Python numpy 模块，cdouble() 实例源码
我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.cdouble()。
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def do(self, a, b):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
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_complex_scalar_complex_cast(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_equal(complex(x), 1+2j)
def test_complex_boolean_cast(self):
# Ticket #2218
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = np.array([0, 0+0.5j, 0.5+0j], dtype=tp)
assert_equal(x.astype(bool), np.array([0, 1, 1], dtype=bool))
assert_(np.any(x))
assert_(np.all(x[1:]))
def test_precisions_consistent(self):
z = 1 + 1j
for f in self.funcs:
fcf = f(np.csingle(z))
fcd = f(np.cdouble(z))
fcl = f(np.clongdouble(z))
assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s' % f)
assert_almost_equal(fcl, fcd, decimal=15, err_msg='fch-fcl %s' % f)
def test_complex_types():
"""Check formatting of complex types.
This is only for the str function, and only for simple types.
The precision of np.float and np.longdouble aren't the same as the
python float precision.
"""
for t in [np.complex64, np.cdouble, np.clongdouble]:
yield check_complex_type, t
def test_complex_type_print():
"""Check formatting when using print """
for t in [np.complex64, np.cdouble, np.clongdouble]:
yield check_complex_type_print, t
def test_export_record(self):
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
asbytes('aaaa'), 'bbbb', asbytes(' '), True, 1.0)],
dtype=dt)
y = memoryview(x)
assert_equal(y.shape, (1,))
assert_equal(y.ndim, 1)
assert_equal(y.suboffsets, EMPTY)
sz = sum([np.dtype(b).itemsize for a, b in dt])
if np.dtype('l').itemsize == 4:
assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
else:
assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
# Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides
if not (np.ones(1).strides[0] == np.iinfo(np.intp).max):
assert_equal(y.strides, (sz,))
assert_equal(y.itemsize, sz)
def test_array(self):
a = np.array([], float)
self.check_roundtrips(a)
a = np.array([[1, 2], [3, 4]], float)
self.check_roundtrips(a)
a = np.array([[1, 2], [3, 4]], int)
self.check_roundtrips(a)
a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.csingle)
self.check_roundtrips(a)
a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.cdouble)
self.check_roundtrips(a)
def test_basic(self):
ai32 = np.array([[1, 2], [3, 4]], dtype=np.int32)
af16 = np.array([[1, 2], [3, 4]], dtype=np.float16)
af32 = np.array([[1, 2], [3, 4]], dtype=np.float32)
af64 = np.array([[1, 2], [3, 4]], dtype=np.float64)
acs = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.csingle)
acd = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.cdouble)
assert_(common_type(ai32) == np.float64)
assert_(common_type(af16) == np.float16)
assert_(common_type(af32) == np.float32)
assert_(common_type(af64) == np.float64)
assert_(common_type(acs) == np.csingle)
assert_(common_type(acd) == np.cdouble)
def get_complex_dtype(dtype):
return {single: csingle, double: cdouble,
csingle: csingle, cdouble: cdouble}[dtype]
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.solve(x, x).dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.inv(x).dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.eigvals(x).dtype, dtype)
x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
assert_equal(linalg.eigvals(x).dtype, get_complex_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w, v = np.linalg.eig(x)
assert_equal(w.dtype, dtype)
assert_equal(v.dtype, dtype)
x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
w, v = np.linalg.eig(x)
assert_equal(w.dtype, get_complex_dtype(dtype))
assert_equal(v.dtype, get_complex_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_zero(self):
assert_equal(linalg.det([[0.0]]), 0.0)
assert_equal(type(linalg.det([[0.0]])), double)
assert_equal(linalg.det([[0.0j]]), 0.0)
assert_equal(type(linalg.det([[0.0j]])), cdouble)
assert_equal(linalg.slogdet([[0.0]]), (0.0, -inf))
assert_equal(type(linalg.slogdet([[0.0]])[0]), double)
assert_equal(type(linalg.slogdet([[0.0]])[1]), double)
assert_equal(linalg.slogdet([[0.0j]]), (0.0j, -inf))
assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble)
assert_equal(type(linalg.slogdet([[0.0j]])[1]), double)
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(np.linalg.det(x).dtype, dtype)
ph, s = np.linalg.slogdet(x)
assert_equal(s.dtype, get_real_dtype(dtype))
assert_equal(ph.dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w = np.linalg.eigvalsh(x)
assert_equal(w.dtype, get_real_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w, v = np.linalg.eigh(x)
assert_equal(w.dtype, get_real_dtype(dtype))
assert_equal(v.dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
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_complex_scalar_complex_cast(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_equal(complex(x), 1+2j)
def test_complex_boolean_cast(self):
# Ticket #2218
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = np.array([0, 0+0.5j, 0.5+0j], dtype=tp)
assert_equal(x.astype(bool), np.array([0, 1, 1], dtype=bool))
assert_(np.any(x))
assert_(np.all(x[1:]))
def test_precisions_consistent(self):
z = 1 + 1j
for f in self.funcs:
fcf = f(np.csingle(z))
fcd = f(np.cdouble(z))
fcl = f(np.clongdouble(z))
assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s' % f)
assert_almost_equal(fcl, fcd, decimal=15, err_msg='fch-fcl %s' % f)
def test_complex_types():
"""Check formatting of complex types.
