Befator-Inc-Firmen-Netzwerk/MINGW-packages
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases 1 Wiki Activity
Files
fba6c7fde85eaec281dc4b425f4d00a2b475521f
MINGW-packages/mingw-w64-python-numba/numpy-2.4.patch
Christoph Reiter 7167b1fcb4 python-numpy: Update to 2.4.0 (#26966) 
* python-numpy: Update to 2.4.0

* numba: add numpy 2.4 support
2026-01-02 20:59:30 +01:00

323 lines
12 KiB
Diff
Raw Blame History

diff --git a/numba/__init__.py b/numba/__init__.py
index 33f752018..7d3b3ae8d 100644
--- a/numba/__init__.py
+++ b/numba/__init__.py
@@ -39,8 +39,8 @@ def _ensure_critical_deps():
f"{numpy_version[0]}.{numpy_version[1]}.")
raise ImportError(msg)
- if numpy_version > (2, 3):
- msg = (f"Numba needs NumPy 2.3 or less. Got NumPy "
+ if numpy_version > (2, 4):
+ msg = (f"Numba needs NumPy 2.4 or less. Got NumPy "
f"{numpy_version[0]}.{numpy_version[1]}.")
raise ImportError(msg)
diff --git a/numba/np/arraymath.py b/numba/np/arraymath.py
index 6b031d789..75e9869a1 100644
--- a/numba/np/arraymath.py
+++ b/numba/np/arraymath.py
@@ -2222,7 +2222,7 @@ def get_d_impl(x, dx):
return impl
-@overload(np.trapz)
+@overload(np.trapezoid)
def np_trapz(y, x=None, dx=1.0):
if isinstance(y, (types.Number, types.Boolean)):
@@ -4941,12 +4941,12 @@ def jit_np_setdiff1d(ar1, ar2, assume_unique=False):
else:
ar1 = np.unique(ar1)
ar2 = np.unique(ar2)
- return ar1[np.in1d(ar1, ar2, assume_unique=True, invert=True)]
+ return ar1[np.isin(ar1, ar2, assume_unique=True, invert=True)]
return np_setdiff1d_impl
-@overload(np.in1d)
+@overload(np.isin)
def jit_np_in1d(ar1, ar2, assume_unique=False, invert=False):
if not (type_can_asarray(ar1) or type_can_asarray(ar2)):
raise TypingError('in1d: first two args must be array-like')
@@ -4958,6 +4958,8 @@ def jit_np_in1d(ar1, ar2, assume_unique=False, invert=False):
def np_in1d_impl(ar1, ar2, assume_unique=False, invert=False):
# https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L525 # noqa: E501
+ ar1 = np.asarray(ar1)
+ shape = ar1.shape
# Ravel both arrays, behavior for the first array could be different
ar1 = np.asarray(ar1).ravel()
ar2 = np.asarray(ar2).ravel()
@@ -4974,7 +4976,7 @@ def jit_np_in1d(ar1, ar2, assume_unique=False, invert=False):
mask = np.zeros(len(ar1), dtype=np.bool_)
for a in ar2:
mask |= (ar1 == a)
- return mask
+ return mask.reshape(shape)
# Otherwise use sorting
if not assume_unique:
@@ -5008,27 +5010,8 @@ def jit_np_in1d(ar1, ar2, assume_unique=False, invert=False):
# return ret[:len(ar1)]
if assume_unique:
- return ret[:len(ar1)]
+ return ret[:len(ar1)].reshape(shape)
else:
- return ret[inv_idx]
+ return ret[inv_idx].reshape(shape)
return np_in1d_impl
-
-
-@overload(np.isin)
-def jit_np_isin(element, test_elements, assume_unique=False, invert=False):
- if not (type_can_asarray(element) or type_can_asarray(test_elements)):
- raise TypingError('isin: first two args must be array-like')
- if not (isinstance(assume_unique, (types.Boolean, bool))):
- raise TypingError('isin: Argument "assume_unique" must be boolean')
- if not (isinstance(invert, (types.Boolean, bool))):
- raise TypingError('isin: Argument "invert" must be boolean')
-
- # https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L889 # noqa: E501
- def np_isin_impl(element, test_elements, assume_unique=False, invert=False):
-
- element = np.asarray(element)
- return np.in1d(element, test_elements, assume_unique=assume_unique,
- invert=invert).reshape(element.shape)
-
- return np_isin_impl
