Nothing
inst/doc/lpjml-data.R
In lpjmlkit: Toolkit for Basic LPJmL Handling
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Read monthly runoff with meta file.
# runoff <- read_io("./output/mrunoff.bin.json")
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Read monthly runoff data with header.
# runoff <- read_io("./output/mrunoff.clm",
# # If the clm version is lower than 4 set nbands and nstep
# # manually so that month dimension is recognized correctly.
# nbands = 1,
# nstep = 12,
# # Useful additional information that is not needed to read the
# # Data.
# variable = "runoff",
# descr = "monthly runoff",
# unit = "mm/month")
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Read monthly runoff raw data.
# runoff <- read_io("./output/mrunoff.bin",
# # Specify all meta data if they differ from the function
# # default values.
# ...)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# runoff$data
# # , , band = 1
# #
# # time
# # cell 1901-01-31 1901-02-28 1901-03-31 1901-04-30
# # 0 2.427786e+02 1.265680e+02 2.279087e+02 2.027685e+02
# # 1 4.189225e-14 1.032507e-16 0.000000e+00 0.000000e+00
# # 2 3.860512e-14 0.000000e+00 0.000000e+00 0.000000e+00
# # 3 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
# # 4 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
# # 5 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# runoff$meta
# # $sim_name "lu_cf"
# # $source "LPJmL C Version 5.3.001"
# # $history "./LPJmL_internal/bin/lpjml ./configurations/config_lu_cf.json"
# # $variable "runoff"
# # $descr "monthly runoff"
# # $unit "mm/month"
# # $nbands 1
# # $nyear 111
# # $firstyear 1901
# # $lastyear 2011
# # $nstep 12
# # $timestep 1
# # $ncell 67420
# # $firstcell 0
# # $cellsize_lon 0.5
# # $cellsize_lat 0.5
# # $datatype "float"
# # $scalar 1
# # $order "cellseq"
# # $bigendian FALSE
# # $format "raw"
# # $filename "runoff.bin"
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Self print; also via print(runoff).
# runoff
# # $meta |>
# # .$sim_name "lu_cf"
# # .$variable "runoff"
# # .$descr "monthly runoff"
# # .$unit "mm/month"
# # .$nbands 1
# # .$nyear 111
# # .$nstep 12
# # .$timestep 1
# # .$ncell 67420
# # .$cellsize_lon 0.5
# # .$cellsize_lat 0.5
# # Note: not printing all meta data, use $meta to get all.
# # $data |>
# # dimnames() |>
# # .$cell "0" "1" "2" "3" ... "67419"
# # .$time ""1901-01-31" "1901-02-28" "1901-03-31" "1901-04-30" ... "2011-12-31"
# # .$band "1"
# # $summary()
# # 1
# # Min. : 0.0000
# # 1st Qu.: 0.0619
# # Median : 4.4320
# # Mean : 28.7658
# # 3rd Qu.: 27.5627
# # Max. :2840.9602
# # Note: summary is not weighted by grid area.
#
# # Return the dimension length of $data array; dimnames function is also available.
# dim(runoff)
# # cell time band
# # 67420 1332 1
#
# # Plot as maps or time series, depending on the dimensions.
# plot(runoff)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Object- oriented (R6 class) notation (assigning grid directly to runoff)
# runoff$add_grid()
#
# # Common R notation (overwriting the original object)
# runoff <- add_grid(runoff)
#
# # Use the read_io arguments if a grid file cannot be detected automatically.
# runoff <- add_grid(runoff, "./output/grid.clm")
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Transform into lon and lat dimensions. If add_grid has not been executed
