# Python numpy.iscomplex() Examples

The following are 15 code examples for showing how to use numpy.iscomplex(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
def test_eigs(par):
"""Eigenvalues and condition number estimate with ARPACK
"""
# explicit=True
diag = np.arange(par['nx'], 0, -1) +\
par['imag'] * np.arange(par['nx'], 0, -1)
Op = MatrixMult(np.vstack((np.diag(diag),
np.zeros((par['ny'] - par['nx'], par['nx'])))))
eigs = Op.eigs()
assert_array_almost_equal(diag[:eigs.size], eigs, decimal=3)

cond = Op.cond()
assert_array_almost_equal(np.real(cond), par['nx'], decimal=3)

# explicit=False
Op = Diagonal(diag, dtype=par['dtype'])
if par['ny'] > par['nx']:
Op = VStack([Op, Zero(par['ny'] - par['nx'], par['nx'])])
eigs = Op.eigs()
assert_array_almost_equal(diag[:eigs.size], eigs, decimal=3)

# uselobpcg cannot be used for square non-symmetric complex matrices
if np.iscomplex(Op):
eigs1 = Op.eigs(uselobpcg=True)
assert_array_almost_equal(eigs, eigs1, decimal=3)

cond = Op.cond()
assert_array_almost_equal(np.real(cond), par['nx'], decimal=3)

# uselobpcg cannot be used for square non-symmetric complex matrices
if np.iscomplex(Op):
cond1 = Op.cond(uselobpcg=True, niter=100)
assert_array_almost_equal(np.real(cond), np.real(cond1), decimal=3) 
Example 2
def _ll_nbin(self, params, alpha, Q=0):
if np.any(np.iscomplex(params)) or np.iscomplex(alpha):
gamma_ln = loggamma
else:
gamma_ln = gammaln
endog = self.endog
mu = self.predict(params)
size = 1/alpha * mu**Q
prob = size/(size+mu)
coeff = (gamma_ln(size+endog) - gamma_ln(endog+1) -
gamma_ln(size))
llf = coeff + size*np.log(prob) + endog*np.log(1-prob)
return llf 
Example 3
def iscomplex(x):
return array_ops.imag(x) != 0 
Example 4
def t_REAL(self, t):
r'(([0-9]+|([0-9]+)?\.[0-9]+|[0-9]+\.)[eE][+-]?[0-9]+)|(([0-9]+)?\.[0-9]+|[0-9]+\.)'
if np.iscomplex(t):
return t.real
else:
return t 
Example 5
def iscomplex(x):
"""Returns a bool array, where True if input element is complex.

What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.

Args:
x (cupy.ndarray): Input array.

Returns:
cupy.ndarray: Boolean array of the same shape as x.

.. seealso::
:func:isreal, :func:iscomplexobj

Examples
--------
>>> cupy.iscomplex(cupy.array([1+1j, 1+0j, 4.5, 3, 2, 2j]))
array([ True, False, False, False, False,  True])

"""
if numpy.isscalar(x):
return numpy.iscomplex(x)
if not isinstance(x, cupy.ndarray):
return cupy.asarray(numpy.iscomplex(x))
if x.dtype.kind == 'c':
return x.imag != 0
return cupy.zeros(x.shape, bool) 
Example 6
def iscomplexobj(x):
"""Check for a complex type or an array of complex numbers.

The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, iscomplexobj evaluates to True.

Args:
x (cupy.ndarray): Input array.

Returns:
bool: The return value, True if x is of a complex type or
has at least one complex element.

.. seealso::
:func:isrealobj, :func:iscomplex

Examples
--------
>>> cupy.iscomplexobj(cupy.array([3, 1+0j, True]))
True
>>> cupy.iscomplexobj(cupy.array([3, 1, True]))
False

"""
if not isinstance(x, cupy.ndarray):
return numpy.iscomplexobj(x)
return x.dtype.kind == 'c' 
Example 7
def isreal(x):
"""Returns a bool array, where True if input element is real.

