Python numpy.isrealobj() Examples
The following are 30 code examples for showing how to use numpy.isrealobj(). 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
| Project: recruit Author: Frank-qlu File: test_polynomial.py License: Apache License 2.0 | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 2
| Project: lambda-packs Author: ryfeus File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 3
| Project: vnpy_crypto Author: birforce File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 4
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 5
| Project: GraphicDesignPatternByPython Author: Relph1119 File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 6
| Project: DeepFormants Author: MLSpeech File: extract_features.py License: MIT License | 6 votes |
|
def atal(x, order, num_coefs):
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
a, e, kk = lpc(x, order)
c = np.zeros(num_coefs)
c[0] = a[0]
for m in range(1, order+1):
c[m] = - a[m]
for k in range(1, m):
c[m] += (float(k)/float(m)-1)*a[k]*c[m-k]
for m in range(order+1, num_coefs):
for k in range(1, order+1):
c[m] += (float(k)/float(m)-1)*a[k]*c[m-k]
return c
Example 7
| Project: pb_bss Author: fgnt File: gmm.py License: MIT License | 6 votes |
|
def predict(self, x):
"""
Args:
x: Shape (N, D)
Returns: Affiliation with shape (K, N)
"""
N, D = x.shape
assert np.isrealobj(x), x.dtype
labels = self.kmeans.predict(x)
affiliations = labels_to_one_hot(
labels, self.kmeans.n_clusters, axis=-2, keepdims=False,
dtype=x.dtype
)
assert affiliations.shape == (self.kmeans.n_clusters, N)
return affiliations
Example 8
| Project: pb_bss Author: fgnt File: gaussian.py License: MIT License | 6 votes |
|
def fit(self, y, saliency=None, covariance_type="full"):
"""
Args:
y: Shape (..., N, D)
saliency: Importance weighting for each observation, shape (..., N)
covariance_type: Either 'full', 'diagonal', or 'spherical'
Returns:
"""
assert np.isrealobj(y), y.dtype
if saliency is not None:
assert is_broadcast_compatible(y.shape[:-1], saliency.shape), (
y.shape, saliency.shape
)
return self._fit(y, saliency=saliency, covariance_type=covariance_type)
Example 9
| Project: pb_bss Author: fgnt File: gcacgmm.py License: MIT License | 6 votes |
|
def predict(self, observation, embedding):
"""
Args:
observation: Shape (F, T, D)
embedding: Shape (F, T, E)
Returns:
affiliation: Shape (F, K, T)
"""
assert np.iscomplexobj(observation), observation.dtype
assert np.isrealobj(embedding), embedding.dtype
observation = observation / np.maximum(
np.linalg.norm(observation, axis=-1, keepdims=True),
np.finfo(observation.dtype).tiny,
)
affiliation, quadratic_form = self._predict(observation, embedding)
return affiliation
Example 10
| Project: pumpp Author: bmcfee File: tags.py License: ISC License | 6 votes |
|
def inverse(self, encoded, duration=None):
'''Inverse static tag transformation'''
ann = jams.Annotation(namespace=self.namespace, duration=duration)
if np.isrealobj(encoded):
detected = (encoded >= 0.5)
else:
detected = encoded
for vd in self.encoder.inverse_transform(np.atleast_2d(detected))[0]:
vid = np.flatnonzero(self.encoder.transform(np.atleast_2d(vd)))
ann.append(time=0,
duration=duration,
value=vd,
confidence=encoded[vid])
return ann
Example 11
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: test_polynomial.py License: Apache License 2.0 | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 12
| Project: pySINDy Author: luckystarufo File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 13
| Project: fluids Author: CalebBell File: pychebfun.py License: MIT License | 6 votes |
|
def polyval(self, chebcoeff):
"""
Compute the interpolation values at Chebyshev points.
chebcoeff: Chebyshev coefficients
"""
N = len(chebcoeff)
if N == 1:
return chebcoeff
data = even_data(chebcoeff)/2
data[0] *= 2
data[N-1] *= 2
fftdata = 2*(N-1)*fftpack.ifft(data, axis=0)
complex_values = fftdata[:N]
# convert to real if input was real
if np.isrealobj(chebcoeff):
values = np.real(complex_values)
else:
values = complex_values
return values
Example 14
| Project: fluids Author: CalebBell File: pychebfun.py License: MIT License | 6 votes |
|
def dct(data):
"""
Compute DCT using FFT
"""
N = len(data)//2
fftdata = fftpack.fft(data, axis=0)[:N+1]
fftdata /= N
fftdata[0] /= 2.
fftdata[-1] /= 2.
if np.isrealobj(data):
data = np.real(fftdata)
else:
data = fftdata
return data
# ----------------------------------------------------------------
# Add overloaded operators
# ----------------------------------------------------------------
Example 15
| Project: arlpy Author: org-arl File: signal.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def correlate_periodic(a, v=None):
"""Cross-correlation of two 1-dimensional periodic sequences.
a and v must be sequences with the same length. If v is not specified, it is
assumed to be the same as a (i.e. the function computes auto-correlation).
