Solved the issue with repeating diffractometry analyses by wrapper functions.
Caught some errors and made some improvements in the other functions.master
Taha Ahmed
13 years ago
parent
4fff82be0c
commit
beab0b9e64
@ -0,0 +1,64 @@
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RamanWrapper <-
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function(data.exp, run, override = FALSE,
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kerpk = 1, fitmaxiter = 50, gam = 0.6, scl.factor = 0.1) {
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# the override flag is currently not used
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print("... Started RamanWrapper")
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# check if Ramanpk has already completed successfully for the current job
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current.dirname <- getwd()
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print(current.dirname)
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current.filename <- "raman-peak-data.rda"
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ramandatafile <- paste(current.dirname, current.filename, sep = "/")
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if (file.exists(ramandatafile)) {
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print("... Started if-clause 1")
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# File already exists
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# return the data using load() or data()
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load(file = ramandatafile)
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if (run > length(ramres)) {
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print("... Started if-clause 1:1")
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# the it does not really exist
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ramres[[run]] <- Ramanpk(data.exp,
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kerpk = kerpk,
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fitmaxiter = fitmaxiter,
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gam = gam,
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scl.factor = scl.factor)
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save(ramres, file = ramandatafile)
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print("... Ended if-clause 1:1")
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}
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print("... Ended if-clause 1")
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return(ramres)
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} else {
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print("... Started else-clause 1")
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if (!exists("ramres")) {
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ramres <- list()
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print("... ramres list created")
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}
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# Need to call Ramanpk() and save its results to file as above
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ramres[[run]] <- Ramanpk(data.exp,
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kerpk = kerpk,
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fitmaxiter = fitmaxiter,
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gam = gam,
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scl.factor = scl.factor)
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save(ramres, file = ramandatafile)
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print("... Ended else-clause 1")
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return(ramres)
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}
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}
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@ -0,0 +1,4 @@
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I wanted to run the \texttt{diffractometry} peak-analysis code (which can be both time-consuming and prone to stray errors halting compilation) without the need to re-run the peak analysis every time the document is re-compiled.
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For that purpose, I created this wrapper function.
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Its job is to call \Rfun{Ramanpk()} only if necessary (i.e., peak analysis has not already been performed). That is made possible by saving the results of a successful analysis to a \texttt{raman-peak-data.rda} file in the directory of the report.
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@ -1,95 +1,104 @@
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source("/home/taha/chepec/chetex/common/R/common.R")
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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, gam = 0.6, scl.factor = 0.1) {
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Ramanpk <-
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function(data.exp, kerpk = 1, fitmaxiter = 50, gam = 0.6, scl.factor = 0.1) {
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Raman.base <- baselinefit(Raman.exp, tau = 2.0, gam = gam, scl.factor = scl.factor, maxwdth = 200)
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print("... Starting baseline fitting")
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data.basl <- baselinefit(data.exp,
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tau = 2.0,
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gam = gam,
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scl.factor = scl.factor,
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maxwdth = 200)
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print("... Ended baseline fitting")
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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$npks)
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data.pks <- data.frame()
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data.pks.basl <- data.frame()
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data.pks.pmg <- data.frame()
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data.pks.spl <- data.frame()
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peaks <- 1:length(data.basl$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.pks <- rbind(data.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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data.exp[data.basl$indlsep[s]:data.basl$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.pks.basl <- rbind(data.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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x = data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], 1],
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y = data.basl$baseline$basisl[data.basl$indlsep[s]:data.basl$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.pks.pmg <- rbind(data.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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x = data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], 1],
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y = data.basl$pmg$fn[data.basl$indlsep[s]:data.basl$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.pks.spl <- rbind(data.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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x = data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], 1],
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y = data.basl$spl$reg[data.basl$indlsep[s]:data.basl$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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names(data.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$npks)
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data.fit <- list() # holds pkdecompint output
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data.fit.fitpk <- data.frame() # contains fitting curves
