foxes_opt.problems.layout.geom_layouts.objectives¶

Classes¶

`MaxDensity`	Maximal turbine density objective
`MaxGridSpacing`	Maximal grid spacing objective
`MeMiMaDist`	Mean-min-max distance objective
`OFixN`	Fixed number of turbines objective
`OMaxN`	Maximal number of turbines objective
`OMinN`	Minimal number of turbines objective

Module Contents¶

class foxes_opt.problems.layout.geom_layouts.objectives.MaxDensity(problem: iwopy.Problem, dfactor: int = 1, min_dist: float | None = None, name: str = 'max_density')[source]¶

Bases: iwopy.Objective

Maximal turbine density objective for purely geometrical layouts problems.

Parameters:¶

problem: The underlying geometrical layout optimization problem
dfactor: Delta factor for grid spacing
min_dist: The minimal distance
name: The constraint name

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)

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)

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

Initialize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags: Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

classmethod new(objective_type: str, *args: object, **kwargs: object) → Objective¶

Run-time objective function factory.

Parameters:¶

objective_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 : int = 1¶

property initialized : bool¶

Flag for finished initialization

Returns:¶

initialized: True if initialization has been done

min_dist¶

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 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.objectives.MaxGridSpacing(problem: iwopy.Problem, name: str = 'max_dxdy')[source]¶

Bases: iwopy.Objective

Maximal grid spacing objective for purely geometrical layouts problems.

Parameters:¶

problem: The underlying geometrical layout optimization problem
name: The constraint name

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)

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)

initialize(verbosity: int = 0) → None¶

Initialize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags: Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

classmethod new(objective_type: str, *args: object, **kwargs: object) → Objective¶

Run-time objective function factory.

Parameters:¶

objective_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

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 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.objectives.MeMiMaDist(problem: iwopy.Problem, scale: float = 500.0, c1: int = 1, c2: int = 1, c3: int = 1, name: str = 'MiMaMean')[source]¶

Bases: iwopy.Objective

Mean-min-max distance objective for purely geometrical layouts problems.

Parameters:¶

problem: The underlying geometrical layout optimization problem
scale: The distance scale
c1: Parameter for mean weighting
c2: Parameter for max diff weighting
c3: Parameter for min diff weighting
name: The constraint name

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)

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)

initialize(verbosity: int = 0) → None¶

Initialize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags: Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

classmethod new(objective_type: str, *args: object, **kwargs: object) → Objective¶

Run-time objective function factory.

Parameters:¶

objective_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)

c1 : int = 1¶

c2 : int = 1¶

c3 : int = 1¶

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¶

scale : float = 500.0¶

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.objectives.OFixN(problem: iwopy.Problem, N: int, name: str = 'ofixN')[source]¶

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

Fixed number of turbines objective for purely geometrical layouts problems.

Parameters:¶

problem: The underlying geometrical layout optimization problem
N: The number of turbines
name: The constraint name

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)

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)

initialize(verbosity: int = 0) → None¶

Initialize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags: Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

classmethod new(objective_type: str, *args: object, **kwargs: object) → Objective¶

Run-time objective function factory.

Parameters:¶

objective_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¶

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.objectives.OMaxN(problem: iwopy.Problem, name: str = 'maxN')[source]¶

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

Maximal number of turbines objective for purely geometrical layouts problems.

Parameters:¶

problem: The underlying geometrical layout optimization problem
name: The constraint name

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)

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)

initialize(verbosity: int = 0) → None¶

Initialize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags: Bool array for component maximization, shape

n_components() → int[source]¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

classmethod new(objective_type: str, *args: object, **kwargs: object) → Objective¶

Run-time objective function factory.

Parameters:¶

objective_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

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 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.objectives.OMinN(problem: iwopy.Problem, name: str = 'ominN')[source]¶

Bases: OMaxN

Minimal number of turbines objective for purely geometrical layouts problems.

Parameters:¶

problem: The underlying geometrical layout optimization problem
name: The constraint name

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¶

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¶

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)

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)

initialize(verbosity: int = 0) → None¶

Initialize the object.

Parameters:¶

verbosity: The verbosity level, 0 = silent

maximize() → list[bool][source]¶

Returns flag for maximization of each component.

Returns:¶

flags: Bool array for component maximization, shape

n_components() → int¶

Returns the number of components of the function.

Returns:¶

value: The number of components.

classmethod new(objective_type: str, *args: object, **kwargs: object) → Objective¶

Run-time objective function factory.

Parameters:¶

objective_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

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 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