R: Transform a CW-OSL curve into a pLM-OSL curve via...
CW2pLMi
R Documentation
Transform a CW-OSL curve into a pLM-OSL curve via interpolation under linear
modulation conditions
Description
Transforms a conventionally measured continuous-wave (CW) OSL-curve into a
pseudo linearly modulated (pLM) curve under linear modulation conditions
using the interpolation procedure described by Bos & Wallinga (2012).
Usage
CW2pLMi(values, P)
Arguments
values
RLum.Data.Curve or
data.frame (required):
RLum.Data.Curve or data.frame with measured
curve data of type stimulation time (t) (values[,1]) and measured
counts (cts) (values[,2])
P
vector (optional): stimulation time in seconds. If no
value is given the optimal value is estimated automatically (see details).
Greater values of P produce more points in the rising tail of the curve.
Details
The complete procedure of the transformation is given in Bos & Wallinga
(2012). The input data.frame consists of two columns: time (t) and
count values (CW(t))
Nomenclature
P = stimulation time (s) 1/P = stimulation rate
(1/s)
Internal transformation steps
(1) log(CW-OSL) values (2)
Calculate t' which is the transformed time:
t' = 1/2*1/P*t^2
(3) Interpolate CW(t'), i.e. use the log(CW(t)) to obtain the count values
for the transformed time (t'). Values beyond min(t) and max(t)
produce NA values.
(4) Select all values for t' <
min(t), i.e. values beyond the time resolution of t. Select the first
two values of the transformed data set which contain no NA values and
use these values for a linear fit using lm.
(5)
Extrapolate values for t' < min(t) based on the previously obtained
fit parameters.
(6) Transform values using
pLM(t) = t/P*CW(t')
(7) Combine values and truncate all values for t' > max(t)
The number of values for t' < min(t) depends on the stimulation
period (P) and therefore on the stimulation rate 1/P. To avoid the
production of too many artificial data at the raising tail of the determined
pLM curves it is recommended to use the automatic estimation routine for
P, i.e. provide no own value for P.
Value
The function returns the same data type as the input data type with
the transformed curve values.
list(list("RLum.Data.Curve"))
package
RLum object with two additional info elements:
$CW2pLMi.x.t
: transformed time values
$CW2pLMi.method
: used method for the production of the new data points
Function version
0.3.1 (2015-11-29 17:27:48)
Note
According to Bos & Wallinga (2012) the number of extrapolated points
should be limited to avoid artificial intensity data. If P is
provided manually and more than two points are extrapolated, a warning
message is returned.
Author(s)
Sebastian Kreutzer, IRAMAT-CRP2A, Universite Bordeaux
Montaigne
Based on comments and suggestions from: Adrie J.J. Bos,
Delft University of Technology, The Netherlands
R Luminescence Package Team
References
Bos, A.J.J. & Wallinga, J., 2012. How to visualize quartz OSL
signal components. Radiation Measurements, 47, 752-758.
Further Reading
Bulur, E., 1996. An Alternative Technique For
Optically Stimulated Luminescence (OSL) Experiment. Radiation Measurements,
26, 701-709.
Bulur, E., 2000. A simple transformation for converting CW-OSL curves to
LM-OSL curves. Radiation Measurements, 32, 141-145.
##(1)
##load CW-OSL curve data
data(ExampleData.CW_OSL_Curve, envir = environment())
##transform values
values.transformed <- CW2pLMi(ExampleData.CW_OSL_Curve)
##plot
plot(values.transformed$x, values.transformed$y.t, log = "x")
##(2) - produce Fig. 4 from Bos & Wallinga (2012)
##load data
data(ExampleData.CW_OSL_Curve, envir = environment())
values <- CW_Curve.BosWallinga2012
##open plot area
plot(NA, NA,
xlim = c(0.001,10),
ylim = c(0,8000),
ylab = "pseudo OSL (cts/0.01 s)",
xlab = "t [s]",
log = "x",
main = "Fig. 4 - Bos & Wallinga (2012)")
values.t <- CW2pLMi(values, P = 1/20)
lines(values[1:length(values.t[,1]),1],CW2pLMi(values, P = 1/20)[,2],
col = "red", lwd = 1.3)
text(0.03,4500,"LM", col = "red", cex = .8)
values.t <- CW2pHMi(values, delta = 40)
lines(values[1:length(values.t[,1]),1],CW2pHMi(values, delta = 40)[,2],
col = "black", lwd = 1.3)
text(0.005,3000,"HM", cex =.8)
values.t <- CW2pPMi(values, P = 1/10)
lines(values[1:length(values.t[,1]),1], CW2pPMi(values, P = 1/10)[,2],
col = "blue", lwd = 1.3)
text(0.5,6500,"PM", col = "blue", cex = .8)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Luminescence)
Welcome to the R package Luminescence version 0.6.0 [Built: 2016-05-30 16:47:30 UTC]
The true age: 'How many roads...'
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Luminescence/CW2pLMi.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CW2pLMi
> ### Title: Transform a CW-OSL curve into a pLM-OSL curve via interpolation
> ### under linear modulation conditions
> ### Aliases: CW2pLMi
> ### Keywords: manip
>
> ### ** Examples
>
>
>
> ##(1)
> ##load CW-OSL curve data
> data(ExampleData.CW_OSL_Curve, envir = environment())
>
> ##transform values
> values.transformed <- CW2pLMi(ExampleData.CW_OSL_Curve)
>
> ##plot
> plot(values.transformed$x, values.transformed$y.t, log = "x")
>
> ##(2) - produce Fig. 4 from Bos & Wallinga (2012)
> ##load data
> data(ExampleData.CW_OSL_Curve, envir = environment())
> values <- CW_Curve.BosWallinga2012
>
> ##open plot area
> plot(NA, NA,
+ xlim = c(0.001,10),
+ ylim = c(0,8000),
+ ylab = "pseudo OSL (cts/0.01 s)",
+ xlab = "t [s]",
+ log = "x",
+ main = "Fig. 4 - Bos & Wallinga (2012)")
>
>
> values.t <- CW2pLMi(values, P = 1/20)
> lines(values[1:length(values.t[,1]),1],CW2pLMi(values, P = 1/20)[,2],
+ col = "red", lwd = 1.3)
> text(0.03,4500,"LM", col = "red", cex = .8)
>
> values.t <- CW2pHMi(values, delta = 40)
Warning message:
In CW2pHMi(values, delta = 40) :
56 values have been found and replaced the mean of the nearest values.
> lines(values[1:length(values.t[,1]),1],CW2pHMi(values, delta = 40)[,2],
+ col = "black", lwd = 1.3)
Warning message:
In CW2pHMi(values, delta = 40) :
56 values have been found and replaced the mean of the nearest values.
> text(0.005,3000,"HM", cex =.8)
>
> values.t <- CW2pPMi(values, P = 1/10)
Warning message:
In CW2pPMi(values, P = 1/10) :
t' is beyond the time resolution. Only two data points have been extrapolated, the first 3 points have been set to 0!
> lines(values[1:length(values.t[,1]),1], CW2pPMi(values, P = 1/10)[,2],
+ col = "blue", lwd = 1.3)
Warning message:
In CW2pPMi(values, P = 1/10) :
t' is beyond the time resolution. Only two data points have been extrapolated, the first 3 points have been set to 0!
> text(0.5,6500,"PM", col = "blue", cex = .8)
>
>
>
>
>
>
>
> dev.off()
null device
1
>