foxes_opt.constraints.min_dist¶

Classes¶

`MinDistConstraint`	Turbines must keep at least a minimal
`MinDistLocalConstraint`	Turbines must keep at least a minimal spatial distance,

Module Contents¶

class foxes_opt.constraints.min_dist.MinDistConstraint(problem: foxes_opt.core.farm_opt_problem.FarmOptProblem, min_dist: float, min_dist_unit: str = 'm', name: str = 'dist', 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

Turbines must keep at least a minimal spatial distance.

Parameters:¶

problem: The underlying optimization problem
min_dist: The minimal distance
min_dist_unit: The minimal distance unit, either m or D
name: The name of the constraint
sel_turbines: The selected turbines
infer_vars: Whether to infer the float variables automatically
check_only_selected: Whether to check distances only among selected turbines
kwargs: Additional parameters for iwopy.Constraint

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

Select pairs that can approach the spacing limit within bounds.

Parameters:¶

vars_float: Initial values in this constraint’s float-variable order.
lower, upper: Enforced coordinate bounds in the same order, in metres. Unselected turbine positions remain fixed.

Returns:¶

components: Stable pair-component indices whose coordinate boxes can approach the nominal minimum distance. Omitted pairs are separated throughout the box. Component metadata, full values and feasibility checks remain unchanged. Unbounded coordinates are handled conservatively. Unsupported variable parameterizations, variable diameters and state-dependent coordinates retain all pairs.

Raises:¶

ValueError: If the minimum-distance unit is neither m nor D.

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:¶

deriv: The derivative values, shape: (n_sel_components,)

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

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int: The integer variable values, shape: (n_vars_int,)
vars_float: The float variable values, shape: (n_vars_float,)
problem_results: The results of the variable application to the problem
components: The selected components or None for all

Returns:¶

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

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

Calculate values for all individuals of a population.

Parameters:¶

vars_int: The integer variable values, shape: (n_pop, n_vars_int)
vars_float: The float variable values, shape: (n_pop, n_vars_float)
problem_results: The results of the variable application to the problem
components: The selected components or None for all

Returns:¶

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

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]¶

Return turbines associated with constraint components.

Parameters:¶

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

Returns:¶

turbines: The associated 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 constaint.

Parameters:¶

verbosity: The verbosity level, 0 = silent

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

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[tuple[int, int], numpy.dtype[Any]][source]¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: The dependencies of components on function variables, shape

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

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized: True if initialization has been done

min_dist : float¶

min_dist_unit : str = 'm'¶

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

class foxes_opt.constraints.min_dist.MinDistLocalConstraint(problem: foxes_opt.core.farm_opt_problem.FarmOptProblem, min_dist: float, min_dist_unit: str = 'm', name: str = 'dist', sel_turbines: list[int] | None = None, infer_vars: bool = True, check_only_selected: bool = False, max_move: float | None = None, **kwargs: Any)[source]¶

Bases: MinDistConstraint

Turbines must keep at least a minimal spatial distance, checking only pairs that can violate the distance in a local move problem.

Parameters:¶

problem: The underlying local move optimization problem
min_dist: The minimal distance
min_dist_unit: The minimal distance unit, either m or D
name: The name of the constraint
sel_turbines: The selected turbines
infer_vars: Whether to infer the float variables automatically
check_only_selected: Whether to check distances only among selected turbines
max_move: The maximal local displacement per selected turbine. If None, infer from problem.radius or problem.square_length.
kwargs: Additional parameters for iwopy.Constraint

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

Select pairs that can approach the spacing limit within bounds.

Parameters:¶

vars_float: Initial values in this constraint’s float-variable order.
lower, upper: Enforced coordinate bounds in the same order, in metres. Unselected turbine positions remain fixed.

Returns:¶

components: Stable pair-component indices whose coordinate boxes can approach the nominal minimum distance. Omitted pairs are separated throughout the box. Component metadata, full values and feasibility checks remain unchanged. Unbounded coordinates are handled conservatively. Unsupported variable parameterizations, variable diameters and state-dependent coordinates retain all pairs.

Raises:¶

ValueError: If the minimum-distance unit is neither m nor D.

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¶

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:¶

deriv: The derivative values, shape: (n_sel_components,)

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

Calculate values for a single individual of the underlying problem.

Parameters:¶

vars_int: The integer variable values, shape: (n_vars_int,)
vars_float: The float variable values, shape: (n_vars_float,)
problem_results: The results of the variable application to the problem
components: The selected components or None for all

Returns:¶

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

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

Calculate values for all individuals of a population.

Parameters:¶

vars_int: The integer variable values, shape: (n_pop, n_vars_int)
vars_float: The float variable values, shape: (n_pop, n_vars_float)
problem_results: The results of the variable application to the problem
components: The selected components or None for all

Returns:¶

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

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]¶

Return turbines associated with constraint components.

Parameters:¶

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

Returns:¶

turbines: The associated 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 constaint.

Parameters:¶

verbosity: The verbosity level, 0 = silent

n_components() → int¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

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[tuple[int, int], numpy.dtype[Any]]¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps: The dependencies of components on function variables, shape

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

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized: True if initialization has been done

max_move = None¶

min_dist : float¶

min_dist_unit : str = 'm'¶

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