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#*******************************************************************************
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# Title: Example script to create 's2dv_cube' objects
# Author: Eva Rifà Rovira
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# Date: 16/01/2024
#*******************************************************************************
Eva Rifà
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# This example shows how to create an 's2dv_cube' object.
# There are two ways of creating an 's2dv_cube' object. The first way is
# to use the function s2dv_cube(): create an 's2dv_cube' from scratch with any
# data. In the second example we see the other method with the function
# CST_Start(). This function is to create an 's2dv_cube' from a
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# 'startR_array' or 'load' object.
#-------------------------------------------------------------------------------
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# Needed packages
################################################################################
#-----------------------------------------------------
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# Example 1: Function s2dv_cube() from defined data
#-----------------------------------------------------
# (1.1) Minimal use case, with s2dv_cube function
# We define the array with named dimensions:
# In this example we use the function s2dv_cube() to create an object of class
# 's2dv_cube' with the correct structure.
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dat <- array(1:100, dim = c(time = 10, lat = 4, lon = 10))
# We define the coordinates as a list of vectors:
coords <- list(time = 1:10, lat = 43:40, lon = 0:9)
# The metadata:
metadata <- list(tas = list(level = '2m'),
lon = list(cdo_grid_name = 'r360x181'),
lat = list(cdo_grid_name = 'r360x181'))
# 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)
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:
# > 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 ...
#-----------------------------------------------------
#-----------------------------------------------------
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# (2.1) Using CST_Start
# For this case, we use a random example.
# 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())
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$ensemble
# [1] 1 2
# attr(,"indices")
# [1] TRUE
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# Now we take a look into the Dates array. It must have the time dimensions
# of the data.
dim(res$attrs$Dates)
# sdate time
# 2 2
# To see the nested list structure of the object, we just need to use the
# function str():
str(res)
# (2.1) Using as.s2dv_cube() function
# We'll load the data with Start and then we'll transform the 'startR_array'
# to 's2dv_cube' object with the function as.s2dv_cube(). We are going
# to load the same data as before:
repos1 <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$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 ...
# ...
################################################################################