| 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: | David LeBauer [aut, cre], Mike Dietze [aut], Rob Kooper [aut], Shawn Serbin [aut], Elizabeth Cowdery [aut], Ankur Desai [aut], Istem Fer [aut], Alexey Shiklomanov [aut], Tony Gardella [aut], Chris Black [aut], Liam Burke [aut], Ryan Kelly [aut], Dan Wang [aut], Carl Davidson [aut], Xiaohui Feng [aut], Shashank Singh [aut], University of Illinois, NCSA [cph] |
| Maintainer: | David LeBauer <[email protected]> |
| License: | BSD_3_clause + file LICENSE |
| Version: | 1.7.2.9000 |
| Built: | 2024-06-27 20:30:10 UTC |
| Source: | https://github.com/PecanProject/pecan |
append.covariate appends a data frame of covariates as a new column in a data frame
of trait data.
In the event a trait has several covariates available, the first one found
(i.e. lowest row number) will take precedence
append.covariate(data, column.name, covariates.data)append.covariate(data, column.name, covariates.data)
data |
trait dataframe that will be appended to. |
column.name |
name of the covariate as it will appear in the appended column |
covariates.data |
one or more tables of covariate data, ordered by the precedence they will assume in the event a trait has covariates across multiple tables. All tables must contain an 'id' and 'level' column, at minimum. |
Carl Davidson, Ryan Kelly
Apply Arrhenius scaling to 25 degC for temperature-dependent traits
arrhenius.scaling.traits( data, covariates, temp.covariates, new.temp = 25, missing.temp = 25 )arrhenius.scaling.traits( data, covariates, temp.covariates, new.temp = 25, missing.temp = 25 )
data |
data frame of data to scale, as returned by query.data() |
covariates |
data frame of covariates, as returned by query.covariates(). Note that data with no matching covariates will be unchanged. |
temp.covariates |
names of covariates used to adjust for temperature; if length > 1, order matters (first will be used preferentially) |
new.temp |
the reference temperature for the scaled traits. Curerntly 25 degC |
missing.temp |
the temperature assumed for traits with no covariate found. Curerntly 25 degC |
Carl Davidson, David LeBauer, Ryan Kelly
Change treatments to sequential integers
assign.treatments(data)assign.treatments(data)
data |
input data |
Assigns all control treatments the same value, then assigns unique treatments within each site. Each site is required to have a control treatment. The algorithm (incorrectly) assumes that each site has a unique set of experimental treatments. This assumption is required by the data in BETYdb that does not always consistently name treatments or quantity them in the managements table. Also it avoids having the need to estimate treatment by site interactions in the meta analysis model. This model uses data in the control treatment to estimate model parameters so the impact of the assumption is minimal.
dataframe with sequential treatments
David LeBauer, Carl Davidson, Alexey Shiklomanov
Convert BETY variable names to MsTMIP and subsequently PEcAn standard names
bety2pecan(vars_bety)bety2pecan(vars_bety)
vars_bety |
data frame with variable names and units |
Betsy Cowdery
Connect to bety using current PEcAn configuration
betyConnect(php.config = "../../web/config.php")betyConnect(php.config = "../../web/config.php")
php.config |
Path to 'config.php' |
Check two lists. Identical does not work since one can be loaded from the database and the other from a CSV file.
check.lists(x, y, filename = "species.csv")check.lists(x, y, filename = "species.csv")
x |
first list |
y |
second list |
filename |
one of "species.csv" or "cultivars.csv" |
true if two lists are the same
Rob Kooper
Creates a new pft that is a duplicate of an existing pft, including relationships with priors, species, and cultivars (if any) of the existing pft. This function mimics the 'clone pft' button in the PFTs record view page in the BETYdb web interface for PFTs that aggregate >=1 species, but adds the ability to clone the cultivar associations.
clone_pft(parent.pft.name, new.pft.name, new.pft.definition, settings)clone_pft(parent.pft.name, new.pft.name, new.pft.definition, settings)
parent.pft.name |
name of PFT to duplicate |
new.pft.name |
name for new PFT. Must not be the same as parent.pft.name |
new.pft.definition |
text for the new PFT's definition field. |
settings |
PEcAn settings list, used only for BETYdb connection parameters |
ID of the newly created pft in database, creates new PFT as a side effect
David LeBauer, Chris Black
## Not run: clone_pft(parent.pft.name = "tempdecid", new.pft.name = "mytempdecid", new.pft.definition = "mytempdecid is a new pft", settings = pecan_settings_list) ## End(Not run)## Not run: clone_pft(parent.pft.name = "tempdecid", new.pft.name = "mytempdecid", new.pft.definition = "mytempdecid is a new pft", settings = pecan_settings_list) ## End(Not run)
convert_input is a relatively generic function that applies the function fcn and inserts a record of it into the database. It is primarily designed for converting meteorological data between formats and can be used on observed data, forecasts, and ensembles of forecasts.
To minimize downloading and storing duplicate data, it first checks to see if a given file is already in the
database before applying fcn.
convert_input( input.id, outfolder, formatname, mimetype, site.id, start_date, end_date, pkg, fcn, con = con, host, browndog, write = TRUE, format.vars, overwrite = FALSE, exact.dates = FALSE, allow.conflicting.dates = TRUE, insert.new.file = FALSE, pattern = NULL, forecast = FALSE, ensemble = FALSE, ensemble_name = NULL, dbparms = NULL, ... )convert_input( input.id, outfolder, formatname, mimetype, site.id, start_date, end_date, pkg, fcn, con = con, host, browndog, write = TRUE, format.vars, overwrite = FALSE, exact.dates = FALSE, allow.conflicting.dates = TRUE, insert.new.file = FALSE, pattern = NULL, forecast = FALSE, ensemble = FALSE, ensemble_name = NULL, dbparms = NULL, ... )
input.id |
The database id of the input file of the parent of the file being processed here. The parent will have the same data, but in a different format. |
outfolder |
The directory where files generated by functions called by convert_input will be placed |
formatname |
data product specific format name |
mimetype |
data product specific file format |
site.id |
The id of the site |
start_date |
Start date of the data being requested or processed |
end_date |
End date of the data being requested or processed |
pkg |
The package that the function being executed is in (as a string) |
fcn |
The function to be executed if records of the output file aren't found in the database. (as a string) |
con |
Database connection object |
host |
Named list identifying the machine where conversion should be performed.
Currently only |
browndog |
List of information related to browndog conversion. NULL if browndog is not to be used for conversion |
write |
Logical: Write new file records to the database? |
format.vars |
Passed on as arguments to |
overwrite |
Logical: If a file already exists, create a fresh copy? Passed along to fcn. |
exact.dates |
Ignore time-span appending and enforce exact start and end dates on the database input file? (logical) |
allow.conflicting.dates |
Should overlapping years ignore time-span appending and exist as separate input files? (logical) |
insert.new.file |
Logical: force creation of a new database record even if one already exists? |
pattern |
A regular expression, passed to |
forecast |
Logical: Is the data product a forecast? |
ensemble |
An integer representing the number of ensembles, or FALSE if it data product is not an ensemble. |
ensemble_name |
If convert_input is being called iteratively for each ensemble, ensemble_name contains the identifying name/number for that ensemble. |
dbparms |
list of parameters to use for opening a database connection |
... |
Additional arguments, passed unchanged to |
A list of two BETY IDs (input.id, dbfile.id) identifying a pre-existing file if one was available, or a newly created file if not. Each id may be a vector of ids if the function is processing an entire ensemble at once.
convert_input executes the function fcn in package pkg via PEcAn.remote::remote.execute.R. All additional arguments passed to convert_input (...) are in turn passed along to fcn as arguments. In addition, several named arguments to convert_input are passed along to fcn. The command to execute fcn is built as a string.
