| Title: | PEcAn Functions Used for Ecological Forecasts and Reanalysis |
|---|---|
| Description: | The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. |
| Authors: | Mike Dietze [aut, cre], David LeBauer [aut], Xiaohui Feng [ctb], Dan Wang [ctb], Carl Davidson [ctb], Rob Kooper [ctb], Alexey Shiklomanov [ctb], University of Illinois, NCSA [cph] |
| Maintainer: | Mike Dietze <[email protected]> |
| License: | BSD_3_clause + file LICENSE |
| Version: | 1.8.0.9000 |
| Built: | 2024-11-04 07:32:54 UTC |
| Source: | https://github.com/PecanProject/pecan |
Contains data from 246 Fluxnet sites. Variables include aboveground and belowground biomass in various pools, plus soil texture/chemistry/horizonation/C&N stocks.
BADMBADM
## 'BADM' A data frame with 12,300 rows and 13 columns:
Fluxnet code for the site
site coordinates
Measurement date
TODO
category, eg abovground biomass or soil chemistry
key and value for each measured variable
numeric IDs and names for the Level 1 and level 2 ecoregions where this site is located
Originally from Fluxnet <https://fluxnet.org/badm-data-product/>, but the provenence and age of this specific file is not clear.
BADM_IC_process
BADM_IC_process(settings, dir, overwrite = TRUE)BADM_IC_process(settings, dir, overwrite = TRUE)
settings |
pecan xml settings |
dir |
output dir which you want to store the IC netcdf file |
overwrite |
Flag for overwriting the IC file. |
a list of paths to generated and stored IC files.
builds the JAGS data object for the tree ring / inventory fusion code also sets all the priors
buildJAGSdata_InventoryRings(combined, inc.unit.conv = 0.1)buildJAGSdata_InventoryRings(combined, inc.unit.conv = 0.1)
combined |
object returned from matchInventoryRings. Matrix with both increment and plot data |
inc.unit.conv |
conversion factor from loaded increments to cm (of radius) |
list
Michael Dietze
tree core QAQC
Clean_Tucson(file)Clean_Tucson(file)
file |
WinDendro output |
Converts .rds files into pool netcdf files.
cohort2pool(dat, allom_param = NULL, dbh_name = "DBH")cohort2pool(dat, allom_param = NULL, dbh_name = "DBH")
dat |
veg_info file |
allom_param |
parameters for allometric equation, a and b. Based on base-10 log-log linear model (power law) |
dbh_name |
Default is "DBH". This is the column name in the veg_file that represents DBH. May differ depending on data source. |
cohort2pool function Calculates total biomass using veg cohort file.
Saloni Shah
## Not run: veg_file <- "~/downloads/FFT_site_1-25665/FFT.2008.veg.rds" cohort2pool(veg_File = veg_file, allom_param = NULL) ## End(Not run)## Not run: veg_file <- "~/downloads/FFT_site_1-25665/FFT.2008.veg.rds" cohort2pool(veg_File = veg_file, allom_param = NULL) ## End(Not run)
Adapts the dataone::getDataPackage workflow to allow users to download data from the DataONE federation by simply entering the doi or associated package id
dataone_download( id, filepath = "/fs/data1/pecan.data/dbfiles", CNode = "PROD", lazyLoad = FALSE, quiet = FALSE )dataone_download( id, filepath = "/fs/data1/pecan.data/dbfiles", CNode = "PROD", lazyLoad = FALSE, quiet = FALSE )
id |
"The identifier of a package, package metadata or other package member" – dataone r |
filepath |
path to where files will be stored |
CNode |
character, passed to 'dataone::CNode' |
lazyLoad |
"A logical value. If TRUE, then only package member system metadata is downloaded and not data. The default is FALSE." – dataone R |
quiet |
"A 'logical'. If TRUE (the default) then informational messages will not be printed." – dataone R |
Liam P Burke, [email protected]
## Not run: dataone_download(id = "doi:10.6073/pasta/63ad7159306bc031520f09b2faefcf87", filepath = "/fs/data1/pecan.data/dbfiles") ## End(Not run)## Not run: dataone_download(id = "doi:10.6073/pasta/63ad7159306bc031520f09b2faefcf87", filepath = "/fs/data1/pecan.data/dbfiles") ## End(Not run)
Download NEON Soil Water Content and Soil Salinity data by date and site name
download_NEON_soilmoist( site, avg = "all", var = "all", startdate = NA, enddate = NA, outdir )download_NEON_soilmoist( site, avg = "all", var = "all", startdate = NA, enddate = NA, outdir )
site |
four letter NEON site code name(s). If no site is specified, it will download all of them (chr) (e.g "BART" or c("SRER", "KONA", "BART")) |
avg |
averaging interval (minutes): 1, 30, or both ("all") . default returns both |
var |
variable of interest: "SWC" (soil water content) or "SIC" (soil ion content) or both ("all") default returns both. Both variables will be saved in outdir automatically (chr) |
startdate |
start date as YYYY-mm. If left empty, all data available will be downloaded (chr) |
enddate |
start date as YYYY-mm. If left empty, all data available will be downloaded (chr) |
outdir |
out directory to store the following data: .rds list files of SWC and SIC data for each site and sensor position, sensor positions .csv for each site, variable description .csv file, readme .csv file |
List of specified variable(s) AND prints the path to output folder
Juliette Bateman
## Not run: test <- download_NEON_soilmoisture( site = c("SRER", "BART", "KONA"), avg = 30, var = "SWC", startdate = "2019-01", enddate = "2020-01", outdir = getwd()) ## End(Not run)## Not run: test <- download_NEON_soilmoisture( site = c("SRER", "BART", "KONA"), avg = 30, var = "SWC", startdate = "2019-01", enddate = "2020-01", outdir = getwd()) ## End(Not run)
Uses resource_map and dataone::getPackage to download the data into a BagItFile. Then utils::unzip unzips the data and stores in the user's directory.
download_package_rm( resource_map, directory, CNode = "PROD", download_format = "application/bagit-097", overwrite_directory = TRUE )download_package_rm( resource_map, directory, CNode = "PROD", download_format = "application/bagit-097", overwrite_directory = TRUE )
resource_map |
the resource map that corresponds to the given data package |
directory |
location that download.packages places the data |
CNode |
defaults to "PROD" |
download_format |
typically "application/bagit-097". Other possible formats currently unknown. |
overwrite_directory |
boolean that indicates whether or not the function should overwrite the directory |
results of download
Download CDS soil moisture data for the SDA workflow.
