def __init__(self,
problem_params,
dtype_u=particles,
dtype_f=acceleration):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: particle data type (will be passed to parent class)
dtype_f: acceleration data type (will be passed to parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = ['k', 'phase', 'amp']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# invoke super init, passing nparts, dtype_u and dtype_f
super(harmonic_oscillator, self).__init__(1, dtype_u, dtype_f,
problem_params)
if self.params.phase != 0.0:
raise ProblemError('Phase != 0 not implemented yet')
if self.params.amp != 1.0:
raise ProblemError('amp != 1 not implemented yet')
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'nu', 'freq']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (key, str(problem_params.keys()))
raise ParameterError(msg)
# make sure parameters have the correct form
if problem_params['freq'] >= 0 and problem_params['freq'] % 2 != 0:
raise ProblemError('need even number of frequencies due to periodic BCs')
if problem_params['nvars'] % 2 != 0:
raise ProblemError('the setup requires nvars = 2^p per dimension')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(heat1d_periodic, self).__init__(init=problem_params['nvars'], dtype_u=dtype_u, dtype_f=dtype_f,
params=problem_params)
# compute dx (equal in both dimensions) and get discretization matrix A
self.dx = 1.0 / self.params.nvars
self.A = self.__get_A(self.params.nvars, self.params.nu, self.dx)
def __init__(self, problem_params, dtype_u=mesh, dtype_f=rhs_imex_mesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed to parent class)
dtype_f: mesh data type wuth implicit and explicit parts (will be passed to parent class)
"""
if 'L' not in problem_params:
problem_params['L'] = 1.0
if 'init_type' not in problem_params:
problem_params['init_type'] = 'circle'
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'nu', 'eps', 'L', 'radius']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if len(problem_params['nvars']) != 2:
raise ProblemError('this is a 2d example, got %s' %
problem_params['nvars'])
if problem_params['nvars'][0] != problem_params['nvars'][1]:
raise ProblemError('need a square domain, got %s' %
problem_params['nvars'])
if problem_params['nvars'][0] % 2 != 0:
raise ProblemError('the setup requires nvars = 2^p per dimension')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(allencahn2d_imex, self).__init__(init=problem_params['nvars'],
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
self.dx = self.params.L / self.params.nvars[
0] # could be useful for hooks, too.
self.xvalues = np.array([
i * self.dx - self.params.L / 2.0
for i in range(self.params.nvars[0])
])
kx = np.zeros(self.init[0])
ky = np.zeros(self.init[1] // 2 + 1)
kx[:int(self.init[0] / 2) + 1] = 2 * np.pi / self.params.L * np.arange(
0,
int(self.init[0] / 2) + 1)
kx[int(self.init[0] / 2) + 1:] = 2 * np.pi / self.params.L * \
np.arange(int(self.init[0] / 2) + 1 - self.init[0], 0)
ky[:] = 2 * np.pi / self.params.L * np.arange(0, self.init[1] // 2 + 1)
xv, yv = np.meshgrid(kx, ky, indexing='ij')
self.lap = -xv**2 - yv**2
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = [
'nvars', 'nu', 'lambda0', 'newton_maxiter', 'newton_tol',
'interval'
]
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if (problem_params['nvars'] + 1) % 2 != 0:
raise ProblemError('setup requires nvars = 2^p - 1')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(generalized_fisher,
self).__init__(problem_params['nvars'], dtype_u, dtype_f,
problem_params)
# compute dx and get discretization matrix A
self.dx = (self.params.interval[1] -
self.params.interval[0]) / (self.params.nvars + 1)
self.A = self.__get_A(self.params.nvars, self.dx)
def __init__(self, problem_params, dtype_u=mesh, dtype_f=mesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type for solution
dtype_f: mesh data type for RHS
"""
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'nu', 'freq']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if (problem_params['nvars'] + 1) % 2 != 0:
raise ProblemError('setup requires nvars = 2^p - 1')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(heat1d, self).__init__(init=problem_params['nvars'],
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
# compute dx and get discretization matrix A
self.dx = 1.0 / (self.params.nvars + 1)
self.A = self.__get_A(self.params.nvars, self.params.nu, self.dx)
def __init__(self, problem_params, dtype_u=mesh, dtype_f=rhs_comp2_mesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = [
'nvars', 'dw', 'eps', 'newton_maxiter', 'newton_tol', 'interval',
'radius'
]
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if (problem_params['nvars']) % 2 != 0:
raise ProblemError('setup requires nvars = 2^p')
if 'stop_at_nan' not in problem_params:
problem_params['stop_at_nan'] = True
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(allencahn_periodic_multiimplicit,
