diff --git a/404.html b/404.html index d5ea44cdc..e1f8b5ab4 100644 --- a/404.html +++ b/404.html @@ -20,7 +20,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
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+ + + + + + + diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html new file mode 100644 index 000000000..7b6abdef8 --- /dev/null +++ b/CONTRIBUTING.html @@ -0,0 +1,81 @@ + +NA • Luminescence + Skip to contents + + +
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NA

+ Source: CONTRIBUTING.md +
+ + +

Contributing to ‘Luminescence’ is simple and straightforward.

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Workflow

+
  • +Create a fork of the ‘Luminescence’ repository
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Ground rules

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In case you have questions, please contact the package maintainers before creating a pull request.

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+ + + + + + + diff --git a/LICENSE-text.html b/LICENSE-text.html index b2c054461..b10d0d545 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -7,7 +7,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+ + + + +
+
+ +

Assessing crosstalk in single-grain luminescence detection

+

Luc Steinbuch, +Anna-Maartje de Boer, Wageningen University & Research

+ +

February 2025

+ + Source: vignettes/crosstalk.Rmd +
crosstalk.Rmd
+
+ + + +
+

Crosstalk +

+

This vignette explores the crosstalk-related functions in the +“Luminescence” package, related to research paper “A novel tool to +assess crosstalk in single-grain luminescence detection”, by +Anna-Maartje de Boer, Luc Steinbuch, Gerard Heuvelink and Jakob +Wallinga. Crosstalk in single-grain luminescence imaging (EMCCD) is the +overlapping of luminescence signals from adjacent grains on a +single-grain disc. The actual signal on one grain location influences +the observed signal on a neighboring grain location which happens to be +on the same measurement disc (one “position” in the reader). In this +research and the shown functions, we define “neighboring” as rook-wise +(horizontally and vertically) only, and we assume that all measurement +discs have a regular grid of 10x10 grain locations.

+
+library(Luminescence)
+#> Welcome to the R package Luminescence version 1.0.0 [Built: 2025-02-21 15:22:31 UTC]
+#> Luminescence data to Bayesian process: 'Don't you ever touch me again.'
+
+
+

Explore imaged luminescence signals from a single-grain disc (one +“position” in the reader) +

+

All grain location observations on one single-grain measurement disc +are represented by a vector of 100 numbers. Here we simulate such a disc +by randomly selecting from two normal distributions. The prefix “vn_” +means: a vector of numbers:

+
+
+vn_simulated <- sample(x = c(rnorm(n = 30, mean = 2000, sd = 500),
+                             rnorm(n = 70, mean = 20, sd = 1)),
+                       size = 100)
+
+vn_simulated <- round(vn_simulated) # Because photons are discrete
+
+head(vn_simulated, n = 25)
+#>  [1] 1723   23   21   19   21 2175   22   19   21   19 3177   21   18 2985   21
+#> [16]   21   21 2282   21   19   19 2362   20   19   21
+
+hist(vn_simulated,
+     main = "Simulated signal (histogram)",
+     xlab = "Photon counts",
+     ylab = "Frequency",
+     breaks = 30
+     )
+

+
+

plot_SingleGrainDisc +

+

Let’s visualize the disc/position:

+
+
+par(mar = c(1, 4, 6, 4))
+
+plot_SingleGrainDisc(object = vn_simulated,
+          main = "Simulated signal (measurement disc)"
+          )
+

+

Let’s calculate Moran’s I, and the associated pseudo-p, of our +randomly ordered simulated disc:

+
+
+calc_MoransI(object = vn_simulated)
+#> [1] -0.002584057
+
+calc_MoransI(object = vn_simulated, compute_pseudo_p = TRUE)
+#> [1] 0.447
+

what changes if we add a serious amount, say 10%, of crosstalk?

+
+
+vn_simulated_with_crosstalk <- apply_Crosstalk(object = vn_simulated,
+                                              n_crosstalk = 0.10)
+
+vn_simulated_with_crosstalk <- round(vn_simulated_with_crosstalk)
+
+hist(vn_simulated_with_crosstalk,
+     main = "Simulated signal with crosstalk (histogram)",
+     xlab = "Photon counts",
+     ylab = "Frequency",
+     breaks = 30
+     )
+

+
+
+plot_SingleGrainDisc(object = vn_simulated_with_crosstalk,
+          main = "Simulated signal with crosstalk (measurement disc)")
+

+
+
+calc_MoransI(object = vn_simulated_with_crosstalk)
+#> [1] 0.176199
+
+calc_MoransI(object = vn_simulated_with_crosstalk, compute_pseudo_p = TRUE)
+#> [1] 0.007
+

Let’s try several amounts of simulated crosstalk:

+
+
+
+df_MoransI <- data.frame(crosstalk = seq(from = 0,
+                    to = 0.30,
+                    length.out=50))
+
+df_MoransI$MoransI <- NA
+df_MoransI$pseudo_p <- NA
+
+old.opts <- options(warn = -1) # silence warnings from compute_pseudo_p
+for (i in 1:nrow(df_MoransI))
+{
+
+  vn_simulated_with_crosstalk <- apply_Crosstalk(object = vn_simulated,
+                                              n_crosstalk = df_MoransI$crosstalk[i])
+
+  df_MoransI$MoransI[i]  <- calc_MoransI(object = vn_simulated_with_crosstalk)
+  df_MoransI$pseudo_p[i] <-
+    calc_MoransI(object = vn_simulated_with_crosstalk, compute_pseudo_p = TRUE)
+}
+options(old.opts) # restore the default options
+
+n_expected_I_no_spatial_autocorr <- calc_MoransI(1:100,
+                                                 spatial_autocorrelation = FALSE)
+
+##
+
+plot(x = df_MoransI$crosstalk,
+     y = df_MoransI$MoransI,
+     ylim = range(
+          pretty(x = c(df_MoransI$MoransI, n_expected_I_no_spatial_autocorr))
+          ),
+     ## Set ylim manually to make sure the value for I for no crosstalk is visible
+     xlab = "Amount of added crosstalk",
+     ylab = "Calculated Moran's I"
+     )
+graphics::grid()
+abline(h = n_expected_I_no_spatial_autocorr,
+       col = "purple")
+
+legend(x = "topleft",
+       legend = "Expected I if no spatial autocorrelation",
+       lty = "solid",
+       col = "purple",
+       cex = 0.8)
+

+
+
+plot(x = df_MoransI$crosstalk,
+     y = df_MoransI$pseudo_p,
+     xlab = "Amount of added crosstalk",
+     ylab = "Generated pseudo-p of related Moran's I")
+graphics::grid()
+

+

Please note that above two plots are subject to randomness; for a +good assessment many simulations have to be performed.

+
+
+
+

Moran scatterplot +

+

A way to visualise spatial auto-correlation is the Moran +scatterplot:

+
+
+
+plot_MoranScatterplot(object = vn_simulated,
+           main = "Moran scatterplot, simulated signal without crosstalk")
+

+
+
+vn_simulated_with_crosstalk <- apply_Crosstalk(object = vn_simulated,
+                                              n_crosstalk = 0.25)
+vn_simulated_with_crosstalk <- round(vn_simulated_with_crosstalk)
+
+plot_MoranScatterplot(object = vn_simulated_with_crosstalk,
+           main = "Moran scatterplot, simulated signal with added crosstalk")
+

+

The plot area is divided into four quadrants using the mean in each +dimension; the South-west and North-east quadrant represent a +contribution to a positive spatial autocorrelation, while the North-west +and South-east quadrants indicate a negative spatial correlation. +Between the point, a least square line (which slopes indicates, but not +exactly represents, Moran’s I) is added, as well as an 1:1 line (which +indicates a Moran’s I of around 1, suggesting a perfect positive spatial +correlation).

+

The internal function .get_Neighbours() was until now +used on the background, but we can explicitly call it to generate a +dataframe with all positions which are rook connected to each other +(note that we need to use the Luminescence::: prefix as +this function is not exported by the package):

+
+
+    vn_simulated_with_holes <- c(rnorm(30, mean = 10, sd = 5),
+                              rnorm(30, mean = 500, sd = 50),
+                              rep(NA, times = 40)
+                              )
+
+df_Neighbours <- Luminescence:::.get_Neighbours(object = vn_simulated_with_holes)
+
+head(df_Neighbours)
+#>    location neighbour weight
+#> 11       11         1      1
+#> 12       12         2      1
+#> 13       13         3      1
+#> 14       14         4      1
+#> 15       15         5      1
+#> 16       16         6      1
+

And we can plot the first disc, while indicating which borders are +taken into account. The corresponding +adjacent_grain_locations is calculated in the +background:

+
+
+    plot_SingleGrainDisc(object = vn_simulated_with_holes,
+              show_neighbours = TRUE,
+              show_location_ids = TRUE)
+

+Also other functions, such as calc_MoransI(), work as long +as the remaining grid does not become too sparse. Note that in this +context all observations, even “islands” who do not border any other +grains, are used for the calculation of Moran’s I.

