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R/section.R
In oce: Analysis of Oceanographic Data
Defines functions
longitudeTighten
addSpine
as.section
sectionSmooth
sectionGrid
read.section
sectionAddStation
sectionSort
Documented in
addSpine
as.section
longitudeTighten
read.section
sectionAddStation
sectionGrid
sectionSmooth
sectionSort
# vim:textwidth=120:expandtab:shiftwidth=4:softtabstop=4:foldmethod=marker
#' Class to Store Hydrographic Section Data
#'
#' This class stores data from oceanographic section surveys.
#'
#' Sections can be read with [read.section()] or created with
#' [read.section()] or created from CTD objects by using
#' [as.section()] or by adding a ctd station to an existing section with
#' [sectionAddStation()].
#'
#' Sections may be sorted with [sectionSort()], subsetted with
#' [subset,section-method()], smoothed with [sectionSmooth()], and
#' gridded with [sectionGrid()]. A "spine" may be added to a section
#' with [addSpine()]. Sections may be summarized with
#' [summary,section-method()] and plotted
#' with [plot,section-method()].
#'
#' The sample dataset [section()] contains data along WOCE line A03.
#'
#' @templateVar class section
#'
#' @templateVar dataExample {}
#'
#' @templateVar metadataExample Examples that are of common interest include `stationId`, `longitude`, `latitude` and `time`.
#'
#' @template slot_summary
#'
#' @template slot_put
#'
#' @template slot_get
#'
#' @examples
#' library(oce)
#' data(section)
#' plot(section[["station", 1]])
#' pairs(cbind(z=-section[["pressure"]],T=section[["temperature"]],S=section[["salinity"]]))
#' # T profiles for first few stations in section, at common scale
#' par(mfrow=c(3,3))
#' Tlim <- range(section[["temperature"]])
#' ylim <- rev(range(section[["pressure"]]))
#' for (stn in section[["station", 1:9]]) {
#' plotProfile(stn, xtype="potential temperature", ylim=ylim, Tlim=Tlim)
#' }
#'
#' @author Dan Kelley
#'
#' @family classes provided by oce
#' @family things related to section data
setClass("section", contains="oce")
#' Hydrographic section
#'
#' This is line A03 (ExpoCode 90CT40_1, with nominal sampling date 1993-09-11).
#' The chief scientist was Tereschenkov of SOI, working aboard the Russian ship
#' Multanovsky, undertaking a westward transect from the Mediterranean outflow
#' region across to North America, with a change of heading in the last few dozen
#' stations to run across the nominal Gulf Stream axis.
#' The data flags follow the "WHP Bottle"convention, set by
#' [initializeFlagScheme,section-method()] to `"WHP bottle"`. This convention
#' used to be described at the link
#' `https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/whp/exchange/exchange_format_desc.htm`
#' but that was found to fail in December 2020.
#'
#' @section Speculation on a timing error:
#' In May 2022, it was discovered that the times in this dataset are not fully
#' sequential, at two spots. This might be a reporting error. Station 41 has
#' time listed as 1993-10-03T00:06:00 and that leads to a time reversal.
#' However, if that time were actually on the day before, then
#' the time reversal would vanish, and the inter-station timing of
#' about 5 to 6 hours would be recovered. A similar pattern is seen at station
#' 45. Of course, this hypothesis of incorrect recording is difficult to test,
#' for data taken thirty years ago.
#'
#' @examples
#' library(oce)
#' # Gulf Stream
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 5000, 100))
#' plot(GSg, span=1500) # increase span to show more coastline
#'
#' @name section
#'
#' @docType data
#'
#' @usage data(section)
#'
#' @source This is based on the WOCE file named `a03_hy1.csv`, downloaded
#' from `https://cchdo.ucsd.edu/cruise/90CT40_1`, 13 April 2015.
#'
#' @family datasets provided with oce
#' @family things related to section data
NULL
setMethod(f="initialize",
signature="section",
definition=function(.Object, filename="", sectionId="", ...) {
.Object <- callNextMethod(.Object, ...)
.Object@metadata$units <- NULL # senseless keeping these from oce()
.Object@metadata$flags <- NULL # senseless keeping these from oce()
.Object@metadata$filename <- filename
.Object@metadata$sectionId <- sectionId
.Object@processingLog$time <- presentTime()
.Object@processingLog$value <- "create 'section' object"
return(.Object)
})
# DEVELOPERS: please pattern functions and documentation on this, for uniformity.
# DEVELOPERS: You will need to change the docs, and the 3 spots in the code
# DEVELOPERS: marked '# DEVELOPER 1:', etc.
#' @title Handle flags in Section Objects
#'
#' @details
#' The default for `flags` is based on
#' calling [defaultFlags()] based on the
#' `metadata` in the first station in the section. If the
#' other stations have different flag schemes (which seems highly
#' unlikely for archived data), this will not work well, and indeed
#' the only way to proceed would be to use [handleFlags,ctd-method()]
#' on the stations, individually.
#'
#' @param object A [section-class] object.
#'
#' @template handleFlagsTemplate
#'
#' @references
#' The following link used to work, but was found to fail in December 2020.
#' 1. `https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/whp/exchange/exchange_format_desc.htm`
#'
#' @examples
#' library(oce)
#' data(section)
#' section2 <- handleFlags(section, flags=c(1,3:9))
#' par(mfrow=c(2, 1))
#' plotTS(section)
#' plotTS(section2)
#'
#' @family things related to section data
#' @aliases handleFlags.section
setMethod("handleFlags",
signature=c(object="section", flags="ANY", actions="ANY", where="ANY", debug="ANY"),
definition=function(object, flags=NULL, actions=NULL, where=where, debug=getOption("oceDebug")) {
# DEVELOPER 1: alter the next comment to explain your setup
if (is.null(flags)) {
flags <- defaultFlags(object[["station", 1]])
if (is.null(flags))
stop("must supply 'flags', or use initializeFlagScheme() on the section object first")
}
if (is.null(actions)) {
actions <- list("NA") # DEVELOPER 2: alter this line to suit a new data class
names(actions) <- names(flags)
}
if (any(sort(names(actions)) != sort(names(flags))))
stop("names of flags and actions must match")
res <- object
for (i in seq_along(res@data$station))
res@data$station[[i]] <- handleFlags(res@data$station[[i]], flags, actions, where=where, debug)
res
})
#' @templateVar class section
#' @templateVar details This applies `initializeFlagScheme` for each `ctd` station within the `stations` element of the `data` slot.
#' @template initializeFlagSchemeTemplate
#'
#' @examples
#'\dontrun{
#' data(section)
#' section <- read.section("a03_hy1.csv", sectionId="a03", institute="SIO",
#' ship="R/V Professor Multanovskiy", scientist="Vladimir Tereschenov")
#' sectionWithFlags <- initializeFlagScheme(section, "WHP bottle")
#' station1 <- sectionWithFlags[["station", 1]]
#' str(station1[["flagScheme"]])
#'}
setMethod("initializeFlagScheme",
c(object="section", name="ANY", mapping="ANY", default="ANY", update="ANY", debug="ANY"),
function(object, name=NULL, mapping=NULL, default=NULL, update=NULL, debug=getOption("oceDebug"))
{
res <- object
for (i in seq_along(object@data$station)) {
res@data$station[[i]] <- initializeFlagScheme(object@data$station[[i]],
name=name,
mapping=mapping,
default=default,
update=update,
debug=debug-1)
}
res@processingLog <-
processingLogAppend(res@processingLog,
paste("initializeFlagScheme(object",
", name=\"", name, "\"",
", mapping=", gsub("[ ]*", "", paste(as.character(deparse(mapping)))), ")",
", default=", gsub("[ ]*", "", paste(as.character(deparse(default)))), ")",
sep=""))
res
})
#' Summarize a Section Object
#'
#' Pertinent summary information is presented, including station locations,
#' distance along trac, etc.
#'
#' @param object An object of class `"section"`, usually, a result of a call
#' to [read.section()], [read.oce()], or
#' [as.section()].
#'
#' @param ... Further arguments passed to or from other methods.
#'
#' @return `NULL`
#'
#' @examples
#' library(oce)
#' data(section)
#' summary(section)
#'
#' @family things related to section data
#'
#' @author Dan Kelley
#' @aliases summary.section
setMethod(f="summary",
signature="section",
definition=function(object, ...) {
numStations <- length(object@data$station)
cat("Section Summary\n---------------\n\n")
cat("* Source: \"", object@metadata$filename, "\"\n", sep="")
cat("* ID: \"", object@metadata$sectionId, "\"\n", sep="")
if (numStations > 0) {
cat("* Overview of stations\n")
cat(sprintf("%5s %5s %9s %9s %7s %5s %16s\n", "Index", "ID", "Lon", "Lat", "Levels", "Depth", "Time"))
for (i in seq_len(numStations)) {
thisStn <- object@data$station[[i]]
id <- if (!is.null(thisStn@metadata$station) && "" != thisStn@metadata$station) {
thisStn@metadata$station
} else {
""
}
depth <- if ("waterDepth" %in% names(thisStn@metadata)) {
thisStn@metadata$waterDepth
} else {
NA
}
time <- if ("startTime" %in% names(thisStn@metadata)) {
thisStn[["startTime"]][1]
} else {
thisStn[["time"]][1]
}
cat(sprintf("%5d %5s %9.4f %9.4f %7.0f %5.0f %16s\n",
i, id,
thisStn[["longitude"]][1], thisStn[["latitude"]][1],
length(thisStn@data$pressure), depth,
format(time, "%Y-%m-%d %H:%M")))
}
names <- names(object@data$station[[1]]@metadata$flags)
if (!is.null(names)) {
cat("* Data-quality flags\n")
width <- 1 + max(nchar(names))
# assume all stations have identical flags
for (name in names) {
padding <- rep(" ", width - nchar(name))
cat(" ", name, ":", padding, sep="")
flags <- NULL
flagName <- paste0(name, "Flag")
for (i in seq_len(numStations)) {
flags <- c(flags, as.numeric(object@data$station[[i]][[flagName]]))
}
flagTable <- table(flags)
flagTableLength <- length(flagTable)
if (flagTableLength) {
for (i in seq_len(flagTableLength)) {
cat("\"", names(flagTable)[i], "\"", " ", flagTable[i], "", sep="")
if (i != flagTableLength) cat(", ") else cat("\n")
}
}
}
}
} else {
cat("* No stations\n")
}
if ("spine" %in% names(object@metadata)) {
if (2 == length(object@metadata$spine) &&
2 == sum(c("latitude", "longitude") %in% names(object@metadata$spine))) {
spine <- object@metadata$spine
cat("* Section spine\n")
cat(sprintf(" %8s %8s\n", "Lon", "Lat"))
for (i in seq_along(spine$longitude)) {
cat(sprintf(" %8.4f %8.4f\n", spine$longitude[i], spine$latitude[i]))
}
} else {
warning("malformed section spine")
}
}
processingLogShow(object)
invisible(NULL)
})
#' Extract Something From a Section Object
#'
#' @param x a [section-class] object.
#'
#' @templateVar class section
#'
#' @section Details of the Specialized Method:
#'
#' There are several possibilities, depending on the nature of `i`.
#'
#' * If `i` is `"?"`, then the return value is a list containing four items,
#' each of which is a character vector holding the names of things that can be
#' accessed with `[[`. This list is compiled by examining all the stations in
#' the object, and reporting an entry if it is found in any one of them. The
#' `data` and `metadata` items hold the names of entries in the object's data
#' and metadata slots, respectively. The `dataDerived` and `metadataDerived`
#' items hold data-like and metadata-like things that can be derived from these.
#'
#' * If `i` is `"station"`, then `[[` will return a [list()] of [ctd-class]
#' objects holding the station data. If `j` is also given, it specifies a
#' station (or set of stations) to be returned. if `j` contains just a single
#' value, then that station is returned, but otherwise a list is returned. If
#' `j` is an integer, then the stations are specified by index, but if it is
#' character, then stations are specified by the names stored within their
#' metadata. (Missing stations yield `NULL` in the return value.)
#'
#' * If `i` is `"station ID"`, then the IDs of the stations in the
#' section are returned.
#'
#' * If `i` is `"dynamic height"`, then an estimate of dynamic
#' height is returned, as calculated with [`swDynamicHeight`]`(x)`.
#'
#' * If `i` is `"distance"`, then the distance along the section is
#' returned, using [geodDist()].
#'
#' * If `i` is `"depth"`, then a vector containing the depths
#' of the stations is returned.
#'
#' * If `i` is `"z"`, then a vector containing the z
#' coordinates is returned.
#'
#' * If `i` is `"theta"` or `"potential temperature"`, then
#' the potential temperatures of all the stations are returned in one
#' vector. Similarly, `"spice"` returns the property known
#' as spice, using [swSpice()].
#'
#' * If `i` is a string ending with `"Flag"`, then the characters
#' prior to that ending are taken to be the name of a variable contained
#' within the stations in the section. If this flag is available in
#' the first station of the section, then the flag values are looked
#' up for every station.
#'
#' If `j` is `"byStation"`, then a list is returned, with
#' one (unnamed) item per station.
#'
#' If `j` is `"grid:distance-pressure"` or `"grid:time-pressure"`, then a gridded
#' representation of `i` is returned, as a list with elements:
#' `distance` (in km) or `time` (in POSIXct); `pressure` (in dbar) and
#' `field` (in whatever unit is used for `i`). See the
#' examples in the documentation for [plot,section-method()].
#'
#' @examples
#' data(section)
#' length(section[["latitude"]])
#' length(section[["latitude", "byStation"]])
#' # Vector of all salinities, for all stations
#' Sv <- section[["salinity"]]
#' # List of salinities, grouped by station
#' Sl <- section[["salinity", "byStation"]]
#' # First station salinities
#' Sl[[1]]
#'
#' @template sub_subTemplate
#'
#' @author Dan Kelley
#'
#' @family things related to section data
setMethod(f="[[",
signature(x="section", i="ANY", j="ANY"),
definition=function(x, i, j, ...) {
# This is broken down into 3 cases.
#
# Case 1. Things that can be computed without looking deeply
# enough within @data$station to determine computable things.
# This determination is a bit slow, and is taken up in SECTION 2.
#
# Case 1.1 "station".
if (i == "station") {
# All stations.
if (missing(j))
return(x@data$station)
# A subset of stations, specified with j, which is either a
# character value for station ID(s) or a numeric value for
# sequence number(s).
nj <- length(j)
if (is.character(j)) { # station ID(s)
stationNames <- unlist(lapply(x@data$station,
function(station) station@metadata$station))
if (nj == 1L) {
w <- which(stationNames == j)
res <- if (length(w)) x@data$station[[w[1]]] else NULL
} else {
res <- vector("list", nj)
for (jj in seq_len(nj)) {
w <- which(stationNames == j[jj])
res[[jj]] <- if (length(w)) x@data$station[[w[1]]] else NULL
}
}
} else { # sequence number(s)
if (nj == 1L) {
res <- x@data$station[[j]]
} else {
res <- vector("list", nj)
for (jj in seq_len(nj)) {
res[[jj]] <- x@data$station[[j[jj]]]
}
}
}
return(res)
}
# Case 1.2: data-quality flags.
if (1 == length(grep(".*Flag$", i))) {
baseName <- gsub("Flag$", "", i)
# FIXME: should check all stations, not just the first
if (baseName %in% names(x@data$station[[1]]@metadata$flags)) {
return(unlist(lapply(x@data$station, function(ctd) ctd[[i]])))
} else {
stop("First station lacks a '", baseName, "' flag, so assuming the same for all.")
}
}
# Case 1.3: things stored in section@metadata.
if (i %in% names(x@metadata)) {
if (i %in% c("longitude", "latitude")) {
if (!missing(j) && j == "byStation") {
return(x@metadata[[i]])
} else {
res <- NULL
for (stn in seq_along(x@data$station)) {
res <- c(res, rep(x@data$station[[stn]]@metadata[[i]], length(x@data$station[[stn]][["salinity"]])))
}
return(res)
}
} else {
return(x@metadata[[i]])
}
}
# Case 1.4: station IDs.
if (i == "station ID") {
return(unlist(lapply(x@data$station, function(stn) stn[["station"]])))
}
# Case 1.5: dynamic height.
if (i == "dynamic height") {
return(swDynamicHeight(x))
}
# Case 1.6: distance along track.
if (i == "distance") {
res <- NULL
for (stn in seq_along(x@data$station)) {
distance <- geodDist(x@data$station[[stn]][["longitude"]][1],
x@data$station[[stn]][["latitude"]][1],
x@data$station[[1]][["longitude"]][1],
x@data$station[[1]][["latitude"]][1])
if (!missing(j) && j == "byStation") {
res <- c(res, distance)
} else {
res <- c(res, rep(distance, length(x@data$station[[stn]]@data$temperature)))
}
}
return(res)
}
# Case 1.7: time.
if (i == "time") {
return(numberAsPOSIXct(unlist(lapply(x@data$station, function(stn) stn[["time"]]))))
}
# Case 2. We are now done with things that can be determined by
# looking one level deep. To extract individual data, we will
# need to know what is in each of the stations (and what can be
# computed from this content).
metadataStn <- dataStn <- metadataDerivedStn <- dataDerivedStn <- NULL
for (station in x@data$station) {
q <- station[["?"]]
metadataStn <- c(metadataStn, q$metadata)
metadataDerivedStn <- c(metadataDerivedStn, q$metadataDerived)
dataStn <- c(dataStn, q$data)
dataDerivedStn <- c(dataDerivedStn, q$dataDerived)
}
metadataStn <- sort(unique(metadataStn))
metadataDerivedStn <- sort(c(
"distance",
paste("station", "ID"),
paste("dynamic", "height"),
unique(metadataDerivedStn)))
dataStn <- sort(unique(dataStn))
dataDerivedStn <- sort(unique(dataDerivedStn))
# Case 2.1: indication of available values for i.
if (i == "?") {
return(list(metadata=metadataStn,
metadataDerived=metadataDerivedStn,
data=dataStn,
dataDerived=dataDerivedStn))
}
# Case 2.2: something inside stations (or computable from such).
res <- NULL
nstation <- length(x@data$station)
if (i %in% c(dataStn, dataDerivedStn)) {
if (!missing(j) && substr(j, 1, 4) == "grid") {
if (j %in% c("grid:distance-pressure", "grid:time-pressure")) {
numStations <- length(x@data$station)
p1 <- x[["station", 1]][["pressure"]]
np1 <- length(p1)
field <- matrix(NA, nrow=numStations, ncol=np1)
if (numStations > 1) {
field[1, ] <- x[["station", 1]][[i]]
for (istn in 2:numStations) {
pi <- x[["station", istn]][["pressure"]]
if (length(pi) != np1 || any(pi != p1)) {
warning("returning NULL because this section is not gridded")
return(NULL)
}
field[istn, ] <- x[["station", istn]][[i]]
}
if (j == "grid:distance-pressure") {
res <- list(distance=x[["distance", "byStation"]], pressure=p1, field=field)
} else if (j == "grid:time-pressure") {
res <- list(time=x[["time", "byStation"]], pressure=p1, field=field)
}
return(res)
} else {
warning("returning NULL because this section contains only 1 station")
return(NULL)
}
} else {
warning("only grid:distance-pressure and grid:time-pressure are permitted")
return(NULL)
}
} else {
# Note that nitrite and nitrate might be computed, not stored
if (!missing(j) && j == "byStation") {
res <- vector("list", nstation)
for (istation in seq_len(nstation)) {
res[[istation]] <- x@data$station[[istation]][[i]]
}
} else {
res <- NULL
for (station in x[["station"]]) {
res <- c(res, station[[i]])
}
}
return(res)
}
}
# Case 3: unknown, so drop down a level.
callNextMethod()
}) # [[
#' Replace Parts of a Section Object
#'
#' @param x a [section-class] object.
#'
#' @template sub_subsetTemplate
#'
#' @examples
#' # 1. Change section ID from a03 to A03
#' data(section)
#' section[["sectionId"]]
#' section[["sectionId"]] <- toupper(section[["sectionId"]])
#' section[["sectionId"]]
#' # 2. Add a millidegree to temperatures at station 10
#' section[["station", 10]][["temperature"]] <-
#' 1e-3 + section[["station", 10]][["temperature"]]
#'
#' @author Dan Kelley
#'
#' @family things related to section data
setMethod(f="[[<-",
signature(x="section", i="ANY", j="ANY"),
definition=function(x, i, j, ..., value) {
if (i == "station") {
if (missing(j)) {
x@data$station <- value
} else {
x@data$station[[j]] <- value
}
x
} else {
callNextMethod(x=x, i=i, j=j, ...=..., value=value) # [[<-
}
})
setMethod(f="show",
signature="section",
definition=function(object) {
id <- object@metadata$sectionId
n <- length(object@data$station)
if (n == 0) {
cat("Section has no stations\n")
} else {
if (is.null(id) || id == "") {
cat("Unnamed section has ", n, " stations:\n", sep="")
} else {
cat("Section '", id, "' has ", n, " stations:\n", sep="")
}
cat(sprintf("%5s %5s %8s %8s %7s %5s\n", "Index", "ID", "Lon", "Lat", "Levels", "Depth"))
for (i in 1:n) {
thisStn <- object@data$station[[i]]
id <- if (!is.null(thisStn@metadata$station) && "" != thisStn@metadata$station) {
thisStn@metadata$station
} else {
""
}
depth <- if (is.null(thisStn@metadata$waterDepth)) {
max(thisStn@data$pressure, na.rm=TRUE)
} else {
thisStn@metadata$waterDepth
}
cat(sprintf("%5d %5s %8.3f %8.3f %7.0f %5.0f\n",
i, id,
thisStn[["longitude"]][1], thisStn[["latitude"]][1],
length(thisStn@data$pressure), depth))
}
}
})
#' Subset a Section Object
#'
#' Return a subset of a section object.
#'
#' This function is used to subset data within the
#' stations of a section, or to choose a subset of the stations
#' themselves. The first case is handled with the `subset` argument,
#' while the second is handled if `...` contains a vector named
#' `indices`. Either `subset` or `indices` must be
#' provided, but not both.
#'
#' **In the "subset" method**, `subset` indicates
#' either stations to be kept, or data to be kept within the stations.
#'
#' The first step in processing is to check for the presence of certain
#' key words in the `subset` expression. If `distance`
#' is present, then stations are selected according to a condition on the
#' distance (in km) from the first station to the given station (Example 1).
#' If either `longitude` or `latitude` is given, then
#' stations are selected according to the stated condition (Example 2).
#' If `stationId` is present, then selection is in terms of the
#' station ID (not the sequence number) is used (Example 3). In all of these
#' cases, stations are either selected in their entirety or dropped
#' in their entirety.
#'
#' If none of these keywords is present, then the `subset` expression is
#' evaluated in the context of the `data` slot of each of the CTD stations
#' stored within `x`. (Note that this slot does not normally
#' contain any of the keywords that are listed in the previous
#' paragraph; it does, then odd results may occur.) Each station is examined
#' in turn, with `subset` being evaluated individually in each. The evaluation
#' produces a logical vector. If that vector has length 1 (Examples 4 and 5)
#' then the station is retained or discarded, accordingly. If the vector is longer,
#' then the logical vector is used as a sieve to subsample that individual CTD
#' profile.
#'
#' **In the "indices" method**, stations are selected using
#' `indices`, which may be a vector of integers that indicate sequence
#' number, or a logical vector, again indicating which stations to keep.
#'
#' @param x a [section-class] object.
#'
#' @param subset an optional indication of either the stations to be kept,
#' or the data to be kept within the stations. See \dQuote{Details}.
#'
#' @param ... optional arguments, of which only the first is examined. The
#' possibilities for this argument are `indices`, which must be a
#' vector of station indices (see Example 6), or `within`, which must be
#' a list or data frame, containing items named either `x` and `y`
#' or `longitude` and `latitude` (see Example 7). If `within`
#' is given, then `subset` is ignored.
#'
#' @return A [section-class] object.
#'
#' @examples
#' library(oce)
#' data(section)
#'
#' # Example 1. Stations within 500 km of the first station
#' starting <- subset(section, distance < 500)
#'
#' # Example 2. Stations east of 50W
#' east <- subset(section, longitude > (-50))
#'
#' # Example 3. Gulf Stream
#' GS <- subset(section, 113<=stationId&stationId<=129)
#'
#' # Example 4. Only stations with more than 5 pressure levels
#' long <- subset(section, length(pressure) > 5)
#'
#' # Example 5. Only stations that have some data in top 50 dbar
#' surfacing <- subset(section, min(pressure) < 50)
#'
#' # Example 6. Similar to #4, but done in more detailed way
#' long <- subset(section,
#' indices=unlist(lapply(section[["station"]],
#' function(s)
#' 5 < length(s[["pressure"]]))))
#'
#' # Example 7. Subset by a polygon determined with locator()
#'\dontrun{
#' par(mfrow=c(2, 1))
#' plot(section, which="map")
#' bdy <- locator(4) # choose a polygon near N. America
#' GS <- subset(section, within=bdy)
#' plot(GS, which="map")
#'}
#'
#' @author Dan Kelley
#'
#' @family functions that subset oce objects
#' @family things related to section data
#' @aliases subset.section
setMethod(f="subset",
signature="section",
definition=function(x, subset, ...) {
subsetString <- paste(deparse(substitute(expr=subset, env=environment())), collapse=" ")
res <- x
dots <- list(...)
dotsNames <- names(dots)
indicesGiven <- length(dots) && ("indices" %in% dotsNames)
withinGiven <- length(dots) && ("within" %in% dotsNames)
debug <- getOption("oceDebug")
if (length(dots) && ("debug" %in% names(dots)))
debug <- dots$debug
if (indicesGiven) {
# select a portion of the stations
if (!missing(subset))
stop("cannot specify both 'subset' and 'indices'")
oceDebug(debug, "subsetting by indices\n")
res <- new("section")
indices <- dots$indices
n <- length(indices)
if (is.logical(indices))
indices <- (1:n)[indices]
if (min(indices) < 1)
stop("cannot have negative indices")
if (max(indices) > length(x@data$station))
stop("cannot indices exceeding # stations")
stn <- x@metadata$stationId[indices]
lat <- x@metadata$lat[indices]
lon <- x@metadata$lon[indices]
time <- x@metadata$time[indices]
station <- vector("list", length(indices))
for (i in seq_along(indices))
station[[i]] <- x@data$station[[indices[i]]]
data <- list(station=station)
res@metadata$stationId <- stn
res@metadata$longitude <- lon
res@metadata$latitude <- lat
res@metadata$time <- time
res@data <- data
res@processingLog <- x@processingLog
res@processingLog <- processingLogAppend(res@processingLog,
paste("subset(x, indices=c(", paste(dots$indices, collapse=","), "))", sep=""))
} else if (withinGiven) {
polygon <- dots$within
if (!is.data.frame(polygon) && !is.list(polygon))
stop("'within' must be a data frame or a polygon")
polygonNames <- names(polygon)
lonp <- if ("x" %in% polygonNames) {
polygon$x
} else if ("longitude" %in% polygonNames) {
polygon$longitude
} else {
stop("'within' must contain either 'x' or 'longitude'")
}
latp <- if ("y" %in% polygonNames) {
polygon$y
} else if ("latitude" %in% polygonNames) {
polygon$latitude
} else {
stop("'within' must contain either 'y' or 'latitude'")
}
lon <- x[["longitude", "byStation"]]
lat <- x[["latitude", "byStation"]]
polyNew <- sf::st_polygon(list(outer=cbind(c(lonp, lonp[1]), c(latp, latp[1]))))
pointsNew <- sf::st_multipoint(cbind(lon, lat))
inside <- sf::st_intersection(pointsNew, polyNew)
keep <- matrix(pointsNew %in% inside, ncol=2)[, 1]
res <- x
res@metadata$stationId <- x@metadata$stationId[keep]
res@metadata$longitude <- x@metadata$longitude[keep]
res@metadata$latitude <- x@metadata$latitude[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- x@data$station[keep]
res@processingLog <- processingLogAppend(res@processingLog,
paste("subset(x, within) kept ", sum(keep), " of ",
length(keep), " stations", sep=""))
} else {
if (missing(subset))
stop("must give 'subset' or (in ...) 'indices'")
oceDebug(debug, "subsetting by 'subset'\n")
res <- x
if (grepl("stationId", subsetString)) {
keep <- eval(expr=substitute(expr=subset, env=environment()),
envir=data.frame(stationId=as.numeric(x@metadata$stationId)))
res@metadata$stationId <- x@metadata$stationId[keep]
res@metadata$longitude <- x@metadata$longitude[keep]
res@metadata$latitude <- x@metadata$latitude[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- x@data$station[keep]
res@processingLog <- processingLogAppend(res@processingLog, paste("subset(x, subset=", subsetString, ")", sep=""))
} else if (grepl("distance", subsetString)) {
l <- list(distance=geodDist(res))
keep <- eval(expr=substitute(expr=subset, env=environment()), envir=l, enclos=parent.frame(2))
res@metadata$longitude <- res@metadata$longitude[keep]
res@metadata$latitude <- res@metadata$latitude[keep]
res@metadata$stationId <- res@metadata$stationId[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- res@data$station[keep]
} else if (grepl("levels", subsetString)) {
levels <- unlist(lapply(x[["station"]], function(stn) length(stn[["pressure"]])))
keep <- eval(expr=substitute(expr=subset, env=environment()), envir=list(levels=levels))
res@metadata$longitude <- res@metadata$longitude[keep]
res@metadata$latitude <- res@metadata$latitude[keep]
res@metadata$stationId <- res@metadata$stationId[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- res@data$station[keep]
} else if (grepl("latitude", subsetString) || grepl("longitude", subsetString)) {
n <- length(x@data$station)
keep <- vector(length=n)
for (i in 1:n) {
keep[i] <- eval(expr=substitute(expr=subset, env=environment()), envir=x@data$station[[i]]@metadata, enclos=parent.frame(2))
}
nn <- sum(keep)
station <- vector("list", nn)
stn <- NULL # we can later index this to accumulate
lon <- NULL
lat <- NULL
time <- NULL
j <- 1
for (i in 1:n) {
if (keep[i]) {
stn[j] <- x@metadata$stationId[i]
lon[j] <- x@metadata$longitude[i]
lat[j] <- x@metadata$latitude[i]
station[[j]] <- x@data$station[[i]]
time[[j]] <- x@data$time[[i]]
j <- j + 1
}
}
res <- new("section")
res@data$station <- station
res@metadata$header <- x@metadata$header
res@metadata$sectionId <- x@metadata$sectionId
res@metadata$stationId <- stn
res@metadata$longitude <- lon
res@metadata$latitude <- lat
res@metadata$time <- time
res@processingLog <- x@processingLog
} else {
res <- new("section")
res@data$station <- list()
res@metadata$header <- x@metadata$header
res@metadata$sectionId <- NULL # R will let us index into these later
res@metadata$stationId <- NULL
res@metadata$longitude <- NULL
res@metadata$latitude <- NULL
res@metadata$time <- NULL
res@processingLog <- x@processingLog
n <- length(x@data$station)
j <- 1
for (i in 1:n) {
r <- eval(expr=substitute(expr=subset, env=environment()), envir=x@data$station[[i]]@data, enclos=parent.frame(2))
oceDebug(debug, "i=", i, ", j=", j, ", sum(r)=", sum(r), "\n", sep="")
if (sum(r) > 0) {
# copy whole station ...
res@data$station[[j]] <- x@data$station[[i]]
# ... but if we are looking for a subset, go through the data fields and do that
if (length(r) > 1) {
# Select certain levels. This occurs e.g. for
# subset(sec, S > 35)
# but not for station-by-station selection, e.g. as a result of
# subset(sec, length(S) > 3)
# since the length of the latter is 1, which means to copy
# the whole station.
for (field in names(res@data$station[[j]]@data)) {
oceDebug(debug, " field='", field, "', i=", i, ", j=", j, " (case A)\n", sep="")
res@data$station[[j]]@data[[field]] <- res@data$station[[j]]@data[[field]][r]
}
}
# copy section metadata
res@metadata$stationId[j] <- x@metadata$stationId[i]
res@metadata$latitude[j] <- x@metadata$latitude[i]
res@metadata$longitude[j] <- x@metadata$longitude[i]
res@metadata$time[j] <- x@metadata$time[i]
j <- j + 1
} else {
oceDebug(debug, " skipping this station\n")
}
}
}
res@processingLog <- processingLogAppend(res@processingLog, paste("subset(x, subset=", subsetString, ")", sep=""))
}
res
})
#' Sort a Section
#'
#' Sections created with [as.section()] have "stations" that are in the
#' order of the CTD objects (or filenames for such objects) provided. Sometimes,
#' this is not the desired order, e.g. if file names discovered with
#' [dir()] are in an order that makes no sense. (For example, a
#' practioner might name stations `"stn1"`, `"stn2"`, etc., not
#' realizing that this will yield an unhelpful ordering, by file name, if there
#' are more than 9 stations.) The purpose of `sectionSort` is to permit
#' reordering the constituent stations in sensible ways.
#'
#' @param section A `section` object containing the section whose stations
#' are to be sorted.
#'
#' @param by An optional string indicating how to reorder. If not provided,
#' `"stationID"` will be assumed. Other choices are `"distance"`, for
#' distance from the first station, `"longitude"`, for longitude,
#' `"latitude"` for latitude, and `"time"`, for time.
#'
#' @param decreasing A logical value indicating whether to sort in decreasing
#' order. The default is FALSE. (Thanks to Martin Renner for adding this
#' parameter.)
#'
#' @return object A [section-class] object that has been smoothed,
#' so its data fields will station-to-station variation than
#' is the case for the input section, \code{x}.
#'
#' @examples
#' library(oce)
#' data(section)
#' sectionByLongitude <- sectionSort(section, by="longitude")
#' head(section)
#' head(sectionByLongitude)
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionSort <- function(section, by, decreasing=FALSE)
{
if (missing(by)) {
by <- "stationId"
} else {
byChoices <- c("stationId", "distance", "longitude", "latitude", "time", "spine")
iby <- match(by, byChoices, nomatch=0)
if (0 == iby)
stop('unknown by value "', by, '"; should be one of: ', paste(byChoices, collapse=" "))
by <- byChoices[iby]
}
res <- section
if (by == "stationId") {
o <- order(section@metadata$stationId, decreasing=decreasing)
} else if (by == "distance") {
o <- order(section[["distance", "byStation"]], decreasing=decreasing)
} else if (by == "longitude") {
o <- order(section[["longitude", "byStation"]], decreasing=decreasing)
} else if (by == "latitude") {
o <- order(section[["latitude", "byStation"]], decreasing=decreasing)
} else if (by == "time") {
# FIXME: should check to see if startTime exists first?
times <- unlist(lapply(section@data$station, function(x) x@metadata$startTime))
o <- order(times, decreasing=decreasing)
} else if (by == "spine") {
stop("not implemented for spine")
} else {
o <- seq_along(section[["station"]]) # can't get here, actually
}
res@metadata$stationId <- res@metadata$stationId[o]
res@metadata$longitude <- res@metadata$longitude[o]
res@metadata$latitude <- res@metadata$latitude[o]
if ("time" %in% names(res@metadata)) {
res@metadata$time <- res@metadata$time[o]
}
res@data$station <- res@data$station[o]
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
res
}
#' Add a CTD Station to a Section
#'
#' Add a CTD profile to an existing section.
