Handle scalar and 1d dims

movement/analysis/kinematics.py

@@ -220,6 +220,10 @@ def cdist(
     core_dim = "individuals" if dim == "keypoints" else "keypoints"
     elem1 = getattr(a, dim).item()
     elem2 = getattr(b, dim).item()
+    if a.coords[core_dim].ndim == 0:
+        a = a.expand_dims(core_dim).transpose("time", "space", core_dim)
+    if b.coords[core_dim].ndim == 0:
+        b = b.expand_dims(core_dim).transpose("time", "space", core_dim)
     result = xr.apply_ufunc(
         _cdist,
         a,
@@ -235,7 +239,8 @@ def cdist(
             elem2: getattr(a, core_dim).values,
         }
     )
-    return result
+    # Drop any squeezed coordinates
+    return result.squeeze(drop=True)
 
 
 def compute_interindividual_distances(

tests/test_unit/test_kinematics.py

@@ -1,4 +1,5 @@
 import itertools
+from contextlib import nullcontext as does_not_raise
 
 import numpy as np
 import pytest
@@ -227,7 +228,9 @@ def test_approximate_derivative_with_invalid_order(order):
         ),
     ],
 )
-def test_cdist(dim, pairs, expected_data, pairwise_distances_dataset):
+def test_cdist_with_known_values(
+    dim, pairs, expected_data, pairwise_distances_dataset
+):
     """Test the computation of pairwise distances with known values."""
     core_dim = "keypoints" if dim == "individuals" else "individuals"
     input_dataarray = pairwise_distances_dataset.position
@@ -249,6 +252,55 @@ def test_cdist(dim, pairs, expected_data, pairwise_distances_dataset):
     )
 
 
+@pytest.mark.parametrize(
+    "selection_fn",
+    [
+        lambda position: (
+            position.sel(individuals="ind1"),
+            position.sel(individuals="ind2"),
+        ),  # individuals dim is scalar
+        lambda position: (
+            position.where(
+                position.individuals == "ind1", drop=True
+            ).squeeze(),
+            position.where(
+                position.individuals == "ind2", drop=True
+            ).squeeze(),
+        ),  # individuals dim is 1D
+        lambda position: (
+            position.sel(individuals="ind1", keypoints="key1"),
+            position.sel(individuals="ind2", keypoints="key1"),
+        ),  # both individuals and keypoints dims are scalar
+        lambda position: (
+            position.where(position.keypoints == "key1", drop=True).sel(
+                individuals="ind1"
+            ),
+            position.where(position.keypoints == "key1", drop=True).sel(
+                individuals="ind2"
+            ),
+        ),  # keypoints dim is 1D
+    ],
+    ids=[
+        "dim_has_ndim_0",
+        "dim_has_ndim_1",
+        "core_dim_has_ndim_0",
+        "core_dim_has_ndim_1",
+    ],
+)
+def test_cdist_with_single_dim_inputs(
+    pairwise_distances_dataset, selection_fn
+):
+    """Test that the computation of pairwise distances
+    works regardless of whether the input DataArrays have
+    ```dim``` and ```core_dim``` being either scalar (ndim=0)
+    or 1D (ndim=1).
+    """
+    position = pairwise_distances_dataset.position
+    a, b = selection_fn(position)
+    with does_not_raise():
+        kinematics.cdist(a, b, "individuals")
+
+
 def expected_pairwise_distances(pairs, input_ds, dim):
     """Return a list of the expected data variable names
     for pairwise distances tests.