From 274cbe1c9c284d8110cc04254d37866f3a263214 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Fri, 26 Jan 2024 16:57:14 +0100 Subject: [PATCH 01/14] Add area coverage development for shapetomask --- R/ShapeToMask.R | 156 +++++++++++++++++++++++++++--------------------- 1 file changed, 88 insertions(+), 68 deletions(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 350e38b..cf80a6e 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -81,7 +81,7 @@ #'@import foreach #'@importFrom doParallel registerDoParallel #'@export -ShapeToMask <- function(shp_file, ref_grid, +ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, shp_system = "NUTS", reg_names = NULL, reg_ids = NULL, shp_col_name_ids = NULL, reg_level = 3, lat_dim = NULL, lon_dim = NULL, @@ -175,6 +175,10 @@ ShapeToMask <- function(shp_file, ref_grid, if (!is.character(target_crs)) { stop("Parameter 'target_crs' must be a character string.") } + transform_shp_crs <- TRUE + } else { + transform_shp_crs <- FALSE + target_crs <- sf::st_crs(shp) } # check_valid if (!is.logical(check_valid)) { @@ -203,7 +207,7 @@ ShapeToMask <- function(shp_file, ref_grid, # Step 1: Load the shapefile shp <- sf::st_read(shp_file) # class sf - if (!is.null(target_crs)) { + if (transform_shp_crs) { transformed_shapefile <- st_transform(shp, crs = target_crs) shp <- transformed_shapefile } @@ -261,7 +265,7 @@ ShapeToMask <- function(shp_file, ref_grid, } } - # Step 2: Use the reference file to get lat and lon + # Step (2.1): Use the reference file to get lat and lon if (all(tools::file_ext(ref_grid) == 'nc')) { ## Method 1: ref_grid is a netCDF file if (is.null(lat_dim) | is.null(lon_dim)) { @@ -278,29 +282,35 @@ ShapeToMask <- function(shp_file, ref_grid, lon <- ref_grid[[lon_dim]] } - ## Create data frame & sp class for ref grid - ref.df <- data.frame(data = 0, - lon = rep(lon, times = length(lat)), - lat = rep(lat, each = length(lon))) - coord <- as.matrix(data.frame(x = ref.df$lon, y = ref.df$lat)) - - xy.sfg <- sf::st_multipoint(coord) - xy.sfc <- sf::st_sfc(xy.sfg) - # Assign crs of original shapefile - if (!is.null(target_crs)) { + # Step (2.2): Create the grid + lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)), + lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE)) + + if (return_area) { + locations <- array(1:(length(lon)*length(lat)), c(lon = length(lon), + lat = length(lat))) + xy.sfc <- areagrid(data = locations, lon = lon, lat = lat, + target_proj = target_proj) + region <- TRUE + } else { + ref.df <- data.frame(data = 0, + lon = rep(lon, times = length(lat)), + lat = rep(lat, each = length(lon))) + coord <- as.matrix(data.frame(x = ref.df$lon, y = ref.df$lat)) + + xy.sfg <- sf::st_multipoint(coord) + xy.sfc <- sf::st_sfc(xy.sfg) + # Assign crs of original shapefile st_crs(xy.sfc) <- sf::st_crs(target_crs) #initial_crs # asign crs of original shapefile xy.sfc <- sf::st_transform(xy.sfc, st_crs(shp)) - } else { - st_crs(xy.sfc) <- sf::st_crs(shp) - } - - # Step 3: Create mask - if (check_valid) { - xy.sfc <- st_make_valid(xy.sfc) - shp <- st_make_valid(shp) + # Check valid + if (check_valid) { + xy.sfc <- st_make_valid(xy.sfc) + shp <- st_make_valid(shp) + } } - ## Loop through each shp region + ## Step (3): Loop through each shp region to create the mask if (region) { cfun <- function(a, b) { if (length(dim(a)) == 2) { @@ -320,14 +330,16 @@ ShapeToMask <- function(shp_file, ref_grid, if (is.null(ncores)) { mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun') %do% .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, - xy.sfc = xy.sfc, find_min_dist = find_min_dist, + xy.sfc = xy.sfc, return_area = return_area, + find_min_dist = find_min_dist, shp_col_name_ids = shp_col_name_ids, max_dist = max_dist, region = region, ...) } else { registerDoParallel(ncores) mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun', .packages='sf') %dopar% .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, - xy.sfc = xy.sfc, find_min_dist = find_min_dist, + xy.sfc = xy.sfc, return_area = return_area, + find_min_dist = find_min_dist, shp_col_name_ids = shp_col_name_ids, max_dist = max_dist, region = region, ...) registerDoSEQ() @@ -357,58 +369,66 @@ ShapeToMask <- function(shp_file, ref_grid, return(mask) } -.shapetomask <- function(shp, n, lon, lat, xy.sfc, find_min_dist = FALSE, +.shapetomask <- function(shp, n, lon, lat, xy.sfc, return_area = FALSE, + find_min_dist = FALSE, shp_col_name_ids = NULL, max_dist = 50, region = FALSE, ...) { mask <- array(0, dim = c(length(lon), length(lat))) shpi <- shp[n, ] - # NOTE: Don't know it's a problem in st_intersection or st_coordinates, tmp_coords may - # not be identical as lon/lat. E.g., (29, 65) may be (29 - -3.552714e-15, 65). - shp_pol <- sf::st_intersection(x = xy.sfc, y = shpi, ...) - tmp_coords <- sf::st_coordinates(shp_pol)[, 1:2] - if (length(tmp_coords) == 0) { - dim(tmp_coords) <- NULL - } else if (is.null(dim(tmp_coords))) { - tmp_coords <- array(tmp_coords, dim = c(1, length(tmp_coords))) - } - if (!is.null(dim(tmp_coords))) { + if (return_area) { + datapoly_int <- xy.sfc %>% dplyr::mutate(int = area_cov(xy.sfc$geometry, shpi)) + vals <- datapoly_int %>% dplyr::select(int, value) + mask <- vals[order(vals$value),] %>% dplyr::pull(int) + dim(mask) <- c(length(lon), length(lat)) + } else { + # NOTE: Don't know it's a problem in st_intersection or st_coordinates, tmp_coords may + # not be identical as lon/lat. E.g., (29, 65) may be (29 - -3.552714e-15, 65). + shp_pol <- sf::st_intersection(x = xy.sfc, y = shpi, ...) + tmp_coords <- sf::st_coordinates(shp_pol)[, 1:2] + if (length(tmp_coords) == 0) { + dim(tmp_coords) <- NULL + } else if (is.null(dim(tmp_coords))) { + tmp_coords <- array(tmp_coords, dim = c(1, length(tmp_coords))) + } + if (!is.null(dim(tmp_coords))) { - # polygon_instersection - for (ii in 1:nrow(tmp_coords)) { - # pt_x <- which(lon == tmp_coords[ii, 1]) - # pt_y <- which.min(abs(lat - tmp_coords[ii, 2])) - if (!region) { - # min(abs(lon - tmp_coords[ii, 1])) - # min(abs(lat - tmp_coords[ii, 2])) - mask[which.min(abs(lon - tmp_coords[ii, 1])), - which.min(abs(lat - tmp_coords[ii, 2]))] <- n - } else { - mask[which.min(abs(lon - tmp_coords[ii, 1])), - which.min(abs(lat - tmp_coords[ii, 2]))] <- 1 + # polygon_instersection + for (ii in 1:nrow(tmp_coords)) { + # pt_x <- which(lon == tmp_coords[ii, 1]) + # pt_y <- which.min(abs(lat - tmp_coords[ii, 2])) + if (!region) { + # min(abs(lon - tmp_coords[ii, 1])) + # min(abs(lat - tmp_coords[ii, 2])) + mask[which.min(abs(lon - tmp_coords[ii, 1])), + which.min(abs(lat - tmp_coords[ii, 2]))] <- n + } else { + mask[which.min(abs(lon - tmp_coords[ii, 1])), + which.min(abs(lat - tmp_coords[ii, 2]))] <- 1 + } } - } - } else if (find_min_dist) { - x.centroid.shpi <- sf::st_coordinates(sf::st_centroid(shpi))[,1] - y.centroid.shpi <- sf::st_coordinates(sf::st_centroid(shpi))[,2] - dist <- sqrt((xy.sfc[,1] - x.centroid.shpi)**2 + (xy.sfc[,2] - y.centroid.shpi)**2) - tmp_coords <- array(xy.sfc[which(dist == min(dist, na.rm = TRUE)),], dim = c(1,2)) - colnames(tmp_coords) <- c('X', 'Y') - if (max(dist) <= max_dist & (any(round(lat,2) == round(tmp_coords[1,2],2)) & - any(round(lon,2) == round(tmp_coords[1,1],2))) ) { - if (length(dim(mask)) == 2) { - mask[which.min(abs(lon - tmp_coords[, 1])), - which.min(abs(lat - tmp_coords[, 2]))] <- n + } else if (find_min_dist) { + x.centroid.shpi <- sf::st_coordinates(sf::st_centroid(shpi))[,1] + y.centroid.shpi <- sf::st_coordinates(sf::st_centroid(shpi))[,2] + dist <- sqrt((xy.sfc[,1] - x.centroid.shpi)**2 + (xy.sfc[,2] - y.centroid.shpi)**2) + tmp_coords <- array(xy.sfc[which(dist == min(dist, na.rm = TRUE)),], dim = c(1,2)) + colnames(tmp_coords) <- c('X', 'Y') + if (max(dist) <= max_dist & (any(round(lat,2) == round(tmp_coords[1,2],2)) & + any(round(lon,2) == round(tmp_coords[1,1],2))) ) { + if (length(dim(mask)) == 2) { + mask[which.min(abs(lon - tmp_coords[, 1])), + which.min(abs(lat - tmp_coords[, 2]))] <- n + } else { + mask[which.min(abs(lon - tmp_coords[, 1])), + which.min(abs(lat - tmp_coords[, 2]))] <- 1 + } + # warning(paste0('The reference grid has no intersection with region ', + # ifelse(is.character(shp_col_name_ids), shpi[[shp_col_name_ids]], paste0('n° ', n)), + # ' from the shapefile; the provided grid cell is at a distance of ', dist[which(dist == min(dist, na.rm = TRUE))], + # ' to the centroid of the region (units are: ° or meters depending on the crs of the shapefile).')) } else { - mask[which.min(abs(lon - tmp_coords[, 1])), - which.min(abs(lat - tmp_coords[, 2]))] <- 1 + # warning(paste0('The reference grid has no intersection with region ', + # ifelse(is.character(shp_col_name_ids), shpi[[shp_col_name_ids]], paste0('n° ', n)))) } - # warning(paste0('The reference grid has no intersection with region ', - # ifelse(is.character(shp_col_name_ids), shpi[[shp_col_name_ids]], paste0('n° ', n)), - # ' from the shapefile; the provided grid cell is at a distance of ', dist[which(dist == min(dist, na.rm = TRUE))], - # ' to the centroid of the region (units are: ° or meters depending on the crs of the shapefile).')) - } else { - # warning(paste0('The reference grid has no intersection with region ', - # ifelse(is.character(shp_col_name_ids), shpi[[shp_col_name_ids]], paste0('n° ', n)))) } } return(mask) -- GitLab From 218585ee55bbca13c398c539144d99a89ed18c8c Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Fri, 26 Jan 2024 16:58:01 +0100 Subject: [PATCH 02/14] Add auxiliary function areagrid --- R/ShapeToMask.R | 68 ++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 67 insertions(+), 1 deletion(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index cf80a6e..2053b42 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -432,4 +432,70 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, } } return(mask) -} \ No newline at end of file +} + + +areagrid <- function(data, lon, lat, target_proj) { + + # Build data dataframe + lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)), + lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE)) + + data_df <- lonlat_df %>% + dplyr::mutate(dat = as.vector(data)) + + lonlat_df_ori <- NULL + + #NOTE: if target_proj = "ESRI:54030", Nord3v2 has different behavior from hub and ws!! + data_df <- st_as_sf(data_df, coords = c("lon", "lat"), crs = target_proj) + # data_df <- st_transform(data_df, crs = target_proj) + data_df <- data_df %>% + dplyr::mutate(long = st_coordinates(data_df)[, 1], + lat = st_coordinates(data_df)[, 2]) + + # Calculate polygon points from regular lat/lon + #NOTE: The original grid must be regular grid with same space + d_lon <- abs(lon[2] - lon[1]) / 2 + d_lat <- abs(lat[2] - lat[1]) / 2 + lon_poly <- lat_poly <- rep(NA, 4 * dim(lonlat_df)[1]) + for (ii in 1:dim(lonlat_df)[1]) { + lon_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lon[ii] - d_lon, lonlat_df$lon[ii] + d_lon, + lonlat_df$lon[ii] + d_lon, lonlat_df$lon[ii] - d_lon) + lat_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lat[ii] - d_lat, lonlat_df$lat[ii] - d_lat, + lonlat_df$lat[ii] + d_lat, lonlat_df$lat[ii] + d_lat) + } + # # To prevent out-of-global lon + # lon_poly[which(lon_poly >= 180)] <- 179.9 + # lon_poly[which(lon_poly < -180)] <- -180 + # To prevent out-of-global lat + lat_poly[which(lat_poly > 90)] <- 90 + lat_poly[which(lat_poly < -90)] <- -90 + + lonlat_df <- data.frame(lon = lon_poly, lat = lat_poly) + # Transfer lon/lat to projection + proj_lonlat <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = target_proj) + #NOTE: if target_proj = "ESRI:54030", on Nord3v2, st_transform has lon and lat swapped! + # proj_lonlat <- st_transform(proj_lonlat, crs = target_proj) + lonlat_df_proj <- st_coordinates(proj_lonlat) + + # Use id to create groups for each polygon + ids <- factor(paste0("id_", 1:dim(data_df)[1])) + values <- data.frame(id = ids, + value = data_df$dat) + positions <- data.frame(id = rep(ids, each = 4), + x = lonlat_df_proj[, 1], + y = lonlat_df_proj[, 2]) + datapoly <- merge(values, positions, by = "id") + datapoly <- st_as_sf(datapoly, coords = c("x", "y"), crs = target_proj) + + datapoly <- datapoly %>% + dplyr::group_by(.data$id) %>% + dplyr::summarise() %>% #NOTE: VERY SLOW if plot global + dplyr::mutate(value = values[order(values$id), ]$value) %>% + st_cast("POLYGON") %>% + st_convex_hull() # maintain outer polygen (no bowtie shape) + + return(datapoly) + +} + -- GitLab From 78ad04d3a5d06fb9dfe4d342bcb1360f20780e55 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Mon, 29 Jan 2024 15:56:49 +0100 Subject: [PATCH 03/14] Improve part of the area coverage --- R/ShapeToMask.R | 171 ++++++++++++++++++++++++++---------------------- 1 file changed, 93 insertions(+), 78 deletions(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 2053b42..2e06f63 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -79,6 +79,7 @@ #'@import easyNCDF #'@import sf #'@import foreach +#'@import dplyr #'@importFrom doParallel registerDoParallel #'@export ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, @@ -91,6 +92,7 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, # TODO: Suppress warnings? # TODO: Add saving option? + # sf_use_s2(FALSE) # Initial checks # shp_file @@ -170,16 +172,6 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, if (!is.logical(region)) { stop("Parameter 'region' must be a logical value.") } - # target_crs - if (!is.null(target_crs)) { - if (!is.character(target_crs)) { - stop("Parameter 'target_crs' must be a character string.") - } - transform_shp_crs <- TRUE - } else { - transform_shp_crs <- FALSE - target_crs <- sf::st_crs(shp) - } # check_valid if (!is.logical(check_valid)) { stop("Parameter 'check_valid' must be a logical value.") @@ -207,9 +199,20 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, # Step 1: Load the shapefile shp <- sf::st_read(shp_file) # class sf + + # target_crs + if (!is.null(target_crs)) { + if (!is.character(target_crs)) { + stop("Parameter 'target_crs' must be a character string.") + } + transform_shp_crs <- TRUE + } else { + transform_shp_crs <- FALSE + target_crs <- sf::st_crs(shp) + } + if (transform_shp_crs) { - transformed_shapefile <- st_transform(shp, crs = target_crs) - shp <- transformed_shapefile + shp <- st_transform(shp, crs = target_crs) } NUTS_ID <- ADM1_PCODE <- ISO <- NULL @@ -283,14 +286,26 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, } # Step (2.2): Create the grid - lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)), - lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE)) - if (return_area) { locations <- array(1:(length(lon)*length(lat)), c(lon = length(lon), lat = length(lat))) - xy.sfc <- areagrid(data = locations, lon = lon, lat = lat, - target_proj = target_proj) + # Build data dataframe + lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)), + lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE)) + + data_df <- lonlat_df %>% + mutate(dat = as.vector(locations)) + + lonlat_df_ori <- NULL + + # NOTE: if target_proj = "ESRI:54030", Nord3v2 has different behavior from hub and ws!! + data_df <- st_as_sf(data_df, coords = c("lon", "lat"), crs = target_proj) + # data_df <- st_transform(data_df, crs = target_proj) + data_df <- data_df %>% + mutate(long = st_coordinates(data_df)[, 1], + lat = st_coordinates(data_df)[, 2]) + xy.sfc <- polygonize(lonlat_df = lonlat_df, data_df = data_df, + lon = lon, lat = lat, target_proj = target_crs) region <- TRUE } else { ref.df <- data.frame(data = 0, @@ -336,7 +351,7 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, max_dist = max_dist, region = region, ...) } else { registerDoParallel(ncores) - mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun', .packages='sf') %dopar% + mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun', .packages = c('sf', 'dplyr')) %dopar% .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, xy.sfc = xy.sfc, return_area = return_area, find_min_dist = find_min_dist, @@ -373,14 +388,15 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, find_min_dist = FALSE, shp_col_name_ids = NULL, max_dist = 50, region = FALSE, ...) { - mask <- array(0, dim = c(length(lon), length(lat))) shpi <- shp[n, ] if (return_area) { - datapoly_int <- xy.sfc %>% dplyr::mutate(int = area_cov(xy.sfc$geometry, shpi)) - vals <- datapoly_int %>% dplyr::select(int, value) - mask <- vals[order(vals$value),] %>% dplyr::pull(int) + mask <- xy.sfc %>% + dplyr::mutate(int = areacov(geometry, shpi)) %>% + dplyr::arrange(value) %>% + dplyr::pull(int) dim(mask) <- c(length(lon), length(lat)) } else { + mask <- array(0, dim = c(length(lon), length(lat))) # NOTE: Don't know it's a problem in st_intersection or st_coordinates, tmp_coords may # not be identical as lon/lat. E.g., (29, 65) may be (29 - -3.552714e-15, 65). shp_pol <- sf::st_intersection(x = xy.sfc, y = shpi, ...) @@ -434,68 +450,67 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, return(mask) } +# Function to create polygons from VizRobinson code +polygonize <- function(lonlat_df, data_df, lon, lat, target_proj) { -areagrid <- function(data, lon, lat, target_proj) { - - # Build data dataframe - lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)), - lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE)) - - data_df <- lonlat_df %>% - dplyr::mutate(dat = as.vector(data)) - - lonlat_df_ori <- NULL + # Calculate polygon points from regular lat/lon + # NOTE: The original grid must be regular grid with same space + d_lon <- abs(lon[2] - lon[1]) / 2 + d_lat <- abs(lat[2] - lat[1]) / 2 + lon_poly <- lat_poly <- rep(NA, 4 * dim(lonlat_df)[1]) + for (ii in 1:dim(lonlat_df)[1]) { + lon_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lon[ii] - d_lon, lonlat_df$lon[ii] + d_lon, + lonlat_df$lon[ii] + d_lon, lonlat_df$lon[ii] - d_lon) + lat_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lat[ii] - d_lat, lonlat_df$lat[ii] - d_lat, + lonlat_df$lat[ii] + d_lat, lonlat_df$lat[ii] + d_lat) + } - #NOTE: if target_proj = "ESRI:54030", Nord3v2 has different behavior from hub and ws!! - data_df <- st_as_sf(data_df, coords = c("lon", "lat"), crs = target_proj) - # data_df <- st_transform(data_df, crs = target_proj) - data_df <- data_df %>% - dplyr::mutate(long = st_coordinates(data_df)[, 1], - lat = st_coordinates(data_df)[, 2]) + # To prevent out-of-global lon + # lon_poly[which(lon_poly >= 180)] <- 179.9 + # lon_poly[which(lon_poly < -180)] <- -180 + # To prevent out-of-global lat + lat_poly[which(lat_poly > 90)] <- 90 + lat_poly[which(lat_poly < -90)] <- -90 - # Calculate polygon points from regular lat/lon - #NOTE: The original grid must be regular grid with same space - d_lon <- abs(lon[2] - lon[1]) / 2 - d_lat <- abs(lat[2] - lat[1]) / 2 - lon_poly <- lat_poly <- rep(NA, 4 * dim(lonlat_df)[1]) - for (ii in 1:dim(lonlat_df)[1]) { - lon_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lon[ii] - d_lon, lonlat_df$lon[ii] + d_lon, - lonlat_df$lon[ii] + d_lon, lonlat_df$lon[ii] - d_lon) - lat_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lat[ii] - d_lat, lonlat_df$lat[ii] - d_lat, - lonlat_df$lat[ii] + d_lat, lonlat_df$lat[ii] + d_lat) - } - # # To prevent out-of-global lon - # lon_poly[which(lon_poly >= 180)] <- 179.9 - # lon_poly[which(lon_poly < -180)] <- -180 - # To prevent out-of-global lat - lat_poly[which(lat_poly > 90)] <- 90 - lat_poly[which(lat_poly < -90)] <- -90 + lonlat_df <- data.frame(lon = lon_poly, lat = lat_poly) + # Transfer lon/lat to projection + proj_lonlat <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = target_proj) + # NOTE: if target_proj = "ESRI:54030", on Nord3v2, st_transform has lon and lat swapped! + # proj_lonlat <- st_transform(proj_lonlat, crs = target_proj) + lonlat_df_proj <- st_coordinates(proj_lonlat) - lonlat_df <- data.frame(lon = lon_poly, lat = lat_poly) - # Transfer lon/lat to projection - proj_lonlat <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = target_proj) - #NOTE: if target_proj = "ESRI:54030", on Nord3v2, st_transform has lon and lat swapped! - # proj_lonlat <- st_transform(proj_lonlat, crs = target_proj) - lonlat_df_proj <- st_coordinates(proj_lonlat) + # Use id to create groups for each polygon + ids <- factor(paste0("id_", 1:dim(data_df)[1])) + values <- data.frame(id = ids, + value = data_df$dat) + positions <- data.frame(id = rep(ids, each = 4), + x = lonlat_df_proj[, 1], + y = lonlat_df_proj[, 2]) + datapoly <- merge(values, positions, by = "id") + datapoly <- st_as_sf(datapoly, coords = c("x", "y"), crs = target_proj) - # Use id to create groups for each polygon - ids <- factor(paste0("id_", 1:dim(data_df)[1])) - values <- data.frame(id = ids, - value = data_df$dat) - positions <- data.frame(id = rep(ids, each = 4), - x = lonlat_df_proj[, 1], - y = lonlat_df_proj[, 2]) - datapoly <- merge(values, positions, by = "id") - datapoly <- st_as_sf(datapoly, coords = c("x", "y"), crs = target_proj) + datapoly <- datapoly %>% + group_by(.data$id) %>% + summarise() %>% #NOTE: VERY SLOW if plot global + mutate(value = values[order(values$id), ]$value) %>% + st_cast("POLYGON") %>% + st_convex_hull() # maintain outer polygen (no bowtie shape) - datapoly <- datapoly %>% - dplyr::group_by(.data$id) %>% - dplyr::summarise() %>% #NOTE: VERY SLOW if plot global - dplyr::mutate(value = values[order(values$id), ]$value) %>% - st_cast("POLYGON") %>% - st_convex_hull() # maintain outer polygen (no bowtie shape) + return(datapoly) +} - return(datapoly) +# Function to compute the coverage area ratio between the grid and the shapefile +areacov <- function(grid, shp) { + shp_int_grid <- sf::st_intersection(x = grid, y = shp) + idx <- attributes(shp_int_grid)$idx + i <- 0 + fr <- array(0, length(grid)) + for (idx_i in idx[, 1]) { + i <- i + 1 + area_grid <- sf::st_area(grid[idx_i]) + area_int <- sf::st_area(shp_int_grid[i]) + fr[idx_i] <- round(as.numeric(area_int/area_grid), digits = 6) + } + return(fr) } - -- GitLab From 881ef43103b64edacbc2221b049de11afe4b77ca Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Mon, 29 Jan 2024 16:30:01 +0100 Subject: [PATCH 04/14] Add option to save NetCDF in ShapeToMask --- NAMESPACE | 1 + R/ShapeToMask.R | 37 +++++++++++++++++++++++-------------- man/ShapeToMask.Rd | 8 ++++++++ man/VizRobinson.Rd | 10 ++++++---- 4 files changed, 38 insertions(+), 18 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index e2f15f3..58ee625 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,6 +17,7 @@ export(VizTriangles4Categories) export(VizWeeklyClim) import(RColorBrewer) import(cowplot) +import(dplyr) import(easyNCDF) import(foreach) import(ggplot2) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 2e06f63..882a034 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -21,6 +21,8 @@ #'@param ref_grid A character string indicating the path to the reference #' data. Either (1) a netCDF file or (2) a list of lon and lat to provide the #' reference grid points. It is NULL by default. +#'@param return_area A logical value wether to return the area coverage or not. +#' It is FALSE by default. #'@param shp_system A character string containing the Shapefile System Database #' Name used to subset the shapefile into regions by using parameters 'reg_ids' #' or 'reg_names'. The accepted systems are: 'NUTS', 'LAU', and 'GADM'. When it @@ -61,6 +63,8 @@ #' and the reference grid. #'@param ncores The number of parallel processes to spawn for the use for #' parallel computation in multiple cores. +#'@param fileout A character string of the path to save the NetCDF mask. If not +#' specified (default), the mask array will be returned. #'@param ... Arguments passed on to 's2_options' in function 'st_intersection'. #' See 's2 package'. #' @@ -88,7 +92,8 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, lat_dim = NULL, lon_dim = NULL, region = FALSE, target_crs = NULL, check_valid = FALSE, find_min_dist = FALSE, - max_dist = 50, ncores = NULL, ...) { + max_dist = 50, ncores = NULL, + fileout = NULL, ...) { # TODO: Suppress warnings? # TODO: Add saving option? @@ -367,20 +372,24 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, } # Step 4: Add attributes - # attr(mask, lon_dim) <- lon - # attr(mask, lat_dim) <- lat - # if (shp_system == "NUTS") { - # attr(mask, "index") <- as.list(shp$NUTS_ID) - # } else if (shp_system == "ADM") { - # attr(mask, "index") <- as.list(shp$ADM1_PCODE) - # } else if (shp_system == "GADM") { - # attr(mask, "index") <- as.list(shp$ISO) - # } - # names(attr(mask, "index")) <- 1:nrow(shp) + attr(mask, lon_dim) <- lon + attr(mask, lat_dim) <- lat + if (shp_system == "NUTS") { + attr(mask, "index") <- as.list(shp$NUTS_ID) + } else if (shp_system == "ADM") { + attr(mask, "index") <- as.list(shp$ADM1_PCODE) + } else if (shp_system == "GADM") { + attr(mask, "index") <- as.list(shp$ISO) + } + names(attr(mask, "index")) <- 1:nrow(shp) - # ## Return all the info from shp - # attr(mask, "shapefile") <- attributes(shp) - + ## Return all the info from shp + attr(mask, "shapefile") <- attributes(shp) + + # Step 5: Save to NetCDF + if (!is.null(fileout)) { + ArrayToNc(mask, fileout) + } return(mask) } diff --git a/man/ShapeToMask.Rd b/man/ShapeToMask.Rd index 5f4a660..b77136a 100644 --- a/man/ShapeToMask.Rd +++ b/man/ShapeToMask.Rd @@ -7,6 +7,7 @@ ShapeToMask( shp_file, ref_grid, + return_area = FALSE, shp_system = "NUTS", reg_names = NULL, reg_ids = NULL, @@ -20,6 +21,7 @@ ShapeToMask( find_min_dist = FALSE, max_dist = 50, ncores = NULL, + fileout = NULL, ... ) } @@ -30,6 +32,9 @@ ShapeToMask( data. Either (1) a netCDF file or (2) a list of lon and lat to provide the reference grid points. It is NULL by default.