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# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/pylint-dev/astroid/blob/main/LICENSE
# Copyright (c) https://github.com/pylint-dev/astroid/blob/main/CONTRIBUTORS.txt
import unittest
try:
import numpy # pylint: disable=unused-import
HAS_NUMPY = True
except ImportError:
HAS_NUMPY = False
from astroid import bases, builder, nodes
@unittest.skipUnless(HAS_NUMPY, "This test requires the numpy library.")
class NumpyBrainCoreUmathTest(unittest.TestCase):
"""Test of all members of numpy.core.umath module."""
one_arg_ufunc = (
"arccos",
"arccosh",
"arcsin",
"arcsinh",
"arctan",
"arctanh",
"cbrt",
"conj",
"conjugate",
"cosh",
"deg2rad",
"degrees",
"exp2",
"expm1",
"fabs",
"frexp",
"isfinite",
"isinf",
"log",
"log1p",
"log2",
"logical_not",
"modf",
"negative",
"positive",
"rad2deg",
"radians",
"reciprocal",
"rint",
"sign",
"signbit",
"spacing",
"square",
"tan",
"tanh",
"trunc",
)
two_args_ufunc = (
"add",
"bitwise_and",
"bitwise_or",
"bitwise_xor",
"copysign",
"divide",
"divmod",
"equal",
"float_power",
"floor_divide",
"fmax",
"fmin",
"fmod",
"gcd",
"greater",
"heaviside",
"hypot",
"lcm",
"ldexp",
"left_shift",
"less",
"logaddexp",
"logaddexp2",
"logical_and",
"logical_or",
"logical_xor",
"maximum",
"minimum",
"multiply",
"nextafter",
"not_equal",
"power",
"remainder",
"right_shift",
"subtract",
"true_divide",
)
all_ufunc = one_arg_ufunc + two_args_ufunc
constants = ("e", "euler_gamma")
def _inferred_numpy_attribute(self, func_name):
node = builder.extract_node(
f"""
import numpy.core.umath as tested_module
func = tested_module.{func_name:s}
func"""
)
return next(node.infer())
def test_numpy_core_umath_constants(self):
"""Test that constants have Const type."""
for const in self.constants:
with self.subTest(const=const):
inferred = self._inferred_numpy_attribute(const)
self.assertIsInstance(inferred, nodes.Const)
def test_numpy_core_umath_constants_values(self):
"""Test the values of the constants."""
exact_values = {"e": 2.718281828459045, "euler_gamma": 0.5772156649015329}
for const in self.constants:
with self.subTest(const=const):
inferred = self._inferred_numpy_attribute(const)
self.assertEqual(inferred.value, exact_values[const])
def test_numpy_core_umath_functions(self):
"""Test that functions have FunctionDef type."""
for func in self.all_ufunc:
with self.subTest(func=func):
inferred = self._inferred_numpy_attribute(func)
self.assertIsInstance(inferred, bases.Instance)
def test_numpy_core_umath_functions_one_arg(self):
"""Test the arguments names of functions."""
exact_arg_names = [
"self",
"x",
"out",
"where",
"casting",
"order",
"dtype",
"subok",
]
for func in self.one_arg_ufunc:
with self.subTest(func=func):
inferred = self._inferred_numpy_attribute(func)
self.assertEqual(
inferred.getattr("__call__")[0].argnames(), exact_arg_names
)
def test_numpy_core_umath_functions_two_args(self):
"""Test the arguments names of functions."""
exact_arg_names = [
"self",
"x1",
"x2",
"out",
"where",
"casting",
"order",
"dtype",
"subok",
]
for func in self.two_args_ufunc:
with self.subTest(func=func):
inferred = self._inferred_numpy_attribute(func)
self.assertEqual(
inferred.getattr("__call__")[0].argnames(), exact_arg_names
)
def test_numpy_core_umath_functions_kwargs_default_values(self):
"""Test the default values for keyword arguments."""
exact_kwargs_default_values = [None, True, "same_kind", "K", None, True]
for func in self.one_arg_ufunc + self.two_args_ufunc:
with self.subTest(func=func):
inferred = self._inferred_numpy_attribute(func)
default_args_values = [
default.value
for default in inferred.getattr("__call__")[0].args.defaults
]
self.assertEqual(default_args_values, exact_kwargs_default_values)
def _inferred_numpy_func_call(self, func_name, *func_args):
node = builder.extract_node(
f"""
import numpy as np
func = np.{func_name:s}
func()
"""
)
return node.infer()
def test_numpy_core_umath_functions_return_type(self):
"""Test that functions which should return a ndarray do return it."""
ndarray_returning_func = [
f for f in self.all_ufunc if f not in ("frexp", "modf")
]
for func_ in ndarray_returning_func:
with self.subTest(typ=func_):
inferred_values = list(self._inferred_numpy_func_call(func_))
self.assertTrue(
len(inferred_values) == 1,
msg="Too much inferred values ({}) for {:s}".format(
inferred_values[-1].pytype(), func_
),
)
self.assertTrue(
inferred_values[0].pytype() == ".ndarray",
msg=f"Illicit type for {func_:s} ({inferred_values[-1].pytype()})",
)
def test_numpy_core_umath_functions_return_type_tuple(self):
"""Test that functions which should return a pair of ndarray do return it."""
ndarray_returning_func = ("frexp", "modf")
for func_ in ndarray_returning_func:
with self.subTest(typ=func_):
inferred_values = list(self._inferred_numpy_func_call(func_))
self.assertTrue(
len(inferred_values) == 1,
msg=f"Too much inferred values ({inferred_values}) for {func_:s}",
)
self.assertTrue(
inferred_values[-1].pytype() == "builtins.tuple",
msg=f"Illicit type for {func_:s} ({inferred_values[-1].pytype()})",
)
self.assertTrue(
len(inferred_values[0].elts) == 2,
msg=f"{func_} should return a pair of values. That's not the case.",
)
for array in inferred_values[-1].elts:
effective_infer = [m.pytype() for m in array.inferred()]
self.assertTrue(
".ndarray" in effective_infer,
msg=(
f"Each item in the return of {func_} should be inferred"
f" as a ndarray and not as {effective_infer}"
),
)
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