Merge remote-tracking branch 'upstream/hotfixes' into release

Author: fit-alessandro-berti

pm4py/statistics/attributes/common/get.py

@@ -118,6 +118,26 @@ def get_kde_numeric_attribute(values, parameters=None):
             Parameters.GRAPH_POINTS, parameters, 200
         )
         values = np.sort(values)
+        
+        # Check if we have enough unique values for KDE
+        unique_values = np.unique(values)
+        if len(unique_values) < 2:
+            # Handle edge case: not enough unique values for KDE
+            if len(unique_values) == 0:
+                # No values at all
+                return [], []
+            else:
+                # Single unique value - create a simple representation
+                single_val = float(unique_values[0])
+                # Create a small range around the single value for visualization
+                eps = max(abs(single_val) * 0.01, 1e-6) if single_val != 0 else 1.0
+                xs = np.linspace(single_val - eps, single_val + eps, graph_points)
+                # Create a spike at the single value
+                ys = np.zeros(graph_points)
+                mid_idx = graph_points // 2
+                ys[mid_idx] = 1.0
+                return xs.tolist(), ys.tolist()
+        
         density = gaussian_kde(values)
 
         # ensure we have at least two points for each spacing
@@ -209,6 +229,27 @@ def get_kde_date_attribute(values, parameters=None):
         int_values = sorted(
             [x.replace(tzinfo=None).timestamp() for x in red_values]
         )
+        
+        # Check if we have enough unique values for KDE
+        unique_int_values = np.unique(int_values)
+        if len(unique_int_values) < 2:
+            # Handle edge case: not enough unique values for KDE
+            if len(unique_int_values) == 0:
+                # No values at all
+                return [[], []]
+            else:
+                # Single unique value - create a simple representation
+                single_val = float(unique_int_values[0])
+                # Create a small time range around the single value (1 hour range)
+                time_eps = 3600  # 1 hour in seconds
+                xs = np.linspace(single_val - time_eps, single_val + time_eps, graph_points)
+                xs_transf = pd.to_datetime(xs * 10**9, unit="ns")
+                # Create a spike at the single value
+                ys = np.zeros(graph_points)
+                mid_idx = graph_points // 2
+                ys[mid_idx] = 1.0
+                return [xs_transf, ys.tolist()]
+        
         density = gaussian_kde(int_values)
         xs = np.linspace(min(int_values), max(int_values), graph_points)
         xs_transf = pd.to_datetime(xs * 10**9, unit="ns")

pm4py/statistics/traces/generic/common/case_duration.py

@@ -61,6 +61,17 @@ def get_kde_caseduration(duration_values, parameters=None):
             Parameters.GRAPH_POINTS, parameters, 200
         )
         duration_values = sorted(duration_values)
+        
+        # Check if we have enough data points for KDE
+        if len(duration_values) < 2:
+            # Return empty or single-point data
+            if len(duration_values) == 0:
+                return [[], []]
+            else:
+                # Single value case - return a simple representation
+                single_val = duration_values[0]
+                return [[single_val], [1.0]]
+        
         density = gaussian_kde(duration_values)
         xs1 = list(
             np.linspace(