API: Update numpy exceptions imports

scipy/_lib/_util.py

@@ -11,16 +11,25 @@
     Optional,
     Union,
     TYPE_CHECKING,
+    Type,
     TypeVar,
 )
 
 import numpy as np
 
+
+AxisError: Type[Exception]
+ComplexWarning: Type[Warning]
+VisibleDeprecationWarning: Type[Warning]
+
 if np.lib.NumpyVersion(np.__version__) >= '1.25.0':
-    from numpy.exceptions import AxisError
+    from numpy.exceptions import (
+        AxisError, ComplexWarning, VisibleDeprecationWarning  # noqa: F401
+    )
 else:
-    from numpy import AxisError
-
+    from numpy import (
+        AxisError, ComplexWarning, VisibleDeprecationWarning  # noqa: F401
+    )
 
 
 IntNumber = Union[int, np.integer]

scipy/interpolate/tests/test_bsplines.py

@@ -1,3 +1,4 @@
+import os
 import operator
 import itertools
 
@@ -17,7 +18,7 @@
                                         _woodbury_algorithm, _periodic_knots,
                                          _make_interp_per_full_matr)
 import scipy.interpolate._fitpack_impl as _impl
-import os
+from scipy._lib._util import AxisError
 
 
 class TestBSpline:
@@ -412,7 +413,7 @@ def test_axis(self, axis):
 
         # -c.ndim <= axis < c.ndim
         for ax in [-c.ndim - 1, c.ndim]:
-            assert_raises(np.AxisError, BSpline,
+            assert_raises(AxisError, BSpline,
                           **dict(t=t, c=c, k=k, axis=ax))
 
         # derivative, antiderivative keeps the axis

scipy/interpolate/tests/test_rgi.py

@@ -12,6 +12,8 @@
                                NearestNDInterpolator, LinearNDInterpolator)
 
 from scipy.sparse._sputils import matrix
+from scipy._lib._util import ComplexWarning
+
 
 parametrize_rgi_interp_methods = pytest.mark.parametrize(
     "method", ['linear', 'nearest', 'slinear', 'cubic', 'quintic', 'pchip']
@@ -869,7 +871,7 @@ def test_complex_spline2fd(self):
 
         sample = np.array([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
                            [1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
-        with assert_warns(np.ComplexWarning):
+        with assert_warns(ComplexWarning):
             interpn(points, values, sample, method='splinef2d')
 
     @pytest.mark.parametrize(

scipy/io/matlab/tests/test_mio.py

@@ -34,6 +34,8 @@
     MatFile5Writer, MatFile5Reader, varmats_from_mat, to_writeable,
     EmptyStructMarker)
 import scipy.io.matlab._mio5_params as mio5p
+from scipy._lib._util import VisibleDeprecationWarning
+
 
 test_data_path = pjoin(dirname(__file__), 'data')
 
@@ -1315,7 +1317,7 @@ def test_gh_17992(tmp_path):
     list_of_arrays = [array_one, array_two]
     # warning suppression only needed for NumPy < 1.24.0
     with np.testing.suppress_warnings() as sup:
-        sup.filter(np.VisibleDeprecationWarning)
+        sup.filter(VisibleDeprecationWarning)
         savemat(outfile,
                 {'data': list_of_arrays},
                 long_field_names=True,

scipy/linalg/_decomp_ldl.py

@@ -1,8 +1,9 @@
 from warnings import warn
 
 import numpy as np
-from numpy import (atleast_2d, ComplexWarning, arange, zeros_like, imag, diag,
+from numpy import (atleast_2d, arange, zeros_like, imag, diag,
                    iscomplexobj, tril, triu, argsort, empty_like)
+from scipy._lib._util import ComplexWarning
 from ._decomp import _asarray_validated
 from .lapack import get_lapack_funcs, _compute_lwork
 

scipy/linalg/tests/test_decomp_ldl.py

@@ -4,9 +4,9 @@
                    complex64, complex128)
 from numpy.random import rand, randint, seed
 from scipy.linalg import ldl
+from scipy._lib._util import ComplexWarning
 import pytest
 from pytest import raises as assert_raises, warns
-from numpy import ComplexWarning
 
 
 def test_args():

