From dc562e8381f86eadd4ca3f57a6cbf5572c28b31c Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Wed, 7 Dec 2022 11:14:32 +0000 Subject: [PATCH 001/119] preliminary internal functions --- DESCRIPTION | 1 + R/delta_compute.R | 44 +++++++++++++++++++++++ R/delta_downscale.R | 61 +++++++++++++++++++++++++++++++ R/idw_interp.R | 43 ++++++++++++++++++++++ R/make_binary_mask.R | 15 ++++++++ R/make_land_mask.R | 62 ++++++++++++++++++++++++++++++++ inst/temp/_delta_downscale_3.R | 12 ------- inst/temp/a4_delta_downscale.Rmd | 40 ++++++++++++++------- man/delta_compute.Rd | 34 ++++++++++++++++++ man/delta_downscale.Rd | 36 +++++++++++++++++++ man/idw_interp.Rd | 25 +++++++++++++ man/make_binary_mask.Rd | 17 +++++++++ man/make_land_mask.Rd | 30 ++++++++++++++++ 13 files changed, 395 insertions(+), 25 deletions(-) create mode 100644 R/delta_compute.R create mode 100644 R/delta_downscale.R create mode 100644 R/idw_interp.R create mode 100644 R/make_binary_mask.R create mode 100644 R/make_land_mask.R create mode 100644 man/delta_compute.Rd create mode 100644 man/delta_downscale.Rd create mode 100644 man/idw_interp.Rd create mode 100644 man/make_binary_mask.Rd create mode 100644 man/make_land_mask.Rd diff --git a/DESCRIPTION b/DESCRIPTION index d5253577..b7eb8c6a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -27,6 +27,7 @@ Depends: R (>= 4.0.0) Imports: curl, + gstat, ncdf4, terra (>= 1.6.41), utils diff --git a/R/delta_compute.R b/R/delta_compute.R new file mode 100644 index 00000000..a21ee75c --- /dev/null +++ b/R/delta_compute.R @@ -0,0 +1,44 @@ +#' Compute a delta raster. +#' +#' This function generates a delta (difference) raster, computed as the difference between +#' model estimates (`x`) and some observations (`high_res_obs`). `x` is a +#' \code{terra::SpatRaster} of the variable we want to downscale, and it +#' can contain multiple time steps. `ref_time` sets the time slice for which +#' the delta should be computed. +#' +#' If `obs` has a higher +#' resolution than `x`, the latter is interpolated using a bilinear algorithm. +#' For areas that are present in some time slices, but not in the observations +#' (e.g. due to sea level change), the delta map is extended to cover the maximum +#' cumulative land mask (over all time steps) using idw. +#' +#' @param x a \code{terra::SpatRaster} for the variable of interest, with all +#' time steps of interest +#' @param ref_time the time (BP) of the slice that is used to compute the delta +#' @param obs the observations +#' @returns a \code{terra::SpatRaster} of the delta +#' @keywords internal + +delta_compute <- function(x, ref_time, obs) { + message("This function is still under development; do not use it for real analysis") + ref_index <- which(time_bp(x)==ref_time) + if(length(ref_index)!=1){ + stop("ref_time should be a time in x") + } + x_modern<-x[[ref_index]] + if (terra::ext(obs)!=terra::ext(x_modern)){ + stop("x_modern and high_res_obs don't have the same extent") + } + # disaggregate the x_modern SpatRaster to the resolution of + # the high res with "bilinear" interpolation + x_modern_high<-disagg(x_modern, fact = terra::res( x_modern)/terra::res(obs), + method="bilinear") + # compute anomalies against the modern + delta <- obs - x_modern_high + # mask for maximum land extent + max_land <- max(x,na.rm=TRUE) + max_land <- disagg(max_land, fact = terra::res( x)/terra::res(obs), + method="near") + delta_interp <- idw_interp(delta,max_land) + return(delta_interp) +} diff --git a/R/delta_downscale.R b/R/delta_downscale.R new file mode 100644 index 00000000..cc9f530b --- /dev/null +++ b/R/delta_downscale.R @@ -0,0 +1,61 @@ +#' Downscale using the delta method +#' +#' The delta method computes the difference between an observed raster and the +#' equivalent predictions from a model for a given time step, and then applies +#' that difference (delta) to all other time steps. You will first need to +#' create the delta raster with \code{pastclim::delta_compute}, and thus use it +#' as an argument for this function. +#' +#' It is possible to also provide a high resoultion landmask to this function. +#' For cells which are not included in the original simulation (e.g. because +#' the landmask was discretised at lower resolution), an idw algorightm is used +#' to interpolate the missing values. +#' +#' @param x a \code{terra::SpatRaster} for the variable of interest, with all +#' time steps of interest +#' @param delta_rast a \code{terra::SpatRaster} generated by +#' \code{pastclim::delta_compute} +#' @param x_landmask_high a \code{terra::SpatRaster} with the same number of layers +#' as x. If left NULL, the original landmask of x is used. +#' @returns a \code{terra::SpatRaster} of the downscaled variable, where each +#' layers is a time step. +#' @keywords internal + + +delta_downscale <- function(x, delta_rast, x_landmask_high=NULL) { + message("This function is still under development; do not use it for real analysis") + # check that extent and resolutions are compatible + if (terra::ext(delta_rast)!=terra::ext(x)){ + stop("x and delta_rast don't have the same extent") + } + if (!is.null(x_landmask_high)){ + if (!all((terra::ncol(delta_rast)!=terra::ncol(x_landmask_high)), + (terra::nrow(delta_rast)!=terra::nrow(x_landmask_high)))){ + stop("delta_rast and x_landmask_high should have the same number of rows\n", + "and columns") + } + } + + # downscale x with bilinear + x_high <- disagg(x, fact = terra::res( x)/terra::res(delta_rast), + method="bilinear") + + # apply the delta_rast to x + x_high <- x_high + delta_rast + + ##TODO + ## Now loop over each layer of x_high, and run idw_gap to match the high + ## resolution mask + + if(!is.null(x_landmask_high)){ + #refine the landmask in x_high + # browser() + x_high <- mask(x_high,x_landmask_high) + # fill in any gaps that resulted from this step with idw + for (i in 1:terra::nlyr(x_high)){ + x_high[[i]] <- idw_interp(x_high[[i]],x_landmask_high[[i]]) + } + } + + return(x_high) +} diff --git a/R/idw_interp.R b/R/idw_interp.R new file mode 100644 index 00000000..05b59dd9 --- /dev/null +++ b/R/idw_interp.R @@ -0,0 +1,43 @@ +#' Interpolate x to match mask y +#' +#' Fill in x to match cells available in y, using iwd (inverse-weighted +#' distance) interpolation. Interpolation is fitted using \code{gstat::gstat}; +#' the default parameters for \code{gstat::gstat} are nmax=7 and idp = .5, but +#' can be changed by providing arguments to this function (which will be passed +#' to \code{gstat::gstat}). See \code{gstat::gstat} for details on the available +#' parameters and their meaning. +#' +#' @param x the \code{terra::SpatRaster} of the variable of interest +#' @param y the \code{terra::SpatRaster} the reference mask defining which +#' cells should have values +#' @param ... paramters to be passed to \code{gstat::gstat} +#' +#' @keywords internal + +idw_interp <- function(x, y, ...){ + # first mask x with y (i.e. remove parts of y not found in the mask y) + x<-terra::mask(x,y) + x_bin <- make_binary_mask(x) + y_bin <- make_binary_mask(y) + # delta gap (pixels for which we don't have a delta values) + x_gap <- y_bin - x_bin + x_gap[x_gap==0]<-NA + x_df <- terra::as.data.frame(x,xy=TRUE,na.rm=TRUE) + x_gap_df <- terra::as.data.frame(x_gap, xy=TRUE, na.rm=TRUE) + # if there is not gap between the values and the mask, just return the values + if (nrow(x_gap_df)==0){ + return(x) + } + names(x_df)[3] <-"this_var" + names(x_gap_df)[3] <-"this_var" + # interpolate those gaps with idw (time consuming...) + # add ... to the function to be able to take additional params to gstat + idw_obj <- gstat::gstat(formula = this_var~1, locations = ~x+y, data = x_df, nmax=7, + set=list(idp = .5), ...) + idw_pred <- predict(idw_obj, newdata = x_gap_df)[,-4] # remove the last column + x_gap_vals <- terra::rast(idw_pred,type="xyz",crs=crs(x)) + x_gap_vals <- terra::extend(x_gap_vals, x) + # now extend x to include the x_gap_vals + x_extended <- sum(x,x_gap_vals,na.rm=TRUE) + return(x_extended) +} diff --git a/R/make_binary_mask.R b/R/make_binary_mask.R new file mode 100644 index 00000000..0c03f1d0 --- /dev/null +++ b/R/make_binary_mask.R @@ -0,0 +1,15 @@ +#' Create a binary mask +#' +#' Create a binary mask from a raster: NAs are converted to 0s, and +#' any other value to 1. +#' +#' @param x a \code{terra::SpatRaster} +#' returns a \code{terra::SpatRaster} with 0s and 1s +#' +#' @keywords internal + +make_binary_mask <- function (x){ + x[!is.na(x)]<-1 + x[is.na(x)]<-0 + return(x) +} \ No newline at end of file diff --git a/R/make_land_mask.R b/R/make_land_mask.R new file mode 100644 index 00000000..6d98c025 --- /dev/null +++ b/R/make_land_mask.R @@ -0,0 +1,62 @@ +#' Create a land mask +#' +#' Create a land mask for a given time step. The land mask is based on the simple +#' logic of moving the ocean up and down given the current topography/bathymetry. +#' Note that this approach ignores any rebound due to changing mass and distribution of +#' icesheets. The landmask will show internal lakes/seas as land, as their level +#' is unrelated to the general sea level. If you have specific reconstructions +#' of internal lakes (or want to simply reuse their current extents), you will +#' have to add them onto the masks generated by this function. +#' +#' @param topo_rast a \code{terra::SpatRaster} with topography +#' @param time_bp the time of interest +#' @param sea_level sea level at the time of interest (if left to NULL, this is +#' computed using Spratt 2016) +#' @returns a \code{terra::SpatRaster} of 0s (sea) and 1s (land), where the +#' layers are different times +#' +#' @keywords internal + +make_land_mask <- function(topo_rast, time_bp, sea_level = NULL) { + message("This function is still under development; do not use it for real analysis") + if (is.null(sea_level)){ + # get sea level from Spratt 2016 + sea_level_info <- utils::read.table(system.file("extdata/sea_level_spratt2016.txt", + package="pastclim"), header=TRUE) + time_calkaBP <- -time_bp/1000 + ## TODO this is not safe, we should be getting the closest values + ## or even better interpolate + sea_level <- sea_level_info$SeaLev_longPC1[match(time_calkaBP, sea_level_info$age_calkaBP)] + } else { # check that we have as many sea level estimates as times + if (length(time_bp)!=length(sea_level)){ + stop ("time_bp and sea_level should have the same number of elements") + } + } + land_mask <- NULL + for (i in 1:length(time_bp)) { + topography_bin<-topo_rast + topography_bin[topography_bin>sea_level[i]]<-NA + topography_bin[!is.na(topography_bin)]<-1 + sea_patches<-patches(topography_bin) + modal_vector <- function(x) { + ux <- unique(x) + ux[which.max(tabulate(match(x, ux)))] + } + patch_to_get <- modal_vector(values(sea_patches)) + sea_patches[is.na(sea_patches)]<- -1 + sea_patches[sea_patches != patch_to_get] <- -1 + sea_patches[sea_patches>0]<- NA + sea_patches <- -sea_patches + names(sea_patches) <- "mask" + if (is.null(land_mask)){ + land_mask<-sea_patches + } else { + add(land_mask)<-sea_patches + } + } + # TODO work around problem in terra (fixed in dev) + #terra::time(land_mask, tstep="years") <- (time_bp+1950) + terra::time(land_mask, tstep="") <- (time_bp+1950) + return(land_mask) + +} diff --git a/inst/temp/_delta_downscale_3.R b/inst/temp/_delta_downscale_3.R index 27d501d3..9a67bbe0 100644 --- a/inst/temp/_delta_downscale_3.R +++ b/inst/temp/_delta_downscale_3.R @@ -150,19 +150,7 @@ make_land_mask <- function(topo_rast, time_bp, sea_level = NULL) { } -#' Create a binary mask -#' -#' Create a binary mask where NAs are 0 and values are 1 -#' -#' @param x a \code{terra::SpatRaster} -#' -#' @keywords internal -make_binary_mask <- function (x){ - x[!is.na(x)]<-1 - x[is.na(x)]<-0 - return(x) -} #' Interpolate x to match mask y #' diff --git a/inst/temp/a4_delta_downscale.Rmd b/inst/temp/a4_delta_downscale.Rmd index c25c8200..0c084a9d 100644 --- a/inst/temp/a4_delta_downscale.Rmd +++ b/inst/temp/a4_delta_downscale.Rmd @@ -65,45 +65,59 @@ plot(high_res_obs) ``` We need to make sure that the extent of the modern observations is the same as the -extent of the model reconstructions +extent of the model reconstructions: + ```{r} ext(high_res_obs)==ext(model_rast) ``` If that was not the case, we would use `terra::crop` to match the extents. -We also need a high resolution topographic/bathymetric map to reconstruct past -coastlines following sea level change: + +We also need a high resolution global relief map (i.e. integrating both +topographic and bathymetric values) to reconstruct past +coastlines following sea level change. We will use an upscaled version of the +ETOPO1 dataset (upscaled to 5 min resolution to keep the file smaller): + ```{r} topo_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif",package="pastclim") topo_rast <- terra::rast(topo_file) ``` -This raster also needs to have the same extent as the model reconstructions: +This raster also needs to have the same extent as the model reconstructions, +as well as the same resolution of the high resolution climate observations: + ```{r} -ext(topo_rast)==ext(model_rast) +ext(topo_rast) == ext(model_rast) + +ext(topo_rast) == ext(high_res_obs) +ncol(topo_rast) == ncol(high_res_obs) +nrow(topo_rast) == nrow(high_res_obs) ``` #TEMPORARY UNTIL THIS FUNCTIONS ARE INTEGRATED INTO PASTCLIM -We now need to load the draft functions for downscaling: -```{r} -source(system.file("/temp/_delta_downscale_3.R",package="pastclim")) -``` +Note that we will use the draft functions for downscaling, which can be accessed +by usign the prefix `pastclim:::`. The `:::` access internal functions. You will +also get a message reminding you that these functions are not final and thus +should not be used for real analysis. We can now generate a high resolution land mask for the periods of interest. By default, we use the sea level reconstructions from Spratt et al 2016, but a different reference can be used by setting sea levels for each time step (see the man page for `make_land_mask` for details): + ```{r, fig.width=6, fig.height=5} -high_res_mask <- make_land_mask(topo_rast = topo_rast, +high_res_mask <- pastclim:::make_land_mask(topo_rast = topo_rast, time_bp = time_bp(model_rast)) plot(high_res_mask, main=time_bp(high_res_mask)) ``` -We can now compute a delta raster and use it to downscale the model reconstructions: +We can now compute a delta raster and use it to downscale the model +reconstructions: + ```{r} -delta_rast<-delta_compute(x=model_rast, ref_time = 0, obs = high_res_obs) -model_downscaled <- delta_downscale (x = model_rast, delta_rast = delta_rast, +delta_rast<-pastclim:::delta_compute(x=model_rast, ref_time = 0, obs = high_res_obs) +model_downscaled <- pastclim:::delta_downscale (x = model_rast, delta_rast = delta_rast, x_landmask_high = high_res_mask) model_downscaled ``` diff --git a/man/delta_compute.Rd b/man/delta_compute.Rd new file mode 100644 index 00000000..db20f8a1 --- /dev/null +++ b/man/delta_compute.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/delta_compute.R +\name{delta_compute} +\alias{delta_compute} +\title{Compute a delta raster.} +\usage{ +delta_compute(x, ref_time, obs) +} +\arguments{ +\item{x}{a \code{terra::SpatRaster} for the variable of interest, with all +time steps of interest} + +\item{ref_time}{the time (BP) of the slice that is used to compute the delta} + +\item{obs}{the observations} +} +\value{ +a \code{terra::SpatRaster} of the delta +} +\description{ +This function generates a delta (difference) raster, computed as the difference between +model estimates (`x`) and some observations (`high_res_obs`). `x` is a +\code{terra::SpatRaster} of the variable we want to downscale, and it +can contain multiple time steps. `ref_time` sets the time slice for which +the delta should be computed. +} +\details{ +If `obs` has a higher +resolution than `x`, the latter is interpolated using a bilinear algorithm. +For areas that are present in some time slices, but not in the observations +(e.g. due to sea level change), the delta map is extended to cover the maximum +cumulative land mask (over all time steps) using idw. +} +\keyword{internal} diff --git a/man/delta_downscale.Rd b/man/delta_downscale.Rd new file mode 100644 index 00000000..345ee33f --- /dev/null +++ b/man/delta_downscale.