| CST_PeriodAccumulation {CSIndicators} | R Documentation |
Period Accumulation on 's2dv_cube' objects
Description
Period Accumulation computes the sum (accumulation) of a given variable in a period. Providing precipitation data, two agriculture indices can be obtained by using this function:
'SprR', Spring Total Precipitation: The total precipitation from April 21th to June 21st.
'HarR', Harvest Total Precipitation: The total precipitation from August 21st to October 21st.
Usage
CST_PeriodAccumulation(
data,
start = NULL,
end = NULL,
time_dim = "time",
rollwidth = NULL,
sdate_dim = "sdate",
frequency = "monthly",
na.rm = FALSE,
ncores = NULL
)
Arguments
data |
An 's2dv_cube' object as provided function |
start |
An optional parameter to defined the initial date of the period
to select from the data by providing a list of two elements: the initial
date of the period and the initial m onth of the period. By default it is
set to NULL and the indicator is computed using all the data provided in
|
end |
An optional parameter to defined the final date of the period to
select from the data by providing a list of two elements: the final day of
the period and the final month of the period. By default it is set to NULL
and the indicator is computed using all the data provided in |
time_dim |
A character string indicating the name of the dimension to
compute the indicator. By default, it is set to 'time'. More than one
dimension name matching the dimensions provided in the object
|
rollwidth |
An optional parameter to indicate the number of time steps the rolling sum is applied to. If it is positive, the rolling sum is applied backwards 'time_dim', if it is negative, it will be forward it. When this parameter is NULL, the sum is applied over all 'time_dim', in a specified period. It is NULL by default. |
sdate_dim |
(Only needed when rollwidth is used). A character string indicating the name of the start date dimension to compute the rolling accumulation. By default, it is set to 'sdate'. |
frequency |
(Only needed when rollwidth is used). A character string indicating the time frequency of the data to apply the rolling accumulation. It can be 'daily' or 'monthly'. If it is set to 'monthly', values from continuous months will be accumulated; if it is 'daliy', values from continuous days will be accumulated. It is set to 'monthly' by default. |
na.rm |
A logical value indicating whether to ignore NA values (TRUE) or not (FALSE). |
ncores |
An integer indicating the number of cores to use in parallel computation. |
Details
There are two possible ways of performing the accumulation. The default one is by accumulating a variable over a dimension specified with 'time_dim'. To chose a specific time period, 'start' and 'end' must be used. The other method is by using 'rollwidth' parameter. When this parameter is a positive integer, the cumulative backward sum is applied to the time dimension. If it is negative, the rolling sum is applied backwards. This function is build to be compatible with other tools in that work with 's2dv_cube' object class. The input data must be this object class. If you don't work with 's2dv_cube', see PeriodAccumulation.
Value
An 's2dv_cube' object containing the accumulated data in the element
data. If parameter 'rollwidth' is not used, it will have the dimensions
of the input parameter 'data' except the dimension where the accumulation has
been computed (specified with 'time_dim'). If 'rollwidth' is used, it will be
of same dimensions as input data. The 'Dates' array is updated to the
dates corresponding to the beginning of the aggregated time period. A new
element called 'time_bounds' will be added into the 'attrs' element in the
's2dv_cube' object. It consists of a list containing two elements, the start
and end dates of the aggregated period with the same dimensions of 'Dates'
element. If 'rollwidth' is used, it will contain the same dimensions of
parameter 'data' and the other elements of the 's2dv_cube' will not be
modified.
Examples
exp <- NULL
exp$data <- array(rnorm(216)*200, dim = c(dataset = 1, member = 2, sdate = 3,
ftime = 9, lat = 2, lon = 2))
class(exp) <- 's2dv_cube'
TP <- CST_PeriodAccumulation(exp, time_dim = 'ftime')
exp$data <- array(rnorm(5 * 3 * 214 * 2),
c(memb = 5, sdate = 3, ftime = 214, lon = 2))
Dates <- c(seq(as.Date("01-05-2000", format = "%d-%m-%Y"),
as.Date("30-11-2000", format = "%d-%m-%Y"), by = 'day'),
seq(as.Date("01-05-2001", format = "%d-%m-%Y"),
as.Date("30-11-2001", format = "%d-%m-%Y"), by = 'day'),
seq(as.Date("01-05-2002", format = "%d-%m-%Y"),
as.Date("30-11-2002", format = "%d-%m-%Y"), by = 'day'))
dim(Dates) <- c(sdate = 3, ftime = 214)
exp$attrs$Dates <- Dates
SprR <- CST_PeriodAccumulation(exp, start = list(21, 4), end = list(21, 6),
time_dim = 'ftime')
dim(SprR$data)
head(SprR$attrs$Dates)
HarR <- CST_PeriodAccumulation(exp, start = list(21, 8), end = list(21, 10),
time_dim = 'ftime')
dim(HarR$data)
head(HarR$attrs$Dates)