:param a: input sequence #1
:param v: input sequence #2
:returns: discrete periodic cross-correlation of a and v
"""
a_fft = _np.fft.fft(_np.asarray(a))
if v is None:
v_cfft = a_fft.conj()
else:
v_cfft = _np.fft.fft(_np.asarray(v)).conj()
x = _np.fft.ifft(a_fft * v_cfft)
if _np.isrealobj(a) and (v is None or _np.isrealobj(v)):
x = x.real
return x
Example 16
| Project: spectrum Author: cokelaer File: linear_prediction.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def rc2is(k):
"""Convert reflection coefficients to inverse sine parameters.
:param k: reflection coefficients
:return: inverse sine parameters
.. seealso:: :func:`is2rc`, :func:`rc2poly`, :func:`rc2acC`, :func:`rc2lar`.
Reference: J.R. Deller, J.G. Proakis, J.H.L. Hansen, "Discrete-Time
Processing of Speech Signals", Prentice Hall, Section 7.4.5.
"""
assert numpy.isrealobj(k), 'Inverse sine parameters not defined for complex reflection coefficients.'
if max(numpy.abs(k)) >= 1:
raise ValueError('All reflection coefficients should have magnitude less than unity.')
return (2/numpy.pi)*numpy.arcsin(k)
Example 17
| Project: spectrum Author: cokelaer File: linear_prediction.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def rc2lar(k):
"""Convert reflection coefficients to log area ratios.
:param k: reflection coefficients
:return: inverse sine parameters
The log area ratio is defined by G = log((1+k)/(1-k)) , where the K
parameter is the reflection coefficient.
.. seealso:: :func:`lar2rc`, :func:`rc2poly`, :func:`rc2ac`, :func:`rc2ic`.
:References:
[1] J. Makhoul, "Linear Prediction: A Tutorial Review," Proc. IEEE, Vol.63, No.4, pp.561-580, Apr 1975.
"""
assert numpy.isrealobj(k), 'Log area ratios not defined for complex reflection coefficients.'
if max(numpy.abs(k)) >= 1:
raise ValueError('All reflection coefficients should have magnitude less than unity.')
# Use the relation, atanh(x) = (1/2)*log((1+k)/(1-k))
return -2 * numpy.arctanh(-numpy.array(k))
Example 18
| Project: mxnet-lambda Author: awslabs File: test_polynomial.py License: Apache License 2.0 | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 19
| Project: spins-b Author: stanfordnqp File: creator_em.py License: GNU General Public License v3.0 | 6 votes |
|
def grad(self, input_vals: List[np.ndarray],
grad_val: np.ndarray) -> List[np.ndarray]:
"""Computes gradient via a adjoint calculation.
Args:
input_vals: List of the input values.
grad_val: Gradient of the output.
Returns:
Gradient.
"""
omega = 2 * np.pi / self._wlen
efields = self._simulate(input_vals[0])
B = omega**2 * scipy.sparse.diags(efields, 0)
d = self._simulate_adjoint(input_vals[0],
np.conj(grad_val) / (-1j * omega))
total_df_dz = np.conj(np.transpose(d)) @ B
# If this is a function that maps from real to complex, we have to
# to take the real part to make gradient real.
if np.isrealobj(input_vals[0]):
total_df_dz = np.real(total_df_dz)
return [total_df_dz]
Example 20
| Project: postpic Author: skuschel File: datahandling.py License: GNU General Public License v3.0 | 6 votes |
|
def _shift_grid_by_linear(self, dx):
axes = sorted(dx.keys())
shift = np.zeros(len(self.axes))
for i, d in dx.items():
shift[i] = d
shift_px = shift/self.spacing
ret = copy.copy(self)
if np.isrealobj(self.matrix):
ret.matrix = spnd.shift(self.matrix, -shift_px, order=1, mode='nearest')
else:
real, imag = self.matrix.real.copy(), self.matrix.imag.copy()
ret.matrix = np.empty_like(matrix)
spnd.shift(real, -shift_px, output=ret.matrix.real, order=1, mode='nearest')
spnd.shift(imag, -shift_px, output=ret.matrix.imag, order=1, mode='nearest')
for i in axes:
ret.axes[i] = Axis(grid_node=self.axes[i].grid_node + dx[i],
grid=self.axes[i].grid + dx[i])
return ret
Example 21
| Project: sporco Author: bwohlberg File: __init__.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def _fftconv(a, b, axes=(0, 1)):
"""Patched version of :func:`sporco.fft.fftconv`."""
