desc.objectives.CoilSetMinDistance

class desc.objectives.CoilSetMinDistance(coil, target=None, bounds=None, weight=1, normalize=True, normalize_target=True, loss_function=None, deriv_mode='auto', grid=None, name='coil-coil minimum distance')Source

Target the minimum distance between coils in a coilset.

Will yield one value per coil in the coilset, which is the minimum distance to another coil in that coilset.

Parameters:

• coil (CoilSet) – Coil(s) that are to be optimized.

• target (float, ndarray, optional) – Target value(s) of the objective. Only used if bounds is None. Must be broadcastable to Objective.dim_f. If array, it has to be flattened according to the number of inputs.

• bounds (tuple of float, ndarray, optional) – Lower and upper bounds on the objective. Overrides target. Both bounds must be broadcastable to to Objective.dim_f

• weight (float, ndarray, optional) – Weighting to apply to the Objective, relative to other Objectives. Must be broadcastable to to Objective.dim_f

• 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. be set to True.

• loss_function ({None, 'mean', 'min', 'max'}, optional) – Loss function to apply to the objective values once computed. This loss function is called on the raw compute value, before any shifting, scaling, or normalization. Operates over all coils, not each individial coil.

• deriv_mode ({"auto", "fwd", "rev"}) – Specify how to compute jacobian matrix, either forward mode or reverse mode AD. “auto” selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively.

• grid (Grid, list, optional) – Collocation grid used to discretize each coil. Defaults to the default grid for the given coil-type, see coils.py and curve.py for more details. If a list, must have the same structure as coils.

• name (str, optional) – Name of the objective function.

Methods

build([use_jit, verbose])	Build constant arrays.
compute(params[, constants])	Compute minimum distances between coils.
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.
jit()	Apply JIT to compute methods, or re-apply after updating self.
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, **kwargs)	Print the value of the objective.
save(file_name[, file_format, file_mode])	Save the 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.