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MAINT: Replace setting of array shape by reshape operation #29314

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ab87fb4
MAINT: Replace setting of array shape by reshape operation
eendebakpt Jul 3, 2025
4937228
MAINT: Replace setting of array shape by reshape operation
eendebakpt Jul 3, 2025
28442ba
MAINT: Replace setting of array shape by reshape operation
eendebakpt Jul 3, 2025
f1bcc7e
fix tests
eendebakpt Jul 3, 2025
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5 changes: 2 additions & 3 deletions numpy/_core/numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1106,10 +1106,9 @@ def tensordot(a, b, axes=2):

An extended example taking advantage of the overloading of + and \\*:

>>> a = np.array(range(1, 9))
>>> a.shape = (2, 2, 2)
>>> a = np.array(range(1, 9)).reshape((2, 2, 2))
>>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)
>>> A.shape = (2, 2)
>>> A = A.reshape((2, 2))
>>> a; A
array([[[1, 2],
[3, 4]],
Expand Down
70 changes: 32 additions & 38 deletions numpy/_core/tests/test_einsum.py
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Expand Up @@ -231,21 +231,20 @@ def __rmul__(self, other):
def test_einsum_views(self):
# pass-through
for do_opt in [True, False]:
a = np.arange(6)
a.shape = (2, 3)
a = np.arange(6).reshape((2, 3))

b = np.einsum("...", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)

b = np.einsum(a, [Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)

b = np.einsum("ij", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, a)

b = np.einsum(a, [0, 1], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, a)

# output is writeable whenever input is writeable
Expand All @@ -256,115 +255,110 @@ def test_einsum_views(self):
assert_(not b.flags['WRITEABLE'])

# transpose
a = np.arange(6)
a.shape = (2, 3)
a = np.arange(6).reshape((2, 3))

b = np.einsum("ji", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, a.T)

b = np.einsum(a, [1, 0], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, a.T)

# diagonal
a = np.arange(9)
a.shape = (3, 3)
a = np.arange(9).reshape((3, 3))

b = np.einsum("ii->i", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[i, i] for i in range(3)])

b = np.einsum(a, [0, 0], [0], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[i, i] for i in range(3)])

# diagonal with various ways of broadcasting an additional dimension
a = np.arange(27)
a.shape = (3, 3, 3)
a = np.arange(27).reshape((3, 3, 3))

b = np.einsum("...ii->...i", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [[x[i, i] for i in range(3)] for x in a])

b = np.einsum(a, [Ellipsis, 0, 0], [Ellipsis, 0], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [[x[i, i] for i in range(3)] for x in a])

b = np.einsum("ii...->...i", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(2, 0, 1)])

b = np.einsum(a, [0, 0, Ellipsis], [Ellipsis, 0], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(2, 0, 1)])

b = np.einsum("...ii->i...", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[:, i, i] for i in range(3)])

b = np.einsum(a, [Ellipsis, 0, 0], [0, Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[:, i, i] for i in range(3)])

b = np.einsum("jii->ij", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[:, i, i] for i in range(3)])

b = np.einsum(a, [1, 0, 0], [0, 1], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[:, i, i] for i in range(3)])

b = np.einsum("ii...->i...", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a.transpose(2, 0, 1)[:, i, i] for i in range(3)])

b = np.einsum(a, [0, 0, Ellipsis], [0, Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a.transpose(2, 0, 1)[:, i, i] for i in range(3)])

b = np.einsum("i...i->i...", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a.transpose(1, 0, 2)[:, i, i] for i in range(3)])

b = np.einsum(a, [0, Ellipsis, 0], [0, Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a.transpose(1, 0, 2)[:, i, i] for i in range(3)])

b = np.einsum("i...i->...i", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(1, 0, 2)])

b = np.einsum(a, [0, Ellipsis, 0], [Ellipsis, 0], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(1, 0, 2)])

# triple diagonal
a = np.arange(27)
a.shape = (3, 3, 3)
a = np.arange(27).reshape((3, 3, 3))

b = np.einsum("iii->i", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[i, i, i] for i in range(3)])

b = np.einsum(a, [0, 0, 0], [0], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, [a[i, i, i] for i in range(3)])

