FEM: code forating, solver fenics tools, get rid of not needed indent
This commit is contained in:
Bernd Hahnebach
@@ -27,250 +27,255 @@ __url__ = "http://www.freecadweb.org"
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# \ingroup FEM
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import FreeCAD
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import numpy as np
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try:
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import fenics
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except ImportError:
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FreeCAD.Console.PrintError("No Fenics modules found, please install them.")
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else:
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import numpy as np
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FreeCAD.Console.PrintError("No Fenics modules found, please install them.\n")
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raise Exception("No Fenics modules found, please install them.\n")
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class XDMFReader(object):
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class XDMFReader(object):
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"""
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Reads XDMF file and provides unified interface for returning
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cell functions or facet functions.
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"""
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def __init__(self, xdmffilename):
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"""
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Reads XDMF file and provides unified interface for returning
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cell functions or facet functions.
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Sets filename and sets mesh instance to None.
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"""
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def __init__(self, xdmffilename):
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"""
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Sets filename and sets mesh instance to None.
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"""
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self.xdmffilename = xdmffilename
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self.mesh = None
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self.xdmffilename = xdmffilename
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self.mesh = None
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def resetMesh(self):
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"""
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Resets mesh instance to None.
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"""
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self.mesh = None
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def resetMesh(self):
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"""
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Resets mesh instance to None.
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"""
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self.mesh = None
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def readMesh(self):
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"""
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If mesh instance is None, read mesh instance from file denoted
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by filename property.
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"""
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# TODO: implement mesh read in for open file
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if self.mesh is None:
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xdmffile = fenics.XDMFFile(self.xdmffilename)
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self.mesh = fenics.Mesh()
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xdmffile.read(self.mesh)
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xdmffile.close()
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def readMesh(self):
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"""
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If mesh instance is None, read mesh instance from file denoted
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by filename property.
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"""
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# TODO: implement mesh read in for open file
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if self.mesh is None:
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xdmffile = fenics.XDMFFile(self.xdmffilename)
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self.mesh = fenics.Mesh()
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xdmffile.read(self.mesh)
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xdmffile.close()
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def readCellExpression(
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self,
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def readCellExpression(
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self,
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group_value_dict,
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value_type="scalar",
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overlap=lambda x: x[0],
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*args,
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**kwargs
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):
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"""
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Reads cell expression and returns it.
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"""
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value_type_dictionary = {
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"scalar": ScalarCellExpressionFromXDMF,
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"vector2d": Vector2DCellExpressionFromXDMF,
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"vector3d": Vector3DCellExpressionFromXDMF}
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self.readMesh()
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xdmffile = fenics.XDMFFile(self.xdmffilename)
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cf = value_type_dictionary[value_type.lower()](
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group_value_dict,
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value_type="scalar",
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overlap=lambda x: x[0],
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*args,
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**kwargs
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):
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"""
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Reads cell expression and returns it.
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"""
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value_type_dictionary = {
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"scalar": ScalarCellExpressionFromXDMF,
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"vector2d": Vector2DCellExpressionFromXDMF,
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"vector3d": Vector3DCellExpressionFromXDMF}
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self.readMesh()
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xdmffile = fenics.XDMFFile(self.xdmffilename)
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cf = value_type_dictionary[value_type.lower()](
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group_value_dict,
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overlap=overlap,
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*args, **kwargs
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overlap=overlap,
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*args, **kwargs
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)
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cf.init()
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for (key, value) in cf.group_value_dict.items():
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cf.markers[key] = fenics.MeshFunction(
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"size_t",
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self.mesh,
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self.mesh.topology().dim()
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)
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cf.init()
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for (key, value) in cf.group_value_dict.items():
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cf.markers[key] = fenics.MeshFunction(
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"size_t",
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self.mesh,
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self.mesh.topology().dim()
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)
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xdmffile.read(cf.markers[key], key)
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cf.dx[key] = fenics.Measure(
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"dx",
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domain=self.mesh,
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subdomain_data=cf.markers[key]
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)
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xdmffile.close()
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return cf
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xdmffile.read(cf.markers[key], key)
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cf.dx[key] = fenics.Measure(
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"dx",
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domain=self.mesh,
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subdomain_data=cf.markers[key]
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)
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xdmffile.close()
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return cf
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def readFacetFunction(self, group_value_dict, *args, **kwargs):
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"""
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Reads facet function and returns it.
