Changes between Version 7 and Version 8 of udg/ecoms/RPackage/examples/continentalSelection

Timestamp:

Jun 17, 2014 4:45:27 PM (8 years ago)

Author:

juaco

Comment:

--

udg/ecoms/RPackage/examples/continentalSelection

@@ -41 +41 @@
 [[Image(image-20140617-130616.png)]]
 
+It is possible to load now the reference observations for the spatio-temporal domain selected, using the same values in the corresponding arguments:
+
+{{{
+> ex2.obs <- loadECOMS(dataset = "WFDEI", var = "tasmin", lonLim = c(-15,35), latLim = c(32, 75), season = c(12,1,2), years = 2001:2010)
+[2014-06-17 16:28:13] Defining homogeneization parameters for variable "tasmin"
+[2014-06-17 16:28:13] Defining geo-location parameters
+[2014-06-17 16:28:13] Defining time selection parameters
+[2014-06-17 16:28:32] Done
+> print(object.size(ex2.obs), units = "Mb")
+60.6 Mb
+}}}
+
+This is the map of the observed mean minimum surface temperature observed for DJF 2001-2010:
+
+{{{
+> observed <- apply(ex2.obs$Data, FUN = mean, MARGIN = c(1,2))
+> x.obs <- ex2.obs$xyCoords$x
+> y.obs <- ex2.obs$xyCoords$y
+> image.plot(x.obs, y.obs, observed, asp = 1, xlab = "", ylab = "", main = "Mean minimum surface temp observed")
+> world(add=TRUE)
+}}}
+
+[[Image(image-20140617-163609.png)]]
+
+Note that WFDEI provides data for land areas only.
+
+
+
+
 
 
@@ -49 +78 @@
 
 
-{{{#!comment
-= Alternative visualization tools: Monsoon in the Indian subcontinent
 
-So far we have shown plotting examples using the trellis plots generated by the `spplot` method. In this examples we show alternative plotting options using more standard R plotting functions for gridded data. To this aim, we load the precipitation data of 1997 for the lead month 1 forecast over the Indian subcontinent, considering the monsoon season from June to September:
-
-{{{
-monsoon <- loadSeasonalForecast("CFS", var="tp", members=16, lonLim=c(65,92), latLim=c(5,37), season=6:9, years=1997)
-}}}
-
-The georeferencing of the data is stored as a `SpatialGrid`, which has several convenient attributes for an effective description of a gridded field, including the possibility of defining a coordinate reference system, highly useful -sometimes indispensable- for many geospatial operations.
-Plotting objects inheriting from this class or related classes (e.g. `SpatialGridDataFrame`) is straightforward using the `spplot` methods, as described in [wiki:../Trellis the previous example], but it is not directly usable as input for other plotting methods in R.
-Next, we present some typical plotting functions and how to extract the spatial coordinates in a suitable format for plotting.
-
-The total precipitation field is calculated as the total accumulated precipitation during the selected period for each model grid cell:
-
-{{{
-tp <- colSums(monsoon$MemberData$Member_16) 
-}}}
-
-And this is the classical way of displaying the data using the `spplot` method:
-
-{{{
-sgdf <- SpatialGridDataFrame(monsoon$LonLatCoords, as.data.frame(tp))
-data(world_map)
-wl <- as(world_map, "SpatialLines")
-wlines <- list("sp.lines", wl)
-spplot(sgdf, scales = list(draw = TRUE), sp.layout = list(wlines), col.regions = rev(topo.colors(41)))
-}}}
-
-[[Image(spplot.png)]]
-
-It is also straightforward to represent the precipitation using contour lines:
-
-{{{
-spplot(sgdf, scales = list(draw = TRUE), contour = TRUE, col = "red", col.regions = rev(topo.colors(41)))
-}}}
-
-[[Image(spplot_contour.png)]]
-
-
-
-The help files of functions `image` and `contour` for instance, indicate the type of data structure required for displaying three-dimensional or spatial data (''images'') by many R standard functions.
-Essentially, this is a list of elements specifying the x and y positions of the elements to be displayed as a grid, being the field z a matrix of values whose positions coincide with those of the x and y elements.
-
-As an illustration, the following function produces such a list from an input `SpatialGridDataFrame`, by indicating also the corresponding data column to be represented, as in the argument `zcol` of `spplot`. This function takes care of the appropriate ordering of the data for spatial consistency. Note that by default, if `zcol` is omitted, the function will represent the first column of the data slot.
-
-{{{
-sgdf2xyz <- function(sgdf, zcol = 1) {
-      coords <- coordinates(sgdf)
-      z <- slot(sgdf, "data")[ ,zcol]
-      aux <- cbind(coords, z)
-      aux.ordered <- aux[order(aux[ ,2], aux[ ,1]), ]
-      x <- unique(aux.ordered[ ,1])
-      y <- unique(aux.ordered[ ,2])
-      z <- t(matrix(aux.ordered[ ,3], nrow = length(y), ncol = length(x), byrow = TRUE))
-      xyz.list <- list("x" = x, "y" = y, "z" = z)
-      return(xyz.list)
-}
-}}}
-
-We create the xyz object for data visualization using several basic R functions:
-
-{{{
-xyz <- sgdf2xyz(sgdf)
-}}}
-
-== `image` function
-{{{
-image(xyz, asp = 1, col = rev(topo.colors(21)))
-lines(wl)
-}}}
-
-[[Image(image.png)]]
-
-
-== `contour` function
-
-`contour` can be used alone or in combination with other plots by setting the argument `add = TRUE`
-
-{{{
-par(mfrow = c(1,2))
-contour(xyz, levels=seq(0,4000,200), labcex = 1.2)
-image(xyz, col = terrain.colors(21))
-contour(xyz, col = "blue", add = TRUE)
-# Use dev.off() To restore the original par settings
-}}}
-
-[[Image(contour.png)]]
-
-== `filled.contour` function
-
-Filled contour produces a nice output with a graduated colorbar, but placing lines or other elements on the plot is not straightforward...
-
-
-[[Image(filled_contour.png)]]
-