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/delta_downscale.R +\name{delta_downscale} +\alias{delta_downscale} +\title{Downscale using the delta method} +\usage{ +delta_downscale(x, delta_rast, x_landmask_high = NULL) +} +\arguments{ +\item{x}{a \code{terra::SpatRaster} for the variable of interest, with all +time steps of interest} + +\item{delta_rast}{a \code{terra::SpatRaster} generated by +\code{pastclim::delta_compute}} + +\item{x_landmask_high}{a \code{terra::SpatRaster} with the same number of layers +as x. If left NULL, the original landmask of x is used.} +} +\value{ +a \code{terra::SpatRaster} of the downscaled variable, where each +layers is a time step. +} +\description{ +The delta method computes the difference between an observed raster and the +equivalent predictions from a model for a given time step, and then applies +that difference (delta) to all other time steps. You will first need to +create the delta raster with \code{pastclim::delta_compute}, and thus use it +as an argument for this function. +} +\details{ +It is possible to also provide a high resoultion landmask to this function. +For cells which are not included in the original simulation (e.g. because +the landmask was discretised at lower resolution), an idw algorightm is used +to interpolate the missing values. +} +\keyword{internal} diff --git a/man/idw_interp.Rd b/man/idw_interp.Rd new file mode 100644 index 00000000..d45bec1d --- /dev/null +++ b/man/idw_interp.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/idw_interp.R +\name{idw_interp} +\alias{idw_interp} +\title{Interpolate x to match mask y} +\usage{ +idw_interp(x, y, ...) +} +\arguments{ +\item{x}{the \code{terra::SpatRaster} of the variable of interest} + +\item{y}{the \code{terra::SpatRaster} the reference mask defining which +cells should have values} + +\item{...}{paramters to be passed to \code{gstat::gstat}} +} +\description{ +Fill in x to match cells available in y, using iwd (inverse-weighted +distance) interpolation. Interpolation is fitted using \code{gstat::gstat}; +the default parameters for \code{gstat::gstat} are nmax=7 and idp = .5, but +can be changed by providing arguments to this function (which will be passed +to \code{gstat::gstat}). See \code{gstat::gstat} for details on the available +parameters and their meaning. +} +\keyword{internal} diff --git a/man/make_binary_mask.Rd b/man/make_binary_mask.Rd new file mode 100644 index 00000000..d9a61e87 --- /dev/null +++ b/man/make_binary_mask.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/make_binary_mask.R +\name{make_binary_mask} +\alias{make_binary_mask} +\title{Create a binary mask} +\usage{ +make_binary_mask(x) +} +\arguments{ +\item{x}{a \code{terra::SpatRaster} +returns a \code{terra::SpatRaster} with 0s and 1s} +} +\description{ +Create a binary mask from a raster: NAs are converted to 0s, and +any other value to 1. +} +\keyword{internal} diff --git a/man/make_land_mask.Rd b/man/make_land_mask.Rd new file mode 100644 index 00000000..aad6174b --- /dev/null +++ b/man/make_land_mask.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/make_land_mask.R +\name{make_land_mask} +\alias{make_land_mask} +\title{Create a land mask} +\usage{ +make_land_mask(topo_rast, time_bp, sea_level = NULL) +} +\arguments{ +\item{topo_rast}{a \code{terra::SpatRaster} with topography} + +\item{time_bp}{the time of interest} + +\item{sea_level}{sea level at the time of interest (if left to NULL, this is +computed using Spratt 2016)} +} +\value{ +a \code{terra::SpatRaster} of 0s (sea) and 1s (land), where the +layers are different times +} +\description{ +Create a land mask for a given time step. The land mask is based on the simple +logic of moving the ocean up and down given the current topography/bathymetry. +Note that this approach ignores any rebound due to changing mass and distribution of +icesheets. The landmask will show internal lakes/seas as land, as their level +is unrelated to the general sea level. If you have specific reconstructions +of internal lakes (or want to simply reuse their current extents), you will +have to add them onto the masks generated by this function. +} +\keyword{internal} From 4ba037e73c3fb245eb2105fc7e1814d59c5e722c Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Wed, 7 Dec 2022 14:51:16 +0000 Subject: [PATCH 002/119] download reliefs --- DESCRIPTION | 5 ++-- NEWS.md | 2 +- R/delta_downscale.R | 4 +-- R/download_relief.R | 46 +++++++++++++++++++++++++++++++ R/make_land_mask.R | 9 +++--- README.md | 7 +++-- a4_delta_downscale.pdf | Bin 0 -> 682734 bytes inst/temp/a4_delta_downscale.Rmd | 42 ++++++++++++++++------------ man/download_relief.Rd | 30 ++++++++++++++++++++ man/make_land_mask.Rd | 7 +++-- 10 files changed, 119 insertions(+), 33 deletions(-) create mode 100644 R/download_relief.R create mode 100644 a4_delta_downscale.pdf create mode 100644 man/download_relief.Rd diff --git a/DESCRIPTION b/DESCRIPTION index b7eb8c6a..039c7e9e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: pastclim Type: Package Title: Manipulate Time Series of Palaeoclimate Reconstructions -Version: 1.2.2 +Version: 1.2.2.9000 Authors@R: c( person("Michela", "Leonardi", role = "aut"), person(c("Emily","Y."), "Hallet", role = "ctb"), @@ -34,8 +34,9 @@ Imports: Suggests: rmarkdown, knitr, - sf, + marmap, ggplot2, + sf, testthat (>= 3.0.0) VignetteBuilder: knitr Config/testthat/edition: 3 diff --git a/NEWS.md b/NEWS.md index a3399a21..18f6782e 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,7 @@ # pastclim 1.2.2 * Update of Krapp2021 files to make them compatible with how terra now handles time. Users will have to redownload datasets. Old files can be removed with - 'clean_data_path()' + `clean_data_path()`. # pastclim 1.2.1 * Small updates for CRAN submission. diff --git a/R/delta_downscale.R b/R/delta_downscale.R index cc9f530b..5741f571 100644 --- a/R/delta_downscale.R +++ b/R/delta_downscale.R @@ -29,8 +29,8 @@ delta_downscale <- function(x, delta_rast, x_landmask_high=NULL) { stop("x and delta_rast don't have the same extent") } if (!is.null(x_landmask_high)){ - if (!all((terra::ncol(delta_rast)!=terra::ncol(x_landmask_high)), - (terra::nrow(delta_rast)!=terra::nrow(x_landmask_high)))){ + if (!all((terra::ncol(delta_rast)==terra::ncol(x_landmask_high)), + (terra::nrow(delta_rast)==terra::nrow(x_landmask_high)))){ stop("delta_rast and x_landmask_high should have the same number of rows\n", "and columns") } diff --git a/R/download_relief.R b/R/download_relief.R new file mode 100644 index 00000000..591e9407 --- /dev/null +++ b/R/download_relief.R @@ -0,0 +1,46 @@ +#' Download a relief dataset. +#' +#' This function downloads a relief (topography+bathymetry) dataset from the +#' ETOPO2022 dataset. +#' +#' This function uses \code{marmap::getNOAA.bathy} to download the data, and +#' then converts them into a \code{terra::SpatRaster} in a format compatible +#' with `pastclim` +#' +#' @param rast_template a \code{terra::SpatRaster} providing the extent and +#' resolution to be downloaded. This raster needs to have identical vertical +#' and horizontal resolution, and standard lat/long projection. +#' @param ... additional parameters to be passed to \code{marmap::getNOAA.bathy} +#' to customise how files are stored. See the manpage for that function for +#' details +#' @returns a \code{terra::SpatRaster} with the relief for the chosen region +#' +#' @keywords internal + +download_relief <- function(rast_template, ...) { + + if(!requireNamespace("marmap", quietly=TRUE)){ + message("This function requires the 'marmap' package; install it with:\n", + "install.packages('marmap')") + return(invisible()) + } + # check that this works, I think there might be issues with decimal places + if (terra::res(rast_template)[1]!=terra::res(rast_template)[2]){ + stop("rast_template should have identical vertical and horizontal resolution") + } + #browser() + relief_bathy<- marmap::getNOAA.bathy(lon1 = terra::ext(rast_template)[1], + lon2 = terra::ext(rast_template)[2], + lat1 = terra::ext(rast_template)[3], + lat2 = terra::ext(rast_template)[4], + # note that resolution in marmap is in minuts, and in degrees for terra + resolution = res(rast_template)[1]*60, ...) + relief_rast <- terra::rast(marmap::as.raster(relief_bathy)) + longnames(relief_rast)<-"relief from ETOPO2022" + units(relief_rast)<-"m" + return(relief_rast) + #relief_terra <- bathy_to_spatraster(relief_bathy) + # ideally check if there are any differences (due to rounding problems or corners), + # and then reshape the relief to match the template exactly + +} diff --git a/R/make_land_mask.R b/R/make_land_mask.R index 6d98c025..ec6e4a0e 100644 --- a/R/make_land_mask.R +++ b/R/make_land_mask.R @@ -1,14 +1,15 @@ #' Create a land mask #' #' Create a land mask for a given time step. The land mask is based on the simple -#' logic of moving the ocean up and down given the current topography/bathymetry. +#' logic of moving the ocean up and down given the current relief profile ( +#' topography+bathymetry, i.e. the elevation both above and below sea level). #' Note that this approach ignores any rebound due to changing mass and distribution of #' icesheets. The landmask will show internal lakes/seas as land, as their level #' is unrelated to the general sea level. If you have specific reconstructions #' of internal lakes (or want to simply reuse their current extents), you will #' have to add them onto the masks generated by this function. #' -#' @param topo_rast a \code{terra::SpatRaster} with topography +#' @param relief_rast a \code{terra::SpatRaster} with relief #' @param time_bp the time of interest #' @param sea_level sea level at the time of interest (if left to NULL, this is #' computed using Spratt 2016) @@ -17,7 +18,7 @@ #' #' @keywords internal -make_land_mask <- function(topo_rast, time_bp, sea_level = NULL) { +make_land_mask <- function(relief_rast, time_bp, sea_level = NULL) { message("This function is still under development; do not use it for real analysis") if (is.null(sea_level)){ # get sea level from Spratt 2016 @@ -34,7 +35,7 @@ make_land_mask <- function(topo_rast, time_bp, sea_level = NULL) { } land_mask <- NULL for (i in 1:length(time_bp)) { - topography_bin<-topo_rast + topography_bin<-relief_rast topography_bin[topography_bin>sea_level[i]]<-NA topography_bin[!is.na(topography_bin)]<-1 sea_patches<-patches(topography_bin) diff --git a/README.md b/README.md index 13b24262..3bcff876 100644 --- a/README.md +++ b/README.md @@ -32,13 +32,14 @@ devtools::install_github("EvolEcolGroup/pastclim") ## Overview of functionality On its dedicated [website](https://evolecolgroup.github.io/pastclim/), you can find -Articles giving you a step-by-step [overview of the package](https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html), and how -to build and use [custom datasets](https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html). There is also a [cheatsheet](https://evolecolgroup.github.io/pastclim/pastclim_cheatsheet.pdf). +Articles giving you a step-by-step [overview of the package](https://evolecolgroup.github.io/pastclim/articles/a0_pastclim_overview.html), as well as a [cheatsheet](https://evolecolgroup.github.io/pastclim/pastclim_cheatsheet.pdf). Pastclim currently includes data from Beyer et al 2020, a reconstruction of climate based on the HadCM3 model for the last 120k years, and Krapp et al 2021, which covers the last 800k years. The reconstructions are bias-corrected and downscaled to 0.5 degree. More details on these datasets -can be found [here](https://evolecolgroup.github.io/pastclim/articles/a1_available_datasets.html). +can be found [here](https://evolecolgroup.github.io/pastclim/articles/a1_available_datasets.html). There are also instructions +on how +to build and use [custom datasets](https://evolecolgroup.github.io/pastclim/articles/a2_custom_datasets.html). 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The `:::` access internal functions. You will +also get a message reminding you that these functions are not final and thus +should not be used for real analysis. + + ## Downscaling a dataset in pastclim `pastclim` The `terra` package can downscale reconstructions using a bilinear method. Other `R` @@ -56,7 +62,7 @@ We can see how that the reconstructions are rather coarse (the Example dataset uses 1x1 degree cells). We now need a set of high resolutions observations for the variable of interest that we will use to generate the delta raster used to downscale reconstructions. We will use data from -worldclim2 at 5 minute resolution (but other datasets such as CHELSA would be +Worldclim2 at 5 minute resolution (but other datasets such as CHELSA would be equally suitable). ```{r, fig.width=4, fig.height=4} high_res_file <- system.file("extdata/wc2.1_5m_bio_1_sea.tif",package="pastclim") @@ -75,39 +81,39 @@ If that was not the case, we would use `terra::crop` to match the extents. We also need a high resolution global relief map (i.e. integrating both topographic and bathymetric values) to reconstruct past -coastlines following sea level change. We will use an upscaled version of the -ETOPO1 dataset (upscaled to 5 min resolution to keep the file smaller): +coastlines following sea level change. The relief raster will need to have +the same extent and resolution as the high resolution observations. We +can download one (based on ETOPO2022) with: ```{r} -topo_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif",package="pastclim") -topo_rast <- terra::rast(topo_file) +relief_rast <- pastclim:::download_relief(high_res_obs) + ``` -This raster also needs to have the same extent as the model reconstructions, +```{r eval=FALSE, results="hide",echo=FALSE} +relief_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif",package="pastclim") +relief_rast <- terra::rast(relief_file) +``` + +We can quickly confirm that the resulting relief raster has the same extent +as the model reconstructions, as well as the same resolution of the high resolution climate observations: ```{r} -ext(topo_rast) == ext(model_rast) +ext(relief_rast) == ext(model_rast) -ext(topo_rast) == ext(high_res_obs) -ncol(topo_rast) == ncol(high_res_obs) -nrow(topo_rast) == nrow(high_res_obs) +ext(relief_rast) == ext(high_res_obs) +ncol(relief_rast) == ncol(high_res_obs) +nrow(relief_rast) == nrow(high_res_obs) ``` -#TEMPORARY UNTIL THIS FUNCTIONS ARE INTEGRATED INTO PASTCLIM - -Note that we will use the draft functions for downscaling, which can be accessed -by usign the prefix `pastclim:::`. The `:::` access internal functions. You will -also get a message reminding you that these functions are not final and thus -should not be used for real analysis. - We can now generate a high resolution land mask for the periods of interest. By default, we use the sea level reconstructions from Spratt et al 2016, but a different reference can be used by setting sea levels for each time step (see the man page for `make_land_mask` for details): ```{r, fig.width=6, fig.height=5} -high_res_mask <- pastclim:::make_land_mask(topo_rast = topo_rast, +high_res_mask <- pastclim:::make_land_mask(relief_rast = relief_rast, time_bp = time_bp(model_rast)) plot(high_res_mask, main=time_bp(high_res_mask)) ``` diff --git a/man/download_relief.Rd b/man/download_relief.Rd new file mode 100644 index 00000000..0c791c09 --- /dev/null +++ b/man/download_relief.