diff --git a/numba/tests/test_exceptions.py b/numba/tests/test_exceptions.py
index e6855c4ff..71c913963 100644
--- a/numba/tests/test_exceptions.py
+++ b/numba/tests/test_exceptions.py
@@ -189,7 +189,7 @@ class TestRaising(TestCase):
self.check_against_python(flags, pyfunc, cfunc, MyError, 1)
self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
self.check_against_python(flags, pyfunc, cfunc,
- np.linalg.linalg.LinAlgError, 3)
+ np.linalg.LinAlgError, 3)
def test_raise_class_nopython(self):
self.check_raise_class(flags=no_pyobj_flags)
@@ -207,7 +207,7 @@ class TestRaising(TestCase):
self.check_against_python(flags, pyfunc, cfunc, clazz, 1)
self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
self.check_against_python(flags, pyfunc, cfunc,
- np.linalg.linalg.LinAlgError, 3)
+ np.linalg.LinAlgError, 3)
def test_raise_instance_objmode(self):
self.check_raise_instance(flags=force_pyobj_flags)
@@ -374,7 +374,7 @@ class TestRaising(TestCase):
self.check_against_python(flags, pyfunc, cfunc, ValueError, 2,
'world')
self.check_against_python(flags, pyfunc, cfunc,
- np.linalg.linalg.LinAlgError, 3, 'linalg')
+ np.linalg.LinAlgError, 3, 'linalg')
def test_raise_instance_with_runtime_args_objmode(self):
self.check_raise_instance_with_runtime_args(flags=force_pyobj_flags)
diff --git a/numba/tests/test_extending.py b/numba/tests/test_extending.py
index f8608a6e6..86a4d177e 100644
--- a/numba/tests/test_extending.py
+++ b/numba/tests/test_extending.py
@@ -2124,7 +2124,7 @@ class TestNumbaInternalOverloads(TestCase):
# 1 to get violations reported to STDOUT
# 2 to get a verbose output of everything that was checked and its state
# reported to STDOUT.
- DEBUG = 0
+ DEBUG = 1
# np.random.* does not have a signature exposed to `inspect`... so
# custom parse the docstrings.
diff --git a/numba/tests/test_np_functions.py b/numba/tests/test_np_functions.py
index f76b3ff9c..5a6449529 100644
--- a/numba/tests/test_np_functions.py
+++ b/numba/tests/test_np_functions.py
@@ -347,19 +347,19 @@ def extract(condition, arr):
def np_trapz(y):
- return np.trapz(y)
+ return np.trapezoid(y)
def np_trapz_x(y, x):
- return np.trapz(y, x)
+ return np.trapezoid(y, x)
def np_trapz_dx(y, dx):
- return np.trapz(y, dx=dx)
+ return np.trapezoid(y, dx=dx)
def np_trapz_x_dx(y, x, dx):
- return np.trapz(y, x, dx)
+ return np.trapezoid(y, x, dx)
def np_trapezoid(y):
@@ -510,22 +510,6 @@ def np_setdiff1d_3(a, b, assume_unique=False):
return np.setdiff1d(a, b, assume_unique)
-def np_in1d_2(a, b):
- return np.in1d(a, b)
-
-
-def np_in1d_3a(a, b, assume_unique=False):
- return np.in1d(a, b, assume_unique=assume_unique)
-
-
-def np_in1d_3b(a, b, invert=False):
- return np.in1d(a, b, invert=invert)
-
-
-def np_in1d_4(a, b, assume_unique=False, invert=False):
- return np.in1d(a, b, assume_unique, invert)
-
-
def np_isin_2(a, b):
return np.isin(a, b)
@@ -6634,122 +6618,6 @@ class TestNPFunctions(MemoryLeakMixin, TestCase):
with self.assertRaises(TypingError):
np_nbfunc(a, "foo", True)
- @staticmethod
- def _in1d_arrays():
- yield (List.empty_list(types.float64),
- List.empty_list(types.float64)) # two empty arrays
- yield [1], List.empty_list(types.float64) # empty right
- yield List.empty_list(types.float64), [1] # empty left
- yield [1], [2] # singletons - False
- yield [1], [1] # singletons - True
- yield [1, 2], [1]
- yield [1, 2, 2], [2, 2]
- yield [1, 2, 2], [2, 2, 3]
- yield [1, 2], [2, 1]
- yield [1, 2, 3], [1, 2, 3]
- yield [2, 3, 4, 0], [3, 1]
- yield [2, 3], np.arange(20) # Test the "sorting" method.
