foxes_opt.constraints.nearest_group¶

Classes¶

`NearestGroupConstraint`	Keep every turbine in a sufficiently large nearby connected group.

Module Contents¶

class foxes_opt.constraints.nearest_group.NearestGroupConstraint(problem: foxes_opt.core.farm_opt_problem.FarmOptProblem, max_nearest_dist: float, min_nearest_group_size: int, name: str = 'nearest_group', sel_turbines: list[int] | None = None, infer_vars: bool = True, check_only_selected: bool = False, **kwargs: Any)[source]¶

Bases: foxes_opt.core.farm_constraint.FarmConstraint

Keep every turbine in a sufficiently large nearby connected group.

For every selected turbine, this constraint finds the smallest connection radius at which its distance graph component reaches the required group size. The constraint value is that radius minus max_nearest_dist. Requiring a group size of at least two also enforces the nearest-turbine upper bound when min_nearest_group_size is one.

The connection radius is piecewise equal to the length of one graph edge. Its analytical derivative follows that active edge. At equal edge lengths, a deterministic active branch is used.

Parameters:¶

problem: The underlying optimization problem.
max_nearest_dist: Maximum graph-edge distance for connected turbine groups.
min_nearest_group_size: Minimum number of turbines in every connected group.
name: The name of the constraint.
sel_turbines: The selected turbines.
infer_vars: Whether to infer the float variables automatically.
check_only_selected: Whether groups may contain only selected turbines. By default, fixed turbines can contribute to a selected turbine’s group.
kwargs: Additional parameters for iwopy.Constraint.

active_components(vars_float: numpy.ndarray, lower: numpy.ndarray, upper: numpy.ndarray) → numpy.ndarray¶

Select components that can bind within a guarded variable box.

Parameters:¶

vars_float: Initial values in this function’s float-variable order.
lower, upper: Enforced variable bounds in the same order. Infinite bounds must be treated conservatively.

Returns:¶

components: Unique local component indices. The default retains all. Overrides may omit a component only when it is guaranteed feasible throughout the box, not merely at the initial point. Full evaluation and feasibility contracts remain unchanged.

add_to_layout_figure(ax: matplotlib.axes.Axes, **kwargs: Any) → matplotlib.axes.Axes¶

Add to a layout figure

Parameters:¶

ax: The figure axis

ana_deriv(vars_int: numpy.ndarray, vars_float: numpy.ndarray, var: int, components: collections.abc.Sequence[int] | numpy.ndarray | None = None) → numpy.ndarray[source]¶

Calculate the analytical derivative for one float variable.

Parameters:¶

vars_int: The integer variable values.
vars_float: The float variable values.
var: The float variable index.
components: The selected components, or None for all.

Returns:¶

derivative: The derivative values for the selected components.

calc_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: Any, components: collections.abc.Sequence[int] | numpy.ndarray | None = None) → numpy.ndarray[source]¶

Calculate constraint values for one individual.

Parameters:¶

vars_int: The integer variable values.
vars_float: The float variable values.
problem_results: The results of applying the variables to the problem.
components: The selected components, or None for all.

Returns:¶

values: The selected constraint values.

calc_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: Any, components: collections.abc.Sequence[int] | numpy.ndarray | None = None) → numpy.ndarray[source]¶

Calculate constraint values for a population.

Parameters:¶

vars_int: The integer variable values.
vars_float: The float variable values.
problem_results: The results of applying the population to the problem.
components: The selected components, or None for all.

Returns:¶

values: The selected constraint values for each individual.

check_individual(constraint_values: numpy.ndarray, verbosity: int = 0) → numpy.ndarray¶

Check if the constraints are fullfilled for the given individual.

Parameters:¶

constraint_values: The constraint values, shape: (n_components,)
verbosity: The verbosity level, 0 = silent

Returns:¶

values: The boolean result, shape: (n_components,)

check_population(constraint_values: numpy.ndarray, verbosity: int = 0) → numpy.ndarray¶

Check if the constraints are fullfilled for the given population.

