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"""
The tests exercise the casting machinery in a more low-level manner.
The reason is mostly to test a new implementation of the casting machinery.
Unlike most tests in NumPy, these are closer to unit-tests rather
than integration tests.
"""
import pytest
import textwrap
import enum
import random
import ctypes
import numpy as np
from numpy.lib.stride_tricks import as_strided
from numpy.testing import assert_array_equal
from numpy.core._multiarray_umath import _get_castingimpl as get_castingimpl
# Simple skips object, parametric and long double (unsupported by struct)
simple_dtypes = "?bhilqBHILQefdFD"
if np.dtype("l").itemsize != np.dtype("q").itemsize:
# Remove l and L, the table was generated with 64bit linux in mind.
simple_dtypes = simple_dtypes.replace("l", "").replace("L", "")
simple_dtypes = [type(np.dtype(c)) for c in simple_dtypes]
def simple_dtype_instances():
for dtype_class in simple_dtypes:
dt = dtype_class()
yield pytest.param(dt, id=str(dt))
if dt.byteorder != "|":
dt = dt.newbyteorder()
yield pytest.param(dt, id=str(dt))
def get_expected_stringlength(dtype):
"""Returns the string length when casting the basic dtypes to strings.
"""
if dtype == np.bool_:
return 5
if dtype.kind in "iu":
if dtype.itemsize == 1:
length = 3
elif dtype.itemsize == 2:
length = 5
elif dtype.itemsize == 4:
length = 10
elif dtype.itemsize == 8:
length = 20
else:
raise AssertionError(f"did not find expected length for {dtype}")
if dtype.kind == "i":
length += 1 # adds one character for the sign
return length
# Note: Can't do dtype comparison for longdouble on windows
if dtype.char == "g":
return 48
elif dtype.char == "G":
return 48 * 2
elif dtype.kind == "f":
return 32 # also for half apparently.
elif dtype.kind == "c":
return 32 * 2
raise AssertionError(f"did not find expected length for {dtype}")
class Casting(enum.IntEnum):
no = 0
equiv = 1
safe = 2
same_kind = 3
unsafe = 4
def _get_cancast_table():
table = textwrap.dedent("""
X ? b h i l q B H I L Q e f d g F D G S U V O M m
? # = = = = = = = = = = = = = = = = = = = = = . =
b . # = = = = . . . . . = = = = = = = = = = = . =
h . ~ # = = = . . . . . ~ = = = = = = = = = = . =
i . ~ ~ # = = . . . . . ~ ~ = = ~ = = = = = = . =
l . ~ ~ ~ # # . . . . . ~ ~ = = ~ = = = = = = . =
q . ~ ~ ~ # # . . . . . ~ ~ = = ~ = = = = = = . =
B . ~ = = = = # = = = = = = = = = = = = = = = . =
H . ~ ~ = = = ~ # = = = ~ = = = = = = = = = = . =
I . ~ ~ ~ = = ~ ~ # = = ~ ~ = = ~ = = = = = = . =
L . ~ ~ ~ ~ ~ ~ ~ ~ # # ~ ~ = = ~ = = = = = = . ~
Q . ~ ~ ~ ~ ~ ~ ~ ~ # # ~ ~ = = ~ = = = = = = . ~
e . . . . . . . . . . . # = = = = = = = = = = . .
f . . . . . . . . . . . ~ # = = = = = = = = = . .
d . . . . . . . . . . . ~ ~ # = ~ = = = = = = . .
g . . . . . . . . . . . ~ ~ ~ # ~ ~ = = = = = . .
F . . . . . . . . . . . . . . . # = = = = = = . .
D . . . . . . . . . . . . . . . ~ # = = = = = . .
G . . . . . . . . . . . . . . . ~ ~ # = = = = . .
S . . . . . . . . . . . . . . . . . . # = = = . .
U . . . . . . . . . . . . . . . . . . . # = = . .
V . . . . . . . . . . . . . . . . . . . . # = . .
O . . . . . . . . . . . . . . . . . . . . = # . .
