def __init__(self, c, funcgen_tol, funcgen_order):
# c: boundary coordinates, complex
self.c = c
self.cx = self.c.real.copy()
self.cy = self.c.imag.copy()
# get some useful boundary-related information
self.n = self.c.size
self.dt = 2 * np.pi / self.n
self.ts = np.arange(self.n) * self.dt
self.ik = np.fft.fftfreq(self.n, 1.0 / self.n)
# get derivatives of this
self.cp = fourier_derivative_1d(f=self.c, d=1, ik=self.ik)
self.cpp = fourier_derivative_1d(f=self.c, d=2, ik=self.ik)
# get direct fourier evaluator for this
self.dirf_c = periodic_interp1d(self.c)
self.dirf_cp = periodic_interp1d(self.cp)
self.dirf_cpp = periodic_interp1d(self.cpp)
# build function generator representations of these functions
self.funcgen_c = FunctionGenerator(self.dirf_c,
0,
2 * np.pi,
funcgen_tol,
n=funcgen_order)
self.funcgen_cp = FunctionGenerator(self.dirf_cp,
0,
2 * np.pi,
funcgen_tol,
n=funcgen_order)
self.funcgen_cpp = FunctionGenerator(self.dirf_cpp,
0,
2 * np.pi,
funcgen_tol,
n=funcgen_order)
def compute_local_coordinates_nufft_old(cx,
cy,
x,
y,
newton_tol=1e-12,
guess_ind=None,
verbose=False,
max_iterations=30):
"""
Find using the coordinates:
x = X + r n_x
y = Y + r n_y
"""
xshape = x.shape
yshape = y.shape
x = x.flatten()
y = y.flatten()
n = cx.shape[0]
dt = 2 * np.pi / n
ts = np.arange(n) * dt
ik = 1j * np.fft.fftfreq(n, dt / (2 * np.pi))
# tangent vectors
xp = fourier_derivative_1d(f=cx, d=1, ik=ik, out='f')
yp = fourier_derivative_1d(f=cy, d=1, ik=ik, out='f')
# speed
sp = np.sqrt(xp * xp + yp * yp)
isp = 1.0 / sp
# unit tangent vectors
tx = xp * isp
ty = yp * isp
# unit normal vectors
nx = ty
ny = -tx
# derivatives of the normal vector
nxp = fourier_derivative_1d(f=nx, d=1, ik=ik, out='f')
nyp = fourier_derivative_1d(f=ny, d=1, ik=ik, out='f')
# interpolation routines for the necessary objects
if have_better_fourier:
def interp(f):
return _interp2(f)
else:
def interp(f):
return _interp(np.fft.fft(f), x.size)
nx_i = interp(nx)
ny_i = interp(ny)
nxp_i = interp(nxp)
nyp_i = interp(nyp)
x_i = interp(cx)
y_i = interp(cy)
xp_i = interp(xp)
yp_i = interp(yp)
# functions for coordinate transform and its jacobian
def f(t, r):
x = x_i(t) + r * nx_i(t)
y = y_i(t) + r * ny_i(t)
return x, y
def Jac(t, r):
dxdt = xp_i(t) + r * nxp_i(t)
dydt = yp_i(t) + r * nyp_i(t)
dxdr = nx_i(t)
dydr = ny_i(t)
J = np.zeros((t.shape[0], 2, 2), dtype=float)
J[:, 0, 0] = dxdt
J[:, 0, 1] = dydt
J[:, 1, 0] = dxdr
J[:, 1, 1] = dydr
return J.transpose((0, 2, 1))
# brute force find of guess_inds if not provided (slow!)
if guess_ind is None:
xd = x - cx[:, None]
yd = y - cy[:, None]
dd = xd**2 + yd**2
guess_ind = dd.argmin(axis=0)
# get starting points (initial guess for t and r)
t = ts[guess_ind]
cxg = cx[guess_ind]
cyg = cy[guess_ind]
xdg = x - cxg
ydg = y - cyg
r = np.sqrt(xdg**2 + ydg**2)
