def test_binary_cast_scalar_scalar_with_out_with_where(
self, xp, op, in1, in2, out, where):
out = xp.array(out)
where = xp.array(where)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore',
over='ignore',
invalid='ignore'):
with numpy.warnings.catch_warnings():
numpy.warnings.simplefilter('ignore', numpy.ComplexWarning)
func = getattr(xp, op)
y = func(in1, in2, out=out, where=where)
if xp is numpy and op in float16_op_set:
if isinstance(in1, bool) and \
isinstance(in2, bool):
y = func(in1,
in2,
out=out,
where=where,
dtype='f4')
return y
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_binary_cast_array_array_with_out_with_where_broadcast(
self, xp, op, in1, in2, out, where):
in1 = xp.array(in1)
in2 = xp.array(in2)
out = xp.array(out)
where = xp.array(where)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore',
over='ignore',
invalid='ignore'):
with numpy.warnings.catch_warnings():
numpy.warnings.simplefilter('ignore', numpy.ComplexWarning)
func = getattr(xp, op)
y = func(in1, in2, out=out, where=where)
if xp is numpy and op in float16_op_set:
if in1.dtype.kind == 'b' and in2.dtype.kind == 'b':
y = func(in1,
in2,
out=out,
where=where,
dtype='f4')
return y
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_unary_cast_scalar_with_dtype(self, xp, op, in1, dtype):
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore', over='ignore', invalid='ignore'):
func = getattr(xp, op)
y = func(in1, dtype=dtype)
if xp is nlcpy:
nlcpy.request.flush()
return y
def check_typecast(self, val, dtype):
operators = [
operator.add, operator.sub, operator.mul, operator.truediv]
for op in operators:
with testing.NumpyError(divide='ignore', invalid='ignore'):
a = op(val, (testing.shaped_arange((5,), numpy, dtype) - 2))
b = op(val, (testing.shaped_arange((5,), nlcpy, dtype) - 2))
self.assertEqual(a.dtype, b.dtype)
def test_unary_cast_array(self, xp, op, in1):
in1 = xp.array(in1)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore', over='ignore', invalid='ignore'):
func = getattr(xp, op)
y = func(in1)
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_unary_cast_array_with_out_with_dtype(self, xp, op, in1, out, dtype):
in1 = xp.array(in1)
out = xp.array(out)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore', over='ignore', invalid='ignore'):
func = getattr(xp, op)
y = func(in1, out=out, dtype=dtype)
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_binary_cast_scalar_scalar_with_out_with_dtype(
self, xp, op, in1, in2, out):
out = xp.array(out)
dtype = out.dtype
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore',
over='ignore',
invalid='ignore'):
func = getattr(xp, op)
y = func(in1, in2, out=out, dtype=dtype)
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_unary_cast_scalar_with_out(self, xp, op, in1, out):
out = xp.array(out)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore', over='ignore', invalid='ignore'):
func = getattr(xp, op)
y = func(in1, out=out)
if xp is numpy and op in float16_op_set:
if isinstance(in1, bool):
y = func(in1, out=out, dtype='f4')
return y
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_unary_cast_scalar_with_out_with_where_with_dtype_broadcast(
self, xp, op, in1, out, where, dtype):
out = xp.array(out)
where = xp.array(where)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore', over='ignore', invalid='ignore'):
with numpy.warnings.catch_warnings():
numpy.warnings.simplefilter('ignore', numpy.ComplexWarning)
func = getattr(xp, op)
y = func(in1, out=out, where=where, dtype=dtype)
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_unary_cast_array_with_out_broadcast(self, xp, op, in1, out):
in1 = xp.array(in1)
out = xp.array(out)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore', over='ignore', invalid='ignore'):
func = getattr(xp, op)
y = func(in1, out=out)
if xp is numpy and op in float16_op_set:
if in1.dtype == numpy.dtype('bool'):
y = func(in1, out=out, dtype='f4')
return y
