Package 'lpjmlkit'

Description

A collection of basic functions to facilitate the work with the Dynamic Global Vegetation Model (DGVM) Lund-Potsdam-Jena managed Land (LPJmL) hosted at the Potsdam Institute for Climate Impact Research (PIK). It provides functions for performing LPJmL simulations, as well as reading, processing and writing model-related data such as inputs and outputs or configuration files.

Author(s)

Maintainer: Jannes Breier [email protected] (ORCID)

Authors:

• Sebastian Ostberg [email protected] (ORCID)

• Stephen Björn Wirth [email protected] (ORCID)

• Sara Minoli [email protected] (ORCID)

• Fabian Stenzel [email protected] (ORCID)

• David Hötten [email protected]

• Christoph Müller [email protected] (ORCID)

See Also

Useful links:

• https://github.com/PIK-LPJmL/lpjmlkit

• doi:10.5281/zenodo.7773134

• Report bugs at https://github.com/PIK-LPJmL/lpjmlkit/issues

Description

Function to add a grid to an LPJmLData object. The function acts as a read_io() wrapper for the grid file and adds it as an LPJmLData object itself to the ⁠$grid⁠ attribute of the main object.

Usage

add_grid(x, ...)

Arguments

Details

Important:

• If "file_type" == "raw" prescribe variable = "grid" to ensure data are recognized as a grid.

• Do not use read_io() argument subset here. add_grid will use the subset of the parent LPJmLData object x.

Value

A copy of x (LPJmLData object) with added ⁠$grid⁠ attribute.

Examples

## Not run: 

# Read in vegetation carbon data with meta file
vegc <- read_io(filename = "./vegc.bin.json")

# Add grid as attribute (via grid file in output directory)
vegc_with_grid <- add_grid(vegc)

## End(Not run)

Description

Function to coerce (convert) an LPJmLData object into a pure array. Pure - because LPJmLData stores the data already as an array which can be accessed via ⁠$data⁠. as_array provides additional functionality to subset or aggregate the array.

Usage

as_array(x, subset = NULL, aggregate = NULL, ...)

Arguments

Value

an array with dimensions of object ⁠$data⁠ with applied subset and aggregate functionality as well as dim and dimnames from the LPJmLData object.

Examples

## Not run: 

vegc <- read_io(filename = "./vegc.bin.json")

# Returns array attribute of LPJmLData object directly
vegc$data
#        time
# cell      1901-12-31   1902-12-31   1903-12-31   1904-12-31   1905-12-31
#   0     1.362730e+04 1.363163e+04 1.364153e+04 1.365467e+04 1.366689e+04
#   1     1.201350e+02 1.158988e+02 1.101675e+02 1.214204e+02 1.062658e+02
#   2     1.334261e+02 1.210387e+02 1.218128e+02 1.183210e+02 1.159934e+02
#   3     9.744530e+01 9.586801e+01 8.365642e+01 8.193731e+01 7.757602e+01
#   4     7.592700e+01 7.821202e+01 6.798551e+01 6.632317e+01 5.691082e+01
#   5     1.106748e+01 1.137272e+01 1.196524e+01 1.131316e+01 9.924266e+0

# Returns two-dimensional array with timeseries for the mean across cells
# 27410:27415
as_array(vegc,
         subset = list(cell = 27410:27415),
         aggregate = list(cell = mean))

#             band
# time                1
#   1901-12-31 1995.959
#   1902-12-31 1979.585
#   1903-12-31 1978.054
#   1904-12-31 1935.623
#   1905-12-31 1938.805


## End(Not run)

Description

Function to coerce (convert) an LPJmLMetaData object into an LPJmL header object. More information at create_header().

Usage

as_header(x, silent = FALSE)

Arguments

Value

An LPJmL header object. More information at create_header().

Examples

## Not run: 

vegc_meta <- read_meta(filename = "./vegc.bin.json")

# Returns a list object with the structure of an LPJmL header
as_header(vegc_meta)
# $name
# [1] "LPJDUMMY"
#
# $header
#      version        order    firstyear        nyear    firstcell
#          4.0          4.0       1901.0        200.0          0.0
#        ncell       nbands cellsize_lon       scalar cellsize_lat
#      67420.0          1.0          0.5          1.0          0.5
#     datatype        nstep     timestep
#          3.0          1.0          1.0
#
# $endian
# [1] "little"


## End(Not run)

Description

Function to coerce (convert) an LPJmLMetaData object into a list.

Usage

as_list(x)

Arguments

Value

A list

Examples

## Not run: 

vegc_meta <- read_meta(filename = "./vegc.bin.json")

# Returns one dimensional array with timeseries for cells `27410:27415`
as_list(vegc_meta)
# $sim_name
# [1] "lu_cf"
#
# $source
# [1] "LPJmL C Version 5.3.001"
#
# $variable
# [1] "vegc"
#
# $descr
# [1] "vegetation carbon"
#
# $unit
# [1] "gC/m2"
#
# $nbands
# [1] 1
#
# ...


## End(Not run)

Description

Function to coerce (convert) an LPJmLData object into a raster or brick object that allows for any GIS-based raster operations. Read more about the raster package at https://rspatial.github.io/raster/reference/raster-package.html. The successor package of raster is called terra: https://rspatial.org/.

Usage

as_raster(x, subset = NULL, aggregate = NULL, ...)

Arguments

Details

The ⁠$grid⁠ attribute is required for spatial transformation. When using file_type = "meta", grid data are usually read automatically via add_grid() if the grid file is present in the same directory. Otherwise, add_grid() has to be called explicitly with the path to a matching grid file. Supports either multiple bands or multiple time steps. Use subset or aggregate to reduce data with multiple bands and time steps.

Value

A raster or brick object with spatial extent and coordinates based on internal ⁠$grid⁠ attribute and containing a lon/lat representation of x$data. If multiple bands or time steps exist, a brick is created. Further meta information such as the lon/lat resolution are extracted from ⁠$meta⁠.

Examples

## Not run: 

vegc <- read_io(filename = "./vegc.bin.json")

# Returns a RasterBrick for all data
as_raster(vegc)
# class      : RasterBrick
# dimensions : 280, 720, 201600, 200  (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
# source     : memory
# names      : X1901.12.31, X1902.12.31, X1903.12.31, X1904.12.31, ...
# min values :           0,           0,           0,           0, ...
# max values :    28680.72,    28662.49,    28640.29,    28634.03, ...


## End(Not run)

Description

Function to coerce (convert) an LPJmLData object into a rast object that allows GIS-based raster operations. Read more about the terra package at https://rspatial.org/.

Usage

as_terra(x, subset = NULL, aggregate = NULL, ...)

Arguments

Details

The ⁠$grid⁠ attribute is required for spatial transformation. When using file_type = "meta", grid data are usually read automatically via add_grid() if the grid file is present in the same directory. Otherwise, add_grid() has to be called explicitly with the path to a matching grid file. Supports either multiple bands or multiple time steps. Use subset or aggregate to reduce data with multiple bands and time steps.

