update SI

CDCgov · Dec 18, 2024 · 3d624dc · 3d624dc
1 parent 677d222
commit 3d624dc
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Showing 2 changed files with 25 additions and 19 deletions.
diff --git a/manuscript/Project.toml b/manuscript/Project.toml
@@ -1,4 +1,5 @@
 [deps]
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
 DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1"

diff --git a/manuscript/index.qmd b/manuscript/index.qmd
@@ -24,9 +24,9 @@ index_location = @__DIR__()
 Pkg.activate(index_location)
 Pkg.resolve()
 Pkg.instantiate()
-Pkg.add(["CairoMakie", "JLD2", "DataFramesMeta", "DrWatson"])
+Pkg.add(["CairoMakie", "JLD2", "DataFramesMeta", "DrWatson", "CSV"])
 
-using DataFramesMeta, JLD2
+using DataFramesMeta, JLD2, CSV
 
 ```
 
@@ -66,23 +66,28 @@ We noted that for a substantial number of the model configurations there were mo
 priorpred_dir = joinpath(@__DIR__(),"..", "pipeline/data/priorpredictive/")
 priorpred_datafiles = readdir(priorpred_dir) |>
   fns -> filter(fn -> contains(fn, ".jld2"), fns) #filter for .jld2 files
-
-priorpred_outcomes_df = mapreduce(vcat, priorpred_datafiles) do fn
-  D = load(joinpath(priorpred_dir, fn))
-  igp = D["inference_config"]["igp"]
-  latent_model = D["inference_config"]["latent_model"]
-  gi_mean = D["inference_config"]["gi_mean"]
-  T1, T2 = split(D["inference_config"]["tspan"], "_")
-  runsuccess = D["priorpredictive"] .== "Pass"
-  df = DataFrame(
-    infection_gen_proc = igp,
-    latent_model = latent_model,
-    gi_mean = gi_mean,
-    T1 = T1,
-    T2 = T2,
-    T_diff = parse(Int, T2) - parse(Int, T1),
-    runsuccess = runsuccess,
-    )
+priorpred_outcomes_df = DataFrame()
+if !isfile(joinpath(index_location, "pass_fail_rdn1.csv"))
+  priorpred_outcomes_df = mapreduce(vcat, priorpred_datafiles) do fn
+    D = load(joinpath(priorpred_dir, fn))
+    igp = D["inference_config"]["igp"]
+    latent_model = D["inference_config"]["latent_model"]
+    gi_mean = D["inference_config"]["gi_mean"]
+    T1, T2 = split(D["inference_config"]["tspan"], "_")
+    runsuccess = D["priorpredictive"] .== "Pass"
+    df = DataFrame(
+      infection_gen_proc = igp,
+      latent_model = latent_model,
+      gi_mean = gi_mean,
+      T1 = T1,
+      T2 = T2,
+      T_diff = parse(Int, T2) - parse(Int, T1),
+      runsuccess = runsuccess,
+      )
+  end
+  CSV.write(joinpath(index_location, "pass_fail_rdn1.csv"), priorpred_outcomes_df)
+else
+  priorpred_outcomes_df = CSV.File(joinpath(index_location, "pass_fail_rdn1.csv")) |> DataFrame
 end
 ```