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* initial refactor * Update plotting.jl * refactor plotting for DRY * Figure 2
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,91 +1,75 @@ | ||
| function _make_captions!(df, scenario_dict, target_dict) | ||
| scenario_titles = [scenario_dict[scenario].title for scenario in df.Scenario] | ||
| target_titles = [target_dict[target].title for target in df.Target] | ||
| df.Scenario_Target .= scenario_titles .* "\n" .* target_titles | ||
| return nothing | ||
| function _fig2_pred_filter(predictions, scenario, target, latent_model, horizon; | ||
| true_gi_choice, used_gi_choice, horizon_diff = 7) | ||
| df = predictions |> | ||
| df -> @subset(df, :Latent_Model.==latent_model) |> | ||
| df -> @subset(df, :True_GI_Mean.==true_gi_choice) |> | ||
| df -> @subset(df, :Used_GI_Mean.==used_gi_choice) |> | ||
| df -> @subset(df, | ||
| horizon-horizon_diff.<(:target_times.-:Reference_Time).<=horizon) |> | ||
| df -> @subset(df, :Scenario.==scenario) |> | ||
| df -> @subset(df, :Target.==target) | ||
| return df | ||
| end | ||
|
|
||
| function _figure_two_truth_data( | ||
| truth_df, scenario_dict, target_dict; true_gi_choice, gi_choices = [ | ||
| 2.0, 10.0, 20.0]) | ||
| _truth_df = mapreduce(vcat, gi_choices) do used_gi | ||
| df = deepcopy(truth_df) | ||
| df.Used_GI_Mean .= used_gi | ||
| df | ||
| function figuretwo( | ||
| prediction_df, truth_data_df, scenarios, targets, horizon; | ||
| scenario_dict, target_dict, latent_model_dict, | ||
| latent_model = "ar", igps = ["DirectInfections", "ExpGrowthRate", "Renewal"], | ||
| true_gi_choice = 10.0, other_gi_choices = [2.0, 10.0, 20.0], data_color = :black, | ||
| colors = [:red, :blue, :green], iqr_alpha = 0.3, horizon_diff = 7) | ||
| fig = Figure(; size = (1000, 800 * length(scenarios))) | ||
| axs = mapreduce(vcat, enumerate(scenarios)) do (i, scenario) | ||
| n = length(targets) | ||
| Label(fig[(n * (i - 1) + 1):(n * i), 0], | ||
| scenario_dict[scenario].title, rotation = pi / 2, fontsize = 36) | ||
| mapreduce(hcat, enumerate(targets)) do (j, target) | ||
| map(enumerate(other_gi_choices)) do (k, used_gi_choice) | ||
| row = j + (i - 1) * length(targets) | ||
| ax = Axis(fig[row, k]) | ||
| # #Filter the data for fig2 panels | ||
| pred_df = _fig2_pred_filter( | ||
| prediction_df, scenario, target, latent_model, horizon; | ||
| true_gi_choice, used_gi_choice, horizon_diff) | ||
| truth_df = _fig_truth_filter( | ||
| truth_data_df, scenario, target; true_gi_choice) | ||
| # #Plot onto axes | ||
| _plot_predictions!(ax, pred_df; igps, colors, iqr_alpha) | ||
| _plot_truth!(ax, truth_df; color = data_color) | ||
| vlines!(ax, pred_df.Reference_Time |> unique, color = :black, | ||
| linestyle = :dash, label = "Reference time") | ||
| # axes | ||
| if row == 1 | ||
| if k == 1 | ||
| ax.title = "Underestimating mean GI" | ||
| elseif k == 2 | ||
| ax.title = "Good estimation of GI" | ||
| elseif k == 3 | ||
| ax.title = "Overestimating mean GI" | ||
| end | ||
| end | ||
| if row == length(targets) * length(scenarios) | ||
| ax.xlabel = "Time" | ||
| end | ||
| if k == 1 | ||
| ax.ylabel = target_dict[target].title | ||
| end | ||
| ax.limits = ( | ||
| (minimum(pred_df.Reference_Time) - horizon_diff, | ||
| maximum(pred_df.Reference_Time) + 1), | ||
| nothing) | ||
| ax.xticks = vcat(minimum(pred_df.Reference_Time) - horizon_diff, | ||
| pred_df.Reference_Time |> unique) | ||
| ax | ||
| end | ||
| end | ||
| end | ||
| _make_captions!(_truth_df, scenario_dict, target_dict) | ||
|
|
||
