Automatically format with JuliaFormatter

src/item_banks.jl

@@ -77,7 +77,7 @@ function plot_item_bank(item_bank::AbstractItemBank;
         labeller = index_labeller,
         zero_symmetric = false,
         include_outcome_toggles = true,
-        include_item_toggles = false,)
+        include_item_toggles = false)
     fig = Figure()
     ax = Axis(fig[1, 1])
     lim_lo, lim_hi = _item_bank_domain(DomainType(item_bank),
@@ -165,11 +165,12 @@ function make_grid(::OneDimContinuousDomain, item_bank, lim_lo, lim_hi, num_poin
     range(lim_lo, lim_hi, length = num_points)
 end
 
-function make_grid(::VectorContinuousDomain, item_bank, lim_lo::AbstractVector, lim_hi::AbstractVector, num_points)
+function make_grid(::VectorContinuousDomain, item_bank,
+        lim_lo::AbstractVector, lim_hi::AbstractVector, num_points)
     prod = Iterators.product((
         range(lo, hi, length = num_points)
-        for (lo, hi)
-        in zip(lim_lo, lim_hi)
+    for (lo, hi)
+    in zip(lim_lo, lim_hi)
     )...)
     reshape(collect.(prod), :)
 end
@@ -194,7 +195,7 @@ function plot_item_bank_comparison(item_banks::AbstractVector;
         labeller = index_labeller,
         include_outcome_toggles = true,
         include_item_toggles = false,
-        ignore_domain_indices = [],)
+        ignore_domain_indices = [])
     fig = Figure()
     ax = Axis(fig[1, 1])
     # Get limits
@@ -209,7 +210,8 @@ function plot_item_bank_comparison(item_banks::AbstractVector;
         lim_hi = max(lim_hi, ib_lim_hi)
     end
     # Plot lines
-    outcomes = Array{Union{Makie.Lines, Makie.Heatmap}}(undef, length(item_banks), 2, length(items))
+    outcomes = Array{Union{Makie.Lines, Makie.Heatmap}}(
+        undef, length(item_banks), 2, length(items))
     some_heatmap = nothing
     for (ibi, item_bank) in enumerate(item_banks)
         ax = Axis(fig[ibi, 1])
@@ -234,7 +236,8 @@ function plot_item_bank_comparison(item_banks::AbstractVector;
     outcome_grid = include_outcome_toggles ? draw_outcome_toggles!(fig[1, 2], [1, 2]) :
                    nothing
     item_grid = include_item_toggles ?
-                draw_item_toggles!(fig[include_outcome_toggles ? length(item_banks) : 1, 2],
+                draw_item_toggles!(
+        fig[include_outcome_toggles ? length(item_banks) : 1, 2],
         items,
         labeller) : nothing
     # Connect widgets

src/makie_extensions.jl

@@ -1,6 +1,5 @@
 using Makie: make_block_docstring  # This is used by @Block
 
-
 # Allows hline! on AlgebraOfGraphics plots. May be better way in future.
 # See: https://github.com/JuliaPlots/AlgebraOfGraphics.jl/issues/299
 function aog_hlines!(fg::AlgebraOfGraphics.FigureGrid, args...; kws...)

src/recorder.jl

@@ -158,43 +158,47 @@ function eachmatcol(xs::Vector)
     xs
 end
 
-function save_sampled(xs::Nothing, integrator, recorder::CatRecorder, tracked_responses, ir, item_correct)
+function save_sampled(
+        xs::Nothing, integrator, recorder::CatRecorder, tracked_responses, ir, item_correct)
     # Just skip saving in this case
 end
 
-function save_sampled(xs::Nothing, integrator::RiemannEnumerationIntegrator, recorder::CatRecorder, tracked_responses, ir, item_correct)
+function save_sampled(xs::Nothing, integrator::RiemannEnumerationIntegrator,
+        recorder::CatRecorder, tracked_responses, ir, item_correct)
     # In this case, the item bank is probably sampled so we can use that
 
     # Save likelihoods
     dist_est = distribution_estimator(recorder.ability_estimator)
     denom = normdenom(integrator, dist_est, tracked_responses)
     recorder.likelihoods[:, recorder.col_idx] = function_ys(
         Aggregators.pdf(
-            dist_est,
-            tracked_responses
-        )
+        dist_est,
+        tracked_responses
+    )
     ) ./ denom
     raw_denom = normdenom(integrator, recorder.raw_estimator, tracked_responses)
     recorder.raw_likelihoods[:, recorder.col_idx] = function_ys(
         Aggregators.pdf(
-            recorder.raw_estimator,
-            tracked_responses
-        )
+        recorder.raw_estimator,
+        tracked_responses
+    )
     ) ./ raw_denom
 
     # Save item responses
     recorder.item_responses[:, recorder.col_idx] = item_ys(ir, item_correct)
 end
 
-function save_sampled(xs, integrator, recorder::CatRecorder, tracked_responses, ir, item_correct)
+function save_sampled(
+        xs, integrator, recorder::CatRecorder, tracked_responses, ir, item_correct)
     # Save likelihoods
     dist_est = distribution_estimator(recorder.ability_estimator)
     denom = normdenom(integrator, dist_est, tracked_responses)
     recorder.likelihoods[:, recorder.col_idx] = Aggregators.pdf.(Ref(dist_est),
         Ref(tracked_responses),
         eachmatcol(xs)) ./ denom
     raw_denom = normdenom(integrator, recorder.raw_estimator, tracked_responses)
-    recorder.raw_likelihoods[:, recorder.col_idx] = Aggregators.pdf.(Ref(recorder.raw_estimator),
+    recorder.raw_likelihoods[:, recorder.col_idx] = Aggregators.pdf.(
+        Ref(recorder.raw_estimator),
         Ref(tracked_responses),
         eachmatcol(xs)) ./ raw_denom
 
@@ -224,7 +228,8 @@ function (recorder::CatRecorder)(tracked_responses, resp_idx, terminating)
     item_index = tracked_responses.responses.indices[end]
     item_correct = tracked_responses.responses.values[end] > 0
     ir = ItemResponse(tracked_responses.item_bank, item_index)
-    save_sampled(recorder.xs, recorder.integrator, recorder, tracked_responses, ir, item_correct)
+    save_sampled(
+        recorder.xs, recorder.integrator, recorder, tracked_responses, ir, item_correct)
 
     # Save item parameters
     params = item_params(tracked_responses.item_bank, item_index)
@@ -286,7 +291,7 @@ function lh_evolution_interactive(recorder; abilities = nothing)
         "raw ability estimate",
         "actual ability",
         "current item response",
-        "previous responses",
+        "previous responses"
     ]
     toggles = [Toggle(conv_dist_fig, active = true) for _ in toggle_labels]
     labels = [Label(conv_dist_fig, lift(x -> x ? "Hide $l" : "Show $l", t.active))