windfarmGA (version 5.0.0)

get_grids: Map layouts to grid coordinates

Description

Map a population of layouts to grid coordinates. Accepts either an integer matrix of unique cell IDs (n turbines x individuals) or a legacy binary matrix (n_gridcells x individuals).

Usage

get_grids(layouts, grid)

Value

Returns a list of all individuals with X and Y coordinates and the grid cell ID.

Arguments

layouts

Binary matrix (legacy) or integer matrix of grid IDs (n turbines x individuals)

grid

Indexed grid from grid_area()

See Also

Other Helper Functions: grid_area(), hexa_area(), isSpatial(), permutations(), read_power_curve(), splitAt(), wind_from_series(), wind_from_uv(), windata_format()

Examples

# \donttest{
## Create a random rectangular shapefile
library(sf)
area <- sf::st_as_sf(sf::st_sfc(
  sf::st_polygon(list(cbind(
    c(0, 0, 2000, 2000, 0),
    c(0, 2000, 2000, 0, 0)
  ))),
  crs = 3035
))

## Calculate a Grid and an indexed data.frame with coordinates and
## grid cell Ids.
Grid1 <- grid_area(area = area, size = 200, prop = 1)
Grid <- Grid1[[1]]

startsel <- init_population(Grid, 10, 20)
wind <- data.frame(ws = 12, wd = 0)
wind <- list(wind, probab = 100)
fit <- fitness(
  population = startsel, reference_height = 100, rotor_height = 100,
  surface_roughness = 0.3, area = area, rotor = 20,
  wind = wind, terrain = FALSE
)
allparks <- do.call("rbind", fit)

## SELECTION (n unique cell IDs per individual)
selec6best <- selection(fit, Grid, 2, TRUE, 6, "VAR")

## Set-crossover and swap-mutation keep exactly n turbines.
cross_ids <- set_crossover(selec6best[[1]], Grid[, "ID"], uplimit = 20)
mut_ids <- swap_mutation(cross_ids, Grid[, "ID"], p = 0.2)

## Look up XY coordinates for the next fitness evaluation.
getRectV <- get_grids(mut_ids, Grid)
fit <- fitness(
  population = getRectV, reference_height = 100, rotor_height = 100,
  surface_roughness = 0.3, area = area, rotor = 20,
  wind = wind, terrain = FALSE
)
head(fit)
# }