#'
#' @section Historical note:
#' Until March 2015, this operation was carried out with the `+` operator,
#' but at that time, the syntax was flagged by the development version of R, so it
#' was changed to the present form.
#'
#' @param section A section to which a station is to be added.
#'
#' @param station A ctd object holding data for the station to be added.
#'
#' @aliases sectionAddCtd
#' @return A [section-class] object.
#'
#' @examples
#' library(oce)
#' data(ctd)
#' ctd2 <- ctd
#' ctd2[["temperature"]] <- ctd2[["temperature"]] + 0.5
#' ctd2[["latitude"]] <- ctd2[["latitude"]] + 0.1
#' section <- as.section(c("ctd", "ctd2"))
#' ctd3 <- ctd
#' ctd3[["temperature"]] <- ctd[["temperature"]] + 1
#' ctd3[["latitude"]] <- ctd[["latitude"]] + 0.1
#' ctd3[["station"]] <- "Stn 3"
#' sectionAddStation(section, ctd3)
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionAddStation <- function(section, station)
{
if (missing(section))
stop("must provide a section to which the ctd is to be added")
if (!inherits(section, "section"))
stop("'section' is not a 'section' object")
if (missing(station))
return(section)
if (!inherits(station, "ctd"))
stop("'station' is not a 'ctd' object")
res <- section
n.orig <- length(section@data$station)
s <- vector("list", n.orig + 1)
for (i in 1:n.orig) {
s[[i]] <- section@data$station[[i]]
}
s[[n.orig + 1]] <- station
res@data$station <- s
res@metadata$longitude <- c(res@metadata$longitude, station@metadata$longitude)
res@metadata$latitude <- c(res@metadata$latitude, station@metadata$latitude)
res@metadata$stationId <- c(res@metadata$stationId, station@metadata$station)
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
res
}
sectionAddCtd <- sectionAddStation
#' Plot a section Object
#'
#' Creates a summary plot for a CTD section, with one panel for each value of
#' `which`.
#'
#' The type of plot is governed by `which`, as follows.
#' * `which=0` or `"potential temperature"` for potential temperature contours
#' * `which=1` or `"temperature"` for in-situ temperature contours (the default)
#' * `which=2` or `"salinity"` for salinity contours
#' * `which=3` or `"sigmaTheta"` for sigma-theta contours
#' * `which=4` or `"nitrate"` for nitrate concentration contours
#' * `which=5` or `"nitrite"` for nitrite concentration contours
#' * `which=6` or `"oxygen"` for oxygen concentration contours
#' * `which=7` or `"phosphate"` for phosphate concentration contours
#' * `which=8` or `"silicate"` for silicate concentration contours
#' * `which=9` or `"u"` for eastward velocity
#' * `which=10` or `"uz"` for vertical derivative of eastward velocity
#' * `which=11` or `"v"` for northward velocity
#' * `which=12` or `"vz"` for vertical derivative of northward velocity
#' * `which=20` or `"data"` for a dot for each data location
#' * `which=99` or `"map"` for a location map
#'
#' The y-axis for the contours is pressure, plotted in the conventional reversed
#' form, so that the water surface appears at the top of the plot. The x-axis is
#' more complicated. If `at` is not supplied, then the routine calculates x
#' as the distance between the first station in the section and each of the other
#' stations. (This will produce an error if the stations are not ordered
#' geographically, because the [contour()] routine cannot handle
#' non-increasing axis coordinates.) If `at` is specified, then it is taken
#' to be the location, in arbitrary units, along the x-axis of labels specified by
#' `labels`; the way this works is designed to be the same as for
#' [axis()].
#'
#' @param x a [section-class] object.
#'
#' @param which a list of desired plot types, as explained in \dQuote{Details}.
#' There may be up to four panels in total, and the desired plots are placed in
#' these panels, in reading order. If only one panel is plotted, `par` is
#' not adjusted, which makes it easy to add to the plot with subsequent plotting
#' commands.
#'
#' @template eosTemplate
#'
#' @param at If `NULL` (the default), the x axis will indicate the distance
#' of the stations from the first in the section. (This may give errors in the
#' contouring routine, if the stations are not present in a geographical order.)
#' If a list, then it indicates the values at which stations will be plotted.
#'
#' @param labels Either a logical, indicating whether to put labels on the x axis,
#' or a vector that is a list of labels to be placed at the x positions indicated
#' by `at`.
#'
#' @param grid If `TRUE`, points are drawn at data locations.
#'
#' @param contourLevels Optional contour levels.
#'
#' @param contourLabels Optional contour labels.
#'
#' @param stationIndices Optional list of the indices of stations to use. Note
#' that an index is *not* a station number, e.g. to show the first 4
#' stations, use `station.indices=1:4`.
#'
#' @param coastline Either a [coastline-class] object to be used,
#' or a string. In the second case, the permitted
#' choices are `"best"` (the default) to pick
#' a variant that suits the scale, `"coastlineWorld"` for the coarse
#' version that is provided by \CRANpkg{oce},
#' `"coastlineWorldMedium"` or `"coastlineWorldFine"` for two
#' coastlines provided by the \CRANpkg{ocedata} package, or `"none"`, to avoid
#' drawing a coastline.
#'
#' @param colLand colour used to fill in land areas if `which` is `"map"`;
#' ignored otherwise.
#'
#' @param xlim Optional limit for x axis (only in sections, not map).
#'
#' @param ylim Optional limit for y axis (only in sections, not map)
#'
#' @param zlim,zbreaks,zcol Elements that control colours for `image` and `points`
#' plot types, i.e. if `ztype` is either `"points"` or `"image"`.
#' `zlim` is a two-element numerical vector specifying the
#' limit on the plotted field. If not provided, it defaults to the data
#' range.
#' `zbreaks` controls the colour breaks, in a manner that is similar to
#' the [image()] parameter named `breaks`. If not provided, `zbreaks` is
#' inferred from `zlim`.
#' `zcol`, which controls the colour scheme, may be a vector of colours
#' (of length 1 less than `zbreaks`), or a function that takes an
#' integer as its sole argument and returns that number of colours.
#' If not provided, `zcol` defaults to [oceColorsViridis()].
#' These three parameters are used in Example 6, an illustration of
#' Atlantic salinity along 36N.
#'
#' @param map.xlim,map.ylim Optional limits for station map; `map.ylim` is
#' ignored if `map.xlim` is provided.
#'
#' @param clongitude,clatitude,span Optional map centre position and span (km).
#'
#' @param projection Parameter specifying map
#' projection; see [mapPlot()]. If `projection="automatic"`,
#' however, a projection is devised from the data, with `stereographic` if
#' the mean latitude exceeds 70N and `mollweide` otherwise.
#'
#' @param xtype Type of x axis, for contour plots, either `"distance"` for
#' distance (in km) to the first point in the section, `"track"` for distance
#' along the cruise track, `"longitude"`, `"latitude"`,
#' `"time"` or `"spine"` (distance along a spine that was added
#' with [addSpine()]). Note that if the x values are not in order, they will be put in
#' order, and since that might not make physical sense, a warning will be issued.
#'
#' @param longitude0,latitude0 Location of the point from which distance is measured.
#' These values are ignored unless `xtype` is `"distance"`.
#'
#' @param ytype Type of y axis for contour plots, either `"pressure"` for
#' pressure (in dbar, with zero at the surface) or `"depth"` for depth (in m
#' below the surface, calculated from pressure with [swDepth()]).
#'
#' @param ztype String indicating whether to how to indicate the "z"
#' data (in the R sense, i.e. this could be salinity, temperature, etc; it does
#' not mean the vertical coordinate) The choices are: `"contour"` for
#' contours, `"image"` for an image (drawn with [imagep()] with
#' `filledContours=TRUE`), or `"points"` to draw points.
#' In the first two cases, the data must be gridded, with identical pressures at
#' each station.
#'
#' @param legend.loc Location of legend, as supplied to [legend()], or
#' set to the empty string to avoid plotting a legend.
#'
#' @param legend.text character value indicating the text for the legend.
#' If this is NULL (the default) then the legend is automatically
#' constructed by [labelWithUnit()], based on the value of `which`.
#'
#' @param showStations Logical indicating whether to draw station numbers on maps.
#'
#' @param showStart Logical indicating whether to indicate the first station with
#'
#' @param stationTicks A logical value indicating whether to indicate station
#' locations with ticks at the top margin of cross-section plots. Setting this
#' parameter to `FALSE` frees the user up to do their own labelling
#' at this spot.
#'
#' @param showBottom An indication of whether (and how) to indicate the ocean bottom.
#' If `FALSE`, then the bottom is not rendered. If `TRUE`, then it
#' is rendered with a gray polygon. If `showBottom` is a character string,
#' then there are three possibilities: is the string is `"polygon"` then
#' a polygon is drawn, if it is `"lines"` then a line is drawn, and if it
#' is `"points"` then points are drawn. If `showBottom` is
#' a [topo-class] object,
#' then the station locations are
#' interpolated to that topography and the results are shown with a polygon
#' (and, for this case, note that the section data and the topography must
#' use the same longitude convention, either 0 to 360 or -180 to 180.)
#' In this last case, the interpolation is set at a grid that is roughly
#' in accordance with the resolution of the latitudes in the `topo` object.
#' See \dQuote{Examples}.
#'
#' @param showBottom a value indicating whether (and how) to indicate the
#' ocean bottom on cross-section views. There are three possibilities.
#' (a) If `showBottom` is `FALSE`, then the bottom is not rendered. If it
#' is `TRUE`, then the bottom is rendered with a gray polygon.
#' (b) If `showBottom` is the character value `"polygon"`, then a polygon is drawn,
#' and similarly lines are drawn for `"lines"`, and points for `"points"`.
#' (c) If `showBottom` is a [topo-class] object, then the station locations are
#' interpolated to that topography and the results are shown with a polygon.
#' See \dQuote{Examples}.
#'
#' @param showSpine logical value used if `which="map"`. If `showSpine` is
#' `TRUE` and `section` has had a spine added with [addSpine()], then
#' the spine is drawn in blue.
#'
#' @param drawPalette logical value indicating whether to draw a palette when `ztype="image"`
#' ignored otherwise.
#'
#' @param axes Logical value indicating whether to draw axes.
#'
#' @param mgp A 3-element numerical vector to use for `par(mgp)`, and also for
#' `par(mar)`, computed from this. If not provided, this defaults to
#' [`getOption`]`("oceMgp")`.
#'
#' @param mar Value to be used with [`par`]`("mar")`. If not provided,
#' a default is set up.
#'
#' @param col Color for line types. If not provided, this defaults to
#' [`par`]`("col")`. See `zcol`, for `ztype="image"` and `ztype="points"`.
#'
#' @param cex Numerical character-expansion factor, which defaults to [`par`]`("cex")`.
#'
#' @param pch Indication of symbol type; defaults to [`par`]`("pch")` for
#' non-map or to 3 for map.
#'
#' @param lwd line width; defaults to [`par`]`("lwd")`.
#'
#' @param labcex Size of characters in contour labels (passed to
#' [contour()]).
#'
#' @template debugShortTemplate
#'
#' @param ... Optional arguments passed to the contouring function.
#'
#' @return If the original section was gridded, the return value is that section.
#' Otherwise, the gridded section that was constructed for the plot is returned.
#' In both cases, the value is returned silently. The
#' purpose of returning the section is to enable subsequent processing
#' of the grid, including adding elements to the plot (see example 5).
#'
#' @seealso The documentation for [section-class] explains the
#' structure of section objects, and also outlines the other functions dealing
#' with them.
#'
#' @section Ancillary Examples:
#'
#' The following examples were once part of the \dQuote{Examples}
#' section, but were moved here in May 2022, to reduce the build-check
#' time for CRAN submission.
#'```
#' library(oce)
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 2000, 100))
#'
#' # Gulf Stream, salinity data and contoured
#' par(mfrow=c(2, 1))
#' plot(GS, which=1, ylim=c(2000, 0), ztype="points",
#' zbreaks=seq(0,30,2), pch=20, cex=3)
#' plot(GSg, which=1, ztype="image", zbreaks=seq(0,30,2))
#'
#' # Gulf Stream, temperature grid (image) and data (dots)
#' par(mfrow=c(1, 1))
#' plot(GSg, which=1, ztype="image")
#' T <- GS[["temperature"]]
#' col <- oceColorsViridis(100)[rescale(T, rlow=1, rhigh=100)]
#' points(GS[["distance"]],GS[["depth"]],pch=20,cex=3,col="white")
#' points(GS[["distance"]],GS[["depth"]],pch=20,cex=2.5,col=col)
#'
#' # 4. Image of temperature, with a high-salinity contour on top;
#' # note the Mediterranean water.
#' sec <- plot(section, which="temperature", ztype="image")
#' S <- sec[["salinity", "grid:distance-pressure"]]
#' contour(S$distance, S$pressure, S$field, level=35.8, lwd=3, add=TRUE)
#'
#' # 5. Contours of salinity, with dots for high pressure and spice
#' plot(section, which="salinity")
#' distance <- section[["distance"]]
#' depth <- section[["depth"]]
#' spice <- section[["spice"]]
#' look <- spice > 1.8 & depth > 500
#' points(distance[look], depth[look], col="red")
#'
#' # Image of Absolute Salinity, with 4-minute bathymetry
#' # It's easy to calculate the desired area for the bathymetry,
#' # but for brevity we'll hard-code it. Note that download.topo()
#' # requires the "ncdf4" package to have been installed.
#' if (requireNamespace("ncdf4")) {
#' f <- download.topo(west=-80, east=0, south=35, north=40, resolution=4)
#' t <- read.topo(f)
#' plot(section, which="SA", xtype="longitude", ztype="image", showBottom=t)
#' }
#'
#' # Temperature with salinity added in red
#' plot(GSg, which="temperature")
#' distance <- GSg[["distance", "byStation"]]
#' depth <- GSg[["station", 1]][["depth"]]
#' S <- matrix(GSg[["salinity"]], byrow=TRUE, nrow=length(GSg[["station"]]))
#' contour(distance, depth, S, col=2, add=TRUE)
#'
#' # Image with controlled colours
#' plot(GSg, which="salinity", ztype="image",
#' zlim=c(35, 37.5),
#' zbreaks=seq(35, 37.5, 0.25),
#' zcol=oceColorsTurbo)
#'```
#'
#' @examples
#' library(oce)
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 2000, 100))
#'
#' # Gulf Stream, salinity and temperature contours
#' plot(GSg, which=c("salinity", "temperature"))
#'
#' # Gulf Stream, Temperature image
#' plot(GSg, which="temperature", ztype="image",
#' zbreaks=seq(0,25,2), zcol=oceColorsTemperature)
#'
#' @author Dan Kelley, with help from Clark Richards and Chantelle Layton.
#'
#' @family functions that plot oce data
#' @family things related to section data
#'
#' @aliases plot.section
setMethod(f="plot", signature=signature("section"),
definition=function(x,
which=c(1, 2, 3, 99), eos,
at=NULL, labels=TRUE, grid=FALSE,
contourLevels=NULL, contourLabels=NULL,
stationIndices, coastline="best", colLand="gray",
xlim=NULL, ylim=NULL, zlim=NULL, zbreaks=NULL, zcol=NULL,
map.xlim=NULL, map.ylim=NULL, clongitude, clatitude, span, projection=NULL,
xtype="distance", ytype="depth", ztype="contour", longitude0, latitude0,
legend.loc="bottomright", legend.text=NULL,
showStations=FALSE, showStart=TRUE, stationTicks=TRUE, showBottom=TRUE,
showSpine=TRUE, drawPalette=TRUE,
axes=TRUE, mgp, mar, col, cex, pch, lwd, labcex=par("cex"),
debug=getOption("oceDebug"),
...)
{
debug <- if (debug > 4) 4 else floor(0.5 + debug)
if (missing(eos)) eos <- getOption("oceEOS", default="gsw")
zlimOrig <- zlim
xtype <- match.arg(xtype,
c("distance", "track", "longitude", "latitude", "time", "spine"))
ytype <- match.arg(ytype,
c("depth", "pressure"))
ztype <- match.arg(ztype,
c("contour", "image", "points"))
# Ensure that ylim makes sense
if (!is.null(ylim)) {
if (length(ylim) != 2L)
stop("In plot,section-method() : length(ylim) must be 2, but it is ", length(ylim))
if (any(ylim < 0.0))
stop("In plot,section-method() : ylim elements cannot be negative, but ylim=c(", ylim[1], ",", ylim[2], ") was provided")
if (ylim[1] <= ylim[2])
stop("In plot,section-method() require ylim[2]<ylim[1], but ylim=c(", ylim[1], ",", ylim[2], ") was provided")
}
# nolint start object_usage_linter
drawPoints <- ztype == "points"
# nolint end object_usage_linter
if (!inherits(coastline, "coastline")) {
coastline <- match.arg(coastline,
c("best", "coastlineWorld", "coastlineWorldMedium", "coastlineWorldFine", "none"))
}
if (missing(mgp))
mgp <- getOption("oceMgp")
if (missing(mar))
mar <- c(mgp[1]+1, mgp[1]+1.5, mgp[2]+1, mgp[2]+0.5)
if (missing(col))
col <- par("col")
if (missing(cex))
cex <- par("cex")
if (missing(pch))
pch <- if (length(which) == 1L && (which == "map" || which == 99)) 3 else par("pch")
if (missing(lwd))
lwd <- par("lwd")
# L and R are used much later, for constructing labels
# nolint start object_usage_linter
L <- if (getOption("oceUnitBracket") == "[") " [" else " ("
R <- if (getOption("oceUnitBracket") == "[") "]" else ")"
# nolint end object_usage_linter
# Make 'which' be character, to simplify following code
#oceDebug(debug, "which=c(", paste(which, collapse=","), ")\n")
lw <- length(which)
legend.text <- rep(legend.text, lw)
if (is.numeric(which)) {
which[which==0] <- "potential temperature"
which[which==1] <- "temperature"
which[which==2] <- "salinity"
which[which==3] <- "sigmaTheta"
which[which==4] <- "nitrate"
which[which==5] <- "nitrite"
which[which==6] <- "oxygen"
which[which==7] <- "phosphate"
which[which==8] <- "silicate"
which[which==9] <- "u"
which[which==10] <- "uz"
which[which==11] <- "v"
which[which==12] <- "vz"
which[which==20] <- "data"
which[which==99] <- "map"
}
if (any(is.na(which)))
stop("cannot have NA values in which, but it is ", paste(which, collapse=","))
#oceDebug(debug, "which=c(", paste(which, collapse=","), ")\n")
oceDebug(debug, "plot.section(, ..., which=c(",
paste(which, collapse=","), "), eos=\"", eos, "\"",
", xtype=", xtype,
", ztype=", ztype,
", xlim=c(", paste(xlim, collapse=","), ")",
", ylim=c(", paste(ylim, collapse=","), ")",
", \", ...) {\n", sep="", unindent=1)
# Contour and image plots, e.g. for which="temperature" and so forth, require
# gridded data.
#2092 message("DAN ", vectorShow(ztype))
#2092 message("DAN ", vectorShow(which))
if (which[1] != "data" && which[1] != "map") {
#2092 message("DAN will it grid???")
p1 <- x[["station", 1]][["pressure"]]
numStations <- length(x@data$station)
for (ix in 2:numStations) {
thisStation <- x@data$station[[ix]]
thisPressure <- thisStation[["pressure"]]
if ("points" != ztype && !identical(p1, thisPressure)) {
#2092 message("DAN gridding")
oceDebug(debug, "gridding section because pressures at station ", ix, " differ from those at station 1\n")
x <- sectionGrid(x, debug=debug-1)
break
}
}
}
res <- x # will now be gridded (either originally or through above code)
# Trim stations that have zero good data FIXME: brittle to addition of new metadata
haveData <- unlist(lapply(x@data$station, function(stn) 0 < length(stn[["pressure"]])))
x@data$station <- x@data$station[haveData]
x@metadata$stationId <- x@metadata$stationId[haveData]
x@metadata$latitude <- x@metadata$latitude[haveData]
x@metadata$longitude <- x@metadata$longitude[haveData]
x@metadata$time <- x@metadata$time[haveData]
if (xtype != "time") {
oceDebug(debug, "xtype=\"", xtype, "\" is not \"time\", so checking whether longitude and latitude vary\n")
if (!all(is.finite(x@metadata$longitude)))
stop("In plot(): with xtype=\"", xtype, "\", all stations must have finite longitude", call.=FALSE)
if (!all(is.finite(x@metadata$latitude)))
stop("In plot(): with xtype=\"", xtype, "\", all stations must have finite latitude", call.=FALSE)
if (0.0 == diff(range(x@metadata$longitude)) && 0.0 == diff(range(x@metadata$latitude)))
stop("In plot(): with xtype=\"", xtype, "\", either longitude or latitude must vary", call.=FALSE)
if (0.0 == diff(range(x@metadata$longitude)) && xtype == "longitude")
stop("In plot(): with xtype=\"", xtype, "\", longitude must vary", call.=FALSE)
if (0.0 == diff(range(x@metadata$latitude)) && xtype == "latitude")
stop("In plot(): with xtype=\"", xtype, "\", latitude must vary", call.=FALSE)
}
plotSubsection <- function(xx, yy, zz,
which.xtype, which.ytype,
variable="temperature", vtitle="T", unit=NULL,
eos=getOption("oceEOS", default="gsw"),
indicate.stations=TRUE,
contourLevels=NULL, contourLabels=NULL,
showStations=FALSE,
xlim=NULL, ylim=NULL,
clongitude, clatitude, span,
projection=NULL,
zbreaks=NULL, zcol=NULL,
ztype=c("contour", "image", "points"),
legend=TRUE,
axes=TRUE,
col=par("col"), cex=par("cex"),
pch=if (as.character(variable) == "map") 3 else par("pch"), lwd=par("lwd"),
debug=0,
...)
{
oceDebug(debug, "plotSubsection(variable=\"", variable, "\"",
", eos=\"", eos, "\"",
", which.xtype=\"", which.xtype, "\"",
", xlim=c(", paste(xlim, collapse=","), ")",
", ylim=c(", paste(ylim, collapse=","), ")",
", col=", if (missing(col)) "(missing)" else col,
", cex=", if (missing(cex)) "(missing)" else cex,
", pch=", if (missing(pch)) "(missing)" else pch,
", ztype=\"", ztype,
", zcol=", if (missing(zcol)) "(missing)" else "(provided)",
", span=", if (missing(span)) "(missing)" else span,
", showStations=", showStations,
", axes=", axes, ", ...) {\n", sep="", unindent=1)
ztype <- match.arg(ztype)
drawPoints <- "points" == ztype
omar <- par("mar")
xIsTime <- inherits(xx, "POSIXt")
isMap <- as.character(variable) == "map"
if (isMap) {
oceDebug(debug, "is a map\n")
lat <- array(NA_real_, numStations)
lon <- array(NA_real_, numStations)
for (i in 1:numStations) {
thisStation <- x[["station", stationIndices[i]]]
lon[i] <- mean(thisStation[["longitude"]], na.rm=TRUE)
lat[i] <- mean(thisStation[["latitude"]], na.rm=TRUE)
}
# lon[lon<0] <- lon[lon<0] + 360
asp <- 1 / cos(mean(range(lat, na.rm=TRUE))*pi/180)
latm <- mean(lat, na.rm=TRUE)
lonm <- mean(lon, na.rm=TRUE)
if (missing(span)) {
lonr <- lonm + sqrt(2) * (range(lon, na.rm=TRUE) - mean(lon, na.rm=TRUE)) # expand range
latr <- latm + sqrt(2) * (range(lat, na.rm=TRUE) - mean(lat, na.rm=TRUE))
} else {
lonr <- lonm + span / 111.1 * c(-0.5, 0.5) / cos(pi/180*latm) / sqrt(2)
latr <- latm + span / 111.1 * c(-0.5, 0.5) / sqrt(2)
}
# FIXME: this coastline code is reproduced in section.R; it should be DRY
haveCoastline <- FALSE
if (inherits(coastline, "coastline")) {
haveCoastline <- TRUE
oceDebug(debug, "using coastline object given as an argument\n")
} else {
if (!is.character(coastline))
stop("coastline must be a character string")
haveOcedata <- requireNamespace("ocedata", quietly=TRUE)
if (coastline == "best") {
if (haveOcedata) {
bestcoastline <- coastlineBest(lonRange=lonr, latRange=latr)
oceDebug(debug, "'best' coastline is: \"", bestcoastline, '\"\n', sep="")
if (bestcoastline == "coastlineWorld") {
data(list=bestcoastline, package="oce", envir=environment())
} else {
data(list=bestcoastline, package="ocedata", envir=environment())
}
coastline <- get(bestcoastline)
} else {
oceDebug(debug, "using \"coastlineWorld\" because ocedata package not installed\n")
data("coastlineWorld", package="oce", envir=environment())
coastline <- get("coastlineWorld")
}
haveCoastline <- TRUE
} else {
if (coastline != "none") {
if (coastline == "coastlineWorld") {
data("coastlineWorld", package="oce", envir=environment())
coastline <- get("coastlineWorld")
} else if (haveOcedata && coastline == "coastlineWorldFine") {
data("coastlineWorldFine", package="ocedata", envir=environment())
coastline <- get("coastlineWorldFine")
} else if (haveOcedata && coastline == "coastlineWorldMedium") {
data("coastlineWorldMedium", package="ocedata", envir=environment())
coastline <- get("coastlineWorldMedium")
} else {
stop("there is no built-in coastline file of name \"", coastline, "\"")
}
haveCoastline <- TRUE
}
}
}
# FIXME: I think both should have missing() means auto-pick and NULL means none
if (!is.null(projection)) {
stnlats <- x[["latitude", "byStation"]]
stnlons <- x[["longitude", "byStation"]]
if (is.null(map.xlim)) map.xlim <- range(stnlons)
if (is.null(map.ylim)) map.ylim <- range(stnlats)
id <- pmatch(projection, "automatic")
if (!is.na(id)) {
meanlat <- mean(stnlats, na.rm=TRUE)
#meanlon <- mean(stnlons, na.rm=TRUE)
# NOTE: mercator messes up filling for data(section) but mollweide is okay
projection <- if (meanlat > 70) "stereographic" else "mollweide"
#orientation <- c(90, meanlon, 0)
oceDebug(debug, "using", projection, "projection (chosen automatically)\n")
} else {
oceDebug(debug, "using", projection, "projection (specified)\n")
}
mapPlot(coastline, longitudelim=map.xlim, latitudelim=map.ylim, projection=projection, col=colLand)
spine <- x[["spine"]]
if (showSpine && !is.null(spine)) {
mapLines(spine$longitude, spine$latitude, col=4, lwd=1.4*par("lwd"))
}
mapPoints(x[["longitude", "byStation"]], x[["latitude", "byStation"]],
cex=cex, col=col, pch=pch, lwd=lwd)
if (xtype == "distance" && showStart) {
mapPoints(lon[1], lat[1], col=col, pch=22, cex=3*par("cex"), lwd=1/2)
}
return() # early return
} else {
# draw without map projection
if (!is.null(map.xlim)) {
map.xlim <- sort(map.xlim)
plot(lonr, latr, xlim=map.xlim, asp=asp, type="n",
xlab=gettext("Longitude", domain="R-oce"),
ylab=gettext("Latitude", domain="R-oce"))
} else if (!is.null(map.ylim)) {
map.ylim <- sort(map.ylim)
plot(lonr, latr, ylim=map.ylim, asp=asp, type="n",
xlab=gettext("Longitude", domain="R-oce"),
ylab=gettext("Latitude", domain="R-oce"))
} else {
plot(lonr, latr, asp=asp, type="n",
xlab=gettext("Longitude", domain="R-oce"),
ylab=gettext("Latitude", domain="R-oce"))
}
}
if (haveCoastline) {
if (!is.null(coastline@metadata$fillable) && coastline@metadata$fillable) {
polygon(coastline[["longitude"]], coastline[["latitude"]], col=colLand, lwd=3/4)
polygon(coastline[["longitude"]]+360, coastline[["latitude"]], col=colLand, lwd=3/4)
# redraw box, if we have axes. This is necessary because polygon will color
# over the axis box, if land goes past the edge of the view
if (axes) {
box()
}
} else {
lines(coastline[["longitude"]], coastline[["latitude"]], col="darkgray")
lines(coastline[["longitude"]]+360, coastline[["latitude"]], col="darkgray")
}
}
spine <- x[["spine"]]
if (showSpine && !is.null(spine))
lines(spine$longitude, spine$latitude, col="blue", lwd=1.4*par("lwd"))
# draw station trace (or skip it, if white was requested)
if (col[1] != "white")
lines(lon, lat, col="lightgray")
# replot with shifted longitude
points(lon, lat, col=col, pch=pch, cex=cex, lwd=lwd)
points(lon - 360, lat, col=col, pch=pch, cex=cex, lwd=lwd)
if (showStations) {
stationId <- x[["station ID"]]
text(lon, lat, stationId, pos=2, cex=cex)
text(lon-360, lat, stationId, pos=2, cex=cex)
}
if (xtype == "distance" && showStart) {
points(lon[1], lat[1], col=col, pch=22, cex=3*par("cex"), lwd=lwd)
points(lon[1] - 360, col=col, lat[1], pch=22, cex=3*par("cex"), lwd=lwd)
}
if (indicate.stations) {
dy <- 5.0*mean(diff(sort(x@metadata$latitude)), na.rm=TRUE)
dx <- 5.0*mean(diff(sort(x@metadata$longitude)), na.rm=TRUE)
ylab <- x@metadata$latitude[1] - dy*sign(x@metadata$latitude[2] - x@metadata$latitude[1])
xlab <- x@metadata$longitude[1] - dx*sign(x@metadata$longitude[2] - x@metadata$longitude[1])
xlab <- x@metadata$longitude[numStations] - dx * sign(x@metadata$longitude[numStations-1] - x@metadata$longitude[numStations])
ylab <- x@metadata$latitude[numStations] - dy * sign(x@metadata$latitude[numStations-1] - x@metadata$latitude[numStations])
}
} else {
# not isMap
oceDebug(debug, "not a map\n")
z <- x[[variable]]
zAllMissing <- all(is.na(z))
# Use try() to quiet warnings if all data are NA
zRANGE <- try(range(z, na.rm=TRUE), silent=TRUE)
if ((drawPoints || ztype == "image") && !zAllMissing) {
if (is.null(zbreaks)) {
oceDebug(debug, "zbreaks was not given; inferring from data\n")
if (is.null(zlim)) {
zbreaks <- if (is.null(zcol) || is.function(zcol)) {
seq(zRANGE[1], zRANGE[2], length.out=200)
} else {
seq(zRANGE[1], zRANGE[2], length.out=length(zcol) + 1)
}
oceDebug(debug, "zlim was not given; ", vectorShow(zbreaks))
} else {
zbreaks <- seq(zlim[1], zlim[2], length.out=200)
oceDebug(debug, "zlim was given; ", vectorShow(zbreaks))
}
} else {
oceDebug(debug, "zbreaks was given\n")
}
nbreaks <- length(zbreaks)
if (nbreaks > 0) {
if (is.null(zcol))
zcol <- oceColorsViridis(nbreaks - 1)
if (is.function(zcol))
zcol <- zcol(nbreaks - 1)
if (is.null(zlim))
zlim <- range(zbreaks, na.rm=TRUE) # FIXME: is this used/ok?
if (drawPalette) {
# draw triangles if data extend past either end of the palette
drawTriangles <- c(zRANGE[1] < zlim[1], zRANGE[2] > zlim[2])
drawPalette(zlim=zlim, breaks=zbreaks, col=zcol,
drawTriangles=drawTriangles)
}
}
}
# FIXME: contours don't get to plot edges
xxrange <- range(xx, na.rm=TRUE)
yyrange <- range(yy, na.rm=TRUE)
if (is.null(ylim))
ylim <- yyrange
par(xaxs="i", yaxs="i")
ylab <- if ("ylab" %in% names(list(...))) {
list(...)$ylab
} else {
if (which.ytype==1) {
resizableLabel("p")
} else {
resizableLabel("depth")
}
}
if (is.null(at)) {
if ("xlab" %in% names(list(...))) {
xlab <- list(...)$xlab
} else {
xlab <- switch(which.xtype,
resizableLabel("distance km"),
resizableLabel("along-track distance km"),
gettext("Longitude", domain="R-oce"),
gettext("Latitude", domain="R-oce"),
gettext("Time", domain="R-oce"),
resizableLabel("along-spine distance km"))
}
oceDebug(debug, vectorShow(yyrange))
if (!isMap) {
yyrange <- rev(yyrange)
oceDebug(debug, "reversed yyrange to be ", vectorShow(yyrange))
ylim <- rev(sort(ylim))
oceDebug(debug, "reversed ylim to be ", vectorShow(ylim))
}
plot(xxrange, yyrange, xaxs="i", yaxs="i", xlim=xlim, ylim=ylim,
xlab=xlab, ylab=ylab, col="white", axes=FALSE)
if (axes) {
oceDebug(debug, "drawing axes (before contouring etc)\n")
axis(4L, labels=FALSE)
ytics <- axis(2L, labels=FALSE)
axis(2L, at=ytics, labels=ytics)
# If constructing labels for time, need to check xlim
if (xIsTime) {
if (!is.null(xlim)) {
# FIXME: might need to check whether/how xx used later
XX <- xx[xlim[1] <= xx & xx <= xlim[2]]
axis(1L, at=pretty(XX), labels=pretty(XX))
} else {
axis(1L, at=pretty(xx), labels=pretty(xx))
}
} else {
axis(1L)
}
}
box()
} else {
plot(xxrange, yyrange,
xaxs="i", yaxs="i",
xlim=xlim, ylim=ylim,
col="white",
xlab="", ylab=ylab, axes=FALSE)
if (axes) {
axis(1, at=at, labels=labels)
axis(2)
axis(4, labels=FALSE)
}
box()
}
# Bottom trace
usr <- par("usr")
graph.bottom <- usr[3]
waterDepth <- NULL
# For ztype == "points", plot the points. Otherwise, collect them in zz
# for the contour or image plot.
for (i in seq_len(numStations)) {
#2092 message("DAN 1758 i=", i)
thisStation <- x[["station", i]]
p <- thisStation[["pressure"]] # assume that we always have pressure
np <- length(p)
if (variable != "data") {
# CAUTION: the assignment to 'v' and 'zz' is tricky:
# 1. not all datasets will have computed items (e.g. density and potential
# temperature) so we compute them, discarding any data. (Is that sensible?)
# 2. things are different in gsw and unesco, and someone might say
# "potential temperature" in either system, so which do we compute??
# 3. there was some code reworking in early May 2019, relating to issues:
# https://github.com/dankelley/oce/issues/1539
# and
# https://github.com/dankelley/oce/issues/1540
# and there is a chance of breakage starting at that time.
v <- thisStation[[variable]]
if (is.null(v))
v <- rep(NA, length(p))
if (drawPoints) {
p <- thisStation[["pressure"]]
points(rep(xx[i], np), -p, pch=pch, cex=cex,
col=zcol[rescale(v, xlow=zlim[1], xhigh=zlim[2], rlow=1, rhigh=nbreaks)])
} else {
#2092 message(vectorShow(dim(zz)))
#2092 message(vectorShow(v))
zz[i, ] <- rev(v)
}
}
if (grid && !drawPoints)
points(rep(xx[i], length(yy)), yy, col="gray", pch=20, cex=1/3)
#temp <- x@data$station[[stationIndices[i]]]@data$temperature
#len <- length(temp)
wd <- if ("waterDepth" %in% names(x@data$station[[stationIndices[i]]]@metadata)
&& is.finite(x@data$station[[stationIndices[i]]]@metadata$waterDepth))
x@data$station[[stationIndices[i]]]@metadata$waterDepth else NA
#in.land <- which(is.na(x@data$station[[stationIndices[i]]]@data$temperature[-3])) # skip first 3 points
waterDepth <- c(waterDepth, wd)
#2092 message(sprintf("DAN i=%d x=%.2f depth=%.0f", i, xx[i], wd))
}
if (!grid && axes && stationTicks)
Axis(side=3, at=xx, labels=FALSE, tcl=-1/3, lwd=0.5) # station locations
#2092 message("DAN next is xx. Is it in order of station or axis?")
bottom.x <- c(xx[1], xx, xx[length(xx)])
bottom.y <- if (any(is.finite(waterDepth)))
c(graph.bottom, waterDepth, graph.bottom)
else
rep(NA, length(bottom.x)+2)
# Put x in order, if it's not already
xx[!is.finite(xx)] <- NA # for issue 1583: grid larger than data range can get NaN values
#<20230601> ox <- order(xx)
ox <- seq_along(xx) #<20230601>
xxOrig <- xx
ii <- seq_along(xxOrig) # so we can use it later for drawing bottoms
#2092 message("DAN 1")
#2092 print(head(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 print(tail(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 lines(bottom.x, bottom.y, col=2, lwd=2)
if (any(xx[ox] != xx, na.rm=TRUE)) { # for issue 1583: handle the NA just inserted
#2092 message("DAN BARK")
xx <- xx[ox]
zz <- zz[ox, ] # FIXME keep this???
