diff --git a/CHI.R b/CHI.R index 682c01f..8d7920f 100644 --- a/CHI.R +++ b/CHI.R @@ -501,19 +501,19 @@ lsv2df <- function(datafilename, wearea = 1) { ## wearea: (optional) area of working electrode (in square centimeter) ## Value: ## Dataframe with the following columns (and no extra attributes): - ## $ sampleid : chr - ## $ segment : num - ## $ potential : num - ## $ current : num - ## $ charge : num - ## $ currentdensity : num - ## $ chargedensity : num - ## $ InitE : num - ## $ FinalE : num - ## $ ScanRate : num - ## $ SampleInterval : num - ## $ QuietTime : num - ## $ Sensitivity : num + ## $ sampleid : chr (id) + ## $ segment : num (id) + ## $ potential : num (measure) + ## $ current : num (measure) + ## $ charge : num (measure) + ## $ currentdensity : num (measure) + ## $ chargedensity : num (measure) + ## $ InitE : num (id) + ## $ FinalE : num (id) + ## $ ScanRate : num (id) + ## $ SampleInterval : num (id) + ## $ QuietTime : num (id) + ## $ Sensitivity : num (id) ## Note: ## The CH Instruments 760 potentiostat records all data ## using standard SI units, therefore this function diff --git a/INCA.R b/INCA.R new file mode 100644 index 0000000..5eb83ea --- /dev/null +++ b/INCA.R @@ -0,0 +1,212 @@ +# INCA.R +# Functions to read and manipulate data from the Oxford INCA EDS +# Taha Ahmed, April 2011 + +# CONTENTS +source("/home/taha/chepec/chetex/common/R/common.R") +# >>>> eds2df +# >>>> edspk + + + + +################################################## +################### eds2df ####################### +################################################## +eds2df <- function(edstxtfile) { + ## Description: + ## Reads EDS textfile from INCA EDS. + ## Stores data in data frame. + ## Usage: + ## eds2df(edstxtfile) + ## Arguments: + ## edstxtfile: character string, the full filename + ## (with path) to one txt file. + ## Value: + ## A dataframe + # + incatxt <- file(edstxtfile, "r") + edsfile <- readLines(incatxt, n = -1) #read all lines of input file + close(incatxt) + # + sampleid <- ProvideSampleId(edstxtfile) + # + rgxp.comment <- "^\\#" + # + numrow.idx <- regexpr(rgxp.comment, edsfile) + # scrap the match.length attribute + attr(numrow.idx, "match.length") <- NULL + # + i <- seq(1, length(numrow.idx) - 1, 1) + j <- seq(2, length(numrow.idx), 1) + # Start index of data range + start.idx <- which(numrow.idx[i] == 1 & numrow.idx[j] != 1) + 1 + # End index of the data range + end.idx <- which(numrow.idx[i] != 1 & numrow.idx[j] == 1) + # + zz <- textConnection(edsfile[start.idx:end.idx], "r") + # + ff <- data.frame() + ff <- data.frame(stringsAsFactors = FALSE, + sampleid = sampleid, + matrix(scan(zz, what = numeric(), sep = ","), ncol = 2, byrow = T)) + close(zz) + names(ff) <- c("sampleid", "energy", "counts") + # + ### Collect attributes of this experiment + # XUnit + position.XUnit <- regexpr("^\\#XUNITS", edsfile) + XUnit <- as.character(strsplit(edsfile[which(position.XUnit == 1)], ":\\s")[[1]][2]) + ff$XUnit <- XUnit + # YUnit + position.YUnit <- regexpr("^\\#YUNITS", edsfile) + YUnit <- as.character(strsplit(edsfile[which(position.YUnit == 1)], ":\\s")[[1]][2]) + ff$YUnit <- YUnit + # Date + position.Date <- regexpr("^\\#DATE", edsfile) + Date <- strsplit(edsfile[which(position.Date == 1)], ":\\s")[[1]][2] + ff$Date <- Date + # Time + position.Time <- regexpr("^\\#TIME", edsfile) + Time <- strsplit(edsfile[which(position.Time == 1)], ":\\s")[[1]][2] + ff$Time <- Time + # XPerChannel + position.XPerChannel <- regexpr("^\\#XPERCHAN", edsfile) + XPerChannel <- as.numeric(strsplit(edsfile[which(position.XPerChannel == 1)], ":\\s")[[1]][2]) + ff$XPerChannel <- XPerChannel + # Offset + position.Offset <- regexpr("^\\#OFFSET", edsfile) + Offset <- as.numeric(strsplit(edsfile[which(position.Offset == 1)], ":\\s")[[1]][2]) + ff$Offset <- Offset + # ChOffset + position.ChOffset <- regexpr("^\\#CHOFFSET", edsfile) + ChOffset <- as.numeric(strsplit(edsfile[which(position.ChOffset == 1)], ":\\s")[[1]][2]) + ff$ChOffset <- ChOffset + # LiveTime + position.LiveTime <- regexpr("^\\#LIVETIME", edsfile) + LiveTime <- as.numeric(strsplit(edsfile[which(position.LiveTime == 1)], ":\\s")[[1]][2]) + ff$LiveTime <- LiveTime + # DeadTime is calculated from: REALTIME - LIVETIME + position.RealTime <- regexpr("^\\#REALTIME", edsfile) + RealTime <- as.numeric(strsplit(edsfile[which(position.RealTime == 1)], ":\\s")[[1]][2]) + DeadTime <- RealTime - LiveTime + ff$DeadTime <- DeadTime + # BeamEnergy + position.BeamEnergy <- regexpr("^\\#BEAMKV", edsfile) + BeamEnergy <- as.numeric(strsplit(edsfile[which(position.BeamEnergy == 1)], ":\\s")[[1]][2]) + ff$BeamEnergy <- BeamEnergy + # + return(ff) +} + + + + +################################################## +#################### edspk ####################### +################################################## +edspk <- function(eds.exp, kerpk = 1, fitmaxiter = 50) { + + eds.base <- baselinefit(eds.exp, tau=2.0, gam=1.0, scl.factor=3.0, maxwdth=0.20) + + # This loop deals with the output from baselinefit() + # It makes a "melted" dataframe in long form for each + # separated peak for some baseline parameters + eds.pks <- data.frame() + eds.pks.basl <- data.frame() + eds.pks.pmg <- data.frame() + eds.pks.spl <- data.frame() + peaks <- 1:length(eds.base$npks) + for (s in peaks) { + # recorded data in long form by separated peak + eds.pks <- rbind(eds.pks, # column names assigned after loop + data.frame(peak = factor(peaks[s]), + kernel = NA, + eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s], ])) + # the calculated baseline in long form by separated peak + eds.pks.basl <- rbind(eds.pks.basl, + data.frame(peak = factor(peaks[s]), + kernel = NA, + x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]], + y = eds.base$baseline$basisl[eds.base$indlsep[s]:eds.base$indrsep[s]])) + # the taut string estimation in long form by separated peak + eds.pks.pmg <- rbind(eds.pks.pmg, + data.frame(peak = factor(peaks[s]), + kernel = NA, + x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]], + y = eds.base$pmg$fn[eds.base$indlsep[s]:eds.base$indrsep[s]])) + # the weighted smoothed spline in long form by separated peak + eds.pks.spl <- rbind(eds.pks.spl, + data.frame(peak = factor(peaks[s]), + kernel = NA, + x = eds.exp[eds.base$indlsep[s]:eds.base$indrsep[s]], + y = eds.base$spl$reg[eds.base$indlsep[s]:eds.base$indrsep[s]])) + } + # Column names assigned to d.pks + names(eds.pks) <- c("peak", "kernel", "x", "y") + + + # This loop calls pkdecompint() on each peak separately + # It makes a "melted" dataframe in long form for: + eds.fit <- list() # holds pkdecompint output + eds.fit.fitpk <- data.frame() # contains fitting curves + eds.fit.parpk <- data.frame() # physical parameters by peak and kernel + eds.nobasl <- data.frame() # data with baseline removed + peaks <- 1:length(eds.base$npks) + for (s in peaks) { + ######## PKDECOMPINT ######## + if (length(kerpk) > 1) { + # set number of kernels per peak manually + eds.fit[[s]] <- pkdecompint(eds.base, intnum = s, + k = kerpk[s], maxiter = fitmaxiter) + } else { + # use number of kernels determined by baselinefit() + eds.fit[[s]] <- pkdecompint(eds.base, intnum = s, + k = eds.base$npks[s], maxiter = fitmaxiter) + } + # Setup the dataframe that makes up the peak table + for (kernel in 1:eds.fit[[s]]$num.ker) { + eds.fit.parpk <- rbind(eds.fit.parpk, + data.frame(peak = factor(eds.fit[[s]]$intnr), + kernel = factor(kernel), + x = eds.fit[[s]]$parpks[kernel, "loc"], + height = eds.fit[[s]]$parpks[kernel, "height"], + area = eds.fit[[s]]$parpks[kernel, "intens"], + fwhm = eds.fit[[s]]$parpks[kernel, "FWHM"], + m = eds.fit[[s]]$parpks[kernel, "m"], + accept = eds.fit[[s]]$accept)) + eds.fit.fitpk <- rbind(eds.fit.fitpk, + data.frame(peak = factor(peaks[s]), + kernel = factor(kernel), + x = eds.fit[[s]]$x, + y = eds.fit[[s]]$fitpk[kernel, ])) + } + eds.nobasl <- rbind(eds.nobasl, + data.frame(peak = factor(peaks[s]), + x = eds.fit[[s]]$x, + y = eds.fit[[s]]$y)) + } + + + + return(list(eds.base = eds.base, + eds.peaks = eds.pks, + eds.fit.parpk = eds.fit.parpk, + eds.fit.fitpk = eds.fit.fitpk, + eds.nobasl = eds.nobasl)) + +} + + + + + + + + + + + + + + diff --git a/Renishaw.R b/Renishaw.R index dbde806..fbb5157 100644 --- a/Renishaw.R +++ b/Renishaw.R @@ -5,6 +5,7 @@ # CONTENTS