There are two kinds of database records (in different tables) that represent a given data file in the file system. An input file contains information about the contents of the data file. A dbfile contains machine spacific information for a given input file, such as the file path. Because duplicates of data files for a given input can be on multiple different machines, there can be more than one dbfile for a given input file.
By default, convert_input tries to optimize the download of most data products by only downloading the years of data not present on the current machine. (For example, if files for 2004-2008 exist for a given data product exist on this machine and the user requests 2006-2010, the function will only download data for 2009 and 2010). In year-long data files, each year exists as a separate file. The database input file contains records of the bounds of the range stored by those years. The data optimization can be turned off by overriding the default values for exact.dates and allow.conflicting.dates.
If the flag forecast is TRUE, convert_input treats data as if it were forecast data. Forecast data do not undergo time span appending.
convert_input has the capability to handle ensembles of met data. If ensemble = an integer > 1, convert_input checks the database for records of all ensemble members, and calls fcn if at least one is missing. convert_input assumes that fcn will return records for all ensembles. convert_input can also be called iteratevely for each ensemble member. In this case ensemble_name contains the unique identifying name/number of the ensemble to be processed.
Betsy Cowdery, Michael Dietze, Ankur Desai, Tony Gardella, Luke Dramko
Merge local data frame into SQL table
db_merge_into(values, table, con, by = NULL, drop = FALSE, ...)db_merge_into(values, table, con, by = NULL, drop = FALSE, ...)
values |
'data.frame' of values to write to SQL database |
table |
Name of target SQL table, as character |
con |
Database connection object |
by |
Character vector of columns by which to perform merge. Defaults to all columns in 'values' |
drop |
logical. If 'TRUE' (default), drop columns not found in SQL table. |
... |
Arguments passed on to
|
Data frame: Inner join of SQL table and input data frame (as unevaluated "lazy query" table)
irisdb <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") dplyr::copy_to(irisdb, iris[1:10,], name = "iris", overwrite = TRUE) db_merge_into(iris[1:12,], "iris", irisdb) dplyr::tbl(irisdb, "iris") %>% dplyr::count()irisdb <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") dplyr::copy_to(irisdb, iris[1:10,], name = "iris", overwrite = TRUE) db_merge_into(iris[1:12,], "iris", irisdb) dplyr::tbl(irisdb, "iris") %>% dplyr::count()
Close a previously opened connection to a database.
db.close(con, showWarnings = TRUE)db.close(con, showWarnings = TRUE)
con |
database connection to be closed |
showWarnings |
logical: report possible issues with connection? |
'TRUE', invisibly (see [DBI::dbDisconnect()])
Rob Kooper
## Not run: db.close(con) ## End(Not run)## Not run: db.close(con) ## End(Not run)
Useful to only run tests that depend on database when a connection exists
db.exists(params, write = TRUE, table = NA)db.exists(params, write = TRUE, table = NA)
params |
database connection information |
write |
logical: test whether we have write access? |
table |
name of database table to check |
TRUE if database connection works; else FALSE
David LeBauer, Rob Kooper
Returns if the queries should be shown that are being executed
db.getShowQueries()db.getShowQueries()
will return TRUE if queries are shown
Rob Kooper
Create a connection to a database using the specified parameters. The 'params' list will be passed as arguments to [DBI::dbConnect()].
db.open(params)db.open(params)
params |
Named list of database connection options. See details |
Typical arguments are as follows: - 'driver' – The name of the database driver. Only '"PostgreSQL"' and '"Postgres"' are supported. If no driver is specified, default to '"PostgreSQL"'. - 'user' – The database username. For local instances of PEcAn, this is usually '"bety"'. - 'password' – The database password. For local instances of PEcAn, this is usually '"bety"'. - 'host' – The database hostname. For local instances of PEcAn, this is usually '"localhost"'. Inside the PEcAn Docker stack, this may be '"postgres"'. - 'port' (optional) – The port for accessing the database. If omitted, this will use the PostgreSQL default (5432).
Database connection object
Rob Kooper
## Not run: db.open(settings$database$bety) ## End(Not run)## Not run: db.open(settings$database$bety) ## End(Not run)
Prints the number of connections opened as well as any connections that have never been closes.
db.print.connections()db.print.connections()
Rob Kooper
## Not run: db.print.connections() ## End(Not run)## Not run: db.print.connections() ## End(Not run)
Given a connection and a query, will return a query as a data frame. Either con or params need to be specified. If both are specified it will use con.
db.query(query, con = NULL, params = NULL, values = NULL)db.query(query, con = NULL, params = NULL, values = NULL)
query |
SQL query string |
con |
Database connection object |
params |
Named list of database connection parameters. See 'params' argument to [db.open()]. |
values |
If using prepared statements, a list of values to substitute into the query. If 'NULL' (default), execute the query directly. See this function's "Details", and documentation for [DBI::dbBind()]. |
This function supports prepared statements, which provide a way to pass data into SQL queries without the risk of SQL injection attacks. Whenever you are tempted to do something like this:
“' db.query(paste0( "SELECT * FROM table WHERE mycol = ", somevalue, " AND othercol = ", othervalue ), con = con) “'
...use a prepared query instead:
“' db.query( "SELECT * FROM table WHERE mycol = $1 AND othercol = $2", values = list(somevalue, othervalue), con = con ) “'
Besides preventing SQL injections, prepared statements also ensure that the input and target types are compatible.
Prepared statements provide an efficient way to operate on multiple values at once. For example, the following will return all the models whose revision is either "git", "46", or "unk":
“' db.query( "SELECT * FROM models WHERE revision = $1", values = list(c("git", "46", "unk")), con = con ) “'
...and here is an example of inserting multiple values of a given trait for a given species:
“' db.query( "INSERT INTO traits (specie_id, variable_id, mean, n) VALUES ($1, $2, $3)", values = list(938, 396, c(1.7, 3.9, 4.5), 1), con = con ) “'
Note that prepared statements **can not be used to select tables or columns**. In other words, the following _will not work_ because of the following placeholders, the only valid one is '$5':
“' # This will not work! db.query( "SELECT $1, $2 FROM $3 WHERE $4 = $5", values = list("col1", "col2", "mytable", "somecolumn", "somevalue") ) “'
Note that prepared statements **are not supported by the 'RPostgreSQL' package**; only by the newer 'RPostgres' package.
