Python numpy.sinh() Examples

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 

def __init__(self, img, percentiles=[1, 99]):
        """Create norm that is linear between lower and upper percentile of img
        Parameters
        ----------
        img: array_like
            Image to normalize
        percentile: array_like, default=[1,99]
            Lower and upper percentile to consider. Pixel values below will be
            set to zero, above to saturated.
        """
        assert len(percentiles) == 2
        vmin, vmax = np.percentile(img, percentiles)
        # solution for beta assumes flat spectrum at vmax
        stretch = vmax - vmin
        beta = stretch / np.sinh(1)
        super().__init__(minimum=vmin, stretch=stretch, Q=beta) 

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 

def tauStep(dtau, v0, x0, t0, g):
        ## linear step in proper time of clock.
        ## If an object has proper acceleration g and starts at position x0 with speed v0 at time t0
        ## as seen from an inertial frame, then return the new v, x, t after proper time dtau has elapsed.
        

        ## Compute how much t will change given a proper-time step of dtau
        gamma = (1. - v0**2)**-0.5
        if g == 0:
            dt = dtau * gamma
        else:
            v0g = v0 * gamma
            dt = (np.sinh(dtau * g + np.arcsinh(v0g)) - v0g) / g
        
        #return v0 + dtau * g, x0 + v0*dt, t0 + dt
        v1, x1, t1 = Simulation.hypTStep(dt, v0, x0, t0, g)
        return v1, x1, t0+dt 

def _eq_10_42(lam_1, lam_2, t_12):
    """
    Equation (10.42) of Functions of Matrices: Theory and Computation.

    Notes
    -----
    This is a helper function for _fragment_2_1 of expm_2009.
    Equation (10.42) is on page 251 in the section on Schur algorithms.
    In particular, section 10.4.3 explains the Schur-Parlett algorithm.
    expm([[lam_1, t_12], [0, lam_1])
    =
    [[exp(lam_1), t_12*exp((lam_1 + lam_2)/2)*sinch((lam_1 - lam_2)/2)],
    [0, exp(lam_2)]
    """

    # The plain formula t_12 * (exp(lam_2) - exp(lam_2)) / (lam_2 - lam_1)
    # apparently suffers from cancellation, according to Higham's textbook.
    # A nice implementation of sinch, defined as sinh(x)/x,
    # will apparently work around the cancellation.
    a = 0.5 * (lam_1 + lam_2)
    b = 0.5 * (lam_1 - lam_2)
    return t_12 * np.exp(a) * _sinch(b) 

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 

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 

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 

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) 

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) 

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) 

def matrix_elem(n, r, m):
    """Matrix element corresponding to squeezed density matrix[n, m]"""
    eps = 1e-10

    if n % 2 != m % 2:
        return 0.0

    if r == 0.0:
        return np.complex(n == m)  # delta function

    k = np.arange(m % 2, min([m, n]) + 1, 2)
    res = np.sum(
        (-1) ** ((n - k) / 2)
        * np.exp(
            (lg(m + 1) + lg(n + 1)) / 2
            - lg(k + 1)
            - lg((m - k) / 2 + 1)
            - lg((n - k) / 2 + 1)
        )
        * (np.sinh(r) / 2 + eps) ** ((n + m - 2 * k) / 2)
        / (np.cosh(r) ** ((n + m + 1) / 2))
    )
    return res 

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) 

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) 

def test_number_expectation_two_mode_squeezed(self, setup_backend, tol, batch_size):
        """Tests the expectation value of photon numbers when there is correlation"""
        if batch_size is not None:
            pytest.skip("Does not support batch mode")
        backend = setup_backend(3)
        state = backend.state()
        r = 0.2
        phi = 0.0
        backend.prepare_squeezed_state(r, phi, 0)
        backend.prepare_squeezed_state(-r, phi, 2)
        backend.beamsplitter(np.pi/4, np.pi, 0, 2)
        state = backend.state()
        nbar = np.sinh(r) ** 2

        res = state.number_expectation([2, 0])
        assert np.allclose(res[0], 2 * nbar ** 2 + nbar, atol=tol, rtol=0)

        res = state.number_expectation([0])
        assert np.allclose(res[0], nbar, atol=tol, rtol=0)

        res = state.number_expectation([2])
        assert np.allclose(res[0], nbar, atol=tol, rtol=0) 

def _eq_10_42(lam_1, lam_2, t_12):
    """
    Equation (10.42) of Functions of Matrices: Theory and Computation.

    Notes
    -----
    This is a helper function for _fragment_2_1 of expm_2009.
    Equation (10.42) is on page 251 in the section on Schur algorithms.
    In particular, section 10.4.3 explains the Schur-Parlett algorithm.
    expm([[lam_1, t_12], [0, lam_1])
    =
    [[exp(lam_1), t_12*exp((lam_1 + lam_2)/2)*sinch((lam_1 - lam_2)/2)],
    [0, exp(lam_2)]
    """

    # The plain formula t_12 * (exp(lam_2) - exp(lam_2)) / (lam_2 - lam_1)
    # apparently suffers from cancellation, according to Higham's textbook.
    # A nice implementation of sinch, defined as sinh(x)/x,
    # will apparently work around the cancellation.
    a = 0.5 * (lam_1 + lam_2)
    b = 0.5 * (lam_1 - lam_2)
    return t_12 * np.exp(a) * _sinch(b) 

def cheb2ap(N, rs):
    """Return (z,p,k) zero, pole, gain for Nth order Chebyshev type II lowpass
    analog filter prototype with `rs` decibels of ripple in the stopband.

