add univariate snensitivity plots, some output code, added the model size and...

add univariate snensitivity plots, some output code, added the model size and length to parameter tuple. New output is in /CovidAlertVaccinationModel/plots/univariate

CovidAlertVaccinationModel/src/CovidAlertVaccinationModel.jl

@@ -25,6 +25,8 @@ using CSV
 import Pandas: read_csv
 using DataFrames
 using StaticArrays
+import LightGraphs.neighbors
+
 export intervalsmodel, hh, ws, rest, abm
 
 const DNDEBUG = false
@@ -37,7 +39,7 @@ end
 
 const durmax = 144
 const PACKAGE_FOLDER = dirname(dirname(pathof(CovidAlertVaccinationModel)))
-const RNG = Xoroshiro128Star(1)
+const RNG = Xoroshiro128Star()
 
 const color_palette = palette(:seaborn_pastel) #color theme for the plots
 #consts that let give us nicer names for the indices
@@ -48,7 +50,6 @@ include("ABM/contact_vectors.jl")
 include("ABM/mixing_distributions.jl")
 include("ABM/mixing_graphs.jl")
 include("ABM/agents.jl")
-include("ABM/plotting.jl")
 include("ABM/model_setup.jl")
 include("ABM/output.jl")
 include("ABM/solve.jl")

CovidAlertVaccinationModel/test/ABM/abm_test.jl

-
-using CovidAlertVaccinationModel
-using CovidAlertVaccinationModel:ModelSolution,AgentDemographic,mean,AgentStatus,get_u_0,get_parameters,solve!, DebugRecorder
-using Random
-const model_sizes = [100,1000,5000]
-const dem_cat = AgentDemographic.size -1
-
-const steps = 300
-const reps = 5
-#network generation
-#covidalert
-
-@testset "mixing matrices, size: $sz" for sz in model_sizes
-    for rep = 1:reps
-        m = ModelSolution(10,get_parameters(),sz)
-        ws_dist = m.ws_matrix_tuple
-        r_dist = m.rest_matrix_tuple 
-        index_vec =m.index_vectors
-        @testset "workschool" for i in dem_cat, j in dem_cat
-            for t in 1:length(ws_dist)
-                @test mean(ws_dist[t][i,j])*length(index_vec[i]) == mean(ws_dist[t][j,i])*length(index_vec[j])
-            end
-        end
-        @testset "rest" for i in dem_cat, j in dem_cat
-            for t in 1:length(ws_dist)
-                @test mean(r_dist[t][i,j])*length(index_vec[i]) == mean(r_dist[t][j,i])*length(index_vec[j])
-            end
-        end
-    end
-end
-
-@testset "status counter, size: $sz" for sz in model_sizes
-    for rep = 1:reps
-        Random.seed!(CovidAlertVaccinationModel.RNG,1)
-        m = ModelSolution(steps,get_parameters(),sz)
-        recording = DebugRecorder(steps)
-        output = solve!(m,recording )
-        @show recording.Total_I
-        @show recording.recorded_status_totals[2,:]
-        @test all(recording.Total_S .== recording.recorded_status_totals[1,:])
-        @test all(recording.Total_I .== recording.recorded_status_totals[2,:])
-        @test all(recording.Total_R .== recording.recorded_status_totals[3,:])
-        @test all(recording.Total_V .== recording.recorded_status_totals[4,:])
-    end
-end
-
-
-# function vaccinator_opinion_test(model,vac_strategy, π_base; rng = Xoroshiro128Plus()) 
-#     params = merge(get_parameters(),(I_0_fraction = 0.0,π_base))
-#     steps = 300
-#     sol1,_ = solve!(u_0,params,steps,model,vac_strategy);
-#     total_infections = count(x->x == AgentStatus(3),sol1[end])
-#     display(total_infections)
-#     return total_infections
-# end
-
-
-# function infection_rate_test(model, inf_parameter; rng = Xoroshiro128Plus()) 
-#     steps = 300
-#     # display(params)
-#     sol1,_ = solve!(params,steps,model,vaccinate_uniformly!);
-#     total_infections = count(x->x == AgentStatus(3),sol1[end])
-
-#     # display(total_infections)
-#     return total_infections
-# end
-
-# function test_comparison(f,xpts,comparison)
-#     xpts_sorted = sort(xpts)
-#     ypts = ThreadsX.map(f,xpts)
-#     return all(comparison(ypts[i],ypts[i+1]) for i in 1:length(ypts)-1)
-# end
-
-# @testset "vaccination efficacy $sz" for (m,sz) in zip(deepcopy(agent_models),model_sizes)
-#     @show vac_rate_test(m,vaccination_strategies[1],vaccination_rates[1])
-#     @testset "strategy" for strat in vaccination_strategies
-#         @test test_comparison(x->vac_rate_test(m,strat,x),vaccination_rates,>)
-#     end
-# end
-
-# @testset "infection efficacy $sz" for (m,sz) in zip(deepcopy(agent_models),model_sizes)
-#     @test test_comparison(x->infection_rate_test(m,x),infection_rates,<)
-# end
-
-
-# @testset "infection efficacy $sz" for (m,sz) in zip(deepcopy(agent_models),model_sizes)
-#     @test test_comparison(x->infection_rate_test(m,x),infection_rates,<)
-# end
-
-
-
-
-

CovidAlertVaccinationModel/test/ABM/mixing_test.jl

+
+using CovidAlertVaccinationModel
+using CovidAlertVaccinationModel:ModelSolution,AgentDemographic,mean,AgentStatus,get_u_0,get_parameters,solve!, DebugRecorder
+using Random
+using Plots
+using ThreadsX
+using OnlineStats
+const model_sizes = [1000,5000]
+ const dem_cat = AgentDemographic.size -1
+ const samples = 10
+
+#WRITE MORE TESTS
+
+@testset "mixing matrices, size: $sz" for sz in model_sizes
+    for rep = 1:samples
+        m = ModelSolution(100,get_parameters(),sz)
+        ws_dist = m.ws_matrix_tuple
+        r_dist = m.rest_matrix_tuple 
+        index_vec =m.index_vectors
+        @testset "workschool" for i in dem_cat, j in dem_cat
+            for t in 1:length(ws_dist)
+                @test mean(ws_dist[t][i,j])*length(index_vec[i]) == mean(ws_dist[t][j,i])*length(index_vec[j])
+            end
+        end
+        @testset "rest" for i in dem_cat, j in dem_cat
+            for t in 1:length(ws_dist)
+                @test mean(r_dist[t][i,j])*length(index_vec[i]) == mean(r_dist[t][j,i])*length(index_vec[j])
+            end
+        end
+    end
+end
+
+
+

CovidAlertVaccinationModel/test/runtests.jl

@@ -4,5 +4,5 @@ using Test
 using ThreadsX
 import StatsBase.mean
 
-include("ABM/abm_test.jl")
+include("ABM/mixing_test.jl")
 include("IntervalsModel/intervals_model_test.jl")
\ No newline at end of file