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data.fit.parpk <- data.frame() # physical parameters by peak and kernel
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data.fit.basl <- data.frame() # data with baseline removed
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peaks <- 1:length(data.basl$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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data.fit[[s]] <- pkdecompint(data.basl, 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$npks[s], maxiter = fitmaxiter)
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data.fit[[s]] <- pkdecompint(data.basl, intnum = s,
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k = data.basl$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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for (kernel in 1:data.fit[[s]]$num.ker) {
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data.fit.parpk <- rbind(data.fit.parpk,
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data.frame(peak = factor(data.fit[[s]]$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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x = data.fit[[s]]$parpks[kernel, "loc"],
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height = data.fit[[s]]$parpks[kernel, "height"],
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area = data.fit[[s]]$parpks[kernel, "intens"],
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fwhm = data.fit[[s]]$parpks[kernel, "FWHM"],
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m = data.fit[[s]]$parpks[kernel, "m"],
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accept = data.fit[[s]]$accept))
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data.fit.fitpk <- rbind(data.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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x = data.fit[[s]]$x,
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y = data.fit[[s]]$fitpk[kernel, ]))
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}
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Raman.nobasl <- rbind(Raman.nobasl,
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data.fit.basl <- rbind(data.fit.basl,
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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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x = data.fit[[s]]$x,
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y = data.fit[[s]]$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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return(list(data.basl = data.basl,
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data.peaks = data.pks,
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data.fit.parpk = data.fit.parpk,
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data.fit.fitpk = data.fit.fitpk,
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data.fit.basl = data.fit.basl))
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}
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@ -0,0 +1,65 @@
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SiliconWrapper <-
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function(data.exp, run, override = FALSE,
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kerpk = 1, fitmaxiter = 50, gam = 0.6, scl.factor = 0.1) {
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# the override flag is currently not used
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print("... Started SiliconWrapper")
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# check if Ramanpk has already completed successfully for the current job
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current.dirname <- getwd()
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print(current.dirname)
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current.filename <- "silicon-peak-data.rda"
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ramandatafile <- paste(current.dirname, current.filename, sep = "/")
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if (file.exists(ramandatafile) && !override) {
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print("... Started if-clause 1")
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# File already exists
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# return the data using load() or data()
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load(file = ramandatafile)
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if (run > length(ramres)) {
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print("... Started if-clause 1:1")
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# the it does not really exist
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ramres[[run]] <- Ramanpk(data.exp,
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kerpk = kerpk,
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fitmaxiter = fitmaxiter,
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gam = gam,
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scl.factor = scl.factor)
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save(ramres, file = ramandatafile)
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print("... Ended if-clause 1:1")
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}
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print("... Ended if-clause 1")
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return(ramres)
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} else {
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# File does not exist, or override requested
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print("... Started else-clause 1")
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if (!exists("ramres")) {
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ramres <- list()
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print("... ramres list created")
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}
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# Need to call Ramanpk() and save its results to file as above
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ramres[[run]] <- Ramanpk(data.exp,
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kerpk = kerpk,
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fitmaxiter = fitmaxiter,
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gam = gam,
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scl.factor = scl.factor)
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save(ramres, file = ramandatafile)
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print("... Ended else-clause 1")
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return(ramres)
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}
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}
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@ -0,0 +1,104 @@
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xrfpk <-
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function(data.exp, kerpk = 1, fitmaxiter = 50, gam = 0.6, scl.factor = 0.1, maxwdth = 200) {
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print("... Starting baseline fitting")
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data.basl <- baselinefit(data.exp,
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tau = 2.0,
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gam = gam,
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scl.factor = scl.factor,
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maxwdth = maxwdth)
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print("... Ended baseline fitting")
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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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data.pks <- data.frame()
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data.pks.basl <- data.frame()
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data.pks.pmg <- data.frame()
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data.pks.spl <- data.frame()
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peaks <- 1:length(data.basl$npks)
