def _convert_to_scalar_function(coef, label):
import core as cr
if not callable(coef):
register_base(label, cr.Function(lambda z: coef), overwrite=True)
elif isinstance(coef, cr.Function):
register_base(label, coef, overwrite=True)
else:
register_base(label, cr.Function(coef), overwrite=True)
return ph.ScalarFunction(label)
def _simplify_product(a, b):
# try to simplify expression containing ScalarFunctions
scalar_func = None
other_func = None
for obj1, obj2 in [(a, b), (b, a)]:
if isinstance(obj1, ScalarFunction):
scalar_func = obj1
if isinstance(obj2, (FieldVariable, TestFunction, ScalarFunction)):
other_func = obj2
break
if scalar_func and other_func:
s_func = get_base(scalar_func.data["func_lbl"], scalar_func.order[1])
o_func = get_base(other_func.data["func_lbl"], other_func.order[1])
if s_func.shape != o_func.shape:
if s_func.shape[0] == 1:
# only one function provided, use it for all others
s_func = s_func[[0 for i in range(o_func.shape[0])]]
else:
raise ValueError("Cannot simplify Product due to dimension mismatch!")
new_func = np.asarray([func.scale(scale_func) for func, scale_func in zip(o_func, s_func)])
new_name = new_func.tostring()
register_base(new_name, new_func)
if isinstance(other_func, (ScalarFunction, TestFunction)):
a = other_func.__class__(function_label=new_name, order=other_func.order[1],
location=other_func.location)
elif isinstance(other_func, FieldVariable):
# overwrite spatial derivative order, since derivation has been performed
a = FieldVariable(function_label=new_name, weight_label=other_func.data["weight_lbl"],
order=(other_func.order[0], 0), location=other_func.location)
b = None
return a, b
