R/merge_locs.R
In ks905383/quantproj: Project Climate Data Based on Changes in Distribution Shape
Defines functions
combine.locs
Documented in
combine.locs
#' Re-merge quantile map projections
#'
#' Concatenate the individual projection files calculated through separate calls
#' to \code{build.projections} into a single file, removing duplicates along the
#' way.
#'
#' @section Output Files:
#' The file is saved as both an .RData file and a NetCDF file. The final output
#' in the NetCDF file is a \code{[loc x time]} array (variable name
#' \code{[defaults$filevar]}), with variables \code{lat} and \code{lon} being
#' \code{[loc x 1]} vectors giving the location. The file also includes a variable
#' \code{base_idx} giving, for each projected day, the linear index in the
#' original base data that was used as the base day for the projection.
#'
#'
#' @section Beware:
#' Files are searched for in the directory code{[base.data.dir]/output}, with
#' \code{[base.data.dir]} set by \code{defaults}. As a result, this code depends
#' on the desired temporary files being the only files in that directory with
#' the filename pattern searched for (from the \code{file.list} variable in the
#' code).
#'
#' @param defaults the output from \code{\link{various_defaults}}
#' @param comments if desired, a 'comments' global attribute will be
#' added to the NetCDF file, with the string input to this
#' option as the body
#' @param output.type how the time series is exported into the netcdf file; either
#' "linear" (default), as an [idx x time] array, or "grid," as a
#' [lon x lat x time] array
#' @param lat_vec,lon_vec if \code{output.type=="grid"}, this allows the specification
#' of the output grid (useful if the projected data doesn't have values for
#' the whole global/etc. grid, or if you just want a subset)
#'
#' @importFrom rlist list.select
#' @importFrom ncdf4 ncdim_def ncvar_def nc_create ncvar_put ncatt_put nc_close
combine.locs <- function(defaults,comments=character(),output.type="linear",
lat_vec=numeric(),lon_vec=numeric()) {
#------ Set Output Filename -------------------------------------------------
# Get better string filename summary for the projection base year choosing
if (defaults$index.type=="resampling_rep") {
proj.method <- paste0("resample",defaults$resampling.timescale)
proj.desc <- paste0("resampling ",defaults$resampling.timescale," with replacement")
} else if (defaults$index.type=="resampling") {
proj.method <- paste0("NoRepResample",defaults$resampling.timescale)
proj.desc <- paste0("resampling ",defaults$resampling.timescale," without replacement")
} else if (defaults$index.type=="raw") {
proj.method <- "projectraw"
proj.desc <- paste0("projecting ",defaults$resampling.timescale,"-by-",defaults$resampling.timescale," in order")
} else if (defaults$index.type=="raw_mean") {
proj.method <- "projectbyavg"
proj.desc <- paste0("projecting by the change in the quantiles averaged over the time periods")
}
# Set output filename
fn <- paste0(defaults$base.data.dir,"output/",
defaults$filevar,"_day_",defaults$base.name,
"_",defaults$mod.name,"proj_",proj.method,"_",
paste0(paste0(defaults$proj.year.range,collapse="0101-"),"1231"))
if (defaults$bootstrapping) {
fn <- paste0(fn,defaults$block.size,"block",defaults$nboots,"runs")
}
#------ Find Output Files ----------------------------------------------------
if (defaults$bootstrapping) {
file.list <- dir(path=paste0(defaults$base.data.dir,"output/"),
paste0(defaults$filevar,"_day_",defaults$base.name,".*",
paste0(defaults$proj.year.range,collapse="-"),".*_",
defaults$block.size,"block",defaults$nboots,"runs",
defaults$fn.suffix,".RData")
)
} else {
file.list <- dir(path=paste0(defaults$base.data.dir,"output/"),
paste0(defaults$filevar,"_day_",defaults$base.name,".*",
paste0(defaults$proj.year.range,collapse="-"),".*",
defaults$fn.suffix,".RData")
)
}
#------ Load and Concatenate Files -------------------------------------------
# Initialize list
output.all <- list()
# Load and concatenate files
for (filen in file.list) {
# Load output
load(paste0(defaults$base.data.dir,"output/",filen))
# Separate out resampling indices
if (filen==file.list[1]) {
base.idxs <- output.map[[1]]$base.idxs
}
# Attach
output.all <- c(output.all,list.select(output.map,lat,lon,global_loc,reg,proj.data))
}
# Remove duplicated latlon combinations
dup.idxs <- duplicated(cbind(list.select(output.all,lat),list.select(output.all,lon)))
output.all <- output.all[!dup.idxs]
# Rename back to output.map
output.map <- output.all; rm(list=c("output.all"))
# Save as .RData file
save(file=paste0(fn,defaults$fn.suffix,".RData"),output.map)
print(paste0(fn,defaults$fn.suffix,".RData saved!"))