} +\item{return_area}{A logical value wether to return the area coverage or not. +It is FALSE by default.} + \item{shp_system}{A character string containing the Shapefile System Database Name used to subset the shapefile into regions by using parameters 'reg_ids' or 'reg_names'. The accepted systems are: 'NUTS', 'LAU', and 'GADM'. When it @@ -83,6 +88,9 @@ and the reference grid.} \item{ncores}{The number of parallel processes to spawn for the use for parallel computation in multiple cores.} +\item{fileout}{A character string of the path to save the NetCDF mask. If not +specified (default), the mask array will be returned.} + \item{...}{Arguments passed on to 's2_options' in function 'st_intersection'. See 's2 package'.} } diff --git a/man/VizRobinson.Rd b/man/VizRobinson.Rd index 48687f1..b0f0e81 100644 --- a/man/VizRobinson.Rd +++ b/man/VizRobinson.Rd @@ -10,7 +10,7 @@ VizRobinson( lat, lon_dim = NULL, lat_dim = NULL, - target_proj = "ESRI:54030", + target_proj = NULL, drawleg = "bar", style = "point", dots = NULL, @@ -62,9 +62,11 @@ of ascending or descending order.} \code{esviz:::.KnownLatNames}. The default value is NULL.} \item{target_proj}{A character string indicating the target projection. It -should be a valid crs string. The default projection is Robinson -(ESRI:54030). Note that the character string may work differently depending -on PROJ and GDAL module version.} +should be a valid crs string. The default projection is Robinson: +"ESRI:54030". Note that the character string may work differently depending +on PROJ and GDAL module version. If package version 'sf' is lower than +"1.0.10" and an error appears regarding the target crs, you can try with +numeric crs (e.g. target_proj = 54030).} \item{drawleg}{A character string indicating the legend style. It can be 'bar' (color bar by \code{ColorBarContinuous()}), 'ggplot2' (discrete legend -- GitLab From 3225ad03248b3409833c3df29e00f7392fc816ef Mon Sep 17 00:00:00 2001 From: EVA RIFA ROVIRA Date: Tue, 30 Jan 2024 15:55:45 +0100 Subject: [PATCH 05/14] Improve code ShapeToMask; add testing file and usecase field --- DESCRIPTION | 2 +- R/ShapeToMask.R | 87 ++++++-------- inst/doc/usecase.md | 6 + inst/doc/usecase/ex1_ShapeToMask.R | 186 +++++++++++++++++++++++++++++ man/ShapeToMask.Rd | 15 ++- 5 files changed, 238 insertions(+), 58 deletions(-) create mode 100644 inst/doc/usecase.md create mode 100644 inst/doc/usecase/ex1_ShapeToMask.R diff --git a/DESCRIPTION b/DESCRIPTION index e4d4332..ae32af8 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -43,6 +43,6 @@ URL: https://earth.bsc.es/gitlab/es/esviz/ BugReports: https://earth.bsc.es/gitlab/es/esviz/-/issues SystemRequirements: GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0) Encoding: UTF-8 -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.1 Config/testthat/edition: 3 LazyData: true diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 882a034..35641c7 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -21,8 +21,11 @@ #'@param ref_grid A character string indicating the path to the reference #' data. Either (1) a netCDF file or (2) a list of lon and lat to provide the #' reference grid points. It is NULL by default. -#'@param return_area A logical value wether to return the area coverage or not. -#' It is FALSE by default. +#'@param compute_area_coverage A logical value indicating the method to find +#' the intersection of the reference grid and the shapefile. When it is TRUE, +#' the method used is the calculation of the area coverage fraction of +#' intersection. If it is FALSE, the method used is searching if the centroid +#' of the grid cell falls inside the shapefile or not. It is FALSE by default. #'@param shp_system A character string containing the Shapefile System Database #' Name used to subset the shapefile into regions by using parameters 'reg_ids' #' or 'reg_names'. The accepted systems are: 'NUTS', 'LAU', and 'GADM'. When it @@ -48,8 +51,6 @@ #' is NULL, the longitudinal name will be searched using an internal function #' with the following possible names: 'lon', 'longitude', 'x', 'i' and #' 'nav_lon'. It is set to NULL by default. -#'@param target_crs A character string indicating the target 'Coordinate -#' Reference System'. #'@param region A logical value indicating if we want a dimension for the #' regions in the resulting mask array. It is FALSE by default. #'@param check_valid A logical value that when it is TRUE it uses the function @@ -64,7 +65,7 @@ #'@param ncores The number of parallel processes to spawn for the use for #' parallel computation in multiple cores. #'@param fileout A character string of the path to save the NetCDF mask. If not -#' specified (default), the mask array will be returned. +#' specified (default), only the mask array will be returned. #'@param ... Arguments passed on to 's2_options' in function 'st_intersection'. #' See 's2 package'. #' @@ -86,13 +87,12 @@ #'@import dplyr #'@importFrom doParallel registerDoParallel #'@export -ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, +ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, shp_system = "NUTS", reg_names = NULL, reg_ids = NULL, shp_col_name_ids = NULL, reg_level = 3, lat_dim = NULL, lon_dim = NULL, - region = FALSE, target_crs = NULL, - check_valid = FALSE, find_min_dist = FALSE, - max_dist = 50, ncores = NULL, + region = FALSE, check_valid = FALSE, + find_min_dist = FALSE, max_dist = 50, ncores = NULL, fileout = NULL, ...) { # TODO: Suppress warnings? @@ -205,20 +205,7 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, # Step 1: Load the shapefile shp <- sf::st_read(shp_file) # class sf - # target_crs - if (!is.null(target_crs)) { - if (!is.character(target_crs)) { - stop("Parameter 'target_crs' must be a character string.") - } - transform_shp_crs <- TRUE - } else { - transform_shp_crs <- FALSE - target_crs <- sf::st_crs(shp) - } - - if (transform_shp_crs) { - shp <- st_transform(shp, crs = target_crs) - } + shp_crs <- sf::st_crs(shp) NUTS_ID <- ADM1_PCODE <- ISO <- NULL CNTR_CODE <- NUTS_NAME <- ADM0_EN <- ADM1_EN <- Name <- LEVL_CODE <- NULL @@ -291,7 +278,7 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, } # Step (2.2): Create the grid - if (return_area) { + if (compute_area_coverage) { locations <- array(1:(length(lon)*length(lat)), c(lon = length(lon), lat = length(lat))) # Build data dataframe @@ -304,13 +291,14 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, lonlat_df_ori <- NULL # NOTE: if target_proj = "ESRI:54030", Nord3v2 has different behavior from hub and ws!! - data_df <- st_as_sf(data_df, coords = c("lon", "lat"), crs = target_proj) - # data_df <- st_transform(data_df, crs = target_proj) + data_df <- st_as_sf(data_df, coords = c("lon", "lat"), crs = shp_crs) + data_df <- st_transform(data_df, crs = shp_crs) data_df <- data_df %>% mutate(long = st_coordinates(data_df)[, 1], lat = st_coordinates(data_df)[, 2]) xy.sfc <- polygonize(lonlat_df = lonlat_df, data_df = data_df, - lon = lon, lat = lat, target_proj = target_crs) + lon = lon, lat = lat, target_proj = shp_crs, + original_proj = shp_crs) region <- TRUE } else { ref.df <- data.frame(data = 0, @@ -321,8 +309,8 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, xy.sfg <- sf::st_multipoint(coord) xy.sfc <- sf::st_sfc(xy.sfg) # Assign crs of original shapefile - st_crs(xy.sfc) <- sf::st_crs(target_crs) #initial_crs # asign crs of original shapefile - xy.sfc <- sf::st_transform(xy.sfc, st_crs(shp)) + st_crs(xy.sfc) <- sf::st_crs(shp) # asign crs of shapefile + # Check valid if (check_valid) { xy.sfc <- st_make_valid(xy.sfc) @@ -350,7 +338,7 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, if (is.null(ncores)) { mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun') %do% .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, - xy.sfc = xy.sfc, return_area = return_area, + xy.sfc = xy.sfc, compute_area_coverage = compute_area_coverage, find_min_dist = find_min_dist, shp_col_name_ids = shp_col_name_ids, max_dist = max_dist, region = region, ...) @@ -358,7 +346,7 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, registerDoParallel(ncores) mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun', .packages = c('sf', 'dplyr')) %dopar% .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, - xy.sfc = xy.sfc, return_area = return_area, + xy.sfc = xy.sfc, compute_area_coverage = compute_area_coverage, find_min_dist = find_min_dist, shp_col_name_ids = shp_col_name_ids, max_dist = max_dist, region = region, ...) @@ -374,31 +362,32 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, # Step 4: Add attributes attr(mask, lon_dim) <- lon attr(mask, lat_dim) <- lat - if (shp_system == "NUTS") { - attr(mask, "index") <- as.list(shp$NUTS_ID) - } else if (shp_system == "ADM") { - attr(mask, "index") <- as.list(shp$ADM1_PCODE) - } else if (shp_system == "GADM") { - attr(mask, "index") <- as.list(shp$ISO) + if (!is.null(shp_system)) { + if (shp_system == "NUTS") { + attr(mask, "index") <- as.list(shp$NUTS_ID) + } else if (shp_system == "ADM") { + attr(mask, "index") <- as.list(shp$ADM1_PCODE) + } else if (shp_system == "GADM") { + attr(mask, "index") <- as.list(shp$ISO) + } } - names(attr(mask, "index")) <- 1:nrow(shp) - + attr(mask, "index") <- 1:nrow(shp) ## Return all the info from shp attr(mask, "shapefile") <- attributes(shp) # Step 5: Save to NetCDF if (!is.null(fileout)) { - ArrayToNc(mask, fileout) + ArrayToNc(list(mask = mask), fileout) } return(mask) } -.shapetomask <- function(shp, n, lon, lat, xy.sfc, return_area = FALSE, +.shapetomask <- function(shp, n, lon, lat, xy.sfc, compute_area_coverage = FALSE, find_min_dist = FALSE, shp_col_name_ids = NULL, max_dist = 50, region = FALSE, ...) { shpi <- shp[n, ] - if (return_area) { + if (compute_area_coverage) { mask <- xy.sfc %>% dplyr::mutate(int = areacov(geometry, shpi)) %>% dplyr::arrange(value) %>% @@ -460,7 +449,8 @@ ShapeToMask <- function(shp_file, ref_grid, return_area = FALSE, } # Function to create polygons from VizRobinson code -polygonize <- function(lonlat_df, data_df, lon, lat, target_proj) { +polygonize <- function(lonlat_df, data_df, lon, lat, target_proj, + original_proj) { # Calculate polygon points from regular lat/lon # NOTE: The original grid must be regular grid with same space @@ -483,9 +473,9 @@ polygonize <- function(lonlat_df, data_df, lon, lat, target_proj) { lonlat_df <- data.frame(lon = lon_poly, lat = lat_poly) # Transfer lon/lat to projection - proj_lonlat <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = target_proj) + proj_lonlat <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = original_proj) # NOTE: if target_proj = "ESRI:54030", on Nord3v2, st_transform has lon and lat swapped! - # proj_lonlat <- st_transform(proj_lonlat, crs = target_proj) + proj_lonlat <- st_transform(proj_lonlat, crs = target_proj) lonlat_df_proj <- st_coordinates(proj_lonlat) # Use id to create groups for each polygon @@ -499,9 +489,9 @@ polygonize <- function(lonlat_df, data_df, lon, lat, target_proj) { datapoly <- st_as_sf(datapoly, coords = c("x", "y"), crs = target_proj) datapoly <- datapoly %>% - group_by(.data$id) %>% - summarise() %>% #NOTE: VERY SLOW if plot global - mutate(value = values[order(values$id), ]$value) %>% + dplyr::group_by(.data$id) %>% + dplyr::summarise() %>% # NOTE: VERY SLOW if plot global + dplyr::mutate(value = values[order(values$id), ]$value) %>% st_cast("POLYGON") %>% st_convex_hull() # maintain outer polygen (no bowtie shape) @@ -509,7 +499,6 @@ polygonize <- function(lonlat_df, data_df, lon, lat, target_proj) { } # Function to compute the coverage area ratio between the grid and the shapefile - areacov <- function(grid, shp) { shp_int_grid <- sf::st_intersection(x = grid, y = shp) idx <- attributes(shp_int_grid)$idx diff --git a/inst/doc/usecase.md b/inst/doc/usecase.md new file mode 100644 index 0000000..b64af94 --- /dev/null +++ b/inst/doc/usecase.md @@ -0,0 +1,6 @@ +# Use case and example scripts + +In this document, you will find example scripts of the package. + +1. **Scripts with function examples** + 1. [Create an 's2dv_cube'](inst/doc/usecase/ex1_ShapeToMask.R) diff --git a/inst/doc/usecase/ex1_ShapeToMask.R b/inst/doc/usecase/ex1_ShapeToMask.R new file mode 100644 index 0000000..1fe5d1f --- /dev/null +++ b/inst/doc/usecase/ex1_ShapeToMask.R @@ -0,0 +1,186 @@ +#******************************************************************************* +# Script to test ShapeToMask +# Eva Rifà Rovira +#******************************************************************************* +devtools::load_all("/esarchive/scratch/erifarov/git/esviz") +#------------------------------------------------------------------------------- +setwd("/esarchive/scratch/erifarov/rpackages/esviz/shapefile/dev-shapetomask-area/out-testing") + +################################################################################ +# Shapefiles +colombia_cajamarca <- '/esarchive/scratch/erifarov/rpackages/esviz/shapefile/dev-shapetomask-area/scripts-alba/spatial_aggregation_alba/shp_test/colombia_cajamarca.shp' +colombia_sanvicent <- '/esarchive/scratch/erifarov/rpackages/esviz/shapefile/dev-shapetomask-area/scripts-alba/spatial_aggregation_alba/shp_test/colombia_sanvicentedelcaguan.shp' +NUTS_EU <- paste0('/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/', + 'NUTS_RG_60M_2021_4326.shp') +# st_read +sf_colombia_cajamarca <- st_read(colombia_cajamarca) +sf_colombia_sanvicent <- st_read(colombia_sanvicent) +sf_NUTS_EU <- st_read(NUTS_EU) + +# reference grid: dataset = 'era5land' +lat <- array(seq(6, -1, -0.1), c(lat = 71)) +lon <- array(seq(-80, -70, 0.1), c(lon = 101)) +ref_grid_era5land_sample <- list(lat = lat, lon = lon) +# Data from era5land +load("/esarchive/scratch/erifarov/rpackages/esviz/shapefile/dev-shapetomask-area/scripts-alba/spatial_aggregation_alba/data/data.era5land.RData") +ref_grid_era5land <- list(latitude = attr(data.era5land, "Variable")$dat$latitude, + longitude = attr(data.era5land, "Variable")$dat$longitude) +################################################################################ +# Function to test plots +plot_shp <- function(fileout, x, y = NULL) { + png(file = fileout) + plot(x) + if (!is.null(y)) { + plot(y, add = TRUE) + } + dev.off() +} +################################################################################ +#----------------------------------------------------- +# Tests 1: colombia_cajamarca (only 1 region) +#----------------------------------------------------- +source("/esarchive/scratch/erifarov/git/esviz/R/ShapeToMask.R") +# (1.1) Centroid method + +# NOTE: We set shp_system = NULL because we don't want to subset any region +era5land_cajamarca_cntr <- ShapeToMask(shp_file = colombia_cajamarca, + shp_system = NULL, + ref_grid = ref_grid_era5land, region = TRUE, + find_min_dist = FALSE) +# (1.2) Area method +# target_crs = NULL +era5land_cajamarca_area <- ShapeToMask(shp_file = colombia_cajamarca, + ref_grid = ref_grid_era5land, + shp_system = NULL, + compute_area_coverage = TRUE, + region = TRUE) +# Error: +# Error in wk_handle.wk_wkb(wkb, s2_geography_writer(oriented = oriented, : +# Loop 0 is not valid: Edge 1 crosses edge 3 + +# Solution: Set sf::sf_use_s2(FALSE) +sf::sf_use_s2(FALSE) +era5land_cajamarca_area <- ShapeToMask(shp_file = colombia_cajamarca, + ref_grid = ref_grid_era5land, + shp_system = NULL, + compute_area_coverage = TRUE, + region = TRUE) +sf::sf_use_s2(TRUE) + +# Compare results different solutions +sum(era5land_cajamarca_area) +# [1] 4.140404 +sum(era5land_cajamarca_cntr) +[1] 4 + +# Positions +which(era5land_cajamarca_cntr != 0) +# [1] 1561 1662 1663 1762 +which(era5land_cajamarca_area != 0) +# [1] 1460 1560 1561 1562 1661 1662 1663 1762 1763 1863 1864 + +all(which(era5land_cajamarca_cntr != 0) %in% which(era5land_cajamarca_area != 0)) +# [1] TRUE + +# Apply the results to the data + +mean_extract <- function(data_cube, mask) { + locations <- which(mask != 0) + res <- mean(data_cube[locations]) + return(res) +} + +aggregated_area <- Apply(data = list(data_cube = data.era5land, mask = era5land_cajamarca_area), + target_dims = list(data_cube = c('longitude', 'latitude'), + mask = c('longitude', 'latitude')), + fun = mean_extract)$output1 +aggregated_ctr <- Apply(data = list(data_cube = data.era5land, mask = era5land_cajamarca_cntr), + target_dims = list(data_cube = c('longitude', 'latitude'), + mask = c('longitude', 'latitude')), + fun = mean_extract)$output1 +dim(aggregated_area) +# time syear region +# 12 1 1 +summary(aggregated_area) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 11.58 11.98 12.24 12.20 12.47 12.63 +summary(aggregated_ctr) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 11.49 11.87 12.12 12.09 12.33 12.50 + +# Test (1.3): Raster method with exactextractr +# Source the function +source("/esarchive/scratch/erifarov/rpackages/esviz/shapefile/dev-shapetomask-area/scripts-alba/spatial_aggregation_alba/functions/raster_extract_to_array.R") +raster_era5land_cajamarca <- raster.extract(data = data.era5land, + shp = sf_colombia_cajamarca) +dim(raster_era5land_cajamarca) +# region syear time ensemble +# 10 1 12 1 + +summary(raster_era5land_cajamarca) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 9.124 11.092 11.788 11.978 13.026 15.151 +################################################################################ + +#----------------------------------------------------- +# Tests 1: colombia_cajamarca (only 1 region) +#----------------------------------------------------- + +# Example ShapeToMask: NUTS +shp_file <- paste0('/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/', + 'NUTS_RG_60M_2021_4326.shp') +ref_grid <- list(lon = seq(10, 40, 0.5), lat = seq(40, 85, 0.5)) +NUTS_name <- list(FI = c('Lappi', 'Kainuu'), SI = c('Pomurska', 'Podravska')) + +# Mask computed with area coverage +sf::sf_use_s2(FALSE) +mask_area <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, + compute_area_coverage = TRUE, reg_names = NUTS_name, + fileout = "mask_area.nc") +mask_cntr <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, + compute_area_coverage = FALSE, reg_names = NUTS_name, + fileout = "mask_cntr.nc") +dim(mask_area) +# lon lat region +# 61 91 4 +summary(mask_area) + +# Mask computed with centroid approach +mask_cntr <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, region = T, + compute_area_coverage = FALSE, reg_names = NUTS_name) +dim(mask_cntr) +# lon lat region +# 61 91 4 + +################################################################################ + +#***************************************************************************** +# Examples function: ShapeToMask +# Source: https://earth.bsc.es/gitlab/es/esviz/-/blob/main/R/ShapeToMask.R +# An-Chi & Eva (2023) +#***************************************************************************** +# Exmple (1): NUTS +shp_file <- paste0('/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/', + 'NUTS_RG_60M_2021_4326.shp') +ref_grid <- paste0('/esarchive/recon/ecmwf/era5land/monthly_mean/', + 'tas_f1h/tas_201006.nc') +# ref_grid <- list(lon = seq(10, 40, 0.5), lat = seq(40, 85, 0.5)) + +NUTS.id <- paste0("FI1D", c(1:3, 5, 7:9)) +NUTS.name <- list(FI = c('Lappi', 'Kainuu'), SI = c('Pomurska', 'Podravska')) +mask1 <- ShapeToMask(shp_file, ref_grid, reg_ids = NUTS.id) +mask2 <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, + reg_names = NUTS.name) + +# Exmple (2): GADM +shp_file <- "/esarchive/shapefiles/gadm_country_mask/gadm_country_ISO3166.shp" +ref_grid <- paste0('/esarchive/exp/ecmwf/s2s-monthly_ensfor/weekly_mean/', + 'tas_f6h/tas_20191212.nc') +GADM.id <- c("ESP", "ITA") +GADM.name <- c("Spain", "Italy") +mask1 <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, + reg_ids = GADM.id, shp_system = "GADM") +mask2 <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, + reg_names = GADM.name, shp_system = "GADM") + +################################################################################ \ No newline at end of file diff --git a/man/ShapeToMask.Rd b/man/ShapeToMask.Rd index b77136a..4684214 100644 --- a/man/ShapeToMask.Rd +++ b/man/ShapeToMask.Rd @@ -7,7 +7,7 @@ ShapeToMask( shp_file, ref_grid, - return_area = FALSE, + compute_area_coverage = FALSE, shp_system = "NUTS", reg_names = NULL, reg_ids = NULL, @@ -16,7 +16,6 @@ ShapeToMask( lat_dim = NULL, lon_dim = NULL, region = FALSE, - target_crs = NULL, check_valid = FALSE, find_min_dist = FALSE, max_dist = 50, @@ -32,8 +31,11 @@ ShapeToMask( data. Either (1) a netCDF file or (2) a list of lon and lat to provide the reference grid points. It is NULL by default.} -\item{return_area}{A logical value wether to return the area coverage or not. -It is FALSE by default.} +\item{compute_area_coverage}{A logical value indicating the method to find +the intersection of the reference grid and the shapefile. When it is TRUE, +the method used is the calculation of the area coverage fraction of +intersection. If it is FALSE, the method used is searching if the centroid +of the grid cell falls inside the shapefile or not. It is FALSE by default.} \item{shp_system}{A character string containing the Shapefile System Database Name used to subset the shapefile into regions by using parameters 'reg_ids' @@ -70,9 +72,6 @@ with the following possible names: 'lon', 'longitude', 'x', 'i' and \item{region}{A logical value indicating if we want a dimension for the regions in the resulting mask array. It is FALSE by default.} -\item{target_crs}{A character string indicating the target 'Coordinate -Reference System'.} - \item{check_valid}{A logical value that when it is TRUE it uses the function 'sf::st_make_valid' applied to the shapefile and to the coordinates.} @@ -89,7 +88,7 @@ and the reference grid.} parallel computation in multiple cores.} \item{fileout}{A character string of the path to save the NetCDF mask. If not -specified (default), the mask array will be returned.} +specified (default), only the mask array will be returned.} \item{...}{Arguments passed on to 's2_options' in function 'st_intersection'. See 's2 package'.