AlkantarClanX12
Your IP : 18.188.175.66
import sys
import numpy as np
from numpy.core._rational_tests import rational
import pytest
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_raises, assert_warns,
HAS_REFCOUNT
)
def test_array_array():
tobj = type(object)
ones11 = np.ones((1, 1), np.float64)
tndarray = type(ones11)
# Test is_ndarray
assert_equal(np.array(ones11, dtype=np.float64), ones11)
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(tndarray)
np.array(ones11)
assert_equal(old_refcount, sys.getrefcount(tndarray))
# test None
assert_equal(np.array(None, dtype=np.float64),
np.array(np.nan, dtype=np.float64))
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(tobj)
np.array(None, dtype=np.float64)
assert_equal(old_refcount, sys.getrefcount(tobj))
# test scalar
assert_equal(np.array(1.0, dtype=np.float64),
np.ones((), dtype=np.float64))
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(np.float64)
np.array(np.array(1.0, dtype=np.float64), dtype=np.float64)
assert_equal(old_refcount, sys.getrefcount(np.float64))
# test string
S2 = np.dtype((bytes, 2))
S3 = np.dtype((bytes, 3))
S5 = np.dtype((bytes, 5))
assert_equal(np.array(b"1.0", dtype=np.float64),
np.ones((), dtype=np.float64))
assert_equal(np.array(b"1.0").dtype, S3)
assert_equal(np.array(b"1.0", dtype=bytes).dtype, S3)
assert_equal(np.array(b"1.0", dtype=S2), np.array(b"1."))
assert_equal(np.array(b"1", dtype=S5), np.ones((), dtype=S5))
# test string
U2 = np.dtype((str, 2))
U3 = np.dtype((str, 3))
U5 = np.dtype((str, 5))
assert_equal(np.array("1.0", dtype=np.float64),
np.ones((), dtype=np.float64))
assert_equal(np.array("1.0").dtype, U3)
assert_equal(np.array("1.0", dtype=str).dtype, U3)
assert_equal(np.array("1.0", dtype=U2), np.array(str("1.")))
assert_equal(np.array("1", dtype=U5), np.ones((), dtype=U5))
builtins = getattr(__builtins__, '__dict__', __builtins__)
assert_(hasattr(builtins, 'get'))
# test memoryview
dat = np.array(memoryview(b'1.0'), dtype=np.float64)
assert_equal(dat, [49.0, 46.0, 48.0])
assert_(dat.dtype.type is np.float64)
dat = np.array(memoryview(b'1.0'))
assert_equal(dat, [49, 46, 48])
assert_(dat.dtype.type is np.uint8)
# test array interface
a = np.array(100.0, dtype=np.float64)
o = type("o", (object,),
dict(__array_interface__=a.__array_interface__))
assert_equal(np.array(o, dtype=np.float64), a)
# test array_struct interface
a = np.array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('f0', int), ('f1', float), ('f2', str)])
o = type("o", (object,),
dict(__array_struct__=a.__array_struct__))
## wasn't what I expected... is np.array(o) supposed to equal a ?
## instead we get a array([...], dtype=">V18")
assert_equal(bytes(np.array(o).data), bytes(a.data))
# test array
o = type("o", (object,),
dict(__array__=lambda *x: np.array(100.0, dtype=np.float64)))()
assert_equal(np.array(o, dtype=np.float64), np.array(100.0, np.float64))
# test recursion
nested = 1.5
for i in range(np.MAXDIMS):
nested = [nested]
# no error
np.array(nested)
# Exceeds recursion limit
assert_raises(ValueError, np.array, [nested], dtype=np.float64)
