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# Copyright (c) 2017-2021 hippo91 <guillaume.peillex@gmail.com>
# Copyright (c) 2017-2018, 2020 Claudiu Popa <pcmanticore@gmail.com>
# Copyright (c) 2018 Bryce Guinta <bryce.paul.guinta@gmail.com>
# Copyright (c) 2019 Ashley Whetter <ashley@awhetter.co.uk>
# Copyright (c) 2021 Pierre Sassoulas <pierre.sassoulas@gmail.com>
# Copyright (c) 2021 Marc Mueller <30130371+cdce8p@users.noreply.github.com>
# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/PyCQA/astroid/blob/main/LICENSE
import unittest
try:
import numpy # pylint: disable=unused-import
HAS_NUMPY = True
except ImportError:
HAS_NUMPY = False
from astroid import Uninferable, builder, nodes
from astroid.brain.brain_numpy_utils import (
NUMPY_VERSION_TYPE_HINTS_SUPPORT,
_get_numpy_version,
numpy_supports_type_hints,
)
@unittest.skipUnless(HAS_NUMPY, "This test requires the numpy library.")
class NumpyBrainCoreNumericTypesTest(unittest.TestCase):
"""
Test of all the missing types defined in numerictypes module.
"""
all_types = [
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
"float96",
"complex64",
"complex128",
"complex192",
"timedelta64",
"datetime64",
"unicode_",
"str_",
"bool_",
"bool8",
"byte",
"int8",
"bytes0",
"bytes_",
"cdouble",
"cfloat",
"character",
"clongdouble",
"clongfloat",
"complexfloating",
"csingle",
"double",
"flexible",
"floating",
"half",
"inexact",
"int0",
"longcomplex",
"longdouble",
"longfloat",
"short",
"signedinteger",
"single",
"singlecomplex",
"str0",
"ubyte",
"uint",
"uint0",
"uintc",
"uintp",
"ulonglong",
"unsignedinteger",
"ushort",
"void0",
]
def _inferred_numpy_attribute(self, attrib):
node = builder.extract_node(
f"""
import numpy.core.numerictypes as tested_module
missing_type = tested_module.{attrib:s}"""
)
return next(node.value.infer())
def test_numpy_core_types(self):
"""
Test that all defined types have ClassDef type.
"""
for typ in self.all_types:
with self.subTest(typ=typ):
inferred = self._inferred_numpy_attribute(typ)
self.assertIsInstance(inferred, nodes.ClassDef)
def test_generic_types_have_methods(self):
"""
Test that all generic derived types have specified methods
"""
generic_methods = [
"all",
"any",
"argmax",
"argmin",
"argsort",
"astype",
"base",
"byteswap",
"choose",
"clip",
"compress",
"conj",
"conjugate",
"copy",
"cumprod",
"cumsum",
"data",
"diagonal",
"dtype",
"dump",
"dumps",
"fill",
"flags",
"flat",
"flatten",
"getfield",
"imag",
"item",
"itemset",
"itemsize",
"max",
"mean",
"min",
"nbytes",
"ndim",
"newbyteorder",
"nonzero",
"prod",
"ptp",
"put",
"ravel",
"real",
"repeat",
"reshape",
"resize",
"round",
"searchsorted",
"setfield",
"setflags",
"shape",
"size",
"sort",
"squeeze",
"std",
"strides",
"sum",
"swapaxes",
"take",
"tobytes",
"tofile",
"tolist",
"tostring",
"trace",
"transpose",
"var",
"view",
]
for type_ in (
"bool_",
"bytes_",
"character",
"complex128",
"complex192",
"complex64",
"complexfloating",
"datetime64",
"flexible",
"float16",
"float32",
"float64",
"float96",
"floating",
"generic",
"inexact",
"int16",
"int32",
"int32",
"int64",
"int8",
"integer",
"number",
"signedinteger",
"str_",
"timedelta64",
"uint16",
"uint32",
"uint32",
"uint64",
"uint8",
"unsignedinteger",
"void",
):
with self.subTest(typ=type_):
inferred = self._inferred_numpy_attribute(type_)
for meth in generic_methods:
with self.subTest(meth=meth):
self.assertTrue(meth in {m.name for m in inferred.methods()})
def test_generic_types_have_attributes(self):
"""
Test that all generic derived types have specified attributes
"""
generic_attr = [
"base",
"data",
"dtype",
"flags",
"flat",
"imag",
"itemsize",
"nbytes",
"ndim",
"real",
"size",
"strides",
]
for type_ in (
"bool_",
"bytes_",
"character",
"complex128",
"complex192",
"complex64",
