def mass_V0_B(T, p, bc, Bz0):
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
NbaseN = Nel + p - bc*p
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
basisN = bsp.basis_ders_on_quad_grid(T, p, pts, 0)
M = np.zeros((NbaseN, 2*p + 1))
for ie in range(Nel):
for il in range(p + 1):
for jl in range(p + 1):
value = 0.
for q in range(p + 1):
value += wts[ie, q] * basisN[ie, il, 0, q] * basisN[ie, jl, 0, q] * Bz0(pts[ie, q])
M[(ie + il)%NbaseN, p + jl - il] += value
indices = np.indices((NbaseN, 2*p + 1))
shift = np.arange(NbaseN) - p
row = indices[0].flatten()
col = (indices[1] + shift[:, None])%NbaseN
M = spa.csr_matrix((M.flatten(), (row, col.flatten())), shape=(NbaseN, NbaseN))
M.eliminate_zeros()
return M
def GRAD(T, p, bc):
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
NbaseN = Nel + p - bc*p
if bc == True:
G = np.zeros((NbaseN, NbaseN))
for i in range(NbaseN):
G[i, i] = -1.
if i < NbaseN - 1:
G[i, i + 1] = 1.
G[-1, 0] = 1.
return G
else:
G = np.zeros((NbaseN - 1, NbaseN))
for i in range(NbaseN - 1):
G[i, i] = -1.
G[i, i + 1] = 1.
return G
def L2_prod_V1(T, p, bc, fun):
t = T[1:-1]
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
NbaseN = Nel + p - bc*p
NbaseD = NbaseN - (1 - bc)
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
basisD = bsp.basis_ders_on_quad_grid(t, p - 1, pts, 0, normalize=True)
f_int = np.zeros(NbaseD)
for ie in range(Nel):
for il in range(p):
value = 0.
for q in range(p + 1):
value += wts[ie, q]*fun(pts[ie, q])*basisD[ie, il, 0, q]
f_int[(ie + il)%NbaseD] += value
return f_int
def matrixAssembly_V1(p, Nbase, T, bc):
"""
Computes the 1d mass matrix in the space V1.
Parameters
----------
p : int
spline degree
Nbase : int
number of spline functions
T : np.array
knot vector
bc : boolean
boundary conditions (True = periodic, False = homogeneous Dirichlet, None = no boundary conditions)
Returns
-------
M : 2d np.array
mass matrix in V1
"""
t = T[1:-1]
el_b = bsp.breakpoints(T, p)
ne = len(el_b) - 1
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
d = 0
basis = bsp.basis_ders_on_quad_grid(t, p - 1, pts, d, normalize=True)
M = np.zeros((Nbase - 1, Nbase - 1))
for ie in range(ne):
for il in range(p):
for jl in range(p):
i = ie + il
j = ie + jl
value_m = 0.
for g in range(p + 1):
value_m += wts[ie, g] * basis[ie, il, 0, g] * basis[ie, jl,
0, g]
M[i, j] += value_m
if bc == True:
M[:p - 1, :] += M[-p + 1:, :]
M[:, :p - 1] += M[:, -p + 1:]
M = M[:M.shape[0] - p + 1, :M.shape[1] - p + 1]
return M
def __init__(self, T, p, bc):
self.p = p
self.T = T
self.bc = bc
self.greville = bsp.greville(T, p, bc)
self.breakpoints = bsp.breakpoints(T, p)
self.Nbase = len(self.breakpoints) - 1 + p - bc*p
self.pts_loc, self.wts_loc = np.polynomial.legendre.leggauss(p)
self.interpolation_V0 = bsp.collocation_matrix(T, p, self.greville, bc)
self.histopolation_V1 = histo(T, p, bc, self.greville)
def L2_prod_V1(fun, p, Nbase, T):
"""
Computes the L2 scalar product of the function 'fun' with the B-splines of the space V1
using a quadrature rule of order p + 1.
