R/pbToDf.R

Defines functions as.data.frame.PamBinary pbToDf

Documented in pbToDf

#' @title Convert a PamBinary Object to Data Frame
#' 
#' @description Converts a PamBinary object into a data frame. The data.frame
#'   will combine all of the data from the \code{data} part of the PamBinary
#'   object, but will not include annotations data, click waveforms, DIFAR demux
#'   data, or contours from the WMD detector. These are skipped because they are
#'   either inconsistent in their size, or are large objects. The function 
#'   \code{pbToDf} is also called when \code{as.data.frame} is called on a PamBinary
#'   class object.
#'   
#' @param pb a PamBinary class object created by 
#'   \code{\link[PamBinaries]{loadPamguardBinaryFile}}
#' @param templateNames if using the click template classifier, the names of the species
#'   for the click templates. These will be used as the names of the columns in the
#'   dataframe, and the length of this must exactly match the number of templates used.
#'   Will add columns for the threshold, match, and reject correlation values for each
#'   template name provided
#' 
#' @return a data.frame containing most of the binary data read in. Will not
#'   contain most annotation data, click waveforms, DIFAR demux data, or contour
#'   information from WMD detector. These are skipped because they are either
#'   incosistent in their size, or are large objects. Click template classifier
#'   information will be included if \code{templateNames} are supplied. If binary
#'   is from noise band monitor, noise data will be stored in columns noiseMean,
#'   noisePeak, and octaveBands, and the resulting dataframe will have a row for 
#'   each separate octave band stored
#' 
#' @author Taiki Sakai \email{taiki.sakai@@noaa.gov}
#' 
#' @examples 
#' 
#' # load the data
#' clickFile <- system.file('extdata', 'Click.pgdf', package='PamBinaries')
#' clickData <- loadPamguardBinaryFile(clickFile)
#' # two methods two convert to a dataframe
#' head(pbToDf(clickData))
#' head(data.frame(clickData))
#' 
#' @importFrom dplyr bind_rows
#' @export
#' 
pbToDf <- function(pb, templateNames = NULL) {
    # skip long data
    skip <- c('annotations', 'wave', 'contour', 'contWidth', 'sliceData', 'demuxData', 'noise', 'excess', 'energy', 'points')
    good <- FALSE
    fileName <- NULL
    # Case 1: either a PamBinary class, or has the right pieces but not the class
    if('PamBinary' %in% class(pb) ||
       all(c('data', 'fileInfo') %in% names(pb))) {
        justData <- pb$data
        fileName <- pb$fileInfo$fileName
        good <- TRUE
    }
    # Case 2: Just the $data part, we should do it anyway but warn.
    if(all(c('flagBitMap', 'identifier') %in% names(pb[[1]]))) {
        warning('It looks like you input just the $data, please use',
                'the entire "PamBinary" output next time.', call.=FALSE)
        justData <- pb
        good <- TRUE
    }
    if(length(justData) == 0) {
        return(NULL)
    }
    keepIx <- !(names(justData[[1]]) %in% skip)
    if(!good) {
        stop('Input does not look like a PamBinaray output.')
    }
    result <- bind_rows(lapply(justData, function(x) {
        if(all(c('noise', 'nBands') %in% names(x))) {
            tmp <- x[keepIx]
            tmp <- data.frame(list(tmp, octaveBand = 1:x$nBands))
            tmp$noiseMean <- x$noise[1, ]
            tmp$noisePeak <- x$noise[2, ]
            # tmp$octaveBand <- 1:x$nBands
            tmp
        } else if(!is.null(templateNames)) {
            ct <- unlist(x$annotations$mclassification)
            if(length(ct) < (3 *  length(templateNames))) {
                ct <- c(ct, rep(NA, 3 * length(templateNames) - length(ct)))
            } else if(length(ct) > (3 * length(templateNames))) {
                msg <- paste0('Insufficient number of template names provided',
                              ' (found ', length(ct)/3, ' but only ', length(templateNames),
                              ' provided)')
                stop(msg, call. = FALSE)
            }
            names(ct) <- paste0(templateNames, '_', rep(c('thresh', 'match', 'reject'), each = length(templateNames)))
            c(x[keepIx], ct)
        } else {
            x[keepIx]
        }
    }))
    
    if(any(is.na(result[ncol(result)]))) {
        if(is.null(fileName)) {
            warning('Some classification templates were missing, or the number of template names provided does not match',
                    ' number of templates present. Missing values have been set to NA.', call. = FALSE)
        } else {
            warning('Some classification templates were missing, or the number of template names provided does not match',
                    ' number of templates present. Missing values have been set to NA.\n',
                    'Issue found in binary file: ', fileName, call. = FALSE)
        }
    }
    result
}

#' @export
#' 
as.data.frame.PamBinary <- function(x, ...) {
    pbToDf(x)
}

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PamBinaries documentation built on April 30, 2022, 1:08 a.m.