source("/home/taha/chepec/chetex/common/R/common.R") # >>>> Raman2df +# >>>> Ramanpk @@ -25,11 +26,111 @@ Raman2df <- function(datafilename) { # ff <- data.frame(NULL) zz <- textConnection(chifile, "r") - ff <- rbind(ff, data.frame(sampleid, + ff <- rbind(ff, data.frame(stringsAsFactors = FALSE, + sampleid, matrix(scan(zz, what = numeric(), sep = "\t"), ncol = 2, byrow = T))) close(zz) names(ff) <- c("sampleid", "shift", "counts") - # + # Re-order by increasing shift + ff <- ff[order(ff$shift), ] + # And fix the row.names + row.names(ff) <- seq(1, dim(ff)[1]) + ## return(ff) } + + +################################################## +################## Ramanpk ####################### +################################################## +Ramanpk <- function(Raman.exp, kerpk = 1, fitmaxiter = 50) { + + Raman.base <- baselinefit(Raman.exp, tau=2.0, gam=1.0, scl.factor=1.2, maxwdth=400) + + # This loop deals with the output from baselinefit() + # It makes a "melted" dataframe in long form for each + # separated peak for some baseline parameters + Raman.pks <- data.frame() + Raman.pks.basl <- data.frame() + Raman.pks.pmg <- data.frame() + Raman.pks.spl <- data.frame() + peaks <- 1:length(Raman.base$npks) + for (s in peaks) { + # recorded data in long form by separated peak + Raman.pks <- rbind(Raman.pks, # column names assigned after loop + data.frame(peak = factor(peaks[s]), + kernel = NA, + Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s], ])) + # the calculated baseline in long form by separated peak + Raman.pks.basl <- rbind(Raman.pks.basl, + data.frame(peak = factor(peaks[s]), + kernel = NA, + x = Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s]], + y = Raman.base$baseline$basisl[Raman.base$indlsep[s]:Raman.base$indrsep[s]])) + # the taut string estimation in long form by separated peak + Raman.pks.pmg <- rbind(Raman.pks.pmg, + data.frame(peak = factor(peaks[s]), + kernel = NA, + x = Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s]], + y = Raman.base$pmg$fn[Raman.base$indlsep[s]:Raman.base$indrsep[s]])) + # the weighted smoothed spline in long form by separated peak + Raman.pks.spl <- rbind(Raman.pks.spl, + data.frame(peak = factor(peaks[s]), + kernel = NA, + x = Raman.exp[Raman.base$indlsep[s]:Raman.base$indrsep[s]], + y = Raman.base$spl$reg[Raman.base$indlsep[s]:Raman.base$indrsep[s]])) + } + # Column names assigned to d.pks + names(Raman.pks) <- c("peak", "kernel", "x", "y") + + + # This loop calls pkdecompint() on each peak separately + # It makes a "melted" dataframe in long form for: + Raman.fit <- list() # holds pkdecompint output + Raman.fit.fitpk <- data.frame() # contains fitting curves + Raman.fit.parpk <- data.frame() # physical parameters by peak and kernel + Raman.nobasl <- data.frame() # data with baseline removed + peaks <- 1:length(Raman.base$npks) + for (s in peaks) { + ######## PKDECOMPINT ######## + if (length(kerpk) > 1) { + # set number of kernels per peak manually + Raman.fit[[s]] <- pkdecompint(Raman.base, intnum = s, + k = kerpk[s], maxiter = fitmaxiter) + } else { + # use number of kernels determined by baselinefit() + Raman.fit[[s]] <- pkdecompint(Raman.base, intnum = s, + k = Raman.base$npks[s], maxiter = fitmaxiter) + } + # Setup the dataframe that makes up the peak table + for (kernel in 1:Raman.fit[[s]]$num.ker) { + Raman.fit.parpk <- rbind(Raman.fit.parpk, + data.frame(peak = factor(Raman.fit[[s]]$intnr), + kernel = factor(kernel), + x = Raman.fit[[s]]$parpks[kernel, "loc"], + height = Raman.fit[[s]]$parpks[kernel, "height"], + area = Raman.fit[[s]]$parpks[kernel, "intens"], + fwhm = Raman.fit[[s]]$parpks[kernel, "FWHM"], + m = Raman.fit[[s]]$parpks[kernel, "m"], + accept = Raman.fit[[s]]$accept)) + Raman.fit.fitpk <- rbind(Raman.fit.fitpk, + data.frame(peak = factor(peaks[s]), + kernel = factor(kernel), + x = Raman.fit[[s]]$x, + y = Raman.fit[[s]]$fitpk[kernel, ])) + } + Raman.nobasl <- rbind(Raman.nobasl, + data.frame(peak = factor(peaks[s]), + x = Raman.fit[[s]]$x, + y = Raman.fit[[s]]$y)) + } + + + + return(list(Raman.base = Raman.base, + Raman.peaks = Raman.pks, + Raman.fit.parpk = Raman.fit.parpk, + Raman.fit.fitpk = Raman.fit.fitpk, + Raman.nobasl = Raman.nobasl)) +}