if xp is nlcpy:
nlcpy.request.flush()
return y
def check_typecast(self, val, dtype):
operators = [
operator.add, operator.sub, operator.mul, operator.truediv
]
mask = [0, 1, 0, 1, 0]
nval = testing.shaped_arange((5, ), numpy, dtype) - 2
vval = testing.shaped_arange((5, ), nlcpy, dtype) - 2
nval = numpy.ma.array(nval, mask=mask)
vval = nlcpy.ma.array(vval, mask=mask)
for op in operators:
with testing.NumpyError(divide='ignore', invalid='ignore'):
a = op(val, nval)
b = op(val, vval)
self.assertEqual(a.dtype, b.dtype)
def test_binary_broadcast_array_array_with_where(self, xp, op, in1, in2,
out, where):
in1 = xp.array(in1)
in2 = xp.array(in2)
out = xp.array(out)
where = xp.array(where)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore',
over='ignore',
invalid='ignore'):
func = getattr(xp, op)
y = func(in1, in2, out=out, where=where)
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_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: Fix this: xp.power(0j, 0)
# numpy => 1+0j
# nlcpy => nan + nanj
c_arg1 = dtype1 in complex_types
if c_arg1 and (np1 == 0j).any() and (np2 == 0).any():
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: Write a separate test to check both NumPy and nlcpy
# 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')
y = func(arg1, arg2)
# NumPy returns different values (nan/inf) on division by zero
# depending on the architecture.
# As it is not possible for nlcpy 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_binary_cast_array_array_with_out(self, xp, op, in1, in2, out):
in1 = xp.array(in1)
in2 = xp.array(in2)
out = xp.array(out)
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore',
over='ignore',
invalid='ignore'):
func = getattr(xp, op)
y = func(in1, in2, out=out)
if xp is numpy and op in float16_op_set:
if in1.dtype.kind == 'b' and in2.dtype.kind == 'b':
y = func(in1, in2, out=out, dtype='f4')
return y
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_binary_cast_array_array_with_out_with_where_with_dtype(
self, xp, op, in1, in2, out, where):
in1 = xp.array(in1)
in2 = xp.array(in2)
out = xp.array(out)
where = xp.array(where)
dtype = out.dtype
with testing.NumpyError(divide='ignore'):
with testing.NlcpyError(divide='ignore',
over='ignore',
invalid='ignore'):
with numpy.warnings.catch_warnings():
numpy.warnings.simplefilter('ignore', numpy.ComplexWarning)
func = getattr(xp, op)
y = func(in1, in2, out=out, where=where, dtype=dtype)
if xp is nlcpy:
nlcpy.request.flush()
return y
def test_imod_array(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_array_op(operator.imod)
def test_ifloordiv_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(operator.ifloordiv, no_complex=True)
def test_itruediv_array(self):
with testing.NumpyError(divide='ignore'):
self.check_array_array_op(operator.itruediv)
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_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_floordiv(self):
with testing.NumpyError(divide='ignore'):
self.check_array_doubly_broadcasted_op(operator.floordiv,
no_complex=True)
def test_log2(self):
with testing.NumpyError(divide='ignore'):
self.check_unary('log2', no_complex=True)
def test_itruediv_array(self):
with testing.NumpyError(divide='ignore'):
self.check_ma_ndarray_op('__itruediv__')
def test_mod_scalar(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_scalar_op(operator.mod)
def test_rmod_scalarzero(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_scalarzero_op(operator.mod, swap=True)
def test_truediv_ma(self):
with testing.NumpyError(divide='ignore'):
self.check_ma_ma_op('__truediv__')
def test_doubly_broadcasted_mod(self):
with testing.NumpyError(divide='ignore', invalid='ignore'):
self.check_array_doubly_broadcasted_op(operator.mod)
def test_truediv_scalar(self):
with testing.NumpyError(divide='ignore'):
self.check_array_scalar_op(operator.truediv)
def test_log(self):
with testing.NumpyError(divide='ignore'):
self.check_unary('log')