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"""
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self.readMesh()
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xdmffile = fenics.XDMFFile(self.xdmffilename)
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ff = FacetFunctionFromXDMF(group_value_dict, *args, **kwargs)
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ff.init()
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for (key, value) in ff.group_value_dict.items():
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ff.markers[key] = fenics.MeshFunction(
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"size_t",
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self.mesh,
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self.mesh.topology().dim() - 1
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)
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xdmffile.read(ff.markers[key], key)
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ff.marked[key] = value.get("marked", 1)
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ff.ds[key] = fenics.Measure(
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"ds",
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domain=self.mesh,
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subdomain_data=ff.markers[key]
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)
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ff.bcs[key] = value
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xdmffile.close()
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return ff
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def readFacetFunction(self, group_value_dict, *args, **kwargs):
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"""
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Reads facet function and returns it.
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"""
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self.readMesh()
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xdmffile = fenics.XDMFFile(self.xdmffilename)
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ff = FacetFunctionFromXDMF(group_value_dict, *args, **kwargs)
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ff.init()
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for (key, value) in ff.group_value_dict.items():
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ff.markers[key] = fenics.MeshFunction(
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"size_t",
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self.mesh,
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self.mesh.topology().dim() - 1
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)
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xdmffile.read(ff.markers[key], key)
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ff.marked[key] = value.get("marked", 1)
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ff.ds[key] = fenics.Measure(
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"ds",
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domain=self.mesh,
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subdomain_data=ff.markers[key]
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)
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ff.bcs[key] = value
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xdmffile.close()
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return ff
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class CellExpressionFromXDMF(object):
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"""
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Creates cell function expression from XDMF file.
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"""
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def __init__(
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class CellExpressionFromXDMF(object):
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"""
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Creates cell function expression from XDMF file.
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"""
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def __init__(
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self, group_value_dict,
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default=lambda x: 0.,
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0],
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**kwargs
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):
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self.init()
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self.group_value_dict = group_value_dict
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self.check_marked = check_marked
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self.overlap = overlap
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self.default = default
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def init(self):
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self.markers = {}
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self.dx = {}
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def assign_values(self, values, to_assign):
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values[:] = to_assign
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def eval_cell_backend(self, values, x, cell):
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values_list = [
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func(x) for (key, func) in self.group_value_dict.items()
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if self.check_marked(self.markers[key][cell.index])
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]
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return_value = self.overlap(values_list)
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if values_list:
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self.assign_values(values, return_value)
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else:
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self.assign_values(values, self.default(x))
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# TODO: python classes much slower than JIT compilation
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# ***********************************
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# * Sub classes due to value_shape method which is not of dynamical return type
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# * Also the assignment of values is to be done by reference. Therefore it has to be
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# * overloaded.
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# ***********************************
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class ScalarCellExpressionFromXDMF(fenics.Expression, CellExpressionFromXDMF):
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def __init__(
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self,
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group_value_dict,
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default=lambda x: 0.,
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0],
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**kwargs
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):
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CellExpressionFromXDMF.__init__(
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self, group_value_dict,
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default=lambda x: 0.,
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0],
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**kwargs
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):
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self.init()
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self.group_value_dict = group_value_dict
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self.check_marked = check_marked
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self.overlap = overlap
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self.default = default
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default=default,
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check_marked=check_marked,
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overlap=overlap
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)
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def init(self):
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self.markers = {}
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self.dx = {}
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def eval_cell(self, values, x, cell):
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self.eval_cell_backend(values, x, cell)
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def assign_values(self, values, to_assign):
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values[:] = to_assign
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def value_shape(self):
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return ()
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def eval_cell_backend(self, values, x, cell):
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values_list = [
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func(x) for (key, func) in self.group_value_dict.items()
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if self.check_marked(self.markers[key][cell.index])
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]
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return_value = self.overlap(values_list)
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class Vector3DCellExpressionFromXDMF(fenics.Expression, CellExpressionFromXDMF):
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if values_list:
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self.assign_values(values, return_value)
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else:
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self.assign_values(values, self.default(x))
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def __init__(
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self,
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group_value_dict,
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default=lambda x: np.zeros((3,)),
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0], **kwargs
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):
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CellExpressionFromXDMF.__init__(
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self, group_value_dict,
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default=default,
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check_marked=check_marked,
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overlap=overlap
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)
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# TODO: python classes much slower than JIT compilation
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def eval_cell(self, values, x, cell):
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self.eval_cell_backend(values, x, cell)
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# ***********************************
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# * Sub classes due to value_shape method which is not of dynamical return type
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# * Also the assignment of values is to be done by reference. Therefore it has to be
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# * overloaded.