download.SM_CDS( outfolder, time.points, overwrite = FALSE, auto.create.key = FALSE )download.SM_CDS( outfolder, time.points, overwrite = FALSE, auto.create.key = FALSE )
outfolder |
physical paths to where the unziped soil moisture files are downloaded. |
time.points |
A vector contains each time point within the start and end date. |
overwrite |
flag determine if we want to overwrite existing files when downloading. |
auto.create.key |
flag determine if we want to automatically create the credential file. |
Introduction on how to play with the CDS python API to correctly build the python environment with the cdsapi installed, you need to follow those steps. 1. Install miniconda. create a directory to install minicaonda 'mkdir -p ~/miniconda3' 2. Download latest miniconda version. 'wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh' 3. run the install script. 'bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3' 4. delete the intall script. 'rm -rf ~/miniconda3/miniconda.sh' 5. add a conda initialize to your bash '~/miniconda3/bin/conda init bash' 6. Verify the installaton, you need to restart your session first. 'conda list' 7. Create Python environment. 'conda update conda' 'conda create -n myenv python=3.9 –yes' 8. Activate your python env. 'conda activate myenv' 9. Install the cdsapi package. 'pip install cdsapi' in the meantime, you might encounter several issues saying XXXX dependency is not available. to solve this issue, you just need to install those dependencies before hand. 10. Create CDS account. go to 'https://cds.climate.copernicus.eu/api-how-to#install-the-cds-api-key' website. create an account. 11. Create CDS personel token. run this function. go to 'https://cds.climate.copernicus.eu/api-how-to#install-the-cds-api-key' website. copy and paste url and key to the prompt window.
A vector containing file paths to the downloaded files.
Dongchen Zhang
Sampling/ensemble module
ens_veg_module( getveg.id, dbparms, input_veg, outfolder, machine, start_date, end_date, n.ensemble, new_site, host )ens_veg_module( getveg.id, dbparms, input_veg, outfolder, machine, start_date, end_date, n.ensemble, new_site, host )
getveg.id |
list, input.id and dbfile.id of the IC file in intermediate pecan standard |
dbparms |
list, settings$database info reqired for opening a connection to DB |
input_veg |
list, this is a sublist of settings$run$inputs that has info about source, id, metadata of the requested IC file |
outfolder |
path to where the processed files will be written |
machine |
data frame, DB info regarding localhost machine id/hostname etc. |
start_date |
date in "YYYY-MM-DD" format, in case of source==FIA it's the settings$run$start.date, otherwise start_date of the IC file in DB |
end_date |
date in "YYYY-MM-DD" format, in case of source==FIA it's the settings$run$end.date, otherwise end_date of the IC file in DB |
n.ensemble |
integer, ensemble member number |
new_site |
data frame, id/lat/lon/name info about the site |
host |
list, host info as in settings$host, host$name forced to be "localhost" upstream |
Istem Fer
This function is designed to find the level1 and level2 code ecoregions for a given lat and long. You can learn more about ecoregions here: https://www.epa.gov/eco-research/ecoregions.
EPA_ecoregion_finder(Lat, Lon, folder.path = NULL)EPA_ecoregion_finder(Lat, Lon, folder.path = NULL)
Lat |
numeric latitude |
Lon |
numeric longitude |
folder.path |
path to the directory where you store the shape files of L1 and L2 ecoregion maps. |
a dataframe with codes corresponding to level1 and level2 codes as two columns
extract_FIA
extract_FIA(lon, lat, start_date, end_date, gridres = 0.075, dbparms, ...)extract_FIA(lon, lat, start_date, end_date, gridres = 0.075, dbparms, ...)
lon |
site longitude |
lat |
site latitude |
start_date |
"YYYY-MM-DD" |
end_date |
"YYYY-MM-DD" |
gridres |
taken from input_veg, DEFAULT = 0.075 |
dbparms |
taken from settings object |
... |
Additional parameters |
Istem Fer
extract_NEON_veg
extract_NEON_veg( lon, lat, start_date, end_date, store_dir, neonsites = NULL, ... )extract_NEON_veg( lon, lat, start_date, end_date, store_dir, neonsites = NULL, ... )
lon |
site longitude, passed from ic_process |
lat |
site latitude, passed from ic_process |
start_date |
"YYYY-MM-DD", used to download NEON datasets for desired time period |
end_date |
"YYYY_MM_DD", used to download NEON datasets for desired time period |
store_dir |
location where you want to store downloaded NEON files |
neonsites |
prepared datasets table from NEON using neonstore::neon_sites(api = "https://data.neonscience.org/api/v0", .token = Sys.getenv("NEON_TOKEN")) |
... |
Additional parameters |
veg_info object to be passed to extract_veg within ic_process
Alexis Helgeson and Michael Dietze
start_date = as.Date("2020-01-01") end_date = as.Date("2021-09-01")start_date = as.Date("2020-01-01") end_date = as.Date("2021-09-01")
Extract CDS soil moisture data for the SDA workflow.
extract_SM_CDS( site_info, time.points, in.path, out.path = NULL, allow.download = TRUE, search_window = 10 )extract_SM_CDS( site_info, time.points, in.path, out.path = NULL, allow.download = TRUE, search_window = 10 )
site_info |
Bety list of site info including site_id, lon, and lat. |
time.points |
A vector contains each time point within the start and end date. |
in.path |
physical paths to where the unziped soil moisture files are downloaded. |
out.path |
Where the final CSV file will be stored. |
allow.download |
Flag determine if we want to automatic download files if they are not available. |
search_window |
time length (days) for locate available soil moisture values. |
A data frame containing soil moisture and sd for each site and each time step.
