fix: replace np.nan checks with pd.isna for better NaN handling

Author: richrobe

src/biopsykit/signals/icg/event_extraction/_b_point_miljkovic2022.py

@@ -107,7 +107,7 @@ def extract(
             # Get the C-Point location at the current heartbeat id
             c_point = c_points[idx]
 
-            if c_point is np.nan:
+            if pd.isna(c_point):
                 b_points.loc[idx, "b_point_sample"] = np.nan
                 b_points.loc[idx, "nan_reason"] = "c_point_nan"
                 continue

src/biopsykit/signals/icg/event_extraction/_b_point_pale2021.py

@@ -118,13 +118,13 @@ def extract(
             # Get the C-Point location at the current heartbeat id
             c_point = c_points[idx]
 
-            icg_slice = icg.iloc[data["start_sample"] : c_point]
-
-            if c_point is np.nan:
+            if pd.isna(c_point):
                 b_points.loc[idx, "b_point_sample"] = np.nan
                 b_points.loc[idx, "nan_reason"] = "c_point_nan"
                 continue
 
+            icg_slice = icg.iloc[data["start_sample"] : c_point]
+
             # Calculate the search window based on the C-point location and amplitude
             # start of the search window is 80 ms before the C-point
             search_window_start = c_point - int(0.08 * sampling_rate_hz)
@@ -140,6 +140,11 @@ def extract(
             # B_min is the minimum of the signal in the search window
             b_point_min = data["start_sample"] + np.argmin(icg_slice)
 
+            if (search_window_end - search_window_start) <= 2:
+                # If the search window is too small, set the B-point to the minimum of the signal in the search window
+                b_points.loc[idx, "b_point_sample"] = b_point_min
+                continue
+
             # slice derivative to the search window
             icg_2nd_der_slice = icg_2nd_der[search_window_start:search_window_end]
 

src/biopsykit/stats/stats.py

@@ -1144,9 +1144,7 @@ def _remove_a_b(data: pd.DataFrame) -> pd.DataFrame:
         return data.reorder_levels(names_new)
 
     @staticmethod
-    def _format_latex_table_index(
-        data: pd.DataFrame, index_kws: dict[str, Any], show_a_b: bool | None = False
-    ):  # pylint:disable=too-many-branches
+    def _format_latex_table_index(data: pd.DataFrame, index_kws: dict[str, Any], show_a_b: bool | None = False):  # pylint:disable=too-many-branches
         index_italic = index_kws.get("index_italic", True)
         index_level_order = index_kws.get("index_level_order")
         index_value_order = index_kws.get("index_value_order")