This is only for the str function, and only for simple types.
The precision of np.float and np.longdouble aren't the same as the
python float precision.
"""
for t in [np.complex64, np.cdouble, np.clongdouble]:
yield check_complex_type, t
def test_complex_type_print():
"""Check formatting when using print """
for t in [np.complex64, np.cdouble, np.clongdouble]:
yield check_complex_type_print, t
def test_export_record(self):
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
asbytes('aaaa'), 'bbbb', asbytes(' '), True, 1.0)],
dtype=dt)
y = memoryview(x)
assert_equal(y.shape, (1,))
assert_equal(y.ndim, 1)
assert_equal(y.suboffsets, EMPTY)
sz = sum([np.dtype(b).itemsize for a, b in dt])
if np.dtype('l').itemsize == 4:
assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
else:
assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
# Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides
if not (np.ones(1).strides[0] == np.iinfo(np.intp).max):
assert_equal(y.strides, (sz,))
assert_equal(y.itemsize, sz)
def test_array(self):
a = np.array([], float)
self.check_roundtrips(a)
a = np.array([[1, 2], [3, 4]], float)
self.check_roundtrips(a)
a = np.array([[1, 2], [3, 4]], int)
self.check_roundtrips(a)
a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.csingle)
self.check_roundtrips(a)
a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.cdouble)
self.check_roundtrips(a)
def test_basic(self):
ai32 = np.array([[1, 2], [3, 4]], dtype=np.int32)
af16 = np.array([[1, 2], [3, 4]], dtype=np.float16)
af32 = np.array([[1, 2], [3, 4]], dtype=np.float32)
af64 = np.array([[1, 2], [3, 4]], dtype=np.float64)
acs = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.csingle)
acd = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.cdouble)
assert_(common_type(ai32) == np.float64)
assert_(common_type(af16) == np.float16)
assert_(common_type(af32) == np.float32)
assert_(common_type(af64) == np.float64)
assert_(common_type(acs) == np.csingle)
assert_(common_type(acd) == np.cdouble)
def get_complex_dtype(dtype):
return {single: csingle, double: cdouble,
csingle: csingle, cdouble: cdouble}[dtype]
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.solve(x, x).dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.inv(x).dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.eigvals(x).dtype, dtype)
x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
assert_equal(linalg.eigvals(x).dtype, get_complex_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w, v = np.linalg.eig(x)
assert_equal(w.dtype, dtype)
assert_equal(v.dtype, dtype)
x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
w, v = np.linalg.eig(x)
assert_equal(w.dtype, get_complex_dtype(dtype))
assert_equal(v.dtype, get_complex_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_zero(self):
assert_equal(linalg.det([[0.0]]), 0.0)
assert_equal(type(linalg.det([[0.0]])), double)
assert_equal(linalg.det([[0.0j]]), 0.0)
assert_equal(type(linalg.det([[0.0j]])), cdouble)
assert_equal(linalg.slogdet([[0.0]]), (0.0, -inf))
assert_equal(type(linalg.slogdet([[0.0]])[0]), double)
assert_equal(type(linalg.slogdet([[0.0]])[1]), double)
assert_equal(linalg.slogdet([[0.0j]]), (0.0j, -inf))
assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble)
assert_equal(type(linalg.slogdet([[0.0j]])[1]), double)
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(np.linalg.det(x).dtype, dtype)
ph, s = np.linalg.slogdet(x)
assert_equal(s.dtype, get_real_dtype(dtype))
assert_equal(ph.dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w = np.linalg.eigvalsh(x)
assert_equal(w.dtype, get_real_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w, v = np.linalg.eigh(x)
assert_equal(w.dtype, get_real_dtype(dtype))
assert_equal(v.dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def Sequence_Mask(\
self,
pipelineitem
):
if self.pipeline_started == True:
title = "Sequence " + pipelineitem.treeitem['name']
self.ancestor.GetPage(0).queue_info.put("Preparing mask array...")