Value

A rast object with spatial extent and coordinates based on internal ⁠$grid⁠ attribute and containing a lon/lat representation of x$data. Further meta information such as the lon/lat resolution is extracted from ⁠$meta⁠.

Examples

## Not run: 

vegc <- read_io(filename = "./vegc.bin.json")

# Returns a SpatRaster for all data
as_terra(vegc)
# ...

## End(Not run)

Description

Function to coerce (convert) an LPJmLData object into a tibble (modern data.frame). Read more about tibbles at https://r4ds.had.co.nz/tibbles.html. Please make sure to call lpjmlkit::as_tibble() explicitly when also using the tidyverse packages tibble or dplyr.

Usage

## S3 method for class 'LPJmLData'
as_tibble(x, subset = NULL, aggregate = NULL, value_name = "value", ...)

Arguments

Value

a tibble with columns corresponding to dimension naming of the LPJmLData$data array and values in one value column.

Examples

## Not run: 

vegc <- read_io(filename = "./vegc.bin.json")

# Returns two-dimensional tibble representation of vegc$data.
as_tibble(vegc)
#   cell  time       band    value
#   <fct> <fct>      <fct>   <dbl>
# 1 0     1901-12-31 1     13627.
# 2 1     1901-12-31 1       120.
# 3 2     1901-12-31 1       133.
# 4 3     1901-12-31 1        97.4
# 5 4     1901-12-31 1        75.9
# 6 5     1901-12-31 1        11.1


## End(Not run)

Description

Subset an array with the supplied dimnames and - if defined - replace values.

Usage

asub(x, ..., drop = TRUE)

Arguments

Value

array (or vector if drop = TRUE and only one dimension is left) of the selected subset of x.

Examples

my_array <- array(1,
                  dim = c(cell = 67, month = 12, band = 3),
                  dimnames = list(cell = 0:66,
                                month = 1:12,
                                band = c("band1", "band2", "band3")))
my_subset <- asub(my_array,
                  band = c("band1", "band3"))
dimnames(my_subset)[3]
# $ band
#   [1] "band1"
#   [2] "band3"

Description

Calculate the cell area of LPJmL cells based on an LPJmLData object or latitude coordinates and grid resolution. Uses a spherical representation of the Earth.

Usage

calc_cellarea(
  x,
  cellsize_lon = 0.5,
  cellsize_lat = cellsize_lon,
  earth_radius = 6371000.785,
  return_unit = "m2"
)

Arguments

Value

A vector or array matching the space dimension(s) of x if x is an LPJmLData object. A vector of the same length as x if x is a vector of latitude coordinates. Cell areas are returned in the unit return_unit.

Examples

grid <- matrix(
  data = c(-179.75, 89.75, -0.25, 0.25, 0.25, -0.25, 179.75, -89.75),
  ncol = 2,
  byrow = TRUE,
  dimnames = list(NULL, c("lon", "lat"))
)
gridarea <- calc_cellarea(grid[,"lat"])

Description

Check if created LPJmL config JSON files (write_config()) are valid and are ready to be used for simulations using lpjcheck for multiple files.

Usage

check_config(
  x,
  model_path = ".",
  sim_path = NULL,
  return_output = FALSE,
  raise_error = FALSE,
  output_path = NULL
)

Arguments

Value

NULL.

Examples

## Not run: 
library(tibble)
library(lpjmlkit)

model_path <- "./LPJmL_internal"
sim_path <-"./my_runs"


# Basic usage
my_params <- tibble(
  sim_name = c("scen1", "scen2"),
  random_seed = c(12, 404),
  `pftpar[[1]]$name` = c("first_tree", NA),
  `param$k_temp` = c(NA, 0.03),
  new_phenology = c(TRUE, FALSE)
)

config_details <- write_config(
  x = my_params,
  model_path = model_path,
  sim_path = sim_path
)

check_config(x = config_details,
  model_path = model_path,
  sim_path = sim_path,
  return_output = FALSE
)

## End(Not run)

Description

Create a header from scratch in the format required by write_header().

Usage

create_header(
  name = "LPJGRID",
  version = 3,
  order = 1,
  firstyear = 1901,
  nyear = 1,
  firstcell = 0,
  ncell,
  nbands = 2,
  cellsize_lon = 0.5,
  scalar = 1,
  cellsize_lat = cellsize_lon,
  datatype = 3,
  nstep = 1,
  timestep = 1,
  endian = .Platform$endian,
  verbose = TRUE
)

Arguments

Details

File headers in input files are used by LPJmL to determine the structure of the file and how to read it. They can also be used to describe the structure of output files.

Header names usually start with "LPJ" followed by a word or abbreviation describing the type of input/output data. See LPJmL code for valid header names.

The version number determines the amount of header information included in the file. All versions save the header name and header attributes 'version', 'order', 'firstyear', 'nyear', 'firstcell', 'ncell', and 'nbands'. Header versions 2, 3 and 4 add header attributes 'cellsize_lon' and 'scalar'. Header versions 3 and 4 add header attributes 'cellsize_lat' and 'datatype'. Header version 4 adds attributes 'nstep' and 'timestep'.

Valid values for order are 1 / "cellyear", 2 / "yearcell", 3 / "cellindex", and 4 / "cellseq". The default for LPJmL input files is 1. The default for LPJmL output files is 4, except for grid output files which also use 1.

By default, input files contain data for all cells, indicated by setting the firstcell index to 0. If firstcell > 0, LPJmL assumes the first firstcell cells to be missing in the data.

Valid codes for the datatype attribute and the corresponding LPJmL data types are: 0 / "byte" (LPJ_BYTE), 1 / "short" (LPJ_SHORT), 2 / "int" (LPJ_INT), 3 / "float" (LPJ_FLOAT), 4 / "double" (LPJ_DOUBLE).

The default parameters of the function are valid for grid input files using LPJ_FLOAT data type.

Value

The function returns a list with 3 components:

• name: The header name, e.g. "LPJGRID".

• header: Vector of header values ('version', 'order', 'firstyear', 'nyear', 'firstcell', 'ncell', 'nbands', 'cellsize_lon', 'scalar', 'cellsize_lat', 'datatype', 'nstep', 'timestep').

• endian: Endian used to write binary data, either "little" or "big".

See Also

• read_header() for reading headers from LPJmL input/output files.

• write_header() for writing headers to files.

Examples

header <- create_header(
  name = "LPJGRID",
  version = 3,
  order = 1,
  firstyear = 1901,
  nyear = 1,
  firstcell = 0,
  ncell = 67420,
  nbands = 2,
  cellsize_lon = 0.5,
  scalar = 1.0,
  cellsize_lat = 0.5,
  datatype = 3,
  nstep = 1,
  timestep = 1,
  endian = .Platform$endian,
  verbose = TRUE
)

Description

This utility function tries to detect automatically if a provided file is of "clm", "meta", or "raw" file type. NetCDFs and simple text formats such as ".txt" or ".csv" are also detected.

