Python numpy.cosh方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def psi2c2c3(self, psi0):

        c2 = np.zeros(len(psi0))
        c3 = np.zeros(len(psi0))

        psi12 = np.sqrt(np.abs(psi0))
        pos = psi0 >= 0
        neg = psi0 < 0
        if np.any(pos):
            c2[pos] = (1 - np.cos(psi12[pos]))/psi0[pos]
            c3[pos] = (psi12[pos] - np.sin(psi12[pos]))/psi12[pos]**3.
        if any(neg):
            c2[neg] = (1 - np.cosh(psi12[neg]))/psi0[neg]
            c3[neg] = (np.sinh(psi12[neg]) - psi12[neg])/psi12[neg]**3.

        tmp = c2+c3 == 0
        if any(tmp):
            c2[tmp] = 1./2.
            c3[tmp] = 1./6.

        return c2,c3 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def c2c3(psi):  # Stumpff functions definitions

    c2, c3 = 0, 0

    if np.any(psi > 1e-6):
        c2 = (1 - np.cos(np.sqrt(psi))) / psi
        c3 = (np.sqrt(psi) - np.sin(np.sqrt(psi))) / np.sqrt(psi ** 3)

    if np.any(psi < -1e-6):
        c2 = (1 - np.cosh(np.sqrt(-psi))) / psi
        c3 = (np.sinh(np.sqrt(-psi)) - np.sqrt(-psi)) / np.sqrt(-psi ** 3)

    if np.any(abs(psi) <= 1e-6):
        c2 = 0.5
        c3 = 1. / 6.

    return c2, c3 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_swish_grad(self):
        def swish_grad(x, beta):
            return (
                beta * (2 - beta * x * np.tanh(beta * x / 2)) / (1 + np.cosh(beta * x))
            )

        x = testing_utils.generate_real_values_with_zeros(shape=(8, 3, 3, 16))
        tf_x = tf.Variable(x)
        with tf.GradientTape() as tape:
            activation = lq.quantizers.SwishSign()(tf_x)
        grad = tape.gradient(activation, tf_x)
        np.testing.assert_allclose(grad.numpy(), swish_grad(x, beta=5.0))

        with tf.GradientTape() as tape:
            activation = lq.quantizers.SwishSign(beta=10.0)(tf_x)
        grad = tape.gradient(activation, tf_x)
        np.testing.assert_allclose(grad.numpy(), swish_grad(x, beta=10.0)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def fields(x,y,z, kx, ky, kz, B0):
    k1 =  -B0*kx/ky
    k2 = -B0*kz/ky
    kx_x = kx*x
    ky_y = ky*y
    kz_z = kz*z
    cosx = np.cos(kx_x)
    sinhy = np.sinh(ky_y)
    cosz = np.cos(kz_z)
    Bx = k1*np.sin(kx_x)*sinhy*cosz #// here kx is only real
    By = B0*cosx*np.cosh(ky_y)*cosz
    Bz = k2*cosx*sinhy*np.sin(kz_z)
    #Bx = ne.evaluate("k1*sin(kx*x)*sinhy*cosz")
    #By = ne.evaluate("B0*cosx*cosh(ky*y)*cosz")
    #Bz = ne.evaluate("k2*cosx*sinhy*sin(kz*z)")
    return Bx, By, Bz 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_CXgate_decomp_equal(self, setup_eng, s, tol):
        """Tests that the CXgate gives the same transformation as its decomposition."""
        eng, prog = setup_eng(2)

        r = np.arcsinh(-s / 2)
        y = -1 / np.cosh(r)
        x = -np.tanh(r)
        theta = np.arctan2(y, x) / 2

        with prog.context as q:
            ops.CXgate(s) | q
            # run decomposition with reversed arguments
            ops.BSgate(-(np.pi / 2 + theta), 0) | q
            ops.Sgate(r, np.pi) | q[0]
            ops.Sgate(r, 0) | q[1]
            ops.BSgate(-theta, 0) | q

        eng.run(prog)
        assert np.all(eng.backend.is_vacuum(tol)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def TMS(r, phi):
    """Two-mode squeezing.