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/download_relief.R +\name{download_relief} +\alias{download_relief} +\title{Download a relief dataset.} +\usage{ +download_relief(rast_template, ...) +} +\arguments{ +\item{rast_template}{a \code{terra::SpatRaster} providing the extent and +resolution to be downloaded. This raster needs to have identical vertical +and horizontal resolution, and standard lat/long projection.} + +\item{...}{additional parameters to be passed to \code{marmap::getNOAA.bathy} +to customise how files are stored. See the manpage for that function for +details} +} +\value{ +a \code{terra::SpatRaster} with the relief for the chosen region +} +\description{ +This function downloads a relief (topography+bathymetry) dataset from the +ETOPO2022 dataset. +} +\details{ +This function uses \code{marmap::getNOAA.bathy} to download the data, and +then converts them into a \code{terra::SpatRaster} in a format compatible +with `pastclim` +} +\keyword{internal} diff --git a/man/make_land_mask.Rd b/man/make_land_mask.Rd index aad6174b..19512ee0 100644 --- a/man/make_land_mask.Rd +++ b/man/make_land_mask.Rd @@ -4,10 +4,10 @@ \alias{make_land_mask} \title{Create a land mask} \usage{ -make_land_mask(topo_rast, time_bp, sea_level = NULL) +make_land_mask(relief_rast, time_bp, sea_level = NULL) } \arguments{ -\item{topo_rast}{a \code{terra::SpatRaster} with topography} +\item{relief_rast}{a \code{terra::SpatRaster} with relief} \item{time_bp}{the time of interest} @@ -20,7 +20,8 @@ layers are different times } \description{ Create a land mask for a given time step. The land mask is based on the simple -logic of moving the ocean up and down given the current topography/bathymetry. +logic of moving the ocean up and down given the current relief profile ( +topography+bathymetry, i.e. the elevation both above and below sea level). Note that this approach ignores any rebound due to changing mass and distribution of icesheets. The landmask will show internal lakes/seas as land, as their level is unrelated to the general sea level. 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From 8518c594d3e0ba16297ecc65da2fb6486e534ac2 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Wed, 7 Dec 2022 16:49:11 +0000 Subject: [PATCH 004/119] fix for terra bug on cran --- R/make_land_mask.R | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/R/make_land_mask.R b/R/make_land_mask.R index ec6e4a0e..41d6917f 100644 --- a/R/make_land_mask.R +++ b/R/make_land_mask.R @@ -55,9 +55,13 @@ make_land_mask <- function(relief_rast, time_bp, sea_level = NULL) { add(land_mask)<-sea_patches } } - # TODO work around problem in terra (fixed in dev) - #terra::time(land_mask, tstep="years") <- (time_bp+1950) - terra::time(land_mask, tstep="") <- (time_bp+1950) + # work around problem in terra defining time units for negative values + # (fixed in dev after 1.6.47) + if (utils::packageVersion("terra")<"1.6.48"){ + terra::time(land_mask, tstep="") <- (time_bp+1950) + } else { + terra::time(land_mask, tstep="years") <- (time_bp+1950) + } return(land_mask) } From 74d58447b38728f691fff3190a44f0f669b23576 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Wed, 7 Dec 2022 21:22:35 +0000 Subject: [PATCH 005/119] ideas for monthly vars --- R/make_land_mask.R | 11 ++++++----- inst/temp/a4_delta_downscale.Rmd | 24 ++++++++++++++---------- inst/temp/monthly_vars.R | 16 ++++++++++++++++ 3 files changed, 36 insertions(+), 15 deletions(-) create mode 100644 inst/temp/monthly_vars.R diff --git a/R/make_land_mask.R b/R/make_land_mask.R index 41d6917f..bcda7254 100644 --- a/R/make_land_mask.R +++ b/R/make_land_mask.R @@ -35,10 +35,11 @@ make_land_mask <- function(relief_rast, time_bp, sea_level = NULL) { } land_mask <- NULL for (i in 1:length(time_bp)) { - topography_bin<-relief_rast - topography_bin[topography_bin>sea_level[i]]<-NA - topography_bin[!is.na(topography_bin)]<-1 - sea_patches<-patches(topography_bin) + # create binary relief map for areas above and below the relevant sea level + relief_bin<-relief_rast + relief_bin[relief_bin>sea_level[i]]<-NA + relief_bin[!is.na(relief_bin)]<-1 + sea_patches<-patches(relief_bin) modal_vector <- function(x) { ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] @@ -55,7 +56,7 @@ make_land_mask <- function(relief_rast, time_bp, sea_level = NULL) { add(land_mask)<-sea_patches } } - # work around problem in terra defining time units for negative values + # work around problem of old terra failing to set time units for negative values # (fixed in dev after 1.6.47) if (utils::packageVersion("terra")<"1.6.48"){ terra::time(land_mask, tstep="") <- (time_bp+1950) diff --git a/inst/temp/a4_delta_downscale.Rmd b/inst/temp/a4_delta_downscale.Rmd index 1fcac0e0..e8f0dfe8 100644 --- a/inst/temp/a4_delta_downscale.Rmd +++ b/inst/temp/a4_delta_downscale.Rmd @@ -41,7 +41,7 @@ First, we extract a time series for the variable and region of interest: library(terra) library(pastclim) sea_ext<- terra::ext(100, 130, -15, 20) -model_series <- region_series(bio_variables = "bio01",dataset="Example", +model_series <- region_series(bio_variables = "bio01", dataset = "Example", ext = sea_ext) model_series ``` @@ -55,7 +55,7 @@ model_rast ``` And we can now plot it: ```{r, fig.width=6, fig.height=5} -plot(model_rast, main=time_bp(model_rast)) +plot(model_rast, main = time_bp(model_rast)) ``` We can see how that the reconstructions are rather coarse (the Example dataset @@ -65,8 +65,9 @@ generate the delta raster used to downscale reconstructions. We will use data fr Worldclim2 at 5 minute resolution (but other datasets such as CHELSA would be equally suitable). ```{r, fig.width=4, fig.height=4} -high_res_file <- system.file("extdata/wc2.1_5m_bio_1_sea.tif",package="pastclim") -high_res_obs<-terra::rast(high_res_file) +high_res_file <- system.file("extdata/wc2.1_5m_bio_1_sea.tif", + package = "pastclim") +high_res_obs <- terra::rast(high_res_file) plot(high_res_obs) ``` @@ -91,7 +92,8 @@ relief_rast <- pastclim:::download_relief(high_res_obs) ``` ```{r eval=FALSE, results="hide",echo=FALSE} -relief_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif",package="pastclim") +relief_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif", + package = "pastclim") relief_rast <- terra::rast(relief_file) ``` @@ -122,19 +124,21 @@ We can now compute a delta raster and use it to downscale the model reconstructions: ```{r} -delta_rast<-pastclim:::delta_compute(x=model_rast, ref_time = 0, obs = high_res_obs) -model_downscaled <- pastclim:::delta_downscale (x = model_rast, delta_rast = delta_rast, - x_landmask_high = high_res_mask) +delta_rast<-pastclim:::delta_compute(x=model_rast, ref_time = 0, + obs = high_res_obs) +model_downscaled <- pastclim:::delta_downscale (x = model_rast, + delta_rast = delta_rast, + x_landmask_high = high_res_mask) model_downscaled ``` Let's inspect the resulting data: ```{r, fig.width=6, fig.height=5} -plot(model_downscaled, main=time_bp(model_downscaled)) +plot(model_downscaled, main = time_bp(model_downscaled)) ``` And, as a reminder, the original reconstructions (note that the colour scales are not the same! `terra` chooses a scale for each time step based on the time specific range): ```{r, fig.width=6, fig.height=5} -plot(model_rast, main=time_bp(model_rast)) +plot(model_rast, main = time_bp(model_rast)) ``` diff --git a/inst/temp/monthly_vars.R b/inst/temp/monthly_vars.R new file mode 100644 index 00000000..462f4d39 --- /dev/null +++ b/inst/temp/monthly_vars.R @@ -0,0 +1,16 @@ +library(geodata) +path_wc <- "../../project_temp/climate_downscale/worldclim_monthly" +wc_tavg_5<-worldclim_global(var="tavg", res=5, path=path_wc, version="2.1") +# to read in again the tiffs +path_wc <- file.path(path_wc,"wc2.1_5m") +list_of_tiffs <- file.path(path_wc,dir(path_wc)) +wc_tavg_5<-terra::rast(list_of_tiffs) +# better to save it as a netcdf +writeCDF(wc_tavg_5, file.path(get_data_path(),"wc2.1_5m_tavg.nc")) + +One month +foo<-wc_tavg_5[[1]] + + +##get a tile +foo<-worldclim_tile("tavg",lon=0,lat=52,path=tempdir()) \ No newline at end of file From 269bfa1a6a28d7402dc5a95c60d93848eaf46c9a Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Fri, 9 Dec 2022 16:59:21 +0000 Subject: [PATCH 006/119] First implementation of bioclim_vars --- DESCRIPTION | 1 + NAMESPACE | 3 + R/bioclim_vars.R | 207 +++++++++++++++++++++++++++++++++++ R/df_from_region_series.R | 1 - inst/temp/monthly_vars.R | 48 ++++++-- man/bioclim_vars-methods.Rd | 66 +++++++++++ man/df_from_region_series.Rd | 8 +- man/dot-cv.Rd | 24 ++++ 8 files changed, 340 insertions(+), 18 deletions(-) create mode 100644 R/bioclim_vars.R create mode 100644 man/bioclim_vars-methods.Rd create mode 100644 man/dot-cv.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 039c7e9e..a6eefa75 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -28,6 +28,7 @@ Depends: Imports: curl, gstat, + methods, ncdf4, terra (>= 1.6.41), utils diff --git a/NAMESPACE b/NAMESPACE index c3f7feeb..1f420466 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +export(bioclim_vars) export(check_dataset_path) export(clean_data_path) export(climate_for_locations) @@ -30,4 +31,6 @@ export(time_bp) export(time_series_for_locations) export(update_dataset_list) export(var_labels) +exportMethods(bioclim_vars) import(terra) +importFrom(methods,setGeneric) diff --git a/R/bioclim_vars.R b/R/bioclim_vars.R new file mode 100644 index 00000000..a359db9d --- /dev/null +++ b/R/bioclim_vars.R @@ -0,0 +1,207 @@ +#' Compute the BIOCLIM variables +#' +#' To compute BIOCLIM variables from +#' monthly average temperature and precipitation data. For modern data, +#' this variables are generally computed using min and maximum temperature, +#' but for many paleoclimatic reconstructions one average temperature is +#' avaliable. Most variables, with the exception of BIO02 and BIO03, can +#' be rephrased meaningfully in terms of mean temperature. +#' This function is a modified version of \code{predicts::bcvars}. +#' The variables are: +#' BIO01 = Annual Mean Temperature +#' BIO04 = Temperature Seasonality (standard deviation *100) +#' BIO05 = Max Temperature of Warmest Month +#' BIO06 = Min Temperature of Coldest Month +#' BIO07 = Temperature Annual Range (P5-P6) +#' BIO08 = Mean Temperature of Wettest Quarter +#' BIO09 = Mean Temperature of Driest Quarter +#' BIO10 = Mean Temperature of Warmest Quarter +#' BIO11 = Mean Temperature of Coldest Quarter +#' BIO12 = Annual Precipitation +#' BIO13 = Precipitation of Wettest Month +#' BIO14 = Precipitation of Driest Month +#' BIO15 = Precipitation Seasonality (Coefficient of Variation) +#' BIO16 = Precipitation of Wettest Quarter +#' BIO17 = Precipitation of Driest Quarter +#' BIO18 = Precipitation of Warmest Quarter +#' BIO19 = Precipitation of Coldest Quarter +#' +#' These summary Bioclimatic variables are after: +#' Nix, 1986. A biogeographic analysis of Australian elapid snakes. In: R. Longmore (ed.). +#' Atlas of elapid snakes of Australia. Australian Flora and Fauna Series 7. +#' Australian Government Publishing Service, Canberra. +#' +# and Expanded following the ANUCLIM manual +#' +#' +#' @param tavg monthly average temperatures, as a matrix or +#' a \code{terra::SpatRaster} +#' @param prec monthly precipitation, as a matrix or +#' a \code{terra::SpatRaster} +#' @param ... additional variables for specific methods +#' @returns an array or a \code{terra::SpatRaster} where each layer +#' is a bioclim variable +#' @docType methods +#' @rdname bioclim_vars-methods +#' @importFrom methods setGeneric +#' @export + +#if (!isGeneric("bioclim_vars")) { +methods::setGeneric("bioclim_vars", function(prec, tavg, ...){ + methods::standardGeneric("bioclim_vars") +}) + +#' @rdname bioclim_vars-methods +#' @export +methods::setMethod("bioclim_vars", signature(prec="numeric", tavg="numeric"), + function(prec, tavg) { + bioclim_vars(t(as.matrix(prec)), t(as.matrix(tavg))) + } +) + +#' @param filename filename where the raster can be stored. +#' @rdname bioclim_vars-methods +#' @export +methods::setMethod("bioclim_vars", signature(prec="SpatRaster", tavg="SpatRaster"), + function(prec, tavg, filename="", ...) { + + if (nlyr(prec) != 12) stop("nlyr(prec) is not 12") + if (nlyr(tavg) != 12) stop("nlyr(tavg) is not 12") + + + x <- c(prec, tavg) + readStart(x) + on.exit(readStop(x)) + nc <- ncol(x) + out <- rast(prec, nlyr=17) + names(out) = c("bio01",paste0("bio0", 4:9), + paste0("bio",10:19)) + b <- writeStart(out, filename, ...) + for (i in 1:b$n) { + d <- readValues(x, b$row[i], b$nrows[i], 1, nc, TRUE, FALSE) + p <- bioclim_vars(d[,1:12], d[,13:24]) + writeValues(out, p, b$row[i], b$nrows[i]) + } + writeStop(out) + return(out) + } +) + + +#' @rdname bioclim_vars-methods +#' @export +methods::setMethod("bioclim_vars", signature(prec="matrix", tavg="matrix"), + function(prec, tavg) { + + if (nrow(prec) != nrow(tavg) ) { + stop("prec and tavg should have same length") + } + + if (ncol(prec) != ncol(tavg)) { + stop("prec and tavg should have same number of variables (columns)") + } + + # can"t have missing values in a row + nas <- apply(prec, 1, function(x){ any(is.na(x)) } ) + nas <- nas | apply(tavg, 1, function(x){ any(is.na(x)) } ) + p <- matrix(nrow=nrow(prec), ncol=17) + colnames(p) = c("bio01",paste0("bio0", 4:9), + paste0("bio",10:19)) + if (all(nas)) { return(p) } + + prec[nas,] <- NA + tavg[nas,] <- NA + + window <- function(x) { + lng <- length(x) + x <- c(x, x[1:3]) + m <- matrix(ncol=3, nrow=lng) + for (i in 1:3) { m[,i] <- x[i:(lng+i-1)] } + apply(m, MARGIN=1, FUN=sum) + } + + # P1. Annual Mean Temperature + p[,"bio01"] <- apply(tavg,1,mean) + # P4. Temperature Seasonality (standard deviation) + p[,"bio04"] <- 100 * apply(tavg, 1, stats::sd) + # P5. Max Temperature of Warmest Period + p[,"bio05"] <- apply(tavg,1, max) + # P6. Min Temperature of Coldest Period + p[,"bio06"] <- apply(tavg, 1, min) + # P7. Temperature Annual Range (P5-P6) + p[,"bio07"] <- p[,"bio05"] - p[,"bio06"] + # P12. Annual Precipitation + p[,"bio12"] <- apply(prec, 1, sum) + # P13. Precipitation of Wettest Period + p[,"bio13"] <- apply(prec, 1, max) + # P14. Precipitation of Driest Period + p[,"bio14"] <- apply(prec, 1, min) + # P15. Precipitation Seasonality(Coefficient of Variation) + # the "1 +" is to avoid strange CVs for areas where mean rainfaill is < 1) + p[,"bio15"] <- apply(prec+1, 1, .cv) + + # precip by quarter (3 months) + wet <- t(apply(prec, 1, window)) + # P16. Precipitation of Wettest Quarter + p[,"bio16"] <- apply(wet, 1, max) + # P17. Precipitation of Driest Quarter + p[,"bio17"] <- apply(wet, 1, min) + tmp <- t(apply(tavg, 1, window)) / 3 + + if (all(is.na(wet))) { + p[,"bio08"] <- NA + p[,"bio09"] <- NA + } else { + # P8. Mean Temperature of Wettest Quarter + wetqrt <- cbind(1:nrow(p), as.integer(apply(wet, 1, which.max))) + p[,"bio08"] <- tmp[wetqrt] + # P9. Mean Temperature of Driest Quarter + dryqrt <- cbind(1:nrow(p), as.integer(apply(wet, 1, which.min))) + p[,"bio09"] <- tmp[dryqrt] + } + # P10 Mean Temperature of Warmest Quarter + p[,"bio10"] <- apply(tmp, 1, max) + + # P11 Mean Temperature of Coldest Quarter + p[,"bio11"] <- apply(tmp, 1, min) + + if (all(is.na(tmp))) { + p[,"bio18"] <- NA + p[,"bio19"] <- NA + } else { + # P18. Precipitation of Warmest Quarter + hot <- cbind(1:nrow(p), as.integer(apply(tmp, 1, which.max))) + p[,"bio18"] <- wet[hot] + # P19. Precipitation of Coldest Quarter + cold <- cbind(1:nrow(p), as.integer(apply(tmp, 1, which.min))) + p[,"bio19"] <- wet[cold] + } + + return(p) + } +) + +#' Coefficient of variables (expressed as percentage) +#' +#' R function to compute the coefficient of variation +#' (expressed as a percentage). If there is only a single value, stats::sd = NA. +#' However, one could argue that cv =0; and NA may break the code that +#' receives it. The function returns 0 if the mean is close to zero. +#' +#' This is ODD: abs to avoid