Testing improvements

Author: dcherian

flox/aggregations.py

@@ -49,7 +49,7 @@ def _normalize_dtype(dtype, array_dtype, fill_value=None):
             dtype = np.floating
         else:
             dtype = array_dtype
-    elif dtype is np.floating:
+    if dtype is np.floating:
         # mean, std, var always result in floating
         # but we preserve the array's dtype if it is floating
         if array_dtype.kind in "fcmM":

tests/__init__.py

@@ -92,6 +92,9 @@ def assert_equal(a, b):
     elif has_xarray and isinstance(a, xr_types) or isinstance(b, xr_types):
         xr.testing.assert_identical(a, b)
     elif has_dask and isinstance(a, dask_array_type) or isinstance(b, dask_array_type):
+        # sometimes it's nice to see values and shapes
+        # rather than being dropped into some file in dask
+        np.testing.assert_allclose(a, b)
         # does some validation of the dask graph
         da.utils.assert_eq(a, b, equal_nan=True)
     else:

tests/test_core.py

@@ -307,29 +307,30 @@ def test_numpy_reduce_axis_subset(engine):
     # TODO: add NaNs
     by = labels2d
     array = np.ones_like(by)
-    result, _ = groupby_reduce(array, by, "count", axis=1, engine=engine)
+    kwargs = dict(func="count", engine=engine)
+    result, _ = groupby_reduce(array, by, **kwargs, axis=1)
     assert_equal(result, [[2, 3], [2, 3]])
 
     by = np.broadcast_to(labels2d, (3, *labels2d.shape))
     array = np.ones_like(by)
-    result, _ = groupby_reduce(array, by, "count", axis=1, engine=engine)
+    result, _ = groupby_reduce(array, by, **kwargs, axis=1)
     subarr = np.array([[1, 1], [1, 1], [0, 2], [1, 1], [1, 1]])
     expected = np.tile(subarr, (3, 1, 1))
     assert_equal(result, expected)
 
-    result, _ = groupby_reduce(array, by, "count", axis=2, engine=engine)
+    result, _ = groupby_reduce(array, by, **kwargs, axis=2)
     subarr = np.array([[2, 3], [2, 3]])
     expected = np.tile(subarr, (3, 1, 1))
     assert_equal(result, expected)
 
-    result, _ = groupby_reduce(array, by, "count", axis=(1, 2), engine=engine)
+    result, _ = groupby_reduce(array, by, **kwargs, axis=(1, 2))
     expected = np.array([[4, 6], [4, 6], [4, 6]])
     assert_equal(result, expected)
 
-    result, _ = groupby_reduce(array, by, "count", axis=(2, 1), engine=engine)
+    result, _ = groupby_reduce(array, by, **kwargs, axis=(2, 1))
     assert_equal(result, expected)
 
-    result, _ = groupby_reduce(array, by[0, ...], "count", axis=(1, 2), engine=engine)
+    result, _ = groupby_reduce(array, by[0, ...], **kwargs, axis=(1, 2))
     expected = np.array([[4, 6], [4, 6], [4, 6]])
     assert_equal(result, expected)
 
@@ -343,7 +344,7 @@ def test_dask_reduce_axis_subset():
         result, _ = groupby_reduce(
             da.from_array(array, chunks=(2, 3)),
             da.from_array(by, chunks=(2, 2)),
-            "count",
+            func="count",
             axis=1,
             expected_groups=[0, 2],
         )
@@ -357,7 +358,7 @@ def test_dask_reduce_axis_subset():
         result, _ = groupby_reduce(
             da.from_array(array, chunks=(1, 2, 3)),
             da.from_array(by, chunks=(2, 2, 2)),
-            "count",
+            func="count",
             axis=1,
             expected_groups=[0, 2],
             fill_value=123,
@@ -370,7 +371,7 @@ def test_dask_reduce_axis_subset():
         result, _ = groupby_reduce(
             da.from_array(array, chunks=(1, 2, 3)),
             da.from_array(by, chunks=(2, 2, 2)),
-            "count",
+            func="count",
             axis=2,
             expected_groups=[0, 2],
         )
@@ -380,7 +381,7 @@ def test_dask_reduce_axis_subset():
         groupby_reduce(
             da.from_array(array, chunks=(1, 3, 2)),
             da.from_array(by, chunks=(2, 2, 2)),
-            "count",
+            func="count",
             axis=2,
         )
 
@@ -448,7 +449,7 @@ def _maybe_chunk(arr):
     result, _ = groupby_reduce(
         _maybe_chunk(array),
         _maybe_chunk(by),
-        "count",
+        func="count",
         expected_groups=groups,
         axis=axis,
         fill_value=0,