def callback_add_to_solution(q, qbc, ghostlayer_width, size_x, size_y, size_z, position_x, position_y, position_z, currentTime):
dim = get_dimension( q )
#TODO 3D: Adjust subdivision_factor to 3D
# Set up grid information for current patch
if dim is 2:
subdivision_factor_x = q.shape[1]
subdivision_factor_y = q.shape[2]
unknowns_per_subcell = q.shape[0]
dim_x = pyclaw.Dimension('x', position_x, position_x + size_x, subdivision_factor_x)
dim_y = pyclaw.Dimension('y', position_y, position_y + size_y, subdivision_factor_y)
patch = pyclaw.geometry.Patch((dim_x, dim_y))
elif dim is 3:
subdivision_factor_x = q.shape[1]
subdivision_factor_y = q.shape[2]
subdivision_factor_z = q.shape[3]
unknowns_per_subcell = q.shape[0]
dim_x = pyclaw.Dimension('x', position_x, position_x + size_x, subdivision_factor_x)
dim_y = pyclaw.Dimension('y', position_y, position_y + size_y, subdivision_factor_y)
dim_z = pyclaw.Dimension('z', position_z, position_z + size_z, subdivision_factor_z)
patch = pyclaw.geometry.Patch((dim_x, dim_y,dim_z))
state = pyclaw.State(patch, unknowns_per_subcell)
state.q[:] = q[:]
state.t = currentTime
self.gathered_patches.append(patch)
self.gathered_states.append(state)
def callback_initialization(q, qbc, aux, subdivision_factor_x0, subdivision_factor_x1, subdivision_factor_x2, unknowns_per_subcell, aux_fields_per_subcell, size_x, size_y, size_z, position_x, position_y, position_z):
import clawpack.pyclaw as pyclaw
dim = get_dimension(q)
if dim is 2:
self.dim_x = pyclaw.Dimension('x',position_x,position_x + size_x,subdivision_factor_x0)
self.dim_y = pyclaw.Dimension('y',position_y,position_y + size_y,subdivision_factor_x1)
domain = pyclaw.Domain([self.dim_x,self.dim_y])
elif dim is 3:
self.dim_x = pyclaw.Dimension('x',position_x,position_x + size_x,subdivision_factor_x0)
self.dim_y = pyclaw.Dimension('y',position_y,position_y + size_y,subdivision_factor_x1)
self.dim_z = pyclaw.Dimension('z',position_z,position_z + size_z,subdivision_factor_x2)
domain = pyclaw.Domain([self.dim_x,self.dim_y,self.dim_z])
subgrid_state = pyclaw.State(domain, unknowns_per_subcell, aux_fields_per_subcell)
subgrid_state.q = q
if(aux_fields_per_subcell > 0):
subgrid_state.aux = aux
subgrid_state.problem_data = self.solution.state.problem_data
self.q_initialization(subgrid_state)
if(self.aux_initialization != None and aux_fields_per_subcell > 0):
self.aux_initialization(subgrid_state)
#Steer refinement
if not self.refinement_criterion == None:
return self.refinement_criterion(subgrid_state)
else:
return self.initial_minimal_mesh_width
def __init__(self, solver, global_state, q, qbc, aux, position, size, subdivision_factor_x0, subdivision_factor_x1, subdivision_factor_x2, unknowns_per_cell, aux_fields_per_cell, current_time):
r"""
Initializes this subgrid solver. It get's all information to prepare a domain and state for a
single subgrid.
:Input:
- *solver* - (:class:`pyclaw.Solver`) The PyClaw-solver used for advancing this subgrid in time.
- *global_state* - (:class:`pyclaw.State`) The global state. This is not the state used for
for the actual solving of the timestep on the subgrid.
- *q* - The array storing the current solution.
- *qbc* - The array storing the solution of the last timestep including the ghostlayer.
- *position* - A d-dimensional tuple holding the position of the grid in the computational domain.
This measures the continuous real-world position in floats, not the integer position
in terms of cells.
- *size* - A d-dimensional tuple holding the size of the grid in the computational domain. This
measures the continuous real-world size in floats, not the size in terms of cells.
- *subdivision_factor* - The number of cells in one dimension of this subgrid. At the moment only
square subgrids are allowed, so the total number of cells in the subgrid
(excluding the ghostlayer) is subdivision_factor x subdivision_factor.
- *unknowns_per_cell* - The number of equations or unknowns that are stored per cell of the subgrid.