+
+
+

Add borders with certain weight +

+

One can manually change df_Neighbours, for example add +the diagonal borders and attain a certain weight to it, and add it as +argument to almost all functions. Assume that we want to add a few +diagonal borders with weight 1/sqrt(2) to a full disc (note that the +standard relative weight for rook borders is set to one):

+
+
+df_Neighbours_with_diag <- Luminescence:::.get_Neighbours(object = vn_simulated)
+
+
+for (i in c(1:9, 11:19, 21:29) )
+{
+  df_Neighbours_with_diag <- rbind(df_Neighbours_with_diag,
+                                            c(i, i+11, 1/sqrt(2))
+                                          )
+}
+
+tail(df_Neighbours_with_diag)
+#>     location neighbour    weight
+#> 202       24        35 0.7071068
+#> 203       25        36 0.7071068
+#> 204       26        37 0.7071068
+#> 205       27        38 0.7071068
+#> 206       28        39 0.7071068
+#> 207       29        40 0.7071068
+
+
+plot_SingleGrainDisc(object = vn_simulated,
+          df_neighbours = df_Neighbours_with_diag,
+          show_neighbours = TRUE,
+          show_location_ids = TRUE)
+

+

To exclude all border effects, we can use the +ignore_borders option available in +calc_MoransI() and plot_SingleGrainDisc(), +which can take out all border rows and columns when computing the data +frame of neighbours:

+
+
+vn_values_to_show <-
+      sample(x = c(rnorm(n = 30, mean = 2000, sd = 500),
+                   rnorm(n = 70, mean = 20, sd = 1)),
+             size = 100)
+
+## Set the outer rows to NA before adding crosstalk
+vn_disc_border_locations <- c(1:10,
+                              91:100,
+                              seq(from = 11, to = 81, by = 10),
+                              seq(from = 20, to = 90, by = 10)
+                             )
+vn_values_to_show[vn_disc_border_locations] <- NA
+
+vn_values_to_show <- apply_Crosstalk(object = vn_values_to_show,
+                                     n_crosstalk = 0.15)
+
+calc_MoransI(object = vn_values_to_show)
+#> [1] 0.1406872
+
+plot_SingleGrainDisc(object = vn_values_to_show,
+                     show_neighbours = TRUE,
+                     ignore_borders = TRUE)
+

+
+
+calc_MoransI(object = vn_values_to_show,
+             ignore_borders = TRUE)
+#> [1] 0.1406872
+
+
+

Plot disc options +

+
+
+plot_SingleGrainDisc(object = vn_simulated,
+          main = "",
+          legend = TRUE,
+          show_coordinates = TRUE,
+          show_location_ids = TRUE,
+          show_positioning_holes = FALSE)
+

+

When there is a wide range in values, it can be helpful to apply a +logarithmic scale in plotting (note that the default is “square +root”):

+
+
+plot_SingleGrainDisc(object = vn_simulated,
+          main = "Linear scale",
+          legend = TRUE,
+          show_coordinates = FALSE,
+          show_location_ids = FALSE,
+          show_positioning_holes = TRUE,
+          str_transform = "lin")
+

+
+
+
+plot_SingleGrainDisc(object = vn_simulated,
+          main = "Logarithmic scale",
+          legend = TRUE,
+          show_coordinates = FALSE,
+          show_location_ids = FALSE,
+          show_positioning_holes = TRUE,
+          str_transform = "log")
+

+
+
+

Moran scatterplot options +

+
+
+
+vn_simulated <- c(rnorm(75, mean = 10, sd = 5),
+                  rnorm(25, mean = 500, sd = 50)  )
+vn_simulated <- sample(size = 100, vn_simulated)
+
+vn_simulated_with_crosstalk <- apply_Crosstalk(object = vn_simulated,
+                                               n_crosstalk = 0.15)
+
+## Base use
+plot_MoranScatterplot(object = vn_simulated,
+                      main = "Without crosstalk")
+

+
+
+
+plot_MoranScatterplot(object = vn_simulated_with_crosstalk,
+                      main = "With crosstalk")
+

+
+
+
+## Layout options
+plot_MoranScatterplot(object = vn_simulated_with_crosstalk,
+           pch = "show_location_ids",
+           legend = FALSE,
+           log = "xy",
+           main = "With location ID's; and with log scales"
+           )
+

+
+
+plot_MoranScatterplot(object = vn_simulated_with_crosstalk,
+           pch = "show_n_neighbours",
+           legend = FALSE,
+           str_y_def = "weighted_sum",
+           main = "With number of neighbours, and other y calculation"
+           )
+

+
+
+

Moran’s I function options +

+

The function calc_MoransI() can return many intermediate +calculation numbers:

+
+
+
+calc_MoransI(object = 1:100,
+             return_intermediate_values = TRUE)
+#> $n
+#> [1] 100
+#> 
+#> $n_mean
+#> [1] 50.5
+#> 
+#> $n_population_variance
+#> [1] 833.25
+#> 
+#> $n_sum_similarities
+#> [1] 133320
+#> 
+#> $n_sum_weights
+#> [1] 180
+#> 
+#> $n_average_auto_correlation
+#> [1] 740.6667
+#> 
+#> $n_moransI
+#> [1] 0.8888889
+

If the weights (and thus the spatial pattern under investigation) and +the number of observations remain the same, this can be useful to +understand what is happening. For example, if we add crosstalk, we can +see that the population variance in most cases slightly increases (the +values are indeed spatially smoothed, but the average increases) but the +spatial autocorrelation strongly increases:

+
+
+vn_simulated <- sample(x = c(rnorm(n = 30, mean = 2000, sd = 500),
+                             rnorm(n = 70, mean = 20, sd = 1)),
+                       size = 100)
+vn_simulated <- round(vn_simulated)
+
+vn_simulated_with_crosstalk <- apply_Crosstalk(object = vn_simulated,
+                                               n_crosstalk = 0.20)
+vn_simulated_with_crosstalk <- round(vn_simulated_with_crosstalk)
+
+
+df_compare <-
+  data.frame(Case = c("Without crosstalk", "With crosstalk"),
+             MoransI =  c(calc_MoransI(object = vn_simulated),
+                          calc_MoransI(object = vn_simulated_with_crosstalk)
+                          ),
+             PopulationVar =
+               c(calc_MoransI(object = vn_simulated,
+                 return_intermediate_values = TRUE)$n_population_variance,
+                 calc_MoransI(object = vn_simulated_with_crosstalk,
+                 return_intermediate_values = TRUE)$n_population_variance),
+             SpatialAutoCor =
+               c(calc_MoransI(object = vn_simulated,
+                 return_intermediate_values = TRUE)$n_average_auto_correlation,
+                 calc_MoransI(object = vn_simulated_with_crosstalk,
+                 return_intermediate_values = TRUE)$n_average_auto_correlation)
+      )
+
+df_compare[,2] <-  round(df_compare[,2],2)
+
+(df_compare)
+#>                Case MoransI PopulationVar SpatialAutoCor
+#> 1 Without crosstalk   -0.11      849694.4      -97084.16
+#> 2    With crosstalk    0.34      884920.6      303927.55
+
+
+
+ + + + +
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+
+
+ +

Articles

+
+ +
+

All vignettes

+
+ +
Assessing crosstalk in single-grain luminescence detection
+
+
+
+ + +
+ + + + + + + diff --git a/authors.html b/authors.html index df851440a..6556e667f 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ Luminescence - 0.9.26 + 1.0.0 - - - - - -
-
-
- -

Transform a CW-OSL curve into a pHM-OSL curve via interpolation under hyperbolic modulation conditions

- Source: R/CW2pHMi.R -
CW2pHMi.Rd
-
- -
-

This function transforms a conventionally measured continuous-wave (CW) -OSL-curve to a pseudo hyperbolic modulated (pHM) curve under hyperbolic -modulation conditions using the interpolation procedure described by Bos & -Wallinga (2012).

-
- -
-

Usage

- 
CW2pHMi(values, delta)
-
- -
-

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]).

- - -
delta
-

vector (optional): -stimulation rate parameter, if no value is given, the optimal value is -estimated automatically (see details). Smaller values of delta produce more -points in the rising tail of -the curve.

- -
-
-

Value

-

The function returns the same data type as the input data type with -the transformed curve values.

-

RLum.Data.Curve

-
$CW2pHMi.x.t: transformed time values
$CW2pHMi.method: used method for the production of the new data points

data.frame

-
$x: time
$y.t: transformed count values
$x.t: transformed time values
$method: used method for the production of the new data points
-
-

Details

-

The complete procedure of the transformation is described in Bos & Wallinga -(2012). The input data.frame consists of two columns: time (t) and -count values (CW(t))

-

Internal transformation steps

-

(1) log(CW-OSL) values

-

(2) -Calculate t' which is the transformed time: -$$t' = t-(1/\delta)*log(1+\delta*t)$$

-

(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 -$$pHM(t) = (\delta*t/(1+\delta*t))*c*CW(t')$$ -$$c = (1+\delta*P)/\delta*P$$ -$$P = length(stimulation~period)$$

-

(7) Combine all values and truncate all values for t' > max(t)

-

NOTE: -The number of values for t' < min(t) depends on the stimulation rate -parameter delta. To avoid the production of too many artificial data -at the raising tail of the determined pHM curve, it is recommended to use -the automatic estimation routine for delta, i.e. provide no value for -delta.

-
-
-

Note

-

According to Bos & Wallinga (2012), the number of extrapolated points -should be limited to avoid artificial intensity data. If delta is -provided manually and more than two points are extrapolated, a warning -message is returned.

-

The function approx may produce some Inf and NaN data. -The function tries to manually interpolate these values by calculating -the mean using the adjacent channels. If two invalid values are succeeding, -the values are removed and no further interpolation is attempted. -In every case a warning message is shown.

-
-
-

Function version

-

0.2.2

-
-
-

How to cite

-

Kreutzer, S., 2024. CW2pHMi(): Transform a CW-OSL curve into a pHM-OSL curve via interpolation under hyperbolic modulation conditions. Function version 0.2.2. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

-
-
-

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.

-
-
-

See also

-

CW2pLM, CW2pLMi, CW2pPMi, fit_LMCurve, lm, -RLum.Data.Curve

-
-
-

Author

-

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
-Based on comments and suggestions from:
-Adrie J.J. Bos, Delft University of Technology, The Netherlands -, RLum Developer Team

-
- -
-

Examples

- 

-##(1) - simple transformation
-
-##load CW-OSL curve data
-data(ExampleData.CW_OSL_Curve, envir = environment())
-
-##transform values
-values.transformed<-CW2pHMi(ExampleData.CW_OSL_Curve)
-
-##plot
-plot(values.transformed$x, values.transformed$y.t, log = "x")
-
-
-##(2) - load CW-OSL curve from BIN-file and plot transformed values
-
-##load BINfile
-#BINfileData<-readBIN2R("[path to BIN-file]")
-data(ExampleData.BINfileData, envir = environment())
-
-##grep first CW-OSL curve from ALQ 1
-curve.ID<-CWOSL.SAR.Data@METADATA[CWOSL.SAR.Data@METADATA[,"LTYPE"]=="OSL" &
-                                    CWOSL.SAR.Data@METADATA[,"POSITION"]==1
-                                  ,"ID"]
-
-curve.HIGH<-CWOSL.SAR.Data@METADATA[CWOSL.SAR.Data@METADATA[,"ID"]==curve.ID[1]
-                                    ,"HIGH"]
-
-curve.NPOINTS<-CWOSL.SAR.Data@METADATA[CWOSL.SAR.Data@METADATA[,"ID"]==curve.ID[1]
-                                       ,"NPOINTS"]
-
-##combine curve to data set
-
-curve<-data.frame(x = seq(curve.HIGH/curve.NPOINTS,curve.HIGH,
-                          by = curve.HIGH/curve.NPOINTS),
-                  y=unlist(CWOSL.SAR.Data@DATA[curve.ID[1]]))
-
-
-##transform values
-
-curve.transformed <- CW2pHMi(curve)
-
-##plot curve
-plot(curve.transformed$x, curve.transformed$y.t, log = "x")
-
-
-
-##(3) - 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: [CW2pHMi()] 56 values have been found and replaced by 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: [CW2pHMi()] 56 values have been found and replaced by the mean of the nearest values
-text(0.005,3000,"HM", cex=.8)
-
-values.t<-CW2pPMi(values, P=1/10)
-#> Warning: 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: 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)
-
-
-
-
-
- - -
- - - - - - + + + + + + + diff --git a/reference/CW2pLM.html b/reference/CW2pLM.html index 500f41fd9..faf5ad61d 100644 --- a/reference/CW2pLM.html +++ b/reference/CW2pLM.html @@ -1,152 +1,8 @@ - -Transform a CW-OSL curve into a pLM-OSL curve — CW2pLM • Luminescence - Skip to contents - - -
-
-
- -

Transform a CW-OSL curve into a pLM-OSL curve

- Source: R/CW2pLM.R -
CW2pLM.Rd
-
- -
-

Transforms a conventionally measured continuous-wave (CW) curve into a -pseudo linearly modulated (pLM) curve using the equations given in Bulur -(2000).

