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@ -2,6 +2,8 @@ source("/home/taha/chepec/chetex/common/R/common/ProvideSampleId.R")
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xrfspectro2df <- function(smpfile) {
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xrfspectro2df <- function(smpfile) {
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## Description:
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## Description:
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## Total remake of xrfspectro2df(). Idea is to accomodate all 6 possible
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## datasets of each SMP file, plus the attributes.
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## Reads XRF textfile from XLAB SPECTRO XRF.
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## Reads XRF textfile from XLAB SPECTRO XRF.
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## Stores data in data frame and parameters in an attributed dataframe.
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## Stores data in data frame and parameters in an attributed dataframe.
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## Usage:
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## Usage:
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@ -11,76 +13,193 @@ xrfspectro2df <- function(smpfile) {
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## (with path) to one SMP file (ASCII).
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## (with path) to one SMP file (ASCII).
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## Value:
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## Value:
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## A dataframe with attributed dataframe
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## A dataframe with attributed dataframe
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#
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#### ONLY BOTHER WITH THE FIRST MEASUREMENT IN THE SMP-FILE.
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filecon <- file(smpfile, "r")
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filecon <- file(smpfile, "r")
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smpcontents <- readLines(filecon, n = -1) #read all lines of input file
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smpcontents <- readLines(filecon, n = -1) #read all lines of input file
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close(filecon)
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close(filecon)
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#
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sampleid <- ProvideSampleId(smpfile)
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# Parameter table
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#
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# Those are the parameter we may access later in this function
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rgxp.data <- "^Kanal\\s[\\d]+:"
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xrf.param <- data.frame(stringsAsFactors = FALSE,
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#
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matrix(c("Method", "^Method:",
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numrow.idx <- regexpr(rgxp.data, smpcontents, perl = TRUE)
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"Job", "^Job:",
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# scrap the match.length attribute
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"Status", "^Status:",
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attr(numrow.idx, "match.length") <- NULL
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"Description", "^Description:",
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#
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"Date", "^Date\\sof\\sMeasurement:",
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# Determine how many columns the data contains
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"Measurements", "^Measurements:",
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smpdata.cols <- length(strsplit(smpcontents[which(numrow.idx == 1)][1], "\t")[[1]]) - 1
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"Voltage", "^Voltage:",
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# While we are at it, save row count to a variable as well
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"Current", "^Current:",
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smpdata.rows <- length(smpcontents[which(numrow.idx == 1)])
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"Target", "^Target:",
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# strip prefix off each data row
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"Duration", "^Meas\\.\\sDuration:",
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#smpdata <- matrix(NA, ncol = smpdata.cols, nrow = smpdata.rows)
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"Impulse", "^Imp\\.\\sRate:",
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smpdata.txt <- vector(length = smpdata.rows)
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"DeadTime", "^Rel\\.\\sDead\\sTime:",
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for (i in 1:smpdata.rows) {
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"FirstChannel", "^First\\sChannel:",
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smpdata.txt[i] <- strsplit(smpcontents[which(numrow.idx == 1)][i], ":")[[1]][2]
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"LastChannel", "^Last\\sChannel:",
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"PeakTime", "^Peak\\sTime:",
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"Gain", "^Gain:",
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"ZeroPeak", "^Zero\\sPeak:",
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"Data", "^Kanal\\s[\\d]+:"),
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ncol = 2, byrow = T))
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names(xrf.param) <- c("parameter", "regexp")
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# Data table
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# Contains the regexp used for identifiying rows containing data
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xrf.data <- data.frame(stringsAsFactors = FALSE,
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matrix(c("Data", "^Kanal\\s[\\d]+:"), ncol = 2, byrow = T))
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names(xrf.data) <- c("parameter", "regexp")
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# Find out how many measurements we have in this
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# file by accessing the Measurements field
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n_measurements <- as.numeric(strsplit(gsub("^\\t", "",
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strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Measurements", select = "regexp")$regexp,
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smpcontents) == 1)], ":")[[1]][2]), "\\t")[[1]])
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# If more than one measurement, issue warning
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if (n_measurements > 1) {
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warning(paste(paste(n_measurements, " measurements detected in ",
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basename(smpfile), sep = ""),
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"Only the first measurement will be recorded",
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sep = "\n", collapse = ""))
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}
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}
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smpdata.txt.clean <- gsub("^\\s", "",
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gsub("\\t", " ", smpdata.txt))
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zz <- textConnection(smpdata.txt.clean, "r")
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ff <- data.frame(stringsAsFactors = FALSE,
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sampleid = sampleid,
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channel = seq(1, smpdata.rows),
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matrix(scan(zz, what = numeric(), sep = " "),
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ncol = smpdata.cols, byrow = TRUE))
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close(zz)
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names(ff) <- c("sampleid", "channel", paste("Y", seq(1, smpdata.cols), sep = ""))
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#
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# How many rows of data?
