maps.py

string = 'a*x**r+b*x+c'
expression = sympy.sympify(string)
expr = hs.model.components1D.Expression(string, 'test')


data = np.random.random((5,300))
s = hs.signals.Signal1D(data)
m = s.create_model()
m.append(expr)

r,a,c = expr.r, expr.a, expr.c
r.value = 0
r.free = False
a.value = 10
a.free = True
c.value = 20

a.assign_current_value_to_all()
r.assign_current_value_to_all()
c.assign_current_value_to_all()

subs = tuple()
names = tuple()
values = tuple()
# handles the case when a fixed parameter has a value such that the expression becomes independent of x
for para in set(expr.parameters) - set(expr.free_parameters):
# for simultaneous multifit, this will only be safe if all values in the map are equal:
    if False:# Simultaneous multifit scenario
        values_map = para.map['values']
        assert (values_map == values_map.flat[0]).all(), 'Please use para.assign_current_value_to_all() on parameter {}'.format(para) # flat handles the case where para is multidimensional
    names += (para.name, )
    values += (para.value, )
    subs += ((para.name, para.value),)
new_expression = expression.subs(subs)
constant, not_constant = extract_constant_part_of_expression(new_expression, 'x')
names = tuple()
values = tuple()
for symbol in constant.free_symbols:
    para = getattr(expr, str(symbol))
    names += (para.name, )
    if False: # Simultaneous multifit scenario
        values += (para.map['values'], )
    else:
        values += (para.value, )
constant2 = lambdify(names, constant, 'numpy')(*values)