# Try with lists...
# float32
assert_equal(np.array([None] * 10, dtype=np.float32),
np.full((10,), np.nan, dtype=np.float32))
assert_equal(np.array([[None]] * 10, dtype=np.float32),
np.full((10, 1), np.nan, dtype=np.float32))
assert_equal(np.array([[None] * 10], dtype=np.float32),
np.full((1, 10), np.nan, dtype=np.float32))
assert_equal(np.array([[None] * 10] * 10, dtype=np.float32),
np.full((10, 10), np.nan, dtype=np.float32))
# float64
assert_equal(np.array([None] * 10, dtype=np.float64),
np.full((10,), np.nan, dtype=np.float64))
assert_equal(np.array([[None]] * 10, dtype=np.float64),
np.full((10, 1), np.nan, dtype=np.float64))
assert_equal(np.array([[None] * 10], dtype=np.float64),
np.full((1, 10), np.nan, dtype=np.float64))
assert_equal(np.array([[None] * 10] * 10, dtype=np.float64),
np.full((10, 10), np.nan, dtype=np.float64))
assert_equal(np.array([1.0] * 10, dtype=np.float64),
np.ones((10,), dtype=np.float64))
assert_equal(np.array([[1.0]] * 10, dtype=np.float64),
np.ones((10, 1), dtype=np.float64))
assert_equal(np.array([[1.0] * 10], dtype=np.float64),
np.ones((1, 10), dtype=np.float64))
assert_equal(np.array([[1.0] * 10] * 10, dtype=np.float64),
np.ones((10, 10), dtype=np.float64))
# Try with tuples
assert_equal(np.array((None,) * 10, dtype=np.float64),
np.full((10,), np.nan, dtype=np.float64))
assert_equal(np.array([(None,)] * 10, dtype=np.float64),
np.full((10, 1), np.nan, dtype=np.float64))
assert_equal(np.array([(None,) * 10], dtype=np.float64),
np.full((1, 10), np.nan, dtype=np.float64))
assert_equal(np.array([(None,) * 10] * 10, dtype=np.float64),
np.full((10, 10), np.nan, dtype=np.float64))
assert_equal(np.array((1.0,) * 10, dtype=np.float64),
np.ones((10,), dtype=np.float64))
assert_equal(np.array([(1.0,)] * 10, dtype=np.float64),
np.ones((10, 1), dtype=np.float64))
assert_equal(np.array([(1.0,) * 10], dtype=np.float64),
np.ones((1, 10), dtype=np.float64))
assert_equal(np.array([(1.0,) * 10] * 10, dtype=np.float64),
np.ones((10, 10), dtype=np.float64))
@pytest.mark.parametrize("array", [True, False])
def test_array_impossible_casts(array):
# All builtin types can be forcibly cast, at least theoretically,
# but user dtypes cannot necessarily.
rt = rational(1, 2)
if array:
rt = np.array(rt)
with assert_raises(TypeError):
np.array(rt, dtype="M8")
# TODO: remove when fastCopyAndTranspose deprecation expires
@pytest.mark.parametrize("a",
(
np.array(2), # 0D array
np.array([3, 2, 7, 0]), # 1D array
np.arange(6).reshape(2, 3) # 2D array
),
)
def test_fastCopyAndTranspose(a):
with pytest.deprecated_call():
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert b.flags.owndata
def test_array_astype():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Default behavior: allows unsafe casts, keeps memory layout,
# always copies.
b = a.astype('i4')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('i4'))
assert_equal(a.strides, b.strides)
b = a.T.astype('i4')
assert_equal(a.T, b)
assert_equal(b.dtype, np.dtype('i4'))
assert_equal(a.T.strides, b.strides)
b = a.astype('f4')
assert_equal(a, b)
assert_(not (a is b))
# copy=False parameter can sometimes skip a copy
b = a.astype('f4', copy=False)
assert_(a is b)
# order parameter allows overriding of the memory layout,
# forcing a copy if the layout is wrong
b = a.astype('f4', order='F', copy=False)
assert_equal(a, b)
assert_(not (a is b))
assert_(b.flags.f_contiguous)
b = a.astype('f4', order='C', copy=False)
assert_equal(a, b)
assert_(a is b)
assert_(b.flags.c_contiguous)
# casting parameter allows catching bad casts
b = a.astype('c8', casting='safe')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('c8'))
assert_raises(TypeError, a.astype, 'i4', casting='safe')
# subok=False passes through a non-subclassed array
b = a.astype('f4', subok=0, copy=False)
assert_(a is b)
class MyNDArray(np.ndarray):
pass
a = np.array([[0, 1, 2], [3, 4, 5]], dtype='f4').view(MyNDArray)
# subok=True passes through a subclass
b = a.astype('f4', subok=True, copy=False)
assert_(a is b)
# subok=True is default, and creates a subtype on a cast
b = a.astype('i4', copy=False)
assert_equal(a, b)
assert_equal(type(b), MyNDArray)
# subok=False never returns a subclass
b = a.astype('f4', subok=False, copy=False)
assert_equal(a, b)
assert_(not (a is b))
assert_(type(b) is not MyNDArray)