very small (or zero) mean with e.g. -5:5 +#' +#' @param x a vector of values +#' @returns the cv +#' @keywords internal + +.cv <- function(x) { + m <- mean(abs(x)) + if (is.na(m)){return(NA)} + if (m == 0) { + return(0) + } else { + return(100 * stats::sd(x) / m) + } +} + + \ No newline at end of file diff --git a/R/df_from_region_series.R b/R/df_from_region_series.R index d084aee0..70dca348 100644 --- a/R/df_from_region_series.R +++ b/R/df_from_region_series.R @@ -1,5 +1,4 @@ #' Extract data frame from a region series -#' #' Extract the climatic information from a region series and organise them as #' a data frame. #' diff --git a/inst/temp/monthly_vars.R b/inst/temp/monthly_vars.R index 462f4d39..3c3cd5d6 100644 --- a/inst/temp/monthly_vars.R +++ b/inst/temp/monthly_vars.R @@ -1,16 +1,40 @@ -library(geodata) +# the code below downloads the full worldclim dataset. Another option is to use +# worldclim_tile("tavg",lon=0,lat=52,path=tempdir()) +# to only get part of the world + path_wc <- "../../project_temp/climate_downscale/worldclim_monthly" -wc_tavg_5<-worldclim_global(var="tavg", res=5, path=path_wc, version="2.1") -# to read in again the tiffs -path_wc <- file.path(path_wc,"wc2.1_5m") -list_of_tiffs <- file.path(path_wc,dir(path_wc)) -wc_tavg_5<-terra::rast(list_of_tiffs) -# better to save it as a netcdf -writeCDF(wc_tavg_5, file.path(get_data_path(),"wc2.1_5m_tavg.nc")) +wc_res <- 5 # one of 10,5,2.5,0.5 minutes + +library(geodata) +library(pastclim) + +# load the observations (and download them if needed) +if (!file.exists(file.path(base_path_wc,"wc_tavg_5_monthly.nc"))){ + wc_tavg_5<-geodata::worldclim_global(var="tavg", res=wc_res, path=tempdir(), version="2.1") + terra::writeCDF(wc_tavg_5,file.path(base_path_wc,"wc_tavg_5_monthly.nc"), + compression=9) +} else { + wc_tavg_5<-terra::rast(file.path(base_path_wc,"wc_tavg_5_monthly.nc")) +} + +if (!file.exists(file.path(base_path_wc,"wc_prec_5_monthly.nc"))){ + wc_prec_5<-geodata::worldclim_global(var="prec", res=wc_res, path=tempdir(), version="2.1") + terra::writeCDF(wc_prec_5,file.path(base_path_wc,"wc_prec_5_monthly.nc"), + compression=9) +} else { + wc_prec_5<-terra::rast(file.path(base_path_wc,"wc_prec_5_monthly.nc")) +} + +# now set the extent for SEA +sea_ext<- terra::ext(100, 110, 0, 10) +# crop the observations for this extent +high_res_tavg <- terra::crop(wc_tavg_5, sea_ext) +high_res_prec <- terra::crop(wc_prec_5, sea_ext) -One month -foo<-wc_tavg_5[[1]] +bioclim_pres <- bioclim_vars(tavg = high_res_tavg, prec = high_res_prec) +tavg_series <- region_series(bio_variables = c(paste0("temperature_0",1:9),paste0("temperature_",10:12)), + time_bp = get_time_steps(dataset = "Example"), + dataset = "Beyer2020", + ext = sea_ext) -##get a tile -foo<-worldclim_tile("tavg",lon=0,lat=52,path=tempdir()) \ No newline at end of file diff --git a/man/bioclim_vars-methods.Rd b/man/bioclim_vars-methods.Rd new file mode 100644 index 00000000..b5235199 --- /dev/null +++ b/man/bioclim_vars-methods.Rd @@ -0,0 +1,66 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bioclim_vars.R +\docType{methods} +\name{bioclim_vars} +\alias{bioclim_vars} +\alias{bioclim_vars,numeric,numeric-method} +\alias{bioclim_vars,SpatRaster,SpatRaster-method} +\alias{bioclim_vars,matrix,matrix-method} +\title{Compute the BIOCLIM variables} +\usage{ +bioclim_vars(prec, tavg, ...) + +\S4method{bioclim_vars}{numeric,numeric}(prec, tavg) + +\S4method{bioclim_vars}{SpatRaster,SpatRaster}(prec, tavg, filename = "", ...) + +\S4method{bioclim_vars}{matrix,matrix}(prec, tavg) +} +\arguments{ +\item{prec}{monthly precipitation, as a matrix or +a \code{terra::SpatRaster}} + +\item{tavg}{monthly average temperatures, as a matrix or +a \code{terra::SpatRaster}} + +\item{...}{additional variables for specific methods} + +\item{filename}{filename where the raster can be stored.} +} +\value{ +an array or a \code{terra::SpatRaster} where each layer +is a bioclim variable +} +\description{ +To compute BIOCLIM variables from +monthly average temperature and precipitation data. For modern data, +this variables are generally computed using min and maximum temperature, +but for many paleoclimatic reconstructions one average temperature is +avaliable. Most variables, with the exception of BIO02 and BIO03, can +be rephrased meaningfully in terms of mean temperature. +This function is a modified version of \code{predicts::bcvars}. +The variables are: +BIO01 = Annual Mean Temperature +BIO04 = Temperature Seasonality (standard deviation *100) +BIO05 = Max Temperature of Warmest Month +BIO06 = Min Temperature of Coldest Month +BIO07 = Temperature Annual Range (P5-P6) +BIO08 = Mean Temperature of Wettest Quarter +BIO09 = Mean Temperature of Driest Quarter +BIO10 = Mean Temperature of Warmest Quarter +BIO11 = Mean Temperature of Coldest Quarter +BIO12 = Annual Precipitation +BIO13 = Precipitation of Wettest Month +BIO14 = Precipitation of Driest Month +BIO15 = Precipitation Seasonality (Coefficient of Variation) +BIO16 = Precipitation of Wettest Quarter +BIO17 = Precipitation of Driest Quarter +BIO18 = Precipitation of Warmest Quarter +BIO19 = Precipitation of Coldest Quarter +} +\details{ +These summary Bioclimatic variables are after: +Nix, 1986. A biogeographic analysis of Australian elapid snakes. In: R. Longmore (ed.). + Atlas of elapid snakes of Australia. Australian Flora and Fauna Series 7. + Australian Government Publishing Service, Canberra. +} diff --git a/man/df_from_region_series.Rd b/man/df_from_region_series.Rd index 992a86c1..0036ceba 100644 --- a/man/df_from_region_series.Rd +++ b/man/df_from_region_series.Rd @@ -2,7 +2,9 @@ % Please edit documentation in R/df_from_region_series.R \name{df_from_region_series} \alias{df_from_region_series} -\title{Extract data frame from a region series} +\title{Extract data frame from a region series +Extract the climatic information from a region series and organise them as +a data frame.} \usage{ df_from_region_series(x, xy = TRUE) } @@ -17,9 +19,5 @@ a data.frame where each cell each raster layer (i.e. timestep) is a row, and the available variables are columns. } \description{ -Extract the climatic information from a region series and organise them as -a data frame. -} -\details{ To extract a data frame from a region slice, see \code{df_from_region_slice}. } diff --git a/man/dot-cv.Rd b/man/dot-cv.Rd new file mode 100644 index 00000000..1e5be011 --- /dev/null +++ b/man/dot-cv.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bioclim_vars.R +\name{.cv} +\alias{.cv} +\title{Coefficient of variables (expressed as percentage)} +\usage{ +.cv(x) +} +\arguments{ +\item{x}{a vector of values} +} +\value{ +the cv +} +\description{ +R function to compute the coefficient of variation +(expressed as a percentage). If there is only a single value, stats::sd = NA. +However, one could argue that cv =0; and NA may break the code that +receives it. The function returns 0 if the mean is close to zero. +} +\details{ +This is ODD: abs to avoid very small (or zero) mean with e.g. -5:5 +} +\keyword{internal} From 5b8720d3c4324829cf51bd8ac75ab3feab7d534b Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Fri, 9 Dec 2022 21:29:59 +0000 Subject: [PATCH 007/119] biovars for sds --- R/bioclim_vars.R | 36 +++++++++++++++++++++++++++++++++++ R/delta_compute.R | 3 +++ inst/temp/monthly_vars.R | 38 +++++++++++++++++++++++++++++++++++-- man/bioclim_vars-methods.Rd | 8 ++++++-- 4 files changed, 81 insertions(+), 4 deletions(-) diff --git a/R/bioclim_vars.R b/R/bioclim_vars.R index a359db9d..03b43c55 100644 --- a/R/bioclim_vars.R +++ b/R/bioclim_vars.R @@ -7,6 +7,8 @@ #' avaliable. Most variables, with the exception of BIO02 and BIO03, can #' be rephrased meaningfully in terms of mean temperature. #' This function is a modified version of \code{predicts::bcvars}. +#' +#' #' The variables are: #' BIO01 = Annual Mean Temperature #' BIO04 = Temperature Seasonality (standard deviation *100) @@ -88,6 +90,40 @@ methods::setMethod("bioclim_vars", signature(prec="SpatRaster", tavg="SpatRaster ) +#' @param filename filename where the raster can be stored. +#' @rdname bioclim_vars-methods +#' @export +methods::setMethod("bioclim_vars", signature(prec="SpatRasterDataset", tavg="SpatRasterDataset"), + function(prec, tavg, filename="", ...) { + if (!all(is_region_series(prec),is_region_series(tavg))){ + "prec and tavg should be generated with region_series" + } + if (!all((nlyr(prec)[1]==nlyr(tavg)[1]), + (time_bp(prec[[1]])==time_bp(tavg[[1]])))){ + stop("prec and tavg should have the same time steps") + } + time_slices <- time_bp(prec[[1]]) + # loop over the time slices + for (i in 1:length(time_slices)){ + #browser() + prec_slice <- slice_region_series(prec, time_bp = time_slices[i]) + tavg_slice <- slice_region_series(tavg, time_bp = time_slices[i]) + biovars_slice<-bioclim_vars(prec_slice, tavg_slice) + # TODO add years as time step when terra is fixed + time(biovars_slice)<-rep(time_slices[i],terra::nlyr(biovars_slice)) + if (i==1){ + biovars_list<-split(biovars_slice, f=1:17) + } else { + for (x in 1:(terra::nlyr(biovars_slice))){ + biovars_list[[x]] <- c(biovars_list[[x]], biovars_slice[[x]]) + } + } + } + # return the variables as a SpatRasterDataset + return(terra::sds(biovars_list)) +}) + + #' @rdname bioclim_vars-methods #' @export methods::setMethod("bioclim_vars", signature(prec="matrix", tavg="matrix"), diff --git a/R/delta_compute.R b/R/delta_compute.R index a21ee75c..9d317fec 100644 --- a/R/delta_compute.R +++ b/R/delta_compute.R @@ -26,6 +26,9 @@ delta_compute <- function(x, ref_time, obs) { stop("ref_time should be a time in x") } x_modern<-x[[ref_index]] + if (terra::nlyr(obs)>1){ + stop("obs should only contain one layer of observations") + } if (terra::ext(obs)!=terra::ext(x_modern)){ stop("x_modern and high_res_obs don't have the same extent") } diff --git a/inst/temp/monthly_vars.R b/inst/temp/monthly_vars.R index 3c3cd5d6..3b6fff46 100644 --- a/inst/temp/monthly_vars.R +++ b/inst/temp/monthly_vars.R @@ -30,11 +30,45 @@ sea_ext<- terra::ext(100, 110, 0, 10) # crop the observations for this extent high_res_tavg <- terra::crop(wc_tavg_5, sea_ext) high_res_prec <- terra::crop(wc_prec_5, sea_ext) - +# check that we can compute bioclim variables for the present bioclim_pres <- bioclim_vars(tavg = high_res_tavg, prec = high_res_prec) - +# Get timeseries (only for a limited number of time steps, equivalent to the Example dataset) tavg_series <- region_series(bio_variables = c(paste0("temperature_0",1:9),paste0("temperature_",10:12)), time_bp = get_time_steps(dataset = "Example"), dataset = "Beyer2020", ext = sea_ext) +prec_series <- region_series(bio_variables = c(paste0("precipitation_0",1:9),paste0("precipitation_",10:12)), + time_bp = get_time_steps(dataset = "Example"), + dataset = "Beyer2020", + ext = sea_ext) +# now get the relief data and create landmasks +relief_rast <- pastclim:::download_relief(high_res_tavg) +high_res_mask <- pastclim:::make_land_mask(relief_rast = relief_rast, + time_bp = time_bp(model_rast)) +# now we need to downscale the two series of monthly variables +# we will have to downscale one month at a time, put all the months into +# a list, and then combine them into a SpatRasterDataset +# start with temperature +tavg_downscaled_list<-list() +for (i in 1:12){ + delta_rast<-pastclim:::delta_compute(x=tavg_series[[i]], ref_time = 0, + obs = high_res_tavg[[i]]) + tavg_downscaled_list[[i]] <- pastclim:::delta_downscale (x = tavg_series[[i]], + delta_rast = delta_rast, + x_landmask_high = high_res_mask) +} +tavg_downscaled <- terra::sds(tavg_downscaled_list) + +prec_downscaled_list<-list() +for (i in 1:12){ + delta_rast<-pastclim:::delta_compute(x=prec_series[[i]], ref_time = 0, + obs = high_res_prec[[i]]) + prec_downscaled_list[[i]] <- pastclim:::delta_downscale (x = prec_series[[i]], + delta_rast = delta_rast, + x_landmask_high = high_res_mask) +} +prec_downscaled <- terra::sds(prec_downscaled_list) + +# now create the biovariables +bioclim_downscaled<-bioclim_vars(tavg =tavg_downscaled, prec = prec_downscaled) diff --git a/man/bioclim_vars-methods.Rd b/man/bioclim_vars-methods.Rd index b5235199..f4746d82 100644 --- a/man/bioclim_vars-methods.Rd +++ b/man/bioclim_vars-methods.Rd @@ -5,6 +5,7 @@ \alias{bioclim_vars} \alias{bioclim_vars,numeric,numeric-method} \alias{bioclim_vars,SpatRaster,SpatRaster-method} +\alias{bioclim_vars,SpatRasterDataset,SpatRasterDataset-method} \alias{bioclim_vars,matrix,matrix-method} \title{Compute the BIOCLIM variables} \usage{ @@ -14,6 +15,8 @@ bioclim_vars(prec, tavg, ...) \S4method{bioclim_vars}{SpatRaster,SpatRaster}(prec, tavg, filename = "", ...) +\S4method{bioclim_vars}{SpatRasterDataset,SpatRasterDataset}(prec, tavg, filename = "", ...) + \S4method{bioclim_vars}{matrix,matrix}(prec, tavg) } \arguments{ @@ -39,6 +42,8 @@ but for many paleoclimatic reconstructions one average temperature is avaliable. Most variables, with the exception of BIO02 and BIO03, can be rephrased meaningfully in terms of mean temperature. This function is a modified version of \code{predicts::bcvars}. +} +\details{ The variables are: BIO01 = Annual Mean Temperature BIO04 = Temperature Seasonality (standard deviation *100) @@ -57,8 +62,7 @@ BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter -} -\details{ + These summary Bioclimatic variables are after: Nix, 1986. A biogeographic analysis of Australian elapid snakes. In: R. Longmore (ed.). Atlas of elapid snakes of Australia. Australian Flora and Fauna Series 7. From d2a12664503afa179f69cabc8c9b94544781d4b8 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Fri, 9 Dec 2022 21:42:33 +0000 Subject: [PATCH 008/119] improve docs for methods [skip ci] --- R/bioclim_vars.R | 13 ++++--------- man/bioclim_vars-methods.Rd | 9 +++------ 2 files changed, 7 insertions(+), 15 deletions(-) diff --git a/R/bioclim_vars.R b/R/bioclim_vars.R index 03b43c55..72387044 100644 --- a/R/bioclim_vars.R +++ b/R/bioclim_vars.R @@ -3,12 +3,11 @@ #' To compute BIOCLIM variables from #' monthly average temperature and precipitation data. For modern data, #' this variables are generally computed using min and maximum temperature, -#' but for many paleoclimatic reconstructions one average temperature is +#' but for many paleoclimatic reconstructions only average temperature is #' avaliable. Most variables, with the exception of BIO02 and BIO03, can #' be rephrased meaningfully in terms of mean temperature. #' This function is a modified version of \code{predicts::bcvars}. #' -#' #' The variables are: #' BIO01 = Annual Mean Temperature #' BIO04 = Temperature Seasonality (standard deviation *100) @@ -36,13 +35,10 @@ # and Expanded following the ANUCLIM manual #' #' -#' @param tavg monthly average temperatures, as a matrix or -#' a \code{terra::SpatRaster} -#' @param prec monthly precipitation, as a matrix or -#' a \code{terra::SpatRaster} +#' @param tavg monthly average temperatures +#' @param prec monthly precipitation #' @param ... additional variables for specific methods -#' @returns an array or a \code{terra::SpatRaster} where each layer -#' is a bioclim variable +#' @returns the bioclim variables #' @docType methods #' @rdname bioclim_vars-methods #' @importFrom methods setGeneric @@ -105,7 +101,6 @@ methods::setMethod("bioclim_vars", signature(prec="SpatRasterDataset", tavg="Spa time_slices <- time_bp(prec[[1]]) # loop over the time slices for (i in 1:length(time_slices)){ - #browser() prec_slice <- slice_region_series(prec, time_bp = time_slices[i]) tavg_slice <- slice_region_series(tavg, time_bp = time_slices[i]) biovars_slice<-bioclim_vars(prec_slice, tavg_slice) diff --git a/man/bioclim_vars-methods.Rd b/man/bioclim_vars-methods.Rd index f4746d82..537a1d12 100644 --- a/man/bioclim_vars-methods.Rd +++ b/man/bioclim_vars-methods.Rd @@ -20,19 +20,16 @@ bioclim_vars(prec, tavg, ...) \S4method{bioclim_vars}{matrix,matrix}(prec, tavg) } \arguments{ -\item{prec}{monthly precipitation, as a matrix or -a \code{terra::SpatRaster}} +\item{prec}{monthly precipitation} -\item{tavg}{monthly average temperatures, as a matrix or -a \code{terra::SpatRaster}} +\item{tavg}{monthly average temperatures} \item{...