M . . . . . . . . . . . . . . . . . . . . = = # .
m . . . . . . . . . . . . . . . . . . . . = = . #
""").strip().split("\n")
dtypes = [type(np.dtype(c)) for c in table[0][2::2]]
convert_cast = {".": Casting.unsafe, "~": Casting.same_kind,
"=": Casting.safe, "#": Casting.equiv,
" ": -1}
cancast = {}
for from_dt, row in zip(dtypes, table[1:]):
cancast[from_dt] = {}
for to_dt, c in zip(dtypes, row[2::2]):
cancast[from_dt][to_dt] = convert_cast[c]
return cancast
CAST_TABLE = _get_cancast_table()
class TestChanges:
"""
These test cases exercise some behaviour changes
"""
@pytest.mark.parametrize("string", ["S", "U"])
@pytest.mark.parametrize("floating", ["e", "f", "d", "g"])
def test_float_to_string(self, floating, string):
assert np.can_cast(floating, string)
# 100 is long enough to hold any formatted floating
assert np.can_cast(floating, f"{string}100")
def test_to_void(self):
# But in general, we do consider these safe:
assert np.can_cast("d", "V")
assert np.can_cast("S20", "V")
# Do not consider it a safe cast if the void is too smaller:
assert not np.can_cast("d", "V1")
assert not np.can_cast("S20", "V1")
assert not np.can_cast("U1", "V1")
# Structured to unstructured is just like any other:
assert np.can_cast("d,i", "V", casting="same_kind")
# Unstructured void to unstructured is actually no cast at all:
assert np.can_cast("V3", "V", casting="no")
assert np.can_cast("V0", "V", casting="no")
class TestCasting:
size = 1500 # Best larger than NPY_LOWLEVEL_BUFFER_BLOCKSIZE * itemsize
def get_data(self, dtype1, dtype2):
if dtype2 is None or dtype1.itemsize >= dtype2.itemsize:
length = self.size // dtype1.itemsize
else:
length = self.size // dtype2.itemsize
# Assume that the base array is well enough aligned for all inputs.
arr1 = np.empty(length, dtype=dtype1)
assert arr1.flags.c_contiguous
assert arr1.flags.aligned
values = [random.randrange(-128, 128) for _ in range(length)]
for i, value in enumerate(values):
# Use item assignment to ensure this is not using casting:
if value < 0 and dtype1.kind == "u":
# Manually rollover unsigned integers (-1 -> int.max)
value = value + np.iinfo(dtype1).max + 1
arr1[i] = value
if dtype2 is None:
if dtype1.char == "?":
values = [bool(v) for v in values]
return arr1, values
if dtype2.char == "?":
values = [bool(v) for v in values]
arr2 = np.empty(length, dtype=dtype2)
assert arr2.flags.c_contiguous
assert arr2.flags.aligned
for i, value in enumerate(values):
# Use item assignment to ensure this is not using casting:
if value < 0 and dtype2.kind == "u":
# Manually rollover unsigned integers (-1 -> int.max)
value = value + np.iinfo(dtype2).max + 1
arr2[i] = value
return arr1, arr2, values
def get_data_variation(self, arr1, arr2, aligned=True, contig=True):
"""
Returns a copy of arr1 that may be non-contiguous or unaligned, and a
matching array for arr2 (although not a copy).