# # before it is called implicitly.
# runoff <- transform(runoff, to = "lon_lat")
# runoff
# # [...]
# # $data |>
# # dimnames() |>
# # .$lat "-55.75" "-55.25" "-54.75" "-54.25" ... "83.75"
# # .$lon "-179.75" "-179.25" "-178.75" "-178.25" ... "179.75"
# # .$time "1901-01-31" "1901-02-28" "1901-03-31" "1901-04-30" ... "2011-12-31"
# # .$band "1"
# # [...]
#
# # Transform into year and month dimensions (day not available for monthly
# # runoff)
# runoff <- transform(runoff, to = "year_month_day")
# runoff
# # [...]
# # $data |>
# # dimnames() |>
# # .$lat "-55.75" "-55.25" "-54.75" "-54.25" ... "83.75"
# # .$lon "-179.75" "-179.25" "-178.75" "-178.25" ... "179.75"
# # .$month "1" "2" "3" "4" ... "12"
# # .$year "1901" "1902" "1903" "1904" ... "2011"
# # .$band "1"
# # [...]
#
# # Transform back to original dimensions.
# runoff <- transform(runoff, to = c("cell", "time"))
# runoff
# # [...]
# # $data |>
# # dimnames() |>
# # .$cell "0" "1" "2" "3" ... "67419"
# # .$time "1901-01-31" "1901-02-28" "1901-03-31" "1901-04-30" ... "2100-12-31"
# # .$band "1"
# # [...]
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Subset by dimnames (character string).
# runoff <- subset(runoff, time = "1991-05-31")
# runoff
# # $meta |>
# # .$nyear 1
# # .$ncell 67420
# # .$subset TRUE
# # [...]
# # Note: not printing all meta data, use $meta to get all.
# # $data |>
# # dimnames() |>
# # .$cell "0" "1" "2" "3" ... "67419"
# # .$time "1991-05-31"
# # .$band "1"
# # [...]
#
# # Subset by indices
# runoff <- subset(runoff, cell = 28697:28700)
# runoff
# # $meta |>
# # .$nyear 1
# # .$ncell 4
# # .$subset TRUE
# # [...]
# # Note: not printing all meta data, use $meta to get all.
# # $data |>
# # dimnames() |>
# # .$cell "28696" "28697" "28698" "28699"
# # .$time "1991-05-31"
# # .$band "1"
# # [...]
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Export as an array with subset of first 6 time steps and aggregation along
# # the dimension cell (mean).
# as_array(runoff,
# subset = list(time = 1:6),
# aggregate = list(cell = mean))
# # band
# # time 1
# # 1901-01-31 19.49611
# # 1901-02-28 20.28368
# # 1901-03-31 27.93595
# # 1901-04-30 36.90505
# # 1901-05-31 39.38885
# # 1901-06-30 32.80252
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Export as a tibble with subset of first 6 time steps
# as_tibble(runoff, subset = list(time = 1:6))
# # # A tibble: 404,520 × 4
# # cell time band value
# # <fct> <fct> <fct> <dbl>
# # 1 0 1901-01-31 1 184.
# # 2 1 1901-01-31 1 0
# # 3 2 1901-01-31 1 0
# # 4 3 1901-01-31 1 0
# # 5 4 1901-01-31 1 0
# # 6 5 1901-01-31 1 0
# # 7 6 1901-01-31 1 0
# # 8 7 1901-01-31 1 0
# # 9 8 1901-01-31 1 0
# # 10 9 1901-01-31 1 0
# # # … with 404,510 more rows
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Export the first time step as a RasterLayer object from the raster package.
# as_raster(runoff, subset = list(time = 1))
# # class : RasterLayer
# # dimensions : 280, 720, 201600 (nrow, ncol, ncell)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # crs : +proj=longlat +datum=WGS84 +no_defs # nolint:commented_code_linter.