Usage

detect_io_type(filename)

Arguments

Value

Character vector of length 1 giving the file type:

• "cdf" for a NetCDF file (classic or NetCDF4/HDF5 format).

• "clm" for a binary LPJmL input/output file with header.

• "meta" for a JSON meta file describing a binary LPJmL input/output file.

• "raw" for a binary LPJmL input/output file without header. This is also the default if no other file type can be recognized.

• "text" for any type of text-only file, e.g. ".txt" or ".csv"

Examples

## Not run: 
detect_io_type(filename = "filename.clm")
# [1] "clm"

## End(Not run)

Description

Function to get the dimensions of the data array of an LPJmLData object.

Usage

## S3 method for class 'LPJmLData'
dim(x)

Arguments

Value

For the default method, either NULL or a numeric vector, which is coerced to integer (by truncation).

Description

Function to get the dimnames (list) of the data array of an LPJmLData object.

Usage

## S3 method for class 'LPJmLData'
dimnames(x)

Arguments

Value

A list of the same length as dim(x). Components are character vectors with positive length of the respective dimension of x.

Description

Function to search for a file containing a specific variable in a specific directory.

Usage

find_varfile(searchdir, variable = "grid", strict = FALSE)

Arguments

Details

This function looks for file names in searchdir that match the pattern parameter in its list.files() call. Files of type "meta" are preferred. Files of type "clm" are also accepted. The function returns an error if no suitable file or multiple files are found.

Value

Character string with the file name of a matched file, including the full path.

Description

This function returns the cell index from a grid file based on the provided extent or coordinates. If neither extent nor coordinates are provided, the full grid will be returned. If both extent and coordinates are provided, the function will stop and ask for only one of them. The extent should be a vector of length 4 in the form c(lonmin, lonmax, latmin, latmax). If the extent is not in the correct form, the function will swap the values to correct it.

Usage

get_cellindex(grid_filename, extent = NULL, coordinates = NULL)

Arguments

Details

The function reads a grid file specified by grid_filename and creates a data frame with columns for longitude, latitude, and cell number. The cell number is a sequence from 1 to the number of rows in the data frame.

If an extent is provided, the function filters the cells to include only those within the specified longitude and latitude range. The extent should be a numeric vector of length 4 in the form c(lonmin, lonmax, latmin, latmax).

If a list of coordinates is provided, the function filters the cells to include only those that match the specified coordinates. The coordinates should be a list of two character vectors representing the longitude and latitude values as for subset().

If both extent and coordinates are provided, the function will stop and ask for only one of them. If neither extent nor coordinates are provided, the function will return the cell numbers for all cells in the grid.

The function also includes checks for input types and values, and gives specific error messages for different error conditions. For example, it checks if the grid_filename exists, if the extent vector has the correct length, and if the coordinates list contains two vectors of equal length.

Value

The cell index from the grid file based on the provided extent or coordinates.

Examples

## Not run: 
get_cellindex(
  grid_filename = "my_grid.bin.json",
  extent = c(-123.25, -122.75, 49.25, 49.75) # (lonmin, lonmax, latmin, latmax)
)
get_cellindex(
  grid_filename = "my_grid.bin.json",
  coordinates = list(lon = c(-123.25, -122.75), lat = c(49.25, 49.75))
)

## End(Not run)

Description

Provides information on the data type used in an LPJmL input/output file based on the 'datatype' attribute included in the file header.

Usage

get_datatype(header, fail = TRUE)

Arguments

Value

On success, the function returns a list object with three components:

• type: R data type; can be used with what parameter of readBin().

• size: size of data type; can be used with size parameter of readBin().

• signed: whether or not the data type is signed; can be used with signed parameter of readBin().

If fail = FALSE, the function returns NULL if an invalid datatype is provided.

See Also

• read_header() for reading headers from LPJmL input/output files.

• create_header() for creating headers from scratch.

• get_headersize() for determining the size of file headers.

Examples

## Not run: 
# Read file header
header <- read_header("filename.clm")
# Open file for reading
fp <- file("filename.clm", "rb")
# Skip over file header
seek(fp, get_headersize(header))
# Read in file data
file_data <- readBin(
  fp,
  what = get_datatype(header)$type,
  size = get_datatype(header)$size,
  signed = get_datatype(header)$signed,
  n = header$header["ncell"] * header$header["nbands"] *
      header$header["nyear"] * header$header["nstep"],
  endian = header[["endian"]]
)
# Close file
close(fp)

## End(Not run)

Description

Convenience function to extract information from a header object as returned by read_header() or create_header(). Returns one item per call.

Usage

get_header_item(header, item)

Arguments

Value

Requested header item. Character string in case of "name" and "endian", otherwise numeric value.

See Also

• create_header() for creating headers from scratch and for a more detailed description of the LPJmL header format.

• read_header() for reading headers from LPJmL input/output files.

Examples

## Not run: 
# Read file header
header <- read_header("filename.clm")
nyear <- get_header_item(header = header, item = "nyear")

## End(Not run)

Description

Returns the size in bytes of an LPJmL file header based on a header list object read by read_header() or generated by create_header().

Usage

get_headersize(header)

Arguments

Value

Integer value giving the size of the header in bytes. This can be used when seeking in the file or to calculate the expected total file size in combination with the number of included data values and the data type.

See Also

• read_header() for reading a header from an LPJmL input/output file.

• create_header() for creating a header from scratch.

Examples

## Not run: 
header <- read_header("filename.clm")
size <- get_headersize(header)
# Open file for reading
fp <- file("filename.clm", "rb")
# Skip over file header
seek(fp, size)
# Add code to read data from file

## End(Not run)

Description

Function to get the length of the data array of an LPJmLData object.

Usage

## S3 method for class 'LPJmLData'
length(x)

Arguments

Value

A non-negative integer or numeric (which will be rounded down).

Description

A data container for LPJmL input and output. Container - because an LPJmLData object is an environment in which the data array as well as the meta data are stored after read_io(). The data array can be accessed via ⁠$data⁠, the meta data via ⁠$meta⁠. The enclosing environment is locked and cannot be altered by any other than the available modify methods to ensure its integrity and validity. Use base stats methods like print(), summary.LPJmLData() or plot.LPJmLData() to get insights and export methods like as_tibble() or as_raster() to export it into common working formats.

Active bindings

Methods

Public methods

• LPJmLData$add_grid()

• LPJmLData$subset()

• LPJmLData$transform()

• LPJmLData$as_array()

• LPJmLData$as_tibble()

• LPJmLData$as_raster()

• LPJmLData$as_terra()

• LPJmLData$plot()

• LPJmLData$length()

• LPJmLData$dim()

• LPJmLData$dimnames()

• LPJmLData$summary()

• LPJmLData$print()

• LPJmLData$.__set_data__()

• LPJmLData$.__set_grid__()

• LPJmLData$new()

• LPJmLData$clone()

Method add_grid()

Method to add a grid to an LPJmLData object. See also add_grid

Usage

LPJmLData$add_grid(...)

