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))