}{additional variables for specific methods} \item{filename}{filename where the raster can be stored.} } \value{ -an array or a \code{terra::SpatRaster} where each layer -is a bioclim variable +the bioclim variables } \description{ To compute BIOCLIM variables from From e58c898f70b7f7a99182cb74f2a95fa9c3f11e61 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Fri, 9 Dec 2022 22:41:12 +0000 Subject: [PATCH 009/119] Fix possible problems with relief extent --- R/download_relief.R | 16 +++++++++++----- inst/temp/monthly_vars.R | 18 +++++++++++++++--- 2 files changed, 26 insertions(+), 8 deletions(-) diff --git a/R/download_relief.R b/R/download_relief.R index 591e9407..597c3f9c 100644 --- a/R/download_relief.R +++ b/R/download_relief.R @@ -24,8 +24,9 @@ download_relief <- function(rast_template, ...) { "install.packages('marmap')") return(invisible()) } + #browser() # check that this works, I think there might be issues with decimal places - if (terra::res(rast_template)[1]!=terra::res(rast_template)[2]){ + if (!all.equal(terra::res(rast_template)[1],terra::res(rast_template)[2])){ stop("rast_template should have identical vertical and horizontal resolution") } #browser() @@ -34,13 +35,18 @@ download_relief <- function(rast_template, ...) { lat1 = terra::ext(rast_template)[3], lat2 = terra::ext(rast_template)[4], # note that resolution in marmap is in minuts, and in degrees for terra - resolution = res(rast_template)[1]*60, ...) + resolution = terra::res(rast_template)[1]*60, ...) relief_rast <- terra::rast(marmap::as.raster(relief_bathy)) longnames(relief_rast)<-"relief from ETOPO2022" units(relief_rast)<-"m" - return(relief_rast) - #relief_terra <- bathy_to_spatraster(relief_bathy) # ideally check if there are any differences (due to rounding problems or corners), - # and then reshape the relief to match the template exactly + # and then resample the relief to match the template exactly + if (!all(terra::ext(relief_rast)==terra::ext(rast_template), + terra::ncol(relief_rast)==terra::ncol(rast_template), + terra::nrow(relief_rast)==terra::nrow(rast_template))){ + relief_rast <- terra::resample(relief_rast,rast_template) + } + return(relief_rast) + } diff --git a/inst/temp/monthly_vars.R b/inst/temp/monthly_vars.R index 3b6fff46..506d5e3c 100644 --- a/inst/temp/monthly_vars.R +++ b/inst/temp/monthly_vars.R @@ -2,7 +2,7 @@ # worldclim_tile("tavg",lon=0,lat=52,path=tempdir()) # to only get part of the world -path_wc <- "../../project_temp/climate_downscale/worldclim_monthly" +base_path_wc <- "../../project_temp/climate_downscale/worldclim_monthly" wc_res <- 5 # one of 10,5,2.5,0.5 minutes library(geodata) @@ -26,7 +26,7 @@ if (!file.exists(file.path(base_path_wc,"wc_prec_5_monthly.nc"))){ } # now set the extent for SEA -sea_ext<- terra::ext(100, 110, 0, 10) +sea_ext<- terra::ext(110, 120, -5, 5) # crop the observations for this extent high_res_tavg <- terra::crop(wc_tavg_5, sea_ext) high_res_prec <- terra::crop(wc_prec_5, sea_ext) @@ -45,7 +45,7 @@ prec_series <- region_series(bio_variables = c(paste0("precipitation_0",1:9),pas # now get the relief data and create landmasks relief_rast <- pastclim:::download_relief(high_res_tavg) high_res_mask <- pastclim:::make_land_mask(relief_rast = relief_rast, - time_bp = time_bp(model_rast)) + time_bp = get_time_steps(dataset = "Example")) # now we need to downscale the two series of monthly variables # we will have to downscale one month at a time, put all the months into # a list, and then combine them into a SpatRasterDataset @@ -72,3 +72,15 @@ prec_downscaled <- terra::sds(prec_downscaled_list) # now create the biovariables bioclim_downscaled<-bioclim_vars(tavg =tavg_downscaled, prec = prec_downscaled) + + +## write some files for a future vignette +# terra::writeCDF(tavg_series,file.path(path_wc,"tavg_series.nc"),compression=9) +# terra::writeCDF(prec_series,file.path(path_wc,"prec_series.nc"),compression=9) +# terra::writeCDF(relief_rast,file.path(path_wc,"relief.nc"),compression=9) +# terra::writeCDF(high_res_tavg,file.path(path_wc,"high_res_tavg.nc"),compression=9) +# terra::writeCDF(high_res_prec,file.path(path_wc,"high_res_prec.nc"),compression=9) + +# suggested range for a vignette: +#sea_ext<- terra::ext(110, 120, -5, 5) + From 7eb94f3fa7fe1df87396c23a8e7f4850cf980979 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Fri, 9 Dec 2022 23:33:36 +0000 Subject: [PATCH 010/119] new vignette for downscaling --- inst/temp/a4_delta_downscale_b.Rmd | 233 +++++++++++++++++++++++++++++ man/bioclim_vars-methods.Rd | 2 +- 2 files changed, 234 insertions(+), 1 deletion(-) create mode 100644 inst/temp/a4_delta_downscale_b.Rmd diff --git a/inst/temp/a4_delta_downscale_b.Rmd b/inst/temp/a4_delta_downscale_b.Rmd new file mode 100644 index 00000000..6a04aba8 --- /dev/null +++ b/inst/temp/a4_delta_downscale_b.Rmd @@ -0,0 +1,233 @@ +--- +title: "delta downscaling" +output: rmarkdown::pdf_document +#output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{delta downscaling} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +# THIS IS WORK IN PROGRESS, DO NOT USE FOR ANY ANALYSIS + +Note that we will use the draft functions for downscaling, which can be accessed +by usign the prefix `pastclim:::`. The `:::` access internal functions. You will +also get a message reminding you that these functions are not final and thus +should not be used for real analysis. + + +## Delta downscaling a dataset in `pastclim` + +The `terra` package can downscale reconstructions using a bilinear method. Other `R` +packages also offer various approaches to downscale rasters. + +For palaeoclimate reconstructions, the delta method has been shown to be very effective +(Beyer et al, REF), but sea level changes require some care on how to apply such a method. +`pastclim` includes functions to use the delta method for downscaling. We will focus on South East +Asia, as this area was greatly affected by sea level changes over the last glaciation. Specifically, we will look at the area around Borneo (for real applications, we would +reccomend using a bigger extent in areas of large changes in land extent, as +interpolating over a small extent can lead to greater artefacts; for this example, +we keep the extent small to reduce computational time). + +## An example for one variable + +When downscaling, it is generally a better idea to downscale the monthly variables, +and then recompute the BIOCLIM variables. We will usethe monthly variables +from the Beyer2020 dataset, and focus on the time steps that are also available +in the Example dataset (i.e. a subset of the full Beyer2020 dataset, to reduce +computational time): +```{r} +library(terra) +library(pastclim) +sea_ext<- terra::ext(110, 120, -5, 5) +tavg_vars <- c(paste0("temperature_0",1:9),paste0("temperature_",10:12)) +time_steps <- get_time_steps(dataset = "Example") +``` +The series for this region could be generated with the following command: +```{r} +tavg_series <- region_series(bio_variables =tavg_vars, + time_bp = time_steps, + dataset = "Beyer2020", + ext = sea_ext) +``` +In this vignette, we will fetch pre-generated series stored in the package +```{r} +#fetch the data + +``` + +Downscaling is performed one variable at a time. We will start with temperature in January. +So, we first need to extract the `SpatRaster` of model low resolution data from the `SpatRasterDataset`: +```{r} +tavg_model_lres_rast <- tavg_series$temperature_01 +tavg_model_lres_rast +``` +And we can now plot it: +```{r, fig.width=6, fig.height=5} +plot(tavg_model_lres_rast, main = time_bp(tavg_model_lres_rast)) +``` + +We can see how that the reconstructions are rather coarse (the Beyer2020 dataset +uses 0.5x0.5 degree cells). We now need a set of +high resolutions observations for the variable of interest that we will use to +generate the delta raster used to downscale reconstructions. We will use data from +Worldclim2 at 5 minute resolution (but other datasets such as CHELSA would be +equally suitable). +```{r, fig.width=4, fig.height=4} +high_res_file <- "../../../../project_temp/climate_downscale/worldclim_monthly/wc_tavg_5_monthly.nc" +tavg_obs_hres_series <- terra::rast(high_res_file) +tavg_obs_hres_series <- terra::crop(tavg_obs_hres_series, sea_ext) +# extract the january raster +tavg_obs_hres_rast <- tavg_obs_hres_series[[1]] +plot(tavg_obs_hres_rast) +``` + +We need to make sure that the extent of the modern observations is the same as the +extent of the model reconstructions: + +```{r} +ext(tavg_obs_hres_rast)==ext(tavg_model_lres_rast) +``` + +If that was not the case, we would use `terra::crop` to match the extents. + +We also need a high resolution global relief map (i.e. integrating both +topographic and bathymetric values) to reconstruct past +coastlines following sea level change. The relief raster will need to have +the same extent and resolution as the high resolution observations. We +can download one (based on ETOPO2022) with: + +```{r} +relief_rast <- pastclim:::download_relief(tavg_obs_hres_rast) + +``` + +```{r eval=FALSE, results="hide",echo=FALSE} +relief_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif", + package = "pastclim") +relief_rast <- terra::rast(relief_file) +``` + +We can quickly confirm that the resulting relief raster has the same extent +as the model reconstructions, +as well as the same resolution of the high resolution climate observations: + +```{r} +ext(relief_rast) == ext(tavg_model_lres_rast) + +ext(relief_rast) == ext(tavg_obs_hres_rast) +ncol(relief_rast) == ncol(tavg_obs_hres_rast) +nrow(relief_rast) == nrow(tavg_obs_hres_rast) +``` + +We can now generate a high resolution land mask for the periods of interest. By +default, we use the sea level reconstructions from Spratt et al 2016, but a different +reference can be used by setting sea levels for each time step (see the man page +for `make_land_mask` for details): + +```{r, fig.width=6, fig.height=5} +high_res_mask <- pastclim:::make_land_mask(relief_rast = relief_rast, + time_bp = time_bp(tavg_model_lres_rast)) +plot(high_res_mask, main=time_bp(high_res_mask)) +``` + +We can now compute a delta raster and use it to downscale the model +reconstructions: + +```{r} +delta_rast<-pastclim:::delta_compute(x=tavg_model_lres_rast, ref_time = 0, + obs = tavg_obs_hres_rast) +model_downscaled <- pastclim:::delta_downscale (x = tavg_model_lres_rast, + delta_rast = delta_rast, + x_landmask_high = high_res_mask) +model_downscaled +``` + +Let's inspect the resulting data: +```{r, fig.width=6, fig.height=5} +plot(model_downscaled, main = time_bp(model_downscaled)) +``` + +And, as a reminder, the original reconstructions (note that the colour scales are not +the same! `terra` chooses a scale for each time step based on the time specific range): +```{r, fig.width=6, fig.height=5} +plot(tavg_model_lres_rast, main = time_bp(tavg_model_lres_rast)) +``` + +## Computing the bioclim variables +To compute the bioclim variables, we need to repeat the procedure above for +temperature and precipitation for all months. Let us start with temperature. We +loop over each month, create a `SpatRaster` of downscaled temperature, add it to +a list, and finally conver the list into a `SpatRasterDataset` + +```{r} +tavg_downscaled_list<-list() +for (i in 1:12){ + delta_rast<-pastclim:::delta_compute(x=tavg_series[[i]], ref_time = 0, + obs = tavg_obs_hres_series[[i]]) + tavg_downscaled_list[[i]] <- pastclim:::delta_downscale (x = tavg_series[[i]], + delta_rast = delta_rast, + x_landmask_high = high_res_mask) +} +tavg_downscaled <- terra::sds(tavg_downscaled_list) +``` + +Quickly inspect the resulting dataset: +```{r} +tavg_downscaled +``` +As expected, we have 12 months (subdatasets), each with 5 time steps. + +We now need to create a series for precipitation: +```{r} +prec_vars <- c(paste0("precipitation_0",1:9),paste0("precipitation_",10:12)) +prec_series <- region_series(bio_variables = prec_vars, + time_bp = time_steps, + dataset = "Beyer2020", + ext = sea_ext) +``` + +Get some high resolution observations: +```{r, fig.width=4, fig.height=4} +high_res_file <- "../../../../project_temp/climate_downscale/worldclim_monthly/wc_prec_5_monthly.nc" +prec_obs_hres_series <- terra::rast(high_res_file) +prec_obs_hres_series <- terra::crop(prec_obs_hres_series, sea_ext) +# extract the january raster +prec_obs_hres_rast <- prec_obs_hres_series[[1]] +plot(prec_obs_hres_rast) +``` + +And finally downscale precipitation: +```{r} +prec_downscaled_list<-list() +for (i in 1:12){ + delta_rast<-pastclim:::delta_compute(x=prec_series[[i]], ref_time = 0, + obs = prec_obs_hres_series[[i]]) + prec_downscaled_list[[i]] <- pastclim:::delta_downscale (x = prec_series[[i]], + delta_rast = delta_rast, + x_landmask_high = high_res_mask) +} +prec_downscaled <- terra::sds(prec_downscaled_list) +``` + +We are now ready to compute the bioclim variables: +```{r} +bioclim_downscaled<-bioclim_vars(tavg =tavg_downscaled, prec = prec_downscaled) +``` + +Let's inspect the object: +```{r} +bioclim_downscaled +``` + +And plot the first variable (bio01): +```{r} +plot(bioclim_downscaled[[1]],main=time(bioclim_downscaled[[1]])) +``` \ No newline at end of file diff --git a/man/bioclim_vars-methods.Rd b/man/bioclim_vars-methods.Rd index 537a1d12..79abbcca 100644 --- a/man/bioclim_vars-methods.Rd +++ b/man/bioclim_vars-methods.Rd @@ -35,7 +35,7 @@ the bioclim variables To compute BIOCLIM variables from monthly average temperature and precipitation data. For modern data, this variables are generally computed using min and maximum temperature, -but for many paleoclimatic reconstructions one average temperature is +but for many paleoclimatic reconstructions only average temperature is avaliable. Most variables, with the exception of BIO02 and BIO03, can be rephrased meaningfully in terms of mean temperature. This function is a modified version of \code{predicts::bcvars}. From 40189e8b914bc1041e47e9e13440718a74ed0da7 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Sat, 10 Dec 2022 09:28:48 +0000 Subject: [PATCH 011/119] worldclim helper functions --- R/download_worldclim.R | 50 ++++++++++++++++++ R/load_worldclim.R | 37 +++++++++++++ inst/temp/a4_delta_downscale_b.Rmd | 20 ++++--- inst/temp/a4_delta_downscale_b.pdf | Bin 0 -> 308837 bytes ...elta_downscale_create_files_for_vignette.R | 13 +++++ man/download_worldclim.Rd | 37 +++++++++++++ man/load_worldclim.Rd | 34 ++++++++++++ 7 files changed, 185 insertions(+), 6 deletions(-) create mode 100644 R/download_worldclim.R create mode 100644 R/load_worldclim.R create mode 100644 inst/temp/a4_delta_downscale_b.pdf create mode 100644 inst/temp/delta_downscale_create_files_for_vignette.R create mode 100644 man/download_worldclim.Rd create mode 100644 man/load_worldclim.Rd diff --git a/R/download_worldclim.R b/R/download_worldclim.R new file mode 100644 index 00000000..eeb3b868 --- /dev/null +++ b/R/download_worldclim.R @@ -0,0 +1,50 @@ +#' Download a WorldClim modern observations. +#' +#' This function downloads annual and monthly variables from the Worldclim 2.1 dataset. +#' These variables are saved in a format that can be read by +#' \code{load_worldclim}, and easily used for delta downscaling paleoclimate +#' observations +#' +#' This function uses \code{geodata::worldclim_global} to download the data, and +#' then converts them into a \code{terra::SpatRaster} in a format compatible +#' with `pastclim` +#' +#' @param var character Valid variables names are "tmin", "tmax", "tavg", +#' "prec", "wind", "vapr", and "bio". +#' @param