0.1.0.post1

(acc)^-1

accinv - Python project for inverse modeling of accelerator lattices

Note: This project is in proof-of-concept stage and therefore lacks more advanced features of some of the implemented methods.

In its current state, the package provides functionality for inverse modeling of linear optics via fitting of orbit response matrix (ORM) data, typically referred to as linear optics from closed orbits. It supports cpymad as a backend.

The main class is accinv.loco.Loco which requires one of the models from accinv.model, as well as a method for computing Jacobians, as an argument. Two methods for Jacobian computation are available.

• AnalyticalJacobianMethod: This method uses an analytical formula to compute the Jacobian of the ORM with respect to quadrupole gradient errors and BPM and steerer gain errors. The data for the analytical formula is obtained from a single Twiss call for the current lattice configuration.
• NumericalMJacobianMethod: This method uses a finite difference approximation scheme to compute the Jacobian of the ORM with respect to the quadrupole gradient errors. Thus, the number of ORMs that will be computed is proportional to the number of quadrupoles.

The inverse modeling process can be started by creating a Loco object and calling its run method:

from accinv.jacobian import AnalyticalJacobianMethod
from accinv.loco import Loco, OrmMeasurement
from accinv.model import Madx

model = Madx(path='path/to/script.madx')
loco = Loco(
    model_and_jacobian_method=(model, AnalyticalJacobianMethod),
    quadrupoles=[...],  # names of quadrupoles
    hbpms=[...],        # names of horizontal BPMs
    hsteerers=[...],    # names of horizontal steerers
    vbpms=[...],        # names of vertical BPMs
    vsteerers=[...],    # names of vertical steerers
    orm_measurement=OrmMeasurement(
      orm=np.load('path/to/measured_orm.npy'),
      uncertainty=np.load('path/to/orm_uncertainty.npy'),
  ),
)
result = loco.run()

Please consider the documentation of the Loco class for more details.