new naming for precipitation datasets (tp-->pr)

change NCEP source to UDG

R/climatologyVariogram.R

@@ -34,10 +34,10 @@
 #' @importFrom graphics abline plot text grid
 #' @importFrom stats var
 #' @examples
-#' data("EOBS_Iberia_tp")
+#' data("EOBS_Iberia_pr")
 #' # We compute the mean annual DJF precipitation
 #' aggr.fun <- list(FUN = "sum")
-#' annual.tp <- aggregateGrid(EOBS_Iberia_tp, aggr.m = aggr.fun, aggr.y = aggr.fun)
+#' annual.tp <- aggregateGrid(EOBS_Iberia_pr, aggr.m = aggr.fun, aggr.y = aggr.fun)
 #' # Now the winter precipitation climatology is computed
 #' tp.clim <- climatology(annual.tp)
 #' plotClimatology(tp.clim,

R/grid2mopa.R

@@ -37,8 +37,8 @@
 #' # A raster stack from a multigrid
 #' # A raster stack from a multigrid
 #' data("EOBS_Iberia_tas")
-#' data("EOBS_Iberia_tp")
-#' multigrid <- makeMultiGrid(EOBS_Iberia_tas, EOBS_Iberia_tp)
+#' data("EOBS_Iberia_pr")
+#' multigrid <- makeMultiGrid(EOBS_Iberia_tas, EOBS_Iberia_pr)
 #' multigridaggr <- aggregateGrid(multigrid, aggr.y = list(FUN = "mean"))
 #' ras <- grid2mopa(multigridaggr)
 #' require(sp)

R/helpers.R

@@ -42,16 +42,16 @@
 #' str(getGrid(VALUE_Iberia_tas))
 #' 
 #' # Interpolate NCEP onto the System4 grid:
-#' data("CFS_Iberia_tp")
-#' NCEP_interpolated <- interpGrid(NCEP_Iberia_hus850, getGrid(CFS_Iberia_tp), "bilinear")
+#' data("CFS_Iberia_pr")
+#' NCEP_interpolated <- interpGrid(NCEP_Iberia_hus850, getGrid(CFS_Iberia_pr), "bilinear")
 #' # Note the warnings because of the non-overlapping domain extents (longitudes)
 #' plotClimatology(climatology(NCEP_Iberia_hus850), backdrop.theme = "countries")
 #' plotClimatology(climatology(NCEP_interpolated), backdrop.theme = "countries")
 #' str(getGrid(NCEP_interpolated))
-#' str(getGrid(CFS_Iberia_tp))
+#' str(getGrid(CFS_Iberia_pr))
 #' 
 #' # The other way round, using nearest neighbour interpolation:
-#' CFS_interpolated <- interpGrid(CFS_Iberia_tp, getGrid(NCEP_Iberia_hus850))
+#' CFS_interpolated <- interpGrid(CFS_Iberia_pr, getGrid(NCEP_Iberia_hus850))
 #' plotClimatology(climatology(CFS_interpolated), backdrop.theme = "countries")
 #' # In this case, the mismatch in domain extent occurs in the longitudes and latitudes
 
@@ -425,16 +425,16 @@ selectPar.pplyFun <- function(parallel.pars, .pplyFUN = c("apply", "lapply", "sa
 #' @author J Bedia
 #' @examples 
 #' data("VALUE_Iberia_tas")
-#' data("VALUE_Iberia_tp")
-#' checkDim(VALUE_Iberia_tas, VALUE_Iberia_tp) # ok, go on
+#' data("VALUE_Iberia_pr")
+#' checkDim(VALUE_Iberia_tas, VALUE_Iberia_pr) # ok, go on
 #' data("EOBS_Iberia_tas")
-#' data("EOBS_Iberia_tp")
-#' checkDim(EOBS_Iberia_tas, EOBS_Iberia_tp) # ok, go on
+#' data("EOBS_Iberia_pr")
+#' checkDim(EOBS_Iberia_tas, EOBS_Iberia_pr) # ok, go on
 #' data("NCEP_Iberia_psl")
-#' try(checkDim(EOBS_Iberia_tas, EOBS_Iberia_tp, NCEP_Iberia_psl)) # inconsistent dimensions
+#' try(checkDim(EOBS_Iberia_tas, EOBS_Iberia_pr, NCEP_Iberia_psl)) # inconsistent dimensions
 #' data("CFS_Iberia_tas")
 #' try(checkDim(NCEP_Iberia_psl, CFS_Iberia_tas, dimensions = "member")) 
-#' checkDim(VALUE_Iberia_tas, VALUE_Iberia_tp) # ok, go on
+#' checkDim(VALUE_Iberia_tas, VALUE_Iberia_pr) # ok, go on
 
