def mapNtoDD_left_s0(lo, ld, g, c, s0=()):
no = lo.n
nd = ld.n
Kpd = ly.layerpotSD(s=ld)
Kpo = ly.layerpotSD(s=lo)
Kpd[np.diag_indices(nd)] = Kpd[np.diag_indices(nd)] + 0.5 * c
Kpo[np.diag_indices(no)] = Kpo[np.diag_indices(no)] + 0.5
Kd2o = ly.layerpotSD(s=ld, t=lo)
Ko2d = ly.layerpotSD(s=lo, t=ld)
row1 = np.concatenate((Kpo.T, Kd2o.T)).T
row2 = np.concatenate((Ko2d.T, Kpd.T)).T
# row1 = lo.SL.T.dot(row1)
Ks = np.concatenate((row1, row2))
if g.ndim == 1:
gz = np.concatenate((lo.SL.T.dot(g), np.zeros(nd)))
elif g.ndim == 2:
nt = g.shape[1]
gz = np.concatenate((lo.SL.T.dot(g), np.zeros((nd, nt))))
if setups.mapNtoD_left == False:
if g.ndim == 1:
gz = np.concatenate((g, np.zeros(nd)))
elif g.ndim == 2:
nt = g.shape[1]
gz = np.concatenate((g, np.zeros((nd, nt))))
gz[0] = 0
Ks[0, :no] = lo.s0
Ks[0, no:] = 0
phi = linalg.lstsq(Ks, gz)[0]
# (lu, piv) = linalg.lu_factor(Ks)
# phi = linalg.lu_solve((lu, piv), gz)
return phi
def mapNtoDD0(lo, ld, g, c, s0):
no = lo.n
nd = ld.n
# --- matrix: shape = (lo.n + ld.n, lo.n + ld.n) ---
Kpd = ly.layerpotSD(s=ld)
Kpo = ly.layerpotSD(s=lo)
Kpd[np.diag_indices(nd)] = Kpd[np.diag_indices(nd)] + 0.5 * c
Kpo[np.diag_indices(no)] = Kpo[np.diag_indices(no)] + 0.5
Kd2o = ly.layerpotSD(s=ld, t=lo)
Ko2d = ly.layerpotSD(s=lo, t=ld)
row1 = np.concatenate((Kpo.T, Kd2o.T)).T
row2 = np.concatenate((Ko2d.T, Kpd.T)).T
Ks = np.concatenate((row1, row2))
if verbose:
print(dbgg.CYAN, end='')
print(' condition num = %.5e' %
numpy.linalg.cond(np.array(Ks, float)))
print(' determninant= ', numpy.linalg.det(np.array(Ks, float)))
print(dbgg.RESET, end='')
# --- rhs: [g, 0] with shape = (lo.n + ld.n) ---
if g.ndim == 1:
gz = np.concatenate((g, np.zeros(nd)))
elif g.ndim == 2:
nt = g.shape[1]
gz = np.concatenate((g, np.zeros((nd, nt))))
# --- linear system resolution : () * phi = g ---
phi = linalg.lstsq(Ks, gz)[0]
return phi
def mapNtoDD_correctedinfirst(lo, ld, g, c, s0):
no = lo.n
nd = ld.n
Kpd = ly.layerpotSD(s=ld)
Kpo = ly.layerpotSD(s=lo)
Kpd[np.diag_indices(nd)] = Kpd[np.diag_indices(nd)] + 0.5 * c
Kpo[np.diag_indices(no)] = Kpo[np.diag_indices(no)] + 0.5
Kd2o = ly.layerpotSD(s=ld, t=lo)
Ko2d = ly.layerpotSD(s=lo, t=ld)
row1 = np.concatenate((Kpo.T, Kd2o.T)).T
row2 = np.concatenate((Ko2d.T, Kpd.T)).T
Ks = np.concatenate((row1, row2))
if verbose:
print('mapNtoD condition num = %.5e' %
numpy.linalg.cond(np.array(Ks, float)))
print('mapNtoD determninant= ', numpy.linalg.det(np.array(Ks, float)))
if g.ndim == 1:
gz = np.concatenate((g, np.zeros(nd)))
elif g.ndim == 2:
nt = g.shape[1]
gz = np.concatenate((g, np.zeros((nd, nt))))
mean_reduced = lo.w[1:].dot(gz[1:no]) / lo.w[0]
gz[0] = -mean_reduced
phi = linalg.lstsq(Ks, gz)[0]
# (lu, piv) = linalg.lu_factor(Ks)
# phi = linalg.lu_solve((lu, piv), gz)
return phi
def mapNtoD(lo, ld, g, c, s0=()):
print("!! not revisioned !!")
