Fit routines break r_values

[s-kuberski]

import pyerrors as pe
def f(a, x):
    return a[0] + a[1] * x

a1 = pe.pseudo_Obs(1.1, .1, 'a|1')
a2 = pe.pseudo_Obs(1.2, .1, 'a|2')
a = pe.merge_obs([a1, a2])
b = pe.pseudo_Obs(2, .001, 'b')
c = pe.pseudo_Obs(3, .001, 'c')

y = [a, b, c]
[o.gamma_method() for o in y]
fitp = pe.fits.least_squares([1, 2, 3], y, f)
print(fitp)
for o in fitp:
    print(o.value, o.r_values)

gives

Fit with 2 parameters
Method: Levenberg-Marquardt
`xtol` termination condition is satisfied.
chisquare/d.o.f.: 4.5022309160171705
Goodness of fit:
χ²/d.o.f. = 4.502231
Fit parameters:
0	 0.0002(36)
1	 0.9999(14)

0.0002401189830877699 {'a|1': 1.1000000000000003, 'a|2': 1.2, 'b': 1.15, 'c': 1.15}
0.9999099553831112 {'a|1': 1.1000000000000003, 'a|2': 1.2, 'b': 1.15, 'c': 1.15}

which is clearly wrong.

pyerrors/pyerrors/fits.py

Line 617 in a324a7f

result.append(derived_observable(lambda x, **kwargs: x[0], list(y), man_grad=list(deriv[i])))

[s-kuberski]

Something like

result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * fit_result.x[i], list(y), man_grad=list(deriv[i])))

seems to resolve the issue.