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143 lines
5.6 KiB
R
143 lines
5.6 KiB
R
# Renishaw.R
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# Functions to read data from the Renishaw Raman spectrometer
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# Taha Ahmed, Feb 2011
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# CONTENTS
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source("/home/taha/chepec/chetex/common/R/common.R")
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# >>>> Raman2df
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# >>>> Ramanpk
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##################################################
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################### Raman2df #######################
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##################################################
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Raman2df <- function(datafilename) {
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# Function description: for reading Raman spectrum into dataframe
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#
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datafile <- file(datafilename, "r")
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chifile <- readLines(datafile, n = -1) #read all lines of input file
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close(datafile)
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#
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#####
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sampleid <- ProvideSampleId(datafilename)
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#
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ff <- data.frame(NULL)
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zz <- textConnection(chifile, "r")
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ff <- rbind(ff, data.frame(stringsAsFactors = FALSE,
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sampleid,
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matrix(scan(zz, what = numeric(), sep = "\t"),
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ncol = 2, byrow = T)))
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close(zz)
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names(ff) <- c("sampleid", "shift", "counts")
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# Re-order by increasing shift
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ff <- ff[order(ff$shift), ]
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# And fix the row.names
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row.names(ff) <- seq(1, dim(ff)[1])
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# Re-calculate the spectrum with evenly spaced points
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# (so that the peak-finding algorithms of diffractometry package may be used)
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ff$cshift <- approx(x = ff$shift, y = ff$counts,
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method = "linear", n = length(ff$shift))$x
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ff$ccounts <- approx(x = ff$shift, y = ff$counts,
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method = "linear", n = length(ff$shift))$y
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##
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return(ff)
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}
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##################################################
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################## Ramanpk #######################
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##################################################
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Ramanpk <- function(Raman.exp, kerpk = 1, fitmaxiter = 50) {
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Raman.base <- baselinefit(Raman.exp, tau=2.0, gam=1.0, scl.factor=1.2, maxwdth=400)
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# This loop deals with the output from baselinefit()
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# It makes a "melted" dataframe in long form for each
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# separated peak for some baseline parameters
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Raman.pks <- data.frame()
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Raman.pks.basl <- data.frame()
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Raman.pks.pmg <- data.frame()
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Raman.pks.spl <- data.frame()
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peaks <- 1:length(Raman.base$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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Raman.pks <- rbind(Raman.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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Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s], ]))
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# the calculated baseline in long form by separated peak
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Raman.pks.basl <- rbind(Raman.pks.basl,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s]],
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y = Raman.base$baseline$basisl[Raman.base$indlsep[s]:Raman.base$indrsep[s]]))
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# the taut string estimation in long form by separated peak
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Raman.pks.pmg <- rbind(Raman.pks.pmg,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s]],
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y = Raman.base$pmg$fn[Raman.base$indlsep[s]:Raman.base$indrsep[s]]))
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# the weighted smoothed spline in long form by separated peak
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Raman.pks.spl <- rbind(Raman.pks.spl,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s]],
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y = Raman.base$spl$reg[Raman.base$indlsep[s]:Raman.base$indrsep[s]]))
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}
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# Column names assigned to d.pks
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names(Raman.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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Raman.fit <- list() # holds pkdecompint output
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Raman.fit.fitpk <- data.frame() # contains fitting curves
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Raman.fit.parpk <- data.frame() # physical parameters by peak and kernel
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Raman.nobasl <- data.frame() # data with baseline removed
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peaks <- 1:length(Raman.base$npks)
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for (s in peaks) {
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######## PKDECOMPINT ########
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if (length(kerpk) > 1) {
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# set number of kernels per peak manually
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Raman.fit[[s]] <- pkdecompint(Raman.base, intnum = s,
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k = kerpk[s], maxiter = fitmaxiter)
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} else {
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# use number of kernels determined by baselinefit()
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Raman.fit[[s]] <- pkdecompint(Raman.base, intnum = s,
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k = Raman.base$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:Raman.fit[[s]]$num.ker) {
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Raman.fit.parpk <- rbind(Raman.fit.parpk,
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data.frame(peak = factor(Raman.fit[[s]]$intnr),
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kernel = factor(kernel),
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x = Raman.fit[[s]]$parpks[kernel, "loc"],
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height = Raman.fit[[s]]$parpks[kernel, "height"],
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area = Raman.fit[[s]]$parpks[kernel, "intens"],
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fwhm = Raman.fit[[s]]$parpks[kernel, "FWHM"],
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m = Raman.fit[[s]]$parpks[kernel, "m"],
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accept = Raman.fit[[s]]$accept))
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Raman.fit.fitpk <- rbind(Raman.fit.fitpk,
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data.frame(peak = factor(peaks[s]),
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kernel = factor(kernel),
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x = Raman.fit[[s]]$x,
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y = Raman.fit[[s]]$fitpk[kernel, ]))
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}
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Raman.nobasl <- rbind(Raman.nobasl,
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data.frame(peak = factor(peaks[s]),
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x = Raman.fit[[s]]$x,
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y = Raman.fit[[s]]$y))
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}
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return(list(Raman.base = Raman.base,
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Raman.peaks = Raman.pks,
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Raman.fit.parpk = Raman.fit.parpk,
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Raman.fit.fitpk = Raman.fit.fitpk,
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Raman.nobasl = Raman.nobasl))
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}
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