def param_canon(expr, real_args, imag_args, real2imag):
if expr.is_real():
return expr, None
elif expr.is_imag():
imag = CallbackParam(lambda: np.imag(expr.value), expr.shape)
return None, imag
else:
real = CallbackParam(lambda: np.real(expr.value), expr.shape)
imag = CallbackParam(lambda: np.imag(expr.value), expr.shape)
return (real, imag)
def canonicalize(self):
"""Represent the atom as an affine objective and conic constraints.
"""
# Constant atoms are treated as a leaf.
if self.is_constant():
# Parameterized expressions are evaluated later.
if self.parameters():
param = CallbackParam(lambda: self.value, self.shape)
return param.canonical_form
# Non-parameterized expressions are evaluated immediately.
else:
return Constant(self.value).canonical_form
else:
arg_objs = []
constraints = []
for arg in self.args:
obj, constr = arg.canonical_form
arg_objs.append(obj)
constraints += constr
# Special info required by the graph implementation.
data = self.get_data()
graph_obj, graph_constr = self.graph_implementation(arg_objs,
self.shape,
data)
return graph_obj, constraints + graph_constr
def canonicalize_expr(self, expr, args):
if isinstance(expr, Expression) and not expr.variables():
# Parameterized expressions are evaluated in a subsequent
# reduction.
if expr.parameters():
param = CallbackParam(lambda: expr.value, expr.shape)
return param, []
# Non-parameterized expressions are evaluated immediately.
else:
return Constant(expr.value), []
elif type(expr) in self.canon_methods:
return self.canon_methods[type(expr)](expr, args)
else:
return expr.copy(args), []