Arguments

Method subset()

Method to use dimension names of LPJmLData$data array directly to subset each dimension to match the supplied vectors.

Usage

LPJmLData$subset(...)

Arguments

Method transform()

Method to transform inner LPJmLData$data array into another space or time format.

Usage

LPJmLData$transform(...)

Arguments

Method as_array()

Method to coerce (convert) an LPJmLData object into an array.

Usage

LPJmLData$as_array(...)

Arguments

Method as_tibble()

Method to coerce (convert) an LPJmLData object into a tibble (modern data.frame).

Usage

LPJmLData$as_tibble(...)

Arguments

Method as_raster()

Method to coerce (convert) an LPJmLData object into a raster or brick object that can be used for any GIS-based raster operations.

Usage

LPJmLData$as_raster(...)

Arguments

Method as_terra()

Method to coerce (convert) an LPJmLData object into a rast object that can be used for any GIS-based raster operations.

Usage

LPJmLData$as_terra(...)

Arguments

Method plot()

Method to plot a time-series or raster map of an LPJmLData object.

Usage

LPJmLData$plot(...)

Arguments

Method length()

Method to get the length of the data array of an LPJmLData object.
See also length.

Usage

LPJmLData$length()

Method dim()

Method to get the dimensions of the data array of an LPJmLData object.
See also dim.

Usage

LPJmLData$dim()

Method dimnames()

Method to get the dimnames (list) of the data array of an LPJmLData object.

Usage

LPJmLData$dimnames(...)

Arguments

Method summary()

Method to get the summary of the data array of an LPJmLData object.

Usage

LPJmLData$summary(...)

Arguments

Method print()

Method to print the LPJmLData object.
See also print.

Usage

LPJmLData$print()

Method .__set_data__()

!Internal method only to be used for package development!

Usage

LPJmLData$.__set_data__(data)

Arguments

Method .__set_grid__()

!Internal method only to be used for package development!

Usage

LPJmLData$.__set_grid__(grid)

Arguments

Method new()

!Internal method only to be used for package development!

Usage

LPJmLData$new(data, meta_data = NULL)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

LPJmLData$clone(deep = FALSE)

Arguments

Description

A dedicated data class for an LPJmL input or output grid. LPJmLGridData serves the spatial reference for any LPJmLData objects and matches its spatial dimensions ("cell" or "lon", "lat") when attached as an grid attribute to it.\ LPJmLGridData holds the information which longitude and latitude correspond to each cell center assuming WGS84 as the coordinate reference system or the corresponding cell index when the data comes with longitude and latitude dimension. As in LPJmLData the data array can be accessed via ⁠$data⁠, the meta data via ⁠$meta⁠.

Super class

lpjmlkit::LPJmLData -> LPJmLGridData

Methods

Public methods

• LPJmLGridData$add_grid()

• LPJmLGridData$plot()

• LPJmLGridData$new()

• LPJmLGridData$print()

• LPJmLGridData$clone()

Method add_grid()

! Not allowed to add a grid to an LPJmLGridData object.

Usage

LPJmLGridData$add_grid(...)

Arguments

Method plot()

! No plot function available for LPJmLGridData object. Use as_raster() or as_terra() (and plot()) to visualize the grid.

Usage

LPJmLGridData$plot(...)

Arguments

Method new()

!Internal method only to be used for package development!

Usage

LPJmLGridData$new(lpjml_data)

Arguments

Method print()

Method to print the LPJmLGridData.
See also print

Usage

LPJmLGridData$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

LPJmLGridData$clone(deep = FALSE)

Arguments

Description

A meta data container for LPJmL input and output meta data. Container - because an LPJmLMetaData object is an environment in which the meta data are stored after read_meta() (or read_io()). Each attribute can be accessed via ⁠$<attribute>⁠. To get an overview over available attributes, print the object or export it as a list as_list(). The enclosing environment is locked and cannot be altered.

Active bindings

Methods

Public methods

• LPJmLMetaData$as_list()

• LPJmLMetaData$as_header()

• LPJmLMetaData$print()

• LPJmLMetaData$.__init_grid__()

• LPJmLMetaData$.__update_subset__()

• LPJmLMetaData$.__transform_time_format__()

• LPJmLMetaData$.__transform_space_format__()

• LPJmLMetaData$.__set_attribute__()

• LPJmLMetaData$new()

• LPJmLMetaData$clone()

Method as_list()

Method to coerce (convert) an LPJmLMetaData object into a list.
See also as_list().

Usage

LPJmLMetaData$as_list()

Method as_header()

Method to coerce (convert) an LPJmLMetaData object into an LPJmL binary file header. More information about file headers at create_header()).

Usage

LPJmLMetaData$as_header(...)

Arguments

Method print()

Method to print an LPJmLMetaData object. See also print.

Usage

LPJmLMetaData$print(all = TRUE, spaces = "")

Arguments

Method .__init_grid__()

!Internal method only to be used for package development!

Usage

LPJmLMetaData$.__init_grid__()

Method .__update_subset__()

!Internal method only to be used for package development!

Usage

LPJmLMetaData$.__update_subset__(
  subset,
  cell_dimnames = NULL,
  time_dimnames = NULL,
  year_dimnames = NULL
)

Arguments

Method .__transform_time_format__()

!Internal method only to be used for package development!

Usage

LPJmLMetaData$.__transform_time_format__(time_format)

Arguments

Method .__transform_space_format__()

!Internal method only to be used for package development!

Usage

LPJmLMetaData$.__transform_space_format__(space_format)

Arguments

Method .__set_attribute__()

!Internal method only to be used for package development!

Usage

LPJmLMetaData$.__set_attribute__(key, value)

Arguments

Method new()

Create a new LPJmLMetaData object.

Usage

LPJmLMetaData$new(x, additional_attributes = list(), data_dir = NULL)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

LPJmLMetaData$clone(deep = FALSE)

Arguments

Description

Compiles the LPJmL source code and creates an executable by executing "make all" on the operating system shell.

Usage

make_lpjml(
  model_path = ".",
  parallel_cores = NULL,
  make_clean = FALSE,
  raise_error = TRUE,
  debug = NULL
)

Arguments

Value

A list with process status, see run.

Examples

## Not run: 
model_path <- "./LPJmL_internal"
make_lpjml(model_path = model_path)

## End(Not run)

Description

Function to plot a time-series or raster map of an LPJmLData object.

Usage

## S3 method for class 'LPJmLData'
plot(x, subset = NULL, aggregate = NULL, raster_extent = NULL, ...)

Arguments

Details

Depending on the dimensions of the LPJmLData object's internal data array the plot will be a ...