Dongchen Zhang
Extract soil data from gssurgo
extract_soil_gssurgo( outdir, lat, lon, size = 1, radius = 500, depths = c(0.15, 0.3, 0.6) )extract_soil_gssurgo( outdir, lat, lon, size = 1, radius = 500, depths = c(0.15, 0.3, 0.6) )
outdir |
Output directory for writing down the netcdf file |
lat |
Latitude |
lon |
Longitude |
size |
Ensemble size |
radius |
radius in meters is used to take soil type samples around the site |
depths |
Standard set of soil depths in m to create the ensemble of soil profiles with. |
It returns the address for the generated soil netcdf file
Hamze Dokoohaki
## Not run: outdir <- "~/paleon/envTest" lat <- 40 lon <- -80 PEcAn.data.land::extract_soil_gssurgo(outdir, lat, lon) ## End(Not run)## Not run: outdir <- "~/paleon/envTest" lat <- 40 lon <- -80 PEcAn.data.land::extract_soil_gssurgo(outdir, lat, lon) ## End(Not run)
Extract soil data from the gridpoint closest to a location
extract_soil_nc(in.file, outdir, lat, lon)extract_soil_nc(in.file, outdir, lat, lon)
in.file |
path to netcdf file containing soil data |
outdir |
directory in which to write netcdf file of extracted data. Output filename will be the same as input filename. |
lat, lon
|
location in decimal degrees. Data will be extracted from the point in 'in.file' that is nearest this |
path to netCDF file containing extracted data
## Not run: in.file <- "~/paleon/env_paleon/soil/paleon_soil.nc" outdir <- "~/paleon/envTest" lat <- 40 lon <- -80 PEcAn.data.land::extract_soil_nc(in.file,outdir,lat,lon) ## End(Not run)## Not run: in.file <- "~/paleon/env_paleon/soil/paleon_soil.nc" outdir <- "~/paleon/envTest" lat <- 40 lon <- -80 PEcAn.data.land::extract_soil_nc(in.file,outdir,lat,lon) ## End(Not run)
Function queries a DB to extract veg info downstream
extract_veg( new_site, start_date, end_date, source, gridres, format_name = NULL, machine_host, dbparms, outfolder, overwrite = FALSE, input_veg = input_veg, ... )extract_veg( new_site, start_date, end_date, source, gridres, format_name = NULL, machine_host, dbparms, outfolder, overwrite = FALSE, input_veg = input_veg, ... )
new_site |
new_site object passed from ic_process includes lat, lon, id, and name |
start_date |
"YYYY-MM-DD" |
end_date |
"YYYY-MM-DD" |
source |
taken from input$source, passed from ic_process |
gridres |
only used for source = "FIA" |
format_name |
DEFAULT=NULL |
machine_host |
passed from ic_process |
dbparms |
taken from settings object, passed from ic_process |
outfolder |
passed from ic_process, location where to store files |
overwrite |
DEFAULT = FALSE |
input_veg |
passed from input object in ic_process |
... |
Additional parameters |
results object to be passed back to get.veg.module
Istem Fer and Alexis Helgeson
extract.stringCode
extract.stringCode(x, extractor = from.TreeCode)extract.stringCode(x, extractor = from.TreeCode)
x |
string to decode |
extractor |
function to apply |
Create pss/css files based on data in the fia database
fia.to.psscss( settings, lat = as.numeric(settings$run$site$lat), lon = as.numeric(settings$run$site$lon), year = lubridate::year(settings$run$start.date), gridres = 0.075, min.year = year - 5, max.year = year + 5, overwrite = FALSE )fia.to.psscss( settings, lat = as.numeric(settings$run$site$lat), lon = as.numeric(settings$run$site$lon), year = lubridate::year(settings$run$start.date), gridres = 0.075, min.year = year - 5, max.year = year + 5, overwrite = FALSE )
settings |
PEcAn settings object |
lat, lon
|
site location in decimal degrees. Defults to values passed in 'settings'. |
year |
defaults to year of start date passed in settings |
gridres |
grid resolution in degrees |
min.year, max.year
|
limits on years of FIA data to look for |
overwrite |
logical: regenerate files already in the database? |
modified settings, invisibly
Mike Dietze, Rob Kooper, Ryan Kelly
This function is for formatting purposes. It simply inserts the doi or id that the user wishes to query into Solr format so that it is compatible with the dataoneR query functionality in the PEcAn function
format_identifier(id)format_identifier(id)
id |
the doi or other identifier linked to the package in DataONE |
returns the id in the proper format for querying the DataONE Federation (using solrQuery syntax)
Liam P Burke, [email protected]
from.TreeCode
from.TreeCode(x)from.TreeCode(x)
x |
string to decode |
Locates data in DataONE and returns the resource_map or a message indicating that there is no corresponding resource_map for the given id
get_resource_map(id, CNode = "PROD")get_resource_map(id, CNode = "PROD")
id |
the doi or other identifier linked to the package in DataONE |
CNode |
default is "PROD" |
return the resource_map or a message indicating that there is no corresponding resource_map for the given id
Load/extract + match species module
get_veg_module( input_veg, outfolder, start_date, end_date, dbparms, new_site, host, machine_host, overwrite )get_veg_module( input_veg, outfolder, start_date, end_date, dbparms, new_site, host, machine_host, overwrite )
input_veg |
list, this is a sublist of settings$run$inputs that has info about source, id, metadata of the requested IC file |
outfolder |
path to where the processed files will be written |
start_date |
date in "YYYY-MM-DD" format, in case of source==FIA it's the settings$run$start.date, otherwise start_date of the IC file in DB |
end_date |
date in "YYYY-MM-DD" format, in case of source==FIA it's the settings$run$end.date, otherwise end_date of the IC file in DB |
dbparms |
list, settings$database info reqired for opening a connection to DB |
new_site |
data frame, id/lat/lon/name info about the site |
host |
list, host info as in settings$host, host$name forced to be "localhost" upstream |
machine_host |
local machine hostname, e.g. "pecan2.bu.edu" |
overwrite |
logical flag for convert_input |
Istem Fer
Function to extract attribute information from vector or raster data layer.
get.attributes(file, coords)get.attributes(file, coords)
file |
vector or raster layer |
coords |
vector containin xmin,ymin,xmax,ymax defing the bounding box for subset |
Shawn P. Serbin
## Not run: file <- Sys.glob(file.path(R.home(), 'library', 'PEcAn.data.land','data','*.kml')) out <- get.attributes(file=file,coords=c(-95,42,-84,47)) print(out) ## End(Not run)## Not run: file <- Sys.glob(file.path(R.home(), 'library', 'PEcAn.data.land','data','*.kml')) out <- get.attributes(file=file,coords=c(-95,42,-84,47)) print(out) ## End(Not run)
Get Soil
get.soil(lat, lon, soil.nc = soil.nc)get.soil(lat, lon, soil.nc = soil.nc)
lat |
latitude |
lon |
longitude |
soil.nc |
netCDFe file with soil data |
usda soil class
David LeBauer
This function queries the gSSURGO database for a series of map unit keys
gSSURGO.Query( mukeys, fields = c("chorizon.sandtotal_r", "chorizon.silttotal_r", "chorizon.claytotal_r") )gSSURGO.Query( mukeys, fields = c("chorizon.sandtotal_r", "chorizon.silttotal_r", "chorizon.claytotal_r") )
mukeys |
map unit key from gssurgo |
fields |
a character vector of the fields to be extracted. See details and the default argument to find out how to define fields. |
Full documention of available tables and their relationships can be found here www.sdmdataaccess.nrcs.usda.gov/QueryHelp.aspx There have been occasions where NRCS made some minor changes to the structure of the API which this code is where those changes need to be implemneted here. Fields need to be defined with their associate tables. For example, sandtotal is a field in chorizon table which needs to be defined as chorizon.sandotal_(r/l/h), where r stands for the representative value, l stands for low and h stands for high. At the moment fields from mapunit, component, muaggatt, and chorizon tables can be extracted.