-
- -
-

Usage

- 
CW2pLM(values)
-
- -
-

Arguments

- - -
values
-

RLum.Data.Curve or data.frame (required): -RLum.Data.Curve data object. Alternatively, a data.frame of the measured -curve data of type stimulation time (t) (values[,1]) and measured counts (cts) -(values[,2]) can be provided.

- -
-
-

Value

-

The function returns the same data type as the input data type with -the transformed curve values (data.frame or RLum.Data.Curve).

-
-
-

Details

-

According to Bulur (2000) the curve data are transformed by introducing two -new parameters P (stimulation period) and u (transformed time):

-

$$P=2*max(t)$$ $$u=\sqrt{(2*t*P)}$$

-

The new count values are then calculated by -$$ctsNEW = cts(u/P)$$

-

and the returned data.frame is produced by: data.frame(u,ctsNEW)

-

The output of the function can be further used for LM-OSL fitting.

-
-
-

Note

-

The transformation is recommended for curves recorded with a channel -resolution of at least 0.05 s/channel.

-
-
-

Function version

-

0.4.1

-
-
-

How to cite

-

Kreutzer, S., 2024. CW2pLM(): Transform a CW-OSL curve into a pLM-OSL curve. Function version 0.4.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

-
-
-

References

-

Bulur, E., 2000. A simple transformation for converting CW-OSL -curves to LM-OSL curves. Radiation Measurements, 32, 141-145.

-

Further Reading

-

Bulur, E., 1996. An Alternative Technique For Optically Stimulated -Luminescence (OSL) Experiment. Radiation Measurements, 26, 701-709.

-
-
-

See also

-

CW2pHMi, CW2pLMi, CW2pPMi, fit_LMCurve, lm, -RLum.Data.Curve

-
-
-

Author

-

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) -, RLum Developer Team

-
- -
-

Examples

- 

-##read curve from CWOSL.SAR.Data transform curve and plot values
-data(ExampleData.BINfileData, envir = environment())
-
-##read id for the 1st OSL curve
-id.OSL <- CWOSL.SAR.Data@METADATA[CWOSL.SAR.Data@METADATA[,"LTYPE"] == "OSL","ID"]
-
-##produce x and y (time and count data for the data set)
-x<-seq(CWOSL.SAR.Data@METADATA[id.OSL[1],"HIGH"]/CWOSL.SAR.Data@METADATA[id.OSL[1],"NPOINTS"],
-       CWOSL.SAR.Data@METADATA[id.OSL[1],"HIGH"],
-       by = CWOSL.SAR.Data@METADATA[id.OSL[1],"HIGH"]/CWOSL.SAR.Data@METADATA[id.OSL[1],"NPOINTS"])
-y <- unlist(CWOSL.SAR.Data@DATA[id.OSL[1]])
-values <- data.frame(x,y)
-
-##transform values
-values.transformed <- CW2pLM(values)
-
-##plot
-plot(values.transformed)
-
-
-
-
-
- - -
- - - - - - + + + + + + + diff --git a/reference/CW2pLMi.html b/reference/CW2pLMi.html index 3b71734f4..3dd124e1c 100644 --- a/reference/CW2pLMi.html +++ b/reference/CW2pLMi.html @@ -1,218 +1,8 @@ - -Transform a CW-OSL curve into a pLM-OSL curve via interpolation under linear modulation conditions — CW2pLMi • Luminescence - Skip to contents - - -
-
-
- -

Transform a CW-OSL curve into a pLM-OSL curve via interpolation under linear modulation conditions

- Source: R/CW2pLMi.R -
CW2pLMi.Rd
-
- -
-

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.

- -
-
-

Value

-

The function returns the same data type as the input data type with -the transformed curve values.

-

RLum.Data.Curve

-
$CW2pLMi.x.t: transformed time values
$CW2pLMi.method: used method for the production of the new data points
-
-

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)

-

NOTE: -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.

-
-
-

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.

-
-
-

Function version

-

0.3.1

-
-
-

How to cite

-

Kreutzer, S., 2024. CW2pLMi(): Transform a CW-OSL curve into a pLM-OSL curve via interpolation under linear modulation conditions. Function version 0.3.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

-
-
-

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.

-
-
-

See also

-

CW2pLM, CW2pHMi, CW2pPMi, fit_LMCurve, -RLum.Data.Curve

-
-
-

Author

-

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

-

Based on comments and suggestions from:
-Adrie J.J. Bos, Delft University of Technology, The Netherlands -, RLum Developer Team

-
- -
-

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: [CW2pHMi()] 56 values have been found and replaced by 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: [CW2pHMi()] 56 values have been found and replaced by the mean of the nearest values
-text(0.005,3000,"HM", cex =.8)
-
-values.t <- CW2pPMi(values, P = 1/10)
-#> Warning: 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: 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)
-
-
-
-
-
- - -
- - - - - - + + + + + + + diff --git a/reference/CW2pPMi.html b/reference/CW2pPMi.html index a885b20c6..27d6ce526 100644 --- a/reference/CW2pPMi.html +++ b/reference/CW2pPMi.html @@ -1,220 +1,8 @@ - -Transform a CW-OSL curve into a pPM-OSL curve via interpolation under parabolic modulation conditions — CW2pPMi • Luminescence - Skip to contents - - -
-
-
- -

Transform a CW-OSL curve into a pPM-OSL curve via interpolation under parabolic modulation conditions

- Source: R/CW2pPMi.R -
CW2pPMi.Rd
-
- -
-

Transforms a conventionally measured continuous-wave (CW) OSL-curve into a -pseudo parabolic modulated (pPM) curve under parabolic modulation conditions -using the interpolation procedure described by Bos & Wallinga (2012).

-
- -
-

Usage

- 
CW2pPMi(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 period 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.

- -
-
-

Value

-

The function returns the same data type as the input data type with -the transformed curve values.

-

RLum.Data.Curve

-
$CW2pPMi.x.t: transformed time values
$CW2pPMi.method: used method for the production of the new data points

data.frame

-
$x: time
$y.t: transformed count values
$x.t: transformed time values
$method: used method for the production of the new data points
-
-

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: -$$t' = (1/3)*(1/P^2)t^3$$

-

(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. The extrapolation is limited to two values. Other values at -the beginning of the transformed curve are set to 0.

-

(6) -Transform values using -$$pLM(t) = t^2/P^2*CW(t')$$

-

(7) -Combine all values and truncate all values for t' > max(t)

-

NOTE: -The number of values for t' < min(t) depends on the stimulation -period P. To avoid the production of too many artificial data at the -raising tail of the determined pPM curve, it is recommended to use the -automatic estimation routine for P, i.e. provide no value for -P.

-
-
-

Note

-

According to Bos & Wallinga (2012), the number of extrapolated points -should be limited to avoid artificial intensity data. If P is -provided manually, not more than two points are extrapolated.

-
-
-

Function version

-

0.2.1

-
-
-

How to cite

-

Kreutzer, S., 2024. CW2pPMi(): Transform a CW-OSL curve into a pPM-OSL curve via interpolation under parabolic modulation conditions. Function version 0.2.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

-
-
-

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.

-
-
-

See also

-

CW2pLM, CW2pLMi, CW2pHMi, fit_LMCurve, RLum.Data.Curve

-
-
-

Author

-

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

-

Based on comments and suggestions from:
-Adrie J.J. Bos, Delft University of Technology, The Netherlands -, RLum Developer Team

-
- -
-

Examples

- 

-
-##(1)
-##load CW-OSL curve data
-data(ExampleData.CW_OSL_Curve, envir = environment())
-
-##transform values
-values.transformed <- CW2pPMi(ExampleData.CW_OSL_Curve)
-#> Warning: t' is beyond the time resolution. Only two data points have been extrapolated, the first 0 points have been set to 0!
-
-##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: [CW2pHMi()] 56 values have been found and replaced by 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: [CW2pHMi()] 56 values have been found and replaced by the mean of the nearest values
-text(0.005,3000,"HM", cex = .8)
-
-values.t <- CW2pPMi(values, P = 1/10)
-#> Warning: 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: 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)
-
-
-
-
-
- - -
- - - - - - + + + + + + + diff --git a/reference/ExampleData.Al2O3C.html b/reference/ExampleData.Al2O3C.html index 528e90a99..a90a7d538 100644 --- a/reference/ExampleData.Al2O3C.html +++ b/reference/ExampleData.Al2O3C.html @@ -13,7 +13,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Example equivalent dose data from mortar samples

+ Source: R/Luminescence-package.R +
ExampleData.MortarData.Rd
+
+ +
+

Arbitrary data to test the function calc_EED_Model

+
+ + +
+

Format

+

Two data.frames containing De and De error

+
+
+

Source

+

Arbitrary measurements.