#<20230601> ii <- ii[ox]
bottom.x <- c(min(xxOrig), xxOrig[ox], max(xxOrig))
bottom.y <- c(graph.bottom, waterDepth[ox], graph.bottom)
}
#2092 message("DAN 2")
#2092 print(head(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 print(tail(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 lines(bottom.x, bottom.y, col=3, lwd=2, lty=2)
# Construct xm, ym and zm that can be contoured. That requires
# unique and increasing x and y values.
# cannot contour with duplicates in x or y; the former is the only problem
xx.unique <- c(TRUE, 0 != diff(xx))
yy.unique <- c(TRUE, 0 != diff(yy))
xx.unique <- xx.unique & !is.na(xx.unique)
yy.unique <- yy.unique & !is.na(yy.unique)
# contour() requires x and y to be increasing, and pressure/depth are not.
if (diff(yy[yy.unique][1:2]) < 0.0) {
oceDebug(debug, "flipping matrix for contouring\n")
# must flip in y
xm <- xx[xx.unique]
ym <- yy[yy.unique]
zm <- zz[xx.unique, yy.unique]
j <- rev(seq_along(ym))
ym <- ym[j]
zm <- zm[, j]
} else {
oceDebug(debug, "no need to flip matrix for contouring\n")
xm <- xx[xx.unique]
ym <- yy[yy.unique]
zm <- zz[xx.unique, yy.unique]
}
# a problem with mbari data revealed that we need to chop NA valaues too
if (variable == "data") {
#2092 message(vectorShow(which.xtype))
for (i in 1:numStations) {
thisStation <- x[["station", i]]
pressure <- thisStation[["pressure"]]
if (which.xtype == 3000) {
longitude <- mean(thisStation[["longitude"]], na.rm=TRUE)
points(rep(longitude, length(pressure)), pressure, cex=cex, pch=pch, col=col)
} else {
# FIXME: shouldn't the next line work for all types??
points(rep(xx[i], length(pressure)), pressure, cex=cex, pch=pch, col=col)
}
}
} else if (!drawPoints) {
# Use try() to quiet warnings if all data are NA
if (zAllMissing) {
if (nchar(legend.loc))
legend(legend.loc, legend=vtitle, bg="white", x.intersp=0, y.intersp=0.5, cex=1)
return()
}
# nolint start object_usage_linter
zrange <- try(range(zz[xx.unique, yy.unique], na.rm=TRUE), silent=TRUE)
# nolint end object_usage_linter
if (!is.null(contourLevels) && !is.null(contourLabels)) {
oceDebug(debug, "user-supplied contourLevels: ", contourLevels, "\n")
if (ztype == "contour") {
oceDebug(debug, "about to call contour() near L1885\n")
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
contour(x=xm[oxm], y=ym[oym], z=zm[oxm, oym],
ylim=if (is.null(ylim)) rev(range(ym)) else ylim,
axes=FALSE, add=TRUE, levels=contourLevels, labels=contourLabels,
col=col, xaxs="i", yaxs="i", labcex=labcex, ...)
} else if (ztype == "image") {
oceDebug(debug, "about to .filled.contour (LOC 1)\n")
# issue 2083 (https://github.com/dankelley/oce/issues/2083)
# FIXME: ensure that similare is done at location 1 also
nbreaks <- length(zbreaks)
if (is.function(zcol))
zcol <- zcol(nbreaks + 1)
zmRange <- range(zm, na.rm=TRUE)
zbreaksNEW <- zbreaks
zcolNEW <- zcol
if (zbreaks[1] > zmRange[1]) {
zbreaksNEW <- c(zmRange[1], zbreaks)
zcolNEW <- c(zcol[1], zcol)
}
if (zbreaks[nbreaks] < zmRange[2]) {
zbreaksNEW <- c(zbreaksNEW, zmRange[2])
zcolNEW <- c(zcolNEW, tail(zcolNEW, 1))
}
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
.filled.contour(x=xm[oxm], y=ym[oym], z=zm[oxm, oym], levels=zbreaksNEW, col=zcolNEW)
} else {
stop("unknown ztype: \"", ztype, "\" [2]")
}
} else {
oceDebug(debug, "automatically-calculated contourLevels\n")
zrange <- range(zz[xx.unique, yy.unique], na.rm=TRUE)
if (ztype == "contour") {
zzrange <- range(zz[xx.unique, yy.unique], na.rm=TRUE)
if (any(!is.finite(zzrange)))
stop("cannot draw a contour diagram because all values are NA or Inf")
oceDebug(debug, "about to call contour() near L1908\n")
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
contour(x=xm[oxm], y=ym[oym], z=zm[oxm, oym], add=TRUE, col=col, labcex=labcex, ...)
} else if (ztype == "image") {
oceDebug(debug, "about to .filled.contour (LOC 2)\n")
# issue 2083 (https://github.com/dankelley/oce/issues/2083)
# FIXME: ensure that similare is done at location 1 also
nbreaks <- length(zbreaks)
if (is.function(zcol))
zcol <- zcol(nbreaks + 1)
zmRange <- range(zm, na.rm=TRUE)
zbreaksNEW <- zbreaks
zcolNEW <- zcol
if (zbreaks[1] > zmRange[1]) {
zbreaksNEW <- c(zmRange[1], zbreaks)
zcolNEW <- c(zcol[1], zcol)
}
if (zbreaks[nbreaks] < zmRange[2]) {
zbreaksNEW <- c(zbreaksNEW, zmRange[2])
zcolNEW <- c(zcolNEW, tail(zcolNEW, 1))
}
#2092 print(zbreaksNEW)
#2092 str(xm)
#2092 str(ym)
#2092 str(zm)
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
#2092 message("DAN about to filled.contour")
.filled.contour(x=xm[oxm], y=ym[oym], z=zm[oxm,oym], levels=zbreaksNEW, col=zcolNEW)
} else if (ztype == "points") {
# nothing to do now
} else {
stop("unknown ztype: \"", ztype, "\" [3]")
}
}
}
if (is.character(showBottom) || (is.logical(showBottom) && showBottom)) {
type <- "polygon"
if (is.character(showBottom)) {
type <- showBottom
}
#2092 message("DAN about to plot")
#2092 print(head(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 print(tail(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 message("DAN KLUDGE!! reversing bottom.x")
#2092 message("DAN ORIG ", vectorShow(head(bottom.x)))
#2092 #???bottom.x<-rev(bottom.x) # KLUDGE
#2092 message("DAN LATER ", vectorShow(head(bottom.x)))
#2092 message("DAN ", vectorShow(xtype))
if (length(bottom.x) == length(bottom.y)) {
bottom <- par("usr")[3]
if (type == "polygon") {
polygon(bottom.x, bottom.y, col=gray(0.25, alpha=0.2))
} else if (type == "lines") {
for (s in seq_along(bottom.x))
lines(rep(bottom.x[s], 2), c(bottom.y[s], bottom), col="lightgray")
} else if (type == "points") {
for (s in seq_along(bottom.x))
points(rep(bottom.x[s], 2), c(bottom.y[s], bottom), col="lightgray")
}
}
box()
} else if (inherits(showBottom, "topo")) {
oceDebug(debug, "using a topo object for the bottom\n")
# Fine longitude and latitude: roughly
topoResolution <- geodDist(0, 0, 0, diff(showBottom[["latitude"]][1:2]))
#cat(vectorShow(topoResolution))
slon <- x[["longitude", "byStation"]]
slat <- x[["latitude", "byStation"]]
sectionSpan <- geodDist(min(slon, na.rm=TRUE), min(slat, na.rm=TRUE),
max(slon, na.rm=TRUE), max(slat, na.rm=TRUE))
#cat(vectorShow(sectionSpan))
nin <- length(slon)
# double up on resolution, although perhaps not needed
nout <- as.integer(1 + 2*sectionSpan / topoResolution)
#cat(vectorShow(nout))
blon <- approx(seq_len(nin), slon[ii], n=nout)$y
#cat(vectorShow(blon))
blat <- approx(seq_len(nin), slat[ii], n=nout)$y
#cat(vectorShow(blat))
by <- topoInterpolate(blon, blat, showBottom)
bx <- approx(seq_len(nin), xx, n=nout)$y
bx <- c(bx[1], bx, tail(bx, 1))
usr3 <- par("usr")[3]
#cat(vectorShow(usr))
by <- c(usr3, by, usr3)
polygon(bx, by, col="lightgray")
}
if (axes) {
if (xIsTime) {
if (!is.null(xlim)) {
# FIXME: might need to check whether/how xx used later
XX <- xx[xlim[1] <= xx & xx <= xlim[2]]
axis(1, at=pretty(XX), labels=pretty(XX))
} else {
axis(1, at=pretty(xx), labels=pretty(xx))
}
}
}
if (nchar(legend.loc)) {
legend(legend.loc, legend=vtitle, bg="white", x.intersp=0, y.intersp=0.5, cex=1)
}
usr <- par("usr")
#par("usr"=c(usr[1], usr[2], -usr[3], usr[4]))
}
par(mar=omar)
oceDebug(debug, "} # plotSubsection()\n", unindent=1)
}
opar <- par(no.readonly=TRUE)
if (length(which) > 1)
on.exit(par(opar))
which.xtype <- match(xtype, c("distance", "track", "longitude", "latitude", "time", "spine"), nomatch=0)
if (0 == which.xtype)
stop('xtype must be one of: "distance", "track", "longitude", "latitude", "time", or "spine", not "', xtype, '" as provided')
which.ytype <- pmatch(ytype, c("pressure", "depth"), nomatch=0)
if (missing(stationIndices)) {
numStations <- length(x@data$station)
stationIndices <- 1:numStations
} else {
numStations <- length(stationIndices)
}
if (numStations < 2)
stop("In plot() :\n cannot plot a section containing fewer than 2 stations", call.=FALSE)
firstStation <- x@data$station[[stationIndices[1]]]
num.depths <- length(firstStation@data$pressure)
zz <- array(NA_real_, dim=c(numStations, num.depths))
xx <- rep(NA, numStations)
yy <- rep(NA, num.depths)
if (is.null(at)) {
lon0 <- if (missing(longitude0)) mean(firstStation[["longitude"]], na.rm=TRUE) else longitude0
lat0 <- if (missing(latitude0)) mean(firstStation[["latitude"]], na.rm=TRUE) else latitude0
for (ix in 1:numStations) {
j <- stationIndices[ix]
if (which.xtype == 1) { # distance from first station
xx[ix] <- geodDist(lon0, lat0,
mean(x@data$station[[j]][["longitude"]], na.rm=TRUE),
mean(x@data$station[[j]][["latitude"]], na.rm=TRUE))
} else if (which.xtype == 2) { # distance along the cruise track
if (ix == 1) {
xx[ix] <- 0
} else {
xx[ix] <- xx[ix-1] + geodDist(mean(x@data$station[[stationIndices[ix-1]]][["longitude"]], na.rm=TRUE),
mean(x@data$station[[stationIndices[ix-1]]][["latitude"]], na.rm=TRUE),
mean(x@data$station[[j]][["longitude"]], na.rm=TRUE),
mean(x@data$station[[j]][["latitude"]], na.rm=TRUE))
}
} else if (which.xtype == 3) { # longitude
xx[ix] <- mean(x@data$station[[j]][["longitude"]], na.rm=TRUE)
} else if (which.xtype == 4) { # latitude
xx[ix] <- mean(x@data$station[[j]][["latitude"]], na.rm=TRUE)
} else if (which.xtype == 5) { # time
# use ix if there are no times.
if (!is.null(x@data$station[[j]]@metadata$startTime)) {
xx[ix] <- as.POSIXct(x@data$station[[j]]@metadata$startTime)
} else if (!is.null(x@metadata$time[[j]])) {
xx[ix] <- x@metadata$time[[j]]
} else {
xx[ix] <- ix
if (ix == 1) {
warning("In plot,section-method() :",
"\n section stations do not contain startTime; using integers for time axis", call.=FALSE)
}
}
}
}
} else {
xx <- at
}
if (which.xtype == 5) {
xx <- numberAsPOSIXct(xx)
} else if (which.xtype == 6) {
# see https://github.com/dankelley/oce-issues/blob/master/16xx/1678
if (!("spine" %in% names(x@metadata)))
stop("In plot,section-metod() :\n this section has no spine; use addSpine() to add a spine", call.=FALSE)
spine <- x@metadata$spine
# Parametric lon=lon(s), at=lat(s)
# nolint start object_usage_linter
s <- seq(0, 1, length.out=length(spine$longitude))
# nolint end object_usage_linter
lonfun <- approxfun(spine$longitude ~ s)
latfun <- approxfun(spine$latitude ~ s)
# Create many points on the spine
spineSegments <- 1000
ss <- seq(0, 1, length.out=spineSegments)
stnLon <- x[["longitude", "byStation"]]
stnLat <- x[["latitude", "byStation"]]
closest <- rep(NA, length=length(stnLon))
# find distance (used in following loop; uses global 'i')
for (i in seq_along(stnLon)) {
closest[i] <- which.min(sapply(ss,
function(t) {
lonSpine <- lonfun(t)
latSpine <- latfun(t)
geodDist(lonSpine, latSpine, stnLon[i], stnLat[i])
}))
}
# Map points back to the spine
longitudeRemapped <- lonfun(ss[closest])
latitudeRemapped <- latfun(ss[closest])
xx <- geodDist(longitudeRemapped, latitudeRemapped, alongPath=TRUE)
}
# Grid is regular (so need only first station) unless which=="data"
# FIXME: why checking just first which[] value?
if (which.ytype == 1) {
if (!is.na(which[1]) && which[1] == "data" || ztype == "points") {
yy <- c(max(x[["pressure"]]), 0)
oceDebug(debug, "ytype case 1A near L2062 ", vectorShow(yy))
} else {
yy <- rev(x@data$station[[stationIndices[1]]]@data$pressure)
oceDebug(debug, "ytype case 1B near L2065 ", vectorShow(yy))
}
} else if (which.ytype == 2) {
if (!is.na(which[1]) && which[1] == "data" || ztype == "points") {
yy <- c(max(x[["pressure"]], na.rm=TRUE), 0)
oceDebug(debug, "ytype case 2A near L2070 ", vectorShow(yy))
} else {
yy <- rev(swDepth(x@data$station[[stationIndices[1]]]@data$pressure))
oceDebug(debug, "ytype case 2B near L2073 ", vectorShow(yy))
}
} else {
stop("unknown ytype")
}
oceDebug(debug, "near L2078 ", vectorShow(yy))
par(mgp=mgp, mar=mar)
if (lw > 1) {
if (lw > 2) {
layout(matrix(1:4, nrow=2, byrow=TRUE))
} else {
layout(matrix(1:2, nrow=2, byrow=TRUE))
}
}
L <- if (getOption("oceUnitBracket") == "[") " [" else " ("
R <- if (getOption("oceUnitBracket") == "[") "]" else ")"
available <- sort(unique(c("data", "map", unlist(c(x[["?"]][c("data", "dataDerived")])))))
for (w in 1:lw) {
oceDebug(debug, "handling which[", w, "]=\"", which[w], "\"\n", sep="")
if (!which[w] %in% available)
stop("in plot(section) : which='", which[w], "' is not available; please try one of c(\"",
paste(available, collapse="\",\""),
"\")", call.=FALSE)
station1 <- x[["station", 1]]
#OLD unit <- station1[[paste(which[w], "Unit", sep="")]][[1]] # FIXME: what if not in that station?
if (!missing(contourLevels)) {
# contourLevels given
oceDebug(debug, "contourLevels was given\n")
if (is.null(contourLabels)) {
contourLabels <- format(contourLevels)
}
vtitle <- labelWithUnit(which[w], unit=station1[[paste0(which[w], "Unit")]])
plotSubsection(xx, yy, zz,
which.xtype=which.xtype, which.ytype=which.ytype, variable=which[w], eos=eos,
vtitle=if (is.null(legend.text[w])) vtitle else legend.text[w],
levels=contourLevels, labels=contourLabels, xlim=xlim, ylim=ylim, ztype=ztype,
axes=axes, col=col, debug=debug-1, ...)
} else {
# contourLevels not given
oceDebug(debug, "contourLevels was not given\n")
if (which[w] != "map" && which[w] != 99) {
vtitle <- labelWithUnit(which[w], unit=station1[[paste0(which[w], "Unit")]])
plotSubsection(xx, yy, zz,
which.xtype=which.xtype,
which.ytype=which.ytype,
variable=which[w], eos=eos,
vtitle=if (is.null(legend.text[w])) vtitle else legend.text[w],
xlim=xlim, ylim=ylim, ztype=ztype, zbreaks=zbreaks, zcol=zcol, axes=axes,
pch=pch, cex=cex, col=col,
debug=debug-1, ...)
}
}
if (!is.na(which[w]) && which[w] == 20) {
plotSubsection(xx, yy, zz,
which.xtype=which.xtype, which.ytype=which.ytype,
variable="data", vtitle="", unit=NULL,
xlim=xlim, ylim=ylim, cex=cex, pch=pch, col=col, legend=FALSE,
debug=debug-1, ...)
}
if (!is.na(which[w]) && (which[w] == 99 || which[w] == "map")) {
oceDebug(debug, "plotting a map\n")
plotSubsection(xx, yy, zz,
which.xtype=which.xtype, which.ytype=which.ytype,
variable="map", vtitle="", unit=NULL,
indicate.stations=FALSE,
clongitude=clongitude, clatitude=clatitude, span=span,
projection=projection,
debug=debug-1,
col=col, pch=pch, cex=cex, lwd=lwd,
...)
}
}
oceDebug(debug, "} # plot.section()\n", unindent=1)
invisible(res)
})
#' Read a Section File
#'
#' Read a file that contains a series of `ctd` profiles that make up an
#' oceanographic section.
#' Only *exchange BOT* comma-separated value format is permitted at this time,
#' but other formats may be added later. It should also be noted that the parsing
#' scheme was developed after inspection of the A03 data set (see Examples). This
#' may cause problems if the format is not universal. For example, the header must
#' name the salinity column "`CTDSAL`"; if not, salinity values will not be
#' read from the file.
#'
#' @section Disambiguating salinity:
#' WOCE datasets commonly have a column named `CTDSAL` for salinity inferred
#' from a CTD and `SALNTY` (not a typo) for salinity derived from bottle data.
#' If only one of these is present in the data file, the data will be called
#' `salinity` in the `data` slot of the return value. However, if both
#' are present, then `CTDSAL` is stored as `salinity` and `SALNTY`
#' is stored as `salinityBottle`.
#'
#' @param file A file containing a set of CTD observations. At present, only the
#' *exchange BOT* format is accepted (see \dQuote{Details}).
#'
#' @param directory A character string indicating the name of a directory that
#' contains a set of CTD files that hold individual stations in the section.
#'
#' @param sectionId Optional string indicating the name for the section. If not
#' provided, the section ID is determined by examination of the file header.
#'
#' @param ship Name of the ship carrying out the sampling.
#'
#' @param scientist Name of chief scientist aboard ship.
#'
#' @param institute Name of chief scientist's institute.
#'
#' @param flags Ignored, and deprecated (will be disallowed in a future version).
#'
#' @param missingValue Numerical value used to indicate missing data.
#'
#' @template encodingTemplate
#'
#' @template debugTemplate
#'
#' @param processingLog If provided, the action item to be stored in the log. This
#' is typically only provided for internal calls; the default that it provides is
#' better for normal calls by a user.
#'
#' @return A [section-class] object.
#'
#' @references
#' Several repository sites provide section data. A reasonably stable example is
#' `https://cchdo.ucsd.edu`, but a search on `"WOCE bottle data"` should
#' turn up other sites, if this ceases to exist. Only
#' the so-called *exchange BOT* data format can be processed by [read.section()]
#' at this time. Data names are inferred from column headings using
#' [woceNames2oceNames()].
#'
#' @author Dan Kelley
#'
#' @family things related to section data
read.section <- function(file,
directory,
sectionId="",
flags,
ship="",
scientist="",
institute="",
missingValue=-999,
encoding="latin1",
debug=getOption("oceDebug"),
processingLog)
{
if (missing(file))
stop("must supply 'file'")
if (is.character(file)) {
if (!file.exists(file))
stop("cannot find file '", file, "'")
if (0L == file.info(file)$size)
stop("empty file '", file, "'")
}
oceDebug(debug, "read.section(file=\"", file, "\", ...) {\n", unindent=1)
if (!missing(directory)) {
if (!missing(file))
stop("cannot specify both 'file' and 'directory'")
files <- list.files(directory)
nstations <- length(files)
stations <- vector("list", nstations)
for (i in seq_along(files)) {
name <- paste(directory, files[i], sep="/")
stations[[i]] <- ctdTrim(read.oce(name))
}
return(as.section(stations))
}
if (is.character(file)) {
filename <- file
file <- file(file, "r", encoding=encoding)
on.exit(close(file))
}
if (!inherits(file, "connection"))
stop("argument `file' must be a character string or connection")
res <- new("section")
if (!isOpen(file)) {
filename <- "(connection)"
open(file, "r", encoding=encoding)
on.exit(close(file))
}
if (!missing(flags))
warning("'flags' is ignored, and will be disallowed in an upcoming CRAN release")
# Skip header
lines <- readLines(file)
if ("BOTTLE" != substr(lines[1], 1, 6))
stop("only type \"BOTTLE\" understood, but got header line\n", lines[1], "\n")
if (nchar(sectionId) < 1)
sectionId <- substr(lines[1], 8, nchar(lines[1]))
n <- length(lines)
header <- lines[1]
for (l in (2:n)) {
oceDebug(debug>4, lines[l], "\n")
if ("#" != substr(lines[l], 1, 1)) {
header <- c(header, lines[l])
break
}
}
header.length <- l + 1
ccc <- textConnection(lines[header.length - 1])
var.names <- scan(ccc, sep=",", what="", quiet=TRUE)
dataNamesOriginal <- var.names
close(ccc)
ccc <- textConnection(lines[header.length])
var.units <- scan(ccc, sep=",", what="", quiet=TRUE)
close(ccc)
if (length(var.units) != length(var.names))
stop("length mismatch in variable lists")
header <- lines[1:header.length]
nd <- n - header.length - 1
nv <- length(var.names)
col.start <- 3 # FIXME: why is this value used? It fails on some (Arctic) data.
col.start <- 1
data <- array(dim=c(nd, nv - col.start + 1))
flags <- list()
stnSectionId <- vector("character", nd)
stnId <- vector("character", nd)
for (l in seq(header.length + 1, n-1L)) {
# last line is END_DATA
contents <- strsplit(lines[l], split=",")[[1]]
stnSectionId[l - header.length] <- sub(" *", "", contents[2])
stnId[l - header.length] <- sub("^ *", "", contents[3])
data[l - header.length, ] <- contents[col.start:nv]
# FIXME: maybe should just scan this thing; it might work better anyway
}
if (length(which(var.names=="DATE"))) {
stn.date <- as.character(data[, which(var.names=="DATE") - col.start + 1])
} else {
stop("no column named \"DATE\"")
}
if (length(which(var.names=="TIME"))) {
stn.time <- as.character(data[, which(var.names=="TIME") - col.start + 1])
} else {
stop("no column named \"TIME\"")
}
# nolint start (long lines)
# EXPOCODE,SECT_ID,STNNBR,CASTNO,SAMPNO,BTLNBR,BTLNBR_FLAG_W,DATE,TIME,LATITUDE,LONGITUDE,DEPTH,CTDPRS,CTDTMP,CTDSAL,CTDSAL_FLAG_W,SALNTY,SALNTY_FLAG_W,OXYGEN,OXYGEN_FLAG_W,SILCAT,SILCAT_FLAG_W,NITRIT,NITRIT_FLAG_W,NO2+NO3,NO2+NO3_FLAG_W,PHSPHT,PHSPHT_FLAG_W
# nolint end (long lines)
waterDepth <- as.numeric(data[, which(var.names=="DEPTH") - col.start + 1])
waterDepth <- ifelse(waterDepth == missingValue, NA, waterDepth)
# FIXME: we have both 'latitude' and 'lat'; this is too confusing
longitude <- as.numeric(data[, which(var.names=="LONGITUDE") - col.start + 1])
latitude <- as.numeric(data[, which(var.names=="LATITUDE") - col.start + 1])
stationId <- data[, which(var.names=="STNNBR") - col.start + 1]
stationId <- sub(" *$", "", sub("^ *", "", stationId)) #remove blanks
stationList <- unique(stationId)
numStations <- length(stationList)
station <- vector("list", numStations)
stn <- vector("character", numStations)
lon <- vector("numeric", numStations)
lat <- vector("numeric", numStations)
time <- vector("numeric", numStations) # has to be numeric
# tref <- as.POSIXct("2000-01-01 00:00:00", tz="UTC")
# trefn <- as.numeric(tref)
# We will trim out metadata columns, in assembling the 'data' slot of
# the ctd objects that will make up the section. The pattern below is based
# on one particular file (provided with oce in inst/extdata), which has
# the following column names.
# "EXPOCODE" "SECT_ID" "STNNBR" "CASTNO" "SAMPNO" "BTLNBR" "BTLNBR_FLAG_W"
# "DATE" "TIME" "LATITUDE" "LONGITUDE" "DEPTH" "CTDPRS" "CTDTMP"
# "CTDSAL" "CTDSAL_FLAG_W" "SALNTY" "SALNTY_FLAG_W" "OXYGEN" "OXYGEN_FLAG_W"
colSkip <- var.names %in% c("EXPOCODE", "SECT_ID", "STNNBR", "CASTNO", "SAMPNO",
"BTLNBR", "BTLNBR_FLAG_W",
"DATE", "TIME", "LATITUDE", "LONGITUDE", "DEPTH")
dataNamesOriginal <- as.list(var.names[!colSkip])
dataNames <- woceNames2oceNames(var.names)[!colSkip]
names(dataNamesOriginal) <- dataNames
dataUnits <- list()
for (idata in seq_along(dataNames)) {
n <- dataNames[idata]
dataUnits[[dataNames[idata]]] <- unitFromString(var.units[!colSkip][idata])
}
# Names and units are the same for every station, so determine them
# before going through the data.
for (i in 1:numStations) {
select <- which(stationId == stationList[i])
# "199309232222"
# "1993-09-23 22:22:00"
time[i] <- as.numeric(strptime(paste(stn.date[select[1]], stn.time[select[1]], sep=""), "%Y%m%d%H%M", tz="UTC"))
stn[i] <- sub("^ *", "", stationId[select[1]])
oceDebug(debug, "reading station i=", i, ", stn=", stn[i], "\n")
lon[i] <- longitude[select[1]]
lat[i] <- latitude[select[1]]
select <- which(stationId == stationList[i])
thisStation <- new("ctd")
thisStation@data <- list() # start over, then insert one by one
# colNames <- oceNames[!colSkip]
DATA <- data[, !colSkip]
isFlag <- rep(TRUE, sum(!colSkip))
for (idata in seq_along(dataNames)) {
# Split flags into metadata
isFlag[idata] <- grepl("Flag$", dataNames[idata])
if (isFlag[idata]) {
thisStation@metadata$flags[[gsub("Flag$", "", dataNames[idata])]] <- as.numeric(DATA[select, idata])
} else {
tmp <- as.numeric(DATA[select, idata])
tmp[tmp == missingValue] <- NA
thisStation@data[[dataNames[idata]]] <- tmp
}
}
thisStation@metadata$dataNamesOriginal <- dataNamesOriginal
thisStation@metadata$src <- filename
thisStation@metadata$startTime <- numberAsPOSIXct(time[i])
thisStation@metadata$longitude <- lon[i]
thisStation@metadata$latitude <- lat[i]
thisStation@metadata$stn <- sub("^ *", "", stationId[select[1]])
thisStation@metadata$time <- as.numeric(strptime(paste(stn.date[select[1]], stn.time[select[1]], sep=""), "%Y%m%d%H%M", tz="UTC"))
thisStation@metadata$station <- sub("^ *", "", stationId[select[1]])
thisStation@metadata$longitude <- longitude[select[1]]
thisStation@metadata$latitude <- latitude[select[1]]
thisStation@metadata$waterDepth <- waterDepth[select[1]]
thisStation@metadata$units <- dataUnits
oceDebug(debug, " ", length(select), " levels ", lat[i], "N,", lon[i], "W", " (", format(thisStation@metadata$startTime), ")\n")
station[[i]] <- thisStation
}
res@metadata$header <- header
res@metadata$sectionId <- sectionId
res@metadata$stationId <- stn
res@metadata$longitude <- lon
res@metadata$latitude <- lat
res@metadata$time <- numberAsPOSIXct(time)
res@metadata$filename <- filename
res@data <- list(station=station)
if (missing(processingLog))
processingLog <- paste(deparse(match.call()), sep="", collapse="")
res@processingLog <- processingLogAppend(res@processingLog, processingLog)
oceDebug(debug, "} # read.section()\n", unindent=1)
res
}
#' Grid a Section in Pressure Space
#'
#' Grid a section, by interpolating to fixed pressure levels. The
#' `"approx"`, `"boxcar"` and `"lm"` methods are described in the
#' documentation for [ctdDecimate()], which is used to do this
#' processing.
#'
#' The default `"approx"` method is best for bottle data, the
#' `"boxcar"` is best for ctd data, and the `"lm"` method is probably
#' too slow to recommend for exploratory work, in which it is common to do trials
#' with a variety of `"p"` values.
#'
#' The stations in the returned value have flags with names that match those
#' of the corresponding stations in the original `section`, but the values
#' of these flags are all set to `NA`. This recognizes that it makes
#' no sense to grid flag values, but that there is merit in initializing
#' a flag system, for possible use in later processing steps.
#'
#' @param section A `section` object containing the section to be gridded.
#'
#' @param p Optional indication of the pressure levels to which interpolation
#' should be done. If this is not supplied, the pressure levels will be
#' calculated based on the typical spacing in the ctd profiles stored within
#' `section`. If `p="levitus"`, then pressures will be set to be those
#' of the Levitus atlas, given by [standardDepths()].
#' If `p` is a single numerical value,
#' it is taken as the number of subdivisions to use in a call to [seq()]
#' that has range from 0 to the maximum pressure in `section`. Finally, if a
#' vector numerical values is provided, perhaps. constructed with [seq()]
#' or [`standardDepths`]`(5)` (as in the examples),
#' then it is used as is, after trimming any values that exceed the maximum
#' pressure in the station data stored within `section`.
#'
#' @param method The method to use to decimate data within the stations; see
#' [ctdDecimate()], which is used for the decimation.
#'
#' @param trim Logical value indicating whether to trim gridded pressures
#' to the range of the data in `section`.
#'
#' @template debugTemplate
#'
#' @param ... Optional arguments to be supplied to [ctdDecimate()],
#' e.g. `rule` controls extrapolation beyond the observed pressure range,
#' in the case where `method` equals `"approx"`.
#'
#' @return A [section-class] object that contains stations in which
#' the pressure values match identically, and that has all
#' flags set to `NA`.
#'
#' @examples
#' # Gulf Stream
#' library(oce)
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 5000, 100))
#' plot(GSg, which="temperature")
# plot(GSg, map.xlim=c(-80,-60))
# # Show effects of various depth schemes
# par(mfrow=c(3, 1))
# default <- sectionGrid(GS)
# approxML <- sectionGrid(GS, method="approxML")
# standardDepths5 <- sectionGrid(GS, p=standardDepths(5))
# plot(default, which="temperature", ztype="image", ylim=c(200,0))
# mtext("default sectionGrid()")
# plot(approxML, which="temperature", ztype="image", ylim=c(200,0))
# mtext("sectionGrid(..., method=\"approxML\")")
# plot(standardDepths5, which="temperature", ztype="image", ylim=c(200,0))
# mtext("sectionGrid(..., p=standardDepths(5))")
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionGrid <- function(section, p, method="approx", trim=TRUE, debug=getOption("oceDebug"), ...)