# # source : memory
# # names : runoff
# # values : -1.682581e-13, 671.8747 (min, max)
#
# # Export the first time step as a terra SpatRaster object.
# as_terra(runoff, subset = list(time = 1))
# # class : SpatRaster
# # dimensions : 280, 720, 1 (nrow, ncol, nlyr)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # coord. ref. : lon/lat WGS 84 (EPSG:4326)
# # source : memory
# # name : runoff
# # min value : -1.682581e-13
# # max value : 6.718747e+02
# # unit : mm/month # nolint:commented_code_linter.
#
# # Export the first 4 times step as a RasterBrick object.
# as_raster(runoff, subset = list(time = 1:4))
# # class : RasterBrick
# # dimensions : 280, 720, 201600, 4 (nrow, ncol, ncell, nlayers)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # crs : +proj=longlat +datum=WGS84 +no_defs # nolint:commented_code_linter.
# # source : memory
# # names : X1901.01.31, X1901.02.28, X1901.03.31, X1901.04.30
# # min values : -1.682581e-13, -1.750495e-13, -2.918900e-13, -1.516298e-13
# # max values : 671.8747, 785.2363, 828.2853, 987.4359
#
# # Export the first 4 time steps as a terra SpatRaster object.
# as_terra(runoff, subset = list(time = 1:4))
# # class : SpatRaster
# # dimensions : 280, 720, 4 (nrow, ncol, nlyr)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # coord. ref. : lon/lat WGS 84 (EPSG:4326)
# # source : memory
# # names : 1901-01-31, 1901-02-28, 1901-03-31, 1901-04-30
# # min values : -1.682581e-13, -1.750495e-13, -2.918900e-13, -1.516298e-13
# # max values : 6.718747e+02, 7.852363e+02, 8.282853e+02, 9.874359e+02
# # unit : mm/month, mm/month, mm/month, mm/month
# # time (days) : 1901-01-31 to 1901-04-30
## ----setup, echo = TRUE, eval = FALSE, highlight = TRUE-----------------------
# library(lpjmlkit)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# npp <- read_io(filename = "./output/npp.bin.json",
# subset = list(year = 1970:2011))
#
# # Transform "time" into "year" and "month" dimensions.
# npp$transform(to = "year_month_day")
#
# # Plot timeseries with aggregated cell and month dimensions. Note that spatial
# # aggregation across cells is not area-weighted.
# plot(npp,
# aggregate = list(cell = mean, month = sum))
#
# # Also available as data array.
# global_npp_trend <- as_array(npp,
# aggregate = list(cell = mean, month = sum))
#
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# runoff <- read_io(filename = "./output/runoff.bin.json",
# subset = list(year = 2002:2011))
#
# # Usage of pipe operator operator |> (%>% via package magrittr R version < 4.1)
# runoff |>
# # Transform the time and space dimensions ...
# transform(to = c("year_month_day", "lon_lat")) |>
# # ... to subset summer months as well as northern hemisphere (positive)
# # latitudes.
# subset(month = 6:9,
# lat = seq(0.25, 83.75, by = 0.5)) |>
# # for plotting sum up summer month and take the average over the years
# plot(aggregate = list(year = mean, month = sum))
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# gpp <- read_io(filename = "./output/gpp.bin.json",
# subset = list(year = 2002:2011))
#
# # Transform into lon_lat format.
# gpp$transform(to = "lon_lat")
#
# # Plot GPP per latitude.
# plot(gpp, aggregate = list(time = mean, lon = mean))
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Coordinates for cells around Potsdam.
# coordinates <- tibble::tibble(lat = c(52.25, 52.400922, 53.25),
# lon = c(12.75, 13.03638, 12.75))
#
# # Complete pipe notation, from reading to plotting data.
# read_io(
# filename = glue("./cftfrac.bin.json"),
# subset = list(year = 2000:2018)
# ) |>
# transform(to = "lon_lat") |>
# # Special case for subsetting of lat and lon pairs
# subset(coords = coordinates) |>
# # Mean across spatial dimensions
# plot(aggregate = list(lon = mean, lat = mean))
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# my_copy <- lpjml_data
# # Instead use:
# my_copy <- lpjml_data$clone(deep = TRUE)
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Read monthly runoff with meta file.