Parameters:¶

constraint_values: The constraint values, shape: (n_pop, n_components,)
verbosity: The verbosity level, 0 = silent

Returns:¶

values: The boolean result, shape: (n_pop, n_components)

component_turbines(components: collections.abc.Sequence[int] | numpy.ndarray | None = None) → set[int][source]¶

Return turbines represented by constraint components.

Parameters:¶

components: The selected component indices, or None for all components.

Returns:¶

turbines: The represented turbine indices.

finalize(verbosity: int = 0, /) → None¶

finalize(*, verbosity: int = 0) → None

Finalize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

finalize_individual(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: object, verbosity: int = 1) → numpy.ndarray¶

Finalization, given the champion data.

Parameters:¶

vars_int: The optimal integer variable values, shape: (n_vars_int,)
vars_float: The optimal float variable values, shape: (n_vars_float,)
problem_results: The results of the variable application to the problem
verbosity: The verbosity level, 0 = silent

Returns:¶

values: The component values, shape: (n_components,)

finalize_population(vars_int: numpy.ndarray, vars_float: numpy.ndarray, problem_results: object, verbosity: int = 1) → numpy.ndarray¶

Finalization, given the final population data.

Parameters:¶

vars_int: The integer variable values of the final generation, shape: (n_pop, n_vars_int)
vars_float: The float variable values of the final generation, shape: (n_pop, n_vars_float)
problem_results: The results of the variable application to the problem
verbosity: The verbosity level, 0 = silent

Returns:¶

values: The component values, shape: (n_pop, n_components)

get_bounds() → tuple[numpy.ndarray, numpy.ndarray]¶

Returns the bounds for all components.

Non-existing bounds are expressed by np.inf.

Returns:¶

min: The lower bounds, shape: (n_components,)
max: The upper bounds, shape: (n_components,)

initialize(verbosity: int = 0) → None[source]¶

Initialize the constraint.

Parameters:¶

verbosity: The verbosity level, where zero is silent.

n_components() → int[source]¶

Return the number of constraint components.

Returns:¶

value: The number of selected turbines.

classmethod new(constraint_type: str, *args: Any, **kwargs: Any) → Any¶

Run-time farm constraint factory.

Parameters:¶

constraint_type: The selected derived class name
args: Additional parameters for the constructor
kwargs: Additional parameters for the constructor

classmethod print_models() → None¶: Prints all model names.

rename_vars_float(varmap: collections.abc.Mapping[str | int, str]) → None¶

Rename float variables.

Parameters:¶

varmap: The name mapping. Key: old name str, Value: new name str

rename_vars_int(varmap: collections.abc.Mapping[str | int, str]) → None¶

Rename integer variables.

Parameters:¶

varmap: The name mapping. Key: old name str, Value: new name str

vardeps_float() → numpy.ndarray[source]¶

Return float-variable dependencies for all components.

Returns:¶

dependencies: The dependency mask, with shape (n_components, n_vars_float).

vardeps_int() → numpy.ndarray¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: The dependencies of components on function variables, shape: (n_components, n_vars_int)

check_only_selected = False¶

property component_names : list[str]¶

The names of the components

Returns:¶

names: The component names

property farm : foxes.core.WindFarm¶

The wind farm

Returns:¶

value: The wind farm

infer_vars = True¶

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized: True if initialization has been done

max_nearest_dist¶

min_nearest_group_size¶

property n_sel_turbines : int¶

The numer of selected turbines

Returns:¶

value: The numer of selected turbines

property n_vars_float : int¶

The number of float variables

Returns:¶

n: The number of float variables

property n_vars_int : int¶

The number of int variables

Returns:¶

n: The number of int variables

name = 'Base'¶

problem¶

property sel_turbines : list[int]¶

The list of selected turbines

Returns:¶

value: The list of selected turbines

tol = 1e-05¶

property var_names_float : list[str]¶

The names of the float variables

Returns:¶

names: The float variable names

property var_names_int : list[str]¶

The names of the integer variables

Returns:¶

names: The integer variable names