 
 checkDim <- function(..., dimensions = c("var", "member", "time", "lat", "lon")) {
@@ -461,9 +461,9 @@ checkDim <- function(..., dimensions = c("var", "member", "time", "lat", "lon"))
 #' @export
 #' @examples 
 #' data("EOBS_Iberia_tas")
-#' data("EOBS_Iberia_tp")
+#' data("EOBS_Iberia_pr")
 #' grid1 <- subsetGrid(EOBS_Iberia_tas, season = 1)
-#' grid2 <- subsetGrid(EOBS_Iberia_tp, season = 1)
+#' grid2 <- subsetGrid(EOBS_Iberia_pr, season = 1)
 #' checkSeason(grid1, grid2) # ok, go on
 #' \donttest{
 #' try(checkSeason(grid1, grid2, EOBS_Iberia_tas)) # oops
@@ -475,8 +475,8 @@ checkDim <- function(..., dimensions = c("var", "member", "time", "lat", "lon"))
 #' 
 #' #Station data
 #' data("VALUE_Iberia_tas")
-#' data("VALUE_Iberia_tp")
-#' checkSeason(VALUE_Iberia_tas, VALUE_Iberia_tp)
+#' data("VALUE_Iberia_pr")
+#' checkSeason(VALUE_Iberia_tas, VALUE_Iberia_pr)
 
 
 checkSeason <- function(...) {
@@ -694,12 +694,12 @@ array3Dto2Dmat.stations <- function(array3D) {
 #' @family get.helpers
 #' @export
 #' @examples 
-#' data("CFS_Iberia_tp")
-#' getVarNames(CFS_Iberia_tp)
-#' getVarNames(CFS_Iberia_tp, "long")
+#' data("CFS_Iberia_pr")
+#' getVarNames(CFS_Iberia_pr)
+#' getVarNames(CFS_Iberia_pr, "long")
 #' ## Example with a multigrid
 #' data("CFS_Iberia_tas")
-#' mg <- makeMultiGrid(CFS_Iberia_tas, CFS_Iberia_tp)
+#' mg <- makeMultiGrid(CFS_Iberia_tas, CFS_Iberia_pr)
 #' getVarNames(mg)
 #' getVarNames(mg, "long")
 #' ## Example with station data

R/interpGrid.R

@@ -46,11 +46,11 @@
 #' @export
 #' @examples 
 #' # boreal winter (DJF) precipitation data for the Iberian Peninsula and the period 1983-2002
-#' data(EOBS_Iberia_tp)
-#' plotClimatology(climatology(EOBS_Iberia_tp))
+#' data(EOBS_Iberia_pr)
+#' plotClimatology(climatology(EOBS_Iberia_pr))
 #' # Bilinear interpolation to a regular grid of 0.5 degree 
 #' # resolution centered in the Iberian Peninsula
-#' t1 <- interpGrid(EOBS_Iberia_tp, new.coordinates = list(x = seq(-10,5,.5),
+#' t1 <- interpGrid(EOBS_Iberia_pr, new.coordinates = list(x = seq(-10,5,.5),
 #'                                                         y = seq(36,44,.5)),
 #'                  method = "bilinear",
 #'                  bilin.method = "akima")
@@ -59,29 +59,29 @@
 #' attributes(t1$xyCoords)
 #' 
 #' # Using the coordinate information of another grid via getGrid()
-#' data(NCEP_Iberia_tp)
-#' t2 <- interpGrid(EOBS_Iberia_tp, new.coordinates = getGrid(NCEP_Iberia_tp),
+#' data(NCEP_Iberia_pr)
+#' t2 <- interpGrid(EOBS_Iberia_pr, new.coordinates = getGrid(NCEP_Iberia_pr),
 #'                  method = "nearest")
 #' plotClimatology(climatology(t2), backdrop.theme = "countries")
 #' 
 #' #From station data to grid
-#' data(VALUE_Iberia_tp)
-#' plotClimatology(climatology(VALUE_Iberia_tp), backdrop.theme = "countries")
-#' t3 <- interpGrid(VALUE_Iberia_tp, new.coordinates = getGrid(EOBS_Iberia_tp),
+#' data(VALUE_Iberia_pr)
+#' plotClimatology(climatology(VALUE_Iberia_pr), backdrop.theme = "countries")
+#' t3 <- interpGrid(VALUE_Iberia_pr, new.coordinates = getGrid(EOBS_Iberia_pr),
 #'                  method = "bilinear")
 #' plotClimatology(climatology(t3), backdrop.theme = "countries")
 #' 
 #' #From grid to station data
-#' t4 <- interpGrid(EOBS_Iberia_tp, new.coordinates = getGrid(VALUE_Iberia_tp),
+#' t4 <- interpGrid(EOBS_Iberia_pr, new.coordinates = getGrid(VALUE_Iberia_pr),
 #'                  method = "nearest")
 #' plotClimatology(climatology(t4), backdrop.theme = "countries")
-#' t5 <- interpGrid(EOBS_Iberia_tp, 
+#' t5 <- interpGrid(EOBS_Iberia_pr, 
 #'                new.coordinates = list(x = c(-6.7, -4.5, 2.5), 
 #'                                       y = c(41.8, 40, 39)))
 #' plotClimatology(climatology(t5), backdrop.theme = "countries")
 #' 
 #' #From grid to a single point or station
-#' t6 <- interpGrid(grid = EOBS_Iberia_tp, 
+#' t6 <- interpGrid(grid = EOBS_Iberia_pr, 
 #'                  new.coordinates = list(x = -6.7, y = 41.8))
 #' str(t6$Data)
 

