def esa_edge_2d_line(omega, x0, xs, alpha=3/2*np.pi, Nc=None, c=None):
"""Line source by two-dimensional ESA for an edge-shaped secondary source
distribution constisting of monopole line sources.
One leg of the secondary sources have to be located on the x-axis (y0=0),
the edge at the origin.
Derived from :cite:`Spors2016`
Parameters
----------
omega : float
Angular frequency.
x0 : int(N, 3) array_like
Sequence of secondary source positions.
xs : (3,) array_like
Position of synthesized line source.
alpha : float, optional
Outer angle of edge.
Nc : int, optional
Number of elements for series expansion of driving function. Estimated
if not given.
c : float, optional
Speed of sound
Returns
-------
(N,) numpy.ndarray
Complex weights of secondary sources.
"""
x0 = np.asarray(x0)
k = util.wavenumber(omega, c)
phi_s = np.arctan2(xs[1], xs[0])
if phi_s < 0:
phi_s = phi_s + 2*np.pi
r_s = np.linalg.norm(xs)
L = x0.shape[0]
r = np.linalg.norm(x0, axis=1)
phi = np.arctan2(x0[:, 1], x0[:, 0])
phi = np.where(phi < 0, phi+2*np.pi, phi)
if Nc is None:
Nc = np.ceil(2 * k * np.max(r) * alpha/np.pi)
epsilon = np.ones(Nc) # weights for series expansion
epsilon[0] = 2
d = np.zeros(L, dtype=complex)
idx = (r <= r_s)
for m in np.arange(Nc):
nu = m*np.pi/alpha
f = 1/epsilon[m] * np.sin(nu*phi_s) * np.cos(nu*phi) * nu/r
d[idx] = d[idx] + f[idx] * jn(nu, k*r[idx]) * hankel2(nu, k*r_s)
d[~idx] = d[~idx] + f[~idx] * jn(nu, k*r_s) * hankel2(nu, k*r[~idx])
d[phi > 0] = -d[phi > 0]
return -1j*np.pi/alpha * d
def scatter_dielectric_cylinder(R,
phi,
wavelength,
radius,
er=1.0,
ur=1.0,
E0=1.0,
N=N0):
'''Scattered field from a dielectric cylinder'''
Ezs = 0
Ezint = 0
k = 2 * np.pi / wavelength
kd = k * np.sqrt(ur * er)
a = radius
for n in range(-N, N):
hn2 = hankel2(n, k * a)
dr = 0.001 * k * a
hn2p = (hankel2(n, k * a + dr) - hankel2(n, k * a - dr)) / (2 * dr)
an_n = np.sqrt(ur) * jvp(n, k * a) * jv(n, kd * a) - np.sqrt(er) * jv(
n, k * a) * jvp(n, kd * a)
an_d = np.sqrt(ur) * hn2p * jv(n, kd * a) - np.sqrt(er) * hn2 * jvp(
n, kd * a)
an = -(1.j)**-n * an_n / an_d
Ezs += E0 * an * hankel2(n, k * R) * np.exp(1.j * n * phi)
cn_n = 2 * np.sqrt(ur)
cn_d = np.sqrt(ur) * hn2p * jv(n, kd * a) - np.sqrt(er) * hn2 * jvp(
n, kd * a)
cn = (1.j)**-(n + 1) / (np.pi * k * a) * cn_n / cn_d
Ezint += E0 * cn * jv(n, kd * R) * np.exp(1.j * n * phi)
Ezs[R < a] = Ezint[R < a]
return Ezint, Ezs
def get_weight_vector():
_, valid_freq_array = get_valid_freq_array_and_index()
kr_array = 2 * np.pi * valid_freq_array / c.speed_of_sound * c.array_radius
n = 3 # warning: magic number
weight_vector = 1 / kr_array ** 2 / (np.sqrt(np.pi / (2 * kr_array)) * (
n / kr_array * hankel2(n + 1 / 2, kr_array) - hankel2(n + 3 / 2, kr_array)))
return np.abs(weight_vector)
def get_bn(freq, order):
kr = 2 * np.pi * freq * c.array_radius / c.speed_of_sound + (
freq == 0) * np.finfo(float).eps
_t = sqrt(np.pi / (2 * kr)) * (order / kr * hankel2(order + 1 / 2, kr) -
hankel2(order + 3 / 2, kr))
bn = 1j**(order - 1) / (kr**2 * _t)
return bn
def construct_g_D(pos_D, k, n): # pos_D : N X 2 vector containing position of each grid # n : size of each grid # k : wavevector a = (n**2/np.pi)**0.5 N = np.shape(pos_D)[0] L = np.int32(N**0.5) pos_D_x, pos_D_y = np.reshape(pos_D[:,0], [L,L]), np.reshape(pos_D[:,1], [L,L]) g_D = np.zeros([2*L-1,2*L-1],dtype=np.complex128) g_D[0,0] = -1j*0.5*(np.pi*k*a*sc.hankel2(1,k*a) - 2*1j) for i in range(L): for j in range(1,L): rho_ij = np.abs(pos_D_x[i,j]-pos_D_x[0,0])**2 + np.abs(pos_D_y[i,j]-pos_D_y[0,0])**2 rho_ij = np.float32(rho_ij**0.5) g_D[i,j] = -1j*0.5*np.pi*k*a*sc.j1(k*a)*sc.hankel2(0,k*rho_ij) for i in range(1,L): rho_ij = np.abs(pos_D_x[i,0]-pos_D_x[0,0])**2 + np.abs(pos_D_y[i,0]-pos_D_y[0,0])**2 rho_ij = np.float32(rho_ij**0.5) g_D[i,0] = -1j*0.5*np.pi*k*a*sc.j1(k*a)*sc.hankel2(0,k*rho_ij) for i in range(L,2*L-1): for j in range(L, 2*L-1): g_D[i,j] = g_D[abs(i+1-2*L),abs(j+1-2*L)] for i in range(L, 2*L-1): for j in range(L): g_D[i,j] = g_D[abs(i+1-2*L),j] for i in range(L): for j in range(L, 2*L-1): g_D[i,j] = g_D[i,abs(j+1-2*L)] g_D_fft = fftpack.fftn(g_D) g_D_fft_conj = fftpack.fftn(g_D.conj()) return g_D, g_D_fft, g_D_fft_conj
def admitant_2d_matrix_element_bm(mesh, row_idx, col_idx, z0, k, coupling_sign):
