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FEM: fenics #0003038: removed old fenics interface classes

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joha2
2018-08-06 22:22:28 +02:00
committed by wmayer
parent ee8a525c07
commit bf45030595
1 changed files with 3 additions and 115 deletions
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118
src/Mod/Fem/femsolver/fenics/fenics_tools.py
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@@ -120,21 +120,15 @@ else:
self.dx = {}
def assign_values(self, values, to_assign):
raise NotImplementedError()
values[:] = to_assign
def eval_cell_backend(self, values, x, cell):
values_list = [func(x) for (key, func) in self.group_value_dict.items()
if self.check_marked(self.markers[key][cell.index])]
if values_list:
#values[0] = values_list[0][0]
#values[1] = values_list[0][1]
#values[2] = values_list[0][2]
if values_list:
self.assign_values(values, values_list[0])
# TODO: improve for vectorial data
# TODO: fix value assignment for overlap
# according to priority, mean, or standard
# TODO: python classes much slower than JIT compilation
@@ -160,9 +154,6 @@ else:
def eval_cell(self, values, x, cell):
self.eval_cell_backend(values, x, cell)
def assign_values(self, values, to_assign):
values[0] = to_assign[0]
def value_shape(self):
return ()
@@ -176,13 +167,6 @@ else:
def eval_cell(self, values, x, cell):
self.eval_cell_backend(values, x, cell)
print("cell index %s values in frontend: %s" % (str(cell.index), str(values)))
def assign_values(self, values, to_assign):
values[0] = to_assign[0]
values[1] = to_assign[1]
values[2] = to_assign[2]
def value_shape(self):
return (3,)
@@ -199,10 +183,6 @@ else:
def eval_cell(self, values, x, cell):
self.eval_cell_backend(values, x, cell)
def assign_values(self, values, to_assign):
values[0] = to_assign[0]
values[1] = to_assign[1]
def value_shape(self):
return (2,)
@@ -231,95 +211,3 @@ else:
# boundary conditions for the general case (i.e. vector, tensor)
# ***********************************************************************
# * old interface classes
# ***********************************************************************
class CellExpressionXDMF(fenics.Expression):
def __init__(self, xdmffilename, group_value_dict, group_priority_dict={}, default=0., check_marked=(lambda x: x == 1), **kwargs):
self.group_priority_dict = group_priority_dict
self.check_marked = check_marked
self.default = default
self.readXDMFfile(xdmffilename, group_value_dict)
def readXDMFfile(self, xdmffilename, group_value_dict):
"""
Initialization of CellExpressionXDMF by reading an XDMF file.
@param: xdmffilename: path to xdmf file
@param: group_value_dict: {"groupname":function(x)}
function(x) is a function which is evaluated at the marked positions of the cells
"""
xdmffile = fenics.XDMFFile(xdmffilename)
self.group_value_dict = group_value_dict
self.mesh = fenics.Mesh()
xdmffile.read(self.mesh)
self.markers = {}
self.dx = {}
for (key, value) in self.group_value_dict.items():
# Fenics interface here: create cell function of type int for every group
# TODO: examine whether int is appropriate or this class could be generalized
self.markers[key] = fenics.CellFunction("size_t", self.mesh)
xdmffile.read(self.markers[key], key)
self.dx[key] = fenics.Measure("dx", domain=self.mesh, subdomain_data=self.markers[key])
xdmffile.close()
def eval_cell(self, values, x, cell):
values_list = []
for (key, func) in self.group_value_dict.items():
if self.check_marked(self.markers[key][cell.index]):
values_list.append(func(x))
if values_list:
values[0] = values_list[0]
# TODO: improve for vectorial data
# TODO: fix value assignment for overlap
# according to priority, mean, or standard
# TODO: python classes much slower than JIT compilation
else:
values[0] = self.default
# def value_shape(self):
# return self.shape
class FacetFunctionXDMF(object):
def __init__(self, xdmffilename, group_value_dict):
"""
Initialization of FacetFunctionXDMF by reading an XDMF file.
@param: xdmffilename: path to xdmf file
@param: group_value_dict: {"groupname":{"type":"Dirichlet|Neumann|Robin", "value":u_D|du_N|(r,s), "marked":1}, ...}
If group_value_dict contains no "marked" entry for the groupname, "marked" is set to 1 by default.
"""
self.readXDMFFile(xdmffilename, group_value_dict)
def readXDMFFile(self, xdmffilename, group_value_dict):
xdmffile = fenics.XDMFFile(xdmffilename)
self.group_value_dict = group_value_dict
self.mesh = fenics.Mesh()
xdmffile.read(self.mesh)
self.markers = {}
self.marked = {}
self.ds = {}
self.bcs = {}
for (key, value) in self.group_value_dict.items():
# Fenics interface here: create facet function of type size_t (positive int) for every group
# TODO: examine whether size_t is appropriate or this class could be generalized
self.markers[key] = fenics.FacetFunction("size_t", self.mesh)
xdmffile.read(self.markers[key], key)
self.marked[key] = value.get("marked", 1)
self.ds[key] = fenics.Measure("ds", domain=self.mesh, subdomain_data=self.markers[key])
self.bcs[key] = value
xdmffile.close()
def getDirichletBCs(self, vectorspace, *args, **kwargs):
dbcs = []
for (dict_key, dict_value) in self.bcs.items():
if dict_value["type"] == 'Dirichlet':
bc = fenics.DirichletBC(vectorspace, dict_value["value"], self.markers[dict_key], dict_value.get("marked", 1), *args, **kwargs)
dbcs.append(bc)
return dbcs
# TODO: write some functions to return integrals for Neumann and Robin
# boundary conditions for the general case (i.e. vector, tensor)