a dataframe with soil properties. Units can be looked up from database documentation
## Not run: PEcAn.data.land::gSSURGO.Query( mukeys = 2747727, fields = c( "chorizon.cec7_r", "chorizon.sandtotal_r", "chorizon.silttotal_r","chorizon.claytotal_r", "chorizon.om_r","chorizon.hzdept_r","chorizon.frag3to10_r", "chorizon.dbovendry_r","chorizon.ph1to1h2o_r", "chorizon.cokey","chorizon.chkey")) ## End(Not run)## Not run: PEcAn.data.land::gSSURGO.Query( mukeys = 2747727, fields = c( "chorizon.cec7_r", "chorizon.sandtotal_r", "chorizon.silttotal_r","chorizon.claytotal_r", "chorizon.om_r","chorizon.hzdept_r","chorizon.frag3to10_r", "chorizon.dbovendry_r","chorizon.ph1to1h2o_r", "chorizon.cokey","chorizon.chkey")) ## End(Not run)
Extract ISCN SOC initial conditions from existing ISCN database.
IC_ISCN_SOC(site_info, ens = 100, ecoregion.path = NULL)IC_ISCN_SOC(site_info, ens = 100, ecoregion.path = NULL)
site_info |
Bety list of site info including site_id, lon, and lat. |
ens |
ensemble number. |
ecoregion.path |
path to the directory where you store the shape files of L1 and L2 ecoregion maps. |
A data frame containing sampled SOC, each row represent each site.
Dongchen Zhang
ic_process
ic_process(settings, input, dir, overwrite = FALSE)ic_process(settings, input, dir, overwrite = FALSE)
settings |
pecan settings list |
input |
Taken from settings$run$inputs. This should include id, path, and source |
dir |
settings$database$dbfiles |
overwrite |
Default = FALSE. whether to force ic_process to proceed |
Istem Fer, Hamze Dokoohaki
Uses dataone::query from dataoneR to query DataONE. Prints result if data exists
id_resolveable(id, return_result = TRUE, CNode = "PROD")id_resolveable(id, return_result = TRUE, CNode = "PROD")
id |
the doi or other identifier linked to the package in DataONE |
return_result |
boolean that returns or suppresses result of query. defaults to TRUE. |
CNode |
CNode="PROD" |
returns message indicating wether or not the id resolves to data in the DataONE federation and information about said data.
this code fuses forest inventory data with tree growth data (tree ring or dendrometer band) for the same plots. Code is a rewrite of Clark et al 2007 Ecol Appl into JAGS
InventoryGrowthFusion( data, cov.data = NULL, time_data = NULL, n.iter = 5000, n.chunk = n.iter, n.burn = min(n.chunk, 2000), random = NULL, fixed = NULL, time_varying = NULL, burnin_plot = FALSE, save.jags = "IGF.txt", z0 = NULL, save.state = TRUE, restart = NULL )InventoryGrowthFusion( data, cov.data = NULL, time_data = NULL, n.iter = 5000, n.chunk = n.iter, n.burn = min(n.chunk, 2000), random = NULL, fixed = NULL, time_varying = NULL, burnin_plot = FALSE, save.jags = "IGF.txt", z0 = NULL, save.state = TRUE, restart = NULL )
data |
list of data inputs |
cov.data |
covariate data |
time_data |
required if time_varying is provided |
n.iter |
total number of iterations across all chunks |
n.chunk |
number of MCMC steps to evaluate at a time. Will only return LAST. If restarting, second number in vector is chunk to start from |
n.burn |
number of steps to automatically discard as burn-in |
random |
whether or not to include random effects |
fixed |
formula for fixed effects |
time_varying |
formula for time-varying effects |
burnin_plot |
logical: display a plot of the burnin steps? |
save.jags |
logical: Save the generated JAGS script? |
z0 |
initial conditions for state variable |
save.state |
whether or not to include inferred DBH in output (can be large). Enter numeric value to save.state periodically (in terms of n.chunk) |
restart |
final mcmc.list from previous execution. NULL for new run. TRUE to save final state for new run. |
an mcmc.list object
Requires JAGS
InventoryGrowthFusionDiagnostics
InventoryGrowthFusionDiagnostics(jags.out, combined = NULL)InventoryGrowthFusionDiagnostics(jags.out, combined = NULL)
jags.out |
output mcmc.list from InventoryGrowthFusion |
combined |
data output from matchInventoryRings |
Michael Dietze
Contains 200 ensemble SOC data from 43 level 2 eco-regions across North America. Variable include SOC densities in g/cm2.
iscn_sociscn_soc
## 'iscn_soc' A data frame with 200 rows and 43 columns:
1 to 200 ensemble members
43 level 2 ecoregion codes across North America
https://iscn.fluxdata.org/wp-content/uploads/sites/23/2019/05/ISCN_ALL_DATA_DATASET_1-1.xlsx
uses 'PEcAn.benchmark::load_data()' to get veg data
load_veg( new_site, start_date, end_date, source_id, source, icmeta = NULL, format_name = NULL, machine_host, dbparms, outfolder, overwrite = FALSE, ... )load_veg( new_site, start_date, end_date, source_id, source, icmeta = NULL, format_name = NULL, machine_host, dbparms, outfolder, overwrite = FALSE, ... )
new_site |
list passed to 'load_data' |
start_date, end_date
|
date range to look up |
source_id |
input id to look up in DB |
source |
name of data source (used in file naming) |
icmeta |
metadata for initial conditions |
format_name |
file format to look for |
machine_host |
hostname of machine where the data lives |
dbparms |
parameters to use when opening connection to database |
outfolder |
path to write results |
overwrite |
Logical: replace existing files? NOTE: Currently ignored! |
... |
Additional arguments, currently ignored |
Istem Fer
Matches BETYdb species IDs to model-specific PFTs
match_pft( bety_species_id, pfts, query = NULL, con = NULL, allow_missing = FALSE, model = NULL )match_pft( bety_species_id, pfts, query = NULL, con = NULL, allow_missing = FALSE, model = NULL )
bety_species_id |
vector of BETYdb species IDs |
pfts |
settings$pfts. List of pfts with database matching based on name |
query |
Default is NULL. query to BETY db. |
con |
database connection, if NULL use traits package |
allow_missing |
flag to indicate that settings file does not need to match exactly |
model |
Default is NULL. This is the BETY model ID for matching pfts to the correct model. |
table of BETYdb PFT IDs matched to species IDs
Mike Dietze, Istem Fer
Parses species codes in input data and matches them with the BETY species ID.