• single map plot: more than 8 "cell"s or "lat" & "lon" dimensions available)

• multiple maps plot: length of one time (e.g."time", "year", "month") or "band" dimension > 1.

• time series plot: less than 9 "cell"s

• lat/lon plot: a subsetted/aggregated "lat" or "lon" dimension

The plot can only handle 2-3 dimensions. Use arguments subset and aggregate to modify x$data to the desired plot type. If more than three dimensions have length > 1,' plot will return an error and suggest to reduce the number of dimensions.

Note that the plot function aims to provide a quick overview of the data rather than create publication-ready graphs.

Value

No return value; called for side effects.

Examples

## Not run: 

vegc <- read_io(filename = "./vegc.bin.json")

# Plot first 9 years starting from 1901 as a raster plot
plot(vegc)

# Plot raster with mean over the whole time series
plot(vegc,
     aggregate = list(time = mean))

# Plot only year 2010 as a raster
plot(vegc,
     subset = list(time = "2010"))

# Plot first 10 time steps as global mean time series. Note: Aggregation
# across cells is not area-weighted.
plot(vegc,
     subset = list(time = 1:10),
     aggregate = list(cell = mean))

# Plot time series for cells with LPJmL index 27410 - 27415 (C indices start
#    at 0 in contrast to R indices starting at 1).
plot(vegc,
     subset = list(cell = 27411:27416))


## End(Not run)

Description

Reads a configuration (config) file (compilable csjon/js file or json file) and turns it into a nested list object.

Usage

read_config(filename, from_restart = FALSE, macro = "")

Arguments

Value

A nested list object representing the LPJmL configuration read from filename.

Examples

## Not run: 
 config <- read_config(filename = "config_spinup.json")

 config[["version"]]
 # [1] "5.3"

 config[["pftpar"]][[1]][["name"]]
 # [1] "tropical broadleaved evergreen tree"

 config[["input"]][["coord"]][["name"]]
 # [1] "input_VERSION2/grid.bin"

 # visualize configuration as tree view
 View(config)

## End(Not run)

Description

Generic function to read LPJmL input & output files in different formats. Depending on the format, arguments can be automatically detected or have to be passed as individual arguments.

Usage

read_grid(...)

Arguments

Details

See read_io for more details.

Value

An LPJmLGridData object.

Examples

## Not run: 
my_grid <- read_io("grid.bin.json")


## End(Not run)

Description

Reads a header from an LPJmL clm file. CLM is the default format used for LPJmL input files and can also be used for output files.

Usage

read_header(filename, force_version = NULL, verbose = TRUE)

Arguments

Value

The function returns a list with 3 components:

• name: Header name, e.g. "LPJGRID"; describes the type of data in the file.

• header: Vector of header values ('version', 'order', 'firstyear', 'nyear', 'firstcell', 'ncell', 'nbands', 'cellsize_lon', 'scalar', 'cellsize_lat', 'datatype', 'nstep', 'timestep') describing the file structure. If header version is <4, the header is partially filled with default values.

• endian: Endianness of file ("little" or "big").

See Also

• create_header() for a more detailed description of the LPJmL header format.

• write_header() for writing headers to files.

Examples

## Not run: 
header <- read_header("filename.clm")

## End(Not run)

Description

Generic function to read LPJmL input & output files in different formats. Depending on the format, arguments can be automatically detected or have to be passed as individual arguments.

Usage

read_io(
  filename,
  subset = list(),
  band_names = NULL,
  dim_order = c("cell", "time", "band"),
  file_type = NULL,
  version = NULL,
  order = NULL,
  firstyear = NULL,
  nyear = NULL,
  firstcell = NULL,
  ncell = NULL,
  nbands = NULL,
  cellsize_lon = NULL,
  scalar = NULL,
  cellsize_lat = NULL,
  datatype = NULL,
  nstep = NULL,
  timestep = NULL,
  endian = NULL,
  variable = NULL,
  descr = NULL,
  unit = NULL,
  name = NULL,
  silent = FALSE
)

Arguments

Details

The file_type determines which arguments are mandatory or optional. filename must always be provided. file_type is usually detected automatically. Supply only if detected file_type is incorrect.

In case of file_type = "meta", if any of the function arguments not listed as "mandatory" are provided and are already set in the JSON file, a warning is given, but they are still overwritten. Normally, you would only set meta attributes not set in the JSON file.

In case of file_type = "clm", function arguments not listed as "optional" are usually determined automatically from the file header included in the clm file. Users may still provide any of these arguments to overwrite values read from the file header, e.g. when they know that the values in the file header are wrong. Also, clm headers with versions < 4 do not contain all header attributes, with missing attributes filled with default values that may not be correct for all files.

In case of file_type = "raw", files do not contain any information about their structure. Users should provide all arguments not listed as "optional". Otherwise, default values valid for LPJmL standard outputs are used for arguments not supplied by the user. For example, the default firstyear is 1901, the default for nyear, nbands, nstep, and timestep is 1.

subset can be a list containing one or several named elements. Allowed names are "band", "cell", and "year".

• "year" can be used to return data for a subset of one or several years included in the file. Integer indices can be between 1 and nyear. If subsetting by actual calendar years (starting at firstyear) a character vector has to be supplied.

• "band" can be used to return data for a subset of one or several bands included in the file. These can be specified either as integer indices or as a character vector if bands are named.

• "cell" can be used to return data for a subset of cells. Note that integer indices start counting at 1, whereas character indices start counting at the value of firstcell (usually 0).

Value

An LPJmLData object.

Examples

## Not run: 
# First case: meta file. Reads meta information from "my_file.json" and
# data from binary file linked in "my_file.json". Normally does not require
# any additional arguments.
my_data <- read_io("my_file.json")

# Suppose that file data has two bands named "wheat" and "rice". `band_names`
# are included in the JSON meta file. Select only the "wheat" band during
# reading and discard the "rice" band. Also, read only data for years
# 1910-1920.
my_data_wheat <- read_io(
  "my_file.json",
  subset = list(band = "wheat", year = as.character(seq(1910, 1920)))
)

# Read data from clm file. This includes a header describing the file
# structure.
my_data_clm <- read_io("my_file.clm")

# Suppose that "my_file.clm" has two bands containing data for "wheat" and
# "rice". Assign names to them manually since the header does not include a
# `band_names` attribute.
my_data_clm <- read_io("my_file.clm", band_names = c("wheat", "rice"))

# Once `band_names` are set, subsetting by name is possible also for
# file_type = "clm"
my_data_wheat <- read_io(
  "my_file.clm",
  band_names = c("wheat", "rice"),
  subset = list(band = "wheat", year = as.character(seq(1910, 1920)))
)

# Read data from raw binary file. All information about file structure needs
# to be supplied. Use default values except for nyear (1 by default), and
# nbands (also 1 by default).
my_data <- read_io("my_file.bin", nyear = 100, nbands = 2)

# Supply band_names to be able to subset by name
my_data_wheat <- read_io(
  "my_file.bin",
  band_names = c("wheat", "rice"), # length needs to correspond to `nbands`
  subset = list(band = "wheat", year = as.character(seq(1910, 1920))),
  nyear = 100,
  nbands = 2,
)

## End(Not run)

Description

Reads a meta JSON file or the header of a binary LPJmL input or output file.