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for (s in peaks) {
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# recorded data in long form by separated peak
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data.pks <- rbind(data.pks, # column names assigned after loop
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], ]))
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# the calculated baseline in long form by separated peak
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data.pks.basl <- rbind(data.pks.basl,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], 1],
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y = data.basl$baseline$basisl[data.basl$indlsep[s]:data.basl$indrsep[s]]))
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# the taut string estimation in long form by separated peak
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data.pks.pmg <- rbind(data.pks.pmg,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], 1],
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y = data.basl$pmg$fn[data.basl$indlsep[s]:data.basl$indrsep[s]]))
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# the weighted smoothed spline in long form by separated peak
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data.pks.spl <- rbind(data.pks.spl,
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data.frame(peak = factor(peaks[s]),
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kernel = NA,
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x = data.exp[data.basl$indlsep[s]:data.basl$indrsep[s], 1],
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y = data.basl$spl$reg[data.basl$indlsep[s]:data.basl$indrsep[s]]))
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}
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# Column names assigned to d.pks
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names(data.pks) <- c("peak", "kernel", "x", "y")
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# This loop calls pkdecompint() on each peak separately
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# It makes a "melted" dataframe in long form for:
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data.fit <- list() # holds pkdecompint output
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data.fit.fitpk <- data.frame() # contains fitting curves
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data.fit.parpk <- data.frame() # physical parameters by peak and kernel
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data.fit.basl <- data.frame() # data with baseline removed
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peaks <- 1:length(data.basl$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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data.fit[[s]] <- pkdecompint(data.basl, 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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data.fit[[s]] <- pkdecompint(data.basl, intnum = s,
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k = data.basl$npks[s], maxiter = fitmaxiter)
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}
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# Setup the dataframe that makes up the peak table
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for (kernel in 1:data.fit[[s]]$num.ker) {
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data.fit.parpk <- rbind(data.fit.parpk,
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data.frame(peak = factor(data.fit[[s]]$intnr),
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kernel = factor(kernel),
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x = data.fit[[s]]$parpks[kernel, "loc"],
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height = data.fit[[s]]$parpks[kernel, "height"],
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area = data.fit[[s]]$parpks[kernel, "intens"],
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fwhm = data.fit[[s]]$parpks[kernel, "FWHM"],
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m = data.fit[[s]]$parpks[kernel, "m"],
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accept = data.fit[[s]]$accept))
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data.fit.fitpk <- rbind(data.fit.fitpk,
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data.frame(peak = factor(peaks[s]),
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kernel = factor(kernel),
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x = data.fit[[s]]$x,
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y = data.fit[[s]]$fitpk[kernel, ]))
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}
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data.fit.basl <- rbind(data.fit.basl,
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data.frame(peak = factor(peaks[s]),
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x = data.fit[[s]]$x,
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y = data.fit[[s]]$y))
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}
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return(list(data.basl = data.basl,
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data.peaks = data.pks,
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data.fit.parpk = data.fit.parpk,
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data.fit.fitpk = data.fit.fitpk,
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data.fit.basl = data.fit.basl))
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}
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@ -0,0 +1,67 @@
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xrfpkWrapper <-
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function(data.exp, run, override = FALSE,
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kerpk = 1, fitmaxiter = 100, gam = 0.64, scl.factor = 0.06, maxwdth = 10) {
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# kerpk = 1, fitmaxiter = 50, gam = 0.6, scl.factor = 0.1) {
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# the override flag is IN USE
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print("... Started xrfpkWrapper")
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# check if xrfpk has already completed successfully for the current job
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current.dirname <- getwd()
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print(current.dirname)
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current.filename <- "xrf-peak-data.rda"
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xrfdatafile <- paste(current.dirname, current.filename, sep = "/")
|
||||
|
||||
|
||||
|
||||
if (file.exists(xrfdatafile) && !override) {
|
||||
print("... Started if-clause 1")
|
||||
|
||||
# File already exists
|
||||
# return the data using load() or data()
|
||||
|
||||
load(file = xrfdatafile)
|
||||
|
||||
if (run > length(xrfres)) {
|
||||
|
||||
print("... Started if-clause 1:1")
|
||||
|
||||
# the it does not really exist
|
||||
xrfres[[run]] <- xrfpk(data.exp,
|
||||
kerpk = kerpk,
|
||||
fitmaxiter = fitmaxiter,
|
||||
gam = gam,
|
||||
scl.factor = scl.factor,
|
||||
maxwdth = maxwdth)
|
||||
save(xrfres, file = xrfdatafile)
|
||||
|
||||
print("... Ended if-clause 1:1")
|
||||
}
|
||||
|
||||
print("... Ended if-clause 1")
|
||||
|
||||
return(xrfres)
|
||||
} else {
|
||||
|
||||
print("... Started else-clause 1")
|
||||
|
||||
if (!exists("xrfres")) {
|
||||
xrfres <- list()
|
||||
print("... xrfres list created")
|
||||
}
|
||||
|
||||
# Need to call xrfpk() and save its results to file as above
|
||||
xrfres[[run]] <- xrfpk(data.exp,
|
||||
kerpk = kerpk,
|
||||
fitmaxiter = fitmaxiter,
|
||||
gam = gam,
|
||||
scl.factor = scl.factor,
|
||||
maxwdth = maxwdth)
|
||||
save(xrfres, file = xrfdatafile)
|
||||
|
||||
print("... Ended else-clause 1")
|
||||
|
||||
return(xrfres)
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,3 @@
|
||||
I have been attempting to fine-tune the parameters of the \texttt{baselinefit()} function.
