Closes #5269: alignment tests for arkouda.numpy.pdarraymanipulation

Author: ajpotts

pytest.ini

@@ -52,6 +52,7 @@ testpaths =
     tests/numpy/err_test.py
     tests/numpy/manipulation_functions_test.py
     tests/numpy/alignment_verification/operators_alignment.py
+    tests/numpy/alignment_verification/pdarraymanipulation_alignment.py
     tests/numpy/numeric_test.py
     tests/numpy/numpy_test.py
     tests/numpy/pdarrayclass_test.py

tests/numpy/alignment_verification/pdarraymanipulation_alignment.py

@@ -0,0 +1,236 @@
+import numpy as np
+import pytest
+
+import arkouda as ak
+
+from arkouda.numpy.pdarrayclass import pdarray
+from arkouda.numpy.pdarraymanipulation import append, delete, hstack, vstack
+
+
+# -----------------------------
+# Helpers
+# -----------------------------
+def _ak_to_np(x):
+    """
+    Convert Arkouda pdarray (possibly nested / multi-d) to a NumPy array.
+    We prefer to_ndarray() when available; fall back to to_list().
+    """
+    # Many Arkouda objects support to_ndarray(); pdarray does.
+    if hasattr(x, "to_ndarray"):
+        return x.to_ndarray()
+    return np.array(x.to_list())
+
+
+def _assert_np_equal(got, exp):
+    got_np = _ak_to_np(got) if isinstance(got, pdarray) else np.asarray(got)
+    exp_np = np.asarray(exp)
+
+    assert got_np.shape == exp_np.shape
+    assert got_np.dtype == exp_np.dtype
+
+    # Handle NaNs in a NumPy-version-stable way
+    if np.issubdtype(exp_np.dtype, np.floating) or np.issubdtype(exp_np.dtype, np.complexfloating):
+        np.testing.assert_allclose(got_np, exp_np, rtol=0, atol=0, equal_nan=True)
+    else:
+        np.testing.assert_array_equal(got_np, exp_np)
+
+
+def _mk_cases_1d_same_len():
+    return [
+        (np.array([1, 2, 3], dtype=np.int64), np.array([4, 5, 6], dtype=np.int64)),
+        (np.array([1, 2, 3], dtype=np.int64), np.array([4.5, 5.5, 6.5], dtype=np.float64)),
+        (np.array([np.nan, 1.0], dtype=np.float64), np.array([2.0, np.nan], dtype=np.float64)),
+        (np.array([True, False], dtype=bool), np.array([False, True], dtype=bool)),
+        (np.array([], dtype=np.int64), np.array([], dtype=np.int64)),
+    ]
+
+
+def _mk_cases_2d():
+    return [
+        (np.array([[1], [2], [3]], dtype=np.int64), np.array([[4], [5], [6]], dtype=np.int64)),
+        (np.array([[1, 2]], dtype=np.int64), np.array([[3, 4]], dtype=np.int64)),
+        (np.array([[1, 2]], dtype=np.int64), np.array([[3.0, 4.0]], dtype=np.float64)),
+        (np.array([[np.nan, 1.0]], dtype=np.float64), np.array([[2.0, np.nan]], dtype=np.float64)),
+    ]
+
+
+def _to_ak(x: np.ndarray):
+    # ak.array handles numpy arrays; for multi-d it yields Arkouda "multi-d" pdarray-like.
+    return ak.array(x)
+
+
+# -----------------------------
+# hstack alignment
+# -----------------------------
+@pytest.mark.parametrize("a,b", _mk_cases_1d_same_len())
+def test_hstack_1d_alignment(a, b):
+    ak_a, ak_b = _to_ak(a), _to_ak(b)
+
+    got = hstack((ak_a, ak_b))
+    exp = np.hstack((a, b))
+
+    _assert_np_equal(got, exp)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize("a,b", _mk_cases_2d())
+def test_hstack_2d_alignment(a, b):
+    ak_a, ak_b = _to_ak(a), _to_ak(b)
+
+    got = hstack((ak_a, ak_b))
+    exp = np.hstack((a, b))
+
+    _assert_np_equal(got, exp)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+def test_hstack_dim_mismatch_raises():
+    a = _to_ak(np.array([1, 2, 3], dtype=np.int64))
+    b = _to_ak(np.array([[4], [5], [6]], dtype=np.int64))
+    with pytest.raises(ValueError, match="same number of dimensions"):
+        hstack((a, b))
+
+
+def test_hstack_casting_not_supported():
+    a = _to_ak(np.array([1, 2, 3], dtype=np.int64))
+    b = _to_ak(np.array([4, 5], dtype=np.int64))
+    with pytest.raises(NotImplementedError):
+        hstack((a, b), casting="unsafe")
+
+
+# -----------------------------
+# vstack alignment
+# -----------------------------
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize("a,b", _mk_cases_1d_same_len())
+def test_vstack_1d_alignment(a, b):
+    ak_a, ak_b = _to_ak(a), _to_ak(b)
+    got = vstack((ak_a, ak_b))
+    exp = np.vstack((a, b))
+    _assert_np_equal(got, exp)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize("a,b", _mk_cases_2d())
+def test_vstack_2d_alignment(a, b):
+    ak_a, ak_b = _to_ak(a), _to_ak(b)
+
+    got = vstack((ak_a, ak_b))
+    exp = np.vstack((a, b))
+
+    _assert_np_equal(got, exp)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+def test_vstack_dim_mismatch_raises():
+    a_np = np.array([1, 2, 3], dtype=np.int64)
+    b_np = np.array([[4], [5], [6]], dtype=np.int64)
+
+    a = _to_ak(a_np)
+    b = _to_ak(b_np)
+
+    # NumPy: must raise for mismatched dimensions
+    with pytest.raises(ValueError):
+        np.vstack((a_np, b_np))
+
+    # Arkouda: currently raises RuntimeError from server; message is shape-related
+    with pytest.raises((ValueError, RuntimeError), match="same shape|shape except|concatenation axis"):
+        vstack((a, b))
+
+
+def test_vstack_casting_not_supported():
+    a = _to_ak(np.array([1, 2, 3], dtype=np.int64))
+    b = _to_ak(np.array([4, 5], dtype=np.int64))
+    with pytest.raises(NotImplementedError):
+        vstack((a, b), casting="unsafe")
+
+
+# -----------------------------
+# delete alignment
+# -----------------------------
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize(
+    "arr,obj,axis",
+    [
+        # 1D basics
+        (np.array([1, 2, 3, 4], dtype=np.int64), 0, None),
+        (np.array([1, 2, 3, 4], dtype=np.int64), -1, None),
+        (np.array([1, 2, 3, 4], dtype=np.int64), slice(0, 4, 2), None),
+        (np.array([1, 2, 3, 4], dtype=np.int64), [1, 3], None),
+        (np.array([1, 2, 3, 4], dtype=np.int64), np.array([True, False, True, False]), None),
+        # 2D axis cases
+        (np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64), 0, 0),
+        (np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64), 1, 1),
+        (np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64), slice(0, 3, 2), 1),
+        (np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64), [0, 1], 0),
+    ],
+)
+def test_delete_alignment(arr, obj, axis):
+    ak_arr = _to_ak(arr)
+
+    # convert obj to ak where relevant
+    if isinstance(obj, np.ndarray) and obj.dtype == bool:
+        ak_obj = ak.array(obj.tolist())
+    elif isinstance(obj, (list, tuple)):
+        ak_obj = obj  # delete() accepts Sequence[int]/Sequence[bool]
+    else:
+        ak_obj = obj  # int or slice
+
+    got = delete(ak_arr, ak_obj, axis=axis)
+    exp = np.delete(arr, obj, axis=axis)
+
+    _assert_np_equal(got, exp)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+def test_delete_axis_none_flattens_like_numpy():
+    arr = np.array([[1, 2], [3, 4]], dtype=np.int64)
+    ak_arr = _to_ak(arr)
+
+    got = delete(ak_arr, [1, 3], axis=None)
+    exp = np.delete(arr, [1, 3], axis=None)
+
+    _assert_np_equal(got, exp)
+
+
+# -----------------------------
+# append alignment
+# -----------------------------
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize(
+    "arr,values,axis",
+    [
+        # axis=None -> flatten both (NumPy behavior)
+        (np.array([1, 2, 3], dtype=np.int64), np.array([[4, 5], [6, 7]], dtype=np.int64), None),
+        (np.array([[1, 2], [3, 4]], dtype=np.int64), np.array([5, 6], dtype=np.int64), None),
+        # axis specified -> shapes must align except on axis
+        (np.array([[1, 2], [3, 4]], dtype=np.int64), np.array([[5, 6]], dtype=np.int64), 0),
+        (np.array([[1, 2], [3, 4]], dtype=np.int64), np.array([[5], [6]], dtype=np.int64), 1),
+        # dtype promotion
+        (np.array([1, 2, 3], dtype=np.int64), np.array([4.5], dtype=np.float64), None),
+    ],
+)
+def test_append_alignment(arr, values, axis):
+    ak_arr = _to_ak(arr)
+    ak_values = _to_ak(values)
+
+    got = append(ak_arr, ak_values, axis=axis)
+    exp = np.append(arr, values, axis=axis)
+
+    _assert_np_equal(got, exp)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+def test_append_axis_dim_mismatch_raises():
+    arr = _to_ak(np.array([1, 2, 3], dtype=np.int64))
+    values = _to_ak(np.array([[4], [5]], dtype=np.int64))
+    with pytest.raises(ValueError, match="same number of dimensions"):
+        append(arr, values, axis=0)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+def test_append_axis_out_of_bounds_raises():
+    arr = _to_ak(np.array([[1, 2], [3, 4]], dtype=np.int64))
+    values = _to_ak(np.array([[5, 6]], dtype=np.int64))
+    with pytest.raises(ValueError, match="out of bounds"):
+        append(arr, values, axis=5)