res numeric Valid resolutions are 10, 5, 2.5, +#' and 0.5 (minutes of a degree) +#' @param path character. Path where to download the data to, If left NULL, the data +#' will be downloaded from the directory returned by \code{get_data_path()} +#' @param version character or numeric. WorldClim version number. Only "2.1" supported at the moment +#' @param ... additional arguments passed to download.file +#' @returns a \code{terra::SpatRaster} with the requested worldclim variable +#' +#' @keywords internal + +download_worldclim <- function(var, res, path=NULL, version="2.1", ...) { + + if(!requireNamespace("geodata", quietly=TRUE)){ + message("This function requires the 'geodata' package; install it with:\n", + "install.packages('marmap')") + return(invisible()) + } + if (is.null(path)){ + path <- get_data_path() + } else { + if (!dir.exists(path)){ + stop("the provided path does not exist") + } + } + #browser() + wc_file_name <- paste0("wc",version,"_",res,"m_",var,".nc") + wc_full_path<-file.path(path,wc_file_name) + if (file.exists(wc_full_path)){ + message(wc_full_path," already exists") + return(FALSE) + } + + wc_rast<-geodata::worldclim_global(var=var, res=res, path=tempdir(), version="2.1") + terra::writeCDF(wc_rast,file.path(path, wc_file_name), + compression=9) + return(TRUE) +} diff --git a/R/load_worldclim.R b/R/load_worldclim.R new file mode 100644 index 00000000..aa9bcc3c --- /dev/null +++ b/R/load_worldclim.R @@ -0,0 +1,37 @@ +#' Load a variable from WorldClim modern observations. +#' +#' This function loads previously downloaded annual and monthly variables +#' from the Worldclim 2.1 dataset. To save the variables in a compatible +#' format, use \code{download_worldclim} +#' +#' The function assumes that the file name is in the format wc*version*_*res*m_*var*.nc. +#' For example, average temperature downloaded at 10 minutes resolution will be +#' *wc21_10m_tavg.nc*. +#' +#' @param var character Valid variables names are "tmin", "tmax", "tavg", +#' "prec", "wind", "vapr", and "bio". +#' @param res numeric Valid resolutions are 10, 5, 2.5, +#' and 0.5 (minutes of a degree) +#' @param path character. Path where the dataset is stored. If left NULL, the data +#' will be downloaded from the directory returned by \code{get_data_path()} +#' @param version character or numeric. WorldClim version number. Only "2.1" supported at the moment +#' @returns a \code{terra::SpatRaster} with the requested worldclim variable +#' +#' @keywords internal + +load_worldclim <- function(var, res, path=NULL, version="2.1", ...) { + + if (is.null(path)){ + path <- get_data_path() + } else { + if (!dir.exists(path)){ + stop("the provided path does not exist") + } + } + wc_file_name <- paste0("wc",version,"_",res,"m_",var,".nc") + wc_full_path<-file.path(path,wc_file_name) + if (!file.exists(wc_full_path)){ + stop(wc_full_path," does not exist; use download_wordclim() to download it") + } + return(terra::rast(wc_full_path)) +} diff --git a/inst/temp/a4_delta_downscale_b.Rmd b/inst/temp/a4_delta_downscale_b.Rmd index 6a04aba8..718cb215 100644 --- a/inst/temp/a4_delta_downscale_b.Rmd +++ b/inst/temp/a4_delta_downscale_b.Rmd @@ -78,11 +78,19 @@ We can see how that the reconstructions are rather coarse (the Beyer2020 dataset uses 0.5x0.5 degree cells). We now need a set of high resolutions observations for the variable of interest that we will use to generate the delta raster used to downscale reconstructions. We will use data from -Worldclim2 at 5 minute resolution (but other datasets such as CHELSA would be -equally suitable). +WorldClim2 at 10 minute resolution (but other datasets such as CHELSA would be +equally suitable). For WorldClim, there are some convenient functions to download +and then load variables: +```{r} +pastclim:::download_worldclim("tavg",10) +``` +Once the variable is downloaded, we can load it at any time with: +```{r} +tavg_obs_hres_series <- pastclim:::load_worldclim("tavg",10) +``` + +We want to crop these reconstructions to the extent of interest ```{r, fig.width=4, fig.height=4} -high_res_file <- "../../../../project_temp/climate_downscale/worldclim_monthly/wc_tavg_5_monthly.nc" -tavg_obs_hres_series <- terra::rast(high_res_file) tavg_obs_hres_series <- terra::crop(tavg_obs_hres_series, sea_ext) # extract the january raster tavg_obs_hres_rast <- tavg_obs_hres_series[[1]] @@ -196,8 +204,8 @@ prec_series <- region_series(bio_variables = prec_vars, Get some high resolution observations: ```{r, fig.width=4, fig.height=4} -high_res_file <- "../../../../project_temp/climate_downscale/worldclim_monthly/wc_prec_5_monthly.nc" -prec_obs_hres_series <- terra::rast(high_res_file) 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b/inst/temp/delta_downscale_create_files_for_vignette.R new file mode 100644 index 00000000..4d15ef29 --- /dev/null +++ b/inst/temp/delta_downscale_create_files_for_vignette.R @@ -0,0 +1,13 @@ +# script to generate files for delta downscaling vignette +library(terra) +library(pastclim) +sea_ext<- terra::ext(110, 120, -5, 5) +tavg_vars <- c(paste0("temperature_0",1:9),paste0("temperature_",10:12)) +time_steps <- get_time_steps(dataset = "Example") +tavg_series <- region_series(bio_variables =tavg_vars, + time_bp = time_steps, + dataset = "Beyer2020", + ext = sea_ext) + +# create download_worldclim(vars,res,version,path) +# and load_worldclim(vars, res, version, path) \ No newline at end of file diff --git a/man/download_worldclim.Rd b/man/download_worldclim.Rd new file mode 100644 index 00000000..cbf42767 --- /dev/null +++ b/man/download_worldclim.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/download_worldclim.R +\name{download_worldclim} +\alias{download_worldclim} +\title{Download a WorldClim modern observations.} +\usage{ +download_worldclim(var, res, path = NULL, version = "2.1", ...) +} +\arguments{ +\item{var}{character Valid variables names are "tmin", "tmax", "tavg", +"prec", "wind", "vapr", and "bio".} + +\item{res}{numeric Valid resolutions are 10, 5, 2.5, +and 0.5 (minutes of a degree)} + +\item{path}{character. Path where to download the data to, If left NULL, the data +will be downloaded from the directory returned by \code{get_data_path()}} + +\item{version}{character or numeric. WorldClim version number. Only "2.1" supported at the moment} + +\item{...}{additional arguments passed to download.file} +} +\value{ +a \code{terra::SpatRaster} with the requested worldclim variable +} +\description{ +This function downloads annual and monthly variables from the Worldclim 2.1 dataset. +These variables are saved in a format that can be read by +\code{load_worldclim}, and easily used for delta downscaling paleoclimate +observations +} +\details{ +This function uses \code{geodata::worldclim_global} to download the data, and +then converts them into a \code{terra::SpatRaster} in a format compatible +with `pastclim` +} +\keyword{internal} diff --git a/man/load_worldclim.Rd b/man/load_worldclim.Rd new file mode 100644 index 00000000..46cccd46 --- /dev/null +++ b/man/load_worldclim.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/load_worldclim.R +\name{load_worldclim} +\alias{load_worldclim} +\title{Load a variable from WorldClim modern observations.} +\usage{ +load_worldclim(var, res, path = NULL, version = "2.1", ...) +} +\arguments{ +\item{var}{character Valid variables names are "tmin", "tmax", "tavg", +"prec", "wind", "vapr", and "bio".} + +\item{res}{numeric Valid resolutions are 10, 5, 2.5, +and 0.5 (minutes of a degree)} + +\item{path}{character. Path where the dataset is stored. If left NULL, the data +will be downloaded from the directory returned by \code{get_data_path()}} + +\item{version}{character or numeric. WorldClim version number. Only "2.1" supported at the moment} +} +\value{ +a \code{terra::SpatRaster} with the requested worldclim variable +} +\description{ +This function loads previously downloaded annual and monthly variables +from the Worldclim 2.1 dataset. To save the variables in a compatible +format, use \code{download_worldclim} +} +\details{ +The function assumes that the file name is in the format wc*version*_*res*m_*var*.nc. +For example, average temperature downloaded at 10 minutes resolution will be +*wc21_10m_tavg.nc*. +} +\keyword{internal} From 898d9b9e9c5eb8651d63110ba71993e40d63ecf6 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Mon, 12 Dec 2022 10:20:20 +0000 Subject: [PATCH 012/119] initial files for ds there are some issues with timing in terra [skip ci] --- R/make_land_mask.R | 1 + inst/extdata/delta/prec_obs_hres_all.nc | Bin 0 -> 52180 bytes inst/extdata/delta/prec_series.nc | Bin 0 -> 118409 bytes inst/extdata/delta/relief.nc | Bin 0 -> 26119 bytes inst/extdata/delta/tavg_obs_hres_all.nc | Bin 0 -> 97068 bytes inst/extdata/delta/tavg_series.nc | Bin 0 -> 117928 bytes .../ETOPO1_Ice_c_gmt4_5min_sea.tif | Bin inst/temp/a4_delta_downscale_b.Rmd | 62 +++++++++++------- inst/temp/a4_delta_downscale_b.pdf | Bin 308837 -> 0 bytes .../figure-latex/unnamed-chunk-10-1.pdf | Bin 0 -> 11068 bytes .../figure-latex/unnamed-chunk-15-1.pdf | Bin 0 -> 7806 bytes .../figure-latex/unnamed-chunk-6-1.pdf | Bin 0 -> 18638 bytes ...elta_downscale_create_files_for_vignette.R | 50 +++++++++++++- inst/{extdata => temp}/wc2.1_5m_bio_1_sea.tif | Bin 14 files changed, 90 insertions(+), 23 deletions(-) create mode 100644 inst/extdata/delta/prec_obs_hres_all.nc create mode 100644 inst/extdata/delta/prec_series.nc create mode 100644 inst/extdata/delta/relief.nc create mode 100644 inst/extdata/delta/tavg_obs_hres_all.nc create mode 100644 inst/extdata/delta/tavg_series.nc rename inst/{extdata => temp}/ETOPO1_Ice_c_gmt4_5min_sea.tif (100%) delete mode 100644 inst/temp/a4_delta_downscale_b.pdf create mode 100644 inst/temp/a4_delta_downscale_b_files/figure-latex/unnamed-chunk-10-1.pdf create mode 100644 inst/temp/a4_delta_downscale_b_files/figure-latex/unnamed-chunk-15-1.pdf create mode 100644 inst/temp/a4_delta_downscale_b_files/figure-latex/unnamed-chunk-6-1.pdf rename inst/{extdata => temp}/wc2.1_5m_bio_1_sea.tif (100%) diff --git a/R/make_land_mask.R b/R/make_land_mask.R index bcda7254..bbb7ab1d 100644 --- a/R/make_land_mask.R +++ b/R/make_land_mask.R @@ -58,6 +58,7 @@ make_land_mask <- function(relief_rast, time_bp, sea_level = NULL) { } # work around problem of old terra failing to set time units for negative values # (fixed in dev after 1.6.47) + browser() if (utils::packageVersion("terra")<"1.6.48"){ terra::time(land_mask, tstep="") <- (time_bp+1950) } else { diff --git a/inst/extdata/delta/prec_obs_hres_all.nc b/inst/extdata/delta/prec_obs_hres_all.nc new file mode 100644 index 0000000000000000000000000000000000000000..b6dc3da74eba6439a4536396ceb6f4c919776f66 GIT binary patch literal 52180 zcmeFX2UJttw)l&npman)I)Z>olioo@L^^^9N(ntu1BBiJA0j9Mf&$Vl^o}S<4ZR2? z(tGbU^w2_H0^vL7o_o*u-#gxWcZ~OjYm8)N|5lr8uDR!)Ywf&#^xz>GF#|FF<;!^I 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z@3tL|%bYCE(ioW53nQkW9mdnuGw|zb%rY+Im|?KNzD+D|-4gQ3^`3iOD#l*=dkQ6B z`7eas|m!)?y zZIYF zZV3e$h*F#No&=O=YY{1HgAOj?;t3=)sLBA^QF04aeJ!*?k?4O=U}q zg*ElBA#xVB_)Fnh#UfZra>u&3w<+ipM_1a4nz)ZC=X9Qxf<#VZ^=D#sJv)l;wA)QQqYB@>(&X{Y2(*7m4%#0Kp4d AasU7T literal 0 HcmV?d00001 diff --git a/inst/extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif b/inst/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif similarity index 100% rename from inst/extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif rename to inst/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif diff --git a/inst/temp/a4_delta_downscale_b.Rmd b/inst/temp/a4_delta_downscale_b.Rmd index 718cb215..5961ee2b 100644 --- a/inst/temp/a4_delta_downscale_b.Rmd +++ b/inst/temp/a4_delta_downscale_b.Rmd @@ -50,16 +50,19 @@ sea_ext<- terra::ext(110, 120, -5, 5) tavg_vars <- c(paste0("temperature_0",1:9),paste0("temperature_",10:12)) time_steps <- get_time_steps(dataset = "Example") ``` -The series for this region could be generated with the following command: -```{r} +The series for this region can be generated with the following command (note +that you will need the appropriate variables for the Beyer2020 dataset downloaded +on your computer, use `download_dataset` if needed): +```{r eval=FALSE} tavg_series <- region_series(bio_variables =tavg_vars, time_bp = time_steps, dataset = "Beyer2020", ext = sea_ext) ``` -In this vignette, we will fetch pre-generated series stored in the package -```{r} -#fetch the data + +```{r echo=FALSE, results="hide"} +tavg_series <- terra::sds(system.file("extdata/delta/tavg_series.nc", + package="pastclim")) ``` @@ -81,19 +84,25 @@ generate the delta raster used to downscale reconstructions. We will use data fr WorldClim2 at 10 minute resolution (but other datasets such as CHELSA would be equally suitable). For WorldClim, there are some convenient functions to download and then load variables: -```{r} +```{r eval=FALSE} pastclim:::download_worldclim("tavg",10) ``` Once the variable is downloaded, we can load it at any time with: -```{r} -tavg_obs_hres_series <- pastclim:::load_worldclim("tavg",10) +```{r eval=FALSE} +tavg_obs_hres_all<- pastclim:::load_worldclim("tavg",10) ``` +```{r echo=FALSE, results="hide"} +tavg_obs_hres_all<- terra::rast(system.file("extdata/delta/tavg_obs_hres_all.nc", + package="pastclim")) +``` + + We want to crop these reconstructions to the extent of interest ```{r, fig.width=4, fig.height=4} -tavg_obs_hres_series <- terra::crop(tavg_obs_hres_series, sea_ext) +tavg_obs_hres_all <- terra::crop(tavg_obs_hres_all, sea_ext) # extract the january raster -tavg_obs_hres_rast <- tavg_obs_hres_series[[1]] +tavg_obs_hres_rast <- tavg_obs_hres_all[[1]] plot(tavg_obs_hres_rast) ``` @@ -112,15 +121,14 @@ coastlines following sea level change. The relief raster will need to have the same extent and resolution as the high resolution observations. We can download one (based on ETOPO2022) with: -```{r} +```{r eval=FALSE} relief_rast <- pastclim:::download_relief(tavg_obs_hres_rast) ``` -```{r eval=FALSE, results="hide",echo=FALSE} -relief_file <- system.file("extdata/ETOPO1_Ice_c_gmt4_5min_sea.tif", - package = "pastclim") -relief_rast <- terra::rast(relief_file) +```{r echo=FALSE, results="hide"} +relief_rast <- terra::rast(system.file("extdata/delta/relief.nc", + package="pastclim")) ``` We can quickly confirm that the resulting relief raster has the same extent @@ -194,7 +202,7 @@ tavg_downscaled As expected, we have 12 months (subdatasets), each with 5 time steps. We now need to create a series for precipitation: -```{r} +```{r eval=FALSE} prec_vars <- c(paste0("precipitation_0",1:9),paste0("precipitation_",10:12)) prec_series <- region_series(bio_variables = prec_vars, time_bp = time_steps, @@ -202,14 +210,24 @@ prec_series <- region_series(bio_variables = prec_vars, ext = sea_ext) ``` +```{r echo=FALSE, results="hide"} +prec_vars <- c(paste0("precipitation_0",1:9),paste0("precipitation_",10:12)) +prec_series <- terra::sds(system.file("extdata/delta/prec_series.nc", + package="pastclim")) + +``` + Get some high resolution observations: -```{r, fig.width=4, fig.height=4} +```{r, eval=FALSE} pastclim:::download_worldclim("prec",10) -prec_obs_hres_series <- pastclim:::load_worldclim("prec",10) -prec_obs_hres_series <- terra::crop(prec_obs_hres_series, sea_ext) -# extract the january raster -prec_obs_hres_rast <- prec_obs_hres_series[[1]] -plot(prec_obs_hres_rast) +prec_obs_hres_all <- pastclim:::load_worldclim("prec",10) +prec_obs_hres_all <- terra::crop(prec_obs_hres_all, sea_ext) +``` + +```{r echo=FALSE, results="hide"} +prec_obs_hres_all <-terra::rast(system.file("extdata/delta/prec_obs_hres_all.nc", + package="pastclim")) + ``` And finally downscale precipitation: diff --git a/inst/temp/a4_delta_downscale_b.pdf b/inst/temp/a4_delta_downscale_b.pdf deleted file mode 100644 index 9fc325e7ad25df69a2e7d9535a39f20237ffaf43..