match_species_id( input_codes, format_name = "custom", bety = NULL, translation_table = NULL, ... )match_species_id( input_codes, format_name = "custom", bety = NULL, translation_table = NULL, ... )
input_codes |
Character vector of species codes |
format_name |
Species code format name (see details) |
bety |
BETY connection object |
translation_table |
Data frame with custom translation table (see details). |
... |
additional arguments, currently ignored |
format_name can be one of the following:
usdaUSDA Plants database symbol (e.g. QURU, TSCA)
fiaFIA species code
latin_nameScientific name, as "Genus species"; must match exactly and unambiguously to scientificname field in BETY
customA data frame matching BETY IDs (column name bety_species_id) to input codes (column name input_code). This data frame must be passed via the translation_table argument.
data.frame containing the following columns:
input_codeCharacter provided as input
bety_species_idBig integer species ID, unique and specific to BETY
genusGenus part of Latin name, from BETY
speciesSpecies part of Latin name, from BETY
Alexey Shiklomanov <[email protected]>, Istem Fer
## Not run: con <- PEcAn.DB::db.open(list( driver = "Postgres", dbname = 'bety', user = 'bety', password = 'bety', host = 'localhost') ) input_codes <- c('ACRU', 'PIMA', 'TSCA') format_name <- 'usda' match_species_id(input_codes = input_codes, format_name = format_name, bety = con) ## End(Not run)## Not run: con <- PEcAn.DB::db.open(list( driver = "Postgres", dbname = 'bety', user = 'bety', password = 'bety', host = 'localhost') ) input_codes <- c('ACRU', 'PIMA', 'TSCA') format_name <- 'usda' match_species_id(input_codes = input_codes, format_name = format_name, bety = con) ## End(Not run)
matchInventoryRings
matchInventoryRings( trees, rings, extractor = "TreeCode", nyears = 30, coredOnly = TRUE )matchInventoryRings( trees, rings, extractor = "TreeCode", nyears = 30, coredOnly = TRUE )
trees, rings
|
codes from which to extract IDs |
extractor |
function to call, specified without its initial 'to.' e.g. "TreeCode" calls 'to.TreeCode' |
nyears |
number of years to extract |
coredOnly |
logical: Only include trees with data from 2000? |
Convert a matric potential to a soil moisture
mpot2smoist( mpot, soil_water_potential_at_saturation, soil_hydraulic_b, volume_fraction_of_water_in_soil_at_saturation )mpot2smoist( mpot, soil_water_potential_at_saturation, soil_hydraulic_b, volume_fraction_of_water_in_soil_at_saturation )
mpot |
water potential (cm H2O) |
soil_water_potential_at_saturation |
water potential when soil is saturated (cm H2O) |
soil_hydraulic_b |
pore-size distribution parameter for Campbell (1974) water content model |
volume_fraction_of_water_in_soil_at_saturation |
VSWC when soil is saturated (numeric in range 0-1) |
volumetric soil water content
netcdf.writer.BADAM
netcdf.writer.BADM(lat, long, siteid, outdir, ens)netcdf.writer.BADM(lat, long, siteid, outdir, ens)
lat |
numeric latitude |
long |
numeric longitude |
siteid |
site id as a string |
outdir |
output dir which you want to store the IC netcdf file |
ens |
ensemble members, passed on to 'pool_ic_list2netcdf' |
a dataframe with file, host, mimetype, formatname, startdate, enddate and dbfile.name columns
parse.MatrixNames
parse.MatrixNames(w, pre = "x", numeric = FALSE)parse.MatrixNames(w, pre = "x", numeric = FALSE)
w |
mcmc object containing matrix outputs |
pre |
prefix (variable name) for the matrix variable to be extracted |
numeric |
boolean, whether to coerce class to numeric |
matrix
Michael Dietze
Given a vector of root size thresholds (lower bound of each) and a vector of corresponding root carbon values, partition_roots checks if the input can be partitioned along the .002 m threshold between fine and coarse roots and returns a list containing the summed values for fine and coarse. If there are fewer than two thresholds or none within .0005 m of .002 m, returns NULL. Meant to be used in conjunction with standard variable root_carbon_content with rtsize dimension, extracted from netcdf.
partition_roots(roots, rtsize)partition_roots(roots, rtsize)
roots |
vector of root carbon values in kg C m-2 |
rtsize |
vector of lower bounds of root size class thresholds in m, length greater than one and equal to roots. Must contain threshold within .0005 m of .002 m |
list containing summed fine root and coarse root carbon (2 values)
Anne Thomas
convert composite ring & census data into AGB
plot2AGB(combined, out, outfolder, allom.stats, unit.conv = 0.02)plot2AGB(combined, out, outfolder, allom.stats, unit.conv = 0.02)
combined |
data frame merging plot inventory and tree ring data |
out |
MCMC samples for diameter (sample x tree) |
outfolder |
output folder for graphs & data |
allom.stats |
Allometry statistics computed by 'AllomAve' |
unit.conv |
area conversion from sum(kg/tree) to kg/area |
Mike Dietze [email protected]
Converts input list containing standard dimensions and variables (named values) for initial conditions to a netcdf file, input to pool-based models.