+
+
+

References

+

unpublished data

+
+ +
+

Examples

+ 

+##load data
+data(ExampleData.MortarData, envir = environment())
+
+##plot data
+plot(MortarData)
+
+
+
+
+
+ + +
+ + + + + + + diff --git a/reference/ExampleData.RLum.Analysis.html b/reference/ExampleData.RLum.Analysis.html index 4e8011e14..9b72a58d4 100644 --- a/reference/ExampleData.RLum.Analysis.html +++ b/reference/ExampleData.RLum.Analysis.html @@ -9,7 +9,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Melt RLum-class objects into a flat data.frame

+ Source: R/Luminescence-generics.R +
melt_RLum.Rd
+
+ +
+

Melt RLum-class objects into a flat data.frame

+
+ +
+

Usage

+ 
melt_RLum(object, ...)
+
+# S4 method for class 'list'
+melt_RLum(object, ...)
+
+ +
+

Arguments

+ + +
object
+

RLum (required): +S4 object of class RLum

+ + +
...
+

further arguments passed to the specific class method

+ +
+
+

Value

+

A flat data.frame.

+
+
+

Functions

+ +

  • melt_RLum(list): Returns a list a single data.frame

    • +
+
+

Function version

+

0.1.0

+
+
+

See also

+

RLum.Data.Curve, RLum.Analysis

+
+
+

Author

+

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) +, RLum Developer Team

+
+
+

How to cite

+

Kreutzer, S., 2025. melt_RLum(): Melt RLum-class objects into a flat data.frame. Function version 0.1.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+ +
+