|
||||
|
||||
Increasing the number of iterations above 100 have been useless. Small changes in \texttt{gam} leads to noticeable changes in the peak and kernel distribution.
|
||||
@ -0,0 +1,86 @@
|
||||
source("/home/taha/chepec/chetex/common/R/common/ProvideSampleId.R")
|
||||
|
||||
xrfspectro2df <- function(smpfile) {
|
||||
## Description:
|
||||
## Reads XRF textfile from XLAB SPECTRO XRF.
|
||||
## Stores data in data frame and parameters in an attributed dataframe.
|
||||
## Usage:
|
||||
## xrfspectro2df(smpfile)
|
||||
## Arguments:
|
||||
## smpfile: character string, the full filename
|
||||
## (with path) to one SMP file (ASCII).
|
||||
## Value:
|
||||
## A dataframe with attributed dataframe
|
||||
#
|
||||
filecon <- file(smpfile, "r")
|
||||
smpcontents <- readLines(filecon, n = -1) #read all lines of input file
|
||||
close(filecon)
|
||||
#
|
||||
sampleid <- ProvideSampleId(smpfile)
|
||||
#
|
||||
rgxp.data <- "^Kanal\\s[\\d]+:"
|
||||
#
|
||||
numrow.idx <- regexpr(rgxp.data, smpcontents, perl = TRUE)
|
||||
# scrap the match.length attribute
|
||||
attr(numrow.idx, "match.length") <- NULL
|
||||
#
|
||||
# Determine how many columns the data contains
|
||||
smpdata.cols <- length(strsplit(smpcontents[which(numrow.idx == 1)][1], "\t")[[1]]) - 1
|
||||
# While we are at it, save row count to a variable as well
|
||||
smpdata.rows <- length(smpcontents[which(numrow.idx == 1)])
|
||||
# strip prefix off each data row
|
||||
#smpdata <- matrix(NA, ncol = smpdata.cols, nrow = smpdata.rows)
|
||||
smpdata.txt <- vector(length = smpdata.rows)
|
||||
for (i in 1:smpdata.rows) {
|
||||
smpdata.txt[i] <- strsplit(smpcontents[which(numrow.idx == 1)][i], ":")[[1]][2]
|
||||
}
|
||||
smpdata.txt.clean <- gsub("^\\s", "",
|
||||
gsub("\\t", " ", smpdata.txt))
|
||||
|
||||
zz <- textConnection(smpdata.txt.clean, "r")
|
||||
ff <- data.frame(stringsAsFactors = FALSE,
|
||||
sampleid = sampleid,
|
||||
channel = seq(1, smpdata.rows),
|
||||
matrix(scan(zz, what = numeric(), sep = " "),
|
||||
ncol = smpdata.cols, byrow = TRUE))
|
||||
close(zz)
|
||||
names(ff) <- c("sampleid", "channel", paste("Y", seq(1, smpdata.cols), sep = ""))
|
||||
|
||||
|
||||
#
|
||||
### Collect attributes of this experiment
|
||||
SMPattrEdit <- matrix(c("Voltage", "^Voltage:",
|
||||
"Current", "^Current:",
|
||||
"Target", "^Target:",
|
||||
"Duration", "^Meas\\.\\sDuration:",
|
||||
"Impulse", "^Imp\\.\\sRate:",
|
||||
"DeadTime", "^Rel\\.\\sDead\\sTime:",
|
||||
"FirstChannel", "^First\\sChannel:",
|
||||
"LastChannel", "^Last\\sChannel:",
|
||||
"PeakTime", "^Peak\\sTime:",
|
||||
"Gain", "^Gain:",
|
||||
"ZeroPeak", "^Zero\\sPeak:"),
|
||||
ncol = 2, byrow = T)
|
||||
|
||||
SMPattr <- matrix(NA, nrow = smpdata.cols + 1, ncol = dim(SMPattrEdit)[1])
|
||||
|
||||
for (c in 1:dim(SMPattrEdit)[1]) {
|
||||
SMPattr[1, c] <- SMPattrEdit[c, 1]
|
||||
SMPattr[2:dim(SMPattr)[1], c] <-
|
||||
matrix(strsplit(gsub("^\\t", "",
|
||||
strsplit(smpcontents[which(regexpr(SMPattrEdit[c, 2], smpcontents) == 1)],
|
||||
":")[[1]][2]), "\\t")[[1]], ncol = smpdata.cols)
|
||||
}
|
||||
SMPdf <- data.frame(stringsAsFactors = FALSE,
|
||||
SMPattr[2:dim(SMPattr)[1], ])
|
||||
colnames(SMPdf) <- SMPattr[1, ]
|
||||
|
||||
|
||||
### Now calculate the energy (keV) scale (convert from channels to energy)
|
||||
ff$X <- ff$channel * (as.numeric(SMPdf$Gain[1]) / as.numeric(SMPdf$LastChannel[1]))
|
||||
|
||||
# Attach parameters to returned dataframe
|
||||
attr(ff, "parameters") <- SMPdf
|
||||
#
|
||||
return(ff)
