_vaccinators,
-            avg_weighted_degree
+            avg_weighted_degree,
+            avg_weighted_degree_of_vaccinator_belief
         )
     end
 end

CovidAlertVaccinationModel/src/ABM/plotting.jl

@@ -24,6 +24,7 @@ function plot_model(varname,univariate_series, output_list::Vector{T},p) where T
         (x_axis, data.daily_immunized_by_age.O, "new O vaccinations each day"),
         (x_axis, data.daily_total_notifications, "total notifications each day"),
         (x_axis, data.daily_total_notified_agents, "total notified agents each day"),
+        (x_axis, data.avg_weighted_degree_of_vaccinator_belief, "Average weighted degree of vaccinators"),
         zoom((x_axis, data.daily_cases_by_age.Y, "Daily Y cases (zoomed in)")),
         zoom((x_axis, data.daily_cases_by_age.M, "Daily M cases (zoomed in)")),
         zoom((x_axis, data.daily_cases_by_age.O, "Daily O cases (zoomed in)")),

CovidAlertVaccinationModel/src/ABM/solve.jl

@@ -184,6 +184,10 @@ function agents_step!(t,modelsol,vaccinate_today)
         elseif immunization_countdown[agent]>0
             immunization_countdown[agent] -= 1
         end
+
+        if u_vac[agent]
+            fit!(output_data.avg_weighted_degree_of_vaccinator_belief[t],weighted_degree_of_i)
+        end
         update_vaccination_opinion_state!(t, agent,agent_demo, modelsol,vaccinate_today, total_infected,π_base_vec,weighted_degree_of_i)
     end
 

CovidAlertVaccinationModel/test/ABM/output_test.jl

 using CovidAlertVaccinationModel:vaccination_data,ymo_vac,ymo_attack_rate
 using OnlineStats
 using Plots
-const samples = 10
+const samples = 50
 using Random
 
 @testset "default parameters output" begin