delete initialisation --- not used

EpiAware/src/EpiAware.jl

@@ -20,16 +20,16 @@ This module provides functionality for calculating Rt (effective reproduction nu
 module EpiAware
 
 using Distributions,
-      Turing,
-      LogExpFunctions,
-      LinearAlgebra,
-      SparseArrays,
-      Random,
-      ReverseDiff,
-      Optim,
-      Parameters,
-      QuadGK,
-      DataFramesMeta
+    Turing,
+    LogExpFunctions,
+    LinearAlgebra,
+    SparseArrays,
+    Random,
+    ReverseDiff,
+    Optim,
+    Parameters,
+    QuadGK,
+    DataFramesMeta
 
 # Exported utilities
 export create_discrete_pmf, spread_draws

EpiAware/src/epimodel.jl

@@ -68,8 +68,8 @@ and `g_i` is the generation interval.
 """
 function (epimodel::Renewal)(recent_incidence, Rt)
     new_incidence = Rt * dot(recent_incidence, epimodel.data.gen_int)
-    return (
-        [new_incidence; recent_incidence[1:(epimodel.data.len_gen_int - 1)]], new_incidence)
+    return ([new_incidence; recent_incidence[1:(epimodel.data.len_gen_int - 1)]],
+        new_incidence)
 end
 
 function generate_latent_infs(epimodel::AbstractEpiModel, latent_process)

EpiAware/src/models.jl

@@ -5,8 +5,7 @@
         observation_model::AbstractObservationModel,
         pos_shift = 1e-6)
     #Latent process
-    @submodel latent_process, latent_process_aux = generate_latent_process(
-        latent_process_model,
+    @submodel latent_process, latent_process_aux = generate_latent_process(latent_process_model,
         time_steps)
 
     #Transform into infections

EpiAware/test/test_epimodel.jl

@@ -78,8 +78,8 @@ end
     @test ks_test_pval > 1e-6 #Very unlikely to fail if the model is correctly implemented
 
     #Check that the generated incidence is correct given correct initialisation
-    mdl_incidence = generated_quantities(
-        EpiAware.generate_latent_infs(rt_model, rt), (init_incidence = log_init,))
+    mdl_incidence = generated_quantities(EpiAware.generate_latent_infs(rt_model, rt),
+        (init_incidence = log_init,))
     @test mdl_incidence ≈ recent_incidence
 end
 
@@ -98,8 +98,8 @@ end
     expected_incidence = exp.(log_init_scale .+ log_incidence)
 
     #Check log_init is sampled from the correct distribution
-    sample_init_inc = sample(
-        EpiAware.generate_latent_infs(direct_inf_model, log_incidence), Prior(), 1000) |>
+    sample_init_inc = sample(EpiAware.generate_latent_infs(direct_inf_model, log_incidence),
+        Prior(), 1000) |>
                       chn -> chn[:init_incidence] |>
                              Array |>
                              vec
@@ -108,8 +108,8 @@ end
     @test ks_test_pval > 1e-6 #Very unlikely to fail if the model is correctly implemented
 
     #Check that the generated incidence is correct given correct initialisation
-    mdl_incidence = generated_quantities(
-        EpiAware.generate_latent_infs(direct_inf_model, log_incidence),
+    mdl_incidence = generated_quantities(EpiAware.generate_latent_infs(direct_inf_model,
+            log_incidence),
         (init_incidence = log_init_scale,))
 
     @test mdl_incidence ≈ expected_incidence
@@ -139,8 +139,8 @@ end
     initial_incidence = [1.0, 1.0, 1.0]#aligns with initial exp growth rate of 0.
 
     #Check log_init is sampled from the correct distribution
-    @time sample_init_inc = sample(
-        EpiAware.generate_latent_infs(renewal_model, log_Rt), Prior(), 1000) |>
+    @time sample_init_inc = sample(EpiAware.generate_latent_infs(renewal_model, log_Rt),
+        Prior(), 1000) |>
                             chn -> chn[:init_incidence] |>
                                    Array |>
                                    vec
@@ -150,8 +150,8 @@ end
 
     #Check that the generated incidence is correct given correct initialisation
     #Check first three days "by hand"
-    mdl_incidence = generated_quantities(
-        EpiAware.generate_latent_infs(renewal_model, log_Rt), (init_incidence = 0.0,))
+    mdl_incidence = generated_quantities(EpiAware.generate_latent_infs(renewal_model,
+            log_Rt), (init_incidence = 0.0,))
 
     day1_incidence = dot(initial_incidence, gen_int) * Rt[1]
     day2_incidence = dot(initial_incidence, gen_int) * Rt[2]

EpiAware/test/test_models.jl

@@ -26,8 +26,8 @@
         Δd = Δd)
 
     # Create full epi model and sample from it
-    test_mdl = make_epi_inference_model(
-        y_t, time_horizon; epimodel = epimodel, latent_process_model = rwp,
+    test_mdl = make_epi_inference_model(y_t, time_horizon; epimodel = epimodel,
+        latent_process_model = rwp,
         observation_model = obs_model, pos_shift)
     gen = generated_quantities(test_mdl, rand(test_mdl))
 

EpiAware/test/test_utilities.jl

@@ -64,7 +64,7 @@ end
     @testset "Test case 5" begin
         dist = Exponential(1.0)
         expected_pmf_uncond = [exp(-1)
-                               [(1 - exp(-1)) * (exp(1) - 1) * exp(-s) for s in 1:9]]
+            [(1 - exp(-1)) * (exp(1) - 1) * exp(-s) for s in 1:9]]
         expected_pmf = expected_pmf_uncond ./ sum(expected_pmf_uncond)
         pmf = create_discrete_pmf(dist; Δd = 1.0, D = 10.0)
         @test expected_pmf≈pmf atol=1e-15
@@ -100,10 +100,10 @@ end
         delay_int = [0.2, 0.5, 0.3]
         time_horizon = 5
         expected_K = SparseMatrixCSC([0.2 0 0 0 0
-                                      0.5 0.2 0 0 0
-                                      0.3 0.5 0.2 0 0
-                                      0 0.3 0.5 0.2 0
-                                      0 0 0.3 0.5 0.2])
+            0.5 0.2 0 0 0
+            0.3 0.5 0.2 0 0
+            0 0.3 0.5 0.2 0
+            0 0 0.3 0.5 0.2])
         K = EpiAware.generate_observation_kernel(delay_int, time_horizon)
         @test K == expected_K
     end