self).__init__(problem_params, dtype_u, dtype_f)
self.A -= sp.eye(self.init) * 0.0 / self.params.eps**2
def build_f(self, f, part, t):
"""
Helper function to assemble the correct right-hand side out of B and E field
Args:
f (dtype_f): the field values
part (dtype_u): the current particles data
t (float): the current time
Returns:
acceleration: correct RHS of type acceleration
"""
if not isinstance(part, particles):
raise ProblemError('something is wrong during build_f, got %s' %
type(part))
N = self.params.nparts
rhs = acceleration((3, self.params.nparts))
for n in range(N):
rhs.values[:, n] = part.q[n] / part.m[n] * (
f.elec.values[:, n] +
np.cross(part.vel.values[:, n], f.magn.values[:, n]))
return rhs
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = [
'nvars', 'nu', 'eps', 'newton_maxiter', 'newton_tol', 'lin_tol',
'lin_maxiter', 'radius'
]
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if len(problem_params['nvars']) != 2:
raise ProblemError('this is a 2d example, got %s' %
problem_params['nvars'])
if problem_params['nvars'][0] != problem_params['nvars'][1]:
raise ProblemError('need a square domain, got %s' %
problem_params['nvars'])
if problem_params['nvars'][0] % 2 != 0:
raise ProblemError('the setup requires nvars = 2^p per dimension')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(allencahn_fullyimplicit,
self).__init__(problem_params['nvars'], dtype_u, dtype_f,
problem_params)
# compute dx and get discretization matrix A
self.dx = 1.0 / self.params.nvars[0]
self.A = self.__get_A(self.params.nvars, self.dx)
self.xvalues = np.array(
[i * self.dx - 0.5 for i in range(self.params.nvars[0])])
self.newton_itercount = 0
self.lin_itercount = 0
self.newton_ncalls = 0
self.lin_ncalls = 0
def u_init(self):
"""
Routine to compute the starting values for the particles
Returns:
dtype_u: particle set filled with initial data
"""
u0 = self.params.u0
N = self.params.nparts
u = self.dtype_u((3, N))
if u0[2][0] is not 1 or u0[3][0] is not 1:
raise ProblemError('so far only q = m = 1 is implemented')
# set first particle to u0
u.pos.values[0, 0] = u0[0][0]
u.pos.values[1, 0] = u0[0][1]
u.pos.values[2, 0] = u0[0][2]
u.vel.values[0, 0] = u0[1][0]
u.vel.values[1, 0] = u0[1][1]
u.vel.values[2, 0] = u0[1][2]
u.q[0] = u0[2][0]
u.m[0] = u0[3][0]
# initialize random seed
np.random.seed(N)
comx = u.pos.values[0, 0]
comy = u.pos.values[1, 0]
comz = u.pos.values[2, 0]
for n in range(1, N):
# draw 3 random variables in [-1,1] to shift positions
r = np.random.random_sample(3) - 1
u.pos.values[0, n] = r[0] + u0[0][0]
u.pos.values[1, n] = r[1] + u0[0][1]
u.pos.values[2, n] = r[2] + u0[0][2]
# draw 3 random variables in [-5,5] to shift velocities
r = np.random.random_sample(3) - 5
u.vel.values[0, n] = r[0] + u0[1][0]
u.vel.values[1, n] = r[1] + u0[1][1]
u.vel.values[2, n] = r[2] + u0[1][2]
u.q[n] = u0[2][0]
u.m[n] = u0[3][0]
# gather positions to check center
comx += u.pos.values[0, n]
comy += u.pos.values[1, n]
comz += u.pos.values[2, n]
# print('Center of positions:',comx/N,comy/N,comz/N)
return u
def __get_A(N, c, dx, order, type):
"""
Helper function to assemble FD matrix A in sparse format
Args:
N (int): number of dofs
c (float): diffusion coefficient
dx (float): distance between two spatial nodes
order (int): specifies order of discretization
type (string): upwind or centered differences
Returns:
scipy.sparse.csc_matrix: matrix A in CSC format
"""
coeff = None
stencil = None
zero_pos = None
if type == 'center':
if order == 2:
stencil = [-1.0, 0.0, 1.0]
zero_pos = 2
coeff = 1.0 / 2.0
else:
raise ProblemError("Order " + str(order) + " not implemented.")
else:
if order == 1:
stencil = [-1.0, 1.0]
coeff = 1.0
zero_pos = 2
else:
raise ProblemError("Order " + str(order) + " not implemented.")
offsets = [pos - zero_pos + 1 for pos in range(len(stencil))]
A = sp.diags(stencil, offsets, shape=(N, N), format='csc')
A *= c * coeff * (1.0 / dx)
return A
def __init__(self,
problem_params,
dtype_u=dedalus_field,
dtype_f=rhs_imex_dedalus_field):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
if 'comm' not in problem_params:
problem_params['comm'] = None
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'nu', 'freq', 'comm']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if problem_params['freq'] % 2 != 0:
raise ProblemError('setup requires freq to be an equal number')
xbasis = de.Fourier('x',
problem_params['nvars'][0],
interval=(0, 1),
dealias=1)
ybasis = de.Fourier('y',
problem_params['nvars'][1],
interval=(0, 1),
dealias=1)
domain = de.Domain([xbasis, ybasis],
grid_dtype=np.float64,
comm=problem_params['comm'])
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(heat2d_dedalus_forced, self).__init__(init=domain,
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
self.x = self.init.grid(0, scales=1)
self.y = self.init.grid(1, scales=1)
self.rhs = self.dtype_u(self.init, val=0.0)