#------ Save as NetCDF file! -------------------------------------------------
# Get variables (all the messiness below is to make sure NaNs stay NaNs. I don't know why they're there, but that's a question for soon after)
lon <- unlist(lapply(list.select(output.map,lon),function(x) as.numeric(as.character(x))))
lat <- unlist(lapply(list.select(output.map,lat),function(x) as.numeric(as.character(x))))
global_loc <- unlist(lapply(list.select(output.map,global_loc),function(x) as.numeric(as.character(x))))
Raw <- t(matrix(unlist(list.select(output.map,proj.data)),ncol=length(global_loc),byrow=FALSE))
rm(output.map)
if (output.type=="linear") {
# Dimensions
latdim <- ncdim_def("lat","deg",lat)
londim <- ncdim_def("lon","deg",lon)
locdim <- ncdim_def("global_loc","idx",global_loc,longname="global location index")
timedim <- ncdim_def("time","days",1:ncol(Raw),calendar="noleap")
# Variables
tas <- ncvar_def("tas","K",list(locdim,timedim),NaN,longname="Near-Surface Air Temperature",prec="double")
latv <- ncvar_def("lat","deg",locdim,NaN,longname="latitude",prec="double")
lonv <- ncvar_def("lon","deg",locdim,NaN,longname="longitude",prec="double")
idxv <- ncvar_def("base_idx","days",timedim,NaN,longname="day index of base climate",prec="integer")
locv <- ncvar_def("global_loc","idx",locdim,NaN,longname="global location index",prec="integer")
# Create netcdf file
if (defaults$bootstrapping) {
# Uhhhhhh, i don't think this should be [locdim]...
#runid <- ncvar_def("run_id","id",locdim,NaN,longname="bootstrap run index",prec="integer")
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(lonv,latv,idxv,runid,tas))
} else {
#ncout <- nc_create(paste0(fn,".nc"),list(latv,lonv,idxv,locv,tas))
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(lonv,latv,idxv,tas))
}
# add global attributes (BEFORE ADDING THE MAIN DATA; WEIRD BUG IN NCDF4 OTHERWISE CAUSES ISSUES)
ncatt_put(ncout,0,"variable_short",defaults$filevar)
ncatt_put(ncout,0,"variable_long","Near-Surface Air Temperature")
ncatt_put(ncout,0,"frequency","day")
ncatt_put(ncout,0,"experiment",paste0(defaults$mod.name,"-projected"))
ncatt_put(ncout,0,"range",paste0(defaults$proj.year.range,collapse="-"))
ncatt_put(ncout,0,"model_id",defaults$base.name)
ncatt_put(ncout,0,"projection",paste0("Projected from ",paste0(defaults$base.year.range,collapse="-"),
" using day-by-day quantile maps from the ",defaults$mod.name," ensemble. "))
if (length(comments)>0) {
ncatt_put(ncout,0,"comments",comments)
}
ncatt_put(ncout,0,"projection_method",proj.desc)
ncatt_put(ncout,0,"attribution","This file was created using the 'quantproj' code package by Kevin Schwarzwald based on methodology and code by Matz Haugen and Haugen et al. (2018).")
