desc.objectives.FixCoilCurrent

class desc.objectives.FixCoilCurrent(coil, target=None, bounds=None, weight=1, normalize=True, normalize_target=True, indices=True, name='fixed coil current')Source 

Fixes current(s) in a Coil or CoilSet.

Parameters:

coil (Coil) – Coil(s) that will be optimized to satisfy the Objective.
target (dict of {float, ndarray}, optional) – Target value(s) of the objective. Only used if bounds is None. Should have the same tree structure as coil.params. Default is target=coil.current.
bounds (tuple of dict {float, ndarray}, optional) – Lower and upper bounds on the objective. Overrides target. Should have the same tree structure as coil.params. Default is target=coil.current.
weight (dict of {float, ndarray}, optional) – Weighting to apply to the Objective, relative to other Objectives. Should be a scalar or have the same tree structure as coil.params.
normalize (bool, optional) – Whether to compute the error in physical units or non-dimensionalize.
normalize_target (bool, optional) – Whether target and bounds should be normalized before comparing to computed values. If normalize is True and the target is in physical units, this should also be set to True.
indices (nested list of bool, optional) – Pytree of bool specifying which coil currents to fix. See the example for how to use this on a mixed coil set. If True/False fixes all/none of the coil currents.
name (str, optional) – Name of the objective function.

Examples

import numpy as np
from desc.coils import (
    CoilSet, FourierPlanarCoil, FourierRZCoil, FourierXYZCoil, MixedCoilSet
)
from desc.objectives import FixCoilCurrent

# toroidal field coil set with 4 coils
tf_coil = FourierPlanarCoil(
    current=3, center=[2, 0, 0], normal=[0, 1, 0], r_n=[1]
)
tf_coilset = CoilSet.linspaced_angular(tf_coil, n=4)
# vertical field coil set with 3 coils
vf_coil = FourierRZCoil(current=-1, R_n=3, Z_n=-1)
vf_coilset = CoilSet.linspaced_linear(
    vf_coil, displacement=[0, 0, 2], n=3, endpoint=True
)
# another single coil
xyz_coil = FourierXYZCoil(current=2)
# full coil set with TF coils, VF coils, and other single coil
full_coilset = MixedCoilSet((tf_coilset, vf_coilset, xyz_coil))

# fix the current of the 1st & 3rd TF coil
# fix none of the currents in the VF coil set
# fix the current of the other coil
obj = FixCoilCurrent(
    full_coilset, indices=[[True, False, True, False], False, True]
)

Methods

`build`([use_jit, verbose])	Build constant arrays.
`compute`(params[, constants])	Compute fixed degree of freedom errors.
`compute_scalar`(args, *kwargs)	Compute the scalar form of the objective.
`compute_scaled`(args, *kwargs)	Compute and apply weighting and normalization.
`compute_scaled_error`(args, *kwargs)	Compute and apply the target/bounds, weighting, and normalization.
`compute_unscaled`(args, *kwargs)	Compute the raw value of the objective.
`copy`([deepcopy])	Return a (deep)copy of this object.
`equiv`(other)	Compare equivalence between DESC objects.
`grad`(args, *kwargs)	Compute gradient vector of self.compute_scalar wrt x.
`hess`(args, *kwargs)	Compute Hessian matrix of self.compute_scalar wrt x.
`jac_scaled`(args, *kwargs)	Compute Jacobian matrix of self.compute_scaled wrt x.
`jac_scaled_error`(args, *kwargs)	Compute Jacobian matrix of self.compute_scaled_error wrt x.
`jac_unscaled`(args, *kwargs)	Compute Jacobian matrix of self.compute_unscaled wrt x.
`jvp_scaled`(v, x[, constants])	Compute Jacobian-vector product of self.compute_scaled.
`jvp_scaled_error`(v, x[, constants])	Compute Jacobian-vector product of self.compute_scaled_error.
`jvp_unscaled`(v, x[, constants])	Compute Jacobian-vector product of self.compute_unscaled.
`load`(load_from[, file_format])	Initialize from file.
`print_value`(args[, args0])	Print the value of the objective and return a dict of values.
`save`(file_name[, file_format, file_mode])	Save the object.
`update_target`(thing)	Update target values using an Optimizable object.
`xs`(*things)	Return a tuple of args required by this objective from optimizable things.

Attributes

`bounds`	Lower and upper bounds of the objective.
`built`	Whether the transforms have been precomputed (or not).
`constants`	Constant parameters such as transforms and profiles.
`dim_f`	Number of objective equations.
`fixed`	Whether the objective fixes individual parameters (or linear combo).
`linear`	Whether the objective is a linear function (or nonlinear).
`name`	Name of objective (str).
`normalization`	normalizing scale factor.
`scalar`	Whether default "compute" method is a scalar or vector.
`target`	Target value(s) of the objective.
`things`	Optimizable things that this objective is tied to.
`weight`	Weighting to apply to the Objective, relative to other Objectives.