# ---------------------------------------------------
# --- building matrix ---
no = lo.n
nd = ld.n
Kpd = ly.layerpotSD(s=ld)
Kpo = ly.layerpotSD(s=lo)
Kpd[np.diag_indices(nd)] = Kpd[np.diag_indices(nd)] + 0.5 * c
Kpo[np.diag_indices(no)] = Kpo[np.diag_indices(no)] + 0.5
Kd2o = ly.layerpotSD(s=ld, t=lo)
Ko2d = ly.layerpotSD(s=lo, t=ld)
# ---------------------------------------------------
if s0 == ():
s0 = np.ones(no)
S = linf.gramschmidt(s0=s0)
Kpo = Kpo.dot(S)
Ko2d = Ko2d.dot(S)
row1 = np.concatenate((Kpo.T, Kd2o.T)).T
row2 = np.concatenate((Ko2d.T, Kpd.T)).T
Ks = np.concatenate((lo.SL.T.dot(row1), row2))
# Ks = np.concatenate(( row1, row2 ))
Ks1 = Ks[1::, 1::]
if verbose:
print(' not true matrix (to fix)')
print(dbgg.CYAN, end='')
print(' condition num = %.5e' %
numpy.linalg.cond(np.array(Ks, float)))
print(' determinant = %.5e' % numpy.linalg.det(np.array(Ks, float)))
print(dbgg.RESET, end='')
(lu, piv) = linalg.lu_factor(Ks1)
if g.ndim == 1:
gs = np.concatenate((lo.SL.T.dot(g), np.zeros(nd)))
# gs = np.concatenate(( g, np.zeros(nd) ))
gs1 = gs[1::]
phi1 = linalg.lu_solve((lu, piv), gs1)
if verbose:
print(' residual = ', numpy.linalg.norm(Ks1.dot(phi1) - gs1))
print(' residual2 = ',
numpy.linalg.norm(row2[:, 1::].dot(phi1) - gs1[-nd::]))
phi = np.concatenate(([0], phi1))
elif g.ndim == 2:
nt = g.shape[1]
gs = np.concatenate((lo.SL.T.dot(g), np.zeros((nd, nt))))
# gs = np.concatenate(( g, np.zeros((nd, nt)) ))
gs1 = gs[1::]
# gs2t = gs2.T
# phi2 = np.empty((nt, no + nd - 1))
# for k in range(nt):
# phi2[k] = linalg.lu_solve((lu, piv), gs2t[k])
# time.sleep(0.001)
# phi2 = phi2.T
phi1 = linalg.lu_solve((lu, piv), gs1)
phi = np.concatenate((np.zeros((1, nt)), phi1))
else:
print('Error dimensions for gs1 in mapNtoD')
return np.concatenate((S.dot(phi[0:no]), phi[no::]))
def __init__(self, pieces=[], more=0):
self.pc = pieces
self.s = pieces
self.n = sum([len(pk.x) for pk in self.pc])
self.x = np.array([z for p in pieces for z in p.x])
self.nx = np.array([nx for p in pieces for nx in p.nx])
self.speed = np.array([sp for p in pieces
for sp in p.speed]) * len(pieces)
self.kappa = np.array([k for p in pieces for k in p.kappa])
self.w = np.array([w for p in pieces for w in p.w])
self.condc = np.array([c for p in pieces for c in p.condc])
# else: # no itial position
# self.n = sum([len(pk.x) for pk in self.pc]) - len(pieces)
# self.x = np.array([z for p in pieces for z in p.x])
# self.nx = np.array([nx for p in pieces for nx in p.nx])
# self.speed = np.array([sp for p in pieces for sp in p.speed])
# self.kappa = np.array([k for p in pieces for k in p.kappa])
# self.w = np.array([w for p in pieces for w in p.w])
# # miscellaneous
if more:
print("# Boundary.__init__ : more = 1")
K = ly.layerpotSD(s=self)
nu, s0 = linf.eigmaxpower(K)