n, ns = mesh.normals[row_idx], mesh.normals[col_idx]
r = mesh.centers[row_idx]
corners, admittance = mesh.corners[col_idx], mesh.admittances[col_idx]
singular = row_idx == col_idx
if singular:
element_vector = corners[1] - corners[0]
length = np.sqrt(element_vector.dot(element_vector))
lk2 = length * k / 2
return (
+(z0 * admittance * np.pi * length * k / 8)
* (+hankel2(0, lk2) * struve(-1, lk2) + hankel2(1, lk2) * struve(0, lk2))
- coupling_sign
* fixed_quad(regularized_hypersingular_bm_part, 0, lk2)[0]
/ 2
+ coupling_sign * 2j / (np.pi * k * length)
+ (1 - coupling_sign * z0 * admittance) / 2
)
else:
def integral_function(rs):
return (
hs_2d(ns, k, r, rs)
- 1j * k * z0 * admittance * g_2d(k, r, rs)
+ coupling_sign * z0 * admittance * h_2d(n, k, r, rs)
+ coupling_sign * 1j / k * hypersingular(k, r, rs, n, ns)
)
return line_integral(integral_function, corners[0], corners[1], singular)
def hypersingular(k, r, rs, n, ns):
vector = r - rs
distance = np.sqrt(vector.dot(vector))
kdist = k * distance
return (1j * k / (4 * distance ** 2)) * (
distance * hankel2(1, kdist) * n.dot(ns)
- k * hankel2(2, kdist) * n.dot(vector) * ns.dot(vector)
)
def scatter_conducting_cylinder(R, phi, wavelength, radius, E0=1.0, N=N0):
'''Scattered field from a conducting cylinder'''
Ez = 0
k = 2 * np.pi / wavelength
for n in range(-N, N):
an = -(1.j)**-n * jv(n, k * radius) / hankel2(n, k * radius)
Ez += E0 * an * hankel2(n, k * R) * np.exp(1.j * n * phi)
Ez[R < radius] = 0
return Ez
def theo_fun(k):
'''Returns Theodorsen function at a reduced frequency k'''
H1 = scsp.hankel2(1, k)
H0 = scsp.hankel2(0, k)
C = H1 / (H1 + j * H0)
return C
def sMatrixElementCoefficients(k, n1, n2, m):
A = -n1 * hankel2(m, n2 * k) * jvp(m, n1 * k) + n2 * jn(m, n1 * k) * h2vp(
m, n2 * k)
B = n1 * hankel1(m, n2 * k) * jvp(m, n1 * k) - n2 * jn(m, n1 * k) * h1vp(
m, n2 * k)
Ap = -n1 * n1 * jvp(m, n1 * k, 2) * hankel2(m, n2 * k) + n2 * n2 * jn(
m, n1 * k) * h2vp(m, n2 * k, 2)
Bp = n1 * n1 * jvp(m, n1 * k, 2) * hankel1(m, n2 * k) - n2 * n2 * jn(
m, n1 * k) * h1vp(m, n2 * k, 2)
return A, B, Ap, Bp
def compute_total_field(x,
y,
radius,
an,
cn,
N,
wavenumber_b,
wavenumber_d,
magnitude,
theta=None):
"""Summarize the method."""
E0 = magnitude
kb, kd = wavenumber_b, wavenumber_d
a = radius
if theta is None:
rho, phi = cart2pol(x, y)
et = np.zeros(rho.shape, dtype=complex)
i = 0
for n in range(-N, N + 1):
et[rho > a] = et[rho > a] + (E0 * 1j**(-n) *
(jv(n, kb * rho[rho > a]) + an[i] *
hankel2(n, kb * rho[rho > a])) *
np.exp(1j * n * phi[rho > a]))
et[rho <= a] = et[rho <= a] + (E0 * 1j**(-n) * cn[i] * jv(
n, kd * rho[rho <= a]) * np.exp(1j * n * phi[rho <= a]))
i += 1
else:
S = theta.size
et = np.zeros((x.size, S), dtype=complex)
for s in range(S):
xp, yp = rotate_axis(theta[s], x.reshape(-1), y.reshape(-1))
rho, phi = cart2pol(xp, yp)
i = 0
for n in range(-N, N + 1):
et[rho > a,
s] = et[rho > a, s] + (E0 * 1j**(-n) *
(jv(n, kb * rho[rho > a]) + an[i] *
hankel2(n, kb * rho[rho > a])) *
np.exp(1j * n * phi[rho > a]))
et[rho <= a,
s] = et[rho <= a, s] + (E0 * 1j**(-n) * cn[i] * jv(
n, kd * rho[rho <= a]) * np.exp(1j * n * phi[rho <= a]))
i += 1
return et
def test_cylindrical_hankel_2():
order = np.random.randint(0, 6)
value = np.random.rand(1).item() * 10
assert (roundComplex(complex(NumCpp.cyclic_hankel_2_Scaler(order, value)), NUM_DECIMALS_ROUND) ==
roundComplex(sp.hankel2(order, value), NUM_DECIMALS_ROUND))
shapeInput = np.random.randint(20, 100, [2, ])
shape = NumCpp.Shape(shapeInput[0].item(), shapeInput[1].item())
order = np.random.randint(0, 6)
value = NumCpp.NdArray(shape)
valuePy = np.random.rand(shape.rows, shape.cols) * 10
value.setArray(valuePy)
assert np.array_equal(roundComplexArray(NumCpp.cyclic_hankel_2_Array(order, value), NUM_DECIMALS_ROUND),
roundComplexArray(sp.hankel2(order, valuePy), NUM_DECIMALS_ROUND))
def f0(x, v):
if v == 0:
return f(x)
elif v == 1:
return spec.jn(10, x)
elif v == 2:
return spec.hankel1(0, x)
elif v == 3:
return spec.hankel1(10, x)
elif v == 4:
return spec.hankel2(0, x)
elif v == 5:
return spec.hankel2(10, x)
else:
return spec.airy(x)
def radar_cross_section(frequency, radius, incident_angle, observation_angle,
number_of_modes):
"""
Calculate the bistatic radar cross section for the infinite cylinder with oblique incidence.
