fixed silly distribution adjust bug

CovidAlertVaccinationModel/Manifest.toml

@@ -8,9 +8,9 @@ version = "1.0.1"
 
 [[AbstractMCMC]]
 deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"]
-git-tree-sha1 = "21279159f6be4b2fd00e1a4a1f736893100408fc"
+git-tree-sha1 = "06e46b94299b6d0d9fb4f7833c7b1df3df6f9678"
 uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
-version = "3.2.0"
+version = "3.0.0"
 
 [[AbstractTrees]]
 git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5"
@@ -520,8 +520,8 @@ uuid = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 version = "0.6.3"
 
 [[KissABC]]
-deps = ["AbstractMCMC", "Distributed", "Distributions", "MonteCarloMeasurements", "Random"]
-path = "/home/peterj/Projects/PRs/KissABC.jl/"
+deps = ["AbstractMCMC", "Distributions", "MonteCarloMeasurements", "Random"]
+git-tree-sha1 = "693d4bd4cbb9ad6515414e0935bbd646b15fc16a"
 uuid = "9c9dad79-530a-4643-a18b-2704674d4108"
 version = "3.0.1"
 

CovidAlertVaccinationModel/abm_fit_parameters.jl

-using Distributed
-addprocs(15)
-@everywhere using CovidAlertVaccinationModel
+using CovidAlertVaccinationModel
 CovidAlertVaccinationModel.fit_parameters(CovidAlertVaccinationModel.get_parameters())
\ No newline at end of file

CovidAlertVaccinationModel/prem_mixing_approx.jl

-using CovidAlertVaccinationModel:ModelSolution, get_parameters, neighbors, GraphEdge, get_weight
+using CovidAlertVaccinationModel:abm,ModelSolution, get_parameters, neighbors, GraphEdge, get_weight
 function approximate_mixing_matricies()
-    sol = abm(get_parameters(),nothing)
+    p = get_parameters()
+    sol = abm(p,nothing)
     mean_mixing = zeros(3,3)
-    display(mean_mixing)
-    display(sol.inf_network)
+    display(p.O_distribution_shift)
     for (node_i,demo_i) in enumerate(sol.demographics)
         for g_list in sol.inf_network.graph_list
             for g in g_list
@@ -19,6 +19,7 @@ function approximate_mixing_matricies()
     display(mean_mixing)
     return sol
 end
-approximate_mixing_matricies()
+approximate_mixing_matricies();
+println("dasdlkjasdas")
 #rerun with weights
 #
\ No newline at end of file

CovidAlertVaccinationModel/src/ABM/mixing_distributions.jl

@@ -8,7 +8,9 @@ function adjust_distributions_mean!(distribution_matrix,mean_shift_percentage)
     for i in eachindex(distribution_matrix)
         dist = distribution_matrix[i]
         new_mean = mean(dist) + mean_shift_percentage
+
         distribution_matrix[i] = from_mean(typeof(dist), new_mean)
+
     end
 end
 

CovidAlertVaccinationModel/src/ABM/model_setup.jl

@@ -83,19 +83,23 @@ mutable struct ModelSolution{T,InfNet,SocNet,WSMixingDist,RestMixingDist}
         demographics,base_network,index_vectors = generate_population(num_households)
         nodes = length(demographics)
         pop_sizes = length.(index_vectors)
+        ws_mixing_tuple_preshift = deepcopy(workschool_mixing)
+        rest_mixing_tuple_preshift = deepcopy(rest_mixing)
 
-        map(workschool_mixing) do md
+        for md in ws_mixing_tuple_preshift
             adjust_distributions_mean!(md[1:3,3],params.O_distribution_shift)
-            adjust_distributions_mean!(md[3,1:3],params.O_distribution_shift)
+            adjust_distributions_mean!(md[3,1:2],params.O_distribution_shift)#dont shift OO twice
         end
-        map(rest_mixing) do md
+        for md in rest_mixing_tuple_preshift
             adjust_distributions_mean!( md[1:3,3],params.O_distribution_shift)
-            adjust_distributions_mean!( md[3,1:3],params.O_distribution_shift)
+            adjust_distributions_mean!( md[3,1:2],params.O_distribution_shift) #dont shift OO twice
         end
+
         map_symmetrize(m_tuple) = map(md -> symmetrize_means(pop_sizes,md), m_tuple)
 
-        ws_matrix_tuple = map_symmetrize(workschool_mixing) 
-        rest_matrix_tuple = map_symmetrize(rest_mixing)
+        ws_matrix_tuple = map_symmetrize(ws_mixing_tuple_preshift) 
+        rest_matrix_tuple = map_symmetrize(rest_mixing_tuple_preshift)
+
         is_app_user = app_users(demographics,params.app_user_fraction)
         app_user_list = zeros(nodes)
         app_user_index = findall(==(true),is_app_user)