From ff13cc1d05ece0e873727b282d3b4230ef98cda3 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Tue, 5 Aug 2025 09:51:38 +1000 Subject: [PATCH 01/10] Start cleaning up VoCC --- DESCRIPTION | 5 +- NAMESPACE | 4 +- R/dVoCC.R | 85 ++++++++++++++++++++++++----- R/spatGrad.R | 52 +++++++----------- man/dVoCC.Rd | 1 - man/spatGrad.Rd | 2 +- man/trajClas.Rd | 138 ------------------------------------------------ man/trajLine.Rd | 67 ----------------------- readme.md | 1 + 9 files changed, 96 insertions(+), 259 deletions(-) delete mode 100644 man/trajClas.Rd delete mode 100644 man/trajLine.Rd diff --git a/DESCRIPTION b/DESCRIPTION index e725390..99099dd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -21,7 +21,7 @@ Imports: geosphere, ggplot2, magrittr, - parallel, + parallelly, RColorBrewer, rlang, sp, @@ -33,7 +33,6 @@ Suggests: knitr, mapplots, ncdf4, - prettydoc, rasterVis, repmis, rmarkdown, @@ -43,6 +42,6 @@ BugReports: https://github.com/JorGarMol/VoCC/issues License: AGPL (>= 3) Encoding: UTF-8 LazyData: true -RoxygenNote: 7.3.1 +RoxygenNote: 7.3.2 VignetteBuilder: knitr, rmarkdown diff --git a/NAMESPACE b/NAMESPACE index 2ef3380..3eb78da 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,10 +11,8 @@ export(shiftTime) export(spatGrad) export(sumSeries) export(tempTrend) -export(trajClas) -export(trajLine) -export(voccTraj) importFrom(data.table,":=") importFrom(foreach,"%dopar%") importFrom(magrittr,"%>%") importFrom(rlang,.data) +importFrom(stats,na.omit) diff --git a/R/dVoCC.R b/R/dVoCC.R index caa6ddc..7bc7ccf 100644 --- a/R/dVoCC.R +++ b/R/dVoCC.R @@ -58,7 +58,6 @@ #' terra::plot(r1) #' #' \dontrun{ -#' #' # Cell-specific, distance-unrestricted climate analogue velocity based on least-cost path distances #' # First, create the conductance matrix (all land cells considered to have conductance of 1) #' r <- JapTC @@ -84,7 +83,7 @@ dVoCC <- function(clim, n, tdiff, method = "Single", climTol, geoTol, geoDis <- climDis <- ang <- vel <- target <- cid <- NULL # Fix devtools check warnings - if (distfun == "Euclidean" & lonlat == TRUE) { + if (distfun == "Euclidean" && lonlat == TRUE) { print("Error: Euclidean distances specified for unprojected coordinates") stop() } @@ -99,17 +98,24 @@ dVoCC <- function(clim, n, tdiff, method = "Single", climTol, geoTol, # matrix with the future climatic values for all cells fut <- dat[, seq(2, (2 * n), by = 2), with = FALSE] - # set things up for parallel processing - cores <- parallel::detectCores() - ncores <- cores[1] - 1 - cuts <- cut(1:nrow(dat), ncores, labels = FALSE) - cl <- parallel::makeCluster(ncores) + # Determine optimal number of cores, ensuring we don't exceed data rows + ncores <- parallelly::availableCores(constraints = "connections", omit = 2) + ncores <- min(ncores, nrow(dat)) # Don't use more cores than data rows + ncores <- max(ncores, 1) # Ensure at least 1 core + + # Only use parallel processing if we have multiple cores and sufficient data + if (ncores > 1 && nrow(dat) > ncores) { + cuts <- cut(seq_len(nrow(dat)), ncores, labels = FALSE) + cl <- parallelly::makeClusterPSOCK(ncores, autoStop = TRUE) + + doParallel::registerDoParallel(cl) - doParallel::registerDoParallel(cl) + result <- foreach::foreach(a = seq_len(ncores), + .combine = rbind, + .packages = c("terra", "gdistance", "geosphere", "data.table"), + .multicombine = TRUE) %dopar% { - result <- foreach::foreach(x = 1:ncores, .combine = rbind, .packages = c("terra", "gdistance", "geosphere", "data.table"), .multicombine = TRUE) %dopar% { - a <- x - Dat <- dat[cuts == a, ] + Dat <- dat[cuts == a, ] resu <- data.table::data.table( focal = Dat$cid, @@ -177,7 +183,62 @@ dVoCC <- function(clim, n, tdiff, method = "Single", climTol, geoTol, } return(resu) } - parallel::stopCluster(cl) + } else { + # Sequential processing for small datasets or limited cores + result <- data.table::data.table( + focal = dat$cid, + target = as.integer(NA), + climDis = as.double(NA), + geoDis = as.double(NA), + ang = as.double(NA), + vel = as.double(NA) + ) + + for (i in seq_len(nrow(dat))) { + # for each focal cell subset target cell analogues (within ClimTol) + pres <- as.numeric(dat[i, seq(1, (2 * n), by = 2), with = FALSE]) + dif <- data.table::data.table(sweep(fut, 2, pres, "-")) + + # Identify future analogue cells + if (method == "Single") { # Ohlemuller et al 2006 / Hamann et al 2015 + upper <- colnames(dif) + l <- lapply(upper, function(x) call("<", call("abs", as.name(x)), climTol[grep(x, colnames(dif))])) + ii <- Reduce(function(c1, c2) substitute(.c1 & .c2, list(.c1 = c1, .c2 = c2)), l) + anacid <- dat$cid[dif[eval(ii), which = TRUE]] # cids analogue cells + } + + if (method == "Variable") { # Garcia Molinos et al. 2017 + climTol <- as.numeric(dat[i, ((2 * n) + 1):(3 * n), with = FALSE]) # focal cell tolerance + upper <- colnames(dif) + l <- lapply(upper, function(x) call("<", call("abs", as.name(x)), climTol[grep(x, colnames(dif))])) + ii <- Reduce(function(c1, c2) substitute(.c1 & .c2, list(.c1 = c1, .c2 = c2)), l) + anacid <- dat$cid[dif[eval(ii), which = TRUE]] # cids analogue cells + } + + # LOCATE CLOSEST ANALOGUE + if (length(anacid) > 0) { + # check which of those are within distance and get the analogue at minimum distance + if (distfun == "Euclidean") { + d <- stats::dist(cbind(dat$x[i], dat$y[i]), cbind(dat$x[dat$cid %in% anacid], dat$y[dat$cid %in% anacid])) + } # in x/y units + if (distfun == "GreatCircle") { + d <- (geosphere::distHaversine(cbind(dat$x[i], dat$y[i]), cbind(dat$x[dat$cid %in% anacid], dat$y[dat$cid %in% anacid]))) / 1000 + } # in km + + an <- anacid[d < geoTol] # cids analogue cells within search radius + dis <- d[d < geoTol] # distance to candidate analogues + if (length(an) > 0) { + result[i, target := an[which.min(dis)]] # cid of geographically closest climate analogue + if (method == "Single") { + result[i, climDis := mean(as.numeric(dif[which(anacid == result[i, target]), ]))] + } # mean clim difference for the closest analogue + result[i, geoDis := min(dis)] + result[i, ang := geosphere::bearing(dat[i, c("x", "y")], dat[cid == result[i, target], c("x", "y")])] + result[i, vel := result$geoDis[i] / tdiff] + } + } + } + } return(result) } diff --git a/R/spatGrad.R b/R/spatGrad.R index 179833a..3369ff8 100644 --- a/R/spatGrad.R +++ b/R/spatGrad.R @@ -21,6 +21,7 @@ #' @seealso{\code{\link{tempTrend}}, \code{\link{gVoCC}}} #' #' @importFrom rlang .data +#' @importFrom stats na.omit #' #' @export #' @author Jorge Garcia Molinos, David S. Schoeman, and Michael T. Burrows @@ -40,11 +41,13 @@ #' terra::plot(sg) #' spatGrad <- function(r, th = -Inf, projected = FALSE) { + # Fix devtools check warnings + "." <- NULL gradNS1 <- gradNS2 <- gradNS3 <- gradNS4 <- gradNS5 <- gradNS6 <- gradWE1 <- gradWE2 <- gradWE3 <- gradWE4 <- gradWE5 <- gradWE6 <- NULL sy <- sx <- NSgrad <- WEgrad <- NULL clim <- climE <- climN <- climNE <- climNW <- climS <- climSE <- climSW <- climW <- climFocal <- NULL - to <- code <- i.to <- LAT <- angle <- Grad <- NULL + to <- code <- i.to <- LAT <- angle <- Grad <- .SD <- NULL if (terra::nlyr(r) > 1) { r <- terra::mean(r, na.rm = TRUE) @@ -55,11 +58,8 @@ spatGrad <- function(r, th = -Inf, projected = FALSE) { # Create a columns for focal and each of its 8 adjacent cells y <- data.table::data.table(terra::adjacent(r, 1:terra::ncell(r), directions = 8, pairs = TRUE)) - y <- stats::na.omit(y[, climFocal := terra::values(r)[from]][order(from, to)]) # Get value for focal cell, order the table by raster sequence and omit NAs (land cells) - - # TODO JDE added in na.rm = TRUE as I was getting NaN. I can't test if this behaviour has changed from raster.... - # On second thought I am not sure if NAs are valid here. It gives errors below when calculating weighted means - y[, clim := terra::values(r, na.rm = TRUE)[to]] # Insert values for adjacent cells + y <- na.omit(y[, climFocal := terra::values(r)[from]][order(from, to)]) # Get value for focal cell, order the table by raster sequence and omit NAs (land cells) + y[, clim := terra::values(r)[to]] # Insert values for adjacent cells y[, sy := terra::rowFromCell(r, from) - terra::rowFromCell(r, to)] # Column to identify rows in the raster (N = 1, mid = 0, S = -1) y[, sx := terra::colFromCell(r, to) - terra::colFromCell(r, from)] # Same for columns (E = 1, mid = 0, W = -1) y[sx > 1, sx := -1] # Sort out the W-E wrap at the dateline, part I @@ -67,14 +67,9 @@ spatGrad <- function(r, th = -Inf, projected = FALSE) { y[, code := paste0(sx, sy)] # Make a unique code for each of the eight neighbouring cells # Code cells with positions - y[ - list( - code = c("10", "-10", "-11", "-1-1", "11", "1-1", "01", "0-1"), - to = c("climE", "climW", "climNW", "climSW", "climNE", "climSE", "climN", "climS") - ), - on = "code", - code := i.to - ] + y[.(code = c("10", "-10", "-11", "-1-1", "11", "1-1", "01", "0-1"), + to = c("climE", "climW", "climNW", "climSW", "climNE", "climSE", "climN", "climS")), + on = "code", code := i.to] y <- data.table::dcast(y[, c("from", "code", "clim")], from ~ code, value.var = "clim") y[, climFocal := terra::values(r)[from]] # Put climFocal back in y[, LAT := terra::yFromCell(r, from)] # Add focal cell latitude @@ -93,7 +88,8 @@ spatGrad <- function(r, th = -Inf, projected = FALSE) { y[, gradWE5 := (climE - climFocal) / (cos(co * CircStats::rad(LAT)) * (d * re[1]))] y[, gradWE6 := (climSE - climS) / (cos(co * CircStats::rad(LAT - re[2])) * (d * re[1]))] - # NS gradients difference in temperatures for each northern and southern pairs divided by the distance between them (111.325 km per degC *re[2] degC) + # NS gradients difference in temperatures for each northern and southern pairs divided by + # the distance between them (111.325 km per degC *re[2] degC) # Positive values indicate an increase in sst from S to N (i.e., in line with the Cartesian y axis) y[, gradNS1 := (climNW - climW) / (d * re[2])] y[, gradNS2 := (climN - climFocal) / (d * re[2])] @@ -102,29 +98,16 @@ spatGrad <- function(r, th = -Inf, projected = FALSE) { y[, gradNS5 := (climFocal - climS) / (d * re[2])] y[, gradNS6 := (climE - climSE) / (d * re[2])] - - for (nn in 1:365){ - - print(nn) - - print(stats::weighted.mean(y[nn,12:17], w = c(1, 2, 1, 1, 2, 1), na.rm = TRUE)) - - - } - - - browser() - # Calulate NS and WE gradients. NOTE: for angles to work (at least using simple positive and negative values on Cartesian axes), - # S-N & W-E gradients need to be positive. - # JDE Notes: 1 in apply = operate over rows - # Lots of NAs in clim. Can these be removed? Should they be? Chat to Dave S - y[, WEgrad := apply(data.table::.SD, 1, function(x) stats::weighted.mean(x, w = c(1, 2, 1, 1, 2, 1), na.rm = TRUE)), .SDcols = gradWE1:gradWE6] - y[, NSgrad := apply(data.table::.SD, 1, function(x) stats::weighted.mean(x, c(1, 2, 1, 1, 2, 1), na.rm = T)), .SDcols = 18:23] + # Calulate NS and WE gradients. + # NOTE: for angles to work (at least using simple positive and negative values on Cartesian axes), + # S-N & W-E gradients need to be positive) + y[, WEgrad := apply(.SD, 1, function(x) stats::weighted.mean(x, c(1, 2, 1, 1, 2, 1), na.rm = TRUE)), .SDcols = 12:17] + y[, NSgrad := apply(.SD, 1, function(x) stats::weighted.mean(x, c(1, 2, 1, 1, 2, 1), na.rm = TRUE)), .SDcols = 18:23] y[is.na(WEgrad) & !is.na(NSgrad), WEgrad := 0L] # Where NSgrad does not exist, but WEgrad does, make NSgrad 0 y[!is.na(WEgrad) & is.na(NSgrad), NSgrad := 0L] # same the other way around # Calculate angles of gradients (degrees) - adjusted for quadrant (0 deg is North) - y[, angle := angulo(data.table::.SD$WEgrad, data.table::.SD$NSgrad), .SDcols = c("WEgrad", "NSgrad")] + y[, angle := angulo(.SD$WEgrad, .SD$NSgrad), .SDcols = c("WEgrad", "NSgrad")] # Calculate the vector sum of gradients (C/km) y[, Grad := sqrt(apply(cbind((y$WEgrad^2), (y$NSgrad^2)), 1, sum, na.rm = TRUE))] @@ -139,5 +122,6 @@ spatGrad <- function(r, th = -Inf, projected = FALSE) { rGrad[rGrad[] < th] <- th output <- c(rGrad, rAng) names(output) <- c("Grad", "Ang") + return(output) } diff --git a/man/dVoCC.Rd b/man/dVoCC.Rd index fcbece5..4c32c9d 100644 --- a/man/dVoCC.Rd +++ b/man/dVoCC.Rd @@ -75,7 +75,6 @@ r1[avocc1$focal] <- avocc1$vel terra::plot(r1) \dontrun{ - # Cell-specific, distance-unrestricted climate analogue velocity based on least-cost path distances # First, create the conductance matrix (all land cells considered to have conductance of 1) r <- JapTC diff --git a/man/spatGrad.Rd b/man/spatGrad.Rd index 7348782..6b3da32 100644 --- a/man/spatGrad.Rd +++ b/man/spatGrad.Rd @@ -11,7 +11,7 @@ spatGrad(r, th = -Inf, projected = FALSE) Alternatively, a \code{raster} with the annual climatic values averaged over the period of interest.} -\item{th}{\code{Integer} indicating a lower thershold to truncate the spatial +\item{th}{\code{Integer} indicating a lower threshold to truncate the spatial gradient with. Use -Inf (default) if no threshold required.} \item{projected}{\code{Logical} is the source raster in a projected coordinate system? diff --git a/man/trajClas.Rd b/man/trajClas.Rd deleted file mode 100644 index f18e690..0000000 --- a/man/trajClas.Rd +++ /dev/null @@ -1,138 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trajClas.R -\name{trajClas} -\alias{trajClas} -\title{Climate velocity trajectory classification} -\usage{ -trajClas( - traj, - vel, - ang, - mn, - trajSt, - tyr, - nmL, - smL, - Nend, - Nst, - NFT, - DateLine = FALSE -) -} -\arguments{ -\item{traj}{\code{data.frame} as retuned by voccTraj containing the coordinates -and identification number for each trajectory.} - -\item{vel}{\code{raster} with the magnitude of gradient-based climate velocity.} - -\item{ang}{\code{raster} with velocity angles.} - -\item{mn}{\code{raster} with mean climatic values for the study period.} - -\item{trajSt}{\code{integer} number of trajectories starting from each cell or spatial unit.} - -\item{tyr}{\code{integer} number of years comprising the projected period.} - -\item{nmL}{\code{numeric} upper threshold (distance units as per vel object) up to which -a trajectory is considered to have traveled a negligible distance over the study period (non-moving).} - -\item{smL}{\code{numeric} upper threshold up to which a trajectory is considered to have traveled a small -distance over the study period (slow-moving).} - -\item{Nend}{\code{numeric} the percentage of trajectories ending to be used as threshold in the classification.} - -\item{Nst}{\code{numeric} the percentage of trajectories starting to be used as threshold in the classification.} - -\item{NFT}{\code{numeric} the percentage of trajectories flowing through to be used as threshold in the classification.} - -\item{DateLine}{\code{logical} does the raster extent cross the international date line? (default "FALSE").} -} -\value{ -A \code{raster.stack} containing the trajectory classification ("TrajClas"), -as well as those based on trajectory length ("ClassL"; 1 non-moving, 2 slow-moving, 3 fast-moving cells), -boundrary ("BounS") and internal sinks ("IntS"), and the proportion of trajectories ending("PropEnd"), -flowing through ("PropFT") and starting ("PropSt"). The trajectory classes ("TrajClas") are (1) non-moving, -(2) slow-moving, (3) internal sinks, (4) boundary sinks, (5) sources, (6) relative sinks, (7) corridors, -(8) divergence and (9) convergence. -} -\description{ -Function for the spatial classification of cells based on VoCC trajectories after Burrows et al. (2014). The function performs -a hierarchical sequential classification based on length of trajectories, geographical features, and the relative abundance of -trajectories ending in, starting from and flowing through each cell. Essentially, cells are first classified as non-moving, -slow-moving and fast-moving relative to the distance a trajectory will cover over the projection period based on local climate velocities. -Two types of climate sinks are then identified among the fast-moving cells: (i) boundary (e.g., coastal) cells disconnected from cooler (warmer) -neighbouring cells under a locally warming (cooling) climate, and (ii) locations with endorheic spatial gradients where the velocity angles of -neighbouring cells converge towards their central point of intersection. Finally, the remaining cells are classified by reference to the total -number of trajectories per cell based on the proportions of the number of trajectories starting from (Nst), ending in (Nend), and flowing -through (NFT) a cell over the period. Based on these proportions, cells are classified into five classes: (1) climate sources, when no -trajectories end in a cell (Nend = 0); (2) relative climate sinks, when the relative number of trajectories ending in a cell is high and the -proportion of starting trajectories is low; (3) corridors as cells with a high proportion of trajectories passing through; and (4) divergence -and (5) convergence cells identified from the remaining cells as those where fewer/more trajectories ended than started in that -cell, respectively. -} -\examples{ - -HSST <- VoCC_get_data("HSST.tif") - -# input raster layers -yrSST <- sumSeries(HSST, - p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), - fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years" -) - -mn <- terra::mean(yrSST, na.rm = TRUE) -tr <- tempTrend(yrSST, th = 10) -sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) -v <- gVoCC(tr, sg) -vel <- v[[1]] -ang <- v[[2]] - -# Get the set of starting cells for the trajectories and calculate trajectories -# at 1/4-deg resolution (16 trajectories per 1-deg cell) -mnd <- terra::disagg(mn, 4) -veld <- terra::disagg(vel, 4) -angd <- terra::disagg(ang, 4) -lonlat <- stats::na.omit(data.frame( - terra::xyFromCell(veld, 1:terra::ncell(veld)), - veld[], angd[], mnd[] -))[, 1:2] - -traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) - -# Generate the trajectory-based classification -clas <- trajClas(traj, vel, ang, mn, - trajSt = 16, tyr = 50, nmL = 20, smL = 100, - Nend = 45, Nst = 15, NFT = 70, DateLine = FALSE -) - -# Define first the colour palette for the full set of categories -my_col <- c( - "gainsboro", "darkseagreen1", "coral4", "firebrick2", "mediumblue", "darkorange1", - "magenta1", "cadetblue1", "yellow1" -) -# Keep only the categories present in our raster -my_col <- my_col[sort(unique(clas[[7]][]))] - -# Classify raster / build attribute table -clasr <- ratify(clas[[7]]) -rat_r <- levels(clasr)[[1]] -rat_r$trajcat <- c( - "N-M", "S-M", "IS", "BS", "Srce", - "RS", "Cor", "Div", "Con" -)[sort(unique(clas[[7]][]))] -levels(clasr) <- rat_r -# Produce the plot using the rasterVis levelplot function -rasterVis::levelplot(clasr, - col.regions = my_col, - xlab = NULL, ylab = NULL, scales = list(draw = FALSE) -) -} -\references{ -\href{https://www.nature.com/articles/nature12976}{Burrows et al. 2014}. Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507, 492-495. -} -\seealso{ -{\code{\link{voccTraj}}} -} -\author{ -Jorge Garcia Molinos -} diff --git a/man/trajLine.Rd b/man/trajLine.Rd deleted file mode 100644 index 50c03a4..0000000 --- a/man/trajLine.Rd +++ /dev/null @@ -1,67 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trajLine.R -\name{trajLine} -\alias{trajLine} -\title{Climate velocity trajectory spatial lines} -\usage{ -trajLine(x, projx = "EPSG::4326") -} -\arguments{ -\item{x}{\code{data.frame} containing the coordinates (x, y) of the constituent -points and identification number (trajIDs) for each trajectory as returned by VoCCTraj.} - -\item{projx}{\code{CRS} detailing the coordinate reference system of the input data -(default geographic CRS).} -} -\value{ -A \code{SpatialLinesDataFrame} with one line per trajectory as specified in x. -To avoid artifacts, trajectories crossing the date line need to be split into two segments. -Where the trajectory on one side of the date line is only composed of a single point, -the trajectory won't be displayed (no line object created). The function assumes -a -180 to 180 longitudinal arrangement. -} -\description{ -Create a spatial line data frame object from trajectory points. -} -\examples{ - -HSST <- VoCC_get_data("HSST.tif") - -yrSST <- sumSeries(HSST, - p = "1969-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), - fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years" -) -tr <- tempTrend(yrSST, th = 10) -sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) -v <- gVoCC(tr, sg) -vel <- v[[1]] -ang <- v[[2]] - -# calculate the annual SST mean over the period -mn <- terra::mean(yrSST, na.rm = TRUE) - -# get the set of starting cells for the trajectories -lonlat <- stats::na.omit(data.frame( - terra::xyFromCell(vel, 1:terra::ncell(vel)), - vel[], ang[], mn[] -))[, 1:2] - -# Calculate trajectories. -traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) - -# create a spatial line data frame from traj -lns <- trajLine(x = traj) -terra::plot(mn) -terra::plot(lns, add = TRUE) - -\dontrun{ -# Export as ESRI shape file -terra::writeVector(lns, filename = "velTraj", filetype = "ESRI Shapefile") -} -} -\seealso{ -{\code{\link{voccTraj}}} -} -\author{ -Jorge Garcia Molinos -} diff --git a/readme.md b/readme.md index d270af4..6caeaa4 100644 --- a/readme.md +++ b/readme.md @@ -4,6 +4,7 @@ # VoCC: The Velocity of Climate Change and related climatic metrics + Jorge Garcia Molinos et al. 18 July 2019 From 7ce714aa0dea44a383d2372b2e73600e19fd3e48 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Tue, 5 Aug 2025 09:52:03 +1000 Subject: [PATCH 02/10] Add Daves traj code --- R/voccTraj.R | 363 +++++++++++++++++++++++++++++++ data-raw/voccTraj.R | 516 -------------------------------------------- 2 files changed, 363 insertions(+), 516 deletions(-) create mode 100644 R/voccTraj.R delete mode 100644 data-raw/voccTraj.R diff --git a/R/voccTraj.R b/R/voccTraj.R new file mode 100644 index 0000000..033471a --- /dev/null +++ b/R/voccTraj.R @@ -0,0 +1,363 @@ +#' Climate velocity trajectories +#' +#' Function to calculate vocc trajectories after Burrows et al (2014). Trajectories +#' are calculated by propagating climatic isopleths using the magnitude and direction of +#' local (cell) velocities. This is a slightly modified version of the original +#' Burrows et al. (2014) approach in that iterations of a trajectory are based on +#' cumulative time traveled instead of using fixed time steps. +#' +#' @param lonlat \code{data.frame} with the longitude and latitude (in decimal degrees) +#' of the points to project. +#' @param vel \code{raster} with the magnitude of gradient-based climate velocity. +#' @param ang \code{raster} with velocity angles in degrees. +#' @param mn \code{raster} with the overall mean climatic value over the period of interest. +#' @param tyr \code{integer} temporal length of the period of interest. +#' @param trajID \code{integer} specifying the identifiers for the trajectories. +#' @param correct \code{logical} does the input raster need to be corrected to account for cropped margins? +#' Unless the raster extent is global, calculation of trajectories will throw an error at the margins +#' as the trajectories go beyond the raster extent (no input values). To avoid this, an option is given for +#' expanding the extent by the resolution of the raster (1 column/row) with NAs. Note that those trajectories +#' reaching the extent limits will be artificially bounced back so should be discarded at that point. +#' Alternatively, users may choose to crop to a larger extent to the domain of interest (appropriately +#' defined by lonlat), so the extra extent buffer for those trajectories getting to the border +#' of the raster. +#' +#' @return a \code{data.frame} containing the coordinates ("x", "y") of the constituent +#' points and identification number ("trajIDs") for each trajectory. +#' +#' @references \href{https://www.nature.com/articles/nature12976}{Burrows et al. 2014}. Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507, 492-495. +#' +#' @seealso{\code{\link{gVoCC}}, \code{\link{trajClas}}} +#' @export +#' @author Jorge Garcia Molinos, David S. Schoeman and Michael T. Burrows +#' @examples +#' +#' HSST <- VoCC_get_data("HSST.tif") +#' +#' yrSST <- sumSeries(HSST, +#' p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), +#' fun = function(x) colMeans(x, na.rm = TRUE), +#' freqin = "months", freqout = "years" +#' ) +#' +#' # Long-term local climatic trends +#' tr <- tempTrend(yrSST, th = 10) +#' +#' # Local spatial climatic gradients +#' sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) +#' +#' # Gradient-based climate velocity +#' v <- gVoCC(tr, sg) +#' vel <- v[[1]] +#' ang <- v[[2]] +#' +#' # Calculate the annual SST mean over the period +#' mn <- terra::mean(yrSST, na.rm = TRUE) +#' +#' # Get the set of starting cells for the trajectories +#' lonlat <- stats::na.omit(data.frame( +#' terra::xyFromCell(vel, 1:terra::ncell(vel)), +#' vel[], ang[], mn[] +#' ))[, 1:2] +#' +#' # Calculate trajectories +#' # The following throws an error due to the trajectories moving beyond the raster extent +#' traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50) +#' +#' # This accounts for the extent issue +#' traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) +#' +#' +voccTraj <- function(lonlat, + vel, ang, mn, + vel_c, ang_c, mn_c, + fine_coast, lk_up, + tstep, tyr = 50) { + + # Setup ------------------------------------------------------------------- + + y_res <- x_res <- res(vel)[1] # Set resolution of operations + + # Constrain max velocity to avoid stepping over grid squares + max_vel = 111.325*x_res/tstep + vel[vel > max_vel] <- max_vel + vel[vel < -max_vel] <- -max_vel + vel_c[vel_c > max_vel] <- max_vel + vel_c[vel_c < -max_vel] <- -max_vel + + lonlat <- lonlat %>% + dplyr::select(x, y) %>% # Collect just lon and lat (in case there's anything else there) + as.data.frame() + tcells <- terra::cellFromXY(vel, lonlat) # # Cell IDs of starting cells + n <- nrow(lonlat) # Get number of cells in your sequence + sflags <- rep(NA, n) # Set a string of cells that change from NA to 1 where the trajectory sticks + + # Set up variables to catch results, allocating the right amount of memory + llon <- numeric((n * tyr / tstep) + n) # Needs to have a set of starting lons, plus one more set for each time step + llat <- numeric((n * tyr / tstep) + n) # Needs to have a set of starting lats, plus one more set for each time step + # cellIDs <- rep(tcells, ((tyr / tstep) + 1)) # Needs to have a set of starting cells, plus one more set for each time step—just as a reference + cellIDs <- rep(1:n, ((tyr / tstep) + 1)) # Needs to have a set of starting cells, plus one more set for each time step—just as a reference + cellIDend <- numeric((n * tyr / tstep) + n) # Needs to have a set of ending cells, plus one more set for each time step + # coast <- llon != 0 # Needs to have a set of starting coastal flags, plus one more set for each time step...set up as boolean + flags <- numeric((n * tyr / tstep) + n) # Needs to have a set of starting flags, plus one more set for each time step + Steps <- numeric((n * tyr / tstep) + n) # Needs to have a set of starting steps, plus one more set for each time step + + # Populate the first n slots with starting points + llon[1:n] <- lonlat[,1] + llat[1:n] <- lonlat[,2] + cellIDend[1:n] <- tcells + # coast[1:n] <- scoast + flags[1:n] <- NA + Steps[1:n] <- 0 + + # Get coarse cellIDs that exist in fine raster + fn_lk_up <- lk_up[] %>% + na.omit() %>% + unique() + + # Helper functions -------------------------------------------------------- + + # Trajectory helper functions + + # Function for to grep min distance + mfind <- function(rw){ + X <- which.min(rw) + return(X) + } + + # Function for to extract corner coordinates per row + mplace <- function(rw){ + X <- rw[(rw[1]+1)] + Y <- rw[rw[1]] + return(c(X, Y)) + } + + # Simple circular functions + deg2rad <- function(deg) return(deg*pi/180) + rad2deg <- function(rad) return(180*rad/pi) + + # A function to find the nearest coastal cell + get_nearest_coastal_cell <- function(x, y, tccell,...) { + t_block <- which(lk_up[] == tccell) # The cellIDs of lk_up + cst_pts <- terra::xyFromCell(lk_up, t_block) %>% + as.data.frame() %>% + st_as_sf(coords = c("x", "y"), crs = crs(rast())) + # Here, we can't search for corners and add random "fuzz" to we collect 10 random points (if there are 10), and select the nearest of those, instead. If there are 10 or fewer, just pick the nearest + if(nrow(cst_pts) > 10) { + cst_pts <- cst_pts[sample(1:nrow(cst_pts), 10, replace = FALSE),] + } # The unspoken "else" is that we just retain the cst_pts we have + pt <- st_as_sf(data.frame(x = x, y = y), coords = c("x", "y"), crs = crs(rast())) # The point departed from + nearest <- st_distance(cst_pts, pt) %>% + which.min() + out <- cst_pts[nearest,] %>% + st_coordinates() %>% + as.data.frame() %>% + dplyr::rename(x = 1, y = 2) + return(out) + } + + # Find new destination, given velocity (km/yr), angle (º), time step (yr) and initial coordinates (ºlon, ºlat); max allowed jump is 1 deg + # vell = vel[fcells] %>% pull(1); angg = ang[fcells] %>% pull(1); timestep = tstep; ll = llold + destcoords <- function(vell, angg, timestep, ll, y_res, x_res){ + latshift <- (abs(vell) * timestep * cos(deg2rad(angg))) / 111.325 # Calculate shift in lat + latnew <- ll[,2] + latshift # Find new lat...first approximation + lonshift <- (abs(vell) * timestep * sin(deg2rad(angg))) / (111.325 * cos(deg2rad(latnew))) # Shift in lon + # Limit large longitudinal jumps at high latitudes + # Because we constrain velocity to be no more than 12 * y_res, all problems will be with lonshift + # Limit lonshift to at most 1 cell + lonshift[lonshift > x_res] <- x_res + lonshift[lonshift < -x_res] <- -x_res + # Now on that basis, adjust latshift + x_gt <- which(lonshift == x_res) # Indices for adjusted lon shifts + latshift[x_gt] <- ((x_res*111.325 * cos(deg2rad(ll[x_gt,2])))/tan(deg2rad(angg[x_gt])))/111.325 # Using trig on distances + x_lt <- which(lonshift == -x_res) # Indices for adjusted lon shifts + latshift[x_lt] <- ((x_res*111.325 * cos(deg2rad(ll[x_lt,2])))/tan(deg2rad(angg[x_lt])))/111.325 # Using trig on distances + latnew <- ll[,2] + latshift # Find new lat by adding the adjusted lats + # Stop new lat from jumping the poles + latnew[latnew > 90] <- 90 + latnew[latnew < -90] <- -90 + # Adjust lon + lonnew <- ll[,1] + lonshift # Find new lon...first approximation + # Adjust for dateline jumps + lonnew <- lonnew - (360 * floor((lonnew + 180) / 360)) + + return(data.frame(lonnew, latnew) %>% + setNames(c("dlon", "dlat"))) + } + + # Function for to find closest cooler/warmer cell within the union of two levels of 8-cell adjacency from the "departed" and "destination" cells + get_dest_cell <- function(rw) { # A to-from cell pair and the buffer around a cell on land that can be searched for a suitable cell + # Find clumps of ocean; the rule is that you can't jump from one clump to another, because this would mean passing over unbroken land + xy <- terra::xyFromCell(mn, as.vector(as.matrix(rw))) %>% + as.data.frame() + bfr = ((y_res*111.325) + 1) * 1000 # Set buffer to be 1 grid-square width at the equator + 1 km + xy <- xy %>% + st_as_sf(coords = c("x", "y"), crs = "EPSG:4326") + sp_buffer <- st_buffer(st_as_sf(xy), bfr) # Remembering that buffer is in metres + buffer_zone <- terra::extract(mn_c, sp_buffer, cells = TRUE, xy = TRUE, touches = TRUE) %>% + dplyr::select(-ID) %>% + distinct() %>% + dplyr::rename(sst = climatology) %>% + dplyr::select(x, y, sst, cell) %>% + drop_na(sst) + clumped <- buffer_zone %>% + dplyr::select(-cell) %>% + rast(crs = "EPSG:4326") %>% + terra::patches(directions = 8, allowGaps = FALSE) + # Which clump did I start in? + r1 <- rw[1] %>% + unlist() %>% + as.vector() # We use this cell a lot, so let's just make it an object + from_clump <- terra::extract(clumped, terra::xyFromCell(mn, r1)) %>% + unlist() %>% + as.vector() + # What are the coordinates of cells within the search area that fall in the clump I came from? + search_xy <- terra::xyFromCell(clumped, which(clumped[] == from_clump)) %>% + as.data.frame() #**** + search_cells <- terra::cellFromXY(mn, search_xy) # Which cells are these + # Check if any of these are NOT in in EITHER fine coast or mn, and eliminate, if needed + to_keep <- c(which(!is.na(terra::extract(mn, search_xy, ID = FALSE) %>% + pull(1))), # Coords in the coarse grid + which(search_cells %in% fn_lk_up)) %>% + unique() + search_xy1 <- search_xy[to_keep,] %>% + as.data.frame() %>% + distinct() + # Get the ssts in the cells to search + or <- terra::extract(mn_c, search_xy1, cells = TRUE, xy = TRUE) %>% + dplyr::rename(sst = 2, x = 4, y = 5) + # If velocity is positive, find nearest cell that is cooler, if there is one + if(unlist(vel_c[r1]) > 0) { + o <- or %>% + dplyr::filter(sst < unlist(mn_c[r1])) %>% + na.omit() + if(nrow(o) == 0) { + dest_cell <- NA # Set condition with which to ID stuck cells + } else { + pt <- terra::xyFromCell(mn_c, r1) %>% + data.frame() + dest_cell <- st_distance(st_as_sf(pt, coords = c("x", "y"), crs = crs(rast())), + st_as_sf(data.frame(o), coords = c("x", "y"), crs = crs(rast()))) %>% + which.min() %>% + o$cell[.] + } + } else { # Otherwise, find nearest cell that is warmer, if there is one + o <- or %>% + dplyr::filter(sst > unlist(mn_c[r1])) %>% + na.omit() + if(nrow(o) == 0) { + dest_cell <- NA # Set condition with which to ID stuck cells + } else { + pt <- terra::xyFromCell(mn_c, r1) %>% + data.frame() + dest_cell <- st_distance(st_as_sf(pt, coords = c("x", "y"), crs = crs(rast())), + st_as_sf(o, coords = c("x", "y"), crs = crs(rast()))) %>% + which.min() %>% + o$cell[.] + } + } + return(dest_cell) + } + + # Loop -------------------------------------------------------------------- + + # Loop through the trajectories + for(i in 1:(tyr/tstep)) { # 1:(tyr/tstep) + llold <- lonlat # Take a copy of lonlat + fcells <- terra::cellFromXY(vel,llold) # Get the cells that the trajectories start in + # Pull velocity and angle from the "inland" versions, because we always ensure that we can find destination cells for departures within the coastal "blind spot" + vc <- vel_c[fcells] %>% pull(1) + ac <- ang_c[fcells] %>% pull(1) + llc <- llold + # Get new locations + lonlat <- destcoords(vc, ac, tstep, llc, y_res, x_res) # Extract lon and lat of landing point + tcells <- terra::cellFromXY(vel,lonlat) # Get the cells that the trajectories end in + sflags[which(is.na(tcells))] <- 1 # Sets the trajectory to "stuck" if it exits the velocity field (i.e., tcells == NA) + # Bounce stuck cells + stuck_cells <- which(!is.na(sflags)) + lonlat[stuck_cells,] <- llold[stuck_cells,] # Bounce the corresponding coordinates across for "stuck" cells + tcells[stuck_cells] <- fcells[stuck_cells] # Bounce the corresponding cells across for "stuck" cells + # Deal with cells that end on land + onland <- which(is.na(vel[tcells])) %>% # Identify which rows of velend are on land, provided that they are not stuck + dplyr::setdiff(stuck_cells) # Ignoring stuck cells + if(length(onland) > 0){ # Only bother if there is at least one cell that returns a NA velocity = land + # Here, you need to check whether it really *IS* on land, or whether it is just in the coastal "blind spot" + fn <- terra::extract(fine_coast, lonlat[onland,], ID = FALSE) %>% + pull(1) + onland <- onland[which(is.na(fn))] + if(length(onland) > 0) { + # Collect the stuff we need here for cells that are really onland + fpos <- llold[onland,] + tpos <- lonlat[onland,] + SFlags <- sflags[onland] + fcell <- fcells[onland] + tcell <- tcells[onland] + ft <- data.frame(fcell = fcell, tcell = tcell, code = paste(fcell, tcell, sep = " "), ref = 1:length(fcell)) + ttcell <- apply(ft[,1:2], 1, get_dest_cell) + # Filter "stuck" flags here + stuck <- which(is.na(ttcell)) + unstuck <- which(!is.na(ttcell)) + SFlags[stuck] <- 1 # Adjust flags + # Make data frame to catch data needed to find new positions + ttpos <- data.frame(x = rep(NA, length(onland)), y = rep(NA, length(onland))) + ttpos[stuck,] <- fpos[stuck,] # If they're stuck, pass on starting points + ttcell[stuck] <- fcell[stuck] # If they're stuck, pass on starting cells + if(length(unstuck) > 0) { + ttdat <- data.frame(ttcell = ttcell[unstuck], loncent = xFromCell(vel, ttcell[unstuck]), latcent = yFromCell(vel, ttcell[unstuck])) %>% # Start building coordinates + mutate(e = NA, w = NA, n = NA, s = NA, dlon = NA, dlat = NA) # To facilitate finding corners, if we need them + # Separate cells in the coastal blind spot from those that are not + which_coast <- which(ttdat$ttcell %in% fn_lk_up) + which_not_coast <- which(!