:param frequency: The frequency of the incident energy (Hz).
:param radius: The radius of the cylinder (m).
:param incident_angle: The angle of incidence from z-axis (deg).
:param observation_angle: The observation angle (deg).
:param number_of_modes: The number of terms to take in the summation.
:return: The bistatic radar cross section for the infinite cylinder (m^2).
"""
# Wavelength
wavelength = c / frequency
# Wavenumber
k = 2.0 * pi / wavelength
modes = range(number_of_modes + 1)
# Initialize the sum
s_tm = 0
s_te = 0
phi = radians(observation_angle)
theta_i = radians(incident_angle)
# Argument for Bessel and Hankel functions
z = k * radius * sin(theta_i)
for n in modes:
en = 2.0
if n == 0:
en = 1.0
an = jv(n, z) / hankel2(n, z)
jp = n * jv(n, z) / z - jv(n + 1, z)
hp = n * hankel2(n, z) / z - hankel2(n + 1, z)
bn = -jp / hp
s_tm += en * an * cos(n * phi)
s_te += en * bn * cos(n * phi)
rcs_tm = 2.0 * wavelength / (pi * sin(theta_i)) * abs(s_tm)**2
rcs_te = 2.0 * wavelength / (pi * sin(theta_i)) * abs(s_te)**2
return rcs_te, rcs_tm
def my_Hankel(n, k, abs_R):
z = k * k
if (np.imag(z) > 0):
ret = sf.hankel1(n, k * abs_R)
else:
ret = -sf.hankel2(n, k * abs_R)
return ret
def _minimum_norm(self, et):
"""Evaluate the Minimum Norm formulation.
In this formulation, the trial function is defined as the kernel
function.
Parameters
----------
et : :class:`numpy.ndarray`
Total field matrix.
"""
N = self._u.size
Q, P = self.discretization
kb = self.configuration.kb
Kpq = np.zeros((P * Q, N), dtype=complex)
s = 0
for i in range(P * Q):
R = np.sqrt((self._xpq[i] - self._u)**2 +
(self._ypq[i] - self._v)**2)
Kpq[i, :] = -kb**2 * 1j / 4 * hankel2(0, kb * R) * et[:, s]
if s == self.configuration.NS - 1:
s = 0
else:
s += 1
return Kpq
def _get_kernel(self, et, resolution):
r"""Compute kernel function.
Evaluate the kernel function of the integral operator. The
kernel is defined as:
.. math:: K(x, y, u, v) = j\omega\mu_b E(\phi, u, v)\frac{j}{4}
H_0^{(2)}(k_b|\sqrt{(x-u)^2 + (y-v)^2}|)
Parameters
----------
et : :class:`numpy.ndarray`
Total field matrix.
resolution : 2-tuple
Image discretization in y and x directions,
respectively.
"""
NS = self.configuration.NS
kb = self.configuration.kb
K = np.zeros(self._R.shape, dtype=complex)
L = et.shape[1]
s = 0
for i in range(K.shape[0]):
K[i, :] = -kb**2 * 1j / 4 * hankel2(0, kb * self._R[i, :]) * et[:,
s]
# Matching the measurement-source indexation
if s == L - 1:
s = 0
else:
s += 1
return K
def line_dipole(omega, x0, n0, grid, *, c=None):
r"""Line source with dipole characteristics parallel to the z-axis.
Parameters
----------
omega : float
Frequency of source.
x0 : (3,) array_like
Position of source. Note: third component of x0 is ignored.
x0 : (3,) array_like
Normal vector of the source.
grid : triple of array_like
The grid that is used for the sound field calculations.
See `sfs.util.xyz_grid()`.
c : float, optional
Speed of sound.
Notes
-----
.. math::
G(\x-\x_0,\w) = \frac{\i k}{4} \Hankel{2}{1}{\wc|\x-\x_0|} \cos{\phi}
"""
k = _util.wavenumber(omega, c)
x0 = _util.asarray_1d(x0)[:2] # ignore z-components
n0 = _util.asarray_1d(n0)[:2]
grid = _util.as_xyz_components(grid)
dx = grid[:2] - x0
r = _np.linalg.norm(dx)
p = 1j * k / 4 * _special.hankel2(1, k * r) * _np.inner(dx, n0) / r
return _duplicate_zdirection(p, grid)
def _spherical_hankel(n, z, kind):
if kind == 1:
hankel = _spspecial.hankel1(n + 0.5, z)
elif kind == 2:
hankel = _spspecial.hankel2(n + 0.5, z)
hankel = np.sqrt(np.pi / 2 / z) * hankel
return hankel
def get_coefficients(Nterms, wavenumber_b, wavenumber_d, radius, epsilon_d,
epsilon_b):
"""Summarize the method."""
