Add option to include weights in horizontal and vertical dispersion for the fitting
Fix specific bug when jacobian calculation is parallelized and a single column is calculated in a single CPU core. This caused a situation that val = np.array(float_value) is created, then val[0] was being called and returning an error. Using np.atleast_1d instead of np.array in these cases fixes the bug.
Add option to fit sextupoles in families (constrained knobs).
Code standardization to always refer to integrated gradients KL and KsL
Fix in report creation timestamp and get data from loco input depending on dictionary keys
val = np.array(float_value)is created, thenval[0]was being called and returning an error. Usingnp.atleast_1dinstead ofnp.arrayin these cases fixes the bug.