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, **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
kwargs: Additional parameters for iwopy.Constraint

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, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array: The integer variable values, shape: (n_vars_int,)
vars_float: np.array: The float variable values, shape: (n_vars_float,)
var: int: The index of the differentiation float variable
components: list of int: The selected components, or None for all

Returns:¶

deriv: numpy.ndarray: 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, verbosity=0)¶

Check if the constraints are fullfilled for the given individual.

Parameters:¶

constraint_values: np.array: The constraint values, shape: (n_components,)
verbosity: int: The verbosity level, 0 = silent
Returns
values: np.array
——-: The boolean result, shape: (n_components,)

check_population(constraint_values, verbosity=0)¶

Check if the constraints are fullfilled for the given population.

Parameters:¶

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

Returns:¶

values: np.array: The boolean result, shape: (n_pop, n_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int: The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

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

Returns:¶

values: np.array: The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

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

Returns:¶

values: np.array: The component values, shape: (n_pop, n_components)

get_bounds()¶

Returns the bounds for all components.

Non-existing bounds are expressed by np.inf.

Returns:¶

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

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int: 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)¶

Rename float variables.

Parameters:¶

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

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict: 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()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool: The dependencies of components on function variables, shape: (n_components, n_vars_int)

D = None¶

property component_names¶

The names of the components

Returns:¶

names: list of str: The component names

disc_inside = False¶

property farm : foxes.core.WindFarm¶

The wind farm

Returns:¶

value: The wind farm

geometry¶

property initialized¶

Flag for finished initialization

Returns:¶

bool: 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¶

The number of float variables

Returns:¶

n: int: The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int: The number of int variables

name¶

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¶

The names of the float variables

Returns:¶

names: list of str: The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str: 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

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, vars_float, var, components=None)¶

Calculates the analytic derivative, if possible.

Use numpy.nan if analytic derivatives cannot be calculated.

Parameters:¶

vars_int: np.array: The integer variable values, shape: (n_vars_int,)
vars_float: np.array: The float variable values, shape: (n_vars_float,)
var: int: The index of the differentiation float variable
components: list of int: The selected components, or None for all

Returns:¶

deriv: numpy.ndarray: 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, verbosity=0)¶

Check if the constraints are fullfilled for the given individual.

Parameters:¶

constraint_values: np.array: The constraint values, shape: (n_components,)
verbosity: int: The verbosity level, 0 = silent
Returns
values: np.array
——-: The boolean result, shape: (n_components,)

check_population(constraint_values, verbosity=0)¶

Check if the constraints are fullfilled for the given population.

Parameters:¶

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

Returns:¶

values: np.array: The boolean result, shape: (n_pop, n_components)

finalize(verbosity=0)¶

Finalize the object.

Parameters:¶

verbosity: int: The verbosity level, 0 = silent

finalize_individual(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the champion data.

Parameters:¶

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

Returns:¶

values: np.array: The component values, shape: (n_components,)

finalize_population(vars_int, vars_float, problem_results, verbosity=1)¶

Finalization, given the final population data.

Parameters:¶

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

Returns:¶

values: np.array: The component values, shape: (n_pop, n_components)

get_bounds()¶

Returns the bounds for all components.

Non-existing bounds are expressed by np.inf.

Returns:¶

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

initialize(verbosity=0)¶

Initialize the object.

Parameters:¶

verbosity: int: 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)¶

Rename float variables.

Parameters:¶

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

rename_vars_int(varmap)¶

Rename integer variables.

Parameters:¶

varmap: dict: 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()¶

Gets the dependencies of all components on the function int variables

Returns:¶

deps: numpy.ndarray of bool: The dependencies of components on function variables, shape: (n_components, n_vars_int)

D = None¶

property component_names¶

The names of the components

Returns:¶

names: list of str: The component names

disc_inside = False¶

property farm : foxes.core.WindFarm¶

The wind farm

Returns:¶

value: The wind farm

geometry¶

property initialized¶

Flag for finished initialization

Returns:¶

bool: 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¶

The number of float variables

Returns:¶

n: int: The number of float variables

property n_vars_int¶

The number of int variables

Returns:¶

n: int: The number of int variables

name¶

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¶

The names of the float variables

Returns:¶

names: list of str: The float variable names

property var_names_int¶

The names of the integer variables

Returns:¶

names: list of str: The integer variable names