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 308837 zcmd431z1(x);|g&Al)U>AxL+JG}0m6u;~uzkd_7kX{14<8>FP!bT`r+f^^-5uf8AW zocG-G{_l70eJ;m(J|4p!BY&4bxgst5i=6o7?{KJ^1?Gn zgRD&)O^KPA**S=R{=qYdnOQo5?1>q~EcG2hA|OK>BM>}4KfHsZJxJdQ-esm=-O6FM z7VUOlH$$>no&`G+LNZFaBvsPw&GxBzO*K!VbjiwFYKdikQYUgy*Im=qqwZ`Wt_TO2 ztByz5Qyu#i3~EEIMqF@|2NAvu*G-N5mrJaOl%(Htg0p%~3`n!O2Ss=lUYv-+WM8c6 zF3iQIrVV@*bUSu?5$CL1RYxCc&UJHkqU*&OUj8olno$v5v`>UovQt_zibTEhBz>?r zmnBXj(p&i8hBbDfV@=of&6|Nj@8*=iuMIBFuJ%q&eNqD2ha(pqY$&&`PG4#AXnmjC z3aq#AhL?qWxq2PF 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zHV%*x=yw=OebgTa_B)J~6PR560S2@)Cm{bP7zaBr5Bdql{U>HumE4i`H2>Q z@%#eg=H~cW7Zx6FU^e@cTo#sJb!KH@0VZ~T)Q=SmSpO3Y_(aby`~Wmyiu*@eHdgMR zeZ|Vg4*rE7I~X Date: Wed, 14 Dec 2022 12:10:06 +0000 Subject: [PATCH 013/119] move ds draft vignette --- DESCRIPTION | 1 + R/download_worldclim.R | 2 +- .../temp/ETOPO1_Ice_c_gmt4_5min_sea.tif | Bin {inst => data-raw}/temp/_delta_downscale_3.R | 0 {inst => data-raw}/temp/a4_delta_downscale.Rmd | 0 .../temp/a4_delta_downscale_b.Rmd | 0 .../delta_downscale_create_files_for_vignette.R | 0 {inst => data-raw}/temp/monthly_vars.R | 0 {inst => data-raw}/temp/wc2.1_5m_bio_1_sea.tif | Bin .../figure-latex/unnamed-chunk-10-1.pdf | Bin 11068 -> 0 bytes .../figure-latex/unnamed-chunk-15-1.pdf | Bin 7806 -> 0 bytes .../figure-latex/unnamed-chunk-6-1.pdf | Bin 18638 -> 0 bytes 12 files changed, 2 insertions(+), 1 deletion(-) rename {inst => data-raw}/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif (100%) rename {inst => data-raw}/temp/_delta_downscale_3.R (100%) rename {inst => data-raw}/temp/a4_delta_downscale.Rmd (100%) rename {inst => data-raw}/temp/a4_delta_downscale_b.Rmd (100%) rename {inst => data-raw}/temp/delta_downscale_create_files_for_vignette.R (100%) rename {inst => data-raw}/temp/monthly_vars.R (100%) rename {inst => data-raw}/temp/wc2.1_5m_bio_1_sea.tif (100%) delete mode 100644 inst/temp/a4_delta_downscale_b_files/figure-latex/unnamed-chunk-10-1.pdf delete mode 100644 inst/temp/a4_delta_downscale_b_files/figure-latex/unnamed-chunk-15-1.pdf delete mode 100644 inst/temp/a4_delta_downscale_b_files/figure-latex/unnamed-chunk-6-1.pdf diff --git a/DESCRIPTION b/DESCRIPTION index 8e578d8d..3d598718 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -34,6 +34,7 @@ Imports: utils Suggests: rmarkdown, + geodata, knitr, marmap, ggplot2, diff --git a/R/download_worldclim.R b/R/download_worldclim.R index eeb3b868..1c83b3a1 100644 --- a/R/download_worldclim.R +++ b/R/download_worldclim.R @@ -25,7 +25,7 @@ download_worldclim <- function(var, res, path=NULL, version="2.1", ...) { if(!requireNamespace("geodata", quietly=TRUE)){ message("This function requires the 'geodata' package; install it with:\n", - "install.packages('marmap')") + "install.packages('geodata')") return(invisible()) } if (is.null(path)){ diff --git a/inst/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif b/data-raw/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif similarity index 100% rename from inst/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif rename to data-raw/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif diff --git a/inst/temp/_delta_downscale_3.R b/data-raw/temp/_delta_downscale_3.R similarity index 100% rename from inst/temp/_delta_downscale_3.R rename to data-raw/temp/_delta_downscale_3.R diff --git a/inst/temp/a4_delta_downscale.Rmd b/data-raw/temp/a4_delta_downscale.Rmd similarity index 100% rename from inst/temp/a4_delta_downscale.Rmd rename to data-raw/temp/a4_delta_downscale.Rmd diff --git a/inst/temp/a4_delta_downscale_b.Rmd b/data-raw/temp/a4_delta_downscale_b.Rmd similarity index 100% rename from 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zHV%*x=yw=OebgTa_B)J~6PR560S2@)Cm{bP7zaBr5Bdql{U>HumE4i`H2>Q z@%#eg=H~cW7Zx6FU^e@cTo#sJb!KH@0VZ~T)Q=SmSpO3Y_(aby`~Wmyiu*@eHdgMR zeZ|Vg4*rE7I~X Date: Wed, 14 Dec 2022 12:48:25 +0000 Subject: [PATCH 014/119] fix rounding bug for resolution --- R/delta_compute.R | 4 +- R/delta_downscale.R | 2 +- R/make_land_mask.R | 5 +- .../ETOPO1_Ice_c_gmt4_5min_sea.tif | Bin .../{temp => ds_draft}/_delta_downscale_3.R | 0 .../{temp => ds_draft}/a4_delta_downscale.Rmd | 0 .../a4_delta_downscale_b.Rmd | 6 +- ...elta_downscale_create_files_for_vignette.R | 5 +- .../{temp => ds_draft}/wc2.1_5m_bio_1_sea.tif | Bin data-raw/temp/monthly_vars.R | 86 ------------------ inst/extdata/delta/prec_obs_hres_all.nc | Bin 52180 -> 52180 bytes inst/extdata/delta/prec_series.nc | Bin 118409 -> 118409 bytes inst/extdata/delta/relief.nc | Bin 26119 -> 26119 bytes inst/extdata/delta/tavg_obs_hres_all.nc | Bin 97068 -> 97068 bytes inst/extdata/delta/tavg_series.nc | Bin 117928 -> 117928 bytes 15 files changed, 11 insertions(+), 97 deletions(-) rename data-raw/{temp => ds_draft}/ETOPO1_Ice_c_gmt4_5min_sea.tif (100%) rename data-raw/{temp => ds_draft}/_delta_downscale_3.R (100%) rename data-raw/{temp => ds_draft}/a4_delta_downscale.Rmd (100%) rename data-raw/{temp => ds_draft}/a4_delta_downscale_b.Rmd (97%) rename data-raw/{temp => ds_draft}/delta_downscale_create_files_for_vignette.R (97%) rename data-raw/{temp => ds_draft}/wc2.1_5m_bio_1_sea.tif (100%) delete mode 100644 data-raw/temp/monthly_vars.R diff --git a/R/delta_compute.R b/R/delta_compute.R index 9d317fec..daba6cdf 100644 --- a/R/delta_compute.R +++ b/R/delta_compute.R @@ -34,13 +34,13 @@ delta_compute <- function(x, ref_time, obs) { } # disaggregate the x_modern SpatRaster to the resolution of # the high res with "bilinear" interpolation - x_modern_high<-disagg(x_modern, fact = terra::res( x_modern)/terra::res(obs), + x_modern_high<-disagg(x_modern, fact = round(terra::res( x_modern)/terra::res(obs)), method="bilinear") # compute anomalies against the modern delta <- obs - x_modern_high # mask for maximum land extent max_land <- max(x,na.rm=TRUE) - max_land <- disagg(max_land, fact = terra::res( x)/terra::res(obs), + max_land <- disagg(max_land, fact = round(terra::res( x)/terra::res(obs)), method="near") delta_interp <- idw_interp(delta,max_land) return(delta_interp) diff --git a/R/delta_downscale.R b/R/delta_downscale.R index 5741f571..6e379630 100644 --- a/R/delta_downscale.R +++ b/R/delta_downscale.R @@ -37,7 +37,7 @@ delta_downscale <- function(x, delta_rast, x_landmask_high=NULL) { } # downscale x with bilinear - x_high <- disagg(x, fact = terra::res( x)/terra::res(delta_rast), + x_high <- disagg(x, fact = round(terra::res( x)/terra::res(delta_rast)), method="bilinear") # apply the delta_rast to x diff --git a/R/make_land_mask.R b/R/make_land_mask.R index bbb7ab1d..31c28ff2 100644 --- a/R/make_land_mask.R +++ b/R/make_land_mask.R @@ -57,9 +57,8 @@ make_land_mask <- function(relief_rast, time_bp, sea_level = NULL) { } } # work around problem of old terra failing to set time units for negative values - # (fixed in dev after 1.6.47) - browser() - if (utils::packageVersion("terra")<"1.6.48"){ + # (fixed in dev in 1.6.49) + if (utils::packageVersion("terra")<"1.6.49"){ terra::time(land_mask, tstep="") <- (time_bp+1950) } else { terra::time(land_mask, tstep="years") <- (time_bp+1950) diff --git a/data-raw/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif b/data-raw/ds_draft/ETOPO1_Ice_c_gmt4_5min_sea.tif similarity index 100% rename from data-raw/temp/ETOPO1_Ice_c_gmt4_5min_sea.tif rename to data-raw/ds_draft/ETOPO1_Ice_c_gmt4_5min_sea.tif diff --git a/data-raw/temp/_delta_downscale_3.R b/data-raw/ds_draft/_delta_downscale_3.R similarity index 100% rename from data-raw/temp/_delta_downscale_3.R rename to data-raw/ds_draft/_delta_downscale_3.R diff --git a/data-raw/temp/a4_delta_downscale.Rmd b/data-raw/ds_draft/a4_delta_downscale.Rmd similarity index 100% rename from data-raw/temp/a4_delta_downscale.Rmd rename to data-raw/ds_draft/a4_delta_downscale.Rmd diff --git a/data-raw/temp/a4_delta_downscale_b.Rmd b/data-raw/ds_draft/a4_delta_downscale_b.Rmd similarity index 97% rename from data-raw/temp/a4_delta_downscale_b.Rmd rename to data-raw/ds_draft/a4_delta_downscale_b.Rmd index 5961ee2b..65fd26cc 100644 --- a/data-raw/temp/a4_delta_downscale_b.Rmd +++ b/data-raw/ds_draft/a4_delta_downscale_b.Rmd @@ -18,7 +18,7 @@ knitr::opts_chunk$set( # THIS IS WORK IN PROGRESS, DO NOT USE FOR ANY ANALYSIS Note that we will use the draft functions for downscaling, which can be accessed -by usign the prefix `pastclim:::`. The `:::` access internal functions. You will +by using the prefix `pastclim:::`. The `:::` accesses internal functions. You will also get a message reminding you that these functions are not final and thus should not be used for real analysis. @@ -187,7 +187,7 @@ a list, and finally conver the list into a `SpatRasterDataset` tavg_downscaled_list<-list() for (i in 1:12){ delta_rast<-pastclim:::delta_compute(x=tavg_series[[i]], ref_time = 0, - obs = tavg_obs_hres_series[[i]]) + obs = tavg_obs_hres_all[[i]]) tavg_downscaled_list[[i]] <- pastclim:::delta_downscale (x = tavg_series[[i]], delta_rast = delta_rast, x_landmask_high = high_res_mask) @@ -235,7 +235,7 @@ And finally downscale precipitation: prec_downscaled_list<-list() for (i in 1:12){ delta_rast<-pastclim:::delta_compute(x=prec_series[[i]], ref_time = 0, - obs = prec_obs_hres_series[[i]]) + obs = prec_obs_hres_all[[i]]) prec_downscaled_list[[i]] <- pastclim:::delta_downscale (x = prec_series[[i]], delta_rast = delta_rast, x_landmask_high = high_res_mask) diff --git a/data-raw/temp/delta_downscale_create_files_for_vignette.R b/data-raw/ds_draft/delta_downscale_create_files_for_vignette.R similarity index 97% rename from data-raw/temp/delta_downscale_create_files_for_vignette.R rename to data-raw/ds_draft/delta_downscale_create_files_for_vignette.R index 81a70bf2..58de08b6 100644 --- a/data-raw/temp/delta_downscale_create_files_for_vignette.R +++ b/data-raw/ds_draft/delta_downscale_create_files_for_vignette.R @@ -1,10 +1,11 @@ # script to generate files for delta downscaling vignette library(terra) library(pastclim) -if (!dir.exists("../extdata")){ +delta_dir <- "../../inst/extdata/delta/" +if (!dir.exists(delta_dir)){ stop("your working directory was not set in the directory of this script") } -delta_dir <- "../extdata/delta" + dir.create(delta_dir,showWarnings = FALSE) sea_ext<- terra::ext(110, 120, -5, 5) tavg_vars <- c(paste0("temperature_0",1:9),paste0("temperature_",10:12)) diff --git a/data-raw/temp/wc2.1_5m_bio_1_sea.tif b/data-raw/ds_draft/wc2.1_5m_bio_1_sea.tif similarity index 100% rename from data-raw/temp/wc2.1_5m_bio_1_sea.tif rename to data-raw/ds_draft/wc2.1_5m_bio_1_sea.tif diff --git a/data-raw/temp/monthly_vars.R b/data-raw/temp/monthly_vars.R deleted file mode 100644 index 506d5e3c..00000000 --- a/data-raw/temp/monthly_vars.R +++ /dev/null @@ -1,86 +0,0 @@ -# the code below downloads the full worldclim dataset. Another option is to use -# worldclim_tile("tavg",lon=0,lat=52,path=tempdir()) -# to only get part of the world - -base_path_wc <- "../../project_temp/climate_downscale/worldclim_monthly" -wc_res <- 5 # one of 10,5,2.5,0.5 minutes - -library(geodata) -library(pastclim) - -# load the observations (and download them if needed) -if (!file.exists(file.path(base_path_wc,"wc_tavg_5_monthly.nc"))){ - wc_tavg_5<-geodata::worldclim_global(var="tavg", res=wc_res, path=tempdir(), version="2.1") - terra::writeCDF(wc_tavg_5,file.path(base_path_wc,"wc_tavg_5_monthly.nc"), - compression=9) -} else { - wc_tavg_5<-terra::rast(file.path(base_path_wc,"wc_tavg_5_monthly.nc")) -} - -if (!file.exists(file.path(base_path_wc,"wc_prec_5_monthly.nc"))){ - wc_prec_5<-geodata::worldclim_global(var="prec", res=wc_res, path=tempdir(), version="2.1") - terra::writeCDF(wc_prec_5,file.path(base_path_wc,"wc_prec_5_monthly.nc"), - compression=9) -} else { - wc_prec_5<-terra::rast(file.path(base_path_wc,"wc_prec_5_monthly.nc")) -} - -# now set the extent for SEA -sea_ext<- terra::ext(110, 120, -5, 5) -# crop the observations for this extent -high_res_tavg <- terra::crop(wc_tavg_5, sea_ext) -high_res_prec <- terra::crop(wc_prec_5, sea_ext) -# check that we can compute bioclim variables for the present -bioclim_pres <- bioclim_vars(tavg = high_res_tavg, prec = high_res_prec) -# Get timeseries (only for a limited number of time steps, equivalent to the Example dataset) -tavg_series <- region_series(bio_variables = c(paste0("temperature_0",1:9),paste0("temperature_",10:12)), - time_bp = get_time_steps(dataset = "Example"), - dataset = "Beyer2020", - ext = sea_ext) - -prec_series <- region_series(bio_variables = c(paste0("precipitation_0",1:9),paste0("precipitation_",10:12)), - time_bp = get_time_steps(dataset = "Example"), - dataset = "Beyer2020", - ext = sea_ext) -# now get the relief data and create landmasks -relief_rast <- pastclim:::download_relief(high_res_tavg) -high_res_mask <- pastclim:::make_land_mask(relief_rast = relief_rast, - time_bp = get_time_steps(dataset = "Example")) -# now we need to downscale the two series of monthly variables -# we will have to downscale one month at a time, put all the months into -# a list, and then combine them into a SpatRasterDataset -# start with temperature -tavg_downscaled_list<-list() -for (i in 1:12){ - delta_rast<-pastclim:::delta_compute(x=tavg_series[[i]], ref_time = 0, - obs = high_res_tavg[[i]]) - tavg_downscaled_list[[i]] <- pastclim:::delta_downscale (x = tavg_series[[i]], - delta_rast = delta_rast, - x_landmask_high = high_res_mask) -} -tavg_downscaled <- terra::sds(tavg_downscaled_list) - -prec_downscaled_list<-list() -for (i in 1:12){ - delta_rast<-pastclim:::delta_compute(x=prec_series[[i]], ref_time = 0, - obs = high_res_prec[[i]]) - prec_downscaled_list[[i]] <- pastclim:::delta_downscale (x = prec_series[[i]], - delta_rast = delta_rast, - x_landmask_high = high_res_mask) -} -prec_downscaled <- terra::sds(prec_downscaled_list) - -# now create the biovariables -bioclim_downscaled<-bioclim_vars(tavg =tavg_downscaled, prec = prec_downscaled) - - -## write some files for a future vignette -# terra::writeCDF(tavg_series,file.path(path_wc,"tavg_series.nc"),compression=9) -# terra::writeCDF(prec_series,file.path(path_wc,"prec_series.nc"),compression=9) -# terra::writeCDF(relief_rast,file.path(path_wc,"relief.nc"),compression=9) -# terra::writeCDF(high_res_tavg,file.path(path_wc,"high_res_tavg.nc"),compression=9) -# terra::writeCDF(high_res_prec,file.path(path_wc,"high_res_prec.nc"),compression=9) - -# suggested range for a vignette: -#sea_ext<- terra::ext(110, 120, -5, 5) - diff --git a/inst/extdata/delta/prec_obs_hres_all.nc b/inst/extdata/delta/prec_obs_hres_all.nc index b6dc3da74eba6439a4536396ceb6f4c919776f66..1bffc51467cac3071c45b484eebf05f3e74c1822 100644 GIT binary patch delta 30 mcmcaIo%zai<_)t?Gg?lbb6ShfM8VL=%Fxux&`7YaHwXa1Hww`J delta 30 lcmcaIo%zai<_)t?Gg?fZb6ShfNWsv+3W$u&3_rcN4FJQ`3^f1% diff --git a/inst/extdata/delta/prec_series.nc b/inst/extdata/delta/prec_series.nc index ab3ef654e8ee9f0d9720e0364021fae1a30e5ae8..d3e2af181da8965f146bfceed942db0008248601 100644 GIT binary patch delta 44 zcmeC2%icMcy60WGwfIaF42`S|O|1+p?gwSNZx&=}VcqU0&uG0K05K{J AD*ylh delta 44 ycmeC2%icMcy60WGwfKw_3=OP+$k>#j`9|huL6#QQ?SAr%*6RT+4h;eT diff --git a/inst/extdata/delta/relief.nc b/inst/extdata/delta/relief.nc index a7fc2f09b0539a23adee2b2010aa069f684be2cc..e168af0ad21a179ce2a69e97d4a382fece9aba6e 100644 GIT binary patch delta 30 mcmZoa!`Oa?al^S(M$5_PQ?>X^6by~53{9;JER~-;SquQS5(`ZL delta 30 kcmZoa!`Oa?al^S(MvKYkQ?