"complexfloating",
"datetime64",
"flexible",
"float16",
"float32",
"float64",
"float96",
"floating",
"generic",
"inexact",
"int16",
"int32",
"int32",
"int64",
"int8",
"integer",
"number",
"signedinteger",
"str_",
"timedelta64",
"uint16",
"uint32",
"uint32",
"uint64",
"uint8",
"unsignedinteger",
"void",
):
with self.subTest(typ=type_):
inferred = self._inferred_numpy_attribute(type_)
for attr in generic_attr:
with self.subTest(attr=attr):
self.assertNotEqual(len(inferred.getattr(attr)), 0)
def test_number_types_have_unary_operators(self):
"""
Test that number types have unary operators
"""
unary_ops = ("__neg__",)
for type_ in (
"float64",
"float96",
"floating",
"int16",
"int32",
"int32",
"int64",
"int8",
"integer",
"number",
"signedinteger",
"uint16",
"uint32",
"uint32",
"uint64",
"uint8",
"unsignedinteger",
):
with self.subTest(typ=type_):
inferred = self._inferred_numpy_attribute(type_)
for attr in unary_ops:
with self.subTest(attr=attr):
self.assertNotEqual(len(inferred.getattr(attr)), 0)
def test_array_types_have_unary_operators(self):
"""
Test that array types have unary operators
"""
unary_ops = ("__neg__", "__invert__")
for type_ in ("ndarray",):
with self.subTest(typ=type_):
inferred = self._inferred_numpy_attribute(type_)
for attr in unary_ops:
with self.subTest(attr=attr):
self.assertNotEqual(len(inferred.getattr(attr)), 0)
def test_datetime_astype_return(self):
"""
Test that the return of astype method of the datetime object
is inferred as a ndarray.
PyCQA/pylint#3332
"""
node = builder.extract_node(
"""
import numpy as np
import datetime
test_array = np.datetime64(1, 'us')
test_array.astype(datetime.datetime)
"""
)
licit_array_types = ".ndarray"
inferred_values = list(node.infer())
self.assertTrue(
len(inferred_values) == 1,
msg="Too much inferred value for datetime64.astype",
)
self.assertTrue(
inferred_values[-1].pytype() in licit_array_types,
msg="Illicit type for {:s} ({})".format(
"datetime64.astype", inferred_values[-1].pytype()
),
)
@unittest.skipUnless(
HAS_NUMPY and numpy_supports_type_hints(),
f"This test requires the numpy library with a version above {NUMPY_VERSION_TYPE_HINTS_SUPPORT}",
)
def test_generic_types_are_subscriptables(self):
"""
Test that all types deriving from generic are subscriptables
"""
for type_ in (
"bool_",
"bytes_",
"character",
"complex128",
"complex192",
"complex64",
"complexfloating",
"datetime64",
"flexible",
"float16",
"float32",
"float64",
"float96",
"floating",
"generic",
"inexact",
"int16",
"int32",
"int32",
"int64",
"int8",
"integer",
"number",
"signedinteger",
"str_",
"timedelta64",
"uint16",
"uint32",
"uint32",
"uint64",
"uint8",
"unsignedinteger",
"void",
):
with self.subTest(type_=type_):
src = f"""
import numpy as np
np.{type_}[int]
"""
node = builder.extract_node(src)
cls_node = node.inferred()[0]
self.assertIsInstance(cls_node, nodes.ClassDef)
self.assertEqual(cls_node.name, type_)
@unittest.skipIf(
HAS_NUMPY, "Those tests check that astroid does not crash if numpy is not available"
)
class NumpyBrainUtilsTest(unittest.TestCase):
"""
This class is dedicated to test that astroid does not crash
if numpy module is not available
"""
def test_get_numpy_version_do_not_crash(self):
"""
Test that the function _get_numpy_version doesn't crash even if numpy is not installed
"""
self.assertEqual(_get_numpy_version(), ("0", "0", "0"))
def test_numpy_object_uninferable(self):
"""
Test that in case numpy is not available, then a numpy object is uninferable
but the inference doesn't lead to a crash
"""
src = """
import numpy as np
np.number[int]
"""
node = builder.extract_node(src)
cls_node = node.inferred()[0]
self.assertIs(cls_node, Uninferable)
if __name__ == "__main__":
unittest.main()
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