# runoff <- read_io("./output/mrunoff.bin.json")
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Read monthly runoff data with header.
# runoff <- read_io("./output/mrunoff.clm",
# # If the clm version is lower than 4 set nbands and nstep
# # manually so that month dimension is recognized correctly.
# nbands = 1,
# nstep = 12,
# # Useful additional information that is not needed to read the
# # Data.
# variable = "runoff",
# descr = "monthly runoff",
# unit = "mm/month")
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Read monthly runoff raw data.
# runoff <- read_io("./output/mrunoff.bin",
# # Specify all meta data if they differ from the function
# # default values.
# ...)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# runoff$data
# # , , band = 1
# #
# # time
# # cell 1901-01-31 1901-02-28 1901-03-31 1901-04-30
# # 0 2.427786e+02 1.265680e+02 2.279087e+02 2.027685e+02
# # 1 4.189225e-14 1.032507e-16 0.000000e+00 0.000000e+00
# # 2 3.860512e-14 0.000000e+00 0.000000e+00 0.000000e+00
# # 3 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
# # 4 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
# # 5 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# runoff$meta
# # $sim_name "lu_cf"
# # $source "LPJmL C Version 5.3.001"
# # $history "./LPJmL_internal/bin/lpjml ./configurations/config_lu_cf.json"
# # $variable "runoff"
# # $descr "monthly runoff"
# # $unit "mm/month"
# # $nbands 1
# # $nyear 111
# # $firstyear 1901
# # $lastyear 2011
# # $nstep 12
# # $timestep 1
# # $ncell 67420
# # $firstcell 0
# # $cellsize_lon 0.5
# # $cellsize_lat 0.5
# # $datatype "float"
# # $scalar 1
# # $order "cellseq"
# # $bigendian FALSE
# # $format "raw"
# # $filename "runoff.bin"
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Self print; also via print(runoff).
# runoff
# # $meta |>
# # .$sim_name "lu_cf"
# # .$variable "runoff"
# # .$descr "monthly runoff"
# # .$unit "mm/month"
# # .$nbands 1
# # .$nyear 111
# # .$nstep 12
# # .$timestep 1
# # .$ncell 67420
# # .$cellsize_lon 0.5
# # .$cellsize_lat 0.5
# # Note: not printing all meta data, use $meta to get all.
# # $data |>
# # dimnames() |>
# # .$cell "0" "1" "2" "3" ... "67419"
# # .$time ""1901-01-31" "1901-02-28" "1901-03-31" "1901-04-30" ... "2011-12-31"
# # .$band "1"
# # $summary()
# # 1
# # Min. : 0.0000
# # 1st Qu.: 0.0619
# # Median : 4.4320
# # Mean : 28.7658
# # 3rd Qu.: 27.5627
# # Max. :2840.9602
# # Note: summary is not weighted by grid area.
#
# # Return the dimension length of $data array; dimnames function is also available.
# dim(runoff)
# # cell time band
# # 67420 1332 1
#
# # Plot as maps or time series, depending on the dimensions.
# plot(runoff)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Object- oriented (R6 class) notation (assigning grid directly to runoff)
# runoff$add_grid()
#
# # Common R notation (overwriting the original object)
# runoff <- add_grid(runoff)
#
# # Use the read_io arguments if a grid file cannot be detected automatically.
# runoff <- add_grid(runoff, "./output/grid.clm")
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Transform into lon and lat dimensions. If add_grid has not been executed