>Yv6buclfXLWXtow>40JF&oQUCw| diff --git a/inst/extdata/delta/tavg_obs_hres_all.nc b/inst/extdata/delta/tavg_obs_hres_all.nc index 7a43f6f6f5c3177bde2c593282764c15b6fd87d0..2fba66eaa14e3f8143b3e505e1d5ef1063f7723c 100644 GIT binary patch delta 33 pcmZ4Ujdjg8)`l&NpQ{)xr+=wp)Z#NyFf_6 Date: Wed, 14 Dec 2022 13:51:45 +0000 Subject: [PATCH 015/119] silence idw messages --- R/delta_downscale.R | 20 +++++++++++++---- R/idw_interp.R | 19 +++++++++++----- data-raw/ds_draft/a4_delta_downscale_b.Rmd | 6 ++--- man/delta_downscale.Rd | 26 +++++++++++++++++++--- man/idw_interp.Rd | 13 +++++++++-- 5 files changed, 67 insertions(+), 17 deletions(-) diff --git a/R/delta_downscale.R b/R/delta_downscale.R index 6e379630..2cea3ca1 100644 --- a/R/delta_downscale.R +++ b/R/delta_downscale.R @@ -8,8 +8,10 @@ #' #' It is possible to also provide a high resoultion landmask to this function. #' For cells which are not included in the original simulation (e.g. because -#' the landmask was discretised at lower resolution), an idw algorightm is used -#' to interpolate the missing values. +#' the landmask was discretised at lower resolution), an inverse +#' distance weighted algorithm (as implemented in \code{gstat::gstat}) is used +#' to interpolate the missing values. See the manpage for \code{gstat::gstat} +#' for more parameters that can change the behaviour of the iwd interpolation. #' #' @param x a \code{terra::SpatRaster} for the variable of interest, with all #' time steps of interest @@ -17,12 +19,21 @@ #' \code{pastclim::delta_compute} #' @param x_landmask_high a \code{terra::SpatRaster} with the same number of layers #' as x. If left NULL, the original landmask of x is used. +#' @param nmax the number of nearest observations that should be used for a +#' kriging prediction or simulation, where nearest is defined in terms +#' of the space of the spatial locations (see \code{gstat::gstat} for details) +#' @param set named list with optional parameters to be passed to gstat +#' (only set commands of gstat are allowed, and not all of them may be +#' relevant; see the gstat manual for gstat stand-alone, URL and more details +#' in the \code{gstat::gstat} help page) +#' @param ... further parameters to be passed to \code{gstat::gstat} #' @returns a \code{terra::SpatRaster} of the downscaled variable, where each #' layers is a time step. #' @keywords internal -delta_downscale <- function(x, delta_rast, x_landmask_high=NULL) { +delta_downscale <- function(x, delta_rast, x_landmask_high=NULL, + nmax=7, set=list(idp = .5), ...) { message("This function is still under development; do not use it for real analysis") # check that extent and resolutions are compatible if (terra::ext(delta_rast)!=terra::ext(x)){ @@ -53,7 +64,8 @@ delta_downscale <- function(x, delta_rast, x_landmask_high=NULL) { x_high <- mask(x_high,x_landmask_high) # fill in any gaps that resulted from this step with idw for (i in 1:terra::nlyr(x_high)){ - x_high[[i]] <- idw_interp(x_high[[i]],x_landmask_high[[i]]) + x_high[[i]] <- idw_interp(x_high[[i]],x_landmask_high[[i]], + nmax=nmax, set=set, ...) } } diff --git a/R/idw_interp.R b/R/idw_interp.R index 05b59dd9..29ecfd83 100644 --- a/R/idw_interp.R +++ b/R/idw_interp.R @@ -10,11 +10,18 @@ #' @param x the \code{terra::SpatRaster} of the variable of interest #' @param y the \code{terra::SpatRaster} the reference mask defining which #' cells should have values -#' @param ... paramters to be passed to \code{gstat::gstat} +#' @param nmax the number of nearest observations that should be used for a +#' kriging prediction or simulation, where nearest is defined in terms +#' of the space of the spatial locations (see \code{gstat::gstat} for details) +#' @param set named list with optional parameters to be passed to gstat +#' (only set commands of gstat are allowed, and not all of them may be +#' relevant; see the gstat manual for gstat stand-alone, URL and more details +#' in the \code{gstat::gstat} help page) +#' @param ... further parameters to be passed to \code{gstat::gstat} #' #' @keywords internal -idw_interp <- function(x, y, ...){ +idw_interp <- function(x, y, nmax=7, set=list(idp = .5), ...){ # first mask x with y (i.e. remove parts of y not found in the mask y) x<-terra::mask(x,y) x_bin <- make_binary_mask(x) @@ -32,9 +39,11 @@ idw_interp <- function(x, y, ...){ names(x_gap_df)[3] <-"this_var" # interpolate those gaps with idw (time consuming...) # add ... to the function to be able to take additional params to gstat - idw_obj <- gstat::gstat(formula = this_var~1, locations = ~x+y, data = x_df, nmax=7, - set=list(idp = .5), ...) - idw_pred <- predict(idw_obj, newdata = x_gap_df)[,-4] # remove the last column + idw_obj <- gstat::gstat(formula = this_var~1, locations = ~x+y, + data = x_df, nmax=nmax, + set=set, ...) + idw_pred <- predict(idw_obj, newdata = x_gap_df, + debug.level=0)[,-4] # remove the last column x_gap_vals <- terra::rast(idw_pred,type="xyz",crs=crs(x)) x_gap_vals <- terra::extend(x_gap_vals, x) # now extend x to include the x_gap_vals diff --git a/data-raw/ds_draft/a4_delta_downscale_b.Rmd b/data-raw/ds_draft/a4_delta_downscale_b.Rmd index 65fd26cc..767edbf4 100644 --- a/data-raw/ds_draft/a4_delta_downscale_b.Rmd +++ b/data-raw/ds_draft/a4_delta_downscale_b.Rmd @@ -226,7 +226,7 @@ prec_obs_hres_all <- terra::crop(prec_obs_hres_all, sea_ext) ```{r echo=FALSE, results="hide"} prec_obs_hres_all <-terra::rast(system.file("extdata/delta/prec_obs_hres_all.nc", - package="pastclim")) + package = "pastclim")) ``` @@ -245,7 +245,7 @@ prec_downscaled <- terra::sds(prec_downscaled_list) We are now ready to compute the bioclim variables: ```{r} -bioclim_downscaled<-bioclim_vars(tavg =tavg_downscaled, prec = prec_downscaled) +bioclim_downscaled<-bioclim_vars(tavg = tavg_downscaled, prec = prec_downscaled) ``` Let's inspect the object: @@ -255,5 +255,5 @@ bioclim_downscaled And plot the first variable (bio01): ```{r} -plot(bioclim_downscaled[[1]],main=time(bioclim_downscaled[[1]])) +plot(bioclim_downscaled[[1]], main = time(bioclim_downscaled[[1]])) ``` \ No newline at end of file diff --git a/man/delta_downscale.Rd b/man/delta_downscale.Rd index 345ee33f..6f927b4c 100644 --- a/man/delta_downscale.Rd +++ b/man/delta_downscale.Rd @@ -4,7 +4,14 @@ \alias{delta_downscale} \title{Downscale using the delta method} \usage{ -delta_downscale(x, delta_rast, x_landmask_high = NULL) +delta_downscale( + x, + delta_rast, + x_landmask_high = NULL, + nmax = 7, + set = list(idp = 0.5), + ... +) } \arguments{ \item{x}{a \code{terra::SpatRaster} for the variable of interest, with all @@ -15,6 +22,17 @@ time steps of interest} \item{x_landmask_high}{a \code{terra::SpatRaster} with the same number of layers as x. If left NULL, the original landmask of x is used.} + +\item{nmax}{the number of nearest observations that should be used for a +kriging prediction or simulation, where nearest is defined in terms +of the space of the spatial locations (see \code{gstat::gstat} for details)} + +\item{set}{named list with optional parameters to be passed to gstat +(only set commands of gstat are allowed, and not all of them may be +relevant; see the gstat manual for gstat stand-alone, URL and more details +in the \code{gstat::gstat} help page)} + +\item{...}{further parameters to be passed to \code{gstat::gstat}} } \value{ a \code{terra::SpatRaster} of the downscaled variable, where each @@ -30,7 +48,9 @@ as an argument for this function. \details{ It is possible to also provide a high resoultion landmask to this function. For cells which are not included in the original simulation (e.g. because -the landmask was discretised at lower resolution), an idw algorightm is used -to interpolate the missing values. +the landmask was discretised at lower resolution), an inverse +distance weighted algorithm (as implemented in \code{gstat::gstat}) is used +to interpolate the missing values. See the manpage for \code{gstat::gstat} +for more parameters that can change the behaviour of the iwd interpolation. } \keyword{internal} diff --git a/man/idw_interp.Rd b/man/idw_interp.Rd index d45bec1d..614f81d2 100644 --- a/man/idw_interp.Rd +++ b/man/idw_interp.Rd @@ -4,7 +4,7 @@ \alias{idw_interp} \title{Interpolate x to match mask y} \usage{ -idw_interp(x, y, ...) +idw_interp(x, y, nmax = 7, set = list(idp = 0.5), ...) } \arguments{ \item{x}{the \code{terra::SpatRaster} of the variable of interest} @@ -12,7 +12,16 @@ idw_interp(x, y, ...) \item{y}{the \code{terra::SpatRaster} the reference mask defining which cells should have values} -\item{...}{paramters to be passed to \code{gstat::gstat}} +\item{nmax}{the number of nearest observations that should be used for a +kriging prediction or simulation, where nearest is defined in terms +of the space of the spatial locations (see \code{gstat::gstat} for details)} + +\item{set}{named list with optional parameters to be passed to gstat +(only set commands of gstat are allowed, and not all of them may be +relevant; see the gstat manual for gstat stand-alone, URL and more details +in the \code{gstat::gstat} help page)} + +\item{...}{further parameters to be passed to \code{gstat::gstat}} } \description{ Fill in x to match cells available in y, using iwd (inverse-weighted From 3c21ca865647541708d18df7009fc0c262eb4e00 Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Wed, 14 Dec 2022 17:32:49 +0000 Subject: [PATCH 016/119] Update namespace --- NAMESPACE | 1 - 1 file changed, 1 deletion(-) diff --git a/NAMESPACE b/NAMESPACE index d5fa00f4..88e3e36c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,7 +1,6 @@ # Generated by roxygen2: do not edit by hand export(bioclim_vars) -export(check_dataset_path) export(clean_data_path) export(climate_for_locations) export(climate_for_time_slice) From 7c9cf7178fcd95dc58f74955e4962559b8c7dd1d Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Thu, 15 Dec 2022 11:35:05 +0000 Subject: [PATCH 017/119] initial implementation of a remote test --- tests/testthat/test_remote_files.R | 46 ++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 tests/testthat/test_remote_files.R diff --git a/tests/testthat/test_remote_files.R b/tests/testthat/test_remote_files.R new file mode 100644 index 00000000..b380fe28 --- /dev/null +++ b/tests/testthat/test_remote_files.R @@ -0,0 +1,46 @@ +## This is a resource intensive test. It downloads all files in the dataset_list +## and then validates them. It is only run if the appropriate environment +## variable is set, and thus skipped most of the time +## To set the environment variable, use: +## Sys.setenv(PASTCLIM_TEST = "download_full") +## remember to unset it once you are done +## Sys.unsetenv("PASTCLIM_TEST") + + +# set up data path for this test +data_path <- file.path(tempdir(),"pastclim_data") +unlink(data_path, recursive = TRUE) # it should not exist, but remove it just in case +# set data path +set_data_path(path_to_nc = data_path, + ask = FALSE, + write_config = FALSE, + copy_example = TRUE) +################################################################################ +test_that("download and validate all files", { + skip_if(Sys.getenv("PASTCLIM_TEST")!="download_full") + # download all files for each dataset + all_datasets <- get_available_datasets() + all_datasets <- all_datasets[!all_datasets %in% "Example"] + for (i_dataset in all_datasets){ + expect_true(download_dataset(dataset = i_dataset)) + } + # now check that the files we downloaded are valid + for (i_file in list.files(get_data_path())){ + expect_true(validate_nc(i_file)) + } + # check that the variables in the table are found in the respective files + meta_table <- getOption("pastclim.dataset_list") + for (i_row in 1:nrow(meta_table)){ + nc_in <- ncdf4::nc_open(file.path(in_dir, meta_table$file_name[i])) + # check below if !! works to unquote the expression + expect_true(!!meta_table$ncvar[i] %in% names(nc_in$var)) + ncdf4::nc_close(nc_in) + } + # for each dataset, check that all variables cover the same extent and have + # the same missing values +} +) + +################################################################################ +# clean up for the next test +unlink(data_path, recursive = TRUE) From 0ef82709f9b21c6c71156b109225831f14bf9ebc Mon Sep 17 00:00:00 2001 From: Andrea Manica Date: Thu, 15 Dec 2022 17:00:37 +0000 Subject: [PATCH 018/119] direct casting from bathy to spatraster --- R/bathy_to_spatraster.R | 32 +++++++++++++++++++++ R/download_relief.R | 20 ++++++------- data-raw/ds_draft/a4_delta_downscale_b.pdf | Bin 0 -> 293597 bytes man/bathy_to_spatraster.Rd | 19 ++++++++++++ man/download_relief.Rd | 2 +- 5 files changed, 62 insertions(+), 11 deletions(-) create mode 100644 R/bathy_to_spatraster.R create mode 100644 data-raw/ds_draft/a4_delta_downscale_b.pdf create mode 100644 man/bathy_to_spatraster.Rd diff --git a/R/bathy_to_spatraster.R b/R/bathy_to_spatraster.R new file mode 100644 index 00000000..ddf60355 --- /dev/null +++ b/R/bathy_to_spatraster.R @@ -0,0 +1,32 @@ +#' Cast bathy to SpatRaster +#' +#' This function converts a \code{marmap::bathy} object to +#' a \code{terra::SpatRaster}. +#' +#' @param bathy a \code{marmap::bathy} to convert +#' @returns a \code{terra::SpatRaster} with the relief for the chosen region +#' +#' @keywords internal + + +bathy_to_spatraster <- function(bathy) { + if(!requireNamespace("marmap", quietly=TRUE)){ + message("This function requires the 'marmap' package; install it with:\n", + "install.packages('marmap')") + return(invisible()) + } + + if (!inherits(bathy,"bathy")) stop("Object is not of class bathy") + + lat <- as.numeric(colnames(bathy)) + lon <- as.numeric(rownames(bathy)) + + r <- terra::rast(ncol = nrow(bathy), nrow = ncol(bathy), + xmin = min(lon), xmax = max(lon), + ymin = min(lat), ymax = max(lat)) + terra::values(r) <- as.vector(bathy[,rev(1:ncol(bathy))]) + + return(r) + +} + diff --git a/R/download_relief.R b/R/download_relief.R index 597c3f9c..a254dc35 100644 --- a/R/download_relief.R +++ b/R/download_relief.R @@ -4,7 +4,7 @@ #' ETOPO2022 dataset. #' #' This function uses \code{marmap::getNOAA.bathy} to download the data, and -#' then converts them into a \code{terra::SpatRaster} in a format compatible +#' then converts them into a \code{terra::SpatRaster} formatted to be compatible #' with `pastclim` #' #' @param rast_template a \code{terra::SpatRaster} providing the extent and @@ -24,21 +24,19 @@ download_relief <- function(rast_template, ...) { "install.packages('marmap')") return(invisible()) } - #browser() - # check that this works, I think there might be issues with decimal places + + # check that this works, I think there might be issues with decimal places if (!all.equal(terra::res(rast_template)[1],terra::res(rast_template)[2])){ stop("rast_template should have identical vertical and horizontal resolution") } - #browser() + relief_bathy<- marmap::getNOAA.bathy(lon1 = terra::ext(rast_template)[1], lon2 = terra::ext(rast_template)[2], lat1 = terra::ext(rast_template)[3], lat2 = terra::ext(rast_template)[4], - # note that resolution in marmap is in minuts, and in degrees for terra + # note that resolution in marmap is in minutes, and in degrees for terra resolution = terra::res(rast_template)[1]*60, ...) - relief_rast <- terra::rast(marmap::as.raster(relief_bathy)) - longnames(relief_rast)<-"relief from ETOPO2022" - units(relief_rast)<-"m" + relief_rast <- bathy_to_spatraster(relief_bathy) # ideally check if there are any differences (due to rounding problems or corners), # and then resample the relief to match the template exactly if (!all(terra::ext(relief_rast)==terra::ext(rast_template), @@ -46,7 +44,9 @@ download_relief <- function(rast_template, ...) { terra::nrow(relief_rast)==terra::nrow(rast_template))){ relief_rast <- terra::resample(relief_rast,rast_template) } + longnames(relief_rast)<-"relief from ETOPO2022" + varnames(relief_rast)<-"relief" + names(relief_rast)<-"relief" + units(relief_rast)<-"m" return(relief_rast) - - } diff --git a/data-raw/ds_draft/a4_delta_downscale_b.pdf b/data-raw/ds_draft/a4_delta_downscale_b.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c725f736d9d347e5d4a5cdc2bceb3de7cbbff39f GIT binary patch literal 293597 zcmd431yo&2);|~^xJ%HWL4&(XZ~{Ss6C5t??(Po3HNgqN-66QUTY@_Tg4-POlK0Ww zuczOCzMfgbT9>jdzrCyWK6TEmbID|dMQIu7SmDSjEOkxbczEFGr9c*j zc1A=@%nU36qubC5&@>Q; 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