self.problem = de.IVP(domain=self.init, variables=['u'])
self.problem.parameters['nu'] = self.params.nu
self.problem.add_equation(
"dt(u) - nu * dx(dx(u)) - nu * dy(dy(u)) = 0")
self.solver = self.problem.build_solver(de.timesteppers.SBDF1)
self.u = self.solver.state['u']
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
if 'L' not in problem_params:
problem_params['L'] = 1.0
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'c', 'freq', 'nu', 'L']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if (problem_params['nvars']) % 2 != 0:
raise ProblemError('setup requires nvars = 2^p')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(advectiondiffusion1d_imex,
self).__init__(init=problem_params['nvars'],
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
self.xvalues = np.array([
i * self.params.L / self.params.nvars - self.params.L / 2.0
for i in range(self.params.nvars)
])
kx = np.zeros(self.init // 2 + 1)
for i in range(0, len(kx)):
kx[i] = 2 * np.pi / self.params.L * i
self.ddx = kx * 1j
self.lap = -kx**2
rfft_in = pyfftw.empty_aligned(self.init, dtype='float64')
fft_out = pyfftw.empty_aligned(self.init // 2 + 1, dtype='complex128')
ifft_in = pyfftw.empty_aligned(self.init // 2 + 1, dtype='complex128')
irfft_out = pyfftw.empty_aligned(self.init, dtype='float64')
self.rfft_object = pyfftw.FFTW(rfft_in,
fft_out,
direction='FFTW_FORWARD')
self.irfft_object = pyfftw.FFTW(ifft_in,
irfft_out,
direction='FFTW_BACKWARD')
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
if 'comm' not in problem_params:
problem_params['comm'] = PETSc.COMM_WORLD
if 'sol_tol' not in problem_params:
problem_params['sol_tol'] = 1E-10
if 'sol_maxiter' not in problem_params:
problem_params['sol_maxiter'] = None
essential_keys = ['nvars', 'nu', 'freq', 'comm']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (key, str(problem_params.keys()))
raise ParameterError(msg)
# make sure parameters have the correct form
if len(problem_params['nvars']) != 2:
raise ProblemError('this is a 2d example, got %s' % problem_params['nvars'])
# create DMDA object which will be used for all grid operations
da = PETSc.DMDA().create([problem_params['nvars'][0], problem_params['nvars'][1]], stencil_width=1,
comm=problem_params['comm'])
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(heat2d_petsc_forced, self).__init__(init=da, dtype_u=dtype_u, dtype_f=dtype_f, params=problem_params)
# compute dx, dy and get local ranges
self.dx = 1.0 / (self.params.nvars[0] - 1)
self.dy = 1.0 / (self.params.nvars[1] - 1)
(self.xs, self.xe), (self.ys, self.ye) = self.init.getRanges()
# compute discretization matrix A and identity
self.A = self.__get_A()
self.Id = self.__get_Id()
# setup solver
self.ksp = PETSc.KSP()
self.ksp.create(comm=self.params.comm)
self.ksp.setType('cg')
pc = self.ksp.getPC()
pc.setType('none')
self.ksp.setInitialGuessNonzero(True)
self.ksp.setFromOptions()
self.ksp.setTolerances(rtol=self.params.sol_tol, atol=self.params.sol_tol, max_it=self.params.sol_maxiter)
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'c', 'freq']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if (problem_params['nvars']) % 2 != 0:
raise ProblemError('setup requires nvars = 2^p')
if problem_params['freq'] >= 0 and problem_params['freq'] % 2 != 0:
raise ProblemError(
'need even number of frequencies due to periodic BCs')
if 'order' not in problem_params:
problem_params['order'] = 1
if 'type' not in problem_params:
problem_params['type'] = 'upwind'
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(advection1d, self).__init__(init=problem_params['nvars'],
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
# compute dx and get discretization matrix A
self.dx = 1.0 / self.params.nvars
self.A = self.__get_A(self.params.nvars, self.params.c, self.dx,
self.params.order, self.params.type)
def __get_A(N, nu, dx, ndim, order):
"""
Helper function to assemble FD matrix A in sparse format
Args:
N (list): number of dofs
nu (float): diffusion coefficient
dx (float): distance between two spatial nodes
ndim (int): number of dimensions
Returns:
scipy.sparse.csc_matrix: matrix A in CSC format
"""
if order == 2:
stencil = [1, -2, 1]
zero_pos = 2
elif order == 4:
stencil = [-1 / 12, 4 / 3, -5 / 2, 4 / 3, -1 / 12]
zero_pos = 3
elif order == 6:
stencil = [
1 / 90, -3 / 20, 3 / 2, -49 / 18, 3 / 2, -3 / 20, 1 / 90
]
zero_pos = 4
elif order == 8:
stencil = [
-1 / 560, 8 / 315, -1 / 5, 8 / 5, -205 / 72, 8 / 5, -1 / 5,
8 / 315, -1 / 560
]
zero_pos = 5
else:
raise ProblemError(
f'wrong order given, has to be 2, 4, 6, or 8, got {order}')
dstencil = np.concatenate((stencil, np.delete(stencil, zero_pos - 1)))
offsets = np.concatenate(
([N[0] - i - 1 for i in reversed(range(zero_pos - 1))],
[i - zero_pos + 1 for i in range(zero_pos - 1, len(stencil))]))
doffsets = np.concatenate(
(offsets, np.delete(offsets, zero_pos - 1) - N[0]))
