cost_distance: replace mutable default target_values=[] with None sentinel (#3340)

xrspatial/cost_distance.py

@@ -1204,7 +1204,7 @@ def cost_distance(
     friction: xr.DataArray,
     x: str = "x",
     y: str = "y",
-    target_values: list = [],
+    target_values: list = None,
     max_cost: float = np.inf,
     connectivity: int = 8,
 ) -> xr.DataArray:
@@ -1229,7 +1229,7 @@ def cost_distance(
         Name of the y coordinate.
     target_values : list, optional
         Specific pixel values in *raster* to treat as sources.
-        If empty, all non-zero finite pixels are sources.
+        If not provided, all non-zero finite pixels are sources.
     max_cost : float, default=np.inf
         Maximum accumulated cost.  Pixels whose least-cost path exceeds
         this budget are set to NaN.  A finite value enables efficient
@@ -1255,6 +1255,9 @@ def cost_distance(
     if connectivity not in (4, 8):
         raise ValueError("connectivity must be 4 or 8")
 
+    if target_values is None:
+        target_values = []
+
     cellsize_x, cellsize_y = get_dataarray_resolution(raster)
     cellsize_x = abs(float(cellsize_x))
     cellsize_y = abs(float(cellsize_y))

xrspatial/tests/test_cost_distance.py

@@ -304,6 +304,48 @@ def test_target_values(backend):
     np.testing.assert_allclose(out[0, 1], 2.0, atol=1e-5)
 
 
+def test_target_values_default_is_none_sentinel():
+    """target_values default is the None sentinel, not a shared mutable list.
+
+    Regression for issue #3340: a mutable default (``list = []``) shares one
+    list object across calls. cost_distance uses the ``None`` sentinel like
+    proximity()/allocation() do.
+    """
+    import inspect
+
+    default = inspect.signature(cost_distance).parameters['target_values'].default
+    assert default is None
+
+
+def test_target_values_none_matches_empty_list():
+    """Omitting target_values, passing None, and passing [] are equivalent.
+
+    All three mean "every non-zero finite pixel is a source".
+    """
+    source = np.array([
+        [7.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 1.0],
+    ])
+    friction_data = np.ones((3, 3))
+    raster = _make_raster(source)
+    friction = _make_raster(friction_data)
+
+    out_omitted = _compute(cost_distance(raster, friction))
+    out_none = _compute(cost_distance(raster, friction, target_values=None))
+    out_empty = _compute(cost_distance(raster, friction, target_values=[]))
+
+    np.testing.assert_array_equal(
+        np.nan_to_num(out_omitted), np.nan_to_num(out_none)
+    )
+    np.testing.assert_array_equal(
+        np.nan_to_num(out_omitted), np.nan_to_num(out_empty)
+    )
+    # Both non-zero finite pixels are sources (cost 0).
+    assert out_omitted[0, 0] == 0.0
+    assert out_omitted[2, 2] == 0.0
+
+
 # -----------------------------------------------------------------------
 # Lazy coordinate arrays for dask input
 # -----------------------------------------------------------------------