# Add variables
ncvar_put(ncout,latv,lat)
ncvar_put(ncout,lonv,lon)
ncvar_put(ncout,idxv,base.idxs)
ncvar_put(ncout,tas,Raw)
if (defaults$bootstrapping) {
ncvar_put(ncout,runid,rep(1:defaults$nboots,length(unique(lat))))
}
} else if (output.type=="grid") {
# Get the grid vectors (ignores gaps - so if there are pixels with lat = 1 2 5 6,
# the vector will be c(1 2 5 6))
if (length(lon_vec)==0) {lon_vec <- sort(unique(lon))}
if (length(lat_vec)==0) {lat_vec <- sort(unique(lat))}
if (max(diff(lat_vec))/median(unique(diff(lat_vec)))>2 || max(diff(lon_vec))/median(unique(diff(lon_vec)))>2) {
warning("grid may be discontinuous (not every lat or lon value is sequential); suggest inputting target grid using the lon_vec and lat_vec options")
}
# Assign the values from the original [Raw] vector to the grid, by longitude band
Raw_out <- array(data=NA,c(length(lon_vec),length(lat_vec),dim(Raw)[2]))
global_loc_out <- array(data=NA,c(length(lon_vec),length(lat_vec)))
for (lon_idx in 1:length(lon_vec)) {
match.idx <- match(lat_vec,lat[which(lon==lon_vec[lon_idx])])
# This includes support for the ordering in lat_vec being different than in lat
Raw_out[lon_idx,which(!is.na(match.idx)),] <- Raw[which(lon==lon_vec[lon_idx])[match.idx[!is.na(match.idx)]],]
global_loc_out[lon_idx,which(!is.na(match.idx))] <- global_loc[which(lon==lon_vec[lon_idx])[match.idx[!is.na(match.idx)]]]
}
Raw <- Raw_out; rm(Raw_out)
# Dimensions
latdim <- ncdim_def("lat","deg",lat_vec)
londim <- ncdim_def("lon","deg",lon_vec)
locdim <- ncdim_def("global_loc","idx",global_loc,longname="global location index")
timedim <- ncdim_def("time","days",1:dim(Raw)[3],calendar="noleap")
# Variables
tas <- ncvar_def("tas","K",list(londim,latdim,timedim),NaN,longname="Near-Surface Air Temperature",prec="double")
latv <- ncvar_def("lat","deg",latdim,NaN,longname="latitude",prec="double")
lonv <- ncvar_def("lon","deg",londim,NaN,longname="longitude",prec="double")
idxv <- ncvar_def("base_idx","days",timedim,NaN,longname="day index of base climate",prec="integer")
locv <- ncvar_def("global_loc","idx",list(londim,latdim),NaN,longname="global location index",prec="integer")
# Create netcdf file
if (defaults$bootstrapping) {
# Uhhhhhh, i don't think this should be [locdim]...
#runid <- ncvar_def("run_id","id",locdim,NaN,longname="bootstrap run index",prec="integer")
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(idxv,runid,locv,tas))
} else {
#ncout <- nc_create(paste0(fn,".nc"),list(latv,lonv,idxv,locv,tas))
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(idxv,locv,tas))
}
# add global attributes (BEFORE ADDING THE MAIN DATA; WEIRD BUG IN NCDF4 OTHERWISE CAUSES ISSUES)
ncatt_put(ncout,0,"variable_short",defaults$filevar)
ncatt_put(ncout,0,"variable_long","Near-Surface Air Temperature")
ncatt_put(ncout,0,"frequency","day")
ncatt_put(ncout,0,"experiment",paste0(defaults$mod.name,"-projected"))
ncatt_put(ncout,0,"range",paste0(defaults$proj.year.range,collapse="-"))
ncatt_put(ncout,0,"model_id",defaults$base.name)
ncatt_put(ncout,0,"projection",paste0("Projected from ",paste0(defaults$base.year.range,collapse="-"),
" using day-by-day quantile maps from the ",defaults$mod.name," ensemble. "))
if (length(comments)>0) {
ncatt_put(ncout,0,"comments",comments)
}
ncatt_put(ncout,0,"projection_method",proj.desc)
ncatt_put(ncout,0,"attribution","This file was created using the 'quantproj' code package by Kevin Schwarzwald based on methodology and code by Matz Haugen and Haugen et al. (2018).")
# Add variables to netcdf file
ncvar_put(ncout,idxv,base.idxs)
ncvar_put(ncout,locv,global_loc_out)
ncvar_put(ncout,tas,Raw)
if (defaults$bootstrapping) {
ncvar_put(ncout,runid,rep(1:defaults$nboots,length(unique(lat))))
}
} else {
error(paste0("output type '",output.type,"' unrecognized"))
}
# close the file, writing data to disk
nc_close(ncout)
# Fin
print(paste0(fn,defaults$fn.suffix,".nc saved!"))
invisible()
}
ks905383/quantproj documentation built on Nov. 1, 2020, 9:12 p.m.
