A regular grid within a boundary geometry.

This optimization problem does not involve wind farms.

Attributes¶

boundary: foxes.utils.geom2d.AreaGeometry

The boundary geometry

min_dist: float

The minimal distance between points

n_grids: int

The number of grids

n_max: int

The maximal number of points

n_row_max: int

The maximal number of points in a row

max_dist: float

The maximal distance between points

D: float

The diameter of circle fully within boundary

Public members¶

GeomRegGrids(boundary, min_dist, n_grids, n_max=None, ...)[source]

Constructor.

initialize(verbosity=1)[source]

Initialize the object.

var_names_int()[source]

The names of int variables.

initial_values_int()[source]

The initial values of the int variables.

min_values_int()[source]

The minimal values of the integer variables.

max_values_int()[source]

The maximal values of the integer variables.

var_names_float()[source]

The names of float variables.

initial_values_float()[source]

The initial values of the float variables.

min_values_float()[source]

The minimal values of the float variables.

max_values_float()[source]

The maximal values of the float variables.

apply_individual(vars_int, vars_float)[source]

Apply new variables to the problem.

apply_population(vars_int, vars_float)[source]

Apply new variables to the problem, for a whole population.

get_fig(xy=None, valid=None, ax=None, title=None, ...)[source]

Return plotly figure axis.

INT_INF = -999999

property n_vars_int

The number of int variables

property n_vars_float

The number of float variables

add_objective(objective, varmap_int=None, varmap_float=None, ...)

Add an objective to the problem.

add_constraint(constraint, varmap_int=None, varmap_float=None, ...)

Add a constraint to the problem.

property min_values_constraints

Gets the minimal values of constraints

property max_values_constraints

Gets the maximal values of constraints

property constraints_tol

Gets the tolerance values of constraints

property n_objectives

The total number of objectives, i.e., the sum of all components

property n_constraints

The total number of constraints, i.e., the sum of all components

calc_gradients(vars_int, vars_float, func, components, ivars, ...)

The actual gradient calculation, not to be called directly (call get_gradients instead).

get_gradients(vars_int, vars_float, func=None, components=None, ...)

Obtain gradients of a function that is linked to the problem.

property maximize_objs

Flags for objective maximization

evaluate_individual(vars_int, vars_float, ret_prob_res=False)

Evaluate a single individual of the problem.

evaluate_population(vars_int, vars_float, ret_prob_res=False)

Evaluate all individuals of a population.

check_constraints_individual(constraint_values, verbosity=0)

Check if the constraints are fullfilled for the given individual.

check_constraints_population(constraint_values, verbosity=0)

Check if the constraints are fullfilled for the given population.

finalize_individual(vars_int, vars_float, verbosity=1)

Finalization, given the champion data.

finalize_population(vars_int, vars_float, verbosity=0)

Finalization, given the final population data.

prob_res_einsum_individual(prob_res_list, coeffs)

Calculate the einsum of problem results

prob_res_einsum_population(prob_res_list, coeffs)

Calculate the einsum of problem results

__str__()

Get info string

property initialized

Flag for finished initialization

finalize(verbosity=0)

Finalize the object.