# Make sure converting from string object to fixed length string
# does not truncate.
a = np.array([b'a'*100], dtype='O')
b = a.astype('S')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('S100'))
a = np.array(['a'*100], dtype='O')
b = a.astype('U')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('U100'))
# Same test as above but for strings shorter than 64 characters
a = np.array([b'a'*10], dtype='O')
b = a.astype('S')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('S10'))
a = np.array(['a'*10], dtype='O')
b = a.astype('U')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('U10'))
a = np.array(123456789012345678901234567890, dtype='O').astype('S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array(123456789012345678901234567890, dtype='O').astype('U')
assert_array_equal(a, np.array('1234567890' * 3, dtype='U30'))
a = np.array([123456789012345678901234567890], dtype='O').astype('S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array([123456789012345678901234567890], dtype='O').astype('U')
assert_array_equal(a, np.array('1234567890' * 3, dtype='U30'))
a = np.array(123456789012345678901234567890, dtype='S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array(123456789012345678901234567890, dtype='U')
assert_array_equal(a, np.array('1234567890' * 3, dtype='U30'))
a = np.array('a\u0140', dtype='U')
b = np.ndarray(buffer=a, dtype='uint32', shape=2)
assert_(b.size == 2)
a = np.array([1000], dtype='i4')
assert_raises(TypeError, a.astype, 'S1', casting='safe')
a = np.array(1000, dtype='i4')
assert_raises(TypeError, a.astype, 'U1', casting='safe')
# gh-24023
assert_raises(TypeError, a.astype)
@pytest.mark.parametrize("dt", ["S", "U"])
def test_array_astype_to_string_discovery_empty(dt):
# See also gh-19085
arr = np.array([""], dtype=object)
# Note, the itemsize is the `0 -> 1` logic, which should change.
# The important part the test is rather that it does not error.
assert arr.astype(dt).dtype.itemsize == np.dtype(f"{dt}1").itemsize
# check the same thing for `np.can_cast` (since it accepts arrays)
assert np.can_cast(arr, dt, casting="unsafe")
assert not np.can_cast(arr, dt, casting="same_kind")
# as well as for the object as a descriptor:
assert np.can_cast("O", dt, casting="unsafe")
@pytest.mark.parametrize("dt", ["d", "f", "S13", "U32"])
def test_array_astype_to_void(dt):
dt = np.dtype(dt)
arr = np.array([], dtype=dt)
assert arr.astype("V").dtype.itemsize == dt.itemsize
def test_object_array_astype_to_void():
# This is different to `test_array_astype_to_void` as object arrays
# are inspected. The default void is "V8" (8 is the length of double)
arr = np.array([], dtype="O").astype("V")
assert arr.dtype == "V8"
@pytest.mark.parametrize("t",
np.sctypes['uint'] + np.sctypes['int'] + np.sctypes['float']
)
def test_array_astype_warning(t):
# test ComplexWarning when casting from complex to float or int
a = np.array(10, dtype=np.complex_)
assert_warns(np.ComplexWarning, a.astype, t)
@pytest.mark.parametrize(["dtype", "out_dtype"],
[(np.bytes_, np.bool_),
(np.str_, np.bool_),
(np.dtype("S10,S9"), np.dtype("?,?"))])
def test_string_to_boolean_cast(dtype, out_dtype):
"""
Currently, for `astype` strings are cast to booleans effectively by
calling `bool(int(string)`. This is not consistent (see gh-9875) and
will eventually be deprecated.
"""
arr = np.array(["10", "10\0\0\0", "0\0\0", "0"], dtype=dtype)
expected = np.array([True, True, False, False], dtype=out_dtype)
assert_array_equal(arr.astype(out_dtype), expected)
@pytest.mark.parametrize(["dtype", "out_dtype"],
[(np.bytes_, np.bool_),
(np.str_, np.bool_),
(np.dtype("S10,S9"), np.dtype("?,?"))])
def test_string_to_boolean_cast_errors(dtype, out_dtype):
"""
These currently error out, since cast to integers fails, but should not
error out in the future.
