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FEM: fenics #0003038: added overlap parameter and removed assign_values from interfaces

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This commit is contained in:
joha2
2018-08-06 22:22:33 +02:00
committed by wmayer
parent a80630adea
commit 19432b0f0b
1 changed files with 17 additions and 20 deletions
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37
src/Mod/Fem/femsolver/fenics/fenics_tools.py
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@@ -59,7 +59,7 @@ else:
xdmffile.read(self.mesh)
xdmffile.close()
def readCellExpression(self, group_value_dict, value_type="scalar", *args, **kwargs):
def readCellExpression(self, group_value_dict, value_type="scalar", overlap=lambda x: x[0], *args, **kwargs):
"""
Reads cell expression and returns it.
"""
@@ -71,15 +71,12 @@ else:
self.readMesh()
xdmffile = fenics.XDMFFile(self.xdmffilename)
val_shape = group_value_dict.values()[0].value_shape()
print("read cell expression with value shape: %s" % (str(val_shape),))
cf = value_type_dictionary[value_type.lower()](group_value_dict,
val_shape=val_shape,
cf = value_type_dictionary[value_type.lower()](group_value_dict,
overlap=overlap,
*args, **kwargs)
cf.init()
for (key, value) in cf.group_value_dict.items():
cf.markers[key] = fenics.MeshFunction("size_t", self.mesh, self.mesh.topology().dim())
# fenics.CellFunction("size_t", self.mesh) # deprecated
xdmffile.read(cf.markers[key], key)
cf.dx[key] = fenics.Measure("dx", domain=self.mesh, subdomain_data=cf.markers[key])
xdmffile.close()
@@ -95,7 +92,6 @@ else:
ff.init()
for (key, value) in ff.group_value_dict.items():
ff.markers[key] = fenics.MeshFunction("size_t", self.mesh, self.mesh.topology().dim() - 1)
# fenics.FacetFunction("size_t", self.mesh) # deprecated
xdmffile.read(ff.markers[key], key)
ff.marked[key] = value.get("marked", 1)
ff.ds[key] = fenics.Measure("ds", domain=self.mesh, subdomain_data=ff.markers[key])
@@ -109,10 +105,12 @@ else:
"""
def __init__(self, group_value_dict,
default=lambda x: 0.,
check_marked=(lambda x: x == 1), **kwargs):
check_marked=(lambda x: x == 1), overlap=lambda x: x[0],
**kwargs):
self.init()
self.group_value_dict = group_value_dict
self.check_marked = check_marked
self.overlap = overlap
self.default = default
def init(self):
@@ -126,16 +124,15 @@ else:
values_list = [func(x) for (key, func) in self.group_value_dict.items()
if self.check_marked(self.markers[key][cell.index])]
return_value = self.overlap(values_list)
if values_list:
self.assign_values(values, values_list[0])
# TODO: fix value assignment for overlap
# according to priority, mean, or standard
# TODO: python classes much slower than JIT compilation
self.assign_values(values, return_value)
else:
self.assign_values(values, self.default(x))
print("cell index %s values in backend: %s" % (str(cell.index), str(values)))
# TODO: python classes much slower than JIT compilation
# ***********************************
# * Sub classes due to value_shape method which is not of dynamical return type
@@ -147,9 +144,9 @@ else:
def __init__(self, group_value_dict,
default=lambda x: 0.,
check_marked=(lambda x: x == 1), **kwargs):
check_marked=(lambda x: x == 1), overlap=lambda x: x[0], **kwargs):
CellExpressionFromXDMF.__init__(self, group_value_dict,
default=default, check_marked=check_marked)
default=default, check_marked=check_marked, overlap=overlap)
def eval_cell(self, values, x, cell):
self.eval_cell_backend(values, x, cell)
@@ -161,9 +158,9 @@ else:
def __init__(self, group_value_dict,
default=lambda x: np.zeros((3,)),
check_marked=(lambda x: x == 1), **kwargs):
check_marked=(lambda x: x == 1), overlap=lambda x: x[0], **kwargs):
CellExpressionFromXDMF.__init__(self, group_value_dict,
default=default, check_marked=check_marked)
default=default, check_marked=check_marked, overlap=overlap)
def eval_cell(self, values, x, cell):
self.eval_cell_backend(values, x, cell)
@@ -175,9 +172,9 @@ else:
def __init__(self, group_value_dict,
default=lambda x: np.zeros((2,)),
check_marked=(lambda x: x == 1), **kwargs):
check_marked=(lambda x: x == 1), overlap=lambda x: x[0], **kwargs):
CellExpressionFromXDMF.__init__(self, group_value_dict,
default=default, check_marked=check_marked)
default=default, check_marked=check_marked, overlap=overlap)
def eval_cell(self, values, x, cell):