| truth_plotting_data = _truth_df |> | ||
| df -> @subset(df, :True_GI_Mean.==true_gi_choice) |> | ||
| df -> @transform(df, :Data="Truth data") |> data | ||
| plt_truth = truth_plotting_data * | ||
| mapping(:target_times => "T", :target_values => "Process values", | ||
| row = :Scenario_Target, | ||
| col = :Used_GI_Mean => renamer([2.0 => "Underestimate GI", | ||
| 10.0 => "Good GI", 20.0 => "Overestimate GI"]), | ||
| color = :Data => AlgebraOfGraphics.scale(:color2)) * | ||
| visual(AlgebraOfGraphics.Scatter) | ||
| return plt_truth | ||
| end | ||
|
|
||
| function _figure_two_scenario( | ||
| analysis_df, igp, scenario_dict, target_dict; true_gi_choice, | ||
| lower_sym = :q_025, upper_sym = :q_975) | ||
| min_ref_time = minimum(analysis_df.Reference_Time) | ||
| early_df = analysis_df |> | ||
| df -> @subset(df, :Reference_Time.==min_ref_time) |> | ||
| df -> @subset(df, :IGP_Model.==igp) |> | ||
| df -> @subset(df, :True_GI_Mean.==true_gi_choice) |> | ||
| df -> @subset(df, :target_times.<=min_ref_time - 7) | ||
|
|
||
| seqn_df = analysis_df |> | ||
| df -> @subset(df, :True_GI_Mean.==true_gi_choice) |> | ||
| df -> @subset(df, :IGP_Model.==igp) |> | ||
| df -> @subset(df, | ||
| :Reference_Time .- :target_times.∈fill(0:6, size(df, 1))) | ||
|
|
||
| full_df = vcat(early_df, seqn_df) | ||
| _make_captions!(full_df, scenario_dict, target_dict) | ||
|
|
||
| model_plotting_data = full_df |> data | ||
|
|
||
| plt_model = model_plotting_data * | ||
| mapping(:target_times => "T", :q_5 => "Process values", | ||
| row = :Scenario_Target, | ||
| col = :Used_GI_Mean => renamer([2.0 => "Underestimate GI", | ||
| 10.0 => "Good GI", 20.0 => "Overestimate GI"]), | ||
| color = :Latent_Model => "Latent models") * | ||
| mapping(lower = lower_sym, upper = upper_sym) * | ||
| visual(LinesFill) | ||
|
|
||
| return plt_model | ||
| end | ||
|
|
||
| function figuretwo(truth_df, analysis_df, igp, scenario_dict, | ||
| target_dict; fig_kws = (; size = (1000, 2800)), | ||
| true_gi_choice = 10.0, gi_choices = [2.0, 10.0, 20.0]) | ||
|
|
||
| # Perform checks on the dataframes | ||
| _dataframe_checks(truth_df, analysis_df, scenario_dict) | ||
|
|
||
| f_td = _figure_two_truth_data( | ||
| truth_df, scenario_dict, target_dict; true_gi_choice, gi_choices) | ||
| f_mdl = _figure_two_scenario( | ||
| analysis_df, igp, scenario_dict, target_dict; true_gi_choice) | ||
|
|
||
| fg = draw(f_mdl + f_td; facet = (; linkyaxes = :none), | ||
| legend = (; orientation = :horizontal, position = :bottom), | ||
| figure = fig_kws, | ||
| axis = (; xlabel = "T", ylabel = "Process values")) | ||
| for g in fg.grid[1:3:end, :] | ||
| g.axis.limits = (nothing, target_dict["rt"].ylims) | ||
| end | ||
| for g in fg.grid[2:3:end, :] | ||
| g.axis.limits = (nothing, target_dict["Rt"].ylims) | ||
| end | ||
| for g in fg.grid[3:3:end, :] | ||
| g.axis.limits = (nothing, target_dict["log_I_t"].ylims) | ||
| end | ||
|
|
||
| return fg | ||
| leg = Legend(fig[length(targets) * length(scenarios) + 1, 1:2], | ||
| last(axs), "Infection generating process"; | ||
| orientation = :horizontal, tellwidth = false, framevisible = false) | ||
| lab = Label(fig[length(targets) * length(scenarios) + 1, length(other_gi_choices)], | ||
| "Latent model for \n infection generating\n process: $(latent_model_dict[latent_model].title) \n True mean GI: $(true_gi_choice) days \n Horizon: $(horizon) days"; | ||
| tellwidth = false, | ||
| fontsize = 18) | ||
| resize_to_layout!(fig) | ||
| fig | ||
| end |
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