|
||||
}
|
||||
@ -1,25 +1,34 @@
|
||||
##################################################
|
||||
################ ProvideSampleId #################
|
||||
##################################################
|
||||
ProvideSampleId <- function (fullpathwithfilename) {
|
||||
### OBS! Only very rudimentary error-checking.
|
||||
### If the filename is formatted as \w*-\w*-\w*, we use the middle segment,
|
||||
### otherwise we use the whole string (excluding the extension)
|
||||
# Extract the name of the parent directory of the datafilename argument
|
||||
substrateid <- basename(dirname(fullpathwithfilename))
|
||||
# Extract the name of the method from the filename-part
|
||||
# First split the filename over all hyphens
|
||||
nameparts <- strsplit(basename(fullpathwithfilename), "-")[[1]]
|
||||
# If the number of nameparts exceed 3, save the whole filename as methodid, otherwise use the middle part
|
||||
if (length(nameparts) > 3) {
|
||||
# We need to lose the file extension from the last namepart
|
||||
nameparts[length(nameparts)] <- strsplit(nameparts[length(nameparts)], "\\.")[[1]][1]
|
||||
methodid <- paste(nameparts, collapse = "-")
|
||||
} else {
|
||||
methodid <- nameparts[2]
|
||||
}
|
||||
# Make an informative sampleid
|
||||
sampleid <- paste(substrateid, methodid, sep = "-")
|
||||
#
|
||||
return(sampleid)
|
||||
ProvideSampleId <- function (pathexpfile) {
|
||||
# Returns a "unique" sample ID when supplied
|
||||
# with a path to an experimental file.
|
||||
## Note: the sample ID must derive directly from the file or path.
|
||||
|
||||
sampleid <- sub("\\.[\\w]+$", "", basename(pathexpfile), perl = TRUE)
|
||||
|
||||
#### The code below is the old ProvideSampleId() function
|
||||
# ### OBS! Only very rudimentary error-checking.
|
||||
# ### If the filename is formatted as \w*-\w*-\w*, we use the middle segment,
|
||||
# ### otherwise we use the whole string (excluding the extension)
|
||||
# # Extract the name of the parent directory of the datafilename argument
|
||||
# substrateid <- basename(dirname(fullpathwithfilename))
|
||||
# # Extract the name of the method from the filename-part
|
||||
# # First split the filename over all hyphens
|
||||
# nameparts <- strsplit(basename(fullpathwithfilename), "-")[[1]]
|
||||
# # If the number of nameparts exceed 3, save the whole filename
|
||||
# # as methodid, otherwise use the middle part
|
||||
# if (length(nameparts) > 3) {
|
||||
# # We need to lose the file extension from the last namepart
|
||||
# nameparts[length(nameparts)] <-
|
||||
# strsplit(nameparts[length(nameparts)], "\\.")[[1]][1]
|
||||
# methodid <- paste(nameparts, collapse = "-")
|
||||
# } else {
|
||||
# methodid <- nameparts[2]
|
||||
# }
|
||||
# # Make an informative sampleid
|
||||
# sampleid <- paste(substrateid, methodid, sep = "-")
|
||||
# #
|
||||
# return(sampleid)
|
||||
####
|
||||
|
||||
return(sampleid)
|
||||
}
|
||||
@ -0,0 +1,6 @@
|
||||
The following function, \Rfun{ProvideSampleId()}, strives to supply a unique sample ID from any path supplied to it. Of course, a certain structure on the part of the path and filename are assumed, namely:
|
||||
\begin{itemize}
|
||||
\item That the filename (not including the extension) consists of three alphanumeric strings separated by hyphens.
|
||||
\item That\ldots
|
||||
\end{itemize}
|
||||
|
||||
Loading…
Reference in New Issue