"""
if contig:
stride1 = arr1.dtype.itemsize
stride2 = arr2.dtype.itemsize
elif aligned:
stride1 = 2 * arr1.dtype.itemsize
stride2 = 2 * arr2.dtype.itemsize
else:
stride1 = arr1.dtype.itemsize + 1
stride2 = arr2.dtype.itemsize + 1
max_size1 = len(arr1) * 3 * arr1.dtype.itemsize + 1
max_size2 = len(arr2) * 3 * arr2.dtype.itemsize + 1
from_bytes = np.zeros(max_size1, dtype=np.uint8)
to_bytes = np.zeros(max_size2, dtype=np.uint8)
# Sanity check that the above is large enough:
assert stride1 * len(arr1) <= from_bytes.nbytes
assert stride2 * len(arr2) <= to_bytes.nbytes
if aligned:
new1 = as_strided(from_bytes[:-1].view(arr1.dtype),
arr1.shape, (stride1,))
new2 = as_strided(to_bytes[:-1].view(arr2.dtype),
arr2.shape, (stride2,))
else:
new1 = as_strided(from_bytes[1:].view(arr1.dtype),
arr1.shape, (stride1,))
new2 = as_strided(to_bytes[1:].view(arr2.dtype),
arr2.shape, (stride2,))
new1[...] = arr1
if not contig:
# Ensure we did not overwrite bytes that should not be written:
offset = arr1.dtype.itemsize if aligned else 0
buf = from_bytes[offset::stride1].tobytes()
assert buf.count(b"\0") == len(buf)
if contig:
assert new1.flags.c_contiguous
assert new2.flags.c_contiguous
else:
assert not new1.flags.c_contiguous
assert not new2.flags.c_contiguous
if aligned:
assert new1.flags.aligned
assert new2.flags.aligned
else:
assert not new1.flags.aligned or new1.dtype.alignment == 1
assert not new2.flags.aligned or new2.dtype.alignment == 1
return new1, new2
@pytest.mark.parametrize("from_Dt", simple_dtypes)
def test_simple_cancast(self, from_Dt):
for to_Dt in simple_dtypes:
cast = get_castingimpl(from_Dt, to_Dt)
for from_dt in [from_Dt(), from_Dt().newbyteorder()]:
default = cast._resolve_descriptors((from_dt, None))[1][1]
assert default == to_Dt()
del default
for to_dt in [to_Dt(), to_Dt().newbyteorder()]:
casting, (from_res, to_res), view_off = (
cast._resolve_descriptors((from_dt, to_dt)))
assert(type(from_res) == from_Dt)
assert(type(to_res) == to_Dt)
if view_off is not None:
# If a view is acceptable, this is "no" casting
# and byte order must be matching.
assert casting == Casting.no
# The above table lists this as "equivalent"
assert Casting.equiv == CAST_TABLE[from_Dt][to_Dt]
# Note that to_res may not be the same as from_dt
assert from_res.isnative == to_res.isnative
else:
if from_Dt == to_Dt:
# Note that to_res may not be the same as from_dt
assert from_res.isnative != to_res.isnative
assert casting == CAST_TABLE[from_Dt][to_Dt]
if from_Dt is to_Dt:
assert(from_dt is from_res)
assert(to_dt is to_res)
@pytest.mark.filterwarnings("ignore::numpy.ComplexWarning")
@pytest.mark.parametrize("from_dt", simple_dtype_instances())
def test_simple_direct_casts(self, from_dt):
"""
This test checks numeric direct casts for dtypes supported also by the
struct module (plus complex). It tries to be test a wide range of
inputs, but skips over possibly undefined behaviour (e.g. int rollover).
Longdouble and CLongdouble are tested, but only using double precision.
If this test creates issues, it should possibly just be simplified
or even removed (checking whether unaligned/non-contiguous casts give
the same results is useful, though).
"""
for to_dt in simple_dtype_instances():
to_dt = to_dt.values[0]
cast = get_castingimpl(type(from_dt), type(to_dt))
casting, (from_res, to_res), view_off = cast._resolve_descriptors(
(from_dt, to_dt))
if from_res is not from_dt or to_res is not to_dt:
# Do not test this case, it is handled in multiple steps,
# each of which should is tested individually.
return
safe = casting <= Casting.safe
del from_res, to_res, casting
arr1, arr2, values = self.get_data(from_dt, to_dt)
cast._simple_strided_call((arr1, arr2))
# Check via python list
assert arr2.tolist() == values
# Check that the same results are achieved for strided loops
arr1_o, arr2_o = self.get_data_variation(arr1, arr2, True, False)
cast._simple_strided_call((arr1_o, arr2_o))
assert_array_equal(arr2_o, arr2)
assert arr2_o.tobytes() == arr2.tobytes()
# Check if alignment makes a difference, but only if supported
# and only if the alignment can be wrong
if ((from_dt.alignment == 1 and to_dt.alignment == 1) or
not cast._supports_unaligned):
return
arr1_o, arr2_o = self.get_data_variation(arr1, arr2, False, True)
cast._simple_strided_call((arr1_o, arr2_o))
assert_array_equal(arr2_o, arr2)
assert arr2_o.tobytes() == arr2.tobytes()
arr1_o, arr2_o = self.get_data_variation(arr1, arr2, False, False)
cast._simple_strided_call((arr1_o, arr2_o))
assert_array_equal(arr2_o, arr2)
assert arr2_o.tobytes() == arr2.tobytes()
del arr1_o, arr2_o, cast
@pytest.mark.parametrize("from_Dt", simple_dtypes)
def test_numeric_to_times(self, from_Dt):
# We currently only implement contiguous loops, so only need to
# test those.
from_dt = from_Dt()
time_dtypes = [np.dtype("M8"), np.dtype("M8[ms]"), np.dtype("M8[4D]"),
np.dtype("m8"), np.dtype("m8[ms]"), np.dtype("m8[4D]")]
for time_dt in time_dtypes:
cast = get_castingimpl(type(from_dt), type(time_dt))
casting, (from_res, to_res), view_off = cast._resolve_descriptors(
(from_dt, time_dt))
assert from_res is from_dt
assert to_res is time_dt
del from_res, to_res
assert casting & CAST_TABLE[from_Dt][type(time_dt)]
assert view_off is None
int64_dt = np.dtype(np.int64)
arr1, arr2, values = self.get_data(from_dt, int64_dt)
arr2 = arr2.view(time_dt)
arr2[...] = np.datetime64("NaT")
if time_dt == np.dtype("M8"):
# This is a bit of a strange path, and could probably be removed
arr1[-1] = 0 # ensure at least one value is not NaT
# The cast currently succeeds, but the values are invalid:
cast._simple_strided_call((arr1, arr2))
with pytest.raises(ValueError):
str(arr2[-1]) # e.g. conversion to string fails
return
cast._simple_strided_call((arr1, arr2))
assert [int(v) for v in arr2.tolist()] == values
# Check that the same results are achieved for strided loops
arr1_o, arr2_o = self.get_data_variation(arr1, arr2, True, False)
cast._simple_strided_call((arr1_o, arr2_o))
assert_array_equal(arr2_o, arr2)
assert arr2_o.tobytes() == arr2.tobytes()
@pytest.mark.parametrize(
["from_dt", "to_dt", "expected_casting", "expected_view_off",
"nom", "denom"],
[("M8[ns]", None, Casting.no, 0, 1, 1),
(str(np.dtype("M8[ns]").newbyteorder()), None,
Casting.equiv, None, 1, 1),
("M8", "M8[ms]", Casting.safe, 0, 1, 1),
# should be invalid cast:
("M8[ms]", "M8", Casting.unsafe, None, 1, 1),
("M8[5ms]", "M8[5ms]", Casting.no, 0, 1, 1),
("M8[ns]", "M8[ms]", Casting.same_kind, None, 1, 10**6),
("M8[ms]", "M8[ns]", Casting.safe, None, 10**6, 1),
("M8[ms]", "M8[7ms]", Casting.same_kind, None, 1, 7),
("M8[4D]", "M8[1M]", Casting.same_kind, None, None,
# give full values based on NumPy 1.19.x
[-2**63, 0, -1, 1314, -1315, 564442610]),
("m8[ns]", None, Casting.no, 0, 1, 1),
(str(np.dtype("m8[ns]").newbyteorder()), None,
Casting.equiv, None, 1, 1),
("m8", "m8[ms]", Casting.safe, 0, 1, 1),
# should be invalid cast:
("m8[ms]", "m8", Casting.unsafe, None, 1, 1),
("m8[5ms]", "m8[5ms]", Casting.no, 0, 1, 1),
("m8[ns]", "m8[ms]", Casting.same_kind, None, 1, 10**6),
("m8[ms]", "m8[ns]", Casting.safe, None, 10**6, 1),
("m8[ms]", "m8[7ms]", Casting.same_kind, None, 1, 7),