Parameters
----------
fun : callable
function for scalar product
p : int
spline degree
Nbase : int
number of spline functions
T : np.array
knot vector
Returns
-------
f_int : np.array
the result of the integration with each basis function
"""
t = T[1:-1]
el_b = bsp.breakpoints(T, p)
ne = len(el_b) - 1
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
d = 0
basis = bsp.basis_ders_on_quad_grid(t, p - 1, pts, d, normalize=True)
f_int = np.zeros(Nbase - 1)
for ie in range(ne):
for il in range(p):
i = ie + il
value = 0.
for g in range(p + 1):
value += wts[ie, g] * fun(pts[ie, g]) * basis[ie, il, 0, g]
f_int[i] += value
return f_int
def mass_V1(T, p, bc):
t = T[1:-1]
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
NbaseN = Nel + p - bc*p
NbaseD = NbaseN - (1 - bc)
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
basisD = bsp.basis_ders_on_quad_grid(t, p - 1, pts, 0, normalize=True)
M = np.zeros((NbaseD, 2*p + 1))
for ie in range(Nel):
for il in range(p):
for jl in range(p):
value = 0.
for q in range(p + 1):
value += wts[ie, q] * basisD[ie, il, 0, q] * basisD[ie, jl, 0, q]
M[(ie + il)%NbaseD, p + jl - il] += value
indices = np.indices((NbaseD, 2*p + 1))
shift = np.arange(NbaseD) - p
row = indices[0].flatten()
col = (indices[1] + shift[:, None])%NbaseD
M = spa.csr_matrix((M.flatten(), (row, col.flatten())), shape=(NbaseD, NbaseD))
M.eliminate_zeros()
return M
def L2_prod_V0(T, p, bc, fun):
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
NbaseN = Nel + p - bc*p
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
basisN = bsp.basis_ders_on_quad_grid(T, p, pts, 0)
f_int = np.zeros(NbaseN)
for ie in range(Nel):
for il in range(p + 1):
value = 0.
for q in range(p + 1):
value += wts[ie, q] * fun(pts[ie, q]) * basisN[ie, il, 0, q]
f_int[(ie + il)%NbaseN] += value
return f_int
def histo(T, p, bc, grev):
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
t = T[1:-1]
Nbase = Nel + p - bc*p
if bc == True:
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p - 1)
ne = Nbase
D = np.zeros((ne, ne))
if p%2 != 0:
grid = el_b
pts, wts = bsp.quadrature_grid(grid, pts_loc, wts_loc)
col_quad = bsp.collocation_matrix(t, p - 1, pts.flatten(), bc, normalize=True)
for ie in range(Nel):
for il in range(p):
i = (ie + il)%ne
for k in range(p - 1):
D[ie, i] += wts[ie, k]*col_quad[ie*(p - 1) + k, i]
return D
else:
grid = np.linspace(0., el_b[-1], 2*Nel + 1)
pts, wts = bsp.quadrature_grid(grid, pts_loc, wts_loc)
col_quad = bsp.collocation_matrix(t, p - 1, pts.flatten(), bc, normalize=True)
for iee in range(2*Nel):
for il in range(p):
ie = int(iee/2)
ie_grev = int(np.ceil(iee/2) - 1)
i = (ie + il)%ne
for k in range(p - 1):
D[ie_grev, i] += wts[iee, k]*col_quad[iee*(p - 1) + k, i]
return D
else:
ng = len(grev)
col_quad = bsp.collocation_matrix(T, p, grev, bc)
D = np.zeros((ng - 1, Nbase - 1))
for i in range(ng - 1):
for j in range(max(i - p + 1, 1), min(i + p + 3, Nbase)):
s = 0.
for k in range(j):
s += col_quad[i, k] - col_quad[i + 1, k]
D[i, j - 1] = s
return D
def matrixAssembly_V0(p, Nbase, T, bc):
"""
Computes the 1d mass and advection matrix in the space V0.
Parameters
----------
p : int
spline degree
Nbase : int
number of spline functions
T : np.array
knot vector
bc : boolean
boundary conditions (True = periodic, False = homogeneous Dirichlet, None = no boundary conditions)
Returns
-------
M : 2d np.array
mass matrix in V0
D : 2d np.array
advection matrix in V0
"""
el_b = bsp.breakpoints(T, p)
ne = len(el_b) - 1
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
d = 1
basis = bsp.basis_ders_on_quad_grid(T, p, pts, d)
M = np.zeros((Nbase, Nbase))
C = np.zeros((Nbase, Nbase))
for ie in range(ne):
for il in range(p + 1):
for jl in range(p + 1):
i = ie + il
j = ie + jl
value_m = 0.
value_c = 0.