    Args:
        r (float): squeezing magnitude
        phi (float): rotation parameter

    Returns:
        array: symplectic transformation matrix
    """
    cp = np.cos(phi)
    sp = np.sin(phi)
    ch = np.cosh(r)
    sh = np.sinh(r)

    S = np.array(
        [
            [ch, cp * sh, 0, sp * sh],
            [cp * sh, ch, sp * sh, 0],
            [0, sp * sh, ch, -cp * sh],
            [sp * sh, 0, -cp * sh, ch],
        ]
    )

    return S 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def _squeezing(r, phi, mode, num_modes):
    """Squeezing in the phase space.

    Args:
        r (float): squeezing magnitude
        phi (float): rotation parameter
        mode (int): mode it is applied to
        num_modes (int): total number of modes in the system

    Returns:
        array: symplectic transformation matrix
    """
    cp = np.cos(phi)
    sp = np.sin(phi)
    ch = np.cosh(r)
    sh = np.sinh(r)

    S = np.array([[ch - cp * sh, -sp * sh], [-sp * sh, ch + cp * sh]])

    return expand(S, mode, num_modes) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_squeezed_state_gaussian(self, r, phi, hbar, tol):
        """test squeezed state returns correct means and covariance"""
        means, cov = utils.squeezed_state(r, phi, basis="gaussian", hbar=hbar)

        cov_expected = (hbar / 2) * np.array(
            [
                [
                    np.cosh(2 * r) - np.cos(phi) * np.sinh(2 * r),
                    -2 * np.cosh(r) * np.sin(phi) * np.sinh(r),
                ],
                [
                    -2 * np.cosh(r) * np.sin(phi) * np.sinh(r),
                    np.cosh(2 * r) + np.cos(phi) * np.sinh(2 * r),
                ],
            ]
        )

        assert np.all(means == np.zeros([2]))
        assert np.allclose(cov, cov_expected, atol=tol, rtol=0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_displaced_squeezed_state_gaussian(self, r_d, phi_d, r_s, phi_s, hbar, tol):
        """test displaced squeezed state returns correct means and covariance"""
        means, cov = utils.displaced_squeezed_state(r_d, phi_d, r_s, phi_s, basis="gaussian", hbar=hbar)

        a = r_d * np.exp(1j * phi_d)
        means_expected = np.array([[a.real, a.imag]]) * np.sqrt(2 * hbar)
        cov_expected = (hbar / 2) * np.array(
            [
                [
                    np.cosh(2 * r_s) - np.cos(phi_s) * np.sinh(2 * r_s),
                    -2 * np.cosh(r_s) * np.sin(phi_s) * np.sinh(r_s),
                ],
                [
                    -2 * np.cosh(r_s) * np.sin(phi_s) * np.sinh(r_s),
                    np.cosh(2 * r_s) + np.cos(phi_s) * np.sinh(2 * r_s),
                ],
            ]
        )

        assert np.allclose(means, means_expected, atol=tol, rtol=0)
        assert np.allclose(cov, cov_expected, atol=tol, rtol=0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_displaced_squeezed_state_fock(self, r_d, phi_d, r_s, phi_s, hbar, cutoff, tol):
        """test displaced squeezed state returns correct Fock basis state vector"""
        state = utils.displaced_squeezed_state(r_d, phi_d, r_s, phi_s, basis="fock", fock_dim=cutoff, hbar=hbar)
        a = r_d * np.exp(1j * phi_d)

        if r_s == 0:
            pytest.skip("test only non-zero squeezing")

        n = np.arange(cutoff)
        gamma = a * np.cosh(r_s) + np.conj(a) * np.exp(1j * phi_s) * np.sinh(r_s)
        coeff = np.diag(
            (0.5 * np.exp(1j * phi_s) * np.tanh(r_s)) ** (n / 2) / np.sqrt(fac(n) * np.cosh(r_s))
        )

        expected = H(gamma / np.sqrt(np.exp(1j * phi_s) * np.sinh(2 * r_s)), coeff)
        expected *= np.exp(
            -0.5 * np.abs(a) ** 2 - 0.5 * np.conj(a) ** 2 * np.exp(1j * phi_s) * np.tanh(r_s)
        )

        assert np.allclose(state, expected, atol=tol, rtol=0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_squeezed_coherent(setup_backend, hbar, tol):
    """Test Wigner function for a squeezed coherent state
    matches the analytic result"""
    backend = setup_backend(1)
    backend.prepare_coherent_state(np.abs(A), np.angle(A), 0)
    backend.squeeze(R, PHI, 0)

    state = backend.state()
    W = state.wigner(0, XVEC, XVEC)
    rot = rotm(PHI / 2)