n = np.arange(-Nterms, Nterms + 1)
kb, kd = wavenumber_b, wavenumber_d
a = radius
an = (-jv(n, kb * a) / hankel2(n, kb * a) *
((epsilon_d * jvp(n, kd * a) /
(epsilon_b * kd * a * jv(n, kd * a)) - jvp(n, kb * a) /
(kb * a * jv(n, kb * a))) /
(epsilon_d * jvp(n, kd * a) /
(epsilon_b * kd * a * jv(n, kd * a)) - h2vp(n, kb * a) /
(kb * a * hankel2(n, kb * a)))))
cn = 1 / jv(n, kd * a) * (jv(n, kb * a) + an * hankel2(n, kb * a))
return an, cn
def construct_G_D(pos_D, k, n):
# pos_D : N X 2 vector containing position of each grid
# n : size of each grid
# k : wavevector
a = (n**2 / np.pi)**0.5
N = np.shape(pos_D)[0]
G_D = np.zeros([N, N], dtype=np.complex128)
for i in range(N):
for j in range(N):
if i == j:
G_D[i, j] = -1j * 0.5 * (np.pi * k * a * sc.hankel2(1, k * a) -
2 * 1j)
else:
rho_ij = linalg.norm(pos_D[i, :] - pos_D[j, :], 2)
G_D[i,
j] = -1j * 0.5 * np.pi * k * a * sc.j1(k * a) * sc.hankel2(
0, k * rho_ij)
return G_D
def um(r, m):
T = 1000
f = 1 / T
omega = 2 * np.pi * f
a = 3480.
rs = 6271.
omega = 0.05
bbeta = 6.5
mu = 80
if r < a:
um = 0
return um
elif a <= r and r < rs:
um = -1. / (16 * mu) * 1j * (
2 * spf.hankel1(m, omega * r / bbeta) + a * omega *
(-spf.hankel1(m - 1, a * omega / bbeta) +
spf.hankel1(m + 1, a * omega / bbeta)) *
spf.hankel2(m, r * omega / bbeta) /
(a * omega * spf.hankel2(m - 1, a * omega / bbeta) -
m * bbeta * spf.hankel2(m, a * omega / bbeta))) * spf.hankel2(
m, rs * omega / bbeta)
# um = -1./(16*mu)*1j*(2*spf.hankel1(m,r*omega/bbeta)+ a*omega*(-spf.hankel1(m-1,a*omega/bbeta)+spf.hankel1(m+1,a*omega/bbeta))*spf.hankel2(m,r*omega/bbeta) / (a*omega*spf.hankel2(m-1,a*omega/bbeta)-m*bbeta*spf.hankel2(m,a*omega/bbeta)) )*spf.hankel2(m,rs*omega/bbeta)
# *spf.hankel1(m,omega*rs/bbeta)*(2*spf.hankel2(m,omega*r/bbeta)+omega*a*spf.hankel1(m,omega*r/bbeta)*(spf.hankel2(m+1,omega*a/bbeta)-spf.hankel2(m-1,omega*a/bbeta))/(omega*a*spf.hankel1(m-1,omega*a/bbeta)-m*bbeta*spf.hankel1(m,omega*a/bbeta)))
# 1./(16*mu)*1j*spf.hankel1(m,omega*rs/bbeta)*(2*spf.hankel2(m,omega*r/bbeta)+omega*a*spf.hankel1(m,omega*r/bbeta)*(spf.hankel2(m+1,omega*a/bbeta)-spf.hankel2(m-1,omega*a/bbeta))/(omega*a*spf.hankel1(m-1,omega*a/bbeta)-m*bbeta*spf.hankel1(m,omega*a/bbeta)))
return um
elif rs <= r:
um = -1 / 16. * 1j * a * omega * spf.hankel2(m, r * omega / bbeta) * (
(-spf.hankel1(m - 1, a * omega / bbeta) +
spf.hankel1(m + 1, a * omega / bbeta)) * spf.hankel2(
m, rs * omega / bbeta) + spf.hankel1(m, rs * omega / bbeta) *
(spf.hankel2(m - 1, a * omega / bbeta) -
spf.hankel2(m + 1, a * omega / bbeta))) / (
a * mu * omega * spf.hankel2(m - 1, a * omega / bbeta) -
m * bbeta * mu * spf.hankel2(m, a * omega / bbeta))
#-1./16.*a*omega*spf.hankel2(m,r*omega/bbeta)*((-spf.hankel1(m-1,a*omega/bbeta)+spf.hankel1(m+1,a*omega/bbeta))*spf.hankel2(m,rs*omega/bbeta)+spf.hankel1(m,rs*omega)*(spf.hankel2(m-1,a*omega/bbeta)-spf.hankel2(m+1,a*omega/bbeta)))/(a*mu*omega*spf.hankel2(m-1,a*omega/bbeta)-m*bbeta*mu*spf.hankel2(m,a*omega/bbeta))
# -1/(16*mu)*1j*a*omega*spf.hankel1(m,omega*r/bbeta)*((-spf.hankel1(m-1,a*omega/bbeta)+spf.hankel1(m+1,a*omega/bbeta))*spf.hankel2(m,rs*omega/bbeta)+spf.hankel1(m,rs*omega/bbeta)*(spf.hankel2(m-1,a*omega/bbeta)-spf.hankel2(m+1,a*omega/bbeta)))/(a*omega*spf.hankel1(m-1,omega*a/bbeta)-m*bbeta*spf.hankel1(m,a*omega/bbeta))
#-1/(16*mu)*1j*a*omega*spf.hankel1(m,omega*r/bbeta)*((-spf.hankel1(m-1,a*omega/bbeta)+spf.hankel1(m+1,a*omega/bbeta))*spf.hankel2(m,rs*omega/bbeta)+spf.hankel1(m,rs*omega/bbeta)*(spf.hankel2(m-1,a*omega/bbeta)-spf.hankel2(m+1,a*omega/bbeta)))/(a*omega*spf.hankel1(m-1,omega*a/bbeta)-m*bbeta*spf.hankel1(m,a*omega/bbeta))
return um
def directwave(wav, trav, nt, dt, nfft=None, dist=None, kind='2d'):
r"""Analytical direct wave
Compute analytical 2d or 3d Green's function in frequency domain given
a wavelet ``wav``, traveltime curve ``trav`` and distance ``dist``
(for 3d case only).