data frame with query results
Rob Kooper, Alexey Shiklomanov
## Not run: db.query("SELECT count(id) FROM traits;", params = settings$database$bety) # Prepared statements con <- db.open(settings$database$bety) db.query( "SELECT * FROM table WHERE mycol = $1 AND othercol = $2", values = list(somevalue, othervalue), con = con ) # Select multiple values at once; rbind the result db.query( "SELECT * FROM models WHERE revision = $1", values = list(c("git", "46", "unk")), con = con ) # Efficiently insert multiple values into a table db.query( "INSERT INTO traits (specie_id, variable_id, mean, n) VALUES ($1, $2, $3, $4)", values = list(938, 396, rnorm(1000), 1), con = con ) ## End(Not run)## Not run: db.query("SELECT count(id) FROM traits;", params = settings$database$bety) # Prepared statements con <- db.open(settings$database$bety) db.query( "SELECT * FROM table WHERE mycol = $1 AND othercol = $2", values = list(somevalue, othervalue), con = con ) # Select multiple values at once; rbind the result db.query( "SELECT * FROM models WHERE revision = $1", values = list(c("git", "46", "unk")), con = con ) # Efficiently insert multiple values into a table db.query( "INSERT INTO traits (specie_id, variable_id, mean, n) VALUES ($1, $2, $3, $4)", values = list(938, 396, rnorm(1000), 1), con = con ) ## End(Not run)
Sets if the queries should be shown that are being executed
db.showQueries(show)db.showQueries(show)
show |
set to TRUE to show the queries, FALSE by default |
Useful to print queries when debuging SQL statements
Rob Kooper
List files associated with a container and machine exist in 'dbfiles' table
dbfile.check( type, container.id, con, hostname = PEcAn.remote::fqdn(), machine.check = TRUE, return.all = FALSE )dbfile.check( type, container.id, con, hostname = PEcAn.remote::fqdn(), machine.check = TRUE, return.all = FALSE )
type |
The type of 'dbfile', as a character. Must be either "Input", "Posterior", or "Model". |
container.id |
the ID of container type. E.g. if 'type' is "Input", this will correspond to the 'id' column from the 'inputs' table. |
con |
database connection object |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
machine.check |
setting to check for file on named host, otherwise will check for any file given container id |
return.all |
(Logical) If 'TRUE', return all files. If 'FALSE', return only the most recent files. |
'data.frame' with the id, filename and pathname of all the files that are associated
Rob Kooper, Alexey Shiklomanov
## Not run: dbfile.check("Input", 7, dbcon) ## End(Not run)## Not run: dbfile.check("Input", 7, dbcon) ## End(Not run)
These functions check the dbfiles and machines tables to see if the file
exists, and return the container_id (dbfile.id) or full filename
with path (dbfile.file) to the first one found.
If none is found, both will return NA.
dbfile.file(type, id, con, hostname = PEcAn.remote::fqdn()) dbfile.id(type, file, con, hostname = PEcAn.remote::fqdn())dbfile.file(type, id, con, hostname = PEcAn.remote::fqdn()) dbfile.id(type, file, con, hostname = PEcAn.remote::fqdn())
type |
the type of dbfile (Input, Posterior) |
id |
the id of container type |
con |
database connection object |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
file |
the full pathname to the file |
filename on host, or NA if none found
dbfile.file(): Return full path to file from the dbfiles table
dbfile.id(): Return id to container type given a filename
Rob Kooper
## Not run: dbfile.file('Input', 7, dbcon) ## End(Not run) ## Not run: dbfile.id('Model', '/usr/local/bin/sipnet', dbcon) ## End(Not run)## Not run: dbfile.file('Input', 7, dbcon) ## End(Not run) ## Not run: dbfile.id('Model', '/usr/local/bin/sipnet', dbcon) ## End(Not run)
Function to check to see if a file exists in the dbfiles table as an input
dbfile.input.check( siteid, startdate = NULL, enddate = NULL, mimetype, formatname, parentid = NA, con, hostname = PEcAn.remote::fqdn(), exact.dates = FALSE, pattern = NULL, return.all = FALSE )dbfile.input.check( siteid, startdate = NULL, enddate = NULL, mimetype, formatname, parentid = NA, con, hostname = PEcAn.remote::fqdn(), exact.dates = FALSE, pattern = NULL, return.all = FALSE )
siteid |
the id of the site that this data is applicable to |
startdate |
the start date of the data stored in the file |
enddate |
the end date of the data stored in the file |
mimetype |
the mime-type of the file |
formatname |
the name of the format to distinguish between simmilair mime-types |
parentid |
the id of the parent of the input |
con |
database connection object |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
exact.dates |
setting to include start and end date in input query |
pattern |
text to seach for in the file name (default NULL = no check). |
return.all |
(Logical) If 'TRUE', return all files. If 'FALSE', return only the most recent files. |
This will check the dbfiles, inputs, machines and formats tables to see if the file exists
data.frame with the id, filename and pathname of the input that is requested
Rob Kooper, Tony Gardella, Hamze Dokoohaki
## Not run: dbfile.input.check(siteid, startdate, enddate, 'application/x-RData', 'traits', dbcon) ## End(Not run)## Not run: dbfile.input.check(siteid, startdate, enddate, 'application/x-RData', 'traits', dbcon) ## End(Not run)
Function to insert a file into the dbfiles table as an input
dbfile.input.insert( in.path, in.prefix, siteid, startdate, enddate, mimetype, formatname, parentid = NA, con, hostname = PEcAn.remote::fqdn(), allow.conflicting.dates = FALSE, ens = FALSE )dbfile.input.insert( in.path, in.prefix, siteid, startdate, enddate, mimetype, formatname, parentid = NA, con, hostname = PEcAn.remote::fqdn(), allow.conflicting.dates = FALSE, ens = FALSE )
in.path |
path to the directory containing the file to be inserted |
in.prefix |
initial portion of the filename that does not vary by date. Does not include directory; specify that as part of in.path |
siteid |
the id of the site that this data is applicable to |
startdate |
the start date of the data stored in the file |
enddate |
the end date of the data stored in the file |
mimetype |
the mime-type of the file |
formatname |
the name of the format to distinguish between simmilair mime-types |
parentid |
the id of the parent of the input |
con |
database connection object |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
allow.conflicting.dates |
Whether to allow a new input record with same siteid, name, and format but different start/end dates |
ens |
In case of ensembles we could let to have more than one file associated with one input. |
This will write into the dbfiles, inputs, machines and formats the required data to store the file
data.frame with the id, filename and pathname of the input that is requested
Rob Kooper, Betsy Cowdery
## Not run: dbfile.input.insert( in.path = 'trait.data.Rdata', in.prefix = siteid, startdate = startdate, enddate = enddate, mimetype = 'application/x-RData', formatname = 'traits', con = dbcon) ## End(Not run)## Not run: dbfile.input.insert( in.path = 'trait.data.Rdata', in.prefix = siteid, startdate = startdate, enddate = enddate, mimetype = 'application/x-RData', formatname = 'traits', con = dbcon) ## End(Not run)
Function to insert a file into the dbfiles table