Examples

+ 
data(ExampleData.XSYG, envir = environment())
+melt_RLum(OSL.SARMeasurement[[2]][[1]])
+#>          X  Y       TYPE                                UID
+#> 1      0.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 2      0.2  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 3      0.3  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 4      0.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 5      0.5 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 6      0.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 7      0.7 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 8      0.8 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 9      0.9 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 10     1.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 11     1.1 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 12     1.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 13     1.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 14     1.4  3 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 15     1.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 16     1.6 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 17     1.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 18     1.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 19     1.9 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 20     2.0  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 21     2.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 22     2.2  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 23     2.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 24     2.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 25     2.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 26     2.6 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 27     2.7 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 28     2.8  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 29     2.9 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 30     3.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 31     3.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 32     3.2 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 33     3.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 34     3.4 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 35     3.5  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 36     3.6 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 37     3.7 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 38     3.8 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 39     3.9 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 40     4.0  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 41     4.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 42     4.2 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 43     4.3 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 44     4.4  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 45     4.5 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 46     4.6  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 47     4.7 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 48     4.8  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 49     4.9 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 50     5.0 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 51     5.1 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 52     5.2 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 53     5.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 54     5.4 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 55     5.5 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 56     5.6 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 57     5.7 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 58     5.8 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 59     5.9 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 60     6.0  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 61     6.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 62     6.2 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 63     6.3 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 64     6.4 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 65     6.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 66     6.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 67     6.7 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 68     6.8  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 69     6.9 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 70     7.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 71     7.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 72     7.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 73     7.3 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 74     7.4  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 75     7.5  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 76     7.6  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 77     7.7  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 78     7.8 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 79     7.9 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 80     8.0 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 81     8.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 82     8.2 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 83     8.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 84     8.4 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 85     8.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 86     8.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 87     8.7 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 88     8.8  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 89     8.9 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 90     9.0  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 91     9.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 92     9.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 93     9.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 94     9.4 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 95     9.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 96     9.6 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 97     9.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 98     9.8 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 99     9.9  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 100   10.0 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 101   10.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 102   10.2 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 103   10.3 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 104   10.4 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 105   10.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 106   10.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 107   10.7  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 108   10.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 109   10.9 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 110   11.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 111   11.1 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 112   11.2  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 113   11.3  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 114   11.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 115   11.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 116   11.6  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 117   11.7 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 118   11.8 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 119   11.9 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 120   12.0  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 121   12.1 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 122   12.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 123   12.3 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 124   12.4 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 125   12.5  3 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 126   12.6 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 127   12.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 128   12.8  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 129   12.9 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 130   13.0  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 131   13.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 132   13.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 133   13.3 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 134   13.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 135   13.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 136   13.6  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 137   13.7 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 138   13.8 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 139   13.9 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 140   14.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 141   14.1 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 142   14.2  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 143   14.3 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 144   14.4  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 145   14.5 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 146   14.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 147   14.7 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 148   14.8 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 149   14.9 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 150   15.0  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 151   15.1 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 152   15.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 153   15.3 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 154   15.4 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 155   15.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 156   15.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 157   15.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 158   15.8 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 159   15.9 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 160   16.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 161   16.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 162   16.2  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 163   16.3  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 164   16.4  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 165   16.5 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 166   16.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 167   16.7  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 168   16.8 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 169   16.9 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 170   17.0  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 171   17.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 172   17.2  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 173   17.3  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 174   17.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 175   17.5 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 176   17.6 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 177   17.7 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 178   17.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 179   17.9 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 180   18.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 181   18.1  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 182   18.2  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 183   18.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 184   18.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 185   18.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 186   18.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 187   18.7 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 188   18.8  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 189   18.9 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 190   19.0 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 191   19.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 192   19.2  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 193   19.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 194   19.4 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 195   19.5 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 196   19.6 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 197   19.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 198   19.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 199   19.9 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 200   20.0 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 201   20.1 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 202   20.2 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 203   20.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 204   20.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 205   20.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 206   20.6 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 207   20.7 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 208   20.8 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 209   20.9 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 210   21.0  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 211   21.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 212   21.2 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 213   21.3 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 214   21.4 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 215   21.5 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 216   21.6 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 217   21.7  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 218   21.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 219   21.9 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 220   22.0  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 221   22.1 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 222   22.2 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 223   22.3 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 224   22.4  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 225   22.5  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 226   22.6 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 227   22.7 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 228   22.8  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 229   22.9 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 230   23.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 231   23.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 232   23.2 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 233   23.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 234   23.4 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 235   23.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 236   23.6  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 237   23.7 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 238   23.8 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 239   23.9 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 240   24.0  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 241   24.1  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 242   24.2 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 243   24.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 244   24.4  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 245   24.5 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 246   24.6 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 247   24.7 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 248   24.8  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 249   24.9 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 250   25.0 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 251   25.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 252   25.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 253   25.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 254   25.4 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 255   25.5 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 256   25.6 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 257   25.7 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 258   25.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 259   25.9 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 260   26.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 261   26.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 262   26.2 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 263   26.3 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 264   26.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 265   26.5  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 266   26.6  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 267   26.7 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 268   26.8 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 269   26.9 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 270   27.0 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 271   27.1 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 272   27.2 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 273   27.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 274   27.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 275   27.5 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 276   27.6 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 277   27.7  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 278   27.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 279   27.9  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 280   28.0 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 281   28.1 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 282   28.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 283   28.3 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 284   28.4 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 285   28.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 286   28.6 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 287   28.7  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 288   28.8  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 289   28.9  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 290   29.0  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 291   29.1  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 292   29.2  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 293   29.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 294   29.4 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 295   29.5 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 296   29.6  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 297   29.7 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 298   29.8 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 299   29.9 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 300   30.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 301   30.1 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 302   30.2  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 303   30.3 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 304   30.4 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 305   30.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 306   30.6 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 307   30.7  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 308   30.8  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 309   30.9  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 310   31.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 311   31.1 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 312   31.2 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 313   31.3 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 314   31.4 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 315   31.5  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 316   31.6 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 317   31.7 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 318   31.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 319   31.9  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 320   32.0 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 321   32.1 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 322   32.2  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 323   32.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 324   32.4  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 325   32.5  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 326   32.6 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 327   32.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 328   32.8 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 329   32.9  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 330   33.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 331   33.1 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 332   33.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 333   33.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 334   33.4 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 335   33.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 336   33.6 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 337   33.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 338   33.8 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 339   33.9  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 340   34.0  2 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 341   34.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 342   34.2 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 343   34.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 344   34.4 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 345   34.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 346   34.6  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 347   34.7 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 348   34.8 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 349   34.9 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 350   35.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 351   35.1 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 352   35.2 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 353   35.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 354   35.4 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 355   35.5  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 356   35.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 357   35.7 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 358   35.8 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 359   35.9  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 360   36.0  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 361   36.1 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 362   36.2 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 363   36.3 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 364   36.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 365   36.5  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 366   36.6 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 367   36.7  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 368   36.8 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 369   36.9  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 370   37.0  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 371   37.1  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 372   37.2  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 373   37.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 374   37.4 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 375   37.5 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 376   37.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 377   37.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 378   37.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 379   37.9 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 380   38.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 381   38.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 382   38.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 383   38.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 384   38.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 385   38.5  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 386   38.6 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 387   38.7  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 388   38.8 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 389   38.9 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 390   39.0  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 391   39.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 392   39.2 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 393   39.3 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 394   39.4 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 395   39.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 396   39.6  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 397   39.7 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 398   39.8  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 399   39.9  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 400   40.0  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 401   40.1 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 402   40.2 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 403   40.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 404   40.4 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 405   40.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 406   40.6  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 407   40.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 408   40.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 409   40.9  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 410   41.0 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 411   41.1 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 412   41.2 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 413   41.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 414   41.4  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 415   41.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 416   41.6 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 417   41.7  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 418   41.8  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 419   41.9 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 420   42.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 421   42.1  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 422   42.2  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 423   42.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 424   42.4 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 425   42.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 426   42.6 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 427   42.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 428   42.8  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 429   42.9 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 430   43.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 431   43.1  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 432   43.2  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 433   43.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 434   43.4 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 435   43.5 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 436   43.6  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 437   43.7  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 438   43.8  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 439   43.9 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 440   44.0 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 441   44.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 442   44.2 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 443   44.3 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 444   44.4 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 445   44.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 446   44.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 447   44.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 448   44.8  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 449   44.9 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 450   45.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 451   45.1 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 452   45.2  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 453   45.3 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 454   45.4 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 455   45.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 456   45.6 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 457   45.7 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 458   45.8  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 459   45.9 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 460   46.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 461   46.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 462   46.2 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 463   46.3 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 464   46.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 465   46.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 466   46.6  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 467   46.7  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 468   46.8 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 469   46.9  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 470   47.0 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 471   47.1 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 472   47.2 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 473   47.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 474   47.4  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 475   47.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 476   47.6  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 477   47.7  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 478   47.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 479   47.9 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 480   48.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 481   48.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 482   48.2  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 483   48.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 484   48.4 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 485   48.5 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 486   48.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 487   48.7 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 488   48.8 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 489   48.9 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 490   49.0 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 491   49.1 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 492   49.2 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 493   49.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 494   49.4 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 495   49.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 496   49.6  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 497   49.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 498   49.8 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 499   49.9  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 500   50.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 501   50.1 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 502   50.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 503   50.3 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 504   50.4  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 505   50.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 506   50.6 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 507   50.7  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 508   50.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 509   50.9 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 510   51.0 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 511   51.1 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 512   51.2 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 513   51.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 514   51.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 515   51.5 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 516   51.6 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 517   51.7 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 518   51.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 519   51.9 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 520   52.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 521   52.1 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 522   52.2 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 523   52.3  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 524   52.4 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 525   52.5 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 526   52.6 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 527   52.7 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 528   52.8  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 529   52.9 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 530   53.0  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 531   53.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 532   53.2 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 533   53.3 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 534   53.4  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 535   53.5 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 536   53.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 537   53.7 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 538   53.8 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 539   53.9 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 540   54.0 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 541   54.1 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 542   54.2  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 543   54.3 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 544   54.4  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 545   54.5 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 546   54.6  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 547   54.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 548   54.8 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 549   54.9  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 550   55.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 551   55.1  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 552   55.2 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 553   55.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 554   55.4 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 555   55.5  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 556   55.6 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 557   55.7 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 558   55.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 559   55.9  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 560   56.0 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 561   56.1  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 562   56.2 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 563   56.3  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 564   56.4 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 565   56.5 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 566   56.6  4 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 567   56.7  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 568   56.8 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 569   56.9 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 570   57.0 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 571   57.1 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 572   57.2  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 573   57.3  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 574   57.4 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 575   57.5 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 576   57.6 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 577   57.7  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 578   57.8  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 579   57.9  5 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 580   58.0 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 581   58.1  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 582   58.2 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 583   58.3 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 584   58.4 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 585   58.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 586   58.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 587   58.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 588   58.8 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 589   58.9 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 590   59.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 591   59.1 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 592   59.2  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 593   59.3 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 594   59.4  8 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 595   59.5  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 596   59.6 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 597   59.7 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 598   59.8  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 599   59.9 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 600   60.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 601   60.1 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 602   60.2 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 603   60.3 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 604   60.4  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 605   60.5 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 606   60.6 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 607   60.7  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 608   60.8 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 609   60.9  0 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 610   61.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 611   61.1  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 612   61.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 613   61.3 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 614   61.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 615   61.5 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 616   61.6 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 617   61.7 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 618   61.8 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 619   61.9 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 620   62.0 11 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 621   62.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 622   62.2 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 623   62.3  7 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 624   62.4 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 625   62.5 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 626   62.6 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 627   62.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 628   62.8  6 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 629   62.9 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 630   63.0 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 631   63.1 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 632   63.2 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 633   63.3 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 634   63.4 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 635   63.5 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 636   63.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 637   63.7  9 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 638   63.8 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 639   63.9 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 640   64.0 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 641   64.1 10 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 642   64.2 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 643   64.3 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 644   64.4 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 645   64.5 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 646   64.6 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 647   64.7 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 648   64.8 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 649   64.9 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 650   65.0 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 651   65.1 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 652   65.2 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 653   65.3 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 654   65.4 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 655   65.5 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 656   65.6 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 657   65.7 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 658   65.8 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 659   65.9 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 660   66.0 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 661   66.1 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 662   66.2 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 663   66.3 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 664   66.4 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 665   66.5 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 666   66.6 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 667   66.7 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 668   66.8 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 669   66.9 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 670   67.0 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 671   67.1 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 672   67.2 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 673   67.3 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 674   67.4 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 675   67.5 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 676   67.6 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 677   67.7 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 678   67.8 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 679   67.9 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 680   68.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 681   68.1 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 682   68.2 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 683   68.3 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 684   68.4 12 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 685   68.5 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 686   68.6 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 687   68.7 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 688   68.8 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 689   68.9 15 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 690   69.0 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 691   69.1 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 692   69.2 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 693   69.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 694   69.4 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 695   69.5 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 696   69.6 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 697   69.7 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 698   69.8 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 699   69.9 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 700   70.0 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 701   70.1 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 702   70.2 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 703   70.3 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 704   70.4 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 705   70.5 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 706   70.6 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 707   70.7 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 708   70.8 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 709   70.9 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 710   71.0 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 711   71.1 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 712   71.2 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 713   71.3 18 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 714   71.4 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 715   71.5 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 716   71.6 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 717   71.7 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 718   71.8 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 719   71.9 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 720   72.0 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 721   72.1 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 722   72.2 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 723   72.3 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 724   72.4 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 725   72.5 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 726   72.6 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 727   72.7 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 728   72.8 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 729   72.9 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 730   73.0 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 731   73.1 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 732   73.2 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 733   73.3 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 734   73.4 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 735   73.5 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 736   73.6 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 737   73.7 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 738   73.8 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 739   73.9 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 740   74.0 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 741   74.1 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 742   74.2 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 743   74.3 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 744   74.4 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 745   74.5 40 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 746   74.6 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 747   74.7 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 748   74.8 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 749   74.9 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 750   75.0 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 751   75.1 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 752   75.2 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 753   75.3 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 754   75.4 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 755   75.5 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 756   75.6 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 757   75.7 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 758   75.8 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 759   75.9 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 760   76.0 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 761   76.1 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 762   76.2 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 763   76.3 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 764   76.4 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 765   76.5 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 766   76.6 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 767   76.7 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 768   76.8 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 769   76.9 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 770   77.0 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 771   77.1 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 772   77.2 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 773   77.3 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 774   77.4 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 775   77.5 40 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 776   77.6 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 777   77.7 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 778   77.8 48 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 779   77.9 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 780   78.0 47 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 781   78.1 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 782   78.2 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 783   78.3 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 784   78.4 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 785   78.5 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 786   78.6 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 787   78.7 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 788   78.8 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 789   78.9 47 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 790   79.0 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 791   79.1 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 792   79.2 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 793   79.3 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 794   79.4 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 795   79.5 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 796   79.6 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 797   79.7 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 798   79.8 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 799   79.9 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 800   80.0 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 801   80.1 47 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 802   80.2 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 803   80.3 40 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 804   80.4 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 805   80.5 47 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 806   80.6 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 807   80.7 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 808   80.8 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 809   80.9 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 810   81.0 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 811   81.1 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 812   81.2 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 813   81.3 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 814   81.4 59 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 815   81.5 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 816   81.6 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 817   81.7 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 818   81.8 48 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 819   81.9 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 820   82.0 48 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 821   82.1 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 822   82.2 63 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 823   82.3 48 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 824   82.4 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 825   82.5 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 826   82.6 59 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 827   82.7 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 828   82.8 56 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 829   82.9 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 830   83.0 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 831   83.1 70 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 832   83.2 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 833   83.3 63 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 834   83.4 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 835   83.5 55 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 836   83.6 59 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 837   83.7 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 838   83.8 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 839   83.9 62 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 840   84.0 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 841   84.1 59 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 842   84.2 55 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 843   84.3 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 844   84.4 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 845   84.5 73 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 846   84.6 70 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 847   84.7 59 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 848   84.8 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 849   84.9 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 850   85.0 75 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 851   85.1 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 852   85.2 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 853   85.3 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 854   85.4 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 855   85.5 62 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 856   85.6 62 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 857   85.7 47 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 858   85.8 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 859   85.9 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 860   86.0 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 861   86.1 69 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 862   86.2 64 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 863   86.3 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 864   86.4 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 865   86.5 65 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 866   86.6 72 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 867   86.7 62 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 868   86.8 65 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 869   86.9 62 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 870   87.0 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 871   87.1 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 872   87.2 69 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 873   87.3 70 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 874   87.4 71 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 875   87.5 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 876   87.6 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 877   87.7 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 878   87.8 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 879   87.9 82 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 880   88.0 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 881   88.1 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 882   88.2 62 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 883   88.3 64 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 884   88.4 65 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 885   88.5 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 886   88.6 55 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 887   88.7 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 888   88.8 66 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 889   88.9 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 890   89.0 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 891   89.1 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 892   89.2 68 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 893   89.3 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 894   89.4 67 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 895   89.5 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 896   89.6 70 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 897   89.7 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 898   89.8 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 899   89.9 71 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 900   90.0 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 901   90.1 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 902   90.2 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 903   90.3 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 904   90.4 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 905   90.5 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 906   90.6 66 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 907   90.7 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 908   90.8 58 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 909   90.9 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 910   91.0 63 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 911   91.1 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 912   91.2 70 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 913   91.3 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 914   91.4 48 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 915   91.5 60 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 916   91.6 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 917   91.7 52 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 918   91.8 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 919   91.9 56 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 920   92.0 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 921   92.1 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 922   92.2 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 923   92.3 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 924   92.4 57 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 925   92.5 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 926   92.6 53 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 927   92.7 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 928   92.8 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 929   92.9 61 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 930   93.0 65 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 931   93.1 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 932   93.2 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 933   93.3 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 934   93.4 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 935   93.5 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 936   93.6 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 937   93.7 48 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 938   93.8 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 939   93.9 56 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 940   94.0 51 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 941   94.1 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 942   94.2 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 943   94.3 64 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 944   94.4 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 945   94.5 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 946   94.6 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 947   94.7 54 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 948   94.8 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 949   94.9 49 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 950   95.0 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 951   95.1 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 952   95.2 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 953   95.3 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 954   95.4 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 955   95.5 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 956   95.6 59 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 957   95.7 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 958   95.8 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 959   95.9 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 960   96.0 40 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 961   96.1 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 962   96.2 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 963   96.3 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 964   96.4 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 965   96.5 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 966   96.6 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 967   96.7 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 968   96.8 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 969   96.9 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 970   97.0 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 971   97.1 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 972   97.2 45 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 973   97.3 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 974   97.4 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 975   97.5 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 976   97.6 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 977   97.7 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 978   97.8 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 979   97.9 42 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 980   98.0 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 981   98.1 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 982   98.2 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 983   98.3 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 984   98.4 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 985   98.5 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 986   98.6 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 987   98.7 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 988   98.8 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 989   98.9 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 990   99.0 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 991   99.1 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 992   99.2 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 993   99.3 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 994   99.4 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 995   99.5 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 996   99.6 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 997   99.7 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 998   99.8 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 999   99.9 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1000 100.0 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1001 100.1 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1002 100.2 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1003 100.3 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1004 100.4 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1005 100.5 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1006 100.6 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1007 100.7 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1008 100.8 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1009 100.9 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1010 101.0 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1011 101.1 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1012 101.2 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1013 101.3 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1014 101.4 46 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1015 101.5 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1016 101.6 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1017 101.7 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1018 101.8 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1019 101.9 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1020 102.0 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1021 102.1 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1022 102.2 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1023 102.3 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1024 102.4 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1025 102.5 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1026 102.6 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1027 102.7 40 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1028 102.8 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1029 102.9 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1030 103.0 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1031 103.1 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1032 103.2 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1033 103.3 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1034 103.4 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1035 103.5 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1036 103.6 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1037 103.7 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1038 103.8 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1039 103.9 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1040 104.0 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1041 104.1 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1042 104.2 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1043 104.3 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1044 104.4 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1045 104.5 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1046 104.6 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1047 104.7 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1048 104.8 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1049 104.9 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1050 105.0 13 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1051 105.1 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1052 105.2 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1053 105.3 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1054 105.4 50 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1055 105.5 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1056 105.6 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1057 105.7 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1058 105.8 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1059 105.9 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1060 106.0 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1061 106.1 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1062 106.2 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1063 106.3 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1064 106.4 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1065 106.5 44 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1066 106.6 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1067 106.7 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1068 106.8 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1069 106.9 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1070 107.0 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1071 107.1 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1072 107.2 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1073 107.3 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1074 107.4 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1075 107.5 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1076 107.6 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1077 107.7 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1078 107.8 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1079 107.9 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1080 108.0 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1081 108.1 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1082 108.2 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1083 108.3 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1084 108.4 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1085 108.5 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1086 108.6 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1087 108.7 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1088 108.8 16 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1089 108.9 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1090 109.0 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1091 109.1 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1092 109.2 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1093 109.3 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1094 109.4 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1095 109.5 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1096 109.6 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1097 109.7 40 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1098 109.8 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1099 109.9 19 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1100 110.0 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1101 110.1 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1102 110.2 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1103 110.3 47 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1104 110.4 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1105 110.5 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1106 110.6 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1107 110.7 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1108 110.8 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1109 110.9 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1110 111.0 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1111 111.1 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1112 111.2 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1113 111.3 38 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1114 111.4 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1115 111.5 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1116 111.6 14 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1117 111.7 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1118 111.8 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1119 111.9 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1120 112.0 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1121 112.1 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1122 112.2 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1123 112.3 31 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1124 112.4 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1125 112.5 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1126 112.6 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1127 112.7 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1128 112.8 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1129 112.9 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1130 113.0 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1131 113.1 43 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1132 113.2 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1133 113.3 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1134 113.4 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1135 113.5 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1136 113.6 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1137 113.7 41 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1138 113.8 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1139 113.9 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1140 114.0 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1141 114.1 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1142 114.2 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1143 114.3 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1144 114.4 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1145 114.5 17 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1146 114.6 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1147 114.7 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1148 114.8 22 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1149 114.9 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1150 115.0 30 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1151 115.1 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1152 115.2 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1153 115.3 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1154 115.4 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1155 115.5 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1156 115.6 29 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1157 115.7 39 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1158 115.8 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1159 115.9 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1160 116.0 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1161 116.1 28 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1162 116.2 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1163 116.3 37 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1164 116.4 23 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1165 116.5 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1166 116.6 20 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1167 116.7 36 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1168 116.8 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1169 116.9 21 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1170 117.0 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1171 117.1 34 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1172 117.2 32 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1173 117.3 25 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1174 117.4 24 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1175 117.5 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1176 117.6 35 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1177 117.7 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1178 117.8 26 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1179 117.9 33 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+#> 1180 118.0 27 TL (UVVIS) 2016-01-30-10:54.0.226260372670367
+
+
+
+
+ + +
+ + + + + + + diff --git a/reference/merge_RLum.Analysis.html b/reference/merge_RLum.Analysis.html index 411ac2101..d4a2a8af8 100644 --- a/reference/merge_RLum.Analysis.html +++ b/reference/merge_RLum.Analysis.html @@ -9,7 +9,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Merge function for RLum.Data.Spectrum S4 class objects