A = sp.diags(dstencil, doffsets, shape=(N[0], N[0]), format='csc')
# stencil = [1, -2, 1]
# A = sp.diags(stencil, [-1, 0, 1], shape=(N[0], N[0]), format='csc')
if ndim == 2:
A = sp.kron(A, sp.eye(N[0])) + sp.kron(sp.eye(N[1]), A)
elif ndim == 3:
A = sp.kron(A, sp.eye(N[1] * N[0])) + sp.kron(sp.eye(N[2] * N[1]), A) + \
sp.kron(sp.kron(sp.eye(N[2]), A), sp.eye(N[0]))
A *= nu / (dx**2)
return A
def u_exact(self, t):
"""
Routine to compute the exact trajectory at time t (only for single-particle setup)
Args:
t (float): current time
Returns:
dtype_u: particle type containing the exact position and velocity
"""
# some abbreviations
wE = self.params.omega_E
wB = self.params.omega_B
N = self.params.nparts
u0 = self.params.u0
if N != 1:
raise ProblemError('u_exact is only valid for a single particle')
u = self.dtype_u((3, 1))
wbar = np.sqrt(2) * wE
# position and velocity in z direction is easy to compute
u.pos.values[2, 0] = u0[0][2] * np.cos(
wbar * t) + u0[1][2] / wbar * np.sin(wbar * t)
u.vel.values[2, 0] = -u0[0][2] * wbar * np.sin(
wbar * t) + u0[1][2] * np.cos(wbar * t)
# define temp. variables to compute complex position
Op = 1 / 2 * (wB + np.sqrt(wB**2 - 4 * wE**2))
Om = 1 / 2 * (wB - np.sqrt(wB**2 - 4 * wE**2))
Rm = (Op * u0[0][0] + u0[1][1]) / (Op - Om)
Rp = u0[0][0] - Rm
Im = (Op * u0[0][1] - u0[1][0]) / (Op - Om)
Ip = u0[0][1] - Im
# compute position in complex notation
w = np.complex(Rp, Ip) * np.exp(-np.complex(0, Op * t)) + np.complex(
Rm, Im) * np.exp(-np.complex(0, Om * t))
# compute velocity as time derivative of the position
dw = -1j * Op * np.complex(Rp, Ip) * \
np.exp(-np.complex(0, Op * t)) - 1j * Om * np.complex(Rm, Im) * np.exp(-np.complex(0, Om * t))
# get the appropriate real and imaginary parts
u.pos.values[0, 0] = w.real
u.vel.values[0, 0] = dw.real
u.pos.values[1, 0] = w.imag
u.vel.values[1, 0] = dw.imag
return u
def __init__(self, problem_params, dtype_u=mesh, dtype_f=mesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
essential_keys = [
'nvars', 'dw', 'eps', 'newton_maxiter', 'newton_tol', 'interval',
'radius'
]
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if (problem_params['nvars']) % 2 != 0:
raise ProblemError('setup requires nvars = 2^p')
if 'stop_at_nan' not in problem_params:
problem_params['stop_at_nan'] = True
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(allencahn_periodic_fullyimplicit,
self).__init__(problem_params['nvars'], dtype_u, dtype_f,
problem_params)
# compute dx and get discretization matrix A
self.dx = (self.params.interval[1] -
self.params.interval[0]) / self.params.nvars
self.xvalues = np.array([
self.params.interval[0] + i * self.dx
for i in range(self.params.nvars)
])
self.A = self.__get_A(self.params.nvars, self.dx)
self.newton_itercount = 0
self.lin_itercount = 0
self.newton_ncalls = 0
self.lin_ncalls = 0
def __init__(self, problem_params, dtype_u=pmesh_datatype, dtype_f=rhs_imex_pmesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: pmesh data type (will be passed to parent class)
dtype_f: pmesh data type wuth implicit and explicit parts (will be passed to parent class)
"""
if 'L' not in problem_params:
problem_params['L'] = 1.0
if 'init_type' not in problem_params:
problem_params['init_type'] = 'circle'
if 'comm' not in problem_params:
problem_params['comm'] = None
if 'dw' not in problem_params:
problem_params['dw'] = 0.0
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'eps', 'L', 'radius', 'dw']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (key, str(problem_params.keys()))
raise ParameterError(msg)
if not (isinstance(problem_params['nvars'], tuple) and len(problem_params['nvars']) > 1):
raise ProblemError('Need at least two dimensions')
# Creating ParticleMesh structure
self.pm = ParticleMesh(BoxSize=problem_params['L'], Nmesh=list(problem_params['nvars']), dtype='f8',
plan_method='measure', comm=problem_params['comm'])
# create test RealField to get the local dimensions (there's probably a better way to do that)
tmp = self.pm.create(type='real')
# invoke super init, passing the communicator and the local dimensions as init
super(allencahn_imex, self).__init__(init=(self.pm.comm, tmp.value.shape), dtype_u=dtype_u, dtype_f=dtype_f,
params=problem_params)
# Need this for diagnostics
self.dx = self.params.L / problem_params['nvars'][0]
self.dy = self.params.L / problem_params['nvars'][1]
self.xvalues = [i * self.dx - problem_params['L'] / 2 for i in range(problem_params['nvars'][0])]
self.yvalues = [i * self.dy - problem_params['L'] / 2 for i in range(problem_params['nvars'][1])]
def solve_system(self, rhs, dt, u0, t):
"""
Simple Newton solver for the nonlinear system
Args:
rhs (dtype_f): right-hand side for the nonlinear system
dt (float): abbrev. for the node-to-node stepsize (or any other factor required)
u0 (dtype_u): initial guess for the iterative solver
t (float): current time (e.g. for time-dependent BCs)
Returns:
dtype_u: solution u