Usage

read_meta(filename, ...)

Arguments

Value

An LPJmLMetaData object.

Examples

## Not run: 
 meta <- read_meta(filename = "mpft_npp.bin.json")

 meta$sim_name
 # [1] "LPJmL Run"

 meta$firstcell
 # [1] 27410

 meta$band_names[1]
 # [1] "tropical broadleaved evergreen tree"

## End(Not run)

Description

Runs LPJmL using "config_*.json" files written by write_config(). write_config() returns a tibble that can be used as an input (see x). It contains the details to run single or multiple (dependent/subsequent) model runs.

Usage

run_lpjml(
  x,
  model_path = ".",
  sim_path = NULL,
  run_cmd = "srun --propagate",
  parallel_cores = 1,
  write_stdout = FALSE,
  raise_error = TRUE,
  output_path = NULL
)

Arguments

Details

x: A tibble for x that has been generated by write_config() and can look like the following examples can supplied:

To perform subsequent or rather dependent runs the optional run parameter "dependency" needs to be provided within the initial tibble supplied as param to write_config().

As a shortcut it is also possible to provide the config file "config_*.json" as a character string or multiple config files as a character string vector directly as the x argument to run_lpjml.
Also be aware that the order of the supplied config files is important (e.g. make sure the spin-up run is run before the transient one).

run_cmd: The run_cmd argument is used to define the command to execute LPJmL. This is needed because the LPJmL executable can not directly be used on all machines. Which command has to be used depends on the software installed. Further information on this can be found in the INSTALL file of LPJmL. To determine the correct command, check the lpj_submit.sh file in the bin directory of LPJmL. Using PIK infrastrucure the command is srun for the hpc2015 and mpirun for the hpc2024. To facilitate usage on the interactive (login) nodes, no command is needed for hpc2015. For the hpc2024 the command remains mpirun (in these cases run_lpjml adjusts run_cmd accordingly).

Value

See x, extended by columns "type", "job_id" and "status".

Examples

## Not run: 
library(tibble)

model_path <- "./LPJmL_internal"
sim_path <-"./my_runs"

# Basic usage
my_params1 <- tibble(
  sim_name = c("scen1", "scen2"),
  startgrid = c(27410, 27410),
  river_routing = c(FALSE, FALSE),
  random_seed = c(42, 404),
  `pftpar[[1]]$name` = c("first_tree", NA),
  `param$k_temp` = c(NA, 0.03),
  new_phenology = c(TRUE, FALSE)
)

config_details1 <- write_config(my_params1, model_path, sim_path)

 run_details1 <- run_lpjml(
  x = config_details1,
  model_path = model_path,
  sim_path = sim_path
)

run_details1
#   sim_name      job_id   status
#   <chr>           <int>  <chr>
# 1 scen1              NA  run
# 2 scen2              NA  run


# With run parameters dependency and order being set (also less other
#   parameters than in previous example)
my_params2 <- tibble(
  sim_name = c("scen1", "scen2"),
  startgrid = c(27410, 27410),
  river_routing = c(FALSE, FALSE),
  random_seed = c(42, 404),
  dependency = c(NA, "scen1_spinup")
)

config_details2 <- write_config(my_params2, model_path, sim_path)

run_details2 <- run_lpjml(config_details2, model_path, sim_path)

run_details2
#   sim_name        order dependency   type       job_id   status
#   <chr>           <dbl> <chr>        <chr>      <chr>    <chr>
# 1 scen1_spinup        1 NA           simulation NA       run
# 2 scen1_transient     2 scen1_spinup simulation NA       run


# Same but by using the pipe operator
library(magrittr)

run_details2 <- tibble(
  sim_name = c("scen1_spinup", "scen1_transient"),
  random_seed = as.integer(c(1, 42)),
  dependency = c(NA, "scen1_spinup")
) %>%
  write_config(model_path, sim_path) %>%
  run_lpjml(model_path, sim_path)


# Shortcut approaches
run_details3 <- run_lpjml(
  x = "./config_scen1_transient.json",
  model_path = model_path,
  sim_path = sim_path
)

run_details4 <- run_lpjml(
  c("./config_scen1_spinup.json", "./config_scen1_transient.json"),
  model_path,
  sim_path
)


## End(Not run)

Description

Convenience function to set information in a header object as returned by read_header() or create_header(). One or several

Usage

set_header_item(header, ...)

Arguments

Value

Header header where header items supplied through the ellipsis have been changed.

See Also

• create_header() for creating headers from scratch and for a more detailed description of the LPJmL header format.

• read_header() for reading headers from files.

Examples

header <- create_header(
  name = "LPJGRID",
  version = 3,
  order = 1,
  firstyear = 1901,
  nyear = 1,
  firstcell = 0,
  ncell = 67420,
  nbands = 2,
  cellsize_lon = 0.5,
  scalar = 1.0,
  cellsize_lat = 0.5,
  datatype = 3,
  nstep = 1,
  timestep = 1,
  endian = .Platform$endian,
  verbose = TRUE
)

header
# $name
# [1] "LPJGRID"
#
# $header
#      version        order    firstyear        nyear    firstcell        ncell
#          3.0          1.0       1901.0          1.0          0.0      67420.0
#        nbands cellsize_lon       scalar cellsize_lat     datatype       nstep
#          2.0          0.5          1.0          0.5          3.0          1.0
#     timestep
#          1.0
#
# $endian
# [1] "little"

# Change number of cells to 1
set_header_item(header = header, ncell = 1)
# $name
# [1] "LPJGRID"
#
# $header
#      version        order    firstyear        nyear    firstcell        ncell
#          3.0          1.0       1901.0          1.0          0.0          1.0
#        nbands cellsize_lon       scalar cellsize_lat     datatype       nstep
#          2.0          0.5          1.0          0.5          3.0          1.0
#     timestep
#          1.0
#
# $endian
# [1] "little"

Description

LPJmL simulations are submitted to SLURM using "config*.json" files written by write_config(). write_config() returns a tibble that can be used as an input (see x). It serves the details to submit single or multiple (dependent/subsequent) model simulations.

Usage

submit_lpjml(
  x,
  model_path,
  sim_path = NULL,
  group = "",
  sclass = "short",
  ntasks = 256,
  wtime = "",
  blocking = "",
  constraint = "",
  slurm_options = list(),
  no_submit = FALSE,
  output_path = NULL
)

Arguments

Details

A tibble for x that has been generated by write_config() and can look like the following examples can supplied:

To perform subsequent or rather dependent simulations the optional run parameter "dependency" needs to be provided within the initial tibble supplied as param to write_config().