pool_ic_list2netcdf(input, outdir, siteid, ens = NA)pool_ic_list2netcdf(input, outdir, siteid, ens = NA)
input |
list with two elements: list of netcdf dimensions (dims, with named values) and list of variables (vals, with named values) |
outdir |
directory to write netcdf file |
siteid |
site id |
ens |
Default is NA. Ensemble members. |
Anne Thomas
Converts netcdf containing standard dimensions and variables for pool-based initial conditions, created by pool_ic_list2netcdf, back into list format
pool_ic_netcdf2list(nc.path)pool_ic_netcdf2list(nc.path)
nc.path |
path to netcdf file containing standard dimensions and variables |
list with two elements: list of netcdf dimensions (dims, with named values) and list of variables (vals, with named values)
Anne Thomas
Calculates pools from given initial condition values, deriving complements where necessary/possible if given TotLivBiomass
prepare_pools(nc.path, constants = NULL)prepare_pools(nc.path, constants = NULL)
nc.path |
path to netcdf file containing standard dimensions and variables; currently supports these variables: TotLivBiom, leaf_carbon_content, LAI, AbvGrndWood, root_carbon_content, fine_root_carbon_content, coarse_root_carbon_content, litter_carbon_content, soil_organic_carbon_content, soil_carbon_content, wood_debris_carbon_content |
constants |
list of constants; must include SLA in m2 / kg C if providing LAI for leaf carbon |
list of pool values in kg C / m2 with generic names
Anne Thomas
Match species to PFTs + veg2model module
put_veg_module( getveg.id, dbparms, input_veg, pfts, outfolder, n.ensemble, dir, machine, model, start_date, end_date, new_site, host, overwrite )put_veg_module( getveg.id, dbparms, input_veg, pfts, outfolder, n.ensemble, dir, machine, model, start_date, end_date, new_site, host, overwrite )
getveg.id |
list, input.id and dbfile.id of the IC file in intermediate pecan standard |
dbparms |
list, settings$database info reqired for opening a connection to DB |
input_veg |
list, this is a sublist of settings$run$inputs that has info about source, id, metadata of the requested IC file |
pfts |
list, same as settings$pfts |
outfolder |
path to where the processed files will be written |
n.ensemble |
integer, ensemble member number |
dir |
dir path to dbfiles on local machine |
machine |
data frame, DB info regarding localhost machine id/hostname etc. |
model |
model name, e.g. "ED2" |
start_date |
date in "YYYY-MM-DD" format, in case of source==FIA it's the settings$run$start.date, otherwise start_date of the IC file in DB |
end_date |
date in "YYYY-MM-DD" format, in case of source==FIA it's the settings$run$end.date, otherwise end_date of the IC file in DB |
new_site |
data frame, id/lat/lon/name info about the site |
host |
list, host info as in settings$host, host$name forced to be "localhost" upstream |
overwrite |
logical flag for convert_input |
Istem Fer
wrapper around read.tucson that loads a whole directory of tree ring files and calls a 'clean' function that removes redundant records (WinDendro can sometimes create duplicate records when editing)
Read_Tucson(folder)Read_Tucson(folder)
folder |
path to read files from. Will read all files at this path matching "TXT", "rwl", or "rw" |
This function returns a dataframe of plant biomass, root and soil carbon for a set of lat and long coordinates. This function first finds the level1 and level2 ecoregions for the given coordinates, and then tries to filter BADM database for those eco-regions. If no data found in the BADM database for the given lat/longs eco-regions, then all the data in the database will be used to return the initial condition. All the variables are also converted to kg/m^2.
Read.IC.info.BADM(lat, long)Read.IC.info.BADM(lat, long)
lat |
numeric latitude |
long |
numeric longitude |
a dataframe with 7 columns of Site, Variable, Date, Organ, AGB, soil_organic_carbon_content, litter_carbon_content. Variable in the return object refers to what this value was called inside BADM database.
## Not run: badm_test <- Read.IC.info.BADM(45.805925,-90.07961) ## End(Not run)## Not run: badm_test <- Read.IC.info.BADM(45.805925,-90.07961) ## End(Not run)
sample_ic
sample_ic( in.path, in.name, start_date, end_date, outfolder, n.ensemble, machine_host, source, bin_var = "DBH", bin_size = 10, bin_herb_soil = TRUE, ... )sample_ic( in.path, in.name, start_date, end_date, outfolder, n.ensemble, machine_host, source, bin_var = "DBH", bin_size = 10, bin_herb_soil = TRUE, ... )
in.path |
path to folder of the file to be sampled |
in.name |
file name of the file to be sampled |
start_date |
date in "YYYY-MM-DD" format |
end_date |
date in "YYYY-MM-DD" format |
outfolder |
dir path, whete to write the file |
n.ensemble |
integer, ensemble member number |
machine_host |
localhost name, e.g. "pecan2.bu.edu" |
source |
string to appear in file names, e.g. "PalEON" |
bin_var |
variable you would like to sample by, DEFAULT is DBH |
bin_size |
bin size for sampling, DEFAULT is 10 |
bin_herb_soil |
if we want to use bin size for both herb and soil sampling |
... |
Other inputs |
Istem Fer
This function determines the soil class number based on the fraction of sand, clay, and silt
sclass(sandfrac, clayfrac)sclass(sandfrac, clayfrac)
sandfrac, clayfrac
|
numeric vectors with values in range 0 to 1. Silt fraction is assumed to be the difference between (sand+clay) and 1 |
vector of integers identifying textural class of each input layer. Possible values are 1 through 17; NB these are NOT the same class boundaries as the 12 USDA soil texture classes.