if cp.isrealobj(a) and cp.isrealobj(b):
fft = cp.fft.rfftn
ifft = cp.fft.irfftn
else:
fft = cp.fft.fftn
ifft = cp.fft.ifftn
dims = cp.maximum(cp.asarray([a.shape[i] for i in axes]),
cp.asarray([b.shape[i] for i in axes]))
dims = [int(d) for d in dims]
af = fft(a, dims, axes)
bf = fft(b, dims, axes)
return ifft(af * bf, dims, axes)
# Construct sporco.cupy.fft
Example 22
| Project: elasticintel Author: securityclippy File: test_polynomial.py License: GNU General Public License v3.0 | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 23
| Project: coffeegrindsize Author: jgagneastro File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 24
| Project: PyRAT Author: birgander2 File: SVA.py License: Mozilla Public License 2.0 | 6 votes |
|
def filter(self, array, *args, **kwargs):
# 1-D Real Arrays
if array.ndim == 1 and np.isrealobj(array):
return self.svafilter(array, self.ov)
# 1-D Complex Arrays
if array.ndim == 1 and np.iscomplexobj(array):
return self.sva1D(array, self.ov)
# 2-D Complex Arrays
if array.ndim == 2 and np.iscomplexobj(array):
return self.sva2D(array, self.ov)
# 3-D Complex Arrays
if array.ndim == 3 and np.iscomplexobj(array):
p = array.shape
for k in range(0,p[0]):
array[k,:,:] = self.sva2D(array[k,:,:], self.ov)
return array
else:
print(" ERROR: Bad input.")
return None
Example 25
| Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda Author: PacktPublishing File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 26
| Project: twitter-stock-recommendation Author: alvarobartt File: test_polynomial.py License: MIT License | 6 votes |
|
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
Example 27
| Project: scaper Author: justinsalamon File: util.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def is_real_number(num):
'''
Check if a value is a real scalar by aggregating several numpy checks.
Parameters
----------
num : any type
The parameter to check
Returns
------
check : bool
True if ```num``` is a real scalar, False otherwise.
'''
if (not np.isreal(num) or
not np.isrealobj(num) or
not np.isscalar(num)):
return False
else:
return True
Example 28
| Project: lambda-packs Author: ryfeus File: matfuncs.py License: MIT License | 5 votes |
|
def _maybe_real(A, B, tol=None):
"""
Return either B or the real part of B, depending on properties of A and B.
The motivation is that B has been computed as a complicated function of A,
and B may be perturbed by negligible imaginary components.
If A is real and B is complex with small imaginary components,
then return a real copy of B. The assumption in that case would be that
the imaginary components of B are numerical artifacts.
Parameters
----------
A : ndarray
Input array whose type is to be checked as real vs. complex.
B : ndarray
Array to be returned, possibly without its imaginary part.
tol : float
Absolute tolerance.
Returns
-------
out : real or complex array
Either the input array B or only the real part of the input array B.
"""
# Note that booleans and integers compare as real.
if np.isrealobj(A) and np.iscomplexobj(B):
if tol is None:
tol = {0:feps*1e3, 1:eps*1e6}[_array_precision[B.dtype.char]]
if np.allclose(B.imag, 0.0, atol=tol):
B = B.real
return B
###############################################################################
# Matrix functions.
Example 29
| Project: trax Author: google File: math_ops.py License: Apache License 2.0 | 5 votes |
|
def isrealobj(x): return not iscomplexobj(x)
Example 30
| Project: GraphicDesignPatternByPython Author: Relph1119 File: matfuncs.py License: MIT License | 5 votes |
|
def _maybe_real(A, B, tol=None):
"""
Return either B or the real part of B, depending on properties of A and B.
The motivation is that B has been computed as a complicated function of A,
and B may be perturbed by negligible imaginary components.
If A is real and B is complex with small imaginary components,
then return a real copy of B. The assumption in that case would be that
the imaginary components of B are numerical artifacts.
Parameters
----------
A : ndarray
Input array whose type is to be checked as real vs. complex.
B : ndarray
Array to be returned, possibly without its imaginary part.
tol : float
Absolute tolerance.
Returns
-------
out : real or complex array
Either the input array B or only the real part of the input array B.
"""
# Note that booleans and integers compare as real.
if np.isrealobj(A) and np.iscomplexobj(B):
if tol is None:
tol = {0:feps*1e3, 1:eps*1e6}[_array_precision[B.dtype.char]]
if np.allclose(B.imag, 0.0, atol=tol):
B = B.real
return B
###############################################################################
# Matrix functions.