self.s0 = s0
self.S = linf.gramschmidt(s0=s0)
# self.Sw = linf.gramschmidtw(s=self, s0=s0)
self.SL = linf.gramschmidt(s0=self.w)
# self.B = linf.gramschmidtw(s=self, s0=np.ones(self.n))
self.B = linf.gramschmidt(s0=np.ones(self.n))
self.BX, self.BXinv = get_Basis(self.n)
self.BY, self.BYinv = get_Basis(self.n)
def mapNtoD0_left_s0(l, g, s0=()):
'''
This function computes interior laplace problem with neumann data
- l: segment or boundary of the region where there are data
- g: neumann data
(Kp + 0.5 I) * phi = g
'''
n = l.n
# --- matrix ---
Kp = ly.layerpotSD(s=l)
Kp[np.diag_indices(n)] = Kp[np.diag_indices(n)] + 0.5
if verbose:
print(dbgg.CYAN, end='')
print(' condition num = %.5e' %
numpy.linalg.cond(np.array(Kp, float)))
print(' determinant = %.5e' % numpy.linalg.det(np.array(Kp, float)))
print(dbgg.RESET, end='')
Kps = l.SL.T.dot(Kp)
# --- rhs ---
gs = l.SL.T.dot(g)
# --- correction s0 in first line ---
gs[0] = 0
Kps[0, :] = l.s0 # potrebbe essere anche l.w ? vedi vecchio func_left in vecchio script
# --- linear system resolution : (Kp + 0.5 I) * phi = g ---
phi = linalg.lstsq(Kps, gs)[0]
if verbose and gs.ndim == 1:
print(' residual', max(abs(np.array(Kps.dot(phi) - gs))))
return phi
def mapNtoD0_left(l, g, s0=()):
'''
This function computes interior laplace problem with neumann data
- l: segment or boundary of the region where there are data
- g: neumann data
(Kp + 0.5 I) * phi = g
'''
n = l.n
# --- matrix ---
Kp = ly.layerpotSD(s=l)
Kp[np.diag_indices(n)] = Kp[np.diag_indices(n)] + 0.5
if verbose:
print(dbgg.CYAN, end='')
print(' condition num = %.5e' %
numpy.linalg.cond(np.array(Kp, float)))
print(' determinant = %.5e' % numpy.linalg.det(np.array(Kp, float)))
print(dbgg.RESET, end='')
Kps = l.SL.T.dot(Kp)
# --- rhs ---
gs = l.SL.T.dot(g)
# --- linear system resolution : (Kp + 0.5 I) * phi = g ---
phi = linalg.lstsq(Kps, gs)[0]
print(" g ndim =", gs.shape)
if verbose and gs.ndim == 1:
print(' residual', max(abs(np.array(Kps.dot(phi) - gs))))
return phi
def mapNtoD00(l, g, s0=()):
'''
This function computes interior laplace problem with neumann data
- l: segment or boundary of the region where there are data
- g: neumann data
(Kp + 0.5 I) * phi = g
'''
n = l.n
# --- matrix ---
Kp = ly.layerpotSD(s=l)
Kp[np.diag_indices(n)] = Kp[np.diag_indices(n)] + 0.5
if verbose:
print(dbgg.CYAN, end='')
print(' condition num = %.5e' %
numpy.linalg.cond(np.array(Kp, float)))
print(' determinant = %.5e' % numpy.linalg.det(np.array(Kp, float)))
print(dbgg.RESET, end='')
# --- rhs ---
# --- linear system resolution : (Kp + 0.5 I) * phi = g ---
phi = linalg.lstsq(Kp, g)[0]
# (lu, piv) = linalg.lu_factor(Kp)
# phi = linalg.lu_solve((lu, piv), g)
return phi
def mapNtoD0(l, g, s0=()):
print("!! not revisioned !!")