sclass(0.3,0.3)sclass(0.3,0.3)
Convert ESRI shapefile (*.shp) to keyhole markup language (KML) file format
shp2kml( dir, ext, kmz = FALSE, proj4 = NULL, color = NULL, NameField = NULL, out.dir = NULL )shp2kml( dir, ext, kmz = FALSE, proj4 = NULL, color = NULL, NameField = NULL, out.dir = NULL )
dir |
Directory of GIS shapefiles to convert to kml/kmz |
ext |
File extension for files to convert to kml/kmz. Defaults to ESRI shapefile, '.shp'. [Place holder for other potential vector files to conver to kml] |
kmz |
TRUE/FALSE. Option to write out file as a compressed kml. Requires zip utility |
proj4 |
OPTIONAL. Define output proj4 projection string. If set, input vector will be reprojected to desired projection. Not yet implemented. |
color |
OPTIONAL. Fill color for output kml/kmz file |
NameField |
OPTIONAL. Define names for individual features in KML/KMZ file |
out.dir |
OPTIONAL. Output directory for converted files |
Shawn P. Serbin
## Not run: dir <- Sys.glob(file.path(R.home(), 'library', 'PEcAn.data.land','data')) out.dir <- path.expand('~/temp') shp2kml(dir,'.shp',kmz=FALSE,NameField='STATE',out.dir=out.dir) system(paste('rm -r ',out.dir)) ## End(Not run)## Not run: dir <- Sys.glob(file.path(R.home(), 'library', 'PEcAn.data.land','data')) out.dir <- path.expand('~/temp') shp2kml(dir,'.shp',kmz=FALSE,NameField='STATE',out.dir=out.dir) system(paste('rm -r ',out.dir)) ## End(Not run)
Default parameters for calculating soil properties from sand & clay content
soil_classsoil_class
## 'soil_class' A list with 26 entries:
thermal conductivity, W m^-1 K^-1
heat capacity, J m^-3 K^-1
relative conductivity factor
hydraulic conductance at field capacity, mm day^-1
gravity acceleration, m s^-2
Abbreviations for each of 18 soil texture classes, e.g. "SiL", "LSa"
Names for 18 soil texture classes, e.g. "Sand", "Silty clay"
soil water potential when air-dry, MPa
soil water potential at critical water content, MPa
soil water potential at wilting point, MPa
list of 18 lists, each giving minimum and maximum sand/silt/clay contents for a soil texture class
list of 18 lists, each giving corner points in the soil texture triangle for a soil texture class
data frame with 13 rows and 21 columns, giving default parameter values for 13 named soil textures
critical water content (fractional soil moisture at which plants start dropping leaves), m^3 m^-3
default volume fraction of sand in each of 18 soil texture classes
default volume fraction of clay in each of 18 soil texture classes
The hydraulic parameters are derived from Cosby et al 1984, "A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils", Water Resources Research 20(6): 682-690. This implementation comes from one provided by the ED2 model, plus 'texture.csv' from a source not recorded. Package 'PEcAn.linkages' contains an identical texture.csv, also with no obvious source label. See also comments in soil_utils.R
Estimate soil parameters from texture class or sand/silt/clay
soil_params( soil_type = NULL, sand = NULL, silt = NULL, clay = NULL, bulk = NULL )soil_params( soil_type = NULL, sand = NULL, silt = NULL, clay = NULL, bulk = NULL )
soil_type |
USDA Soil Class. See Details |
sand |
percent sand |
silt |
percent silt |
clay |
percent clay |
bulk |
soil bulk density (optional, kg m-3) |
* Specify _either_ soil_type or sand/silt/clay. soil_type will be ignored if sand/silt/clay is provided * If only 2 out of sand/silt/clay are provided, it will be assumed they sum to 100 * Valid soil class options: "Sand","Loamy sand","Sandy loam","Silt loam","Loam", "Sandy clay loam","Silty clay loam","Clayey loam", "Sandy clay","Silty clay","Clay","Peat","Bedrock", "Silt","Heavy clay","Clayey sand","Clayey silt" * Based on ED2/R-utils/soilutils.r * Hydraulics based on Cosby et al 1984, using table 4 and equation 1 (which is incorrect it should be saturated moisture potential over moisture potential)
list of soil hydraulic and thermal parameters
sand <- c(0.3, 0.4, 0.5) clay <- c(0.3, 0.3, 0.3) soil_params(sand=sand,clay=clay)sand <- c(0.3, 0.4, 0.5) clay <- c(0.3, 0.3, 0.3) soil_params(sand=sand,clay=clay)
Module for managing soil texture extraction
soil_process(settings, input, dbfiles, overwrite = FALSE, run.local = TRUE)soil_process(settings, input, dbfiles, overwrite = FALSE, run.local = TRUE)
settings |
PEcAn settings list |
input |
PEcAn input list |
dbfiles |
directory to write database files |
overwrite |
overwrite previous results (boolean) |
run.local |
logical: Run only on the current machine? If FALSE, runs on 'settings$host' (which might turn out to be the current machine) |
path to soil file
Given SSURGO names for soil properties, looks up their standard units. Note that names must match exactly.
soil.units(varname = NA)soil.units(varname = NA)
varname |
character vector. See details |
Supported variables are:
soil_depth
soil_cec
fraction_of_clay_in_soil
fraction_of_sand_in_soil
fraction_of_silt_in_soil
fraction_of_gravel_in_soil
volume_fraction_of_water_in_soil_at_saturation
volume_fraction_of_water_in_soil_at_field_capacity
volume_fraction_of_condensed_water_in_dry_soil
volume_fraction_of_condensed_water_in_soil_at_wilting_point
soilC
soil_ph
soil_bulk_density
soil_type
soil_hydraulic_b
soil_water_potential_at_saturation
soil_hydraulic_conductivity_at_saturation
thcond0
thcond1
thcond2
thcond3
soil_thermal_conductivity
soil_thermal_conductivity_at_saturation
soil_thermal_capacity
soil_albedo
character matrix with columns var and unit
soil.units("soil_albedo")soil.units("soil_albedo")
A table of standard names and units can be displayed by running soil.units() without any arguements
soil2netcdf(soil.data, new.file)soil2netcdf(soil.data, new.file)
soil.data |
List of soil variables in standard names & units. Minimum is soil_depth and two of [sand, silt, clay]. Bulk density encouraged. |
new.file |
filename (including path) for output |
soil_params is called internally to estimate additional soil physical parameters from sand/silt/clay & bulk density. Will not overwrite any provided values
Need to expand to alternatively take soil_type (texture class) as an input
On output, soil_type named class is converted to a number because netCDF is a pain for storing strings. Conversion back can be done by load(system.file ("data/soil_class.RData",package = "PEcAn.data.land")) and then soil.name[soil_n]
none
## Not run: soil.data <- list(fraction_of_sand_in_soil = c (0.3,0.4,0.5), fraction_of_clay_in_soil = c(0.3,0.3,0.3), soil_depth = c (0.2,0.5,1.0)) soil2netcdf(soil.data,"soil.nc") ## End(Not run)## Not run: soil.data <- list(fraction_of_sand_in_soil = c (0.3,0.4,0.5), fraction_of_clay_in_soil = c(0.3,0.3,0.3), soil_depth = c (0.2,0.5,1.0)) soil2netcdf(soil.data,"soil.nc") ## End(Not run)
soilgrids_soilC_extract function A function to extract total soil organic carbon for a single or group of lat/long locationsbased on user-defined site location from SoilGrids250m version 2.0 : https://soilgrids.org
