def visit_InputElement(self, node):
index = self.gen_array_macro(
node.grid.name,
map(
lambda x, y: cpp_ast.BinOp(cpp_ast.CName(x), "+",
cpp_ast.CNumber(y)), self.dim_vars,
node.offset_list))
return cpp_ast.Subscript("_my_" + node.grid.name, index)
def declare_child(self, child, idx):
if child.is_scalar():
return cpp_ast.Subscript(self.ref_name(), idx)
else:
return cpp_ast.BinOp(cpp_ast.CName(self.ref_name()), '+',
cpp_ast.BinOp(idx, '*', self.element_size()))
def declare_child(self, child, idx):
return cpp_ast.Subscript(cpp_ast.CName(self.ref_name()), idx)
def visit_InputElementExprIndex(self, node):
return cpp_ast.Subscript("_my_" + node.grid.name,
self.visit(node.index))
def visit_OutputElement(self, node):
return cpp_ast.Subscript("_my_" + self.output_grid_name,
self.output_index_var)
def __init__(self, weight_index, target):
self.left = cpp_ast.Subscript(cpp_ast.CName('_blb_weights'),
weight_index)
self.right = target
self.op = '*'
super(cpp_ast.BinOp, self).__init__()
def visit_Subscript(self, node):
index = self.visit(node.slice)
value = self.visit(node.value)
if type(node.ctx) == ast.Load:
if type(value) == cpp_ast.CName:
target_model = self.get_or_create_model(value)
if target_model.is_scalar():
raise ValueError(
'Invalid target for indexing: %s is scalar' %
value.name)
child_model = target_model.branch(idx=index.name)
if not child_model.weight_index:
child_model.weight_with(index.name)
ret = cpp_ast.Subscript(value, index)
if child_model.is_scalar() and self.weighted:
ret = WeightOp(child_model.weight_index, ret)
self.data_model[ret] = child_model
return ret
else:
self.data_model[ret] = child_model
return ret
elif type(value) == cpp_ast.Subscript:
target_model = self.get_or_create_model(value)
if target_model.is_scalar():
raise ValueError('Invalid target for indexing: %s' %
value.name)
child_model = target_model.branch()
new_index = cpp_ast.CName(
'(((%d)*(%s))+(%s))' %
(target_model.dimensions[0], value.index.generate(),
index.generate()))
ret = cpp_ast.Subscript(value, new_index)
if child_model.is_scalar() and self.weighted:
# This isn't fully general, and in fact only works for two level indexing.
ret = WeightOp(
child_model.weight_index
if child_model.weight_index else index, value)
self.data_model[ret] = child_model
return ret
else:
ret = cpp_ast.Subscript(value.value, new_index)
self.data_model[ret] = child_model
return ret
else:
raise ValueError('Invalid target for indexing: %s' %
str(value))
else:
#Nothing fancy here, just make sure everything gets written out right
if type(value) == cpp_ast.CName:
return cpp_ast.Subscipt(value, index)
elif type(value) == cpp_ast.Subscript:
if not target_model or target_model.is_scalar():
raise ValueError('Invalid target for indexing: %s' %
value.name)
child_model = target_model.branch()
new_index = cpp_ast.CName(
'(((%d)*(%s))+(%s))' %
(target_model.dimensions[0], value.index.generate(),
index.generate()))
return cpp_ast.Subscript(value.value, new_index)
else:
raise ValueError('Invalid target for indexing: %s' %
str(value))