Parameters
----------
wav : :obj:`numpy.ndarray`
Wavelet in time domain to apply to direct arrival when created
using ``trav``. Phase will be discarded resulting in a zero-phase
wavelet with same amplitude spectrum as provided by ``wav``
trav : :obj:`numpy.ndarray`
Traveltime of first arrival from subsurface point to
surface receivers of size :math:`\lbrack nr \times 1 \rbrack`
nt : :obj:`float`, optional
Number of samples in time
dt : :obj:`float`, optional
Sampling in time
nfft : :obj:`int`, optional
Number of samples in fft time (if ``None``, :math:`nfft=nt`)
dist: :obj:`numpy.ndarray`
Distance between subsurface point to
surface receivers of size :math:`\lbrack nr \times 1 \rbrack`
kind : :obj:`str`, optional
2-dimensional (``2d``) or 3-dimensional (``3d``)
Returns
-------
direct : :obj:`numpy.ndarray`
Direct arrival in time domain of size
:math:`\lbrack nt \times nr \rbrack`
"""
nr = len(trav)
nfft = nt if nfft is None or nfft < nt else nfft
W = np.abs(np.fft.rfft(wav, nfft)) * dt
f = 2 * np.pi * np.arange(nfft) / (dt * nfft)
direct = np.zeros((nfft, nr), dtype=np.complex128)
for it in range(len(W)):
if kind == '2d':
#direct[it] = W[it] * 1j * f[it] * np.exp(-1j * ((f[it] * trav) \
# + np.sign(f[it]) * np.pi / 4)) / \
# np.sqrt(8 * np.pi * np.abs(f[it]) * trav + 1e-10)
direct[it] = W[it] * 1j * f[it] * (-1j) * \
hankel2(0, f[it] * trav + 1e-10) / 4
else:
direct[it] = W[it] * np.exp(
-1j * f[it] * trav) / (4 * np.pi * dist)
direct = np.fft.irfft(direct, nfft, axis=0) / dt
direct = np.real(direct[:nt])
return direct
def calc_coefficiencts(k, radius, z0, amplitude, admittance, max_order):
orders = np.arange(max_order + 1)
thetas = np.pi * orders / len(orders)
rhs = (amplitude * np.exp(1j * k * radius * np.cos(thetas)) *
(np.cos(thetas) / z0 - admittance))
matrix = np.array([[
np.cos(n * theta) *
(1j * h2vp(n, k * radius) / z0 + admittance * hankel2(n, k * radius))
for n in orders
] for theta in thetas])
return np.linalg.solve(matrix, rhs)
def theo_fun(k):
r"""Returns the value of Theodorsen's function at a reduced frequency :math:`k`.
.. math:: \mathcal{C}(jk) = \frac{H_1^{(2)}(k)}{H_1^{(2)}(k) + jH_0^{(2)}(k)}
where :math:`H_0^{(2)}(k)` and :math:`H_1^{(2)}(k)` are Hankel functions of the second kind.
Args:
k (np.array): Reduced frequency/frequencies at which to evaluate the function.
Returns:
np.array: Value of Theodorsen's function evaluated at the desired reduced frequencies.
"""
H1 = scsp.hankel2(1, k)
H0 = scsp.hankel2(0, k)
C = H1 / (H1 + j * H0)
return C
def besselh(n, k, z):
""" Hankel function
n - real order
k - kind (1,2)
z - complex argument
"""
if k == 2:
return special.hankel2(n,z)
elif 0 == 1:
return special.hankel1(n,z)
else:
raise TypeError("Only second or first kind.")
def construct_G_S(pos_D, pos_S, k, n):
M = np.shape(pos_S)[0]
N = np.shape(pos_D)[0]
G_S = np.zeros([M, N], dtype=np.complex128)
a = (n**2 / np.pi)**0.5
for i in range(M):
for j in range(N):
rho_ij = linalg.norm(pos_S[i, :] - pos_D[j, :], 2)
G_S[i, j] = -1j * 0.5 * np.pi * k * a * sc.j1(k * a) * sc.hankel2(
0, k * rho_ij)
return G_S
def cyl_modal_coefs(N, kr, arrayType):
"""
Modal coefficients for rigid or open cylindrical array
Parameters
----------
N : int
Maximum spherical harmonic expansion order.
kr: ndarray
Wavenumber-radius product. Dimension = (l).
arrayType: str
'open' or 'rigid'.
Returns
-------
b_N : ndarray
Modal coefficients. Dimension = (l, N+1)
Raises
-----
TypeError, ValueError: if method arguments mismatch in type, dimension or value.
Notes
-----
The `arrayType` options are:
- 'open' for open array of omnidirectional sensors
- 'rigid' for sensors mounted on a rigid baffle.
"""
_validate_int('N', N)
_validate_ndarray_1D('kr', kr, positive=True)
_validate_string('arrayType', arrayType, choices=['open', 'rigid'])
b_N = np.zeros((kr.size, N + 1), dtype='complex')
for n in range(N + 1):
if arrayType is 'open':
b_N[:, n] = np.power(1j, n) * jv(n, kr)
elif arrayType is 'rigid':
jn = jv(n, kr)
jnprime = jvp(n, kr, 1)
hn = hankel2(n, kr)
hnprime = h2vp(n, kr, 1)
temp = np.power(1j, n) * (jn - (jnprime / hnprime) * hn)
temp[np.where(kr == 0)] = 1 if n == 0 else 0.
b_N[:, n] = temp
return b_N
def line_velocity(omega, x0, grid, *, c=None, rho0=None):
"""Velocity of line source parallel to the z-axis.
