chore: port to numpy 2.0

pyproject.toml

@@ -30,7 +30,7 @@ classifiers = [
 ]
 dynamic = ["version"]
 dependencies = [
-  "awkward>=2.5.0",
+  "awkward>=2.6.7",
 ]
 
 [project.optional-dependencies]

src/ragged/_helper_functions.py

@@ -0,0 +1,20 @@
+# BSD 3-Clause License; see https://github.com/scikit-hep/ragged/blob/main/LICENSE
+from __future__ import annotations
+
+import numpy as np
+
+
+def regularise_to_float(t: np.dtype, /) -> np.dtype:
+    # Ensure compatibility with numpy 2.0.0
+    if np.__version__ >= "2.1":
+        # Just pass and return the input type if the numpy version is not 2.0.0
+        return t
+
+    if t in [np.int8, np.uint8, np.bool_, bool]:
+        return np.float16
+    elif t in [np.int16, np.uint16]:
+        return np.float32
+    elif t in [np.int32, np.uint32, np.int64, np.uint64]:
+        return np.float64
+    else:
+        return t

src/ragged/_spec_array_object.py

@@ -244,9 +244,9 @@ def __str__(self) -> str:
         if len(self._shape) == 0:
             return f"{self._impl}"
         elif len(self._shape) == 1:
-            return f"{ak._prettyprint.valuestr(self._impl, 1, 80)}"
+            return f"{ak.prettyprint.valuestr(self._impl, 1, 80)}"
         else:
-            prep = ak._prettyprint.valuestr(self._impl, 20, 80 - 4)[1:-1].replace(
+            prep = ak.prettyprint.valuestr(self._impl, 20, 80 - 4)[1:-1].replace(
                 "\n ", "\n    "
             )
             return f"[\n    {prep}\n]"
@@ -259,9 +259,9 @@ def __repr__(self) -> str:
         if len(self._shape) == 0:
             return f"ragged.array({self._impl})"
         elif len(self._shape) == 1:
-            return f"ragged.array({ak._prettyprint.valuestr(self._impl, 1, 80 - 14)})"
+            return f"ragged.array({ak.prettyprint.valuestr(self._impl, 1, 80 - 14)})"
         else:
-            prep = ak._prettyprint.valuestr(self._impl, 20, 80 - 4)[1:-1].replace(
+            prep = ak.prettyprint.valuestr(self._impl, 20, 80 - 4)[1:-1].replace(
                 "\n ", "\n    "
             )
             return f"ragged.array([\n    {prep}\n])"

src/ragged/_spec_elementwise_functions.py

@@ -10,6 +10,7 @@
 
 import numpy as np
 
+from ._helper_functions import regularise_to_float
 from ._spec_array_object import _box, _unbox, array
 
 
@@ -414,7 +415,7 @@ def ceil(x: array, /) -> array:
     https://data-apis.org/array-api/latest/API_specification/generated/array_api.ceil.html
     """
 
-    return _box(type(x), np.ceil(*_unbox(x)), dtype=x.dtype)
+    return _box(type(x), np.ceil(*_unbox(x)), dtype=regularise_to_float(x.dtype))
 
 
 def conj(x: array, /) -> array:
@@ -586,7 +587,7 @@ def floor(x: array, /) -> array:
     https://data-apis.org/array-api/latest/API_specification/generated/array_api.floor.html
     """
 
-    return _box(type(x), np.floor(*_unbox(x)), dtype=x.dtype)
+    return _box(type(x), np.floor(*_unbox(x)), dtype=regularise_to_float(x.dtype))
 
 
 def floor_divide(x1: array, x2: array, /) -> array:

tests/test_spec_elementwise_functions.py

@@ -14,13 +14,27 @@
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
-    import numpy.array_api as xp
 
 import pytest
 
 import ragged
+from ragged._helper_functions import regularise_to_float
+
+has_complex_dtype = True
+numpy_has_array_api = False
 
 devices = ["cpu"]
+
+try:
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        import numpy.array_api as xp
+
+        numpy_has_array_api = True
+        has_complex_dtype = np.dtype("complex128") in xp._dtypes._all_dtypes
+except ModuleNotFoundError:
+    import numpy as xp  # noqa: ICN001
+
 try:
     import cupy as cp
 