scipy/optimize/_milp.py

@@ -1,6 +1,7 @@
 import warnings
 import numpy as np
 from scipy.sparse import csc_array, vstack, issparse
+from scipy._lib._util import VisibleDeprecationWarning
 from ._highs._highs_wrapper import _highs_wrapper  # type: ignore[import]
 from ._constraints import LinearConstraint, Bounds
 from ._optimize import OptimizeResult
@@ -43,7 +44,7 @@ def _constraints_to_components(constraints):
             # argument could be a single tuple representing a LinearConstraint
             try:
                 constraints = [LinearConstraint(*constraints)]
-            except (TypeError, ValueError, np.VisibleDeprecationWarning):
+            except (TypeError, ValueError, VisibleDeprecationWarning):
                 # argument was not a tuple representing a LinearConstraint
                 pass
 

scipy/optimize/tests/test__linprog_clean_inputs.py

@@ -5,6 +5,7 @@
 from numpy.testing import assert_, assert_allclose, assert_equal
 from pytest import raises as assert_raises
 from scipy.optimize._linprog_util import _clean_inputs, _LPProblem
+from scipy._lib._util import VisibleDeprecationWarning
 from copy import deepcopy
 from datetime import date
 
@@ -113,7 +114,7 @@ def test_inconsistent_dimensions():
     assert_raises(ValueError, _clean_inputs, _LPProblem(c=c, A_eq=Agood, b_eq=bbad))
     assert_raises(ValueError, _clean_inputs, _LPProblem(c=c, bounds=boundsbad))
     with np.testing.suppress_warnings() as sup:
-        sup.filter(np.VisibleDeprecationWarning, "Creating an ndarray from ragged")
+        sup.filter(VisibleDeprecationWarning, "Creating an ndarray from ragged")
         assert_raises(ValueError, _clean_inputs,
                       _LPProblem(c=c, bounds=[[1, 2], [2, 3], [3, 4], [4, 5, 6]]))
 
@@ -245,7 +246,7 @@ def test_bad_bounds():
     assert_raises(ValueError, _clean_inputs, lp._replace(bounds=(1, 2, 2)))
     assert_raises(ValueError, _clean_inputs, lp._replace(bounds=[(1, 2, 2)]))
     with np.testing.suppress_warnings() as sup:
-        sup.filter(np.VisibleDeprecationWarning, "Creating an ndarray from ragged")
+        sup.filter(VisibleDeprecationWarning, "Creating an ndarray from ragged")
         assert_raises(ValueError, _clean_inputs,
                       lp._replace(bounds=[(1, 2), (1, 2, 2)]))
     assert_raises(ValueError, _clean_inputs, lp._replace(bounds=[(1, 2), (1, 2), (1, 2)]))

scipy/optimize/tests/test_linprog.py

@@ -12,6 +12,7 @@
 from scipy.optimize._numdiff import approx_derivative
 from scipy.sparse.linalg import MatrixRankWarning
 from scipy.linalg import LinAlgWarning
+from scipy._lib._util import VisibleDeprecationWarning
 import scipy.sparse
 import pytest
 
@@ -487,7 +488,7 @@ def f(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None):
         # Test ill-formatted bounds
         assert_raises(ValueError, f, [1, 2, 3], bounds=[(1, 2), (3, 4)])
         with np.testing.suppress_warnings() as sup:
-            sup.filter(np.VisibleDeprecationWarning, "Creating an ndarray from ragged")
+            sup.filter(VisibleDeprecationWarning, "Creating an ndarray from ragged")
             assert_raises(ValueError, f, [1, 2, 3], bounds=[(1, 2), (3, 4), (3, 4, 5)])
         assert_raises(ValueError, f, [1, 2, 3], bounds=[(1, -2), (1, 2)])
 

scipy/signal/tests/test_signaltools.py

@@ -31,6 +31,7 @@
                                       _group_poles)
 from scipy.signal._upfirdn import _upfirdn_modes
 from scipy._lib import _testutils
+from scipy._lib._util import ComplexWarning
 
 
 class _TestConvolve:
@@ -297,7 +298,7 @@ def test_fillvalue(self):
     def test_fillvalue_errors(self):
         msg = "could not cast `fillvalue` directly to the output "
         with np.testing.suppress_warnings() as sup:
-            sup.filter(np.ComplexWarning, "Casting complex values")
+            sup.filter(ComplexWarning, "Casting complex values")
             with assert_raises(ValueError, match=msg):
                 convolve2d([[1]], [[1, 2]], fillvalue=1j)
 

