foxes_opt.constraints.min_dist¶
Classes¶
Turbines must keep at least a minimal |
Module Contents¶
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class foxes_opt.constraints.min_dist.MinDistConstraint(problem: foxes_opt.core.farm_opt_problem.FarmOptProblem, min_dist: float, min_dist_unit: str =
'm', name: str ='dist', sel_turbines: list[int] | None =None, **kwargs: Any)[source]¶ Bases:
foxes_opt.core.farm_constraint.FarmConstraintTurbines must keep at least a minimal spatial distance.
- Parameters:¶
- problem
The underlying optimization problem
- min_dist
The minimal distance
- min_dist_unit
The minimal distance unit, either m or D
- name
The name of the constraint
- sel_turbines
The selected turbines
- 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
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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,)
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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,)
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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)
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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,)
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check_population(constraint_values, verbosity=
0)¶ Check if the constraints are fullfilled for the given population.
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finalize(verbosity=
0)¶ Finalize the object.
- Parameters:¶
- verbosity: int
The verbosity level, 0 = silent
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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,)
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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,)
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initialize(verbosity: int =
0) None[source]¶ Initialize the constaint.
- Parameters:¶
- verbosity
The verbosity level, 0 = silent
- n_components() int[source]¶
Returns the number of components of the function.
- Returns:¶
- value
The number of components.
- classmethod new(constraint_type: str, *args: Any, **kwargs: Any) Any¶
Run-time farm constraint factory.
- Parameters:¶
- constraint_type
The selected derived class name
- args
Additional parameters for the constructor
- kwargs
Additional parameters for the constructor
- 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[Any]][source]¶
Gets the dependencies of all components on the function float variables
- Returns:¶
- deps
The dependencies of components on function variables, shape
- vardeps_int()¶
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)
- property component_names¶
The names of the components
- Returns:¶
- names: list of str
The component names
- 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
- name¶
- problem¶
- property sel_turbines : list[int]¶
The list of selected turbines
- Returns:¶
- value
The list of selected turbines
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tol =
1e-05¶