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Defines functions combine.locs
Documented in combine.locs
#' Re-merge quantile map projections
#'
#' Concatenate the individual projection files calculated through separate calls
#' to \code{build.projections} into a single file, removing duplicates along the
#' way.
#'
#' @section Output Files:
#' The file is saved as both an .RData file and a NetCDF file. The final output
#' in the NetCDF file is a \code{[loc x time]} array (variable name
#' \code{[defaults$filevar]}), with variables \code{lat} and \code{lon} being
#' \code{[loc x 1]} vectors giving the location. The file also includes a variable
#' \code{base_idx} giving, for each projected day, the linear index in the
#' original base data that was used as the base day for the projection.
#'
#'
#' @section Beware:
#' Files are searched for in the directory code{[base.data.dir]/output}, with
#' \code{[base.data.dir]} set by \code{defaults}. As a result, this code depends
#' on the desired temporary files being the only files in that directory with
#' the filename pattern searched for (from the \code{file.list} variable in the
#' code).
#'
#' @param defaults the output from \code{\link{various_defaults}}
#' @param comments if desired, a 'comments' global attribute will be
#' added to the NetCDF file, with the string input to this
#' option as the body
#' @param output.type how the time series is exported into the netcdf file; either
#' "linear" (default), as an [idx x time] array, or "grid," as a
#' [lon x lat x time] array
#' @param lat_vec,lon_vec if \code{output.type=="grid"}, this allows the specification
#' of the output grid (useful if the projected data doesn't have values for
#' the whole global/etc. grid, or if you just want a subset)
#'
#' @importFrom rlist list.select
#' @importFrom ncdf4 ncdim_def ncvar_def nc_create ncvar_put ncatt_put nc_close
combine.locs <- function(defaults,comments=character(),output.type="linear",
lat_vec=numeric(),lon_vec=numeric()) {
#------ Set Output Filename -------------------------------------------------
# Get better string filename summary for the projection base year choosing
if (defaults$index.type=="resampling_rep") {
proj.method <- paste0("resample",defaults$resampling.timescale)
proj.desc <- paste0("resampling ",defaults$resampling.timescale," with replacement")
} else if (defaults$index.type=="resampling") {
proj.method <- paste0("NoRepResample",defaults$resampling.timescale)
proj.desc <- paste0("resampling ",defaults$resampling.timescale," without replacement")
} else if (defaults$index.type=="raw") {
proj.method <- "projectraw"
proj.desc <- paste0("projecting ",defaults$resampling.timescale,"-by-",defaults$resampling.timescale," in order")
} else if (defaults$index.type=="raw_mean") {
proj.method <- "projectbyavg"
proj.desc <- paste0("projecting by the change in the quantiles averaged over the time periods")
}
# Set output filename
fn <- paste0(defaults$base.data.dir,"output/",
defaults$filevar,"_day_",defaults$base.name,
"_",defaults$mod.name,"proj_",proj.method,"_",
paste0(paste0(defaults$proj.year.range,collapse="0101-"),"1231"))
if (defaults$bootstrapping) {
fn <- paste0(fn,defaults$block.size,"block",defaults$nboots,"runs")
}
#------ Find Output Files ----------------------------------------------------
if (defaults$bootstrapping) {
file.list <- dir(path=paste0(defaults$base.data.dir,"output/"),
paste0(defaults$filevar,"_day_",defaults$base.name,".*",
paste0(defaults$proj.year.range,collapse="-"),".*_",
defaults$block.size,"block",defaults$nboots,"runs",
defaults$fn.suffix,".RData")
)
} else {
file.list <- dir(path=paste0(defaults$base.data.dir,"output/"),
paste0(defaults$filevar,"_day_",defaults$base.name,".*",
paste0(defaults$proj.year.range,collapse="-"),".*",
defaults$fn.suffix,".RData")
)
}
#------ Load and Concatenate Files -------------------------------------------
# Initialize list
output.all <- list()
# Load and concatenate files
for (filen in file.list) {
# Load output
load(paste0(defaults$base.data.dir,"output/",filen))
# Separate out resampling indices
if (filen==file.list[1]) {
base.idxs <- output.map[[1]]$base.idxs
}
# Attach
output.all <- c(output.all,list.select(output.map,lat,lon,global_loc,reg,proj.data))
}
# Remove duplicated latlon combinations
dup.idxs <- duplicated(cbind(list.select(output.all,lat),list.select(output.all,lon)))
output.all <- output.all[!dup.idxs]
# Rename back to output.map
output.map <- output.all; rm(list=c("output.all"))
# Save as .RData file
save(file=paste0(fn,defaults$fn.suffix,".RData"),output.map)
print(paste0(fn,defaults$fn.suffix,".RData saved!"))