(ttdat$ttcell %in% fn_lk_up)) + # For non-coastal cells + if(length(which_not_coast) > 0) { + corner_block_size <- 0.25*x_res + nc_ttdat <- ttdat[which_not_coast,] # ttdat for non-coastal cells + nc_ttdat$e <- nc_ttdat$loncent + (0.5 * x_res) - (runif(1, 0, 1) * corner_block_size) + nc_ttdat$w <- nc_ttdat$loncent - (0.5 * x_res) + (runif(1, 0, 1) * corner_block_size) + nc_ttdat$n <- nc_ttdat$latcent + (0.5 * y_res) - (runif(1, 0, 1) * corner_block_size) + nc_ttdat$s <- nc_ttdat$latcent - (0.5 * y_res) + (runif(1, 0, 1) * corner_block_size) + coords <- with(nc_ttdat, cbind(n, e, n, w, s, w, s, e)) # NE, NW, SW, SE corners' coordinates + # Find distances from departure point to corners of ttcell + get_dist <- function(y1, x1, y2, x2) { + pt1 <- st_as_sf(data.frame(x = x1, y = y1), coords = c("x", "y"), crs = crs(rast())) + pt2 <- st_as_sf(data.frame(x = x2, y = y2), coords = c("x", "y"), crs = crs(rast())) + out <- st_distance(pt1, pt2, by_element = TRUE) %>% + as.vector() + return(out) + } + corners <- data.frame(ne = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,1], coords[,2])) %>% + mutate(nw = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,3], coords[,4]), + sw = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,5], coords[,6]), + se = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,7], coords[,8])) + cornset <- apply(corners, 1, mfind)*2 # Identify which corners for each onland cell. Have to mul by 2 to shift along correctly. + cornset <- cbind(cornset, coords) # Add in coordinates + ttdat[which_not_coast,8:9] <- data.frame(t(apply(cornset, 1, mplace))) # Extract coordinates of correct corner + } + # For coastal cells + if(length(which_coast) > 0) { + coastal_dat <- ttdat[which_coast,2:3] %>% + mutate(x = fpos[which_coast,1], y = fpos[which_coast,2], + tccell = ttdat[which_coast,]$ttcell) + ttdat[which_coast,8:9] <- pmap_df(coastal_dat, get_nearest_coastal_cell) + } + ttpos[unstuck,] <- ttdat[,8:9] + ttcell[unstuck] <- terra::cellFromXY(mn, ttpos[unstuck,]) + } + # Collect results + lonlat[onland,] <- ttpos + tcells[onland] <- ttcell + sflags[onland] <- SFlags + } + } + llon[((i * n) + 1): ((i * n) + n)] <- lonlat[,1] # Add lon to the list + llat[((i * n) + 1): ((i * n) + n)] <- lonlat[,2]# Add lat to the list + cellIDend[((i * n) + 1): ((i * n) + n)] <- tcells # Add cellIDs to the list + flags[((i * n) + 1): ((i * n) + n)] <- sflags # Add flags to the list + Steps[((i * n) + 1): ((i * n) + n)] <- i # Capture the time_step + print(c(i, length(onland), round(100*i/(tyr/tstep), 3), date())) # Keep a reference of progress + } + + return(cbind(Steps, llon, llat, cellIDs, cellIDend, flags) %>% + as_tibble()) +} + diff --git a/data-raw/voccTraj.R b/data-raw/voccTraj.R deleted file mode 100644 index bf800b0..0000000 --- a/data-raw/voccTraj.R +++ /dev/null @@ -1,516 +0,0 @@ -#' Climate velocity trajectories -#' -#' Function to calculate vocc trajectories after Burrows et al (2014). Trajectories -#' are calculated by propagating climatic isopleths using the magnitude and direction of -#' local (cell) velocities. This is a slightly modified version of the original -#' Burrows et al. (2014) approach in that iterations of a trajectory are based on -#' cumulative time traveled instead of using fixed time steps. -#' -#' @param lonlat \code{data.frame} with the longitude and latitude (in decimal degrees) -#' of the points to project. -#' @param vel \code{raster} with the magnitude of gradient-based climate velocity. -#' @param ang \code{raster} with velocity angles in degrees. -#' @param mn \code{raster} with the overall mean climatic value over the period of interest. -#' @param tyr \code{integer} temporal length of the period of interest. -#' @param trajID \code{integer} specifying the identifiers for the trajectories. -#' @param correct \code{logical} does the input raster need to be corrected to account for cropped margins? -#' Unless the raster extent is global, calculation of trajectories will throw an error at the margins -#' as the trajectories go beyond the raster extent (no input values). To avoid this, an option is given for -#' expanding the extent by the resolution of the raster (1 column/row) with NAs. Note that those trajectories -#' reaching the extent limits will be artificially bounced back so should be discarded at that point. -#' Alternatively, users may choose to crop to a larger extent to the domain of interest (appropriately -#' defined by lonlat), so the extra extent buffer for those trajectories getting to the border -#' of the raster. -#' -#' @return a \code{data.frame} containing the coordinates ("x", "y") of the constituent -#' points and identification number ("trajIDs") for each trajectory. -#' -#' @references \href{https://www.nature.com/articles/nature12976}{Burrows et al. 2014}. Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507, 492-495. -#' -#' @seealso{\code{\link{gVoCC}}, \code{\link{trajClas}}} -#' @export -#' @author Jorge Garcia Molinos, David S. Schoeman and Michael T. Burrows -#' @examples -#' -#' HSST <- VoCC_get_data("HSST.tif") -#' -#' yrSST <- sumSeries(HSST, -#' p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), -#' fun = function(x) colMeans(x, na.rm = TRUE), -#' freqin = "months", freqout = "years" -#' ) -#' -#' # Long-term local climatic trends -#' tr <- tempTrend(yrSST, th = 10) -#' -#' # Local spatial climatic gradients -#' sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) -#' -#' # Gradient-based climate velocity -#' v <- gVoCC(tr, sg) -#' vel <- v[[1]] -#' ang <- v[[2]] -#' -#' # Calculate the annual SST mean over the period -#' mn <- terra::mean(yrSST, na.rm = TRUE) -#' -#' # Get the set of starting cells for the trajectories -#' lonlat <- stats::na.omit(data.frame( -#' terra::xyFromCell(vel, 1:terra::ncell(vel)), -#' vel[], ang[], mn[] -#' ))[, 1:2] -#' -#' # Calculate trajectories -#' # The following throws an error due to the trajectories moving beyond the raster extent -#' traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50) -#' -#' # This accounts for the extent issue -#' traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) -#' -voccTraj <- function(lonlat, vel, ang, mn, tyr, trajID = 1:nrow(lonlat), correct = FALSE) { - - if (correct == TRUE) { - e <- terra::ext(vel) + (rep(terra::res(vel), each = 2) * c(1, 1, 1, 1)) - vel <- terra::extend(vel, e, fill = NA) - ang <- terra::extend(ang, e, fill = NA) - mn <- terra::extend(mn, e, fill = NA) - } - - # make sure all raster has consistent NAs - vel[is.na(ang)] <- NA - vel[is.na(mn)] <- NA - ang[is.na(vel)] <- NA - mn[is.na(vel)] <- NA - - # Set up variables to catch results, allocating the right amount of memory - nc <- nrow(lonlat) - remaining <- rep(tyr, nc) # String containing the time remaining for each trajectory - llon <- rep(NA, (nc * tyr) + nc) # Starting lons, plus one more set for each iteration - llat <- rep(NA, (nc * tyr) + nc) - rem <- rep(NA, (nc * tyr) + nc) # this is to keep track of trajectories trapped in internal sinks - - # populate the first n slots with starting points - llon[1:nc] <- lonlat[, 1] - llat[1:nc] <- lonlat[, 2] - rem[1:nc] <- remaining - i <- 0 # set the iteration counter - land <- "N" # set to not hit land - bounce <- "N" # set to not bounced - - # Calculate the trajectories - pb <- utils::txtProgressBar(min = 0, max = 100, style = 3) - while (sum(remaining <= 0) != nc) { # while there is at least one trajectory active - - utils::setTxtProgressBar(pb, 100 * (sum(remaining <= 0) / nc)) - - llold <- lonlat # Take a copy of lonlat (starting cell xy) - resto <- which(remaining > 0) # index with remaining active trajectories - - fcells <- terra::cellFromXY(vel, llold) # focal cells - - # Extract lon and lat of landing point for the remaining active trajectories - # limit the displacement to 2 cell lengths to reduce later the number of intermediate points - dth <- max(terra::res(vel)) * 222000 - dis <- data.table::fifelse(dth < (abs(vel[fcells[resto]]) * remaining[resto] * 1000), # If - dth, # Do this - (abs(vel[fcells[resto]]) * remaining[resto] * 1000)[, 1]) # else - - - # JDE - The error occurs here but doesn't start here. ang[fcells[resto]][, 1] is a NA and therefore NA is introduced into lonlat - # latlon is not NA, but it seems somewhere a land cell or a NA velocity cell is introduced into llold - # I think this starts on the previous iteration. Obviously a land cell is not corrected. I think this is in Step 4.... - - # distance input in meters. The function adjusts internally for -180-180 and pole crossings - lonlat[resto, ] <- as.data.frame( - geosphere::destPoint(p = llold[resto, ], b = ang[fcells[resto]][, 1], d = dis) - ) - - tcells <- terra::cellFromXY(vel, lonlat) - - # Step 1. where the trajectory is still in the same cell by tyr, it has terminated. ----- - # Flag those trajectories by resetting the reminding time to 0 to get them out of the next iteration. - remaining[fcells == tcells] <- 0 - if (sum(remaining == 0) == nc) { - break # to avoid error when only 1 trajectory is left and finishes inside a cell - } - - # Step 2. For the rest, get the last point in the starting cell and the first point in a new cell ----- - resto <- which(remaining > 0) # update resto - d <- round((geosphere::distGeo(llold[resto, ], lonlat[resto, ]) / 1000), 0) - d[d == 0] <- 1 - - - Trajxy <- splitLine(A = llold[resto, ], B = lonlat[resto, ], nn = d) - - # now get a list with the cells for the points in each trajectory - if (is.list(Trajxy)) { - Trajcells <- lapply(Trajxy, terra::cellFromXY, object = vel) - # the first new cell in the trajectory - index <- lapply(Trajcells, function(x) which(x != x[1])[1]) - newcell <- mapply(function(X, Y) { - X[Y] - }, X = Trajcells, Y = index) - - # the coordinates for that first point out of the focal cell - newxy <- as.data.frame(t(mapply(function(X, Y) { - X[Y, ] - }, X = Trajxy, Y = index))) - - # the coordinates for the last focal cell point - oldxy <- as.data.frame(t(mapply(function(X, Y) { - X[Y - 1, ] - }, X = Trajxy, Y = index))) - } else { # if 1 trajectory the output from splitLine is a single matrix instead of a list - Trajcells <- apply(Trajxy, 1, terra::cellFromXY, object = vel) - newcell <- Trajcells[which(Trajcells != Trajcells[1])[1]] - newxy <- data.frame(x = Trajxy[which(Trajcells != Trajcells[1])[1], 1], y = Trajxy[which(Trajcells != Trajcells[1])[1], 2]) - oldxy <- data.frame(x = Trajxy[(which(Trajcells != Trajcells[1])[1]) - 1, 1], y = Trajxy[(which(Trajcells != Trajcells[1])[1]) - 1, 2]) - } - - # starting and destination cell ids - oldcell <- fcells[resto] - - # Get the new velocity at the new cells - velend <- terra::extract(vel, newcell) - - # set remaining time for each of the running trajectories - remaining[resto][is.na(velend)] <- remaining[resto][is.na(velend)] - ((geosphere::distGeo(llold[resto, ][is.na(velend), ], oldxy[is.na(velend), ]) / 1000) / abs(vel[fcells[resto]][is.na(velend)])) - remaining[resto][!is.na(velend)] <- remaining[resto][!is.na(velend)] - ((geosphere::distGeo(llold[resto, ][!is.na(velend), ], newxy[!is.na(velend), ]) / 1000) / abs(vel[fcells[resto]][!is.na(velend)])) - - # For those ending in marine cells update lonlat info to the new point - lonlat[resto, ][!is.na(velend), ] <- newxy[!is.na(velend), ] - - # For those ending in land cells update lonlat info to the last marine point - lonlat[resto, ][is.na(velend), ] <- oldxy[is.na(velend), ] - - # Step 3. From step 3 onwards check if the trajectory has bounced back to the origin cell. ----- - # If so, redirect along cell border. - if (i >= 1) { - current <- newcell[!is.na(velend)] # current cell - last <- oldcell[!is.na(velend)] # last cell - if (land == "Y" & bounce == "Y") { # Given the counter increases each time the trajectory table is updated, - # where some traj hit land AND bounced in the previous iteration the values were repeated twice for all cells. Hence, need to go 3 steps back instead of 1 - print("Option 1: Go back 3 steps") - last2 <- terra::cellFromXY(vel, cbind(llon[(((i - 3) * nc) + 1):(((i - 3) * nc) + nc)][resto][!is.na(velend)], llat[(((i - 3) * nc) + 1):(((i - 3) * nc) + nc)][resto][!is.na(velend)])) # last but one cell - land <- "N" # set back to no hit land - bounce <- "N" - } else if (xor(land == "Y", bounce == "Y")) { # if one of the two conditions then need to go two steps back - print("Option 2: Go back 2 steps") - last2 <- terra::cellFromXY(vel, cbind(llon[(((i - 2) * nc) + 1):(((i - 2) * nc) + nc)][resto][!is.na(velend)], llat[(((i - 2) * nc) + 1):(((i - 2) * nc) + nc)][resto][!is.na(velend)])) - land <- "N" - bounce <- "N" - } else { # if non of the two, then just one step back - print("Option 3: Go back 1 step") - last2 <- terra::cellFromXY(vel, cbind(llon[(((i - 1) * nc) + 1):(((i - 1) * nc) + nc)][resto][!is.na(velend)], llat[(((i - 1) * nc) + 1):(((i - 1) * nc) + nc)][resto][!is.na(velend)])) - } - - # Identify the bouncing trajectories - ind <- which(current == last2 & current != last) - if (length(ind) > 0) { - bounce <- "Y" - - # take the mean velocity from the appropriate lat/lon vel components. - vb <- mapply( - function(X, Y) { - ifelse(abs(X - Y) > 1, # if - mean(c(as.numeric((vel[X] * sin(pi * ang[X] / 180))), as.numeric((vel[Y] * sin(pi * ang[Y] / 180))))), # do this - mean(c(as.numeric((vel[X] * cos(pi * ang[X] / 180))), as.numeric((vel[Y] * cos(pi * ang[Y] / 180))))) # else do this - )}, - X = current[ind], Y = last[ind] - ) - - # take the corresponding angle (0/180 / 90/270 for lat / lon movements) - ab <- mapply(function(X, Y, Z) { - ifelse(abs(X - Y) > 1 & Z > 0, # if - 90, # do this - ifelse(abs(X - Y) > 1 & Z < 0, # else - 270, # do this - ifelse(abs(X - Y) == 1 & Z > 0, - 0, # do this - 180 # else - )))}, - X = current[ind], Y = last[ind], Z = vb) - - if (is.matrix(ab)) { - ab <- ab[, 1] - } - - # Extract lon and lat of point previous to bounce and new destination point for the remaining active trajectories - p <- oldxy[!is.na(velend), ][ind, ] - dis <- ifelse(dth < (abs(vb) * remaining[resto][!is.na(velend)][ind] * 1000), - dth, - (abs(vb) * remaining[resto][!is.na(velend)][ind] * 1000)) - destp <- as.data.frame(geosphere::destPoint(p, ab, dis)) - - # where the trajectory is still in the same cell by tyr, it has terminated. Flag those trajectories by resetting the reminding time to 0 to get them out of the next iteration. - same <- which(terra::cellFromXY(vel, p) == terra::cellFromXY(vel, destp)) - remaining[resto][!is.na(velend)][ind][same] <- 0 - - # For the rest, get the correct destination point - rest <- which(terra::cellFromXY(vel, p) != terra::cellFromXY(vel, destp)) # update rest - - if (length(rest) > 0) { - - d <- round((geosphere::distGeo(p[rest, ], destp[rest, ]) / 1000), 0) - d[d == 0] <- 1 - - Trajxy <- splitLine(A = p[rest, ], B = destp[rest, ], nn = d) - - # now get a list with the cells for the points in each trajectory - if (is.list(Trajxy)) { - Trajcells <- lapply(Trajxy, terra::cellFromXY, object = vel) - - # the first new cell in the trajectory - index <- lapply(Trajcells, function(x) which(x != x[1])[1]) - - # update the coordinates for that first point out of the focal cell - newxy[!is.na(velend), ][ind, ][rest, ] <- as.data.frame(t(mapply(function(X, Y) { - X[Y, ] - }, X = Trajxy, Y = index))) - - # update points info for the old position in case the trajectory hit land and need to be repositioned - i <- i + 1 - llon[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 1] - llat[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 2] - llon[((i * nc) + 1):((i * nc) + nc)][resto][!is.na(velend)][ind][rest] <- oldxy[!is.na(velend), ][ind, ][rest, 1] - llat[((i * nc) + 1):((i * nc) + nc)][resto][!is.na(velend)][ind][rest] <- oldxy[!is.na(velend), ][ind, ][rest, 2] - - # update the coordinates for the last focal cell point - oldxy[!is.na(velend), ][ind, ][rest, ] <- as.data.frame(t(mapply(function(X, Y) { - X[Y - 1, ] - }, X = Trajxy, Y = index))) - - } else { # if all the trajectories have same number of points the output from gc - # Intermediate is a matrix instead of a list - - # Trajcells <- apply(Trajxy, 1, terra::cellFromXY, object = vel) - Trajcells <- apply(vel, 1, terra::cellFromXY, xy = Trajxy) - - newxy[!is.na(velend), ][ind, ][rest, ] <- data.frame(x = Trajxy[which(Trajcells != Trajcells[1])[1], 1], y = Trajxy[which(Trajcells != Trajcells[1])[1], 2]) - i <- i + 1 - llon[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 1] # necessary to not leave the other cells as NAs - llat[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 2] - llon[((i * nc) + 1):((i * nc) + nc)][resto][!is.na(velend)][ind][rest] <- oldxy[!is.na(velend), ][ind, ][rest, 1] - llat[((i * nc) + 1):((i * nc) + nc)][resto][!is.na(velend)][ind][rest] <- oldxy[!is.na(velend), ][ind, ][rest, 2] - oldxy[!is.na(velend), ][ind, ][rest, ] <- data.frame(x = Trajxy[(which(Trajcells != Trajcells[1])[1]) - 1, 1], y = Trajxy[(which(Trajcells != Trajcells[1])[1]) - 1, 2]) - } - - # Update main traj info - # get remaining time for each of the running trajectories - remaining[resto][!is.na(velend)][ind][rest] <- remaining[resto][!is.na(velend)][ind][rest] - ((geosphere::distGeo(newxy[!is.na(velend), ][ind, ][rest, ], p[rest, ]) / 1000) / abs(vb[rest])) - rem[((i * nc) + 1):((i * nc) + nc)] <- remaining - lonlat[resto, ][!is.na(velend), ][ind, ][rest, ] <- newxy[!is.na(velend), ][ind, ][rest, ] - - # update the velocity at new cells (some of the redirected bouncing trajectories might have ended in a land cell) - newcell[!is.na(velend)][ind][rest] <- terra::cellFromXY(vel, newxy[!is.na(velend), ][ind, ][rest, ]) - velend[!is.na(velend)][ind][rest] <- terra::extract(vel, terra::cellFromXY(vel, newxy[!is.na(velend), ][ind, ][rest, ])) - } - } - } - - # Step 4. For those traj ending on land redirect the trajectories if possible ----- - if (sum(is.na(velend)) > 0) { - - land <- "Y" # to know if land was hit in the next loop when looking at bouncing trajectories - onland <- which(is.na(velend)) # Identify which rows of velend are on land - - # break and produce error if trajectories go beyond raster extent - if (anyNA(newcell[onland])) { - stop("Trajectories beyond raster extent - set correct = TRUE or limit lonlat to a sufficiently smaller domain relative to current raster extent") - } - - # For each cell that ends on land, look for a suitable target cell... - # Make list of candidate cell IDs: overlap between cells adjacent - # to "from" (fcells[is.na(sflags)][onland]) AND "to" (newcell[onland])cells - # that are in the direction of movement (any other cells would mean "un-natural" movements). - ccells <- suppressWarnings(mapply(function(X, Y) { - Z <- as.numeric(terra::intersect(terra::adjacent(vel, X, directions = 8), terra::adjacent(vel, Y, directions = 8))) - Z[!is.na(vel[Z])] - }, X = oldcell[onland], Y = newcell[onland], SIMPLIFY = FALSE)) - - # Now check if a suitable cell, other than the focal cell, is available along the line of movement - # given departure direction. Diagonals included. - # First, calculate the velocity associated to each focal cell with which to - # move along the cell border given direction - # Then - # if transfer is vertical (upper/lower cell) use horizontal velocity component, - # if transfer is horizontal, use vertical component - # negative values indicate E-W and N-S movements - - v <- mapply(function(X, Y) { - ifelse(abs(X - Y) > 1, abs(vel[Y]) * sin(pi * ang[Y] / 180), abs(vel[Y]) * cos(pi * ang[Y] / 180)) - }, X = newcell[onland], Y = oldcell[onland]) - - a <- rep(NA, length(v)) # to store angles for border movement - - for (k in 1:length(v)) { - target <- newcell[onland][k] - focal <- oldcell[onland][k] - - if ((target - focal) > 1 & v[k] > 0) { # vertical transfer, horizontal movement - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal + 1, focal + ncol(vel) + 1)) # JDE Focal cell + 1 and focal diagonal on next column (ncol + a) - a[k] <- 90 # Then set angle - } else if ((target - focal) > 1 & v[k] < 0) { - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal - 1, focal + ncol(vel) - 1)) - a[k] <- 270 - } else if ((target - focal) < -1 & v[k] > 0) { - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal + 1, focal - ncol(vel) + 1)) - a[k] <- 90 - } else if ((target - focal) < -1 & v[k] < 0) { - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal - 1, focal - ncol(vel) - 1)) - a[k] <- 270 - } else if ((target - focal) == 1 & v[k] > 0) { # horizontal transfer, vertical movement - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal - ncol(vel), focal - ncol(vel) + 1)) - a[k] <- 0 - } else if ((target - focal) == 1 & v[k] < 0) { - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal + ncol(vel), focal + ncol(vel) + 1)) - a[k] <- 180 - } else if ((target - focal) == -1 & v[k] > 0) { - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal - ncol(vel), focal - ncol(vel) - 1)) - a[k] <- 0 - } else if ((target - focal) == -1 & v[k] < 0) { - ccells[[k]] <- subset(ccells[[k]], ccells[[k]] %in% c(focal + ncol(vel), focal + ncol(vel) - 1)) - a[k] <- 180 - } - } - - rm(k) - - # Flag out the cells for which no suitable potential neighbours are available - # (all potential neighbours are NA; the trajectory has nowhere to go) - empty <- as.logical(lapply(ccells, function(x) identical(x, numeric(0)))) - remaining[resto][onland][empty] <- 0 - lonlat[resto, ][onland, ][empty, ] <- oldxy[onland, ][empty, ] - - # if there are cells with available neighbours - if (sum(!empty) > 0) { - - # select those cells - leftcell <- ccells[!empty] - focal <- oldcell[onland][!empty] - target <- newcell[onland][!empty] - nxy <- newxy[onland, ][!empty, ] - oxy <- oldxy[onland, ][!empty, ] - vleft <- as.numeric(v[!empty]) - aleft <- a[!empty] - - # Check if there is an actual suitable neighbour - # sst at focal and neighbouring cells - - sst <- lapply(leftcell, function(x) as.numeric(terra::extract(mn, x, raw = TRUE))) - - # if warming (cooling), need a suitable neighbour with cooler (warmer) local temperatures - for (k in 1:length(leftcell)) { - - # sst (list) is the sst from the mn climate layer for the adjoining cell - # focal is the focal cell number - # mn is the climate temp layer - # mn[focal[k]] is the temperature at the focal cell - # sst[[k]] is the sst in the adjoining cells - leftcell[k] <- ifelse(vel[focal[k]] > 0 & sum(sst[[k]] < mn[focal[k]]) > 0, # if - leftcell[[k]][which.min(sst[[k]])], # do this - ifelse(vel[focal[k]] < 0 & sum(sst[[k]] > mn[focal[k]]) > 0, # else - leftcell[[k]][which.max(sst[[k]])], # do this - NA)) # else - } # End finding a suitable cell - - # Flag cells for which no suitable neighbours are available (no suitable sst) - remaining[resto][onland][!empty][is.na(as.numeric(leftcell))] <- 0 - lonlat[resto, ][onland, ][!empty, ][is.na(as.numeric(leftcell)), ] <- oldxy[onland, ][!empty, ][is.na(as.numeric(leftcell)), ] - - # For those traj with a suitable neighbour, relocate the trajectory to the nearest newcell point and calculate the time needed to reach it - if (length(target[!is.na(as.numeric(leftcell))]) > 0) { - dudcent <- terra::xyFromCell(vel, target[!is.na(as.numeric(leftcell))]) # Coordinates of dud target cell on land - newcent <- terra::xyFromCell(vel, as.numeric(leftcell[!is.na(as.numeric(leftcell))])) # Coordinates of new target cell - oldcent <- terra::xyFromCell(vel, focal[!is.na(as.numeric(leftcell))]) # Coordinates of departure cell - loncent <- data.frame(oldx = oldcent[, 1], NAx = dudcent[, 1], newx = newcent[, 1]) # An object containing the longitudes of the cells involved - needed for fixing dateline - - for (k in 1:nrow(loncent)) { - - # Remove sign change for dateline, if needed - if (abs(max(loncent[k, ]) - min(loncent[k, ])) > 180) { - loncent[k, ][loncent[k, ] < 0] <- loncent[k, ][loncent[k, ] < 0] + 360 - } - - # Figure out position of dividing longitude - loncent$lonline[k] <- ifelse(abs(loncent[k, 2] - loncent[k, 3]) > abs(loncent[k, 2] - loncent[k, 1]), # if lon difference between NA cell and new cell is larger than NA to old cell - mean(c(loncent[k, 1], loncent[k, 3])), mean(c(loncent[k, 1], loncent[k, 2])) - ) - - loncent$lonline[k] <- loncent$lonline[k] - (360 * floor((loncent$lonline[k] + 180) / 360)) # Return to -180o to 180o format - loncent$lonline[k] <- ifelse(loncent$lonline[k] == 180 && newcent[k, 1] < 0, -180, loncent$lonline[k]) - loncent$lonline[k] <- ifelse(loncent$lonline[k] == -180 && newcent[k, 1] > 0, 180, loncent$lonline[k]) # Arrange lonline to accommodate dateline - loncent$latline[k] <- ifelse(abs(dudcent[k, 2] - newcent[k, 2]) > abs(dudcent[k, 2] - oldcent[k, 2]), mean(c(oldcent[k, 2], newcent[k, 2])), mean(c(oldcent[k, 2], dudcent[k, 2]))) - loncent$dirlon[k] <- ifelse(newcent[k, 1] > loncent$lonline[k], 1, -1) # Adjust direction of movement relative to lonline - loncent$dirlat[k] <- ifelse(newcent[k, 2] > loncent$latline[k], 1, -1) # Same for latline - loncent$lonnew[k] <- loncent$lonline[k] + (loncent$dirlon[k] * 0.0001) # add a small offset to put the traj on the new cell - loncent$latnew[k] <- loncent$latline[k] + (loncent$dirlat[k] * 0.0001) - loncent$latnew[k] <- ifelse(loncent$latnew[k] > 90, 90, ifelse(loncent$latnew[k] < -90, -90, loncent$latnew[k])) - loncent$lonnew[k] <- loncent$lonnew[k] - (360 * floor((loncent$lonnew[k] + 180) / 360)) - } - - loncent$oldcell <- focal[!is.na(as.numeric(leftcell))] - loncent$lonold <- oxy[!is.na(as.numeric(leftcell)), 1] # oxy <- old xy - loncent$latold <- oxy[!is.na(as.numeric(leftcell)), 2] - loncent$dis <- (geosphere::distGeo(oxy[!is.na(as.numeric(leftcell)), ], loncent[, 8:9]) / 1000) # distance from dold to new point - loncent$dur <- loncent$dis / abs(vleft[!is.na(as.numeric(leftcell))]) # time taken to get there - loncent$remaining <- remaining[resto][onland][!empty][!is.na(as.numeric(leftcell))] - loncent$dur - - # where remaining is < 0, the trajectory terminates before reaching new destination. - # Flag those traj out and place them in the corresponding final coordinates. - if (sum(loncent$remaining < 0) > 0) { - loncent[loncent$remaining < 0, 8:9] <- as.matrix(geosphere::destPoint( - oxy[!is.na(as.numeric(leftcell)), ][loncent$remaining < 0, ], aleft[!is.na(as.numeric(leftcell))][loncent$remaining < 0], - (abs(vleft[!is.na(as.numeric(leftcell))][loncent$remaining < 0]) * (remaining[resto][onland][!empty][!is.na(as.numeric(leftcell))][loncent$remaining < 0]) * 1000) - )) - loncent$remaining[loncent$remaining < 0] <- 0 - } - - # Finally, update lonlat - i <- i + 1 # increase the counter by 1 - - # first input the point before hitting land - lonlat[resto, ][onland, ][!empty, ][!is.na(as.numeric(leftcell)), ] <- loncent[, 11:12] - llon[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 1] # Add final lon to the list - llat[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 2] # Add final lat to the list - - # then the new point to carry the trajectory on with - remaining[resto][onland][!empty][!is.na(as.numeric(leftcell))] <- loncent$remaining - rem[((i * nc) + 1):((i * nc) + nc)] <- remaining - lonlat[resto, ][onland, ][!empty, ][!is.na(as.numeric(leftcell)), ] <- loncent[, 8:9] - - } - } - } # End of Step 4. There should be no land cells after Step 4 - - # Step 5. Update register before moving to the next projection step ---- - i <- i + 1 # increase the counter by 1 - llon[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 1] # Add final lon to the list - llat[((i * nc) + 1):((i * nc) + nc)] <- lonlat[, 2] # Add final lat to the list - rem[((i * nc) + 1):((i * nc) + nc)] <- remaining - - # Step 6. Check for trajectories trapped in internal sinks and terminate them ---- - if (i >= 8) { - cs <- which(remaining != 0) - # index the trajectories trapped in a sink (those for which distance travelled over the last 4 iterations is less than 0.5 km) - if (length(cs) > 0) { - ind <- which(unlist(lapply(cs, function(x) all(utils::tail(abs(diff(rem[seq(x, by = nc, length.out = i + 1)], 1))[abs(diff(rem[seq(x, by = nc, length.out = i + 1)], 1)) != 0], 4) < 0.5)))) - # Terminate trapped trajectories - if (length(ind) > 0) { - remaining[cs][ind] <- 0 - } - } - } # End Step 6 - - } # End of while remaining - close(pb) - - - # prepare output - trajIDs <- rep(trajID, (length(llon) / nc)) # rep(initialcells,(length(llon)/nc)) - traj <- stats::na.omit(data.frame(x = llon, y = llat, trajIDs = trajIDs)) - # remove duplicated points (to keep track of the trajectory's ID all trajectories are repeated over iterations even if they had terminated) - traj <- traj[!duplicated(traj), ] - - return(traj) -} From ec27da75a3fa96ba58092eabf9c1d0681cdba2f0 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Tue, 5 Aug 2025 09:52:57 +1000 Subject: [PATCH 03/10] Style voccTraj --- R/voccTraj.R | 167 +++++++++++++++++++++++++++------------------------ 1 file changed, 89 insertions(+), 78 deletions(-) diff --git a/R/voccTraj.R b/R/voccTraj.R index 033471a..70b708c 100644 --- a/R/voccTraj.R +++ b/R/voccTraj.R @@ -67,26 +67,24 @@ #' # This accounts for the extent issue #' traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) #' -#' voccTraj <- function(lonlat, - vel, ang, mn, - vel_c, ang_c, mn_c, - fine_coast, lk_up, - tstep, tyr = 50) { - + vel, ang, mn, + vel_c, ang_c, mn_c, + fine_coast, lk_up, + tstep, tyr = 50) { # Setup ------------------------------------------------------------------- y_res <- x_res <- res(vel)[1] # Set resolution of operations # Constrain max velocity to avoid stepping over grid squares - max_vel = 111.325*x_res/tstep + max_vel <- 111.325 * x_res / tstep vel[vel > max_vel] <- max_vel vel[vel < -max_vel] <- -max_vel vel_c[vel_c > max_vel] <- max_vel vel_c[vel_c < -max_vel] <- -max_vel lonlat <- lonlat %>% - dplyr::select(x, y) %>% # Collect just lon and lat (in case there's anything else there) + dplyr::select(x, y) %>% # Collect just lon and lat (in case there's anything else there) as.data.frame() tcells <- terra::cellFromXY(vel, lonlat) # # Cell IDs of starting cells n <- nrow(lonlat) # Get number of cells in your sequence @@ -103,8 +101,8 @@ voccTraj <- function(lonlat, Steps <- numeric((n * tyr / tstep) + n) # Needs to have a set of starting steps, plus one more set for each time step # Populate the first n slots with starting points - llon[1:n] <- lonlat[,1] - llat[1:n] <- lonlat[,2] + llon[1:n] <- lonlat[, 1] + llat[1:n] <- lonlat[, 2] cellIDend[1:n] <- tcells # coast[1:n] <- scoast flags[1:n] <- NA @@ -120,36 +118,40 @@ voccTraj <- function(lonlat, # Trajectory helper functions # Function for to grep min distance - mfind <- function(rw){ + mfind <- function(rw) { X <- which.min(rw) return(X) } # Function for to extract corner coordinates per row - mplace <- function(rw){ - X <- rw[(rw[1]+1)] + mplace <- function(rw) { + X <- rw[(rw[1] + 1)] Y <- rw[rw[1]] return(c(X, Y)) } # Simple circular functions - deg2rad <- function(deg) return(deg*pi/180) - rad2deg <- function(rad) return(180*rad/pi) + deg2rad <- function(deg) { + return(deg * pi / 180) + } + rad2deg <- function(rad) { + return(180 * rad / pi) + } # A function to find the nearest coastal cell - get_nearest_coastal_cell <- function(x, y, tccell,...) { + get_nearest_coastal_cell <- function(x, y, tccell, ...) { t_block <- which(lk_up[] == tccell) # The cellIDs of lk_up cst_pts <- terra::xyFromCell(lk_up, t_block) %>% as.data.frame() %>% st_as_sf(coords = c("x", "y"), crs = crs(rast())) # Here, we can't search for corners and add random "fuzz" to we collect 10 random points (if there are 10), and select the nearest of those, instead. If there are 10 or fewer, just pick the nearest - if(nrow(cst_pts) > 10) { - cst_pts <- cst_pts[sample(1:nrow(cst_pts), 10, replace = FALSE),] + if (nrow(cst_pts) > 10) { + cst_pts <- cst_pts[sample(1:nrow(cst_pts), 10, replace = FALSE), ] } # The unspoken "else" is that we just retain the cst_pts we have pt <- st_as_sf(data.frame(x = x, y = y), coords = c("x", "y"), crs = crs(rast())) # The point departed from nearest <- st_distance(cst_pts, pt) %>% which.min() - out <- cst_pts[nearest,] %>% + out <- cst_pts[nearest, ] %>% st_coordinates() %>% as.data.frame() %>% dplyr::rename(x = 1, y = 2) @@ -158,9 +160,9 @@ voccTraj <- function(lonlat, # Find new destination, given velocity (km/yr), angle (º), time step (yr) and initial coordinates (ºlon, ºlat); max allowed jump is 1 deg # vell = vel[fcells] %>% pull(1); angg = ang[fcells] %>% pull(1); timestep = tstep; ll = llold - destcoords <- function(vell, angg, timestep, ll, y_res, x_res){ + destcoords <- function(vell, angg, timestep, ll, y_res, x_res) { latshift <- (abs(vell) * timestep * cos(deg2rad(angg))) / 111.325 # Calculate shift in lat - latnew <- ll[,2] + latshift # Find new lat...first approximation + latnew <- ll[, 2] + latshift # Find new lat...first approximation lonshift <- (abs(vell) * timestep * sin(deg2rad(angg))) / (111.325 * cos(deg2rad(latnew))) # Shift in lon # Limit large longitudinal jumps at high latitudes # Because we constrain velocity to be no more than 12 * y_res, all problems will be with lonshift @@ -169,20 +171,20 @@ voccTraj <- function(lonlat, lonshift[lonshift < -x_res] <- -x_res # Now on that basis, adjust latshift x_gt <- which(lonshift == x_res) # Indices for adjusted lon shifts - latshift[x_gt] <- ((x_res*111.325 * cos(deg2rad(ll[x_gt,2])))/tan(deg2rad(angg[x_gt])))/111.325 # Using trig on distances + latshift[x_gt] <- ((x_res * 111.325 * cos(deg2rad(ll[x_gt, 2]))) / tan(deg2rad(angg[x_gt]))) / 111.325 # Using trig on distances x_lt <- which(lonshift == -x_res) # Indices for adjusted lon shifts - latshift[x_lt] <- ((x_res*111.325 * cos(deg2rad(ll[x_lt,2])))/tan(deg2rad(angg[x_lt])))/111.325 # Using trig on distances - latnew <- ll[,2] + latshift # Find new lat by adding the adjusted lats + latshift[x_lt] <- ((x_res * 111.325 * cos(deg2rad(ll[x_lt, 2]))) / tan(deg2rad(angg[x_lt]))) / 111.325 # Using trig on distances + latnew <- ll[, 2] + latshift # Find