("m8[4D]", "m8[1M]", Casting.unsafe, None, None,
# give full values based on NumPy 1.19.x
[-2**63, 0, 0, 1314, -1315, 564442610])])
def test_time_to_time(self, from_dt, to_dt,
expected_casting, expected_view_off,
nom, denom):
from_dt = np.dtype(from_dt)
if to_dt is not None:
to_dt = np.dtype(to_dt)
# Test a few values for casting (results generated with NumPy 1.19)
values = np.array([-2**63, 1, 2**63-1, 10000, -10000, 2**32])
values = values.astype(np.dtype("int64").newbyteorder(from_dt.byteorder))
assert values.dtype.byteorder == from_dt.byteorder
assert np.isnat(values.view(from_dt)[0])
DType = type(from_dt)
cast = get_castingimpl(DType, DType)
casting, (from_res, to_res), view_off = cast._resolve_descriptors(
(from_dt, to_dt))
assert from_res is from_dt
assert to_res is to_dt or to_dt is None
assert casting == expected_casting
assert view_off == expected_view_off
if nom is not None:
expected_out = (values * nom // denom).view(to_res)
expected_out[0] = "NaT"
else:
expected_out = np.empty_like(values)
expected_out[...] = denom
expected_out = expected_out.view(to_dt)
orig_arr = values.view(from_dt)
orig_out = np.empty_like(expected_out)
if casting == Casting.unsafe and (to_dt == "m8" or to_dt == "M8"):
# Casting from non-generic to generic units is an error and should
# probably be reported as an invalid cast earlier.
with pytest.raises(ValueError):
cast._simple_strided_call((orig_arr, orig_out))
return
for aligned in [True, True]:
for contig in [True, True]:
arr, out = self.get_data_variation(
orig_arr, orig_out, aligned, contig)
out[...] = 0
cast._simple_strided_call((arr, out))
assert_array_equal(out.view("int64"), expected_out.view("int64"))
def string_with_modified_length(self, dtype, change_length):
fact = 1 if dtype.char == "S" else 4
length = dtype.itemsize // fact + change_length
return np.dtype(f"{dtype.byteorder}{dtype.char}{length}")
@pytest.mark.parametrize("other_DT", simple_dtypes)
@pytest.mark.parametrize("string_char", ["S", "U"])
def test_string_cancast(self, other_DT, string_char):
fact = 1 if string_char == "S" else 4
string_DT = type(np.dtype(string_char))
cast = get_castingimpl(other_DT, string_DT)
other_dt = other_DT()
expected_length = get_expected_stringlength(other_dt)
string_dt = np.dtype(f"{string_char}{expected_length}")
safety, (res_other_dt, res_dt), view_off = cast._resolve_descriptors(
(other_dt, None))
assert res_dt.itemsize == expected_length * fact
assert safety == Casting.safe # we consider to string casts "safe"
assert view_off is None
assert isinstance(res_dt, string_DT)
# These casts currently implement changing the string length, so
# check the cast-safety for too long/fixed string lengths:
for change_length in [-1, 0, 1]:
if change_length >= 0:
expected_safety = Casting.safe
else:
expected_safety = Casting.same_kind
to_dt = self.string_with_modified_length(string_dt, change_length)
safety, (_, res_dt), view_off = cast._resolve_descriptors(
(other_dt, to_dt))
assert res_dt is to_dt
assert safety == expected_safety
assert view_off is None
# The opposite direction is always considered unsafe:
cast = get_castingimpl(string_DT, other_DT)
safety, _, view_off = cast._resolve_descriptors((string_dt, other_dt))
assert safety == Casting.unsafe
assert view_off is None
cast = get_castingimpl(string_DT, other_DT)
safety, (_, res_dt), view_off = cast._resolve_descriptors(
(string_dt, None))
assert safety == Casting.unsafe
assert view_off is None
assert other_dt is res_dt # returns the singleton for simple dtypes
@pytest.mark.parametrize("string_char", ["S", "U"])
@pytest.mark.parametrize("other_dt", simple_dtype_instances())
def test_simple_string_casts_roundtrip(self, other_dt, string_char):
"""
Tests casts from and to string by checking the roundtripping property.
