def __init__(self, a, b):
CompoundTensorOperator.__init__(self, (a, b))
fi, fid = merge_nonoverlapping_indices(a, b)
self.ufl_free_indices = fi
self.ufl_index_dimensions = fid
def __init__(self, a, b):
CompoundTensorOperator.__init__(self, (a, b))
fi, fid = merge_nonoverlapping_indices(a, b)
self.ufl_free_indices = fi
self.ufl_index_dimensions = fid
def __new__(cls, a, b):
ash, bsh = a.ufl_shape, b.ufl_shape
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero(ash + bsh, fi, fid)
if ash == () or bsh == ():
return a * b
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
ash, bsh = a.ufl_shape, b.ufl_shape
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero(ash + bsh, fi, fid)
if ash == () or bsh == ():
return Conj(a) * b
return CompoundTensorOperator.__new__(cls)
def __init__(self, a, b):
# sort operands for unique representation, must be independent
# of various counts etc. as explained in cmp_expr
a, b = sorted_expr((a, b))
CompoundTensorOperator.__init__(self, (a, b))
fi, fid = merge_nonoverlapping_indices(a, b)
self.ufl_free_indices = fi
self.ufl_index_dimensions = fid
def __new__(cls, a, b):
# Checks
ash, bsh = a.ufl_shape, b.ufl_shape
if ash != bsh:
error("Shapes do not match: %s and %s." % (ufl_err_str(a), ufl_err_str(b)))
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero((), fi, fid)
elif ash == ():
return a*b
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
ash = a.ufl_shape
bsh = b.ufl_shape
# Checks
if not (len(ash) == 1 and ash == bsh):
error("Cross product requires arguments of rank 1, got %s and %s." % (
ufl_err_str(a), ufl_err_str(b)))
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero(ash, fi, fid)
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
ash = a.ufl_shape
bsh = b.ufl_shape
# Checks
if not (len(ash) == 1 and ash == bsh):
error("Cross product requires arguments of rank 1, got %s and %s." % (
ufl_err_str(a), ufl_err_str(b)))
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero(ash, fi, fid)
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
# Checks
ash, bsh = a.ufl_shape, b.ufl_shape
if ash != bsh:
error("Shapes do not match: %s and %s." % (ufl_err_str(a), ufl_err_str(b)))
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero((), fi, fid)
elif ash == ():
return a*Conj(b)
# sort operands for unique representation,
# must be independent of various counts etc.
# as explained in cmp_expr
if (a, b) != tuple(sorted_expr((a, b))):
return Conj(Inner(b, a))
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
# Checks
ash, bsh = a.ufl_shape, b.ufl_shape
if ash != bsh:
error("Shapes do not match: %s and %s." % (ufl_err_str(a), ufl_err_str(b)))
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero((), fi, fid)
elif ash == ():
return a * Conj(b)
# sort operands for unique representation,
# must be independent of various counts etc.
# as explained in cmp_expr
if (a, b) != tuple(sorted_expr((a, b))):
return Conj(Inner(b, a))
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
ash = a.ufl_shape
bsh = b.ufl_shape
ar, br = len(ash), len(bsh)
scalar = (ar == 0 and br == 0)
# Checks
if not ((ar >= 1 and br >= 1) or scalar):
error("Dot product requires non-scalar arguments, "
"got arguments with ranks %d and %d." % (ar, br))
if not (scalar or ash[-1] == bsh[0]):
error("Dimension mismatch in dot product.")
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
shape = ash[:-1] + bsh[1:]
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero(shape, fi, fid)
elif scalar: # TODO: Move this to def dot()?
return a * b
return CompoundTensorOperator.__new__(cls)
def __new__(cls, a, b):
ash = a.ufl_shape
bsh = b.ufl_shape
ar, br = len(ash), len(bsh)
scalar = (ar == 0 and br == 0)
# Checks
if not ((ar >= 1 and br >= 1) or scalar):
error("Dot product requires non-scalar arguments, "
"got arguments with ranks %d and %d." % (ar, br))
if not (scalar or ash[-1] == bsh[0]):
error("Dimension mismatch in dot product.")
# Simplification
if isinstance(a, Zero) or isinstance(b, Zero):
shape = ash[:-1] + bsh[1:]
fi, fid = merge_nonoverlapping_indices(a, b)
return Zero(shape, fi, fid)
elif scalar: # TODO: Move this to def dot()?
return a * b
return CompoundTensorOperator.__new__(cls)