To use different SLURM settings for each run the optional SLURM options "sclass", "ntasks", "wtime", "blocking"orconstraint⁠can also be supplied to the initial \link[tibble]{tibble} supplied as⁠param⁠ to [⁠write_config()⁠]. These overwrite the (default) SLURM arguments (⁠sclass⁠, ⁠ntasks⁠, ⁠wtime⁠, ⁠blockingor constraint) supplied to submit_lpjml.

As a shortcut it is also possible to provide the config file "config_*.json" as a character string or multiple config files as a character string vector directly as the x argument to submit_lpjml.
With this approach, run parameters or SLURM options cannot be taken into account.

Value

See x, extended by columns "type", "job_id" and "status".

Examples

## Not run: 
library(tibble)

model_path <- "./LPJmL_internal"
sim_path <-"./my_runs"


# Basic usage
my_params <- tibble(
 sim_name = c("scen1", "scen2"),
 random_seed = as.integer(c(42, 404)),
  `pftpar[[1]]$name` = c("first_tree", NA),
  `param$k_temp` = c(NA, 0.03),
 new_phenology = c(TRUE, FALSE)
)

config_details <- write_config(my_params, model_path, sim_path)

 run_details <- submit_lpjml(
  x = config_details,
  model_path = model_path,
  sim_path = sim_path
)

run_details
#   sim_name      job_id   status
#   <chr>           <int>  <chr>
# 1 scen1        21235215  submitted
# 2 scen2        21235216  submitted


# With run parameter dependency and SLURM option wtime being
#   set (also less other parameters than in previous example)
my_params <- tibble(
  sim_name = c("scen1", "scen2"),
  random_seed = as.integer(c(42, 404)),
  dependency = c(NA, "scen1_spinup"),
  wtime = c("8:00:00", "4:00:00"),
)

config_details2 <- write_config(my_params2, model_path, sim_path)

run_details2 <- submit_lpjml(config_details2, model_path, sim_path)

run_details2
#   sim_name        order dependency   wtime   type       job_id   status
#   <chr>           <dbl> <chr>        <chr>   <chr>      <chr>    <chr>
# 1 scen1_spinup        1 NA           8:00:00 simulation 22910240 submitted
# 2 scen1_transient     2 scen1_spinup 4:00:00 simulation 22910241 submitted


# Same but by using the pipe operator
library(magrittr)

run_details <- tibble(
  sim_name = c("scen1_spinup", "scen1_transient"),
  random_seed = as.integer(c(1, 42)),
  dependency = c(NA, "scen1_spinup"),
  wtime = c("8:00:00", "4:00:00"),
) %>%
  write_config(model_path, sim_path) %>%
  submit_lpjml(model_path, sim_path)


# Shortcut approach
run_details <- submit_lpjml(
  x = "./config_scen1_transient.json",
  model_path = model_path,
  sim_path = sim_path
)

run_details <- submit_lpjml(
  c("./config_scen1_spinup.json", "./config_scen1_transient.json"),
  model_path,
  sim_path
)


## End(Not run)

Description

Function to extract a subset of the full data in an LPJmLData object by applying selections along one or several of its dimensions.

Usage

## S3 method for class 'LPJmLData'
subset(x, ...)

Arguments

Value

An LPJmLData object with dimensions resulting from the selection in subset. Meta data are updated as well.

Examples

## Not run: 

vegc <- read_io(filename = "./vegc.bin.json")

# Subset cells by index
subset(vegc, cell = seq(27410, 27415))
# [...]
# $data |>
#   dimnames() |>
#     .$cell  "27409" "27410" "27411" "27412" "27413" "27414"
#     .$time  "1901-12-31" "1902-12-31" "1903-12-31" "1904-12-31" ...
#     .$band  "1"
# [...]

# Subset time by character vector
subset(vegc, time = c("2001-12-31", "2002-12-31", "2003-12-31"))
# [...]
# $data |>
#   dimnames() |>
#     .$cell  "0" "1" "2" "3" ... "67419"
#     .$time  "2001-12-31" "2002-12-31" "2003-12-31"
#     .$band  "1"
# [...]

## End(Not run)

Description

Function to get the summary of the data array of an LPJmLData object. See also summary.

Usage

## S3 method for class 'LPJmLData'
summary(object, ...)

Arguments

Value

Summary for object of class matrix (see summary) for selected dimension(s) and if defined subset.

Description

Function to transform an LPJmLData data object into another space or another time format. Combinations of space and time formats are also possible.

Usage

transform(x, to)

Arguments

Value

An LPJmLData object in the selected format.

Examples

## Not run: 

runoff <- read_io(filename = "runoff.bin.json",
                  subset = list(year = as.character(1991:2000)))

# Transform into space format "lon_lat". This assumes a "grid.bin.json" file
# is present in the same directory as "runoff.bin.json".
transform(runoff, to = "lon_lat")
# [...]
# $data |>
#   dimnames() |>
#     .$lat  "-55.75" "-55.25" "-54.75" "-54.25" ... "83.75"
#     .$lon  "-179.75" "-179.25" "-178.75" "-178.25" ... "179.75"
#     .$time  "1991-01-31" "1991-02-28" "1991-03-31" "1991-04-30" ...
#     .$band  "1"
# [...]

# Transform time format from a single time dimension into separate dimensions
# for years, months, and days. Dimensions for time steps not present in the
# data are omitted, i.e. no "day" dimension for monthly data.
transform(runoff, to = "year_month_day")
# [...]
# $data |>
#   dimnames() |>
#     .$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"
#     .$year  "1991" "1992" "1993" "1994" ... "2000"
#     .$band  "1"
# [...]

## End(Not run)

Description

Requires a tibble (modern data.frame class) in a specific format (see details & examples) to write the model configuration file "config_*.json". Each row in the tibble corresponds to a model run. The generated "config_*.json" is based on a cjson file (e.g. "lpjml_config.cjson").

