def test_complex_scalar_warning(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_warns(np.ComplexWarning, float, x)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
assert_equal(float(x), float(x.real))
python类ComplexWarning()的实例源码
def test_iter_buffered_cast_byteswapped():
# Test that buffering can handle a cast which requires swap->cast->swap
a = np.arange(10, dtype='f4').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
try:
warnings.simplefilter("ignore", np.ComplexWarning)
a = np.arange(10, dtype='f8').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='unsafe',
op_dtypes=[np.dtype('c8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f8'))
finally:
warnings.simplefilter("default", np.ComplexWarning)
def test_complex_warning(self):
x = np.array([1, 2])
y = np.array([1-2j, 1+2j])
with warnings.catch_warnings():
warnings.simplefilter("error", np.ComplexWarning)
assert_raises(np.ComplexWarning, x.__setitem__, slice(None), y)
assert_equal(x, [1, 2])
def test_complex_scalar_warning(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_warns(np.ComplexWarning, float, x)
with suppress_warnings() as sup:
sup.filter(np.ComplexWarning)
assert_equal(float(x), float(x.real))
def test_iter_buffered_cast_byteswapped():
# Test that buffering can handle a cast which requires swap->cast->swap
a = np.arange(10, dtype='f4').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
with suppress_warnings() as sup:
sup.filter(np.ComplexWarning)
a = np.arange(10, dtype='f8').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='unsafe',
op_dtypes=[np.dtype('c8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f8'))
def test_complex_warning(self):
x = np.array([1, 2])
y = np.array([1-2j, 1+2j])
with warnings.catch_warnings():
warnings.simplefilter("error", np.ComplexWarning)
assert_raises(np.ComplexWarning, x.__setitem__, slice(None), y)
assert_equal(x, [1, 2])
def test_complex_scalar_warning(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_warns(np.ComplexWarning, float, x)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
assert_equal(float(x), float(x.real))
def test_iter_buffered_cast_byteswapped():
# Test that buffering can handle a cast which requires swap->cast->swap
a = np.arange(10, dtype='f4').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
try:
warnings.simplefilter("ignore", np.ComplexWarning)
a = np.arange(10, dtype='f8').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='unsafe',
op_dtypes=[np.dtype('c8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f8'))
finally:
warnings.simplefilter("default", np.ComplexWarning)
def test_complex_warning(self):
x = np.array([1, 2])
y = np.array([1-2j, 1+2j])
with warnings.catch_warnings():
warnings.simplefilter("error", np.ComplexWarning)
assert_raises(np.ComplexWarning, x.__setitem__, slice(None), y)
assert_equal(x, [1, 2])
def conv(self, img, kern, mode="valid", dilation=1):
"""
Basic slow Python 2D or 3D convolution for DebugMode
"""
if not imported_scipy_signal:
raise NotImplementedError(
"AbstractConv perform requires the python package"
" for scipy.signal to be installed.")
if not (mode in ('valid', 'full')):
raise ValueError(
'invalid mode {}, which must be either '
'"valid" or "full"'.format(mode))
if isinstance(dilation, integer_types):
dilation = (dilation,) * self.convdim
if len(dilation) != self.convdim:
raise ValueError(
'invalid dilation {}, expected {} values'.format(dilation,
self.convdim))
out_shape = get_conv_output_shape(img.shape, kern.shape,
mode, [1] * self.convdim, dilation)
out = numpy.zeros(out_shape, dtype=img.dtype)
dil_kern_shp = kern.shape[:-self.convdim] + tuple(
(kern.shape[-self.convdim + i] - 1) * dilation[i] + 1
for i in range(self.convdim))
dilated_kern = numpy.zeros(dil_kern_shp, dtype=kern.dtype)
dilated_kern[(slice(None), slice(None)) +
tuple(slice(None, None, dilation[i]) for i in range(self.convdim))
] = kern
if self.convdim == 2:
val = _valfrommode(mode)
bval = _bvalfromboundary('fill')
with warnings.catch_warnings():
warnings.simplefilter('ignore', numpy.ComplexWarning)
for b in xrange(img.shape[0]):
for n in xrange(kern.shape[0]):
for im0 in xrange(img.shape[1]):
# some cast generates a warning here
out[b, n, ...] += _convolve2d(img[b, im0, ...],
dilated_kern[n, im0, ...],
1, val, bval, 0)
elif self.convdim == 3:
for b in xrange(img.shape[0]):
for n in xrange(kern.shape[0]):
for im0 in xrange(img.shape[1]):
out[b, n, ...] += convolve(img[b, im0, ...],
dilated_kern[n, im0, ...],
mode)
else:
raise NotImplementedError('only 2D and 3D convolution are implemented')
return out
