Often in CTD profiling, the goal is to isolate only the downcast, discarding measurements made in the air, in an equilibration phase in which the device is held below the water surface, and then the upcast phase that follows the downcast. This is handled reasonably well by ctdTrim with method="downcast", although it is almost always best to use plotScan() to investigate the data, and then use the method="index" or method="scan" method based on visual inspection of the data.

ctdTrim(
x,
method,
removeDepthInversions = FALSE,
parameters = NULL,
indices = FALSE,
debug = getOption("oceDebug")
)

## Arguments

x a ctd object. A string (or a vector of two strings) specifying the trimming method, or a function to be used to determine data indices to keep. If method is not provided, "downcast" is assumed. See “Details”. Logical value indicating whether to remove any levels at which depth is less than, or equal to, a depth above. (This is needed if the object is to be assembled into a section, unless ctdDecimate() will be used, which will remove the inversions. A list whose elements depend on the method; see “Details”. Logical value indicating what to return. If indices=FALSE (the default), then the return value is a subsetted ctd object. If indices=TRUE, then the return value is a logical vector that could be used to subset the data with subset,ctd-method() or to set data-quality flags. an integer specifying whether debugging information is to be printed during the processing. This is a general parameter that is used by many oce functions. Generally, setting debug=0 turns off the printing, while higher values suggest that more information be printed. If one function calls another, it usually reduces the value of debug first, so that a user can often obtain deeper debugging by specifying higher debug values.

## Value

Either a ctd object of or a logical vector of length matching the data. The first option is the default. The second option, achieved by setting indices=FALSE, may be useful in constructing data flags to be inserted into the object.

## Details

ctdTrim begins by examining the pressure differences between subsequent samples. If these are all of the same value, then the input ctd object is returned, unaltered. This handles the case of pressure-binned data. However, if the pressure difference varies, a variety of approaches are taken to trimming the dataset.

• If method[1] is "downcast" then an attempt is made to keep only data for which the CTD is descending. This is done in stages, with variants based on method[2], if supplied.

1. The pressure data are despiked with a smooth() filter with method "3R". This removes wild spikes that arise from poor instrument connections, etc.

2. Step 2. If no parameters are given, then any data with negative pressures are deleted. If there is a parameter named pmin, then that pressure (in decibars) is used instead as the lower limit. This is a commonly-used setup, e.g. ctdTrim(ctd, parameters=list(pmin=1)) removes the top decibar (roughly 1m) from the data. Specifying pmin is a simple way to remove near-surface data, such as a shallow equilibration phase, and if specified will cause ctdTrim to skip step 4 below.

3. The maximum pressure is determined, and data acquired subsequent to that point are deleted. This removes the upcast and any subsequent data.

4. If the pmin parameter is not specified, an attempt is made to remove an initial equilibrium phase by a regression of pressure on scan number. There are three variants to this, depending on the value of the second method element. If method is "A" (or not given), the procedure is to call nls() to fit a piecewise linear model of pressure as a function of scan, in which pressure is constant for scan less than a critical value, and then linearly varying for with scan. This is meant to handle the common situation in which the CTD is held at roughly constant depth (typically a metre or so) to equilibrate, before it is lowered through the water column. If method is "B", the procedure is similar, except that the pressure in the surface region is taken to be zero (this does not make much sense, but it might help in some cases). Note that, prior to early 2016, method "B" was called method "C"; the old "B" method was judged useless and so it was removed.

• If method="upcast", a sort of reverse of "downcast" is used. This was added in late April 2017 and has not been well tested yet.

• If method="sbe", a method similar to that described in the SBE Data Processing manual is used to remove the "soak" period at the beginning of a cast (see Section 6 under subsection "Loop Edit"). The method is based on the soak procedure whereby the instrument sits at a fixed depth for a period of time, after which it is raised toward the surface before beginning the actual downcast. This enables equilibration of the sensors while still permitting reasonably good near-surface data. Parameters for the method can be passed using the parameters argument, which include minSoak (the minimum depth for the soak) and maxSoak the maximum depth of the soak. The method finds the minimum pressure prior to the maxSoak value being passed, each of which occurring after the scan in which the minSoak value was reached. For the method to work, the pre-cast pressure minimum must be less than the minSoak value. The default values of minSoak and maxSoak are 1 and 20 dbar, respectively.

• If method="index" or "scan", then each column of data is subsetted according to the value of parameters. If the latter is a logical vector of length matching data column length, then it is used directly for subsetting. If parameters is a numerical vector with two elements, then the index or scan values that lie between parameters[1] and parameters[2] (inclusive) are used for subsetting. The two-element method is probably the most useful, with the values being determined by visual inspection of the results of plotScan(). While this may take a minute or two, the analyst should bear in mind that a deep-water CTD profile might take 6 hours, corresponding to ship-time costs exceeding a week of salary.

• If method="range" then data are selected based on the value of the column named parameters$item. This may be by range or by critical value. By range: select values between parameters$from (the lower limit) and parameters$to (the upper limit) By critical value: select if the named column exceeds the value. For example, ctd2 <- ctdTrim(ctd, "range", parameters=list(item="scan", from=5)) starts at scan number 5 and continues to the end, while ctdTrim(ctd,"range",parameters=list(item="scan",from=5,to=100)) also starts at scan 5, but extends only to scan 100. • If method is a function, then it must return a vector of logical() values, computed based on two arguments: data (a list()), and parameters as supplied to ctdTrim. Both inferWaterDepth and removeInversions are ignored in the function case. See “Examples”. ## References The Seabird CTD instrument is described at http://www.seabird.com/products/spec_sheets/19plusdata.htm. Seasoft V2: SBE Data Processing, SeaBird Scientific, 05/26/2016 ## See also Other things related to ctd data: CTD_BCD2014666_008_1_DN.ODF.gz, [[,ctd-method, [[<-,ctd-method, as.ctd(), cnvName2oceName(), ctd-class, ctd.cnv, ctdDecimate(), ctdFindProfiles(), ctdRaw, ctd, d200321-001.ctd, d201211_0011.cnv, handleFlags,ctd-method, initialize,ctd-method, initializeFlagScheme,ctd-method, oceNames2whpNames(), oceUnits2whpUnits(), plot,ctd-method, plotProfile(), plotScan(), plotTS(), read.ctd.itp(), read.ctd.odf(), read.ctd.odv(), read.ctd.sbe(), read.ctd.woce.other(), read.ctd.woce(), read.ctd(), setFlags,ctd-method, subset,ctd-method, summary,ctd-method, woceNames2oceNames(), woceUnit2oceUnit(), write.ctd() ## Author Dan Kelley and Clark Richards ## Examples if (FALSE) { library(oce) data(ctdRaw) plot(ctdRaw) # barely recognizable, due to pre- and post-cast junk plot(ctdTrim(ctdRaw)) # looks like a real profile ... plot(ctdDecimate(ctdTrim(ctdRaw),method="boxcar")) # ... smoothed # Demonstrate use of a function. The scan limits were chosen # by using locator(2) on a graph made by plotScan(ctdRaw). trimByIndex<-function(data, parameters) { parameters[1] < data$scan & data\$scan < parameters[2]
}
trimmed <- ctdTrim(ctdRaw, trimByIndex, parameters=c(130, 380))
plot(trimmed)
}