If element has complex type with zero complex part, the return value
for that element is True.

Args:
x (cupy.ndarray): Input array.

Returns:
cupy.ndarray: Boolean array of same shape as x.

.. seealso::
:func:iscomplex, :func:isrealobj

Examples
--------
>>> cupy.isreal(cp.array([1+1j, 1+0j, 4.5, 3, 2, 2j]))
array([False,  True,  True,  True,  True, False])

"""
if numpy.isscalar(x):
return numpy.isreal(x)
if not isinstance(x, cupy.ndarray):
return cupy.asarray(numpy.isreal(x))
if x.dtype.kind == 'c':
return x.imag == 0
return cupy.ones(x.shape, bool) 
Example 8
def test_outer_product(self, n, dtype):
r"""Check that hafnian(x \otimes x) = hafnian(J_2n)*prod(x)"""
x = np.random.rand(2 * n) + 1j * np.random.rand(2 * n)

if not np.iscomplex(dtype()):
x = x.real

x = dtype(x)
A = np.outer(x, x)

rpt = np.ones([2 * n], dtype=np.int32)
haf = hafnian_repeated(A, rpt)
expected = np.prod(x) * fac(2 * n) / (fac(n) * (2 ** n))
assert np.allclose(haf, expected) 
Example 9
def set_filters(self, filters, padding_type='SAME'):
"""
Given a list of temporal 1D filters of variable size, this method creates a
list of nn.conv1d objects that collectively form the filter bank.

:param filters: list, collection of filters each a np.ndarray
:param padding_type: str, should be SAME or VALID
:return:
"""

assert isinstance(filters, list)

self._filters = [None]*len(filters)
for ind, filt in enumerate(filters):

assert filt.dtype in (np.float32, np.float64, np.complex64, np.complex128)

if np.iscomplex(filt).any():
chn_out = 2
filt_weights = np.asarray([np.real(filt), np.imag(filt)], np.float32)
else:
chn_out = 1
filt_weights = filt.astype(np.float32)[None,:]

filt_weights = np.expand_dims(filt_weights, 1)  # append chn_in dimension
filt_size = filt_weights.shape[-1]              # filter length

conv.weight.data = torch.from_numpy(filt_weights)

if self._cuda: conv.cuda()
self._filters[ind] = conv 
Example 10
def test_iscomplex(self):
self.check(np.iscomplex)
assert np.iscomplex([1. + 1j]*u.m) 
Example 11
def find_zero_crossing_quadratic(x1, y1, x2, y2, x3, y3, eps=1.0):
""" Find zero crossing using quadratic approximation along 1d line"""
# compute coords along 1d line
v = x2 - x1
v = v / np.linalg.norm(v)
if v[v != 0].shape[0] == 0:
logging.error('Difference is 0. Probably a bug')

t1 = 0
t2 = (x2 - x1)[v != 0] / v[v != 0]
t2 = t2[0]
t3 = (x3 - x1)[v != 0] / v[v != 0]
t3 = t3[0]

x1_row = np.array([t1 ** 2, t1, 1])
x2_row = np.array([t2 ** 2, t2, 1])
x3_row = np.array([t3 ** 2, t3, 1])
X = np.array([x1_row, x2_row, x3_row])
y_vec = np.array([y1, y2, y3])
try:
w = np.linalg.solve(X, y_vec)
except np.linalg.LinAlgError:
logging.error('Singular matrix. Probably a bug')
return None

# get positive roots
possible_t = np.roots(w)
t_zc = None
for i in range(possible_t.shape[0]):
if 0 <= possible_t[i] <= 10 and not np.iscomplex(possible_t[i]):
t_zc = possible_t[i]