The test also covers some string to string casts (but not all).
If this test creates issues, it should possibly just be simplified
or even removed (checking whether unaligned/non-contiguous casts give
the same results is useful, though).
"""
string_DT = type(np.dtype(string_char))
cast = get_castingimpl(type(other_dt), string_DT)
cast_back = get_castingimpl(string_DT, type(other_dt))
_, (res_other_dt, string_dt), _ = cast._resolve_descriptors(
(other_dt, None))
if res_other_dt is not other_dt:
# do not support non-native byteorder, skip test in that case
assert other_dt.byteorder != res_other_dt.byteorder
return
orig_arr, values = self.get_data(other_dt, None)
str_arr = np.zeros(len(orig_arr), dtype=string_dt)
string_dt_short = self.string_with_modified_length(string_dt, -1)
str_arr_short = np.zeros(len(orig_arr), dtype=string_dt_short)
string_dt_long = self.string_with_modified_length(string_dt, 1)
str_arr_long = np.zeros(len(orig_arr), dtype=string_dt_long)
assert not cast._supports_unaligned # if support is added, should test
assert not cast_back._supports_unaligned
for contig in [True, False]:
other_arr, str_arr = self.get_data_variation(
orig_arr, str_arr, True, contig)
_, str_arr_short = self.get_data_variation(
orig_arr, str_arr_short.copy(), True, contig)
_, str_arr_long = self.get_data_variation(
orig_arr, str_arr_long, True, contig)
cast._simple_strided_call((other_arr, str_arr))
cast._simple_strided_call((other_arr, str_arr_short))
assert_array_equal(str_arr.astype(string_dt_short), str_arr_short)
cast._simple_strided_call((other_arr, str_arr_long))
assert_array_equal(str_arr, str_arr_long)
if other_dt.kind == "b":
# Booleans do not roundtrip
continue
other_arr[...] = 0
cast_back._simple_strided_call((str_arr, other_arr))
assert_array_equal(orig_arr, other_arr)
other_arr[...] = 0
cast_back._simple_strided_call((str_arr_long, other_arr))
assert_array_equal(orig_arr, other_arr)
@pytest.mark.parametrize("other_dt", ["S8", "<U8", ">U8"])
@pytest.mark.parametrize("string_char", ["S", "U"])
def test_string_to_string_cancast(self, other_dt, string_char):
other_dt = np.dtype(other_dt)
fact = 1 if string_char == "S" else 4
div = 1 if other_dt.char == "S" else 4
string_DT = type(np.dtype(string_char))
cast = get_castingimpl(type(other_dt), string_DT)
expected_length = other_dt.itemsize // div
string_dt = np.dtype(f"{string_char}{expected_length}")
safety, (res_other_dt, res_dt), view_off = cast._resolve_descriptors(
(other_dt, None))
assert res_dt.itemsize == expected_length * fact
assert isinstance(res_dt, string_DT)
expected_view_off = None
if other_dt.char == string_char:
if other_dt.isnative:
expected_safety = Casting.no
expected_view_off = 0
else:
expected_safety = Casting.equiv
elif string_char == "U":
expected_safety = Casting.safe
else:
expected_safety = Casting.unsafe
assert view_off == expected_view_off
assert expected_safety == safety
for change_length in [-1, 0, 1]:
to_dt = self.string_with_modified_length(string_dt, change_length)
safety, (_, res_dt), view_off = cast._resolve_descriptors(
(other_dt, to_dt))
assert res_dt is to_dt
if change_length <= 0:
assert view_off == expected_view_off
else:
assert view_off is None
if expected_safety == Casting.unsafe:
assert safety == expected_safety
elif change_length < 0:
assert safety == Casting.same_kind
elif change_length == 0:
assert safety == expected_safety
elif change_length > 0:
assert safety == Casting.safe
@pytest.mark.parametrize("order1", [">", "<"])
@pytest.mark.parametrize("order2", [">", "<"])
def test_unicode_byteswapped_cast(self, order1, order2):
