def __init__(self,mesh,parameters = default_parameters()): if parameters["primal_solver"] == "Newton": NewtonFSI.__init__(self,mesh) FixedPointFSI.__init__(self,mesh) elif parameters["primal_solver"] == "fixpoint": FixedPointFSI.__init__(self,mesh) else: raise Exception("Only 'fixpoint' and 'Newton' are possible values \ for the parameter 'primal_solver'")
def __init__(self, mesh, parameters=default_parameters()): if parameters["primal_solver"] == "Newton": NewtonFSI.__init__(self, mesh) FixedPointFSI.__init__(self, mesh) elif parameters["primal_solver"] == "fixpoint": FixedPointFSI.__init__(self, mesh) else: raise Exception("Only 'fixpoint' and 'Newton' are possible values \ for the parameter 'primal_solver'")
def solve(self, parameters=default_parameters()): "Solve and return computed solution (u_F, p_F, U_S, P_S, U_M, P_M)" # Create submeshes and mappings (only first time) if self.Omega is None: # Refine original mesh mesh = self._original_mesh for i in range(parameters["num_initial_refinements"]): mesh = refine(mesh) # Initialize meshes self.init_meshes(mesh, parameters) # Create solver solver = FSISolver(self) # Solve return solver.solve(parameters)
# analysis - specifically, the station list, settings for determining close # neighbors, and settings for finding optimally correlated neighbors. default_params = dict(nstns=21, mindist=200.0, distinc=200.0, numsrt=21, numcorr=21, begyr=1900, endyr=2001, data_src="raw", variable="avg", corrlim=0.1, minpair=14, benchmark=True, project="benchmark") params = parameters.default_parameters(**default_params) pprint.pprint(params) # Read in station data (download if necessary) all_series, all_stations = ushcn_io.get_ushcn_data(params) if not hasattr(params, 'stations'): station_ids = sorted(random.sample(all_stations.keys(), params.nstns)) else: station_ids = sorted(params.stations) stations = dict(zip(station_ids, [all_stations[s] for s in station_ids])) series_list = [all_series[station] for station in station_ids] series = dict(zip([s.coop_id for s in series_list], series_list))