Parameters
----------
omega : float
Frequency of source.
x0 : (3,) array_like
Position of source. Note: third component of x0 is ignored.
grid : triple of array_like
The grid that is used for the sound field calculations.
See `sfs.util.xyz_grid()`.
c : float, optional
Speed of sound.
Returns
-------
`XyzComponents`
Particle velocity at positions given by *grid*.
Examples
--------
The particle velocity can be plotted on top of the sound pressure:
.. plot::
:context: close-figs
v = sfs.fd.source.line_velocity(omega, x0, vgrid)
sfs.plot2d.amplitude(p * normalization_line, grid)
sfs.plot2d.vectors(v * normalization_line, vgrid)
plt.title("Sound Pressure and Particle Velocity")
"""
if c is None:
c = _default.c
if rho0 is None:
rho0 = _default.rho0
k = _util.wavenumber(omega, c)
x0 = _util.asarray_1d(x0)[:2] # ignore z-component
grid = _util.as_xyz_components(grid)
offset = grid[:2] - x0
r = _np.linalg.norm(offset)
v = -1 / (4 * c * rho0) * _special.hankel2(1, k * r)
v = [v * o / r for o in offset]
assert v[0].shape == v[1].shape
if len(grid) > 2:
v.append(_np.zeros_like(v[0]))
return _util.XyzComponents([_duplicate_zdirection(vi, grid) for vi in v])
def eq(x, r, n, coef):
"""
计算均衡矩阵中各元素数值
argument:
x: 某一单一频率值
r: 球形阵列半径
n: 阶数
coef: 均衡系数
return:
out：均衡矩阵的一个元素
"""
kr = 2 * np.pi * x * r / 344 + (x == 0) * np.finfo(float).eps
hnde = math.sqrt(np.pi /
(2 * kr)) * (n / kr * special.hankel2(n + 1 / 2, kr) -
special.hankel2(n + 3 / 2, kr))
bn = (-1j)**(n + 1) * (-1)**n / (kr**2 * hnde)
# bn = 1j/(kr**2*hnde)
out = np.conjugate(bn) / ((abs(bn))**2 + coef**2)
#out = (np.conjugate(1j/hnde)*(kr**2))/((coef**2)*(kr**4)-1/(hnde**2))/(-1j)**n
return out
def itf_worker(self,input_array,lock_input_array,output_dictionary,lock_output_dictionary,itf2): while len(input_array)>0: time_fi=0 if len(input_array)>0: with lock_input_array: if len(input_array)>0: time_fi=input_array.pop(0) if time_fi!=0: itf1 = -1j*pi*self.CONST_MU0*time_fi/2/self.magnetic_altitude(time_fi)/self.phase_velocity(time_fi) itf3 = special.hankel2([1],[2*pi*self.r*time_fi/self.phase_velocity(time_fi)]) itf4 = exp(-self.attenuation_factor(time_fi)*self.r) output_res=itf1*itf3*itf4*itf2 with lock_output_dictionary: output_dictionary[time_fi]=output_res
def wfs_2d_line(omega, x0, n0, xs, c=None):
"""Line source by 2-dimensional WFS.
::
D(x0,k) = j/2 k (x0-xs) n0 / |x0-xs| * H1(k |x0-xs|)
"""
x0 = util.asarray_of_rows(x0)
n0 = util.asarray_of_rows(n0)
xs = util.asarray_1d(xs)
k = util.wavenumber(omega, c)
ds = x0 - xs
r = np.linalg.norm(ds, axis=1)
return -1j/2 * k * inner1d(ds, n0) / r * hankel2(1, k * r)
def sdm_25d_plane(omega, x0, n0, n=[0, 1, 0], xref=[0, 0, 0], c=None):
"""Plane wave by 2.5-dimensional SDM.
The secondary sources have to be located on the x-axis (y0=0).
Eq.(3.79) from :cite:`Ahrens2012`::
D_2.5D(x0,w) =
"""
x0 = util.asarray_of_rows(x0)
n0 = util.asarray_of_rows(n0)
n = util.normalize_vector(n)
xref = util.asarray_1d(xref)
k = util.wavenumber(omega, c)
return 4j * np.exp(-1j*k*n[1]*xref[1]) / hankel2(0, k*n[1]*xref[1]) * \
np.exp(-1j*k*n[0]*x0[:, 0])
def sdm_2d_line(omega, x0, n0, xs, c=None):
"""Line source by two-dimensional SDM.
The secondary sources have to be located on the x-axis (y0=0).
Derived from :cite:`Spors2009`, Eq.(9), Eq.(4)::
D(x0,k) =
"""
x0 = util.asarray_of_rows(x0)
n0 = util.asarray_of_rows(n0)
xs = util.asarray_1d(xs)
k = util.wavenumber(omega, c)
ds = x0 - xs
r = np.linalg.norm(ds, axis=1)
return - 1j/2 * k * xs[1] / r * hankel2(1, k * r)
def hankel2(n, z):
"""Bessel function of third kind (Hankel function) of order n at kr.
Wraps scipy.special.hankel2(n, z)
Parameters
----------
n : array_like
Order
z: array_like
Argument
Returns
-------
H2 : array_like
Values of Hankel function of order n at position z
"""
return scy.hankel2(n, z)
def sdm_25d_point(omega, x0, n0, xs, xref=[0, 0, 0], c=None):
"""Point source by 2.5-dimensional SDM.