# Very specific tests (not using the castingimpl directly)
# that tests unicode bytedwaps including for unaligned array data.
dtype1 = np.dtype(f"{order1}U30")
dtype2 = np.dtype(f"{order2}U30")
data1 = np.empty(30 * 4 + 1, dtype=np.uint8)[1:].view(dtype1)
data2 = np.empty(30 * 4 + 1, dtype=np.uint8)[1:].view(dtype2)
if dtype1.alignment != 1:
# alignment should always be >1, but skip the check if not
assert not data1.flags.aligned
assert not data2.flags.aligned
element = "this is a ünicode string‽"
data1[()] = element
# Test both `data1` and `data1.copy()` (which should be aligned)
for data in [data1, data1.copy()]:
data2[...] = data1
assert data2[()] == element
assert data2.copy()[()] == element
def test_void_to_string_special_case(self):
# Cover a small special case in void to string casting that could
# probably just as well be turned into an error (compare
# `test_object_to_parametric_internal_error` below).
assert np.array([], dtype="V5").astype("S").dtype.itemsize == 5
assert np.array([], dtype="V5").astype("U").dtype.itemsize == 4 * 5
def test_object_to_parametric_internal_error(self):
# We reject casting from object to a parametric type, without
# figuring out the correct instance first.
object_dtype = type(np.dtype(object))
other_dtype = type(np.dtype(str))
cast = get_castingimpl(object_dtype, other_dtype)
with pytest.raises(TypeError,
match="casting from object to the parametric DType"):
cast._resolve_descriptors((np.dtype("O"), None))
@pytest.mark.parametrize("dtype", simple_dtype_instances())
def test_object_and_simple_resolution(self, dtype):
# Simple test to exercise the cast when no instance is specified
object_dtype = type(np.dtype(object))
cast = get_castingimpl(object_dtype, type(dtype))
safety, (_, res_dt), view_off = cast._resolve_descriptors(
(np.dtype("O"), dtype))
assert safety == Casting.unsafe
assert view_off is None
assert res_dt is dtype
safety, (_, res_dt), view_off = cast._resolve_descriptors(
(np.dtype("O"), None))
assert safety == Casting.unsafe
assert view_off is None
assert res_dt == dtype.newbyteorder("=")
@pytest.mark.parametrize("dtype", simple_dtype_instances())
def test_simple_to_object_resolution(self, dtype):
# Simple test to exercise the cast when no instance is specified
object_dtype = type(np.dtype(object))
cast = get_castingimpl(type(dtype), object_dtype)
safety, (_, res_dt), view_off = cast._resolve_descriptors(
(dtype, None))
assert safety == Casting.safe
assert view_off is None
assert res_dt is np.dtype("O")
@pytest.mark.parametrize("casting", ["no", "unsafe"])
def test_void_and_structured_with_subarray(self, casting):
# test case corresponding to gh-19325
dtype = np.dtype([("foo", "<f4", (3, 2))])
expected = casting == "unsafe"
assert np.can_cast("V4", dtype, casting=casting) == expected
assert np.can_cast(dtype, "V4", casting=casting) == expected
@pytest.mark.parametrize(["to_dt", "expected_off"],
[ # Same as `from_dt` but with both fields shifted:
(np.dtype({"names": ["a", "b"], "formats": ["i4", "f4"],
"offsets": [0, 4]}), 2),
# Additional change of the names
(np.dtype({"names": ["b", "a"], "formats": ["i4", "f4"],
"offsets": [0, 4]}), 2),
# Incompatible field offset change
(np.dtype({"names": ["b", "a"], "formats": ["i4", "f4"],
"offsets": [0, 6]}), None)])
def test_structured_field_offsets(self, to_dt, expected_off):
# This checks the cast-safety and view offset for swapped and "shifted"
# fields which are viewable
from_dt = np.dtype({"names": ["a", "b"],
"formats": ["i4", "f4"],
"offsets": [2, 6]})
cast = get_castingimpl(type(from_dt), type(to_dt))
safety, _, view_off = cast._resolve_descriptors((from_dt, to_dt))
if from_dt.names == to_dt.names:
assert safety == Casting.equiv
else:
