foxes_opt.problems.layout.geom_layouts.constraints¶

Classes¶

Module Contents¶

class foxes_opt.problems.layout.geom_layouts.constraints.Boundary(problem: iwopy.Problem, n_turbines: int | None = None, D: float | None = None, name: str = 'boundary', **kwargs: Any)[source]¶

Bases: iwopy.Constraint

Boundary constraint for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

n_turbines

The number of turbines

D

The rotor diameter

name

The constraint name

kwargs

Additioal parameters for the base class

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.

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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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)

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 object.

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: object, **kwargs: object) → Constraint¶

Run-time constraint factory.

Parameters:¶

constraint_type

The selected derived class name

args

Additional parameters for constructor

kwargs

Additional parameters for constructor

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¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps

The dependencies of components on function variables, 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)

D¶

property component_names : list[str]¶

The names of the components

Returns:¶

names

The component names

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized

True if initialization has been done

n_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¶

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.problems.layout.geom_layouts.constraints.CFixN(problem: iwopy.Problem, N: int, name: str = 'cfixN', **kwargs: Any)[source]¶

Bases: foxes_opt.core.zero_derivatives.ZeroFloatDerivatives, iwopy.Constraint

Fixed number of turbines constraint for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

N

The number of turbines

name

The constraint name

kwargs

Additioal parameters for the base class

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.

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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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)

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 object.

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: object, **kwargs: object) → Constraint¶

Run-time constraint factory.

Parameters:¶

constraint_type

The selected derived class name

args

Additional parameters for constructor

kwargs

Additional parameters for constructor

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¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps

The dependencies of components on function variables, 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)

N¶

property component_names : list[str]¶

The names of the components

Returns:¶

names

The component names

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized

True if initialization has been done

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¶

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.problems.layout.geom_layouts.constraints.CMaxN(problem: iwopy.Problem, N: int, name: str = 'cmaxN', **kwargs: Any)[source]¶

Bases: foxes_opt.core.zero_derivatives.ZeroFloatDerivatives, iwopy.Constraint

Maximal number of turbines constraint for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

N

The maximal number of turbines

name

The constraint name

kwargs

Additioal parameters for the base class

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.

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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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)

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 object.

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: object, **kwargs: object) → Constraint¶

Run-time constraint factory.

Parameters:¶

constraint_type

The selected derived class name

args

Additional parameters for constructor

kwargs

Additional parameters for constructor

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¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps

The dependencies of components on function variables, 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)

N¶

property component_names : list[str]¶

The names of the components

Returns:¶

names

The component names

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized

True if initialization has been done

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¶

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.problems.layout.geom_layouts.constraints.CMinDensity(problem: iwopy.Problem, min_value: float, dfactor: int = 1, name: str = 'min_density')[source]¶

Bases: iwopy.Constraint

Minimal turbine density constraint for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

min_value

The minimal turbine density

dfactor

Delta factor for grid spacing

name

The constraint name

kwargs

Additioal parameters for the base class

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.

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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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)

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) → None[source]¶

Initialize the object.

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: object, **kwargs: object) → Constraint¶

Run-time constraint factory.

Parameters:¶

constraint_type

The selected derived class name

args

Additional parameters for constructor

kwargs

Additional parameters for constructor

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¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps

The dependencies of components on function variables, 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)

property component_names : list[str]¶

The names of the components

Returns:¶

names

The component names

dfactor = 1¶

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized

True if initialization has been done

min_value¶

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¶

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.problems.layout.geom_layouts.constraints.CMinN(problem: iwopy.Problem, N: int, name: str = 'cminN', **kwargs: Any)[source]¶

Bases: foxes_opt.core.zero_derivatives.ZeroFloatDerivatives, iwopy.Constraint

Minimal number of turbines constraint for purely geometrical layouts problems.

Initialize the constraint.

Parameters:¶

args

Positional parameters for the base class.

tol

Feasibility tolerance applied to every constraint component.

kwargs

Keyword parameters for the base class.

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.

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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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)

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 object.

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: object, **kwargs: object) → Constraint¶

Run-time constraint factory.

Parameters:¶

constraint_type

The selected derived class name

args

Additional parameters for constructor

kwargs

Additional parameters for constructor

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¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps

The dependencies of components on function variables, 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)

N¶

property component_names : list[str]¶

The names of the components

Returns:¶

names

The component names

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized

True if initialization has been done

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¶

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.problems.layout.geom_layouts.constraints.MinDist(problem: iwopy.Problem, min_dist: float | None = None, n_turbines: int | None = None, name: str = 'min_dist', **kwargs: Any)[source]¶

Bases: iwopy.Constraint

Minimal distance constraint for purely geometrical layouts problems.

Parameters:¶

problem

The underlying geometrical layout optimization problem

min_dist

The minimal distance between turbines

n_turbines

The number of turbines

name

The constraint name

kwargs

Additioal parameters for the base class

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.

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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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: tuple[numpy.ndarray, numpy.ndarray], cmpnts: 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)

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: object, **kwargs: object) → Constraint¶

Run-time constraint factory.

Parameters:¶

constraint_type

The selected derived class name

args

Additional parameters for constructor

kwargs

Additional parameters for constructor

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¶

Gets the dependencies of all components on the function float variables

Returns:¶

deps

The dependencies of components on function variables, 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)

property component_names : list[str]¶

The names of the components

Returns:¶

names

The component names

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized

True if initialization has been done

min_dist¶

n_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¶

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