#------ Save as NetCDF file! -------------------------------------------------
# Get variables (all the messiness below is to make sure NaNs stay NaNs. I don't know why they're there, but that's a question for soon after)
lon <- unlist(lapply(list.select(output.map,lon),function(x) as.numeric(as.character(x))))
lat <- unlist(lapply(list.select(output.map,lat),function(x) as.numeric(as.character(x))))
global_loc <- unlist(lapply(list.select(output.map,global_loc),function(x) as.numeric(as.character(x))))
Raw <- t(matrix(unlist(list.select(output.map,proj.data)),ncol=length(global_loc),byrow=FALSE))
rm(output.map)
if (output.type=="linear") {
# Dimensions
latdim <- ncdim_def("lat","deg",lat)
londim <- ncdim_def("lon","deg",lon)
locdim <- ncdim_def("global_loc","idx",global_loc,longname="global location index")
timedim <- ncdim_def("time","days",1:ncol(Raw),calendar="noleap")
# Variables
tas <- ncvar_def("tas","K",list(locdim,timedim),NaN,longname="Near-Surface Air Temperature",prec="double")
latv <- ncvar_def("lat","deg",locdim,NaN,longname="latitude",prec="double")
lonv <- ncvar_def("lon","deg",locdim,NaN,longname="longitude",prec="double")
idxv <- ncvar_def("base_idx","days",timedim,NaN,longname="day index of base climate",prec="integer")
locv <- ncvar_def("global_loc","idx",locdim,NaN,longname="global location index",prec="integer")
# Create netcdf file
if (defaults$bootstrapping) {
# Uhhhhhh, i don't think this should be [locdim]...
#runid <- ncvar_def("run_id","id",locdim,NaN,longname="bootstrap run index",prec="integer")
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(lonv,latv,idxv,runid,tas))
} else {
#ncout <- nc_create(paste0(fn,".nc"),list(latv,lonv,idxv,locv,tas))
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(lonv,latv,idxv,tas))
}
# add global attributes (BEFORE ADDING THE MAIN DATA; WEIRD BUG IN NCDF4 OTHERWISE CAUSES ISSUES)
ncatt_put(ncout,0,"variable_short",defaults$filevar)
ncatt_put(ncout,0,"variable_long","Near-Surface Air Temperature")
ncatt_put(ncout,0,"frequency","day")
ncatt_put(ncout,0,"experiment",paste0(defaults$mod.name,"-projected"))
ncatt_put(ncout,0,"range",paste0(defaults$proj.year.range,collapse="-"))
ncatt_put(ncout,0,"model_id",defaults$base.name)
ncatt_put(ncout,0,"projection",paste0("Projected from ",paste0(defaults$base.year.range,collapse="-"),
" using day-by-day quantile maps from the ",defaults$mod.name," ensemble. "))
if (length(comments)>0) {
ncatt_put(ncout,0,"comments",comments)
}
ncatt_put(ncout,0,"projection_method",proj.desc)
ncatt_put(ncout,0,"attribution","This file was created using the 'quantproj' code package by Kevin Schwarzwald based on methodology and code by Matz Haugen and Haugen et al. (2018).")