@@ -374,17 +388,34 @@ def test_ceil(device, x):
     assert xp.ceil(first(x)).dtype == result.dtype
 
 
+@pytest.mark.skipif(
+    not numpy_has_array_api,
+    reason=f"testing only in numpy version 1, but got numpy version {np.__version__}",
+)
 @pytest.mark.parametrize("device", devices)
-def test_ceil_int(device, x_int):
+def test_ceil_int_1(device, x_int):
     result = ragged.ceil(x_int.to_device(device))
     assert type(result) is type(x_int)
     assert result.shape == x_int.shape
-    assert xp.ceil(first(x_int)) == first(result)
-    assert xp.ceil(first(x_int)).dtype == result.dtype
 
 
 @pytest.mark.skipif(
-    np.dtype("complex128") not in xp._dtypes._all_dtypes,
+    numpy_has_array_api,
+    reason=f"testing only in numpy version 2, but got numpy version {np.__version__}",
+)
+@pytest.mark.parametrize("device", devices)
+def test_ceil_int_2(device, x_int):
+    result = ragged.ceil(x_int.to_device(device))
+    assert type(result) is type(x_int)
+    assert result.shape == x_int.shape
+    assert xp.ceil(first(x_int)) == first(result).astype(
+        regularise_to_float(first(result).dtype)
+    )
+    assert xp.ceil(first(x_int)).dtype == regularise_to_float(result.dtype)
+
+
+@pytest.mark.skipif(
+    not has_complex_dtype,
     reason=f"complex not allowed in np.array_api version {np.__version__}",
 )
 @pytest.mark.parametrize("device", devices)
@@ -487,13 +518,32 @@ def test_floor(device, x):
     assert xp.floor(first(x)).dtype == result.dtype
 
 
+@pytest.mark.skipif(
+    not numpy_has_array_api,
+    reason=f"testing only in numpy version 1, but got numpy version {np.__version__}",
+)
 @pytest.mark.parametrize("device", devices)
-def test_floor_int(device, x_int):
+def test_floor_int_1(device, x_int):
+    result = ragged.floor(
+        x_int.to_device(device)
+    )  # always returns float64 regardless of x_int.dtype
+    assert type(result) is type(x_int)
+    assert result.shape == x_int.shape
+
+
+@pytest.mark.skipif(
+    numpy_has_array_api,
+    reason=f"testing only in numpy version 2, but got numpy version {np.__version__}",
+)
+@pytest.mark.parametrize("device", devices)
+def test_floor_int_2(device, x_int):
     result = ragged.floor(x_int.to_device(device))
     assert type(result) is type(x_int)
     assert result.shape == x_int.shape
-    assert xp.floor(first(x_int)) == first(result)
-    assert xp.floor(first(x_int)).dtype == result.dtype
+    assert xp.floor(first(x_int)) == np.asarray(first(result)).astype(
+        regularise_to_float(first(result).dtype)
+    )
+    assert xp.floor(first(x_int)).dtype == regularise_to_float(result.dtype)
 
 
 @pytest.mark.parametrize("device", devices)
@@ -571,7 +621,7 @@ def test_greater_equal_method(device, x, y):
 
 
 @pytest.mark.skipif(
-    np.dtype("complex128") not in xp._dtypes._all_dtypes,
+    not has_complex_dtype,
     reason=f"complex not allowed in np.array_api version {np.__version__}",
 )
 @pytest.mark.parametrize("device", devices)
@@ -838,7 +888,7 @@ def test_pow_inplace_method(device, x, y):
 
 
 @pytest.mark.skipif(
-    np.dtype("complex128") not in xp._dtypes._all_dtypes,
+    not has_complex_dtype,
     reason=f"complex not allowed in np.array_api version {np.__version__}",
 )
 @pytest.mark.parametrize("device", devices)
@@ -888,7 +938,7 @@ def test_round(device, x):
 
 
 @pytest.mark.skipif(
-    np.dtype("complex128") not in xp._dtypes._all_dtypes,
+    not has_complex_dtype,
     reason=f"complex not allowed in np.array_api version {np.__version__}",
 )
 @pytest.mark.parametrize("device", devices)