Integration#90
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… Bee issue #6) For multi-objective runs the per-objective 1D plots were each drawn at that objective's own expected minimum when no selectedPoint was supplied, so the quality and cost rows described different factor settings. Default the selected_point to the Pareto front's compromise point (front_x_data[best_idx]) when the caller sends none, so both objectives' 1D plots are evaluated at the same point. emit_pareto_data now returns that optimal point for the caller to reuse. An explicit selectedPoint (a clicked point) still takes precedence.
… (Brownie Bee issue #10) get_Brownie_Bee_1d_plot computes the histogram mean/std via model.predict() on the raw selected point, but the GP is fitted in transformed space. For all-numeric spaces the raw point lands outside the normalized domain, so the prediction collapses to the prior mean — the histogram stayed constant across every Pareto point (while the per-factor plots, which transform x_eval via dependence(), moved). The _get_brownie_bee_1d_plot_safe workaround only transformed for categorical points, leaving numeric spaces (e.g. cfps-multi) broken. Recompute the histogram in emit_json_single_plots via model.predict(space.transform([selected_point])); the default (selected_point is None) path still reuses the expected_minimum-derived value, which was already correct. Adds a numeric-only regression test.
front_x_data[best_idx].tolist() returns Any (numpy is untyped), but the function is declared -> list | None. Cast the return so `mypy optimizerapi` (the CI type-check step) passes. Pre-existing since d204b2d; surfaced now because it blocks the build.
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Fix more issues from the integration test