Commitin

src/ert/gui/plottery/plots/std_dev.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -28,40 +28,102 @@ def plot(
         if layer is not None:
             vmin: float = np.inf
             vmax: float = -np.inf
-            axes = []
-            images: List[npt.NDArray[np.float32]] = []
+            heatmaps = []
+            boxplot_axes = []
+
+            # Adjusted gridspec to allocate less space to boxplots
+            gridspec = figure.add_gridspec(2, ensemble_count, height_ratios=[3, 1])
+
             for i, ensemble in enumerate(plot_context.ensembles(), start=1):
-                ax = figure.add_subplot(1, ensemble_count, i)
-                axes.append(ax)
+                # Create two subplots: one for heatmap, one for boxplot
+                ax_heat = figure.add_subplot(
+                    gridspec[0, i - 1]
+                )  # Heatmap in the upper row
+                ax_box = figure.add_subplot(
+                    gridspec[1, i - 1]
+                )  # Boxplot in the lower row
+
                 data = std_dev_data[ensemble.name]
                 if data.size == 0:
-                    ax.set_axis_off()
-                    ax.text(
+                    ax_heat.set_axis_off()
+                    ax_box.set_axis_off()
+                    ax_heat.text(
                         0.5,
                         0.5,
                         f"No data for {ensemble.experiment_name} : {ensemble.name}",
                         ha="center",
                         va="center",
                     )
                 else:
-                    images.append(data)
                     vmin = min(vmin, float(np.min(data)))
                     vmax = max(vmax, float(np.max(data)))
-                ax.set_title(
+
+                    # Create heatmap with aspect='equal' to make it quadratic
+                    im = ax_heat.imshow(data, cmap="viridis", aspect="equal")
+                    heatmaps.append(im)
+
+                    # Create boxplot
+                    ax_box.boxplot(data.flatten(), vert=False, widths=0.5)
+                    boxplot_axes.append(ax_box)
+
+                    # Calculate min, mean, and max
+                    min_value = np.min(data)
+                    mean_value = np.mean(data)
+                    max_value = np.max(data)
+
+                    # Create a combined annotation in the top-right corner
+                    annotation_text = f"Min: {min_value:.2f}\nMean: {mean_value:.2f}\nMax: {max_value:.2f}"
+                    ax_box.annotate(
+                        annotation_text,
+                        xy=(1, 1),  # Top-right corner
+                        xycoords="axes fraction",
+                        ha="right",
+                        va="top",
+                        fontsize=10,
+                        fontweight="bold",
+                        bbox={
+                            "facecolor": "white",
+                            "edgecolor": "black",
+                            "boxstyle": "round,pad=0.3",
+                        },
+                    )
+
+                    # Remove the frame around the boxplot, except for the bottom spine
+                    ax_box.spines["top"].set_visible(False)
+                    ax_box.spines["right"].set_visible(False)
+                    ax_box.spines["left"].set_visible(False)
+                    ax_box.spines["bottom"].set_visible(True)
+
+                    # Remove y-axis ticks and labels
+                    ax_box.set_yticks([])
+                    ax_box.set_yticklabels([])
+
+                    # Only show xlabel for the bottom subplot
+                    ax_heat.set_xlabel("")
+                    ax_box.set_xlabel("Standard Deviation")
+
+                    # Add colorbar using _colorbar method
+                    self._colorbar(im)
+
+                ax_heat.set_title(
                     f"{ensemble.experiment_name} : {ensemble.name} layer={layer}",
                     wrap=True,
                 )
 
+            # Apply normalization to all heatmaps after determining global vmin and vmax
             norm = plt.Normalize(vmin, vmax)
-            for ax, data in zip(axes, images):
-                if data is not None:
-                    im = ax.imshow(data, norm=norm, cmap="viridis")
-                    self._colorbar(im)
+            for im in heatmaps:
+                im.set_norm(norm)
+
+            # Set x-limits for all boxplots with padding
+            padding = 0.05 * (vmax - vmin)  # 5% padding on each side
+            for ax_box in boxplot_axes:
+                ax_box.set_xlim(vmin - padding, vmax + padding)
+
             figure.tight_layout()
 
     @staticmethod
     def _colorbar(mappable: Any) -> Any:
-        # https://joseph-long.com/writing/colorbars/
         last_axes = plt.gca()
         ax = mappable.axes
         assert ax is not None