Increment DPPL to 0.31 and reduce testenv deps

.github/workflows/test-EpiAware.yaml

@@ -32,3 +32,5 @@ jobs:
       - uses: julia-actions/cache@v1
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
+        with:
+          depwarn: 'yes'

EpiAware/Project.toml

@@ -32,7 +32,7 @@ AdvancedHMC = "0.6"
 DataFramesMeta = "0.15"
 Distributions = "0.25"
 DocStringExtensions = "0.9"
-DynamicPPL = "0.30"
+DynamicPPL = "0.31"
 FillArrays = "1.11"
 LinearAlgebra = ">= 1.9"
 LogExpFunctions = "0.3"

EpiAware/test/EpiAwareBase/EpiMethod.jl

@@ -12,3 +12,7 @@
         @test method.sampler == sampler
     end
 end
+
+@testitem "trigger" begin
+    @test true
+end

EpiAware/test/EpiObsModels/StackObservationModels.jl

@@ -17,7 +17,7 @@
 end
 
 @testitem "StackObervationModels generate_observations works as expected" begin
-    using Turing, DynamicPPL, DataFrames
+    using Turing, DynamicPPL
 
     struct TestObs <: AbstractTuringObservationModel
         mean::Float64
@@ -48,11 +48,11 @@ end
 
     # extract samples for cases.y_t and deaths_y_t
     # from the chain of samples (not using generated_quantities)
+    # flatten the samples
     function extract_obs(samples, obs_name)
         obs = group(samples, obs_name) |>
-              DataFrame |>
-              x -> stack(x, Not(:iteration, :chain)) |>
-                   x -> x[!, :value]
+              Array |>
+              x -> x[:]
         return obs
     end
 

EpiAware/test/EpiObsModels/modifiers/LatentDelay.jl

@@ -165,7 +165,7 @@ end
 @testitem "LatentDelay parameter recovery with mix of IGP + latent processes: Poisson errors + EpiProblem interface" begin
     using Random, Turing, Distributions, LinearAlgebra, DynamicPPL, StatsBase, ReverseDiff,
           Suppressor, LogExpFunctions
-    # using PairPlots, CairoMakie
+
     Random.seed!(1234)
 
     #Set up model testing matrix

EpiAware/test/EpiObsModels/modifiers/ascertainment/helpers.jl

@@ -1,5 +1,5 @@
 @testitem "ascertainment_dayofweek correctly constructs a day of week ascertainment model" begin
-    using DynamicPPL, LogExpFunctions, Turing, DataFrames
+    using DynamicPPL, LogExpFunctions, Turing
 
     struct ExpectedObs <: AbstractTuringObservationModel
         model::AbstractTuringObservationModel

EpiAware/test/Project.toml

@@ -1,18 +1,14 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-FFMPEG = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
-LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
@@ -22,10 +18,8 @@ ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 TestItems = "1c621080-faea-4a02-84b6-bbd5e436b8fe"
-Transducers = "28d57a85-8fef-5791-bfe6-a80928e7c999"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

EpiAware/test/doctest.jl

@@ -1,4 +1,4 @@
 @testitem "Run docstring tests" begin
     using Documenter
-    doctest(EpiAware)
+    doctest(EpiAware; fix = true)
 end