# if no positive roots find min
if np.abs(w[0]) < 1e-10:
return None

if t_zc is None:
t_zc = -w[1] / (2 * w[0])

if t_zc < -eps or t_zc > eps:
return None

x_zc = x1 + t_zc * v
return x_zc 
Example 12
def find_zero_crossing_quadratic(x1, y1, x2, y2, x3, y3, eps = 1.0):
""" Find zero crossing using quadratic approximation along 1d line"""
# compute coords along 1d line
v = x2 - x1
v = v / np.linalg.norm(v)
if v[v!=0].shape[0] == 0:
logging.error('Difference is 0. Probably a bug')

t1 = 0
t2 = (x2 - x1)[v!=0] / v[v!=0]
t2 = t2[0]
t3 = (x3 - x1)[v!=0] / v[v!=0]
t3 = t3[0]

x1_row = np.array([t1**2, t1, 1])
x2_row = np.array([t2**2, t2, 1])
x3_row = np.array([t3**2, t3, 1])
X = np.array([x1_row, x2_row, x3_row])
y_vec = np.array([y1, y2, y3])
try:
w = np.linalg.solve(X, y_vec)
except np.linalg.LinAlgError:
logging.error('Singular matrix. Probably a bug')
return None

# get positive roots
possible_t = np.roots(w)
t_zc = None
for i in range(possible_t.shape[0]):
if possible_t[i] >= 0 and possible_t[i] <= 10 and not np.iscomplex(possible_t[i]):
t_zc = possible_t[i]

# if no positive roots find min
if np.abs(w[0]) < 1e-10:
return None

if t_zc is None:
t_zc = -w[1] / (2 * w[0])

if t_zc < -eps or t_zc > eps:
return None

x_zc = x1 + t_zc * v
return x_zc 
Example 13
def filter(self, array, *args, **kwargs):
arr1 = array[0]
arr2 = array[1]
ny, nx = arr1.shape
dy, dx = 2 ** (int(np.log(min(ny, 4096)) / np.log(2))), 2 ** (int(np.log(min(nx, 4096)) / np.log(2)))
offset = coreg(arr1[(ny - dy) / 2:(ny + dy) / 2, (nx - dx) / 2:(nx + dx) / 2],
arr2[(ny - dy) / 2:(ny + dy) / 2, (nx - dx) / 2:(nx + dx) / 2])
logging.info('Global offset : ' + str(int(offset[0])) + ' / ' + str(int(offset[1])))

paty = np.arange(12) * ny / 12
patx = np.arange(12) * nx / 12
dy = 2 ** (int(np.log(ny / 12) / np.log(2)))
dx = 2 ** (int(np.log(nx / 12) / np.log(2)))
offy = np.zeros((10, 10))
offx = np.zeros((10, 10))

for y, yp in enumerate(paty[1:11]):
for x, xp in enumerate(patx[1:11]):
amp1 = np.abs(arr1[yp:yp + dy, xp:xp + dx])
amp2 = np.abs(
arr2[yp - int(offset[0]):yp + dy - int(offset[0]), xp - int(offset[1]):xp + dx - int(offset[1])])

foo = coreg(amp1, amp2, sub=True)
offy[y, x] = foo[0] + offset[0]
offx[y, x] = foo[1] + offset[1]

# pdb.set_trace()
xx, yy = np.meshgrid(patx[1:11], paty[1:11])
cx = polyfit2d(yy.flatten(), xx.flatten(), offx.flatten(), order=2)
cy = polyfit2d(yy.flatten(), xx.flatten(), offy.flatten(), order=2)
#cx = polyfit2d(xx.flatten(), yy.flatten(), offx.flatten(), order=3)
#cy = polyfit2d(xx.flatten(), yy.flatten(), offy.flatten(), order=3)

ny, nx = arr2.shape
xx, yy = np.meshgrid(np.arange(nx), np.arange(ny))
px = polyval2d(yy, xx, cx)
py = polyval2d(yy, xx, cy)
#px = polyval2d(xx, yy, cx)
#py = polyval2d(xx, yy, cy)
if np.iscomplex(arr2):
arr2 = ndimage.map_coordinates(arr2.real, np.rollaxis(np.dstack([yy - py, xx - px]), 2)) + \
1j * ndimage.map_coordinates(arr2.imag, np.rollaxis(np.dstack([yy - py, xx - px]), 2))
else:
arr2 = ndimage.map_coordinates(arr2, np.rollaxis(np.dstack([yy - py, xx - px]), 2))
#pdb.set_trace()
#arr2 = np.roll(np.roll(arr2, int(offset[0]), axis=0), int(offset[1]), axis=1)

