A couple of improvements in plotting routines.

optimas/diagnostics/exploration_diagnostics.py

@@ -593,6 +593,7 @@ def plot_history(
         select: Optional[Dict] = None,
         sort: Optional[Dict] = None,
         top: Optional[Dict] = None,
+        parnames_as_titles: Optional[bool] = True,
         show_top_evaluation_indices: Optional[bool] = False,
         show_legend: Optional[bool] = False,
         subplot_spec: Optional[SubplotSpec] = None,
@@ -617,6 +618,9 @@ def plot_history(
             e.g. {'f': False} sort simulations according to f descendingly.
         top: int, optional
             Highlight the 'top' evaluations of every objective.
+        parnames_as_titles : bool, optional
+            Whether to show the parameter names as titles (instead of labels)
+            for a more compact layout.
         show_top_evaluation_indices : bool, optional
             Whether to show the indices of the top evaluations.
         show_legend : bool, optional
@@ -674,13 +678,25 @@ def plot_history(
 
         # Make figure
         nplots = len(parnames)
+        if parnames_as_titles is True:
+            hspace = 0.40
+        else:
+            hspace = None
+
         if subplot_spec is None:
             fig = plt.figure(**figure_kw)
-            gs = GridSpec(nplots, 2, width_ratios=[0.8, 0.2], wspace=0.05)
+            gs = GridSpec(
+                nplots, 2, width_ratios=[0.8, 0.2], wspace=0.05, hspace=hspace
+            )
         else:
             fig = plt.gcf()
             gs = GridSpecFromSubplotSpec(
-                nplots, 2, subplot_spec, width_ratios=[0.8, 0.2], wspace=0.05
+                nplots,
+                2,
+                subplot_spec,
+                width_ratios=[0.8, 0.2],
+                wspace=0.05,
+                hspace=hspace,
             )
 
         # Actual plotting
@@ -792,7 +808,15 @@ def plot_history(
             ax_histy.set_ylim(ax_scatter.get_ylim())
 
             # Tuning axes and labels
-            ax_scatter.set_ylabel(parnames[i])
+            if parnames_as_titles is True:
+                ax_scatter.set_title(
+                    parnames[i].replace("_", " "),
+                    fontdict={"fontsize": "x-small"},
+                    loc="right",
+                    pad=2,
+                )
+            else:
+                ax_scatter.set_ylabel(parnames[i])
 
             if i != nplots - 1:
                 ax_scatter.tick_params(labelbottom=False)

optimas/utils/ax/ax_model_manager.py

@@ -9,6 +9,7 @@
 from matplotlib.figure import Figure
 from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec, SubplotSpec
 from matplotlib.axes import Axes
+from mpl_toolkits.axes_grid1 import make_axes_locatable
 
 # Ax utilities for model building
 try:
@@ -358,6 +359,7 @@ def plot_contour(
         range_x: Optional[List[float]] = None,
         range_y: Optional[List[float]] = None,
         mode: Optional[Literal["mean", "sem", "both"]] = "mean",
+        cbar_location: Optional[Literal["top", "right"]] = "top",
         show_trials: Optional[bool] = True,
         show_contour: Optional[bool] = True,
         show_contour_labels: Optional[bool] = False,
@@ -393,6 +395,8 @@ def plot_contour(
         mode : str, optional.
             Whether to plot the ``"mean"`` of the model, the standard error of
             the mean ``"sem"``, or ``"both"``. By default, ``"mean"``.
+        cbar_location : str, optional.
+            Set position of the colorbar. By default, ``"top"``.
         show_trials : bool
             Whether to show the trials used to build the model. By default,
             ``True``.
@@ -503,7 +507,9 @@ def plot_contour(
             # colormesh
             pcolormesh_kw = dict(pcolormesh_kw or {})
             im = ax.pcolormesh(xaxis, yaxis, f, shading="auto", **pcolormesh_kw)
-            cbar = plt.colorbar(im, ax=ax, location="top")
+            divider = make_axes_locatable(ax)
+            cax = divider.append_axes(cbar_location, size="2.5%", pad=0.1)
+            cbar = plt.colorbar(im, cax=cax, location=cbar_location)
             cbar.set_label(labels[i])
             ax.set(xlabel=param_x, ylabel=param_y)
             # contour
@@ -518,9 +524,7 @@ def plot_contour(
                     linestyles="solid",
                 )
                 if show_contour_labels:
-                    ax.clabel(
-                        cset, inline=True, fmt="%1.1f", fontsize="xx-small"
-                    )
+                    ax.clabel(cset, inline=True, fontsize="xx-small")
             if show_trials:
                 ax.scatter(
                     trials[param_x], trials[param_y], s=8, c="black", marker="o"