upgrade to pandas 2.0 groupby (#21)

- Fix outer value lookup with vector data (#19)
- Fix compatibility with groupby from pandas 2.0

CHANGELOG.rst

@@ -1,6 +1,12 @@
 Changelog
 =========
 
+Version 3.1.5
+-------------
+
+- Fix outer value lookup with vector data (#19)
+- Fix compatibility with groupby from pandas 2.0
+
 Version 3.1.4
 -------------
 - switch hemisphere constants to be in line with Allen Institute

tests/test_voxel_data.py

@@ -27,10 +27,49 @@ def test_lookup():
     assert_raises(VoxcellError, v.lookup, [[2, 10]])
 
 
-def test_lookup_vector_data():
-    raw = np.array([[[11], [12]], [[21], [22]]])
-    v = test_module.VoxelData(raw, (2, 2))
-    assert_array_equal(v.lookup([[1, 1], [3, 3]]), [[11], [22]])
+@pytest.mark.parametrize(
+    "raw, voxel_dimensions, coordinates, outer_value, expected",
+    [
+        (
+            [[[11, 11], [12, 12]], [[21, 21], [22, 22]]],
+            (2, 2),
+            [[1, 1], [3, 3]],
+            None,
+            [[11, 11], [22, 22]],
+        ),
+        (
+            [[[11, 11], [12, 12]], [[21, 21], [22, 22]]],
+            (2, 2),
+            [[10, 1]],
+            [10, 10],
+            [[10, 10]],
+        ),
+        (
+            np.zeros((1, 1, 1)),
+            (1, 1, 1),
+            [(-1, -1, -1)],
+            0,
+            [0],
+        ),
+        (
+            np.zeros((1, 1, 1, 1)),
+            (1, 1, 1),
+            [(-1, -1, -1)],
+            0,
+            [(0,)],
+        ),
+        (
+            np.zeros((1, 1, 1, 2)),
+            (1, 1, 1),
+            [(-1, -1, -1)],
+            (0, 0),
+            [(0, 0)],
+        ),
+    ],
+)
+def test_lookup_vector_data(raw, voxel_dimensions, coordinates, outer_value, expected):
+    voxel_data = test_module.VoxelData(np.array(raw), voxel_dimensions)
+    assert_array_equal(voxel_data.lookup(coordinates, outer_value), expected)
 
 
 def test_positions_to_indices():

voxcell/traits.py

@@ -105,12 +105,7 @@ def assign_conditional(self, positions, preassigned):
 
         unique_assigned = preassigned.drop_duplicates()
         for values_comb in unique_assigned.values:
-
-            if len(values_comb) == 1:
-                values_comb = values_comb[0]
-                hashable = values_comb
-            else:
-                hashable = tuple(values_comb)
+            hashable = tuple(values_comb)
 
             if hashable in subsections:
                 subdist = subsections[hashable]
@@ -179,6 +174,9 @@ def split(self, attributes):
         """
         grouped_distributions = {}
         for attr_values, traits in self.traits.groupby(attributes):
+            # this is backwards compatibility: https://github.com/pandas-dev/pandas/issues/42795
+            if len(attributes) == 1:
+                attr_values = tuple(attr_values)
 
             dists = self.distributions.iloc[traits.index]
 

voxcell/voxel_data.py

@@ -176,7 +176,11 @@ def lookup(self, positions, outer_value=None):
         voxel_idx = self.positions_to_indices(positions, strict=outer_value is None)
         outer_mask = np.any(voxel_idx == VoxelData.OUT_OF_BOUNDS, axis=-1)
         if np.any(outer_mask):
-            result = np.full(voxel_idx.shape[:-1], outer_value)
+            result = np.full(
+                voxel_idx.shape[:-1] + self.payload_shape,
+                outer_value,
+                dtype=self.raw.dtype
+            )
             inner_mask = np.logical_not(outer_mask)  # pylint: disable=assignment-from-no-return
             result[inner_mask] = self._lookup_by_indices(voxel_idx[inner_mask])
         else: