Closes #5294: alignment tests for arkouda.numpy.random.legacy

pytest.ini

@@ -53,6 +53,7 @@ testpaths =
     tests/numpy/manipulation_functions_test.py
     tests/numpy/alignment_verification/manipulation_functions_alignment.py
     tests/numpy/alignment_verification/operators_alignment.py
+    tests/numpy/alignment_verification/random_legacy_alignment.py
     tests/numpy/numeric_test.py
     tests/numpy/numpy_test.py
     tests/numpy/pdarrayclass_test.py

tests/numpy/alignment_verification/random_legacy_alignment.py

@@ -0,0 +1,147 @@
+import numpy as np
+import pytest
+
+import arkouda as ak
+
+
+def _to_np(x):
+    if hasattr(x, "to_ndarray"):
+        return x.to_ndarray()
+    return ak.to_numpy(x)
+
+
+def _assert_in_range(arr, lo, hi, *, inclusive_hi=False):
+    a = np.asarray(arr)
+    assert np.all(a >= lo)
+    if inclusive_hi:
+        assert np.all(a <= hi)
+    else:
+        assert np.all(a < hi)
+
+
+def _assert_shape(arr, shape):
+    a = np.asarray(arr)
+    assert a.shape == tuple(shape)
+
+
+def _numpy_randomstate_randint(low, high, size, dtype):
+    rs = np.random.RandomState(0)
+    return rs.randint(low=low, high=high, size=size, dtype=dtype)
+
+
+@pytest.mark.skip_if_rank_not_compiled([2, 3])
+@pytest.mark.parametrize("shape", [(1,), (10,), (2, 3), (4, 1, 5)])
+def test_ak_rand_shape_and_range(shape):
+    # Arkouda: rand is under ak.random
+    ak.random.seed(12345)
+    out = ak.random.rand(*shape)
+    out_np = _to_np(out)
+    _assert_shape(out_np, shape)
+    # Accept [0,1] to tolerate rare inclusive-high implementations
+    _assert_in_range(out_np, 0.0, 1.0, inclusive_hi=True)
+
+
+def test_ak_rand_scalar():
+    ak.random.seed(12345)
+    x = ak.random.rand()
+    assert isinstance(x, (float, np.floating))
+    assert 0.0 <= float(x) <= 1.0
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize("size", [0, 1, 10, (2, 3)])
+def test_randint_int64_shape_dtype_and_bounds(size):
+    low, high = 3, 17
+    ak.random.seed(2468)
+
+    # Arkouda API name: usually randint() under ak.random
+    out = ak.random.randint(low, high, size=size)
+
+    out_np = _to_np(out)
+
+    if isinstance(size, tuple):
+        _assert_shape(out_np, size)
+    else:
+        _assert_shape(out_np, (size,))
+
+    assert np.issubdtype(out_np.dtype, np.integer)
+    _assert_in_range(out_np, low, high, inclusive_hi=False)
+
+
+def _numpy_bool_randint_error(low, high):
+    rs = np.random.RandomState(0)
+    with pytest.raises(ValueError) as e:
+        rs.randint(low=low, high=high, size=10, dtype=bool)
+    return str(e.value)
+
+
+@pytest.mark.parametrize(
+    "low,high",
+    [
+        (0, 0),
+        (0, -1),
+        (-1, 2),
+        (0, 3),
+        (1, 1),
+        (1, 0),
+    ],
+)
+def test_randint_bool_validation_messages_match_numpy(low, high):
+    expected_msg = _numpy_bool_randint_error(low, high)
+
+    ak.random.seed(0)
+    with pytest.raises(ValueError) as e_ak:
+        ak.random.randint(low, high, size=10, dtype="bool")
+
+    actual = str(e_ak.value)
+    if actual != expected_msg:
+        pytest.xfail(
+            f"Arkouda randint(dtype=bool) error msg mismatch for low={low}, high={high}: "
+            f"expected={expected_msg!r}, got={actual!r}"
+            f" Issue #5295."
+        )
+
+    assert actual == expected_msg
+
+
+@pytest.mark.skip_if_rank_not_compiled([2])
+@pytest.mark.parametrize("size", [0, 1, 10, (2, 3)])
+def test_uniform_shape_and_range(size):
+    ak.random.seed(1357)
+
+    # Many Arkouda builds use ak.random.uniform(low, high, size)
+    out = ak.random.uniform(low=2.5, high=7.5, size=size)
+
+    out_np = _to_np(out)
+
+    if isinstance(size, tuple):
+        _assert_shape(out_np, size)
+    else:
+        _assert_shape(out_np, (size,))
+
+    _assert_in_range(out_np, 2.5, 7.5, inclusive_hi=True)
+
+
+def test_standard_normal_basic_moments_are_reasonable():
+    n = 20000
+    ak.random.seed(4242)
+
+    # Many Arkouda builds use ak.random.standard_normal(size)
+    out = ak.random.standard_normal(n)
+
+    x = _to_np(out).astype(np.float64)
+    assert abs(float(x.mean())) < 0.05
+    assert abs(float(x.var()) - 1.0) < 0.08
+
+
+def test_random_api_scalar_and_vector_range():
+    ak.random.seed(123)
+    x = ak.random.random()
+    assert isinstance(x, (float, np.floating))
+    assert 0.0 <= float(x) < 1.0
+
+    ak.random.seed(123)
+    y = ak.random.random(1000)
+    y_np = _to_np(y)
+    _assert_shape(y_np, (1000,))
+    _assert_in_range(y_np, 0.0, 1.0, inclusive_hi=False)