# # before it is called implicitly.
# runoff <- transform(runoff, to = "lon_lat")
# runoff
# # [...]
# # $data |>
# # dimnames() |>
# # .$lat "-55.75" "-55.25" "-54.75" "-54.25" ... "83.75"
# # .$lon "-179.75" "-179.25" "-178.75" "-178.25" ... "179.75"
# # .$time "1901-01-31" "1901-02-28" "1901-03-31" "1901-04-30" ... "2011-12-31"
# # .$band "1"
# # [...]
#
# # Transform into year and month dimensions (day not available for monthly
# # runoff)
# runoff <- transform(runoff, to = "year_month_day")
# runoff
# # [...]
# # $data |>
# # dimnames() |>
# # .$lat "-55.75" "-55.25" "-54.75" "-54.25" ... "83.75"
# # .$lon "-179.75" "-179.25" "-178.75" "-178.25" ... "179.75"
# # .$month "1" "2" "3" "4" ... "12"
# # .$year "1901" "1902" "1903" "1904" ... "2011"
# # .$band "1"
# # [...]
#
# # Transform back to original dimensions.
# runoff <- transform(runoff, to = c("cell", "time"))
# runoff
# # [...]
# # $data |>
# # dimnames() |>
# # .$cell "0" "1" "2" "3" ... "67419"
# # .$time "1901-01-31" "1901-02-28" "1901-03-31" "1901-04-30" ... "2100-12-31"
# # .$band "1"
# # [...]
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Subset by dimnames (character string).
# runoff <- subset(runoff, time = "1991-05-31")
# runoff
# # $meta |>
# # .$nyear 1
# # .$ncell 67420
# # .$subset TRUE
# # [...]
# # Note: not printing all meta data, use $meta to get all.
# # $data |>
# # dimnames() |>
# # .$cell "0" "1" "2" "3" ... "67419"
# # .$time "1991-05-31"
# # .$band "1"
# # [...]
#
# # Subset by indices
# runoff <- subset(runoff, cell = 28697:28700)
# runoff
# # $meta |>
# # .$nyear 1
# # .$ncell 4
# # .$subset TRUE
# # [...]
# # Note: not printing all meta data, use $meta to get all.
# # $data |>
# # dimnames() |>
# # .$cell "28696" "28697" "28698" "28699"
# # .$time "1991-05-31"
# # .$band "1"
# # [...]
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Export as an array with subset of first 6 time steps and aggregation along
# # the dimension cell (mean).
# as_array(runoff,
# subset = list(time = 1:6),
# aggregate = list(cell = mean))
# # band
# # time 1
# # 1901-01-31 19.49611
# # 1901-02-28 20.28368
# # 1901-03-31 27.93595
# # 1901-04-30 36.90505
# # 1901-05-31 39.38885
# # 1901-06-30 32.80252
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Export as a tibble with subset of first 6 time steps
# as_tibble(runoff, subset = list(time = 1:6))
# # # A tibble: 404,520 × 4
# # cell time band value
# # <fct> <fct> <fct> <dbl>
# # 1 0 1901-01-31 1 184.
# # 2 1 1901-01-31 1 0
# # 3 2 1901-01-31 1 0
# # 4 3 1901-01-31 1 0
# # 5 4 1901-01-31 1 0
# # 6 5 1901-01-31 1 0
# # 7 6 1901-01-31 1 0
# # 8 7 1901-01-31 1 0
# # 9 8 1901-01-31 1 0
# # 10 9 1901-01-31 1 0
# # # … with 404,510 more rows
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Export the first time step as a RasterLayer object from the raster package.
# as_raster(runoff, subset = list(time = 1))
# # class : RasterLayer
# # dimensions : 280, 720, 201600 (nrow, ncol, ncell)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # crs : +proj=longlat +datum=WGS84 +no_defs # nolint:commented_code_linter.
# # source : memory
# # names : runoff
# # values : -1.682581e-13, 671.8747 (min, max)
#
# # Export the first time step as a terra SpatRaster object.
# as_terra(runoff, subset = list(time = 1))
# # class : SpatRaster
# # dimensions : 280, 720, 1 (nrow, ncol, nlyr)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # coord. ref. : lon/lat WGS 84 (EPSG:4326)
# # source : memory
# # name : runoff
# # min value : -1.682581e-13
# # max value : 6.718747e+02
# # unit : mm/month # nolint:commented_code_linter.
#
# # Export the first 4 times step as a RasterBrick object.
# as_raster(runoff, subset = list(time = 1:4))
# # class : RasterBrick
# # dimensions : 280, 720, 201600, 4 (nrow, ncol, ncell, nlayers)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # crs : +proj=longlat +datum=WGS84 +no_defs # nolint:commented_code_linter.
