test: add performance benchmark script for hull cropping

Author: NullPointer-cell

benchmark_cropping_local.py

@@ -0,0 +1,41 @@
+import time
+import numpy as np
+import xarray as xr
+from mllam_data_prep.ops.cropping import (
+    create_convex_hull_mask,
+    distance_to_convex_hull_boundary,
+)
+
+def benchmark_cropping(grid_size=1000):
+    print(f"--- Benchmarking grid size: {grid_size}x{grid_size} ({grid_size**2} points) ---")
+    
+    lon_vals = np.linspace(-180, 180, grid_size)
+    lat_vals = np.linspace(-90, 90, grid_size)
+    xs, ys = np.meshgrid(lon_vals, lat_vals)
+    lon_da = xr.DataArray(xs.flatten(), dims="grid_index")
+    lat_da = xr.DataArray(ys.flatten(), dims="grid_index")
+    ds = xr.Dataset(coords={"longitude": lon_da, "latitude": lat_da})
+
+    ref_lon_vals = np.linspace(0, 20, 20)
+    ref_lat_vals = np.linspace(40, 60, 20)
+    r_xs, r_ys = np.meshgrid(ref_lon_vals, ref_lat_vals)
+    ref_lon_da = xr.DataArray(r_xs.flatten(), dims="grid_index")
+    ref_lat_da = xr.DataArray(r_ys.flatten(), dims="grid_index")
+    ds_ref = xr.Dataset(coords={"longitude": ref_lon_da, "latitude": ref_lat_da})
+
+    print("Benchmarking create_convex_hull_mask...")
+    t0 = time.time()
+    da_mask, chull_lat_lons = create_convex_hull_mask(ds, ds_ref)
+    mask_time = time.time() - t0
+    num_inside = int(da_mask.sum())
+    print(f"-> Hull Mask Time: {mask_time:.3f} seconds (Found {num_inside} inside)")
+
+    print("Benchmarking distance_to_convex_hull_boundary...")
+    t1 = time.time()
+    da_mindist = distance_to_convex_hull_boundary(ds, ds_ref)
+    dist_time = time.time() - t1
+    print(f"-> Arc Distance Time: {dist_time:.3f} seconds")
+    
+if __name__ == "__main__":
+    benchmark_cropping(grid_size=100) # 10k points baseline
+    benchmark_cropping(grid_size=500) # 250k points scale test