The secondary sources have to be located on the x-axis (y0=0).
Driving funcnction from :cite:`Spors2010`, Eq.(24)::
D(x0,k) =
"""
x0 = util.asarray_of_rows(x0)
n0 = util.asarray_of_rows(n0)
xs = util.asarray_1d(xs)
xref = util.asarray_1d(xref)
k = util.wavenumber(omega, c)
ds = x0 - xs
r = np.linalg.norm(ds, axis=1)
return 1/2 * 1j * k * np.sqrt(xref[1] / (xref[1] - xs[1])) * \
xs[1] / r * hankel2(1, k * r)
def line_dipole(omega, x0, n0, grid, *, c=None):
r"""Line source with dipole characteristics parallel to the z-axis.
Note: third component of x0 is ignored.
Notes
-----
.. math::
G(\x-\x_0,\w) = \frac{\i k}{4} \Hankel{2}{1}{\wc|\x-\x_0|} \cos{\phi}
"""
k = _util.wavenumber(omega, c)
x0 = _util.asarray_1d(x0)[:2] # ignore z-components
n0 = _util.asarray_1d(n0)[:2]
grid = _util.as_xyz_components(grid)
dx = grid[:2] - x0
r = _np.linalg.norm(dx)
p = 1j*k/4 * _special.hankel2(1, k * r) * _np.inner(dx, n0) / r
return _duplicate_zdirection(p, grid)
def line_velocity(omega, x0, grid, *, c=None, rho0=None):
"""Velocity of line source parallel to the z-axis.
Returns
-------
`XyzComponents`
Particle velocity at positions given by *grid*.
Examples
--------
The particle velocity can be plotted on top of the sound pressure:
.. plot::
:context: close-figs
v = sfs.fd.source.line_velocity(omega, x0, vgrid)
sfs.plot2d.amplitude(p * normalization_line, grid)
sfs.plot2d.vectors(v * normalization_line, vgrid)
plt.title("Sound Pressure and Particle Velocity")
"""
if c is None:
c = _default.c
if rho0 is None:
rho0 = _default.rho0
k = _util.wavenumber(omega, c)
x0 = _util.asarray_1d(x0)[:2] # ignore z-component
grid = _util.as_xyz_components(grid)
offset = grid[:2] - x0
r = _np.linalg.norm(offset)
v = -1/(4 * c * rho0) * _special.hankel2(1, k * r)
v = [v * o / r for o in offset]
assert v[0].shape == v[1].shape
if len(grid) > 2:
v.append(_np.zeros_like(v[0]))
return _util.XyzComponents([_duplicate_zdirection(vi, grid) for vi in v])
def line_dipole(omega, x0, n0, grid, c=None):
"""Line source with dipole characteristics parallel to the z-axis.
Note: third component of x0 is ignored.
::
(2)
G(x-x0, w) = jk/4 H1 (w/c |x-x0|) cos(phi)
"""
k = util.wavenumber(omega, c)
x0 = util.asarray_1d(x0)
x0 = x0[:2] # ignore z-component
n0 = n0[:2]
grid = util.asarray_of_arrays(grid)
dx = grid[:2] - x0
r = np.linalg.norm(dx)
p = 1j*k/4 * special.hankel2(1, k * r) * np.inner(dx, n0) / r
p = _duplicate_zdirection(p, grid)
return np.squeeze(p)
def line(omega, x0, n0, grid, c=None):
"""Line source parallel to the z-axis.
Note: third component of x0 is ignored.
::
(2)
G(x-x0, w) = -j/4 H0 (w/c |x-x0|)
Examples
--------
.. plot::
:context: close-figs
p_line = sfs.mono.source.line(omega, x0, None, grid)
sfs.plot.soundfield(p_line, grid)
plt.title("Line Source at {} m".format(x0[:2]))
Normalization ...
.. plot::
:context: close-figs
p_line *= np.exp(-1j*7*np.pi/4) / np.sqrt(1/(8*np.pi*omega/sfs.defs.c))
sfs.plot.soundfield(p_line, grid)
plt.title("Line Source at {} m (normalized)".format(x0[:2]))
"""
k = util.wavenumber(omega, c)
x0 = util.asarray_1d(x0)
x0 = x0[:2] # ignore z-component
grid = util.asarray_of_arrays(grid)
r = np.linalg.norm(grid[:2] - x0)
p = -1j/4 * special.hankel2(0, k * r)
p = _duplicate_zdirection(p, grid)
return np.squeeze(p)
def nfchoa_2d_plane(omega, x0, r0, n=[0, 1, 0], max_order=None, c=None):
r"""Plane wave by two-dimensional NFC-HOA.
.. math::
D(\phi_0, \omega) =
-\frac{2\i}{\pi r_0}
\sum_{m=-M}^M
\frac{\i^{-m}}{\Hankel{2}{m}{\wc r_0}}
\e{\i m (\phi_0 - \phi_\text{pw})}
See http://sfstoolbox.org/#equation-D.nfchoa.pw.2D.