"""
mu = self.params.mu
# create new mesh object from u0 and set initial values for iteration
u = self.dtype_u(u0)
x1 = u.values[0]
x2 = u.values[1]
# start newton iteration
n = 0
res = 99
while n < self.params.newton_maxiter:
# form the function g with g(u) = 0
g = np.array([
x1 - dt * x2 - rhs.values[0],
x2 - dt * (mu * (1 - x1**2) * x2 - x1) - rhs.values[1]
])
# if g is close to 0, then we are done
res = np.linalg.norm(g, np.inf)
if res < self.params.newton_tol or np.isnan(res):
break
# prefactor for dg/du
c = 1.0 / (-2 * dt**2 * mu * x1 * x2 - dt**2 - 1 + dt * mu *
(1 - x1**2))
# assemble dg/du
dg = c * np.array([[dt * mu * (1 - x1**2) - 1, -dt],
[2 * dt * mu * x1 * x2 + dt, -1]])
# newton update: u1 = u0 - g/dg
u.values -= np.dot(dg, g)
# set new values and increase iteration count
x1 = u.values[0]
x2 = u.values[1]
n += 1
if np.isnan(res) and self.params.stop_at_nan:
raise ProblemError(
'Newton got nan after %i iterations, aborting...' % n)
elif np.isnan(res):
self.logger.warning('Newton got nan after %i iterations...' % n)
if n == self.params.newton_maxiter:
raise ProblemError(
'Newton did not converge after %i iterations, error is %s' %
(n, res))
return u
def __init__(self, problem_params, dtype_u, dtype_f):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
if 'L' not in problem_params:
problem_params['L'] = 1.0
if 'init_type' not in problem_params:
problem_params['init_type'] = 'circle'
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'nu', 'eps', 'L', 'radius']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# we assert that nvars looks very particular here.. this will be necessary for coarsening in space later on
if len(problem_params['nvars']) != 2:
raise ProblemError('this is a 2d example, got %s' %
problem_params['nvars'])
if problem_params['nvars'][0] != problem_params['nvars'][1]:
raise ProblemError('need a square domain, got %s' %
problem_params['nvars'])
if problem_params['nvars'][0] % 2 != 0:
raise ProblemError('the setup requires nvars = 2^p per dimension')
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(allencahn2d_imex, self).__init__(init=problem_params['nvars'],
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
dx = self.params.L / self.params.nvars[0]
self.xvalues = np.array([
i * dx - self.params.L / 2.0 for i in range(self.params.nvars[0])
])
kx = np.zeros(self.init[0])
ky = np.zeros(self.init[1])
for i in range(0, int(self.init[0] / 2) + 1):
kx[i] = 2 * np.pi / self.params.L * i
for i in range(0, int(self.init[1] / 2) + 1):
ky[i] = 2 * np.pi / self.params.L * i
for i in range(int(self.init[0] / 2) + 1, self.init[0]):
kx[i] = 2 * np.pi / self.params.L * (-self.init[0] + i)
for i in range(int(self.init[1] / 2) + 1, self.init[1]):
ky[i] = 2 * np.pi / self.params.L * (-self.init[1] + i)
self.lap = np.zeros(self.init)
for i in range(self.init[0]):
for j in range(self.init[1]):
self.lap[i, j] = -kx[i]**2 - ky[j]**2
# TODO: cleanup and move to real-valued FFT
fft_in = pyfftw.empty_aligned(self.init, dtype='complex128')
fft_out = pyfftw.empty_aligned(self.init, dtype='complex128')
ifft_in = pyfftw.empty_aligned(self.init, dtype='complex128')
ifft_out = pyfftw.empty_aligned(self.init, dtype='complex128')
self.fft_object = pyfftw.FFTW(fft_in,
fft_out,
direction='FFTW_FORWARD',
axes=(0, 1))
self.ifft_object = pyfftw.FFTW(ifft_in,
ifft_out,
direction='FFTW_BACKWARD',
axes=(0, 1))
def solve_system_2(self, rhs, factor, u0, t):
"""
Simple linear solver for (I-factor*A)u = rhs
Args:
rhs (dtype_f): right-hand side for the linear system
factor (float): abbrev. for the local stepsize (or any other factor required)
u0 (dtype_u): initial guess for the iterative solver
t (float): current time (e.g. for time-dependent BCs)
Returns:
dtype_u: solution as mesh
"""
u = self.dtype_u(u0).values
eps2 = self.params.eps**2
dw = self.params.dw
Id = sp.eye(self.params.nvars)
# start newton iteration
n = 0
res = 99
while n < self.params.newton_maxiter:
# print(n)
# form the function g with g(u) = 0
g = u - rhs.values - factor * (-2.0 / eps2 * u * (1.0 - u) *
(1.0 - 2.0 * u) - 6.0 * dw * u *
(1.0 - u) +
0.0 / self.params.eps**2 * u)
# if g is close to 0, then we are done
res = np.linalg.norm(g, np.inf)
if res < self.params.newton_tol:
break
# assemble dg
dg = Id - factor * (-2.0 / eps2 * sp.diags(
(1.0 - u) * (1.0 - 2.0 * u) - u * ((1.0 - 2.0 * u) + 2.0 *
(1.0 - u)),
offsets=0) - 6.0 * dw * sp.diags(
(1.0 - u) - u, offsets=0) + 0.0 / self.params.eps**2 * Id)
# newton update: u1 = u0 - g/dg
u -= spsolve(dg, g)
# u -= gmres(dg, g, x0=z, tol=self.params.lin_tol)[0]
# increase iteration count
n += 1
if np.isnan(res) and self.params.stop_at_nan:
raise ProblemError(
'Newton got nan after %i iterations, aborting...' % n)
elif np.isnan(res):
self.logger.warning('Newton got nan after %i iterations...' % n)
if n == self.params.newton_maxiter:
self.logger.warning(
'Newton did not converge after %i iterations, error is %s' %
