foxes_opt.constraints.area_geometry¶

Classes¶

`AreaGeometryConstraint`	Constrains turbine positions to the inside
`FarmBoundaryConstraint`	Constrains turbine positions to the inside of

Module Contents¶

class foxes_opt.constraints.area_geometry.AreaGeometryConstraint(problem: foxes_opt.core.farm_opt_problem.FarmOptProblem, name: str, geometry: foxes.utils.geom2d.AreaGeometry, sel_turbines: list[int] | None = None, disc_inside: bool = False, D: float | None = None, infer_vars: bool = True, **kwargs: Any)[source]¶

Bases: foxes_opt.core.farm_constraint.FarmConstraint

Constrains turbine positions to the inside of a given area geometry.

Parameters:¶

problem: The underlying optimization problem
name: The name of the constraint
geometry: The area geometry
sel_turbines: The selected turbines
disc_inside: Ensure full rotor disc inside boundary
D: Use this radius for rotor disc inside condition
infer_vars: Whether to infer the float variables automatically
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:¶

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 constraint.

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[numpy.bool_]][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)

D = None¶

property component_names : list[str]¶

The names of the components

Returns:¶

names: The component names

disc_inside = False¶

property farm : foxes.core.WindFarm¶

The wind farm

Returns:¶

value: The wind farm

geometry¶

infer_vars = True¶

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized: True if initialization has been done

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.area_geometry.FarmBoundaryConstraint(problem: foxes_opt.core.farm_opt_problem.FarmOptProblem, name: str = 'boundary', **kwargs: Any)[source]¶

Bases: AreaGeometryConstraint

Constrains turbine positions to the inside of the wind farm boundary

Parameters:¶

problem: The underlying optimization problem
name: The name of the constraint
kwargs: Additional parameters for AreaGeometryConstraint

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¶

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¶

Initialize the constraint.

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[numpy.bool_]]¶

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)

D = None¶

property component_names : list[str]¶

The names of the components

Returns:¶

names: The component names

disc_inside = False¶

property farm : foxes.core.WindFarm¶

The wind farm

Returns:¶

value: The wind farm

geometry¶

infer_vars = True¶

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized: True if initialization has been done

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