- yield [2, 3], np.tile(np.arange(5), 4)
-
- def test_in1d_2(self):
- np_pyfunc = np_in1d_2
- np_nbfunc = njit(np_pyfunc)
-
- def check(ar1, ar2):
- if isinstance(ar1, list):
- ar1 = List(ar1)
- if isinstance(ar2, list):
- ar2 = List(ar2)
- expected = np_pyfunc(ar1, ar2)
- got = np_nbfunc(ar1, ar2)
- self.assertPreciseEqual(expected, got, msg=f"ar1={ar1}, ar2={ar2}")
-
- for a, b in self._in1d_arrays():
- check(a, b)
-
- def test_in1d_3a(self):
- np_pyfunc = np_in1d_3a
- np_nbfunc = njit(np_pyfunc)
-
- def check(ar1, ar2, assume_unique=False):
- if isinstance(ar1, list):
- ar1 = List(ar1)
- if isinstance(ar2, list):
- ar2 = List(ar2)
- expected = np_pyfunc(ar1, ar2, assume_unique)
- got = np_nbfunc(ar1, ar2, assume_unique)
- self.assertPreciseEqual(expected, got, msg=f"ar1={ar1}, ar2={ar2}")
-
- for a, b in self._in1d_arrays():
- check(a, b)
- if len(np.unique(a)) == len(a) and len(np.unique(b)) == len(b):
- check(a, b, assume_unique=True)
-
- def test_in1d_3b(self):
- np_pyfunc = np_in1d_3b
- np_nbfunc = njit(np_pyfunc)
-
- def check(ar1, ar2, invert=False):
- if isinstance(ar1, list):
- ar1 = List(ar1)
- if isinstance(ar2, list):
- ar2 = List(ar2)
- expected = np_pyfunc(ar1, ar2, invert)
- got = np_nbfunc(ar1, ar2, invert)
- self.assertPreciseEqual(expected, got, msg=f"ar1={ar1}, ar2={ar2}")
-
- for a, b in self._in1d_arrays():
- check(a, b, invert=False)
- check(a, b, invert=True)
-
- def test_in1d_4(self):
- np_pyfunc = np_in1d_4
- np_nbfunc = njit(np_pyfunc)
-
- def check(ar1, ar2, assume_unique=False, invert=False):
- if isinstance(ar1, list):
- ar1 = List(ar1)
- if isinstance(ar2, list):
- ar2 = List(ar2)
- expected = np_pyfunc(ar1, ar2, assume_unique, invert)
- got = np_nbfunc(ar1, ar2, assume_unique, invert)
- self.assertPreciseEqual(expected, got, msg=f"ar1={ar1}, ar2={ar2}")
-
- for a, b in self._in1d_arrays():
- check(a, b, invert=False)
- check(a, b, invert=True)
- if len(np.unique(a)) == len(a) and len(np.unique(b)) == len(b):
- check(a, b, assume_unique=True, invert=False)
- check(a, b, assume_unique=True, invert=True)
-
- def test_in1d_errors(self):
- np_pyfunc = np_in1d_4
- np_nbfunc = njit(np_pyfunc)
-
- a = np.array([1])
- b = np.array([2])
- x = np_nbfunc(a, b)
- self.assertPreciseEqual(x, np.array([False]))
-
- self.disable_leak_check()
- with self.assertRaises(TypingError):
- np_nbfunc(a, b, "foo", False)
- with self.assertRaises(TypingError):
- np_nbfunc(a, b, False, "foo")
- with self.assertRaises(TypingError):
- np_nbfunc("foo", b, True, False)
- with self.assertRaises(TypingError):
- np_nbfunc(a, "foo", True, False)
-
- @njit()
- def np_in1d_kind(a, b, kind):
- return np.in1d(a, b, kind=kind)
-
- with self.assertRaises(TypingError):
- np_in1d_kind(a, b, kind=None)
- with self.assertRaises(TypingError):
- np_in1d_kind(a, b, kind="table")
-
@classmethod
def _isin_arrays(cls):
if REDUCED_TESTING:
diff --git a/setup.py b/setup.py
index 9eaa191cb..a5febef1e 100644
--- a/setup.py
+++ b/setup.py
@@ -23,7 +23,7 @@ min_python_version = "3.10"
max_python_version = "3.15" # exclusive
min_numpy_build_version = "2.0.0rc1"
min_numpy_run_version = "1.22"
-max_numpy_run_version = "2.4"
+max_numpy_run_version = "2.5"
min_llvmlite_version = "0.46.0dev0"
max_llvmlite_version = "0.47"
Reference in New Issue View Git Blame Copy Permalink