new lat by adding the adjusted lats # Stop new lat from jumping the poles latnew[latnew > 90] <- 90 latnew[latnew < -90] <- -90 # Adjust lon - lonnew <- ll[,1] + lonshift # Find new lon...first approximation + lonnew <- ll[, 1] + lonshift # Find new lon...first approximation # Adjust for dateline jumps lonnew <- lonnew - (360 * floor((lonnew + 180) / 360)) return(data.frame(lonnew, latnew) %>% - setNames(c("dlon", "dlat"))) + setNames(c("dlon", "dlat"))) } # Function for to find closest cooler/warmer cell within the union of two levels of 8-cell adjacency from the "departed" and "destination" cells @@ -190,7 +192,7 @@ voccTraj <- function(lonlat, # Find clumps of ocean; the rule is that you can't jump from one clump to another, because this would mean passing over unbroken land xy <- terra::xyFromCell(mn, as.vector(as.matrix(rw))) %>% as.data.frame() - bfr = ((y_res*111.325) + 1) * 1000 # Set buffer to be 1 grid-square width at the equator + 1 km + bfr <- ((y_res * 111.325) + 1) * 1000 # Set buffer to be 1 grid-square width at the equator + 1 km xy <- xy %>% st_as_sf(coords = c("x", "y"), crs = "EPSG:4326") sp_buffer <- st_buffer(st_as_sf(xy), bfr) # Remembering that buffer is in metres @@ -216,28 +218,32 @@ voccTraj <- function(lonlat, as.data.frame() #**** search_cells <- terra::cellFromXY(mn, search_xy) # Which cells are these # Check if any of these are NOT in in EITHER fine coast or mn, and eliminate, if needed - to_keep <- c(which(!is.na(terra::extract(mn, search_xy, ID = FALSE) %>% - pull(1))), # Coords in the coarse grid - which(search_cells %in% fn_lk_up)) %>% + to_keep <- c( + which(!is.na(terra::extract(mn, search_xy, ID = FALSE) %>% + pull(1))), # Coords in the coarse grid + which(search_cells %in% fn_lk_up) + ) %>% unique() - search_xy1 <- search_xy[to_keep,] %>% + search_xy1 <- search_xy[to_keep, ] %>% as.data.frame() %>% distinct() # Get the ssts in the cells to search or <- terra::extract(mn_c, search_xy1, cells = TRUE, xy = TRUE) %>% dplyr::rename(sst = 2, x = 4, y = 5) # If velocity is positive, find nearest cell that is cooler, if there is one - if(unlist(vel_c[r1]) > 0) { + if (unlist(vel_c[r1]) > 0) { o <- or %>% dplyr::filter(sst < unlist(mn_c[r1])) %>% na.omit() - if(nrow(o) == 0) { + if (nrow(o) == 0) { dest_cell <- NA # Set condition with which to ID stuck cells } else { pt <- terra::xyFromCell(mn_c, r1) %>% data.frame() - dest_cell <- st_distance(st_as_sf(pt, coords = c("x", "y"), crs = crs(rast())), - st_as_sf(data.frame(o), coords = c("x", "y"), crs = crs(rast()))) %>% + dest_cell <- st_distance( + st_as_sf(pt, coords = c("x", "y"), crs = crs(rast())), + st_as_sf(data.frame(o), coords = c("x", "y"), crs = crs(rast())) + ) %>% which.min() %>% o$cell[.] } @@ -245,13 +251,15 @@ voccTraj <- function(lonlat, o <- or %>% dplyr::filter(sst > unlist(mn_c[r1])) %>% na.omit() - if(nrow(o) == 0) { + if (nrow(o) == 0) { dest_cell <- NA # Set condition with which to ID stuck cells } else { pt <- terra::xyFromCell(mn_c, r1) %>% data.frame() - dest_cell <- st_distance(st_as_sf(pt, coords = c("x", "y"), crs = crs(rast())), - st_as_sf(o, coords = c("x", "y"), crs = crs(rast()))) %>% + dest_cell <- st_distance( + st_as_sf(pt, coords = c("x", "y"), crs = crs(rast())), + st_as_sf(o, coords = c("x", "y"), crs = crs(rast())) + ) %>% which.min() %>% o$cell[.] } @@ -262,56 +270,56 @@ voccTraj <- function(lonlat, # Loop -------------------------------------------------------------------- # Loop through the trajectories - for(i in 1:(tyr/tstep)) { # 1:(tyr/tstep) + for (i in 1:(tyr / tstep)) { # 1:(tyr/tstep) llold <- lonlat # Take a copy of lonlat - fcells <- terra::cellFromXY(vel,llold) # Get the cells that the trajectories start in + fcells <- terra::cellFromXY(vel, llold) # Get the cells that the trajectories start in # Pull velocity and angle from the "inland" versions, because we always ensure that we can find destination cells for departures within the coastal "blind spot" vc <- vel_c[fcells] %>% pull(1) ac <- ang_c[fcells] %>% pull(1) llc <- llold # Get new locations lonlat <- destcoords(vc, ac, tstep, llc, y_res, x_res) # Extract lon and lat of landing point - tcells <- terra::cellFromXY(vel,lonlat) # Get the cells that the trajectories end in - sflags[which(is.na(tcells))] <- 1 # Sets the trajectory to "stuck" if it exits the velocity field (i.e., tcells == NA) + tcells <- terra::cellFromXY(vel, lonlat) # Get the cells that the trajectories end in + sflags[which(is.na(tcells))] <- 1 # Sets the trajectory to "stuck" if it exits the velocity field (i.e., tcells == NA) # Bounce stuck cells stuck_cells <- which(!is.na(sflags)) - lonlat[stuck_cells,] <- llold[stuck_cells,] # Bounce the corresponding coordinates across for "stuck" cells + lonlat[stuck_cells, ] <- llold[stuck_cells, ] # Bounce the corresponding coordinates across for "stuck" cells tcells[stuck_cells] <- fcells[stuck_cells] # Bounce the corresponding cells across for "stuck" cells # Deal with cells that end on land - onland <- which(is.na(vel[tcells])) %>% # Identify which rows of velend are on land, provided that they are not stuck + onland <- which(is.na(vel[tcells])) %>% # Identify which rows of velend are on land, provided that they are not stuck dplyr::setdiff(stuck_cells) # Ignoring stuck cells - if(length(onland) > 0){ # Only bother if there is at least one cell that returns a NA velocity = land + if (length(onland) > 0) { # Only bother if there is at least one cell that returns a NA velocity = land # Here, you need to check whether it really *IS* on land, or whether it is just in the coastal "blind spot" - fn <- terra::extract(fine_coast, lonlat[onland,], ID = FALSE) %>% + fn <- terra::extract(fine_coast, lonlat[onland, ], ID = FALSE) %>% pull(1) onland <- onland[which(is.na(fn))] - if(length(onland) > 0) { + if (length(onland) > 0) { # Collect the stuff we need here for cells that are really onland - fpos <- llold[onland,] - tpos <- lonlat[onland,] + fpos <- llold[onland, ] + tpos <- lonlat[onland, ] SFlags <- sflags[onland] fcell <- fcells[onland] tcell <- tcells[onland] ft <- data.frame(fcell = fcell, tcell = tcell, code = paste(fcell, tcell, sep = " "), ref = 1:length(fcell)) - ttcell <- apply(ft[,1:2], 1, get_dest_cell) + ttcell <- apply(ft[, 1:2], 1, get_dest_cell) # Filter "stuck" flags here stuck <- which(is.na(ttcell)) unstuck <- which(!is.na(ttcell)) SFlags[stuck] <- 1 # Adjust flags # Make data frame to catch data needed to find new positions ttpos <- data.frame(x = rep(NA, length(onland)), y = rep(NA, length(onland))) - ttpos[stuck,] <- fpos[stuck,] # If they're stuck, pass on starting points + ttpos[stuck, ] <- fpos[stuck, ] # If they're stuck, pass on starting points ttcell[stuck] <- fcell[stuck] # If they're stuck, pass on starting cells - if(length(unstuck) > 0) { - ttdat <- data.frame(ttcell = ttcell[unstuck], loncent = xFromCell(vel, ttcell[unstuck]), latcent = yFromCell(vel, ttcell[unstuck])) %>% # Start building coordinates + if (length(unstuck) > 0) { + ttdat <- data.frame(ttcell = ttcell[unstuck], loncent = xFromCell(vel, ttcell[unstuck]), latcent = yFromCell(vel, ttcell[unstuck])) %>% # Start building coordinates mutate(e = NA, w = NA, n = NA, s = NA, dlon = NA, dlat = NA) # To facilitate finding corners, if we need them # Separate cells in the coastal blind spot from those that are not which_coast <- which(ttdat$ttcell %in% fn_lk_up) which_not_coast <- which(!(ttdat$ttcell %in% fn_lk_up)) # For non-coastal cells - if(length(which_not_coast) > 0) { - corner_block_size <- 0.25*x_res - nc_ttdat <- ttdat[which_not_coast,] # ttdat for non-coastal cells + if (length(which_not_coast) > 0) { + corner_block_size <- 0.25 * x_res + nc_ttdat <- ttdat[which_not_coast, ] # ttdat for non-coastal cells nc_ttdat$e <- nc_ttdat$loncent + (0.5 * x_res) - (runif(1, 0, 1) * corner_block_size) nc_ttdat$w <- nc_ttdat$loncent - (0.5 * x_res) + (runif(1, 0, 1) * corner_block_size) nc_ttdat$n <- nc_ttdat$latcent + (0.5 * y_res) - (runif(1, 0, 1) * corner_block_size) @@ -325,39 +333,42 @@ voccTraj <- function(lonlat, as.vector() return(out) } - corners <- data.frame(ne = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,1], coords[,2])) %>% - mutate(nw = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,3], coords[,4]), - sw = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,5], coords[,6]), - se = get_dist(fpos[which_not_coast,2], fpos[which_not_coast,1], coords[,7], coords[,8])) - cornset <- apply(corners, 1, mfind)*2 # Identify which corners for each onland cell. Have to mul by 2 to shift along correctly. + corners <- data.frame(ne = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 1], coords[, 2])) %>% + mutate( + nw = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 3], coords[, 4]), + sw = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 5], coords[, 6]), + se = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 7], coords[, 8]) + ) + cornset <- apply(corners, 1, mfind) * 2 # Identify which corners for each onland cell. Have to mul by 2 to shift along correctly. cornset <- cbind(cornset, coords) # Add in coordinates - ttdat[which_not_coast,8:9] <- data.frame(t(apply(cornset, 1, mplace))) # Extract coordinates of correct corner + ttdat[which_not_coast, 8:9] <- data.frame(t(apply(cornset, 1, mplace))) # Extract coordinates of correct corner } # For coastal cells - if(length(which_coast) > 0) { - coastal_dat <- ttdat[which_coast,2:3] %>% - mutate(x = fpos[which_coast,1], y = fpos[which_coast,2], - tccell = ttdat[which_coast,]$ttcell) - ttdat[which_coast,8:9] <- pmap_df(coastal_dat, get_nearest_coastal_cell) + if (length(which_coast) > 0) { + coastal_dat <- ttdat[which_coast, 2:3] %>% + mutate( + x = fpos[which_coast, 1], y = fpos[which_coast, 2], + tccell = ttdat[which_coast, ]$ttcell + ) + ttdat[which_coast, 8:9] <- pmap_df(coastal_dat, get_nearest_coastal_cell) } - ttpos[unstuck,] <- ttdat[,8:9] - ttcell[unstuck] <- terra::cellFromXY(mn, ttpos[unstuck,]) + ttpos[unstuck, ] <- ttdat[, 8:9] + ttcell[unstuck] <- terra::cellFromXY(mn, ttpos[unstuck, ]) } # Collect results - lonlat[onland,] <- ttpos + lonlat[onland, ] <- ttpos tcells[onland] <- ttcell sflags[onland] <- SFlags } } - llon[((i * n) + 1): ((i * n) + n)] <- lonlat[,1] # Add lon to the list - llat[((i * n) + 1): ((i * n) + n)] <- lonlat[,2]# Add lat to the list - cellIDend[((i * n) + 1): ((i * n) + n)] <- tcells # Add cellIDs to the list - flags[((i * n) + 1): ((i * n) + n)] <- sflags # Add flags to the list - Steps[((i * n) + 1): ((i * n) + n)] <- i # Capture the time_step - print(c(i, length(onland), round(100*i/(tyr/tstep), 3), date())) # Keep a reference of progress + llon[((i * n) + 1):((i * n) + n)] <- lonlat[, 1] # Add lon to the list + llat[((i * n) + 1):((i * n) + n)] <- lonlat[, 2] # Add lat to the list + cellIDend[((i * n) + 1):((i * n) + n)] <- tcells # Add cellIDs to the list + flags[((i * n) + 1):((i * n) + n)] <- sflags # Add flags to the list + Steps[((i * n) + 1):((i * n) + n)] <- i # Capture the time_step + print(c(i, length(onland), round(100 * i / (tyr / tstep), 3), date())) # Keep a reference of progress } return(cbind(Steps, llon, llat, cellIDs, cellIDend, flags) %>% - as_tibble()) + as_tibble()) } - From 1b907666892fd92879916b5217191670f42f9008 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Wed, 27 Aug 2025 16:27:56 +1000 Subject: [PATCH 04/10] New version of VoCC --- .gitignore | 2 + DESCRIPTION | 5 +- Meta/vignette.rds | Bin 202 -> 189 bytes NAMESPACE | 1 - R/climPCA.R | 4 +- R/climPlot.R | 8 +- R/dVoCC.R | 4 +- R/gVoCC.R | 3 +- R/shiftTime.R | 2 + R/spatGrad.R | 3 +- R/sumSeries.R | 2 + R/tempTrend.R | 3 +- data-raw/do_trajectories.R | 35 ++ {R => data-raw}/utils.R | 0 {R => data-raw}/voccTraj.R | 58 ++- docs/404.html | 103 +++++ docs/LICENSE.html | 265 ++++++++++++ docs/articles/VoCC.html | 387 ++++++++++++++++++ .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 206340 bytes .../figure-html/unnamed-chunk-8-2.png | Bin 0 -> 35585 bytes docs/articles/index.html | 76 ++++ docs/authors.html | 114 ++++++ docs/bootstrap-toc.css | 60 +++ docs/bootstrap-toc.js | 159 +++++++ docs/docsearch.css | 148 +++++++ docs/docsearch.js | 85 ++++ docs/index.html | 163 ++++++++ docs/link.svg | 12 + docs/pkgdown.css | 384 +++++++++++++++++ docs/pkgdown.js | 108 +++++ docs/pkgdown.yml | 6 + docs/reference/EEZ.html | 90 ++++ docs/reference/HSST.html | 94 +++++ docs/reference/JapTC.html | 101 +++++ docs/reference/angulo.html | 100 +++++ docs/reference/climPCA.html | 150 +++++++ docs/reference/climPlot.html | 146 +++++++ docs/reference/dVoCC.html | 209 ++++++++++ docs/reference/gVoCC.html | 135 ++++++ docs/reference/index.html | 133 ++++++ docs/reference/pipe.html | 99 +++++ docs/reference/resTime.html | 156 +++++++ docs/reference/shiftTime.html | 139 +++++++ docs/reference/spatGrad.html | 144 +++++++ docs/reference/splitLine.html | 103 +++++ docs/reference/sumSeries.html | 193 +++++++++ docs/reference/tempTrend.html | 134 ++++++ docs/sitemap.xml | 25 ++ man/VoCC_get_data.Rd | 24 -- man/climPCA.Rd | 4 +- man/climPlot.Rd | 3 +- man/dVoCC.Rd | 2 +- man/gVoCC.Rd | 3 +- man/shiftTime.Rd | 2 + man/spatGrad.Rd | 3 +- man/sumSeries.Rd | 2 + man/tempTrend.Rd | 3 +- man/voccTraj.Rd | 82 ---- vignettes/{VoCC_Tutorial.Rmd => VoCC.Rmd} | 157 ++++--- 59 files changed, 4437 insertions(+), 199 deletions(-) create mode 100644 data-raw/do_trajectories.R rename {R => data-raw}/utils.R (100%) rename {R => data-raw}/voccTraj.R (93%) create mode 100644 docs/404.html create mode 100644 docs/LICENSE.html create mode 100644 docs/articles/VoCC.html create mode 100644 docs/articles/VoCC_files/figure-html/unnamed-chunk-8-1.png create mode 100644 docs/articles/VoCC_files/figure-html/unnamed-chunk-8-2.png create mode 100644 docs/articles/index.html create mode 100644 docs/authors.html create mode 100644 docs/bootstrap-toc.css create mode 100644 docs/bootstrap-toc.js create mode 100644 docs/docsearch.css create mode 100644 docs/docsearch.js create mode 100644 docs/index.html create mode 100644 docs/link.svg create mode 100644 docs/pkgdown.css create mode 100644 docs/pkgdown.js create mode 100644 docs/pkgdown.yml create mode 100644 docs/reference/EEZ.html create mode 100644 docs/reference/HSST.html create mode 100644 docs/reference/JapTC.html create mode 100644 docs/reference/angulo.html create mode 100644 docs/reference/climPCA.html create mode 100644 docs/reference/climPlot.html create mode 100644 docs/reference/dVoCC.html create mode 100644 docs/reference/gVoCC.html create mode 100644 docs/reference/index.html create mode 100644 docs/reference/pipe.html create mode 100644 docs/reference/resTime.html create mode 100644 docs/reference/shiftTime.html create mode 100644 docs/reference/spatGrad.html create mode 100644 docs/reference/splitLine.html create mode 100644 docs/reference/sumSeries.html create mode 100644 docs/reference/tempTrend.html create mode 100644 docs/sitemap.xml delete mode 100644 man/VoCC_get_data.Rd delete mode 100644 man/voccTraj.Rd rename vignettes/{VoCC_Tutorial.Rmd => VoCC.Rmd} (68%) diff --git a/.gitignore b/.gitignore index 728e389..97e604b 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,5 @@ .Rhistory .RData .DS_Store +/doc/ +/Meta/ diff --git a/DESCRIPTION b/DESCRIPTION index 99099dd..11b8cdb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,10 +33,13 @@ Suggests: knitr, mapplots, ncdf4, + patchwork, rasterVis, repmis, rmarkdown, - scales + scales, + tidyterra, + VoCCdata URL: https://github.com/JorGarMol/VoCC BugReports: https://github.com/JorGarMol/VoCC/issues License: AGPL (>= 3) diff --git a/Meta/vignette.rds b/Meta/vignette.rds index 2529a6e6bb3e2bbfd4747635371f4facefe082fd..baf81b9578b657646fcbdf244e188e45bb88f9eb 100644 GIT binary patch literal 189 zcmV;u07CyCiwFP!000001B>8dU|?WkU||K4%s?iyFpyvaVgVp#WME<71kxN~`OeOI zLAfbt;w&I>G%-%F+KiIi95e|wh#^3Ggu$W^S&%7ANUB)#5_40F(X_I=q!y&+rGSM0 zVb{<2AKl5`sg>pVMKC=mYS>`TcgxI4h1naDSpw%U2e`OFnT$c`Zon`f#r>Q``Q>^r rKk$OWwgE`|2Lg1DCg&s;qkEJqC9xz?FRciuALMQTcRGyaodEy<$wX8P literal 202 zcmV;*05$&~iwFP!0000028-ZgU|?WkU}j}xU}6R`nT3G_8xRWsF(U&D11FFc49ju3CrriH^hwX!_F2&M-`4I5Mqi(6(+D$L%H%n~?DTyVCdTB*K{UCP(06S5|2EhRU E0FZr69{>OV diff --git a/NAMESPACE b/NAMESPACE index 3eb78da..6039111 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,7 +1,6 @@ # Generated by roxygen2: do not edit by hand export("%>%") -export(VoCC_get_data) export(climPCA) export(climPlot) export(dVoCC) diff --git a/R/climPCA.R b/R/climPCA.R index 668f323..7356993 100644 --- a/R/climPCA.R +++ b/R/climPCA.R @@ -22,7 +22,7 @@ #' @export #' @author Jorge Garcia Molinos #' @examples -#' +#' \dontrun{ #' JapTC <- VoCC_get_data("JapTC.tif") #' #' comp <- climPCA(JapTC[[c(1, 3, 5)]], JapTC[[c(2, 4, 6)]], @@ -31,6 +31,8 @@ #' # Create a data frame with the necessary variables in the required order (see climAna? for details) #' clim <- comp[[2]][, c(2, 4, 3, 5, 1)] #' clim[, c("x", "y")] <- terra::xyFromCell(JapTC[[1]], clim$cid) +#' } +#' climPCA <- function(climp, climf, trans = function(x) log(x), cen = TRUE, sc = TRUE, th = 0.8) { # get a data table with the pooled values (current/future) of the clim variables clim <- data.table::data.table(rbind(terra::values(climp), terra::values(climf))) diff --git a/R/climPlot.R b/R/climPlot.R index f7904aa..73f979f 100644 --- a/R/climPlot.R +++ b/R/climPlot.R @@ -17,7 +17,7 @@ #' @export #' @author Jorge Garcia Molinos and Naoki H. Kumagai #' @examples -#' +#' \dontrun{ #' JapTC <- VoCC_get_data("JapTC.tif") #' #' # Plot climate space for the two first variables(annual precipitation and maximum temperature) @@ -32,7 +32,6 @@ #' y.name = "Temperature max (°C)" #' ) #' -#' \dontrun{ #' # output plots can be saved as: #' ggplot2::ggsave( #' plot = out, filename = file.path(getwd(), "example_plot.pdf"), @@ -44,6 +43,7 @@ climPlot <- function(xy, x.binSize, y.binSize, x.name = "V1", y.name = "V2") { yp <- xy[, 3] xf <- xy[, 2] yf <- xy[, 4] + # bins per axis x.nbins <- floor((abs(range(xp, xf)[2] - range(xp, xf)[1])) / x.binSize) y.nbins <- floor((abs(range(yp, yf)[2] - range(yp, yf)[1])) / y.binSize) @@ -102,7 +102,7 @@ climPlot <- function(xy, x.binSize, y.binSize, x.name = "V1", y.name = "V2") { Freq2D <- data.frame(x = x.bin, y = rep(y.bin, each = length(x.bin)), freq = Freq2D) Freq2D <- Freq2D[!is.na(Freq2D$freq), ] - panelAB <- ggplot2::ggplot(Freq2Dpf, ggplot2::aes(x = x, y = y, fill = freq)) + + panelAB <- ggplot2::ggplot(Freq2Dpf, ggplot2::aes(x = .data$x, y = .data$y, fill = freq)) + ggplot2::geom_raster() + ggplot2::scale_fill_gradientn( colors = r, @@ -121,7 +121,7 @@ climPlot <- function(xy, x.binSize, y.binSize, x.name = "V1", y.name = "V2") { strip.text = ggplot2::element_blank() ) - panelC <- ggplot2::ggplot(Freq2D, ggplot2::aes(x = x, y = y, fill = freq)) + + panelC <- ggplot2::ggplot(Freq2D, ggplot2::aes(x = .data$x, y = .data$y, fill = freq)) + ggplot2::geom_raster() + ggplot2::scale_fill_manual(values = c("#56B4E9", "#009E73", "#D55E00"), name = "Climate type") + ggplot2::labs(x = x.name, y = y.name) diff --git a/R/dVoCC.R b/R/dVoCC.R index 7bc7ccf..a1d8b4c 100644 --- a/R/dVoCC.R +++ b/R/dVoCC.R @@ -41,6 +41,7 @@ #' @export #' @author Jorge Garcia Molinos #' @examples +#' \dontrun{ #' JapTC <- VoCC_get_data("JapTC.tif") #' #' # Create a data frame with the necessary variables in the required order @@ -57,7 +58,6 @@ #' r1[avocc1$focal] <- avocc1$vel #' terra::plot(r1) #' -#' \dontrun{ #' # Cell-specific, distance-unrestricted climate analogue velocity based on least-cost path distances #' # First, create the conductance matrix (all land cells considered to have conductance of 1) #' r <- JapTC @@ -81,7 +81,7 @@ dVoCC <- function(clim, n, tdiff, method = "Single", climTol, geoTol, distfun = "GreatCircle", trans = NA, lonlat = TRUE) { - geoDis <- climDis <- ang <- vel <- target <- cid <- NULL # Fix devtools check warnings + geoDis <- climDis <- ang <- vel <- target <- cid <- a <- NULL # Fix devtools check warnings if (distfun == "Euclidean" && lonlat == TRUE) { print("Error: Euclidean distances specified for unprojected coordinates") diff --git a/R/gVoCC.R b/R/gVoCC.R index 1e1d55e..8a9e21d 100644 --- a/R/gVoCC.R +++ b/R/gVoCC.R @@ -19,7 +19,7 @@ #' @author Jorge Garcia Molinos #' #' @examples -#' +#' \dontrun{ #' HSST <- VoCC_get_data("HSST.tif") #' yrSST <- sumSeries(HSST, #' p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), @@ -32,6 +32,7 @@ #' #' v <- gVoCC(tr, sg) #' terra::plot(v) +#' } #' gVoCC <- function(tempTrend, spatGrad) { VoCC <- tempTrend[[1]] / spatGrad[[1]] diff --git a/R/shiftTime.R b/R/shiftTime.R index 2c1a725..5148bc5 100644 --- a/R/shiftTime.R +++ b/R/shiftTime.R @@ -21,10 +21,12 @@ #' @export #' @author Jorge Garcia Molinos and Michael T. Burrows #' @examples +#' \dontrun{ #' HSST <- VoCC_get_data("HSST.tif") #' Apr <- shiftTime(HSST, yr1 = 1960, yr2 = 2009, yr0 = 1955, th = 10, m = 4) #' #' terra::plot(Apr) +#' } shiftTime <- function(r, yr1, yr2, yr0, th, m) { # 1. Long term trends in monthly values (e.g. deg/year if temperature) m1 <- ((yr1 - yr0) * 12) + m diff --git a/R/spatGrad.R b/R/spatGrad.R index 3369ff8..60b7e87 100644 --- a/R/spatGrad.R +++ b/R/spatGrad.R @@ -26,7 +26,7 @@ #' @export #' @author Jorge Garcia Molinos, David S. Schoeman, and Michael T. Burrows #' @examples -#' +#' \dontrun{ #' HSST <- VoCC_get_data("HSST.tif") #' #' yrSST <- sumSeries(HSST, @@ -39,6 +39,7 @@ #' sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) #' #' terra::plot(sg) +#' } #' spatGrad <- function(r, th = -Inf, projected = FALSE) { diff --git a/R/sumSeries.R b/R/sumSeries.R index b1c264f..3de447a 100644 --- a/R/sumSeries.R +++ b/R/sumSeries.R @@ -28,6 +28,7 @@ #' @export #' @author Jorge Garcia Molinos #' @examples +#' \dontrun{ #' # Monthly mean SST (HadISST) data for Europe Jan-1950 to Dec-2010 #' #' HSST <- VoCC_get_data("HSST.tif") @@ -65,6 +66,7 @@ #' p = "1969-01/2009-12", yr0 = "1950-01-01", l = terra::nlyr(HSST), #' fun = myf, freqin = "months", freqout = "other" #' ) +#' } #' sumSeries <- function(r, p, yr0, l = terra::nlyr(r), fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years") { # construct xts object diff --git a/R/tempTrend.R b/R/tempTrend.R index 6b27c99..638fb53 100644 --- a/R/tempTrend.R +++ b/R/tempTrend.R @@ -19,7 +19,7 @@ #' @export #' @author Jorge Garcia Molinos and Christopher J. Brown #' @examples -#' +#' \dontrun{ #' HSST <- VoCC_get_data("HSST.tif") #' #' yrSST <- sumSeries(HSST, @@ -32,6 +32,7 @@ #' tr <- tempTrend(yrSST, th = 10) #' #' terra::plot(tr) +#' } tempTrend <- function(r, th) { y <- terra::values(r) ocean <- which(rowSums(is.na(y)) != ncol(y)) # remove land cells diff --git a/data-raw/do_trajectories.R b/data-raw/do_trajectories.R new file mode 100644 index 0000000..13d7455 --- /dev/null +++ b/data-raw/do_trajectories.R @@ -0,0 +1,35 @@ +# Run trajectories for a file --------------------------------------------- + +do_trajectories <- function(raster_stack, start_points, nyears) { + # Get the rasters we need + stacks <- build_coastal_mask(f_m = "OISST_Mask_data/mask.nc", + f_cm = "/Volumes/Jet_drive_too/Coastal_masks/coarseAusmask.nc", + f_fm = "/Volumes/Jet_drive_too/Coastal_masks/fineAusmask.nc", + rast_stk = raster_stack) + fine_coast <- stacks[[3]] + vel <- stacks[[1]][[1]] + ang <- stacks[[1]][[2]] + mn <- stacks[[1]][[3]] + vel_c <- stacks[[2]][[1]] + ang_c <- stacks[[2]][[2]] + mn_c <- stacks[[2]][[3]] + lk_up <- stacks$look_up + + lonlat <- start_points[,1:2] %>% + mutate(ID = 1:nrow(.)) %>% + as_tibble() %>% + mutate(vel = terra::extract(vel, .[,1:2], ID = FALSE) %>% + pull(voccMag), + i_coast = terra::extract(fine_coast, .[,1:2], ID = FALSE) %>% + pull(tos), + to_drop = ifelse(is.na(vel) & is.na(i_coast), NA, 1)) %>% + drop_na(to_drop) %>% + dplyr::select(x, y, ID) + out <- traject(lonlat, + vel = vel, ang = ang, mn = mn, + vel_c = vel_c, ang_c = ang_c, mn_c = mn_c, + fine_coast = fine_coast, + lk_up = lk_up, + tstep = 1/12, tyr = nyears) + return(out) +} diff --git a/R/utils.R b/data-raw/utils.R similarity index 100% rename from R/utils.R rename to data-raw/utils.R diff --git a/R/voccTraj.R b/data-raw/voccTraj.R similarity index 93% rename from R/voccTraj.R rename to data-raw/voccTraj.R index 70b708c..9691499 100644 --- a/R/voccTraj.R +++ b/data-raw/voccTraj.R @@ -11,16 +11,13 @@ #' @param vel \code{raster} with the magnitude of gradient-based climate velocity. #' @param ang \code{raster} with velocity angles in degrees. #' @param mn \code{raster} with the overall mean climatic value over the period of interest. +#' @param vel_c +#' @param ang_c +#' @param mn_c +#' @param fine_coast +#' @param lk_up +#' @param tstep #' @param tyr \code{integer} temporal length of the period of interest. -#' @param trajID \code{integer} specifying the identifiers for the trajectories. -#' @param correct \code{logical} does the input raster need to be corrected to account for cropped margins? -#' Unless the raster extent is global, calculation of trajectories will throw an error at the margins -#' as the trajectories go beyond the raster extent (no input values). To avoid this, an option is given for -#' expanding the extent by the resolution of the raster (1 column/row) with NAs. Note that those trajectories -#' reaching the extent limits will be artificially bounced back so should be discarded at that point. -#' Alternatively, users may choose to crop to a larger extent to the domain of interest (appropriately -#' defined by lonlat), so the extra extent buffer for those trajectories getting to the border -#' of the raster. #' #' @return a \code{data.frame} containing the coordinates ("x", "y") of the constituent #' points and identification number ("trajIDs") for each trajectory. @@ -31,9 +28,7 @@ #' @export #' @author Jorge Garcia Molinos, David S. Schoeman and Michael T. Burrows #' @examples -#' -#' HSST <- VoCC_get_data("HSST.tif") -#' +#' \dontrun{ #' yrSST <- sumSeries(HSST, #' p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), #' fun = function(x) colMeans(x, na.rm = TRUE), @@ -66,6 +61,7 @@ #' #' # This accounts for the extent issue #' traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) +#' } #' voccTraj <- function(lonlat, vel, ang, mn, @@ -86,6 +82,7 @@ voccTraj <- function(lonlat, lonlat <- lonlat %>% dplyr::select(x, y) %>% # Collect just lon and lat (in case there's anything else there) as.data.frame() + tcells <- terra::cellFromXY(vel, lonlat) # # Cell IDs of starting cells n <- nrow(lonlat) # Get number of cells in your sequence sflags <- rep(NA, n) # Set a string of cells that change from NA to 1 where the trajectory sticks @@ -144,10 +141,12 @@ voccTraj <- function(lonlat, cst_pts <- terra::xyFromCell(lk_up, t_block) %>% as.data.frame() %>% st_as_sf(coords = c("x", "y"), crs = crs(rast())) + # Here, we can't search for corners and add random "fuzz" to we collect 10 random points (if there are 10), and select the nearest of those, instead. If there are 10 or fewer, just pick the nearest if (nrow(cst_pts) > 10) { cst_pts <- cst_pts[sample(1:nrow(cst_pts), 10, replace = FALSE), ] } # The unspoken "else" is that we just retain the cst_pts we have + pt <- st_as_sf(data.frame(x = x, y = y), coords = c("x", "y"), crs = crs(rast())) # The point departed from nearest <- st_distance(cst_pts, pt) %>% which.min() @@ -158,7 +157,8 @@ voccTraj <- function(lonlat, return(out) } - # Find new destination, given velocity (km/yr), angle (º), time step (yr) and initial coordinates (ºlon, ºlat); max allowed jump is 1 deg + # Find new destination, given velocity (km/yr), angle (º), time step (yr) and + # initial coordinates (ºlon, ºlat); max allowed jump is 1 deg # vell = vel[fcells] %>% pull(1); angg = ang[fcells] %>% pull(1); timestep = tstep; ll = llold destcoords <- function(vell, angg, timestep, ll, y_res, x_res) { latshift <- (abs(vell) * timestep * cos(deg2rad(angg))) / 111.325 # Calculate shift in lat @@ -184,28 +184,34 @@ voccTraj <- function(lonlat, lonnew <- lonnew - (360 * floor((lonnew + 180) / 360)) return(data.frame(lonnew, latnew) %>% - setNames(c("dlon", "dlat"))) + setNames(c("dlon", "dlat"))) } - # Function for to find closest cooler/warmer cell within the union of two levels of 8-cell adjacency from the "departed" and "destination" cells + # Function to find closest cooler/warmer cell within the union of two levels + # of 8-cell adjacency from the "departed" and "destination" cells get_dest_cell <- function(rw) { # A to-from cell pair and the buffer around a cell on land that can be searched for a suitable cell + # Find clumps of ocean; the rule is that you can't jump from one clump to another, because this would mean passing over unbroken land xy <- terra::xyFromCell(mn, as.vector(as.matrix(rw))) %>% as.data.frame() bfr <- ((y_res * 111.325) + 1) * 1000 # Set buffer to be 1 grid-square width at the equator + 1 km xy <- xy %>% st_as_sf(coords = c("x", "y"), crs = "EPSG:4326") + sp_buffer <- st_buffer(st_as_sf(xy), bfr) # Remembering that buffer is in metres + buffer_zone <- terra::extract(mn_c, sp_buffer, cells = TRUE, xy = TRUE, touches = TRUE) %>% dplyr::select(-ID) %>% distinct() %>% dplyr::rename(sst = climatology) %>% dplyr::select(x, y, sst, cell) %>% drop_na(sst) + clumped <- buffer_zone %>% dplyr::select(-cell) %>% rast(crs = "EPSG:4326") %>% terra::patches(directions = 8, allowGaps = FALSE) + # Which clump did I start in? r1 <- rw[1] %>% unlist() %>% @@ -213,20 +219,23 @@ voccTraj <- function(lonlat, from_clump <- terra::extract(clumped, terra::xyFromCell(mn, r1)) %>% unlist() %>% as.vector() + # What are the coordinates of cells within the search area that fall in the clump I came from? search_xy <- terra::xyFromCell(clumped, which(clumped[] == from_clump)) %>% as.data.frame() #**** search_cells <- terra::cellFromXY(mn, search_xy) # Which cells are these + # Check if any of these are NOT in in EITHER fine coast or mn, and eliminate, if needed to_keep <- c( which(!is.na(terra::extract(mn, search_xy, ID = FALSE) %>% - pull(1))), # Coords in the coarse grid + pull(1))), # Coords in the coarse grid which(search_cells %in% fn_lk_up) ) %>% unique() search_xy1 <- search_xy[to_keep, ] %>% as.data.frame() %>% distinct() + # Get the ssts in the cells to search or <- terra::extract(mn_c, search_xy1, cells = TRUE, xy = TRUE) %>% dplyr::rename(sst = 2, x = 4, y = 5) @@ -251,6 +260,7 @@ voccTraj <- function(lonlat, o <- or %>% dplyr::filter(sst > unlist(mn_c[r1])) %>% na.omit() + if (nrow(o) == 0) { dest_cell <- NA # Set condition with which to ID stuck cells } else { @@ -273,26 +283,32 @@ voccTraj <- function(lonlat, for (i in 1:(tyr / tstep)) { # 1:(tyr/tstep) llold <- lonlat # Take a copy of lonlat fcells <- terra::cellFromXY(vel, llold) # Get the cells that the trajectories start in + # Pull velocity and angle from the "inland" versions, because we always ensure that we can find destination cells for departures within the coastal "blind spot" vc <- vel_c[fcells] %>% pull(1) ac <- ang_c[fcells] %>% pull(1) llc <- llold + # Get new locations lonlat <- destcoords(vc, ac, tstep, llc, y_res, x_res) # Extract lon and lat of landing point tcells <- terra::cellFromXY(vel, lonlat) # Get the cells that the trajectories end in sflags[which(is.na(tcells))] <- 1 # Sets the trajectory to "stuck" if it exits the velocity field (i.e., tcells == NA) + # Bounce stuck cells stuck_cells <- which(!is.na(sflags)) lonlat[stuck_cells, ] <- llold[stuck_cells, ] # Bounce the corresponding coordinates across for "stuck" cells tcells[stuck_cells] <- fcells[stuck_cells] # Bounce the corresponding cells across for "stuck" cells + # Deal with cells that end on land onland <- which(is.na(vel[tcells])) %>% # Identify which rows of velend are on land, provided that they are not stuck dplyr::setdiff(stuck_cells) # Ignoring stuck cells + if (length(onland) > 0) { # Only bother if there is at least one cell that returns a NA velocity = land # Here, you need to check whether it really *IS* on land, or whether it is just in the coastal "blind spot" fn <- terra::extract(fine_coast, lonlat[onland, ], ID = FALSE) %>% pull(1) onland <- onland[which(is.na(fn))] + if (length(onland) > 0) { # Collect the stuff we need here for cells that are really onland fpos <- llold[onland, ] @@ -310,12 +326,14 @@ voccTraj <- function(lonlat, ttpos <- data.frame(x = rep(NA, length(onland)), y = rep(NA, length(onland))) ttpos[stuck, ] <- fpos[stuck, ] # If they're stuck, pass on starting points ttcell[stuck] <- fcell[stuck] # If they're stuck, pass on starting cells + if (length(unstuck) > 0) { ttdat <- data.frame(ttcell = ttcell[unstuck], loncent = xFromCell(vel, ttcell[unstuck]), latcent = yFromCell(vel, ttcell[unstuck])) %>% # Start building coordinates mutate(e = NA, w = NA, n = NA, s = NA, dlon = NA, dlat = NA) # To facilitate finding corners, if we need them # Separate cells in the coastal blind spot from those that are not which_coast <- which(ttdat$ttcell %in% fn_lk_up) which_not_coast <- which(!(ttdat$ttcell %in% fn_lk_up)) + # For non-coastal cells if (length(which_not_coast) > 0) { corner_block_size <- 0.25 * x_res @@ -325,6 +343,7 @@ voccTraj <- function(lonlat, nc_ttdat$n <- nc_ttdat$latcent + (0.5 * y_res) - (runif(1, 0, 1) * corner_block_size) nc_ttdat$s <- nc_ttdat$latcent - (0.5 * y_res) + (runif(1, 0, 1) * corner_block_size) coords <- with(nc_ttdat, cbind(n, e, n, w, s, w, s, e)) # NE, NW, SW, SE corners' coordinates + # Find distances from departure point to corners of ttcell get_dist <- function(y1, x1, y2, x2) { pt1 <- st_as_sf(data.frame(x = x1, y = y1), coords = c("x", "y"), crs = crs(rast())) @@ -333,16 +352,19 @@ voccTraj <- function(lonlat, as.vector() return(out) } + corners <- data.frame(ne = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 1], coords[, 2])) %>% mutate( nw = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 3], coords[, 4]), sw = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 5], coords[, 6]), se = get_dist(fpos[which_not_coast, 2], fpos[which_not_coast, 1], coords[, 7], coords[, 8]) ) + cornset <- apply(corners, 1, mfind) * 2 # Identify which corners for each onland cell. Have to mul by 2 to shift along correctly. cornset <- cbind(cornset, coords) # Add in coordinates ttdat[which_not_coast, 8:9] <- data.frame(t(apply(cornset, 1, mplace))) # Extract coordinates of correct corner } + # For coastal cells if (length(which_coast) > 0) { coastal_dat <- ttdat[which_coast, 2:3] %>% @@ -355,12 +377,14 @@ voccTraj <- function(lonlat, ttpos[unstuck, ] <- ttdat[, 8:9] ttcell[unstuck] <- terra::cellFromXY(mn, ttpos[unstuck, ]) } + # Collect results lonlat[onland, ] <- ttpos tcells[onland] <- ttcell sflags[onland] <- SFlags } } + llon[((i * n) + 1):((i * n) + n)] <- lonlat[, 1] # Add lon to the list llat[((i * n) + 1):((i * n) + n)] <- lonlat[, 2] # Add lat to the list cellIDend[((i * n) + 1):((i * n) + n)] <- tcells # Add cellIDs to the list @@ -370,5 +394,5 @@ voccTraj <- function(lonlat, } return(cbind(Steps, llon, llat, cellIDs, cellIDend, flags) %>% - as_tibble()) + as_tibble()) } diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..142ebb3 --- /dev/null +++ b/docs/404.html @@ -0,0 +1,103 @@ + + + + + + + +Page not found (404) • VoCC + + + + + + + + + + + +
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Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