filename_in = pipelineitem.input_filename.objectpath.GetValue()
filename_out = pipelineitem.output_filename.objectpath.GetValue()
frac_max = float(pipelineitem.max.value.GetValue())
frac_min = float(pipelineitem.min.value.GetValue())
try:
array = LoadArray(self, filename_in)
except:
msg = "Could not load array."
wx.CallAfter(self.UserMessage, title, msg)
self.pipeline_started = False
return
else:
mask = numpy.asarray(array, dtype=numpy.cdouble, order='C')
from ..lib.prfftw import rangereplace
rangereplace(mask, frac_min, frac_max, 0.0, 1.0)
try:
SaveArray(self, filename_out,mask)
except:
msg = "Could not save array."
wx.CallAfter(self.UserMessage, title, msg)
self.pipeline_started = False
return
def WrapArray2(array):
if array.shape[2] == 1:
b1 = numpy.array_split( numpy.array_split( array, 2, axis=0 )[0], 2, axis=1 )[0]
b2 = numpy.array_split( numpy.array_split( array, 2, axis=0 )[0], 2, axis=1 )[1]
b3 = numpy.array_split( numpy.array_split( array, 2, axis=0 )[1], 2, axis=1 )[0]
b4 = numpy.array_split( numpy.array_split( array, 2, axis=0 )[1], 2, axis=1 )[1]
v1 = numpy.vstack((b4,b2))
v2 = numpy.vstack((b3,b1))
arrayfinal = numpy.array(numpy.hstack((v1,v2)), dtype=numpy.cdouble, copy=True, order='C')
return arrayfinal
else:
b1 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[0], 2, axis=1 )[0], 2, axis=2 )[0]
b2 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[0], 2, axis=1 )[0], 2, axis=2 )[1]
b3 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[0], 2, axis=1 )[1], 2, axis=2 )[0]
b4 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[0], 2, axis=1 )[1], 2, axis=2 )[1]
b5 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[1], 2, axis=1 )[0], 2, axis=2 )[0]
b6 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[1], 2, axis=1 )[0], 2, axis=2 )[1]
b7 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[1], 2, axis=1 )[1], 2, axis=2 )[0]
b8 = numpy.array_split( numpy.array_split( numpy.array_split( array, 2, axis=0 )[1], 2, axis=1 )[1], 2, axis=2 )[1]
v1 = numpy.vstack((b8,b4))
v2 = numpy.vstack((b7,b3))
v3 = numpy.vstack((b6,b2))
v4 = numpy.vstack((b5,b1))
h1 = numpy.hstack((v1,v3))
h2 = numpy.hstack((v2,v4))
arrayfinal = numpy.array(numpy.dstack((h1,h2)), dtype=numpy.cdouble, copy=True, order='C')
return arrayfinal
def NewArray(self,x,y,z):
try:
array = numpy.zeros((x,y,z), dtype=numpy.cdouble, order='C')
except MemoryError:
self.ancestor.GetPage(0).queue_info.put("Could not create array. Insufficient memory.")
raise MemoryError
else:
return array
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_complex_scalar_complex_cast(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_equal(complex(x), 1+2j)
def test_complex_boolean_cast(self):
# Ticket #2218
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = np.array([0, 0+0.5j, 0.5+0j], dtype=tp)
assert_equal(x.astype(bool), np.array([0, 1, 1], dtype=bool))
assert_(np.any(x))
assert_(np.all(x[1:]))
def test_precisions_consistent(self):
z = 1 + 1j
for f in self.funcs:
fcf = f(np.csingle(z))
fcd = f(np.cdouble(z))
fcl = f(np.clongdouble(z))
assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s' % f)
assert_almost_equal(fcl, fcd, decimal=15, err_msg='fch-fcl %s' % f)