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# ***********************************
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def value_shape(self):
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return (3,)
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class ScalarCellExpressionFromXDMF(fenics.Expression, CellExpressionFromXDMF):
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def __init__(
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class Vector2DCellExpressionFromXDMF(fenics.Expression, CellExpressionFromXDMF):
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def __init__(
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self,
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group_value_dict,
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default=lambda x: np.zeros((2,)),
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0],
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**kwargs
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):
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CellExpressionFromXDMF.__init__(
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self,
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group_value_dict,
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default=lambda x: 0.,
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0],
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**kwargs
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):
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CellExpressionFromXDMF.__init__(
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self, group_value_dict,
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default=default,
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check_marked=check_marked,
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overlap=overlap
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)
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default=default,
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check_marked=check_marked,
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overlap=overlap
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)
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def eval_cell(self, values, x, cell):
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self.eval_cell_backend(values, x, cell)
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def eval_cell(self, values, x, cell):
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self.eval_cell_backend(values, x, cell)
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def value_shape(self):
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return ()
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def value_shape(self):
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return (2,)
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class Vector3DCellExpressionFromXDMF(fenics.Expression, CellExpressionFromXDMF):
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def __init__(
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self,
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group_value_dict,
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default=lambda x: np.zeros((3,)),
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0], **kwargs
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):
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CellExpressionFromXDMF.__init__(
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self, group_value_dict,
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default=default,
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check_marked=check_marked,
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overlap=overlap
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)
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class FacetFunctionFromXDMF(object):
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"""
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Creates facet function from XDMF file.
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"""
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def __init__(self, group_value_dict, *args, **kwargs):
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self.group_value_dict = group_value_dict
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self.init()
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def eval_cell(self, values, x, cell):
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self.eval_cell_backend(values, x, cell)
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def init(self):
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self.markers = {}
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self.marked = {}
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self.ds = {}
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self.bcs = {}
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def value_shape(self):
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return (3,)
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class Vector2DCellExpressionFromXDMF(fenics.Expression, CellExpressionFromXDMF):
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def __init__(
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self,
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group_value_dict,
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default=lambda x: np.zeros((2,)),
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check_marked=(lambda x: x == 1),
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overlap=lambda x: x[0],
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**kwargs
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):
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CellExpressionFromXDMF.__init__(
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self,
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group_value_dict,
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default=default,
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check_marked=check_marked,
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overlap=overlap
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)
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def eval_cell(self, values, x, cell):
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self.eval_cell_backend(values, x, cell)
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def value_shape(self):
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return (2,)
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class FacetFunctionFromXDMF(object):
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"""
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Creates facet function from XDMF file.
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"""
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def __init__(self, group_value_dict, *args, **kwargs):
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self.group_value_dict = group_value_dict
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self.init()
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def init(self):
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self.markers = {}
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self.marked = {}
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self.ds = {}
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self.bcs = {}
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def getDirichletBCs(self, vectorspace, *args, **kwargs):
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dbcs = []
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for (dict_key, dict_value) in self.bcs.items():
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if dict_value["type"] == "Dirichlet":
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bc = fenics.DirichletBC(
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vectorspace,
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dict_value["value"],
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self.markers[dict_key],
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dict_value.get("marked", 1),
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*args, **kwargs
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)
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dbcs.append(bc)
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return dbcs
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# TODO: write some functions to return integrals for Neumann and Robin
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# boundary conditions for the general case (i.e. vector, tensor)
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def getDirichletBCs(self, vectorspace, *args, **kwargs):
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dbcs = []
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for (dict_key, dict_value) in self.bcs.items():
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if dict_value["type"] == "Dirichlet":
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bc = fenics.DirichletBC(
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vectorspace,
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dict_value["value"],
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self.markers[dict_key],
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dict_value.get("marked", 1),
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*args, **kwargs
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)
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dbcs.append(bc)
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return dbcs
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# TODO: write some functions to return integrals for Neumann and Robin
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# boundary conditions for the general case (i.e. vector, tensor)
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## @}
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