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+

Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

+
+ +
+

Site built with pkgdown 2.1.3.

+
+ +
+ + + + + + + + diff --git a/docs/articles/VoCC.html b/docs/articles/VoCC.html new file mode 100644 index 0000000..e863c8a --- /dev/null +++ b/docs/articles/VoCC.html @@ -0,0 +1,387 @@ + + + + + + + +VoCC • VoCC + + + + + + + + + + + +
+
+ + + + +
+
+
+

VoCC

+ + + Source: vignettes/VoCC.Rmd + + +
+ + + +

This vignette provides the code to reproduce the examples for the R +package VoCC as presented in Garcia Molinos et al. (2019). Refer to the +paper and the function documentation for details on function options, +considerations on the argument choices and interepretation of +output.

+

For this tutorial we need the following packages (if not installed +get them first):

+ +

We also need a few data sets that can be accessed from the +VoCCdata package.

+
+library(VoCCdata)
+
+

Example 1: Prediction of biogeographical shifts +

+

We have a look first at the “marshift” global data set containing +reported range shifts in marine species corresponding to given periods +of time.

+
+str(marshift)
+#> 'data.frame':    343 obs. of  6 variables:
+#>  $ lat       : num  49.7 53.8 53.8 40 43 ...
+#>  $ long      : num  -4.33 5 5 1 -9.3 -1.4 -9.3 -71.7 -71.7 -71.7 ...
+#>  $ timespan  : int  30 40 40 55 35 35 84 39 26 54 ...
+#>  $ years_data: int  23 40 40 15 4 2 5 2 2 2 ...
+#>  $ taxa      : Factor w/ 12 levels "Benthic algae",..: 6 6 6 6 3 3 4 5 5 5 ...
+#>  $ Shift     : num  536.1 65.6 95.9 40 10 ...
+

Next, we calculate the gradient- and distance-based velocities +(1960-2009), using the HadiSST data set, from which we will later +extract the corresponding values for each observed shift.

+
+HadiSST <- terra::rast(system.file("extdata", "HadiSST.tif", package = "VoCCdata"))
+
+# monthly to annual averages
+r <- sumSeries(HadiSST, p = "1960-01/2009-12", yr0 = "1955-01-01", 
+               l = terra::nlyr(HadiSST), 
+               fun = function(x) colMeans(x, na.rm = TRUE), 
+               freqin = "months", freqout = "years")
+# temporal trend
+vt <- tempTrend(r, th = 10)
+# spatial gradient
+vg <- spatGrad(r, th = 0.0001, projected = FALSE)
+# climate velocity
+gv <- gVoCC(vt, vg)
+
+# Now the distance-based velocities
+# Take 1960-1970 as base period against 2000-2009
+r2 <- c(terra::mean(r[[1:10]], na.rm = TRUE), terra::mean(r[[41:50]], na.rm = TRUE))
+
+# prepare the data frame with the necessary variables
+clim <- na.omit(data.frame(terra::values(r2), cid = 1:terra::ncell(r)))
+
+clim[, c("x", "y")] <- terra::xyFromCell(r, clim$cid)
+# 1965-2004 (40 yr), 500 km search radius
+
+v <- dVoCC(clim, n = 1, tdiff = 40, method = "Single", climTol = 0.1, geoTol = 500, distfun = "GreatCircle", trans = NA, lonlat = TRUE)
+

Next, we extract the mean velocity estimates for each reported shift +by taking the average of all grid cell values within a circle of radius +equal to the reported range-shift distance. These are then used to fit +the simple linear regression models of observed range shifts against +climate velocity. Distance-based velocities are strictly positive by +definition, so to compare like with like we change first their sign to +negative where present local climates are warmer than their future +analogues.

+
+# Change sign as needed and create the distance-based velocity raster
+ind <- which(terra::extract(r2[[1]], v$focal) > terra::extract(r2[[2]], v$target))
+v$velBis <- v$vel
+v$velBis[ind] <- v$vel[ind] * -1
+# put output in raster format
+dv <- terra::rast(gv)
+dv[v$focal] <- v$velBis
+
+# Create point geometries and buffer them
+coords <- terra::vect(cbind(marshift$long, marshift$lat), crs = "EPSG:4326")
+buffer_size <- marshift$Shift * (marshift$timespan / 10) * 1000
+
+marshift$GV <- terra::extract(abs(gv[[1]]), terra::buffer(coords, buffer_size), 
+                              fun = mean, na.rm = TRUE)$voccMag # TODO The other option is voccAng but they seem the same. Check data above.
+
+marshift$DV <- terra::extract(abs(dv), terra::buffer(coords, buffer_size), 
+                              fun = mean, na.rm = TRUE)$voccMag  # The other option is voccAng
+
+# fit the regression models
+Mgv <- lm(Shift^(1 / 4) ~ I((GV * 10)^(1 / 4)), data = marshift, weights = years_data)
+Mdv <- lm(Shift^(1 / 4) ~ I((DV * 10)^(1 / 4)), data = marshift, weights = years_data)
+summary(Mgv)
+#> 
+#> Call:
+#> lm(formula = Shift^(1/4) ~ I((GV * 10)^(1/4)), data = marshift, 
+#>     weights = years_data)
+#> 
+#> Weighted Residuals:
+#>     Min      1Q  Median      3Q     Max 
+#> -9.3704 -2.3595 -0.7102  1.7944 14.8587 
+#> 
+#> Coefficients:
+#>                    Estimate Std. Error t value Pr(>|t|)    
+#> (Intercept)          0.9542     0.2467   3.869 0.000131 ***
+#> I((GV * 10)^(1/4))   0.6028     0.1006   5.994 5.22e-09 ***
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual standard error: 3.821 on 340 degrees of freedom
+#>   (1 observation deleted due to missingness)
+#> Multiple R-squared:  0.09558,    Adjusted R-squared:  0.09292 
+#> F-statistic: 35.93 on 1 and 340 DF,  p-value: 5.217e-09
+summary(Mdv)
+#> 
+#> Call:
+#> lm(formula = Shift^(1/4) ~ I((DV * 10)^(1/4)), data = marshift, 
+#>     weights = years_data)
+#> 
+#> Weighted Residuals:
+#>     Min      1Q  Median      3Q     Max 
+#> -9.9702 -2.5047 -0.8118  1.6606 15.5742 
+#> 
+#> Coefficients:
+#>                    Estimate Std. Error t value Pr(>|t|)    
+#> (Intercept)          0.4845     0.3668   1.321    0.187    
+#> I((DV * 10)^(1/4))   0.7660     0.1455   5.266  2.5e-07 ***
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual standard error: 3.883 on 335 degrees of freedom
+#>   (6 observations deleted due to missingness)
+#> Multiple R-squared:  0.07644,    Adjusted R-squared:  0.07369 
+#> F-statistic: 27.73 on 1 and 335 DF,  p-value: 2.502e-07
+

Produce the observed vs predicted scatterplots with regression lines +(Fig. 2 in Garcia Molinos et al. 2019).

+
+# first compare both velocities
+
+p1 <- ggplot() + 
+  geom_spatraster(data = gv[[1]]) + 
+  scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-50, 50)) +
+  ggtitle("Gradient-based vocc") +
+  scale_x_continuous(expand = c(0,0)) + 
+  scale_y_continuous(expand = c(0,0))
+
+p2 <- ggplot() + 
+  geom_spatraster(data = gv[[1]]) + 
+  scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-20, 20)) +
+  ggtitle("Distance-based vocc") +
+  scale_x_continuous(expand = c(0,0)) + 
+  scale_y_continuous(expand = c(0,0))
+
+wrap_plots(p1, p2, ncol = 1)
+

+
+
+# scatter plots with the resulting regression line
+p1 <- ggplot(na.omit(marshift), aes(x = I((GV * 10)^(1 / 4)), y = Shift^(1 / 4))) +
+  geom_point(color = "grey") +
+  geom_smooth(method = lm, se = FALSE) +
+  theme_classic() +
+  scale_color_brewer(palette = "Accent") +
+  labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)")
+p2 <- ggplot(na.omit(marshift), aes(x = I((DV * 10)^(1 / 4)), y = Shift^(1 / 4))) +
+  geom_point(color = "grey") +
+  geom_smooth(method = lm, se = FALSE) +
+  theme_classic() +
+  scale_color_brewer(palette = "Accent") +
+  labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)")
+wrap_plots(p1, p2, nrow = 1)
+#> `geom_smooth()` using formula = 'y ~ x'
+#> `geom_smooth()` using formula = 'y ~ x'
+

+
+
+

Example 2: Analysis of climate exposure and connectivity in the +Western Pacific Ocean +

+

THIS CODE IS NOT OPERATIONAL YET. IN PARTICULAR, +voccTraj HAS NOT BEEN UPDATED FOR terra. +Standby for updates.

+

In this example we use climate velocity trajectories (based on +1960-2009 mean annual SST) to analyse climate connectivity in the +Western Pacific region and calculate the residence time corresponding to +the exclusive economic zones in the region as an index of climatic +exposure. First, we arrange the raster layers for analysis.

+
+# prepare raster layers
+vel <- gv[[1]]
+ang <- gv[[2]]
+mn <- app(r, mean, na.rm = T)
+
+# generate a velocity layer centered and cropped to study region to extract the initial coordinates for the trajectories from
+x1 <- crop(gv[[1]], ext(-180, 0, -90, 90))
+x2 <- crop(gv[[1]], ext(0, 180, -90, 90))
+ext(x1) <- c(180, 360, -90, 90)
+velc <- merge(x1, x2)
+# crop to the desired extent
+# display restricted to +180 longitude to avoid plotting issues with date line crossing
+velc <- crop(velc, c(90, 180, -32, 33))
+

We can now populate the data frame with the cell centroid coordinates +for the trajectories and associated input data

+
+lonlat <- data.frame(terra::xyFromCell(velc, 1:ncell(velc)))
+lonlat$vel <- terra::extract(vel, lonlat, ID = FALSE)
+lonlat$ang <- terra::extract(ang, lonlat[, 1:2], ID = FALSE)
+lonlat$mn <- terra::extract(mn, lonlat[, 1:2], ID = FALSE)
+lonlat <- na.omit(lonlat)
+

Let’s calculate the trajectories with parallel processing to +demonstrate how this can be used to speed things up (especially useful +when dealing with fine resolutions or large extents).

