Apply circular distance mask to max_distance output

The window extraction uses a square (Chebyshev) region but max_distance
is a Euclidean radius.  Add a circular mask after computing the window
result so cells at the corners beyond max_distance are set to INVISIBLE.
Update tests to account for the circular boundary.

Author: brendancol

xrspatial/tests/test_viewshed.py

@@ -281,9 +281,14 @@ def test_viewshed_dask_max_distance_lazy_output():
     assert isinstance(v.data, da.Array)
     assert v.shape == (ny, nx)
     # Only compute a small slice to verify correctness
-    center = v.isel(y=slice(49990, 50011), x=slice(49990, 50011)).values
-    assert center[10, 10] == 180.0  # observer cell
-    assert (center > INVISIBLE).all()  # flat terrain, all visible
+    # max_distance=10 → radius_cells=10 → window is obs ± 10
+    center = v.isel(y=slice(49989, 50012), x=slice(49989, 50012)).values
+    # Observer is at index 11 within this 23-cell slice
+    assert center[11, 11] == 180.0  # observer cell
+    # Cells within the circle should be visible (flat terrain, observer up)
+    assert center[11, 13] > INVISIBLE  # 2 cells away
+    # Corner (49989,49989) is sqrt(11^2+11^2) ≈ 15.6 from observer → outside
+    assert center[0, 0] == INVISIBLE
 
 
 def test_viewshed_numpy_max_distance():
@@ -342,10 +347,15 @@ def test_viewshed_max_distance_matches_full(backend):
     dist_vals = v_dist.values if isinstance(v_dist.data, np.ndarray) \
         else v_dist.data.get()
 
-    # Within the window, results should match
+    # Within the circular radius, results should match
     obs_r, obs_c = 5, 4
+    max_d = 3.5
     for r in range(max(0, obs_r - 3), min(ny, obs_r + 4)):
         for c in range(max(0, obs_c - 3), min(nx, obs_c + 4)):
+            dr = (r - obs_r) * 1.0  # ns_res = 1
+            dc = (c - obs_c) * 1.0  # ew_res = 1
+            if np.sqrt(dr**2 + dc**2) > max_d:
+                continue  # outside circle — correctly INVISIBLE
             np.testing.assert_allclose(
                 dist_vals[r, c], full_vals[r, c],
                 atol=0.03,

xrspatial/viewshed.py

@@ -2090,6 +2090,19 @@ def _viewshed_windowed(raster, x, y, observer_elev, target_elev,
         local_result = _viewshed_cpu(
             window, x, y, observer_elev, target_elev)
 
+    # Mask cells beyond max_distance (the window is a square, not a circle)
+    win_y = local_result.coords['y'].values
+    win_x = local_result.coords['x'].values
+    wx, wy = np.meshgrid(win_x, win_y)
+    dist_sq = (wx - x) ** 2 + (wy - y) ** 2
+    outside = dist_sq > max_distance ** 2
+    if isinstance(local_result.data, np.ndarray):
+        local_result.values[outside] = INVISIBLE
+    else:
+        # cupy path
+        import cupy as cp
+        local_result.data[cp.asarray(outside)] = INVISIBLE
+
     # Embed in full-size INVISIBLE output, preserving array type
     is_dask = has_dask_array() and isinstance(raster.data, da.Array)