# Add variables
ncvar_put(ncout,latv,lat)
ncvar_put(ncout,lonv,lon)
ncvar_put(ncout,idxv,base.idxs)
ncvar_put(ncout,tas,Raw)
if (defaults$bootstrapping) {
ncvar_put(ncout,runid,rep(1:defaults$nboots,length(unique(lat))))
}
} else if (output.type=="grid") {
# Get the grid vectors (ignores gaps - so if there are pixels with lat = 1 2 5 6,
# the vector will be c(1 2 5 6))
if (length(lon_vec)==0) {lon_vec <- sort(unique(lon))}
if (length(lat_vec)==0) {lat_vec <- sort(unique(lat))}
if (max(diff(lat_vec))/median(unique(diff(lat_vec)))>2 || max(diff(lon_vec))/median(unique(diff(lon_vec)))>2) {
warning("grid may be discontinuous (not every lat or lon value is sequential); suggest inputting target grid using the lon_vec and lat_vec options")
}
# Assign the values from the original [Raw] vector to the grid, by longitude band
Raw_out <- array(data=NA,c(length(lon_vec),length(lat_vec),dim(Raw)[2]))
global_loc_out <- array(data=NA,c(length(lon_vec),length(lat_vec)))
for (lon_idx in 1:length(lon_vec)) {
match.idx <- match(lat_vec,lat[which(lon==lon_vec[lon_idx])])
# This includes support for the ordering in lat_vec being different than in lat
Raw_out[lon_idx,which(!is.na(match.idx)),] <- Raw[which(lon==lon_vec[lon_idx])[match.idx[!is.na(match.idx)]],]
global_loc_out[lon_idx,which(!is.na(match.idx))] <- global_loc[which(lon==lon_vec[lon_idx])[match.idx[!is.na(match.idx)]]]
}
Raw <- Raw_out; rm(Raw_out)
# Dimensions
latdim <- ncdim_def("lat","deg",lat_vec)
londim <- ncdim_def("lon","deg",lon_vec)
locdim <- ncdim_def("global_loc","idx",global_loc,longname="global location index")
timedim <- ncdim_def("time","days",1:dim(Raw)[3],calendar="noleap")
# Variables
tas <- ncvar_def("tas","K",list(londim,latdim,timedim),NaN,longname="Near-Surface Air Temperature",prec="double")
latv <- ncvar_def("lat","deg",latdim,NaN,longname="latitude",prec="double")
lonv <- ncvar_def("lon","deg",londim,NaN,longname="longitude",prec="double")
idxv <- ncvar_def("base_idx","days",timedim,NaN,longname="day index of base climate",prec="integer")
locv <- ncvar_def("global_loc","idx",list(londim,latdim),NaN,longname="global location index",prec="integer")
# Create netcdf file
if (defaults$bootstrapping) {
# Uhhhhhh, i don't think this should be [locdim]...
#runid <- ncvar_def("run_id","id",locdim,NaN,longname="bootstrap run index",prec="integer")
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(idxv,runid,locv,tas))
} else {
#ncout <- nc_create(paste0(fn,".nc"),list(latv,lonv,idxv,locv,tas))
ncout <- nc_create(paste0(fn,defaults$fn.suffix,".nc"),list(idxv,locv,tas))
}
# add global attributes (BEFORE ADDING THE MAIN DATA; WEIRD BUG IN NCDF4 OTHERWISE CAUSES ISSUES)
ncatt_put(ncout,0,"variable_short",defaults$filevar)
ncatt_put(ncout,0,"variable_long","Near-Surface Air Temperature")
ncatt_put(ncout,0,"frequency","day")
ncatt_put(ncout,0,"experiment",paste0(defaults$mod.name,"-projected"))
ncatt_put(ncout,0,"range",paste0(defaults$proj.year.range,collapse="-"))
ncatt_put(ncout,0,"model_id",defaults$base.name)
ncatt_put(ncout,0,"projection",paste0("Projected from ",paste0(defaults$base.year.range,collapse="-"),
" using day-by-day quantile maps from the ",defaults$mod.name," ensemble. "))
if (length(comments)>0) {
ncatt_put(ncout,0,"comments",comments)
}
ncatt_put(ncout,0,"projection_method",proj.desc)
ncatt_put(ncout,0,"attribution","This file was created using the 'quantproj' code package by Kevin Schwarzwald based on methodology and code by Matz Haugen and Haugen et al. (2018).")
# Add variables to netcdf file
ncvar_put(ncout,idxv,base.idxs)
ncvar_put(ncout,locv,global_loc_out)
ncvar_put(ncout,tas,Raw)
if (defaults$bootstrapping) {
ncvar_put(ncout,runid,rep(1:defaults$nboots,length(unique(lat))))
}
} else {
error(paste0("output type '",output.type,"' unrecognized"))
}
# close the file, writing data to disk
nc_close(ncout)
# Fin
print(paste0(fn,defaults$fn.suffix,".nc saved!"))
invisible()
}
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