(n, res))
self.newton_ncalls += 1
self.newton_itercount += n
me = self.dtype_u(self.init)
me.values = u
return me
def solve_system(self, rhs, factor, u0, t):
"""
Simple Newton solver
Args:
rhs (dtype_f): right-hand side for the nonlinear system
factor (float): abbrev. for the node-to-node stepsize (or any other factor required)
u0 (dtype_u): initial guess for the iterative solver
t (float): current time (required here for the BC)
Returns:
dtype_u: solution u
"""
u = self.dtype_u(u0)
nu = self.params.nu
lambda0 = self.params.lambda0
# set up boundary values to embed inner points
lam1 = lambda0 / 2.0 * ((nu / 2.0 + 1)**0.5 + (nu / 2.0 + 1)**(-0.5))
sig1 = lam1 - np.sqrt(lam1**2 - lambda0**2)
ul = (1 + (2**(nu / 2.0) - 1) *
np.exp(-nu / 2.0 * sig1 *
(self.params.interval[0] + 2 * lam1 * t)))**(-2.0 / nu)
ur = (1 + (2**(nu / 2.0) - 1) *
np.exp(-nu / 2.0 * sig1 *
(self.params.interval[1] + 2 * lam1 * t)))**(-2.0 / nu)
# start newton iteration
n = 0
res = 99
while n < self.params.newton_maxiter:
# form the function g with g(u) = 0
uext = np.concatenate(([ul], u.values, [ur]))
g = u.values - \
factor * (self.A.dot(uext)[1:-1] + lambda0 ** 2 * u.values * (1 - u.values ** nu)) - rhs.values
# if g is close to 0, then we are done
res = np.linalg.norm(g, np.inf)
if res < self.params.newton_tol:
break
# assemble dg
dg = sp.eye(self.params.nvars) - factor * \
(self.A[1:-1, 1:-1] + sp.diags(lambda0 ** 2 - lambda0 ** 2 * (nu + 1) * u.values ** nu, offsets=0))
# newton update: u1 = u0 - g/dg
u.values -= spsolve(dg, g)
# increase iteration count
n += 1
if n == self.params.newton_maxiter:
raise ProblemError(
'Newton did not converge after %i iterations, error is %s' %
(n, res))
return u
def solve_system(self, rhs, factor, u0, t):
"""
Simple Newton solver
Args:
rhs (dtype_f): right-hand side for the nonlinear system
factor (float): abbrev. for the node-to-node stepsize (or any other factor required)
u0 (dtype_u): initial guess for the iterative solver
t (float): current time (required here for the BC)
Returns:
dtype_u: solution u
"""
u = self.dtype_u(u0).values
eps2 = self.params.eps**2
dw = self.params.dw
Id = sp.eye(self.params.nvars)
v = 3.0 * np.sqrt(2) * self.params.eps * self.params.dw
self.uext.values[0] = 0.5 * (1 + np.tanh(
(self.params.interval[0] - v * t) /
(np.sqrt(2) * self.params.eps)))
self.uext.values[-1] = 0.5 * (1 + np.tanh(
(self.params.interval[1] - v * t) /
(np.sqrt(2) * self.params.eps)))
A = self.A[1:-1, 1:-1]
# start newton iteration
n = 0
res = 99
while n < self.params.newton_maxiter:
# print(n)
# form the function g with g(u) = 0
self.uext.values[1:-1] = u[:]
g = u - rhs.values \
- factor * (self.A.dot(self.uext.values)[1:-1] - 2.0 / eps2 * u * (1.0 - u) * (1.0 - 2.0 * u) -
6.0 * dw * u * (1.0 - u))
# if g is close to 0, then we are done
res = np.linalg.norm(g, np.inf)
if res < self.params.newton_tol:
break
# assemble dg
dg = Id - factor * (A - 2.0 / eps2 * sp.diags(
(1.0 - u) * (1.0 - 2.0 * u) - u * ((1.0 - 2.0 * u) + 2.0 *
(1.0 - u)),
offsets=0) - 6.0 * dw * sp.diags((1.0 - u) - u, offsets=0))
# newton update: u1 = u0 - g/dg
u -= spsolve(dg, g)
# u -= gmres(dg, g, x0=z, tol=self.params.lin_tol)[0]
# increase iteration count
n += 1
if np.isnan(res) and self.params.stop_at_nan:
raise ProblemError(
'Newton got nan after %i iterations, aborting...' % n)
elif np.isnan(res):
self.logger.warning('Newton got nan after %i iterations...' % n)
if n == self.params.newton_maxiter:
self.logger.warning(
'Newton did not converge after %i iterations, error is %s' %
(n, res))
self.newton_ncalls += 1
self.newton_itercount += n
me = self.dtype_u(self.init)
me.values = u
return me
def __get_A(N, c, dx, order, type):
"""
Helper function to assemble FD matrix A in sparse format
Args:
N (int): number of dofs
c (float): diffusion coefficient
dx (float): distance between two spatial nodes
order (int): specifies order of discretization
type (string): upwind or centered differences
Returns:
scipy.sparse.csc_matrix: matrix A in CSC format
"""
coeff = None
stencil = None
zero_pos = None
if type == 'center':
if order == 2:
stencil = [-1.0, 0.0, 1.0]
zero_pos = 2
coeff = 1.0 / 2.0
elif order == 4:
stencil = [1.0, -8.0, 0.0, 8.0, -1.0]
zero_pos = 3
coeff = 1.0 / 12.0
elif order == 6:
stencil = [-1.0, 9.0, -45.0, 0.0, 45.0, -9.0, 1.0]
zero_pos = 4
coeff = 1.0 / 60.0
else:
raise ProblemError("Order " + str(order) + " not implemented.")
else:
if order == 1:
stencil = [-1.0, 1.0]
coeff = 1.0
zero_pos = 2
elif order == 2:
stencil = [1.0, -4.0, 3.0]
coeff = 1.0 / 2.0
zero_pos = 3
elif order == 3:
stencil = [1.0, -6.0, 3.0, 2.0]
coeff = 1.0 / 6.0
zero_pos = 3
elif order == 4:
stencil = [-5.0, 30.0, -90.0, 50.0, 15.0]
coeff = 1.0 / 60.0
zero_pos = 4
elif order == 5:
stencil = [3.0, -20.0, 60.0, -120.0, 65.0, 12.0]
coeff = 1.0 / 60.0
zero_pos = 5
else:
raise ProblemError("Order " + str(order) + " not implemented.")