# how about we re-sign r?
nxg = nx[guess_ind]
nyg = ny[guess_ind]
xcr1 = cxg + r * nxg
ycr1 = cyg + r * nyg
d1 = np.hypot(xcr1 - x, ycr1 - y)
xcr2 = cxg - r * nxg
ycr2 = cyg - r * nyg
d2 = np.hypot(xcr2 - x, ycr2 - y)
better2 = d1 > d2
r[better2] *= -1
# begin Newton iteration
xo, yo = f(t, r)
remx = xo - x
remy = yo - y
rem = np.abs(np.sqrt(remx**2 + remy**2)).max()
if verbose:
print('Newton tol: {:0.2e}'.format(rem))
iteration = 0
while rem > newton_tol:
J = Jac(t, r)
delt = -np.linalg.solve(J, np.column_stack([remx, remy]))
line_factor = 1.0
while True:
t_new, r_new = t + line_factor * delt[:,
0], r + line_factor * delt[:,
1]
try:
xo, yo = f(t_new, r_new)
remx = xo - x
remy = yo - y
rem_new = np.sqrt(remx**2 + remy**2).max()
testit = True
except:
testit = False
if testit and ((rem_new < (1 - 0.5 * line_factor) * rem)
or line_factor < 1e-4):
t = t_new
# put theta back in [0, 2 pi]
t[t < 0] += 2 * np.pi
t[t > 2 * np.pi] -= 2 * np.pi
r = r_new
rem = rem_new
break
line_factor *= 0.5
if verbose:
print('Newton tol: {:0.2e}'.format(rem))
iteration += 1
if iteration > max_iterations:
raise Exception(
'Exceeded maximum number of iterations solving for coordinates .'
)
return t, r
def compute_local_coordinates_polyi_centering(cx,
cy,
x,
y,
gi,
newton_tol=1e-12,
verbose=None,
max_iterations=30):
"""
Find using the coordinates:
x = X + r n_x
y = Y + r n_y
"""
xshape = x.shape
x = x.flatten()
y = y.flatten()
gi = gi.flatten()
xsize = x.size
n = cx.shape[0]
dt = 2 * np.pi / n
ts = np.arange(n) * dt
ik = 1j * np.fft.fftfreq(n, dt / (2 * np.pi))
# tangent vectors
xp = fourier_derivative_1d(f=cx, d=1, ik=ik, out='f')
yp = fourier_derivative_1d(f=cy, d=1, ik=ik, out='f')
# speed
sp = np.sqrt(xp * xp + yp * yp)
isp = 1.0 / sp
# unit tangent vectors
tx = xp * isp
ty = yp * isp
# unit normal vectors
nx = ty
ny = -tx
# brute force find of guess_inds if not provided (slow!)
guess_ind = np.empty(x.size, dtype=int)
multi_guess_ind_finder_centering(cx, cy, x, y, guess_ind, gi, 10)
# get starting points (initial guess for t and r)
initial_ts = ts[guess_ind]
# run the multi-newton solver
t = _multi_newton(initial_ts, x, y, newton_tol, cx, cy, verbose,
max_iterations)
# need to determine the sign now
X, Y = np.zeros_like(x), np.zeros_like(x)
XP, YP = np.zeros_like(x), np.zeros_like(x)
multi_polyi_p(cx, t, X, XP)
multi_polyi_p(cy, t, Y, YP)
ISP = 1.0 / np.sqrt(XP * XP + YP * YP)
NX = YP / ISP
NY = -XP / ISP
r = np.hypot(X - x, Y - y)
xe1 = X + r * NX
ye1 = Y + r * NY
err1 = np.hypot(xe1 - x, ye1 - y)
xe2 = X - r * NX
ye2 = Y - r * NY
err2 = np.hypot(xe2 - x, ye2 - y)
sign = (err1 < err2).astype(int) * 2 - 1
return t, r * sign
def compute_local_coordinates_nufft(cx,
cy,
x,
y,
newton_tol=1e-12,
guess_ind=None,
verbose=False,
max_iterations=30):
"""
Find using the coordinates:
x = X + r n_x
y = Y + r n_y
"""
xshape = x.shape
yshape = y.shape
x = x.flatten()
y = y.flatten()
n = cx.shape[0]
dt = 2 * np.pi / n
ts = np.arange(n) * dt
ik = 1j * np.fft.fftfreq(n, dt / (2 * np.pi))
# tangent vectors
xp = fourier_derivative_1d(f=cx, d=1, ik=ik, out='f')
yp = fourier_derivative_1d(f=cy, d=1, ik=ik, out='f')
xpp = fourier_derivative_1d(f=cx, d=2, ik=ik, out='f')
ypp = fourier_derivative_1d(f=cy, d=2, ik=ik, out='f')
# speed
sp = np.sqrt(xp * xp + yp * yp)
isp = 1.0 / sp
# unit tangent vectors
tx = xp * isp
ty = yp * isp
# unit normal vectors
nx = ty
ny = -tx
# interpolation routines for the necessary objects
if have_better_fourier:
def interp(f):
return _interp2(f)
else:
def interp(f):
return _interp(f, x.size)
nc_i = interp(nx + 1j * ny)
c_i = interp(cx + 1j * cy)
cp_i = interp(xp + 1j * yp)
cpp_i = interp(xpp + 1j * ypp)
# function for computing (d^2)_s and its derivative
def f(t, x, y):
C = c_i(t)
X = C.real
Y = C.imag
Cp = cp_i(t)
Xp = Cp.real
Yp = Cp.imag
return Xp * (X - x) + Yp * (Y - y), X, Y, Xp, Yp
def fp(t, x, y, X, Y, Xp, Yp):
Cpp = cpp_i(t)