R/localScaling.R

@@ -133,10 +133,10 @@
 #' lines(lc.ref.m$Data[,,,15,15], col = "red")
 #' 
 #' # An example using the "ratio" type:
-#' data("EOBS_Iberia_tp")
-#' grid <- subsetGrid(EOBS_Iberia_tp, years = 1999)
-#' base <- subsetGrid(EOBS_Iberia_tp, years = 1998)
-#' ref <- subsetGrid(EOBS_Iberia_tp, years = 2000)
+#' data("EOBS_Iberia_pr")
+#' grid <- subsetGrid(EOBS_Iberia_pr, years = 1999)
+#' base <- subsetGrid(EOBS_Iberia_pr, years = 1998)
+#' ref <- subsetGrid(EOBS_Iberia_pr, years = 2000)
 #' fun <- list(FUN = "sum")
 #' # We plot the climatologies (total accumulated precip) to have a visual comparison of
 #' # the input grid, the grid used as base and the reference

R/makeMultiGrid.R

@@ -77,8 +77,8 @@
 #' # Load three different multimember grids with the same spatiotemporal ranges:
 #' data("CFS_Iberia_tas")
 #' data("CFS_Iberia_hus850")
-#' data("CFS_Iberia_tp")
-#' mm.mf <- makeMultiGrid(CFS_Iberia_tas, CFS_Iberia_hus850, CFS_Iberia_tp)
+#' data("CFS_Iberia_pr")
+#' mm.mf <- makeMultiGrid(CFS_Iberia_tas, CFS_Iberia_hus850, CFS_Iberia_pr)
 #' # Different fields can not be plotted together in the same plot directly.
 #' # subsetGrid and plotClimatology can be conveniently used to that aim, if needed. For instance:
 # tas <- subsetGrid(mm.mf, var = "tas")

R/plotClimatology.R

@@ -126,8 +126,8 @@
 #'                 scales = list(draw = TRUE))                
 #' 
 #' # Station data:
-#' data("VALUE_Iberia_tp")
-#' plotClimatology(climatology(VALUE_Iberia_tp), backdrop.theme = "countries")
+#' data("VALUE_Iberia_pr")
+#' plotClimatology(climatology(VALUE_Iberia_pr), backdrop.theme = "countries")
 
 
 

R/prinComp.R

@@ -142,9 +142,9 @@
 #' str(pca.mm)
 #' 
 #' # The most complex situation comes from multimember multigrids:
-#' data("CFS_Iberia_tp", "CFS_Iberia_tas")
+#' data("CFS_Iberia_pr", "CFS_Iberia_tas")
 #' # Now the multimember multigrid is constructed
-#' mm.multigrid <- makeMultiGrid(CFS_Iberia_tas, CFS_Iberia_tp)
+#' mm.multigrid <- makeMultiGrid(CFS_Iberia_tas, CFS_Iberia_pr)
 #' # Use different n.eofs for each variable:
 #' pca.mm.mf <- prinComp(mm.multigrid, n.eofs = c(3,5))