+
+cores <- detectCores()
+ncores <- cores[1] - 1
+cuts <- cut(1:nrow(lonlat), ncores)
+cl <- makeCluster(ncores)
+registerDoParallel(cl)
+traj <- foreach(x = levels(cuts), .combine = rbind, .packages = c("raster", "sp", "rgeos", "geosphere", "rgdal", "VoCC"), .multicombine = TRUE) %dopar% {
+  voccTraj(lonlat[cuts == x, ], vel, ang, mn, tyr = 50, trajID = as.numeric(rownames(lonlat[cuts == x, ])), correct = FALSE)
+}
+stopCluster(cl)
+

Plot them over the climate velocities and the EEZ polygons from the +EEZs data set (Fig. 3a in Garcia Molinos et al. 2019)

+
+# simplify polygons to speed plotting up
+eez_simp <- rgeos::gSimplify(EEZs, tol = 0.5, topologyPreserve = TRUE)
+plot(velc)
+# create the spatial object with the trajectories and plot them together with the EEZ polygons
+lns <- trajLine(x = traj)
+plot(lns, add = TRUE)
+plot(eez_simp, col = scales::alpha(rgb(211, 211, 211, maxColorValue = 255), 0.5), add = TRUE)
+

We now calulcate the trajectory classes and residence times for each +EEZ using the traj25 data set.

+
+# get the set of starting cells for the trajectories and calculate trajectories
+# at 1/4-deg resolution (16 trajectories per 1-deg cell)
+r <- disaggregate(mn, 4)
+lonlat <- na.omit(data.frame(terra::xyFromCell(vel, 1:ncell(vel)), vel[], ang[], mn[]))[, 1:2]
+traj25 <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE)
+# classify trajectories (16 trajectories starting from each 1-deg cell cell)
+clas <- trajClas(traj25, vel, ang, mn, trajSt = 16, tyr = 50, nmL = 20, smL = 100, Nend = 45, Nst = 15, NFT = 70)
+# Extract proportions by categories for each EEZ
+v <- data.table(terra::extract(clas[[7]], EEZs, df = TRUE))
+v[, TrajClas := as.character(TrajClas)]
+v[, ID := as.ordered(ID)]
+# proportions by class
+d <- prop.table(table(v), 1)
+# residence times by EEZ
+EEZa <- resTime(EEZs, vel, areapg = NA)
+

Finally let’s plot the category proportions as pie charts on top of +each EEZ, the size of the chart being proportional to their respective +residence time (Fig. 3b in Garcia Molinos et al. 2019).

+
+D <- data.table(d) # put data in long format
+# add EEZ names for reference
+D[, name := as.character(EEZs$Territory1)[as.numeric(ID)]]
+D[, RT := as.character(EEZa$resTim)[as.numeric(ID)]]
+# prepare data frame to plot the pie charts with
+dt <- as.data.frame.matrix(d)
+dt$country <- as.character(EEZs$Territory1)
+dt[, c("x", "y")] <- coordinates(EEZs)
+dt$RT <- EEZa$resTim
+# generate the plot
+plot(velc)
+plot(eez_simp, add = TRUE)
+mycol <- c(scales::alpha(rgb(192, 192, 192, maxColorValue = 255), 0.5), scales::alpha(rgb(204, 255, 204, maxColorValue = 255), 0.5), scales::alpha(rgb(255, 153, 51, maxColorValue = 255), 0.5), scales::alpha(rgb(255, 51, 51, maxColorValue = 255), 0.5), scales::alpha(rgb(51, 51, 255, maxColorValue = 255), 0.5), scales::alpha(rgb(204, 102, 0, maxColorValue = 255), 0.5), scales::alpha(rgb(204, 0, 204, maxColorValue = 255), 0.5), scales::alpha(rgb(255, 255, 51, maxColorValue = 255), 0.5), scales::alpha(rgb(153, 204, 255, maxColorValue = 255), 0.5))
+# mylab = c("Non-moving", "Slow-moving", "Internal Sink", "Boundary sink",
+#         "Source", "Internal sink","Corridor", "Divergence", "Convergence")
+for (i in 1:35) {
+  add.pie(z = as.numeric(dt[i, 1:9]), x = dt[i, "x"], y = dt[i, "y"], radius = log(dt[i, "RT"]), col = mycol, labels = "")
+}
+
+
+

References +

+

García Molinos, J., Schoeman, D. S., Brown, C. J. and Burrows, M. T. +(2019), VoCC: An R package for calculating the velocity of climate +change and related climatic metrics. Methods Ecol Evol. doi:10.1111/2041-210X.13295

+
+
+ + + +
+ + + +
+

+

Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

+
+ +
+

+

Site built with pkgdown 2.1.3.

+
+ +
+
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zkibIG62;DthjarDo*OH#3skuYtsV`)v6QF{`pCzLzdv;2%*MKYRd%qIX)3Vc2Q~MxG^crj*dVm>Vf}%JbE(pOwYzWhn{I2#9|7Ob&L-^IB@*Je*vr5$OHfY literal 0 HcmV?d00001 diff --git a/docs/articles/index.html b/docs/articles/index.html new file mode 100644 index 0000000..0828f76 --- /dev/null +++ b/docs/articles/index.html @@ -0,0 +1,76 @@ + +Articles • VoCC + + +

+
+ + + +
+
+
+

Articles

+
+ +
+

All vignettes

+

+ +
VoCC
+
+
+
+
+ + +
+

Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

+
+ +
+

Site built with pkgdown 2.1.3.

+
+ +
+ + + + + + + + diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 0000000..e1b10ae --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,114 @@ + +Authors and Citation • VoCC + + +
+
+ + + +
+
+
+
+

Authors and Citation

+
+ + +
  • +

    Jorge Garcia Molinos. Author, maintainer. +

    +
    • +
  • +

    David S. Schoeman. Author. +

    +
    • +
  • +

    Christopher J. Brown. Author. +

    +
    • +
  • +

    Michael T. Burrows. Author. +

    +
    • +
  • +

    Naoki H. Kumagai. Contributor. +

    +
    • +
+
+
+

Citation

+ Source: DESCRIPTION +
+
+ + +

Garcia Molinos J, S. Schoeman D, J. Brown C, T. Burrows M (2025). +VoCC: The Velocity of Climate Change and related climatic metrics. +R package version 1.0.1, https://github.com/JorGarMol/VoCC. +

+ 
@Manual{,
+  title = {VoCC: The Velocity of Climate Change and related climatic metrics},
+  author = {Jorge {Garcia Molinos} and David {S. Schoeman} and Christopher {J. Brown} and Michael {T. Burrows}},
+  year = {2025},
+  note = {R package version 1.0.1},
+  url = {https://github.com/JorGarMol/VoCC},
+}
+ +
+ +
+ + + +
+

Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

+
+ +
+

Site built with pkgdown 2.1.3.

+
+ +
+ + + + + + + + diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000..5a85941 --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000..1cdd573 --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $('
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+ $("#search-input").focus(); + } + }); + + $(document).ready(function() { + // do keyword highlighting + /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ + var mark = function() { + + var referrer = document.URL ; + var paramKey = "q" ; + + if (referrer.indexOf("?") !== -1) { + var qs = referrer.substr(referrer.indexOf('?') + 1); + var qs_noanchor = qs.split('#')[0]; + var qsa = qs_noanchor.split('&'); + var keyword = ""; + + for (var i = 0; i < qsa.length; i++) { + var currentParam = qsa[i].split('='); + + if (currentParam.length !== 2) { + continue; + } + + if (currentParam[0] == paramKey) { + keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); + } + } + + if (keyword !== "") { + $(".contents").unmark({ + done: function() { + $(".contents").mark(keyword); + } + }); + } + } + }; + + mark(); + }); +}); + +/* Search term highlighting ------------------------------*/ + +function matchedWords(hit) { + var words = []; + + var hierarchy = hit._highlightResult.hierarchy; + // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..fb9d291 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,163 @@ + + + + + + + +The Velocity of Climate Change and related climatic metrics • VoCC + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    VoCC: The Velocity of Climate Change and related climatic metrics +

    + + +

    Jorge Garcia Molinos et al. 18 July 2019

    +

    This package is now in release version (v 1.0.0). Please contact me for questions or feed-back.

    +
    +

    Installation +

    +

    You can install the development version of VoCC from GitHub with:

    +
    +# install.packages("devtools")
+devtools::install_github("JorGarMol/VoCC")
    +
    +
    +

    Example +

    +
    +
    +

    Citation +

    +

    To cite the package itself please use:

    +
    +

    García Molinos, J., Schoeman, D. S., Brown, C. J. and Burrows, M. T. (2019). VoCC: The Velocity of Climate Change and related climatic metrics. R package version 1.0.0. https://doi.org/10.5281/zenodo.3382092

    +
    +

    The following paper explains the package functionality and provides the examples covered by the code in the package vignette

    +
    +

    García Molinos, J., Schoeman, D. S., Brown, C. J. and Burrows, M. T. (2019), VoCC: An R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol Evol. doi:10.1111/2041-210X.13295

    +
    +
    +
    +
    + + +
    + + +
    +

    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    +

    Site built with pkgdown 2.1.3.

    +
    + +
    +
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+ + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } + } + + // Add class to parent
  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $("div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 0000000..44eb1cd --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,6 @@ +pandoc: 3.7.0.2 +pkgdown: 2.1.3 +pkgdown_sha: ~ +articles: + VoCC: VoCC.html +last_built: 2025-08-27T06:12Z diff --git a/docs/reference/EEZ.html b/docs/reference/EEZ.html new file mode 100644 index 0000000..1d253de --- /dev/null +++ b/docs/reference/EEZ.html @@ -0,0 +1,90 @@ + +Icelandic Exclusive Economic Zone (EEZ) — EEZ • VoCC + + +
    +
    + + + +
    +
    +
    +

    Icelandic Exclusive Economic Zone (EEZ)

    + Source: R/data.R + +
    + +
    +

    Icelandic Exclusive Economic Zone (v10).

    +
    + + +
    +

    Format

    +

    A spatial polygon data frame containing the Icenalndic EEZ +(200 NM).

    +
    +
    +

    Source

    +

    Marineregions.org +http://www.marineregions.org/downloads.php

    +
    + +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/HSST.html b/docs/reference/HSST.html new file mode 100644 index 0000000..9d2add4 --- /dev/null +++ b/docs/reference/HSST.html @@ -0,0 +1,94 @@ + +Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010 — HSST • VoCC + + +
    +
    + + + +
    +
    +
    +

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    + Source: R/data.R + +
    + +
    +

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    +
    + + +
    +

    Format

    +

    A raster stack with 672 layers containing mean monthly SSTs (C) +at 1-deg resolution for the period Jan 1955 to Dec 2010 for the Atlantic waters surrounding Iceland.

    +
    +
    +

    Source

    +

    Hadley Centre data set HadISST 1.1 (data accessed May 2018).

    +
    +
    +

    References

    +

    Rayner et al. 2003. Global analyses of sea +surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res.Vol. 108: 4407.

    +
    + +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/JapTC.html b/docs/reference/JapTC.html new file mode 100644 index 0000000..125bc92 --- /dev/null +++ b/docs/reference/JapTC.html @@ -0,0 +1,101 @@ + +Multivariate Terraclimate data for Japan — JapTC • VoCC + + +
    +
    + + + +
    +
    +
    +

    Multivariate Terraclimate data for Japan

    + Source: R/data.R + +
    + +
    +

    Historical (1960-1969) and present (2008-2017) multivariate 1/24 +Terraclimate data set (Abatzoglou et al. 2018), extracted for the Japanese +archipelago and including 3 variables: mean annual monthly precipitation (mm), +maximum and minimum temperatures (C).

    +
    + + +
    +

    Format

    +

    A raster stack with 9 layers for the historical (1960-1969) and current (2008-2017) +average (AnMn) and standard deviation (AnMnSD) of the mean annual (AnMn) precipitation (Ppr), +maximum (Tmax) and minimum (Tmin) temperature.

    +
    +
    +

    Source

    +

    Terraclimate data set.

    +
    +
    +

    References

    +

    Abatzoglou et al. 2018. Terraclimate, a high-resolution global +dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data, 5: 170191

    +
    + +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/angulo.html b/docs/reference/angulo.html new file mode 100644 index 0000000..95b8a11 --- /dev/null +++ b/docs/reference/angulo.html @@ -0,0 +1,100 @@ + +Internal. Angle associated to the spatial gradient — angulo • VoCC + + +
    +
    + + + +
    +
    +
    +

    Internal. Angle associated to the spatial gradient

    + Source: R/angulo.R + +
    + +
    +

    Internal. Angle associated to the spatial gradient

    +
    + +
    + 
    angulo(dx, dy)
    +
    + +
    +

    Arguments

    + + +
    dx
    +

    numeric giving the longitudinal gradient component

    + + +
    dy
    +

    numeric giving the latitudinal gradient component

    + +
    +
    +

    Author

    +

    Jorge Garcia Molinos and David S. Schoeman +angulo()

    +
    + +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/climPCA.html b/docs/reference/climPCA.html new file mode 100644 index 0000000..6bd21bd --- /dev/null +++ b/docs/reference/climPCA.html @@ -0,0 +1,150 @@ + +Reduce dimensionality of climate predictors via Principal Component Analysis — climPCA • VoCC + + +
    +
    + + + +
    +
    +
    +

    Reduce dimensionality of climate predictors via Principal Component Analysis

    + Source: R/climPCA.R + +
    + +
    +

    Function to extract the first n principal components explaining a predefined total amount of variance among climatic variables. +These components can subsequently be used as synthetic climatic variables to reduce dimensionality in climate-analogue methods.

    +
    + +
    + 
    climPCA(
+  climp,
+  climf,
+  trans = function(x) log(x),
+  cen = TRUE,
+  sc = TRUE,
+  th = 0.8
+)
    +
    + +
    +

    Arguments

    + + +
    climp
    +

    raster.stack with one layer for each climatic variable with the values for present or baseline conditions.

    + + +
    climf
    +

    raster.stack with one layer for each climatic variable with the values for future conditions.

    + + +
    trans
    +

    function specifying the type of transformation to be applied prior to the PCA. +Specify NA where no transformation is required (default log(x)).

    + + +
    cen
    +

    logical should the variables be centered prior to the PCA? (default TRUE).

    + + +
    sc
    +

    logical should the variables be scaled prior to the PCA? (default TRUE).

    + + +
    th
    +

    numeric threshold giving the minimum amount of total variance that should be explained by the principal components extracted.

    + +
    +
    +

    Value

    +

    a list containing (i) the output from the PCA (call to 'prcomp'), and +(ii) a table with the present/future cell values for the principal components accounting +for the specified percentage of total variance (th).

    +
    +
    +

    See also

    +

    dVoCC, climPlot

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+JapTC <- VoCC_get_data("JapTC.tif")
+
+comp <- climPCA(JapTC[[c(1, 3, 5)]], JapTC[[c(2, 4, 6)]],
+                trans = NA, cen = TRUE, sc = TRUE, th = 0.85)
+summary(comp[[1]]) # first two components explain >90% of variance
+# Create a data frame with the necessary variables in the required order (see climAna? for details)
+clim <- comp[[2]][, c(2, 4, 3, 5, 1)]
+clim[, c("x", "y")] <- terra::xyFromCell(JapTC[[1]], clim$cid)
+} # }
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/climPlot.html b/docs/reference/climPlot.html new file mode 100644 index 0000000..5c13c9b --- /dev/null +++ b/docs/reference/climPlot.html @@ -0,0 +1,146 @@ + +Binned scatter plot for 2-dimensional climate space — climPlot • VoCC + + +
    +
    + + + +
    +
    +
    +

    Binned scatter plot for 2-dimensional climate space

    + Source: R/climPlot.R + +
    + +
    +

    Function to create a binned scatter plot of two climate variables.

    +
    + +
    + 
    climPlot(xy, x.binSize, y.binSize, x.name = "V1", y.name = "V2")
    +
    + +
    +

    Arguments

    + + +
    xy
    +

    data.frame with cells as rows and 4 columns representing the present and future local values for the two variables (V1p, V1f, V2p, V2f).

    + + +
    x.binSize
    +

    numeric the bin size for the first variable.

    + + +
    y.binSize
    +

    numeric the bin size for the second variable.

    + + +
    x.name
    +

    character the variable name for the first variable. Used to label the plot.

    + + +
    y.name
    +

    character the variable name for the second variable. Used to label the plot.

    + +
    +
    +

    Value

    +

    A series of plot objects displaying the (i) present and (ii) future +cell frequency for each combination of local climates, +and (iii) the location of remnant, novel and disappearing climates between both periods.

    +
    +
    +

    See also

    +

    dVoCC, climPCA

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos and Naoki H. Kumagai

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+JapTC <- VoCC_get_data("JapTC.tif")
+
+# Plot climate space for the two first variables(annual precipitation and maximum temperature)
+xy <- stats::na.omit(data.frame(
+  terra::values(JapTC[[1]]),
+  terra::values(JapTC[[2]]),
+  terra::values(JapTC[[3]]), terra::values(JapTC[[4]])
+))
+
+out <- climPlot(xy,
+  x.binSize = 5, y.binSize = 0.2, x.name = "Precipitation (mm)",
+  y.name = "Temperature max (°C)"
+)
+
+# output plots can be saved as:
+ggplot2::ggsave(
+  plot = out, filename = file.path(getwd(), "example_plot.pdf"),
+  width = 17, height = 17, unit = "cm"
+)
+} # }
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/dVoCC.html b/docs/reference/dVoCC.html new file mode 100644 index 0000000..582301e --- /dev/null +++ b/docs/reference/dVoCC.html @@ -0,0 +1,209 @@ + +Distance-based velocity based on geographically closest climate analogue — dVoCC • VoCC + + +
    +
    + + + +
    +
    +
    +

    Distance-based velocity based on geographically closest climate analogue

    + Source: R/dVoCC.R + +
    + +
    +

    Function to calculate the geographically closest climate analogues and related distance-based velocity. Cell analogues +are identified by comparing the baseline climatic conditions at each focal cell with those existing for all +other (target) cells in the future by reference to a specified climatic threshold. The function allows for the +specification of search distances and incorporates both least-cost path and Great Circle (as-the-crow-flies) distances.

    +
    + +
    + 
    dVoCC(
+  clim,
+  n,
+  tdiff,
+  method = "Single",
+  climTol,
+  geoTol,
+  distfun = "GreatCircle",
+  trans = NA,
+  lonlat = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    clim
    +

    data.frame with the value for the climatic parameters (columns) by cell (rows), arranged as follows (see examples below): +The first 2n columns must contain the present and future values for each of the n climatic variables (V1p, V1f, V2p, V2f,...). +Where cell-specific analogue thresholds (see "variable" in "method" below) are to be calculated, the next (2n+1:3n) columns +should contain the standard deviation (or any other measure of climatic variability) of each variable for the baseline period. +These columns are not required if using the "Single" method. The last three columns of the table should contain an identifyier and centroid coordinates of each cell.

    + + +
    n
    +

    integer defining the number of climatic variables.

    + + +
    tdiff
    +

    integer defining the number of years (or other temporal unit) between periods.

    + + +
    method
    +

    ch)aracter string specifying the analogue method to be used. 'Single': a constant, single analogue threshold +for each climate variable is applied to all cells (Ohlemuller et al. 2006, Hamann et al. 2015); climate analogy corresponds to target cells +with values below the specified threshold for each climatic variable. 'Variable': a cell-specific climate threshold is used for each climatic variable +to determine the climate analogues associated with each cell by reference to its baseline climatic variability (Garcia Molinos et al. 2017).

    + + +
    climTol
    +

    numeric a vector of length n giving the tolerance threshold defining climate analogue conditions for each climatic variable. +If a cell-specific threshold is being used, this function parameter should be passed as NA.

    + + +
    geoTol
    +

    integer impose a geographical distance threshold (in km for lat/lon or map units if projected). +If used, the pool of potential climate analogues will be limited to cells within that distance from the focal cell.

    + + +
    distfun
    +

    character string specifying the function to be used for estimating distances +between focal and target cells. Either 'Euclidean', 'GreatCircle' (Great Circle Distances). +'LeastCost' (Least Cost Path Distances) is NOT CURRENTLY IMPLEMENTED. LeastCost requires a transition matrix supplied to the function via de 'trans' argument.

    + + +
    trans
    +

    TransitionLayer NOT CURRENTLY IMPLEMENTED gdistance object to be used for the analogue search if distfun = 'LeastCost'.

    + + +
    lonlat
    +

    logical is the analysis to be done in unprojected (lon/lat) coordinates?

    + +
    +
    +

    Value

    +

    A data.frame containing the cell id of the future analogue for each focal cell (NA = no analogue available), +together with the climatic ("climDis") and geographical ("geoDis") distances in input units, +the bearing ("ang", degrees North), and resulting climate velocity ("vel", km/yr). Mean climatic distances are returned for multivariate analogues.

    +
    +
    +

    References

    +

    Ohlemuller et al. 2006. Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931-2100. Global Ecology and Biogeography, 15, 395-405.
    Hamann et al. 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology, 21, 997-1004.
    Garcia Molinos et al. 2017. Improving the interpretability of climate landscape metrics: An ecological risk analysis of Japan's Marine Protected Areas. Global Change Biology, 23, 4440-4452.

    +
    +
    +

    See also

    +

    climPCA, climPlot

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+JapTC <- VoCC_get_data("JapTC.tif")
+
+# Create a data frame with the necessary variables in the required order
+clim <- stats::na.omit(data.frame(terra::values(JapTC), cid = 1:terra::ncell(JapTC)))
+clim[, c("x", "y")] <- terra::xyFromCell(JapTC, clim$cid)
+
+# Constant threshold, distance-restricted velocity based on geographical distances
+avocc1 <- dVoCC(clim,
+  n = 3, tdiff = 40, method = "Single", climTol = c(10, 0.1, 0.1),
+  geoTol = 160, distfun = "GreatCircle", trans = NA, lonlat = TRUE
+)
+
+r1 <- JapTC
+r1[avocc1$focal] <- avocc1$vel
+terra::plot(r1)
+
+# Cell-specific, distance-unrestricted climate analogue velocity based on least-cost path distances
+# First, create the conductance matrix (all land cells considered to have conductance of 1)
+r <- JapTC
+r[!is.na(JapTC[[1]])] <- 1
+h8 <- gdistance::transition(r, transitionFunction = mean, directions = 8)
+h8 <- gdistance::geoCorrection(h8, type = "c")
+
+# Now calculate the analogue velocity using the baseline SD for each variable as analogue threshold
+avocc2 <- dVoCC(clim,
+  n = 3, tdiff = 40, method = "Variable", climTol = NA, geoTol = Inf,
+  distfun = "LeastCost", trans = h8, lonlat = TRUE
+)
+
+# Plot results
+r1 <- r2 <- JapTC
+r1[avocc1$focal] <- avocc1$vel
+r2[avocc2$focal] <- avocc2$vel
+terra::plot(c(r1, r2))
+} # }
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/gVoCC.html b/docs/reference/gVoCC.html new file mode 100644 index 0000000..02d77e3 --- /dev/null +++ b/docs/reference/gVoCC.html @@ -0,0 +1,135 @@ + +Gradient-based climate velocity — gVoCC • VoCC + + +
    +
    + + + +
    +
    +
    +

    Gradient-based climate velocity

    + Source: R/gVoCC.R + +
    + +
    +

    Function to calculate the velocity of climate change after Burrows et al. (2011) +based on local climatic temporal trends and spatial gradients.

    +
    + +
    + 
    gVoCC(tempTrend, spatGrad)
    +
    + +
    +

    Arguments

    + + +
    tempTrend
    +

    The output from the tempTrend function containing the long-term linear climatic trends.

    + + +
    spatGrad
    +

    The output from the spatGrad function containing the magnitudes and angles for the spatial climatic gradient.

    + +
    +
    +

    Value

    +

    A RasterStack containing the climate velocity magnitude ("voccMag", +km/yr for unprojected rasters and spatial unit/year for projected rasters) and angle("voccAng" in +degrees north: 0N, 90E, 180S and 270W).

    +
    +
    +

    References

    +

    Burrows et al. 2011. The pace of shifting climate +in marine and terrestrial ecosystems. Science, 334, 652-655.

    +
    +
    +

    See also

    +

    tempTrend, spatGrad

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+HSST <- VoCC_get_data("HSST.tif")
+yrSST <- sumSeries(HSST,
+  p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST),
+  fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years"
+)
+tr <- tempTrend(yrSST, th = 10)
+sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE)
+
+# Magnitude and angle of the climate velocity (km/yr) 1960-2009
+
+v <- gVoCC(tr, sg)
+terra::plot(v)
+} # }
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..3ea2346 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,133 @@ + +Package index • VoCC + + +
    +
    + + + +
    +
    +
    +

    Reference

    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    All functions

    +

    +
    +

    EEZ

    +

    Icelandic Exclusive Economic Zone (EEZ)

    +

    HSST

    +

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    +

    JapTC

    +

    Multivariate Terraclimate data for Japan

    +

    angulo()

    +

    Internal. Angle associated to the spatial gradient

    +

    climPCA()

    +

    Reduce dimensionality of climate predictors via Principal Component Analysis

    +

    climPlot()

    +

    Binned scatter plot for 2-dimensional climate space

    +

    dVoCC()

    +

    Distance-based velocity based on geographically closest climate analogue

    +

    gVoCC()

    +

    Gradient-based climate velocity

    +

    resTime()

    +

    Climatic residence time of a polygon

    +

    shiftTime()

    +

    Shift in timing of seasonal climatology

    +

    spatGrad()

    +

    Local spatial climatic gradients

    +

    splitLine()

    +

    Internal. Split a line segment defined by points A-B into n parts

    +

    sumSeries()

    +

    Summarize climatic series to higher temporal resolution

    +

    tempTrend()

    +

    Long-term local climatic trends

    + + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html new file mode 100644 index 0000000..cc95e9b --- /dev/null +++ b/docs/reference/pipe.html @@ -0,0 +1,99 @@ + +Pipe operator — %>% • VoCC + + +
    +
    + + + +
    +
    +
    +

    Pipe operator

    + Source: R/utils-pipe.R + +
    + +
    +

    See magrittr::%>% for details.

    +
    + +
    + 
    lhs %>% rhs
    +
    + +
    +

    Arguments

    + + +
    lhs
    +

    A value or the magrittr placeholder.

    + + +
    rhs
    +

    A function call using the magrittr semantics.

    + +
    +
    +

    Value

    +

    The result of calling `rhs(lhs)`.

    +
    + +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/resTime.html b/docs/reference/resTime.html new file mode 100644 index 0000000..0bea70a --- /dev/null +++ b/docs/reference/resTime.html @@ -0,0 +1,156 @@ + +Climatic residence time of a polygon — resTime • VoCC + + +
    +
    + + + +
    +
    +
    +

    Climatic residence time of a polygon

    + Source: R/resTime.R + +
    + +
    +

    Function to calculate VoCC-based residence time of isotherms within a polygon after Loaire et al. (2009)

    +
    + +
    + 
    resTime(pg, vel, areapg = NA)
    +
    + +
    +

    Arguments

    + + +
    pg
    +

    SpatialPolygon or a SpatialPolygonsDataFrame containing the polygons for which +the residence time is to be calculated. The polygons must be on the same coordinate system as vel.

    + + +
    vel
    +

    raster with climate velocity (km/year) for the period of interest.

    + + +
    areapg
    +

    vector with the area (in km2) of the polygons. Use NA (default) to calculate internally if field not avilable.

    + +
    +
    +

    Value

    +

    a data.frame containing for each polygon its ID, mean velocity (km/yr), +diameter of the equivalent circle (km), and residence time (years) as the ratio D/vel.

    +
    +
    +

    References

    +

    Loarie et al. 2009. The velocity of climate change. Nature, 462, 1052-1055.

    +
    +
    +

    See also

    +

    gVoCC

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos

    +
    + +
    +

    Examples

    + 
    
+# Load example Exclusive Economic Zone polygon
+if (FALSE) { # \dontrun{
+EEZ <- VoCC_get_data("EEZ.gpkg")
+HSST <- VoCC_get_data("HSST.tif")
+
+yrSST <- sumSeries(HSST,
+  p = "1969-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST),
+  fun = function(x) colMeans(x, na.rm = TRUE),
+  freqin = "months", freqout = "years"
+)
+tr <- tempTrend(yrSST, th = 10)
+sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE)
+v <- gVoCC(tr, sg)
+vel <- v[[1]]
+
+# Calculating area internally
+a1 <- resTime(EEZ, vel, areapg = NA)
+a1
+
+# Using the area field from the polygon data table
+a2 <- resTime(EEZ, vel, areapg = as.numeric(as.numeric(levels(EEZ$Area_km2))[EEZ$Area_km2]))
+a2
+
+# Using a user defined polygon
+x_coord <- c(-28, -20, -20.3, -25.5)
+y_coord <- c(60, 61, 63, 62)
+p <- Polygon(cbind(x_coord, y_coord))
+sps <- SpatialPolygons(list(Polygons(list(p), 1)))
+a3 <- resTime(sps, vel, areapg = NA)
+
+terra::plot(vel)
+terra::plot(EEZ, add = TRUE)
+terra::plot(sps, add = TRUE)
+} # }
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/shiftTime.html b/docs/reference/shiftTime.html new file mode 100644 index 0000000..b22a4cc --- /dev/null +++ b/docs/reference/shiftTime.html @@ -0,0 +1,139 @@ + +Shift in timing of seasonal climatology — shiftTime • VoCC + + +
    +
    + + + +
    +
    +
    +

    Shift in timing of seasonal climatology

    + Source: R/shiftTime.R + +
    + +
    +

    Function to calculate the seasonal shift in the arriving of typical seasonal climates +for a given period of interest as per Burrows et al. (2011).

    +
    + +
    + 
    shiftTime(r, yr1, yr2, yr0, th, m)
    +
    + +
    +

    Arguments

    + + +
    r
    +

    stack with monthly values of the climatic +variable for the period of interest.

    + + +
    yr1
    +

    integer specifying the initial year for the period of interest.

    + + +
    yr2
    +

    integer specifying the end year for the period of interest.

    + + +
    yr0
    +

    integer specifying the first year in the series.

    + + +
    th
    +

    integer minimum number of non NAs in the series needed to +calculate the trend (default 3).

    + + +
    m
    +

    integer number (1-12) of the month for which the shift is to be calculated

    + +
    +
    +

    Value

    +

    a stack with the long-term monthly trend (C/year for temperature in degrees; "mTrend"), +seasonal rate of change (C/month; "seaRate"), and seasonal shift (day/decade; "seaShift").

    +
    +
    +

    References

    +

    Burrows et al. 2011. The pace of +shifting climate in marine and terrestrial ecosystems. Science, 334, 652-655.

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos and Michael T. Burrows

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+HSST <- VoCC_get_data("HSST.tif")
+Apr <- shiftTime(HSST, yr1 = 1960, yr2 = 2009, yr0 = 1955, th = 10, m = 4)
+
+terra::plot(Apr)
+} # }
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/spatGrad.html b/docs/reference/spatGrad.html new file mode 100644 index 0000000..7600ef9 --- /dev/null +++ b/docs/reference/spatGrad.html @@ -0,0 +1,144 @@ + +Local spatial climatic gradients — spatGrad • VoCC + + +
    +
    + + + +
    +
    +
    +

    Local spatial climatic gradients

    + Source: R/spatGrad.R + +
    + +
    +

    Function to calculate the magnitude and direction of the spatial gradient +associated to a climatic variable after Burrows et al. (2011). This trend is +to be used for the calculation of the gradient-based climate velocity using gVoCC.

    +
    + +
    + 
    spatGrad(r, th = -Inf, projected = FALSE)
    +
    + +
    +

    Arguments

    + + +
    r
    +

    RasterStack with the annual climatic values for the period of interest. +Alternatively, a raster with the annual climatic values averaged +over the period of interest.

    + + +
    th
    +

    Integer indicating a lower threshold to truncate the spatial +gradient with. Use -Inf (default) if no threshold required.