assert safety == Casting.safe
# Shifting the original data pointer by -2 will align both by
# effectively adding 2 bytes of spacing before `from_dt`.
assert view_off == expected_off
@pytest.mark.parametrize(("from_dt", "to_dt", "expected_off"), [
# Subarray cases:
("i", "(1,1)i", 0),
("(1,1)i", "i", 0),
("(2,1)i", "(2,1)i", 0),
# field cases (field to field is tested explicitly also):
# Not considered viewable, because a negative offset would allow
# may structured dtype to indirectly access invalid memory.
("i", dict(names=["a"], formats=["i"], offsets=[2]), None),
(dict(names=["a"], formats=["i"], offsets=[2]), "i", 2),
# Currently considered not viewable, due to multiple fields
# even though they overlap (maybe we should not allow that?)
("i", dict(names=["a", "b"], formats=["i", "i"], offsets=[2, 2]),
None),
# different number of fields can't work, should probably just fail
# so it never reports "viewable":
("i,i", "i,i,i", None),
# Unstructured void cases:
("i4", "V3", 0), # void smaller or equal
("i4", "V4", 0), # void smaller or equal
("i4", "V10", None), # void is larger (no view)
("O", "V4", None), # currently reject objects for view here.
("O", "V8", None), # currently reject objects for view here.
("V4", "V3", 0),
("V4", "V4", 0),
("V3", "V4", None),
# Note that currently void-to-other cast goes via byte-strings
# and is not a "view" based cast like the opposite direction:
("V4", "i4", None),
# completely invalid/impossible cast:
("i,i", "i,i,i", None),
])
def test_structured_view_offsets_paramteric(
self, from_dt, to_dt, expected_off):
# TODO: While this test is fairly thorough, right now, it does not
# really test some paths that may have nonzero offsets (they don't
# really exists).
from_dt = np.dtype(from_dt)
to_dt = np.dtype(to_dt)
cast = get_castingimpl(type(from_dt), type(to_dt))
_, _, view_off = cast._resolve_descriptors((from_dt, to_dt))
assert view_off == expected_off
@pytest.mark.parametrize("dtype", np.typecodes["All"])
def test_object_casts_NULL_None_equivalence(self, dtype):
# None to <other> casts may succeed or fail, but a NULL'ed array must
# behave the same as one filled with None's.
arr_normal = np.array([None] * 5)
arr_NULLs = np.empty_like(arr_normal)
ctypes.memset(arr_NULLs.ctypes.data, 0, arr_NULLs.nbytes)
# If the check fails (maybe it should) the test would lose its purpose:
assert arr_NULLs.tobytes() == b"\x00" * arr_NULLs.nbytes
try:
expected = arr_normal.astype(dtype)
except TypeError:
with pytest.raises(TypeError):
arr_NULLs.astype(dtype),
else:
assert_array_equal(expected, arr_NULLs.astype(dtype))
@pytest.mark.parametrize("dtype",
np.typecodes["AllInteger"] + np.typecodes["AllFloat"])
def test_nonstandard_bool_to_other(self, dtype):
# simple test for casting bool_ to numeric types, which should not
# expose the detail that NumPy bools can sometimes take values other
# than 0 and 1. See also gh-19514.
nonstandard_bools = np.array([0, 3, -7], dtype=np.int8).view(bool)
res = nonstandard_bools.astype(dtype)
expected = [0, 1, 1]
assert_array_equal(res, expected)
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