print(g.shape)
# ---------------------------------------------------
# --- building matrix ---
n = l.n
Kp = ly.layerpotSD(s=l)
Kp[np.diag_indices(n)] = Kp[np.diag_indices(n)] + 0.5
# ---------------------------------------------------
if verbose:
print(dbgg.CYAN, end='')
print(' condition num = %.5e' %
numpy.linalg.cond(np.array(Kp, float)))
print(' determinant = %.5e' % numpy.linalg.det(np.array(Kp, float)))
print(dbgg.RESET, end='')
if s0 == ():
s0 = np.ones(n)
S = l.S
# Kps = S.T.dot(Kp.dot(S))
Kps = l.SL.T.dot(Kp.dot(S))
Kps1 = Kps[1::, 1::]
# gs = S.T.dot(g)
gs = l.SL.T.dot(g)
gs1 = gs[1::]
# phi2 = linalg.solve(Kps2, gs2)
(lu, piv) = linalg.lu_factor(Kps1)
if gs1.ndim == 1:
phi1 = linalg.lu_solve((lu, piv), gs1)
# phi1 = linalg.lstsq(Kps1, gs1)[0]
phi = np.concatenate(([0], phi1))
if verbose:
print('residual = ', numpy.linalg.norm(Kps1.dot(phi1) - gs1))
elif gs1.ndim == 2:
nt = g.shape[1]
# gs2t = gs2.T
# phi2 = np.empty((len(gs2t), n - 1 ))
# for k in range(len(gs2t)):
# phi2[k] = linalg.lu_solve((lu, piv), gs2t[k])
# time.sleep(0.001)
# phi2 = phi2.T
phi1 = linalg.lu_solve((lu, piv), gs1)
# phi1 = linalg.lstsq(Kps1, gs1)[0]
phi = np.concatenate((np.zeros((1, nt)), phi1))
else:
print(' Error: dimensions for gs in mapNtoD0')
# phi2 = scipy.sparse.linalg.cg(Kps2, gs2)[0]
# phi = linalg.solve(Kps, gs) # check error
return S.dot(phi)
def computeLLdiff(ld, so, T, c):
if T == ():
T = so.BX
allpsi0 = dpb.mapNtoD0(so, T, so.s0)
Lo = ly.layerpotSD(s=so, t=ld)
rhsdiff = Lo.dot(allpsi0)
allpsi = dpb.mapNtoDdiff(so, ld, rhsdiff, c, so.s0)
Lo = ly.layerpotS(s=so)
Ld = ly.layerpotS(s=ld, t=so)
L = Lo.dot(allpsi[0:so.n]) + Ld.dot(allpsi[so.n::])
# means = sum(np.diagflat(so.w).dot(L)) / sum(so.w) # correct? strange sum by rows
means = np.ones(so.n).dot(np.diagflat(so.w).dot(L)) / sum(so.w)
L = L - np.array([means for k in range(so.n)])
return L
def computeU(allpsi, ld, so, sb):
kerS = ly.layerpotS(s=ld, t=so)
kerSD = ly.layerpotSD(s=ld, t=so)
U_psi = kerS.dot(allpsi)
U_psi_nu = kerSD.dot(allpsi)
U_psi = U_psi.T.dot(np.diag(so.w))
U_psi_nu = U_psi_nu.T.dot(np.diag(so.w))
U = ly.layerpotS(s=so, t=sb)
U_nu = ly.layerpotD(s=so, t=sb)
U = U + U_psi
U_nu = U_nu + U_psi_nu
return (U, U_nu)
def computeR(allpsi, ld, so, sb, sv=(), testset=1):
if sv == ():
sv = sb
nso, nsb = so.n, sb.n
U, U_nu = computeU(allpsi, ld, so, sb)
testset = 1
if testset == 1 or testset == 3:
V1 = ly.layerpotS(s=sv, t=so)
V1_nu = ly.layerpotSD(s=sv, t=so)
R1 = U.dot(V1_nu) - U_nu.dot(V1)
R = R1
if testset == 2 or testset == 3:
V2 = ly.layerpotD(s=sv, t=so)
V2_nu = ly.layerpotDD(s=sv, t=so)
R2 = U.dot(V2_nu) - U_nu.dot(V2)
R = R2
if testset == 3:
R = np.concatenate((R1.T, R2.T)).T
if verbose:
print(' R.shape = ', R.shape)
return (R, U, U_nu)
def __init__(self,
n,
Z_args=((), (), (), ()),
f_inargs=((), ()),
quad='ps',
aff=(0, 1),
sign=1,
more=1,
h=2.0):
'''
This constructor initializes these fields
- x: (complex = R^2) curve points
- dx: (complex = R^2) curve derivative = tangent unit vect * speed
- speed: (complex = R^2) curve speed
Quadrature
- 'p': periodic
- 'ps': periodic shifted, meaning all points are shifted by half step
- 'gf': graded full (with initial point)
'''