    + + +
    projected
    +

    Logical is the source raster in a projected coordinate system? +If FALSE (default) a correction will be made to account for latitudinal distortion.

    + +
    +
    +

    Value

    +

    A RasterStack with the magnitude of the spatial gradient +(Grad in C per km for unprojected rasters and C per spatial unit for projected rasters), +and the associated angle (Ang in degrees).

    +
    +
    +

    References

    +

    Burrows et al. 2011. The pace of shifting climate in marine and terrestrial ecosystems. Science, 334, 652-655.

    +
    +
    +

    See also

    +

    tempTrend, gVoCC

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos, David S. Schoeman, and Michael T. Burrows

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+HSST <- VoCC_get_data("HSST.tif")
+
+yrSST <- sumSeries(HSST,
+  p = "1969-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST),
+  fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years"
+)
+
+# Spatial gradient (magnitude and angle) for the average mean annual SST.
+
+sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE)
+
+terra::plot(sg)
+} # }
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/splitLine.html b/docs/reference/splitLine.html new file mode 100644 index 0000000..bdda379 --- /dev/null +++ b/docs/reference/splitLine.html @@ -0,0 +1,103 @@ + +Internal. Split a line segment defined by points A-B into n parts — splitLine • VoCC + + +
    +
    + + + +
    +
    +
    +

    Internal. Split a line segment defined by points A-B into n parts

    + Source: R/splitLine.R + +
    + +
    +

    Internal. Split a line segment defined by points A-B into n parts

    +
    + +
    + 
    splitLine(A, B, n)
    +
    + +
    +

    Arguments

    + + +
    A
    +

    numeric giving coordinates of first point

    + + +
    B
    +

    numeric giving coordinates of second point

    + + +
    n
    +

    numeric number of segments to divide the distance between points with

    + +
    +
    +

    Author

    +

    Jorge Garcia Molinos

    +
    + +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/sumSeries.html b/docs/reference/sumSeries.html new file mode 100644 index 0000000..33288aa --- /dev/null +++ b/docs/reference/sumSeries.html @@ -0,0 +1,193 @@ + +Summarize climatic series to higher temporal resolution — sumSeries • VoCC + + +
    +
    + + + +
    +
    +
    +

    Summarize climatic series to higher temporal resolution

    + Source: R/sumSeries.R + +
    + +
    +

    Function to convert climatic series (provided as RasterStack) into a +coarser time frequency series for a period of interest. This function transforms the RasterStack +into an xts time series object to extract the values for the period of interest and +apply some summary function. It is mainly a wrapper from the apply. function family +in the package xts (Ryan and Ulrich 2017).

    +
    + +
    + 
    sumSeries(
+  r,
+  p,
+  yr0,
+  l = terra::nlyr(r),
+  fun = function(x) colMeans(x, na.rm = TRUE),
+  freqin = "months",
+  freqout = "years"
+)
    +
    + +
    +

    Arguments

    + + +
    r
    +

    RasterStack containing the time series of the climatic variable.

    + + +
    p
    +

    character string defining the period to extract for the calculation +of the series (see examples).

    + + +
    yr0
    +

    character string specifying the first (yr0) year in the series (see examples).

    + + +
    l
    +

    integer length of the input time series.

    + + +
    fun
    +

    logical summary function to be computed. Summary functions need to be applied by cell (columns) +so should have the structure 'function(x) apply(x, 2, function(y))'. For convenience, sumSeries imports +colMaxs, and colMins from package ‘matrixStats’ (Bengtsson 2018) so they can be called in directly.

    + + +
    freqin
    +

    character string specifying the original time frequency of the series.

    + + +
    freqout
    +

    character string specifying the desired time frequency of the new series. +Must be one of the following: "weeks", "months", "quarters", "years", "other". Argument "other" +allows for user-defined functions to be applied on the 'xts' time series object over the period of interest (see examples).

    + +
    +
    +

    Value

    +

    A RasterStack with the new series.

    +
    +
    +

    References

    +

    Ray and Ulrich. 2017. xts: eXtensible Time Series. R package version 0.10-1.
    Bengtsson 2018. matrixStats: Functions that Apply to Rows and Columns +of Matrices (and to Vectors). R package version 0.53.1.

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# Monthly mean SST (HadISST) data for Europe Jan-1950 to Dec-2010
+
+HSST <- VoCC_get_data("HSST.tif")
+
+# Calculate mean annual monthly SST
+
+yrSST <- sumSeries(HSST,
+  p = "1969-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST),
+  fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years"
+)
+
+# Extract Jul Aug mean SST each year (xts months are indexed from 0 to 11)
+
+myf <- function(x, m = c(7, 8)) {
+  x[xts::.indexmon(x) %in% (m - 1)]
+}
+
+JlAugSST <- sumSeries(HSST,
+  p = "1969-01/2009-12", yr0 = "1950-01-01", l = terra::nlyr(HSST),
+  fun = myf, freqin = "months", freqout = "other"
+)
+
+# Same but calculating the annual variance of the two months
+
+myf <- function(x, m = c(7, 8)) {
+  x1 <- x[xts::.indexmon(x) %in% (m - 1)]
+  xts::apply.yearly(x1, function(y) {
+    apply(y, 2, function(y) {
+      var(y, na.rm = TRUE)
+    })
+  })
+}
+
+meanJASST <- sumSeries(HSST,
+  p = "1969-01/2009-12", yr0 = "1950-01-01", l = terra::nlyr(HSST),
+  fun = myf, freqin = "months", freqout = "other"
+)
+} # }
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/tempTrend.html b/docs/reference/tempTrend.html new file mode 100644 index 0000000..d3d32eb --- /dev/null +++ b/docs/reference/tempTrend.html @@ -0,0 +1,134 @@ + +Long-term local climatic trends — tempTrend • VoCC + + +
    +
    + + + +
    +
    +
    +

    Long-term local climatic trends

    + Source: R/tempTrend.R + +
    + +
    +

    Function to calculate temporal trend from a raster series +of a climatic variable. This trend is to be used for the calculation of the +gradient-based climate velocity using gVoCC.

    +
    + +
    + 
    tempTrend(r, th)
    +
    + +
    +

    Arguments

    + + +
    r
    +

    RasterStack containing a time series of (annual, seasonal, monthly...) values of +the climatic variable for the period of interest.

    + + +
    th
    +

    Integer minimum number of observations in the series needed to +calculate the trend at each cell.

    + +
    +
    +

    Value

    +

    A RasterStack containing the cell-specific temporal trends +extracted from simple linear regressions of the climatic variable against time +("slpTrends" in degree Celsius per year), together with their standard +errors ("seTrends") and statistical significance ("sigTrends").

    +
    +
    +

    See also

    +

    spatGrad, gVoCC

    +
    +
    +

    Author

    +

    Jorge Garcia Molinos and Christopher J. Brown

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+HSST <- VoCC_get_data("HSST.tif")
+
+yrSST <- sumSeries(HSST,
+  p = "1969-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST),
+  fun = function(x) colMeans(x, na.rm = TRUE), freqin = "months", freqout = "years"
+)
+
+# Mean annual SST trend (minimum threshold of 10 years of data), with SE and p-values.
+
+tr <- tempTrend(yrSST, th = 10)
+
+terra::plot(tr)
+} # }
+
    +
    +
    + +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    + +
    +

    Site built with pkgdown 2.1.3.

    +
    + +
    + + + + + + + + diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..646b56e --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,25 @@ + +/404.html +/LICENSE.html +/articles/VoCC.html +/articles/index.html +/authors.html +/index.html +/reference/EEZ.html +/reference/HSST.html +/reference/JapTC.html +/reference/angulo.html +/reference/climPCA.html +/reference/climPlot.html +/reference/dVoCC.html +/reference/gVoCC.html +/reference/index.html +/reference/pipe.html +/reference/resTime.html +/reference/shiftTime.html +/reference/spatGrad.html +/reference/splitLine.html +/reference/sumSeries.html +/reference/tempTrend.html + + diff --git a/man/VoCC_get_data.Rd b/man/VoCC_get_data.Rd deleted file mode 100644 index 805801c..0000000 --- a/man/VoCC_get_data.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/utils.R -\name{VoCC_get_data} -\alias{VoCC_get_data} -\title{Return data location and data if requested} -\usage{ -VoCC_get_data(path = NULL) -} -\arguments{ -\item{path}{Filename of data if known.} -} -\value{ -The path as a character string, or a dataset -} -\description{ -Return data location and data if requested -} -\examples{ - -VoCC_get_data() - -HSST <- VoCC_get_data(path = "HSST.tif") - -} diff --git a/man/climPCA.Rd b/man/climPCA.Rd index decba4a..a90d99e 100644 --- a/man/climPCA.Rd +++ b/man/climPCA.Rd @@ -37,7 +37,7 @@ Function to extract the first n principal components explaining a predefined tot These components can subsequently be used as synthetic climatic variables to reduce dimensionality in climate-analogue methods. } \examples{ - +\dontrun{ JapTC <- VoCC_get_data("JapTC.tif") comp <- climPCA(JapTC[[c(1, 3, 5)]], JapTC[[c(2, 4, 6)]], @@ -46,6 +46,8 @@ summary(comp[[1]]) # first two components explain >90\% of variance # Create a data frame with the necessary variables in the required order (see climAna? for details) clim <- comp[[2]][, c(2, 4, 3, 5, 1)] clim[, c("x", "y")] <- terra::xyFromCell(JapTC[[1]], clim$cid) +} + } \seealso{ {\code{\link{dVoCC}}, \code{\link{climPlot}}} diff --git a/man/climPlot.Rd b/man/climPlot.Rd index 2f19aa1..e0d6e40 100644 --- a/man/climPlot.Rd +++ b/man/climPlot.Rd @@ -26,7 +26,7 @@ and (iii) the location of remnant, novel and disappearing climates between both Function to create a binned scatter plot of two climate variables. } \examples{ - +\dontrun{ JapTC <- VoCC_get_data("JapTC.tif") # Plot climate space for the two first variables(annual precipitation and maximum temperature) @@ -41,7 +41,6 @@ out <- climPlot(xy, y.name = "Temperature max (°C)" ) -\dontrun{ # output plots can be saved as: ggplot2::ggsave( plot = out, filename = file.path(getwd(), "example_plot.pdf"), diff --git a/man/dVoCC.Rd b/man/dVoCC.Rd index 4c32c9d..04b25e5 100644 --- a/man/dVoCC.Rd +++ b/man/dVoCC.Rd @@ -58,6 +58,7 @@ other (target) cells in the future by reference to a specified climatic threshol specification of search distances and incorporates both least-cost path and Great Circle (as-the-crow-flies) distances. } \examples{ +\dontrun{ JapTC <- VoCC_get_data("JapTC.tif") # Create a data frame with the necessary variables in the required order @@ -74,7 +75,6 @@ r1 <- JapTC r1[avocc1$focal] <- avocc1$vel terra::plot(r1) -\dontrun{ # Cell-specific, distance-unrestricted climate analogue velocity based on least-cost path distances # First, create the conductance matrix (all land cells considered to have conductance of 1) r <- JapTC diff --git a/man/gVoCC.Rd b/man/gVoCC.Rd index fb4bd5f..ce2d787 100644 --- a/man/gVoCC.Rd +++ b/man/gVoCC.Rd @@ -21,7 +21,7 @@ Function to calculate the velocity of climate change after Burrows et al. (2011) based on local climatic temporal trends and spatial gradients. } \examples{ - +\dontrun{ HSST <- VoCC_get_data("HSST.tif") yrSST <- sumSeries(HSST, p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), @@ -34,6 +34,7 @@ sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) v <- gVoCC(tr, sg) terra::plot(v) +} } \references{ diff --git a/man/shiftTime.Rd b/man/shiftTime.Rd index 1d80904..af67b85 100644 --- a/man/shiftTime.Rd +++ b/man/shiftTime.Rd @@ -30,11 +30,13 @@ Function to calculate the seasonal shift in the arriving of typical seasonal cli for a given period of interest as per Burrows et al. (2011). } \examples{ +\dontrun{ HSST <- VoCC_get_data("HSST.tif") Apr <- shiftTime(HSST, yr1 = 1960, yr2 = 2009, yr0 = 1955, th = 10, m = 4) terra::plot(Apr) } +} \references{ \href{http://science.sciencemag.org/content/334/6056/652}{Burrows et al. 2011}. The pace of shifting climate in marine and terrestrial ecosystems. Science, 334, 652-655. diff --git a/man/spatGrad.Rd b/man/spatGrad.Rd index 6b3da32..95c155a 100644 --- a/man/spatGrad.Rd +++ b/man/spatGrad.Rd @@ -28,7 +28,7 @@ associated to a climatic variable after Burrows et al. (2011). This trend is to be used for the calculation of the gradient-based climate velocity using gVoCC. } \examples{ - +\dontrun{ HSST <- VoCC_get_data("HSST.tif") yrSST <- sumSeries(HSST, @@ -41,6 +41,7 @@ yrSST <- sumSeries(HSST, sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) terra::plot(sg) +} } \references{ diff --git a/man/sumSeries.Rd b/man/sumSeries.Rd index 1ace44a..5d4faa8 100644 --- a/man/sumSeries.Rd +++ b/man/sumSeries.Rd @@ -45,6 +45,7 @@ apply some summary function. It is mainly a wrapper from the \code{apply.} funct in the package xts (Ryan and Ulrich 2017). } \examples{ +\dontrun{ # Monthly mean SST (HadISST) data for Europe Jan-1950 to Dec-2010 HSST <- VoCC_get_data("HSST.tif") @@ -82,6 +83,7 @@ meanJASST <- sumSeries(HSST, p = "1969-01/2009-12", yr0 = "1950-01-01", l = terra::nlyr(HSST), fun = myf, freqin = "months", freqout = "other" ) +} } \references{ diff --git a/man/tempTrend.Rd b/man/tempTrend.Rd index 612c14d..efe33ab 100644 --- a/man/tempTrend.Rd +++ b/man/tempTrend.Rd @@ -25,7 +25,7 @@ of a climatic variable. This trend is to be used for the calculation of the gradient-based climate velocity using gVoCC. } \examples{ - +\dontrun{ HSST <- VoCC_get_data("HSST.tif") yrSST <- sumSeries(HSST, @@ -39,6 +39,7 @@ tr <- tempTrend(yrSST, th = 10) terra::plot(tr) } +} \seealso{ {\code{\link{spatGrad}}, \code{\link{gVoCC}}} } diff --git a/man/voccTraj.Rd b/man/voccTraj.Rd deleted file mode 100644 index fd4125b..0000000 --- a/man/voccTraj.Rd +++ /dev/null @@ -1,82 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/voccTraj.R -\name{voccTraj} -\alias{voccTraj} -\title{Climate velocity trajectories} -\usage{ -voccTraj(lonlat, vel, ang, mn, tyr, trajID = 1:nrow(lonlat), correct = FALSE) -} -\arguments{ -\item{lonlat}{\code{data.frame} with the longitude and latitude (in decimal degrees) -of the points to project.} - -\item{vel}{\code{raster} with the magnitude of gradient-based climate velocity.} - -\item{ang}{\code{raster} with velocity angles in degrees.} - -\item{mn}{\code{raster} with the overall mean climatic value over the period of interest.} - -\item{tyr}{\code{integer} temporal length of the period of interest.} - -\item{trajID}{\code{integer} specifying the identifiers for the trajectories.} - -\item{correct}{\code{logical} does the input raster need to be corrected to account for cropped margins? -Unless the raster extent is global, calculation of trajectories will throw an error at the margins -as the trajectories go beyond the raster extent (no input values). To avoid this, an option is given for -expanding the extent by the resolution of the raster (1 column/row) with NAs. Note that those trajectories -reaching the extent limits will be artificially bounced back so should be discarded at that point. -Alternatively, users may choose to crop to a larger extent to the domain of interest (appropriately -defined by lonlat), so the extra extent buffer for those trajectories getting to the border -of the raster.} -} -\value{ -a \code{data.frame} containing the coordinates ("x", "y") of the constituent -points and identification number ("trajIDs") for each trajectory. -} -\description{ -Function to calculate vocc trajectories after Burrows et al (2014). Trajectories -are calculated by propagating climatic isopleths using the magnitude and direction of -local (cell) velocities. This is a slightly modified version of the original Burrows et al. (2014) -approach in that iterations of a trajectory are based on cumulative time travelled instead of using fixed time steps. -} -\examples{ - -HSST <- VoCC_get_data("HSST.tif") - -yrSST <- sumSeries(HSST, - p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HSST), - fun = function(x) colMeans(x, na.rm = TRUE), - freqin = "months", freqout = "years" -) -tr <- tempTrend(yrSST, th = 10) -sg <- spatGrad(yrSST, th = 0.0001, projected = FALSE) -v <- gVoCC(tr, sg) -vel <- v[[1]] -ang <- v[[2]] - -# Calculate the annual SST mean over the period -mn <- mean(yrSST, na.rm = T) - -# Get the set of starting cells for the trajectories -lonlat <- stats::na.omit(data.frame( - terra::xyFromCell(vel, 1:terra::ncell(vel)), - vel[], ang[], mn[] -))[, 1:2] - -# Calculate trajectories -# The following throws an error due to the trajectories moving beyond the raster extent -traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50) - -# This accounts for the extent issue -traj <- voccTraj(lonlat, vel, ang, mn, tyr = 50, correct = TRUE) - -} -\references{ -\href{https://www.nature.com/articles/nature12976}{Burrows et al. 2014}. Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507, 492-495. -} -\seealso{ -{\code{\link{gVoCC}}, \code{\link{trajClas}}} -} -\author{ -Jorge Garcia Molinos, David S. Schoeman and Michael T. Burrows -} diff --git a/vignettes/VoCC_Tutorial.Rmd b/vignettes/VoCC.Rmd similarity index 68% rename from vignettes/VoCC_Tutorial.Rmd rename to vignettes/VoCC.Rmd index 1242d4e..5c4d144 100644 --- a/vignettes/VoCC_Tutorial.Rmd +++ b/vignettes/VoCC.Rmd @@ -1,15 +1,10 @@ --- -title: "VoCC_Tutorial" -author: "Jorge García Molinos" -date: "06.05.2019" -output: - prettydoc::html_pretty: - highlight: github - theme: tactile +title: "VoCC" +output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{VoCC_Tutorial} + %\VignetteIndexEntry{VoCC} %\VignetteEngine{knitr::rmarkdown} - %\VignetteIndexEntry{VoCC_Tutorial} + %\VignetteIndexEntry{VoCC} \usepackage[utf8]{inputenc} --- @@ -18,6 +13,9 @@ vignette: > collapse = TRUE, comment = "#>" ) + +# Force sequential processing to avoid parallel processing issues during package checking +Sys.setenv("R_PARALLELLY_AVAILABLECORES_FALLBACK" = "1") ``` @@ -26,32 +24,18 @@ This vignette provides the code to reproduce the examples for the R package VoCC For this tutorial we need the following packages (if not installed get them first): ```{r message=FALSE} -# devtools::install_github("JorGarMol/VoCC", dependencies = TRUE, build_vignettes = TRUE) -devtools::load_all() -# suppressWarnings(library(VoCC)) -# suppressWarnings(library(rasterVis)) -# suppressWarnings(library(gridExtra)) -# suppressWarnings(library(doParallel)) -# suppressWarnings(library(foreach)) -# suppressWarnings(library(scales)) -# suppressWarnings(library(data.table)) -# suppressWarnings(library(mapplots)) -# suppressWarnings(library(ggplot2)) -# suppressWarnings(library(repmis)) +library(VoCC) +# devtools::load_all() +library(terra) +library(tidyterra) +library(ggplot2) +library(patchwork) ``` -We also need a few data sets that can be downloaded into R from the Github Data repository (https://github.com/JorGarMol/VoCC_data_sets) companion to the package. See the repository readme file for description of the data sets. +We also need a few data sets that can be accessed from the `VoCCdata` package. ```{r message=FALSE} -# load(url("https://github.com/JorGarMol/VoCC_data_sets/blob/master/Data/HadiSST.rda?raw=true") # HSST data set -HadiSST <- VoCC_get_data(path = "HSST.tif") - -# source_data("https://github.com/JorGarMol/VoCC_data_sets/blob/master/Data/EZZs.rda?raw=true") # EEZs data set -EEZ <- VoCC_get_data(path = "EEZ.gpkg") - -load(url("https://github.com/JorGarMol/VoCC_data_sets/blob/master/Data/marshift.rda?raw=true")) # marshift data set - -# load(url("https://github.com/JorGarMol/VoCC_data_sets/blob/master/Data/traj25.rda?raw=true")) # traj25 data set +library(VoCCdata) ``` @@ -63,9 +47,11 @@ We have a look first at the “marshift” global data set containing reported r str(marshift) ``` -Next, we calculate the gradient- and distance-based velocities (1960-2009), using the HSST data set, from which we will later extract the corresponding values for each observed shift. +Next, we calculate the gradient- and distance-based velocities (1960-2009), using the HadiSST data set, from which we will later extract the corresponding values for each observed shift. ```{r} +HadiSST <- terra::rast(system.file("extdata", "HadiSST.tif", package = "VoCCdata")) + # monthly to annual averages r <- sumSeries(HadiSST, p = "1960-01/2009-12", yr0 = "1955-01-01", l = terra::nlyr(HadiSST), @@ -80,11 +66,14 @@ gv <- gVoCC(vt, vg) # Now the distance-based velocities # Take 1960-1970 as base period against 2000-2009 -r2 <- c(mean(r[[1:10]], na.rm = TRUE), mean(r[[41:50]], na.rm = TRUE)) +r2 <- c(terra::mean(r[[1:10]], na.rm = TRUE), terra::mean(r[[41:50]], na.rm = TRUE)) + # prepare the data frame with the necessary variables -clim <- na.omit(data.frame(terra::values(r2), cid = 1:ncell(r))) +clim <- na.omit(data.frame(terra::values(r2), cid = 1:terra::ncell(r))) + clim[, c("x", "y")] <- terra::xyFromCell(r, clim$cid) # 1965-2004 (40 yr), 500 km search radius + v <- dVoCC(clim, n = 1, tdiff = 40, method = "Single", climTol = 0.1, geoTol = 500, distfun = "GreatCircle", trans = NA, lonlat = TRUE) ``` @@ -92,18 +81,56 @@ Next, we extract the mean velocity estimates for each reported shift by taking t ```{r} # Change sign as needed and create the distance-based velocity raster -ind <- which(r2[[1]][v$focal] > r2[[2]][v$target]) +ind <- which(terra::extract(r2[[1]], v$focal) > terra::extract(r2[[2]], v$target)) v$velBis <- v$vel v$velBis[ind] <- v$vel[ind] * -1 # put output in raster format dv <- terra::rast(gv) dv[v$focal] <- v$velBis -marshift$GV <- with(marshift, terra::extract(abs(gv[[1]]), cbind(long, lat), buffer = (Shift * (timespan / 10) * 1000), fun = mean, na.rm = TRUE, small = TRUE)) -marshift$DV <- with(marshift, terra::extract(abs(dv), cbind(long, lat), buffer = (Shift * (timespan / 10) * 1000), fun = mean, na.rm = TRUE, small = TRUE)) -# retrieve the closest marine cell for those centroids falling on land -marshift$GV <- with(marshift, ifelse(is.na(GV), gv[[1]][which.min(replace(distanceFromPoints(gv[[1]], cbind(long, lat)), is.na(gv[[1]]), NA))], GV)) -marshift$DV <- with(marshift, ifelse(is.na(DV), dv[which.min(replace(distanceFromPoints(dv, cbind(long, lat)), is.na(dv), NA))], DV)) +# Create point geometries and buffer them +coords <- terra::vect(cbind(marshift$long, marshift$lat), crs = "EPSG:4326") +buffer_size <- marshift$Shift * (marshift$timespan / 10) * 1000 + +marshift$GV <- terra::extract(abs(gv[[1]]), terra::buffer(coords, buffer_size), + fun = mean, na.rm = TRUE)$voccMag # TODO The other option is voccAng but they seem the same. Check data above. + +marshift$DV <- terra::extract(abs(dv), terra::buffer(coords, buffer_size), + fun = mean, na.rm = TRUE)$voccMag # The other option is voccAng +``` + +```{r include=FALSE} +# # retrieve the closest marine cell for those centroids falling on land +# # For points that didn't get values (NA), find nearest valid cells +# na_gv <- is.na(marshift$GV) +# na_dv <- is.na(marshift$DV) +# +# if(any(na_gv)) { +# # Create a mask of valid cells and find nearest valid cell for each NA point +# valid_cells_gv <- !is.na(gv[[1]]) +# for(i in which(na_gv)) { +# distances <- terra::distance(coords[i], gv[[1]], unit = "m") +# # Find the closest non-NA cell +# valid_distances <- terra::mask(distances, valid_cells_gv) +# min_cell <- which.min(terra::values(valid_distances)) +# marshift$GV[i] <- terra::values(gv[[1]])[min_cell] +# } +# } +# +# if(any(na_dv)) { +# # Create a mask of valid cells and find nearest valid cell for each NA point +# valid_cells_dv <- !is.na(dv) +# for(i in which(na_dv)) { +# distances <- terra::distance(coords[i], dv, unit = "m") +# # Find the closest non-NA cell +# valid_distances <- terra::mask(distances, valid_cells_dv) +# min_cell <- which.min(terra::values(valid_distances)) +# marshift$DV[i] <- terra::values(dv)[min_cell] +# } +# } +``` + +```{r} # fit the regression models Mgv <- lm(Shift^(1 / 4) ~ I((GV * 10)^(1 / 4)), data = marshift, weights = years_data) Mdv <- lm(Shift^(1 / 4) ~ I((DV * 10)^(1 / 4)), data = marshift, weights = years_data) @@ -115,11 +142,23 @@ Produce the observed vs predicted scatterplots with regression lines (Fig. 2 in ```{r} # first compare both velocities -my.at <- seq(-50, 50, by = 5) -p1 <- rasterVis::levelplot(gv[[1]], par.settings = BuRdTheme, at = my.at, main = "Gradient-based vocc", margin = FALSE) -my.at <- seq(-20, 20, by = 2) -p2 <- rasterVis::levelplot(dv, par.settings = BuRdTheme, at = my.at, main = "Distance-based vocc", margin = FALSE) -gridExtra::grid.arrange(p1, p2, ncol = 1) + +p1 <- ggplot() + + geom_spatraster(data = gv[[1]]) + + scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-50, 50)) + + ggtitle("Gradient-based vocc") + + scale_x_continuous(expand = c(0,0)) + + scale_y_continuous(expand = c(0,0)) + +p2 <- ggplot() + + geom_spatraster(data = gv[[1]]) + + scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-20, 20)) + + ggtitle("Distance-based vocc") + + scale_x_continuous(expand = c(0,0)) + + scale_y_continuous(expand = c(0,0)) + +wrap_plots(p1, p2, ncol = 1) + # scatter plots with the resulting regression line p1 <- ggplot(na.omit(marshift), aes(x = I((GV * 10)^(1 / 4)), y = Shift^(1 / 4))) + geom_point(color = "grey") + @@ -133,22 +172,26 @@ p2 <- ggplot(na.omit(marshift), aes(x = I((DV * 10)^(1 / 4)), y = Shift^(1 / 4)) theme_classic() + scale_color_brewer(palette = "Accent") + labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)") -grid.arrange(p1, p2, nrow = 1) +wrap_plots(p1, p2, nrow = 1) ``` # Example 2: Analysis of climate exposure and connectivity in the Western Pacific Ocean + +THIS CODE IS NOT OPERATIONAL YET. IN PARTICULAR, `voccTraj` HAS NOT BEEN UPDATED FOR `terra`. Standby for updates. + In this example we use climate velocity trajectories (based on 1960-2009 mean annual SST) to analyse climate connectivity in the Western Pacific region and calculate the residence time corresponding to the exclusive economic zones in the region as an index of climatic exposure. First, we arrange the raster layers for analysis. ```{r} # prepare raster layers vel <- gv[[1]] ang <- gv[[2]] -mn <- calc(r, mean, na.rm = T) +mn <- app(r, mean, na.rm = T) + # generate a velocity layer centered and cropped to study region to extract the initial coordinates for the trajectories from -x1 <- crop(gv[[1]], extent(-180, 0, -90, 90)) -x2 <- crop(gv[[1]], extent(0, 180, -90, 90)) -extent(x1) <- c(180, 360, -90, 90) +x1 <- crop(gv[[1]], ext(-180, 0, -90, 90)) +x2 <- crop(gv[[1]], ext(0, 180, -90, 90)) +ext(x1) <- c(180, 360, -90, 90) velc <- merge(x1, x2) # crop to the desired extent # display restricted to +180 longitude to avoid plotting issues with date line crossing @@ -159,15 +202,15 @@ We can now populate the data frame with the cell centroid coordinates for the tr ```{r} lonlat <- data.frame(terra::xyFromCell(velc, 1:ncell(velc))) -lonlat$vel <- terra::extract(vel, lonlat) -lonlat$ang <- terra::extract(ang, lonlat[, 1:2]) -lonlat$mn <- terra::extract(mn, lonlat[, 1:2]) +lonlat$vel <- terra::extract(vel, lonlat, ID = FALSE) +lonlat$ang <- terra::extract(ang, lonlat[, 1:2], ID = FALSE) +lonlat$mn <- terra::extract(mn, lonlat[, 1:2], ID = FALSE) lonlat <- na.omit(lonlat) ``` Let's calculate the trajectories with parallel processing to demonstrate how this can be used to speed things up (especially useful when dealing with fine resolutions or large extents). -```{r} +```{r, eval=FALSE} cores <- detectCores() ncores <- cores[1] - 1 cuts <- cut(1:nrow(lonlat), ncores) @@ -181,7 +224,7 @@ stopCluster(cl) Plot them over the climate velocities and the EEZ polygons from the EEZs data set (Fig. 3a in Garcia Molinos et al. 2019) -```{r} +```{r, eval=FALSE} # simplify polygons to speed plotting up eez_simp <- rgeos::gSimplify(EEZs, tol = 0.5, topologyPreserve = TRUE) plot(velc) @@ -193,7 +236,7 @@ plot(eez_simp, col = scales::alpha(rgb(211, 211, 211, maxColorValue = 255), 0.5) We now calulcate the trajectory classes and residence times for each EEZ using the traj25 data set. -```{r} +```{r, eval=FALSE} # get the set of starting cells for the trajectories and calculate trajectories # at 1/4-deg resolution (16 trajectories per 1-deg cell) r <- disaggregate(mn, 4) @@ -213,7 +256,7 @@ EEZa <- resTime(EEZs, vel, areapg = NA) Finally let's plot the category proportions as pie charts on top of each EEZ, the size of the chart being proportional to their respective residence time (Fig. 3b in Garcia Molinos et al. 2019). -```{r} +```{r, eval=FALSE} D <- data.table(d) # put data in long format # add EEZ names for reference D[, name := as.character(EEZs$Territory1)[as.numeric(ID)]] From 6452c051fe6016ef57dd98d4d54767af115c5205 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Wed, 27 Aug 2025 21:22:48 +1000 Subject: [PATCH 05/10] Update site --- .Rbuildignore | 3 + .gitignore | 1 + DESCRIPTION | 6 +- _pkgdown.yml | 4 + docs/404.html | 108 ++++++--------- docs/LICENSE.html | 81 +++++------ docs/articles/VoCC.html | 156 ++++++++++----------- docs/articles/index.html | 79 +++++------ docs/authors.html | 105 ++++++--------- docs/index.html | 109 ++++++--------- docs/pkgdown.js | 184 ++++++++++++++++--------- docs/pkgdown.yml | 2 +- docs/reference/EEZ.html | 105 ++++++--------- docs/reference/HSST.html | 109 +++++++-------- docs/reference/JapTC.html | 114 +++++++--------- docs/reference/angulo.html | 108 +++++++-------- docs/reference/climPCA.html | 123 ++++++++--------- docs/reference/climPlot.html | 120 +++++++---------- docs/reference/dVoCC.html | 129 ++++++++---------- docs/reference/gVoCC.html | 127 ++++++++--------- docs/reference/index.html | 247 ++++++++++++++++++---------------- docs/reference/pipe.html | 108 +++++++-------- docs/reference/resTime.html | 124 ++++++++--------- docs/reference/shiftTime.html | 123 ++++++++--------- docs/reference/spatGrad.html | 128 ++++++++---------- docs/reference/splitLine.html | 108 +++++++-------- docs/reference/sumSeries.html | 126 ++++++++--------- docs/reference/tempTrend.html | 124 ++++++++--------- vignettes/VoCC.Rmd | 23 +++- 29 files changed, 1291 insertions(+), 1593 deletions(-) create mode 100644 _pkgdown.yml diff --git a/.Rbuildignore b/.Rbuildignore index b8ad5dc..94d5c04 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -10,3 +10,6 @@ ^data-raw$ ^README\.Rmd$ ^LICENSE\.md$ +^_pkgdown\.yml$ +^docs$ +^pkgdown$ diff --git a/.gitignore b/.gitignore index 97e604b..1354af4 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,4 @@ .DS_Store /doc/ /Meta/ +docs diff --git a/DESCRIPTION b/DESCRIPTION index 11b8cdb..c897d9a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: VoCC Title: The Velocity of Climate Change and related climatic metrics -Version: 1.0.1 +Version: 0.0.1 Authors@R: c(person("Jorge", "Garcia Molinos", email = "jorgegmolinos@arc.hokudai.ac.jp", role = c("aut", "cre")), person("David", "S. Schoeman", email = "", role = "aut"), person("Christopher", "J. Brown", email = "", role = "aut"), @@ -29,13 +29,9 @@ Imports: terra, xts Suggests: - gridExtra, knitr, - mapplots, ncdf4, patchwork, - rasterVis, - repmis, rmarkdown, scales, tidyterra, diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..d71acfb --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,4 @@ +url: ~ +template: + bootstrap: 5 + diff --git a/docs/404.html b/docs/404.html index 142ebb3..bd6f0bf 100644 --- a/docs/404.html +++ b/docs/404.html @@ -4,100 +4,72 @@ - + Page not found (404) • VoCC - - - - - - - + + + + + - - - -
    -
    -
    -

    Page not found (404)

    +
    Content not found. Please use links in the navbar. -
    - - - +
    - -
    -

    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    +
    - diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 59ada21..9e898d8 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -1,49 +1,35 @@ -GNU Affero General Public License • VoCC +GNU Affero General Public License • VoCC + Skip to contents -
    -
    -
    -
    +
    +
    +
    +
    +

    GNU Affero General Public License

    + Source: LICENSE.md
    @@ -236,26 +222,19 @@

    How to Apply These Terms

    -
    +
    - +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    - - -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    -

    Site built with pkgdown 2.1.3.

