Allow an ndarray of integers as a shape.

sparse/_coo/core.py

@@ -241,12 +241,11 @@ def __init__(
         if self.data.ndim != 1:
             raise ValueError("data must be a scalar or 1-dimensional.")
 
-        if shape and not self.coords.size:
-            self.coords = np.zeros(
-                (len(shape) if isinstance(shape, Iterable) else 1, 0), dtype=np.intp
-            )
-
         if shape is None:
+            warnings.warn(
+                "shape should be provided. This will raise a ValueError in the future.",
+                DeprecationWarning,
+            )
             if self.coords.nbytes:
                 shape = tuple((self.coords.max(axis=1) + 1))
             else:
@@ -255,6 +254,18 @@ def __init__(
         if not isinstance(shape, Iterable):
             shape = (shape,)
 
+        if isinstance(shape, np.ndarray):
+            shape = tuple(shape)
+
+        if shape and not self.coords.size:
+            warnings.warn(
+                "coords should be an ndarray. This will raise a ValueError in the future.",
+                DeprecationWarning,
+            )
+            self.coords = np.zeros(
+                (len(shape) if isinstance(shape, Iterable) else 1, 0), dtype=np.intp
+            )
+
         super().__init__(shape, fill_value=fill_value)
         if idx_dtype:
             if not can_store(idx_dtype, max(shape)):

sparse/tests/test_coo.py

@@ -1672,3 +1672,10 @@ def test_scalar_elemwise():
     x1 = s1.todense()
 
     assert_eq(s1 * x2, x1 * x2)
+
+
+def test_array_as_shape():
+    coords = [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
+    data = [10, 20, 30, 40, 50]
+
+    s = sparse.COO(coords, data, shape=np.array((5, 5)))