Raman spectrum are now sorted by increasing Raman shift by Raman2df().
INCA.R deals with EDS data. Minor updates to CHI.R (check the diff logs for details).master
Taha Ahmed
14 years ago
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# INCA.R
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# Functions to read and manipulate data from the Oxford INCA EDS
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# Taha Ahmed, April 2011
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# CONTENTS
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source("/home/taha/chepec/chetex/common/R/common.R")
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# >>>> eds2df
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# >>>> edspk
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##################################################
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################### eds2df #######################
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##################################################
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eds2df <- function(edstxtfile) {
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## Description:
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## Reads EDS textfile from INCA EDS.
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## Stores data in data frame.
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## Usage:
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## eds2df(edstxtfile)
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## Arguments:
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## edstxtfile: character string, the full filename
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## (with path) to one txt file.
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## Value:
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## A dataframe
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#
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incatxt <- file(edstxtfile, "r")
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edsfile <- readLines(incatxt, n = -1) #read all lines of input file
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close(incatxt)
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#
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sampleid <- ProvideSampleId(edstxtfile)
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#
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rgxp.comment <- "^\\#"
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#
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numrow.idx <- regexpr(rgxp.comment, edsfile)
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# scrap the match.length attribute
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attr(numrow.idx, "match.length") <- NULL
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#
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i <- seq(1, length(numrow.idx) - 1, 1)
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j <- seq(2, length(numrow.idx), 1)
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# Start index of data range
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start.idx <- which(numrow.idx[i] == 1 & numrow.idx[j] != 1) + 1
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# End index of the data range
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end.idx <- which(numrow.idx[i] != 1 & numrow.idx[j] == 1)
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#
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zz <- textConnection(edsfile[start.idx:end.idx], "r")
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#
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ff <- data.frame()
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ff <- data.frame(stringsAsFactors = FALSE,
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sampleid = sampleid,
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matrix(scan(zz, what = numeric(), sep = ","), ncol = 2, byrow = T))
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close(zz)
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names(ff) <- c("sampleid", "energy", "counts")
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#
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### Collect attributes of this experiment
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# XUnit
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position.XUnit <- regexpr("^\\#XUNITS", edsfile)
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XUnit <- as.character(strsplit(edsfile[which(position.XUnit == 1)], ":\\s")[[1]][2])
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ff$XUnit <- XUnit
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# YUnit
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position.YUnit <- regexpr("^\\#YUNITS", edsfile)
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YUnit <- as.character(strsplit(edsfile[which(position.YUnit == 1)], ":\\s")[[1]][2])
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ff$YUnit <- YUnit
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# Date
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position.Date <- regexpr("^\\#DATE", edsfile)
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Date <- strsplit(edsfile[which(position.Date == 1)], ":\\s")[[1]][2]
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ff$Date <- Date
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# Time
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position.Time <- regexpr("^\\#TIME", edsfile)
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Time <- strsplit(edsfile[which(position.Time == 1)], ":\\s")[[1]][2]
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ff$Time <- Time
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# XPerChannel
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position.XPerChannel <- regexpr("^\\#XPERCHAN", edsfile)
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XPerChannel <- as.numeric(strsplit(edsfile[which(position.XPerChannel == 1)], ":\\s")[[1]][2])
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ff$XPerChannel <- XPerChannel
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# Offset
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position.Offset <- regexpr("^\\#OFFSET", edsfile)
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Offset <- as.numeric(strsplit(edsfile[which(position.Offset == 1)], ":\\s")[[1]][2])
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ff$Offset <- Offset
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# ChOffset
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position.ChOffset <- regexpr("^\\#CHOFFSET", edsfile)
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ChOffset <- as.numeric(strsplit(edsfile[which(position.ChOffset == 1)], ":\\s")[[1]][2])
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ff$ChOffset <- ChOffset
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# LiveTime
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position.LiveTime <- regexpr("^\\#LIVETIME", edsfile)
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LiveTime <- as.numeric(strsplit(edsfile[which(position.LiveTime == 1)], ":\\s")[[1]][2])
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ff$LiveTime <- LiveTime
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# DeadTime is calculated from: REALTIME - LIVETIME
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position.RealTime <- regexpr("^\\#REALTIME", edsfile)
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RealTime <- as.numeric(strsplit(edsfile[which(position.RealTime == 1)], ":\\s")[[1]][2])