dbfile.insert( in.path, in.prefix, type, id, con, reuse = TRUE, hostname = PEcAn.remote::fqdn() )dbfile.insert( in.path, in.prefix, type, id, con, reuse = TRUE, hostname = PEcAn.remote::fqdn() )
in.path |
Path to file directory |
in.prefix |
Filename prefix (not including directory) |
type |
One of "Model", "Posterior", "Input" |
id |
container_id of the input to be modified |
con |
database connection object |
reuse |
logical: If a record already exists, use it or create a new one? |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
This will write into the dbfiles and machines the required data to store the file
id of the file that is written
Rob Kooper, Ryan Kelly
## Not run: dbfile.insert('somefile.txt', 'Input', 7, dbcon) ## End(Not run)## Not run: dbfile.insert('somefile.txt', 'Input', 7, dbcon) ## End(Not run)
This function will move dbfiles - clim or nc - from one location to another on the same machine and update BETY
dbfile.move(old.dir, new.dir, file.type, siteid = NULL, register = FALSE)dbfile.move(old.dir, new.dir, file.type, siteid = NULL, register = FALSE)
old.dir |
directory with files to be moved |
new.dir |
directory where files should be moved |
file.type |
what type of files are being moved |
siteid |
needed to register files that arent already in BETY |
register |
if file isn't already in BETY, should it be registered? |
print statement of how many files were moved, registered, or have symbolic links
kzarada
## Not run: dbfile.move( old.dir = "/fs/data3/kzarada/pecan.data/dbfiles/NOAA_GEFS_site_0-676", new.dir = '/projectnb/dietzelab/pecan.data/dbfiles/NOAA_GEFS_site_0-676' file.type= clim, siteid = 676, register = TRUE ) ## End(Not run)## Not run: dbfile.move( old.dir = "/fs/data3/kzarada/pecan.data/dbfiles/NOAA_GEFS_site_0-676", new.dir = '/projectnb/dietzelab/pecan.data/dbfiles/NOAA_GEFS_site_0-676' file.type= clim, siteid = 676, register = TRUE ) ## End(Not run)
Function to check to see if a file exists in the dbfiles table as an input
dbfile.posterior.check( pft, mimetype, formatname, con, hostname = PEcAn.remote::fqdn() )dbfile.posterior.check( pft, mimetype, formatname, con, hostname = PEcAn.remote::fqdn() )
pft |
the name of the pft that this data is applicable to |
mimetype |
the mime-type of the file |
formatname |
the name of the format to distinguish between simmilair mime-types |
con |
database connection object |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
This will check the dbfiles, inputs, machines and formats tables to see if the file exists
data.frame with the id, filename and pathname of the posterior that is requested
Rob Kooper
## Not run: dbfile.posterior.check(pft, 'application/x-RData', 'traits', dbcon) ## End(Not run)## Not run: dbfile.posterior.check(pft, 'application/x-RData', 'traits', dbcon) ## End(Not run)
Function to insert a file into the dbfiles table as a posterior
dbfile.posterior.insert( filename, pft, mimetype, formatname, con, hostname = PEcAn.remote::fqdn() )dbfile.posterior.insert( filename, pft, mimetype, formatname, con, hostname = PEcAn.remote::fqdn() )
filename |
the name of the file to be inserted |
pft |
the name of the pft that this data is applicable to |
mimetype |
the mime-type of the file |
formatname |
the name of the format to distinguish between simmilair mime-types |
con |
database connection object |
hostname |
the name of the host where the file is stored, this will default to the name of the current machine |
This will write into the dbfiles, posteriors, machines and formats the require data to store the file
data.frame with the id, filename and pathname of the posterior that is requested
Rob Kooper
## Not run: dbfile.posterior.insert('trait.data.Rdata', pft, 'application/x-RData', 'traits', dbcon) ## End(Not run)## Not run: dbfile.posterior.insert('trait.data.Rdata', pft, 'application/x-RData', 'traits', dbcon) ## End(Not run)
Database host information
dbHostInfo(bety)dbHostInfo(bety)
bety |
BETYdb connection, as opened by 'betyConnect()' |
Convenience function to fix hostname if localhost
default_hostname(hostname)default_hostname(hostname)
hostname |
character |
hostname
Performs an arithmetic function, FUN, over a series of traits and returns the result as a derived trait. Traits must be specified as either lists or single row data frames, and must be either single data points or normally distributed. In the event one or more input traits are normally distributed, the resulting distribution is approximated by numerical simulation. The output trait is effectively a copy of the first input trait with modified mean, stat, and n.
derive.trait(FUN, ..., input = list(...), var.name = NA, sample.size = 10^6)derive.trait(FUN, ..., input = list(...), var.name = NA, sample.size = 10^6)
FUN |
arithmetic function |
... |
traits that will be supplied to FUN as input |
input |
list of trait inputs. See examples |
var.name |
name to use in output |
sample.size |
number of random samples generated by rnorm for normally distributed trait input |
a copy of the first input trait with mean, stat, and n reflecting the derived trait
input <- list(x = data.frame(mean = 1, stat = 1, n = 1)) derive.trait(FUN = identity, input = input, var.name = 'x')input <- list(x = data.frame(mean = 1, stat = 1, n = 1)) derive.trait(FUN = identity, input = input, var.name = 'x')
Equivalent to derive.trait(), but operates over a series of trait datasets,
as opposed to individual trait rows. See derive.trait; for more information.
derive.traits( FUN, ..., input = list(...), match.columns = c("citation_id", "site_id", "specie_id"), var.name = NA, sample.size = 10^6 )derive.traits( FUN, ..., input = list(...), match.columns = c("citation_id", "site_id", "specie_id"), var.name = NA, sample.size = 10^6 )
FUN |
arithmetic function |
... |
trait datasets that will be supplied to FUN as input |
input |
list of trait inputs. See examples in |
match.columns |
in the event more than one trait dataset is supplied, this specifies the columns that identify a unique data point |
var.name |
name to use in output |
sample.size |
where traits are normally distributed with a given |
a copy of the first input trait with modified mean, stat, and n
Count rows of a data frame
dplyr.count(df)dplyr.count(df)
df |
Data frame of which to count length |
Convert number to scientific notation pretty expression
fancy_scientific(l)fancy_scientific(l)
l |
Number to convert to scientific notation |
Queries data from the trait database and transforms statistics to SE
fetch.stats2se(connection, query)fetch.stats2se(connection, query)
connection |
connection to trait database |
query |
to send to databse |
Performs query and then uses transformstats to convert miscellaneous statistical summaries
to SE
dataframe with trait data
<unknown>
used in query.trait.data; transformstats performs transformation calculations
Function to filter out upper canopy leaves
filter_sunleaf_traits(data, covariates)filter_sunleaf_traits(data, covariates)
data |
input data |
covariates |
covariate data |
David LeBauer
Retrieves database connection parameters stored in any of the environment variables known by Postgres, using defaults from '...' for parameters not set in the environment. In a standard PEcAn installation only a few of these parameters will ever be set, but we check all of them anyway in case you need to do anything unusual.
get_postgres_envvars(...)get_postgres_envvars(...)