    -
    - -
    - +
    diff --git a/docs/articles/VoCC.html b/docs/articles/VoCC.html index e863c8a..3f07ebf 100644 --- a/docs/articles/VoCC.html +++ b/docs/articles/VoCC.html @@ -4,77 +4,65 @@ - + VoCC • VoCC - - - - - - - + + + + + - + + Skip to contents -
    -
    -
    -
    -

    VoCC

    + +
    +
    + +

    VoCC

    Source: vignettes/VoCC.Rmd - - +
    VoCC.Rmd
    +

    THIS IS A WORK IN PROGRESS TO CONVERT THE OLD RASTER-BASED +VOCC PACKAGE TO TERRA. NOT EVERYTHING WORKS YET. IN PARTICULAR +CONNECTIVITY IS NOT IMPLEMENTED SO ONLY EXAMPLE 1 BELOW +WORKS.

    This vignette provides the code to reproduce the examples for the R package VoCC as presented in Garcia Molinos et al. (2019). Refer to the paper and the function documentation for details on function options, @@ -84,7 +72,6 @@

    VoCC

    get them first):

     library(VoCC)
-# devtools::load_all()
 library(terra)
 library(tidyterra)
 library(ggplot2)
@@ -166,7 +153,6 @@ 
    Example 1: Prediction of
 
     # fit the regression models
 Mgv <- lm(Shift^(1 / 4) ~ I((GV * 10)^(1 / 4)), data = marshift, weights = years_data)
-Mdv <- lm(Shift^(1 / 4) ~ I((DV * 10)^(1 / 4)), data = marshift, weights = years_data)
 summary(Mgv)
 #> 
 #> Call:
@@ -187,7 +173,9 @@ 
    Example 1: Prediction of
 #> Residual standard error: 3.821 on 340 degrees of freedom
 #>   (1 observation deleted due to missingness)
 #> Multiple R-squared:  0.09558,    Adjusted R-squared:  0.09292 
-#> F-statistic: 35.93 on 1 and 340 DF,  p-value: 5.217e-09
+#> F-statistic: 35.93 on 1 and 340 DF,  p-value: 5.217e-09
    
+
    +Mdv <- lm(Shift^(1 / 4) ~ I((DV * 10)^(1 / 4)), data = marshift, weights = years_data)
 summary(Mdv)
 #> 
 #> Call:
@@ -211,58 +199,59 @@ 
    Example 1: Prediction of
 #> F-statistic: 27.73 on 1 and 335 DF,  p-value: 2.502e-07
    
 
    Produce the observed vs predicted scatterplots with regression lines
 (Fig. 2 in Garcia Molinos et al. 2019).
    
-
    +
     # first compare both velocities
 
 p1 <- ggplot() + 
   geom_spatraster(data = gv[[1]]) + 
-  scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-50, 50)) +
+  scale_fill_distiller(palette = "RdBu", direction = -1, limits = c(-50, 50)) +
   ggtitle("Gradient-based vocc") +
   scale_x_continuous(expand = c(0,0)) + 
   scale_y_continuous(expand = c(0,0))
 
 p2 <- ggplot() + 
   geom_spatraster(data = gv[[1]]) + 
-  scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-20, 20)) +
+  scale_fill_distiller(palette = "RdBu", direction = -1, limits = c(-20, 20)) +
   ggtitle("Distance-based vocc") +
   scale_x_continuous(expand = c(0,0)) + 
   scale_y_continuous(expand = c(0,0))
 
 wrap_plots(p1, p2, ncol = 1)
    
-
    
-
    -
-# scatter plots with the resulting regression line
-p1 <- ggplot(na.omit(marshift), aes(x = I((GV * 10)^(1 / 4)), y = Shift^(1 / 4))) +
+
    
+
    +# scatter plots with the resulting regression line
+p1 <- ggplot(na.omit(marshift), aes(x = (GV * 10)^(1 / 4), y = Shift^(1 / 4))) +
   geom_point(color = "grey") +
   geom_smooth(method = lm, se = FALSE) +
   theme_classic() +
   scale_color_brewer(palette = "Accent") +
   labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)")
-p2 <- ggplot(na.omit(marshift), aes(x = I((DV * 10)^(1 / 4)), y = Shift^(1 / 4))) +
+
+p2 <- ggplot(na.omit(marshift), aes(x = (DV * 10)^(1 / 4), y = Shift^(1 / 4))) +
   geom_point(color = "grey") +
   geom_smooth(method = lm, se = FALSE) +
   theme_classic() +
   scale_color_brewer(palette = "Accent") +
   labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)")
+
 wrap_plots(p1, p2, nrow = 1)
 #> `geom_smooth()` using formula = 'y ~ x'
 #> `geom_smooth()` using formula = 'y ~ x'
    
-
    
+
    
 
    
 
    
 
    Example 2: Analysis of climate exposure and connectivity in the
 Western Pacific Ocean
 
    
-
    THIS CODE IS NOT OPERATIONAL YET. IN PARTICULAR,
+
    THIS CODE IS NOT OPERATIONAL YET. IN PARTICULAR,
 voccTraj HAS NOT BEEN UPDATED FOR terra.
-Standby for updates.
    
+Standby for updates.
    
 
    In this example we use climate velocity trajectories (based on
 1960-2009 mean annual SST) to analyse climate connectivity in the
 Western Pacific region and calculate the residence time corresponding to
 the exclusive economic zones in the region as an index of climatic
 exposure. First, we arrange the raster layers for analysis.
    
-
    +
     # prepare raster layers
 vel <- gv[[1]]
 ang <- gv[[2]]
@@ -278,7 +267,7 @@ 
    velc <- crop(velc, c(90, 180, -32, 33))
    
 
    We can now populate the data frame with the cell centroid coordinates
 for the trajectories and associated input data
    
-
    +
     lonlat <- data.frame(terra::xyFromCell(velc, 1:ncell(velc)))
 lonlat$vel <- terra::extract(vel, lonlat, ID = FALSE)
 lonlat$ang <- terra::extract(ang, lonlat[, 1:2], ID = FALSE)
@@ -287,7 +276,7 @@ 
    +
     cores <- detectCores()
 ncores <- cores[1] - 1
 cuts <- cut(1:nrow(lonlat), ncores)
@@ -299,7 +288,7 @@ 
    stopCluster(cl)
    
 
    Plot them over the climate velocities and the EEZ polygons from the
 EEZs data set (Fig. 3a in Garcia Molinos et al. 2019)
    
-
    +
     # simplify polygons to speed plotting up
 eez_simp <- rgeos::gSimplify(EEZs, tol = 0.5, topologyPreserve = TRUE)
 plot(velc)
@@ -309,7 +298,7 @@ 
    plot(eez_simp, col = scales::alpha(rgb(211, 211, 211, maxColorValue = 255), 0.5), add = TRUE)
    
 
    We now calulcate the trajectory classes and residence times for each
 EEZ using the traj25 data set.
    
-
    +
     # get the set of starting cells for the trajectories and calculate trajectories
 # at 1/4-deg resolution (16 trajectories per 1-deg cell)
 r <- disaggregate(mn, 4)
@@ -328,7 +317,7 @@ 
    +
     D <- data.table(d) # put data in long format
 # add EEZ names for reference
 D[, name := as.character(EEZs$Territory1)[as.numeric(ID)]]
@@ -355,33 +344,26 @@ 
    Referencesdoi:10.1111/2041-210X.13295
    
 
    
-  
    
-
-  
-
+
    -
    -

    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    +
    - diff --git a/docs/articles/index.html b/docs/articles/index.html index 0828f76..9bc9b67 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,72 +1,55 @@ -Articles • VoCC - - -
    -
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    +
    +
    +
    +
    +

    Articles

    All vignettes

    -

    +
    VoCC
    -
    -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/authors.html b/docs/authors.html index e1b10ae..a429f4b 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -1,52 +1,38 @@ -Authors and Citation • VoCC - - -
    -
    -
    -
    -
    -

    Authors and Citation

    -
    +
    +
    +
    +
    + +

    Authors and Citation

    +
    + +
    +

    Authors

    • Jorge Garcia Molinos. Author, maintainer. @@ -69,42 +55,37 @@

      Authors and Citation

    -
    -
    -

    Citation

    - Source: DESCRIPTION -
    -
    +
    +

    Citation

    +

    Source: DESCRIPTION

    -

    Garcia Molinos J, S. Schoeman D, J. Brown C, T. Burrows M (2025). +

    Garcia Molinos J, S. Schoeman D, J. Brown C, T. Burrows M (2025). VoCC: The Velocity of Climate Change and related climatic metrics. -R package version 1.0.1, https://github.com/JorGarMol/VoCC. +R package version 0.0.1, https://github.com/JorGarMol/VoCC.

    - 
    @Manual{,
+      
    @Manual{,
   title = {VoCC: The Velocity of Climate Change and related climatic metrics},
   author = {Jorge {Garcia Molinos} and David {S. Schoeman} and Christopher {J. Brown} and Michael {T. Burrows}},
   year = {2025},
-  note = {R package version 1.0.1},
+  note = {R package version 0.0.1},
   url = {https://github.com/JorGarMol/VoCC},
 }
    
+
    -
    - -
    - +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/index.html b/docs/index.html index fb9d291..d59a276 100644 --- a/docs/index.html +++ b/docs/index.html @@ -4,68 +4,49 @@ - + The Velocity of Climate Change and related climatic metrics • VoCC - - - - - - + + + + + + - - - - -
    -
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    -
    +
    +
    +
    +

    VoCC: The Velocity of Climate Change and related climatic metrics

    @@ -97,10 +78,7 @@

    Citation

    -
    - - - diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 6f0eee4..1a99c65 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -2,83 +2,43 @@ (function($) { $(function() { - $('.navbar-fixed-top').headroom(); + $('nav.navbar').headroom(); - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") }); - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); + if ($('#toc').length) { + $('body').scrollspy({ + target: '#toc', + offset: $("nav.navbar").outerHeight() + 1 + }); } - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); - return(haystack.length); - } + $('[data-bs-toggle="tooltip"]').tooltip(); /* Clipboard --------------------------*/ function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); + var tooltipOriginalTitle=element.getAttribute('data-bs-original-title'); + element.setAttribute('data-bs-original-title', msg); $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); + element.setAttribute('data-bs-original-title', tooltipOriginalTitle); } if(ClipboardJS.isSupported()) { $(document).ready(function() { - var copyButton = ""; + var copyButton = ""; $("div.sourceCode").addClass("hasCopyButton"); @@ -89,20 +49,114 @@ $('.btn-copy-ex').tooltip({container: 'body'}); // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + var clipboard = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); - clipboardBtnCopies.on('success', function(e) { + clipboard.on('success', function(e) { changeTooltipMessage(e.trigger, 'Copied!'); e.clearSelection(); }); - clipboardBtnCopies.on('error', function() { + clipboard.on('error', function(e) { changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); }); + }); } + + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main#main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; + } + } + callback(items); +} + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
    ${s.title}
    `; + } else if (s.previous_headings == "") { + return `${s.dir} >
    ${s.title}
    > ${s.what}`; + } else { + return `${s.dir} >
    ${s.title}
    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); })(window.jQuery || window.$) + +document.addEventListener('keydown', function(event) { + // Check if the pressed key is '/' + if (event.key === '/') { + event.preventDefault(); // Prevent any default action associated with the '/' key + document.getElementById('search-input').focus(); // Set focus to the search input + } +}); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 44eb1cd..9a23dc3 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,4 +3,4 @@ pkgdown: 2.1.3 pkgdown_sha: ~ articles: VoCC: VoCC.html -last_built: 2025-08-27T06:12Z +last_built: 2025-08-27T10:58Z diff --git a/docs/reference/EEZ.html b/docs/reference/EEZ.html index 1d253de..f005b12 100644 --- a/docs/reference/EEZ.html +++ b/docs/reference/EEZ.html @@ -1,86 +1,67 @@ -Icelandic Exclusive Economic Zone (EEZ) — EEZ • VoCC - - -
    -
    -
    -
    -

    Icelandic Exclusive Economic Zone (EEZ)

    - Source: R/data.R - + + + +
    +
    +
    +
    + +

    Icelandic Exclusive Economic Zone (EEZ)

    + Source: R/data.R +
    EEZ.Rd
    -
    +

    Icelandic Exclusive Economic Zone (v10).

    -
    -

    Format

    +
    +

    Format

    A spatial polygon data frame containing the Icenalndic EEZ (200 NM).

    -
    -

    Source

    +
    +

    Source

    Marineregions.org http://www.marineregions.org/downloads.php

    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/HSST.html b/docs/reference/HSST.html index 9d2add4..f112b0d 100644 --- a/docs/reference/HSST.html +++ b/docs/reference/HSST.html @@ -1,90 +1,71 @@ -Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010 — HSST • VoCC - - -
    -
    -
    -
    -

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    - Source: R/data.R - + + + +
    +
    +
    +
    + +

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    + Source: R/data.R +
    HSST.Rd
    -
    +

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    -
    -

    Format

    +
    +

    Format

    A raster stack with 672 layers containing mean monthly SSTs (C) at 1-deg resolution for the period Jan 1955 to Dec 2010 for the Atlantic waters surrounding Iceland.

    -
    -

    Source

    +
    +

    Source

    Hadley Centre data set HadISST 1.1 (data accessed May 2018).

    -
    -

    References

    +
    +

    References

    Rayner et al. 2003. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res.Vol. 108: 4407.

    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/JapTC.html b/docs/reference/JapTC.html index 125bc92..2f3b101 100644 --- a/docs/reference/JapTC.html +++ b/docs/reference/JapTC.html @@ -1,57 +1,45 @@ -Multivariate Terraclimate data for Japan — JapTC • VoCCMultivariate Terraclimate data for Japan — JapTC • VoCC - - -
    -
    -
    -
    -

    Multivariate Terraclimate data for Japan

    - Source: R/data.R - + + + + + +
    +
    +
    +
    + +

    Multivariate Terraclimate data for Japan

    + Source: R/data.R +
    JapTC.Rd
    -
    +

    Historical (1960-1969) and present (2008-2017) multivariate 1/24 Terraclimate data set (Abatzoglou et al. 2018), extracted for the Japanese archipelago and including 3 variables: mean annual monthly precipitation (mm), @@ -59,39 +47,35 @@

    Multivariate Terraclimate data for Japan

    -
    -

    Format

    +
    +

    Format

    A raster stack with 9 layers for the historical (1960-1969) and current (2008-2017) average (AnMn) and standard deviation (AnMnSD) of the mean annual (AnMn) precipitation (Ppr), maximum (Tmax) and minimum (Tmin) temperature.

    -
    -

    Source

    +
    +

    Source

    Terraclimate data set.

    -
    -

    References

    +
    +

    References

    Abatzoglou et al. 2018. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data, 5: 170191

    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/angulo.html b/docs/reference/angulo.html index 95b8a11..4a1a4e2 100644 --- a/docs/reference/angulo.html +++ b/docs/reference/angulo.html @@ -1,63 +1,49 @@ -Internal. Angle associated to the spatial gradient — angulo • VoCC - - -
    -
    -
    -
    -

    Internal. Angle associated to the spatial gradient

    - Source: R/angulo.R - + + + +
    +
    +
    +
    + +

    Internal. Angle associated to the spatial gradient

    + Source: R/angulo.R +
    angulo.Rd
    -
    +

    Internal. Angle associated to the spatial gradient

    -
    +
    +

    Usage

    angulo(dx, dy)
    -
    -

    Arguments

    +
    +

    Arguments

    dx
    @@ -68,29 +54,25 @@

    Arguments

    numeric giving the latitudinal gradient component

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos and David S. Schoeman angulo()

    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/climPCA.html b/docs/reference/climPCA.html index 6bd21bd..effb0a6 100644 --- a/docs/reference/climPCA.html +++ b/docs/reference/climPCA.html @@ -1,60 +1,47 @@ -Reduce dimensionality of climate predictors via Principal Component Analysis — climPCA • VoCC - - -
    -
    -
    -
    -

    Reduce dimensionality of climate predictors via Principal Component Analysis

    - Source: R/climPCA.R - + + + +
    +
    +
    +
    + +

    Reduce dimensionality of climate predictors via Principal Component Analysis

    + Source: R/climPCA.R +
    climPCA.Rd
    -
    +

    Function to extract the first n principal components explaining a predefined total amount of variance among climatic variables. These components can subsequently be used as synthetic climatic variables to reduce dimensionality in climate-analogue methods.

    -
    +
    +

    Usage

    climPCA(
   climp,
   climf,
@@ -65,8 +52,8 @@ 
    Reduce dimensionality of climate predictors via Principal Component Analysis
 )
    -
    -

    Arguments

    +
    +

    Arguments

    climp
    @@ -94,23 +81,23 @@

    Arguments

    numeric threshold giving the minimum amount of total variance that should be explained by the principal components extracted.

    -
    -

    Value

    +
    +

    Value

    a list containing (i) the output from the PCA (call to 'prcomp'), and (ii) a table with the present/future cell values for the principal components accounting for the specified percentage of total variance (th).

    -
    -

    See also

    +
    +

    See also

    dVoCC, climPlot

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 JapTC <- VoCC_get_data("JapTC.tif")
 
@@ -124,23 +111,19 @@ 
    Examples
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/climPlot.html b/docs/reference/climPlot.html index 5c13c9b..428b4fa 100644 --- a/docs/reference/climPlot.html +++ b/docs/reference/climPlot.html @@ -1,63 +1,49 @@ -Binned scatter plot for 2-dimensional climate space — climPlot • VoCC - - -
    -
    -
    -
    -

    Binned scatter plot for 2-dimensional climate space

    - Source: R/climPlot.R - + + + +
    +
    +
    +
    + +

    Binned scatter plot for 2-dimensional climate space

    + Source: R/climPlot.R +
    climPlot.Rd
    -
    +

    Function to create a binned scatter plot of two climate variables.

    -
    +
    +

    Usage

    climPlot(xy, x.binSize, y.binSize, x.name = "V1", y.name = "V2")
    -
    -

    Arguments

    +
    +

    Arguments

    xy
    @@ -80,23 +66,23 @@

    Arguments

    character the variable name for the second variable. Used to label the plot.

    -
    -

    Value

    +
    +

    Value

    A series of plot objects displaying the (i) present and (ii) future cell frequency for each combination of local climates, and (iii) the location of remnant, novel and disappearing climates between both periods.

    -
    -

    See also

    +
    +

    See also

    dVoCC, climPCA

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos and Naoki H. Kumagai

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 JapTC <- VoCC_get_data("JapTC.tif")
 
@@ -120,23 +106,19 @@ 
    Examples
    
 } # }
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/dVoCC.html b/docs/reference/dVoCC.html index 582301e..8a2b0af 100644 --- a/docs/reference/dVoCC.html +++ b/docs/reference/dVoCC.html @@ -1,64 +1,53 @@ -Distance-based velocity based on geographically closest climate analogue — dVoCC • VoCCDistance-based velocity based on geographically closest climate analogue — dVoCC • VoCC - - -
    -
    -
    -
    -

    Distance-based velocity based on geographically closest climate analogue

    - Source: R/dVoCC.R - + + + + + +
    +
    +
    +
    + +

    Distance-based velocity based on geographically closest climate analogue

    + Source: R/dVoCC.R +
    dVoCC.Rd
    -
    +

    Function to calculate the geographically closest climate analogues and related distance-based velocity. Cell analogues are identified by comparing the baseline climatic conditions at each focal cell with those existing for all other (target) cells in the future by reference to a specified climatic threshold. The function allows for the specification of search distances and incorporates both least-cost path and Great Circle (as-the-crow-flies) distances.

    -
    +
    +

    Usage

    dVoCC(
   clim,
   n,
@@ -72,8 +61,8 @@ 
    Distance-based velocity based on geographically closest climate analogue
    )
    -
    -

    Arguments

    +
    +

    Arguments

    clim
    @@ -123,27 +112,27 @@

    Arguments

    logical is the analysis to be done in unprojected (lon/lat) coordinates?

    -
    -

    Value

    +
    +

    Value

    A data.frame containing the cell id of the future analogue for each focal cell (NA = no analogue available), together with the climatic ("climDis") and geographical ("geoDis") distances in input units, the bearing ("ang", degrees North), and resulting climate velocity ("vel", km/yr). Mean climatic distances are returned for multivariate analogues.

    -
    -

    References

    +
    +

    References

    Ohlemuller et al. 2006. Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931-2100. Global Ecology and Biogeography, 15, 395-405.
    Hamann et al. 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology, 21, 997-1004.
    Garcia Molinos et al. 2017. Improving the interpretability of climate landscape metrics: An ecological risk analysis of Japan's Marine Protected Areas. Global Change Biology, 23, 4440-4452.

    -
    -

    See also

    +
    +

    See also

    climPCA, climPlot

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 JapTC <- VoCC_get_data("JapTC.tif")
 
@@ -183,23 +172,19 @@ 
    Examples
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/gVoCC.html b/docs/reference/gVoCC.html index 02d77e3..4674fdb 100644 --- a/docs/reference/gVoCC.html +++ b/docs/reference/gVoCC.html @@ -1,65 +1,52 @@ -Gradient-based climate velocity — gVoCC • VoCC - - -
    -
    -
    -
    -

    Gradient-based climate velocity

    - Source: R/gVoCC.R - + + + +
    +
    +
    +
    + +

    Gradient-based climate velocity

    + Source: R/gVoCC.R +
    gVoCC.Rd
    -
    +

    Function to calculate the velocity of climate change after Burrows et al. (2011) based on local climatic temporal trends and spatial gradients.

    -
    +
    +

    Usage

    gVoCC(tempTrend, spatGrad)
    -
    -

    Arguments

    +
    +

    Arguments

    tempTrend
    @@ -70,28 +57,28 @@

    Arguments

    The output from the spatGrad function containing the magnitudes and angles for the spatial climatic gradient.

    -
    -

    Value

    +
    +

    Value

    A RasterStack containing the climate velocity magnitude ("voccMag", km/yr for unprojected rasters and spatial unit/year for projected rasters) and angle("voccAng" in degrees north: 0N, 90E, 180S and 270W).

    -
    -

    References

    +
    +

    References

    Burrows et al. 2011. The pace of shifting climate in marine and terrestrial ecosystems. Science, 334, 652-655.

    -
    -

    See also

    +
    +

    See also

    tempTrend, spatGrad

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 HSST <- VoCC_get_data("HSST.tif")
 yrSST <- sumSeries(HSST,
@@ -109,23 +96,19 @@ 
    Examples
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/index.html b/docs/reference/index.html index 3ea2346..3e59af6 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,129 +1,144 @@ -Package index • VoCC - - -
    -
    -
    -
    -

    Reference

    + + + + + +
    +
    +
    +
    + +

    Package index

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    All functions

    -

    -
    -

    EEZ

    -

    Icelandic Exclusive Economic Zone (EEZ)

    -

    HSST

    -

    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010

    -

    JapTC

    -

    Multivariate Terraclimate data for Japan

    -

    angulo()

    -

    Internal. Angle associated to the spatial gradient

    -

    climPCA()

    -

    Reduce dimensionality of climate predictors via Principal Component Analysis

    -

    climPlot()

    -

    Binned scatter plot for 2-dimensional climate space

    -

    dVoCC()

    -

    Distance-based velocity based on geographically closest climate analogue

    -

    gVoCC()

    -

    Gradient-based climate velocity

    -

    resTime()

    -

    Climatic residence time of a polygon

    -

    shiftTime()

    -

    Shift in timing of seasonal climatology

    -

    spatGrad()

    -

    Local spatial climatic gradients

    -

    splitLine()

    -

    Internal. Split a line segment defined by points A-B into n parts

    -

    sumSeries()

    -

    Summarize climatic series to higher temporal resolution

    -

    tempTrend()

    -

    Long-term local climatic trends

    - - -
    +
    +

    All functions

    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -
    -

    Site built with pkgdown 2.1.3.

    + +
    + + + + +
    + + EEZ + +
    +
    Icelandic Exclusive Economic Zone (EEZ)
    +
    + + HSST + +
    +
    Monthly mean sea surface temperatures (SST) around Iceland for 1955-2010
    +
    + + JapTC + +
    +
    Multivariate Terraclimate data for Japan
    +
    + + angulo() + +
    +
    Internal. Angle associated to the spatial gradient
    +
    + + climPCA() + +
    +
    Reduce dimensionality of climate predictors via Principal Component Analysis
    +
    + + climPlot() + +
    +
    Binned scatter plot for 2-dimensional climate space
    +
    + + dVoCC() + +
    +
    Distance-based velocity based on geographically closest climate analogue
    +
    + + gVoCC() + +
    +
    Gradient-based climate velocity
    +
    + + resTime() + +
    +
    Climatic residence time of a polygon
    +
    + + shiftTime() + +
    +
    Shift in timing of seasonal climatology
    +
    + + spatGrad() + +
    +
    Local spatial climatic gradients
    +
    + + splitLine() + +
    +
    Internal. Split a line segment defined by points A-B into n parts
    +
    + + sumSeries() + +
    +
    Summarize climatic series to higher temporal resolution
    +
    + + tempTrend() + +
    +
    Long-term local climatic trends
    +
    +
    + + +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    +
    +

    Site built with pkgdown 2.1.3.

    +
    +
    diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index cc95e9b..274b2e4 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -1,63 +1,49 @@ -Pipe operator — %>% • VoCC - - -
    -
    -
    -
    -

    Pipe operator

    - Source: R/utils-pipe.R - + + + +
    +
    +
    +
    + +

    Pipe operator

    + Source: R/utils-pipe.R +
    pipe.Rd
    -
    +

    See magrittr::%>% for details.

    -
    +
    +

    Usage

    lhs %>% rhs
    -
    -

    Arguments

    +
    +

    Arguments

    lhs
    @@ -68,28 +54,24 @@

    Arguments

    A function call using the magrittr semantics.

    -
    -

    Value

    +
    +

    Value

    The result of calling `rhs(lhs)`.

    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/resTime.html b/docs/reference/resTime.html index 0bea70a..433f344 100644 --- a/docs/reference/resTime.html +++ b/docs/reference/resTime.html @@ -1,63 +1,49 @@ -Climatic residence time of a polygon — resTime • VoCC - - -
    -
    -
    -
    -

    Climatic residence time of a polygon

    - Source: R/resTime.R - + + + +
    +
    +
    +
    + +

    Climatic residence time of a polygon

    + Source: R/resTime.R +
    resTime.Rd
    -
    +

    Function to calculate VoCC-based residence time of isotherms within a polygon after Loaire et al. (2009)

    -
    +
    +

    Usage

    resTime(pg, vel, areapg = NA)
    -
    -

    Arguments

    +
    +

    Arguments

    pg
    @@ -73,26 +59,26 @@

    Arguments

    vector with the area (in km2) of the polygons. Use NA (default) to calculate internally if field not avilable.

    -
    -

    Value

    +
    +

    Value

    a data.frame containing for each polygon its ID, mean velocity (km/yr), diameter of the equivalent circle (km), and residence time (years) as the ratio D/vel.

    -
    -

    References

    +
    +

    References

    Loarie et al. 2009. The velocity of climate change. Nature, 462, 1052-1055.

    -
    -

    See also

    +
    +

    See also

    gVoCC

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos

    -
    -

    Examples

    +
    +

    Examples

    
 # Load example Exclusive Economic Zone polygon
 if (FALSE) { # \dontrun{
@@ -130,23 +116,19 @@ 
    Examples
    
 } # }
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/shiftTime.html b/docs/reference/shiftTime.html index b22a4cc..ce47934 100644 --- a/docs/reference/shiftTime.html +++ b/docs/reference/shiftTime.html @@ -1,65 +1,52 @@ -Shift in timing of seasonal climatology — shiftTime • VoCC - - -
    -
    -
    -
    -

    Shift in timing of seasonal climatology

    - Source: R/shiftTime.R - + + + +
    +
    +
    +
    + +

    Shift in timing of seasonal climatology

    + Source: R/shiftTime.R +
    shiftTime.Rd
    -
    +

    Function to calculate the seasonal shift in the arriving of typical seasonal climates for a given period of interest as per Burrows et al. (2011).

    -
    +
    +

    Usage

    shiftTime(r, yr1, yr2, yr0, th, m)
    -
    -

    Arguments

    +
    +

    Arguments

    r
    @@ -88,23 +75,23 @@

    Arguments

    integer number (1-12) of the month for which the shift is to be calculated

    -
    -

    Value

    +
    +

    Value

    a stack with the long-term monthly trend (C/year for temperature in degrees; "mTrend"), seasonal rate of change (C/month; "seaRate"), and seasonal shift (day/decade; "seaShift").

    -
    -

    References

    +
    +

    References

    Burrows et al. 2011. The pace of shifting climate in marine and terrestrial ecosystems. Science, 334, 652-655.

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos and Michael T. Burrows

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 HSST <- VoCC_get_data("HSST.tif")
 Apr <- shiftTime(HSST, yr1 = 1960, yr2 = 2009, yr0 = 1955, th = 10, m = 4)
@@ -113,23 +100,19 @@ 
    Examples
    
 } # }
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/spatGrad.html b/docs/reference/spatGrad.html index 7600ef9..e117514 100644 --- a/docs/reference/spatGrad.html +++ b/docs/reference/spatGrad.html @@ -1,67 +1,55 @@ -Local spatial climatic gradients — spatGrad • VoCCLocal spatial climatic gradients — spatGrad • VoCC - - -
    -
    -
    -
    -

    Local spatial climatic gradients

    - Source: R/spatGrad.R - + + + +
    +
    +
    +
    + +

    Local spatial climatic gradients

    + Source: R/spatGrad.R +
    spatGrad.Rd
    -
    +

    Function to calculate the magnitude and direction of the spatial gradient associated to a climatic variable after Burrows et al. (2011). This trend is to be used for the calculation of the gradient-based climate velocity using gVoCC.

    -
    +
    +

    Usage

    spatGrad(r, th = -Inf, projected = FALSE)
    -
    -

    Arguments

    +
    +

    Arguments

    r
    @@ -80,27 +68,27 @@

    Arguments

    If FALSE (default) a correction will be made to account for latitudinal distortion.

