Python project for inverse modeling of accelerator lattices
Project description
(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.
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