... |
defaults for parameters not found in the environment, in 'name = value' form |
The list of environment variables we check is taken from the [Postgres 12 manual](https://postgresql.org/docs/12/libpq-envars.html), but it should apply to older Postgres versions as well. Note that this function only looks for environment variables that control connection parameters; it does not retrieve any of the variables related to per-session behavior (e.g. PGTZ, PGSYSCONFDIR).
list of connection parameters suitable for passing on to 'db.open'
host <- Sys.getenv("PGHOST") # to restore environment after demo Sys.unsetenv("PGHOST") get_postgres_envvars()$host # NULL get_postgres_envvars(host = "default", port = 5432)$host # "default" # defaults are ignored for a variable that exists Sys.setenv(PGHOST = "localhost") get_postgres_envvars()$host # "localhost" get_postgres_envvars(host = "postgres")$host # still "localhost" # To override a set variable, edit the returned list before using it con_parms <- get_postgres_envvars() con_parms$host # "localhost" con_parms$host <- "postgres" # db.open(con_parms) Sys.setenv(PGHOST = host)host <- Sys.getenv("PGHOST") # to restore environment after demo Sys.unsetenv("PGHOST") get_postgres_envvars()$host # NULL get_postgres_envvars(host = "default", port = 5432)$host # "default" # defaults are ignored for a variable that exists Sys.setenv(PGHOST = "localhost") get_postgres_envvars()$host # "localhost" get_postgres_envvars(host = "postgres")$host # still "localhost" # To override a set variable, edit the returned list before using it con_parms <- get_postgres_envvars() con_parms$host # "localhost" con_parms$host <- "postgres" # db.open(con_parms) Sys.setenv(PGHOST = host)
Get vector of run IDs for a given workflow ID
get_run_ids(bety, workflow_id)get_run_ids(bety, workflow_id)
bety |
BETYdb connection, as opened by 'betyConnect()' |
workflow_id |
Workflow ID |
Get data frame of users and IDs
get_users(bety)get_users(bety)
bety |
BETYdb connection, as opened by 'betyConnect()' |
Get vector of variable names for a particular workflow and run ID
get_var_names(bety, workflow_id, run_id, remove_pool = TRUE)get_var_names(bety, workflow_id, run_id, remove_pool = TRUE)
bety |
BETYdb connection, as opened by 'betyConnect()' |
workflow_id |
Workflow ID |
run_id |
Run ID |
remove_pool |
logical: ignore variables with 'pools' in their names? |
Get vector of workflow IDs
get_workflow_ids(bety, query, all.ids = FALSE)get_workflow_ids(bety, query, all.ids = FALSE)
bety |
BETYdb connection, as opened by 'betyConnect()' |
query |
Named vector or list of workflow IDs |
all.ids |
logical: return a list of all workflow_ids in the BETY database, or just those that are part of the query? |
Retrieve id from a table matching query
get.id(table, colnames, values, con, create = FALSE, dates = TRUE)get.id(table, colnames, values, con, create = FALSE, dates = TRUE)
table |
name of table |
colnames |
names of one or more columns used in where clause |
values |
values to be queried in fields corresponding to colnames |
con |
database connection object, |
create |
logical: make a record if none found? |
dates |
Ignored. Formerly indicated whether to set created_at and updated_at timestamps when 'create' was TRUE, but the database now always sets them automatically |
will numeric
David LeBauer
## Not run: pftid <- get.id("pfts", "name", "salix", con) pftid <- get.id("pfts", c("name", "modeltype_id"), c("ebifarm.salix", 1), con) ## End(Not run)## Not run: pftid <- get.id("pfts", "name", "salix", con) pftid <- get.id("pfts", c("name", "modeltype_id"), c("ebifarm.salix", 1), con) ## End(Not run)
This will use the following items from settings: - 'settings$pfts' - 'settings$model$type' - 'settings$database$bety' - 'settings$database$dbfiles' - 'settings$meta.analysis$update'
get.trait.data( pfts, modeltype, dbfiles, database, forceupdate, write = FALSE, trait.names = NULL )get.trait.data( pfts, modeltype, dbfiles, database, forceupdate, write = FALSE, trait.names = NULL )
pfts |
the list of pfts to get traits for |
modeltype |
type of model that is used, this is is used to distinguish between different PFTs with the same name. |
dbfiles |
location where previous results are found |
database |
database connection parameters (see 'params' argument to [db.query()]) |
forceupdate |
(Logical) If 'TRUE', force a database update whether or not it is needed. If 'FALSE', only update if an update is needed. |
write |
(Logical) If 'TRUE' updated posteriors will be written to BETYdb. Defaults to FALSE. |
trait.names |
Character vector of trait names to search. If 'NULL' (default), use all traits that have a prior for at least one of the 'pfts'. |
list of PFTs with update posteriorids
David LeBauer, Shawn Serbin, Alexey Shiklomanov
Get trait data from the database for a single PFT
get.trait.data.pft( pft, modeltype, dbfiles, dbcon, trait.names, forceupdate = FALSE, write = FALSE )get.trait.data.pft( pft, modeltype, dbfiles, dbcon, trait.names, forceupdate = FALSE, write = FALSE )
pft |
list of settings for the pft whose traits to retrieve. See details |
modeltype |
type of model that is used, this is used to distinguish between different pfts with the same name. |
dbfiles |
location where previous results are found |
dbcon |
database connection |
trait.names |
list of trait names to retrieve |
forceupdate |
set this to true to force an update, auto will check to see if an update is needed. |
write |
(Logical) If 'TRUE' updated posteriors will be written to BETYdb. Defaults to FALSE. |
'pft' should be a list containing at least 'name' and 'outdir', and optionally 'posteriorid' and 'constants'. BEWARE: All existing files in 'outir' will be deleted!
updated pft with posteriorid
David LeBauer, Shawn Serbin, Rob Kooper
First, subset to matching columns. Then, make sure the local and SQL column classes match, coercing local to SQL as necessary (or throwing an error). Then, build an SQL string for the insert statement. Finally, insert into the database.
insert_table(values, table, con, coerce_col_class = TRUE, drop = TRUE)insert_table(values, table, con, coerce_col_class = TRUE, drop = TRUE)
values |
'data.frame' of values to write to SQL database |
table |
Name of target SQL table, as character |
con |
Database connection object |
coerce_col_class |
logical, whether or not to coerce local data columns to SQL classes. Default = 'TRUE.' |
drop |
logical. If 'TRUE' (default), drop columns not found in SQL table. |
data frame with query results
irisdb <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") dplyr::copy_to(irisdb, iris[1,], name = "iris", overwrite = TRUE) insert_table(iris[-1,], "iris", irisdb) dplyr::tbl(irisdb, "iris")irisdb <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") dplyr::copy_to(irisdb, iris[1,], name = "iris", overwrite = TRUE) insert_table(iris[-1,], "iris", irisdb) dplyr::tbl(irisdb, "iris")
Insert Format and Format-Variable Records
insert.format.vars( con, format_name, mimetype_id, notes = NULL, header = TRUE, skip = 0, formats_variables = NULL, suppress = TRUE )insert.format.vars( con, format_name, mimetype_id, notes = NULL, header = TRUE, skip = 0, formats_variables = NULL, suppress = TRUE )
con |
SQL connection to BETYdb |
format_name |
The name of the format. Type: character string. |
mimetype_id |
The id associated with the mimetype of the format. Type: integer. |
notes |
Additional description of the format: character string. |
header |
Boolean that indicates the presence of a header in the format. Defaults to "TRUE". |
skip |
Integer that indicates the number of lines to skip in the header. Defaults to 0. |
formats_variables |
A 'tibble' consisting of entries that correspond to columns in the formats-variables table. See Details for further information. |
suppress |
Boolean that suppresses or allows a test for an existing variable id. This test is inconvenient in applications where the variable_ids are already known. |
The formats_variables argument must be a 'tibble' and be structured in a specific format so that the SQL query functions properly. All arguments should be passed as vectors so that each entry will correspond with a specific row. All empty values should be specified as NA.