{
oceDebug(debug, "sectionGrid(section, p, ",
"method=\"", if (is.function(method)) "(function)" else method, "\", ",
"p=", if (missing(p)) "(missing)" else paste("c(", paste(p, collapse=","), ")", sep=""), ", ",
"trim=", trim, ", ...) {\n",
sep="", unindent=1)
n <- length(section@data$station)
nxg <- n
oceDebug(debug, "have ", nxg, " stations in this section\n")
pMax <- max(section[["pressure"]], na.rm=TRUE)
if (missing(p)) {
dplist <- vector("numeric", n)
for (i in 1:n) {
p <- section@data$station[[i]]@data$pressure
dplist[i] <- mean(diff(p), na.rm=TRUE)
}
dp <- mean(dplist, na.rm=TRUE) / 5 # make it a little smaller
pt <- pretty(c(0, pMax), n=min(200, floor(abs(pMax / dp))))
oceDebug(debug, "p not given, so inferring from data\n")
} else {
if (length(p) == 1) {
if (p=="levitus") {
pt <- standardDepths()
} else {
if (!is.numeric(p))
stop("p must be numeric")
if (p <= 0)
stop("p must be a positive number")
pt <- seq(0, pMax, p)
}
} else {
pt <- p
}
}
oceDebug(debug, vectorShow(pt))
if (trim) {
# allow one extra level, for bracketing
w <- which(pt > pMax)
if (length(w)) {
pt <- pt[1:w[1]]
}
oceDebug(debug, "trimmed ", vectorShow(pt))
}
# BUG should handle all variables (but how to interpolate on a flag?)
res <- section
nstation <- length(res[["station"]])
nyg <- length(pt)
# The core of the work -- use ctdDecimate() on each station
for (i in seq_len(nstation)) {
oceDebug(debug, "decimating station i=", i, "\n")
suppressWarnings(
res@data$station[[i]] <- ctdDecimate(section@data$station[[i]],
p=pt, method=method, debug=debug-1, ...))
}
# Find all units in *any* station, and then insert them into *all* stations.
units <- list()
for (i in seq_len(nstation)) {
for (unitName in names(section@data$station[[i]]@metadata$units)) {
if (!(unitName %in% names(units))) {
units[unitName] <- section@data$station[[i]]@metadata$units[unitName]
}
}
}
for (i in seq_len(nxg)) {
res@data$station[[i]]@metadata$units <- units
res@data$station[[i]]@metadata$flags <- list()
for (flagname in names(section@data$station[[i]]@metadata$flags)) {
# Note that the flags are named after those of the flags for *that particular*
# station in the input object. This is different from sectionSmooth().
res@data$station[[i]]@metadata$flags[[flagname]] <- rep(NA, nyg)
}
}
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
oceDebug(debug, "} # sectionGrid\n", unindent=1)
res
}
#' Smooth a Section
#'
#' Smooth a section, in any of several ways, working either in the vertical
#' direction or in both the vertical and lateral directions.
#'
#' This function produces smoothed fields that might be useful in
#' simplifying graphical elements created with [plot,section-method()].
#' As with any smoothing method, a careful analyst will compare the results
#' against the raw data, e.g. using [plot,section-method()].
#' In addition the problem of falsely widening narrow features such as
#' fronts, there is also the potential to get unphysical results with
#' spars sampling near topographic features such as bottom slopes and ridges.
#'
#' The `method` argument selects between three possible methods.
#'
#' * For `method="spline"`, i.e. the default, the section is smoothed
#' laterally, using [smooth.spline()] on individual pressure levels.
#' (If the pressure levels do not match up, [sectionGrid()] should
#' be used first to create a `section` object that can be used here.)
#' The `df` argument sets the degree of freedom of the spline, with
#' larger values indicating less smoothing.
#'
#' * For `method="barnes"`, smoothing is done across
#' both horizontal and vertical coordinates, using [interpBarnes()].
#' The output station locations are computed by linear interpolation of
#' input locations, using [approx()] on the original
#' longitudes and longitudes of stations, with the independent variable
#' being the distance along the track, computed with [geodDist()].
#' The values of `xg`, `yg`, `xgl` and `ygl` control
#' the smoothing.
#'
#' * For `method="kriging"`, smoothing is done across
#' both horizontal and vertical coordinates, using `autoKrige()` from
#' the \CRANpkg{automap} package (along with support from the
#' \CRANpkg{sp} package to format the data). Note that the format of
#' the value returned by `autoKrige()` has changed over the years,
#' and `method="kriging"` can only handle two particular formats,
#' one of which is the result from version 1.1.9 of
#' \CRANpkg{automap}.
#'
#' * If `method` is a function, then that function is applied to
#' the (distance, pressure) data for each variable at a grid defined by
#' `xg`, `xgl`, `yg` and `ygl`. The function must
#' be of the form `function(x,y,z,xg,xr,yg,yr)`, and must
#' return a matrix of the gridded result, with first index indicating
#' the "grid" station number and second index indicating "grid" pressure.
#' The `x` value that is supplied to this function is set as
#' the distance along the section, as computed with [geodDist()],
#' and repeated for each of the points at each station. The corresponding
#' pressures are provided in `y`, and the value to be gridded is
#' in `z`, which may be `temperature` on one call to the function,
#' `salinity` on another call, etc. The other quantities
#' have the meanings as described below.
#'
#' @param section A `section` object containing the section to be smoothed.
#' For `method="spline"`, the pressure levels must match for each station in
#' the section.
#'
#' @param method A string or a function that specifies the method to use; see \dQuote{Details}.
#'
#' @param x Optional numerical vector, of the same length as the number of stations in `section`,
#' which will be used in gridding in the lateral direction. If not provided, this
#' defaults to [`geodDist`]`(section)`.
#'
#' @param xg,xgl ignored in the `method="spline"` case, but passed to
#' [interpBarnes()] if `method="barnes"`, to kriging
#' functions if `method="kriging"`, or to `method` itself, if it
#' is a function.
#' If `xg` is supplied, it defines the x component of the grid, which by
#' default is the terms of station distances, x, along the track of the section. (Note
#' that the grid `xg` is trimmed to the range of the data `x`, because otherwise
#' it would be impossible to interpolate between stations to infer water depth,
#' longitude, and latitude, which are all stored within the stations in the
#' returned `section` object.)
#' Alternatively, if `xgl` is supplied, the x grid is established using [seq()],
#' to span the data with `xgl` elements. If neither of these is supplied, the output
#' x grid will equal the input x grid.
#'
#' @param yg,ygl similar to `xg` and `xgl`, but for pressure. (Note that
#' trimming to the input `y` is not done, as it is for `xg` and `x`.)
#' If `yg` is not given, it is determined from the deepest station in the section.
#' If `ygl` was not given, then a grid is constructed to span the pressures
#' of that deepest station with `ygl` elements. On the other hand,
#' if `ygl` was not given, then the y grid will constructed from the
#' pressure levels in the deepest station.
#'
#' @param xr,yr influence ranges in x (along-section distance) and y (pressure),
#' passed to [interpBarnes()] if `method="barnes"` or to
#' `method`, if the latter is a function. If missing, `xr` defaults to
#' 1.5X the median inter-station distance and `yr` defaults to 0.2X
#' the pressure range. Since these defaults have changed over the evolution
#' of `sectionSmooth`, analysts ought to supply `xr` and `yr`
#' in the function call, tailoring them to particular applications, and
#' making the code more resistant to changes in `sectionSmooth`.
#'
#' @param df Degree-of-freedom parameter, passed to [smooth.spline()]
#' if `method="spline"`, and ignored otherwise. If `df` is not provided,
#' it defaults to 1/5-th of the number of stations containing non-`NA`
#' data at the particular pressure level being processed, as `sectionSmooth`
#' works its way through the water column.
#'
#' @param gamma,iterations,trim,pregrid Values passed to
#' [interpBarnes()], if `method="barnes"`, and
#' ignored otherwise. `gamma` is the factor by which
#' `xr` and `yr` are reduced on each of succeeding iterations.
#' `iterations` is the number of iterations to do.
#' `trim` controls whether the gridded data are set to
#' `NA` in regions with sparse data
#' coverage. `pregrid` controls whether data are to be
#' pre-gridded with [binMean2D()] before being passed to
#' [interpBarnes()].
#'
#' @param debug A flag that turns on debugging. Set to 1 to get a moderate amount
#' of debugging information, or to 2 to get more.
#'
#' @param ... Optional extra arguments, passed to either
#' [smooth.spline()], if `method="spline"`, and ignored otherwise.
#'
#' @return A [section-class] object of that has been smoothed in some way.
#' Every data field that is in even a single station of the input object
#' is inserted into every station in the returned value, and therefore
#' the units represent all the units in any of the stations, as do the
#' flag names. However, the flags are all set to `NA` values.
#'
#' @examples
#' # Unsmoothed (Gulf Stream)
#' library(oce)
#' data(section)
#' gs <- subset(section, 115<=stationId&stationId<=125)
#' par(mfrow=c(2, 2))
#'
#' plot(gs, which="temperature")
#' mtext("Original data, without smoothing", line=0.5)
#'
#' # Spline
#' gsg <- sectionGrid(gs, p=seq(0, 5000, 100))
#' gsSpline <- sectionSmooth(gsg, "spline")
#' plot(gsSpline, which="temperature")
#' mtext("sectionSmooth(..., method=\"spline\")", line=0.5)
#'
#' # Barnes
#' gsBarnes <- sectionSmooth(gs, "barnes", xr=50, yr=200)
#' plot(gsBarnes, which="temperature")
#' mtext("sectionSmooth(..., method=\"barnes\")", line=0.5)
#'
#' # Kriging. This is commented-out because one of the CRAN
#' # check machines produces an error (converted from a warning)
#' # and I am really not clear on whether dontrun will be
#' # sufficient to solve the problem.
#' #> if (requireNamespace("automap", quietly=TRUE)
#' #> && requireNamespace("sf", quietly=TRUE)) {
#' #> gsKriging <- sectionSmooth(gs, "kriging", xr=50, yr=200)
#' #> plot(gsKriging, which="temperature")
#' #> mtext("sectionSmooth(..., method=\"kriging\")", line=0.5)
#' #> }
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionSmooth <- function(section, method="spline",
x,
xg, yg, xgl, ygl, xr, yr,
df, gamma=0.5, iterations=2, trim=0, pregrid=FALSE,
debug=getOption("oceDebug"), ...)
{
if (!inherits(section, "section"))
stop("method is only for objects of class '", "section", "'")
if (!is.function(method) && !(is.character(method) && (method %in% c("barnes", "kriging", "spline"))))
stop('method must be "barnes", "kriging", "spline", or an R function')
# pin debug, since we only call one function, interpBarnes() that uses debug
debug <- if (debug > 2) 2 else if (debug < 0) 0 else debug
oceDebug(debug, "sectionSmooth(section,method=\"",
if (is.character(method)) method else "(function)", "\", ...) {\n", sep="", unindent=1)
stations <- section[["station"]]
nstn <- length(stations)
if (nstn < 2) {
warning("station has <2 stations, so sectionSmooth() is returning it, unaltered")
return(x)
}
xrGiven <- !missing(xr)
yrGiven <- !missing(yr)
dfGiven <- !missing(df)
if (missing(x))
x <- geodDist(section)
nx <- length(x)
if (nx != length(section@data$station))
stop("length(x)=", nx, " does not match number of stations=", length(section@data$station))
# Rearange the input stations into order set by x.
o <- order(x)
x <- x[o]
section@data$station <- section@data$station[o]
P <- section[["pressure"]]
maxPressure <- max(P, na.rm=TRUE)
if (missing(xg)) {
xg <- if (missing(xgl)) x else seq(min(x), max(x), length.out=xgl)
oceDebug(debug, "defaulted", vectorShow(xg))
} else {
oceDebug(debug, "user-supplied", vectorShow(xg))
}
if (missing(yg)) {
deepest <- which.max(unlist(lapply(section[["station"]], function(ctd) max(ctd[["pressure"]], na.rm=TRUE))))
yg <- if (missing(ygl)) section[["station", deepest]][["pressure"]] else seq(0, maxPressure, length.out=ygl)
oceDebug(debug, "defaulted", vectorShow(yg))
} else {
oceDebug(debug, "user-supplied", vectorShow(yg))
}
# Trim xg to the data range in x (issue 1583)
oceDebug(debug, "original data", vectorShow(x))
keep <- min(x) <= xg & xg <= max(x)
oceDebug(debug, "before trimming", vectorShow(xg))
xg <- xg[keep]
oceDebug(debug, "after trimming", vectorShow(xg))
stn1pressure <- stations[[1]][["pressure"]]
if (identical(method, "spline") && !identical(yg, stn1pressure))
stop("for method=\"spline\", yg must match the pressure vector in first station")
nxg <- length(xg)
nyg <- length(yg)
# varsAll holds the names of all variables in the section.
res <- section
varsAll <- unique(unlist(lapply(stations, function(ctd) names(ctd[["data"]]))))
# vars holds just the names of variables that get smoothed. First, we remove
# things that simply cannot be smoothed...
vars <- varsAll[!varsAll %in% c("flag", "quality", "scan", "time")]
# ..., second, we remove things that will be computed from 'x' and 'xg'.
vars <- vars[!vars %in% c("latitude", "longitude", "pressure")]
# ... finally, remove 'depth', which is kind of a surrogate for pressure, I think.
vars <- vars[!vars %in% c("depth")]
flagnames <- unique(unlist(lapply(stations, function(ctd) names(ctd@metadata$flags))))
# start with existing station, to get processing log, section ID, etc., but
# recreate @data$station
res@data$station <- list("vector", nxg)
for (istn in seq_len(nxg)) {
res@data$station[[istn]] <- new("ctd")
res@data$station[[istn]][["pressure"]] <- yg
}
if (is.character(method) && method == "spline") {
oceDebug(debug, "using spline method\n")
# Since we are smoothing along lines of constant pressure, we must
# first ensure that the stations have identical pressures.
npressure <- length(stn1pressure)
for (istn in 2:nstn) {
thisp <- stations[[istn]][["pressure"]]
if (length(thisp) != npressure || any(thisp != stn1pressure)) {
stop("pressure mismatch between station 1 and station", istn, "; try using sectionGrid() first")
}
}
# The work will be done in matrices, for code clarity and speed.
VAR <- array(double(), dim=c(nstn, npressure))
oceDebug(debug, "dim matrix for input grid=", paste(dim(VAR), collapse="X"), "\n")
for (var in vars) {
oceDebug(debug, "smoothing '", var, "'\n", sep="")
for (istn in seq_len(nx))
VAR[istn, ] <- if (var %in% names(stations[[istn]][["data"]])) stations[[istn]][[var]] else rep(NA, npressure)
# Smooth at each pressure value (turn off warnings because smooth.spline is confusingly chatty)
owarn <- options("warn")$warn
options(warn=-1)
VARS <- apply(VAR, 2,
function(varj)
{
ok <- is.finite(varj)
varjok <- varj[ok]
xok <- x[ok]
nok <- sum(ok)
if (!dfGiven) {
df <- floor(nok / 3)
}
if (df > 1) {
predict(smooth.spline(xok, varjok, df=df), xg)$y
} else {
if (nok > 2) approx(xok, varjok, xout=xg, rule=1)$y else rep(NA, nxg)
}
})
options(warn=owarn)
oceDebug(debug, "dim matrix for output grid=", paste(dim(VARS), collapse="X"), "\n")
for (istn in seq_len(nxg))
res@data$station[[istn]][[var]] <- VARS[istn, ]
}
} else {
if (is.character(method)) {
if (method == "barnes") {
oceDebug(debug, "using method=\"barnes\"\n")
} else if (method == "kriging") {
oceDebug(debug, "using method=\"kriging\"\n")
} else {
stop("unknown string method=\"", method, "\"; it must be \"barnes\" or \"kriging\"")
}
} else if (is.function(method)) {
oceDebug(debug, "using method=(function)\n")
} else {
stop("method must be a string or a function")
}
# either "barnes" or a function
# Find names of all variables in all stations; previous to 2019 May 2,
# we only got names from the first station.
XI <- geodDist(section)
X <- unlist(lapply(seq_along(XI), function(i) rep(XI[i], length(section[["station", i]][["pressure"]]))))
# Set up defaults for xr and yr, if not specified in function call.
if (!xrGiven) {
xr <- 1.5 * median(diff(sort(x)))
oceDebug(debug, "xr defaulting to", xr, "(1.5X the median distance between stations)\n")
}
if (!yrGiven) {
yr <- 0.2 * diff(range(P, na.rm=TRUE))
oceDebug(debug, "yr defaulting to", yr, "(0.2X the pressure range across all stations)\n")
}
# Smooth each variable separately
for (var in vars) {
v <- NULL
oceDebug(debug, "smoothing '", var, "' near section.R:2908\n", sep="")
# collect data
v <- unlist(lapply(section[["station"]],
function(CTD)
if (var %in% names(CTD[["data"]])) {
CTD[[var]]
} else {
rep(NA, length(CTD[["pressure"]]))
}))
# ignore NA values (for e.g. a station that lacks a particular variable)
ok <- is.finite(X) & is.finite(P) & is.finite(v)
if (is.character(method)) {
if (method == "barnes") {
smu <- interpBarnes(X[ok], P[ok], v[ok], xg=xg, yg=yg, xgl=length(xg), ygl=length(yg),
xr=xr, yr=yr, gamma=gamma, iterations=iterations, trim=trim,
debug=debug-1)
# rename to match names if method is a function.
smu$z <- smu$zg
smu$x <- smu$xg
smu$y <- smu$yg
if (all(is.na(smu$z)))
warning("All \"", var, "\" data are NA, so gridded field is a matrix of NA values\n")
} else if (method == "kriging") {
if (requireNamespace("automap", quietly=TRUE) && requireNamespace("sf", quietly=TRUE)) {
krigFunction <- function(x, y, z, xg, xr, yg, yr) {
# Scale by xr and yr to perhaps improve numerics
data <- sf::st_as_sf(data.frame(x=x/xr, y=y/yr, z=z), coords=c("x", "y"))
grid <- sf::st_as_sf(expand.grid(xg=xg/xr, yg=yg/yr), coords=c("xg", "yg"))
# silence kriging, which is distractingly chatty
owarn <- options("warn")$warn
options(warn=-1)
capture.output({
K <- automap::autoKrige(z~1, remove_duplicates=TRUE, input_data=data, new_data=grid)
})
options(warn=owarn)
# Try multiple styles of autoKrige() return values. This is not documented, so
# the styles are reverse-engineered based on observed values. Of course,
# this is a risky endeavour.
if (!"krige_output" %in% names(K))
stop("malformed return value from automap::autoKrige()")
krige_output <- K$krige_output
# Handle format (K$krige_output@data$var1.pred) from some automap prior to 1.1.9
if ("data" %in% slotNames(krige_output)) {
oceDebug(debug, "old autoKrige() output format\n")
return(matrix(K$krige_output@data$var1.pred, nrow=length(xg), ncol=length(yg)))
}
# Handle format (K$krige_output$var1.pred) from automap-1.1.9 (seen Apr 2023).
if ("var1.pred" %in% names(krige_output)) {
oceDebug(debug, "handling automap-1.1.9 autoKrige() format (April 2023)\n")
return(matrix(K$krige_output$var1.pred, nrow=length(xg), ncol=length(yg)))
}
# Have a previously unseen format.
stop("malformed return value from automap::autoKrige()")
}
smu <- list(z=krigFunction(X[ok], P[ok], v[ok], xg=xg, xr=xr, yg=yg, yr=yr), x=xg, y=yg)
} else {
stop('method="kriging" requires packages "automap" and "sf" to be installed\n')
}
} else {
stop('method must be "barnes", "kriging", "spline", or an R function.')
}
} else {
# method is not a character. It must be a function, but let's check again, anyway.
if (is.function(method)) {
smu <- list(z=method(x=X[ok], y=P[ok], z=v[ok], xg=xg, xr=xr, yg=yg, yr=yr), x=xg, y=yg)
} else {
stop('method must be "barnes", "kriging", "spline", or a function')
}
}
for (istn in seq_len(nxg)) {
res@data$station[[istn]]@data[[var]] <- smu$z[istn, ]
res@data$station[[istn]]@data[["pressure"]] <- smu$y
}
}
}
oceDebug(debug, "smoothing portion completed (near section.R line 2920)\n")
# Set up section-level and station-level metadata
res@metadata$longitude <- approx(x, section[["longitude", "byStation"]], xg)$y
res@metadata$latitude <- approx(x, section[["latitude", "byStation"]], xg)$y
if (any(!is.finite(res@metadata$longitude)))
warning("some gridded longitudes are NA\n")
if (any(!is.finite(res@metadata$latitude)))
warning("some gridded latitudes are NA\n")
for (i in seq_along(xg)) {
res@data$station[[i]]@metadata$longitude <- res@metadata$longitude[i]
res@data$station[[i]]@metadata$latitude <- res@metadata$latitude[i]
res@data$station[[i]]@metadata$stationId <- if (nxg < 10) {
sprintf("x%d", istn)
} else if (nxg < 100) {
sprintf("x%02d", istn)
} else if (nxg < 1000) {
sprintf("x%03d", istn)
} else if (nxg < 10000) {
sprintf("x%04d", istn)
} else {
sprintf("x%d", istn) # Just give up on fanciness
}
}
nstation <- length(res[["station"]])
waterDepthOriginal <- unlist(lapply(section[["station"]], function(STN) STN[["waterDepth"]]))
if (length(waterDepthOriginal) > 0) {
waterDepthNew <- approx(x, waterDepthOriginal, xg, rule=2)$y
for (i in seq_len(nstation))
res@data$station[[i]]@metadata$waterDepth <- waterDepthNew[i]
}
oceDebug(debug > 3, "about to create units\n")
# Insert uniform units and flags into each station.
units <- list()
for (i in seq_along(section[["station"]])) {
oceDebug(debug > 3, "station i=", i, ", nstation=", nstation, ", length(section@data$section)=", length(section@data$station), "\n", sep="")
for (unitName in names(section@data$station[[i]]@metadata$units)) {
oceDebug(debug > 3, "unitName='", unitName, "'\n", sep="")
if (!(unitName %in% names(units))) {
oceDebug(debug > 3, " ... installing unitName='", unitName, "'\n", sep="")
units[unitName] <- section@data$station[[i]]@metadata$units[unitName]
oceDebug(debug > 3, " ... installation was ok\n")
}
}
}
oceDebug(debug > 3, "installing units\n")
oceDebug(debug > 3, "length(res@data$station)=", length(res@data$station), "\n")
oceDebug(debug > 3, "nxg=", nxg, "\n")
for (i in seq_len(nxg)) {
res@data$station[[i]]@metadata$units <- units
# Note that we put in flags for *all* variables in the output file. This
# is different from the action in sectionGrid(), which inserts flags
# tailored to individual stations. However, smoothing creates new stations,
# and puts *all* variables in *each* station, so we need flags for everything.
res@data$station[[i]]@metadata$flags <- list()
for (flagname in flagnames) {
res@data$station[[i]]@metadata$flags[[flagname]] <- rep(NA, nyg)
}
}
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
oceDebug(debug, "} # sectionSmooth()\n", unindent=1)
res
}
#' Create a Section
#'
#' Create a section based on columnar data, or a set of [oce-class]
#' objects that can be coerced to a section. There are three cases.
#'
#' Case 1. If the first argument is a numerical vector, then it is taken to be the
#' salinity, and [factor()] is applied to `station` to break the
#' data up into chunks that are assembled into [ctd-class] objects with
#' [as.ctd()] and combined to make a [section-class] object
#' to be returned. This mode of operation is provided as a convenience for datasets
#' that are already partly processed; if original CTD data are available, the next
#' mode is preferred, because it permits the storage of much more data and metadata
#' in the CTD object.
#'
#' Case 2. If the first argument is a list containing oce objects, then those
#' objects are taken as profiles of something. A requirement for this
#' to work is that every element of the list contains both `longitude`
#' and `latitude` in either the `metadata` or `data` slot (in
#' the latter case, the mean value is recorded in the section object)
#' and that every element also contains `pressure` in its `data` slot.
#'
#' Case 3. If the first argument is a [argo-class] object, then
#' the profiles it contains are turned into [ctd-class] objects,
#' and these are assembled into a section to be returned.
#'
#' @param salinity This may be a numerical vector, in which case it is interpreted
#' as the salinity, and the other arguments are used for the other components of
#' [ctd-class] objects. Alternatively, it may be one of a variety of
#' other objects from which the CTD objects can be inferred, in which case the
#' other arguments are ignored; see \dQuote{Details}.
#'
#' @param temperature Temperature, in a vector holding values for all stations.
#'
#' @param pressure Pressure, in a vector holding values for all stations.
#'
#' @param longitude Longitude, in a vector holding values for all stations.
#'
#' @param latitude Latitude, in a vector holding values for all stations.
#'
#' @param station Station identifiers, in a vector holding values for all stations.
#'
#' @param sectionId Section identifier.
#'
#' @param debug an integer value that controls whether `as.section()` prints information
#' during its work. The function works quietly if this is 0 and prints out some
#' information if it is positive.
#'
#' @return An object of [section-class].
#'
#' @examples
#' library(oce)
#' data(ctd)
#' # vector of names of CTD objects
#' fake <- ctd
#' fake[["temperature"]] <- ctd[["temperature"]] + 0.5
#' fake[["salinity"]] <- ctd[["salinity"]] + 0.1
#' fake[["longitude"]] <- ctd[["longitude"]] + 0.01
#' fake[["station"]] <- "fake"
#' sec1 <- as.section(c("ctd", "fake"))
#' summary(sec1)
#' # vector of CTD objects
#' ctds <- vector("list", 2)
#' ctds[[1]] <- ctd
#' ctds[[2]] <- fake
#' sec2 <- as.section(ctds)
#' summary(sec2)
#' # argo data (a subset)
#' data(argo)
#' sec3 <- as.section(subset(argo, profile<5))
#' summary(sec3)
#'
#' @author Dan Kelley
#'
#' @family things related to section data
as.section <- function(salinity, temperature, pressure, longitude, latitude, station, sectionId="", debug=getOption("oceDebug"))
{
debug <- as.integer(min(1, max(0, debug))) # make it be 0 or 1
oceDebug(debug, "as.section() {\n", sep="", style="bold", unindent=1)
if (missing(salinity))
stop("argument 'salinity' is missing")
res <- new("section", sectionId="")
if (is.numeric(salinity)) {
if (missing(temperature))
stop("must provide temperature")
if (missing(temperature))
stop("must provide temperature")
if (missing(pressure))
stop("must provide pressure")
if (missing(longitude))
stop("must provide longitude")
if (missing(latitude))
stop("must provide latitude")
if (missing(station))
stop("must provide station")
stationFactor <- factor(station)
stationLevels <- levels(stationFactor)
nstation <- length(stationLevels)
ctds <- vector("list", nstation)
for (i in 1:nstation) {
# message("NUMERIC CASE. i: ", i, ", name:", stationLevels[i])
look <- station==stationLevels[i]
ctds[[i]] <- as.ctd(salinity[look], temperature[look], pressure[look],
longitude=longitude[look][1],
latitude=latitude[look][1],
station=stationLevels[i])
}
} else if (inherits(salinity, "argo")) {
tmp <- salinity
nstation <- length(tmp[["longitude"]])
station <- 1:nstation
longitude <- tmp[["longitude"]]
latitude <- tmp[["latitude"]]
salinity <- tmp[["salinity"]]
temperature <- tmp[["temperature"]]
pressure <- tmp[["pressure"]]
stationFactor <- factor(station)
stationLevels <- levels(stationFactor)
ctds <- vector("list", nstation)
# N will hold the names of extra data for the CTDs
N <- names(tmp@data)
if ("time" %in% N) N <- N[-which(N=="time")]
if ("longitude" %in% N) N <- N[-which(N=="longitude")]
if ("latitude" %in% N) N <- N[-which(N=="latitude")]
if ("salinity" %in% N) N <- N[-which(N=="salinity")]
if ("temperature" %in% N) N <- N[-which(N=="temperature")]
if ("pressure" %in% N) N <- N[-which(N=="pressure")]
time <- tmp[["time"]]
for (i in 1:nstation) {
ctds[[i]] <- as.ctd(salinity[, i], temperature[, i], pressure[, i],
longitude=longitude[i], latitude=latitude[i],
startTime=as.POSIXct(time[i]), station=as.character(station[i]))
for (Ni in seq_along(N)) {
ctds[[i]] <- oceSetData(ctds[[i]], name=N[Ni], value=tmp[[N[Ni]]][, i],
unit=tmp[["units"]][[N[Ni]]])
}
}
} else if (inherits(salinity, "list")) {
oceDebug(debug, "first argument is a list (assumed to be a list of oce objects)\n")
thelist <- salinity # prevent accidental overwriting
if (!length(thelist))
stop("no data in this list")
if (inherits(thelist[[1]], "oce")) {
nstation <- length(salinity)
ctds <- vector("list", nstation)
badDepths <- NULL
for (i in seq_len(nstation)) {
oceDebug(debug, "processing item", i, "of", nstation, "\n")
if (!("pressure" %in% names(thelist[[i]]@data)))
stop("cannot create a section from this first argument, because its ", i, "-th element lacks pressure")
# Replace NA water depth with highest pressure. Note that this action is skipped
# if there is no water depth (e.g. if the first argument is a list of Argo objects).
# See https://github.com/dankelley/oce/issues/1797
if ("waterDepth" %in% names(thelist[[i]]@metadata)) {
if (is.na(thelist[[i]]@metadata$waterDepth)) {
thelist[[i]]@metadata$waterDepth <- max(thelist[[i]]@data$pressure, na.rm=TRUE)
badDepths <- c(badDepths, i)
}
} else {
thelist[[i]]@metadata$waterDepth <- NA
}
ctds[[i]] <- as.ctd(thelist[[i]])
}
if (length(badDepths)) {
warning("estimated waterDepth as max(pressure) for CTDs numbered ",
paste(abbreviateVector(badDepths), collapse=" "))
}
} else {
stop("first argument must be a salinity vector, or a list of oce objects")
}
} else if (is.character(salinity)) {
# vector of names of CTD objects
nstation <- length(salinity)
ctds <- vector("list", nstation)
for (i in 1:nstation) {
ctds[[i]] <- get(salinity[i], parent.frame())
}
} else {
stop("first argument is not understood")
}
# In each case, we now have a vector of CTD objects.
res@metadata$sectionId <- ""
res@metadata$stationId <- unlist(lapply(ctds, function(x) x[["station"]][1]))
res@metadata$longitude <- unlist(lapply(ctds, function(x) mean(x[["longitude"]], na.rm=TRUE)))
res@metadata$latitude <- unlist(lapply(ctds, function(x) mean(x[["latitude"]], na.rm=TRUE)))
res@metadata$time <- numberAsPOSIXct(unlist(lapply(ctds, function(x) x[["time"]][1])))
res@data <- list(station=ctds)
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
oceDebug(debug, "} # as.section()\n", sep="", style="bold", unindent=1)
res
}
#' Add a spine to a section object
#'
#' The purpose of this is to permit plotting with `xtype="spine"`, so that
#' the section plot will display the distance of stations projected
#' onto the spine.
#'
#' @param section a [section-class] object.
#' @param spine either a list or a data frame, containing numeric items named
#' `longitude` and `latitude`, defining a path along the spine.
#' @template debugTemplate
#'
#' @return A [section-class] object with a spine added.
#'
#' @examples
#' library(oce)
#' data(section)
#' eastern <- subset(section, longitude < (-65))
#' spine <- list(longitude=c(-74.5, -69.2, -55),
#' latitude=c(38.6, 36.25, 36.25))
#' easternWithSpine <- addSpine(eastern, spine)
#'\dontrun{
#' # plot(easternWithSpine, which="map")
#' # plot(easternWithSpine, xtype="distance", which="temperature")
#' # plot(easternWithSpine, xtype="spine", which="temperature")
#'}
#'
#' @author Dan Kelley
addSpine <- function(section, spine, debug=getOption("oceDebug"))
{
oceDebug(debug, "addSpine(..., spine=", argShow(spine), ") {\n", sep="", style="bold", unindent=1)
if (missing(section))
stop("must provide 'section' argument")
if (!inherits(section, "section"))
stop("'section' must be a section object, e.g. created by read.section() or as.section()")
if (missing(spine))
stop("must provide 'spine' argument")
res <- section
if (2 == length(spine) && 2 == sum(c("latitude", "longitude") %in% names(spine))) {
if (length(spine$longitude) != length(spine$latitude))
stop("unequal lengths of spine longitude (", length(spine$longitude),
") and latitude (", length(spine$latitude), ")")
if (length(spine$longitude) < 2)
stop("length of spine longitude must exceed 2, but it is ", length(spine$longitude))
res@metadata$spine <- spine
} else {
stop("'spine' must be a list or data frame containing two items, named 'longitude' and 'latitude'")
}
oceDebug(debug, "} # addSpine()\n", sep="", style="bold", unindent=1)
res
}
#' Try to Reduce Section Longitude Range
#'
#' [longitudeTighten] shifts some longitudes in its first argument by 360 degrees, if doing
#' so will reduce the overall longitude span.
#'
#' This function can be helpful in cases where the CTD stations within a section
#' cross the cut point of the longitude convention, which otherwise might
#' yield ugly plots if [plot,section-method()] is used with `xtype="longitude"`.
#' This problem does occur with CTD objects ordered by time of sampling,
#' but was observed in December 2020 for a GO-SHIPS dataset downloaded from
#' `https://cchdo.ucsd.edu/data/15757/a10_1992_ct1`.
#'
#' @param section a [section-class] object.
#'
#' @return A [section-class] object based on its first argument, but with
#' longitudes shifted in its `metadata` slot, and also in the `metadata` slots
#' of each of the [ctd-class] objects that are stored in the `station` item
#' in its `data` slot.
#'
#' @author Dan Kelley
longitudeTighten <- function(section)
{
if (!inherits(section, "section"))
stop("'section' must be an object created with read.section() or as.section()")
res <- section
longitude <- section[["longitude", "byStation"]]
longitudeShifted <- ifelse(longitude > 180, longitude - 360, longitude)
if (diff(range(longitude)) > diff(range(longitudeShifted)))
longitude <- longitudeShifted
res <- oceSetMetadata(res, "longitude", longitude, "longitudeTighten")
ctds <- section@data$station
for (i in seq_along(ctds))
ctds[[i]]@metadata$longitude <- longitude[i]
res@data$station <- ctds
res
}
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Defines functions longitudeTighten addSpine as.section sectionSmooth sectionGrid read.section sectionAddStation sectionSort
Documented in addSpine as.section longitudeTighten read.section sectionAddStation sectionGrid sectionSmooth sectionSort
# vim:textwidth=120:expandtab:shiftwidth=4:softtabstop=4:foldmethod=marker
#' Class to Store Hydrographic Section Data
#'
#' This class stores data from oceanographic section surveys.
