def test_remainder(self):
with testing.NumpyError(divide='ignore'):
self.check_binary('remainder', no_complex=True)
self.check_raises_with_numpy_input(2, 'remainder')
def test_rmod_scalarzero(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_scalarzero_op(operator.mod, swap=True)
def test_rtruediv_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(operator.truediv, swap=True)
def test_doubly_broadcasted_truediv(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_doubly_broadcasted_op(operator.truediv)
def test_doubly_broadcasted_divmod1(self):
with testing.NumpyError(divide='ignore'):
self.check_array_doubly_broadcasted_op(
lambda x, y: divmod(x, y)[1],
no_complex=True)
def test_floordiv_array(self):
with testing.NumpyError(divide='ignore'):
self.check_array_array_op(operator.floordiv, no_complex=True)
def test_broadcasted_itruediv(self):
with testing.NumpyError(divide='ignore'):
self.check_array_broadcasted_op(operator.itruediv)
def test_doubly_broadcasted_divmod0(self):
with testing.NumpyError(divide='ignore'):
self.check_array_doubly_broadcasted_op(
lambda x, y: divmod(x, y)[0])
def test_floordiv_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(operator.floordiv)
def test_divide_negative(self):
with testing.NumpyError(divide='ignore'):
self.check_binary_negative('divide')
def test_divmod0_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(lambda x, y: divmod(x, y)[0])
def test_divide(self):
with testing.NumpyError(divide='ignore'):
self.check_binary('divide')
self.check_raises_with_numpy_input(2, 'divide')
def test_reciprocal(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_unary('reciprocal')
self.check_raises_with_numpy_input(1, 'reciprocal')
def test_remainder_negative(self):
with testing.NumpyError(divide='ignore'):
self.check_binary_negative_no_complex('remainder')
def test_rdivmod1_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(lambda x, y: divmod(x, y)[1], swap=True,
no_complex=True)
def test_norm(self, xp, dtype):
a = testing.shaped_arange(self.shape, xp, dtype)
with testing.NumpyError(divide='ignore'):
return xp.linalg.norm(a, self.ord, self.axis, self.keepdims)
def test_itruediv_array(self):
with testing.NumpyError(divide='ignore'):
self.check_array_array_op(operator.itruediv)
def test_ifloordiv_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(operator.ifloordiv, no_complex=True)
def test_divmod1_array(self):
with testing.NumpyError(divide='ignore'):
self.check_array_array_op(lambda x, y: divmod(x, y)[1])
def test_div_array(self):
if six.PY3:
return
with testing.NumpyError(divide='ignore'):
self.check_array_array_op(operator.div)
def test_broadcasted_ifloordiv(self):
if '1.16.1' <= numpy.lib.NumpyVersion(numpy.__version__) < '1.18.0':
self.skipTest("NumPy Issue #12927")
with testing.NumpyError(divide='ignore'):
self.check_array_broadcasted_op(operator.ifloordiv,
no_complex=True)
def test_doubly_broadcasted_div(self):
if six.PY3:
return
with testing.NumpyError(divide='ignore'):
self.check_array_doubly_broadcasted_op(operator.div)
def test_doubly_broadcasted_floordiv(self):
with testing.NumpyError(divide='ignore'):
self.check_array_doubly_broadcasted_op(operator.floordiv,
no_complex=True)
def test_idiv_scalar(self):
if six.PY3:
return
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(operator.idiv)
def test_mod_scalar(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_scalar_op(operator.mod)
def impl(x):
with testing.NumpyError(divide='ignore', invalid='ignore'):
return getattr(xp, self.func)(x)
def test_imod_array(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_array_op(operator.imod)
def check_binary(self, xp):
arg1 = self.arg1
arg2 = self.arg2
np1 = numpy.asarray(arg1)
np2 = numpy.asarray(arg2)
dtype1 = np1.dtype
dtype2 = np2.dtype
if self.name == 'power':
# TODO(niboshi): Fix this: power(0, 1j)
# numpy => 1+0j
# cupy => 0j
if dtype2 in complex_types and (np1 == 0).any():
return xp.array(True)
# TODO(niboshi): Fix this: xp.power(0j, 0)
# numpy => 1+0j
# cupy => 0j
c_arg1 = dtype1 in complex_types
if c_arg1 and (np1 == 0j).any() and (np2 == 0).any():
return xp.array(True)
# TODO(niboshi): Fix this: xp.add(0j, xp.array([2.], 'f')).dtype
# numpy => complex64
# cupy => complex128
if isinstance(arg1, complex):
if dtype2 in (numpy.float16, numpy.float32):
return xp.array(True)
if isinstance(arg1, numpy.ndarray):
arg1 = xp.asarray(arg1)
if isinstance(arg2, numpy.ndarray):
arg2 = xp.asarray(arg2)
# NumPy>=1.13.0 does not support subtraction between booleans
# TODO(niboshi): Write a separate test to check both NumPy and CuPy
# raise TypeError.
if testing.numpy_satisfies('>=1.13.0') and self.name == 'subtract':
if dtype1 == numpy.bool_ and dtype2 == numpy.bool_:
return xp.array(True)
func = getattr(xp, self.name)
with testing.NumpyError(divide='ignore'):
with numpy.warnings.catch_warnings():
numpy.warnings.filterwarnings('ignore')
if self.use_dtype:
y = func(arg1, arg2, dtype=self.dtype)
else:
y = func(arg1, arg2)
# TODO(niboshi): Fix this. If rhs is a Python complex,
# numpy returns complex64
# cupy returns complex128
if xp is cupy and isinstance(arg2, complex):
if dtype1 in (numpy.float16, numpy.float32):
y = y.astype(numpy.complex64)
# NumPy returns different values (nan/inf) on division by zero
# depending on the architecture.
# As it is not possible for CuPy to replicate this behavior, we ignore
# the difference here.
if self.name in ('floor_divide', 'remainder'):
if y.dtype in (float_types + complex_types) and (np2 == 0).any():
y = xp.asarray(y)
y[y == numpy.inf] = numpy.nan
y[y == -numpy.inf] = numpy.nan
return y
def test_broadcasted_mod(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_broadcasted_op(operator.mod)
def test_fmod_negative(self):
with testing.NumpyError(divide='ignore'):
self.check_binary_negative_no_complex('fmod')