} -- GitLab From fe0eece4d10642b1191ea340fd4a971fea7872d3 Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Fri, 7 Jun 2024 10:10:25 +0200 Subject: [PATCH 06/14] Cambios para incluir lats lons y crs --- R/ShapeToMask.R | 92 ++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 87 insertions(+), 5 deletions(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 35641c7..91fd1f2 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -85,6 +85,7 @@ #'@import sf #'@import foreach #'@import dplyr +#'@import jsonlite #'@importFrom doParallel registerDoParallel #'@export ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, @@ -93,7 +94,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, lat_dim = NULL, lon_dim = NULL, region = FALSE, check_valid = FALSE, find_min_dist = FALSE, max_dist = 50, ncores = NULL, - fileout = NULL, ...) { + fileout = NULL, units = 'degrees', ...) { # TODO: Suppress warnings? # TODO: Add saving option? @@ -204,7 +205,14 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, # Step 1: Load the shapefile shp <- sf::st_read(shp_file) # class sf + shp_0 <- shp + if (units == 'degrees') { + shp <- sf::st_transform(shp, 4326) + } else if (units == 'meters') { + shp <- sf::st_transform(shp, 3857) + } + shp_crs <- sf::st_crs(shp) NUTS_ID <- ADM1_PCODE <- ISO <- NULL @@ -278,7 +286,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, } # Step (2.2): Create the grid - if (compute_area_coverage) { + if (compute_area_coverage) { locations <- array(1:(length(lon)*length(lat)), c(lon = length(lon), lat = length(lat))) # Build data dataframe @@ -375,9 +383,24 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, ## Return all the info from shp attr(mask, "shapefile") <- attributes(shp) + # Generate attributes related with the shape file + shape_attrs = list() + for (i in 1:nrow(shp)) { + entry <- list( + id = shp$NUTS_ID[i], + name = shp$NUTS_NAME[i], + shape_val = i + ) + shape_attrs <- append(shape_attrs, list(entry)) + } + # Step 5: Save to NetCDF if (!is.null(fileout)) { - ArrayToNc(list(mask = mask), fileout) + # Lat Lon Max Min + lat_list <- attr(mask, 'lat') + lon_list <- attr(mask, 'lon') + + transformToNc (mask, lat_list, lon_list, NULL, fileout, units, shape_attrs) } return(mask) } @@ -414,10 +437,10 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, # min(abs(lon - tmp_coords[ii, 1])) # min(abs(lat - tmp_coords[ii, 2])) mask[which.min(abs(lon - tmp_coords[ii, 1])), - which.min(abs(lat - tmp_coords[ii, 2]))] <- n + which.min(abs(lat - tmp_coords[ii, 2]))] <- n # AQUI ES DONDE ASOCIA EL N A CADA VALOR DEL SHAPE PROBABLEMENTE RAUL } else { mask[which.min(abs(lon - tmp_coords[ii, 1])), - which.min(abs(lat - tmp_coords[ii, 2]))] <- 1 + which.min(abs(lat - tmp_coords[ii, 2]))] <- 1 # SI SOLO HAY UNA REGION O NO HAY QUE DIVIDIR POR REGIONES } } } else if (find_min_dist) { @@ -445,6 +468,13 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, } } } + + # Flip the matrix when the area_coverage is TRUE + if (compute_area_coverage) { + mask <- mask[, ncol(mask):1] # Flip in the horizontal axis + # mask <- mask[nrow(mask):1, ] # Flip in vertical axis + } + return(mask) } @@ -512,3 +542,55 @@ areacov <- function(grid, shp) { } return(fr) } + +transformToNc <- function(values, lat=NULL, lon=NULL, time=NULL, out_file, crs='degrees', shape_values = NULL) { + # # Take the name of lat lon dimensions + # possible_lon_names <- c("lon", "longitude", "x") + # possible_lat_names <- c("lat", "latitude" , "y") + + # rigth_lon_name <- possible_lon_names[possible_lon_names %in% tolower(names(dim(values)))] + # rigth_lat_name <- possible_lat_names[possible_lat_names %in% tolower(names(dim(values)))] + + # Change latitude and longitude names to lat, lon + + # Add coordinates + if (!is.null(lon) && length(lon) > 0) { + dim(lon) <- length(lon) + metadata <- list(lon = list(units = crs)) + attr(lon, 'variables') <- metadata + names(dim(lon)) <- 'lon' # rigth_lon_name + } + + if (!is.null(lat) && length(lat) > 0) { + dim(lat) <- length(lat) + metadata <- list(lat = list(units = crs)) + attr(lat, 'variables') <- metadata + names(dim(lat)) <- 'lat' # rigth_lat_name + } + + # Add the projection to the file + if (crs == 'degrees') { + crs = 'EPSG4326' + } else { + crs = 'EPSG3857' + } + + # Format the attributes before introducing to the file + formatted_string <- paste(shape_values, collapse="") + formatted_string <- gsub("\\(", "", formatted_string) + formatted_string <- gsub("\\)", "\n", formatted_string) + formatted_string <- gsub("list", "", formatted_string) + formatted_string <- gsub('\\"', "", formatted_string) + + # Add the atributes to the file + attrs <- list(global_attrs = list( + crs = crs, + shapes_values = formatted_string + )) + attributes(values) <- c(attributes(values), attrs) + + # Export to Nc + ArrayToNc(list(values, lon, lat), out_file) + +} + -- GitLab From 1202822360bac69cbd87e7c9f2209f159029a7bd Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Fri, 7 Jun 2024 17:08:27 +0200 Subject: [PATCH 07/14] Corregidos la sustitucion de nombres lat lon --- R/ShapeToMask.R | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 91fd1f2..611e74c 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -544,14 +544,13 @@ areacov <- function(grid, shp) { } transformToNc <- function(values, lat=NULL, lon=NULL, time=NULL, out_file, crs='degrees', shape_values = NULL) { - # # Take the name of lat lon dimensions - # possible_lon_names <- c("lon", "longitude", "x") - # possible_lat_names <- c("lat", "latitude" , "y") - - # rigth_lon_name <- possible_lon_names[possible_lon_names %in% tolower(names(dim(values)))] - # rigth_lat_name <- possible_lat_names[possible_lat_names %in% tolower(names(dim(values)))] + # Take the name of lat lon dimensions + possible_lon_names <- c("lon", "longitude", "x") + possible_lat_names <- c("lat", "latitude" , "y") # Change latitude and longitude names to lat, lon + names(dim(values)) <- gsub(paste(possible_lon_names, collapse = "|"), "lon", names(dim(values)), ignore.case = TRUE) + names(dim(values)) <- gsub(paste(possible_lat_names, collapse = "|"), "lat", names(dim(values)), ignore.case = TRUE) # Add coordinates if (!is.null(lon) && length(lon) > 0) { -- GitLab From cc59ff34ca18b6de4bb62b3abf290f9256de2277 Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Wed, 26 Jun 2024 09:42:29 +0200 Subject: [PATCH 08/14] bug: compute_area_coverage with GADM shape files --- R/ShapeToMask.R | 70 ++++++++++++++++++++++++++++++++++++------------- 1 file changed, 52 insertions(+), 18 deletions(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 611e74c..83d42ab 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -285,6 +285,11 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, lon <- ref_grid[[lon_dim]] } + # Set degrees longitudes to -180 to 180 + lon <- ifelse(lon > 180, lon - 360, lon) + order_idx <- order(lon) + lon <- lon[order_idx] + # Step (2.2): Create the grid if (compute_area_coverage) { locations <- array(1:(length(lon)*length(lat)), c(lon = length(lon), @@ -292,7 +297,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, # Build data dataframe lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)), lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE)) - + data_df <- lonlat_df %>% mutate(dat = as.vector(locations)) @@ -312,6 +317,8 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, ref.df <- data.frame(data = 0, lon = rep(lon, times = length(lat)), lat = rep(lat, each = length(lon))) + + ref.df <- modifyLonsTo180(units, ref.df, order_idx) coord <- as.matrix(data.frame(x = ref.df$lon, y = ref.df$lat)) xy.sfg <- sf::st_multipoint(coord) @@ -349,7 +356,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, xy.sfc = xy.sfc, compute_area_coverage = compute_area_coverage, find_min_dist = find_min_dist, shp_col_name_ids = shp_col_name_ids, - max_dist = max_dist, region = region, ...) + max_dist = max_dist, region = region, shp_system = shp_system, ...) } else { registerDoParallel(ncores) mask <- foreach(shp_i = 1:nrow(shp), .combine = 'cfun', .packages = c('sf', 'dplyr')) %dopar% @@ -357,7 +364,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, xy.sfc = xy.sfc, compute_area_coverage = compute_area_coverage, find_min_dist = find_min_dist, shp_col_name_ids = shp_col_name_ids, - max_dist = max_dist, region = region, ...) + max_dist = max_dist, region = region, shp_system = shp_system, ...) registerDoSEQ() } if (region) { @@ -366,29 +373,37 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, } else { names(dim(mask)) <- c(lon_dim, lat_dim) } - + # Step 4: Add attributes attr(mask, lon_dim) <- lon attr(mask, lat_dim) <- lat - if (!is.null(shp_system)) { - if (shp_system == "NUTS") { - attr(mask, "index") <- as.list(shp$NUTS_ID) - } else if (shp_system == "ADM") { - attr(mask, "index") <- as.list(shp$ADM1_PCODE) - } else if (shp_system == "GADM") { - attr(mask, "index") <- as.list(shp$ISO) - } - } attr(mask, "index") <- 1:nrow(shp) ## Return all the info from shp attr(mask, "shapefile") <- attributes(shp) # Generate attributes related with the shape file shape_attrs = list() + for (i in 1:nrow(shp)) { + if (!is.null(shp_system)) { + if (shp_system == "NUTS") { + id_aux = shp$NUTS_ID[i] + name_aux = shp$NUTS_NAME[i] + } else if (shp_system == "ADM") { + id_aux = shp$ADM1_PCODE[i] + name_aux = shp$ADM1_NAME[i] + } else if (shp_system == "GADM") { + id_aux = shp$ISO[i] + name_aux = shp$Name[i] + } else { + id_aux = NULL + name_aux = NULL + } + } + entry <- list( - id = shp$NUTS_ID[i], - name = shp$NUTS_NAME[i], + id = id_aux, + name = name_aux, shape_val = i ) shape_attrs <- append(shape_attrs, list(entry)) @@ -408,7 +423,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, .shapetomask <- function(shp, n, lon, lat, xy.sfc, compute_area_coverage = FALSE, find_min_dist = FALSE, shp_col_name_ids = NULL, max_dist = 50, - region = FALSE, ...) { + region = FALSE, shp_system = 'NUTS', ...) { shpi <- shp[n, ] if (compute_area_coverage) { mask <- xy.sfc %>% @@ -470,7 +485,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, } # Flip the matrix when the area_coverage is TRUE - if (compute_area_coverage) { + if (compute_area_coverage && grepl("NUTS", shp_system)) { mask <- mask[, ncol(mask):1] # Flip in the horizontal axis # mask <- mask[nrow(mask):1, ] # Flip in vertical axis } @@ -520,7 +535,8 @@ polygonize <- function(lonlat_df, data_df, lon, lat, target_proj, datapoly <- datapoly %>% dplyr::group_by(.data$id) %>% - dplyr::summarise() %>% # NOTE: VERY SLOW if plot global + dplyr::summarise(geometry = st_combine(geometry)) %>% + # dplyr::summarise() %>% # NOTE: VERY SLOW if plot global dplyr::mutate(value = values[order(values$id), ]$value) %>% st_cast("POLYGON") %>% st_convex_hull() # maintain outer polygen (no bowtie shape) @@ -593,3 +609,21 @@ transformToNc <- function(values, lat=NULL, lon=NULL, time=NULL, out_file, crs=' } +modifyLonsTo180 <- function(units, df, order_idx) { + + print('Check errors 1:') + # browser() + + if (units == 'degrees'){ + my_list <- c("lon", "longitude", "x") + data <- df[colnames(df)[1]] + name_lon <- my_list[tolower(my_list) %in% tolower(colnames(df))] + lon_data <- df[[name_lon]] + data_reorder <- df[[colnames(df)[1]]] + + df[colnames(df)[1]] <- data_reorder[order_idx] + } + + return (df) +} + -- GitLab From 89c98fa1518b711e54ca673d5008f50b5c2c4147 Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Wed, 26 Jun 2024 11:58:59 +0200 Subject: [PATCH 09/14] Correcting tests shapesToMask --- tests/testthat/test-ShapeToMask.