return arr2 
Example 14
def cwplot(fb_est, rx, t, fs: int, fn) -> None:
#%% time
fg, axs = subplots(1, 2, figsize=(12, 6))
ax = axs[0]
ax.plot(t, rx.T.real)
ax.set_xlabel("time [sec]")
ax.set_ylabel("amplitude")
#%% periodogram
if DTPG >= (t[-1] - t[0]):
dt = (t[-1] - t[0]) / 4
else:
dt = DTPG

dtw = 2 * dt  #  seconds to window
tstep = ceil(dt * fs)
wind = ceil(dtw * fs)

f, Sraw = signal.welch(
rx.ravel(), fs, nperseg=wind, noverlap=tstep, nfft=Nfft, return_onesided=False
)

if np.iscomplex(rx).any():
f = np.fft.fftshift(f)
Sraw = np.fft.fftshift(Sraw)

ax = axs[1]
ax.plot(f, Sraw, "r", label="raw signal")

fc_est = f[Sraw.argmax()]

# ax.set_yscale('log')
ax.set_xlim([fc_est - 200, fc_est + 200])
ax.set_xlabel("frequency [Hz]")
ax.set_ylabel("amplitude")
ax.legend()

esttxt = ""

if fn is None:  # simulation
ax.axvline(ft + fb0, color="red", linestyle="--", label="true freq.")
esttxt += f"true: {ft+fb0} Hz "

for e in fb_est:
ax.axvline(e, color="blue", linestyle="--", label="est. freq.")

esttxt += " est: " + str(fb_est) + " Hz"

ax.set_title(esttxt) 
Example 15
def cw_est(rx, fs: int, Ntone: int, method: str = "esprit", usepython=False, useall=False):
"""
estimate beat frequency using subspace frequency estimation techniques.
This is much faster in Fortran, but to start using Python alone doesn't require compiling Fortran.

ESPRIT and RootMUSIC are two popular subspace techniques.

Matlab's rootmusic is a far inferior FFT-based method with very poor accuracy vs. my implementation.
"""
assert isinstance(method, str)
method = method.lower()

tic = time()
if method == "esprit":
#%% ESPRIT
if rx.ndim == 2:
assert usepython, "Fortran not yet configured for multi-pulse case"
Ntone *= 2

if usepython or (Sc is None and Sr is None):
print("Python ESPRIT")
fb_est, sigma = esprit(rx, Ntone, Nblockest, fs)
elif np.iscomplex(rx).any():
print("Fortran complex64 ESPRIT")
fb_est, sigma = Sc.subspace.esprit(rx, Ntone, Nblockest, fs)
else:  # real signal
print("Fortran float32 ESPRIT")
fb_est, sigma = Sr.subspace.esprit(rx, Ntone, Nblockest, fs)

fb_est = abs(fb_est)
#%% ROOTMUSIC
elif method == "rootmusic":
fb_est, sigma = rootmusic(rx, Ntone, Nblockest, fs)
else:
raise ValueError(f"unknown estimation method: {method}")
print(f"computed via {method} in {time()-tic:.1f} seconds.")
#%% improvised process for CW only without notch filter
# assumes first two results have largest singular values (from SVD)
if not useall:
i = sigma > 0.001  # arbitrary
fb_est = fb_est[i]
sigma = sigma[i]

#        if fb_est.size>1:
#            ii = np.argpartition(sigma, Ntone-1)[:Ntone-1]
#            fb_est = fb_est[ii]
#            sigma = sigma[ii]

return fb_est, sigma