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DeadTime <- RealTime - LiveTime
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ff$DeadTime <- DeadTime
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# BeamEnergy
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position.BeamEnergy <- regexpr("^\\#BEAMKV", edsfile)
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BeamEnergy <- as.numeric(strsplit(edsfile[which(position.BeamEnergy == 1)], ":\\s")[[1]][2])
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ff$BeamEnergy <- BeamEnergy
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#
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return(ff)
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}
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##################################################
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#################### edspk #######################
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##################################################
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edspk <- function(eds.exp, kerpk = 1, fitmaxiter = 50) {
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eds.base <- baselinefit(eds.exp, tau=2.0, gam=1.0, scl.factor=3.0, maxwdth=0.20)
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# This loop deals with the output from baselinefit()
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# It makes a "melted" dataframe in long form for each
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# separated peak for some baseline parameters
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eds.pks <- data.frame()
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eds.pks.basl <- data.frame()
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eds.pks.pmg <- data.frame()
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eds.pks.spl <- data.frame()
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peaks <- 1:length(eds.base$npks)
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for (s in peaks) {
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# recorded data in long form by separated peak
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eds.pks <- rbind(eds.pks, # column names assigned after loop
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s], ]))
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# the calculated baseline in long form by separated peak
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eds.pks.basl <- rbind(eds.pks.basl,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]],
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y = eds.base$baseline$basisl[eds.base$indlsep[s]:eds.base$indrsep[s]]))
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# the taut string estimation in long form by separated peak
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eds.pks.pmg <- rbind(eds.pks.pmg,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]],
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y = eds.base$pmg$fn[eds.base$indlsep[s]:eds.base$indrsep[s]]))
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# the weighted smoothed spline in long form by separated peak
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eds.pks.spl <- rbind(eds.pks.spl,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]],
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y = eds.base$spl$reg[eds.base$indlsep[s]:eds.base$indrsep[s]]))
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}
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# Column names assigned to d.pks
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names(eds.pks) <- c("peak", "kernel", "x", "y")
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# This loop calls pkdecompint() on each peak separately
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# It makes a "melted" dataframe in long form for:
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eds.fit <- list() # holds pkdecompint output
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eds.fit.fitpk <- data.frame() # contains fitting curves
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eds.fit.parpk <- data.frame() # physical parameters by peak and kernel
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eds.nobasl <- data.frame() # data with baseline removed
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peaks <- 1:length(eds.base$npks)
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for (s in peaks) {
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######## PKDECOMPINT ########
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if (length(kerpk) > 1) {
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# set number of kernels per peak manually
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eds.fit[[s]] <- pkdecompint(eds.base, intnum = s,
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k = kerpk[s], maxiter = fitmaxiter)
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} else {
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# use number of kernels determined by baselinefit()
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eds.fit[[s]] <- pkdecompint(eds.base, intnum = s,
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k = eds.base$npks[s], maxiter = fitmaxiter)
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}
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# Setup the dataframe that makes up the peak table
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for (kernel in 1:eds.fit[[s]]$num.ker) {
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eds.fit.parpk <- rbind(eds.fit.parpk,
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data.frame(peak = factor(eds.fit[[s]]$intnr),
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kernel = factor(kernel),
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x = eds.fit[[s]]$parpks[kernel, "loc"],
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height = eds.fit[[s]]$parpks[kernel, "height"],
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area = eds.fit[[s]]$parpks[kernel, "intens"],
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fwhm = eds.fit[[s]]$parpks[kernel, "FWHM"],
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m = eds.fit[[s]]$parpks[kernel, "m"],
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accept = eds.fit[[s]]$accept))
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eds.fit.fitpk <- rbind(eds.fit.fitpk,
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data.frame(peak = factor(peaks[s]),
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kernel = factor(kernel),
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x = eds.fit[[s]]$x,
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y = eds.fit[[s]]$fitpk[kernel, ]))
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}
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eds.nobasl <- rbind(eds.nobasl,
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data.frame(peak = factor(peaks[s]),
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x = eds.fit[[s]]$x,
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y = eds.fit[[s]]$y))
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}
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return(list(eds.base = eds.base,
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eds.peaks = eds.pks,
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eds.fit.parpk = eds.fit.parpk,
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eds.fit.fitpk = eds.fit.fitpk,
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eds.nobasl = eds.nobasl))
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}
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