#******************************************************************************* # Title: Example script to create 's2dv_cube' objects # Author: Eva Rifà Rovira # Date: 16/01/2024 #******************************************************************************* # This example shows how to create an 's2dv_cube' object. # There are two ways of creating an 's2dv_cube' object. # (1) With the function s2dv_cube(): create it from scratch with any data. # (2) With the function CST_Start(). This function returns an 's2dv_cube' # from an 'startR_array'. # Needed packages library(CSTools) library(startR) ################################################################################ #----------------------------------------------------- # Example 1: Function s2dv_cube() from defined data #----------------------------------------------------- # Minimal use case, with s2dv_cube function. # In this example we use the function s2dv_cube() to create an object of class # 's2dv_cube' with the correct structure. # (1) We define the array with named dimensions: dat <- array(1:100, dim = c(time = 10, lat = 4, lon = 10)) # (2) We define the coordinates as a list of vectors: coords <- list(time = 1:10, lat = 43:40, lon = 0:9) # (3) The metadata: metadata <- list(tas = list(level = '2m'), lon = list(cdo_grid_name = 'r360x181'), lat = list(cdo_grid_name = 'r360x181')) # (4) The creation of Dates array. # First the initial date: ini_date <- as.POSIXct('2010-01-01', format = '%Y-%m-%d') # The sequence of dates dates <- seq(ini_date, by = 'days', length.out = 10) # We define the dates dimensions dim(dates) <- c(time = 10) # (5) We call the function s2dv_cube() dat_cube <- s2dv_cube(data = dat, coords = coords, varName = 'tas', metadata = metadata, Dates = dates, when = "2019-10-23 19:15:29 CET", source_files = c("/path/to/file1.nc", "/path/to/file2.nc"), Datasets = 'test_dataset') # We print the result to see the 's2dv_cube' structure: # > dat_cube # 's2dv_cube' # Data [ 1, 2, 3, 4, 5, 6, 7, 8 ... ] # Dimensions ( time = 10, lat = 4, lon = 10 ) # Coordinates # * time : 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 # * lat : 43, 42, 41, 40 # * lon : 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 # Attributes # Dates : 2010-01-01 2010-01-02 2010-01-03 2010-01-04 2010-01-05 ... # varName : tas # metadata : # tas # other : level # lon # other : cdo_grid_name # lat # other : cdo_grid_name # Datasets : test_dataset # when : 2019-10-23 19:15:29 CET # source_files : /path/to/file1.nc ... #----------------------------------------------------- # Example 2: Function as.s2dv_cube() #----------------------------------------------------- # (1) Example using CST_Start # NOTE 1: CST_Start() is just a wrapper of function Start() with the # transformation to 's2dv_cube' object. # NOTE 2: In order that the input argument auxiliary functions from startR # work, we need to call them explicitly the startR namespace. # (e.g. startR::indices()) # We just need to define a CST_Start call with all the information: repos1 <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" repos2 <- "/esarchive/exp/ecmwf/system4_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" res <- CST_Start(dat = list(list(name = 'system4_m1', path = repos2), list(name = 'system5_m1', path = repos1)), var = c('tas', 'sfcWind'), sdate = c('20160101', '20170101'), ensemble = startR::indices(1:2), time = startR::indices(1:2), lat = startR::indices(1:10), lon = startR::indices(1:10), synonims = list(lat = c('lat', 'latitude'), lon = c('lon', 'longitude')), return_vars = list(time = 'sdate', longitude = 'dat', latitude = 'dat'), metadata_dims = c('dat', 'var'), retrieve = TRUE) # Now we can explore the object: # 1st level names(res) # "data" "dims" "coords" "attrs" dim(res$data) # dat var sdate ensemble time lat lon # 2 2 2 2 2 10 10 res$coords$lon # [1] 0.000000 0.703125 1.406250 2.109375 2.812500 3.515625 4.218750 4.921875 # [9] 5.625000 6.328125 attr(res$coords$lon, 'indices') # [1] FALSE # NOTE: The attribute 'indices' is FALSE, it means that the longitude elements # are the actual values of longitude coordinate. res$coords$ensemble # [1] 1 2 # attr(,"indices") # [1] TRUE # Now we take a look into the Dates array. It must have the time dimensions # of the data. dim(res$attrs$Dates) # sdate time # 2 2 # To see the nested list structure of the object, we just need to use the # function str(): str(res) #----------------------------------------------------- # (2) Example using as.s2dv_cube() function # We'll load the data with Start and then we'll transform the 'startR_array' # to 's2dv_cube' object with the function as.s2dv_cube(). We are going # to load the same data as before, with the same call: repos1 <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" repos2 <- "/esarchive/exp/ecmwf/system4_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" res <- Start(dat = list(list(name = 'system4_m1', path = repos2), list(name = 'system5_m1', path = repos1)), var = c('tas', 'sfcWind'), sdate = c('20160101', '20170101'), ensemble = startR::indices(1:2), time = startR::indices(1:2), lat = startR::indices(1:10), lon = startR::indices(1:10), synonims = list(lat = c('lat', 'latitude'), lon = c('lon', 'longitude')), return_vars = list(time = 'sdate', longitude = 'dat', latitude = 'dat'), metadata_dims = c('dat', 'var'), retrieve = TRUE) # Now, we use the function as.s2dv_cube() to transform the 'startR_array' # into an 's2dv_cube': res_cube <- as.s2dv_cube(res) # If we call directly the object directly into the terminal, we can see # all the elements nicely: # > res_cube # 's2dv_cube' # Data [ 248.241973876953, 247.365753173828, 6.80753087997437, 5.46453714370728, 247.256896972656, 248.500869750977, 6.25862503051758, 5.76889991760254 ... ] # Dimensions ( dat = 2, var = 2, sdate = 2, ensemble = 2, time = 2, lat = 10, lon = 10 ) # Coordinates # * dat : system4_m1, system5_m1 # * var : tas, sfcWind # * sdate : 20160101, 20170101 # ensemble : 1, 2 # time : 1, 2 # * lat : 89.4628215685774, 88.7669513528422, 88.0669716474306, 87.366063433082, 86.6648030134408, 85.9633721608804, 85.2618460607126, 84.5602613830534, 83.8586381286076, 83.1569881285417 # * lon : 0, 0.703125, 1.40625, 2.109375, 2.8125, 3.515625, 4.21875, 4.921875, 5.625, 6.328125 # Attributes # Dates : 2016-02-01 2017-02-01 2016-03-01 2017-03-01 # varName : tas sfcWind # metadata : # time # units : hours since 2016-01-01 00:00:00 # other : ndims, size, standard_name, calendar # lon # units : degrees_east # long name : longitude # other : ndims, size, standard_name, axis # lat # units : degrees_north # long name : latitude # other : ndims, size, standard_name, axis # tas # units : K # long name : 2 metre temperature # other : prec, dim, unlim, make_missing_value, missval, hasAddOffset, hasScaleFact, code, table, grid_type # sfcWind # units : m s**-1 # long name : 10 meter windspeed # other : prec, dim, unlim, make_missing_value, missval, hasAddOffset, hasScaleFact, code, table, grid_type # Datasets : system4_m1 ... # when : 2024-01-17 11:38:27 # source_files : /esarchive/exp/ecmwf/system4_m1/monthly_mean/tas_f6h/tas_20160101.nc ... # load_parameters : # ( system4_m1 ) : dat = system4_m1, var = tas ..., sdate = 20160101 ... # ... ################################################################################