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### Collect attributes of this experiment
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n_rowsdata <-
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SMPattrEdit <- matrix(c("Voltage", "^Voltage:",
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length(which(regexpr(subset(xrf.data, parameter == "Data",
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"Current", "^Current:",
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select = "regexp")$regexp, smpcontents, perl = TRUE) == 1))
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"Target", "^Target:",
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"Duration", "^Meas\\.\\sDuration:",
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# Build an empty matrix big enough to hold all data
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"Impulse", "^Imp\\.\\sRate:",
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# (i.e., ncol = 3, and nrow = n_rowsdata * n_measurements)
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"DeadTime", "^Rel\\.\\sDead\\sTime:",
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data.long <- data.frame(matrix(NA, ncol = 5, nrow = 6 * n_rowsdata))
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"FirstChannel", "^First\\sChannel:",
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names(data.long) <- c("sampleid", "measurement", "channel", "energy", "counts")
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"LastChannel", "^Last\\sChannel:",
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"PeakTime", "^Peak\\sTime:",
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"Gain", "^Gain:",
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data.mtx <- matrix(NA, ncol = 6, nrow = n_rowsdata)
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"ZeroPeak", "^Zero\\sPeak:"),
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for (j in 1:n_rowsdata) {
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ncol = 2, byrow = T)
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data.mtx[j, ] <- as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.data, parameter == "Data",
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select = "regexp")$regexp, smpcontents, perl = TRUE) == 1)], ":\\t")[[j]][2], "\\t")[[1]])
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SMPattr <- matrix(NA, nrow = smpdata.cols + 1, ncol = dim(SMPattrEdit)[1])
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for (c in 1:dim(SMPattrEdit)[1]) {
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SMPattr[1, c] <- SMPattrEdit[c, 1]
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SMPattr[2:dim(SMPattr)[1], c] <-
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matrix(strsplit(gsub("^\\t", "",
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strsplit(smpcontents[which(regexpr(SMPattrEdit[c, 2], smpcontents) == 1)],
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":")[[1]][2]), "\\t")[[1]], ncol = smpdata.cols)
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}
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}
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SMPdf <- data.frame(stringsAsFactors = FALSE,
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SMPattr[2:dim(SMPattr)[1], ])
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colnames(SMPdf) <- SMPattr[1, ]
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# Sampleid to column 1
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data.long[, 1] <- rep(ProvideSampleId(smpfile), n_rowsdata)
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# Channel to column 3
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### Now calculate the energy (keV) scale (convert from channels to energy)
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data.long[, 3] <- rep(seq(1, n_rowsdata), dim(data.mtx)[2])
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ff$X <- ff$channel * (as.numeric(SMPdf$Gain[1]) / as.numeric(SMPdf$LastChannel[1]))
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for (c in 1:6) {
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# Measurement no. in column 2
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data.long[((c * n_rowsdata) - n_rowsdata + 1):(((c + 1) * n_rowsdata) - n_rowsdata), 2] <- rep(c, n_rowsdata)
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# Counts in column 5
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data.long[((c * n_rowsdata) - n_rowsdata + 1):(((c + 1) * n_rowsdata) - n_rowsdata), 5] <- data.mtx[, c]
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}
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# Drop all rows with measurement-number not equal to 1
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data.long <- subset(data.long, measurement == 1)
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# Fetch the measurement parameters
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data.long[, subset(xrf.param, parameter == "Date")$parameter] <-
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rep(substr(strsplit(gsub("^\\t", "", strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Date")$regexp, smpcontents) == 1)], ":")[[1]][2]),
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"\\t")[[1]][1], 1, 8), n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Method")$parameter] <-
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rep(strsplit(gsub("^\\t", "", strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Method")$regexp, smpcontents) == 1)], ":")[[1]][2]),
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"\\t")[[1]][1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Job")$parameter] <-
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rep(strsplit(gsub("^\\t", "", strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Job")$regexp, smpcontents) == 1)], ":")[[1]][2]),
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"\\t")[[1]][1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Status")$parameter] <-
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rep(strsplit(gsub("^\\t", "", strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Status")$regexp, smpcontents) == 1)], ":")[[1]][2]),
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"\\t")[[1]][1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Description")$parameter] <-
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rep(gsub("^\\t", "", strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Description")$regexp, smpcontents) == 1)], ":")[[1]][2]),
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n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Voltage")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Voltage")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Current")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Current")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Target")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Target")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Duration")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Duration")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Impulse")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Impulse")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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# Attach parameters to returned dataframe
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data.long[, subset(xrf.param, parameter == "DeadTime")$parameter] <-
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attr(ff, "parameters") <- SMPdf
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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#
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parameter == "DeadTime")$regexp, smpcontents, perl = TRUE) == 1)],
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return(ff)
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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}
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data.long[, subset(xrf.param, parameter == "FirstChannel")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "FirstChannel")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "LastChannel")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "LastChannel")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "PeakTime")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "PeakTime")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "Gain")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "Gain")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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data.long[, subset(xrf.param, parameter == "ZeroPeak")$parameter] <-
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rep(as.numeric(strsplit(strsplit(smpcontents[which(regexpr(subset(xrf.param,
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parameter == "ZeroPeak")$regexp, smpcontents, perl = TRUE) == 1)],
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":\\t")[[1]][2], "\\t")[[1]])[1], n_rowsdata)
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# Convert channel into energy scale
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# Using the following assumptions:
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# 1. Zero peak is always the strongest (highest) peak in the spectrum
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# The channel with maximum counts should correspond to 0 keV
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# This gives a one-channel deviation from what the instrument shows
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# for a 12.5 keV range measurement using 1024 channels (so far)
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# This is good enough for our purposes, since the peak energies for most
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# ions do not match with reference values without a correction term anyway.
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max.channel <- which(data.long$counts == max(data.long$counts))
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data.long$energy <- (data.long$channel * (data.long$Gain / data.long$LastChannel)) -
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((max.channel / data.long$LastChannel) * data.long$Gain)
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# Save the maxchannel to the returned dataframe
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data.long$ZeroChannel <- rep(max.channel, n_rowsdata)
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# Calculate energy from channel # this is no longer viable
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#data.long$energy <- (data.long$channel * (data.long$Gain / data.long$LastChannel)) -
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# ((24 / data.long$LastChannel) * data.long$Gain)
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return(data.long)
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}
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Taha Ahmed
13 years ago