for g in range(p + 1):
value_m += wts[ie, g] * basis[ie, il, 0, g] * basis[ie, jl,
0, g]
value_c += wts[ie, g] * basis[ie, il, 0, g] * basis[ie, jl,
1, g]
M[i, j] += value_m
C[i, j] += value_c
if bc == True:
M[:p, :] += M[-p:, :]
M[:, :p] += M[:, -p:]
M = M[:M.shape[0] - p, :M.shape[1] - p]
C[:p, :] += C[-p:, :]
C[:, :p] += C[:, -p:]
C = C[:C.shape[0] - p, :C.shape[1] - p]
elif bc == False:
M = M[1:-1, 1:-1]
C = C[1:-1, 1:-1]
return M, C
def histopolation_matrix_1d(p, Nbase, T, grev, bc):
"""
Computest the 1d histopolation matrix.
Parameters
----------
p : int
spline degree
Nbase : int
number of spline functions
T : np.array
knot vector
grev : np.array
greville points
bc : boolean
boundary conditions (True = periodic, False = homogeneous Dirichlet, None = no boundary conditions)
Returns
-------
D : 2d np.array
histopolation matrix
"""
el_b = bsp.breakpoints(T, p)
Nel = len(el_b) - 1
t = T[1:-1]
if bc == True:
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p - 1)
ne = Nbase - p
D = np.zeros((ne, ne))
if p % 2 != 0:
grid = el_b
pts, wts = bsp.quadrature_grid(grid, pts_loc, wts_loc)
col_quad = bsp.collocation_matrix(t,
p - 1,
pts.flatten(),
bc,
normalize=True)
for ie in range(Nel):
for il in range(p):
i = (ie + il) % ne
for k in range(p - 1):
D[ie, i] += wts[ie, k] * col_quad[ie * (p - 1) + k, i]
return D
else:
grid = np.linspace(0., el_b[-1], 2 * Nel + 1)
pts, wts = bsp.quadrature_grid(grid, pts_loc, wts_loc)
col_quad = bsp.collocation_matrix(t,
p - 1,
pts.flatten(),
bc,
normalize=True)
for iee in range(2 * Nel):
for il in range(p):
ie = int(iee / 2)
ie_grev = int(np.ceil(iee / 2) - 1)
i = (ie + il) % ne
for k in range(p - 1):
D[ie_grev,
i] += wts[iee, k] * col_quad[iee * (p - 1) + k, i]
return D
else:
ng = len(grev)
col_quad = bsp.collocation_matrix(T, p, grev, bc)
D = np.zeros((ng - 1, Nbase - 1))
for i in range(ng - 1):
for j in range(max(i - p + 1, 1), min(i + p + 3, Nbase)):
s = 0.
for k in range(j):
s += col_quad[i, k] - col_quad[i + 1, k]
D[i, j - 1] = s
return D
def matrixAssembly_backgroundField(p, Nbase, T, bc, B_background_z):
"""
Computes the 1d mass matrix weighted with some background field in the space V0.
Parameters
----------
p : int
spline degree
Nbase : int
number of spline functions
T : np.array
knot vector
bc : boolean
boundary conditions (True = periodic, False = homogeneous Dirichlet, None = no boundary conditions)
B_background_z : callable
the variation of the background magnetic field in z-direction
Returns
-------
D : 2d np.array
mass matrix in V0
"""
el_b = bsp.breakpoints(T, p)
ne = len(el_b) - 1
pts_loc, wts_loc = np.polynomial.legendre.leggauss(p + 1)
pts, wts = bsp.quadrature_grid(el_b, pts_loc, wts_loc)
d = 0
basis = bsp.basis_ders_on_quad_grid(T, p, pts, d)
D = np.zeros((Nbase, Nbase))
for ie in range(ne):
for il in range(p + 1):
for jl in range(p + 1):
i = ie + il
j = ie + jl
value_d = 0.
for g in range(p + 1):
value_d += wts[ie, g] * basis[ie, il, 0, g] * basis[
ie, jl, 0, g] * B_background_z(pts[ie, g])
D[i, j] += value_d
if bc == True:
D[:p, :] += D[-p:, :]
D[:, :p] += D[:, -p:]
D = D[:D.shape[0] - p, :D.shape[1] - p]
elif bc == False:
D = D[1:-1, 1:-1]
return D