"""
x0 = util.asarray_of_rows(x0)
k = util.wavenumber(omega, c)
n = util.normalize_vector(n)
phi, _, r = util.cart2sph(*n)
phi0 = util.cart2sph(*x0.T)[0]
M = _max_order_circular_harmonics(len(x0), max_order)
d = 0
for m in range(-M, M + 1):
d += 1j**-m / hankel2(m, k * r0) * np.exp(1j * m * (phi0 - phi))
return -2j / (np.pi*r0) * d
def hankel2p(m, z):
return hankel2(m - 1, z) - hankel2(m, z) * m / z
def hankel(j, nu, z):
if j==1:
return hankel1(nu, z)
elif j==2:
return hankel2(nu, z)
raise ValueError("j must be either 1 or 2")
if __name__ == '__main__': mesh = [25, 50, 100, 200, 300,500,1000,2000] convergence = np.zeros((0,2)) for nPoints in mesh: y = homoCircle(nPoints, 1.0, 2.0, 1.0, 1.0, 1) scatMat = y.computeScatteringMatrix(y.Mmax) analScatMat = np.zeros(2*y.Mmax+1, dtype=complex) zc = y.nc*y.k zo = y.no*y.k eta = y.nc/y.no for i in range(2*y.Mmax+1): m = i-y.Mmax num = -(eta*jvp(m,zc)*hankel2(m,zo)-jn(m,zc)*h2vp(m,zo)) den = eta*jvp(m,zc)*hankel1(m,zo)-jn(m,zc)*h1vp(m,zo) analScatMat[i] = num/den err = np.amax(np.abs(np.diag(analScatMat)-scatMat)) print(err) print(analScatMat) # -- Mean areas of triangles convergence = np.insert(convergence, len(convergence), [np.mean(y.areas), err],axis=0) fig1 = plt.figure(figsize=(5,3)) ax1 = fig1.add_subplot(111) plt.plot(convergence[:,0],convergence[:,1], ls='--', marker='o', label=r"Numerical Error") p = np.polyfit(np.log(convergence[:,0]),np.log(convergence[:,1]),1) ynew = np.exp(np.polyval(p,np.log(convergence[:,0])))
def line_dirichlet_edge(omega, x0, grid, alpha=3/2*np.pi, Nc=None, c=None):
"""Line source scattered at an edge with Dirichlet boundary conditions.
:cite:`Moser2012`, eq.(10.18/19)
Parameters
----------
omega : float
Angular frequency.
x0 : (3,) array_like
Position of line source.
grid : triple of array_like
The grid that is used for the sound field calculations.
See `sfs.util.xyz_grid()`.
alpha : float, optional
Outer angle of edge.
Nc : int, optional
Number of elements for series expansion of driving function.
Estimated if not given.
c : float, optional
Speed of sound
Returns
-------
numpy.ndarray
Complex pressure at grid positions.
"""
k = util.wavenumber(omega, c)
x0 = util.asarray_1d(x0)
phi_s = np.arctan2(x0[1], x0[0])
if phi_s < 0:
phi_s = phi_s + 2*np.pi
r_s = np.linalg.norm(x0)
grid = util.XyzComponents(grid)
r = np.linalg.norm(grid[:2])
phi = np.arctan2(grid[1], grid[0])
phi = np.where(phi < 0, phi+2*np.pi, phi)
if Nc is None:
Nc = np.ceil(2 * k * np.max(r) * alpha/np.pi)
epsilon = np.ones(Nc) # weights for series expansion
epsilon[0] = 2
p = np.zeros((grid[0].shape[1], grid[1].shape[0]), dtype=complex)
idxr = (r <= r_s)
idxa = (phi <= alpha)
for m in np.arange(Nc):
nu = m*np.pi/alpha
f = 1/epsilon[m] * np.sin(nu*phi_s) * np.sin(nu*phi)
p[idxr & idxa] = p[idxr & idxa] + f[idxr & idxa] * \
special.jn(nu, k*r[idxr & idxa]) * special.hankel2(nu, k*r_s)
p[~idxr & idxa] = p[~idxr & idxa] + f[~idxr & idxa] * \
special.jn(nu, k*r_s) * special.hankel2(nu, k*r[~idxr & idxa])
p = p * -1j*np.pi/alpha
pl = line(omega, x0, None, grid, c=c)
p[~idxa] = pl[~idxa]
return p
def ratioHankelFunctions(m1, m2, z): return hankel2(m2,z)/hankel1(m1,z)
rt = np.sqrt(rti/pi)
print(dt*180/pi,rti,rt)
theta = np.linspace(0,2*pi - dt, nodeTot)
r = rd * np.sqrt(
( np.cos(theta[0]) - np.cos(theta) )**2 +
( np.sin(theta[0]) - np.sin(theta) )**2
)
## build coefficient matrix
r = linalg.toeplitz(r)
z = 1j * pi**2 * rt * (er - 1) * (
special.jv(1, 2*pi*rt) *
special.hankel2(0, 2 * pi * r)
)
#diagonal
di = np.diag_indices(nodeTot)
z[di] = 1 + (er-1) * .5j * ( 2 * pi**2 * rt * special.hankel2(1, 2*pi*rt) - 2j )
cv = ei * np.exp(-2j * pi * rd * np.cos(theta))
# plt.matshow(np.imag(z))
#solve for field
e = linalg.solve(z, cv)
def sMatrixElementCoefficients(k,n1,n2,m): A = -n1*hankel2(m,n2*k)*jvp(m,n1*k)+n2*jn(m,n1*k)*h2vp(m,n2*k) B = n1*hankel1(m,n2*k)*jvp(m,n1*k)-n2*jn(m,n1*k)*h1vp(m,n2*k) Ap = -n1*n1*jvp(m,n1*k,2)*hankel2(m,n2*k)+n2*n2*jn(m,n1*k)*h2vp(m,n2*k,2) Bp = n1*n1*jvp(m,n1*k,2)*hankel1(m,n2*k)-n2*n2*jn(m,n1*k)*h1vp(m,n2*k,2) return A, B, Ap, Bp
def hankel2d(n, z):
"""Derivative of hankel2 (from Wolfram MathWorld)"""
return 0.5 * (hankel2(n-1, z) - hankel2(n+1, z))
def p_cmpx(r,t):
return ( 1j*rho_0*c*u_0 * hankel2(0,r*k) / hankel2(1,a*k) ) * exp(1j*w*t)
def u_cmpx(r,t):
return ( u_0 * hankel2(1,r*k) / hankel2(1,a*k) ) * exp(1j*w*t)