dstencil = np.concatenate((stencil, np.delete(stencil, zero_pos - 1)))
offsets = np.concatenate(
([N - i - 1 for i in reversed(range(zero_pos - 1))],
[i - zero_pos + 1 for i in range(zero_pos - 1, len(stencil))]))
doffsets = np.concatenate(
(offsets, np.delete(offsets, zero_pos - 1) - N))
A = sp.diags(dstencil, doffsets, shape=(N, N), format='csc')
A *= -c * coeff * (1.0 / dx)
return A
def __init__(self,
problem_params,
dtype_u=parallel_mesh,
dtype_f=parallel_imex_mesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: fft data type (will be passed to parent class)
dtype_f: fft data type wuth implicit and explicit parts (will be passed to parent class)
"""
if 'L' not in problem_params:
problem_params['L'] = 1.0
if 'init_type' not in problem_params:
problem_params['init_type'] = 'circle'
if 'comm' not in problem_params:
problem_params['comm'] = None
if 'dw' not in problem_params:
problem_params['dw'] = 0.0
# these parameters will be used later, so assert their existence
essential_keys = ['nvars', 'eps', 'L', 'radius', 'dw', 'spectral']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
if not (isinstance(problem_params['nvars'], tuple)
and len(problem_params['nvars']) > 1):
raise ProblemError('Need at least two dimensions')
# Creating FFT structure
ndim = len(problem_params['nvars'])
axes = tuple(range(ndim))
self.fft = PFFT(problem_params['comm'],
list(problem_params['nvars']),
axes=axes,
dtype=np.float,
collapse=True)
# get test data to figure out type and dimensions
tmp_u = newDistArray(self.fft, problem_params['spectral'])
# invoke super init, passing the communicator and the local dimensions as init
super(allencahn_imex,
self).__init__(init=(tmp_u.shape, problem_params['comm'],
tmp_u.dtype),
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
L = np.array([self.params.L] * ndim, dtype=float)
# get local mesh
X = np.ogrid[self.fft.local_slice(False)]
N = self.fft.global_shape()
for i in range(len(N)):
X[i] = (X[i] * L[i] / N[i])
self.X = [np.broadcast_to(x, self.fft.shape(False)) for x in X]
# get local wavenumbers and Laplace operator
s = self.fft.local_slice()
N = self.fft.global_shape()
k = [np.fft.fftfreq(n, 1. / n).astype(int) for n in N[:-1]]
k.append(np.fft.rfftfreq(N[-1], 1. / N[-1]).astype(int))
K = [ki[si] for ki, si in zip(k, s)]
Ks = np.meshgrid(*K, indexing='ij', sparse=True)
Lp = 2 * np.pi / L
for i in range(ndim):
Ks[i] = (Ks[i] * Lp[i]).astype(float)
K = [np.broadcast_to(k, self.fft.shape(True)) for k in Ks]
K = np.array(K).astype(float)
self.K2 = np.sum(K * K, 0, dtype=float)
# Need this for diagnostics
self.dx = self.params.L / problem_params['nvars'][0]
self.dy = self.params.L / problem_params['nvars'][1]
def __init__(self, problem_params, dtype_u=mesh, dtype_f=mesh):
"""
Initialization routine
Args:
problem_params (dict): custom parameters for the example
dtype_u: mesh data type (will be passed parent class)
dtype_f: mesh data type (will be passed parent class)
"""
# these parameters will be used later, so assert their existence
if 'order' not in problem_params:
problem_params['order'] = 2
if 'lintol' not in problem_params:
problem_params['lintol'] = 1E-12
if 'liniter' not in problem_params:
problem_params['liniter'] = 10000
if 'direct_solver' not in problem_params:
problem_params['direct_solver'] = False
essential_keys = [
'nvars', 'c', 'freq', 'type', 'order', 'ndim', 'lintol', 'liniter',
'direct_solver'
]
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (
key, str(problem_params.keys()))
raise ParameterError(msg)
# make sure parameters have the correct form
if problem_params['ndim'] > 3:
raise ProblemError(
f'can work with up to three dimensions, got {problem_params["ndim"]}'
)
if type(problem_params['freq']) is not tuple or len(
problem_params['freq']) != problem_params['ndim']:
raise ProblemError(
f'need {problem_params["ndim"]} frequencies, got {problem_params["freq"]}'
)
for freq in problem_params['freq']:
if freq % 2 != 0:
raise ProblemError(
'need even number of frequencies due to periodic BCs')
if type(problem_params['nvars']) is not tuple or len(
problem_params['nvars']) != problem_params['ndim']:
raise ProblemError(
f'need {problem_params["ndim"]} nvars, got {problem_params["nvars"]}'
)
for nvars in problem_params['nvars']:
if nvars % 2 != 0:
raise ProblemError(
'the setup requires nvars = 2^p per dimension')
if problem_params['nvars'][1:] != problem_params['nvars'][:-1]:
raise ProblemError('need a square domain, got %s' %
problem_params['nvars'])
# invoke super init, passing number of dofs, dtype_u and dtype_f
super(advectionNd_periodic,
self).__init__(init=problem_params['nvars'],
dtype_u=dtype_u,
dtype_f=dtype_f,
params=problem_params)
# compute dx (equal in both dimensions) and get discretization matrix A
self.dx = 1.0 / self.params.nvars[0]
self.A = self.__get_A(self.params.nvars, self.params.c, self.dx,
