Fix: Inaccurate visualization of parameter distributions (#1327)

* Fix: Inaccurate visualization of parameter distributions

Author: HansKallekleiv

CHANGELOG.md

@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - [#1297](https://github.com/equinor/webviz-subsurface/pull/1297) - Added mean for mc sensitivities in the tornadotable in `VolumetricAnalysis`.
-
+- [#1327](https://github.com/equinor/webviz-subsurface/pull/1327) - Added histograms to parameter distributions in `ParameterAnalysis`.
 ## [0.2.30] - 2024-06-10
 
 ### Added

webviz_subsurface/plugins/_parameter_analysis/_types.py

@@ -2,6 +2,7 @@
 
 
 class VisualizationType(StrEnum):
+    HISTOGRAM = "histogram"
     DISTRIBUTION = "distribution"
     BOX = "box"
     STAT_TABLE = "stat-table"

webviz_subsurface/plugins/_parameter_analysis/_utils/_parameters_model.py

@@ -8,6 +8,8 @@
 from webviz_subsurface._figures import create_figure
 from webviz_subsurface._models.parameter_model import ParametersModel as Pmodel
 
+from .._types import VisualizationType
+
 
 class ParametersModel:
     """Class to process and visualize ensemble parameter data"""
@@ -163,22 +165,23 @@ def make_grouped_plot(
         self,
         ensembles: list,
         parameters: List[Any],
-        plot_type: str = "distribution",
+        plot_type: VisualizationType = VisualizationType.DISTRIBUTION,
     ) -> go.Figure:
         """Create subplots for selected parameters"""
         df = self.dataframe_melted.copy()
         df = df[df["ENSEMBLE"].isin(ensembles)]
         df = df[df["PARAMETER"].isin(parameters)]
         df = self._sort_parameters_col(df, parameters)
 
-        return (
+        figure = (
             create_figure(
                 plot_type=plot_type,
                 data_frame=df,
                 x="VALUE",
                 facet_col="PARAMETER",
                 color="ENSEMBLE",
                 color_discrete_sequence=self.colorway,
+                barmode="overlay",
             )
             .update_xaxes(matches=None)
             .for_each_trace(
@@ -189,6 +192,11 @@ def make_grouped_plot(
                 )
             )
         )
+        # Use bingroup=None so that Plotly calculates bins per trace
+        # This also means that individual ensembles will have separate binning.
+        if plot_type == VisualizationType.HISTOGRAM:
+            figure.update_traces(bingroup=None)
+        return figure
 
     def get_stat_value(self, parameter: str, ensemble: str, stat_column: str) -> float:
         """

webviz_subsurface/plugins/_parameter_analysis/_views/_parameter_distributions_view/_settings/_visualization_type.py

@@ -20,6 +20,7 @@ def layout(self) -> List[Component]:
             wcc.RadioItems(
                 id=self.register_component_unique_id(self.Ids.VISUALIZATION_TYPE),
                 options=[
+                    {"label": "Histogram", "value": VisualizationType.HISTOGRAM},
                     {
                         "label": "Distribution plots",
                         "value": VisualizationType.DISTRIBUTION,
@@ -30,7 +31,7 @@ def layout(self) -> List[Component]:
                         "value": VisualizationType.STAT_TABLE,
                     },
                 ],
-                value=VisualizationType.DISTRIBUTION,
+                value=VisualizationType.HISTOGRAM,
                 vertical=True,
             )
         ]