Usage

write_config(
  x,
  model_path,
  sim_path = NULL,
  output_list = c(),
  output_list_timestep = "annual",
  output_format = NULL,
  cjson_filename = "lpjml_config.cjson",
  parallel_cores = 4,
  debug = FALSE,
  params = NULL,
  output_path = NULL,
  js_filename = NULL
)

Arguments

Details

Supply a tibble for x, in which each row represents a configuration (config) for an LPJmL simulation.
Here a config refers to a precompiled "lpjml_config.cjson" file (or file name provided as cjson_filename argument) which already contains all the information from the mandatory cjson files. The precompilation is done internally by write_config().
write_config() uses the column names of param as keys for the config json using the same syntax as lists, e.g. "k_temp" from "param.js" can be accessed with "param$k_temp" or ⁠"param[["k_temp"]]"⁠ as the column name. (The former point-style syntax - "param.k_temp" - is still valid but deprecated)
For each run and thus each row, this value has to be specified in the tibble. If the original value should instead be used, insert NA.
Each run can be identified via the "sim_name", which is mandatory to specify.

my_params1 <- tibble(
  sim_name = c("scenario1", "scenario2"),
  random_seed = c(42, 404),
  `pftpar[[1]]$name` = c("first_tree", NA),
  `param$k_temp` = c(NA, 0.03),
  new_phenology = c(TRUE, FALSE)
)

my_params1
# A tibble: 2 x 5
#   sim_name random_seed `pftpar[[1]]$name` `param$k_temp` new_phenology
#   <chr>          <dbl> <chr>                <dbl> <lgl>
# 1 scenario1         42 first_tree           NA    TRUE
# 2 scenario2        404 NA                    0.03 FALSE

Simulation sequences

To set up spin-up and transient runs, where transient runs are dependent on the spin-up(s), a parameter "dependency" has to be defined as a column in the tibble that links simulations with each other using the "sim_name".
Do not manually set "-DFROM_RESTART" when using "dependency". The same applies for LPJmL config settings "restart", "write_restart", "write_restart_filename", "restart_filename", which are set automatically by this function. This way multiple runs can be performed in succession and build a conceivably endless chain or tree.

# With dependent runs.
my_params3 <- tibble(
 sim_name = c("scen1_spinup", "scen1_transient"),
 random_seed = c(42, 404),
 dependency = c(NA, "scen1_spinup")
)
my_params3
# A tibble: 2 x 4
#   sim_name        random_seed order dependency
#   <chr>                 <int> <lgl> <chr>
# 1 scen1_spinup             42 FALSE NA
# 2 scen1_transient         404 TRUE  scen1_spinup

SLURM options

Another feature is to define SLURM options for each simulation (row) separately. For example, users may want to set a lower wall clock limit (wtime) for the transient run than the spin-up run to get a higher priority in the SLURM queue. This can be achieved by supplying this option as a parameter to param.
6 options are available, namely sclass, ntasks, wtime, blocking, constraint and slurm_options. Use as arguments for [submit_lpjml()⁠].\cr If specified in ⁠param⁠, they overwrite the corresponding function arguments in [⁠submit_lpjml()'].

my_params4 <- tibble(
 sim_name = c("scen1_spinup", "scen1_transient"),
 random_seed = c(42, 404),
 dependency = c(NA, "scen1_spinup"),
 wtime = c("8:00:00", "2:00:00")
)

my_params4
# A tibble: 2 x 5
#   sim_name        random_seed order dependency   wtime
#   <chr>                 <int> <lgl> <chr>        <chr>
# 1 scen1_spinup             42 FALSE NA           8:00:00
# 2 scen1_transient         404 TRUE  scen1_spinup 2:00:00

Use of macros

To set a macro (e.g. "MY_MACRO" or "CHECKPOINT") provide it as a column of the tibble as you would do with a flag in the shell: "-DMY_MACRO" "-DCHECKPOINT".
Wrap macros in backticks or tibble will raise an error, as starting an object definition with "-" is not allowed in R.

my_params2 <- tibble(
  sim_name = c("scen1_spinup", "scen1_transient"),
  random_seed = c(42, 404),
  `-DMY_MACRO` = c(TRUE, FALSE),
)

my_params2
# A tibble: 2 x 3
#   sim_name        random_seed `-DMY_MACRO`
#   <chr>                 <int> <lgl>
# 1 scen1_spinup             42 TRUE
# 2 scen1_transient         404 FALSE

In short

• write_config() creates subdirectories within the sim_path directory

  • "./configurations" to store the config files.

  • "./output" to store the output within subdirectories for each sim_name.

  • "./restart" to store the restart files within subdirectories for each sim_name.

• The list syntax (e.g. pftpar[[1]]$name) allows to create column names and thus keys for accessing values in the config json.

• The column "sim_name" is mandatory (used as an identifier).

• The run parameter "dependency" is optional but enables interdependent consecutive runs using submit_lpjml().

• SLURM options in param allow to use different values per run.

• If NA is specified as cell value the original value is used.

• R booleans/logical constants TRUE and FALSE are to be used for boolean parameters in the config json.

• Value types need to be set correctly, e.g. no strings where numeric values are expected.

Value

tibble with at least one column named "sim_name". Run parameters "order" and "dependency" are included if defined in x. tibble in this format is required for submit_lpjml().

Examples

## Not run: 
library(tibble)

model_path <- "./LPJmL_internal"
sim_path <-"./my_runs"


# Basic usage
my_params <- tibble(
  sim_name = c("scen1", "scen2"),
  random_seed = c(12, 404),
  `pftpar[[1]]$name` = c("first_tree", NA),
  `param$k_temp` = c(NA, 0.03),
  new_phenology = c(TRUE, FALSE)
)

config_details <- write_config(
  x = my_params,
  model_path = model_path,
  sim_path = sim_path
)

config_details
# A tibble: 2 x 1
#   sim_name
#   <chr>
# 1 scen1
# 2 scen2

# Usage with dependency
my_params <- tibble(
 sim_name = c("scen1_spinup", "scen1_transient"),
 random_seed = c(42, 404),
 dependency = c(NA, "scen1_spinup")
)

config_details <- write_config(
  x = my_params,
  model_path = model_path,
  sim_path = sim_path
)

config_details
# A tibble: 2 x 3
#   sim_name        order dependency
#   <chr>           <dbl> <chr>
# 1 scen1_spinup        1 NA
# 2 scen1_transient     2 scen1_spinup


my_params <- tibble(
 sim_name = c("scen1_spinup", "scen1_transient"),
 random_seed = c(42, 404),
 dependency = c(NA, "scen1_spinup"),
 wtime = c("8:00:00", "2:00:00")
)

config_details <- write_config(
  x = my_params,
  model_path = model_path,
  sim_path = sim_path
)

config_details
# A tibble: 2 x 4
#   sim_name        order dependency   wtime
#   <chr>           <dbl> <chr>        <chr>
# 1 scen1_spinup        1 NA           8:00:00
# 2 scen1_transient     2 scen1_spinup 2:00:00


## End(Not run)

Description

Write an LPJmL clm header to a file. The header has to be a list following the structure returned by read_header() or create_header(). The function will fail if the output file exists already unless overwrite is set to TRUE.

Usage

write_header(filename, header, overwrite = FALSE)

Arguments

Value

Returns filename invisibly.

See Also

• create_header() for creating headers from scratch and for a more detailed description of the LPJmL header format.

• read_header() for reading headers from files.

Examples

## Not run: 
header <- read_header(filename = "old_filename.clm")
write_header(
  filename = "new_filename.clm",
  header = header,
  overwrite = FALSE
)

## End(Not run)