#'
#' Sections can be read with [read.section()] or created with
#' [read.section()] or created from CTD objects by using
#' [as.section()] or by adding a ctd station to an existing section with
#' [sectionAddStation()].
#'
#' Sections may be sorted with [sectionSort()], subsetted with
#' [subset,section-method()], smoothed with [sectionSmooth()], and
#' gridded with [sectionGrid()]. A "spine" may be added to a section
#' with [addSpine()]. Sections may be summarized with
#' [summary,section-method()] and plotted
#' with [plot,section-method()].
#'
#' The sample dataset [section()] contains data along WOCE line A03.
#'
#' @templateVar class section
#'
#' @templateVar dataExample {}
#'
#' @templateVar metadataExample Examples that are of common interest include `stationId`, `longitude`, `latitude` and `time`.
#'
#' @template slot_summary
#'
#' @template slot_put
#'
#' @template slot_get
#'
#' @examples
#' library(oce)
#' data(section)
#' plot(section[["station", 1]])
#' pairs(cbind(z=-section[["pressure"]],T=section[["temperature"]],S=section[["salinity"]]))
#' # T profiles for first few stations in section, at common scale
#' par(mfrow=c(3,3))
#' Tlim <- range(section[["temperature"]])
#' ylim <- rev(range(section[["pressure"]]))
#' for (stn in section[["station", 1:9]]) {
#' plotProfile(stn, xtype="potential temperature", ylim=ylim, Tlim=Tlim)
#' }
#'
#' @author Dan Kelley
#'
#' @family classes provided by oce
#' @family things related to section data
setClass("section", contains="oce")
#' Hydrographic section
#'
#' This is line A03 (ExpoCode 90CT40_1, with nominal sampling date 1993-09-11).
#' The chief scientist was Tereschenkov of SOI, working aboard the Russian ship
#' Multanovsky, undertaking a westward transect from the Mediterranean outflow
#' region across to North America, with a change of heading in the last few dozen
#' stations to run across the nominal Gulf Stream axis.
#' The data flags follow the "WHP Bottle"convention, set by
#' [initializeFlagScheme,section-method()] to `"WHP bottle"`. This convention
#' used to be described at the link
#' `https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/whp/exchange/exchange_format_desc.htm`
#' but that was found to fail in December 2020.
#'
#' @section Speculation on a timing error:
#' In May 2022, it was discovered that the times in this dataset are not fully
#' sequential, at two spots. This might be a reporting error. Station 41 has
#' time listed as 1993-10-03T00:06:00 and that leads to a time reversal.
#' However, if that time were actually on the day before, then
#' the time reversal would vanish, and the inter-station timing of
#' about 5 to 6 hours would be recovered. A similar pattern is seen at station
#' 45. Of course, this hypothesis of incorrect recording is difficult to test,
#' for data taken thirty years ago.
#'
#' @examples
#' library(oce)
#' # Gulf Stream
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 5000, 100))
#' plot(GSg, span=1500) # increase span to show more coastline
#'
#' @name section
#'
#' @docType data
#'
#' @usage data(section)
#'
#' @source This is based on the WOCE file named `a03_hy1.csv`, downloaded
#' from `https://cchdo.ucsd.edu/cruise/90CT40_1`, 13 April 2015.
#'
#' @family datasets provided with oce
#' @family things related to section data
NULL
setMethod(f="initialize",
signature="section",
definition=function(.Object, filename="", sectionId="", ...) {
.Object <- callNextMethod(.Object, ...)
.Object@metadata$units <- NULL # senseless keeping these from oce()
.Object@metadata$flags <- NULL # senseless keeping these from oce()
.Object@metadata$filename <- filename
.Object@metadata$sectionId <- sectionId
.Object@processingLog$time <- presentTime()
.Object@processingLog$value <- "create 'section' object"
return(.Object)
})
# DEVELOPERS: please pattern functions and documentation on this, for uniformity.
# DEVELOPERS: You will need to change the docs, and the 3 spots in the code
# DEVELOPERS: marked '# DEVELOPER 1:', etc.
#' @title Handle flags in Section Objects
#'
#' @details
#' The default for `flags` is based on
#' calling [defaultFlags()] based on the
#' `metadata` in the first station in the section. If the
#' other stations have different flag schemes (which seems highly
#' unlikely for archived data), this will not work well, and indeed
#' the only way to proceed would be to use [handleFlags,ctd-method()]
#' on the stations, individually.
#'
#' @param object A [section-class] object.
#'
#' @template handleFlagsTemplate
#'
#' @references
#' The following link used to work, but was found to fail in December 2020.
#' 1. `https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/whp/exchange/exchange_format_desc.htm`
#'
#' @examples
#' library(oce)
#' data(section)
#' section2 <- handleFlags(section, flags=c(1,3:9))
#' par(mfrow=c(2, 1))
#' plotTS(section)
#' plotTS(section2)
#'
#' @family things related to section data
#' @aliases handleFlags.section
setMethod("handleFlags",
signature=c(object="section", flags="ANY", actions="ANY", where="ANY", debug="ANY"),
definition=function(object, flags=NULL, actions=NULL, where=where, debug=getOption("oceDebug")) {
# DEVELOPER 1: alter the next comment to explain your setup
if (is.null(flags)) {
flags <- defaultFlags(object[["station", 1]])
if (is.null(flags))
stop("must supply 'flags', or use initializeFlagScheme() on the section object first")
}
if (is.null(actions)) {
actions <- list("NA") # DEVELOPER 2: alter this line to suit a new data class
names(actions) <- names(flags)
}
if (any(sort(names(actions)) != sort(names(flags))))
stop("names of flags and actions must match")
res <- object
for (i in seq_along(res@data$station))
res@data$station[[i]] <- handleFlags(res@data$station[[i]], flags, actions, where=where, debug)
res
})
#' @templateVar class section
#' @templateVar details This applies `initializeFlagScheme` for each `ctd` station within the `stations` element of the `data` slot.
#' @template initializeFlagSchemeTemplate
#'
#' @examples
#'\dontrun{
#' data(section)
#' section <- read.section("a03_hy1.csv", sectionId="a03", institute="SIO",
#' ship="R/V Professor Multanovskiy", scientist="Vladimir Tereschenov")
#' sectionWithFlags <- initializeFlagScheme(section, "WHP bottle")
#' station1 <- sectionWithFlags[["station", 1]]
#' str(station1[["flagScheme"]])
#'}
setMethod("initializeFlagScheme",
c(object="section", name="ANY", mapping="ANY", default="ANY", update="ANY", debug="ANY"),
function(object, name=NULL, mapping=NULL, default=NULL, update=NULL, debug=getOption("oceDebug"))
{
res <- object
for (i in seq_along(object@data$station)) {
res@data$station[[i]] <- initializeFlagScheme(object@data$station[[i]],
name=name,
mapping=mapping,
default=default,
update=update,
debug=debug-1)
}
res@processingLog <-
processingLogAppend(res@processingLog,
paste("initializeFlagScheme(object",
", name=\"", name, "\"",
", mapping=", gsub("[ ]*", "", paste(as.character(deparse(mapping)))), ")",
", default=", gsub("[ ]*", "", paste(as.character(deparse(default)))), ")",
sep=""))
res
})
#' Summarize a Section Object
#'
#' Pertinent summary information is presented, including station locations,
#' distance along trac, etc.
#'
#' @param object An object of class `"section"`, usually, a result of a call
#' to [read.section()], [read.oce()], or
#' [as.section()].
#'
#' @param ... Further arguments passed to or from other methods.
#'
#' @return `NULL`
#'
#' @examples
#' library(oce)
#' data(section)
#' summary(section)
#'
#' @family things related to section data
#'
#' @author Dan Kelley
#' @aliases summary.section
setMethod(f="summary",
signature="section",
definition=function(object, ...) {
numStations <- length(object@data$station)
cat("Section Summary\n---------------\n\n")
cat("* Source: \"", object@metadata$filename, "\"\n", sep="")
cat("* ID: \"", object@metadata$sectionId, "\"\n", sep="")
if (numStations > 0) {
cat("* Overview of stations\n")
cat(sprintf("%5s %5s %9s %9s %7s %5s %16s\n", "Index", "ID", "Lon", "Lat", "Levels", "Depth", "Time"))
for (i in seq_len(numStations)) {
thisStn <- object@data$station[[i]]
id <- if (!is.null(thisStn@metadata$station) && "" != thisStn@metadata$station) {
thisStn@metadata$station
} else {
""
}
depth <- if ("waterDepth" %in% names(thisStn@metadata)) {
thisStn@metadata$waterDepth
} else {
NA
}
time <- if ("startTime" %in% names(thisStn@metadata)) {
thisStn[["startTime"]][1]
} else {
thisStn[["time"]][1]
}
cat(sprintf("%5d %5s %9.4f %9.4f %7.0f %5.0f %16s\n",
i, id,
thisStn[["longitude"]][1], thisStn[["latitude"]][1],
length(thisStn@data$pressure), depth,
format(time, "%Y-%m-%d %H:%M")))
}
names <- names(object@data$station[[1]]@metadata$flags)
if (!is.null(names)) {
cat("* Data-quality flags\n")
width <- 1 + max(nchar(names))
# assume all stations have identical flags
for (name in names) {
padding <- rep(" ", width - nchar(name))
cat(" ", name, ":", padding, sep="")
flags <- NULL
flagName <- paste0(name, "Flag")
for (i in seq_len(numStations)) {
flags <- c(flags, as.numeric(object@data$station[[i]][[flagName]]))
}
flagTable <- table(flags)
flagTableLength <- length(flagTable)
if (flagTableLength) {
for (i in seq_len(flagTableLength)) {
cat("\"", names(flagTable)[i], "\"", " ", flagTable[i], "", sep="")
if (i != flagTableLength) cat(", ") else cat("\n")
}
}
}
}
} else {
cat("* No stations\n")
}
if ("spine" %in% names(object@metadata)) {
if (2 == length(object@metadata$spine) &&
2 == sum(c("latitude", "longitude") %in% names(object@metadata$spine))) {
spine <- object@metadata$spine
cat("* Section spine\n")
cat(sprintf(" %8s %8s\n", "Lon", "Lat"))
for (i in seq_along(spine$longitude)) {
cat(sprintf(" %8.4f %8.4f\n", spine$longitude[i], spine$latitude[i]))
}
} else {
warning("malformed section spine")
}
}
processingLogShow(object)
invisible(NULL)
})
#' Extract Something From a Section Object
#'
#' @param x a [section-class] object.
#'
#' @templateVar class section
#'
#' @section Details of the Specialized Method:
#'
#' There are several possibilities, depending on the nature of `i`.
#'
#' * If `i` is `"?"`, then the return value is a list containing four items,
#' each of which is a character vector holding the names of things that can be
#' accessed with `[[`. This list is compiled by examining all the stations in
#' the object, and reporting an entry if it is found in any one of them. The
#' `data` and `metadata` items hold the names of entries in the object's data
#' and metadata slots, respectively. The `dataDerived` and `metadataDerived`
#' items hold data-like and metadata-like things that can be derived from these.
#'
#' * If `i` is `"station"`, then `[[` will return a [list()] of [ctd-class]
#' objects holding the station data. If `j` is also given, it specifies a
#' station (or set of stations) to be returned. if `j` contains just a single
#' value, then that station is returned, but otherwise a list is returned. If
#' `j` is an integer, then the stations are specified by index, but if it is
#' character, then stations are specified by the names stored within their
#' metadata. (Missing stations yield `NULL` in the return value.)
#'
#' * If `i` is `"station ID"`, then the IDs of the stations in the
#' section are returned.
#'
#' * If `i` is `"dynamic height"`, then an estimate of dynamic
#' height is returned, as calculated with [`swDynamicHeight`]`(x)`.
#'
#' * If `i` is `"distance"`, then the distance along the section is
#' returned, using [geodDist()].
#'
#' * If `i` is `"depth"`, then a vector containing the depths
#' of the stations is returned.
#'
#' * If `i` is `"z"`, then a vector containing the z
#' coordinates is returned.
#'
#' * If `i` is `"theta"` or `"potential temperature"`, then
#' the potential temperatures of all the stations are returned in one
#' vector. Similarly, `"spice"` returns the property known
#' as spice, using [swSpice()].
#'
#' * If `i` is a string ending with `"Flag"`, then the characters
#' prior to that ending are taken to be the name of a variable contained
#' within the stations in the section. If this flag is available in
#' the first station of the section, then the flag values are looked
#' up for every station.
#'
#' If `j` is `"byStation"`, then a list is returned, with
#' one (unnamed) item per station.
#'
#' If `j` is `"grid:distance-pressure"` or `"grid:time-pressure"`, then a gridded
#' representation of `i` is returned, as a list with elements:
#' `distance` (in km) or `time` (in POSIXct); `pressure` (in dbar) and
#' `field` (in whatever unit is used for `i`). See the
#' examples in the documentation for [plot,section-method()].
#'
#' @examples
#' data(section)
#' length(section[["latitude"]])
#' length(section[["latitude", "byStation"]])
#' # Vector of all salinities, for all stations
#' Sv <- section[["salinity"]]
#' # List of salinities, grouped by station
#' Sl <- section[["salinity", "byStation"]]
#' # First station salinities
#' Sl[[1]]
#'
#' @template sub_subTemplate
#'
#' @author Dan Kelley
#'
#' @family things related to section data
setMethod(f="[[",
signature(x="section", i="ANY", j="ANY"),
definition=function(x, i, j, ...) {
# This is broken down into 3 cases.
#
# Case 1. Things that can be computed without looking deeply
# enough within @data$station to determine computable things.
# This determination is a bit slow, and is taken up in SECTION 2.
#
# Case 1.1 "station".
if (i == "station") {
# All stations.
if (missing(j))
return(x@data$station)
# A subset of stations, specified with j, which is either a
# character value for station ID(s) or a numeric value for
# sequence number(s).
nj <- length(j)
if (is.character(j)) { # station ID(s)
stationNames <- unlist(lapply(x@data$station,
function(station) station@metadata$station))
if (nj == 1L) {
w <- which(stationNames == j)
res <- if (length(w)) x@data$station[[w[1]]] else NULL
} else {
res <- vector("list", nj)
for (jj in seq_len(nj)) {
w <- which(stationNames == j[jj])
res[[jj]] <- if (length(w)) x@data$station[[w[1]]] else NULL
}
}
} else { # sequence number(s)
if (nj == 1L) {
res <- x@data$station[[j]]
} else {
res <- vector("list", nj)
for (jj in seq_len(nj)) {
res[[jj]] <- x@data$station[[j[jj]]]
}
}
}
return(res)
}
# Case 1.2: data-quality flags.
if (1 == length(grep(".*Flag$", i))) {
baseName <- gsub("Flag$", "", i)
# FIXME: should check all stations, not just the first
if (baseName %in% names(x@data$station[[1]]@metadata$flags)) {
return(unlist(lapply(x@data$station, function(ctd) ctd[[i]])))
} else {
stop("First station lacks a '", baseName, "' flag, so assuming the same for all.")
}
}
# Case 1.3: things stored in section@metadata.
if (i %in% names(x@metadata)) {
if (i %in% c("longitude", "latitude")) {
if (!missing(j) && j == "byStation") {
return(x@metadata[[i]])
} else {
res <- NULL
for (stn in seq_along(x@data$station)) {
res <- c(res, rep(x@data$station[[stn]]@metadata[[i]], length(x@data$station[[stn]][["salinity"]])))
}
return(res)
}
} else {
return(x@metadata[[i]])
}
}
# Case 1.4: station IDs.
if (i == "station ID") {
return(unlist(lapply(x@data$station, function(stn) stn[["station"]])))
}
# Case 1.5: dynamic height.
if (i == "dynamic height") {
return(swDynamicHeight(x))
}
# Case 1.6: distance along track.
if (i == "distance") {
res <- NULL
for (stn in seq_along(x@data$station)) {
distance <- geodDist(x@data$station[[stn]][["longitude"]][1],
x@data$station[[stn]][["latitude"]][1],
x@data$station[[1]][["longitude"]][1],
x@data$station[[1]][["latitude"]][1])
if (!missing(j) && j == "byStation") {
res <- c(res, distance)
} else {
res <- c(res, rep(distance, length(x@data$station[[stn]]@data$temperature)))
}
}
return(res)
}
# Case 1.7: time.
if (i == "time") {
return(numberAsPOSIXct(unlist(lapply(x@data$station, function(stn) stn[["time"]]))))
}
# Case 2. We are now done with things that can be determined by
# looking one level deep. To extract individual data, we will
# need to know what is in each of the stations (and what can be
# computed from this content).
metadataStn <- dataStn <- metadataDerivedStn <- dataDerivedStn <- NULL
for (station in x@data$station) {
q <- station[["?"]]
metadataStn <- c(metadataStn, q$metadata)
metadataDerivedStn <- c(metadataDerivedStn, q$metadataDerived)
dataStn <- c(dataStn, q$data)
dataDerivedStn <- c(dataDerivedStn, q$dataDerived)
}
metadataStn <- sort(unique(metadataStn))
metadataDerivedStn <- sort(c(
"distance",
paste("station", "ID"),
paste("dynamic", "height"),
unique(metadataDerivedStn)))
dataStn <- sort(unique(dataStn))
dataDerivedStn <- sort(unique(dataDerivedStn))
# Case 2.1: indication of available values for i.
if (i == "?") {
return(list(metadata=metadataStn,
metadataDerived=metadataDerivedStn,
data=dataStn,
dataDerived=dataDerivedStn))
}
# Case 2.2: something inside stations (or computable from such).
res <- NULL
nstation <- length(x@data$station)
if (i %in% c(dataStn, dataDerivedStn)) {
if (!missing(j) && substr(j, 1, 4) == "grid") {
if (j %in% c("grid:distance-pressure", "grid:time-pressure")) {
numStations <- length(x@data$station)
p1 <- x[["station", 1]][["pressure"]]
np1 <- length(p1)
field <- matrix(NA, nrow=numStations, ncol=np1)
if (numStations > 1) {
field[1, ] <- x[["station", 1]][[i]]
for (istn in 2:numStations) {
pi <- x[["station", istn]][["pressure"]]
if (length(pi) != np1 || any(pi != p1)) {
warning("returning NULL because this section is not gridded")
return(NULL)
}
field[istn, ] <- x[["station", istn]][[i]]
}
if (j == "grid:distance-pressure") {
res <- list(distance=x[["distance", "byStation"]], pressure=p1, field=field)
} else if (j == "grid:time-pressure") {
res <- list(time=x[["time", "byStation"]], pressure=p1, field=field)
}
return(res)
} else {
warning("returning NULL because this section contains only 1 station")
return(NULL)
}
} else {
warning("only grid:distance-pressure and grid:time-pressure are permitted")
return(NULL)
}
} else {
# Note that nitrite and nitrate might be computed, not stored
if (!missing(j) && j == "byStation") {
res <- vector("list", nstation)
for (istation in seq_len(nstation)) {
res[[istation]] <- x@data$station[[istation]][[i]]
}
} else {
res <- NULL
for (station in x[["station"]]) {
res <- c(res, station[[i]])
}
}
return(res)
}
}
# Case 3: unknown, so drop down a level.
callNextMethod()
}) # [[
#' Replace Parts of a Section Object
#'
#' @param x a [section-class] object.
#'
#' @template sub_subsetTemplate
#'
#' @examples
#' # 1. Change section ID from a03 to A03
#' data(section)
#' section[["sectionId"]]
#' section[["sectionId"]] <- toupper(section[["sectionId"]])
#' section[["sectionId"]]
#' # 2. Add a millidegree to temperatures at station 10
#' section[["station", 10]][["temperature"]] <-
#' 1e-3 + section[["station", 10]][["temperature"]]
#'
#' @author Dan Kelley
#'
#' @family things related to section data
setMethod(f="[[<-",
signature(x="section", i="ANY", j="ANY"),
definition=function(x, i, j, ..., value) {
if (i == "station") {
if (missing(j)) {
x@data$station <- value
} else {
x@data$station[[j]] <- value
}
x
} else {
callNextMethod(x=x, i=i, j=j, ...=..., value=value) # [[<-
}
})
setMethod(f="show",
signature="section",
definition=function(object) {
id <- object@metadata$sectionId
n <- length(object@data$station)
if (n == 0) {
cat("Section has no stations\n")
} else {
if (is.null(id) || id == "") {
cat("Unnamed section has ", n, " stations:\n", sep="")
} else {
cat("Section '", id, "' has ", n, " stations:\n", sep="")
}
cat(sprintf("%5s %5s %8s %8s %7s %5s\n", "Index", "ID", "Lon", "Lat", "Levels", "Depth"))
for (i in 1:n) {
thisStn <- object@data$station[[i]]
id <- if (!is.null(thisStn@metadata$station) && "" != thisStn@metadata$station) {
thisStn@metadata$station
} else {
""
}
depth <- if (is.null(thisStn@metadata$waterDepth)) {
max(thisStn@data$pressure, na.rm=TRUE)
} else {
thisStn@metadata$waterDepth
}
cat(sprintf("%5d %5s %8.3f %8.3f %7.0f %5.0f\n",
i, id,
thisStn[["longitude"]][1], thisStn[["latitude"]][1],
length(thisStn@data$pressure), depth))
}
}
})
#' Subset a Section Object
#'
#' Return a subset of a section object.
#'
#' This function is used to subset data within the
#' stations of a section, or to choose a subset of the stations
#' themselves. The first case is handled with the `subset` argument,
#' while the second is handled if `...` contains a vector named
#' `indices`. Either `subset` or `indices` must be
#' provided, but not both.
#'
#' **In the "subset" method**, `subset` indicates
#' either stations to be kept, or data to be kept within the stations.
#'
#' The first step in processing is to check for the presence of certain
#' key words in the `subset` expression. If `distance`
#' is present, then stations are selected according to a condition on the
#' distance (in km) from the first station to the given station (Example 1).
#' If either `longitude` or `latitude` is given, then
#' stations are selected according to the stated condition (Example 2).
#' If `stationId` is present, then selection is in terms of the
#' station ID (not the sequence number) is used (Example 3). In all of these
#' cases, stations are either selected in their entirety or dropped
#' in their entirety.
#'
#' If none of these keywords is present, then the `subset` expression is
#' evaluated in the context of the `data` slot of each of the CTD stations
#' stored within `x`. (Note that this slot does not normally
#' contain any of the keywords that are listed in the previous
#' paragraph; it does, then odd results may occur.) Each station is examined
#' in turn, with `subset` being evaluated individually in each. The evaluation
#' produces a logical vector. If that vector has length 1 (Examples 4 and 5)
#' then the station is retained or discarded, accordingly. If the vector is longer,
#' then the logical vector is used as a sieve to subsample that individual CTD
#' profile.
#'
#' **In the "indices" method**, stations are selected using
#' `indices`, which may be a vector of integers that indicate sequence
#' number, or a logical vector, again indicating which stations to keep.
#'
#' @param x a [section-class] object.
#'
#' @param subset an optional indication of either the stations to be kept,
#' or the data to be kept within the stations. See \dQuote{Details}.
#'
#' @param ... optional arguments, of which only the first is examined. The
#' possibilities for this argument are `indices`, which must be a
#' vector of station indices (see Example 6), or `within`, which must be
#' a list or data frame, containing items named either `x` and `y`
#' or `longitude` and `latitude` (see Example 7). If `within`
#' is given, then `subset` is ignored.
#'
#' @return A [section-class] object.
#'
#' @examples
#' library(oce)
#' data(section)
#'
#' # Example 1. Stations within 500 km of the first station
#' starting <- subset(section, distance < 500)
#'
#' # Example 2. Stations east of 50W
#' east <- subset(section, longitude > (-50))
#'
#' # Example 3. Gulf Stream
#' GS <- subset(section, 113<=stationId&stationId<=129)
#'
#' # Example 4. Only stations with more than 5 pressure levels
#' long <- subset(section, length(pressure) > 5)
#'
#' # Example 5. Only stations that have some data in top 50 dbar
#' surfacing <- subset(section, min(pressure) < 50)
#'
#' # Example 6. Similar to #4, but done in more detailed way
#' long <- subset(section,
#' indices=unlist(lapply(section[["station"]],
#' function(s)
#' 5 < length(s[["pressure"]]))))
#'
#' # Example 7. Subset by a polygon determined with locator()
#'\dontrun{
#' par(mfrow=c(2, 1))
#' plot(section, which="map")
#' bdy <- locator(4) # choose a polygon near N. America
#' GS <- subset(section, within=bdy)
#' plot(GS, which="map")
#'}
#'
#' @author Dan Kelley
#'
#' @family functions that subset oce objects
#' @family things related to section data
#' @aliases subset.section
setMethod(f="subset",
signature="section",
definition=function(x, subset, ...) {
subsetString <- paste(deparse(substitute(expr=subset, env=environment())), collapse=" ")
res <- x
dots <- list(...)
dotsNames <- names(dots)
indicesGiven <- length(dots) && ("indices" %in% dotsNames)
withinGiven <- length(dots) && ("within" %in% dotsNames)
debug <- getOption("oceDebug")
if (length(dots) && ("debug" %in% names(dots)))
debug <- dots$debug
if (indicesGiven) {
# select a portion of the stations
if (!missing(subset))
stop("cannot specify both 'subset' and 'indices'")
oceDebug(debug, "subsetting by indices\n")
res <- new("section")
indices <- dots$indices
n <- length(indices)
if (is.logical(indices))
indices <- (1:n)[indices]
if (min(indices) < 1)
stop("cannot have negative indices")
if (max(indices) > length(x@data$station))
stop("cannot indices exceeding # stations")
stn <- x@metadata$stationId[indices]
lat <- x@metadata$lat[indices]
lon <- x@metadata$lon[indices]
time <- x@metadata$time[indices]
station <- vector("list", length(indices))
for (i in seq_along(indices))
station[[i]] <- x@data$station[[indices[i]]]
data <- list(station=station)
res@metadata$stationId <- stn
res@metadata$longitude <- lon
res@metadata$latitude <- lat
res@metadata$time <- time
res@data <- data
res@processingLog <- x@processingLog
res@processingLog <- processingLogAppend(res@processingLog,
paste("subset(x, indices=c(", paste(dots$indices, collapse=","), "))", sep=""))
} else if (withinGiven) {
polygon <- dots$within
if (!is.data.frame(polygon) && !is.list(polygon))
stop("'within' must be a data frame or a polygon")
polygonNames <- names(polygon)
lonp <- if ("x" %in% polygonNames) {
polygon$x
} else if ("longitude" %in% polygonNames) {
polygon$longitude
} else {
stop("'within' must contain either 'x' or 'longitude'")
}
latp <- if ("y" %in% polygonNames) {
polygon$y
} else if ("latitude" %in% polygonNames) {
polygon$latitude
} else {
stop("'within' must contain either 'y' or 'latitude'")
}
lon <- x[["longitude", "byStation"]]
lat <- x[["latitude", "byStation"]]
polyNew <- sf::st_polygon(list(outer=cbind(c(lonp, lonp[1]), c(latp, latp[1]))))
pointsNew <- sf::st_multipoint(cbind(lon, lat))
inside <- sf::st_intersection(pointsNew, polyNew)
keep <- matrix(pointsNew %in% inside, ncol=2)[, 1]
res <- x
res@metadata$stationId <- x@metadata$stationId[keep]
res@metadata$longitude <- x@metadata$longitude[keep]
res@metadata$latitude <- x@metadata$latitude[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- x@data$station[keep]
res@processingLog <- processingLogAppend(res@processingLog,
paste("subset(x, within) kept ", sum(keep), " of ",
length(keep), " stations", sep=""))
} else {
if (missing(subset))
stop("must give 'subset' or (in ...) 'indices'")
oceDebug(debug, "subsetting by 'subset'\n")
res <- x
if (grepl("stationId", subsetString)) {
keep <- eval(expr=substitute(expr=subset, env=environment()),
envir=data.frame(stationId=as.numeric(x@metadata$stationId)))
res@metadata$stationId <- x@metadata$stationId[keep]
res@metadata$longitude <- x@metadata$longitude[keep]
res@metadata$latitude <- x@metadata$latitude[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- x@data$station[keep]
res@processingLog <- processingLogAppend(res@processingLog, paste("subset(x, subset=", subsetString, ")", sep=""))
} else if (grepl("distance", subsetString)) {
l <- list(distance=geodDist(res))
keep <- eval(expr=substitute(expr=subset, env=environment()), envir=l, enclos=parent.frame(2))
res@metadata$longitude <- res@metadata$longitude[keep]
res@metadata$latitude <- res@metadata$latitude[keep]
res@metadata$stationId <- res@metadata$stationId[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- res@data$station[keep]
} else if (grepl("levels", subsetString)) {
levels <- unlist(lapply(x[["station"]], function(stn) length(stn[["pressure"]])))
keep <- eval(expr=substitute(expr=subset, env=environment()), envir=list(levels=levels))
res@metadata$longitude <- res@metadata$longitude[keep]
res@metadata$latitude <- res@metadata$latitude[keep]
res@metadata$stationId <- res@metadata$stationId[keep]
res@metadata$time <- x@metadata$time[keep]
res@data$station <- res@data$station[keep]
} else if (grepl("latitude", subsetString) || grepl("longitude", subsetString)) {
n <- length(x@data$station)
keep <- vector(length=n)
for (i in 1:n) {
keep[i] <- eval(expr=substitute(expr=subset, env=environment()), envir=x@data$station[[i]]@metadata, enclos=parent.frame(2))
}
nn <- sum(keep)
station <- vector("list", nn)
stn <- NULL # we can later index this to accumulate
lon <- NULL
lat <- NULL
time <- NULL
j <- 1
for (i in 1:n) {
if (keep[i]) {
stn[j] <- x@metadata$stationId[i]
lon[j] <- x@metadata$longitude[i]
lat[j] <- x@metadata$latitude[i]
station[[j]] <- x@data$station[[i]]
time[[j]] <- x@data$time[[i]]
j <- j + 1
}
}
res <- new("section")
res@data$station <- station
res@metadata$header <- x@metadata$header
res@metadata$sectionId <- x@metadata$sectionId
res@metadata$stationId <- stn
res@metadata$longitude <- lon
res@metadata$latitude <- lat
res@metadata$time <- time
res@processingLog <- x@processingLog
} else {
res <- new("section")
res@data$station <- list()
res@metadata$header <- x@metadata$header
res@metadata$sectionId <- NULL # R will let us index into these later
res@metadata$stationId <- NULL
res@metadata$longitude <- NULL
res@metadata$latitude <- NULL
res@metadata$time <- NULL
res@processingLog <- x@processingLog
n <- length(x@data$station)
j <- 1
for (i in 1:n) {
r <- eval(expr=substitute(expr=subset, env=environment()), envir=x@data$station[[i]]@data, enclos=parent.frame(2))
oceDebug(debug, "i=", i, ", j=", j, ", sum(r)=", sum(r), "\n", sep="")
if (sum(r) > 0) {
# copy whole station ...
res@data$station[[j]] <- x@data$station[[i]]
# ... but if we are looking for a subset, go through the data fields and do that
if (length(r) > 1) {
# Select certain levels. This occurs e.g. for
# subset(sec, S > 35)
# but not for station-by-station selection, e.g. as a result of
# subset(sec, length(S) > 3)
# since the length of the latter is 1, which means to copy
# the whole station.
for (field in names(res@data$station[[j]]@data)) {
oceDebug(debug, " field='", field, "', i=", i, ", j=", j, " (case A)\n", sep="")
res@data$station[[j]]@data[[field]] <- res@data$station[[j]]@data[[field]][r]
}
}
# copy section metadata
res@metadata$stationId[j] <- x@metadata$stationId[i]
res@metadata$latitude[j] <- x@metadata$latitude[i]
res@metadata$longitude[j] <- x@metadata$longitude[i]
res@metadata$time[j] <- x@metadata$time[i]
j <- j + 1
} else {
oceDebug(debug, " skipping this station\n")
}
}
}
res@processingLog <- processingLogAppend(res@processingLog, paste("subset(x, subset=", subsetString, ")", sep=""))
}
res
})
#' Sort a Section
#'
#' Sections created with [as.section()] have "stations" that are in the
#' order of the CTD objects (or filenames for such objects) provided. Sometimes,
#' this is not the desired order, e.g. if file names discovered with
#' [dir()] are in an order that makes no sense. (For example, a
#' practioner might name stations `"stn1"`, `"stn2"`, etc., not
#' realizing that this will yield an unhelpful ordering, by file name, if there
#' are more than 9 stations.) The purpose of `sectionSort` is to permit
#' reordering the constituent stations in sensible ways.
#'
#' @param section A `section` object containing the section whose stations
#' are to be sorted.
#'
#' @param by An optional string indicating how to reorder. If not provided,
#' `"stationID"` will be assumed. Other choices are `"distance"`, for
#' distance from the first station, `"longitude"`, for longitude,
#' `"latitude"` for latitude, and `"time"`, for time.
#'
#' @param decreasing A logical value indicating whether to sort in decreasing
#' order. The default is FALSE. (Thanks to Martin Renner for adding this
#' parameter.)
#'
#' @return object A [section-class] object that has been smoothed,
#' so its data fields will station-to-station variation than
#' is the case for the input section, \code{x}.
#'
#' @examples
#' library(oce)
#' data(section)
#' sectionByLongitude <- sectionSort(section, by="longitude")
#' head(section)
#' head(sectionByLongitude)
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionSort <- function(section, by, decreasing=FALSE)
{
if (missing(by)) {
by <- "stationId"
} else {
byChoices <- c("stationId", "distance", "longitude", "latitude", "time", "spine")
iby <- match(by, byChoices, nomatch=0)
if (0 == iby)
stop('unknown by value "', by, '"; should be one of: ', paste(byChoices, collapse=" "))
by <- byChoices[iby]
}
res <- section
if (by == "stationId") {
o <- order(section@metadata$stationId, decreasing=decreasing)
} else if (by == "distance") {
o <- order(section[["distance", "byStation"]], decreasing=decreasing)
} else if (by == "longitude") {
o <- order(section[["longitude", "byStation"]], decreasing=decreasing)
} else if (by == "latitude") {
o <- order(section[["latitude", "byStation"]], decreasing=decreasing)
} else if (by == "time") {
# FIXME: should check to see if startTime exists first?
times <- unlist(lapply(section@data$station, function(x) x@metadata$startTime))
o <- order(times, decreasing=decreasing)
} else if (by == "spine") {
stop("not implemented for spine")
} else {
o <- seq_along(section[["station"]]) # can't get here, actually
}
res@metadata$stationId <- res@metadata$stationId[o]
res@metadata$longitude <- res@metadata$longitude[o]
res@metadata$latitude <- res@metadata$latitude[o]
if ("time" %in% names(res@metadata)) {
res@metadata$time <- res@metadata$time[o]
}
res@data$station <- res@data$station[o]
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
res
}
#' Add a CTD Station to a Section
#'
#' Add a CTD profile to an existing section.