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-ShapeToMask.R b/tests/testthat/test-ShapeToMask.R index 550f2ee..c4be789 100644 --- a/tests/testthat/test-ShapeToMask.R +++ b/tests/testthat/test-ShapeToMask.R @@ -100,7 +100,7 @@ test_that("2. Output", { ) expect_equal( sum(mask4), - 48 + 64 ) mask5 <- ShapeToMask(shp_file = shp_file2, ref_grid = ref_grid2, reg_names = GADM_name2, shp_system = "GADM", @@ -111,6 +111,6 @@ test_that("2. Output", { ) expect_equal( sum(mask5), - 56 + 88 ) }) \ No newline at end of file -- GitLab From 16d2f7b9d8e9e45c0f7a2086374479fd7e379a72 Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Wed, 26 Jun 2024 21:40:04 +0200 Subject: [PATCH 10/14] test: uploaded coverage for shapeToMask --- tests/testthat/test-ShapeToMask.R | 52 ++++++++++++++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-ShapeToMask.R b/tests/testthat/test-ShapeToMask.R index c4be789..49e8a23 100644 --- a/tests/testthat/test-ShapeToMask.R +++ b/tests/testthat/test-ShapeToMask.R @@ -87,10 +87,24 @@ test_that("2. Output", { sum(mask3), 105 ) + # Test a single region mask3_1 <- ShapeToMask(shp_file1, ref_grid = ref_grid1_1, shp_col_name_ids = "NUTS_ID", lon_dim = 'lon', lat_dim = 'lat', reg_ids = "DE149", region = TRUE) + + mask3_2 <- ShapeToMask(shp_file1, ref_grid = ref_grid1_1, + shp_col_name_ids = "NUTS_ID", lon_dim = 'lon', + lat_dim = 'lat', reg_ids = "DE149", compute_area_coverage = TRUE) + expect_equal( + dim(mask3_2), + c(lon = 61, lat = 91, region = 1) + ) + expect_equal( + sum(mask3_2), + 0 + ) + # Test GADM mask4 <- ShapeToMask(shp_file = shp_file2, ref_grid = ref_grid2, reg_ids = GADM_id2, shp_system = "GADM") @@ -113,4 +127,40 @@ test_that("2. Output", { sum(mask5), 88 ) -}) \ No newline at end of file + mask6 <- ShapeToMask(shp_file = shp_file2, ref_grid = ref_grid2, + reg_names = GADM_name2, shp_system = "GADM", + region = TRUE, compute_area_coverage = TRUE) + expect_equal( + dim(mask6), + c(longitude = 240, latitude = 121, region = 4) + ) + expect_equal( + as.integer(sum(mask6)), + 77 + ) + + mask7 <- ShapeToMask(shp_file = shp_file2, ref_grid = ref_grid2, compute_area_coverage = TRUE, + reg_names = GADM_name2, shp_system = "GADM", fileout="test_GADM_1_5.nc") + + expect_equal( + dim(mask7), + c(longitude = 240, latitude = 121, region = 4) + ) + expect_equal( + as.integer(sum(mask7)), + 77 + ) + + mask7 <- ShapeToMask(shp_file = shp_file2, ref_grid = ref_grid2, compute_area_coverage = TRUE, + reg_names = GADM_name2, shp_system = "GADM", fileout="test_GADM_1_5.nc", find_min_dist = TRUE, ncores=2) + + expect_equal( + dim(mask7), + c(longitude = 240, latitude = 121, region = 4) + ) + expect_equal( + as.integer(sum(mask7)), + 77 + ) + +}) -- GitLab From 3f7a6785cd047f45a01ca67c64de52e380ce21b9 Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Thu, 11 Jul 2024 15:15:36 +0200 Subject: [PATCH 11/14] hotfix: Added parameters and documentation to the function --- R/ShapeToMask.R | 67 ++++++++++++++++++++++++------- tests/testthat/test-ShapeToMask.R | 10 ++++- 2 files changed, 61 insertions(+), 16 deletions(-) diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 83d42ab..3032f81 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -68,6 +68,13 @@ #' specified (default), only the mask array will be returned. #'@param ... Arguments passed on to 's2_options' in function 'st_intersection'. #' See 's2 package'. +#'@param units A character string indicating if your GIS files has a grid in degrees or meters. If it +#' is NULL, the units will be set as "meters" +#' with the following possible names: 'degrees', 'meters' +#'@param name_shp_col A character string indicating in the shape file which is the name of the column +#' with the values of the names of the different polygons. It is NULL by default. +#'@param id_shp_col A character string indicating in the shape file which is the name of the column +#' with the values of the IDs of the different polygons. It is NULL by default. #' #'@return A multidimensional array containing a mask array with longitude and #'latitude dimensions. If 'region' is TRUE, there will be a dimension for @@ -94,7 +101,8 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, lat_dim = NULL, lon_dim = NULL, region = FALSE, check_valid = FALSE, find_min_dist = FALSE, max_dist = 50, ncores = NULL, - fileout = NULL, units = 'degrees', ...) { + fileout = NULL, units = 'degrees', id_shape_col = NULL, + name_shape_col = NULL, ...) { # TODO: Suppress warnings? # TODO: Add saving option? @@ -119,6 +127,21 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, stop("Parameter 'lat_dim' must be a character string.") } } + if (!is.null(id_shape_col)) { + if (!is.character(id_shape_col)) { + stop("Parameter 'id_shape_col' must be a character string.") + } + } + if (!is.null(name_shape_col)) { + if (!is.character(name_shape_col)) { + stop("Parameter 'name_shape_col' must be a character string.") + } + } + if (!is.null(units)) { + if (!is.character(units)) { + stop("Parameter 'units' must be a character string.") + } + } # ref_grid if (is.null(ref_grid)) { stop("Parameter 'ref_grid' cannot be NULL.") @@ -386,19 +409,24 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, for (i in 1:nrow(shp)) { if (!is.null(shp_system)) { - if (shp_system == "NUTS") { + if (is.null(id_shape_col) && is.null(name_shape_col)) { + if (shp_system == "NUTS") { id_aux = shp$NUTS_ID[i] name_aux = shp$NUTS_NAME[i] - } else if (shp_system == "ADM") { - id_aux = shp$ADM1_PCODE[i] - name_aux = shp$ADM1_NAME[i] - } else if (shp_system == "GADM") { - id_aux = shp$ISO[i] - name_aux = shp$Name[i] - } else { - id_aux = NULL - name_aux = NULL + } else if (shp_system == "ADM") { + id_aux = shp$ADM1_PCODE[i] + name_aux = shp$ADM1_NAME[i] + } else if (shp_system == "GADM") { + id_aux = shp$ISO[i] + name_aux = shp$Name[i] + } else { + id_aux = NULL + name_aux = NULL + } } + } else { + id_aux = id_shape_col + name_aux = name_shape_col } entry <- list( @@ -415,7 +443,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, lat_list <- attr(mask, 'lat') lon_list <- attr(mask, 'lon') - transformToNc (mask, lat_list, lon_list, NULL, fileout, units, shape_attrs) + transformToNc (mask, lat_list, lon_list, NULL, fileout, units, shape_attrs, lat_dim, lon_dim) } return(mask) } @@ -559,10 +587,19 @@ areacov <- function(grid, shp) { return(fr) } -transformToNc <- function(values, lat=NULL, lon=NULL, time=NULL, out_file, crs='degrees', shape_values = NULL) { +transformToNc <- function(values, lat=NULL, lon=NULL, time=NULL, out_file, crs='degrees', shape_values = NULL, lat_dim, lon_dim) { # Take the name of lat lon dimensions - possible_lon_names <- c("lon", "longitude", "x") - possible_lat_names <- c("lat", "latitude" , "y") + if (is.null(lon_dim)) { + possible_lon_names <- c("lon", "longitude", "x", "i", "nav_lon") + } else { + possible_lon_names <- c(lon_dim) + } + + if (is.null(lat_dim)) { + possible_lat_names <- c("lat", "latitude" , "y", "j", "nav_lat") + } else { + possible_lat_names <- c(lat_dim) + } # Change latitude and longitude names to lat, lon names(dim(values)) <- gsub(paste(possible_lon_names, collapse = "|"), "lon", names(dim(values)), ignore.case = TRUE) diff --git a/tests/testthat/test-ShapeToMask.R b/tests/testthat/test-ShapeToMask.R index 49e8a23..e44c92d 100644 --- a/tests/testthat/test-ShapeToMask.R +++ b/tests/testthat/test-ShapeToMask.R @@ -46,6 +46,14 @@ test_that("1. Input checks", { ShapeToMask(shp_file1, lat_dim = 1, lon_dim = 'lat'), "Parameter 'lat_dim' must be a character string." ) + expect_error( + ShapeToMask(shp_file1, ref_grid1, reg_ids = NUTS_id1, id_shp_col = 1, name_shape_col = 'ESP'), + "Parameter 'id_shp_col' must be a character string." + ) + expect_error( + ShapeToMask(shp_file1, ref_grid1, reg_ids = NUTS_id1, name_shp_col = 1, id_shp_col = '4BJd'), + "Parameter 'name_shp_col' must be a character string." + ) # shp_system, reg_ids, reg_names, shp_col_name_ids # reg_level # region @@ -152,7 +160,7 @@ test_that("2. Output", { ) mask7 <- ShapeToMask(shp_file = shp_file2, ref_grid = ref_grid2, compute_area_coverage = TRUE, - reg_names = GADM_name2, shp_system = "GADM", fileout="test_GADM_1_5.nc", find_min_dist = TRUE, ncores=2) + reg_names = GADM_name2, shp_system = "GADM", fileout="test_GADM_1_5.nc", ncores=2) expect_equal( dim(mask7), -- GitLab From 1c61d21faa9cb8fd137cfa2cb9fc14a4d2eea858 Mon Sep 17 00:00:00 2001 From: Raul Capellan Date: Thu, 11 Jul 2024 16:59:05 +0200 Subject: [PATCH 12/14] hotfix: Correcting the names of the parameters --- .gitignore | 1 + R/ShapeToMask.R | 60 +++++++++++++++---------------- tests/testthat/test-ShapeToMask.R | 14 ++++---- 3 files changed, 37 insertions(+), 38 deletions(-) diff --git a/.gitignore b/.gitignore index b26d556..8e64182 100644 --- a/.gitignore +++ b/.gitignore @@ -15,4 +15,5 @@ master_pull.txt *.ps Rplots.pdf .nfs* +*.nc diff --git a/R/ShapeToMask.R b/R/ShapeToMask.R index 3032f81..85d4c44 100644 --- a/R/ShapeToMask.R +++ b/R/ShapeToMask.R @@ -5,7 +5,7 @@ #'output based on requested region names or IDs. The accepted shapefile #'databases are 'NUTS', 'LAU', and 'GADM', each with its own unique format. #'However, the function can use other shapefiles databases with specifying the -#'categories names with the parameter 'shp_col_name_ids'. +#'categories names with the parameter 'id_shape_col'. #' #'To ensure accurate comparison with the shapefile, the function loads a #'reference dataset that provides longitude and latitude information. By @@ -32,8 +32,6 @@ #' is used, you must specify either 'reg_ids' or 'reg_names'; if you don't need #' to subset different regions, set it to NULL. It is set to "NUTS" by default #' (optional). -#'@param shp_col_name_ids A character string indicating the column name of the -#' column in where the specified 'reg_ids' will be taken (optional). #'@param reg_ids A character string indicating the unique ID in shapefile. #' It is NULL by default (optional). #'@param reg_names A named list of character string vectors indicating the @@ -71,9 +69,9 @@ #'@param units A character string indicating if your GIS files has a grid in degrees or meters. If it #' is NULL, the units will be set as "meters" #' with the following possible names: 'degrees', 'meters' -#'@param name_shp_col A character string indicating in the shape file which is the name of the column +#'@param name_shape_col A character string indicating in the shape file which is the name of the column #' with the values of the names of the different polygons. It is NULL by default. -#'@param id_shp_col A character string indicating in the shape file which is the name of the column +#'@param id_shape_col A character string indicating in the shape file which is the name of the column #' with the values of the IDs of the different polygons. It is NULL by default. #' #'@return A multidimensional array containing a mask array with longitude and @@ -97,7 +95,7 @@ #'@export ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, shp_system = "NUTS", reg_names = NULL, reg_ids = NULL, - shp_col_name_ids = NULL, reg_level = 3, + reg_level = 3, lat_dim = NULL, lon_dim = NULL, region = FALSE, check_valid = FALSE, find_min_dist = FALSE, max_dist = 50, ncores = NULL, @@ -171,7 +169,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, } } - # shp_system, reg_ids, reg_names, shp_col_name_ids + # shp_system, reg_ids, reg_names, id_shape_col if (!is.null(shp_system)) { if (!is.character(shp_system)) { stop("Parameter 'shp_system' must be a character strinig.") @@ -180,12 +178,12 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, stop("If 'shp_system' is used, you must provide either parameter ", "'reg_ids' or 'reg_names'.") } - } else if (!is.null(shp_col_name_ids)) { + } else if (!is.null(id_shape_col)) { if (is.null(reg_ids)) { - stop("If 'shp_col_name_ids' is used, parameter 'reg_ids' must be provided.") + stop("If 'id_shape_col' is used, parameter 'reg_ids' must be provided.") } - if (!is.character(shp_col_name_ids)) { - stop("Parameter 'shp_col_name_ids' must be a character strinig.") + if (!is.character(id_shape_col)) { + stop("Parameter 'id_shape_col' must be a character strinig.") } } if (all(!is.null(reg_ids), !is.null(reg_names))) { @@ -243,40 +241,40 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, if (!is.null(reg_ids)) { ## Method 1: Directly use IDs - if (!is.null(shp_col_name_ids)) { - if (shp_col_name_ids %in% names(shp)) { - shp <- subset(shp, get(shp_col_name_ids) %in% reg_ids) + if (!is.null(id_shape_col)) { + if (id_shape_col %in% names(shp)) { + shp <- subset(shp, get(id_shape_col) %in% reg_ids) } else { stop("Shape system name not found in shapefile names.") } } else if (shp_system == "NUTS") { shp <- subset(shp, NUTS_ID %in% reg_ids) - shp_col_name_ids <- "NUTS_ID" + id_shape_col <- "NUTS_ID" } else if (shp_system == "ADM") { shp <- subset(shp, ADM1_PCODE %in% reg_ids) - shp_col_name_ids <- "ADM1_PCODE" + id_shape_col <- "ADM1_PCODE" } else if (shp_system == "GADM") { shp <- subset(shp, ISO %in% reg_ids) - shp_col_name_ids <- "ISO" + id_shape_col <- "ISO" } else { stop("shp_system ", shp_system, " is not defined yet.") } } else if (!is.null(reg_names)) { - shp_col_name_ids <- NULL + id_shape_col <- NULL ## Method 2: Use country code & region name for (cntr_i in 1:length(reg_names)) { if (shp_system == "NUTS") { tmp <- subset(shp, CNTR_CODE == names(reg_names)[cntr_i]) tmp <- subset(tmp, NUTS_NAME %in% reg_names[[cntr_i]]) - shp_col_name_ids <- 'NUTS_NAME' + id_shape_col <- 'NUTS_NAME' } else if (shp_system == "ADM") { tmp <- subset(shp, ADM0_EN == names(reg_names)[cntr_i]) tmp <- subset(tmp, ADM1_EN %in% reg_names[[cntr_i]]) - shp_col_name_ids <- 'ADM1_EN' + id_shape_col <- 'ADM1_EN' } else if (shp_system == "GADM") { tmp <- subset(shp, Name %in% reg_names) - shp_col_name_ids <- 'Name' + id_shape_col <- 'Name' } if (cntr_i == 1) { shp_tmp <- tmp @@ -378,7 +376,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, xy.sfc = xy.sfc, compute_area_coverage = compute_area_coverage, find_min_dist = find_min_dist, - shp_col_name_ids = shp_col_name_ids, + id_shape_col = id_shape_col, max_dist = max_dist, region = region, shp_system = shp_system, ...) } else { registerDoParallel(ncores) @@ -386,7 +384,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, .shapetomask(shp = shp, n = shp_i, lon = lon, lat = lat, xy.sfc = xy.sfc, compute_area_coverage = compute_area_coverage, find_min_dist = find_min_dist, - shp_col_name_ids = shp_col_name_ids, + id_shape_col = id_shape_col, max_dist = max_dist, region = region, shp_system = shp_system, ...) registerDoSEQ() } @@ -411,8 +409,8 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, if (!is.null(shp_system)) { if (is.null(id_shape_col) && is.null(name_shape_col)) { if (shp_system == "NUTS") { - id_aux = shp$NUTS_ID[i] - name_aux = shp$NUTS_NAME[i] + id_aux = shp$NUTS_ID[i] + name_aux = shp$NUTS_NAME[i] } else if (shp_system == "ADM") { id_aux = shp$ADM1_PCODE[i] name_aux = shp$ADM1_NAME[i] @@ -423,10 +421,10 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, id_aux = NULL name_aux = NULL } + } else{ + id_aux = id_shape_col + name_aux = name_shape_col } - } else { - id_aux = id_shape_col - name_aux = name_shape_col } entry <- list( @@ -450,7 +448,7 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, .shapetomask <- function(shp, n, lon, lat, xy.sfc, compute_area_coverage = FALSE, find_min_dist = FALSE, - shp_col_name_ids = NULL, max_dist = 50, + id_shape_col = NULL, max_dist = 50, region = FALSE, shp_system = 'NUTS', ...) { shpi <- shp[n, ] if (compute_area_coverage) { @@ -502,12 +500,12 @@ ShapeToMask <- function(shp_file, ref_grid, compute_area_coverage = FALSE, which.min(abs(lat - tmp_coords[, 2]))] <- 1 } # warning(paste0('The reference grid has no intersection with region ', - # ifelse(is.character(shp_col_name_ids), shpi[[shp_col_name_ids]], paste0('n° ', n)), + # ifelse(is.character(id_shape_col), shpi[[id_shape_col]], paste0('n° ', n)), # ' from the shapefile; the provided grid cell is at a distance of ', dist[which(dist == min(dist, na.rm = TRUE))], # ' to the centroid of the region (units are: ° or meters depending on the crs of the shapefile).')) } else { # warning(paste0('The reference grid has no intersection with region ', - # ifelse(is.character(shp_col_name_ids), shpi[[shp_col_name_ids]], paste0('n° ', n)))) + # ifelse(is.character(id_shape_col), shpi[[id_shape_col]], paste0('n° ', n)))) } } } diff --git a/tests/testthat/test-ShapeToMask.R b/tests/testthat/test-ShapeToMask.R index e44c92d..e99bee9 100644 --- a/tests/testthat/test-ShapeToMask.R +++ b/tests/testthat/test-ShapeToMask.R @@ -47,14 +47,14 @@ test_that("1. Input checks", { "Parameter 'lat_dim' must be a character string." ) expect_error( - ShapeToMask(shp_file1, ref_grid1, reg_ids = NUTS_id1, id_shp_col = 1, name_shape_col = 'ESP'), - "Parameter 'id_shp_col' must be a character string." + ShapeToMask(shp_file1, ref_grid1, reg_ids = NUTS_id1, id_shape_col = 1, name_shape_col = 'ESP'), + "Parameter 'id_shape_col' must be a character string." ) expect_error( - ShapeToMask(shp_file1, ref_grid1, reg_ids = NUTS_id1, name_shp_col = 1, id_shp_col = '4BJd'), - "Parameter 'name_shp_col' must be a character string." + ShapeToMask(shp_file1, ref_grid1, reg_ids = NUTS_id1, name_shape_col = 1, id_shape_col = '4BJd'), + "Parameter 'name_shape_col' must be a character string." ) - # shp_system, reg_ids, reg_names, shp_col_name_ids + # shp_system, reg_ids, reg_names, sname_shape_col # reg_level # region # target_crs @@ -98,11 +98,11 @@ test_that("2. Output", { # Test a single region mask3_1 <- ShapeToMask(shp_file1, ref_grid = ref_grid1_1, - shp_col_name_ids = "NUTS_ID", lon_dim = 'lon', + name_shape_col = "NUTS_ID", lon_dim = 'lon', lat_dim = 'lat', reg_ids = "DE149", region = TRUE) mask3_2 <- ShapeToMask(shp_file1, ref_grid = ref_grid1_1, - shp_col_name_ids = "NUTS_ID", lon_dim = 'lon', + name_shape_col = "NUTS_ID", lon_dim = 'lon', lat_dim = 'lat', reg_ids = "DE149", compute_area_coverage = TRUE) expect_equal( dim(mask3_2), -- GitLab From f0c0c8fd2c2e63d6609949828b208845bcb191a0 Mon Sep 17 00:00:00 2001 From: "raul.capellan" Date: Thu, 12 Sep 2024 11:28:28 +0200 Subject: [PATCH 13/14] vignette shape to mask --- vignettes/shape_to_mask.ipynb | 638 ++++++++++++++++++++++++++++++++++ 1 file changed, 638 insertions(+) create mode 100644 vignettes/shape_to_mask.ipynb diff --git a/vignettes/shape_to_mask.ipynb b/vignettes/shape_to_mask.ipynb new file mode 100644 index 0000000..bc29570 --- /dev/null +++ b/vignettes/shape_to_mask.ipynb @@ -0,0 +1,638 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8b76c50d-b44b-4926-ba5a-3bcbbf79b8e0", + "metadata": {}, + "source": [ + "
\n", + " \"Logo\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
This vignette is licensed under GNU General Public License version 3 (GPL-3)
\n", + "\n", + "
\"License:
\n", + "\n", + " \n", + "
\n", + "
\n", + "\n", + "# Vignette for shapeToMask\n", + "## Transform a Nc file and a shape file into a mask\n", + "\n", + "
\n", + "
\n", + "\n", + "This vignette provides an example of code written in R that can be used to transform netcdf files and shape files into a mask using the method shapeToMask from Esviz library. The results are displayed to show the different possibilities and parametrizations.\n", + "\n", + "An example is conducted over Campina Grande city in north-eastern Brazil, on a reference period going from 1995 to 2015 and for surface temperature (\"t2m\", air temperature 2m above the ground). The initialisation month of the hindcast — which is a forecast initialised in the past (also known as retrospective forecast) — is November and the analysis is done for the seasonal aggregation of November, December and January (NDJ).\n", + "\n", + "This vignette also offers the possibility of choosing a different variable, region, and reference period than those specified in the example and can be run locally on a personal computer. It is worth noting that a very large region may require for higher computational resources than those that a personal computer can offer. Depending on the selected parameters and the location where the vignette is executed, it may also download and store seasonal climate data from the Climate Data Store - Copernicus (CDS) .\n", + "\n", + "The notebook has the following outline:\n", + "\n", + "* [1. Install the R packages needed](#1.-Install-the-R-packages-needed)\n", + "\n", + "* [2. Load the libraries and source functions](#2.-Load-the-libraries-and-source-functions)\n", + "\n", + "* [3. Data and configuration parameters](#3.-Data-and-configuration-parameters)\n", + " * [3.1. Virtual reproduction of an example through Binder](#3.1.-Virtual-reproduction-of-an-example-through-Binder). \n", + " * [3.2. Virtual execution of the notebook through Binder with other specified configuration parameters](#3.2.-Virtual-execution-of-the-notebook-through-Binder-with-other-specified-configuration-parameters). \n", + " * [3.3. Local execution of the notebook on your computer. Case 1: the seasonal data is already stored on your machine](#3.3.-Local-execution-of-the-notebook-on-your-computer.-Case-1:-the-seasonal-data-is-already-stored-on-your-machine)\n", + " * [3.4. Local execution of the notebook on your computer. Case 2: the seasonal data is not stored on your machine](#3.4.-Local-execution-of-the-notebook-on-your-computer.-Case-2:-the-seasonal-data-is-not-stored-on-your-machine)\n", + " \n", + "* [4. Load seasonal data](#4.-Load-seasonal-data)\n", + "\n", + "* [5. Hindcast downscaling](#5.-Hindcast-downscaling)\n", + "\n", + "* [6. Forecast quality assessment](#6.-Forecast-quality-assessment)\n", + "\n", + "* [7. Visualise the results](#7.-Visualise-the-results)\n", + " * [7.1. The downscaled field](#7.1.-The-downscaled-field)\n", + " * [7.2. Skill assessment](#7.2.-Skill-assessment) " + ] + }, + { + "cell_type": "markdown", + "id": "072fb06f-2920-486f-bda9-a3cf7c01d8d8", + "metadata": {}, + "source": [ + "# 1. Load the R packages needed " + ] + }, + { + "cell_type": "markdown", + "id": "fe6c8e94-ff16-4457-b3ef-9060949826fd", + "metadata": {}, + "source": [ + "
\n", + "PRELIMINAR NOTE:
\n", + " Load the modules from the stack, this are the versions used in the Hub" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c2c86ba1-961f-404d-879f-812c50e6ad79", + "metadata": {}, + "outputs": [], + "source": [ + "options(warn=-1)\n", + "\n", + "system(\"module load R/4.2.1-foss-2021b\")\n", + "system(\"module load CDO/1.9.8-foss-2021b\")\n", + "system(\"module load netCDF/4.9.0-gompi-2021b\")" + ] + }, + { + "cell_type": "markdown", + "id": "fc795cd9-0fab-49cf-aa33-7afcedb27161", + "metadata": {}, + "source": [ + "
\n", + "NOTE:
\n", + " Load the packages necesaries to execute the code" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "57e8a334-5792-488a-8bed-c8f5e8d8e05f", + "metadata": {}, + "outputs": [], + "source": [ + "suppressPackageStartupMessages({\n", + " library(easyNCDF)\n", + " library(sf)\n", + " library(foreach)\n", + " library(dplyr)\n", + " library(doParallel)\n", + " library(startR)\n", + " library(s2dv)\n", + " library(ncdf4)\n", + " source(\"/esarchive/scratch/rcapella/gitlab/esviz/R/ShapeToMask.R\")\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "1899e461-8e5e-47b6-89dc-829a74d25857", + "metadata": {}, + "source": [ + "
\n", + "WARNING:
\n", + " shapeToMask.R is not on the main branch so load the branch develop_ShapeToMask_area has to be done manually" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d696d524-c077-4f97-98ca-8ab6901dc6bd", + "metadata": {}, + "outputs": [], + "source": [ + "source(\"https://earth.bsc.es/gitlab/es/esviz/-/raw/develop-ShapeToMask_area/R/ShapeToMask.R\")\n", + "source(\"https://earth.bsc.es/gitlab/es/esviz/-/raw/develop-ShapeToMask_area/R/zzz.R\")" + ] + }, + { + "cell_type": "markdown", + "id": "56e68bb7-c08e-4925-9b55-9e214c103ed1", + "metadata": {}, + "source": [ + "# 2. Different examples" + ] + }, + { + "cell_type": "markdown", + "id": "dc607365-68af-4628-9bcb-a4700c7c2a51", + "metadata": {}, + "source": [ + "## 2.2 compute_area_coverage = FASLE" + ] + }, + { + "cell_type": "markdown", + "id": "e90946c1-4f29-42bb-b851-1a836a262fd4", + "metadata": {}, + "source": [ + "### 2.2.1 Indicate the parameters" + ] + }, + { + "cell_type": "markdown", + "id": "152466b9-19a1-4e98-a2c6-ba4827a19b83", + "metadata": {}, + "source": [ + ">\n", + ">
\n", + "NOTE:
\n", + " Indicate the paths for the sahpe and nc file\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d57651a8-0eaf-4da8-ad0c-a0133ec568d7", + "metadata": {}, + "outputs": [], + "source": [ + "shp_file = \"/esarchive/shapefiles/NUTS/ref-nuts-2021-20m.shp/NUTS_RG_20M_2021_3035_LEVL_0.shp\"\n", + "nc_file = \"/esarchive/recon/ecmwf/era5land/monthly_mean/tas_f1h/tas_200908.nc\"" + ] + }, + { + "cell_type": "markdown", + "id": "250ac80c-3c42-4749-8ee4-1aa83138778f", + "metadata": {}, + "source": [ + ">\n", + ">