+ Source: R/merge_RLum.Data.Spectrum.R +
merge_RLum.Data.Spectrum.Rd
+
+ +
+

This function allows to merge RLum.Data.Spectrum objects in +different ways without modifying the original objects.

+
+ +
+

Usage

+ 
merge_RLum.Data.Spectrum(
+  object,
+  merge.method = "mean",
+  method.info,
+  max.temp.diff = 0.1
+)
+
+ +
+

Arguments

+ + +
object
+

list of RLum.Data.Spectrum (required): +list of objects to be merged.

+ + +
merge.method
+

character (required): +method for combining of the objects, e.g. 'mean' (default), 'median', +'sum', see details for +further information and allowed methods. Note: Elements in slot info will +be taken from the first object in the list.

+ + +
method.info
+

numeric (optional): +allows to specify how info elements of the input objects are combined, +e.g. 1 means that just the elements from the first object are kept, +2 keeps only the info elements from the 2 object etc. +If nothing is provided all elements are combined.

+ + +
max.temp.diff
+

numeric (with default): +maximum difference in the time/temperature values between the spectra to +be merged: when differences exceed this threshold value, the merging +occurs but a warning is raised.

+ +
+
+

Value

+

Returns an RLum.Data.Spectrum object.

+
+
+

Details

+

Supported merge operations are:

+

"mean" (default)

+

The mean over the cell values is calculated using the function +rowMeans.

+

"median"

+

The median over the cell values is calculated using the function +matrixStats::rowMedians.

+

"sum"

+

All cell values will be summed up using the function rowSums.

+

"sd"

+

The standard deviation over the cell values is calculated using the function +matrixStats::rowSds.

+

"var"

+

The variance over the cell values is calculated using the function +matrixStats::rowVars.

+

"min"

+

The min values from the cell values is chosen using the function +matrixStats::rowMins.

+

"max"

+

The max values from the cell values is chosen using the function +matrixStats::rowMins.

+

"append" (only for RLum.Data.Curve)

+

Appends cell values of all curves to one combined data curve. The channel width +is automatically re-calculated, but requires a constant channel width of the +original data.

+

"-"

+

The cell sums of the last objects are subtracted from the first object.

+

"*"

+

The cell sums of the last objects are multiplied with the first object.

+

"/"

+

Values of the first object are divided by cell sums of the last objects.

+
+
+

Note

+

The information from the slot recordType is taken from the first +object in the input list. The slot +'curveType' is filled with the name merged.

+
+
+

S3-generic support

+ + + +

This function is fully operational via S3-generics: ++, -, /, *, merge

+
+
+

Function version

+

0.1.1

+
+
+

See also

+

merge_RLum, RLum.Data.Spectrum

+
+
+

Author

+

Marco Colombo, Institute of Geography, Heidelberg University (Germany) +Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) +, RLum Developer Team

+
+
+

How to cite

+

Colombo, M., Kreutzer, S., 2025. merge_RLum.Data.Spectrum(): Merge function for RLum.Data.Spectrum S4 class objects. Function version 0.1.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+ +
+

Examples

+ 

+## load example data
+data(ExampleData.XSYG, envir = environment())
+
+## plot single curve
+plot_RLum(TL.Spectrum)
+
+
+## sum two copies of the same curve
+merged <- merge_RLum.Data.Spectrum(list(TL.Spectrum, TL.Spectrum),
+                                   merge.method = "sum")
+plot_RLum(merged)
+
+
+
+
+
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/merge_RLum.Results.html b/reference/merge_RLum.Results.html index c3dd4a76a..47b214d53 100644 --- a/reference/merge_RLum.Results.html +++ b/reference/merge_RLum.Results.html @@ -11,7 +11,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Safe manipulation of object metadata

+ Source: R/Luminescence-generics.R +
metadata.Rd
+
+ +
+

Generic functions for manipulation of metadata in Risoe.BINfileData, +RLum.Analysis and RLum.Data objects.

+
+ +
+

Usage

+ 
add_metadata(object, ...) <- value
+
+rename_metadata(object, ...) <- value
+
+replace_metadata(object, ...) <- value
+
+ +
+

Arguments

+ + +
object
+

(required) object to manipulate

+ + +
...
+

further arguments passed to the specific class method

+ + +
value
+

the value to be assigned

+ +
+
+

See also

+

RLum.Data, RLum.Analysis, Risoe.BINfileData

+
+
+

Author

+

Marco Colombo, Institute of Geography, Heidelberg University (Germany) +, RLum Developer Team

+
+
+

How to cite

+

Colombo, M., 2025. add_metadata<-(): Safe manipulation of object metadata. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+ +
+

Examples

+ 

+## load example data
+data(ExampleData.BINfileData, envir = environment())
+
+## show data
+CWOSL.SAR.Data
+#> 
+#> [Risoe.BINfileData object]
+#> 
+#> 	BIN/BINX version:     03
+#> 	Object date:          060920, 070920, 080920, 090920, 100920
+#> 	User:                 Default
+#> 	System ID:            0 (unknown)
+#> 	Overall records:      720
+#> 	Records type:         IRSL	(n = 24)
+#> 	                      OSL	(n = 336)
+#> 	                      TL	(n = 360)
+#> 	Position range:       1 : 24
+#> 	Run range:            1 : 8
+
+## add a new field
+add_metadata(CWOSL.SAR.Data,
+             info_element = "INSTITUTE") <- "Heidelberg University"
+
+## rename a field
+rename_metadata(CWOSL.SAR.Data,
+                info_element = "INSTITUTE") <- "INSTITUTION"
+
+## replace all LTYPE to RSL
+## but only for the first position
+replace_metadata(
+ object = CWOSL.SAR.Data,
+ info_element = "LTYPE",
+ subset = (POSITION == 1)) <- "RSL"
+
+## replacing a field with NULL allows to remove that field
+replace_metadata(CWOSL.SAR.Data,
+                 info_element = "PREVIOUS") <- NULL
+
+## show the modified data
+CWOSL.SAR.Data
+#> 
+#> [Risoe.BINfileData object]
+#> 
+#> 	BIN/BINX version:     03
+#> 	Object date:          060920, 070920, 080920, 090920, 100920
+#> 	User:                 Default
+#> 	System ID:            0 (unknown)
+#> 	Overall records:      720
+#> 	Records type:         IRSL	(n = 23)
+#> 	                      OSL	(n = 322)
+#> 	                      RSL	(n = 30)
+#> 	                      TL	(n = 345)
+#> 	Position range:       1 : 24
+#> 	Run range:            1 : 8
+
+
+
+
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/methods_RLum.html b/reference/methods_RLum.html index 67dadaeb2..f2bdef602 100644 --- a/reference/methods_RLum.html +++ b/reference/methods_RLum.html @@ -15,7 +15,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Plot a dose-response curve for luminescence data (Lx/Tx against dose)

+ Source: R/plot_DoseResponseCurve.R +
plot_DoseResponseCurve.Rd
+
+ +
+

A dose-response curve is produced for luminescence measurements using a +regenerative or additive protocol as implemented in fit_DoseResponseCurve.