scipy/sparse/_base.py

@@ -2,6 +2,7 @@
 from warnings import warn
 
 import numpy as np
+from scipy._lib._util import VisibleDeprecationWarning
 
 from ._sputils import (asmatrix, check_reshape_kwargs, check_shape,
                        get_sum_dtype, isdense, isscalarlike,
@@ -714,7 +715,7 @@ def __pow__(self, *args, **kwargs):
     @property
     def A(self) -> np.ndarray:
         if isinstance(self, sparray):
-            warn(np.VisibleDeprecationWarning(
+            warn(VisibleDeprecationWarning(
                 "`.A` is deprecated and will be removed in v1.13.0. "
                 "Use `.toarray()` instead."
             ))
@@ -727,7 +728,7 @@ def T(self):
     @property
     def H(self):
         if isinstance(self, sparray):
-            warn(np.VisibleDeprecationWarning(
+            warn(VisibleDeprecationWarning(
                 "`.H` is deprecated and will be removed in v1.13.0. "
                 "Please use `.T.conjugate()` instead."
             ))

scipy/sparse/linalg/_dsolve/tests/test_linsolve.py

@@ -21,6 +21,7 @@
 import scipy.sparse
 
 from scipy._lib._testutils import check_free_memory
+from scipy._lib._util import ComplexWarning
 
 
 sup_sparse_efficiency = suppress_warnings()
@@ -154,7 +155,7 @@ def test_call_with_cast_to_complex_with_umfpack(self):
         solve = factorized(self.A)
         b = random.rand(4)
         for t in [np.complex64, np.complex128]:
-            assert_warns(np.ComplexWarning, solve, b.astype(t))
+            assert_warns(ComplexWarning, solve, b.astype(t))
 
     @pytest.mark.skipif(not has_umfpack, reason="umfpack not available")
     def test_assume_sorted_indices_flag(self):

scipy/sparse/tests/test_array_api.py

@@ -3,6 +3,8 @@
 import numpy.testing as npt
 import scipy.sparse
 import scipy.sparse.linalg as spla
+from scipy._lib._util import VisibleDeprecationWarning
+
 
 sparray_types = ('bsr', 'coo', 'csc', 'csr', 'dia', 'dok', 'lil')
 
@@ -160,13 +162,13 @@ def test_dense_divide(A):
 
 @parametrize_sparrays
 def test_no_A_attr(A):
-    with pytest.warns(np.VisibleDeprecationWarning):
+    with pytest.warns(VisibleDeprecationWarning):
         A.A
 
 
 @parametrize_sparrays
 def test_no_H_attr(A):
-    with pytest.warns(np.VisibleDeprecationWarning):
+    with pytest.warns(VisibleDeprecationWarning):
         A.H
 
 

scipy/sparse/tests/test_base.py

@@ -20,7 +20,7 @@ class for generic tests" section.
 import numpy as np
 from numpy import (arange, zeros, array, dot, asarray,
                    vstack, ndarray, transpose, diag, kron, inf, conjugate,
-                   int8, ComplexWarning)
+                   int8)
 
 import random
 from numpy.testing import (assert_equal, assert_array_equal,
@@ -39,6 +39,7 @@ class for generic tests" section.
 from scipy.sparse.linalg import splu, expm, inv
 
 from scipy._lib.decorator import decorator
+from scipy._lib._util import ComplexWarning
 
 import pytest
 

scipy/stats/_axis_nan_policy.py

@@ -7,7 +7,7 @@
 import numpy as np
 from functools import wraps
 from scipy._lib._docscrape import FunctionDoc, Parameter
-from scipy._lib._util import _contains_nan
+from scipy._lib._util import _contains_nan, AxisError
 import inspect
 
 
@@ -42,8 +42,8 @@ def _broadcast_shapes(shapes, axis=None):
         axis = np.atleast_1d(axis)
         axis_int = axis.astype(int)
         if not np.array_equal(axis_int, axis):
-            raise np.AxisError('`axis` must be an integer, a '
-                               'tuple of integers, or `None`.')
+            raise AxisError('`axis` must be an integer, a '
+                            'tuple of integers, or `None`.')
         axis = axis_int
 