(Required) Vector of integers.
(Optional) Vector of character strings. The variable name in the imported data need only be specified if it differs from the BETY variable name.
(Optional) Vector of type character string. Should be in a format parseable by the udunits library and need only be secified if the units of the data in the file differ from the BETY standard.
(Optional) Vector of character strings. Storage type need only be specified if the variable is stored in a format other than would be expected (e.g. if numeric values are stored as quoted character strings). Additionally, storage_type stores POSIX codes that are used to store any time variables (e.g. a column with a 4-digit year would be %Y). See also [base::strptime]
Vector of integers that list the column numbers associated with variables in a dataset. Required for text files that lack headers.
format_id
Liam Burke ([email protected])
## Not run: con <- PEcAn.DB::betyConnect() formats_variables_tibble <- tibble::tibble( variable_id = c(411, 135, 382), name = c("NPP", NA, "YEAR"), unit = c("g C m-2 yr-1", NA, NA), storage_type = c(NA, NA, "%Y"), column_number = c(2, NA, 4)) insert.format.vars( con = con, format_name = "LTER-HFR-103", mimetype_id = 1090, notes = "NPP from Harvard Forest.", header = FALSE, skip = 0, formats_variables = formats_variables_tibble) ## End(Not run)## Not run: con <- PEcAn.DB::betyConnect() formats_variables_tibble <- tibble::tibble( variable_id = c(411, 135, 382), name = c("NPP", NA, "YEAR"), unit = c("g C m-2 yr-1", NA, NA), storage_type = c(NA, NA, "%Y"), column_number = c(2, NA, 4)) insert.format.vars( con = con, format_name = "LTER-HFR-103", mimetype_id = 1090, notes = "NPP from Harvard Forest.", header = FALSE, skip = 0, formats_variables = formats_variables_tibble) ## End(Not run)
Load data for a single run of the model
load_data_single_run(bety, workflow_id, run_id)load_data_single_run(bety, workflow_id, run_id)
bety |
BETYdb connection, as opened by 'betyConnect()' |
workflow_id |
Workflow ID |
run_id |
Run ID |
Match names of local data frame to SQL table
match_colnames(values, table, con)match_colnames(values, table, con)
values |
'data.frame' of values to write to SQL database |
table |
Name of target SQL table, as character |
con |
Database connection object |
Match column names and classes between local and SQL table
match_dbcols(values, table, con, coerce_col_class = TRUE, drop = TRUE)match_dbcols(values, table, con, coerce_col_class = TRUE, drop = TRUE)
values |
'data.frame' of values to write to SQL database |
table |
Name of target SQL table, as character |
con |
Database connection object |
coerce_col_class |
logical, whether or not to coerce local data columns to SQL classes. Default = 'TRUE.' |
drop |
logical. If 'TRUE' (default), drop columns not found in SQL table. |
'values' 'data.frame' with column names and classes matched to SQL
This is identical to the query performed by 'web/03-inputs.php' to populate the list of available sites. It may be useful for debugging, or otherwise replicating PEcAn web interface behavior.
met_inputs(dbcon, site_id, model_id, hostname)met_inputs(dbcon, site_id, model_id, hostname)
dbcon |
Database connection object |
site_id |
Site ID (from 'sites' table) |
model_id |
Model ID (from 'models') table |
hostname |
Hostname of machine |
'data.frame' of available met inputs
Alexey Shiklomanov
Convert netcdf number of days to a datetime
ncdays2date(time, unit)ncdays2date(time, unit)
time |
number of time units elapsed since origin |
unit |
string containing CF-style time unit including origin (e.g. "days since 2010-01-01") |
adds a list of species to a pft based on USDA Plants acronyms
pft.add.spp(pft, acronym = NULL, ID = NULL, test = TRUE, con = NULL, ...)pft.add.spp(pft, acronym = NULL, ID = NULL, test = TRUE, con = NULL, ...)
pft |
String name of the PFT in the database |
acronym |
Specie's Symbols. see http://plants.usda.gov |
ID |
Species IDs in Bety. You can provide either IDs or Symbols as input, if you provide both ID and acronym, only acronym will be used. |
test |
Runs the function in test mode. No species are actually added, but checks are run for existing species-PFT pairs, unmatched acronyms, missing species, or duplicate species |
con |
Database connection object. |
... |
optional arguements for connecting to database (e.g. password, user name, database) |
This function is used to add PFT-Species pairs to the database table 'pfts_species'. In the initial implementation the PFT has to be defined already and the species are added based on their USDA Symbol (genus/species acronym). Multiple species can be added at once but only one PFT at a time.
The Symbols object are
Function does not return a value but does print out diagnostic statements.
Michael C. Dietze, Dongchen Zhang
Retrieve PFT ID, name, and type from BETY
query_pfts(dbcon, pft_names, modeltype = NULL, strict = FALSE)query_pfts(dbcon, pft_names, modeltype = NULL, strict = FALSE)
dbcon |
Database connection object |
pft_names |
character vector of PFT names |
modeltype |
character. If specified, only returns PFTs matching this modeltype. If NULL, considers all modeltypes. |
strict |
(Logical) If 'TRUE', throw an error if any of the input 'pft_names/ids' or 'traits' are missing from the output. If 'FALSE' (default), only throw a warning. |
'data.frame' containing PFT ID ('id'), type ('pft_type'), and name ('name').
Alexey Shiklomanov, Chris Black
Query priors using prepared statements
query_priors( pft_names = NULL, traits = NULL, pft_ids = NULL, expand = TRUE, strict = FALSE, ... )query_priors( pft_names = NULL, traits = NULL, pft_ids = NULL, expand = TRUE, strict = FALSE, ... )
pft_names |
Character vector of PFT names ('name' column of BETY 'pfts' table). You cannot pass both this and 'pft_ids'. |
traits |
Character vector of trait names ('name' column of BETY 'traits' table). If 'NULL' (default), return information for all traits available for that PFT. |
pft_ids |
Numeric vector of PFT IDs ('id' column of BETY 'pfts' table). You cannot pass both this and 'pft_names'. |
expand |
(Logical) If 'TRUE' (default), search every trait-PFT combination. If 'FALSE', assume that input traits and PFTs are paired. |
strict |
(Logical) If 'TRUE', throw an error if any of the input 'pft_names/ids' or 'traits' are missing from the output. If 'FALSE' (default), only throw a warning. |
... |
Additional arguments to [db.query()] |
'data.frame' containing prior information for the given PFTs and traits.
