[JTH] little edits to format code

Author: tausiaj

bluemath_tk/topo_bathy/swan_grid.py

@@ -1,14 +1,15 @@
 import numpy as np
 import xarray as xr
 
+
 def generate_grid_parameters(
     bathy_data: xr.DataArray,
     alpc: float = 0,
     xpc: float = None,
     ypc: float = None,
     xlenc: float = None,
     ylenc: float = None,
-    buffer_distance: float = None, 
+    buffer_distance: float = None,
 ) -> dict:
     """
     Generate grid parameters for the SWAN model based on bathymetry.
@@ -31,29 +32,15 @@ def generate_grid_parameters(
     -------
     dict
         Dictionary with grid configuration for SWAN input.
-
-    
-    Contact
-    -------
-    @
-    """
     """
-    Generate the grid parameters for the SWAN model.
 
-    Returns
-    -------
-    dict
-        Grid parameters for the SWAN model.
-    """
     # Determine coordinate system based on coordinate names
     coord_names = list(bathy_data.coords)
 
     # Get coordinate variables
     if any(name in ["lon", "longitude"] for name in coord_names):
         x_coord = next(name for name in coord_names if name in ["lon", "longitude"])
         y_coord = next(name for name in coord_names if name in ["lat", "latitude"])
-        is_geographic = True
-        # coord_type = 'geographic'
     else:
         x_coord = next(
             name for name in coord_names if name in ["x", "X", "cx", "easting"]
@@ -62,7 +49,6 @@ def generate_grid_parameters(
             name for name in coord_names if name in ["y", "Y", "cy", "northing"]
         )
 
-
     # Get resolution from cropped data
     grid_resolution_x = abs(
         bathy_data[x_coord][1].values - bathy_data[x_coord][0].values
@@ -93,7 +79,7 @@ def generate_grid_parameters(
         x = rotated[:, 0] + xpc
         y = rotated[:, 1] + ypc
         corners = np.column_stack([x, y])
-        
+
         x_min = np.min(x) - buffer_distance
         x_max = np.max(x) + buffer_distance
         y_min = np.min(y) - buffer_distance
@@ -128,8 +114,6 @@ def generate_grid_parameters(
         return grid_parameters, cropped, corners
 
     else:
-
-        
         # Compute parameters from full bathymetry
         grid_parameters = {
             "xpc": float(np.nanmin(bathy_data[x_coord])),  # origin x
@@ -145,7 +129,7 @@ def generate_grid_parameters(
             "myc": len(bathy_data[y_coord]) - 1,  # num mesh y
             "xpinp": float(np.nanmin(bathy_data[x_coord])),  # origin x
             "ypinp": float(np.nanmin(bathy_data[y_coord])),  # origin y
-            "alpinp":0,  # x-axis direction
+            "alpinp": 0,  # x-axis direction
             "mxinp": len(bathy_data[x_coord]) - 1,  # num mesh x
             "myinp": len(bathy_data[y_coord]) - 1,  # num mesh y
             "dxinp": float(
@@ -154,6 +138,5 @@ def generate_grid_parameters(
             "dyinp": float(
                 abs(bathy_data[y_coord][1].values - bathy_data[y_coord][0].values)
             ),  # resolution y
-        }       
+        }
         return grid_parameters
-

bluemath_tk/waves/calibration.py

@@ -354,7 +354,6 @@ def _create_vec_direc(self, waves: np.ndarray, direcs: np.ndarray) -> np.ndarray
                 else:
                     bin_idx = int(direcs[i] / self.direction_bin_size)
                 data[i, bin_idx] = waves[i]
-      
 