"""
for invalid in ["False", "True", "", "\0", "non-empty"]:
arr = np.array([invalid], dtype=dtype)
with assert_raises(ValueError):
arr.astype(out_dtype)
@pytest.mark.parametrize("str_type", [str, bytes, np.str_, np.unicode_])
@pytest.mark.parametrize("scalar_type",
[np.complex64, np.complex128, np.clongdouble])
def test_string_to_complex_cast(str_type, scalar_type):
value = scalar_type(b"1+3j")
assert scalar_type(value) == 1+3j
assert np.array([value], dtype=object).astype(scalar_type)[()] == 1+3j
assert np.array(value).astype(scalar_type)[()] == 1+3j
arr = np.zeros(1, dtype=scalar_type)
arr[0] = value
assert arr[0] == 1+3j
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
def test_none_to_nan_cast(dtype):
# Note that at the time of writing this test, the scalar constructors
# reject None
arr = np.zeros(1, dtype=dtype)
arr[0] = None
assert np.isnan(arr)[0]
assert np.isnan(np.array(None, dtype=dtype))[()]
assert np.isnan(np.array([None], dtype=dtype))[0]
assert np.isnan(np.array(None).astype(dtype))[()]
def test_copyto_fromscalar():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Simple copy
np.copyto(a, 1.5)
assert_equal(a, 1.5)
np.copyto(a.T, 2.5)
assert_equal(a, 2.5)
# Where-masked copy
mask = np.array([[0, 1, 0], [0, 0, 1]], dtype='?')
np.copyto(a, 3.5, where=mask)
assert_equal(a, [[2.5, 3.5, 2.5], [2.5, 2.5, 3.5]])
mask = np.array([[0, 1], [1, 1], [1, 0]], dtype='?')
np.copyto(a.T, 4.5, where=mask)
assert_equal(a, [[2.5, 4.5, 4.5], [4.5, 4.5, 3.5]])
def test_copyto():
a = np.arange(6, dtype='i4').reshape(2, 3)
# Simple copy
np.copyto(a, [[3, 1, 5], [6, 2, 1]])
assert_equal(a, [[3, 1, 5], [6, 2, 1]])
# Overlapping copy should work
np.copyto(a[:, :2], a[::-1, 1::-1])
assert_equal(a, [[2, 6, 5], [1, 3, 1]])
# Defaults to 'same_kind' casting
assert_raises(TypeError, np.copyto, a, 1.5)
# Force a copy with 'unsafe' casting, truncating 1.5 to 1
np.copyto(a, 1.5, casting='unsafe')
assert_equal(a, 1)
# Copying with a mask
np.copyto(a, 3, where=[True, False, True])
assert_equal(a, [[3, 1, 3], [3, 1, 3]])
# Casting rule still applies with a mask
assert_raises(TypeError, np.copyto, a, 3.5, where=[True, False, True])
# Lists of integer 0's and 1's is ok too
np.copyto(a, 4.0, casting='unsafe', where=[[0, 1, 1], [1, 0, 0]])
assert_equal(a, [[3, 4, 4], [4, 1, 3]])
# Overlapping copy with mask should work
np.copyto(a[:, :2], a[::-1, 1::-1], where=[[0, 1], [1, 1]])
assert_equal(a, [[3, 4, 4], [4, 3, 3]])
# 'dst' must be an array
assert_raises(TypeError, np.copyto, [1, 2, 3], [2, 3, 4])
def test_copyto_permut():
# test explicit overflow case
pad = 500
l = [True] * pad + [True, True, True, True]
r = np.zeros(len(l)-pad)
d = np.ones(len(l)-pad)
mask = np.array(l)[pad:]
np.copyto(r, d, where=mask[::-1])
# test all permutation of possible masks, 9 should be sufficient for
# current 4 byte unrolled code
power = 9
d = np.ones(power)
for i in range(2**power):
r = np.zeros(power)
l = [(i & x) != 0 for x in range(power)]
mask = np.array(l)
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=mask[::-1])
assert_array_equal(r == 1, l[::-1])
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::2])
assert_array_equal(r[::2] == 1, l[::2])
assert_equal(r[::2].sum(), sum(l[::2]))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::-2])
assert_array_equal(r[::2] == 1, l[::-2])
assert_equal(r[::2].sum(), sum(l[::-2]))
for c in [0xFF, 0x7F, 0x02, 0x10]:
r = np.zeros(power)
mask = np.array(l)
imask = np.array(l).view(np.uint8)
imask[mask != 0] = c
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=True)
assert_equal(r.sum(), r.size)
r = np.ones(power)
d = np.zeros(power)
np.copyto(r, d, where=False)
assert_equal(r.sum(), r.size)
def test_copy_order():
a = np.arange(24).reshape(2, 1, 3, 4)
b = a.copy(order='F')
c = np.arange(24).reshape(2, 1, 4, 3).swapaxes(2, 3)
def check_copy_result(x, y, ccontig, fcontig, strides=False):
assert_(not (x is y))
assert_equal(x, y)
assert_equal(res.flags.c_contiguous, ccontig)
assert_equal(res.flags.f_contiguous, fcontig)
# Validate the initial state of a, b, and c
assert_(a.flags.c_contiguous)
assert_(not a.flags.f_contiguous)
assert_(not b.flags.c_contiguous)
assert_(b.flags.f_contiguous)
assert_(not c.flags.c_contiguous)
assert_(not c.flags.f_contiguous)
# Copy with order='C'
res = a.copy(order='C')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = b.copy(order='C')
