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
parent 2287b7f57f
commit aadd16e605

26
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

212
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))
}

@ -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))
}

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