         return data
 
@@ -562,7 +561,9 @@ def correct(
                     correction_coeffs[n_part, :] = np.array(
                         [
                             self.calibration_params["sea_correction"][
-                                int(peak_direction / self.direction_bin_size) if peak_direction < 360 else 0 #TODO: Check if this with Javi
+                                int(peak_direction / self.direction_bin_size)
+                                if peak_direction < 360
+                                else 0  # TODO: Check if this with Javi
                             ]
                             for peak_direction in peak_directions.isel(
                                 part=n_part
@@ -573,7 +574,9 @@ def correct(
                     correction_coeffs[n_part, :] = np.array(
                         [
                             self.calibration_params["swell_correction"][
-                                int(peak_direction / self.direction_bin_size) if peak_direction < 360 else 0 #TODO: Check if this with Javi
+                                int(peak_direction / self.direction_bin_size)
+                                if peak_direction < 360
+                                else 0  # TODO: Check if this with Javi
                             ]
                             for peak_direction in peak_directions.isel(
                                 part=n_part
@@ -599,7 +602,9 @@ def correct(
                 * np.array(
                     [
                         self.calibration_params["sea_correction"][
-                            int(peak_direction / self.direction_bin_size) if peak_direction < 360 else 0
+                            int(peak_direction / self.direction_bin_size)
+                            if peak_direction < 360
+                            else 0
                         ]
                         for peak_direction in corrected_data["Dirsea"]
                     ]
@@ -613,7 +618,9 @@ def correct(
                     * np.array(
                         [
                             self.calibration_params["swell_correction"][
-                                int(peak_direction / self.direction_bin_size) if peak_direction < 360 else 0
+                                int(peak_direction / self.direction_bin_size)
+                                if peak_direction < 360
+                                else 0
                             ]
                             for peak_direction in corrected_data[f"Dirswell{n_part}"]
                         ]
@@ -764,7 +771,7 @@ def plot_calibration_results(self) -> Tuple[Figure, list]:
         valid_mask = np.isfinite(sea_dirs) & np.isfinite(sea_heights)
         sea_dirs_valid = sea_dirs[valid_mask]
         sea_heights_valid = sea_heights[valid_mask]
-        
+
         if len(sea_dirs_valid) > 0:
             x, y, z = density_scatter(sea_dirs_valid, sea_heights_valid)
             ax5.scatter(x, y, c=z, s=3, cmap="jet")
@@ -784,7 +791,7 @@ def plot_calibration_results(self) -> Tuple[Figure, list]:
         valid_mask = np.isfinite(swell_dirs) & np.isfinite(swell_heights)
         swell_dirs_valid = swell_dirs[valid_mask]
         swell_heights_valid = swell_heights[valid_mask]
-        
+
         if len(swell_dirs_valid) > 0:
             x, y, z = density_scatter(swell_dirs_valid, swell_heights_valid)
             ax6.scatter(x, y, c=z, s=3, cmap="jet")

bluemath_tk/waves/superpoint.py

@@ -56,36 +56,48 @@ def load_station_data(ds: xr.Dataset) -> xr.DataArray:
     superpoint_dataarray = xr.zeros_like(
         stations_data.isel({stations_dimension_name: 0})
     )
-    
+
     if overlap_angle == 0:
         for station_id, (dir_min, dir_max) in sectors_for_each_station.items():
             if dir_min < dir_max:
-                mask = (stations_data["dir"] >= dir_min) & (stations_data["dir"] < dir_max)
+                mask = (stations_data["dir"] >= dir_min) & (
+                    stations_data["dir"] < dir_max
+                )
             else:
                 # Handle wrap-around (e.g., 350° to 10°)
-                mask = (stations_data["dir"] >= dir_min) | (stations_data["dir"] < dir_max)
-            superpoint_dataarray += stations_data.sel({stations_dimension_name: station_id}).where(mask, 0.0)
-    
+                mask = (stations_data["dir"] >= dir_min) | (
+                    stations_data["dir"] < dir_max
+                )
+            superpoint_dataarray += stations_data.sel(
+                {stations_dimension_name: station_id}
+            ).where(mask, 0.0)
+
     else:
         # With overlap - expand sectors and average overlaps
         directions = stations_data["dir"]
         count_array = xr.zeros_like(superpoint_dataarray)  # Counter for overlaps
-        
+
         for station_id, (dir_min, dir_max) in sectors_for_each_station.items():
             station_data = stations_data.sel({stations_dimension_name: station_id})
-            
+
             # Expand sector boundaries by overlap_angle
-            if (dir_max - dir_min) < 0:  
-                mask = (directions >= dir_min - overlap_angle) | (directions <= dir_max + overlap_angle)
-            else:  
-                mask = (directions >= dir_min - overlap_angle) & (directions <= dir_max + overlap_angle)
-            
+            if (dir_max - dir_min) < 0:
+                mask = (directions >= dir_min - overlap_angle) | (
+                    directions <= dir_max + overlap_angle
+                )
+            else:
+                mask = (directions >= dir_min - overlap_angle) & (
+                    directions <= dir_max + overlap_angle
+                )
+
             # Add contribution where mask is true
             superpoint_dataarray += station_data.where(mask, 0.0)
             count_array += xr.where(mask, 1, 0)
-        
+
         # Average where there are overlaps (count > 1)
         overlap_mask = count_array > 1
-        superpoint_dataarray = xr.where(overlap_mask, superpoint_dataarray / count_array, superpoint_dataarray)
+        superpoint_dataarray = xr.where(
+            overlap_mask, superpoint_dataarray / count_array, superpoint_dataarray
+        )
 
     return superpoint_dataarray