Fixed typing/mypy issues (#907)

webviz_subsurface/_providers/ensemble_summary_provider/_resampling.py

@@ -84,7 +84,7 @@ def interpolate_backfill(
     # Finds the leftmost valid insertion indices for the values x in xp
     indices = np.searchsorted(xp, x, side="left")
 
-    padded_y = np.concatenate((yp, [yright]))
+    padded_y = np.concatenate((yp, np.array([yright])))
 
     ret_arr = padded_y[indices]
 

webviz_subsurface/_utils/simulation_timeseries.py

@@ -54,13 +54,13 @@ def calc_series_statistics(
     refaxis is used if another column than DATE should be used to groupby.
     """
     # Invert p10 and p90 due to oil industry convention.
-    def p10(x: List[float]) -> List[float]:
+    def p10(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=90)
 
-    def p90(x: List[float]) -> List[float]:
+    def p90(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=10)
 
-    def p50(x: List[float]) -> List[float]:
+    def p50(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=50)
 
     # Calculate statistics, ignoring NaNs.

webviz_subsurface/plugins/_line_plotter_fmu/controllers/build_figure.py

@@ -246,10 +246,10 @@ def calc_series_statistics(
     refaxis is used if another column than DATE should be used to groupby.
     """
     # Invert p10 and p90 due to oil industry convention.
-    def p10(x: List[float]) -> List[float]:
+    def p10(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=90)
 
-    def p90(x: List[float]) -> List[float]:
+    def p90(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=10)
 
     # Calculate statistics, ignoring NaNs.

webviz_subsurface/plugins/_simulation_time_series/utils/vector_statistics.py

@@ -28,13 +28,13 @@ def create_vectors_statistics_df(vectors_df: pd.DataFrame) -> pd.DataFrame:
     )
 
     # Invert p10 and p90 due to oil industry convention.
-    def p10(x: List[float]) -> List[float]:
+    def p10(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=90)
 
-    def p90(x: List[float]) -> List[float]:
+    def p90(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=10)
 
-    def p50(x: List[float]) -> List[float]:
+    def p50(x: List[float]) -> np.floating:
         return np.nanpercentile(x, q=50)
 
     statistics_df: pd.DataFrame = (

webviz_subsurface/plugins/_volumetric_analysis/controllers/comparison_controllers.py

@@ -312,7 +312,7 @@ def create_comparison_df(
 
 def compute_highlighted_col(
     df: pd.DataFrame, response: str, value1: str, selections: dict
-) -> list:
+) -> np.ndarray:
     highlight_mask = (df[response][value1] > selections["Ignore <"]) & (
         df[response]["diff (%)"].abs() > selections["Accept value"]
     )