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Grid-Generate_clusters.R
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Grid-Generate_clusters.R
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#########################################################
# This module generates cluster locations
# Edited: 17.1.2022 From spatial dataframe to use sf
# Lauri Vesa
#########################################################
# Too many arguments - will refactor
generate_clusters <- function(cluster_start_random,
cluster_hexagonal,
cluster_hexagonal_dist,
map_projection,
min_x,
max_x,
min_y,
max_y,
cluster_x,
cluster_y,
cluster_x_distance,
cluster_y_distance,
first_cluster_id
) {
# Check parameters
if (max_x <= min_x) stop("Max x must be greater than min x")
if (max_y <= min_y) stop("Max y must be greater than min y")
if (cluster_x_distance <= 0) stop("Cluster x distance must be greater than zero")
if (cluster_y_distance <= 0) stop("Cluster y distance must be greater than zero")
is.even <- function(x) x %% 2 == 0
if (cluster_hexagonal==TRUE & cluster_hexagonal_dist==TRUE) {
cluster_y_distance = sqrt(3/4) * cluster_x_distance
}
# if starting point is random, get random coordinates
cluster_x <- ifelse( !cluster_start_random, min_x + cluster_x,
min_x + runif(1, min = 0, max = 0.99999) * cluster_x_distance )
cluster_y <- ifelse( !cluster_start_random, max_y + cluster_y,
max_y - runif(1, min = 0, max = 0.99999) * cluster_y_distance )
i_cols <- round((max_x - min_x) / cluster_x_distance + 0.5)
i_rows <- round((max_y - min_y) / cluster_y_distance + 0.5)
DT_Cluster <- as.data.frame(matrix(0, ncol = 3, nrow = i_cols * i_rows))
colnames(DT_Cluster)[1] <- "Cluster_ID" ; colnames(DT_Cluster)[2] <- "X" ; colnames(DT_Cluster)[3] <- "Y"
DT_Cluster$RowNo <- rep(seq(1, i_rows, 1), each = i_cols)
DT_Cluster$ColNo <- rep(seq(1, i_cols, 1), times = i_rows)
## +++++++++++++++++++++++++++++++++++++++++++++++++++++
## Clusters are numbered in east-west direction
## Generate cluster centre X coordinates -----
DT_Cluster$X <- rep(seq(cluster_x, cluster_x + (i_cols - 1) * cluster_x_distance, cluster_x_distance), times = i_rows)
## Generate cluster centre Y coordinates -----
DT_Cluster$Y <- rep(seq(cluster_y, cluster_y - (i_rows - 1) * cluster_y_distance, -cluster_y_distance), each = i_cols)
DT_Cluster$Cluster_ID <- seq(first_cluster_id, first_cluster_id + i_cols * i_rows - 1, 1)
# write.csv(DT_Cluster,"d:/tmp/t.csv")
# hexagonal grid
if (cluster_hexagonal==TRUE) {
DT_Cluster$is_even <- is.even( rep(seq(1, i_rows), each = i_cols) )
DT_Cluster$X[ DT_Cluster$is_even ] <- DT_Cluster$X[ DT_Cluster$is_even ] + 0.5 * cluster_x_distance
DT_Cluster$is_even <- NULL
}
print("CLUSTER: Step 1")
if (map_projection == 4326) {
DT_Cluster <- dplyr::mutate(DT_Cluster, zone = (trunc((X + 180) / 6) + 1))
DT_Cluster$zone <- as.integer(DT_Cluster$zone)
DT_Cluster$hemisphere <- ifelse( DT_Cluster$Y < 0, "S", "N" )
DT_Cluster$zone <- paste(DT_Cluster$zone, DT_Cluster$hemisphere, sep="" )
DT_Cluster$hemisphere <- NULL
UTM_list <- unique(DT_Cluster$zone)
clusterdata <- split(DT_Cluster, DT_Cluster$zone)
for (i in 1:length(clusterdata)) {
zone_str <- as.character(names(clusterdata)[i])
zone <- as.integer(substr(zone_str, 0, nchar(zone_str)-1))
hemispere <- substr(zone_str, nchar(zone_str), nchar(zone_str))
df <- data.frame(clusterdata[[i]])
# https://stackoverflow.com/questions/49181715/how-to-make-a-data-frame-into-a-simple-features-data-frame
# projcrs <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
projcrs <- ifelse(hemispere=='S', 32700 + zone, 32600 + zone)
data <- st_as_sf(x = df,
coords = c("X", "Y"),
crs = paste0("EPSG:", projcrs),
remove = FALSE)
# coordinates(data) <- c("X", "Y")
# proj4string(data) <- map_proj4string
# ress <- spTransform(data, conv_string)
ress <- st_drop_geometry(data)
ress$zone <- NULL
ress$ColNo <- NULL
ress$RowNo <- NULL
names(ress)[names(ress) == 'X'] <- 'X_map'
names(ress)[names(ress) == 'Y'] <- 'Y_map'
if (i == 1) {
resAll <- ress
} else {
resAll <- rbind(resAll, ress)
}
}
DT_Cluster <- merge(x=DT_Cluster, y=resAll, by="Cluster_ID", all.x = TRUE)
rm(clusterdata)
rm(ress)
} else {
DT_Cluster$X_map <- as.numeric(DT_Cluster$X)
DT_Cluster$Y_map <- as.numeric(DT_Cluster$Y)
}
#####################################
# print(str(DT_Cluster))
print("CLUSTER: Step 2")
sf_Cluster <- st_as_sf( x = DT_Cluster,
coords = c("X", "Y"),
crs = st_crs(map_projection)$proj4string )
if (map_projection != 4326) {
sf_Cluster <- st_transform( sf_Cluster, 4326 )
}
print("CLUSTER: Step 3")
# if (!any(names(DT_Cluster) == 'X')) {
# DT_Cluster@data$X <- DT_Cluster$X
# DT_Cluster@data$Y <- DT_Cluster$Y
# }
coord_list <- st_coordinates(sf_Cluster)
DT_Cluster <- st_drop_geometry(sf_Cluster)
DT_Cluster$X <- coord_list[,1]
DT_Cluster$Y <- coord_list[,2]
rm(coord_list)
DT_Cluster <- DT_Cluster %>%
dplyr::mutate(zone= (trunc((X + 180) / 6)) + 1 ) %>%
dplyr::mutate(hemisphere = ifelse( Y < 0, "S", "N" )) %>%
dplyr::mutate(zone = paste(zone, hemisphere, sep="" )) %>%
dplyr::select(-hemisphere)
sf_Cluster <- st_as_sf( x = DT_Cluster,
coords = c("X", "Y"),
crs = "EPSG:4326",
remove = FALSE)
print("CLUSTER: Step 4")
return(sf_Cluster)
}