#'
#' @section Historical note:
#' Until March 2015, this operation was carried out with the `+` operator,
#' but at that time, the syntax was flagged by the development version of R, so it
#' was changed to the present form.
#'
#' @param section A section to which a station is to be added.
#'
#' @param station A ctd object holding data for the station to be added.
#'
#' @aliases sectionAddCtd
#' @return A [section-class] object.
#'
#' @examples
#' library(oce)
#' data(ctd)
#' ctd2 <- ctd
#' ctd2[["temperature"]] <- ctd2[["temperature"]] + 0.5
#' ctd2[["latitude"]] <- ctd2[["latitude"]] + 0.1
#' section <- as.section(c("ctd", "ctd2"))
#' ctd3 <- ctd
#' ctd3[["temperature"]] <- ctd[["temperature"]] + 1
#' ctd3[["latitude"]] <- ctd[["latitude"]] + 0.1
#' ctd3[["station"]] <- "Stn 3"
#' sectionAddStation(section, ctd3)
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionAddStation <- function(section, station)
{
if (missing(section))
stop("must provide a section to which the ctd is to be added")
if (!inherits(section, "section"))
stop("'section' is not a 'section' object")
if (missing(station))
return(section)
if (!inherits(station, "ctd"))
stop("'station' is not a 'ctd' object")
res <- section
n.orig <- length(section@data$station)
s <- vector("list", n.orig + 1)
for (i in 1:n.orig) {
s[[i]] <- section@data$station[[i]]
}
s[[n.orig + 1]] <- station
res@data$station <- s
res@metadata$longitude <- c(res@metadata$longitude, station@metadata$longitude)
res@metadata$latitude <- c(res@metadata$latitude, station@metadata$latitude)
res@metadata$stationId <- c(res@metadata$stationId, station@metadata$station)
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
res
}
sectionAddCtd <- sectionAddStation
#' Plot a section Object
#'
#' Creates a summary plot for a CTD section, with one panel for each value of
#' `which`.
#'
#' The type of plot is governed by `which`, as follows.
#' * `which=0` or `"potential temperature"` for potential temperature contours
#' * `which=1` or `"temperature"` for in-situ temperature contours (the default)
#' * `which=2` or `"salinity"` for salinity contours
#' * `which=3` or `"sigmaTheta"` for sigma-theta contours
#' * `which=4` or `"nitrate"` for nitrate concentration contours
#' * `which=5` or `"nitrite"` for nitrite concentration contours
#' * `which=6` or `"oxygen"` for oxygen concentration contours
#' * `which=7` or `"phosphate"` for phosphate concentration contours
#' * `which=8` or `"silicate"` for silicate concentration contours
#' * `which=9` or `"u"` for eastward velocity
#' * `which=10` or `"uz"` for vertical derivative of eastward velocity
#' * `which=11` or `"v"` for northward velocity
#' * `which=12` or `"vz"` for vertical derivative of northward velocity
#' * `which=20` or `"data"` for a dot for each data location
#' * `which=99` or `"map"` for a location map
#'
#' The y-axis for the contours is pressure, plotted in the conventional reversed
#' form, so that the water surface appears at the top of the plot. The x-axis is
#' more complicated. If `at` is not supplied, then the routine calculates x
#' as the distance between the first station in the section and each of the other
#' stations. (This will produce an error if the stations are not ordered
#' geographically, because the [contour()] routine cannot handle
#' non-increasing axis coordinates.) If `at` is specified, then it is taken
#' to be the location, in arbitrary units, along the x-axis of labels specified by
#' `labels`; the way this works is designed to be the same as for
#' [axis()].
#'
#' @param x a [section-class] object.
#'
#' @param which a list of desired plot types, as explained in \dQuote{Details}.
#' There may be up to four panels in total, and the desired plots are placed in
#' these panels, in reading order. If only one panel is plotted, `par` is
#' not adjusted, which makes it easy to add to the plot with subsequent plotting
#' commands.
#'
#' @template eosTemplate
#'
#' @param at If `NULL` (the default), the x axis will indicate the distance
#' of the stations from the first in the section. (This may give errors in the
#' contouring routine, if the stations are not present in a geographical order.)
#' If a list, then it indicates the values at which stations will be plotted.
#'
#' @param labels Either a logical, indicating whether to put labels on the x axis,
#' or a vector that is a list of labels to be placed at the x positions indicated
#' by `at`.
#'
#' @param grid If `TRUE`, points are drawn at data locations.
#'
#' @param contourLevels Optional contour levels.
#'
#' @param contourLabels Optional contour labels.
#'
#' @param stationIndices Optional list of the indices of stations to use. Note
#' that an index is *not* a station number, e.g. to show the first 4
#' stations, use `station.indices=1:4`.
#'
#' @param coastline Either a [coastline-class] object to be used,
#' or a string. In the second case, the permitted
#' choices are `"best"` (the default) to pick
#' a variant that suits the scale, `"coastlineWorld"` for the coarse
#' version that is provided by \CRANpkg{oce},
#' `"coastlineWorldMedium"` or `"coastlineWorldFine"` for two
#' coastlines provided by the \CRANpkg{ocedata} package, or `"none"`, to avoid
#' drawing a coastline.
#'
#' @param colLand colour used to fill in land areas if `which` is `"map"`;
#' ignored otherwise.
#'
#' @param xlim Optional limit for x axis (only in sections, not map).
#'
#' @param ylim Optional limit for y axis (only in sections, not map)
#'
#' @param zlim,zbreaks,zcol Elements that control colours for `image` and `points`
#' plot types, i.e. if `ztype` is either `"points"` or `"image"`.
#' `zlim` is a two-element numerical vector specifying the
#' limit on the plotted field. If not provided, it defaults to the data
#' range.
#' `zbreaks` controls the colour breaks, in a manner that is similar to
#' the [image()] parameter named `breaks`. If not provided, `zbreaks` is
#' inferred from `zlim`.
#' `zcol`, which controls the colour scheme, may be a vector of colours
#' (of length 1 less than `zbreaks`), or a function that takes an
#' integer as its sole argument and returns that number of colours.
#' If not provided, `zcol` defaults to [oceColorsViridis()].
#' These three parameters are used in Example 6, an illustration of
#' Atlantic salinity along 36N.
#'
#' @param map.xlim,map.ylim Optional limits for station map; `map.ylim` is
#' ignored if `map.xlim` is provided.
#'
#' @param clongitude,clatitude,span Optional map centre position and span (km).
#'
#' @param projection Parameter specifying map
#' projection; see [mapPlot()]. If `projection="automatic"`,
#' however, a projection is devised from the data, with `stereographic` if
#' the mean latitude exceeds 70N and `mollweide` otherwise.
#'
#' @param xtype Type of x axis, for contour plots, either `"distance"` for
#' distance (in km) to the first point in the section, `"track"` for distance
#' along the cruise track, `"longitude"`, `"latitude"`,
#' `"time"` or `"spine"` (distance along a spine that was added
#' with [addSpine()]). Note that if the x values are not in order, they will be put in
#' order, and since that might not make physical sense, a warning will be issued.
#'
#' @param longitude0,latitude0 Location of the point from which distance is measured.
#' These values are ignored unless `xtype` is `"distance"`.
#'
#' @param ytype Type of y axis for contour plots, either `"pressure"` for
#' pressure (in dbar, with zero at the surface) or `"depth"` for depth (in m
#' below the surface, calculated from pressure with [swDepth()]).
#'
#' @param ztype String indicating whether to how to indicate the "z"
#' data (in the R sense, i.e. this could be salinity, temperature, etc; it does
#' not mean the vertical coordinate) The choices are: `"contour"` for
#' contours, `"image"` for an image (drawn with [imagep()] with
#' `filledContours=TRUE`), or `"points"` to draw points.
#' In the first two cases, the data must be gridded, with identical pressures at
#' each station.
#'
#' @param legend.loc Location of legend, as supplied to [legend()], or
#' set to the empty string to avoid plotting a legend.
#'
#' @param legend.text character value indicating the text for the legend.
#' If this is NULL (the default) then the legend is automatically
#' constructed by [labelWithUnit()], based on the value of `which`.
#'
#' @param showStations Logical indicating whether to draw station numbers on maps.
#'
#' @param showStart Logical indicating whether to indicate the first station with
#'
#' @param stationTicks A logical value indicating whether to indicate station
#' locations with ticks at the top margin of cross-section plots. Setting this
#' parameter to `FALSE` frees the user up to do their own labelling
#' at this spot.
#'
#' @param showBottom An indication of whether (and how) to indicate the ocean bottom.
#' If `FALSE`, then the bottom is not rendered. If `TRUE`, then it
#' is rendered with a gray polygon. If `showBottom` is a character string,
#' then there are three possibilities: is the string is `"polygon"` then
#' a polygon is drawn, if it is `"lines"` then a line is drawn, and if it
#' is `"points"` then points are drawn. If `showBottom` is
#' a [topo-class] object,
#' then the station locations are
#' interpolated to that topography and the results are shown with a polygon
#' (and, for this case, note that the section data and the topography must
#' use the same longitude convention, either 0 to 360 or -180 to 180.)
#' In this last case, the interpolation is set at a grid that is roughly
#' in accordance with the resolution of the latitudes in the `topo` object.
#' See \dQuote{Examples}.
#'
#' @param showBottom a value indicating whether (and how) to indicate the
#' ocean bottom on cross-section views. There are three possibilities.
#' (a) If `showBottom` is `FALSE`, then the bottom is not rendered. If it
#' is `TRUE`, then the bottom is rendered with a gray polygon.
#' (b) If `showBottom` is the character value `"polygon"`, then a polygon is drawn,
#' and similarly lines are drawn for `"lines"`, and points for `"points"`.
#' (c) If `showBottom` is a [topo-class] object, then the station locations are
#' interpolated to that topography and the results are shown with a polygon.
#' See \dQuote{Examples}.
#'
#' @param showSpine logical value used if `which="map"`. If `showSpine` is
#' `TRUE` and `section` has had a spine added with [addSpine()], then
#' the spine is drawn in blue.
#'
#' @param drawPalette logical value indicating whether to draw a palette when `ztype="image"`
#' ignored otherwise.
#'
#' @param axes Logical value indicating whether to draw axes.
#'
#' @param mgp A 3-element numerical vector to use for `par(mgp)`, and also for
#' `par(mar)`, computed from this. If not provided, this defaults to
#' [`getOption`]`("oceMgp")`.
#'
#' @param mar Value to be used with [`par`]`("mar")`. If not provided,
#' a default is set up.
#'
#' @param col Color for line types. If not provided, this defaults to
#' [`par`]`("col")`. See `zcol`, for `ztype="image"` and `ztype="points"`.
#'
#' @param cex Numerical character-expansion factor, which defaults to [`par`]`("cex")`.
#'
#' @param pch Indication of symbol type; defaults to [`par`]`("pch")` for
#' non-map or to 3 for map.
#'
#' @param lwd line width; defaults to [`par`]`("lwd")`.
#'
#' @param labcex Size of characters in contour labels (passed to
#' [contour()]).
#'
#' @template debugShortTemplate
#'
#' @param ... Optional arguments passed to the contouring function.
#'
#' @return If the original section was gridded, the return value is that section.
#' Otherwise, the gridded section that was constructed for the plot is returned.
#' In both cases, the value is returned silently. The
#' purpose of returning the section is to enable subsequent processing
#' of the grid, including adding elements to the plot (see example 5).
#'
#' @seealso The documentation for [section-class] explains the
#' structure of section objects, and also outlines the other functions dealing
#' with them.
#'
#' @section Ancillary Examples:
#'
#' The following examples were once part of the \dQuote{Examples}
#' section, but were moved here in May 2022, to reduce the build-check
#' time for CRAN submission.
#'```
#' library(oce)
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 2000, 100))
#'
#' # Gulf Stream, salinity data and contoured
#' par(mfrow=c(2, 1))
#' plot(GS, which=1, ylim=c(2000, 0), ztype="points",
#' zbreaks=seq(0,30,2), pch=20, cex=3)
#' plot(GSg, which=1, ztype="image", zbreaks=seq(0,30,2))
#'
#' # Gulf Stream, temperature grid (image) and data (dots)
#' par(mfrow=c(1, 1))
#' plot(GSg, which=1, ztype="image")
#' T <- GS[["temperature"]]
#' col <- oceColorsViridis(100)[rescale(T, rlow=1, rhigh=100)]
#' points(GS[["distance"]],GS[["depth"]],pch=20,cex=3,col="white")
#' points(GS[["distance"]],GS[["depth"]],pch=20,cex=2.5,col=col)
#'
#' # 4. Image of temperature, with a high-salinity contour on top;
#' # note the Mediterranean water.
#' sec <- plot(section, which="temperature", ztype="image")
#' S <- sec[["salinity", "grid:distance-pressure"]]
#' contour(S$distance, S$pressure, S$field, level=35.8, lwd=3, add=TRUE)
#'
#' # 5. Contours of salinity, with dots for high pressure and spice
#' plot(section, which="salinity")
#' distance <- section[["distance"]]
#' depth <- section[["depth"]]
#' spice <- section[["spice"]]
#' look <- spice > 1.8 & depth > 500
#' points(distance[look], depth[look], col="red")
#'
#' # Image of Absolute Salinity, with 4-minute bathymetry
#' # It's easy to calculate the desired area for the bathymetry,
#' # but for brevity we'll hard-code it. Note that download.topo()
#' # requires the "ncdf4" package to have been installed.
#' if (requireNamespace("ncdf4")) {
#' f <- download.topo(west=-80, east=0, south=35, north=40, resolution=4)
#' t <- read.topo(f)
#' plot(section, which="SA", xtype="longitude", ztype="image", showBottom=t)
#' }
#'
#' # Temperature with salinity added in red
#' plot(GSg, which="temperature")
#' distance <- GSg[["distance", "byStation"]]
#' depth <- GSg[["station", 1]][["depth"]]
#' S <- matrix(GSg[["salinity"]], byrow=TRUE, nrow=length(GSg[["station"]]))
#' contour(distance, depth, S, col=2, add=TRUE)
#'
#' # Image with controlled colours
#' plot(GSg, which="salinity", ztype="image",
#' zlim=c(35, 37.5),
#' zbreaks=seq(35, 37.5, 0.25),
#' zcol=oceColorsTurbo)
#'```
#'
#' @examples
#' library(oce)
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 2000, 100))
#'
#' # Gulf Stream, salinity and temperature contours
#' plot(GSg, which=c("salinity", "temperature"))
#'
#' # Gulf Stream, Temperature image
#' plot(GSg, which="temperature", ztype="image",
#' zbreaks=seq(0,25,2), zcol=oceColorsTemperature)
#'
#' @author Dan Kelley, with help from Clark Richards and Chantelle Layton.
#'
#' @family functions that plot oce data
#' @family things related to section data
#'
#' @aliases plot.section
setMethod(f="plot", signature=signature("section"),
definition=function(x,
which=c(1, 2, 3, 99), eos,
at=NULL, labels=TRUE, grid=FALSE,
contourLevels=NULL, contourLabels=NULL,
stationIndices, coastline="best", colLand="gray",
xlim=NULL, ylim=NULL, zlim=NULL, zbreaks=NULL, zcol=NULL,
map.xlim=NULL, map.ylim=NULL, clongitude, clatitude, span, projection=NULL,
xtype="distance", ytype="depth", ztype="contour", longitude0, latitude0,
legend.loc="bottomright", legend.text=NULL,
showStations=FALSE, showStart=TRUE, stationTicks=TRUE, showBottom=TRUE,
showSpine=TRUE, drawPalette=TRUE,
axes=TRUE, mgp, mar, col, cex, pch, lwd, labcex=par("cex"),
debug=getOption("oceDebug"),
...)
{
debug <- if (debug > 4) 4 else floor(0.5 + debug)
if (missing(eos)) eos <- getOption("oceEOS", default="gsw")
zlimOrig <- zlim
xtype <- match.arg(xtype,
c("distance", "track", "longitude", "latitude", "time", "spine"))
ytype <- match.arg(ytype,
c("depth", "pressure"))
ztype <- match.arg(ztype,
c("contour", "image", "points"))
# Ensure that ylim makes sense
if (!is.null(ylim)) {
if (length(ylim) != 2L)
stop("In plot,section-method() : length(ylim) must be 2, but it is ", length(ylim))
if (any(ylim < 0.0))
stop("In plot,section-method() : ylim elements cannot be negative, but ylim=c(", ylim[1], ",", ylim[2], ") was provided")
if (ylim[1] <= ylim[2])
stop("In plot,section-method() require ylim[2]<ylim[1], but ylim=c(", ylim[1], ",", ylim[2], ") was provided")
}
# nolint start object_usage_linter
drawPoints <- ztype == "points"
# nolint end object_usage_linter
if (!inherits(coastline, "coastline")) {
coastline <- match.arg(coastline,
c("best", "coastlineWorld", "coastlineWorldMedium", "coastlineWorldFine", "none"))
}
if (missing(mgp))
mgp <- getOption("oceMgp")
if (missing(mar))
mar <- c(mgp[1]+1, mgp[1]+1.5, mgp[2]+1, mgp[2]+0.5)
if (missing(col))
col <- par("col")
if (missing(cex))
cex <- par("cex")
if (missing(pch))
pch <- if (length(which) == 1L && (which == "map" || which == 99)) 3 else par("pch")
if (missing(lwd))
lwd <- par("lwd")
# L and R are used much later, for constructing labels
# nolint start object_usage_linter
L <- if (getOption("oceUnitBracket") == "[") " [" else " ("
R <- if (getOption("oceUnitBracket") == "[") "]" else ")"
# nolint end object_usage_linter
# Make 'which' be character, to simplify following code
#oceDebug(debug, "which=c(", paste(which, collapse=","), ")\n")
lw <- length(which)
legend.text <- rep(legend.text, lw)
if (is.numeric(which)) {
which[which==0] <- "potential temperature"
which[which==1] <- "temperature"
which[which==2] <- "salinity"
which[which==3] <- "sigmaTheta"
which[which==4] <- "nitrate"
which[which==5] <- "nitrite"
which[which==6] <- "oxygen"
which[which==7] <- "phosphate"
which[which==8] <- "silicate"
which[which==9] <- "u"
which[which==10] <- "uz"
which[which==11] <- "v"
which[which==12] <- "vz"
which[which==20] <- "data"
which[which==99] <- "map"
}
if (any(is.na(which)))
stop("cannot have NA values in which, but it is ", paste(which, collapse=","))
#oceDebug(debug, "which=c(", paste(which, collapse=","), ")\n")
oceDebug(debug, "plot.section(, ..., which=c(",
paste(which, collapse=","), "), eos=\"", eos, "\"",
", xtype=", xtype,
", ztype=", ztype,
", xlim=c(", paste(xlim, collapse=","), ")",
", ylim=c(", paste(ylim, collapse=","), ")",
", \", ...) {\n", sep="", unindent=1)
# Contour and image plots, e.g. for which="temperature" and so forth, require
# gridded data.
#2092 message("DAN ", vectorShow(ztype))
#2092 message("DAN ", vectorShow(which))
if (which[1] != "data" && which[1] != "map") {
#2092 message("DAN will it grid???")
p1 <- x[["station", 1]][["pressure"]]
numStations <- length(x@data$station)
for (ix in 2:numStations) {
thisStation <- x@data$station[[ix]]
thisPressure <- thisStation[["pressure"]]
if ("points" != ztype && !identical(p1, thisPressure)) {
#2092 message("DAN gridding")
oceDebug(debug, "gridding section because pressures at station ", ix, " differ from those at station 1\n")
x <- sectionGrid(x, debug=debug-1)
break
}
}
}
res <- x # will now be gridded (either originally or through above code)
# Trim stations that have zero good data FIXME: brittle to addition of new metadata
haveData <- unlist(lapply(x@data$station, function(stn) 0 < length(stn[["pressure"]])))
x@data$station <- x@data$station[haveData]
x@metadata$stationId <- x@metadata$stationId[haveData]
x@metadata$latitude <- x@metadata$latitude[haveData]
x@metadata$longitude <- x@metadata$longitude[haveData]
x@metadata$time <- x@metadata$time[haveData]
if (xtype != "time") {
oceDebug(debug, "xtype=\"", xtype, "\" is not \"time\", so checking whether longitude and latitude vary\n")
if (!all(is.finite(x@metadata$longitude)))
stop("In plot(): with xtype=\"", xtype, "\", all stations must have finite longitude", call.=FALSE)
if (!all(is.finite(x@metadata$latitude)))
stop("In plot(): with xtype=\"", xtype, "\", all stations must have finite latitude", call.=FALSE)
if (0.0 == diff(range(x@metadata$longitude)) && 0.0 == diff(range(x@metadata$latitude)))
stop("In plot(): with xtype=\"", xtype, "\", either longitude or latitude must vary", call.=FALSE)
if (0.0 == diff(range(x@metadata$longitude)) && xtype == "longitude")
stop("In plot(): with xtype=\"", xtype, "\", longitude must vary", call.=FALSE)
if (0.0 == diff(range(x@metadata$latitude)) && xtype == "latitude")
stop("In plot(): with xtype=\"", xtype, "\", latitude must vary", call.=FALSE)
}
plotSubsection <- function(xx, yy, zz,
which.xtype, which.ytype,
variable="temperature", vtitle="T", unit=NULL,
eos=getOption("oceEOS", default="gsw"),
indicate.stations=TRUE,
contourLevels=NULL, contourLabels=NULL,
showStations=FALSE,
xlim=NULL, ylim=NULL,
clongitude, clatitude, span,
projection=NULL,
zbreaks=NULL, zcol=NULL,
ztype=c("contour", "image", "points"),
legend=TRUE,
axes=TRUE,
col=par("col"), cex=par("cex"),
pch=if (as.character(variable) == "map") 3 else par("pch"), lwd=par("lwd"),
debug=0,
...)
{
oceDebug(debug, "plotSubsection(variable=\"", variable, "\"",
", eos=\"", eos, "\"",
", which.xtype=\"", which.xtype, "\"",
", xlim=c(", paste(xlim, collapse=","), ")",
", ylim=c(", paste(ylim, collapse=","), ")",
", col=", if (missing(col)) "(missing)" else col,
", cex=", if (missing(cex)) "(missing)" else cex,
", pch=", if (missing(pch)) "(missing)" else pch,
", ztype=\"", ztype,
", zcol=", if (missing(zcol)) "(missing)" else "(provided)",
", span=", if (missing(span)) "(missing)" else span,
", showStations=", showStations,
", axes=", axes, ", ...) {\n", sep="", unindent=1)
ztype <- match.arg(ztype)
drawPoints <- "points" == ztype
omar <- par("mar")
xIsTime <- inherits(xx, "POSIXt")
isMap <- as.character(variable) == "map"
if (isMap) {
oceDebug(debug, "is a map\n")
lat <- array(NA_real_, numStations)
lon <- array(NA_real_, numStations)
for (i in 1:numStations) {
thisStation <- x[["station", stationIndices[i]]]
lon[i] <- mean(thisStation[["longitude"]], na.rm=TRUE)
lat[i] <- mean(thisStation[["latitude"]], na.rm=TRUE)
}
# lon[lon<0] <- lon[lon<0] + 360
asp <- 1 / cos(mean(range(lat, na.rm=TRUE))*pi/180)
latm <- mean(lat, na.rm=TRUE)
lonm <- mean(lon, na.rm=TRUE)
if (missing(span)) {
lonr <- lonm + sqrt(2) * (range(lon, na.rm=TRUE) - mean(lon, na.rm=TRUE)) # expand range
latr <- latm + sqrt(2) * (range(lat, na.rm=TRUE) - mean(lat, na.rm=TRUE))
} else {
lonr <- lonm + span / 111.1 * c(-0.5, 0.5) / cos(pi/180*latm) / sqrt(2)
latr <- latm + span / 111.1 * c(-0.5, 0.5) / sqrt(2)
}
# FIXME: this coastline code is reproduced in section.R; it should be DRY
haveCoastline <- FALSE
if (inherits(coastline, "coastline")) {
haveCoastline <- TRUE
oceDebug(debug, "using coastline object given as an argument\n")
} else {
if (!is.character(coastline))
stop("coastline must be a character string")
haveOcedata <- requireNamespace("ocedata", quietly=TRUE)
if (coastline == "best") {
if (haveOcedata) {
bestcoastline <- coastlineBest(lonRange=lonr, latRange=latr)
oceDebug(debug, "'best' coastline is: \"", bestcoastline, '\"\n', sep="")
if (bestcoastline == "coastlineWorld") {
data(list=bestcoastline, package="oce", envir=environment())
} else {
data(list=bestcoastline, package="ocedata", envir=environment())
}
coastline <- get(bestcoastline)
} else {
oceDebug(debug, "using \"coastlineWorld\" because ocedata package not installed\n")
data("coastlineWorld", package="oce", envir=environment())
coastline <- get("coastlineWorld")
}
haveCoastline <- TRUE
} else {
if (coastline != "none") {
if (coastline == "coastlineWorld") {
data("coastlineWorld", package="oce", envir=environment())
coastline <- get("coastlineWorld")
} else if (haveOcedata && coastline == "coastlineWorldFine") {
data("coastlineWorldFine", package="ocedata", envir=environment())
coastline <- get("coastlineWorldFine")
} else if (haveOcedata && coastline == "coastlineWorldMedium") {
data("coastlineWorldMedium", package="ocedata", envir=environment())
coastline <- get("coastlineWorldMedium")
} else {
stop("there is no built-in coastline file of name \"", coastline, "\"")
}
haveCoastline <- TRUE
}
}
}
# FIXME: I think both should have missing() means auto-pick and NULL means none
if (!is.null(projection)) {
stnlats <- x[["latitude", "byStation"]]
stnlons <- x[["longitude", "byStation"]]
if (is.null(map.xlim)) map.xlim <- range(stnlons)
if (is.null(map.ylim)) map.ylim <- range(stnlats)
id <- pmatch(projection, "automatic")
if (!is.na(id)) {
meanlat <- mean(stnlats, na.rm=TRUE)
#meanlon <- mean(stnlons, na.rm=TRUE)
# NOTE: mercator messes up filling for data(section) but mollweide is okay
projection <- if (meanlat > 70) "stereographic" else "mollweide"
#orientation <- c(90, meanlon, 0)
oceDebug(debug, "using", projection, "projection (chosen automatically)\n")
} else {
oceDebug(debug, "using", projection, "projection (specified)\n")
}
mapPlot(coastline, longitudelim=map.xlim, latitudelim=map.ylim, projection=projection, col=colLand)
spine <- x[["spine"]]
if (showSpine && !is.null(spine)) {
mapLines(spine$longitude, spine$latitude, col=4, lwd=1.4*par("lwd"))
}
mapPoints(x[["longitude", "byStation"]], x[["latitude", "byStation"]],
cex=cex, col=col, pch=pch, lwd=lwd)
if (xtype == "distance" && showStart) {
mapPoints(lon[1], lat[1], col=col, pch=22, cex=3*par("cex"), lwd=1/2)
}
return() # early return
} else {
# draw without map projection
if (!is.null(map.xlim)) {
map.xlim <- sort(map.xlim)
plot(lonr, latr, xlim=map.xlim, asp=asp, type="n",
xlab=gettext("Longitude", domain="R-oce"),
ylab=gettext("Latitude", domain="R-oce"))
} else if (!is.null(map.ylim)) {
map.ylim <- sort(map.ylim)
plot(lonr, latr, ylim=map.ylim, asp=asp, type="n",
xlab=gettext("Longitude", domain="R-oce"),
ylab=gettext("Latitude", domain="R-oce"))
} else {
plot(lonr, latr, asp=asp, type="n",
xlab=gettext("Longitude", domain="R-oce"),
ylab=gettext("Latitude", domain="R-oce"))
}
}
if (haveCoastline) {
if (!is.null(coastline@metadata$fillable) && coastline@metadata$fillable) {
polygon(coastline[["longitude"]], coastline[["latitude"]], col=colLand, lwd=3/4)
polygon(coastline[["longitude"]]+360, coastline[["latitude"]], col=colLand, lwd=3/4)
# redraw box, if we have axes. This is necessary because polygon will color
# over the axis box, if land goes past the edge of the view
if (axes) {
box()
}
} else {
lines(coastline[["longitude"]], coastline[["latitude"]], col="darkgray")
lines(coastline[["longitude"]]+360, coastline[["latitude"]], col="darkgray")
}
}
spine <- x[["spine"]]
if (showSpine && !is.null(spine))
lines(spine$longitude, spine$latitude, col="blue", lwd=1.4*par("lwd"))
# draw station trace (or skip it, if white was requested)
if (col[1] != "white")
lines(lon, lat, col="lightgray")
# replot with shifted longitude
points(lon, lat, col=col, pch=pch, cex=cex, lwd=lwd)
points(lon - 360, lat, col=col, pch=pch, cex=cex, lwd=lwd)
if (showStations) {
stationId <- x[["station ID"]]
text(lon, lat, stationId, pos=2, cex=cex)
text(lon-360, lat, stationId, pos=2, cex=cex)
}
if (xtype == "distance" && showStart) {
points(lon[1], lat[1], col=col, pch=22, cex=3*par("cex"), lwd=lwd)
points(lon[1] - 360, col=col, lat[1], pch=22, cex=3*par("cex"), lwd=lwd)
}
if (indicate.stations) {
dy <- 5.0*mean(diff(sort(x@metadata$latitude)), na.rm=TRUE)
dx <- 5.0*mean(diff(sort(x@metadata$longitude)), na.rm=TRUE)
ylab <- x@metadata$latitude[1] - dy*sign(x@metadata$latitude[2] - x@metadata$latitude[1])
xlab <- x@metadata$longitude[1] - dx*sign(x@metadata$longitude[2] - x@metadata$longitude[1])
xlab <- x@metadata$longitude[numStations] - dx * sign(x@metadata$longitude[numStations-1] - x@metadata$longitude[numStations])
ylab <- x@metadata$latitude[numStations] - dy * sign(x@metadata$latitude[numStations-1] - x@metadata$latitude[numStations])
}
} else {
# not isMap
oceDebug(debug, "not a map\n")
z <- x[[variable]]
zAllMissing <- all(is.na(z))
# Use try() to quiet warnings if all data are NA
zRANGE <- try(range(z, na.rm=TRUE), silent=TRUE)
if ((drawPoints || ztype == "image") && !zAllMissing) {
if (is.null(zbreaks)) {
oceDebug(debug, "zbreaks was not given; inferring from data\n")
if (is.null(zlim)) {
zbreaks <- if (is.null(zcol) || is.function(zcol)) {
seq(zRANGE[1], zRANGE[2], length.out=200)
} else {
seq(zRANGE[1], zRANGE[2], length.out=length(zcol) + 1)
}
oceDebug(debug, "zlim was not given; ", vectorShow(zbreaks))
} else {
zbreaks <- seq(zlim[1], zlim[2], length.out=200)
oceDebug(debug, "zlim was given; ", vectorShow(zbreaks))
}
} else {
oceDebug(debug, "zbreaks was given\n")
}
nbreaks <- length(zbreaks)
if (nbreaks > 0) {
if (is.null(zcol))
zcol <- oceColorsViridis(nbreaks - 1)
if (is.function(zcol))
zcol <- zcol(nbreaks - 1)
if (is.null(zlim))
zlim <- range(zbreaks, na.rm=TRUE) # FIXME: is this used/ok?
if (drawPalette) {
# draw triangles if data extend past either end of the palette
drawTriangles <- c(zRANGE[1] < zlim[1], zRANGE[2] > zlim[2])
drawPalette(zlim=zlim, breaks=zbreaks, col=zcol,
drawTriangles=drawTriangles)
}
}
}
# FIXME: contours don't get to plot edges
xxrange <- range(xx, na.rm=TRUE)
yyrange <- range(yy, na.rm=TRUE)
if (is.null(ylim))
ylim <- yyrange
par(xaxs="i", yaxs="i")
ylab <- if ("ylab" %in% names(list(...))) {
list(...)$ylab
} else {
if (which.ytype==1) {
resizableLabel("p")
} else {
resizableLabel("depth")
}
}
if (is.null(at)) {
if ("xlab" %in% names(list(...))) {
xlab <- list(...)$xlab
} else {
xlab <- switch(which.xtype,
resizableLabel("distance km"),
resizableLabel("along-track distance km"),
gettext("Longitude", domain="R-oce"),
gettext("Latitude", domain="R-oce"),
gettext("Time", domain="R-oce"),
resizableLabel("along-spine distance km"))
}
oceDebug(debug, vectorShow(yyrange))
if (!isMap) {
yyrange <- rev(yyrange)
oceDebug(debug, "reversed yyrange to be ", vectorShow(yyrange))
ylim <- rev(sort(ylim))
oceDebug(debug, "reversed ylim to be ", vectorShow(ylim))
}
plot(xxrange, yyrange, xaxs="i", yaxs="i", xlim=xlim, ylim=ylim,
xlab=xlab, ylab=ylab, col="white", axes=FALSE)
if (axes) {
oceDebug(debug, "drawing axes (before contouring etc)\n")
axis(4L, labels=FALSE)
ytics <- axis(2L, labels=FALSE)
axis(2L, at=ytics, labels=ytics)
# If constructing labels for time, need to check xlim
if (xIsTime) {
if (!is.null(xlim)) {
# FIXME: might need to check whether/how xx used later
XX <- xx[xlim[1] <= xx & xx <= xlim[2]]
axis(1L, at=pretty(XX), labels=pretty(XX))
} else {
axis(1L, at=pretty(xx), labels=pretty(xx))
}
} else {
axis(1L)
}
}
box()
} else {
plot(xxrange, yyrange,
xaxs="i", yaxs="i",
xlim=xlim, ylim=ylim,
col="white",
xlab="", ylab=ylab, axes=FALSE)
if (axes) {
axis(1, at=at, labels=labels)
axis(2)
axis(4, labels=FALSE)
}
box()
}
# Bottom trace
usr <- par("usr")
graph.bottom <- usr[3]
waterDepth <- NULL
# For ztype == "points", plot the points. Otherwise, collect them in zz
# for the contour or image plot.
for (i in seq_len(numStations)) {
#2092 message("DAN 1758 i=", i)
thisStation <- x[["station", i]]
p <- thisStation[["pressure"]] # assume that we always have pressure
np <- length(p)
if (variable != "data") {
# CAUTION: the assignment to 'v' and 'zz' is tricky:
# 1. not all datasets will have computed items (e.g. density and potential
# temperature) so we compute them, discarding any data. (Is that sensible?)
# 2. things are different in gsw and unesco, and someone might say
# "potential temperature" in either system, so which do we compute??
# 3. there was some code reworking in early May 2019, relating to issues:
# https://github.com/dankelley/oce/issues/1539
# and
# https://github.com/dankelley/oce/issues/1540
# and there is a chance of breakage starting at that time.
v <- thisStation[[variable]]
if (is.null(v))
v <- rep(NA, length(p))
if (drawPoints) {
p <- thisStation[["pressure"]]
points(rep(xx[i], np), -p, pch=pch, cex=cex,
col=zcol[rescale(v, xlow=zlim[1], xhigh=zlim[2], rlow=1, rhigh=nbreaks)])
} else {
#2092 message(vectorShow(dim(zz)))
#2092 message(vectorShow(v))
zz[i, ] <- rev(v)
}
}
if (grid && !drawPoints)
points(rep(xx[i], length(yy)), yy, col="gray", pch=20, cex=1/3)
#temp <- x@data$station[[stationIndices[i]]]@data$temperature
#len <- length(temp)
wd <- if ("waterDepth" %in% names(x@data$station[[stationIndices[i]]]@metadata)
&& is.finite(x@data$station[[stationIndices[i]]]@metadata$waterDepth))
x@data$station[[stationIndices[i]]]@metadata$waterDepth else NA
#in.land <- which(is.na(x@data$station[[stationIndices[i]]]@data$temperature[-3])) # skip first 3 points
waterDepth <- c(waterDepth, wd)
#2092 message(sprintf("DAN i=%d x=%.2f depth=%.0f", i, xx[i], wd))
}
if (!grid && axes && stationTicks)
Axis(side=3, at=xx, labels=FALSE, tcl=-1/3, lwd=0.5) # station locations
#2092 message("DAN next is xx. Is it in order of station or axis?")
bottom.x <- c(xx[1], xx, xx[length(xx)])
bottom.y <- if (any(is.finite(waterDepth)))
c(graph.bottom, waterDepth, graph.bottom)
else
rep(NA, length(bottom.x)+2)
# Put x in order, if it's not already
xx[!is.finite(xx)] <- NA # for issue 1583: grid larger than data range can get NaN values
#<20230601> ox <- order(xx)
ox <- seq_along(xx) #<20230601>
xxOrig <- xx
ii <- seq_along(xxOrig) # so we can use it later for drawing bottoms
#2092 message("DAN 1")
#2092 print(head(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 print(tail(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 lines(bottom.x, bottom.y, col=2, lwd=2)
if (any(xx[ox] != xx, na.rm=TRUE)) { # for issue 1583: handle the NA just inserted
#2092 message("DAN BARK")
xx <- xx[ox]
zz <- zz[ox, ] # FIXME keep this???