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

    """
    de = 1.0 / sqrt(10 ** (0.1 * rs) - 1)
    mu = arcsinh(1.0 / de) / N

    if N % 2:
        n = numpy.concatenate((numpy.arange(1, N - 1, 2),
                               numpy.arange(N + 2, 2 * N, 2)))
    else:
        n = numpy.arange(1, 2 * N, 2)

    z = conjugate(1j / cos(n * pi / (2.0 * N)))
    p = exp(1j * (pi * numpy.arange(1, 2 * N, 2) / (2.0 * N) + pi / 2.0))
    p = sinh(mu) * p.real + 1j * cosh(mu) * p.imag
    p = 1.0 / p
    k = (numpy.prod(-p, axis=0) / numpy.prod(-z, axis=0)).real
    return z, p, k 

def Dm(self, z, cm=False, meter=False, pc=False, kpc=False, mpc=False):
        Ok = self.Ok()
        sOk = num.sqrt(num.abs(Ok))
        Dc = self.Dc(z)
        Dh = self.Dh()

        conversion = self.lengthConversion(cm=cm,
                                           meter=meter,
                                           pc=pc,
                                           kpc=kpc,
                                           mpc=mpc)

        if Ok > 0:
            return Dh / sOk * num.sinh(sOk * Dc / Dh) * conversion
        elif Ok == 0:
            return Dc * conversion
        else:
            return Dh / sOk * num.sin(sOk * Dc / Dh) * conversion

    # Angular diameter distance
    # Ratio of an objects physical transvserse size to its angular size in radians 

def numpy_sinh(a):
  return np.sinh(a) 

def tfe_sinh(t):
  return tf.sinh(t) 

def cosh(y, x):
  d[x] = d[y] * numpy.sinh(x) 

def sinh(y, x):
  d[x] = d[y] * numpy.cosh(x) 

def tcosh(z, x):
  d[z] = d[x] * numpy.sinh(x) 

def tau_davies(Z, fmax, t, y, dy, normalization="standard", dH=1, dK=3):
    """tau factor for estimating Davies bound (Baluev 2008, Table 1)"""
    N = len(t)
    NH = N - dH  # DOF for null hypothesis
    NK = N - dK  # DOF for periodic hypothesis
    Dt = _weighted_var(t, dy)
    Teff = np.sqrt(4 * np.pi * Dt)
    W = fmax * Teff
    if normalization == "psd":
        # 'psd' normalization is same as Baluev's z
        return W * np.exp(-Z) * np.sqrt(Z)
    elif normalization == "standard":
        # 'standard' normalization is Z = 2/NH * z_1
        return (
            _gamma(NH)
            * W
            * (1 - Z) ** (0.5 * (NK - 1))
            * np.sqrt(0.5 * NH * Z)
        )
    elif normalization == "model":
        # 'model' normalization is Z = 2/NK * z_2
        return _gamma(NK) * W * (1 + Z) ** (-0.5 * NK) * np.sqrt(0.5 * NK * Z)
    elif normalization == "log":
        # 'log' normalization is Z = 2/NK * z_3
        return (
            _gamma(NK)
            * W
            * np.exp(-0.5 * Z * (NK - 0.5))
            * np.sqrt(NK * np.sinh(0.5 * Z))
        )
    else:
        raise NotImplementedError("normalization={0}".format(normalization)) 

def test_testUfuncs1(self):
        # Test various functions such as sin, cos.
        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
        assert_(eq(np.cos(x), cos(xm)))
        assert_(eq(np.cosh(x), cosh(xm)))
        assert_(eq(np.sin(x), sin(xm)))
        assert_(eq(np.sinh(x), sinh(xm)))
        assert_(eq(np.tan(x), tan(xm)))
        assert_(eq(np.tanh(x), tanh(xm)))
        with np.errstate(divide='ignore', invalid='ignore'):
            assert_(eq(np.sqrt(abs(x)), sqrt(xm)))
            assert_(eq(np.log(abs(x)), log(xm)))
            assert_(eq(np.log10(abs(x)), log10(xm)))
        assert_(eq(np.exp(x), exp(xm)))
        assert_(eq(np.arcsin(z), arcsin(zm)))
        assert_(eq(np.arccos(z), arccos(zm)))
        assert_(eq(np.arctan(z), arctan(zm)))
        assert_(eq(np.arctan2(x, y), arctan2(xm, ym)))
        assert_(eq(np.absolute(x), absolute(xm)))
        assert_(eq(np.equal(x, y), equal(xm, ym)))
        assert_(eq(np.not_equal(x, y), not_equal(xm, ym)))
        assert_(eq(np.less(x, y), less(xm, ym)))
        assert_(eq(np.greater(x, y), greater(xm, ym)))
        assert_(eq(np.less_equal(x, y), less_equal(xm, ym)))
        assert_(eq(np.greater_equal(x, y), greater_equal(xm, ym)))
        assert_(eq(np.conjugate(x), conjugate(xm)))
        assert_(eq(np.concatenate((x, y)), concatenate((xm, ym))))
        assert_(eq(np.concatenate((x, y)), concatenate((x, y))))
        assert_(eq(np.concatenate((x, y)), concatenate((xm, y))))
        assert_(eq(np.concatenate((x, y, x)), concatenate((x, ym, x)))) 