## Not run: con <- db.open(...) # No trait provided, so return all available traits pdat <- query_priors( c("temperate.Early_Hardwood", "temperate.North_Mid_Hardwood", "temperate.Late_Hardwood"), con = con ) # Traits provided, so restrict to only those traits. Note that # because `expand = TRUE`, this will search for these traits for # every PFT. pdat2 <- query_priors( c("Optics.Temperate_Early_Hardwood", "Optics.Temperate_Mid_Hardwood", "Optics.Temperate_Late_Hardwood"), c("leaf_reflect_vis", "leaf_reflect_nir"), con = con ) # With `expand = FALSE`, search the first trait for the first PFT, # the second trait for the second PFT, etc. Note that this means # PFT and trait input vectors must be the same length. pdat2 <- query_priors( c("Optics.Temperate_Early_Hardwood", "Optics.Temperate_Early_Hardwood", "Optics.Temperate_Mid_Hardwood", "Optics.Temperate_Late_Hardwood"), c("leaf_reflect_vis", "leaf_reflect_nir", "leaf_reflect_vis", "leaf_reflect_nir"), con = con ) ## End(Not run)## Not run: con <- db.open(...) # No trait provided, so return all available traits pdat <- query_priors( c("temperate.Early_Hardwood", "temperate.North_Mid_Hardwood", "temperate.Late_Hardwood"), con = con ) # Traits provided, so restrict to only those traits. Note that # because `expand = TRUE`, this will search for these traits for # every PFT. pdat2 <- query_priors( c("Optics.Temperate_Early_Hardwood", "Optics.Temperate_Mid_Hardwood", "Optics.Temperate_Late_Hardwood"), c("leaf_reflect_vis", "leaf_reflect_nir"), con = con ) # With `expand = FALSE`, search the first trait for the first PFT, # the second trait for the second PFT, etc. Note that this means # PFT and trait input vectors must be the same length. pdat2 <- query_priors( c("Optics.Temperate_Early_Hardwood", "Optics.Temperate_Early_Hardwood", "Optics.Temperate_Mid_Hardwood", "Optics.Temperate_Late_Hardwood"), c("leaf_reflect_vis", "leaf_reflect_nir", "leaf_reflect_vis", "leaf_reflect_nir"), con = con ) ## End(Not run)
Queries covariates from database for a given vector of trait id's
query.covariates(trait.ids, con = NULL, ...)query.covariates(trait.ids, con = NULL, ...)
trait.ids |
list of trait ids |
con |
database connection |
... |
extra arguments |
David LeBauer
fetch.stats2se;Function to query data from database for specific species and convert stat to SE
query.data( trait, spstr, con, extra.columns = paste("ST_X(ST_CENTROID(sites.geometry)) AS lon,", "ST_Y(ST_CENTROID(sites.geometry)) AS lat, "), store.unconverted = FALSE, ids_are_cultivars = FALSE, ... )query.data( trait, spstr, con, extra.columns = paste("ST_X(ST_CENTROID(sites.geometry)) AS lon,", "ST_Y(ST_CENTROID(sites.geometry)) AS lat, "), store.unconverted = FALSE, ids_are_cultivars = FALSE, ... )
trait |
trait to query from the database |
spstr |
IDs of species to query from, as a single comma-separated string |
con |
database connection |
extra.columns |
other query terms to pass in. If unspecified, retrieves latitude and longitude |
store.unconverted |
determines whether or not a copy of the mean and stat fields are returned with _unconverted appended to the column names |
ids_are_cultivars |
if TRUE, ids is a vector of cultivar IDs, otherwise they are species IDs |
... |
extra arguments |
David LeBauer, Carl Davidson
used in query.trait.data; fetch.stats2se; transformstats performs transformation calculations
Get file path given id and machine
query.file.path(input.id, host_name, con)query.file.path(input.id, host_name, con)
input.id |
database id of the file ("container") to find |
host_name |
character: machine where the file lives |
con |
: database connection |
Betsy Cowdery
Look up names and units of input variables from a format id or input id
query.format.vars(bety, input.id = NA, format.id = NA, var.ids = NA)query.format.vars(bety, input.id = NA, format.id = NA, var.ids = NA)
bety |
database connection |
input.id, format.id
|
numeric. Defaults to format.id if both provided |
var.ids |
optional vector of variable IDs. If provided, limits results to these variables |
Betsy Cowdery, Ankur Desai, Istem Fer
Given a PFT name and optionally a modeltype, finds its pft_id and returns the cultivars associated with it.
query.pft_cultivars(pft, modeltype = NULL, con)query.pft_cultivars(pft, modeltype = NULL, con)
pft |
string pft name |
modeltype |
type of model that is used, this is used to distinguish between different pfts with the same name. |
con |
database connection |
A PFT is allowed to have associated species or associated
cultivars, but not both. If this function returns no results, try checking
your PFT with query.pft_species instead.
Note that the cultivars associated with one PFT *are* allowed to come from
multiple species, if desired.
tibble containing names and ids for each cultivar and the species it comes from
select plant id's associated with pft
query.pft_species(pft, modeltype = NULL, con)query.pft_species(pft, modeltype = NULL, con)
pft |
string pft name |
modeltype |
type of model that is used, this is used to distinguish between different pfts with the same name. |
con |
database connection |
data.frame containing id, genus, species, scientificname of each species associated with pft
David LeBauer
## Not run: query.pft_species('ebifarm.pavi') query.pft_species(settings = read.settings("pecan.xml")) ## End(Not run)## Not run: query.pft_species('ebifarm.pavi') query.pft_species(settings = read.settings("pecan.xml")) ## End(Not run)
Query priors associated with a plant functional type and a set of traits.
query.priors(pft, trstr = NULL, con = NULL, ...)query.priors(pft, trstr = NULL, con = NULL, ...)
pft |
ID number of the PFT in the database |
trstr |
String of traits to query priors for. If passed as a character vector, it will be concatenated to a single string using [PEcAn.utils::vecpaste()]. |
con |
Database connection object. |
... |
Optional arguments for connecting to database (e.g. password, user name, database). |
If neither 'con' nor '...' are provided, this will try to connect to BETY using a 'settings' object in the current environment.
'data.frame' of priors for each trait and the given PFT.
David LeBauer, Alexey Shiklomanov
## Not run: con <- db.open(...) query.priors("ebifarm.pavi", c("SLA", "Vcmax", "leaf_width"), con = con) ## End(Not run)## Not run: con <- db.open(...) query.priors("ebifarm.pavi", c("SLA", "Vcmax", "leaf_width"), con = con) ## End(Not run)
Given site_id, return site table
query.site(site.id, con)query.site(site.id, con)
site.id |
The id of the site |
con |
: database connection |
Betsy Cowdery
Extracts data from database for a given trait and set of species,
converts all statistics to summary statistics, and prepares a dataframe for use in meta-analysis.
For Vcmax and SLA data, only data collected between April and July are queried, and only data collected from the top of the canopy (canopy height > 0.66).