check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)
res = c.copy(order='C')
check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)
res = np.copy(a, order='C')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = np.copy(b, order='C')
check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)
res = np.copy(c, order='C')
check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)
# Copy with order='F'
res = a.copy(order='F')
check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)
res = b.copy(order='F')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = c.copy(order='F')
check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)
res = np.copy(a, order='F')
check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)
res = np.copy(b, order='F')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = np.copy(c, order='F')
check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)
# Copy with order='K'
res = a.copy(order='K')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = b.copy(order='K')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = c.copy(order='K')
check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)
res = np.copy(a, order='K')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = np.copy(b, order='K')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = np.copy(c, order='K')
check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)
def test_contiguous_flags():
a = np.ones((4, 4, 1))[::2,:,:]
a.strides = a.strides[:2] + (-123,)
b = np.ones((2, 2, 1, 2, 2)).swapaxes(3, 4)
def check_contig(a, ccontig, fcontig):
assert_(a.flags.c_contiguous == ccontig)
assert_(a.flags.f_contiguous == fcontig)
# Check if new arrays are correct:
check_contig(a, False, False)
check_contig(b, False, False)
check_contig(np.empty((2, 2, 0, 2, 2)), True, True)
check_contig(np.array([[[1], [2]]], order='F'), True, True)
check_contig(np.empty((2, 2)), True, False)
check_contig(np.empty((2, 2), order='F'), False, True)
# Check that np.array creates correct contiguous flags:
check_contig(np.array(a, copy=False), False, False)
check_contig(np.array(a, copy=False, order='C'), True, False)
check_contig(np.array(a, ndmin=4, copy=False, order='F'), False, True)
# Check slicing update of flags and :
check_contig(a[0], True, True)
check_contig(a[None, ::4, ..., None], True, True)
check_contig(b[0, 0, ...], False, True)
check_contig(b[:, :, 0:0, :, :], True, True)
# Test ravel and squeeze.
check_contig(a.ravel(), True, True)
check_contig(np.ones((1, 3, 1)).squeeze(), True, True)
def test_broadcast_arrays():
# Test user defined dtypes
a = np.array([(1, 2, 3)], dtype='u4,u4,u4')
b = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4')
result = np.broadcast_arrays(a, b)
assert_equal(result[0], np.array([(1, 2, 3), (1, 2, 3), (1, 2, 3)], dtype='u4,u4,u4'))
assert_equal(result[1], np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4'))
@pytest.mark.parametrize(["shape", "fill_value", "expected_output"],
[((2, 2), [5.0, 6.0], np.array([[5.0, 6.0], [5.0, 6.0]])),
((3, 2), [1.0, 2.0], np.array([[1.0, 2.0], [1.0, 2.0], [1.0, 2.0]]))])
def test_full_from_list(shape, fill_value, expected_output):
output = np.full(shape, fill_value)
assert_equal(output, expected_output)
def test_astype_copyflag():
# test the various copyflag options
arr = np.arange(10, dtype=np.intp)
res_true = arr.astype(np.intp, copy=True)
assert not np.may_share_memory(arr, res_true)
res_always = arr.astype(np.intp, copy=np._CopyMode.ALWAYS)
assert not np.may_share_memory(arr, res_always)
res_false = arr.astype(np.intp, copy=False)
# `res_false is arr` currently, but check `may_share_memory`.
assert np.may_share_memory(arr, res_false)
res_if_needed = arr.astype(np.intp, copy=np._CopyMode.IF_NEEDED)
# `res_if_needed is arr` currently, but check `may_share_memory`.
assert np.may_share_memory(arr, res_if_needed)
res_never = arr.astype(np.intp, copy=np._CopyMode.NEVER)
assert np.may_share_memory(arr, res_never)
# Simple tests for when a copy is necessary:
res_false = arr.astype(np.float64, copy=False)
assert_array_equal(res_false, arr)
res_if_needed = arr.astype(np.float64,
copy=np._CopyMode.IF_NEEDED)
assert_array_equal(res_if_needed, arr)
assert_raises(ValueError, arr.astype, np.float64,
copy=np._CopyMode.NEVER)