# swap axes
a = np.arange(24)
a.shape = (2, 3, 4)
a = np.arange(24).reshape((2, 3, 4))

b = np.einsum("ijk->jik", a, optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, a.swapaxes(0, 1))

b = np.einsum(a, [0, 1, 2], [1, 0, 2], optimize=do_opt)
assert_(b.base is a)
assert_(b.base is a.base)
assert_equal(b, a.swapaxes(0, 1))

def check_einsum_sums(self, dtype, do_opt=False):
Expand Down
22 changes: 9 additions & 13 deletions numpy/_core/tests/test_multiarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2281,8 +2281,7 @@ def test_sort_axis(self):

def test_sort_size_0(self):
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
a = np.array([]).reshape((3, 2, 1, 0))
for axis in range(-a.ndim, a.ndim):
msg = f'test empty array sort with axis={axis}'
assert_equal(np.sort(a, axis=axis), a, msg)
Expand Down Expand Up @@ -2543,8 +2542,7 @@ def test_argsort(self):
assert_equal(a.copy().argsort(), c)

# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
a = np.array([]).reshape((3, 2, 1, 0))
for axis in range(-a.ndim, a.ndim):
msg = f'test empty array argsort with axis={axis}'
assert_equal(np.argsort(a, axis=axis),
Expand Down Expand Up @@ -2861,8 +2859,7 @@ def test_partition_integer(self):
def test_partition_empty_array(self, kth_dtype):
# check axis handling for multidimensional empty arrays
kth = np.array(0, dtype=kth_dtype)[()]
a = np.array([])
a.shape = (3, 2, 1, 0)
a = np.array([]).reshape((3, 2, 1, 0))
for axis in range(-a.ndim, a.ndim):
msg = f'test empty array partition with axis={axis}'
assert_equal(np.partition(a, kth, axis=axis), a, msg)
Expand All @@ -2873,8 +2870,7 @@ def test_partition_empty_array(self, kth_dtype):
def test_argpartition_empty_array(self, kth_dtype):
# check axis handling for multidimensional empty arrays
kth = np.array(0, dtype=kth_dtype)[()]
a = np.array([])
a.shape = (3, 2, 1, 0)
a = np.array([]).reshape((3, 2, 1, 0))
for axis in range(-a.ndim, a.ndim):
msg = f'test empty array argpartition with axis={axis}'
assert_equal(np.partition(a, kth, axis=axis),
Expand Down Expand Up @@ -5358,7 +5354,7 @@ def test_ip_types(self):
unchecked_types = [bytes, str, np.void]

x = np.random.random(24) * 100
x.shape = 2, 3, 4
x = x.reshape((2, 3, 4))
for types in np._core.sctypes.values():
for T in types:
if T not in unchecked_types:
Expand All @@ -5369,20 +5365,20 @@ def test_ip_types(self):

def test_raise(self):
x = np.random.random(24) * 100
x.shape = 2, 3, 4
x = x.reshape((2, 3, 4))
assert_raises(IndexError, x.take, [0, 1, 2], axis=0)
assert_raises(IndexError, x.take, [-3], axis=0)
assert_array_equal(x.take([-1], axis=0)[0], x[1])

def test_clip(self):
x = np.random.random(24) * 100
x.shape = 2, 3, 4
x = x.reshape((2, 3, 4))
assert_array_equal(x.take([-1], axis=0, mode='clip')[0], x[0])
assert_array_equal(x.take([2], axis=0, mode='clip')[0], x[1])

def test_wrap(self):
x = np.random.random(24) * 100
x.shape = 2, 3, 4
x = x.reshape((2, 3, 4))
assert_array_equal(x.take([-1], axis=0, mode='wrap')[0], x[1])
assert_array_equal(x.take([2], axis=0, mode='wrap')[0], x[0])
assert_array_equal(x.take([3], axis=0, mode='wrap')[0], x[1])
Expand Down Expand Up @@ -5962,7 +5958,7 @@ class TestFlat:
def setup_method(self):
a0 = np.arange(20.0)
a = a0.reshape(4, 5)
a0.shape = (4, 5)
a0 = a0.reshape((4, 5))
a.flags.writeable = False
self.a = a
self.b = a[::2, ::2]
Expand Down
4 changes: 2 additions & 2 deletions numpy/lib/_format_impl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -879,10 +879,10 @@ def read_array(fp, allow_pickle=False, pickle_kwargs=None, *,
)