#<20230601> ii <- ii[ox]
bottom.x <- c(min(xxOrig), xxOrig[ox], max(xxOrig))
bottom.y <- c(graph.bottom, waterDepth[ox], graph.bottom)
}
#2092 message("DAN 2")
#2092 print(head(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 print(tail(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 lines(bottom.x, bottom.y, col=3, lwd=2, lty=2)
# Construct xm, ym and zm that can be contoured. That requires
# unique and increasing x and y values.
# cannot contour with duplicates in x or y; the former is the only problem
xx.unique <- c(TRUE, 0 != diff(xx))
yy.unique <- c(TRUE, 0 != diff(yy))
xx.unique <- xx.unique & !is.na(xx.unique)
yy.unique <- yy.unique & !is.na(yy.unique)
# contour() requires x and y to be increasing, and pressure/depth are not.
if (diff(yy[yy.unique][1:2]) < 0.0) {
oceDebug(debug, "flipping matrix for contouring\n")
# must flip in y
xm <- xx[xx.unique]
ym <- yy[yy.unique]
zm <- zz[xx.unique, yy.unique]
j <- rev(seq_along(ym))
ym <- ym[j]
zm <- zm[, j]
} else {
oceDebug(debug, "no need to flip matrix for contouring\n")
xm <- xx[xx.unique]
ym <- yy[yy.unique]
zm <- zz[xx.unique, yy.unique]
}
# a problem with mbari data revealed that we need to chop NA valaues too
if (variable == "data") {
#2092 message(vectorShow(which.xtype))
for (i in 1:numStations) {
thisStation <- x[["station", i]]
pressure <- thisStation[["pressure"]]
if (which.xtype == 3000) {
longitude <- mean(thisStation[["longitude"]], na.rm=TRUE)
points(rep(longitude, length(pressure)), pressure, cex=cex, pch=pch, col=col)
} else {
# FIXME: shouldn't the next line work for all types??
points(rep(xx[i], length(pressure)), pressure, cex=cex, pch=pch, col=col)
}
}
} else if (!drawPoints) {
# Use try() to quiet warnings if all data are NA
if (zAllMissing) {
if (nchar(legend.loc))
legend(legend.loc, legend=vtitle, bg="white", x.intersp=0, y.intersp=0.5, cex=1)
return()
}
# nolint start object_usage_linter
zrange <- try(range(zz[xx.unique, yy.unique], na.rm=TRUE), silent=TRUE)
# nolint end object_usage_linter
if (!is.null(contourLevels) && !is.null(contourLabels)) {
oceDebug(debug, "user-supplied contourLevels: ", contourLevels, "\n")
if (ztype == "contour") {
oceDebug(debug, "about to call contour() near L1885\n")
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
contour(x=xm[oxm], y=ym[oym], z=zm[oxm, oym],
ylim=if (is.null(ylim)) rev(range(ym)) else ylim,
axes=FALSE, add=TRUE, levels=contourLevels, labels=contourLabels,
col=col, xaxs="i", yaxs="i", labcex=labcex, ...)
} else if (ztype == "image") {
oceDebug(debug, "about to .filled.contour (LOC 1)\n")
# issue 2083 (https://github.com/dankelley/oce/issues/2083)
# FIXME: ensure that similare is done at location 1 also
nbreaks <- length(zbreaks)
if (is.function(zcol))
zcol <- zcol(nbreaks + 1)
zmRange <- range(zm, na.rm=TRUE)
zbreaksNEW <- zbreaks
zcolNEW <- zcol
if (zbreaks[1] > zmRange[1]) {
zbreaksNEW <- c(zmRange[1], zbreaks)
zcolNEW <- c(zcol[1], zcol)
}
if (zbreaks[nbreaks] < zmRange[2]) {
zbreaksNEW <- c(zbreaksNEW, zmRange[2])
zcolNEW <- c(zcolNEW, tail(zcolNEW, 1))
}
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
.filled.contour(x=xm[oxm], y=ym[oym], z=zm[oxm, oym], levels=zbreaksNEW, col=zcolNEW)
} else {
stop("unknown ztype: \"", ztype, "\" [2]")
}
} else {
oceDebug(debug, "automatically-calculated contourLevels\n")
zrange <- range(zz[xx.unique, yy.unique], na.rm=TRUE)
if (ztype == "contour") {
zzrange <- range(zz[xx.unique, yy.unique], na.rm=TRUE)
if (any(!is.finite(zzrange)))
stop("cannot draw a contour diagram because all values are NA or Inf")
oceDebug(debug, "about to call contour() near L1908\n")
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
contour(x=xm[oxm], y=ym[oym], z=zm[oxm, oym], add=TRUE, col=col, labcex=labcex, ...)
} else if (ztype == "image") {
oceDebug(debug, "about to .filled.contour (LOC 2)\n")
# issue 2083 (https://github.com/dankelley/oce/issues/2083)
# FIXME: ensure that similare is done at location 1 also
nbreaks <- length(zbreaks)
if (is.function(zcol))
zcol <- zcol(nbreaks + 1)
zmRange <- range(zm, na.rm=TRUE)
zbreaksNEW <- zbreaks
zcolNEW <- zcol
if (zbreaks[1] > zmRange[1]) {
zbreaksNEW <- c(zmRange[1], zbreaks)
zcolNEW <- c(zcol[1], zcol)
}
if (zbreaks[nbreaks] < zmRange[2]) {
zbreaksNEW <- c(zbreaksNEW, zmRange[2])
zcolNEW <- c(zcolNEW, tail(zcolNEW, 1))
}
#2092 print(zbreaksNEW)
#2092 str(xm)
#2092 str(ym)
#2092 str(zm)
# ensure ordered values (required by this contouring function)
oxm <- order(xm)
oym <- order(ym)
#2092 message("DAN about to filled.contour")
.filled.contour(x=xm[oxm], y=ym[oym], z=zm[oxm,oym], levels=zbreaksNEW, col=zcolNEW)
} else if (ztype == "points") {
# nothing to do now
} else {
stop("unknown ztype: \"", ztype, "\" [3]")
}
}
}
if (is.character(showBottom) || (is.logical(showBottom) && showBottom)) {
type <- "polygon"
if (is.character(showBottom)) {
type <- showBottom
}
#2092 message("DAN about to plot")
#2092 print(head(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 print(tail(data.frame(bottom.x=bottom.x, bottom.y=bottom.y), 2))
#2092 message("DAN KLUDGE!! reversing bottom.x")
#2092 message("DAN ORIG ", vectorShow(head(bottom.x)))
#2092 #???bottom.x<-rev(bottom.x) # KLUDGE
#2092 message("DAN LATER ", vectorShow(head(bottom.x)))
#2092 message("DAN ", vectorShow(xtype))
if (length(bottom.x) == length(bottom.y)) {
bottom <- par("usr")[3]
if (type == "polygon") {
polygon(bottom.x, bottom.y, col=gray(0.25, alpha=0.2))
} else if (type == "lines") {
for (s in seq_along(bottom.x))
lines(rep(bottom.x[s], 2), c(bottom.y[s], bottom), col="lightgray")
} else if (type == "points") {
for (s in seq_along(bottom.x))
points(rep(bottom.x[s], 2), c(bottom.y[s], bottom), col="lightgray")
}
}
box()
} else if (inherits(showBottom, "topo")) {
oceDebug(debug, "using a topo object for the bottom\n")
# Fine longitude and latitude: roughly
topoResolution <- geodDist(0, 0, 0, diff(showBottom[["latitude"]][1:2]))
#cat(vectorShow(topoResolution))
slon <- x[["longitude", "byStation"]]
slat <- x[["latitude", "byStation"]]
sectionSpan <- geodDist(min(slon, na.rm=TRUE), min(slat, na.rm=TRUE),
max(slon, na.rm=TRUE), max(slat, na.rm=TRUE))
#cat(vectorShow(sectionSpan))
nin <- length(slon)
# double up on resolution, although perhaps not needed
nout <- as.integer(1 + 2*sectionSpan / topoResolution)
#cat(vectorShow(nout))
blon <- approx(seq_len(nin), slon[ii], n=nout)$y
#cat(vectorShow(blon))
blat <- approx(seq_len(nin), slat[ii], n=nout)$y
#cat(vectorShow(blat))
by <- topoInterpolate(blon, blat, showBottom)
bx <- approx(seq_len(nin), xx, n=nout)$y
bx <- c(bx[1], bx, tail(bx, 1))
usr3 <- par("usr")[3]
#cat(vectorShow(usr))
by <- c(usr3, by, usr3)
polygon(bx, by, col="lightgray")
}
if (axes) {
if (xIsTime) {
if (!is.null(xlim)) {
# FIXME: might need to check whether/how xx used later
XX <- xx[xlim[1] <= xx & xx <= xlim[2]]
axis(1, at=pretty(XX), labels=pretty(XX))
} else {
axis(1, at=pretty(xx), labels=pretty(xx))
}
}
}
if (nchar(legend.loc)) {
legend(legend.loc, legend=vtitle, bg="white", x.intersp=0, y.intersp=0.5, cex=1)
}
usr <- par("usr")
#par("usr"=c(usr[1], usr[2], -usr[3], usr[4]))
}
par(mar=omar)
oceDebug(debug, "} # plotSubsection()\n", unindent=1)
}
opar <- par(no.readonly=TRUE)
if (length(which) > 1)
on.exit(par(opar))
which.xtype <- match(xtype, c("distance", "track", "longitude", "latitude", "time", "spine"), nomatch=0)
if (0 == which.xtype)
stop('xtype must be one of: "distance", "track", "longitude", "latitude", "time", or "spine", not "', xtype, '" as provided')
which.ytype <- pmatch(ytype, c("pressure", "depth"), nomatch=0)
if (missing(stationIndices)) {
numStations <- length(x@data$station)
stationIndices <- 1:numStations
} else {
numStations <- length(stationIndices)
}
if (numStations < 2)
stop("In plot() :\n cannot plot a section containing fewer than 2 stations", call.=FALSE)
firstStation <- x@data$station[[stationIndices[1]]]
num.depths <- length(firstStation@data$pressure)
zz <- array(NA_real_, dim=c(numStations, num.depths))
xx <- rep(NA, numStations)
yy <- rep(NA, num.depths)
if (is.null(at)) {
lon0 <- if (missing(longitude0)) mean(firstStation[["longitude"]], na.rm=TRUE) else longitude0
lat0 <- if (missing(latitude0)) mean(firstStation[["latitude"]], na.rm=TRUE) else latitude0
for (ix in 1:numStations) {
j <- stationIndices[ix]
if (which.xtype == 1) { # distance from first station
xx[ix] <- geodDist(lon0, lat0,
mean(x@data$station[[j]][["longitude"]], na.rm=TRUE),
mean(x@data$station[[j]][["latitude"]], na.rm=TRUE))
} else if (which.xtype == 2) { # distance along the cruise track
if (ix == 1) {
xx[ix] <- 0
} else {
xx[ix] <- xx[ix-1] + geodDist(mean(x@data$station[[stationIndices[ix-1]]][["longitude"]], na.rm=TRUE),
mean(x@data$station[[stationIndices[ix-1]]][["latitude"]], na.rm=TRUE),
mean(x@data$station[[j]][["longitude"]], na.rm=TRUE),
mean(x@data$station[[j]][["latitude"]], na.rm=TRUE))
}
} else if (which.xtype == 3) { # longitude
xx[ix] <- mean(x@data$station[[j]][["longitude"]], na.rm=TRUE)
} else if (which.xtype == 4) { # latitude
xx[ix] <- mean(x@data$station[[j]][["latitude"]], na.rm=TRUE)
} else if (which.xtype == 5) { # time
# use ix if there are no times.
if (!is.null(x@data$station[[j]]@metadata$startTime)) {
xx[ix] <- as.POSIXct(x@data$station[[j]]@metadata$startTime)
} else if (!is.null(x@metadata$time[[j]])) {
xx[ix] <- x@metadata$time[[j]]
} else {
xx[ix] <- ix
if (ix == 1) {
warning("In plot,section-method() :",
"\n section stations do not contain startTime; using integers for time axis", call.=FALSE)
}
}
}
}
} else {
xx <- at
}
if (which.xtype == 5) {
xx <- numberAsPOSIXct(xx)
} else if (which.xtype == 6) {
# see https://github.com/dankelley/oce-issues/blob/master/16xx/1678
if (!("spine" %in% names(x@metadata)))
stop("In plot,section-metod() :\n this section has no spine; use addSpine() to add a spine", call.=FALSE)
spine <- x@metadata$spine
# Parametric lon=lon(s), at=lat(s)
# nolint start object_usage_linter
s <- seq(0, 1, length.out=length(spine$longitude))
# nolint end object_usage_linter
lonfun <- approxfun(spine$longitude ~ s)
latfun <- approxfun(spine$latitude ~ s)
# Create many points on the spine
spineSegments <- 1000
ss <- seq(0, 1, length.out=spineSegments)
stnLon <- x[["longitude", "byStation"]]
stnLat <- x[["latitude", "byStation"]]
closest <- rep(NA, length=length(stnLon))
# find distance (used in following loop; uses global 'i')
for (i in seq_along(stnLon)) {
closest[i] <- which.min(sapply(ss,
function(t) {
lonSpine <- lonfun(t)
latSpine <- latfun(t)
geodDist(lonSpine, latSpine, stnLon[i], stnLat[i])
}))
}
# Map points back to the spine
longitudeRemapped <- lonfun(ss[closest])
latitudeRemapped <- latfun(ss[closest])
xx <- geodDist(longitudeRemapped, latitudeRemapped, alongPath=TRUE)
}
# Grid is regular (so need only first station) unless which=="data"
# FIXME: why checking just first which[] value?
if (which.ytype == 1) {
if (!is.na(which[1]) && which[1] == "data" || ztype == "points") {
yy <- c(max(x[["pressure"]]), 0)
oceDebug(debug, "ytype case 1A near L2062 ", vectorShow(yy))
} else {
yy <- rev(x@data$station[[stationIndices[1]]]@data$pressure)
oceDebug(debug, "ytype case 1B near L2065 ", vectorShow(yy))
}
} else if (which.ytype == 2) {
if (!is.na(which[1]) && which[1] == "data" || ztype == "points") {
yy <- c(max(x[["pressure"]], na.rm=TRUE), 0)
oceDebug(debug, "ytype case 2A near L2070 ", vectorShow(yy))
} else {
yy <- rev(swDepth(x@data$station[[stationIndices[1]]]@data$pressure))
oceDebug(debug, "ytype case 2B near L2073 ", vectorShow(yy))
}
} else {
stop("unknown ytype")
}
oceDebug(debug, "near L2078 ", vectorShow(yy))
par(mgp=mgp, mar=mar)
if (lw > 1) {
if (lw > 2) {
layout(matrix(1:4, nrow=2, byrow=TRUE))
} else {
layout(matrix(1:2, nrow=2, byrow=TRUE))
}
}
L <- if (getOption("oceUnitBracket") == "[") " [" else " ("
R <- if (getOption("oceUnitBracket") == "[") "]" else ")"
available <- sort(unique(c("data", "map", unlist(c(x[["?"]][c("data", "dataDerived")])))))
for (w in 1:lw) {
oceDebug(debug, "handling which[", w, "]=\"", which[w], "\"\n", sep="")
if (!which[w] %in% available)
stop("in plot(section) : which='", which[w], "' is not available; please try one of c(\"",
paste(available, collapse="\",\""),
"\")", call.=FALSE)
station1 <- x[["station", 1]]
#OLD unit <- station1[[paste(which[w], "Unit", sep="")]][[1]] # FIXME: what if not in that station?
if (!missing(contourLevels)) {
# contourLevels given
oceDebug(debug, "contourLevels was given\n")
if (is.null(contourLabels)) {
contourLabels <- format(contourLevels)
}
vtitle <- labelWithUnit(which[w], unit=station1[[paste0(which[w], "Unit")]])
plotSubsection(xx, yy, zz,
which.xtype=which.xtype, which.ytype=which.ytype, variable=which[w], eos=eos,
vtitle=if (is.null(legend.text[w])) vtitle else legend.text[w],
levels=contourLevels, labels=contourLabels, xlim=xlim, ylim=ylim, ztype=ztype,
axes=axes, col=col, debug=debug-1, ...)
} else {
# contourLevels not given
oceDebug(debug, "contourLevels was not given\n")
if (which[w] != "map" && which[w] != 99) {
vtitle <- labelWithUnit(which[w], unit=station1[[paste0(which[w], "Unit")]])
plotSubsection(xx, yy, zz,
which.xtype=which.xtype,
which.ytype=which.ytype,
variable=which[w], eos=eos,
vtitle=if (is.null(legend.text[w])) vtitle else legend.text[w],
xlim=xlim, ylim=ylim, ztype=ztype, zbreaks=zbreaks, zcol=zcol, axes=axes,
pch=pch, cex=cex, col=col,
debug=debug-1, ...)
}
}
if (!is.na(which[w]) && which[w] == 20) {
plotSubsection(xx, yy, zz,
which.xtype=which.xtype, which.ytype=which.ytype,
variable="data", vtitle="", unit=NULL,
xlim=xlim, ylim=ylim, cex=cex, pch=pch, col=col, legend=FALSE,
debug=debug-1, ...)
}
if (!is.na(which[w]) && (which[w] == 99 || which[w] == "map")) {
oceDebug(debug, "plotting a map\n")
plotSubsection(xx, yy, zz,
which.xtype=which.xtype, which.ytype=which.ytype,
variable="map", vtitle="", unit=NULL,
indicate.stations=FALSE,
clongitude=clongitude, clatitude=clatitude, span=span,
projection=projection,
debug=debug-1,
col=col, pch=pch, cex=cex, lwd=lwd,
...)
}
}
oceDebug(debug, "} # plot.section()\n", unindent=1)
invisible(res)
})
#' Read a Section File
#'
#' Read a file that contains a series of `ctd` profiles that make up an
#' oceanographic section.
#' Only *exchange BOT* comma-separated value format is permitted at this time,
#' but other formats may be added later. It should also be noted that the parsing
#' scheme was developed after inspection of the A03 data set (see Examples). This
#' may cause problems if the format is not universal. For example, the header must
#' name the salinity column "`CTDSAL`"; if not, salinity values will not be
#' read from the file.
#'
#' @section Disambiguating salinity:
#' WOCE datasets commonly have a column named `CTDSAL` for salinity inferred
#' from a CTD and `SALNTY` (not a typo) for salinity derived from bottle data.
#' If only one of these is present in the data file, the data will be called
#' `salinity` in the `data` slot of the return value. However, if both
#' are present, then `CTDSAL` is stored as `salinity` and `SALNTY`
#' is stored as `salinityBottle`.
#'
#' @param file A file containing a set of CTD observations. At present, only the
#' *exchange BOT* format is accepted (see \dQuote{Details}).
#'
#' @param directory A character string indicating the name of a directory that
#' contains a set of CTD files that hold individual stations in the section.
#'
#' @param sectionId Optional string indicating the name for the section. If not
#' provided, the section ID is determined by examination of the file header.
#'
#' @param ship Name of the ship carrying out the sampling.
#'
#' @param scientist Name of chief scientist aboard ship.
#'
#' @param institute Name of chief scientist's institute.
#'
#' @param flags Ignored, and deprecated (will be disallowed in a future version).
#'
#' @param missingValue Numerical value used to indicate missing data.
#'
#' @template encodingTemplate
#'
#' @template debugTemplate
#'
#' @param processingLog If provided, the action item to be stored in the log. This
#' is typically only provided for internal calls; the default that it provides is
#' better for normal calls by a user.
#'
#' @return A [section-class] object.
#'
#' @references
#' Several repository sites provide section data. A reasonably stable example is
#' `https://cchdo.ucsd.edu`, but a search on `"WOCE bottle data"` should
#' turn up other sites, if this ceases to exist. Only
#' the so-called *exchange BOT* data format can be processed by [read.section()]
#' at this time. Data names are inferred from column headings using
#' [woceNames2oceNames()].
#'
#' @author Dan Kelley
#'
#' @family things related to section data
read.section <- function(file,
directory,
sectionId="",
flags,
ship="",
scientist="",
institute="",
missingValue=-999,
encoding="latin1",
debug=getOption("oceDebug"),
processingLog)
{
if (missing(file))
stop("must supply 'file'")
if (is.character(file)) {
if (!file.exists(file))
stop("cannot find file '", file, "'")
if (0L == file.info(file)$size)
stop("empty file '", file, "'")
}
oceDebug(debug, "read.section(file=\"", file, "\", ...) {\n", unindent=1)
if (!missing(directory)) {
if (!missing(file))
stop("cannot specify both 'file' and 'directory'")
files <- list.files(directory)
nstations <- length(files)
stations <- vector("list", nstations)
for (i in seq_along(files)) {
name <- paste(directory, files[i], sep="/")
stations[[i]] <- ctdTrim(read.oce(name))
}
return(as.section(stations))
}
if (is.character(file)) {
filename <- file
file <- file(file, "r", encoding=encoding)
on.exit(close(file))
}
if (!inherits(file, "connection"))
stop("argument `file' must be a character string or connection")
res <- new("section")
if (!isOpen(file)) {
filename <- "(connection)"
open(file, "r", encoding=encoding)
on.exit(close(file))
}
if (!missing(flags))
warning("'flags' is ignored, and will be disallowed in an upcoming CRAN release")
# Skip header
lines <- readLines(file)
if ("BOTTLE" != substr(lines[1], 1, 6))
stop("only type \"BOTTLE\" understood, but got header line\n", lines[1], "\n")
if (nchar(sectionId) < 1)
sectionId <- substr(lines[1], 8, nchar(lines[1]))
n <- length(lines)
header <- lines[1]
for (l in (2:n)) {
oceDebug(debug>4, lines[l], "\n")
if ("#" != substr(lines[l], 1, 1)) {
header <- c(header, lines[l])
break
}
}
header.length <- l + 1
ccc <- textConnection(lines[header.length - 1])
var.names <- scan(ccc, sep=",", what="", quiet=TRUE)
dataNamesOriginal <- var.names
close(ccc)
ccc <- textConnection(lines[header.length])
var.units <- scan(ccc, sep=",", what="", quiet=TRUE)
close(ccc)
if (length(var.units) != length(var.names))
stop("length mismatch in variable lists")
header <- lines[1:header.length]
nd <- n - header.length - 1
nv <- length(var.names)
col.start <- 3 # FIXME: why is this value used? It fails on some (Arctic) data.
col.start <- 1
data <- array(dim=c(nd, nv - col.start + 1))
flags <- list()
stnSectionId <- vector("character", nd)
stnId <- vector("character", nd)
for (l in seq(header.length + 1, n-1L)) {
# last line is END_DATA
contents <- strsplit(lines[l], split=",")[[1]]
stnSectionId[l - header.length] <- sub(" *", "", contents[2])
stnId[l - header.length] <- sub("^ *", "", contents[3])
data[l - header.length, ] <- contents[col.start:nv]
# FIXME: maybe should just scan this thing; it might work better anyway
}
if (length(which(var.names=="DATE"))) {
stn.date <- as.character(data[, which(var.names=="DATE") - col.start + 1])
} else {
stop("no column named \"DATE\"")
}
if (length(which(var.names=="TIME"))) {
stn.time <- as.character(data[, which(var.names=="TIME") - col.start + 1])
} else {
stop("no column named \"TIME\"")
}
# nolint start (long lines)
# EXPOCODE,SECT_ID,STNNBR,CASTNO,SAMPNO,BTLNBR,BTLNBR_FLAG_W,DATE,TIME,LATITUDE,LONGITUDE,DEPTH,CTDPRS,CTDTMP,CTDSAL,CTDSAL_FLAG_W,SALNTY,SALNTY_FLAG_W,OXYGEN,OXYGEN_FLAG_W,SILCAT,SILCAT_FLAG_W,NITRIT,NITRIT_FLAG_W,NO2+NO3,NO2+NO3_FLAG_W,PHSPHT,PHSPHT_FLAG_W
# nolint end (long lines)
waterDepth <- as.numeric(data[, which(var.names=="DEPTH") - col.start + 1])
waterDepth <- ifelse(waterDepth == missingValue, NA, waterDepth)
# FIXME: we have both 'latitude' and 'lat'; this is too confusing
longitude <- as.numeric(data[, which(var.names=="LONGITUDE") - col.start + 1])
latitude <- as.numeric(data[, which(var.names=="LATITUDE") - col.start + 1])
stationId <- data[, which(var.names=="STNNBR") - col.start + 1]
stationId <- sub(" *$", "", sub("^ *", "", stationId)) #remove blanks
stationList <- unique(stationId)
numStations <- length(stationList)
station <- vector("list", numStations)
stn <- vector("character", numStations)
lon <- vector("numeric", numStations)
lat <- vector("numeric", numStations)
time <- vector("numeric", numStations) # has to be numeric
# tref <- as.POSIXct("2000-01-01 00:00:00", tz="UTC")
# trefn <- as.numeric(tref)
# We will trim out metadata columns, in assembling the 'data' slot of
# the ctd objects that will make up the section. The pattern below is based
# on one particular file (provided with oce in inst/extdata), which has
# the following column names.
# "EXPOCODE" "SECT_ID" "STNNBR" "CASTNO" "SAMPNO" "BTLNBR" "BTLNBR_FLAG_W"
# "DATE" "TIME" "LATITUDE" "LONGITUDE" "DEPTH" "CTDPRS" "CTDTMP"
# "CTDSAL" "CTDSAL_FLAG_W" "SALNTY" "SALNTY_FLAG_W" "OXYGEN" "OXYGEN_FLAG_W"
colSkip <- var.names %in% c("EXPOCODE", "SECT_ID", "STNNBR", "CASTNO", "SAMPNO",
"BTLNBR", "BTLNBR_FLAG_W",
"DATE", "TIME", "LATITUDE", "LONGITUDE", "DEPTH")
dataNamesOriginal <- as.list(var.names[!colSkip])
dataNames <- woceNames2oceNames(var.names)[!colSkip]
names(dataNamesOriginal) <- dataNames
dataUnits <- list()
for (idata in seq_along(dataNames)) {
n <- dataNames[idata]
dataUnits[[dataNames[idata]]] <- unitFromString(var.units[!colSkip][idata])
}
# Names and units are the same for every station, so determine them
# before going through the data.
for (i in 1:numStations) {
select <- which(stationId == stationList[i])
# "199309232222"
# "1993-09-23 22:22:00"
time[i] <- as.numeric(strptime(paste(stn.date[select[1]], stn.time[select[1]], sep=""), "%Y%m%d%H%M", tz="UTC"))
stn[i] <- sub("^ *", "", stationId[select[1]])
oceDebug(debug, "reading station i=", i, ", stn=", stn[i], "\n")
lon[i] <- longitude[select[1]]
lat[i] <- latitude[select[1]]
select <- which(stationId == stationList[i])
thisStation <- new("ctd")
thisStation@data <- list() # start over, then insert one by one
# colNames <- oceNames[!colSkip]
DATA <- data[, !colSkip]
isFlag <- rep(TRUE, sum(!colSkip))
for (idata in seq_along(dataNames)) {
# Split flags into metadata
isFlag[idata] <- grepl("Flag$", dataNames[idata])
if (isFlag[idata]) {
thisStation@metadata$flags[[gsub("Flag$", "", dataNames[idata])]] <- as.numeric(DATA[select, idata])
} else {
tmp <- as.numeric(DATA[select, idata])
tmp[tmp == missingValue] <- NA
thisStation@data[[dataNames[idata]]] <- tmp
}
}
thisStation@metadata$dataNamesOriginal <- dataNamesOriginal
thisStation@metadata$src <- filename
thisStation@metadata$startTime <- numberAsPOSIXct(time[i])
thisStation@metadata$longitude <- lon[i]
thisStation@metadata$latitude <- lat[i]
thisStation@metadata$stn <- sub("^ *", "", stationId[select[1]])
thisStation@metadata$time <- as.numeric(strptime(paste(stn.date[select[1]], stn.time[select[1]], sep=""), "%Y%m%d%H%M", tz="UTC"))
thisStation@metadata$station <- sub("^ *", "", stationId[select[1]])
thisStation@metadata$longitude <- longitude[select[1]]
thisStation@metadata$latitude <- latitude[select[1]]
thisStation@metadata$waterDepth <- waterDepth[select[1]]
thisStation@metadata$units <- dataUnits
oceDebug(debug, " ", length(select), " levels ", lat[i], "N,", lon[i], "W", " (", format(thisStation@metadata$startTime), ")\n")
station[[i]] <- thisStation
}
res@metadata$header <- header
res@metadata$sectionId <- sectionId
res@metadata$stationId <- stn
res@metadata$longitude <- lon
res@metadata$latitude <- lat
res@metadata$time <- numberAsPOSIXct(time)
res@metadata$filename <- filename
res@data <- list(station=station)
if (missing(processingLog))
processingLog <- paste(deparse(match.call()), sep="", collapse="")
res@processingLog <- processingLogAppend(res@processingLog, processingLog)
oceDebug(debug, "} # read.section()\n", unindent=1)
res
}
#' Grid a Section in Pressure Space
#'
#' Grid a section, by interpolating to fixed pressure levels. The
#' `"approx"`, `"boxcar"` and `"lm"` methods are described in the
#' documentation for [ctdDecimate()], which is used to do this
#' processing.
#'
#' The default `"approx"` method is best for bottle data, the
#' `"boxcar"` is best for ctd data, and the `"lm"` method is probably
#' too slow to recommend for exploratory work, in which it is common to do trials
#' with a variety of `"p"` values.
#'
#' The stations in the returned value have flags with names that match those
#' of the corresponding stations in the original `section`, but the values
#' of these flags are all set to `NA`. This recognizes that it makes
#' no sense to grid flag values, but that there is merit in initializing
#' a flag system, for possible use in later processing steps.
#'
#' @param section A `section` object containing the section to be gridded.
#'
#' @param p Optional indication of the pressure levels to which interpolation
#' should be done. If this is not supplied, the pressure levels will be
#' calculated based on the typical spacing in the ctd profiles stored within
#' `section`. If `p="levitus"`, then pressures will be set to be those
#' of the Levitus atlas, given by [standardDepths()].
#' If `p` is a single numerical value,
#' it is taken as the number of subdivisions to use in a call to [seq()]
#' that has range from 0 to the maximum pressure in `section`. Finally, if a
#' vector numerical values is provided, perhaps. constructed with [seq()]
#' or [`standardDepths`]`(5)` (as in the examples),
#' then it is used as is, after trimming any values that exceed the maximum
#' pressure in the station data stored within `section`.
#'
#' @param method The method to use to decimate data within the stations; see
#' [ctdDecimate()], which is used for the decimation.
#'
#' @param trim Logical value indicating whether to trim gridded pressures
#' to the range of the data in `section`.
#'
#' @template debugTemplate
#'
#' @param ... Optional arguments to be supplied to [ctdDecimate()],
#' e.g. `rule` controls extrapolation beyond the observed pressure range,
#' in the case where `method` equals `"approx"`.
#'
#' @return A [section-class] object that contains stations in which
#' the pressure values match identically, and that has all
#' flags set to `NA`.
#'
#' @examples
#' # Gulf Stream
#' library(oce)
#' data(section)
#' GS <- subset(section, 113<=stationId&stationId<=129)
#' GSg <- sectionGrid(GS, p=seq(0, 5000, 100))
#' plot(GSg, which="temperature")
# plot(GSg, map.xlim=c(-80,-60))
# # Show effects of various depth schemes
# par(mfrow=c(3, 1))
# default <- sectionGrid(GS)
# approxML <- sectionGrid(GS, method="approxML")
# standardDepths5 <- sectionGrid(GS, p=standardDepths(5))
# plot(default, which="temperature", ztype="image", ylim=c(200,0))
# mtext("default sectionGrid()")
# plot(approxML, which="temperature", ztype="image", ylim=c(200,0))
# mtext("sectionGrid(..., method=\"approxML\")")
# plot(standardDepths5, which="temperature", ztype="image", ylim=c(200,0))
# mtext("sectionGrid(..., p=standardDepths(5))")
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionGrid <- function(section, p, method="approx", trim=TRUE, debug=getOption("oceDebug"), ...)