# in vecchie versioni c'era una diversa ipotesi di costruttore (vedi parametri)
Z, Zp, Zpp, args = Z_args
f, inargs = f_inargs
if f != () and f != ():
(Z, Zp, Zpp, args) = f(*inargs)
# -------------------------------------------------------------------
# ------------------- Quadrature: t ---------------------------------
if quad == 'p' or quad == 'ps':
self.t = np.array([float(k) / n for k in range(n)], float)
if quad == 'ps':
self.t = self.t + 1. / 2 / n
elif quad == 'gp' or quad == 'g' or quad == 'gf':
temp_s = np.array([float(k) / n for k in range(n)], float)
if quad == 'gf':
pass
else:
# no initial point with w=0
temp_s = temp_s[1:]
n = n - 1
#
self.t = graded.w(2.0 * pi * temp_s) / 2 / pi
self.a = graded.wp(2.0 * pi * temp_s)
else:
self.t = np.array([float(k) / (n - 1) for k in range(n)], float)
# -------------------------------------------------------------------
# trapezoidal nodes
self.n = n
self.x = np.array([Z(t, *args, aff=aff) for t in self.t], np.cfloat)
self.dx = np.array([Zp(t, *args, aff=aff) for t in self.t], np.cfloat)
self.speed = np.array(abs(self.dx), float)
self.nx = np.array(-1j * self.dx / self.speed, np.cfloat) * sign
self.ddx = np.array([Zpp(t, *args, aff=aff) for t in self.t],
np.cfloat)
# s.kappa = -real(conj(-1i*dZdt).*s.Zpp(s.t)) ./ s.speed.^3; %curvature
self.kappa = -np.real(np.conj(-1j * self.dx) * self.ddx) / (
self.speed**3) # signed curvature
# -------------------------------------------------------------------
# ------------------- Quadrature: w ---------------------------------
if quad == 'p' or quad == 'ps':
self.w = np.array([sp / n for sp in self.speed], float)
elif quad == 'gp' or quad == 'g' or quad == 'gf':
self.speed = self.speed * self.a
# self.w = np.array([sp / (n) for sp in self.speed], float)
if quad == 'gp' or quad == 'g':
self.w = np.array([sp / (n + 1) for sp in self.speed], float)
print('graded mesh in segment, n = ', n, ' w.size =',
self.w.size)
elif quad == 'gf':
self.w = np.array([sp / n for sp in self.speed], float)
print('graded FULL mesh in segment, n = ', n, ' w.size =',
self.w.size)
# self.w = np.array([sp / n for sp in self.speed], float) * self.a
else:
self.w = np.array([sp / (n - 1) for sp in self.speed], float)
self.w[0] = self.w[0] * 0.5
self.w[-1] = self.w[-1] * 0.5
# -------------------------------------------------------------------
# -------------------------------------------------------------------
# miscellaneous
if more:
K = ly.layerpotSD(s=self)
nu, s0 = linf.eigmaxpower(
K
) # eigenvector with max. modulus eigenvalues: it's density which makes constant simple layer
self.s0 = s0
self.S = linf.gramschmidt(s0=s0) #
# self.Sw = linf.gramschmidtw(s=self, s0=s0)
self.SL = linf.gramschmidt(s0=self.w) #
# self.B = linf.gramschmidtw(s=self, s0=np.ones(self.n))
# self.B = linf.gramschmidt(s0=np.ones(self.n))
self.BT = get_basis_trigonometric(self.n)
self.BX, self.BXinv = get_Basis(self.n)
self.BY, self.BYinv = get_Basis(self.n)
# -------------------------------------------------------------------
condc = const_from_conductivity(h)
self.condc = np.array([condc for t in self.t], np.cfloat)
# ---- save properties ----------------------------------------------
self.name = f_inargs[0]
self.params = f_inargs[1]
self.aff = aff
return # __init__