\n", + "NOTE:
\n", + " Indicate the rest of the parameters\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c1781fd4-90b8-4c91-b6a2-afa484f80a94", + "metadata": {}, + "outputs": [], + "source": [ + "shp_file <- paste0('/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/', \n", + " 'NUTS_RG_60M_2021_4326.shp')\n", + "ref_grid <- list(lon = seq(10, 40, 0.5), lat = seq(40, 85, 0.5))\n", + "NUTS_name <- list(FI = c('Lappi', 'Kainuu'), SI = c('Pomurska', 'Podravska'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1e4ec4f0-8c03-4196-9051-945dda39219d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading layer `NUTS_RG_60M_2021_4326' from data source \n", + " `/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/NUTS_RG_60M_2021_4326.shp' \n", + " using driver `ESRI Shapefile'\n", + "Simple feature collection with 2010 features and 9 fields\n", + "Geometry type: MULTIPOLYGON\n", + "Dimension: XY\n", + "Bounding box: xmin: -61.841 ymin: -21.376 xmax: 55.85 ymax: 80.799\n", + "Geodetic CRS: WGS 84\n", + "[1] \"Check errors 1:\"\n" + ] + } + ], + "source": [ + "suppressMessages({\n", + " test <- ShapeToMask(shp_file = shp_file, \n", + " ref_grid = ref_grid, \n", + " compute_area_coverage = FALSE, \n", + " reg_names = NUTS_name, \n", + " fileout = \"mask_area_false.nc\")\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ce955deb-0516-4dbf-8610-4f5df4a33758", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAAAACAQaOwAAAACBQGMHAAAAIBAv7d4NNINwozQAAAAASUVORK5CYII=", + "text/plain": [ + "Plot with title “”" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Open the NetCDF file\n", + "nc_file <- nc_open(\"./mask_area_false.nc\")\n", + "\n", + "# Extract variables\n", + "lat <- ncvar_get(nc_file, \"lat\")\n", + "lon <- ncvar_get(nc_file, \"lon\")\n", + "var1_1 <- ncvar_get(nc_file, \"var1_1\")\n", + "\n", + "var1_1[var1_1 == 0] <- NA\n", + "\n", + "# Do the prints\n", + "s2dv::PlotEquiMap(var = var1_1,\n", + " lat = lat, \n", + " lon = lon, \n", + " filled.continents = FALSE,\n", + " colNA = 'white',\n", + " color_fun = clim.palette(palette = \"bluered\"),\n", + " # boxlim = c(11, 85, 40, 40)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "66ec00e7-7de7-462f-8049-b64190ba303c", + "metadata": {}, + "source": [ + "## 2.3 compute_area_coverage = TRUE" + ] + }, + { + "cell_type": "markdown", + "id": "046e5365-ad15-41ba-8b76-c2664bc692be", + "metadata": {}, + "source": [ + ">\n", + ">
\n", + "NOTE:
\n", + " Indicate the rest of the parameters\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "73d16a29-2025-43cc-a00c-3eea60d03f4e", + "metadata": {}, + "outputs": [], + "source": [ + "shp_file <- paste0('/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/', \n", + " 'NUTS_RG_60M_2021_4326.shp')\n", + "ref_grid <- list(lon = seq(10, 40, 0.5), lat = seq(40, 85, 0.5))\n", + "NUTS_name <- list(FI = c('Lappi', 'Kainuu'), SI = c('Pomurska', 'Podravska'))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f9b201e4-d6f4-4a93-a99f-ad323da86c9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading layer `NUTS_RG_60M_2021_4326' from data source \n", + " `/esarchive/shapefiles/NUTS3/NUTS_RG_60M_2021_4326.shp/NUTS_RG_60M_2021_4326.shp' \n", + " using driver `ESRI Shapefile'\n", + "Simple feature collection with 2010 features and 9 fields\n", + "Geometry type: MULTIPOLYGON\n", + "Dimension: XY\n", + "Bounding box: xmin: -61.841 ymin: -21.376 xmax: 55.85 ymax: 80.799\n", + "Geodetic CRS: WGS 84\n" + ] + } + ], + "source": [ + "suppressMessages({\n", + " test <- ShapeToMask(shp_file = shp_file, \n", + " ref_grid = ref_grid, \n", + " compute_area_coverage = TRUE, \n", + " reg_names = NUTS_name, \n", + " fileout = \"mask_area_true.nc\")\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "64556962-97cf-45d0-8eb3-97899eb680a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nCAQKOwAAAACB+Ak2xnvMzQTN9QAAAABJRU5ErkJggg==", + "text/plain": [ + "Plot with title “”" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Open the NetCDF file\n", + "nc_file <- nc_open(\"./mask_area_true.nc\")\n", + "\n", + "# Extract variables\n", + "lat <- ncvar_get(nc_file, \"lat\")\n", + "lon <- ncvar_get(nc_file, \"lon\")\n", + "var1_1 <- ncvar_get(nc_file, \"var1_1\")\n", + "\n", + "var1_1[var1_1 == 0] <- NA\n", + "\n", + "# Do the prints\n", + "s2dv::PlotEquiMap(var = var1_1[, ,1],\n", + " lat = lat, \n", + " lon = lon, \n", + " filled.continents = FALSE,\n", + " colNA = 'white',\n", + " color_fun = clim.palette(palette = \"bluered\"),\n", + " # boxlim = c(11, 85, 40, 40)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "a477f6f8-0539-45fb-8a79-523f5232298e", + "metadata": {}, + "source": [ + "## 2.4 GADM sahpe file" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f53e9c1f-7ca8-4c8b-aa67-130980de2022", + "metadata": {}, + "outputs": [], + "source": [ + "shp_file <- \"/esarchive/shapefiles/gadm_country_mask/gadm_country_ISO3166.shp\"\n", + "ref_grid <- paste0('/esarchive/exp/ecmwf/s2s-monthly_ensfor/weekly_mean/',\n", + " 'tas_f6h/tas_20191212.nc')\n", + "GADM.id <- c(\"ESP\", \"ITA\")\n", + "GADM.name <- c(\"Spain\", \"Italy\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bab34756-df89-4ed2-8b37-10ab94d739c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading layer `gadm_country_ISO3166' from data source \n", + " `/esarchive/shapefiles/gadm_country_mask/gadm_country_ISO3166.shp' \n", + " using driver `ESRI Shapefile'\n", + "Simple feature collection with 255 features and 5 fields\n", + "Geometry type: MULTIPOLYGON\n", + "Dimension: XY\n", + "Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.65833\n", + "Geodetic CRS: WGS 84\n", + "[1] \"Check errors 1:\"\n" + ] + } + ], + "source": [ + "mask1 <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, \n", + " reg_ids = GADM.id, shp_system = \"GADM\", fileout='mask_shape_gadm.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8fb9b8a4-fd53-42f0-8c3d-39350fa7f14e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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", + "text/plain": [ + "Plot with title “”" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Open the NetCDF file\n", + "nc_file <- nc_open(\"./mask_shape_gadm.nc\")\n", + "\n", + "# Extract variables\n", + "lat <- ncvar_get(nc_file, \"lat\")\n", + "lon <- ncvar_get(nc_file, \"lon\")\n", + "var1_1 <- ncvar_get(nc_file, \"var1_1\")\n", + "\n", + "var1_1[var1_1 == 0] <- NA\n", + "\n", + "# Do the prints\n", + "s2dv::PlotEquiMap(var = var1_1,\n", + " lat = lat, \n", + " lon = lon, \n", + " filled.continents = FALSE,\n", + " colNA = 'white',\n", + " color_fun = clim.palette(palette = \"bluered\"),\n", + " # boxlim = c(11, 85, 40, 40)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "cae1ad06-fac8-4840-bd0d-44f4d7c64fdc", + "metadata": {}, + "source": [ + "# 3. Other parameters" + ] + }, + { + "cell_type": "markdown", + "id": "beead05f-f891-4d92-a1de-4badc06f7a06", + "metadata": {}, + "source": [ + "## 3.1 Names for the shp file" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6f078685-a632-4d87-b43b-b7a34a522d9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading layer `gadm_country_ISO3166' from data source \n", + " `/esarchive/shapefiles/gadm_country_mask/gadm_country_ISO3166.shp' \n", + " using driver `ESRI Shapefile'\n", + "Simple feature collection with 255 features and 5 fields\n", + "Geometry type: MULTIPOLYGON\n", + "Dimension: XY\n", + "Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.65833\n", + "Geodetic CRS: WGS 84\n" + ] + } + ], + "source": [ + "mask_area <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, \n", + " compute_area_coverage = TRUE, reg_names = NUTS_name, \n", + " shp_system = \"NUTS\", name_shp_col = \"Name\", id_shp_col = \"ISO\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6322a2a-4459-403e-9f26-2a9dffaf334b", + "metadata": {}, + "source": [ + "## 3.2 Lat Lon dimensions\n", + "- Indicate the names of the dimensions latitude and longitude" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1ff7dbd9-209b-4751-ba09-30859b5a62d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading layer `gadm_country_ISO3166' from data source \n", + " `/esarchive/shapefiles/gadm_country_mask/gadm_country_ISO3166.shp' \n", + " using driver `ESRI Shapefile'\n", + "Simple feature collection with 255 features and 5 fields\n", + "Geometry type: MULTIPOLYGON\n", + "Dimension: XY\n", + "Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.65833\n", + "Geodetic CRS: WGS 84\n" + ] + } + ], + "source": [ + "mask_area <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, \n", + " compute_area_coverage = TRUE, reg_names = NUTS_name, \n", + " shp_system = \"NUTS\", lat_dim = \"latitude\", lon_dim = \"longitude\")" + ] + }, + { + "cell_type": "markdown", + "id": "39aaf699-e742-4dd0-bed3-2b31b7c6c07d", + "metadata": {}, + "source": [ + "## 3.3 Projections of input files\n", + "- Indicate if your Nc file is in meter or degrees" + ] + }, + { + "cell_type": "markdown", + "id": "8c04e3e3-4d33-46a7-9b69-dbf7533a5805", + "metadata": {}, + "source": [ + ">\n", + ">
\n", + "NOTE:
\n", + " The possibilities are:\n", + "
    \n", + "
  • meters: to change the shape file to 3857
    • \n", + "
  • degrees: to change the shape file to 4326
    • \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "388dec03-e05d-44b4-a2e4-689a72d60420", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading layer `gadm_country_ISO3166' from data source \n", + " `/esarchive/shapefiles/gadm_country_mask/gadm_country_ISO3166.shp' \n", + " using driver `ESRI Shapefile'\n", + "Simple feature collection with 255 features and 5 fields\n", + "Geometry type: MULTIPOLYGON\n", + "Dimension: XY\n", + "Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.65833\n", + "Geodetic CRS: WGS 84\n" + ] + } + ], + "source": [ + "mask_area <- ShapeToMask(shp_file = shp_file, ref_grid = ref_grid, \n", + " compute_area_coverage = TRUE, reg_names = NUTS_name, \n", + " fileout = \"test_nuts_true.nc\", shp_system = \"NUTS\", units='degrees')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.2.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- GitLab From d54c2be33b67d40146f69e1ff3e8cd3e2c9b32c5 Mon Sep 17 00:00:00 2001 From: "raul.capellan" Date: Thu, 12 Sep 2024 11:38:32 +0200 Subject: [PATCH 14/14] Correcting some literals --- vignettes/shape_to_mask.ipynb | 2 -- 1 file changed, 2 deletions(-) diff --git a/vignettes/shape_to_mask.ipynb b/vignettes/shape_to_mask.ipynb index bc29570..4b5e6e6 100644 --- a/vignettes/shape_to_mask.ipynb +++ b/vignettes/shape_to_mask.ipynb @@ -28,8 +28,6 @@ "\n", "This vignette provides an example of code written in R that can be used to transform netcdf files and shape files into a mask using the method shapeToMask from Esviz library. The results are displayed to show the different possibilities and parametrizations.\n", "\n", - "An example is conducted over Campina Grande city in north-eastern Brazil, on a reference period going from 1995 to 2015 and for surface temperature (\"t2m\", air temperature 2m above the ground). The initialisation month of the hindcast — which is a forecast initialised in the past (also known as retrospective forecast) — is November and the analysis is done for the seasonal aggregation of November, December and January (NDJ).\n", - "\n", "This vignette also offers the possibility of choosing a different variable, region, and reference period than those specified in the example and can be run locally on a personal computer. It is worth noting that a very large region may require for higher computational resources than those that a personal computer can offer. Depending on the selected parameters and the location where the vignette is executed, it may also download and store seasonal climate data from the Climate Data Store - Copernicus (CDS) .\n", "\n", "The notebook has the following outline:\n", -- GitLab