+
+ +
+

Usage

+ 
plot_DoseResponseCurve(
+  object,
+  plot_extended = TRUE,
+  plot_singlePanels = FALSE,
+  cex.global = 1,
+  verbose = TRUE,
+  ...
+)
+
+ +
+

Arguments

+ + +
object
+

RLum.Results (required): +An object produced by fit_DoseResponseCurve.

+ + +
plot_extended
+

logical (with default): +If TRUE, 3 plots on one plot area are provided:

  1. growth curve,

    2. +

  2. histogram from Monte Carlo error simulation and

    3. +

  3. a test dose response plot.

    4. +

If FALSE, just the growth curve will be plotted.

+ + +
plot_singlePanels
+

logical (with default): +single plot output (TRUE/FALSE) to allow for plotting the results in +single plot windows. Ignored if plot_extended = FALSE.

+ + +
cex.global
+

numeric (with default): +global scaling factor.

+ + +
verbose
+

logical (with default): +enable/disable output to the terminal.

+ + +
...
+

Further graphical parameters to be passed (supported: +main, mtext, xlim, ylim, xlab, ylab, legend, reg_points_pch, +density_polygon (TRUE/FALSE), density_polygon_col, density_rug (TRUE/FALSE)), +box (TRUE/FALSE)

+ +
+
+

Value

+

A plot (or a series of plots) is produced.

+
+
+

Function version

+

1.0.2

+
+
+

How to cite

+

Kreutzer, S., Dietze, M., Colombo, M., 2025. plot_DoseResponseCurve(): Plot a dose-response curve for luminescence data (Lx/Tx against dose). Function version 1.0.2. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+
+

References

+

Berger, G.W., Huntley, D.J., 1989. Test data for exponential fits. Ancient TL 7, 43-46.

+

Guralnik, B., Li, B., Jain, M., Chen, R., Paris, R.B., Murray, A.S., Li, S.-H., Pagonis, P., +Herman, F., 2015. Radiation-induced growth and isothermal decay of infrared-stimulated luminescence +from feldspar. Radiation Measurements 81, 224-231.

+

Pagonis, V., Kitis, G., Chen, R., 2020. A new analytical equation for the dose response of dosimetric materials, +based on the Lambert W function. Journal of Luminescence 225, 117333. doi:10.1016/j.jlumin.2020.117333

+
+
+

See also

+

fit_DoseResponseCurve

+
+
+

Author

+

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
+Michael Dietze, GFZ Potsdam (Germany)
+Marco Colombo, Institute of Geography, Heidelberg University (Germany) +, RLum Developer Team

+
+ +
+

Examples

+ 

+##(1) plot dose-response curve for a dummy dataset
+data(ExampleData.LxTxData, envir = environment())
+fit <- fit_DoseResponseCurve(LxTxData)
+#> [fit_DoseResponseCurve()] Fit: EXP (interpolation) | De = 1737.88 | D01 = 1766.07
+plot_DoseResponseCurve(fit)
+
+
+##(1b) horizontal plot arrangement
+layout(mat = matrix(c(1,1,2,3), ncol = 2))
+plot_DoseResponseCurve(fit, plot_singlePanels = TRUE)
+
+
+##(2) plot the dose-response curve with pdf output - uncomment to use
+##pdf(file = "~/Dose_Response_Curve_Dummy.pdf", paper = "special")
+plot_DoseResponseCurve(fit)
+
+##dev.off()
+
+##(3) plot the growth curve with pdf output - uncomment to use, single output
+##pdf(file = "~/Dose_Response_Curve_Dummy.pdf", paper = "special")
+plot_DoseResponseCurve(fit, plot_singlePanels = TRUE)
+##dev.off()
+
+##(4) plot resulting function for given interval x
+x <- seq(1,10000, by = 100)
+plot(
+ x = x,
+ y = eval(fit$Formula),
+ type = "l"
+)
+
+
+
+
+
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/plot_FilterCombinations-1.png b/reference/plot_FilterCombinations-1.png index d4f269358..157d288ec 100644 Binary files a/reference/plot_FilterCombinations-1.png and b/reference/plot_FilterCombinations-1.png differ diff --git a/reference/plot_FilterCombinations-2.png b/reference/plot_FilterCombinations-2.png index 0b1714756..1a24504ea 100644 Binary files a/reference/plot_FilterCombinations-2.png and b/reference/plot_FilterCombinations-2.png differ diff --git a/reference/plot_FilterCombinations-3.png b/reference/plot_FilterCombinations-3.png index 130f71d28..538849f96 100644 Binary files a/reference/plot_FilterCombinations-3.png and b/reference/plot_FilterCombinations-3.png differ diff --git a/reference/plot_FilterCombinations.html b/reference/plot_FilterCombinations.html index 7952fc70a..f5c5e485a 100644 --- a/reference/plot_FilterCombinations.html +++ b/reference/plot_FilterCombinations.html @@ -1,9 +1,101 @@ Plot filter combinations along with the (optional) net transmission window — plot_FilterCombinations • Luminescence +With that a standardised output is reached and a net transmission window can be shown. +Calculations +Net transmission window +The net transmission window of two filters is approximated by +$$T_{final} = T_{1} * T_{2}$$ +Optical density +$$OD = -log10(T)$$ +Total optical density +$$OD_{total} = OD_{1} + OD_{2}$$ +Please consider using own calculations for more precise values. +How to provide input data? +CASE 1 +The function expects that all filter values are either of type matrix or data.frame +with two columns. The first columns contains the wavelength, the second the relative transmission +(but not in percentage, i.e. the maximum transmission can be only become 1). +In this case only the transmission window is show as provided. Changes in filter thickness and +reflection factor are not considered. +CASE 2 +The filter data itself are provided as list element containing a matrix or +data.frame and additional information on the thickness of the filter, e.g., +list(filter1 = list(filter_matrix, d = 2)). +The given filter data are always considered as standard input and the filter thickness value +is taken into account by +$$Transmission = Transmission^(d)$$ +with d given in the same dimension as the original filter data. +CASE 3 +Same as CASE 2 but additionally a reflection factor P is provided, e.g., +list(filter1 = list(filter_matrix, d = 2, P = 0.9)). +The final transmission becomes: +$$Transmission = Transmission^(d) * P$$ +Advanced plotting parameters +The following further non-common plotting parameters can be passed to the function: + +ArgumentDatatypeDescription +legendlogicalenable/disable legend +legend.poscharacterchange legend position (graphics::legend) +legend.textcharactersame as the argument legend in (graphics::legend) +net_transmission.colcolcolour of net transmission window polygon +net_transmission.col_linescolcolour of net transmission window polygon lines +net_transmission.densitynumericspecify line density in the transmission polygon +gridlistfull list of arguments that can be passed to the function graphics::grid + + + +For further modifications standard additional R plot functions are recommend, e.g., the legend +can be fully customised by disabling the standard legend and use the function graphics::legend +instead."> Skip to contents @@ -11,7 +103,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Moran Scatter Plot: Visualizing Spatial Dependency

+ Source: R/plot_MoranScatterplot.R +
plot_MoranScatterplot.Rd
+
+ +
+

Scatter plot, with on the x axis the original grain signal and on the y axis +the weighted mean of the neighbour grain signals. The plot area is divided into four quadrants, +and also a least square line (which slopes indicates, but not exactly represents, Moran's I) and +an 1:1 line (which indicates a Moran's I of around 1).

+
+ +
+

Usage

+ 
plot_MoranScatterplot(
+  object,
+  df_neighbours = NULL,
+  str_y_def = "mean_neighbours",
+  ...
+)
+
+ +
+

Arguments

+ + +
object
+

RLum.Results or numeric (required): containing a numerical vector of length 100, +representing one or more measurement discs ("positions") in a reader. +Each element in the vector represents one grain hole location on a disc.

+ + +
df_neighbours
+

data.frame (with default) Data frame indicating +which borders to consider, and their respective weights (see the description +provided for calc_MoransI). If NULL (default), this is constructed +automatically by the internal function .get_Neighbours.

+ + +
str_y_def
+

character (with default) Calculation of y position. Defaults to "mean_neighbours" +which is the plain mean of all neighbour values and the most illustrative. The other option is "weighted_sum", +which means the sum of the border weights times the neighbour values, which is actually closer +to the way Moran's I is by default calculated in this package.

+ + +
...
+

Other parameters to be forwarded to the base R plot functions. +legend (TRUE/FALSE) to enable/disable the legend. +Note that xlab (x axis label), ylab (y axis label) and cex (scaling +value) are given default values. Because of sometimes large value +differences, log = "x", log = "y" and log = "xy"are supported. +In case of negative values and logarithmic plotting, values are increased +so the smallest value to plot is 1. Summary elements such as means, least +square line etc. will still be based on the linear case. +pch accepts options "show_location_ids"(plots grain location id's),"show_n_neighbours"(indicates numbers of neighbours) and the normal base plotpch` options.

+ +
+
+

Value

+

Returns (invisibly) a data frame with plotting coordinates and +grain location id's, for creating user-defined plots.

+
+
+

Details

+

Note that this function plots on the y-axis the mean of the neighbours, while the function +calc_MoransI by default will for its global calculation weight every border the same. So, grain locations +with 1, 2 or 3 neighbours will appear higher on the y-axis than their influence on Moran's I justify – apart +from scaling, this explains a part of the differences of Moran's scatter plots between different packages. +Also note that island' grain locations (=those not bordering other grains) are left out of these plots but +might still influence Moran's I calculations.

+
+
+

How to cite

+

Boer, A.d., Steinbuch, L., 2025. plot_MoranScatterplot(): Moran Scatter Plot: Visualizing Spatial Dependency. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+
+

References

+

de Boer, A-M., Steinbuch, L., Heuvelink, G.B.M., Wallinga, J., 2025. +A novel tool to assess crosstalk in single-grain luminescence detection. +Submitted.