# # source : memory
# # names : X1901.01.31, X1901.02.28, X1901.03.31, X1901.04.30
# # min values : -1.682581e-13, -1.750495e-13, -2.918900e-13, -1.516298e-13
# # max values : 671.8747, 785.2363, 828.2853, 987.4359
#
# # Export the first 4 time steps as a terra SpatRaster object.
# as_terra(runoff, subset = list(time = 1:4))
# # class : SpatRaster
# # dimensions : 280, 720, 4 (nrow, ncol, nlyr)
# # resolution : 0.5, 0.5 (x, y)
# # extent : -180, 180, -56, 84 (xmin, xmax, ymin, ymax)
# # coord. ref. : lon/lat WGS 84 (EPSG:4326)
# # source : memory
# # names : 1901-01-31, 1901-02-28, 1901-03-31, 1901-04-30
# # min values : -1.682581e-13, -1.750495e-13, -2.918900e-13, -1.516298e-13
# # max values : 6.718747e+02, 7.852363e+02, 8.282853e+02, 9.874359e+02
# # unit : mm/month, mm/month, mm/month, mm/month
# # time (days) : 1901-01-31 to 1901-04-30
## ----setup, echo = TRUE, eval = FALSE, highlight = TRUE-----------------------
# library(lpjmlkit)
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# npp <- read_io(filename = "./output/npp.bin.json",
# subset = list(year = 1970:2011))
#
# # Transform "time" into "year" and "month" dimensions.
# npp$transform(to = "year_month_day")
#
# # Plot timeseries with aggregated cell and month dimensions. Note that spatial
# # aggregation across cells is not area-weighted.
# plot(npp,
# aggregate = list(cell = mean, month = sum))
#
# # Also available as data array.
# global_npp_trend <- as_array(npp,
# aggregate = list(cell = mean, month = sum))
#
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# runoff <- read_io(filename = "./output/runoff.bin.json",
# subset = list(year = 2002:2011))
#
# # Usage of pipe operator operator |> (%>% via package magrittr R version < 4.1)
# runoff |>
# # Transform the time and space dimensions ...
# transform(to = c("year_month_day", "lon_lat")) |>
# # ... to subset summer months as well as northern hemisphere (positive)
# # latitudes.
# subset(month = 6:9,
# lat = seq(0.25, 83.75, by = 0.5)) |>
# # for plotting sum up summer month and take the average over the years
# plot(aggregate = list(year = mean, month = sum))
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# gpp <- read_io(filename = "./output/gpp.bin.json",
# subset = list(year = 2002:2011))
#
# # Transform into lon_lat format.
# gpp$transform(to = "lon_lat")
#
# # Plot GPP per latitude.
# plot(gpp, aggregate = list(time = mean, lon = mean))
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# # Coordinates for cells around Potsdam.
# coordinates <- tibble::tibble(lat = c(52.25, 52.400922, 53.25),
# lon = c(12.75, 13.03638, 12.75))
#
# # Complete pipe notation, from reading to plotting data.
# read_io(
# filename = glue("./cftfrac.bin.json"),
# subset = list(year = 2000:2018)
# ) |>
# transform(to = "lon_lat") |>
# # Special case for subsetting of lat and lon pairs
# subset(coords = coordinates) |>
# # Mean across spatial dimensions
# plot(aggregate = list(lon = mean, lat = mean))
#
## ---- echo = TRUE, eval = FALSE, highlight = TRUE-----------------------------
# my_copy <- lpjml_data
# # Instead use:
# my_copy <- lpjml_data$clone(deep = TRUE)
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