def test_testUfuncRegression(self):
        f_invalid_ignore = [
            'sqrt', 'arctanh', 'arcsin', 'arccos',
            'arccosh', 'arctanh', 'log', 'log10', 'divide',
            'true_divide', 'floor_divide', 'remainder', 'fmod']
        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
                  'sin', 'cos', 'tan',
                  'arcsin', 'arccos', 'arctan',
                  'sinh', 'cosh', 'tanh',
                  'arcsinh',
                  'arccosh',
                  'arctanh',
                  'absolute', 'fabs', 'negative',
                  'floor', 'ceil',
                  'logical_not',
                  'add', 'subtract', 'multiply',
                  'divide', 'true_divide', 'floor_divide',
                  'remainder', 'fmod', 'hypot', 'arctan2',
                  'equal', 'not_equal', 'less_equal', 'greater_equal',
                  'less', 'greater',
                  'logical_and', 'logical_or', 'logical_xor']:
            try:
                uf = getattr(umath, f)
            except AttributeError:
                uf = getattr(fromnumeric, f)
            mf = getattr(np.ma, f)
            args = self.d[:uf.nin]
            with np.errstate():
                if f in f_invalid_ignore:
                    np.seterr(invalid='ignore')
                if f in ['arctanh', 'log', 'log10']:
                    np.seterr(divide='ignore')
                ur = uf(*args)
                mr = mf(*args)
            assert_(eq(ur.filled(0), mr.filled(0), f))
            assert_(eqmask(ur.mask, mr.mask)) 

def test_basic_ufuncs(self):
        # Test various functions such as sin, cos.
        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
        assert_equal(np.cos(x), cos(xm))
        assert_equal(np.cosh(x), cosh(xm))
        assert_equal(np.sin(x), sin(xm))
        assert_equal(np.sinh(x), sinh(xm))
        assert_equal(np.tan(x), tan(xm))
        assert_equal(np.tanh(x), tanh(xm))
        assert_equal(np.sqrt(abs(x)), sqrt(xm))
        assert_equal(np.log(abs(x)), log(xm))
        assert_equal(np.log10(abs(x)), log10(xm))
        assert_equal(np.exp(x), exp(xm))
        assert_equal(np.arcsin(z), arcsin(zm))
        assert_equal(np.arccos(z), arccos(zm))
        assert_equal(np.arctan(z), arctan(zm))
        assert_equal(np.arctan2(x, y), arctan2(xm, ym))
        assert_equal(np.absolute(x), absolute(xm))
        assert_equal(np.angle(x + 1j*y), angle(xm + 1j*ym))
        assert_equal(np.angle(x + 1j*y, deg=True), angle(xm + 1j*ym, deg=True))
        assert_equal(np.equal(x, y), equal(xm, ym))
        assert_equal(np.not_equal(x, y), not_equal(xm, ym))
        assert_equal(np.less(x, y), less(xm, ym))
        assert_equal(np.greater(x, y), greater(xm, ym))
        assert_equal(np.less_equal(x, y), less_equal(xm, ym))
        assert_equal(np.greater_equal(x, y), greater_equal(xm, ym))
        assert_equal(np.conjugate(x), conjugate(xm)) 

def test_testUfuncRegression(self):
        # Tests new ufuncs on MaskedArrays.
        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
                  'sin', 'cos', 'tan',
                  'arcsin', 'arccos', 'arctan',
                  'sinh', 'cosh', 'tanh',
                  'arcsinh',
                  'arccosh',
                  'arctanh',
                  'absolute', 'fabs', 'negative',
                  'floor', 'ceil',
                  'logical_not',
                  'add', 'subtract', 'multiply',
                  'divide', 'true_divide', 'floor_divide',
                  'remainder', 'fmod', 'hypot', 'arctan2',
                  'equal', 'not_equal', 'less_equal', 'greater_equal',
                  'less', 'greater',
                  'logical_and', 'logical_or', 'logical_xor',
                  ]:
            try:
                uf = getattr(umath, f)
            except AttributeError:
                uf = getattr(fromnumeric, f)
            mf = getattr(numpy.ma.core, f)
            args = self.d[:uf.nin]
            ur = uf(*args)
            mr = mf(*args)
            assert_equal(ur.filled(0), mr.filled(0), f)
            assert_mask_equal(ur.mask, mr.mask, err_msg=f) 

def _ppf(self, q, a, b):
        return np.sinh((_norm_ppf(q) - a) / b) 

def _entropy(self, a):
        return a / sinh(a) - log(tanh(a/2.0))