     # First, ensure all shapes have same number of dimensions by prepending 1s.
@@ -59,10 +59,10 @@ def _broadcast_shapes(shapes, axis=None):
         if axis[-1] >= n_dims or axis[0] < 0:
             message = (f"`axis` is out of bounds "
                        f"for array of dimension {n_dims}")
-            raise np.AxisError(message)
+            raise AxisError(message)
 
         if len(np.unique(axis)) != len(axis):
-            raise np.AxisError("`axis` must contain only distinct elements")
+            raise AxisError("`axis` must contain only distinct elements")
 
         removed_shapes = new_shapes[:, axis]
         new_shapes = np.delete(new_shapes, axis, axis=1)

scipy/stats/_stats_py.py

@@ -43,7 +43,8 @@
 from scipy.ndimage import _measurements
 from scipy.optimize import milp, LinearConstraint
 from scipy._lib._util import (check_random_state, MapWrapper, _get_nan,
-                              rng_integers, _rename_parameter, _contains_nan)
+                              rng_integers, _rename_parameter, _contains_nan,
+                              AxisError)
 
 import scipy.special as special
 from scipy import linalg
@@ -4124,9 +4125,9 @@ def f_oneway(*samples, axis=0):
     num_groups = len(samples)
 
     # We haven't explicitly validated axis, but if it is bad, this call of
-    # np.concatenate will raise np.AxisError.  The call will raise ValueError
-    # if the dimensions of all the arrays, except the axis dimension, are not
-    # the same.
+    # np.concatenate will raise np.exceptions.AxisError. The call will raise
+    # ValueError if the dimensions of all the arrays, except the axis 
+    # dimension, are not the same.
     alldata = np.concatenate(samples, axis=axis)
     bign = alldata.shape[axis]
 
@@ -7676,7 +7677,7 @@ def _get_len(a, axis, msg):
     try:
         n = a.shape[axis]
     except IndexError:
-        raise np.AxisError(axis, a.ndim, msg) from None
+        raise AxisError(axis, a.ndim, msg) from None
     return n
 
 

scipy/stats/tests/test_axis_nan_policy.py

@@ -13,6 +13,7 @@
 from numpy.testing import assert_allclose, assert_equal, suppress_warnings
 from scipy import stats
 from scipy.stats._axis_nan_policy import _masked_arrays_2_sentinel_arrays
+from scipy._lib._util import AxisError
 
 
 def unpack_ttest_result(res):
@@ -971,13 +972,13 @@ def test_other_axis_tuples(axis):
 
     if len(set(axis)) != len(axis):
         message = "`axis` must contain only distinct elements"
-        with pytest.raises(np.AxisError, match=re.escape(message)):
+        with pytest.raises(AxisError, match=re.escape(message)):
             stats.mannwhitneyu(x, y, axis=axis_original)
         return
 
     if axis[0] < 0 or axis[-1] > 2:
         message = "`axis` is out of bounds for array of dimension 3"
-        with pytest.raises(np.AxisError, match=re.escape(message)):
+        with pytest.raises(AxisError, match=re.escape(message)):
             stats.mannwhitneyu(x, y, axis=axis_original)
         return
 

scipy/stats/tests/test_stats.py

@@ -35,6 +35,7 @@
 from numpy.lib import NumpyVersion
 from scipy.stats._axis_nan_policy import _broadcast_concatenate
 from scipy.stats._stats_py import _permutation_distribution_t
+from scipy._lib._util import AxisError
 
 
 """ Numbers in docstrings beginning with 'W' refer to the section numbers
@@ -2891,7 +2892,7 @@ def test_axis(self):
         assert_equal(stats.iqr(d, axis=(1, 3))[2, 2],
                      stats.iqr(d[2, :, 2,:].ravel()))
 
-        assert_raises(np.AxisError, stats.iqr, d, axis=4)
+        assert_raises(AxisError, stats.iqr, d, axis=4)
         assert_raises(ValueError, stats.iqr, d, axis=(0, 0))
 
     def test_rng(self):
@@ -7089,7 +7090,7 @@ def test_too_few_inputs(self, args):
     def test_axis_error(self):
         a = np.ones((3, 4))
         b = np.ones((5, 4))
-        with assert_raises(np.AxisError):
+        with assert_raises(AxisError):
             stats.f_oneway(a, b, axis=2)
 
     def test_bad_shapes(self):