| ctdFindProfiles {oce} | R Documentation |
Examine the pressure record looking for extended periods of either ascent or descent, and return either indices to these events or a vector of CTD records containing the events.
ctdFindProfiles(x, cutoff = 0.5, minLength = 10, minHeight = 0.1 *
diff(range(x[["pressure"]])), smoother = smooth.spline,
direction = c("descending", "ascending"), breaks, arr.ind = FALSE,
distinct, debug = getOption("oceDebug"), ...)
x |
A |
cutoff |
criterion on pressure difference; see “Details”. |
minLength |
lower limit on number of points in candidate profiles. |
minHeight |
lower limit on height of candidate profiles. |
smoother |
The smoothing function to use for identifying down/up
casts. The default is |
direction |
String indicating the travel direction to be selected. |
breaks |
optional integer vector indicating the indices of last
datum in each profile stored within |
arr.ind |
Logical indicating whether the array indices should be returned; the alternative is to return a vector of ctd objects. |
distinct |
An optional string indicating how to identify profiles
by unique values. Use |
debug |
an integer specifying whether debugging information is
to be printed during the processing. This is a general parameter that
is used by many |
... |
Optional extra arguments that are passed to the smoothing function, |
The method works by examining the pressure record. First, this is smoothed using
smoother() (see “Arguments”), and then the result is first-differenced
using diff. Median values of the positive and
negative first-difference values are then multiplied by cutoff. This establishes criteria
for any given point to be in an ascending profile, a descending profile, or a non-profile.
Contiguous regions are then found, and those that have fewer than minLength points are
discarded. Then, those that have pressure ranges less than minHeight are discarded.
Caution: this method is not well-suited to all datasets. For example, the default
value of smoother is smooth.spline, and this works well for just a few
profiles, but poorly for a tow-yo with a long sequence of profiles; in the latter case,
it can be preferable to use simpler smoothers (see “Examples”). Also, depending
on the sampling protocol, it is often necessary to pass the resultant profiles through
ctdTrim, to remove artifacts such as an equilibration phase, etc.
Generally, one is well-advised to use the present function for a quick look at the data,
relying on e.g. plotScan to identify profiles visually, for a final product.
If arr.ind=TRUE, a data frame with columns start and end, the indices
of the downcasts. Otherwise, a vector of ctd objects. In this second case,
the station names are set to a form like "10/3", for the third profile within an
original ctd object with station name "10", or to "3", if the original
ctd object had no station name defined.
Dan Kelley and Clark Richards
The documentation for ctd-class explains the structure
of CTD objects, and also outlines the other functions dealing with them.
Other things related to ctd data: [[,ctd-method,
[[<-,ctd-method, as.ctd,
cnvName2oceName, ctd-class,
ctdDecimate, ctdRaw,
ctdTrim, ctd,
handleFlags,ctd-method,
initialize,ctd-method,
initializeFlagScheme,ctd-method,
oceNames2whpNames,
oceUnits2whpUnits,
plot,ctd-method, plotProfile,
plotScan, plotTS,
read.ctd.itp, read.ctd.odf,
read.ctd.sbe,
read.ctd.woce.other,
read.ctd.woce, read.ctd,
setFlags,ctd-method,
subset,ctd-method,
summary,ctd-method,
woceNames2oceNames,
woceUnit2oceUnit, write.ctd
## Not run:
library(oce)
## Example 1.
d <- read.csv("towyow.csv", header=TRUE)
towyow <- as.ctd(d$salinity, d$temperature, d$pressure)
casts <- ctdFindProfiles(towyow)
par(mfrow=c(length(casts), 3))
for (cast in casts) {
plotProfile(cast, "salinity")
plotProfile(cast, "temperature")
plotTS(cast, type='o')
}
## Example 2.
## Using a moving average to smooth pressure, instead of the default
## smooth.spline() method. This avoids a tendency of smooth.spline()
## to smooth out the profiles in a tow-yo with many (dozens or more) cycles.
movingAverage <- function(x, n = 11, ...)
{
f <- rep(1/n, n)
stats::filter(x, f, ...)
}
casts <- ctdFindProfiles(towyo, smoother=movingAverage)
## Example 3: glider data, with profiles separated by >10dbar jump.
breaks <- which(diff(ctd[["pressure"]]) > 10))
profiles <- ctdFindProfiles(ctd, breaks=breaks)
## End(Not run)