Add option to disable histogram

LLNL · Nov 15, 2024 · af9ccf7 · af9ccf7
1 parent 6ee258a
commit af9ccf7
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Showing 2 changed files with 37 additions and 32 deletions.
diff --git a/thicket/external/console.py b/thicket/external/console.py
@@ -54,7 +54,8 @@ def render(self, roots, dataframe, **kwargs):
         self.min_value = kwargs["min_value"]
         self.max_value = kwargs["max_value"]
         self.indices = kwargs["indices"]
-        self.full_df = kwargs["full_df"]
+        self.hist_data = kwargs["hist_data"]
+        self.histogram = kwargs["histogram"]
 
         if self.color:
             self.colors = self.colors_enabled
@@ -274,36 +275,37 @@ def render_frame(self, node, dataframe, indent="", child_indent=""):
                 indent=indent, metric_str=metric_str, name_str=name_str
             )
 
-            nprofs = len(self.full_df.loc[df_index])
-            # Auto choose num bins
-            nintervals = math.ceil(math.sqrt(nprofs))
-            # Add min/max to tree
-            min = self.full_df.loc[df_index].min()
-            max = self.full_df.loc[df_index].max()
-            result += f" ({min:.{self.precision}f}, {max:.{self.precision}f})"
-            # Define unicode bars
-            bar_list = [
-                "_",
-                "\u2581",
-                "\u2582",
-                "\u2583",
-                "\u2584",
-                "\u2585",
-                "\u2586",
-                "\u2587",
-                "\u2588",
-            ]
-            # Compute histogram intervals using pandas binning
-            binned = pd.cut(self.full_df.loc[df_index], bins=nintervals)
-            hist = binned.value_counts().sort_index()
-            # Normalize values to the number of bars
-            normalized_hist = (
-                (len(bar_list) - 1) * (hist - hist.min()) / (hist.max() - hist.min())
-            )
-            normalized_hist = normalized_hist.apply(np.ceil).astype(int)
-            # Add histogram to tree
-            for idx in normalized_hist.values:
-                result += bar_list[idx]
+            if self.histogram:
+                nprofs = len(self.hist_data.loc[df_index])
+                # Auto choose num bins
+                nintervals = math.ceil(math.sqrt(nprofs))
+                # Add min/max to tree
+                min = self.hist_data.loc[df_index].min()
+                max = self.hist_data.loc[df_index].max()
+                result += f" ({min:.{self.precision}f}, {max:.{self.precision}f}) "
+                # Define unicode bars
+                bar_list = [
+                    "_",
+                    "\u2581",
+                    "\u2582",
+                    "\u2583",
+                    "\u2584",
+                    "\u2585",
+                    "\u2586",
+                    "\u2587",
+                    "\u2588",
+                ]
+                # Compute histogram intervals using pandas binning
+                binned = pd.cut(self.hist_data.loc[df_index], bins=nintervals)
+                hist = binned.value_counts().sort_index()
+                # Normalize values to the number of bars
+                normalized_hist = (
+                    (len(bar_list) - 1) * (hist - hist.min()) / (hist.max() - hist.min())
+                )
+                normalized_hist = normalized_hist.apply(np.ceil).astype(int)
+                # Add histogram to tree
+                for idx in normalized_hist.values:
+                    result += bar_list[idx]
 
             if self.context in dataframe.columns:
                 result += " {c.faint}{context}{c.end}\n".format(

diff --git a/thicket/thicket.py b/thicket/thicket.py
@@ -990,6 +990,7 @@ def tree(
         min_value=None,
         max_value=None,
         indices=None,
+        histogram=True,
     ):
         """Visualize the Thicket as a tree
 
@@ -1011,6 +1012,7 @@ def tree(
             min_value (int, optional): Overwrites the min value for the coloring legend. Defaults to None.
             max_value (int, optional): Overwrites the max value for the coloring legend. Defaults to None.
             indices(tuple, list, optional): Index/indices to display on the DataFrame. Defaults to None.
+            histogram (bool, optional): Whether to show a histogram next to each node of the data for all profiles. Defaults to True.
 
         Returns:
             (str): String representation of the tree, ready to print
@@ -1122,7 +1124,8 @@ def tree(
             min_value=min_value,
             max_value=max_value,
             indices=idx_dict,
-            full_df=self.dataframe[metric_column],
+            hist_data=self.dataframe[metric_column],
+            histogram=histogram,
         )
 
     @staticmethod