    -
    -

    Value

    +
    +

    Value

    A RasterStack with the magnitude of the spatial gradient (Grad in C per km for unprojected rasters and C per spatial unit for projected rasters), and the associated angle (Ang in degrees).

    -
    -

    References

    +
    +

    References

    Burrows et al. 2011. The pace of shifting climate in marine and terrestrial ecosystems. Science, 334, 652-655.

    -
    -

    See also

    +
    +

    See also

    tempTrend, gVoCC

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos, David S. Schoeman, and Michael T. Burrows

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 HSST <- VoCC_get_data("HSST.tif")
 
@@ -118,23 +106,19 @@ 
    Examples
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/splitLine.html b/docs/reference/splitLine.html index bdda379..22df91f 100644 --- a/docs/reference/splitLine.html +++ b/docs/reference/splitLine.html @@ -1,63 +1,49 @@ -Internal. Split a line segment defined by points A-B into n parts — splitLine • VoCC - - -
    -
    -
    -
    -

    Internal. Split a line segment defined by points A-B into n parts

    - Source: R/splitLine.R - + + + +
    +
    +
    +
    + +

    Internal. Split a line segment defined by points A-B into n parts

    + Source: R/splitLine.R +
    splitLine.Rd
    -
    +

    Internal. Split a line segment defined by points A-B into n parts

    -
    +
    +

    Usage

    splitLine(A, B, n)
    -
    -

    Arguments

    +
    +

    Arguments

    A
    @@ -72,28 +58,24 @@

    Arguments

    numeric number of segments to divide the distance between points with

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos

    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/sumSeries.html b/docs/reference/sumSeries.html index 33288aa..ce16cce 100644 --- a/docs/reference/sumSeries.html +++ b/docs/reference/sumSeries.html @@ -1,58 +1,47 @@ -Summarize climatic series to higher temporal resolution — sumSeries • VoCCSummarize climatic series to higher temporal resolution — sumSeries • VoCC - - -
    -
    -
    -
    -

    Summarize climatic series to higher temporal resolution

    - Source: R/sumSeries.R - + + + + + +
    +
    +
    +
    + +

    Summarize climatic series to higher temporal resolution

    + Source: R/sumSeries.R +
    sumSeries.Rd
    -
    +

    Function to convert climatic series (provided as RasterStack) into a coarser time frequency series for a period of interest. This function transforms the RasterStack into an xts time series object to extract the values for the period of interest and @@ -60,7 +49,8 @@

    Summarize climatic series to higher temporal resolution

    in the package xts (Ryan and Ulrich 2017).

    -
    +
    +

    Usage

    sumSeries(
   r,
   p,
@@ -72,8 +62,8 @@ 
    Summarize climatic series to higher temporal resolution
    
 )
    -
    -

    Arguments

    +
    +

    Arguments

    r
    @@ -109,22 +99,22 @@

    Arguments

    allows for user-defined functions to be applied on the 'xts' time series object over the period of interest (see examples).

    -
    -

    Value

    +
    +

    Value

    A RasterStack with the new series.

    -
    -

    References

    +
    +

    References

    Ray and Ulrich. 2017. xts: eXtensible Time Series. R package version 0.10-1.
    Bengtsson 2018. matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors). R package version 0.53.1.

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 # Monthly mean SST (HadISST) data for Europe Jan-1950 to Dec-2010
 
@@ -167,23 +157,19 @@ 
    Examples
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/docs/reference/tempTrend.html b/docs/reference/tempTrend.html index d3d32eb..a872b1c 100644 --- a/docs/reference/tempTrend.html +++ b/docs/reference/tempTrend.html @@ -1,67 +1,55 @@ -Long-term local climatic trends — tempTrend • VoCCLong-term local climatic trends — tempTrend • VoCC - - -
    -
    -
    -
    -

    Long-term local climatic trends

    - Source: R/tempTrend.R - + + + +
    +
    +
    +
    + +

    Long-term local climatic trends

    + Source: R/tempTrend.R +
    tempTrend.Rd
    -
    +

    Function to calculate temporal trend from a raster series of a climatic variable. This trend is to be used for the calculation of the gradient-based climate velocity using gVoCC.

    -
    +
    +

    Usage

    tempTrend(r, th)
    -
    -

    Arguments

    +
    +

    Arguments

    r
    @@ -74,24 +62,24 @@

    Arguments

    calculate the trend at each cell.

    -
    -

    Value

    +
    +

    Value

    A RasterStack containing the cell-specific temporal trends extracted from simple linear regressions of the climatic variable against time ("slpTrends" in degree Celsius per year), together with their standard errors ("seTrends") and statistical significance ("sigTrends").

    -
    -

    See also

    +
    +

    See also

    spatGrad, gVoCC

    -
    -

    Author

    +
    +

    Author

    Jorge Garcia Molinos and Christopher J. Brown

    -
    -

    Examples

    +
    +

    Examples

    if (FALSE) { # \dontrun{
 HSST <- VoCC_get_data("HSST.tif")
 
@@ -108,23 +96,19 @@ 
    Examples
    
 } # }
    -
    - -
    +
    -
    -

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    +
    +

    Developed by Jorge Garcia Molinos, David S. Schoeman, Christopher J. Brown, Michael T. Burrows.

    -
    -

    Site built with pkgdown 2.1.3.

    +
    +

    Site built with pkgdown 2.1.3.

    -
    - +
    diff --git a/vignettes/VoCC.Rmd b/vignettes/VoCC.Rmd index 5c4d144..bb92f71 100644 --- a/vignettes/VoCC.Rmd +++ b/vignettes/VoCC.Rmd @@ -18,6 +18,7 @@ vignette: > Sys.setenv("R_PARALLELLY_AVAILABLECORES_FALLBACK" = "1") ``` +**THIS IS A WORK IN PROGRESS TO CONVERT THE OLD RASTER-BASED VOCC PACKAGE TO TERRA. NOT EVERYTHING WORKS YET. IN PARTICULAR CONNECTIVITY IS NOT IMPLEMENTED SO ONLY EXAMPLE 1 BELOW WORKS.** This vignette provides the code to reproduce the examples for the R package VoCC as presented in Garcia Molinos et al. (2019). Refer to the paper and the function documentation for details on function options, considerations on the argument choices and interepretation of output. @@ -25,7 +26,6 @@ For this tutorial we need the following packages (if not installed get them firs ```{r message=FALSE} library(VoCC) -# devtools::load_all() library(terra) library(tidyterra) library(ggplot2) @@ -133,8 +133,12 @@ marshift$DV <- terra::extract(abs(dv), terra::buffer(coords, buffer_size), ```{r} # fit the regression models Mgv <- lm(Shift^(1 / 4) ~ I((GV * 10)^(1 / 4)), data = marshift, weights = years_data) -Mdv <- lm(Shift^(1 / 4) ~ I((DV * 10)^(1 / 4)), data = marshift, weights = years_data) summary(Mgv) +``` + + +```{r} +Mdv <- lm(Shift^(1 / 4) ~ I((DV * 10)^(1 / 4)), data = marshift, weights = years_data) summary(Mdv) ``` @@ -145,40 +149,45 @@ Produce the observed vs predicted scatterplots with regression lines (Fig. 2 in p1 <- ggplot() + geom_spatraster(data = gv[[1]]) + - scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-50, 50)) + + scale_fill_distiller(palette = "RdBu", direction = -1, limits = c(-50, 50)) + ggtitle("Gradient-based vocc") + scale_x_continuous(expand = c(0,0)) + scale_y_continuous(expand = c(0,0)) p2 <- ggplot() + geom_spatraster(data = gv[[1]]) + - scale_fill_distiller(palette = "RdBu", direction = 1, limits = c(-20, 20)) + + scale_fill_distiller(palette = "RdBu", direction = -1, limits = c(-20, 20)) + ggtitle("Distance-based vocc") + scale_x_continuous(expand = c(0,0)) + scale_y_continuous(expand = c(0,0)) wrap_plots(p1, p2, ncol = 1) +``` + +```{r} # scatter plots with the resulting regression line -p1 <- ggplot(na.omit(marshift), aes(x = I((GV * 10)^(1 / 4)), y = Shift^(1 / 4))) + +p1 <- ggplot(na.omit(marshift), aes(x = (GV * 10)^(1 / 4), y = Shift^(1 / 4))) + geom_point(color = "grey") + geom_smooth(method = lm, se = FALSE) + theme_classic() + scale_color_brewer(palette = "Accent") + labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)") -p2 <- ggplot(na.omit(marshift), aes(x = I((DV * 10)^(1 / 4)), y = Shift^(1 / 4))) + + +p2 <- ggplot(na.omit(marshift), aes(x = (DV * 10)^(1 / 4), y = Shift^(1 / 4))) + geom_point(color = "grey") + geom_smooth(method = lm, se = FALSE) + theme_classic() + scale_color_brewer(palette = "Accent") + labs(x = "Predicted shift (x^1/4; km/yr)", y = "Observed shift (y^1/4; km/yr)") + wrap_plots(p1, p2, nrow = 1) ``` # Example 2: Analysis of climate exposure and connectivity in the Western Pacific Ocean -THIS CODE IS NOT OPERATIONAL YET. IN PARTICULAR, `voccTraj` HAS NOT BEEN UPDATED FOR `terra`. Standby for updates. +**THIS CODE IS NOT OPERATIONAL YET. IN PARTICULAR, `voccTraj` HAS NOT BEEN UPDATED FOR `terra`. Standby for updates.** In this example we use climate velocity trajectories (based on 1960-2009 mean annual SST) to analyse climate connectivity in the Western Pacific region and calculate the residence time corresponding to the exclusive economic zones in the region as an index of climatic exposure. First, we arrange the raster layers for analysis. From 87dad126b8706cd3d3006b423dcf829fd8b4648a Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Thu, 28 Aug 2025 11:14:49 +1000 Subject: [PATCH 06/10] Update site --- _pkgdown.yml | 7 +- docs/404.html | 2 +- docs/LICENSE.html | 4 +- docs/articles/VoCC.html | 2 +- .../figure-html/unnamed-chunk-8-1.png | Bin 206340 -> 0 bytes .../figure-html/unnamed-chunk-8-2.png | Bin 35585 -> 0 bytes docs/articles/index.html | 4 +- docs/authors.html | 4 +- docs/bootstrap-toc.css | 60 --- docs/bootstrap-toc.js | 159 -------- docs/docsearch.css | 148 ------- docs/docsearch.js | 85 ---- docs/index.html | 2 +- docs/pkgdown.css | 384 ------------------ docs/pkgdown.yml | 2 +- docs/reference/EEZ.html | 4 +- docs/reference/HSST.html | 4 +- docs/reference/JapTC.html | 4 +- docs/reference/angulo.html | 4 +- docs/reference/climPCA.html | 4 +- docs/reference/climPlot.html | 4 +- docs/reference/dVoCC.html | 4 +- docs/reference/gVoCC.html | 4 +- docs/reference/index.html | 4 +- docs/reference/pipe.html | 4 +- docs/reference/resTime.html | 4 +- docs/reference/shiftTime.html | 4 +- docs/reference/spatGrad.html | 4 +- docs/reference/splitLine.html | 4 +- docs/reference/sumSeries.html | 4 +- docs/reference/tempTrend.html | 4 +- 31 files changed, 48 insertions(+), 879 deletions(-) delete mode 100644 docs/articles/VoCC_files/figure-html/unnamed-chunk-8-1.png delete mode 100644 docs/articles/VoCC_files/figure-html/unnamed-chunk-8-2.png delete mode 100644 docs/bootstrap-toc.css delete mode 100644 docs/bootstrap-toc.js delete mode 100644 docs/docsearch.css delete mode 100644 docs/docsearch.js delete mode 100644 docs/pkgdown.css diff --git a/_pkgdown.yml b/_pkgdown.yml index d71acfb..455962f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,4 +1,9 @@ url: ~ template: bootstrap: 5 - + bslib: + toc: true + toc-expand: 3 +navbar: + structure: + right: [github, search] diff --git a/docs/404.html b/docs/404.html index bd6f0bf..2848c1e 100644 --- a/docs/404.html +++ b/docs/404.html @@ -33,10 +33,10 @@
  • Reference
      • +
      • -
    diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 9e898d8..29d5c4f 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -17,9 +17,9 @@ diff --git a/docs/articles/VoCC.html b/docs/articles/VoCC.html index 3f07ebf..8095854 100644 --- a/docs/articles/VoCC.html +++ b/docs/articles/VoCC.html @@ -33,10 +33,10 @@
  • Reference
      • +
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zkibIG62;DthjarDo*OH#3skuYtsV`)v6QF{`pCzLzdv;2%*MKYRd%qIX)3Vc2Q~MxG^crj*dVm>Vf}%JbE(pOwYzWhn{I2#9|7Ob&L-^IB@*Je*vr5$OHfY diff --git a/docs/articles/index.html b/docs/articles/index.html index 9bc9b67..5ca0849 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,9 +17,9 @@

    diff --git a/docs/authors.html b/docs/authors.html index a429f4b..2ab43f1 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,9 +17,9 @@ diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css deleted file mode 100644 index 5a85941..0000000 --- a/docs/bootstrap-toc.css +++ /dev/null @@ -1,60 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ - -/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ - -/* All levels of nav */ -nav[data-toggle='toc'] .nav > li > a { - display: block; - padding: 4px 20px; - font-size: 13px; - font-weight: 500; - color: #767676; -} -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 19px; - color: #563d7c; - text-decoration: none; - background-color: transparent; - border-left: 1px solid #563d7c; -} -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 18px; - font-weight: bold; - color: #563d7c; - background-color: transparent; - border-left: 2px solid #563d7c; -} - -/* Nav: second level (shown on .active) */ -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} -nav[data-toggle='toc'] .nav .nav > li > a { - padding-top: 1px; - padding-bottom: 1px; - padding-left: 30px; - font-size: 12px; - font-weight: normal; -} -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 29px; -} -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 28px; - font-weight: 500; -} - -/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ -nav[data-toggle='toc'] .nav > .active > ul { - display: block; -} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js deleted file mode 100644 index 1cdd573..0000000 --- a/docs/bootstrap-toc.js +++ /dev/null @@ -1,159 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ -(function() { - 'use strict'; - - window.Toc = { - helpers: { - // return all matching elements in the set, or their descendants - findOrFilter: function($el, selector) { - // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ - // http://stackoverflow.com/a/12731439/358804 - var $descendants = $el.find(selector); - return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); - }, - - generateUniqueIdBase: function(el) { - var text = $(el).text(); - var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); - return anchor || el.tagName.toLowerCase(); - }, - - generateUniqueId: function(el) { - var anchorBase = this.generateUniqueIdBase(el); - for (var i = 0; ; i++) { - var anchor = anchorBase; - if (i > 0) { - // add suffix - anchor += '-' + i; - } - // check if ID already exists - if (!document.getElementById(anchor)) { - return anchor; - } - } - }, - - generateAnchor: function(el) { - if (el.id) { - return el.id; - } else { - var anchor = this.generateUniqueId(el); - el.id = anchor; - return anchor; - } - }, - - createNavList: function() { - return $('
      '); - }, - - createChildNavList: function($parent) { - var $childList = this.createNavList(); - $parent.append($childList); - return $childList; - }, - - generateNavEl: function(anchor, text) { - var $a = $(''); - $a.attr('href', '#' + anchor); - $a.text(text); - var $li = $('
    • '); - $li.append($a); - return $li; - }, - - generateNavItem: function(headingEl) { - var anchor = this.generateAnchor(headingEl); - var $heading = $(headingEl); - var text = $heading.data('toc-text') || $heading.text(); - return this.generateNavEl(anchor, text); - }, - - // Find the first heading level (`

      `, then `

      `, etc.) that has more than one element. Defaults to 1 (for `

      `). - getTopLevel: function($scope) { - for (var i = 1; i <= 6; i++) { - var $headings = this.findOrFilter($scope, 'h' + i); - if ($headings.length > 1) { - return i; - } - } - - return 1; - }, - - // returns the elements for the top level, and the next below it - getHeadings: function($scope, topLevel) { - var topSelector = 'h' + topLevel; - - var secondaryLevel = topLevel + 1; - var secondarySelector = 'h' + secondaryLevel; - - return this.findOrFilter($scope, topSelector + ',' + secondarySelector); - }, - - getNavLevel: function(el) { - return parseInt(el.tagName.charAt(1), 10); - }, - - populateNav: function($topContext, topLevel, $headings) { - var $context = $topContext; - var $prevNav; - - var helpers = this; - $headings.each(function(i, el) { - var $newNav = helpers.generateNavItem(el); - var navLevel = helpers.getNavLevel(el); - - // determine the proper $context - if (navLevel === topLevel) { - // use top level - $context = $topContext; - } else if ($prevNav && $context === $topContext) { - // create a new level of the tree and switch to it - $context = helpers.createChildNavList($prevNav); - } // else use the current $context - - $context.append($newNav); - - $prevNav = $newNav; - }); - }, - - parseOps: function(arg) { - var opts; - if (arg.jquery) { - opts = { - $nav: arg - }; - } else { - opts = arg; - } - opts.$scope = opts.$scope || $(document.body); - return opts; - } - }, - - // accepts a jQuery object, or an options object - init: function(opts) { - opts = this.helpers.parseOps(opts); - - // ensure that the data attribute is in place for styling - opts.$nav.attr('data-toggle', 'toc'); - - var $topContext = this.helpers.createChildNavList(opts.$nav); - var topLevel = this.helpers.getTopLevel(opts.$scope); - var $headings = this.helpers.getHeadings(opts.$scope, topLevel); - this.helpers.populateNav($topContext, topLevel, $headings); - } - }; - - $(function() { - $('nav[data-toggle="toc"]').each(function(i, el) { - var $nav = $(el); - Toc.init($nav); - }); - }); -})(); diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; - padding-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { - float: none; - width: auto; - padding: 0; - text-align: left -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content { - float: none; - width: auto; - padding: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content::before { - display: none -} - -.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { - padding-top: .75rem; - margin-top: .75rem; - border-top: 1px solid rgba(0, 0, 0, .1) -} - -.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { - display: block; - padding: .1rem 1rem; - margin-bottom: 0.1; - font-size: 1.0em; - font-weight: 400 - /* display: none */ -} - -.algolia-autocomplete .algolia-docsearch-suggestion--title { - display: block; - padding: .25rem 1rem; - margin-bottom: 0; - font-size: 0.9em; - font-weight: 400 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--text { - padding: 0 1rem .5rem; - margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/docsearch.js b/docs/docsearch.js deleted file mode 100644 index b35504c..0000000 --- a/docs/docsearch.js +++ /dev/null @@ -1,85 +0,0 @@ -$(function() { - - // register a handler to move the focus to the search bar - // upon pressing shift + "/" (i.e. "?") - $(document).on('keydown', function(e) { - if (e.shiftKey && e.keyCode == 191) { - e.preventDefault(); - $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html index d59a276..8b9ec05 100644 --- a/docs/index.html +++ b/docs/index.html @@ -35,10 +35,10 @@
    • Reference
        • +
        • -
      diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index 80ea5b8..0000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,384 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; -} -footer .pkgdown { - text-align: right; -} -footer p { - margin-bottom: 0; -} - -img.icon { - float: right; -} - -/* Ensure in-page images don't run outside their container */ -.contents img { - max-width: 100%; - height: auto; -} - -/* Fix bug in bootstrap (only seen in firefox) */ -summary { - display: list-item; -} - -/* Typographic tweaking ---------------------------------*/ - -.contents .page-header { - margin-top: calc(-60px + 1em); -} - -dd { - margin-left: 3em; -} - -/* Section anchors ---------------------------------*/ - -a.anchor { - display: none; - margin-left: 5px; - width: 20px; - height: 20px; - - background-image: url(./link.svg); - background-repeat: no-repeat; - background-size: 20px 20px; - background-position: center center; -} - -h1:hover .anchor, -h2:hover .anchor, -h3:hover .anchor, -h4:hover .anchor, -h5:hover .anchor, -h6:hover .anchor { - display: inline-block; -} - -/* Fixes for fixed navbar --------------------------*/ - -.contents h1, .contents h2, .contents h3, .contents h4 { - padding-top: 60px; - margin-top: -40px; -} - -/* Navbar submenu --------------------------*/ - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu>.dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover>.dropdown-menu { - display: block; -} - -.dropdown-submenu>a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #cccccc; - margin-top: 5px; - margin-right: -10px; -} - -.dropdown-submenu:hover>a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left>.dropdown-menu { - left: -100%; - margin-left: 10px; - border-radius: 6px 0 6px 6px; -} - -/* Sidebar --------------------------*/ - -#pkgdown-sidebar { - margin-top: 30px; - position: -webkit-sticky; - position: sticky; - top: 70px; -} - -#pkgdown-sidebar h2 { - font-size: 1.5em; - margin-top: 1em; -} - -#pkgdown-sidebar h2:first-child { - margin-top: 0; -} - -#pkgdown-sidebar .list-unstyled li { - margin-bottom: 0.5em; -} - -/* bootstrap-toc tweaks ------------------------------------------------------*/ - -/* All levels of nav */ - -nav[data-toggle='toc'] .nav > li > a { - padding: 4px 20px 4px 6px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; -} - -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 5px; - color: inherit; - border-left: 1px solid #878787; -} - -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 5px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; - border-left: 2px solid #878787; -} - -/* Nav: second level (shown on .active) */ - -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} - -nav[data-toggle='toc'] .nav .nav > li > a { - padding-left: 16px; - font-size: 1.35rem; -} - -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 15px; -} - -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 15px; - font-weight: 500; - font-size: 1.35rem; -} - -/* orcid ------------------------------------------------------------------- */ - -.orcid { - font-size: 16px; - color: #A6CE39; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top; min-width: 100px} -.ref-index .icon {width: 40px;} -.ref-index .alias {width: 40%;} -.ref-index-icons .alias {width: calc(40% - 40px);} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre, code, pre code { - background-color: #f8f8f8; - color: #333; -} -pre, pre code { - white-space: pre-wrap; - word-break: break-all; - overflow-wrap: break-word; -} - -pre { - border: 1px solid #eee; -} - -pre .img, pre .r-plt { - margin: 5px 0; -} - -pre .img img, pre .r-plt img { - background-color: #fff; -} - -code a, pre a { - color: #375f84; -} - -a.sourceLine:hover { - text-decoration: none; -} - -.fl {color: #1514b5;} -.fu {color: #000000;} /* function */ -.ch,.st {color: #036a07;} /* string */ -.kw {color: #264D66;} /* keyword */ -.co {color: #888888;} /* comment */ - -.error {font-weight: bolder;} -.warning {font-weight: bolder;} - -/* Clipboard --------------------------*/ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} - -/* Section anchors --------------------------------- - Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 -*/ - -div.csl-bib-body { } -div.csl-entry { - clear: both; -} -.hanging-indent div.csl-entry { - margin-left:2em; - text-indent:-2em; -} -div.csl-left-margin { - min-width:2em; - float:left; -} -div.csl-right-inline { - margin-left:2em; - padding-left:1em; -} -div.csl-indent { - margin-left: 2em; -} diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 9a23dc3..4f84c37 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,4 +3,4 @@ pkgdown: 2.1.3 pkgdown_sha: ~ articles: VoCC: VoCC.html -last_built: 2025-08-27T10:58Z +last_built: 2025-08-27T22:02Z diff --git a/docs/reference/EEZ.html b/docs/reference/EEZ.html index f005b12..9cc5385 100644 --- a/docs/reference/EEZ.html +++ b/docs/reference/EEZ.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/HSST.html b/docs/reference/HSST.html index f112b0d..6043445 100644 --- a/docs/reference/HSST.html +++ b/docs/reference/HSST.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/JapTC.html b/docs/reference/JapTC.html index 2f3b101..b6296f4 100644 --- a/docs/reference/JapTC.html +++ b/docs/reference/JapTC.html @@ -23,9 +23,9 @@ diff --git a/docs/reference/angulo.html b/docs/reference/angulo.html index 4a1a4e2..b23fad4 100644 --- a/docs/reference/angulo.html +++ b/docs/reference/angulo.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/climPCA.html b/docs/reference/climPCA.html index effb0a6..8e0382b 100644 --- a/docs/reference/climPCA.html +++ b/docs/reference/climPCA.html @@ -19,9 +19,9 @@ diff --git a/docs/reference/climPlot.html b/docs/reference/climPlot.html index 428b4fa..6fc9958 100644 --- a/docs/reference/climPlot.html +++ b/docs/reference/climPlot.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/dVoCC.html b/docs/reference/dVoCC.html index 8a2b0af..022e5dd 100644 --- a/docs/reference/dVoCC.html +++ b/docs/reference/dVoCC.html @@ -23,9 +23,9 @@ diff --git a/docs/reference/gVoCC.html b/docs/reference/gVoCC.html index 4674fdb..1f5ee2f 100644 --- a/docs/reference/gVoCC.html +++ b/docs/reference/gVoCC.html @@ -19,9 +19,9 @@ diff --git a/docs/reference/index.html b/docs/reference/index.html index 3e59af6..725f29b 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 274b2e4..bf4467a 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/resTime.html b/docs/reference/resTime.html index 433f344..0489630 100644 --- a/docs/reference/resTime.html +++ b/docs/reference/resTime.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/shiftTime.html b/docs/reference/shiftTime.html index ce47934..7045537 100644 --- a/docs/reference/shiftTime.html +++ b/docs/reference/shiftTime.html @@ -19,9 +19,9 @@ diff --git a/docs/reference/spatGrad.html b/docs/reference/spatGrad.html index e117514..c446588 100644 --- a/docs/reference/spatGrad.html +++ b/docs/reference/spatGrad.html @@ -21,9 +21,9 @@ diff --git a/docs/reference/splitLine.html b/docs/reference/splitLine.html index 22df91f..96eaf93 100644 --- a/docs/reference/splitLine.html +++ b/docs/reference/splitLine.html @@ -17,9 +17,9 @@ diff --git a/docs/reference/sumSeries.html b/docs/reference/sumSeries.html index ce16cce..5597606 100644 --- a/docs/reference/sumSeries.html +++ b/docs/reference/sumSeries.html @@ -25,9 +25,9 @@ diff --git a/docs/reference/tempTrend.html b/docs/reference/tempTrend.html index a872b1c..23ed686 100644 --- a/docs/reference/tempTrend.html +++ b/docs/reference/tempTrend.html @@ -21,9 +21,9 @@ From 3c96eb66eee5985eaf26991bf542d1eaefd26569 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Thu, 28 Aug 2025 12:25:39 +1000 Subject: [PATCH 07/10] Update website --- .gitignore | 1 - DESCRIPTION | 4 +- docs/404.html | 2 +- docs/LICENSE.html | 4 +- docs/articles/VoCC.html | 6 +- .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 120239 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 211565 bytes docs/articles/index.html | 2 +- docs/authors.html | 8 +- .../bootstrap-5.3.1/bootstrap.bundle.min.js | 7 + .../bootstrap.bundle.min.js.map | 1 + docs/deps/bootstrap-5.3.1/bootstrap.min.css | 5 + .../bootstrap-toc-1.0.1/bootstrap-toc.min.js | 5 + .../deps/clipboard.js-2.0.11/clipboard.min.js | 7 + docs/deps/data-deps.txt | 13 + docs/deps/font-awesome-6.5.2/css/all.css | 8028 ++++++++++++ docs/deps/font-awesome-6.5.2/css/all.min.css | 9 + docs/deps/font-awesome-6.5.2/css/v4-shims.css | 2194 ++++ .../font-awesome-6.5.2/css/v4-shims.min.css | 6 + .../webfonts/fa-brands-400.ttf | Bin 0 -> 209128 bytes .../webfonts/fa-brands-400.woff2 | Bin 0 -> 117852 bytes .../webfonts/fa-regular-400.ttf | Bin 0 -> 67860 bytes .../webfonts/fa-regular-400.woff2 | Bin 0 -> 25392 bytes .../webfonts/fa-solid-900.ttf | Bin 0 -> 420332 bytes .../webfonts/fa-solid-900.woff2 | Bin 0 -> 156400 bytes .../webfonts/fa-v4compatibility.ttf | Bin 0 -> 10832 bytes .../webfonts/fa-v4compatibility.woff2 | Bin 0 -> 4792 bytes docs/deps/headroom-0.11.0/headroom.min.js | 7 + .../headroom-0.11.0/jQuery.headroom.min.js | 7 + docs/deps/jquery-3.6.0/jquery-3.6.0.js | 10881 ++++++++++++++++ docs/deps/jquery-3.6.0/jquery-3.6.0.min.js | 2 + docs/deps/jquery-3.6.0/jquery-3.6.0.min.map | 1 + .../search-1.0.0/autocomplete.jquery.min.js | 7 + docs/deps/search-1.0.0/fuse.min.js | 9 + docs/deps/search-1.0.0/mark.min.js | 7 + docs/index.html | 6 +- docs/katex-auto.js | 14 + docs/lightswitch.js | 85 + docs/pkgdown.yml | 2 +- docs/reference/EEZ.html | 4 +- docs/reference/HSST.html | 4 +- docs/reference/JapTC.html | 4 +- docs/reference/angulo.html | 4 +- docs/reference/climPCA.html | 4 +- docs/reference/climPlot.html | 4 +- docs/reference/dVoCC.html | 4 +- docs/reference/gVoCC.html | 4 +- docs/reference/index.html | 2 +- docs/reference/pipe.html | 4 +- docs/reference/resTime.html | 4 +- docs/reference/shiftTime.html | 4 +- docs/reference/spatGrad.html | 4 +- docs/reference/splitLine.html | 4 +- docs/reference/sumSeries.html | 4 +- docs/reference/tempTrend.html | 4 +- docs/search.json | 1 + 56 files changed, 21344 insertions(+), 49 deletions(-) create mode 100644 docs/articles/VoCC_files/figure-html/unnamed-chunk-10-1.png create mode 100644 docs/articles/VoCC_files/figure-html/unnamed-chunk-9-1.png create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.min.css create mode 100644 docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js create mode 100644 docs/deps/clipboard.js-2.0.11/clipboard.min.js create mode 100644 docs/deps/data-deps.txt create mode 100644 docs/deps/font-awesome-6.5.2/css/all.css create mode 100644 docs/deps/font-awesome-6.5.2/css/all.min.css create mode 100644 docs/deps/font-awesome-6.5.2/css/v4-shims.css create mode 100644 docs/deps/font-awesome-6.5.2/css/v4-shims.min.css create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.ttf create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2 create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.ttf create mode 100644 docs/deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.woff2 create mode 100644 docs/deps/headroom-0.11.0/headroom.min.js create mode 100644 docs/deps/headroom-0.11.0/jQuery.headroom.min.js create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.js create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.min.js create mode 100644 docs/deps/jquery-3.6.0/jquery-3.6.0.min.map create mode 100644 docs/deps/search-1.0.0/autocomplete.jquery.min.js create mode 100644 docs/deps/search-1.0.0/fuse.min.js create mode 100644 docs/deps/search-1.0.0/mark.min.js create mode 100644 docs/katex-auto.js create mode 100644 docs/lightswitch.js create mode 100644 docs/search.json diff --git a/.gitignore b/.gitignore index 1354af4..97e604b 100644 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,3 @@ .DS_Store /doc/ /Meta/ -docs diff --git a/DESCRIPTION b/DESCRIPTION index c897d9a..2d8b5f4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -36,8 +36,8 @@ Suggests: scales, tidyterra, VoCCdata -URL: https://github.com/JorGarMol/VoCC -BugReports: https://github.com/JorGarMol/VoCC/issues +URL: https://mathmarecol.github.io/VoCC/ +BugReports: https://github.com/MathMarEcol/VoCC/issues License: AGPL (>= 3) Encoding: UTF-8 LazyData: true diff --git a/docs/404.html b/docs/404.html index 2848c1e..baaac0b 100644 --- a/docs/404.html +++ b/docs/404.html @@ -33,7 +33,7 @@
    • Reference
        • -
        • +
        • diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 29d5c4f..e3b0ddb 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -17,7 +17,7 @@ @@ -29,7 +29,7 @@

        GNU Affero General Public License

        - Source: LICENSE.md + Source: LICENSE.md
        diff --git a/docs/articles/VoCC.html b/docs/articles/VoCC.html index 8095854..55dfa0c 100644 --- a/docs/articles/VoCC.html +++ b/docs/articles/VoCC.html @@ -33,7 +33,7 @@
      • Reference