"""
self.solver = solver
self.dimension = get_dimension(q)
if self.dimension is 2:
self.dim_x = pyclaw.Dimension('x',position[0],position[0] + size[0],subdivision_factor_x0)
self.dim_y = pyclaw.Dimension('y',position[1],position[1] + size[1],subdivision_factor_x1)
domain = pyclaw.Domain([self.dim_x,self.dim_y])
elif self.dimension is 3:
self.dim_x = pyclaw.Dimension('x',position[0],position[0] + size[0],subdivision_factor_x0)
self.dim_y = pyclaw.Dimension('y',position[1],position[1] + size[1],subdivision_factor_x1)
self.dim_z = pyclaw.Dimension('z',position[2],position[2] + size[2],subdivision_factor_x2)
domain = pyclaw.Domain([self.dim_x,self.dim_y,self.dim_z])
subgrid_state = pyclaw.State(domain, unknowns_per_cell, aux_fields_per_cell)
subgrid_state.q = q
subgrid_state.aux = aux
subgrid_state.problem_data = global_state.problem_data
self.solution = pyclaw.Solution(subgrid_state, domain)
self.solution.t = current_time
self.solver.bc_lower[0] = pyclaw.BC.custom
self.solver.bc_upper[0] = pyclaw.BC.custom
self.solver.bc_lower[1] = pyclaw.BC.custom
self.solver.bc_upper[1] = pyclaw.BC.custom
if self.dimension is 3:
self.solver.bc_lower[2] = pyclaw.BC.custom
self.solver.bc_upper[2] = pyclaw.BC.custom
self.qbc = qbc
self.solver.user_bc_lower = self.user_bc_lower
self.solver.user_bc_upper = self.user_bc_upper
def setup(self, solution):
r"""
Initialize a Solver object. This method loads the library of Peano and prepares the initial mesh.
See :class:`Solver` for full documentation
"""
dim = get_dimension( solution.q )
logging.getLogger('peanoclaw').info("Loading Peano-library...")
self.libpeano = CDLL(self.get_lib_path(dim))
logging.getLogger('peanoclaw').info("Peano loaded successfully.")
self.libpeano.pyclaw_peano_new.restype = c_void_p
self.libpeano.pyclaw_peano_destroy.argtypes = [c_void_p]
self.libpeano.pyclaw_peano_evolveToTime.argtypes = [c_double, c_void_p, c_void_p, c_void_p]
self.bc_lower = self.solver.bc_lower[:]
self.bc_upper = self.solver.bc_upper[:]
self.user_bc_lower = self.solver.user_bc_lower
self.user_bc_upper = self.solver.user_bc_upper
# Get parameters for Peano
dimensions = solution.state.grid.dimensions
subdivision_factor_x0 = solution.state.grid.dimensions[0].num_cells
subdivision_factor_x1 = solution.state.grid.dimensions[1].num_cells
if dim is 3:
subdivision_factor_x2 = solution.state.grid.dimensions[2].num_cells
else:
subdivision_factor_x2 = 0
number_of_unknowns = solution.state.num_eqn
number_of_auxiliar_fields = solution.state.num_aux
ghostlayer_width = self.num_ghost
import os, sys
configuration_file = os.path.join(sys.path[0], 'peanoclaw-config.xml')
use_dimensional_splitting_optimization = self.solver.dimensional_split
#self.solver.setup(solution)
self.solution = solution
if dim is 2:
domain_position_x2 = 0
domain_size_x2 = 0
else:
domain_position_x2 = dimensions[2].lower
domain_size_x2 = dimensions[2].upper - dimensions[2].lower
self.libpeano.pyclaw_peano_new.argtypes = [ c_double, #Initial mesh width
c_double, #Domain position X0
c_double, #Domain position X1
c_double, #Domain position X2
c_double, #Domain size X0
c_double, #Domain size X1
c_double, #Domain size X2
c_int, #Subdivision factor X0
c_int, #Subdivision factor X1
c_int, #Subdivision factor X2
c_int, #Number of unknowns
c_int, #Number of auxiliar fields
c_int, #Ghostlayer width
c_double, #Initial timestep size
c_char_p, #Config file
c_bool, #Use dimensional splitting
c_void_p, #q Initialization callback
c_void_p, #Boundary condition callback
c_void_p] #Solver callback
self.peano = self.libpeano.pyclaw_peano_new(c_double(self.initial_minimal_mesh_width),
c_double(dimensions[0].lower),
c_double(dimensions[1].lower),
c_double(domain_position_x2),
c_double(dimensions[0].upper - dimensions[0].lower),
c_double(dimensions[1].upper - dimensions[1].lower),
c_double(domain_size_x2),
subdivision_factor_x0,
subdivision_factor_x1,
subdivision_factor_x2,
number_of_unknowns,
number_of_auxiliar_fields,
ghostlayer_width,
self.solver.dt_initial,
c_char_p(configuration_file),
use_dimensional_splitting_optimization,
self.initialization_callback,
self.boundary_condition_callback,
self.solver_callback)
# Set PeanoSolution
import clawpack.peanoclaw as peanoclaw
if(isinstance(solution, peanoclaw.Solution)):
solution.peano = self.peano
solution.libpeano = self.libpeano
else:
logging.getLogger('peanoclaw').warning("Use peanoclaw.Solution instead of pyclaw.Solution together with peanoclaw.Solver to provide plotting functionality.")
#Causes Ctrl+C to quit Peano
signal.signal(signal.SIGINT, signal.SIG_DFL)