    # exact wigner function
    alpha = A * np.cosh(R) - np.conjugate(A) * np.exp(1j * PHI) * np.sinh(R)
    mu = np.array([alpha.real, alpha.imag]) * np.sqrt(2 * hbar)
    cov = np.diag([np.exp(-2 * R), np.exp(2 * R)])
    cov = np.dot(rot, np.dot(cov, rot.T)) * hbar / 2.0
    Wexact = wigner(GRID, mu, cov)

    assert np.allclose(W, Wexact, atol=0.01, rtol=0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_two_mode_squeezing(self, setup_backend, r, p, cutoff, pure, tol):
        r""" Test two-mode squeezing on vacuum-state for both pure states and
        mixed states with the amplitude given by
	        :math:`\delta_{kl} \frac{e^{in\phi} \tanh^n{r}}{\cosh{r}}`
        """

        backend = setup_backend(2)
        backend.two_mode_squeeze(r, p, 0, 1)

        state = backend.state()

        if pure:
            for k in it.product(range(cutoff), repeat=2):
                tmsv = get_amplitude(k, r, p)
                assert np.allclose(state.data[k], tmsv, atol=tol, rtol=0)
        else:
            for k in it.product(range(cutoff), repeat=2):
                for l in it.product(range(cutoff), repeat=2):
                    t = (k[0], l[0], k[1], l[1])
                    tmsv2 = get_amplitude(k, r, p) * np.conj(get_amplitude(l, r, p))

                    assert np.allclose(state.data[t], tmsv2, atol=tol, rtol=0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def test_squeezed_coherent(self, setup_backend, hbar, batch_size, tol):
        """Test squeezed coherent state has correct mean and variance"""
        # quadrature rotation angle
        backend = setup_backend(1)
        qphi = 0.78

        backend.prepare_displaced_squeezed_state(np.abs(a), np.angle(a), r, phi, 0)

        state = backend.state()
        res = np.array(state.quad_expectation(0, phi=qphi)).T

        xphi_mean = (a.real * np.cos(qphi) + a.imag * np.sin(qphi)) * np.sqrt(2 * hbar)
        xphi_var = (np.cosh(2 * r) - np.cos(phi - 2 * qphi) * np.sinh(2 * r)) * hbar / 2
        res_exact = np.array([xphi_mean, xphi_var])

        if batch_size is not None:
            res_exact = np.tile(res_exact, batch_size)

        assert np.allclose(res.flatten(), res_exact.flatten(), atol=tol, rtol=0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def testAcoshFunction(self):
        ma5 = MovingAverage(5, 'close')
        holder = Acosh(ma5)

        sampleClose = np.cosh(self.sampleClose)

        for i, close in enumerate(sampleClose):
            data = {'close': close}
            ma5.push(data)
            holder.push(data)

            expected = math.acosh(ma5.result())
            calculated = holder.result()
            self.assertAlmostEqual(calculated, expected, 12, "at index {0:d}\n"
                                                             "expected:   {1:f}\n"
                                                             "calculated: {2:f}".format(i, expected, calculated)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cosh [as 別名]
def cheb1ap(N, rp):
    """Return (z,p,k) zero, pole, gain for Nth order Chebyshev type I lowpass
    analog filter prototype with `rp` decibels of ripple in the passband.

    The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1,
    defined as the point at which the gain first drops below -`rp`.

    """
    z = numpy.array([])
    eps = numpy.sqrt(10 ** (0.1 * rp) - 1.0)
    n = numpy.arange(1, N + 1)
    mu = 1.0 / N * numpy.log((1.0 + numpy.sqrt(1 + eps * eps)) / eps)
    theta = pi / 2.0 * (2 * n - 1.0) / N
    p = (-numpy.sinh(mu) * numpy.sin(theta) +
         1j * numpy.cosh(mu) * numpy.cos(theta))
    k = numpy.prod(-p, axis=0).real
    if N % 2 == 0:
        k = k / sqrt((1 + eps * eps))
    return z, p, k