Issue 541: Changelog for prior changes and fix the data ingestion step (

#548)

* Update changelog.md

* Update make_model_priors.jl

* Make `define_epiprob` more modular

_make_epidata can also be reused elsewhere

* restructure analysis dataframe func

* fix make_prediction_dataframe_from_output

And add simple unit test with committed test data

* Revert "fix make_prediction_dataframe_from_output"

This reverts commit 7e6238b.

* Reapply "fix make_prediction_dataframe_from_output"

This reverts commit 14d29b4.

* script to generate prediction dataframes

* create_prediction_df refactor + fix

* update to analyses failures

* Update create_prediction_dataframe.jl

pipeline/scripts/create_prediction_dataframe.jl

@@ -0,0 +1,58 @@
+using EpiAwarePipeline, EpiAware, AlgebraOfGraphics, JLD2, DrWatson, DataFramesMeta,
+      Statistics, Distributions, DrWatson, CSV
+
+## Define scenarios
+scenarios = ["measures_outbreak", "smooth_outbreak", "smooth_endemic", "rough_endemic"]
+
+## Define true GI means
+true_gi_means = [2.0, 10.0, 20.0]
+
+## Load the prediction dataframes or record fails
+failed_configs = Dict[]
+
+dfs = mapreduce(vcat, scenarios) do scenario
+    mapreduce(vcat, true_gi_means) do true_gi_mean
+        target_str = "truth_gi_mean_" * string(true_gi_mean) * "_"
+        files = readdir(datadir("epiaware_observables/" * scenario)) |>
+                strs -> filter(s -> occursin("jld2", s), strs) |>
+                        strs -> filter(s -> occursin(target_str, s), strs)
+
+        mapreduce(vcat, files) do filename
+            output = load(joinpath(datadir("epiaware_observables"), scenario, filename))
+            try
+                make_prediction_dataframe_from_output(output, true_gi_mean)
+            catch e
+                @warn "Error in $filename"
+                push!(failed_configs, output["inference_config"])
+                return DataFrame()
+            end
+        end
+    end
+end
+
+## Gather the failed data
+failed_df = mapreduce(vcat, failed_configs) do D
+    igp = D["igp"] |> str -> split(str, ".")[end]
+    latent_model = D["latent_model"]
+    gi_mean = D["gi_mean"]
+    T1, T2 = split(D["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
+
+##
+grped_failed_df = failed_df |>
+                  df -> @groupby(df, :infection_gen_proc, :latent_model) |>
+                        gd -> @combine(gd, :n_fail=sum(1 .- :runsuccess))
+
+## Save the prediction and failed dataframes
+CSV.write(plotsdir("plotting_data/predictions.csv"), dfs)
+CSV.write(plotsdir("plotting_data/failed_preds.csv"), failed_df)