Merge pull request #4 from dcherian/zero-chunks

have property tests support zero-sized chunks

src/zarr/testing/strategies.py

@@ -85,17 +85,24 @@ def numpy_arrays(
 @st.composite  # type: ignore[misc]
 def np_array_and_chunks(
     draw: st.DrawFn, *, arrays: st.SearchStrategy[np.ndarray] = numpy_arrays
-) -> tuple[np.ndarray, tuple[int]]:  # type: ignore[type-arg]
+) -> tuple[np.ndarray, tuple[int, ...]]:  # type: ignore[type-arg]
     """A hypothesis strategy to generate small sized random arrays.
 
     Returns: a tuple of the array and a suitable random chunking for it.
     """
     array = draw(arrays)
     # We want this strategy to shrink towards arrays with smaller number of chunks
     # 1. st.integers() shrinks towards smaller values. So we use that to generate number of chunks
-    numchunks = draw(st.tuples(*[st.integers(min_value=1, max_value=size) for size in array.shape]))
+    numchunks = draw(
+        st.tuples(
+            *[st.integers(min_value=0 if size == 0 else 1, max_value=size) for size in array.shape]
+        )
+    )
     # 2. and now generate the chunks tuple
-    chunks = tuple(size // nchunks for size, nchunks in zip(array.shape, numchunks, strict=True))
+    chunks = tuple(
+        size // nchunks if nchunks > 0 else 0
+        for size, nchunks in zip(array.shape, numchunks, strict=True)
+    )
     return (array, chunks)
 
 

tests/test_properties.py

@@ -6,7 +6,7 @@
 
 import hypothesis.extra.numpy as npst  # noqa: E402
 import hypothesis.strategies as st  # noqa: E402
-from hypothesis import given  # noqa: E402
+from hypothesis import assume, given  # noqa: E402
 
 from zarr.testing.strategies import arrays, basic_indices, numpy_arrays, zarr_formats  # noqa: E402
 
@@ -35,11 +35,13 @@ def test_basic_indexing(data: st.DataObject) -> None:
 @given(data=st.data())
 def test_vindex(data: st.DataObject) -> None:
     zarray = data.draw(arrays())
+    # integer_array_indices can't handle 0-size dimensions.
+    assume(all(s > 0 for s in zarray.shape))
     nparray = zarray[:]
 
     indexer = data.draw(
         npst.integer_array_indices(
-            shape=nparray.shape, result_shape=npst.array_shapes(max_dims=None)
+            shape=nparray.shape, result_shape=npst.array_shapes(min_side=1, max_dims=None)
         )
     )
     actual = zarray.vindex[indexer]