+
+
+

Author

+

Anna-Maartje de Boer, Luc Steinbuch, Wageningen University & Research, 2025 +, RLum Developer Team

+
+ +
+

Examples

+ 
plot_MoranScatterplot(1:100)
+
+
+
+
+
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/plot_NRt-10.png b/reference/plot_NRt-10.png index 0616ce0c7..45b0fa743 100644 Binary files a/reference/plot_NRt-10.png and b/reference/plot_NRt-10.png differ diff --git a/reference/plot_NRt-11.png b/reference/plot_NRt-11.png index a14df96af..096e5dd63 100644 Binary files a/reference/plot_NRt-11.png and b/reference/plot_NRt-11.png differ diff --git a/reference/plot_NRt-12.png b/reference/plot_NRt-12.png index c93109e32..42727fdae 100644 Binary files a/reference/plot_NRt-12.png and b/reference/plot_NRt-12.png differ diff --git a/reference/plot_NRt-7.png b/reference/plot_NRt-7.png index cd25ba78d..3119c7e3c 100644 Binary files a/reference/plot_NRt-7.png and b/reference/plot_NRt-7.png differ diff --git a/reference/plot_NRt-8.png b/reference/plot_NRt-8.png index cc474ffc1..423397df3 100644 Binary files a/reference/plot_NRt-8.png and b/reference/plot_NRt-8.png differ diff --git a/reference/plot_NRt-9.png b/reference/plot_NRt-9.png index 111111fb7..d6d4462e4 100644 Binary files a/reference/plot_NRt-9.png and b/reference/plot_NRt-9.png differ diff --git a/reference/plot_NRt.html b/reference/plot_NRt.html index 21411c1dd..ffd47c1f6 100644 --- a/reference/plot_NRt.html +++ b/reference/plot_NRt.html @@ -1,7 +1,23 @@ Visualise natural/regenerated signal ratios — plot_NRt • Luminescence +as shown in Steffen et al. 2009. +This function accepts the individual curve data in many different formats. If +data is a list, each element of the list must contain a two +column data.frame or matrix containing the XY data of the curves +(time and counts). Alternatively, the elements can be objects of class +RLum.Data.Curve. +Input values can also be provided as a data.frame or matrix where +the first column contains the time values and each following column contains +the counts of each curve."> Skip to contents @@ -9,7 +25,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Plot a disc with its values

+ Source: R/plot_SingleGrainDisc.R +
plot_SingleGrainDisc.Rd
+
+ +
+

Shows a schematic representation of the physical appearance +of one disc (one position in the reader) +and illustrates the measured or calculated values per grain location.

+
+ +
+

Usage

+ 
plot_SingleGrainDisc(
+  object,
+  show_coordinates = FALSE,
+  show_location_ids = FALSE,
+  show_neighbours = FALSE,
+  show_positioning_holes = TRUE,
+  df_neighbours = NULL,
+  ignore_borders = FALSE,
+  str_transform = "sqrt",
+  ...
+)
+
+ +
+

Arguments

+ + +
object
+

RLum.Results or numeric (required): the values +to show, should have length 100.

+ + +
show_coordinates
+

logical (with default): Show coordinates (1..10) +in x and in y direction. Defaults to FALSE.

+ + +
show_location_ids
+

logical (with default): Show id with every +grain location (1..100). Defaults to FALSE.

+ + +
show_neighbours
+

logical (with default): Show which +neighbour connections are taken into account if calculating Moran's I. +This makes sense when there are NA observations, or when a non-standard +neighbour setting is defined.

+ + +
show_positioning_holes
+

logical (with default): Show the 3 +positioning holes for orientation. Defaults to TRUE.

+ + +
df_neighbours
+

data.frame (with default): only relevant if +show_neighbours is TRUE. Data frame indicating which borders to +consider, and their respective weights (see the description provided for +calc_MoransI). If NULL (default), this is constructed automatically by +the internal function .get_Neighbours.

+ + +
ignore_borders
+

logical (with default): whether only grain +locations that do not lie on the border of the disc should be considered +(FALSE by default). Thus if TRUE, only the inner 8x8 grain locations +rather than the full 10x10 are considered. Ignored if df_neighbours is +not NULL or if show_neighbours = FALSE.

+ + +
str_transform
+

character (with default): The observed value of each individual grain is +reflected in the size of a triangle (or other dot-like element). To account for large value differences, +the transformation from value to triangle size can be "lin" (linear), "log" (logarithmic) and "sqrt" +(square root). Defaults to "sqrt", so that the surface is linear to the value. Note that +the log and sqrt transformations can come with an addition to avoid negative values. When the legend +is shown, the actual lower, middle and upper values are printed.

+ + +
...
+

other arguments to be given to the base R plot function, such +as main, col and pch. legend can be used to enable/disable the +legend (FALSE by default).

+ +
+
+

Details

+

Depending of the available plotting space, some optional elements might have not enough room +to be displayed. As this function is wrapped around the base plot function, one can also choose to add elements +manually.

+
+
+

How to cite

+

Boer, A.d., Steinbuch, L., 2025. plot_SingleGrainDisc(): Plot a disc with its values. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+
+

References

+

de Boer, A-M., Steinbuch, L., Heuvelink, G.B.M., Wallinga, J., 2025. +A novel tool to assess crosstalk in single-grain luminescence detection. +Submitted.

+
+
+

Author

+

Anna-Maartje de Boer, Luc Steinbuch, Wageningen University & Research, 2025 +, RLum Developer Team

+
+ +
+

Examples

+ 

+plot_SingleGrainDisc(1:100)
+
+
+
+
+
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/plot_ViolinPlot.html b/reference/plot_ViolinPlot.html index 69f45d365..a4d3e43ab 100644 --- a/reference/plot_ViolinPlot.html +++ b/reference/plot_ViolinPlot.html @@ -2,7 +2,7 @@ Create a violin plot — plot_ViolinPlot • LuminescenceLuminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Sort data for RLum S4-class objects

+ Source: R/Luminescence-generics.R +
sort_RLum.Rd
+
+ +
+

The function provides a generalised access point for specific +RLum objects. Depending on the input object, the corresponding +function will be selected. +Allowed arguments can be found in the documentations of the corresponding +RLum class.

+
+ +
+

Usage

+ 
sort_RLum(object, ...)
+
+ +
+

Arguments

+ + +
object
+

RLum or Risoe.BINfileData (required): +S4 object of class RLum.Analysis or Risoe.BINfileData

+ + +
...
+

further arguments passed to the specific class method

+ +
+
+

Value

+

An object of the same type as the input object provided.

+
+
+

Function version

+

0.1.0

+
+
+

See also

+

RLum.Analysis, Risoe.BINfileData

+
+
+

Author

+

Marco Colombo, Institute of Geography, Heidelberg University (Germany) +, RLum Developer Team

+
+
+

How to cite

+

Colombo, M., 2025. sort_RLum(): Sort data for RLum S4-class objects. Function version 0.1.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+ +
+

Examples

+ 

+## load example data
+data(ExampleData.XSYG, envir = environment())
+obj <- OSL.SARMeasurement$Sequence.Object[1:9]
+
+sort_RLum(obj, slot = "recordType")
+#> 
+#>  [RLum.Analysis-class]
+#> 	 originator: read_XSYG2R()
+#> 	 protocol: SAR
+#> 	 additional info elements:  0
+#> 	 number of records: 9
+#> 	 .. : RLum.Data.Curve : 9
+#> 	 .. .. : #1 OSL (NA) <> #2 OSL (NA) <> #3 OSL (NA) <> #4 OSL (NA) <> #5 OSL (UVVIS) 
+#> 	 .. .. : #6 TL (NA) <> #7 TL (NA) <> #8 TL (UVVIS)
+#> 	 .. .. : #9 irradiation (NA)
+sort_RLum(obj, info_element = "curveDescripter")
+#> 
+#>  [RLum.Analysis-class]
+#> 	 originator: read_XSYG2R()
+#> 	 protocol: SAR
+#> 	 additional info elements:  0
+#> 	 number of records: 9
+#> 	 .. : RLum.Data.Curve : 9
+#> 	 .. .. : #1 TL (NA) <> #2 TL (NA) 
+#> 	 .. .. : #3 OSL (NA) <> #4 OSL (NA) 
+#> 	 .. .. : #5 TL (UVVIS) 
+#> 	 .. .. : #6 OSL (UVVIS) <> #7 OSL (NA) <> #8 OSL (NA)
+#> 	 .. .. : #9 irradiation (NA)
+
+
+
+
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/structure_RLum.html b/reference/structure_RLum.html index 4e6f45584..da5f2381e 100644 --- a/reference/structure_RLum.html +++ b/reference/structure_RLum.html @@ -1,5 +1,13 @@ -General structure function for RLum S4 class objects — structure_RLum • Luminescence +General structure function for RLum S4 class objects — structure_RLum • Luminescence Skip to contents @@ -7,7 +15,7 @@ Luminescence - 0.9.26 + 1.0.0 + + + + + +
+
+
+ +

Convenience data visualisation function

+ Source: R/Luminescence-generics.R +
view.Rd
+
+ +
+

Invokes the utils::View function tailored to objects in the package. +If started from RStudio, it uses the RStudio viewer.

+
+ +
+

Usage

+ 
view(object, ...)
+
+ +
+

Arguments

+ + +
object
+

(required) object to view

+ + +
...
+

further arguments passed to the specific class method

+ +
+
+

Value

+

NULL and opens the data viewer.

+
+
+

See also

+

utils::View()

+
+
+

Author

+

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) +, RLum Developer Team

+
+
+

How to cite

+

Kreutzer, S., 2025. view(): Convenience data visualisation function. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.0. https://r-lum.github.io/Luminescence/

+
+ +
+ + +
+

Developed by Sebastian Kreutzer, Christoph Burow, Michael Dietze, Margret C. Fuchs, Christoph Schmidt, Manfred Fischer, Johannes Friedrich, Norbert Mercier, Anne Philippe, Svenja Riedesel, Martin Autzen, Dirk Mittelstrass, Harrison J. Gray, Jean-Michel Galharret, Marco Colombo, Luc Steinbuch, Anna-Maartje de Boer.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/reference/write_R2BIN.html b/reference/write_R2BIN.html index a0c6e9cef..73abd7028 100644 --- a/reference/write_R2BIN.html +++ b/reference/write_R2BIN.html @@ -9,7 +9,7 @@ Luminescence - 0.9.26 + 1.0.0