For Vcmax and root_respiration_rate, data are scaled
converted from measurement temperature to via the arrhenius equation.
query.trait.data( trait, spstr, con = NULL, update.check.only = FALSE, ids_are_cultivars = FALSE, ... )query.trait.data( trait, spstr, con = NULL, update.check.only = FALSE, ids_are_cultivars = FALSE, ... )
trait |
is the trait name used in the database, stored in variables.name |
spstr |
is the species.id integer or string of integers associated with the species |
con |
database connection object |
update.check.only |
if TRUE, returns results but does not print summaries |
ids_are_cultivars |
if TRUE, the IDs in spstr are cultivar IDs, otherwise they are species IDs. Passed on to |
... |
unused currently |
dataframe ready for use in meta-analysis
David LeBauer, Carl Davidson, Shawn Serbin
## Not run: settings <- read.settings() query.trait.data("Vcmax", "938", con = con) ## End(Not run)## Not run: settings <- read.settings() query.trait.data("Vcmax", "938", con = con) ## End(Not run)
Query available trait data associated with a given pft and a list of traits
query.traits( ids, priors, con, update.check.only = FALSE, ids_are_cultivars = FALSE )query.traits( ids, priors, con, update.check.only = FALSE, ids_are_cultivars = FALSE )
ids |
vector of species or cultivar id's from trait database |
priors |
vector of parameters for which priors have been specified |
con |
database connection object |
update.check.only |
if TRUE, returns results but does not print summaries |
ids_are_cultivars |
if TRUE, ids is a vector of cultivar IDs, otherwise they are species IDs |
list of dataframes, each with data for one trait
David LeBauer, Carl Davidson, Shawn Serbin
## Not run: con <- db.open(your_settings_here) species <- query.pft_species('ebifarm.c4crop') spstr <- vecpaste(species$id) trvec <- c('leafN', 'SLA') trait.data <- query.traits(spstr, trvec, con) ## End(Not run)## Not run: con <- db.open(your_settings_here) species <- query.pft_species('ebifarm.c4crop') spstr <- vecpaste(species$id) trvec <- c('leafN', 'SLA') trait.data <- query.traits(spstr, trvec, con) ## End(Not run)
fetch.stats2se;Function to query yields data from database for specific species and convert stat to SE
query.yields( trait = "yield", spstr, extra.columns = "", con = NULL, ids_are_cultivars = FALSE, ... )query.yields( trait = "yield", spstr, extra.columns = "", con = NULL, ids_are_cultivars = FALSE, ... )
trait |
yield trait to query |
spstr |
species to query for yield data |
extra.columns |
other query terms to pass in. Optional |
con |
database connection |
ids_are_cultivars |
if TRUE, spstr contains cultivar IDs, otherwise they are species IDs |
... |
extra arguments |
<unknown>
used in query.trait.data; fetch.stats2se; transformstats performs transformation calculations
Get table of runs corresponding to a workflow
runs(bety, workflow_id)runs(bety, workflow_id)
bety |
BETYdb connection, as opened by 'betyConnect()' |
workflow_id |
Workflow ID |
Requires the 'rcrossref' package.
search_reference_single(query, limit = 1, min_score = 85)search_reference_single(query, limit = 1, min_score = 85)
query |
Citation string (length 1) to search for DOI |
limit |
Number of results to return |
min_score |
Minimum match score. Default (85) is fairly strict. |
'data.frame' containing crossref information converted to match bety citations table.
Requires the 'rcrossref' package.
search_references(queries, ...)search_references(queries, ...)
queries |
Character vector of queries |
... |
Arguments passed on to
|
'data.frame' containing crossref information converted to match bety citations table.
Stamp start and stop times of runs
stamp_started(con, run) stamp_finished(con, run)stamp_started(con, run) stamp_finished(con, run)
con |
BETY database connection |
run |
(numeric) run ID |
'NULL'
Symmetric set difference of two data frames
symmetric_setdiff( x, y, xname = "x", yname = "y", namecol = "source", simplify_types = TRUE )symmetric_setdiff( x, y, xname = "x", yname = "y", namecol = "source", simplify_types = TRUE )
x, y
|
'data.frame's to compare |
xname |
Label for data in x but not y. Default = "x" |
yname |
Label for data in y but not x. Default = "y" |
namecol |
Name of label column. Default = "source". |
simplify_types |
(Logical) If 'TRUE', coerce anything that isn't numeric to character, to facilitate comparison. |
'data.frame' of data not common to x and y, with additional column ('namecol') indicating whether data are only in x ('xname') or y ('yname')
xdf <- data.frame(a = c("a", "b", "c"), b = c(1, 2, 3), stringsAsFactors = FALSE) ydf <- data.frame(a = c("a", "b", "d"), b = c(1, 2.5, 3), stringsAsFactors = FALSE) symmetric_setdiff(xdf, ydf)xdf <- data.frame(a = c("a", "b", "c"), b = c(1, 2, 3), stringsAsFactors = FALSE) ydf <- data.frame(a = c("a", "b", "d"), b = c(1, 2.5, 3), stringsAsFactors = FALSE) symmetric_setdiff(xdf, ydf)
sample from normal distribution, given summary stats
take.samples(summary, sample.size = 10^6)take.samples(summary, sample.size = 10^6)
summary |
data.frame with values of mean and sd |
sample.size |
number of samples to take |
sample of length sample.size
David LeBauer, Carl Davidson
## return the mean when stat = NA take.samples(summary = data.frame(mean = 10, stat = NA)) ## return vector of length \code{sample.size} from N(mean,stat) take.samples(summary = data.frame(mean = 10, stat = 10), sample.size = 10)## return the mean when stat = NA take.samples(summary = data.frame(mean = 10, stat = NA)) ## return vector of length \code{sample.size} from N(mean,stat) take.samples(summary = data.frame(mean = 10, stat = 10), sample.size = 10)
The TRY file is huge and unnecessarily long, which makes it difficult to work with. The resulting SQLite database is much smaller on disk, and can be read much faster thanks to lazy evaluation.
try2sqlite(try_files, sqlite_file = "try.sqlite")try2sqlite(try_files, sqlite_file = "try.sqlite")
try_files |
Character vector of file names containing TRY data. Multiple files are combined with 'data.table::rbindlist'. |
sqlite_file |
Target SQLite database file name, as character. |
The resulting TRY SQLite database contains the following tables: - 'values' – The actual TRY data. Links to all other tables through ID columns. - 'traits' – Description of trait and data names. Links to 'values' through 'DataID'. Similar to BETY 'variables' table. - 'datasets' – Description of datasets and references/citations. Links to 'values' through 'DatasetID' and 'ReferenceID'. - 'species' – Species. Links to 'values' through 'AccSpeciesID'.
Get vector of variable names for a particular workflow and run ID
var_names_all(bety, workflow_id, run_id)var_names_all(bety, workflow_id, run_id)
bety |
BETYdb connection, as opened by 'betyConnect()' |
workflow_id |
Workflow ID |
run_id |
Run ID |
Get single workflow by workflow_id
workflow(bety, workflow_id)workflow(bety, workflow_id)
bety |
BETYdb connection, as opened by 'betyConnect()' |
workflow_id |
Workflow ID |
list of workflows that exist
workflows(bety, ensemble = FALSE)workflows(bety, ensemble = FALSE)
bety |
BETYdb connection, as opened by 'betyConnect()' |
ensemble |
Logical. Use workflows from ensembles table. |