metaclipR.Aggregation: Directed metadata graph construction for Transformations of...
In Predictia/metaclipR: A climate4R extension for METACLIP provenance description

Description Usage Arguments Details Value Author(s) References See Also Examples

Build a directed metadata graph describing a Transformation on a climate4R grid that is a subset of Dataset.

1 2	metaclipR.Aggregation(package = "transformeR", version = "1.3.2", graph, fun = "aggregateGrid", arg.list = NULL)

`package`	package
`version`	version
`graph`	A previous metaclipR data structure from which the current step follows
`fun`	function name. Unused (set to `"aggregateGrid"`)
`arg.list`	Argument list. See details

This function takes as reference the semantics defined in the Data Source and Transformation ontology defined in the Metaclip Framework (http://metaclip.predictia.es/).

Argument list

The following list of arguments is required to define an aggregation:

aggr.mem = list(FUN = NULL)
aggr.d = list(FUN = NULL)
aggr.m = list(FUN = NULL)
aggr.y = list(FUN = NULL)
aggr.lat = list(FUN = NULL)
weight.by.lat = TRUE
aggr.lon = list(FUN = NULL)

The different arguments are explained in the the help page of aggregateGrid

A named list with the updated graph in element "graph" and the parent node name ("parentnodename"), needed for linking subsequent operations.

D. San Martín, J. Bedia

Climate4R page at University of Cantabria

Other transformation: metaclip.graph.Command, metaclipR.AnomalyCalculation, metaclipR.Climatology, metaclipR.Dataset, metaclipR.Ensemble, metaclipR.Regridding, metaclipR.etccdi

## Not run: 
require(transformeR)
require(igraph)
pkg <- "transformeR"
v <- "1.1.1"
# Assume a given hindcast DatasetSubset (we first simplify a)
data("CFS_Iberia_hus850")
DS <- subsetGrid(CFS_Iberia_hus850, members = 1:3, years = 1989:1991)
graph <- metaclipR.DatasetSubset(package = pkg,
                                 version = v,
                                 arg.list = list(members = 1:3,
                                                 years = 1989:1991),
                                 fun = "subsetGrid",
                                 output = "DS")
# An aggregation is performed on the example data. In this case, 
# The forecast is aggregated by members (ensemble mean), and in space
# (along longitude and latitude) to obtain a spatial mean. Original data is daily,
# and an annual aggregation is performed, considering the monthly means and the
# annual maxima:
fun <- "aggregateGrid"
arg.list <- list("aggr.mem" = list(FUN = "mean", na.rm = TRUE),
                 "aggr.d" = list(FUN = NULL),
                 "aggr.m" = list(FUN = "mean", na.rm = TRUE),
                 "aggr.y" = list(FUN = "max", na.rm = TRUE),
                 "aggr.lat" = list(FUN = "mean", na.rm = TRUE),
                 "aggr.lon" = list(FUN = "mean", na.rm = TRUE),
                 "weight.by.lat" = TRUE)
# The aggregation is undertaken with transformeR::subsetGrid:
arg.list[["grid"]] <- DS
out <- do.call(fun, arg.list)
# This is how metadata is encoded:
# 1.) We identify the origin node from which the first transformation hangs:
graph$parentnodename
# 2.) metaclipR.Aggregation is called:
graph <- metaclipR.Aggregation(package = pkg,
                               version = v,
                               graph = graph,
                               fun = fun,
                               arg.list = arg.list)
# And this is the metadata description stored in the igraph-class object:
plot(graph$graph)

## End(Not run)