if fortran_order:
array.shape = shape[::-1]
array = array.reshape(shape[::-1])
array = array.transpose()
else:
array.shape = shape
array = array.reshape(shape)

return array

Expand Down
5 changes: 4 additions & 1 deletion numpy/lib/_function_base_impl.py
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Expand Up @@ -1317,7 +1317,10 @@ def gradient(f, *varargs, axis=None, edge_order=1):
# fix the shape for broadcasting
shape = np.ones(N, dtype=int)
shape[axis] = -1
a.shape = b.shape = c.shape = shape

a = a.reshape(shape)
b = b.reshape(shape)
c = c.reshape(shape)
# 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:]
out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] \
+ c * f[tuple(slice4)]
Expand Down
3 changes: 1 addition & 2 deletions numpy/lib/tests/test_arrayterator.py
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Expand Up @@ -14,8 +14,7 @@ def test():
ndims = randint(5) + 1
shape = tuple(randint(10) + 1 for dim in range(ndims))
els = reduce(mul, shape)
a = np.arange(els)
a.shape = shape
a = np.arange(els).reshape(shape)

buf_size = randint(2 * els)
b = Arrayterator(a, buf_size)
Expand Down
9 changes: 3 additions & 6 deletions numpy/linalg/_linalg.py
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Expand Up @@ -317,8 +317,7 @@ def tensorsolve(a, b, axes=None):
Examples
--------
>>> import numpy as np
>>> a = np.eye(2*3*4)
>>> a.shape = (2*3, 4, 2, 3, 4)
>>> a = np.eye(2*3*4).reshape((2*3, 4, 2, 3, 4))
>>> rng = np.random.default_rng()
>>> b = rng.normal(size=(2*3, 4))
>>> x = np.linalg.tensorsolve(a, b)
Expand Down Expand Up @@ -495,8 +494,7 @@ def tensorinv(a, ind=2):
Examples
--------
>>> import numpy as np
>>> a = np.eye(4*6)
>>> a.shape = (4, 6, 8, 3)
>>> a = np.eye(4*6).reshape((4, 6, 8, 3))
>>> ainv = np.linalg.tensorinv(a, ind=2)
>>> ainv.shape
(8, 3, 4, 6)
Expand All @@ -505,8 +503,7 @@ def tensorinv(a, ind=2):
>>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
True

>>> a = np.eye(4*6)
>>> a.shape = (24, 8, 3)
>>> a = np.eye(4*6).reshape((24, 8, 3))
>>> ainv = np.linalg.tensorinv(a, ind=1)
>>> ainv.shape
(8, 3, 24)
Expand Down
9 changes: 3 additions & 6 deletions numpy/linalg/tests/test_linalg.py
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Expand Up @@ -2203,8 +2203,7 @@ def test_non_square_handling(self, arr, ind):
((24, 8, 3), 1),
])
def test_tensorinv_shape(self, shape, ind):
a = np.eye(24)
a.shape = shape
a = np.eye(24).reshape(shape)
ainv = linalg.tensorinv(a=a, ind=ind)
expected = a.shape[ind:] + a.shape[:ind]
actual = ainv.shape
Expand All @@ -2214,15 +2213,13 @@ def test_tensorinv_shape(self, shape, ind):
0, -2,
])
def test_tensorinv_ind_limit(self, ind):
a = np.eye(24)
a.shape = (4, 6, 8, 3)
a = np.eye(24).reshape((4, 6, 8, 3))
with assert_raises(ValueError):
linalg.tensorinv(a=a, ind=ind)

def test_tensorinv_result(self):
# mimic a docstring example
a = np.eye(24)
a.shape = (24, 8, 3)
a = np.eye(24).reshape((24, 8, 3))
ainv = linalg.tensorinv(a, ind=1)
b = np.ones(24)
assert_allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
Expand Down
2 changes: 1 addition & 1 deletion numpy/linalg/tests/test_regression.py
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Expand Up @@ -33,7 +33,7 @@ def test_eig_build(self):
1.51971555e-15 + 0.j,
-1.51308713e-15 + 0.j])
a = arange(13 * 13, dtype=float64)
a.shape = (13, 13)
a = a.reshape((13, 13))
a = a % 17
va, ve = linalg.eig(a)
va.sort()
Expand Down
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