soilgrids_soilC_extract(site_info, outdir = NULL, verbose = TRUE)soilgrids_soilC_extract(site_info, outdir = NULL, verbose = TRUE)
site_info |
A dataframe of site info containing the BETYdb site ID, site name, latitude, and longitude, e.g. (site_id, site_name, lat, lon) |
outdir |
Optional. Provide the results as a CSV file (soilgrids_soilC_data.csv) |
verbose |
Provide progress feedback to the terminal? TRUE/FALSE |
a dataframe containing the total soil carbon values and the corresponding standard deviation values (uncertainties) for each location Output column names are c("Site_ID","Site_Name","Latitude","Longitude", "Total_soilC","Std_soilC")
Qianyu Li, Shawn P. Serbin
## Not run: # Example 1 - using the modex.bnl.gov BETYdb and site IDs to extract data db <- 'betydb' host_db <- 'modex.bnl.gov' db_port <- '5432' db_user <- 'bety' db_password <- 'bety' bety <- list(user='bety', password='bety', host=host_db, dbname='betydb', driver=RPostgres::Postgres(),write=FALSE) con <- DBI::dbConnect(drv=bety$driver, dbname=bety$dbname, host=bety$host, password=bety$password, user=bety$user) suppressWarnings(site_qry <- glue::glue_sql("SELECT *, ST_X(ST_CENTROID(geometry)) AS lon, ST_Y(ST_CENTROID(geometry)) AS lat FROM sites WHERE id IN ({ids*})", ids = c("676","622","678","766","764"), .con = con)) suppressWarnings(qry_results.1 <- DBI::dbSendQuery(con,site_qry)) suppressWarnings(qry_results.2 <- DBI::dbFetch(qry_results.1)) DBI::dbClearResult(qry_results.1) DBI::dbDisconnect(con) site_info <- qry_results.2 verbose <- TRUE system.time(result_soc <- PEcAn.data.land::soilgrids_soilC_extract(site_info=site_info, verbose=verbose)) result_soc ## End(Not run)## Not run: # Example 1 - using the modex.bnl.gov BETYdb and site IDs to extract data db <- 'betydb' host_db <- 'modex.bnl.gov' db_port <- '5432' db_user <- 'bety' db_password <- 'bety' bety <- list(user='bety', password='bety', host=host_db, dbname='betydb', driver=RPostgres::Postgres(),write=FALSE) con <- DBI::dbConnect(drv=bety$driver, dbname=bety$dbname, host=bety$host, password=bety$password, user=bety$user) suppressWarnings(site_qry <- glue::glue_sql("SELECT *, ST_X(ST_CENTROID(geometry)) AS lon, ST_Y(ST_CENTROID(geometry)) AS lat FROM sites WHERE id IN ({ids*})", ids = c("676","622","678","766","764"), .con = con)) suppressWarnings(qry_results.1 <- DBI::dbSendQuery(con,site_qry)) suppressWarnings(qry_results.2 <- DBI::dbFetch(qry_results.1)) DBI::dbClearResult(qry_results.1) DBI::dbDisconnect(con) site_info <- qry_results.2 verbose <- TRUE system.time(result_soc <- PEcAn.data.land::soilgrids_soilC_extract(site_info=site_info, verbose=verbose)) result_soc ## End(Not run)
Prepare Soilgrids SoilC data for the SDA workflow.
Soilgrids_SoilC_prep( site_info, start_date, end_date, time_points, outdir = NULL, export_csv = FALSE )Soilgrids_SoilC_prep( site_info, start_date, end_date, time_points, outdir = NULL, export_csv = FALSE )
site_info |
Bety list of site info including site_id, lon, and lat. |
start_date |
Start date of SDA workflow. |
end_date |
End date of SDA workflow. |
time_points |
A vector contains each time point within the start and end date. |
outdir |
Where the final CSV file will be stored. |
export_csv |
Decide if we want to export the CSV file. |
A data frame containing AGB median and sd for each site and each time step.
Dongchen Zhang
Function to subset and clip a GIS vector or raster layer by a bounding box or clip/subset layer (e.g. shapefile/KML)
subset_layer( file, coords = NULL, sub.layer = NULL, clip = FALSE, out.dir = NULL, out.name = NULL )subset_layer( file, coords = NULL, sub.layer = NULL, clip = FALSE, out.dir = NULL, out.name = NULL )
file |
input file to be subset |
coords |
vector with xmin,ymin,xmax,ymax defing the bounding box for subset |
sub.layer |
Vector layer defining the subset region |
clip |
clip geometries to bounding box/subset layer? TRUE/FALSE |
out.dir |
output directory for subset layer. Defaults to location of input file. Can also set to 'pwd' |
out.name |
filename for subset layer. Defaults to original filename with the suffix *.sub |
Shawn P. Serbin
## Not run: # Test dataset file <- Sys.glob(file.path(R.home(), 'library', 'PEcAn.data.land','data','*.shp')) out.dir <- path.expand('~/temp') # with clipping enabled subset_layer(file=file,coords=c(-95,42,-84,47),clip=TRUE,out.dir=out.dir) # without clipping enables subset_layer(file=file,coords=c(-95,42,-84,47),out.dir=out.dir) system(paste('rm -r',out.dir,sep='')) ## End(Not run)## Not run: # Test dataset file <- Sys.glob(file.path(R.home(), 'library', 'PEcAn.data.land','data','*.shp')) out.dir <- path.expand('~/temp') # with clipping enabled subset_layer(file=file,coords=c(-95,42,-84,47),clip=TRUE,out.dir=out.dir) # without clipping enables subset_layer(file=file,coords=c(-95,42,-84,47),out.dir=out.dir) system(paste('rm -r',out.dir,sep='')) ## End(Not run)
to.Tag
to.Tag(SITE, PLOT, SUBPLOT, TAG = NULL)to.Tag(SITE, PLOT, SUBPLOT, TAG = NULL)
SITE, PLOT, SUBPLOT
|
ignored |
TAG |
string (or coercible to) |
to.TreeCode
to.TreeCode(SITE, PLOT, SUBPLOT, TAG = NULL)to.TreeCode(SITE, PLOT, SUBPLOT, TAG = NULL)
SITE, PLOT, SUBPLOT, TAG
|
strings (or coercible to) |
write_ic
write_ic( in.path, in.name, start_date, end_date, outfolder, model, new_site, pfts, source = input_veg$source, overwrite = FALSE, n.ensemble, host.inputargs, ... )write_ic( in.path, in.name, start_date, end_date, outfolder, model, new_site, pfts, source = input_veg$source, overwrite = FALSE, n.ensemble, host.inputargs, ... )
in.path |
file path to rds file with IC data |
in.name |
file name of IC data |
start_date |
YYYY-MM-DD |
end_date |
YYYY-MM-DD |
outfolder |
Location to store function outputs |
model |
BETY model ID |
new_site |
Site info including lat, lon, and BETT site ID |
pfts |
list settings$pfts. |
source |
Data source as saved in the BETY db |
overwrite |
DEfault is FALSE. Option to overwrite existing files. |
n.ensemble |
number of ensemble members |
host.inputargs |
host info taken from settings object |
... |
Additional parameters |
Istem Fer
Function to save intermediate rds file
write_veg(outfolder, start_date, veg_info, source)write_veg(outfolder, start_date, veg_info, source)
outfolder |
output folder |
start_date |
start date |
veg_info |
vegetation data to be saved |
source |
name of data source (used in file naming) |