Xpp = Cpp.real
Ypp = Cpp.imag
return Xpp * (X - x) + Ypp * (Y - y) + Xp * Xp + Yp * Yp
# brute force find of guess_inds if not provided (slow!)
if guess_ind is None:
guess_ind = np.empty(x.size, dtype=int)
multi_guess_ind_finder(cx, cy, x, y, guess_ind)
# get starting guess
t = ts[guess_ind]
# begin Newton iteration
rem, X, Y, Xp, Yp = f(t, x, y)
mrem = np.abs(rem).max()
if verbose:
print('Newton tol: {:0.2e}'.format(mrem))
iteration = 0
while mrem > newton_tol:
J = fp(t, x, y, X, Y, Xp, Yp)
delt = -rem / J
line_factor = 1.0
while True:
t_new = t + line_factor * delt
rem_new, X, Y, Xp, Yp = f(t_new, x, y)
mrem_new = np.abs(rem_new).max()
# try:
# rem_new, X, Y, Xp, Yp = f(t_new, x, y)
# mrem_new = np.abs(rem_new).max()
# testit = True
# except:
# testit = False
testit = True
if testit and ((mrem_new < (1 - 0.5 * line_factor) * mrem)
or line_factor < 1e-4):
t = t_new
# put theta back in [0, 2 pi]
t[t < 0] += 2 * np.pi
t[t > 2 * np.pi] -= 2 * np.pi
rem = rem_new
mrem = mrem_new
break
line_factor *= 0.5
if verbose:
print('Newton tol: {:0.2e}'.format(mrem))
iteration += 1
if iteration > max_iterations:
raise Exception(
'Exceeded maximum number of iterations solving for coordinates .'
)
# need to determine the sign now
C = c_i(t)
X = C.real
Y = C.imag
if True: # use nx, ny from guess_inds to determine sign
NX = nx[guess_ind]
NY = ny[guess_ind]
else: # interpolate to get these
NC = nc_i(t)
NX = NC.real
NY = NC.imag
r = np.hypot(X - x, Y - y)
xe1 = X + r * NX
ye1 = Y + r * NY
err1 = np.hypot(xe1 - x, ye1 - y)
xe2 = X - r * NX
ye2 = Y - r * NY
err2 = np.hypot(xe2 - x, ye2 - y)
sign = (err1 < err2).astype(int) * 2 - 1
return t, r * sign
def compute_local_coordinates_polyi_old(cx,
cy,
x,
y,
newton_tol=1e-12,
guess_ind=None,
verbose=False,
max_iterations=30):
"""
Find using the coordinates:
x = X + r n_x
y = Y + r n_y
"""
xshape = x.shape
x = x.flatten()
y = y.flatten()
xsize = x.size
n = cx.shape[0]
dt = 2 * np.pi / n
ts = np.arange(n) * dt
ik = 1j * np.fft.fftfreq(n, dt / (2 * np.pi))
# tangent vectors
xp = fourier_derivative_1d(f=cx, d=1, ik=ik, out='f')
yp = fourier_derivative_1d(f=cy, d=1, ik=ik, out='f')
# speed
sp = np.sqrt(xp * xp + yp * yp)
isp = 1.0 / sp
# unit tangent vectors
tx = xp * isp
ty = yp * isp
# unit normal vectors
nx = ty
ny = -tx
# derivatives of the normal vector
nxp = fourier_derivative_1d(f=nx, d=1, ik=ik, out='f')
nyp = fourier_derivative_1d(f=ny, d=1, ik=ik, out='f')
# brute force find of guess_inds if not provided (slow!)
if guess_ind is None:
xd = x - cx[:, None]
yd = y - cy[:, None]
dd = xd**2 + yd**2
guess_ind = dd.argmin(axis=0)
# get starting points (initial guess for t and r)
initial_ts = ts[guess_ind]
cxg = cx[guess_ind]
cyg = cy[guess_ind]
xdg = x - cxg
ydg = y - cyg
initial_rs = np.sqrt(xdg**2 + ydg**2)
# how about we re-sign r?
nxg = nx[guess_ind]
nyg = ny[guess_ind]
xcr1 = cxg + initial_rs * nxg
ycr1 = cyg + initial_rs * nyg
d1 = np.hypot(xcr1 - x, ycr1 - y)
xcr2 = cxg - initial_rs * nxg
ycr2 = cyg - initial_rs * nyg
d2 = np.hypot(xcr2 - x, ycr2 - y)
better2 = d1 > d2
initial_rs[better2] *= -1
# run the multi-newton solver
all_t, all_r = _multi_newton_old(initial_ts, initial_rs, x, y, newton_tol,
cx, cy, nx, ny, xp, yp, nxp, nyp, verbose,
max_iterations)
return all_t, all_r