self.params.ndim, self.params.type,
self.params.order)
xvalues = np.array([i * self.dx for i in range(self.params.nvars[0])])
self.xv = np.meshgrid(*[xvalues for _ in range(self.params.ndim)])
self.Id = sp.eye(np.prod(self.params.nvars), format='csc')
def solve_system_2(self, rhs, factor, u0, t):
"""
Newton-Solver for the second component
Args:
rhs (dtype_f): right-hand side for the linear system
factor (float) : abbrev. for the node-to-node stepsize (or any other factor required)
u0 (dtype_u): initial guess for the iterative solver (not used here so far)
t (float): current time (e.g. for time-dependent BCs)
Returns:
dtype_u: solution as mesh
"""
u = self.dtype_u(u0)
if self.params.spectral:
tmpu = newDistArray(self.fft, False)
tmpv = newDistArray(self.fft, False)
tmpu[:] = self.fft.backward(u[..., 0], tmpu)
tmpv[:] = self.fft.backward(u[..., 1], tmpv)
tmprhsu = newDistArray(self.fft, False)
tmprhsv = newDistArray(self.fft, False)
tmprhsu[:] = self.fft.backward(rhs[..., 0], tmprhsu)
tmprhsv[:] = self.fft.backward(rhs[..., 1], tmprhsv)
else:
tmpu = u[..., 0]
tmpv = u[..., 1]
tmprhsu = rhs[..., 0]
tmprhsv = rhs[..., 1]
# start newton iteration
n = 0
res = 99
while n < self.params.newton_maxiter:
# print(n, res)
# form the function g with g(u) = 0
tmpgu = tmpu - tmprhsu - factor * (-tmpu * tmpv**2 + self.params.A)
tmpgv = tmpv - tmprhsv - factor * (tmpu * tmpv**2)
# if g is close to 0, then we are done
res = max(np.linalg.norm(tmpgu, np.inf),
np.linalg.norm(tmpgv, np.inf))
if res < self.params.newton_tol:
break
# assemble dg
dg00 = 1 - factor * (-tmpv**2)
dg01 = -factor * (-2 * tmpu * tmpv)
dg10 = -factor * (tmpv**2)
dg11 = 1 - factor * (2 * tmpu * tmpv)
# interleave and unravel to put into sparse matrix
dg00I = np.ravel(np.kron(dg00, np.array([1, 0])))
dg01I = np.ravel(np.kron(dg01, np.array([1, 0])))
dg10I = np.ravel(np.kron(dg10, np.array([1, 0])))
dg11I = np.ravel(np.kron(dg11, np.array([0, 1])))
# put into sparse matrix
dg = sp.diags(dg00I, offsets=0) + sp.diags(dg11I, offsets=0)
dg += sp.diags(dg01I, offsets=1, shape=dg.shape) + sp.diags(
dg10I, offsets=-1, shape=dg.shape)
# interleave g terms to apply inverse to it
g = np.kron(tmpgu.flatten(), np.array([1, 0])) + np.kron(
tmpgv.flatten(), np.array([0, 1]))
# invert dg matrix
b = sp.linalg.spsolve(dg, g)
# update real-space vectors
tmpu[:] -= b[::2].reshape(self.params.nvars)
tmpv[:] -= b[1::2].reshape(self.params.nvars)
# increase iteration count
n += 1
if np.isnan(res) and self.params.stop_at_nan:
raise ProblemError(
'Newton got nan after %i iterations, aborting...' % n)
elif np.isnan(res):
self.logger.warning('Newton got nan after %i iterations...' % n)
if n == self.params.newton_maxiter:
self.logger.warning(
'Newton did not converge after %i iterations, error is %s' %
(n, res))
# self.newton_ncalls += 1
# self.newton_itercount += n
me = self.dtype_u(self.init)
if self.params.spectral:
me[..., 0] = self.fft.forward(tmpu)
me[..., 1] = self.fft.forward(tmpv)
else:
me[..., 0] = tmpu
me[..., 1] = tmpv
return me
def __get_A(N, c, dx, ndim, type, order):
"""
Helper function to assemble FD matrix A in sparse format
Args:
N (list): number of dofs
nu (float): diffusion coefficient
dx (float): distance between two spatial nodes
type (str): disctretization type
ndim (int): number of dimensions
Returns:
scipy.sparse.csc_matrix: matrix A in CSC format
"""
coeff = None
stencil = None
zero_pos = None
if type == 'center':
if order == 2:
stencil = [-1.0, 0.0, 1.0]
zero_pos = 2
coeff = 1.0 / 2.0
elif order == 4:
stencil = [1.0, -8.0, 0.0, 8.0, -1.0]
zero_pos = 3
coeff = 1.0 / 12.0
elif order == 6:
stencil = [-1.0, 9.0, -45.0, 0.0, 45.0, -9.0, 1.0]
zero_pos = 4
coeff = 1.0 / 60.0
else:
raise ProblemError("Order " + str(order) + " not implemented.")
else:
if order == 1:
stencil = [-1.0, 1.0]
coeff = 1.0
zero_pos = 2
elif order == 2:
stencil = [1.0, -4.0, 3.0]
coeff = 1.0 / 2.0
zero_pos = 3
elif order == 3:
stencil = [1.0, -6.0, 3.0, 2.0]
coeff = 1.0 / 6.0
zero_pos = 3
elif order == 4:
stencil = [-5.0, 30.0, -90.0, 50.0, 15.0]
coeff = 1.0 / 60.0
zero_pos = 4
elif order == 5:
stencil = [3.0, -20.0, 60.0, -120.0, 65.0, 12.0]
coeff = 1.0 / 60.0
zero_pos = 5
else:
raise ProblemError("Order " + str(order) + " not implemented.")
dstencil = np.concatenate((stencil, np.delete(stencil, zero_pos - 1)))
offsets = np.concatenate(
([N[0] - i - 1 for i in reversed(range(zero_pos - 1))],
[i - zero_pos + 1 for i in range(zero_pos - 1, len(stencil))]))
doffsets = np.concatenate(
(offsets, np.delete(offsets, zero_pos - 1) - N[0]))
A = coeff * sp.diags(
dstencil, doffsets, shape=(N[0], N[0]), format='csc')
if ndim == 2:
A = sp.kron(A, sp.eye(N[0])) + sp.kron(sp.eye(N[1]), A)
elif ndim == 3:
A = sp.kron(A, sp.eye(N[1] * N[0])) + sp.kron(sp.eye(N[2] * N[1]), A) + \
sp.kron(sp.kron(sp.eye(N[2]), A), sp.eye(N[0]))
A *= -c * (1.0 / dx)
return A