{
oceDebug(debug, "sectionGrid(section, p, ",
"method=\"", if (is.function(method)) "(function)" else method, "\", ",
"p=", if (missing(p)) "(missing)" else paste("c(", paste(p, collapse=","), ")", sep=""), ", ",
"trim=", trim, ", ...) {\n",
sep="", unindent=1)
n <- length(section@data$station)
nxg <- n
oceDebug(debug, "have ", nxg, " stations in this section\n")
pMax <- max(section[["pressure"]], na.rm=TRUE)
if (missing(p)) {
dplist <- vector("numeric", n)
for (i in 1:n) {
p <- section@data$station[[i]]@data$pressure
dplist[i] <- mean(diff(p), na.rm=TRUE)
}
dp <- mean(dplist, na.rm=TRUE) / 5 # make it a little smaller
pt <- pretty(c(0, pMax), n=min(200, floor(abs(pMax / dp))))
oceDebug(debug, "p not given, so inferring from data\n")
} else {
if (length(p) == 1) {
if (p=="levitus") {
pt <- standardDepths()
} else {
if (!is.numeric(p))
stop("p must be numeric")
if (p <= 0)
stop("p must be a positive number")
pt <- seq(0, pMax, p)
}
} else {
pt <- p
}
}
oceDebug(debug, vectorShow(pt))
if (trim) {
# allow one extra level, for bracketing
w <- which(pt > pMax)
if (length(w)) {
pt <- pt[1:w[1]]
}
oceDebug(debug, "trimmed ", vectorShow(pt))
}
# BUG should handle all variables (but how to interpolate on a flag?)
res <- section
nstation <- length(res[["station"]])
nyg <- length(pt)
# The core of the work -- use ctdDecimate() on each station
for (i in seq_len(nstation)) {
oceDebug(debug, "decimating station i=", i, "\n")
suppressWarnings(
res@data$station[[i]] <- ctdDecimate(section@data$station[[i]],
p=pt, method=method, debug=debug-1, ...))
}
# Find all units in *any* station, and then insert them into *all* stations.
units <- list()
for (i in seq_len(nstation)) {
for (unitName in names(section@data$station[[i]]@metadata$units)) {
if (!(unitName %in% names(units))) {
units[unitName] <- section@data$station[[i]]@metadata$units[unitName]
}
}
}
for (i in seq_len(nxg)) {
res@data$station[[i]]@metadata$units <- units
res@data$station[[i]]@metadata$flags <- list()
for (flagname in names(section@data$station[[i]]@metadata$flags)) {
# Note that the flags are named after those of the flags for *that particular*
# station in the input object. This is different from sectionSmooth().
res@data$station[[i]]@metadata$flags[[flagname]] <- rep(NA, nyg)
}
}
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
oceDebug(debug, "} # sectionGrid\n", unindent=1)
res
}
#' Smooth a Section
#'
#' Smooth a section, in any of several ways, working either in the vertical
#' direction or in both the vertical and lateral directions.
#'
#' This function produces smoothed fields that might be useful in
#' simplifying graphical elements created with [plot,section-method()].
#' As with any smoothing method, a careful analyst will compare the results
#' against the raw data, e.g. using [plot,section-method()].
#' In addition the problem of falsely widening narrow features such as
#' fronts, there is also the potential to get unphysical results with
#' spars sampling near topographic features such as bottom slopes and ridges.
#'
#' The `method` argument selects between three possible methods.
#'
#' * For `method="spline"`, i.e. the default, the section is smoothed
#' laterally, using [smooth.spline()] on individual pressure levels.
#' (If the pressure levels do not match up, [sectionGrid()] should
#' be used first to create a `section` object that can be used here.)
#' The `df` argument sets the degree of freedom of the spline, with
#' larger values indicating less smoothing.
#'
#' * For `method="barnes"`, smoothing is done across
#' both horizontal and vertical coordinates, using [interpBarnes()].
#' The output station locations are computed by linear interpolation of
#' input locations, using [approx()] on the original
#' longitudes and longitudes of stations, with the independent variable
#' being the distance along the track, computed with [geodDist()].
#' The values of `xg`, `yg`, `xgl` and `ygl` control
#' the smoothing.
#'
#' * For `method="kriging"`, smoothing is done across
#' both horizontal and vertical coordinates, using `autoKrige()` from
#' the \CRANpkg{automap} package (along with support from the
#' \CRANpkg{sp} package to format the data). Note that the format of
#' the value returned by `autoKrige()` has changed over the years,
#' and `method="kriging"` can only handle two particular formats,
#' one of which is the result from version 1.1.9 of
#' \CRANpkg{automap}.
#'
#' * If `method` is a function, then that function is applied to
#' the (distance, pressure) data for each variable at a grid defined by
#' `xg`, `xgl`, `yg` and `ygl`. The function must
#' be of the form `function(x,y,z,xg,xr,yg,yr)`, and must
#' return a matrix of the gridded result, with first index indicating
#' the "grid" station number and second index indicating "grid" pressure.
#' The `x` value that is supplied to this function is set as
#' the distance along the section, as computed with [geodDist()],
#' and repeated for each of the points at each station. The corresponding
#' pressures are provided in `y`, and the value to be gridded is
#' in `z`, which may be `temperature` on one call to the function,
#' `salinity` on another call, etc. The other quantities
#' have the meanings as described below.
#'
#' @param section A `section` object containing the section to be smoothed.
#' For `method="spline"`, the pressure levels must match for each station in
#' the section.
#'
#' @param method A string or a function that specifies the method to use; see \dQuote{Details}.
#'
#' @param x Optional numerical vector, of the same length as the number of stations in `section`,
#' which will be used in gridding in the lateral direction. If not provided, this
#' defaults to [`geodDist`]`(section)`.
#'
#' @param xg,xgl ignored in the `method="spline"` case, but passed to
#' [interpBarnes()] if `method="barnes"`, to kriging
#' functions if `method="kriging"`, or to `method` itself, if it
#' is a function.
#' If `xg` is supplied, it defines the x component of the grid, which by
#' default is the terms of station distances, x, along the track of the section. (Note
#' that the grid `xg` is trimmed to the range of the data `x`, because otherwise
#' it would be impossible to interpolate between stations to infer water depth,
#' longitude, and latitude, which are all stored within the stations in the
#' returned `section` object.)
#' Alternatively, if `xgl` is supplied, the x grid is established using [seq()],
#' to span the data with `xgl` elements. If neither of these is supplied, the output
#' x grid will equal the input x grid.
#'
#' @param yg,ygl similar to `xg` and `xgl`, but for pressure. (Note that
#' trimming to the input `y` is not done, as it is for `xg` and `x`.)
#' If `yg` is not given, it is determined from the deepest station in the section.
#' If `ygl` was not given, then a grid is constructed to span the pressures
#' of that deepest station with `ygl` elements. On the other hand,
#' if `ygl` was not given, then the y grid will constructed from the
#' pressure levels in the deepest station.
#'
#' @param xr,yr influence ranges in x (along-section distance) and y (pressure),
#' passed to [interpBarnes()] if `method="barnes"` or to
#' `method`, if the latter is a function. If missing, `xr` defaults to
#' 1.5X the median inter-station distance and `yr` defaults to 0.2X
#' the pressure range. Since these defaults have changed over the evolution
#' of `sectionSmooth`, analysts ought to supply `xr` and `yr`
#' in the function call, tailoring them to particular applications, and
#' making the code more resistant to changes in `sectionSmooth`.
#'
#' @param df Degree-of-freedom parameter, passed to [smooth.spline()]
#' if `method="spline"`, and ignored otherwise. If `df` is not provided,
#' it defaults to 1/5-th of the number of stations containing non-`NA`
#' data at the particular pressure level being processed, as `sectionSmooth`
#' works its way through the water column.
#'
#' @param gamma,iterations,trim,pregrid Values passed to
#' [interpBarnes()], if `method="barnes"`, and
#' ignored otherwise. `gamma` is the factor by which
#' `xr` and `yr` are reduced on each of succeeding iterations.
#' `iterations` is the number of iterations to do.
#' `trim` controls whether the gridded data are set to
#' `NA` in regions with sparse data
#' coverage. `pregrid` controls whether data are to be
#' pre-gridded with [binMean2D()] before being passed to
#' [interpBarnes()].
#'
#' @param debug A flag that turns on debugging. Set to 1 to get a moderate amount
#' of debugging information, or to 2 to get more.
#'
#' @param ... Optional extra arguments, passed to either
#' [smooth.spline()], if `method="spline"`, and ignored otherwise.
#'
#' @return A [section-class] object of that has been smoothed in some way.
#' Every data field that is in even a single station of the input object
#' is inserted into every station in the returned value, and therefore
#' the units represent all the units in any of the stations, as do the
#' flag names. However, the flags are all set to `NA` values.
#'
#' @examples
#' # Unsmoothed (Gulf Stream)
#' library(oce)
#' data(section)
#' gs <- subset(section, 115<=stationId&stationId<=125)
#' par(mfrow=c(2, 2))
#'
#' plot(gs, which="temperature")
#' mtext("Original data, without smoothing", line=0.5)
#'
#' # Spline
#' gsg <- sectionGrid(gs, p=seq(0, 5000, 100))
#' gsSpline <- sectionSmooth(gsg, "spline")
#' plot(gsSpline, which="temperature")
#' mtext("sectionSmooth(..., method=\"spline\")", line=0.5)
#'
#' # Barnes
#' gsBarnes <- sectionSmooth(gs, "barnes", xr=50, yr=200)
#' plot(gsBarnes, which="temperature")
#' mtext("sectionSmooth(..., method=\"barnes\")", line=0.5)
#'
#' # Kriging. This is commented-out because one of the CRAN
#' # check machines produces an error (converted from a warning)
#' # and I am really not clear on whether dontrun will be
#' # sufficient to solve the problem.
#' #> if (requireNamespace("automap", quietly=TRUE)
#' #> && requireNamespace("sf", quietly=TRUE)) {
#' #> gsKriging <- sectionSmooth(gs, "kriging", xr=50, yr=200)
#' #> plot(gsKriging, which="temperature")
#' #> mtext("sectionSmooth(..., method=\"kriging\")", line=0.5)
#' #> }
#'
#' @author Dan Kelley
#'
#' @family things related to section data
sectionSmooth <- function(section, method="spline",
x,
xg, yg, xgl, ygl, xr, yr,
df, gamma=0.5, iterations=2, trim=0, pregrid=FALSE,
debug=getOption("oceDebug"), ...)
{
if (!inherits(section, "section"))
stop("method is only for objects of class '", "section", "'")
if (!is.function(method) && !(is.character(method) && (method %in% c("barnes", "kriging", "spline"))))
stop('method must be "barnes", "kriging", "spline", or an R function')
# pin debug, since we only call one function, interpBarnes() that uses debug
debug <- if (debug > 2) 2 else if (debug < 0) 0 else debug
oceDebug(debug, "sectionSmooth(section,method=\"",
if (is.character(method)) method else "(function)", "\", ...) {\n", sep="", unindent=1)
stations <- section[["station"]]
nstn <- length(stations)
if (nstn < 2) {
warning("station has <2 stations, so sectionSmooth() is returning it, unaltered")
return(x)
}
xrGiven <- !missing(xr)
yrGiven <- !missing(yr)
dfGiven <- !missing(df)
if (missing(x))
x <- geodDist(section)
nx <- length(x)
if (nx != length(section@data$station))
stop("length(x)=", nx, " does not match number of stations=", length(section@data$station))
# Rearange the input stations into order set by x.
o <- order(x)
x <- x[o]
section@data$station <- section@data$station[o]
P <- section[["pressure"]]
maxPressure <- max(P, na.rm=TRUE)
if (missing(xg)) {
xg <- if (missing(xgl)) x else seq(min(x), max(x), length.out=xgl)
oceDebug(debug, "defaulted", vectorShow(xg))
} else {
oceDebug(debug, "user-supplied", vectorShow(xg))
}
if (missing(yg)) {
deepest <- which.max(unlist(lapply(section[["station"]], function(ctd) max(ctd[["pressure"]], na.rm=TRUE))))
yg <- if (missing(ygl)) section[["station", deepest]][["pressure"]] else seq(0, maxPressure, length.out=ygl)
oceDebug(debug, "defaulted", vectorShow(yg))
} else {
oceDebug(debug, "user-supplied", vectorShow(yg))
}
# Trim xg to the data range in x (issue 1583)
oceDebug(debug, "original data", vectorShow(x))
keep <- min(x) <= xg & xg <= max(x)
oceDebug(debug, "before trimming", vectorShow(xg))
xg <- xg[keep]
oceDebug(debug, "after trimming", vectorShow(xg))
stn1pressure <- stations[[1]][["pressure"]]
if (identical(method, "spline") && !identical(yg, stn1pressure))
stop("for method=\"spline\", yg must match the pressure vector in first station")
nxg <- length(xg)
nyg <- length(yg)
# varsAll holds the names of all variables in the section.
res <- section
varsAll <- unique(unlist(lapply(stations, function(ctd) names(ctd[["data"]]))))
# vars holds just the names of variables that get smoothed. First, we remove
# things that simply cannot be smoothed...
vars <- varsAll[!varsAll %in% c("flag", "quality", "scan", "time")]
# ..., second, we remove things that will be computed from 'x' and 'xg'.
vars <- vars[!vars %in% c("latitude", "longitude", "pressure")]
# ... finally, remove 'depth', which is kind of a surrogate for pressure, I think.
vars <- vars[!vars %in% c("depth")]
flagnames <- unique(unlist(lapply(stations, function(ctd) names(ctd@metadata$flags))))
# start with existing station, to get processing log, section ID, etc., but
# recreate @data$station
res@data$station <- list("vector", nxg)
for (istn in seq_len(nxg)) {
res@data$station[[istn]] <- new("ctd")
res@data$station[[istn]][["pressure"]] <- yg
}
if (is.character(method) && method == "spline") {
oceDebug(debug, "using spline method\n")
# Since we are smoothing along lines of constant pressure, we must
# first ensure that the stations have identical pressures.
npressure <- length(stn1pressure)
for (istn in 2:nstn) {
thisp <- stations[[istn]][["pressure"]]
if (length(thisp) != npressure || any(thisp != stn1pressure)) {
stop("pressure mismatch between station 1 and station", istn, "; try using sectionGrid() first")
}
}
# The work will be done in matrices, for code clarity and speed.
VAR <- array(double(), dim=c(nstn, npressure))
oceDebug(debug, "dim matrix for input grid=", paste(dim(VAR), collapse="X"), "\n")
for (var in vars) {
oceDebug(debug, "smoothing '", var, "'\n", sep="")
for (istn in seq_len(nx))
VAR[istn, ] <- if (var %in% names(stations[[istn]][["data"]])) stations[[istn]][[var]] else rep(NA, npressure)
# Smooth at each pressure value (turn off warnings because smooth.spline is confusingly chatty)
owarn <- options("warn")$warn
options(warn=-1)
VARS <- apply(VAR, 2,
function(varj)
{
ok <- is.finite(varj)
varjok <- varj[ok]
xok <- x[ok]
nok <- sum(ok)
if (!dfGiven) {
df <- floor(nok / 3)
}
if (df > 1) {
predict(smooth.spline(xok, varjok, df=df), xg)$y
} else {
if (nok > 2) approx(xok, varjok, xout=xg, rule=1)$y else rep(NA, nxg)
}
})
options(warn=owarn)
oceDebug(debug, "dim matrix for output grid=", paste(dim(VARS), collapse="X"), "\n")
for (istn in seq_len(nxg))
res@data$station[[istn]][[var]] <- VARS[istn, ]
}
} else {
if (is.character(method)) {
if (method == "barnes") {
oceDebug(debug, "using method=\"barnes\"\n")
} else if (method == "kriging") {
oceDebug(debug, "using method=\"kriging\"\n")
} else {
stop("unknown string method=\"", method, "\"; it must be \"barnes\" or \"kriging\"")
}
} else if (is.function(method)) {
oceDebug(debug, "using method=(function)\n")
} else {
stop("method must be a string or a function")
}
# either "barnes" or a function
# Find names of all variables in all stations; previous to 2019 May 2,
# we only got names from the first station.
XI <- geodDist(section)
X <- unlist(lapply(seq_along(XI), function(i) rep(XI[i], length(section[["station", i]][["pressure"]]))))
# Set up defaults for xr and yr, if not specified in function call.
if (!xrGiven) {
xr <- 1.5 * median(diff(sort(x)))
oceDebug(debug, "xr defaulting to", xr, "(1.5X the median distance between stations)\n")
}
if (!yrGiven) {
yr <- 0.2 * diff(range(P, na.rm=TRUE))
oceDebug(debug, "yr defaulting to", yr, "(0.2X the pressure range across all stations)\n")
}
# Smooth each variable separately
for (var in vars) {
v <- NULL
oceDebug(debug, "smoothing '", var, "' near section.R:2908\n", sep="")
# collect data
v <- unlist(lapply(section[["station"]],
function(CTD)
if (var %in% names(CTD[["data"]])) {
CTD[[var]]
} else {
rep(NA, length(CTD[["pressure"]]))
}))
# ignore NA values (for e.g. a station that lacks a particular variable)
ok <- is.finite(X) & is.finite(P) & is.finite(v)
if (is.character(method)) {
if (method == "barnes") {
smu <- interpBarnes(X[ok], P[ok], v[ok], xg=xg, yg=yg, xgl=length(xg), ygl=length(yg),
xr=xr, yr=yr, gamma=gamma, iterations=iterations, trim=trim,
debug=debug-1)
# rename to match names if method is a function.
smu$z <- smu$zg
smu$x <- smu$xg
smu$y <- smu$yg
if (all(is.na(smu$z)))
warning("All \"", var, "\" data are NA, so gridded field is a matrix of NA values\n")
} else if (method == "kriging") {
if (requireNamespace("automap", quietly=TRUE) && requireNamespace("sf", quietly=TRUE)) {
krigFunction <- function(x, y, z, xg, xr, yg, yr) {
# Scale by xr and yr to perhaps improve numerics
data <- sf::st_as_sf(data.frame(x=x/xr, y=y/yr, z=z), coords=c("x", "y"))
grid <- sf::st_as_sf(expand.grid(xg=xg/xr, yg=yg/yr), coords=c("xg", "yg"))
# silence kriging, which is distractingly chatty
owarn <- options("warn")$warn
options(warn=-1)
capture.output({
K <- automap::autoKrige(z~1, remove_duplicates=TRUE, input_data=data, new_data=grid)
})
options(warn=owarn)
# Try multiple styles of autoKrige() return values. This is not documented, so
# the styles are reverse-engineered based on observed values. Of course,
# this is a risky endeavour.
if (!"krige_output" %in% names(K))
stop("malformed return value from automap::autoKrige()")
krige_output <- K$krige_output
# Handle format (K$krige_output@data$var1.pred) from some automap prior to 1.1.9
if ("data" %in% slotNames(krige_output)) {
oceDebug(debug, "old autoKrige() output format\n")
return(matrix(K$krige_output@data$var1.pred, nrow=length(xg), ncol=length(yg)))
}
# Handle format (K$krige_output$var1.pred) from automap-1.1.9 (seen Apr 2023).
if ("var1.pred" %in% names(krige_output)) {
oceDebug(debug, "handling automap-1.1.9 autoKrige() format (April 2023)\n")
return(matrix(K$krige_output$var1.pred, nrow=length(xg), ncol=length(yg)))
}
# Have a previously unseen format.
stop("malformed return value from automap::autoKrige()")
}
smu <- list(z=krigFunction(X[ok], P[ok], v[ok], xg=xg, xr=xr, yg=yg, yr=yr), x=xg, y=yg)
} else {
stop('method="kriging" requires packages "automap" and "sf" to be installed\n')
}
} else {
stop('method must be "barnes", "kriging", "spline", or an R function.')
}
} else {
# method is not a character. It must be a function, but let's check again, anyway.
if (is.function(method)) {
smu <- list(z=method(x=X[ok], y=P[ok], z=v[ok], xg=xg, xr=xr, yg=yg, yr=yr), x=xg, y=yg)
} else {
stop('method must be "barnes", "kriging", "spline", or a function')
}
}
for (istn in seq_len(nxg)) {
res@data$station[[istn]]@data[[var]] <- smu$z[istn, ]
res@data$station[[istn]]@data[["pressure"]] <- smu$y
}
}
}
oceDebug(debug, "smoothing portion completed (near section.R line 2920)\n")
# Set up section-level and station-level metadata
res@metadata$longitude <- approx(x, section[["longitude", "byStation"]], xg)$y
res@metadata$latitude <- approx(x, section[["latitude", "byStation"]], xg)$y
if (any(!is.finite(res@metadata$longitude)))
warning("some gridded longitudes are NA\n")
if (any(!is.finite(res@metadata$latitude)))
warning("some gridded latitudes are NA\n")
for (i in seq_along(xg)) {
res@data$station[[i]]@metadata$longitude <- res@metadata$longitude[i]
res@data$station[[i]]@metadata$latitude <- res@metadata$latitude[i]
res@data$station[[i]]@metadata$stationId <- if (nxg < 10) {
sprintf("x%d", istn)
} else if (nxg < 100) {
sprintf("x%02d", istn)
} else if (nxg < 1000) {
sprintf("x%03d", istn)
} else if (nxg < 10000) {
sprintf("x%04d", istn)
} else {
sprintf("x%d", istn) # Just give up on fanciness
}
}
nstation <- length(res[["station"]])
waterDepthOriginal <- unlist(lapply(section[["station"]], function(STN) STN[["waterDepth"]]))
if (length(waterDepthOriginal) > 0) {
waterDepthNew <- approx(x, waterDepthOriginal, xg, rule=2)$y
for (i in seq_len(nstation))
res@data$station[[i]]@metadata$waterDepth <- waterDepthNew[i]
}
oceDebug(debug > 3, "about to create units\n")
# Insert uniform units and flags into each station.
units <- list()
for (i in seq_along(section[["station"]])) {
oceDebug(debug > 3, "station i=", i, ", nstation=", nstation, ", length(section@data$section)=", length(section@data$station), "\n", sep="")
for (unitName in names(section@data$station[[i]]@metadata$units)) {
oceDebug(debug > 3, "unitName='", unitName, "'\n", sep="")
if (!(unitName %in% names(units))) {
oceDebug(debug > 3, " ... installing unitName='", unitName, "'\n", sep="")
units[unitName] <- section@data$station[[i]]@metadata$units[unitName]
oceDebug(debug > 3, " ... installation was ok\n")
}
}
}
oceDebug(debug > 3, "installing units\n")
oceDebug(debug > 3, "length(res@data$station)=", length(res@data$station), "\n")
oceDebug(debug > 3, "nxg=", nxg, "\n")
for (i in seq_len(nxg)) {
res@data$station[[i]]@metadata$units <- units
# Note that we put in flags for *all* variables in the output file. This
# is different from the action in sectionGrid(), which inserts flags
# tailored to individual stations. However, smoothing creates new stations,
# and puts *all* variables in *each* station, so we need flags for everything.
res@data$station[[i]]@metadata$flags <- list()
for (flagname in flagnames) {
res@data$station[[i]]@metadata$flags[[flagname]] <- rep(NA, nyg)
}
}
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
oceDebug(debug, "} # sectionSmooth()\n", unindent=1)
res
}
#' Create a Section
#'
#' Create a section based on columnar data, or a set of [oce-class]
#' objects that can be coerced to a section. There are three cases.
#'
#' Case 1. If the first argument is a numerical vector, then it is taken to be the
#' salinity, and [factor()] is applied to `station` to break the
#' data up into chunks that are assembled into [ctd-class] objects with
#' [as.ctd()] and combined to make a [section-class] object
#' to be returned. This mode of operation is provided as a convenience for datasets
#' that are already partly processed; if original CTD data are available, the next
#' mode is preferred, because it permits the storage of much more data and metadata
#' in the CTD object.
#'
#' Case 2. If the first argument is a list containing oce objects, then those
#' objects are taken as profiles of something. A requirement for this
#' to work is that every element of the list contains both `longitude`
#' and `latitude` in either the `metadata` or `data` slot (in
#' the latter case, the mean value is recorded in the section object)
#' and that every element also contains `pressure` in its `data` slot.
#'
#' Case 3. If the first argument is a [argo-class] object, then
#' the profiles it contains are turned into [ctd-class] objects,
#' and these are assembled into a section to be returned.
#'
#' @param salinity This may be a numerical vector, in which case it is interpreted
#' as the salinity, and the other arguments are used for the other components of
#' [ctd-class] objects. Alternatively, it may be one of a variety of
#' other objects from which the CTD objects can be inferred, in which case the
#' other arguments are ignored; see \dQuote{Details}.
#'
#' @param temperature Temperature, in a vector holding values for all stations.
#'
#' @param pressure Pressure, in a vector holding values for all stations.
#'
#' @param longitude Longitude, in a vector holding values for all stations.
#'
#' @param latitude Latitude, in a vector holding values for all stations.
#'
#' @param station Station identifiers, in a vector holding values for all stations.
#'
#' @param sectionId Section identifier.
#'
#' @param debug an integer value that controls whether `as.section()` prints information
#' during its work. The function works quietly if this is 0 and prints out some
#' information if it is positive.
#'
#' @return An object of [section-class].
#'
#' @examples
#' library(oce)
#' data(ctd)
#' # vector of names of CTD objects
#' fake <- ctd
#' fake[["temperature"]] <- ctd[["temperature"]] + 0.5
#' fake[["salinity"]] <- ctd[["salinity"]] + 0.1
#' fake[["longitude"]] <- ctd[["longitude"]] + 0.01
#' fake[["station"]] <- "fake"
#' sec1 <- as.section(c("ctd", "fake"))
#' summary(sec1)
#' # vector of CTD objects
#' ctds <- vector("list", 2)
#' ctds[[1]] <- ctd
#' ctds[[2]] <- fake
#' sec2 <- as.section(ctds)
#' summary(sec2)
#' # argo data (a subset)
#' data(argo)
#' sec3 <- as.section(subset(argo, profile<5))
#' summary(sec3)
#'
#' @author Dan Kelley
#'
#' @family things related to section data
as.section <- function(salinity, temperature, pressure, longitude, latitude, station, sectionId="", debug=getOption("oceDebug"))
{
debug <- as.integer(min(1, max(0, debug))) # make it be 0 or 1
oceDebug(debug, "as.section() {\n", sep="", style="bold", unindent=1)
if (missing(salinity))
stop("argument 'salinity' is missing")
res <- new("section", sectionId="")
if (is.numeric(salinity)) {
if (missing(temperature))
stop("must provide temperature")
if (missing(temperature))
stop("must provide temperature")
if (missing(pressure))
stop("must provide pressure")
if (missing(longitude))
stop("must provide longitude")
if (missing(latitude))
stop("must provide latitude")
if (missing(station))
stop("must provide station")
stationFactor <- factor(station)
stationLevels <- levels(stationFactor)
nstation <- length(stationLevels)
ctds <- vector("list", nstation)
for (i in 1:nstation) {
# message("NUMERIC CASE. i: ", i, ", name:", stationLevels[i])
look <- station==stationLevels[i]
ctds[[i]] <- as.ctd(salinity[look], temperature[look], pressure[look],
longitude=longitude[look][1],
latitude=latitude[look][1],
station=stationLevels[i])
}
} else if (inherits(salinity, "argo")) {
tmp <- salinity
nstation <- length(tmp[["longitude"]])
station <- 1:nstation
longitude <- tmp[["longitude"]]
latitude <- tmp[["latitude"]]
salinity <- tmp[["salinity"]]
temperature <- tmp[["temperature"]]
pressure <- tmp[["pressure"]]
stationFactor <- factor(station)
stationLevels <- levels(stationFactor)
ctds <- vector("list", nstation)
# N will hold the names of extra data for the CTDs
N <- names(tmp@data)
if ("time" %in% N) N <- N[-which(N=="time")]
if ("longitude" %in% N) N <- N[-which(N=="longitude")]
if ("latitude" %in% N) N <- N[-which(N=="latitude")]
if ("salinity" %in% N) N <- N[-which(N=="salinity")]
if ("temperature" %in% N) N <- N[-which(N=="temperature")]
if ("pressure" %in% N) N <- N[-which(N=="pressure")]
time <- tmp[["time"]]
for (i in 1:nstation) {
ctds[[i]] <- as.ctd(salinity[, i], temperature[, i], pressure[, i],
longitude=longitude[i], latitude=latitude[i],
startTime=as.POSIXct(time[i]), station=as.character(station[i]))
for (Ni in seq_along(N)) {
ctds[[i]] <- oceSetData(ctds[[i]], name=N[Ni], value=tmp[[N[Ni]]][, i],
unit=tmp[["units"]][[N[Ni]]])
}
}
} else if (inherits(salinity, "list")) {
oceDebug(debug, "first argument is a list (assumed to be a list of oce objects)\n")
thelist <- salinity # prevent accidental overwriting
if (!length(thelist))
stop("no data in this list")
if (inherits(thelist[[1]], "oce")) {
nstation <- length(salinity)
ctds <- vector("list", nstation)
badDepths <- NULL
for (i in seq_len(nstation)) {
oceDebug(debug, "processing item", i, "of", nstation, "\n")
if (!("pressure" %in% names(thelist[[i]]@data)))
stop("cannot create a section from this first argument, because its ", i, "-th element lacks pressure")
# Replace NA water depth with highest pressure. Note that this action is skipped
# if there is no water depth (e.g. if the first argument is a list of Argo objects).
# See https://github.com/dankelley/oce/issues/1797
if ("waterDepth" %in% names(thelist[[i]]@metadata)) {
if (is.na(thelist[[i]]@metadata$waterDepth)) {
thelist[[i]]@metadata$waterDepth <- max(thelist[[i]]@data$pressure, na.rm=TRUE)
badDepths <- c(badDepths, i)
}
} else {
thelist[[i]]@metadata$waterDepth <- NA
}
ctds[[i]] <- as.ctd(thelist[[i]])
}
if (length(badDepths)) {
warning("estimated waterDepth as max(pressure) for CTDs numbered ",
paste(abbreviateVector(badDepths), collapse=" "))
}
} else {
stop("first argument must be a salinity vector, or a list of oce objects")
}
} else if (is.character(salinity)) {
# vector of names of CTD objects
nstation <- length(salinity)
ctds <- vector("list", nstation)
for (i in 1:nstation) {
ctds[[i]] <- get(salinity[i], parent.frame())
}
} else {
stop("first argument is not understood")
}
# In each case, we now have a vector of CTD objects.
res@metadata$sectionId <- ""
res@metadata$stationId <- unlist(lapply(ctds, function(x) x[["station"]][1]))
res@metadata$longitude <- unlist(lapply(ctds, function(x) mean(x[["longitude"]], na.rm=TRUE)))
res@metadata$latitude <- unlist(lapply(ctds, function(x) mean(x[["latitude"]], na.rm=TRUE)))
res@metadata$time <- numberAsPOSIXct(unlist(lapply(ctds, function(x) x[["time"]][1])))
res@data <- list(station=ctds)
res@processingLog <- processingLogAppend(res@processingLog, paste(deparse(match.call()), sep="", collapse=""))
oceDebug(debug, "} # as.section()\n", sep="", style="bold", unindent=1)
res
}
#' Add a spine to a section object
#'
#' The purpose of this is to permit plotting with `xtype="spine"`, so that
#' the section plot will display the distance of stations projected
#' onto the spine.
#'
#' @param section a [section-class] object.
#' @param spine either a list or a data frame, containing numeric items named
#' `longitude` and `latitude`, defining a path along the spine.
#' @template debugTemplate
#'
#' @return A [section-class] object with a spine added.
#'
#' @examples
#' library(oce)
#' data(section)
#' eastern <- subset(section, longitude < (-65))
#' spine <- list(longitude=c(-74.5, -69.2, -55),
#' latitude=c(38.6, 36.25, 36.25))
#' easternWithSpine <- addSpine(eastern, spine)
#'\dontrun{
#' # plot(easternWithSpine, which="map")
#' # plot(easternWithSpine, xtype="distance", which="temperature")
#' # plot(easternWithSpine, xtype="spine", which="temperature")
#'}
#'
#' @author Dan Kelley
addSpine <- function(section, spine, debug=getOption("oceDebug"))
{
oceDebug(debug, "addSpine(..., spine=", argShow(spine), ") {\n", sep="", style="bold", unindent=1)
if (missing(section))
stop("must provide 'section' argument")
if (!inherits(section, "section"))
stop("'section' must be a section object, e.g. created by read.section() or as.section()")
if (missing(spine))
stop("must provide 'spine' argument")
res <- section
if (2 == length(spine) && 2 == sum(c("latitude", "longitude") %in% names(spine))) {
if (length(spine$longitude) != length(spine$latitude))
stop("unequal lengths of spine longitude (", length(spine$longitude),
") and latitude (", length(spine$latitude), ")")
if (length(spine$longitude) < 2)
stop("length of spine longitude must exceed 2, but it is ", length(spine$longitude))
res@metadata$spine <- spine
} else {
stop("'spine' must be a list or data frame containing two items, named 'longitude' and 'latitude'")
}
oceDebug(debug, "} # addSpine()\n", sep="", style="bold", unindent=1)
res
}
#' Try to Reduce Section Longitude Range
#'
#' [longitudeTighten] shifts some longitudes in its first argument by 360 degrees, if doing
#' so will reduce the overall longitude span.
#'
#' This function can be helpful in cases where the CTD stations within a section
#' cross the cut point of the longitude convention, which otherwise might
#' yield ugly plots if [plot,section-method()] is used with `xtype="longitude"`.
#' This problem does occur with CTD objects ordered by time of sampling,
#' but was observed in December 2020 for a GO-SHIPS dataset downloaded from
#' `https://cchdo.ucsd.edu/data/15757/a10_1992_ct1`.
#'
#' @param section a [section-class] object.
#'
#' @return A [section-class] object based on its first argument, but with
#' longitudes shifted in its `metadata` slot, and also in the `metadata` slots
#' of each of the [ctd-class] objects that are stored in the `station` item
#' in its `data` slot.
#'
#' @author Dan Kelley
longitudeTighten <- function(section)
{
if (!inherits(section, "section"))
stop("'section' must be an object created with read.section() or as.section()")
res <- section
longitude <- section[["longitude", "byStation"]]
longitudeShifted <- ifelse(longitude > 180, longitude - 360, longitude)
if (diff(range(longitude)) > diff(range(longitudeShifted)))
longitude <- longitudeShifted
res <- oceSetMetadata(res, "longitude", longitude, "longitudeTighten")
ctds <- section@data$station
for (i in seq_along(ctds))
ctds[[i]]@metadata$longitude <- longitude[i]
res@data$station <- ctds
res
}
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